我必须为分类数据分配标签。让我们考虑一下虹膜的例子:
import pandas as pd
import numpy as np
from sklearn.datasets import load_iris
iris = load_iris()
print "targets: ", np.unique(iris.target)
print "targets: ", iris.target.shape
print "target_names: ", np.unique(iris.target_names)
print "target_names: ", iris.target_names.shape
将打印出来:
目标:[0 1 2]目标:(150L,)target_names:[' setosa' '云芝' ' virginica'] target_names:(3L,)
为了产生所需的标签我使用pandas.Categorical.from_codes:
print pd.Categorical.from_codes(iris.target, iris.target_names)
[setosa,setosa,setosa,setosa,setosa,...,virginica,virginica, virginica,virginica,virginica]长度:150类别(3,对象): [setosa,versicolor,virginica]
让我们尝试一个不同的例子:
# I define new targets
target = np.array([123,123,54,123,123,54,2,54,2])
target = np.array([1,1,3,1,1,3,2,3,2])
target_names = np.array(['paglia','gioele','papa'])
#---
print "targets: ", np.unique(target)
print "targets: ", target.shape
print "target_names: ", np.unique(target_names)
print "target_names: ", target_names.shape
如果我再次尝试转换标签中的分类值:
print pd.Categorical.from_codes(target, target_names)
我收到错误消息:
C:\用户\ ianni \ Anaconda2 \ lib中\站点包\大熊猫\芯\ categorical.pyc in from_codes(cls,代码,类别,有序) 459 460如果len(代码)和(codes.max()> = len(类别)或codes.min()< -1): - > 461引发ValueError("代码需要介于-1和&#34之间; 462" len(类别)-1") 463
ValueError:代码需要介于-1和len(类别)-1之间
你知道为什么吗?
答案 0 :(得分:1)
你知道为什么吗?
如果您将仔细查看错误追溯:
In [128]: pd.Categorical.from_codes(target, target_names)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-128-c2b4f6ac2369> in <module>()
----> 1 pd.Categorical.from_codes(target, target_names)
~\Anaconda3_5.0\envs\py36\lib\site-packages\pandas\core\categorical.py in from_codes(cls, codes, categories, ordered)
619
620 if len(codes) and (codes.max() >= len(categories) or codes.min() < -1):
--> 621 raise ValueError("codes need to be between -1 and "
622 "len(categories)-1")
623
ValueError: codes need to be between -1 and len(categories)-1
您将看到满足以下条件:
codes.max() >= len(categories)
在你的情况下:
In [133]: target.max() >= len(target_names)
Out[133]: True
换句话说,pd.Categorical.from_codes()
期望codes
为从0
到len(categories) - 1
解决方法:
In [173]: target
Out[173]: array([123, 123, 54, 123, 123, 54, 2, 54, 2])
helper dicts:
In [174]: mapping = dict(zip(np.unique(target), np.arange(len(target_names))))
In [175]: mapping
Out[175]: {2: 0, 54: 1, 123: 2}
In [176]: reverse_mapping = {v:k for k,v in mapping.items()}
In [177]: reverse_mapping
Out[177]: {0: 2, 1: 54, 2: 123}
建立分类系列:
In [178]: ser = pd.Categorical.from_codes(pd.Series(target).map(mapping), target_names)
In [179]: ser
Out[179]:
[papa, papa, gioele, papa, papa, gioele, paglia, gioele, paglia]
Categories (3, object): [paglia, gioele, papa]
反向映射:
In [180]: pd.Series(ser.codes).map(reverse_mapping)
Out[180]:
0 123
1 123
2 54
3 123
4 123
5 54
6 2
7 54
8 2
dtype: int64