Added a progress-command (and newly trained weights)

caliGraph.py

@@ -718,6 +718,25 @@ def readBooksAnalysis(G, minRating=0, showAllTags=True, removeUnconnected=False,
     scaleOpinionsByRating(G)
     addScoreToLabels(G)
 
+def progress(G, minimum=3.5):
+    bookCount = 0
+    readCount = 0
+    toReadCount = 0
+    for n in list(G.nodes):
+        node = G.nodes[n]
+        if node['t'] == 'book':
+            bookCount += 1
+            if node['rating'] != None:
+                readCount += 1
+            elif 'score' in node and (node['score'] >= minimum or node['std']==0.0):
+                toReadCount += 1
+    perc = round(readCount / (toReadCount+readCount) * 100, 2)
+    print('Books in libary: '+str(bookCount))
+    print('Read Books: '+str(readCount))
+    print('Unread Books: '+str(bookCount-readCount))
+    print('Recommended Books (score > '+str(round(minimum, 2))+'): '+str(toReadCount))
+    print('Progress: '+str(perc)+'%')
+
 
 def analyze(G, type_name, name, dist=2.7):
     from fuzzywuzzy import fuzz
@@ -935,6 +954,9 @@ def cliInterface():
     p_train.add_argument('-g', type=float, default=0.1, help='learning rate gamma')
     p_train.add_argument('--full', action="store_true")
 
+    p_prog = cmds.add_parser('progress', description="TODO", aliases=[])
+    p_prog.add_argument('-m', type=float, default=7, help='Mimimum Score to read')
+
     p_full = cmds.add_parser('full', description="TODO", aliases=[])
 
     args = parser.parse_args()
@@ -966,6 +988,9 @@ def cliInterface():
         analyze(G, args.type, args.name, args.d)
     elif args.cmd=="full":
         fullGraph(G, not args.keep_top_lists)
+    elif args.cmd=="progress":
+        progress(G, args.m)
+        return
     else:
         raise Exception("Bad")
 

neuralWeights.json

@@ -1 +1 @@
-{"topList": 0.46904994817058576, "recommender": 0.5105580165380674, "author": 1.2017599208712688, "series": 0.08101540889409455, "tag": 0.01759005368430446, "mu": 0.6921620983533125, "sigma": 0.016831028550919676, "se": 0.3363180764359938, "bias": 0.15960517748561318}
+{"topList": 0.45743192655139997, "recommender": 0.5079140172269819, "author": 1.2111137699117849, "series": 0.06399278731591332, "tag": 0.015332649025594572, "mu": 0.7090073197262562, "sigma": 0.015801805052114225, "se": 0.338754954784782, "bias": 0.16354975767988336}