本文整理匯總了Python中sklearn.metrics.cluster.adjusted_mutual_info_score方法的典型用法代碼示例。如果您正苦於以下問題：Python cluster.adjusted_mutual_info_score方法的具體用法？Python cluster.adjusted_mutual_info_score怎麽用？Python cluster.adjusted_mutual_info_score使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類sklearn.metrics.cluster的用法示例。
在下文中一共展示了cluster.adjusted_mutual_info_score方法的8個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: test_perfect_matches
# 需要導入模塊: from sklearn.metrics import cluster [as 別名]
# 或者: from sklearn.metrics.cluster import adjusted_mutual_info_score [as 別名]
def test_perfect_matches():
for score_func in score_funcs:
assert_equal(score_func([], []), 1.0)
assert_equal(score_func([0], [1]), 1.0)
assert_equal(score_func([0, 0, 0], [0, 0, 0]), 1.0)
assert_equal(score_func([0, 1, 0], [42, 7, 42]), 1.0)
assert_equal(score_func([0., 1., 0.], [42., 7., 42.]), 1.0)
assert_equal(score_func([0., 1., 2.], [42., 7., 2.]), 1.0)
assert_equal(score_func([0, 1, 2], [42, 7, 2]), 1.0)
score_funcs_with_changing_means = [
normalized_mutual_info_score,
adjusted_mutual_info_score,
]
means = {"min", "geometric", "arithmetic", "max"}
for score_func in score_funcs_with_changing_means:
for mean in means:
assert score_func([], [], mean) == 1.0
assert score_func([0], [1], mean) == 1.0
assert score_func([0, 0, 0], [0, 0, 0], mean) == 1.0
assert score_func([0, 1, 0], [42, 7, 42], mean) == 1.0
assert score_func([0., 1., 0.], [42., 7., 42.], mean) == 1.0
assert score_func([0., 1., 2.], [42., 7., 2.], mean) == 1.0
assert score_func([0, 1, 2], [42, 7, 2], mean) == 1.0 示例2: test_future_warning
# 需要導入模塊: from sklearn.metrics import cluster [as 別名]
# 或者: from sklearn.metrics.cluster import adjusted_mutual_info_score [as 別名]
def test_future_warning():
score_funcs_with_changing_means = [
normalized_mutual_info_score,
adjusted_mutual_info_score,
]
warning_msg = "The behavior of "
args = [0, 0, 0], [0, 0, 0]
for score_func in score_funcs_with_changing_means:
assert_warns_message(FutureWarning, warning_msg, score_func, *args) 示例3: test_adjusted_mutual_info_score
# 需要導入模塊: from sklearn.metrics import cluster [as 別名]
# 或者: from sklearn.metrics.cluster import adjusted_mutual_info_score [as 別名]
def test_adjusted_mutual_info_score():
# Compute the Adjusted Mutual Information and test against known values
labels_a = np.array([1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3])
labels_b = np.array([1, 1, 1, 1, 2, 1, 2, 2, 2, 2, 3, 1, 3, 3, 3, 2, 2])
# Mutual information
mi = mutual_info_score(labels_a, labels_b)
assert_almost_equal(mi, 0.41022, 5)
# with provided sparse contingency
C = contingency_matrix(labels_a, labels_b, sparse=True)
mi = mutual_info_score(labels_a, labels_b, contingency=C)
assert_almost_equal(mi, 0.41022, 5)
# with provided dense contingency
C = contingency_matrix(labels_a, labels_b)
mi = mutual_info_score(labels_a, labels_b, contingency=C)
assert_almost_equal(mi, 0.41022, 5)
# Expected mutual information
n_samples = C.sum()
emi = expected_mutual_information(C, n_samples)
assert_almost_equal(emi, 0.15042, 5)
# Adjusted mutual information
ami = adjusted_mutual_info_score(labels_a, labels_b)
assert_almost_equal(ami, 0.27502, 5)
ami = adjusted_mutual_info_score([1, 1, 2, 2], [2, 2, 3, 3])
assert_equal(ami, 1.0)
# Test with a very large array
a110 = np.array([list(labels_a) * 110]).flatten()
