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05_3_CSA_if-idf.py
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05_3_CSA_if-idf.py
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import numpy as np
from queue import Queue, LifoQueue, PriorityQueue
import datetime
import re
import os
from tqdm import tqdm
import time
from collections import Counter
from operator import itemgetter
import json
from tools import general
def check_path(full_paht):
print("data_name location: %s" % (full_paht))
assert os.path.exists(full_paht), ('The following path does not exist, please check...\n %s' % (full_paht))
def tf(data_dir, data_name, save_file=False):
print('\n\n\n', '*' * 20, data_name, '*' * 20)
full_path = os.path.join(data_dir, data_name)
check_path(full_path)
list_activitydata = os.listdir(full_path)
savedir = os.path.join(data_dir, '../..', 'tf', data_name)
for str_activitydata in list_activitydata:
print('activity：%s' % (str_activitydata))
with open(os.path.join(full_path, str_activitydata), 'r') as fr:
dict_data = json.load(fr)
int_total_num_activities = 0
int_total_num_sensors = 0
A_type = 0
P_type = 0
T_type = 0
M_type = 0
D_type = 0
L_type = 0
I_type = 0
E_type = 0
dict_total_sensors = {}
for activity_index in dict_data:
int_total_num_activities += 1
for k, v in dict_data[activity_index].items():
if k[0] == 'A':
A_type += v
elif k[0] == 'P':
P_type += v
elif k[0] == 'T':
T_type += v
elif k[0] == 'M':
M_type += v
elif k[0] == 'D':
D_type += v
elif k[0] == 'L':
L_type += v
elif k[0] == 'I':
I_type += v
elif k[0] == 'E':
E_type += v
dict_total_sensors.update({
'str_activitydata': str_activitydata,
'int_total_num_activities': int_total_num_activities,
'A_type': A_type,
'P_type': P_type,
'T_type': T_type,
'M_type': M_type,
'D_type': D_type,
'L_type': L_type,
'I_type': I_type,
'E_type': E_type,
})
if not os.path.exists(savedir):
os.makedirs(savedir)
with open(os.path.join(savedir, str_activitydata), 'w', encoding="utf-8") as fw:
json_total_sensors = json.dumps(dict_total_sensors)
fw.writelines(json_total_sensors)
print('TF calculation: %s' % os.path.join(savedir, str_activitydata))
# break
pass
def df(data_dir, data_name, save_file=False):
print('\n\n\n', '*' * 20, data_name, '*' * 20)
full_path = os.path.join(data_dir, data_name)
check_path(full_path)
begin_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S.%f")
list_activitydata = os.listdir(full_path)
savedir = os.path.join(data_dir, '..', 'df', data_name)
for str_activitydata in list_activitydata:
print('activity：%s' % (str_activitydata))
with open(os.path.join(full_path, str_activitydata), 'r', encoding="utf-8") as fr:
lines = fr.readlines()
int_count_activities = 0
A_type = 0
P_type = 0
T_type = 0
M_type = 0
D_type = 0
L_type = 0
I_type = 0
E_type = 0
A_flag = False
P_flag = False
T_flag = False
M_flag = False
D_flag = False
L_flag = False
I_flag = False
E_flag = False
dict_df = {}
for i, line in enumerate(lines):
f_info = line.split()
if len(f_info) < 5:
A_flag = False
P_flag = False
T_flag = False
M_flag = False
D_flag = False
L_flag = False
I_flag = False
E_flag = False
int_count_activities += 1
elif f_info[3][0] == 'A' and A_flag is False:
A_type += 1
A_flag = True
elif f_info[3][0] == 'P' and P_flag is False:
P_type += 1
P_flag = True
elif f_info[3][0] == 'T' and T_flag is False:
T_type += 1
T_flag = True
elif f_info[3][0] == 'M' and M_flag is False:
M_type += 1
M_flag = True
elif f_info[3][0] == 'D' and D_flag is False:
D_type += 1
D_flag = True
elif f_info[3][0] == 'L' and L_flag is False:
L_type += 1
L_flag = True
