test_size=0.20, random_state=10) XtrS, params = ml.rescale(X_train) Xvas, _ = ml.rescale(X_test, params) from imblearn.under_sampling import RandomUnderSampler rus = RandomUnderSampler(random_state=42) X_res, y_res = rus.fit_resample(XtrS, y_train) mlp = MLPClassifier(solver='sgd', max_iter=2000) mlp.hidden_layer_sizes = (100, 100, 100) mlp.activation = 'logistic' mlp.learning_rate_init = 0.1 mlp.learning_rate = 'adaptive' mlp.verbose = True mlp.fit(X_res, y_res) print(mlp.score(Xvas, y_test)) Xte = np.genfromtxt( 'C:\\Users\\radad\\OneDrive\\Desktop\\cs178\\CS178-Kaggle-Competition\\X_test.txt', delimiter=None) Yte = np.vstack((np.arange(Xte.shape[0]), mlp.predict_proba(Xte)[:, 1])).T np.savetxt( 'C:\\Users\\radad\\OneDrive\\Desktop\\cs178\\CS178-Kaggle-Competition\\Y_submit.txt', Yte, '%d, %.2f', header='ID,Prob1',
import os from sklearn.neural_network import MLPClassifier from sklearn.metrics import accuracy_score, classification_report from loading_dataset import load_dataset import matplotlib.pyplot as plt from random import shuffle images_test, labels_test = load_dataset("../training_dataset/", 160) data = images_test.reshape(len(images_test), -1) classifier = MLPClassifier(solver="sgd") classifier.activation = "relu" classifier.learning_rate_init = 0.001 classifier.learning_rate = "adaptive" print(classifier) classifier.fit(data, labels_test) images_test, labels_test = load_dataset("../test_dataset/", 160, False) data_test = images_test.reshape(len(images_test), -1) expected = labels_test predicted = classifier.predict(data_test) accuracy = accuracy_score(expected, predicted) print("****************** average_score : " + str(accuracy))