Skip to content

[MRG] Simplify super() calls #12812

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 14 commits into from
Jan 10, 2019
Merged

[MRG] Simplify super() calls #12812

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
14 commits
Select commit Hold shift + click to select a range
df9ca33
[MRG] Simplify super() calls
JarnoRFB Dec 18, 2018
1d7e122
Simplify super calls
JarnoRFB Dec 30, 2018
f1e4900
Simplify some leftover super calls
JarnoRFB Dec 31, 2018
28725c7
Merge branch 'master' into simpler-super-calls-for-dummies
JarnoRFB Jan 3, 2019
625fdd3
Fix lint error
JarnoRFB Jan 3, 2019
2298457
Revert super calls in base
JarnoRFB Jan 3, 2019
9b7b8e2
Revert second __getstate__ super call in isotonic
JarnoRFB Jan 9, 2019
85181a3
Merge branch 'master' into simpler-super-calls-for-dummies
JarnoRFB Jan 9, 2019
1baf496
Revert fixes
JarnoRFB Jan 9, 2019
8182f87
Simplify super calls in base and isotonic
JarnoRFB Jan 9, 2019
ef436ce
Revert changes in externals
JarnoRFB Jan 10, 2019
8ad92ab
Merge branch 'master' into simpler-super-calls-for-dummies
JarnoRFB Jan 10, 2019
4d18469
Format arguments a bit nicer in lable_propagation
JarnoRFB Jan 10, 2019
c36128d
Fix lint error
JarnoRFB Jan 10, 2019
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
2 changes: 1 addition & 1 deletion doc/modules/feature_extraction.rst
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -961,7 +961,7 @@ Some tips and tricks:
...
>>> class CustomVectorizer(CountVectorizer):
... def build_tokenizer(self):
... tokenize = super(CustomVectorizer, self).build_tokenizer()
... tokenize = super().build_tokenizer()
... return lambda doc: list(to_british(tokenize(doc)))
...
>>> print(CustomVectorizer().build_analyzer()(u"color colour")) # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS
Expand Down
2 changes: 1 addition & 1 deletion examples/bicluster/plot_bicluster_newsgroups.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -51,7 +51,7 @@ def number_normalizer(tokens):

class NumberNormalizingVectorizer(TfidfVectorizer):
def build_tokenizer(self):
tokenize = super(NumberNormalizingVectorizer, self).build_tokenizer()
tokenize = super().build_tokenizer()
return lambda doc: list(number_normalizer(tokenize(doc)))


Expand Down
4 changes: 2 additions & 2 deletions sklearn/base.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -244,7 +244,7 @@ def __repr__(self):

def __getstate__(self):
try:
state = super(BaseEstimator, self).__getstate__()
state = super().__getstate__()
except AttributeError:
state = self.__dict__.copy()

Expand All @@ -264,7 +264,7 @@ def __setstate__(self, state):
self.__class__.__name__, pickle_version, __version__),
UserWarning)
try:
super(BaseEstimator, self).__setstate__(state)
super().__setstate__(state)
except AttributeError:
self.__dict__.update(state)

Expand Down
34 changes: 17 additions & 17 deletions sklearn/cluster/bicluster.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -281,14 +281,14 @@ class SpectralCoclustering(BaseSpectral):
def __init__(self, n_clusters=3, svd_method='randomized',
n_svd_vecs=None, mini_batch=False, init='k-means++',
n_init=10, n_jobs=None, random_state=None):
super(SpectralCoclustering, self).__init__(n_clusters,
svd_method,
n_svd_vecs,
mini_batch,
init,
n_init,
n_jobs,
random_state)
super().__init__(n_clusters,
svd_method,
n_svd_vecs,
mini_batch,
init,
n_init,
n_jobs,
random_state)

def _fit(self, X):
normalized_data, row_diag, col_diag = _scale_normalize(X)
Expand Down Expand Up @@ -429,20 +429,20 @@ def __init__(self, n_clusters=3, method='bistochastic',
n_components=6, n_best=3, svd_method='randomized',
n_svd_vecs=None, mini_batch=False, init='k-means++',
n_init=10, n_jobs=None, random_state=None):
super(SpectralBiclustering, self).__init__(n_clusters,
svd_method,
n_svd_vecs,
mini_batch,
init,
n_init,
n_jobs,
random_state)
super().__init__(n_clusters,
svd_method,
n_svd_vecs,
mini_batch,
init,
n_init,
n_jobs,
random_state)
self.method = method
self.n_components = n_components
self.n_best = n_best

