def test_check_estimator_clones():
# check that check_estimator doesn't modify the estimator it receives
from sklearn.datasets import load_iris
iris = load_iris()
for Estimator in [GaussianMixture, LinearRegression,
RandomForestClassifier, NMF, SGDClassifier,
MiniBatchKMeans]:
with ignore_warnings(category=(FutureWarning, DeprecationWarning)):
# when 'est = SGDClassifier()'
est = Estimator()
set_checking_parameters(est)
set_random_state(est)
# without fitting
old_hash = _joblib.hash(est)
check_estimator(est)
assert_equal(old_hash, _joblib.hash(est))
with ignore_warnings(category=(FutureWarning, DeprecationWarning)):
# when 'est = SGDClassifier()'
est = Estimator()
set_checking_parameters(est)
set_random_state(est)
# with fitting
est.fit(iris.data + 10, iris.target)
old_hash = _joblib.hash(est)
check_estimator(est)
assert_equal(old_hash, _joblib.hash(est))
def test_check_estimator_clones():
# check that check_estimator doesn't modify the estimator it receives
from sklearn.datasets import load_iris
iris = load_iris()
for Estimator in [GaussianMixture, LinearRegression,
RandomForestClassifier, NMF, SGDClassifier,
MiniBatchKMeans]:
with ignore_warnings(category=(FutureWarning, DeprecationWarning)):
# when 'est = SGDClassifier()'
est = Estimator()
set_checking_parameters(est)
set_random_state(est)
# without fitting
old_hash = _joblib.hash(est)
check_estimator(est)
assert_equal(old_hash, _joblib.hash(est))
with ignore_warnings(category=(FutureWarning, DeprecationWarning)):
# when 'est = SGDClassifier()'
est = Estimator()
set_checking_parameters(est)
set_random_state(est)
# with fitting
est.fit(iris.data + 10, iris.target)
old_hash = _joblib.hash(est)
check_estimator(est)
assert_equal(old_hash, _joblib.hash(est))
def check_estimators_overwrite_params(name, estimator_orig):
X, y = make_blobs(random_state=0, n_samples=9)
y = (y > 1).astype(int)
# some want non-negative input
X -= X.min()
X = pairwise_estimator_convert_X(X, estimator_orig, kernel=rbf_kernel)
estimator = clone(estimator_orig)
y = multioutput_estimator_convert_y_2d(estimator, y)
set_random_state(estimator)
# Make a physical copy of the original estimator parameters before fitting.
params = estimator.get_params()
original_params = deepcopy(params)
# Fit the model
estimator.fit(X, y)
# Compare the state of the model parameters with the original parameters
new_params = estimator.get_params()
for param_name, original_value in original_params.items():
new_value = new_params[param_name]
# We should never change or mutate the internal state of input
# parameters by default. To check this we use the joblib.hash function
# that introspects recursively any subobjects to compute a checksum.
# The only exception to this rule of immutable constructor parameters
# is possible RandomState instance but in this check we explicitly
# fixed the random_state params recursively to be integer seeds.
assert_equal(
_joblib.hash(new_value), _joblib.hash(original_value),
"Estimator %s should not change or mutate "
" the parameter %s from %s to %s during fit." %
(name, param_name, original_value, new_value))
def fit(self, X, y):
self.training_size_ = X.shape[0]
self.training_hash_ = _joblib.hash(X)
def fit(self, X, y):
self.training_size_ = X.shape[0]
self.training_hash_ = _joblib.hash(X)
