doc string

multiview_platform/mono_multi_view_classifiers/monoview/monoview_utils.py

@@ -18,12 +18,35 @@ __status__ = "Prototype"  # Production, Development, Prototype
 # __date__ = 2016 - 03 - 25
 
 def change_label_to_minus(y):
+    """
+    Change the label 0 to minus one
+
+    Parameters
+    ----------
+    y :
+
+    Returns
+    -------
+    label y with -1 instead of 0
+
+    """
     minus_y = np.copy(y)
     minus_y[np.where(y == 0)] = -1
     return minus_y
 
 
 def change_label_to_zero(y):
+    """
+    Change the label -1 to 0
+
+    Parameters
+    ----------
+    y
+
+    Returns
+    -------
+
+    """
     zeroed_y = np.copy(y)
     zeroed_y[np.where(y == -1)] = 0
     return zeroed_y

multiview_platform/mono_multi_view_classifiers/monoview_classifiers/adaboost.py

@@ -17,12 +17,44 @@ classifier_class_name = "Adaboost"
 
 class Adaboost(AdaBoostClassifier, BaseMonoviewClassifier):
     """
-    This class implement a Classifier with adaboost algorithm.
+    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 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 :
 
     """
 
     def __init__(self, random_state=None, n_estimators=50,
                  base_estimator=None, **kwargs):
+
         super(Adaboost, self).__init__(
             random_state=random_state,
             n_estimators=n_estimators,
@@ -39,6 +71,23 @@ 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()
         super(Adaboost, self).fit(X, y, sample_weight=sample_weight)
         end = time.time()
@@ -51,10 +100,31 @@ class Adaboost(AdaBoostClassifier, BaseMonoviewClassifier):
         return self
 
     def canProbas(self):
-        """Used to know if the classifier can return label probabilities"""
+        """
+        Used to know if the classifier can return label probabilities
+
+        Returns
+        -------
+        True
+        """
         return True
 
     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 = super(Adaboost, self).predict(X)
         end = time.time()

multiview_platform/mono_multi_view_classifiers/monoview_classifiers/adaboost_graalpy.py

@@ -15,6 +15,26 @@ classifier_class_name = "AdaboostGraalpy"
 class AdaBoostGP(BaseEstimator, ClassifierMixin, BaseBoost):
     """Scikit-Learn compatible AdaBoost classifier. Original code by Pascal Germain, adapted by Jean-Francis Roy.
 
+
+    Parameters
+    ----------
+
+    n_iterations : int, optional
+        The number of iterations of the algorithm. Defaults to 200.
+
+    iterations_to_collect_as_hyperparameters : list
+        Iteration numbers to collect while learning, that will be converted as hyperparameter values at evaluation time.
+        Defaults to None.
+    classifiers_generator : Transformer, optional
+        A transformer to convert input samples in voters' outputs. Default: Decision stumps transformer, with 10 stumps
+        per attributes.
+    callback_function : function, optional
+        A function to call at each iteration that is supplied learning information. Defaults to None.
+
+    n_stumps : int ( default : 10)
+
+    self_complemented : boolean (default : True
+
     Attributes
     ----------
     n_iterations : int, optional
@@ -34,6 +54,7 @@ class AdaBoostGP(BaseEstimator, ClassifierMixin, BaseBoost):
                  iterations_to_collect_as_hyperparameters=True,
                  classifiers_generator=None, callback_function=None,
                  n_stumps=10, self_complemented=True):
+
         self.n_iterations = n_iterations
         self.n_stumps = n_stumps
         self.iterations_to_collect_as_hyperparameters = iterations_to_collect_as_hyperparameters
@@ -158,9 +179,37 @@ class AdaBoostGP(BaseEstimator, ClassifierMixin, BaseBoost):
 
 
 class AdaboostGraalpy(AdaBoostGP, BaseMonoviewClassifier):
+    """AdaboostGraalpy
+
+    Parameters
+    ----------
+    random_state : int seed, RandomState instance, or None (default=None)
+        The seed of the pseudo random number generator to use when
+        shuffling the data.
+
+    n_iterations : in number of iterations (default : 200)
+
+    n_stumps : int (default 1)
+
+    kwargs :  others arguments
+
 