def _check_parameters(self):
super(SpectralBiclustering, self)._check_parameters()
super()._check_parameters()
legal_methods = ('bistochastic', 'scale', 'log')
if self.method not in legal_methods:
raise ValueError("Unknown method: '{0}'. method must be"
Expand Down
2 changes: 1 addition & 1 deletion sklearn/cluster/hierarchical.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -929,7 +929,7 @@ def __init__(self, n_clusters=2, affinity="euclidean",
memory=None,
connectivity=None, compute_full_tree='auto',
linkage='ward', pooling_func=np.mean):
super(FeatureAgglomeration, self).__init__(
super().__init__(
n_clusters=n_clusters, memory=memory, connectivity=connectivity,
compute_full_tree=compute_full_tree, linkage=linkage,
affinity=affinity)
Expand Down
2 changes: 1 addition & 1 deletion sklearn/cluster/k_means_.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -1458,7 +1458,7 @@ def __init__(self, n_clusters=8, init='k-means++', max_iter=100,
random_state=None, tol=0.0, max_no_improvement=10,
init_size=None, n_init=3, reassignment_ratio=0.01):

super(MiniBatchKMeans, self).__init__(
super().__init__(
n_clusters=n_clusters, init=init, max_iter=max_iter,
verbose=verbose, random_state=random_state, tol=tol, n_init=n_init)

Expand Down
4 changes: 2 additions & 2 deletions sklearn/compose/tests/test_target.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -246,11 +246,11 @@ class DummyCheckerListRegressor(DummyRegressor):

def fit(self, X, y, sample_weight=None):
assert isinstance(X, list)
return super(DummyCheckerListRegressor, self).fit(X, y, sample_weight)
return super().fit(X, y, sample_weight)

def predict(self, X):
assert isinstance(X, list)
return super(DummyCheckerListRegressor, self).predict(X)
return super().predict(X)


def test_transform_target_regressor_ensure_y_array():
Expand Down
4 changes: 2 additions & 2 deletions sklearn/covariance/elliptic_envelope.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -88,7 +88,7 @@ class EllipticEnvelope(MinCovDet, OutlierMixin):
def __init__(self, store_precision=True, assume_centered=False,
support_fraction=None, contamination=0.1,
random_state=None):
super(EllipticEnvelope, self).__init__(
super().__init__(
store_precision=store_precision,
assume_centered=assume_centered,
support_fraction=support_fraction,
Expand All @@ -107,7 +107,7 @@ def fit(self, X, y=None):
not used, present for API consistency by convention.

"""
super(EllipticEnvelope, self).fit(X)
super().fit(X)
self.offset_ = np.percentile(-self.dist_, 100. * self.contamination)
return self

Expand Down
4 changes: 2 additions & 2 deletions sklearn/covariance/graph_lasso_.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -337,7 +337,7 @@ class GraphicalLasso(EmpiricalCovariance):

def __init__(self, alpha=.01, mode='cd', tol=1e-4, enet_tol=1e-4,
max_iter=100, verbose=False, assume_centered=False):
super(GraphicalLasso, self).__init__(assume_centered=assume_centered)
super().__init__(assume_centered=assume_centered)
self.alpha = alpha
self.mode = mode
self.tol = tol
Expand Down Expand Up @@ -581,7 +581,7 @@ class GraphicalLassoCV(GraphicalLasso):
def __init__(self, alphas=4, n_refinements=4, cv='warn', tol=1e-4,
enet_tol=1e-4, max_iter=100, mode='cd', n_jobs=None,
verbose=False, assume_centered=False):
super(GraphicalLassoCV, self).__init__(
super().__init__(
mode=mode, tol=tol, verbose=verbose, enet_tol=enet_tol,
max_iter=max_iter, assume_centered=assume_centered)
self.alphas = alphas
Expand Down
8 changes: 4 additions & 4 deletions sklearn/covariance/shrunk_covariance_.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -126,8 +126,8 @@ class ShrunkCovariance(EmpiricalCovariance):
"""
def __init__(self, store_precision=True, assume_centered=False,
shrinkage=0.1):
super(ShrunkCovariance, self).__init__(store_precision=store_precision,
assume_centered=assume_centered)
super().__init__(store_precision=store_precision,
assume_centered=assume_centered)
self.shrinkage = shrinkage

