Mixing
Update Machine Learning models in Mllib dataframe based PySpark (2.2.0)
I have built a Machine Learning model based on clustering, & now just want to update it with new data periodically (on daily basis). I am using PySpark Mlib, & not able to find any method in Spark for this need.
Note, required method 'partial_fit' available in scikit-learn, but not in Spark.
I am not in favor of appending new data & then re-built the model on daily basis, as it will increase data size & will be computationally expensive.
Please suggest me an effective way for model update or on-line learning using Spark Mllib?
apache-spark machine-learning pyspark cluster-analysis apache-spark-ml
edited Dec 13 '18 at 16:39
desertnaut
16.3k63566
asked Nov 19 '18 at 12:05
bioinformatician
143722
add a comment |
I have built a Machine Learning model based on clustering, & now just want to update it with new data periodically (on daily basis). I am using PySpark Mlib, & not able to find any method in Spark for this need.
Note, required method 'partial_fit' available in scikit-learn, but not in Spark.
I am not in favor of appending new data & then re-built the model on daily basis, as it will increase data size & will be computationally expensive.
Please suggest me an effective way for model update or on-line learning using Spark Mllib?
apache-spark machine-learning pyspark cluster-analysis apache-spark-ml
edited Dec 13 '18 at 16:39
desertnaut
16.3k63566
asked Nov 19 '18 at 12:05
bioinformatician
143722
5
In general case you cannot. Some models (especially in the old API) might have some methods that enable such process, but this exception not rule, and would be applicable only for small subset of iterative algorithms. Also there are a few legacy streaming implementations (regression models, k-means).
– user6910411
Nov 19 '18 at 12:34
See StreamingLinearAlgorithm, StreamingKMeans and parameters like initialWeights in LinearRegressionWithSGD.run
– user6910411
Nov 19 '18 at 12:43
@user6910411 Thanks for comments, could you please suggest me ways how models are being updated in industry (particularly on-line learning) dealing with massive big data?
– bioinformatician
Nov 22 '18 at 8:41
4
I concur with @user6910411 This is not possible with Apache Spark. And for the records, sklearn or other machine learning libraries can scale with right amount of resources you don't always need Spark.
– eliasah
Dec 11 '18 at 14:54
add a comment |
I have built a Machine Learning model based on clustering, & now just want to update it with new data periodically (on daily basis). I am using PySpark Mlib, & not able to find any method in Spark for this need.
Note, required method 'partial_fit' available in scikit-learn, but not in Spark.
I am not in favor of appending new data & then re-built the model on daily basis, as it will increase data size & will be computationally expensive.
Please suggest me an effective way for model update or on-line learning using Spark Mllib?
apache-spark machine-learning pyspark cluster-analysis apache-spark-ml
edited Dec 13 '18 at 16:39
desertnaut
16.3k63566
asked Nov 19 '18 at 12:05
bioinformatician
143722
I have built a Machine Learning model based on clustering, & now just want to update it with new data periodically (on daily basis). I am using PySpark Mlib, & not able to find any method in Spark for this need.
Note, required method 'partial_fit' available in scikit-learn, but not in Spark.
I am not in favor of appending new data & then re-built the model on daily basis, as it will increase data size & will be computationally expensive.
Please suggest me an effective way for model update or on-line learning using Spark Mllib?
apache-spark machine-learning pyspark cluster-analysis apache-spark-ml
apache-spark machine-learning pyspark cluster-analysis apache-spark-ml
edited Dec 13 '18 at 16:39
desertnaut
16.3k63566
asked Nov 19 '18 at 12:05
bioinformatician
143722
edited Dec 13 '18 at 16:39
desertnaut
16.3k63566
asked Nov 19 '18 at 12:05
bioinformatician
143722
edited Dec 13 '18 at 16:39
desertnaut
16.3k63566
edited Dec 13 '18 at 16:39
desertnaut
16.3k63566
edited Dec 13 '18 at 16:39
desertnaut
16.3k63566
16.3k63566
asked Nov 19 '18 at 12:05
bioinformatician
143722
asked Nov 19 '18 at 12:05
bioinformatician
143722
asked Nov 19 '18 at 12:05
bioinformatician
143722
143722
5
In general case you cannot. Some models (especially in the old API) might have some methods that enable such process, but this exception not rule, and would be applicable only for small subset of iterative algorithms. Also there are a few legacy streaming implementations (regression models, k-means).
