-
Notifications
You must be signed in to change notification settings - Fork 1
/
exp_dist_range.py
93 lines (78 loc) · 3.96 KB
/
exp_dist_range.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
import pandas as pd
from sklearn.cluster import KMeans
from preprocessing import features as fts
import os
from sklearn.metrics import davies_bouldin_score, silhouette_score
from preprocessing.dunn_index import bootstrap_sampling
import numpy as np
dist_size = 21 # fixed number of observations
nc_size = 26 # number of clusters to be tested
eval = {}
print('Reading Dataset')
data_file = f'./data/preprocessed/DCAIS_[30]_None-mmsi_region_[46, 51, -130, -122.5]_01-04_to_30-06_trips.csv'
dataset_raw = pd.read_csv(data_file, parse_dates=['time'], low_memory=False)
dataset_raw['time'] = dataset_raw['time'].astype('datetime64[ns]')
dataset_raw = dataset_raw.sort_values(by=['trajectory', "time"])
dataset_raw['control'] = dataset_raw['status'].copy()
dataset_raw.loc[dataset_raw[dataset_raw['status'] == 7].index, 'control'] = 1
dataset_raw.loc[dataset_raw[dataset_raw['status'] != 7].index, 'control'] = 0
for dist_eps in range(2, dist_size):
# for dist_eps in [1/1000, 10/1000, 100/1000]:
dataset = dataset_raw.copy()
folder = f'./results/dist_{dist_eps}/'
if not os.path.exists(folder):
os.makedirs(folder)
os.makedirs(f'{folder}/data')
features_path = f'{folder}/data/features_window_dist_{dist_eps}.csv'
print(features_path)
if not os.path.exists(features_path):
print('Getting Features')
# features_all = fts.get_all_features(dataset, n_dirs=n_dirs, win=window, eps=seconds)
features = fts.get_features_distance(dataset, eps=dist_eps)
features.to_csv(features_path, index=False)
else:
print('Reading features file')
features = pd.read_csv(features_path)
data_cl = features[['ma_d_acceleration', 'msum_d_roc']]
eval[dist_eps] = {}
for nc in range(2, nc_size):
file_name = f'{folder}/fishing_{nc}_{dist_eps}.csv'
if not os.path.exists(file_name):
# Clustering
print(f'Clustering - {nc}')
model = KMeans(nc).fit(data_cl)
labels = model.labels_
data_cl['labels'] = labels
dataset['labels'] = labels
print('Pos-processing')
# labels2 = fts.posprocessing(labels, min_points=seconds)
labels2 = fts.posprocessing_2(dataset, min_points=5)['labels']
data_cl['labels_pos'] = labels2
dataset['labels_pos'] = labels2
# Saving
# index of a few trajectories to quickly evaluate
trajs = dataset[dataset['status'] == 7]['trajectory'].unique()
sample_fishing = dataset[dataset['trajectory'].isin(trajs)]
sample_fishing.to_csv(f'{folder}/sample_fishing_{nc}_{dist_eps}.csv', index=False)
dataset.to_csv(file_name, index=False)
else:
print('Reading clustering files')
dataset = pd.read_csv(file_name)
sample_fishing = pd.read_csv(f'{folder}/sample_fishing_{nc}_{dist_eps}.csv')
test = dataset[dataset['trajectory'].isin([213, 117, 145, 26, 11])]
if not os.path.exists(f'{folder}/test/'):
os.makedirs(f'{folder}/test/')
test.to_csv(f'{folder}/test/test_{nc}_{dist_eps}.csv', index=False)
print('Computing metrics')
DBI_l = davies_bouldin_score(features[['ma_d_acceleration', 'msum_d_roc']], dataset['labels'])
print(f'DBI data = {DBI_l}')
DBI_lp = davies_bouldin_score(features[['ma_d_acceleration', 'msum_d_roc']], dataset['labels_pos'])
print(f'DBI pos data = {DBI_lp}')
# dunn, sil = bootstrap_sampling(features[['ma_t_acceleration', 'msum_t_roc']], dataset['labels'])
# dunn_lp, sil_lp = bootstrap_sampling(features[['ma_t_acceleration', 'msum_t_roc']], dataset['labels_pos'])
eval[dist_eps][nc] = [DBI_l, DBI_lp]
# eval[seconds][nc] = [DBI_l, DBI_lp, dunn, dunn_lp, sil, sil_lp]
eval2 = pd.DataFrame.from_dict(eval)
eval2.to_csv(f'./results/dist/measures2.csv')
eval2 = pd.DataFrame.from_dict(eval)
eval2.to_csv(f'./results/dist/measures2.csv')