Refactor tabular data to use classification targets handling

CHANGELOG.md

@@ -54,6 +54,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - Fixed `InstanceSegmentationData.from_voc` ([#1102](https://github.com/PyTorchLightning/lightning-flash/pull/1102))
 
+- Fixed a bug when loading tabular data for prediction without a target field / column ([#1114](https://github.com/PyTorchLightning/lightning-flash/pull/1114))
+
 ### Removed
 
 ## [0.6.0] - 2021-13-12

docs/source/api/tabular.rst

@@ -20,6 +20,9 @@ ______________
     ~classification.model.TabularClassifier
     ~classification.data.TabularClassificationData
 
+    classification.input.TabularClassificationDataFrameInput
+    classification.input.TabularClassificationCSVInput
+
 Regression
 __________
 
@@ -31,6 +34,9 @@ __________
     ~regression.model.TabularRegressor
     ~regression.data.TabularRegressionData
 
+    regression.input.TabularRegressionDataFrameInput
+    regression.input.TabularRegressionCSVInput
+
 Forecasting
 ___________
 
@@ -56,5 +62,4 @@ __________________
     ~data.TabularData
 
     input.TabularDataFrameInput
-    input.TabularCSVInput
     input.TabularDeserializer

flash/tabular/classification/cli.py

@@ -54,7 +54,7 @@ def tabular_classification():
             "categorical_fields",
             "num_features",
             "cat_dims",
-            "output_dim",
+            "num_classes",
         },
     )
 

flash/tabular/classification/data.py

@@ -11,8 +11,101 @@
 # 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, Type, Union
+
+from flash.core.data.data_pipeline import DataPipelineState
+from flash.core.data.io.input import Input
+from flash.core.data.io.input_transform import INPUT_TRANSFORM_TYPE, InputTransform
+from flash.core.utilities.imports import _PANDAS_AVAILABLE
+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
+
 
 class TabularClassificationData(TabularData):
-    is_regression = False
+    @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,
+        input_cls: Type[Input] = TabularClassificationDataFrameInput,
+        transform_kwargs: Optional[Dict] = None,
+        **data_module_kwargs: Any,
+    ) -> "TabularClassificationData":
+        ds_kw = dict(
+            data_pipeline_state=DataPipelineState(),
+            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
+
+        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,
+        )
+
+    @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,
+        input_cls: Type[Input] = TabularClassificationCSVInput,
+        transform_kwargs: Optional[Dict] = None,
+        **data_module_kwargs: Any,
+    ) -> "TabularClassificationData":
+        ds_kw = dict(
+            data_pipeline_state=DataPipelineState(),
+            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
+
+        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,
+        )

flash/tabular/classification/input.py

@@ -0,0 +1,62 @@
+# 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 import DataKeys
+from flash.core.data.io.classification_input import ClassificationInput
+from flash.core.data.utilities.data_frame import read_csv, resolve_targets
+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
+
+
+class TabularClassificationDataFrameInput(TabularDataFrameInput, ClassificationInput):
+    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,
+    ):
+        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)
+            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
+
+
+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,
+    ):
+        if file is not None:
+            return super().load_data(read_csv(file), categorical_fields, numerical_fields, target_fields, parameters)

flash/tabular/classification/model.py

@@ -37,7 +37,7 @@ class TabularClassifier(ClassificationAdapterTask):
         embedding_sizes: List of (num_classes, emb_dim) to form categorical embeddings.
         cat_dims: Number of distinct values for each categorical column
         num_features: Number of columns in table
-        output_dim: Number of classes to classify
+        num_classes: Number of classes to classify
         backbone: name of the model to use
         loss_fn: Loss function for training, defaults to cross entropy.
         optimizer: Optimizer to use for training.
@@ -59,7 +59,7 @@ def __init__(
         categorical_fields: list,
         cat_dims: list,
         num_features: int,
-        output_dim: int,
+        num_classes: int,
         backbone: str = "tabnet",
         loss_fn: Callable = F.cross_entropy,
         optimizer: OPTIMIZER_TYPE = "Adam",
@@ -77,7 +77,7 @@ def __init__(
             categorical_fields=categorical_fields,
             cat_dims=cat_dims,
             num_features=num_features,
-            output_dim=output_dim,
+            output_dim=num_classes,
             backbone=backbone,
             backbone_kwargs=backbone_kwargs,
             loss_fn=loss_fn,
@@ -102,7 +102,7 @@ def from_data(cls, datamodule, **kwargs) -> "TabularClassifier":
             categorical_fields=datamodule.categorical_fields,
             cat_dims=datamodule.cat_dims,
             num_features=datamodule.num_features,
-            output_dim=datamodule.output_dim,
+            num_classes=datamodule.num_classes,
             **kwargs,
         )
         return model

