dask_ml.preprocessing.MinMaxScaler

class dask_ml.preprocessing.MinMaxScaler(feature_range=(0, 1), *, copy=True)

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).

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))

New in version 0.17: scale_ attribute.

data_min_ : ndarray of shape (n_features,)

Per feature minimum seen in the data

New in version 0.17: data_min_

data_max_ : ndarray of shape (n_features,)

Per feature maximum seen in the data

New in version 0.17: data_max_

data_range_ : ndarray of shape (n_features,)

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

New in version 0.17: data_range_

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.

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.

Examples

>>> from sklearn.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. ]]

Methods

fit(X, pandas.core.frame.DataFrame, …) 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, …) Undo the scaling of X according to feature_range.
partial_fit(X, pandas.core.frame.DataFrame, …) Online computation of min and max on X for later scaling.
set_params(**params) Set the parameters of this estimator.
transform(X, pandas.core.frame.DataFrame, …) Scale features of X according to feature_range.
__init__(feature_range=(0, 1), *, copy=True)

Initialize self. See help(type(self)) for accurate signature.

fit(X: Union[ArrayLike, pandas.core.frame.DataFrame, dask.dataframe.core.DataFrame], y: Union[ArrayLike, dask.dataframe.core.Series, pandas.core.series.Series, None] = None) → dask_ml.preprocessing.data.MinMaxScaler

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

Parameters:
X : array-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.

y : None

Ignored.

Returns:
self : object

Fitted scaler.

fit_transform(X, y=None, **fit_params)

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters:
X : {array-like, sparse matrix, dataframe} of shape (n_samples, n_features)
y : ndarray of shape (n_samples,), default=None

Target values.

**fit_params : dict

Additional fit parameters.

Returns:
X_new : ndarray array of shape (n_samples, n_features_new)

Transformed array.

get_params(deep=True)

Get parameters for this estimator.

Parameters:
deep : bool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:
params : mapping of string to any

Parameter names mapped to their values.

inverse_transform(X: Union[ArrayLike, pandas.core.frame.DataFrame, dask.dataframe.core.DataFrame], y: Union[ArrayLike, dask.dataframe.core.Series, pandas.core.series.Series, None] = None, copy: Optional[bool] = None) → Union[ArrayLike, pandas.core.frame.DataFrame, dask.dataframe.core.DataFrame]

Undo the scaling of X according to feature_range.

Parameters:
X : array-like of shape (n_samples, n_features)

Input data that will be transformed. It cannot be sparse.

Returns:
Xt : array-like of shape (n_samples, n_features)

Transformed data.

partial_fit(X: Union[ArrayLike, pandas.core.frame.DataFrame, dask.dataframe.core.DataFrame], y: Union[ArrayLike, dask.dataframe.core.Series, pandas.core.series.Series, None] = None)

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:
X : array-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.

y : None

Ignored.

Returns:
self : object

Transformer instance.

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:
**params : dict

Estimator parameters.

Returns:
self : object

Estimator instance.

transform(X: Union[ArrayLike, pandas.core.frame.DataFrame, dask.dataframe.core.DataFrame], y: Union[ArrayLike, dask.dataframe.core.Series, pandas.core.series.Series, None] = None, copy: Optional[bool] = None) → Union[ArrayLike, pandas.core.frame.DataFrame, dask.dataframe.core.DataFrame]

Scale features of X according to feature_range.

Parameters:
X : array-like of shape (n_samples, n_features)

Input data that will be transformed.

Returns:
Xt : array-like of shape (n_samples, n_features)

Transformed data.