Python sklearn.base 模块，ClusterMixin() 实例源码
我们从Python开源项目中，提取了以下3个代码示例，用于说明如何使用sklearn.base.ClusterMixin()。
def _global_clustering(self, X=None):
"""
Global clustering for the subclusters obtained after fitting
"""
clusterer = self.n_clusters
centroids = self.subcluster_centers_
compute_labels = (X is not None) and self.compute_labels
# Preprocessing for the global clustering.
not_enough_centroids = False
if isinstance(clusterer, int):
clusterer = AgglomerativeClustering(
n_clusters=self.n_clusters)
# There is no need to perform the global clustering step.
if len(centroids) < self.n_clusters:
not_enough_centroids = True
elif (clusterer is not None and not
hasattr(clusterer, 'fit_predict')):
raise ValueError("n_clusters should be an instance of "
"ClusterMixin or an int")
# To use in predict to avoid recalculation.
self._subcluster_norms = row_norms(
self.subcluster_centers_, squared=True)
if clusterer is None or not_enough_centroids:
self.subcluster_labels_ = np.arange(len(centroids))
if not_enough_centroids:
warnings.warn(
"Number of subclusters found (%d) by Birch is less "
"than (%d). Decrease the threshold."
% (len(centroids), self.n_clusters))
else:
# The global clustering step that clusters the subclusters of
# the leaves. It assumes the centroids of the subclusters as
# samples and finds the final centroids.
self.subcluster_labels_ = clusterer.fit_predict(
self.subcluster_centers_)
if compute_labels:
self.labels_ = self.predict(X)
def _generate_bases_test(est, pd_est):
def test(self):
self.assertTrue(isinstance(pd_est, FrameMixin), pd_est)
self.assertFalse(isinstance(est, FrameMixin))
self.assertTrue(isinstance(pd_est, base.BaseEstimator))
try:
mixins = [
base.ClassifierMixin,
base.ClusterMixin,
base.BiclusterMixin,
base.TransformerMixin,
base.DensityMixin,
base.MetaEstimatorMixin,
base.ClassifierMixin,
base.RegressorMixin]
except:
if _sklearn_ver > 17:
raise
mixins = [
base.ClassifierMixin,
base.RegressorMixin]
for mixin in mixins:
self.assertEqual(
isinstance(pd_est, mixin),
isinstance(est,
mixin)
return test
def get_params_for_est(estimator, name):
'''Choose initialization parameters for an estimator for auto-testing'''
is_classifier = ClassifierMixin in estimator.__mro__
is_cluster = ClusterMixin in estimator.__mro__
is_ensemble = BaseEnsemble in estimator.__mro__
uses_counts = any(c in name for c in USES_COUNTS)
as_1d = name in REQUIRES_1D
args, params, _ = get_args_kwargs_defaults(estimator.__init__)
est_keys = set(('estimator', 'base_estimator', 'estimators'))
est_keys = (set(params) | set(args)) & est_keys
if is_classifier:
score_func = feat.f_classif
else:
score_func = feat.f_regression
for key in est_keys:
if name == 'SelectFromModel':
params[key] = sklearn.linear_model.LassoCV()
elif is_classifier:
params[key] = sklearn.tree.DecisionTreeClassifier()
else:
params[key] = sklearn.tree.DecisionTreeRegressor()
if key == 'estimators':
params[key] = [(str(_), clone(params[key])) for _ in range(10)]
kw = dict(is_classifier=is_classifier, is_cluster=is_cluster,
is_ensemble=is_ensemble, uses_counts=uses_counts)
if 'score_func' in params:
params['score_func'] = score_func
X, y = make_X_y(**kw)
return X, y, kw
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