– user6910411
Nov 19 '18 at 12:34
See StreamingLinearAlgorithm, StreamingKMeans and parameters like initialWeights in LinearRegressionWithSGD.run
– user6910411
Nov 19 '18 at 12:43
@user6910411 Thanks for comments, could you please suggest me ways how models are being updated in industry (particularly on-line learning) dealing with massive big data?
– bioinformatician
Nov 22 '18 at 8:41
4
I concur with @user6910411 This is not possible with Apache Spark. And for the records, sklearn or other machine learning libraries can scale with right amount of resources you don't always need Spark.
– eliasah
Dec 11 '18 at 14:54
add a comment |
5
In general case you cannot. Some models (especially in the old API) might have some methods that enable such process, but this exception not rule, and would be applicable only for small subset of iterative algorithms. Also there are a few legacy streaming implementations (regression models, k-means).
– user6910411
Nov 19 '18 at 12:34
See StreamingLinearAlgorithm, StreamingKMeans and parameters like initialWeights in LinearRegressionWithSGD.run
– user6910411
Nov 19 '18 at 12:43
@user6910411 Thanks for comments, could you please suggest me ways how models are being updated in industry (particularly on-line learning) dealing with massive big data?
– bioinformatician
Nov 22 '18 at 8:41
4
I concur with @user6910411 This is not possible with Apache Spark. And for the records, sklearn or other machine learning libraries can scale with right amount of resources you don't always need Spark.
– eliasah
Dec 11 '18 at 14:54
5
5
In general case you cannot. Some models (especially in the old API) might have some methods that enable such process, but this exception not rule, and would be applicable only for small subset of iterative algorithms. Also there are a few legacy streaming implementations (regression models, k-means).
– user6910411
Nov 19 '18 at 12:34
In general case you cannot. Some models (especially in the old API) might have some methods that enable such process, but this exception not rule, and would be applicable only for small subset of iterative algorithms. Also there are a few legacy streaming implementations (regression models, k-means).
– user6910411
Nov 19 '18 at 12:34
See StreamingLinearAlgorithm, StreamingKMeans and parameters like initialWeights in LinearRegressionWithSGD.run
– user6910411
Nov 19 '18 at 12:43
See StreamingLinearAlgorithm, StreamingKMeans and parameters like initialWeights in LinearRegressionWithSGD.run
– user6910411
Nov 19 '18 at 12:43
@user6910411 Thanks for comments, could you please suggest me ways how models are being updated in industry (particularly on-line learning) dealing with massive big data?
– bioinformatician
Nov 22 '18 at 8:41
@user6910411 Thanks for comments, could you please suggest me ways how models are being updated in industry (particularly on-line learning) dealing with massive big data?
– bioinformatician
Nov 22 '18 at 8:41
4
4
I concur with @user6910411 This is not possible with Apache Spark. And for the records, sklearn or other machine learning libraries can scale with right amount of resources you don't always need Spark.
– eliasah
Dec 11 '18 at 14:54
I concur with @user6910411 This is not possible with Apache Spark. And for the records, sklearn or other machine learning libraries can scale with right amount of resources you don't always need Spark.
– eliasah
Dec 11 '18 at 14:54
add a comment |
1 Answer
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You cannot update arbitrary models.
On a few select models this works. On some it works if you accept some loss in a accuracy. But on other models, the only way is to rebuild it completely.
For example support vector machines. The model only stores the support vectors. When updating, you would also need all the non-support vectors in order to find the optimal model.
