Fix cv related

README.md

@@ -12,8 +12,11 @@ git submodule update --init --recursive
    See [feature-extraction/atlasnet/README.md](feature-extraction/atlasnet/README.md) for instructions.
 
 2. **Feature decoding** — Decode DNN features from fMRI data using linear decoders.
-   See [feature-decoding/README.md](feature-decoding/README.md) for instructions.
-
-3. **Reconstruction** — Reconstruct 3D shapes from true or decoded features using AtlasNet.
-   See [reconstruction/atlasnet/README.md](reconstruction/atlasnet/README.md) for instructions.
+   This includes cross-validation training and prediction for estimating the
+   decoded-feature scaling factor. See
+   [feature-decoding/README.md](feature-decoding/README.md) for instructions.
 
+3. **Feature scaling and reconstruction** — Scale decoded features using the
+   cross-validation estimate, then reconstruct 3D shapes from true or scaled
+   decoded features using AtlasNet. See
+   [reconstruction/atlasnet/README.md](reconstruction/atlasnet/README.md) for instructions.

feature-decoding/README.md

@@ -57,6 +57,32 @@ python scripts/predict_feature_fastl2lir.py train-3d-natural-objects_rep3_fmap_t
 
 Output: decoded features saved to the path specified by `decoded_feature.path` in the config.
 
+### Cross-validation for feature scaling
+
+Cross-validation predictions on the training dataset are used to estimate the
+standard deviation of decoded features for feature scaling.
+
+#### 1. Train cross-validation decoders
+
+```bash
+uv run python scripts/cv_train_decoder_fastl2lir.py config/cv_train-3d-natural-objects-image_rep3_fmap_fmriprep_5000voxel_atlasnet.yaml
+```
+
+Output: cross-validation decoders are saved to the path specified by
+`decoder.path` in the CV config.
+
+#### 2. Decode cross-validation features
+
+```bash
+uv run python scripts/cv_predict_feature_fastl2lir.py config/cv_train-3d-natural-objects-image_rep3_fmap_fmriprep_5000voxel_atlasnet.yaml
+```
+
+Output: cross-validated training features are saved to the path specified by
+`decoded_feature.path` in the CV config.
+
+The cross-validated features are subsequently used for feature scaling. See
+[the AtlasNet reconstruction instructions](../reconstruction/atlasnet/README.md#feature-scaling).
+
 ## Config files
 
 Config files are located in `config/`. Each file specifies a combination of training and test datasets.

reconstruction/atlasnet/README.md

@@ -20,6 +20,29 @@ The AtlasNet model weights must be placed at:
 data/models/atlasnet/network_crtd.pth
 ```
 
+## Feature scaling
+
+Before reconstructing shapes from decoded features, scale the decoded test
+features using the standard deviation estimated from cross-validation predictions.
+
+First, complete:
+
+1. Regular feature decoder training and feature prediction.
+2. Cross-validation decoder training and prediction described in
+   [feature-decoding/README.md](../../feature-decoding/README.md#cross-validation-for-feature-scaling).
+
+Then run:
+
+```bash
+uv run feature_scaling.py
+```
+
+For every experiment listed in `feature_scaling.py`, the scaled features are saved to:
+
+```text
+data/decoded-features/{experiment}_scaled_traincvstd/atlasnet/
+```
+
 ## Usage
 
 ```bash
@@ -51,9 +74,12 @@ true/
 
 ### From decoded features
 
-Input: `data/decoded-features/{experiment}/atlasnet/`
+Input: `data/decoded-features/{experiment}_scaled_traincvstd/atlasnet/`
+
+Output: `data/reconstruction/atlasnet_encoder_bn5/decoded/{experiment}_scaled_traincvstd/{subject}/{roi}/`
 
-Output: `data/reconstruction/atlasnet_encoder_bn5/decoded/{experiment}/{subject}/{roi}/`
+`recon_from_features.py` reconstructs shapes only from the scaled decoded-feature
+datasets listed in `decoded_datasets`.
 
 ```
 decoded/

reconstruction/atlasnet/recon_from_features.py

@@ -152,11 +152,11 @@ def reconstruct_all(recon, features, output_dir, source_layer, subject=None, roi
 
     # Decoded features
     decoded_datasets = [
-        'train-3d-natural-objects_rep3_test-3d-natural-objects_rep8_fmap_fmriprep_5000voxel_fastl2lir_alpha5000',
-        'train-3d-natural-objects_rep3_test-3d-artificial-objects-image_rep8_fmap_fmriprep_5000voxel_fastl2lir_alpha5000',
-        'train-3d-natural-objects_rep3_test-3d-artificial-objects-rds_rep8_fmap_fmriprep_5000voxel_fastl2lir_alpha5000',
-        'train-3d-natural-objects_rep3_test-3d-contour-matched-rds-horizontal-shape-variants_rep8_fmap_fmriprep_5000voxel_fastl2lir_alpha5000',
-        'train-3d-natural-objects_rep3_test-3d-contour-matched-rds-thin-tilt-variants_rep8_fmap_fmriprep_5000voxel_fastl2lir_alpha5000',
+        'train-3d-natural-objects_rep3_test-3d-natural-objects_rep8_fmap_fmriprep_5000voxel_fastl2lir_alpha5000_scaled_traincvstd',
+        'train-3d-natural-objects_rep3_test-3d-artificial-objects-image_rep8_fmap_fmriprep_5000voxel_fastl2lir_alpha5000_scaled_traincvstd',
+        'train-3d-natural-objects_rep3_test-3d-artificial-objects-rds_rep8_fmap_fmriprep_5000voxel_fastl2lir_alpha5000_scaled_traincvstd',
+        'train-3d-natural-objects_rep3_test-3d-contour-matched-rds-horizontal-shape-variants_rep8_fmap_fmriprep_5000voxel_fastl2lir_alpha5000_scaled_traincvstd',
+        'train-3d-natural-objects_rep3_test-3d-contour-matched-rds-thin-tilt-variants_rep8_fmap_fmriprep_5000voxel_fastl2lir_alpha5000_scaled_traincvstd',
     ]
 
     subjects = ['S1', 'S2', 'S3', 'S4', 'S5']