from sklearn.grid_search import GridSearchCV from data.TrafficFeeder import TrafficFeeder from experiments.initializer import * from utils.SplitTrainTest import SplitTrainTest param_dicts = { "Q": np.arange(0.01, 0.1, step=0.01), "epsilon": np.arange(0.1, 1.0, step=0.05) } n_sliding_window = 4 n_periodic = 1 n_input = n_sliding_window + n_periodic neural_shape = [n_input, 15, 1] dataFeeder = TrafficFeeder() X, y = dataFeeder.fetch_traffic_training(n_sliding_window, n_periodic, (40, 46)) estimator = ACOEstimator() archive_solution = construct_solution(estimator.number_of_solutions, neural_shape) cv = SplitTrainTest(X.shape[0]) fit_param = {'neural_shape': neural_shape, "archive": archive_solution} # estimator.fit(X,y,**fit_param) gs = GridSearchCV(estimator, param_grid=param_dicts, cv=cv, n_jobs=-1, fit_params=fit_param, scoring='mean_squared_error') gs.fit(X, y) print gs.best_estimator_ print gs.best_estimator_.score(X, y)
plt.show() if __name__ == '__main__': best_estimator = None best_score = np.Inf for loop in np.arange(1, 20): n_input = 4 n_periodic = 1 n_hidden = 15 neural_shape = [n_input + n_periodic, n_hidden, 1] Q = 0.09 epsilon = 0.55 dataFeeder = TrafficFeeder() X_train, y_train = dataFeeder.fetch_traffic_training( n_input, 1, (40, 46)) X_test, y_test = dataFeeder.fetch_traffic_test(n_input, 1, (46, 48)) # retrieve = [n_input+1,(X_train,y_train,X_test,y_test)] acoNet = ACOEstimator(Q=Q, epsilon=epsilon) fit_param = {"neural_shape": neural_shape} acoNet.fit(X_train, y_train, **fit_param) fit_param["weights_matrix"] = acoNet.best_archive neuralNet = NeuralFlowRegressor() neuralNet.fit(X_train, y_train, **fit_param) y_pred = dataFeeder.convert(neuralNet.predict(X_test)) score = np.sqrt(mean_squared_error(y_pred, y_test)) if (score < best_score): best_estimator = acoNet print score # plot_fig(y_pred,y_test) print best_score, best_estimator
import numpy as np if __name__ == '__main__': np.random.seed(7) n_input = 4 n_periodic = 0 n_hidden = 15 neural_shape = [n_input + n_periodic, n_hidden, 1] cross_rate = 0.6 mutation_rate = 0.04 pop_size = 50 dataFeeder = TrafficFeeder() X_train, y_train = dataFeeder.fetch_traffic_training( n_input, n_periodic, (40, 47)) X_test, y_test = dataFeeder.fetch_traffic_test(n_input, n_periodic, (48, 50)) # retrieve = [n_input+1,(X_train,y_train,X_test,y_test)] gaEstimator = GAEstimator(cross_rate=cross_rate, mutation_rate=mutation_rate, pop_size=pop_size) fit_param = {"neural_shape": neural_shape} gaEstimator.fit(X_train, y_train, **fit_param) fit_param["weights_matrix"] = gaEstimator.best_archive neuralNet = NeuralFlowRegressor() neuralNet.fit(X_train, y_train, **fit_param) y_pred = dataFeeder.convert(neuralNet.predict(X_test)) print "done" print sqrt(mean_squared_error(y_pred, y_test)) plot_figure(y_pred, y_test)