wehub-resource-sync
55 lines
1.8 KiB
Python
55 lines
1.8 KiB
Python
from __future__ import division, print_function
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import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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import progressbar
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from mlfromscratch.utils import train_test_split, standardize, to_categorical
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from mlfromscratch.utils import mean_squared_error, accuracy_score, Plot
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from mlfromscratch.utils.loss_functions import SquareLoss
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from mlfromscratch.utils.misc import bar_widgets
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from mlfromscratch.supervised_learning import GradientBoostingRegressor
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def main():
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print ("-- Gradient Boosting Regression --")
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# Load temperature data
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data = pd.read_csv('mlfromscratch/data/TempLinkoping2016.txt', sep="\t")
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time = np.atleast_2d(data["time"].values).T
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temp = np.atleast_2d(data["temp"].values).T
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X = time.reshape((-1, 1)) # Time. Fraction of the year [0, 1]
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X = np.insert(X, 0, values=1, axis=1) # Insert bias term
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y = temp[:, 0] # Temperature. Reduce to one-dim
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5)
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model = GradientBoostingRegressor()
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model.fit(X_train, y_train)
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y_pred = model.predict(X_test)
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y_pred_line = model.predict(X)
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# Color map
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cmap = plt.get_cmap('viridis')
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mse = mean_squared_error(y_test, y_pred)
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print ("Mean Squared Error:", mse)
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# Plot the results
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m1 = plt.scatter(366 * X_train[:, 1], y_train, color=cmap(0.9), s=10)
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m2 = plt.scatter(366 * X_test[:, 1], y_test, color=cmap(0.5), s=10)
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m3 = plt.scatter(366 * X_test[:, 1], y_pred, color='black', s=10)
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plt.suptitle("Regression Tree")
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plt.title("MSE: %.2f" % mse, fontsize=10)
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plt.xlabel('Day')
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plt.ylabel('Temperature in Celcius')
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plt.legend((m1, m2, m3), ("Training data", "Test data", "Prediction"), loc='lower right')
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plt.show()
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if __name__ == "__main__":
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main() |