Python sklearn.cluster 模块，AffinityPropagation() 实例源码
我们从Python开源项目中，提取了以下27个代码示例，用于说明如何使用sklearn.cluster.AffinityPropagation()。
def compare_clusters(X,Y,method='spectral',s=10000):
A = (X/np.linalg.norm(X,axis=0)).T
A[np.isnan(A)] = 0
B = (Y/np.linalg.norm(Y,axis=0)).T
B[np.isnan(B)] = 0
random_samples = np.zeros(A.shape[0],dtype=np.bool)
random_samples[:min(s,A.shape[0])] = True
np.random.shuffle(random_samples)
A = A[random_samples]
B = B[random_samples]
dA = 1 - A.dot(A.T)
dA = np.exp(-dA**2/2.)
dB = 1 - B.dot(B.T)
dB = np.exp(-dB**2/2.)
del A,B
if method == 'spectral':
n = max(5,min(30,X.shape[1]/50))
lA = SpectralClustering(n_clusters=n,affinity='precomputed').fit_predict(dA)
lB = SpectralClustering(n_clusters=n,affinity='precomputed').fit_predict(dB)
elif method == 'ap':
lA = AffinityPropagation(affinity='precomputed').fit_predict(dA)
lB = AffinityPropagation(affinity='precomputed').fit_predict(dB)
return adjusted_mutual_info_score(lA,lB)
def constructParallelograms(dataset):
'''
@params
dataset is a list of points to find clusters in
returns a list of the parallelograms found.
'''
af = AffinityPropagation().fit(dataset)
print(af.cluster_centers_, af.labels_, len(af.cluster_centers_))
clusters = []
count = 0
while (count < len(af.cluster_centers_)):
pointlist = af.cluster_centers_[count].tolist()
clusters += [Point(pointlist[0], pointlist[1])]
count += 1
print(clusters)
return extrapolateParallelogram(clusters[0], clusters[1], clusters[2])
def test_clusterer_enforcement(self):
"""
Assert that only clustering estimators can be passed to cluster viz
"""
nomodels = [
SVC, SVR, Ridge, RidgeCV, LinearRegression, RandomForestClassifier
]
for nomodel in nomodels:
with self.assertRaises(YellowbrickTypeError):
visualizer = ClusteringscoreVisualizer(nomodel())
models = [
KMeans, MiniBatchKMeans, AffinityPropagation, MeanShift, DBSCAN, Birch
]
for model in models:
try:
visualizer = ClusteringscoreVisualizer(model())
except YellowbrickTypeError:
self.fail("Could not pass clustering estimator to visualizer")
def clusterSimilarityWithSklearnAPC(data_file,damping=0.9,max_iter=200,convergence_iter=15,preference='min'):
"""
Compare Sparse Affinity Propagation (SAP) result with SKlearn Affinity Propagation (AP) Clustering result.
Please note that convergence condition for Sklearn AP is "no change in the number of estimated clusters",
for SAP the condition is "no change in the cluster assignment".
So SAP may take more iterations and the there will be slightly difference in final cluster assignment (exemplars for each sample).
"""
# loading data
simi_mat=loadMatrix(data_file)
simi_mat_dense=simi_mat.todense()
# get preference
if preference=='min':
preference=np.min(simi_mat_dense)
elif preference=='median':
preference=np.median(simi_mat_dense)
print('{0},start SKlearn Affinity Propagation'.format(datetime.Now()))
af=AffinityPropagation(damping=damping, preference=preference, affinity='precomputed',verbose=True)
af.fit(simi_mat_dense)
cluster_centers_indices,labels = af.cluster_centers_indices_,af.labels_
sk_exemplars=np.asarray([cluster_centers_indices[i] for i in labels])
print('{0},start Fast Sparse Affinity Propagation Cluster'.format(datetime.Now()))
sap=SAP(preference=preference,convergence_iter=convergence_iter,max_iter=max_iter,damping=damping,verboseIter=100)
sap_exemplars=sap.fit_predict(simi_mat_dense)
# Caculate similarity between sk_exemplars and sap_exemplars
exemplars_similarity=sparseAP_cy.arrSamePercent(np.array(sk_exemplars), np.array(sap_exemplars))
return exemplars_similarity
def testDense():
"""
Test dense similarity matrix,Compare FSAPC result with SKlearn Affinity Propagation (AP) Clustering result
"""
dense_similarity_matrix_file=os.path.join(os.path.dirname(os.path.abspath(__file__)),'FaceClusteringSimilarities.txt')
exemplars_similarity=clusterSimilarityWithSklearnAPC(data_file=dense_similarity_matrix_file,preference='min')
print("Exemplar label similarity between sklearn.cluster.AffinityPropagation and SAP is: {0}".format(exemplars_similarity))
def affinity(fig):
global X_iris, geo
ax = fig.add_subplot(geo + 3, projection='3d', title='affinity')
affinity = cluster.AffinityPropagation(preference=-50)
affinity.fit(X_iris)
res = affinity.labels_
for n, i in enumerate(X_iris):
ax.scatter(*i[: 3], c='bgrcmyk'[res[n] % 7], marker='o')
ax.set_xlabel('X Label')
ax.set_ylabel('Y Label')
ax.set_zlabel('Z Label')
return res
def compute_affinity(item):
text, f_idx, table_name, f_sql = item
tokens = text.split()
# Find out which tokens are defined
valid_tokens = [w for w in tokens if w in M]
collections.Counter(valid_tokens)
labels = np.array(list(set(valid_tokens)))
token_clf_index = np.array([M.word2index[w]
for w in labels])
if not labels.size:
msg = "Document has no valid tokens! This is problem."
raise ValueError(msg)
V = np.array([M[w] for w in labels])
DV = cdist(V, metric='cosine')
# Values are sometimes "slightly" less than zero due to rounding
DV[DV < 0] = 0
cluster_args = {"damping": damping}
cluster = cluster_clf(**cluster_args)
y_labels = cluster.fit_predict(DV)
data = {}
data = {
"token_clf_index": token_clf_index,
"y_labels": y_labels,
}
return f_idx, f_sql, data
def get_cluster_assignments(sim_matrix, parameters):
"""
(np.array,list of int) -> list of int
sim_matrix: list of list of float -- similarity matrix between exemplars
parameters: list of parameters in the format ["method:method_name",
"algo:algo_name","k:num_clusters","damping:damping"]
where order doesn't matter
(k and damping only relevant for certain clustering methods)
the possible values for each parameter are listed in the
function below.
Returns a list of integers. The integer at each index of the list corresponds
to the cluster number of the exemplar at the same index in sim_matrix.
"""
algorithm = next((re.split(':',f)[1] for f in parameters if f[:4] == 'algo'), 'ap')
# from { 'hierarchical','kmeans','ap','ward' }
method = next((re.split(':',f)[1] for f in parameters if f[:6] == 'method'), 'single')
# from {'single','complete','average'} (only relevant for hierarchical clustering)
kMk = next((int(re.split(':',f)[1]) for f in parameters if f[:1] == 'k'), 8)
# any integer <= the data length
damping = next((re.split(':',f)[1] for f in parameters if f[:4] == 'damping'), 0.5)
# only relevant for AP -- in [0.5,1]
#
if algorithm == 'hierarchical':
clustering = hierarchy.linkage(sim_matrix, method)
k = get_k(clustering, 20)
cluster_assignments = hierarchy.fcluster(clustering, k, criterion = 'maxclust')-1
elif algorithm == 'kmeans':
cluster_assignments = KMeans(n_clusters = kMk).fit_predict(sim_matrix)
elif algorithm == 'ap':
cluster_assignments = AffinityPropagation().fit_predict(sim_matrix)
elif algorithm == 'ward':
clustering = hierarchy.ward(sim_matrix)
k = get_k(clustering, criterion = 'maxclust')-1
return cluster_assignments
def affinity_propagation_clusters(similarity_matrix):
return AffinityPropagation(affinity='precomputed').fit(similarity_matrix)
def main():
centers = get_list('out_center.txt')
labels = get_list('142-label.txt')
judge(centers, labels)
n_class = int(len(centers) * 0.18)
est = KMeans(n_clusters=n_class, max_iter=1000)
est.fit(centers)
new_list = []
for x, y in est.cluster_centers_:
min_num = 10000
min_x = -1
min_y = -1
for x_, y_ in centers:
dist = distance(x, y, x_, y_)
if (dist < min_num) or (min_x == -1):
min_num = dist
min_x = x_
min_y = y_
new_list.append([min_x, min_y])
judge(new_list, labels)
judge(est.cluster_centers_, labels)
# db = DBSCAN(eps=0.3,min_samples=180).fit(centers)
# print(db.core_sample_indices_)
# judge(new_list,labels)
# print(est.cluster_centers_)
# save_list('result.txt',est.cluster_centers_)
# af = AffinityPropagation(preference=180).fit(centers)
# judge(af.cluster_centers_,labels)
def __init__(self, damping=.5, max_iter=200,
copy=True, preference=None, affinity='euclidean',
verbose=False, convergence_percentage=0.999999):
super(AffinityPropagation, self).__init__(
damping=damping,
max_iter=max_iter,
convergence_iter=convergence_iter,
copy=copy,
verbose=verbose,
preference=preference,
affinity=affinity)
self.convergence_percentage = convergence_percentage
def fit(self, X, **kwargs):
"""Apply affinity propagation clustering.
