[FIX] update report output from text to csv

classifiers.py

@@ -22,8 +22,6 @@ classifiers = [
 
 
 param_grid_svm = {'C':[1,10,100,1000],'gamma':[1,0.1,0.001,0.0001], 'kernel':['linear','rbf']}
-#param_grid_svm = {'C':[1,10],'gamma':[1], 'kernel':['linear','rbf']}
-#param_grid_svm = [{'kernel': ['rbf'], 'gamma': [1e-3, 1e-4], 'C': [1, 10, 100, 1000]}, {'kernel': ['linear'], 'C': [1, 10, 100, 1000]}]
 param_grid_decisionTree = { 'criterion' : ['gini', 'entropy'], 'max_depth':range(5,10), 'min_samples_split': range(5,10), 'min_samples_leaf': range(1,5) }
 param_grid_rfc = { 'n_estimators': [200, 500], 'max_features': ['auto', 'sqrt', 'log2'], 'max_depth' : [4,5,6,7,8], 'criterion' :['gini', 'entropy'] }
 param_grid_lr = {"C":np.logspace(-3,3,7), "penalty":["l1","l2"]}

experimentsClassicClassifiers.py

@@ -143,10 +143,10 @@ for columnInput in [columnText, 'firstParagraph']:
             file_name_report = columnInput + '_' +feature_technique_name + '_' + clf_name
 
             report, accuracy, weighted_avg = evaluate_model(clf, test_x, valid_y, y_pred, valid_y, [str(e) for e in encoder.transform(encoder.classes_)],  encoder.classes_, os.path.join('reports', columnClass, dir_name_report, file_name_report)+'.pdf')
+            report.to_csv(os.path.join('reports', columnClass, dir_name_report, file_name_report+'.csv'))
             with open(os.path.join('reports', columnClass, dir_name_report, file_name_report+'.txt'), 'w') as f:
 
                 sys.stdout = f # Change the standard output to the file we created.
-                print(report)
                 print('accuracy : {}'.format(accuracy))
                 print('weighted_Precision : {}'.format(weighted_avg['precision']))
                 print('weighted_Recall    : {}'.format(weighted_avg['recall']))