Datasets:
File size: 10,032 Bytes
cd61af8 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 |
import pandas as pd
import os
import json
from sklearn.preprocessing import StandardScaler
import argparse
# this function put on the good format the date from the nlp dataset
def to_date(my_date):
set_month= {'Jan': '01', 'Feb': '02', 'Mar': '03', 'Apr': '04', 'May': '05', 'Jun': '06',
'Jul': '07', 'Aug': '08', 'Sep': '09', 'Oct': '10', 'Nov': '11', 'Dec': '12'}
month = set_month[my_date[4:7]]
day = my_date[8:10]
year = my_date[26:30]
final_date = year+'-'+month+'-'+day
return final_date
# open the knowledge base of the corresponding state with the corresponding category
def open_state_file(dataset_path: str, STATE: str, CATEGORY: str) -> list[str]:
tags = set()
with open('./utils/Keywords/'+STATE+'_keywords_'+CATEGORY+'_no_dupe.txt', 'r') as fp:
for line in fp:
# remove linebreak from a current name
# linebreak is the last character of each line
x = line[:-1]
# add current item to the list
tags.add(x.lower())
return tags
# the function take a time series dataframe (df) and return another dataset with all the data put in one parameter
# (Window) except the date and the label which are in different columns
# window size is equal to the size of the window
def window_creation(df: object, window_size: int, timestamp_colum: str) -> object:
"""
Create a TS window for each date. Distance of sliding window is 1.
df: Dataframe of a whole TS record in one state. Each row represents the record on one date.
window_size: Size of TS window.
label_column: Name of the column of label
timestamp_colum: Name of the column of time stamp
"""
df = df.reset_index()
df = df.drop(columns=['index'])
good_columns = list(df.columns)
good_columns.remove(timestamp_colum)
window_df = df[good_columns]
scaler = StandardScaler()
window_df = pd.DataFrame(scaler.fit_transform(window_df), columns=window_df.columns)
date_df = df[timestamp_colum]
window_data = []
label_data = []
date_data = []
for i in range(len(df)-window_size):
my_window = window_df.loc[i:i+window_size-1]
window_data.append(my_window.to_numpy())
date_data.append((date_df.loc[i+window_size-1]))
return_value = pd.DataFrame({'Date': date_data, 'Window': window_data})
return return_value
# the function that link the NLP data and the time series data for English dataset
# crisis_to_link is one row from crisis knowledge
# directory is the path of NLP data
# dataset_path is the path of time series data
# window_size is the size of the window for time series data
# fillNA is the value to fill the missing value
def linker(
crisis_to_link: object,
corpus_path: str, # path of corpus directory
ts_dataset_path: str, # path of ts data directory
window_size: int,
date_column: str,
fillNA : int) -> object:
# list of states associated with the crisis
type_of_crisis = crisis_to_link['Crisis']
states = crisis_to_link['Places'].replace('[', '').replace(']', '').split(',')
# features to keep
features = ['date', 'mean_speed_wind', 'precipitation', 'snow_fall', 'snow_depth', 'temp_max', 'temp_min', 'temp_mean',
'wind_dir_angle_2min', 'wind_dir_angle_5sec', 'wind_max_speed_2min', 'wind_max_speed_5sec']
df_return = pd.DataFrame({'Date': [], 'Text': [],'Crisis_Type' :[], 'Window': [], 'label_humanitarian': [], 'label_useful' : [], 'label_urgent' : [], 'label_sudden' : []})
for state in states:
# Open the time series file corresponding to the state
ts_df = pd.read_csv(ts_dataset_path + '/' + state + '_Time_Series_Data.csv')
ts_df = ts_df[features].sort_values(by='date') # Frequency of TS is per day.
ts_df = ts_df.fillna(fillNA)
# Window Creation
window_df = window_creation(ts_df, window_size, date_column)
#since New Zealand has no keyword, we skip this part
if state != 'New_Zealand' :
# we get the keywords to link the location mention to the state
tags_city = open_state_file(ts_dataset_path, state, 'city')
tags_county = open_state_file(ts_dataset_path, state, 'county')
tags_state = open_state_file(ts_dataset_path, state, 'state')
date = []
text = []
label_humanitarian = []
label_useful = []
label_urgent = []
label_sudden = []
window_data = []
crisis_label = []
# Process NLP data and link to time series data
for root, dirs, files in os.walk(corpus_path + '/' + crisis_to_link['Path_name']):
for fil in files:
if fil.endswith('.jsonl'):
with open(os.path.join(root,fil), 'r') as json_file:
json_list = list(json_file) # a list of '{}', each {} is a tweet and its features.
