API make a few estimators' inits kw-only (scikit-learn#16474)

sklearn/calibration.py

@@ -27,6 +27,7 @@
 from .isotonic import IsotonicRegression
 from .svm import LinearSVC
 from .model_selection import check_cv
+from .utils.validation import _deprecate_positional_args
 
 
 class CalibratedClassifierCV(BaseEstimator, ClassifierMixin,
@@ -98,7 +99,8 @@ class CalibratedClassifierCV(BaseEstimator, ClassifierMixin,
     .. [4] Predicting Good Probabilities with Supervised Learning,
            A. Niculescu-Mizil & R. Caruana, ICML 2005
     """
-    def __init__(self, base_estimator=None, method='sigmoid', cv=None):
+    @_deprecate_positional_args
+    def __init__(self, base_estimator=None, *, method='sigmoid', cv=None):
         self.base_estimator = base_estimator
         self.method = method
         self.cv = cv
@@ -275,7 +277,8 @@ class _CalibratedClassifier:
     .. [4] Predicting Good Probabilities with Supervised Learning,
            A. Niculescu-Mizil & R. Caruana, ICML 2005
     """
-    def __init__(self, base_estimator, method='sigmoid', classes=None):
+    @_deprecate_positional_args
+    def __init__(self, base_estimator, *, method='sigmoid', classes=None):
         self.base_estimator = base_estimator
         self.method = method
         self.classes = classes

sklearn/discriminant_analysis.py

@@ -11,7 +11,6 @@
 
 import warnings
 import numpy as np
-from .exceptions import ChangedBehaviorWarning
 from scipy import linalg
 from scipy.special import expit
 
@@ -24,6 +23,7 @@
 from .utils.multiclass import check_classification_targets
 from .utils.extmath import softmax
 from .preprocessing import StandardScaler
+from .utils.validation import _deprecate_positional_args
 
 
 __all__ = ['LinearDiscriminantAnalysis', 'QuadraticDiscriminantAnalysis']
@@ -246,8 +246,8 @@ class LinearDiscriminantAnalysis(BaseEstimator, LinearClassifierMixin,
     >>> print(clf.predict([[-0.8, -1]]))
     [1]
     """
-
-    def __init__(self, solver='svd', shrinkage=None, priors=None,
+    @_deprecate_positional_args
+    def __init__(self, *, solver='svd', shrinkage=None, priors=None,
                  n_components=None, store_covariance=False, tol=1e-4):
         self.solver = solver
         self.shrinkage = shrinkage
@@ -618,8 +618,8 @@ class QuadraticDiscriminantAnalysis(ClassifierMixin, BaseEstimator):
     sklearn.discriminant_analysis.LinearDiscriminantAnalysis: Linear
         Discriminant Analysis
     """
-
-    def __init__(self, priors=None, reg_param=0., store_covariance=False,
+    @_deprecate_positional_args
+    def __init__(self, *, priors=None, reg_param=0., store_covariance=False,
                  tol=1.0e-4):
         self.priors = np.asarray(priors) if priors is not None else None
         self.reg_param = reg_param

sklearn/dummy.py

@@ -18,7 +18,7 @@
 from .utils.stats import _weighted_percentile
 from .utils.multiclass import class_distribution
 from .utils import deprecated
-
+from .utils.validation import _deprecate_positional_args
 
 class DummyClassifier(MultiOutputMixin, ClassifierMixin, BaseEstimator):
     """
@@ -98,8 +98,8 @@ class DummyClassifier(MultiOutputMixin, ClassifierMixin, BaseEstimator):
     >>> dummy_clf.score(X, y)
     0.75
     """
-
-    def __init__(self, strategy="warn", random_state=None,
+    @_deprecate_positional_args
+    def __init__(self, *, strategy="warn", random_state=None,
                  constant=None):
         self.strategy = strategy
         self.random_state = random_state
@@ -453,8 +453,8 @@ class DummyRegressor(MultiOutputMixin, RegressorMixin, BaseEstimator):
     >>> dummy_regr.score(X, y)
     0.0
     """
-
-    def __init__(self, strategy="mean", constant=None, quantile=None):
+    @_deprecate_positional_args
+    def __init__(self, *, strategy="mean", constant=None, quantile=None):
         self.strategy = strategy
         self.constant = constant
         self.quantile = quantile

