Merge pull request #38 from lukapecnik/additional_features

Additional features

COMPONENTS.md

@@ -5,7 +5,12 @@
 * Extremely Randomized Trees (ExtremelyRandomizedTrees),
 * Linear SVC (LinearSVC),
 * Multi Layer Perceptron (MultiLayerPerceptron),
-* Random Forest Classifier (RandomForestClassifier).
+* Random Forest Classifier (RandomForest),
+* Decision Tree Classifier (DecisionTree),
+* K-Neighbors Classifier (KNeighbors),
+* Gaussian Process Classifier (GaussianProcess),
+* Gaussian Naive Bayes (GaussianNB),
+* Quadratic Discriminant Analysis (QuadraticDiscriminantAnalysis).
 
 ### Feature Selection Algorithms
 
@@ -24,7 +29,10 @@
 ### Feature Transformation Algorithms
 
 * Normalizer (Normalizer),
-* Standard Scaler (StandardScaler).
+* Standard Scaler (StandardScaler),
+* Maximum Absolute Scaler (MaxAbsScaler),
+* Quantile Transformer (QuantileTransformer),
+* Robust Scaler (RobustScaler).
 
 ### Fitness Functions based on
 

README.rst

@@ -108,7 +108,12 @@ Classifiers
 -  Extremely Randomized Trees (ExtremelyRandomizedTrees),
 -  Linear SVC (LinearSVC),
 -  Multi Layer Perceptron (MultiLayerPerceptron),
--  Random Forest Classifier (RandomForestClassifier).
+-  Random Forest Classifier (RandomForest),
+-  Decision Tree Classifier (DecisionTree),
+-  K-Neighbors Classifier (KNeighbors),
+-  Gaussian Process Classifier (GaussianProcess),
+-  Gaussian Naive Bayes (GaussianNB),
+-  Quadratic Discriminant Analysis (QuadraticDiscriminantAnalysis).
 
 Feature Selection Algorithms
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -130,10 +135,13 @@ Feature Transformation Algorithms
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 -  Normalizer (Normalizer),
--  Standard Scaler (StandardScaler).
+-  Standard Scaler (StandardScaler),
+-  Maximum Absolute Scaler (MaxAbsScaler),
+-  Quantile Transformer (QuantileTransformer),
+-  Robust Scaler (RobustScaler).
 
-Fitness Functions
-~~~~~~~~~~~~~~~~~
+Fitness Functions based on
+~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 -  Accuracy (Accuracy),
 -  Cohen's kappa (CohenKappa),

docs/getting_started.rst

@@ -71,7 +71,12 @@ Classifiers
 * Extremely Randomized Trees (ExtremelyRandomizedTrees),
 * Linear SVC (LinearSVC),
 * Multi Layer Perceptron (MultiLayerPerceptron),
-* Random Forest Classifier (RandomForestClassifier).
+* Random Forest Classifier (RandomForest),
+* Decision Tree Classifier (DecisionTree),
+* K-Neighbors Classifier (KNeighbors),
+* Gaussian Process Classifier (GaussianProcess),
+* Gaussian Naive Bayes (GaussianNB),
+* Quadratic Discriminant Analysis (QuadraticDiscriminantAnalysis).
 
 Feature Selection Algorithms
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -93,16 +98,29 @@ Feature Transformation Algorithms
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 * Normalizer (Normalizer),
-* Standard Scaler (StandardScaler).
+* Standard Scaler (StandardScaler),
+* Maximum Absolute Scaler (MaxAbsScaler),
+* Quantile Transformer (QuantileTransformer),
+* Robust Scaler (RobustScaler).
 
-Fitness Functions
-^^^^^^^^^^^^^^^^^
+Fitness Functions based on
+^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 * Accuracy (Accuracy),
 * Cohen's kappa (CohenKappa),
 * F1-Score (F1),
 * Precision (Precision).
 
