sklearn：sklearn.preprocessing的MinMaxScaler简介、使用方法之详细攻略
【摘要】 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
MinMaxScaler底层代码
MinMaxScaler的使用方法
1、基础案例
MinMaxScaler简介
MinMaxSca...
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
|
MinMaxScaler底层代码
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class MinMaxScaler Found at: sklearn.preprocessing.data
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class MinMaxScaler(BaseEstimator, TransformerMixin):
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def __init__(self, feature_range=(0, 1), copy=True):
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self.feature_range = feature_range
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self.copy = copy
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def _reset(self):
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"""Reset internal data-dependent state of the scaler, if
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necessary.
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__init__ parameters are not touched.
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"""
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# Checking one attribute is enough, becase they are all set
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together
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# in partial_fit
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if hasattr(self, 'scale_'):
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del self.scale_
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del self.min_
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del self.n_samples_seen_
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del self.data_min_
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del self.data_max_
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del self.data_range_
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def fit(self, X, y=None):
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"""Compute the minimum and maximum to be used for later
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scaling.
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Parameters
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----------
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X : array-like, shape [n_samples, n_features]
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The data used to compute the per-feature minimum and
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maximum
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used for later scaling along the features axis.
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"""
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# Reset internal state before fitting
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self._reset()
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return self.partial_fit(X, y)
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def partial_fit(self, X, y=None):
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"""Online computation of min and max on X for later scaling.
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All of X is processed as a single batch. This is intended for
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cases
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when `fit` is not feasible due to very large number of
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`n_samples`
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or because X is read from a continuous stream.
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Parameters
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----------
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X : array-like, shape [n_samples, n_features]
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The data used to compute the mean and standard deviation
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used for later scaling along the features axis.
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y : Passthrough for ``Pipeline`` compatibility.
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"""
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feature_range = self.feature_range
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if feature_range[0] >= feature_range[1]:
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raise ValueError(
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"Minimum of desired feature range must be smaller"
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" than maximum. Got %s." %
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str(feature_range))
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if sparse.issparse(X):
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raise TypeError("MinMaxScaler does no support sparse
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input. "
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"You may consider to use MaxAbsScaler instead.")
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X = check_array(X, copy=self.copy, warn_on_dtype=True,
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estimator=self, dtype=FLOAT_DTYPES)
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data_min = np.min(X, axis=0)
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data_max = np.max(X, axis=0)
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# First pass
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if not hasattr(self, 'n_samples_seen_'):
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self.n_samples_seen_ = X.shape[0]
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else:
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data_min = np.minimum(self.data_min_, data_min)
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data_max = np.maximum(self.data_max_, data_max)
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self.n_samples_seen_ += X.shape[0] # Next steps
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data_range = data_max - data_min
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self.scale_ = (feature_range[1] - feature_range[0]) /
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_handle_zeros_in_scale(data_range)
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self.min_ = feature_range[0] - data_min * self.scale_
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self.data_min_ = data_min
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self.data_max_ = data_max
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self.data_range_ = data_range
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return self
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def transform(self, X):
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"""Scaling features of X according to feature_range.
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Parameters
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----------
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X : array-like, shape [n_samples, n_features]
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Input data that will be transformed.
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"""
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check_is_fitted(self, 'scale_')
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X = check_array(X, copy=self.copy, dtype=FLOAT_DTYPES)
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X *= self.scale_
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X += self.min_
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return X
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def inverse_transform(self, X):
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"""Undo the scaling of X according to feature_range.
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Parameters
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----------
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X : array-like, shape [n_samples, n_features]
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Input data that will be transformed. It cannot be sparse.
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"""
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check_is_fitted(self, 'scale_')
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X = check_array(X, copy=self.copy, dtype=FLOAT_DTYPES)
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X -= self.min_
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X /= self.scale_
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return X
MinMaxScaler的使用方法
1、基础案例
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>>> from sklearn.preprocessing import MinMaxScaler
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>>>
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>>> data = [[-1, 2], [-0.5, 6], [0, 10], [1, 18]]
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>>> scaler = MinMaxScaler()
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>>> print(scaler.fit(data))
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MinMaxScaler(copy=True, feature_range=(0, 1))
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>>> print(scaler.data_max_)
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[ 1. 18.]
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>>> print(scaler.transform(data))
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[[ 0. 0. ]
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[ 0.25 0.25]
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[ 0.5 0.5 ]
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[ 1. 1. ]]
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>>> print(scaler.transform([[2, 2]]))
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[[ 1.5 0. ]]
文章来源: yunyaniu.blog.csdn.net，作者：一个处女座的程序猿，版权归原作者所有，如需转载，请联系作者。
原文链接：yunyaniu.blog.csdn.net/article/details/104719182
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