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test.py
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from app_logger import logging
from app_pipeline import TrainingPipeline
from app_entity.model_factory import ModelFactory,get_sample_model_config_yaml_file
import os
from app_src.stage_02_data_transformation import DataTransformation
if __name__ == '__main__':
try:
TrainingPipeline().start_training_pipeline()
#get_sample_model_config_yaml_file("logs")
# model_config_file = os.path.join("config", "model.yaml")
# model_factory = ModelFactory(model_config_path=model_config_file)
# file_path = r'D:\Project\housing_prediciton\housing\artifact\data_transformation\2022-06-30-16-53-38\transformed_data\train\housing_transformed.npy'
# dataset = DataTransformation.load_numpy_array_data(file_path=file_path)
# X, y = dataset[:, :-1], dataset[:, -1]
# best_model = model_factory.get_best_model(X, y)
# print(best_model)
except Exception as e:
logging.info(e)