本文整理汇总了Python中sklearn.metrics.silhouette_score方法的典型用法代码示例。如果您正苦于以下问题：Python metrics.silhouette_score方法的具体用法？Python metrics.silhouette_score怎么用？Python metrics.silhouette_score使用的例子？那么恭喜您, 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模块sklearn.metrics的用法示例。

在下文中一共展示了metrics.silhouette_score方法的28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，您的评价将有助于我们的系统推荐出更棒的Python代码示例。

示例1: silhouette_score

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# 需要导入模块: from sklearn import metrics [as 别名]

# 或者: from sklearn.metrics import silhouette_score [as 别名]

def silhouette_score(phate_op, n_clusters, random_state=None, **kwargs):

"""Compute the Silhouette score on KMeans on the PHATE potential

Parameters

----------

phate_op : phate.PHATE

Fitted PHATE operator

n_clusters : int

Number of clusters.

random_state : int or None, optional (default: None)

Random seed for k-means

Returns

-------

score : float

"""

cluster_labels = kmeans(phate_op, n_clusters=n_clusters, random_state=random_state, **kwargs)

return metrics.silhouette_score(phate_op.diff_potential, cluster_labels)

开发者ID:KrishnaswamyLab，项目名称:PHATE，代码行数:20，

示例2: calc_scores

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# 需要导入模块: from sklearn import metrics [as 别名]

# 或者: from sklearn.metrics import silhouette_score [as 别名]

def calc_scores(cls, model, data, min_clusters, max_clusters, random_state=0):

silhouettes = []

davieses = []

calinskies = []

if model.__class__.__name__ == 'HierarchicalClustering':

linkage_matrix = model.fit(data)

else:

linkage_matrix = None

for nc in range(min_clusters, max_clusters + 1):

model.n_clusters = nc

model.random_state = random_state

pred_labels = model.fit_predict(data)

silhouettes.append(silhouette_score(data, pred_labels, random_state=random_state))

davieses.append(davies_bouldin_score(data, pred_labels))

calinskies.append(calinski_harabasz_score(data, pred_labels))

sil_nc = np.argmax(silhouettes) + min_clusters

dav_nc = np.argmin(davieses) + min_clusters

cal_nc = np.argmax(calinskies) + min_clusters

return silhouettes, sil_nc, davieses, dav_nc, calinskies, cal_nc, linkage_matrix

开发者ID:canard0328，项目名称:malss，代码行数:23，

示例3: clustering_scores

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# 需要导入模块: from sklearn import metrics [as 别名]

# 或者: from sklearn.metrics import silhouette_score [as 别名]

def clustering_scores(self, prediction_algorithm: str = "knn") -> Tuple:

if self.gene_dataset.n_labels > 1:

latent, _, labels = self.get_latent()

if prediction_algorithm == "knn":

labels_pred = KMeans(

self.gene_dataset.n_labels, n_init=200

).fit_predict(

latent

) # n_jobs>1 ?

elif prediction_algorithm == "gmm":

gmm = GMM(self.gene_dataset.n_labels)

gmm.fit(latent)

labels_pred = gmm.predict(latent)

asw_score = silhouette_score(latent, labels)

nmi_score = NMI(labels, labels_pred)

ari_score = ARI(labels, labels_pred)

uca_score = unsupervised_clustering_accuracy(labels, labels_pred)[0]

logger.debug(

"Clustering Scores:\nSilhouette: %.4f\nNMI: %.4f\nARI: %.4f\nUCA: %.4f"

% (asw_score, nmi_score, ari_score, uca_score)

)

return asw_score, nmi_score, ari_score, uca_score

开发者ID:YosefLab，项目名称:scVI，代码行数:25，

示例4: _cluster_plot

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# 需要导入模块: from sklearn import metrics [as 别名]

