more tests

bengrn/base.py

@@ -4,10 +4,13 @@
 
 import json
 import logging
+import os
 import os.path
+import tarfile
 import urllib.request
 from typing import Optional, Union
 
+import gdown
 import gseapy as gp
 import matplotlib.pyplot as plt
 import numpy as np
@@ -29,9 +32,6 @@
 from sklearn.linear_model import LogisticRegression, RidgeClassifier
 from sklearn.metrics import PrecisionRecallDisplay, auc, precision_recall_curve
 from sklearn.model_selection import train_test_split
-import gdown
-import os
-import tarfile
 
 from .tools import GENIE3
 
@@ -409,13 +409,13 @@ def get_scenicplus(
 
 
 def get_sroy_gt(
-    get: str = "main", join: str = "outer", species: str = "human", gt: str = "full"
+    get: str = "mine", join: str = "outer", species: str = "human", gt: str = "full"
 ) -> GRNAnnData:
     """
     This function retrieves the ground truth data from the McCall et al.'s paper.
 
     Args:
-        get (str): The specific dataset to retrieve. Options include "main", "liu", and "chen".
+        get (str): The specific dataset to retrieve. Options include "mine", "liu", and "chen".
         join (str, optional): The type of join to be performed when concatenating the data. Default is "outer".
         species (str, optional): The species of the dataset. Default is "human".
         gt (str, optional): The type of ground truth data to retrieve. Options include "full", "chip", and "ko". Default is "full".

bengrn/tools/genie3.py

@@ -17,7 +17,7 @@ def compute_feature_importances(estimator):
             for e in estimator.estimators_
         ]
         importances = array(importances)
-        return sum(importances, axis=0) / len(estimator)
+        return importances.sum(0) / len(estimator.estimators_)
 
 
 def get_link_list(

tests/test_base.py

@@ -1,12 +1,22 @@
 import os
 
 import numpy as np
+import pandas as pd
 import pytest
 import scanpy as sc
 from grnndata import GRNAnnData
 from scipy.sparse import csr_matrix
 
-from bengrn.base import NAME, BenGRN
+from bengrn.base import (
+    NAME,
+    BenGRN,
+    compute_epr,
+    compute_genie3,
+    get_GT_db,
+    get_perturb_gt,
+    get_sroy_gt,
+    train_classifier,
+)
 
 
 def test_base():
@@ -20,5 +30,30 @@ def test_base():
         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)}")