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implemented neuralBins (performance is bad...)

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Dominik Moritz Roth 2022-02-22 10:37:16 +01:00
parent 880cb6ba7e
commit bd53a83058
1 changed files with 53 additions and 45 deletions
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caliGraph.py
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@ -7,6 +7,8 @@ import copy
import random
import requests
from collections import defaultdict
import numpy as np
import pandas as pd
from scipy.stats import norm
@ -404,9 +406,9 @@ def scoreOpinions(G, globMu, globStd):
node['score'] = None
def scoreUnread(G, globMu, globStd):
neuralBins = defaultdict(list)
feedbacks = [globMu-globStd, globMu+globStd]
for n in list(G.nodes):
feedbacks = [globMu]
ws = [['mu']]
node = G.nodes[n]
if node['t'] == 'book':
if node['rating'] == None:
@ -414,42 +416,41 @@ def scoreUnread(G, globMu, globStd):
for adj in adjacens:
adjNode = G.nodes[adj]
if 'score' in adjNode and adjNode['score'] != None:
w = [adjNode['t'], G[n][adj]['weight'] if 'weight' in G[n][adj] else 1]
w = adjNode['t']
for fb in adjNode['feedbacks']:
neuralBins[w].append(fb)
feedbacks.append(fb)
ws.append(w)
if len(feedbacks):
node['mean'], node['std'] = norm.fit(feedbacks)
node['median'] = np.percentile(feedbacks, [50], method='linear')[0]
node['se'] = globStd / math.sqrt(len(feedbacks))
feedbacks.append(node['pagerank_score'])
ws.append(['pagerank'])
#feedbacks.append(10/math.ln10(10+node['tgb_rank']) if 'tgb_rank' in node else 0)
#ws.append(['tgb_rank'])
feedbacks.append(node['std'])
ws.append(['sigma'])
#feedbacks.append(node['median'])
#ws.append(['median'])
#feedbacks.append(node['se'])
#ws.append(['se'])
feedbacks.append(globMu)
ws.append(['bias'])
node['score'] = sum([fb*getWeightForType(w[0], w[1] if len(w)>1 else 1) for fb, w in zip(feedbacks, ws)])/sum([getWeightForType(w[0], w[1] if len(w)>1 else 1) for w in ws])
node['_act'] = feedbacks
node['_wgh'] = ws
else:
node['score'] = globMu + errorFac*globStd + len(feedbacks)*0.0000000001
neuralBins['mean'] = [node['mean']]
neuralBins['sigma'] = [node['std']]
neuralBins['median'] = [node['median']]
neuralBins['se'] = [node['se']]
neuralBins['pagerank'] = [node['pagerank_score']]
if 'tgb_rank' in node:
neuralBins['tgbrank'] = [10/math.ln10(10+node['tgb_rank'])]
neuralBins['bias'] = [globMu]
score = 0
nb = dict(neuralBins)
act = {}
for b in nb:
act[b] = sum(nb[b])/len(nb[b])
score += act[b] * getWeightForType(b)
score /= sum([abs(getWeightForType(b)) for b in nb])
node['score'] = math.tanh(score/10)*10
node['_act'] = act
if 'series' in node:
if node['series_index'] == 1.0:
node['score'] += 0.000000001
def getWeightForType(nodeType, edgeWeight=1):
def getWeightForType(nodeType):
global weights
w = weights[nodeType]
if nodeType == 'topList':
return edgeWeight*w
else:
return w
if nodeType not in weights:
weights[nodeType] = 0.1
saveWeights(weights)
print('[i] neuralWeights-Vector extended with >'+nodeType+'<')
return weights[nodeType]
def printBestList(G, t='book', num=-1):
bestlist = []
@ -1198,7 +1199,7 @@ def findNewBooks(G, books, mu, num=-1, minRecSco=5):
# while batchSize is implemented, we only get a good gonvergence when we disable it (batchSize=-1)
# but might be necessary to enable later for a larger libary for better training performance...
# maybe try again for 128 books?
def evaluateFitness(books, batchSize=-1, debugPrint=False):
def evaluateFitness(books, batchSize=16, debugPrint=False):
global weights
G = buildBookGraph(books)
graphAddAuthors(G, books)
@ -1213,12 +1214,13 @@ def evaluateFitness(books, batchSize=-1, debugPrint=False):
linSepLoss = []
errSq = []
gradient = {}
for wt in weights:
gradient[wt] = 0
for w in weights:
gradient[w] = 0
mu, sigma = genScores(G, books)
for b in G.nodes:
batch = random.sample(ratedBooks, batchSize) if batchSize!=-1 and len(ratedBooks) > batchSize else ratedBooks
if b in batch:
for b in G.nodes:
if b in ratedBooks:
node = G.nodes[b]
rating = G.nodes[b]['rating']
G.nodes[b]['rating'] = None
_, _ = genScores(G, books, calcPagerank=False)
@ -1227,17 +1229,20 @@ def evaluateFitness(books, batchSize=-1, debugPrint=False):
else:
errSq.append((rating - G.nodes[b]['score'])**2)
G.nodes[b]['rating'] = rating
if b in batch:
for wt in weights:
scoreB = sum([a*(1.001 if wt==w[0] else 1)*weights[w[0]]*(w[1] if len(w)>1 else 1) for a,w in zip(G.nodes[b]['_act'], G.nodes[b]['_wgh'])])/sum([(1.001 if wt==w[0] else 1)*weights[w[0]]*(w[1] if len(w)>1 else 1) for w in G.nodes[b]['_wgh']])
scoreB = 0
for w in node['_act']:
scoreB += node['_act'][w] * (getWeightForType(w) + (0.001 if wt==w else 0))
scoreB /= sum([abs(getWeightForType(w)) for w in node['_act']])
scoreB = math.tanh(scoreB/10)*10
gradient[wt] += ((rating - G.nodes[b]['score'])**2 - (rating - scoreB)**2)*1000
regressionLoss = sum([max(0,abs(w)-1)**2 for w in weights.values()]) # no punishment if w within -1 and 1
for wt in weights:
if abs(weights[wt]) > 1.0:
gradient[wt] -= weights[wt]*10
else:
gradient[wt] -= weights[wt]*1
gradient[wt] -= weights[wt]*3
for g in gradient:
gradient[g] /= len(errSq)
gradient[g] /= len(batch)
if debugPrint:
print(sum(errSq)/len(errSq), 0.001*regressionLoss)
fit = sum(errSq)/len(errSq) + 0.001*regressionLoss
@ -1253,7 +1258,7 @@ def train(initGamma, full=True):
books = loadBooksFromDB()
bestWeights = copy.copy(weights)
mse, gradient = evaluateFitness(books)
delta = sum(gradient[g]**2 for g in gradient)
delta = math.sqrt(sum(gradient[g]**2 for g in gradient)/len(gradient))
best_mse = mse
stagLen = 0
goal = 1.0e-4
@ -1266,7 +1271,10 @@ def train(initGamma, full=True):
print({'mse': mse, 'gamma': gamma, 'delta': delta})
delta = sum(gradient[g]**2 for g in gradient)
for wt in weights:
if wt in gradient:
weights[wt] += gamma*gradient[wt]/math.sqrt(delta)
#else:
# del weights[wt]
mse, gradient = evaluateFitness(books)
if mse < last_mse:
gamma = gamma*1.25