def test_one_estimator_print_change_only(print_changed_only):
pca = PCA(n_components=10)
with config_context(print_changed_only=print_changed_only):
pca_repr = str(pca)
html_output = estimator_html_repr(pca)
assert pca_repr in html_output
def test_fallback_exists():
"""Check that repr fallback is in the HTML."""
pca = PCA(n_components=10)
html_output = estimator_html_repr(pca)
assert (f'<div class="sk-text-repr-fallback"><pre>{html.escape(str(pca))}'
in html_output)
def test_show_arrow_pipeline():
"""Show arrow in pipeline for top level in pipeline"""
pipe = Pipeline([("scale", StandardScaler()),
("log_Reg", LogisticRegression())])
html_output = estimator_html_repr(pipe)
assert ('class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline'
in html_output)
def test_duck_typing_nested_estimator():
# Test duck typing metaestimators with GP
kernel = RationalQuadratic(length_scale=1.0, alpha=0.1)
gp = GaussianProcessRegressor(kernel=kernel)
html_output = estimator_html_repr(gp)
assert f"<pre>{str(kernel)}" in html_output
assert f"<pre>{str(gp)}" in html_output
def test_stacking_regressor(final_estimator):
reg = StackingRegressor(estimators=[("svr", LinearSVR())],
final_estimator=final_estimator)
html_output = estimator_html_repr(reg)
assert str(reg.estimators[0][0]) in html_output
assert "LinearSVR</label>" in html_output
if final_estimator is None:
assert "RidgeCV</label>" in html_output
else:
assert final_estimator.__class__.__name__ in html_output
def test_duck_typing_nested_estimator():
# Test duck typing metaestimators with random search
kernel_ridge = KernelRidge(kernel=ExpSineSquared())
param_distributions = {"alpha": [1, 2]}
kernel_ridge_tuned = RandomizedSearchCV(
kernel_ridge,
param_distributions=param_distributions,
)
html_output = estimator_html_repr(kernel_ridge_tuned)
assert "estimator: KernelRidge</label>" in html_output
def test_ovo_classifier_duck_typing_meta():
# Test duck typing metaestimators with OVO
ovo = OneVsOneClassifier(LinearSVC(penalty="l1"))
html_output = estimator_html_repr(ovo)
# inner estimators do not show changes
with config_context(print_changed_only=True):
assert f"<pre>{str(ovo.estimator)}" in html_output
assert "LinearSVC</label>" in html_output
# outer estimator
assert f"<pre>{str(ovo)}" in html_output
def test_birch_duck_typing_meta():
# Test duck typing meta estimators with Birch
birch = Birch(n_clusters=AgglomerativeClustering(n_clusters=3))
html_output = estimator_html_repr(birch)
# inner estimators do not show changes
with config_context(print_changed_only=True):
assert f"<pre>{str(birch.n_clusters)}" in html_output
assert "AgglomerativeClustering</label>" in html_output
# outer estimator contains all changes
assert f"<pre>{str(birch)}" in html_output
def test_stacking_classsifer(final_estimator):
estimators = [('mlp', MLPClassifier(alpha=0.001)),
('tree', DecisionTreeClassifier())]
clf = StackingClassifier(
estimators=estimators, final_estimator=final_estimator)
html_output = estimator_html_repr(clf)
assert str(clf) in html_output
# If final_estimator's default changes from LogisticRegression
# this should be updated
if final_estimator is None:
assert "LogisticRegression(" in html_output
else:
assert final_estimator.__class__.__name__ in html_output
def test_estimator_html_repr_pipeline():
num_trans = Pipeline(
steps=[("pass",
"passthrough"), ("imputer", SimpleImputer(strategy="median"))])
cat_trans = Pipeline(steps=[
("imputer",
SimpleImputer(strategy="constant", missing_values="empty")),
("one-hot", OneHotEncoder(drop="first")),
])
preprocess = ColumnTransformer([
("num", num_trans, ["a", "b", "c", "d", "e"]),
("cat", cat_trans, [0, 1, 2, 3]),
])
feat_u = FeatureUnion([