# 或者: from sklearn.metrics import silhouette_score [as 别名]

def _cluster_plot(self, embedding, labels):

silhouette = silhouette_score(embedding.squeeze(), labels)

chs = calinski_harabaz_score(embedding.squeeze(), labels)

dbs = davies_bouldin_score(embedding.squeeze(), labels)

n_labels = len(set(labels))

self.writer.add_scalar(f"silhouette {n_labels}", silhouette, self.step_id)

self.writer.add_scalar(f"chs {n_labels}", chs, self.step_id)

self.writer.add_scalar(f"dbs {n_labels}", dbs, self.step_id)

indices = list(range(len(labels)))

random.shuffle(indices)

samples_to_plot = indices[:1000]

sample_labels = [labels[idx] for idx in samples_to_plot]

sample_embedding = embedding[samples_to_plot]

pca = PCA(2).fit_transform(sample_embedding.squeeze())

fig, ax = plt.subplots()

ax.scatter(pca[:, 0], pca[:, 1], c=sample_labels, cmap="tab20")

self.writer.add_figure(f"clustering {n_labels}", fig, self.step_id)

开发者ID:mjendrusch，项目名称:torchsupport，代码行数:22，

示例5: test_silhouette

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# 需要导入模块: from sklearn import metrics [as 别名]

# 或者: from sklearn.metrics import silhouette_score [as 别名]

def test_silhouette():

# this test checks wether combat can align data from several gaussians

# it checks this by computing the silhouette coefficient in a pca embedding

# load in data

adata = sc.datasets.blobs()

# apply combat

sc.pp.combat(adata, 'blobs')

# compute pca

sc.tl.pca(adata)

X_pca = adata.obsm['X_pca']

# compute silhouette coefficient in pca

sh = silhouette_score(X_pca[:, :2], adata.obs['blobs'].values)

assert sh < 0.1

开发者ID:theislab，项目名称:scanpy，代码行数:20，

示例6: _find_optimal_clustering

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# 需要导入模块: from sklearn import metrics [as 别名]

# 或者: from sklearn.metrics import silhouette_score [as 别名]

def _find_optimal_clustering(self,clusterings):

max_score = float('-inf')

max_clustering = None

for clustering in clusterings:

labeled_vectors = [(node.vector,cluster_idx) for cluster_idx in range(len(clustering)) for node in _get_cluster_nodes(clustering[cluster_idx][1]) ]

vectors,labels = [np.array(x) for x in zip(*labeled_vectors)]

if np.in1d([1],labels)[0]:

score = silhouette_score(vectors,labels,metric='cosine')

else:

continue # silhouette doesn't work with just one cluster

if score > max_score:

max_score = score

max_clustering = clustering

return list(zip(*max_clustering))[1] if max_clustering else list(zip(*clusterings[0]))[1]

开发者ID:texta-tk，项目名称:texta，代码行数:19，

示例7: bench_k_means

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# 需要导入模块: from sklearn import metrics [as 别名]

# 或者: from sklearn.metrics import silhouette_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')))

开发者ID:HoussemCharf，项目名称:FunUtils，代码行数:28，

示例8: printClustersSummary

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# 需要导入模块: from sklearn import metrics [as 别名]

# 或者: from sklearn.metrics import silhouette_score [as 别名]

def printClustersSummary(data, labels, centroids):

'''

Helper method to automate models assessment

'''

print('Pseudo_F: ', pseudo_F(data, labels, centroids))

print('Davis-Bouldin: ',

davis_bouldin(data, labels, centroids))

print('Silhouette score: ',

mt.silhouette_score(data, np.array(labels),

metric='euclidean'))

开发者ID:drabastomek，项目名称:practicalDataAnalysisCookbook，代码行数:12，

示例9: find_best_n_clusters

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# 需要导入模块: from sklearn import metrics [as 别名]

# 或者: from sklearn.metrics import silhouette_score [as 别名]

def find_best_n_clusters(data, clusterer, max_n_clusters, random_state, **kwargs):

"""

Finds best number of clusters for KMeans and Gaussian Mixture.

