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kmeans.py
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import os
import numpy as np
import gc
from sklearn.metrics import normalized_mutual_info_score as nmi
from autowarp_np import Trained_distance
'''
Time series clustering
K-means + learned distance metric
'''
class K_means_learned:
def __init__(self, num_cluster, random_state=None, max_iter=300):
self.num_cluster = num_cluster
self.random_seed = random_state
self.max_iter = max_iter
def set_my_metric(self, len_ts_a, len_ts_b, alpha, gamma, epsilon):
self.my_distance_metric = Trained_distance(len_ts_a, len_ts_b, alpha, gamma, epsilon)
def fit(self, train_data):
#set a seed
if type(train_data) != type([]):
train_data = list(train_data)
import random
if self.random_seed is not None:
random.seed(self.random_seed)
centroids=random.sample(train_data, self.num_cluster)
for n in range(self.max_iter):
print('iter num: {} of {}'.format(n, self.max_iter-1))
assignments={}
#assign data points to clusters
for idx, data in enumerate(train_data):
#print('\ttraining sample: {} of {}'.format(idx, len(train_data)-1))
min_dist=float('inf')
closest_clust=None
#分别计算样本idx距离几个中心的距离
for c_idx, center in enumerate(centroids):
cur_dist = self.my_distance_metric.caclulate_distance(data, center)
#cur_dist = edr_dist_py(data, center, epsilon=0.5)
if cur_dist < min_dist:
min_dist = cur_dist
closest_clust = c_idx
#将样本idx分配到某个簇中
#assignments, key = center_idx, value = a list of sample index
if closest_clust not in assignments:
assignments[closest_clust] = []
assignments[closest_clust].append(idx)
#至此，样本idx被分配到一个簇中，recalculate centroids of clusters
for center_idx in assignments:#迭代key
cluster_sum = 0
for k in assignments[center_idx]:#迭代簇中的样本
cluster_sum += train_data[k]
centroids[center_idx] = [m / len(assignments[center_idx]) for m in cluster_sum]
self.centroids = centroids
print('centroids: ')#shape: [n_cluster, data_dim]
for c_idx in range(self.num_cluster):
print('center of {}^th cluster: {}'.format(c_idx, centroids[c_idx]))
print('shape of centroids: ', np.array(self.centroids).shape)
print('\n#################################\n\n')
return np.array(self.centroids)
def predict(self, test_data):
if type(test_data) != type([]):
test_data = list(test_data)
pred_label = []
for idx, data in enumerate(test_data):
#print('\ttest sample: {} of {}'.format(idx, len(test_data)))
min_dist=float('inf')
closest_clust=None
for c_idx, center in enumerate(self.centroids):
cur_dist = self.my_distance_metric.caclulate_distance(data, center)
#cur_dist = edr_dist_py(data, center, epsilon=0.5)
if cur_dist < min_dist:
min_dist = cur_dist
closest_clust = c_idx
#至此，样本已经被分配到一个簇
pred_label.append(closest_clust)
pred_label = np.array(pred_label)
return pred_label
def fit_and_test_every_epoch(self, train_data, test_data, test_label):
print('START K-MEANS')
#性能下降的次数
decrease_num = 0
nmi_old = None
#set a seed
if type(train_data) != type([]):
train_data = list(train_data)
import random
if self.random_seed is not None:
random.seed(self.random_seed)
centroids=random.sample(train_data, self.num_cluster)
best_nmi = 0
best_epoch = None
for n in range(self.max_iter):
print('iter num: {} of {}'.format(n, self.max_iter - 1))
assignments={}
#assign data points to clusters
for idx, data in enumerate(train_data):
#print('\ttraining sample: {} of {}'.format(idx, len(train_data) - 1))
min_dist=float('inf')
closest_clust=None
#分别计算样本idx距离几个中心的距离
for c_idx, center in enumerate(centroids):
cur_dist = self.my_distance_metric.caclulate_distance(data, center)
#cur_dist = edr_dist_py(data, center, epsilon=0.5)
if cur_dist < min_dist:
min_dist = cur_dist
closest_clust = c_idx
#将样本idx分配到某个簇中
#assignments, key = center_idx, value = a list of sample index
if closest_clust not in assignments:
assignments[closest_clust] = []
assignments[closest_clust].append(idx)
#至此，样本idx被分配到一个簇中，recalculate centroids of clusters
for center_idx in assignments:#迭代key
cluster_sum = 0
for k in assignments[center_idx]:#迭代簇中的样本
cluster_sum += train_data[k]
centroids[center_idx] = [m / len(assignments[center_idx]) for m in cluster_sum]
self.centroids = centroids
#至此，一轮结束
labels = self.predict(test_data)
nmi_ = nmi(test_label, labels, average_method='arithmetic')
print('epoch: {}, nmi: {}, decrease_num: {}'.format(n, nmi_, decrease_num))
if nmi_old is None:
nmi_old = nmi_
else:
#如果当前的性能小于上一次的，下降次数加1
if nmi_ <= nmi_old:
decrease_num += 1
#如果当前的性能大于等于上一次的，清零下降次数
else:
decrease_num = 0
nmi_old = nmi_
#如果有五次下降，则停止迭代
if decrease_num > 5:
break
if nmi_ > best_nmi:
best_nmi = nmi_
best_epoch = n
print('best_nmi_kmeans: ', best_nmi, 'epoch:', best_epoch)