Loading human_needs_assigner.py 0 → 100644 +167 −0 Original line number Diff line number Diff line #!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Tue Mar 23 02:03:59 2021 @author: SWP-Group This file assigns the Human Needs via the generated output paths. Since the output paths are ranked according to the "strength" (the strongest and best paths appear at the top), these can be taken as indications for human needs annotations The input data is the output of the system and the golddata, both as in csv file format. These files are then processed via pandas module and necessary changes are made to the both the files (such as adjusting strings accordingly) This file creates the necessary output to run the "human_needs_evaluation.py" file and evaluate the results of the system """ import pandas as pd #output_final.csv file should be in the same directory, else insert filepath output_df = pd.read_csv("output_final.csv", sep=';', error_bad_lines=False) index_list = output_df.index.tolist() #output["Essay"] += 1 essay numbers must correspond #print(index_list) essay_list = output_df["Essay"].tolist() path_list = output_df["Path"].tolist() #gold_final.csv should be in same directory, else insert filepath gold_df = pd.read_csv("gold_final.csv", sep=';', error_bad_lines=False) #gold_df["Essay] += 1 maslow_gold = gold_df["Maslow"].tolist() reiss_gold = gold_df["Reiss"].tolist() def replacer(list_string): """ Function to replace all the unnecessary characters in the paths in order to further process the data and return clean strings This allows the proper format for the paths of the subgraphs in order to assign human needs Parameters ---------- list_string : list a list containing strings of words (here: conceptnet paths as strings) Returns ------- str strings cleaned from unwanted and unnecessary characters and tokens """ text = list_string.replace("[", "").replace("]", "").replace('\'', "").replace("\"", "").replace(",", "") return text.split() # create maslow and reiss human needs maslow_human_needs = ["physiological needs", "stability", "love/belonging", "esteem", "spiritual growth"] reiss_motives = ["food", "rest", "health", "save_money", "order", "safety", "romance", "belonging", "family", "contact", "competition", "honor", "approval", "status", "power", "curiosity", "serenity", "idealism", "independent"] # create a cleaned list of paths cleaned_paths = [replacer(path) for path in path_list] def assign_reiss(path_list): """ Assigns Reiss motive to a a graph consisting of a list of its paths Since paths are ordered according to their rankings, first found reiss motive is assigned due to conjecture that it must be the most fitting human need Parameters ---------- path_list : list The entire list of subraphs which consist of their paths paths are split into their single units as strings Returns ------- None. """ temp_list = [] human_needs = [] for path in path_list: for word in path: if word in reiss_motives: temp_list.append(word) human_needs.append(temp_list[0]) temp_list = [] return human_needs #assign reiss human needs for every graph via its top ranked path reiss_needs = assign_reiss(cleaned_paths) #maslow needs list to assign maslow need accordingly physiological_needs = ['food', 'rest'] safety = ['health', 'save_money', 'order', 'safety'] love_belonging = ['love', 'belonging', 'family', 'contact'] esteem = ['competition','honor', 'approval', 'status', 'power'] spiritual_growth = ['curiosity', 'serenity','idealism', 'independent'] def assign_maslow(reiss_list): """ Function that assigns corresponding maslow human need given its reiss motive Parameters ---------- reiss_list : list list containing assigned reiss human need for every essay (via the top ranked graphpath) Returns ------- maslow_needs : list list containing corresponding maslow human needs """ maslow_needs = [] for r in reiss_needs: if r in physiological_needs: maslow_needs.append(maslow_human_needs[0]) elif r in safety: maslow_needs.append(maslow_human_needs[1]) elif r in love_belonging: maslow_needs.append(maslow_human_needs[2]) elif r in esteem: maslow_needs.append(maslow_human_needs[3]) else: maslow_needs.append(maslow_human_needs[4]) return maslow_needs #Assign maslow category maslow_needs = assign_maslow(reiss_needs) # create joint list of reiss and maslow hn_list_full = list(zip(maslow_needs, reiss_needs)) #post-processing for evaluation in reiss reiss_needs = [w.replace("independent", "independence") for w in reiss_needs] reiss_needs = [w.replace("save_money", "savings") for w in reiss_needs] #post-processing for evaluation in maslow maslow_needs = [w.replace("love / belonging", "love/belonging") for w in maslow_needs] # post-processing of gold data maslow_gold = [w.replace("love / belonging", "love/belonging") for w in maslow_gold] # add columns accordingly output_df["Maslow_predict"] = maslow_needs output_df["Reiss_predict"] = reiss_needs """ The necessary data (all of type list) for evaluation are: maslow_needs: contains maslow human needs assigned by system (i.e. system output) reiss_needs: contains reiss motives assigned by system maslow_gold: contains gold data for maslow human needs reiss_gold: contains gold data for reiss motives These variables need to be imported in order to run evaluation """ Loading
