ENH: Add default losses to KerasClassifier and KerasRegressor

.github/workflows/tests.yaml

@@ -22,7 +22,6 @@ jobs:
       - uses: pre-commit/action@v2.0.0
 
   TestStable:
-    needs: Linting
     name: Ubuntu / Python ${{ matrix.python-version }} / TensorFlow Stable / Scikit-Learn Stable
     runs-on: ubuntu-latest
     strategy:
@@ -55,7 +54,6 @@ jobs:
       - uses: codecov/codecov-action@v1
 
   TestDev:
-    needs: Linting
     name: Ubuntu / Python ${{ matrix.python-version }} / TensorFlow Nightly / Scikit-Learn Nightly
     runs-on: ubuntu-latest
     strategy:
@@ -98,7 +96,6 @@ jobs:
       - uses: codecov/codecov-action@v1
 
   TestOldest:
-    needs: Linting
     name: Ubuntu / Python ${{ matrix.python-version }} / TF ${{ matrix.tf-version }} / Scikit-Learn ${{ matrix.sklearn-version }}
     runs-on: ubuntu-latest
     strategy:
@@ -135,7 +132,6 @@ jobs:
       - uses: codecov/codecov-action@v1
 
   TestOSs:
-    needs: Linting
     name: ${{ matrix.os }} / Python ${{ matrix.python-version }} / TF Stable / Scikit-Learn Stable
     runs-on: ${{ matrix.os }}-latest
     strategy:

docs/source/notebooks/DataTransformers.md

@@ -316,7 +316,6 @@ from sklearn.metrics import accuracy_score
 
 
 class MultiOutputClassifier(KerasClassifier):
-
     @property
     def target_encoder(self):
         return MultiOutputTransformer()
@@ -340,7 +339,8 @@ X = StandardScaler().fit_transform(X)
 ```
 
 ```python
-clf = MultiOutputClassifier(model=get_clf_model, verbose=0, random_state=0)
+clf = MultiOutputClassifier(model=get_clf_model, verbose=0, random_state=0,
+loss=None)
 
 clf.fit(X, y_sklearn).score(X, y_sklearn)
 ```
@@ -432,7 +432,8 @@ Note that we did **not** implement `inverse_transform` (that is, we did not pass
 ### 4.3 Test regressor
 
 ```python
-reg = MultiInputRegressor(model=get_reg_model, verbose=0, random_state=0)
+reg = MultiInputRegressor(model=get_reg_model, verbose=0, random_state=0,
+loss=None)
 
 X_sklearn = np.column_stack(X)
 
@@ -528,6 +529,7 @@ clf = MultiDimensionalClassifier(
     batch_size=128,
     validation_split=0.1,
     random_state=0,
+    loss=None,
 )
 ```
 

scikeras/_types.py

@@ -0,0 +1,18 @@
+from typing import Callable, List, Type, Union
+
+import numpy as np
+import tensorflow as tf
+import tensorflow.keras as keras
+
+from tensorflow.keras.callbacks import Callback as TF_Callback
+from tensorflow.keras.losses import Loss as TF_Loss
+from tensorflow.keras.metrics import Metric as TF_Metric
+from tensorflow.keras.optimizers import Optimizer as TF_Optimizer
+
+
+Model = Union[Callable[..., keras.Model], keras.Model]
+RandomState = Union[int, np.random.RandomState]
+Optimizer = Union[str, TF_Optimizer, Type[TF_Optimizer]]
+Loss = Union[str, TF_Loss, Type[TF_Loss], Callable]
+Metrics = Union[List[Union[str, TF_Metric, Type[TF_Metric], Callable]]]
+Callbacks = Union[List[Union[TF_Callback, Type[TF_Callback]]]]