Doc auto classifiers

README.rst

 .. |pipeline| image:: https://gitlab.lis-lab.fr/baptiste.bauvin/summit/badges/master/pipeline.svg
     :alt: Pipeline status
 
-.. image:: https://img.shields.io/badge/License-GPL%20v3-blue.svg
+.. |license| image:: https://img.shields.io/badge/License-GPL%20v3-blue.svg
     :target: http://www.gnu.org/licenses/gpl-3.0
     :alt: License: GPL v3
 
-|pipeline|
-
-.. image:: https://gitlab.lis-lab.fr/baptiste.bauvin/summit/badges/master/coverage.svg
+.. |coverage| image:: https://gitlab.lis-lab.fr/baptiste.bauvin/summit/badges/master/coverage.svg
     :target: http://baptiste.bauvin.pages.lis-lab.fr/summit/coverage/index.html
     :alt: Coverage
 
+|pipeline| |license| |coverage|
+
+
+
 
 Supervised MultiModal Integration Tool's Readme
 ===============================================
@@ -109,7 +111,7 @@ For your first go with SuMMIT, you can run it on simulated data with
 
 This will run the benchmark of `documentation's Example 1 <http://baptiste.bauvin.pages.lis-lab.fr/summit/tutorials/example1.html>`_.
 
-For more information about the examples, see the `documentation <http://baptiste.bauvin.pages.lis-lab.fr/summit/>`_.
+For more information about the examples, see the `documentation <http://baptiste.bauvin.pages.lis-lab.fr/summit/index.html>`_.
 Results will, by default, be stored in the results directory of the installation path :
 ``path/to/summit/multiview_platform/examples/results``.
 

docs/source/classifiers.rst

-.. toctree::
-    :maxdepth: 1
-
-    autoapi/summit/multiview_platform/monoview_classifiers/index
-    autoapi/summit/multiview_platform/multiview_classifiers/index
\ No newline at end of file

docs/source/index.rst

@@ -20,7 +20,8 @@ This documentation consists in a short read me, with instructions to install and
 .. toctree::
    :maxdepth: 2
 
-   classifiers
+   autoapi/summit/multiview_platform/monoview_classifiers/index
+   autoapi/summit/multiview_platform/multiview_classifiers/index
 
 
 Read me

summit/multiview_platform/monoview_classifiers/adaboost.py

-""" Ada"""
 import os
 import time
 
@@ -19,38 +18,7 @@ classifier_class_name = "Adaboost"
 
 class Adaboost(AdaBoostClassifier, BaseMonoviewClassifier):
     """
-    This class implement a Classifier with adaboost algorithm inherit from sklearn
-    AdaBoostClassifier
-
-    Parameters
-    ----------
-
-    random_state : int seed, RandomState instance, or None (default=None)
-        The seed of the pseudo random number multiview_generator to use when
-        shuffling the data.
-
-    n_estimators : int number of estimators
-
-    base_estimator :
-
-    kwargs : others arguments
-
-
-    Attributes
-    ----------
-    param_name :
-
-    classed_params :
-
-    distribs :
-
-    weird_strings :
-
-    plotted_metric : selection of metric to plot
-
-    plotted_metric_name : name of the metric to plot
-
-    step_predictions :
+    This class is an adaptation of scikit-learn's `AdaBoostClassifier <https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostClassifier.html#sklearn.ensemble.AdaBoostClassifier>`_
 
     """
 
@@ -75,23 +43,6 @@ class Adaboost(AdaBoostClassifier, BaseMonoviewClassifier):
         self.step_predictions = None
 
     def fit(self, X, y, sample_weight=None):
-        """
-        Fit adaboost model
-
-        Parameters
-        ----------
-        X : {array-like, sparse matrix}, shape (n_samples, n_features)
-
-        y :  { array-like, shape (n_samples,)
-            Target values class labels in classification
-
-        sample_weight :
-
-        Returns
-        -------
-        self : object
-            Returns self.
-        """
         begin = time.time()
         AdaBoostClassifier.fit(self, X, y, sample_weight=sample_weight)
         end = time.time()
@@ -104,21 +55,6 @@ class Adaboost(AdaBoostClassifier, BaseMonoviewClassifier):
         return self
 
