docs(decomposer): improve error messages

tests/models/test_decomposer.py

@@ -229,7 +229,7 @@ def test_raise_warning_for_low_init_rank_reduction(mock_data_array):
     decomposer = Decomposer(
         n_modes=target_variance, init_rank_reduction=init_rank_reduction
     )
-    warn_msg = ".*components were computed which explain.*of the variance but.*of explained variance was requested. Consider increasing the `init_rank_reduction`"
+    warn_msg = "Dataset has .* components, explaining .* of the variance. However, .*explained variance was requested. Please consider increasing `init_rank_reduction`"
     with pytest.warns(UserWarning, match=warn_msg):
         decomposer.fit(mock_data_array)
 
@@ -243,7 +243,7 @@ def test_compute_at_least_one_component(mock_data_array):
     )
 
     # Warning is raised to indicate that the value of init_rank_reduction is too low
-    warn_msg = "`init_rank_reduction=.*` is too low and results in zero components. One component will be computed instead."
+    warn_msg = "`init_rank_reduction=.*` is too low resulting in zero components. One component will be computed instead."
     with pytest.warns(UserWarning, match=warn_msg):
         decomposer.fit(mock_data_array)
 

xeofs/models/decomposer.py

@@ -95,14 +95,14 @@ def fit(self, X, dims=("sample", "feature")):
             self.n_modes_precompute = int(rank * self.init_rank_reduction)
             if self.n_modes_precompute < 1:
                 warnings.warn(
-                    f"`init_rank_reduction={self.init_rank_reduction}` is too low and results in zero components. One component will be computed instead."
+                    f"`init_rank_reduction={self.init_rank_reduction}` is too low resulting in zero components. One component will be computed instead."
                 )
                 self.n_modes_precompute = 1
 
         # TODO(nicrie): perhaps we can just set n_modes to rank if it is larger than rank (possible solution for #158)
         if self.n_modes_precompute > rank:
             raise ValueError(
-                f"n_modes must be smaller or equal to the rank of the data object (rank={rank})"
+                f"n_modes must be less than or equal to the rank of the dataset (rank = {rank})."
             )
 
         # Check if data is small enough to use exact SVD
@@ -212,7 +212,7 @@ def fit(self, X, dims=("sample", "feature")):
             )
             if n_modes_required > self.n_modes_precompute:
                 warnings.warn(
-                    f"{self.n_modes_precompute} components were computed which explain {total_explained_variance:.2%} of the variance but {self.n_modes:.2%} of explained variance was requested. Consider increasing the `init_rank_reduction`."
+                    f"Dataset has {self.n_modes_precompute} components, explaining {total_explained_variance:.2%} of the variance. However, {self.n_modes:.2%} explained variance was requested. Please consider increasing `init_rank_reduction`."
                 )
                 n_modes_required = self.n_modes_precompute