sklearn.preprocessing
.MinMaxScaler¶
-
class
sklearn.preprocessing.
MinMaxScaler
(feature_range=(0, 1), copy=True)[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.
The transformation is calculated as:
X_scaled = scale * X + min - X.min(axis=0) * scale where scale = (max - min) / (X.max(axis=0) - X.min(axis=0))
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))
.. versionadded:: 0.17 scale_ attribute.data_min_
(ndarray of shape (n_features,)) Per feature minimum seen in the data .. versionadded:: 0.17 data_min_
data_max_
(ndarray of shape (n_features,)) Per feature maximum seen in the data .. versionadded:: 0.17 data_max_
data_range_
(ndarray of shape (n_features,)) Per feature range
(data_max_ - data_min_)
seen in the data .. versionadded:: 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[, y])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.
Undo the scaling of X according to feature_range.
partial_fit
(X[, y])Online computation of min and max on X for later scaling.
set_params
(**params)Set the parameters of this estimator.
transform
(X)Scale features of X according to feature_range.
-
__init__
(feature_range=(0, 1), copy=True)[source]¶ Initialize self. See help(type(self)) for accurate signature.
-
fit
(X, y=None)[source]¶ 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)[source]¶ 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 : numpy array of shape [n_samples, n_features]
Training set.
y : numpy array of shape [n_samples]
Target values.
**fit_params : dict
Additional fit parameters.
- Returns
X_new : numpy array of shape [n_samples, n_features_new]
Transformed array.
-
get_params
(deep=True)[source]¶ 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)[source]¶ 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, y=None)[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 ofn_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)[source]¶ 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.