+Categorical Feature Encoders
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+* One-Hot Encoder (OneHotEncoder).
+
+Feature Imputers
+^^^^^^^^^^^^^^^^
+
+* Simple Imputer (SimpleImputer).
+
 Optimization Algorithms
 ^^^^^^^^^^^^^^^^^^^^^^^
 

niaaml/__init__.py

@@ -27,4 +27,4 @@
 ]
 
 __project__ = 'niaaml'
-__version__ = '1.0.0rc5'
+__version__ = '1.0.0rc6'

niaaml/classifiers/__init__.py

@@ -5,6 +5,11 @@
 from niaaml.classifiers.ada_boost import AdaBoost
 from niaaml.classifiers.extremely_randomized_trees import ExtremelyRandomizedTrees
 from niaaml.classifiers.bagging import Bagging
+from niaaml.classifiers.decision_tree import DecisionTree
+from niaaml.classifiers.k_neighbors import KNeighbors
+from niaaml.classifiers.gaussian_process import GaussianProcess
+from niaaml.classifiers.gaussian_naive_bayes import GaussianNB
+from niaaml.classifiers.quadratic_driscriminant_analysis import QuadraticDiscriminantAnalysis
 from niaaml.classifiers.utility import ClassifierFactory
 
 __all__ = [
@@ -15,5 +20,10 @@
     'AdaBoost',
     'Bagging',
     'ExtremelyRandomizedTrees',
+    'DecisionTree',
+    'KNeighbors',
+    'GaussianProcess',
+    'GaussianNB',
+    'QuadraticDiscriminantAnalysis',
     'ClassifierFactory'
 ]

niaaml/classifiers/decision_tree.py

@@ -0,0 +1,69 @@
+from niaaml.classifiers.classifier import Classifier
+from niaaml.utilities import MinMax
+from niaaml.utilities import ParameterDefinition
+from sklearn.tree import DecisionTreeClassifier as DTC
+import numpy as np
+
+__all__ = ['DecisionTree']
+
+class DecisionTree(Classifier):
+    r"""Implementation of decision tree classifier.
+    
+    Date:
+        2020
+
+    Author:
+        Luka Pečnik
+
+    License:
+        MIT
+
+    See Also:
+        * :class:`niaaml.classifiers.Classifier`
+    """
+    Name = 'Decision Tree Classifier'
+
+    def __init__(self, **kwargs):
+        r"""Initialize DecisionTree instance.
+        """
+        self._params = dict(
+            criterion = ParameterDefinition(['gini', 'entropy']),
+            splitter = ParameterDefinition(['best', 'random'])
+        )
+        self.__decision_tree_classifier = DTC()
+
+    def set_parameters(self, **kwargs):
+        r"""Set the parameters/arguments of the algorithm.
+        """
+        self.__decision_tree_classifier.set_params(**kwargs)
+
+    def fit(self, x, y, **kwargs):
+        r"""Fit DecisionTree.
+
+        Arguments:
+            x (pandas.core.frame.DataFrame): n samples to classify.
+            y (pandas.core.series.Series): n classes of the samples in the x array.
+
+        Returns:
+            None
+        """
+        self.__decision_tree_classifier.fit(x, y)
+
+    def predict(self, x, **kwargs):
+        r"""Predict class for each sample (row) in x.
+
+        Arguments:
+            x (pandas.core.frame.DataFrame): n samples to classify.
+
+        Returns:
+            pandas.core.series.Series: n predicted classes.
+        """
+        return self.__decision_tree_classifier.predict(x)
+
+    def to_string(self):
+        r"""User friendly representation of the object.
+
+        Returns:
+            str: User friendly representation of the object.
+        """
+        return Classifier.to_string(self).format(name=self.Name, args=self._parameters_to_string(self.__decision_tree_classifier.get_params()))

