add one hot to numeric input transform (#1517)

Summary:
Pull Request resolved: #1517

see title

Differential Revision: https://internalfb.com/D41482322

fbshipit-source-id: db4d684f6cbbe8af5ae6b257a949da0610871bd8

Author: sdaulton

botorch/models/transforms/input.py

@@ -31,6 +31,7 @@
 from torch import nn, Tensor
 from torch.distributions import Kumaraswamy
 from torch.nn import Module, ModuleDict
+from torch.nn.functional import one_hot
 
 
 class InputTransform(ABC):
@@ -1358,3 +1359,133 @@ def _expanded_perturbations(self, X: Tensor) -> Tensor:
         else:
             p = p(X) if self.indices is None else p(X[..., self.indices])
         return p.transpose(-3, -2)  # p is batch_shape x n_p x n x d
+
+
+class OneHotToNumeric(InputTransform, Module):
+    r"""Transform categorical parameters from a one-hot to a numeric representation.
+
+    This assumes that the categoricals are the trailing dimensions.
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        categorical_features: Optional[Dict[int, int]] = None,
+        transform_on_train: bool = False,
+        transform_on_eval: bool = True,
+        transform_on_fantasize: bool = False,
+    ) -> None:
+        r"""Initialize.
+
+        Args:
+            dim: The dimension of the one-hot-encoded input.
+            categorical_features: A dictionary mapping the starting index of each
+                categorical feature to its cardinality. This assumes that categoricals
+                are one-hot encoded.
+            transform_on_train: A boolean indicating whether to apply the
+                transforms in train() mode. Default: False.
+            transform_on_eval: A boolean indicating whether to apply the
+                transform in eval() mode. Default: True.
+            transform_on_fantasize: A boolean indicating whether to apply the
+                transform when called from within a `fantasize` call. Default: False.
+
+        Returns:
+            A `batch_shape x n x d'`-dim tensor of where the one-hot encoded
+            categoricals are transformed to integer representation.
+        """
+        super().__init__()
+        self.transform_on_train = transform_on_train
+        self.transform_on_eval = transform_on_eval
+        self.transform_on_fantasize = transform_on_fantasize
+        categorical_features = categorical_features or {}
+        # sort by starting index
+        self.categorical_features = OrderedDict(
+            sorted(categorical_features.items(), key=lambda x: x[0])
+        )
+        if len(self.categorical_features) > 0:
+            self.categorical_start_idx = min(self.categorical_features.keys())
+            # check that the trailing dimensions are categoricals
+            end = self.categorical_start_idx
+            err_msg = (
+                f"{self.__class__.__name__} requires that the categorical "
+                "parameters are the rightmost elements."
+            )
+            for start, card in self.categorical_features.items():
+                # the end of one one-hot representation should be followed
+                # by the start of the next
+                if end != start:
+                    raise ValueError(err_msg)
+                end = start + card
+            if end != dim:
+                # check end
+                raise ValueError(err_msg)
+            # the numeric representation dimension is the total number of parameters
+            # (continuous, integer, and categorical)
+            self.numeric_dim = self.categorical_start_idx + len(categorical_features)
+
+    def transform(self, X: Tensor) -> Tensor:
+        r"""Transform the categorical inputs into integer representation.
+
+        Args:
+            X: A `batch_shape x n x d`-dim tensor of inputs.
+
+        Returns:
+            A `batch_shape x n x d'`-dim tensor of where the one-hot encoded
+            categoricals are transformed to integer representation.
+        """
+        if len(self.categorical_features) > 0:
+            X_numeric = X[..., : self.numeric_dim].clone()
+            idx = self.categorical_start_idx
+            for start, card in self.categorical_features.items():
+                X_numeric[..., idx] = X[..., start : start + card].argmax(dim=-1)
+                idx += 1
+            return X_numeric
+        return X
+
+    def untransform(self, X: Tensor) -> Tensor:
+        r"""Transform the categoricals from integer representation to one-hot.
+
+        Args:
+            X: A `batch_shape x n x d'`-dim tensor of transformed inputs, where
+                the categoricals are represented as integers.
+
+        Returns:
+            A `batch_shape x n x d`-dim tensor of inputs, where the categoricals
+            have been transformed to one-hot representation.
+        """
+        if len(self.categorical_features) > 0:
+            self.numeric_dim
+            one_hot_categoricals = [
+                # note that self.categorical_features is sorted by the starting index
+                # in one-hot representation
+                one_hot(
+                    X[..., idx - len(self.categorical_features)].long(),
+                    num_classes=cardinality,
+                )
+                for idx, cardinality in enumerate(self.categorical_features.values())
+            ]
+            X = torch.cat(
+                [
+                    X[..., : self.categorical_start_idx],
+                    *one_hot_categoricals,
+                ],
+                dim=-1,
+            )
+        return X
+
+    def equals(self, other: InputTransform) -> bool:
+        r"""Check if another input transform is equivalent.
+
+        Args:
+            other: Another input transform.
+
+        Returns:
+            A boolean indicating if the other transform is equivalent.
+        """
+        return (
+            type(self) == type(other)
+            and (self.transform_on_train == other.transform_on_train)
+            and (self.transform_on_eval == other.transform_on_eval)
+            and (self.transform_on_fantasize == other.transform_on_fantasize)
+            and self.categorical_features == other.categorical_features
+        )

