Pre-reformat

multiview_platform/MonoMultiViewClassifiers/Monoview/Additions/CGDescUtils.py

@@ -2,12 +2,10 @@ import scipy
 import logging
 import numpy as np
 import numpy.ma as ma
-from collections import defaultdict
 import math
 from sklearn.utils.validation import check_is_fitted
 from sklearn.base import BaseEstimator, ClassifierMixin
 import time
-import matplotlib.pyplot as plt
 
 from .BoostUtils import StumpsClassifiersGenerator, sign, BaseBoost, \
     getInterpretBase, get_accuracy_graph, TreeClassifiersGenerator
@@ -22,7 +20,7 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
                  c_bound_choice=True, random_start=True,
                  n_stumps=1, use_r=True, c_bound_sol=True,
                  plotted_metric=Metrics.zero_one_loss, save_train_data=True,
-                 test_graph=True, mincq_tracking=False):
+                 test_graph=True, mincq_tracking=True):
         super(ColumnGenerationClassifierQar, self).__init__()
         r"""
 
@@ -157,6 +155,7 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
         return signs_array
 
     def step_predict(self, classification_matrix):
+        """Used to predict with each step of the greedy algorithm to analyze its performance increase"""
         if classification_matrix.shape != self.train_shape:
             self.step_decisions = np.zeros(classification_matrix.shape)
             self.mincq_step_decisions = np.zeros(classification_matrix.shape)
@@ -200,14 +199,14 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
 
         self.train_metrics.append(train_metric)
         self.bounds.append(bound)
-        if self.mincq_tracking:
+        if self.mincq_tracking: # Used to compute the optimal c-bound distribution on the chose set
             from ...MonoviewClassifiers.MinCQ import MinCqLearner
-            mincq = MinCqLearner(10e-3, "stumps", n_stumps_per_attribute=1)
+            mincq = MinCqLearner(10e-3, "stumps", n_stumps_per_attribute=1, self_complemented=False)
             training_set = self.classification_matrix[:, self.chosen_columns_]
             mincq.fit(training_set, y)
             mincq_pred = mincq.predict(training_set)
             self.mincq_learners.append(mincq)
-            self.mincq_train_metrics.append(self.plotted_metric.score(y, mincq_pred))
+            self.mincq_train_metrics.append(self.plotted_metric.score(y, change_label_to_minus(mincq_pred)))
             self.mincq_weights.append(mincq.majority_vote._weights)
             self.mincq_c_bounds.append(mincq.majority_vote.cbound_value(training_set, y.reshape((y.shape[0],))))
 
@@ -243,7 +242,7 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
             (self.n_total_examples, 1))
 
     def choose_new_voter(self, y_kernel_matrix, formatted_y):
-        """Used to chhoose the voter according to the specified criterion (margin or C-Bound"""
+        """Used to choose the voter according to the specified criterion (margin or C-Bound"""
         if self.c_bound_choice:
             sol, new_voter_index = self._find_new_voter(y_kernel_matrix,
                                                         formatted_y)
@@ -505,7 +504,6 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
                 step_mincq_test_metrics.append(self.plotted_metric.score(y_test,
                                                                          self.mincq_step_decisions[:,
                                                                          step_index]))
-            # step_mincq_test_metrics = np.array(step_mincq_test_metrics)
             np.savetxt(directory + "mincq_step_test_metrics.csv",
                        step_mincq_test_metrics,
                        delimiter=',')

multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/MinCQ.py

@@ -86,6 +86,8 @@ class MinCqLearner(BaseEstimator, ClassifierMixin):
         if (np.unique(y)!= [-1,1]).any():
             y_reworked = np.copy(y)
             y_reworked[np.where(y_reworked==0)] = -1
+        else:
+            y_reworked = y
 
         assert self.voters_type in ['stumps', 'kernel', 'manual'], "MinCqLearner: voters_type must be 'stumps', 'kernel' or 'manual'"
 

multiview_platform/MonoMultiViewClassifiers/utils/execution.py

@@ -390,7 +390,7 @@ def parseTheArgs(arguments):
                                          type=int,
                                          action='store',
                                          help='Number of stumps inthe pregenerated dataset',
-                                         default=2)
+                                         default=3)
 
     groupLasso = parser.add_argument_group('Lasso arguments')
     groupLasso.add_argument('--LA_n_iter', metavar='INT', type=int,