88 lines
2.6 KiB
Python
88 lines
2.6 KiB
Python
import os
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import pandas as pd
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import jsgf
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from unidecode import unidecode
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import string
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from collections import defaultdict
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def decode_prompt(prompt):
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prompt_decoded = unidecode(prompt)
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translator = str.maketrans('', '', string.punctuation)
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prompt_decoded = prompt_decoded.translate(translator)
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return prompt_decoded
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grammar = jsgf.parse_grammar_file('book.jsgf')
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data_files = []
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for filename in os.listdir("data"):
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f = os.path.join("data", filename)
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if os.path.isfile(f):
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data_files.append(pd.read_csv(f, sep='\t', header=None))
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recognized = 0
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unrecognized = 0
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true_positives = 0
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false_positives = 0
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false_negatives = 0
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acts_recognized = defaultdict(int)
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acts_not_recognized = defaultdict(int)
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for df in data_files:
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if len(df.columns) == 3:
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df.columns = ["agent", "message", "act"]
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elif len(df.columns) == 2:
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df.columns = ["agent", "message"]
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else:
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continue
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user_speech_rows = df[df['agent'] == "user"]
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user_speeches = user_speech_rows["message"]
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entries_count = len(user_speeches)
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parsed = user_speeches.apply(
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lambda x: bool(grammar.find_matching_rules(decode_prompt(x))))
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true_count = parsed.sum()
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false_count = len(parsed) - true_count
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recognized += true_count
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unrecognized += false_count
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for line, correct in zip(df.iterrows(), parsed):
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acts_recognized[line[1]['act'].split('(')[0]] += int(correct)
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acts_not_recognized[line[1]['act'].split('(')[0]] += int(not(correct))
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print(f"Recognized user utterances: {recognized}")
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print(f"Unrecognized user utterances: {unrecognized}")
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print(f"Accuracy: {recognized/(recognized+unrecognized)}")
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precision_per_class = {}
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recall_per_class = {}
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for act in acts_recognized.keys():
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true_positives = acts_recognized[act]
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false_negatives = acts_not_recognized[act]
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false_positives = recognized - true_positives
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precision = true_positives / (true_positives + false_positives) if (true_positives + false_positives) != 0 else 0
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recall = true_positives / (true_positives + false_negatives) if (true_positives + false_negatives) != 0 else 0
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precision_per_class[act] = precision
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recall_per_class[act] = recall
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average_precision = sum(precision_per_class.values()) / len(precision_per_class)
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average_recall = sum(recall_per_class.values()) / len(recall_per_class)
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print("\nPrecision per class:")
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for act, precision in precision_per_class.items():
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print(f"{act}: {precision}")
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print("\nRecall per class:")
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for act, recall in recall_per_class.items():
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print(f"{act}: {recall}")
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print(f"\nAverage Precision: {average_precision}")
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print(f"Average Recall: {average_recall}")
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