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shixiaowen03
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recommendation evaluation metrics
1 parent 6e6ad9e commit 6289093

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Lines changed: 346 additions & 82 deletions

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‎.idea/workspace.xml‎

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‎recommendation/Basic-Evaluation-metrics/AUC.py‎

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import numpy as np
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label_all = np.random.randint(0,2,[10,1]).tolist()
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pred_all = np.random.random((10,1)).tolist()
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print(label_all)
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print(pred_all)
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posNum = len(list(filter(lambda s: s[0] == 1, label_all)))
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if (posNum > 0):
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negNum = len(label_all) - posNum
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sortedq = sorted(enumerate(pred_all), key=lambda x: x[1])
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posRankSum = 0
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for j in range(len(pred_all)):
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if (label_all[j][0] == 1):
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posRankSum += list(map(lambda x: x[0], sortedq)).index(j) + 1
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auc = (posRankSum - posNum * (posNum + 1) / 2) / (posNum * negNum)
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print("auc:", auc)

‎recommendation/Basic-Evaluation-metrics/HR.py‎

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def hit(gt_items, pred_items):
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count = 0
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for item in pred_items:
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if item in gt_items:
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count += 1
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return count

‎recommendation/Basic-Evaluation-metrics/MAP.py‎

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def AP(ranked_list, ground_truth):
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"""Compute the average precision (AP) of a list of ranked items
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"""
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hits = 0
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sum_precs = 0
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for n in range(len(ranked_list)):
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if ranked_list[n] in ground_truth:
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hits += 1
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sum_precs += hits / (n + 1.0)
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if hits > 0:
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return sum_precs / len(ground_truth)
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else:
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return 0
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‎recommendation/Basic-Evaluation-metrics/MRR.py‎

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import numpy as np
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def mrr(gt_items, pred_items):
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for index,item in enumerate(pred_items):
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if item in gt_items:
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return 1/index
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‎recommendation/Basic-Evaluation-metrics/NDCG.py‎

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import numpy as np
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def ndcg(pred_rel):
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dcg = 0
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for (index,rel) in enumerate(pred_rel):
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dcg += (rel * np.reciprocal(np.log2(index+2)))
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print("dcg " + str(dcg))
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idcg = 0
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for(index,rel) in enumerate(sorted(pred_rel,reverse=True)):
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idcg += (rel * np.reciprocal(np.log2(index+2)))
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print("idcg " + str(idcg))
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return dcg/idcg
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pred_rel = [3,1,2,3,2]
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print("ndcg " + str(ndcg(pred_rel)))
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