Well, this is exactly the kind of question that could really do with some experiments & code snippets provided...
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...
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})
And here are the results:
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
Numpy:
# 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_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:
# 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
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...
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...