ium_487187/train.py

from sacred import Experiment
from sacred.observers import MongoObserver
import os
import mlflow
import mlflow.keras
from mlflow.models.signature import infer_signature
from mlflow.models import Model

os.environ["SACRED_NO_GIT"] = "1"

ex = Experiment('s487187-training', interactive=True, save_git_info=False)
ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred'))

@ex.config
def my_config():
    data_file = 'data.csv'
    model_file = 'model'
    epochs = 10
    batch_size = 32
    test_size = 0.2
    random_state = 42


@ex.capture
def train_model(data_file, model_file, epochs, batch_size, test_size, random_state):
    import pandas as pd
    from sklearn.model_selection import train_test_split
    from sklearn.preprocessing import MinMaxScaler
    import tensorflow as tf
    from imblearn.over_sampling import SMOTE

    with mlflow.start_run():

        smote = SMOTE(random_state=random_state)
        data = pd.read_csv(data_file, sep=';')

        data = pd.get_dummies(data, columns=['Sex', 'Medal'])
        data = data.drop(columns=['Name', 'Team', 'NOC', 'Games', 'Year', 'Season', 'City', 'Sport', 'Event'])

        scaler = MinMaxScaler()
        data = pd.DataFrame(scaler.fit_transform(data), columns=data.columns)

        X = data.filter(regex='Sex|Age')
        y = data.filter(regex='Medal')
        y = pd.get_dummies(y)

        X = X.fillna(0)
        y = y.fillna(0)

        y = y.values

        X_resampled, y_resampled = smote.fit_resample(X, y)
        X_train, X_test, y_train, y_test = train_test_split(X_resampled, y_resampled, test_size=test_size, random_state=random_state)

        model = tf.keras.models.Sequential()
        model.add(tf.keras.layers.Dense(64, input_dim=X_train.shape[1], activation='relu')) 
        model.add(tf.keras.layers.Dense(32, activation='relu'))
        model.add(tf.keras.layers.Dense(y.shape[1], activation='softmax')) 

        model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

        model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size)
        loss, accuracy = model.evaluate(X_test, y_test)

        mlflow.log_metric("loss", loss)
        mlflow.log_metric("accuracy", accuracy)

        signature = infer_signature(X_train, model.predict(X_train))
        input_example = Model.log_input_example(X_train.iloc[0])

        mlflow.keras.log_model(model, model_file, signature=signature, input_example=input_example)

        return accuracy  

@ex.main
def run_experiment():
    accuracy = train_model()
    ex.log_scalar('accuracy', accuracy)  
    ex.add_artifact('model.h5')  

ex.run()