59 lines
1.9 KiB
Python
59 lines
1.9 KiB
Python
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import pandas as pd
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from sklearn.model_selection import train_test_split
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from tensorflow.keras import layers
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import numpy as np
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import tensorflow as tf
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def normalization(label):
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return 0 if label == False else 1
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def main():
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data = pd.read_csv('Amazon_Consumer_Reviews.csv', header=0, sep=',')
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column_names = ['reviews.doRecommend', 'reviews.title']
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data = data[column_names]
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data_train, data_test = train_test_split(data, train_size=0.6, random_state=1)
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data_test, data_val = train_test_split(data_test, test_size=0.5, random_state=1)
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train_labels = [normalization(x) for x in np.array(data_train['reviews.doRecommend'])]
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train_examples = np.array(data_train['reviews.title'])
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test_examples = np.array(data_test['reviews.title'])
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test_labels = [normalization(x) for x in np.array(data_test['reviews.doRecommend'])]
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val_labels = [normalization(x) for x in np.array(data_val['reviews.doRecommend'])]
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val_examples = np.array(data_val['reviews.title'])
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# print("Training entries: {}, test entries: {}".format(len(data_train), len(data_test)))
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# print(train_examples)
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# print(train_labels)
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model = tf.keras.Sequential([
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layers.Input(shape=(12,)),
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layers.Dense(32),
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layers.Dense(16),
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layers.Dense(2, activation='softmax')
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])
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model.summary()
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model.compile(
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loss=tf.losses.BinaryCrossentropy(),
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optimizer=tf.optimizers.Adam(),
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metrics=[tf.keras.metrics.BinaryAccuracy()])
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history = model.fit(train_examples, train_labels,
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epochs=40,
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batch_size=512,
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validation_data=(val_examples, val_labels),
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verbose=1)
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results = model.evaluate(test_examples, test_labels)
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file = open('results.txt', 'w')
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file.write('test loss: ' + str(results[0]) + '\n' + 'test accuracy: ' + str(results[1]))
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file.close()
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if __name__ == '__main__':
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main()
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