elif f_info[3][0] == 'I' and I_flag is False:
I_type += 1
I_flag = True
elif f_info[3][0] == 'E' and E_flag is False:
E_type += 1
E_flag = True
dict_df.update({
str(int_count_activities): {
'A_type': A_type,
'P_type': P_type,
'T_type': T_type,
'M_type': M_type,
'D_type': D_type,
'L_type': L_type,
'I_type': I_type,
'E_type': E_type,
}
})
if not os.path.exists(savedir):
os.makedirs(savedir)
with open(os.path.join(savedir, str_activitydata + '.json'), 'w') as fw:
json_total_sensors = json.dumps(dict_df)
fw.writelines(json_total_sensors)
print('DF calculation, save in: %s' % os.path.join(savedir, str_activitydata + '.json'))
pass
def tf_df(data_dir, data_name, save_file=False, multiply=3):
print('\n\n\n', '*' * 20, data_name, '*' * 20)
tf_dir = os.path.join(data_dir, '..', 'tf')
df_dir = os.path.join(data_dir, '..', 'df')
tf_full_path = os.path.join(tf_dir, data_name)
check_path(tf_full_path)
df_full_path = os.path.join(df_dir, data_name)
check_path(df_full_path)
list_activitydata = os.listdir(tf_full_path)
dict_total_tf = {}
dict_total_df = {}
for activitydata in list_activitydata:
# TF
tf_d = open(os.path.join(tf_full_path, activitydata), 'r')
dict_tf = json.load(tf_d)
# The frequency of occurrence has been calculated in the document, so the total frequency is counted here
count_sensors = dict_tf['A_type'] + dict_tf['P_type'] + dict_tf['T_type'] + dict_tf['M_type'] + dict_tf[
'D_type'] + dict_tf['L_type'] + dict_tf['I_type'] + dict_tf['E_type']
dict_total_tf.update({dict_tf['str_activitydata'].split('.')[0]:
{
'int_total_num_activities': dict_tf['int_total_num_activities'],
'A_type_frequency': dict_tf['A_type'] / count_sensors,
'P_type_frequency': dict_tf['P_type'] / count_sensors,
'T_type_frequency': dict_tf['T_type'] / count_sensors,
'M_type_frequency': dict_tf['M_type'] / count_sensors,
'D_type_frequency': dict_tf['D_type'] / count_sensors,
'L_type_frequency': dict_tf['L_type'] / count_sensors,
'I_type_frequency': dict_tf['I_type'] / count_sensors,
'E_type_frequency': dict_tf['E_type'] / count_sensors,
}
})
# DF
df_d = open(os.path.join(df_full_path, activitydata), 'r')
dict_df = json.load(df_d)
if len(dict_df) > 1:
print('There is a problem. There was only one, and there were still a few behaviors')
exit(-1)
for k in dict_df:
dict_total_df.update({dict_tf['str_activitydata'].split('.')[0]: {
'int_total_num_activities': dict_tf['int_total_num_activities'],
'A_type_recall': dict_df[k]['A_type'] / dict_tf['int_total_num_activities'],
'P_type_recall': dict_df[k]['P_type'] / dict_tf['int_total_num_activities'],
'T_type_recall': dict_df[k]['T_type'] / dict_tf['int_total_num_activities'],
'M_type_recall': dict_df[k]['M_type'] / dict_tf['int_total_num_activities'],
'D_type_recall': dict_df[k]['D_type'] / dict_tf['int_total_num_activities'],
'L_type_recall': dict_df[k]['L_type'] / dict_tf['int_total_num_activities'],
'I_type_recall': dict_df[k]['I_type'] / dict_tf['int_total_num_activities'],
'E_type_recall': dict_df[k]['E_type'] / dict_tf['int_total_num_activities']
}
})
df_d.close()
tf_d.close()
dict_total_tf_df = {}
dict_total_tf_df_norm = {} # normalization # 归一化
for activity in dict_total_tf:
dict_total_tf_df.update({activity: {
'A_tf_df': (1 if dict_total_tf[activity]['A_type_frequency'] == 0 or dict_total_tf[activity][
'A_type_frequency'] == 1 else -np.log(dict_total_tf[activity]['A_type_frequency']) *
dict_total_tf[activity]['A_type_frequency']) * pow(
dict_total_df[activity]['A_type_recall'], multiply),
'P_tf_df': (1 if dict_total_tf[activity]['P_type_frequency'] == 0 or dict_total_tf[activity][