Parameters

-------

data: pd.DataFrame

Dataframe with features for clustering with index as in ``retention_config.index_col``

clusterer: sklearn clusterer class

For instance, ``sklearn.cluster.KMeans`` or ``sklearn.mixture.GaussianMixture``.

max_n_clusters: int

Maximal number of clusters for searching.

random_state: int

Random state for clusterer.

Returns

-------

Optimal keyword arguments for clustering method.

Return type

------

Dict

"""

args = {i: j for i, j in kwargs.items() if i in clusterer.get_params(clusterer)}

if 'n_clusters' in clusterer.get_params(clusterer):

kms = True

else:

kms = False

args.pop('n_clusters' if kms else 'n_components', None)

args.update({'random_state': random_state})

score = {}

for i in range(2, max_n_clusters + 1):

args.update({'n_clusters' if kms else 'n_components': i})

km = clusterer(**args)

score[i] = silhouette_score(data, km.fit_predict(data), metric='cosine')

best = pd.Series(score).idxmax()

args.update({'n_clusters' if kms else 'n_components': best})

print(f'Best number of clusters is {best}')

return args

开发者ID:retentioneering，项目名称:retentioneering-tools，代码行数:41，

示例10: calc_all_metrics

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# 需要导入模块: from sklearn import metrics [as 别名]

# 或者: from sklearn.metrics import silhouette_score [as 别名]

def calc_all_metrics(data, km):

"""

Calculates all quality metrics: Cluster Stability Index, Silhouette score, Homogeneity, distances for clustering.

Parameters

--------

data: pd.DataFrame

Dataframe with features for clustering indexed as in ``retention_config.index_col``

km:

Already fitted clusterer.

Returns

--------

Metrics scores

Return type

--------

Dict

"""

res = {}

cl = km.labels_

res['mean_pd'] = calc_mean_pd(data, cl)

if hasattr(km, 'cluster_centers_'):

res['mean_fc'] = calc_mean_dist_from_center(data, km)

if len(set(cl)) > 1:

res['silhouette'] = silhouette_score(data, cl, metric='cosine')

return res

开发者ID:retentioneering，项目名称:retentioneering-tools，代码行数:29，

示例11: test

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# 需要导入模块: from sklearn import metrics [as 别名]

# 或者: from sklearn.metrics import silhouette_score [as 别名]

def test():

parser = argparse.ArgumentParser()

parser.add_argument("File")

args = parser.parse_args()

info = fh.get_function_information(args.File)

#info = fh.get_arg_funcs(args.File)

info = trim_funcs(info, args.File)

vect, func_sparse = funcs_to_sparse(info)

transformer = Normalizer().fit(func_sparse)

func_sparse = transformer.transform(func_sparse)

#svd = TruncatedSVD(random_state=2)

svd = TruncatedSVD(n_components=5, n_iter=7, random_state=42)

func_sparse = svd.fit_transform(func_sparse)

scores = []

clust_count = []

for x in range(2, 20):

result = KMeans(n_clusters=x, random_state=2).fit(func_sparse)

score = silhouette_score(func_sparse, result.labels_, metric="cosine")

scores.append(score)

clust_count.append(x)

print("Clusters {:<3} | Silhoette Score : {}".format(x, score))

plt.plot(clust_count, scores)

plt.xlabel("Cluster Centroid Count")

plt.ylabel("Silhoette Score")

plt.grid = True

plt.show()

pass

开发者ID:ChrisTheCoolHut，项目名称:Firmware_Slap，代码行数:43，

示例12: single_cluster

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# 需要导入模块: from sklearn import metrics [as 别名]

# 或者: from sklearn.metrics import silhouette_score [as 别名]

def single_cluster(all_functions, centroid_count=2):

vect, func_sparse = funcs_to_sparse(all_functions)

transformer = Normalizer().fit(func_sparse)

func_sparse = transformer.transform(func_sparse)

