Better rankings-list in cli and newly trained net

caliGraph.py

@@ -446,17 +446,21 @@ def getWeightForType(nodeType, edgeWeight=1):
     else:
         return w
 
-def printBestList(G, num=-1):
+def printBestList(G, t='book', num=-1):
+    print(t)
     bestlist = []
     for n in list(G.nodes):
         node = G.nodes[n]
-        if node['t'] == 'book':
+        if node['t'] == t:
             if 'score' in node and node['score'] != None:
                 bestlist.append(node)
-    bestlist.sort(key=lambda node: node['score'], reverse=True)
+    bestlist.sort(key=lambda node: node['score'] + 0.00001*(node['se'] if 'se' in node else 0), reverse=True)
     for i, book in enumerate(bestlist):
-        print("["+str(i+1).zfill(int((math.log10(num) if num!=-1 else 3)+1))+"] "+book['title'] +
+        if t=='book':
-              " ("+" & ".join(book['authors'])+"): {:.5f}".format(book['score']))
+            line = book['title'] + " ("+" & ".join(book['authors'])+")"+": {:.5f}".format(book['score'])
+        else:
+            line = book['label']
+        print("["+str(i+1).zfill(int((math.log10(num) if num!=-1 else 3)+1))+"] "+line)
         if num!=-1 and i == num-1:
             break
 
@@ -804,10 +808,18 @@ def recommenderCompetence(G):
     removeSeries(G)
     removeTopLists(G)
 
+    removeEdge(G)
+    removeDangling(G, alsoBooks=True)
+
     scaleBooksByRating(G)
     scaleOpinionsByRating(G)
     addScoreToLabels(G)
     
+    for n in list(G.nodes):
+        node = G.nodes[n]
+        if node['t'] == 'recommender':
+            node['score'] -= node['se'] * 1
+
 def readBooksAnalysis(G, minRating=0, showAllTags=True, removeUnconnected=False, removeTopListsB=True):
     removeUnread(G)
     removeBad(G, minRating)
@@ -1170,6 +1182,8 @@ def cliInterface():
         train(args.g, args.full)
         exit()
 
+    bestListT = 'book'
+
     G, books = buildFullGraph(darkMode=args.dark_mode)
     mu, std = genScores(G, books)
 
@@ -1199,20 +1213,20 @@ def cliInterface():
     elif args.cmd=="full":
         fullGraph(G, not args.keep_top_lists)
     elif args.cmd=="competence":
+        bestListT = 'recommender'
         recommenderCompetence(G)
     elif args.cmd=="shell":
         shell(G, books, mu, std)
-    elif args.cmd=="competence":
-        recommenderCompetence(G)
-        return
     elif args.cmd=="progress":
         progress(G, args.m)
         return
     elif args.cmd=="newBooks":
+        bestListT = 'newBook'
         newBooks(G, books, args.n, mu, std)
     else:
         raise Exception("Bad")
 
+
     if not args.keep_priv:
         removePriv(G)
     if args.remove_read:
@@ -1226,7 +1240,7 @@ def cliInterface():
         removeEdge(G)
 
     if not args.no_list:
-        printBestList(G)
+        printBestList(G, t=bestListT)
     if not args.no_web and not args.cmd in ['listScores']:
         genAndShowHTML(G, darkMode=args.dark_mode)
 

neuralWeights.json

@@ -1 +1 @@
-{"topList": 0.020357663923899313, "recommender": 0.3428529069584538, "author": 1.288739952769999, "series": 0.6484880619815447, "tag": 0.026296102297284726, "pagerank": 0.011025414086463162, "mu": 1.1052719819984915, "sigma": 0.5964842267419479, "bias": 0.0346189601467517}
+{"topList": 0.008877350797718757, "recommender": 0.3310976523405012, "author": 1.2297781106971182, "series": 0.6295690756530204, "tag": 0.029071357518799894, "pagerank": 0.011066788616739529, "mu": 1.1289465103781409, "sigma": 0.6694694643304658, "bias": 0.03536567416040619}