+    Attributes
+    ----------
+    param_names :
+
+    distribs :
+
+    weird_strings :
+
+    n_stumps :
+
+    nbCores :
+
+    """
     def __init__(self, random_state=None, n_iterations=200, n_stumps=1,
                  **kwargs):
+
         super(AdaboostGraalpy, self).__init__(
             n_iterations=n_iterations,
             n_stumps=n_stumps
@@ -177,10 +226,28 @@ class AdaboostGraalpy(AdaBoostGP, BaseMonoviewClassifier):
             self.nbCores = kwargs["nbCores"]
 
     def canProbas(self):
-        """Used to know if the classifier can return label probabilities"""
+        """
+        Used to know if the classifier can return label probabilities
+
+        Returns
+        -------
+        True in any case
+        """
         return True
 
     def getInterpret(self, directory, y_test):
+        """
+
+        Parameters
+        ----------
+        directory :
+
+        y_test :
+
+        Returns
+        -------
+        retur string of interpret
+        """
         np.savetxt(directory + "train_metrics.csv", self.losses, delimiter=',')
         np.savetxt(directory + "y_test_step.csv", self.test_preds,
                    delimiter=',')

multiview_platform/mono_multi_view_classifiers/monoview_classifiers/adaboost_pregen.py

@@ -19,7 +19,55 @@ classifier_class_name = "AdaboostPregen"
 
 class AdaboostPregen(AdaBoostClassifier, BaseMonoviewClassifier,
                      PregenClassifier):
+    """
 
+    Parameters
+    ----------
+    random_state : int seed, RandomState instance, or None (default=None)
+        The seed of the pseudo random number generator to use when
+        shuffling the data.
+
+    n_estimators : int number of estimators (default : 50)
+
+    base_estimator :
+
+    n_stumps : int (default : 1)
+
+    estimators_generator : str, (default : "Stumps")
+
+    max_depth : int (default : 1)
+
+    self_complemeted : bool, (default : True)
+
+    kwargs : others arguments
+
+
+    Attributes
+    ----------
+
+    param_names : list of parameters names
+
+    classed_params :  list of parameters names
+
+    distribs :
+
+    weird_strings :
+
+    plotted_metric
+
+    plotted_metric_name : str name of plotted  metric
+
+    step_predictions :
+
+    estimators_generator :
+
+    max_depth :
+
+    n_stumps :
+
+    self_complemented :
+
+    """
     def __init__(self, random_state=None, n_estimators=50,
                  base_estimator=None, n_stumps=1, estimators_generator="Stumps",
                  max_depth=1, self_complemeted=True,
@@ -48,6 +96,21 @@ class AdaboostPregen(AdaBoostClassifier, BaseMonoviewClassifier,
         self.self_complemented = self_complemeted
 
     def fit(self, X, y, sample_weight=None):
+        """
+        Fit the AdaboostPregen
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix}, shape (n_samples, n_features)
+            For kernel="precomputed", the expected shape of X is
+            (n_samples_test, n_samples_train).
+        y :  { array-like, shape (n_samples,)
+            Target values class labels in classification
+
+        sample_weight :
+
+
+        """
         begin = time.time()
         pregen_X, pregen_y = self.pregen_voters(X, y)
         super(AdaboostPregen, self).fit(pregen_X, pregen_y,
@@ -68,10 +131,29 @@ class AdaboostPregen(AdaBoostClassifier, BaseMonoviewClassifier,
                                 range(self.estimator_errors_.shape[0])])
 
     def canProbas(self):
-        """Used to know if the classifier can return label probabilities"""
+        """
+        Used to know if the classifier can return label probabilities
+
+        Returns
+        -------
+        True
+        """
         return True
 
     def predict(self, X):
+        """
+
+        Parameters
+        ----------
+
+        X : {array-like, sparse matrix}, shape (n_samples, n_features)
+            For kernel="precomputed", the expected shape of X is
+            (n_samples_test, n_samples_train).
+
+        Returns
+        -------
+
+        """
         begin = time.time()
         pregen_X, _ = self.pregen_voters(X)
         pred = super(AdaboostPregen, self).predict(pregen_X)

multiview_platform/mono_multi_view_classifiers/monoview_classifiers/cb_boost.py

@@ -5,9 +5,34 @@ from ..monoview.monoview_utils import BaseMonoviewClassifier, CustomRandint
 classifier_class_name = "CBBoost"
 
 class CBBoost(CBBoostClassifier, BaseMonoviewClassifier):
+    """
 
