Tabular regression task and example

flash/core/classification.py

@@ -39,8 +39,8 @@ def binary_cross_entropy_with_logits(x: torch.Tensor, y: torch.Tensor) -> torch.
 
 
 class ClassificationMixin:
+    @staticmethod
     def _build(
-        self,
         num_classes: Optional[int] = None,
         loss_fn: Optional[Callable] = None,
         metrics: Union[torchmetrics.Metric, Mapping, Sequence, None] = None,
@@ -72,7 +72,7 @@ def __init__(
         **kwargs,
     ) -> None:
 
-        metrics, loss_fn = ClassificationMixin._build(self, num_classes, loss_fn, metrics, multi_label)
+        metrics, loss_fn = ClassificationMixin._build(num_classes, loss_fn, metrics, multi_label)
 
         super().__init__(
             *args,
@@ -95,7 +95,7 @@ def __init__(
         **kwargs,
     ) -> None:
 
-        metrics, loss_fn = ClassificationMixin._build(self, num_classes, loss_fn, metrics, multi_label)
+        metrics, loss_fn = ClassificationMixin._build(num_classes, loss_fn, metrics, multi_label)
 
         super().__init__(
             *args,
@@ -257,7 +257,7 @@ def __init__(
     def serialize(
         self,
         sample: Any,
-    ) -> Union[Classification, Classifications, Dict[str, Any], Dict[str, Any]]:
+    ) -> Union[Classification, Classifications, Dict[str, Any]]:
         pred = sample[DefaultDataKeys.PREDS] if isinstance(sample, Dict) else sample
         pred = torch.tensor(pred)
 

flash/core/regression.py

@@ -0,0 +1,59 @@
+# 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 Callable, Mapping, Optional, Sequence, Union
+
+import torch
+import torch.nn.functional as F
+import torchmetrics
+
+from flash.core.data.process import Serializer
+from flash.core.model import Task
+
+
+class RegressionMixin:
+    @staticmethod
+    def _build(
+        loss_fn: Optional[Callable] = None,
+        metrics: Union[torchmetrics.Metric, Mapping, Sequence, None] = None,
+    ):
+        metrics = metrics or torchmetrics.MeanSquaredError()
+        loss_fn = loss_fn or F.mse_loss
+
+        return metrics, loss_fn
+
+    def to_metrics_format(self, x: torch.Tensor) -> torch.Tensor:
+        if getattr(self.hparams, "multi_label", False):
+            return torch.sigmoid(x)
+        return torch.softmax(x, dim=1)
+
+
+class RegressionTask(Task, RegressionMixin):
+    def __init__(
+        self,
+        *args,
+        loss_fn: Optional[Callable] = None,
+        metrics: Union[torchmetrics.Metric, Mapping, Sequence, None] = None,
+        serializer: Optional[Union[Serializer, Mapping[str, Serializer]]] = None,
+        **kwargs,
+    ) -> None:
+
+        metrics, loss_fn = RegressionMixin._build(loss_fn, metrics)
+
+        super().__init__(
+            *args,
+            loss_fn=loss_fn,
+            metrics=metrics,
+            serializer=serializer,
+            **kwargs,
+        )

flash/tabular/__init__.py

@@ -5,4 +5,4 @@
     TabularForecastingDataFrameDataSource,
     TabularForecastingPreprocess,
 )
-from flash.tabular.regression import TabularRegressionData  # noqa: F401
+from flash.tabular.regression import TabularRegressionData, TabularRegressor  # noqa: F401

flash/tabular/classification/model.py

@@ -40,7 +40,7 @@ class TabularClassifier(ClassificationTask):
             package, a custom metric inherenting from `torchmetrics.Metric`, a callable function or a list/dict
             containing a combination of the aforementioned. In all cases, each metric needs to have the signature
             `metric(preds,target)` and return a single scalar tensor. Defaults to :class:`torchmetrics.Accuracy`.
-        learning_rate: Learning rate to use for training, defaults to `1e-3`
+        learning_rate: Learning rate to use for training.
         multi_label: Whether the targets are multi-label or not.
         serializer: The :class:`~flash.core.data.process.Serializer` to use when serializing prediction outputs.
         **tabnet_kwargs: Optional additional arguments for the TabNet model, see

flash/tabular/regression/__init__.py

@@ -1 +1,2 @@
 from flash.tabular.regression.data import TabularRegressionData  # noqa: F401
+from flash.tabular.regression.model import TabularRegressor  # noqa: F401