     def predict(self, X):
-        """
-
-        Parameters
-        ----------
-        X : {array-like, sparse matrix}, shape (n_samples, n_features)
-            Training vectors, where n_samples is the number of samples
-            and n_features is the number of features.
-            For kernel="precomputed", the expected shape of X is
-            (n_samples, n_samples).
-
-        Returns
-        -------
-        predictions : ndarray of shape (n_samples, )
-            The estimated labels.
-        """
         begin = time.time()
         pred = AdaBoostClassifier.predict(self, X)
         end = time.time()

summit/multiview_platform/monoview_classifiers/decision_tree.py

@@ -11,9 +11,15 @@ classifier_class_name = "DecisionTree"
 
 
 class DecisionTree(DecisionTreeClassifier, BaseMonoviewClassifier):
+    """
+    This class is an adaptation of scikit-learn's `DecisionTreeClassifier <https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html>`_
+
+
+    """
 
     def __init__(self, random_state=None, max_depth=None,
                  criterion='gini', splitter='best', **kwargs):
+
         DecisionTreeClassifier.__init__(self,
                                         max_depth=max_depth,
                                         criterion=criterion,

summit/multiview_platform/monoview_classifiers/gradient_boosting.py

@@ -24,6 +24,11 @@ class CustomDecisionTreeGB(DecisionTreeClassifier):
 
 
 class GradientBoosting(GradientBoostingClassifier, BaseMonoviewClassifier):
+    """
+     This class is an adaptation of scikit-learn's `GradientBoostingClassifier <https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html>`_
+
+
+     """
 
     def __init__(self, random_state=None, loss="exponential", max_depth=1.0,
                  n_estimators=100,

summit/multiview_platform/monoview_classifiers/knn.py

@@ -12,17 +12,9 @@ classifier_class_name = "KNN"
 
 class KNN(KNeighborsClassifier, BaseMonoviewClassifier):
     """
-    Implement extention of KNeighborsClassifier of sklearn
-    for the usage of the summit.
-
-    Parameters
-    ----------
-    random_state
-    n_neighbors
-    weights
-    algorithm
-    p
-    kwargs
+     This class is an adaptation of scikit-learn's `KNeighborsClassifier <https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html>`_
+
+
      """
 
     def __init__(self, random_state=None, n_neighbors=5,

summit/multiview_platform/monoview_classifiers/lasso.py

@@ -13,36 +13,8 @@ classifier_class_name = "Lasso"
 
 class Lasso(LassoSK, BaseMonoviewClassifier):
     """
+     This class is an adaptation of scikit-learn's `Lasso <https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Lasso.html>`_
 
-    Parameters
-    ----------
-    random_state :
-
-    alpha : float, optional
-        Constant that multiplies the L1 term. Defaults to 1.0.
-        ``alpha = 0`` is equivalent to an ordinary least square, solved
-        by the :class:`LinearRegression` object. For numerical
-        reasons, using ``alpha = 0`` is with the Lasso object is
-        not advised
-        and you should prefer the LinearRegression object. (default( : 10)
-
-    max_iter :  int The maximum number of iterations (default : 10)
-
-    warm_start : bool, optional
-        When set to True, reuse the solution of the previous call to fit as
-        initialization, otherwise, just erase the previous solution.
-
-    kwargs : others arguments
-
-    Attributes
-    ----------
-    param_name :
-
-    classed_params :
-
-    distribs :
-
-    weird_strings :
 
      """
 

summit/multiview_platform/monoview_classifiers/random_forest.py

@@ -11,47 +11,15 @@ classifier_class_name = "RandomForest"
 
 
 class RandomForest(RandomForestClassifier, BaseMonoviewClassifier):
-    """RandomForest Classifier Class
-
-    Parameters
-    ----------
-    random_state : int seed, RandomState instance, or None (default=None)
-        The seed of the pseudo random number multiview_generator to use when
-        shuffling the data.
-
-    n_estimators : int (default : 10) number of estimators
-
-    max_depth : int , optional (default :  None) maximum of depth
-
-    criterion : criteria (default : 'gini')
-
-    kwargs : others arguments
-
-
-    Attributes
-    ----------
-    param_names :
-
-    distribs :
-
-    classed_params :
+    """
+    This class is an adaptation of scikit-learn's `RandomForestClassifier <https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html>`_
 
