Source code for flash.tabular.classification.data
# Copyright The PyTorch Lightning team.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# http://www.apache.org/licenses/LICENSE-2.0
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from typing import Any, Dict, List, Optional, Type, Union
from flash.core.data.io.input import Input
from flash.core.data.io.input_transform import INPUT_TRANSFORM_TYPE, InputTransform
from flash.core.data.utilities.classification import TargetFormatter
from flash.core.utilities.imports import _PANDAS_AVAILABLE, _TABULAR_TESTING
from flash.core.utilities.stages import RunningStage
from flash.tabular.classification.input import TabularClassificationCSVInput, TabularClassificationDataFrameInput
from flash.tabular.data import TabularData
if _PANDAS_AVAILABLE:
from pandas.core.frame import DataFrame
else:
DataFrame = object
# Skip doctests if requirements aren't available
if not _TABULAR_TESTING:
__doctest_skip__ = ["TabularClassificationData", "TabularClassificationData.*"]
[docs]class TabularClassificationData(TabularData):
"""The ``TabularClassificationData`` class is a :class:`~flash.core.data.data_module.DataModule` with a set of
classmethods for loading data for tabular classification."""
[docs] @classmethod
def from_data_frame(
cls,
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: Optional[Dict[str, Any]] = None,
train_data_frame: Optional[DataFrame] = None,
val_data_frame: Optional[DataFrame] = None,
test_data_frame: Optional[DataFrame] = None,
predict_data_frame: Optional[DataFrame] = None,
train_transform: INPUT_TRANSFORM_TYPE = InputTransform,
val_transform: INPUT_TRANSFORM_TYPE = InputTransform,
test_transform: INPUT_TRANSFORM_TYPE = InputTransform,
predict_transform: INPUT_TRANSFORM_TYPE = InputTransform,
target_formatter: Optional[TargetFormatter] = None,
input_cls: Type[Input] = TabularClassificationDataFrameInput,
transform_kwargs: Optional[Dict] = None,
**data_module_kwargs: Any,
) -> "TabularClassificationData":
"""Creates a :class:`~flash.tabular.classification.data.TabularClassificationData` object from the given
data frames.
.. note::
The ``categorical_fields``, ``numerical_fields``, and ``target_fields`` do not need to be provided if
``parameters`` are passed instead. These can be obtained from the
:attr:`~flash.tabular.data.TabularData.parameters` attribute of the
:class:`~flash.tabular.data.TabularData` object that contains your training data.
The targets will be extracted from the ``target_fields`` in the data frames and can be in any of our
:ref:`supported classification target formats <formatting_classification_targets>`.
To learn how to customize the transforms applied for each stage, read our
:ref:`customizing transforms guide <customizing_transforms>`.
Args:
categorical_fields: The fields (column names) in the data frames containing categorical data.
numerical_fields: The fields (column names) in the data frames containing numerical data.
target_fields: The field (column name) or list of fields in the data frames containing the targets.
parameters: Parameters to use if ``categorical_fields``, ``numerical_fields``, and ``target_fields`` are not
provided (e.g. when loading data for inference or validation).
train_data_frame: The DataFrame to use when training.
val_data_frame: The DataFrame to use when validating.
test_data_frame: The DataFrame to use when testing.
predict_data_frame: The DataFrame to use when predicting.
train_transform: The :class:`~flash.core.data.io.input_transform.InputTransform` type to use when training.
val_transform: The :class:`~flash.core.data.io.input_transform.InputTransform` type to use when validating.
test_transform: The :class:`~flash.core.data.io.input_transform.InputTransform` type to use when testing.
predict_transform: The :class:`~flash.core.data.io.input_transform.InputTransform` type to use when
predicting.
target_formatter: Optionally provide a :class:`~flash.core.data.utilities.classification.TargetFormatter` to
control how targets are handled. See :ref:`formatting_classification_targets` for more details.
input_cls: The :class:`~flash.core.data.io.input.Input` type to use for loading the data.
transform_kwargs: Dict of keyword arguments to be provided when instantiating the transforms.
data_module_kwargs: Additional keyword arguments to provide to the
:class:`~flash.core.data.data_module.DataModule` constructor.