+    Parameters
+    ----------
+    random_state : int seed, RandomState instance, or None (default=None)
+        The seed of the pseudo random number generator to use when
+        shuffling the data.
+
+    n_max_iterations :
+
+    n_stumps :
+
+    kwargs : others arguments
+
+    Attributes
+    ----------
+    param_names : names of parameter used for hyper parameter search
+
+    distribs :
+
+    classed_params :
+
+    weird_strings :
+
+    """
     def __init__(self, random_state=None, n_max_iterations=500, n_stumps=1,
                  **kwargs):
+
         super(CBBoost, self).__init__(n_max_iterations=n_max_iterations,
                                      random_state=random_state,
                                      self_complemented=True,
@@ -25,13 +50,39 @@ class CBBoost(CBBoostClassifier, BaseMonoviewClassifier):
         self.weird_strings = {}
 
     def canProbas(self):
-        """Used to know if the classifier can return label probabilities"""
+        """
+        Used to know if the classifier can return label probabilities
+
+        Returns
+        -------
+        True
+        """
         return True
 
     def getInterpret(self, directory, y_test):
+        """
+        return interpretation string
+
+        Parameters
+        ----------
+
+        directory :
+
+        y_test :
+
+        Returns
+        -------
+
+        """
         return self.getInterpretCBBoost(directory, y_test)
 
     def get_name_for_fusion(self):
+        """
+
+        Returns
+        -------
+        string name of fusion
+        """
         return "CBB"
 
 

multiview_platform/mono_multi_view_classifiers/monoview_classifiers/cg_desc.py

@@ -5,10 +5,23 @@ from ..monoview.monoview_utils import BaseMonoviewClassifier, CustomRandint
 classifier_class_name = "CGDesc"
 
 class CGDesc(ColumnGenerationClassifierQar, BaseMonoviewClassifier):
+    """
 
+    Parameters
+    ----------
+    random_state
+    n_max_iterations
+    n_stumps
+    estimators_generator
+    twice_the_same
+    max_depth
+    kwargs
+    
+    """
     def __init__(self, random_state=None, n_max_iterations=500, n_stumps=1,
                  estimators_generator="Stumps", twice_the_same=True, max_depth=1,
                  **kwargs):
+
         super(CGDesc, self).__init__(n_max_iterations=n_max_iterations,
                                      random_state=random_state,
                                      self_complemented=True,

multiview_platform/mono_multi_view_classifiers/monoview_classifiers/knn.py

@@ -11,9 +11,22 @@ classifier_class_name = "KNN"
 
 
 class KNN(KNeighborsClassifier, BaseMonoviewClassifier):
+    """
+    Implement extention of KNeighborsClassifier of sklearn
+    for the usage of the multiview_platform.
 
+    Parameters
+    ----------
+    random_state
+    n_neighbors
+    weights
+    algorithm
+    p
+    kwargs
+    """
     def __init__(self, random_state=None, n_neighbors=5,
                  weights='uniform', algorithm='auto', p=2, **kwargs):
+
         super(KNN, self).__init__(
             n_neighbors=n_neighbors,
             weights=weights,

multiview_platform/mono_multi_view_classifiers/monoview_classifiers/lasso.py

@@ -12,9 +12,42 @@ __status__ = "Prototype"  # Production, Development, Prototype
 classifier_class_name = "Lasso"
 
 class Lasso(LassoSK, BaseMonoviewClassifier):
+    """
 
+    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 :
+
+    """
     def __init__(self, random_state=None, alpha=1.0,
                  max_iter=10, warm_start=False, **kwargs):
+
         super(Lasso, self).__init__(
             alpha=alpha,
             max_iter=max_iter,
@@ -40,12 +73,31 @@ class Lasso(LassoSK, BaseMonoviewClassifier):
         return signed
 
     def canProbas(self):
-        """Used to know if the classifier can return label probabilities"""
+        """
+        Used to know if the classifier can return label probabilities
+
+        Returns
+        -------
+        False
+        """
         return False
 
     def getInterpret(self, directory, y_test):
-        interpretString = ""
-        return interpretString
+        """
+        return the interpreted string
+
+        Parameters
+        ----------
+        directory :
+
+        y_test : 
+
+        Returns
+        -------
+        interpreted string, str interpret_string
+        """
+        interpret_string = ""
+        return interpret_string
 
