系统之间的随机种子是否兼容? [英] Are random seeds compatible between systems?
问题描述
我使用python的sklearn软件包创建了一个随机森林模型，在其中我将种子设置为例如1234.为了生产模型，我们使用pyspark.如果我要传递相同的超参数和相同的种子值(即1234)，它将得到相同的结果吗?
I made a random forest model using python's sklearn package where I set the seed to for example to 1234. To productionise models, we use pyspark. If I was to pass the same hyperparmeters and same seed value, i.e. 1234, will it get the same results?
基本上，随机种子数在不同系统之间起作用吗?
Basically, do random seed numbers work between different systems?
推荐答案
好吧，这正是真的可以通过一些实验完成的问题&提供的代码段...
Well, this is exactly the kind of question that could really do with some experiments & code snippets provided...
无论如何，似乎一般的答案是坚定的否:不仅在Python和Spark MLlib之间，甚至在Spark子模块之间，或者在Python&脾气暴躁...
Anyway, it seems that the general answer is a firm no: not only between Python and Spark MLlib, but even between Spark sub-modules, or between Python & Numpy...
这是一些可复制的代码，可在 Databricks社区云(其中pyspark已导入并已初始化相关上下文):
Here is some reproducible code, run in the Databricks community cloud (where pyspark is already imported & the relevant contexts initialized):
import sys
import random
import pandas as pd
import numpy as np
from pyspark.sql.functions import rand, randn
from pyspark.mllib import random as r # avoid conflict with native Python random module
print("Spark version " + spark.version)
print("Python version %s.%s.%s" % sys.version_info[:3])
print("Numpy version " + np.version.version)
# Spark version 2.3.1
# Python version 3.5.2
# Numpy version 1.11.1
s = 1234 # RNG seed
# Spark SQL random module:
spark_df = sqlContext.range(0, 10)
spark_df = spark_df.select("id", randn(seed=s).alias("normal"), rand(seed=s).alias("uniform"))
# Python 3 random module:
random.seed(s)
x = [random.uniform(0,1) for i in range(10)] # random.rand() gives exact same results
random.seed(s)
y = [random.normalvariate(0,1) for i in range(10)]
df = pd.DataFrame({'uniform':x, 'normal':y})
# numpy random module
np.random.seed(s)
xx = np.random.uniform(size=10) # again, np.random.rand(10) gives exact same results
np.random.seed(s)
yy = np.random.randn(10)
numpy_df = pd.DataFrame({'uniform':xx, 'normal':yy})
# Spark MLlib random module
rdd_uniform = r.RandomRDDs.uniformRDD(sc, 10, seed=s).collect()
rdd_normal = r.RandomRDDs.normalRDD(sc, 10, seed=s).collect()
rdd_df = pd.DataFrame({'uniform':rdd_uniform, 'normal':rdd_normal})
这是结果:
Python原生3:
Native Python 3:
# df
normal uniform
0 1.430825 0.966454
1 1.803801 0.440733
2 0.321290 0.007491
3 0.599006 0.910976
4 -0.700891 0.939269
5 0.233350 0.582228
6 -0.613906 0.671563
7 -1.622382 0.083938
8 0.131975 0.766481
9 0.191054 0.236810
脾气暴躁:
# numpy_df
normal uniform
0 0.471435 0.191519
1 -1.190976 0.622109
2 1.432707 0.437728
3 -0.312652 0.785359
4 -0.720589 0.779976
5 0.887163 0.272593
6 0.859588 0.276464
7 -0.636524 0.801872
8 0.015696 0.958139
9 -2.242685 0.875933
Spark SQL:
Spark SQL:
# spark_df.show()
+---+--------------------+-------------------+
| id| normal| uniform|
+---+--------------------+-------------------+
| 0| 0.9707422835368164| 0.9499610869333489|
| 1| 0.3641589200870126| 0.9682554532421536|
| 2|-0.22282955491417034|0.20293463923130883|
| 3|-0.00607734375219...|0.49540111648680385|
| 4| -0.603246393509015|0.04350782074761239|
| 5|-0.12066287904491797|0.09390549680302918|
| 6| 0.2899567922101867| 0.6789838400775526|
| 7| 0.5827830892516723| 0.6560703836291193|
| 8| 1.351649207673346| 0.7750229279150739|
| 9| 0.5286035772104091| 0.6075560897646175|
+---+--------------------+-------------------+
Spark MLlib:
Spark MLlib:
# rdd_df
normal uniform
0 -0.957840 0.259282
1 0.742598 0.674052
2 0.225768 0.707127
3 1.109644 0.850683
4 -0.269745 0.414752
5 -0.148916 0.494394
6 0.172857 0.724337
7 -0.276485 0.252977
8 -0.963518 0.356758
9 1.366452 0.703145
当然，即使以上结果相同，也不能保证scikit-learn中的Random Forest的结果与pyspark Random Forest的结果完全相同 ...
Of course, even if the above results were identical, this would be no guarantee that results from, say, Random Forest in scikit-learn, would be exactly identical to the results of pyspark Random Forest...
尽管答案是否定的，但我真的看不到它如何影响任何ML系统的部署，即，如果结果关键地取决于RNG，那么肯定可以不对...
Despite the negative answer, I really cannot see how that affects the deployment of any ML system, i.e. if the results depend crucially on the RNG, then something is definitely not right...
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