mars.learn.preprocessing.MinMaxScaler#

class mars.learn.preprocessing.MinMaxScaler(feature_range=(0, 1), copy=True, clip=False)[source]#

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.

Parameters

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_

Type: 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))

Type: Tensor of shape (n_features,)

data_min_#

Per feature minimum seen in the data

Type: ndarray of shape (n_features,)

data_max_#

Per feature maximum seen in the data

Type: ndarray of shape (n_features,)

data_range_#

Per feature range (data_max_ - data_min_) seen in the data

Type: ndarray of shape (n_features,)

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.

Type: 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__(feature_range=(0, 1), copy=True, clip=False)[source]#

Methods

`__init__`([feature_range, copy, clip])
`fit`(X[, y, session, run_kwargs])	Compute the minimum and maximum to be used for later scaling.
`fit_transform`(X[, y])	Fit to data, then transform it.
`get_params`([deep])	Get parameters for this estimator.
`inverse_transform`(X[, session, run_kwargs])	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 the parameters of this estimator.
`transform`(X[, session, run_kwargs])	Scale features of X according to feature_range.