'P_type_frequency'] == 1 else -np.log(dict_total_tf[activity]['P_type_frequency']) *
dict_total_tf[activity]['P_type_frequency']) * pow(
dict_total_df[activity]['P_type_recall'], multiply),
'T_tf_df': (1 if dict_total_tf[activity]['T_type_frequency'] == 0 or dict_total_tf[activity][
'T_type_frequency'] == 1 else -np.log(dict_total_tf[activity]['T_type_frequency']) *
dict_total_tf[activity]['T_type_frequency']) * pow(
dict_total_df[activity]['T_type_recall'], multiply),
'M_tf_df': (1 if dict_total_tf[activity]['M_type_frequency'] == 0 or dict_total_tf[activity][
'M_type_frequency'] == 1 else -np.log(dict_total_tf[activity]['M_type_frequency']) *
dict_total_tf[activity]['M_type_frequency']) * pow(
dict_total_df[activity]['M_type_recall'], multiply),
'D_tf_df': (1 if dict_total_tf[activity]['D_type_frequency'] == 0 or dict_total_tf[activity][
'D_type_frequency'] == 1 else -np.log(dict_total_tf[activity]['D_type_frequency']) *
dict_total_tf[activity]['D_type_frequency']) * pow(
dict_total_df[activity]['D_type_recall'], multiply),
'L_tf_df': (1 if dict_total_tf[activity]['L_type_frequency'] == 0 or dict_total_tf[activity][
'L_type_frequency'] == 1 else -np.log(dict_total_tf[activity]['L_type_frequency']) *
dict_total_tf[activity]['L_type_frequency']) * pow(
dict_total_df[activity]['L_type_recall'], multiply),
'I_tf_df': (1 if dict_total_tf[activity]['I_type_frequency'] == 0 or dict_total_tf[activity][
'I_type_frequency'] == 1 else -np.log(dict_total_tf[activity]['I_type_frequency']) *
dict_total_tf[activity]['I_type_frequency']) * pow(
dict_total_df[activity]['I_type_recall'], multiply),
'E_tf_df': (1 if dict_total_tf[activity]['E_type_frequency'] == 0 or dict_total_tf[activity][
'E_type_frequency'] == 1 else -np.log(dict_total_tf[activity]['E_type_frequency']) *
dict_total_tf[activity]['E_type_frequency']) * pow(
dict_total_df[activity]['E_type_recall'], multiply),
}
})
list_data = []
norm_data = []
for values in dict_total_tf_df[activity].values():
list_data.append(values)
for x in np.array(list_data):
norm_data.append(
float((x - np.min(np.array(list_data))) / (np.max(np.array(list_data) - np.min(np.array(list_data))))))
dict_total_tf_df_norm[activity] = {
'A_tf_df': norm_data[0],
'P_tf_df': norm_data[1],
'T_tf_df': norm_data[2],
'M_tf_df': norm_data[3],
'D_tf_df': norm_data[4],
'L_tf_df': norm_data[5],
'I_tf_df': norm_data[6],
'E_tf_df': norm_data[7],
}
json_dict_total_tf_df = json.dumps(dict_total_tf_df)
json_dict_total_tf_df_norm = json.dumps(dict_total_tf_df_norm)
save_dir = os.path.join(data_dir, '..', 'tfidf')
if not os.path.exists(save_dir):
os.makedirs(save_dir)
with open(os.path.join(save_dir, data_name), 'w') as fw:
fw.writelines(json_dict_total_tf_df)
print('save in: %s' % (os.path.join(save_dir, data_name)))
with open(os.path.join(save_dir, data_name + '-norm'), 'w') as fw:
fw.writelines(json_dict_total_tf_df_norm)
print('save in %s: ' % (os.path.join(save_dir, data_name + '-norm')))
pass
if __name__ == '__main__':
opts = general.load_config()
data_dir = os.path.join(opts["datasets"]["base_dir"], 'tfidf', 'log')
data_names = ['cairo', 'kyoto7', 'kyoto8', 'kyoto11', 'milan']
data_names = opts["datasets"]["names"]
for data_name in data_names:
tf(data_dir, data_name, save_file=False) # TF calculation
pass
data_dir = os.path.join(opts["datasets"]["base_dir"], 'cutdata')
for data_name in data_names:
df(data_dir, data_name, save_file=False) # DF calculation
pass
for data_name in data_names:
multiply = opts["tfidf"]["power"]
tf_df(data_dir, data_name, save_file=False, multiply=multiply)
pass
print('Finish all!')