# MinMaxScaler¶

class ibex.sklearn.preprocessing.MinMaxScaler(feature_range=(0, 1), copy=True)

Bases: sklearn.preprocessing.data.MinMaxScaler, ibex._base.FrameMixin

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Transforms 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, i.e. 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 : boolean, optional, default True
Set to False to perform inplace row normalization and avoid a copy (if the input is already a numpy array).
min_ : ndarray, shape (n_features,)
scale_ : ndarray, shape (n_features,)

Per feature relative scaling of the data.

New in version 0.17: scale_ attribute.

data_min_ : ndarray, shape (n_features,)

Per feature minimum seen in the data

New in version 0.17: data_min_

data_max_ : ndarray, shape (n_features,)

Per feature maximum seen in the data

New in version 0.17: data_max_

data_range_ : ndarray, shape (n_features,)

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

New in version 0.17: data_range_

>>> from sklearn.preprocessing import MinMaxScaler
>>>
>>> data = [[-1, 2], [-0.5, 6], [0, 10], [1, 18]]
>>> scaler = MinMaxScaler()
>>> print(scaler.fit(data))
MinMaxScaler(copy=True, feature_range=(0, 1))
>>> 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. ]]


minmax_scale: Equivalent function without the estimator API.

For a comparison of the different scalers, transformers, and normalizers, see examples/preprocessing/plot_all_scaling.py.

fit(X, y=None)[source]

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

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

X : array-like, shape [n_samples, n_features]
The data used to compute the per-feature minimum and maximum used for later scaling along the features axis.
fit_transform(X, y=None, **fit_params)

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Fit to data, then transform it.

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

X : numpy array of shape [n_samples, n_features]
Training set.
y : numpy array of shape [n_samples]
Target values.
X_new : numpy array of shape [n_samples, n_features_new]
Transformed array.
inverse_transform(X)[source]

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Undo the scaling of X according to feature_range.

X : array-like, shape [n_samples, n_features]
Input data that will be transformed. It cannot be sparse.
partial_fit(X, y=None)[source]

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

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.

X : array-like, shape [n_samples, n_features]
The data used to compute the mean and standard deviation used for later scaling along the features axis.

y : Passthrough for Pipeline compatibility.

transform(X)[source]

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Scaling features of X according to feature_range.

X : array-like, shape [n_samples, n_features]
Input data that will be transformed.