Returns:
The constructed :class:`~flash.tabular.classification.data.TabularClassificationData`.
Examples
________
.. testsetup::
>>> from pandas import DataFrame
>>> train_data = DataFrame.from_dict({
... "animal": ["cat", "dog", "cat"],
... "friendly": ["yes", "yes", "no"],
... "weight": [6, 10, 5],
... })
>>> predict_data = DataFrame.from_dict({
... "friendly": ["yes", "no", "yes"],
... "weight": [7, 12, 5],
... })
We have a DataFrame ``train_data`` with the following contents:
.. doctest::
>>> train_data.head(3)
animal friendly weight
0 cat yes 6
1 dog yes 10
2 cat no 5
and a DataFrame ``predict_data`` with the following contents:
.. doctest::
>>> predict_data.head(3)
friendly weight
0 yes 7
1 no 12
2 yes 5
.. doctest::
>>> from flash import Trainer
>>> from flash.tabular import TabularClassifier, TabularClassificationData
>>> datamodule = TabularClassificationData.from_data_frame(
... "friendly",
... "weight",
... "animal",
... train_data_frame=train_data,
... predict_data_frame=predict_data,
... batch_size=4,
... )
>>> datamodule.num_classes
2
>>> datamodule.labels
['cat', 'dog']
>>> model = TabularClassifier.from_data(datamodule, backbone="tabnet")
>>> trainer = Trainer(fast_dev_run=True)
>>> trainer.fit(model, datamodule=datamodule) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
Training...
>>> trainer.predict(model, datamodule=datamodule) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
Predicting...
.. testcleanup::
>>> del train_data
>>> del predict_data
"""
ds_kw = dict(
target_formatter=target_formatter,
transform_kwargs=transform_kwargs,
input_transforms_registry=cls.input_transforms_registry,
categorical_fields=categorical_fields,
numerical_fields=numerical_fields,
target_fields=target_fields,
parameters=parameters,
)
train_input = input_cls(RunningStage.TRAINING, train_data_frame, transform=train_transform, **ds_kw)
ds_kw["parameters"] = train_input.parameters if train_input else parameters
ds_kw["target_formatter"] = getattr(train_input, "target_formatter", None)
return cls(
train_input,
input_cls(RunningStage.VALIDATING, val_data_frame, transform=val_transform, **ds_kw),
input_cls(RunningStage.TESTING, test_data_frame, transform=test_transform, **ds_kw),
input_cls(RunningStage.PREDICTING, predict_data_frame, transform=predict_transform, **ds_kw),
**data_module_kwargs,
)
[docs] @classmethod
def from_csv(
cls,
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: Optional[Dict[str, Any]] = None,
train_file: Optional[str] = None,
val_file: Optional[str] = None,
test_file: Optional[str] = None,
predict_file: Optional[str] = None,
train_transform: INPUT_TRANSFORM_TYPE = InputTransform,
val_transform: INPUT_TRANSFORM_TYPE = InputTransform,
test_transform: INPUT_TRANSFORM_TYPE = InputTransform,
predict_transform: INPUT_TRANSFORM_TYPE = InputTransform,
target_formatter: Optional[TargetFormatter] = None,
input_cls: Type[Input] = TabularClassificationCSVInput,
transform_kwargs: Optional[Dict] = None,
**data_module_kwargs: Any,
) -> "TabularClassificationData":
"""Creates a :class:`~flash.tabular.classification.data.TabularClassificationData` object from the given
CSV files.
.. note::
The ``categorical_fields``, ``numerical_fields``, and ``target_fields`` do not need to be provided if
``parameters`` are passed instead. These can be obtained from the
:attr:`~flash.tabular.data.TabularData.parameters` attribute of the
:class:`~flash.tabular.data.TabularData` object that contains your training data.
The targets will be extracted from the ``target_fields`` columns in the CSV files and can be in any of our
:ref:`supported classification target formats <formatting_classification_targets>`.
