making it fail

Makefile

@@ -64,6 +64,7 @@ virtualenv:       ## Create a virtual environment.
 	@uv venv
 	@source .venv/bin/activate
 	@make install
+	@echo "!!! Please run 'source .venv/bin/activate' to enable the environment !!!"
 
 .PHONY: release
 release:          ## Create a new tag for release.

bengrn/base.py

@@ -29,6 +29,7 @@
 from ctxcore.rnkdb import FeatherRankingDatabase as RankingDatabase
 from grnndata import GRNAnnData, from_adata_and_longform, from_scope_loomfile, utils
 from scipy.sparse import csc_matrix, csr_matrix
+from scipy.sparse import issparse
 from sklearn.linear_model import LogisticRegression, RidgeClassifier
 from sklearn.metrics import PrecisionRecallDisplay, auc, precision_recall_curve
 from sklearn.model_selection import train_test_split
@@ -203,9 +204,10 @@ def scprint_benchmark(self, elems=["Central", "Regulators", "Targets"]):
                 except KeyError:
                     pass
             istrue = metrics.get("TF_enr", False)
-            istrue = istrue or (
-                res.res2d.loc[res.res2d.Term == "0__TFs", "FDR q-val"].iloc[0] < 0.1
-            )
+            if len(res.res2d.loc[res.res2d.Term == "0__TFs"]) > 0:
+                istrue = istrue or (
+                    res.res2d.loc[res.res2d.Term == "0__TFs", "FDR q-val"].iloc[0] < 0.1
+                )
             metrics.update({"TF_enr": istrue})
         if self.doplot:
             print("_________________________________________")
@@ -608,6 +610,7 @@ def get_perturb_gt(
         GRNAnnData: The Gene Regulatory Network data as a GRNAnnData object.
     """
     if not os.path.exists(filename_bh):
+        os.makedirs(os.path.dirname(filename_bh), exist_ok=True)
         urllib.request.urlretrieve(url_bh, filename_bh)
     pert = pd.read_csv(filename_bh)
     pert = pert.set_index("Unnamed: 0").T
@@ -711,7 +714,7 @@ def compute_genie3(
     Returns:
         GRNAnnData: The Gene Regulatory Network data computed using the GENIE3 algorithm.
     """
-    mat = np.asarray(adata.X.todense())
+    mat = np.asarray(adata.X.toarray() if issparse(adata.X) else adata.X)
     names = adata.var_names[mat.sum(0) > 0].tolist()
     var = adata.var[mat.sum(0) > 0]
     mat = mat[:, mat.sum(0) > 0]
@@ -759,6 +762,9 @@ def get_GT_db(
         net = dc.get_dorothea(organism=organism)
     elif name == "omnipath":
         if not os.path.exists(FILEDIR + "/../data/omnipath.parquet"):
+            os.makedirs(
+                os.path.dirname(FILEDIR + "/../data/omnipath.parquet"), exist_ok=True
+            )
             from omnipath.interactions import AllInteractions
             from omnipath.requests import Annotations
 
@@ -992,7 +998,6 @@ def load_genes(organisms: Union[str, list] = "NCBITaxon:9606"):  # "NCBITaxon:10
         genesdf = bt.Gene.filter(
             organism_id=bt.Organism.filter(ontology_id=organism).first().id
         ).df()
-        genesdf = genesdf[~genesdf["public_source_id"].isna()]
         genesdf = genesdf.drop_duplicates(subset="ensembl_gene_id")
         genesdf = genesdf.set_index("ensembl_gene_id").sort_index()
         # mitochondrial genes
@@ -1004,8 +1009,7 @@ def load_genes(organisms: Union[str, list] = "NCBITaxon:9606"):  # "NCBITaxon:10
         genesdf["organism"] = organism
         organismdf.append(genesdf)
     organismdf = pd.concat(organismdf)
-    organismdf.drop(
-        columns=["source_id", "run_id", "created_by_id", "updated_at", "stable_id"],
-        inplace=True,
-    )
+    for col in ["source_id", "run_id", "created_by_id", "updated_at", "stable_id"]:
+        if col in organismdf.columns:
+            organismdf.drop(columns=[col], inplace=True)
     return organismdf

