tests interpret

GenNet_utils/Create_network.py

@@ -232,7 +232,7 @@ def create_network_from_csv(datapath,
         model = K.layers.Reshape(input_shape=(inputsize,), target_shape=(inputsize, 1))(input_layer)
 
     for i in range(len(columns) - 1):
-        matrix_ones = np.ones(len(network_csv[[columns[i], columns[i + 1]]]), np.bool)
+        matrix_ones = np.ones(len(network_csv[[columns[i], columns[i + 1]]]), bool)
         matrix_coord = (network_csv[columns[i]].values, network_csv[columns[i + 1]].values)
         if i == 0:
             matrixshape = (inputsize, network_csv[columns[i + 1]].max() + 1)

GenNet_utils/Interpret.py

@@ -39,7 +39,7 @@ def get_NID_scores(args):
     print("Interpreting with NID:")
     model, masks = load_trained_network(args)
 
-    print(model.summary())
+    G    print(model.summary())
 
     if args.layer == None:
         if args.onehot == 1:

GenNet_utils/Train_network.py

@@ -321,3 +321,4 @@ def load_trained_network(args):
 
 
 
+

requirements_GenNet.txt

@@ -3,7 +3,7 @@ joblib>=0.16.0
 Markdown>=3.2.1
 matplotlib>=3.3.2
 jupyter>=1.0.0
-numpy==1.21
+numpy>=1.21
 pandas>=0.25.3
 Pillow>=7.2.0
 plotly>=4.12.0
@@ -21,5 +21,5 @@ statsmodels
 shap
 psutil
 kaleido
-tensorflow==2.2.0
+tensorflow
 bitarray

tests/test_interpret.py

@@ -0,0 +1,31 @@
+import os
+import sys
+import pandas as pd
+import shutil
+from os.path import dirname, abspath
+import os
+sys.path.insert(1, os.getcwd())
+from GenNet_utils.Interpret import interpret
+
+# TODO: add test without covariates
+# TODO add test with covariates for regression + classification
+# TODO add test with multiple genotype files.
+# test randomnesss after .. epoch shuffles.
+# ToDO add test for each file.
+
+class ArgparseSimulator():
+    def __init__(self,
+                 resultpath = "/trinity/home/avanhilten/repositories/epistasis/prototyping/GenNet_realLife_data/GenNet/results/GenNet_experiment_2_/",
+                 type = "NID",
+                 layer = None
+                 ):
+
+        self.resultpath = resultpath
+        self.type = type
+        self.layer = layer
+
+
+args = ArgparseSimulator()
+print("done")
+
+interpret(args)