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Stronger linear seperation

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Dominik Moritz Roth 2021-09-26 14:31:00 +02:00
parent 147a78302f
commit 9126bbcc14
1 changed files with 6 additions and 5 deletions
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caliGraph.py
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@ -545,8 +545,8 @@ def addScoreToLabels(G):
if 'rating' in node and node['rating'] != None: if 'rating' in node and node['rating'] != None:
node['label'] += " ("+str(node['rating'])+")" node['label'] += " ("+str(node['rating'])+")"
else: else:
if 'score' in node and node['score'] != None: if 'score' in node and node['score'] != None and 'se' in node:
node['label'] += " (~{:.2f}±{:.1f})".format(node['score'], node['se'] if 'se' in node else 0.0) node['label'] += " (~{:.2f}±{:.1f})".format(node['score'], node['se'])
else: else:
node['label'] += " (~0±∞)" node['label'] += " (~0±∞)"
@ -775,7 +775,7 @@ def waveFlow(G, node, n, dist, menge, firstEdge=False):
if node in bestlist or node in keeplist: if node in bestlist or node in keeplist:
waveFlow(G, node, m, dist, menge, firstEdge=firstEdge) waveFlow(G, node, m, dist, menge, firstEdge=firstEdge)
def evaluateFitness(books): def evaluateFitness(books, debugPrint=False):
global weights global weights
G = buildBookGraph(books) G = buildBookGraph(books)
graphAddAuthors(G, books) graphAddAuthors(G, books)
@ -808,8 +808,9 @@ def evaluateFitness(books):
# reward seperation linearly # reward seperation linearly
linSepLoss.append(abs(score - mu)) linSepLoss.append(abs(score - mu))
regressionLoss = sum([(1-w)**2 for w in weights.values()]) regressionLoss = sum([(1-w)**2 for w in weights.values()])
print(sum(errSq)/len(errSq), 0.005*regressionLoss, 0.2*boundsLoss/len(ratedBooks), 0.5*sum(linSepLoss)/len(linSepLoss)) if debugPrint:
return sum(errSq)/len(errSq) + 0.005*regressionLoss + 0.2*boundsLoss/len(ratedBooks) - 0.5*sum(linSepLoss)/len(linSepLoss) 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):
global weights global weights