-    weird_strings :
 
     """
 
     def __init__(self, random_state=None, n_estimators=10,
                  max_depth=None, criterion='gini', **kwargs):
-        """
 
-        Parameters
-        ----------
-        random_state
-        n_estimators
-        max_depth
-        criterion
-        kwargs
-        """
         RandomForestClassifier.__init__(self,
                                         n_estimators=n_estimators,
                                         max_depth=max_depth,
@@ -68,17 +36,7 @@ class RandomForest(RandomForestClassifier, BaseMonoviewClassifier):
 
     def get_interpretation(self, directory, base_file_name, y_test,
                            multiclass=False):
-        """
-
-        Parameters
-        ----------
-        directory
-        y_test
 
-        Returns
-        -------
-        string for interpretation interpret_string
-        """
         interpret_string = ""
         interpret_string += self.get_feature_importance(directory,
                                                         base_file_name)

summit/multiview_platform/monoview_classifiers/sgd.py

@@ -12,30 +12,8 @@ classifier_class_name = "SGD"
 
 class SGD(SGDClassifier, BaseMonoviewClassifier):
     """
+    This class is an adaptation of scikit-learn's `SGDClassifier <https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html>`_
 
-    Parameters
-    ----------
-    random_state : int seed, RandomState instance, or None (default=None)
-        The seed of the pseudo random number multiview_generator to use when
-        shuffling the data.
-
-    loss : str , (default = "hinge")
-    penalty : str, (default = "l2")
-
-    alpha : float, (default = 0.0001)
-
-    kwargs : other arguments
-
-
-    Attributes
-    ----------
-    param_names :
-
-    distribs :
-
-    classed_params :
-
-    weird_strings :
 
     """
 

summit/multiview_platform/monoview_classifiers/svm_linear.py

@@ -11,20 +11,10 @@ classifier_class_name = "SVMLinear"
 
 
 class SVMLinear(SVCClassifier, BaseMonoviewClassifier):
-    """SVMLinear
-
-    Parameters
-    ----------
-    random_state : int seed, RandomState instance, or None (default=None)
-        The seed of the pseudo random number multiview_generator to use when
-        shuffling the data.
-
-
-    C : float, optional (default=1.0)
-        Penalty parameter C of the error term.
-
-    kwargs : others arguments
+    """
+    This class is an adaptation of scikit-learn's `SVC <https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html>`_
 
+    Here, it is the linear kernel version
     """
 
     def __init__(self, random_state=None, C=1.0, **kwargs):

summit/multiview_platform/monoview_classifiers/svm_poly.py

@@ -13,30 +13,9 @@ classifier_class_name = "SVMPoly"
 
 class SVMPoly(SVCClassifier, BaseMonoviewClassifier):
     """
-    Class of SVMPoly for SVC Classifier
+    This class is an adaptation of scikit-learn's `SVC <https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html>`_
 
-    Parameters
-    ----------
-    random_state : int seed, RandomState instance, or None (default=None)
-        The seed of the pseudo random number multiview_generator to use when
-        shuffling the data.
-
-
-    C : float, optional (default=1.0)
-        Penalty parameter C of the error term.
-
-
-    degree :
-
-    kwargs : others arguments
-
-
-    Attributes
-    ----------
-
-    param_names : list of parameters names
-
-    distribs :  list of random_state distribution
+    Here, it is the polynomial kernel version
     """
 
     def __init__(self, random_state=None, C=1.0, degree=3, **kwargs):

summit/multiview_platform/monoview_classifiers/svm_rbf.py

@@ -12,24 +12,9 @@ classifier_class_name = "SVMRBF"
 
 class SVMRBF(SVCClassifier, BaseMonoviewClassifier):
     """
-    class SVMRBF for classifier SVCC
+    This class is an adaptation of scikit-learn's `SVC <https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html>`_
 
