80 lines
2.3 KiB
Python
80 lines
2.3 KiB
Python
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import sys
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import pandas as pd
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import numpy as np
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import tensorflow as tf
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import os.path
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from sacred import Experiment
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from sacred.observers import FileStorageObserver, MongoObserver
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from tensorflow import keras
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from tensorflow.keras import layers
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from tensorflow.keras.layers.experimental import preprocessing
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exp = Experiment("s434704", interactive=False, save_git_info=False)
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exp.observers.append(FileStorageObserver("sacred_file"))
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exp.observers.append(MongoObserver(url='mongodb://mongo_user:mongo_password_IUM_2021@172.17.0.1:27017', db_name="sacred"))
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@exp.config
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def my_config():
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verbose = 0
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epochs = 100
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@exp.capture
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def training(verbose, epochs, _log):
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pd.set_option("display.max_columns", None)
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# Wczytanie danych
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train_data = pd.read_csv("./MoviesOnStreamingPlatforms_updated.train")
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# Stworzenie modelu
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columns_to_use = ['Year', 'Runtime', 'Netflix']
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train_X = tf.convert_to_tensor(train_data[columns_to_use])
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train_Y = tf.convert_to_tensor(train_data[["IMDb"]])
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normalizer = preprocessing.Normalization(input_shape=[3,])
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normalizer.adapt(train_X)
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model = keras.Sequential([
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keras.Input(shape=(len(columns_to_use),)),
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normalizer,
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layers.Dense(30, activation='relu'),
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layers.Dense(10, activation='relu'),
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layers.Dense(25, activation='relu'),
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layers.Dense(1)
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])
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model.compile(loss='mean_absolute_error',
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optimizer=tf.keras.optimizers.Adam(0.001),
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metrics=[tf.keras.metrics.RootMeanSquaredError()])
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params = f"Verbose: {verbose}, Epochs: {epochs}"
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_log.info(params)
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model.fit(train_X, train_Y, verbose=verbose, epochs=epochs)
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model.save('linear_regression.h5')
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# Evaluation
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test_data = pd.read_csv("./MoviesOnStreamingPlatforms_updated.test")
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columns_to_use = ['Year', 'Runtime', 'Netflix']
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test_X = tf.convert_to_tensor(test_data[columns_to_use])
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test_Y = tf.convert_to_tensor(test_data[["IMDb"]])
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scores = model.evaluate(x=test_X,
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y=test_Y)
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evaluation_info = f"RMSE: {scores[1]}"
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_log.info(evaluation_info)
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@exp.automain
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def run(verbose, epochs, _run):
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training()
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runner = exp.run()
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exp.add_source_file("./training.py")
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exp.add_artifact("linear_regression.h5")
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