That is why it is fairly common to build new models every night, for example.
Streaming is quite overrated. In particular k-means. Total nonsense to do online k-meand with "big" (lol) data. Because the new point have next to zero effect, you may just as well do a batch every night. These are just academic toys with no relevancy.
answered Dec 16 '18 at 20:04
Anony-Mousse
57.2k796159
add a comment |
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votes
You cannot update arbitrary models.
On a few select models this works. On some it works if you accept some loss in a accuracy. But on other models, the only way is to rebuild it completely.
For example support vector machines. The model only stores the support vectors. When updating, you would also need all the non-support vectors in order to find the optimal model.
That is why it is fairly common to build new models every night, for example.
Streaming is quite overrated. In particular k-means. Total nonsense to do online k-meand with "big" (lol) data. Because the new point have next to zero effect, you may just as well do a batch every night. These are just academic toys with no relevancy.
answered Dec 16 '18 at 20:04
Anony-Mousse
57.2k796159
add a comment |
You cannot update arbitrary models.
On a few select models this works. On some it works if you accept some loss in a accuracy. But on other models, the only way is to rebuild it completely.
For example support vector machines. The model only stores the support vectors. When updating, you would also need all the non-support vectors in order to find the optimal model.
That is why it is fairly common to build new models every night, for example.
Streaming is quite overrated. In particular k-means. Total nonsense to do online k-meand with "big" (lol) data. Because the new point have next to zero effect, you may just as well do a batch every night. These are just academic toys with no relevancy.
answered Dec 16 '18 at 20:04
Anony-Mousse
57.2k796159
add a comment |
You cannot update arbitrary models.
On a few select models this works. On some it works if you accept some loss in a accuracy. But on other models, the only way is to rebuild it completely.
For example support vector machines. The model only stores the support vectors. When updating, you would also need all the non-support vectors in order to find the optimal model.
That is why it is fairly common to build new models every night, for example.
Streaming is quite overrated. In particular k-means. Total nonsense to do online k-meand with "big" (lol) data. Because the new point have next to zero effect, you may just as well do a batch every night. These are just academic toys with no relevancy.
answered Dec 16 '18 at 20:04
Anony-Mousse
57.2k796159
You cannot update arbitrary models.
On a few select models this works. On some it works if you accept some loss in a accuracy. But on other models, the only way is to rebuild it completely.
For example support vector machines. The model only stores the support vectors. When updating, you would also need all the non-support vectors in order to find the optimal model.
That is why it is fairly common to build new models every night, for example.
Streaming is quite overrated. In particular k-means. Total nonsense to do online k-meand with "big" (lol) data. Because the new point have next to zero effect, you may just as well do a batch every night. These are just academic toys with no relevancy.
answered Dec 16 '18 at 20:04
Anony-Mousse
57.2k796159
answered Dec 16 '18 at 20:04
Anony-Mousse
57.2k796159
answered Dec 16 '18 at 20:04
Anony-Mousse
57.2k796159
answered Dec 16 '18 at 20:04
Anony-Mousse
57.2k796159
57.2k796159
add a comment |
add a comment |
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In general case you cannot. Some models (especially in the old API) might have some methods that enable such process, but this exception not rule, and would be applicable only for small subset of iterative algorithms. Also there are a few legacy streaming implementations (regression models, k-means).
– user6910411
Nov 19 '18 at 12:34
See StreamingLinearAlgorithm, StreamingKMeans and parameters like initialWeights in LinearRegressionWithSGD.run
– user6910411
Nov 19 '18 at 12:43
@user6910411 Thanks for comments, could you please suggest me ways how models are being updated in industry (particularly on-line learning) dealing with massive big data?
– bioinformatician
Nov 22 '18 at 8:41
4
I concur with @user6910411 This is not possible with Apache Spark. And for the records, sklearn or other machine learning libraries can scale with right amount of resources you don't always need Spark.
– eliasah
Dec 11 '18 at 14:54