以等概率从 Pandas 组中随机选择——意外行为 [英] Randomly selecting from Pandas groups with equal probability -- unexpected behavior
问题描述
我尝试从 12 个独特的组中随机抽样,每个组都有不同数量的观察结果.我想从整个群体(数据帧)中随机抽样,每组具有相同的被选中概率.最简单的例子是具有 2 个组的数据框.
分组概率0 一个 0.251 0.252 b 0.5
using np.random.choice(df['groups'], p=df['probability'], size=100)
现在每次迭代都有 50% 的机会选择 group a
并且有 50% 的机会选择 group b
为了得出我使用的公式的概率:
(1./num_groups)/size_of_groups
或在 Python 中:
num_groups = len(df['groups'].unique()) # 2size_of_groups = df.groupby('label').size() # {a: 2, b: 1}(1./num_groups)/size_of_groups
哪个返回
组0.250.50
这很好用,直到我超过 10 个独特的组,之后我开始得到奇怪的分布.这是一个小例子:
np.random.seed(1234)组大小 = 12组 = np.arange(group_size)概率 = np.random.uniform(size=group_size)probs = probs/probs.sum()g = np.random.choice(groups, size=10000, p=probs)df = pd.DataFrame({'groups': g})prob_map = ((1./len(df['groups'].unique()))/df.groupby('groups').size()).to_dict()df['probability'] = df['groups'].map(prob_map)plt.hist(np.random.choice(df['groups'], p=df['probability'], size=10000, replace=True))plt.xticks(np.arange(group_size))plt.show()
我希望样本量足够大,分布相当均匀,但是当组数为 11+ 时,我得到了这些翅膀.如果我将 group_size
变量更改为 10 或更低,我确实得到了所需的均匀分布.
我不知道问题是出在我计算概率的公式上,还是出在浮点精度问题上?任何人都知道实现此目的的更好方法,或此示例的修复程序?
提前致谢!
您正在使用
plt.rcParams['hist.bins']10
<小时>
通过 group_size
作为 bins
参数.
plt.hist(np.random.choice(df['groups'], p=df['probability'], size=10000, replace=True),bins=group_size)
I have 12 unique groups that I am trying to randomly sample from, each with a different number of observations. I want to randomly sample from the entire population (dataframe) with each group having the same probability of being selected from. The simplest example of this would be a dataframe with 2 groups.
groups probability
0 a 0.25
1 a 0.25
2 b 0.5
using np.random.choice(df['groups'], p=df['probability'], size=100)
Each iteration will now have a 50% chance of selecting group a
and a 50% chance of selecting group b
To come up with the probabilities I used the formula:
(1. / num_groups) / size_of_groups
or in Python:
num_groups = len(df['groups'].unique()) # 2
size_of_groups = df.groupby('label').size() # {a: 2, b: 1}
(1. / num_groups) / size_of_groups
Which returns
groups
a 0.25
b 0.50
This works great until I get past 10 unique groups, after which I start getting weird distributions. Here is a small example:
np.random.seed(1234)
group_size = 12
groups = np.arange(group_size)
probs = np.random.uniform(size=group_size)
probs = probs / probs.sum()
g = np.random.choice(groups, size=10000, p=probs)
df = pd.DataFrame({'groups': g})
prob_map = ((1. / len(df['groups'].unique())) / df.groupby('groups').size()).to_dict()
df['probability'] = df['groups'].map(prob_map)
plt.hist(np.random.choice(df['groups'], p=df['probability'], size=10000, replace=True))
plt.xticks(np.arange(group_size))
plt.show()
I would expect a fairly uniform distribution with a large enough sample size, but I am getting these wings when the number of groups is 11+. If I change the group_size
variable to 10 or lower, I do get the desired uniform distribution.
I can't tell if the problem is with my formula for calculating the probabilities, or possibly a floating point precision problem? Anyone know a better way to accomplish this, or a fix for this example?
Thanks in advance!
you are using hist
which defaults to 10
bins...
plt.rcParams['hist.bins']
10
pass group_size
as the bins
parameter.
plt.hist(
np.random.choice(df['groups'], p=df['probability'], size=10000, replace=True),
bins=group_size)
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