# Setear Features
X = ip.getTextureFeature(15, 15)
# Setear Parametros para tunear
tuned_parameters = [{'n_estimators': [300, 500, 850, 1000, 1500, 2000, 5000]}]
# Setear Clasificador para tuner
classifier = RandomForestClassifier()
#############################################################
######## DE ACA PARA ABAJO NO HACE FALTA TOCAR NADA #########
#############################################################
#scores = ['precision', 'recall']
scores = ['precision']
Y = ip.getImagesClass()
X_train, X_test, y_train, y_test = train_test_split(
X, Y, test_size=Constants.TEST_SIZE, random_state=Constants.RANDOM_STATE)
for score in scores:
print("# Tuning hyper-parameters for %s" % score)
print()
clf = GridSearchCV(classifier,
tuned_parameters,
cv=10,
scoring='%s_weighted' % score)
clf.fit(X_train, y_train)
print("Best parameters set found on development set:")
print()
print(clf.best_params_)
print()
print("Grid scores on development set:")
# Setear Features
X = ip.getTextureFeature(15, 15)
# Setear Parametros para tunear
tuned_parameters = [{'n_estimators': [300, 500, 850, 1000, 1500, 2000, 5000]}]
# Setear Clasificador para tuner
classifier = RandomForestClassifier()
#############################################################
######## DE ACA PARA ABAJO NO HACE FALTA TOCAR NADA #########
#############################################################
#scores = ['precision', 'recall']
scores = ['precision']
Y = ip.getImagesClass()
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=Constants.TEST_SIZE, random_state=Constants.RANDOM_STATE)
for score in scores:
print("# Tuning hyper-parameters for %s" % score)
print()
clf = GridSearchCV(classifier, tuned_parameters, cv=10, scoring='%s_weighted' % score)
clf.fit(X_train, y_train)
print("Best parameters set found on development set:")
print()
print(clf.best_params_)
print()
print("Grid scores on development set:")
print()
for params, mean_score, scores in clf.grid_scores_:
print("%0.3f (+/-%0.03f) for %r"
% (mean_score, scores.std() * 2, params))
