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@mrksr
Last active June 12, 2020 14:45
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gpflow-predict-partial
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"sns.set()\n",
"%matplotlib inline\n",
"plt.rcParams['figure.figsize'] = (10, 4)\n",
"plt.rcParams['figure.dpi'] = 100"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"import GPflow\n",
"from partial_test import PartialGPR"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Data"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"X = np.mgrid[0:5*np.pi:75j][:, None]\n",
"Y = 0.5 * X + np.sin(X) + np.random.normal(scale=0.25, size=X.shape)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7f7deb2c0048>]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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XdI+19ksRrA0AAAAAAmdaZK39qqSvRrAWAAAAAJiDhtYAAAAAUi1wpgUAgFbj+74Gh8sq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\nYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f7deb37e780>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(X, Y, 'o')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Model"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"kernel = GPflow.kernels.Linear(1) + GPflow.kernels.Cosine(1)\n",
"model = PartialGPR(X, Y, kernel)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
" fun: 17.327876317403231\n",
" hess_inv: <4x4 LbfgsInvHessProduct with dtype=float64>\n",
" jac: array([ -9.64702181e-07, 4.08881555e-06, -2.35491575e-05,\n",
" -5.42490385e-05])\n",
" message: b'CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH'\n",
" nfev: 30\n",
" nit: 19\n",
" status: 0\n",
" success: True\n",
" x: array([-1.26845335, -0.30029396, 0.54495587, -2.62561349])"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.optimize()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table id='params' width=100%><tr><td>Name</td><td>values</td><td>prior</td><td>constraint</td></tr><tr><td>name.kern.linear.variance</td><td>[ 0.24784989]</td><td>None</td><td>+ve</td></tr><tr><td>name.kern.cosine.variance</td><td>[ 0.55423116]</td><td>None</td><td>+ve</td></tr><tr><td>name.kern.cosine.lengthscales</td><td>[ 1.00229777]</td><td>None</td><td>+ve</td></tr><tr><td>name.likelihood.variance</td><td>[ 0.06989577]</td><td>None</td><td>+ve</td></tr></table>"
],
"text/plain": [
"<partial_test.PartialGPR at 0x7f7deb0c1710>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"Xt = np.mgrid[0:5*np.pi:200j][:, None]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"mu, _ = model.predict_f(Xt)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x7f7dd6490c18>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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st7EPBSUtARAXF9/t+9deeyMpKam88MJz7Nu3l/j4BAoLR/H9718JwDnnnE9x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\nTGlKWkREREREZEpT0iIiIiIiIlPa/wcc58W7YSnPQwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f7dd651d438>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(X, Y, 'o')\n",
"plt.plot(Xt, mu)\n",
"plt.legend(['Observations', 'Mean Prediction'])"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"mu_cos, _ = model.predict_f_partial(Xt, kern=model.kern.cosine)\n",
"mu_lin, _ = model.predict_f_partial(Xt, kern=model.kern.linear)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x7f7dd5cccb00>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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8/RyOWJwL7itQyUNtdTvjJu6sMUlLDCcm3NBhTZGLx/UjPirIo7thNdVQyWqc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\nCCFi2v8HZIwwRugVERYAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f7dd5d4f5c0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(X, Y, 'o')\n",
"plt.plot(Xt, mu_lin)\n",
"plt.plot(Xt, mu_cos)\n",
"plt.plot(Xt, mu_lin + mu_cos)\n",
"plt.legend(['Observations', 'Linear Part', 'Periodic Part', 'Sum of Parts'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
import tensorflow as tf
from GPflow.gpr import GPR
from GPflow.param import AutoFlow
from GPflow._settings import settings
float_type = settings.dtypes.float_type
class PartialGPR(GPR):
def build_predict(self, Xnew, full_cov=False, kx_kern=None):
"""
Xnew is a data matrix, point at which we want to predict
This method computes
p(F* | Y )
where F* are points on the GP at Xnew, Y are noisy observations at X.
"""
if kx_kern is None:
kx_kern = self.kern
Kx = kx_kern.K(self.X, Xnew)
K = self.kern.K(self.X) + tf.eye(tf.shape(self.X)[0], dtype=float_type) * self.likelihood.variance
L = tf.cholesky(K)
A = tf.matrix_triangular_solve(L, Kx, lower=True)
V = tf.matrix_triangular_solve(L, self.Y - self.mean_function(self.X))
fmean = tf.matmul(A, V, transpose_a=True) + self.mean_function(Xnew)
if full_cov:
fvar = self.kern.K(Xnew) - tf.matmul(A, A, transpose_a=True)
shape = tf.stack([1, 1, tf.shape(self.Y)[1]])
fvar = tf.tile(tf.expand_dims(fvar, 2), shape)
else:
fvar = self.kern.Kdiag(Xnew) - tf.reduce_sum(tf.square(A), 0)
fvar = tf.tile(tf.reshape(fvar, (-1, 1)), [1, tf.shape(self.Y)[1]])
return fmean, fvar
@AutoFlow(Xnew=(float_type, [None, None]))
def predict_f_partial(self, Xnew, full_cov=False, kern=None):
return self.build_predict(Xnew, full_cov, kern)
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