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test_pal_gpflowgpr.py
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test_pal_gpflowgpr.py
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# -*- coding: utf-8 -*-
# Copyright 2020 PyePAL authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Testing the PALGPflowGPR class"""
import numpy as np
from pyepal.pal.pal_gpflowgpr import PALGPflowGPR
def test_pal_gpflow(binh_korn_points):
"""Test basic functionality of the PALGpy class"""
import gpflow # pylint:disable=import-outside-toplevel
X_binh_korn, y_binh_korn = binh_korn_points # pylint:disable=invalid-name
X_binh_korn = ( # pylint:disable=invalid-name
X_binh_korn - X_binh_korn.mean()
) / X_binh_korn.std() # pylint:disable=invalid-name
y_binh_korn = (
y_binh_korn - y_binh_korn.mean()
) / y_binh_korn.std() + 0.01 * np.random.rand()
def build_model(x, y): # pylint:disable=invalid-name
k = gpflow.kernels.RationalQuadratic()
m = gpflow.models.GPR( # pylint:disable=invalid-name
data=(x, y), kernel=k, mean_function=None
)
return m
sample_idx = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 50, 60, 70])
model_0 = build_model(X_binh_korn[sample_idx], y_binh_korn[sample_idx])
model_1 = build_model(X_binh_korn[sample_idx], y_binh_korn[sample_idx])
palinstance = PALGPflowGPR(
X_binh_korn,
[model_0, model_1],
2,
beta_scale=1,
epsilon=0.01,
delta=0.01,
opt_kwargs={"maxiter": 50},
)
palinstance.cross_val_points = 0
palinstance.update_train_set(sample_idx, y_binh_korn[sample_idx])
idx = palinstance.run_one_step()
assert idx[0] not in [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 50, 60, 70]
assert palinstance.number_sampled_points > 0
assert sum(palinstance.discarded) == 0