mars.learn.preprocessing.MinMaxScaler

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

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.

参数

• 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

实际案例

>>> 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. ]]

参见

minmax_scale

Equivalent function without the estimator API.

提示

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)

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.

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