def test_grids_list_get(self):
iris = load_iris()
client = DjangoClient()
response = client.get(reverse('grids_list'))
self.assertEqual(200, response.status_code)
self.assertEqual(0, len(response.data))
gs1 = ATGridSearchCV(tree.DecisionTreeClassifier(), {
'criterion': ['gini', 'entropy'],
'max_depth': range(1, 6),
'max_features': ['auto', 'log2']
},
webserver_url=self.live_server_url)
wait(gs1.fit(iris.data, iris.target))
response = client.get(reverse('grids_list'))
self.assertEqual(200, response.status_code)
self.assertEqual(1, len(response.data))
gs2 = ATGridSearchCV(tree.ExtraTreeClassifier(), {
'criterion': ['gini', 'entropy'],
'max_depth': range(1, 6),
'max_features': ['auto', 'log2']
},
webserver_url=self.live_server_url)
wait(gs2.fit(iris.data, iris.target))
response = client.get(reverse('grids_list'))
self.assertEqual(200, response.status_code)
self.assertEqual(2, len(response.data))
def test_grid_search_iid(self):
# test the iid parameter
# noise-free simple 2d-data
X, y = make_blobs(centers=[[0, 0], [1, 0], [0, 1], [1, 1]],
random_state=0,
cluster_std=0.1,
shuffle=False,
n_samples=80)
# split dataset into two folds that are not iid
# first one contains data of all 4 blobs, second only from two.
mask = np.ones(X.shape[0], dtype=np.bool)
mask[np.where(y == 1)[0][::2]] = 0
mask[np.where(y == 2)[0][::2]] = 0
# this leads to perfect classification on one fold and a score of 1/3 on
# the other
svm = SVC(kernel='linear')
# create "cv" for splits
cv = [[mask, ~mask], [~mask, mask]]
# once with iid=True (default)
grid_search = ATGridSearchCV(svm,
param_grid={'C': [.1, 10]},
cv=cv,
webserver_url=self.live_server_url)
wait(grid_search.fit(X, y))
first = grid_search.grid_scores_[0]
if first.parameters['C'] != 10:
first = grid_search.grid_scores_[1]
assert_equal(first.parameters['C'], 10)
assert_array_almost_equal(first.cv_validation_scores, [1, 1. / 3.])
# for first split, 1/4 of dataset is in test, for second 3/4.
# take weighted average
assert_almost_equal(first.mean_validation_score,
1 * 1. / 4. + 1. / 3. * 3. / 4.)
# once with iid=False
grid_search = ATGridSearchCV(svm,
param_grid={'C': [.1, 10]},
cv=cv,
iid=False,
webserver_url=self.live_server_url)
wait(grid_search.fit(X, y))
first = grid_search.grid_scores_[0]
if first.parameters['C'] != 10:
first = grid_search.grid_scores_[1]
assert_equal(first.parameters['C'], 10)
# scores are the same as above
assert_array_almost_equal(first.cv_validation_scores, [1, 1. / 3.])
# averaged score is just mean of scores
assert_almost_equal(first.mean_validation_score,
np.mean(first.cv_validation_scores))
def test_cvscore_post(self):
examples_file, label_file = _create_dataset()
ds, _ = DataSet.objects.get_or_create(
name='TEST',
examples=SimpleUploadedFile(examples_file.name,
examples_file.read()),
labels=SimpleUploadedFile(label_file.name, label_file.read()))
gs_1 = ATGridSearchCV(ensemble.RandomForestClassifier(), {
'criterion': ['gini', 'entropy'],
'max_depth': range(1, 21),
'max_features': ['auto', 'log2', 'sqrt', None]
},
dataset=ds.pk,
webserver_url=self.live_server_url)
params = {'criterion': 'gini', 'max_depth': 3, 'max_features': 'log2'}
res = fit_and_save(ensemble.RandomForestClassifier(**params),
X=numpy.genfromtxt(ds.examples, delimiter=','),
y=numpy.genfromtxt(ds.labels, delimiter=','),
parameters=params,
uuid=gs_1._uuid,
url=gs_1.webserver_url)
self.assertEqual(res.status_code, 201)
self.assertEqual(CVResult.objects.count(), 1)
self.assertEqual(
CVResult.objects.get(id=res.json()['id']).scores.all().count(), 1)
def test_grid_search_score_consistency(self):
# test that correct scores are used
clf = LinearSVC(random_state=0)
X, y = make_blobs(random_state=0, centers=2)
