25 lines
824 B
Python
25 lines
824 B
Python
import pandas as pd
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import numpy as np
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import tensorflow as tf
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# Load the test data
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test_data = pd.read_csv('./beer_reviews_test.csv')
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X_test = test_data[['review_aroma', 'review_appearance', 'review_palate', 'review_taste']]
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y_test = test_data['review_overall']
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# Load the model
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model = tf.keras.models.load_model('beer_review_sentiment_model.h5')
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# Make predictions
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predictions = model.predict(X_test)
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# Check the shape of the predictions
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print(f'Predictions shape: {predictions.shape}')
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# If predictions have more than one dimension, select only one dimension
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if len(predictions.shape) > 1:
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predictions = predictions[:, 0]
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# Save predictions and actual data to a CSV file
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results = pd.DataFrame({'Predictions': predictions, 'Actual': y_test})
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results.to_csv('beer_review_sentiment_predictions.csv', index=False) |