import sys import mlflow import pandas as pd from sklearn.metrics import mean_squared_error from sklearn.model_selection import train_test_split from tensorflow.keras.callbacks import EarlyStopping from tensorflow.keras.layers import Dense, Dropout from tensorflow.keras.models import Sequential def prepare_model(train_size_param, test_size_param, epochs, batch_size): movies_data = pd.read_csv("train.csv", error_bad_lines=False) movies_data.drop(movies_data.columns[0], axis=1, inplace=True) movies_data.dropna(inplace=True) X = movies_data.drop("rating", axis=1) Y = movies_data["rating"] X_train, X_test, Y_train, Y_test = train_test_split( X, Y, test_size=test_size_param, random_state=42 ) test_df = pd.read_csv("test.csv") test_df.drop(test_df.columns[0], axis=1, inplace=True) x_test = test_df.drop("rating", axis=1) y_test = test_df["rating"] # Set up model model = Sequential() model.add(Dense(8, activation="relu")) model.add(Dropout(0.5)) model.add(Dense(3, activation="relu")) model.add(Dropout(0.5)) model.add(Dense(1)) model.compile(optimizer="adam", loss="mse") early_stop = EarlyStopping(monitor="val_loss", mode="min", verbose=1, patience=10) model.fit( x=X_train.values, y=Y_train.values, validation_data=(X_test, Y_test.values), batch_size=batch_size, epochs=epochs, callbacks=[early_stop], ) y_pred = model.predict(x_test.values) input_example = X_test.values[10] rmse = mean_squared_error(y_test, y_pred) model.save("model_movies") return model, rmse, X_train, input_example train_size_param = float(sys.argv[1]) if len(sys.argv) > 1 else 0.8 test_size_param = float(sys.argv[2]) if len(sys.argv) > 1 else 0.2 epochs = int(sys.argv[3]) if len(sys.argv) > 1 else 400 batch_size = int(sys.argv[4]) if len(sys.argv) > 1 else 128 with mlflow.start_run(): mlflow.log_param("train size", train_size_param) mlflow.log_param("test size", test_size_param) mlflow.log_param("epochs", epochs) mlflow.log_param("batch size", batch_size) model, rmse, X_train, input_example = prepare_model( train_size_param=train_size_param, test_size_param=test_size_param, epochs=epochs, batch_size=batch_size, ) mlflow.log_metric("RMSE", rmse) signature = mlflow.models.signature.infer_signature(X_train.values, model.predict(X_train.values)) mlflow.keras.save_model(model, "movies_imdb", input_example=input_example, signature=signature)