sklearn.preprocessing.LabelEncoder¶
- class sklearn.preprocessing.LabelEncoder¶
Encode labels with value between 0 and n_classes-1.
Examples
LabelEncoder can be used to normalize labels.
>>> 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])
It can also be used to transform non-numerical labels (as long as they are hashable and comparable) to numerical labels.
>>> 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']
Attributes
classes_ array of shape (n_class,) Holds the label for each class. Methods
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. - __init__()¶
x.__init__(...) initializes x; see help(type(x)) for signature
- fit(y)¶
Fit label encoder
Parameters : y : array-like of shape (n_samples,)
Target values.
Returns : self : returns an instance of self.
- fit_transform(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]
- get_params(deep=True)¶
Get parameters for this estimator.
Parameters : deep: boolean, optional :
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns : params : mapping of string to any
Parameter names mapped to their values.
- inverse_transform(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]
- set_params(**params)¶
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.
Returns : self :
- transform(y)¶
Transform labels to normalized encoding.
Parameters : y : array-like of shape [n_samples]
Target values.
Returns : y : array-like of shape [n_samples]