根据给定的分布对数据帧进行采样

时间:2015-10-13 07:58:19

标签: python pandas graphlab sframe

如何基于给定的类\标签分布值对pandas数据帧或graphlab sframe进行采样,例如:我想对具有label \ class列的数据帧进行采样,以选择行,使得每个类标签被均等地提取,从而具有每个类标签的相似频率对应于类标签的均匀分布。或者最好是根据我们想要的班级分布来获取样本。

+------+-------+-------+
| col1 | clol2 | class |
+------+-------+-------+
| 4    | 45    | A     |
+------+-------+-------+
| 5    | 66    | B     |
+------+-------+-------+
| 5    | 6     | C     |
+------+-------+-------+
| 4    | 6     | C     |
+------+-------+-------+
| 321  | 1     | A     |
+------+-------+-------+
| 32   | 432   | B     |
+------+-------+-------+
| 5    | 3     | B     |
+------+-------+-------+

given a huge dataframe like above and the required frequency distribution like below:
+-------+--------------+
| class | nostoextract |
+-------+--------------+
| A     | 2            |
+-------+--------------+
| B     | 2            |
+-------+--------------+
| C     | 2            |
+-------+--------------+


以上应基于第二帧中的给定频率分布从第一个数据帧中提取行,其中频率计数值在nostoextract列中给出,以给出一个采样帧,其中每个类最多出现2次。如果找不到足够的课程来满足所需的数量,应该忽略并继续。生成的数据帧将用于基于决策树的分类器。

正如评论员所说,采样数据帧必须包含nostoextract相应类的不同实例?除非没有足够的给定类示例,否则您只需要使用所有可用的示例。

3 个答案:

答案 0 :(得分:4)

您可以将您的第一个数据帧拆分为特定于类的子数据帧,然后随意采样吗?

即。

dfa = df[df['class']=='A']
dfb = df[df['class']=='B']
dfc = df[df['class']=='C']
....

然后,当您在dfa,dfb,dfc上拆分/创建/过滤后,根据需要从顶部选择一个数字(如果数据框没有任何特定的排序模式)

 dfasamplefive = dfa[:5]

或者使用先前评论者描述的样本方法直接采样随机样本:

dfasamplefive = dfa.sample(n=5)

如果这符合您的需求,剩下要做的就是自动完成整个过程,从您拥有的控制数据帧中提取要采样的数字,作为包含所需样本数量的第二个数据帧。

答案 1 :(得分:3)

我认为这可以解决您的问题:

import pandas as pd

data = pd.DataFrame({'cols1':[4, 5, 5, 4, 321, 32, 5],
                     'clol2':[45, 66, 6, 6, 1, 432, 3],
                     'class':['A', 'B', 'C', 'C', 'A', 'B', 'B']})

freq = pd.DataFrame({'class':['A', 'B', 'C'],
                     'nostoextract':[2, 2, 2], })

def bootstrap(data, freq):
    freq = freq.set_index('class')

    # This function will be applied on each group of instances of the same
    # class in `data`.
    def sampleClass(classgroup):
        cls = classgroup['class'].iloc[0]
        nDesired = freq.nostoextract[cls]
        nRows = len(classgroup)

        nSamples = min(nRows, nDesired)
        return classgroup.sample(nSamples)

    samples = data.groupby('class').apply(sampleClass)

    # If you want a new index with ascending values
    # samples.index = range(len(samples))

    # If you want an index which is equal to the row in `data` where the sample
    # came from
    samples.index = samples.index.get_level_values(1)

    # If you don't change it then you'll have a multiindex with level 0
    # being the class and level 1 being the row in `data` where
    # the sample came from.

    return samples

print(bootstrap(data,freq))

打印:

  class  clol2  cols1
0     A     45      4
4     A      1    321
1     B     66      5
5     B    432     32
3     C      6      4
2     C      6      5

如果您不希望按类别排序结果,最后可以permute

答案 2 :(得分:1)

这是SFrame的解决方案。它不是完全您想要的,因为它会随机采样点,因此结果不一定恰好具有您指定的行数。一个确切的方法可能会随机地对数据进行随机抽取,然后为给定的类获取第一个k行,但这会让你非常接近。

import random
import graphlab as gl

## Construct data.
sf = gl.SFrame({'col1': [4, 5, 5, 4, 321, 32, 5],
                'col2': [45, 66, 6, 6, 1, 432, 3],
                'class': ['A', 'B', 'C', 'C', 'A', 'B', 'B']})

freq = gl.SFrame({'class': ['A', 'B', 'C'],
                  'number': [3, 1, 0]})

## Count how many instances of each class and compute a sampling
#  probability.
grp = sf.groupby('class', gl.aggregate.COUNT)
freq = freq.join(grp, on ='class', how='left')
freq['prob'] = freq.apply(lambda x: float(x['number']) / x['Count'])

## Join the sampling probability back to the original data.
sf = sf.join(freq[['class', 'prob']], on='class', how='left')

## Sample the original data, then subset.
sf['sample_mask'] = sf.apply(lambda x: 1 if random.random() <= x['prob'] 
                             else 0)
sf2 = sf[sf['sample_mask'] == 1]

在我的示例运行中,我碰巧得到了我指定的确切数量的样本,但同样,这个解决方案无法保证。

>>> sf2
+-------+------+------+
| class | col1 | col2 |
+-------+------+------+
|   A   |  4   |  45  |
|   A   | 321  |  1   |
|   B   |  32  | 432  |
+-------+------+------+