TextClassificationData from_dataframe

CHANGELOG.md

@@ -12,6 +12,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - Added `vissl` training_strategies for `ImageEmbedder` ([#682](https://github.com/PyTorchLightning/lightning-flash/pull/682))
 
+- Added support for `from_data_frame` to `TextClassificationData` ([#785](https://github.com/PyTorchLightning/lightning-flash/pull/785))
+
 ### Changed
 
 - Changed the default `num_workers` on linux to `0` (matching the default for other OS) ([#759](https://github.com/PyTorchLightning/lightning-flash/pull/759))

flash/text/classification/data.py

@@ -12,20 +12,23 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 from functools import partial
-from typing import Any, Callable, Dict, List, Mapping, Optional, Sequence, Tuple, Union
+from typing import Any, Callable, Dict, List, Mapping, Optional, Sequence, Tuple, Type, Union
 
+import pandas as pd
 import torch
 from torch import Tensor
+from torch.utils.data.sampler import Sampler
 
 import flash
 from flash.core.data.auto_dataset import AutoDataset
+from flash.core.data.callback import BaseDataFetcher
 from flash.core.data.data_module import DataModule
 from flash.core.data.data_source import DataSource, DefaultDataSources, LabelsState
 from flash.core.data.process import Deserializer, Postprocess, Preprocess
 from flash.core.utilities.imports import _TEXT_AVAILABLE, requires
 
 if _TEXT_AVAILABLE:
-    from datasets import DatasetDict, load_dataset
+    from datasets import Dataset, DatasetDict, load_dataset
     from transformers import AutoTokenizer, default_data_collator
     from transformers.modeling_outputs import SequenceClassifierOutput
 
@@ -215,6 +218,66 @@ def __setstate__(self, state):
         self.tokenizer = AutoTokenizer.from_pretrained(self.backbone, use_fast=True)
 
 
+class TextDataFrameDataSource(TextDataSource):
+    @staticmethod
+    def _multilabel_target(targets, element):
+        targets = [element.pop(target) for target in targets]
+        element["labels"] = targets
+        return element
+
+    def load_data(
+        self,
+        data: Union[Tuple[pd.DataFrame, Union[str, List[str]], Union[str, List[str]]], Tuple[List[str], List[str]]],
+        dataset: Optional[Any] = None,
+        columns: Union[List[str], Tuple[str]] = ("input_ids", "attention_mask", "labels"),
+    ) -> Union[Sequence[Mapping[str, Any]]]:
+        df, input, target = data
+        hf_dataset = Dataset.from_pandas(df)
+
+        if not self.predicting:
+            if isinstance(target, List):
+                # multi-target
+                dataset.multi_label = True
+                hf_dataset = hf_dataset.map(partial(self._multilabel_target, target))
+                dataset.num_classes = len(target)
+                self.set_state(LabelsState(target))
+            else:
+                dataset.multi_label = False
+                if self.training:
+                    labels = list(sorted(list(set(hf_dataset[target]))))
+                    dataset.num_classes = len(labels)
+                    self.set_state(LabelsState(labels))
+
+                labels = self.get_state(LabelsState)
+
+                # convert labels to ids
+                if labels is not None:
+                    labels = labels.labels
+                    label_to_class_mapping = {v: k for k, v in enumerate(labels)}
+                    hf_dataset = hf_dataset.map(partial(self._transform_label, label_to_class_mapping, target))
+
+                # Hugging Face models expect target to be named ``labels``.
+                if target != "labels":
+                    hf_dataset.rename_column_(target, "labels")
+
+        hf_dataset = hf_dataset.map(partial(self._tokenize_fn, input=input), batched=True)
+        hf_dataset.set_format("torch", columns=columns)
+
+        return hf_dataset
+
+    def predict_load_data(self, data: Any, dataset: AutoDataset):
+        return self.load_data(data, dataset, columns=["input_ids", "attention_mask"])
+
+    def __getstate__(self):  # TODO: Find out why this is being pickled
+        state = self.__dict__.copy()
+        state.pop("tokenizer")
+        return state
+
+    def __setstate__(self, state):
+        self.__dict__.update(state)
+        self.tokenizer = AutoTokenizer.from_pretrained(self.backbone, use_fast=True)
+
+
 class TextSentencesDataSource(TextDataSource):
     def __init__(self, backbone: str, max_length: int = 128):
         super().__init__(backbone, max_length=max_length)
@@ -266,6 +329,7 @@ def __init__(
             data_sources={
                 DefaultDataSources.CSV: TextCSVDataSource(self.backbone, max_length=max_length),
                 DefaultDataSources.JSON: TextJSONDataSource(self.backbone, max_length=max_length),
+                "data_frame": TextDataFrameDataSource(self.backbone, max_length=max_length),
                 "sentences": TextSentencesDataSource(self.backbone, max_length=max_length),
             },
             default_data_source="sentences",
@@ -313,3 +377,76 @@ class TextClassificationData(DataModule):
     @property
     def backbone(self) -> Optional[str]:
         return getattr(self.preprocess, "backbone", None)
+
+    @classmethod
+    def from_data_frame(
+        cls,
+        input_field: str,
+        target_fields: Union[str, Sequence[str]],
+        train_data_frame: Optional[pd.DataFrame] = None,
+        val_data_frame: Optional[pd.DataFrame] = None,
+        test_data_frame: Optional[pd.DataFrame] = None,
+        predict_data_frame: Optional[pd.DataFrame] = None,
+        train_transform: Optional[Union[Callable, List, Dict[str, Callable]]] = None,
+        val_transform: Optional[Union[Callable, List, Dict[str, Callable]]] = None,
+        test_transform: Optional[Union[Callable, List, Dict[str, Callable]]] = None,
+        predict_transform: Optional[Dict[str, Callable]] = None,
+        data_fetcher: Optional[BaseDataFetcher] = None,
+        preprocess: Optional[Preprocess] = None,
+        val_split: Optional[float] = None,
+        batch_size: int = 4,
+        num_workers: int = 0,
+        sampler: Optional[Type[Sampler]] = None,
+        **preprocess_kwargs: Any,
+    ) -> "DataModule":
+        """Creates a :class:`~flash.image.classification.data.ImageClassificationData` object from the given pandas
+        ``DataFrame`` objects.
+
+        Args:
+            input_field: The field (column) in the pandas ``DataFrame`` to use for the input.
+            target_fields: The field or fields (columns) in the pandas ``DataFrame`` to use for the target.
+            train_data_frame: The pandas ``DataFrame`` containing the training data.
+            val_data_frame: The pandas ``DataFrame`` containing the validation data.
+            test_data_frame: The pandas ``DataFrame`` containing the testing data.
+            predict_data_frame: The pandas ``DataFrame`` containing the data to use when predicting.
+            train_transform: The dictionary of transforms to use during training which maps
+                :class:`~flash.core.data.process.Preprocess` hook names to callable transforms.
+            val_transform: The dictionary of transforms to use during validation which maps
+                :class:`~flash.core.data.process.Preprocess` hook names to callable transforms.
+            test_transform: The dictionary of transforms to use during testing which maps
+                :class:`~flash.core.data.process.Preprocess` hook names to callable transforms.
+            predict_transform: The dictionary of transforms to use during predicting which maps
+                :class:`~flash.core.data.process.Preprocess` hook names to callable transforms.
+            data_fetcher: The :class:`~flash.core.data.callback.BaseDataFetcher` to pass to the
+                :class:`~flash.core.data.data_module.DataModule`.
+            preprocess: The :class:`~flash.core.data.data.Preprocess` to pass to the
+                :class:`~flash.core.data.data_module.DataModule`. If ``None``, ``cls.preprocess_cls``
+                will be constructed and used.
+            val_split: The ``val_split`` argument to pass to the :class:`~flash.core.data.data_module.DataModule`.
+            batch_size: The ``batch_size`` argument to pass to the :class:`~flash.core.data.data_module.DataModule`.
+            num_workers: The ``num_workers`` argument to pass to the :class:`~flash.core.data.data_module.DataModule`.
+            sampler: The ``sampler`` to use for the ``train_dataloader``.
+            preprocess_kwargs: Additional keyword arguments to use when constructing the preprocess. Will only be used
+                if ``preprocess = None``.
+
+        Returns:
+            The constructed data module.
+        """
+        return cls.from_data_source(
+            "data_frame",
+            (train_data_frame, input_field, target_fields),
+            (val_data_frame, input_field, target_fields),
+            (test_data_frame, input_field, target_fields),
+            (predict_data_frame, input_field, target_fields),
+            train_transform=train_transform,
+            val_transform=val_transform,
+            test_transform=test_transform,
+            predict_transform=predict_transform,
+            data_fetcher=data_fetcher,
+            preprocess=preprocess,
+            val_split=val_split,
+            batch_size=batch_size,
+            num_workers=num_workers,
+            sampler=sampler,
+            **preprocess_kwargs,
+        )

