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Added a regression-loss (push weights towards 1)

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Dominik Moritz Roth 2021-09-24 19:12:09 +02:00
parent 9d6f37af45
commit 5a4e48d86c
1 changed files with 12 additions and 10 deletions
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caliGraph.py
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@ -341,7 +341,7 @@ def scoreOpinions(G, globMu, globStd):
def scoreUnread(G, globMu, globStd):
for n in list(G.nodes):
feedbacks = [globMu]
weights = [getWeightForType('mu')]
ws = [getWeightForType('mu')]
node = G.nodes[n]
if node['t'] == 'book':
if node['rating'] == None:
@ -352,16 +352,16 @@ def scoreUnread(G, globMu, globStd):
w = getWeightForType(adjNode['t'], G[n][adj]['weight'] if 'weight' in G[n][adj] else 1)
for fb in adjNode['feedbacks']:
feedbacks.append(fb)
weights.append(w)
ws.append(w)
if len(feedbacks):
node['mean'], node['std'] = norm.fit(feedbacks)
node['se'] = globStd / math.sqrt(len(feedbacks))
feedbacks.append(node['std'])
weights.append(getWeightForType('sigma'))
ws.append(getWeightForType('sigma'))
feedbacks.append(node['se'])
weights.append(getWeightForType('se'))
node['score'] = sum([fb*w for fb, w in zip(feedbacks, weights)])/len(feedbacks)
#node['score'] = sum([fb*w for fb, w in zip(feedbacks, weights)])/sum(weights)
ws.append(getWeightForType('se'))
#node['score'] = sum([fb*w for fb, w in zip(feedbacks, weights)])/len(feedbacks)
node['score'] = sum([fb*w for fb, w in zip(feedbacks, ws)])/len(feedbacks)
else:
node['score'] = globMu + errorFac*globStd + len(feedbacks)*0.0000000001
if 'series' in node:
@ -683,6 +683,7 @@ def waveFlow(G, node, n, dist, menge, firstEdge=False):
waveFlow(G, node, m, dist, menge, firstEdge=firstEdge)
def evaluateFitness(books):
global weights
G = buildBookGraph(books)
graphAddAuthors(G, books)
graphAddRecommenders(G, books)
@ -703,7 +704,8 @@ def evaluateFitness(books):
if rating > 10.0:
errSq[-1] *= 1.5
G.nodes[m]['rating'] = rating
return sum(errSq) / len(errSq)
regressionLoss = sum([(1-w)**2 for w in weights.values()])
return sum(errSq)/len(errSq) + regressionLoss/1000
def train(gamma = 0.1):
global weights
@ -720,15 +722,15 @@ def train(gamma = 0.1):
if gamma < 0.01:
while random.random() < 0.5:
attr = random.choice(w)
weights[attr] = -0.1+random.random()*1.5
weights[attr] = -0.1+random.random()*1.5+random.random()
else:
weights[attr] += delta
if attr not in ['sigma', 'mu', 'se']:
weights[attr] = min(max(0, weights[attr]), 3)
weights[attr] = min(max(0.0, weights[attr]), 3.0)
mse = evaluateFitness(books)
if mse < best_mse: # got better
saveWeights(weights)
gamma = max(gamma*1.75, 0.001)
gamma = min(max(gamma*1.75, 0.001), 0.5)
bestWeights = copy.copy(weights)
best_mse = mse
delta *= 2