def test_2():
try:
from mixtape.markovstatemodel import MarkovStateModel
except ImportError as e:
raise SkipTest(e)
X = [np.random.randint(2, size=10), np.random.randint(2, size=11)]
out = fit_and_score_estimator(
MarkovStateModel(), {'verbose': False}, cv=2, X=X, y=None, verbose=0)
np.testing.assert_array_equal(out['n_train_samples'], [11, 10])
np.testing.assert_array_equal(out['n_test_samples'], [10, 11])
def test_2():
try:
from msmbuilder.msm import MarkovStateModel
except ImportError as e:
raise SkipTest(e)
X = [np.random.randint(2, size=10), np.random.randint(2, size=11)]
out = fit_and_score_estimator(
MarkovStateModel(), {'verbose': False}, cv=2, X=X, y=None, verbose=0)
np.testing.assert_array_equal(out['n_train_samples'], [11, 10])
np.testing.assert_array_equal(out['n_test_samples'], [10, 11])
def test_1():
X, y = make_regression(n_features=10)
lasso = Lasso()
params = {'alpha': 2}
cv = 6
out = fit_and_score_estimator(lasso, params, cv=cv, X=X, y=y, verbose=0)
param_grid = dict((k, [v]) for k, v in iteritems(params))
g = GridSearchCV(estimator=lasso, param_grid=param_grid, cv=cv)
g.fit(X, y)
np.testing.assert_almost_equal(
out['mean_test_score'], g.grid_scores_[0].mean_validation_score)
assert np.all(out['test_scores'] == g.grid_scores_[0].cv_validation_scores)
def test_1():
X, y = make_regression(n_features=10)
lasso = Lasso()
params = {'alpha': 2}
cv = 6
out = fit_and_score_estimator(lasso, params, cv=cv, X=X, y=y, verbose=0)
param_grid = dict((k, [v]) for k, v in iteritems(params))
g = GridSearchCV(estimator=lasso, param_grid=param_grid, cv=cv)
g.fit(X, y)
np.testing.assert_almost_equal(
out['mean_test_score'], g.grid_scores_[0].mean_validation_score)
assert np.all(out['test_scores'] == g.grid_scores_[0].cv_validation_scores)
def test_1():
X, y = make_regression(n_features=10)
lasso = Lasso()
params = {'alpha': 2}
cv = 6
out = fit_and_score_estimator(lasso, params, cv=cv, X=X, y=y, verbose=0)
param_grid = dict((k, [v]) for k, v in iteritems(params))
g = GridSearchCV(estimator=lasso, param_grid=param_grid, cv=cv)
g.fit(X, y)
np.testing.assert_almost_equal(out['mean_test_score'],
g.cv_results_['mean_test_score'][0])
test_scores = np.hstack(
[g.cv_results_['split{}_test_score'.format(i)] for i in range(cv)])
assert np.all(out['test_scores'] == test_scores)