-    Parameters
-    ----------
-    random_state : int seed, RandomState instance, or None (default=None)
-        The seed of the pseudo random number multiview_generator to use when
-        shuffling the data.
-
-    C :
-
-    kwargs : others arguments
-
-    Attributes
-    ----------
-
-    param_names : list of parameters names
-
-    distribs :  list of random_state distribution
+    Here, it is the RBF kernel version
     """
 
     def __init__(self, random_state=None, C=1.0, **kwargs):

summit/multiview_platform/multiview_classifiers/bayesian_inference_fusion.py

@@ -8,6 +8,11 @@ classifier_class_name = "BayesianInferenceClassifier"
 
 
 class BayesianInferenceClassifier(LateFusionClassifier):
+
+    """
+
+    """
+
     def __init__(self, random_state, classifiers_names=None,
                  classifier_configs=None, nb_cores=1, weights=None,
                  rs=None):

summit/multiview_platform/multiview_classifiers/difficulty_fusion.py

@@ -8,6 +8,11 @@ classifier_class_name = "DifficultyFusion"
 
 class DifficultyFusion(GlobalDiversityFusionClassifier):
 
+    """
+    This classifier is inspired by Kuncheva, Ludmila & Whitaker, Chris. (2000). Measures of Diversity in Classifier Ensembles.
+    It find the subset of monoview classifiers with the best difficulty
+    """
+
     def diversity_measure(self, classifiers_decisions, combination, y):
         _, nb_view, nb_samples = classifiers_decisions.shape
         scores = np.zeros((nb_view, nb_samples), dtype=int)

summit/multiview_platform/multiview_classifiers/disagree_fusion.py

@@ -8,6 +8,11 @@ classifier_class_name = "DisagreeFusion"
 
 class DisagreeFusion(CoupleDiversityFusionClassifier):
 
+    """
+    This classifier is inspired by Kuncheva, Ludmila & Whitaker, Chris. (2000). Measures of Diversity in Classifier Ensembles.
+    It find the subset of monoview classifiers with the best disagreement
+    """
+
     def diversity_measure(self, first_classifier_decision,
                           second_classifier_decision, _):
         return np.logical_xor(first_classifier_decision,

summit/multiview_platform/multiview_classifiers/double_fault_fusion.py

@@ -8,6 +8,13 @@ classifier_class_name = "DoubleFaultFusion"
 
 class DoubleFaultFusion(CoupleDiversityFusionClassifier):
 
+    """
+    This classifier is inspired by
+    Kuncheva, Ludmila & Whitaker, Chris. (2000). Measures of Diversity in
+    Classifier Ensembles.
+    It find the subset of monoview classifiers with the best double fault
+    """
+
     def diversity_measure(self, first_classifier_decision,
                           second_classifier_decision, y):
         return np.logical_and(np.logical_xor(first_classifier_decision, y),

summit/multiview_platform/multiview_classifiers/entropy_fusion.py

@@ -8,6 +8,11 @@ classifier_class_name = "EntropyFusion"
 
 class EntropyFusion(GlobalDiversityFusionClassifier):
 
+    """
+    This classifier is inspired by Kuncheva, Ludmila & Whitaker, Chris. (2000). Measures of Diversity in Classifier Ensembles.
+    It find the subset of monoview classifiers with the best entropy
+    """
+
     def diversity_measure(self, classifiers_decisions, combination, y):
         _, nb_view, nb_samples = classifiers_decisions.shape
         scores = np.zeros((nb_view, nb_samples), dtype=int)

summit/multiview_platform/multiview_classifiers/majority_voting_fusion.py

@@ -12,6 +12,11 @@ class VotingIndecision(Exception):
 
 
 class MajorityVoting(LateFusionClassifier):
+
+    """
+    This classifier is a late fusion that builds a majority vote between the views
+    """
+
     def __init__(self, random_state, classifiers_names=None,
                  classifier_configs=None, weights=None, nb_cores=1, rs=None):
         self.need_probas = False

summit/multiview_platform/multiview_classifiers/svm_jumbo_fusion.py

@@ -8,6 +8,11 @@ classifier_class_name = "SVMJumboFusion"
 
 class SVMJumboFusion(BaseJumboFusion):
 
+    """
+    This classifier learns monoview classifiers on each view and then uses an
+    SVM on their decisions to aggregate them.
+    """
+
     def __init__(self, random_state=None, classifiers_names=None,
                  classifier_configs=None, nb_cores=1, weights=None,
                  nb_monoview_per_view=1, C=1.0, kernel="rbf", degree=2,