Update hyperparameters-fitting.py

code/models/hyperparameters-fitting.py

@@ -19,27 +19,23 @@ from sklearn.neural_network import MLPClassifier
 from sklearn.svm import SVC
 from sklearn.tree import DecisionTreeClassifier
 
-def output_datasets(dataset,filtering): 
-    """Gives the features and labels to train the model on depending on the dataset considered"""
-    if dataset == "Dropout_1":
-        # Import of csv database from the feature engineering R code
-        db_cluster = pd.read_csv("dropout_cluster.csv", sep=",")
-        db = pd.read_csv("dropout_sin_cluster.csv", sep=",")
-        # Features to be selected from the feature filtering step
-        features_cluster = (pd.read_csv("dropout_cluster_FSS.csv",sep=",")).columns.values
-        features = (pd.read_csv("dropout_sin_cluster_FSS.csv",sep=",")).columns.values
-
-    else:
-        # Import of csv database from the feature engineering R code
-        db_cluster = pd.read_csv("relapse_cluster.csv", sep=",")
-        db = pd.read_csv("relapse_sin_cluster.csv", sep=",")
-        # Features to be selected from the feature filtering step
-        features_cluster = (pd.read_csv("relapse_cluster_FSS.csv",sep=",")).columns.values
-        features = (pd.read_csv("relapse_sin_cluster_FSS.csv",sep=",")).columns.values
+def output_datasets(filtering): 
+    # Import of csv database from the feature engineering R code
+    db_cluster = pd.read_csv("data/dropout_cluster.csv", sep=",")
+    db = pd.read_csv("data/dropout.csv", sep=",")
+    # Features to be selected from the feature filtering step
+    features_cluster = (pd.read_csv("data/FSS/featsGR_cluster.csv",sep=",")).columns.values
+    features = (pd.read_csv("data/FSS/featsGR.csv",sep=",")).columns.values
+    # # Import of csv database from the feature engineering R code
+    # db_cluster = pd.read_csv("dropout_cluster.csv", sep=",")
+    # db = pd.read_csv("dropout_sin_cluster.csv", sep=",")
+    # # Features to be selected from the feature filtering step
+    # features_cluster = (pd.read_csv("dropout_cluster_FSS.csv",sep=",")).columns.values
+    # features = (pd.read_csv("dropout_sin_cluster_FSS.csv",sep=",")).columns.values
 
     # Creation of train and test sets for the dataset without cluster
-    sin_cluster_data_label = db[dataset]
-    sin_cluster_data_features = db.drop(dataset, axis=1) #elimination of the output from the training set
+    sin_cluster_data_label = db["Dropout_1"]
+    sin_cluster_data_features = db.drop("Dropout_1", axis=1) #elimination of the output from the training set
     columns_to_be_changed = sin_cluster_data_features.select_dtypes(exclude='number').columns.values
     sin_cluster_data_features = pd.get_dummies(sin_cluster_data_features, columns=columns_to_be_changed) #use of one hot encoding for categorical variables
     if filtering == "FSS" : #selection of features in case the filtering is activated for the dataset
@@ -48,8 +44,8 @@ def output_datasets(dataset,filtering):
     sin_cluster_data_label.replace({False: 0, True: 1}, inplace=True) #convertion of boolean by integers for the features set (necessary for newest numpy versions)
 
     # Creation of train and test sets for the dataset with cluster (same steps)
-    cluster_data_label = db_cluster[dataset]
-    cluster_data_features = db_cluster.drop(dataset, axis=1)
+    cluster_data_label = db_cluster["Dropout_1"]
+    cluster_data_features = db_cluster.drop("Dropout_1", axis=1)
     columns_to_be_changed = cluster_data_features.select_dtypes(exclude='number').columns.values
     cluster_data_features = pd.get_dummies(cluster_data_features, columns=columns_to_be_changed)
     if filtering == "FSS" :
@@ -60,7 +56,6 @@ def output_datasets(dataset,filtering):
     return sin_cluster_data_features, sin_cluster_data_label, cluster_data_features, cluster_data_label
 
 if __name__ == '__main__':
-    datasets = ["Dropout_1"] #select the dataset to train on
     filtering = ["FSS","noFSS"] #select whether the dataset has been through the filtering step or not 
     scorings = ("roc_auc","f1","neg_log_loss") #scorings to be used for model evaluation
     
@@ -77,28 +72,27 @@ if __name__ == '__main__':
     cv = StratifiedKFold(n_splits=10, shuffle=True, random_state=1) #the cross-validation protocole used
     
     for f in filtering : 
-        for d in datasets:
-            sin_cluster_data_features, sin_cluster_data_label, cluster_data_features, cluster_data_label = output_datasets(d,f)
-            cluster_params = pd.DataFrame(index=['SVM','NN','LR','Bagging','RF','Boosting','Tree'], columns=['Parameters','Score']) #dataframe to save the results in for the cluster dataset
-            sin_cluster_params = cluster_params.copy(deep=True) #dataframe to save the results in for the cluster dataset
-            for k in models :
-                model = models[k]
-                parameters = hyperparameters[k]
-                pipeline = Pipeline(steps=[('r', resample), ('m', model)])
-                search = RandomizedSearchCV(pipeline, param_distributions=parameters, cv=cv, n_jobs=1, scoring='precision')
+        sin_cluster_data_features, sin_cluster_data_label, cluster_data_features, cluster_data_label = output_datasets(f)
+        cluster_params = pd.DataFrame(index=['SVM','NN','LR','Bagging','RF','Boosting','Tree'], columns=['Parameters','Score']) #dataframe to save the results in for the cluster dataset
+        sin_cluster_params = cluster_params.copy(deep=True) #dataframe to save the results in for the cluster dataset
+        for k in models :
+            model = models[k]
+            parameters = hyperparameters[k]
+            pipeline = Pipeline(steps=[('r', resample), ('m', model)])
+            search = RandomizedSearchCV(pipeline, param_distributions=parameters, cv=cv, n_jobs=1, scoring='precision')
 
-                search.fit(sin_cluster_data_features.values, sin_cluster_data_label.values)
-                print(search.best_params_)
-                print(search.best_score_)
-                sin_cluster_params.at[k,'Parameters']=search.best_params_
-                sin_cluster_params.at[k,'Score']=round(search.best_score_,4)
+            search.fit(sin_cluster_data_features.values, sin_cluster_data_label.values)
+            print(search.best_params_)
+            print(search.best_score_)
+            sin_cluster_params.at[k,'Parameters']=search.best_params_
+            sin_cluster_params.at[k,'Score']=round(search.best_score_,4)
 
-                search.fit(cluster_data_features.values, cluster_data_label.values)
-                print(search.best_params_)
-                print(search.best_score_)
-                cluster_params.at[k,'Parameters']=search.best_params_
-                cluster_params.at[k,'Score']=round(search.best_score_,4)
-        
-            #Download of results as csv files 
-            cluster_params.to_csv("Results_2_"+d+"_Cluster_"+f+".csv")
-            sin_cluster_params.to_csv("Results_2_"+d+"_sin_Cluster_"+f+".csv")
+            search.fit(cluster_data_features.values, cluster_data_label.values)
+            print(search.best_params_)
+            print(search.best_score_)
+            cluster_params.at[k,'Parameters']=search.best_params_
+            cluster_params.at[k,'Score']=round(search.best_score_,4)
+    
+        #Download of results as csv files 
+        cluster_params.to_csv("Results_2_Dropout_1_Cluster_"+f+".csv")
+        sin_cluster_params.to_csv("Results_2_Dropout_1_sin_Cluster_"+f+".csv")