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* rid of str variables for types in preprocessor * improved define column_types function in data_types.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,68 +1,69 @@ | ||
| import numpy as np | ||
|
|
||
| from fedot.core.data.data import OutputData | ||
| from fedot.core.operations.operation import Operation | ||
| from fedot.core.repository.dataset_types import DataTypesEnum | ||
| from fedot.core.repository.operation_types_repository import OperationTypesRepository | ||
| from fedot.core.repository.tasks import TaskTypesEnum | ||
|
|
||
|
|
||
| class Model(Operation): | ||
| """Class with ``fit``/``predict`` methods defining the evaluation strategy for the task | ||
| Args: | ||
| operation_type: name of the model | ||
| """ | ||
|
|
||
| def __init__(self, operation_type: str): | ||
| super().__init__(operation_type=operation_type) | ||
| self.operations_repo = OperationTypesRepository('model') | ||
|
|
||
| @staticmethod | ||
| def assign_tabular_column_types(output_data: OutputData, output_mode: str) -> OutputData: | ||
| """Assign types for tabular data obtained from model predictions.\n | ||
| By default, all types of model predictions for tabular data can be clearly defined | ||
| """ | ||
| if output_data.data_type is not DataTypesEnum.table: | ||
| # No column data types info for non-tabular data | ||
| return output_data | ||
|
|
||
| is_regression_task = output_data.task.task_type is TaskTypesEnum.regression | ||
| is_ts_forecasting_task = output_data.task.task_type is TaskTypesEnum.ts_forecasting | ||
|
|
||
| predict_shape = np.array(output_data.predict).shape | ||
| # Add information about features | ||
| if is_regression_task or is_ts_forecasting_task: | ||
| if len(predict_shape) < 2: | ||
| column_info = {'features': [str(float)] * predict_shape[0]} | ||
| else: | ||
| column_info = {'features': [str(float)] * predict_shape[1]} | ||
| else: | ||
| if len(predict_shape) < 2: | ||
| output_data.predict = output_data.predict.reshape((-1, 1)) | ||
| predict_shape = output_data.predict.shape | ||
| # Classification task or clustering | ||
| if output_mode == 'labels': | ||
| column_info = {'features': [str(int)] * predict_shape[1]} | ||
| else: | ||
| column_info = {'features': [str(float)] * predict_shape[1]} | ||
|
|
||
| # Add information about target | ||
| target_shape = output_data.target.shape if output_data.target is not None else None | ||
| if target_shape is None: | ||
| # There is no target column in output data | ||
| output_data.supplementary_data.column_types = column_info | ||
| return output_data | ||
|
|
||
| if is_regression_task or is_ts_forecasting_task: | ||
| if len(target_shape) > 1: | ||
| column_info.update({'target': [str(float)] * target_shape[1]}) | ||
| else: | ||
| # Array present "time series" | ||
| column_info.update({'target': [str(float)] * len(output_data.target)}) | ||
| else: | ||
| # Classification task or clustering | ||
| column_info.update({'target': [str(int)] * predict_shape[1]}) | ||
|
|
||
| output_data.supplementary_data.column_types = column_info | ||
| return output_data | ||
| import numpy as np | ||
|
|
||
| from fedot.core.data.data import OutputData | ||
| from fedot.core.operations.operation import Operation | ||
| from fedot.core.repository.dataset_types import DataTypesEnum | ||
| from fedot.core.repository.operation_types_repository import OperationTypesRepository | ||
| from fedot.core.repository.tasks import TaskTypesEnum | ||
| from fedot.preprocessing.data_types import TYPE_TO_ID | ||
|
|
||
|
|
||
| class Model(Operation): | ||
| """Class with ``fit``/``predict`` methods defining the evaluation strategy for the task | ||
| Args: | ||
| operation_type: name of the model | ||
| """ | ||
|
|
||
| def __init__(self, operation_type: str): | ||
| super().__init__(operation_type=operation_type) | ||
| self.operations_repo = OperationTypesRepository('model') | ||
|
|
||
| @staticmethod | ||
| def assign_tabular_column_types(output_data: OutputData, output_mode: str) -> OutputData: | ||
| """Assign types for tabular data obtained from model predictions.\n | ||
| By default, all types of model predictions for tabular data can be clearly defined | ||
| """ | ||
| if output_data.data_type is not DataTypesEnum.table: | ||
| # No column data types info for non-tabular data | ||
| return output_data | ||
|
|
||
| is_regression_task = output_data.task.task_type is TaskTypesEnum.regression | ||
| is_ts_forecasting_task = output_data.task.task_type is TaskTypesEnum.ts_forecasting | ||
|
|
||
| predict_shape = np.array(output_data.predict).shape | ||
| # Add information about features | ||
| if is_regression_task or is_ts_forecasting_task: | ||
| if len(predict_shape) < 2: | ||
| column_info = {'features': [TYPE_TO_ID[float]] * predict_shape[0]} | ||
| else: | ||
| column_info = {'features': [TYPE_TO_ID[float]] * predict_shape[1]} | ||
| else: | ||
| if len(predict_shape) < 2: | ||
| output_data.predict = output_data.predict.reshape((-1, 1)) | ||
| predict_shape = output_data.predict.shape | ||
| # Classification task or clustering | ||
| if output_mode == 'labels': | ||
| column_info = {'features': [TYPE_TO_ID[int]] * predict_shape[1]} | ||
| else: | ||
| column_info = {'features': [TYPE_TO_ID[float]] * predict_shape[1]} | ||
|
|
||
| # Add information about target | ||
| target_shape = output_data.target.shape if output_data.target is not None else None | ||
| if target_shape is None: | ||
| # There is no target column in output data | ||
| output_data.supplementary_data.column_types = column_info | ||
| return output_data | ||
|
|
||
| if is_regression_task or is_ts_forecasting_task: | ||
| if len(target_shape) > 1: | ||
| column_info.update({'target': [TYPE_TO_ID[float]] * target_shape[1]}) | ||
| else: | ||
| # Array present "time series" | ||
| column_info.update({'target': [TYPE_TO_ID[float]] * len(output_data.target)}) | ||
| else: | ||
| # Classification task or clustering | ||
| column_info.update({'target': [TYPE_TO_ID[int]] * predict_shape[1]}) | ||
|
|
||
| output_data.supplementary_data.column_types = column_info | ||
| return output_data |
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