def main(): print("-- Regression Tree --") # Load temperature data data = pd.read_csv('mlfromscratch/data/TempLinkoping2016.txt', sep="\t") time = np.atleast_2d(data["time"].values).T temp = np.atleast_2d(data["temp"].values).T X = standardize(time) # Time. Fraction of the year [0, 1] y = temp[:, 0] # Temperature. Reduce to one-dim X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3) model = RegressionTree() model.fit(X_train, y_train) y_pred = model.predict(X_test) y_pred_line = model.predict(X) # Color map cmap = plt.get_cmap('viridis') mse = mean_squared_error(y_test, y_pred) print("Mean Squared Error:", mse) # Plot the results # Plot the results m1 = plt.scatter(366 * X_train, y_train, color=cmap(0.9), s=10) m2 = plt.scatter(366 * X_test, y_test, color=cmap(0.5), s=10) m3 = plt.scatter(366 * X_test, y_pred, color='black', s=10) plt.suptitle("Regression Tree") plt.title("MSE: %.2f" % mse, fontsize=10) plt.xlabel('Day') plt.ylabel('Temperature in Celcius') plt.legend((m1, m2, m3), ("Training data", "Test data", "Prediction"), loc='lower right') plt.show()
def main(): print ("-- Regression Tree --") # Load temperature data data = pd.read_csv('mlfromscratch/data/TempLinkoping2016.txt', sep="\t") time = np.atleast_2d(data["time"].values).T temp = np.atleast_2d(data["temp"].values).T X = standardize(time) # Time. Fraction of the year [0, 1] y = temp[:, 0] # Temperature. Reduce to one-dim X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3) model = RegressionTree() model.fit(X_train, y_train) y_pred = model.predict(X_test) y_pred_line = model.predict(X) # Color map cmap = plt.get_cmap('viridis') mse = mean_squared_error(y_test, y_pred) print ("Mean Squared Error:", mse) # Plot the results # Plot the results m1 = plt.scatter(366 * X_train, y_train, color=cmap(0.9), s=10) m2 = plt.scatter(366 * X_test, y_test, color=cmap(0.5), s=10) m3 = plt.scatter(366 * X_test, y_pred, color='black', s=10) plt.suptitle("Regression Tree") plt.title("MSE: %.2f" % mse, fontsize=10) plt.xlabel('Day') plt.ylabel('Temperature in Celcius') plt.legend((m1, m2, m3), ("Training data", "Test data", "Prediction"), loc='lower right') plt.show()