29 lines
680 B
Python
29 lines
680 B
Python
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import pandas as pd
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import numpy as np
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from tensorflow import keras
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from sklearn.metrics import accuracy_score, f1_score
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import matplotlib.pyplot as plt
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model = keras.models.load_model('trained_model')
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test_df = pd.read_csv('test.csv')
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test_x = test_df['reviews.text'].to_numpy()
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test_y = test_df['reviews.doRecommend'].to_numpy()
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# print(test_y.shape)
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# print(test_x.shape)
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predictions = model.predict(test_x)
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predictions = [1 if p > 0.5 else 0 for p in predictions]
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accuracy = accuracy_score(test_y, predictions)
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f1 = f1_score(test_y, predictions)
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file = open('evaluation.txt', 'w')
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file.writelines(accuracy.__str__() + '\n')
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file.writelines(f1.__str__())
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file.close()
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