def fit(self, X, y=None):
Expand Down Expand Up @@ -401,8 +401,8 @@ class LedoitWolf(EmpiricalCovariance):
"""
def __init__(self, store_precision=True, assume_centered=False,
block_size=1000):
super(LedoitWolf, self).__init__(store_precision=store_precision,
assume_centered=assume_centered)
super().__init__(store_precision=store_precision,
assume_centered=assume_centered)
self.block_size = block_size

def fit(self, X, y=None):
Expand Down
8 changes: 4 additions & 4 deletions sklearn/cross_decomposition/cca_.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -101,7 +101,7 @@ class CCA(_PLS):

def __init__(self, n_components=2, scale=True,
max_iter=500, tol=1e-06, copy=True):
super(CCA, self).__init__(n_components=n_components, scale=scale,
deflation_mode="canonical", mode="B",
norm_y_weights=True, algorithm="nipals",
max_iter=max_iter, tol=tol, copy=copy)
super().__init__(n_components=n_components, scale=scale,
deflation_mode="canonical", mode="B",
norm_y_weights=True, algorithm="nipals",
max_iter=max_iter, tol=tol, copy=copy)
4 changes: 2 additions & 2 deletions sklearn/cross_decomposition/pls_.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -588,7 +588,7 @@ class PLSRegression(_PLS):

def __init__(self, n_components=2, scale=True,
max_iter=500, tol=1e-06, copy=True):
super(PLSRegression, self).__init__(
super().__init__(
n_components=n_components, scale=scale,
deflation_mode="regression", mode="A",
norm_y_weights=False, max_iter=max_iter, tol=tol,
Expand Down Expand Up @@ -734,7 +734,7 @@ class PLSCanonical(_PLS):

def __init__(self, n_components=2, scale=True, algorithm="nipals",
max_iter=500, tol=1e-06, copy=True):
super(PLSCanonical, self).__init__(
super().__init__(
n_components=n_components, scale=scale,
deflation_mode="canonical", mode="A",
norm_y_weights=True, algorithm=algorithm,
Expand Down
2 changes: 1 addition & 1 deletion sklearn/decomposition/fastica_.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -463,7 +463,7 @@ def my_g(x):
def __init__(self, n_components=None, algorithm='parallel', whiten=True,
fun='logcosh', fun_args=None, max_iter=200, tol=1e-4,
w_init=None, random_state=None):
super(FastICA, self).__init__()
super().__init__()
if max_iter < 1:
raise ValueError("max_iter should be greater than 1, got "
"(max_iter={})".format(max_iter))
Expand Down
2 changes: 1 addition & 1 deletion sklearn/decomposition/sparse_pca.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -348,7 +348,7 @@ def __init__(self, n_components=None, alpha=1, ridge_alpha=0.01,
n_iter=100, callback=None, batch_size=3, verbose=False,
shuffle=True, n_jobs=None, method='lars', random_state=None,
normalize_components=False):
super(MiniBatchSparsePCA, self).__init__(
super().__init__(
n_components=n_components, alpha=alpha, verbose=verbose,
ridge_alpha=ridge_alpha, n_jobs=n_jobs, method=method,
random_state=random_state,
Expand Down
4 changes: 2 additions & 2 deletions sklearn/dummy.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -349,7 +349,7 @@ def score(self, X, y, sample_weight=None):
"""
if X is None:
X = np.zeros(shape=(len(y), 1))
return super(DummyClassifier, self).score(X, y, sample_weight)
return super().score(X, y, sample_weight)


class DummyRegressor(BaseEstimator, RegressorMixin):
Expand Down Expand Up @@ -548,4 +548,4 @@ def score(self, X, y, sample_weight=None):
"""
if X is None:
X = np.zeros(shape=(len(y), 1))
return super(DummyRegressor, self).score(X, y, sample_weight)
return super().score(X, y, sample_weight)
10 changes: 5 additions & 5 deletions sklearn/ensemble/bagging.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -202,7 +202,7 @@ def __init__(self,
n_jobs=None,
random_state=None,
verbose=0):
super(BaseBagging, self).__init__(
super().__init__(
base_estimator=base_estimator,
n_estimators=n_estimators)

