58 lines
1.4 KiB
Python
58 lines
1.4 KiB
Python
"""
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A comparison of different methods in GLM
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Data comes from a random square matrix.
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"""
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from datetime import datetime
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import numpy as np
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from sklearn import linear_model
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from sklearn.utils.bench import total_seconds
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if __name__ == '__main__':
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import pylab as pl
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n_iter = 40
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time_ridge = np.empty(n_iter)
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time_ols = np.empty(n_iter)
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time_lasso = np.empty(n_iter)
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dimensions = 500 * np.arange(1, n_iter + 1)
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for i in range(n_iter):
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print 'Iteration %s of %s' % (i, n_iter)
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n_samples, n_features = 10 * i + 3, 10 * i + 3
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X = np.random.randn(n_samples, n_features)
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Y = np.random.randn(n_samples)
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start = datetime.now()
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ridge = linear_model.Ridge(alpha=1.)
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ridge.fit(X, Y)
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time_ridge[i] = total_seconds(datetime.now() - start)
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start = datetime.now()
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ols = linear_model.LinearRegression()
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ols.fit(X, Y)
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time_ols[i] = total_seconds(datetime.now() - start)
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start = datetime.now()
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lasso = linear_model.LassoLars()
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lasso.fit(X, Y)
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time_lasso[i] = total_seconds(datetime.now() - start)
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pl.xlabel('Dimesions')
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pl.ylabel('Time (in seconds)')
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pl.plot(dimensions, time_ridge, color='r')
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pl.plot(dimensions, time_ols, color='g')
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pl.plot(dimensions, time_lasso, color='b')
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pl.legend(['Ridge', 'OLS', 'LassoLars'])
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pl.axis('tight')
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pl.show()
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