tests/text/classification/test_data.py

@@ -14,12 +14,14 @@
 import os
 from pathlib import Path
 
+import pandas as pd
 import pytest
 
 from flash.core.utilities.imports import _TEXT_AVAILABLE
 from flash.text import TextClassificationData
 from flash.text.classification.data import (
     TextCSVDataSource,
+    TextDataFrameDataSource,
     TextDataSource,
     TextFileDataSource,
     TextJSONDataSource,
@@ -51,6 +53,11 @@
 """
 
 
+TEST_DATA_FRAME_DATA = pd.DataFrame(
+    {"sentence": ["this is a sentence one", "this is a sentence two", "this is a sentence three"], "lab": [0, 1, 0]},
+)
+
+
 def csv_data(tmpdir):
     path = Path(tmpdir) / "data.csv"
     path.write_text(TEST_CSV_DATA)
@@ -123,6 +130,17 @@ def test_from_json_with_field(tmpdir):
     assert "input_ids" in batch
 
 
+@pytest.mark.skipif(os.name == "nt", reason="Huggingface timing out on Windows")
+@pytest.mark.skipif(not _TEXT_TESTING, reason="text libraries aren't installed.")
+def test_from_data_frame():
+    dm = TextClassificationData.from_data_frame(
+        "sentence", "lab", backbone=TEST_BACKBONE, train_data_frame=TEST_DATA_FRAME_DATA, batch_size=1
+    )
+    batch = next(iter(dm.train_dataloader()))
+    assert batch["labels"].item() in [0, 1]
+    assert "input_ids" in batch
+
+
 @pytest.mark.skipif(_TEXT_AVAILABLE, reason="text libraries are installed.")
 def test_text_module_not_found_error():
     with pytest.raises(ModuleNotFoundError, match="[text]"):
@@ -138,6 +156,7 @@ def test_text_module_not_found_error():
         (TextFileDataSource, {"filetype": "csv"}),
         (TextCSVDataSource, {}),
         (TextJSONDataSource, {}),
+        (TextDataFrameDataSource, {}),
         (TextSentencesDataSource, {}),
     ],
 )