Merge branch 'set_shape_basis_method' into improve_transformer_api

src/nemos/basis/_basis_mixin.py

@@ -307,11 +307,7 @@ def _compute_features(self, *xi: NDArray | Tsd | TsdFrame | TsdTensor):
         structure: a single (X, y) pair for the transformer, a number of time series for the Basis.
 
         """
-        if self.kernel_ is None:
-            raise ValueError(
-                "You must call `setup_basis` before `_compute_features`! "
-                "Convolution kernel is not set."
-            )
+        self._check_has_kernel()
         # before calling the convolve, check that the input matches
         # the expectation. We can check xi[0] only, since convolution
         # is applied at the end of the recursion on the 1D basis, ensuring len(xi) == 1.
@@ -457,8 +453,8 @@ def _check_convolution_kwargs(conv_kwargs: dict):
     def _check_has_kernel(self) -> None:
         """Check that the kernel is pre-computed."""
         if self.kernel_ is None:
-            raise ValueError(
-                "You must call `_set_kernel` before `_compute_features` for Conv basis."
+            raise RuntimeError(
+                "You must call `setup_basis` before `_compute_features` for Conv basis."
             )
 
 
@@ -517,7 +513,7 @@ def __init__(self, basis1: Basis, basis2: Basis):
             *(bas2._input_shape_ for bas2 in basis2._iterate_over_components()),
         )
         # if all bases where set, then set input for composition.
-        set_bases = (s is not None for s in shapes)
+        set_bases = [s is not None for s in shapes]
 
         if all(set_bases):
             # pass down the input shapes

tests/test_basis.py

@@ -152,6 +152,15 @@ def method(self):
             pass
 
     assert CustomSubClass().method.__doc__ == "My extra text.\nMy custom method."
+    with pytest.raises(AttributeError, match="CustomClass has no attribute"):
+
+        class CustomSubClass2(CustomClass):
+            @custom_add_docstring("unknown", cls=CustomClass)
+            def method(self):
+                """My custom method."""
+                pass
+
+        CustomSubClass2()
 
 
 @pytest.mark.parametrize(
@@ -241,18 +250,34 @@ def test_expected_output_compute_features(basis_instance, super_class):
             ),
             OrthExponentialBasis,
         ),
+        (
+            basis.OrthExponentialConv(
+                10, decay_rates=np.arange(1, 11), window_size=12, label="a"
+            )
+            * basis.RaisedCosineLogConv(10, window_size=11, label="b"),
+            OrthExponentialBasis,
+        ),
+        (
+            basis.OrthExponentialConv(
+                10, decay_rates=np.arange(1, 11), window_size=12, label="a"
+            )
+            + basis.RaisedCosineLogConv(10, window_size=11, label="b"),
+            OrthExponentialBasis,
+        ),
     ],
 )
 def test_expected_output_split_by_feature(basis_instance, super_class):
-    x = super_class.compute_features(basis_instance, np.linspace(0, 1, 100))
+    inp = [np.linspace(0, 1, 100)] * basis_instance._n_input_dimensionality
+    x = super_class.compute_features(basis_instance, *inp)
     xdict = super_class.split_by_feature(basis_instance, x)
     xxdict = basis_instance.split_by_feature(x)
     assert xdict.keys() == xxdict.keys()
-    xx = xxdict["label"]
-    x = xdict["label"]
-    nans = np.isnan(x.sum(axis=(1, 2)))
-    assert np.all(np.isnan(xx[nans]))
-    np.testing.assert_array_equal(xx[~nans], x[~nans])
+    for k in xdict.keys():
+        xx = xxdict[k]
+        x = xdict[k]
+        nans = np.isnan(x.sum(axis=(1, 2)))
+        assert np.all(np.isnan(xx[nans]))
+        np.testing.assert_array_equal(xx[~nans], x[~nans])
 
 
 @pytest.mark.parametrize(
@@ -1236,7 +1261,7 @@ def test_transform_fails(self, cls):
             n_basis_funcs=5, window_size=5, **extra_decay_rates(cls["conv"], 5)
         )
         with pytest.raises(
-            ValueError, match="You must call `setup_basis` before `_compute_features`"
+            RuntimeError, match="You must call `setup_basis` before `_compute_features`"
         ):
             bas._compute_features(np.linspace(0, 1, 10))
 
@@ -1579,6 +1604,15 @@ def test_minimum_number_of_basis_required_is_matched(
                     n_basis_funcs=n_basis_funcs, order=order, **kwargs
                 )
                 basis_obj.compute_features(np.linspace(0, 1, 10))
+
+            # test the setter valuerror
+            if (order > 1) & (n_basis_funcs > 1):
+                basis_obj = self.cls[mode](n_basis_funcs=20, order=order, **kwargs)
+                with pytest.raises(
+                    ValueError,
+                    match=rf"{self.cls[mode].__name__} `order` parameter cannot be larger than",
+                ):
+                    basis_obj.n_basis_funcs = n_basis_funcs
         else:
             basis_obj = self.cls[mode](
                 n_basis_funcs=n_basis_funcs, order=order, **kwargs
@@ -2210,6 +2244,41 @@ def test_number_of_required_inputs_compute_features(
         with expectation:
             basis_obj.compute_features(*inputs)
 
+    @pytest.mark.parametrize("basis_a", list_all_basis_classes())
+    @pytest.mark.parametrize("basis_b", list_all_basis_classes())
+    @pytest.mark.parametrize("n_basis_a", [5])
+    @pytest.mark.parametrize("n_basis_b", [6])
+    @pytest.mark.parametrize("window_size", [10])
+    def test_warn_partial_setup(
+        self,
+        n_basis_a,
+        n_basis_b,
+        basis_a,
+        basis_b,
+        window_size,
+        basis_class_specific_params,
+    ):
+        basis_a_obj = self.instantiate_basis(
+            n_basis_a, basis_a, basis_class_specific_params, window_size=window_size
+        )
+        basis_b_obj = self.instantiate_basis(
+            n_basis_b, basis_b, basis_class_specific_params, window_size=window_size
+        )
+
+        basis_a_obj.set_input_shape(*([1] * basis_a_obj._n_input_dimensionality))
+        with pytest.warns(UserWarning, match="Only some of the basis where"):
+            basis_a_obj + basis_b_obj
+
+        # check that if both set addition is fine
+        basis_b_obj.set_input_shape(*([1] * basis_b_obj._n_input_dimensionality))
+        basis_a_obj + basis_b_obj
+
+        basis_a_obj = self.instantiate_basis(
+            n_basis_a, basis_a, basis_class_specific_params, window_size=window_size
+        )
+        with pytest.warns(UserWarning, match="Only some of the basis where"):
+            basis_a_obj + basis_b_obj
+
     @pytest.mark.parametrize("sample_size", [11, 20])
     @pytest.mark.parametrize("basis_a", list_all_basis_classes())
     @pytest.mark.parametrize("basis_b", list_all_basis_classes())
@@ -2661,7 +2730,7 @@ def test_transform_fails(
             context = does_not_raise()
         else:
             context = pytest.raises(
-                ValueError,
+                RuntimeError,
                 match="You must call `setup_basis` before `_compute_features`",
             )
         with context:
@@ -3589,7 +3658,7 @@ def test_transform_fails(
             context = does_not_raise()
         else:
             context = pytest.raises(
-                ValueError,
+                RuntimeError,
                 match="You must call `setup_basis` before `_compute_features`",
             )
         with context: