Update app.py

app.py

@@ -70,7 +70,7 @@ def GRUModel(trainX, trainy, testX, testy, epochs=1000, batch_size=64, learning_
     # Apply feature selection using Random Forest Regressor
     if feature_selection:
         # Use RandomForestRegressor to rank features by importance
-        rf = RandomForestRegressor(n_estimators=100, random_state=42)
+        rf = RandomForestRegressor(n_estimators=100, random_state=60)
         rf.fit(trainX, trainy)
         
         # Select features with importance greater than a threshold (e.g., mean importance)
@@ -159,7 +159,7 @@ def GRUModel(trainX, trainy, testX, testy, epochs=1000, batch_size=64, learning_
 #-----------------------------------------------------------DeepMap-------------------------------------------------------------------------------
 def CNNModel(trainX, trainy, testX, testy, epochs=1000, batch_size=64, learning_rate=0.0001, l1_reg=0.0001, l2_reg=0.0001, dropout_rate=0.3,feature_selection=True):
     if feature_selection:
-        rf=RandomForestRegressor(n_estimators=100,random_state=42)
+        rf=RandomForestRegressor(n_estimators=100,random_state=60)
         rf.fit(trainX,trainy)
         
         selector=SelectFromModel(rf, threshold="mean",prefit=True)
@@ -227,7 +227,7 @@ def CNNModel(trainX, trainy, testX, testy, epochs=1000, batch_size=64, learning_
 #-------------------------------------------------------------------------Random Forest----------------------------------------------------
 def RFModel(trainX, trainy, testX, testy, n_estimators=100, max_depth=None,feature_selection=True):
     if feature_selection:
-        rf=RandomForestRegressor(n_estimators=100, random_state=42)
+        rf=RandomForestRegressor(n_estimators=100, random_state=60)
         rf.fit(trainX, trainy)
         selector=SelectFromModel(rf, threshold="mean", prefit=True)
         trainX=selector.transform(trainX)
@@ -257,7 +257,7 @@ def RFModel(trainX, trainy, testX, testy, n_estimators=100, max_depth=None,featu
 #------------------------------------------------------------------------------XGboost---------------------------------------------------------------
 def XGBoostModel(trainX, trainy, testX, testy,learning_rate,min_child_weight,feature_selection=True, n_estimators=100, max_depth=None):
     if feature_selection:
-        rf=RandomForestRegressor(n_estimators=100,random_state=42)
+        rf=RandomForestRegressor(n_estimators=100,random_state=60)
         rf.fit(trainX,trainy)
         selector=SelectFromModel(rf,threshold="mean",prefit=True)
         trainX=selector.transform(trainX)
@@ -381,7 +381,7 @@ def NestedKFoldCrossValidation(
 
     # Feature selection
     if feature_selection:
-        rf = RandomForestRegressor(n_estimators=100, random_state=65)
+        rf = RandomForestRegressor(n_estimators=1000, random_state=60)
         rf.fit(training_genotypic_data_merged, phenotypic_info)
         selector = SelectFromModel(rf, threshold="mean", prefit=True)
         training_genotypic_data_merged = selector.transform(training_genotypic_data_merged)