wehub-resource-sync
82 lines
2.9 KiB
Python
82 lines
2.9 KiB
Python
from __future__ import print_function
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import matplotlib.pyplot as plt
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import numpy as np
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import pandas as pd
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# Import helper functions
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from mlfromscratch.supervised_learning import PolynomialRidgeRegression
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from mlfromscratch.utils import k_fold_cross_validation_sets, normalize, mean_squared_error
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from mlfromscratch.utils import train_test_split, polynomial_features, Plot
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def main():
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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 = data["temp"].values
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X = time # fraction of the year [0, 1]
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y = temp
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4)
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poly_degree = 15
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# Finding regularization constant using cross validation
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lowest_error = float("inf")
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best_reg_factor = None
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print ("Finding regularization constant using cross validation:")
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k = 10
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for reg_factor in np.arange(0, 0.1, 0.01):
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cross_validation_sets = k_fold_cross_validation_sets(
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X_train, y_train, k=k)
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mse = 0
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for _X_train, _X_test, _y_train, _y_test in cross_validation_sets:
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model = PolynomialRidgeRegression(degree=poly_degree,
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reg_factor=reg_factor,
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learning_rate=0.001,
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n_iterations=10000)
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model.fit(_X_train, _y_train)
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y_pred = model.predict(_X_test)
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_mse = mean_squared_error(_y_test, y_pred)
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mse += _mse
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mse /= k
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# Print the mean squared error
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print ("\tMean Squared Error: %s (regularization: %s)" % (mse, reg_factor))
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# Save reg. constant that gave lowest error
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if mse < lowest_error:
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best_reg_factor = reg_factor
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lowest_error = mse
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# Make final prediction
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model = PolynomialRidgeRegression(degree=poly_degree,
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reg_factor=best_reg_factor,
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learning_rate=0.001,
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n_iterations=10000)
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model.fit(X_train, y_train)
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y_pred = model.predict(X_test)
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mse = mean_squared_error(y_test, y_pred)
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print ("Mean squared error: %s (given by reg. factor: %s)" % (lowest_error, best_reg_factor))
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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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# Plot the results
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m1 = plt.scatter(366 * X_train, y_train, color=cmap(0.9), s=10)
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m2 = plt.scatter(366 * X_test, y_test, color=cmap(0.5), s=10)
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plt.plot(366 * X, y_pred_line, color='black', linewidth=2, label="Prediction")
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plt.suptitle("Polynomial Ridge Regression")
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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), ("Training data", "Test data"), loc='lower right')
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plt.show()
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if __name__ == "__main__":
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main()
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