mars.learn.preprocessing.MinMaxScaler¶
- class mars.learn.preprocessing.MinMaxScaler(feature_range=(0, 1), copy=True, clip=False)[源代码]¶
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
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.
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.