From f3240147d5dedadb2b90b2a16ba52f06021e67f5 Mon Sep 17 00:00:00 2001
From: Dominik Roth <dominik.roth.dev@gmail.com>
Date: Sun, 26 Sep 2021 15:52:54 +0200
Subject: [PATCH] training now terminates earlier when stagnating (can be
 disabled via flag)

---
 caliGraph.py | 12 +++++++++---
 1 file changed, 9 insertions(+), 3 deletions(-)

diff --git a/caliGraph.py b/caliGraph.py
index 6526929..c69c4e1 100755
--- a/caliGraph.py
+++ b/caliGraph.py
@@ -638,7 +638,7 @@ def recommendNBooksTagBased(G, mu, std, n, removeTopListsB=True):
 
 def recommendNBooks(G, mu, std, n, removeTopListsB=True, removeUselessRecommenders=True):
     removeRestOfSeries(G)
-    removeBad(G, mu-std*2-1)
+    removeBad(G, mu-std-1)
     removeKeepBest(G, int(n*2) + 5, maxDistForRead=2)
     removeEdge(G)
     removeHighSpanTags(G, 12)
@@ -812,7 +812,7 @@ def evaluateFitness(books, debugPrint=False):
         print(sum(errSq)/len(errSq), 0.005*regressionLoss, 0.2*boundsLoss/len(ratedBooks), 1.0*sum(linSepLoss)/len(linSepLoss))
     return sum(errSq)/len(errSq) + 0.005*regressionLoss + 0.2*boundsLoss/len(ratedBooks) - 1.0*sum(linSepLoss)/len(linSepLoss)
 
-def train(gamma = 1):
+def train(gamma = 1, maxEmptySteps=-1):
     global weights
     books = loadBooksFromDB()
     bestWeights = copy.copy(weights)
@@ -820,6 +820,7 @@ def train(gamma = 1):
     w = list(weights.keys())
     attr = random.choice(w)
     delta = gamma * (-0.5 + (0.75 + 0.25*random.random()))
+    emptyStepsLeft = maxEmptySteps
 
     while gamma > 1.0e-08:
         print({'mse': best_mse, 'w': weights, 'gamma': gamma})
@@ -841,11 +842,15 @@ def train(gamma = 1):
             delta *= 2
             if random.random() < 0.10:
                 attr = random.choice(w)
+            emptyStepsLeft = maxEmptySteps
         else:
             weights = copy.copy(bestWeights)
             gamma *= 0.8
             attr = random.choice(w)
             delta = gamma * (-0.5 + (0.75 + 0.25*random.random()))
+            emptyStepsLeft -= 1
+        if emptyStepsLeft == 0:
+            return
 
 def saveWeights(weights):
     with open('neuralWeights.json', 'w') as f:
@@ -887,13 +892,14 @@ def cliInterface():
 
     p_train = cmds.add_parser('train', description="TODO", aliases=[])
     p_train.add_argument('-g', type=float, default=1, help='learning rate gamma')
+    p_train.add_argument('--full', action="store_true")
 
     p_full = cmds.add_parser('full', description="TODO", aliases=[])
 
     args = parser.parse_args()
 
     if args.cmd=="train":
-        train(args.g)
+        train(args.g, -1 if args.full else 32)
         exit()
 
     G, books = buildFullGraph()