add edge noise

docs/source/release_notes.rst

@@ -1,7 +1,8 @@
 Release notes
 =============
 
-1.0.4 (02.01.2024)
+1.0.4 (17.01.2024)
+-------------------------------------------------
 
 - Moved the Global score summaries of ``SpatialBivariate`` from .uns to .var
 
@@ -15,9 +16,9 @@ Release notes
 
 - Global results of ``SpatialBivariate`` will now be saved to ``.var``
 
-- LIANA's spatial methods will now work with non-aligned AnnData objects, i.e. when observations across modalities are not aligned.
-= Added ``li.ut.interpolate_adata`` utility function to interpolate the data to a common space.
-= MISTy will also work with directly non-aligned data with spatial connectivities from one modality to the other being passed via ``obsm`` rather than ``obsp``. Making use of ``li.ut.spatial_neighbors`` by passing reference coordinates.
+- Added ``li.ut.interpolate_adata`` utility function to interpolate the data to a common space.
+
+- MISTy will also work with directly non-aligned data with spatial connectivities from one modality to the other being passed via ``obsm`` rather than ``obsp``. Making use of ``li.ut.spatial_neighbors`` by passing reference coordinates.
 
 - Fixed a bug where ``li.ut.obsm_to_adata`` would assign var as a method rather than DataFrame
 
@@ -33,8 +34,12 @@ Release notes
 
 - ``li.rs.explode_complexes`` is now consistently exported to ``li.rs`` (as previous versions)
 
+- ``li.mt.find_causalnet``: changed the noise assigned to nodes to be proportional to the minimum penalty of the model. Also, added noise to the edges to avoid multiple solutions to the same problem.
+
+
 1.0.3 (06.11.2023)
 -------------------------------------------------
+
 - Added ``filterby`` and ``filter_lambda`` parameters to ``li.pl.interactions`` and ``li.pl.target_metrics`` to allow filtering of interactions and metrics, respectively.
 
 - Removed unnecessary ``stat`` parameter from ``li.pl.contributions``

liana/method/fun/_causalnet.py

@@ -172,7 +172,7 @@ def find_causalnet(
     # assign 0 penalties to input/output nodes, missing_penalty to missing nodes
     # add a small amount of noise to the penalties to ensure reproducible solutions
     rng = np.random.default_rng(seed=seed)
-    c_node_penalties = {k: node_penalties.get(k, missing_penalty) + rng.uniform(0, 0.00001)
+    c_node_penalties = {k: node_penalties.get(k, missing_penalty) + rng.uniform(min_penalty/100, min_penalty/10)
                         if k not in measured_nodes else 0.0 for k in prior_graph.vertices}
 
     _logg("Building CORNETO problem...", verbose=verbose)
@@ -184,6 +184,11 @@ def find_causalnet(
         edge_penalty=edge_penalty
     )
 
+    # E is the variable with 1 if edge activates or inhibits, 0 otherwise
+    E = P.symbols['reaction_sends_activation_c0'] + P.symbols['reaction_sends_inhibition_c0']
+    W = rng.uniform(edge_penalty/100, edge_penalty/10, size=E.shape)
+    P.add_objectives(W.T @ E)
+
     _logg(f"Solving with {solver}...", verbose=verbose)
     if (solver=='scipy') and verbose:
         kwargs.update(scipy_options=dict(disp='true'))
@@ -201,6 +206,7 @@ def find_causalnet(
         _logg(f" - {s}: {o.value}", verbose=verbose)
     rows, cols = cn.methods.carnival.export_results(P, G, input_node_scores, output_node_scores)
     df = pd.DataFrame(rows, columns=cols)
+
     return df, P
 
 

liana/tests/test_causalnet.py

@@ -26,10 +26,11 @@ def test_caulsalnet():
                                      output_scores,
                                      node_weights=node_weights,
                                      verbose=False,
-                                     solver='scipy'
+                                     solver='scipy',
+                                     seed=1337
                                      )
 
-    assert problem.weights == [1.0, 0.01, 1.0]
+    assert problem.weights == [1.0, 0.01, 1.0, 1.0]
     assert df_res['source_pred_val'].values.sum() == 5
     assert df_res['target_pred_val'].values.sum() == 8
     assert df_res[df_res['source_type']=='input']['source'].values[0] == 'I2'