Added 'competence' command (displays recommenders and justifications of

their scores)

caliGraph.py

@@ -1,4 +1,4 @@
-#!./.venv/bin/python3.9
+#!./.venv/bin/python3.10
 import os
 import json
 import math
@@ -291,6 +291,17 @@ def removeRecommenders(G):
         if node['t'] == 'recommender':
             G.remove_node(n)
 
+def removeAuthors(G):
+    for n in list(G.nodes):
+        node = G.nodes[n]
+        if node['t'] == 'author':
+            G.remove_node(n)
+
+def removeSeries(G):
+    for n in list(G.nodes):
+        node = G.nodes[n]
+        if node['t'] == 'series':
+            G.remove_node(n)
 
 def removeRestOfSeries(G):
     for n in list(G.nodes):
@@ -703,6 +714,18 @@ def fullGraph(G, removeTopListsB=True):
     addScoreToLabels(G)
 
 
+def recommenderCompetence(G):
+    #removeRead(G)
+    removeUnread(G)
+    removeTags(G)
+    removeAuthors(G)
+    removeSeries(G)
+    removeTopLists(G)
+
+    scaleBooksByRating(G)
+    scaleOpinionsByRating(G)
+    addScoreToLabels(G)
+
 def readBooksAnalysis(G, minRating=0, showAllTags=True, removeUnconnected=False, removeTopListsB=True):
     removeUnread(G)
     removeBad(G, minRating)
@@ -873,7 +896,7 @@ def train(initGamma, full=True):
     best_mse = mse
     stagLen = 0
 
-    while gamma > 3.0e-05 and delta > 3.0e-05 or best_mse > 3:
+    while gamma > 3.0e-05 and delta > 5.0e-05 or best_mse > 10:
         last_mse = mse
         print({'mse': mse, 'gamma': gamma, 'delta': delta})
         delta = sum(gradient[g]**2 for g in gradient)
@@ -957,6 +980,8 @@ def cliInterface():
     p_prog = cmds.add_parser('progress', description="TODO", aliases=[])
     p_prog.add_argument('-m', type=float, default=7, help='Mimimum Score to read')
 
+    p_comp = cmds.add_parser('competence', description="TODO", aliases=[])
+
     p_full = cmds.add_parser('full', description="TODO", aliases=[])
 
     args = parser.parse_args()
@@ -988,6 +1013,8 @@ def cliInterface():
         analyze(G, args.type, args.name, args.d)
     elif args.cmd=="full":
         fullGraph(G, not args.keep_top_lists)
+    elif args.cmd=="competence":
+        recommenderCompetence(G)
     elif args.cmd=="progress":
         progress(G, args.m)
         return