("pca", PCA(n_components=1)),
(
"tsvd",
Pipeline([
("first", TruncatedSVD(n_components=3)),
("select", SelectPercentile()),
]),
),
])
clf = VotingClassifier([
("lr", LogisticRegression(solver="lbfgs", random_state=1)),
("mlp", MLPClassifier(alpha=0.001)),
])
pipe = Pipeline([("preprocessor", preprocess), ("feat_u", feat_u),
("classifier", clf)])
html_output = estimator_html_repr(pipe)
# top level estimators show estimator with changes
assert str(pipe) in html_output
for _, est in pipe.steps:
assert f'<div class="sk-toggleable__content"><pre>{str(est)}' in html_output
# low level estimators do not show changes
with config_context(print_changed_only=True):
assert str(num_trans["pass"]) in html_output
assert "passthrough</label>" in html_output
assert str(num_trans["imputer"]) in html_output
for _, _, cols in preprocess.transformers:
assert f"<pre>{cols}</pre>" in html_output
# feature union
for name, _ in feat_u.transformer_list:
assert f"<label>{name}</label>" in html_output
pca = feat_u.transformer_list[0][1]
assert f"<pre>{str(pca)}</pre>" in html_output
tsvd = feat_u.transformer_list[1][1]
first = tsvd["first"]
select = tsvd["select"]
assert f"<pre>{str(first)}</pre>" in html_output
assert f"<pre>{str(select)}</pre>" in html_output
# voting classifier
for name, est in clf.estimators:
assert f"<label>{name}</label>" in html_output
assert f"<pre>{str(est)}</pre>" in html_output
def _repr_mimebundle_(self, **kwargs):
"""Mime bundle used by jupyter kernels to display estimator"""
output = {"text/plain": repr(self)}
if get_config()["display"] == 'diagram':
output["text/html"] = estimator_html_repr(self)
return output
def _repr_html_inner(self):
"""This function is returned by the @property `_repr_html_` to make
`hasattr(estimator, "_repr_html_") return `True` or `False` depending
on `get_config()["display"]`.
"""
return estimator_html_repr(self)
def test_estimator_html_repr_pipeline():
num_trans = Pipeline(steps=[
('pass', 'passthrough'),
('imputer', SimpleImputer(strategy='median'))
])
cat_trans = Pipeline(steps=[
('imputer', SimpleImputer(strategy='constant',
missing_values='empty')),
('one-hot', OneHotEncoder(drop='first'))
])
preprocess = ColumnTransformer([
('num', num_trans, ['a', 'b', 'c', 'd', 'e']),
('cat', cat_trans, [0, 1, 2, 3])
])
feat_u = FeatureUnion([
('pca', PCA(n_components=1)),
('tsvd', Pipeline([('first', TruncatedSVD(n_components=3)),
('select', SelectPercentile())]))
])
clf = VotingClassifier([
('lr', LogisticRegression(solver='lbfgs', random_state=1)),
('mlp', MLPClassifier(alpha=0.001))
])
pipe = Pipeline([
('preprocessor', preprocess), ('feat_u', feat_u), ('classifier', clf)
])
html_output = estimator_html_repr(pipe)
# top level estimators show estimator with changes
assert str(pipe) in html_output
for _, est in pipe.steps:
assert (f"<div class=\"sk-toggleable__content\">"
f"<pre>{str(est)}") in html_output
# low level estimators do not show changes
with config_context(print_changed_only=True):
assert str(num_trans['pass']) in html_output
assert 'passthrough</label>' in html_output
assert str(num_trans['imputer']) in html_output
for _, _, cols in preprocess.transformers:
assert f"<pre>{cols}</pre>" in html_output
# feature union
for name, _ in feat_u.transformer_list:
assert f"<label>{name}</label>" in html_output
pca = feat_u.transformer_list[0][1]
assert f"<pre>{str(pca)}</pre>" in html_output
tsvd = feat_u.transformer_list[1][1]
first = tsvd['first']
select = tsvd['select']
assert f"<pre>{str(first)}</pre>" in html_output
assert f"<pre>{str(select)}</pre>" in html_output
# voting classifer
for name, est in clf.estimators:
assert f"<label>{name}</label>" in html_output
assert f"<pre>{str(est)}</pre>" in html_output