Create affinity matrix from negative euclidean distances if required.
Parameters
----------
X: array-like or sparse matrix,
shape (n_samples,n_features) or (n_samples,n_samples)
Data matrix or,if affinity is ``precomputed``,matrix of
similarities / affinities.
"""
if not issparse(X):
return super(AffinityPropagation, self).fit(X, **kwargs)
# Since X is sparse,this converts it in a coo_matrix if required
X = check_array(X, accept_sparse='coo')
if self.affinity == "precomputed":
self.affinity_matrix_ = X
elif self.affinity == "euclidean":
self.affinity_matrix_ = coo_matrix(
-euclidean_distances(X, squared=True))
else:
raise ValueError("Affinity must be 'precomputed' or "
"'euclidean'. Got %s instead"
% str(self.affinity))
self.cluster_centers_indices_, self.labels_, self.n_iter_ = \
sparse_ap(
self.affinity_matrix_, self.preference, max_iter=self.max_iter,
convergence_iter=self.convergence_iter, damping=self.damping,
copy=self.copy, verbose=self.verbose, return_n_iter=True,
convergence_percentage=self.convergence_percentage)
if self.affinity != "precomputed":
self.cluster_centers_ = X.data[self.cluster_centers_indices_].copy()
return self
def make_aa_clustering(self, short_filenames, input_texts):
output_dir = self.output_dir + 'affinity_propagation/'
if not os.path.exists(output_dir):
os.makedirs(output_dir)
if self.need_tf_idf:
self.signals.PrintInfo.emit("?????? TF-IDF...")
idf_filename = output_dir + 'tf_idf.csv'
msg = self.calculate_and_write_tf_idf(idf_filename, input_texts)
self.signals.PrintInfo.emit(msg)
vectorizer = CountVectorizer()
X = vectorizer.fit_transform(input_texts)
svd = TruncatedSVD(2)
normalizer = normalizer(copy=False)
lsa = make_pipeline(svd, normalizer)
X = lsa.fit_transform(X)
aa_clusterizator = AffinityPropagation(damping=self.aa_damping,
max_iter=self.aa_max_iter,
convergence_iter=self.aa_no_change_stop)
predict_result = aa_clusterizator.fit_predict(X)
self.signals.PrintInfo.emit('\n??????? ?? ??????????:\n')
clasters_output = ''
for claster_index in range(max(predict_result) + 1):
clasters_output += ('??????? ' + str(claster_index) + ':\n')
for predict, document in zip(predict_result, short_filenames):
if predict == claster_index:
clasters_output += (' ' + str(document) + '\n')
clasters_output += '\n'
self.signals.PrintInfo.emit(clasters_output)
self.signals.PrintInfo.emit('????????? ?:' + str(output_dir + 'clusters.txt'))
writeStringToFile(clasters_output, output_dir + 'clusters.txt')
self.draw_clusters_plot(X, predict_result, short_filenames)
def find_number_of_sources(cosine_distance):
cos_dist = np.resize(cosine_distance, new_shape = (len(cosine_distance), len(cosine_distance)))
ap = AP(affinity = 'precomputed').fit(cos_dist)
counter = Counter(ap.labels_).most_common()
source = 0
for i in xrange(len(counter)):
if counter[i][1] == counter[0][1]:
source += 1
return source
def affinity_propagation(feature_matrix):
sim = feature_matrix * feature_matrix.T
sim = sim.todense()
ap = AffinityPropagation()
ap.fit(sim)
clusters = ap.labels_
return ap, clusters
# get clusters using affinity propagation
def cluster_keyterms(keyterms, word2vec_model):
'''
This function takes a list of keyterms,filters out only the words that can be used in the model
and clusters them
:param
