import numpy as np
from typing import Optional, Tuple
from sklearn.exceptions import NotFittedError
from dtaianomaly.preprocessing.Preprocessor import Preprocessor
[docs]
class MinMaxScaler(Preprocessor):
"""
Rescale raw time series to a [0, 1] via min-max scaling. The
minimum and maximum is computed on a training set, after which
these values can be used to transform a new time series. Therefore,
there is no guarantee that the values of the transformed test set
will actually be in the range [0, 1].
For multivariate time series, each attribute will be normalized
independently, i.e., the minimum and maximum of each attribute in
the transformed time series will 0 and 1, respectively.
If the minimum and maximum of an attribute is the same (the time
series consists of only one value), then the transformation will
not do anything.
Attributes
----------
min_: array-like of shape (n_attributes)
The minimum value in each attribute of the training data.
max_: array-like of shape (n_attributes)
The maximum value in each attribute of the training data.
Raises
------
NotFittedError
If the `transform` method is called before fitting this MinMaxScaler.
"""
min_: np.array
max_: np.array
def _fit(self, X: np.ndarray, y: Optional[np.ndarray] = None) -> 'MinMaxScaler':
if len(X.shape) == 1 or X.shape[1] == 1:
# univariate case
self.min_ = np.array([np.nanmin(X)])
self.max_ = np.array([np.nanmax(X)])
else:
# multivariate case
self.min_ = np.nanmin(X, axis=0)
self.max_ = np.nanmax(X, axis=0)
# Adjust to deal with constant attributes
constant_attributes = (self.min_ == self.max_)
self.min_ = np.where(constant_attributes, 0, self.min_)
self.max_ = np.where(constant_attributes, 1, self.max_)
return self
def _transform(self, X: np.ndarray, y: Optional[np.ndarray] = None) -> Tuple[np.ndarray, Optional[np.ndarray]]:
if not (hasattr(self, 'min_') and hasattr(self, 'max_')):
raise NotFittedError(f'Call `fit` before using transform on {str(self)}')
if not ((len(X.shape) == 1 and self.min_.shape[0] == 1) or X.shape[1] == self.min_.shape[0]):
raise AttributeError(f'Trying to min max scale a time series with {X.shape[0]} attributes while it was fitted on {self.min_.shape[0]} attributes!')
X_ = (X - self.min_) / (self.max_ - self.min_)
return X_, y