54 lines
1.4 KiB
Python
54 lines
1.4 KiB
Python
import os
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import pandas as pd
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import re
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from NaturalLanguageAnalyzer import NaturalLanguageAnalyzer
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data_directory = 'data'
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file_list = os.listdir(data_directory)
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dfs = []
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for file_name in file_list:
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file_path = os.path.join(data_directory, file_name)
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df = pd.read_csv(file_path, sep='\t', encoding='utf-8')
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df_user = df[df['role'] == 'user'].drop('role', axis=1)
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dfs.append(df_user)
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combined_df = pd.concat(dfs, ignore_index=True)
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change_act_format = {
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"thankyou": "thank",
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"bye": "thank",
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"hello": "greet",
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"inform": "inform",
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"request": "request",
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"reqmore": "request"
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}
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correct = 0
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incorrect = 0
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for text, ground_act in zip(combined_df["value"].values, combined_df["act"].values):
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nla = NaturalLanguageAnalyzer()
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nla_output = nla.process(text)
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predicted_act = set([i[0] for i in nla_output])
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pattern = re.compile(r'([^(&]+)(?=\()')
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matches = re.findall(pattern, ground_act)
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ground_act_processed = set()
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for match in matches:
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if match in change_act_format:
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ground_act_processed.add(change_act_format[match])
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for i in ground_act_processed:
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if i in predicted_act:
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correct += 1
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else:
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incorrect += 1
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print("Predicted:", predicted_act)
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print("Ground truth:", ground_act_processed)
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print()
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accuracy = correct/(correct+incorrect)
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print("Accuracy: ", accuracy)
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