diff --git a/multiview_platform/MonoMultiViewClassifiers/Monoview/Additions/QarBoostUtils.py b/multiview_platform/MonoMultiViewClassifiers/Monoview/Additions/QarBoostUtils.py
index 7402dfc63ee74466c3b01d854b5a17fb1084fa24..5fdb396fd98608cc0464e661b3f188cc441b02f9 100644
--- a/multiview_platform/MonoMultiViewClassifiers/Monoview/Additions/QarBoostUtils.py
+++ b/multiview_platform/MonoMultiViewClassifiers/Monoview/Additions/QarBoostUtils.py
@@ -87,14 +87,13 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
self.epsilons = []
self.example_weights_ = [self.example_weights]
self.train_metrics = []
- self.gammas = []
self.bounds = []
self.previous_votes = []
self.previous_margins = [np.multiply(y,y)]
self.n_total_hypotheses_ = n
self.n_total_examples = m
- self.n_max_iterations = n
+ self.n_max_iterations = 100
self.break_cause = " the maximum number of iterations was attained."
for k in range(min(n, self.n_max_iterations if self.n_max_iterations is not None else np.inf)):
@@ -105,54 +104,32 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
first_voter_index = self.random_state.choice(self.get_possible(y_kernel_matrix, y))
else:
first_voter_index, _ = self._find_best_weighted_margin(y_kernel_matrix)
+
self.chosen_columns_.append(first_voter_index)
self.new_voter = self.classification_matrix[:, first_voter_index].reshape((m,1))
self.previous_vote = self.new_voter
self.weighted_sum = self.new_voter
- # We update the weights of the examples according to the error of the fisrt voter
- epsilon = self._compute_epsilon(y)
- if epsilon == 0. or math.log((1 - epsilon) / epsilon) == math.inf:
- self.break_cause = " epsilon was too small"
- self.weights_ = [1.0]
- self.train_accuracies = [1.0]
- break
- self.epsilons.append(epsilon)
- if self.divided_ponderation:
- self.q = (1 / (self.n_max_iterations - k)) * math.log((1 - epsilon) / epsilon)
- else:
- # self.q = math.log((1 - epsilon) / epsilon)
- self.q = math.log((1 + epsilon) / (1-epsilon))
- self.weights_.append(self.q)
-
- # Update the boosting variables
- self._update_example_weights(y)
- self.example_weights_.append(self.example_weights)
- self.previous_margins.append(np.multiply(y, self.previous_vote))
- self.train_metrics.append(self.plotted_metric.score(y, np.sign(self.previous_vote)))
- self.gammas.append(accuracy_score(y, np.sign(self.previous_vote))-0.5)
- self.bounds.append(math.exp(-2*self.gammas[-1]**2))
- continue
-
- # Print dynamicly the step and the error of the current classifier
- print("{}/{}, eps :{}".format(k, self.n_max_iterations, self.epsilons[-1]), end="\r")
-
- # Find best weak hypothesis given example_weights. Select the one that has the lowest minimum
- # C-bound with the previous vote or the one with the best weighted margin
- if self.c_bound_choice:
- sol, new_voter_index = self._find_new_voter(y_kernel_matrix, y)
else:
- new_voter_index, sol = self._find_best_weighted_margin(y_kernel_matrix)
+ # Print dynamically the step and the error of the current classifier
+ print("{}/{}, eps :{}".format(k, self.n_max_iterations, self.epsilons[-1]), end="\r")
- # If the new voter selector could not find one, break the loop
- if type(sol) == str:
- self.break_cause = new_voter_index # " no more hypothesis were able to improve the boosted vote."
- break
+ # Find best weak hypothesis given example_weights. Select the one that has the lowest minimum
+ # C-bound with the previous vote or the one with the best weighted margin
+ if self.c_bound_choice:
+ sol, new_voter_index = self._find_new_voter(y_kernel_matrix, y)
+ else:
+ new_voter_index, sol = self._find_best_weighted_margin(y_kernel_matrix)
- # Append the weak hypothesis.
- self.chosen_columns_.append(new_voter_index)
- self.new_voter = self.classification_matrix[:, new_voter_index].reshape((m, 1))
+ # If the new voter selector could not find one, break the loop
+ if type(sol) == str:
+ self.break_cause = new_voter_index #
+ break
+
+ # Append the weak hypothesis.
