407a5112ab430db6f23fc4317a7400ada797d274,examples/bench.py,,,#,5

Before Change

import mord
import numpy as np

for n_samples in range(40, 200, 20):
    X, y = datasets.make_classification(n_samples=n_samples, n_features=10000,
        n_classes=5, n_informative=5)
    clf1 = linear_model.LogisticRegressionCV(solver="lbfgs",
                                            multi_class="multinomial")

    clf2 = grid_search.GridSearchCV(mord.LogisticIT(), {"alpha": np.logspace(
        -3, 3, 10)})
    cv = cross_validation.StratifiedShuffleSplit(y, test_size=0.3)
    print(cross_validation.cross_val_score(clf1, X, y, cv=cv).mean())
    print(cross_validation.cross_val_score(clf2, X, y, cv=cv).mean())
    print()

After Change

      metrics.mean_absolute_error(clf1.predict(X), y))


clf2 = mord.LogisticAT(alpha=1.)
clf2.fit(X, y)
print("Mean Absolute Error of LogisticAT %s" %
      metrics.mean_absolute_error(clf2.predict(X), y))

Instances