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41 lines
1.5 KiB
Python
41 lines
1.5 KiB
Python
#
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# Copyright (c) 2019 Intel Corporation
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import numpy as np
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class DoublyRobust(object):
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@staticmethod
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def evaluate(ope_shared_stats: 'OpeSharedStats') -> tuple:
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"""
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Run the off-policy evaluator to get a score for the goodness of the new policy, based on the dataset,
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which was collected using other policy(ies).
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Papers:
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https://arxiv.org/abs/1103.4601
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https://arxiv.org/pdf/1612.01205 (some more clearer explanations)
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:return: the evaluation score
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"""
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ips = np.mean(ope_shared_stats.rho_all_dataset * ope_shared_stats.all_rewards)
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dm = np.mean(ope_shared_stats.all_v_values_reward_model_based)
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dr = np.mean(ope_shared_stats.rho_all_dataset *
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(ope_shared_stats.all_rewards - ope_shared_stats.all_reward_model_rewards[
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range(len(ope_shared_stats.all_actions)), ope_shared_stats.all_actions])) + dm
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return ips, dm, dr
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