 
 # def formatCmdArgs(args):

multiview_platform/mono_multi_view_classifiers/monoview_classifiers/min_cq.py

@@ -444,6 +444,13 @@ class DecisionStumpVoter(Voter):
 
     direction : int (-1 or 1)
         Used to reverse classification decision
+
+    Attributes
+    ----------
+
+    attribute_index :
+    threshold :
+    direction :
     """
 
     def __init__(self, attribute_index, threshold, direction=1):
@@ -508,6 +515,19 @@ class StumpsVotersGenerator(VotersGenerator):
 
     def generate(self, X, y=None, self_complemented=False,
                  only_complements=False):
+        """
+
+        Parameters
+        ----------
+        X
+        y
+        self_complemented
+        only_complements
+
+        Returns
+        -------
+
+        """
         voters = []
         if len(X) != 0:
             for i in range(len(X[0])):

multiview_platform/mono_multi_view_classifiers/monoview_classifiers/min_cq_graalpy.py

@@ -9,14 +9,22 @@ classifier_class_name = "MinCQGraalpy"
 
 class MinCQGraalpy(RegularizedBinaryMinCqClassifier, BaseMonoviewClassifier):
     """
+    MinCQGraalpy extend of ``RegularizedBinaryMinCqClassifier ``
 
     Parameters
     ----------
-    random_state
-    mu
-    self_complemented
-    n_stumps_per_attribute
-    kwargs
+    random_state : int seed, RandomState instance, or None (default=None)
+        The seed of the pseudo random number generator to use when
+        shuffling the data.
+
+    mu :  float, (default: 0.01)
+
+    self_complemented : bool (default : True)
+
+    n_stumps_per_attribute : (default: =1
+
+    kwargs : others arguments
+
 
     Attributes
     ----------
@@ -30,7 +38,7 @@ class MinCQGraalpy(RegularizedBinaryMinCqClassifier, BaseMonoviewClassifier):
 
     weird_strings
 
-    nbCores
+    nbCores : number of cores
 
     """
     def __init__(self, random_state=None, mu=0.01, self_complemented=True,

multiview_platform/mono_multi_view_classifiers/monoview_classifiers/min_cq_graalpy_tree.py

@@ -12,13 +12,35 @@ class MinCQGraalpyTree(RegularizedBinaryMinCqClassifier,
 
     Parameters
     ----------
-    random_state
-    mu
-    self_complemented
-    n_stumps_per_attribute
-    max_depth
-    kwargs
+    random_state :
 
+    mu : (default : 0.01)
+
+    self_complemented :  ( default : True)
+
+    n_stumps_per_attribute : int ( default : 1)
+    max_depth :
+
+    kwargs : others parameters
+
+
+    Attributes
+    ----------
+    param_name :
+
+    distribs :
+
+    classed_params :
+
+    n_stumps_per_attribute : int
+
+    weird_strings :
+
+    max_depth :
+
+    random_state :
+
+    nbCores :
     """
     def __init__(self, random_state=None, mu=0.01, self_complemented=True,
                  n_stumps_per_attribute=1, max_depth=2, **kwargs):
@@ -44,10 +66,28 @@ class MinCQGraalpyTree(RegularizedBinaryMinCqClassifier,
             self.nbCores = kwargs["nbCores"]
 
     def canProbas(self):
-        """Used to know if the classifier can return label probabilities"""
+        """
+        Used to know if the classifier can return label probabilities
+
+        Returns
+        -------
+        True
+        """
         return True
 
     def set_params(self, **params):
+        """
+        set parameter in the input dictionary
+
+        Parameters
+        ----------
+        params : dict parameter to set
+
+        Returns
+        -------
+        self : object
+            Returns self.
+        """
         self.mu = params["mu"]
         self.random_state = params["random_state"]
         self.n_stumps_per_attribute = params["n_stumps_per_attribute"]
@@ -55,11 +95,35 @@ class MinCQGraalpyTree(RegularizedBinaryMinCqClassifier,
         return self
 
     def get_params(self, deep=True):
+        """
+        get parameter
+
+        Parameters
+        ----------
+        deep : (boolean (default : True) not used
+
+        Returns
+        -------
+        dictionary of parameter as key and its values
+        """
         return {"random_state": self.random_state, "mu": self.mu,
                 "n_stumps_per_attribute": self.n_stumps_per_attribute,
                 "max_depth": self.max_depth}
 
     def getInterpret(self, directory, y_test):
+        """
+
+        Parameters
+        ----------
+        directory :
+
+        y_test :
+
+
+        Returns
+        -------
+        string  for interpretation interpret_string
+        """
         interpret_string = "Cbound on train :" + str(self.train_cbound)
         np.savetxt(directory + "times.csv", np.array([self.train_time, 0]))
         # interpret_string += "Train C_bound value : "+str(self.cbound_train)

multiview_platform/mono_multi_view_classifiers/monoview_classifiers/random_forest.py

@@ -14,25 +14,28 @@ class RandomForest(RandomForestClassifier, BaseMonoviewClassifier):
 
     Parameters
     ----------
-    random_state
+    random_state : int seed, RandomState instance, or None (default=None)
+        The seed of the pseudo random number generator to use when
+        shuffling the data.
 