# svd = TruncatedSVD(random_state=2)

# svd = TruncatedSVD(n_components=5, n_iter=7, random_state=42)

# func_sparse = svd.fit_transform(func_sparse)

labels = []

result = KMeans(n_clusters=centroid_count, random_state=2).fit(func_sparse)

score = silhouette_score(func_sparse,

result.labels_,

metric="cosine",

random_state=2,

sample_size=5000)

labels.append(result.labels_)

print("Clusters {:<3} | Silhoette Score : {}".format(

centroid_count, score))

return result.labels_

开发者ID:ChrisTheCoolHut，项目名称:Firmware_Slap，代码行数:29，

示例13: get_single_cluster

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# 需要导入模块: from sklearn import metrics [as 别名]

# 或者: from sklearn.metrics import silhouette_score [as 别名]

def get_single_cluster(all_functions, centroid_count=2):

return_dict = {}

vect, func_sparse = funcs_to_sparse(all_functions)

transformer = Normalizer().fit(func_sparse)

func_sparse = transformer.transform(func_sparse)

# svd = TruncatedSVD(random_state=2)

# svd = TruncatedSVD(n_components=5, n_iter=7, random_state=42)

# func_sparse = svd.fit_transform(func_sparse)

labels = []

result = KMeans(n_clusters=centroid_count, random_state=2).fit(func_sparse)

score = silhouette_score(func_sparse,

result.labels_,

metric="cosine",

random_state=2,

sample_size=5000)

labels.append(result.labels_)

#print("Clusters {:<3} | Silhoette Score : {}".format(centroid_count, score))

return_dict['count'] = centroid_count

return_dict['score'] = score

return_dict['labels'] = result.labels_

return return_dict

开发者ID:ChrisTheCoolHut，项目名称:Firmware_Slap，代码行数:32，

示例14: n_cluster_embeddings

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# 需要导入模块: from sklearn import metrics [as 别名]

# 或者: from sklearn.metrics import silhouette_score [as 别名]

def n_cluster_embeddings(self, features=None, n_clusters=3, method='ac'):

'''

clusters the nodes based on embedding features

features = None (use DGI generated embeddings)

'''

if method == 'ac':

clustering = AgglomerativeClustering(n_clusters=n_clusters, affinity='euclidean',\

linkage='ward')

clustering.fit(self.embeddings if features is None else features)

self.labels = clustering.labels_

self.score = silhouette_score(self.embeddings if features is None else features,\

self.labels)

return {'labels': self.labels, 'score': self.score}

开发者ID:plkmo，项目名称:NLP_Toolkit，代码行数:15，

示例15: evaluate

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# 需要导入模块: from sklearn import metrics [as 别名]

# 或者: from sklearn.metrics import silhouette_score [as 别名]

def evaluate(k):

km = kmeans[k]

score = silhouette_score(train_offsets, km.labels_, metric='euclidean', random_state=RANDOM_SEED)

print('Silhouette score for k=%d is %f.' % (k, score))

return (k, score)

开发者ID:nlpub，项目名称:hyperstar，代码行数:7，

示例16: sk_kmeans

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# 需要导入模块: from sklearn import metrics [as 别名]

# 或者: from sklearn.metrics import silhouette_score [as 别名]

def sk_kmeans(core): #, kval=3

solrURL = "http://localhost:8983/solr/" + core

solrInstance = Solr(solrURL)

list_of_points = []

docs = solrInstance.query_iterator(query="*:*", start=0)

for doc in docs:

list_of_points.append(Vector(doc['id'], doc))

list_of_Dicts = (point.features for point in list_of_points)

df = pd.DataFrame(list_of_Dicts)

df = df.fillna(0)

silhouettes = {}

for k in range(2, 10):

kmeans = KMeans(n_clusters=k,

init='k-means++',

max_iter=300, # k-means convergence

n_init=10, # find global minima

n_jobs=-2, # parallelize

)

labels = kmeans.fit_predict(df)

silhouettes[k] = silhouette_score(df, labels)

return str(silhouettes)

