Add JSD metric

src/metrics/jsd/config.vsh.yaml

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+__merge__: ../../api/comp_metric.yaml
+
+name: jsd
+info:
+  metrics:
+    - name: jensen_shannon_distance
+      label: Jensen-Shannon Distance
+      summary: "Jensen-Shannon Distance measure the similarity between to probability distributions."
+      description: |
+        The Jensen-Shannon Distance, which is the square root of Jensen-Shannon Divergence is a symmetric method for measuring the similarity between two probability distributions. The similarity between the distributions is greater when the Jensen-Shannon distance is closer to zero.
+      reference: 10.1109/18.61115
+      documentation_url: https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.jensenshannon.html
+      repository_url: https://github.com/scipy/scipy/
+      min: 0
+      max: 1
+      maximize: false
+
+resources:
+  - type: python_script
+    path: script.py
+
+engines:
+  - type: docker
+    image: ghcr.io/openproblems-bio/base_images/python:1.1.0
+    setup:
+      - type: python
+        packages: [numpy, scipy]
+
+runners:
+  - type: executable
+  - type: nextflow
+    directives:
+      label: [midtime, midmem, midcpu]

src/metrics/jsd/script.py

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+import anndata as ad
+import numpy as np
+from scipy.spatial.distance import jensenshannon
+
+## VIASH START
+par = {
+  'input_method': 'resources_test/spatial_decomposition/cxg_mouse_pancreas_atlas/output.h5ad',
+  'input_solution': 'resources_test/spatial_decomposition/cxg_mouse_pancreas_atlas/solution.h5ad',
+  'output': 'score.h5ad'
+}
+meta = {
+  'name': 'r2'
+}
+## VIASH END
+
+print('Reading input files', flush=True)
+input_method = ad.read_h5ad(par['input_method'])
+input_solution = ad.read_h5ad(par['input_solution'])
+
+print('Compute metrics', flush=True)
+jsd = jensenshannon(input_solution.obsm['proportions_true'], input_method.obsm['proportions_pred'], axis=0)
+uns_metric_ids = [ 'jsd' ]
+uns_metric_values = [ np.mean(jsd) ]
+
+print("Write output AnnData to file", flush=True)
+output = ad.AnnData(
+  uns={
+    'dataset_id': input_method.uns['dataset_id'],
+    'method_id': input_method.uns['method_id'],
+    'metric_ids': uns_metric_ids,
+    'metric_values': uns_metric_values
+  }
+)
+output.write_h5ad(par['output'], compression='gzip')