Revert "implemented neuralBins (performance is bad...)"
This reverts commit bd53a83058.
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b10fcac016
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7c16b8044e
99
caliGraph.py
99
caliGraph.py
@ -7,8 +7,6 @@ import copy
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import random
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import requests
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from collections import defaultdict
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import numpy as np
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import pandas as pd
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from scipy.stats import norm
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@ -409,9 +407,9 @@ def scoreOpinions(G, globMu, globStd):
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node['score'] = node['score'] / 2 + node['gpr_score']
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def scoreUnread(G, globMu, globStd):
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neuralBins = defaultdict(list)
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feedbacks = [globMu-globStd, globMu+globStd]
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for n in list(G.nodes):
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feedbacks = [globMu]
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ws = [['mu']]
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node = G.nodes[n]
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if node['t'] == 'book':
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if node['rating'] == None:
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@ -419,42 +417,42 @@ def scoreUnread(G, globMu, globStd):
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for adj in adjacens:
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adjNode = G.nodes[adj]
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if 'score' in adjNode and adjNode['score'] != None:
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w = adjNode['t']
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w = [adjNode['t'], G[n][adj]['weight'] if 'weight' in G[n][adj] else 1]
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for fb in adjNode['feedbacks']:
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neuralBins[w].append(fb)
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feedbacks.append(fb)
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node['mean'], node['std'] = norm.fit(feedbacks)
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node['median'] = np.percentile(feedbacks, [50], method='linear')[0]
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node['se'] = globStd / math.sqrt(len(feedbacks))
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neuralBins['mean'] = [node['mean']]
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neuralBins['sigma'] = [node['std']]
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neuralBins['median'] = [node['median']]
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neuralBins['se'] = [node['se']]
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neuralBins['pagerank'] = [node['pagerank_score']]
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neuralBins['gpr_score'] = [node['gpr_score']]
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if 'tgb_rank' in node:
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neuralBins['tgbrank'] = [10/math.ln10(10+node['tgb_rank'])]
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neuralBins['bias'] = [globMu]
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score = 0
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nb = dict(neuralBins)
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act = {}
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for b in nb:
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act[b] = sum(nb[b])/len(nb[b])
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score += act[b] * getWeightForType(b)
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score /= sum([abs(getWeightForType(b)) for b in nb])
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node['score'] = math.tanh(score/10)*10
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node['_act'] = act
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ws.append(w)
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if len(feedbacks):
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node['mean'], node['std'] = norm.fit(feedbacks)
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node['median'] = np.percentile(feedbacks, [50], method='linear')[0]
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node['se'] = globStd / math.sqrt(len(feedbacks))
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feedbacks.append(node['pagerank_score'])
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ws.append(['pagerank'])
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#feedbacks.append(10/math.ln10(10+node['tgb_rank']) if 'tgb_rank' in node else 0)
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#ws.append(['tgb_rank'])
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feedbacks.append(node['std'])
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ws.append(['sigma'])
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#feedbacks.append(node['median'])
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#ws.append(['median'])
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#feedbacks.append(node['se'])
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#ws.append(['se'])
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feedbacks.append(globMu)
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ws.append(['bias'])
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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])
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node['_act'] = feedbacks
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node['_wgh'] = ws
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else:
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node['score'] = globMu + errorFac*globStd + len(feedbacks)*0.0000000001
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if 'series' in node:
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if node['series_index'] == 1.0:
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node['score'] += 0.000000001
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def getWeightForType(nodeType):
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def getWeightForType(nodeType, edgeWeight=1):
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global weights
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if nodeType not in weights:
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weights[nodeType] = 0.1
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saveWeights(weights)
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print('[i] neuralWeights-Vector extended with >'+nodeType+'<')
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return weights[nodeType]
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w = weights[nodeType]
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if nodeType == 'topList':
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return edgeWeight*w
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else:
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return w
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def printBestList(G, t='book', num=-1):
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bestlist = []
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@ -1204,7 +1202,7 @@ def findNewBooks(G, books, mu, num=-1, minRecSco=5):
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# while batchSize is implemented, we only get a good gonvergence when we disable it (batchSize=-1)
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# but might be necessary to enable later for a larger libary for better training performance...
