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bench_sgd_regression.py
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bench_sgd_regression.py
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# Author: Peter Prettenhofer <peter.prettenhofer@gmail.com>
# License: BSD 3 clause
import numpy as np
import matplotlib.pyplot as plt
import gc
from time import time
from sklearn.linear_model import Ridge, SGDRegressor, ElasticNet
from sklearn.metrics import mean_squared_error
from sklearn.datasets import make_regression
"""
Benchmark for SGD regression
Compares SGD regression against coordinate descent and Ridge
on synthetic data.
"""
print(__doc__)
if __name__ == "__main__":
list_n_samples = np.linspace(100, 10000, 5).astype(int)
list_n_features = [10, 100, 1000]
n_test = 1000
max_iter = 1000
noise = 0.1
alpha = 0.01
sgd_results = np.zeros((len(list_n_samples), len(list_n_features), 2))
elnet_results = np.zeros((len(list_n_samples), len(list_n_features), 2))
ridge_results = np.zeros((len(list_n_samples), len(list_n_features), 2))
asgd_results = np.zeros((len(list_n_samples), len(list_n_features), 2))
for i, n_train in enumerate(list_n_samples):
for j, n_features in enumerate(list_n_features):
X, y, coef = make_regression(
n_samples=n_train + n_test,
n_features=n_features,
noise=noise,
coef=True,
)
X_train = X[:n_train]
y_train = y[:n_train]
X_test = X[n_train:]
y_test = y[n_train:]
print("=======================")
print("Round %d %d" % (i, j))
print("n_features:", n_features)
print("n_samples:", n_train)
# Shuffle data
idx = np.arange(n_train)
np.random.seed(13)
np.random.shuffle(idx)
X_train = X_train[idx]
y_train = y_train[idx]
std = X_train.std(axis=0)
mean = X_train.mean(axis=0)
X_train = (X_train - mean) / std
X_test = (X_test - mean) / std
std = y_train.std(axis=0)
mean = y_train.mean(axis=0)
y_train = (y_train - mean) / std
y_test = (y_test - mean) / std
gc.collect()
print("- benchmarking ElasticNet")
clf = ElasticNet(alpha=alpha, l1_ratio=0.5, fit_intercept=False)
tstart = time()
clf.fit(X_train, y_train)
elnet_results[i, j, 0] = mean_squared_error(clf.predict(X_test), y_test)
elnet_results[i, j, 1] = time() - tstart
gc.collect()
print("- benchmarking SGD")
clf = SGDRegressor(
alpha=alpha / n_train,
fit_intercept=False,
max_iter=max_iter,
learning_rate="invscaling",
eta0=0.01,
power_t=0.25,
tol=1e-3,
)
tstart = time()
clf.fit(X_train, y_train)
sgd_results[i, j, 0] = mean_squared_error(clf.predict(X_test), y_test)
sgd_results[i, j, 1] = time() - tstart
gc.collect()
print("max_iter", max_iter)
print("- benchmarking A-SGD")
clf = SGDRegressor(
alpha=alpha / n_train,
fit_intercept=False,
max_iter=max_iter,
learning_rate="invscaling",
eta0=0.002,
power_t=0.05,
tol=1e-3,
average=(max_iter * n_train // 2),
)
tstart = time()
clf.fit(X_train, y_train)
asgd_results[i, j, 0] = mean_squared_error(clf.predict(X_test), y_test)
asgd_results[i, j, 1] = time() - tstart
gc.collect()
print("- benchmarking RidgeRegression")
clf = Ridge(alpha=alpha, fit_intercept=False)
tstart = time()
clf.fit(X_train, y_train)
ridge_results[i, j, 0] = mean_squared_error(clf.predict(X_test), y_test)
ridge_results[i, j, 1] = time() - tstart
# Plot results
i = 0
m = len(list_n_features)
plt.figure("scikit-learn SGD regression benchmark results", figsize=(5 * 2, 4 * m))
for j in range(m):
plt.subplot(m, 2, i + 1)
plt.plot(list_n_samples, np.sqrt(elnet_results[:, j, 0]), label="ElasticNet")
plt.plot(list_n_samples, np.sqrt(sgd_results[:, j, 0]), label="SGDRegressor")
plt.plot(list_n_samples, np.sqrt(asgd_results[:, j, 0]), label="A-SGDRegressor")
plt.plot(list_n_samples, np.sqrt(ridge_results[:, j, 0]), label="Ridge")
plt.legend(prop={"size": 10})
plt.xlabel("n_train")
plt.ylabel("RMSE")
plt.title("Test error - %d features" % list_n_features[j])
i += 1
plt.subplot(m, 2, i + 1)
plt.plot(list_n_samples, np.sqrt(elnet_results[:, j, 1]), label="ElasticNet")
plt.plot(list_n_samples, np.sqrt(sgd_results[:, j, 1]), label="SGDRegressor")
plt.plot(list_n_samples, np.sqrt(asgd_results[:, j, 1]), label="A-SGDRegressor")
plt.plot(list_n_samples, np.sqrt(ridge_results[:, j, 1]), label="Ridge")
plt.legend(prop={"size": 10})
plt.xlabel("n_train")
plt.ylabel("Time [sec]")
plt.title("Training time - %d features" % list_n_features[j])
i += 1
plt.subplots_adjust(hspace=0.30)
plt.show()