How to speed up jaro-winkler process in python (multithread? pyspark?)
I am using jaro-winkler to do scoring for text similarity in python 3. My comparing set is relatively large (>470,000 rows). So each time, the loop need to calculate score for each strings with those 470,000 items, which made my process very slow.
Is there anyway I can speed up the process? I am thinking to use multithread, multiprocessing, or pyspark. But I am not sure how to do so. Any suggestions? Thanks in advance.
python python-3.x multithreading pyspark multiprocessing
asked Nov 11 at 16:52
Zoe
1
add a comment |
I am using jaro-winkler to do scoring for text similarity in python 3. My comparing set is relatively large (>470,000 rows). So each time, the loop need to calculate score for each strings with those 470,000 items, which made my process very slow.
Is there anyway I can speed up the process? I am thinking to use multithread, multiprocessing, or pyspark. But I am not sure how to do so. Any suggestions? Thanks in advance.
python python-3.x multithreading pyspark multiprocessing
asked Nov 11 at 16:52
Zoe
1
Have been able to make it work for a smaller set? Like 100 rows?
– Ramazan Polat
Nov 11 at 17:42
that will decrease the accuracy I feel...
– Zoe
Nov 12 at 15:07
yes it will, but you will know it works before optimizing it.
– Ramazan Polat
Nov 12 at 22:06
add a comment |
I am using jaro-winkler to do scoring for text similarity in python 3. My comparing set is relatively large (>470,000 rows). So each time, the loop need to calculate score for each strings with those 470,000 items, which made my process very slow.
Is there anyway I can speed up the process? I am thinking to use multithread, multiprocessing, or pyspark. But I am not sure how to do so. Any suggestions? Thanks in advance.
python python-3.x multithreading pyspark multiprocessing
asked Nov 11 at 16:52
Zoe
1
I am using jaro-winkler to do scoring for text similarity in python 3. My comparing set is relatively large (>470,000 rows). So each time, the loop need to calculate score for each strings with those 470,000 items, which made my process very slow.
Is there anyway I can speed up the process? I am thinking to use multithread, multiprocessing, or pyspark. But I am not sure how to do so. Any suggestions? Thanks in advance.
python python-3.x multithreading pyspark multiprocessing
python python-3.x multithreading pyspark multiprocessing
asked Nov 11 at 16:52
Zoe
1
asked Nov 11 at 16:52
Zoe
1
asked Nov 11 at 16:52
Zoe
1
asked Nov 11 at 16:52
Zoe
1
asked Nov 11 at 16:52
Zoe
1
1
Have been able to make it work for a smaller set? Like 100 rows?
– Ramazan Polat
Nov 11 at 17:42
that will decrease the accuracy I feel...
– Zoe
Nov 12 at 15:07
yes it will, but you will know it works before optimizing it.
– Ramazan Polat
Nov 12 at 22:06
add a comment |
Have been able to make it work for a smaller set? Like 100 rows?
– Ramazan Polat
Nov 11 at 17:42
that will decrease the accuracy I feel...
– Zoe
Nov 12 at 15:07
yes it will, but you will know it works before optimizing it.
– Ramazan Polat
Nov 12 at 22:06
Have been able to make it work for a smaller set? Like 100 rows?
– Ramazan Polat
Nov 11 at 17:42
Have been able to make it work for a smaller set? Like 100 rows?
– Ramazan Polat
Nov 11 at 17:42
that will decrease the accuracy I feel...
– Zoe
Nov 12 at 15:07
that will decrease the accuracy I feel...
– Zoe
Nov 12 at 15:07
yes it will, but you will know it works before optimizing it.
– Ramazan Polat
Nov 12 at 22:06
yes it will, but you will know it works before optimizing it.
– Ramazan Polat
Nov 12 at 22:06
add a comment |
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Have been able to make it work for a smaller set? Like 100 rows?
– Ramazan Polat
Nov 11 at 17:42
that will decrease the accuracy I feel...
– Zoe
Nov 12 at 15:07
yes it will, but you will know it works before optimizing it.
– Ramazan Polat
Nov 12 at 22:06