human_needs_assigner.py 0 → 100644 +167 −0 Original line number Diff line number Diff line #!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Tue Mar 23 02:03:59 2021 @author: SWP-Group This file assigns the Human Needs via the generated output paths. Since the output paths are ranked according to the "strength" (the strongest and best paths appear at the top), these can be taken as indications for human needs annotations The input data is the output of the system and the golddata, both as in csv file format. These files are then processed via pandas module and necessary changes are made to the both the files (such as adjusting strings accordingly) This file creates the necessary output to run the "human_needs_evaluation.py" file and evaluate the results of the system """ import pandas as pd #output_final.csv file should be in the same directory, else insert filepath output_df = pd.read_csv("output_final.csv", sep=';', error_bad_lines=False) index_list = output_df.index.tolist() #output["Essay"] += 1 essay numbers must correspond #print(index_list) essay_list = output_df["Essay"].tolist() path_list = output_df["Path"].tolist() #gold_final.csv should be in same directory, else insert filepath gold_df = pd.read_csv("gold_final.csv", sep=';', error_bad_lines=False) #gold_df["Essay] += 1 maslow_gold = gold_df["Maslow"].tolist() reiss_gold = gold_df["Reiss"].tolist() def replacer(list_string): """ Function to replace all the unnecessary characters in the paths in order to further process the data and return clean strings This allows the proper format for the paths of the subgraphs in order to assign human needs Parameters ---------- list_string : list a list containing strings of words (here: conceptnet paths as strings) Returns ------- str strings cleaned from unwanted and unnecessary characters and tokens """ text = list_string.replace("[", "").replace("]", "").replace('\'', "").replace("\"", "").replace(",", "") return text.split() # create maslow and reiss human needs maslow_human_needs = ["physiological needs", "stability", "love/belonging", "esteem", "spiritual growth"] reiss_motives = ["food", "rest", "health", "save_money", "order", "safety", "romance", "belonging", "family", "contact", "competition", "honor", "approval", "status", "power", "curiosity", "serenity", "idealism", "independent"] # create a cleaned list of paths cleaned_paths = [replacer(path) for path in path_list] def assign_reiss(path_list): """ Assigns Reiss motive to a a graph consisting of a list of its paths Since paths are ordered according to their rankings, first found reiss motive is assigned due to conjecture that it must be the most fitting human need Parameters ---------- path_list : list The entire list of subraphs which consist of their paths paths are split into their single units as strings Returns ------- None. """ temp_list = [] human_needs = [] for path in path_list: for word in path: if word in reiss_motives: temp_list.append(word) human_needs.append(temp_list[0]) temp_list = [] return human_needs #assign reiss human needs for every graph via its top ranked path reiss_needs = assign_reiss(cleaned_paths) #maslow needs list to assign maslow need accordingly physiological_needs = ['food', 'rest'] safety = ['health', 'save_money', 'order', 'safety'] love_belonging = ['love', 'belonging', 'family', 'contact'] esteem = ['competition','honor', 'approval', 'status', 'power'] spiritual_growth = ['curiosity', 'serenity','idealism', 'independent'] def assign_maslow(reiss_list): """ Function that assigns corresponding maslow human need given its reiss motive Parameters ---------- reiss_list : list list containing assigned reiss human need for every essay (via the top ranked graphpath) Returns ------- maslow_needs : list list containing corresponding maslow human needs """ maslow_needs = [] for r in reiss_needs: if r in physiological_needs: maslow_needs.append(maslow_human_needs[0]) elif r in safety: maslow_needs.append(maslow_human_needs[1]) elif r in love_belonging: maslow_needs.append(maslow_human_needs[2]) elif r in esteem: maslow_needs.append(maslow_human_needs[3]) else: maslow_needs.append(maslow_human_needs[4]) return maslow_needs #Assign maslow category maslow_needs = assign_maslow(reiss_needs) # create joint list of reiss and maslow hn_list_full = list(zip(maslow_needs, reiss_needs)) #post-processing for evaluation in reiss reiss_needs = [w.replace("independent", "independence") for w in reiss_needs] reiss_needs = [w.replace("save_money", "savings") for w in reiss_needs] #post-processing for evaluation in maslow maslow_needs = [w.replace("love / belonging", "love/belonging") for w in maslow_needs] # post-processing of gold data maslow_gold = [w.replace("love / belonging", "love/belonging") for w in maslow_gold] # add columns accordingly output_df["Maslow_predict"] = maslow_needs output_df["Reiss_predict"] = reiss_needs """ The necessary data (all of type list) for evaluation are: maslow_needs: contains maslow human needs assigned by system (i.e. system output) reiss_needs: contains reiss motives assigned by system maslow_gold: contains gold data for maslow human needs reiss_gold: contains gold data for reiss motives These variables need to be imported in order to run evaluation """
