Removed easy_mkl

multiview_platform/mono_multi_view_classifiers/multiview_classifiers/easy_mkl.py

-from MKLpy.algorithms import EasyMKL
-from MKLpy.metrics import pairwise
-from MKLpy.lists import HPK_generator
-from MKLpy.algorithms.komd import KOMD
-import numpy as np
-
-from ..multiview.multiview_utils import BaseMultiviewClassifier, get_examples_views_indices
-from ..utils.hyper_parameter_search import CustomUniform
-
-
-classifier_class_name = "EasyMKLClassifier"
-
-class EasyMKLClassifier(BaseMultiviewClassifier, EasyMKL):
-
-    def __init__(self, random_state=None, degrees=1, lam=0.1,
-                 learner=KOMD(lam=0.1), generator=HPK_generator(n=10),
-                 multiclass_strategy='ova', verbose=False):
-        super().__init__(random_state)
-        super(BaseMultiviewClassifier, self).__init__(lam=lam,
-                                                      learner=learner,
-                                                      generator=generator,
-                                                      multiclass_strategy=multiclass_strategy,
-                                                      verbose=verbose)
-        self.degrees = degrees
-        self.param_names = ["lam", "degrees"]
-        self.distribs = [CustomUniform(), DegreesGenerator()]
-
-    def fit(self, X, y, train_indices=None, view_indices=None ):
-        train_indices, view_indices = get_examples_views_indices(X,
-                                                                  train_indices,
-                                                                  view_indices)
-        if isinstance(self.degrees, DegreesDistribution):
-            self.degrees = self.degrees.draw(len(view_indices))
-        elif isinstance(int, self.degrees):
-            self.degrees = [self.degrees for _ in range(len(view_indices))]
-
-        kernels = [pairwise.homogeneous_polynomial_kernel(X.get_v(view_indices[index],
-                                                                  train_indices),
-                                                          degree=degree)
-                   for index, degree in enumerate(self.degrees)]
-        return super(EasyMKLClassifier, self).fit(kernels, y[train_indices])
-
-    def predict(self, X, example_indices=None, view_indices=None):
-        example_indices, view_indices = get_examples_views_indices(X,
-                                                                  example_indices,
-                                                                  view_indices)
-        kernels = [
-            pairwise.homogeneous_polynomial_kernel(X.get_v(view_indices[index],
-                                                           example_indices),
-                                                   degree=degree)
-            for index, degree in enumerate(self.degrees)]
-        return super(EasyMKLClassifier, self).predict(kernels,)
-
-
-class DegreesGenerator:
-
-    def __init__(self):
-        pass
-
-    def rvs(self, random_state=None):
-        return DegreesDistribution(seed=random_state.randint(1))
-
-
-class DegreesDistribution:
-
-    def __init__(self, seed=42):
-        self.random_state=np.random.RandomState(seed)
-
-    def draw(self, nb_view):
-        return self.random_state.randint(low=5,high=10,size=nb_view)