开发者ID:chrismattmann，项目名称:tika-similarity，代码行数:33，

示例17: evaluate_performance

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# 需要导入模块: from sklearn import metrics [as 别名]

# 或者: from sklearn.metrics import silhouette_score [as 别名]

def evaluate_performance(data, labels, metric='euclidean'):

score = skmetrics.silhouette_score(data, labels, metric=metric)

print('Labels:', labels)

print('Score:', score)

return score

开发者ID:melqkiades，项目名称:yelp，代码行数:8，

示例18: labeled_val_fun

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# 需要导入模块: from sklearn import metrics [as 别名]

# 或者: from sklearn.metrics import silhouette_score [as 别名]

def labeled_val_fun(u_feats, l_feats, l_targets, k):

if device=='cuda':

torch.cuda.empty_cache()

l_num=len(l_targets)

kmeans = K_Means(k, pairwise_batch_size=256)

kmeans.fit_mix(torch.from_numpy(u_feats).to(device), torch.from_numpy(l_feats).to(device), torch.from_numpy(l_targets).to(device))

cat_pred = kmeans.labels_.cpu().numpy()

u_pred = cat_pred[l_num:]

silh_score = silhouette_score(u_feats, u_pred)

return silh_score, cat_pred

开发者ID:k-han，项目名称:DTC，代码行数:12，

示例19: labeled_val_fun

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# 需要导入模块: from sklearn import metrics [as 别名]

# 或者: from sklearn.metrics import silhouette_score [as 别名]

def labeled_val_fun(u_feats, l_feats, l_targets, k):

if device=='cuda':

torch.cuda.empty_cache()

l_num=len(l_targets)

kmeans = K_Means(k, pairwise_batch_size = 200)

kmeans.fit_mix(torch.from_numpy(u_feats).to(device), torch.from_numpy(l_feats).to(device), torch.from_numpy(l_targets).to(device))

cat_pred = kmeans.labels_.cpu().numpy()

u_pred = cat_pred[l_num:]

silh_score = silhouette_score(u_feats, u_pred)

del kmeans

return silh_score, cat_pred

开发者ID:k-han，项目名称:DTC，代码行数:13，

示例20: test_gmm

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# 需要导入模块: from sklearn import metrics [as 别名]

# 或者: from sklearn.metrics import silhouette_score [as 别名]

def test_gmm():

sil = pyclust.validate.Silhouette()

sil_score = sil.score(X, ypred, sample_size=None)

print(sil_score[0])

print(sil.sample_scores[:10])

print(silhouette_score(X, ypred, sample_size=None))

print(silhouette_samples(X, ypred)[:10])

开发者ID:vmirly，项目名称:pyclust，代码行数:13，

示例21: get_clusters

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# 需要导入模块: from sklearn import metrics [as 别名]

# 或者: from sklearn.metrics import silhouette_score [as 别名]

def get_clusters(self, features_2d):

""" Mapping instances to clusters, using silhouette-scores to determine

number of cluster.

Returns

-------

paths: List[str]

paths to plots

"""

# get silhouette scores for k_means with 2 to 12 clusters

# use number of clusters with highest silhouette score

best_score, best_n_clusters = -1, -1

min_clusters, max_clusters = 2, min(features_2d.shape[0], 12)

clusters = None

for n_clusters in range(min_clusters, max_clusters):

km = KMeans(n_clusters=n_clusters)

y_pred = km.fit_predict(features_2d)

score = silhouette_score(features_2d, y_pred)

if score > best_score:

best_n_clusters = n_clusters

best_score = score

clusters = y_pred

self.logger.debug("%d clusters detected using silhouette scores",

best_n_clusters)

cluster_dict = {n: [] for n in range(best_n_clusters)}

for i, c in enumerate(clusters):

cluster_dict[c].append(self.insts[i])

self.logger.debug("Distribution over clusters: %s",

str({k: len(v) for k, v in cluster_dict.items()}))

return clusters, cluster_dict

开发者ID:automl，项目名称:CAVE，代码行数:36，

示例22: bench_k_means

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# 需要导入模块: from sklearn import metrics [as 别名]

