sidpy fitter refactor AIC BIC

Author: ramav87

sidpy/proc/fitter_refactor.py

@@ -561,7 +561,6 @@ def fit_worker(data_block, guess_block, x_in, ind_dims, num_params):
 
         return self.transform_to_sidpy(computed_result)
 
-
     def transform_to_sidpy(self, fit_dask_array):
         """
         Convert the fit results into sidpy.Dataset(s).
@@ -593,7 +592,7 @@ def transform_to_sidpy(self, fit_dask_array):
         else:
             raise ValueError(f"Unknown cov_mode={cov_mode!r}. Use None, 'full', 'diag', or 'stderr'.")
 
-        metrics_size = 2 if return_metrics else 0
+        metrics_size = 4 if return_metrics else 0
         expected = self.num_params + cov_size + metrics_size
 
         if total_channels != expected:

tests/proc/test_fitter_refactor.py

@@ -190,7 +190,8 @@ def test_2d_fit_execution(self):
             use_kmeans=False, 
             fit_parameter_labels=self.param_labels,
             return_cov=True,
-            loss='linear'
+            loss='linear',
+            return_metrics=False
         )
 
         # --- Assertions ---
@@ -267,7 +268,8 @@ def test_cov_mode_diag_returns_param_length_per_pixel(self):
         res_params, res_cov_diag = fitter.do_fit(
             use_kmeans=False,
             cov_mode='diag',
-            loss='linear'
+            loss='linear',
+            return_metrics=False
         )
 
         # Params should be (nx, ny, 6)
@@ -314,7 +316,8 @@ def test_cov_mode_stderr_nonnegative_and_shape(self):
         res_params, res_stderr = fitter.do_fit(
             use_kmeans=False,
             cov_mode='stderr',
-            loss='linear'
+            loss='linear',
+            return_metrics=False
         )
 
         assert res_params.shape == (nx, ny, 6)
@@ -367,7 +370,7 @@ def test_metrics_perfect_fit_r2_one_rmse_zero(self):
         assert np.nanmin(r2) > 0.999
         assert np.nanmax(rmse) < 1e-6
 
-    def test_aic_bic_increase_with_noise_on_same_model():
+    def test_aic_bic_increase_with_noise_on_same_model(self):
         import sidpy as sid
         rng = np.random.default_rng(0)
 
@@ -484,7 +487,7 @@ def make_dataset(arr, title):
             )
         fitter.setup_calc()
         params, metrics = fitter.do_fit()
-        assert metrics.shape[-1] == 2
+        assert metrics.shape[-1] == 4