本文整理汇总了Python中sklearn.metrics.completeness_score方法的典型用法代码示例。如果您正苦于以下问题：Python metrics.completeness_score方法的具体用法？Python metrics.completeness_score怎么用？Python metrics.completeness_score使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类sklearn.metrics的用法示例。
在下文中一共展示了metrics.completeness_score方法的6个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: bench_k_means
# 需要导入模块: from sklearn import metrics [as 别名]
# 或者: from sklearn.metrics import completeness_score [as 别名]
def bench_k_means(estimator, name, data):
estimator.fit(data)
# A short explanation for every score:
# homogeneity: each cluster contains only members of a single class (range 0 - 1)
# completeness: all members of a given class are assigned to the same cluster (range 0 - 1)
# v_measure: harmonic mean of homogeneity and completeness
# adjusted_rand: similarity of the actual values and their predictions,
# ignoring permutations and with chance normalization
# (range -1 to 1, -1 being bad, 1 being perfect and 0 being random)
# adjusted_mutual_info: agreement of the actual values and predictions, ignoring permutations
# (range 0 - 1, with 0 being random agreement and 1 being perfect agreement)
# silhouette: uses the mean distance between a sample and all other points in the same class,
# as well as the mean distance between a sample and all other points in the nearest cluster
# to calculate a score (range: -1 to 1, with the former being incorrect,
# and the latter standing for highly dense clustering.
# 0 indicates overlapping clusters.
print('%-9s \t%i \thomogeneity: %.3f \tcompleteness: %.3f \tv-measure: %.3f \tadjusted-rand: %.3f \t'
'adjusted-mutual-info: %.3f \tsilhouette: %.3f'
% (name, estimator.inertia_,
metrics.homogeneity_score(y, estimator.labels_),
metrics.completeness_score(y, estimator.labels_),
metrics.v_measure_score(y, estimator.labels_),
metrics.adjusted_rand_score(y, estimator.labels_),
metrics.adjusted_mutual_info_score(y, estimator.labels_),
metrics.silhouette_score(data, estimator.labels_,
metric='euclidean'))) 示例2: completeness_kmeans_scorer
# 需要导入模块: from sklearn import metrics [as 别名]
# 或者: from sklearn.metrics import completeness_score [as 别名]
def completeness_kmeans_scorer(self, min_similarity):
return self.kmeans_scorer(
metrics.completeness_score,
min_similarity
) 示例3: completeness_dbscan_scorer
# 需要导入模块: from sklearn import metrics [as 别名]
# 或者: from sklearn.metrics import completeness_score [as 别名]
def completeness_dbscan_scorer(self, min_similarity):
return self.dbscan_scorer(
metrics.completeness_score,
min_similarity
) 示例4: bench_k_means
# 需要导入模块: from sklearn import metrics [as 别名]
# 或者: from sklearn.metrics import completeness_score [as 别名]
def bench_k_means(estimator, name, data):
t0 = time()
estimator.fit(data)
print('% 9s %.2fs %i %.3f %.3f %.3f %.3f %.3f %.3f'
% (name, (time() - t0), estimator.inertia_,
metrics.homogeneity_score(labels, estimator.labels_),
metrics.completeness_score(labels, estimator.labels_),
metrics.v_measure_score(labels, estimator.labels_),
metrics.adjusted_rand_score(labels, estimator.labels_),
metrics.adjusted_mutual_info_score(labels, estimator.labels_),
metrics.silhouette_score(data, estimator.labels_,
metric='euclidean',
sample_size=sample_size))) 示例5: test_completeness_score
# 需要导入模块: from sklearn import metrics [as 别名]
# 或者: from sklearn.metrics import completeness_score [as 别名]
def test_completeness_score(self):
result = self.df.metrics.completeness_score()
expected = metrics.completeness_score(self.target, self.pred)
self.assertEqual(result, expected) 示例6: test_KMeans_scores
# 需要导入模块: from sklearn import metrics [as 别名]
# 或者: from sklearn.metrics import completeness_score [as 别名]
def test_KMeans_scores(self):
digits = datasets.load_digits()
df = pdml.ModelFrame(digits)
scaled = pp.scale(digits.data)
df.data = df.data.pp.scale()
self.assert_numpy_array_almost_equal(df.data.values, scaled)
clf1 = cluster.KMeans(init='k-means++', n_clusters=10,
n_init=10, random_state=self.random_state)
clf2 = df.cluster.KMeans(init='k-means++', n_clusters=10,
n_init=10, random_state=self.random_state)
clf1.fit(scaled)
df.fit_predict(clf2)
expected = m.homogeneity_score(digits.target, clf1.labels_)
self.assertEqual(df.metrics.homogeneity_score(), expected)
expected = m.completeness_score(digits.target, clf1.labels_)
self.assertEqual(df.metrics.completeness_score(), expected)
expected = m.v_measure_score(digits.target, clf1.labels_)
self.assertEqual(df.metrics.v_measure_score(), expected)
expected = m.adjusted_rand_score(digits.target, clf1.labels_)
self.assertEqual(df.metrics.adjusted_rand_score(), expected)
expected = m.homogeneity_score(digits.target, clf1.labels_)
self.assertEqual(df.metrics.homogeneity_score(), expected)
expected = m.silhouette_score(scaled, clf1.labels_, metric='euclidean',
sample_size=300, random_state=self.random_state)
result = df.metrics.silhouette_score(metric='euclidean', sample_size=300,
random_state=self.random_state)
self.assertAlmostEqual(result, expected)