def evaluate_validation(classifier, epoch):
if not epoch % 50:
print 'Epoch:', epoch, '.Validation:', calculateAuc(
classifier, x_test, y_test)
if not epoch % 50:
print 'Epoch:', epoch, '. Train:', calculateAuc(
classifier, x_train, y_train)
print classifier.layer_sizes
def evaluate_validation(classifier, epoch):
if not epoch % 50:
print 'Epoch:', epoch, '.Validation:', calculateAuc(classifier, x_test, y_test)
if not epoch % 50:
print 'Epoch:', epoch, '. Train:', calculateAuc(classifier, x_train, y_train)
print classifier.layer_sizes
#
# classifiers = [sklearn.linear_model.LogisticRegression()] #
#
# classifiers = [
# sklearn.ensemble.RandomForestClassifier(n_estimators=200, n_jobs=-1),
# ]
print "training set shape:", x_train.shape
for i, classifier in enumerate(classifiers):
print '-------------------- trying iteration ', i, '---------------------'
try:
print classifier
classifier.fit(x_train, y_train)
predictions = classifier.predict_proba(x_kaggle)[:, 1]
output = '\n'.join([str(p) for p in predictions])
n_submission = str(i + 6000) + 'submission-best-rf.csv'
f = open('/home/blazej/projects/whale-kaggle/submission' + n_submission + '.csv', 'w')
f.write(output)
f.close()
print '.Validation:', calculateAuc(classifier, x_test, y_test)
except BaseException as e:
print 'Had error', e
verbose=1),
GradientBoostingClassifier(),
]
#
# classifiers = [sklearn.linear_model.LogisticRegression()] #
#
# classifiers = [
# sklearn.ensemble.RandomForestClassifier(n_estimators=200, n_jobs=-1),
# ]
print "training set shape:", x_train.shape
for i, classifier in enumerate(classifiers):
print '-------------------- trying iteration ', i, '---------------------'
try:
print classifier
classifier.fit(x_train, y_train)
predictions = classifier.predict_proba(x_kaggle)[:, 1]
output = '\n'.join([str(p) for p in predictions])
n_submission = str(i + 6000) + 'submission-best-rf.csv'
f = open(
'/home/blazej/projects/whale-kaggle/submission' + n_submission +
'.csv', 'w')
f.write(output)
f.close()
print '.Validation:', calculateAuc(classifier, x_test, y_test)
except BaseException as e:
print 'Had error', e
