def test_search_patience(c, s, a, b):
X, y = make_classification(n_samples=100, n_features=5, chunks=(10, 5))
class ConstantClassifier(SGDClassifier):
def score(*args, **kwargs):
return 0.5
model = ConstantClassifier(tol=1e-3)
params = {
"alpha": np.logspace(-2, 10, 100),
"l1_ratio": np.linspace(0.01, 1, 200)
}
search = IncrementalSearch(model,
params,
n_initial_parameters=10,
patience=2)
yield search.fit(X, y, classes=[0, 1])
assert search.history_results_
for d in search.history_results_:
assert d["partial_fit_calls"] <= 3
assert isinstance(search.best_estimator_, SGDClassifier)
assert search.best_score_ > 0
assert "visualize" not in search.__dict__
X_test, y_test = yield c.compute([X, y])
search.predict(X_test)
search.score(X_test, y_test)
def test_small(c, s, a, b):
X, y = make_classification(n_samples=100, n_features=5, chunks=(10, 5))
model = SGDClassifier(tol=1e-3, penalty="elasticnet")
params = {"alpha": [0.1, 0.5, 0.75, 1.0]}
search = IncrementalSearch(model, params, n_initial_parameters="grid")
yield search.fit(X, y, classes=[0, 1])
X_, = yield c.compute([X])
search.predict(X_)