| Title: | R Interface for the 'H2O' Scalable Machine Learning Platform |
|---|---|
| Description: | R interface for 'H2O', the scalable open source machine learning platform that offers parallelized implementations of many supervised and unsupervised machine learning algorithms such as Generalized Linear Models (GLM), Gradient Boosting Machines (including XGBoost), Random Forests, Deep Neural Networks (Deep Learning), Stacked Ensembles, Naive Bayes, Generalized Additive Models (GAM), ANOVA GLM, Cox Proportional Hazards, K-Means, PCA, ModelSelection, Word2Vec, as well as a fully automatic machine learning algorithm (H2O AutoML). |
| Authors: | Tomas Fryda [aut, cre], Erin LeDell [aut], Navdeep Gill [aut], Spencer Aiello [aut], Anqi Fu [aut], Arno Candel [aut], Cliff Click [aut], Tom Kraljevic [aut], Tomas Nykodym [aut], Patrick Aboyoun [aut], Michal Kurka [aut], Michal Malohlava [aut], Sebastien Poirier [aut], Wendy Wong [aut], Ludi Rehak [ctb], Eric Eckstrand [ctb], Brandon Hill [ctb], Sebastian Vidrio [ctb], Surekha Jadhawani [ctb], Amy Wang [ctb], Raymond Peck [ctb], Jan Gorecki [ctb], Matt Dowle [ctb], Yuan Tang [ctb], Lauren DiPerna [ctb], Veronika Maurerova [ctb], Yuliia Syzon [ctb], Adam Valenta [ctb], Marek Novotny [ctb], H2O.ai [cph, fnd] |
| Maintainer: | Tomas Fryda <[email protected]> |
| License: | Apache License (== 2.0) |
| Version: | 3.44.0.3 |
| Built: | 2024-09-08 06:41:29 UTC |
| Source: | CRAN |
This is a package for running H2O via its REST API from within R. To communicate with a H2O instance, the version of the R package must match the version of H2O. When connecting to a new H2O cluster, it is necessary to re-run the initializer.
| Package: | h2o |
| Type: | Package |
| Version: | 3.44.0.3 |
| Branch: | rel-3.44.0 |
| Date: | Wed Dec 20 16:35:40 UTC 2023 |
| License: | Apache License (== 2.0) |
| Depends: | R (>= 2.13.0), RCurl, jsonlite, statmod, tools, methods, utils |
H2O is the scalable open source machine learning platform that offers parallelized implementations of many supervised and unsupervised machine learning algorithms such as Generalized Linear Models (GLM), Gradient Boosting Machines (including XGBoost), Random Forests, Deep Neural Networks (Deep Learning), Stacked Ensembles, Naive Bayes, Generalized Additive Models (GAM), ANOVA GLM, Maximum R GLM (maxrglm), Cox Proportional Hazards, K-Means, PCA, Word2Vec, as well as a fully automatic machine learning algorithm (H2O AutoML). As an example, to run GLM, call h2o.glm with the H2O parsed data and parameters (response variable, error distribution, etc.) as arguments.
This package enables the use of the H2O machine learning platform commands in R. To use H2O from R, you must start or connect to the "H2O cluster", the term we use to describe the backend H2O Java engine. To run H2O on your local machine, call h2o.init without any arguments, and H2O will be automatically launched at localhost:54321, where the IP is "127.0.0.1" and the port is 54321. If you have the H2O cluster running on a remote machine (e.g. AWS EC2), you must provide the IP and port of the remote machine as arguments to the h2o.init call.
Note that no actual data is stored in the R workspace; and no actual work is carried out by R. R only saves the named objects, which uniquely identify the data set, model, etc on the server. When the user makes a request, R queries the server via the REST API, which returns a JSON file with the relevant information that R then displays in the console.
Maintainer: Tomas Fryda <[email protected]>
Append a <key,value> pair to a list.
.addParm(parms, k, v).addParm(parms, k, v)
parms |
a list to add the <k,v> pair to |
k |
a key, typically the name of some algorithm parameter |
v |
a value, the value of the algorithm parameter |
Contained here are a set of helper methods that perform type checking on the value passed in.
Stop with a user friendly message if a user is missing the ggplot2 package or has an old version of it.
.check_for_ggplot2(version = "3.0.0").check_for_ggplot2(version = "3.0.0")
version |
minimal required ggplot2 version |
Collapse a character vector into a ','-sep array of the form: [thing1,thing2,...]
.collapse(v).collapse(v)
v |
Character vector. |
Consolidation works in the following way: 1. if varimp variable is in x => add it to consolidated_varimps 2. for all remaining varimp variables: 1. find the longest prefix of varimp variable that is in x and add it to the consolidated varimp 2. if there was no match, throw an error 3. normalize the consolidated_varimps so they sum up to 1
.consolidate_varimps(model).consolidate_varimps(model)
model |
H2OModel |
sorted named vector
models
Create a leaderboard like data frame for models
.create_leaderboard(models_info, leaderboard_frame, top_n = 20).create_leaderboard(models_info, leaderboard_frame, top_n = 20)
models_info |
H2OAutoML object or list of models |
leaderboard_frame |
when provided with list of models, use this frame to calculate metrics |
top_n |
create leaderboard with just top_n models |
a data.frame
A helper function that makes it easier to override/add params in a function call.
.customized_call(fun, ..., overridable_defaults = NULL, overrides = NULL).customized_call(fun, ..., overridable_defaults = NULL, overrides = NULL)
fun |
Function to be called |
... |
Parameters that can't be overridden |
overridable_defaults |
List of parameters and values that can be overridden |
overrides |
Parameters to add/override. |
result of fun
fuzzy_col_name with a column name that exists in cols.Tries to match a fuzzy_col_name with a column name that exists in cols.
.find_appropriate_column_name(fuzzy_col_name, cols).find_appropriate_column_name(fuzzy_col_name, cols)
fuzzy_col_name |
a name to be decoded |
cols |
vector of columns that contain all possible column names, i.e., decode fuzzy_col_name must be in cols |
a correct column name
Get the algoritm used by the model_or_model_id
.get_algorithm(model_or_model_id, treat_xrt_as_algorithm = FALSE).get_algorithm(model_or_model_id, treat_xrt_as_algorithm = FALSE)
model_or_model_id |
Model object or a string containing model id |
treat_xrt_as_algorithm |
Try to find out if a model is XRT and if so report it as xrt |
algorithm name
Get a mapping between columns and their domains
.get_domain_mapping(model).get_domain_mapping(model)
model |
an h2o model |
list containing a mapping from column to its domains (levels)
Get feature count sorted by the count descending.
.get_feature_count(column).get_feature_count(column)
column |
H2OFrame column |
named vector with feature counts
Get first of family models
.get_first_of_family(models, all_stackedensembles = FALSE).get_first_of_family(models, all_stackedensembles = FALSE)
models |
models or model ids |
all_stackedensembles |
if TRUE, select all stacked ensembles |
Capabilities endpoints
.h2o.__ALL_CAPABILITIES.h2o.__ALL_CAPABILITIES
An object of class character of length 1.
Warn if there are sick nodes.
.h2o.__checkConnectionHealth().h2o.__checkConnectionHealth()
H2OFrame Manipulation
.h2o.__CREATE_FRAME.h2o.__CREATE_FRAME
An object of class character of length 1.
Decryption Endpoints
.h2o.__DECRYPTION_SETUP.h2o.__DECRYPTION_SETUP
An object of class character of length 1.
Removal Endpoints
.h2o.__DKV.h2o.__DKV
An object of class character of length 1.
Export Files Endpoint Generator
.h2o.__EXPORT_FILES(frame).h2o.__EXPORT_FILES(frame)
frame |
H2OFrame |
Inspect/Summary Endpoints
.h2o.__FRAMES.h2o.__FRAMES
An object of class character of length 1.
Import/Export Endpoints
.h2o.__IMPORT.h2o.__IMPORT
An object of class character of length 1.
Administrative Endpoints
.h2o.__JOBS.h2o.__JOBS
An object of class character of length 1.
Log and Echo Endpoint
.h2o.__LOGANDECHO.h2o.__LOGANDECHO
An object of class character of length 1.
Model Builder Endpoint Generator
.h2o.__MODEL_BUILDERS(algo).h2o.__MODEL_BUILDERS(algo)
algo |
Cannonical identifier of H2O algorithm. |
Model Metrics Endpoint
.h2o.__MODEL_METRICS(model, data).h2o.__MODEL_METRICS(model, data)
model |
H2OModel. |
data |
H2OFrame. |
Model Endpoint
.h2o.__MODELS.h2o.__MODELS
An object of class character of length 1.
Parse Endpoints
.h2o.__PARSE_SETUP.h2o.__PARSE_SETUP
An object of class character of length 1.
Rapids Endpoint
.h2o.__RAPIDS.h2o.__RAPIDS
An object of class character of length 1.
The API endpoints for interacting with H2O via REST are named here.
.h2o.__REST_API_VERSION.h2o.__REST_API_VERSION
An object of class integer of length 1.
Additionally, environment variables for the H2O package are named here. Endpoint Version
Segment Models Builder Endpoint Generator
.h2o.__SEGMENT_MODELS_BUILDERS(algo).h2o.__SEGMENT_MODELS_BUILDERS(algo)
algo |
Cannonical identifier of H2O algorithm. |
Word2Vec Endpoints
.h2o.__W2V_SYNONYMS.h2o.__W2V_SYNONYMS
An object of class character of length 1.
Just like doRawGET but fills in the default h2oRestApiVersion if none is provided
.h2o.doGET(h2oRestApiVersion, urlSuffix, parms, ...).h2o.doGET(h2oRestApiVersion, urlSuffix, parms, ...)
h2oRestApiVersion |
(Optional) A version number to prefix to the urlSuffix. If no version is provided, a default version is chosen for you. |
urlSuffix |
The partial URL suffix to add to the calculated base URL for the instance |
parms |
(Optional) Parameters to include in the request |
... |
(Optional) Additional parameters. |
A list object as described above
Just like doRawPOST but fills in the default h2oRestApiVersion if none is provided
.h2o.doPOST(h2oRestApiVersion, urlSuffix, parms, ...).h2o.doPOST(h2oRestApiVersion, urlSuffix, parms, ...)
h2oRestApiVersion |
(Optional) A version number to prefix to the urlSuffix. If no version is provided, a default version is chosen for you. |
urlSuffix |
The partial URL suffix to add to the calculated base URL for the instance |
parms |
(Optional) Parameters to include in the request |
... |
(Optional) Additional parameters. |
A list object as described above
Does not do any I/O level error checking. Caller must do its own validations. Does not modify the response payload in any way. Log the request and response if h2o.startLogging() has been called.
.h2o.doRawGET( conn = h2o.getConnection(), h2oRestApiVersion, urlSuffix, parms, ... ).h2o.doRawGET( conn = h2o.getConnection(), h2oRestApiVersion, urlSuffix, parms, ... )
conn |
H2OConnection |
h2oRestApiVersion |
(Optional) A version number to prefix to the urlSuffix. If no version is provided, the version prefix is skipped. |
urlSuffix |
The partial URL suffix to add to the calculated base URL for the instance |
parms |
(Optional) Parameters to include in the request |
... |
(Optional) Additional parameters. |
The return value is a list as follows: $url – Final calculated URL. $postBody – The body of the POST request from client to server. $curlError – TRUE if a socket-level error occurred. FALSE otherwise. $curlErrorMessage – If curlError is TRUE a message about the error. $httpStatusCode – The HTTP status code. Usually 200 if the request succeeded. $httpStatusMessage – A string describing the httpStatusCode. $payload – The raw response payload as a character vector.
A list object as described above
Does not do any I/O level error checking. Caller must do its own validations. Does not modify the response payload in any way. Log the request and response if h2o.startLogging() has been called.
.h2o.doRawPOST( conn = h2o.getConnection(), h2oRestApiVersion, urlSuffix, parms, fileUploadInfo, ... ).h2o.doRawPOST( conn = h2o.getConnection(), h2oRestApiVersion, urlSuffix, parms, fileUploadInfo, ... )
conn |
H2OConnection |
h2oRestApiVersion |
(Optional) A version number to prefix to the urlSuffix. If no version is provided, the version prefix is skipped. |
urlSuffix |
The partial URL suffix to add to the calculated base URL for the instance |
parms |
(Optional) Parameters to include in the request |
fileUploadInfo |
(Optional) Information to POST (NOTE: changes Content-type from XXX-www-url-encoded to multi-part). Use fileUpload(normalizePath("/path/to/file")). |
... |
(Optional) Additional parameters. |
The return value is a list as follows: $url – Final calculated URL. $postBody – The body of the POST request from client to server. $curlError – TRUE if a socket-level error occurred. FALSE otherwise. $curlErrorMessage – If curlError is TRUE a message about the error. $httpStatusCode – The HTTP status code. Usually 200 if the request succeeded. $httpStatusMessage – A string describing the httpStatusCode. $payload – The raw response payload as a character vector.
A list object as described above
This function validates that no CURL error occurred and that the HTTP response code is successful. If a failure occurred, then stop() is called with an error message. Since all necessary error checking is done inside this call, the valid payload is directly returned if the function successfully finishes without calling stop().
.h2o.doSafeGET(h2oRestApiVersion, urlSuffix, parms, ...).h2o.doSafeGET(h2oRestApiVersion, urlSuffix, parms, ...)
h2oRestApiVersion |
(Optional) A version number to prefix to the urlSuffix. If no version is provided, a default version is chosen for you. |
urlSuffix |
The partial URL suffix to add to the calculated base URL for the instance |
parms |
(Optional) Parameters to include in the request |
... |
(Optional) Additional parameters. |
The raw response payload as a character vector
This function validates that no CURL error occurred and that the HTTP response code is successful. If a failure occurred, then stop() is called with an error message. Since all necessary error checking is done inside this call, the valid payload is directly returned if the function successfully finishes without calling stop().
.h2o.doSafePOST(h2oRestApiVersion, urlSuffix, parms, fileUploadInfo, ...).h2o.doSafePOST(h2oRestApiVersion, urlSuffix, parms, fileUploadInfo, ...)
h2oRestApiVersion |
(Optional) A version number to prefix to the urlSuffix. If no version is provided, a default version is chosen for you. |
urlSuffix |
The partial URL suffix to add to the calculated base URL for the instance |
parms |
(Optional) Parameters to include in the request |
fileUploadInfo |
(Optional) Information to POST (NOTE: changes Content-type from XXX-www-url-encoded to multi-part). Use fileUpload(normalizePath("/path/to/file")). |
... |
(Optional) Additional parameters. |
The raw response payload as a character vector
Check if Progress Bar is Enabled
.h2o.is_progress().h2o.is_progress()
Locate a file given the pattern <bucket>/<path/to/file> e.g. h2o:::.h2o.locate("smalldata/iris/iris22.csv") returns the absolute path to iris22.csv
.h2o.locate(pathStub, root.parent = NULL).h2o.locate(pathStub, root.parent = NULL)
pathStub |
relative path |
root.parent |
search root directory |
Note: The underlying implementation is not distributed and can only handle limited size of data. For internal use only.
.h2o.perfect_auc(probs, acts).h2o.perfect_auc(probs, acts)
probs |
An H2OFrame holding vector of probabilities. |
acts |
An H2OFrame holding vector of actuals. |
Map of operations known to H2O
.h2o.primitives.h2o.primitives
An object of class character of length 39.
model coefficients?Has the model coefficients?
.has_model_coefficients(model).has_model_coefficients(model)
model |
Either a linear model with coefficients => TRUE, or something else => FALSE |
boolean
Has the model variable importance?
.has_varimp(model).has_varimp(model)
model |
model or a string containing model id |
boolean
Is the model considered to be interpretable, i.e., simple enough.
.interpretable(model).interpretable(model)
model |
model or a string containing model id |
boolean
model an H2O model?Is the model an H2O model?
.is_h2o_model(model).is_h2o_model(model)
model |
Either H2O model/model id => TRUE, or something else => FALSE |
boolean
model a Tree-based H2O Model?Is the model a Tree-based H2O Model?
.is_h2o_tree_model(model).is_h2o_tree_model(model)
model |
Either tree-based H2O model/model id => TRUE, or something else => FALSE |
boolean
Check if we are plotting in to r notebook.
.is_plotting_to_rnotebook().is_plotting_to_rnotebook()
boolean
Enhance leaderboard with per-model predictions.
.leaderboard_for_row(models_info, newdata, row_index, top_n = 20).leaderboard_for_row(models_info, newdata, row_index, top_n = 20)
models_info |
models_info object |
newdata |
H2OFrame |
row_index |
index of the inspected row |
top_n |
leaderboard will contain top_n models |
H2OFrame
Min-max normalization.
.min_max(col).min_max(col)
col |
numeric vector |
normalized numeric vector
When provided with list of models it will extract model ids. When provided with model ids it won't change anything. Works for mixed list as well.
.model_ids(models).model_ids(models)
models |
list or vector of models/model_ids |
a vector of model_ids
The H2O Package Environment
.pkg.env.pkg.env
An object of class environment of length 4.
Plot variable importances with ggplot2
.plot_varimp(model, top_n = 10).plot_varimp(model, top_n = 10)
model |
H2OModel |
top_n |
Plot just top_n features |
list of variable importance, groupped variable importance, and variable importance plot
object to a "standardized" list containing models, and
their properties such as x, y, whether it is a (multinomial) clasification or not etc.Do basic validation and transform object to a "standardized" list containing models, and
their properties such as x, y, whether it is a (multinomial) clasification or not etc.
.process_models_or_automl( object, newdata, require_single_model = FALSE, require_multiple_models = FALSE, top_n_from_AutoML = NA, only_with_varimp = FALSE, best_of_family = FALSE, require_newdata = TRUE, check_x_y_consistency = TRUE ).process_models_or_automl( object, newdata, require_single_model = FALSE, require_multiple_models = FALSE, top_n_from_AutoML = NA, only_with_varimp = FALSE, best_of_family = FALSE, require_newdata = TRUE, check_x_y_consistency = TRUE )
object |
Can be a single model/model_id, vector of model_id, list of models, H2OAutoML object |
newdata |
An H2OFrame with the same format as training frame |
require_single_model |
If true, make sure we were provided only one model |
require_multiple_models |
If true, make sure we were provided at least two models |
top_n_from_AutoML |
If set, don't return more than top_n models (applies only for AutoML object) |
only_with_varimp |
If TRUE, return only models that have variable importance |
best_of_family |
If TRUE, return only the best of family models; if FALSE return all models in |
require_newdata |
If TRUE, require newdata to be specified; otherwise allow NULL instead, this can be used when there is no need to know if the problem is (multinomial) classification. |
check_x_y_consistency |
If TRUE, make sure that when given a list of models all models have the same X and y. Defaults to TRUE. |
a list with the following names leader, is_automl, models,
is_classification, is_multinomial_classification, x, y, model
Shortens model ids if possible (iff there will be same amount of unique model_ids as before)
.shorten_model_ids(model_ids).shorten_model_ids(model_ids)
model_ids |
character vector |
character vector
Collected here are the various methods used by the h2o-R package to communicate with the H2O backend. There are methods for checking cluster health, polling, and inspecting objects in the H2O store.
.skip_if_not_developer().skip_if_not_developer()
Convert to quantiles when provided with numeric vector. When col is a factor vector assign uniformly value between 0 and 1 to each level.
.uniformize(col).uniformize(col)
col |
vector |
vector with values between 0 and 1
Get variable importance in a standardized way.
.varimp(model).varimp(model)
model |
H2OModel |
A named vector
Used to verify data, x, y and turn into the appropriate things
.verify_dataxy(data, x, y, autoencoder = FALSE).verify_dataxy(data, x, y, autoencoder = FALSE)
data |
H2OFrame |
x |
features |
y |
response |
autoencoder |
autoencoder flag |
Logical and for H2OFrames
`&&`(x, y)`&&`(x, y)
x |
An H2OFrame object |
y |
An H2OFrame object |
Starting H2O For examples
## Not run: if (Sys.info()['sysname'] == "Darwin" && Sys.info()['release'] == '13.4.0') { quit(save = "no") } else { h2o.init(nthreads = 2) } ## End(Not run)## Not run: if (Sys.info()['sysname'] == "Darwin" && Sys.info()['release'] == '13.4.0') { quit(save = "no") } else { h2o.init(nthreads = 2) } ## End(Not run)
Method for apply on H2OFrame objects.
apply(X, MARGIN, FUN, ...)apply(X, MARGIN, FUN, ...)
X |
an H2OFrame object on which |
MARGIN |
the vector on which the function will be applied over, either
|
FUN |
the function to be applied. |
... |
optional arguments to |
Produces a new H2OFrame of the output of the applied function. The output is stored in H2O so that it can be used in subsequent H2O processes.
apply for the base generic
## Not run: library(h2o) h2o.init() iris_hf <- as.h2o(iris) summary(apply(iris_hf, 2, sum)) ## End(Not run)## Not run: library(h2o) h2o.init() iris_hf <- as.h2o(iris) summary(apply(iris_hf, 2, sum)) ## End(Not run)
Convert an H2OFrame to a String
## S3 method for class 'H2OFrame' as.character(x, ...)## S3 method for class 'H2OFrame' as.character(x, ...)
x |
An H2OFrame object |
... |
Further arguments to be passed from or to other methods. |
## Not run: library(h2o) h2o.init() pretrained <- as.h2o(data.frame( C1 = c("a", "b"), C2 = c(0, 1), C3 = c(1, 0), C4 = c(0.2, 0.8), stringsAsFactors = FALSE)) pretrained_w2v <- h2o.word2vec(pre_trained = pretrained, vec_size = 3) words <- as.character(as.h2o(c("b", "a", "c", NA, "a"))) vecs <- h2o.transform(pretrained_w2v, words = words) ## End(Not run)## Not run: library(h2o) h2o.init() pretrained <- as.h2o(data.frame( C1 = c("a", "b"), C2 = c(0, 1), C3 = c(1, 0), C4 = c(0.2, 0.8), stringsAsFactors = FALSE)) pretrained_w2v <- h2o.word2vec(pre_trained = pretrained, vec_size = 3) words <- as.character(as.h2o(c("b", "a", "c", NA, "a"))) vecs <- h2o.transform(pretrained_w2v, words = words) ## End(Not run)
Downloads the H2O data and then scans it in to an R data frame.
## S3 method for class 'H2OFrame' as.data.frame(x, ...)## S3 method for class 'H2OFrame' as.data.frame(x, ...)
x |
An H2OFrame object. |
... |
Further arguments to be passed down from other methods. |
Method as.data.frame.H2OFrame will use fread if data.table package is installed in required version.
## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(path = prostate_path) as.data.frame(prostate) ## End(Not run)## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(path = prostate_path) as.data.frame(prostate) ## End(Not run)
Converts a collection of Segment Models to a data.frame
## S3 method for class 'H2OSegmentModels' as.data.frame(x, ...)## S3 method for class 'H2OSegmentModels' as.data.frame(x, ...)
x |
Object of class H2OSegmentModels. |
... |
Further arguments to be passed down from other methods. |
Returns data.frame with result of segment model training.
## Not run: library(h2o) h2o.init() iris_hf <- as.h2o(iris) models <- h2o.train_segments(algorithm = "gbm", segment_columns = "Species", x = c(1:3), y = 4, training_frame = iris_hf, ntrees = 5, max_depth = 4) as.data.frame(models) ## End(Not run)## Not run: library(h2o) h2o.init() iris_hf <- as.h2o(iris) models <- h2o.train_segments(algorithm = "gbm", segment_columns = "Species", x = c(1:3), y = 4, training_frame = iris_hf, ntrees = 5, max_depth = 4) as.data.frame(models) ## End(Not run)
Convert column/columns in the current frame to categoricals.
as.factor(x)as.factor(x)
x |
a column from an H2OFrame data set. |
## Not run: library(h2o) h2o.init() # Single column cars <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv" df <- h2o.importFile(cars) df["cylinders"] <- as.factor(df["cylinders"]) h2o.describe(df["cylinders"]) # Multiple columns cars <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv" df <- h2o.importFile(cars) df[c("cylinders","economy_20mpg")] <- as.factor(df[c("cylinders","economy_20mpg")]) h2o.describe(df[c("cylinders","economy_20mpg")]) ## End(Not run)## Not run: library(h2o) h2o.init() # Single column cars <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv" df <- h2o.importFile(cars) df["cylinders"] <- as.factor(df["cylinders"]) h2o.describe(df["cylinders"]) # Multiple columns cars <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv" df <- h2o.importFile(cars) df[c("cylinders","economy_20mpg")] <- as.factor(df[c("cylinders","economy_20mpg")]) h2o.describe(df[c("cylinders","economy_20mpg")]) ## End(Not run)
Import R object to the H2O cluster.
as.h2o(x, destination_frame = "", ...) ## Default S3 method: as.h2o(x, destination_frame = "", ...) ## S3 method for class 'H2OFrame' as.h2o(x, destination_frame = "", ...) ## S3 method for class 'data.frame' as.h2o(x, destination_frame = "", use_datatable = TRUE, ...) ## S3 method for class 'Matrix' as.h2o(x, destination_frame = "", use_datatable = TRUE, ...)as.h2o(x, destination_frame = "", ...) ## Default S3 method: as.h2o(x, destination_frame = "", ...) ## S3 method for class 'H2OFrame' as.h2o(x, destination_frame = "", ...) ## S3 method for class 'data.frame' as.h2o(x, destination_frame = "", use_datatable = TRUE, ...) ## S3 method for class 'Matrix' as.h2o(x, destination_frame = "", use_datatable = TRUE, ...)
x |
An |
destination_frame |
A string with the desired name for the H2OFrame |
... |
arguments passed to method arguments. |
use_datatable |
allow usage of data.table |
Method as.h2o.data.frame will use fwrite if data.table package is installed in required version.
To speedup execution time for large sparse matrices, use h2o datatable. Make sure you have installed and imported data.table and slam packages. Turn on h2o datatable by options("h2o.use.data.table"=TRUE)
https://h2o.ai/blog/2016/fast-csv-writing-for-r/
## Not run: library(h2o) h2o.init() iris_hf <- as.h2o(iris) euro_hf <- as.h2o(euro) letters_hf <- as.h2o(letters) state_hf <- as.h2o(state.x77) iris_hf_2 <- as.h2o(iris_hf) stopifnot(is.h2o(iris_hf), dim(iris_hf) == dim(iris), is.h2o(euro_hf), dim(euro_hf) == c(length(euro), 1L), is.h2o(letters_hf), dim(letters_hf) == c(length(letters), 1L), is.h2o(state_hf), dim(state_hf) == dim(state.x77), is.h2o(iris_hf_2), dim(iris_hf_2) == dim(iris_hf)) if (requireNamespace("Matrix", quietly=TRUE)) { data <- rep(0, 100) data[(1:10) ^ 2] <- 1:10 * pi m <- matrix(data, ncol = 20, byrow = TRUE) m <- Matrix::Matrix(m, sparse = TRUE) m_hf <- as.h2o(m) stopifnot(is.h2o(m_hf), dim(m_hf) == dim(m)) } ## End(Not run)## Not run: library(h2o) h2o.init() iris_hf <- as.h2o(iris) euro_hf <- as.h2o(euro) letters_hf <- as.h2o(letters) state_hf <- as.h2o(state.x77) iris_hf_2 <- as.h2o(iris_hf) stopifnot(is.h2o(iris_hf), dim(iris_hf) == dim(iris), is.h2o(euro_hf), dim(euro_hf) == c(length(euro), 1L), is.h2o(letters_hf), dim(letters_hf) == c(length(letters), 1L), is.h2o(state_hf), dim(state_hf) == dim(state.x77), is.h2o(iris_hf_2), dim(iris_hf_2) == dim(iris_hf)) if (requireNamespace("Matrix", quietly=TRUE)) { data <- rep(0, 100) data[(1:10) ^ 2] <- 1:10 * pi m <- matrix(data, ncol = 20, byrow = TRUE) m <- Matrix::Matrix(m, sparse = TRUE) m_hf <- as.h2o(m) stopifnot(is.h2o(m_hf), dim(m_hf) == dim(m)) } ## End(Not run)
Convert an H2OFrame to a matrix
## S3 method for class 'H2OFrame' as.matrix(x, ...)## S3 method for class 'H2OFrame' as.matrix(x, ...)
x |
An H2OFrame object |
... |
Further arguments to be passed down from other methods. |
## Not run: library(h2o) h2o.init() iris_hf <- as.h2o(iris) describe <- h2o.describe(iris_hf) mins = as.matrix(apply(iris_hf, 2, min)) print(mins) ## End(Not run)## Not run: library(h2o) h2o.init() iris_hf <- as.h2o(iris) describe <- h2o.describe(iris_hf) mins = as.matrix(apply(iris_hf, 2, min)) print(mins) ## End(Not run)
Converts an H2O column into a numeric value column. If the column type is enum and you want to convert it to numeric, you should first convert it to character then convert it to numeric. Otherwise, the values may be converted to underlying factor values, not the expected mapped values.
as.numeric(x)as.numeric(x)
x |
a column from an H2OFrame data set. |
## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(path = prostate_path) prostate[, 2] <- as.factor (prostate[, 2]) prostate[, 2] <- as.numeric(prostate[, 2]) ## End(Not run)## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(path = prostate_path) prostate[, 2] <- as.factor (prostate[, 2]) prostate[, 2] <- as.numeric(prostate[, 2]) ## End(Not run)
Convert an H2OFrame to a vector
## S3 method for class 'H2OFrame' as.vector(x,mode)## S3 method for class 'H2OFrame' as.vector(x,mode)
x |
An H2OFrame object |
mode |
Mode to coerce vector to |
## Not run: library(h2o) h2o.init() iris_hf <- as.h2o(iris) cor_R <- cor(as.matrix(iris[, 1])) cor_h2o <- cor(iris_hf[, 1]) iris_R_cor <- cor(iris[, 1:4]) iris_H2O_cor <- as.data.frame(cor(iris_hf[, 1:4])) h2o_vec <- as.vector(unlist(iris_H2O_cor)) r_vec <- as.vector(unlist(iris_R_cor)) ## End(Not run)## Not run: library(h2o) h2o.init() iris_hf <- as.h2o(iris) cor_R <- cor(as.matrix(iris[, 1])) cor_h2o <- cor(iris_hf[, 1]) iris_R_cor <- cor(iris[, 1:4]) iris_H2O_cor <- as.data.frame(cor(iris_hf[, 1:4])) h2o_vec <- as.vector(unlist(iris_H2O_cor)) r_vec <- as.vector(unlist(iris_R_cor)) ## End(Not run)
Temperature, soil moisture, runoff, and other environmental measurements from the Australia coast. The data is available from https://cs.colby.edu/courses/S11/cs251/labs/lab07/AustraliaSubset.csv.
A data frame with 251 rows and 8 columns
Works like match.arg but ignores case
case_insensitive_match_arg(arg, choices)case_insensitive_match_arg(arg, choices)
arg |
argument to match that should be declared as a character vector containing possible values |
choices |
argument to choose from (OPTIONAL) |
matched arg
Returns the column names of an H2OFrame
colnames(x, do.NULL = TRUE, prefix = "col")colnames(x, do.NULL = TRUE, prefix = "col")
x |
An H2OFrame object. |
do.NULL |
logical. If FALSE and names are NULL, names are created. |
prefix |
for created names. |
## Not run: library(h2o) h2o.init() iris_hf <- as.h2o(iris) colnames(iris_hf) # Returns "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" "Species" ## End(Not run)## Not run: library(h2o) h2o.init() iris_hf <- as.h2o(iris) colnames(iris_hf) # Returns "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" "Species" ## End(Not run)
Returns the number of rows and columns for an H2OFrame object.
## S3 method for class 'H2OFrame' dim(x)## S3 method for class 'H2OFrame' dim(x)
x |
An H2OFrame object. |
dim for the base R method.
## Not run: library(h2o) h2o.init() iris_hf <- as.h2o(iris) dim(iris_hf) ## End(Not run)## Not run: library(h2o) h2o.init() iris_hf <- as.h2o(iris) dim(iris_hf) ## End(Not run)
Set column names of an H2O Frame
## S3 method for class 'H2OFrame' dimnames(x)## S3 method for class 'H2OFrame' dimnames(x)
x |
An H2OFrame |
## Not run: library(h2o) h2o.init() n <- 2000 # Generate variables V1, ... V10 X <- matrix(rnorm(10 * n), n, 10) # y = +1 if sum_i x_{ij}^2 > chisq median on 10 df y <- rep(-1, n) y[apply(X*X, 1, sum) > qchisq(.5, 10)] <- 1 # Assign names to the columns of X: dimnames(X)[[2]] <- c("V1", "V2", "V3", "V4", "V5", "V6", "V7", "V8", "V9", "V10") ## End(Not run)## Not run: library(h2o) h2o.init() n <- 2000 # Generate variables V1, ... V10 X <- matrix(rnorm(10 * n), n, 10) # y = +1 if sum_i x_{ij}^2 > chisq median on 10 df y <- rep(-1, n) y[apply(X*X, 1, sum) > qchisq(.5, 10)] <- 1 # Assign names to the columns of X: dimnames(X)[[2]] <- c("V1", "V2", "V3", "V4", "V5", "V6", "V7", "V8", "V9", "V10") ## End(Not run)
Retrieve the number of occurrences of each feature for given observations Available for GBM, Random Forest and Isolation Forest models.
feature_frequencies.H2OModel(object, newdata, ...) h2o.feature_frequencies(object, newdata, ...)feature_frequencies.H2OModel(object, newdata, ...) h2o.feature_frequencies(object, newdata, ...)
object |
a fitted H2OModel object for which prediction is desired |
newdata |
An H2OFrame object in which to look for variables with which to predict. |
... |
additional arguments to pass on. |
Returns an H2OFrame contain per-feature frequencies on the predict path for each input row.
h2o.gbm and h2o.randomForest for model
generation in h2o.
CHeck to see if the column names/indices entered is valid for the dataframe given. This is an internal function
generate_col_ind(data, by)generate_col_ind(data, by)
data |
The H2OFrame whose column names or indices are entered as a list |
by |
The column names/indices in a list. |
Get the seed from H2OModel which was used during training. If a user does not set the seed parameter before training, the seed is autogenerated. It returns seed as the string if the value is bigger than the integer. For example, an autogenerated seed is always long so that the seed in R is a string.
get_seed.H2OModel(object) h2o.get_seed(object)get_seed.H2OModel(object) h2o.get_seed(object)
object |
a fitted H2OModel object. |
Returns seed to be used during training a model. Could be numeric or string.
## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(path = prostate_path) prostate$CAPSULE <- as.factor(prostate$CAPSULE) prostate_gbm <- h2o.gbm(3:9, "CAPSULE", prostate) seed <- h2o.get_seed(prostate_gbm) ## End(Not run)## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(path = prostate_path) prostate$CAPSULE <- as.factor(prostate$CAPSULE) prostate_gbm <- h2o.gbm(3:9, "CAPSULE", prostate) seed <- h2o.get_seed(prostate_gbm) ## End(Not run)
Compute the absolute value of x
h2o.abs(x)h2o.abs(x)
x |
An H2OFrame object. |
MathFun for the base R implementation, abs().
## Not run: library(h2o) h2o.init() url <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/smtrees.csv" smtrees_hf <- h2o.importFile(url) smtrees_df <- read.csv( "https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/smtrees.csv") model <- h2o.gbm(x = c("girth", "height"), y = "vol", ntrees = 3, max_depth = 1, distribution = "gaussian", min_rows = 2, learn_rate = .1, training_frame = smtrees_hf) pred <- as.data.frame(predict(model, newdata = smtrees_hf)) diff <- pred - smtrees_df[, 4] diff_abs <- abs(diff) print(diff_abs) ## End(Not run)## Not run: library(h2o) h2o.init() url <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/smtrees.csv" smtrees_hf <- h2o.importFile(url) smtrees_df <- read.csv( "https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/smtrees.csv") model <- h2o.gbm(x = c("girth", "height"), y = "vol", ntrees = 3, max_depth = 1, distribution = "gaussian", min_rows = 2, learn_rate = .1, training_frame = smtrees_hf) pred <- as.data.frame(predict(model, newdata = smtrees_hf)) diff <- pred - smtrees_df[, 4] diff_abs <- abs(diff) print(diff_abs) ## End(Not run)
Compute the arc cosine of x
h2o.acos(x)h2o.acos(x)
x |
An H2OFrame object. |
Trig for the base R implementation, acos().
## Not run: h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(path = prostate_path) h2o.acos(prostate[, 2]) ## End(Not run)## Not run: h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(path = prostate_path) h2o.acos(prostate[, 2]) ## End(Not run)
Builds an AdaBoost model on an H2OFrame.
h2o.adaBoost( x, y, training_frame, model_id = NULL, ignore_const_cols = TRUE, categorical_encoding = c("AUTO", "Enum", "OneHotInternal", "OneHotExplicit", "Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited"), weights_column = NULL, nlearners = 50, weak_learner = c("AUTO", "DRF", "GLM", "GBM", "DEEP_LEARNING"), learn_rate = 0.5, weak_learner_params = NULL, seed = -1 )h2o.adaBoost( x, y, training_frame, model_id = NULL, ignore_const_cols = TRUE, categorical_encoding = c("AUTO", "Enum", "OneHotInternal", "OneHotExplicit", "Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited"), weights_column = NULL, nlearners = 50, weak_learner = c("AUTO", "DRF", "GLM", "GBM", "DEEP_LEARNING"), learn_rate = 0.5, weak_learner_params = NULL, seed = -1 )
x |
(Optional) A vector containing the names or indices of the predictor variables to use in building the model. If x is missing, then all columns except y are used. |
y |
The name or column index of the response variable in the data. The response must be either a numeric or a categorical/factor variable. If the response is numeric, then a regression model will be trained, otherwise it will train a classification model. |
training_frame |
Id of the training data frame. |
model_id |
Destination id for this model; auto-generated if not specified. |
ignore_const_cols |
|
categorical_encoding |
Encoding scheme for categorical features Must be one of: "AUTO", "Enum", "OneHotInternal", "OneHotExplicit", "Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited". Defaults to AUTO. |
weights_column |
Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0. |
nlearners |
Number of AdaBoost weak learners. Defaults to 50. |
weak_learner |
Choose a weak learner type. Defaults to AUTO, which means DRF. Must be one of: "AUTO", "DRF", "GLM", "GBM", "DEEP_LEARNING". Defaults to AUTO. |
learn_rate |
Learning rate (from 0.0 to 1.0) Defaults to 0.5. |
weak_learner_params |
Customized parameters for the weak_learner algorithm. E.g list(ntrees=3, max_depth=2, histogram_type='UniformAdaptive')) |
seed |
Seed for random numbers (affects certain parts of the algo that are stochastic and those might or might not be enabled by default). Defaults to -1 (time-based random number). |
Creates a H2OModel object of the right type.
predict.H2OModel for prediction
## Not run: library(h2o) h2o.init() # Import the airlines dataset f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv" data <- h2o.importFile(f) # Set predictors and response; set response as a factor data["CAPSULE"] <- as.factor(data["CAPSULE"]) predictors <- c("AGE","RACE","DPROS","DCAPS","PSA","VOL","GLEASON") response <- "CAPSULE" # Train the AdaBoost model h2o_adaboost <- h2o.adaBoost(x = predictors, y = response, training_frame = data, seed = 1234) ## End(Not run)## Not run: library(h2o) h2o.init() # Import the airlines dataset f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv" data <- h2o.importFile(f) # Set predictors and response; set response as a factor data["CAPSULE"] <- as.factor(data["CAPSULE"]) predictors <- c("AGE","RACE","DPROS","DCAPS","PSA","VOL","GLEASON") response <- "CAPSULE" # Train the AdaBoost model h2o_adaboost <- h2o.adaBoost(x = predictors, y = response, training_frame = data, seed = 1234) ## End(Not run)
Retrieves the AECU value from an H2OBinomialUpliftMetrics. You need to specificy the type of AECU using metric parameter. Defaults "qini". Qini AECU equals the Qini value. If "train" and "valid" parameters are FALSE (default), then the training AECU value is returned. If more than one parameter is set to TRUE, then a named vector of AECUs are returned, where the names are "train", "valid".
h2o.aecu(object, train = FALSE, valid = FALSE, metric = "qini")h2o.aecu(object, train = FALSE, valid = FALSE, metric = "qini")
object |
|
train |
Retrieve the training AECU |
valid |
Retrieve the validation AECU |
metric |
Specify metric of AECU. Posibilities are "qini", "lift", "gain", defaults "qini". |
## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/uplift/criteo_uplift_13k.csv" train <- h2o.importFile(f) train$treatment <- as.factor(train$treatment) train$conversion <- as.factor(train$conversion) model <- h2o.upliftRandomForest(training_frame=train, x=sprintf("f%s",seq(0:10)), y="conversion", ntrees=10, max_depth=5, treatment_column="treatment", auuc_type="AUTO") perf <- h2o.performance(model, train=TRUE) h2o.aecu(perf) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/uplift/criteo_uplift_13k.csv" train <- h2o.importFile(f) train$treatment <- as.factor(train$treatment) train$conversion <- as.factor(train$conversion) model <- h2o.upliftRandomForest(training_frame=train, x=sprintf("f%s",seq(0:10)), y="conversion", ntrees=10, max_depth=5, treatment_column="treatment", auuc_type="AUTO") perf <- h2o.performance(model, train=TRUE) h2o.aecu(perf) ## End(Not run)
Retrieves the all types of AECU value in a table from an H2OBinomialUpliftMetrics. If "train" and "valid" parameters are FALSE (default), then the training AECU values are returned. If more than one parameter is set to TRUE, then a named vector of AECU values are returned, where the names are "train", "valid".
h2o.aecu_table(object, train = FALSE, valid = FALSE)h2o.aecu_table(object, train = FALSE, valid = FALSE)
object |
|
train |
Retrieve the training AECU values table |
valid |
Retrieve the validation AECU values table |
## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/uplift/criteo_uplift_13k.csv" train <- h2o.importFile(f) train$treatment <- as.factor(train$treatment) train$conversion <- as.factor(train$conversion) model <- h2o.upliftRandomForest(training_frame=train, x=sprintf("f%s",seq(0:10)), y="conversion", ntrees=10, max_depth=5, treatment_column="treatment", auuc_type="AUTO") perf <- h2o.performance(model, train=TRUE) h2o.aecu_table(perf) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/uplift/criteo_uplift_13k.csv" train <- h2o.importFile(f) train$treatment <- as.factor(train$treatment) train$conversion <- as.factor(train$conversion) model <- h2o.upliftRandomForest(training_frame=train, x=sprintf("f%s",seq(0:10)), y="conversion", ntrees=10, max_depth=5, treatment_column="treatment", auuc_type="AUTO") perf <- h2o.performance(model, train=TRUE) h2o.aecu_table(perf) ## End(Not run)
Retrieve an aggregated frame from the Aggregator model and use it to create a new frame.
h2o.aggregated_frame(model)h2o.aggregated_frame(model)
model |
an H2OClusteringModel corresponding from a |
## Not run: library(h2o) h2o.init() df <- h2o.createFrame(rows = 100, cols = 5, categorical_fraction = 0.6, integer_fraction = 0, binary_fraction = 0, real_range = 100, integer_range = 100, missing_fraction = 0) target_num_exemplars = 1000 rel_tol_num_exemplars = 0.5 encoding = "Eigen" agg <- h2o.aggregator(training_frame = df, target_num_exemplars = target_num_exemplars, rel_tol_num_exemplars = rel_tol_num_exemplars, categorical_encoding = encoding) # Use the aggregated frame to create a new dataframe new_df <- h2o.aggregated_frame(agg) ## End(Not run)## Not run: library(h2o) h2o.init() df <- h2o.createFrame(rows = 100, cols = 5, categorical_fraction = 0.6, integer_fraction = 0, binary_fraction = 0, real_range = 100, integer_range = 100, missing_fraction = 0) target_num_exemplars = 1000 rel_tol_num_exemplars = 0.5 encoding = "Eigen" agg <- h2o.aggregator(training_frame = df, target_num_exemplars = target_num_exemplars, rel_tol_num_exemplars = rel_tol_num_exemplars, categorical_encoding = encoding) # Use the aggregated frame to create a new dataframe new_df <- h2o.aggregated_frame(agg) ## End(Not run)
Builds an Aggregated Frame of an H2OFrame.
h2o.aggregator( training_frame, x, model_id = NULL, ignore_const_cols = TRUE, target_num_exemplars = 5000, rel_tol_num_exemplars = 0.5, transform = c("NONE", "STANDARDIZE", "NORMALIZE", "DEMEAN", "DESCALE"), categorical_encoding = c("AUTO", "Enum", "OneHotInternal", "OneHotExplicit", "Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited"), save_mapping_frame = FALSE, num_iteration_without_new_exemplar = 500, export_checkpoints_dir = NULL )h2o.aggregator( training_frame, x, model_id = NULL, ignore_const_cols = TRUE, target_num_exemplars = 5000, rel_tol_num_exemplars = 0.5, transform = c("NONE", "STANDARDIZE", "NORMALIZE", "DEMEAN", "DESCALE"), categorical_encoding = c("AUTO", "Enum", "OneHotInternal", "OneHotExplicit", "Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited"), save_mapping_frame = FALSE, num_iteration_without_new_exemplar = 500, export_checkpoints_dir = NULL )
training_frame |
Id of the training data frame. |
x |
A vector containing the |
model_id |
Destination id for this model; auto-generated if not specified. |
ignore_const_cols |
|
target_num_exemplars |
Targeted number of exemplars Defaults to 5000. |
rel_tol_num_exemplars |
Relative tolerance for number of exemplars (e.g, 0.5 is +/- 50 percents) Defaults to 0.5. |
transform |
Transformation of training data Must be one of: "NONE", "STANDARDIZE", "NORMALIZE", "DEMEAN", "DESCALE". Defaults to NORMALIZE. |
categorical_encoding |
Encoding scheme for categorical features Must be one of: "AUTO", "Enum", "OneHotInternal", "OneHotExplicit", "Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited". Defaults to AUTO. |
save_mapping_frame |
|
num_iteration_without_new_exemplar |
The number of iterations to run before aggregator exits if the number of exemplars collected didn't change Defaults to 500. |
export_checkpoints_dir |
Automatically export generated models to this directory. |
## Not run: library(h2o) h2o.init() df <- h2o.createFrame(rows = 100, cols = 5, categorical_fraction = 0.6, integer_fraction = 0, binary_fraction = 0, real_range = 100, integer_range = 100, missing_fraction = 0) target_num_exemplars = 1000 rel_tol_num_exemplars = 0.5 encoding = "Eigen" agg <- h2o.aggregator(training_frame = df, target_num_exemplars = target_num_exemplars, rel_tol_num_exemplars = rel_tol_num_exemplars, categorical_encoding = encoding) ## End(Not run)## Not run: library(h2o) h2o.init() df <- h2o.createFrame(rows = 100, cols = 5, categorical_fraction = 0.6, integer_fraction = 0, binary_fraction = 0, real_range = 100, integer_range = 100, missing_fraction = 0) target_num_exemplars = 1000 rel_tol_num_exemplars = 0.5 encoding = "Eigen" agg <- h2o.aggregator(training_frame = df, target_num_exemplars = target_num_exemplars, rel_tol_num_exemplars = rel_tol_num_exemplars, categorical_encoding = encoding) ## End(Not run)
Retrieves the AIC value. If "train", "valid", and "xval" parameters are FALSE (default), then the training AIC value is returned. If more than one parameter is set to TRUE, then a named vector of AICs are returned, where the names are "train", "valid" or "xval".
h2o.aic(object, train = FALSE, valid = FALSE, xval = FALSE)h2o.aic(object, train = FALSE, valid = FALSE, xval = FALSE)
object |
An H2OModel or H2OModelMetrics. |
train |
Retrieve the training AIC |
valid |
Retrieve the validation AIC |
xval |
Retrieve the cross-validation AIC |
## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(path = prostate_path) p_sid <- h2o.runif(prostate) prostate_train <- prostate[p_sid > .2,] prostate_glm <- h2o.glm(x = 3:7, y = 2, training_frame = prostate_train) aic_basic <- h2o.aic(prostate_glm) print(aic_basic) ## End(Not run)## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(path = prostate_path) p_sid <- h2o.runif(prostate) prostate_train <- prostate[p_sid > .2,] prostate_glm <- h2o.glm(x = 3:7, y = 2, training_frame = prostate_train) aic_basic <- h2o.aic(prostate_glm) print(aic_basic) ## End(Not run)
Given a set of logical vectors, are all of the values true?
h2o.all(x)h2o.all(x)
x |
An H2OFrame object. |
all for the base R implementation.
Detect anomalies in an H2O dataset using an H2O deep learning model with auto-encoding.
h2o.anomaly(object, data, per_feature = FALSE)h2o.anomaly(object, data, per_feature = FALSE)
object |
An H2OAutoEncoderModel object that represents the model to be used for anomaly detection. |
data |
An H2OFrame object. |
per_feature |
Whether to return the per-feature squared reconstruction error |
Returns an H2OFrame object containing the reconstruction MSE or the per-feature squared error.
h2o.deeplearning for making an H2OAutoEncoderModel.
## Not run: library(h2o) h2o.init() prostate_path = system.file("extdata", "prostate.csv", package = "h2o") prostate = h2o.importFile(path = prostate_path) prostate_dl = h2o.deeplearning(x = 3:9, training_frame = prostate, autoencoder = TRUE, hidden = c(10, 10), epochs = 5, seed = 1) prostate_anon = h2o.anomaly(prostate_dl, prostate) head(prostate_anon) prostate_anon_per_feature = h2o.anomaly(prostate_dl, prostate, per_feature = TRUE) head(prostate_anon_per_feature) ## End(Not run)## Not run: library(h2o) h2o.init() prostate_path = system.file("extdata", "prostate.csv", package = "h2o") prostate = h2o.importFile(path = prostate_path) prostate_dl = h2o.deeplearning(x = 3:9, training_frame = prostate, autoencoder = TRUE, hidden = c(10, 10), epochs = 5, seed = 1) prostate_anon = h2o.anomaly(prostate_dl, prostate) head(prostate_anon) prostate_anon_per_feature = h2o.anomaly(prostate_dl, prostate, per_feature = TRUE) head(prostate_anon_per_feature) ## End(Not run)
H2O ANOVAGLM is used to calculate Type III SS which is used to evaluate the contributions of individual predictors and their interactions to a model. Predictors or interactions with negligible contributions to the model will have high p-values while those with more contributions will have low p-values.
h2o.anovaglm( x, y, training_frame, model_id = NULL, seed = -1, ignore_const_cols = TRUE, score_each_iteration = FALSE, offset_column = NULL, weights_column = NULL, family = c("AUTO", "gaussian", "binomial", "fractionalbinomial", "quasibinomial", "poisson", "gamma", "tweedie", "negativebinomial"), tweedie_variance_power = 0, tweedie_link_power = 1, theta = 0, solver = c("AUTO", "IRLSM", "L_BFGS", "COORDINATE_DESCENT_NAIVE", "COORDINATE_DESCENT", "GRADIENT_DESCENT_LH", "GRADIENT_DESCENT_SQERR"), missing_values_handling = c("MeanImputation", "Skip", "PlugValues"), plug_values = NULL, compute_p_values = TRUE, standardize = TRUE, non_negative = FALSE, max_iterations = 0, link = c("family_default", "identity", "logit", "log", "inverse", "tweedie", "ologit"), prior = 0, alpha = NULL, lambda = c(0), lambda_search = FALSE, stopping_rounds = 0, stopping_metric = c("AUTO", "deviance", "logloss", "MSE", "RMSE", "MAE", "RMSLE", "AUC", "AUCPR", "lift_top_group", "misclassification", "mean_per_class_error", "custom", "custom_increasing"), early_stopping = FALSE, stopping_tolerance = 0.001, balance_classes = FALSE, class_sampling_factors = NULL, max_after_balance_size = 5, max_runtime_secs = 0, save_transformed_framekeys = FALSE, highest_interaction_term = 0, nparallelism = 4, type = 0 )h2o.anovaglm( x, y, training_frame, model_id = NULL, seed = -1, ignore_const_cols = TRUE, score_each_iteration = FALSE, offset_column = NULL, weights_column = NULL, family = c("AUTO", "gaussian", "binomial", "fractionalbinomial", "quasibinomial", "poisson", "gamma", "tweedie", "negativebinomial"), tweedie_variance_power = 0, tweedie_link_power = 1, theta = 0, solver = c("AUTO", "IRLSM", "L_BFGS", "COORDINATE_DESCENT_NAIVE", "COORDINATE_DESCENT", "GRADIENT_DESCENT_LH", "GRADIENT_DESCENT_SQERR"), missing_values_handling = c("MeanImputation", "Skip", "PlugValues"), plug_values = NULL, compute_p_values = TRUE, standardize = TRUE, non_negative = FALSE, max_iterations = 0, link = c("family_default", "identity", "logit", "log", "inverse", "tweedie", "ologit"), prior = 0, alpha = NULL, lambda = c(0), lambda_search = FALSE, stopping_rounds = 0, stopping_metric = c("AUTO", "deviance", "logloss", "MSE", "RMSE", "MAE", "RMSLE", "AUC", "AUCPR", "lift_top_group", "misclassification", "mean_per_class_error", "custom", "custom_increasing"), early_stopping = FALSE, stopping_tolerance = 0.001, balance_classes = FALSE, class_sampling_factors = NULL, max_after_balance_size = 5, max_runtime_secs = 0, save_transformed_framekeys = FALSE, highest_interaction_term = 0, nparallelism = 4, type = 0 )
x |
(Optional) A vector containing the names or indices of the predictor variables to use in building the model. If x is missing, then all columns except y are used. |
y |
The name or column index of the response variable in the data. The response must be either a numeric or a categorical/factor variable. If the response is numeric, then a regression model will be trained, otherwise it will train a classification model. |
training_frame |
Id of the training data frame. |
model_id |
Destination id for this model; auto-generated if not specified. |
seed |
Seed for random numbers (affects certain parts of the algo that are stochastic and those might or might not be enabled by default). Defaults to -1 (time-based random number). |
ignore_const_cols |
|
score_each_iteration |
|
offset_column |
Offset column. This will be added to the combination of columns before applying the link function. |
weights_column |
Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0. |
family |
Family. Use binomial for classification with logistic regression, others are for regression problems. Must be one of: "AUTO", "gaussian", "binomial", "fractionalbinomial", "quasibinomial", "poisson", "gamma", "tweedie", "negativebinomial". Defaults to AUTO. |
tweedie_variance_power |
Tweedie variance power Defaults to 0. |
tweedie_link_power |
Tweedie link power Defaults to 1. |
theta |
Theta Defaults to 0. |
solver |
AUTO will set the solver based on given data and the other parameters. IRLSM is fast on on problems with small number of predictors and for lambda-search with L1 penalty, L_BFGS scales better for datasets with many columns. Must be one of: "AUTO", "IRLSM", "L_BFGS", "COORDINATE_DESCENT_NAIVE", "COORDINATE_DESCENT", "GRADIENT_DESCENT_LH", "GRADIENT_DESCENT_SQERR". Defaults to IRLSM. |
missing_values_handling |
Handling of missing values. Either MeanImputation, Skip or PlugValues. Must be one of: "MeanImputation", "Skip", "PlugValues". Defaults to MeanImputation. |
plug_values |
Plug Values (a single row frame containing values that will be used to impute missing values of the training/validation frame, use with conjunction missing_values_handling = PlugValues) |
compute_p_values |
|
standardize |
|
non_negative |
|
max_iterations |
Maximum number of iterations Defaults to 0. |
link |
Link function. Must be one of: "family_default", "identity", "logit", "log", "inverse", "tweedie", "ologit". Defaults to family_default. |
prior |
Prior probability for y==1. To be used only for logistic regression iff the data has been sampled and the mean of response does not reflect reality. Defaults to 0. |
alpha |
Distribution of regularization between the L1 (Lasso) and L2 (Ridge) penalties. A value of 1 for alpha represents Lasso regression, a value of 0 produces Ridge regression, and anything in between specifies the amount of mixing between the two. Default value of alpha is 0 when SOLVER = 'L-BFGS'; 0.5 otherwise. |
lambda |
Regularization strength Defaults to c(0.0). |
lambda_search |
|
stopping_rounds |
Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable) Defaults to 0. |
stopping_metric |
Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client. Must be one of: "AUTO", "deviance", "logloss", "MSE", "RMSE", "MAE", "RMSLE", "AUC", "AUCPR", "lift_top_group", "misclassification", "mean_per_class_error", "custom", "custom_increasing". Defaults to AUTO. |
early_stopping |
|
stopping_tolerance |
Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much) Defaults to 0.001. |
balance_classes |
|
class_sampling_factors |
Desired over/under-sampling ratios per class (in lexicographic order). If not specified, sampling factors will be automatically computed to obtain class balance during training. Requires balance_classes. |
max_after_balance_size |
Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires balance_classes. Defaults to 5.0. |
max_runtime_secs |
Maximum allowed runtime in seconds for model training. Use 0 to disable. Defaults to 0. |
save_transformed_framekeys |
|
highest_interaction_term |
Limit the number of interaction terms, if 2 means interaction between 2 columns only, 3 for three columns and so on... Default to 2. Defaults to 0. |
nparallelism |
Number of models to build in parallel. Default to 4. Adjust according to your system. Defaults to 4. |
type |
Refer to the SS type 1, 2, 3, or 4. We are currently only supporting 3 Defaults to 0. |
## Not run: h2o.init() # Run ANOVA GLM of VOL ~ CAPSULE + RACE prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(path = prostate_path) prostate$CAPSULE <- as.factor(prostate$CAPSULE) model <- h2o.anovaglm(y = "VOL", x = c("CAPSULE","RACE"), training_frame = prostate) ## End(Not run)## Not run: h2o.init() # Run ANOVA GLM of VOL ~ CAPSULE + RACE prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(path = prostate_path) prostate$CAPSULE <- as.factor(prostate$CAPSULE) model <- h2o.anovaglm(y = "VOL", x = c("CAPSULE","RACE"), training_frame = prostate) ## End(Not run)
Given a set of logical vectors, is at least one of the values true?
h2o.any(x)h2o.any(x)
x |
An H2OFrame object. |
all for the base R implementation.
## Not run: library(h2o) h2o.init() f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_train.csv" iris <- h2o.importFile(f) h2o.any(iris[, 1] < 1000) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_train.csv" iris <- h2o.importFile(f) h2o.any(iris[, 1] < 1000) ## End(Not run)
Determines if any column of an H2OFrame object contains categorical data.
h2o.anyFactor(x)h2o.anyFactor(x)
x |
An |
Returns a logical value indicating whether any of the columns in x are factors.
## Not run: library(h2o) h2o.init() iris_hf <- as.h2o(iris) h2o.anyFactor(iris_hf) ## End(Not run)## Not run: library(h2o) h2o.init() iris_hf <- as.h2o(iris) h2o.anyFactor(iris_hf) ## End(Not run)
This function is mostly for internal purposes, but may occasionally be useful for direct access to the backend H2O server.
It has same parameters as :meth:H2OConnection.request <h2o.backend.H2OConnection.request>.
h2o.api(endpoint, params = NULL, json = NULL)h2o.api(endpoint, params = NULL, json = NULL)
endpoint |
A H2O REST API endpoint. |
params |
A list of params passed in the url. |
json |
A list of params passed as a json payload. |
REST API endpoints can be obtained using:
endpoints <- sapply(h2o.api("GET /3/Metadata/endpoints")$routes, function(r) paste(r$http_method, r$url_pattern))
For a given route, the supported params can be otained using:
parameters <- sapply(h2o.api("GET /3/Metadata/schemas/{route$input_schema}")$schemas[[1]]$fields, function(f) { l <-list(); l[f$name] <- f$help; l })
The parsed response.
## Not run: res <- h2o.api("GET /3/NetworkTest") res$table ## End(Not run)## Not run: res <- h2o.api("GET /3/NetworkTest") res$table ## End(Not run)
Sorts H2OFrame by the columns specified. H2OFrame can contain String columns but should not sort on any String columns. Otherwise, an error will be thrown. To sort column c1 in descending order, do desc(c1). Returns a new H2OFrame, like dplyr::arrange.
h2o.arrange(x, ...)h2o.arrange(x, ...)
x |
The H2OFrame input to be sorted. |
... |
The column names to sort by. |
## Not run: library(h2o) h2o.init() f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_train.csv" iris <- h2o.importFile(f) h2o.arrange(iris, "species","petal_len","petal_wid") ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_train.csv" iris <- h2o.importFile(f) h2o.arrange(iris, "species","petal_len","petal_wid") ## End(Not run)
Functions to convert between character representations and objects of class "Date" representing calendar dates.
h2o.as_date(x, format, ...)h2o.as_date(x, format, ...)
x |
H2OFrame column of strings or factors to be converted |
format |
A character string indicating date pattern |
... |
Further arguments to be passed from or to other methods. |
## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/jira/v-11-eurodate.csv" hdf <- h2o.importFile(f) h2o.as_date(hdf["ds5"], "%d.%m.%y %H:%M") ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/jira/v-11-eurodate.csv" hdf <- h2o.importFile(f) h2o.as_date(hdf["ds5"], "%d.%m.%y %H:%M") ## End(Not run)
Convert H2O Data to Characters
h2o.ascharacter(x)h2o.ascharacter(x)
x |
An H2OFrame object. |
## Not run: library(h2o) h2o.init() f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_train.csv" iris <- h2o.importFile(f) h2o.ascharacter(iris["species"]) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_train.csv" iris <- h2o.importFile(f) h2o.ascharacter(iris["species"]) ## End(Not run)
Convert H2O Data to Factors
h2o.asfactor(x)h2o.asfactor(x)
x |
An H2OFrame object. |
factor for the base R implementation, as.factor().
## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv" cars <- h2o.importFile(f) h2o.asfactor(cars["cylinders"]) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv" cars <- h2o.importFile(f) h2o.asfactor(cars["cylinders"]) ## End(Not run)
If the column type is enum and you want to convert it to numeric, you should first convert it to character then convert it to numeric. Otherwise, the values may be converted to underlying factor values, not the expected mapped values.
h2o.asnumeric(x)h2o.asnumeric(x)
x |
An H2OFrame object. |
numeric for the base R implementation, as.numeric().
## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv" cars <- h2o.importFile(f) h2o.ascharacter(cars) h2o.asnumeric(cars) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv" cars <- h2o.importFile(f) h2o.ascharacter(cars) h2o.asnumeric(cars) ## End(Not run)
Makes a copy of the data frame and gives it the desired key.
h2o.assign(data, key)h2o.assign(data, key)
data |
An H2OFrame object |
key |
The key to be associated with the H2O parsed data object |
## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv" cars <- h2o.importFile(f) dim(cars) split <- h2o.splitFrame(data = cars, ratios = 0.8) train <- h2o.assign(split[[1]], key = "train") test <- h2o.assign(split[[2]], key = "test") dim(train) dim(test) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv" cars <- h2o.importFile(f) dim(cars) split <- h2o.splitFrame(data = cars, ratios = 0.8) train <- h2o.assign(split[[1]], key = "train") test <- h2o.assign(split[[2]], key = "test") dim(train) dim(test) ## End(Not run)
Retrieves ATC from an H2OBinomialUpliftMetrics. If "train" and "valid" parameters are FALSE (default), then the training ATC is returned. If more than one parameter is set to TRUE, then a named vector of ATC values are returned, where the names are "train", "valid".
h2o.atc(object, train = FALSE, valid = FALSE)h2o.atc(object, train = FALSE, valid = FALSE)
object |
An H2OBinomialUpliftMetrics or |
train |
Retrieve the training ATC value |
valid |
Retrieve the validation ATC value |
## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/uplift/criteo_uplift_13k.csv" train <- h2o.importFile(f) train$treatment <- as.factor(train$treatment) train$conversion <- as.factor(train$conversion) model <- h2o.upliftRandomForest(training_frame=train, x=sprintf("f%s",seq(0:10)), y="conversion", ntrees=10, max_depth=5, treatment_column="treatment", auuc_type="AUTO") perf <- h2o.performance(model, train=TRUE) h2o.atc(perf) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/uplift/criteo_uplift_13k.csv" train <- h2o.importFile(f) train$treatment <- as.factor(train$treatment) train$conversion <- as.factor(train$conversion) model <- h2o.upliftRandomForest(training_frame=train, x=sprintf("f%s",seq(0:10)), y="conversion", ntrees=10, max_depth=5, treatment_column="treatment", auuc_type="AUTO") perf <- h2o.performance(model, train=TRUE) h2o.atc(perf) ## End(Not run)
Retrieves ATE from an H2OBinomialUpliftMetrics. If "train" and "valid" parameters are FALSE (default), then the training ATE is returned. If more than one parameter is set to TRUE, then a named vector of ATE values are returned, where the names are "train", "valid".
h2o.ate(object, train = FALSE, valid = FALSE)h2o.ate(object, train = FALSE, valid = FALSE)
object |
An H2OBinomialUpliftMetrics or |
train |
Retrieve the training ATE value |
valid |
Retrieve the validation ATE value |
## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/uplift/criteo_uplift_13k.csv" train <- h2o.importFile(f) train$treatment <- as.factor(train$treatment) train$conversion <- as.factor(train$conversion) model <- h2o.upliftRandomForest(training_frame=train, x=sprintf("f%s",seq(0:10)), y="conversion", ntrees=10, max_depth=5, treatment_column="treatment", auuc_type="AUTO") perf <- h2o.performance(model, train=TRUE) h2o.ate(perf) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/uplift/criteo_uplift_13k.csv" train <- h2o.importFile(f) train$treatment <- as.factor(train$treatment) train$conversion <- as.factor(train$conversion) model <- h2o.upliftRandomForest(training_frame=train, x=sprintf("f%s",seq(0:10)), y="conversion", ntrees=10, max_depth=5, treatment_column="treatment", auuc_type="AUTO") perf <- h2o.performance(model, train=TRUE) h2o.ate(perf) ## End(Not run)
Retrieves ATE from an H2OBinomialUpliftMetrics. If "train" and "valid" parameters are FALSE (default), then the training ATT is returned. If more than one parameter is set to TRUE, then a named vector of ATT values are returned, where the names are "train", "valid".
h2o.att(object, train = FALSE, valid = FALSE)h2o.att(object, train = FALSE, valid = FALSE)
object |
An H2OBinomialUpliftMetrics or |
train |
Retrieve the training ATT value |
valid |
Retrieve the validation ATT value |
## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/uplift/criteo_uplift_13k.csv" train <- h2o.importFile(f) train$treatment <- as.factor(train$treatment) train$conversion <- as.factor(train$conversion) model <- h2o.upliftRandomForest(training_frame=train, x=sprintf("f%s",seq(0:10)), y="conversion", ntrees=10, max_depth=5, treatment_column="treatment", auuc_type="AUTO") perf <- h2o.performance(model, train=TRUE) h2o.att(perf) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/uplift/criteo_uplift_13k.csv" train <- h2o.importFile(f) train$treatment <- as.factor(train$treatment) train$conversion <- as.factor(train$conversion) model <- h2o.upliftRandomForest(training_frame=train, x=sprintf("f%s",seq(0:10)), y="conversion", ntrees=10, max_depth=5, treatment_column="treatment", auuc_type="AUTO") perf <- h2o.performance(model, train=TRUE) h2o.att(perf) ## End(Not run)
Retrieves the AUC value from an H2OBinomialMetrics. If "train", "valid", and "xval" parameters are FALSE (default), then the training AUC value is returned. If more than one parameter is set to TRUE, then a named vector of AUCs are returned, where the names are "train", "valid" or "xval".
h2o.auc(object, train = FALSE, valid = FALSE, xval = FALSE)h2o.auc(object, train = FALSE, valid = FALSE, xval = FALSE)
object |
An H2OBinomialMetrics or H2OMultinomialMetrics object. |
train |
Retrieve the training AUC |
valid |
Retrieve the validation AUC |
xval |
Retrieve the cross-validation AUC |
h2o.giniCoef for the Gini coefficient,
h2o.mse for MSE, and h2o.metric for the
various threshold metrics. See h2o.performance for
creating H2OModelMetrics objects.
## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(prostate_path) prostate[, 2] <- as.factor(prostate[, 2]) model <- h2o.gbm(x = 3:9, y = 2, training_frame = prostate, distribution = "bernoulli") perf <- h2o.performance(model, prostate) h2o.auc(perf) ## End(Not run)## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(prostate_path) prostate[, 2] <- as.factor(prostate[, 2]) model <- h2o.gbm(x = 3:9, y = 2, training_frame = prostate, distribution = "bernoulli") perf <- h2o.performance(model, prostate) h2o.auc(perf) ## End(Not run)
Retrieves the AUCPR value from an H2OBinomialMetrics. If "train", "valid", and "xval" parameters are FALSE (default), then the training AUCPR value is returned. If more than one parameter is set to TRUE, then a named vector of AUCPRs are returned, where the names are "train", "valid" or "xval".
h2o.aucpr(object, train = FALSE, valid = FALSE, xval = FALSE) h2o.pr_auc(object, train = FALSE, valid = FALSE, xval = FALSE)h2o.aucpr(object, train = FALSE, valid = FALSE, xval = FALSE) h2o.pr_auc(object, train = FALSE, valid = FALSE, xval = FALSE)
object |
An H2OBinomialMetrics object. |
train |
Retrieve the training aucpr |
valid |
Retrieve the validation aucpr |
xval |
Retrieve the cross-validation aucpr |
h2o.giniCoef for the Gini coefficient,
h2o.mse for MSE, and h2o.metric for the
various threshold metrics. See h2o.performance for
creating H2OModelMetrics objects.
## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(prostate_path) prostate[, 2] <- as.factor(prostate[, 2]) model <- h2o.gbm(x = 3:9, y = 2, training_frame = prostate, distribution = "bernoulli") perf <- h2o.performance(model, prostate) h2o.aucpr(perf) ## End(Not run)## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(prostate_path) prostate[, 2] <- as.factor(prostate[, 2]) model <- h2o.gbm(x = 3:9, y = 2, training_frame = prostate, distribution = "bernoulli") perf <- h2o.performance(model, prostate) h2o.aucpr(perf) ## End(Not run)
The Automatic Machine Learning (AutoML) function automates the supervised machine learning model training process. AutoML finds the best model, given a training frame and response, and returns an H2OAutoML object, which contains a leaderboard of all the models that were trained in the process, ranked by a default model performance metric.
h2o.automl( x, y, training_frame, validation_frame = NULL, leaderboard_frame = NULL, blending_frame = NULL, nfolds = -1, fold_column = NULL, weights_column = NULL, balance_classes = FALSE, class_sampling_factors = NULL, max_after_balance_size = 5, max_runtime_secs = NULL, max_runtime_secs_per_model = NULL, max_models = NULL, distribution = c("AUTO", "bernoulli", "ordinal", "multinomial", "gaussian", "poisson", "gamma", "tweedie", "laplace", "quantile", "huber", "custom"), stopping_metric = c("AUTO", "deviance", "logloss", "MSE", "RMSE", "MAE", "RMSLE", "AUC", "AUCPR", "lift_top_group", "misclassification", "mean_per_class_error"), stopping_tolerance = NULL, stopping_rounds = 3, seed = NULL, project_name = NULL, exclude_algos = NULL, include_algos = NULL, modeling_plan = NULL, preprocessing = NULL, exploitation_ratio = -1, monotone_constraints = NULL, keep_cross_validation_predictions = FALSE, keep_cross_validation_models = FALSE, keep_cross_validation_fold_assignment = FALSE, sort_metric = c("AUTO", "deviance", "logloss", "MSE", "RMSE", "MAE", "RMSLE", "AUC", "AUCPR", "mean_per_class_error"), export_checkpoints_dir = NULL, verbosity = "warn", ... )h2o.automl( x, y, training_frame, validation_frame = NULL, leaderboard_frame = NULL, blending_frame = NULL, nfolds = -1, fold_column = NULL, weights_column = NULL, balance_classes = FALSE, class_sampling_factors = NULL, max_after_balance_size = 5, max_runtime_secs = NULL, max_runtime_secs_per_model = NULL, max_models = NULL, distribution = c("AUTO", "bernoulli", "ordinal", "multinomial", "gaussian", "poisson", "gamma", "tweedie", "laplace", "quantile", "huber", "custom"), stopping_metric = c("AUTO", "deviance", "logloss", "MSE", "RMSE", "MAE", "RMSLE", "AUC", "AUCPR", "lift_top_group", "misclassification", "mean_per_class_error"), stopping_tolerance = NULL, stopping_rounds = 3, seed = NULL, project_name = NULL, exclude_algos = NULL, include_algos = NULL, modeling_plan = NULL, preprocessing = NULL, exploitation_ratio = -1, monotone_constraints = NULL, keep_cross_validation_predictions = FALSE, keep_cross_validation_models = FALSE, keep_cross_validation_fold_assignment = FALSE, sort_metric = c("AUTO", "deviance", "logloss", "MSE", "RMSE", "MAE", "RMSLE", "AUC", "AUCPR", "mean_per_class_error"), export_checkpoints_dir = NULL, verbosity = "warn", ... )
x |
A vector containing the names or indices of the predictor variables to use in building the model. If x is missing, then all columns except y are used. |
y |
The name or index of the response variable in the model. For classification, the y column must be a factor, otherwise regression will be performed. Indexes are 1-based in R. |
training_frame |
Training frame (H2OFrame or ID). |
validation_frame |
Validation frame (H2OFrame or ID); Optional. This argument is ignored unless the user sets nfolds = 0. If cross-validation is turned off, then a validation frame can be specified and used for early stopping of individual models and early stopping of the grid searches. By default and when nfolds > 1, cross-validation metrics will be used for early stopping and thus validation_frame will be ignored. |
leaderboard_frame |
Leaderboard frame (H2OFrame or ID); Optional. If provided, the Leaderboard will be scored using this data frame intead of using cross-validation metrics, which is the default. |
blending_frame |
Blending frame (H2OFrame or ID) used to train the the metalearning algorithm in Stacked Ensembles (instead of relying on cross-validated predicted values); Optional.
When provided, it also is recommended to disable cross validation by setting |
nfolds |
Number of folds for k-fold cross-validation. Must be >= 2; defaults to 5. Use 0 to disable cross-validation; this will also disable Stacked Ensemble (thus decreasing the overall model performance). |
fold_column |
Column with cross-validation fold index assignment per observation; used to override the default, randomized, 5-fold cross-validation scheme for individual models in the AutoML run. |
weights_column |
Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. |
balance_classes |
|
class_sampling_factors |
Desired over/under-sampling ratios per class (in lexicographic order). If not specified, sampling factors will
be automatically computed to obtain class balance during training. Requires |
max_after_balance_size |
Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires
|
max_runtime_secs |
This argument specifies the maximum time that the AutoML process will run for.
If both |
max_runtime_secs_per_model |
Maximum runtime in seconds dedicated to each individual model training process. Use 0 to disable. Defaults to 0. Note that models constrained by a time budget are not guaranteed reproducible. |
max_models |
Maximum number of models to build in the AutoML process (does not include Stacked Ensembles). Defaults to NULL (no strict limit). Always set this parameter to ensure AutoML reproducibility: all models are then trained until convergence and none is constrained by a time budget. |
distribution |
Distribution function used by algorithms that support it; other algorithms use their defaults. Possible values: "AUTO", "bernoulli", "multinomial", "gaussian", "poisson", "gamma", "tweedie", "laplace", "quantile", "huber", "custom", and for parameterized distributions list form is used to specify the parameter, e.g., |
stopping_metric |
Metric to use for early stopping ("AUTO" is logloss for classification, deviance for regression). Must be one of "AUTO", "deviance", "logloss", "MSE", "RMSE", "MAE", "RMSLE", "AUC", "AUCPR", "lift_top_group", "misclassification", "mean_per_class_error". Defaults to "AUTO". |
stopping_tolerance |
Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much). This value defaults to 0.001 if the dataset is at least 1 million rows; otherwise it defaults to a bigger value determined by the size of the dataset and the non-NA-rate. In that case, the value is computed as 1/sqrt(nrows * non-NA-rate). |
stopping_rounds |
Integer. Early stopping based on convergence of |
seed |
Integer. Set a seed for reproducibility. AutoML can only guarantee reproducibility if |
project_name |
Character string to identify an AutoML project. Defaults to NULL, which means a project name will be auto-generated. More models can be trained and added to an existing AutoML project by specifying the same project name in multiple calls to the AutoML function (as long as the same training frame is used in subsequent runs). |
exclude_algos |
Vector of character strings naming the algorithms to skip during the model-building phase. An example use is |
include_algos |
Vector of character strings naming the algorithms to restrict to during the model-building phase. This can't be used in combination with |
modeling_plan |
List. The list of modeling steps to be used by the AutoML engine (they may not all get executed, depending on other constraints). Optional (Expert usage only). |
preprocessing |
List. The list of preprocessing steps to run. Only 'target_encoding' is currently supported. |
exploitation_ratio |
The budget ratio (between 0 and 1) dedicated to the exploitation (vs exploration) phase. By default, this is set to AUTO (exploitation_ratio=-1) as this is still experimental; to activate it, it is recommended to try a ratio around 0.1. Note that the current exploitation phase only tries to fine-tune the best XGBoost and the best GBM found during exploration. |
monotone_constraints |
List. A mapping representing monotonic constraints. Use +1 to enforce an increasing constraint and -1 to specify a decreasing constraint. |
keep_cross_validation_predictions |
|
keep_cross_validation_models |
|
keep_cross_validation_fold_assignment |
|
sort_metric |
Metric to sort the leaderboard by. For binomial classification choose between "AUC", "AUCPR", "logloss", "mean_per_class_error", "RMSE", "MSE". For regression choose between "mean_residual_deviance", "RMSE", "MSE", "MAE", and "RMSLE". For multinomial classification choose between "mean_per_class_error", "logloss", "RMSE", "MSE". Default is "AUTO". If set to "AUTO", then "AUC" will be used for binomial classification, "mean_per_class_error" for multinomial classification, and "mean_residual_deviance" for regression. |
export_checkpoints_dir |
(Optional) Path to a directory where every model will be stored in binary form. |
verbosity |
Verbosity of the backend messages printed during training; Optional. Must be one of NULL (live log disabled), "debug", "info", "warn", "error". Defaults to "warn". |
... |
Additional (experimental) arguments to be passed through; Optional. |
AutoML trains several models, cross-validated by default, by using the following available algorithms:
XGBoost
GBM (Gradient Boosting Machine)
GLM (Generalized Linear Model)
DRF (Distributed Random Forest)
XRT (eXtremely Randomized Trees)
DeepLearning (Fully Connected Deep Neural Network)
It also applies HPO on the following algorithms:
XGBoost
GBM
DeepLearning
In some cases, there will not be enough time to complete all the algorithms, so some may be missing from the leaderboard.
Finally, AutoML also trains several Stacked Ensemble models at various stages during the run. Mainly two kinds of Stacked Ensemble models are trained:
one of all available models at time t.
one of only the best models of each kind at time t.
Note that Stacked Ensemble models are trained only if there isn't another stacked ensemble with the same base models.
An H2OAutoML object.
## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.importFile(path = prostate_path, header = TRUE) y <- "CAPSULE" prostate[,y] <- as.factor(prostate[,y]) #convert to factor for classification aml <- h2o.automl(y = y, training_frame = prostate, max_runtime_secs = 30) lb <- h2o.get_leaderboard(aml) head(lb) ## End(Not run)## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.importFile(path = prostate_path, header = TRUE) y <- "CAPSULE" prostate[,y] <- as.factor(prostate[,y]) #convert to factor for classification aml <- h2o.automl(y = y, training_frame = prostate, max_runtime_secs = 30) lb <- h2o.get_leaderboard(aml) head(lb) ## End(Not run)
Retrieves the AUUC value from an H2OBinomialUpliftMetrics. If the metric parameter is "AUTO", the type of AUUC depends on auuc_type which was set before training. If you need specific AUUC, set metric parameter. If "train" and "valid" parameters are FALSE (default), then the training AUUC value is returned. If more than one parameter is set to TRUE, then a named vector of AUUCs are returned, where the names are "train", "valid".
h2o.auuc(object, train = FALSE, valid = FALSE, metric = NULL)h2o.auuc(object, train = FALSE, valid = FALSE, metric = NULL)
object |
|
train |
Retrieve the training AUUC |
valid |
Retrieve the validation AUUC |
metric |
Specify the AUUC metric to get specific AUUC. Possibilities are NULL, "qini", "lift", "gain". |
## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/uplift/criteo_uplift_13k.csv" train <- h2o.importFile(f) train$treatment <- as.factor(train$treatment) train$conversion <- as.factor(train$conversion) model <- h2o.upliftRandomForest(training_frame=train, x=sprintf("f%s",seq(0:10)), y="conversion", ntrees=10, max_depth=5, treatment_column="treatment", auuc_type="AUTO") perf <- h2o.performance(model, train=TRUE) h2o.auuc(perf) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/uplift/criteo_uplift_13k.csv" train <- h2o.importFile(f) train$treatment <- as.factor(train$treatment) train$conversion <- as.factor(train$conversion) model <- h2o.upliftRandomForest(training_frame=train, x=sprintf("f%s",seq(0:10)), y="conversion", ntrees=10, max_depth=5, treatment_column="treatment", auuc_type="AUTO") perf <- h2o.performance(model, train=TRUE) h2o.auuc(perf) ## End(Not run)
Retrieves the AUUC value from an H2OBinomialUpliftMetrics. If the metric parameter is "AUTO", the type of AUUC depends on auuc_type which was set before training. If you need specific normalized AUUC, set metric parameter. If "train" and "valid" parameters are FALSE (default), then the training normalized AUUC value is returned. If more than one parameter is set to TRUE, then a named vector of normalized AUUCs are returned, where the names are "train", "valid".
h2o.auuc_normalized(object, train = FALSE, valid = FALSE, metric = NULL)h2o.auuc_normalized(object, train = FALSE, valid = FALSE, metric = NULL)
object |
|
train |
Retrieve the training AUUC |
valid |
Retrieve the validation AUUC |
metric |
Specify the AUUC metric to get specific AUUC. Possibilities are NULL, "qini", "lift", "gain". |
## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/uplift/criteo_uplift_13k.csv" train <- h2o.importFile(f) train$treatment <- as.factor(train$treatment) train$conversion <- as.factor(train$conversion) model <- h2o.upliftRandomForest(training_frame=train, x=sprintf("f%s",seq(0:10)), y="conversion", ntrees=10, max_depth=5, treatment_column="treatment", auuc_type="AUTO") perf <- h2o.performance(model, train=TRUE) h2o.auuc_normalized(perf) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/uplift/criteo_uplift_13k.csv" train <- h2o.importFile(f) train$treatment <- as.factor(train$treatment) train$conversion <- as.factor(train$conversion) model <- h2o.upliftRandomForest(training_frame=train, x=sprintf("f%s",seq(0:10)), y="conversion", ntrees=10, max_depth=5, treatment_column="treatment", auuc_type="AUTO") perf <- h2o.performance(model, train=TRUE) h2o.auuc_normalized(perf) ## End(Not run)
Retrieves the all types of AUUC in a table from an H2OBinomialUpliftMetrics. If "train" and "valid" parameters are FALSE (default), then the training AUUC values are returned. If more than one parameter is set to TRUE, then a named vector of AUUCs are returned, where the names are "train", "valid".
h2o.auuc_table(object, train = FALSE, valid = FALSE)h2o.auuc_table(object, train = FALSE, valid = FALSE)
object |
|
train |
Retrieve the training AUUC table |
valid |
Retrieve the validation AUUC table |
## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/uplift/criteo_uplift_13k.csv" train <- h2o.importFile(f) train$treatment <- as.factor(train$treatment) train$conversion <- as.factor(train$conversion) model <- h2o.upliftRandomForest(training_frame=train, x=sprintf("f%s",seq(0:10)), y="conversion", ntrees=10, max_depth=5, treatment_column="treatment", auuc_type="AUTO") perf <- h2o.performance(model, train=TRUE) h2o.auuc_table(perf) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/uplift/criteo_uplift_13k.csv" train <- h2o.importFile(f) train$treatment <- as.factor(train$treatment) train$conversion <- as.factor(train$conversion) model <- h2o.upliftRandomForest(training_frame=train, x=sprintf("f%s",seq(0:10)), y="conversion", ntrees=10, max_depth=5, treatment_column="treatment", auuc_type="AUTO") perf <- h2o.performance(model, train=TRUE) h2o.auuc_table(perf) ## End(Not run)
Extracts the final training average objective function of a GLM model.
h2o.average_objective(model)h2o.average_objective(model)
model |
an H2OModel object. |
The final training average objective of a GLM model.
## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv" cars <- h2o.importFile(f) predictors <- c("displacement", "power", "weight", "acceleration", "year") response <- "acceleration" cars_model <- h2o.glm(y=response, x=predictors, training_frame = cars, family="gaussian", generate_scoring_history=TRUE) objValue <- h2o.average_objective(cars_model) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv" cars <- h2o.importFile(f) predictors <- c("displacement", "power", "weight", "acceleration", "year") response <- "acceleration" cars_model <- h2o.glm(y=response, x=predictors, training_frame = cars, family="gaussian", generate_scoring_history=TRUE) objValue <- h2o.average_objective(cars_model) ## End(Not run)
Get the between cluster sum of squares. If "train", "valid", and "xval" parameters are FALSE (default), then the training betweenss value is returned. If more than one parameter is set to TRUE, then a named vector of betweenss' are returned, where the names are "train", "valid" or "xval".
h2o.betweenss(object, train = FALSE, valid = FALSE, xval = FALSE)h2o.betweenss(object, train = FALSE, valid = FALSE, xval = FALSE)
object |
An H2OClusteringModel object. |
train |
Retrieve the training between cluster sum of squares |
valid |
Retrieve the validation between cluster sum of squares |
xval |
Retrieve the cross-validation between cluster sum of squares |
## Not run: library(h2o) h2o.init() fr <- h2o.importFile("https://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_train.csv") predictors <- c("sepal_len", "sepal_wid", "petal_len", "petal_wid") km <- h2o.kmeans(x = predictors, training_frame = fr, k = 3, nfolds = 3) h2o.betweenss(km, train = TRUE) ## End(Not run)## Not run: library(h2o) h2o.init() fr <- h2o.importFile("https://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_train.csv") predictors <- c("sepal_len", "sepal_wid", "petal_len", "petal_wid") km <- h2o.kmeans(x = predictors, training_frame = fr, k = 3, nfolds = 3) h2o.betweenss(km, train = TRUE) ## End(Not run)
Return the respective bias vector
h2o.biases(object, vector_id = 1)h2o.biases(object, vector_id = 1)
object |
An H2OModel or H2OModelMetrics |
vector_id |
An integer, ranging from 1 to number of layers + 1, that specifies the bias vector to return. |
## Not run: library(h2o) h2o.init() f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/chicago/chicagoCensus.csv" census <- h2o.importFile(f) census[, 1] <- as.factor(census[, 1]) dl_model <- h2o.deeplearning(x = c(1:3), y = 4, training_frame = census, hidden = c(17, 191), epochs = 1, balance_classes = FALSE, export_weights_and_biases = TRUE) h2o.biases(dl_model, vector_id = 1) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/chicago/chicagoCensus.csv" census <- h2o.importFile(f) census[, 1] <- as.factor(census[, 1]) dl_model <- h2o.deeplearning(x = c(1:3), y = 4, training_frame = census, hidden = c(17, 191), epochs = 1, balance_classes = FALSE, export_weights_and_biases = TRUE) h2o.biases(dl_model, vector_id = 1) ## End(Not run)
bottomN function will will grab the bottom N percent of values of a column and return it in a H2OFrame. Extract the top N percent of values of a column and return it in a H2OFrame.
h2o.bottomN(x, column, nPercent)h2o.bottomN(x, column, nPercent)
x |
an H2OFrame |
column |
is a column name or column index to grab the top N percent value from |
nPercent |
is a bottom percentage value to grab |
An H2OFrame with 2 columns. The first column is the original row indices, second column contains the bottomN values
## Not run: library(h2o) h2o.init() f1 <- "https://s3.amazonaws.com/h2o-public-test-data/bigdata/laptop/jira/TopBottomNRep4.csv.zip" f2 <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/jira/Bottom20Per.csv.zip" data_Frame <- h2o.importFile(f1) bottom_Answer <- h2o.importFile(f2) nPercent <- c(1, 2, 3, 4) frame_Names <- names(data_Frame) nP <- nPercent[sample(1:length(nPercent), 1, replace = FALSE)] col_Index <- sample(1:length(frame_Names), 1, replace = FALSE) h2o.bottomN(data_Frame, frame_Names[col_Index], nP) ## End(Not run)## Not run: library(h2o) h2o.init() f1 <- "https://s3.amazonaws.com/h2o-public-test-data/bigdata/laptop/jira/TopBottomNRep4.csv.zip" f2 <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/jira/Bottom20Per.csv.zip" data_Frame <- h2o.importFile(f1) bottom_Answer <- h2o.importFile(f2) nPercent <- c(1, 2, 3, 4) frame_Names <- names(data_Frame) nP <- nPercent[sample(1:length(nPercent), 1, replace = FALSE)] col_Index <- sample(1:length(frame_Names), 1, replace = FALSE) h2o.bottomN(data_Frame, frame_Names[col_Index], nP) ## End(Not run)
Calculate intersectional fairness metrics.
h2o.calculate_fairness_metrics( model, frame, protected_columns, reference, favorable_class )h2o.calculate_fairness_metrics( model, frame, protected_columns, reference, favorable_class )
model |
H2O Model |
frame |
Frame used to calculate the metrics. |
protected_columns |
List of categorical columns that contain sensitive information such as race, gender, age etc. |
reference |
List of values corresponding to a reference for each protected columns. If set to NULL, it will use the biggest group as the reference. |
favorable_class |
Positive/favorable outcome class of the response. |
Dictionary of frames. One frame is the overview, other frames contain dependence of performance on threshold for each protected group.
## Not run: library(h2o) h2o.init() data <- h2o.importFile(paste0("https://s3.amazonaws.com/h2o-public-test-data/smalldata/", "admissibleml_test/taiwan_credit_card_uci.csv")) x <- c('LIMIT_BAL', 'AGE', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6', 'BILL_AMT1', 'BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6') y <- "default payment next month" protected_columns <- c('SEX', 'EDUCATION') for (col in c(y, protected_columns)) data[[col]] <- as.factor(data[[col]]) splits <- h2o.splitFrame(data, 0.8) train <- splits[[1]] test <- splits[[2]] reference <- c(SEX = "1", EDUCATION = "2") # university educated man favorable_class <- "0" # no default next month gbm <- h2o.gbm(x, y, training_frame = train) h2o.calculate_fairness_metrics(gbm, test, protected_columns = protected_columns, reference = reference, favorable_class = favorable_class) ## End(Not run)## Not run: library(h2o) h2o.init() data <- h2o.importFile(paste0("https://s3.amazonaws.com/h2o-public-test-data/smalldata/", "admissibleml_test/taiwan_credit_card_uci.csv")) x <- c('LIMIT_BAL', 'AGE', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6', 'BILL_AMT1', 'BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6') y <- "default payment next month" protected_columns <- c('SEX', 'EDUCATION') for (col in c(y, protected_columns)) data[[col]] <- as.factor(data[[col]]) splits <- h2o.splitFrame(data, 0.8) train <- splits[[1]] test <- splits[[2]] reference <- c(SEX = "1", EDUCATION = "2") # university educated man favorable_class <- "0" # no default next month gbm <- h2o.gbm(x, y, training_frame = train) h2o.calculate_fairness_metrics(gbm, test, protected_columns = protected_columns, reference = reference, favorable_class = favorable_class) ## End(Not run)
Takes a sequence of H2O data sets and combines them by column
h2o.cbind(...)h2o.cbind(...)
... |
A sequence of H2OFrame arguments. All datasets must exist on the same H2O instance (IP and port) and contain the same number of rows. |
An H2OFrame object containing the combined ... arguments column-wise.
cbind for the base R method.
## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(path = prostate_path) prostate_cbind <- h2o.cbind(prostate, prostate) head(prostate_cbind) ## End(Not run)## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(path = prostate_path) prostate_cbind <- h2o.cbind(prostate, prostate) head(prostate_cbind) ## End(Not run)
ceiling takes a single numeric argument x and returns a numeric vector containing the smallest integers not less than the corresponding elements of x.
h2o.ceiling(x)h2o.ceiling(x)
x |
An H2OFrame object. |
Round for the base R implementation, ceiling().
## Not run: library(h2o) h2o.init() f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_train.csv" iris <- h2o.importFile(f) h2o.ceiling(iris[, 1]) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_train.csv" iris <- h2o.importFile(f) h2o.ceiling(iris[, 1]) ## End(Not run)
Retrieve the Model Centers
h2o.centers(object)h2o.centers(object)
object |
An H2OClusteringModel object. |
## Not run: library(h2o) h2o.init() fr <- h2o.importFile("https://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_train.csv") h2o.ceiling(fr[, 1]) ## End(Not run)## Not run: library(h2o) h2o.init() fr <- h2o.importFile("https://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_train.csv") h2o.ceiling(fr[, 1]) ## End(Not run)
Retrieve the Model Centers STD
h2o.centersSTD(object)h2o.centersSTD(object)
object |
An H2OClusteringModel object. |
## Not run: library(h2o) h2o.init() fr <- h2o.importFile("https://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_train.csv") predictors <- c("sepal_len", "sepal_wid", "petal_len", "petal_wid") km <- h2o.kmeans(x = predictors, training_frame = fr, k = 3, nfolds = 3) h2o.centersSTD(km) ## End(Not run)## Not run: library(h2o) h2o.init() fr <- h2o.importFile("https://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_train.csv") predictors <- c("sepal_len", "sepal_wid", "petal_len", "petal_wid") km <- h2o.kmeans(x = predictors, training_frame = fr, k = 3, nfolds = 3) h2o.centersSTD(km) ## End(Not run)
Retrieve the centroid statistics. If "train" and "valid" parameters are FALSE (default), then the training centroid stats value is returned. If more than one parameter is set to TRUE, then a named list of centroid stats data frames are returned, where the names are "train" or "valid" For cross validation metrics this statistics are not available.
h2o.centroid_stats(object, train = FALSE, valid = FALSE)h2o.centroid_stats(object, train = FALSE, valid = FALSE)
object |
An H2OClusteringModel object. |
train |
Retrieve the training centroid statistics |
valid |
Retrieve the validation centroid statistics |
## Not run: library(h2o) h2o.init() fr <- h2o.importFile("https://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_train.csv") predictors <- c("sepal_len", "sepal_wid", "petal_len", "petal_wid") km <- h2o.kmeans(x = predictors, training_frame = fr, k = 3, nfolds = 3) h2o.centroid_stats(km, train = TRUE) ## End(Not run)## Not run: library(h2o) h2o.init() fr <- h2o.importFile("https://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_train.csv") predictors <- c("sepal_len", "sepal_wid", "petal_len", "petal_wid") km <- h2o.kmeans(x = predictors, training_frame = fr, k = 3, nfolds = 3) h2o.centroid_stats(km, train = TRUE) ## End(Not run)
Clear all H2O R command and error response logs from the local disk. Used primarily for debugging purposes.
h2o.clearLog()h2o.clearLog()
h2o.startLogging, h2o.stopLogging,
h2o.openLog
## Not run: library(h2o) h2o.init() h2o.startLogging() australia_path = system.file("extdata", "australia.csv", package = "h2o") australia = h2o.importFile(path = australia_path) h2o.stopLogging() h2o.clearLog() ## End(Not run)## Not run: library(h2o) h2o.init() h2o.startLogging() australia_path = system.file("extdata", "australia.csv", package = "h2o") australia = h2o.importFile(path = australia_path) h2o.stopLogging() h2o.clearLog() ## End(Not run)
Retrieve the cluster sizes. If "train", "valid", and "xval" parameters are FALSE (default), then the training cluster sizes value is returned. If more than one parameter is set to TRUE, then a named list of cluster size vectors are returned, where the names are "train", "valid" or "xval".
h2o.cluster_sizes(object, train = FALSE, valid = FALSE, xval = FALSE)h2o.cluster_sizes(object, train = FALSE, valid = FALSE, xval = FALSE)
object |
An H2OClusteringModel object. |
train |
Retrieve the training cluster sizes |
valid |
Retrieve the validation cluster sizes |
xval |
Retrieve the cross-validation cluster sizes |
## Not run: library(h2o) h2o.init() fr <- h2o.importFile("https://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_train.csv") predictors <- c("sepal_len", "sepal_wid", "petal_len", "petal_wid") km <- h2o.kmeans(x = predictors, training_frame = fr, k = 3, nfolds = 3) h2o.cluster_sizes(km, train = TRUE) ## End(Not run)## Not run: library(h2o) h2o.init() fr <- h2o.importFile("https://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_train.csv") predictors <- c("sepal_len", "sepal_wid", "petal_len", "petal_wid") km <- h2o.kmeans(x = predictors, training_frame = fr, k = 3, nfolds = 3) h2o.cluster_sizes(km, train = TRUE) ## End(Not run)
Determine if an H2O cluster is up or not
h2o.clusterIsUp(conn = h2o.getConnection())h2o.clusterIsUp(conn = h2o.getConnection())
conn |
H2OConnection object |
TRUE if the cluster is up; FALSE otherwise
Retrieve information on the status of the cluster running H2O.
h2o.clusterStatus()h2o.clusterStatus()
## Not run: h2o.init() h2o.clusterStatus() ## End(Not run)## Not run: h2o.init() h2o.clusterStatus() ## End(Not run)
Note: standardize = True by default. If set to False, then coef() returns the coefficients that are fit directly.
h2o.coef(object, predictorSize = -1)h2o.coef(object, predictorSize = -1)
object |
an H2OModel object. |
predictorSize |
predictor subset size. If specified, will only return model coefficients of that subset size. If not specified will return a lists of model coefficient dicts for all predictor subset size. |
## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv" cars <- h2o.importFile(f) predictors <- c("displacement", "power", "weight", "acceleration", "year") response <- "cylinders" cars_split <- h2o.splitFrame(data = cars, ratios = 0.8, seed = 1234) train <- cars_split[[1]] valid <- cars_split[[2]] cars_glm <- h2o.glm(balance_classes = TRUE, seed = 1234, x = predictors, y = response, training_frame = train, validation_frame = valid) h2o.coef(cars_glm) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv" cars <- h2o.importFile(f) predictors <- c("displacement", "power", "weight", "acceleration", "year") response <- "cylinders" cars_split <- h2o.splitFrame(data = cars, ratios = 0.8, seed = 1234) train <- cars_split[[1]] valid <- cars_split[[2]] cars_glm <- h2o.glm(balance_classes = TRUE, seed = 1234, x = predictors, y = response, training_frame = train, validation_frame = valid) h2o.coef(cars_glm) ## End(Not run)
Return coefficients fitted on the standardized data (requires standardize = True, which is on by default). These coefficients can be used to evaluate variable importance.
h2o.coef_norm(object, predictorSize = -1)h2o.coef_norm(object, predictorSize = -1)
object |
an H2OModel object. |
predictorSize |
predictor subset size. If specified, will only return model coefficients of that subset size. If not specified will return a lists of model coefficient dicts for all predictor subset size. |
## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv" cars <- h2o.importFile(f) predictors <- c("displacement", "power", "weight", "acceleration", "year") response <- "cylinders" cars_split <- h2o.splitFrame(data = cars, ratios = 0.8, seed = 1234) train <- cars_split[[1]] valid <- cars_split[[2]] cars_glm <- h2o.glm(balance_classes = TRUE, seed = 1234, x = predictors, y = response, training_frame = train, validation_frame = valid) h2o.coef_norm(cars_glm) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv" cars <- h2o.importFile(f) predictors <- c("displacement", "power", "weight", "acceleration", "year") response <- "cylinders" cars_split <- h2o.splitFrame(data = cars, ratios = 0.8, seed = 1234) train <- cars_split[[1]] valid <- cars_split[[2]] cars_glm <- h2o.glm(balance_classes = TRUE, seed = 1234, x = predictors, y = response, training_frame = train, validation_frame = valid) h2o.coef_norm(cars_glm) ## End(Not run)
Return the coefficients table with coefficients, standardized coefficients, p-values, z-values and std-error for GLM models
h2o.coef_with_p_values(object)h2o.coef_with_p_values(object)
object |
An H2OModel object. |
## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv" cars <- h2o.importFile(f) predictors <- c("displacement", "power", "weight", "acceleration", "year") response <- "cylinders" cars_split <- h2o.splitFrame(data = cars, ratios = 0.8, seed = 1234) train <- cars_split[[1]] valid <- cars_split[[2]] cars_glm <- h2o.glm(seed = 1234, lambda=0.0, compute_p_values=TRUE, x = predictors, y = response, training_frame = train, validation_frame = valid) h2o.coef_with_p_values(cars_glm) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv" cars <- h2o.importFile(f) predictors <- c("displacement", "power", "weight", "acceleration", "year") response <- "cylinders" cars_split <- h2o.splitFrame(data = cars, ratios = 0.8, seed = 1234) train <- cars_split[[1]] valid <- cars_split[[2]] cars_glm <- h2o.glm(seed = 1234, lambda=0.0, compute_p_values=TRUE, x = predictors, y = response, training_frame = train, validation_frame = valid) h2o.coef_with_p_values(cars_glm) ## End(Not run)
Return column names of an H2OFrame
h2o.colnames(x)h2o.colnames(x)
x |
An H2OFrame object. |
colnames for the base R implementation.
## Not run: library(h2o) h2o.init() frame <- h2o.createFrame(rows = 6, cols = 2, categorical_fraction = 0.0, missing_fraction = 0.7, seed = 123) h2o.colnames(frame) ## End(Not run)## Not run: library(h2o) h2o.init() frame <- h2o.createFrame(rows = 6, cols = 2, categorical_fraction = 0.0, missing_fraction = 0.7, seed = 123) h2o.colnames(frame) ## End(Not run)
Obtain a list of columns that are specified by 'coltype'
h2o.columns_by_type(object, coltype = "numeric", ...)h2o.columns_by_type(object, coltype = "numeric", ...)
object |
H2OFrame object |
coltype |
A character string indicating which column type to filter by. This must be one of the following: "numeric" - Numeric, but not categorical or time "categorical" - Integer, with a categorical/factor String mapping "string" - String column "time" - Long msec since the Unix Epoch - with a variety of display/parse options "uuid" - UUID "bad" - No none-NA rows (triple negative! all NAs or zero rows) |
... |
Ignored |
A list of column indices that correspond to "type"
## Not run: h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(path = prostate_path) h2o.columns_by_type(prostate, coltype = "numeric") ## End(Not run)## Not run: h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(path = prostate_path) h2o.columns_by_type(prostate, coltype = "numeric") ## End(Not run)
Compute weighted gram matrix.
h2o.computeGram( X, weights = "", use_all_factor_levels = FALSE, standardize = TRUE, skip_missing = FALSE )h2o.computeGram( X, weights = "", use_all_factor_levels = FALSE, standardize = TRUE, skip_missing = FALSE )
X |
an H2OModel corresponding to H2O framel. |
weights |
character corresponding to name of weight vector in frame. |
use_all_factor_levels |
logical flag telling h2o whether or not to skip first level of categorical variables during one-hot encoding. |
standardize |
logical flag telling h2o whether or not to standardize data |
skip_missing |
logical flag telling h2o whether skip rows with missing data or impute them with mean |
Retrieve either a single or many confusion matrices from H2O objects.
h2o.confusionMatrix(object, ...) ## S4 method for signature 'H2OModel' h2o.confusionMatrix(object, newdata, valid = FALSE, xval = FALSE, ...) ## S4 method for signature 'H2OModelMetrics' h2o.confusionMatrix(object, thresholds = NULL, metrics = NULL)h2o.confusionMatrix(object, ...) ## S4 method for signature 'H2OModel' h2o.confusionMatrix(object, newdata, valid = FALSE, xval = FALSE, ...) ## S4 method for signature 'H2OModelMetrics' h2o.confusionMatrix(object, thresholds = NULL, metrics = NULL)
object |
Either an H2OModel object or an H2OModelMetrics object. |
... |
Extra arguments for extracting train or valid confusion matrices. |
newdata |
An H2OFrame object that can be scored on. Requires a valid response column. |
valid |
Retrieve the validation metric. |
xval |
Retrieve the cross-validation metric. |
thresholds |
(Optional) A value or a list of valid values between 0.0 and 1.0. This value is only used in the case of H2OBinomialMetrics objects. |
metrics |
(Optional) A metric or a list of valid metrics ("min_per_class_accuracy", "absolute_mcc", "tnr", "fnr", "fpr", "tpr", "precision", "accuracy", "f0point5", "f2", "f1"). This value is only used in the case of H2OBinomialMetrics objects. |
The H2OModelMetrics version of this function will only take H2OBinomialMetrics or H2OMultinomialMetrics objects. If no threshold is specified, all possible thresholds are selected.
Calling this function on H2OModel objects returns a
confusion matrix corresponding to the predict function.
If used on an H2OBinomialMetrics object, returns a list
of matrices corresponding to the number of thresholds specified.
predict for generating prediction frames,
h2o.performance for creating
H2OModelMetrics.
## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(prostate_path) prostate[, 2] <- as.factor(prostate[, 2]) model <- h2o.gbm(x = 3:9, y = 2, training_frame = prostate, distribution = "bernoulli") h2o.confusionMatrix(model, prostate) # Generating a ModelMetrics object perf <- h2o.performance(model, prostate) h2o.confusionMatrix(perf) ## End(Not run)## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(prostate_path) prostate[, 2] <- as.factor(prostate[, 2]) model <- h2o.gbm(x = 3:9, y = 2, training_frame = prostate, distribution = "bernoulli") h2o.confusionMatrix(model, prostate) # Generating a ModelMetrics object perf <- h2o.performance(model, prostate) h2o.confusionMatrix(perf) ## End(Not run)
Connect to a running H2O instance.
h2o.connect( ip = "localhost", port = 54321, strict_version_check = TRUE, proxy = NA_character_, https = FALSE, cacert = NA_character_, insecure = FALSE, username = NA_character_, password = NA_character_, use_spnego = FALSE, cookies = NA_character_, context_path = NA_character_, config = NULL )h2o.connect( ip = "localhost", port = 54321, strict_version_check = TRUE, proxy = NA_character_, https = FALSE, cacert = NA_character_, insecure = FALSE, username = NA_character_, password = NA_character_, use_spnego = FALSE, cookies = NA_character_, context_path = NA_character_, config = NULL )
ip |
Object of class |
port |
Object of class |
strict_version_check |
(Optional) Setting this to FALSE is unsupported and should only be done when advised by technical support. |
proxy |
(Optional) A |
https |
(Optional) Set this to TRUE to use https instead of http. |
cacert |
Path to a CA bundle file with root and intermediate certificates of trusted CAs. |
insecure |
(Optional) Set this to TRUE to disable SSL certificate checking. |
username |
(Optional) Username to login with. |
password |
(Optional) Password to login with. |
use_spnego |
(Optional) Set this to TRUE to enable SPNEGO authentication. |
cookies |
(Optional) Vector(or list) of cookies to add to request. |
context_path |
(Optional) The last part of connection URL: http://<ip>:<port>/<context_path> |
config |
(Optional) A |
an instance of H2OConnection object representing a connection to the running H2O instance.
## Not run: library(h2o) # Try to connect to a H2O instance running at http://localhost:54321/cluster_X #h2o.connect(ip = "localhost", port = 54321, context_path = "cluster_X") # Or #config = list(ip = "localhost", port = 54321, context_path = "cluster_X") #h2o.connect(config = config) # Skip strict version check during connecting to the instance #h2o.connect(config = c(strict_version_check = FALSE, config)) ## End(Not run)## Not run: library(h2o) # Try to connect to a H2O instance running at http://localhost:54321/cluster_X #h2o.connect(ip = "localhost", port = 54321, context_path = "cluster_X") # Or #config = list(ip = "localhost", port = 54321, context_path = "cluster_X") #h2o.connect(config = config) # Skip strict version check during connecting to the instance #h2o.connect(config = c(strict_version_check = FALSE, config)) ## End(Not run)
Compute the correlation matrix of one or two H2OFrames.
h2o.cor(x, y = NULL, na.rm = FALSE, use, method = "Pearson") cor(x, ...)h2o.cor(x, y = NULL, na.rm = FALSE, use, method = "Pearson") cor(x, ...)
x |
An H2OFrame object. |
y |
|
na.rm |
|
use |
An optional character string indicating how to handle missing values. This must be one of the following: "everything" - outputs NaNs whenever one of its contributing observations is missing "all.obs" - presence of missing observations will throw an error "complete.obs" - discards missing values along with all observations in their rows so that only complete observations are used |
method |
|
... |
Further arguments to be passed down from other methods. |
## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(path = prostate_path) cor(prostate$AGE) ## End(Not run)## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(path = prostate_path) cor(prostate$AGE) ## End(Not run)
Compute the cosine of x
h2o.cos(x)h2o.cos(x)
x |
An H2OFrame object. |
Trig for the base R implementation, cos().
## Not run: library(h2o) h2o.init() frame <- h2o.createFrame(rows = 6, cols = 2, categorical_fraction = 0.0, missing_fraction = 0.7, seed = 123) h2o.cos(frame["C1"]) ## End(Not run)## Not run: library(h2o) h2o.init() frame <- h2o.createFrame(rows = 6, cols = 2, categorical_fraction = 0.0, missing_fraction = 0.7, seed = 123) h2o.cos(frame["C1"]) ## End(Not run)
Compute the hyperbolic cosine of x
h2o.cosh(x)h2o.cosh(x)
x |
An H2OFrame object. |
Hyperbolic for the base R implementation, cosh().
## Not run: library(h2o) h2o.init() frame <- h2o.createFrame(rows = 6, cols = 2, categorical_fraction = 0.0, missing_fraction = 0.7, seed = 123) h2o.cosh(frame["C1"]) ## End(Not run)## Not run: library(h2o) h2o.init() frame <- h2o.createFrame(rows = 6, cols = 2, categorical_fraction = 0.0, missing_fraction = 0.7, seed = 123) h2o.cosh(frame["C1"]) ## End(Not run)
Trains a Cox Proportional Hazards Model (CoxPH) on an H2O dataset
h2o.coxph( x, event_column, training_frame, model_id = NULL, start_column = NULL, stop_column = NULL, weights_column = NULL, offset_column = NULL, stratify_by = NULL, ties = c("efron", "breslow"), init = 0, lre_min = 9, max_iterations = 20, interactions = NULL, interaction_pairs = NULL, interactions_only = NULL, use_all_factor_levels = FALSE, export_checkpoints_dir = NULL, single_node_mode = FALSE )h2o.coxph( x, event_column, training_frame, model_id = NULL, start_column = NULL, stop_column = NULL, weights_column = NULL, offset_column = NULL, stratify_by = NULL, ties = c("efron", "breslow"), init = 0, lre_min = 9, max_iterations = 20, interactions = NULL, interaction_pairs = NULL, interactions_only = NULL, use_all_factor_levels = FALSE, export_checkpoints_dir = NULL, single_node_mode = FALSE )
x |
(Optional) A vector containing the names or indices of the predictor variables to use in building the model. If x is missing, then all columns except event_column, start_column and stop_column are used. |
event_column |
The name of binary data column in the training frame indicating the occurrence of an event. |
training_frame |
Id of the training data frame. |
model_id |
Destination id for this model; auto-generated if not specified. |
start_column |
Start Time Column. |
stop_column |
Stop Time Column. |
weights_column |
Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0. |
offset_column |
Offset column. This will be added to the combination of columns before applying the link function. |
stratify_by |
List of columns to use for stratification. |
ties |
Method for Handling Ties. Must be one of: "efron", "breslow". Defaults to efron. |
init |
Coefficient starting value. Defaults to 0. |
lre_min |
Minimum log-relative error. Defaults to 9. |
max_iterations |
Maximum number of iterations. Defaults to 20. |
interactions |
A list of predictor column indices to interact. All pairwise combinations will be computed for the list. |
interaction_pairs |
A list of pairwise (first order) column interactions. |
interactions_only |
A list of columns that should only be used to create interactions but should not itself participate in model training. |
use_all_factor_levels |
|
export_checkpoints_dir |
Automatically export generated models to this directory. |
single_node_mode |
|
## Not run: library(h2o) h2o.init() # Import the heart dataset f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/coxph_test/heart.csv" heart <- h2o.importFile(f) # Set the predictor and response predictor <- "age" response <- "event" # Train a Cox Proportional Hazards model heart_coxph <- h2o.coxph(x = predictor, training_frame = heart, event_column = "event", start_column = "start", stop_column = "stop") ## End(Not run)## Not run: library(h2o) h2o.init() # Import the heart dataset f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/coxph_test/heart.csv" heart <- h2o.importFile(f) # Set the predictor and response predictor <- "age" response <- "event" # Train a Cox Proportional Hazards model heart_coxph <- h2o.coxph(x = predictor, training_frame = heart, event_column = "event", start_column = "start", stop_column = "stop") ## End(Not run)
Creates a data frame in H2O with real-valued, categorical, integer, and binary columns specified by the user.
h2o.createFrame( rows = 10000, cols = 10, randomize = TRUE, value = 0, real_range = 100, categorical_fraction = 0.2, factors = 100, integer_fraction = 0.2, integer_range = 100, binary_fraction = 0.1, binary_ones_fraction = 0.02, time_fraction = 0, string_fraction = 0, missing_fraction = 0.01, response_factors = 2, has_response = FALSE, seed, seed_for_column_types )h2o.createFrame( rows = 10000, cols = 10, randomize = TRUE, value = 0, real_range = 100, categorical_fraction = 0.2, factors = 100, integer_fraction = 0.2, integer_range = 100, binary_fraction = 0.1, binary_ones_fraction = 0.02, time_fraction = 0, string_fraction = 0, missing_fraction = 0.01, response_factors = 2, has_response = FALSE, seed, seed_for_column_types )
rows |
The number of rows of data to generate. |
cols |
The number of columns of data to generate. Excludes the response column if |
randomize |
A logical value indicating whether data values should be randomly generated. This must be TRUE if either |
value |
If |
real_range |
The range of randomly generated real values. |
categorical_fraction |
The fraction of total columns that are categorical. |
factors |
The number of (unique) factor levels in each categorical column. |
integer_fraction |
The fraction of total columns that are integer-valued. |
integer_range |
The range of randomly generated integer values. |
binary_fraction |
The fraction of total columns that are binary-valued. |
binary_ones_fraction |
The fraction of values in a binary column that are set to 1. |
time_fraction |
The fraction of randomly created date/time columns. |
string_fraction |
The fraction of randomly created string columns. |
missing_fraction |
The fraction of total entries in the data frame that are set to NA. |
response_factors |
If |
has_response |
A logical value indicating whether an additional response column should be pre-pended to the final H2O data frame. If set to TRUE, the total number of columns will be |
seed |
A seed used to generate random values when |
seed_for_column_types |
A seed used to generate random column types when |
Returns an H2OFrame object.
## Not run: library(h2o) h2o.init() hf <- h2o.createFrame(rows = 1000, cols = 100, categorical_fraction = 0.1, factors = 5, integer_fraction = 0.5, integer_range = 1, has_response = TRUE) head(hf) summary(hf) hf <- h2o.createFrame(rows = 100, cols = 10, randomize = FALSE, value = 5, categorical_fraction = 0, integer_fraction = 0) summary(hf) ## End(Not run)## Not run: library(h2o) h2o.init() hf <- h2o.createFrame(rows = 1000, cols = 100, categorical_fraction = 0.1, factors = 5, integer_fraction = 0.5, integer_range = 1, has_response = TRUE) head(hf) summary(hf) hf <- h2o.createFrame(rows = 100, cols = 10, randomize = FALSE, value = 5, categorical_fraction = 0, integer_fraction = 0) summary(hf) ## End(Not run)
Retrieve the cross-validation fold assignment
h2o.cross_validation_fold_assignment(object)h2o.cross_validation_fold_assignment(object)
object |
An H2OModel object. |
Returns a H2OFrame
## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv" cars <- h2o.importFile(f) cars["economy_20mpg"] <- as.factor(cars["economy_20mpg"]) predictors <- c("displacement", "power", "weight", "acceleration", "year") response <- "economy_20mpg" cars_split <- h2o.splitFrame(data = cars, ratios = 0.8, seed = 1234) train <- cars_split[[1]] valid <- cars_split[[2]] cars_gbm <- h2o.gbm(x = predictors, y = response, training_frame = train, nfolds = 5, keep_cross_validation_fold_assignment = TRUE, seed = 1234) h2o.cross_validation_fold_assignment(cars_gbm) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv" cars <- h2o.importFile(f) cars["economy_20mpg"] <- as.factor(cars["economy_20mpg"]) predictors <- c("displacement", "power", "weight", "acceleration", "year") response <- "economy_20mpg" cars_split <- h2o.splitFrame(data = cars, ratios = 0.8, seed = 1234) train <- cars_split[[1]] valid <- cars_split[[2]] cars_gbm <- h2o.gbm(x = predictors, y = response, training_frame = train, nfolds = 5, keep_cross_validation_fold_assignment = TRUE, seed = 1234) h2o.cross_validation_fold_assignment(cars_gbm) ## End(Not run)
Retrieve the cross-validation holdout predictions
h2o.cross_validation_holdout_predictions(object)h2o.cross_validation_holdout_predictions(object)
object |
An H2OModel object. |
Returns a H2OFrame
## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv" cars <- h2o.importFile(f) cars["economy_20mpg"] <- as.factor(cars["economy_20mpg"]) predictors <- c("displacement","power","weight","acceleration","year") response <- "economy_20mpg" cars_split <- h2o.splitFrame(data = cars,ratios = 0.8, seed = 1234) train <- cars_split[[1]] valid <- cars_split[[2]] cars_gbm <- h2o.gbm(x = predictors, y = response, training_frame = train, nfolds = 5, keep_cross_validation_predictions = TRUE, seed = 1234) h2o.cross_validation_holdout_predictions(cars_gbm) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv" cars <- h2o.importFile(f) cars["economy_20mpg"] <- as.factor(cars["economy_20mpg"]) predictors <- c("displacement","power","weight","acceleration","year") response <- "economy_20mpg" cars_split <- h2o.splitFrame(data = cars,ratios = 0.8, seed = 1234) train <- cars_split[[1]] valid <- cars_split[[2]] cars_gbm <- h2o.gbm(x = predictors, y = response, training_frame = train, nfolds = 5, keep_cross_validation_predictions = TRUE, seed = 1234) h2o.cross_validation_holdout_predictions(cars_gbm) ## End(Not run)
Retrieve the cross-validation models
h2o.cross_validation_models(object)h2o.cross_validation_models(object)
object |
An H2OModel object. |
Returns a list of H2OModel objects
## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv" cars <- h2o.importFile(f) cars["economy_20mpg"] <- as.factor(cars["economy_20mpg"]) predictors <- c("displacement", "power", "weight", "acceleration", "year") response <- "economy_20mpg" cars_split <- h2o.splitFrame(data = cars, ratios = 0.8, seed = 1234) train <- cars_split[[1]] valid <- cars_split[[2]] cars_gbm <- h2o.gbm(x = predictors, y = response, training_frame = train, nfolds = 5, keep_cross_validation_models = TRUE, seed = 1234) h2o.cross_validation_models(cars_gbm) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv" cars <- h2o.importFile(f) cars["economy_20mpg"] <- as.factor(cars["economy_20mpg"]) predictors <- c("displacement", "power", "weight", "acceleration", "year") response <- "economy_20mpg" cars_split <- h2o.splitFrame(data = cars, ratios = 0.8, seed = 1234) train <- cars_split[[1]] valid <- cars_split[[2]] cars_gbm <- h2o.gbm(x = predictors, y = response, training_frame = train, nfolds = 5, keep_cross_validation_models = TRUE, seed = 1234) h2o.cross_validation_models(cars_gbm) ## End(Not run)
Retrieve the cross-validation predictions
h2o.cross_validation_predictions(object)h2o.cross_validation_predictions(object)
object |
An H2OModel object. |
Returns a list of H2OFrame objects
## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv" cars <- h2o.importFile(f) cars["economy_20mpg"] <- as.factor(cars["economy_20mpg"]) predictors <- c("displacement", "power", "weight", "acceleration", "year") response <- "economy_20mpg" cars_split <- h2o.splitFrame(data = cars, ratios = 0.8, seed = 1234) train <- cars_split[[1]] valid <- cars_split[[2]] cars_gbm <- h2o.gbm(x = predictors, y = response, training_frame = train, nfolds = 5, keep_cross_validation_predictions = TRUE, seed = 1234) h2o.cross_validation_predictions(cars_gbm) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv" cars <- h2o.importFile(f) cars["economy_20mpg"] <- as.factor(cars["economy_20mpg"]) predictors <- c("displacement", "power", "weight", "acceleration", "year") response <- "economy_20mpg" cars_split <- h2o.splitFrame(data = cars, ratios = 0.8, seed = 1234) train <- cars_split[[1]] valid <- cars_split[[2]] cars_gbm <- h2o.gbm(x = predictors, y = response, training_frame = train, nfolds = 5, keep_cross_validation_predictions = TRUE, seed = 1234) h2o.cross_validation_predictions(cars_gbm) ## End(Not run)
Return the cumulative max over a column or across a row
h2o.cummax(x, axis = 0)h2o.cummax(x, axis = 0)
x |
An H2OFrame object. |
axis |
An int that indicates whether to do down a column (0) or across a row (1). |
cumsum for the base R implementation, cummax().
## Not run: library(h2o) h2o.init() frame <- h2o.createFrame(rows = 6, cols = 2, categorical_fraction = 0.0, missing_fraction = 0.7, seed = 123) h2o.cummax(frame, 1) ## End(Not run)## Not run: library(h2o) h2o.init() frame <- h2o.createFrame(rows = 6, cols = 2, categorical_fraction = 0.0, missing_fraction = 0.7, seed = 123) h2o.cummax(frame, 1) ## End(Not run)
Return the cumulative min over a column or across a row
h2o.cummin(x, axis = 0)h2o.cummin(x, axis = 0)
x |
An H2OFrame object. |
axis |
An int that indicates whether to do down a column (0) or across a row (1). |
cumsum for the base R implementation, cummin().
## Not run: library(h2o) h2o.init() frame <- h2o.createFrame(rows = 6, cols = 2, categorical_fraction = 0.0, missing_fraction = 0.7, seed = 123) h2o.cummin(frame, 1) ## End(Not run)## Not run: library(h2o) h2o.init() frame <- h2o.createFrame(rows = 6, cols = 2, categorical_fraction = 0.0, missing_fraction = 0.7, seed = 123) h2o.cummin(frame, 1) ## End(Not run)
Return the cumulative product over a column or across a row
h2o.cumprod(x, axis = 0)h2o.cumprod(x, axis = 0)
x |
An H2OFrame object. |
axis |
An int that indicates whether to do down a column (0) or across a row (1). |
cumsum for the base R implementation, cumprod().
## Not run: library(h2o) h2o.init() frame <- h2o.createFrame(rows = 6, cols = 2, categorical_fraction = 0.0, missing_fraction = 0.7, seed = 123) h2o.cumprod(frame, 1) ## End(Not run)## Not run: library(h2o) h2o.init() frame <- h2o.createFrame(rows = 6, cols = 2, categorical_fraction = 0.0, missing_fraction = 0.7, seed = 123) h2o.cumprod(frame, 1) ## End(Not run)
Return the cumulative sum over a column or across a row
h2o.cumsum(x, axis = 0)h2o.cumsum(x, axis = 0)
x |
An H2OFrame object. |
axis |
An int that indicates whether to do down a column (0) or across a row (1). |
cumsum for the base R implementation.
## Not run: library(h2o) h2o.init() frame <- h2o.createFrame(rows = 6, cols = 2, categorical_fraction = 0.0, missing_fraction = 0.7, seed = 123) h2o.cumsum(frame, 1) ## End(Not run)## Not run: library(h2o) h2o.init() frame <- h2o.createFrame(rows = 6, cols = 2, categorical_fraction = 0.0, missing_fraction = 0.7, seed = 123) h2o.cumsum(frame, 1) ## End(Not run)
Divides the range of the H2O data into intervals and codes the values according to which interval they fall in. The leftmost interval corresponds to the level one, the next is level two, etc.
h2o.cut( x, breaks, labels = NULL, include.lowest = FALSE, right = TRUE, dig.lab = 3, ... ) ## S3 method for class 'H2OFrame' cut( x, breaks, labels = NULL, include.lowest = FALSE, right = TRUE, dig.lab = 3, ... )h2o.cut( x, breaks, labels = NULL, include.lowest = FALSE, right = TRUE, dig.lab = 3, ... ) ## S3 method for class 'H2OFrame' cut( x, breaks, labels = NULL, include.lowest = FALSE, right = TRUE, dig.lab = 3, ... )
x |
An H2OFrame object with a single numeric column. |
breaks |
A numeric vector of two or more unique cut points. |
labels |
Labels for the levels of the resulting category. By default, labels are constructed sing "(a,b]" interval notation. |
include.lowest |
|
right |
|
dig.lab |
Integer which is used when labels are not given, determines the number of digits used in formatting the break numbers. |
... |
Further arguments passed to or from other methods. |
Returns an H2OFrame object containing the factored data with intervals as levels.
## Not run: library(h2o) h2o.init() iris_hf <- as.h2o(iris) summary(iris_hf) # Cut sepal length column into intervals determined by min/max/quantiles sepal_len_cut <- cut(iris_hf$Sepal.Length, c(4.2, 4.8, 5.8, 6, 8)) head(sepal_len_cut) summary(sepal_len_cut) ## End(Not run)## Not run: library(h2o) h2o.init() iris_hf <- as.h2o(iris) summary(iris_hf) # Cut sepal length column into intervals determined by min/max/quantiles sepal_len_cut <- cut(iris_hf$Sepal.Length, c(4.2, 4.8, 5.8, 6, 8)) head(sepal_len_cut) summary(sepal_len_cut) ## End(Not run)
Converts the entries of an H2OFrame object from milliseconds to days of the month (on a 1 to 31 scale).
h2o.day(x) day(x) ## S3 method for class 'H2OFrame' day(x)h2o.day(x) day(x) ## S3 method for class 'H2OFrame' day(x)
x |
An H2OFrame object. |
An H2OFrame object containing the entries of x converted to days of
the month.
## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/jira/v-11-eurodate.csv" hdf <- h2o.importFile(f) h2o.day(hdf["ds9"]) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/jira/v-11-eurodate.csv" hdf <- h2o.importFile(f) h2o.day(hdf["ds9"]) ## End(Not run)
Converts the entries of an H2OFrame object from milliseconds to days of the week (on a 0 to 6 scale).
h2o.dayOfWeek(x) dayOfWeek(x) ## S3 method for class 'H2OFrame' dayOfWeek(x)h2o.dayOfWeek(x) dayOfWeek(x) ## S3 method for class 'H2OFrame' dayOfWeek(x)
x |
An H2OFrame object. |
An H2OFrame object containing the entries of x converted to days of
the week.
## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/jira/v-11-eurodate.csv" hdf <- h2o.importFile(f) h2o.dayOfWeek(hdf["ds9"]) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/jira/v-11-eurodate.csv" hdf <- h2o.importFile(f) h2o.dayOfWeek(hdf["ds9"]) ## End(Not run)
Compute the Discrete Cosine Transform of every row in the H2OFrame
h2o.dct(data, destination_frame, dimensions, inverse = FALSE)h2o.dct(data, destination_frame, dimensions, inverse = FALSE)
data |
An H2OFrame object representing the dataset to transform |
destination_frame |
A frame ID for the result |
dimensions |
An array containing the 3 integer values for height, width, depth of each sample. The product of HxWxD must total up to less than the number of columns. For 1D, use c(L,1,1), for 2D, use C(N,M,1). |
inverse |
Whether to perform the inverse transform |
Returns an H2OFrame object.
## Not run: library(h2o) h2o.init() df <- h2o.createFrame(rows = 1000, cols = 8 * 16 * 24, categorical_fraction = 0, integer_fraction = 0, missing_fraction = 0) df1 <- h2o.dct(data = df, dimensions = c(8 * 16 * 24, 1, 1)) df2 <- h2o.dct(data = df1, dimensions = c(8 * 16 * 24, 1, 1), inverse = TRUE) max(abs(df1 - df2)) df1 <- h2o.dct(data = df, dimensions = c(8 * 16, 24, 1)) df2 <- h2o.dct(data = df1, dimensions = c(8 * 16, 24, 1), inverse = TRUE) max(abs(df1 - df2)) df1 <- h2o.dct(data = df, dimensions = c(8, 16, 24)) df2 <- h2o.dct(data = df1, dimensions = c(8, 16, 24), inverse = TRUE) max(abs(df1 - df2)) ## End(Not run)## Not run: library(h2o) h2o.init() df <- h2o.createFrame(rows = 1000, cols = 8 * 16 * 24, categorical_fraction = 0, integer_fraction = 0, missing_fraction = 0) df1 <- h2o.dct(data = df, dimensions = c(8 * 16 * 24, 1, 1)) df2 <- h2o.dct(data = df1, dimensions = c(8 * 16 * 24, 1, 1), inverse = TRUE) max(abs(df1 - df2)) df1 <- h2o.dct(data = df, dimensions = c(8 * 16, 24, 1)) df2 <- h2o.dct(data = df1, dimensions = c(8 * 16, 24, 1), inverse = TRUE) max(abs(df1 - df2)) df1 <- h2o.dct(data = df, dimensions = c(8, 16, 24)) df2 <- h2o.dct(data = df1, dimensions = c(8, 16, 24), inverse = TRUE) max(abs(df1 - df2)) ## End(Not run)
For each subset of an H2O data set, apply a user-specified function, then combine the results. This is an experimental feature based on plyr::ddply.
h2o.ddply(X, .variables, FUN, ..., .progress = "none")h2o.ddply(X, .variables, FUN, ..., .progress = "none")
X |
An H2OFrame object to be processed. |
.variables |
Variables to split |
FUN |
Function to apply to each subset grouping. |
... |
Additional arguments passed on to |
.progress |
Name of the progress bar to use. #TODO: (Currently unimplemented) |
Returns an H2OFrame object containing the results from the split/apply operation, arranged
## Not run: library(h2o) h2o.init() # Import iris dataset to H2O iris_hf <- as.h2o(iris) # Add function taking mean of Sepal.Length column fun <- function(df) { sum(df[, 1], na.rm = TRUE) / nrow(df) } # Apply function to groups by flower specie # uses h2o's ddply, since iris_hf is an H2OFrame object res <- h2o.ddply(iris_hf, "Species", fun) head(res) ## End(Not run)## Not run: library(h2o) h2o.init() # Import iris dataset to H2O iris_hf <- as.h2o(iris) # Add function taking mean of Sepal.Length column fun <- function(df) { sum(df[, 1], na.rm = TRUE) / nrow(df) } # Apply function to groups by flower specie # uses h2o's ddply, since iris_hf is an H2OFrame object res <- h2o.ddply(iris_hf, "Species", fun) head(res) ## End(Not run)
Builds a Decision Tree model on an H2OFrame.
h2o.decision_tree( x, y, training_frame, model_id = NULL, ignore_const_cols = TRUE, categorical_encoding = c("AUTO", "Enum", "OneHotInternal", "OneHotExplicit", "Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited"), seed = -1, max_depth = 20, min_rows = 10 )h2o.decision_tree( x, y, training_frame, model_id = NULL, ignore_const_cols = TRUE, categorical_encoding = c("AUTO", "Enum", "OneHotInternal", "OneHotExplicit", "Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited"), seed = -1, max_depth = 20, min_rows = 10 )
x |
(Optional) A vector containing the names or indices of the predictor variables to use in building the model. If x is missing, then all columns except y are used. |
y |
The name or column index of the response variable in the data. The response must be either a numeric or a categorical/factor variable. If the response is numeric, then a regression model will be trained, otherwise it will train a classification model. |
training_frame |
Id of the training data frame. |
model_id |
Destination id for this model; auto-generated if not specified. |
ignore_const_cols |
|
categorical_encoding |
Encoding scheme for categorical features Must be one of: "AUTO", "Enum", "OneHotInternal", "OneHotExplicit", "Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited". Defaults to AUTO. |
seed |
Seed for random numbers (affects certain parts of the algo that are stochastic and those might or might not be enabled by default). Defaults to -1 (time-based random number). |
max_depth |
Max depth of tree. Defaults to 20. |
min_rows |
Fewest allowed (weighted) observations in a leaf. Defaults to 10. |
Creates a H2OModel object of the right type.
predict.H2OModel for prediction
## Not run: library(h2o) h2o.init() # Import the airlines dataset f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv" data <- h2o.importFile(f) # Set predictors and response; set response as a factor data["CAPSULE"] <- as.factor(data["CAPSULE"]) predictors <- c("AGE","RACE","DPROS","DCAPS","PSA","VOL","GLEASON") response <- "CAPSULE" # Train the DT model h2o_dt <- h2o.decision_tree(x = predictors, y = response, training_frame = data, seed = 1234) ## End(Not run)## Not run: library(h2o) h2o.init() # Import the airlines dataset f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv" data <- h2o.importFile(f) # Set predictors and response; set response as a factor data["CAPSULE"] <- as.factor(data["CAPSULE"]) predictors <- c("AGE","RACE","DPROS","DCAPS","PSA","VOL","GLEASON") response <- "CAPSULE" # Train the DT model h2o_dt <- h2o.decision_tree(x = predictors, y = response, training_frame = data, seed = 1234) ## End(Not run)
If your source file is encrypted - setup a Decryption Tool and then provide the reference (result of this function) to the import functions.
h2o.decryptionSetup( keystore, keystore_type = "JCEKS", key_alias = NA_character_, password = NA_character_, decrypt_tool = "", decrypt_impl = "water.parser.GenericDecryptionTool", cipher_spec = NA_character_ )h2o.decryptionSetup( keystore, keystore_type = "JCEKS", key_alias = NA_character_, password = NA_character_, decrypt_tool = "", decrypt_impl = "water.parser.GenericDecryptionTool", cipher_spec = NA_character_ )
keystore |
An H2OFrame object referencing a loaded Java Keystore (see example). |
keystore_type |
(Optional) Specification of Keystore type, defaults to JCEKS. |
key_alias |
Which key from the keystore to use for decryption. |
password |
Password to the keystore and the key. |
decrypt_tool |
(Optional) Name of the decryption tool. |
decrypt_impl |
(Optional) Java class name implementing the Decryption Tool. |
cipher_spec |
Specification of a cipher (eg.: AES/ECB/PKCS5Padding). |
h2o.importFile, h2o.parseSetup
## Not run: library(h2o) h2o.init() ks_path <- system.file("extdata", "keystore.jks", package = "h2o") keystore <- h2o.importFile(path = ks_path, parse = FALSE) # don't parse, keep as a binary file cipher <- "AES/ECB/PKCS5Padding" pwd <- "Password123" alias <- "secretKeyAlias" dt <- h2o.decryptionSetup(keystore, key_alias = alias, password = pwd, cipher_spec = cipher) data_path <- system.file("extdata", "prostate.csv.aes", package = "h2o") data <- h2o.importFile(data_path, decrypt_tool = dt) summary(data) ## End(Not run)## Not run: library(h2o) h2o.init() ks_path <- system.file("extdata", "keystore.jks", package = "h2o") keystore <- h2o.importFile(path = ks_path, parse = FALSE) # don't parse, keep as a binary file cipher <- "AES/ECB/PKCS5Padding" pwd <- "Password123" alias <- "secretKeyAlias" dt <- h2o.decryptionSetup(keystore, key_alias = alias, password = pwd, cipher_spec = cipher) data_path <- system.file("extdata", "prostate.csv.aes", package = "h2o") data <- h2o.importFile(data_path, decrypt_tool = dt) summary(data) ## End(Not run)
Extract the non-linear feature from an H2O data set using an H2O deep learning model.
h2o.deepfeatures(object, data, layer)h2o.deepfeatures(object, data, layer)
object |
An H2OModel object that represents the deep learning model to be used for feature extraction. |
data |
An H2OFrame object. |
layer |
Index (integer) of the hidden layer to extract |
Returns an H2OFrame object with as many features as the number of units in the hidden layer of the specified index.
h2o.deeplearning for making H2O Deep Learning models.
## Not run: library(h2o) h2o.init() prostate_path = system.file("extdata", "prostate.csv", package = "h2o") prostate = h2o.importFile(path = prostate_path) prostate_dl = h2o.deeplearning(x = 3:9, y = 2, training_frame = prostate, hidden = c(100, 200), epochs = 5) prostate_deepfeatures_layer1 = h2o.deepfeatures(prostate_dl, prostate, layer = 1) prostate_deepfeatures_layer2 = h2o.deepfeatures(prostate_dl, prostate, layer = 2) head(prostate_deepfeatures_layer1) head(prostate_deepfeatures_layer2) ## End(Not run)## Not run: library(h2o) h2o.init() prostate_path = system.file("extdata", "prostate.csv", package = "h2o") prostate = h2o.importFile(path = prostate_path) prostate_dl = h2o.deeplearning(x = 3:9, y = 2, training_frame = prostate, hidden = c(100, 200), epochs = 5) prostate_deepfeatures_layer1 = h2o.deepfeatures(prostate_dl, prostate, layer = 1) prostate_deepfeatures_layer2 = h2o.deepfeatures(prostate_dl, prostate, layer = 2) head(prostate_deepfeatures_layer1) head(prostate_deepfeatures_layer2) ## End(Not run)
Builds a feed-forward multilayer artificial neural network on an H2OFrame.
h2o.deeplearning( x, y, training_frame, model_id = NULL, validation_frame = NULL, nfolds = 0, keep_cross_validation_models = TRUE, keep_cross_validation_predictions = FALSE, keep_cross_validation_fold_assignment = FALSE, fold_assignment = c("AUTO", "Random", "Modulo", "Stratified"), fold_column = NULL, ignore_const_cols = TRUE, score_each_iteration = FALSE, weights_column = NULL, offset_column = NULL, balance_classes = FALSE, class_sampling_factors = NULL, max_after_balance_size = 5, checkpoint = NULL, pretrained_autoencoder = NULL, overwrite_with_best_model = TRUE, use_all_factor_levels = TRUE, standardize = TRUE, activation = c("Tanh", "TanhWithDropout", "Rectifier", "RectifierWithDropout", "Maxout", "MaxoutWithDropout"), hidden = c(200, 200), epochs = 10, train_samples_per_iteration = -2, target_ratio_comm_to_comp = 0.05, seed = -1, adaptive_rate = TRUE, rho = 0.99, epsilon = 1e-08, rate = 0.005, rate_annealing = 1e-06, rate_decay = 1, momentum_start = 0, momentum_ramp = 1e+06, momentum_stable = 0, nesterov_accelerated_gradient = TRUE, input_dropout_ratio = 0, hidden_dropout_ratios = NULL, l1 = 0, l2 = 0, max_w2 = 3.4028235e+38, initial_weight_distribution = c("UniformAdaptive", "Uniform", "Normal"), initial_weight_scale = 1, initial_weights = NULL, initial_biases = NULL, loss = c("Automatic", "CrossEntropy", "Quadratic", "Huber", "Absolute", "Quantile"), distribution = c("AUTO", "bernoulli", "multinomial", "gaussian", "poisson", "gamma", "tweedie", "laplace", "quantile", "huber"), quantile_alpha = 0.5, tweedie_power = 1.5, huber_alpha = 0.9, score_interval = 5, score_training_samples = 10000, score_validation_samples = 0, score_duty_cycle = 0.1, classification_stop = 0, regression_stop = 1e-06, stopping_rounds = 5, stopping_metric = c("AUTO", "deviance", "logloss", "MSE", "RMSE", "MAE", "RMSLE", "AUC", "AUCPR", "lift_top_group", "misclassification", "mean_per_class_error", "custom", "custom_increasing"), stopping_tolerance = 0, max_runtime_secs = 0, score_validation_sampling = c("Uniform", "Stratified"), diagnostics = TRUE, fast_mode = TRUE, force_load_balance = TRUE, variable_importances = TRUE, replicate_training_data = TRUE, single_node_mode = FALSE, shuffle_training_data = FALSE, missing_values_handling = c("MeanImputation", "Skip"), quiet_mode = FALSE, autoencoder = FALSE, sparse = FALSE, col_major = FALSE, average_activation = 0, sparsity_beta = 0, max_categorical_features = 2147483647, reproducible = FALSE, export_weights_and_biases = FALSE, mini_batch_size = 1, categorical_encoding = c("AUTO", "Enum", "OneHotInternal", "OneHotExplicit", "Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited"), elastic_averaging = FALSE, elastic_averaging_moving_rate = 0.9, elastic_averaging_regularization = 0.001, export_checkpoints_dir = NULL, auc_type = c("AUTO", "NONE", "MACRO_OVR", "WEIGHTED_OVR", "MACRO_OVO", "WEIGHTED_OVO"), custom_metric_func = NULL, gainslift_bins = -1, verbose = FALSE )h2o.deeplearning( x, y, training_frame, model_id = NULL, validation_frame = NULL, nfolds = 0, keep_cross_validation_models = TRUE, keep_cross_validation_predictions = FALSE, keep_cross_validation_fold_assignment = FALSE, fold_assignment = c("AUTO", "Random", "Modulo", "Stratified"), fold_column = NULL, ignore_const_cols = TRUE, score_each_iteration = FALSE, weights_column = NULL, offset_column = NULL, balance_classes = FALSE, class_sampling_factors = NULL, max_after_balance_size = 5, checkpoint = NULL, pretrained_autoencoder = NULL, overwrite_with_best_model = TRUE, use_all_factor_levels = TRUE, standardize = TRUE, activation = c("Tanh", "TanhWithDropout", "Rectifier", "RectifierWithDropout", "Maxout", "MaxoutWithDropout"), hidden = c(200, 200), epochs = 10, train_samples_per_iteration = -2, target_ratio_comm_to_comp = 0.05, seed = -1, adaptive_rate = TRUE, rho = 0.99, epsilon = 1e-08, rate = 0.005, rate_annealing = 1e-06, rate_decay = 1, momentum_start = 0, momentum_ramp = 1e+06, momentum_stable = 0, nesterov_accelerated_gradient = TRUE, input_dropout_ratio = 0, hidden_dropout_ratios = NULL, l1 = 0, l2 = 0, max_w2 = 3.4028235e+38, initial_weight_distribution = c("UniformAdaptive", "Uniform", "Normal"), initial_weight_scale = 1, initial_weights = NULL, initial_biases = NULL, loss = c("Automatic", "CrossEntropy", "Quadratic", "Huber", "Absolute", "Quantile"), distribution = c("AUTO", "bernoulli", "multinomial", "gaussian", "poisson", "gamma", "tweedie", "laplace", "quantile", "huber"), quantile_alpha = 0.5, tweedie_power = 1.5, huber_alpha = 0.9, score_interval = 5, score_training_samples = 10000, score_validation_samples = 0, score_duty_cycle = 0.1, classification_stop = 0, regression_stop = 1e-06, stopping_rounds = 5, stopping_metric = c("AUTO", "deviance", "logloss", "MSE", "RMSE", "MAE", "RMSLE", "AUC", "AUCPR", "lift_top_group", "misclassification", "mean_per_class_error", "custom", "custom_increasing"), stopping_tolerance = 0, max_runtime_secs = 0, score_validation_sampling = c("Uniform", "Stratified"), diagnostics = TRUE, fast_mode = TRUE, force_load_balance = TRUE, variable_importances = TRUE, replicate_training_data = TRUE, single_node_mode = FALSE, shuffle_training_data = FALSE, missing_values_handling = c("MeanImputation", "Skip"), quiet_mode = FALSE, autoencoder = FALSE, sparse = FALSE, col_major = FALSE, average_activation = 0, sparsity_beta = 0, max_categorical_features = 2147483647, reproducible = FALSE, export_weights_and_biases = FALSE, mini_batch_size = 1, categorical_encoding = c("AUTO", "Enum", "OneHotInternal", "OneHotExplicit", "Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited"), elastic_averaging = FALSE, elastic_averaging_moving_rate = 0.9, elastic_averaging_regularization = 0.001, export_checkpoints_dir = NULL, auc_type = c("AUTO", "NONE", "MACRO_OVR", "WEIGHTED_OVR", "MACRO_OVO", "WEIGHTED_OVO"), custom_metric_func = NULL, gainslift_bins = -1, verbose = FALSE )
x |
(Optional) A vector containing the names or indices of the predictor variables to use in building the model. If x is missing, then all columns except y are used. |
y |
The name or column index of the response variable in the data. The response must be either a numeric or a categorical/factor variable. If the response is numeric, then a regression model will be trained, otherwise it will train a classification model. |
training_frame |
Id of the training data frame. |
model_id |
Destination id for this model; auto-generated if not specified. |
validation_frame |
Id of the validation data frame. |
nfolds |
Number of folds for K-fold cross-validation (0 to disable or >= 2). Defaults to 0. |
keep_cross_validation_models |
|
keep_cross_validation_predictions |
|
keep_cross_validation_fold_assignment |
|
fold_assignment |
Cross-validation fold assignment scheme, if fold_column is not specified. The 'Stratified' option will stratify the folds based on the response variable, for classification problems. Must be one of: "AUTO", "Random", "Modulo", "Stratified". Defaults to AUTO. |
fold_column |
Column with cross-validation fold index assignment per observation. |
ignore_const_cols |
|
score_each_iteration |
|
weights_column |
Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0. |
offset_column |
Offset column. This will be added to the combination of columns before applying the link function. |
balance_classes |
|
class_sampling_factors |
Desired over/under-sampling ratios per class (in lexicographic order). If not specified, sampling factors will be automatically computed to obtain class balance during training. Requires balance_classes. |
max_after_balance_size |
Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires balance_classes. Defaults to 5.0. |
checkpoint |
Model checkpoint to resume training with. |
pretrained_autoencoder |
Pretrained autoencoder model to initialize this model with. |
overwrite_with_best_model |
|
use_all_factor_levels |
|
standardize |
|
activation |
Activation function. Must be one of: "Tanh", "TanhWithDropout", "Rectifier", "RectifierWithDropout", "Maxout", "MaxoutWithDropout". Defaults to Rectifier. |
|
Hidden layer sizes (e.g. [100, 100]). Defaults to c(200, 200). |
|
epochs |
How many times the dataset should be iterated (streamed), can be fractional. Defaults to 10. |
train_samples_per_iteration |
Number of training samples (globally) per MapReduce iteration. Special values are 0: one epoch, -1: all available data (e.g., replicated training data), -2: automatic. Defaults to -2. |
target_ratio_comm_to_comp |
Target ratio of communication overhead to computation. Only for multi-node operation and train_samples_per_iteration = -2 (auto-tuning). Defaults to 0.05. |
seed |
Seed for random numbers (affects certain parts of the algo that are stochastic and those might or might not be enabled by default). Note: only reproducible when running single threaded. Defaults to -1 (time-based random number). |
adaptive_rate |
|
rho |
Adaptive learning rate time decay factor (similarity to prior updates). Defaults to 0.99. |
epsilon |
Adaptive learning rate smoothing factor (to avoid divisions by zero and allow progress). Defaults to 1e-08. |
rate |
Learning rate (higher => less stable, lower => slower convergence). Defaults to 0.005. |
rate_annealing |
Learning rate annealing: rate / (1 + rate_annealing * samples). Defaults to 1e-06. |
rate_decay |
Learning rate decay factor between layers (N-th layer: rate * rate_decay ^ (n - 1). Defaults to 1. |
momentum_start |
Initial momentum at the beginning of training (try 0.5). Defaults to 0. |
momentum_ramp |
Number of training samples for which momentum increases. Defaults to 1000000. |
momentum_stable |
Final momentum after the ramp is over (try 0.99). Defaults to 0. |
nesterov_accelerated_gradient |
|
input_dropout_ratio |
Input layer dropout ratio (can improve generalization, try 0.1 or 0.2). Defaults to 0. |
|
Hidden layer dropout ratios (can improve generalization), specify one value per hidden layer, defaults to 0.5. |
|
l1 |
L1 regularization (can add stability and improve generalization, causes many weights to become 0). Defaults to 0. |
l2 |
L2 regularization (can add stability and improve generalization, causes many weights to be small. Defaults to 0. |
max_w2 |
Constraint for squared sum of incoming weights per unit (e.g. for Rectifier). Defaults to 3.4028235e+38. |
initial_weight_distribution |
Initial weight distribution. Must be one of: "UniformAdaptive", "Uniform", "Normal". Defaults to UniformAdaptive. |
initial_weight_scale |
Uniform: -value...value, Normal: stddev. Defaults to 1. |
initial_weights |
A list of H2OFrame ids to initialize the weight matrices of this model with. |
initial_biases |
A list of H2OFrame ids to initialize the bias vectors of this model with. |
loss |
Loss function. Must be one of: "Automatic", "CrossEntropy", "Quadratic", "Huber", "Absolute", "Quantile". Defaults to Automatic. |
distribution |
Distribution function Must be one of: "AUTO", "bernoulli", "multinomial", "gaussian", "poisson", "gamma", "tweedie", "laplace", "quantile", "huber". Defaults to AUTO. |
quantile_alpha |
Desired quantile for Quantile regression, must be between 0 and 1. Defaults to 0.5. |
tweedie_power |
Tweedie power for Tweedie regression, must be between 1 and 2. Defaults to 1.5. |
huber_alpha |
Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1). Defaults to 0.9. |
score_interval |
Shortest time interval (in seconds) between model scoring. Defaults to 5. |
score_training_samples |
Number of training set samples for scoring (0 for all). Defaults to 10000. |
score_validation_samples |
Number of validation set samples for scoring (0 for all). Defaults to 0. |
score_duty_cycle |
Maximum duty cycle fraction for scoring (lower: more training, higher: more scoring). Defaults to 0.1. |
classification_stop |
Stopping criterion for classification error fraction on training data (-1 to disable). Defaults to 0. |
regression_stop |
Stopping criterion for regression error (MSE) on training data (-1 to disable). Defaults to 1e-06. |
stopping_rounds |
Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable) Defaults to 5. |
stopping_metric |
Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client. Must be one of: "AUTO", "deviance", "logloss", "MSE", "RMSE", "MAE", "RMSLE", "AUC", "AUCPR", "lift_top_group", "misclassification", "mean_per_class_error", "custom", "custom_increasing". Defaults to AUTO. |
stopping_tolerance |
Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much) Defaults to 0. |
max_runtime_secs |
Maximum allowed runtime in seconds for model training. Use 0 to disable. Defaults to 0. |
score_validation_sampling |
Method used to sample validation dataset for scoring. Must be one of: "Uniform", "Stratified". Defaults to Uniform. |
diagnostics |
|
fast_mode |
|
force_load_balance |
|
variable_importances |
|
replicate_training_data |
|
single_node_mode |
|
shuffle_training_data |
|
missing_values_handling |
Handling of missing values. Either MeanImputation or Skip. Must be one of: "MeanImputation", "Skip". Defaults to MeanImputation. |
quiet_mode |
|
autoencoder |
|
sparse |
|
col_major |
|
average_activation |
Average activation for sparse auto-encoder. #Experimental Defaults to 0. |
sparsity_beta |
Sparsity regularization. #Experimental Defaults to 0. |
max_categorical_features |
Max. number of categorical features, enforced via hashing. #Experimental Defaults to 2147483647. |
reproducible |
|
export_weights_and_biases |
|
mini_batch_size |
Mini-batch size (smaller leads to better fit, larger can speed up and generalize better). Defaults to 1. |
categorical_encoding |
Encoding scheme for categorical features Must be one of: "AUTO", "Enum", "OneHotInternal", "OneHotExplicit", "Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited". Defaults to AUTO. |
elastic_averaging |
|
elastic_averaging_moving_rate |
Elastic averaging moving rate (only if elastic averaging is enabled). Defaults to 0.9. |
elastic_averaging_regularization |
Elastic averaging regularization strength (only if elastic averaging is enabled). Defaults to 0.001. |
export_checkpoints_dir |
Automatically export generated models to this directory. |
auc_type |
Set default multinomial AUC type. Must be one of: "AUTO", "NONE", "MACRO_OVR", "WEIGHTED_OVR", "MACRO_OVO", "WEIGHTED_OVO". Defaults to AUTO. |
custom_metric_func |
Reference to custom evaluation function, format: 'language:keyName=funcName' |
gainslift_bins |
Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning. Defaults to -1. |
verbose |
|
predict.H2OModel for prediction
## Not run: library(h2o) h2o.init() iris_hf <- as.h2o(iris) iris_dl <- h2o.deeplearning(x = 1:4, y = 5, training_frame = iris_hf, seed=123456) # now make a prediction predictions <- h2o.predict(iris_dl, iris_hf) ## End(Not run)## Not run: library(h2o) h2o.init() iris_hf <- as.h2o(iris) iris_dl <- h2o.deeplearning(x = 1:4, y = 5, training_frame = iris_hf, seed=123456) # now make a prediction predictions <- h2o.predict(iris_dl, iris_hf) ## End(Not run)
Reports the "Flow" style summary rollups on an instance of H2OFrame. Includes information about column types, mins/maxs/missing/zero counts/stds/number of levels
h2o.describe(frame)h2o.describe(frame)
frame |
An H2OFrame object. |
A table with the Frame stats.
## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.importFile(path = prostate_path) h2o.describe(prostate) ## End(Not run)## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.importFile(path = prostate_path) h2o.describe(prostate) ## End(Not run)
Conduct a lag 1 transform on a numeric H2OFrame column
h2o.difflag1(object)h2o.difflag1(object)
object |
H2OFrame object |
Returns an H2OFrame object.
## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv" cars <- h2o.importFile(f) predictors <- c("displacement", "power", "weight", "acceleration", "year") response <- "cylinders" cars_split <- h2o.splitFrame(data = cars, ratios = 0.8, seed = 1234) train <- cars_split[[1]] valid <- cars_split[[2]] cars_gbm <- h2o.gbm(x = predictors, y = response, training_frame = train, validation_frame = valid, nfolds = 5, seed = 1234) h2o.difflag1(cars["cylinders"]) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv" cars <- h2o.importFile(f) predictors <- c("displacement", "power", "weight", "acceleration", "year") response <- "cylinders" cars_split <- h2o.splitFrame(data = cars, ratios = 0.8, seed = 1234) train <- cars_split[[1]] valid <- cars_split[[2]] cars_gbm <- h2o.gbm(x = predictors, y = response, training_frame = train, validation_frame = valid, nfolds = 5, seed = 1234) h2o.difflag1(cars["cylinders"]) ## End(Not run)
Returns the number of rows and columns for an H2OFrame object.
h2o.dim(x)h2o.dim(x)
x |
An H2OFrame object. |
dim for the base R implementation.
## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv" cars <- h2o.importFile(f) h2o.dim(cars) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv" cars <- h2o.importFile(f) h2o.dim(cars) ## End(Not run)
Column names of an H2OFrame
h2o.dimnames(x)h2o.dimnames(x)
x |
An H2OFrame object. |
dimnames for the base R implementation.
## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv" cars <- h2o.importFile(f) h2o.dimnames(cars) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv" cars <- h2o.importFile(f) h2o.dimnames(cars) ## End(Not run)
Create a frame containing aggregations of intersectional fairness across the models.
h2o.disparate_analysis( models, newdata, protected_columns, reference, favorable_class, air_metric = "selectedRatio", alpha = 0.05 )h2o.disparate_analysis( models, newdata, protected_columns, reference, favorable_class, air_metric = "selectedRatio", alpha = 0.05 )
models |
List of H2O Models |
newdata |
H2OFrame |
protected_columns |
List of categorical columns that contain sensitive information such as race, gender, age etc. |
reference |
List of values corresponding to a reference for each protected columns. If set to NULL, it will use the biggest group as the reference. |
favorable_class |
Positive/favorable outcome class of the response. |
air_metric |
Metric used for Adverse Impact Ratio calculation. Defaults to “selectedRatio“. |
alpha |
The alpha level is the probability of rejecting the null hypothesis that the protected group and the reference came from the same population when the null hypothesis is true. |
frame containing aggregations of intersectional fairness across the models
## Not run: library(h2o) h2o.init() data <- h2o.importFile(paste0("https://s3.amazonaws.com/h2o-public-test-data/smalldata/", "admissibleml_test/taiwan_credit_card_uci.csv")) x <- c('LIMIT_BAL', 'AGE', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6', 'BILL_AMT1', 'BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6') y <- "default payment next month" protected_columns <- c('SEX', 'EDUCATION') for (col in c(y, protected_columns)) data[[col]] <- as.factor(data[[col]]) splits <- h2o.splitFrame(data, 0.8) train <- splits[[1]] test <- splits[[2]] reference <- c(SEX = "1", EDUCATION = "2") # university educated man favorable_class <- "0" # no default next month aml <- h2o.automl(x, y, training_frame = train, max_models = 3) h2o.disparate_analysis(aml, test, protected_columns = protected_columns, reference = reference, favorable_class = favorable_class) ## End(Not run)## Not run: library(h2o) h2o.init() data <- h2o.importFile(paste0("https://s3.amazonaws.com/h2o-public-test-data/smalldata/", "admissibleml_test/taiwan_credit_card_uci.csv")) x <- c('LIMIT_BAL', 'AGE', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6', 'BILL_AMT1', 'BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6') y <- "default payment next month" protected_columns <- c('SEX', 'EDUCATION') for (col in c(y, protected_columns)) data[[col]] <- as.factor(data[[col]]) splits <- h2o.splitFrame(data, 0.8) train <- splits[[1]] test <- splits[[2]] reference <- c(SEX = "1", EDUCATION = "2") # university educated man favorable_class <- "0" # no default next month aml <- h2o.automl(x, y, training_frame = train, max_models = 3) h2o.disparate_analysis(aml, test, protected_columns = protected_columns, reference = reference, favorable_class = favorable_class) ## End(Not run)
Compute a pairwise distance measure between all rows of two numeric H2OFrames.
h2o.distance(x, y, measure)h2o.distance(x, y, measure)
x |
An H2OFrame object (large, references). |
y |
An H2OFrame object (small, queries). |
measure |
An optional string indicating what distance measure to use. Must be one of: "l1" - Absolute distance (L1-norm, >=0) "l2" - Euclidean distance (L2-norm, >=0) "cosine" - Cosine similarity (-1...1) "cosine_sq" - Squared Cosine similarity (0...1) |
## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(path = prostate_path) h2o.distance(prostate[11:30, ], prostate[1:10, ], "cosine") ## End(Not run)## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(path = prostate_path) h2o.distance(prostate[11:30, ], prostate[1:10, ], "cosine") ## End(Not run)
Download the model in binary format. The owner of the file saved is the user by which python session was executed.
h2o.download_model( model, path = NULL, export_cross_validation_predictions = FALSE, filename = "" )h2o.download_model( model, path = NULL, export_cross_validation_predictions = FALSE, filename = "" )
model |
An H2OModel |
path |
The path where binary file should be downloaded. Downloaded to current directory by default. |
export_cross_validation_predictions |
A boolean flag indicating whether the download model should be saved with CV Holdout Frame predictions. Default is not to export the predictions. |
filename |
string indicating the file name. |
## Not run: library(h2o) h <- h2o.init() fr <- as.h2o(iris) my_model <- h2o.gbm(x = 1:4, y = 5, training_frame = fr) h2o.download_model(my_model) # save to the current working directory ## End(Not run)## Not run: library(h2o) h <- h2o.init() fr <- as.h2o(iris) my_model <- h2o.gbm(x = 1:4, y = 5, training_frame = fr) h2o.download_model(my_model) # save to the current working directory ## End(Not run)
Download the model in MOJO format.
h2o.download_mojo( model, path = getwd(), get_genmodel_jar = FALSE, genmodel_name = "", genmodel_path = "", filename = "" )h2o.download_mojo( model, path = getwd(), get_genmodel_jar = FALSE, genmodel_name = "", genmodel_path = "", filename = "" )
model |
An H2OModel |
path |
The path where MOJO file should be saved. Saved to current directory by default. |
get_genmodel_jar |
If TRUE, then also download h2o-genmodel.jar and store it in either in the same folder as the MOJO or in “genmodel_path“ if specified. |
genmodel_name |
Custom name of genmodel jar. |
genmodel_path |
Path to store h2o-genmodel.jar. If left blank and “get_genmodel_jar“ is TRUE, then the h2o-genmodel.jar is saved to “path“. |
filename |
string indicating the file name. (Type of file is always .zip) |
Name of the MOJO file written to the path.
## Not run: library(h2o) h <- h2o.init() fr <- as.h2o(iris) my_model <- h2o.gbm(x = 1:4, y = 5, training_frame = fr) h2o.download_mojo(my_model) # save to the current working directory ## End(Not run)## Not run: library(h2o) h <- h2o.init() fr <- as.h2o(iris) my_model <- h2o.gbm(x = 1:4, y = 5, training_frame = fr) h2o.download_mojo(my_model) # save to the current working directory ## End(Not run)
Download the Scoring POJO (Plain Old Java Object) of an H2O Model
h2o.download_pojo( model, path = NULL, getjar = NULL, get_jar = TRUE, jar_name = "" )h2o.download_pojo( model, path = NULL, getjar = NULL, get_jar = TRUE, jar_name = "" )
model |
An H2OModel |
path |
The path to the directory to store the POJO (no trailing slash). If NULL, then print to to console. The file name will be a compilable java file name. |
getjar |
(DEPRECATED) Whether to also download the h2o-genmodel.jar file needed to compile the POJO. This argument is now called 'get_jar'. |
get_jar |
Whether to also download the h2o-genmodel.jar file needed to compile the POJO |
jar_name |
Custom name of genmodel jar. |
If path is NULL, then pretty print the POJO to the console. Otherwise save it to the specified directory and return POJO file name.
## Not run: library(h2o) h <- h2o.init() fr <- as.h2o(iris) my_model <- h2o.gbm(x = 1:4, y = 5, training_frame = fr) h2o.download_pojo(my_model) # print the model to screen # h2o.download_pojo(my_model, getwd()) # save the POJO and jar file to the current working # directory, NOT RUN # h2o.download_pojo(my_model, getwd(), get_jar = FALSE ) # save only the POJO to the current # working directory, NOT RUN h2o.download_pojo(my_model, getwd()) # save to the current working directory ## End(Not run)## Not run: library(h2o) h <- h2o.init() fr <- as.h2o(iris) my_model <- h2o.gbm(x = 1:4, y = 5, training_frame = fr) h2o.download_pojo(my_model) # print the model to screen # h2o.download_pojo(my_model, getwd()) # save the POJO and jar file to the current working # directory, NOT RUN # h2o.download_pojo(my_model, getwd(), get_jar = FALSE ) # save only the POJO to the current # working directory, NOT RUN h2o.download_pojo(my_model, getwd()) # save to the current working directory ## End(Not run)
h2o.downloadAllLogs downloads all H2O log files to local disk in .zip format. Generally used for debugging purposes.
h2o.downloadAllLogs(dirname = ".", filename = NULL)h2o.downloadAllLogs(dirname = ".", filename = NULL)
dirname |
(Optional) A character string indicating the directory that the log file should be saved in. |
filename |
(Optional) A character string indicating the name that the log file should be saved to. Note that the saved format is .zip, so the file name must include the .zip extension. |
## Not run: h2o.downloadAllLogs(dirname='./your_directory_name/', filename = 'autoh2o_log.zip') ## End(Not run)## Not run: h2o.downloadAllLogs(dirname='./your_directory_name/', filename = 'autoh2o_log.zip') ## End(Not run)
Download an H2O data set to a CSV file on the local disk
h2o.downloadCSV(data, filename)h2o.downloadCSV(data, filename)
data |
an H2OFrame object to be downloaded. |
filename |
A string indicating the name that the CSV file should be should be saved to. |
Files located on the H2O server may be very large! Make sure you have enough hard drive space to accomodate the entire file.
## Not run: library(h2o) h2o.init() iris_hf <- as.h2o(iris) file_path <- paste(getwd(), "my_iris_file.csv", sep = .Platform$file.sep) h2o.downloadCSV(iris_hf, file_path) file.info(file_path) file.remove(file_path) ## End(Not run)## Not run: library(h2o) h2o.init() iris_hf <- as.h2o(iris) file_path <- paste(getwd(), "my_iris_file.csv", sep = .Platform$file.sep) h2o.downloadCSV(iris_hf, file_path) file.info(file_path) file.remove(file_path) ## End(Not run)
Drops duplicated rows across specified columns.
h2o.drop_duplicates(frame, columns, keep = "first")h2o.drop_duplicates(frame, columns, keep = "first")
frame |
An H2OFrame object to drop duplicates on. |
columns |
Columns to compare during the duplicate detection process. |
keep |
Which rows to keep. The "first" value (default) keeps the first row and deletes the rest. The "last" keeps the last row. |
## Not run: library(h2o) h2o.init() data <- as.h2o(iris) deduplicated_data <- h2o.drop_duplicates(data, c("Species", "Sepal.Length"), keep = "first") ## End(Not run)## Not run: library(h2o) h2o.init() data <- as.h2o(iris) deduplicated_data <- h2o.drop_duplicates(data, c("Species", "Sepal.Length"), keep = "first") ## End(Not run)
Return the Shannon entropy of a string column. If the string is empty, the entropy is 0.
h2o.entropy(x)h2o.entropy(x)
x |
The column on which to calculate the entropy. |
## Not run: library(h2o) h2o.init() buys <- as.h2o(c("no", "no", "yes", "yes", "yes", "no", "yes", "no", "yes", "yes","no")) buys_entropy <- h2o.entropy(buys) ## End(Not run)## Not run: library(h2o) h2o.init() buys <- as.h2o(c("no", "no", "yes", "yes", "yes", "no", "yes", "no", "yes", "yes","no")) buys_entropy <- h2o.entropy(buys) ## End(Not run)
Compute the exponential function of x
h2o.exp(x)h2o.exp(x)
x |
An H2OFrame object. |
Log for the base R implementation, exp().
