"""Code for scalers."""
# Author: Johann Faouzi <johann.faouzi@gmail.com>
# License: BSD-3-Clause
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.preprocessing import StandardScaler as SklearnStandardScaler
from sklearn.preprocessing import MinMaxScaler as SklearnMinMaxScaler
from sklearn.preprocessing import MaxAbsScaler as SklearnMaxAbsScaler
from sklearn.preprocessing import RobustScaler as SklearnRobustScaler
from sklearn.utils.validation import check_array
[docs]class StandardScaler(BaseEstimator, TransformerMixin):
"""Standardize time series by removing mean and scaling to unit variance.
Parameters
----------
with_mean : bool (default = True)
If True, center the data before scaling.
with_std : bool (default = True)
If True, scale the data to unit variance.
Examples
--------
>>> from pyts.preprocessing import StandardScaler
>>> X = [[0, 2, 0, 4, 4, 6, 4, 4],
... [1, 0, 3, 2, 2, 2, 0, 2]]
>>> scaler = StandardScaler()
>>> scaler.transform(X)
array([[-1.5, -0.5, -1.5, 0.5, 0.5, 1.5, 0.5, 0.5],
[-0.5, -1.5, 1.5, 0.5, 0.5, 0.5, -1.5, 0.5]])
"""
[docs] def __init__(self, with_mean=True, with_std=True):
self.with_mean = with_mean
self.with_std = with_std
[docs] def fit(self, X=None, y=None):
"""Pass.
Parameters
----------
X
Ignored
y
Ignored
Returns
-------
self : object
"""
return self
[docs]class MinMaxScaler(BaseEstimator, TransformerMixin):
"""Transforms samples by scaling each sample to a given range.
Parameters
----------
sample_range : tuple (min, max) (default = (0, 1))
Desired range of transformed data.
Examples
--------
>>> from pyts.preprocessing import MinMaxScaler
>>> X = [[1, 5, 3, 2, 9, 6, 4, 7],
... [1, -2, 3, 2, 2, 1, 0, 2]]
>>> scaler = MinMaxScaler()
>>> scaler.transform(X)
array([[0. , 0.5 , 0.25 , 0.125, 1. , 0.625, 0.375, 0.75 ],
[0.6 , 0. , 1. , 0.8 , 0.8 , 0.6 , 0.4 , 0.8 ]])
"""
[docs] def __init__(self, sample_range=(0, 1)):
self.sample_range = sample_range
[docs] def fit(self, X=None, y=None):
"""Pass.
Parameters
----------
X
Ignored
y
Ignored
Returns
-------
self : object
"""
return self
[docs]class MaxAbsScaler(BaseEstimator, TransformerMixin):
"""Scale each sample by its maximum absolute value.
Examples
--------
>>> from pyts.preprocessing import MaxAbsScaler
>>> X = [[1, 5, 3, 2, 10, 6, 4, 7],
... [1, -5, 3, 2, 2, 1, 0, 2]]
>>> scaler = MaxAbsScaler()
>>> scaler.transform(X)
array([[ 0.1, 0.5, 0.3, 0.2, 1. , 0.6, 0.4, 0.7],
[ 0.2, -1. , 0.6, 0.4, 0.4, 0.2, 0. , 0.4]])
"""
[docs] def __init__(self):
pass
[docs] def fit(self, X=None, y=None):
"""Pass.
Parameters
----------
X
Ignored
y
Ignored
Returns
-------
self : object
"""
return self
[docs]class RobustScaler(BaseEstimator, TransformerMixin):
"""Scale samples using statistics that are robust to outliers.
This Scaler removes the median and scales the data according to
the quantile range (defaults to IQR: Interquartile Range).
The IQR is the range between the 1st quartile (25th quantile)
and the 3rd quartile (75th quantile).
Centering and scaling happen independently on each sample by
computing the relevant statistics on the timestamps. Median and
interquartile range are used to transform the data.
Standardization of a dataset is a common requirement for many
machine learning estimators. Typically this is done by removing the mean
and scaling to unit variance. However, outliers can often influence the
sample mean / variance in a negative way. In such cases, the median and
the interquartile range often give better results.
Parameters
----------
with_centering : bool (default = True)
If True, center the data before scaling.
with_scaling : bool (default = True)
If True, scale the data to interquartile range.
quantile_range : tuple (q_min, q_max), 0.0 < q_min < q_max < 100.0
Default: (25.0, 75.0) = (1st quantile, 3rd quantile) = IQR
Examples
--------
>>> from pyts.preprocessing import RobustScaler
>>> X = [[1, -2, 4],
... [-2, 1, 1],
... [2, 3, -2]]
>>> scaler = RobustScaler()
>>> scaler.transform(X)
array([[ 0. , -1. , 1. ],
[-2. , 0. , 0. ],
[ 0. , 0.4, -1.6]])
"""
[docs] def __init__(self, with_centering=True, with_scaling=True,
quantile_range=(25.0, 75.0)):
self.with_centering = with_centering
self.with_scaling = with_scaling
self.quantile_range = quantile_range
[docs] def fit(self, X=None, y=None):
"""Pass.
Parameters
----------
X
Ignored
y
Ignored
Returns
-------
self : object
"""
return self
