import numpy as np
import gpflow
import tensorflow as tf
from gpflow.mean_functions import Constant
from gpflow.models import SVGP
from scipy.cluster.vq import kmeans2
from gpflow import Parameter
from gpflow.inducing_variables.inducing_variables import InducingPointsBase
from gpflow import covariances as cov
num_classes=7
node_feats = np.random.randn(2708, 1433)
node_labels = np.sum(node_feats, axis=1)
node_labels=num_classes*(node_labels-np.min(node_labels))/(np.max(node_labels)-np.min(node_labels))
node_labels=(num_classes-1)*np.floor(node_labels)
idx_train, idx_val, idx_test = np.array(range(140)),np.array(range(140,640)),np.array(range(1708,2708))
idx_train, idx_val, idx_test = tf.constant(idx_train), tf.constant(idx_val), tf.constant(idx_test)
X_train, y_train = node_feats[idx_train], node_labels[idx_train]
X_test, y_test = node_feats[idx_test], node_labels[idx_test]
def training_step(X_train, y_train, optimizer, gprocess):
with tf.GradientTape(watch_accessed_variables=False) as tape:
tape.watch(gprocess.trainable_variables)
data=(X_train, y_train)
objective = -gprocess.elbo(data)
gradients = tape.gradient(objective, gprocess.trainable_variables)
optimizer.apply_gradients(zip(gradients, gprocess.trainable_variables))
return objective
def evaluate(X_val, y_val, gprocess):
pred_y, pred_y_var = gprocess.predict_y(X_val)
pred_classes = np.argmax(pred_y.numpy(), axis=-1)
acc = np.mean(pred_classes == y_val)
return acc
def sparse_mat_to_sparse_tensor(sparse_mat):
"""
Converts a scipy csr_matrix to a tensorflow SparseTensor.
"""
coo = sparse_mat.tocoo()
indices = np.stack([coo.row, coo.col], axis=-1)
tensor = tf.sparse.SparseTensor(indices, sparse_mat.data, sparse_mat.shape)
return tensor
class GraphPolynomial(gpflow.kernels.base.Kernel):
"""
GraphPolynomial kernel for node classification as introduced in
Yin Chen Ng, Nicolo Colombo, Ricardo Silva: "Bayesian Semi-supervised
Learning with Graph Gaussian Processes".
"""
def __init__(self, sparse_adj_mat, feature_mat, degree=3.0, variance=1.0,
offset=1.0):
super().__init__([1])
self.degree = degree
self.offset = Parameter(offset, transform=gpflow.utilities.positive())
self.variance = Parameter(variance, transform=gpflow.utilities.positive())
# Pre-compute the P-matrix for transforming the base covariance matrix
# (c.f. paper for details).
sparse_adj_mat[np.diag_indices(sparse_adj_mat.shape[0])] = 1.0
self.sparse_P = sparse_mat_to_sparse_tensor(sparse_adj_mat)
self.sparse_P = self.sparse_P / sparse_adj_mat.sum(axis=1)
self.feature_mat = feature_mat
def K(self, X, Y=None, presliced=False):
X = tf.reshape(tf.cast(X, tf.int32), [-1])
X2 = tf.reshape(tf.cast(Y, tf.int32), [-1]) if Y is not None else X
base_cov = (self.variance * tf.matmul(self.feature_mat, self.feature_mat, transpose_b=True) + self.offset) ** self.degree
cov = tf.sparse.sparse_dense_matmul(self.sparse_P, base_cov)
cov = tf.sparse.sparse_dense_matmul(self.sparse_P, cov, adjoint_b=True)
cov = tf.gather(tf.gather(cov, X, axis=0), X2, axis=1)
# print(f"Kff: {cov.shape}")
return cov
def K_diag(self, X, presliced=False):
return tf.linalg.diag_part(self.K(X))
class NodeInducingPoints(InducingPointsBase):
"""
Set of real-valued inducing points. See parent-class for details.
