metrics.py

#!/usr/bin/env python
# coding: utf-8

# In[1]:


from sklearn.metrics import precision_recall_curve, auc, log_loss

def compute_prauc(pred, gt):
    prec, recall, thresh = precision_recall_curve(gt, pred)
    prauc = auc(recall, prec)
    return prauc

def calculate_ctr(gt):
    positive = len([x for x in gt if x == 1])
    ctr = positive/float(len(gt))
    return ctr

def compute_rce(pred, gt):
    cross_entropy = log_loss(gt, pred)
    data_ctr = calculate_ctr(gt)
    strawman_cross_entropy = log_loss(gt, [data_ctr for _ in range(len(gt))])
    return (1.0 - cross_entropy/strawman_cross_entropy)*100.0

  
#ground_truth = read_predictions("gt.csv") # will return data in the form (tweet_id, user_id, labed (1 or 0))
#predictions = read_predictions("predictions.csv") # will return data in the form (tweet_id, user_id, prediction)