From a62eccffcd611e87a850b9bf38ea699fbea1fb31 Mon Sep 17 00:00:00 2001 From: Felix Wick Date: Fri, 9 Feb 2024 13:33:10 +0100 Subject: [PATCH] prepare for release --- cyclic_boosting/__init__.py | 2 +- docs/source/tutorial.md | 9 ++++----- pyproject.toml | 2 +- 3 files changed, 6 insertions(+), 7 deletions(-) diff --git a/cyclic_boosting/__init__.py b/cyclic_boosting/__init__.py index a251920..3fc51ef 100644 --- a/cyclic_boosting/__init__.py +++ b/cyclic_boosting/__init__.py @@ -99,4 +99,4 @@ "pipeline_CBGenericClassifier", ] -__version__ = "1.3.0" +__version__ = "1.4.0" diff --git a/docs/source/tutorial.md b/docs/source/tutorial.md index e9f395e..2db5c93 100644 --- a/docs/source/tutorial.md +++ b/docs/source/tutorial.md @@ -157,8 +157,7 @@ CB_est.get_feature_contributions(X_test) Below you can find an example of a quantile regression model for three different quantiles, with a subsequent quantile matching (to get a full individual probability distribution from the estimated quantiles) by means of a -Johnson Quantile-Parameterized Distribution (J-QPD) for an arbitrary test -sample: +Johnson Quantile-Parameterized Distribution (J-QPD) for each test sample: ```python from cyclic_boosting.pipelines import pipeline_CBMultiplicativeQuantileRegressor from cyclic_boosting.quantile_matching import J_QPD_S @@ -175,15 +174,15 @@ CB_est_qhigh = pipeline_CBMultiplicativeQuantileRegressor(quantile=0.8) CB_est_qhigh.fit(X_train, y) yhat_qhigh = CB_est_qhigh.predict(X_test) -j_qpd_s_42 = J_QPD_S(0.2, yhat_qlow[42], yhat_qmedian[42], yhat_qhigh[42]) -yhat_42_percentile95 = j_qpd_s_42.ppf(0.95) +j_qpd_s = J_QPD_S(0.2, yhat_qlow, yhat_qmedian, yhat_qhigh) +yhat_percentile95 = j_qpd_s.ppf(0.95) ``` There is also a ready-made end-to-end practical training chain, employing quantile transformations to impose constraints on the target range (for bound or semi-bound scenarios) and maintain the order of symmetric-percentile triplet predictions (from an arbitrary quantile regression method, not restricted to -Cyclic Boosting) used for J-QPD (and its [extensions](https://github.com/Blue-Yonder-OSS/cyclic-boosting/blob/main/docs/JQPDregression.pdf)): +Cyclic Boosting) used for J-QPD: ```python from cyclic_boosting.pipelines import pipeline_CBAdditiveQuantileRegressor from cyclic_boosting.quantile_matching import QPD_RegressorChain diff --git a/pyproject.toml b/pyproject.toml index 1f439aa..3f94d83 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "cyclic-boosting" -version = "1.3.0" +version = "1.4.0" description = "Implementation of Cyclic Boosting machine learning algorithms" authors = ["Blue Yonder GmbH"] packages = [{include = "cyclic_boosting"}]