b110 = np.array([list(labels_b) * 110]).flatten()
ami = adjusted_mutual_info_score(a110, b110)
# This is not accurate to more than 2 places
assert_almost_equal(ami, 0.37, 2) 示例4: test_exactly_zero_info_score
# 需要導入模塊: from sklearn.metrics import cluster [as 別名]
# 或者: from sklearn.metrics.cluster import adjusted_mutual_info_score [as 別名]
def test_exactly_zero_info_score():
# Check numerical stability when information is exactly zero
for i in np.logspace(1, 4, 4).astype(np.int):
labels_a, labels_b = (np.ones(i, dtype=np.int),
np.arange(i, dtype=np.int))
assert_equal(normalized_mutual_info_score(labels_a, labels_b), 0.0)
assert_equal(v_measure_score(labels_a, labels_b), 0.0)
assert_equal(adjusted_mutual_info_score(labels_a, labels_b), 0.0)
assert_equal(normalized_mutual_info_score(labels_a, labels_b), 0.0)
for method in ["min", "geometric", "arithmetic", "max"]:
assert adjusted_mutual_info_score(labels_a, labels_b,
method) == 0.0
assert normalized_mutual_info_score(labels_a, labels_b,
method) == 0.0 示例5: validate
# 需要導入模塊: from sklearn.metrics import cluster [as 別名]
# 或者: from sklearn.metrics.cluster import adjusted_mutual_info_score [as 別名]
def validate( measure, classes, clustering ):
if measure == "nmi":
return normalized_mutual_info_score( classes, clustering )
elif measure == "ami":
return adjusted_mutual_info_score( classes, clustering )
elif measure == "ari":
return adjusted_rand_score( classes, clustering )
log.error("Unknown validation measure: %s" % measure )
return None
# -------------------------------------------------------------- 示例6: clusterscores
# 需要導入模塊: from sklearn.metrics import cluster [as 別名]
# 或者: from sklearn.metrics.cluster import adjusted_mutual_info_score [as 別名]
def clusterscores(self):
target,pred = self.conf2label()
NMI = normalized_mutual_info_score(target,pred)
ARI = adjusted_rand_score(target,pred)
AMI = adjusted_mutual_info_score(target,pred)
return {'NMI':NMI,'ARI':ARI,'AMI':AMI} 示例7: test_exactly_zero_info_score
# 需要導入模塊: from sklearn.metrics import cluster [as 別名]
# 或者: from sklearn.metrics.cluster import adjusted_mutual_info_score [as 別名]
def test_exactly_zero_info_score():
# Check numerical stability when information is exactly zero
for i in np.logspace(1, 4, 4).astype(np.int):
labels_a, labels_b = (np.ones(i, dtype=np.int),
np.arange(i, dtype=np.int))
assert_equal(normalized_mutual_info_score(labels_a, labels_b), 0.0)
assert_equal(v_measure_score(labels_a, labels_b), 0.0)
assert_equal(adjusted_mutual_info_score(labels_a, labels_b), 0.0)
assert_equal(normalized_mutual_info_score(labels_a, labels_b), 0.0) 示例8: evaluate
# 需要導入模塊: from sklearn.metrics import cluster [as 別名]
# 或者: from sklearn.metrics.cluster import adjusted_mutual_info_score [as 別名]
def evaluate( self, partition, clustered_ids ):
# no class info?
if not self.has_class_info():
return {}
# get two clusterings that we can compare
n = len(clustered_ids)
classes_subset = np.zeros( n )
for row in range(n):
classes_subset[row] = self.class_map[clustered_ids[row]]
scores = {}
scores["external-nmi"] = normalized_mutual_info_score( classes_subset, partition )
scores["external-ami"] = adjusted_mutual_info_score( classes_subset, partition )
scores["external-ari"] = adjusted_rand_score( classes_subset, partition )
return scores