GPR using Node2Vec embedding cosine similarity kernels done
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02e912d4ff
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06712ee027
44
caliGraph.py
44
caliGraph.py
@ -389,8 +389,11 @@ def removeUselessSeries(G, minSco=0):
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def scoreOpinions(G, globMu, globStd):
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for n in list(G.nodes):
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node = G.nodes[n]
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feedbacks = []
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if node['t'] not in ['book', 'newBooks']:
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if 'gpr_score' in node:
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feedbacks = [node['gpr_score']]
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else:
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feedbacks = []
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adjacens = list(G.adj[n].keys())
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for adj in adjacens:
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adjNode = G.nodes[adj]
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@ -399,7 +402,6 @@ def scoreOpinions(G, globMu, globStd):
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if len(feedbacks):
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node['mean'], node['std'] = norm.fit(feedbacks)
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node['se'] = globStd / math.sqrt(len(feedbacks))
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ratio = len(feedbacks) / len(adjacens)
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node['score'] = node['mean']
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node['feedbacks'] = feedbacks
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else:
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@ -436,6 +438,11 @@ def scoreUnread(G, globMu, globStd):
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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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if 'gpr_score' in node:
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feedbacks.append(node['gpr_score'])
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ws.append(['gpr_score'])
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feedbacks.append(node['gpr_se'])
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ws.append(['gpr_se'])
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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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@ -809,6 +816,9 @@ def buildFullGraph(darkMode=False):
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graphAddTopLists(G, books, darkMode=darkMode)
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graphAddSeries(G, books, darkMode=darkMode)
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graphAddTags(G, books, darkMode=darkMode)
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genGprScores(G, 'gpr_score', 'gpr_se')
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return G, books
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@ -816,9 +826,8 @@ def genScores(G, books, calcPagerank=True):
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globMu, globStd = calcRecDist(G, books)
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if calcPagerank:
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runPagerank(G)
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genGprScores(G, globMu, globStd, 'score', 'se')
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#scoreOpinions(G, globMu, globStd)
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#scoreUnread(G, globMu, globStd)
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scoreOpinions(G, globMu, globStd)
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scoreUnread(G, globMu, globStd)
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return globMu, globStd
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def addImageToNode(node, cache, shape='circularImage'):
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@ -1111,23 +1120,6 @@ def waveFlow(G, node, n, dist, menge, firstEdge=False):
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if node in bestlist or node in keeplist:
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waveFlow(G, node, m, dist, menge, firstEdge=firstEdge)
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def gensimTokensForLines(lines):
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for i, line in enumerate(lines):
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tokens = gensim.utils.simple_preprocess(line)
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if tokens_only:
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yield tokens
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else:
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# For training data, add tags
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yield gensim.models.doc2vec.TaggedDocument(tokens, [i])
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def buildDoc2Vec(books):
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import gensim
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for n in list(G.nodes):
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node = G.nodes[n]
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if node['t'] == 'book':
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pass
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gensimTokensForLines(lines)
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def shell(G, books, mu, std):
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from ptpython.repl import embed
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embed(globals(), locals())
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@ -1201,7 +1193,7 @@ 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=-1, debugPrint=False):
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def evaluateFitness(books, batchSize=-1, debugPrint=False, doGPR=True):
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global weights
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G = buildBookGraph(books)
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graphAddAuthors(G, books)
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@ -1210,6 +1202,8 @@ def evaluateFitness(books, batchSize=-1, debugPrint=False):
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graphAddSeries(G, books)
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graphAddTags(G, books)
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runPagerank(G)
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if doGPR:
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genGprScores(G, 'gpr_score', 'gpr_se')
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ratedBooks = [n for n in list(G.nodes) if 'rating' in G.nodes[n] and G.nodes[n]['rating'] != None]
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boundsLoss = 0
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@ -1270,7 +1264,7 @@ def train(initGamma, full=True):
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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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weights[wt] += gamma*gradient[wt]/math.sqrt(delta)
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mse, gradient = evaluateFitness(books)
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mse, gradient = evaluateFitness(books, doGPR=False)
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if mse < last_mse:
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gamma = gamma*1.25
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else:
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@ -1307,7 +1301,7 @@ def loadWeights():
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with open('neuralWeights.json', 'r') as f:
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weights = json.loads(f.read())
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except IOError:
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weights = {"topList": 0.15, "recommender": 0.30, "author": 0.70, "series": 0.05, "tag": 0.05, "pagerank": 0.05, "mu": 0.50, "sigma": 0.30, "bias": 0.25} #, "tgb_rank": 0.10}
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weights = {"topList": 0.15, "recommender": 0.30, "author": 0.70, "series": 0.05, "tag": 0.05, "pagerank": 0.05, "mu": 0.50, "sigma": 0.30, "bias": 0.25, "gpr_score": 1.00, "gpr_se": -0.50} #, "tgb_rank": 0.10}
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return weights
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def cliInterface():
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10
py/gp.py
10
py/gp.py
@ -43,33 +43,33 @@ class BookKernel(GenericKernelMixin, Kernel):
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cloned.theta = theta
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return cloned
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def genGprScores(G, globMu, globStd, scoreName='gpr_score', stdName='gpr_std'):
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print('[\] Constructing Vectorizer')
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def genGprScores(G, scoreName='gpr_score', stdName='gpr_std'):
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print('[\] Constructing Feature-Space-Projector')
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node2vec = Node2Vec(G, dimensions=32, walk_length=16, num_walks=128, workers=8)
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print('[\] Fitting Embeddings for Kernel')
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model = node2vec.fit(window=8, min_count=1, batch_words=4)
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wv = model.wv
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print('[\] Constructing Kernel')
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kernel = BookKernel(wv)
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print('[\] Fitting GP')
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X, y = [], []
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for n in G.nodes:
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node = G.nodes[n]
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if 'rating' in node and node['rating']!=None:
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X.append(n)
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y.append(node['rating'])
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print('[\] Fitting GP')
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gpr = GaussianProcessRegressor(kernel=kernel, random_state=3141, alpha=1e-8).fit(X, y)
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print('[\] Inferencing GP')
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X = []
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for n in G.nodes:
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node = G.nodes[n]
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if not 'rating' in node or node['rating']==None:
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X.append(n)
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print('[\] Inferencing GP')
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y, stds = gpr.predict(X, return_std=True)
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i=0
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for n in G.nodes:
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node = G.nodes[n]
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if not 'rating' in node or node['rating']==None:
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s, std = y[i], sum([val[0] for val in stds[i]])
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s, std = y[i], stds[i][i][0]
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i+=1
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node[scoreName], node[stdName] = float(s), float(std)
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