sklearn/isotonic.py

@@ -6,12 +6,13 @@
 import numpy as np
 from scipy import interpolate
 from scipy.stats import spearmanr
+import warnings
+import math
+
 from .base import BaseEstimator, TransformerMixin, RegressorMixin
 from .utils import check_array, check_consistent_length
-from .utils.validation import _check_sample_weight
+from .utils.validation import _check_sample_weight, _deprecate_positional_args
 from ._isotonic import _inplace_contiguous_isotonic_regression, _make_unique
-import warnings
-import math
 
 
 __all__ = ['check_increasing', 'isotonic_regression',
@@ -198,7 +199,8 @@ class IsotonicRegression(RegressorMixin, TransformerMixin, BaseEstimator):
     >>> iso_reg.predict([.1, .2])
     array([1.8628..., 3.7256...])
     """
-    def __init__(self, y_min=None, y_max=None, increasing=True,
+    @_deprecate_positional_args
+    def __init__(self, *, y_min=None, y_max=None, increasing=True,
                  out_of_bounds='nan'):
         self.y_min = y_min
         self.y_max = y_max

sklearn/kernel_approximation.py

@@ -19,7 +19,7 @@
 from .utils.extmath import safe_sparse_dot
 from .utils.validation import check_is_fitted
 from .metrics.pairwise import pairwise_kernels, KERNEL_PARAMS
-from .utils.validation import check_non_negative
+from .utils.validation import check_non_negative, _deprecate_positional_args
 
 
 class RBFSampler(TransformerMixin, BaseEstimator):
@@ -81,8 +81,8 @@ class RBFSampler(TransformerMixin, BaseEstimator):
     Benjamin Recht.
     (https://people.eecs.berkeley.edu/~brecht/papers/08.rah.rec.nips.pdf)
     """
-
-    def __init__(self, gamma=1., n_components=100, random_state=None):
+    @_deprecate_positional_args
+    def __init__(self, *, gamma=1., n_components=100, random_state=None):
         self.gamma = gamma
         self.n_components = n_components
         self.random_state = random_state
@@ -187,8 +187,8 @@ class SkewedChi2Sampler(TransformerMixin, BaseEstimator):
 
     sklearn.metrics.pairwise.chi2_kernel : The exact chi squared kernel.
     """
-
-    def __init__(self, skewedness=1., n_components=100, random_state=None):
+    @_deprecate_positional_args
+    def __init__(self, *, skewedness=1., n_components=100, random_state=None):
         self.skewedness = skewedness
         self.n_components = n_components
         self.random_state = random_state
@@ -318,8 +318,8 @@ class AdditiveChi2Sampler(TransformerMixin, BaseEstimator):
     A. Vedaldi and A. Zisserman, Pattern Analysis and Machine Intelligence,
     2011
     """
-
-    def __init__(self, sample_steps=2, sample_interval=None):
+    @_deprecate_positional_args
+    def __init__(self, *, sample_steps=2, sample_interval=None):
         self.sample_steps = sample_steps
         self.sample_interval = sample_interval
 
@@ -534,8 +534,8 @@ class Nystroem(TransformerMixin, BaseEstimator):
 
     sklearn.metrics.pairwise.kernel_metrics : List of built-in kernels.
     """
-
-    def __init__(self, kernel="rbf", gamma=None, coef0=None, degree=None,
+    @_deprecate_positional_args
+    def __init__(self, kernel="rbf", *, gamma=None, coef0=None, degree=None,
                  kernel_params=None, n_components=100, random_state=None):
         self.kernel = kernel
         self.gamma = gamma

sklearn/utils/validation.py

@@ -1290,7 +1290,7 @@ def inner_f(*args, **kwargs):
             args_msg = ['{}={}'.format(name, arg)
                         for name, arg in zip(kwonly_args[:extra_args],
                                              args[-extra_args:])]
-            warnings.warn("Pass {} as keyword args. From version 0.24 "
+            warnings.warn("Pass {} as keyword args. From version 0.25 "
                           "passing these as positional arguments will "
                           "result in an error".format(", ".join(args_msg)),
                           FutureWarning)