niaaml/classifiers/gaussian_naive_bayes.py

@@ -0,0 +1,66 @@
+from niaaml.classifiers.classifier import Classifier
+from niaaml.utilities import MinMax
+from niaaml.utilities import ParameterDefinition
+from sklearn.naive_bayes import GaussianNB as GNB
+import numpy as np
+
+__all__ = ['GaussianNB']
+
+class GaussianNB(Classifier):
+    r"""Implementation of gaussian Naive Bayes classifier.
+    
+    Date:
+        2020
+
+    Author:
+        Luka Pečnik
+
+    License:
+        MIT
+
+    See Also:
+        * :class:`niaaml.classifiers.Classifier`
+    """
+    Name = 'Gaussian Naive Bayes'
+
+    def __init__(self, **kwargs):
+        r"""Initialize GaussianNB instance.
+        """
+        self.__gaussian_nb = GNB()
+        super(GaussianNB, self).__init__()
+
+    def set_parameters(self, **kwargs):
+        r"""Set the parameters/arguments of the algorithm.
+        """
+        self.__gaussian_nb.set_params(**kwargs)
+
+    def fit(self, x, y, **kwargs):
+        r"""Fit GaussianNB.
+
+        Arguments:
+            x (pandas.core.frame.DataFrame): n samples to classify.
+            y (pandas.core.series.Series): n classes of the samples in the x array.
+
+        Returns:
+            None
+        """
+        self.__gaussian_nb.fit(x, y)
+
+    def predict(self, x, **kwargs):
+        r"""Predict class for each sample (row) in x.
+
+        Arguments:
+            x (pandas.core.frame.DataFrame): n samples to classify.
+
+        Returns:
+            pandas.core.series.Series: n predicted classes.
+        """
+        return self.__gaussian_nb.predict(x)
+
+    def to_string(self):
+        r"""User friendly representation of the object.
+
+        Returns:
+            str: User friendly representation of the object.
+        """
+        return Classifier.to_string(self).format(name=self.Name, args=self._parameters_to_string(self.__gaussian_nb.get_params()))

niaaml/classifiers/gaussian_process.py

@@ -0,0 +1,70 @@
+from niaaml.classifiers.classifier import Classifier
+from niaaml.utilities import MinMax
+from niaaml.utilities import ParameterDefinition
+from sklearn.gaussian_process import GaussianProcessClassifier as GPC
+import numpy as np
+
+__all__ = ['GaussianProcess']
+
+class GaussianProcess(Classifier):
+    r"""Implementation of gaussian process classifier.
+    
+    Date:
+        2020
+
+    Author:
+        Luka Pečnik
+
+    License:
+        MIT
+
+    See Also:
+        * :class:`niaaml.classifiers.Classifier`
+    """
+    Name = 'Gaussian Process Classifier'
+
+    def __init__(self, **kwargs):
+        r"""Initialize GaussianProcess instance.
+        """
+        self._params = dict(
+            max_iter_predict = ParameterDefinition(MinMax(50, 200), np.uint),
+            warm_start = ParameterDefinition([True, False]),
+            multi_class = ParameterDefinition(['one_vs_rest', 'one_vs_one'])
+        )
+        self.__gaussian_process = GPC()
+
+    def set_parameters(self, **kwargs):
+        r"""Set the parameters/arguments of the algorithm.
+        """
+        self.__gaussian_process.set_params(**kwargs)
+
+    def fit(self, x, y, **kwargs):
+        r"""Fit GaussianProcess.
+
+        Arguments:
+            x (pandas.core.frame.DataFrame): n samples to classify.
+            y (pandas.core.series.Series): n classes of the samples in the x array.
+
+        Returns:
+            None
+        """
+        self.__gaussian_process.fit(x, y)
+
+    def predict(self, x, **kwargs):
+        r"""Predict class for each sample (row) in x.
+
+        Arguments:
+            x (pandas.core.frame.DataFrame): n samples to classify.
+
+        Returns:
+            pandas.core.series.Series: n predicted classes.
+        """
+        return self.__gaussian_process.predict(x)
+
+    def to_string(self):
+        r"""User friendly representation of the object.
+
+        Returns:
+            str: User friendly representation of the object.
+        """
+        return Classifier.to_string(self).format(name=self.Name, args=self._parameters_to_string(self.__gaussian_process.get_params()))