ium_434704/sacred_exp.py

import sys

import pandas as pd
import numpy as np
import tensorflow as tf
import os.path

from sacred import Experiment
from sacred.observers import FileStorageObserver, MongoObserver

from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.layers.experimental import preprocessing

exp = Experiment("s434704", interactive=False, save_git_info=False)
exp.observers.append(FileStorageObserver("sacred_file"))
exp.observers.append(MongoObserver(url='mongodb://mongo_user:mongo_password_IUM_2021@172.17.0.1:27017', db_name="sacred"))

@exp.config
def my_config():
    verbose = 0
    epochs = 100

@exp.capture
def training(verbose, epochs, _log, _run):

    pd.set_option("display.max_columns", None)

    # Wczytanie danych
    train_data = pd.read_csv("./MoviesOnStreamingPlatforms_updated.train")

    # Stworzenie modelu 
    columns_to_use = ['Year', 'Runtime', 'Netflix']
    train_X = tf.convert_to_tensor(train_data[columns_to_use])
    train_Y = tf.convert_to_tensor(train_data[["IMDb"]])

    normalizer = preprocessing.Normalization(input_shape=[3,])
    normalizer.adapt(train_X)

    model = keras.Sequential([
        keras.Input(shape=(len(columns_to_use),)),
        normalizer,
        layers.Dense(30, activation='relu'),
        layers.Dense(10, activation='relu'),
        layers.Dense(25, activation='relu'),
        layers.Dense(1)
    ])

    model.compile(loss='mean_absolute_error',
                    optimizer=tf.keras.optimizers.Adam(0.001),
                    metrics=[tf.keras.metrics.RootMeanSquaredError()])

    params = f"Verbose: {verbose}, Epochs: {epochs}"
    _log.info(params)

    model.fit(train_X, train_Y, verbose=verbose, epochs=epochs)

    model.save('linear_regression.h5')

    # Evaluation

    test_data = pd.read_csv("./MoviesOnStreamingPlatforms_updated.test")

    columns_to_use = ['Year', 'Runtime', 'Netflix']
    test_X = tf.convert_to_tensor(test_data[columns_to_use])
    test_Y = tf.convert_to_tensor(test_data[["IMDb"]])

    scores = model.evaluate(x=test_X, 
                        y=test_Y)
    
    _run.log_scalar("training.RMSE", scores[1])

@exp.automain
def run(verbose, epochs):
    training()

runner = exp.run()
exp.add_source_file("./training.py")
exp.add_artifact("linear_regression.h5")