From 5dce33202115b04084f8fe31ae54b7b510dea4d9 Mon Sep 17 00:00:00 2001
From: Baptiste Bauvin <baptiste.bauvin@lis-lab.fr>
Date: Sat, 1 Dec 2018 13:08:38 -0500
Subject: [PATCH] Added init_info_containers
---
.../Monoview/Additions/QarBoostUtils.py | 50 +++++++++----------
1 file changed, 24 insertions(+), 26 deletions(-)
diff --git a/multiview_platform/MonoMultiViewClassifiers/Monoview/Additions/QarBoostUtils.py b/multiview_platform/MonoMultiViewClassifiers/Monoview/Additions/QarBoostUtils.py
index 2a90d024..ed428329 100644
--- a/multiview_platform/MonoMultiViewClassifiers/Monoview/Additions/QarBoostUtils.py
+++ b/multiview_platform/MonoMultiViewClassifiers/Monoview/Additions/QarBoostUtils.py
@@ -15,16 +15,19 @@ from ... import Metrics
class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
def __init__(self, n_max_iterations=None, estimators_generator=None,
- random_state=42, self_complemented=True, twice_the_same=False, c_bound_choice = True, random_start = True, n_stumps_per_attribute=None, use_r=True, plotted_metric=Metrics.zero_one_loss):
+ random_state=42, self_complemented=True, twice_the_same=False,
+ c_bound_choice = True, random_start = True,
+ n_stumps_per_attribute=None, use_r=True,
+ plotted_metric=Metrics.zero_one_loss):
super(ColumnGenerationClassifierQar, self).__init__()
- self.train_time = 0
- self.n_max_iterations = n_max_iterations
- self.estimators_generator = estimators_generator
if type(random_state) is int:
self.random_state = np.random.RandomState(random_state)
else:
self.random_state = random_state
+ self.train_time = 0
+ self.n_max_iterations = n_max_iterations
+ self.estimators_generator = estimators_generator
self.self_complemented = self_complemented
self.twice_the_same = twice_the_same
self.c_bound_choice = c_bound_choice
@@ -55,32 +58,22 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
# Initialization
y[y == 0] = -1
+ self.init_info_containers()
+
self.estimators_generator.fit(X, y)
self.classification_matrix = self._binary_classification_matrix(X)
- self.weights_ = []
- self.infos_per_iteration_ = defaultdict(list)
-
+
m, n = self.classification_matrix.shape
y = y.reshape((m,1))
y_kernel_matrix = np.multiply(y, self.classification_matrix)
- self.collected_weight_vectors_ = {}
- self.collected_dual_constraint_violations_ = {}
self.example_weights = self._initialize_alphas(m).reshape((m,1))
- self.chosen_columns_ = []
- self.fobidden_columns = []
- self.edge_scores = []
- self.c_bounds = []
- self.epsilons = []
- self.example_weights_ = [self.example_weights]
- self.train_metrics = []
- self.bounds = []
- self.previous_votes = []
- self.previous_margins = [np.multiply(y,y)]
+ self.previous_margins.append(np.multiply(y,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."
@@ -180,6 +173,18 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
self.predict_time = end - start
return signs_array
+ def init_info_containers(self):
+ self.weights_ = []
+ self.chosen_columns_ = []
+ self.fobidden_columns = []
+ self.c_bounds = []
+ self.epsilons = []
+ self.example_weights_ = []
+ self.train_metrics = []
+ self.bounds = []
+ self.previous_votes = []
+ self.previous_margins = []
+
def _compute_epsilon(self,y):
"""Updating the error variable, the old fashioned way uses the whole majority vote to update the error"""
ones_matrix = np.zeros(y.shape)
@@ -198,13 +203,6 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
new_weights = self.example_weights.reshape((self.n_total_examples, 1))*np.exp(-self.q*np.multiply(y,self.new_voter))
self.example_weights = new_weights/np.sum(new_weights)
- def _find_best_margin(self, y_kernel_matrix):
- """Used only on the first iteration to select the voter with the largest margin"""
- pseudo_h_values = ma.array(np.sum(y_kernel_matrix, axis=0), fill_value=-np.inf)
- pseudo_h_values[self.fobidden_columns] = ma.masked
- worst_h_index = ma.argmax(pseudo_h_values)
- return worst_h_index
-
def _find_best_weighted_margin(self, y_kernel_matrix, upper_bound=1.0, lower_bound=0.0):
"""Finds the new voter by choosing the one that has the best weighted margin between 0.5 and 0.55
to avoid too god voters that will get all the votes weights"""
--
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