-
Notifications
You must be signed in to change notification settings - Fork 4
/
Clustering_DBSCAN_auto.py
203 lines (190 loc) · 6.91 KB
/
Clustering_DBSCAN_auto.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
'''
Author: Haoyang Ye (Zoe)
'''
import numpy as np
from sklearn.cluster import DBSCAN
from scipy import stats
from scipy.signal import argrelextrema
from astropy.table import Table
def find_center(Data, atom_max, min_samples = 10, eps = 4):
"""
Find the cluster centers for given data using DBSCAN package.
Data: input data, it should be a n*2 array
atom_max: the MCMC results determine the maximum number of the atoms that can be
found.
min_samples: sets the value 'min_samples' in function dbscan, which determines
the minimum grouping sample number
eps: the maximum distance between two samples for them to be considered as in
the same neighborhood
atom: the number of clusters picked by dbscan
xy: data arranged according to clustering results
noise: data that are not selected into any clusters
labels: flags/labels that determine which data belongs to which cluster
centers: cluster centre location (x_i,y_i) of each cluster i
widths: deviation of each clusters
"""
db = DBSCAN(eps, min_samples).fit(Data)
core_samples_mask = np.zeros_like(db.labels_, dtype=bool)
core_samples_mask[db.core_sample_indices_] = True
labels = db.labels_
atom = len(set(labels)) - (1 if -1 in labels else 0) # number of atoms
print ('We find', atom, 'atoms')
if atom > atom_max:
print ('You need to increase the variable: min_samples')
print (atom, 'is even bigger than the possible biggest atom number', atom_max)
unique_labels = set(labels)
noise_label = (labels == -1)
noise = Data[noise_label & ~core_samples_mask]
if len(noise) == 0:
print ('There is no outlier.')
else:
print ('There are ', len(noise), ' outliers. \n')
xy = []
centers = np.zeros((atom,2))
widths = np.zeros((atom,2))
for k in range(atom):
class_member_mask = (labels == k)
xy += [Data[class_member_mask & core_samples_mask]]
centers[k] = np.mean(Data[class_member_mask & core_samples_mask], axis = 0)
widths[k] = np.std(Data[class_member_mask & core_samples_mask], axis = 0)
print ('The', atom, 'cluster centers are \n', centers)
print ('The', atom, 'standard deviation of each clusters are \n', widths)
return atom, xy, noise, labels, centers, widths
def distance(point1, point2):
"""
Euclidean distance from cluster centre 1 and cluster centre 2,
the coordinates of the cluster centres should be located at the
centre of the cell.
>>>distance([0.1, 0.1], [0.2, 0.1])
>>>0.1
>>>distance([0.2, 0.1], [0.2, 0.1])
>>>0.0
"""
point1[0] = np.floor(point1[0]) + 0.5
point1[1] = np.floor(point1[1]) + 0.5
point2[0] = np.floor(point2[0]) + 0.5
point2[1] = np.floor(point2[1]) + 0.5
return np.abs((point1[0] - point2[0]) + complex(0,1)*(point1[1]- point2[1]))
def angle_between(p1, p2):
"""
Angle in degrees between cluster centre 1 and cluster centre 2.
"""
p1x = p1[0]
p1y = p1[1]
p2x = p2[0]
p2y = p2[1]
ang = np.arctan((p1y - p2y)/(p1x - p2x))
return ang/np.pi*180
def distance_list(X,Y):
"""
Euclidean distance from point X and point Y
"""
return [np.sqrt((X[0]-Y[0])**2 + (X[1] - Y[1])**2)]
def distance_N_nearest(X, data, N):
"""
Calculate distances from X to each data, and get the N nearest distances, then
sort the N distances in ascending order.
"""
dis = []
for i in range(len(data)):
dis += distance_list(X, data[i])
return sorted(dis)[1:N+1]
def eps_determin(data, N = 10):
"""
Combine the N nearest distances for each dataset together and sort them in
ascending order.
"""
y_dis = []
for i in range(len(data)):
y_dis += distance_N_nearest(data[i], data, N)
return sorted(y_dis)
def eps_slope_diff(data, N=10, eps_max = 10):
"""
Determine the parameter eps for dbscan.
"""
x_dis = [i for i in range(N*len(data))]
y_dis = eps_determin(data, N)
slope_list = []
slope_mean = []
slope_var = []
ind = 0
for i in range(len(data)):
slope = stats.linregress(x_dis[i*N: i*N + N], y_dis[i*N: i*N + N])[0]
slope_list += [slope]
for i in range(len(slope_list)//N):
slope_mean += [np.mean(slope_list[:i*N+N])]
slope_var += [np.var(slope_list[:i*N+N])]
slope_var = np.diff(slope_var).tolist()
max_ind = argrelextrema(np.asarray(slope_var), np.greater)[0]
eps_cal = y_dis[(max_ind[-1])*N*N]
return max(min(eps_cal,eps_max),1) #if eps_cal < 1, just set eps_cal = 1
if __name__ == "__main__":
import matplotlib.pyplot as plt
# 1. All the input configuration
eps_max = 10
cluster_num = []
d = []
ang = []
eps_list = []
filename = str(input('Enter the MCMC result file name:\n'))
min_samples = int(input('Enter the minimum group number (you can try to start with 10):\n'))
N = 10
# 2. Clustering process
# 2.1 Read in data
print ('Processing ' + filename + '...')
Data_c = Table.read(filename, format="ascii")
Data_vxy = np.zeros(shape=(len(Data_c),2))
Data_vxy[:,0]=Data_c['x'].data
Data_vxy[:,1]=Data_c['y'].data
Flux = Data_c['F'].data
# 2.2 Determine atom_max
n = 0
col_num = []
n_old = Data_c['k'][0]
for n_new in Data_c['k'][1:]:
if n_new==n_old:
n += 1
else:
col_num += [n+1]
n = 0
n_old = n_new
atom_max = int(np.max(col_num))
print ('atom_max', atom_max)
# 2.3 Start DBSCAN algorithm
# N is the min_samples, eps_cal is the epsself.
# eps_slope_diff is the function I made to determine the best eps
#eps_cal = eps_slope_diff(Data_vxy, N, eps_max) # calculate the simultaneous eps
eps_cal = eps_slope_diff(Data_vxy, N, eps_max)
print ('eps=', eps_cal)
eps_list += [eps_cal]
atom, xy, noise, labels, centers, widths = find_center(Data_vxy, atom_max, N, eps_cal)
# 2.4 Plot the results and save
plt.figure()
plt.scatter(noise.transpose()[0], noise.transpose()[1], s = 20, c = 'k', label = "Outlier")
for i in range(atom):
plt.scatter(xy[i][:,0], xy[i][:,1], s = 100, label = "Cluster %d" %i)
for i in range(atom):
plt.scatter(centers[i][0], centers[i][1], s = 80, marker = '*', label = "Centre %d" %i)
plt.grid(True)
plt.legend()
plt.xlabel('Image plane x axis')
plt.ylabel('Image plane y axis')
plt.gca().set_aspect('equal', adjustable='box')
figname = filename + '_DBSCAN_clustering_auto_results.png'
plt.savefig(figname)
if atom == 2:
d += [distance(centers[0], centers[1])]
ang += [angle_between(centers[0], centers[1])]
cluster_num += [len(centers)]
elif atom >= 3:
d += [100]
ang += [100]
cluster_num += [len(centers)]
elif atom == 1:
d += [0]
ang += [0]
cluster_num += [len(centers)]
else:
d += [-100]
ang += [-100]
cluster_num += [len(centers)]