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "benGRN"
-version = "1.2.2"
+version = "1.2.4"
 description = "benchmarking gene regulatory networks"
 authors = [
     {name = "jeremie kalfon", email = "[email protected]"}
@@ -20,13 +20,15 @@ dependencies = [
     "seaborn>=0.11.0",
     "decoupler>=1.2.0",
     "pandas>=2.0.0",
-    "grnndata>=0.1.0",
+    "grnndata>=1.1.4",
     "omnipath>=1.0.0",
     "dask-expr>=1.0.0",
     "gseapy>=0.10.0",
     "bionty>=0.49.0",
     "rich>=13.5.0",
     "gdown>=4.7.1",
+    "setuptools>=58.0.0",
+    "numba>=0.56.0",
 ]
 
 [project.optional-dependencies]
@@ -56,4 +58,10 @@ ignore = ["E501", "E203", "E266", "E265", "F401", "F403"]
 
 [build-system]
 requires = ["hatchling"]
-build-backend = "hatchling.build"
+build-backend = "hatchling.build"
+
+[tool.hatch.build.targets.wheel]
+include = [
+    "/bengrn",
+    "/data",
+]

tests/test_base.py

@@ -26,34 +26,30 @@ def test_base():
     adata = adata[:, np.argsort(-adata.X.sum(axis=0)).tolist()[0][:1000]]
     random_mask = np.random.choice([0, 1], size=random_matrix.shape, p=[0.8, 0.2])
     sparse_random_matrix = csr_matrix(random_matrix * random_mask)
-    try:
-        grn = GRNAnnData(adata.copy(), grn=sparse_random_matrix)
-        grn.var.index = grn.var.symbol.astype(str)
-        _ = BenGRN(grn, doplot=False).scprint_benchmark()
-
-        # Test get_sroy_gt function
-        sroy_gt = get_sroy_gt(get="liu")
-        assert isinstance(
-            sroy_gt, GRNAnnData
-        ), "get_sroy_gt should return a GRNAnnData object"
-
-        # Test get_perturb_gt function
-        perturb_gt = get_perturb_gt()
-        assert isinstance(
-            perturb_gt, GRNAnnData
-        ), "get_perturb_gt should return a GRNAnnData object"
-
-        # Test compute_genie3 function
-        genie3_result = compute_genie3(adata[:, :100], ntrees=10, nthreads=1)
-        assert isinstance(
-            genie3_result, GRNAnnData
-        ), "compute_genie3 should return a GRNAnnData object"
-
-        # Test train_classifier function
-        random_matrix = np.random.rand(4, 10000).reshape(100, 100, 4)
-        subgrn = grn[:, :100]
-        subgrn.varp["GRN"] = random_matrix
-        classifier, metrics, clf = train_classifier(subgrn)
-
-    except Exception as e:
-        pytest.fail(f"An exception occurred: {str(e)}")
+    grn = GRNAnnData(adata.copy(), grn=sparse_random_matrix)
+    grn.var.index = grn.var.symbol.astype(str)
+    _ = BenGRN(grn, doplot=False).scprint_benchmark()
+
+    # Test get_sroy_gt function
+    sroy_gt = get_sroy_gt(get="liu")
+    assert isinstance(
+        sroy_gt, GRNAnnData
+    ), "get_sroy_gt should return a GRNAnnData object"
+
+    # Test get_perturb_gt function
+    perturb_gt = get_perturb_gt()
+    assert isinstance(
+        perturb_gt, GRNAnnData
+    ), "get_perturb_gt should return a GRNAnnData object"
+
+    # Test compute_genie3 function
+    genie3_result = compute_genie3(adata[:, :100], ntrees=10, nthreads=1)
+    assert isinstance(
+        genie3_result, GRNAnnData
+    ), "compute_genie3 should return a GRNAnnData object"
+
+    # Test train_classifier function
+    random_matrix = np.random.rand(4, 10000).reshape(100, 100, 4)
+    subgrn = grn[:, :100]
+    subgrn.varp["GRN"] = random_matrix
+    classifier, metrics, clf = train_classifier(subgrn)