# coding:utf-8
import sklearn.datasets
import sklearn.linear_model
import numpy.random
import numpy.linalg
import matplotlib.pyplot
if __name__ == "__main__":
# Load boston dataset
boston = sklearn.datasets.load_boston()
# Split the dataset with sampleRatio
sampleRatio = 0.5
n_samples = len(boston.target)
sampleBoundary = int(n_samples * sampleRatio)
# Shuffle the whole data
shuffleIdx = range(n_samples)
numpy.random.shuffle(shuffleIdx)
# Make the training data
train_features = boston.data[shuffleIdx[:sampleBoundary]]
train_targets = boston.target[shuffleIdx[:sampleBoundary]]
# Make the testing data
test_features = boston.data[shuffleIdx[sampleBoundary:]]
test_targets = boston.target[shuffleIdx[sampleBoundary:]]
# Train
lasso = sklearn.linear_model.LassoCV(alphas = [0.01, 0.05, 0.1, 0.5, 1.0, 10.0])
lasso.fit(train_features, train_targets)
print("Alpha = ", lasso.alpha_)
# Predict
predict_targets = lasso.predict(test_features)
# Evaluation
n_test_samples = len(test_targets)
X = range(n_test_samples)
error = numpy.linalg.norm(predict_targets - test_targets, ord =1) / n_test_samples
print("Lasso(Boston) Error: %.2f" %(error))
# Draw
matplotlib.pyplot.plot(X, predict_targets, 'r--', label = 'Predict Price')
matplotlib.pyplot.plot(X, test_targets, 'g:', label='True Price')
legend = matplotlib.pyplot.legend()
matplotlib.pyplot.title("Lasso (Boston)")
matplotlib.pyplot.ylabel("Price (1000 U.S.D)")
matplotlib.pyplot.savefig("Lasso (Boston).png", format='png')
matplotlib.pyplot.show()
Lasso
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