def cross_validate(): train, test = load_cross_validation() u = train.pca() X = train.get_pca_features(u) Y = train.get_labels() X2 = test.get_pca_features(u) Y2 = test.get_labels() rf = RandomForestRegressor(n_jobs=-1) model = rf.fit(X, Y) print('Cross validation score: %f' % loss(Y2, model.predict(X2)))
def cross_validate(): print('Cross validate bayes model') train, test = load_cross_validation() X = train.get_features() Y = train.get_labels() X2 = test.get_features() Y2 = test.get_labels() kmeans = KMeans(n_clusters=8) clf = kmeans.fit(X, train.get_multi_labels()) score = check_score(Y, to_labels(clf.predict(X))) print("Train dataset score %f" % score) score = check_score(Y2, to_labels(clf.predict(X2))) print("test dataset score %f" % score)
def cross_validate(): print('Cross validate kmeans model') train, test = load_cross_validation() X = train.get_features() Y = train.get_labels() X2 = test.get_features() Y2 = test.get_labels() kmeans = KMeans(n_clusters=8) clf = kmeans.fit(X, train.get_multi_labels()) score = check_score(Y, to_labels(clf.predict(X))) print("Train dataset score %f" % score) score = check_score(Y2, to_labels(clf.predict(X2))) print("test dataset score %f" % score)
def cross_validate(): print('Cross validate bayes model') train, test = load_cross_validation() X = train.get_features() Y = train.get_labels() X2 = test.get_features() Y2 = test.get_labels() gnb = bayes.MultinomialNB() clf = gnb.fit(X, train.get_multi_labels()) score = check_score(Y, to_labels(clf.predict(X))) print("Train dataset score %f" % score) score = check_score(Y2, to_labels(clf.predict(X2))) print("test dataset score %f" % score)
def cross_validate(params): print('Cross validate with params') print(params) network = NeuralNetwork(params) train, test = load_cross_validation(0.8) u = train.pca() if params.pca: X = train.get_pca_features(u) else: X = train.get_features() Y = train.get_labels() if params.pca: X2 = test.get_pca_features(u) else: X2 = test.get_features() Y2 = test.get_labels() network.fit(X, Y, X2, Y2) score = network.check_score(X, Y) print("Train dataset score %f" % (score / len(X))) score = network.check_score(X2, Y2) print("test dataset score %f" % (score / len(X2))) make_submission(network, params, u)
def cross_validate(params): print('Cross validate with params') print(params) network = NeuralNetwork(params) train, test = load_cross_validation(0.8) u = train.pca() if params.pca: X = train.get_pca_features(u) else: X = train.get_features() Y = train.get_labels() if params.pca: X2 = test.get_pca_features(u) else: X2 = test.get_features() Y2 = test.get_labels() network.fit(X, Y, X2, Y2) score = network.check_score(X, Y) print("Train dataset score %f" % (score/len(X))) score = network.check_score(X2, Y2) print("test dataset score %f" % (score/len(X2))) make_submission(network, params, u)