Cs = [.1, 1, 10]
for score in ['f1', 'roc_auc']:
grid_search = ATGridSearchCV(clf, {'C': Cs},
scoring=score,
webserver_url=self.live_server_url)
wait(grid_search.fit(X, y))
cv = StratifiedKFold(n_folds=3, y=y)
for scores in grid_search.grid_scores_:
C = scores.parameters['C']
clf.set_params(C=C)
scores = scores[2] # get the separate runs from grid scores
i = 0
for train, test in cv:
clf.fit(X[train], y[train])
if score == "f1":
correct_score = f1_score(y[test], clf.predict(X[test]))
elif score == "roc_auc":
dec = clf.decision_function(X[test])
correct_score = roc_auc_score(y[test], dec)
assert_almost_equal(correct_score, scores[i])
i += 1
def test_grid_search_with_multioutput_data(self):
# Test search with multi-output estimator
X, y = make_multilabel_classification(random_state=0)
est_parameters = {"max_depth": [1, 2, 3, 4]}
cv = KFold(y.shape[0], random_state=0)
estimators = [
DecisionTreeRegressor(random_state=0),
DecisionTreeClassifier(random_state=0)
]
# Test with grid search cv
for est in estimators:
grid_search = ATGridSearchCV(est,
est_parameters,
cv=cv,
webserver_url=self.live_server_url)
wait(grid_search.fit(X, y))
for parameters, _, cv_validation_scores in grid_search.grid_scores_:
est.set_params(**parameters)
for i, (train, test) in enumerate(cv):
est.fit(X[train], y[train])
correct_score = est.score(X[test], y[test])
assert_almost_equal(correct_score, cv_validation_scores[i])
def test_dataset_grids_get(self):
reg = linear_model.LinearRegression()
examples_file, label_file = _create_dataset()
ds, _ = DataSet.objects.get_or_create(
name='TEST',
examples=SimpleUploadedFile(examples_file.name,
examples_file.read()),
labels=SimpleUploadedFile(label_file.name, label_file.read()))
gs_, _ = GridSearch.objects.get_or_create(
classifier=reg.__class__.__name__, dataset=ds)
client = DjangoClient()
response = client.get(reverse('dataset_grids', kwargs={'name':
'TEST'}))
self.assertEqual(200, response.status_code)
self.assertEqual(1, len(response.data))
gs_1 = ATGridSearchCV(ensemble.RandomForestClassifier(), {
'criterion': ['gini', 'entropy'],
'max_depth': range(1, 21),
'max_features': ['auto', 'log2', 'sqrt', None]
},
dataset=ds.pk,
webserver_url=self.live_server_url)
wait(gs_1.fit())
response = client.get(reverse('dataset_grids', kwargs={'name':
'TEST'}))
self.assertEqual(200, response.status_code)
self.assertEqual(2, len(response.data))
def test_grid_search(self):
# Test that the best estimator contains the right value for foo_param
clf = MockClassifier()
grid_search = ATGridSearchCV(clf, {'foo_param': [1, 2, 3]},
verbose=3,
webserver_url=self.live_server_url)
# make sure it selects the smallest parameter in case of ties
old_stdout = sys.stdout
sys.stdout = StringIO()
wait(grid_search.fit(X, y))
sys.stdout = old_stdout
self.assertIn(grid_search.best_estimator_.foo_param, [2, 3])
_mock_sort = partial(_sort_grid_scores, param='foo_param')
for idx, tup in enumerate(
sorted(grid_search.grid_scores_, key=cmp_to_key(_mock_sort))):
self.assertEqual(tup[0], {'foo_param': idx + 1},
'%d%s' % (idx, tup))
# Smoke test the score etc:
grid_search.score(X, y)
grid_search.predict_proba(X)
grid_search.decision_function(X)
grid_search.transform(X)
# Test exception handling on scoring
grid_search.scoring = 'sklearn'
self.assertRaises(ValueError, grid_search.fit, X, y)
def test_cvscore_post_multimetric(self):
from sklearn.metrics import make_scorer, accuracy_score, jaccard_similarity_score
examples_file, label_file = _create_dataset()
ds, _ = DataSet.objects.get_or_create(
name='TEST',
examples=SimpleUploadedFile(examples_file.name,
examples_file.read()),
labels=SimpleUploadedFile(label_file.name, label_file.read()))
