63 lines
1.9 KiB
Python
63 lines
1.9 KiB
Python
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import numpy as np
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from sklearn.gaussian_process.kernels import Kernel, Hyperparameter
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from sklearn.gaussian_process.kernels import GenericKernelMixin
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from sklearn.gaussian_process import GaussianProcessRegressor
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#from sklearn.gaussian_process import GaussianProcessClassifier
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from sklearn.base import clone
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class BookKernel(GenericKernelMixin, Kernel):
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def __init__(self, G):
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self.baseline_similarity = 0.5
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self.baseline_similarity_bounds = (1e-5, 1)
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self.G = G
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self.node2vec = Node2Vec(self.G, dimensions=32, walk_length=16, num_walks=256, workers=8)
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self.model = node2vec.fit(window=10, min_count=1, batch_words=4)
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self.wv = self.model.wv
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def _f(self, s1, s2):
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return self.wv.similarity(s1, s2)
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def __call__(self, X, Y=None, eval_gradient=False):
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if Y is None:
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Y = X
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if eval_gradient:
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return (
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np.array([[self._f(x, y) for y in Y] for x in X]),
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)
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else:
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return np.array([[self._f(x, y) for y in Y] for x in X])
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def diag(self, X):
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return self(X)
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def is_stationary(self):
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return False
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def clone_with_theta(self, theta):
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cloned = clone(self)
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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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gpr = GaussianProcessRegressor(kernel=BookKernel(G), random_state=3141)
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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 node['rating']!=None:
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X.append(n)
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y.append(node['rating'])
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gpr.fit(X, y)
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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 node['rating']==None:
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X.append(n)
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y,stds = gpr.predict(X, return_std=True)
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for n in G.nodes:
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node = G.nodes[n]
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if node['rating']==None:
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y, std = y.pop(0), stds.pop(0)
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node[scoreName], node[stdName] = y, std
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