Added network inference files

.gitignore

@@ -47,3 +47,5 @@
 .DS_Store?
 ._*
 *.idea/
+
+Qiery Parser.py

EvaluationMetrics.py

@@ -0,0 +1,68 @@
+#########################################################################################
+# EvaluationMetrics.py
+# Purpose: script contains methods for evaluating link prediction algorithm performance
+# version 1.0
+# date: 01.28.2017
+#########################################################################################
+
+
+# import module/script dependencies
+import random
+import heapq
+
+
+
+def AUC(nonexist_scores, missing_scores):
+    '''
+    Function calculates the probability that a randomly chosen missing link is given a higher score than a randomly
+    chosen nonexistent link using procedures described by Lu & Zhou (2010)
+    (doi:http://dx.doi.org/10.1016/j.physa.2010.11.027T). The function takes a list containing missing, non-existent
+    edges, and predictions, and returns an AUC score. Current method is currently set to perform 1,000 comparisons.
+    :param nonexist_scores: list of nonexistent edges and scores
+    :param missing_scores: list of test edges and scores
+    :return: an integer which is the AUC score for that comparison
+    '''
+
+    # comparisons = len(nonexist_scores)*len(missing_scores) #HOW MANY??
+    comparisons = 1000
+    count = 0.0
+
+    for i in xrange(comparisons):
+        TN = random.sample(nonexist_scores.values(), 1)
+        TP = random.sample(missing_scores.values(), 1)
+
+        if TP > TN:
+            count += 1.0
+        if TP == TN:
+            count += 0.5
+
+    auc = count/comparisons
+
+    return auc
+
+
+
+def KPrecision(auc, scores, testing_edges):
+    '''
+    Function calculates the ratio of relevant items selected from the top n items using procedures described by Lu &
+    Zhou (2010) (doi:http://dx.doi.org/10.1016/j.physa.2010.11.027T). The function takes a list containing missing and
+    non-existent edges, the list of all predictions, and returns a top k-precision score for 20% of scores.
+    :param auc: integer representing AUC score - to indicate whether the top or bottom of list should be assessed
+    :param scores: list of test edges and scores
+    :param testing_edges: list of nonexistent edges and scores
+    :return: an integer which is the precision for the top number of selected links
+    '''
+
+    #get 20% of edges
+    links = [int(len(scores)*0.20) if int(len(scores)*0.20) >= 1 else 1][0]
+
+    if auc < 0.5:
+        # pred_list = heapq.nsmallest(links, set([x[1] for x in scores.items()])) #returns n highest likelihood scores
+        pred_list = heapq.nsmallest(links, scores, key=lambda k: scores[k])
+    else:
+        # pred_list = heapq.nlargest(links, set([x[1] for x in scores.items()])) #returns n highest likelihood scores
+        pred_list = heapq.nlargest(links, scores, key=lambda k: scores[k])
+
+    y_pred = [0 if (i[0], i[1]) in testing_edges else 0 if (i[1], i[0]) in testing_edges else 1 for i in pred_list]
+
+    return float(y_pred.count(0))/links