class LabelEncoder Found at: sklearn.preprocessing._labelclass LabelEncoder(TransformerMixin, BaseEstimator):
    """Encode target labels with value between 0 and n_classes-1.
    This transformer should be used to encode target values, *i.e.* `y`, and not the input `X`.
    Read more in the :ref:`User Guide <preprocessing_targets>`.

""对目标标签进行编码，值在0到n_class -1之间。

这个转换器应该用于编码目标值，*即' y '，而不是输入' X '。

更多内容见:ref: ' User Guide '。

. .versionadded:: 0.12

属性
----------
classes_:形状数组(n_class，)
保存每个类的标签。

例子
--------
“LabelEncoder”可用于规范化标签。

>>> from sklearn import preprocessing
    >>> le = preprocessing.LabelEncoder()
    >>> le.fit([1, 2, 2, 6])
    LabelEncoder()
    >>> le.classes_
    array([1, 2, 6])
    >>> le.transform([1, 1, 2, 6])
    array([0, 0, 1, 2]...)
    >>> le.inverse_transform([0, 0, 1, 2])
    array([1, 1, 2, 6])

它还可以用于将非数字标签(只要它们是可hashable和可比的)转换为数字标签。

>>> le = preprocessing.LabelEncoder()
    >>> le.fit(["paris", "paris", "tokyo", "amsterdam"])
    LabelEncoder()
    >>> list(le.classes_)
    ['amsterdam', 'paris', 'tokyo']
    >>> le.transform(["tokyo", "tokyo", "paris"])
    array([2, 2, 1]...)
    >>> list(le.inverse_transform([2, 2, 1]))
    ['tokyo', 'tokyo', 'paris']

另请参阅
--------
sklearn.preprocessing.OrdinalEncoder :序号编码器:使用序号编码方案编码分类特征。
sklearn.preprocessing.OneHotEncoder :  将分类特性编码为一个热的数字数组。

"""
    def fit(self, y):
        """Fit label encoder

Parameters
        ----------
        y : array-like of shape (n_samples,)
            Target values.

Returns
        -------
        self : returns an instance of self.
        """
        y = column_or_1d(y, warn=True)
        self.classes_ = _encode(y)
        return self
    
    def fit_transform(self, y):
        """Fit label encoder and return encoded labels

Parameters
        ----------
        y : array-like of shape [n_samples]
            Target values.

Returns
        -------
        y : array-like of shape [n_samples]
        """
        y = column_or_1d(y, warn=True)
        self.classes_, y = _encode(y, encode=True)
        return y
    
    def transform(self, y):
        """Transform labels to normalized encoding.

Parameters
        ----------
        y : array-like of shape [n_samples]
            Target values.

Returns
        -------
        y : array-like of shape [n_samples]
        """
        check_is_fitted(self)
        y = column_or_1d(y, warn=True)
        # transform of empty array is empty array
        if _num_samples(y) == 0:
            return np.array([])
        _, y = _encode(y, uniques=self.classes_, encode=True)
        return y
    
    def inverse_transform(self, y):
        """Transform labels back to original encoding.

Parameters
        ----------
        y : numpy array of shape [n_samples]
            Target values.

Returns
        -------
        y : numpy array of shape [n_samples]
        """
        check_is_fitted(self)
        y = column_or_1d(y, warn=True)
        # inverse transform of empty array is empty array
        if _num_samples(y) == 0:
            return np.array([])
        diff = np.setdiff1d(y, np.arange(len(self.classes_)))
        if len(diff):
            raise ValueError(
                "y contains previously unseen labels: %s" % str(diff))
        y = np.asarray(y)
        return self.classes_[y]
    
    def _more_tags(self):
        return {'X_types':['1dlabels']}

fit(y)

Fit label encoder

fit_transform(y)

Fit label encoder and return encoded labels

get_params([deep])

Get parameters for this estimator.

inverse_transform(y)

Transform labels back to original encoding.

set_params(**params)

Set the parameters of this estimator.

transform(y)

Transform labels to normalized encoding.