实现one hot encode的两种方法
Approach 1: You can use get_dummies on pandas dataframe.
# transform a given column into one hot. Use prefix to have multiple dummies
>>> import pandas as pd
>>> df = pd.DataFrame({'A': ['a','b','c'],'B': ['b','a','c']})
>>># Get one hot encoding of columns B...
>>> df
A B
0 a b
1 b a
2 c c
>>> one_hot = pd.get_dummies(df['B'])
>>># Drop columns B as it is now encoded...
>>> df = df.drop('B', axis=1)
>>># Join the encoded df...
>>> df = df.join(one_hot)
>>> df
A a b c
0 a 0 1 0
1 b 1 0 0
2 c 0 0 1
一个定性特征哑编码的demo：
def one_hot(df, cols):
"""
@param df pandas DataFrame
@param cols a list of columns to encode
@return a DataFrame with one-hot encoding
"""
for each in cols:
dummies = pd.get_dummies(df[each], prefix=each, drop_first=False)
df = pd.concat([df, dummies], axis=1)
return df
使用 sklearn进行特征变量哑编码：
>>> from sklearn.preprocessing import OneHotEncoder
>>> enc = OneHotEncoder()
>>> enc.fit([[0, 0, 3], [1,1,0], [0,2,1], [1,0,2]])
OneHotEncoder(categorical_features='all', dtype=, handle_unknown='error', n_values='auto', sparse=True)
>>> enc.n_values_array([2,3,4])
>>> enc.feature_indices_array([0,2,5,9])
>>> enc.transform([[0,1,1]])<1x9 sparse matrix oftype'<class 'numpy.float64'>'with3stored elementsinCompressed Sparse Rowformat
>>> enc.transform([[0,1,1]]).toarray()
array([[ 1., 0., 0., 1., 0., 0., 1., 0., 0.]])
一个保存在全局的Label_Binarizer的demo：
from sklearn.preprocessing import LabelBinarizer
label_binarizer =LabelBinarizer()
label_binarizer.fit(all_your_labels_list)# need to be global or remembered to use it later
def one_hot_encode(x):
"""
One hot encode a list of sample labels. Return a one-hot encoded vector for each label.
: x: List of sample Labels
: return: Numpy array of one-hot encoded labels
"""
return label_binarizer.transform(x)
https://stackoverflow.com/questions/37292872/how-can-i-one-hot-encode-in-python
