Python之sklearn：LabelEncoder函式簡介(編碼與編碼還原)、使用方法、具體案例之詳細攻略

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>`.

""對目標標簽進行編碼，值在0到n_class -1之間，

這個轉換器應該用于編碼目標值，*即' y '，而不是輸入' X '，

更多內容見:ref: ' User Guide '，

. .versionadded:: 0.12

屬性
----------
classes_:形狀陣列(n_class，)
保存每個類的標簽，

例子
--------
“LabelEncoder”可用于規范化標簽，

>>> 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])

它還可以用于將非數字標簽(只要它們是可hashable和可比的)轉換為數字標簽，

>>> 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']

另請參閱
--------
sklearn.preprocessing.OrdinalEncoder :序號編碼器:使用序號編碼方案編碼分類特征，
sklearn.preprocessing.OneHotEncoder : 將分類特性編碼為一個熱的數字陣列，

"""
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.