from datetime import datetime import pandas as pd from sacred import Experiment from sacred.observers import MongoObserver 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 ex = Experiment("file_observer", interactive=False, save_git_info=False) ex.observers.append(MongoObserver(url='mongodb://mongo_user:mongo_password_IUM_2021@localhost:27017', db_name='sacred')) @ex.config def my_config(): train_size_param = 0.8 test_size_param = 0.2 epochs = 400 batch_size = 128 @ex.capture def prepare_model(train_size_param, test_size_param, epochs, batch_size, _run): _run.info["prepare_model_ts"] = str(datetime.now()) 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"] print(X, Y.values) # Split set to train/test 8:2 ratio 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) rmse = mean_squared_error(y_test, y_pred) _run.info["Final Results: "] = rmse model.save("model_movies") return rmse @ex.automain def my_main(train_size_param, test_size_param, epochs, batch_size): print(prepare_model()) r = ex.run() ex.add_artifact("model_movies/saved_model.pb")