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rutgerswiselab · May 3, 2021 · af8d333 · af8d333
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diff --git a/annotation_11-20.txt b/annotation_11-20.txt
diff --git a/cand_pl.npy b/cand_pl.npy
diff --git a/eval_mln.py b/eval_mln.py
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+def read_triplet(file_path):
+    triplet2prob = {}
+    with open(file_path) as fin:
+        for line in fin:
+            triplet = line.strip().split('\t')
+            h, t, r, prob = triplet
+            triplet2prob[(int(h), int(t))] = float(prob)
+    return triplet2prob
+
+import numpy as np
+import torch
+from metrics4rec import evaluate_all
+import gzip
+
+def hidden_gen(file, mln, transe, weight=1):
+    c = 0
+    candidates = np.load(file)['candidates']
+    probs = np.zeros_like(candidates, dtype='float')
+    for i in range(candidates.shape[0]):
+        user = candidates[i, 0]
+        for j in range(1, candidates.shape[1]):
+            item = candidates[i, j]
+            if (user, item) not in mln.keys():
+                c += 1
+            else:
+                #  prob = mln[(user, item)]*weight + transe[(user, item)]
+                prob = mln[(user, item)]
+                probs[i, j] = prob
+    return probs, c
+
+def new_setting(file, mln, transe, weight=1):
+    ui_scores, gt = {}, {}
+    # for tri in mln.keys():
+    #     h1, t1 = tri
+    #     if tri not in transe.keys():
+    #         continue
+    #     if h1 not in ui_scores.keys():
+    #         ui_scores[h1] = {}
+    #     ui_scores[h1][t1] = mln[tri]
+
+    mln_avg = sum(mln.values())/len(mln)
+    for tri in transe.keys():
+        h1, t1 = tri
+        if h1 not in ui_scores.keys():
+            ui_scores[h1] = {}
+        if tri not in mln.keys():
+            mln[tri] = 0
+        ui_scores[h1][t1] = mln[tri]*weight + transe[tri]
+
+    # print(len(ui_scores))
+    with open(file) as fin:
+        for line in fin:
+            h, t, r = line.strip().split('\t')
+            assert r == '0'
+            if int(h) not in gt.keys():
+                gt[int(h)] = []
+            gt[int(h)].append(int(t))
+    evaluate_all(ui_scores, gt, 10)
+    cand = np.load('/common/users/yz956/kg/code/OpenDialKG/hidden50_cpa.npy')
+    # cand = np.load('cand_pl.npy')
+    s2 = {}
+    for i in range(cand.shape[0]):
+        s2[i] = {}
+        for j in range(cand.shape[1]):
+            s2[i][cand[i, j]] = 300 - j
+    # print(s2[0])
+    evaluate_all(s2, gt, 10)
+
+    # c = np.load('/common/users/yz956/kg/code/KBRD/data/cpa/cpa/rec_test_candidate100.npz')['candidates']
+    # m = 0
+    # for i in range(c.shape[0]):
+    #     if c[i][1] in gt[c[i][0]]:
+    #         m += 1
+    # print(m/c.shape[0])
+
+
+mln = read_triplet('/common/users/yz956/kg/code/pLogicNet/record/cpa30/0/mln_10.31.txt')
+transe = read_triplet('/common/users/yz956/kg/code/OpenDialKG/transe_cand_1000.txt')
+transe = read_triplet('/common/users/yz956/kg/code/pLogicNet/record/cpa30/1/annotation_1000_htr.txt')
+new_setting('/common/users/yz956/kg/code/KBRD/data/cpa/cpa/kg_test_triples_Cell_Phones_and_Accessories.txt', mln, transe, 0.3)