Sklearn of Python: introduction of labelencoder function (encoding and coding restoration), usage method, detailed introduction of specific cases

A Virgo procedural ape 2020-11-13 05:48:52
sklearn python introduction labelencoder function


Python And sklearn:LabelEncoder Function introduction ( Coding and coding restoration )、 Usage method 、 Detailed strategy of specific cases

 

 

Catalog

LabelEncoder Function introduction ( Coding and coding restoration )

Methods

LabelEncoder How to use the function

LabelEncoder Specific cases of functions

1、 Basic case

2、 In the absence of data and test There is a new value in the data (train The data didn't show up ) Data in the environment LabelEncoder turn


 

LabelEncoder Function introduction ( Coding and coding restoration )

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>`.

 

"" Encode the target , Values in 0 To n_class -1 Between .

This converter should be used to encode the target value ,* namely ' y ', Instead of typing ' X '.

For more information, see :ref: ' User Guide '.

    .. versionadded:: 0.12
    
    Attributes
    ----------
    classes_ : array of shape (n_class,)
    Holds the label for each class.
    
    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']
    
    See also
    --------
    sklearn.preprocessing.OrdinalEncoder : Encode categorical features using an ordinal encoding scheme.
    sklearn.preprocessing.OneHotEncoder : Encode categorical features as a one-hot numeric array.

. .versionadded:: 0.12

attribute
----------
classes_: Array of shapes (n_class,)
Save tags for each class .

Example
--------
“LabelEncoder” Can be used to normalize tags .

 

    >>> 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 put non numeric tags ( As long as they can hashable And comparable ) Convert to digital tags .

 

    >>> 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']


See also
--------
sklearn.preprocessing.OrdinalEncoder : Serial encoder : Use serial number coding scheme to encode classification features .
sklearn.preprocessing.OneHotEncoder :   Encode the classification properties into a hot array of numbers .

    """
    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']}

 

 

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.

 

 

LabelEncoder How to use the function

import pandas as pd
from sklearn.preprocessing import LabelEncoder
from DataScienceNYY.DataAnalysis import dataframe_fillAnyNull,Dataframe2LabelEncoder
# Structural data
train_data_dict={'Name':[' Zhang San ',' Li Si ',' Wang Wu ',' Zhao Liu ',' Zhang Qi ',' Li ba ',' Wang Shi ','un'],
'Age':[22,23,24,25,22,22,22,None],
'District':[' Beijing ',' Shanghai ',' guangdong ',' Shenzhen ',' Shandong ',' Henan ',' Zhejiang ',' '],
'Job':['CEO','CTO','CFO','COO','CEO','CTO','CEO','']}
test_data_dict={'Name':[' Zhang San ',' Li Si ',' Wang Xi ',None],
'Age':[22,23,22,'un'],
'District':[' Beijing ',' Shanghai ',' guangdong ',''],
'Job':['CEO','CTO','UFO',' ']}
train_data_df = pd.DataFrame(train_data_dict)
test_data_df = pd.DataFrame(test_data_dict)
print(train_data_df,'\n',test_data_df)
# Missing data fill
for col in train_data_df.columns:
train_data_df[col]=dataframe_fillAnyNull(train_data_df,col)
test_data_df[col]=dataframe_fillAnyNull(test_data_df,col)
print(train_data_df,'\n',test_data_df)
# data LabelEncoder turn
train_data,test_data=Dataframe2LabelEncoder(train_data_df,test_data_df)
print(train_data,'\n',test_data)

 

 

 

 

LabelEncoder Specific cases of functions

1、 Basic case

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']

 

 

 

2、 In the absence of data and test There is a new value in the data (train The data didn't show up ) Data in the environment LabelEncoder turn

Reference article Python And sklearn:LabelEncoder How to use the function LabelEncoder The necessary operation before

import numpy as np
from sklearn.preprocessing import LabelEncoder
# Training train data
LE= LabelEncoder()
LE.fit(train_df[col])
#test New values in the data are added to LE.classes_
test_df[col] =test_df[col].map(lambda s:'Unknown' if s not in LE.classes_ else s)
LE.classes_ = np.append(LE.classes_, 'Unknown')
# Transform separately train、test data
train_df[col] = LE.transform(train_df[col])
test_df[col] = LE.transform(test_df[col])

 

 

 

 

 

 

 

 

 

 

 

 

 

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