gs_1 = ATGridSearchCV(tree.DecisionTreeClassifier, {
'criterion': ['gini', 'entropy'],
'max_depth': range(1, 21),
'max_features': ['auto', 'log2', 'sqrt', None]
},
dataset=ds.pk,
webserver_url=self.live_server_url,
scoring={
'Accuracy': make_scorer(accuracy_score),
'Jaccard':
make_scorer(jaccard_similarity_score)
})
params = {'criterion': 'gini', 'max_depth': 3, 'max_features': 'log2'}
res = fit_and_save(tree.DecisionTreeClassifier(**params),
X=numpy.genfromtxt(ds.examples, delimiter=','),
y=numpy.genfromtxt(ds.labels, delimiter=','),
parameters=params,
uuid=gs_1._uuid,
url=gs_1.webserver_url,
scoring={
'Accuracy': make_scorer(accuracy_score),
'Jaccard': make_scorer(jaccard_similarity_score)
})
self.assertEqual(res.status_code, 201)
self.assertEqual(CVResult.objects.count(), 1)
self.assertEqual(
CVResult.objects.get(id=res.json()['id']).scores.all().count(), 2)
def test_grid_search_precomputed_kernel(self):
# Test that grid search works when the input features are given in the
# form of a precomputed kernel matrix
X_, y_ = make_classification(n_samples=200,
n_features=100,
random_state=0)
# compute the training kernel matrix corresponding to the linear kernel
K_train = np.dot(X_[:180], X_[:180].T)
y_train = y_[:180]
clf = SVC(kernel='precomputed')
cv = ATGridSearchCV(clf, {'C': [0.1, 1.0]},
webserver_url=self.live_server_url)
wait(cv.fit(K_train, y_train))
assert_true(cv.best_score_ >= 0)
# compute the test kernel matrix
K_test = np.dot(X_[180:], X_[:180].T)
y_test = y_[180:]
y_pred = cv.predict(K_test)
assert_true(np.mean(y_pred == y_test) >= 0)
# test error is raised when the precomputed kernel is not array-like
# or sparse
assert_raises(ValueError, cv.fit, K_train.tolist(), y_train)
def test_classes__property(self):
# Test that classes_ property matches best_esimator_.classes_
X = np.arange(100).reshape(10, 10)
y = np.array([0] * 5 + [1] * 5)
Cs = [.1, 1, 10]
grid_search = ATGridSearchCV(LinearSVC(random_state=0), {'C': Cs},
webserver_url=self.live_server_url)
wait(grid_search.fit(X, y))
assert_array_equal(grid_search.best_estimator_.classes_,
grid_search.classes_)
# Test that regressors do not have a classes_ attribute
grid_search = ATGridSearchCV(Ridge(), {'alpha': [1.0, 2.0]},
webserver_url=self.live_server_url)
wait(grid_search.fit(X, y))
assert_false(hasattr(grid_search, 'classes_'))
def test_predict_proba_disabled(self):
# Test predict_proba when disabled on estimator.
X = np.arange(20).reshape(5, -1)
y = [0, 0, 1, 1, 1]
clf = SVC(probability=False)
gs = ATGridSearchCV(clf, {}, cv=2, webserver_url=self.live_server_url)
wait(gs.fit(X, y))
assert_false(hasattr(gs, "predict_proba"))
def test_no_refit(self):
# Test that grid search can be used for model selection only
clf = MockClassifier()
grid_search = ATGridSearchCV(clf, {'foo_param': [1, 2, 3]},
refit=False,
webserver_url=self.live_server_url)
wait(grid_search.fit(X, y))
assert_true(hasattr(grid_search, "best_params_"))
def test_trivial_grid_scores(self):
# Test search over a "grid" with only one point.
# Non-regression test: grid_scores_ wouldn't be set by GridSearchCV.
clf = MockClassifier()
grid_search = ATGridSearchCV(clf, {'foo_param': [1]},
webserver_url=self.live_server_url)
wait(grid_search.fit(X, y))
assert_true(hasattr(grid_search, "grid_scores_"))
def test_transform_inverse_transform_round_trip(self):
clf = MockClassifier()
grid_search = ATGridSearchCV(clf, {'foo_param': [1, 2, 3]},
verbose=3,
webserver_url=self.live_server_url)
wait(grid_search.fit(X, y))
X_round_trip = grid_search.inverse_transform(grid_search.transform(X))
assert_array_equal(X, X_round_trip)
def test_grid_search_failing_classifier(self):
