Created
March 30, 2017 18:37
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import time | |
import sys | |
from sklearn import ensemble, datasets, model_selection, metrics | |
import numpy as np | |
n_estimators = int(sys.argv[1]) | |
rs = np.random.RandomState(12345) | |
X, y = datasets.make_classification(n_samples=10000, n_features=12, | |
n_informative=12, n_redundant=0, | |
n_repeated=0, random_state=rs) | |
X = X.astype(np.float32) | |
X_train, X_test, y_train, y_test = \ | |
model_selection.train_test_split(X, y, test_size=0.8, | |
random_state=rs) | |
rfc = ensemble.RandomForestClassifier(n_estimators=n_estimators, | |
n_jobs=-1, random_state=rs) | |
time1 = time.perf_counter() | |
rfc.fit(X_train, y_train) | |
time2 = time.perf_counter() | |
proba = rfc.predict_proba(X_test) | |
time3 = time.perf_counter() | |
print("{:5.3f} sec to fit".format(time2-time1)) | |
print("{:5.3f} sec to predict".format(time3-time2)) | |
print("{:5.3f} brier score".format(metrics.brier_score_loss(y_test, proba[:, 1]))) |
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