sklearn：sklearn.preprocessing的MinMaxScaler简介、使用方法之详细攻略
目录
MinMaxScaler简介
MinMaxScaler函数解释
| """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 :ref:`User Guide <preprocessing_scaler>`. |
“”通过将每个特性缩放到给定范围来转换特性。 这个估计量对每个特征进行了缩放和单独转换，使其位于训练集的给定范围内，即在0和1之间。 变换由:: X_std = (X - X.min(axis=0)) / (X.max(axis=0) - X.min(axis=0)) X_scaled = X_std * (max - min) + min 其中，min, max = feature_range。 这种转换经常被用来替代零均值，单位方差缩放。 请参阅:ref: ' User Guide '。</preprocessing_scaler> |
| Parameters ---------- 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). |
参数 feature_range: tuple (min, max)，默认值=(0,1) 所需的转换数据范围。 复制:布尔值，可选，默认为真 设置为False执行插入行规范化并避免复制(如果输入已经是numpy数组)。 |
| Attributes ---------- min_ : ndarray, shape (n_features,) Per feature adjustment for minimum. scale_ : ndarray, shape (n_features,) Per feature relative scaling of the data. .. versionadded:: 0.17 *scale_* attribute. data_min_ : ndarray, shape (n_features,) Per feature minimum seen in the data .. versionadded:: 0.17 *data_min_* data_max_ : ndarray, shape (n_features,) Per feature maximum seen in the data .. versionadded:: 0.17 *data_max_* data_range_ : ndarray, shape (n_features,) Per feature range ``(data_max_ - data_min_)`` seen in the data .. versionadded:: 0.17 *data_range_* |
属性
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MinMaxScaler底层代码
class MinMaxScaler Found at: sklearn.preprocessing.data
class MinMaxScaler(BaseEstimator, TransformerMixin):
def __init__(self, feature_range=(0, 1), copy=True):
self.feature_range = feature_range
self.copy = copy
def _reset(self):
"""Reset internal data-dependent state of the scaler, if
necessary.
__init__ parameters are not touched.
"""
# Checking one attribute is enough, becase they are all set
together
# in partial_fit
if hasattr(self, 'scale_'):
del self.scale_
del self.min_
del self.n_samples_seen_
del self.data_min_
del self.data_max_
del self.data_range_
def fit(self, X, y=None):
"""Compute the minimum and maximum to be used for later
scaling.
Parameters
----------
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.
"""
# Reset internal state before fitting
self._reset()
return self.partial_fit(X, y)
def partial_fit(self, X, y=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, 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.
"""
feature_range = self.feature_range
if feature_range[0] >= feature_range[1]:
raise ValueError(
"Minimum of desired feature range must be smaller"
" than maximum. Got %s." %
str(feature_range))
if sparse.issparse(X):
raise TypeError("MinMaxScaler does no support sparse
input. "
"You may consider to use MaxAbsScaler instead.")
X = check_array(X, copy=self.copy, warn_on_dtype=True,
estimator=self, dtype=FLOAT_DTYPES)
data_min = np.min(X, axis=0)
data_max = np.max(X, axis=0)
# First pass
if not hasattr(self, 'n_samples_seen_'):
self.n_samples_seen_ = X.shape[0]
else:
data_min = np.minimum(self.data_min_, data_min)
data_max = np.maximum(self.data_max_, data_max)
self.n_samples_seen_ += X.shape[0] # Next steps
data_range = data_max - data_min
self.scale_ = (feature_range[1] - feature_range[0]) /
_handle_zeros_in_scale(data_range)
self.min_ = feature_range[0] - data_min * self.scale_
self.data_min_ = data_min
self.data_max_ = data_max
self.data_range_ = data_range
return self
def transform(self, X):
"""Scaling features of X according to feature_range.
Parameters
----------
X : array-like, shape [n_samples, n_features]
Input data that will be transformed.
"""
check_is_fitted(self, 'scale_')
X = check_array(X, copy=self.copy, dtype=FLOAT_DTYPES)
X *= self.scale_
X += self.min_
return X
def inverse_transform(self, X):
"""Undo the scaling of X according to feature_range.
Parameters
----------
X : array-like, shape [n_samples, n_features]
Input data that will be transformed. It cannot be sparse.
"""
check_is_fitted(self, 'scale_')
X = check_array(X, copy=self.copy, dtype=FLOAT_DTYPES)
X -= self.min_
X /= self.scale_
return XMinMaxScaler的使用方法
1、基础案例
>>> 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. ]]