131 lines
4.4 KiB
Python
131 lines
4.4 KiB
Python
"""
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Benchmark for SGD regression
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Compares SGD regression against coordinate descent and Ridge
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on synthetik data.
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"""
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print __doc__
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# Author: Peter Prettenhofer <peter.prettenhofer@gmail.com>
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# License: BSD Style.
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import numpy as np
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import pylab as pl
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import gc
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from time import time
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from sklearn.linear_model import Ridge, SGDRegressor, ElasticNet
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from sklearn.metrics import mean_squared_error
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from sklearn.datasets.samples_generator import make_regression
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if __name__ == "__main__":
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list_n_samples = np.linspace(100, 10000, 5).astype(np.int)
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list_n_features = [10, 100, 1000]
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n_test = 1000
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noise = 0.1
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alpha = 0.01
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sgd_results = np.zeros((len(list_n_samples), len(list_n_features), 2))
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elnet_results = np.zeros((len(list_n_samples), len(list_n_features), 2))
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ridge_results = np.zeros((len(list_n_samples), len(list_n_features), 2))
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for i, n_train in enumerate(list_n_samples):
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for j, n_features in enumerate(list_n_features):
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X, y, coef = make_regression(
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n_samples=n_train + n_test, n_features=n_features,
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noise=noise, coef=True)
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X_train = X[:n_train]
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y_train = y[:n_train]
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X_test = X[n_train:]
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y_test = y[n_train:]
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print "======================="
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print "Round %d %d" % (i, j)
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print "n_features:", n_features
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print "n_samples:", n_train
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# Shuffle data
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idx = np.arange(n_train)
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np.random.seed(13)
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np.random.shuffle(idx)
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X_train = X_train[idx]
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y_train = y_train[idx]
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std = X_train.std(axis=0)
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mean = X_train.mean(axis=0)
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X_train = (X_train - mean) / std
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X_test = (X_test - mean) / std
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std = y_train.std(axis=0)
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mean = y_train.mean(axis=0)
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y_train = (y_train - mean) / std
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y_test = (y_test - mean) / std
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gc.collect()
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print "- benching ElasticNet"
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clf = ElasticNet(alpha=alpha, rho=0.5, fit_intercept=False)
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tstart = time()
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clf.fit(X_train, y_train)
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elnet_results[i, j, 0] = mean_squared_error(clf.predict(X_test),
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y_test)
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elnet_results[i, j, 1] = time() - tstart
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gc.collect()
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print "- benching SGD"
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n_iter = np.ceil(10 ** 4.0 / n_train)
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clf = SGDRegressor(alpha=alpha, fit_intercept=False,
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n_iter=n_iter, learning_rate="invscaling",
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eta0=.01, power_t=0.25)
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tstart = time()
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clf.fit(X_train, y_train)
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sgd_results[i, j, 0] = mean_squared_error(clf.predict(X_test),
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y_test)
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sgd_results[i, j, 1] = time() - tstart
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gc.collect()
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print "- benching RidgeRegression"
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clf = Ridge(alpha=alpha, fit_intercept=False)
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tstart = time()
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clf.fit(X_train, y_train)
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ridge_results[i, j, 0] = mean_squared_error(clf.predict(X_test),
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y_test)
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ridge_results[i, j, 1] = time() - tstart
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# Plot results
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i = 0
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m = len(list_n_features)
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pl.figure(figsize=(5 * 2, 4 * m))
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for j in range(m):
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pl.subplot(m, 2, i + 1)
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pl.plot(list_n_samples, np.sqrt(elnet_results[:, j, 0]),
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label="ElasticNet")
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pl.plot(list_n_samples, np.sqrt(sgd_results[:, j, 0]),
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label="SGDRegressor")
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pl.plot(list_n_samples, np.sqrt(ridge_results[:, j, 0]),
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label="Ridge")
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pl.legend(prop={"size": 10})
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pl.xlabel("n_train")
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pl.ylabel("RMSE")
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pl.title("Test error - %d features" % list_n_features[j])
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i += 1
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pl.subplot(m, 2, i + 1)
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pl.plot(list_n_samples, np.sqrt(elnet_results[:, j, 1]),
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label="ElasticNet")
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pl.plot(list_n_samples, np.sqrt(sgd_results[:, j, 1]),
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label="SGDRegressor")
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pl.plot(list_n_samples, np.sqrt(ridge_results[:, j, 1]),
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label="Ridge")
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pl.legend(prop={"size": 10})
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pl.xlabel("n_train")
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pl.ylabel("Time [sec]")
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pl.title("Training time - %d features" % list_n_features[j])
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i += 1
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pl.subplots_adjust(hspace=.30)
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pl.show()
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