keyterms : list of keyterms in dictionary format. They contain the following details: lemma_string,pos,len,
cvalue,words,tf,lemma_list
word2vec_model : embedding model
:return:
cluster of keyterms
'''
from sklearn import cluster
#filter keyterms to work with the embedding model
filtered_keyterms = filter_keyterms_byVocab(keyterms, word2vec_model.vocab)
X = []
for kt1 in filtered_keyterms:
line = []
for kt2 in filtered_keyterms:
sim = word2vec_model.n_similarity(kt1, kt2)
line.append(sim)
X.append(line)
# preference = [np.amin(X)] * len(filtered_keyterms)
preference = [np.median(X)] * len(filtered_keyterms)
print "Start Affinity Propagation ..."
af = cluster.AffinityPropagation(affinity="precomputed", damping=0.5, preference = preference)
af.fit(X)
print "Finished affinity propagation"
af_cluster_indices = af.cluster_centers_indices_
af_labels = af.labels_
n_clusters = len(af_cluster_indices)
clusters = []
for i in range(n_clusters):
cluster_center_1 = filtered_keyterms[af_cluster_indices[i]]
## compute cluster composition
cluster_members = []
for ktIdx in range(len(af_labels)):
if af_labels[ktIdx] == i:
cluster_members.append(filtered_keyterms[ktIdx])
cluster_data = {
"idx" : i,
"center": cluster_center_1,
"members": cluster_members,
"len": len(cluster_members)
}
clusters.append(cluster_data)
return clusters
# if __name__ == "__main__":
# process_keyterm_clusters(GENERATION_NT_CANDIDATES)
def create_stratum(self, column_names, **kwargs):
'''
Use affinity propagation to find number of strata for each column.
column_names is a list of the covariates to be split into strata and
used for classification. This funciton adds a column to the data frame
for each column as column_name_strata that gives the strata designation
for that variable. The whole data frame is returned.
'''
for colname in column_names:
X = self.data[colname].reshape(-1, 1)
if np.isnan(X).any():
raise ValueError("There are NaN values in self.data[%s] that the \
clustering algorithm can't handle" % colname)
elif np.unique(self.data[colname]).shape[0] <=2:
string_name = colname+'_strata'
self.data[string_name] = self.data[colname].astype(int)
else:
af_model = AP(damping = 0.9)
strata_groups = af_model.fit(X)
#cluster_centers_indices = af.cluster_centers_indices_
#n_clusters_ = len(cluster_centers_indices)
string_name = colname+'_strata'
self.data[string_name] = strata_groups.labels_
return self.data
#In the main function,you need to call create_stratum before create_unique_strata
def makeAffinityProp(X=None, k=-1):
return cluster.AffinityPropagation(damping=.9, preference=-200)
def makeClusterers(X, k=2):
return [('MiniBatchKMeans', makeKMeans(X, k)),
('AffinityPropagation', makeAffinityProp()),
('MeanShift', makeMeanShift(X)),
('SpectralClustering', makeSpectral(X,
('Ward', makeWard(X,
('AgglomerativeAvg', makeAvgLinkage(X,
('AgglomerativeMax', makeMaxLinkage(X,
('AgglomerativeWard', makeWardLinkage(X,
('DBSCAN', makeDBScan())]
def affinity_propagation(location, location_callback):
"""Returns one or more clusters of a set of points,using an affinity
propagation algorithm.
The result is sorted with the first value being the largest cluster.
Returns:
A list of NamedTuples (see get_cluster_named_tuple for a deFinition
of the tuple).
