Correct metric bounds, labels and the logcounts type

CHANGELOG.md

@@ -1,6 +1,10 @@
 # task_spatial_simulators dev
 
 Bug fixes:
+  - `file_dataset_sp.yaml`: `logcounts` is a double, not an integer.
+  - `downstream`: `clustering_ari` was declared -Inf..+Inf and
+    `ctdeconvolute_rmse` 0..+Inf, though both are bounded. Metric labels were
+    the ids repeated back.
   - `splatter` and `symsim`: drop the `try()` around the per-cluster loop, which
     let a failed cluster pass silently and only surfaced later as a length
     mismatch.

src/api/file_dataset_sp.yaml

@@ -13,7 +13,7 @@ info:
         name: counts
         description: Raw counts
         required: true
-      - type: integer
+      - type: double
         name: logcounts
         description: Log-transformed counts
         required: true

src/metrics/downstream/config.vsh.yaml

@@ -5,7 +5,7 @@ name: downstream
 info:
   metrics:
     - name: clustering_ari
-      label: clustering_ari
+      label: Adjusted Rand index
       summary: Adjusted rand index (ARI) measures the similarity between two clusters in real and simulated datasets.
       description: |
         Adjusted Rand Index used in spatial clustering to measure the similarity between two data clusterings, adjusted for chance.
@@ -14,11 +14,11 @@ info:
       links:
         documentation: https://cran.r-project.org/web/packages/aricode/index.html
         repository: https://github.com/jchiquet/aricode
-      min: -Inf
-      max: +Inf
+      min: -1
+      max: 1
       maximize: true
     - name: clustering_nmi
-      label: clustering_nmi
+      label: Normalised mutual information
       summary: Normalized mutual information (NMI) measures of the mutual dependence between the real and simulated spatial clusters.
       description: |
         Normalized Mutual Information used in spatial clustering to measure the agreement between two different clusterings, scaled to [0, 1].
@@ -31,7 +31,7 @@ info:
       max: 1
       maximize: true
     - name: svg_recall
-      label: svg_recall
+      label: SVG recall
       summary: Recall measures the proportion of real SVG correctly identified in the simulated dataset.
       description: |
         Recall used in identifying spatial variable genes, measuring the true positive rate.
@@ -41,7 +41,7 @@ info:
       max: 1
       maximize: true
     - name: svg_precision
-      label: svg_precision
+      label: SVG precision
       summary: Precision measures the proportion of correctly identified items in simulated datasets.
       description: |
         Precision used in identifying spatial variable genes, measuring the accuracy of positive predictions.
@@ -51,7 +51,7 @@ info:
       max: 1
       maximize: true
     - name: ctdeconvolute_rmse
-      label: ctdeconvolute_rmse
+      label: Cell type deconvolution RMSE
       summary: Root Mean Square deviation is calculated between the true and predicted proportion of per cell type. 
       description: |
         Root Mean Squared Error used in cell type deconvolution to measure the difference between observed and predicted values.
@@ -61,10 +61,10 @@ info:
         documentation: https://cran.r-project.org/web/packages/Metrics/Metrics.pdf
         repository: https://github.com/mfrasco/Metrics
       min: 0
-      max: +Inf
+      max: 1
       maximize: false
     - name: ctdeconvolute_jsd
-      label: ctdeconvolute_jsd
+      label: Cell type deconvolution JSD
       summary: Jensen-Shannon divergence (JSD) is calculated between the true and predicted proportion per cell type in all spots.
       description: |
         Jensen-Shannon Divergence used in cell type deconvolution to measure the similarity between two probability distributions.
@@ -77,7 +77,7 @@ info:
       max: 1
       maximize: false
     - name: crosscor_mantel
-      label: crosscor_mantel
+      label: Mantel statistic
       summary: Mantel statistic is the test statistic for the Mantel test, which is a correlation coefficient calculated between bivariate Moran’s I of real dataset and that of in simulation dataset.
       description: |
         Mantel statistic used in spatial cross-correlation to test the correlation between two distance matrices.
@@ -90,7 +90,7 @@ info:
       max: 1
       maximize: true
     - name: crosscor_cosine
-      label: crosscor_cosine
+      label: Cosine similarity
       summary: Cosine similarity measures similarity between bivariate Moran’s I of real dataset and that of in simulation dataset.
       description: |
         Cosine similarity used in spatial cross-correlation to measure the cosine of the angle between two non-zero vectors.