## Not run: library(h2o) h2o.init() frame <- h2o.createFrame(rows = 6, cols = 2, categorical_fraction = 0.0, missing_fraction = 0.7, seed = 123) h2o.exp(frame["C1"]) ## End(Not run)## Not run: library(h2o) h2o.init() frame <- h2o.createFrame(rows = 6, cols = 2, categorical_fraction = 0.0, missing_fraction = 0.7, seed = 123) h2o.exp(frame["C1"]) ## End(Not run)
The H2O Explainability Interface is a convenient wrapper to a number of explainabilty methods and visualizations in H2O. The function can be applied to a single model or group of models and returns a list of explanations, which are individual units of explanation such as a partial dependence plot or a variable importance plot. Most of the explanations are visual (ggplot plots). These plots can also be created by individual utility functions as well.
h2o.explain( object, newdata, columns = NULL, top_n_features = 5, include_explanations = "ALL", exclude_explanations = NULL, plot_overrides = NULL, background_frame = NULL )h2o.explain( object, newdata, columns = NULL, top_n_features = 5, include_explanations = "ALL", exclude_explanations = NULL, plot_overrides = NULL, background_frame = NULL )
object |
A list of H2O models, an H2O AutoML instance, or an H2OFrame with a 'model_id' column (e.g. H2OAutoML leaderboard). |
newdata |
An H2OFrame. |
columns |
A vector of column names or column indices to create plots with. If specified parameter top_n_features will be ignored. |
top_n_features |
An integer specifying the number of columns to use, ranked by variable importance (where applicable). |
include_explanations |
If specified, return only the specified model explanations. (Mutually exclusive with exclude_explanations) |
exclude_explanations |
Exclude specified model explanations. |
plot_overrides |
Overrides for individual model explanations, e.g.
|
background_frame |
Optional frame, that is used as the source of baselines for the marginal SHAP. Setting it enables calculating SHAP in more models but it can be more time and memory consuming. |
List of outputs with class "H2OExplanation"
## Not run: library(h2o) h2o.init() # Import the wine dataset into H2O: f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/wine/winequality-redwhite-no-BOM.csv" df <- h2o.importFile(f) # Set the response response <- "quality" # Split the dataset into a train and test set: splits <- h2o.splitFrame(df, ratios = 0.8, seed = 1) train <- splits[[1]] test <- splits[[2]] # Build and train the model: aml <- h2o.automl(y = response, training_frame = train, max_models = 10, seed = 1) # Create the explanation for whole H2OAutoML object exa <- h2o.explain(aml, test) print(exa) # Create the explanation for the leader model exm <- h2o.explain(aml@leader, test) print(exm) ## End(Not run)## Not run: library(h2o) h2o.init() # Import the wine dataset into H2O: f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/wine/winequality-redwhite-no-BOM.csv" df <- h2o.importFile(f) # Set the response response <- "quality" # Split the dataset into a train and test set: splits <- h2o.splitFrame(df, ratios = 0.8, seed = 1) train <- splits[[1]] test <- splits[[2]] # Build and train the model: aml <- h2o.automl(y = response, training_frame = train, max_models = 10, seed = 1) # Create the explanation for whole H2OAutoML object exa <- h2o.explain(aml, test) print(exa) # Create the explanation for the leader model exm <- h2o.explain(aml@leader, test) print(exm) ## End(Not run)
Explain the behavior of a model or group of models with respect to a single row of data. The function returns a list of explanations, which are individual units of explanation such as a partial dependence plot or a variable importance plot. Most of the explanations are visual (ggplot plots). These plots can also be created by individual utility functions as well.
h2o.explain_row( object, newdata, row_index, columns = NULL, top_n_features = 5, include_explanations = "ALL", exclude_explanations = NULL, plot_overrides = NULL, background_frame = NULL )h2o.explain_row( object, newdata, row_index, columns = NULL, top_n_features = 5, include_explanations = "ALL", exclude_explanations = NULL, plot_overrides = NULL, background_frame = NULL )
object |
A list of H2O models, an H2O AutoML instance, or an H2OFrame with a 'model_id' column (e.g. H2OAutoML leaderboard). |
newdata |
An H2OFrame. |
row_index |
A row index of the instance to explain. |
columns |
A vector of column names or column indices to create plots with. If specified parameter top_n_features will be ignored. |
top_n_features |
An integer specifying the number of columns to use, ranked by variable importance (where applicable). |
include_explanations |
If specified, return only the specified model explanations. (Mutually exclusive with exclude_explanations) |
exclude_explanations |
Exclude specified model explanations. |
plot_overrides |
Overrides for individual model explanations, e.g.,
|
background_frame |
Optional frame, that is used as the source of baselines for the marginal SHAP. Setting it enables calculating SHAP in more models but it can be more time and memory consuming. |
List of outputs with class "H2OExplanation"
## Not run: library(h2o) h2o.init() # Import the wine dataset into H2O: f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/wine/winequality-redwhite-no-BOM.csv" df <- h2o.importFile(f) # Set the response response <- "quality" # Split the dataset into a train and test set: splits <- h2o.splitFrame(df, ratios = 0.8, seed = 1) train <- splits[[1]] test <- splits[[2]] # Build and train the model: aml <- h2o.automl(y = response, training_frame = train, max_models = 10, seed = 1) # Create the explanation for whole H2OAutoML object exa <- h2o.explain_row(aml, test, row_index = 1) print(exa) # Create the explanation for the leader model exm <- h2o.explain_row(aml@leader, test, row_index = 1) print(exm) ## End(Not run)## Not run: library(h2o) h2o.init() # Import the wine dataset into H2O: f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/wine/winequality-redwhite-no-BOM.csv" df <- h2o.importFile(f) # Set the response response <- "quality" # Split the dataset into a train and test set: splits <- h2o.splitFrame(df, ratios = 0.8, seed = 1) train <- splits[[1]] test <- splits[[2]] # Build and train the model: aml <- h2o.automl(y = response, training_frame = train, max_models = 10, seed = 1) # Create the explanation for whole H2OAutoML object exa <- h2o.explain_row(aml, test, row_index = 1) print(exa) # Create the explanation for the leader model exm <- h2o.explain_row(aml@leader, test, row_index = 1) print(exm) ## End(Not run)
Exports an H2OFrame (which can be either VA or FV) to a file. This file may be on the H2O instace's local filesystem, or to HDFS (preface the path with hdfs://) or to S3N (preface the path with s3n://).
h2o.exportFile( data, path, force = FALSE, sep = ",", compression = NULL, parts = 1, header = TRUE, quote_header = TRUE, format = "csv", write_checksum = TRUE )h2o.exportFile( data, path, force = FALSE, sep = ",", compression = NULL, parts = 1, header = TRUE, quote_header = TRUE, format = "csv", write_checksum = TRUE )
data |
An H2OFrame object. |
path |
The path to write the file to. Must include the directory and also filename if exporting to a single file. May be prefaced with hdfs:// or s3n://. Each row of data appears as line of the file. |
force |
logical, indicates how to deal with files that already exist. |
sep |
The field separator character. Values on each line of the file will be separated by this character (default ","). |
compression |
How to compress the exported dataset (default none; gzip, bzip2 and snappy available) |
parts |
integer, number of part files to export to. Default is to write to a single file. Large data can be exported to multiple 'part' files, where each part file contains subset of the data. User can specify the maximum number of part files or use value -1 to indicate that H2O should itself determine the optimal number of files. Parameter path will be considered to be a path to a directory if export to multiple part files is desired. Part files conform to naming scheme 'part-m-?????'. |
header |
logical, indicates whether to write the header line. Default is to include the header in the output file. |
quote_header |
logical, indicates whether column names should be quoted. Default is to use quotes. |
format |
string, one of "csv" or "parquet". Default is "csv". Export to parquet is multipart and H2O itself determines the optimal number of files (1 file per chunk). |
write_checksum |
logical, if supported by the format (e.g. 'parquet'), export will include a checksum file for each exported data file. |
In the case of existing files force = TRUE will overwrite the file.
Otherwise, the operation will fail.
## Not run: library(h2o) h2o.init() iris_hf <- as.h2o(iris) # These aren't real paths # h2o.exportFile(iris_hf, path = "/path/on/h2o/server/filesystem/iris.csv") # h2o.exportFile(iris_hf, path = "hdfs://path/in/hdfs/iris.csv") # h2o.exportFile(iris_hf, path = "s3n://path/in/s3/iris.csv") ## End(Not run)## Not run: library(h2o) h2o.init() iris_hf <- as.h2o(iris) # These aren't real paths # h2o.exportFile(iris_hf, path = "/path/on/h2o/server/filesystem/iris.csv") # h2o.exportFile(iris_hf, path = "hdfs://path/in/hdfs/iris.csv") # h2o.exportFile(iris_hf, path = "s3n://path/in/s3/iris.csv") ## End(Not run)
Exports an H2OModel to HDFS.
h2o.exportHDFS(object, path, force = FALSE)h2o.exportHDFS(object, path, force = FALSE)
object |
an H2OModel class object. |
path |
The path to write the model to. Must include the driectory and filename. |
force |
logical, indicates how to deal with files that already exist. |
## Not run: library(h2o) h2o.init f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/iris/iris_train.csv" train <- h2o.importFile(f) h2o.exportHDFS(train, path = " ", force = FALSE) ## End(Not run)## Not run: library(h2o) h2o.init f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/iris/iris_train.csv" train <- h2o.importFile(f) h2o.exportHDFS(train, path = " ", force = FALSE) ## End(Not run)
Trains an Extended Isolation Forest model
h2o.extendedIsolationForest( training_frame, x, model_id = NULL, ignore_const_cols = TRUE, categorical_encoding = c("AUTO", "Enum", "OneHotInternal", "OneHotExplicit", "Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited"), score_each_iteration = FALSE, score_tree_interval = 0, ntrees = 100, sample_size = 256, extension_level = 0, seed = -1, disable_training_metrics = TRUE )h2o.extendedIsolationForest( training_frame, x, model_id = NULL, ignore_const_cols = TRUE, categorical_encoding = c("AUTO", "Enum", "OneHotInternal", "OneHotExplicit", "Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited"), score_each_iteration = FALSE, score_tree_interval = 0, ntrees = 100, sample_size = 256, extension_level = 0, seed = -1, disable_training_metrics = TRUE )
training_frame |
Id of the training data frame. |
x |
A vector containing the |
model_id |
Destination id for this model; auto-generated if not specified. |
ignore_const_cols |
|
categorical_encoding |
Encoding scheme for categorical features Must be one of: "AUTO", "Enum", "OneHotInternal", "OneHotExplicit", "Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited". Defaults to AUTO. |
score_each_iteration |
|
score_tree_interval |
Score the model after every so many trees. Disabled if set to 0. Defaults to 0. |
ntrees |
Number of Extended Isolation Forest trees. Defaults to 100. |
sample_size |
Number of randomly sampled observations used to train each Extended Isolation Forest tree. Defaults to 256. |
extension_level |
Maximum is N - 1 (N = numCols). Minimum is 0. Extended Isolation Forest with extension_Level = 0 behaves like Isolation Forest. Defaults to 0. |
seed |
Seed for random numbers (affects certain parts of the algo that are stochastic and those might or might not be enabled by default). Defaults to -1 (time-based random number). |
disable_training_metrics |
|
## Not run: library(h2o) h2o.init() # Import the prostate dataset p <- h2o.importFile(path="https://raw.github.com/h2oai/h2o/master/smalldata/logreg/prostate.csv") # Set the predictors predictors <- c("AGE","RACE","DPROS","DCAPS","PSA","VOL","GLEASON") # Build an Extended Isolation forest model model <- h2o.extendedIsolationForest(x = predictors, training_frame = p, model_id = "eif.hex", ntrees = 100, sample_size = 256, extension_level = length(predictors) - 1) # Calculate score score <- h2o.predict(model, p) anomaly_score <- score$anomaly_score # Number in [0, 1] explicitly defined in Equation (1) from Extended Isolation Forest paper # or in paragraph '2 Isolation and Isolation Trees' of Isolation Forest paper anomaly_score <- score$anomaly_score # Average path length of the point in Isolation Trees from root to the leaf mean_length <- score$mean_length ## End(Not run)## Not run: library(h2o) h2o.init() # Import the prostate dataset p <- h2o.importFile(path="https://raw.github.com/h2oai/h2o/master/smalldata/logreg/prostate.csv") # Set the predictors predictors <- c("AGE","RACE","DPROS","DCAPS","PSA","VOL","GLEASON") # Build an Extended Isolation forest model model <- h2o.extendedIsolationForest(x = predictors, training_frame = p, model_id = "eif.hex", ntrees = 100, sample_size = 256, extension_level = length(predictors) - 1) # Calculate score score <- h2o.predict(model, p) anomaly_score <- score$anomaly_score # Number in [0, 1] explicitly defined in Equation (1) from Extended Isolation Forest paper # or in paragraph '2 Isolation and Isolation Trees' of Isolation Forest paper anomaly_score <- score$anomaly_score # Average path length of the point in Isolation Trees from root to the leaf mean_length <- score$mean_length ## End(Not run)
Partial dependence plot per protected group.
h2o.fair_pd_plot(model, newdata, protected_columns, column, autoscale = TRUE)h2o.fair_pd_plot(model, newdata, protected_columns, column, autoscale = TRUE)
model |
H2O Model Object |
newdata |
H2OFrame |
protected_columns |
List of categorical columns that contain sensitive information such as race, gender, age etc. |
column |
String containing column name. |
autoscale |
If “True“, try to guess when to use log transformation on X axis. |
ggplot2 object
## Not run: library(h2o) h2o.init() data <- h2o.importFile(paste0("https://s3.amazonaws.com/h2o-public-test-data/smalldata/", "admissibleml_test/taiwan_credit_card_uci.csv")) x <- c('LIMIT_BAL', 'AGE', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6', 'BILL_AMT1', 'BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6') y <- "default payment next month" protected_columns <- c('SEX', 'EDUCATION') for (col in c(y, protected_columns)) data[[col]] <- as.factor(data[[col]]) splits <- h2o.splitFrame(data, 0.8) train <- splits[[1]] test <- splits[[2]] reference <- c(SEX = "1", EDUCATION = "2") # university educated man favorable_class <- "0" # no default next month gbm <- h2o.gbm(x, y, training_frame = train) h2o.fair_pd_plot(gbm, test, protected_columns, "AGE") ## End(Not run)## Not run: library(h2o) h2o.init() data <- h2o.importFile(paste0("https://s3.amazonaws.com/h2o-public-test-data/smalldata/", "admissibleml_test/taiwan_credit_card_uci.csv")) x <- c('LIMIT_BAL', 'AGE', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6', 'BILL_AMT1', 'BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6') y <- "default payment next month" protected_columns <- c('SEX', 'EDUCATION') for (col in c(y, protected_columns)) data[[col]] <- as.factor(data[[col]]) splits <- h2o.splitFrame(data, 0.8) train <- splits[[1]] test <- splits[[2]] reference <- c(SEX = "1", EDUCATION = "2") # university educated man favorable_class <- "0" # no default next month gbm <- h2o.gbm(x, y, training_frame = train) h2o.fair_pd_plot(gbm, test, protected_columns, "AGE") ## End(Not run)
Plot PR curve per protected group.
h2o.fair_pr_plot(model, newdata, protected_columns, reference, favorable_class)h2o.fair_pr_plot(model, newdata, protected_columns, reference, favorable_class)
model |
H2O Model Object |
newdata |
H2OFrame |
protected_columns |
List of categorical columns that contain sensitive information such as race, gender, age etc. |
reference |
List of values corresponding to a reference for each protected columns. If set to NULL, it will use the biggest group as the reference. |
favorable_class |
Positive/favorable outcome class of the response. |
ggplot2 object
## Not run: library(h2o) h2o.init() data <- h2o.importFile(paste0("https://s3.amazonaws.com/h2o-public-test-data/smalldata/", "admissibleml_test/taiwan_credit_card_uci.csv")) x <- c('LIMIT_BAL', 'AGE', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6', 'BILL_AMT1', 'BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6') y <- "default payment next month" protected_columns <- c('SEX', 'EDUCATION') for (col in c(y, protected_columns)) data[[col]] <- as.factor(data[[col]]) splits <- h2o.splitFrame(data, 0.8) train <- splits[[1]] test <- splits[[2]] reference <- c(SEX = "1", EDUCATION = "2") # university educated man favorable_class <- "0" # no default next month gbm <- h2o.gbm(x, y, training_frame = train) h2o.fair_pr_plot(gbm, test, protected_columns = protected_columns, reference = reference, favorable_class = favorable_class) ## End(Not run)## Not run: library(h2o) h2o.init() data <- h2o.importFile(paste0("https://s3.amazonaws.com/h2o-public-test-data/smalldata/", "admissibleml_test/taiwan_credit_card_uci.csv")) x <- c('LIMIT_BAL', 'AGE', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6', 'BILL_AMT1', 'BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6') y <- "default payment next month" protected_columns <- c('SEX', 'EDUCATION') for (col in c(y, protected_columns)) data[[col]] <- as.factor(data[[col]]) splits <- h2o.splitFrame(data, 0.8) train <- splits[[1]] test <- splits[[2]] reference <- c(SEX = "1", EDUCATION = "2") # university educated man favorable_class <- "0" # no default next month gbm <- h2o.gbm(x, y, training_frame = train) h2o.fair_pr_plot(gbm, test, protected_columns = protected_columns, reference = reference, favorable_class = favorable_class) ## End(Not run)
Plot ROC curve per protected group.
h2o.fair_roc_plot( model, newdata, protected_columns, reference, favorable_class )h2o.fair_roc_plot( model, newdata, protected_columns, reference, favorable_class )
model |
H2O Model Object |
newdata |
H2OFrame |
protected_columns |
List of categorical columns that contain sensitive information such as race, gender, age etc. |
reference |
List of values corresponding to a reference for each protected columns. If set to NULL, it will use the biggest group as the reference. |
favorable_class |
Positive/favorable outcome class of the response. |
ggplot2 object
## Not run: library(h2o) h2o.init() data <- h2o.importFile(paste0("https://s3.amazonaws.com/h2o-public-test-data/smalldata/", "admissibleml_test/taiwan_credit_card_uci.csv")) x <- c('LIMIT_BAL', 'AGE', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6', 'BILL_AMT1', 'BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6') y <- "default payment next month" protected_columns <- c('SEX', 'EDUCATION') for (col in c(y, protected_columns)) data[[col]] <- as.factor(data[[col]]) splits <- h2o.splitFrame(data, 0.8) train <- splits[[1]] test <- splits[[2]] reference <- c(SEX = "1", EDUCATION = "2") # university educated man favorable_class <- "0" # no default next month gbm <- h2o.gbm(x, y, training_frame = train) h2o.fair_roc_plot(gbm, test, protected_columns = protected_columns, reference = reference, favorable_class = favorable_class) ## End(Not run)## Not run: library(h2o) h2o.init() data <- h2o.importFile(paste0("https://s3.amazonaws.com/h2o-public-test-data/smalldata/", "admissibleml_test/taiwan_credit_card_uci.csv")) x <- c('LIMIT_BAL', 'AGE', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6', 'BILL_AMT1', 'BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6') y <- "default payment next month" protected_columns <- c('SEX', 'EDUCATION') for (col in c(y, protected_columns)) data[[col]] <- as.factor(data[[col]]) splits <- h2o.splitFrame(data, 0.8) train <- splits[[1]] test <- splits[[2]] reference <- c(SEX = "1", EDUCATION = "2") # university educated man favorable_class <- "0" # no default next month gbm <- h2o.gbm(x, y, training_frame = train) h2o.fair_roc_plot(gbm, test, protected_columns = protected_columns, reference = reference, favorable_class = favorable_class) ## End(Not run)
SHAP summary plot for one feature with protected groups on y-axis.
h2o.fair_shap_plot( model, newdata, protected_columns, column, autoscale = TRUE, background_frame = NULL )h2o.fair_shap_plot( model, newdata, protected_columns, column, autoscale = TRUE, background_frame = NULL )
model |
H2O Model Object |
newdata |
H2OFrame |
protected_columns |
List of categorical columns that contain sensitive information such as race, gender, age etc. |
column |
String containing column name. |
autoscale |
If TRUE, try to guess when to use log transformation on X axis. |
background_frame |
Optional frame, that is used as the source of baselines for the marginal SHAP. |
list of ggplot2 objects
## Not run: library(h2o) h2o.init() data <- h2o.importFile(paste0("https://s3.amazonaws.com/h2o-public-test-data/smalldata/", "admissibleml_test/taiwan_credit_card_uci.csv")) x <- c('LIMIT_BAL', 'AGE', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6', 'BILL_AMT1', 'BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6') y <- "default payment next month" protected_columns <- c('SEX', 'EDUCATION') for (col in c(y, protected_columns)) data[[col]] <- as.factor(data[[col]]) splits <- h2o.splitFrame(data, 0.8) train <- splits[[1]] test <- splits[[2]] reference <- c(SEX = "1", EDUCATION = "2") # university educated man favorable_class <- "0" # no default next month gbm <- h2o.gbm(x, y, training_frame = train) h2o.fair_shap_plot(gbm, test, protected_columns, "AGE") ## End(Not run)## Not run: library(h2o) h2o.init() data <- h2o.importFile(paste0("https://s3.amazonaws.com/h2o-public-test-data/smalldata/", "admissibleml_test/taiwan_credit_card_uci.csv")) x <- c('LIMIT_BAL', 'AGE', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6', 'BILL_AMT1', 'BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6') y <- "default payment next month" protected_columns <- c('SEX', 'EDUCATION') for (col in c(y, protected_columns)) data[[col]] <- as.factor(data[[col]]) splits <- h2o.splitFrame(data, 0.8) train <- splits[[1]] test <- splits[[2]] reference <- c(SEX = "1", EDUCATION = "2") # university educated man favorable_class <- "0" # no default next month gbm <- h2o.gbm(x, y, training_frame = train) h2o.fair_shap_plot(gbm, test, protected_columns, "AGE") ## End(Not run)
Metrics: Gain - Total gain of each feature or feature interaction. FScore - Amount of possible splits taken on a feature or feature interaction. wFScore - Amount of possible splits taken on a feature or feature interaction weighed by the probability of the splits to take place. Average wFScore - wFScore divided by FScore. Average Gain - Gain divided by FScore. Expected Gain - Total gain of each feature or feature interaction weighed by the probability to gather the gain. Average Tree Index Average Tree Depth
h2o.feature_interaction( model, max_interaction_depth = 100, max_tree_depth = 100, max_deepening = -1 )h2o.feature_interaction( model, max_interaction_depth = 100, max_tree_depth = 100, max_deepening = -1 )
model |
A trained xgboost model. |
max_interaction_depth |
Upper bound for extracted feature interactions depth. Defaults to 100. |
max_tree_depth |
Upper bound for tree depth. Defaults to 100. |
max_deepening |
Upper bound for interaction start deepening (zero deepening => interactions starting at root only). Defaults to -1. |
## Not run: library(h2o) h2o.init() boston <- h2o.importFile( "https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv", destination_frame="boston" ) boston_xgb <- h2o.xgboost(training_frame = boston, y = "medv", seed = 1234) feature_interactions <- h2o.feature_interaction(boston_xgb) ## End(Not run)## Not run: library(h2o) h2o.init() boston <- h2o.importFile( "https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv", destination_frame="boston" ) boston_xgb <- h2o.xgboost(training_frame = boston, y = "medv", seed = 1234) feature_interactions <- h2o.feature_interaction(boston_xgb) ## End(Not run)
Fill NA's in a sequential manner up to a specified limit
h2o.fillna(x, method = "forward", axis = 1, maxlen = 1L)h2o.fillna(x, method = "forward", axis = 1, maxlen = 1L)
x |
an H2OFrame |
method |
A String: "forward" or "backward" |
axis |
An Integer 1 for row-wise fill (default), 2 for column-wise fill |
maxlen |
An Integer for maximum number of consecutive NA's to fill |
An H2OFrame after filling missing values
## Not run: library(h2o) h2o.init() frame_with_nas <- h2o.createFrame(rows = 6, cols = 2, categorical_fraction = 0.0, missing_fraction = 0.7, seed = 123) frame <- h2o.fillna(frame_with_nas, "forward", axis = 1, maxlen = 2L) ## End(Not run)## Not run: library(h2o) h2o.init() frame_with_nas <- h2o.createFrame(rows = 6, cols = 2, categorical_fraction = 0.0, missing_fraction = 0.7, seed = 123) frame <- h2o.fillna(frame_with_nas, "forward", axis = 1, maxlen = 2L) ## End(Not run)
Filter NA Columns
h2o.filterNACols(data, frac = 0.2)h2o.filterNACols(data, frac = 0.2)
data |
A dataset to filter on. |
frac |
The threshold of NAs to allow per column (columns >= this threshold are filtered) |
Returns a numeric vector of indexes that pertain to non-NA columns
## Not run: library(h2o) h2o.init() frame <- h2o.createFrame(rows = 6, cols = 2, categorical_fraction = 0.0, missing_fraction = 0.7, seed = 123) h2o.filterNACols(frame, frac = 0.5) h2o.filterNACols(frame, frac = 0.6) ## End(Not run)## Not run: library(h2o) h2o.init() frame <- h2o.createFrame(rows = 6, cols = 2, categorical_fraction = 0.0, missing_fraction = 0.7, seed = 123) h2o.filterNACols(frame, frac = 0.5) h2o.filterNACols(frame, frac = 0.6) ## End(Not run)
Find the threshold, give the max metric. No duplicate thresholds allowed
h2o.find_row_by_threshold(object, threshold)h2o.find_row_by_threshold(object, threshold)
object |
H2OBinomialMetrics |
threshold |
number between 0 and 1 |
## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv" cars <- h2o.importFile(f) cars["economy_20mpg"] <- as.factor(cars["economy_20mpg"]) predictors <- c("displacement", "power", "weight", "acceleration", "year") response <- "economy_20mpg" cars_split <- h2o.splitFrame(data = cars, ratios = 0.8, seed = 1234) train <- cars_split[[1]] valid <- cars_split[[2]] cars_gbm <- h2o.gbm(x = predictors, y = response, training_frame = train, validation_frame = valid, build_tree_one_node = TRUE , seed = 1234) perf <- h2o.performance(cars_gbm, cars) h2o.find_row_by_threshold(perf, 0.5) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv" cars <- h2o.importFile(f) cars["economy_20mpg"] <- as.factor(cars["economy_20mpg"]) predictors <- c("displacement", "power", "weight", "acceleration", "year") response <- "economy_20mpg" cars_split <- h2o.splitFrame(data = cars, ratios = 0.8, seed = 1234) train <- cars_split[[1]] valid <- cars_split[[2]] cars_gbm <- h2o.gbm(x = predictors, y = response, training_frame = train, validation_frame = valid, build_tree_one_node = TRUE , seed = 1234) perf <- h2o.performance(cars_gbm, cars) h2o.find_row_by_threshold(perf, 0.5) ## End(Not run)
Find the threshold, give the max metric
h2o.find_threshold_by_max_metric(object, metric)h2o.find_threshold_by_max_metric(object, metric)
object |
H2OBinomialMetrics |
metric |
"F1," for example |
## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv" cars <- h2o.importFile(f) cars["economy_20mpg"] <- as.factor(cars["economy_20mpg"]) predictors <- c("displacement", "power", "weight", "acceleration", "year") response <- "economy_20mpg" cars_split <- h2o.splitFrame(data = cars, ratios = 0.8, seed = 1234) train <- cars_split[[1]] valid <- cars_split[[2]] cars_gbm <- h2o.gbm(x = predictors, y = response, training_frame = train, validation_frame = valid, build_tree_one_node = TRUE , seed = 1234) perf <- h2o.performance(cars_gbm, cars) h2o.find_threshold_by_max_metric(perf, "fnr") ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv" cars <- h2o.importFile(f) cars["economy_20mpg"] <- as.factor(cars["economy_20mpg"]) predictors <- c("displacement", "power", "weight", "acceleration", "year") response <- "economy_20mpg" cars_split <- h2o.splitFrame(data = cars, ratios = 0.8, seed = 1234) train <- cars_split[[1]] valid <- cars_split[[2]] cars_gbm <- h2o.gbm(x = predictors, y = response, training_frame = train, validation_frame = valid, build_tree_one_node = TRUE , seed = 1234) perf <- h2o.performance(cars_gbm, cars) h2o.find_threshold_by_max_metric(perf, "fnr") ## End(Not run)
Find synonyms using a word2vec model.
h2o.findSynonyms(word2vec, word, count = 20)h2o.findSynonyms(word2vec, word, count = 20)
word2vec |
A word2vec model. |
word |
A single word to find synonyms for. |
count |
The top 'count' synonyms will be returned. |
## Not run: library(h2o) h2o.init() job_titles <- h2o.importFile( "https://s3.amazonaws.com/h2o-public-test-data/smalldata/craigslistJobTitles.csv", col.names = c("category", "jobtitle"), col.types = c("String", "String"), header = TRUE ) words <- h2o.tokenize(job_titles, " ") vec <- h2o.word2vec(training_frame = words) h2o.findSynonyms(vec, "teacher", count = 20) ## End(Not run)## Not run: library(h2o) h2o.init() job_titles <- h2o.importFile( "https://s3.amazonaws.com/h2o-public-test-data/smalldata/craigslistJobTitles.csv", col.names = c("category", "jobtitle"), col.types = c("String", "String"), header = TRUE ) words <- h2o.tokenize(job_titles, " ") vec <- h2o.word2vec(training_frame = words) h2o.findSynonyms(vec, "teacher", count = 20) ## End(Not run)
floor takes a single numeric argument x and returns a numeric vector containing the largest integers not greater than the corresponding elements of x.
h2o.floor(x)h2o.floor(x)
x |
An H2OFrame object. |
Round for the base R implementation, floor().
## Not run: library(h2o) h2o.init() frame <- h2o.createFrame(rows = 6, cols = 2, categorical_fraction = 0.0, missing_fraction = 0.7, seed = 123) h2o.floor(frame["C2"]) ## End(Not run)## Not run: library(h2o) h2o.init() frame <- h2o.createFrame(rows = 6, cols = 2, categorical_fraction = 0.0, missing_fraction = 0.7, seed = 123) h2o.floor(frame["C2"]) ## End(Not run)
Plot Gains/Lift curves
h2o.gains_lift_plot(object, type = c("both", "gains", "lift"), ...)h2o.gains_lift_plot(object, type = c("both", "gains", "lift"), ...)
object |
Either an H2OModel or H2OModelMetrics |
type |
What curve to plot. One of "both", "gains", "lift". |
... |
Optional arguments |
## Not run: library(h2o) h2o.init() data <- h2o.importFile( path = "https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") model <- h2o.gbm(x = c("Origin", "Distance"), y = "IsDepDelayed", training_frame = data, ntrees = 1) h2o.gains_lift_plot(model) ## End(Not run)## Not run: library(h2o) h2o.init() data <- h2o.importFile( path = "https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") model <- h2o.gbm(x = c("Origin", "Distance"), y = "IsDepDelayed", training_frame = data, ntrees = 1) h2o.gains_lift_plot(model) ## End(Not run)
Plot Gains/Lift curves
## S4 method for signature 'H2OModel' h2o.gains_lift_plot(object, type = c("both", "gains", "lift"), xval = FALSE)## S4 method for signature 'H2OModel' h2o.gains_lift_plot(object, type = c("both", "gains", "lift"), xval = FALSE)
object |
H2OModel object |
type |
What curve to plot. One of "both", "gains", "lift". |
xval |
if TRUE, use cross-validation metrics |
Plot Gains/Lift curves
## S4 method for signature 'H2OModelMetrics' h2o.gains_lift_plot(object, type = c("both", "gains", "lift"))## S4 method for signature 'H2OModelMetrics' h2o.gains_lift_plot(object, type = c("both", "gains", "lift"))
object |
H2OModelMetrics object |
type |
What curve to plot. One of "both", "gains", "lift". |
Retrieve either a single or many Gains/Lift tables from H2O objects.
h2o.gainsLift(object, ...) h2o.gains_lift(object, ...) ## S4 method for signature 'H2OModel' h2o.gainsLift(object, newdata, valid = FALSE, xval = FALSE, ...) ## S4 method for signature 'H2OModelMetrics' h2o.gainsLift(object)h2o.gainsLift(object, ...) h2o.gains_lift(object, ...) ## S4 method for signature 'H2OModel' h2o.gainsLift(object, newdata, valid = FALSE, xval = FALSE, ...) ## S4 method for signature 'H2OModelMetrics' h2o.gainsLift(object)
object |
Either an H2OModel object or an H2OModelMetrics object. |
... |
further arguments to be passed to/from this method. |
newdata |
An H2OFrame object that can be scored on. Requires a valid response column. |
valid |
Retrieve the validation metric. |
xval |
Retrieve the cross-validation metric. |
The H2OModelMetrics version of this function will only take H2OBinomialMetrics objects.
Calling this function on H2OModel objects returns a
Gains/Lift table corresponding to the predict function.
predict for generating prediction frames,
h2o.performance for creating
H2OModelMetrics.
## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(prostate_path) prostate[, 2] <- as.factor(prostate[, 2]) model <- h2o.gbm(x = 3:9, y = 2, distribution = "bernoulli", training_frame = prostate, validation_frame = prostate, nfolds = 3) h2o.gainsLift(model) ## extract training metrics h2o.gainsLift(model, valid = TRUE) ## extract validation metrics (here: the same) h2o.gainsLift(model, xval = TRUE) ## extract cross-validation metrics h2o.gainsLift(model, newdata = prostate) ## score on new data (here: the same) # Generating a ModelMetrics object perf <- h2o.performance(model, prostate) h2o.gainsLift(perf) ## extract from existing metrics object ## End(Not run)## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(prostate_path) prostate[, 2] <- as.factor(prostate[, 2]) model <- h2o.gbm(x = 3:9, y = 2, distribution = "bernoulli", training_frame = prostate, validation_frame = prostate, nfolds = 3) h2o.gainsLift(model) ## extract training metrics h2o.gainsLift(model, valid = TRUE) ## extract validation metrics (here: the same) h2o.gainsLift(model, xval = TRUE) ## extract cross-validation metrics h2o.gainsLift(model, newdata = prostate) ## score on new data (here: the same) # Generating a ModelMetrics object perf <- h2o.performance(model, prostate) h2o.gainsLift(perf) ## extract from existing metrics object ## End(Not run)
Creates a generalized additive model, specified by a response variable, a set of predictors, and a description of the error distribution.
h2o.gam( x, y, training_frame, gam_columns, model_id = NULL, validation_frame = NULL, nfolds = 0, seed = -1, keep_cross_validation_models = TRUE, keep_cross_validation_predictions = FALSE, keep_cross_validation_fold_assignment = FALSE, fold_assignment = c("AUTO", "Random", "Modulo", "Stratified"), fold_column = NULL, ignore_const_cols = TRUE, score_each_iteration = FALSE, offset_column = NULL, weights_column = NULL, family = c("AUTO", "gaussian", "binomial", "quasibinomial", "ordinal", "multinomial", "poisson", "gamma", "tweedie", "negativebinomial", "fractionalbinomial"), tweedie_variance_power = 0, tweedie_link_power = 0, theta = 0, solver = c("AUTO", "IRLSM", "L_BFGS", "COORDINATE_DESCENT_NAIVE", "COORDINATE_DESCENT", "GRADIENT_DESCENT_LH", "GRADIENT_DESCENT_SQERR"), alpha = NULL, lambda = NULL, lambda_search = FALSE, early_stopping = TRUE, nlambdas = -1, standardize = FALSE, missing_values_handling = c("MeanImputation", "Skip", "PlugValues"), plug_values = NULL, compute_p_values = FALSE, remove_collinear_columns = FALSE, splines_non_negative = NULL, intercept = TRUE, non_negative = FALSE, max_iterations = -1, objective_epsilon = -1, beta_epsilon = 1e-04, gradient_epsilon = -1, link = c("family_default", "identity", "logit", "log", "inverse", "tweedie", "ologit"), startval = NULL, prior = -1, cold_start = FALSE, lambda_min_ratio = -1, beta_constraints = NULL, max_active_predictors = -1, interactions = NULL, interaction_pairs = NULL, obj_reg = -1, export_checkpoints_dir = NULL, stopping_rounds = 0, stopping_metric = c("AUTO", "deviance", "logloss", "MSE", "RMSE", "MAE", "RMSLE", "AUC", "AUCPR", "lift_top_group", "misclassification", "mean_per_class_error", "custom", "custom_increasing"), stopping_tolerance = 0.001, balance_classes = FALSE, class_sampling_factors = NULL, max_after_balance_size = 5, max_runtime_secs = 0, custom_metric_func = NULL, num_knots = NULL, spline_orders = NULL, knot_ids = NULL, standardize_tp_gam_cols = FALSE, scale_tp_penalty_mat = FALSE, bs = NULL, scale = NULL, keep_gam_cols = FALSE, store_knot_locations = FALSE, auc_type = c("AUTO", "NONE", "MACRO_OVR", "WEIGHTED_OVR", "MACRO_OVO", "WEIGHTED_OVO"), gainslift_bins = -1 )h2o.gam( x, y, training_frame, gam_columns, model_id = NULL, validation_frame = NULL, nfolds = 0, seed = -1, keep_cross_validation_models = TRUE, keep_cross_validation_predictions = FALSE, keep_cross_validation_fold_assignment = FALSE, fold_assignment = c("AUTO", "Random", "Modulo", "Stratified"), fold_column = NULL, ignore_const_cols = TRUE, score_each_iteration = FALSE, offset_column = NULL, weights_column = NULL, family = c("AUTO", "gaussian", "binomial", "quasibinomial", "ordinal", "multinomial", "poisson", "gamma", "tweedie", "negativebinomial", "fractionalbinomial"), tweedie_variance_power = 0, tweedie_link_power = 0, theta = 0, solver = c("AUTO", "IRLSM", "L_BFGS", "COORDINATE_DESCENT_NAIVE", "COORDINATE_DESCENT", "GRADIENT_DESCENT_LH", "GRADIENT_DESCENT_SQERR"), alpha = NULL, lambda = NULL, lambda_search = FALSE, early_stopping = TRUE, nlambdas = -1, standardize = FALSE, missing_values_handling = c("MeanImputation", "Skip", "PlugValues"), plug_values = NULL, compute_p_values = FALSE, remove_collinear_columns = FALSE, splines_non_negative = NULL, intercept = TRUE, non_negative = FALSE, max_iterations = -1, objective_epsilon = -1, beta_epsilon = 1e-04, gradient_epsilon = -1, link = c("family_default", "identity", "logit", "log", "inverse", "tweedie", "ologit"), startval = NULL, prior = -1, cold_start = FALSE, lambda_min_ratio = -1, beta_constraints = NULL, max_active_predictors = -1, interactions = NULL, interaction_pairs = NULL, obj_reg = -1, export_checkpoints_dir = NULL, stopping_rounds = 0, stopping_metric = c("AUTO", "deviance", "logloss", "MSE", "RMSE", "MAE", "RMSLE", "AUC", "AUCPR", "lift_top_group", "misclassification", "mean_per_class_error", "custom", "custom_increasing"), stopping_tolerance = 0.001, balance_classes = FALSE, class_sampling_factors = NULL, max_after_balance_size = 5, max_runtime_secs = 0, custom_metric_func = NULL, num_knots = NULL, spline_orders = NULL, knot_ids = NULL, standardize_tp_gam_cols = FALSE, scale_tp_penalty_mat = FALSE, bs = NULL, scale = NULL, keep_gam_cols = FALSE, store_knot_locations = FALSE, auc_type = c("AUTO", "NONE", "MACRO_OVR", "WEIGHTED_OVR", "MACRO_OVO", "WEIGHTED_OVO"), gainslift_bins = -1 )
x |
(Optional) A vector containing the names or indices of the predictor variables to use in building the model. If x is missing, then all columns except y are used. |
y |
The name or column index of the response variable in the data. The response must be either a numeric or a categorical/factor variable. If the response is numeric, then a regression model will be trained, otherwise it will train a classification model. |
training_frame |
Id of the training data frame. |
gam_columns |
Arrays of predictor column names for gam for smoothers using single or multiple predictors like {{'c1'},{'c2','c3'},{'c4'},...} |
model_id |
Destination id for this model; auto-generated if not specified. |
validation_frame |
Id of the validation data frame. |
nfolds |
Number of folds for K-fold cross-validation (0 to disable or >= 2). Defaults to 0. |
seed |
Seed for random numbers (affects certain parts of the algo that are stochastic and those might or might not be enabled by default). Defaults to -1 (time-based random number). |
keep_cross_validation_models |
|
keep_cross_validation_predictions |
|
keep_cross_validation_fold_assignment |
|
fold_assignment |
Cross-validation fold assignment scheme, if fold_column is not specified. The 'Stratified' option will stratify the folds based on the response variable, for classification problems. Must be one of: "AUTO", "Random", "Modulo", "Stratified". Defaults to AUTO. |
fold_column |
Column with cross-validation fold index assignment per observation. |
ignore_const_cols |
|
score_each_iteration |
|
offset_column |
Offset column. This will be added to the combination of columns before applying the link function. |
weights_column |
Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0. |
family |
Family. Use binomial for classification with logistic regression, others are for regression problems. Must be one of: "AUTO", "gaussian", "binomial", "quasibinomial", "ordinal", "multinomial", "poisson", "gamma", "tweedie", "negativebinomial", "fractionalbinomial". Defaults to AUTO. |
tweedie_variance_power |
Tweedie variance power Defaults to 0. |
tweedie_link_power |
Tweedie link power Defaults to 0. |
theta |
Theta Defaults to 0. |
solver |
AUTO will set the solver based on given data and the other parameters. IRLSM is fast on on problems with small number of predictors and for lambda-search with L1 penalty, L_BFGS scales better for datasets with many columns. Must be one of: "AUTO", "IRLSM", "L_BFGS", "COORDINATE_DESCENT_NAIVE", "COORDINATE_DESCENT", "GRADIENT_DESCENT_LH", "GRADIENT_DESCENT_SQERR". Defaults to AUTO. |
alpha |
Distribution of regularization between the L1 (Lasso) and L2 (Ridge) penalties. A value of 1 for alpha represents Lasso regression, a value of 0 produces Ridge regression, and anything in between specifies the amount of mixing between the two. Default value of alpha is 0 when SOLVER = 'L-BFGS'; 0.5 otherwise. |
lambda |
Regularization strength |
lambda_search |
|
early_stopping |
|
nlambdas |
Number of lambdas to be used in a search. Default indicates: If alpha is zero, with lambda search set to True, the value of nlamdas is set to 30 (fewer lambdas are needed for ridge regression) otherwise it is set to 100. Defaults to -1. |
standardize |
|
missing_values_handling |
Handling of missing values. Either MeanImputation, Skip or PlugValues. Must be one of: "MeanImputation", "Skip", "PlugValues". Defaults to MeanImputation. |
plug_values |
Plug Values (a single row frame containing values that will be used to impute missing values of the training/validation frame, use with conjunction missing_values_handling = PlugValues) |
compute_p_values |
|
remove_collinear_columns |
|
splines_non_negative |
Valid for I-spline (bs=2) only. True if the I-splines are monotonically increasing (and monotonically non- decreasing) and False if the I-splines are monotonically decreasing (and monotonically non-increasing). If specified, must be the same size as gam_columns. Values for other spline types will be ignored. Default to true. |
intercept |
|
non_negative |
|
max_iterations |
Maximum number of iterations Defaults to -1. |
objective_epsilon |
Converge if objective value changes less than this. Default indicates: If lambda_search is set to True the value of objective_epsilon is set to .0001. If the lambda_search is set to False and lambda is equal to zero, the value of objective_epsilon is set to .000001, for any other value of lambda the default value of objective_epsilon is set to .0001. Defaults to -1. |
beta_epsilon |
Converge if beta changes less (using L-infinity norm) than beta esilon, ONLY applies to IRLSM solver Defaults to 0.0001. |
gradient_epsilon |
Converge if objective changes less (using L-infinity norm) than this, ONLY applies to L-BFGS solver. Default indicates: If lambda_search is set to False and lambda is equal to zero, the default value of gradient_epsilon is equal to .000001, otherwise the default value is .0001. If lambda_search is set to True, the conditional values above are 1E-8 and 1E-6 respectively. Defaults to -1. |
link |
Link function. Must be one of: "family_default", "identity", "logit", "log", "inverse", "tweedie", "ologit". Defaults to family_default. |
startval |
double array to initialize coefficients for GAM. |
prior |
Prior probability for y==1. To be used only for logistic regression iff the data has been sampled and the mean of response does not reflect reality. Defaults to -1. |
cold_start |
|
lambda_min_ratio |
Minimum lambda used in lambda search, specified as a ratio of lambda_max (the smallest lambda that drives all coefficients to zero). Default indicates: if the number of observations is greater than the number of variables, then lambda_min_ratio is set to 0.0001; if the number of observations is less than the number of variables, then lambda_min_ratio is set to 0.01. Defaults to -1. |
beta_constraints |
Beta constraints |
max_active_predictors |
Maximum number of active predictors during computation. Use as a stopping criterion to prevent expensive model building with many predictors. Default indicates: If the IRLSM solver is used, the value of max_active_predictors is set to 5000 otherwise it is set to 100000000. Defaults to -1. |
interactions |
A list of predictor column indices to interact. All pairwise combinations will be computed for the list. |
interaction_pairs |
A list of pairwise (first order) column interactions. |
obj_reg |
Likelihood divider in objective value computation, default is 1/nobs Defaults to -1. |
export_checkpoints_dir |
Automatically export generated models to this directory. |
stopping_rounds |
Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable) Defaults to 0. |
stopping_metric |
Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client. Must be one of: "AUTO", "deviance", "logloss", "MSE", "RMSE", "MAE", "RMSLE", "AUC", "AUCPR", "lift_top_group", "misclassification", "mean_per_class_error", "custom", "custom_increasing". Defaults to AUTO. |
stopping_tolerance |
Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much) Defaults to 0.001. |
balance_classes |
|
class_sampling_factors |
Desired over/under-sampling ratios per class (in lexicographic order). If not specified, sampling factors will be automatically computed to obtain class balance during training. Requires balance_classes. |
max_after_balance_size |
Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires balance_classes. Defaults to 5.0. |
max_runtime_secs |
Maximum allowed runtime in seconds for model training. Use 0 to disable. Defaults to 0. |
custom_metric_func |
Reference to custom evaluation function, format: 'language:keyName=funcName' |
num_knots |
Number of knots for gam predictors. If specified, must specify one for each gam predictor. For monotone I-splines, mininum = 2, for cs spline, minimum = 3. For thin plate, minimum is size of polynomial basis + 2. |
spline_orders |
Order of I-splines or NBSplineTypeI M-splines used for gam predictors. If specified, must be the same size as gam_columns. For I-splines, the spline_orders will be the same as the polynomials used to generate the splines. For M-splines, the polynomials used to generate the splines will be spline_order-1. Values for bs=0 or 1 will be ignored. |
knot_ids |
Array storing frame keys of knots. One for each gam column set specified in gam_columns |
standardize_tp_gam_cols |
|
scale_tp_penalty_mat |
|
bs |
Basis function type for each gam predictors, 0 for cr, 1 for thin plate regression with knots, 2 for monotone I-splines, 3 for NBSplineTypeI M-splines (refer to doc here: https://github.com/h2oai/h2o-3/issues/6926). If specified, must be the same size as gam_columns |
scale |
Smoothing parameter for gam predictors. If specified, must be of the same length as gam_columns |
keep_gam_cols |
|
store_knot_locations |
|
auc_type |
Set default multinomial AUC type. Must be one of: "AUTO", "NONE", "MACRO_OVR", "WEIGHTED_OVR", "MACRO_OVO", "WEIGHTED_OVO". Defaults to AUTO. |
gainslift_bins |
Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning. Defaults to -1. |
## Not run: h2o.init() # Run GAM of CAPSULE ~ AGE + RACE + PSA + DCAPS prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(path = prostate_path) prostate$CAPSULE <- as.factor(prostate$CAPSULE) h2o.gam(y = "CAPSULE", x = c("RACE"), gam_columns = c("PSA"), training_frame = prostate, family = "binomial") ## End(Not run)## Not run: h2o.init() # Run GAM of CAPSULE ~ AGE + RACE + PSA + DCAPS prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(path = prostate_path) prostate$CAPSULE <- as.factor(prostate$CAPSULE) h2o.gam(y = "CAPSULE", x = c("RACE"), gam_columns = c("PSA"), training_frame = prostate, family = "binomial") ## End(Not run)
Builds gradient boosted classification trees and gradient boosted regression trees on a parsed data set. The default distribution function will guess the model type based on the response column type. In order to run properly, the response column must be an numeric for "gaussian" or an enum for "bernoulli" or "multinomial".
h2o.gbm( x, y, training_frame, model_id = NULL, validation_frame = NULL, nfolds = 0, keep_cross_validation_models = TRUE, keep_cross_validation_predictions = FALSE, keep_cross_validation_fold_assignment = FALSE, score_each_iteration = FALSE, score_tree_interval = 0, fold_assignment = c("AUTO", "Random", "Modulo", "Stratified"), fold_column = NULL, ignore_const_cols = TRUE, offset_column = NULL, weights_column = NULL, balance_classes = FALSE, class_sampling_factors = NULL, max_after_balance_size = 5, ntrees = 50, max_depth = 5, min_rows = 10, nbins = 20, nbins_top_level = 1024, nbins_cats = 1024, r2_stopping = Inf, stopping_rounds = 0, stopping_metric = c("AUTO", "deviance", "logloss", "MSE", "RMSE", "MAE", "RMSLE", "AUC", "AUCPR", "lift_top_group", "misclassification", "mean_per_class_error", "custom", "custom_increasing"), stopping_tolerance = 0.001, max_runtime_secs = 0, seed = -1, build_tree_one_node = FALSE, learn_rate = 0.1, learn_rate_annealing = 1, distribution = c("AUTO", "bernoulli", "quasibinomial", "multinomial", "gaussian", "poisson", "gamma", "tweedie", "laplace", "quantile", "huber", "custom"), quantile_alpha = 0.5, tweedie_power = 1.5, huber_alpha = 0.9, checkpoint = NULL, sample_rate = 1, sample_rate_per_class = NULL, col_sample_rate = 1, col_sample_rate_change_per_level = 1, col_sample_rate_per_tree = 1, min_split_improvement = 1e-05, histogram_type = c("AUTO", "UniformAdaptive", "Random", "QuantilesGlobal", "RoundRobin", "UniformRobust"), max_abs_leafnode_pred = Inf, pred_noise_bandwidth = 0, categorical_encoding = c("AUTO", "Enum", "OneHotInternal", "OneHotExplicit", "Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited"), calibrate_model = FALSE, calibration_frame = NULL, calibration_method = c("AUTO", "PlattScaling", "IsotonicRegression"), custom_metric_func = NULL, custom_distribution_func = NULL, export_checkpoints_dir = NULL, in_training_checkpoints_dir = NULL, in_training_checkpoints_tree_interval = 1, monotone_constraints = NULL, check_constant_response = TRUE, gainslift_bins = -1, auc_type = c("AUTO", "NONE", "MACRO_OVR", "WEIGHTED_OVR", "MACRO_OVO", "WEIGHTED_OVO"), interaction_constraints = NULL, auto_rebalance = TRUE, verbose = FALSE )h2o.gbm( x, y, training_frame, model_id = NULL, validation_frame = NULL, nfolds = 0, keep_cross_validation_models = TRUE, keep_cross_validation_predictions = FALSE, keep_cross_validation_fold_assignment = FALSE, score_each_iteration = FALSE, score_tree_interval = 0, fold_assignment = c("AUTO", "Random", "Modulo", "Stratified"), fold_column = NULL, ignore_const_cols = TRUE, offset_column = NULL, weights_column = NULL, balance_classes = FALSE, class_sampling_factors = NULL, max_after_balance_size = 5, ntrees = 50, max_depth = 5, min_rows = 10, nbins = 20, nbins_top_level = 1024, nbins_cats = 1024, r2_stopping = Inf, stopping_rounds = 0, stopping_metric = c("AUTO", "deviance", "logloss", "MSE", "RMSE", "MAE", "RMSLE", "AUC", "AUCPR", "lift_top_group", "misclassification", "mean_per_class_error", "custom", "custom_increasing"), stopping_tolerance = 0.001, max_runtime_secs = 0, seed = -1, build_tree_one_node = FALSE, learn_rate = 0.1, learn_rate_annealing = 1, distribution = c("AUTO", "bernoulli", "quasibinomial", "multinomial", "gaussian", "poisson", "gamma", "tweedie", "laplace", "quantile", "huber", "custom"), quantile_alpha = 0.5, tweedie_power = 1.5, huber_alpha = 0.9, checkpoint = NULL, sample_rate = 1, sample_rate_per_class = NULL, col_sample_rate = 1, col_sample_rate_change_per_level = 1, col_sample_rate_per_tree = 1, min_split_improvement = 1e-05, histogram_type = c("AUTO", "UniformAdaptive", "Random", "QuantilesGlobal", "RoundRobin", "UniformRobust"), max_abs_leafnode_pred = Inf, pred_noise_bandwidth = 0, categorical_encoding = c("AUTO", "Enum", "OneHotInternal", "OneHotExplicit", "Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited"), calibrate_model = FALSE, calibration_frame = NULL, calibration_method = c("AUTO", "PlattScaling", "IsotonicRegression"), custom_metric_func = NULL, custom_distribution_func = NULL, export_checkpoints_dir = NULL, in_training_checkpoints_dir = NULL, in_training_checkpoints_tree_interval = 1, monotone_constraints = NULL, check_constant_response = TRUE, gainslift_bins = -1, auc_type = c("AUTO", "NONE", "MACRO_OVR", "WEIGHTED_OVR", "MACRO_OVO", "WEIGHTED_OVO"), interaction_constraints = NULL, auto_rebalance = TRUE, verbose = FALSE )
x |
(Optional) A vector containing the names or indices of the predictor variables to use in building the model. If x is missing, then all columns except y are used. |
y |
The name or column index of the response variable in the data. The response must be either a numeric or a categorical/factor variable. If the response is numeric, then a regression model will be trained, otherwise it will train a classification model. |
training_frame |
Id of the training data frame. |
model_id |
Destination id for this model; auto-generated if not specified. |
validation_frame |
Id of the validation data frame. |
nfolds |
Number of folds for K-fold cross-validation (0 to disable or >= 2). Defaults to 0. |
keep_cross_validation_models |
|
keep_cross_validation_predictions |
|
keep_cross_validation_fold_assignment |
|
score_each_iteration |
|
score_tree_interval |
Score the model after every so many trees. Disabled if set to 0. Defaults to 0. |
fold_assignment |
Cross-validation fold assignment scheme, if fold_column is not specified. The 'Stratified' option will stratify the folds based on the response variable, for classification problems. Must be one of: "AUTO", "Random", "Modulo", "Stratified". Defaults to AUTO. |
fold_column |
Column with cross-validation fold index assignment per observation. |
ignore_const_cols |
|
offset_column |
Offset column. This will be added to the combination of columns before applying the link function. |
weights_column |
Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0. |
balance_classes |
|
class_sampling_factors |
Desired over/under-sampling ratios per class (in lexicographic order). If not specified, sampling factors will be automatically computed to obtain class balance during training. Requires balance_classes. |
max_after_balance_size |
Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires balance_classes. Defaults to 5.0. |
ntrees |
Number of trees. Defaults to 50. |
max_depth |
Maximum tree depth (0 for unlimited). Defaults to 5. |
min_rows |
Fewest allowed (weighted) observations in a leaf. Defaults to 10. |
nbins |
For numerical columns (real/int), build a histogram of (at least) this many bins, then split at the best point Defaults to 20. |
nbins_top_level |
For numerical columns (real/int), build a histogram of (at most) this many bins at the root level, then decrease by factor of two per level Defaults to 1024. |
nbins_cats |
For categorical columns (factors), build a histogram of this many bins, then split at the best point. Higher values can lead to more overfitting. Defaults to 1024. |
r2_stopping |
r2_stopping is no longer supported and will be ignored if set - please use stopping_rounds, stopping_metric and stopping_tolerance instead. Previous version of H2O would stop making trees when the R^2 metric equals or exceeds this Defaults to 1.797693135e+308. |
stopping_rounds |
Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable) Defaults to 0. |
stopping_metric |
Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client. Must be one of: "AUTO", "deviance", "logloss", "MSE", "RMSE", "MAE", "RMSLE", "AUC", "AUCPR", "lift_top_group", "misclassification", "mean_per_class_error", "custom", "custom_increasing". Defaults to AUTO. |
stopping_tolerance |
Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much) Defaults to 0.001. |
max_runtime_secs |
Maximum allowed runtime in seconds for model training. Use 0 to disable. Defaults to 0. |
seed |
Seed for random numbers (affects certain parts of the algo that are stochastic and those might or might not be enabled by default). Defaults to -1 (time-based random number). |
build_tree_one_node |
|
learn_rate |
Learning rate (from 0.0 to 1.0) Defaults to 0.1. |
learn_rate_annealing |
Scale the learning rate by this factor after each tree (e.g., 0.99 or 0.999) Defaults to 1. |
distribution |
Distribution function Must be one of: "AUTO", "bernoulli", "quasibinomial", "multinomial", "gaussian", "poisson", "gamma", "tweedie", "laplace", "quantile", "huber", "custom". Defaults to AUTO. |
quantile_alpha |
Desired quantile for Quantile regression, must be between 0 and 1. Defaults to 0.5. |
tweedie_power |
Tweedie power for Tweedie regression, must be between 1 and 2. Defaults to 1.5. |
huber_alpha |
Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1). Defaults to 0.9. |
checkpoint |
Model checkpoint to resume training with. |
sample_rate |
Row sample rate per tree (from 0.0 to 1.0) Defaults to 1. |
sample_rate_per_class |
A list of row sample rates per class (relative fraction for each class, from 0.0 to 1.0), for each tree |
col_sample_rate |
Column sample rate (from 0.0 to 1.0) Defaults to 1. |
col_sample_rate_change_per_level |
Relative change of the column sampling rate for every level (must be > 0.0 and <= 2.0) Defaults to 1. |
col_sample_rate_per_tree |
Column sample rate per tree (from 0.0 to 1.0) Defaults to 1. |
min_split_improvement |
Minimum relative improvement in squared error reduction for a split to happen Defaults to 1e-05. |
histogram_type |
What type of histogram to use for finding optimal split points Must be one of: "AUTO", "UniformAdaptive", "Random", "QuantilesGlobal", "RoundRobin", "UniformRobust". Defaults to AUTO. |
max_abs_leafnode_pred |
Maximum absolute value of a leaf node prediction Defaults to 1.797693135e+308. |
pred_noise_bandwidth |
Bandwidth (sigma) of Gaussian multiplicative noise ~N(1,sigma) for tree node predictions Defaults to 0. |
categorical_encoding |
Encoding scheme for categorical features Must be one of: "AUTO", "Enum", "OneHotInternal", "OneHotExplicit", "Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited". Defaults to AUTO. |
calibrate_model |
|
calibration_frame |
Data for model calibration |
calibration_method |
Calibration method to use Must be one of: "AUTO", "PlattScaling", "IsotonicRegression". Defaults to AUTO. |
custom_metric_func |
Reference to custom evaluation function, format: 'language:keyName=funcName' |
custom_distribution_func |
Reference to custom distribution, format: 'language:keyName=funcName' |
export_checkpoints_dir |
Automatically export generated models to this directory. |
in_training_checkpoints_dir |
Create checkpoints into defined directory while training process is still running. In case of cluster shutdown, this checkpoint can be used to restart training. |
in_training_checkpoints_tree_interval |
Checkpoint the model after every so many trees. Parameter is used only when in_training_checkpoints_dir is defined Defaults to 1. |
monotone_constraints |
A mapping representing monotonic constraints. Use +1 to enforce an increasing constraint and -1 to specify a decreasing constraint. |
check_constant_response |
|
gainslift_bins |
Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning. Defaults to -1. |
auc_type |
Set default multinomial AUC type. Must be one of: "AUTO", "NONE", "MACRO_OVR", "WEIGHTED_OVR", "MACRO_OVO", "WEIGHTED_OVO". Defaults to AUTO. |
interaction_constraints |
A set of allowed column interactions. |
auto_rebalance |
|
verbose |
|
predict.H2OModel for prediction
## Not run: library(h2o) h2o.init() # Run regression GBM on australia data australia_path <- system.file("extdata", "australia.csv", package = "h2o") australia <- h2o.uploadFile(path = australia_path) independent <- c("premax", "salmax", "minairtemp", "maxairtemp", "maxsst", "maxsoilmoist", "Max_czcs") dependent <- "runoffnew" h2o.gbm(y = dependent, x = independent, training_frame = australia, ntrees = 3, max_depth = 3, min_rows = 2) ## End(Not run)## Not run: library(h2o) h2o.init() # Run regression GBM on australia data australia_path <- system.file("extdata", "australia.csv", package = "h2o") australia <- h2o.uploadFile(path = australia_path) independent <- c("premax", "salmax", "minairtemp", "maxairtemp", "maxsst", "maxsoilmoist", "Max_czcs") dependent <- "runoffnew" h2o.gbm(y = dependent, x = independent, training_frame = australia, ntrees = 3, max_depth = 3, min_rows = 2) ## End(Not run)
Imports a generic model into H2O. Such model can be used then used for scoring and obtaining additional information about the model. The imported model has to be supported by H2O.
h2o.generic(model_id = NULL, model_key = NULL, path = NULL)h2o.generic(model_id = NULL, model_key = NULL, path = NULL)
model_id |
Destination id for this model; auto-generated if not specified. |
model_key |
Key to the self-contained model archive already uploaded to H2O. |
path |
Path to file with self-contained model archive. |
## Not run: # library(h2o) # h2o.init() # generic_model <- h2o.genericModel(path="/path/to/model.zip", model_id="my_model") # predictions <- h2o.predict(generic_model, dataset) ## End(Not run)## Not run: # library(h2o) # h2o.init() # generic_model <- h2o.genericModel(path="/path/to/model.zip", model_id="my_model") # predictions <- h2o.predict(generic_model, dataset) ## End(Not run)
Usage example: generic_model <- h2o.genericModel(model_file_path = "/path/to/mojo.zip") predictions <- h2o.predict(generic_model, dataset)
h2o.genericModel(mojo_file_path, model_id = NULL)h2o.genericModel(mojo_file_path, model_id = NULL)
mojo_file_path |
Filesystem path to the model imported |
model_id |
Model ID, default is NULL |
Returns H2O Generic Model based on given embedded model
## Not run: # Import default Iris dataset as H2O frame data <- as.h2o(iris) # Train a very simple GBM model features <- c("Sepal.Length", "Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width") original_model <- h2o.gbm(x = features, y = "Species", training_frame = data) # Download the trained GBM model as MOJO (temporary directory used in this example) mojo_original_name <- h2o.download_mojo(model = original_model, path = tempdir()) mojo_original_path <- paste0(tempdir(), "/", mojo_original_name) # Import the MOJO as Generic model generic_model <- h2o.genericModel(mojo_original_path) # Perform scoring with the generic model generic_model_predictions <- h2o.predict(generic_model, data) ## End(Not run)## Not run: # Import default Iris dataset as H2O frame data <- as.h2o(iris) # Train a very simple GBM model features <- c("Sepal.Length", "Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width") original_model <- h2o.gbm(x = features, y = "Species", training_frame = data) # Download the trained GBM model as MOJO (temporary directory used in this example) mojo_original_name <- h2o.download_mojo(model = original_model, path = tempdir()) mojo_original_path <- paste0(tempdir(), "/", mojo_original_name) # Import the MOJO as Generic model generic_model <- h2o.genericModel(mojo_original_path) # Perform scoring with the generic model generic_model_predictions <- h2o.predict(generic_model, data) ## End(Not run)
Get an R object that is a subclass of H2OAutoML
h2o.get_automl(project_name) h2o.getAutoML(project_name)h2o.get_automl(project_name) h2o.getAutoML(project_name)
project_name |
A string indicating the project_name of the automl instance to retrieve. |
Returns an object that is a subclass of H2OAutoML.
## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.importFile(path = prostate_path, header = TRUE) y <- "CAPSULE" prostate[,y] <- as.factor(prostate[,y]) #convert to factor for classification aml <- h2o.automl(y = y, training_frame = prostate, max_runtime_secs = 30, project_name = "prostate") aml2 <- h2o.get_automl("prostate") ## End(Not run)## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.importFile(path = prostate_path, header = TRUE) y <- "CAPSULE" prostate[,y] <- as.factor(prostate[,y]) #convert to factor for classification aml <- h2o.automl(y = y, training_frame = prostate, max_runtime_secs = 30, project_name = "prostate") aml2 <- h2o.get_automl("prostate") ## End(Not run)
Get best model of a given family/algorithm for a given criterion from an AutoML object.
h2o.get_best_model( object, algorithm = c("any", "basemodel", "deeplearning", "drf", "gbm", "glm", "stackedensemble", "xgboost"), criterion = c("AUTO", "AUC", "AUCPR", "logloss", "MAE", "mean_per_class_error", "deviance", "MSE", "predict_time_per_row_ms", "RMSE", "RMSLE", "training_time_ms") )h2o.get_best_model( object, algorithm = c("any", "basemodel", "deeplearning", "drf", "gbm", "glm", "stackedensemble", "xgboost"), criterion = c("AUTO", "AUC", "AUCPR", "logloss", "MAE", "mean_per_class_error", "deviance", "MSE", "predict_time_per_row_ms", "RMSE", "RMSLE", "training_time_ms") )
object |
H2OAutoML object |
algorithm |
One of "any", "basemodel", "deeplearning", "drf", "gbm", "glm", "stackedensemble", "xgboost" |
criterion |
Criterion can be one of the metrics reported in the leaderboard. If set to NULL, the same ordering as in the leaderboard will be used. Avaliable criteria:
The following additional leaderboard information can be also used as a criterion:
|
An H2OModel or NULL if no model of a given family is present
## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.importFile(path = prostate_path, header = TRUE) y <- "CAPSULE" prostate[,y] <- as.factor(prostate[,y]) #convert to factor for classification aml <- h2o.automl(y = y, training_frame = prostate, max_runtime_secs = 30) gbm <- h2o.get_best_model(aml, "gbm") ## End(Not run)## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.importFile(path = prostate_path, header = TRUE) y <- "CAPSULE" prostate[,y] <- as.factor(prostate[,y]) #convert to factor for classification aml <- h2o.automl(y = y, training_frame = prostate, max_runtime_secs = 30) gbm <- h2o.get_best_model(aml, "gbm") ## End(Not run)
Extracts the subset of predictor names that yield the best R2 value for each predictor subset size.
h2o.get_best_model_predictors(model)h2o.get_best_model_predictors(model)
model |
is a H2OModel with algorithm name of modelselection |
Extracts the best R2 values for all predictor subset size.
h2o.get_best_r2_values(model)h2o.get_best_r2_values(model)
model |
is a H2OModel with algorithm name of modelselection |
Extracts the gam column names corresponding to the knot locations from model output if it is enabled.
h2o.get_gam_knot_column_names(model)h2o.get_gam_knot_column_names(model)
model |
is a H2OModel with algorithm name of gam |
Extracts the knot locations from model output if it is enabled.
h2o.get_knot_locations(model, gam_column = NULL)h2o.get_knot_locations(model, gam_column = NULL)
model |
is a H2OModel with algorithm name of gam |
gam_column |
will only extract the knot locations for the specific gam_columns. Else, return all. |
Contrary to the default leaderboard attached to the automl instance, this one can return columns other than the metrics.
h2o.get_leaderboard(object, extra_columns = NULL)h2o.get_leaderboard(object, extra_columns = NULL)
object |
The object for which to return the leaderboard. Currently, only H2OAutoML instances are supported. |
extra_columns |
A string or a list of string specifying which optional columns should be added to the leaderboard. Defaults to None. Currently supported extensions are:
|
An H2OFrame representing the leaderboard.
## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.importFile(path = prostate_path, header = TRUE) y <- "CAPSULE" prostate[,y] <- as.factor(prostate[,y]) #convert to factor for classification aml <- h2o.automl(y = y, training_frame = prostate, max_runtime_secs = 30) lb <- h2o.get_leaderboard(aml) head(lb) ## End(Not run)## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.importFile(path = prostate_path, header = TRUE) y <- "CAPSULE" prostate[,y] <- as.factor(prostate[,y]) #convert to factor for classification aml <- h2o.automl(y = y, training_frame = prostate, max_runtime_secs = 30) lb <- h2o.get_leaderboard(aml) head(lb) ## End(Not run)
Retrieve actual number of trees for tree algorithms
h2o.get_ntrees_actual(object)h2o.get_ntrees_actual(object)
object |
An H2OModel object. |
Extracts the predictor added to model at each step.
h2o.get_predictors_added_per_step(model)h2o.get_predictors_added_per_step(model)
model |
is a H2OModel with algorithm name of modelselection |
Extracts the predictor removed to model at each step.
h2o.get_predictors_removed_per_step(model)h2o.get_predictors_removed_per_step(model)
model |
is a H2OModel with algorithm name of modelselection |
Extracts a list of H2OFrames containing regression influence diagnostics for predictor subsets of various sizes or just one H2OFrame containing regression influence diagnostics for predictor subsets of one fixed size
h2o.get_regression_influence_diagnostics(model, predictorSize = -1)h2o.get_regression_influence_diagnostics(model, predictorSize = -1)
model |
an H2OModel object. |
predictorSize |
predictor subset size. If specified, will only return model coefficients of that subset size. If not specified will return a lists of model coefficient dicts for all predictor subset size. |
## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv" cars <- h2o.importFile(f) predictors <- c("displacement", "power", "weight", "acceleration", "year") response <- "acceleration" cars_model <- h2o.modelSelection(y=response, x=predictors, training_frame = cars, min_predictor_number=2, mode="backward", influence="dfbetas", lambda=0.0, family="gaussian") rid_frame <- h2o.get_regression_influence_diagnostics(cars_model, predictorSize=3) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv" cars <- h2o.importFile(f) predictors <- c("displacement", "power", "weight", "acceleration", "year") response <- "acceleration" cars_model <- h2o.modelSelection(y=response, x=predictors, training_frame = cars, min_predictor_number=2, mode="backward", influence="dfbetas", lambda=0.0, family="gaussian") rid_frame <- h2o.get_regression_influence_diagnostics(cars_model, predictorSize=3) ## End(Not run)
Retrieves an instance of H2OSegmentModels for a given id.
h2o.get_segment_models(segment_models_id)h2o.get_segment_models(segment_models_id)
segment_models_id |
A string indicating the unique segment_models_id |
Returns an object that is a subclass of H2OSegmentModels.
## Not run: library(h2o) h2o.init() iris_hf <- as.h2o(iris) h2o.train_segments(algorithm = "gbm", segment_columns = "Species", segment_models_id="models_by_species", x = c(1:3), y = 4, training_frame = iris_hf, ntrees = 5, max_depth = 4) models <- h2o.get_segment_models("models_by_species") as.data.frame(models) ## End(Not run)## Not run: library(h2o) h2o.init() iris_hf <- as.h2o(iris) h2o.train_segments(algorithm = "gbm", segment_columns = "Species", segment_models_id="models_by_species", x = c(1:3), y = 4, training_frame = iris_hf, ntrees = 5, max_depth = 4) models <- h2o.get_segment_models("models_by_species") as.data.frame(models) ## End(Not run)
Return the variable inflation factors associated with numerical predictors for GLM models.
h2o.get_variable_inflation_factors(object)h2o.get_variable_inflation_factors(object)
object |
An H2OModel object. |
## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv" cars <- h2o.importFile(f) predictors <- c("displacement", "power", "weight", "acceleration", "year") response <- "cylinders" cars_split <- h2o.splitFrame(data = cars, ratios = 0.8, seed = 1234) train <- cars_split[[1]] valid <- cars_split[[2]] cars_glm <- h2o.glm(seed = 1234, lambda=0.0, compute_p_values=TRUE, generate_variable_inflation_factors=TRUE, x = predictors, y = response, training_frame = train, validation_frame = valid) h2o.get_variable_inflation_factors(cars_glm) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv" cars <- h2o.importFile(f) predictors <- c("displacement", "power", "weight", "acceleration", "year") response <- "cylinders" cars_split <- h2o.splitFrame(data = cars, ratios = 0.8, seed = 1234) train <- cars_split[[1]] valid <- cars_split[[2]] cars_glm <- h2o.glm(seed = 1234, lambda=0.0, compute_p_values=TRUE, generate_variable_inflation_factors=TRUE, x = predictors, y = response, training_frame = train, validation_frame = valid) h2o.get_variable_inflation_factors(cars_glm) ## End(Not run)
This function allows setting betas of an existing glm model.
h2o.getAlphaBest(model)h2o.getAlphaBest(model)
model |
an H2OModel corresponding from a |
Attempt to recover an h2o connection.
h2o.getConnection()h2o.getConnection()
Returns an H2OConnection object.
Get the reference to a frame with the given id in the H2O instance.
h2o.getFrame(id)h2o.getFrame(id)
id |
A string indicating the unique frame of the dataset to retrieve. |
## Not run: library(h2o) h2o.init() f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_train.csv" train <- h2o.importFile(f) y <- "species" x <- setdiff(names(train), y) train[, y] <- as.factor(train[, y]) nfolds <- 5 num_base_models <- 2 my_gbm <- h2o.gbm(x = x, y = y, training_frame = train, distribution = "multinomial", ntrees = 10, max_depth = 3, min_rows = 2, learn_rate = 0.2, nfolds = nfolds, fold_assignment = "Modulo", keep_cross_validation_predictions = TRUE, seed = 1) my_rf <- h2o.randomForest(x = x, y = y, training_frame = train, ntrees = 50, nfolds = nfolds, fold_assignment = "Modulo", keep_cross_validation_predictions = TRUE, seed = 1) stack <- h2o.stackedEnsemble(x = x, y = y, training_frame = train, model_id = "my_ensemble_l1", base_models = list(my_gbm@model_id, my_rf@model_id), keep_levelone_frame = TRUE) h2o.getFrame(stack@model$levelone_frame_id$name) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_train.csv" train <- h2o.importFile(f) y <- "species" x <- setdiff(names(train), y) train[, y] <- as.factor(train[, y]) nfolds <- 5 num_base_models <- 2 my_gbm <- h2o.gbm(x = x, y = y, training_frame = train, distribution = "multinomial", ntrees = 10, max_depth = 3, min_rows = 2, learn_rate = 0.2, nfolds = nfolds, fold_assignment = "Modulo", keep_cross_validation_predictions = TRUE, seed = 1) my_rf <- h2o.randomForest(x = x, y = y, training_frame = train, ntrees = 50, nfolds = nfolds, fold_assignment = "Modulo", keep_cross_validation_predictions = TRUE, seed = 1) stack <- h2o.stackedEnsemble(x = x, y = y, training_frame = train, model_id = "my_ensemble_l1", base_models = list(my_gbm@model_id, my_rf@model_id), keep_levelone_frame = TRUE) h2o.getFrame(stack@model$levelone_frame_id$name) ## End(Not run)
Extract the full regularization path from a GLM model (assuming it was run with the lambda search option).
h2o.getGLMFullRegularizationPath(model)h2o.getGLMFullRegularizationPath(model)
model |
an H2OModel corresponding from a |
Note that if neither cross-validation nor a validation frame is used in the grid search, then the training metrics will display in the "get grid" output. If a validation frame is passed to the grid, and nfolds = 0, then the validation metrics will display. However, if nfolds > 1, then cross-validation metrics will display even if a validation frame is provided.
h2o.getGrid(grid_id, sort_by, decreasing, verbose = FALSE)h2o.getGrid(grid_id, sort_by, decreasing, verbose = FALSE)
grid_id |
ID of existing grid object to fetch |
sort_by |
Sort the models in the grid space by a metric. Choices are "logloss", "residual_deviance", "mse", "auc", "accuracy", "precision", "recall", "f1", etc. |
decreasing |
Specify whether sort order should be decreasing |
verbose |
Controls verbosity of the output, if enabled prints out error messages for failed models (default: FALSE) |
## Not run: library(h2o) library(jsonlite) h2o.init() iris_hf <- as.h2o(iris) h2o.grid("gbm", grid_id = "gbm_grid_id", x = c(1:4), y = 5, training_frame = iris_hf, hyper_params = list(ntrees = c(1, 2, 3))) grid <- h2o.getGrid("gbm_grid_id") # Get grid summary summary(grid) # Fetch grid models model_ids <- grid@model_ids models <- lapply(model_ids, function(id) { h2o.getModel(id)}) ## End(Not run)## Not run: library(h2o) library(jsonlite) h2o.init() iris_hf <- as.h2o(iris) h2o.grid("gbm", grid_id = "gbm_grid_id", x = c(1:4), y = 5, training_frame = iris_hf, hyper_params = list(ntrees = c(1, 2, 3))) grid <- h2o.getGrid("gbm_grid_id") # Get grid summary summary(grid) # Fetch grid models model_ids <- grid@model_ids models <- lapply(model_ids, function(id) { h2o.getModel(id)}) ## End(Not run)
Get back-end distributed key/value store id from an H2OFrame.
h2o.getId(x)h2o.getId(x)
x |
An H2OFrame |
The id of the H2OFrame
## Not run: library(h2o) h2o.init() f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_train.csv" iris <- h2o.importFile(f) h2o.getId(iris) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_train.csv" iris <- h2o.importFile(f) h2o.getId(iris) ## End(Not run)
This function allows setting betas of an existing glm model.
h2o.getLambdaBest(model)h2o.getLambdaBest(model)
model |
an H2OModel corresponding from a |
This function allows setting betas of an existing glm model.
h2o.getLambdaMax(model)h2o.getLambdaMax(model)
model |
an H2OModel corresponding from a |
This function allows setting betas of an existing glm model.
h2o.getLambdaMin(model)h2o.getLambdaMin(model)
model |
an H2OModel corresponding from a |
Returns a reference to an existing model in the H2O instance.
h2o.getModel(model_id)h2o.getModel(model_id)
model_id |
A string indicating the unique model_id of the model to retrieve. |
Returns an object that is a subclass of H2OModel.
## Not run: library(h2o) h2o.init() iris_hf <- as.h2o(iris) model_id <- h2o.gbm(x = 1:4, y = 5, training_frame = iris_hf)@model_id model_retrieved <- h2o.getModel(model_id) ## End(Not run)## Not run: library(h2o) h2o.init() iris_hf <- as.h2o(iris) model_id <- h2o.gbm(x = 1:4, y = 5, training_frame = iris_hf)@model_id model_retrieved <- h2o.getModel(model_id) ## End(Not run)
Fetchces a single tree of a H2O model. This function is intended to be used on Gradient Boosting Machine models or Distributed Random Forest models.
h2o.getModelTree( model, tree_number, tree_class = NA, plain_language_rules = "AUTO" )h2o.getModelTree( model, tree_number, tree_class = NA, plain_language_rules = "AUTO" )
model |
Model with trees |
tree_number |
Number of the tree in the model to fetch, starting with 1 |
tree_class |
Name of the class of the tree (if applicable). This value is ignored for regression and binomial response column, as there is only one tree built. As there is exactly one class per categorical level, name of tree's class equals to the corresponding categorical level of response column. |
plain_language_rules |
(Optional) Whether to generate plain language rules. AUTO by default, meaning FALSE for big trees and TRUE for small trees. |
Returns an H2OTree object with detailed information about a tree.
## Not run: library(h2o) h2o.init() f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_train.csv" iris <- h2o.importFile(f) gbm_model <- h2o.gbm(y = "species", training_frame = iris) tree <- h2o.getModelTree(gbm_model, 1, "Iris-setosa") ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_train.csv" iris <- h2o.importFile(f) gbm_model <- h2o.gbm(y = "species", training_frame = iris) tree <- h2o.getModelTree(gbm_model, 1, "Iris-setosa") ## End(Not run)
Get the Time Zone on the H2O cluster Returns a string
h2o.getTimezone()h2o.getTimezone()
Get the types-per-column
h2o.getTypes(x)h2o.getTypes(x)
x |
An H2OFrame |
A list of types per column
## Not run: library(h2o) h2o.init() f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_train.csv" iris <- h2o.importFile(f) h2o.getTypes(iris) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_train.csv" iris <- h2o.importFile(f) h2o.getTypes(iris) ## End(Not run)
Retrieves the GINI coefficient from an H2OBinomialMetrics. If "train", "valid", and "xval" parameters are FALSE (default), then the training GINIvalue is returned. If more than one parameter is set to TRUE, then a named vector of GINIs are returned, where the names are "train", "valid" or "xval".
h2o.giniCoef(object, train = FALSE, valid = FALSE, xval = FALSE)h2o.giniCoef(object, train = FALSE, valid = FALSE, xval = FALSE)
object |
an H2OBinomialMetrics object. |
train |
Retrieve the training GINI Coefficcient |
valid |
Retrieve the validation GINI Coefficcient |
xval |
Retrieve the cross-validation GINI Coefficcient |
h2o.auc for AUC, h2o.giniCoef for the
GINI coefficient, and h2o.metric for the various
threshold metrics. See h2o.performance for creating
H2OModelMetrics objects.
## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(prostate_path) prostate[, 2] <- as.factor(prostate[, 2]) model <- h2o.gbm(x = 3:9, y = 2, training_frame = prostate, distribution = "bernoulli") perf <- h2o.performance(model, prostate) h2o.giniCoef(perf) ## End(Not run)## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(prostate_path) prostate[, 2] <- as.factor(prostate[, 2]) model <- h2o.gbm(x = 3:9, y = 2, training_frame = prostate, distribution = "bernoulli") perf <- h2o.performance(model, prostate) h2o.giniCoef(perf) ## End(Not run)
Fits a generalized linear model, specified by a response variable, a set of predictors, and a description of the error distribution.
h2o.glm( x, y, training_frame, model_id = NULL, validation_frame = NULL, nfolds = 0, checkpoint = NULL, export_checkpoints_dir = NULL, seed = -1, keep_cross_validation_models = TRUE, keep_cross_validation_predictions = FALSE, keep_cross_validation_fold_assignment = FALSE, fold_assignment = c("AUTO", "Random", "Modulo", "Stratified"), fold_column = NULL, random_columns = NULL, ignore_const_cols = TRUE, score_each_iteration = FALSE, score_iteration_interval = -1, offset_column = NULL, weights_column = NULL, family = c("AUTO", "gaussian", "binomial", "fractionalbinomial", "quasibinomial", "ordinal", "multinomial", "poisson", "gamma", "tweedie", "negativebinomial"), rand_family = c("[gaussian]"), tweedie_variance_power = 0, tweedie_link_power = 1, theta = 1e-10, solver = c("AUTO", "IRLSM", "L_BFGS", "COORDINATE_DESCENT_NAIVE", "COORDINATE_DESCENT", "GRADIENT_DESCENT_LH", "GRADIENT_DESCENT_SQERR"), alpha = NULL, lambda = NULL, lambda_search = FALSE, early_stopping = TRUE, nlambdas = -1, standardize = TRUE, missing_values_handling = c("MeanImputation", "Skip", "PlugValues"), plug_values = NULL, compute_p_values = FALSE, dispersion_parameter_method = c("deviance", "pearson", "ml"), init_dispersion_parameter = 1, remove_collinear_columns = FALSE, intercept = TRUE, non_negative = FALSE, max_iterations = -1, objective_epsilon = -1, beta_epsilon = 1e-04, gradient_epsilon = -1, link = c("family_default", "identity", "logit", "log", "inverse", "tweedie", "ologit"), rand_link = c("[identity]", "[family_default]"), startval = NULL, calc_like = FALSE, HGLM = FALSE, prior = -1, cold_start = FALSE, lambda_min_ratio = -1, beta_constraints = NULL, max_active_predictors = -1, interactions = NULL, interaction_pairs = NULL, obj_reg = -1, stopping_rounds = 0, stopping_metric = c("AUTO", "deviance", "logloss", "MSE", "RMSE", "MAE", "RMSLE", "AUC", "AUCPR", "lift_top_group", "misclassification", "mean_per_class_error", "custom", "custom_increasing"), stopping_tolerance = 0.001, balance_classes = FALSE, class_sampling_factors = NULL, max_after_balance_size = 5, max_runtime_secs = 0, custom_metric_func = NULL, generate_scoring_history = FALSE, auc_type = c("AUTO", "NONE", "MACRO_OVR", "WEIGHTED_OVR", "MACRO_OVO", "WEIGHTED_OVO"), dispersion_epsilon = 1e-04, tweedie_epsilon = 8e-17, max_iterations_dispersion = 3000, build_null_model = FALSE, fix_dispersion_parameter = FALSE, generate_variable_inflation_factors = FALSE, fix_tweedie_variance_power = TRUE, dispersion_learning_rate = 0.5, influence = c("dfbetas"), gainslift_bins = -1 )h2o.glm( x, y, training_frame, model_id = NULL, validation_frame = NULL, nfolds = 0, checkpoint = NULL, export_checkpoints_dir = NULL, seed = -1, keep_cross_validation_models = TRUE, keep_cross_validation_predictions = FALSE, keep_cross_validation_fold_assignment = FALSE, fold_assignment = c("AUTO", "Random", "Modulo", "Stratified"), fold_column = NULL, random_columns = NULL, ignore_const_cols = TRUE, score_each_iteration = FALSE, score_iteration_interval = -1, offset_column = NULL, weights_column = NULL, family = c("AUTO", "gaussian", "binomial", "fractionalbinomial", "quasibinomial", "ordinal", "multinomial", "poisson", "gamma", "tweedie", "negativebinomial"), rand_family = c("[gaussian]"), tweedie_variance_power = 0, tweedie_link_power = 1, theta = 1e-10, solver = c("AUTO", "IRLSM", "L_BFGS", "COORDINATE_DESCENT_NAIVE", "COORDINATE_DESCENT", "GRADIENT_DESCENT_LH", "GRADIENT_DESCENT_SQERR"), alpha = NULL, lambda = NULL, lambda_search = FALSE, early_stopping = TRUE, nlambdas = -1, standardize = TRUE, missing_values_handling = c("MeanImputation", "Skip", "PlugValues"), plug_values = NULL, compute_p_values = FALSE, dispersion_parameter_method = c("deviance", "pearson", "ml"), init_dispersion_parameter = 1, remove_collinear_columns = FALSE, intercept = TRUE, non_negative = FALSE, max_iterations = -1, objective_epsilon = -1, beta_epsilon = 1e-04, gradient_epsilon = -1, link = c("family_default", "identity", "logit", "log", "inverse", "tweedie", "ologit"), rand_link = c("[identity]", "[family_default]"), startval = NULL, calc_like = FALSE, HGLM = FALSE, prior = -1, cold_start = FALSE, lambda_min_ratio = -1, beta_constraints = NULL, max_active_predictors = -1, interactions = NULL, interaction_pairs = NULL, obj_reg = -1, stopping_rounds = 0, stopping_metric = c("AUTO", "deviance", "logloss", "MSE", "RMSE", "MAE", "RMSLE", "AUC", "AUCPR", "lift_top_group", "misclassification", "mean_per_class_error", "custom", "custom_increasing"), stopping_tolerance = 0.001, balance_classes = FALSE, class_sampling_factors = NULL, max_after_balance_size = 5, max_runtime_secs = 0, custom_metric_func = NULL, generate_scoring_history = FALSE, auc_type = c("AUTO", "NONE", "MACRO_OVR", "WEIGHTED_OVR", "MACRO_OVO", "WEIGHTED_OVO"), dispersion_epsilon = 1e-04, tweedie_epsilon = 8e-17, max_iterations_dispersion = 3000, build_null_model = FALSE, fix_dispersion_parameter = FALSE, generate_variable_inflation_factors = FALSE, fix_tweedie_variance_power = TRUE, dispersion_learning_rate = 0.5, influence = c("dfbetas"), gainslift_bins = -1 )
x |
(Optional) A vector containing the names or indices of the predictor variables to use in building the model. If x is missing, then all columns except y are used. |
y |
The name or column index of the response variable in the data. The response must be either a numeric or a categorical/factor variable. If the response is numeric, then a regression model will be trained, otherwise it will train a classification model. |
training_frame |
Id of the training data frame. |
model_id |
Destination id for this model; auto-generated if not specified. |
validation_frame |
Id of the validation data frame. |
nfolds |
Number of folds for K-fold cross-validation (0 to disable or >= 2). Defaults to 0. |
checkpoint |
Model checkpoint to resume training with. |
export_checkpoints_dir |
Automatically export generated models to this directory. |
seed |
Seed for random numbers (affects certain parts of the algo that are stochastic and those might or might not be enabled by default). Defaults to -1 (time-based random number). |
keep_cross_validation_models |
|
keep_cross_validation_predictions |
|
keep_cross_validation_fold_assignment |
|
fold_assignment |
Cross-validation fold assignment scheme, if fold_column is not specified. The 'Stratified' option will stratify the folds based on the response variable, for classification problems. Must be one of: "AUTO", "Random", "Modulo", "Stratified". Defaults to AUTO. |
fold_column |
Column with cross-validation fold index assignment per observation. |
random_columns |
random columns indices for HGLM. |
ignore_const_cols |
|
score_each_iteration |
|
score_iteration_interval |
Perform scoring for every score_iteration_interval iterations Defaults to -1. |
offset_column |
Offset column. This will be added to the combination of columns before applying the link function. |
weights_column |
Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0. |
family |
Family. Use binomial for classification with logistic regression, others are for regression problems. Must be one of: "AUTO", "gaussian", "binomial", "fractionalbinomial", "quasibinomial", "ordinal", "multinomial", "poisson", "gamma", "tweedie", "negativebinomial". Defaults to AUTO. |
rand_family |
Random Component Family array. One for each random component. Only support gaussian for now. Must be one of: "[gaussian]". |
tweedie_variance_power |
Tweedie variance power Defaults to 0. |
tweedie_link_power |
Tweedie link power Defaults to 1. |
theta |
Theta Defaults to 1e-10. |
solver |
AUTO will set the solver based on given data and the other parameters. IRLSM is fast on on problems with small number of predictors and for lambda-search with L1 penalty, L_BFGS scales better for datasets with many columns. Must be one of: "AUTO", "IRLSM", "L_BFGS", "COORDINATE_DESCENT_NAIVE", "COORDINATE_DESCENT", "GRADIENT_DESCENT_LH", "GRADIENT_DESCENT_SQERR". Defaults to AUTO. |
alpha |
Distribution of regularization between the L1 (Lasso) and L2 (Ridge) penalties. A value of 1 for alpha represents Lasso regression, a value of 0 produces Ridge regression, and anything in between specifies the amount of mixing between the two. Default value of alpha is 0 when SOLVER = 'L-BFGS'; 0.5 otherwise. |
lambda |
Regularization strength |
lambda_search |
|
early_stopping |
|
nlambdas |
Number of lambdas to be used in a search. Default indicates: If alpha is zero, with lambda search set to True, the value of nlamdas is set to 30 (fewer lambdas are needed for ridge regression) otherwise it is set to 100. Defaults to -1. |
standardize |
|
missing_values_handling |
Handling of missing values. Either MeanImputation, Skip or PlugValues. Must be one of: "MeanImputation", "Skip", "PlugValues". Defaults to MeanImputation. |
plug_values |
Plug Values (a single row frame containing values that will be used to impute missing values of the training/validation frame, use with conjunction missing_values_handling = PlugValues) |
compute_p_values |
|
dispersion_parameter_method |
Method used to estimate the dispersion parameter for Tweedie, Gamma and Negative Binomial only. Must be one of: "deviance", "pearson", "ml". Defaults to pearson. |
init_dispersion_parameter |
Only used for Tweedie, Gamma and Negative Binomial GLM. Store the initial value of dispersion parameter. If fix_dispersion_parameter is set, this value will be used in the calculation of p-values.Default to 1.0. Defaults to 1. |
remove_collinear_columns |
|
intercept |
|
non_negative |
|
max_iterations |
Maximum number of iterations Defaults to -1. |
objective_epsilon |
Converge if objective value changes less than this. Default (of -1.0) indicates: If lambda_search is set to True the value of objective_epsilon is set to .0001. If the lambda_search is set to False and lambda is equal to zero, the value of objective_epsilon is set to .000001, for any other value of lambda the default value of objective_epsilon is set to .0001. Defaults to -1. |
beta_epsilon |
Converge if beta changes less (using L-infinity norm) than beta esilon, ONLY applies to IRLSM solver Defaults to 0.0001. |
gradient_epsilon |
Converge if objective changes less (using L-infinity norm) than this, ONLY applies to L-BFGS solver. Default (of -1.0) indicates: If lambda_search is set to False and lambda is equal to zero, the default value of gradient_epsilon is equal to .000001, otherwise the default value is .0001. If lambda_search is set to True, the conditional values above are 1E-8 and 1E-6 respectively. Defaults to -1. |
link |
Link function. Must be one of: "family_default", "identity", "logit", "log", "inverse", "tweedie", "ologit". Defaults to family_default. |
rand_link |
Link function array for random component in HGLM. Must be one of: "[identity]", "[family_default]". |
startval |
double array to initialize fixed and random coefficients for HGLM, coefficients for GLM. |
calc_like |
|
HGLM |
|
prior |
Prior probability for y==1. To be used only for logistic regression iff the data has been sampled and the mean of response does not reflect reality. Defaults to -1. |
cold_start |
|
lambda_min_ratio |
Minimum lambda used in lambda search, specified as a ratio of lambda_max (the smallest lambda that drives all coefficients to zero). Default indicates: if the number of observations is greater than the number of variables, then lambda_min_ratio is set to 0.0001; if the number of observations is less than the number of variables, then lambda_min_ratio is set to 0.01. Defaults to -1. |
beta_constraints |
Beta constraints |
max_active_predictors |
Maximum number of active predictors during computation. Use as a stopping criterion to prevent expensive model building with many predictors. Default indicates: If the IRLSM solver is used, the value of max_active_predictors is set to 5000 otherwise it is set to 100000000. Defaults to -1. |
interactions |
A list of predictor column indices to interact. All pairwise combinations will be computed for the list. |
interaction_pairs |
A list of pairwise (first order) column interactions. |
obj_reg |
Likelihood divider in objective value computation, default (of -1.0) will set it to 1/nobs Defaults to -1. |
stopping_rounds |
Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable) Defaults to 0. |
stopping_metric |
Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client. Must be one of: "AUTO", "deviance", "logloss", "MSE", "RMSE", "MAE", "RMSLE", "AUC", "AUCPR", "lift_top_group", "misclassification", "mean_per_class_error", "custom", "custom_increasing". Defaults to AUTO. |
stopping_tolerance |
Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much) Defaults to 0.001. |
balance_classes |
|
class_sampling_factors |
Desired over/under-sampling ratios per class (in lexicographic order). If not specified, sampling factors will be automatically computed to obtain class balance during training. Requires balance_classes. |
max_after_balance_size |
Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires balance_classes. Defaults to 5.0. |
max_runtime_secs |
Maximum allowed runtime in seconds for model training. Use 0 to disable. Defaults to 0. |
custom_metric_func |
Reference to custom evaluation function, format: 'language:keyName=funcName' |
generate_scoring_history |
|
auc_type |
Set default multinomial AUC type. Must be one of: "AUTO", "NONE", "MACRO_OVR", "WEIGHTED_OVR", "MACRO_OVO", "WEIGHTED_OVO". Defaults to AUTO. |
dispersion_epsilon |
If changes in dispersion parameter estimation or loglikelihood value is smaller than dispersion_epsilon, will break out of the dispersion parameter estimation loop using maximum likelihood. Defaults to 0.0001. |
tweedie_epsilon |
In estimating tweedie dispersion parameter using maximum likelihood, this is used to choose the lower and upper indices in the approximating of the infinite series summation. Defaults to 8e-17. |
max_iterations_dispersion |
Control the maximum number of iterations in the dispersion parameter estimation loop using maximum likelihood. Defaults to 3000. |
build_null_model |
|
fix_dispersion_parameter |
|
generate_variable_inflation_factors |
|
fix_tweedie_variance_power |
|
dispersion_learning_rate |
Dispersion learning rate is only valid for tweedie family dispersion parameter estimation using ml. It must be > 0. This controls how much the dispersion parameter estimate is to be changed when the calculated loglikelihood actually decreases with the new dispersion. In this case, instead of setting new dispersion = dispersion + change, we set new dispersion = dispersion + dispersion_learning_rate * change. Defaults to 0.5. Defaults to 0.5. |
influence |
If set to dfbetas will calculate the difference in beta when a datarow is included and excluded in the dataset. Must be one of: "dfbetas". |
gainslift_bins |
Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning. Defaults to -1. |
A subclass of H2OModel is returned. The specific subclass depends on the machine
learning task at hand (if it's binomial classification, then an H2OBinomialModel is
returned, if it's regression then a H2ORegressionModel is returned). The default print-
out of the models is shown, but further GLM-specifc information can be queried out of the object. To access
these various items, please refer to the seealso section below. Upon completion of the GLM, the resulting
object has coefficients, normalized coefficients, residual/null deviance, aic, and a host of model metrics
including MSE, AUC (for logistic regression), degrees of freedom, and confusion matrices. Please refer to the
more in-depth GLM documentation available here:
https://h2o-release.s3.amazonaws.com/h2o-dev/rel-shannon/2/docs-website/h2o-docs/index.html#Data+Science+Algorithms-GLM
predict.H2OModel for prediction, h2o.mse, h2o.auc,
h2o.confusionMatrix, h2o.performance, h2o.giniCoef,
h2o.logloss, h2o.varimp, h2o.scoreHistory
## Not run: h2o.init() # Run GLM of CAPSULE ~ AGE + RACE + PSA + DCAPS prostate_path = system.file("extdata", "prostate.csv", package = "h2o") prostate = h2o.importFile(path = prostate_path) h2o.glm(y = "CAPSULE", x = c("AGE", "RACE", "PSA", "DCAPS"), training_frame = prostate, family = "binomial", nfolds = 0, alpha = 0.5, lambda_search = FALSE) # Run GLM of VOL ~ CAPSULE + AGE + RACE + PSA + GLEASON predictors = setdiff(colnames(prostate), c("ID", "DPROS", "DCAPS", "VOL")) h2o.glm(y = "VOL", x = predictors, training_frame = prostate, family = "gaussian", nfolds = 0, alpha = 0.1, lambda_search = FALSE) # GLM variable importance # Also see: # https://github.com/h2oai/h2o/blob/master/R/tests/testdir_demos/runit_demo_VI_all_algos.R bank = h2o.importFile( path="https://s3.amazonaws.com/h2o-public-test-data/smalldata/demos/bank-additional-full.csv" ) predictors = 1:20 target = "y" glm = h2o.glm(x = predictors, y = target, training_frame = bank, family = "binomial", standardize = TRUE, lambda_search = TRUE) h2o.std_coef_plot(glm, num_of_features = 20) ## End(Not run)## Not run: h2o.init() # Run GLM of CAPSULE ~ AGE + RACE + PSA + DCAPS prostate_path = system.file("extdata", "prostate.csv", package = "h2o") prostate = h2o.importFile(path = prostate_path) h2o.glm(y = "CAPSULE", x = c("AGE", "RACE", "PSA", "DCAPS"), training_frame = prostate, family = "binomial", nfolds = 0, alpha = 0.5, lambda_search = FALSE) # Run GLM of VOL ~ CAPSULE + AGE + RACE + PSA + GLEASON predictors = setdiff(colnames(prostate), c("ID", "DPROS", "DCAPS", "VOL")) h2o.glm(y = "VOL", x = predictors, training_frame = prostate, family = "gaussian", nfolds = 0, alpha = 0.1, lambda_search = FALSE) # GLM variable importance # Also see: # https://github.com/h2oai/h2o/blob/master/R/tests/testdir_demos/runit_demo_VI_all_algos.R bank = h2o.importFile( path="https://s3.amazonaws.com/h2o-public-test-data/smalldata/demos/bank-additional-full.csv" ) predictors = 1:20 target = "y" glm = h2o.glm(x = predictors, y = target, training_frame = bank, family = "binomial", standardize = TRUE, lambda_search = TRUE) h2o.std_coef_plot(glm, num_of_features = 20) ## End(Not run)
Builds a generalized low rank decomposition of an H2O data frame
h2o.glrm( training_frame, cols = NULL, model_id = NULL, validation_frame = NULL, ignore_const_cols = TRUE, score_each_iteration = FALSE, representation_name = NULL, loading_name = NULL, transform = c("NONE", "STANDARDIZE", "NORMALIZE", "DEMEAN", "DESCALE"), k = 1, loss = c("Quadratic", "Absolute", "Huber", "Poisson", "Hinge", "Logistic", "Periodic"), loss_by_col = c("Quadratic", "Absolute", "Huber", "Poisson", "Hinge", "Logistic", "Periodic", "Categorical", "Ordinal"), loss_by_col_idx = NULL, multi_loss = c("Categorical", "Ordinal"), period = 1, regularization_x = c("None", "Quadratic", "L2", "L1", "NonNegative", "OneSparse", "UnitOneSparse", "Simplex"), regularization_y = c("None", "Quadratic", "L2", "L1", "NonNegative", "OneSparse", "UnitOneSparse", "Simplex"), gamma_x = 0, gamma_y = 0, max_iterations = 1000, max_updates = 2000, init_step_size = 1, min_step_size = 1e-04, seed = -1, init = c("Random", "SVD", "PlusPlus", "User"), svd_method = c("GramSVD", "Power", "Randomized"), user_y = NULL, user_x = NULL, expand_user_y = TRUE, impute_original = FALSE, recover_svd = FALSE, max_runtime_secs = 0, export_checkpoints_dir = NULL )h2o.glrm( training_frame, cols = NULL, model_id = NULL, validation_frame = NULL, ignore_const_cols = TRUE, score_each_iteration = FALSE, representation_name = NULL, loading_name = NULL, transform = c("NONE", "STANDARDIZE", "NORMALIZE", "DEMEAN", "DESCALE"), k = 1, loss = c("Quadratic", "Absolute", "Huber", "Poisson", "Hinge", "Logistic", "Periodic"), loss_by_col = c("Quadratic", "Absolute", "Huber", "Poisson", "Hinge", "Logistic", "Periodic", "Categorical", "Ordinal"), loss_by_col_idx = NULL, multi_loss = c("Categorical", "Ordinal"), period = 1, regularization_x = c("None", "Quadratic", "L2", "L1", "NonNegative", "OneSparse", "UnitOneSparse", "Simplex"), regularization_y = c("None", "Quadratic", "L2", "L1", "NonNegative", "OneSparse", "UnitOneSparse", "Simplex"), gamma_x = 0, gamma_y = 0, max_iterations = 1000, max_updates = 2000, init_step_size = 1, min_step_size = 1e-04, seed = -1, init = c("Random", "SVD", "PlusPlus", "User"), svd_method = c("GramSVD", "Power", "Randomized"), user_y = NULL, user_x = NULL, expand_user_y = TRUE, impute_original = FALSE, recover_svd = FALSE, max_runtime_secs = 0, export_checkpoints_dir = NULL )
training_frame |
Id of the training data frame. |
cols |
(Optional) A vector containing the data columns on which k-means operates. |
model_id |
Destination id for this model; auto-generated if not specified. |
validation_frame |
Id of the validation data frame. |
ignore_const_cols |
|
score_each_iteration |
|
representation_name |
Frame key to save resulting X |
loading_name |
[Deprecated] Use representation_name instead. Frame key to save resulting X. |
transform |
Transformation of training data Must be one of: "NONE", "STANDARDIZE", "NORMALIZE", "DEMEAN", "DESCALE". Defaults to NONE. |
k |
Rank of matrix approximation Defaults to 1. |
loss |
Numeric loss function Must be one of: "Quadratic", "Absolute", "Huber", "Poisson", "Hinge", "Logistic", "Periodic". Defaults to Quadratic. |
loss_by_col |
Loss function by column (override) Must be one of: "Quadratic", "Absolute", "Huber", "Poisson", "Hinge", "Logistic", "Periodic", "Categorical", "Ordinal". |
loss_by_col_idx |
Loss function by column index (override) |
multi_loss |
Categorical loss function Must be one of: "Categorical", "Ordinal". Defaults to Categorical. |
period |
Length of period (only used with periodic loss function) Defaults to 1. |
regularization_x |
Regularization function for X matrix Must be one of: "None", "Quadratic", "L2", "L1", "NonNegative", "OneSparse", "UnitOneSparse", "Simplex". Defaults to None. |
regularization_y |
Regularization function for Y matrix Must be one of: "None", "Quadratic", "L2", "L1", "NonNegative", "OneSparse", "UnitOneSparse", "Simplex". Defaults to None. |
gamma_x |
Regularization weight on X matrix Defaults to 0. |
gamma_y |
Regularization weight on Y matrix Defaults to 0. |
max_iterations |
Maximum number of iterations Defaults to 1000. |
max_updates |
Maximum number of updates, defaults to 2*max_iterations Defaults to 2000. |
init_step_size |
Initial step size Defaults to 1. |
min_step_size |
Minimum step size Defaults to 0.0001. |
seed |
Seed for random numbers (affects certain parts of the algo that are stochastic and those might or might not be enabled by default). Defaults to -1 (time-based random number). |
init |
Initialization mode Must be one of: "Random", "SVD", "PlusPlus", "User". Defaults to PlusPlus. |
svd_method |
Method for computing SVD during initialization (Caution: Randomized is currently experimental and unstable) Must be one of: "GramSVD", "Power", "Randomized". Defaults to Randomized. |
user_y |
User-specified initial Y |
user_x |
User-specified initial X |
expand_user_y |
|
impute_original |
|
recover_svd |
|
max_runtime_secs |
Maximum allowed runtime in seconds for model training. Use 0 to disable. Defaults to 0. |
export_checkpoints_dir |
Automatically export generated models to this directory. |
an object of class H2ODimReductionModel.
M. Udell, C. Horn, R. Zadeh, S. Boyd (2014). Generalized Low Rank Models[https://arxiv.org/abs/1410.0342]. Unpublished manuscript, Stanford Electrical Engineering Department. N. Halko, P.G. Martinsson, J.A. Tropp. Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions[https://arxiv.org/abs/0909.4061]. SIAM Rev., Survey and Review section, Vol. 53, num. 2, pp. 217-288, June 2011.
h2o.kmeans, h2o.svd, h2o.prcomp
## Not run: library(h2o) h2o.init() australia_path <- system.file("extdata", "australia.csv", package = "h2o") australia <- h2o.uploadFile(path = australia_path) h2o.glrm(training_frame = australia, k = 5, loss = "Quadratic", regularization_x = "L1", gamma_x = 0.5, gamma_y = 0, max_iterations = 1000) ## End(Not run)## Not run: library(h2o) h2o.init() australia_path <- system.file("extdata", "australia.csv", package = "h2o") australia <- h2o.uploadFile(path = australia_path) h2o.glrm(training_frame = australia, k = 5, loss = "Quadratic", regularization_x = "L1", gamma_x = 0.5, gamma_y = 0, max_iterations = 1000) ## End(Not run)
Searches for matches to argument 'pattern' within each element of a string column.
h2o.grep( pattern, x, ignore.case = FALSE, invert = FALSE, output.logical = FALSE )h2o.grep( pattern, x, ignore.case = FALSE, invert = FALSE, output.logical = FALSE )
pattern |
A character string containing a regular expression. |
x |
An H2O frame that wraps a single string column. |
ignore.case |
If TRUE case is ignored during matching. |
invert |
Identify elements that do not match the pattern. |
output.logical |
If TRUE returns logical vector of indicators instead of list of matching positions |
This function has similar semantics as R's native grep function and it supports a subset of its parameters. Default behavior is to return indices of the elements matching the pattern. Parameter 'output.logical' can be used to return a logical vector indicating if the element matches the pattern (1) or not (0).
H2OFrame holding the matching positions or a logical vector if 'output.logical' is enabled.
## Not run: library(h2o) h2o.init() addresses <- as.h2o(c("2307", "Leghorn St", "Mountain View", "CA", "94043")) zip_codes <- addresses[h2o.grep("[0-9]{5}", addresses, output.logical = TRUE),] ## End(Not run)## Not run: library(h2o) h2o.init() addresses <- as.h2o(c("2307", "Leghorn St", "Mountain View", "CA", "94043")) zip_codes <- addresses[h2o.grep("[0-9]{5}", addresses, output.logical = TRUE),] ## End(Not run)
Provides a set of functions to launch a grid search and get its results.
h2o.grid( algorithm, grid_id, x, y, training_frame, ..., hyper_params = list(), is_supervised = NULL, do_hyper_params_check = FALSE, search_criteria = NULL, export_checkpoints_dir = NULL, recovery_dir = NULL, parallelism = 1 )h2o.grid( algorithm, grid_id, x, y, training_frame, ..., hyper_params = list(), is_supervised = NULL, do_hyper_params_check = FALSE, search_criteria = NULL, export_checkpoints_dir = NULL, recovery_dir = NULL, parallelism = 1 )
algorithm |
Name of algorithm to use in grid search (gbm, randomForest, kmeans, glm, deeplearning, naivebayes, pca). |
grid_id |
(Optional) ID for resulting grid search. If it is not specified then it is autogenerated. |
x |
(Optional) A vector containing the names or indices of the predictor variables to use in building the model. If x is missing, then all columns except y are used. |
y |
The name or column index of the response variable in the data. The response must be either a numeric or a categorical/factor variable. If the response is numeric, then a regression model will be trained, otherwise it will train a classification model. |
training_frame |
Id of the training data frame. |
... |
arguments describing parameters to use with algorithm (i.e., x, y, training_frame). Look at the specific algorithm - h2o.gbm, h2o.glm, h2o.kmeans, h2o.deepLearning - for available parameters. |
hyper_params |
List of lists of hyper parameters (i.e., |
is_supervised |
[Deprecated] It is not possible to override default behaviour. (Optional) If specified then override the default heuristic which decides if the given algorithm name and parameters specify a supervised or unsupervised algorithm. |
do_hyper_params_check |
Perform client check for specified hyper parameters. It can be time expensive for large hyper space. |
search_criteria |
(Optional) List of control parameters for smarter hyperparameter search. The list can
include values for: strategy, max_models, max_runtime_secs, stopping_metric, stopping_tolerance, stopping_rounds and
seed. The default strategy 'Cartesian' covers the entire space of hyperparameter combinations. If you want to use
cartesian grid search, you can leave the search_criteria argument unspecified. Specify the "RandomDiscrete" strategy
to get random search of all the combinations of your hyperparameters with three ways of specifying when to stop the
search: max number of models, max time, and metric-based early stopping (e.g., stop if MSE has not improved by 0.0001
over the 5 best models). Examples below:
|
export_checkpoints_dir |
Directory to automatically export grid and its models to. |
recovery_dir |
When specified the grid and all necessary data (frames, models) will be saved to this
directory (use HDFS or other distributed file-system). Should the cluster crash during training, the grid
can be reloaded from this directory via |
parallelism |
Level of Parallelism during grid model building. 1 = sequential building (default). Use the value of 0 for adaptive parallelism - decided by H2O. Any number > 1 sets the exact number of models built in parallel. |
Launch grid search with given algorithm and parameters.
## Not run: library(h2o) library(jsonlite) h2o.init() iris_hf <- as.h2o(iris) grid <- h2o.grid("gbm", x = c(1:4), y = 5, training_frame = iris_hf, hyper_params = list(ntrees = c(1, 2, 3))) # Get grid summary summary(grid) # Fetch grid models model_ids <- grid@model_ids models <- lapply(model_ids, function(id) { h2o.getModel(id)}) ## End(Not run)## Not run: library(h2o) library(jsonlite) h2o.init() iris_hf <- as.h2o(iris) grid <- h2o.grid("gbm", x = c(1:4), y = 5, training_frame = iris_hf, hyper_params = list(ntrees = c(1, 2, 3))) # Get grid summary summary(grid) # Fetch grid models model_ids <- grid@model_ids models <- lapply(model_ids, function(id) { h2o.getModel(id)}) ## End(Not run)
Performs a group by and apply similar to ddply.
h2o.group_by( data, by, ..., gb.control = list(na.methods = NULL, col.names = NULL) )h2o.group_by( data, by, ..., gb.control = list(na.methods = NULL, col.names = NULL) )
data |
an H2OFrame object. |
by |
a list of column names |
... |
any supported aggregate function. See |
gb.control |
a list of how to handle |
In the case of na.methods within gb.control, there are three possible settings.
"all" will include NAs in computation of functions. "rm" will completely
remove all NA fields. "ignore" will remove NAs from the numerator but keep
the rows for computational purposes. If a list smaller than the number of columns groups is
supplied, the list will be padded by "ignore".
Note that to specify a list of column names in the gb.control list, you must add the
col.names argument. Similar to na.methods, col.names will pad the list with
the default column names if the length is less than the number of colums groups supplied.
Supported functions include nrow. This function is required and accepts a string for the
name of the generated column. Other supported aggregate functions accept col and na
arguments for specifying columns and the handling of NAs ("all", "ignore", and
GroupBy object; max calculates the maximum of each column specified in col for each
group of a GroupBy object; mean calculates the mean of each column specified in col
for each group of a GroupBy object; min calculates the minimum of each column specified in
col for each group of a GroupBy object; mode calculates the mode of each column
specified in col for each group of a GroupBy object; sd calculates the standard
deviation of each column specified in col for each group of a GroupBy object; ss
calculates the sum of squares of each column specified in col for each group of a GroupBy
object; sum calculates the sum of each column specified in col for each group of a
GroupBy object; and var calculates the variance of each column specified in col for
each group of a GroupBy object. If an aggregate is provided without a value (for example, as
max in sum(col="X1", na="all").mean(col="X5", na="all").max()), then it is assumed
that the aggregation should apply to all columns except the GroupBy columns. However, operations
will not be performed on String columns. They will be skipped. Note again that
nrow is required and cannot be empty.
Returns a new H2OFrame object with columns equivalent to the number of groups created
## Not run: library(h2o) h2o.init() df <- h2o.importFile(paste("https://s3.amazonaws.com/h2o-public-test-data", "/smalldata/prostate/prostate.csv", sep="")) h2o.group_by(data = df, by = "RACE", nrow("VOL")) ## End(Not run)## Not run: library(h2o) h2o.init() df <- h2o.importFile(paste("https://s3.amazonaws.com/h2o-public-test-data", "/smalldata/prostate/prostate.csv", sep="")) h2o.group_by(data = df, by = "RACE", nrow("VOL")) ## End(Not run)
Creates a copy of the target column in which each string has all occurence of the regex pattern replaced with the replacement substring.
h2o.gsub(pattern, replacement, x, ignore.case = FALSE)h2o.gsub(pattern, replacement, x, ignore.case = FALSE)
pattern |
The pattern to replace. |
replacement |
The replacement pattern. |
x |
The column on which to operate. |
ignore.case |
Case sensitive or not |
## Not run: library(h2o) h2o.init() string_to_gsub <- as.h2o("r tutorial") sub_string <- h2o.gsub("r ", "H2O ", string_to_gsub) ## End(Not run)## Not run: library(h2o) h2o.init() string_to_gsub <- as.h2o("r tutorial") sub_string <- h2o.gsub("r ", "H2O ", string_to_gsub) ## End(Not run)
This statistic can be calculated only for numerical variables. Missing values are supported.
h2o.h(model, frame, variables)h2o.h(model, frame, variables)
model |
A trained gradient-boosting model. |
frame |
A frame that current model has been fitted to. |
variables |
Variables of the interest. |
See Jerome H. Friedman and Bogdan E. Popescu, 2008, "Predictive learning via rule ensembles", *Ann. Appl. Stat.* **2**:916-954, http://projecteuclid.org/download/pdfview_1/euclid.aoas/1223908046, s. 8.1.
## Not run: library(h2o) h2o.init() prostate.hex <- h2o.importFile( "https://s3.amazonaws.com/h2o-public-test-data/smalldata/logreg/prostate.csv", destination_frame="prostate.hex" ) prostate.hex$CAPSULE <- as.factor(prostate.hex$CAPSULE) prostate.hex$RACE <- as.factor(prostate.hex$RACE) prostate.h2o <- h2o.gbm(x = 3:9, y = "CAPSULE", training_frame = prostate.hex, distribution = "bernoulli", ntrees = 100, max_depth = 5, min_rows = 10, learn_rate = 0.1) h_val <- h2o.h(prostate.h2o, prostate.hex, c('DPROS','DCAPS')) ## End(Not run)## Not run: library(h2o) h2o.init() prostate.hex <- h2o.importFile( "https://s3.amazonaws.com/h2o-public-test-data/smalldata/logreg/prostate.csv", destination_frame="prostate.hex" ) prostate.hex$CAPSULE <- as.factor(prostate.hex$CAPSULE) prostate.hex$RACE <- as.factor(prostate.hex$RACE) prostate.h2o <- h2o.gbm(x = 3:9, y = "CAPSULE", training_frame = prostate.hex, distribution = "bernoulli", ntrees = 100, max_depth = 5, min_rows = 10, learn_rate = 0.1) h_val <- h2o.h(prostate.h2o, prostate.hex, c('DPROS','DCAPS')) ## End(Not run)
Returns the first or last rows of an H2OFrame object.
h2o.head(x, n = 6L, m = 200L, ...) ## S3 method for class 'H2OFrame' head(x, n = 6L, m = 200L, ...) h2o.tail(x, n = 6L, m = 200L, ...) ## S3 method for class 'H2OFrame' tail(x, n = 6L, m = 200L, ...)h2o.head(x, n = 6L, m = 200L, ...) ## S3 method for class 'H2OFrame' head(x, n = 6L, m = 200L, ...) h2o.tail(x, n = 6L, m = 200L, ...) ## S3 method for class 'H2OFrame' tail(x, n = 6L, m = 200L, ...)
x |
An H2OFrame object. |
n |
(Optional) A single integer. If positive, number of rows in x to return. If negative, all but the n first/last number of rows in x. |
m |
(Optional) A single integer. If positive, number of columns in x to return. If negative, all but the m first/last number of columns in x. |
... |
Ignored. |
An H2OFrame containing the first or last n rows and m columns of an H2OFrame object.
## Not run: library(h2o) h2o.init(ip <- "localhost", port = 54321, startH2O = TRUE) australia_path <- system.file("extdata", "australia.csv", package = "h2o") australia <- h2o.uploadFile(path = australia_path) # Return the first 10 rows and 6 columns h2o.head(australia, n = 10L, m = 6L) # Return the last 10 rows and 6 columns h2o.tail(australia, n = 10L, m = 6L) # For Jupyter notebook with an R kernel, # view all rows of a data frame options(repr.matrix.max.rows = 600, repr.matrix.max.cols = 200) ## End(Not run)## Not run: library(h2o) h2o.init(ip <- "localhost", port = 54321, startH2O = TRUE) australia_path <- system.file("extdata", "australia.csv", package = "h2o") australia <- h2o.uploadFile(path = australia_path) # Return the first 10 rows and 6 columns h2o.head(australia, n = 10L, m = 6L) # Return the last 10 rows and 6 columns h2o.tail(australia, n = 10L, m = 6L) # For Jupyter notebook with an R kernel, # view all rows of a data frame options(repr.matrix.max.rows = 600, repr.matrix.max.cols = 200) ## End(Not run)
Retrieve HGLM ModelMetrics
h2o.HGLMMetrics(object)h2o.HGLMMetrics(object)
object |
an H2OModel object or H2OModelMetrics. |
Compute a histogram over a numeric column. If breaks=="FD", the MAD is used over the IQR in computing bin width. Note that we do not beautify the breakpoints as R does.
h2o.hist(x, breaks = "Sturges", plot = TRUE)h2o.hist(x, breaks = "Sturges", plot = TRUE)
x |
A single numeric column from an H2OFrame. |
breaks |
Can be one of the following: A string: "Sturges", "Rice", "sqrt", "Doane", "FD", "Scott" A single number for the number of breaks splitting the range of the vec into number of breaks bins of equal width A vector of numbers giving the split points, e.g., c(-50,213.2123,9324834) |
plot |
A logical value indicating whether or not a plot should be generated (default is TRUE). |
## Not run: library(h2o) h2o.init() f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_train.csv" iris <- h2o.importFile(f) h2o.asnumeric(iris["petal_len"]) h2o.hist(iris["petal_len"], breaks = "Sturges", plot = TRUE) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_train.csv" iris <- h2o.importFile(f) h2o.asnumeric(iris["petal_len"]) h2o.hist(iris["petal_len"], breaks = "Sturges", plot = TRUE) ## End(Not run)
If "train", "valid", and "xval" parameters are FALSE (default), then the training Hit Ratios value is returned. If more than one parameter is set to TRUE, then a named list of Hit Ratio tables are returned, where the names are "train", "valid" or "xval".
h2o.hit_ratio_table(object, train = FALSE, valid = FALSE, xval = FALSE)h2o.hit_ratio_table(object, train = FALSE, valid = FALSE, xval = FALSE)
object |
An H2OModel object. |
train |
Retrieve the training Hit Ratio |
valid |
Retrieve the validation Hit Ratio |
xval |
Retrieve the cross-validation Hit Ratio |
## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/iris/iris_wheader.csv" iris <- h2o.importFile(f) iris_split <- h2o.splitFrame(data = iris, ratios = 0.8, seed = 1234) train <- iris_split[[1]] valid <- iris_split[[2]] iris_xgb <- h2o.xgboost(x = 1:4, y = 5, training_frame = train, validation_frame = valid) hrt_iris <- h2o.hit_ratio_table(iris_xgb, valid = TRUE) hrt_iris ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/iris/iris_wheader.csv" iris <- h2o.importFile(f) iris_split <- h2o.splitFrame(data = iris, ratios = 0.8, seed = 1234) train <- iris_split[[1]] valid <- iris_split[[2]] iris_xgb <- h2o.xgboost(x = 1:4, y = 5, training_frame = train, validation_frame = valid) hrt_iris <- h2o.hit_ratio_table(iris_xgb, valid = TRUE) hrt_iris ## End(Not run)
Converts the entries of an H2OFrame object from milliseconds to hours of the day (on a 0 to 23 scale).
h2o.hour(x) hour(x) ## S3 method for class 'H2OFrame' hour(x)h2o.hour(x) hour(x) ## S3 method for class 'H2OFrame' hour(x)
x |
An H2OFrame object. |
An H2OFrame object containing the entries of x converted to hours of
the day.
## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/jira/v-11-eurodate.csv" hdf <- h2o.importFile(f) h2o.hour(hdf["ds9"]) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/jira/v-11-eurodate.csv" hdf <- h2o.importFile(f) h2o.hour(hdf["ds9"]) ## End(Not run)
Individual Conditional Expectation (ICE) plot gives a graphical depiction of the marginal effect of a variable on the response. ICE plots are similar to partial dependence plots (PDP); PDP shows the average effect of a feature while ICE plot shows the effect for a single instance. This function will plot the effect for each decile. In contrast to the PDP, ICE plots can provide more insight, especially when there is stronger feature interaction. Also, the plot shows the original observation values marked by semi-transparent circle on each ICE line. Please note, that the score of the original observation value may differ from score value of underlying ICE line at original observation point as ICE line is drawn as an interpolation of several points.
h2o.ice_plot( model, newdata, column, target = NULL, max_levels = 30, show_pdp = TRUE, binary_response_scale = c("response", "logodds"), centered = FALSE, grouping_column = NULL, output_graphing_data = FALSE, nbins = 100, show_rug = TRUE, ... )h2o.ice_plot( model, newdata, column, target = NULL, max_levels = 30, show_pdp = TRUE, binary_response_scale = c("response", "logodds"), centered = FALSE, grouping_column = NULL, output_graphing_data = FALSE, nbins = 100, show_rug = TRUE, ... )
model |
An H2OModel. |
newdata |
An H2OFrame. |
column |
A feature column name to inspect. |
target |
If multinomial, plot PDP just for |
max_levels |
An integer specifying the maximum number of factor levels to show. Defaults to 30. |
show_pdp |
Option to turn on/off PDP line. Defaults to TRUE. |
binary_response_scale |
Option for binary model to display (on the y-axis) the logodds instead of the actual score. Can be one of: "response", "logodds". Defaults to "response". |
centered |
A boolean whether to center curves around 0 at the first valid x value or not. Defaults to FALSE. |
grouping_column |
A feature column name to group the data and provide separate sets of plots by grouping feature values |
output_graphing_data |
A bool whether to output final graphing data to a frame. Defaults to FALSE. |
nbins |
A number of bins used. Defaults to 100. |
show_rug |
Show rug to visualize the density of the column. Defaults to TRUE. |
... |
Custom parameters. |
A ggplot2 object
## Not run: library(h2o) h2o.init() # Import the wine dataset into H2O: f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/wine/winequality-redwhite-no-BOM.csv" df <- h2o.importFile(f) # Set the response response <- "quality" # Split the dataset into a train and test set: splits <- h2o.splitFrame(df, ratios = 0.8, seed = 1) train <- splits[[1]] test <- splits[[2]] # Build and train the model: gbm <- h2o.gbm(y = response, training_frame = train) # Create the individual conditional expectations plot ice <- h2o.ice_plot(gbm, test, column = "alcohol") print(ice) ## End(Not run)## Not run: library(h2o) h2o.init() # Import the wine dataset into H2O: f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/wine/winequality-redwhite-no-BOM.csv" df <- h2o.importFile(f) # Set the response response <- "quality" # Split the dataset into a train and test set: splits <- h2o.splitFrame(df, ratios = 0.8, seed = 1) train <- splits[[1]] test <- splits[[2]] # Build and train the model: gbm <- h2o.gbm(y = response, training_frame = train) # Create the individual conditional expectations plot ice <- h2o.ice_plot(gbm, test, column = "alcohol") print(ice) ## End(Not run)
Applies conditional statements to numeric vectors in H2O parsed data objects when the data are numeric.
h2o.ifelse(test, yes, no) ifelse(test, yes, no)h2o.ifelse(test, yes, no) ifelse(test, yes, no)
test |
A logical description of the condition to be met (>, <, =, etc...) |
yes |
The value to return if the condition is TRUE. |
no |
The value to return if the condition is FALSE. |
Both numeric and categorical values can be tested. However when returning a yes and no condition both conditions must be either both categorical or numeric.
Returns a vector of new values matching the conditions stated in the ifelse call.
## Not run: library(h2o) h2o.init() australia_path <- system.file("extdata", "australia.csv", package = "h2o") australia <- h2o.importFile(path = australia_path) australia[, 9] <- ifelse(australia[, 3] < 279.9, 1, 0) summary(australia) ## End(Not run)## Not run: library(h2o) h2o.init() australia_path <- system.file("extdata", "australia.csv", package = "h2o") australia <- h2o.importFile(path = australia_path) australia[, 9] <- ifelse(australia[, 3] < 279.9, 1, 0) summary(australia) ## End(Not run)
Import Hive table to H2OFrame in memory. Make sure to start H2O with Hive on classpath. Uses hive-site.xml on classpath to connect to Hive. When database is specified as jdbc URL uses Hive JDBC driver to obtain table metadata. then uses direct HDFS access to import data.
h2o.import_hive_table( database, table, partitions = NULL, allow_multi_format = FALSE )h2o.import_hive_table( database, table, partitions = NULL, allow_multi_format = FALSE )
database |
Name of Hive database (default database will be used by default), can be also a JDBC URL |
table |
name of Hive table to import |
partitions |
a list of lists of strings - partition key column values of partitions you want to import. |
allow_multi_format |
enable import of partitioned tables with different storage formats used. WARNING: this may fail on out-of-memory for tables with a large number of small partitions. |
For example, my_citibike_data = h2o.import_hive_table("default", "citibike20k", partitions = list(c("2017", "01"), c("2017", "02"))) my_citibike_data = h2o.import_hive_table("jdbc:hive2://hive-server:10000/default", "citibike20k", allow_multi_format = TRUE)
Usage example: mojo_model <- h2o.import_mojo(model_file_path = "/path/to/mojo.zip") predictions <- h2o.predict(mojo_model, dataset)
h2o.import_mojo(mojo_file_path, model_id = NULL)h2o.import_mojo(mojo_file_path, model_id = NULL)
mojo_file_path |
Filesystem path to the model imported |
model_id |
Model ID, default is NULL |
Returns H2O Generic Model embedding given MOJO model
## Not run: # Import default Iris dataset as H2O frame data <- as.h2o(iris) # Train a very simple GBM model features <- c("Sepal.Length", "Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width") original_model <- h2o.gbm(x = features, y = "Species", training_frame = data) # Download the trained GBM model as MOJO (temporary directory used in this example) mojo_original_path <- h2o.save_mojo(original_model, path = tempdir()) # Import the MOJO and obtain a Generic model mojo_model <- h2o.import_mojo(mojo_original_path) # Perform scoring with the generic model predictions <- h2o.predict(mojo_model, data) ## End(Not run)## Not run: # Import default Iris dataset as H2O frame data <- as.h2o(iris) # Train a very simple GBM model features <- c("Sepal.Length", "Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width") original_model <- h2o.gbm(x = features, y = "Species", training_frame = data) # Download the trained GBM model as MOJO (temporary directory used in this example) mojo_original_path <- h2o.save_mojo(original_model, path = tempdir()) # Import the MOJO and obtain a Generic model mojo_model <- h2o.import_mojo(mojo_original_path) # Perform scoring with the generic model predictions <- h2o.predict(mojo_model, data) ## End(Not run)
Creates a temporary SQL table from the specified sql_query. Runs multiple SELECT SQL queries on the temporary table concurrently for parallel ingestion, then drops the table. Be sure to start the h2o.jar in the terminal with your downloaded JDBC driver in the classpath: 'java -cp <path_to_h2o_jar>:<path_to_jdbc_driver_jar> water.H2OApp' Also see h2o.import_sql_table. Currently supported SQL databases are MySQL, PostgreSQL, MariaDB, Hive, Oracle and Microsoft SQL Server.
h2o.import_sql_select( connection_url, select_query, username, password, use_temp_table = NULL, temp_table_name = NULL, optimize = NULL, fetch_mode = NULL )h2o.import_sql_select( connection_url, select_query, username, password, use_temp_table = NULL, temp_table_name = NULL, optimize = NULL, fetch_mode = NULL )
connection_url |
URL of the SQL database connection as specified by the Java Database Connectivity (JDBC) Driver. For example, "jdbc:mysql://localhost:3306/menagerie?&useSSL=false" |
select_query |
SQL query starting with 'SELECT' that returns rows from one or more database tables. |
username |
Username for SQL server |
password |
Password for SQL server |
use_temp_table |
Whether a temporary table should be created from select_query |
temp_table_name |
Name of temporary table to be created from select_query |
optimize |
(Optional) Optimize import of SQL table for faster imports. Experimental. Default is true. |
fetch_mode |
(Optional) Set to DISTRIBUTED to enable distributed import. Set to SINGLE to force a sequential read from the database Can be used for databases that do not support OFFSET-like clauses in SQL statements. |
For example, my_sql_conn_url <- "jdbc:mysql://172.16.2.178:3306/ingestSQL?&useSSL=false" select_query <- "SELECT bikeid from citibike20k" username <- "root" password <- "abc123" my_citibike_data <- h2o.import_sql_select(my_sql_conn_url, select_query, username, password)
Imports SQL table into an H2O cluster. Assumes that the SQL table is not being updated and is stable. Runs multiple SELECT SQL queries concurrently for parallel ingestion. Be sure to start the h2o.jar in the terminal with your downloaded JDBC driver in the classpath: 'java -cp <path_to_h2o_jar>:<path_to_jdbc_driver_jar> water.H2OApp' Also see h2o.import_sql_select. Currently supported SQL databases are MySQL, PostgreSQL, MariaDB, Hive, Oracle and Microsoft SQL Server.
h2o.import_sql_table( connection_url, table, username, password, columns = NULL, optimize = NULL, fetch_mode = NULL )h2o.import_sql_table( connection_url, table, username, password, columns = NULL, optimize = NULL, fetch_mode = NULL )
connection_url |
URL of the SQL database connection as specified by the Java Database Connectivity (JDBC) Driver. For example, "jdbc:mysql://localhost:3306/menagerie?&useSSL=false" |
table |
Name of SQL table |
username |
Username for SQL server |
password |
Password for SQL server |
columns |
(Optional) Character vector of column names to import from SQL table. Default is to import all columns. |
optimize |
(Optional) Optimize import of SQL table for faster imports. Default is true. Ignored - use fetch_mode instead. |
fetch_mode |
(Optional) Set to DISTRIBUTED to enable distributed import. Set to SINGLE to force a sequential read from the database Can be used for databases that do not support OFFSET-like clauses in SQL statements. |
For example, my_sql_conn_url <- "jdbc:mysql://172.16.2.178:3306/ingestSQL?&useSSL=false" table <- "citibike20k" username <- "root" password <- "abc123" my_citibike_data <- h2o.import_sql_table(my_sql_conn_url, table, username, password)
Imports files into an H2O cluster. The default behavior is to pass-through to the parse phase automatically.
h2o.importFile( path, destination_frame = "", parse = TRUE, header = NA, sep = "", col.names = NULL, col.types = NULL, na.strings = NULL, decrypt_tool = NULL, skipped_columns = NULL, force_col_types = FALSE, custom_non_data_line_markers = NULL, partition_by = NULL, quotechar = NULL, escapechar = "" ) h2o.importFolder( path, pattern = "", destination_frame = "", parse = TRUE, header = NA, sep = "", col.names = NULL, col.types = NULL, na.strings = NULL, decrypt_tool = NULL, skipped_columns = NULL, force_col_types = FALSE, custom_non_data_line_markers = NULL, partition_by = NULL, quotechar = NULL, escapechar = "\\" ) h2o.importHDFS( path, pattern = "", destination_frame = "", parse = TRUE, header = NA, sep = "", col.names = NULL, na.strings = NULL ) h2o.uploadFile( path, destination_frame = "", parse = TRUE, header = NA, sep = "", col.names = NULL, col.types = NULL, na.strings = NULL, progressBar = FALSE, parse_type = NULL, decrypt_tool = NULL, skipped_columns = NULL, force_col_types = FALSE, quotechar = NULL, escapechar = "\\" )h2o.importFile( path, destination_frame = "", parse = TRUE, header = NA, sep = "", col.names = NULL, col.types = NULL, na.strings = NULL, decrypt_tool = NULL, skipped_columns = NULL, force_col_types = FALSE, custom_non_data_line_markers = NULL, partition_by = NULL, quotechar = NULL, escapechar = "" ) h2o.importFolder( path, pattern = "", destination_frame = "", parse = TRUE, header = NA, sep = "", col.names = NULL, col.types = NULL, na.strings = NULL, decrypt_tool = NULL, skipped_columns = NULL, force_col_types = FALSE, custom_non_data_line_markers = NULL, partition_by = NULL, quotechar = NULL, escapechar = "\\" ) h2o.importHDFS( path, pattern = "", destination_frame = "", parse = TRUE, header = NA, sep = "", col.names = NULL, na.strings = NULL ) h2o.uploadFile( path, destination_frame = "", parse = TRUE, header = NA, sep = "", col.names = NULL, col.types = NULL, na.strings = NULL, progressBar = FALSE, parse_type = NULL, decrypt_tool = NULL, skipped_columns = NULL, force_col_types = FALSE, quotechar = NULL, escapechar = "\\" )
path |
The complete URL or normalized file path of the file to be imported. Each row of data appears as one line of the file. |
destination_frame |
(Optional) The unique hex key assigned to the imported file. If none is given, a key will automatically be generated based on the URL path. |
parse |
(Optional) A logical value indicating whether the file should be parsed after import, for details see h2o.parseRaw. |
header |
(Optional) A logical value indicating whether the first line of the file contains column headers. If left empty, the parser will try to automatically detect this. |
sep |
(Optional) The field separator character. Values on each line of
the file are separated by this character. If |
col.names |
(Optional) An H2OFrame object containing a single delimited line with the column names for the file. |
col.types |
(Optional) A vector to specify whether columns should be forced to a certain type upon import parsing. |
na.strings |
(Optional) H2O will interpret these strings as missing. |
decrypt_tool |
(Optional) Specify a Decryption Tool (key-reference acquired by calling h2o.decryptionSetup. |
skipped_columns |
a list of column indices to be skipped during parsing. |
force_col_types |
(Optional) If TRUE, will force the column types to be either the ones in Parquet schema for Parquet files or the ones specified in column_types. This parameter is used for numerical columns only. Other column settings will happen without setting this parameter. Defaults to FALSE. |
custom_non_data_line_markers |
(Optional) If a line in imported file starts with any character in given string it will NOT be imported. Empty string means all lines are imported, NULL means that default behaviour for given format will be used |
partition_by |
names of the columns the persisted dataset has been partitioned by. |
quotechar |
A hint for the parser which character to expect as quoting character. None (default) means autodetection. |
escapechar |
(Optional) One ASCII character used to escape other characters. |
pattern |
(Optional) Character string containing a regular expression to match file(s) in the folder. |
progressBar |
(Optional) When FALSE, tell H2O parse call to block synchronously instead of polling. This can be faster for small datasets but loses the progress bar. |
parse_type |
(Optional) Specify which parser type H2O will use. Valid types are "ARFF", "XLS", "CSV", "SVMLight" |
h2o.importFile is a parallelized reader and pulls information from the server from a location specified
by the client. The path is a server-side path. This is a fast, scalable, highly optimized way to read data. H2O
pulls the data from a data store and initiates the data transfer as a read operation.
Unlike the import function, which is a parallelized reader, h2o.uploadFile is a push from
the client to the server. The specified path must be a client-side path. This is not scalable and is only
intended for smaller data sizes. The client pushes the data from a local filesystem (for example,
on your machine where R is running) to H2O. For big-data operations, you don't want the data
stored on or flowing through the client.
h2o.importFolder imports an entire directory of files. If the given path is relative, then it
will be relative to the start location of the H2O instance. The default
behavior is to pass-through to the parse phase automatically.
h2o.importHDFS is deprecated. Instead, use h2o.importFile.
h2o.import_sql_select, h2o.import_sql_table, h2o.parseRaw
## Not run: h2o.init(ip = "localhost", port = 54321, startH2O = TRUE) prostate_path = system.file("extdata", "prostate.csv", package = "h2o") prostate = h2o.importFile(path = prostate_path) class(prostate) summary(prostate) #Import files with a certain regex pattern by utilizing h2o.importFolder() #In this example we import all .csv files in the directory prostate_folder prostate_path = system.file("extdata", "prostate_folder", package = "h2o") prostate_pattern = h2o.importFolder(path = prostate_path, pattern = ".*.csv") class(prostate_pattern) summary(prostate_pattern) ## End(Not run)## Not run: h2o.init(ip = "localhost", port = 54321, startH2O = TRUE) prostate_path = system.file("extdata", "prostate.csv", package = "h2o") prostate = h2o.importFile(path = prostate_path) class(prostate) summary(prostate) #Import files with a certain regex pattern by utilizing h2o.importFolder() #In this example we import all .csv files in the directory prostate_folder prostate_path = system.file("extdata", "prostate_folder", package = "h2o") prostate_pattern = h2o.importFolder(path = prostate_path, pattern = ".*.csv") class(prostate_pattern) summary(prostate_pattern) ## End(Not run)
Perform inplace imputation by filling missing values with aggregates computed on the "na.rm'd" vector. Additionally, it's possible to perform imputation based on groupings of columns from within data; these columns can be passed by index or name to the by parameter. If a factor column is supplied, then the method must be "mode".
h2o.impute( data, column = 0, method = c("mean", "median", "mode"), combine_method = c("interpolate", "average", "lo", "hi"), by = NULL, groupByFrame = NULL, values = NULL )h2o.impute( data, column = 0, method = c("mean", "median", "mode"), combine_method = c("interpolate", "average", "lo", "hi"), by = NULL, groupByFrame = NULL, values = NULL )
data |
The dataset containing the column to impute. |
column |
A specific column to impute, default of 0 means impute the whole frame. |
method |
"mean" replaces NAs with the column mean; "median" replaces NAs with the column median; "mode" replaces with the most common factor (for factor columns only); |
combine_method |
If method is "median", then choose how to combine quantiles on even sample sizes. This parameter is ignored in all other cases. |
by |
group by columns |
groupByFrame |
Impute the column col with this pre-computed grouped frame. |
values |
A vector of impute values (one per column). NaN indicates to skip the column |
The default method is selected based on the type of the column to impute. If the column is numeric then "mean" is selected; if it is categorical, then "mode" is selected. Other column types (e.g. String, Time, UUID) are not supported.
an H2OFrame with imputed values
## Not run: h2o.init() iris_hf <- as.h2o(iris) iris_hf[sample(nrow(iris_hf), 40), 5] <- NA # randomly replace 50 values with NA # impute with a group by iris_hf <- h2o.impute(iris_hf, "Species", "mode", by = c("Sepal.Length", "Sepal.Width")) ## End(Not run)## Not run: h2o.init() iris_hf <- as.h2o(iris) iris_hf[sample(nrow(iris_hf), 40), 5] <- NA # randomly replace 50 values with NA # impute with a group by iris_hf <- h2o.impute(iris_hf, "Species", "mode", by = c("Sepal.Length", "Sepal.Width")) ## End(Not run)
The infogram is a graphical information-theoretic interpretability tool which allows the user to quickly spot the core, decision-making variables that uniquely and safely drive the response, in supervised classification problems. The infogram can significantly cut down the number of predictors needed to build a model by identifying only the most valuable, admissible features. When protected variables such as race or gender are present in the data, the admissibility of a variable is determined by a safety and relevancy index, and thus serves as a diagnostic tool for fairness. The safety of each feature can be quantified and variables that are unsafe will be considered inadmissible. Models built using only admissible features will naturally be more interpretable, given the reduced feature set. Admissible models are also less susceptible to overfitting and train faster, while providing similar accuracy as models built using all available features.
h2o.infogram( x, y, training_frame, model_id = NULL, validation_frame = NULL, seed = -1, keep_cross_validation_models = TRUE, keep_cross_validation_predictions = FALSE, keep_cross_validation_fold_assignment = FALSE, nfolds = 0, fold_assignment = c("AUTO", "Random", "Modulo", "Stratified"), fold_column = NULL, ignore_const_cols = TRUE, score_each_iteration = FALSE, offset_column = NULL, weights_column = NULL, standardize = FALSE, distribution = c("AUTO", "bernoulli", "multinomial", "gaussian", "poisson", "gamma", "tweedie", "laplace", "quantile", "huber"), plug_values = NULL, max_iterations = 0, stopping_rounds = 0, stopping_metric = c("AUTO", "deviance", "logloss", "MSE", "RMSE", "MAE", "RMSLE", "AUC", "AUCPR", "lift_top_group", "misclassification", "mean_per_class_error", "custom", "custom_increasing"), stopping_tolerance = 0.001, balance_classes = FALSE, class_sampling_factors = NULL, max_after_balance_size = 5, max_runtime_secs = 0, custom_metric_func = NULL, auc_type = c("AUTO", "NONE", "MACRO_OVR", "WEIGHTED_OVR", "MACRO_OVO", "WEIGHTED_OVO"), algorithm = c("AUTO", "deeplearning", "drf", "gbm", "glm", "xgboost"), algorithm_params = NULL, protected_columns = NULL, total_information_threshold = -1, net_information_threshold = -1, relevance_index_threshold = -1, safety_index_threshold = -1, data_fraction = 1, top_n_features = 50 )h2o.infogram( x, y, training_frame, model_id = NULL, validation_frame = NULL, seed = -1, keep_cross_validation_models = TRUE, keep_cross_validation_predictions = FALSE, keep_cross_validation_fold_assignment = FALSE, nfolds = 0, fold_assignment = c("AUTO", "Random", "Modulo", "Stratified"), fold_column = NULL, ignore_const_cols = TRUE, score_each_iteration = FALSE, offset_column = NULL, weights_column = NULL, standardize = FALSE, distribution = c("AUTO", "bernoulli", "multinomial", "gaussian", "poisson", "gamma", "tweedie", "laplace", "quantile", "huber"), plug_values = NULL, max_iterations = 0, stopping_rounds = 0, stopping_metric = c("AUTO", "deviance", "logloss", "MSE", "RMSE", "MAE", "RMSLE", "AUC", "AUCPR", "lift_top_group", "misclassification", "mean_per_class_error", "custom", "custom_increasing"), stopping_tolerance = 0.001, balance_classes = FALSE, class_sampling_factors = NULL, max_after_balance_size = 5, max_runtime_secs = 0, custom_metric_func = NULL, auc_type = c("AUTO", "NONE", "MACRO_OVR", "WEIGHTED_OVR", "MACRO_OVO", "WEIGHTED_OVO"), algorithm = c("AUTO", "deeplearning", "drf", "gbm", "glm", "xgboost"), algorithm_params = NULL, protected_columns = NULL, total_information_threshold = -1, net_information_threshold = -1, relevance_index_threshold = -1, safety_index_threshold = -1, data_fraction = 1, top_n_features = 50 )
x |
(Optional) A vector containing the names or indices of the predictor variables to use in building the model. If x is missing, then all columns except y are used. |
y |
The name or column index of the response variable in the data. The response must be either a numeric or a categorical/factor variable. If the response is numeric, then a regression model will be trained, otherwise it will train a classification model. |
training_frame |
Id of the training data frame. |
model_id |
Destination id for this model; auto-generated if not specified. |
validation_frame |
Id of the validation data frame. |
seed |
Seed for random numbers (affects certain parts of the algo that are stochastic and those might or might not be enabled by default). Defaults to -1 (time-based random number). |
keep_cross_validation_models |
|
keep_cross_validation_predictions |
|
keep_cross_validation_fold_assignment |
|
nfolds |
Number of folds for K-fold cross-validation (0 to disable or >= 2). Defaults to 0. |
fold_assignment |
Cross-validation fold assignment scheme, if fold_column is not specified. The 'Stratified' option will stratify the folds based on the response variable, for classification problems. Must be one of: "AUTO", "Random", "Modulo", "Stratified". Defaults to AUTO. |
fold_column |
Column with cross-validation fold index assignment per observation. |
ignore_const_cols |
|
score_each_iteration |
|
offset_column |
Offset column. This will be added to the combination of columns before applying the link function. |
weights_column |
Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0. |
standardize |
|
distribution |
Distribution function Must be one of: "AUTO", "bernoulli", "multinomial", "gaussian", "poisson", "gamma", "tweedie", "laplace", "quantile", "huber". Defaults to AUTO. |
plug_values |
Plug Values (a single row frame containing values that will be used to impute missing values of the training/validation frame, use with conjunction missing_values_handling = PlugValues). |
max_iterations |
Maximum number of iterations. Defaults to 0. |
stopping_rounds |
Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable) Defaults to 0. |
stopping_metric |
Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client. Must be one of: "AUTO", "deviance", "logloss", "MSE", "RMSE", "MAE", "RMSLE", "AUC", "AUCPR", "lift_top_group", "misclassification", "mean_per_class_error", "custom", "custom_increasing". Defaults to AUTO. |
stopping_tolerance |
Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much) Defaults to 0.001. |
balance_classes |
|
class_sampling_factors |
Desired over/under-sampling ratios per class (in lexicographic order). If not specified, sampling factors will be automatically computed to obtain class balance during training. Requires balance_classes. |
max_after_balance_size |
Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires balance_classes. Defaults to 5.0. |
max_runtime_secs |
Maximum allowed runtime in seconds for model training. Use 0 to disable. Defaults to 0. |
custom_metric_func |
Reference to custom evaluation function, format: 'language:keyName=funcName' |
auc_type |
Set default multinomial AUC type. Must be one of: "AUTO", "NONE", "MACRO_OVR", "WEIGHTED_OVR", "MACRO_OVO", "WEIGHTED_OVO". Defaults to AUTO. |
algorithm |
Type of machine learning algorithm used to build the infogram. Options include 'AUTO' (gbm), 'deeplearning' (Deep Learning with default parameters), 'drf' (Random Forest with default parameters), 'gbm' (GBM with default parameters), 'glm' (GLM with default parameters), or 'xgboost' (if available, XGBoost with default parameters). Must be one of: "AUTO", "deeplearning", "drf", "gbm", "glm", "xgboost". Defaults to AUTO. |
algorithm_params |
Customized parameters for the machine learning algorithm specified in the algorithm parameter. |
protected_columns |
Columns that contain features that are sensitive and need to be protected (legally, or otherwise), if applicable. These features (e.g. race, gender, etc) should not drive the prediction of the response. |
total_information_threshold |
A number between 0 and 1 representing a threshold for total information, defaulting to 0.1. For a specific feature, if the total information is higher than this threshold, and the corresponding net information is also higher than the threshold “net_information_threshold“, that feature will be considered admissible. The total information is the x-axis of the Core Infogram. Default is -1 which gets set to 0.1. Defaults to -1. |
net_information_threshold |
A number between 0 and 1 representing a threshold for net information, defaulting to 0.1. For a specific feature, if the net information is higher than this threshold, and the corresponding total information is also higher than the total_information_threshold, that feature will be considered admissible. The net information is the y-axis of the Core Infogram. Default is -1 which gets set to 0.1. Defaults to -1. |
relevance_index_threshold |
A number between 0 and 1 representing a threshold for the relevance index, defaulting to 0.1. This is only used when “protected_columns“ is set by the user. For a specific feature, if the relevance index value is higher than this threshold, and the corresponding safety index is also higher than the safety_index_threshold“, that feature will be considered admissible. The relevance index is the x-axis of the Fair Infogram. Default is -1 which gets set to 0.1. Defaults to -1. |
safety_index_threshold |
A number between 0 and 1 representing a threshold for the safety index, defaulting to 0.1. This is only used when protected_columns is set by the user. For a specific feature, if the safety index value is higher than this threshold, and the corresponding relevance index is also higher than the relevance_index_threshold, that feature will be considered admissible. The safety index is the y-axis of the Fair Infogram. Default is -1 which gets set to 0.1. Defaults to -1. |
data_fraction |
The fraction of training frame to use to build the infogram model. Defaults to 1.0, and any value greater than 0 and less than or equal to 1.0 is acceptable. Defaults to 1. |
top_n_features |
An integer specifying the number of columns to evaluate in the infogram. The columns are ranked by variable importance, and the top N are evaluated. Defaults to 50. Defaults to 50. |
The infogram allows the user to quickly spot the admissible decision-making variables that are driving the response. There are two types of infogram plots: Core and Fair Infogram.
The Core Infogram plots all the variables as points on two-dimensional grid of total vs net information. The x-axis is total information, a measure of how much the variable drives the response (the more predictive, the higher the total information). The y-axis is net information, a measure of how unique the variable is. The top right quadrant of the infogram plot is the admissible section; the variables located in this quadrant are the admissible features. In the Core Infogram, the admissible features are the strongest, unique drivers of the response.
If sensitive or protected variables are present in data, the user can specify which attributes should be protected while training using the protected_columns
argument. All non-protected predictor variables will be checked to make sure that there's no information pathway to the response through a protected feature, and
deemed inadmissible if they possess little or no informational value beyond their use as a dummy for protected attributes. The Fair Infogram plots all the features
as points on two-dimensional grid of relevance vs safety. The x-axis is relevance index, a measure of how much the variable drives the response (the more predictive,
the higher the relevance). The y-axis is safety index, a measure of how much extra information the variable has that is not acquired through the protected variables.
In the Fair Infogram, the admissible features are the strongest, safest drivers of the response.
## Not run: h2o.init() # Convert iris dataset to an H2OFrame df <- as.h2o(iris) # Infogram ig <- h2o.infogram(y = "Species", training_frame = df) plot(ig) ## End(Not run)## Not run: h2o.init() # Convert iris dataset to an H2OFrame df <- as.h2o(iris) # Infogram ig <- h2o.infogram(y = "Species", training_frame = df) plot(ig) ## End(Not run)
Train models over subsets selected using infogram
h2o.infogram_train_subset_models( ig, model_fun, training_frame, test_frame, y, protected_columns, reference, favorable_class, feature_selection_metrics = c("safety_index"), metric = "euclidean", air_metric = "selectedRatio", alpha = 0.05, ... )h2o.infogram_train_subset_models( ig, model_fun, training_frame, test_frame, y, protected_columns, reference, favorable_class, feature_selection_metrics = c("safety_index"), metric = "euclidean", air_metric = "selectedRatio", alpha = 0.05, ... )
ig |
Infogram object trained with the same protected columns |
model_fun |
Function that creates models. This can be something like h2o.automl, h2o.gbm, etc. |
training_frame |
Training frame |
test_frame |
Test frame |
y |
Response column |
protected_columns |
Protected columns |
reference |
List of values corresponding to a reference for each protected columns. If set to NULL, it will use the biggest group as the reference. |
favorable_class |
Positive/favorable outcome class of the response. |
feature_selection_metrics |
One or more columns from the infogram@admissible_score. |
metric |
Metric supported by stats::dist which is used to sort the features. |
air_metric |
Metric used for Adverse Impact Ratio calculation. Defaults to “selectedRatio“. |
alpha |
The alpha level is the probability of rejecting the null hypothesis that the protected group and the reference came from the same population when the null hypothesis is true. |
... |
Parameters that are passed to the model_fun. |
frame containing aggregations of intersectional fairness across the models
## Not run: library(h2o) h2o.connect() data <- h2o.importFile(paste0("https://s3.amazonaws.com/h2o-public-test-data/smalldata/", "admissibleml_test/taiwan_credit_card_uci.csv")) x <- c('LIMIT_BAL', 'AGE', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6', 'BILL_AMT1', 'BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6') y <- "default payment next month" protected_columns <- c('SEX', 'EDUCATION') for (col in c(y, protected_columns)) data[[col]] <- as.factor(data[[col]]) splits <- h2o.splitFrame(data, 0.8) train <- splits[[1]] test <- splits[[2]] reference <- c(SEX = "1", EDUCATION = "2") # university educated man favorable_class <- "0" # no default next month ig <- h2o.infogram(x, y, train, protected_columns = protected_columns) print(ig@admissible_score) plot(ig) infogram_models <- h2o.infogram_train_subset_models(ig, h2o.gbm, train, test, y, protected_columns, reference, favorable_class) pf <- h2o.pareto_front(infogram_models, x_metric = "air_min", y_metric = "AUC", optimum = "top right") plot(pf) pf@pareto_front ## End(Not run)## Not run: library(h2o) h2o.connect() data <- h2o.importFile(paste0("https://s3.amazonaws.com/h2o-public-test-data/smalldata/", "admissibleml_test/taiwan_credit_card_uci.csv")) x <- c('LIMIT_BAL', 'AGE', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6', 'BILL_AMT1', 'BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6') y <- "default payment next month" protected_columns <- c('SEX', 'EDUCATION') for (col in c(y, protected_columns)) data[[col]] <- as.factor(data[[col]]) splits <- h2o.splitFrame(data, 0.8) train <- splits[[1]] test <- splits[[2]] reference <- c(SEX = "1", EDUCATION = "2") # university educated man favorable_class <- "0" # no default next month ig <- h2o.infogram(x, y, train, protected_columns = protected_columns) print(ig@admissible_score) plot(ig) infogram_models <- h2o.infogram_train_subset_models(ig, h2o.gbm, train, test, y, protected_columns, reference, favorable_class) pf <- h2o.pareto_front(infogram_models, x_metric = "air_min", y_metric = "AUC", optimum = "top right") plot(pf) pf@pareto_front ## End(Not run)
Attempts to start and/or connect to and H2O instance.
h2o.init( ip = "localhost", port = 54321, name = NA_character_, startH2O = TRUE, forceDL = FALSE, enable_assertions = TRUE, license = NULL, nthreads = -1, max_mem_size = NULL, min_mem_size = NULL, ice_root = tempdir(), log_dir = NA_character_, log_level = NA_character_, strict_version_check = TRUE, proxy = NA_character_, https = FALSE, cacert = NA_character_, insecure = FALSE, username = NA_character_, password = NA_character_, use_spnego = FALSE, cookies = NA_character_, context_path = NA_character_, ignore_config = FALSE, extra_classpath = NULL, jvm_custom_args = NULL, bind_to_localhost = TRUE )h2o.init( ip = "localhost", port = 54321, name = NA_character_, startH2O = TRUE, forceDL = FALSE, enable_assertions = TRUE, license = NULL, nthreads = -1, max_mem_size = NULL, min_mem_size = NULL, ice_root = tempdir(), log_dir = NA_character_, log_level = NA_character_, strict_version_check = TRUE, proxy = NA_character_, https = FALSE, cacert = NA_character_, insecure = FALSE, username = NA_character_, password = NA_character_, use_spnego = FALSE, cookies = NA_character_, context_path = NA_character_, ignore_config = FALSE, extra_classpath = NULL, jvm_custom_args = NULL, bind_to_localhost = TRUE )
ip |
Object of class |
port |
Object of class |
name |
(Optional) A |
startH2O |
(Optional) A |
forceDL |
(Optional) A |
enable_assertions |
(Optional) A |
license |
(Optional) A |
nthreads |
(Optional) Number of threads in the thread pool. This relates very closely to the number of CPUs used. -1 means use all CPUs on the host (Default). A positive integer specifies the number of CPUs directly. This value is only used when R starts H2O. |
max_mem_size |
(Optional) A |
min_mem_size |
(Optional) A |
ice_root |
(Optional) A directory to handle object spillage. The defaul varies by OS. |
log_dir |
(Optional) A directory where H2O server logs are stored. The default varies by OS. |
log_level |
(Optional) The level of logging of H2O server. The default is INFO. |
strict_version_check |
(Optional) Setting this to FALSE is unsupported and should only be done when advised by technical support. |
proxy |
(Optional) A |
https |
(Optional) Set this to TRUE to use https instead of http. |
cacert |
(Optional) Path to a CA bundle file with root and intermediate certificates of trusted CAs. |
insecure |
(Optional) Set this to TRUE to disable SSL certificate checking. |
username |
(Optional) Username to login with. |
password |
(Optional) Password to login with. |
use_spnego |
(Optional) Set this to TRUE to enable SPNEGO authentication. |
cookies |
(Optional) Vector(or list) of cookies to add to request. |
context_path |
(Optional) The last part of connection URL: http://<ip>:<port>/<context_path> |
ignore_config |
(Optional) A |
extra_classpath |
(Optional) A vector of paths to libraries to be added to the Java classpath when H2O is started from R. |
jvm_custom_args |
(Optional) A |
bind_to_localhost |
(Optional) A |
By default, this method first checks if an H2O instance is connectible. If it cannot connect and start = TRUE with ip = "localhost", it will attempt to start and instance of H2O at localhost:54321.
If an open ip and port of your choice are passed in, then this method will attempt to start an H2O instance at that specified ip port.
When initializing H2O locally, this method searches for h2o.jar in the R library resources (system.file("java", "h2o.jar", package = "h2o")), and if the file does not exist, it will automatically attempt to download the correct version from Amazon S3. The user must have Internet access for this process to be successful.
Once connected, the method checks to see if the local H2O R package version matches the version of H2O running on the server. If there is a mismatch and the user indicates she wishes to upgrade, it will remove the local H2O R package and download/install the H2O R package from the server.
this method will load it and return a H2OConnection object containing the IP address and port number of the H2O server.
Users may wish to manually upgrade their package (rather than waiting until being prompted), which requires that they fully uninstall and reinstall the H2O package, and the H2O client package. You must unload packages running in the environment before upgrading. It's recommended that users restart R or R studio after upgrading
H2O R package documentation for more details. h2o.shutdown for shutting down from R.
## Not run: # Try to connect to a local H2O instance that is already running. # If not found, start a local H2O instance from R with the default settings. h2o.init() # Try to connect to a local H2O instance. # If not found, raise an error. h2o.init(startH2O = FALSE) # Try to connect to a local H2O instance that is already running. # If not found, start a local H2O instance from R with 5 gigabytes of memory. h2o.init(max_mem_size = "5g") # Try to connect to a local H2O instance that is already running. # If not found, start a local H2O instance from R that uses 5 gigabytes of memory. h2o.init(max_mem_size = "5g") ## End(Not run)## Not run: # Try to connect to a local H2O instance that is already running. # If not found, start a local H2O instance from R with the default settings. h2o.init() # Try to connect to a local H2O instance. # If not found, raise an error. h2o.init(startH2O = FALSE) # Try to connect to a local H2O instance that is already running. # If not found, start a local H2O instance from R with 5 gigabytes of memory. h2o.init(max_mem_size = "5g") # Try to connect to a local H2O instance that is already running. # If not found, start a local H2O instance from R that uses 5 gigabytes of memory. h2o.init(max_mem_size = "5g") ## End(Not run)
Randomly replaces a user-specified fraction of entries in an H2O dataset with missing values.
h2o.insertMissingValues(data, fraction = 0.1, seed = -1)h2o.insertMissingValues(data, fraction = 0.1, seed = -1)
data |
An H2OFrame object representing the dataset. |
fraction |
A number between 0 and 1 indicating the fraction of entries to replace with missing. |
seed |
A random number used to select which entries to replace with
missing values. Default of |
Returns an H2OFrame object.
This will modify the original dataset. Unless this is intended, this function should only be called on a subset of the original.
## Not run: library(h2o) h2o.init() iris_hf <- as.h2o(iris) summary(iris_hf) iris_miss <- h2o.insertMissingValues(iris_hf, fraction = 0.25) head(iris_miss) summary(iris_miss) ## End(Not run)## Not run: library(h2o) h2o.init() iris_hf <- as.h2o(iris) summary(iris_hf) iris_miss <- h2o.insertMissingValues(iris_hf, fraction = 0.25) head(iris_miss) summary(iris_miss) ## End(Not run)
Produce plots and dataframes related to a single model fairness.
h2o.inspect_model_fairness( model, newdata, protected_columns, reference, favorable_class, metrics = c("auc", "aucpr", "f1", "p.value", "selectedRatio", "total"), background_frame = NULL )h2o.inspect_model_fairness( model, newdata, protected_columns, reference, favorable_class, metrics = c("auc", "aucpr", "f1", "p.value", "selectedRatio", "total"), background_frame = NULL )
model |
H2O Model Object |
newdata |
H2OFrame |
protected_columns |
List of categorical columns that contain sensitive information such as race, gender, age etc. |
reference |
List of values corresponding to a reference for each protected columns. If set to NULL, it will use the biggest group as the reference. |
favorable_class |
Positive/favorable outcome class of the response. |
metrics |
Character vector of metrics to show. |
background_frame |
Optional frame, that is used as the source of baselines for the marginal SHAP. Setting it enables calculating SHAP in more models but it can be more time and memory consuming. |
H2OExplanation object
## Not run: library(h2o) h2o.init() data <- h2o.importFile(paste0("https://s3.amazonaws.com/h2o-public-test-data/smalldata/", "admissibleml_test/taiwan_credit_card_uci.csv")) x <- c('LIMIT_BAL', 'AGE', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6', 'BILL_AMT1', 'BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6') y <- "default payment next month" protected_columns <- c('SEX', 'EDUCATION') for (col in c(y, protected_columns)) data[[col]] <- as.factor(data[[col]]) splits <- h2o.splitFrame(data, 0.8) train <- splits[[1]] test <- splits[[2]] reference <- c(SEX = "1", EDUCATION = "2") # university educated man favorable_class <- "0" # no default next month gbm <- h2o.gbm(x, y, training_frame = train) h2o.inspect_model_fairness(gbm, test, protected_columns = protected_columns, reference = reference, favorable_class = favorable_class) ## End(Not run)## Not run: library(h2o) h2o.init() data <- h2o.importFile(paste0("https://s3.amazonaws.com/h2o-public-test-data/smalldata/", "admissibleml_test/taiwan_credit_card_uci.csv")) x <- c('LIMIT_BAL', 'AGE', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6', 'BILL_AMT1', 'BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6') y <- "default payment next month" protected_columns <- c('SEX', 'EDUCATION') for (col in c(y, protected_columns)) data[[col]] <- as.factor(data[[col]]) splits <- h2o.splitFrame(data, 0.8) train <- splits[[1]] test <- splits[[2]] reference <- c(SEX = "1", EDUCATION = "2") # university educated man favorable_class <- "0" # no default next month gbm <- h2o.gbm(x, y, training_frame = train) h2o.inspect_model_fairness(gbm, test, protected_columns = protected_columns, reference = reference, favorable_class = favorable_class) ## End(Not run)
Creates a data frame in H2O with n-th order interaction features between categorical columns, as specified by the user.
h2o.interaction( data, destination_frame, factors, pairwise, max_factors, min_occurrence )h2o.interaction( data, destination_frame, factors, pairwise, max_factors, min_occurrence )
data |
An H2OFrame object containing the categorical columns. |
destination_frame |
A string indicating the destination key. If empty, this will be auto-generated by H2O. |
factors |
Factor columns (either indices or column names). |
pairwise |
Whether to create pairwise interactions between factors (otherwise create one higher-order interaction). Only applicable if there are 3 or more factors. |
max_factors |
Max. number of factor levels in pair-wise interaction terms (if enforced, one extra catch-all factor will be made) |
min_occurrence |
Min. occurrence threshold for factor levels in pair-wise interaction terms |
Returns an H2OFrame object.
## Not run: library(h2o) h2o.init() # Create some random data my_frame <- h2o.createFrame(rows = 20, cols = 5, seed = -12301283, randomize = TRUE, value = 0, categorical_fraction = 0.8, factors = 10, real_range = 1, integer_fraction = 0.2, integer_range = 10, binary_fraction = 0, binary_ones_fraction = 0.5, missing_fraction = 0.2, response_factors = 1) # Turn integer column into a categorical my_frame[,5] <- as.factor(my_frame[,5]) head(my_frame, 20) # Create pairwise interactions pairwise <- h2o.interaction(my_frame, factors = list(c(1, 2), c("C2", "C3", "C4")), pairwise = TRUE, max_factors = 10, min_occurrence = 1) head(pairwise, 20) h2o.levels(pairwise, 2) # Create 5-th order interaction higherorder <- h2o.interaction(my_frame, factors = c(1, 2, 3, 4, 5), pairwise = FALSE, max_factors = 10000, min_occurrence = 1) head(higherorder, 20) # Limit the number of factors of the "categoricalized" integer column # to at most 3 factors, and only if they occur at least twice head(my_frame[,5], 20) trim_integer_levels <- h2o.interaction(my_frame, factors = "C5", pairwise = FALSE, max_factors = 3, min_occurrence = 2) head(trim_integer_levels, 20) # Put all together my_frame <- h2o.cbind(my_frame, pairwise, higherorder, trim_integer_levels) my_frame head(my_frame, 20) summary(my_frame) ## End(Not run)## Not run: library(h2o) h2o.init() # Create some random data my_frame <- h2o.createFrame(rows = 20, cols = 5, seed = -12301283, randomize = TRUE, value = 0, categorical_fraction = 0.8, factors = 10, real_range = 1, integer_fraction = 0.2, integer_range = 10, binary_fraction = 0, binary_ones_fraction = 0.5, missing_fraction = 0.2, response_factors = 1) # Turn integer column into a categorical my_frame[,5] <- as.factor(my_frame[,5]) head(my_frame, 20) # Create pairwise interactions pairwise <- h2o.interaction(my_frame, factors = list(c(1, 2), c("C2", "C3", "C4")), pairwise = TRUE, max_factors = 10, min_occurrence = 1) head(pairwise, 20) h2o.levels(pairwise, 2) # Create 5-th order interaction higherorder <- h2o.interaction(my_frame, factors = c(1, 2, 3, 4, 5), pairwise = FALSE, max_factors = 10000, min_occurrence = 1) head(higherorder, 20) # Limit the number of factors of the "categoricalized" integer column # to at most 3 factors, and only if they occur at least twice head(my_frame[,5], 20) trim_integer_levels <- h2o.interaction(my_frame, factors = "C5", pairwise = FALSE, max_factors = 3, min_occurrence = 2) head(trim_integer_levels, 20) # Put all together my_frame <- h2o.cbind(my_frame, pairwise, higherorder, trim_integer_levels) my_frame head(my_frame, 20) summary(my_frame) ## End(Not run)
Check Client Mode Connection
h2o.is_client()h2o.is_client()
Compute the iSAX index for a DataFrame which is assumed to be numeric time series data
h2o.isax(x, num_words, max_cardinality, optimize_card = FALSE)h2o.isax(x, num_words, max_cardinality, optimize_card = FALSE)
x |
an H2OFrame |
num_words |
Number of iSAX words for the timeseries. ie granularity along the time series |
max_cardinality |
Maximum cardinality of the iSAX word. Each word can have less than the max |
optimize_card |
An optimization flag that will find the max cardinality regardless of what is passed in for max_cardinality. |
An H2OFrame with the name of time series, string representation of iSAX word, followed by binary representation
https://www.cs.ucr.edu/~eamonn/iSAX_2.0.pdf
https://www.cs.ucr.edu/~eamonn/SAX.pdf
## Not run: library(h2o) h2o.init() df <- h2o.createFrame(rows = 1, cols = 256, randomize = TRUE, value = 0, real_range = 100, categorical_fraction = 0, factors = 0, integer_fraction = 0, integer_range = 100, binary_fraction = 0, binary_ones_fraction = 0, time_fraction = 0, string_fraction = 0, missing_fraction = 0, has_response = FALSE, seed = 123) df2 <- h2o.cumsum(df, axis = 1) h2o.isax(df2, num_words = 10, max_cardinality = 10) ## End(Not run)## Not run: library(h2o) h2o.init() df <- h2o.createFrame(rows = 1, cols = 256, randomize = TRUE, value = 0, real_range = 100, categorical_fraction = 0, factors = 0, integer_fraction = 0, integer_range = 100, binary_fraction = 0, binary_ones_fraction = 0, time_fraction = 0, string_fraction = 0, missing_fraction = 0, has_response = FALSE, seed = 123) df2 <- h2o.cumsum(df, axis = 1) h2o.isax(df2, num_words = 10, max_cardinality = 10) ## End(Not run)
Check if character
h2o.ischaracter(x)h2o.ischaracter(x)
x |
An H2OFrame object. |
character for the base R implementation, is.character().
## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/iris/iris_wheader.csv" iris <- h2o.importFile(f) iris_char <- h2o.ascharacter(iris["class"]) h2o.ischaracter(iris_char) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/iris/iris_wheader.csv" iris <- h2o.importFile(f) iris_char <- h2o.ascharacter(iris["class"]) h2o.ischaracter(iris_char) ## End(Not run)
Check if factor
h2o.isfactor(x)h2o.isfactor(x)
x |
An H2OFrame object. |
factor for the base R implementation, is.factor().
## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv" cars <- h2o.importFile(f) cars["economy_20mpg"] <- as.factor(cars["economy_20mpg"]) h2o.isfactor(cars["economy_20mpg"]) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv" cars <- h2o.importFile(f) cars["economy_20mpg"] <- as.factor(cars["economy_20mpg"]) h2o.isfactor(cars["economy_20mpg"]) ## End(Not run)
Check if numeric
h2o.isnumeric(x)h2o.isnumeric(x)
x |
An H2OFrame object. |
numeric for the base R implementation, is.numeric().
## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/iris/iris_wheader.csv" iris <- h2o.importFile(f) h2o.isnumeric(iris["sepal_len"]) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/iris/iris_wheader.csv" iris <- h2o.importFile(f) h2o.isnumeric(iris["sepal_len"]) ## End(Not run)
Trains an Isolation Forest model
h2o.isolationForest( training_frame, x, model_id = NULL, score_each_iteration = FALSE, score_tree_interval = 0, ignore_const_cols = TRUE, ntrees = 50, max_depth = 8, min_rows = 1, max_runtime_secs = 0, seed = -1, build_tree_one_node = FALSE, mtries = -1, sample_size = 256, sample_rate = -1, col_sample_rate_change_per_level = 1, col_sample_rate_per_tree = 1, categorical_encoding = c("AUTO", "Enum", "OneHotInternal", "OneHotExplicit", "Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited"), stopping_rounds = 0, stopping_metric = c("AUTO", "anomaly_score"), stopping_tolerance = 0.01, export_checkpoints_dir = NULL, contamination = -1, validation_frame = NULL, validation_response_column = NULL )h2o.isolationForest( training_frame, x, model_id = NULL, score_each_iteration = FALSE, score_tree_interval = 0, ignore_const_cols = TRUE, ntrees = 50, max_depth = 8, min_rows = 1, max_runtime_secs = 0, seed = -1, build_tree_one_node = FALSE, mtries = -1, sample_size = 256, sample_rate = -1, col_sample_rate_change_per_level = 1, col_sample_rate_per_tree = 1, categorical_encoding = c("AUTO", "Enum", "OneHotInternal", "OneHotExplicit", "Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited"), stopping_rounds = 0, stopping_metric = c("AUTO", "anomaly_score"), stopping_tolerance = 0.01, export_checkpoints_dir = NULL, contamination = -1, validation_frame = NULL, validation_response_column = NULL )
training_frame |
Id of the training data frame. |
x |
A vector containing the |
model_id |
Destination id for this model; auto-generated if not specified. |
score_each_iteration |
|
score_tree_interval |
Score the model after every so many trees. Disabled if set to 0. Defaults to 0. |
ignore_const_cols |
|
ntrees |
Number of trees. Defaults to 50. |
max_depth |
Maximum tree depth (0 for unlimited). Defaults to 8. |
min_rows |
Fewest allowed (weighted) observations in a leaf. Defaults to 1. |
max_runtime_secs |
Maximum allowed runtime in seconds for model training. Use 0 to disable. Defaults to 0. |
seed |
Seed for random numbers (affects certain parts of the algo that are stochastic and those might or might not be enabled by default). Defaults to -1 (time-based random number). |
build_tree_one_node |
|
mtries |
Number of variables randomly sampled as candidates at each split. If set to -1, defaults (number of predictors)/3. Defaults to -1. |
sample_size |
Number of randomly sampled observations used to train each Isolation Forest tree. Only one of parameters sample_size and sample_rate should be defined. If sample_rate is defined, sample_size will be ignored. Defaults to 256. |
sample_rate |
Rate of randomly sampled observations used to train each Isolation Forest tree. Needs to be in range from 0.0 to 1.0. If set to -1, sample_rate is disabled and sample_size will be used instead. Defaults to -1. |
col_sample_rate_change_per_level |
Relative change of the column sampling rate for every level (must be > 0.0 and <= 2.0) Defaults to 1. |
col_sample_rate_per_tree |
Column sample rate per tree (from 0.0 to 1.0) Defaults to 1. |
categorical_encoding |
Encoding scheme for categorical features Must be one of: "AUTO", "Enum", "OneHotInternal", "OneHotExplicit", "Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited". Defaults to AUTO. |
stopping_rounds |
Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable) Defaults to 0. |
stopping_metric |
Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client. Must be one of: "AUTO", "anomaly_score". Defaults to AUTO. |
stopping_tolerance |
Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much) Defaults to 0.01. |
export_checkpoints_dir |
Automatically export generated models to this directory. |
contamination |
Contamination ratio - the proportion of anomalies in the input dataset. If undefined (-1) the predict function will not mark observations as anomalies and only anomaly score will be returned. Defaults to -1 (undefined). Defaults to -1. |
validation_frame |
Id of the validation data frame. |
validation_response_column |
(experimental) Name of the response column in the validation frame. Response column should be binary and indicate not anomaly/anomaly. |
## Not run: library(h2o) h2o.init() # Import the cars dataset f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv" cars <- h2o.importFile(f) # Set the predictors predictors <- c("displacement", "power", "weight", "acceleration", "year") # Train the IF model cars_if <- h2o.isolationForest(x = predictors, training_frame = cars, seed = 1234, stopping_metric = "anomaly_score", stopping_rounds = 3, stopping_tolerance = 0.1) ## End(Not run)## Not run: library(h2o) h2o.init() # Import the cars dataset f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv" cars <- h2o.importFile(f) # Set the predictors predictors <- c("displacement", "power", "weight", "acceleration", "year") # Train the IF model cars_if <- h2o.isolationForest(x = predictors, training_frame = cars, seed = 1234, stopping_metric = "anomaly_score", stopping_rounds = 3, stopping_tolerance = 0.1) ## End(Not run)
Builds an Isotonic Regression model on an H2OFrame with a single feature (univariate regression).
h2o.isotonicregression( x, y, training_frame, model_id = NULL, validation_frame = NULL, weights_column = NULL, out_of_bounds = c("NA", "clip"), custom_metric_func = NULL, nfolds = 0, keep_cross_validation_models = TRUE, keep_cross_validation_predictions = FALSE, keep_cross_validation_fold_assignment = FALSE, fold_assignment = c("AUTO", "Random", "Modulo", "Stratified"), fold_column = NULL )h2o.isotonicregression( x, y, training_frame, model_id = NULL, validation_frame = NULL, weights_column = NULL, out_of_bounds = c("NA", "clip"), custom_metric_func = NULL, nfolds = 0, keep_cross_validation_models = TRUE, keep_cross_validation_predictions = FALSE, keep_cross_validation_fold_assignment = FALSE, fold_assignment = c("AUTO", "Random", "Modulo", "Stratified"), fold_column = NULL )
x |
(Optional) A vector containing the names or indices of the predictor variables to use in building the model. If x is missing, then all columns except y are used. |
y |
The name or column index of the response variable in the data. The response must be either a numeric or a categorical/factor variable. If the response is numeric, then a regression model will be trained, otherwise it will train a classification model. |
training_frame |
Id of the training data frame. |
model_id |
Destination id for this model; auto-generated if not specified. |
validation_frame |
Id of the validation data frame. |
weights_column |
Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0. |
out_of_bounds |
Method of handling values of X predictor that are outside of the bounds seen in training. Must be one of: "NA", "clip". Defaults to NA. |
custom_metric_func |
Reference to custom evaluation function, format: 'language:keyName=funcName' |
nfolds |
Number of folds for K-fold cross-validation (0 to disable or >= 2). Defaults to 0. |
keep_cross_validation_models |
|
keep_cross_validation_predictions |
|
keep_cross_validation_fold_assignment |
|
fold_assignment |
Cross-validation fold assignment scheme, if fold_column is not specified. The 'Stratified' option will stratify the folds based on the response variable, for classification problems. Must be one of: "AUTO", "Random", "Modulo", "Stratified". Defaults to AUTO. |
fold_column |
Column with cross-validation fold index assignment per observation. |
Creates a H2OModel object of the right type.
predict.H2OModel for prediction
## Not run: library(h2o) h2o.init() N <- 100 x <- seq(N) y <- sample(-50:50, N, replace=TRUE) + 50 * log1p(x) train <- as.h2o(data.frame(x = x, y = y)) isotonic <- h2o.isotonicregression(x = "x", y = "y", training_frame = train) ## End(Not run)## Not run: library(h2o) h2o.init() N <- 100 x <- seq(N) y <- sample(-50:50, N, replace=TRUE) + 50 * log1p(x) train <- as.h2o(data.frame(x = x, y = y)) isotonic <- h2o.isotonicregression(x = "x", y = "y", training_frame = train) ## End(Not run)
Keyed objects allowing to obtain their key.Method on Keyed objects allowing to obtain their key.
h2o.keyof(object) ## S4 method for signature 'Keyed' h2o.keyof(object) ## S4 method for signature 'H2OModel' h2o.keyof(object) ## S4 method for signature 'H2OGrid' h2o.keyof(object) ## S4 method for signature 'H2OFrame' h2o.keyof(object) ## S4 method for signature 'H2OAutoML' h2o.keyof(object)h2o.keyof(object) ## S4 method for signature 'Keyed' h2o.keyof(object) ## S4 method for signature 'H2OModel' h2o.keyof(object) ## S4 method for signature 'H2OGrid' h2o.keyof(object) ## S4 method for signature 'H2OFrame' h2o.keyof(object) ## S4 method for signature 'H2OAutoML' h2o.keyof(object)
object |
A |
the string key holding the persistent object.
Create a k-fold vector useful for H2O algorithms that take a fold_assignments argument.
h2o.kfold_column(data, nfolds, seed = -1)h2o.kfold_column(data, nfolds, seed = -1)
data |
A dataframe against which to create the fold column. |
nfolds |
The number of desired folds. |
seed |
A random seed, -1 indicates that H2O will choose one. |
Returns an H2OFrame object with fold assignments.
## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/iris/iris_wheader.csv" iris <- h2o.importFile(f) kfolds <- h2o.kfold_column(iris, nfolds = 5, seed = 1234) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/iris/iris_wheader.csv" iris <- h2o.importFile(f) kfolds <- h2o.kfold_column(iris, nfolds = 5, seed = 1234) ## End(Not run)
A poor man's profiler, but effective.
h2o.killMinus3()h2o.killMinus3()
Performs k-means clustering on an H2O dataset
h2o.kmeans( training_frame, x, model_id = NULL, validation_frame = NULL, nfolds = 0, keep_cross_validation_models = TRUE, keep_cross_validation_predictions = FALSE, keep_cross_validation_fold_assignment = FALSE, fold_assignment = c("AUTO", "Random", "Modulo", "Stratified"), fold_column = NULL, ignore_const_cols = TRUE, score_each_iteration = FALSE, k = 1, estimate_k = FALSE, user_points = NULL, max_iterations = 10, standardize = TRUE, seed = -1, init = c("Random", "PlusPlus", "Furthest", "User"), max_runtime_secs = 0, categorical_encoding = c("AUTO", "Enum", "OneHotInternal", "OneHotExplicit", "Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited"), export_checkpoints_dir = NULL, cluster_size_constraints = NULL )h2o.kmeans( training_frame, x, model_id = NULL, validation_frame = NULL, nfolds = 0, keep_cross_validation_models = TRUE, keep_cross_validation_predictions = FALSE, keep_cross_validation_fold_assignment = FALSE, fold_assignment = c("AUTO", "Random", "Modulo", "Stratified"), fold_column = NULL, ignore_const_cols = TRUE, score_each_iteration = FALSE, k = 1, estimate_k = FALSE, user_points = NULL, max_iterations = 10, standardize = TRUE, seed = -1, init = c("Random", "PlusPlus", "Furthest", "User"), max_runtime_secs = 0, categorical_encoding = c("AUTO", "Enum", "OneHotInternal", "OneHotExplicit", "Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited"), export_checkpoints_dir = NULL, cluster_size_constraints = NULL )
training_frame |
Id of the training data frame. |
x |
A vector containing the |
model_id |
Destination id for this model; auto-generated if not specified. |
validation_frame |
Id of the validation data frame. |
nfolds |
Number of folds for K-fold cross-validation (0 to disable or >= 2). Defaults to 0. |
keep_cross_validation_models |
|
keep_cross_validation_predictions |
|
keep_cross_validation_fold_assignment |
|
fold_assignment |
Cross-validation fold assignment scheme, if fold_column is not specified. The 'Stratified' option will stratify the folds based on the response variable, for classification problems. Must be one of: "AUTO", "Random", "Modulo", "Stratified". Defaults to AUTO. |
fold_column |
Column with cross-validation fold index assignment per observation. |
ignore_const_cols |
|
score_each_iteration |
|
k |
The max. number of clusters. If estimate_k is disabled, the model will find k centroids, otherwise it will find up to k centroids. Defaults to 1. |
estimate_k |
|
user_points |
This option allows you to specify a dataframe, where each row represents an initial cluster center. The user- specified points must have the same number of columns as the training observations. The number of rows must equal the number of clusters |
max_iterations |
Maximum training iterations (if estimate_k is enabled, then this is for each inner Lloyds iteration) Defaults to 10. |
standardize |
|
seed |
Seed for random numbers (affects certain parts of the algo that are stochastic and those might or might not be enabled by default). Defaults to -1 (time-based random number). |
init |
Initialization mode Must be one of: "Random", "PlusPlus", "Furthest", "User". Defaults to Furthest. |
max_runtime_secs |
Maximum allowed runtime in seconds for model training. Use 0 to disable. Defaults to 0. |
categorical_encoding |
Encoding scheme for categorical features Must be one of: "AUTO", "Enum", "OneHotInternal", "OneHotExplicit", "Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited". Defaults to AUTO. |
export_checkpoints_dir |
Automatically export generated models to this directory. |
cluster_size_constraints |
An array specifying the minimum number of points that should be in each cluster. The length of the constraints array has to be the same as the number of clusters. |
an object of class H2OClusteringModel.
h2o.cluster_sizes, h2o.totss, h2o.num_iterations, h2o.betweenss, h2o.tot_withinss, h2o.withinss, h2o.centersSTD, h2o.centers
## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(path = prostate_path) h2o.kmeans(training_frame = prostate, k = 10, x = c("AGE", "RACE", "VOL", "GLEASON")) ## End(Not run)## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(path = prostate_path) h2o.kmeans(training_frame = prostate, k = 10, x = c("AGE", "RACE", "VOL", "GLEASON")) ## End(Not run)
Retrieves a Kolmogorov-Smirnov metric for given binomial model. The number returned is in range between 0 and 1. K-S metric represents the degree of separation between the positive (1) and negative (0) cumulative distribution functions. Detailed metrics per each group are to be found in the gains-lift table.
h2o.kolmogorov_smirnov(object) ## S4 method for signature 'H2OModelMetrics' h2o.kolmogorov_smirnov(object) ## S4 method for signature 'H2OModel' h2o.kolmogorov_smirnov(object)h2o.kolmogorov_smirnov(object) ## S4 method for signature 'H2OModelMetrics' h2o.kolmogorov_smirnov(object) ## S4 method for signature 'H2OModel' h2o.kolmogorov_smirnov(object)
object |
Either an H2OModel object or an H2OModelMetrics object. |
The H2OModelMetrics version of this function will only take H2OBinomialMetrics objects.
Kolmogorov-Smirnov metric, a number between 0 and 1.
h2o.gainsLift to see detailed K-S metrics per group
## Not run: library(h2o) h2o.init() data <- h2o.importFile( path = "https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") model <- h2o.gbm(x = c("Origin", "Distance"), y = "IsDepDelayed", training_frame = data, ntrees = 1) h2o.kolmogorov_smirnov(model) ## End(Not run)## Not run: library(h2o) h2o.init() data <- h2o.importFile( path = "https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") model <- h2o.gbm(x = c("Origin", "Distance"), y = "IsDepDelayed", training_frame = data, ntrees = 1) h2o.kolmogorov_smirnov(model) ## End(Not run)
Obtain the kurtosis of a column of a parsed H2O data object.
h2o.kurtosis(x, ..., na.rm = TRUE) kurtosis.H2OFrame(x, ..., na.rm = TRUE)h2o.kurtosis(x, ..., na.rm = TRUE) kurtosis.H2OFrame(x, ..., na.rm = TRUE)
x |
An H2OFrame object. |
... |
Further arguments to be passed from or to other methods. |
na.rm |
A logical value indicating whether |
Returns a list containing the kurtosis for each column (NaN for non-numeric columns).
## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(path = prostate_path) h2o.kurtosis(prostate$AGE) ## End(Not run)## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(path = prostate_path) h2o.kurtosis(prostate$AGE) ## End(Not run)
Create learning curve plot for an H2O Model. Learning curves show error metric dependence on learning progress, e.g., RMSE vs number of trees trained so far in GBM. There can be up to 4 curves showing Training, Validation, Training on CV Models, and Cross-validation error.
h2o.learning_curve_plot( model, metric = c("AUTO", "auc", "aucpr", "mae", "rmse", "anomaly_score", "convergence", "custom", "custom_increasing", "deviance", "lift_top_group", "logloss", "misclassification", "negative_log_likelihood", "objective", "sumetaieta02", "loglik"), cv_ribbon = NULL, cv_lines = NULL )h2o.learning_curve_plot( model, metric = c("AUTO", "auc", "aucpr", "mae", "rmse", "anomaly_score", "convergence", "custom", "custom_increasing", "deviance", "lift_top_group", "logloss", "misclassification", "negative_log_likelihood", "objective", "sumetaieta02", "loglik"), cv_ribbon = NULL, cv_lines = NULL )
model |
an H2O model |
metric |
Metric to be used for the learning curve plot. These should mostly correspond with stopping metric. |
cv_ribbon |
if True, plot the CV mean as a and CV standard deviation as a ribbon around the mean, if NULL, it will attempt to automatically determine if this is suitable visualisation |
cv_lines |
if True, plot scoring history for individual CV models, if NULL, it will attempt to automatically determine if this is suitable visualisation |
A ggplot2 object
## Not run: library(h2o) h2o.init() # Import the wine dataset into H2O: f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/wine/winequality-redwhite-no-BOM.csv" df <- h2o.importFile(f) # Set the response response <- "quality" # Split the dataset into a train and test set: splits <- h2o.splitFrame(df, ratios = 0.8, seed = 1) train <- splits[[1]] test <- splits[[2]] # Build and train the model: gbm <- h2o.gbm(y = response, training_frame = train) # Create the learning curve plot learning_curve <- h2o.learning_curve_plot(gbm) print(learning_curve) ## End(Not run)## Not run: library(h2o) h2o.init() # Import the wine dataset into H2O: f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/wine/winequality-redwhite-no-BOM.csv" df <- h2o.importFile(f) # Set the response response <- "quality" # Split the dataset into a train and test set: splits <- h2o.splitFrame(df, ratios = 0.8, seed = 1) train <- splits[[1]] test <- splits[[2]] # Build and train the model: gbm <- h2o.gbm(y = response, training_frame = train) # Create the learning curve plot learning_curve <- h2o.learning_curve_plot(gbm) print(learning_curve) ## End(Not run)
Return the levels from the column requested column.
h2o.levels(x, i)h2o.levels(x, i)
x |
An H2OFrame object. |
i |
Optional, the index of the column whose domain is to be returned. |
levels for the base R method.
## Not run: library(h2o) h2o.init() iris_hf <- as.h2o(iris) h2o.levels(iris_hf, 5) # returns "setosa" "versicolor" "virginica" ## End(Not run)## Not run: library(h2o) h2o.init() iris_hf <- as.h2o(iris) h2o.levels(iris_hf, 5) # returns "setosa" "versicolor" "virginica" ## End(Not run)
List all H2O registered extensions
h2o.list_all_extensions()h2o.list_all_extensions()
List registered API extensions
h2o.list_api_extensions()h2o.list_api_extensions()
List registered core extensions
h2o.list_core_extensions()h2o.list_core_extensions()
Return list of jobs performed by the H2O cluster
h2o.list_jobs()h2o.list_jobs()
Get an list of all model ids present in the cluster
h2o.list_models()h2o.list_models()
Returns a vector of model ids.
## Not run: library(h2o) h2o.init() iris_hf <- as.h2o(iris) model_id <- h2o.gbm(x = 1:4, y = 5, training_frame = iris_hf)@model_id model_id_list <- h2o.list_models() ## End(Not run)## Not run: library(h2o) h2o.init() iris_hf <- as.h2o(iris) model_id <- h2o.gbm(x = 1:4, y = 5, training_frame = iris_hf)@model_id model_id_list <- h2o.list_models() ## End(Not run)
List all of the Time Zones Acceptable by the H2O cluster.
h2o.listTimezones()h2o.listTimezones()
Load frame previously stored in H2O's native format.
h2o.load_frame(frame_id, dir, force = TRUE)h2o.load_frame(frame_id, dir, force = TRUE)
frame_id |
the frame ID of the original frame |
dir |
a filesystem location where to look for frame data |
force |
|
## Not run: library(h2o) h2o.init() prostate_path = system.file("extdata", "prostate.csv", package = "h2o") prostate = h2o.importFile(path = prostate_path) h2o.save_frame(prostate, "/tmp/prostate") prostate.key <- h2o.getId(prostate) h2o.rm(prostate) prostate <- h2o.load_frame(prostate.key, "/tmp/prostate") ## End(Not run)## Not run: library(h2o) h2o.init() prostate_path = system.file("extdata", "prostate.csv", package = "h2o") prostate = h2o.importFile(path = prostate_path) h2o.save_frame(prostate, "/tmp/prostate") prostate.key <- h2o.getId(prostate) h2o.rm(prostate) prostate <- h2o.load_frame(prostate.key, "/tmp/prostate") ## End(Not run)
Returns a reference to the loaded Grid.
h2o.loadGrid(grid_path, load_params_references = FALSE)h2o.loadGrid(grid_path, load_params_references = FALSE)
grid_path |
A character string containing the path to the file with the grid saved. |
load_params_references |
A logical which if true will attemt to reload saved objects referenced by grid parameters (e.g. training frame, calibration frame), will fail if grid was saved without referenced objects. |
## Not run: library(h2o) h2o.init() iris <- as.h2o(iris) ntrees_opts = c(1, 5) learn_rate_opts = c(0.1, 0.01) size_of_hyper_space = length(ntrees_opts) * length(learn_rate_opts) hyper_parameters = list(ntrees = ntrees_opts, learn_rate = learn_rate_opts) # Tempdir is chosen arbitrarily. May be any valid folder on an H2O-supported filesystem. baseline_grid <- h2o.grid("gbm", grid_id="gbm_grid_test", x=1:4, y=5, training_frame=iris, hyper_params = hyper_parameters, export_checkpoints_dir = tempdir()) # Remove everything from the cluster or restart it h2o.removeAll() grid <- h2o.loadGrid(paste0(tempdir(),"/",baseline_grid@grid_id)) ## End(Not run)## Not run: library(h2o) h2o.init() iris <- as.h2o(iris) ntrees_opts = c(1, 5) learn_rate_opts = c(0.1, 0.01) size_of_hyper_space = length(ntrees_opts) * length(learn_rate_opts) hyper_parameters = list(ntrees = ntrees_opts, learn_rate = learn_rate_opts) # Tempdir is chosen arbitrarily. May be any valid folder on an H2O-supported filesystem. baseline_grid <- h2o.grid("gbm", grid_id="gbm_grid_test", x=1:4, y=5, training_frame=iris, hyper_params = hyper_parameters, export_checkpoints_dir = tempdir()) # Remove everything from the cluster or restart it h2o.removeAll() grid <- h2o.loadGrid(paste0(tempdir(),"/",baseline_grid@grid_id)) ## End(Not run)
Load a saved H2O model from disk. (Note that ensemble binary models can now be loaded using this method.)
h2o.loadModel(path)h2o.loadModel(path)
path |
The path of the H2O Model to be imported. |
Returns a H2OModel object of the class corresponding to the type of model loaded.
## Not run: # library(h2o) # h2o.init() # prostate_path = system.file("extdata", "prostate.csv", package = "h2o") # prostate = h2o.importFile(path = prostate_path) # prostate_glm = h2o.glm(y = "CAPSULE", x = c("AGE", "RACE", "PSA", "DCAPS"), # training_frame = prostate, family = "binomial", alpha = 0.5) # glmmodel_path = h2o.saveModel(prostate_glm, dir = "/Users/UserName/Desktop") # glmmodel_load = h2o.loadModel(glmmodel_path) ## End(Not run)## Not run: # library(h2o) # h2o.init() # prostate_path = system.file("extdata", "prostate.csv", package = "h2o") # prostate = h2o.importFile(path = prostate_path) # prostate_glm = h2o.glm(y = "CAPSULE", x = c("AGE", "RACE", "PSA", "DCAPS"), # training_frame = prostate, family = "binomial", alpha = 0.5) # glmmodel_path = h2o.saveModel(prostate_glm, dir = "/Users/UserName/Desktop") # glmmodel_load = h2o.loadModel(glmmodel_path) ## End(Not run)
Compute the logarithm of x
h2o.log(x)h2o.log(x)
x |
An H2OFrame object. |
Log for the base R implementation, log.
## Not run: library(h2o) h2o.init() frame <- h2o.createFrame(rows = 6, cols = 2, categorical_fraction = 0.0, missing_fraction = 0.7, seed = 123) h2o.log(frame) ## End(Not run)## Not run: library(h2o) h2o.init() frame <- h2o.createFrame(rows = 6, cols = 2, categorical_fraction = 0.0, missing_fraction = 0.7, seed = 123) h2o.log(frame) ## End(Not run)
Compute the log10 of x
h2o.log10(x)h2o.log10(x)
x |
An H2OFrame object. |
Log for the base R implementation, log10().
## Not run: library(h2o) h2o.init() frame <- h2o.createFrame(rows = 6, cols = 2, categorical_fraction = 0.0, missing_fraction = 0.7, seed = 123) h2o.log10(frame) ## End(Not run)## Not run: library(h2o) h2o.init() frame <- h2o.createFrame(rows = 6, cols = 2, categorical_fraction = 0.0, missing_fraction = 0.7, seed = 123) h2o.log10(frame) ## End(Not run)
Compute the log1p of x
h2o.log1p(x)h2o.log1p(x)
x |
An H2OFrame object. |
Log for the base R implementation, log1p().
## Not run: library(h2o) h2o.init() frame <- h2o.createFrame(rows = 6, cols = 2, categorical_fraction = 0.0, missing_fraction = 0.7, seed = 123) h2o.log1p(frame) ## End(Not run)## Not run: library(h2o) h2o.init() frame <- h2o.createFrame(rows = 6, cols = 2, categorical_fraction = 0.0, missing_fraction = 0.7, seed = 123) h2o.log1p(frame) ## End(Not run)
Compute the log2 of x
h2o.log2(x)h2o.log2(x)
x |
An H2OFrame object. |
Log for the base R implementation, log2()
## Not run: library(h2o) h2o.init() frame <- h2o.createFrame(rows = 6, cols = 2, categorical_fraction = 0.0, missing_fraction = 0.7, seed = 123) h2o.log2(frame) ## End(Not run)## Not run: library(h2o) h2o.init() frame <- h2o.createFrame(rows = 6, cols = 2, categorical_fraction = 0.0, missing_fraction = 0.7, seed = 123) h2o.log2(frame) ## End(Not run)
This is helpful when running several pieces of work one after the other on a single H2O cluster and you want to make a notation in the H2O server side log where one piece of work ends and the next piece of work begins.
h2o.logAndEcho(message)h2o.logAndEcho(message)
message |
A character string with the message to write to the log. |
h2o.logAndEcho sends a message to H2O for logging. Generally used for debugging purposes.
Retrieves the log likelihood value. If "train", "valid", and "xval" parameters are FALSE (default), then the training log likelihood value is returned. If more than one parameter is set to TRUE, then a named vector of log likelihoods is returned, where the names are "train", "valid" or "xval".
h2o.loglikelihood(object, train = FALSE, valid = FALSE, xval = FALSE)h2o.loglikelihood(object, train = FALSE, valid = FALSE, xval = FALSE)
object |
An H2OModel or H2OModelMetrics. |
train |
Retrieve the training log likelihood |
valid |
Retrieve the validation log likelihood |
xval |
Retrieve the cross-validation log likelihood |
## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(path = prostate_path) p_sid <- h2o.runif(prostate) prostate_train <- prostate[p_sid > .2,] prostate_glm <- h2o.glm(x = 3:7, y = 2, training_frame = prostate_train) ll_basic <- h2o.loglikelihood(prostate_glm) print(ll_basic) ## End(Not run)## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(path = prostate_path) p_sid <- h2o.runif(prostate) prostate_train <- prostate[p_sid > .2,] prostate_glm <- h2o.glm(x = 3:7, y = 2, training_frame = prostate_train) ll_basic <- h2o.loglikelihood(prostate_glm) print(ll_basic) ## End(Not run)
Retrieves the log loss output for a H2OBinomialMetrics or H2OMultinomialMetrics object If "train", "valid", and "xval" parameters are FALSE (default), then the training Log Loss value is returned. If more than one parameter is set to TRUE, then a named vector of Log Losses are returned, where the names are "train", "valid" or "xval".
h2o.logloss(object, train = FALSE, valid = FALSE, xval = FALSE)h2o.logloss(object, train = FALSE, valid = FALSE, xval = FALSE)
object |
a H2OModelMetrics object of the correct type. |
train |
Retrieve the training Log Loss |
valid |
Retrieve the validation Log Loss |
xval |
Retrieve the cross-validation Log Loss |
## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv" cars <- h2o.importFile(f) cars["economy_20mpg"] <- as.factor(cars["economy_20mpg"]) predictors <- c("displacement", "power", "weight", "acceleration", "year") response <- "economy_20mpg" cars_splits <- h2o.splitFrame(data = cars, ratios = .8, seed = 1234) train <- cars_splits[[1]] valid <- cars_splits[[2]] car_drf <- h2o.randomForest(x = predictors, y = response, training_frame = train, validation_frame = valid) h2o.logloss(car_drf, train = TRUE, valid = TRUE) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv" cars <- h2o.importFile(f) cars["economy_20mpg"] <- as.factor(cars["economy_20mpg"]) predictors <- c("displacement", "power", "weight", "acceleration", "year") response <- "economy_20mpg" cars_splits <- h2o.splitFrame(data = cars, ratios = .8, seed = 1234) train <- cars_splits[[1]] valid <- cars_splits[[2]] car_drf <- h2o.randomForest(x = predictors, y = response, training_frame = train, validation_frame = valid) h2o.logloss(car_drf, train = TRUE, valid = TRUE) ## End(Not run)
Accesses a list of object keys in the running instance of H2O.
h2o.ls()h2o.ls()
Returns a list of hex keys in the current H2O instance.
## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(path = prostate_path) h2o.ls() ## End(Not run)## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(path = prostate_path) h2o.ls() ## End(Not run)
Return a copy of the target column with leading characters removed. The set argument is a string specifying the set of characters to be removed. If omitted, the set argument defaults to removing whitespace.
h2o.lstrip(x, set = " ")h2o.lstrip(x, set = " ")
x |
The column whose strings should be lstrip-ed. |
set |
string of characters to be removed |
## Not run: library(h2o) h2o.init() string_to_lstrip <- as.h2o("1234567890") lstrip_string <- h2o.lstrip(string_to_lstrip, "123") #Remove "123" ## End(Not run)## Not run: library(h2o) h2o.init() string_to_lstrip <- as.h2o("1234567890") lstrip_string <- h2o.lstrip(string_to_lstrip, "123") #Remove "123" ## End(Not run)
Retrieves the mean absolute error (MAE) value from an H2O model. If "train", "valid", and "xval" parameters are FALSE (default), then the training MAE value is returned. If more than one parameter is set to TRUE, then a named vector of MAEs are returned, where the names are "train", "valid" or "xval".
h2o.mae(object, train = FALSE, valid = FALSE, xval = FALSE)h2o.mae(object, train = FALSE, valid = FALSE, xval = FALSE)
object |
An H2OModel object. |
train |
Retrieve the training MAE |
valid |
Retrieve the validation set MAE if a validation set was passed in during model build time. |
xval |
Retrieve the cross-validation MAE |
## Not run: library(h2o) h <- h2o.init() fr <- as.h2o(iris) m <- h2o.deeplearning(x = 2:5, y = 1, training_frame = fr) h2o.mae(m) ## End(Not run)## Not run: library(h2o) h <- h2o.init() fr <- as.h2o(iris) m <- h2o.deeplearning(x = 2:5, y = 1, training_frame = fr) h2o.mae(m) ## End(Not run)
Create a leaderboard from a list of models, grids and/or automls.
h2o.make_leaderboard( object, leaderboard_frame, sort_metric = "AUTO", extra_columns = c(), scoring_data = c("AUTO", "train", "valid", "xval") )h2o.make_leaderboard( object, leaderboard_frame, sort_metric = "AUTO", extra_columns = c(), scoring_data = c("AUTO", "train", "valid", "xval") )
object |
List of models, automls, or grids; or just single automl/grid object. |
leaderboard_frame |
Frame used for generating the metrics (optional). |
sort_metric |
Metric used for sorting the leaderboard. |
extra_columns |
What extra columns should be calculated (might require leaderboard_frame). Use "ALL" for all available or list of extra columns. |
scoring_data |
Metrics to be reported in the leaderboard ("xval", "train", or "valid"). Used if no leaderboard_frame is provided. |
data.frame
## Not run: library(h2o) h2o.init() iris_hf <- as.h2o(iris) grid <- h2o.grid("gbm", x = c(1:4), y = 5, training_frame = iris_hf, hyper_params = list(ntrees = c(1, 2, 3))) h2o.make_leaderboard(grid, iris_hf) ## End(Not run)## Not run: library(h2o) h2o.init() iris_hf <- as.h2o(iris) grid <- h2o.grid("gbm", x = c(1:4), y = 5, training_frame = iris_hf, hyper_params = list(ntrees = c(1, 2, 3))) h2o.make_leaderboard(grid, iris_hf) ## End(Not run)
Given predicted values (target for regression, class-1 probabilities or binomial or per-class probabilities for multinomial), compute a model metrics object
h2o.make_metrics( predicted, actuals, domain = NULL, distribution = NULL, weights = NULL, treatment = NULL, auc_type = "NONE", auuc_type = "AUTO", auuc_nbins = -1, custom_auuc_thresholds = NULL )h2o.make_metrics( predicted, actuals, domain = NULL, distribution = NULL, weights = NULL, treatment = NULL, auc_type = "NONE", auuc_type = "AUTO", auuc_nbins = -1, custom_auuc_thresholds = NULL )
predicted |
An H2OFrame containing predictions |
actuals |
An H2OFrame containing actual values |
domain |
Vector with response factors for classification. |
distribution |
Distribution for regression. |
weights |
(optional) An H2OFrame containing observation weights. |
treatment |
(optional, for uplift models only) An H2OFrame containing treatment column for uplift classification. |
auc_type |
(optional) For multinomial classification you have to specify which type of agregated AUC/AUCPR will be used to calculate this metric. |
auuc_type |
(optional) For uplift binomial classification you have to specify which type of AUUC will be used to calculate this metric. Possibilities are gini, lift, gain, AUTO. Default is AUTO which means qini. |
auuc_nbins |
(optional) For uplift binomial classification you can specify number of bins to be used for calculation the AUUC. Default is -1, which means 1000. |
custom_auuc_thresholds |
(optional) For uplift binomial classification you can specify exact thresholds to calculate AUUC. Default is NULL. If the thresholds are not defined, auuc_nbins will be used to calculate new thresholds from the predicted data. |
Returns an object of the H2OModelMetrics subclass.
## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(path = prostate_path) prostate$CAPSULE <- as.factor(prostate$CAPSULE) prostate_gbm <- h2o.gbm(3:9, "CAPSULE", prostate) pred <- h2o.predict(prostate_gbm, prostate)[, 3] ## class-1 probability h2o.make_metrics(pred, prostate$CAPSULE) ## End(Not run)## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(path = prostate_path) prostate$CAPSULE <- as.factor(prostate$CAPSULE) prostate_gbm <- h2o.gbm(3:9, "CAPSULE", prostate) pred <- h2o.predict(prostate_gbm, prostate)[, 3] ## class-1 probability h2o.make_metrics(pred, prostate$CAPSULE) ## End(Not run)
This function allows setting betas of an existing glm model.
h2o.makeGLMModel(model, beta)h2o.makeGLMModel(model, beta)
model |
an H2OModel corresponding from a |
beta |
a new set of betas (a named vector) |
match and %in% return values similar to the base R generic
functions.
h2o.match(x, table, nomatch = 0, incomparables = NULL) match.H2OFrame(x, table, nomatch = 0, incomparables = NULL) x %in% tableh2o.match(x, table, nomatch = 0, incomparables = NULL) match.H2OFrame(x, table, nomatch = 0, incomparables = NULL) x %in% table
x |
a categorical vector from an H2OFrame object with values to be matched. |
table |
an R object to match |
nomatch |
the value to be returned in the case when no match is found. |
incomparables |
a vector of calues that cannot be matched. Any value in
|
Returns a vector of the positions of (first) matches of its first argument in its second
match for base R implementation.
## Not run: h2o.init() iris_hf <- as.h2o(iris) h2o.match(iris_hf[, 5], c("setosa", "versicolor")) ## End(Not run)## Not run: h2o.init() iris_hf <- as.h2o(iris) h2o.match(iris_hf[, 5], c("setosa", "versicolor")) ## End(Not run)
Returns the maxima of the input values.
h2o.max(x, na.rm = FALSE)h2o.max(x, na.rm = FALSE)
x |
An H2OFrame object. |
na.rm |
|
Extremes for the base R implementation, max().
## Not run: library(h2o) h2o.init() f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_train.csv" iris <- h2o.importFile(f) h2o.max(iris["petal_len"], na.rm = TRUE) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_train.csv" iris <- h2o.importFile(f) h2o.max(iris["petal_len"], na.rm = TRUE) ## End(Not run)
Compute the frame's mean by-column (or by-row).
h2o.mean(x, na.rm = FALSE, axis = 0, return_frame = FALSE, ...) ## S3 method for class 'H2OFrame' mean(x, na.rm = FALSE, axis = 0, return_frame = FALSE, ...)h2o.mean(x, na.rm = FALSE, axis = 0, return_frame = FALSE, ...) ## S3 method for class 'H2OFrame' mean(x, na.rm = FALSE, axis = 0, return_frame = FALSE, ...)
x |
An H2OFrame object. |
na.rm |
|
axis |
|
return_frame |
|
... |
Further arguments to be passed from or to other methods. |
Returns a list containing the mean for each column (NaN for non-numeric columns) if return_frame is set to FALSE. If return_frame is set to TRUE, then it will return an H2O frame with means per column or row (depends on axis argument).
Round for base R implementation, mean() and colSums for the base R implementation, colMeans().
## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(path = prostate_path) # Default behavior. Will return list of means per column. h2o.mean(prostate$AGE) # return_frame set to TRUE. This will return an H2O Frame # with mean per row or column (depends on axis argument) h2o.mean(prostate, na.rm = TRUE, axis = 1, return_frame = TRUE) ## End(Not run)## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(path = prostate_path) # Default behavior. Will return list of means per column. h2o.mean(prostate$AGE) # return_frame set to TRUE. This will return an H2O Frame # with mean per row or column (depends on axis argument) h2o.mean(prostate, na.rm = TRUE, axis = 1, return_frame = TRUE) ## End(Not run)
Retrieves the mean per class error from an H2OBinomialMetrics. If "train", "valid", and "xval" parameters are FALSE (default), then the training mean per class error value is returned. If more than one parameter is set to TRUE, then a named vector of mean per class errors are returned, where the names are "train", "valid" or "xval".
h2o.mean_per_class_error(object, train = FALSE, valid = FALSE, xval = FALSE)h2o.mean_per_class_error(object, train = FALSE, valid = FALSE, xval = FALSE)
object |
An H2OBinomialMetrics object. |
train |
Retrieve the training mean per class error |
valid |
Retrieve the validation mean per class error |
xval |
Retrieve the cross-validation mean per class error |
h2o.mse for MSE, and h2o.metric for the
various threshold metrics. See h2o.performance for
creating H2OModelMetrics objects.
## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(prostate_path) prostate[, 2] <- as.factor(prostate[, 2]) model <- h2o.gbm(x = 3:9, y = 2, training_frame = prostate, distribution = "bernoulli") perf <- h2o.performance(model, prostate) h2o.mean_per_class_error(perf) h2o.mean_per_class_error(model, train=TRUE) ## End(Not run)## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(prostate_path) prostate[, 2] <- as.factor(prostate[, 2]) model <- h2o.gbm(x = 3:9, y = 2, training_frame = prostate, distribution = "bernoulli") perf <- h2o.performance(model, prostate) h2o.mean_per_class_error(perf) h2o.mean_per_class_error(model, train=TRUE) ## End(Not run)
Retrieves the Mean Residual Deviance value from an H2O model. If "train", "valid", and "xval" parameters are FALSE (default), then the training Mean Residual Deviance value is returned. If more than one parameter is set to TRUE, then a named vector of Mean Residual Deviances are returned, where the names are "train", "valid" or "xval".
h2o.mean_residual_deviance(object, train = FALSE, valid = FALSE, xval = FALSE)h2o.mean_residual_deviance(object, train = FALSE, valid = FALSE, xval = FALSE)
object |
An H2OModel object. |
train |
Retrieve the training Mean Residual Deviance |
valid |
Retrieve the validation Mean Residual Deviance |
xval |
Retrieve the cross-validation Mean Residual Deviance |
## Not run: library(h2o) h <- h2o.init() fr <- as.h2o(iris) m <- h2o.deeplearning(x = 2:5, y = 1, training_frame = fr) h2o.mean_residual_deviance(m) ## End(Not run)## Not run: library(h2o) h <- h2o.init() fr <- as.h2o(iris) m <- h2o.deeplearning(x = 2:5, y = 1, training_frame = fr) h2o.mean_residual_deviance(m) ## End(Not run)
Compute the median of an H2OFrame.
h2o.median(x, na.rm = TRUE) ## S3 method for class 'H2OFrame' median(x, na.rm = TRUE)h2o.median(x, na.rm = TRUE) ## S3 method for class 'H2OFrame' median(x, na.rm = TRUE)
x |
An H2OFrame object. |
na.rm |
a logical, indicating whether na's are omitted. |
Returns a list containing the median for each column (NaN for non-numeric columns)
## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(path = prostate_path) h2o.median(prostate) ## End(Not run)## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(path = prostate_path) h2o.median(prostate) ## End(Not run)
Pivot the frame designated by the three columns: index, column, and value. Index and column should be of type enum, int, or time. For cases of multiple indexes for a column label, the aggregation method is to pick the first occurrence in the data frame
h2o.melt( x, id_vars, value_vars = NULL, var_name = "variable", value_name = "value", skipna = FALSE )h2o.melt( x, id_vars, value_vars = NULL, var_name = "variable", value_name = "value", skipna = FALSE )
x |
an H2OFrame |
id_vars |
the columns used as identifiers |
value_vars |
what columns will be converted to key-value pairs (optional, if not specified complement to id_vars will be used) |
var_name |
name of the key-column (default: "variable") |
value_name |
name of the value-column (default: "value") |
skipna |
if enabled, do not include NAs in the result (default: FALSE) |
an unpivoted H2OFrame
Merges two H2OFrame objects with the same arguments and meanings as merge() in base R. However, we do not support all=TRUE, all.x=TRUE and all.y=TRUE. The default method is auto and it will default to the radix method. The radix method will return the correct merge result regardless of duplicated rows in the right frame. In addition, the radix method can perform merge even if you have string columns in your frames. If there are duplicated rows in your rite frame, they will not be included if you use the hash method. The hash method cannot perform merge if you have string columns in your left frame. Hence, we consider the radix method superior to the hash method and is the default method to use.
h2o.merge( x, y, by = intersect(names(x), names(y)), by.x = by, by.y = by, all = FALSE, all.x = all, all.y = all, method = "auto" )h2o.merge( x, y, by = intersect(names(x), names(y)), by.x = by, by.y = by, all = FALSE, all.x = all, all.y = all, method = "auto" )
x, y
|
H2OFrame objects |
by |
columns used for merging by default the common names |
by.x |
x columns used for merging by name or number |
by.y |
y columns used for merging by name or number |
all |
TRUE includes all rows in x and all rows in y even if there is no match to the other |
all.x |
If all.x is true, all rows in the x will be included, even if there is no matching row in y, and vice-versa for all.y. |
all.y |
see all.x |
method |
auto(default), radix, hash |
## Not run: library(h2o) h2o.init() left <- data.frame(fruit = c('apple', 'orange', 'banana', 'lemon', 'strawberry', 'blueberry'), color <- c('red', 'orange', 'yellow', 'yellow', 'red', 'blue')) right <- data.frame(fruit = c('apple', 'orange', 'banana', 'lemon', 'strawberry', 'watermelon'), citrus <- c(FALSE, TRUE, FALSE, TRUE, FALSE, FALSE)) left_hf <- as.h2o(left) right_hf <- as.h2o(right) merged <- h2o.merge(left_hf, right_hf, all.x = TRUE) ## End(Not run)## Not run: library(h2o) h2o.init() left <- data.frame(fruit = c('apple', 'orange', 'banana', 'lemon', 'strawberry', 'blueberry'), color <- c('red', 'orange', 'yellow', 'yellow', 'red', 'blue')) right <- data.frame(fruit = c('apple', 'orange', 'banana', 'lemon', 'strawberry', 'watermelon'), citrus <- c(FALSE, TRUE, FALSE, TRUE, FALSE, FALSE)) left_hf <- as.h2o(left) right_hf <- as.h2o(right) merged <- h2o.merge(left_hf, right_hf, all.x = TRUE) ## End(Not run)
A series of functions that retrieve model metric details.
h2o.metric(object, thresholds, metric, transform = NULL) h2o.F0point5(object, thresholds) h2o.F1(object, thresholds) h2o.F2(object, thresholds) h2o.accuracy(object, thresholds) h2o.error(object, thresholds) h2o.maxPerClassError(object, thresholds) h2o.mean_per_class_accuracy(object, thresholds) h2o.mcc(object, thresholds) h2o.precision(object, thresholds) h2o.tpr(object, thresholds) h2o.fpr(object, thresholds) h2o.fnr(object, thresholds) h2o.tnr(object, thresholds) h2o.recall(object, thresholds) h2o.sensitivity(object, thresholds) h2o.fallout(object, thresholds) h2o.missrate(object, thresholds) h2o.specificity(object, thresholds)h2o.metric(object, thresholds, metric, transform = NULL) h2o.F0point5(object, thresholds) h2o.F1(object, thresholds) h2o.F2(object, thresholds) h2o.accuracy(object, thresholds) h2o.error(object, thresholds) h2o.maxPerClassError(object, thresholds) h2o.mean_per_class_accuracy(object, thresholds) h2o.mcc(object, thresholds) h2o.precision(object, thresholds) h2o.tpr(object, thresholds) h2o.fpr(object, thresholds) h2o.fnr(object, thresholds) h2o.tnr(object, thresholds) h2o.recall(object, thresholds) h2o.sensitivity(object, thresholds) h2o.fallout(object, thresholds) h2o.missrate(object, thresholds) h2o.specificity(object, thresholds)
object |
An H2OModelMetrics object of the correct type. |
thresholds |
(Optional) A value or a list of values between 0.0 and 1.0. If not set, then all thresholds will be returned. If "max", then the threshold maximizing the metric will be used. |
metric |
(Optional) the metric to retrieve. If not set, then all metrics will be returned. |
transform |
(Optional) a list describing a transformer for the given metric, if any. e.g. transform=list(op=foo_fn, name="foo") will rename the given metric to "foo" and apply function foo_fn to the metric values. |
Many of these functions have an optional thresholds parameter. Currently only increments of 0.1 are allowed. If not specified, the functions will return all possible values. Otherwise, the function will return the value for the indicated threshold.
Currently, the these functions are only supported by H2OBinomialMetrics objects.
Returns either a single value, or a list of values.
h2o.auc for AUC, h2o.giniCoef for the
GINI coefficient, and h2o.mse for MSE. See
h2o.performance for creating H2OModelMetrics objects.
## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(prostate_path) prostate$CAPSULE <- as.factor(prostate$CAPSULE) model <- h2o.gbm(x = 3:9, y = 2, training_frame = prostate, distribution = "bernoulli") perf <- h2o.performance(model, prostate) h2o.F1(perf) ## End(Not run)## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(prostate_path) prostate$CAPSULE <- as.factor(prostate$CAPSULE) model <- h2o.gbm(x = 3:9, y = 2, training_frame = prostate, distribution = "bernoulli") perf <- h2o.performance(model, prostate) h2o.F1(perf) ## End(Not run)
Returns the minima of the input values.
h2o.min(x, na.rm = FALSE)h2o.min(x, na.rm = FALSE)
x |
An H2OFrame object. |
na.rm |
|
Extremes for the base R implementation, min().
## Not run: library(h2o) h2o.init() f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_train.csv" iris <- h2o.importFile(f) h2o.min(iris["sepal_len"], na.rm = TRUE) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_train.csv" iris <- h2o.importFile(f) h2o.min(iris["sepal_len"], na.rm = TRUE) ## End(Not run)
Compute msec since the Unix Epoch
h2o.mktime( year = 1970, month = 0, day = 0, hour = 0, minute = 0, second = 0, msec = 0 )h2o.mktime( year = 1970, month = 0, day = 0, hour = 0, minute = 0, second = 0, msec = 0 )
year |
Defaults to 1970 |
month |
zero based (months are 0 to 11) |
day |
zero based (days are 0 to 30) |
hour |
hour |
minute |
minute |
second |
second |
msec |
msec |
## Not run: library(h2o) h2o.init() x = as.h2o(c(2018, 3, 2, 6, 32, 0, 0)) h2o.mktime(x) ## End(Not run)## Not run: library(h2o) h2o.init() x = as.h2o(c(2018, 3, 2, 6, 32, 0, 0)) h2o.mktime(x) ## End(Not run)
Get a data.frame containing the correlation between the predictions of the models. For classification, frequency of identical predictions is used. By default, models are ordered by their similarity (as computed by hierarchical clustering).
h2o.model_correlation(object, newdata, top_n = 20, cluster_models = TRUE)h2o.model_correlation(object, newdata, top_n = 20, cluster_models = TRUE)
object |
A list of H2O models, an H2O AutoML instance, or an H2OFrame with a 'model_id' column (e.g. H2OAutoML leaderboard).. |
newdata |
An H2O Frame. Predictions from the models will be generated using this frame, so this should be a holdout set. |
top_n |
(DEPRECATED) Integer specifying the number models shown in the heatmap (used only with an AutoML object, and based on the leaderboard ranking. Defaults to 20. |
cluster_models |
Logical. Order models based on their similarity. Defaults to TRUE. |
A data.frame containing variable importance.
## Not run: library(h2o) h2o.init() # Import the wine dataset into H2O: f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/wine/winequality-redwhite-no-BOM.csv" df <- h2o.importFile(f) # Set the response response <- "quality" # Split the dataset into a train and test set: splits <- h2o.splitFrame(df, ratios = 0.8, seed = 1) train <- splits[[1]] test <- splits[[2]] # Build and train the model: aml <- h2o.automl(y = response, training_frame = train, max_models = 10, seed = 1) # Create the model correlation model_correlation <- h2o.model_correlation(aml, test) print(model_correlation) ## End(Not run)## Not run: library(h2o) h2o.init() # Import the wine dataset into H2O: f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/wine/winequality-redwhite-no-BOM.csv" df <- h2o.importFile(f) # Set the response response <- "quality" # Split the dataset into a train and test set: splits <- h2o.splitFrame(df, ratios = 0.8, seed = 1) train <- splits[[1]] test <- splits[[2]] # Build and train the model: aml <- h2o.automl(y = response, training_frame = train, max_models = 10, seed = 1) # Create the model correlation model_correlation <- h2o.model_correlation(aml, test) print(model_correlation) ## End(Not run)
This plot shows the correlation between the predictions of the models. For classification, frequency of identical predictions is used. By default, models are ordered by their similarity (as computed by hierarchical clustering).
h2o.model_correlation_heatmap( object, newdata, top_n = 20, cluster_models = TRUE, triangular = TRUE )h2o.model_correlation_heatmap( object, newdata, top_n = 20, cluster_models = TRUE, triangular = TRUE )
object |
A list of H2O models, an H2O AutoML instance, or an H2OFrame with a 'model_id' column (e.g. H2OAutoML leaderboard). |
newdata |
An H2O Frame. Predictions from the models will be generated using this frame, so this should be a holdout set. |
top_n |
Integer specifying the number models shown in the heatmap (used only with an AutoML object, and based on the leaderboard ranking. Defaults to 20. |
cluster_models |
Logical. Order models based on their similarity. Defaults to TRUE. |
triangular |
Print just the lower triangular part of correlation matrix. Defaults to TRUE. |
A ggplot2 object.
## Not run: library(h2o) h2o.init() # Import the wine dataset into H2O: f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/wine/winequality-redwhite-no-BOM.csv" df <- h2o.importFile(f) # Set the response response <- "quality" # Split the dataset into a train and test set: splits <- h2o.splitFrame(df, ratios = 0.8, seed = 1) train <- splits[[1]] test <- splits[[2]] # Build and train the model: aml <- h2o.automl(y = response, training_frame = train, max_models = 10, seed = 1) # Create the model correlation heatmap model_correlation_heatmap <- h2o.model_correlation_heatmap(aml, test) print(model_correlation_heatmap) ## End(Not run)## Not run: library(h2o) h2o.init() # Import the wine dataset into H2O: f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/wine/winequality-redwhite-no-BOM.csv" df <- h2o.importFile(f) # Set the response response <- "quality" # Split the dataset into a train and test set: splits <- h2o.splitFrame(df, ratios = 0.8, seed = 1) train <- splits[[1]] test <- splits[[2]] # Build and train the model: aml <- h2o.automl(y = response, training_frame = train, max_models = 10, seed = 1) # Create the model correlation heatmap model_correlation_heatmap <- h2o.model_correlation_heatmap(aml, test) print(model_correlation_heatmap) ## End(Not run)
H2O ModelSelection is used to build the best model with one predictor, two predictors, ... up to max_predictor_number specified in the algorithm parameters when mode=allsubsets. The best model is the one with the highest R2 value. When mode=maxr, the model returned is no longer guaranteed to have the best R2 value.
h2o.modelSelection( x, y, training_frame, model_id = NULL, validation_frame = NULL, nfolds = 0, seed = -1, fold_assignment = c("AUTO", "Random", "Modulo", "Stratified"), fold_column = NULL, ignore_const_cols = TRUE, score_each_iteration = FALSE, score_iteration_interval = 0, offset_column = NULL, weights_column = NULL, family = c("AUTO", "gaussian", "binomial", "fractionalbinomial", "quasibinomial", "poisson", "gamma", "tweedie", "negativebinomial"), link = c("family_default", "identity", "logit", "log", "inverse", "tweedie", "ologit"), tweedie_variance_power = 0, tweedie_link_power = 0, theta = 0, solver = c("AUTO", "IRLSM", "L_BFGS", "COORDINATE_DESCENT_NAIVE", "COORDINATE_DESCENT", "GRADIENT_DESCENT_LH", "GRADIENT_DESCENT_SQERR"), alpha = NULL, lambda = c(0), lambda_search = FALSE, early_stopping = FALSE, nlambdas = 0, standardize = TRUE, missing_values_handling = c("MeanImputation", "Skip", "PlugValues"), plug_values = NULL, compute_p_values = FALSE, remove_collinear_columns = FALSE, intercept = TRUE, non_negative = FALSE, max_iterations = 0, objective_epsilon = -1, beta_epsilon = 1e-04, gradient_epsilon = -1, startval = NULL, prior = 0, cold_start = FALSE, lambda_min_ratio = 0, beta_constraints = NULL, max_active_predictors = -1, obj_reg = -1, stopping_rounds = 0, stopping_metric = c("AUTO", "deviance", "logloss", "MSE", "RMSE", "MAE", "RMSLE", "AUC", "AUCPR", "lift_top_group", "misclassification", "mean_per_class_error", "custom", "custom_increasing"), stopping_tolerance = 0.001, balance_classes = FALSE, class_sampling_factors = NULL, max_after_balance_size = 5, max_runtime_secs = 0, custom_metric_func = NULL, nparallelism = 0, max_predictor_number = 1, min_predictor_number = 1, mode = c("allsubsets", "maxr", "maxrsweep", "backward"), build_glm_model = FALSE, p_values_threshold = 0, influence = c("dfbetas"), multinode_mode = FALSE )h2o.modelSelection( x, y, training_frame, model_id = NULL, validation_frame = NULL, nfolds = 0, seed = -1, fold_assignment = c("AUTO", "Random", "Modulo", "Stratified"), fold_column = NULL, ignore_const_cols = TRUE, score_each_iteration = FALSE, score_iteration_interval = 0, offset_column = NULL, weights_column = NULL, family = c("AUTO", "gaussian", "binomial", "fractionalbinomial", "quasibinomial", "poisson", "gamma", "tweedie", "negativebinomial"), link = c("family_default", "identity", "logit", "log", "inverse", "tweedie", "ologit"), tweedie_variance_power = 0, tweedie_link_power = 0, theta = 0, solver = c("AUTO", "IRLSM", "L_BFGS", "COORDINATE_DESCENT_NAIVE", "COORDINATE_DESCENT", "GRADIENT_DESCENT_LH", "GRADIENT_DESCENT_SQERR"), alpha = NULL, lambda = c(0), lambda_search = FALSE, early_stopping = FALSE, nlambdas = 0, standardize = TRUE, missing_values_handling = c("MeanImputation", "Skip", "PlugValues"), plug_values = NULL, compute_p_values = FALSE, remove_collinear_columns = FALSE, intercept = TRUE, non_negative = FALSE, max_iterations = 0, objective_epsilon = -1, beta_epsilon = 1e-04, gradient_epsilon = -1, startval = NULL, prior = 0, cold_start = FALSE, lambda_min_ratio = 0, beta_constraints = NULL, max_active_predictors = -1, obj_reg = -1, stopping_rounds = 0, stopping_metric = c("AUTO", "deviance", "logloss", "MSE", "RMSE", "MAE", "RMSLE", "AUC", "AUCPR", "lift_top_group", "misclassification", "mean_per_class_error", "custom", "custom_increasing"), stopping_tolerance = 0.001, balance_classes = FALSE, class_sampling_factors = NULL, max_after_balance_size = 5, max_runtime_secs = 0, custom_metric_func = NULL, nparallelism = 0, max_predictor_number = 1, min_predictor_number = 1, mode = c("allsubsets", "maxr", "maxrsweep", "backward"), build_glm_model = FALSE, p_values_threshold = 0, influence = c("dfbetas"), multinode_mode = FALSE )
x |
(Optional) A vector containing the names or indices of the predictor variables to use in building the model. If x is missing, then all columns except y are used. |
y |
The name or column index of the response variable in the data. The response must be either a numeric or a categorical/factor variable. If the response is numeric, then a regression model will be trained, otherwise it will train a classification model. |
training_frame |
Id of the training data frame. |
model_id |
Destination id for this model; auto-generated if not specified. |
validation_frame |
Id of the validation data frame. |
nfolds |
Number of folds for K-fold cross-validation (0 to disable or >= 2). Defaults to 0. |
seed |
Seed for random numbers (affects certain parts of the algo that are stochastic and those might or might not be enabled by default). Defaults to -1 (time-based random number). |
fold_assignment |
Cross-validation fold assignment scheme, if fold_column is not specified. The 'Stratified' option will stratify the folds based on the response variable, for classification problems. Must be one of: "AUTO", "Random", "Modulo", "Stratified". Defaults to AUTO. |
fold_column |
Column with cross-validation fold index assignment per observation. |
ignore_const_cols |
|
score_each_iteration |
|
score_iteration_interval |
Perform scoring for every score_iteration_interval iterations Defaults to 0. |
offset_column |
Offset column. This will be added to the combination of columns before applying the link function. |
weights_column |
Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0. |
family |
Family. For maxr/maxrsweep, only gaussian. For backward, ordinal and multinomial families are not supported Must be one of: "AUTO", "gaussian", "binomial", "fractionalbinomial", "quasibinomial", "poisson", "gamma", "tweedie", "negativebinomial". Defaults to AUTO. |
link |
Link function. Must be one of: "family_default", "identity", "logit", "log", "inverse", "tweedie", "ologit". Defaults to family_default. |
tweedie_variance_power |
Tweedie variance power Defaults to 0. |
tweedie_link_power |
Tweedie link power Defaults to 0. |
theta |
Theta Defaults to 0. |
solver |
AUTO will set the solver based on given data and the other parameters. IRLSM is fast on on problems with small number of predictors and for lambda-search with L1 penalty, L_BFGS scales better for datasets with many columns. Must be one of: "AUTO", "IRLSM", "L_BFGS", "COORDINATE_DESCENT_NAIVE", "COORDINATE_DESCENT", "GRADIENT_DESCENT_LH", "GRADIENT_DESCENT_SQERR". Defaults to IRLSM. |
alpha |
Distribution of regularization between the L1 (Lasso) and L2 (Ridge) penalties. A value of 1 for alpha represents Lasso regression, a value of 0 produces Ridge regression, and anything in between specifies the amount of mixing between the two. Default value of alpha is 0 when SOLVER = 'L-BFGS'; 0.5 otherwise. |
lambda |
Regularization strength Defaults to c(0.0). |
lambda_search |
|
early_stopping |
|
nlambdas |
Number of lambdas to be used in a search. Default indicates: If alpha is zero, with lambda search set to True, the value of nlamdas is set to 30 (fewer lambdas are needed for ridge regression) otherwise it is set to 100. Defaults to 0. |
standardize |
|
missing_values_handling |
Handling of missing values. Either MeanImputation, Skip or PlugValues. Must be one of: "MeanImputation", "Skip", "PlugValues". Defaults to MeanImputation. |
plug_values |
Plug Values (a single row frame containing values that will be used to impute missing values of the training/validation frame, use with conjunction missing_values_handling = PlugValues) |
compute_p_values |
|
remove_collinear_columns |
|
intercept |
|
non_negative |
|
max_iterations |
Maximum number of iterations Defaults to 0. |
objective_epsilon |
Converge if objective value changes less than this. Default (of -1.0) indicates: If lambda_search is set to True the value of objective_epsilon is set to .0001. If the lambda_search is set to False and lambda is equal to zero, the value of objective_epsilon is set to .000001, for any other value of lambda the default value of objective_epsilon is set to .0001. Defaults to -1. |
beta_epsilon |
Converge if beta changes less (using L-infinity norm) than beta esilon, ONLY applies to IRLSM solver Defaults to 0.0001. |
gradient_epsilon |
Converge if objective changes less (using L-infinity norm) than this, ONLY applies to L-BFGS solver. Default (of -1.0) indicates: If lambda_search is set to False and lambda is equal to zero, the default value of gradient_epsilon is equal to .000001, otherwise the default value is .0001. If lambda_search is set to True, the conditional values above are 1E-8 and 1E-6 respectively. Defaults to -1. |
startval |
double array to initialize fixed and random coefficients for HGLM, coefficients for GLM. |
prior |
Prior probability for y==1. To be used only for logistic regression iff the data has been sampled and the mean of response does not reflect reality. Defaults to 0. |
cold_start |
|
lambda_min_ratio |
Minimum lambda used in lambda search, specified as a ratio of lambda_max (the smallest lambda that drives all coefficients to zero). Default indicates: if the number of observations is greater than the number of variables, then lambda_min_ratio is set to 0.0001; if the number of observations is less than the number of variables, then lambda_min_ratio is set to 0.01. Defaults to 0. |
beta_constraints |
Beta constraints |
max_active_predictors |
Maximum number of active predictors during computation. Use as a stopping criterion to prevent expensive model building with many predictors. Default indicates: If the IRLSM solver is used, the value of max_active_predictors is set to 5000 otherwise it is set to 100000000. Defaults to -1. |
obj_reg |
Likelihood divider in objective value computation, default (of -1.0) will set it to 1/nobs Defaults to -1. |
stopping_rounds |
Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable) Defaults to 0. |
stopping_metric |
Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client. Must be one of: "AUTO", "deviance", "logloss", "MSE", "RMSE", "MAE", "RMSLE", "AUC", "AUCPR", "lift_top_group", "misclassification", "mean_per_class_error", "custom", "custom_increasing". Defaults to AUTO. |
stopping_tolerance |
Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much) Defaults to 0.001. |
balance_classes |
|
class_sampling_factors |
Desired over/under-sampling ratios per class (in lexicographic order). If not specified, sampling factors will be automatically computed to obtain class balance during training. Requires balance_classes. |
max_after_balance_size |
Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires balance_classes. Defaults to 5.0. |
max_runtime_secs |
Maximum allowed runtime in seconds for model training. Use 0 to disable. Defaults to 0. |
custom_metric_func |
Reference to custom evaluation function, format: 'language:keyName=funcName' |
nparallelism |
number of models to build in parallel. Defaults to 0.0 which is adaptive to the system capability Defaults to 0. |
max_predictor_number |
Maximum number of predictors to be considered when building GLM models. Defaults to 1. Defaults to 1. |
min_predictor_number |
For mode = 'backward' only. Minimum number of predictors to be considered when building GLM models starting with all predictors to be included. Defaults to 1. Defaults to 1. |
mode |
Mode: Used to choose model selection algorithms to use. Options include 'allsubsets' for all subsets, 'maxr' that uses sequential replacement and GLM to build all models, slow but works with cross-validation, validation frames for more robust results, 'maxrsweep' that uses sequential replacement and sweeping action, much faster than 'maxr', 'backward' for backward selection. Must be one of: "allsubsets", "maxr", "maxrsweep", "backward". Defaults to maxr. |
build_glm_model |
|
p_values_threshold |
For mode='backward' only. If specified, will stop the model building process when all coefficientsp-values drop below this threshold Defaults to 0. |
influence |
If set to dfbetas will calculate the difference in beta when a datarow is included and excluded in the dataset. Must be one of: "dfbetas". |
multinode_mode |
|
## Not run: library(h2o) h2o.init() # Run ModelSelection of VOL ~ all predictors prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(path = prostate_path) prostate$CAPSULE <- as.factor(prostate$CAPSULE) model <- h2o.modelSelection(y="VOL", x=c("RACE","AGE","RACE","DPROS"), training_frame=prostate) ## End(Not run)## Not run: library(h2o) h2o.init() # Run ModelSelection of VOL ~ all predictors prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(path = prostate_path) prostate$CAPSULE <- as.factor(prostate$CAPSULE) model <- h2o.modelSelection(y="VOL", x=c("RACE","AGE","RACE","DPROS"), training_frame=prostate) ## End(Not run)
Provides the method h2o.mojo_predict_csv with which you can predict a MOJO model from R.
h2o.mojo_predict_csv( input_csv_path, mojo_zip_path, output_csv_path = NULL, genmodel_jar_path = NULL, classpath = NULL, java_options = NULL, verbose = F, setInvNumNA = F )h2o.mojo_predict_csv( input_csv_path, mojo_zip_path, output_csv_path = NULL, genmodel_jar_path = NULL, classpath = NULL, java_options = NULL, verbose = F, setInvNumNA = F )
input_csv_path |
Path to input CSV file. |
mojo_zip_path |
Path to MOJO zip downloaded from H2O. |
output_csv_path |
Optional, path to the output CSV file with computed predictions. If NULL (default), then predictions will be saved as prediction.csv in the same folder as the MOJO zip. |
genmodel_jar_path |
Optional, path to genmodel jar file. If NULL (default) then the h2o-genmodel.jar in the same folder as the MOJO zip will be used. |
classpath |
Optional, specifies custom user defined classpath which will be used when scoring. If NULL (default) then the default classpath for this MOJO model will be used. |
java_options |
Optional, custom user defined options for Java. By default '-Xmx4g -XX:ReservedCodeCacheSize=256m' is used. |
verbose |
Optional, if TRUE, then additional debug information will be printed. FALSE by default. |
setInvNumNA |
Optional, if TRUE, then then for an string that cannot be parsed into a number an N/A value will be produced, if false the command will fail. FALSE by default. |
Returns a data.frame containing computed predictions
Provides the method h2o.mojo_predict_df with which you can predict a MOJO model from R.
h2o.mojo_predict_df( frame, mojo_zip_path, genmodel_jar_path = NULL, classpath = NULL, java_options = NULL, verbose = F, setInvNumNA = F )h2o.mojo_predict_df( frame, mojo_zip_path, genmodel_jar_path = NULL, classpath = NULL, java_options = NULL, verbose = F, setInvNumNA = F )
frame |
data.frame to score. |
mojo_zip_path |
Path to MOJO zip downloaded from H2O. |
genmodel_jar_path |
Optional, path to genmodel jar file. If NULL (default) then the h2o-genmodel.jar in the same folder as the MOJO zip will be used. |
classpath |
Optional, specifies custom user defined classpath which will be used when scoring. If NULL (default) then the default classpath for this MOJO model will be used. |
java_options |
Optional, custom user defined options for Java. By default '-Xmx4g -XX:ReservedCodeCacheSize=256m' is used. |
verbose |
Optional, if TRUE, then additional debug information will be printed. FALSE by default. |
setInvNumNA |
Optional, if TRUE, then then for an string that cannot be parsed into a number an N/A value will be produced, if false the command will fail. FALSE by default. |
Returns a data.frame containing computed predictions
Converts the entries of an H2OFrame object from milliseconds to months (on a 1 to 12 scale).
h2o.month(x) month(x) ## S3 method for class 'H2OFrame' month(x)h2o.month(x) month(x) ## S3 method for class 'H2OFrame' month(x)
x |
An H2OFrame object. |
An H2OFrame object containing the entries of x converted to months of
the year.
## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/jira/v-11-eurodate.csv" hdf <- h2o.importFile(f) h2o.month(hdf["ds9"]) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/jira/v-11-eurodate.csv" hdf <- h2o.importFile(f) h2o.month(hdf["ds9"]) ## End(Not run)
Retrieves the mean squared error value from an H2OModelMetrics object. If "train", "valid", and "xval" parameters are FALSE (default), then the training MSEvalue is returned. If more than one parameter is set to TRUE, then a named vector of MSEs are returned, where the names are "train", "valid" or "xval".
h2o.mse(object, train = FALSE, valid = FALSE, xval = FALSE)h2o.mse(object, train = FALSE, valid = FALSE, xval = FALSE)
object |
An H2OModelMetrics object of the correct type. |
train |
Retrieve the training MSE |
valid |
Retrieve the validation MSE |
xval |
Retrieve the cross-validation MSE |
This function only supports H2OBinomialMetrics, H2OMultinomialMetrics, and H2ORegressionMetrics objects.
h2o.auc for AUC, h2o.mse for MSE, and
h2o.metric for the various threshold metrics. See
h2o.performance for creating H2OModelMetrics objects.
## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(prostate_path) prostate[, 2] <- as.factor(prostate[, 2]) model <- h2o.gbm(x = 3:9, y = 2, training_frame = prostate, distribution = "bernoulli") perf <- h2o.performance(model, prostate) h2o.mse(perf) ## End(Not run)## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(prostate_path) prostate[, 2] <- as.factor(prostate[, 2]) model <- h2o.gbm(x = 3:9, y = 2, training_frame = prostate, distribution = "bernoulli") perf <- h2o.performance(model, prostate) h2o.mse(perf) ## End(Not run)
Retrieves the AUC table from an H2OMultinomialMetrics. If "train", "valid", and "xval" parameters are FALSE (default), then the training AUC table is returned. If more than one parameter is set to TRUE, then a named vector of AUC tables are returned, where the names are "train", "valid" or "xval".
h2o.multinomial_auc_table(object, train = FALSE, valid = FALSE, xval = FALSE)h2o.multinomial_auc_table(object, train = FALSE, valid = FALSE, xval = FALSE)
object |
An H2OMultinomialMetrics object. |
train |
Retrieve the training AUC table |
valid |
Retrieve the validation AUC table |
xval |
Retrieve the cross-validation AUC table |
h2o.giniCoef for the Gini coefficient,
h2o.mse for MSE, and h2o.metric for the
various threshold metrics. See h2o.performance for
creating H2OModelMetrics objects.
## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(prostate_path) prostate[, 2] <- as.factor(prostate[, 2]) model <- h2o.gbm(x = 3:9, y = 2, training_frame = prostate, distribution = "bernoulli") perf <- h2o.performance(model, prostate) h2o.multinomial_auc_table(perf) ## End(Not run)## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(prostate_path) prostate[, 2] <- as.factor(prostate[, 2]) model <- h2o.gbm(x = 3:9, y = 2, training_frame = prostate, distribution = "bernoulli") perf <- h2o.performance(model, prostate) h2o.multinomial_auc_table(perf) ## End(Not run)
Retrieves the PR AUC table from an H2OMultinomialMetrics. If "train", "valid", and "xval" parameters are FALSE (default), then the training PR AUC table is returned. If more than one parameter is set to TRUE, then a named vector of PR AUC tables are returned, where the names are "train", "valid" or "xval".
h2o.multinomial_aucpr_table(object, train = FALSE, valid = FALSE, xval = FALSE)h2o.multinomial_aucpr_table(object, train = FALSE, valid = FALSE, xval = FALSE)
object |
An H2OMultinomialMetrics object. |
train |
Retrieve the training PR AUC table |
valid |
Retrieve the validation PR AUC table |
xval |
Retrieve the cross-validation PR AUC table |
h2o.giniCoef for the Gini coefficient,
h2o.mse for MSE, and h2o.metric for the
various threshold metrics. See h2o.performance for
creating H2OModelMetrics objects.
## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(prostate_path) prostate[, 2] <- as.factor(prostate[, 2]) model <- h2o.gbm(x = 3:9, y = 2, training_frame = prostate, distribution = "bernoulli") perf <- h2o.performance(model, prostate) h2o.multinomial_aucpr_table(perf) ## End(Not run)## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(prostate_path) prostate[, 2] <- as.factor(prostate[, 2]) model <- h2o.gbm(x = 3:9, y = 2, training_frame = prostate, distribution = "bernoulli") perf <- h2o.performance(model, prostate) h2o.multinomial_aucpr_table(perf) ## End(Not run)
Remove Rows With NAs
h2o.na_omit(object, ...)h2o.na_omit(object, ...)
object |
H2OFrame object |
... |
Ignored |
Returns an H2OFrame object containing non-NA rows.
## Not run: library(h2o) h2o.init() frame <- h2o.createFrame(rows = 6, cols = 2, categorical_fraction = 0.0, missing_fraction = 0.7, seed = 123) h2o.na_omit(frame) ## End(Not run)## Not run: library(h2o) h2o.init() frame <- h2o.createFrame(rows = 6, cols = 2, categorical_fraction = 0.0, missing_fraction = 0.7, seed = 123) h2o.na_omit(frame) ## End(Not run)
Gives the count of NAs per column.
h2o.nacnt(x)h2o.nacnt(x)
x |
An H2OFrame object. |
Returns a list containing the count of NAs per column
## Not run: library(h2o) h2o.init() iris_hf <- as.h2o(iris) h2o.nacnt(iris_hf) # should return all 0s h2o.insertMissingValues(iris_hf) h2o.nacnt(iris_hf) ## End(Not run)## Not run: library(h2o) h2o.init() iris_hf <- as.h2o(iris) h2o.nacnt(iris_hf) # should return all 0s h2o.insertMissingValues(iris_hf) h2o.nacnt(iris_hf) ## End(Not run)
The naive Bayes classifier assumes independence between predictor variables conditional on the response, and a Gaussian distribution of numeric predictors with mean and standard deviation computed from the training dataset. When building a naive Bayes classifier, every row in the training dataset that contains at least one NA will be skipped completely. If the test dataset has missing values, then those predictors are omitted in the probability calculation during prediction.
h2o.naiveBayes( x, y, training_frame, model_id = NULL, nfolds = 0, seed = -1, fold_assignment = c("AUTO", "Random", "Modulo", "Stratified"), fold_column = NULL, keep_cross_validation_models = TRUE, keep_cross_validation_predictions = FALSE, keep_cross_validation_fold_assignment = FALSE, validation_frame = NULL, ignore_const_cols = TRUE, score_each_iteration = FALSE, balance_classes = FALSE, class_sampling_factors = NULL, max_after_balance_size = 5, laplace = 0, threshold = 0.001, min_sdev = 0.001, eps = 0, eps_sdev = 0, min_prob = 0.001, eps_prob = 0, compute_metrics = TRUE, max_runtime_secs = 0, export_checkpoints_dir = NULL, gainslift_bins = -1, auc_type = c("AUTO", "NONE", "MACRO_OVR", "WEIGHTED_OVR", "MACRO_OVO", "WEIGHTED_OVO") )h2o.naiveBayes( x, y, training_frame, model_id = NULL, nfolds = 0, seed = -1, fold_assignment = c("AUTO", "Random", "Modulo", "Stratified"), fold_column = NULL, keep_cross_validation_models = TRUE, keep_cross_validation_predictions = FALSE, keep_cross_validation_fold_assignment = FALSE, validation_frame = NULL, ignore_const_cols = TRUE, score_each_iteration = FALSE, balance_classes = FALSE, class_sampling_factors = NULL, max_after_balance_size = 5, laplace = 0, threshold = 0.001, min_sdev = 0.001, eps = 0, eps_sdev = 0, min_prob = 0.001, eps_prob = 0, compute_metrics = TRUE, max_runtime_secs = 0, export_checkpoints_dir = NULL, gainslift_bins = -1, auc_type = c("AUTO", "NONE", "MACRO_OVR", "WEIGHTED_OVR", "MACRO_OVO", "WEIGHTED_OVO") )
x |
(Optional) A vector containing the names or indices of the predictor variables to use in building the model. If x is missing, then all columns except y are used. |
y |
The name or column index of the response variable in the data. The response must be either a numeric or a categorical/factor variable. If the response is numeric, then a regression model will be trained, otherwise it will train a classification model. |
training_frame |
Id of the training data frame. |
model_id |
Destination id for this model; auto-generated if not specified. |
nfolds |
Number of folds for K-fold cross-validation (0 to disable or >= 2). Defaults to 0. |
seed |
Seed for random numbers (affects certain parts of the algo that are stochastic and those might or might not be enabled by default). Defaults to -1 (time-based random number). |
fold_assignment |
Cross-validation fold assignment scheme, if fold_column is not specified. The 'Stratified' option will stratify the folds based on the response variable, for classification problems. Must be one of: "AUTO", "Random", "Modulo", "Stratified". Defaults to AUTO. |
fold_column |
Column with cross-validation fold index assignment per observation. |
keep_cross_validation_models |
|
keep_cross_validation_predictions |
|
keep_cross_validation_fold_assignment |
|
validation_frame |
Id of the validation data frame. |
ignore_const_cols |
|
score_each_iteration |
|
balance_classes |
|
class_sampling_factors |
Desired over/under-sampling ratios per class (in lexicographic order). If not specified, sampling factors will be automatically computed to obtain class balance during training. Requires balance_classes. |
max_after_balance_size |
Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires balance_classes. Defaults to 5.0. |
laplace |
Laplace smoothing parameter Defaults to 0. |
threshold |
This argument is deprecated, use 'min_sdev' instead. The minimum standard deviation to use for observations without enough data. Must be at least 1e-10. |
min_sdev |
The minimum standard deviation to use for observations without enough data. Must be at least 1e-10. |
eps |
This argument is deprecated, use 'eps_sdev' instead. A threshold cutoff to deal with numeric instability, must be positive. |
eps_sdev |
A threshold cutoff to deal with numeric instability, must be positive. |
min_prob |
Min. probability to use for observations with not enough data. |
eps_prob |
Cutoff below which probability is replaced with min_prob. |
compute_metrics |
|
max_runtime_secs |
Maximum allowed runtime in seconds for model training. Use 0 to disable. Defaults to 0. |
export_checkpoints_dir |
Automatically export generated models to this directory. |
gainslift_bins |
Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning. Defaults to -1. |
auc_type |
Set default multinomial AUC type. Must be one of: "AUTO", "NONE", "MACRO_OVR", "WEIGHTED_OVR", "MACRO_OVO", "WEIGHTED_OVO". Defaults to AUTO. |
an object of class H2OBinomialModel if the response has two categorical levels, and H2OMultinomialModel otherwise.
## Not run: h2o.init() votes_path <- system.file("extdata", "housevotes.csv", package = "h2o") votes <- h2o.uploadFile(path = votes_path, header = TRUE) h2o.naiveBayes(x = 2:17, y = 1, training_frame = votes, laplace = 3) ## End(Not run)## Not run: h2o.init() votes_path <- system.file("extdata", "housevotes.csv", package = "h2o") votes <- h2o.uploadFile(path = votes_path, header = TRUE) h2o.naiveBayes(x = 2:17, y = 1, training_frame = votes, laplace = 3) ## End(Not run)
Column names of an H2OFrame
h2o.names(x)h2o.names(x)
x |
An H2OFrame object. |
names for the base R implementation.
## Not run: library(h2o) h2o.init() f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_train.csv" iris <- h2o.importFile(f) h2o.names(iris) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_train.csv" iris <- h2o.importFile(f) h2o.names(iris) ## End(Not run)
String length
h2o.nchar(x)h2o.nchar(x)
x |
The column whose string lengths will be returned. |
## Not run: library(h2o) h2o.init() string_to_nchar <- as.h2o("r tutorial") nchar_string <- h2o.nchar(string_to_nchar) ## End(Not run)## Not run: library(h2o) h2o.init() string_to_nchar <- as.h2o("r tutorial") nchar_string <- h2o.nchar(string_to_nchar) ## End(Not run)
Return the number of columns present in x.
h2o.ncol(x)h2o.ncol(x)
x |
An H2OFrame object. |
nrow for the base R implementation, ncol().
## Not run: library(h2o) h2o.init() f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_train.csv" iris <- h2o.importFile(f) h2o.ncol(iris) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_train.csv" iris <- h2o.importFile(f) h2o.ncol(iris) ## End(Not run)
Extracts the final training negative log likelihood of a GLM model.
h2o.negative_log_likelihood(model)h2o.negative_log_likelihood(model)
model |
an H2OModel object. |
The final training negative log likelihood of a GLM model.
## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv" cars <- h2o.importFile(f) predictors <- c("displacement", "power", "weight", "acceleration", "year") response <- "acceleration" cars_model <- h2o.glm(y=response, x=predictors, training_frame = cars, family="gaussian", generate_scoring_history=TRUE) nllValue <- h2o.negative_log_likelihood(cars_model) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv" cars <- h2o.importFile(f) predictors <- c("displacement", "power", "weight", "acceleration", "year") response <- "acceleration" cars_model <- h2o.glm(y=response, x=predictors, training_frame = cars, family="gaussian", generate_scoring_history=TRUE) nllValue <- h2o.negative_log_likelihood(cars_model) ## End(Not run)
View speed with various file sizes.
h2o.networkTest()h2o.networkTest()
Returns a table listing the network speed for 1B, 10KB, and 10MB.
Get the number of factor levels for this frame.
h2o.nlevels(x)h2o.nlevels(x)
x |
An H2OFrame object. |
nlevels for the base R method.
## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv" cars <- h2o.importFile(f) h2o.nlevels(cars) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv" cars <- h2o.importFile(f) h2o.nlevels(cars) ## End(Not run)
Disable Progress Bar
h2o.no_progress(expr)h2o.no_progress(expr)
expr |
When specified, disable progress bar only for the evaluation of the expr and after the evaluation return to the previous setting (default is to show the progress bar), otherwise disable it globally. |
Value of expr if specified, otherwise NULL.
## Not run: library(h2o) h2o.init() h2o.no_progress() f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_wheader.csv" iris <- h2o.importFile(f) iris["class"] <- as.factor(iris["class"]) predictors <- c("sepal_len", "sepal_wid", "petal_len", "petal_wid") splits <- h2o.splitFrame(iris, ratios = 0.8, seed = 1234) train <- splits[[1]] valid <- splits[[2]] iris_km <- h2o.kmeans(x = predictors, training_frame = train, validation_frame = valid, k = 10, estimate_k = TRUE, standardize = FALSE, seed = 1234) ## End(Not run)## Not run: library(h2o) h2o.init() h2o.no_progress() f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_wheader.csv" iris <- h2o.importFile(f) iris["class"] <- as.factor(iris["class"]) predictors <- c("sepal_len", "sepal_wid", "petal_len", "petal_wid") splits <- h2o.splitFrame(iris, ratios = 0.8, seed = 1234) train <- splits[[1]] valid <- splits[[2]] iris_km <- h2o.kmeans(x = predictors, training_frame = train, validation_frame = valid, k = 10, estimate_k = TRUE, standardize = FALSE, seed = 1234) ## End(Not run)
Return the number of rows present in x.
h2o.nrow(x)h2o.nrow(x)
x |
An H2OFrame object. |
nrow for the base R implementation.
## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv" cars <- h2o.importFile(f) h2o.nrow(cars) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv" cars <- h2o.importFile(f) h2o.nrow(cars) ## End(Not run)
If "train", "valid", and "xval" parameters are FALSE (default), then the training null deviance value is returned. If more than one parameter is set to TRUE, then a named vector of null deviances are returned, where the names are "train", "valid" or "xval".
h2o.null_deviance(object, train = FALSE, valid = FALSE, xval = FALSE)h2o.null_deviance(object, train = FALSE, valid = FALSE, xval = FALSE)
object |
An H2OModel or H2OModelMetrics |
train |
Retrieve the training null deviance |
valid |
Retrieve the validation null deviance |
xval |
Retrieve the cross-validation null deviance |
## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.importFile(prostate_path) prostate[, 2] <- as.factor(prostate[, 2]) prostate_glm <- h2o.glm(y = "CAPSULE", x = c("AGE", "RACE", "PSA", "DCAPS"), training_frame = prostate, family = "binomial", nfolds = 0, alpha = 0.5, lambda_search = FALSE) h2o.null_deviance(prostate_glm, train = TRUE) ## End(Not run)## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.importFile(prostate_path) prostate[, 2] <- as.factor(prostate[, 2]) prostate_glm <- h2o.glm(y = "CAPSULE", x = c("AGE", "RACE", "PSA", "DCAPS"), training_frame = prostate, family = "binomial", nfolds = 0, alpha = 0.5, lambda_search = FALSE) h2o.null_deviance(prostate_glm, train = TRUE) ## End(Not run)
If "train", "valid", and "xval" parameters are FALSE (default), then the training null degrees of freedom value is returned. If more than one parameter is set to TRUE, then a named vector of null degrees of freedom are returned, where the names are "train", "valid" or "xval".
h2o.null_dof(object, train = FALSE, valid = FALSE, xval = FALSE)h2o.null_dof(object, train = FALSE, valid = FALSE, xval = FALSE)
object |
An H2OModel or H2OModelMetrics |
train |
Retrieve the training null degrees of freedom |
valid |
Retrieve the validation null degrees of freedom |
xval |
Retrieve the cross-validation null degrees of freedom |
## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.importFile(prostate_path) prostate[, 2] <- as.factor(prostate[, 2]) prostate_glm <- h2o.glm(y = "CAPSULE", x = c("AGE", "RACE", "PSA", "DCAPS"), training_frame = prostate, family = "binomial", nfolds = 0, alpha = 0.5, lambda_search = FALSE) h2o.null_dof(prostate_glm, train = TRUE) ## End(Not run)## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.importFile(prostate_path) prostate[, 2] <- as.factor(prostate[, 2]) prostate_glm <- h2o.glm(y = "CAPSULE", x = c("AGE", "RACE", "PSA", "DCAPS"), training_frame = prostate, family = "binomial", nfolds = 0, alpha = 0.5, lambda_search = FALSE) h2o.null_dof(prostate_glm, train = TRUE) ## End(Not run)
Retrieve the number of iterations.
h2o.num_iterations(object)h2o.num_iterations(object)
object |
An H2OClusteringModel object. |
## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.importFile(prostate_path) prostate[, 2] <- as.factor(prostate[, 2]) prostate_glm <- h2o.glm(y = "CAPSULE", x = c("AGE", "RACE", "PSA", "DCAPS"), training_frame = prostate, family = "binomial", nfolds = 0, alpha = 0.5, lambda_search = FALSE) h2o.num_iterations(prostate_glm) ## End(Not run)## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.importFile(prostate_path) prostate[, 2] <- as.factor(prostate[, 2]) prostate_glm <- h2o.glm(y = "CAPSULE", x = c("AGE", "RACE", "PSA", "DCAPS"), training_frame = prostate, family = "binomial", nfolds = 0, alpha = 0.5, lambda_search = FALSE) h2o.num_iterations(prostate_glm) ## End(Not run)
Find the count of all possible substrings >= 2 chars that are contained in the specified line-separated text file.
h2o.num_valid_substrings(x, path)h2o.num_valid_substrings(x, path)
x |
The column on which to calculate the number of valid substrings. |
path |
Path to text file containing line-separated strings to be referenced. |
Open existing logs of H2O R POST commands and error resposnes on local disk. Used primarily for debugging purposes.
h2o.openLog(type)h2o.openLog(type)
type |
Currently unimplemented. |
h2o.startLogging, h2o.stopLogging,
h2o.clearLog
## Not run: h2o.init() h2o.startLogging() australia_path = system.file("extdata", "australia.csv", package = "h2o") australia = h2o.importFile(path = australia_path) h2o.stopLogging() # Not run to avoid windows being opened during R CMD check # h2o.openLog("Command") # h2o.openLog("Error") ## End(Not run)## Not run: h2o.init() h2o.startLogging() australia_path = system.file("extdata", "australia.csv", package = "h2o") australia = h2o.importFile(path = australia_path) h2o.stopLogging() # Not run to avoid windows being opened during R CMD check # h2o.openLog("Command") # h2o.openLog("Error") ## End(Not run)
Create Pareto front and plot it. Pareto front contains models that are optimal in a sense that for each model in the Pareto front there isn't a model that would be better in both criteria. For example, this can be useful in picking models that are fast to predict and at the same time have high accuracy. For generic data.frames/H2OFrames input the task is assumed to be minimization for both metrics.
h2o.pareto_front( object, leaderboard_frame = NULL, x_metric = c("AUTO", "AUC", "AUCPR", "logloss", "MAE", "mean_per_class_error", "mean_residual_deviance", "MSE", "predict_time_per_row_ms", "RMSE", "RMSLE", "training_time_ms"), y_metric = c("AUTO", "AUC", "AUCPR", "logloss", "MAE", "mean_per_class_error", "mean_residual_deviance", "MSE", "predict_time_per_row_ms", "RMSE", "RMSLE", "training_time_ms"), optimum = c("AUTO", "top left", "top right", "bottom left", "bottom right"), title = NULL, color_col = "algo" )h2o.pareto_front( object, leaderboard_frame = NULL, x_metric = c("AUTO", "AUC", "AUCPR", "logloss", "MAE", "mean_per_class_error", "mean_residual_deviance", "MSE", "predict_time_per_row_ms", "RMSE", "RMSLE", "training_time_ms"), y_metric = c("AUTO", "AUC", "AUCPR", "logloss", "MAE", "mean_per_class_error", "mean_residual_deviance", "MSE", "predict_time_per_row_ms", "RMSE", "RMSLE", "training_time_ms"), optimum = c("AUTO", "top left", "top right", "bottom left", "bottom right"), title = NULL, color_col = "algo" )
object |
H2OAutoML or H2OGrid or a data.frame |
leaderboard_frame |
a frame used for generating the leaderboard (used when |
x_metric |
one of the metrics present in the leaderboard |
y_metric |
one of the metrics present in the leaderboard |
optimum |
location of the optimum on XY plane |
title |
title used for plotting |
color_col |
categorical column in the leaderboard that should be used for coloring the points |
An H2OParetoFront S4 object with plot method and 'pareto_front“ slot
## Not run: library(h2o) h2o.init() # Import the wine dataset into H2O: df <- h2o.importFile("h2o://prostate.csv") # Set the response response <- "CAPSULE" df[[response]] <- as.factor(df[[response]]) # Split the dataset into a train and test set: splits <- h2o.splitFrame(df, ratios = 0.8, seed = 1) train <- splits[[1]] test <- splits[[2]] # Build and train the model: aml <- h2o.automl(y = response, training_frame = train, max_models = 10, seed = 1) # Create the Pareto front pf <- h2o.pareto_front(aml) plot(pf) pf@pareto_front # to retrieve the Pareto front subset of the leaderboard aml2 <- h2o.automl(y = response, training_frame = train, max_models = 10, seed = 42) combined_leaderboard <- h2o.make_leaderboard(list(aml, aml2), test, extra_columns = "ALL") pf_combined <- h2o.pareto_front(combined_leaderboard, x_metric = "predict_time_per_row_ms", y_metric = "rmse", optimum = "bottom left") plot(pf_combined) pf_combined@pareto_front ## End(Not run)## Not run: library(h2o) h2o.init() # Import the wine dataset into H2O: df <- h2o.importFile("h2o://prostate.csv") # Set the response response <- "CAPSULE" df[[response]] <- as.factor(df[[response]]) # Split the dataset into a train and test set: splits <- h2o.splitFrame(df, ratios = 0.8, seed = 1) train <- splits[[1]] test <- splits[[2]] # Build and train the model: aml <- h2o.automl(y = response, training_frame = train, max_models = 10, seed = 1) # Create the Pareto front pf <- h2o.pareto_front(aml) plot(pf) pf@pareto_front # to retrieve the Pareto front subset of the leaderboard aml2 <- h2o.automl(y = response, training_frame = train, max_models = 10, seed = 42) combined_leaderboard <- h2o.make_leaderboard(list(aml, aml2), test, extra_columns = "ALL") pf_combined <- h2o.pareto_front(combined_leaderboard, x_metric = "predict_time_per_row_ms", y_metric = "rmse", optimum = "bottom left") plot(pf_combined) pf_combined@pareto_front ## End(Not run)
The second phase in the data ingestion step.
h2o.parseRaw( data, pattern = "", destination_frame = "", header = NA, sep = "", col.names = NULL, col.types = NULL, na.strings = NULL, blocking = FALSE, parse_type = NULL, chunk_size = NULL, decrypt_tool = NULL, skipped_columns = NULL, force_col_types = FALSE, custom_non_data_line_markers = NULL, partition_by = NULL, quotechar = NULL, escapechar = "" )h2o.parseRaw( data, pattern = "", destination_frame = "", header = NA, sep = "", col.names = NULL, col.types = NULL, na.strings = NULL, blocking = FALSE, parse_type = NULL, chunk_size = NULL, decrypt_tool = NULL, skipped_columns = NULL, force_col_types = FALSE, custom_non_data_line_markers = NULL, partition_by = NULL, quotechar = NULL, escapechar = "" )
data |
An H2OFrame object to be parsed. |
pattern |
(Optional) Character string containing a regular expression to match file(s) in the folder. |
destination_frame |
(Optional) The hex key assigned to the parsed file. |
header |
(Optional) A logical value indicating whether the first row is the column header. If missing, H2O will automatically try to detect the presence of a header. |
sep |
(Optional) The field separator character. Values on each line of
the file are separated by this character. If |
col.names |
(Optional) An H2OFrame object containing a single delimited line with the column names for the file. If skipped_columns are specified, only list column names of columns that are not skipped. |
col.types |
(Optional) A vector specifying the types to attempt to force over columns. If skipped_columns are specified, only list column types of columns that are not skipped. |
na.strings |
(Optional) H2O will interpret these strings as missing. |
blocking |
(Optional) Tell H2O parse call to block synchronously instead of polling. This can be faster for small datasets but loses the progress bar. |
parse_type |
(Optional) Specify which parser type H2O will use. Valid types are "ARFF", "XLS", "CSV", "SVMLight" |
chunk_size |
size of chunk of (input) data in bytes |
decrypt_tool |
(Optional) Specify a Decryption Tool (key-reference acquired by calling h2o.decryptionSetup. |
skipped_columns |
a list of column indices to be excluded from parsing |
force_col_types |
(Optional) if true will force parser to return the exact column types specified in column_types. For parquet, if column_types is not specified, the parquet schema will be used to determine the actual column type. |
custom_non_data_line_markers |
(Optional) If a line in imported file starts with any character in given string it will NOT be imported. Empty string means all lines are imported, NULL means that default behaviour for given format will be used |
partition_by |
(Optional) Names of the columns the persisted dataset has been partitioned by. |
quotechar |
A hint for the parser which character to expect as quoting character. None (default) means autodetection. |
escapechar |
(Optional) One ASCII character used to escape other characters. |
Parse the Raw Data produced by the import phase.
h2o.importFile, h2o.parseSetup
Get a parse setup back for the staged data.
h2o.parseSetup( data, pattern = "", destination_frame = "", header = NA, sep = "", col.names = NULL, col.types = NULL, na.strings = NULL, parse_type = NULL, chunk_size = NULL, decrypt_tool = NULL, skipped_columns = NULL, force_col_types = FALSE, custom_non_data_line_markers = NULL, partition_by = NULL, single_quotes = FALSE, escapechar = "" )h2o.parseSetup( data, pattern = "", destination_frame = "", header = NA, sep = "", col.names = NULL, col.types = NULL, na.strings = NULL, parse_type = NULL, chunk_size = NULL, decrypt_tool = NULL, skipped_columns = NULL, force_col_types = FALSE, custom_non_data_line_markers = NULL, partition_by = NULL, single_quotes = FALSE, escapechar = "" )
data |
An H2OFrame object to be parsed. |
pattern |
(Optional) Character string containing a regular expression to match file(s) in the folder. |
destination_frame |
(Optional) The hex key assigned to the parsed file. |
header |
(Optional) A logical value indicating whether the first row is the column header. If missing, H2O will automatically try to detect the presence of a header. |
sep |
(Optional) The field separator character. Values on each line of
the file are separated by this character. If |
col.names |
(Optional) An H2OFrame object containing a single delimited line with the column names for the file. If skipped_columns are specified, only list column names of columns that are not skipped. |
col.types |
(Optional) A vector specifying the types to attempt to force over columns. If skipped_columns are specified, only list column types of columns that are not skipped. |
na.strings |
(Optional) H2O will interpret these strings as missing. |
parse_type |
(Optional) Specify which parser type H2O will use. Valid types are "ARFF", "XLS", "CSV", "SVMLight" |
chunk_size |
size of chunk of (input) data in bytes |
decrypt_tool |
(Optional) Specify a Decryption Tool (key-reference acquired by calling h2o.decryptionSetup. |
skipped_columns |
a list of column indices to be excluded from parsing |
force_col_types |
(Optional) if true will force parser to return the exact column types specified in column_types. For parquet, if column_types is not specified, the parquet schema will be used to determine the actual column type. |
custom_non_data_line_markers |
(Optional) If a line in imported file starts with any character in given string it will NOT be imported. Empty string means all lines are imported, NULL means that default behaviour for given format will be used |
partition_by |
(Optional) Names of the columns the persisted dataset has been partitioned by. |
single_quotes |
If set to true, the parser expects single quotes. False for double quotes (default). |
escapechar |
(Optional) One ASCII character used to escape other characters. |
Partial dependence plot gives a graphical depiction of the marginal effect of a variable on the response. The effect of a variable is measured in change in the mean response. Note: Unlike randomForest's partialPlot when plotting partial dependence the mean response (probabilities) is returned rather than the mean of the log class probability.
h2o.partialPlot( object, newdata, cols, destination_key, nbins = 20, plot = TRUE, plot_stddev = TRUE, weight_column = -1, include_na = FALSE, user_splits = NULL, col_pairs_2dpdp = NULL, save_to = NULL, row_index = -1, targets = NULL, ... )h2o.partialPlot( object, newdata, cols, destination_key, nbins = 20, plot = TRUE, plot_stddev = TRUE, weight_column = -1, include_na = FALSE, user_splits = NULL, col_pairs_2dpdp = NULL, save_to = NULL, row_index = -1, targets = NULL, ... )
object |
An H2OModel object. |
newdata |
An H2OFrame object used for scoring and constructing the plot. |
cols |
Feature(s) for which partial dependence will be calculated. |
destination_key |
An key reference to the created partial dependence tables in H2O. |
nbins |
Number of bins used. For categorical columns make sure the number of bins exceeds the level count. If you enable add_missing_NA, the returned length will be nbin+1. |
plot |
A logical specifying whether to plot partial dependence table. |
plot_stddev |
A logical specifying whether to add std err to partial dependence plot. |
weight_column |
A string denoting which column of data should be used as the weight column. |
include_na |
A logical specifying whether missing value should be included in the Feature values. |
user_splits |
A two-level nested list containing user defined split points for pdp plots for each column. If there are two columns using user defined split points, there should be two lists in the nested list. Inside each list, the first element is the column name followed by values defined by the user. |
col_pairs_2dpdp |
A two-level nested list like this: col_pairs_2dpdp = list(c("col1_name", "col2_name"), c("col1_name","col3_name"), ...,) where a 2D partial plots will be generated for col1_name, col2_name pair, for col1_name, col3_name pair and whatever other pairs that are specified in the nested list. |
save_to |
Fully qualified prefix of the image files the resulting plots should be saved to, e.g. '/home/user/pdp'. Plots for each feature are saved separately in PNG format, each file receives a suffix equal to the corresponding feature name, e.g. '/home/user/pdp_AGE.png'. If the files already exists, they will be overridden. Files are only saves if plot = TRUE (default). |
row_index |
Row for which partial dependence will be calculated instead of the whole input frame. |
targets |
Target classes for multinomial model. |
... |
Mainly used for backwards compatibility, to allow deprecated parameters. |
Plot and list of calculated mean response tables for each feature requested.
## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(path = prostate_path) prostate[, "CAPSULE"] <- as.factor(prostate[, "CAPSULE"] ) prostate[, "RACE"] <- as.factor(prostate[, "RACE"] ) prostate_gbm <- h2o.gbm(x = c("AGE", "RACE"), y = "CAPSULE", training_frame = prostate, ntrees = 10, max_depth = 5, learn_rate = 0.1) h2o.partialPlot(object = prostate_gbm, newdata = prostate, cols = c("AGE", "RACE")) iris_hex <- as.h2o(iris) iris_gbm <- h2o.gbm(x = c(1:4), y = 5, training_frame = iris_hex) # one target class h2o.partialPlot(object = iris_gbm, newdata = iris_hex, cols="Petal.Length", targets=c("setosa")) # three target classes h2o.partialPlot(object = iris_gbm, newdata = iris_hex, cols="Petal.Length", targets=c("setosa", "virginica", "versicolor")) ## End(Not run)## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(path = prostate_path) prostate[, "CAPSULE"] <- as.factor(prostate[, "CAPSULE"] ) prostate[, "RACE"] <- as.factor(prostate[, "RACE"] ) prostate_gbm <- h2o.gbm(x = c("AGE", "RACE"), y = "CAPSULE", training_frame = prostate, ntrees = 10, max_depth = 5, learn_rate = 0.1) h2o.partialPlot(object = prostate_gbm, newdata = prostate, cols = c("AGE", "RACE")) iris_hex <- as.h2o(iris) iris_gbm <- h2o.gbm(x = c(1:4), y = 5, training_frame = iris_hex) # one target class h2o.partialPlot(object = iris_gbm, newdata = iris_hex, cols="Petal.Length", targets=c("setosa")) # three target classes h2o.partialPlot(object = iris_gbm, newdata = iris_hex, cols="Petal.Length", targets=c("setosa", "virginica", "versicolor")) ## End(Not run)
Partial dependence plot (PDP) gives a graphical depiction of the marginal effect of a variable on the response. The effect of a variable is measured in change in the mean response. PDP assumes independence between the feature for which is the PDP computed and the rest.
h2o.pd_multi_plot( object, newdata, column, best_of_family = TRUE, target = NULL, row_index = NULL, max_levels = 30, show_rug = TRUE )h2o.pd_multi_plot( object, newdata, column, best_of_family = TRUE, target = NULL, row_index = NULL, max_levels = 30, show_rug = TRUE )
object |
Either a list of H2O models/model_ids or an H2OAutoML object. |
newdata |
An H2OFrame. |
column |
A feature column name to inspect. Character string. |
best_of_family |
If TRUE, plot only the best model of each algorithm family; if FALSE, plot all models. Defaults to TRUE. |
target |
If multinomial, plot PDP just for |
row_index |
Optional. Calculate Individual Conditional Expectation (ICE) for row, |
max_levels |
An integer specifying the maximum number of factor levels to show. Defaults to 30. |
show_rug |
Show rug to visualize the density of the column. Defaults to TRUE. |
A ggplot2 object
## Not run: library(h2o) h2o.init() # Import the wine dataset into H2O: f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/wine/winequality-redwhite-no-BOM.csv" df <- h2o.importFile(f) # Set the response response <- "quality" # Split the dataset into a train and test set: splits <- h2o.splitFrame(df, ratios = 0.8, seed = 1) train <- splits[[1]] test <- splits[[2]] # Build and train the model: aml <- h2o.automl(y = response, training_frame = train, max_models = 10, seed = 1) # Create the partial dependence plot pdp <- h2o.pd_multi_plot(aml, test, column = "alcohol") print(pdp) ## End(Not run)## Not run: library(h2o) h2o.init() # Import the wine dataset into H2O: f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/wine/winequality-redwhite-no-BOM.csv" df <- h2o.importFile(f) # Set the response response <- "quality" # Split the dataset into a train and test set: splits <- h2o.splitFrame(df, ratios = 0.8, seed = 1) train <- splits[[1]] test <- splits[[2]] # Build and train the model: aml <- h2o.automl(y = response, training_frame = train, max_models = 10, seed = 1) # Create the partial dependence plot pdp <- h2o.pd_multi_plot(aml, test, column = "alcohol") print(pdp) ## End(Not run)
Partial dependence plot (PDP) gives a graphical depiction of the marginal effect of a variable on the response. The effect of a variable is measured in change in the mean response. PDP assumes independence between the feature for which is the PDP computed and the rest.
h2o.pd_plot( object, newdata, column, target = NULL, row_index = NULL, max_levels = 30, binary_response_scale = c("response", "logodds"), grouping_column = NULL, nbins = 100, show_rug = TRUE )h2o.pd_plot( object, newdata, column, target = NULL, row_index = NULL, max_levels = 30, binary_response_scale = c("response", "logodds"), grouping_column = NULL, nbins = 100, show_rug = TRUE )
object |
An H2O model. |
newdata |
An H2OFrame. Used to generate predictions used in Partial Dependence calculations. |
column |
A feature column name to inspect. Character string. |
target |
If multinomial, plot PDP just for |
row_index |
Optional. Calculate Individual Conditional Expectation (ICE) for row, |
max_levels |
An integer specifying the maximum number of factor levels to show. Defaults to 30. |
binary_response_scale |
Option for binary model to display (on the y-axis) the logodds instead of the actual score. Can be one of: "response", "logodds". Defaults to "response". |
grouping_column |
A feature column name to group the data and provide separate sets of plots by grouping feature values |
nbins |
A number of bins used. Defaults to 100. |
show_rug |
Show rug to visualize the density of the column. Defaults to TRUE. |
A ggplot2 object
## Not run: library(h2o) h2o.init() # Import the wine dataset into H2O: f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/wine/winequality-redwhite-no-BOM.csv" df <- h2o.importFile(f) # Set the response response <- "quality" # Split the dataset into a train and test set: splits <- h2o.splitFrame(df, ratios = 0.8, seed = 1) train <- splits[[1]] test <- splits[[2]] # Build and train the model: gbm <- h2o.gbm(y = response, training_frame = train) # Create the partial dependence plot pdp <- h2o.pd_plot(gbm, test, column = "alcohol") print(pdp) ## End(Not run)## Not run: library(h2o) h2o.init() # Import the wine dataset into H2O: f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/wine/winequality-redwhite-no-BOM.csv" df <- h2o.importFile(f) # Set the response response <- "quality" # Split the dataset into a train and test set: splits <- h2o.splitFrame(df, ratios = 0.8, seed = 1) train <- splits[[1]] test <- splits[[2]] # Build and train the model: gbm <- h2o.gbm(y = response, training_frame = train) # Create the partial dependence plot pdp <- h2o.pd_plot(gbm, test, column = "alcohol") print(pdp) ## End(Not run)
Given a trained h2o model, compute its performance on the given dataset. However, if the dataset does not contain the response/target column, no performance will be returned. Instead, a warning message will be printed.
h2o.performance( model, newdata = NULL, train = FALSE, valid = FALSE, xval = FALSE, data = NULL, auc_type = "NONE", auuc_type = NULL )h2o.performance( model, newdata = NULL, train = FALSE, valid = FALSE, xval = FALSE, data = NULL, auc_type = "NONE", auuc_type = NULL )
model |
An H2OModel object |
newdata |
An H2OFrame. The model will make predictions on this dataset, and subsequently score them. The dataset should match the dataset that was used to train the model, in terms of column names, types, and dimensions. If newdata is passed in, then train, valid, and xval are ignored. |
train |
A logical value indicating whether to return the training metrics (constructed during training). Note: when the trained h2o model uses balance_classes, the training metrics constructed during training will be from the balanced training dataset. For more information visit: https://github.com/h2oai/h2o-3/discussions/15518 |
valid |
A logical value indicating whether to return the validation metrics (constructed during training). |
xval |
A logical value indicating whether to return the cross-validation metrics (constructed during training). |
data |
(DEPRECATED) An H2OFrame. This argument is now called 'newdata'. |
auc_type |
For multinomila model only. Set default multinomial AUC type. Must be one of: "AUTO", "NONE", "MACRO_OVR", "WEIGHTED_OVR", "MACRO_OVO", "WEIGHTED_OVO". Default is "NONE" |
auuc_type |
For binomial model only. Set default AUUC type. Must be one of: "AUTO", "GINI", "GAIN", "LIFT". Default is NULL. |
Returns an object of the H2OModelMetrics subclass.
## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(path = prostate_path) prostate$CAPSULE <- as.factor(prostate$CAPSULE) prostate_gbm <- h2o.gbm(3:9, "CAPSULE", prostate) h2o.performance(model = prostate_gbm, newdata=prostate) ## If model uses balance_classes ## the results from train = TRUE will not match the results from newdata = prostate prostate_gbm_balanced <- h2o.gbm(3:9, "CAPSULE", prostate, balance_classes = TRUE) h2o.performance(model = prostate_gbm_balanced, newdata = prostate) h2o.performance(model = prostate_gbm_balanced, train = TRUE) ## End(Not run)## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(path = prostate_path) prostate$CAPSULE <- as.factor(prostate$CAPSULE) prostate_gbm <- h2o.gbm(3:9, "CAPSULE", prostate) h2o.performance(model = prostate_gbm, newdata=prostate) ## If model uses balance_classes ## the results from train = TRUE will not match the results from newdata = prostate prostate_gbm_balanced <- h2o.gbm(3:9, "CAPSULE", prostate, balance_classes = TRUE) h2o.performance(model = prostate_gbm_balanced, newdata = prostate) h2o.performance(model = prostate_gbm_balanced, train = TRUE) ## End(Not run)
When n_repeats == 1, the result is similar to the one from h2o.varimp(), i.e., it contains the following columns "Relative Importance", "Scaled Importance", and "Percentage".
h2o.permutation_importance( object, newdata, metric = c("AUTO", "AUC", "MAE", "MSE", "RMSE", "logloss", "mean_per_class_error", "PR_AUC"), n_samples = 10000, n_repeats = 1, features = NULL, seed = -1 )h2o.permutation_importance( object, newdata, metric = c("AUTO", "AUC", "MAE", "MSE", "RMSE", "logloss", "mean_per_class_error", "PR_AUC"), n_samples = 10000, n_repeats = 1, features = NULL, seed = -1 )
object |
A trained supervised H2O model. |
newdata |
Training frame of the model which is going to be permuted |
metric |
Metric to be used. One of "AUTO", "AUC", "MAE", "MSE", "RMSE", "logloss", "mean_per_class_error", "PR_AUC". Defaults to "AUTO". |
n_samples |
Number of samples to be evaluated. Use -1 to use the whole dataset. Defaults to 10 000. |
n_repeats |
Number of repeated evaluations. Defaults to 1. |
features |
Character vector of features to include in the permutation importance. Use NULL to include all. |
seed |
Seed for the random generator. Use -1 to pick a random seed. Defaults to -1. |
When n_repeats > 1, the individual columns correspond to the permutation variable importance values from individual runs which corresponds to the "Relative Importance" and also to the distance between the original prediction error and prediction error using a frame with a given feature permuted.
H2OTable with variable importance.
## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.importFile(prostate_path) prostate[, 2] <- as.factor(prostate[, 2]) model <- h2o.gbm(x = 3:9, y = 2, training_frame = prostate, distribution = "bernoulli") h2o.permutation_importance(model, prostate) ## End(Not run)## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.importFile(prostate_path) prostate[, 2] <- as.factor(prostate[, 2]) model <- h2o.gbm(x = 3:9, y = 2, training_frame = prostate, distribution = "bernoulli") h2o.permutation_importance(model, prostate) ## End(Not run)
This method plots either a bar plot or if n_repeats > 1 a box plot and returns the variable importance table.
h2o.permutation_importance_plot( object, newdata, metric = c("AUTO", "AUC", "MAE", "MSE", "RMSE", "logloss", "mean_per_class_error", "PR_AUC"), n_samples = 10000, n_repeats = 1, features = NULL, seed = -1, num_of_features = NULL )h2o.permutation_importance_plot( object, newdata, metric = c("AUTO", "AUC", "MAE", "MSE", "RMSE", "logloss", "mean_per_class_error", "PR_AUC"), n_samples = 10000, n_repeats = 1, features = NULL, seed = -1, num_of_features = NULL )
object |
A trained supervised H2O model. |
newdata |
Training frame of the model which is going to be permuted |
metric |
Metric to be used. One of "AUTO", "AUC", "MAE", "MSE", "RMSE", "logloss", "mean_per_class_error", "PR_AUC". Defaults to "AUTO". |
n_samples |
Number of samples to be evaluated. Use -1 to use the whole dataset. Defaults to 10 000. |
n_repeats |
Number of repeated evaluations. Defaults to 1. |
features |
Character vector of features to include in the permutation importance. Use NULL to include all. |
seed |
Seed for the random generator. Use -1 to pick a random seed. Defaults to -1. |
num_of_features |
The number of features shown in the plot (default is 10 or all if less than 10). |
H2OTable with variable importance.
## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.importFile(prostate_path) prostate[, 2] <- as.factor(prostate[, 2]) model <- h2o.gbm(x = 3:9, y = 2, training_frame = prostate, distribution = "bernoulli") h2o.permutation_importance_plot(model, prostate) ## End(Not run)## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.importFile(prostate_path) prostate[, 2] <- as.factor(prostate[, 2]) model <- h2o.gbm(x = 3:9, y = 2, training_frame = prostate, distribution = "bernoulli") h2o.permutation_importance_plot(model, prostate) ## End(Not run)
Pivot the frame designated by the three columns: index, column, and value. Index and column should be of type enum, int, or time. For cases of multiple indexes for a column label, the aggregation method is to pick the first occurrence in the data frame
h2o.pivot(x, index, column, value)h2o.pivot(x, index, column, value)
x |
an H2OFrame |
index |
the column where pivoted rows should be aligned on |
column |
the column to pivot |
value |
values of the pivoted table |
An H2OFrame with columns from the columns arg, aligned on the index arg, with values from values arg
## Not run: library(h2o) h2o.init() df = h2o.createFrame(rows = 1000, cols = 3, factors = 10, integer_fraction = 1.0/3, categorical_fraction = 1.0/3, missing_fraction = 0.0, seed = 123) df$C3 = h2o.abs(df$C3) h2o.pivot(df, index="C3", column="C2", value="C1") ## End(Not run)## Not run: library(h2o) h2o.init() df = h2o.createFrame(rows = 1000, cols = 3, factors = 10, integer_fraction = 1.0/3, categorical_fraction = 1.0/3, missing_fraction = 0.0, seed = 123) df$C3 = h2o.abs(df$C3) h2o.pivot(df, index="C3", column="C2", value="C1") ## End(Not run)
Principal components analysis of an H2O data frame using the power method to calculate the singular value decomposition of the Gram matrix.
h2o.prcomp( training_frame, x, model_id = NULL, validation_frame = NULL, ignore_const_cols = TRUE, score_each_iteration = FALSE, transform = c("NONE", "STANDARDIZE", "NORMALIZE", "DEMEAN", "DESCALE"), pca_method = c("GramSVD", "Power", "Randomized", "GLRM"), pca_impl = c("MTJ_EVD_DENSEMATRIX", "MTJ_EVD_SYMMMATRIX", "MTJ_SVD_DENSEMATRIX", "JAMA"), k = 1, max_iterations = 1000, use_all_factor_levels = FALSE, compute_metrics = TRUE, impute_missing = FALSE, seed = -1, max_runtime_secs = 0, export_checkpoints_dir = NULL )h2o.prcomp( training_frame, x, model_id = NULL, validation_frame = NULL, ignore_const_cols = TRUE, score_each_iteration = FALSE, transform = c("NONE", "STANDARDIZE", "NORMALIZE", "DEMEAN", "DESCALE"), pca_method = c("GramSVD", "Power", "Randomized", "GLRM"), pca_impl = c("MTJ_EVD_DENSEMATRIX", "MTJ_EVD_SYMMMATRIX", "MTJ_SVD_DENSEMATRIX", "JAMA"), k = 1, max_iterations = 1000, use_all_factor_levels = FALSE, compute_metrics = TRUE, impute_missing = FALSE, seed = -1, max_runtime_secs = 0, export_checkpoints_dir = NULL )
training_frame |
Id of the training data frame. |
x |
A vector containing the |
model_id |
Destination id for this model; auto-generated if not specified. |
validation_frame |
Id of the validation data frame. |
ignore_const_cols |
|
score_each_iteration |
|
transform |
Transformation of training data Must be one of: "NONE", "STANDARDIZE", "NORMALIZE", "DEMEAN", "DESCALE". Defaults to NONE. |
pca_method |
Specify the algorithm to use for computing the principal components: GramSVD - uses a distributed computation of the Gram matrix, followed by a local SVD; Power - computes the SVD using the power iteration method (experimental); Randomized - uses randomized subspace iteration method; GLRM - fits a generalized low-rank model with L2 loss function and no regularization and solves for the SVD using local matrix algebra (experimental) Must be one of: "GramSVD", "Power", "Randomized", "GLRM". Defaults to GramSVD. |
pca_impl |
Specify the implementation to use for computing PCA (via SVD or EVD): MTJ_EVD_DENSEMATRIX - eigenvalue decompositions for dense matrix using MTJ; MTJ_EVD_SYMMMATRIX - eigenvalue decompositions for symmetric matrix using MTJ; MTJ_SVD_DENSEMATRIX - singular-value decompositions for dense matrix using MTJ; JAMA - eigenvalue decompositions for dense matrix using JAMA. References: JAMA - http://math.nist.gov/javanumerics/jama/; MTJ - https://github.com/fommil/matrix-toolkits-java/ Must be one of: "MTJ_EVD_DENSEMATRIX", "MTJ_EVD_SYMMMATRIX", "MTJ_SVD_DENSEMATRIX", "JAMA". |
k |
Rank of matrix approximation Defaults to 1. |
max_iterations |
Maximum training iterations Defaults to 1000. |
use_all_factor_levels |
|
compute_metrics |
|
impute_missing |
|
seed |
Seed for random numbers (affects certain parts of the algo that are stochastic and those might or might not be enabled by default). Defaults to -1 (time-based random number). |
max_runtime_secs |
Maximum allowed runtime in seconds for model training. Use 0 to disable. Defaults to 0. |
export_checkpoints_dir |
Automatically export generated models to this directory. |
an object of class H2ODimReductionModel.
N. Halko, P.G. Martinsson, J.A. Tropp. Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions[http://arxiv.org/abs/0909.4061]. SIAM Rev., Survey and Review section, Vol. 53, num. 2, pp. 217-288, June 2011.
## Not run: library(h2o) h2o.init() australia_path <- system.file("extdata", "australia.csv", package = "h2o") australia <- h2o.uploadFile(path = australia_path) h2o.prcomp(training_frame = australia, k = 8, transform = "STANDARDIZE") ## End(Not run)## Not run: library(h2o) h2o.init() australia_path <- system.file("extdata", "australia.csv", package = "h2o") australia <- h2o.uploadFile(path = australia_path) h2o.prcomp(training_frame = australia, k = 8, transform = "STANDARDIZE") ## End(Not run)
Predict on an H2O Model
h2o.predict(object, newdata, ...)h2o.predict(object, newdata, ...)
object |
a fitted model object for which prediction is desired. |
newdata |
An H2OFrame object in which to look for variables with which to predict. |
... |
additional arguments to pass on. |
Returns an H2OFrame object with probabilites and default predictions.
Provides the method h2o.predict with which you can predict a MOJO or POJO Jar model from R.
h2o.predict_json(model, json, genmodelpath, labels, classpath, javaoptions)h2o.predict_json(model, json, genmodelpath, labels, classpath, javaoptions)
model |
String with file name of MOJO or POJO Jar |
json |
JSON String with inputs to model |
genmodelpath |
(Optional) path name to h2o-genmodel.jar, if not set defaults to same dir as MOJO |
labels |
(Optional) if TRUE then show output labels in result |
classpath |
(Optional) Extra items for the class path of where to look for Java classes, e.g., h2o-genmodel.jar |
javaoptions |
(Optional) Java options string, default if "-Xmx4g" |
Returns an object with the prediction result
## Not run: library(h2o) h2o.predict_json('~/GBM_model_python_1473313897851_6.zip', '{"C7":1}') h2o.predict_json('~/GBM_model_python_1473313897851_6.zip', '{"C7":1}', c(".", "lib")) ## End(Not run)## Not run: library(h2o) h2o.predict_json('~/GBM_model_python_1473313897851_6.zip', '{"C7":1}') h2o.predict_json('~/GBM_model_python_1473313897851_6.zip', '{"C7":1}', c(".", "lib")) ## End(Not run)
Evaluates validity of the given rules on the given data. Returns a frame with a column per each input rule id, representing a flag whether given rule is applied to the observation or not.
h2o.predict_rules(model, frame, rule_ids)h2o.predict_rules(model, frame, rule_ids)
model |
A trained rulefit model. |
frame |
A frame on which rule validity is to be evaluated |
rule_ids |
Rule ids to be evaluated against the frame |
## Not run: library(h2o) h2o.init() titanic <- h2o.importFile( "https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv" ) response = "survived" predictors <- c("age", "sibsp", "parch", "fare", "sex", "pclass") titanic[,response] <- as.factor(titanic[,response]) titanic[,"pclass"] <- as.factor(titanic[,"pclass"]) splits <- h2o.splitFrame(data = titanic, ratios = .8, seed = 1234) train <- splits[[1]] test <- splits[[2]] rfit <- h2o.rulefit(y = response, x = predictors, training_frame = train, validation_frame = test, min_rule_length = 1, max_rule_length = 10, max_num_rules = 100, seed = 1, model_type="rules") h2o.predict_rules(rfit, train, c("M1T0N7, M1T49N7, M1T16N7", "M1T36N7", "M2T19N19")) ## End(Not run)## Not run: library(h2o) h2o.init() titanic <- h2o.importFile( "https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv" ) response = "survived" predictors <- c("age", "sibsp", "parch", "fare", "sex", "pclass") titanic[,response] <- as.factor(titanic[,response]) titanic[,"pclass"] <- as.factor(titanic[,"pclass"]) splits <- h2o.splitFrame(data = titanic, ratios = .8, seed = 1234) train <- splits[[1]] test <- splits[[2]] rfit <- h2o.rulefit(y = response, x = predictors, training_frame = train, validation_frame = test, min_rule_length = 1, max_rule_length = 10, max_num_rules = 100, seed = 1, model_type="rules") h2o.predict_rules(rfit, train, c("M1T0N7, M1T49N7, M1T16N7", "M1T36N7", "M2T19N19")) ## End(Not run)
In the basic setting, this function is equivalent to doing group-by on variable and calculating mean on predicted and actual. In addition to that it also handles NAs in response and weights automatically.
h2o.predicted_vs_actual_by_variable(object, newdata, predicted, variable)h2o.predicted_vs_actual_by_variable(object, newdata, predicted, variable)
object |
A trained supervised H2O model. |
newdata |
Input frame (can be training/test/.. frame). |
predicted |
Frame of predictions for the given input frame. |
variable |
Name of variable to inspect. |
H2OTable
Print An H2OFrame
h2o.print(x, n = 6L)h2o.print(x, n = 6L)
x |
An H2OFrame object |
n |
An (Optional) A single integer. If positive, number of rows in x to return. If negative, all but the n first/last number of rows in x. Anything bigger than 20 rows will require asking the server (first 20 rows are cached on the client). |
## Not run: library() h2o.init() f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_train.csv" iris <- h2o.importFile(f) h2o.print(iris["species"], n = 15) ## End(Not run)## Not run: library() h2o.init() f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_train.csv" iris <- h2o.importFile(f) h2o.print(iris["species"], n = 15) ## End(Not run)
Return the product of all the values present in its arguments.
h2o.prod(x)h2o.prod(x)
x |
An H2OFrame object. |
prod for the base R implementation.
## Not run: library(h2o) h2o.init() f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_train.csv" iris <- h2o.importFile(f) h2o.prod(iris["petal_len"]) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_train.csv" iris <- h2o.importFile(f) h2o.prod(iris["petal_len"]) ## End(Not run)
Project each archetype in an H2O GLRM model into the corresponding feature space from the H2O training frame.
h2o.proj_archetypes(object, data, reverse_transform = FALSE)h2o.proj_archetypes(object, data, reverse_transform = FALSE)
object |
An H2ODimReductionModel object that represents the model containing archetypes to be projected. |
data |
An H2OFrame object representing the training data for the H2O GLRM model. |
reverse_transform |
(Optional) A logical value indicating whether to reverse the transformation from model-building by re-scaling columns and adding back the offset to each column of the projected archetypes. |
Returns an H2OFrame object containing the projection of the archetypes down into the original feature space, where each row is one archetype.
h2o.glrm for making an H2ODimReductionModel.
## Not run: library(h2o) h2o.init() iris_hf <- as.h2o(iris) iris_glrm <- h2o.glrm(training_frame = iris_hf, k = 4, loss = "Quadratic", multi_loss = "Categorical", max_iterations = 1000) iris_parch <- h2o.proj_archetypes(iris_glrm, iris_hf) head(iris_parch) ## End(Not run)## Not run: library(h2o) h2o.init() iris_hf <- as.h2o(iris) iris_glrm <- h2o.glrm(training_frame = iris_hf, k = 4, loss = "Quadratic", multi_loss = "Categorical", max_iterations = 1000) iris_parch <- h2o.proj_archetypes(iris_glrm, iris_hf) head(iris_parch) ## End(Not run)
Alpha version. Supports only binomial classification problems.
h2o.psvm( x, y, training_frame, model_id = NULL, validation_frame = NULL, ignore_const_cols = TRUE, hyper_param = 1, kernel_type = c("gaussian"), gamma = -1, rank_ratio = -1, positive_weight = 1, negative_weight = 1, disable_training_metrics = TRUE, sv_threshold = 1e-04, fact_threshold = 1e-05, feasible_threshold = 0.001, surrogate_gap_threshold = 0.001, mu_factor = 10, max_iterations = 200, seed = -1 )h2o.psvm( x, y, training_frame, model_id = NULL, validation_frame = NULL, ignore_const_cols = TRUE, hyper_param = 1, kernel_type = c("gaussian"), gamma = -1, rank_ratio = -1, positive_weight = 1, negative_weight = 1, disable_training_metrics = TRUE, sv_threshold = 1e-04, fact_threshold = 1e-05, feasible_threshold = 0.001, surrogate_gap_threshold = 0.001, mu_factor = 10, max_iterations = 200, seed = -1 )
x |
(Optional) A vector containing the names or indices of the predictor variables to use in building the model. If x is missing, then all columns except y are used. |
y |
The name or column index of the response variable in the data. The response must be either a binary categorical/factor variable or a numeric variable with values -1/1 (for compatibility with SVMlight format). |
training_frame |
Id of the training data frame. |
model_id |
Destination id for this model; auto-generated if not specified. |
validation_frame |
Id of the validation data frame. |
ignore_const_cols |
|
hyper_param |
Penalty parameter C of the error term Defaults to 1. |
kernel_type |
Type of used kernel Must be one of: "gaussian". Defaults to gaussian. |
gamma |
Coefficient of the kernel (currently RBF gamma for gaussian kernel, -1 means 1/#features) Defaults to -1. |
rank_ratio |
Desired rank of the ICF matrix expressed as an ration of number of input rows (-1 means use sqrt(#rows)). Defaults to -1. |
positive_weight |
Weight of positive (+1) class of observations Defaults to 1. |
negative_weight |
Weight of positive (-1) class of observations Defaults to 1. |
disable_training_metrics |
|
sv_threshold |
Threshold for accepting a candidate observation into the set of support vectors Defaults to 0.0001. |
fact_threshold |
Convergence threshold of the Incomplete Cholesky Factorization (ICF) Defaults to 1e-05. |
feasible_threshold |
Convergence threshold for primal-dual residuals in the IPM iteration Defaults to 0.001. |
surrogate_gap_threshold |
Feasibility criterion of the surrogate duality gap (eta) Defaults to 0.001. |
mu_factor |
Increasing factor mu Defaults to 10. |
max_iterations |
Maximum number of iteration of the algorithm Defaults to 200. |
seed |
Seed for random numbers (affects certain parts of the algo that are stochastic and those might or might not be enabled by default). Defaults to -1 (time-based random number). |
## Not run: library(h2o) h2o.init() # Import the splice dataset f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/splice/splice.svm" splice <- h2o.importFile(f) # Train the Support Vector Machine model svm_model <- h2o.psvm(gamma = 0.01, rank_ratio = 0.1, y = "C1", training_frame = splice, disable_training_metrics = FALSE) ## End(Not run)## Not run: library(h2o) h2o.init() # Import the splice dataset f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/splice/splice.svm" splice <- h2o.importFile(f) # Train the Support Vector Machine model svm_model <- h2o.psvm(gamma = 0.01, rank_ratio = 0.1, y = "C1", training_frame = splice, disable_training_metrics = FALSE) ## End(Not run)
Retrieves the Qini value from an H2OBinomialUpliftMetrics. If "train" and "valid" parameters are FALSE (default), then the training Qini value is returned. If more than one parameter is set to TRUE, then a named vector of Qini values are returned, where the names are "train", "valid".
h2o.qini(object, train = FALSE, valid = FALSE)h2o.qini(object, train = FALSE, valid = FALSE)
object |
An H2OBinomialUpliftMetrics or |
train |
Retrieve the training Qini value |
valid |
Retrieve the validation Qini |
## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/uplift/criteo_uplift_13k.csv" train <- h2o.importFile(f) train$treatment <- as.factor(train$treatment) train$conversion <- as.factor(train$conversion) model <- h2o.upliftRandomForest(training_frame=train, x=sprintf("f%s",seq(0:10)), y="conversion", ntrees=10, max_depth=5, treatment_column="treatment", auuc_type="AUTO") perf <- h2o.performance(model, train=TRUE) h2o.qini(perf) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/uplift/criteo_uplift_13k.csv" train <- h2o.importFile(f) train$treatment <- as.factor(train$treatment) train$conversion <- as.factor(train$conversion) model <- h2o.upliftRandomForest(training_frame=train, x=sprintf("f%s",seq(0:10)), y="conversion", ntrees=10, max_depth=5, treatment_column="treatment", auuc_type="AUTO") perf <- h2o.performance(model, train=TRUE) h2o.qini(perf) ## End(Not run)
Obtain and display quantiles for H2O parsed data.
h2o.quantile( x, probs = c(0.001, 0.01, 0.1, 0.25, 0.333, 0.5, 0.667, 0.75, 0.9, 0.99, 0.999), combine_method = c("interpolate", "average", "avg", "low", "high"), weights_column = NULL, ... ) ## S3 method for class 'H2OFrame' quantile( x, probs = c(0.001, 0.01, 0.1, 0.25, 0.333, 0.5, 0.667, 0.75, 0.9, 0.99, 0.999), combine_method = c("interpolate", "average", "avg", "low", "high"), weights_column = NULL, ... )h2o.quantile( x, probs = c(0.001, 0.01, 0.1, 0.25, 0.333, 0.5, 0.667, 0.75, 0.9, 0.99, 0.999), combine_method = c("interpolate", "average", "avg", "low", "high"), weights_column = NULL, ... ) ## S3 method for class 'H2OFrame' quantile( x, probs = c(0.001, 0.01, 0.1, 0.25, 0.333, 0.5, 0.667, 0.75, 0.9, 0.99, 0.999), combine_method = c("interpolate", "average", "avg", "low", "high"), weights_column = NULL, ... )
x |
An |
probs |
Numeric vector of probabilities with values in [0,1]. |
combine_method |
How to combine quantiles for even sample sizes. Default is to do linear interpolation. E.g., If method is "lo", then it will take the lo value of the quantile. Abbreviations for average, low, and high are acceptable (avg, lo, hi). |
weights_column |
(Optional) String name of the observation weights column in x or an |
... |
Further arguments passed to or from other methods. |
quantile.H2OFrame, a method for the quantile generic. Obtain and return quantiles for
an H2OFrame object.
A vector describing the percentiles at the given cutoffs for the H2OFrame object.
## Not run: # Request quantiles for an H2O parsed data set: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(path = prostate_path) # Request quantiles for a subset of columns in an H2O parsed data set quantile(prostate[, 3]) for(i in 1:ncol(prostate)) quantile(prostate[, i]) ## End(Not run)## Not run: # Request quantiles for an H2O parsed data set: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(path = prostate_path) # Request quantiles for a subset of columns in an H2O parsed data set quantile(prostate[, 3]) for(i in 1:ncol(prostate)) quantile(prostate[, i]) ## End(Not run)
Retrieves the R2 value from an H2O model. Will return R^2 for GLM Models. If "train", "valid", and "xval" parameters are FALSE (default), then the training R2 value is returned. If more than one parameter is set to TRUE, then a named vector of R2s are returned, where the names are "train", "valid" or "xval".
h2o.r2(object, train = FALSE, valid = FALSE, xval = FALSE)h2o.r2(object, train = FALSE, valid = FALSE, xval = FALSE)
object |
An H2OModel object. |
train |
Retrieve the training R2 |
valid |
Retrieve the validation set R2 if a validation set was passed in during model build time. |
xval |
Retrieve the cross-validation R2 |
## Not run: library(h2o) h <- h2o.init() fr <- as.h2o(iris) m <- h2o.glm(x = 2:5, y = 1, training_frame = fr) h2o.r2(m) ## End(Not run)## Not run: library(h2o) h <- h2o.init() fr <- as.h2o(iris) m <- h2o.glm(x = 2:5, y = 1, training_frame = fr) h2o.r2(m) ## End(Not run)
Builds a Random Forest model on an H2OFrame.
h2o.randomForest( x, y, training_frame, model_id = NULL, validation_frame = NULL, nfolds = 0, keep_cross_validation_models = TRUE, keep_cross_validation_predictions = FALSE, keep_cross_validation_fold_assignment = FALSE, score_each_iteration = FALSE, score_tree_interval = 0, fold_assignment = c("AUTO", "Random", "Modulo", "Stratified"), fold_column = NULL, ignore_const_cols = TRUE, offset_column = NULL, weights_column = NULL, balance_classes = FALSE, class_sampling_factors = NULL, max_after_balance_size = 5, ntrees = 50, max_depth = 20, min_rows = 1, nbins = 20, nbins_top_level = 1024, nbins_cats = 1024, r2_stopping = Inf, stopping_rounds = 0, stopping_metric = c("AUTO", "deviance", "logloss", "MSE", "RMSE", "MAE", "RMSLE", "AUC", "AUCPR", "lift_top_group", "misclassification", "mean_per_class_error", "custom", "custom_increasing"), stopping_tolerance = 0.001, max_runtime_secs = 0, seed = -1, build_tree_one_node = FALSE, mtries = -1, sample_rate = 0.632, sample_rate_per_class = NULL, binomial_double_trees = FALSE, checkpoint = NULL, col_sample_rate_change_per_level = 1, col_sample_rate_per_tree = 1, min_split_improvement = 1e-05, histogram_type = c("AUTO", "UniformAdaptive", "Random", "QuantilesGlobal", "RoundRobin", "UniformRobust"), categorical_encoding = c("AUTO", "Enum", "OneHotInternal", "OneHotExplicit", "Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited"), calibrate_model = FALSE, calibration_frame = NULL, calibration_method = c("AUTO", "PlattScaling", "IsotonicRegression"), distribution = c("AUTO", "bernoulli", "multinomial", "gaussian", "poisson", "gamma", "tweedie", "laplace", "quantile", "huber"), custom_metric_func = NULL, export_checkpoints_dir = NULL, check_constant_response = TRUE, gainslift_bins = -1, auc_type = c("AUTO", "NONE", "MACRO_OVR", "WEIGHTED_OVR", "MACRO_OVO", "WEIGHTED_OVO"), verbose = FALSE )h2o.randomForest( x, y, training_frame, model_id = NULL, validation_frame = NULL, nfolds = 0, keep_cross_validation_models = TRUE, keep_cross_validation_predictions = FALSE, keep_cross_validation_fold_assignment = FALSE, score_each_iteration = FALSE, score_tree_interval = 0, fold_assignment = c("AUTO", "Random", "Modulo", "Stratified"), fold_column = NULL, ignore_const_cols = TRUE, offset_column = NULL, weights_column = NULL, balance_classes = FALSE, class_sampling_factors = NULL, max_after_balance_size = 5, ntrees = 50, max_depth = 20, min_rows = 1, nbins = 20, nbins_top_level = 1024, nbins_cats = 1024, r2_stopping = Inf, stopping_rounds = 0, stopping_metric = c("AUTO", "deviance", "logloss", "MSE", "RMSE", "MAE", "RMSLE", "AUC", "AUCPR", "lift_top_group", "misclassification", "mean_per_class_error", "custom", "custom_increasing"), stopping_tolerance = 0.001, max_runtime_secs = 0, seed = -1, build_tree_one_node = FALSE, mtries = -1, sample_rate = 0.632, sample_rate_per_class = NULL, binomial_double_trees = FALSE, checkpoint = NULL, col_sample_rate_change_per_level = 1, col_sample_rate_per_tree = 1, min_split_improvement = 1e-05, histogram_type = c("AUTO", "UniformAdaptive", "Random", "QuantilesGlobal", "RoundRobin", "UniformRobust"), categorical_encoding = c("AUTO", "Enum", "OneHotInternal", "OneHotExplicit", "Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited"), calibrate_model = FALSE, calibration_frame = NULL, calibration_method = c("AUTO", "PlattScaling", "IsotonicRegression"), distribution = c("AUTO", "bernoulli", "multinomial", "gaussian", "poisson", "gamma", "tweedie", "laplace", "quantile", "huber"), custom_metric_func = NULL, export_checkpoints_dir = NULL, check_constant_response = TRUE, gainslift_bins = -1, auc_type = c("AUTO", "NONE", "MACRO_OVR", "WEIGHTED_OVR", "MACRO_OVO", "WEIGHTED_OVO"), verbose = FALSE )
x |
(Optional) A vector containing the names or indices of the predictor variables to use in building the model. If x is missing, then all columns except y are used. |
y |
The name or column index of the response variable in the data. The response must be either a numeric or a categorical/factor variable. If the response is numeric, then a regression model will be trained, otherwise it will train a classification model. |
training_frame |
Id of the training data frame. |
model_id |
Destination id for this model; auto-generated if not specified. |
validation_frame |
Id of the validation data frame. |
nfolds |
Number of folds for K-fold cross-validation (0 to disable or >= 2). Defaults to 0. |
keep_cross_validation_models |
|
keep_cross_validation_predictions |
|
keep_cross_validation_fold_assignment |
|
score_each_iteration |
|
score_tree_interval |
Score the model after every so many trees. Disabled if set to 0. Defaults to 0. |
fold_assignment |
Cross-validation fold assignment scheme, if fold_column is not specified. The 'Stratified' option will stratify the folds based on the response variable, for classification problems. Must be one of: "AUTO", "Random", "Modulo", "Stratified". Defaults to AUTO. |
fold_column |
Column with cross-validation fold index assignment per observation. |
ignore_const_cols |
|
offset_column |
Offset column. This argument is deprecated and has no use for Random Forest. |
weights_column |
Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0. |
balance_classes |
|
class_sampling_factors |
Desired over/under-sampling ratios per class (in lexicographic order). If not specified, sampling factors will be automatically computed to obtain class balance during training. Requires balance_classes. |
max_after_balance_size |
Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires balance_classes. Defaults to 5.0. |
ntrees |
Number of trees. Defaults to 50. |
max_depth |
Maximum tree depth (0 for unlimited). Defaults to 20. |
min_rows |
Fewest allowed (weighted) observations in a leaf. Defaults to 1. |
nbins |
For numerical columns (real/int), build a histogram of (at least) this many bins, then split at the best point Defaults to 20. |
nbins_top_level |
For numerical columns (real/int), build a histogram of (at most) this many bins at the root level, then decrease by factor of two per level Defaults to 1024. |
nbins_cats |
For categorical columns (factors), build a histogram of this many bins, then split at the best point. Higher values can lead to more overfitting. Defaults to 1024. |
r2_stopping |
r2_stopping is no longer supported and will be ignored if set - please use stopping_rounds, stopping_metric and stopping_tolerance instead. Previous version of H2O would stop making trees when the R^2 metric equals or exceeds this Defaults to 1.797693135e+308. |
stopping_rounds |
Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable) Defaults to 0. |
stopping_metric |
Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client. Must be one of: "AUTO", "deviance", "logloss", "MSE", "RMSE", "MAE", "RMSLE", "AUC", "AUCPR", "lift_top_group", "misclassification", "mean_per_class_error", "custom", "custom_increasing". Defaults to AUTO. |
stopping_tolerance |
Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much) Defaults to 0.001. |
max_runtime_secs |
Maximum allowed runtime in seconds for model training. Use 0 to disable. Defaults to 0. |
seed |
Seed for random numbers (affects certain parts of the algo that are stochastic and those might or might not be enabled by default). Defaults to -1 (time-based random number). |
build_tree_one_node |
|
mtries |
Number of variables randomly sampled as candidates at each split. If set to -1, defaults to sqrt{p} for classification and p/3 for regression (where p is the # of predictors Defaults to -1. |
sample_rate |
Row sample rate per tree (from 0.0 to 1.0) Defaults to 0.632. |
sample_rate_per_class |
A list of row sample rates per class (relative fraction for each class, from 0.0 to 1.0), for each tree |
binomial_double_trees |
|
checkpoint |
Model checkpoint to resume training with. |
col_sample_rate_change_per_level |
Relative change of the column sampling rate for every level (must be > 0.0 and <= 2.0) Defaults to 1. |
col_sample_rate_per_tree |
Column sample rate per tree (from 0.0 to 1.0) Defaults to 1. |
min_split_improvement |
Minimum relative improvement in squared error reduction for a split to happen Defaults to 1e-05. |
histogram_type |
What type of histogram to use for finding optimal split points Must be one of: "AUTO", "UniformAdaptive", "Random", "QuantilesGlobal", "RoundRobin", "UniformRobust". Defaults to AUTO. |
categorical_encoding |
Encoding scheme for categorical features Must be one of: "AUTO", "Enum", "OneHotInternal", "OneHotExplicit", "Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited". Defaults to AUTO. |
calibrate_model |
|
calibration_frame |
Data for model calibration |
calibration_method |
Calibration method to use Must be one of: "AUTO", "PlattScaling", "IsotonicRegression". Defaults to AUTO. |
distribution |
Distribution. This argument is deprecated and has no use for Random Forest. |
custom_metric_func |
Reference to custom evaluation function, format: 'language:keyName=funcName' |
export_checkpoints_dir |
Automatically export generated models to this directory. |
check_constant_response |
|
gainslift_bins |
Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning. Defaults to -1. |
auc_type |
Set default multinomial AUC type. Must be one of: "AUTO", "NONE", "MACRO_OVR", "WEIGHTED_OVR", "MACRO_OVO", "WEIGHTED_OVO". Defaults to AUTO. |
verbose |
|
Creates a H2OModel object of the right type.
predict.H2OModel for prediction
## Not run: library(h2o) h2o.init() # Import the cars dataset f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv" cars <- h2o.importFile(f) # Set predictors and response; set response as a factor cars["economy_20mpg"] <- as.factor(cars["economy_20mpg"]) predictors <- c("displacement", "power", "weight", "acceleration", "year") response <- "economy_20mpg" # Train the DRF model cars_drf <- h2o.randomForest(x = predictors, y = response, training_frame = cars, nfolds = 5, seed = 1234) ## End(Not run)## Not run: library(h2o) h2o.init() # Import the cars dataset f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv" cars <- h2o.importFile(f) # Set predictors and response; set response as a factor cars["economy_20mpg"] <- as.factor(cars["economy_20mpg"]) predictors <- c("displacement", "power", "weight", "acceleration", "year") response <- "economy_20mpg" # Train the DRF model cars_drf <- h2o.randomForest(x = predictors, y = response, training_frame = cars, nfolds = 5, seed = 1234) ## End(Not run)
Returns a vector containing the minimum and maximum of all the given arguments.
h2o.range(x, na.rm = FALSE, finite = FALSE)h2o.range(x, na.rm = FALSE, finite = FALSE)
x |
An H2OFrame object. |
na.rm |
|
finite |
|
range for the base R implementation.
## Not run: library(h2o) h2o.init() f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_train.csv" iris <- h2o.importFile(f) h2o.range(iris["petal_len"], na.rm = TRUE, finite = TRUE) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_train.csv" iris <- h2o.importFile(f) h2o.range(iris["petal_len"], na.rm = TRUE, finite = TRUE) ## End(Not run)
This function will add a new column rank where the ranking is produced as follows: 1. sorts the H2OFrame by columns sorted in by columns specified in group_by_cols and sort_cols in the directions specified by the ascending for the sort_cols. The sort directions for the group_by_cols are ascending only. 2. A new rank column is added to the frame which will contain a rank assignment performed next. The user can choose to assign a name to this new column. The default name is New_Rank_column. 3. For each groupby groups, a rank is assigned to the row starting from 1, 2, ... to the end of that group. 4. If sort_cols_sorted is TRUE, a final sort on the frame will be performed frame according to the sort_cols and the sort directions in ascending. If sort_cols_sorted is FALSE (by default), the frame from step 3 will be returned as is with no extra sort. This may provide a small speedup if desired.
h2o.rank_within_group_by( x, group_by_cols, sort_cols, ascending = NULL, new_col_name = "New_Rank_column", sort_cols_sorted = FALSE )h2o.rank_within_group_by( x, group_by_cols, sort_cols, ascending = NULL, new_col_name = "New_Rank_column", sort_cols_sorted = FALSE )
x |
The H2OFrame input to be sorted. |
group_by_cols |
a list of column names or indices to form the groupby groups |
sort_cols |
a list of column names or indices for sorting |
ascending |
a list of Boolean to determine if ascending sort (set to TRUE) is needed for each column in sort_cols (optional). Default is ascending sort for all. To perform descending sort, set value to FALSE |
new_col_name |
new column name for the newly added rank column if specified (optional). Default name is New_Rank_column. |
sort_cols_sorted |
Boolean to determine if the final returned frame is to be sorted according to the sort_cols and sort directions in ascending. Default is FALSE. The following example is generated by Nidhi Mehta. If the input frame is train: ID Group_by_column num data Column_to_arrange_by num_1 fdata 12 1 2941.552 1 3 -3177.9077 1 12 1 2941.552 1 5 -13311.8247 1 12 2 -22722.174 1 3 -3177.9077 1 12 2 -22722.174 1 5 -13311.8247 1 13 3 -12776.884 1 5 -18421.6171 0 13 3 -12776.884 1 4 28080.1607 0 13 1 -6049.830 1 5 -18421.6171 0 13 1 -6049.830 1 4 28080.1607 0 15 3 -16995.346 1 1 -9781.6373 0 16 1 -10003.593 0 3 -61284.6900 0 16 3 26052.495 1 3 -61284.6900 0 16 3 -22905.288 0 3 -61284.6900 0 17 2 -13465.496 1 2 12094.4851 1 17 2 -13465.496 1 3 -11772.1338 1 17 2 -13465.496 1 3 -415.1114 0 17 2 -3329.619 1 2 12094.4851 1 17 2 -3329.619 1 3 -11772.1338 1 17 2 -3329.619 1 3 -415.1114 0 If the following commands are issued: rankedF1 <- h2o.rank_within_group_by(train, c("Group_by_column"), c("Column_to_arrange_by"), c(TRUE)) h2o.summary(rankedF1) The returned frame rankedF1 will look like this: ID Group_by_column num fdata Column_to_arrange_by num_1 fdata.1 New_Rank_column 12 1 2941.552 1 3 -3177.9077 1 1 16 1 -10003.593 0 3 -61284.6900 0 2 13 1 -6049.830 0 4 28080.1607 0 3 12 1 2941.552 1 5 -13311.8247 1 4 13 1 -6049.830 0 5 -18421.6171 0 5 17 2 -13465.496 0 2 12094.4851 1 1 17 2 -3329.619 0 2 12094.4851 1 2 12 2 -22722.174 1 3 -3177.9077 1 3 17 2 -13465.496 0 3 -11772.1338 1 4 17 2 -13465.496 0 3 -415.1114 0 5 17 2 -3329.619 0 3 -11772.1338 1 6 17 2 -3329.619 0 3 -415.1114 0 7 12 2 -22722.174 1 5 -13311.8247 1 8 15 3 -16995.346 1 1 -9781.6373 0 1 16 3 26052.495 0 3 -61284.6900 0 2 16 3 -22905.288 1 3 -61284.6900 0 3 13 3 -12776.884 1 4 28080.1607 0 4 13 3 -12776.884 1 5 -18421.6171 0 5 If the following commands are issued: rankedF1 <- h2o.rank_within_group_by(train, c("Group_by_column"), c("Column_to_arrange_by"), c(TRUE), sort_cols_sorted=TRUE) h2o.summary(rankedF1) The returned frame will be sorted according to sortCols and hence look like this instead: ID Group_by_column num fdata Column_to_arrange_by num_1 fdata.1 New_Rank_column 15 3 -16995.346 1 1 -9781.6373 0 1 17 2 -13465.496 0 2 12094.4851 1 1 17 2 -3329.619 0 2 12094.4851 1 2 12 1 2941.552 1 3 -3177.9077 1 1 12 2 -22722.174 1 3 -3177.9077 1 3 16 1 -10003.593 0 3 -61284.6900 0 2 16 3 26052.495 0 3 -61284.6900 0 2 16 3 -22905.288 1 3 -61284.6900 0 3 17 2 -13465.496 0 3 -11772.1338 1 4 17 2 -13465.496 0 3 -415.1114 0 5 17 2 -3329.619 0 3 -11772.1338 1 6 17 2 -3329.619 0 3 -415.1114 0 7 13 3 -12776.884 1 4 28080.1607 0 4 13 1 -6049.830 0 4 28080.1607 0 3 12 1 2941.552 1 5 -13311.8247 1 4 12 2 -22722.174 1 5 -13311.8247 1 8 13 3 -12776.884 1 5 -18421.6171 0 5 13 1 -6049.830 0 5 -18421.6171 0 5 |
## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip" air <- h2o.importFile(f) group_cols <- c("Distance") sort_cols <- c("IsArrDelayed", "IsDepDelayed") sort_directions <- c(TRUE, FALSE) h2o.rank_within_group_by(x = air, group_by_cols = group_cols, sort_cols = sort_cols, ascending = sort_directions, new_col_name = "New_Rank", sort_cols_sorted = TRUE) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip" air <- h2o.importFile(f) group_cols <- c("Distance") sort_cols <- c("IsArrDelayed", "IsDepDelayed") sort_directions <- c(TRUE, FALSE) h2o.rank_within_group_by(x = air, group_by_cols = group_cols, sort_cols = sort_cols, ascending = sort_directions, new_col_name = "New_Rank", sort_cols_sorted = TRUE) ## End(Not run)
Execute a Rapids expression.
h2o.rapids(expr)h2o.rapids(expr)
expr |
The rapids expression (ascii string) |
## Not run: h2o.rapids('(setproperty "sys.ai.h2o.algos.evaluate_auto_model_parameters" "true")') ## End(Not run)## Not run: h2o.rapids('(setproperty "sys.ai.h2o.algos.evaluate_auto_model_parameters" "true")') ## End(Not run)
Takes a sequence of H2O data sets and combines them by rows
h2o.rbind(...)h2o.rbind(...)
... |
A sequence of H2OFrame arguments. All datasets must exist on the same H2O instance (IP and port) and contain the same number and types of columns. |
An H2OFrame object containing the combined ... arguments row-wise.
cbind for the base R method, rbind().
## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(path = prostate_path) prostate_rbind <- h2o.rbind(prostate, prostate) head(prostate_rbind) dim(prostate) dim(prostate_rbind) ## End(Not run)## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(path = prostate_path) prostate_rbind <- h2o.rbind(prostate, prostate) head(prostate_rbind) dim(prostate) dim(prostate_rbind) ## End(Not run)
Reconstruct the training data and impute missing values from the H2O GLRM model by computing the matrix product of X and Y, and transforming back to the original feature space by minimizing each column's loss function.
h2o.reconstruct(object, data, reverse_transform = FALSE)h2o.reconstruct(object, data, reverse_transform = FALSE)
object |
An H2ODimReductionModel object that represents the model to be used for reconstruction. |
data |
An H2OFrame object representing the training data for the H2O GLRM model. Used to set the domain of each column in the reconstructed frame. |
reverse_transform |
(Optional) A logical value indicating whether to reverse the transformation from model-building by re-scaling columns and adding back the offset to each column of the reconstructed frame. |
Returns an H2OFrame object containing the approximate reconstruction of the training data;
h2o.glrm for making an H2ODimReductionModel.
## Not run: library(h2o) h2o.init() iris_hf <- as.h2o(iris) iris_glrm <- h2o.glrm(training_frame = iris_hf, k = 4, transform = "STANDARDIZE", loss = "Quadratic", multi_loss = "Categorical", max_iterations = 1000) iris_rec <- h2o.reconstruct(iris_glrm, iris_hf, reverse_transform = TRUE) head(iris_rec) ## End(Not run)## Not run: library(h2o) h2o.init() iris_hf <- as.h2o(iris) iris_glrm <- h2o.glrm(training_frame = iris_hf, k = 4, transform = "STANDARDIZE", loss = "Quadratic", multi_loss = "Categorical", max_iterations = 1000) iris_rec <- h2o.reconstruct(iris_glrm, iris_hf, reverse_transform = TRUE) head(iris_rec) ## End(Not run)
The levels of a factor are reordered os that the reference level is at level 0, remaining levels are moved down as needed.
h2o.relevel(x, y)h2o.relevel(x, y)
x |
factor column in h2o frame |
y |
reference level (string) |
new reordered factor column
## Not run: library(h2o) h2o.init() # Convert iris dataset to an H2OFrame iris_hf <- as.h2o(iris) # Look at current ordering of the Species column levels h2o.levels(iris_hf["Species"]) # "setosa" "versicolor" "virginica" # Change the reference level to "virginica" iris_hf["Species"] <- h2o.relevel(x = iris_hf["Species"], y = "virginica") # Observe new ordering h2o.levels(iris_hf["Species"]) # "virginica" "setosa" "versicolor" ## End(Not run)## Not run: library(h2o) h2o.init() # Convert iris dataset to an H2OFrame iris_hf <- as.h2o(iris) # Look at current ordering of the Species column levels h2o.levels(iris_hf["Species"]) # "setosa" "versicolor" "virginica" # Change the reference level to "virginica" iris_hf["Species"] <- h2o.relevel(x = iris_hf["Species"], y = "virginica") # Observe new ordering h2o.levels(iris_hf["Species"]) # "virginica" "setosa" "versicolor" ## End(Not run)
The levels of a factor are reordered so that the most frequency level is at level 0, remaining levels are ordered from the second most frequent to the least frequent.
h2o.relevel_by_frequency(x, weights_column = NULL, top_n = -1)h2o.relevel_by_frequency(x, weights_column = NULL, top_n = -1)
x |
H2O frame with some factor columns |
weights_column |
optional name of weights column |
top_n |
optional number of most frequent levels to move to the top (eg.: for top_n=1 move only the most frequent level) |
new reordered frame
## Not run: library(h2o) h2o.init() # Convert iris dataset to an H2OFrame iris_hf <- as.h2o(iris) # Look at current ordering of the Species column levels h2o.levels(iris_hf["Species"]) # "setosa" "versicolor" "virginica" # Change the reference level to "virginica" iris_hf["Species"] <- h2o.relevel_by_frequency(x = iris_hf["Species"]) # Observe new ordering h2o.levels(iris_hf["Species"]) # "virginica" "versicolor" "setosa" ## End(Not run)## Not run: library(h2o) h2o.init() # Convert iris dataset to an H2OFrame iris_hf <- as.h2o(iris) # Look at current ordering of the Species column levels h2o.levels(iris_hf["Species"]) # "setosa" "versicolor" "virginica" # Change the reference level to "virginica" iris_hf["Species"] <- h2o.relevel_by_frequency(x = iris_hf["Species"]) # Observe new ordering h2o.levels(iris_hf["Species"]) # "virginica" "versicolor" "setosa" ## End(Not run)
Removes the data from the h2o cluster, but does not remove the local references. Retains models, frames and vectors specified in retained_elements argument. Retained elements must be instances/ids of models and frames only. For models retained, training and validation frames are retained as well. Cross validation models of a retained model are NOT retained automatically, those must be specified explicitely.
h2o.removeAll(timeout_secs = 0, retained_elements = c())h2o.removeAll(timeout_secs = 0, retained_elements = c())
timeout_secs |
Timeout in seconds. Default is no timeout. |
retained_elements |
Instances or ids of models and frames to be retained. Combination of instances and ids in the same list is also a valid input. |
## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(path = prostate_path) h2o.ls() h2o.removeAll() h2o.ls() ## End(Not run)## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(path = prostate_path) h2o.ls() h2o.removeAll() h2o.ls() ## End(Not run)
Delete the specified columns from the H2OFrame. Returns an H2OFrame without the specified columns.
h2o.removeVecs(data, cols)h2o.removeVecs(data, cols)
data |
The H2OFrame. |
cols |
The columns to remove. |
h2o.rep_len performs just as rep does. It replicates the values in
x in the H2O backend.
h2o.rep_len(x, length.out)h2o.rep_len(x, length.out)
x |
an H2O frame |
length.out |
non negative integer. The desired length of the output vector. |
Creates an H2OFrame of the same type as x
## Not run: library(h2o) h2o.init() f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_train.csv" iris <- h2o.importFile(f) h2o.rep_len(iris, length.out = 3) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_train.csv" iris <- h2o.importFile(f) h2o.rep_len(iris, length.out = 3) ## End(Not run)
Reset model threshold and return old threshold value.
h2o.reset_threshold(object, threshold)h2o.reset_threshold(object, threshold)
object |
An H2OModel object. |
threshold |
A threshold value from 0 to 1 included. |
Returns the previous threshold used in the model.
## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.importFile(prostate_path) prostate[, 2] <- as.factor(prostate[, 2]) prostate_glm <- h2o.glm(y = "CAPSULE", x = c("AGE", "RACE", "PSA", "DCAPS"), training_frame = prostate, family = "binomial", nfolds = 0, alpha = 0.5, lambda_search = FALSE) old_threshold <- h2o.reset_threshold(prostate_glm, 0.9) ## End(Not run)## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.importFile(prostate_path) prostate[, 2] <- as.factor(prostate[, 2]) prostate_glm <- h2o.glm(y = "CAPSULE", x = c("AGE", "RACE", "PSA", "DCAPS"), training_frame = prostate, family = "binomial", nfolds = 0, alpha = 0.5, lambda_search = FALSE) old_threshold <- h2o.reset_threshold(prostate_glm, 0.9) ## End(Not run)
Do Residual Analysis and plot the fitted values vs residuals on a test dataset. Ideally, residuals should be randomly distributed. Patterns in this plot can indicate potential problems with the model selection, e.g., using simpler model than necessary, not accounting for heteroscedasticity, autocorrelation, etc. If you notice "striped" lines of residuals, that is just an indication that your response variable was integer valued instead of real valued.
h2o.residual_analysis_plot(model, newdata)h2o.residual_analysis_plot(model, newdata)
model |
An H2OModel. |
newdata |
An H2OFrame. Used to calculate residuals. |
A ggplot2 object
## Not run: library(h2o) h2o.init() # Import the wine dataset into H2O: f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/wine/winequality-redwhite-no-BOM.csv" df <- h2o.importFile(f) # Set the response response <- "quality" # Split the dataset into a train and test set: splits <- h2o.splitFrame(df, ratios = 0.8, seed = 1) train <- splits[[1]] test <- splits[[2]] # Build and train the model: gbm <- h2o.gbm(y = response, training_frame = train) # Create the residual analysis plot residual_analysis_plot <- h2o.residual_analysis_plot(gbm, test) print(residual_analysis_plot) ## End(Not run)## Not run: library(h2o) h2o.init() # Import the wine dataset into H2O: f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/wine/winequality-redwhite-no-BOM.csv" df <- h2o.importFile(f) # Set the response response <- "quality" # Split the dataset into a train and test set: splits <- h2o.splitFrame(df, ratios = 0.8, seed = 1) train <- splits[[1]] test <- splits[[2]] # Build and train the model: gbm <- h2o.gbm(y = response, training_frame = train) # Create the residual analysis plot residual_analysis_plot <- h2o.residual_analysis_plot(gbm, test) print(residual_analysis_plot) ## End(Not run)
If "train", "valid", and "xval" parameters are FALSE (default), then the training residual deviance value is returned. If more than one parameter is set to TRUE, then a named vector of residual deviances are returned, where the names are "train", "valid" or "xval".
h2o.residual_deviance(object, train = FALSE, valid = FALSE, xval = FALSE)h2o.residual_deviance(object, train = FALSE, valid = FALSE, xval = FALSE)
object |
An H2OModel or H2OModelMetrics |
train |
Retrieve the training residual deviance |
valid |
Retrieve the validation residual deviance |
xval |
Retrieve the cross-validation residual deviance |
## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.importFile(prostate_path) prostate[, 2] <- as.factor(prostate[, 2]) prostate_glm <- h2o.glm(y = "CAPSULE", x = c("AGE", "RACE", "PSA", "DCAPS"), training_frame = prostate, family = "binomial", nfolds = 0, alpha = 0.5, lambda_search = FALSE) h2o.residual_deviance(prostate_glm, train = TRUE) ## End(Not run)## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.importFile(prostate_path) prostate[, 2] <- as.factor(prostate[, 2]) prostate_glm <- h2o.glm(y = "CAPSULE", x = c("AGE", "RACE", "PSA", "DCAPS"), training_frame = prostate, family = "binomial", nfolds = 0, alpha = 0.5, lambda_search = FALSE) h2o.residual_deviance(prostate_glm, train = TRUE) ## End(Not run)
If "train", "valid", and "xval" parameters are FALSE (default), then the training residual degrees of freedom value is returned. If more than one parameter is set to TRUE, then a named vector of residual degrees of freedom are returned, where the names are "train", "valid" or "xval".
h2o.residual_dof(object, train = FALSE, valid = FALSE, xval = FALSE)h2o.residual_dof(object, train = FALSE, valid = FALSE, xval = FALSE)
object |
An H2OModel or H2OModelMetrics |
train |
Retrieve the training residual degrees of freedom |
valid |
Retrieve the validation residual degrees of freedom |
xval |
Retrieve the cross-validation residual degrees of freedom |
## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.importFile(prostate_path) prostate[, 2] <- as.factor(prostate[, 2]) prostate_glm <- h2o.glm(y = "CAPSULE", x = c("AGE", "RACE", "PSA", "DCAPS"), training_frame = prostate, family = "binomial", nfolds = 0, alpha = 0.5, lambda_search = FALSE) h2o.residual_dof(prostate_glm, train = TRUE) ## End(Not run)## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.importFile(prostate_path) prostate[, 2] <- as.factor(prostate[, 2]) prostate_glm <- h2o.glm(y = "CAPSULE", x = c("AGE", "RACE", "PSA", "DCAPS"), training_frame = prostate, family = "binomial", nfolds = 0, alpha = 0.5, lambda_search = FALSE) h2o.residual_dof(prostate_glm, train = TRUE) ## End(Not run)
Retrieve the results to view the best predictor subsets.
h2o.result(model)h2o.result(model)
model |
H2OModelSelection object |
Returns an H2OFrame object
Triggers auto-recovery resume - this will look into configured recovery dir and resume and tasks that were interrupted by unexpected cluster stopping.
h2o.resume(recovery_dir = NULL)h2o.resume(recovery_dir = NULL)
recovery_dir |
A |
Resume previously stopped grid training.
h2o.resumeGrid(grid_id, recovery_dir = NULL, ...)h2o.resumeGrid(grid_id, recovery_dir = NULL, ...)
grid_id |
ID of existing grid search |
recovery_dir |
When specified the grid and all necessary data (frames, models) will be saved to this
directory (use HDFS or other distributed file-system). Should the cluster crash during training, the grid
can be reloaded from this directory via |
... |
Additional parameters to modify the resumed Grid. |
Remove the h2o Big Data object(s) having the key name(s) from ids.
h2o.rm(ids, cascade = TRUE)h2o.rm(ids, cascade = TRUE)
ids |
The object or hex key associated with the object to be removed or a vector/list of those things. |
cascade |
Boolean, if set to TRUE (default), the object dependencies (e.g. submodels) are also removed. |
## Not run: library(h2o) h2o.init() iris <- as.h2o(iris) model <- h2o.glm(1:4,5,training = iris, family = "multinomial") h2o.rm(iris) ## End(Not run)## Not run: library(h2o) h2o.init() iris <- as.h2o(iris) model <- h2o.glm(1:4,5,training = iris, family = "multinomial") h2o.rm(iris) ## End(Not run)
Retrieves the root mean squared error value from an H2OModelMetrics object. If "train", "valid", and "xval" parameters are FALSE (default), then the training RMSEvalue is returned. If more than one parameter is set to TRUE, then a named vector of RMSEs are returned, where the names are "train", "valid" or "xval".
h2o.rmse(object, train = FALSE, valid = FALSE, xval = FALSE)h2o.rmse(object, train = FALSE, valid = FALSE, xval = FALSE)
object |
An H2OModelMetrics object of the correct type. |
train |
Retrieve the training RMSE |
valid |
Retrieve the validation RMSE |
xval |
Retrieve the cross-validation RMSE |
This function only supports H2OBinomialMetrics, H2OMultinomialMetrics, and H2ORegressionMetrics objects.
h2o.auc for AUC, h2o.mse for RMSE, and
h2o.metric for the various threshold metrics. See
h2o.performance for creating H2OModelMetrics objects.
## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(prostate_path) prostate[, 2] <- as.factor(prostate[, 2]) model <- h2o.gbm(x = 3:9, y = 2, training_frame = prostate, distribution = "bernoulli") perf <- h2o.performance(model, prostate) h2o.rmse(perf) ## End(Not run)## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(prostate_path) prostate[, 2] <- as.factor(prostate[, 2]) model <- h2o.gbm(x = 3:9, y = 2, training_frame = prostate, distribution = "bernoulli") perf <- h2o.performance(model, prostate) h2o.rmse(perf) ## End(Not run)
Retrieves the root mean squared log error (RMSLE) value from an H2O model. If "train", "valid", and "xval" parameters are FALSE (default), then the training rmsle value is returned. If more than one parameter is set to TRUE, then a named vector of rmsles are returned, where the names are "train", "valid" or "xval".
h2o.rmsle(object, train = FALSE, valid = FALSE, xval = FALSE)h2o.rmsle(object, train = FALSE, valid = FALSE, xval = FALSE)
object |
An H2OModel object. |
train |
Retrieve the training rmsle |
valid |
Retrieve the validation set rmsle if a validation set was passed in during model build time. |
xval |
Retrieve the cross-validation rmsle |
## Not run: library(h2o) h <- h2o.init() fr <- as.h2o(iris) m <- h2o.deeplearning(x = 2:5, y = 1, training_frame = fr) h2o.rmsle(m) ## End(Not run)## Not run: library(h2o) h <- h2o.init() fr <- as.h2o(iris) m <- h2o.deeplearning(x = 2:5, y = 1, training_frame = fr) h2o.rmsle(m) ## End(Not run)
Round doubles/floats to the given number of decimal places.
h2o.round(x, digits = 0) round(x, digits = 0)h2o.round(x, digits = 0) round(x, digits = 0)
x |
An H2OFrame object. |
digits |
Number of decimal places to round doubles/floats. Rounding to a negative number of decimal places is |
Round for the base R implementation, round().
## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/coxph_test/heart.csv" heart <- h2o.importFile(f) h2o.round(heart["age"], digits = 3) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/coxph_test/heart.csv" heart <- h2o.importFile(f) h2o.round(heart["age"], digits = 3) ## End(Not run)
Return a copy of the target column with trailing characters removed. The set argument is a string specifying the set of characters to be removed. If omitted, the set argument defaults to removing whitespace.
h2o.rstrip(x, set = " ")h2o.rstrip(x, set = " ")
x |
The column whose strings should be rstrip-ed. |
set |
string of characters to be removed |
## Not run: library(h2o) h2o.init() string_to_rstrip <- as.h2o("1234567890") rstrip_string <- h2o.rstrip(string_to_rstrip, "890") #Remove "890" ## End(Not run)## Not run: library(h2o) h2o.init() string_to_rstrip <- as.h2o("1234567890") rstrip_string <- h2o.rstrip(string_to_rstrip, "890") #Remove "890" ## End(Not run)
This function returns the table with estimated coefficients and language representations (in case it is a rule) for each of the significant baselearners.
h2o.rule_importance(model)h2o.rule_importance(model)
model |
of the interest |
Builds a Distributed RuleFit model on a parsed dataset, for regression or classification.
h2o.rulefit( x, y, training_frame, model_id = NULL, validation_frame = NULL, seed = -1, algorithm = c("AUTO", "DRF", "GBM"), min_rule_length = 3, max_rule_length = 3, max_num_rules = -1, model_type = c("rules_and_linear", "rules", "linear"), weights_column = NULL, distribution = c("AUTO", "bernoulli", "multinomial", "gaussian", "poisson", "gamma", "tweedie", "laplace", "quantile", "huber"), rule_generation_ntrees = 50, auc_type = c("AUTO", "NONE", "MACRO_OVR", "WEIGHTED_OVR", "MACRO_OVO", "WEIGHTED_OVO"), remove_duplicates = TRUE, lambda = NULL, max_categorical_levels = 10 )h2o.rulefit( x, y, training_frame, model_id = NULL, validation_frame = NULL, seed = -1, algorithm = c("AUTO", "DRF", "GBM"), min_rule_length = 3, max_rule_length = 3, max_num_rules = -1, model_type = c("rules_and_linear", "rules", "linear"), weights_column = NULL, distribution = c("AUTO", "bernoulli", "multinomial", "gaussian", "poisson", "gamma", "tweedie", "laplace", "quantile", "huber"), rule_generation_ntrees = 50, auc_type = c("AUTO", "NONE", "MACRO_OVR", "WEIGHTED_OVR", "MACRO_OVO", "WEIGHTED_OVO"), remove_duplicates = TRUE, lambda = NULL, max_categorical_levels = 10 )
x |
(Optional) A vector containing the names or indices of the predictor variables to use in building the model. If x is missing, then all columns except y are used. |
y |
The name or column index of the response variable in the data. The response must be either a numeric or a categorical/factor variable. If the response is numeric, then a regression model will be trained, otherwise it will train a classification model. |
training_frame |
Id of the training data frame. |
model_id |
Destination id for this model; auto-generated if not specified. |
validation_frame |
Id of the validation data frame. |
seed |
Seed for random numbers (affects certain parts of the algo that are stochastic and those might or might not be enabled by default). Defaults to -1 (time-based random number). |
algorithm |
The algorithm to use to generate rules. Must be one of: "AUTO", "DRF", "GBM". Defaults to AUTO. |
min_rule_length |
Minimum length of rules. Defaults to 3. |
max_rule_length |
Maximum length of rules. Defaults to 3. |
max_num_rules |
The maximum number of rules to return. defaults to -1 which means the number of rules is selected by diminishing returns in model deviance. Defaults to -1. |
model_type |
Specifies type of base learners in the ensemble. Must be one of: "rules_and_linear", "rules", "linear". Defaults to rules_and_linear. |
weights_column |
Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0. |
distribution |
Distribution function Must be one of: "AUTO", "bernoulli", "multinomial", "gaussian", "poisson", "gamma", "tweedie", "laplace", "quantile", "huber". Defaults to AUTO. |
rule_generation_ntrees |
Specifies the number of trees to build in the tree model. Defaults to 50. Defaults to 50. |
auc_type |
Set default multinomial AUC type. Must be one of: "AUTO", "NONE", "MACRO_OVR", "WEIGHTED_OVR", "MACRO_OVO", "WEIGHTED_OVO". Defaults to AUTO. |
remove_duplicates |
|
lambda |
Lambda for LASSO regressor. |
max_categorical_levels |
For every categorical feature, only use this many most frequent categorical levels for model training. Only used for categorical_encoding == EnumLimited. Defaults to 10. |
## Not run: library(h2o) h2o.init() # Import the titanic dataset: f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv" coltypes <- list(by.col.name = c("pclass", "survived"), types=c("Enum", "Enum")) df <- h2o.importFile(f, col.types = coltypes) # Split the dataset into train and test splits <- h2o.splitFrame(data = df, ratios = 0.8, seed = 1) train <- splits[[1]] test <- splits[[2]] # Set the predictors and response; set the factors: response <- "survived" predictors <- c("age", "sibsp", "parch", "fare", "sex", "pclass") # Build and train the model: rfit <- h2o.rulefit(y = response, x = predictors, training_frame = train, max_rule_length = 10, max_num_rules = 100, seed = 1) # Retrieve the rule importance: print(rfit@model$rule_importance) # Predict on the test data: h2o.predict(rfit, newdata = test) ## End(Not run)## Not run: library(h2o) h2o.init() # Import the titanic dataset: f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv" coltypes <- list(by.col.name = c("pclass", "survived"), types=c("Enum", "Enum")) df <- h2o.importFile(f, col.types = coltypes) # Split the dataset into train and test splits <- h2o.splitFrame(data = df, ratios = 0.8, seed = 1) train <- splits[[1]] test <- splits[[2]] # Set the predictors and response; set the factors: response <- "survived" predictors <- c("age", "sibsp", "parch", "fare", "sex", "pclass") # Build and train the model: rfit <- h2o.rulefit(y = response, x = predictors, training_frame = train, max_rule_length = 10, max_num_rules = 100, seed = 1) # Retrieve the rule importance: print(rfit@model$rule_importance) # Predict on the test data: h2o.predict(rfit, newdata = test) ## End(Not run)
Creates a vector of random uniform numbers equal in length to the length of the specified H2O dataset.
h2o.runif(x, seed = -1)h2o.runif(x, seed = -1)
x |
An H2OFrame object. |
seed |
A random seed used to generate draws from the uniform distribution. |
A vector of random, uniformly distributed numbers. The elements are between 0 and 1.
## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.importFile(path = prostate_path) s <- h2o.runif(prostate) summary(s) prostate_train <- prostate[s <= 0.8,] prostate_test <- prostate[s > 0.8,] nrow(prostate_train) + nrow(prostate_test) ## End(Not run)## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.importFile(path = prostate_path) s <- h2o.runif(prostate) summary(s) prostate_train <- prostate[s <= 0.8,] prostate_test <- prostate[s > 0.8,] nrow(prostate_train) + nrow(prostate_test) ## End(Not run)
Store frame data in H2O's native format.
h2o.save_frame(x, dir, force = TRUE)h2o.save_frame(x, dir, force = TRUE)
x |
An H2OFrame object |
dir |
a filesystem location where to write frame data (hdfs, nfs) |
force |
|
## Not run: library(h2o) h2o.init() prostate_path = system.file("extdata", "prostate.csv", package = "h2o") prostate = h2o.importFile(path = prostate_path) h2o.save_frame(prostate, "/tmp/prostate") ## End(Not run)## Not run: library(h2o) h2o.init() prostate_path = system.file("extdata", "prostate.csv", package = "h2o") prostate = h2o.importFile(path = prostate_path) h2o.save_frame(prostate, "/tmp/prostate") ## End(Not run)
Save an MOJO (Model Object, Optimized) to disk.
h2o.save_mojo(object, path = "", force = FALSE, filename = "")h2o.save_mojo(object, path = "", force = FALSE, filename = "")
object |
an H2OModel object. |
path |
string indicating the directory the model will be written to. |
force |
logical, indicates how to deal with files that already exist. |
filename |
string indicating the file name. (Type of file is always .zip) |
MOJO will download as a zip file. In the case of existing files force = TRUE
will overwrite the file. Otherwise, the operation will fail.
h2o.saveModel for saving a model to disk as a binary object.
## Not run: # library(h2o) # h2o.init() # prostate <- h2o.uploadFile(path = system.file("extdata", "prostate.csv", package="h2o")) # prostate_glm <- h2o.glm(y = "CAPSULE", x = c("AGE", "RACE", "PSA", "DCAPS"), # training_frame = prostate, family = "binomial", alpha = 0.5) # h2o.save_mojo(object = prostate_glm, path = "/Users/UserName/Desktop", force = TRUE) ## End(Not run)## Not run: # library(h2o) # h2o.init() # prostate <- h2o.uploadFile(path = system.file("extdata", "prostate.csv", package="h2o")) # prostate_glm <- h2o.glm(y = "CAPSULE", x = c("AGE", "RACE", "PSA", "DCAPS"), # training_frame = prostate, family = "binomial", alpha = 0.5) # h2o.save_mojo(object = prostate_glm, path = "/Users/UserName/Desktop", force = TRUE) ## End(Not run)
For example, h2o.save_to_hive(data_frame, "jdbc:hive2://host:10000/database", "table_name") h2o.save_to_hive(data_frame, "jdbc:hive2://host:10000/", "database.table_name", format = "parquet")
h2o.save_to_hive( data, jdbc_url, table_name, format = "csv", table_path = NULL, tmp_path = NULL )h2o.save_to_hive( data, jdbc_url, table_name, format = "csv", table_path = NULL, tmp_path = NULL )
data |
A H2O Frame object to be saved. |
jdbc_url |
Hive JDBC connection URL. |
table_name |
Table name into which to store the data. The table must not exist as it will be created |
format |
Storage format of created Hive table. (default csv, can be csv or parquet) |
table_path |
If specified, the table will be created as an external table and this is where the data |
tmp_path |
Path where to store temporary data. |
Returns a reference to the saved Grid.
h2o.saveGrid( grid_directory, grid_id, save_params_references = FALSE, export_cross_validation_predictions = FALSE )h2o.saveGrid( grid_directory, grid_id, save_params_references = FALSE, export_cross_validation_predictions = FALSE )
grid_directory |
A character string containing the path to the folder for the grid to be saved to. |
grid_id |
A chracter string with identification of the grid to be saved. |
save_params_references |
A logical indicating if objects referenced by grid parameters (e.g. training frame, calibration frame) should also be saved. |
export_cross_validation_predictions |
A logical indicating whether exported model artifacts should also include CV holdout Frame predictions. |
Returns an object that is a subclass of H2OGrid.
## Not run: library(h2o) h2o.init() iris <- as.h2o(iris) ntrees_opts = c(1, 5) learn_rate_opts = c(0.1, 0.01) size_of_hyper_space = length(ntrees_opts) * length(learn_rate_opts) hyper_parameters = list(ntrees = ntrees_opts, learn_rate = learn_rate_opts) # Tempdir is chosen arbitrarily. May be any valid folder on an H2O-supported filesystem. baseline_grid <- h2o.grid(algorithm = "gbm", grid_id = "gbm_grid_test", x = 1:4, y = 5, training_frame = iris, hyper_params = hyper_parameters) grid_path <- h2o.saveGrid(grid_directory = tempdir(), grid_id = baseline_grid@grid_id) # Remove everything from the cluster or restart it h2o.removeAll() grid <- h2o.loadGrid(grid_path) ## End(Not run)## Not run: library(h2o) h2o.init() iris <- as.h2o(iris) ntrees_opts = c(1, 5) learn_rate_opts = c(0.1, 0.01) size_of_hyper_space = length(ntrees_opts) * length(learn_rate_opts) hyper_parameters = list(ntrees = ntrees_opts, learn_rate = learn_rate_opts) # Tempdir is chosen arbitrarily. May be any valid folder on an H2O-supported filesystem. baseline_grid <- h2o.grid(algorithm = "gbm", grid_id = "gbm_grid_test", x = 1:4, y = 5, training_frame = iris, hyper_params = hyper_parameters) grid_path <- h2o.saveGrid(grid_directory = tempdir(), grid_id = baseline_grid@grid_id) # Remove everything from the cluster or restart it h2o.removeAll() grid <- h2o.loadGrid(grid_path) ## End(Not run)
Save an H2OModel to disk. (Note that ensemble binary models can be saved.)
h2o.saveModel( object, path = "", force = FALSE, export_cross_validation_predictions = FALSE, filename = "" )h2o.saveModel( object, path = "", force = FALSE, export_cross_validation_predictions = FALSE, filename = "" )
object |
an H2OModel object. |
path |
string indicating the directory the model will be written to. |
force |
logical, indicates how to deal with files that already exist. |
export_cross_validation_predictions |
logical, indicates whether the exported model artifacts should also include CV Holdout Frame predictions. Default is not to export the predictions. |
filename |
string indicating the file name. |
In the case of existing files force = TRUE will overwrite the file.
Otherwise, the operation will fail.
The owner of the file saved is the user by which H2O cluster was executed.
h2o.loadModel for loading a model to H2O from disk
## Not run: # library(h2o) # h2o.init() # prostate <- h2o.importFile(path = paste("https://raw.github.com", # "h2oai/h2o-2/master/smalldata/logreg/prostate.csv", sep = "/")) # prostate_glm <- h2o.glm(y = "CAPSULE", x = c("AGE", "RACE", "PSA", "DCAPS"), # training_frame = prostate, family = "binomial", alpha = 0.5) # h2o.saveModel(object = prostate_glm, path = "/Users/UserName/Desktop", force = TRUE) ## End(Not run)## Not run: # library(h2o) # h2o.init() # prostate <- h2o.importFile(path = paste("https://raw.github.com", # "h2oai/h2o-2/master/smalldata/logreg/prostate.csv", sep = "/")) # prostate_glm <- h2o.glm(y = "CAPSULE", x = c("AGE", "RACE", "PSA", "DCAPS"), # training_frame = prostate, family = "binomial", alpha = 0.5) # h2o.saveModel(object = prostate_glm, path = "/Users/UserName/Desktop", force = TRUE) ## End(Not run)
Save Model Details of an H2O Model in JSON Format
h2o.saveModelDetails(object, path = "", force = FALSE, filename = "")h2o.saveModelDetails(object, path = "", force = FALSE, filename = "")
object |
an H2OModel object. |
path |
string indicating the directory the model details will be written to. |
force |
logical, indicates how to deal with files that already exist. |
filename |
string indicating the file name. (Type of file is always .json) |
Model Details will download as a JSON file. In the case of existing files force = TRUE
will overwrite the file. Otherwise, the operation will fail.
## Not run: # library(h2o) # h2o.init() # prostate <- h2o.uploadFile(path = system.file("extdata", "prostate.csv", package = "h2o")) # prostate_glm <- h2o.glm(y = "CAPSULE", x = c("AGE", "RACE", "PSA", "DCAPS"), # training_frame = prostate, family = "binomial", alpha = 0.5) # h2o.saveModelDetails(object = prostate_glm, path = "/Users/UserName/Desktop", force = TRUE) ## End(Not run)## Not run: # library(h2o) # h2o.init() # prostate <- h2o.uploadFile(path = system.file("extdata", "prostate.csv", package = "h2o")) # prostate_glm <- h2o.glm(y = "CAPSULE", x = c("AGE", "RACE", "PSA", "DCAPS"), # training_frame = prostate, family = "binomial", alpha = 0.5) # h2o.saveModelDetails(object = prostate_glm, path = "/Users/UserName/Desktop", force = TRUE) ## End(Not run)
Save an MOJO (Model Object, Optimized) to disk.
h2o.saveMojo(object, path = "", force = FALSE)h2o.saveMojo(object, path = "", force = FALSE)
object |
an H2OModel object. |
path |
string indicating the directory the model will be written to. |
force |
logical, indicates how to deal with files that already exist. |
MOJO will download as a zip file. In the case of existing files force = TRUE
will overwrite the file. Otherwise, the operation will fail.
h2o.saveModel for saving a model to disk as a binary object.
## Not run: # library(h2o) # h2o.init() # prostate <- h2o.uploadFile(path = system.file("extdata", "prostate.csv", package="h2o")) # prostate_glm <- h2o.glm(y = "CAPSULE", x = c("AGE", "RACE", "PSA", "DCAPS"), # training_frame = prostate, family = "binomial", alpha = 0.5) # h2o.saveMojo(object = prostate_glm, path = "/Users/UserName/Desktop", force = TRUE) ## End(Not run)## Not run: # library(h2o) # h2o.init() # prostate <- h2o.uploadFile(path = system.file("extdata", "prostate.csv", package="h2o")) # prostate_glm <- h2o.glm(y = "CAPSULE", x = c("AGE", "RACE", "PSA", "DCAPS"), # training_frame = prostate, family = "binomial", alpha = 0.5) # h2o.saveMojo(object = prostate_glm, path = "/Users/UserName/Desktop", force = TRUE) ## End(Not run)
Centers and/or scales the columns of an H2O dataset.
h2o.scale(x, center = TRUE, scale = TRUE, inplace = FALSE)h2o.scale(x, center = TRUE, scale = TRUE, inplace = FALSE)
x |
An H2OFrame object. |
center |
either a |
scale |
either a |
inplace |
a |
## Not run: library(h2o) h2o.init() iris_hf <- as.h2o(iris) summary(iris_hf) # Scale and center all the numeric columns in iris data set iris_scaled <- h2o.scale(iris_hf[, 1:4]) ## End(Not run)## Not run: library(h2o) h2o.init() iris_hf <- as.h2o(iris) summary(iris_hf) # Scale and center all the numeric columns in iris data set iris_scaled <- h2o.scale(iris_hf[, 1:4]) ## End(Not run)
Retrieve Model Score History
h2o.scoreHistory(object)h2o.scoreHistory(object)
object |
An H2OModel object. |
## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv" cars <- h2o.importFile(f) cars["economy_20mpg"] <- as.factor(cars["economy_20mpg"]) predictors <- c("displacement", "power", "weight", "acceleration", "year") response <- "economy_20mpg" cars_split <- h2o.splitFrame(data = cars, ratios = 0.8, seed = 1234) train <- cars_split[[1]] valid <- cars_split[[2]] cars_gbm <- h2o.gbm(x = predictors, y = response, training_frame = train, validation_frame = valid, seed = 1234) h2o.scoreHistory(cars_gbm) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv" cars <- h2o.importFile(f) cars["economy_20mpg"] <- as.factor(cars["economy_20mpg"]) predictors <- c("displacement", "power", "weight", "acceleration", "year") response <- "economy_20mpg" cars_split <- h2o.splitFrame(data = cars, ratios = 0.8, seed = 1234) train <- cars_split[[1]] valid <- cars_split[[2]] cars_gbm <- h2o.gbm(x = predictors, y = response, training_frame = train, validation_frame = valid, seed = 1234) h2o.scoreHistory(cars_gbm) ## End(Not run)
Retrieve GLM Model Score History buried in GAM model
h2o.scoreHistoryGAM(object)h2o.scoreHistoryGAM(object)
object |
An H2OModel object. |
Scree Plot
h2o.screeplot(model, type = c("barplot", "lines"))h2o.screeplot(model, type = c("barplot", "lines"))
model |
A PCA model |
type |
Type of the plot. Either "barplot" or "lines". |
Obtain the standard deviation of a column of data.
h2o.sd(x, na.rm = FALSE) sd(x, na.rm = FALSE)h2o.sd(x, na.rm = FALSE) sd(x, na.rm = FALSE)
x |
An H2OFrame object. |
na.rm |
|
h2o.var for variance, and sd for the base R implementation.
## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(path = prostate_path) sd(prostate$AGE) ## End(Not run)## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(path = prostate_path) sd(prostate$AGE) ## End(Not run)
Retrieve the standard deviations of principal components
h2o.sdev(object)h2o.sdev(object)
object |
An H2ODimReductionModel object. |
## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv" cars <- h2o.importFile(f) predictors <- c("displacement", "power", "weight", "acceleration", "year") cars_pca <- h2o.prcomp(cars, transform = "STANDARDIZE", k = 3, x = predictors, seed = 12345) h2o.sdev(cars_pca) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv" cars <- h2o.importFile(f) predictors <- c("displacement", "power", "weight", "acceleration", "year") cars_pca <- h2o.prcomp(cars, transform = "STANDARDIZE", k = 3, x = predictors, seed = 12345) h2o.sdev(cars_pca) ## End(Not run)
There are no validations done to the credentials. Incorrect credentials are thus revealed with first S3 import call.
h2o.set_s3_credentials(secretKeyId, secretAccessKey, sessionToken = NULL)h2o.set_s3_credentials(secretKeyId, secretAccessKey, sessionToken = NULL)
secretKeyId |
Amazon S3 Secret Key ID (provided by Amazon) |
secretAccessKey |
Amazon S3 Secret Access Key (provided by Amazon) |
sessionToken |
Amazon Session Token (optional, only when using AWS Temporary Credentials) |
Works on a single categorical vector. New domains must be aligned with the old domains. This call has SIDE EFFECTS and mutates the column in place (change of the levels will also affect all the frames that are referencing this column). If you want to make a copy of the column instead, use parameter in.place = FALSE.
h2o.setLevels(x, levels, in.place = TRUE)h2o.setLevels(x, levels, in.place = TRUE)
x |
A single categorical column. |
levels |
A character vector specifying the new levels. The number of new levels must match the number of old levels. |
in.place |
Indicates whether new domain will be directly applied to the column (in place change) or if a copy of the column will be created with the given domain levels. |
## Not run: library(h2o) h2o.init() iris_hf <- as.h2o(iris) new_levels <- c("setosa", "versicolor", "caroliniana") iris_hf$Species <- h2o.setLevels(iris_hf$Species, new_levels, in.place = FALSE) h2o.levels(iris_hf$Species) ## End(Not run)## Not run: library(h2o) h2o.init() iris_hf <- as.h2o(iris) new_levels <- c("setosa", "versicolor", "caroliniana") iris_hf$Species <- h2o.setLevels(iris_hf$Species, new_levels, in.place = FALSE) h2o.levels(iris_hf$Species) ## End(Not run)
Set the Time Zone on the H2O cluster
h2o.setTimezone(tz)h2o.setTimezone(tz)
tz |
The desired timezone. |
## Not run: library(h2o) h2o.init() h2o.setTimezone("America/Juneau") h2o.getTimezone() ## End(Not run)## Not run: library(h2o) h2o.init() h2o.setTimezone("America/Juneau") h2o.getTimezone() ## End(Not run)
SHAP explanation shows contribution of features for a given instance. The sum of the feature contributions and the bias term is equal to the raw prediction of the model, i.e., prediction before applying inverse link function. H2O implements TreeSHAP which when the features are correlated, can increase contribution of a feature that had no influence on the prediction.
h2o.shap_explain_row_plot( model, newdata, row_index, columns = NULL, top_n_features = 10, plot_type = c("barplot", "breakdown"), contribution_type = c("both", "positive", "negative"), background_frame = NULL )h2o.shap_explain_row_plot( model, newdata, row_index, columns = NULL, top_n_features = 10, plot_type = c("barplot", "breakdown"), contribution_type = c("both", "positive", "negative"), background_frame = NULL )
model |
An H2O tree-based model. This includes Random Forest, GBM and XGboost only. Must be a binary classification or regression model. |
newdata |
An H2O Frame, used to determine feature contributions. |
row_index |
Instance row index. |
columns |
List of columns or list of indices of columns to show.
If specified, then the |
top_n_features |
Integer specifying the maximum number of columns to show (ranked by their contributions).
When |
plot_type |
Either "barplot" or "breakdown". Defaults to "barplot". |
contribution_type |
When |
background_frame |
Optional frame, that is used as the source of baselines for the marginal SHAP. |
A ggplot2 object.
## Not run: library(h2o) h2o.init() # Import the wine dataset into H2O: f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/wine/winequality-redwhite-no-BOM.csv" df <- h2o.importFile(f) # Set the response response <- "quality" # Split the dataset into a train and test set: splits <- h2o.splitFrame(df, ratios = 0.8, seed = 1) train <- splits[[1]] test <- splits[[2]] # Build and train the model: gbm <- h2o.gbm(y = response, training_frame = train) # Create the SHAP row explanation plot shap_explain_row_plot <- h2o.shap_explain_row_plot(gbm, test, row_index = 1) print(shap_explain_row_plot) ## End(Not run)## Not run: library(h2o) h2o.init() # Import the wine dataset into H2O: f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/wine/winequality-redwhite-no-BOM.csv" df <- h2o.importFile(f) # Set the response response <- "quality" # Split the dataset into a train and test set: splits <- h2o.splitFrame(df, ratios = 0.8, seed = 1) train <- splits[[1]] test <- splits[[2]] # Build and train the model: gbm <- h2o.gbm(y = response, training_frame = train) # Create the SHAP row explanation plot shap_explain_row_plot <- h2o.shap_explain_row_plot(gbm, test, row_index = 1) print(shap_explain_row_plot) ## End(Not run)
SHAP summary plot shows the contribution of the features for each instance (row of data). The sum of the feature contributions and the bias term is equal to the raw prediction of the model, i.e., prediction before applying inverse link function.
h2o.shap_summary_plot( model, newdata, columns = NULL, top_n_features = 20, sample_size = 1000, background_frame = NULL )h2o.shap_summary_plot( model, newdata, columns = NULL, top_n_features = 20, sample_size = 1000, background_frame = NULL )
model |
An H2O tree-based model. This includes Random Forest, GBM and XGboost only. Must be a binary classification or regression model. |
newdata |
An H2O Frame, used to determine feature contributions. |
columns |
List of columns or list of indices of columns to show.
If specified, then the |
top_n_features |
Integer specifying the maximum number of columns to show (ranked by variable importance). |
sample_size |
Integer specifying the maximum number of observations to be plotted. |
background_frame |
Optional frame, that is used as the source of baselines for the marginal SHAP. |
A ggplot2 object
## Not run: library(h2o) h2o.init() # Import the wine dataset into H2O: f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/wine/winequality-redwhite-no-BOM.csv" df <- h2o.importFile(f) # Set the response response <- "quality" # Split the dataset into a train and test set: splits <- h2o.splitFrame(df, ratios = 0.8, seed = 1) train <- splits[[1]] test <- splits[[2]] # Build and train the model: gbm <- h2o.gbm(y = response, training_frame = train) # Create the SHAP summary plot shap_summary_plot <- h2o.shap_summary_plot(gbm, test) print(shap_summary_plot) ## End(Not run)## Not run: library(h2o) h2o.init() # Import the wine dataset into H2O: f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/wine/winequality-redwhite-no-BOM.csv" df <- h2o.importFile(f) # Set the response response <- "quality" # Split the dataset into a train and test set: splits <- h2o.splitFrame(df, ratios = 0.8, seed = 1) train <- splits[[1]] test <- splits[[2]] # Build and train the model: gbm <- h2o.gbm(y = response, training_frame = train) # Create the SHAP summary plot shap_summary_plot <- h2o.shap_summary_plot(gbm, test) print(shap_summary_plot) ## End(Not run)
Enable Progress Bar
h2o.show_progress(expr)h2o.show_progress(expr)
expr |
When specified enable progress only for the evaluation of the expr and after the evaluation return to the previous setting (default is to show the progress bar), otherwise enable it globally. |
Value of expr if specified, otherwise NULL.
## Not run: library(h2o) h2o.init() h2o.no_progress() f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_wheader.csv" iris <- h2o.importFile(f) iris["class"] <- as.factor(iris["class"]) predictors <- c("sepal_len", "sepal_wid", "petal_len", "petal_wid") splits <- h2o.splitFrame(iris, ratios = 0.8, seed = 1234) train <- splits[[1]] valid <- splits[[2]] h2o.show_progress() iris_km <- h2o.kmeans(x = predictors, training_frame = train, validation_frame = valid, k = 10, estimate_k = TRUE, standardize = FALSE, seed = 1234) ## End(Not run)## Not run: library(h2o) h2o.init() h2o.no_progress() f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_wheader.csv" iris <- h2o.importFile(f) iris["class"] <- as.factor(iris["class"]) predictors <- c("sepal_len", "sepal_wid", "petal_len", "petal_wid") splits <- h2o.splitFrame(iris, ratios = 0.8, seed = 1234) train <- splits[[1]] valid <- splits[[2]] h2o.show_progress() iris_km <- h2o.kmeans(x = predictors, training_frame = train, validation_frame = valid, k = 10, estimate_k = TRUE, standardize = FALSE, seed = 1234) ## End(Not run)
Shut down the specified instance. All data will be lost.
h2o.shutdown(prompt = TRUE)h2o.shutdown(prompt = TRUE)
prompt |
A |
This method checks if H2O is running at the specified IP address and port, and if it is, shuts down that H2O instance.
All data, models, and other values stored on the server will be lost! Only call this function if you and all other clients connected to the H2O server are finished and have saved your work.
Users must call h2o.shutdown explicitly in order to shut down the local H2O instance started by R. If R is closed before H2O, then an attempt will be made to automatically shut down H2O. This only applies to local instances started with h2o.init, not remote H2O servers.
# Don't run automatically to prevent accidentally shutting down a cluster ## Not run: library(h2o) h2o.init() h2o.shutdown() ## End(Not run)# Don't run automatically to prevent accidentally shutting down a cluster ## Not run: library(h2o) h2o.init() h2o.shutdown() ## End(Not run)
Round doubles/floats to the given number of significant digits.
h2o.signif(x, digits = 6) signif(x, digits = 6)h2o.signif(x, digits = 6) signif(x, digits = 6)
x |
An H2OFrame object. |
digits |
Number of significant digits to round doubles/floats. |
Round for the base R implementation, signif().
## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/coxph_test/heart.csv" heart <- h2o.importFile(f) h2o.signif(heart["age"], digits = 3) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/coxph_test/heart.csv" heart <- h2o.importFile(f) h2o.signif(heart["age"], digits = 3) ## End(Not run)
Compute the sine of x
h2o.sin(x)h2o.sin(x)
x |
An H2OFrame object. |
Trig for the base R implementation, sin().
## Not run: library(h2o) h2o.init() frame <- h2o.createFrame(rows = 6, cols = 2, categorical_fraction = 0.0, missing_fraction = 0.7, seed = 123) h2o.sin(frame) ## End(Not run)## Not run: library(h2o) h2o.init() frame <- h2o.createFrame(rows = 6, cols = 2, categorical_fraction = 0.0, missing_fraction = 0.7, seed = 123) h2o.sin(frame) ## End(Not run)
Obtain the skewness of a column of a parsed H2O data object.
h2o.skewness(x, ..., na.rm = TRUE) skewness.H2OFrame(x, ..., na.rm = TRUE)h2o.skewness(x, ..., na.rm = TRUE) skewness.H2OFrame(x, ..., na.rm = TRUE)
x |
An H2OFrame object. |
... |
Further arguments to be passed from or to other methods. |
na.rm |
A logical value indicating whether |
Returns a list containing the skewness for each column (NaN for non-numeric columns).
## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(path = prostate_path) h2o.skewness(prostate$AGE) ## End(Not run)## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(path = prostate_path) h2o.skewness(prostate$AGE) ## End(Not run)
Split an existing H2O data set according to user-specified ratios. The number of subsets is always 1 more than the number of given ratios. Note that this does not give an exact split. H2O is designed to be efficient on big data using a probabilistic splitting method rather than an exact split. For example, when specifying a split of 0.75/0.25, H2O will produce a test/train split with an expected value of 0.75/0.25 rather than exactly 0.75/0.25. On small datasets, the sizes of the resulting splits will deviate from the expected value more than on big data, where they will be very close to exact.
h2o.splitFrame(data, ratios = 0.75, destination_frames, seed = -1)h2o.splitFrame(data, ratios = 0.75, destination_frames, seed = -1)
data |
An H2OFrame object, to be split. |
ratios |
A numeric value or array indicating the ratio of total rows contained in each split. Must total up to less than 1. e.g. c(0.8) for 80/20 split. |
destination_frames |
An array of frame IDs equal to the number of values specified in the ratios array, plus one. |
seed |
Random seed. |
Returns a list of split H2OFrames
## Not run: library(h2o) h2o.init() iris_hf <- as.h2o(iris) iris_split <- h2o.splitFrame(iris_hf, ratios = c(0.2, 0.5)) head(iris_split[[1]]) summary(iris_split[[1]]) ## End(Not run)## Not run: library(h2o) h2o.init() iris_hf <- as.h2o(iris) iris_split <- h2o.splitFrame(iris_hf, ratios = c(0.2, 0.5)) head(iris_split[[1]]) summary(iris_split[[1]]) ## End(Not run)
Compute the square root of x
h2o.sqrt(x)h2o.sqrt(x)
x |
An H2OFrame object. |
MathFun for the base R implementation, sqrt().
## Not run: library(h2o) h2o.init() frame <- h2o.createFrame(rows = 6, cols = 2, categorical_fraction = 0.0, missing_fraction = 0.7, seed = 123) h2o.sqrt(frame) ## End(Not run)## Not run: library(h2o) h2o.init() frame <- h2o.createFrame(rows = 6, cols = 2, categorical_fraction = 0.0, missing_fraction = 0.7, seed = 123) h2o.sqrt(frame) ## End(Not run)
Build a stacked ensemble (aka. Super Learner) using the H2O base learning algorithms specified by the user.
h2o.stackedEnsemble( x, y, training_frame, model_id = NULL, validation_frame = NULL, blending_frame = NULL, base_models = list(), metalearner_algorithm = c("AUTO", "deeplearning", "drf", "gbm", "glm", "naivebayes", "xgboost"), metalearner_nfolds = 0, metalearner_fold_assignment = c("AUTO", "Random", "Modulo", "Stratified"), metalearner_fold_column = NULL, metalearner_params = NULL, metalearner_transform = c("NONE", "Logit"), max_runtime_secs = 0, weights_column = NULL, offset_column = NULL, custom_metric_func = NULL, seed = -1, score_training_samples = 10000, keep_levelone_frame = FALSE, export_checkpoints_dir = NULL, auc_type = c("AUTO", "NONE", "MACRO_OVR", "WEIGHTED_OVR", "MACRO_OVO", "WEIGHTED_OVO"), gainslift_bins = -1 )h2o.stackedEnsemble( x, y, training_frame, model_id = NULL, validation_frame = NULL, blending_frame = NULL, base_models = list(), metalearner_algorithm = c("AUTO", "deeplearning", "drf", "gbm", "glm", "naivebayes", "xgboost"), metalearner_nfolds = 0, metalearner_fold_assignment = c("AUTO", "Random", "Modulo", "Stratified"), metalearner_fold_column = NULL, metalearner_params = NULL, metalearner_transform = c("NONE", "Logit"), max_runtime_secs = 0, weights_column = NULL, offset_column = NULL, custom_metric_func = NULL, seed = -1, score_training_samples = 10000, keep_levelone_frame = FALSE, export_checkpoints_dir = NULL, auc_type = c("AUTO", "NONE", "MACRO_OVR", "WEIGHTED_OVR", "MACRO_OVO", "WEIGHTED_OVO"), gainslift_bins = -1 )
x |
(Optional). A vector containing the names or indices of the predictor variables to use in building the model. If x is missing, then all columns except y are used. Training frame is used only to compute ensemble training metrics. |
y |
The name or column index of the response variable in the data. The response must be either a numeric or a categorical/factor variable. If the response is numeric, then a regression model will be trained, otherwise it will train a classification model. |
training_frame |
Id of the training data frame. |
model_id |
Destination id for this model; auto-generated if not specified. |
validation_frame |
Id of the validation data frame. |
blending_frame |
Frame used to compute the predictions that serve as the training frame for the metalearner (triggers blending mode if provided) |
base_models |
List of models or grids (or their ids) to ensemble/stack together. Grids are expanded to individual models. If not using blending frame, then models must have been cross-validated using nfolds > 1, and folds must be identical across models. |
metalearner_algorithm |
Type of algorithm to use as the metalearner. Options include 'AUTO' (GLM with non negative weights; if validation_frame is present, a lambda search is performed), 'deeplearning' (Deep Learning with default parameters), 'drf' (Random Forest with default parameters), 'gbm' (GBM with default parameters), 'glm' (GLM with default parameters), 'naivebayes' (NaiveBayes with default parameters), or 'xgboost' (if available, XGBoost with default parameters). Must be one of: "AUTO", "deeplearning", "drf", "gbm", "glm", "naivebayes", "xgboost". Defaults to AUTO. |
metalearner_nfolds |
Number of folds for K-fold cross-validation of the metalearner algorithm (0 to disable or >= 2). Defaults to 0. |
metalearner_fold_assignment |
Cross-validation fold assignment scheme for metalearner cross-validation. Defaults to AUTO (which is currently set to Random). The 'Stratified' option will stratify the folds based on the response variable, for classification problems. Must be one of: "AUTO", "Random", "Modulo", "Stratified". |
metalearner_fold_column |
Column with cross-validation fold index assignment per observation for cross-validation of the metalearner. |
metalearner_params |
Parameters for metalearner algorithm |
metalearner_transform |
Transformation used for the level one frame. Must be one of: "NONE", "Logit". Defaults to NONE. |
max_runtime_secs |
Maximum allowed runtime in seconds for model training. Use 0 to disable. Defaults to 0. |
weights_column |
Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0. |
offset_column |
Offset column. This will be added to the combination of columns before applying the link function. |
custom_metric_func |
Reference to custom evaluation function, format: 'language:keyName=funcName' |
seed |
Seed for random numbers; passed through to the metalearner algorithm. Defaults to -1 (time-based random number). |
score_training_samples |
Specify the number of training set samples for scoring. The value must be >= 0. To use all training samples, enter 0. Defaults to 10000. |
keep_levelone_frame |
|
export_checkpoints_dir |
Automatically export generated models to this directory. |
auc_type |
Set default multinomial AUC type. Must be one of: "AUTO", "NONE", "MACRO_OVR", "WEIGHTED_OVR", "MACRO_OVO", "WEIGHTED_OVO". Defaults to AUTO. |
gainslift_bins |
Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning. Defaults to -1. |
## Not run: library(h2o) h2o.init() # Import a sample binary outcome train/test set train <- h2o.importFile("https://s3.amazonaws.com/erin-data/higgs/higgs_train_10k.csv") test <- h2o.importFile("https://s3.amazonaws.com/erin-data/higgs/higgs_test_5k.csv") # Identify predictors and response y <- "response" x <- setdiff(names(train), y) # For binary classification, response should be a factor train[, y] <- as.factor(train[, y]) test[, y] <- as.factor(test[, y]) # Number of CV folds nfolds <- 5 # Train & Cross-validate a GBM my_gbm <- h2o.gbm(x = x, y = y, training_frame = train, distribution = "bernoulli", ntrees = 10, max_depth = 3, min_rows = 2, learn_rate = 0.2, nfolds = nfolds, fold_assignment = "Modulo", keep_cross_validation_predictions = TRUE, seed = 1) # Train & Cross-validate a RF my_rf <- h2o.randomForest(x = x, y = y, training_frame = train, ntrees = 50, nfolds = nfolds, fold_assignment = "Modulo", keep_cross_validation_predictions = TRUE, seed = 1) # Train a stacked ensemble using the GBM and RF above ensemble <- h2o.stackedEnsemble(x = x, y = y, training_frame = train, model_id = "my_ensemble_binomial", base_models = list(my_gbm, my_rf)) ## End(Not run)## Not run: library(h2o) h2o.init() # Import a sample binary outcome train/test set train <- h2o.importFile("https://s3.amazonaws.com/erin-data/higgs/higgs_train_10k.csv") test <- h2o.importFile("https://s3.amazonaws.com/erin-data/higgs/higgs_test_5k.csv") # Identify predictors and response y <- "response" x <- setdiff(names(train), y) # For binary classification, response should be a factor train[, y] <- as.factor(train[, y]) test[, y] <- as.factor(test[, y]) # Number of CV folds nfolds <- 5 # Train & Cross-validate a GBM my_gbm <- h2o.gbm(x = x, y = y, training_frame = train, distribution = "bernoulli", ntrees = 10, max_depth = 3, min_rows = 2, learn_rate = 0.2, nfolds = nfolds, fold_assignment = "Modulo", keep_cross_validation_predictions = TRUE, seed = 1) # Train & Cross-validate a RF my_rf <- h2o.randomForest(x = x, y = y, training_frame = train, ntrees = 50, nfolds = nfolds, fold_assignment = "Modulo", keep_cross_validation_predictions = TRUE, seed = 1) # Train a stacked ensemble using the GBM and RF above ensemble <- h2o.stackedEnsemble(x = x, y = y, training_frame = train, model_id = "my_ensemble_binomial", base_models = list(my_gbm, my_rf)) ## End(Not run)
Begin logging H2o R POST commands and error responses to local disk. Used primarily for debuggin purposes.
h2o.startLogging(file)h2o.startLogging(file)
file |
a character string name for the file, automatically generated |
h2o.stopLogging, h2o.clearLog,
h2o.openLog
## Not run: library(h2o) h2o.init() h2o.startLogging() australia_path = system.file("extdata", "australia.csv", package = "h2o") australia = h2o.importFile(path = australia_path) h2o.stopLogging() ## End(Not run)## Not run: library(h2o) h2o.init() h2o.startLogging() australia_path = system.file("extdata", "australia.csv", package = "h2o") australia = h2o.importFile(path = australia_path) h2o.stopLogging() ## End(Not run)
Plot a GLM model's standardized coefficient magnitudes.
h2o.std_coef_plot(model, num_of_features = NULL)h2o.std_coef_plot(model, num_of_features = NULL)
model |
A trained generalized linear model |
num_of_features |
The number of features to be shown in the plot |
h2o.varimp_plot for variable importances plot of
random forest, GBM, deep learning.
## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.importFile(prostate_path) prostate[, 2] <- as.factor(prostate[, 2]) prostate_glm <- h2o.glm(y = "CAPSULE", x = c("AGE", "RACE", "PSA", "DCAPS"), training_frame = prostate, family = "binomial", nfolds = 0, alpha = 0.5, lambda_search = FALSE) h2o.std_coef_plot(prostate_glm) ## End(Not run)## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.importFile(prostate_path) prostate[, 2] <- as.factor(prostate[, 2]) prostate_glm <- h2o.glm(y = "CAPSULE", x = c("AGE", "RACE", "PSA", "DCAPS"), training_frame = prostate, family = "binomial", nfolds = 0, alpha = 0.5, lambda_search = FALSE) h2o.std_coef_plot(prostate_glm) ## End(Not run)
Halt logging of H2O R POST commands and error responses to local disk. Used primarily for debugging purposes.
h2o.stopLogging()h2o.stopLogging()
h2o.startLogging, h2o.clearLog,
h2o.openLog
## Not run: library(h2o) h2o.init() h2o.startLogging() australia_path = system.file("extdata", "australia.csv", package = "h2o") australia = h2o.importFile(path = australia_path) h2o.stopLogging() ## End(Not run)## Not run: library(h2o) h2o.init() h2o.startLogging() australia_path = system.file("extdata", "australia.csv", package = "h2o") australia = h2o.importFile(path = australia_path) h2o.stopLogging() ## End(Not run)
Display the structure of an H2OFrame object
h2o.str(object, ..., cols = FALSE)h2o.str(object, ..., cols = FALSE)
object |
An H2OFrame. |
... |
Further arguments to be passed from or to other methods. |
cols |
Print the per-column str for the H2OFrame |
## Not run: library(h2o) h2o.init() frame <- h2o.createFrame(rows = 6, cols = 2, categorical_fraction = 0.0, missing_fraction = 0.7, seed = 123) h2o.str(frame, cols = FALSE) ## End(Not run)## Not run: library(h2o) h2o.init() frame <- h2o.createFrame(rows = 6, cols = 2, categorical_fraction = 0.0, missing_fraction = 0.7, seed = 123) h2o.str(frame, cols = FALSE) ## End(Not run)
Compute element-wise string distances between two H2OFrames. Both frames need to have the same shape (N x M) and only contain string/factor columns. Return a matrix (H2OFrame) of shape N x M.
h2o.stringdist( x, y, method = c("lv", "lcs", "qgram", "jaccard", "jw", "soundex"), compare_empty = TRUE )h2o.stringdist( x, y, method = c("lv", "lcs", "qgram", "jaccard", "jw", "soundex"), compare_empty = TRUE )
x |
An H2OFrame |
y |
A comparison H2OFrame |
method |
A string identifier indicating what string distance measure to use. Must be one of: "lv" - Levenshtein distance "lcs" - Longest common substring distance "qgram" - q-gram distance "jaccard" - Jaccard distance between q-gram profiles "jw" - Jaro, or Jaro-Winker distance "soundex" - Distance based on soundex encoding |
compare_empty |
if set to FALSE, empty strings will be handled as NaNs |
## Not run: h2o.init() x <- as.h2o(c("Martha", "Dwayne", "Dixon")) y <- as.character(as.h2o(c("Marhta", "Duane", "Dicksonx"))) h2o.stringdist(x, y, method = "jw") ## End(Not run)## Not run: h2o.init() x <- as.h2o(c("Martha", "Dwayne", "Dixon")) y <- as.character(as.h2o(c("Marhta", "Duane", "Dicksonx"))) h2o.stringdist(x, y, method = "jw") ## End(Not run)
String Split
h2o.strsplit(x, split)h2o.strsplit(x, split)
x |
The column whose strings must be split. |
split |
The pattern to split on. |
An H2OFrame where each column is the outcome of the string split.
## Not run: library(h2o) h2o.init() string_to_split <- as.h2o("Split at every character.") split_string <- h2o.strsplit(string_to_split, "") ## End(Not run)## Not run: library(h2o) h2o.init() string_to_split <- as.h2o("Split at every character.") split_string <- h2o.strsplit(string_to_split, "") ## End(Not run)
Creates a copy of the target column in which each string has the first occurence of the regex pattern replaced with the replacement substring.
h2o.sub(pattern, replacement, x, ignore.case = FALSE)h2o.sub(pattern, replacement, x, ignore.case = FALSE)
pattern |
The pattern to replace. |
replacement |
The replacement pattern. |
x |
The column on which to operate. |
ignore.case |
Case sensitive or not |
## Not run: library(h2o) h2o.init() string_to_sub <- as.h2o("r tutorial") sub_string <- h2o.sub("r ", "H2O ", string_to_sub) ## End(Not run)## Not run: library(h2o) h2o.init() string_to_sub <- as.h2o("r tutorial") sub_string <- h2o.sub("r ", "H2O ", string_to_sub) ## End(Not run)
Returns a copy of the target column that is a substring at the specified start and stop indices, inclusive. If the stop index is not specified, then the substring extends to the end of the original string. If start is longer than the number of characters in the original string, or is greater than stop, an empty string is returned. Negative start is coerced to 0.
h2o.substring(x, start, stop = "[]") h2o.substr(x, start, stop = "[]")h2o.substring(x, start, stop = "[]") h2o.substr(x, start, stop = "[]")
x |
The column on which to operate. |
start |
The index of the first element to be included in the substring. |
stop |
Optional, The index of the last element to be included in the substring. |
## Not run: library(h2o) h2o.init() string_to_substring <- as.h2o("1234567890") substr <- h2o.substring(string_to_substring, 2) #Get substring from second index onwards ## End(Not run)## Not run: library(h2o) h2o.init() string_to_substring <- as.h2o("1234567890") substr <- h2o.substring(string_to_substring, 2) #Get substring from second index onwards ## End(Not run)
Compute the frame's sum by-column (or by-row).
h2o.sum(x, na.rm = FALSE, axis = 0, return_frame = FALSE)h2o.sum(x, na.rm = FALSE, axis = 0, return_frame = FALSE)
x |
An H2OFrame object. |
na.rm |
|
axis |
An int that indicates whether to do down a column (0) or across a row (1). For row or column sums, the |
return_frame |
A boolean that indicates whether to return an H2O frame or one single aggregated value. Default is FALSE. |
sum for the base R implementation.
## Not run: library(h2o) h2o.init() frame <- h2o.createFrame(rows = 6, cols = 2, categorical_fraction = 0.0, missing_fraction = 0.7, seed = 123) h2o.sum(frame["C1"], na.rm = TRUE, axis = 0, return_frame = TRUE) ## End(Not run)## Not run: library(h2o) h2o.init() frame <- h2o.createFrame(rows = 6, cols = 2, categorical_fraction = 0.0, missing_fraction = 0.7, seed = 123) h2o.sum(frame["C1"], na.rm = TRUE, axis = 0, return_frame = TRUE) ## End(Not run)
A method for the summary generic. Summarizes the columns of an H2O data frame or subset of
columns and rows using vector notation (e.g. dataset[row, col]).
h2o.summary(object, factors = 6L, exact_quantiles = FALSE, ...) ## S3 method for class 'H2OFrame' summary(object, factors, exact_quantiles, ...)h2o.summary(object, factors = 6L, exact_quantiles = FALSE, ...) ## S3 method for class 'H2OFrame' summary(object, factors, exact_quantiles, ...)
object |
An H2OFrame object. |
factors |
The number of factors to return in the summary. Default is the top 6. |
exact_quantiles |
Compute exact quantiles or use approximation. Default is to use approximation. |
... |
Further arguments passed to or from other methods. |
By default it uses approximated version of quantiles computation, however, user can modify this behavior by setting up exact_quantiles argument to true.
A table displaying the minimum, 1st quartile, median, mean, 3rd quartile and maximum for each numeric column, and the levels and category counts of the levels in each categorical column.
## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.importFile(path = prostate_path) summary(prostate) summary(prostate$GLEASON) summary(prostate[, 4:6]) summary(prostate, exact_quantiles = TRUE) ## End(Not run)## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.importFile(path = prostate_path) summary(prostate) summary(prostate$GLEASON) summary(prostate[, 4:6]) summary(prostate, exact_quantiles = TRUE) ## End(Not run)
Singular value decomposition of an H2O data frame using the power method
h2o.svd( training_frame, x, destination_key, model_id = NULL, validation_frame = NULL, ignore_const_cols = TRUE, score_each_iteration = FALSE, transform = c("NONE", "STANDARDIZE", "NORMALIZE", "DEMEAN", "DESCALE"), svd_method = c("GramSVD", "Power", "Randomized"), nv = 1, max_iterations = 1000, seed = -1, keep_u = TRUE, u_name = NULL, use_all_factor_levels = TRUE, max_runtime_secs = 0, export_checkpoints_dir = NULL )h2o.svd( training_frame, x, destination_key, model_id = NULL, validation_frame = NULL, ignore_const_cols = TRUE, score_each_iteration = FALSE, transform = c("NONE", "STANDARDIZE", "NORMALIZE", "DEMEAN", "DESCALE"), svd_method = c("GramSVD", "Power", "Randomized"), nv = 1, max_iterations = 1000, seed = -1, keep_u = TRUE, u_name = NULL, use_all_factor_levels = TRUE, max_runtime_secs = 0, export_checkpoints_dir = NULL )
training_frame |
Id of the training data frame. |
x |
A vector containing the |
destination_key |
(Optional) The unique key assigned to the resulting model. Automatically generated if none is provided. |
model_id |
Destination id for this model; auto-generated if not specified. |
validation_frame |
Id of the validation data frame. |
ignore_const_cols |
|
score_each_iteration |
|
transform |
Transformation of training data Must be one of: "NONE", "STANDARDIZE", "NORMALIZE", "DEMEAN", "DESCALE". Defaults to NONE. |
svd_method |
Method for computing SVD (Caution: Randomized is currently experimental and unstable) Must be one of: "GramSVD", "Power", "Randomized". Defaults to GramSVD. |
nv |
Number of right singular vectors Defaults to 1. |
max_iterations |
Maximum iterations Defaults to 1000. |
seed |
Seed for random numbers (affects certain parts of the algo that are stochastic and those might or might not be enabled by default). Defaults to -1 (time-based random number). |
keep_u |
|
u_name |
Frame key to save left singular vectors |
use_all_factor_levels |
|
max_runtime_secs |
Maximum allowed runtime in seconds for model training. Use 0 to disable. Defaults to 0. |
export_checkpoints_dir |
Automatically export generated models to this directory. |
an object of class H2ODimReductionModel.
N. Halko, P.G. Martinsson, J.A. Tropp. Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions[https://arxiv.org/abs/0909.4061]. SIAM Rev., Survey and Review section, Vol. 53, num. 2, pp. 217-288, June 2011.
## Not run: library(h2o) h2o.init() australia_path <- system.file("extdata", "australia.csv", package = "h2o") australia <- h2o.uploadFile(path = australia_path) h2o.svd(training_frame = australia, nv = 8) ## End(Not run)## Not run: library(h2o) h2o.init() australia_path <- system.file("extdata", "australia.csv", package = "h2o") australia <- h2o.uploadFile(path = australia_path) h2o.svd(training_frame = australia, nv = 8) ## End(Not run)
Uses the cross-classifying factors to build a table of counts at each combination of factor levels.
h2o.table(x, y = NULL, dense = TRUE) table.H2OFrame(x, y = NULL, dense = TRUE)h2o.table(x, y = NULL, dense = TRUE) table.H2OFrame(x, y = NULL, dense = TRUE)
x |
An H2OFrame object with at most two columns. |
y |
An H2OFrame similar to x, or |
dense |
A logical for dense representation, which lists only non-zero counts, 1 combination per row. Set to FALSE to expand counts across all combinations. |
Returns a tabulated H2OFrame object.
## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(path = prostate_path) summary(prostate) # Counts of the ages of all patients head(h2o.table(prostate[, 3])) h2o.table(prostate[, 3]) # Two-way table of ages (rows) and race (cols) of all patients head(h2o.table(prostate[, c(3, 4)])) h2o.table(prostate[, c(3, 4)]) ## End(Not run)## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(path = prostate_path) summary(prostate) # Counts of the ages of all patients head(h2o.table(prostate[, 3])) h2o.table(prostate[, 3]) # Two-way table of ages (rows) and race (cols) of all patients head(h2o.table(prostate[, c(3, 4)])) h2o.table(prostate[, c(3, 4)]) ## End(Not run)
Simple Co-Occurrence based tabulation of X vs Y, where X and Y are two Vecs in a given dataset. Uses histogram of given resolution in X and Y. Handles numerical/categorical data and missing values. Supports observation weights.
h2o.tabulate(data, x, y, weights_column = NULL, nbins_x = 50, nbins_y = 50)h2o.tabulate(data, x, y, weights_column = NULL, nbins_x = 50, nbins_y = 50)
data |
An H2OFrame object. |
x |
predictor column |
y |
response column |
weights_column |
(optional) observation weights column |
nbins_x |
number of bins for predictor column |
nbins_y |
number of bins for response column |
Returns two TwoDimTables of 3 columns each count_table: X Y counts response_table: X meanY counts
## Not run: library(h2o) h2o.init() df <- as.h2o(iris) tab <- h2o.tabulate(data = df, x = "Sepal.Length", y = "Petal.Width", weights_column = NULL, nbins_x = 10, nbins_y = 10) plot(tab) ## End(Not run)## Not run: library(h2o) h2o.init() df <- as.h2o(iris) tab <- h2o.tabulate(data = df, x = "Sepal.Length", y = "Petal.Width", weights_column = NULL, nbins_x = 10, nbins_y = 10) plot(tab) ## End(Not run)
Compute the tangent of x
h2o.tan(x)h2o.tan(x)
x |
An H2OFrame object. |
Trig for the base R implementation, tan().
## Not run: library(h2o) h2o.init() frame <- h2o.createFrame(rows = 6, cols = 2, categorical_fraction = 0.0, missing_fraction = 0.7, seed = 123) h2o.tan(frame) ## End(Not run)## Not run: library(h2o) h2o.init() frame <- h2o.createFrame(rows = 6, cols = 2, categorical_fraction = 0.0, missing_fraction = 0.7, seed = 123) h2o.tan(frame) ## End(Not run)
Compute the hyperbolic tangent of x
h2o.tanh(x)h2o.tanh(x)
x |
An H2OFrame object. |
Hyperbolic for the base R implementation, tanh().
## Not run: library(h2o) h2o.init() frame <- h2o.createFrame(rows = 6, cols = 2, categorical_fraction = 0.0, missing_fraction = 0.7, seed = 123) h2o.tanh(frame) ## End(Not run)## Not run: library(h2o) h2o.init() frame <- h2o.createFrame(rows = 6, cols = 2, categorical_fraction = 0.0, missing_fraction = 0.7, seed = 123) h2o.tanh(frame) ## End(Not run)
Applies a target encoding map to an H2OFrame object. Computing target encoding for high cardinality categorical columns can improve performance of supervised learning models. A Target Encoding tutorial is available here: https://github.com/h2oai/h2o-tutorials/blob/master/best-practices/categorical-predictors/target_encoding.md.
h2o.target_encode_apply( data, x, y, target_encode_map, holdout_type, fold_column = NULL, blended_avg = TRUE, noise_level = NULL, seed = -1 )h2o.target_encode_apply( data, x, y, target_encode_map, holdout_type, fold_column = NULL, blended_avg = TRUE, noise_level = NULL, seed = -1 )
data |
An H2OFrame object with which to apply the target encoding map. |
x |
A list containing the names or indices of the variables to encode. A target encoding column will be created for each element in the list. Items in the list can be multiple columns. For example, if 'x = list(c("A"), c("B", "C"))', then the resulting frame will have a target encoding column for A and a target encoding column for B & C (in this case, we group by two columns). |
y |
The name or column index of the response variable in the data. The response variable can be either numeric or binary. |
target_encode_map |
A list of H2OFrame objects that is the results of the |
holdout_type |
The holdout type used. Must be one of: "LeaveOneOut", "KFold", "None". |
fold_column |
(Optional) The name or column index of the fold column in the data. Defaults to NULL (no 'fold_column'). Only required if 'holdout_type' = "KFold". |
blended_avg |
|
noise_level |
(Optional) The amount of random noise added to the target encoding. This helps prevent overfitting. Defaults to 0.01 * range of y. |
seed |
(Optional) A random seed used to generate draws from the uniform distribution for random noise. Defaults to -1. |
Returns an H2OFrame object containing the target encoding per record.
h2o.target_encode_create for creating the target encoding map
## Not run: library(h2o) h2o.init() # Get Target Encoding Frame on bank-additional-full data with numeric `y` data <- h2o.importFile( path = "https://s3.amazonaws.com/h2o-public-test-data/smalldata/demos/bank-additional-full.csv") splits <- h2o.splitFrame(data, seed = 1234) train <- splits[[1]] test <- splits[[2]] mapping <- h2o.target_encode_create(data = train, x = list(c("job"), c("job", "marital")), y = "age") # Apply mapping to the training dataset train_encode <- h2o.target_encode_apply(data = train, x = list(c("job"), c("job", "marital")), y = "age", mapping, holdout_type = "LeaveOneOut") # Apply mapping to a test dataset test_encode <- h2o.target_encode_apply(data = test, x = list(c("job"), c("job", "marital")), y = "age", target_encode_map = mapping, holdout_type = "None") ## End(Not run)## Not run: library(h2o) h2o.init() # Get Target Encoding Frame on bank-additional-full data with numeric `y` data <- h2o.importFile( path = "https://s3.amazonaws.com/h2o-public-test-data/smalldata/demos/bank-additional-full.csv") splits <- h2o.splitFrame(data, seed = 1234) train <- splits[[1]] test <- splits[[2]] mapping <- h2o.target_encode_create(data = train, x = list(c("job"), c("job", "marital")), y = "age") # Apply mapping to the training dataset train_encode <- h2o.target_encode_apply(data = train, x = list(c("job"), c("job", "marital")), y = "age", mapping, holdout_type = "LeaveOneOut") # Apply mapping to a test dataset test_encode <- h2o.target_encode_apply(data = test, x = list(c("job"), c("job", "marital")), y = "age", target_encode_map = mapping, holdout_type = "None") ## End(Not run)
Creates a target encoding map based on group-by columns ('x') and a numeric or binary target column ('y'). Computing target encoding for high cardinality categorical columns can improve performance of supervised learning models. A Target Encoding tutorial is available here: https://github.com/h2oai/h2o-tutorials/blob/master/best-practices/categorical-predictors/target_encoding.md.
h2o.target_encode_create(data, x, y, fold_column = NULL)h2o.target_encode_create(data, x, y, fold_column = NULL)
data |
An H2OFrame object with which to create the target encoding map. |
x |
A list containing the names or indices of the variables to encode. A target encoding map will be created for each element in the list. Items in the list can be multiple columns. For example, if 'x = list(c("A"), c("B", "C"))', then there will be one mapping frame for A and one mapping frame for B & C (in this case, we group by two columns). |
y |
The name or column index of the response variable in the data. The response variable can be either numeric or binary. |
fold_column |
(Optional) The name or column index of the fold column in the data. Defaults to NULL (no 'fold_column'). |
Returns a list of H2OFrame objects containing the target encoding mapping for each column in 'x'.
h2o.target_encode_apply for applying the target encoding mapping to a frame.
## Not run: library(h2o) h2o.init() # Get Target Encoding Map on bank-additional-full data with numeric response data <- h2o.importFile( path = "https://s3.amazonaws.com/h2o-public-test-data/smalldata/demos/bank-additional-full.csv") mapping_age <- h2o.target_encode_create(data = data, x = list(c("job"), c("job", "marital")), y = "age") head(mapping_age) # Get Target Encoding Map on bank-additional-full data with binary response mapping_y <- h2o.target_encode_create(data = data, x = list(c("job"), c("job", "marital")), y = "y") head(mapping_y) ## End(Not run)## Not run: library(h2o) h2o.init() # Get Target Encoding Map on bank-additional-full data with numeric response data <- h2o.importFile( path = "https://s3.amazonaws.com/h2o-public-test-data/smalldata/demos/bank-additional-full.csv") mapping_age <- h2o.target_encode_create(data = data, x = list(c("job"), c("job", "marital")), y = "age") head(mapping_age) # Get Target Encoding Map on bank-additional-full data with binary response mapping_y <- h2o.target_encode_create(data = data, x = list(c("job"), c("job", "marital")), y = "y") head(mapping_y) ## End(Not run)
Transformation of a categorical variable with a mean value of the target variable
h2o.targetencoder( x, y, training_frame, model_id = NULL, fold_column = NULL, columns_to_encode = NULL, keep_original_categorical_columns = TRUE, blending = FALSE, inflection_point = 10, smoothing = 20, data_leakage_handling = c("leave_one_out", "k_fold", "none", "LeaveOneOut", "KFold", "None"), noise = 0.01, seed = -1, ... )h2o.targetencoder( x, y, training_frame, model_id = NULL, fold_column = NULL, columns_to_encode = NULL, keep_original_categorical_columns = TRUE, blending = FALSE, inflection_point = 10, smoothing = 20, data_leakage_handling = c("leave_one_out", "k_fold", "none", "LeaveOneOut", "KFold", "None"), noise = 0.01, seed = -1, ... )
x |
(Optional) A vector containing the names or indices of the predictor variables to use in building the model. If x is missing, then all columns except y are used. |
y |
The name or column index of the response variable in the data. The response must be either a numeric or a categorical/factor variable. If the response is numeric, then a regression model will be trained, otherwise it will train a classification model. |
training_frame |
Id of the training data frame. |
model_id |
Destination id for this model; auto-generated if not specified. |
fold_column |
Column with cross-validation fold index assignment per observation. |
columns_to_encode |
List of categorical columns or groups of categorical columns to encode. When groups of columns are specified, each group is encoded as a single column (interactions are created internally). |
keep_original_categorical_columns |
|
blending |
|
inflection_point |
Inflection point of the sigmoid used to blend probabilities (see 'blending' parameter). For a given categorical value, if it appears less that 'inflection_point' in a data sample, then the influence of the posterior probability will be smaller than the prior. Defaults to 10. |
smoothing |
Smoothing factor corresponds to the inverse of the slope at the inflection point on the sigmoid used to blend probabilities (see 'blending' parameter). If smoothing tends towards 0, then the sigmoid used for blending turns into a Heaviside step function. Defaults to 20. |
data_leakage_handling |
Data leakage handling strategy used to generate the encoding. Supported options are: 1) "none" (default) - no holdout, using the entire training frame. 2) "leave_one_out" - current row's response value is subtracted from the per-level frequencies pre-calculated on the entire training frame. 3) "k_fold" - encodings for a fold are generated based on out-of-fold data. Must be one of: "leave_one_out", "k_fold", "none", "LeaveOneOut", "KFold", "None". Defaults to None. |
noise |
The amount of noise to add to the encoded column. Use 0 to disable noise, and -1 (=AUTO) to let the algorithm determine a reasonable amount of noise. Defaults to 0.01. |
seed |
Seed for random numbers (affects certain parts of the algo that are stochastic and those might or might not be enabled by default). Defaults to -1 (time-based random number). |
... |
Mainly used for backwards compatibility, to allow deprecated parameters. |
## Not run: library(h2o) h2o.init() #Import the titanic dataset f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv" titanic <- h2o.importFile(f) # Set response as a factor response <- "survived" titanic[response] <- as.factor(titanic[response]) # Split the dataset into train and test splits <- h2o.splitFrame(data = titanic, ratios = .8, seed = 1234) train <- splits[[1]] test <- splits[[2]] # Choose which columns to encode encode_columns <- c("home.dest", "cabin", "embarked") # Train a TE model te_model <- h2o.targetencoder(x = encode_columns, y = response, training_frame = train, fold_column = "pclass", data_leakage_handling = "KFold") # New target encoded train and test sets train_te <- h2o.transform(te_model, train) test_te <- h2o.transform(te_model, test) ## End(Not run)## Not run: library(h2o) h2o.init() #Import the titanic dataset f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv" titanic <- h2o.importFile(f) # Set response as a factor response <- "survived" titanic[response] <- as.factor(titanic[response]) # Split the dataset into train and test splits <- h2o.splitFrame(data = titanic, ratios = .8, seed = 1234) train <- splits[[1]] test <- splits[[2]] # Choose which columns to encode encode_columns <- c("home.dest", "cabin", "embarked") # Train a TE model te_model <- h2o.targetencoder(x = encode_columns, y = response, training_frame = train, fold_column = "pclass", data_leakage_handling = "KFold") # New target encoded train and test sets train_te <- h2o.transform(te_model, train) test_te <- h2o.transform(te_model, test) ## End(Not run)
Computes TF-IDF values for each word in given documents.
h2o.tf_idf( frame, document_id_col, text_col, preprocess = TRUE, case_sensitive = TRUE )h2o.tf_idf( frame, document_id_col, text_col, preprocess = TRUE, case_sensitive = TRUE )
frame |
documents or words frame for which TF-IDF values should be computed. |
document_id_col |
index or name of a column containing document IDs. |
text_col |
index or name of a column containing documents if 'preprocess = TRUE' or words if 'preprocess = FALSE'. |
preprocess |
whether input text data should be pre-processed. Defaults to 'TRUE'. |
case_sensitive |
whether input data should be treated as case sensitive. Defaults to 'TRUE'. |
resulting frame with TF-IDF values. Row format: documentID, word, TF, IDF, TF-IDF
Retrieves the thresholds and metric scores table from a H2OBinomialUpliftMetrics or a H2OBinomialMetrics.
h2o.thresholds_and_metric_scores( object, train = FALSE, valid = FALSE, xval = FALSE )h2o.thresholds_and_metric_scores( object, train = FALSE, valid = FALSE, xval = FALSE )
object |
|
train |
Retrieve the training thresholds and metric scores table |
valid |
Retrieve the validation thresholds and metric scores table |
xval |
Retrieve the cross-validation thresholds and metric scores table (only for H2OBinomialMetrics) |
The table contains indices, thresholds, all cumulative uplift values and cumulative number of observations for uplift binomial models or thresholds and maximal metric values for binomial models. If "train" and "valid" parameters are FALSE (default), then the training table is returned. If more than one parameter is set to TRUE, then a named vector of tables is returned, where the names are "train", "valid".
## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/uplift/criteo_uplift_13k.csv" train <- h2o.importFile(f) train$treatment <- as.factor(train$treatment) train$conversion <- as.factor(train$conversion) model <- h2o.upliftRandomForest(training_frame=train, x=sprintf("f%s",seq(0:10)), y="conversion", ntrees=10, max_depth=5, treatment_column="treatment", auuc_type="AUTO") perf <- h2o.performance(model, train=TRUE) h2o.thresholds_and_metric_scores(perf) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/uplift/criteo_uplift_13k.csv" train <- h2o.importFile(f) train$treatment <- as.factor(train$treatment) train$conversion <- as.factor(train$conversion) model <- h2o.upliftRandomForest(training_frame=train, x=sprintf("f%s",seq(0:10)), y="conversion", ntrees=10, max_depth=5, treatment_column="treatment", auuc_type="AUTO") perf <- h2o.performance(model, train=TRUE) h2o.thresholds_and_metric_scores(perf) ## End(Not run)
Converts a given word2vec model into an H2OFrame. The frame represents learned word embeddings
h2o.toFrame(word2vec)h2o.toFrame(word2vec)
word2vec |
A word2vec model. |
## Not run: h2o.init() # Build a dummy word2vec model data <- as.character(as.h2o(c("a", "b", "a"))) w2v_model <- h2o.word2vec(data, sent_sample_rate = 0, min_word_freq = 0, epochs = 1, vec_size = 2) # Transform words to vectors and return average vector for each sentence h2o.toFrame(w2v_model) # -> Frame made of 2 rows and 2 columns ## End(Not run)## Not run: h2o.init() # Build a dummy word2vec model data <- as.character(as.h2o(c("a", "b", "a"))) w2v_model <- h2o.word2vec(data, sent_sample_rate = 0, min_word_freq = 0, epochs = 1, vec_size = 2) # Transform words to vectors and return average vector for each sentence h2o.toFrame(w2v_model) # -> Frame made of 2 rows and 2 columns ## End(Not run)
h2o.tokenize is similar to h2o.strsplit, the difference between them is that h2o.tokenize will store the tokenized text into a single column making it easier for additional processing (filtering stop words, word2vec algo, ...).
h2o.tokenize(x, split)h2o.tokenize(x, split)
x |
The column or columns whose strings to tokenize. |
split |
The regular expression to split on. |
An H2OFrame with a single column representing the tokenized Strings. Original rows of the input DF are separated by NA.
## Not run: library(h2o) h2o.init() string_to_tokenize <- as.h2o("Split at every character and tokenize.") tokenize_string <- h2o.tokenize(as.character(string_to_tokenize), "") ## End(Not run)## Not run: library(h2o) h2o.init() string_to_tokenize <- as.h2o("Split at every character and tokenize.") tokenize_string <- h2o.tokenize(as.character(string_to_tokenize), "") ## End(Not run)
Convert strings to lowercase
h2o.tolower(x)h2o.tolower(x)
x |
An H2OFrame object whose strings should be lower cased |
An H2OFrame with all entries in lowercase format
## Not run: library(h2o) h2o.init() string_to_lower <- as.h2o("ABCDE") lowered_string <- h2o.tolower(string_to_lower) ## End(Not run)## Not run: library(h2o) h2o.init() string_to_lower <- as.h2o("ABCDE") lowered_string <- h2o.tolower(string_to_lower) ## End(Not run)
topBottomN function will will grab the top N percent or botom N percent of values of a column and return it in a H2OFrame.
h2o.topBottomN(x, column, nPercent, grabTopN)h2o.topBottomN(x, column, nPercent, grabTopN)
x |
an H2OFrame |
column |
is a column name or column index to grab the top N percent value from |
nPercent |
a top percentage values to grab |
grabTopN |
if -1 grab bottom percentage, 1 grab top percentage |
An H2OFrame with 2 columns: first column is the original row indices, second column contains the values
Extract the top N percent of values of a column and return it in a H2OFrame.
h2o.topN(x, column, nPercent)h2o.topN(x, column, nPercent)
x |
an H2OFrame |
column |
is a column name or column index to grab the top N percent value from |
nPercent |
is a top percentage value to grab |
An H2OFrame with 2 columns. The first column is the original row indices, second column contains the topN values
## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/bigdata/laptop/jira/TopBottomNRep4.csv.zip" dataset <- h2o.importFile(f) frameNames <- names(dataset) nPercent <- c(1, 2, 3, 4) nP <- nPercent[sample(1:length(nPercent), 1, replace = FALSE)] colIndex <- sample(1:length(frameNames), 1, replace = FALSE) h2o.topN(dataset, frameNames[colIndex], nP) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/bigdata/laptop/jira/TopBottomNRep4.csv.zip" dataset <- h2o.importFile(f) frameNames <- names(dataset) nPercent <- c(1, 2, 3, 4) nP <- nPercent[sample(1:length(nPercent), 1, replace = FALSE)] colIndex <- sample(1:length(frameNames), 1, replace = FALSE) h2o.topN(dataset, frameNames[colIndex], nP) ## End(Not run)
If "train", "valid", and "xval" parameters are FALSE (default), then the training tot_withinss value is returned. If more than one parameter is set to TRUE, then a named vector of tot_withinss' are returned, where the names are "train", "valid" or "xval".
h2o.tot_withinss(object, train = FALSE, valid = FALSE, xval = FALSE)h2o.tot_withinss(object, train = FALSE, valid = FALSE, xval = FALSE)
object |
An H2OClusteringModel object. |
train |
Retrieve the training total within cluster sum of squares |
valid |
Retrieve the validation total within cluster sum of squares |
xval |
Retrieve the cross-validation total within cluster sum of squares |
## Not run: library(h2o) h2o.init() fr <- h2o.importFile("https://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_train.csv") predictors <- c("sepal_len", "sepal_wid", "petal_len", "petal_wid") km <- h2o.kmeans(x = predictors, training_frame = fr, k = 3, nfolds = 3) h2o.tot_withinss(km, train = TRUE) ## End(Not run)## Not run: library(h2o) h2o.init() fr <- h2o.importFile("https://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_train.csv") predictors <- c("sepal_len", "sepal_wid", "petal_len", "petal_wid") km <- h2o.kmeans(x = predictors, training_frame = fr, k = 3, nfolds = 3) h2o.tot_withinss(km, train = TRUE) ## End(Not run)
If "train", "valid", and "xval" parameters are FALSE (default), then the training totss value is returned. If more than one parameter is set to TRUE, then a named vector of totss' are returned, where the names are "train", "valid" or "xval".
h2o.totss(object, train = FALSE, valid = FALSE, xval = FALSE)h2o.totss(object, train = FALSE, valid = FALSE, xval = FALSE)
object |
An H2OClusteringModel object. |
train |
Retrieve the training total sum of squares |
valid |
Retrieve the validation total sum of squares |
xval |
Retrieve the cross-validation total sum of squares |
## Not run: library(h2o) h2o.init() fr <- h2o.importFile("https://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_train.csv") predictors <- c("sepal_len", "sepal_wid", "petal_len", "petal_wid") km <- h2o.kmeans(x = predictors, training_frame = fr, k = 3, nfolds = 3) h2o.totss(km, train = TRUE) ## End(Not run)## Not run: library(h2o) h2o.init() fr <- h2o.importFile("https://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_train.csv") predictors <- c("sepal_len", "sepal_wid", "petal_len", "petal_wid") km <- h2o.kmeans(x = predictors, training_frame = fr, k = 3, nfolds = 3) h2o.totss(km, train = TRUE) ## End(Not run)
Convert strings to uppercase
h2o.toupper(x)h2o.toupper(x)
x |
An H2OFrame object whose strings should be upper cased |
An H2OFrame with all entries in uppercase format
## Not run: library(h2o) h2o.init() string_to_upper <- as.h2o("abcde") upper_string <- h2o.toupper(string_to_upper) ## End(Not run)## Not run: library(h2o) h2o.init() string_to_upper <- as.h2o("abcde") upper_string <- h2o.toupper(string_to_upper) ## End(Not run)
Provides a set of functions to train a group of models on different segments (subpopulations) of the training set.
h2o.train_segments( algorithm, segment_columns, segment_models_id, parallelism = 1, ... )h2o.train_segments( algorithm, segment_columns, segment_models_id, parallelism = 1, ... )
algorithm |
Name of algorithm to use in training segment models (gbm, randomForest, kmeans, glm, deeplearning, naivebayes, psvm, xgboost, pca, svd, targetencoder, aggregator, word2vec, coxph, isolationforest, kmeans, stackedensemble, glrm, gam, anovaglm, modelselection). |
segment_columns |
A list of columns to segment-by. H2O will group the training (and validation) dataset by the segment-by columns and train a separate model for each segment (group of rows). |
segment_models_id |
Identifier for the returned collection of Segment Models. If not specified it will be automatically generated. |
parallelism |
Level of parallelism of bulk model building, it is the maximum number of models each H2O node will be building in parallel, defaults to 1. |
... |
Use to pass along training_frame parameter, x, y, and all non-default parameter values to the algorithm Look at the specific algorithm - h2o.gbm, h2o.glm, h2o.kmeans, h2o.deepLearning - for available parameters. |
Start Segmented-Data bulk Model Training for a given algorithm and parameters.
## Not run: library(h2o) h2o.init() iris_hf <- as.h2o(iris) models <- h2o.train_segments(algorithm = "gbm", segment_columns = "Species", x = c(1:3), y = 4, training_frame = iris_hf, ntrees = 5, max_depth = 4) as.data.frame(models) ## End(Not run)## Not run: library(h2o) h2o.init() iris_hf <- as.h2o(iris) models <- h2o.train_segments(algorithm = "gbm", segment_columns = "Species", x = c(1:3), y = 4, training_frame = iris_hf, ntrees = 5, max_depth = 4) as.data.frame(models) ## End(Not run)
Use H2O Transformation model and apply the underlying transformation
h2o.transform(model, ...)h2o.transform(model, ...)
model |
A trained model representing the transformation strategy |
... |
Transformation model-specific parameters |
Returns an H2OFrame object with data transformed.
Use GRLM to transform a frame.
h2o.transform_frame(model, fr)h2o.transform_frame(model, fr)
model |
H2O GRLM model |
fr |
H2OFrame |
Returns a transformed frame
## Not run: library(h2o) h2o.init() # Import the USArrests dataset into H2O: arrests <- h2o.importFile( "https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/USArrests.csv" ) # Split the dataset into a train and valid set: arrests_splits <- h2o.splitFrame(data = arrests, ratios = 0.8, seed = 1234) train <- arrests_splits[[1]] valid <- arrests_splits[[2]] # Build and train the model: glrm_model = h2o.glrm(training_frame = train, k = 4, loss = "Quadratic", gamma_x = 0.5, gamma_y = 0.5, max_iterations = 700, recover_svd = TRUE, init = "SVD", transform = "STANDARDIZE") # Eval performance: arrests_perf <- h2o.performance(glrm_model) # Generate predictions on a validation set (if necessary): arrests_pred <- h2o.predict(glrm_model, newdata = valid) # Transform the data using the dataset "valid" to retrieve the new coefficients: glrm_transform <- h2o.transform_frame(glrm_model, valid) ## End(Not run)## Not run: library(h2o) h2o.init() # Import the USArrests dataset into H2O: arrests <- h2o.importFile( "https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/USArrests.csv" ) # Split the dataset into a train and valid set: arrests_splits <- h2o.splitFrame(data = arrests, ratios = 0.8, seed = 1234) train <- arrests_splits[[1]] valid <- arrests_splits[[2]] # Build and train the model: glrm_model = h2o.glrm(training_frame = train, k = 4, loss = "Quadratic", gamma_x = 0.5, gamma_y = 0.5, max_iterations = 700, recover_svd = TRUE, init = "SVD", transform = "STANDARDIZE") # Eval performance: arrests_perf <- h2o.performance(glrm_model) # Generate predictions on a validation set (if necessary): arrests_pred <- h2o.predict(glrm_model, newdata = valid) # Transform the data using the dataset "valid" to retrieve the new coefficients: glrm_transform <- h2o.transform_frame(glrm_model, valid) ## End(Not run)
Transform words (or sequences of words) to vectors using a word2vec model.
h2o.transform_word2vec( word2vec, words, aggregate_method = c("NONE", "AVERAGE") )h2o.transform_word2vec( word2vec, words, aggregate_method = c("NONE", "AVERAGE") )
word2vec |
A word2vec model. |
words |
An H2OFrame made of a single column containing source words. |
aggregate_method |
Specifies how to aggregate sequences of words. If method is 'NONE' then no aggregation is performed and each input word is mapped to a single word-vector. If method is 'AVERAGE' then input is treated as sequences of words delimited by NA. Each word of a sequences is internally mapped to a vector and vectors belonging to the same sentence are averaged and returned in the result. |
## Not run: h2o.init() # Build a dummy word2vec model data <- as.character(as.h2o(c("a", "b", "a"))) w2v_model <- h2o.word2vec(data, sent_sample_rate = 0, min_word_freq = 0, epochs = 1, vec_size = 2) # Transform words to vectors without aggregation sentences <- as.character(as.h2o(c("b", "c", "a", NA, "b"))) h2o.transform(w2v_model, sentences) # -> 5 rows total, 2 rows NA ("c" is not in the vocabulary) # Transform words to vectors and return average vector for each sentence h2o.transform(w2v_model, sentences, aggregate_method = "AVERAGE") # -> 2 rows ## End(Not run)## Not run: h2o.init() # Build a dummy word2vec model data <- as.character(as.h2o(c("a", "b", "a"))) w2v_model <- h2o.word2vec(data, sent_sample_rate = 0, min_word_freq = 0, epochs = 1, vec_size = 2) # Transform words to vectors without aggregation sentences <- as.character(as.h2o(c("b", "c", "a", NA, "b"))) h2o.transform(w2v_model, sentences) # -> 5 rows total, 2 rows NA ("c" is not in the vocabulary) # Transform words to vectors and return average vector for each sentence h2o.transform(w2v_model, sentences, aggregate_method = "AVERAGE") # -> 2 rows ## End(Not run)
Applies target encoding to a given dataset
## S4 method for signature 'H2OTargetEncoderModel' h2o.transform( model, data, blending = NULL, inflection_point = -1, smoothing = -1, noise = NULL, as_training = FALSE, ... )## S4 method for signature 'H2OTargetEncoderModel' h2o.transform( model, data, blending = NULL, inflection_point = -1, smoothing = -1, noise = NULL, as_training = FALSE, ... )
model |
A trained model representing the transformation strategy |
data |
An H2OFrame with data to be transformed |
blending |
Use blending during the transformation. Respects model settings when not set. |
inflection_point |
Blending parameter. Only effective when blending is enabled. By default, model settings are respected, if not overridden by this setting. |
smoothing |
Blending parameter. Only effective when blending is enabled. By default, model settings are respected, if not overridden by this setting. |
noise |
An amount of random noise added to the encoding, this helps prevent overfitting. By default, model settings are respected, if not overridden by this setting. |
as_training |
Must be set to True when encoding the training frame. Defaults to False. |
... |
Mainly used for backwards compatibility, to allow deprecated parameters. |
Returns an H2OFrame object with data transformed.
Transform words (or sequences of words) to vectors using a word2vec model.
## S4 method for signature 'H2OWordEmbeddingModel' h2o.transform(model, words, aggregate_method = c("NONE", "AVERAGE"))## S4 method for signature 'H2OWordEmbeddingModel' h2o.transform(model, words, aggregate_method = c("NONE", "AVERAGE"))
model |
A word2vec model. |
words |
An H2OFrame made of a single column containing source words. |
aggregate_method |
Specifies how to aggregate sequences of words. If method is 'NONE' then no aggregation is performed and each input word is mapped to a single word-vector. If method is 'AVERAGE' then input is treated as sequences of words delimited by NA. Each word of a sequences is internally mapped to a vector and vectors belonging to the same sentence are averaged and returned in the result. |
## Not run: h2o.init() # Build a simple word2vec model data <- as.character(as.h2o(c("a", "b", "a"))) w2v_model <- h2o.word2vec(data, sent_sample_rate = 0, min_word_freq = 0, epochs = 1, vec_size = 2) # Transform words to vectors without aggregation sentences <- as.character(as.h2o(c("b", "c", "a", NA, "b"))) h2o.transform(w2v_model, sentences) # -> 5 rows total, 2 rows NA ("c" is not in the vocabulary) # Transform words to vectors and return average vector for each sentence h2o.transform(w2v_model, sentences, aggregate_method = "AVERAGE") # -> 2 rows ## End(Not run)## Not run: h2o.init() # Build a simple word2vec model data <- as.character(as.h2o(c("a", "b", "a"))) w2v_model <- h2o.word2vec(data, sent_sample_rate = 0, min_word_freq = 0, epochs = 1, vec_size = 2) # Transform words to vectors without aggregation sentences <- as.character(as.h2o(c("b", "c", "a", NA, "b"))) h2o.transform(w2v_model, sentences) # -> 5 rows total, 2 rows NA ("c" is not in the vocabulary) # Transform words to vectors and return average vector for each sentence h2o.transform(w2v_model, sentences, aggregate_method = "AVERAGE") # -> 2 rows ## End(Not run)
Trim Space
h2o.trim(x)h2o.trim(x)
x |
The column whose strings should be trimmed. |
## Not run: library(h2o) h2o.init() string_to_trim <- as.h2o("r tutorial") trim_string <- h2o.trim(string_to_trim) ## End(Not run)## Not run: library(h2o) h2o.init() string_to_trim <- as.h2o("r tutorial") trim_string <- h2o.trim(string_to_trim) ## End(Not run)
trunc takes a single numeric argument x and returns a numeric vector containing the integers formed by truncating the values in x toward 0.
h2o.trunc(x)h2o.trunc(x)
x |
An H2OFrame object. |
Round for the base R implementation, trunc().
## Not run: library(h2o) h2o.init() frame <- h2o.createFrame(rows = 6, cols = 2, categorical_fraction = 0.0, missing_fraction = 0.7, seed = 123) h2o.trunc(frame["C1"]) ## End(Not run)## Not run: library(h2o) h2o.init() frame <- h2o.createFrame(rows = 6, cols = 2, categorical_fraction = 0.0, missing_fraction = 0.7, seed = 123) h2o.trunc(frame["C1"]) ## End(Not run)
Extract unique values in the column.
h2o.unique(x, include_nas = FALSE)h2o.unique(x, include_nas = FALSE)
x |
An H2OFrame object. |
include_nas |
If set to TRUE, NAs are included. FALSE (turned off) by default. |
Returns an H2OFrame object.
