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
)