flash/tabular/classification/utils.py

@@ -68,14 +68,10 @@ def _pre_transform(
     codes: Dict,
     mean: DataFrame,
     std: DataFrame,
-    target: str = None,
-    target_codes: Dict = None,
 ) -> DataFrame:
     df = _impute(df, num_cols)
     df = _normalize(df, num_cols, mean=mean, std=std)
     df = _categorize(df, cat_cols, codes=codes)
-    if target_codes and target:
-        df = _categorize(df, [target], codes=target_codes)
     return df
 
 

flash/tabular/data.py

@@ -11,31 +11,18 @@
 # 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, Type, Union
+from typing import Any, Dict, List, Optional
 
 from flash.core.data.data_module import DataModule
-from flash.core.data.data_pipeline import DataPipelineState
-from flash.core.data.io.input import Input
-from flash.core.data.io.input_transform import INPUT_TRANSFORM_TYPE, InputTransform
+from flash.core.data.io.input_transform import InputTransform
 from flash.core.data.io.output_transform import OutputTransform
-from flash.core.utilities.imports import _PANDAS_AVAILABLE
-from flash.core.utilities.stages import RunningStage
-from flash.tabular.input import TabularCSVInput, TabularDataFrameInput
-
-if _PANDAS_AVAILABLE:
-    from pandas.core.frame import DataFrame
-else:
-    DataFrame = object
 
 
 class TabularData(DataModule):
-    """Data module for tabular tasks."""
 
     input_transform_cls = InputTransform
     output_transform_cls = OutputTransform
 
-    is_regression: bool = False
-
     @property
     def parameters(self) -> Optional[Dict[str, Any]]:
         """The parameters dictionary created from the train data when constructing the ``TabularData`` object."""
@@ -70,93 +57,3 @@ def embedding_sizes(self) -> list:
         # embedding_dimensions =  number_of_categories**0.25
         emb_dims = [max(int(n ** 0.25), 16) for n in self.cat_dims]
         return list(zip(self.cat_dims, emb_dims))
-
-    @property
-    def output_dim(self) -> int:
-        return self.num_classes if not self.is_regression else 1
-
-    @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[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,
-        input_cls: Type[Input] = TabularDataFrameInput,
-        transform_kwargs: Optional[Dict] = None,
-        **data_module_kwargs: Any,
-    ) -> "TabularData":
-
-        ds_kw = dict(
-            data_pipeline_state=DataPipelineState(),
-            transform_kwargs=transform_kwargs,
-            input_transforms_registry=cls.input_transforms_registry,
-            categorical_fields=categorical_fields,
-            numerical_fields=numerical_fields,
-            target_field=target_fields,
-            is_regression=cls.is_regression,
-            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
-
-        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,
-        )
-
-    @classmethod
-    def from_csv(
-        cls,
-        categorical_fields: Optional[Union[str, List[str]]] = None,
-        numerical_fields: Optional[Union[str, List[str]]] = None,
-        target_fields: Optional[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,
-        input_cls: Type[Input] = TabularCSVInput,
-        transform_kwargs: Optional[Dict] = None,
-        **data_module_kwargs: Any,
-    ) -> "TabularData":
-
-        ds_kw = dict(
-            data_pipeline_state=DataPipelineState(),
-            transform_kwargs=transform_kwargs,
-            input_transforms_registry=cls.input_transforms_registry,
-            categorical_fields=categorical_fields,
-            numerical_fields=numerical_fields,
-            target_field=target_fields,
-            is_regression=cls.is_regression,
-            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
-
-        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,
-        )