-    n_estimators
+    n_estimators : int (default : 10) number of estimators
 
-    max_depth : int maximum of depth (default : 10)
+    max_depth : int , optional (default :  None) maximum of depth
 
     criterion : criteria (default : 'gini')
 
-    kwargs
+    kwargs : others arguments
+
 
     Attributes
     ----------
-    param_names
+    param_names :
 
-    distribs
+    distribs :
 
-    classed_params
+    classed_params :
 
-    weird_strings
+    weird_strings :
 
     """
     def __init__(self, random_state=None, n_estimators=10,

multiview_platform/mono_multi_view_classifiers/monoview_classifiers/scm_pregen.py

@@ -18,7 +18,10 @@ class SCMPregen(BaseMonoviewClassifier, PregenClassifier, scm):
 
     Parameters
     ----------
-    random_state
+    random_state : int seed, RandomState instance, or None (default=None)
+        The seed of the pseudo random number generator to use when
+        shuffling the data.
+
     model_type
     max_rules
     p
@@ -72,7 +75,7 @@ class SCMPregen(BaseMonoviewClassifier, PregenClassifier, scm):
 
         Returns
         -------
-        parame
+        parameters dictionary
         """
         params = super(SCMPregen, self).get_params(deep)
         params["estimators_generator"] = self.estimators_generator
@@ -90,6 +93,7 @@ class SCMPregen(BaseMonoviewClassifier, PregenClassifier, scm):
         X {array-like, sparse matrix}, shape (n_samples, n_features)
             For kernel="precomputed", the expected shape of X is
             (n_samples_test, n_samples_train).
+
         y : { array-like, shape (n_samples,)
             Target values class labels in classification
 

multiview_platform/mono_multi_view_classifiers/monoview_classifiers/sgd.py

@@ -13,11 +13,28 @@ class SGD(SGDClassifier, BaseMonoviewClassifier):
 
     Parameters
     ----------
-    random_state
-    loss
-    penalty
-    alpha
-    kwargs
+    random_state : int seed, RandomState instance, or None (default=None)
+        The seed of the pseudo random number 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 :
+
     """
     def __init__(self, random_state=None, loss='hinge',
                  penalty='l2', alpha=0.0001, **kwargs):

multiview_platform/mono_multi_view_classifiers/monoview_classifiers/svm_linear.py

@@ -13,9 +13,15 @@ class SVMLinear(SVCClassifier, BaseMonoviewClassifier):
 
     Parameters
     ----------
-    random_state
-    C
-    kwargs
+    random_state : int seed, RandomState instance, or None (default=None)
+        The seed of the pseudo random number generator to use when
+        shuffling the data.
+
+
+    C : float, optional (default=1.0)
+        Penalty parameter C of the error term.
+
+    kwargs : others arguments
 
     """
     def __init__(self, random_state=None, C=1.0, **kwargs):

multiview_platform/mono_multi_view_classifiers/monoview_classifiers/svm_poly.py

@@ -14,13 +14,19 @@ class SVMPoly(SVCClassifier, BaseMonoviewClassifier):
 
     Parameters
     ----------
-    random_state
+    random_state : int seed, RandomState instance, or None (default=None)
+        The seed of the pseudo random number generator to use when
+        shuffling the data.
 
-    C
 
-    degree
+    C : float, optional (default=1.0)
+        Penalty parameter C of the error term.
+
+
+    degree :
+
+    kwargs : others arguments
 
-    kwargs
 
     Attributes
     ----------

multiview_platform/mono_multi_view_classifiers/monoview_classifiers/svm_rbf.py

@@ -14,9 +14,13 @@ class SVMRBF(SVCClassifier, BaseMonoviewClassifier):
 
     Parameters
     ----------
-    random_state
-    C
-    kwargs
+    random_state : int seed, RandomState instance, or None (default=None)
+        The seed of the pseudo random number generator to use when
+        shuffling the data.
+
+    C :
+
+    kwargs : others arguments
 
     Attributes
     ----------