From 9d0667f2cd62be3e76bdb3bbb7ba6744c836ee43 Mon Sep 17 00:00:00 2001
From: Baptiste Bauvin <baptiste.bauvin@lis-lab.fr>
Date: Sat, 1 Dec 2018 13:47:12 -0500
Subject: [PATCH] Added format_X_y
---
.../Monoview/Additions/QarBoostUtils.py | 105 +++++++++++-------
1 file changed, 63 insertions(+), 42 deletions(-)
diff --git a/multiview_platform/MonoMultiViewClassifiers/Monoview/Additions/QarBoostUtils.py b/multiview_platform/MonoMultiViewClassifiers/Monoview/Additions/QarBoostUtils.py
index bfe3cded..eeb92c92 100644
--- a/multiview_platform/MonoMultiViewClassifiers/Monoview/Additions/QarBoostUtils.py
+++ b/multiview_platform/MonoMultiViewClassifiers/Monoview/Additions/QarBoostUtils.py
@@ -55,49 +55,33 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
m,n,y_kernel_matrix = self.init_hypotheses(formatted_X, formatted_y)
- self.example_weights = self._initialize_alphas(m).reshape((m,1))
-
-
- self.previous_margins.append(np.multiply(formatted_y, formatted_y))
- self.example_weights_.append(self.example_weights)
self.n_total_hypotheses_ = n
self.n_total_examples = m
- 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)):
-
- # To choose the first voter, we select the one that has the best margin or a random one..
- if k == 0:
- if self.random_start:
- first_voter_index = self.random_state.choice(self.get_possible(y_kernel_matrix, formatted_y))
- else:
- first_voter_index, _ = self._find_best_weighted_margin(y_kernel_matrix)
+ self.init_boosting(m, formatted_y, y_kernel_matrix)
+ self.break_cause = " the maximum number of iterations was attained."
- self.chosen_columns_.append(first_voter_index)
- self.new_voter = self.classification_matrix[:, first_voter_index].reshape((m,1))
+ for k in range(min(n-1, self.n_max_iterations-1 if self.n_max_iterations is not None else np.inf)):
- self.previous_vote = self.new_voter
- self.weighted_sum = self.new_voter
+ # Print dynamically the step and the error of the current classifier
+ print("{}/{}, eps :{}".format(k+2, 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, formatted_y)
else:
- # Print dynamically the step and the error of the current classifier
- print("{}/{}, eps :{}".format(k, self.n_max_iterations, self.epsilons[-1]), end="\r")
+ new_voter_index, sol = self._find_best_weighted_margin(y_kernel_matrix)
- # 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, formatted_y)
- else:
- new_voter_index, sol = self._find_best_weighted_margin(y_kernel_matrix)
+ # If the new voter selector could not find one, break the loop
+ if type(sol) == str:
+ self.break_cause = new_voter_index #
+ break
- # 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))
- # 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(formatted_y)
@@ -120,19 +104,16 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
self._update_example_weights(formatted_y)
self.example_weights_.append(self.example_weights)
- 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)
+ # 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 + 2, 1))).reshape((m, 1))
+ self.previous_votes.append(self.previous_vote)
+
self.previous_margins.append(np.multiply(formatted_y, self.previous_vote))
self.train_metrics.append(self.plotted_metric.score(formatted_y, np.sign(self.previous_vote)))
# self.bounds.append(np.prod(np.sqrt(1-4*np.square(0.5-np.array(self.epsilons)))))
- if k!=0:
- self.bounds.append(self.bounds[-1]*math.sqrt(1-r**2))
- else:
- self.bounds.append(math.sqrt(1 - r ** 2))
+ self.bounds.append(self.bounds[-1]*math.sqrt(1-r**2))
self.nb_opposed_voters = self.check_opposed_voters()
self.estimators_generator.estimators_ = self.estimators_generator.estimators_[self.chosen_columns_]
@@ -159,6 +140,46 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
self.predict_time = end - start
return signs_array
+ def init_boosting(self, m, y, y_kernel_matrix):
+ self.example_weights = self._initialize_alphas(m).reshape((m, 1))
+
+ self.previous_margins.append(np.multiply(y, y))
+ self.example_weights_.append(self.example_weights)
+ if self.random_start:
+ 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
+
+ epsilon = self._compute_epsilon(y)
+ self.epsilons.append(epsilon)
+
+ r = self._compute_r(y)
+
+ if self.use_r:
+ self.q = 0.5 * math.log((1 + r) / (1 - r))
+ else:
+ self.q = math.log((1 - epsilon) / epsilon)
+ self.weights_.append(self.q)
+
+ # Update the distribution on the examples.
+ 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.bounds.append(math.sqrt(1 - r ** 2))
+
def format_X_y(self, X, y):
if scipy.sparse.issparse(X):
logging.info('Converting to dense matrix.')
--
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