"""
pass
# Init inducing points
inducing_points = kmeans2(node_feats, len_train, minit='points')[0] # use as many inducing points as training samples
#inducing_points = NodeInducingPoints(inducing_points)
# Init GP model
mean_function = Constant()
gprocess = SVGP(kernel, gpflow.likelihoods.MultiClass(num_classes),
inducing_points, mean_function=mean_function,
num_latent=num_classes, whiten=True, q_diag=False)
# Init optimizer
optimizer = tf.optimizers.Adam()
for epoch in range(200):
elbo = -training_step(X_train, y_train, optimizer, gprocess)
elbo = elbo.numpy()
acc = evaluate(X_test, y_test, gprocess)
print(f"{epoch}:\tELBO: {elbo:.5f}\tAcc: {acc:.3f}")
我收到以下错误消息：
KeyError跟踪（最近的呼叫） 持续） 〜\ Anaconda3 \ envs \ tf_gpf_env \ lib \ site-packages \ multipledispatch \ dispatcher.py 在通话中（自己，* args，** kwargs） 268尝试： -> 269 func = self._cache [types] 270，除了KeyError：
KeyError：（，，）
在处理上述异常期间，发生了另一个异常：
NotImplementedError错误回溯（最近的调用 最后） 范围（200）中的纪元为1： ----> 2 elbo = -training_step（X_train，y_train，optimizer，gprocess） 3 elbo = elbo.numpy（） 4 5 acc =评估（idx_test，node_labels [idx_test]，gprocess）
在training_step中（X_train，y_train， 优化器，gprocess） 15 tape.watch（gprocess.trainable_variables） 16个数据=（X_train，y_train） ---> 17个目标= -gprocess.elbo（数据） 18 19 #objective = -gprocess.elbo（X_train，y_train）
c：\ users \ asus \ downloads \ gpflow-develop \ gpflow-develop \ gpflow \ models \ svgp.py 在elbo中（自我，数据） 152这将返回对数边际可能性的证据下界（ELBO）。 153“”“ -> 154 return self.log_marginal_likelihood（data） 155 156 def Forecast_f（self，Xnew：tf.Tensor，full_cov = False，full_output_cov = False）-> tf.Tensor：
c：\ users \ asus \ downloads \ gpflow-develop \ gpflow-develop \ gpflow \ models \ model.py 在log_marginal_likelihood（自己，* args，** kwargs）中 43 44 def log_marginal_likelihood（self，* args，** kwargs）-> tf.Tensor： ---> 45返回self.log_likelihood（* args，** kwargs）+ self.log_prior（） 46 47 def log_prior（self）-> tf.Tensor：
c：\ users \ asus \ downloads \ gpflow-develop \ gpflow-develop \ gpflow \ models \ svgp.py 在log_likelihood中（自己，数据） 138 X，Y =数据 第139章 -> 140 f_mean，f_var = self.predict_f（X，full_cov = False，full_output_cov = False） 141 var_exp = self.likelihood.variational_expectations（f_mean，f_var，Y） 142如果self.num_data不是None：
c：\ users \ asus \ downloads \ gpflow-develop \ gpflow-develop \ gpflow \ models \ svgp.py 在predict_f（自我，Xnew，full_cov，full_output_cov）中 164 full_cov = full_cov， 165 white = self.whiten， -> 166 full_output_cov = full_output_cov） 167＃tf.debugging.assert_positive（var）＃我们真的应该在这里通过测试 168 return mu + self.mean_function（Xnew），var
〜\ Anaconda3 \ envs \ tf_gpf_env \ lib \ site-packages \ multipledispatch \ dispatcher.py 在通话中（自己，* args，** kwargs） 276 self._cache [types] = func 277尝试： -> 278 return func（* args，** kwargs） 279 280，除了MDNotImplementedError：
c：\ users \ asus \ downloads \ gpflow-develop \ gpflow-develop \ gpflow \ conditionals \ conditionals.py 在_conditional（Xnew，inducing_variable，内核，函数，full_cov， full_output_cov，q_sqrt，白色） 54“”“ 55 Kmm = Kuu（归纳变量，内核，抖动= default_jitter（））＃[M，M] ---> 56 Kmn = Kuf（inducing_variable，kernel，Xnew）＃[M，N] 57 Knn =内核（Xnew，full = full_cov） 58 fmean，fvar = base_conditional（Kmn，
〜\ Anaconda3 \ envs \ tf_gpf_env \ lib \ site-packages \ multipledispatch \ dispatcher.py 在通话中（自己，* args，** kwargs） 第273章; 274'找不到％s的签名：<％s>'％ -> 275（self.name，str_signature（types））） 276 self._cache [types] = func 277尝试：
NotImplementedError：找不到Kuf的签名：