"""
pts = location._tuple_points()
if not pts:
return None
X = np.array(pts).reshape((len(pts), len(pts[0])))
if np.any(np.isnan(X)) or not np.all(np.isfinite(X)):
return None
X = Imputer().fit_transform(X)
X = X.astype(np.float32)
afkwargs = {
'damping': 0.5,
'convergence_iter': 15,
'max_iter': 200,
'copy': True,
'preference': None,
'affinity': 'euclidean',
'verbose': False
}
af = AffinityPropagation(**afkwargs).fit(X)
cluster_centers_indices = af.cluster_centers_indices_
clusters = []
for cluster_id, cluster_centre in enumerate(af.cluster_centers_):
locations = []
for j, label in enumerate(af.labels_):
if not label == cluster_id:
continue
locations.append(location.locations[j])
if not locations:
continue
clusters.append(cluster_named_tuple()(label=cluster_id,
centroid=Point(cluster_centre),
location=location_callback(
locations)))
return clusters
def plot_similarity_clusters(desc1, desc2, plot = None):
"""
find similar sounds using Affinity Propagation clusters
:param desc1: first descriptor values
:param desc2: second descriptor values
:returns:
- euclidean_labels: labels of clusters
"""
if plot == True:
print (Fore.magenta + "Clustering")
else:
pass
min_max = preprocessing.scale(np.vstack((desc1,desc2)).T, with_mean=False, with_std=False)
pca = PCA(n_components=2, whiten=True)
y = pca.fit(min_max).transform(min_max)
euclidean = AffinityPropagation(convergence_iter=1800, affinity='euclidean')
euclidean_labels= euclidean.fit_predict(y)
if plot == True:
time.sleep(5)
print (Fore.WHITE + "Cada número representa el grupo al que pertence el sonido como ejemplar de otro/s. El grupo '0' esta coloreado en azul,el grupo '1' esta coloreado en rojo,el grupo '2' esta coloreado en amarillo. Observa el ploteo para ver qué sonidos son ejemplares de otros")
print np.vstack((euclidean_labels,files)).T
time.sleep(6)
plt.scatter(y[euclidean_labels==0,0], y[euclidean_labels==0,1], c='b')
plt.scatter(y[euclidean_labels==1, y[euclidean_labels==1, c='r')
plt.scatter(y[euclidean_labels==2, y[euclidean_labels==2, c='y')
plt.scatter(y[euclidean_labels==3, y[euclidean_labels==3, c='g')
plt.show()
else:
pass
return euclidean_labels
# save clusters files in clusters directory
def compute_affinity_propagation(preference_, X):
# DATA FILLING
#text = io.Input.local_read_text_file(inputFilePath)
#input_array = text.split('\n')
centers = [[1, 1], [-1, -1], [1, -1]]
n_samples = 300
#Make Blobs used for generating of labels_true array
if (X == None):
X, labels_true = make_blobs(n_samples = n_samples, centers=centers, cluster_std=1, random_state=0)
print("Data is none!!!")
print("Generating " + str(n_samples) + " samples")
else :
data, labels_true = make_blobs(n_samples=len(X), random_state=0)
#slist = list()
#for line in X:
# slist.append(line)
#io.Output.write_array_to_txt_file("clustering\\Affinity_Propagation\\input_data1.txt",slist)
#float_array = []
#for line in input_array:
# float_line = [float(i) for i in line.split(' ')]
# float_array.append(float_line)
#X = array(float_array)
af = AffinityPropagation(preference=preference_).fit(X)
cluster_centers_indices = af.cluster_centers_indices_
labels = af.labels_
n_clusters_ = len(cluster_centers_indices)
print('Estimated number of clusters: %d' % n_clusters_)
print("Homogeneity: %0.3f" % metrics.homogeneity_score(labels_true, labels))
print("Completeness: %0.3f" % metrics.completeness_score(labels_true, labels))
print("V-measure: %0.3f" % metrics.v_measure_score(labels_true, labels))
print("Adjusted Rand Index: %0.3f" % metrics.adjusted_rand_score(labels_true, labels))
print("Adjusted Mutual information: %0.3f" % metrics.adjusted_mutual_info_score(labels_true, labels))
# print("Silhouette Coefficient: %0.3f" % metrics.silhouette_score(X,labels,metric='sqeuclidean'))
print("Fowlkes Mallows score: %0.3f" % metrics.fowlkes_mallows_score(labels_true, labels))
plt.close('all')
plt.figure(1)
plt.clf()
colors = cycle('bgrcmykbgrcmykbgrcmykbgrcmyk')
for k, col in zip(range(n_clusters_), colors):
class_members = labels == k
cluster_center = X[cluster_centers_indices[k]]
plt.plot(X[class_members, 0], X[class_members, col + '.')
