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Source code for flash.tabular.classification.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.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Any, Dict, List, Optional, Union

from flash.core.data.io.classification_input import ClassificationInputMixin
from flash.core.data.io.input import DataKeys
from flash.core.data.utilities.classification import TargetFormatter
from flash.core.data.utilities.data_frame import resolve_targets
from flash.core.data.utilities.loading import load_data_frame
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 TabularClassificationDataFrameInput(TabularDataFrameInput, ClassificationInputMixin): def load_data( self, data_frame: DataFrame, categorical_fields: Optional[Union[str, List[str]]] = None, numerical_fields: Optional[Union[str, List[str]]] = None, target_fields: Optional[Union[str, List[str]]] = None, parameters: Dict[str, Any] = None, target_formatter: Optional[TargetFormatter] = None, ): cat_vars, num_vars = self.preprocess(data_frame, categorical_fields, numerical_fields, parameters) if not self.predicting: targets = resolve_targets(data_frame, target_fields) self.load_target_metadata(targets, target_formatter=target_formatter) 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)] def load_sample(self, sample: Dict[str, Any]) -> Any: if DataKeys.TARGET in sample: sample[DataKeys.TARGET] = self.format_target(sample[DataKeys.TARGET]) return sample
[docs]class TabularClassificationCSVInput(TabularClassificationDataFrameInput): def load_data( self, file: Optional[str], categorical_fields: Optional[Union[str, List[str]]] = None, numerical_fields: Optional[Union[str, List[str]]] = None, target_fields: Optional[Union[str, List[str]]] = None, parameters: Dict[str, Any] = None, target_formatter: Optional[TargetFormatter] = None, ): if file is not None: return super().load_data( load_data_frame(file), categorical_fields, numerical_fields, target_fields, parameters, target_formatter )
class TabularClassificationDictInput(TabularClassificationDataFrameInput): def load_data( self, data: Dict[str, Union[Any, List[Any]]], categorical_fields: Optional[Union[str, List[str]]] = None, numerical_fields: Optional[Union[str, List[str]]] = None, target_fields: Optional[Union[str, List[str]]] = None, parameters: Dict[str, Any] = None, target_formatter: Optional[TargetFormatter] = None, ): # Convert the data (dict) to a Pandas DataFrame data_frame = DataFrame.from_dict(data) return super().load_data( data_frame, categorical_fields, numerical_fields, target_fields, parameters, target_formatter ) class TabularClassificationListInput(TabularClassificationDataFrameInput): def load_data( self, data: List[Union[tuple, dict]], categorical_fields: Optional[Union[str, List[str]]] = None, numerical_fields: Optional[Union[str, List[str]]] = None, target_fields: Optional[Union[str, List[str]]] = None, parameters: Dict[str, Any] = None, target_formatter: Optional[TargetFormatter] = None, ): # Convert the data (list of dictionary / tuple) into Pandas DataFrame data_frame = DataFrame.from_records(data) return super().load_data( data_frame, categorical_fields, numerical_fields, target_fields, parameters, target_formatter )

© Copyright 2020-2021, PyTorch Lightning. Revision da42a635.

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