for json_str in json_list: # for each tweet loaded
result = json.loads(json_str)
place = result['location_mentions']
# if the location mention is empty (the tweet does not refer to particular place),
# thanks to the crisis_to_link, we know which crisis this tweet make reference
# (the tweet speak about this crisis) so we assume that if location mention is empty
# we assume that the tweet make a reference to the current state since this state is the localisation of the crisis
#we still have the New Zealand special case
if place == [] or state == 'New_Zealand':
# Put NLP date on the same format as time series date
date_NLP = to_date(result['created_at'])
# Check if there is matching date between time series and tweets.
if list(window_df['Window'][ window_df['Date'] == date_NLP]) != [] :
date.append(date_NLP)
text.append(result['text'])
linked_data = window_df[window_df['Date'] == date[-1]]
my_window = list(linked_data['Window'])[0]
window_data.append(my_window)
# for the label, we take reference from the time series label
label_humanitarian.append(result['humAID_class'])
crisis_label.append(type_of_crisis)
if result['humAID_class'] == 'not_humanitarian' :
label_useful.append('not_humanitarian')
label_urgent.append('not_humanitarian')
else :
label_useful.append('useful')
if result['humAID_class'] == 'rescue_volunteering_or_donation_effort' :
label_urgent.append('not_urgent')
else :
label_urgent.append('urgent')
if result['humAID_class'] == 'not_humanitarian' :
label_sudden.append('Not_Crisis_period')
else :
if type_of_crisis == 'Earthquake' or type_of_crisis == 'WildFire' :
label_sudden.append('Sudden_Crisis')
else :
label_sudden.append('Non_Sudden_Crisis')
else:
for zone in place:
# if the location mention refer to a state and this reference is in our knowledge database
bool1 = zone['type'] == 'State' and zone['text'].lower() in tags_state
# if the location mention refer to a county and this reference is in our knowledge database
bool2 = zone['type'] == 'County' and zone['text'].lower() in tags_county
# if the location mention refer to a city and this reference is in our knowledge database
bool3 = zone['type'] == 'City/town' and zone['text'].lower() in tags_city
if bool1 or bool2 or bool3:
date.append(to_date(result['created_at']))
text.append(result['text'])
linked_data = window_df[window_df['Date'] == date[-1]]
my_window = list(linked_data['Window'])[0]
window_data.append(my_window)
label_humanitarian.append(result['humAID_class'])
crisis_label.append(type_of_crisis)
if result['humAID_class'] == 'not_humanitarian' :
label_useful.append('not_humanitarian')
label_urgent.append('not_humanitarian')
else :
label_useful.append('useful')
if result['humAID_class'] == 'rescue_volunteering_or_donation_effort' :
label_urgent.append('not_urgent')
else :
label_urgent.append('urgent')
if result['humAID_class'] == 'not_humanitarian' :
label_sudden.append('Not_Crisis_period')
else :
if type_of_crisis == 'Earthquake' or type_of_crisis == 'WildFire' :
label_sudden.append('Sudden_Crisis')
else :
label_sudden.append('Non_Sudden_Crisis')
df = pd.DataFrame({'Date': date, 'Text': text,'Crisis_Type' :crisis_label, 'Window': window_data, 'label_humanitarian': label_humanitarian, 'label_useful' : label_useful, 'label_urgent' : label_urgent, 'label_sudden' : label_sudden})
df_return = pd.concat([df_return, df])
return df_return
# loading file
parser = argparse.ArgumentParser()
parser.add_argument("-w", "--window_size",type = int, default = 5, help="size (in number of days) of our daily time series data")
parser.add_argument("-o", "--output_file",type = str, default = './Multi_modal_dataset/CrisisTS_Eng_Personnalized.csv', help="name of your output file")
args = parser.parse_args()
directory_nlp = './Textual_Data/English_Corpus'
directory_time_series = './Time_Series/EnglishTS'
path_knowledge = './utils/crisis_knowledge_eng.csv'
knowledge = pd.read_csv(path_knowledge, sep='\t') # (9, 3), 3 columns represent Crisis, Places, Path_name (tweets)
# for all test crisis in english
All_possible_crisis = ['Hurricane', 'WildFire', 'Flood','Earthquake']
Test_crisis = 'Hurricane'
Train_knowledge = knowledge[knowledge['Crisis'] != Test_crisis]
Test_knowledge = knowledge[knowledge['Crisis'] == Test_crisis]
# link
multi_modal_test_data = pd.DataFrame({'Date': [], 'Text': [],'Crisis_Type' :[], 'Window': [], 'label_humanitarian': [], 'label_useful' : [], 'label_urgent' : [], 'label_sudden' : []})
for index, crisis in knowledge.iterrows():
print('Linking textual data and meteorological data of '+crisis['Path_name']+ ' ...')
multi_modal_test_data = pd.concat([multi_modal_test_data,
linker(crisis, directory_nlp,
directory_time_series,
args.window_size,
'date',
0)])
print('Done !')
multi_modal_test_data.to_csv(args.output_file,sep = '\t')
|