8000 Revert "[MRG] Add pprint for estimators - continued (#11705)" · xhluca/scikit-learn@6353084 · GitHub
[go: up one dir, main page]
Skip to content

Commit 6353084

Browse files
author
Xing
authored
Revert "[MRG] Add pprint for estimators - continued (scikit-learn#11705)"
This reverts commit 5f34303.
1 parent ffdcd08 commit 6353084

30 files changed

+44
-860
lines changed

‎doc/modules/compose.rst

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -76,13 +76,13 @@ filling in the names automatically::
7676

7777
The estimators of a pipeline are stored as a list in the ``steps`` attribute::
7878

79-
>>> pipe.steps[0] # doctest: +NORMALIZE_WHITESPACE
79+
>>> pipe.steps[0]
8080
('reduce_dim', PCA(copy=True, iterated_power='auto', n_components=None, random_state=None,
8181
svd_solver='auto', tol=0.0, whiten=False))
8282

8383
and as a ``dict`` in ``named_steps``::
8484

85-
>>> pipe.named_steps['reduce_dim'] # doctest: +NORMALIZE_WHITESPACE
85+
>>> pipe.named_steps['reduce_dim']
8686
PCA(copy=True, iterated_power='auto', n_components=None, random_state=None,
8787
svd_solver='auto', tol=0.0, whiten=False)
8888

‎doc/modules/linear_model.rst

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -185,7 +185,7 @@ for another implementation::
185185

186186
>>> from sklearn import linear_model
187187
>>> reg = linear_model.Lasso(alpha=0.1)
188-
>>> reg.fit([[0, 0], [1, 1]], [0, 1]) # doctest: +NORMALIZE_WHITESPACE
188+
>>> reg.fit([[0, 0], [1, 1]], [0, 1])
189189
Lasso(alpha=0.1, copy_X=True, fit_intercept=True, max_iter=1000,
190190
normalize=False, positive=False, precompute=False, random_state=None,
191191
selection='cyclic', tol=0.0001, warm_start=False)
@@ -639,7 +639,7 @@ Bayesian Ridge Regression is used for regression::
639639
>>> X = [[0., 0.], [1., 1.], [2., 2.], [3., 3.]]
640640
>>> Y = [0., 1., 2., 3.]
641641
>>> reg = linear_model.BayesianRidge()
642-
>>> reg.fit(X, Y) # doctest: +NORMALIZE_WHITESPACE
642+
>>> reg.fit(X, Y)
643643
BayesianRidge(alpha_1=1e-06, alpha_2=1e-06, compute_score=False, copy_X=True,
644644
fit_intercept=True, lambda_1=1e-06, lambda_2=1e-06, n_iter=300,
645645
normalize=False, tol=0.001, verbose=False)

‎doc/modules/model_evaluation.rst

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -979,7 +979,7 @@ with a svm classifier in a binary class problem::
979979
>>> X = [[0], [1]]
980980
>>> y = [-1, 1]
981981
>>> est = svm.LinearSVC(random_state=0)
982-
>>> est.fit(X, y) # doctest: +NORMALIZE_WHITESPACE
982+
>>> est.fit(X, y)
983983
LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,
984984
intercept_scaling=1, loss='squared_hinge', max_iter=1000,
985985
multi_class='ovr', penalty='l2', random_state=0, tol=0.0001,
@@ -997,7 +997,7 @@ with a svm classifier in a multiclass problem::
997997
>>> Y = np.array([0, 1, 2, 3])
998998
>>> labels = np.array([0, 1, 2, 3])
999999
>>> est = svm.LinearSVC()
1000-
>>> est.fit(X, Y) # doctest: +NORMALIZE_WHITESPACE
1000+
>>> est.fit(X, Y)
10011001
LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,
10021002
intercept_scaling=1, loss='squared_hinge', max_iter=1000,
10031003
multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,

‎doc/modules/preprocessing.rst

Lines changed: 3 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -488,7 +488,7 @@ Continuing the example above::
488488

489489
>>> enc = preprocessing.OneHotEncoder()
490490
>>> X = [['male', 'from US', 'uses Safari'], ['female', 'from Europe', 'uses Firefox']]
491-
>>> enc.fit(X) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
491+
>>> enc.fit(X) # doctest: +ELLIPSIS
492492
OneHotEncoder(categorical_features=None, categories=None,
493493
dtype=<... 'numpy.float64'>, handle_unknown='error',
494494
n_values=None, sparse=True)
@@ -514,7 +514,7 @@ dataset::
514514
>>> # Note that for there are missing categorical values for the 2nd and 3rd
515515
>>> # feature
516516
>>> X = [['male', 'from US', 'uses Safari'], ['female', 'from Europe', 'uses Firefox']]
517-
>>> enc.fit(X) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
517+
>>> enc.fit(X) # doctest: +ELLIPSIS
518518
OneHotEncoder(categorical_features=None,
519519
categories=[...],
520520
dtype=<... 'numpy.float64'>, handle_unknown='error',
@@ -532,7 +532,7 @@ columns for this feature will be all zeros
532532

533533
>>> enc = preprocessing.OneHotEncoder(handle_unknown='ignore')
534534
>>> X = [['male', 'from US', 'uses Safari'], ['female', 'from Europe', 'uses Firefox']]
535-
>>> enc.fit(X) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
535+
>>> enc.fit(X) # doctest: +ELLIPSIS
536536
OneHotEncoder(categorical_features=None, categories=None,
537537
dtype=<... 'nu 10000 mpy.float64'>, handle_unknown='ignore',
538538
n_values=None, sparse=True)

