Added more tests

Author: PauBadiaM

src/decoupler/bm/_run.py

@@ -152,6 +152,9 @@ def _metric_scores(
     verbose: bool,
     **kwargs
 ) -> None:
+    assert isinstance(metrics, (str, list)), 'metrics must be str or list'
+    if isinstance(metrics, str):
+        metrics = [metrics]
     if runby == 'expr':
         m = ('benchmark - evaluating by experiment on:\n' +
         f'n_expr={score.shape[0]}, n_sources={score.shape[1]} across metrics={metrics}')

tests/bm/test_benchmark.py

@@ -0,0 +1,46 @@
+import pandas as pd
+import pytest
+
+import decoupler as dc
+
+
+@pytest.mark.parametrize(
+    'metrics,groupby,runby,sfilt,thr,emin',
+    [
+        ['auc', None, 'expr', False, 0.05, 5],
+        ['auc', None, 'expr', True, 0.05, 5],
+        [['auc'], None, 'expr', False, 0.05, 5],
+        [['auc', 'fscore'], 'group', 'expr', False, 0.05, 5],
+        [['auc', 'fscore', 'qrank'], None, 'source', False, 0.05, 2],
+        [['auc', 'fscore', 'qrank'], 'group', 'source', False, 0.05, 1],
+    ]
+)
+def test_benchmark(
+    bdata,
+    net,
+    metrics,
+    groupby,
+    runby,
+    sfilt,
+    thr,
+    emin,
+):
+    df = dc.bm.benchmark(
+        adata=bdata,
+        net=net,
+        metrics=metrics,
+        groupby=groupby,
+        runby=runby,
+        sfilt=sfilt,
+        thr=thr,
+        emin=emin,
+        kws_decouple={
+            'cons': True,
+            'tmin': 3,
+            'methods': ['ulm', 'zscore']
+        },
+    )
+    assert isinstance(df, pd.DataFrame)
+    cols = {'method', 'metric', 'score'}
+    print(df.columns)
+    assert cols.issubset(df.columns)

tests/conftest.py

@@ -14,14 +14,14 @@ def rng():
 
 @pytest.fixture
 def adata():
-    adata, _ = dc.ds.toy(nobs=40, nvar=20, bval=2, seed=42, verbose=False)
+    adata, _ = dc.ds.toy(nobs=10, nvar=20, bval=2, seed=42, verbose=False)
     adata.layers['counts'] = adata.X.round()
     return adata
 
 
 @pytest.fixture
 def tdata():
-    tdata, _ = dc.ds.toy(nobs=40, nvar=20, bval=2, seed=42, verbose=False, pstime=True)
+    tdata, _ = dc.ds.toy(nobs=10, nvar=20, bval=2, seed=42, verbose=False, pstime=True)
     return tdata
 
 
@@ -46,6 +46,12 @@ def pdata(
     return dc.pp.pseudobulk(adata=adata, sample_col='sample', groups_col='group')
 
 
+@pytest.fixture
+def bdata():
+    adata, _ = dc.ds.toy_bench(nobs=100, nvar=20, bval=2, seed=42, verbose=False)
+    return adata
+
+
 @pytest.fixture
 def deg():
     deg = pd.DataFrame(

tests/mt/test_decouple.py

@@ -4,23 +4,31 @@
 
 
 @pytest.mark.parametrize(
-    'methods,args,consensus',
+    'methods,args,cons,anndata',
     [
-        ['all', dict(), True],
-        ['aucell', dict(aucell=dict(n_up=3)), True],
-        [['ulm'], dict(), False],
-        [['ulm', 'ora'], dict(ulm=dict(), ora=dict(n_up=3)), False]
+        ['all', dict(), True, True],
+        ['aucell', dict(aucell=dict(n_up=3)), True, False],
+        [['ulm'], dict(), False, True],
+        [['ulm', 'ora'], dict(ulm=dict(), ora=dict(n_up=3)), False, False]
     ]
 )
 def test_decouple(
     adata,
     net,
     methods,
     args,
-    consensus,
+    cons,
+    anndata
 ):
-    dc.mt.decouple(data=adata, net=net, methods=methods, args=args, cons=consensus, tmin=0)
-    if consensus:
-        assert 'score_consensus' in adata.obsm
+    if anndata:
+        dc.mt.decouple(data=adata, net=net, methods=methods, args=args, cons=cons, tmin=0)
+        if cons:
+            assert 'score_consensus' in adata.obsm
+        else:
+            assert 'score_consensus' not in adata.obsm
     else:
-        assert 'score_consensus' not in adata.obsm
+        res = dc.mt.decouple(data=adata.to_df(), net=net, methods=methods, args=args, cons=cons, tmin=0)
+        if cons:
+            assert 'score_consensus' in res
+        else:
+            assert 'score_consensus' not in res

tests/pl/test_barplot.py

@@ -20,17 +20,19 @@ def df():
 
 
 @pytest.mark.parametrize(
-    'name,top,vertical',
+    'name,top,vertical,vcenter',
     [
-        ['C1', 2, True],
-        ['C2', 10, False],
+        ['C1', 2, True, None],
+        ['C2', 10, False, -3],
+        ['C2', 10, False, 10],
     ]
 )
 def test_barplot(
     df,
     name,
     top,
     vertical,
+    vcenter,
 ):
     fig = dc.pl.barplot(data=df, name=name, top=top, vertical=vertical, return_fig=True)
     assert isinstance(fig, Figure)

tests/pl/test_order_targets.py

@@ -13,6 +13,7 @@
         ['T3', 'group', -20, 15],
         ['T4', 'group', -1, 20],
         ['T5', 'group', -2, 14],
+        ['T5', 'group', None, None],
     ]
 )
 def test_order_targets(

tests/pp/test_anndata.py

@@ -54,6 +54,7 @@ def test_swap_layer(
         [None, 'mean', True, True],
         ['sample', 'sum', False, True],
         ['sample', 'sum', False, False],
+        ['sample', 'sum', True, False],
         [['dose', 'group'], 'sum', False, True],
         ['group', 'median', False, False],
         ['group', lambda x: np.max(x) - np.min(x), True, True],
@@ -73,8 +74,9 @@ def test_pseudobulk(
     if empty:
         adata.X[:, 3] = 0.
         adata.layers['counts'][:, 3] = 0.
-        adata.X[5, :] = 0.
-        adata.layers['counts'][5, :] = 0.
+        msk = adata.obs['sample'] == 'S01'
+        adata.X[msk, :] = 0.
+        adata.layers['counts'][msk, :] = 0.
     if sparse:
         adata.X = sps.csr_matrix(adata.X)
     if mode == 'sum':

tests/tl/test_rankby_obsm.py

@@ -19,6 +19,9 @@ def test_rankby_obsm(
     key,
     uns_key,
 ):
+    tdata_obsm = tdata_obsm.copy()
+    tdata_obsm.obs['dose'] = 'Low'
+    tdata_obsm.obs.loc[tdata_obsm.obs_names[5], 'dose'] = 'High'
     res = dc.tl.rankby_obsm(tdata_obsm, key=key, uns_key=uns_key)
     if uns_key is None:
         assert isinstance(res, pd.DataFrame)