diff --git a/multiview_platform/mono_multi_view_classifiers/monoview/monoview_utils.py b/multiview_platform/mono_multi_view_classifiers/monoview/monoview_utils.py
index 4ba9f364e48d1f582b22140122a8ab09b80be8d0..e5b0b265e8fff64e63b8366546a6392f2a9ccb09 100644
--- a/multiview_platform/mono_multi_view_classifiers/monoview/monoview_utils.py
+++ b/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
diff --git a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/adaboost.py b/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/adaboost.py
index c52e0bfd831960642fefce6358c95ecfc3db3a80..2b3d6423b56e60d7727609af9e42963fe5250b67 100644
--- a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/adaboost.py
+++ b/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()
diff --git a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/adaboost_graalpy.py b/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/adaboost_graalpy.py
index 6052032a987bae844c5c854ac935a84e5d569099..f298906be826e769406d30fe75bf0e48cfd74a13 100644
--- a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/adaboost_graalpy.py
+++ b/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=',')
diff --git a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/adaboost_pregen.py b/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/adaboost_pregen.py
index 511a5320da3e857974c946412a9c39c2458eb4ac..b29495352ce8a69edeff51d0f30e68b7db835288 100644
--- a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/adaboost_pregen.py
+++ b/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)
diff --git a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/cb_boost.py b/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/cb_boost.py
index b86aee42187bcc172740c3fa8bc227f779dd7bab..6955a79e97c9dc73f024227b36e2a5f897adad58 100644
--- a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/cb_boost.py
+++ b/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"
diff --git a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/cg_desc.py b/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/cg_desc.py
index e5ef93cd1645db0b8d8422112a7d4b6b22353f2e..85e8cdca5ad2d37ba16a48fd2d04f1b32e97f0b0 100644
--- a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/cg_desc.py
+++ b/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,
diff --git a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/knn.py b/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/knn.py
index 5a08934db457d2e3edec08805501f0876594723c..e95be5ac9b45442ad4880f437ae04a12d96ed6d5 100644
--- a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/knn.py
+++ b/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,
diff --git a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/lasso.py b/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/lasso.py
index 30af6f5b1839a68ab13bfe7dab37bda9eb3db1d3..0e8b3c41e7d2404eecc52b393b0ce74a0de0e329 100644
--- a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/lasso.py
+++ b/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):
diff --git a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/min_cq.py b/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/min_cq.py
index 21345552b14dc0c2493e3d92d88a152b46de5a80..6956a8553ec0899bf1dbe69524f5bada8796d44a 100644
--- a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/min_cq.py
+++ b/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])):
diff --git a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/min_cq_graalpy.py b/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/min_cq_graalpy.py
index 90e196aba1fe9b8fb6aa0fbd131ad6f713fd4bd6..ddae76cb8b7400d56fc5335138b917dedad2f431 100644
--- a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/min_cq_graalpy.py
+++ b/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,
diff --git a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/min_cq_graalpy_tree.py b/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/min_cq_graalpy_tree.py
index bc2a228cd46bdb2b73a53a3152caaaf034af2402..3e0f370a6ebe906278d980ecfb0e3b075381a305 100644
--- a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/min_cq_graalpy_tree.py
+++ b/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)
diff --git a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/random_forest.py b/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/random_forest.py
index 8b2caef15dd827e0c30ec3984512d72f6a154c65..95c16e923136a2f984547fc453a2a03515de71e0 100644
--- a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/random_forest.py
+++ b/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,
diff --git a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/scm_pregen.py b/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/scm_pregen.py
index 57026c02e2ba84d4a16deac4a6f8554e02f636fc..363577077cc0a6a22ad577dfa8c0eb891597c160 100644
--- a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/scm_pregen.py
+++ b/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
diff --git a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/sgd.py b/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/sgd.py
index f8a10d6fa5df4f5b39d4c7681fb9bc72303be47a..2418b13f781ee907e7e9f87d616669bf8ecb3f6b 100644
--- a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/sgd.py
+++ b/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):
diff --git a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/svm_linear.py b/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/svm_linear.py
index e26e5796256450cff500f416469900c1d08c1d46..e727fed132ef0a3e5e81e54862627c5991461ff2 100644
--- a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/svm_linear.py
+++ b/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):
diff --git a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/svm_poly.py b/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/svm_poly.py
index 3d389802abb7fe0ba56a384c3e27074aef8db4c2..a745092f23cc583624f9b0aa6d84b7bb903ed545 100644
--- a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/svm_poly.py
+++ b/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
----------
diff --git a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/svm_rbf.py b/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/svm_rbf.py
index 16727901006e0e451b18c82c8f7008f56916701b..b341845377179cdfb17574ba2e4ec8589541bad5 100644
--- a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/svm_rbf.py
+++ b/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
----------