HN Special Issue

compute_metrics.py

 import numpy as np
 import pandas as pd
-from sklearn.metrics import classification_report, accuracy_score as acc, precision_score as prec, recall_score as rec
+from sklearn.metrics import classification_report, accuracy_score as acc, precision_score as prec, recall_score as rec, matthews_corrcoef as mcc
 from sklearn.metrics import confusion_matrix
+
 #%%
-y = pd.read_csv('/home/bizzego/Downloads/predictions_test.csv')
+y = pd.read_csv('/home/bizzego/Downloads/TT/TT_merged_features_B_UNCORR_selected_SVMlinear/predictions_test.csv')
 
 y_true = y['true'].values
 y_pred = y['pred'].values
@@ -11,4 +12,4 @@ y_pred = y['pred'].values
 #%%
 print(classification_report(y_true, y_pred))
 
-print(confusion_matrix(y_true, y_pred))
+print(acc(y_true, y_pred), mcc(y_true, y_pred))

extract_features.ipynb

@@ -185,7 +185,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.7"
+   "version": "3.6.8"
   }
  },
  "nbformat": 4,