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