Expand Down Expand Up @@ -565,7 +565,7 @@ def __init__(self,
random_state=None,
verbose=0):

super(BaggingClassifier, self).__init__(
super().__init__(
base_estimator,
n_estimators=n_estimators,
max_samples=max_samples,
Expand All @@ -580,7 +580,7 @@ def __init__(self,

def _validate_estimator(self):
"""Check the estimator and set the base_estimator_ attribute."""
super(BaggingClassifier, self)._validate_estimator(
super()._validate_estimator(
default=DecisionTreeClassifier())

def _set_oob_score(self, X, y):
Expand Down Expand Up @@ -935,7 +935,7 @@ def __init__(self,
n_jobs=None,
random_state=None,
verbose=0):
super(BaggingRegressor, self).__init__(
super().__init__(
base_estimator,
n_estimators=n_estimators,
max_samples=max_samples,
Expand Down Expand Up @@ -990,7 +990,7 @@ def predict(self, X):

def _validate_estimator(self):
"""Check the estimator and set the base_estimator_ attribute."""
super(BaggingRegressor, self)._validate_estimator(
super()._validate_estimator(
default=DecisionTreeRegressor())

def _set_oob_score(self, X, y):
Expand Down
19 changes: 9 additions & 10 deletions sklearn/ensemble/forest.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -141,7 +141,7 @@ def __init__(self,
verbose=0,
warm_start=False,
class_weight=None):
super(BaseForest, self).__init__(
super().__init__(
base_estimator=base_estimator,
n_estimators=n_estimators,
estimator_params=estimator_params)
Expand Down Expand Up @@ -411,7 +411,7 @@ def __init__(self,
verbose=0,
warm_start=False,
class_weight=None):
super(ForestClassifier, self).__init__(
super().__init__(
base_estimator,
n_estimators=n_estimators,
estimator_params=estimator_params,
Expand Down Expand Up @@ -651,7 +651,7 @@ def __init__(self,
random_state=None,
verbose=0,
warm_start=False):
super(ForestRegressor, self).__init__(
super().__init__(
base_estimator,
n_estimators=n_estimators,
estimator_params=estimator_params,
Expand Down Expand Up @@ -1008,7 +1008,7 @@ def __init__(self,
verbose=0,
warm_start=False,
class_weight=None):
super(RandomForestClassifier, self).__init__(
super().__init__(
base_estimator=DecisionTreeClassifier(),
n_estimators=n_estimators,
estimator_params=("criterion", "max_depth", "min_samples_split",
Expand Down Expand Up @@ -1267,7 +1267,7 @@ def __init__(self,
random_state=None,
verbose=0,
warm_start=False):
super(RandomForestRegressor, self).__init__(
super().__init__(
base_estimator=DecisionTreeRegressor(),
n_estimators=n_estimators,
estimator_params=("criterion", "max_depth", "min_samples_split",
Expand Down Expand Up @@ -1523,7 +1523,7 @@ def __init__(self,
verbose=0,
warm_start=False,
class_weight=None):
super(ExtraTreesClassifier, self).__init__(
super().__init__(
base_estimator=ExtraTreeClassifier(),
n_estimators=n_estimators,
estimator_params=("criterion", "max_depth", "min_samples_split",
Expand Down Expand Up @@ -1746,7 +1746,7 @@ def __init__(self,
random_state=None,
verbose=0,
warm_start=False):
super(ExtraTreesRegressor, self).__init__(
super().__init__(
base_estimator=ExtraTreeRegressor(),
n_estimators=n_estimators,
estimator_params=("criterion", "max_depth", "min_samples_split",
Expand Down Expand Up @@ -1921,7 +1921,7 @@ def __init__(self,
random_state=None,
verbose=0,
warm_start=False):
super(RandomTreesEmbedding, self).__init__(
super().__init__(
base_estimator=ExtraTreeRegressor(),
n_estimators=n_estimators,
estimator_params=("criterion", "max_depth", "min_samples_split",
Expand Down Expand Up @@ -2002,8 +2002,7 @@ def fit_transform(self, X, y=None, sample_weight=None):

rnd = check_random_state(self.random_state)
y = rnd.uniform(size=X.shape[0])
super(RandomTreesEmbedding, self).fit(X, y,
sample_weight=sample_weight)
super().fit(X, y, sample_weight=sample_weight)

self.one_hot_encoder_ = OneHotEncoder(sparse=self.sparse_output,
categories='auto')
Expand Down
Loading