[FIX/ENH] Improve generate_report method to work with fit_transform for the maskers

doc/changes/latest.rst

@@ -32,6 +32,7 @@ Fixes
 
 - Fix pathlib.Path not being counted as Niimg-like object in :func:`~image.new_img_like` (:gh:`3723` by `Maximilian Cosmo Sitter`_).
 
+- Fix ``fit_transform`` behavior to match when ``fit`` method is passed image data (:gh:`3897` by `Yasmin Mzayek`_)
 
 Enhancements
 ------------
@@ -48,6 +49,8 @@ Enhancements
 
 - Add ``vmin`` and ``symmetric_cbar`` arguments to :func:`~nilearn.plotting.plot_img_on_surf` (:gh:`3873` by `Michelle Wang`_).
 
+- Improve ``generate_report`` method of maskers by allowing users to pass a cmap argument for plotting image (:gh:`3897` by `Yasmin Mzayek`_)
+
 Changes
 -------
 

nilearn/maskers/nifti_labels_masker.py

@@ -203,6 +203,8 @@ def __init__(
 
         self.keep_masked_labels = keep_masked_labels
 
+        self.cmap = kwargs.get("cmap", "CMRmap_r")
+
     def generate_report(self):
         """Generate a report."""
         from nilearn.reporting.html_report import generate_report
@@ -299,7 +301,7 @@ def _reporting(self):
                 display = plotting.plot_img(
                     img,
                     black_bg=False,
-                    cmap='CMRmap_r',
+                    cmap=self.cmap,
                 )
                 plt.close()
                 display.add_contours(labels_image, filled=False, linewidths=3)
@@ -334,8 +336,15 @@ def _reporting(self):
     def fit(self, imgs=None, y=None):
         """Prepare signal extraction from regions.
 
-        All parameters are unused, they are for scikit-learn compatibility.
+        Parameters
+        ----------
+        imgs : :obj:`list` of Niimg-like objects
+            See :ref:`extracting_data`.
+            Image data passed to the reporter.
 
+        y : None
+            This parameter is unused. It is solely included for scikit-learn
+            compatibility.
         """
         repr = _utils._repr_niimgs(self.labels_img,
                                    shorten=(not self.verbose))
@@ -451,8 +460,9 @@ def fit_transform(self, imgs, confounds=None, sample_mask=None):
             shape: (number of scans, number of labels)
 
         """
-        return self.fit().transform(imgs, confounds=confounds,
-                                    sample_mask=sample_mask)
+        return self.fit(imgs).transform(
+            imgs, confounds=confounds, sample_mask=sample_mask
+        )
 
     def _check_fitted(self):
         if not hasattr(self, 'labels_img_'):

nilearn/maskers/nifti_maps_masker.py

@@ -190,6 +190,8 @@ def __init__(
 
         self.keep_masked_maps = keep_masked_maps
 
+        self.cmap = kwargs.get("cmap", "CMRmap_r")
+
     def generate_report(self, displayed_maps=10):
         """Generate an HTML report for the current ``NiftiMapsMasker`` object.
 
@@ -343,7 +345,7 @@ def _reporting(self):
                 img,
                 cut_coords=cut_coords[idx],
                 black_bg=False,
-                cmap="CMRmap_r",
+                cmap=self.cmap,
             )
             display.add_overlay(
                 image.index_img(maps_image, idx),
@@ -356,8 +358,15 @@ def _reporting(self):
     def fit(self, imgs=None, y=None):
         """Prepare signal extraction from regions.
 
-        All parameters are unused, they are for scikit-learn compatibility.
+        Parameters
+        ----------
+        imgs : :obj:`list` of Niimg-like objects
+            See :ref:`extracting_data`.
+            Image data passed to the reporter.
 
+        y : None
+            This parameter is unused. It is solely included for scikit-learn
+            compatibility.
         """
         # Load images
         repr = _utils._repr_niimgs(self.mask_img, shorten=(not self.verbose))
@@ -439,7 +448,7 @@ def _check_fitted(self):
 
     def fit_transform(self, imgs, confounds=None, sample_mask=None):
         """Prepare and perform signal extraction."""
-        return self.fit().transform(
+        return self.fit(imgs).transform(
             imgs, confounds=confounds, sample_mask=sample_mask
         )
 

nilearn/maskers/nifti_masker.py

@@ -296,6 +296,8 @@ def __init__(
             k[7:]: v for k, v in kwargs.items() if k.startswith("clean__")
         }
 
+        self.cmap = kwargs.get("cmap", "CMRmap_r")
+
     def generate_report(self):
         """Generate a report of the masker."""
         from nilearn.reporting.html_report import generate_report
@@ -357,7 +359,7 @@ def _reporting(self):
         init_display = plotting.plot_img(
             img,
             black_bg=False,
-            cmap="CMRmap_r",
+            cmap=self.cmap,
         )
         plt.close()
         if mask is not None:
@@ -386,7 +388,7 @@ def _reporting(self):
         final_display = plotting.plot_img(
             resampl_img,
             black_bg=False,
-            cmap="CMRmap_r",
+            cmap=self.cmap,
         )
         plt.close()
         final_display.add_contours(

nilearn/maskers/nifti_spheres_masker.py

@@ -398,8 +398,9 @@ def fit_transform(self, imgs, confounds=None, sample_mask=None):
             shape: (number of scans, number of spheres)
 
         """
-        return self.fit().transform(imgs, confounds=confounds,
-                                    sample_mask=sample_mask)
+        return self.fit(imgs).transform(
+            imgs, confounds=confounds, sample_mask=sample_mask
+        )
 
     def _check_fitted(self):
         if not hasattr(self, "seeds_"):