Shortcuts

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)] 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 ) return None
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 a374dd4f.

Built with Sphinx using a theme provided by Read the Docs.
Read the Docs v: latest
Versions
latest
stable
0.8.2
0.8.1.post0
0.8.1
0.8.0
0.7.5
0.7.4
0.7.3
0.7.2
0.7.1
0.7.0
0.6.0
0.5.2
0.5.1
0.5.0
0.4.0
0.3.2
0.3.1
0.3.0
0.2.3
0.2.2
0.2.1
0.2.0
0.1.0post1
Downloads
html
On Read the Docs
Project Home
Builds

Free document hosting provided by Read the Docs.