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Changelog

Source: NEWS.md

profoc 1.3.3

CRAN release: 2024-09-21

Improvements

  • We adjusted the integration of rcpptimer. This simplifies the code and makes use of the API of rcpptimer 1.2.0 which is expected to be stable.

profoc 1.3.2

CRAN release: 2024-03-25

Improvements

  • The timer functionality of online was moved to a seperate package rcpptimer. This is now added to profoc as a dependency. The timer-related code was removed. This makes the code more modular and easier to maintain. The timer functionality is now also available for other ‘R’ packages and even other languages (i.e. Python, via cpptimer and cppytimer).

profoc 1.3.1

CRAN release: 2024-01-13

Improvements

  • Adjusted the clock.h code so that a larger share of code can be shared between the R and Python versions of that file.
  • clock.h now uses welfords online algorithm to calculate the mean and variance of the timings. SD is reported in the times table.

Fixes

  • Fixed an integer overflow in the clock.h code which caused the package to fail on some systems.
  • Fixed online() function for cases where the regret is exactly zero. This can happen if:
    • Only a single expert is used
    • Only two experts are provided and they both have the same predictions (in the beginning).

profoc 1.3.0

CRAN release: 2024-01-09

Improvements

  • New articles explain how to use methods on online() objects to deploy online learning algorithms in production.
  • The conline C++ class now exposes weights to R.
  • A new article on the usage of the conline C++ class was added.
  • Various functions are now exported to R to allow easier interaction with the conline C++ class. These functions are: init_experts_list(), make_basis_mats and make_hat_mats
  • The code of online() was simplified a bit by utilizing the new init_experts_list() function.
  • Function post_process_model() was improved and is now exposed to be used in conjunction with the conline C++ class.
  • Move aggregation of timings from cppclock.R to clock.h. This make it faster, easier to maintain and simplifies the code (which will be used in python in the future as well).

profoc 1.2.1

CRAN release: 2023-08-25

Improvements

  • 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.)
  • A Get started article was added to the docs.
  • Docs of the development version were added to the website

Fixes

  • This release fixes import / export of of the autoplot() method. In consequence, ggplot2 became a new dependency of this package.

profoc 1.2.0

CRAN release: 2023-06-13

Improvements:

  • Periodic splines and penalties added for smoothing the weights in online().

Internal changes

  • profoc now depends on R >= 4.3.0 to ensure C++17 support.

profoc 1.1.1

CRAN release: 2023-03-02

Fixes:

  • Distribution of the knots is now correct for ncp < 0.

profoc 1.1.0

CRAN release: 2023-01-13

Improvements:

  • New penalty() function which works with equidistant and non-equidistant knots.

Fixes:

  • Calculation of the P-Spline penalty if non-uniform B-Splines are used.

profoc 1.0.0

CRAN release: 2022-12-23

Changes:

  • Now, 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().

Internal changes

  • Minor fixes and improvements to online() to reduce memory usage.

profoc 0.9.5

CRAN release: 2022-12-16

Internal changes

  • Now online() is able to sample from grids of up to 2^64-1 rows.
  • The new cpp sampling function sample_int() works similar to sample.int() and also respects seeds set by set.seed().

profoc 0.9.4

CRAN release: 2022-11-30

Fixes:

  • parametergrids lets you provide custom grids of parameters in online()

Internal changes

  • Significantly improved the initialization efficiency in online() when using large grids of parameters

profoc 0.9.3

CRAN release: 2022-04-21

Fixes:

  • forget_past_performance had no effect in online()
  • Improved and fixed documentation

profoc 0.9.2

CRAN release: 2022-03-17

Fixes:

  • Resolved a problem with plotting multivariate probabilistic models

profoc 0.9.1

CRAN release: 2022-02-25

Changes:

  • Basis matrices are created differently now. This solves an issue where basis functions did not always sum to 1 when non-equidistant knot sequences were used.

profoc 0.9.0

CRAN release: 2022-02-07

Changes:

  • online can now be used with multivariate data
    • Just pass a TxK matrix as y and a TxDxPxK array as experts
  • Smoothing was improved. See the documentation for details on the revised interface.
  • summary.online can be used to obtain selected parameters of online models

Internal changes

  • online uses Rcpp Modules to bundle data and functionality into an exposed C++ class
  • Improvements to plot methods

profoc 0.8.5

CRAN release: 2021-10-22

Changes:

  • initial_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.

Internal changes

  • Internal changes to improve readability
  • Resolve C++ compilation warnings

profoc 0.8.4

CRAN release: 2021-09-15

  • Remove unused C++17 dependency

profoc 0.8.3

CRAN release: 2021-08-15

Changes:

  • 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
    • This weighting scheme can be activated by setting qw_crps=TRUE
    • It defaults to FALSE due to better performance

Internal changes

profoc 0.8.0

CRAN release: 2021-07-28

Changes:

  • First release on CRAN
  • The profoc function was renamed to online for consistency.
  • Added batch function to apply batch-learning.
  • Added oracle function to approximate the oracle.
  • Update, predict, plot, and print methods were added for online and batch objects.

Interface:

  • Unfortunately, we decided to apply significant changes to the API. This likely breaks old code. Please refer to the respective function documentation for more information.

Internal changes:

  • The b-spline basis is now calculated using a fast C++ function imported from the splines2 R package.
  • The source code is now distributed across different files.

profoc 0.7.0

Changes:

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.

Interface:

  • 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).
  • A new parameter 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.
  • A new parameter allow_quantile_crossing defines if quantile crossing is allowed. Defaults to false, which means that predictions will be sorted.

Internal changes:

  • Functions for calculating the b-spline basis are now exported to R as internal functions of the package. They can be accessed using the package:::function notation.

profoc 0.6.0

Changes:

Interface:

  • y must now be a matrix of either T×1\text{T} \times 1 or T×P\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.
  • Renamed forget parameter to forget_regret to underline its reference to the regret.
  • New init_weights parameter. It has to be either a Kx1 or KxP matrix specifying the experts’ starting weights.
  • Add 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.

Internal changes:

  • If more expert forecasts than observations are provided, the excessive expert forecasts are used for prediction using the most recent weights.
  • 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.
  • The pinball_loss and loss_pred functions were replaced by a more flexible function called loss.
  • The weights object is changed from a (T+1×K×P)(\text{T}+1 \times \text{K} \times \text{P}) array to a (T+1×P×K)(\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)
  • Fixed Bug that caused single quantile calculations to fail.
  • Various internal changes to improve readability and performance.

profoc 0.5.2

Changes:

  • Initial release on GitHub