flash/tabular/regression/model.py

@@ -0,0 +1,114 @@
+# 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, Callable, List, Tuple
+
+import torch
+from torch.nn import functional as F
+
+from flash.core.data.data_source import DefaultDataKeys
+from flash.core.regression import RegressionTask
+from flash.core.utilities.imports import _TABULAR_AVAILABLE
+from flash.core.utilities.types import LR_SCHEDULER_TYPE, METRICS_TYPE, OPTIMIZER_TYPE, SERIALIZER_TYPE
+
+if _TABULAR_AVAILABLE:
+    from pytorch_tabnet.tab_network import TabNet
+
+
+class TabularRegressor(RegressionTask):
+    """The ``TabularRegressor`` is a :class:`~flash.Task` for regression tabular data.
+
+    Args:
+        num_features: Number of columns in table (not including target column).
+        embedding_sizes: List of (num_classes, emb_dim) to form categorical embeddings.
+        loss_fn: Loss function for training, defaults to cross entropy.
+        optimizer: Optimizer to use for training.
+        lr_scheduler: The LR scheduler to use during training.
+        metrics: Metrics to compute for training and evaluation. Can either be an metric from the `torchmetrics`
+            package, a custom metric inherenting from `torchmetrics.Metric`, a callable function or a list/dict
+            containing a combination of the aforementioned. In all cases, each metric needs to have the signature
+            `metric(preds,target)` and return a single scalar tensor.
+        learning_rate: Learning rate to use for training.
+        multi_label: Whether the targets are multi-label or not.
+        serializer: The :class:`~flash.core.data.process.Serializer` to use when serializing prediction outputs.
+        **tabnet_kwargs: Optional additional arguments for the TabNet model, see
+            `pytorch_tabnet <https://dreamquark-ai.github.io/tabnet/_modules/pytorch_tabnet/tab_network.html#TabNet>`_.
+    """
+
+    required_extras: str = "tabular"
+
+    def __init__(
+        self,
+        num_features: int,
+        embedding_sizes: List[Tuple[int, int]] = None,
+        loss_fn: Callable = F.cross_entropy,
+        optimizer: OPTIMIZER_TYPE = "Adam",
+        lr_scheduler: LR_SCHEDULER_TYPE = None,
+        metrics: METRICS_TYPE = None,
+        learning_rate: float = 1e-2,
+        serializer: SERIALIZER_TYPE = None,
+        **tabnet_kwargs,
+    ):
+        self.save_hyperparameters()
+
+        cat_dims, cat_emb_dim = zip(*embedding_sizes) if embedding_sizes else ([], [])
+        model = TabNet(
+            input_dim=num_features,
+            output_dim=1,
+            cat_idxs=list(range(len(embedding_sizes))),
+            cat_dims=list(cat_dims),
+            cat_emb_dim=list(cat_emb_dim),
+            **tabnet_kwargs,
+        )
+
+        super().__init__(
+            model=model,
+            loss_fn=loss_fn,
+            optimizer=optimizer,
+            lr_scheduler=lr_scheduler,
+            metrics=metrics,
+            learning_rate=learning_rate,
+            serializer=serializer,
+        )
+
+        self.save_hyperparameters()
+
+    def forward(self, x_in) -> torch.Tensor:
+        # TabNet takes single input, x_in is composed of (categorical, numerical)
+        xs = []
+        for x in x_in:
+            if x.numel():
+                xs.append(x)
+        x = torch.cat(xs, dim=1)
+        return self.model(x)[0]
+
+    def training_step(self, batch: Any, batch_idx: int) -> Any:
+        batch = (batch[DefaultDataKeys.INPUT], batch[DefaultDataKeys.TARGET])
+        return super().training_step(batch, batch_idx)
+
+    def validation_step(self, batch: Any, batch_idx: int) -> Any:
+        batch = (batch[DefaultDataKeys.INPUT], batch[DefaultDataKeys.TARGET])
+        return super().validation_step(batch, batch_idx)
+
+    def test_step(self, batch: Any, batch_idx: int) -> Any:
+        batch = (batch[DefaultDataKeys.INPUT], batch[DefaultDataKeys.TARGET])
+        return super().test_step(batch, batch_idx)
+
+    def predict_step(self, batch: Any, batch_idx: int, dataloader_idx: int = 0) -> Any:
+        batch = batch[DefaultDataKeys.INPUT]
+        return self(batch)
+
+    @classmethod
+    def from_data(cls, datamodule, **kwargs) -> "TabularRegressor":
+        model = cls(datamodule.num_features, datamodule.num_classes, datamodule.embedding_sizes, **kwargs)
+        return model

flash_examples/tabular_regression.py

@@ -0,0 +1,54 @@
+# 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.
+import torch
+
+import flash
+from flash.core.data.utils import download_data
+from flash.tabular import TabularRegressionData, TabularRegressor
+
+# 1. Create the DataModule
+download_data("https://archive.ics.uci.edu/ml/machine-learning-databases/00560/SeoulBikeData.csv", "./data")
+
+datamodule = TabularRegressionData.from_csv(
+    categorical_fields=["Seasons", "Holiday", "Functioning Day"],
+    numerical_fields=[
+        "Hour",
+        "Temperature(�C)",
+        "Humidity(%)",
+        "Wind speed (m/s)",
+        "Wind speed (m/s)",
+        "Visibility (10m)",
+        "Dew point temperature(�C)",
+        "Solar Radiation (MJ/m2)",
+        "Rainfall(mm)",
+        "Snowfall (cm)",
+    ],
+    target_fields="Rented Bike Count",
+    train_file="data/SeoulBikeData.csv",
+    val_split=0.1,
+)
+
+# 2. Build the task
+model = TabularRegressor.from_data(datamodule)
+
+# 3. Create the trainer and train the model
+trainer = flash.Trainer(max_epochs=3, gpus=torch.cuda.device_count())
+trainer.fit(model, datamodule=datamodule)
+
+# 4. Generate predictions from a CSV
+predictions = model.predict("data/SeoulBikeData.csv")
+print(predictions)
+
+# 5. Save the model!
+trainer.save_checkpoint("tabular_regression_model.pt")