Add loss="auto" as the default loss

docs/source/advanced.rst

@@ -1,6 +1,6 @@
-===================================
-Advanced Usage of SciKeras Wrappers
-===================================
+==============
+Advanced Usage
+==============
 
 Wrapper Classes
 ---------------
@@ -128,6 +128,43 @@ offer an easy way to compile and tune compilation parameters. Examples:
 In all cases, returning an un-compiled model is equivalent to
 calling ``model.compile(**compile_kwargs)`` within ``model_build_fn``.
 
+.. _loss-selection:
+
+Loss selection
+++++++++++++++
+
+If you do not explicitly define a loss, SciKeras attempts to find a loss
+that matches the type of target (see :py:func:`sklearn.utils.multiclass.type_of_target`).
+
+For guidance selecting losses in Keras, please see Jason Brownlee's
+excellent article `How to Choose Loss Functions When Training Deep Learning Neural Networks`_
+as well as `Keras Losses docs`_.
+
+Default losses are selected as follows:
+
+Classification
+..............
+
++-----------+-----------+----------+---------------------------------+
+| # outputs | # classes | encoding | loss                            |
++===========+===========+==========+=================================+
+| 1         | <= 2      | any      | binary crossentropy             |
++-----------+-----------+----------+---------------------------------+
+| 1         | >=2       | labels   | sparse categorical crossentropy |
++-----------+-----------+----------+---------------------------------+
+| 1         | >=2       | one-hot  | unsupported                     |
++-----------+-----------+----------+---------------------------------+
+| > 1       |    --     |    --    | unsupported                     |
++-----------+-----------+----------+---------------------------------+
+
+Note that SciKeras will not automatically infer the loss for one-hot encoded targets,
+you would need to explicitly specify `loss="categorical_crossentropy"`.
+
+Regression
+..........
+
+Regression always defaults to mean squared error.
+For multi-output models, Keras will use the sum of each output's loss.
 
 Arguments to ``model_build_fn``
 -------------------------------
@@ -287,3 +324,7 @@ and :class:`scikeras.wrappers.KerasRegressor` respectively. To override these sc
 .. _Keras Callbacks docs: https://www.tensorflow.org/api_docs/python/tf/keras/callbacks
 
 .. _Keras Metrics docs: https://www.tensorflow.org/api_docs/python/tf/keras/metrics
+
+.. _Keras Losses docs: https://www.tensorflow.org/api_docs/python/tf/keras/losses
+
+.. _How to Choose Loss Functions When Training Deep Learning Neural Networks: https://machinelearningmastery.com/how-to-choose-loss-functions-when-training-deep-learning-neural-networks/

docs/source/quickstart.rst

@@ -38,16 +38,25 @@ it on a toy classification dataset using SciKeras
         model.add(keras.layers.Activation("softmax"))
         return model
 
-    clf = KerasClassifier(
-        get_model,
-        loss="sparse_categorical_crossentropy",
-        hidden_layer_dim=100,
-    )
+    clf = KerasClassifier(get_model, hidden_layer_dim=100)
 
     clf.fit(X, y)
     y_proba = clf.predict_proba(X)
 
 
+Note that SciKeras even chooses a loss function and compiles your model.
+To override the default loss, simply specify a loss function:
+
+.. code-block:: diff
+
+    -KerasClassifier(get_model, hidden_layer_dim=100)
+    +KerasClassifier(get_model, loss="categorical_crossentropy")
+
+In this case, you would need to specify the loss since SciKeras
+will not default to categorical crossentropy, even for one-hot
+encoded targets.
+See :ref:`loss-selection` for more details.
+
 In an sklearn Pipeline
 ----------------------
 

scikeras/utils/__init__.py

@@ -14,7 +14,7 @@ def _camel2snake(s: str) -> str:
     return "".join(["_" + c.lower() if c.isupper() else c for c in s]).lstrip("_")
 
 
-def loss_name(loss: Union[str, Loss, Callable]) -> str:
+def loss_name(loss: Union[str, Loss, Callable]) -> Union[str, None]:
     """Retrieves a loss's full name (eg: "mean_squared_error").
 
     Parameters
@@ -25,8 +25,9 @@ def loss_name(loss: Union[str, Loss, Callable]) -> str:
 
     Returns
     -------
-    str
-        String name of the loss.
+    Union[str, None]
+        String name of the loss. String inputs that do not map to a known
+        Keras loss function return `None`.
 
     Notes
     -----
@@ -43,6 +44,8 @@ def loss_name(loss: Union[str, Loss, Callable]) -> str:
     'binary_crossentropy'
     >>> loss_name(losses.binary_crossentropy)
     'binary_crossentropy'
+    >>> loss_name("abcdefg")
+    None
 
     Raises
     ------
@@ -56,13 +59,17 @@ def loss_name(loss: Union[str, Loss, Callable]) -> str:
             "``loss`` must be a string, a function, an instance of ``tf.keras.losses.Loss``"
             " or a type inheriting from ``tf.keras.losses.Loss``"
         )
-    fn_or_cls = keras_loss_get(loss)
+    try:
+        fn_or_cls = keras_loss_get(loss)
+    except ValueError:
+        # unknown loss
+        return None
     if isinstance(fn_or_cls, Loss):
         return _camel2snake(fn_or_cls.__class__.__name__)
     return fn_or_cls.__name__
 
 
-def metric_name(metric: Union[str, Metric, Callable]) -> str:
+def metric_name(metric: Union[str, Metric, Callable]) -> Union[str, None]:
     """Retrieves a metric's full name (eg: "mean_squared_error").
 
     Parameters
@@ -73,8 +80,9 @@ def metric_name(metric: Union[str, Metric, Callable]) -> str:
 
     Returns
     -------
-    str
+    Union[str, None]
         Full name for Keras metric. Ex: "mean_squared_error".
+        String inputs that do not map to a known Keras loss function return `None`.
 
     Notes
     -----
@@ -91,6 +99,8 @@ def metric_name(metric: Union[str, Metric, Callable]) -> str:
     'BinaryCrossentropy'
     >>> metric_name(metrics.binary_crossentropy)
     'binary_crossentropy'
+    >>> metric_name("abcdefg")
+    None
 
     Raises
     ------
@@ -106,7 +116,11 @@ def metric_name(metric: Union[str, Metric, Callable]) -> str:
             " ``tf.keras.metrics.Metric`` or a type inheriting from"
             " ``tf.keras.metrics.Metric``"
         )
-    fn_or_cls = keras_metric_get(metric)
+    try:
+        fn_or_cls = keras_metric_get(metric)
+    except ValueError:
+        # unknown metric
+        return None
     if isinstance(fn_or_cls, Metric):
         return _camel2snake(fn_or_cls.__class__.__name__)
     return fn_or_cls.__name__

scikeras/utils/transformers.py

@@ -154,7 +154,7 @@ def fit(self, y: np.ndarray) -> "ClassifierLabelEncoder":
             "multiclass-multioutput": FunctionTransformer(),
             "multilabel-indicator": FunctionTransformer(),
         }
-        if is_categorical_crossentropy(self.loss):
+        if target_type == "multiclass" and is_categorical_crossentropy(self.loss):
             encoders["multiclass"] = make_pipeline(
                 TargetReshaper(),
                 OneHotEncoder(