TST: compare to sklearn.neural_network

scikeras/utils/transformers.py

@@ -368,7 +368,7 @@ def inverse_transform(self, y: np.ndarray) -> np.ndarray:
             Keras Model predictions cast to the dtype and shape of the input
             targets.
         """
-        if self._y_dtype == np.float64 and y.dtype == np.float32:
+        if y.dtype.name == "float32":
             y = y.astype(np.float64, copy=False)
         y = y.reshape(-1, *self._y_shape[1:])
         return y

scikeras/wrappers.py

@@ -530,7 +530,7 @@ def _check_model_compatibility(self, y: np.ndarray) -> None:
         This is in place to avoid cryptic TF errors.
         """
         # check if this is a multi-output model
-        if hasattr(self, "n_outputs_expected_"):
+        if getattr(self, "n_outputs_expected_", None):
             # n_outputs_expected_ is generated by data transformers
             # we recognize the attribute but do not force it to be
             # generated

tests/multi_output_models.py

@@ -3,47 +3,67 @@
 import numpy as np
 
 from sklearn.utils.multiclass import type_of_target
+from tensorflow.keras.backend import floatx as tf_floatx
 
 from scikeras.utils.transformers import ClassifierLabelEncoder
 from scikeras.wrappers import KerasClassifier
 
 
 class MultiLabelTransformer(ClassifierLabelEncoder):
+    def __init__(
+        self, split: bool = True,
+    ):
+        super().__init__()
+        self.split = split
+
     def fit(self, y: np.ndarray) -> "MultiLabelTransformer":
         self._target_type = type_of_target(y)
-        if self._target_type != "multilabel-indicator":
+        if self._target_type not in ("multilabel-indicator", "multiclass-multioutput"):
             return super().fit(y)
         # y = array([1, 1, 1, 0], [0, 0, 1, 1])
         # each col will be processed as multiple binary classifications
-        self.n_outputs_ = self.n_outputs_expected_ = y.shape[1]
+        self.n_outputs_ = y.shape[1]
+        self.n_outputs_expected_ = None if not self.split else self.n_outputs_
         self._y_dtype = y.dtype
         self.classes_ = [np.array([0, 1])] * y.shape[1]
         self.n_classes_ = [2] * y.shape[1]
         return self
 
     def transform(self, y: np.ndarray) -> List[np.ndarray]:
-        if self._target_type != "multilabel-indicator":
+        if self._target_type not in ("multilabel-indicator", "multiclass-multioutput"):
             return super().transform(y)
-        return np.split(y, y.shape[1], axis=1)
+        y = y.astype(tf_floatx())
+        if self.split:
+            return np.split(y, y.shape[1], axis=1)
+        return y
 
     def inverse_transform(
         self, y: List[np.ndarray], return_proba: bool = False
     ) -> np.ndarray:
-        if self._target_type != "multilabel-indicator":
+        if self._target_type not in ("multilabel-indicator", "multiclass-multioutput"):
             return super().inverse_transform(y, return_proba=return_proba)
-        if not return_proba:
+        if not return_proba and self.split:
             y = [np.argmax(y_, axis=1).astype(self._y_dtype, copy=False) for y_ in y]
-        return np.squeeze(np.column_stack(y))
+        y = np.squeeze(np.column_stack(y))
+        if self._target_type == "multilabel-indicator":
+            # RandomForestClassifier and sklearn's MultiOutputClassifier always return int64
+            # for multilabel-indicator
+            y = y.astype(int)
+        return y
 
 
 class MultiOutputClassifier(KerasClassifier):
     """Extend KerasClassifier with the ability to process
     "multilabel-indicator" by mapping to multiple Keras outputs.
     """
 
+    def __init__(self, model=None, split: bool = True, **kwargs):
+        super().__init__(model=model, **kwargs)
+        self.split = split
+
     @property
     def target_encoder(self) -> MultiLabelTransformer:
-        return MultiLabelTransformer()
+        return MultiLabelTransformer(split=self.split)
 
     def score(self, X, y):
         """Taken from sklearn.multiouput.MultiOutputClassifier