54 lines
2.0 KiB
Python
54 lines
2.0 KiB
Python
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import pandas as pd
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import tensorflow as tf
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import sys
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import mlflow
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from sklearn.metrics import accuracy_score
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mlflow.set_tracking_uri("http://localhost:5000")
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def main():
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train_data = pd.read_csv('./beer_reviews_train.csv')
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X_train = train_data[['review_aroma', 'review_appearance', 'review_palate', 'review_taste']]
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y_train = train_data['review_overall']
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tokenizer = tf.keras.preprocessing.text.Tokenizer(num_words=10000)
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tokenizer.fit_on_texts(X_train)
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X_train_seq = tokenizer.texts_to_sequences(X_train)
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X_train_pad = tf.keras.preprocessing.sequence.pad_sequences(X_train_seq, maxlen=100)
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with mlflow.start_run() as run:
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print("MLflow run experiment_id: {0}".format(run.info.experiment_id))
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print("MLflow run artifact_uri: {0}".format(run.info.artifact_uri))
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model = tf.keras.Sequential([
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tf.keras.layers.Embedding(input_dim=10000, output_dim=16, input_length=100),
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tf.keras.layers.GlobalAveragePooling1D(),
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tf.keras.layers.Dense(16, activation='relu'),
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tf.keras.layers.Dense(1, activation='sigmoid')
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])
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model.compile(optimizer='adam',
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loss='binary_crossentropy',
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metrics=['accuracy'])
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print(sys.argv[1])
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print(sys.argv[2])
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model.fit(X_train_pad, y_train, epochs=int(sys.argv[1]), batch_size=int(sys.argv[2]), validation_split=0.1)
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mlflow.log_param("epochs", int(sys.argv[1]))
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mlflow.log_param("batch_size", int(sys.argv[2]))
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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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predictions = model.predict(X_test).flatten()
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y_test_binary = (y_test >= 3).astype(int)
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accuracy = accuracy_score(y_test_binary, predictions.round())
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mlflow.log_metric("accuracy", accuracy)
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if __name__ == '__main__':
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main()
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