ium_s449288/simple_regression_lab8.py
Kacper Dudzic 1d74959d77
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Update 'simple_regression_lab8.py'
2022-05-14 13:22:33 +02:00

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Python

import tensorflow as tf
from keras import layers
from keras.models import save_model
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import mlflow
import mlflow.keras
from urllib.parse import urlparse
import sys
def train():
# Definicja wartości parametrów treningu
epochs = int(sys.argv[1]) if len(sys.argv) > 1 else 100
units = int(sys.argv[2]) if len(sys.argv) > 2 else 1
learning_rate = float(sys.argv[3]) if len(sys.argv) > 3 else 0.1
# Konfiguracja serwera i nazwy eksperymentu MLflow
mlflow.set_tracking_uri("http://172.17.0.1:5000")
mlflow.set_experiment('s449288')
# Podpięcie treningu do MLflow
with mlflow.start_run() as run:
print('MLflow run experiment_id: {0}'.format(run.info.experiment_id))
print('MLflow run artifact_uri: {0}'.format(run.info.artifact_uri))
# Wczytanie danych
data_train = pd.read_csv('lego_sets_clean_train.csv')
data_test = pd.read_csv('lego_sets_clean_test.csv')
# Wydzielenie zbiorów dla predykcji ceny zestawu na podstawie liczby klocków, którą zawiera
train_piece_counts = np.array(data_train['piece_count'])
train_prices = np.array(data_train['list_price'])
test_piece_counts = np.array(data_test['piece_count'])
test_prices = np.array(data_test['list_price'])
# Normalizacja
normalizer = layers.Normalization(input_shape=[1, ], axis=None)
normalizer.adapt(train_piece_counts)
# Inicjalizacja
model = tf.keras.Sequential([
normalizer,
layers.Dense(units=units)
])
# Kompilacja
model.compile(
optimizer=tf.optimizers.Adam(learning_rate=learning_rate),
loss='mean_absolute_error'
)
# Trening
history = model.fit(
train_piece_counts,
train_prices,
epochs=epochs,
verbose=0,
validation_split=0.2
)
# Wykonanie predykcji na danych ze zbioru testującego
y_pred = model.predict(test_piece_counts)
# Zapis predykcji do pliku
results = pd.DataFrame(
{'test_set_piece_count': test_piece_counts.tolist(),
'predicted_price': [round(a[0], 2) for a in y_pred.tolist()]})
results.to_csv('lego_reg_results.csv', index=False, header=True)
# Zapis modelu do pliku
model.save('lego_reg_model')
# Ewaluacja MAE na potrzeby MLflow (kopia z evaluate.py)
mae = model.evaluate(
test_piece_counts,
test_prices, verbose=0)
# Zapis parametrów i metryk dla MLflow
mlflow.log_param('epochs', epochs)
mlflow.log_param('units', units)
mlflow.log_param('learning_rate', learning_rate)
mlflow.log_metric("mae", mae)
# Logowanie i zapis modelu dla Mlflow
signature = mlflow.models.signature.infer_signature(train_piece_counts, model.predict(train_piece_counts))
tracking_url_type_store = urlparse(mlflow.get_tracking_uri()).scheme
if tracking_url_type_store != 'file':
mlflow.keras.log_model(model, 'lego-model', registered_model_name='TFLegoModel',
signature=signature, input_example=np.array(500))
else:
mlflow.keras.log_model(model, 'model', signature=signature, input_example=np.array(500))
if __name__ == '__main__':
train()