40 lines
1.1 KiB
Python
40 lines
1.1 KiB
Python
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import mlflow
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import mlflow.keras
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import pandas as pd
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import numpy as np
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from tensorflow.keras import Sequential
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from tensorflow.keras.layers import Dense
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from sklearn.preprocessing import MinMaxScaler
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import sys
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# Parameters from the command line
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epochs = int(sys.argv[1])
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batch_size = int(sys.argv[2])
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mlflow.start_run()
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train_data = pd.read_csv('./data/car_prices_train.csv')
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train_data.dropna(inplace=True)
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y_train = train_data['sellingprice'].astype(np.float32)
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X_train = train_data[['year', 'condition', 'transmission']]
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scaler_x = MinMaxScaler()
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X_train['condition'] = scaler_x.fit_transform(X_train[['condition']])
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scaler_y = MinMaxScaler()
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y_train = scaler_y.fit_transform(y_train.values.reshape(-1, 1))
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X_train = pd.get_dummies(X_train, columns=['transmission'])
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model = Sequential([Dense(64, activation='relu'), Dense(32, activation='relu'), Dense(1)])
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model.compile(optimizer='adam', loss='mean_squared_error')
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# Training the model with MLflow tracking
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model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size)
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mlflow.keras.log_model(model, "model")
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mlflow.log_param("epochs", epochs)
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mlflow.log_param("batch_size", batch_size)
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mlflow.end_run()
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