From b1789ccc00db3ea0419834ad8bae3ea025b22cbf Mon Sep 17 00:00:00 2001
From: Baptiste Bauvin <baptiste.bauvin@lis-lab.fr>
Date: Mon, 25 Mar 2019 16:13:04 -0400
Subject: [PATCH] cvxopt_unforced
---
.../Monoview/Additions/CQBoostUtils.py | 50 +++++++++----------
1 file changed, 25 insertions(+), 25 deletions(-)
diff --git a/multiview_platform/MonoMultiViewClassifiers/Monoview/Additions/CQBoostUtils.py b/multiview_platform/MonoMultiViewClassifiers/Monoview/Additions/CQBoostUtils.py
index c7e6dbdb..716ebf8c 100644
--- a/multiview_platform/MonoMultiViewClassifiers/Monoview/Additions/CQBoostUtils.py
+++ b/multiview_platform/MonoMultiViewClassifiers/Monoview/Additions/CQBoostUtils.py
@@ -186,31 +186,31 @@ class ColumnGenerationClassifier(BaseEstimator, ClassifierMixin, BaseBoost):
if previous_w is not None:
qp.initial_values = np.append(previous_w, [0])
- # try:
- solver_result = qp.solve(abstol=1e-10, reltol=1e-10, feastol=1e-10, return_all_information=True)
- w = np.asarray(np.array(solver_result['x']).T[0])[:n_hypotheses]
-
- # The alphas are the Lagrange multipliers associated with the equality constraints (returned as the y vector in CVXOPT).
- dual_variables = np.asarray(np.array(solver_result['y']).T[0])
- alpha = dual_variables[:n_examples]
-
- # Set the dual constraint right-hand side to be equal to the last lagrange multiplier (nu).
- # Hack: do not change nu if the QP didn't fully solve...
- if solver_result['dual slack'] <= 1e-8:
- self.dual_constraint_rhs = dual_variables[-1]
- # logging.info('Updating dual constraint rhs: {}'.format(self.dual_constraint_rhs))
-
- # except:
- # logging.warning('QP Solving failed at iteration {}.'.format(n_hypotheses))
- # if previous_w is not None:
- # w = np.append(previous_w, [0])
- # else:
- # w = np.array([1.0 / n_hypotheses] * n_hypotheses)
- #
- # if previous_alpha is not None:
- # alpha = previous_alpha
- # else:
- # alpha = self._initialize_alphas(n_examples)
+ try:
+ solver_result = qp.solve(abstol=1e-10, reltol=1e-10, feastol=1e-10, return_all_information=True)
+ w = np.asarray(np.array(solver_result['x']).T[0])[:n_hypotheses]
+
+ # The alphas are the Lagrange multipliers associated with the equality constraints (returned as the y vector in CVXOPT).
+ dual_variables = np.asarray(np.array(solver_result['y']).T[0])
+ alpha = dual_variables[:n_examples]
+
+ # Set the dual constraint right-hand side to be equal to the last lagrange multiplier (nu).
+ # Hack: do not change nu if the QP didn't fully solve...
+ if solver_result['dual slack'] <= 1e-8:
+ self.dual_constraint_rhs = dual_variables[-1]
+ # logging.info('Updating dual constraint rhs: {}'.format(self.dual_constraint_rhs))
+
+ except:
+ logging.warning('QP Solving failed at iteration {}.'.format(n_hypotheses))
+ if previous_w is not None:
+ w = np.append(previous_w, [0])
+ else:
+ w = np.array([1.0 / n_hypotheses] * n_hypotheses)
+
+ if previous_alpha is not None:
+ alpha = previous_alpha
+ else:
+ alpha = self._initialize_alphas(n_examples)
return w, alpha
--
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