opt = keras.optimizers.Adam(lr=0.001) if TRAIN: model.compile(loss='mse', optimizer=opt, metrics=[auxilary.coefficientofdetermination]) model.fit(X_train, y_train, validation_data=(X_val, y_val), epochs=2000, batch_size=32, callbacks=[es, mc]) bestModel = load_model('best_model.h5', custom_objects={ 'coefficientofdetermination': auxilary.coefficientofdetermination }) del X_test['id'] X_test = scaler.fit_transform(X_test) X_test = np.nan_to_num(X_test, nan=0) X_test = featureSelection.transform(X_test) y_predictions = bestModel.predict(X_test) y_predictions = np.reshape(y_predictions, y_predictions.shape[0]) auxilary.createSubmissionFiles(y_predictions) model.predict(X_test)
parameters = { 'objective': ['binary:logistic'], 'max_depth': [10], 'min_child_weight': [11], 'n_estimators': [400], 'seed': [1111], 'learning_rate': [0.05], 'max_delta_step': [3], 'num_class': [4] } clf = GridSearchCV(estimator=xgb_model, param_grid=parameters, n_jobs=5, cv=10, scoring=scoreFunction, verbose=2) clf.fit(X_train, y_train) print("Best score of best on validation set: ", clf.best_score_) #0.6670 print("Best Parameters: ", clf.best_params_) #rbf, 10 X_test = scaler.transform(X_test) if FEATURE_SELECTION: X_test = featureSelection.transform(X_test) y_pred_test = clf.predict(X_test) print('Number of 3:', np.count_nonzero(y_pred_test == 3)) auxilary.createSubmissionFiles(y_pred_test)