diff --git a/multiview_platform/MonoMultiViewClassifiers/Monoview/Additions/QarBoostUtils.py b/multiview_platform/MonoMultiViewClassifiers/Monoview/Additions/QarBoostUtils.py
index 7e51a32f9ba08eaf1b993c148e9f1257b583a415..a57f145e4e2de27c8f60252029b649a6683e1742 100644
--- a/multiview_platform/MonoMultiViewClassifiers/Monoview/Additions/QarBoostUtils.py
+++ b/multiview_platform/MonoMultiViewClassifiers/Monoview/Additions/QarBoostUtils.py
@@ -187,6 +187,7 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
"""Is used at each iteration to compute and store all the needed quantities for later analysis"""
self.example_weights_.append(self.example_weights)
self.tau.append(np.sum(np.multiply(self.previous_vote, self.new_voter))/float(self.n_total_examples))
+ print(np.sum(np.multiply(self.previous_vote, self.new_voter))/float(self.n_total_examples))
self.previous_vote += self.q * self.new_voter
self.norm.append(np.linalg.norm(self.previous_vote)**2)
self.previous_votes.append(self.previous_vote)
diff --git a/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/MinCQ.py b/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/MinCQ.py
index ed018d5d73b1a247c264ff6ed311989b5c67decf..b7dcd3cfcc56b6d9c64febbba1dbc7dfb56e3498 100644
--- a/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/MinCQ.py
+++ b/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/MinCQ.py
@@ -75,13 +75,16 @@ class MinCqLearner(BaseEstimator, ClassifierMixin):
X : ndarray, shape=(n_samples, n_features)
Training data
- y : ndarray, shape=(n_samples,), optional
+ y_reworked : ndarray, shape=(n_samples,), optional
Training labels
voters : shape=(n_voters,), optional
A priori generated voters
"""
# Preparation of the majority vote, using a voter generator that depends on class attributes
+ if (np.unique(y)!= [-1,1]).any():
+ y_reworked = np.copy(y)
+ y_reworked[np.where(y_reworked==0)] = -1
assert self.voters_type in ['stumps', 'kernel', 'manual'], "MinCqLearner: voters_type must be 'stumps', 'kernel' or 'manual'"
@@ -111,7 +114,7 @@ class MinCqLearner(BaseEstimator, ClassifierMixin):
elif self.kernel == 'rbf':
voters_generator = KernelVotersGenerator(rbf_kernel, gamma=gamma)
- voters = voters_generator.generate(X, y)
+ voters = voters_generator.generate(X, y_reworked)
if self.log:
logging.info("MinCq training started...")
@@ -125,7 +128,7 @@ class MinCqLearner(BaseEstimator, ClassifierMixin):
if self.log:
logging.info("Preparing QP...")
- self._prepare_qp(X, y)
+ self._prepare_qp(X, y_reworked)
try:
if self.log:
@@ -136,15 +139,16 @@ class MinCqLearner(BaseEstimator, ClassifierMixin):
# See Section 7.1 of [2] for an explanation.
self.majority_vote.weights = np.array([2 * q - 1.0 / n_base_voters for q in solver_weights])
if self.log:
- logging.info("First moment of the margin on the training set: {:.4f}".format(np.mean(y * self.majority_vote.margin(X))))
+ logging.info("First moment of the margin on the training set: {:.4f}".format(np.mean(y_reworked * self.majority_vote.margin(X))))
except Exception as e:
logging.error("{}: Error while solving the quadratic program: {}.".format(str(self), str(e)))
self.majority_vote = None
+ self.cbound_train = self.majority_vote.cbound_value(X, y_reworked)
return self
- def predict(self, X):
+ def predict(self, X, save_data=True):
""" Using previously learned majority vote weights, predict the labels of new data points.
