title: precision_recall_fscore_support returns same values for accuracy, precision and recall
body: <p>I am training a logistic regression classification model and trying to compare the results using confusion matrix, and calculating precision, recall, accuracy
  code is given below</p>
  
      
  
  <pre class="lang-py prettyprint-override"><code># logistic regression classification model
  clf_lr = sklearn.linear_model.LogisticRegression(penalty='l2', class_weight='balanced')
  logistic_fit=clf_lr.fit(TrainX, np.where(TrainY &gt;= delay_threshold,1,0))
  pred = clf_lr.predict(TestX)
  
  # print results
  cm_lr = confusion_matrix(np.where(TestY &gt;= delay_threshold,1,0), pred)
  print("Confusion matrix")
  print(pd.DataFrame(cm_lr))
  report_lr = precision_recall_fscore_support(list(np.where(TestY &gt;= delay_threshold,1,0)), list(pred), average='micro')
  print ("\nprecision = %0.2f, recall = %0.2f, F1 = %0.2f, accuracy = %0.2f\n" % \
             (report_lr[0], report_lr[1], report_lr[2], accuracy_score(list(np.where(TestY &gt;= delay_threshold,1,0)), list(pred))))
  print(pd.DataFrame(cm_lr.astype(np.float64) / cm_lr.sum(axis=1)))
  
  show_confusion_matrix(cm_lr)
  #linear_score = cross_validation.cross_val_score(linear_clf, ArrX, ArrY,cv=10)
  #print linear_score
  </code></pre>
  
  <p>expected results are</p>
  
  <pre class="lang-py prettyprint-override"><code>Confusion matrix
        0     1
  0  4303  2906
  1  1060  1731
  
  precision = 0.37, recall = 0.62, F1 = 0.47, accuracy = 0.60
  
            0         1
  0  0.596893  1.041204
  1  0.147038  0.620208
  </code></pre>
  
  <p>however my outputs are</p>
  
  <pre class="lang-py prettyprint-override"><code>Confusion matrix
        0     1
  0  4234  2891
  1  1097  1778
  
  precision = 0.60, recall = 0.60, F1 = 0.60, accuracy = 0.60
  
            0         1
  0  0.594246  1.005565
  1  0.153965  0.618435
  </code></pre>
  
  <p>how do I get correct results ?</p>