Removed mincq

config_files/config.yml

@@ -110,15 +110,6 @@ cb_boost:
   n_max_iterations: [10]
   n_stumps: [1]
 
-min_cq_graalpy:
-  mu: [0.01]
-  n_stumps_per_attribute: [1]
-
-min_cq_graalpy_tree:
-  mu: [0.01]
-  n_stumps_per_attribute: [1]
-  max_depth: [2]
-
 lasso:
   alpha: [1]
   max_iter: [2]
@@ -126,10 +117,6 @@ lasso:
 gradient_boosting:
   n_estimators: [2]
 
-min_cq:
-  mu: [0.01]
-  n_stumps_per_attribute: [1]
-
 
 ######################################
 # The Multiview Classifier arguments #

multiview_platform/mono_multi_view_classifiers/monoview_classifiers/min_cq_graalpy.py

-import numpy as np
-
-from ..monoview.additions.BoostUtils import StumpsClassifiersGenerator
-from ..monoview.additions.MinCQUtils import RegularizedBinaryMinCqClassifier
-from ..monoview.monoview_utils import BaseMonoviewClassifier, CustomUniform
-
-
-classifier_class_name = "MinCQGraalpy"
-
-class MinCQGraalpy(RegularizedBinaryMinCqClassifier, BaseMonoviewClassifier):
-
-    def __init__(self, random_state=None, mu=0.01, self_complemented=True,
-                 n_stumps_per_attribute=1, **kwargs):
-        super(MinCQGraalpy, self).__init__(mu=mu,
-                                           estimators_generator=StumpsClassifiersGenerator(
-                                               n_stumps_per_attribute=n_stumps_per_attribute,
-                                               self_complemented=self_complemented),
-                                           )
-        self.param_names = ["mu", "n_stumps_per_attribute", "random_state"]
-        self.distribs = [CustomUniform(loc=0.05, state=2.0, multiplier="e-"),
-                         [n_stumps_per_attribute], [random_state]]
-        self.n_stumps_per_attribute = n_stumps_per_attribute
-        self.classed_params = []
-        self.weird_strings = {}
-        self.random_state = random_state
-        if "nbCores" not in kwargs:
-            self.nbCores = 1
-        else:
-            self.nbCores = kwargs["nbCores"]
-
-    def canProbas(self):
-        """Used to know if the classifier can return label probabilities"""
-        return False
-
-    def set_params(self, **params):
-        self.mu = params["mu"]
-        self.random_state = params["random_state"]
-        self.n_stumps_per_attribute = params["n_stumps_per_attribute"]
-        return self
-
-    def get_params(self, deep=True):
-        return {"random_state": self.random_state, "mu": self.mu,
-                "n_stumps_per_attribute": self.n_stumps_per_attribute}
-
-    def getInterpret(self, directory, y_test):
-        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)
-        # y_rework = np.copy(y_test)
-        # y_rework[np.where(y_rework==0)] = -1
-        # interpret_string += "\n Test c_bound value : "+str(self.majority_vote.cbound_value(self.x_test, y_rework))
-        return interpret_string
-
-    def get_name_for_fusion(self):
-        return "MCG"
-
-
-# def formatCmdArgs(args):
-#     """Used to format kwargs for the parsed args"""
-#     kwargsDict = {"mu": args.MCG_mu,
-#                   "n_stumps_per_attribute": args.MCG_stumps}
-#     return kwargsDict
-
-
-def paramsToSet(nIter, random_state):
-    """Used for weighted linear early fusion to generate random search sets"""
-    paramsSet = []
-    for _ in range(nIter):
-        paramsSet.append({})
-    return paramsSet

multiview_platform/mono_multi_view_classifiers/monoview_classifiers/min_cq_graalpy_tree.py

-import numpy as np
-
-from ..monoview.additions.BoostUtils import TreeClassifiersGenerator
-from ..monoview.additions.MinCQUtils import RegularizedBinaryMinCqClassifier
-from ..monoview.monoview_utils import BaseMonoviewClassifier, CustomUniform
-
-classifier_class_name = "MinCQGraalpyTree"
-
-class MinCQGraalpyTree(RegularizedBinaryMinCqClassifier,
-                       BaseMonoviewClassifier):
-
-    def __init__(self, random_state=None, mu=0.01, self_complemented=True,
-                 n_stumps_per_attribute=1, max_depth=2, **kwargs):
-        super(MinCQGraalpyTree, self).__init__(mu=mu,
-                                               estimators_generator=TreeClassifiersGenerator(
-                                                   n_trees=n_stumps_per_attribute,
-                                                   max_depth=max_depth,
-                                                   self_complemented=self_complemented),
-                                               )
-        self.param_names = ["mu", "n_stumps_per_attribute", "random_state",
-                            "max_depth"]
-        self.distribs = [CustomUniform(loc=0.05, state=2.0, multiplier="e-"),
-                         [n_stumps_per_attribute], [random_state], [max_depth]]
-        self.n_stumps_per_attribute = n_stumps_per_attribute
-        self.classed_params = []
-        self.weird_strings = {}
-        self.max_depth = max_depth
-        self.random_state = random_state
-        if "nbCores" not in kwargs:
-            self.nbCores = 1
-        else:
-            self.nbCores = kwargs["nbCores"]
-
-    def canProbas(self):
-        """Used to know if the classifier can return label probabilities"""
-        return True
-
-    def set_params(self, **params):
-        self.mu = params["mu"]
-        self.random_state = params["random_state"]
-        self.n_stumps_per_attribute = params["n_stumps_per_attribute"]
-        self.max_depth = params["max_depth"]
-        return self
-
-    def get_params(self, deep=True):
-        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):
-        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)
-        # y_rework = np.copy(y_test)
-        # y_rework[np.where(y_rework==0)] = -1
-        # interpret_string += "\n Test c_bound value : "+str(self.majority_vote.cbound_value(self.x_test, y_rework))
-        return interpret_string
-
-    def get_name_for_fusion(self):
-        return "MCG"
-
-
-# def formatCmdArgs(args):
-#     """Used to format kwargs for the parsed args"""
-#     kwargsDict = {"mu": args.MCGT_mu,
-#                   "n_stumps_per_attribute": args.MCGT_trees,
-#                   "max_depth": args.MCGT_max_depth}
-#     return kwargsDict
-
-
-def paramsToSet(nIter, randomState):
-    """Used for weighted linear early fusion to generate random search sets"""
-    paramsSet = []
-    for _ in range(nIter):
-        paramsSet.append({})
-    return paramsSet