import os import pandas as pd import jsgf from unidecode import unidecode import string from collections import defaultdict def decode_prompt(prompt): prompt_decoded = unidecode(prompt) translator = str.maketrans('', '', string.punctuation) prompt_decoded = prompt_decoded.translate(translator) return prompt_decoded grammar = jsgf.parse_grammar_file('book.jsgf') data_files = [] for filename in os.listdir("data"): f = os.path.join("data", filename) if os.path.isfile(f): data_files.append(pd.read_csv(f, sep='\t', header=None)) true_positives = 0 false_positives = 0 false_negatives = 0 acts_recognized = defaultdict(int) acts_not_recognized = defaultdict(int) false_negatives = 0 false_positives = 0 for df in data_files: if len(df.columns) == 3: df.columns = ["agent", "message", "act"] elif len(df.columns) == 2: df.columns = ["agent", "message"] else: continue user_speech_rows = df[df['agent'] == "user"] user_speeches = user_speech_rows["message"] entries_count = len(user_speeches) found_rules = user_speeches.apply(lambda x: grammar.find_matching_rules(decode_prompt(x))) for line, rules in zip(df.iterrows(), found_rules): act = line[1]['act'].split('(')[0] if len(rules) > 0: recognized_act = rules[0].name if recognized_act in act: true_positives += 1 else: false_positives += 1 acts_not_recognized[act] += 1 else: false_negatives += 1 acts_not_recognized[act] += 1 accuracy = (true_positives + false_positives) / ((true_positives + false_positives) + sum([x for x in acts_not_recognized.values()])) precision = true_positives / (true_positives + false_positives) if (true_positives + false_positives) != 0 else 0 recall = true_positives / (true_positives + false_negatives) if (true_positives + false_negatives) != 0 else 0 print(f"Accuracy: {accuracy}") print(f"Precision: {precision}") print(f"Recall: {recall}")