ENH: user friendly error if there is no loss function (#86)

scikeras/wrappers.py

@@ -332,6 +332,21 @@ def _build_keras_model(self):
                 compile_kwargs = self._get_compile_kwargs()
             model.compile(**compile_kwargs)
 
+        if not getattr(model, "loss", None) or (
+            isinstance(model.loss, list)
+            and not any(callable(l) or isinstance(l, str) for l in model.loss)
+        ):
+            raise ValueError(
+                "No valid loss function found."
+                " You must provide a loss function to train."
+                "\n\nTo resolve this issue, do one of the following:"
+                "\n 1. Provide a loss function via the loss parameter."
+                "\n 2. Compile your model with a loss function inside the"
+                " model-building method."
+                "\n\nSee https://www.tensorflow.org/api_docs/python/tf/keras/losses"
+                " for more information on Keras losses."
+            )
+
         return model
 
     def _fit_keras_model(self, X, y, sample_weight, warm_start):

tests/test_compile_kwargs.py

@@ -1,7 +1,6 @@
 import numpy as np
 import pytest
 
-from numpy.lib.arraysetops import isin
 from sklearn.datasets import make_classification
 from tensorflow.keras import losses as losses_module
 from tensorflow.keras import metrics as metrics_module

tests/test_errors.py

@@ -4,8 +4,10 @@
 import pytest
 
 from sklearn.exceptions import NotFittedError
+from tensorflow.keras.layers import Dense, Input
+from tensorflow.keras.models import Model
 
-from scikeras.wrappers import BaseWrapper, KerasClassifier, KerasRegressor
+from scikeras.wrappers import KerasClassifier, KerasRegressor
 
 from .mlp_models import dynamic_classifier, dynamic_regressor
 
@@ -131,3 +133,49 @@ def no_bar(foo=42):
     est = wrapper(model=no_bar, bar=42, foo=43)
     with pytest.raises(TypeError, match="got an unexpected keyword argument"):
         est.fit([[1]], [1])
+
+
+@pytest.mark.parametrize("loss", [None, [None]])
+@pytest.mark.parametrize("compile", [True, False])
+def test_no_loss(loss, compile):
+    def get_model(compile, meta, compile_kwargs):
+        inp = Input(shape=(meta["n_features_in_"],))
+        hidden = Dense(10, activation="relu")(inp)
+        out = [
+            Dense(1, activation="sigmoid", name=f"out{i+1}")(hidden)
+            for i in range(meta["n_outputs_"])
+        ]
+        model = Model(inp, out)
+        if compile:
+            model.compile(**compile_kwargs)
+        return model
+
+    est = KerasRegressor(model=get_model, loss=loss, compile=compile)
+    with pytest.raises(ValueError, match="must provide a loss function"):
+        est.fit([[1]], [1])
+
+
+@pytest.mark.parametrize("compile", [True, False])
+def test_no_optimizer(compile):
+    def get_model(compile, meta, compile_kwargs):
+        inp = Input(shape=(meta["n_features_in_"],))
+        hidden = Dense(10, activation="relu")(inp)
+        out = [
+            Dense(1, activation="sigmoid", name=f"out{i+1}")(hidden)
+            for i in range(meta["n_outputs_"])
+        ]
+        model = Model(inp, out)
+        if compile:
+            model.compile(**compile_kwargs)
+        return model
+
+    est = KerasRegressor(
+        model=get_model,
+        loss="categorical_crossentropy",
+        compile=compile,
+        optimizer=None,
+    )
+    with pytest.raises(
+        ValueError, match="Could not interpret optimizer identifier"  # Keras error
+    ):
+        est.fit([[1]], [1])