plt.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col, markeredgecolor='k', markersize=14)
for x in X[class_members]:
plt.plot([cluster_center[0], x[0]], [cluster_center[1], x[1]], col)
plt.title('Estimated number of clusters: %d' % n_clusters_)
plt.show()
def cluster_song_in_playlist(self, playlist_id, cluster_n=5, is_detailed=False):
"""
??????????????
Args:
playlist_id: ??id
cluster_n:???
is_detailed: ???????????
Returns:
??????
"""
playlist_obj = playlist_detail(playlist_id)
song_list = []
vec_list = []
song_info_dict = {}
ap_cluster = AffinityPropagation()
data_process_logger.info('clustering playlist: %s' % playlist_obj['name'])
for item in playlist_obj['tracks']:
song = item['name'].lower()
song_info_dict[song] = {
'name': song,
'artist': item['artists'][0]['name'],
'id': item['id'],
'album_img_url': item['album']['picUrl'],
'site_url': 'http://music.163.com/#/song?id=%s' % item['id']
}
# print song
if song not in song_list:
song_list.append(song)
# print self.song2vec_model.vocab.get(song)
# print self.song2vec_model.syn0norm == None
if self.song2vec_model.vocab.get(song) and len(self.song2vec_model.syn0norm):
song_vec = self.song2vec_model.syn0norm[self.song2vec_model.vocab[song].index]
else:
data_process_logger.warn(
'The song %s of playlist-%s is not in dataset' % (song, playlist_obj['name']))
song_vec = [0 for i in range(self.song2vec_model.vector_size)]
vec_list.append(song_vec)
# song_list = list(song_list)
if len(vec_list) > 1:
cluster_result = ap_cluster.fit(vec_list, song_list)
cluster_array = [[] for i in range(len(cluster_result.cluster_centers_indices_))]
for i in range(len(cluster_result.labels_)):
label = cluster_result.labels_[i]
index = i
cluster_array[label].append(song_list[i])
return cluster_array, playlist_obj['name'], song_info_dict
else:
return [song_list], song_info_dict
def cluster_artist_in_playlist(self, is_detailed=False):
"""
??????????????
Args:
playlist_id: ??id
cluster_n:???
is_detailed: ????????
Returns:
??????
"""
playlist_obj = playlist_detail(playlist_id)
artist_list = []
vec_list = []
ap_cluster = AffinityPropagation()
data_process_logger.info('clustering playlist: %s' % playlist_obj['name'])
for item in playlist_obj['tracks']:
artist = item['artists'][0]['name'].lower()
# print artist
if artist not in artist_list:
artist_list.append(artist)
# print self.song2vec_model.vocab.get(artist)
# print self.song2vec_model.syn0norm == None
if self.artist2vec_model.vocab.get(artist) and len(self.artist2vec_model.syn0norm):
artist_vec = self.artist2vec_model.syn0norm[self.artist2vec_model.vocab[artist].index]
else:
data_process_logger.warn(
'The artist %s of playlist-%s is not in dataset' % (artist, playlist_obj['name']))
artist_vec = [0 for i in range(self.artist2vec_model.vector_size)]
vec_list.append(artist_vec)
# artist_list = list(artist_list)
# vec_list = list(vec_list)
if len(vec_list) > 1:
cluster_result = ap_cluster.fit(vec_list, artist_list)
cluster_array = [[] for i in range(len(cluster_result.cluster_centers_indices_))]
for i in range(len(cluster_result.labels_)):
label = cluster_result.labels_[i]
index = i
cluster_array[label].append(artist_list[i])
return cluster_array, {}
else:
return [artist_list], {}
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