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# Copyright (C) 2019 THL A29 Limited, a Tencent company. All rights reserved.
# 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,
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import numpy as np
from ..base import BaseNumericEncoder
from ...helper import batching, train_required
[docs]class StandarderEncoder(BaseNumericEncoder):
batch_size = 2048
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.mean = None
self.scale = None
[docs] def post_init(self):
from sklearn.preprocessing import StandardScaler
self.standarder = StandardScaler()
[docs] @batching
def train(self, vecs: np.ndarray, *args, **kwargs) -> None:
self.standarder.partial_fit(vecs)
self.mean = self.standarder.mean_.astype('float32')
self.scale = self.standarder.scale_.astype('float32')
[docs] @train_required
@batching
def encode(self, vecs: np.ndarray, *args, **kwargs) -> np.ndarray:
return (vecs - self.mean) / self.scale