test/models/transforms/test_input.py

@@ -21,6 +21,7 @@
     InputTransform,
     Log10,
     Normalize,
+    OneHotToNumeric,
     Round,
     Warp,
 )
@@ -889,6 +890,85 @@ def test_warp_transform(self):
             warp_tf._set_concentration(i=1, value=3.0)
             self.assertTrue((warp_tf.concentration1 == 3.0).all())
 
+    def test_one_hot_to_numeric(self):
+        dim = 8
+        # test exception when categoricals are not the trailing dimensions
+        categorical_features = {0: 2}
+        with self.assertRaises(ValueError):
+            OneHotToNumeric(dim=dim, categorical_features=categorical_features)
+        # categoricals at start and end of X but not in between
+        categorical_features = {0: 3, 6: 2}
+        with self.assertRaises(ValueError):
+            OneHotToNumeric(dim=dim, categorical_features=categorical_features)
+        for dtype in (torch.float, torch.double):
+            categorical_features = {6: 2, 3: 3}
+            tf = OneHotToNumeric(dim=dim, categorical_features=categorical_features)
+            tf.eval()
+            self.assertEqual(tf.categorical_features, {3: 3, 6: 2})
+            cat1_numeric = torch.randint(0, 3, (3,), device=self.device)
+            cat1 = one_hot(cat1_numeric, num_classes=3)
+            cat2_numeric = torch.randint(0, 2, (3,), device=self.device)
+            cat2 = one_hot(cat2_numeric, num_classes=2)
+            cont = torch.rand(3, 3, dtype=dtype, device=self.device)
+            X = torch.cat([cont, cat1, cat2], dim=-1)
+            # test forward
+            X_numeric = tf(X)
+            expected = torch.cat(
+                [
+                    cont,
+                    cat1_numeric.view(-1, 1).to(cont),
+                    cat2_numeric.view(-1, 1).to(cont),
+                ],
+                dim=-1,
+            )
+            self.assertTrue(torch.equal(X_numeric, expected))
+
+            # test untransform
+            X2 = tf.untransform(X_numeric)
+            self.assertTrue(torch.equal(X2, X))
+
+            # test no
+            tf = OneHotToNumeric(dim=dim, categorical_features={})
+            tf.eval()
+            X_tf = tf(X)
+            self.assertTrue(torch.equal(X, X_tf))
+            X2 = tf(X_tf)
+            self.assertTrue(torch.equal(X2, X_tf))
+
+        # test no transform on eval
+        tf2 = OneHotToNumeric(
+            dim=dim, categorical_features=categorical_features, transform_on_eval=False
+        )
+        tf2.eval()
+        X_tf = tf2(X)
+        self.assertTrue(torch.equal(X, X_tf))
+
+        # test no transform on train
+        tf2 = OneHotToNumeric(
+            dim=dim, categorical_features=categorical_features, transform_on_train=False
+        )
+        X_tf = tf2(X)
+        self.assertTrue(torch.equal(X, X_tf))
+        tf2.eval()
+        X_tf = tf2(X)
+        self.assertFalse(torch.equal(X, X_tf))
+
+        # test equals
+        tf3 = OneHotToNumeric(
+            dim=dim, categorical_features=categorical_features, transform_on_train=False
+        )
+        self.assertTrue(tf3.equals(tf2))
+        # test different transform_on_train
+        tf3 = OneHotToNumeric(
+            dim=dim, categorical_features=categorical_features, transform_on_train=True
+        )
+        self.assertFalse(tf3.equals(tf2))
+        # test categorical features
+        tf3 = OneHotToNumeric(
+            dim=dim, categorical_features={}, transform_on_train=False
+        )
+        self.assertFalse(tf3.equals(tf2))
+
 
 class TestAppendFeatures(BotorchTestCase):
     def test_append_features(self):