62 lines
1.9 KiB
Python
62 lines
1.9 KiB
Python
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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import mlflow
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import sys
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from mlflow.tracking import MlflowClient
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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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arguments = sys.argv[1:]
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verbose = int(arguments[0])
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epochs = int(arguments[1])
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# Wczytanie danych
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train_data = pd.read_csv("./MoviesOnStreamingPlatforms_updated.train")
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test_data = pd.read_csv("./MoviesOnStreamingPlatforms_updated.test")
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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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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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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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model.fit(train_X, train_Y, verbose=verbose, epochs=epochs)
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signature = mlflow.models.signature.infer_signature(train_X.numpy(), model.predict(train_X.numpy()))
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input_data = test_X
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# Dane do rejestracji modelu w MlFlow
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mlflow.set_tracking_uri("http://172.17.0.1:5000")
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client = MlflowClient()
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model_name = "s434704"
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with mlflow.start_run():
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mlflow.keras.log_model(model, "movies_on_streaming_platforms_model", registered_model_name="s434704", input_example=input_data.numpy(), signature=signature)
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mlflow.keras.save_model(model, "movies_on_streaming_platforms_model", input_example=input_data.numpy(), signature=signature)
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