# 或者: from sklearn.metrics import silhouette_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)))

开发者ID:PacktPublishing，项目名称:Computer-Vision-with-Python-3，代码行数:15，

示例23: test_silhouette_score

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# 需要导入模块: from sklearn import metrics [as 别名]

# 或者: from sklearn.metrics import silhouette_score [as 别名]

def test_silhouette_score(self):

result = self.df.metrics.silhouette_score()

expected = metrics.silhouette_score(self.data, self.pred)

self.assertAlmostEqual(result, expected)

开发者ID:pandas-ml，项目名称:pandas-ml，代码行数:6，

示例24: test_KMeans_scores

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# 需要导入模块: from sklearn import metrics [as 别名]

# 或者: from sklearn.metrics import silhouette_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)

开发者ID:pandas-ml，项目名称:pandas-ml，代码行数:37，

示例25: run_silhouette_cv_estimator

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# 需要导入模块: from sklearn import metrics [as 别名]

# 或者: from sklearn.metrics import silhouette_score [as 别名]

def run_silhouette_cv_estimator(estimator, x, n_folds=10):

"""

只针对kmean的cv验证，使用silhouette_score对聚类后的结果labels_

进行度量使用silhouette_score，kmean的cv验证只是简单的通过np.random.choice

进行随机筛选x数据进行聚类的silhouette_score度量，并不涉及训练集测试集

:param estimator: keman或者支持estimator.labels_, 只通过if not isinstance(estimator, ClusterMixin)进行过滤

:param x: x特征矩阵

:param n_folds: int，透传KFold参数，切割训练集测试集参数，默认10

:return: eg: array([ 0.693 , 0.652 , 0.6845, 0.6696, 0.6732, 0.6874, 0.668 ,

0.6743, 0.6748, 0.671 ])

"""

if not isinstance(estimator, ClusterMixin):

print('estimator must be ClusterMixin')

return

silhouette_list = list()

# eg: n_folds = 10, len(x) = 150 -> 150 * 0.9 = 135

choice_cnt = int(len(x) * ((n_folds - 1) / n_folds))

choice_source = np.arange(0, x.shape[0])

# 所有执行fit的操作使用clone一个新的

estimator = clone(estimator)

for _ in np.arange(0, n_folds):

# 只是简单的通过np.random.choice进行随机筛选x数据

choice_index = np.random.choice(choice_source, choice_cnt)

x_choice = x[choice_index]

estimator.fit(x_choice)

# 进行聚类的silhouette_score度量

silhouette_score = metrics.silhouette_score(x_choice, estimator.labels_, metric='euclidean')

silhouette_list.append(silhouette_score)

return silhouette_list

开发者ID:bbfamily，项目名称:abu，代码行数:34，

示例26: fit

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# 需要导入模块: from sklearn import metrics [as 别名]

# 或者: from sklearn.metrics import silhouette_score [as 别名]

def fit(self, X, y=None, sample_weight=None):

silhouette_avgs = []

for n_clusters in range(1, self.max_n_clusters):

self.clusterers[n_clusters-1].fit(X, y, sample_weight)

if n_clusters == 1:

silhouette_avgs.append(-1.1) # TODO

else:

silhouette_avgs.append(silhouette_score(X, self.clusterers[n_clusters-1].labels_))

self.best_n_clusters = silhouette_avgs.index(max(silhouette_avgs)) + 1

self.labels_ = self.clusterers[self.best_n_clusters-1].labels_

self.cluster_centers_ = self.clusterers[self.best_n_clusters-1].cluster_centers_

开发者ID:VisualComputingInstitute，项目名称:TrackR-CNN，代码行数:13，

示例27: kmeans

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# 需要导入模块: from sklearn import metrics [as 别名]

# 或者: from sklearn.metrics import silhouette_score [as 别名]

def kmeans(phate_op, n_clusters='auto', max_clusters=10, random_state=None, k=None, **kwargs):

"""KMeans on the PHATE potential

Clustering on the PHATE operator as introduced in Moon et al.

