MinMaxScaler#

class cuml.preprocessing.MinMaxScaler(*args, **kwargs)[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.

Parameters:

feature_rangetuple (min, max), default=(0, 1): Desired range of transformed data.
copybool, default=True: Whether a forced copy will be triggered. If copy=False, a copy might be triggered by a conversion.

Attributes:

min_ndarray of shape (n_features,): Per feature adjustment for minimum. Equivalent to min - X.min(axis=0) * self.scale_
scale_ndarray of shape (n_features,): Per feature relative scaling of the data. Equivalent to (max - min) / (X.max(axis=0) - X.min(axis=0))
data_min_ndarray of shape (n_features,): Per feature minimum seen in the data
data_max_ndarray of shape (n_features,): Per feature maximum seen in the data
data_range_ndarray of shape (n_features,): Per feature range (data_max_ - data_min_) seen in the data
n_samples_seen_int: The number of samples processed by the estimator. It will be reset on new calls to fit, but increments across partial_fit calls.

Methods

`fit`(X[, y])	Compute the minimum and maximum to be used for later scaling.
`inverse_transform`(X)	Undo the scaling of X according to feature_range.
`partial_fit`(X[, y])	Online computation of min and max on X for later scaling.
`transform`(X)	Scale features of X according to feature_range.

See also

minmax_scale: Equivalent function without the estimator API.

Notes

NaNs are treated as missing values: disregarded in fit, and maintained in transform.

Examples

>>> from cuml.preprocessing import MinMaxScaler
>>> import cupy as cp
>>> data = [[-1, 2], [-0.5, 6], [0, 10], [1, 18]]
>>> data = cp.array(data)
>>> 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(cp.array([[2, 2]])))
[[1.5 0. ]]

fit(X, y=None) → MinMaxScaler[source]#

Compute the minimum and maximum to be used for later scaling.

Parameters:

Xarray-like of shape (n_samples, n_features): The data used to compute the per-feature minimum and maximum used for later scaling along the features axis.
yNone: Ignored.

Returns:

selfobject: Fitted scaler.

inverse_transform(X) → CumlArray[source]#

Undo the scaling of X according to feature_range.

Parameters:

Xarray-like of shape (n_samples, n_features): Input data that will be transformed. It cannot be sparse.

Returns:

Xtarray-like of shape (n_samples, n_features): Transformed data.

partial_fit(X, y=None) → MinMaxScaler[source]#

Online computation of min and max on X for later scaling.

All of X is processed as a single batch. This is intended for cases when fit() is not feasible due to very large number of n_samples or because X is read from a continuous stream.

Parameters:

Xarray-like of shape (n_samples, n_features): The data used to compute the mean and standard deviation used for later scaling along the features axis.
yNone: Ignored.

Returns:

selfobject: Transformer instance.

transform(X) → CumlArray[source]#

Scale features of X according to feature_range.

Parameters:

Xarray-like of shape (n_samples, n_features): Input data that will be transformed.

Returns:

Xtarray-like of shape (n_samples, n_features): Transformed data.