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

MinMaxScaler函数解释

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 X
  
 

MinMaxScaler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. ]]
  
 

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原文链接：yunyaniu.blog.csdn.net/article/details/104719182