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@@ -58,3 +58,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
 Multi_modal_dataset/Multi_Modal_FR/Crisis_TS_FR.csv filter=lfs diff=lfs merge=lfs -text
+french_time_series_MERGED.csv filter=lfs diff=lfs merge=lfs -text

Linker_Eng.py

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+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')

Linker_Fr.py

@@ -0,0 +1,138 @@
+import pandas as pd
+import datetime
+import numpy as np
+import datetime
+import pytz
+import warnings
+warnings.filterwarnings('ignore')
+import argparse
+
+#this function put on the good format the date from the time series dataset
+def to_datetime(my_date,set_hour = False):
+	my_date = str(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 = int(my_date[4:6])
+	day = int(my_date[6:8])
+	year = int(my_date[0:4])
+	if set_hour :
+		hour = 0
+	else :
+		hour = int(my_date[8:10])
+	unaware = datetime.datetime(year, month, day, hour)
+	final_date = pytz.utc.localize(unaware)
+	return final_date
+
+def to_datetimeNLP(my_date,set_hour = False):
+	my_date = str(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 = int(my_date[5:7])
+	day = int(my_date[8:10])
+	year = int(my_date[0:4])
+	if set_hour :
+		hour = 0
+	else :
+		hour = int(my_date[11:13])
+		hour = hour - (hour%3)
+	unaware = datetime.datetime(year, month, day,hour)
+	final_date = pytz.utc.localize(unaware)
+	return final_date
+
+
+#the function take a time series dataframe (df) and return an other dataset with all the data put in one parameter (Window) except the date and the label wich are in different columns
+#window size is equal to the size of the window
+def window_creation(df,size_window,timestamp_colum):
+	good_row = list(df.columns)
+	good_row.remove(timestamp_colum)
+	window_df = df[good_row]
+	window_df = window_df.reset_index()
+	window_df = window_df.drop(columns=['index'])
+	window_df = window_df.tail(size_window)
+	window_data = window_df.to_numpy()
+	return window_data
+
+#the function that link the NLP data and the time series data for French dataset
+#text_data is the french NLP dataset on dataframe format
+#crisis_knowledge is a dataframe from the crisis_knowledge CSV
+#time_data is the path to the repertory with time series data
+#window size is equal to the size of the window
+#fillNA is the value to fill the missing value
+def linker(text_data : object,crisis_knowledge : object,my_features : str,window_size : int,label_column_1 : str,label_column_2 : str, date_column : str, fillNA : int) -> object :
+
+	#we join crisis knowledge and NLP data on the name of the crisis
+	text_data = text_data.join(crisis_knowledge.set_index('Crisis Name'), on='event', validate='m:1')
+	text_data['date']  = list(map(to_datetimeNLP,list(text_data['created_at'])))
+	#features to keep for Time series
+	features =['numer_sta','date','pmer','tend','ff','t','u','n']
+	features_clean =['pmer','tend','ff','t','u','n']
+	df_return = pd.DataFrame({'Date' : [],'Text' : [], 'Window' : [], 'label' : []})
+	list_of_date = []
+	list_of_text = []
+	list_of_window = []
+	list_of_crisis_type = []
+	list_of_label_uti = []
+	list_of_label_urg = []
+	list_of_label_int = []
+	list_of_label = []
+	#replace missing values and replace label by number
+	my_features = my_features[features].fillna(fillNA).replace({'mq': fillNA, 'No_Crisis': 0, 'Crisis': 1, 'Ecological': 1, 'Sudden': 2})
+	for clean_f in features_clean :
+		my_features[clean_f] = pd.to_numeric(my_features[clean_f])
+		my_features[clean_f] = (my_features[clean_f] - my_features[clean_f].min()) / (my_features[clean_f].max() - my_features[clean_f].min())
+	#this take from time series data the list of the id for each station
+	set_of_station = set(list(my_features['numer_sta']))
+	my_features['date'] = list(map(to_datetime,list(my_features['date'])))
+	set_of_date = set(list(my_features['date']))
+	dict_by_station = {}
+	i = 0
+	for index,text_line in text_data.iterrows():
+
+		#if the NLP date can be found in the time series date
+		if text_line['date'] in set_of_date :
+
+			list_of_station = eval(text_line['Related station'])
+			for i in range(len(list_of_station)):
+				list_of_station[i] = int(list_of_station[i])
+				#station related to the crisis
+
+			current_station = my_features[my_features['numer_sta'] == list_of_station[0]]
+			current_station = current_station.sort_values(by=['date'])
+			good_date = current_station[current_station['date'] < text_line['date']]
+			list_of_date.append(text_line['date'])
+			list_of_text.append(text_line['text'])
+			list_of_crisis_type.append(text_line['type_crisis'])
+			window = window_creation(good_date,window_size,'date')
+			list_of_window.append(window)
+			list_of_label_uti.append(text_line['utility'])
+			list_of_label_int.append(text_line['humanitarian'])
+			list_of_label_urg.append(text_line['urgency'])
+			if text_line['utility'] == 'Message-NonUtilisable' :
+				list_of_label.append('Not_Crisis_period')
+			else:
+				if text_line['type_crisis'] == 'Flood' or text_line['type_crisis'] == 'Hurricane' or text_line['type_crisis'] == 'Storms':
+					list_of_label.append('Ecological_crisis')
+				else:
+					list_of_label.append('Sudden_Crisis')
+
+	df_return = pd.DataFrame({'Date' : list_of_date,'Text' : list_of_text,'Crisis_Type' : list_of_crisis_type , 'Window' : list_of_window, 'intention' : list_of_label_int, 'urgency' : list_of_label_urg, 'utility' : list_of_label_uti , 'label' : list_of_label})
+	return df_return
+
+parser = argparse.ArgumentParser()
+parser.add_argument("-w", "--window_size",type = int, default = 16, 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_FR_Personnalized.csv', help="name of your output file")
+args = parser.parse_args()
+
+Knowledge = pd.read_csv('./utils/crisis_knowledge_fr.csv',sep="\t")
+
+nlp_csv = pd.read_csv('./Textual_Data/French_Corpus/Kozlwoski.csv',sep ='\t')
+
+time_series = pd.read_csv('./Time_Series/FrenchTS/french_time_series_MERGED.csv',sep = '\t')
+
+nlp_csv = nlp_csv.dropna(subset=['date'])
+
+nlp_csv = nlp_csv.dropna(subset=['humanitarian'])
+
+multi_modal_dataset = linker(nlp_csv ,Knowledge ,time_series , args.window_size,, 'Crisis_Predictability', 'label', 'date', 0)
+
+
+print('Done !')
+multi_modal_test_data.to_csv(args.output_file,sep = '\t')

french_time_series_MERGED.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fb65719c8bb7f9bbf8bc9749b2b0ae278bab5d286a01ad93cc362e83c472befe
+size 377691790