mars.learn.preprocessing.
MinMaxScaler
Transform features by scaling each feature to a given range.
This estimator scales and translates each feature individually such that it is in the given range on the training set, e.g. between zero and one.
The transformation is given by:
X_std = (X - X.min(axis=0)) / (X.max(axis=0) - X.min(axis=0)) X_scaled = X_std * (max - min) + min
where min, max = feature_range.
This transformation is often used as an alternative to zero mean, unit variance scaling.
Read more in the User Guide.
feature_range (tuple (min, max), default=(0, 1)) – Desired range of transformed data.
copy (bool, default=True) – Set to False to perform inplace row normalization and avoid a copy (if the input is already a numpy array).
clip (bool, default=False) – Set to True to clip transformed values of held-out data to provided feature range.
min_
Per feature adjustment for minimum. Equivalent to min - X.min(axis=0) * self.scale_
min - X.min(axis=0) * self.scale_
Tensor of shape (n_features,)
scale_
Per feature relative scaling of the data. Equivalent to (max - min) / (X.max(axis=0) - X.min(axis=0))
(max - min) / (X.max(axis=0) - X.min(axis=0))
data_min_
Per feature minimum seen in the data
ndarray of shape (n_features,)
data_max_
Per feature maximum seen in the data
data_range_
Per feature range (data_max_ - data_min_) seen in the data
(data_max_ - data_min_)
n_samples_seen_
The number of samples processed by the estimator. It will be reset on new calls to fit, but increments across partial_fit calls.
partial_fit
int
Examples
>>> from mars.learn.preprocessing import MinMaxScaler >>> data = [[-1, 2], [-0.5, 6], [0, 10], [1, 18]] >>> scaler = MinMaxScaler() >>> print(scaler.fit(data)) MinMaxScaler() >>> print(scaler.data_max_) [ 1. 18.] >>> print(scaler.transform(data)) [[0. 0. ] [0.25 0.25] [0.5 0.5 ] [1. 1. ]] >>> print(scaler.transform([[2, 2]])) [[1.5 0. ]]
See also
minmax_scale
Equivalent function without the estimator API.
Notes
NaNs are treated as missing values: disregarded in fit, and maintained in transform.
For a comparison of the different scalers, transformers, and normalizers, see examples/preprocessing/plot_all_scaling.py.
__init__
Initialize self. See help(type(self)) for accurate signature.
Methods
__init__([feature_range, copy, clip])
Initialize self.
fit(X[, y, session, run_kwargs])
fit
Compute the minimum and maximum to be used for later scaling.
fit_transform(X[, y])
fit_transform
Fit to data, then transform it.
get_params([deep])
get_params
Get parameters for this estimator.
inverse_transform(X[, session, run_kwargs])
inverse_transform
Undo the scaling of X according to feature_range.
partial_fit(X[, y, session, run_kwargs])
Online computation of min and max on X for later scaling.
set_params(**params)
set_params
Set the parameters of this estimator.
transform(X[, session, run_kwargs])
transform
Scale features of X according to feature_range.