45 lines
1.4 KiB
Python
45 lines
1.4 KiB
Python
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import re
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import os
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import pandas as pd
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import numpy as np
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from nlu_utils import predict_multiple
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from flair.models import SequenceTagger
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def __parse_acts(acts):
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acts_split = acts.split('&')
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remove_slot_regex = "[\(\[].*?[\)\]]"
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return set(re.sub(remove_slot_regex, "", act) for act in acts_split)
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def __parse_predictions(predictions):
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return set(prediction.split('/')[0] for prediction in predictions)
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# Exploratory tests
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frame_model = SequenceTagger.load('frame-model-prod/best-model.pt')
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# slot_model = SequenceTagger.load('slot-model-prod/final-model.pt')
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total_acts = 0
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act_correct_predictions = 0
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slot_correct_predictions = 0
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for file_name in os.listdir('data'):
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if file_name.split('.')[-1] != 'tsv':
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continue
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df = pd.read_csv(f'data/{file_name}', sep='\t', names=['kto', 'treść', 'akt'])
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df = df[df.kto == 'user']
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all_data = np.array(df)
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for row in all_data:
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sentence = row[1]
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acts = __parse_acts(row[2])
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predictions_raw = predict_multiple(frame_model, sentence.split(), 'frame')
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predictions = __parse_predictions(predictions_raw)
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for act in acts:
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total_acts += 1
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if act in predictions:
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act_correct_predictions += 1
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print(f"Accuracy - predicting acts: {(act_correct_predictions / total_acts)*100} ({act_correct_predictions}/{total_acts})")
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