From 7d5fbfc228a396b2d9505bb8392abf0992ae7458 Mon Sep 17 00:00:00 2001
From: Baptiste Bauvin <baptiste.bauvin@lis-lab.fr>
Date: Fri, 25 Jan 2019 08:28:42 -0500
Subject: [PATCH] Before tests
---
.../Monoview/Additions/QarBoostUtils.py | 20 -------------------
.../MonoviewClassifiers/CGreed.py | 2 +-
2 files changed, 1 insertion(+), 21 deletions(-)
diff --git a/multiview_platform/MonoMultiViewClassifiers/Monoview/Additions/QarBoostUtils.py b/multiview_platform/MonoMultiViewClassifiers/Monoview/Additions/QarBoostUtils.py
index 03de9a0e..1909d038 100644
--- a/multiview_platform/MonoMultiViewClassifiers/Monoview/Additions/QarBoostUtils.py
+++ b/multiview_platform/MonoMultiViewClassifiers/Monoview/Additions/QarBoostUtils.py
@@ -72,7 +72,6 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
"twice_the_same",
"c_bound_choice", "random_start",
"n_stumps", "use_r", "c_bound_sol"]
- self.matrix_compute = False
def set_params(self, **params):
self.self_complemented = params["self_complemented"]
@@ -242,7 +241,6 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
"""THis initialization corressponds to the first round of boosting with equal weights for each examples and the voter chosen by it's margin."""
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(
@@ -369,24 +367,6 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
pseudo_h_values[self.chosen_columns_] = ma.masked
return np.argmax(pseudo_h_values), [0]
- def _is_not_too_wrong(self, hypothese, y):
- """Check if the weighted margin is better than random"""
- if self.c_bound_sol:
- return np.sum(hypothese) > 0
- else:
- print(np.average(hypothese.reshape(y.shape), weights=self.example_weights))
- quit()
- weighted_margin = np.average(hypothese.reshape(y.shape), weights=self.example_weights)#ondes matrix, axis=0
- return weighted_margin > 0
-
- def get_possible(self, y_kernel_matrix, y):
- """Get all the indices of the hypothesis that are good enough to be chosen"""
- possibleIndices = []
- for hypIndex, hypothese in enumerate(np.transpose(y_kernel_matrix)):
- if self._is_not_too_wrong(hypothese, y):
- possibleIndices.append(hypIndex)
- return np.array(possibleIndices)
-
def _find_new_voter(self, y_kernel_matrix, y):
"""Here, we solve the two_voters_mincq_problem for each potential new voter,
and select the one that has the smallest minimum"""
diff --git a/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/CGreed.py b/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/CGreed.py
index d534e54a..d9ecbf28 100644
--- a/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/CGreed.py
+++ b/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/CGreed.py
@@ -12,7 +12,7 @@ class CGreed(ColumnGenerationClassifierQar, BaseMonoviewClassifier):
twice_the_same=True,
c_bound_choice=True,
random_start=False,
- n_stumps_per_attribute=1,
+ n_stumps_per_attribute=10,
use_r=True,
c_bound_sol=True
)
--
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