def get_all_estimators():
ignored = (Creme2SKLBase, SKL2CremeBase, compose.FuncTransformer,
compose.Pipeline, compose.Grouper,
ensemble.StackingBinaryClassifier, facto.FFMClassifier,
facto.FFMRegressor, facto.FMClassifier, facto.FMRegressor,
facto.FwFMClassifier, facto.FwFMRegressor, facto.HOFMClassifier,
facto.HOFMRegressor, feature_extraction.Agg,
feature_extraction.TargetAgg,
feature_selection.PoissonInclusion, impute.PreviousImputer,
impute.StatImputer, linear_model.SoftmaxRegression,
meta.PredClipper, meta.TransformedTargetRegressor,
model_selection.SuccessiveHalvingClassifier,
model_selection.SuccessiveHalvingRegressor,
preprocessing.OneHotEncoder, reco.Baseline, reco.BiasedMF,
reco.FunkMF, reco.RandomNormal, sampling.HardSamplingClassifier,
sampling.HardSamplingRegressor, sampling.RandomOverSampler,
sampling.RandomUnderSampler, sampling.RandomSampler,
time_series.Detrender, time_series.GroupDetrender,
time_series.SNARIMAX)
try:
ignored = (*ignored, compat.PyTorch2CremeRegressor)
except AttributeError:
pass
def is_estimator(obj):
return inspect.isclass(obj) and issubclass(obj, base.Estimator)
for submodule in importlib.import_module('creme').__all__:
if submodule == 'base':
continue
for _, obj in inspect.getmembers(
importlib.import_module(f'creme.{submodule}'), is_estimator):
if issubclass(obj, ignored):
continue
elif issubclass(obj, multioutput.RegressorChain):
inst = obj(model=linear_model.LinearRegression())
elif issubclass(obj, multioutput.ClassifierChain):
inst = obj(model=linear_model.LogisticRegression())
elif issubclass(obj, dummy.StatisticRegressor):
inst = obj(statistic=stats.Mean())
elif issubclass(obj, tree.RandomForestClassifier):
inst = obj()
elif issubclass(obj, ensemble.BaggingClassifier):
inst = obj(linear_model.LogisticRegression())
elif issubclass(obj, ensemble.BaggingRegressor):
inst = obj(linear_model.LinearRegression())
elif issubclass(obj, ensemble.AdaBoostClassifier):
inst = obj(linear_model.LogisticRegression())
elif issubclass(obj, ensemble.HedgeRegressor):
inst = obj([
preprocessing.StandardScaler()
| linear_model.LinearRegression(intercept_lr=.1),
preprocessing.StandardScaler()
| linear_model.PARegressor(),
])
elif issubclass(obj, feature_selection.SelectKBest):
inst = obj(similarity=stats.PearsonCorr())
elif issubclass(obj, linear_model.LinearRegression):
inst = preprocessing.StandardScaler() | obj(intercept_lr=.1)
elif issubclass(obj, linear_model.PARegressor):
inst = preprocessing.StandardScaler() | obj()
elif issubclass(obj, multiclass.OneVsRestClassifier):
inst = obj(classifier=linear_model.LogisticRegression())
elif issubclass(obj, multiclass.OneVsOneClassifier):
inst = obj(classifier=linear_model.LogisticRegression())
elif issubclass(obj, multiclass.OutputCodeClassifier):
inst = obj(classifier=linear_model.LogisticRegression(),
code_size=10)
else:
inst = obj()
yield inst
n = stat.window_size
X = [random.random() for _ in range(30)]
for i, x in enumerate(X):
stat.update(x)
if i >= 1:
assert math.isclose(stat.get(),
func(tail(X[:i + 1], n)),
abs_tol=1e-10)
@pytest.mark.parametrize(
'stat, func',
[(stats.Cov(), lambda x, y: np.cov(x, y)[0, 1]),
(stats.PearsonCorr(), lambda x, y: sp_stats.pearsonr(x, y)[0])])
def test_bivariate(stat, func):
# Shhh
np.warnings.filterwarnings('ignore')
X = [random.random() for _ in range(30)]
Y = [random.random() * x for x in X]
for i, (x, y) in enumerate(zip(X, Y)):
stat.update(x, y)
if i >= 1:
assert math.isclose(stat.get(),
func(X[:i + 1], Y[:i + 1]),
abs_tol=1e-10)
else:
inst = obj()
yield inst
@pytest.mark.parametrize('estimator, check', [
pytest.param(estimator, check, id=f'{estimator}:{check.__name__}')
for estimator in list(get_all_estimators()) + [
feature_extraction.TFIDF(),
linear_model.LogisticRegression(),
preprocessing.StandardScaler() | linear_model.LinearRegression(),
preprocessing.StandardScaler() | linear_model.PAClassifier(),
preprocessing.StandardScaler()
| multiclass.OneVsRestClassifier(linear_model.LogisticRegression()),
preprocessing.StandardScaler()
| multiclass.OneVsRestClassifier(linear_model.PAClassifier()),
naive_bayes.GaussianNB(),
preprocessing.StandardScaler(),
cluster.KMeans(n_clusters=5, seed=42),
preprocessing.MinMaxScaler(),
preprocessing.MinMaxScaler() + preprocessing.StandardScaler(),
preprocessing.PolynomialExtender(),
feature_selection.VarianceThreshold(),
feature_selection.SelectKBest(similarity=stats.PearsonCorr())
] for check in utils.estimator_checks.yield_checks(estimator)
])
def test_check_estimator(estimator, check):
check(copy.deepcopy(estimator))
def _default_params(cls):
return {'similarity': stats.PearsonCorr()}
