Update pytest for hypergeometric distribution ROC and PR values

.ipynb_checkpoints/main-checkpoint.py

@@ -9,6 +9,7 @@
 from classes.hypergeometric_distribution_class_V2 import HypergeometricDistributionV2
 from classes.hypergeometric_distribution_class_V3 import HypergeometricDistributionV3
 from classes.hypergeometric_distribution_class_V4 import HypergeometricDistributionV4
+
 import matplotlib.pyplot as plt
 from random import sample
 from pathlib import Path
@@ -45,6 +46,11 @@ def main():
     graph_file_path = Path(dataset_directory_path, "graph.pickle")
     sample_size = 1000
 
+    testing_output_data_path = Path("./output/data/")
+    testing_output_image_path = Path("./output/images/")
+    testing_input_directory_path = Path("./tests/testing-dataset/")
+    testing_graph_file_path = Path(testing_input_directory_path, "graph.pickle")
+
     interactome_columns = [0, 1, 4, 5]
     interactome = read_specific_columns(interactome_path, interactome_columns, "\t")
 
@@ -81,10 +87,10 @@ def main():
 
     results = run_workflow(
         algorithm_classes,
-        dataset_directory_path,
-        graph_file_path,
-        output_data_path,
-        output_image_path,
+        testing_input_directory_path,
+        testing_graph_file_path,
+        testing_output_data_path,
+        testing_output_image_path,
         True,
         True,
     )

main.py

@@ -9,6 +9,7 @@
 from classes.hypergeometric_distribution_class_V2 import HypergeometricDistributionV2
 from classes.hypergeometric_distribution_class_V3 import HypergeometricDistributionV3
 from classes.hypergeometric_distribution_class_V4 import HypergeometricDistributionV4
+
 import matplotlib.pyplot as plt
 from random import sample
 from pathlib import Path
@@ -49,7 +50,7 @@ def main():
     testing_output_image_path = Path("./output/images/")
     testing_input_directory_path = Path("./tests/testing-dataset/")
     testing_graph_file_path = Path(testing_input_directory_path, "graph.pickle")
-
+    
     interactome_columns = [0, 1, 4, 5]
     interactome = read_specific_columns(interactome_path, interactome_columns, "\t")
 

tests/test_pytest.py

@@ -77,6 +77,10 @@ def test_algorithm_workflow():
         "ProteinDegree": 0.825,
         "ProteinDegreeV2": 0.675,
         "ProteinDegreeV3": 0.89,
+        "HypergeometricDistribution": 0.78,
+        "HypergeometricDistributionV2": 0.89,
+        "HypergeometricDistributionV3": 0.675,
+        "HypergeometricDistributionV4": 0.6
     }
 
     pr_results = {
@@ -87,6 +91,10 @@ def test_algorithm_workflow():
         "ProteinDegreeV2": 0.6367757242757243,
         "OverlappingNeighbors": 0.5329058916229968,
         "SampleAlgorithm": 0.4093791854859966,
+        "HypergeometricDistribution": 0.7899246806,
+        "HypergeometricDistributionV2": 0.8519169719,
+        "HypergeometricDistributionV3": 0.7142573629,
+        "HypergeometricDistributionV4": 0.6967847007,
     }
 
     for algorithm, metrics in results.items():