# ATGridSearchCV with on_error != 'raise'
# Ensures that a warning is raised and score reset where appropriate.
X, y = make_classification(n_samples=20, n_features=10, random_state=0)
clf = FailingClassifier()
# refit=False because we only want to check that errors caused by fits
# to individual folds will be caught and warnings raised instead. If
# refit was done, then an exception would be raised on refit and not
# caught by grid_search (expected behavior), and this would cause an
# error in this test.
gs = ATGridSearchCV(clf, [{
'parameter': [0, 1, 2]
}],
scoring='accuracy',
refit=False,
error_score=0.0,
webserver_url=self.live_server_url)
#assert_warns(FitFailedWarning, gs.fit, X, y)
wait(gs.fit(X, y))
# Ensure that grid scores were set to zero as required for those fits
# that are expected to fail.
assert all(
np.all(this_point.cv_validation_scores == 0.0)
for this_point in gs.grid_scores_
if this_point.parameters['parameter'] ==
FailingClassifier.FAILING_PARAMETER)
gs = ATGridSearchCV(clf, [{
'parameter': [0, 1, 2]
}],
scoring='accuracy',
refit=False,
error_score=float('nan'),
webserver_url=self.live_server_url)
# assert_warns(FitFailedWarning, gs.fit, X, y)
wait(gs.fit(X, y))
assert all(
np.all(np.isnan(this_point.cv_validation_scores))
for this_point in gs.grid_scores_
if this_point.parameters['parameter'] ==
FailingClassifier.FAILING_PARAMETER)
def test_grid_search_precomputed_kernel_error_nonsquare(self):
# Test that grid search returns an error with a non-square precomputed
# training kernel matrix
K_train = np.zeros((10, 20))
y_train = np.ones((10, ))
clf = SVC(kernel='precomputed')
cv = ATGridSearchCV(clf, {'C': [0.1, 1.0]},
webserver_url=self.live_server_url)
assert_raises(ValueError, cv.fit, K_train, y_train)
def test_unsupervised_grid_search(self):
# test grid-search with unsupervised estimator
X, y = make_blobs(random_state=0)
km = KMeans(random_state=0)
grid_search = ATGridSearchCV(km,
param_grid=dict(n_clusters=[2, 3, 4]),
scoring='adjusted_rand_score',
webserver_url=self.live_server_url)
wait(grid_search.fit(X, y))
# ARI can find the right number :)
assert_equal(grid_search.best_params_["n_clusters"], 3)
# Now without a score, and without y
grid_search = ATGridSearchCV(km,
param_grid=dict(n_clusters=[2, 3, 4]),
webserver_url=self.live_server_url)
wait(grid_search.fit(X))
assert_equal(grid_search.best_params_["n_clusters"], 4)
def test_ATGridsSearchCV_without_dataset_and_fit(self):
gs = ATGridSearchCV(tree.DecisionTreeClassifier(), {
'criterion': ['gini', 'entropy'],
'max_depth': range(1, 21),
'max_features': ['auto', 'log2', 'sqrt', None]
},
webserver_url=self.live_server_url)
with self.assertRaises(NoDatasetError):
gs.fit()
def test_grid_search_score_method(self):
X, y = make_classification(n_samples=100,
n_classes=2,
flip_y=.2,
random_state=0)
clf = LinearSVC(random_state=0)
grid = {'C': [.1]}
search_no_scoring = ATGridSearchCV(clf,
grid,
scoring=None,
webserver_url=self.live_server_url)
wait(search_no_scoring.fit(X, y))
search_accuracy = ATGridSearchCV(clf,
grid,
scoring='accuracy',
webserver_url=self.live_server_url)
wait(search_accuracy.fit(X, y))
search_no_score_method_auc = ATGridSearchCV(
LinearSVCNoScore(),
grid,
scoring='roc_auc',
webserver_url=self.live_server_url)
wait(search_no_score_method_auc.fit(X, y))
search_auc = ATGridSearchCV(clf,
grid,
scoring='roc_auc',
webserver_url=self.live_server_url)
wait(search_auc.fit(X, y))
# ChangedBehaviourWarning occurred previously (prior to #9005)
score_no_scoring = assert_no_warnings(search_no_scoring.score, X, y)