This is similar to spectral clustering.

Parameters

----------

phate_op : phate.PHATE

Fitted PHATE operator

n_clusters : int, optional (default: 'auto')

Number of clusters.

If 'auto', uses the Silhouette score to determine the optimal number of clusters

max_clusters : int, optional (default: 10)

Maximum number of clusters to test if using the Silhouette score.

random_state : int or None, optional (default: None)

Random seed for k-means

k : deprecated for `n_clusters`

kwargs : additional arguments for `sklearn.cluster.KMeans`

Returns

-------

clusters : np.ndarray

Integer array of cluster assignments

"""

if k is not None:

warnings.warn(

"k is deprecated. Please use n_clusters in future.", FutureWarning

)

n_clusters = k

if not isinstance(phate_op, PHATE):

raise TypeError("Expected phate_op to be of type PHATE. Got {}".format(phate_op))

if phate_op.graph is not None:

if n_clusters == 'auto':

n_clusters = np.arange(2, max_clusters)

silhouette_scores = [silhouette_score(phate_op, k, random_state=random_state, **kwargs) for k in n_clusters]

n_clusters = n_clusters[np.argmax(silhouette_scores)]

return cluster.KMeans(n_clusters, random_state=random_state, **kwargs).fit_predict(

phate_op.diff_potential

)

else:

raise exceptions.NotFittedError(

"This PHATE instance is not fitted yet. Call "

"'fit' with appropriate arguments before "

"using this method."

)

开发者ID:KrishnaswamyLab，项目名称:PHATE，代码行数:48，

示例28: calculate_cluster_scores

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# 需要导入模块: from sklearn import metrics [as 别名]

# 或者: from sklearn.metrics import silhouette_score [as 别名]

def calculate_cluster_scores(x, cluster_labels, output):

with open("%s_scores.log" % output, "w+") as fh:

# Filter out singleton "cluster" (labeled as -1)

filtered_x, filtered_cluster_labels, singletons = ([] for _ in range(3))

cluster_groups = defaultdict(list)

for vec, lab in zip(x, cluster_labels):

if not lab == -1:

filtered_x.append(vec)

filtered_cluster_labels.append(lab)

cluster_groups[lab].append(vec)

else:

singletons.append(vec)

ln = "Number of clustered events: %d/%d (%f%%)\n" % (len(filtered_x), len(filtered_x)+len(singletons),

(len(filtered_x)/(len(filtered_x)+len(singletons)))*100)

print(ln.strip("\n"))

fh.write(ln)

for group in cluster_groups:

n_events = len(cluster_groups[group])

ln = "Cluster %d contains %d events\n" % (group, n_events)

print(ln.strip("\n"))

fh.write(ln)

rmsstd_scores = []

for group in cluster_groups:

rmsstd = calculate_rmsstd(np.array(cluster_groups[group]))

ln = "The RMSSTD score for cluster %d is %f\n" % (group, rmsstd)

print(ln.strip("\n"))

fh.write(ln)

rmsstd_scores.append(rmsstd)

try:

silhouette_avg = silhouette_score(np.array(filtered_x), np.array(filtered_cluster_labels))

ln = "The average silhouette score is : %f\n" % silhouette_avg

print(ln.strip("\n"))

fh.write(ln)

except:

silhouette_avg = float("nan")

ln = "Impossible to calculate silhouette score. Only 1 cluster group identified.\n"

print(ln.strip("\n"))

fh.write(ln)

return silhouette_avg, rmsstd_scores

开发者ID:comprna，项目名称:SUPPA，代码行数:49，

注：本文中的sklearn.metrics.silhouette_score方法示例整理自Github/MSDocs等源码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。