quality_models.__class__.__name__,
mixed_models.__class__.__name__,
subjective_column_name,
],
)
# store prediction result
PublicSupport.save_dataframe(
all_df, os.path.join(output_result_path, "prediction" + datetime.now().strftime("%Y-%m-%d %H.%M.%S"))
)
if len(sys.argv) < 2:
raise ValueError("Usage:", sys.argv[0], " Missing some argument to indicate input files")
json_dict = PublicSupport.read_json(sys.argv[1])
# input folder struct
data_home = os.path.abspath(json_dict["data_home"])
PublicSupport.create_path(data_home)
original_data_home = os.path.join(data_home, json_dict["original_data_home"])
PublicSupport.create_path(original_data_home)
predict_data_home = os.path.join(data_home, json_dict["predict_data_home"])
PublicSupport.create_path(predict_data_home)
feature_data_home = os.path.join(data_home, json_dict["feature_data_home"])
PublicSupport.create_path(feature_data_home)
model_data_home = os.path.join(data_home, json_dict["model_data_home"])
PublicSupport.create_path(model_data_home)
output_result_home = os.path.join(data_home, json_dict["output_result_home"])
PublicSupport.create_path(output_result_home)
preprocessed_folder = json_dict["preprocessed_folder"]
def deserialize_json(path, serialized_id):
json_dict = PublicSupport.read_json(os.path.join(path, serialized_id + ".json"))
return json_dict[RegressionManager.x_dim_name], json_dict[RegressionManager.y_dim_name]
