rcpptimer. This simplifies the code and makes use of the API of rcpptimer 1.2.0 which is expected to be stable.online() objects to deploy online learning algorithms in production.conline C++ class now exposes weights to R.conline C++ class was added.conline C++ class. These functions are: init_experts_list(), make_basis_mats and make_hat_matsonline() was simplified a bit by utilizing the new init_experts_list() function.post_process_model() was improved and is now exposed to be used in conjunction with the conline C++ class.online() outputs now include predictions_got_sorted. A matrix which indicates whether quantile crossing occured and predictions have been sorted.tidy() methods were added to convert weights, predictions and loss objects of online() output to a tibble (for further analysis, plotting etc.)autoplot() method. In consequence, ggplot2 became a new dependency of this package.online().profoc now depends on R >= 4.3.0 to ensure C++17 support.ncp < 0.penalty() function which works with equidistant and non-equidistant knots.online() saves memory by not reporting past_performance and past_predictions_grid. However, the cumulative performance and the most recent predictions w.r.t to the parameter grid are always included in the output. The former is used internally for choosing the best hyperparameter set, and the latter for updating the weights. Depending on the data and the parameter space considered, both objects may get large. You can still opt-in to include them in the output by setting save_past_performance = TRUE and save_past_predictions_grid = TRUE in online().online() to reduce memory usage.online() is able to sample from grids of up to 2^64-1 rows.sample_int() works similar to sample.int() and also respects seeds set by set.seed().parametergrids lets you provide custom grids of parameters in online()online() when using large grids of parametersforget_past_performance had no effect in online()online can now be used with multivariate data
y and a TxDxPxK array as expertssummary.online can be used to obtain selected parameters of online modelsonline uses Rcpp Modules to bundle data and functionality into an exposed C++ classinitial_weights argument is replaced by init
init takes a named list and currently intial_weights and R0 the initial weights and the initial cumulative regret can be provided. They have to be PxK or 1xK.The profoc function was extended:
regret can now be passed as an array as before, or as a list, e.g. list(regret = regret_array, share = 0.2) if the provided regret should be mixed with the regret calculated by online.loss can also be provided as a list, see above.The batch function can now minimize an alternative objection function using the quantile weighted CRPS
qw_crps=TRUEprofoc function was renamed to online for consistency.batch function to apply batch-learning.oracle function to approximate the oracle.online and batch objects.The spline functions where rewritten to add the ability of using a non-equidistant knot sequence and a penalty term defined on the Sobolev space. This change induces breaking changes to small parts of the API.
ndiff defines the degree of differencing for creating the penalty term. For values between 1 and 2 a weighted sum of the difference penalization matrices is used.rel_nseg is replaced by knot_distance ( distance between knots). Defaults to 0.025, which corresponds to the grid steps when knot_distance_power = 1 (the default).knot_distance_power defines if knots are uniformly distributed. Defaults to 1, which corresponds to the equidistant case. Values less than 1 create more knots in the center, while values above 1 concentrate more knots in the tails.allow_quantile_crossing defines if quantile crossing is allowed. Defaults to false, which means that predictions will be sorted.package:::function notation.y must now be a matrix of either $\text{T} \times 1$ or $\text{T} \times \text{P}$.trace specifies whether a progress bar will be printed or not. Default to TRUE.loss_function lets you now specify "quantile", "expectile" or "percentage". All functions are generalized as in Gneitling 2009. The power can be scaled by loss_parameter. The latter defaults to 1, which leads to the well-known quantile, squared, and absolute percentage loss.gradient lets you specify whether the learning algorithm should consider actual loss or a linearized version using the gradient of the loss. Defaults to TRUE (gradient-based learning).forget_performance was added. It defines the share of the past performance that will be ignored when selecting the best parameter combination.forget parameter to forget_regret to underline its reference to the regret.init_weights parameter. It has to be either a Kx1 or KxP matrix specifying the experts' starting weights.lead_time parameter. offset for expert forecasts. Defaults to 0 which means that experts predict t+1 at t. Setting this to h means experts' predictions refer to t+1+h at time t. The weight updates delay accordingly.tau is now optional. It defaults to 1:P/(P+1). A scalar given to tau will be repeated P times. The latter is useful in multivariate settings.pinball_loss and loss_pred functions were replaced by a more flexible function called loss.weights object is changed from a $(\text{T}+1 \times \text{K} \times \text{P})$ array to a $(\text{T}+1 \times \text{P} \times \text{K})$ array to match other objects' dimensions. Now the following indexing scheme is consistent throughout the package: (Time, Probabilities, Experts, Parameter combination)