score_accuracy = assert_no_warnings(search_accuracy.score, X, y)
score_no_score_auc = assert_no_warnings(
search_no_score_method_auc.score, X, y)
score_auc = assert_no_warnings(search_auc.score, X, y)
# ensure the test is sane
assert_true(score_auc < 1.0)
assert_true(score_accuracy < 1.0)
assert_not_equal(score_auc, score_accuracy)
assert_almost_equal(score_accuracy, score_no_scoring)
assert_almost_equal(score_auc, score_no_score_auc)
def test_grid_search_sparse_scoring(self):
X_, y_ = make_classification(n_samples=200,
n_features=100,
random_state=0)
clf = LinearSVC()
cv = ATGridSearchCV(clf, {'C': [0.1, 1.0]},
scoring="f1",
webserver_url=self.live_server_url)
wait(cv.fit(X_[:180], y_[:180]))
y_pred = cv.predict(X_[180:])
C = cv.best_estimator_.C
X_ = sp.csr_matrix(X_)
clf = LinearSVC()
cv = ATGridSearchCV(clf, {'C': [0.1, 1.0]},
scoring="f1",
webserver_url=self.live_server_url)
wait(cv.fit(X_[:180], y_[:180]))
y_pred2 = cv.predict(X_[180:])
C2 = cv.best_estimator_.C
assert_array_equal(y_pred, y_pred2)
assert_equal(C, C2)
# Smoke test the score
# np.testing.assert_allclose(f1_score(cv.predict(X_[:180]), y[:180]),
# cv.score(X_[:180], y[:180]))
# test loss where greater is worse
def f1_loss(y_true_, y_pred_):
return -f1_score(y_true_, y_pred_)
F1Loss = make_scorer(f1_loss, greater_is_better=False)
cv = ATGridSearchCV(clf, {'C': [0.1, 1.0]},
scoring=F1Loss,
webserver_url=self.live_server_url)
wait(cv.fit(X_[:180], y_[:180]))
y_pred3 = cv.predict(X_[180:])
C3 = cv.best_estimator_.C
assert_equal(C, C3)
assert_array_equal(y_pred, y_pred3)
def handle(self, *args, **options):
dataset = self.DATASETS[options['dataset']]()
example_f, labels_f = _create_dataset(dataset)
try:
ds = DataSet.objects.get(name=options['dataset'])
except DataSet.DoesNotExist:
ds = DataSet.objects.create(
name=options['dataset'],
examples=SimpleUploadedFile(example_f.name, example_f.read()),
labels=SimpleUploadedFile(labels_f.name, labels_f.read()))
if options['classifier'] == 'Tree':
gs_tree = ATGridSearchCV(
sklearn.tree.DecisionTreeClassifier(), {
'criterion': ['gini', 'entropy'],
'max_depth': range(1, 6),
'max_features': range(1, len(dataset.data[0]))
},
dataset=ds.name,
webserver_url=options['url'])
futures = gs_tree.fit(dataset.data, dataset.target)
distributed.wait(futures)
elif options['classifier'] == 'Forest':
gs_forest = ATGridSearchCV(
sklearn.ensemble.RandomForestClassifier(), {
'criterion': ['gini', 'entropy'],
'max_depth': range(1, 6),
'max_features': range(1, len(dataset.data[0]))
},
dataset=ds.name,
webserver_url=options['url'])
distributed.wait(gs_forest.fit(dataset.data, dataset.target))
else:
gs_network = ATGridSearchCV(
sklearn.neural_network.MLPClassifier(), {
'solver': ['lbfgs', 'sgd', 'adam'],
'learning_rate': ['constant', 'invscaling', 'adaptive'],
'max_iter': range(200, 2000, 200)
},
dataset=ds.name,
webserver_url=options['url'])
distributed.wait(gs_network.fit(dataset.data, dataset.target))
def test_grid_search_error(self):
# Test that grid search will capture errors on data with different
# length
X_, y_ = make_classification(n_samples=200,
n_features=100,
random_state=0)
clf = LinearSVC()
cv = ATGridSearchCV(clf, {'C': [0.1, 1.0]},
webserver_url=self.live_server_url)
assert_raises(ValueError, cv.fit, X_[:180], y_)
def xtest_grid_search_precomputed_kernel_error_kernel_function(self):
# Test that grid search returns an error when using a kernel_function
X_, y_ = make_classification(n_samples=200,
