update docs for v2.1.24.0

Author: cezary.maszczyk

docs/serve/index.html

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                         v2.1.24.0 (latest)
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docs/serve/v2.1.24.0/.buildinfo

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+# Sphinx build info version 1
+# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
+config: 12f3af7734ee25ce1367bfd71b5b933c
+tags: 645f666f9bcd5a90fca523b33c5a78b7

docs/serve/v2.1.24.0/_images/rst_tutorials_classification_7_0.png

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+
+RuleKit
+========================================
+
+This package is python wrapper for `RuleKit <https://github.com/adaa-polsl/RuleKit/>`__ library (a versatile tool for rule learning). 
+Based on a sequential covering induction algorithm, it is suitable for:
+
+- classification, 
+
+- regression, 
+
+- survival problems.
+
+This wrapper aims to give an easy way to integrate RuleKit functionality into python projects and notebooks.
+All package models are writen to be compatible with scikit learn library.
+
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Table of contents:
+
+   Quick start <./rst/quick_start.rst>
+   Whats new <./rst/whats_new/Changes in this version.ipynb>
+   Tutorials <./rst/tutorials.rst>
+   Code documentation <./rst/autodoc.rst>

docs/serve/v2.1.24.0/_images/rst_tutorials_survival_13_1.png

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+
+Code documentation
+========================================
+
+.. automodule:: rulekit.main
+    :members:
+
+.. toctree::
+    :maxdepth: 1
+    :caption: Table of contents:
+
+    Classification <./autodoc/classification.rst>
+    Regression <./autodoc/regression.rst>
+    Survival <./autodoc/survival.rst>
+    Parameters <./autodoc/params.rst>
+    Rules <./autodoc/rules.rst>
+    Statistics <./autodoc/stats.rst>

docs/serve/v2.1.24.0/_images/rst_tutorials_survival_15_1.png

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+
+Classification
+========================================
+
+.. autoclass:: rulekit.classification.RuleClassifier
+   :members:
+   :inherited-members:
+
+.. autoclass:: rulekit.classification.ExpertRuleClassifier
+   :members:
+   :inherited-members: 

docs/serve/v2.1.24.0/_images/rst_tutorials_survival_35_1.png

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+Parameters
+========================================
+
+
+.. data:: rulekit.operator.Data
+
+   alias of type Union[numpy.ndarray, pandas.core.frame.DataFrame, List]
+
+.. automodule:: rulekit.params
+    :exclude-members: Measures
+
+    .. autoclass:: Measures
+        :show-inheritance:
+
+        .. autoattribute:: Accuracy
+
+        .. autoattribute:: BinaryEntropy
+  
+        .. autoattribute:: C1
+  
+        .. autoattribute:: C2
+  
+        .. autoattribute:: CFoil
+
+        .. autoattribute:: CN2Significnce
+  
+        .. autoattribute:: Correlation
+  
+        .. autoattribute:: Coverage
+  
+        .. autoattribute:: FBayesianConfirmation
+  
+        .. autoattribute:: FMeasure
+  
+        .. autoattribute:: FullCoverage
+  
+        .. autoattribute:: GeoRSS
+  
+        .. autoattribute:: GMeasure
+  
+        .. autoattribute:: InformationGain
+  
+        .. autoattribute:: JMeasure
+  
+        .. autoattribute:: Kappa
+  
+        .. autoattribute:: Klosgen
+  
+        .. autoattribute:: Laplace
+  
+        .. autoattribute:: Lift
+  
+        .. autoattribute:: LogicalSufficiency
+  
+        .. autoattribute:: MEstimate
+  
+        .. autoattribute:: MutualSupport
+  
+        .. autoattribute:: Novelty
+  
+        .. autoattribute:: OddsRatio
+  
+        .. autoattribute:: OneWaySupport
+  
+        .. autoattribute:: PawlakDependencyFactor
+  
+        .. autoattribute:: Q2
+  
+        .. autoattribute:: Precision
+  
+        .. autoattribute:: RelativeRisk
+  
+        .. autoattribute:: Ripper
+  
+        .. autoattribute:: RuleInterest
+  
+        .. autoattribute:: RSS
+  
+        .. autoattribute:: SBayesian
+  
+        .. autoattribute:: Sensitivity
+  
+        .. autoattribute:: Specificity
+  
+        .. autoattribute:: TwoWaySupport
+  
+        .. autoattribute:: WeightedLaplace
+  
+        .. autoattribute:: WeightedRelativeAccuracy
+  
+        .. autoattribute:: YAILS
+  
+        .. autoattribute:: LogRank