## Not run: library(h2o) h2o.init() f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_wheader.csv" iris <- h2o.importFile(f) h2o.unique(iris["class"]) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_wheader.csv" iris <- h2o.importFile(f) h2o.unique(iris["class"]) ## End(Not run)
Builds a Uplift Random Forest model on an H2OFrame.
h2o.upliftRandomForest( x, y, training_frame, treatment_column, model_id = NULL, validation_frame = NULL, score_each_iteration = FALSE, score_tree_interval = 0, ignore_const_cols = TRUE, ntrees = 50, max_depth = 20, min_rows = 1, nbins = 20, nbins_top_level = 1024, nbins_cats = 1024, max_runtime_secs = 0, seed = -1, mtries = -2, sample_rate = 0.632, sample_rate_per_class = NULL, col_sample_rate_change_per_level = 1, col_sample_rate_per_tree = 1, histogram_type = c("AUTO", "UniformAdaptive", "Random", "QuantilesGlobal", "RoundRobin", "UniformRobust"), categorical_encoding = c("AUTO", "Enum", "OneHotInternal", "OneHotExplicit", "Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited"), distribution = c("AUTO", "bernoulli", "multinomial", "gaussian", "poisson", "gamma", "tweedie", "laplace", "quantile", "huber"), check_constant_response = TRUE, custom_metric_func = NULL, uplift_metric = c("AUTO", "KL", "Euclidean", "ChiSquared"), auuc_type = c("AUTO", "qini", "lift", "gain"), auuc_nbins = -1, verbose = FALSE )h2o.upliftRandomForest( x, y, training_frame, treatment_column, model_id = NULL, validation_frame = NULL, score_each_iteration = FALSE, score_tree_interval = 0, ignore_const_cols = TRUE, ntrees = 50, max_depth = 20, min_rows = 1, nbins = 20, nbins_top_level = 1024, nbins_cats = 1024, max_runtime_secs = 0, seed = -1, mtries = -2, sample_rate = 0.632, sample_rate_per_class = NULL, col_sample_rate_change_per_level = 1, col_sample_rate_per_tree = 1, histogram_type = c("AUTO", "UniformAdaptive", "Random", "QuantilesGlobal", "RoundRobin", "UniformRobust"), categorical_encoding = c("AUTO", "Enum", "OneHotInternal", "OneHotExplicit", "Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited"), distribution = c("AUTO", "bernoulli", "multinomial", "gaussian", "poisson", "gamma", "tweedie", "laplace", "quantile", "huber"), check_constant_response = TRUE, custom_metric_func = NULL, uplift_metric = c("AUTO", "KL", "Euclidean", "ChiSquared"), auuc_type = c("AUTO", "qini", "lift", "gain"), auuc_nbins = -1, verbose = FALSE )
x |
(Optional) A vector containing the names or indices of the predictor variables to use in building the model. If x is missing, then all columns except y are used. |
y |
The name or column index of the response variable in the data. The response must be either a numeric or a categorical/factor variable. If the response is numeric, then a regression model will be trained, otherwise it will train a classification model. |
training_frame |
Id of the training data frame. |
treatment_column |
Define the column which will be used for computing uplift gain to select best split for a tree. The column has to divide the dataset into treatment (value 1) and control (value 0) groups. Defaults to treatment. |
model_id |
Destination id for this model; auto-generated if not specified. |
validation_frame |
Id of the validation data frame. |
score_each_iteration |
|
score_tree_interval |
Score the model after every so many trees. Disabled if set to 0. Defaults to 0. |
ignore_const_cols |
|
ntrees |
Number of trees. Defaults to 50. |
max_depth |
Maximum tree depth (0 for unlimited). Defaults to 20. |
min_rows |
Fewest allowed (weighted) observations in a leaf. Defaults to 1. |
nbins |
For numerical columns (real/int), build a histogram of (at least) this many bins, then split at the best point Defaults to 20. |
nbins_top_level |
For numerical columns (real/int), build a histogram of (at most) this many bins at the root level, then decrease by factor of two per level Defaults to 1024. |
nbins_cats |
For categorical columns (factors), build a histogram of this many bins, then split at the best point. Higher values can lead to more overfitting. Defaults to 1024. |
max_runtime_secs |
Maximum allowed runtime in seconds for model training. Use 0 to disable. Defaults to 0. |
seed |
Seed for random numbers (affects certain parts of the algo that are stochastic and those might or might not be enabled by default). Defaults to -1 (time-based random number). |
mtries |
Number of variables randomly sampled as candidates at each split. If set to -1, defaults to sqrt{p} for classification and p/3 for regression (where p is the # of predictors Defaults to -2. |
sample_rate |
Row sample rate per tree (from 0.0 to 1.0) Defaults to 0.632. |
sample_rate_per_class |
A list of row sample rates per class (relative fraction for each class, from 0.0 to 1.0), for each tree |
col_sample_rate_change_per_level |
Relative change of the column sampling rate for every level (must be > 0.0 and <= 2.0) Defaults to 1. |
col_sample_rate_per_tree |
Column sample rate per tree (from 0.0 to 1.0) Defaults to 1. |
histogram_type |
What type of histogram to use for finding optimal split points Must be one of: "AUTO", "UniformAdaptive", "Random", "QuantilesGlobal", "RoundRobin", "UniformRobust". Defaults to AUTO. |
categorical_encoding |
Encoding scheme for categorical features Must be one of: "AUTO", "Enum", "OneHotInternal", "OneHotExplicit", "Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited". Defaults to AUTO. |
distribution |
Distribution function Must be one of: "AUTO", "bernoulli", "multinomial", "gaussian", "poisson", "gamma", "tweedie", "laplace", "quantile", "huber". Defaults to AUTO. |
check_constant_response |
|
custom_metric_func |
Reference to custom evaluation function, format: 'language:keyName=funcName' |
uplift_metric |
Divergence metric used to find best split when building an uplift tree. Must be one of: "AUTO", "KL", "Euclidean", "ChiSquared". Defaults to AUTO. |
auuc_type |
Metric used to calculate Area Under Uplift Curve. Must be one of: "AUTO", "qini", "lift", "gain". Defaults to AUTO. |
auuc_nbins |
Number of bins to calculate Area Under Uplift Curve. Defaults to -1. |
verbose |
|
Creates a H2OModel object of the right type.
predict.H2OModel for prediction
Upload a binary model from the provided local path to the H2O cluster. (H2O model can be saved in a binary form either by saveModel() or by download_model() function.)
h2o.upload_model(path)h2o.upload_model(path)
path |
A path on the machine this python session is currently connected to, specifying the location of the model to upload. |
Returns a new H2OModel object.
h2o.saveModel, h2o.download_model
## Not run: # library(h2o) # h2o.init() # prostate_path = system.file("extdata", "prostate.csv", package = "h2o") # prostate = h2o.importFile(path = prostate_path) # prostate_glm = h2o.glm(y = "CAPSULE", x = c("AGE","RACE","PSA","DCAPS"), # training_frame = prostate, family = "binomial", alpha = 0.5) # glmmodel_path = h2o.download_model(prostate_glm, dir = "/Users/UserName/Desktop") # glmmodel_load = h2o.upload_model(glmmodel_path) ## End(Not run)## Not run: # library(h2o) # h2o.init() # prostate_path = system.file("extdata", "prostate.csv", package = "h2o") # prostate = h2o.importFile(path = prostate_path) # prostate_glm = h2o.glm(y = "CAPSULE", x = c("AGE","RACE","PSA","DCAPS"), # training_frame = prostate, family = "binomial", alpha = 0.5) # glmmodel_path = h2o.download_model(prostate_glm, dir = "/Users/UserName/Desktop") # glmmodel_load = h2o.upload_model(glmmodel_path) ## End(Not run)
Usage example: mojo_model <- h2o.upload_mojo(model_file_path = "/path/to/local/mojo.zip") predictions <- h2o.predict(mojo_model, dataset)
h2o.upload_mojo(mojo_local_file_path, model_id = NULL)h2o.upload_mojo(mojo_local_file_path, model_id = NULL)
mojo_local_file_path |
Filesystem path to the model imported |
model_id |
Model ID, default is NULL |
Returns H2O Generic Model embedding given MOJO model
## Not run: # Import default Iris dataset as H2O frame data <- as.h2o(iris) # Train a very simple GBM model features <- c("Sepal.Length", "Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width") original_model <- h2o.gbm(x = features, y = "Species", training_frame = data) # Download the trained GBM model as MOJO (temporary directory used in this example) mojo_original_name <- h2o.download_mojo(model = original_model, path = tempdir()) mojo_original_path <- paste0(tempdir(), "/", mojo_original_name) # Upload the MOJO from local filesystem and obtain a Generic model mojo_model <- h2o.upload_mojo(mojo_original_path) # Perform scoring with the generic model predictions <- h2o.predict(mojo_model, data) ## End(Not run)## Not run: # Import default Iris dataset as H2O frame data <- as.h2o(iris) # Train a very simple GBM model features <- c("Sepal.Length", "Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width") original_model <- h2o.gbm(x = features, y = "Species", training_frame = data) # Download the trained GBM model as MOJO (temporary directory used in this example) mojo_original_name <- h2o.download_mojo(model = original_model, path = tempdir()) mojo_original_path <- paste0(tempdir(), "/", mojo_original_name) # Upload the MOJO from local filesystem and obtain a Generic model mojo_model <- h2o.upload_mojo(mojo_original_path) # Perform scoring with the generic model predictions <- h2o.predict(mojo_model, data) ## End(Not run)
Compute the variance or covariance matrix of one or two H2OFrames.
h2o.var(x, y = NULL, na.rm = FALSE, use) var(x, y = NULL, na.rm = FALSE, use)h2o.var(x, y = NULL, na.rm = FALSE, use) var(x, y = NULL, na.rm = FALSE, use)
x |
An H2OFrame object. |
y |
|
na.rm |
|
use |
An optional character string indicating how to handle missing values. This must be one of the following: "everything" - outputs NaNs whenever one of its contributing observations is missing "all.obs" - presence of missing observations will throw an error "complete.obs" - discards missing values along with all observations in their rows so that only complete observations are used |
cor for the base R implementation, var(). h2o.sd for standard deviation.
## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(path = prostate_path) var(prostate$AGE) ## End(Not run)## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(path = prostate_path) var(prostate$AGE) ## End(Not run)
Retrieve the variable importance.
h2o.varimp(object, ...)h2o.varimp(object, ...)
object |
An H2O object. |
... |
Additional arguments for specific use-cases. |
## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate_complete.csv.zip" pros <- h2o.importFile(f) response <- "GLEASON" predictors <- c("ID", "AGE", "CAPSULE", "DCAPS", "PSA", "VOL", "DPROS") aml <- h2o.automl(x = predictors, y = response, training_frame = pros, max_runtime_secs = 60) h2o.varimp(aml, top_n = 20) # get variable importance matrix for the top 20 models h2o.varimp(aml@leader) # get variable importance for the leader model ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate_complete.csv.zip" pros <- h2o.importFile(f) response <- "GLEASON" predictors <- c("ID", "AGE", "CAPSULE", "DCAPS", "PSA", "VOL", "DPROS") aml <- h2o.automl(x = predictors, y = response, training_frame = pros, max_runtime_secs = 60) h2o.varimp(aml, top_n = 20) # get variable importance matrix for the top 20 models h2o.varimp(aml@leader) # get variable importance for the leader model ## End(Not run)
Variable importance heatmap shows variable importance across multiple models. Some models in H2O return variable importance for one-hot (binary indicator) encoded versions of categorical columns (e.g. Deep Learning, XGBoost). In order for the variable importance of categorical columns to be compared across all model types we compute a summarization of the the variable importance across all one-hot encoded features and return a single variable importance for the original categorical feature. By default, the models and variables are ordered by their similarity.
h2o.varimp_heatmap(object, top_n = 20, num_of_features = 20)h2o.varimp_heatmap(object, top_n = 20, num_of_features = 20)
object |
A list of H2O models, an H2O AutoML instance, or an H2OFrame with a 'model_id' column (e.g. H2OAutoML leaderboard). |
top_n |
Integer specifying the number models shown in the heatmap (based on leaderboard ranking). Defaults to 20. |
num_of_features |
Integer specifying the number of features shown in the heatmap based on the maximum variable importance across the models. Use NULL for unlimited. Defaults to 20. |
A ggplot2 object.
## Not run: library(h2o) h2o.init() # Import the wine dataset into H2O: f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/wine/winequality-redwhite-no-BOM.csv" df <- h2o.importFile(f) # Set the response response <- "quality" # Split the dataset into a train and test set: splits <- h2o.splitFrame(df, ratios = 0.8, seed = 1) train <- splits[[1]] test <- splits[[2]] # Build and train the model: aml <- h2o.automl(y = response, training_frame = train, max_models = 10, seed = 1) # Create the variable importance heatmap varimp_heatmap <- h2o.varimp_heatmap(aml) print(varimp_heatmap) ## End(Not run)## Not run: library(h2o) h2o.init() # Import the wine dataset into H2O: f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/wine/winequality-redwhite-no-BOM.csv" df <- h2o.importFile(f) # Set the response response <- "quality" # Split the dataset into a train and test set: splits <- h2o.splitFrame(df, ratios = 0.8, seed = 1) train <- splits[[1]] test <- splits[[2]] # Build and train the model: aml <- h2o.automl(y = response, training_frame = train, max_models = 10, seed = 1) # Create the variable importance heatmap varimp_heatmap <- h2o.varimp_heatmap(aml) print(varimp_heatmap) ## End(Not run)
Plot Variable Importances
h2o.varimp_plot(model, num_of_features = NULL)h2o.varimp_plot(model, num_of_features = NULL)
model |
A trained model (accepts a trained random forest, GBM,
or deep learning model, will use |
num_of_features |
The number of features shown in the plot (default is 10 or all if less than 10). |
h2o.std_coef_plot for GLM.
## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.importFile(prostate_path) prostate[, 2] <- as.factor(prostate[, 2]) model <- h2o.gbm(x = 3:9, y = 2, training_frame = prostate, distribution = "bernoulli") h2o.varimp_plot(model) # for deep learning set the variable_importance parameter to TRUE iris_hf <- as.h2o(iris) iris_dl <- h2o.deeplearning(x = 1:4, y = 5, training_frame = iris_hf, variable_importances = TRUE) h2o.varimp_plot(iris_dl) ## End(Not run)## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.importFile(prostate_path) prostate[, 2] <- as.factor(prostate[, 2]) model <- h2o.gbm(x = 3:9, y = 2, training_frame = prostate, distribution = "bernoulli") h2o.varimp_plot(model) # for deep learning set the variable_importance parameter to TRUE iris_hf <- as.h2o(iris) iris_dl <- h2o.deeplearning(x = 1:4, y = 5, training_frame = iris_hf, variable_importances = TRUE) h2o.varimp_plot(iris_dl) ## End(Not run)
Retrieve the variable importance.
## S4 method for signature 'H2OAutoML' h2o.varimp(object, top_n = 20, num_of_features = NULL)## S4 method for signature 'H2OAutoML' h2o.varimp(object, top_n = 20, num_of_features = NULL)
object |
An H2OAutoML object. |
top_n |
Show at most top_n models |
num_of_features |
Integer specifying the number of features returned based on the maximum importance across the models. Use NULL for unlimited. Defaults to NULL. |
## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate_complete.csv.zip" pros <- h2o.importFile(f) response <- "GLEASON" predictors <- c("ID", "AGE", "CAPSULE", "DCAPS", "PSA", "VOL", "DPROS") aml <- h2o.automl(x = predictors, y = response, training_frame = pros, max_runtime_secs = 60) h2o.varimp(aml) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate_complete.csv.zip" pros <- h2o.importFile(f) response <- "GLEASON" predictors <- c("ID", "AGE", "CAPSULE", "DCAPS", "PSA", "VOL", "DPROS") aml <- h2o.automl(x = predictors, y = response, training_frame = pros, max_runtime_secs = 60) h2o.varimp(aml) ## End(Not run)
Retrieve the variable importance.
## S4 method for signature 'H2OFrame' h2o.varimp(object, num_of_features = NULL)## S4 method for signature 'H2OFrame' h2o.varimp(object, num_of_features = NULL)
object |
A leaderboard frame. |
num_of_features |
Integer specifying the number of features returned based on the maximum importance across the models. Use NULL for unlimited. Defaults to NULL. |
## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate_complete.csv.zip" pros <- h2o.importFile(f) response <- "GLEASON" predictors <- c("ID", "AGE", "CAPSULE", "DCAPS", "PSA", "VOL", "DPROS") aml <- h2o.automl(x = predictors, y = response, training_frame = pros, max_runtime_secs = 60) h2o.varimp(aml@leaderboard[1:5,]) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate_complete.csv.zip" pros <- h2o.importFile(f) response <- "GLEASON" predictors <- c("ID", "AGE", "CAPSULE", "DCAPS", "PSA", "VOL", "DPROS") aml <- h2o.automl(x = predictors, y = response, training_frame = pros, max_runtime_secs = 60) h2o.varimp(aml@leaderboard[1:5,]) ## End(Not run)
Retrieve the variable importance.
## S4 method for signature 'H2OModel' h2o.varimp(object)## S4 method for signature 'H2OModel' h2o.varimp(object)
object |
An H2OModel object. |
## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate_complete.csv.zip" pros <- h2o.importFile(f) response <- "GLEASON" predictors <- c("ID", "AGE", "CAPSULE", "DCAPS", "PSA", "VOL", "DPROS") model <- h2o.glm(x = predictors, y = response, training_frame = pros) h2o.varimp(model) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate_complete.csv.zip" pros <- h2o.importFile(f) response <- "GLEASON" predictors <- c("ID", "AGE", "CAPSULE", "DCAPS", "PSA", "VOL", "DPROS") model <- h2o.glm(x = predictors, y = response, training_frame = pros) h2o.varimp(model) ## End(Not run)
Retrieve per-variable split information for a given Isolation Forest model. Output will include: - count - The number of times a variable was used to make a split. - aggregated_split_ratios - The split ratio is defined as "abs(#left_observations - #right_observations) / #before_split". Even splits (#left_observations approx the same as #right_observations) contribute less to the total aggregated split ratio value for the given feature; highly imbalanced splits (eg. #left_observations >> #right_observations) contribute more. - aggregated_split_depths - The sum of all depths of a variable used to make a split. (If a variable is used on level N of a tree, then it contributes with N to the total aggregate.)
h2o.varsplits(object)h2o.varsplits(object)
object |
An Isolation Forest model represented by H2OModel object. |
Converts the entries of an H2OFrame object from milliseconds to weeks of the week year (starting from 1).
h2o.week(x) week(x) ## S3 method for class 'H2OFrame' week(x)h2o.week(x) week(x) ## S3 method for class 'H2OFrame' week(x)
x |
An H2OFrame object. |
An H2OFrame object containing the entries of x converted to weeks of
the week year.
## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/jira/v-11-eurodate.csv" hdf <- h2o.importFile(f) h2o.week(hdf["ds9"]) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/jira/v-11-eurodate.csv" hdf <- h2o.importFile(f) h2o.week(hdf["ds9"]) ## End(Not run)
Retrieve the respective weight matrix
h2o.weights(object, matrix_id = 1)h2o.weights(object, matrix_id = 1)
object |
An H2OModel or H2OModelMetrics |
matrix_id |
An integer, ranging from 1 to number of layers + 1, that specifies the weight matrix to return. |
## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/chicago/chicagoCensus.csv" census <- h2o.importFile(f) census[, 1] <- as.factor(census[, 1]) dl_model <- h2o.deeplearning(x = c(1:3), y = 4, training_frame = census, hidden = c(17, 191), epochs = 1, balance_classes = FALSE, export_weights_and_biases = TRUE) h2o.weights(dl_model, matrix_id = 1) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/chicago/chicagoCensus.csv" census <- h2o.importFile(f) census[, 1] <- as.factor(census[, 1]) dl_model <- h2o.deeplearning(x = c(1:3), y = 4, training_frame = census, hidden = c(17, 191), epochs = 1, balance_classes = FALSE, export_weights_and_biases = TRUE) h2o.weights(dl_model, matrix_id = 1) ## End(Not run)
Give the TRUE indices of a logical object, allowing for array indices.
h2o.which(x)h2o.which(x)
x |
An H2OFrame object. |
Returns an H2OFrame object.
which for the base R method.
## Not run: library(h2o) h2o.init() iris_hf <- as.h2o(iris) h2o.which(iris_hf[, 1] == 4.4) ## End(Not run)## Not run: library(h2o) h2o.init() iris_hf <- as.h2o(iris) h2o.which(iris_hf[, 1] == 4.4) ## End(Not run)
Get the index of the max value in a column or row
h2o.which_max(x, na.rm = TRUE, axis = 0) which.max.H2OFrame(x, na.rm = TRUE, axis = 0) which.min.H2OFrame(x, na.rm = TRUE, axis = 0)h2o.which_max(x, na.rm = TRUE, axis = 0) which.max.H2OFrame(x, na.rm = TRUE, axis = 0) which.min.H2OFrame(x, na.rm = TRUE, axis = 0)
x |
An H2OFrame object. |
na.rm |
|
axis |
|
Returns an H2OFrame object.
which.min for the base R method, which.max().
## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/chicago/chicagoCensus.csv" census <- h2o.importFile(f) census[, 1] <- as.factor(census[, 1]) dl_model <- h2o.deeplearning(x = c(1:3), y = 4, hidden = c(17, 191), epochs = 1, training_frame = census, balance_classes = FALSE, export_weights_and_biases = TRUE) h2o.which_max(census["PER CAPITA INCOME "], na.rm = FALSE, axis = 0) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/chicago/chicagoCensus.csv" census <- h2o.importFile(f) census[, 1] <- as.factor(census[, 1]) dl_model <- h2o.deeplearning(x = c(1:3), y = 4, hidden = c(17, 191), epochs = 1, training_frame = census, balance_classes = FALSE, export_weights_and_biases = TRUE) h2o.which_max(census["PER CAPITA INCOME "], na.rm = FALSE, axis = 0) ## End(Not run)
Get the index of the min value in a column or row
h2o.which_min(x, na.rm = TRUE, axis = 0)h2o.which_min(x, na.rm = TRUE, axis = 0)
x |
An H2OFrame object. |
na.rm |
|
axis |
|
Returns an H2OFrame object.
which.min for the base R method.
## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/chicago/chicagoCensus.csv" census <- h2o.importFile(f) dl_model <- h2o.deeplearning(x = c(1:3), y = 4, hidden = c(17, 191), epochs = 1, training_frame = census, balance_classes = FALSE, export_weights_and_biases = TRUE) h2o.which_min(census["PER CAPITA INCOME "], na.rm = FALSE, axis = 0) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/chicago/chicagoCensus.csv" census <- h2o.importFile(f) dl_model <- h2o.deeplearning(x = c(1:3), y = 4, hidden = c(17, 191), epochs = 1, training_frame = census, balance_classes = FALSE, export_weights_and_biases = TRUE) h2o.which_min(census["PER CAPITA INCOME "], na.rm = FALSE, axis = 0) ## End(Not run)
Get the Within SS
h2o.withinss(object)h2o.withinss(object)
object |
An H2OClusteringModel object. |
Trains a word2vec model on a String column of an H2O data frame
h2o.word2vec( training_frame = NULL, model_id = NULL, min_word_freq = 5, word_model = c("SkipGram", "CBOW"), norm_model = c("HSM"), vec_size = 100, window_size = 5, sent_sample_rate = 0.001, init_learning_rate = 0.025, epochs = 5, pre_trained = NULL, max_runtime_secs = 0, export_checkpoints_dir = NULL )h2o.word2vec( training_frame = NULL, model_id = NULL, min_word_freq = 5, word_model = c("SkipGram", "CBOW"), norm_model = c("HSM"), vec_size = 100, window_size = 5, sent_sample_rate = 0.001, init_learning_rate = 0.025, epochs = 5, pre_trained = NULL, max_runtime_secs = 0, export_checkpoints_dir = NULL )
training_frame |
Id of the training data frame. |
model_id |
Destination id for this model; auto-generated if not specified. |
min_word_freq |
This will discard words that appear less than <int> times Defaults to 5. |
word_model |
The word model to use (SkipGram or CBOW) Must be one of: "SkipGram", "CBOW". Defaults to SkipGram. |
norm_model |
Use Hierarchical Softmax Must be one of: "HSM". Defaults to HSM. |
vec_size |
Set size of word vectors Defaults to 100. |
window_size |
Set max skip length between words Defaults to 5. |
sent_sample_rate |
Set threshold for occurrence of words. Those that appear with higher frequency in the training data will be randomly down-sampled; useful range is (0, 1e-5) Defaults to 0.001. |
init_learning_rate |
Set the starting learning rate Defaults to 0.025. |
epochs |
Number of training iterations to run Defaults to 5. |
pre_trained |
Id of a data frame that contains a pre-trained (external) word2vec model |
max_runtime_secs |
Maximum allowed runtime in seconds for model training. Use 0 to disable. Defaults to 0. |
export_checkpoints_dir |
Automatically export generated models to this directory. |
## Not run: library(h2o) h2o.init() # Import the CraigslistJobTitles dataset job_titles <- h2o.importFile( "https://s3.amazonaws.com/h2o-public-test-data/smalldata/craigslistJobTitles.csv", col.names = c("category", "jobtitle"), col.types = c("String", "String"), header = TRUE ) # Build and train the Word2Vec model words <- h2o.tokenize(job_titles, " ") vec <- h2o.word2vec(training_frame = words) h2o.findSynonyms(vec, "teacher", count = 20) ## End(Not run)## Not run: library(h2o) h2o.init() # Import the CraigslistJobTitles dataset job_titles <- h2o.importFile( "https://s3.amazonaws.com/h2o-public-test-data/smalldata/craigslistJobTitles.csv", col.names = c("category", "jobtitle"), col.types = c("String", "String"), header = TRUE ) # Build and train the Word2Vec model words <- h2o.tokenize(job_titles, " ") vec <- h2o.word2vec(training_frame = words) h2o.findSynonyms(vec, "teacher", count = 20) ## End(Not run)
Builds a eXtreme Gradient Boosting model using the native XGBoost backend.
h2o.xgboost( x, y, training_frame, model_id = NULL, validation_frame = NULL, nfolds = 0, keep_cross_validation_models = TRUE, keep_cross_validation_predictions = FALSE, keep_cross_validation_fold_assignment = FALSE, score_each_iteration = FALSE, fold_assignment = c("AUTO", "Random", "Modulo", "Stratified"), fold_column = NULL, ignore_const_cols = TRUE, offset_column = NULL, weights_column = NULL, stopping_rounds = 0, stopping_metric = c("AUTO", "deviance", "logloss", "MSE", "RMSE", "MAE", "RMSLE", "AUC", "AUCPR", "lift_top_group", "misclassification", "mean_per_class_error", "custom", "custom_increasing"), stopping_tolerance = 0.001, max_runtime_secs = 0, seed = -1, distribution = c("AUTO", "bernoulli", "multinomial", "gaussian", "poisson", "gamma", "tweedie", "laplace", "quantile", "huber"), tweedie_power = 1.5, categorical_encoding = c("AUTO", "Enum", "OneHotInternal", "OneHotExplicit", "Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited"), quiet_mode = TRUE, checkpoint = NULL, export_checkpoints_dir = NULL, ntrees = 50, max_depth = 6, min_rows = 1, min_child_weight = 1, learn_rate = 0.3, eta = 0.3, sample_rate = 1, subsample = 1, col_sample_rate = 1, colsample_bylevel = 1, col_sample_rate_per_tree = 1, colsample_bytree = 1, colsample_bynode = 1, max_abs_leafnode_pred = 0, max_delta_step = 0, monotone_constraints = NULL, interaction_constraints = NULL, score_tree_interval = 0, min_split_improvement = 0, gamma = 0, nthread = -1, save_matrix_directory = NULL, build_tree_one_node = FALSE, parallelize_cross_validation = TRUE, calibrate_model = FALSE, calibration_frame = NULL, calibration_method = c("AUTO", "PlattScaling", "IsotonicRegression"), max_bins = 256, max_leaves = 0, sample_type = c("uniform", "weighted"), normalize_type = c("tree", "forest"), rate_drop = 0, one_drop = FALSE, skip_drop = 0, tree_method = c("auto", "exact", "approx", "hist"), grow_policy = c("depthwise", "lossguide"), booster = c("gbtree", "gblinear", "dart"), reg_lambda = 1, reg_alpha = 0, dmatrix_type = c("auto", "dense", "sparse"), backend = c("auto", "gpu", "cpu"), gpu_id = NULL, gainslift_bins = -1, auc_type = c("AUTO", "NONE", "MACRO_OVR", "WEIGHTED_OVR", "MACRO_OVO", "WEIGHTED_OVO"), scale_pos_weight = 1, eval_metric = NULL, score_eval_metric_only = FALSE, verbose = FALSE )h2o.xgboost( x, y, training_frame, model_id = NULL, validation_frame = NULL, nfolds = 0, keep_cross_validation_models = TRUE, keep_cross_validation_predictions = FALSE, keep_cross_validation_fold_assignment = FALSE, score_each_iteration = FALSE, fold_assignment = c("AUTO", "Random", "Modulo", "Stratified"), fold_column = NULL, ignore_const_cols = TRUE, offset_column = NULL, weights_column = NULL, stopping_rounds = 0, stopping_metric = c("AUTO", "deviance", "logloss", "MSE", "RMSE", "MAE", "RMSLE", "AUC", "AUCPR", "lift_top_group", "misclassification", "mean_per_class_error", "custom", "custom_increasing"), stopping_tolerance = 0.001, max_runtime_secs = 0, seed = -1, distribution = c("AUTO", "bernoulli", "multinomial", "gaussian", "poisson", "gamma", "tweedie", "laplace", "quantile", "huber"), tweedie_power = 1.5, categorical_encoding = c("AUTO", "Enum", "OneHotInternal", "OneHotExplicit", "Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited"), quiet_mode = TRUE, checkpoint = NULL, export_checkpoints_dir = NULL, ntrees = 50, max_depth = 6, min_rows = 1, min_child_weight = 1, learn_rate = 0.3, eta = 0.3, sample_rate = 1, subsample = 1, col_sample_rate = 1, colsample_bylevel = 1, col_sample_rate_per_tree = 1, colsample_bytree = 1, colsample_bynode = 1, max_abs_leafnode_pred = 0, max_delta_step = 0, monotone_constraints = NULL, interaction_constraints = NULL, score_tree_interval = 0, min_split_improvement = 0, gamma = 0, nthread = -1, save_matrix_directory = NULL, build_tree_one_node = FALSE, parallelize_cross_validation = TRUE, calibrate_model = FALSE, calibration_frame = NULL, calibration_method = c("AUTO", "PlattScaling", "IsotonicRegression"), max_bins = 256, max_leaves = 0, sample_type = c("uniform", "weighted"), normalize_type = c("tree", "forest"), rate_drop = 0, one_drop = FALSE, skip_drop = 0, tree_method = c("auto", "exact", "approx", "hist"), grow_policy = c("depthwise", "lossguide"), booster = c("gbtree", "gblinear", "dart"), reg_lambda = 1, reg_alpha = 0, dmatrix_type = c("auto", "dense", "sparse"), backend = c("auto", "gpu", "cpu"), gpu_id = NULL, gainslift_bins = -1, auc_type = c("AUTO", "NONE", "MACRO_OVR", "WEIGHTED_OVR", "MACRO_OVO", "WEIGHTED_OVO"), scale_pos_weight = 1, eval_metric = NULL, score_eval_metric_only = FALSE, verbose = FALSE )
x |
(Optional) A vector containing the names or indices of the predictor variables to use in building the model. If x is missing, then all columns except y are used. |
y |
The name or column index of the response variable in the data. The response must be either a numeric or a categorical/factor variable. If the response is numeric, then a regression model will be trained, otherwise it will train a classification model. |
training_frame |
Id of the training data frame. |
model_id |
Destination id for this model; auto-generated if not specified. |
validation_frame |
Id of the validation data frame. |
nfolds |
Number of folds for K-fold cross-validation (0 to disable or >= 2). Defaults to 0. |
keep_cross_validation_models |
|
keep_cross_validation_predictions |
|
keep_cross_validation_fold_assignment |
|
score_each_iteration |
|
fold_assignment |
Cross-validation fold assignment scheme, if fold_column is not specified. The 'Stratified' option will stratify the folds based on the response variable, for classification problems. Must be one of: "AUTO", "Random", "Modulo", "Stratified". Defaults to AUTO. |
fold_column |
Column with cross-validation fold index assignment per observation. |
ignore_const_cols |
|
offset_column |
Offset column. This will be added to the combination of columns before applying the link function. |
weights_column |
Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0. |
stopping_rounds |
Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable) Defaults to 0. |
stopping_metric |
Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client. Must be one of: "AUTO", "deviance", "logloss", "MSE", "RMSE", "MAE", "RMSLE", "AUC", "AUCPR", "lift_top_group", "misclassification", "mean_per_class_error", "custom", "custom_increasing". Defaults to AUTO. |
stopping_tolerance |
Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much) Defaults to 0.001. |
max_runtime_secs |
Maximum allowed runtime in seconds for model training. Use 0 to disable. Defaults to 0. |
seed |
Seed for random numbers (affects certain parts of the algo that are stochastic and those might or might not be enabled by default). Defaults to -1 (time-based random number). |
distribution |
Distribution function Must be one of: "AUTO", "bernoulli", "multinomial", "gaussian", "poisson", "gamma", "tweedie", "laplace", "quantile", "huber". Defaults to AUTO. |
tweedie_power |
Tweedie power for Tweedie regression, must be between 1 and 2. Defaults to 1.5. |
categorical_encoding |
Encoding scheme for categorical features Must be one of: "AUTO", "Enum", "OneHotInternal", "OneHotExplicit", "Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited". Defaults to AUTO. |
quiet_mode |
|
checkpoint |
Model checkpoint to resume training with. |
export_checkpoints_dir |
Automatically export generated models to this directory. |
ntrees |
(same as n_estimators) Number of trees. Defaults to 50. |
max_depth |
Maximum tree depth (0 for unlimited). Defaults to 6. |
min_rows |
(same as min_child_weight) Fewest allowed (weighted) observations in a leaf. Defaults to 1. |
min_child_weight |
(same as min_rows) Fewest allowed (weighted) observations in a leaf. Defaults to 1. |
learn_rate |
(same as eta) Learning rate (from 0.0 to 1.0) Defaults to 0.3. |
eta |
(same as learn_rate) Learning rate (from 0.0 to 1.0) Defaults to 0.3. |
sample_rate |
(same as subsample) Row sample rate per tree (from 0.0 to 1.0) Defaults to 1. |
subsample |
(same as sample_rate) Row sample rate per tree (from 0.0 to 1.0) Defaults to 1. |
col_sample_rate |
(same as colsample_bylevel) Column sample rate (from 0.0 to 1.0) Defaults to 1. |
colsample_bylevel |
(same as col_sample_rate) Column sample rate (from 0.0 to 1.0) Defaults to 1. |
col_sample_rate_per_tree |
(same as colsample_bytree) Column sample rate per tree (from 0.0 to 1.0) Defaults to 1. |
colsample_bytree |
(same as col_sample_rate_per_tree) Column sample rate per tree (from 0.0 to 1.0) Defaults to 1. |
colsample_bynode |
Column sample rate per tree node (from 0.0 to 1.0) Defaults to 1. |
max_abs_leafnode_pred |
(same as max_delta_step) Maximum absolute value of a leaf node prediction Defaults to 0.0. |
max_delta_step |
(same as max_abs_leafnode_pred) Maximum absolute value of a leaf node prediction Defaults to 0.0. |
monotone_constraints |
A mapping representing monotonic constraints. Use +1 to enforce an increasing constraint and -1 to specify a decreasing constraint. |
interaction_constraints |
A set of allowed column interactions. |
score_tree_interval |
Score the model after every so many trees. Disabled if set to 0. Defaults to 0. |
min_split_improvement |
(same as gamma) Minimum relative improvement in squared error reduction for a split to happen Defaults to 0.0. |
gamma |
(same as min_split_improvement) Minimum relative improvement in squared error reduction for a split to happen Defaults to 0.0. |
nthread |
Number of parallel threads that can be used to run XGBoost. Cannot exceed H2O cluster limits (-nthreads parameter). Defaults to maximum available Defaults to -1. |
save_matrix_directory |
Directory where to save matrices passed to XGBoost library. Useful for debugging. |
build_tree_one_node |
|
parallelize_cross_validation |
|
calibrate_model |
|
calibration_frame |
Data for model calibration |
calibration_method |
Calibration method to use Must be one of: "AUTO", "PlattScaling", "IsotonicRegression". Defaults to AUTO. |
max_bins |
For tree_method=hist only: maximum number of bins Defaults to 256. |
max_leaves |
For tree_method=hist only: maximum number of leaves Defaults to 0. |
sample_type |
For booster=dart only: sample_type Must be one of: "uniform", "weighted". Defaults to uniform. |
normalize_type |
For booster=dart only: normalize_type Must be one of: "tree", "forest". Defaults to tree. |
rate_drop |
For booster=dart only: rate_drop (0..1) Defaults to 0.0. |
one_drop |
|
skip_drop |
For booster=dart only: skip_drop (0..1) Defaults to 0.0. |
tree_method |
Tree method Must be one of: "auto", "exact", "approx", "hist". Defaults to auto. |
grow_policy |
Grow policy - depthwise is standard GBM, lossguide is LightGBM Must be one of: "depthwise", "lossguide". Defaults to depthwise. |
booster |
Booster type Must be one of: "gbtree", "gblinear", "dart". Defaults to gbtree. |
reg_lambda |
L2 regularization Defaults to 1.0. |
reg_alpha |
L1 regularization Defaults to 0.0. |
dmatrix_type |
Type of DMatrix. For sparse, NAs and 0 are treated equally. Must be one of: "auto", "dense", "sparse". Defaults to auto. |
backend |
Backend. By default (auto), a GPU is used if available. Must be one of: "auto", "gpu", "cpu". Defaults to auto. |
gpu_id |
Which GPU(s) to use. |
gainslift_bins |
Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning. Defaults to -1. |
auc_type |
Set default multinomial AUC type. Must be one of: "AUTO", "NONE", "MACRO_OVR", "WEIGHTED_OVR", "MACRO_OVO", "WEIGHTED_OVO". Defaults to AUTO. |
scale_pos_weight |
Controls the effect of observations with positive labels in relation to the observations with negative labels on gradient calculation. Useful for imbalanced problems. Defaults to 1.0. |
eval_metric |
Specification of evaluation metric that will be passed to the native XGBoost backend. |
score_eval_metric_only |
|
verbose |
|
## Not run: library(h2o) h2o.init() # Import the titanic dataset f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv" titanic <- h2o.importFile(f) # Set predictors and response; set response as a factor titanic['survived'] <- as.factor(titanic['survived']) predictors <- setdiff(colnames(titanic), colnames(titanic)[2:3]) response <- "survived" # Split the dataset into train and valid splits <- h2o.splitFrame(data = titanic, ratios = .8, seed = 1234) train <- splits[[1]] valid <- splits[[2]] # Train the XGB model titanic_xgb <- h2o.xgboost(x = predictors, y = response, training_frame = train, validation_frame = valid, booster = "dart", normalize_type = "tree", seed = 1234) ## End(Not run)## Not run: library(h2o) h2o.init() # Import the titanic dataset f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv" titanic <- h2o.importFile(f) # Set predictors and response; set response as a factor titanic['survived'] <- as.factor(titanic['survived']) predictors <- setdiff(colnames(titanic), colnames(titanic)[2:3]) response <- "survived" # Split the dataset into train and valid splits <- h2o.splitFrame(data = titanic, ratios = .8, seed = 1234) train <- splits[[1]] valid <- splits[[2]] # Train the XGB model titanic_xgb <- h2o.xgboost(x = predictors, y = response, training_frame = train, validation_frame = valid, booster = "dart", normalize_type = "tree", seed = 1234) ## End(Not run)
Ask the H2O server whether a XGBoost model can be built. (Depends on availability of native backend.) Returns True if a XGBoost model can be built, or False otherwise.
h2o.xgboost.available()h2o.xgboost.available()
Convert the entries of an H2OFrame object from milliseconds to years, indexed starting from 1900.
h2o.year(x) year(x) ## S3 method for class 'H2OFrame' year(x)h2o.year(x) year(x) ## S3 method for class 'H2OFrame' year(x)
x |
An H2OFrame object. |
This method calls the function of the MutableDateTime class in Java.
An H2OFrame object containing the entries of x converted to years
## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/jira/v-11-eurodate.csv" hdf <- h2o.importFile(f) h2o.year(hdf["ds9"]) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/jira/v-11-eurodate.csv" hdf <- h2o.importFile(f) h2o.year(hdf["ds9"]) ## End(Not run)
This virtual class represents a clustering model built by H2O.
This object has slots for the key, which is a character string that points to the model key existing in the H2O cluster, the data used to build the model (an object of class H2OFrame).
model_idA character string specifying the key for the model fit in the H2O cluster's key-value store.
algorithmA character string specifying the algorithm that was used to fit the model.
parametersA list containing the parameter settings that were used to fit the model that differ from the defaults.
allparametersA list containing all parameters used to fit the model.
modelA list containing the characteristics of the model returned by the algorithm.
The number of points in each cluster.
Total sum of squared error to grand mean.
A vector of within-cluster sum of squared error.
Total within-cluster sum of squared error.
Between-cluster sum of squared error.
This class represents a connection to an H2O cluster.
## S4 method for signature 'H2OConnection' show(object)## S4 method for signature 'H2OConnection' show(object)
object |
an |
Because H2O is not a master-slave architecture, there is no restriction on which H2O node is used to establish the connection between R (the client) and H2O (the server).
A new H2O connection is established via the h2o.init() function, which takes as parameters the 'ip' and 'port' of the machine running an instance to connect with. The default behavior is to connect with a local instance of H2O at port 54321, or to boot a new local instance if one is not found at port 54321.
ipA character string specifying the IP address of the H2O cluster.
portA numeric value specifying the port number of the H2O cluster.
nameA character value specifying the name of the H2O cluster.
proxyA character specifying the proxy path of the H2O cluster.
httpsSet this to TRUE to use https instead of http.
cacertPath to a CA bundle file with root and intermediate certificates of trusted CAs.
insecureSet this to TRUE to disable SSL certificate checking.
usernameUsername to login with.
passwordPassword to login with.
use_spnegoSet this to TRUE to use SPNEGO authentication.
cookiesCookies to add to request
context_pathContext path which is appended to H2O server location.
mutableAn H2OConnectionMutableState object to hold the mutable state for the H2O connection.
This class represents the mutable aspects of a connection to an H2O cluster.
session_idA character string specifying the H2O session identifier.
key_countA integer value specifying count for the number of keys generated for the session_id.
Virtual object representing H2O's CoxPH Model.
## S4 method for signature 'H2OCoxPHModel' show(object) ## S3 method for class 'H2OCoxPHModel' coef(object, ...) ## S3 method for class 'H2OCoxPHModel' extractAIC(fit, scale, k = 2, ...) ## S3 method for class 'H2OCoxPHModel' logLik(object, ...) survfit.H2OCoxPHModel(formula, newdata, ...) ## S3 method for class 'H2OCoxPHModel' vcov(object, ...)## S4 method for signature 'H2OCoxPHModel' show(object) ## S3 method for class 'H2OCoxPHModel' coef(object, ...) ## S3 method for class 'H2OCoxPHModel' extractAIC(fit, scale, k = 2, ...) ## S3 method for class 'H2OCoxPHModel' logLik(object, ...) survfit.H2OCoxPHModel(formula, newdata, ...) ## S3 method for class 'H2OCoxPHModel' vcov(object, ...)
object |
an |
... |
additional arguments to pass on. |
fit |
an |
scale |
optional numeric specifying the scale parameter of the model. |
k |
numeric specifying the weight of the equivalent degrees of freedom. |
formula |
an |
newdata |
an optional |
Wrapper object for summary information compatible with survival package.
## S4 method for signature 'H2OCoxPHModelSummary' show(object) ## S3 method for class 'H2OCoxPHModelSummary' coef(object, ...)## S4 method for signature 'H2OCoxPHModelSummary' show(object) ## S3 method for class 'H2OCoxPHModelSummary' coef(object, ...)
object |
An |
... |
additional arguments to pass on. |
summaryA list containing the a summary compatible with CoxPH summary used in the survival package.
Operators to extract or replace parts of H2OFrame objects.
## S3 method for class 'H2OFrame' data[row, col, drop = TRUE] ## S3 method for class 'H2OFrame' x$name ## S3 method for class 'H2OFrame' x[[i, exact = TRUE]] ## S3 method for class 'H2OFrame' x$name ## S3 method for class 'H2OFrame' x[[i, exact = TRUE]] ## S3 replacement method for class 'H2OFrame' data[row, col, ...] <- value ## S3 replacement method for class 'H2OFrame' data$name <- value ## S3 replacement method for class 'H2OFrame' data[[name]] <- value## S3 method for class 'H2OFrame' data[row, col, drop = TRUE] ## S3 method for class 'H2OFrame' x$name ## S3 method for class 'H2OFrame' x[[i, exact = TRUE]] ## S3 method for class 'H2OFrame' x$name ## S3 method for class 'H2OFrame' x[[i, exact = TRUE]] ## S3 replacement method for class 'H2OFrame' data[row, col, ...] <- value ## S3 replacement method for class 'H2OFrame' data$name <- value ## S3 replacement method for class 'H2OFrame' data[[name]] <- value
data |
object from which to extract element(s) or in which to replace element(s). |
row |
index specifying row element(s) to extract or replace. Indices are numeric or character vectors or empty (missing) or will be matched to the names. |
col |
index specifying column element(s) to extract or replace. |
drop |
Unused |
x |
An H2OFrame |
name |
a literal character string or a name (possibly backtick quoted). |
i |
index |
exact |
controls possible partial matching of |
... |
Further arguments passed to or from other methods. |
value |
To be assigned |
A class to contain the information about grid results
## S4 method for signature 'H2OGrid' show(object)## S4 method for signature 'H2OGrid' show(object)
object |
an |
grid_idthe final identifier of grid
model_idslist of model IDs which are included in the grid object
hyper_nameslist of parameter names used for grid search
failed_paramslist of model parameters which caused a failure during model building, it can contain a null value
failure_detailslist of detailed messages which correspond to failed parameters field
failure_stack_traceslist of stack traces corresponding to model failures reported by failed_params and failure_details fields
failed_raw_paramslist of failed raw parameters
summary_tabletable of models built with parameters and metric information.
H2OModel for the final model types.
wrapper function for instantiating H2OInfogram
H2OInfogram(model_id, ...)H2OInfogram(model_id, ...)
model_id |
is string of H2OModel object |
... |
parameters to algorithm, admissible_features, ... |
A H2OInfogram object
H2OInfogram class contains a subset of what a normal H2OModel will return
model_idstring returned as part of every H2OModel
algorithmstring denoting the algorithm used to build infogram
admissible_featuresstring array denoting all predictor names which pass the cmi and relelvance threshold
admissible_features_validstring array denoting all predictor names which pass the cmi and relelvance threshold from validation frame
admissible_features_xvalstring array denoting all predictor names which pass the cmi and relelvance threshold from cv holdout set
net_information_thresholdnumeric value denoting threshold used for predictor selection
total_information_thresholdnumeric value denoting threshold used for predictor selection
safety_index_thresholdnumeric value denoting threshold used for predictor selection
relevance_index_thresholdnumeric value denoting threshold used for predictor selection
admissible_scoreH2OFrame that contains columns, admissible, admissible_index, relevance, cmi, cmi_raw
admissible_score_validH2OFrame that contains columns, admissible, admissible_index, relevance, cmi, cmi_raw from validation frame
admissible_score_xvalH2OFrame that contains averages of columns, admissible, admissible_index, relevance, cmi, cmi_raw from cv hold-out
This class represents a single leaf node in an H2OTree.
#' @aliases H2OLeafNode
This virtual class represents a model built by H2O.
## S4 method for signature 'H2OModel' show(object)## S4 method for signature 'H2OModel' show(object)
object |
an |
This object has slots for the key, which is a character string that points to the model key existing in the H2O cluster, the data used to build the model (an object of class H2OFrame).
model_idA character string specifying the key for the model fit in the H2O cluster's key-value store.
algorithmA character string specifying the algorithm that were used to fit the model.
parametersA list containing the parameter settings that were used to fit the model that differ from the defaults.
allparametersA list containg all parameters used to fit the model.
paramsA list containing default, set, and actual parameters.
have_pojoA logical indicating whether export to POJO is supported
have_mojoA logical indicating whether export to MOJO is supported
modelA list containing the characteristics of the model returned by the algorithm.
A class to contain the information for background model jobs.
job_keya character key representing the identification of the job process.
model_idthe final identifier for the model
H2OModel for the final model types.
A class for constructing performance measures of H2O models.
## S4 method for signature 'H2OModelMetrics' show(object) ## S4 method for signature 'H2OBinomialMetrics' show(object) ## S4 method for signature 'H2OBinomialUpliftMetrics' show(object) ## S4 method for signature 'H2OMultinomialMetrics' show(object) ## S4 method for signature 'H2OOrdinalMetrics' show(object) ## S4 method for signature 'H2ORegressionMetrics' show(object) ## S4 method for signature 'H2OClusteringMetrics' show(object) ## S4 method for signature 'H2OAutoEncoderMetrics' show(object) ## S4 method for signature 'H2ODimReductionMetrics' show(object) ## S4 method for signature 'H2OAnomalyDetectionMetrics' show(object)## S4 method for signature 'H2OModelMetrics' show(object) ## S4 method for signature 'H2OBinomialMetrics' show(object) ## S4 method for signature 'H2OBinomialUpliftMetrics' show(object) ## S4 method for signature 'H2OMultinomialMetrics' show(object) ## S4 method for signature 'H2OOrdinalMetrics' show(object) ## S4 method for signature 'H2ORegressionMetrics' show(object) ## S4 method for signature 'H2OClusteringMetrics' show(object) ## S4 method for signature 'H2OAutoEncoderMetrics' show(object) ## S4 method for signature 'H2ODimReductionMetrics' show(object) ## S4 method for signature 'H2OAnomalyDetectionMetrics' show(object)
object |
An |
The H2ONode class.
## S4 method for signature 'H2ONode' show(object)## S4 method for signature 'H2ONode' show(object)
object |
an |
idAn integer representing node's unique identifier. Generated by H2O.
levelsA character representing categorical levels on split from parent's node belonging into this node. NULL for root node or non-categorical splits.
#' @aliases H2ONode
A class to contain the information for segment models.
## S4 method for signature 'H2OSegmentModels' show(object)## S4 method for signature 'H2OSegmentModels' show(object)
object |
an |
segment_models_idthe identifier for the segment models collections
A class to contain the information for background segment models jobs.
job_keya character key representing the identification of the job process.
segment_models_idthe final identifier for the segment models collections
H2OSegmentModels for the final segment models types.
This class represents a single non-terminal node in an H2OTree.
thresholdA numeric split threshold, typically when the split column is numerical.
left_childA H2ONodeOrNULL representing the left child node, if a node has one.
right_childA H2ONodeOrNULL representing the right child node, if a node has one.
split_featureA character representing the name of the column this node splits on.
left_levelsA character representing the levels of a categorical feature heading to the left child of this node. NA for non-categorical split.
right_levelsA character representing the levels of a categorical feature heading to the right child of this node. NA for non-categorical split.
na_directionA character representing the direction of NA values. LEFT means NA values go to the left child node, RIGH means NA values go to the right child node.
This class represents a model of a Tree built by one of H2O's algorithms (GBM, Random Forest).
## S4 method for signature 'H2OTree' show(object)## S4 method for signature 'H2OTree' show(object)
object |
an |
root_nodeA H2ONode representing the beginning of the tree behind the model. Allows further tree traversal.
left_childrenAn integer vector with left child nodes of tree's nodes
right_childrenAn integer vector with right child nodes of tree's nodes
node_idsAn integer representing identification number of a node. Node IDs are generated by H2O.
descriptionsA character vector with descriptions for each node to be found in the tree. Contains split threshold if the split is based on numerical column.
For cactegorical splits, it contains list of categorical levels for transition from the parent node.
model_idA character with the name of the model this tree is related to.
tree_numberAn integer representing the order in which the tree has been built in the model.
tree_classA character representing name of tree's class. Number of tree classes equals to the number of levels in categorical response column.
As there is exactly one class per categorical level, name of tree's class equals to the corresponding categorical level of response column.
In case of regression and binomial, the name of the categorical level is ignored can be omitted, as there is exactly one tree built in both cases.
thresholdsA numeric split thresholds. Split thresholds are not only related to numerical splits, but might be present in case of categorical split as well.
featuresA character with names of the feature/column used for the split.
levelsA character representing categorical levels on split from parent's node belonging into this node. NULL for root node or non-categorical splits.
nasA character representing if NA values go to the left node or right node. May be NA if node is a leaf.
predictionsA numeric representing predictions for each node in the graph.
tree_decision_pathA character, plain language rules representation of a trained decision tree
decision_pathsA character representing plain language rules that were used in a particular prediction.
left_cat_splitA character list of categorical levels leading to the left child node. Only present when split is categorical, otherwise none.
right_cat_splitA character list of categorical levels leading to the right child node. Only present when split is categorical, otherwise none.
This data set includes votes for each of the U.S. House of Representatives Congressmen on the 16 key votes identified by the CQA. The CQA lists nine different types of votes: voted for, paired for, and announced for (these three simplified to yea), voted against, paired against, and announced against (these three simplified to nay), voted present, voted present to avoid conflict of interest, and did not vote or otherwise make a position known (these three simplified to an unknown disposition).
A data frame with 435 rows and 17 columns
Congressional Quarterly Almanac, 98th Congress, 2nd session 1984, Volume XL: Congressional Quarterly Inc., Washington, D.C., 1985
Newman, D.J. & Hettich, S. & Blake, C.L. & Merz, C.J. (1998). UCI Repository of machine learning databases [https://www.ics.uci.edu/~mlearn/MLRepository.html]. Irvine, CA: University of California, Department of Information and Computer Science.
H2OInfogram object which in this case is to instantiate and initialize itMethod on H2OInfogram object which in this case is to instantiate and initialize it
## S4 method for signature 'H2OInfogram' initialize(.Object, model_id, ...)## S4 method for signature 'H2OInfogram' initialize(.Object, model_id, ...)
.Object |
An |
model_id |
string returned as part of every H2OModel |
... |
additional arguments to pass on |
A H2OInfogram object
Measurements in centimeters of the sepal length and width and petal length and width, respectively, for three species of iris flowers.
A data frame with 150 rows and 5 columns
Fisher, R. A. (1936) The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7, Part II, 179-188.
The data were collected by Anderson, Edgar (1935). The irises of the Gaspe Peninsula, Bulletin of the American Iris Society, 59, 2-5.
Check if character
is.character(x)is.character(x)
x |
An H2OFrame object |
## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/coxph_test/heart.csv" heart <- h2o.importFile(f) heart["transplant"] <- as.character(heart["transplant"]) is.character(heart["transplant"]) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/coxph_test/heart.csv" heart <- h2o.importFile(f) heart["transplant"] <- as.character(heart["transplant"]) is.character(heart["transplant"]) ## End(Not run)
Check if factor
is.factor(x)is.factor(x)
x |
An H2OFrame object |
## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv" cars <- h2o.importFile(f) cars["economy_20mpg"] <- as.factor(cars["economy_20mpg"]) is.factor(cars["economy_20mpg"]) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv" cars <- h2o.importFile(f) cars["economy_20mpg"] <- as.factor(cars["economy_20mpg"]) is.factor(cars["economy_20mpg"]) ## End(Not run)
Test if object is H2O Frame.
is.h2o(x)is.h2o(x)
x |
An |
## Not run: library(h2o) h2o.init() frame <- h2o.createFrame(rows = 6, cols = 2, categorical_fraction = 0.0, missing_fraction = 0.7, seed = 123) is.h2o(frame) ## End(Not run)## Not run: library(h2o) h2o.init() frame <- h2o.createFrame(rows = 6, cols = 2, categorical_fraction = 0.0, missing_fraction = 0.7, seed = 123) is.h2o(frame) ## End(Not run)
Check if numeric
is.numeric(x)is.numeric(x)
x |
An H2OFrame object |
Base class for all objects having a persistent representation on backend.
H2OTree
Overrides the behavior of length() function on H2OTree class. Returns number of nodes in an H2OTree
## S4 method for signature 'H2OTree' length(x)## S4 method for signature 'H2OTree' length(x)
x |
An |
Logical or for H2OFrames
`||`(x, y)`||`(x, y)
x |
An H2OFrame object |
y |
An H2OFrame object |
Function accessor methods for various H2O output fields.
getParms(object) ## S4 method for signature 'H2OModel' getParms(object) getCenters(object) getCentersStd(object) getWithinSS(object) getTotWithinSS(object) getBetweenSS(object) getTotSS(object) getIterations(object) getClusterSizes(object) ## S4 method for signature 'H2OClusteringModel' getCenters(object) ## S4 method for signature 'H2OClusteringModel' getCentersStd(object) ## S4 method for signature 'H2OClusteringModel' getWithinSS(object) ## S4 method for signature 'H2OClusteringModel' getTotWithinSS(object) ## S4 method for signature 'H2OClusteringModel' getBetweenSS(object) ## S4 method for signature 'H2OClusteringModel' getTotSS(object) ## S4 method for signature 'H2OClusteringModel' getIterations(object) ## S4 method for signature 'H2OClusteringModel' getClusterSizes(object)getParms(object) ## S4 method for signature 'H2OModel' getParms(object) getCenters(object) getCentersStd(object) getWithinSS(object) getTotWithinSS(object) getBetweenSS(object) getTotSS(object) getIterations(object) getClusterSizes(object) ## S4 method for signature 'H2OClusteringModel' getCenters(object) ## S4 method for signature 'H2OClusteringModel' getCentersStd(object) ## S4 method for signature 'H2OClusteringModel' getWithinSS(object) ## S4 method for signature 'H2OClusteringModel' getTotWithinSS(object) ## S4 method for signature 'H2OClusteringModel' getBetweenSS(object) ## S4 method for signature 'H2OClusteringModel' getTotSS(object) ## S4 method for signature 'H2OClusteringModel' getIterations(object) ## S4 method for signature 'H2OClusteringModel' getClusterSizes(object)
object |
an H2OModel class object. |
Column names of an H2OFrame
## S3 method for class 'H2OFrame' names(x)## S3 method for class 'H2OFrame' names(x)
x |
An H2OFrame |
## Not run: library(h2o) h2o.init() frame <- h2o.createFrame(rows = 6, cols = 2, categorical_fraction = 0.0, missing_fraction = 0.7, seed = 123) names(frame) ## End(Not run)## Not run: library(h2o) h2o.init() frame <- h2o.createFrame(rows = 6, cols = 2, categorical_fraction = 0.0, missing_fraction = 0.7, seed = 123) names(frame) ## End(Not run)
Methods for group generic functions and H2O objects.
## S3 method for class 'H2OFrame' Ops(e1, e2) ## S3 method for class 'H2OFrame' Math(x, ...) ## S3 method for class 'H2OFrame' Math(x, ...) ## S3 method for class 'H2OFrame' Math(x, ...) ## S3 method for class 'H2OFrame' Summary(x, ..., na.rm) ## S3 method for class 'H2OFrame' !x ## S3 method for class 'H2OFrame' is.na(x) ## S3 method for class 'H2OFrame' t(x) log(x, ...) log10(x) log2(x) log1p(x) trunc(x, ...) x %*% y nrow.H2OFrame(x) ncol.H2OFrame(x) ## S3 method for class 'H2OFrame' length(x) h2o.length(x) ## S3 replacement method for class 'H2OFrame' names(x) <- value colnames(x) <- value## S3 method for class 'H2OFrame' Ops(e1, e2) ## S3 method for class 'H2OFrame' Math(x, ...) ## S3 method for class 'H2OFrame' Math(x, ...) ## S3 method for class 'H2OFrame' Math(x, ...) ## S3 method for class 'H2OFrame' Summary(x, ..., na.rm) ## S3 method for class 'H2OFrame' !x ## S3 method for class 'H2OFrame' is.na(x) ## S3 method for class 'H2OFrame' t(x) log(x, ...) log10(x) log2(x) log1p(x) trunc(x, ...) x %*% y nrow.H2OFrame(x) ncol.H2OFrame(x) ## S3 method for class 'H2OFrame' length(x) h2o.length(x) ## S3 replacement method for class 'H2OFrame' names(x) <- value colnames(x) <- value
e1 |
object |
e2 |
object |
x |
object |
... |
Further arguments passed to or from other methods. |
na.rm |
logical. whether or not missing values should be removed |
y |
object |
value |
To be assigned |
Plot Pareto front
## S4 method for signature 'H2OParetoFront' plot(x, y, ...)## S4 method for signature 'H2OParetoFront' plot(x, y, ...)
x |
|
y |
missing |
... |
unused |
Plots the Infogram for an H2OInfogram object.
## S3 method for class 'H2OInfogram' plot(x, ...)## S3 method for class 'H2OInfogram' plot(x, ...)
x |
A fitted H2OInfogram object. |
... |
additional arguments to pass on. |
A ggplot2 object.
## Not run: h2o.init() # Convert iris dataset to an H2OFrame train <- as.h2o(iris) # Create and plot infogram ig <- h2o.infogram(y = "Species", training_frame = train) plot(ig) ## End(Not run)## Not run: h2o.init() # Convert iris dataset to an H2OFrame train <- as.h2o(iris) # Create and plot infogram ig <- h2o.infogram(y = "Species", training_frame = train) plot(ig) ## End(Not run)
Plots training set (and validation set if available) scoring history for an H2O Model
## S3 method for class 'H2OModel' plot(x, timestep = "AUTO", metric = "AUTO", ...)## S3 method for class 'H2OModel' plot(x, timestep = "AUTO", metric = "AUTO", ...)
x |
A fitted H2OModel object for which the scoring history plot is desired. |
timestep |
A unit of measurement for the x-axis. |
metric |
A unit of measurement for the y-axis. |
... |
additional arguments to pass on. |
This method dispatches on the type of H2O model to select the correct
scoring history. The timestep and metric arguments are restricted to what is
available in the scoring history for a particular type of model.
Returns a scoring history plot.
h2o.deeplearning, h2o.gbm,
h2o.glm, h2o.randomForest for model
generation in h2o.
## Not run: if (requireNamespace("mlbench", quietly=TRUE)) { library(h2o) h2o.init() df <- as.h2o(mlbench::mlbench.friedman1(10000, 1)) rng <- h2o.runif(df, seed = 1234) train <- df[rng < 0.8,] valid <- df[rng >= 0.8,] gbm <- h2o.gbm(x = 1:10, y = "y", training_frame = train, validation_frame = valid, ntrees = 500, learn_rate = 0.01, score_each_iteration = TRUE) plot(gbm) plot(gbm, timestep = "duration", metric = "deviance") plot(gbm, timestep = "number_of_trees", metric = "deviance") plot(gbm, timestep = "number_of_trees", metric = "rmse") plot(gbm, timestep = "number_of_trees", metric = "mae") } ## End(Not run)## Not run: if (requireNamespace("mlbench", quietly=TRUE)) { library(h2o) h2o.init() df <- as.h2o(mlbench::mlbench.friedman1(10000, 1)) rng <- h2o.runif(df, seed = 1234) train <- df[rng < 0.8,] valid <- df[rng >= 0.8,] gbm <- h2o.gbm(x = 1:10, y = "y", training_frame = train, validation_frame = valid, ntrees = 500, learn_rate = 0.01, score_each_iteration = TRUE) plot(gbm) plot(gbm, timestep = "duration", metric = "deviance") plot(gbm, timestep = "number_of_trees", metric = "deviance") plot(gbm, timestep = "number_of_trees", metric = "rmse") plot(gbm, timestep = "number_of_trees", metric = "mae") } ## End(Not run)
Plots the simple co-occurrence based tabulation of X vs Y as a heatmap, where X and Y are two Vecs in a given dataset. This function requires suggested ggplot2 package.
## S3 method for class 'H2OTabulate' plot(x, xlab = x$cols[1], ylab = x$cols[2], base_size = 12, ...)## S3 method for class 'H2OTabulate' plot(x, xlab = x$cols[1], ylab = x$cols[2], base_size = 12, ...)
x |
An H2OTabulate object for which the heatmap plot is desired. |
xlab |
A title for the x-axis. Defaults to what is specified in the given H2OTabulate object. |
ylab |
A title for the y-axis. Defaults to what is specified in the given H2OTabulate object. |
base_size |
Base font size for plot. |
... |
additional arguments to pass on. |
Returns a ggplot2-based heatmap of co-occurance.
## Not run: library(h2o) h2o.init() df <- as.h2o(iris) tab <- h2o.tabulate(data = df, x = "Sepal.Length", y = "Petal.Width", weights_column = NULL, nbins_x = 10, nbins_y = 10) plot(tab) ## End(Not run)## Not run: library(h2o) h2o.init() df <- as.h2o(iris) tab <- h2o.tabulate(data = df, x = "Sepal.Length", y = "Petal.Width", weights_column = NULL, nbins_x = 10, nbins_y = 10) plot(tab) ## End(Not run)
Default implemntation return H2OFrame shape (#rows, #features + 1) - there is a feature contribution column for each input feature, the last column is the model bias (same value for each row). The sum of the feature contributions and the bias term is equal to the raw prediction of the model. Raw prediction of tree-based model is the sum of the predictions of the individual trees before the inverse link function is applied to get the actual prediction. For Gaussian distribution the sum of the contributions is equal to the model prediction.
predict_contributions.H2OModel( object, newdata, output_format = c("compact", "original"), top_n = 0, bottom_n = 0, compare_abs = FALSE, background_frame = NULL, output_space = FALSE, output_per_reference = FALSE, ... ) h2o.predict_contributions( object, newdata, output_format = c("compact", "original"), top_n = 0, bottom_n = 0, compare_abs = FALSE, background_frame = NULL, output_space = FALSE, output_per_reference = FALSE, ... )predict_contributions.H2OModel( object, newdata, output_format = c("compact", "original"), top_n = 0, bottom_n = 0, compare_abs = FALSE, background_frame = NULL, output_space = FALSE, output_per_reference = FALSE, ... ) h2o.predict_contributions( object, newdata, output_format = c("compact", "original"), top_n = 0, bottom_n = 0, compare_abs = FALSE, background_frame = NULL, output_space = FALSE, output_per_reference = FALSE, ... )
object |
a fitted H2OModel object for which prediction is desired |
newdata |
An H2OFrame object in which to look for variables with which to predict. |
output_format |
Specify how to output feature contributions in XGBoost - XGBoost by default outputs contributions for 1-hot encoded features, specifying a compact output format will produce a per-feature contribution. Defaults to original. |
top_n |
Return only #top_n highest contributions + bias If top_n<0 then sort all SHAP values in descending order If top_n<0 && bottom_n<0 then sort all SHAP values in descending order |
bottom_n |
Return only #bottom_n lowest contributions + bias If top_n and bottom_n are defined together then return array of #top_n + #bottom_n + bias If bottom_n<0 then sort all SHAP values in ascending order If top_n<0 && bottom_n<0 then sort all SHAP values in descending order |
compare_abs |
True to compare absolute values of contributions |
background_frame |
Optional frame, that is used as the source of baselines for the baseline SHAP (when output_per_reference == TRUE) or for the marginal SHAP (when output_per_reference == FALSE). |
output_space |
If TRUE, linearly scale the contributions so that they sum up to the prediction. NOTE: This will result only in approximate SHAP values even if the model supports exact SHAP calculation. NOTE: This will not have any effect if the estimator doesn't use a link function. |
output_per_reference |
If TRUE, return baseline SHAP, i.e., contribution for each data point for each reference from the background_frame. If FALSE, return TreeSHAP if no background_frame is provided, or marginal SHAP if background frame is provided. Can be used only with background_frame. |
... |
additional arguments to pass on. |
Note: Multinomial classification models are currently not supported.
Returns an H2OFrame contain feature contributions for each input row.
h2o.gbm and h2o.randomForest for model
generation in h2o.
## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(path = prostate_path) prostate_gbm <- h2o.gbm(3:9, "AGE", prostate) h2o.predict(prostate_gbm, prostate) # Compute SHAP h2o.predict_contributions(prostate_gbm, prostate) # Compute SHAP and pick the top two highest h2o.predict_contributions(prostate_gbm, prostate, top_n=2) # Compute SHAP and pick the top two lowest h2o.predict_contributions(prostate_gbm, prostate, bottom_n=2) # Compute SHAP and pick the top two highest regardless of the sign h2o.predict_contributions(prostate_gbm, prostate, top_n=2, compare_abs=TRUE) # Compute SHAP and pick the top two lowest regardless of the sign h2o.predict_contributions(prostate_gbm, prostate, bottom_n=2, compare_abs=TRUE) # Compute SHAP values and show them all in descending order h2o.predict_contributions(prostate_gbm, prostate, top_n=-1) # Compute SHAP and pick the top two highest and top two lowest h2o.predict_contributions(prostate_gbm, prostate, top_n=2, bottom_n=2) # Compute Marginal SHAP, this enables looking at the contributions against different # baselines, e.g., older people in the following example h2o.predict_contributions(prostate_gbm, prostate, background_frame=prostate[prostate$AGE > 75, ]) ## End(Not run)## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(path = prostate_path) prostate_gbm <- h2o.gbm(3:9, "AGE", prostate) h2o.predict(prostate_gbm, prostate) # Compute SHAP h2o.predict_contributions(prostate_gbm, prostate) # Compute SHAP and pick the top two highest h2o.predict_contributions(prostate_gbm, prostate, top_n=2) # Compute SHAP and pick the top two lowest h2o.predict_contributions(prostate_gbm, prostate, bottom_n=2) # Compute SHAP and pick the top two highest regardless of the sign h2o.predict_contributions(prostate_gbm, prostate, top_n=2, compare_abs=TRUE) # Compute SHAP and pick the top two lowest regardless of the sign h2o.predict_contributions(prostate_gbm, prostate, bottom_n=2, compare_abs=TRUE) # Compute SHAP values and show them all in descending order h2o.predict_contributions(prostate_gbm, prostate, top_n=-1) # Compute SHAP and pick the top two highest and top two lowest h2o.predict_contributions(prostate_gbm, prostate, top_n=2, bottom_n=2) # Compute Marginal SHAP, this enables looking at the contributions against different # baselines, e.g., older people in the following example h2o.predict_contributions(prostate_gbm, prostate, background_frame=prostate[prostate$AGE > 75, ]) ## End(Not run)
Obtains leaf node assignment from fitted H2O model objects.
predict_leaf_node_assignment.H2OModel( object, newdata, type = c("Path", "Node_ID"), ... ) h2o.predict_leaf_node_assignment( object, newdata, type = c("Path", "Node_ID"), ... )predict_leaf_node_assignment.H2OModel( object, newdata, type = c("Path", "Node_ID"), ... ) h2o.predict_leaf_node_assignment( object, newdata, type = c("Path", "Node_ID"), ... )
object |
a fitted H2OModel object for which prediction is desired |
newdata |
An H2OFrame object in which to look for variables with which to predict. |
type |
choice of either "Path" when tree paths are to be returned (default); or "Node_ID" when the output |
... |
additional arguments to pass on. |
For every row in the test set, return the leaf placements of the row in all the trees in the model. Placements can be represented either by paths to the leaf nodes from the tree root or by H2O's internal identifiers. The order of the rows in the results is the same as the order in which the data was loaded
Returns an H2OFrame object with categorical leaf assignment identifiers for each tree in the model.
h2o.gbm and h2o.randomForest for model
generation in h2o.
## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(path = prostate_path) prostate$CAPSULE <- as.factor(prostate$CAPSULE) prostate_gbm <- h2o.gbm(3:9, "CAPSULE", prostate) h2o.predict(prostate_gbm, prostate) h2o.predict_leaf_node_assignment(prostate_gbm, prostate) ## End(Not run)## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(path = prostate_path) prostate$CAPSULE <- as.factor(prostate$CAPSULE) prostate_gbm <- h2o.gbm(3:9, "CAPSULE", prostate) h2o.predict(prostate_gbm, prostate) h2o.predict_leaf_node_assignment(prostate_gbm, prostate) ## End(Not run)
Obtains predictions from an AutoML object.
## S3 method for class 'H2OAutoML' predict(object, newdata, ...) ## S3 method for class 'H2OAutoML' h2o.predict(object, newdata, ...)## S3 method for class 'H2OAutoML' predict(object, newdata, ...) ## S3 method for class 'H2OAutoML' h2o.predict(object, newdata, ...)
object |
a fitted H2OAutoML object for which prediction is desired |
newdata |
An H2OFrame object in which to look for variables with which to predict. |
... |
additional arguments to pass on. |
This method generated predictions on the leader model from an AutoML run. The order of the rows in the results is the same as the order in which the data was loaded, even if some rows fail (for example, due to missing values or unseen factor levels).
Returns an H2OFrame object with probabilites and default predictions.
Obtains predictions from various fitted H2O model objects.
## S3 method for class 'H2OModel' predict(object, newdata, ...) ## S3 method for class 'H2OModel' h2o.predict(object, newdata, ...)## S3 method for class 'H2OModel' predict(object, newdata, ...) ## S3 method for class 'H2OModel' h2o.predict(object, newdata, ...)
object |
a fitted H2OModel object for which prediction is desired |
newdata |
An H2OFrame object in which to look for variables with which to predict. |
... |
additional arguments to pass on. |
This method dispatches on the type of H2O model to select the correct prediction/scoring algorithm. The order of the rows in the results is the same as the order in which the data was loaded, even if some rows fail (for example, due to missing values or unseen factor levels).
Returns an H2OFrame object with probabilites and default predictions.
h2o.deeplearning, h2o.gbm,
h2o.glm, h2o.randomForest for model
generation in h2o.
## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/insurance.csv" insurance <- h2o.importFile(f) predictors <- colnames(insurance)[1:4] response <- "Claims" insurance['Group'] <- as.factor(insurance['Group']) insurance['Age'] <- as.factor(insurance['Age']) splits <- h2o.splitFrame(data = insurance, ratios = 0.8, seed = 1234) train <- splits[[1]] valid <- splits[[2]] insurance_gbm <- h2o.gbm(x = predictors, y = response, training_frame = train, validation_frame = valid, distribution = "huber", huber_alpha = 0.9, seed = 1234) h2o.predict(insurance_gbm, newdata = insurance) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/insurance.csv" insurance <- h2o.importFile(f) predictors <- colnames(insurance)[1:4] response <- "Claims" insurance['Group'] <- as.factor(insurance['Group']) insurance['Age'] <- as.factor(insurance['Age']) splits <- h2o.splitFrame(data = insurance, ratios = 0.8, seed = 1234) train <- splits[[1]] valid <- splits[[2]] insurance_gbm <- h2o.gbm(x = predictors, y = response, training_frame = train, validation_frame = valid, distribution = "huber", huber_alpha = 0.9, seed = 1234) h2o.predict(insurance_gbm, newdata = insurance) ## End(Not run)
Print An H2OFrame
## S3 method for class 'H2OFrame' print(x, n = 6L, m = 200L, ...)## S3 method for class 'H2OFrame' print(x, n = 6L, m = 200L, ...)
x |
An H2OFrame object |
n |
An (Optional) A single integer. If positive, number of rows in x to return. If negative, all but the n first/last number of rows in x. Anything bigger than 20 rows will require asking the server (first 20 rows are cached on the client). |
m |
An (Optional) A single integer. If positive, number of columns in x to return. If negative, all but the m first/last number of columns in x. |
... |
Further arguments to be passed from or to other methods. |
## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv" cars <- h2o.importFile(f) print(cars, n = 8) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv" cars <- h2o.importFile(f) print(cars, n = 8) ## End(Not run)
This will print a truncated view of the table if there are more than 20 rows.
## S3 method for class 'H2OTable' print(x, header = TRUE, ...)## S3 method for class 'H2OTable' print(x, header = TRUE, ...)
x |
An H2OTable object |
header |
A logical value dictating whether or not the table name should be printed. |
... |
Further arguments passed to or from other methods. |
The original x object
## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv" cars <- h2o.importFile(f) print(cars, header = TRUE) ## End(Not run)## Not run: library(h2o) h2o.init() f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv" cars <- h2o.importFile(f) print(cars, header = TRUE) ## End(Not run)
Baseline exam results on prostate cancer patients from Dr. Donn Young at The Ohio State University Comprehensive Cancer Center.
A data frame with 380 rows and 9 columns
Hosmer and Lemeshow (2000) Applied Logistic Regression: Second Edition.
Range of an H2O Column
## S3 method for class 'H2OFrame' range(..., na.rm = TRUE)## S3 method for class 'H2OFrame' range(..., na.rm = TRUE)
... |
An H2OFrame object. |
na.rm |
ignore missing values |
## Not run: library(h2o) h2o.init() frame <- h2o.createFrame(rows = 6, cols = 2, categorical_fraction = 0.0, missing_fraction = 0.7, seed = 123) range(frame, na.rm = TRUE) ## End(Not run)## Not run: library(h2o) h2o.init() frame <- h2o.createFrame(rows = 6, cols = 2, categorical_fraction = 0.0, missing_fraction = 0.7, seed = 123) range(frame, na.rm = TRUE) ## End(Not run)
Output is frame of size nrow = nrow(original_training_data) and ncol = number_of_trees_in_model+1 in format: row_id tree_1 tree_2 tree_3 0 0 1 1 1 1 1 1 2 1 0 0 3 1 1 0 4 0 1 1 5 1 1 1 6 1 0 0 7 0 1 0 8 0 1 1 9 1 0 0
row_to_tree_assignment.H2OModel(object, original_training_data, ...) h2o.row_to_tree_assignment(object, original_training_data, ...)row_to_tree_assignment.H2OModel(object, original_training_data, ...) h2o.row_to_tree_assignment(object, original_training_data, ...)
object |
a fitted H2OModel object |
original_training_data |
An H2OFrame object that was used for model training. Currently there is no validation of the input. |
... |
additional arguments to pass on. |
Where 1 in the tree_{number} cols means row is used in the tree and 0 means that row is not used. The structure of the output depends on sample_rate or sample_size parameter setup.
Note: Multinomial classification generate tree for each category, each tree use the same sample of the data.
Returns an H2OFrame contain row to tree assignment for each tree and row.
## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(path = prostate_path) prostate_gbm <- h2o.gbm(4:9, "AGE", prostate, sample_rate = 0.6) # Get row to tree assignment h2o.row_to_tree_assignment(prostate_gbm, prostate) ## End(Not run)## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(path = prostate_path) prostate_gbm <- h2o.gbm(4:9, "AGE", prostate, sample_rate = 0.6) # Get row to tree assignment h2o.row_to_tree_assignment(prostate_gbm, prostate) ## End(Not run)
Centers and/or scales the columns of an H2O dataset.
## S3 method for class 'H2OFrame' scale(x, center = TRUE, scale = TRUE)## S3 method for class 'H2OFrame' scale(x, center = TRUE, scale = TRUE)
x |
An H2OFrame object. |
center |
either a |
scale |
either a |
## Not run: library(h2o) h2o.init() iris_hf <- as.h2o(iris) summary(iris_hf) # Scale and center all the numeric columns in iris data set iris_scaled <- scale(iris_hf[, 1:4]) ## End(Not run)## Not run: library(h2o) h2o.init() iris_hf <- as.h2o(iris) summary(iris_hf) # Scale and center all the numeric columns in iris data set iris_scaled <- scale(iris_hf[, 1:4]) ## End(Not run)
Format AutoML object in user-friendly way
## S4 method for signature 'H2OAutoML' show(object)## S4 method for signature 'H2OAutoML' show(object)
object |
an |
Show H2OParetoFront
## S4 method for signature 'H2OParetoFront' show(object)## S4 method for signature 'H2OParetoFront' show(object)
object |
|
The output structure is analogous to the output of h2o.predict_leaf_node_assignment. For each tree t and class c there will be a column Tt.Cc (eg. T3.C1 for tree 3 and class 1). The value will be the corresponding predicted probability of this class by combining the raw contributions of trees T1.Cc,..,TtCc. Binomial models build the trees just for the first class and values in columns Tx.C1 thus correspond to the the probability p0.
staged_predict_proba.H2OModel(object, newdata, ...) h2o.staged_predict_proba(object, newdata, ...)staged_predict_proba.H2OModel(object, newdata, ...) h2o.staged_predict_proba(object, newdata, ...)
object |
a fitted H2OModel object for which prediction is desired |
newdata |
An H2OFrame object in which to look for variables with which to predict. |
... |
additional arguments to pass on. |
Returns an H2OFrame object with predicted probability for each tree in the model.
h2o.gbm and h2o.randomForest for model
generation in h2o.
## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(path = prostate_path) prostate$CAPSULE <- as.factor(prostate$CAPSULE) prostate_gbm <- h2o.gbm(3:9, "CAPSULE", prostate) h2o.predict(prostate_gbm, prostate) h2o.staged_predict_proba(prostate_gbm, prostate) ## End(Not run)## Not run: library(h2o) h2o.init() prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") prostate <- h2o.uploadFile(path = prostate_path) prostate$CAPSULE <- as.factor(prostate$CAPSULE) prostate_gbm <- h2o.gbm(3:9, "CAPSULE", prostate) h2o.predict(prostate_gbm, prostate) h2o.staged_predict_proba(prostate_gbm, prostate) ## End(Not run)
Display the structure of an H2OFrame object
## S3 method for class 'H2OFrame' str(object, ..., cols = FALSE)## S3 method for class 'H2OFrame' str(object, ..., cols = FALSE)
object |
An H2OFrame. |
... |
Further arguments to be passed from or to other methods. |
cols |
Print the per-column str for the H2OFrame |
Format AutoML object in user-friendly way
## S4 method for signature 'H2OAutoML' summary(object)## S4 method for signature 'H2OAutoML' summary(object)
object |
an |
Summary method for H2OCoxPHModel objects
## S4 method for signature 'H2OCoxPHModel' summary(object, conf.int = 0.95, scale = 1)## S4 method for signature 'H2OCoxPHModel' summary(object, conf.int = 0.95, scale = 1)
object |
an |
conf.int |
a specification of the confidence interval. |
scale |
a scale. |
Format grid object in user-friendly way
## S4 method for signature 'H2OGrid' summary(object, show_stack_traces = FALSE)## S4 method for signature 'H2OGrid' summary(object, show_stack_traces = FALSE)
object |
an |
show_stack_traces |
a flag to show stack traces for model failures |
Print the Model Summary
## S4 method for signature 'H2OModel' summary(object, ...)## S4 method for signature 'H2OModel' summary(object, ...)
object |
An H2OModel object. |
... |
further arguments to be passed on (currently unimplemented) |
Testing availability of optional package, its version, and extra global default. This function is used internally. It is exported and documented because user can control behavior of the function by global option.
use.package( package, version = "1.9.8"[package == "data.table"], use = getOption("h2o.use.data.table", TRUE)[package == "data.table"] )use.package( package, version = "1.9.8"[package == "data.table"], use = getOption("h2o.use.data.table", TRUE)[package == "data.table"] )
package |
character scalar name of a package that we Suggests or Enhances on. |
version |
character scalar required version of a package. |
use |
logical scalar, extra escape option, to be used as global option. |
We use this function to control csv read/write with optional data.table package.
Currently data.table is enabled by default for some operations, to disable it set options("h2o.use.data.table"=FALSE).
It is possible to control just fread or fwrite with options("h2o.fread"=FALSE, "h2o.fwrite"=FALSE).
h2o.fread and h2o.fwrite options are not handled in this function but next to fread and fwrite calls.
as.h2o.data.frame, as.data.frame.H2OFrame
op <- options("h2o.use.data.table" = TRUE) if (use.package("data.table")) { cat("optional package data.table 1.9.8+ is available\n") } else { cat("optional package data.table 1.9.8+ is not available\n") } options(op)op <- options("h2o.use.data.table" = TRUE) if (use.package("data.table")) { cat("optional package data.table 1.9.8+ is available\n") } else { cat("optional package data.table 1.9.8+ is not available\n") } options(op)
The musculoskeletal model, experimental data, settings files, and results for three-dimensional, muscle-actuated simulations at walking speed as described in Hamner and Delp (2013). Simulations were generated using OpenSim 2.4. The data is available from https://simtk.org/frs/index.php?group_id=603.
A data frame with 151 rows and 124 columns
Hamner, S.R., Delp, S.L. Muscle contributions to fore-aft and vertical body mass center accelerations over a range of running speeds. Journal of Biomechanics, vol 46, pp 780-787. (2013)
Shutdown H2O cluster after examples run
## Not run: library(h2o) h2o.init() h2o.shutdown(prompt = FALSE) Sys.sleep(3) ## End(Not run)## Not run: library(h2o) h2o.init() h2o.shutdown(prompt = FALSE) Sys.sleep(3) ## End(Not run)