Coverage for src/bob/fusion/base/preprocessor/ZNorm.py: 60%

1import numpy as np 

2 

3from sklearn.preprocessing import StandardScaler 

4 

5# to fix the sphinx docs 

6StandardScaler.__module__ = "sklearn.preprocessing" 

7 

8 

9class ZNorm(StandardScaler): 

10 """ZNorm feature scaler 

11 This scaler works just like :any:`sklearn.preprocessing.StandardScaler` but 

12 only takes the zero effort impostors into account when estimating the mean 

13 and standard deviation. You should not use this scaler when PAD scores are 

14 present. 

15 """ 

16 

17 def __init__(self, copy=True, **kwargs): 

18 """Initialize self. See help(type(self)) for accurate signature.""" 

19 super(ZNorm, self).__init__( 

20 copy=copy, with_mean=True, with_std=True, **kwargs 

21 ) 

22 

23 def fit(self, X, y=None): 

24 """Estimates the mean and standard deviation of samples. 

25 Only positive samples are used in estimation. 

26 """ 

27 # the fitting is done only on negative samples 

28 if y is not None: 

29 X = np.asarray(X)[~y, ...] 

30 return super(ZNorm, self).fit(X)