def construct_classifier(self):
logger.info("Creating calibrated...")
return CalibratedClassifierCV(
svm.SVC(probability=True,
C=self.clf_best.best_params_['C'],
kernel="rbf",
gamma=self.clf_best.best_params_['gamma']))
def grid_search(self, x, y):
if self.clf_best is not None:
logger.info("Best result already exist")
return
logger.info("Searching classifier best parameters")
self.clf_best = self.perform_grid_search(x, y)
def fit(self, x, y):
Utilities.count_occurences(y)
self.grid_search(x, y)
self.clf = self.construct_classifier()
logger.info("Training...")
return self.clf.fit(x, y)
def perform_grid_search(self, x, y):
svc = svm.LinearSVC()
c = [0.001, 0.01, 0.1, 1, 10]
params = [{'C': c}]
clf = sklearn.model_selection.GridSearchCV(svc,
param_grid=params,
n_jobs=-1)
clf.fit(x, y)
logger.info("Best parameters found:")
logger.info(clf.best_score_)
logger.info(clf.best_params_)
return clf
def perform_grid_search(self, x, y):
clf = svm.SVC(probability=True)
c = [0.001, 0.01, 0.1, 1, 10]
gamma = [0.001, 0.01, 0.1, 1]
params = [{'C': c, 'gamma': gamma, 'kernel': ['rbf']}]
clf = sklearn.model_selection.GridSearchCV(clf,
param_grid=params,
n_jobs=-1)
clf.fit(x, y)
logger.info("Best parameters found:")
logger.info(clf.best_score_)
logger.info(clf.best_params_)
return clf
def construct_classifier(self):
logger.info("Creating calibrated...")
return CalibratedClassifierCV(
svm.LinearSVC(C=self.clf_best.best_params_['C']))