+ self.chosen_columns_.append(new_voter_index)
+ self.new_voter = self.classification_matrix[:, new_voter_index].reshape((m, 1))
# Generate the new weight for the new voter
epsilon = self._compute_epsilon(y)
@@ -165,20 +142,25 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
self.q = (1/(self.n_max_iterations-k))*math.log((1 - epsilon) / epsilon)
else:
self.q = math.log((1 - epsilon) / epsilon)
- # self.q = math.log((1 + epsilon) / (1 - epsilon))
self.weights_.append(self.q)
# Update the distribution on the examples.
self._update_example_weights(y)
self.example_weights_.append(self.example_weights)
- # Update the "previous vote" to prepare for the next iteration
- self.previous_vote = np.matmul(self.classification_matrix[:, self.chosen_columns_],
- np.array(self.weights_).reshape((k + 1, 1))).reshape((m, 1))
- self.previous_votes.append(self.previous_vote)
+ if k != 0:
+ # Update the "previous vote" to prepare for the next iteration
+ self.previous_vote = np.matmul(self.classification_matrix[:, self.chosen_columns_],
+ np.array(self.weights_).reshape((k + 1, 1))).reshape((m, 1))
+ self.previous_votes.append(self.previous_vote)
self.previous_margins.append(np.multiply(y, self.previous_vote))
self.train_metrics.append(self.plotted_metric.score(y, np.sign(self.previous_vote)))
- self.bounds.append(np.prod(np.sqrt(1-4*np.square(0.5-np.array(self.epsilons)))))
+ # self.bounds.append(np.prod(np.sqrt(1-4*np.square(0.5-np.array(self.epsilons)))))
+ r = self._compute_r(y)
+ if k!=0:
+ self.bounds.append(self.bounds[-1]*math.sqrt(1-r**2))
+ else:
+ self.bounds.append(math.sqrt(1 - r ** 2))
self.nb_opposed_voters = self.check_opposed_voters()
self.estimators_generator.estimators_ = self.estimators_generator.estimators_[self.chosen_columns_]
@@ -214,6 +196,12 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
epsilon = np.average(ones_matrix, weights=self.example_weights, axis=0)
return epsilon
+ def _compute_r(self, y):
+ ones_matrix = np.ones(y.shape)
+ ones_matrix[np.multiply(y, self.new_voter.reshape(y.shape)) < 0] = -1 # can np.divide if needed
+ r = np.average(ones_matrix, weights=self.example_weights, axis=0)
+ return r
+
def _update_example_weights(self, y):
"""Old fashioned exaple weights update uses the whole majority vote, the other way uses only the last voter."""
if self.old_fashioned:
@@ -314,7 +302,7 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
weighted_next_column = np.multiply(next_column.reshape((m,1)), self.example_weights.reshape((m,1)))
self.B2 = np.sum(weighted_next_column ** 2)
- self.B1 = np.sum(2 * weighted_next_column * weighted_next_column)
+ self.B1 = np.sum(2 * weighted_next_column * weighted_previous_sum)
self.B0 = np.sum(weighted_previous_sum ** 2)
M2 = np.sum(np.multiply(np.matmul(weighted_next_column, np.transpose(weighted_next_column)), zero_diag))
diff --git a/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/QarBoostNC.py b/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/QarBoostNC.py
index 98e27afd3294050e2bea48ba5f7a76f43b1216bb..34dc7bb0ff662f5badadbd7367d6f6c939a4818f 100644
--- a/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/QarBoostNC.py
+++ b/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/QarBoostNC.py
@@ -9,15 +9,15 @@ class QarBoostNC(ColumnGenerationClassifierQar, BaseMonoviewClassifier):
def __init__(self, random_state=None, **kwargs):
super(QarBoostNC, self).__init__(
random_state=random_state,
- self_complemented=False,
+ self_complemented=True,
twice_the_same=False,
old_fashioned=False,
previous_vote_weighted=False,
- c_bound_choice=True,
+ c_bound_choice=False,
random_start=True,
- two_wieghts_problem=False,
+ two_wieghts_problem=True,
divided_ponderation=False,
- n_stumps_per_attribute=1
+ n_stumps_per_attribute=100
)
self.param_names = []
self.distribs = []