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План
Как работает
Реализован интерфейс fit/predict в родительском классе FedotLightGBMtImplementation
Код
```py class FedotLightGBMImplementation(ModelImplementation): __operation_params = ['n_jobs', 'use_eval_set'] def __init__(self, params: Optional[OperationParameters] = None): super().__init__(params) self.model_params = {k: v for k, v in self.params.to_dict().items() if k not in self.__operation_params} self.model = None def fit(self, input_data: InputData): input_data = input_data.get_not_encoded_data() if self.params.get('use_eval_set'): train_input, eval_input = train_test_data_setup(input_data) train_input = self.convert_to_dataframe(train_input) eval_input = self.convert_to_dataframe(eval_input) train_x, train_y = train_input.drop(columns=['target']), train_input['target'] eval_x, eval_y = eval_input.drop(columns=['target']), eval_input['target'] if self.classes_ is None: eval_metric = 'rmse' elif len(self.classes_) < 3: eval_metric = 'auc' else: eval_metric = 'multi_logloss' self.model.fit(X=train_x, y=train_y, eval_set=[(eval_x, eval_y)], eval_metric=eval_metric) else: train_data = self.convert_to_dataframe(input_data) train_x, train_y = train_data.drop(columns=['target']), train_data['target'] self.model.fit(X=train_x, y=train_y) return self.model def predict(self, input_data: InputData): input_data = self.convert_to_dataframe(input_data.get_not_encoded_data()) train_x = input_data.drop(columns=['target']) prediction = self.model.predict(train_x) return prediction ```Интерфейс fit/predict не поддерживает работу с внутренним типом данных
lightgbm.Dataset, поэтому необходимо было найти обходной путь. В данном случае был использован тип данныхpandas.DataFrame.Внутри интерфейса идёт преобразование
InputDataвpandas.DataFrame(categorical_idxстановятсяcategory, аnumerical_idxстановятсяfloat)Код
```py @staticmethod def convert_to_dataframe(data: Optional[InputData]): dataframe = pd.DataFrame(data=data.features, columns=data.features_names) dataframe['target'] = data.target if data.categorical_idx is not None: for col in dataframe.columns[data.categorical_idx]: dataframe[col] = dataframe[col].astype('category') if data.numerical_idx is not None: for col in dataframe.columns[data.numerical_idx]: dataframe[col] = dataframe[col].astype('float') return dataframe ```