ium_464962/sacred_model.py

from sacred import Experiment
from sacred.observers import FileStorageObserver, MongoObserver
import pandas as pd
import numpy as np
from tensorflow.keras import Sequential
from tensorflow.keras.layers import Dense
from sklearn.preprocessing import MinMaxScaler

ex = Experiment("464962")
ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred'))
ex.observers.append(FileStorageObserver('my_runs'))


@ex.config
def my_config():
    data_path = './data/car_prices_train.csv'
    epochs = 20
    batch_size = 32
    model_path = './car_prices_predict_model.h5'


@ex.main
def train_model(data_path, epochs, batch_size, model_path, _run):
    train_data = pd.read_csv(data_path)
    train_data.dropna(inplace=True)

    y_train = train_data['sellingprice'].astype(np.float32)
    X_train = train_data[['year', 'condition', 'transmission']]
    scaler_x = MinMaxScaler()
    X_train['condition'] = scaler_x.fit_transform(X_train[['condition']])
    scaler_y = MinMaxScaler()
    y_train = scaler_y.fit_transform(y_train.values.reshape(-1, 1))
    X_train = pd.get_dummies(X_train, columns=['transmission'])

    model = Sequential([
        Dense(64, activation='relu'),
        Dense(32, activation='relu'),
        Dense(1)
    ])

    model.compile(optimizer='adam', loss='mean_squared_error')
    model.summary(print_fn=lambda x: _run.info.setdefault('model_summary', []).append(x))
    history = model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size)

    for epoch, loss in enumerate(history.history['loss']):
        _run.log_scalar("loss", loss, epoch)

    model.save(model_path)


if __name__ == "__main__":
    ex.run()