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bench_glm.py
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bench_glm.py
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"""
A comparison of different methods in GLM
Data comes from a random square matrix.
"""
from datetime import datetime
import numpy as np
from sklearn import linear_model
if __name__ == "__main__":
import matplotlib.pyplot as plt
n_iter = 40
time_ridge = np.empty(n_iter)
time_ols = np.empty(n_iter)
time_lasso = np.empty(n_iter)
dimensions = 500 * np.arange(1, n_iter + 1)
for i in range(n_iter):
print("Iteration %s of %s" % (i, n_iter))
n_samples, n_features = 10 * i + 3, 10 * i + 3
X = np.random.randn(n_samples, n_features)
Y = np.random.randn(n_samples)
start = datetime.now()
ridge = linear_model.Ridge(alpha=1.0)
ridge.fit(X, Y)
time_ridge[i] = (datetime.now() - start).total_seconds()
start = datetime.now()
ols = linear_model.LinearRegression()
ols.fit(X, Y)
time_ols[i] = (datetime.now() - start).total_seconds()
start = datetime.now()
lasso = linear_model.LassoLars()
lasso.fit(X, Y)
time_lasso[i] = (datetime.now() - start).total_seconds()
plt.figure("scikit-learn GLM benchmark results")
plt.xlabel("Dimensions")
plt.ylabel("Time (s)")
plt.plot(dimensions, time_ridge, color="r")
plt.plot(dimensions, time_ols, color="g")
plt.plot(dimensions, time_lasso, color="b")
plt.legend(["Ridge", "OLS", "LassoLars"], loc="upper left")
plt.axis("tight")
plt.show()