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