def test_online_batch_consistent():
# Batch
batch = preprocessing.StandardScaler() | multiclass.OneVsRestClassifier(
linear_model.LogisticRegression())
dataset = datasets.ImageSegments()
batch_metric = metrics.MacroF1()
for i, x in enumerate(pd.read_csv(dataset.path, chunksize=1)):
y = x.pop("category")
y_pred = batch.predict_many(x)
batch.learn_many(x, y)
for yt, yp in zip(y, y_pred):
if yp is not None:
batch_metric.update(yt, yp)
if i == 30:
break
# Online
online = preprocessing.StandardScaler() | multiclass.OneVsRestClassifier(
linear_model.LogisticRegression())
online_metric = metrics.MacroF1()
X = pd.read_csv(dataset.path)
Y = X.pop("category")
for i, (x, y) in enumerate(stream.iter_pandas(X, Y)):
y_pred = online.predict_one(x)
online.learn_one(x, y)
if y_pred is not None:
online_metric.update(y, y_pred)
if i == 30:
break
assert online_metric.get() == batch_metric.get()
continue
params = obj._unit_test_params()
yield obj(**params)
@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(),
feature_extraction.PolynomialExtender(),
(feature_extraction.PolynomialExtender()
| preprocessing.StandardScaler()
| linear_model.LinearRegression()),
feature_selection.VarianceThreshold(),
feature_selection.SelectKBest(similarity=stats.PearsonCorr()),
] for check in utils.estimator_checks.yield_checks(estimator)
if check.__name__ not in estimator._unit_test_skips()