Parameters
@@ -162,8 +166,12 @@ class MinCqLearner(BaseEstimator, ClassifierMixin):
if self.majority_vote is None:
logging.error("{}: Error while predicting: MinCq has not been fit or fitting has failed. Will output invalid labels".format(str(self)))
return np.zeros((len(X),))
+ if save_data:
+ self.x_test = X
- return self.majority_vote.vote(X)
+ vote = self.majority_vote.vote(X)
+ vote[np.where(vote==-1)] = 0
+ return vote
def predict_proba(self, X):
""" Using previously learned majority vote weights, predict the labels of new data points with a confidence
@@ -516,38 +524,39 @@ class KernelVotersGenerator(VotersGenerator):
class MinCQ(MinCqLearner, BaseMonoviewClassifier):
- def __init__(self, random_state=None, **kwargs):
- def __init__(self, random_state=None, mu=0.01, epsilon=1e-06, **kwargs):
- super(MinCQ, self).__init__(
- random_state=random_state,
- mu=mu,
- voters_type='stumps',
- n_stumps_per_attribute = 1
- )
- self.param_names = ["mu"]
- self.distribs = [CustomUniform(loc=0.5, state=1.0, multiplier="e-"),
- ]
- self.classed_params = []
- self.weird_strings = {}
- if "nbCores" not in kwargs:
- self.nbCores = 1
- else:
- self.nbCores = kwargs["nbCores"]
+ def __init__(self, random_state=None, mu=0.01, epsilon=1e-06, **kwargs):
+ super(MinCQ, self).__init__(mu=mu,
+ voters_type='stumps',
+ n_stumps_per_attribute = 10
+ )
+ self.param_names = ["mu"]
+ self.distribs = [CustomUniform(loc=0.5, state=1.0, multiplier="e-"),
+ ]
+ self.classed_params = []
+ self.weird_strings = {}
+ if "nbCores" not in kwargs:
+ self.nbCores = 1
+ else:
+ self.nbCores = kwargs["nbCores"]
def canProbas(self):
"""Used to know if the classifier can return label probabilities"""
return True
def getInterpret(self, directory, y_test):
- return getInterpretBase(directory, y_test, "MinCq", self.majority_vote.weights)
+ interpret_string = "Train C_bound value : "+str(self.cbound_train)
+ y_rework = np.copy(y_test)
+ y_rework[np.where(y_rework==0)] = -1
+ interpret_string += "\n Test c_bound value : "+str(self.majority_vote.cbound_value(self.x_test, y_rework))
+ return interpret_string
def get_name_for_fusion(self):
- return "QBN2"
+ return "MCQ"
def formatCmdArgs(args):
"""Used to format kwargs for the parsed args"""
- kwargsDict = {}
+ kwargsDict = {"mu":args.MCQ_mu,}
return kwargsDict
diff --git a/multiview_platform/MonoMultiViewClassifiers/utils/execution.py b/multiview_platform/MonoMultiViewClassifiers/utils/execution.py
index 0bd1241e9bcd13b5d687b7222f67610e40309dfd..a22ccb90881fd1b9ba7065df008507648e3e1399 100644
--- a/multiview_platform/MonoMultiViewClassifiers/utils/execution.py
+++ b/multiview_platform/MonoMultiViewClassifiers/utils/execution.py
@@ -202,6 +202,13 @@ def parseTheArgs(arguments):
help='Set the n_estimators_parameter for Gradient Boosting',
default=100)
+ groupMinCQ = parser.add_argument_group(
+ 'MinCQ arguments')
+ groupMinCQ.add_argument('--MCQ_mu', metavar='float', type=int,
+ action='store',
+ help='Set the mu_parameter for MinCQ',
+ default=1e-3)
+
groupQarBoostv3 = parser.add_argument_group('QarBoostv3 arguments')
groupQarBoostv3.add_argument('--QarB3_mu', metavar='FLOAT', type=float, action='store',
help='Set the mu parameter for QarBoostv3', default=0.001)