‎doc/tutorial/statistical_inference/model_selection.rst

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -267,7 +267,7 @@ parameter automatically by cross-validation::
267267
>>> diabetes = datasets.load_diabetes()
268268
>>> X_diabetes = diabetes.data
269269
>>> y_diabetes = diabetes.target
270-
>>> lasso.fit(X_diabetes, y_diabetes) # doctest: +NORMALIZE_WHITESPACE
270+
>>> lasso.fit(X_diabetes, y_diabetes)
271271
LassoCV(alphas=None, copy_X=True, cv=3, eps=0.001, fit_intercept=True,
272272
max_iter=1000, n_alphas=100, n_jobs=None, normalize=False,
273273
positive=False, precompute='auto', random_state=None,

‎doc/tutorial/statistical_inference/supervised_learning.rst

Lines changed: 0 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -334,7 +334,6 @@ application of Occam's razor: *prefer simpler models*.
334334
>>> best_alpha = alphas[scores.index(max(scores))]
335335
>>> regr.alpha = best_alpha
336336
>>> regr.fit(diabetes_X_train, diabetes_y_train)
337-
... # doctest: +NORMALIZE_WHITESPACE
338337
Lasso(alpha=0.025118864315095794, copy_X=True, fit_intercept=True,
339338
max_iter=1000, normalize=False, positive=False, precompute=False,
340339
random_state=None, selection='cyclic', tol=0.0001, warm_start=False)

‎doc/tutorial/statistical_inference/unsupervised_learning.rst

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -274,7 +274,7 @@ data by projecting on a principal subspace.
274274

275275
>>> from sklearn import decomposition
276276
>>> pca = decomposition.PCA()
277-
>>> pca.fit(X) # doctest: +NORMALIZE_WHITESPACE
277+
>>> pca.fit(X)
278278
PCA(copy=True, iterated_power='auto', n_components=None, random_state=None,
279279
svd_solver='auto', tol=0.0, whiten=False)
280280
>>> print(pca.explained_variance_) # doctest: +SKIP

‎doc/whats_new/v0.21.rst

Lines changed: 1 addition & 8 deletions
Original file line numberDiff line numberDiff line change
@@ -200,19 +200,12 @@ Support for Python 3.4 and below has been officially dropped.
200200
``max_depth`` by 1 while expanding the tree if ``max_leaf_nodes`` and
201201
``max_depth`` were both specified by the user. Please note that this also
202202
affects all ensemble methods using decision trees.
203-
:issue:`12344` by :user:`Adrin Jalali <adrinjalali>`.
203+
:pr:`12344` by :user:`Adrin Jalali <adrinjalali>`.
204204

205205

206206
Multiple modules
207207
................
208208

209-
- The `__repr__()` method of all estimators (used when calling
210-
`print(estimator)`) has been entirely re-written, building on Python's
211-
pretty printing standard library. All parameters are printed by default,
212-
but this can be altered with the ``print_changed_only`` option in
213-
:func:`sklearn.set_config`. :issue:`11705` by :user:`Nicolas Hug
214-
<NicolasHug>`.
215-
216209
Changes to estimator checks
217210
---------------------------
218211

‎examples/plot_changed_only_pprint_parameter.py

Lines changed: 0 additions & 30 deletions
This file was deleted.

‎sklearn/_config.py

Lines changed: 2 additions & 14 deletions
Original file line numberDiff line numberDiff line change
@@ -5,8 +5,7 @@
55

66
_global_config = {
77
'assume_finite': bool(os.environ.get('SKLEARN_ASSUME_FINITE', False)),
8-
'working_memory': int(os.environ.get('SKLEARN_WORKING_MEMORY', 1024)),
9-
'print_changed_only': False,
8+
'working_memory': int(os.environ.get('SKLEARN_WORKING_MEMORY', 1024))
109
}
1110

1211

@@ -21,8 +20,7 @@ def get_config():
2120
return _global_config.copy()
2221

2322

24-
def set_config(assume_finite=None, working_memory=None,
25-
print_changed_only=None):
23+
def set_config(assume_finite=None, working_memory=None):
2624
"""Set global scikit-learn configuration
2725
2826
.. versionadded:: 0.19
@@ -45,21 +43,11 @@ def set_config(assume_finite=None, working_memory=None,
4543
4644
.. versionadded:: 0.20
4745
48-
print_changed_only : bool, optional
49-
If True, only the parameters that were set to non-default
50-
values will be printed when printing an estimator. For example,
51-
``print(SVC())`` while True will only print 'SVC()' while the default
52-
behaviour would be to print 'SVC(C=1.0, cache_size=200, ...)' with
53-
all the non-changed parameters.
54-
55-
.. versionadded:: 0.21
5646
"""
5747
if assume_finite is not None:
5848
_global_config['assume_finite'] = assume_finite
5949
if working_memory is not None:
6050
_global_config['working_memory'] = working_memory
61-
if print_changed_only is not None:
62-
_global_config['print_changed_only'] = print_changed_only
6351

6452

6553
@contextmanager

0 commit comments

Comments
 (0)
0