To learn how to customize the transforms applied for each stage, read our
:ref:`customizing transforms guide <customizing_transforms>`.
Args:
categorical_fields: The fields (column names) in the CSV files containing categorical data.
numerical_fields: The fields (column names) in the CSV files containing numerical data.
target_fields: The field (column name) or list of fields in the CSV files containing the targets.
parameters: Parameters to use if ``categorical_fields``, ``numerical_fields``, and ``target_fields`` are not
provided (e.g. when loading data for inference or validation).
train_file: The path to the CSV file to use when training.
val_file: The path to the CSV file to use when validating.
test_file: The path to the CSV file to use when testing.
predict_file: The path to the CSV file to use when predicting.
train_transform: The :class:`~flash.core.data.io.input_transform.InputTransform` type to use when training.
val_transform: The :class:`~flash.core.data.io.input_transform.InputTransform` type to use when validating.
test_transform: The :class:`~flash.core.data.io.input_transform.InputTransform` type to use when testing.
predict_transform: The :class:`~flash.core.data.io.input_transform.InputTransform` type to use when
predicting.
target_formatter: Optionally provide a :class:`~flash.core.data.utilities.classification.TargetFormatter` to
control how targets are handled. See :ref:`formatting_classification_targets` for more details.
input_cls: The :class:`~flash.core.data.io.input.Input` type to use for loading the data.
transform_kwargs: Dict of keyword arguments to be provided when instantiating the transforms.
data_module_kwargs: Additional keyword arguments to provide to the
:class:`~flash.core.data.data_module.DataModule` constructor.
Returns:
The constructed :class:`~flash.tabular.classification.data.TabularClassificationData`.
Examples
________
.. testsetup::
>>> from pandas import DataFrame
>>> DataFrame.from_dict({
... "animal": ["cat", "dog", "cat"],
... "friendly": ["yes", "yes", "no"],
... "weight": [6, 10, 5],
... }).to_csv("train_data.csv")
>>> predict_data = DataFrame.from_dict({
... "friendly": ["yes", "no", "yes"],
... "weight": [7, 12, 5],
... }).to_csv("predict_data.csv")
We have a ``train_data.csv`` with the following contents:
.. code-block::
animal,friendly,weight
cat,yes,6
dog,yes,10
cat,no,5
and a ``predict_data.csv`` with the following contents:
.. code-block::
friendly,weight
yes,7
no,12
yes,5
.. doctest::
>>> from flash import Trainer
>>> from flash.tabular import TabularClassifier, TabularClassificationData
>>> datamodule = TabularClassificationData.from_csv(
... "friendly",
... "weight",
... "animal",
... train_file="train_data.csv",
... predict_file="predict_data.csv",
... batch_size=4,
... )
>>> datamodule.num_classes
2
>>> datamodule.labels
['cat', 'dog']
>>> model = TabularClassifier.from_data(datamodule, backbone="tabnet")
>>> trainer = Trainer(fast_dev_run=True)
>>> trainer.fit(model, datamodule=datamodule) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
Training...
>>> trainer.predict(model, datamodule=datamodule) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
Predicting...
.. testcleanup::
>>> import os
>>> os.remove("train_data.csv")
>>> os.remove("predict_data.csv")
"""
ds_kw = dict(
target_formatter=target_formatter,
transform_kwargs=transform_kwargs,
input_transforms_registry=cls.input_transforms_registry,
categorical_fields=categorical_fields,
numerical_fields=numerical_fields,
target_fields=target_fields,
parameters=parameters,
)
train_input = input_cls(RunningStage.TRAINING, train_file, transform=train_transform, **ds_kw)
ds_kw["parameters"] = train_input.parameters if train_input else parameters
ds_kw["target_formatter"] = getattr(train_input, "target_formatter", None)
return cls(
train_input,
input_cls(RunningStage.VALIDATING, val_file, transform=val_transform, **ds_kw),
input_cls(RunningStage.TESTING, test_file, transform=test_transform, **ds_kw),
input_cls(RunningStage.PREDICTING, predict_file, transform=predict_transform, **ds_kw),
**data_module_kwargs,
)