docs/serve/v2.1.24.0/_sources/index.rst.txt

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+Regression
+==========
+
+.. autoclass:: rulekit.regression.RuleRegressor
+   :members:
+   :inherited-members:
+
+.. autoclass:: rulekit.regression.ExpertRuleRegressor
+   :members:
+   :inherited-members: 

docs/serve/v2.1.24.0/_sources/rst/autodoc.rst.txt

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+Rules
+=====
+
+.. automodule:: rulekit.rules
+    :members:

docs/serve/v2.1.24.0/_sources/rst/autodoc/classification.rst.txt

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+Statistics
+==========
+
+.. automodule:: rulekit.stats
+    :members:

docs/serve/v2.1.24.0/_sources/rst/autodoc/params.rst.txt

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+Survival
+==========
+
+.. autoclass:: rulekit.survival.SurvivalRules
+   :members:
+   :inherited-members:
+
+.. autoclass:: rulekit.survival.ExpertSurvivalRules
+   :members:
+   :inherited-members: 

docs/serve/v2.1.24.0/_sources/rst/autodoc/regression.rst.txt

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+
+Quick start
+========================================
+
+.. warning::
+    This package is a wrapper for Java library, and requires Java Development Kit version 8 
+    or later to be installed on the computer. Both Open JDK and Oracle implementations are supported.
+
+    If you don't have JDK installed on your computer you can quickly set it up using 
+    :ref:`install-jdk<https://pypi.org/project/install-jdk/>` package.
+
+    .. code-block:: bash
+        
+        pip install install-jdk
+
+    .. code-block:: python
+
+        import jdk
+
+        jdk.install('11', jre=True)
+
+Installation
+-------------
+
+.. code-block:: bash
+
+    pip install rulekit
+
+.. note::
+
+To check if everything was installed correctly call:
+
+.. code-block:: python
+
+    import rulekit
+    rulekit.__version__
+
+It should run without errors and print package version.
+
+Package usage
+--------------------
+
+Now we are finally ready to use rulekit package and its models.
+
+.. code-block:: python
+
+    from  sklearn import  datasets
+    from rulekit import RuleKit
+    from rulekit.classification import RuleClassifier
+
+    iris = datasets.load_iris()
+    X = iris.data
+    y = iris.target
+
+    classifier = RuleClassifier()
+    classifier.fit(X, y)
+
+    prediction = classifier.predict(X)
+
+    from sklearn.metrics import accuracy_score
+
+    print('Accuracy: ', accuracy_score(y, prediction))
+    
+
+As you may noticed, training and usage of rulekit models is the same as in scikit learn. This 
+mean you easily can use scikit: metrics, cross-validation, hyper-parameters tuning etc.
+
+
+For more examples head to :doc:`Tutorials <./tutorials>` section.

docs/serve/v2.1.24.0/_sources/rst/autodoc/rules.rst.txt

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+
+Tutorials
+=========
+
+.. toctree::
+    :maxdepth: 1
+    :caption: Table of contents:
+
+    Classification <./tutorials/classification.ipynb>
+    Regression <./tutorials/regression.ipynb>
+    Survival <./tutorials/survival.ipynb>
+    Expert Rules <./tutorials/expert_rules.ipynb>