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# maybe try again for 128 books?
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def evaluateFitness(books, batchSize=16, debugPrint=False):
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def evaluateFitness(books, batchSize=-1, debugPrint=False):
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global weights
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G = buildBookGraph(books)
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graphAddAuthors(G, books)
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@ -1219,13 +1217,12 @@ def evaluateFitness(books, batchSize=16, debugPrint=False):
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linSepLoss = []
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errSq = []
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gradient = {}
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for w in weights:
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gradient[w] = 0
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for wt in weights:
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gradient[wt] = 0
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mu, sigma = genScores(G, books)
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batch = random.sample(ratedBooks, batchSize) if batchSize!=-1 and len(ratedBooks) > batchSize else ratedBooks
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for b in G.nodes:
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if b in ratedBooks:
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node = G.nodes[b]
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batch = random.sample(ratedBooks, batchSize) if batchSize!=-1 and len(ratedBooks) > batchSize else ratedBooks
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if b in batch:
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rating = G.nodes[b]['rating']
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G.nodes[b]['rating'] = None
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_, _ = genScores(G, books, calcPagerank=False)
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@ -1234,20 +1231,17 @@ def evaluateFitness(books, batchSize=16, debugPrint=False):
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else:
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errSq.append((rating - G.nodes[b]['score'])**2)
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G.nodes[b]['rating'] = rating
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if b in batch:
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for wt in weights:
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scoreB = 0
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for w in node['_act']:
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scoreB += node['_act'][w] * (getWeightForType(w) + (0.001 if wt==w else 0))
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scoreB /= sum([abs(getWeightForType(w)) for w in node['_act']])
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scoreB = math.tanh(scoreB/10)*10
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gradient[wt] += ((rating - G.nodes[b]['score'])**2 - (rating - scoreB)**2)*1000
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for wt in weights:
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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']])
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gradient[wt] += ((rating - G.nodes[b]['score'])**2 - (rating - scoreB)**2)*1000
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regressionLoss = sum([max(0,abs(w)-1)**2 for w in weights.values()]) # no punishment if w within -1 and 1
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for wt in weights:
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if abs(weights[wt]) > 1.0:
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gradient[wt] -= weights[wt]*3
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gradient[wt] -= weights[wt]*10
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else:
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gradient[wt] -= weights[wt]*1
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for g in gradient:
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gradient[g] /= len(batch)
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gradient[g] /= len(errSq)
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if debugPrint:
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print(sum(errSq)/len(errSq), 0.001*regressionLoss)
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fit = sum(errSq)/len(errSq) + 0.001*regressionLoss
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@ -1263,7 +1257,7 @@ def train(initGamma, full=True):
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books = loadBooksFromDB()
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bestWeights = copy.copy(weights)
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mse, gradient = evaluateFitness(books)
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delta = math.sqrt(sum(gradient[g]**2 for g in gradient)/len(gradient))
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delta = sum(gradient[g]**2 for g in gradient)
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best_mse = mse
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stagLen = 0
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goal = 1.0e-4
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@ -1276,10 +1270,7 @@ def train(initGamma, full=True):
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print({'mse': mse, 'gamma': gamma, 'delta': delta})
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delta = sum(gradient[g]**2 for g in gradient)
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for wt in weights:
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if wt in gradient:
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weights[wt] += gamma*gradient[wt]/math.sqrt(delta)
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#else:
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# del weights[wt]
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weights[wt] += gamma*gradient[wt]/math.sqrt(delta)
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mse, gradient = evaluateFitness(books)
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if mse < last_mse:
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gamma = gamma*1.25
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