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spark.mllib有没有办法使用 groupBy 子句或某种聚合方法从 pyspark 中的库中应用 KS 测试？例如，我有一个df带有列的数据框ID，RESULT如下所示：

+-------+------+
|     ID|RESULT|
+-------+------+
|3648296|  2.73|
|3648296|  9.64|
|3648189|  0.03|
|3648189|  0.03|
|3648296|  2.51|
|3648189|  0.01|
|3648296|  1.75|
|3648296| 30.23|
|3648189|  0.02|
|3648189|  0.02|
|3648189|  0.02|
|3648296|  3.28|
|3648296| 32.55|
|3648296|  2.32|
|3648296| 34.58|
|3648296| 29.22|
|3648189|  0.02|
|3648296|  1.36|
|3648296|  1.64|
|3648296|  1.17|
+-------+------+

有2个IDs 3648296，3648189每个对应的RESULT值都在几十万的数量级。是否可以像这样应用 groupBy 函数：

from pyspark.mllib.stat import Statistics

normtest=df.groupBy('ID').Statistics.kolmogorovSmirnovTest(df.RESULT, "norm", 0, 1)

这样我得到一个输出数据框，如：

+-------+---------+----------+
|     ID|p-value  |statistic |
+-------+---------+----------+
|3648296|some val | some val |
|3648189|some val | some val |
+-------+---------+----------+

这可能吗？

4

1 回答 1

2

这可以通过对数据进行分箱，然后对分箱数据（即直方图）执行Kolmogorov-Smirnov 检验来解决。它不会产生最大距离，但如果您的有效分布是平滑的，那么结果应该足够接近。

通过对结果进行分桶，您可以确保一次仅将有限数量的项目（桶的数量）加载到内存中。

首先，我们需要实现 kstest 的直方图版本：

import numpy as np

def hist_kstest(hist: np.array, bin_edges: np.array, cdf):
    i = hist.cumsum()
    n = i[-1]

    bin_right_edges = bin_edges[1:]
    cdf_vals = cdf(bin_right_edges)
    
    statistic = np.max([
        cdf_vals - (i-1) / n,
        i / n - cdf_vals
    ])
    pvalue = stats.distributions.kstwo.sf(statistic, n)
    return statistic, pvalue

然后按如下方式使用它：

from pyspark.sql import functions as F, types as T
from pyspark.ml.feature import QuantileDiscretizer
import pandas as pd
import numpy as np
from scipy import stats

# Choose the number of buckets. It depends on your memory
# availability and affects the accuracy of the test.
num_buckets = 1_000

# Choose the null hypothesis (H0)
h0_cdf = stats.norm(0, 1).cdf

# Bucket the result and get the buckets' edges
bucketizer = QuantileDiscretizer(
    numBuckets=num_buckets, inputCol='RESULT', outputCol='result_bucket'
).setHandleInvalid("keep").fit(df)
buckets = np.array(bucketizer.getSplits())

def kstest(key, pdf: pd.DataFrame):
    pdf.sort_values('result_bucket', inplace=True)
    hist = pdf['count'].to_numpy()
    # Some of the buckets might not appear in all the groups, so
    # we filter buckets that are not available.
    bin_edges = buckets[[0, *(pdf['result_bucket'].to_numpy() + 1)]]
    statistic, pvalue = hist_kstest(hist, bin_edges, h0_cdf)
    return pd.DataFrame([[*key, statistic, pvalue]])

df = bucketizer.transform(df).groupBy("ID", "result_bucket").agg(
    F.count("*").alias("count")
).groupby("ID").applyInPandas(kstest, "ID long, statistic double, pvalue double")
于 2021-08-25T19:03:53.140 回答