n_features=100,
random_state=0)
kernel_function = lambda x1, x2: np.dot(x1, x2.T)
clf = SVC(kernel=kernel_function)
cv = ATGridSearchCV(clf, {'C': [0.1, 1.0]},
webserver_url=self.live_server_url)
wait(cv.fit(X_, y_))
assert_raises(ValueError, cv.fit, X_, y_)
def test_gridsearch_nd(self):
# Pass X as list in GridSearchCV
X_4d = np.arange(10 * 5 * 3 * 2).reshape(10, 5, 3, 2)
y_3d = np.arange(10 * 7 * 11).reshape(10, 7, 11)
check_X = lambda x: x.shape[1:] == (5, 3, 2)
check_y = lambda x: x.shape[1:] == (7, 11)
clf = CheckingClassifier(check_X=check_X, check_y=check_y)
grid_search = ATGridSearchCV(clf, {'foo_param': [1, 2, 3]},
webserver_url=self.live_server_url)
wait(grid_search.fit(X_4d, y_3d))
assert_true(hasattr(grid_search, "grid_scores_"))
def test_y_as_list(self):
# Pass y as list in GridSearchCV
X = np.arange(100).reshape(10, 10)
y = np.array([0] * 5 + [1] * 5)
clf = CheckingClassifier(check_y=lambda x: isinstance(x, list))
cv = KFold(n=len(X), n_folds=3)
grid_search = ATGridSearchCV(clf, {'foo_param': [1, 2, 3]},
cv=cv,
webserver_url=self.live_server_url)
wait(grid_search.fit(X, y.tolist()))
assert_true(hasattr(grid_search, "grid_scores_"))
def test_grid_search_allows_nans(self):
# Test ATGridSearchCV with Imputer
X = np.arange(20, dtype=np.float64).reshape(5, -1)
X[2, :] = np.nan
y = [0, 0, 1, 1, 1]
p = Pipeline([
('imputer', Imputer(strategy='mean', missing_values='NaN')),
('classifier', MockClassifier()),
])
gs = ATGridSearchCV(p, {'classifier__foo_param': [1, 2, 3]},
cv=2,
webserver_url=self.live_server_url)
wait(gs.fit(X, y))
def test_ATGridSearchCV_no_dataset(self):
iris = load_iris()
grid_size = 2 * 20 * 4
gs = ATGridSearchCV(tree.DecisionTreeClassifier(), {
'criterion': ['gini', 'entropy'],
'max_depth': range(1, 21),
'max_features': ['auto', 'log2', 'sqrt', None]
},
webserver_url=self.live_server_url)
wait(gs.fit(iris.data, iris.target))
self.assertAlmostEqual(
grid_size,
GridSearch.objects.get(uuid=gs._uuid).results.count(),
delta=5)
def test_grid_detail(self):
iris = load_iris()
client = DjangoClient()
gs1 = ATGridSearchCV(tree.DecisionTreeClassifier(), {
'criterion': ['gini', 'entropy'],
'max_depth': range(1, 6),
'max_features': ['auto', 'log2']
},
webserver_url=self.live_server_url)
wait(gs1.fit(iris.data, iris.target))
response = client.get(
reverse('grid_detail', kwargs={'uuid': gs1._uuid}))
self.assertEqual(200, response.status_code)
self.assertEqual(response.data['uuid'], str(gs1._uuid))
def test_grid_search_sparse(self):
# Test that grid search works with both dense and sparse matrices
X_, y_ = make_classification(n_samples=200,
n_features=100,
random_state=0)
clf = LinearSVC()
cv = ATGridSearchCV(clf, {'C': [0.1, 1.0]},
webserver_url=self.live_server_url)
wait(cv.fit(X_[:180], y_[:180]))
y_pred = cv.best_estimator_.predict(X_[180:])
C = cv.best_estimator_.C
X_ = sp.csr_matrix(X_)
clf = LinearSVC()
cv = ATGridSearchCV(clf, {'C': [0.1, 1.0]},
webserver_url=self.live_server_url)
wait(cv.fit(X_[:180].tocoo(), y_[:180]))
y_pred2 = cv.best_estimator_.predict(X_[180:])
C2 = cv.best_estimator_.C
assert_true(np.mean(y_pred == y_pred2) >= .9)
assert_equal(C, C2)
def test_refit(self):
# Regression test for bug in refitting
# Simulates re-fitting a broken estimator; this used to break with
# sparse SVMs.
X = np.arange(100).reshape(10, 10)
y = np.array([0] * 5 + [1] * 5)
clf = ATGridSearchCV(BrokenClassifier(), [{
'parameter': [0, 1]
}],
scoring="precision",
refit=True,
webserver_url=self.live_server_url)
wait(clf.fit(X, y))