Source code for flash.tabular.regression.input
# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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from typing import Any, Dict, List, Optional, Union
import numpy as np
from flash.core.data.io.input import DataKeys
from flash.core.data.utilities.data_frame import read_csv
from flash.core.utilities.imports import _PANDAS_AVAILABLE
from flash.tabular.input import TabularDataFrameInput
if _PANDAS_AVAILABLE:
from pandas.core.frame import DataFrame
else:
DataFrame = object
[docs]class TabularRegressionDataFrameInput(TabularDataFrameInput):
def load_data(
self,
data_frame: DataFrame,
categorical_fields: Optional[Union[str, List[str]]] = None,
numerical_fields: Optional[Union[str, List[str]]] = None,
target_field: Optional[str] = None,
parameters: Dict[str, Any] = None,
):
cat_vars, num_vars = self.preprocess(data_frame, categorical_fields, numerical_fields, parameters)
if not self.predicting:
targets = data_frame[target_field].to_numpy().astype(np.float32)
return [{DataKeys.INPUT: (c, n), DataKeys.TARGET: t} for c, n, t in zip(cat_vars, num_vars, targets)]
else:
return [{DataKeys.INPUT: (c, n)} for c, n in zip(cat_vars, num_vars)]
[docs]class TabularRegressionCSVInput(TabularRegressionDataFrameInput):
def load_data(
self,
file: Optional[str],
categorical_fields: Optional[Union[str, List[str]]] = None,
numerical_fields: Optional[Union[str, List[str]]] = None,
target_field: Optional[str] = None,
parameters: Dict[str, Any] = None,
):
if file is not None:
return super().load_data(read_csv(file), categorical_fields, numerical_fields, target_field, parameters)