8000 fix order of mixin and BaseEstimator · scikit-learn/scikit-learn@8eab08a · GitHub
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fix order of mixin and BaseEstimator
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‎doc/developers/contributing.rst

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@@ -874,7 +874,7 @@ If, for some reason, randomness is needed after ``fit``,
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the RNG should be stored in an attribute ``random_state_``.
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The following example should make this clear::
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class GaussianNoise(BaseEstimator, TransformerMixin):
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class GaussianNoise(TransformerMixin, BaseEstimator):
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"""This estimator ignores its input and returns random Gaussian noise.
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It also does not adhere to all scikit-learn conventions,
@@ -1361,7 +1361,7 @@ the correct interface more easily.
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>>> from sklearn.utils.validation import check_X_y, check_array, check_is_fitted
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>>> from sklearn.utils.multiclass import unique_labels
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>>> from sklearn.metrics import euclidean_distances
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>>> class TemplateClassifier(BaseEstimator, ClassifierMixin):
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>>> class TemplateClassifier(ClassifierMixin, BaseEstimator):
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...
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... def __init__(self, demo_param='demo'):
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... self.demo_param = demo_param

‎examples/compose/plot_column_transformer.py

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@@ -42,7 +42,7 @@
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from sklearn.svm import LinearSVC
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class TextStats(BaseEstimator, TransformerMixin):
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class TextStats(TransformerMixin, BaseEstimator):
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"""Extract features from each document for DictVectorizer"""
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def fit(self, x, y=None):
@@ -54,7 +54,7 @@ def transform(self, posts):
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for text in posts]
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class SubjectBodyExtractor(BaseEstimator, TransformerMixin):
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class SubjectBodyExtractor(TransformerMixin, BaseEstimator):
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"""Extract the subject & body from a usenet post in a single pass.
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Takes a sequence of strings and produces a dict of sequences. Keys are

‎sklearn/calibration.py

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@@ -465,7 +465,7 @@ def grad(AB):
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return AB_[0], AB_[1]
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class _SigmoidCalibration(BaseEstimator, RegressorMixin):
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class _SigmoidCalibration(RegressorMixin, BaseEstimator):
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"""Sigmoid regression model.
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Attributes

‎sklearn/cluster/affinity_propagation_.py

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@@ -233,7 +233,7 @@ def affinity_propagation(S, preference=None, convergence_iter=15, max_iter=200,
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###############################################################################
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class AffinityPropagation(BaseEstimator, ClusterMixin):
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class AffinityPropagation(ClusterMixin, BaseEstimator):
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"""Perform Affinity Propagation Clustering of data.
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Read more in the :ref:`User Guide <affinity_propagation>`.

‎sklearn/cluster/bicluster.py

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@@ -84,7 +84,7 @@ def _log_normalize(X):
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return L - row_avg - col_avg + avg
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class BaseSpectral(BaseEstimator, BiclusterMixin, metaclass=ABCMeta):
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class BaseSpectral(BiclusterMixin, BaseEstimator, metaclass=ABCMeta):
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"""Base class for spectral biclustering."""
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@abstractmethod

‎sklearn/cluster/birch.py

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@@ -319,7 +319,7 @@ def radius(self):
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self.sq_norm_)
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class Birch(BaseEstimator, TransformerMixin, ClusterMixin):
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class Birch(ClusterMixin, TransformerMixin, BaseEstimator):
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"""Implements the Birch clustering algorithm.
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It is a memory-efficient, online-learning algorithm provided as an

‎sklearn/cluster/dbscan_.py

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@@ -190,7 +190,7 @@ def dbscan(X, eps=0.5, min_samples=5, metric='minkowski', metric_params=None,
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return np.where(core_samples)[0], labels
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class DBSCAN(BaseEstimator, ClusterMixin):
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class DBSCAN(ClusterMixin, BaseEstimator):
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"""Perform DBSCAN clustering from vector array or distance matrix.
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DBSCAN - Density-Based Spatial Clustering of Applications with Noise.

‎sklearn/cluster/hierarchical.py

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@@ -652,7 +652,7 @@ def _hc_cut(n_clusters, children, n_leaves):
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###############################################################################
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class AgglomerativeClustering(BaseEstimator, ClusterMixin):
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class AgglomerativeClustering(ClusterMixin, BaseEstimator):
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"""
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Agglomerative Clustering
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‎sklearn/cluster/k_means_.py

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@@ -760,7 +760,7 @@ def _init_centroids(X, k, init, random_state=None, x_squared_norms=None,
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return centers
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class KMeans(BaseEstimator, ClusterMixin, TransformerMixin):
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class KMeans(TransformerMixin, ClusterMixin, BaseEstimator):
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"""K-Means clustering
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Read more in the :ref:`User Guide <k_means>`.

‎sklearn/cluster/mean_shift_.py

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@@ -293,7 +293,7 @@ def get_bin_seeds(X, bin_size, min_bin_freq=1):
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return bin_seeds
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class MeanShift(BaseEstimator, ClusterMixin):
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class MeanShift(ClusterMixin, BaseEstimator):
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"""Mean shift clustering using a flat kernel.
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Mean shift clustering aims to discover "blobs" in a smooth density of

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