release prep

cyclic_boosting/__init__.py

@@ -99,4 +99,4 @@
     "pipeline_CBGenericClassifier",
 ]
 
-__version__ = "1.2.5"
+__version__ = "1.3.0"

docs/source/introduction.md

@@ -43,7 +43,8 @@ possible to use another loss function (also possible for classification tasks).
 An important example for this is optimizing for an arbitrary quantile (e.g.,
 the median), aka [quantile regression](https://cyclic-boosting.readthedocs.io/en/latest/cyclic_boosting.html#module-cyclic_boosting.generic_loss).
 By means of several estimated quantiles, one can also approximate full
-individual probability distributions via [quantile matching](https://cyclic-boosting.readthedocs.io/en/latest/cyclic_boosting.html#module-cyclic_boosting.quantile_matching).
+individual probability distributions via [quantile matching](https://cyclic-boosting.readthedocs.io/en/latest/cyclic_boosting.html#module-cyclic_boosting.quantile_matching)
+(especially Quantile-Parameterized Distributions).
 
 Classification
 --------------

docs/source/tutorial.md

@@ -149,7 +149,8 @@ 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
-quantile-parameterized distribution for an arbitrary test sample:
+Johnson Quantile-Parameterized Distribution (J-QPD) for an arbitrary test
+sample:
 ```python
 from cyclic_boosting.pipelines import pipeline_CBMultiplicativeQuantileRegressor
 from cyclic_boosting.quantile_matching import J_QPD_S
@@ -169,3 +170,22 @@ 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)
 ```
+
+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:
+```python
+from cyclic_boosting.pipelines import pipeline_CBAdditiveQuantileRegressor
+from cyclic_boosting.quantile_matching import QPD_RegressorChain
+
+est = QPD_RegressorChain(
+    pipeline_CBAdditiveQuantileRegressor(quantile=0.5),
+    pipeline_CBAdditiveQuantileRegressor(quantile=0.5),
+    pipeline_CBAdditiveQuantileRegressor(quantile=0.5),
+    "S",
+)
+est.fit(X_train, y)
+yhat_qlow, yhat_qmedian, yhat_qhigh, qpd = est.predict(X_test)
+```

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "cyclic-boosting"
-version = "1.2.5"
+version = "1.3.0"
 description = "Implementation of Cyclic Boosting machine learning algorithms"
 authors = ["Blue Yonder GmbH"]
 packages = [{include = "cyclic_boosting"}]