SVM SMOTE fit_resample() function runs forever with no result2019 Community Moderator ElectionSVM using scikit learn runs endlessly and never completes executionPossible Reason for low Test accuracy and high AUCCan SMOTE be applied over sequence of words (sentences)?SMOTE and multi class oversamplingLogic behind SMOTE-NC?How to avoid resampling part of pipeline on test data (imblearn package, SMOTE)Error with functionSmoteBoost: Should SMOTE be ran individually for each iteration/tree in the boosting?solving multi-class imbalance classification using smote and OSSUsing SMOTE for Synthetic Data generation to improve performance on unbalanced data

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SVM SMOTE fit_resample() function runs forever with no result



2019 Community Moderator ElectionSVM using scikit learn runs endlessly and never completes executionPossible Reason for low Test accuracy and high AUCCan SMOTE be applied over sequence of words (sentences)?SMOTE and multi class oversamplingLogic behind SMOTE-NC?How to avoid resampling part of pipeline on test data (imblearn package, SMOTE)Error with functionSmoteBoost: Should SMOTE be ran individually for each iteration/tree in the boosting?solving multi-class imbalance classification using smote and OSSUsing SMOTE for Synthetic Data generation to improve performance on unbalanced data










2












$begingroup$


Problem



fit_resample(X,y)


is taking too long to complete execution for 2million rows.



Dataset specifications



I have a labeled dataset about network features, where the X(features) and Y(labels) are of shape (2M, 24) and (2M,11) respectively.



i.e. there are over 2million rows in the dataset. there are 24 features, and 11 different classes/labels.



Both X and Y are numpy arrays of float dtype.



Motivation for using SVM SMOTE



Due to class imbalance, I realized SVM SMOTE is a good technique balance it, thereby, giving better classification.



Testing with smaller sub-datasets



To test the performance of my classifier, I started small. I made small subdatasets out of the big 2million row dataset.



It took the following code:-



%%time
sm = SVMSMOTE(random_state=42)
X_res, Y_res = sm.fit_resample(X, Y)


1st dataset contains only 7.5k rows. It took about 800ms to run the cell.
2nd dataset contains 115k rows. It took 20min to execute the cell.



Solution Attempts



My system crashes after running continuously for more than 48hrs, running out of memory.



I've tried some ideas, such as



1. splitting it to run on multiple CPU cores using %%px



No improvement in quicker execution



2. using NVIDIA GPU's



Same as above. Which is more understandable since the _smote.py library functions aren't built with parallel programming for CUDA in mind.



I'm pretty frustrated by the lack of results, and a warm PC. What should I do?










share|improve this question









$endgroup$











  • $begingroup$
    Try a linear SVM. It’s less complex. Also reducing your data set will help
    $endgroup$
    – Jon
    2 hours ago















2












$begingroup$


Problem



fit_resample(X,y)


is taking too long to complete execution for 2million rows.



Dataset specifications



I have a labeled dataset about network features, where the X(features) and Y(labels) are of shape (2M, 24) and (2M,11) respectively.



i.e. there are over 2million rows in the dataset. there are 24 features, and 11 different classes/labels.



Both X and Y are numpy arrays of float dtype.



Motivation for using SVM SMOTE



Due to class imbalance, I realized SVM SMOTE is a good technique balance it, thereby, giving better classification.



Testing with smaller sub-datasets



To test the performance of my classifier, I started small. I made small subdatasets out of the big 2million row dataset.



It took the following code:-



%%time
sm = SVMSMOTE(random_state=42)
X_res, Y_res = sm.fit_resample(X, Y)


1st dataset contains only 7.5k rows. It took about 800ms to run the cell.
2nd dataset contains 115k rows. It took 20min to execute the cell.



Solution Attempts



My system crashes after running continuously for more than 48hrs, running out of memory.



I've tried some ideas, such as



1. splitting it to run on multiple CPU cores using %%px



No improvement in quicker execution



2. using NVIDIA GPU's



Same as above. Which is more understandable since the _smote.py library functions aren't built with parallel programming for CUDA in mind.



I'm pretty frustrated by the lack of results, and a warm PC. What should I do?










share|improve this question









$endgroup$











  • $begingroup$
    Try a linear SVM. It’s less complex. Also reducing your data set will help
    $endgroup$
    – Jon
    2 hours ago













2












2








2





$begingroup$


Problem



fit_resample(X,y)


is taking too long to complete execution for 2million rows.



Dataset specifications



I have a labeled dataset about network features, where the X(features) and Y(labels) are of shape (2M, 24) and (2M,11) respectively.



i.e. there are over 2million rows in the dataset. there are 24 features, and 11 different classes/labels.



Both X and Y are numpy arrays of float dtype.



Motivation for using SVM SMOTE



Due to class imbalance, I realized SVM SMOTE is a good technique balance it, thereby, giving better classification.



Testing with smaller sub-datasets



To test the performance of my classifier, I started small. I made small subdatasets out of the big 2million row dataset.



It took the following code:-



%%time
sm = SVMSMOTE(random_state=42)
X_res, Y_res = sm.fit_resample(X, Y)


1st dataset contains only 7.5k rows. It took about 800ms to run the cell.
2nd dataset contains 115k rows. It took 20min to execute the cell.



Solution Attempts



My system crashes after running continuously for more than 48hrs, running out of memory.



I've tried some ideas, such as



1. splitting it to run on multiple CPU cores using %%px



No improvement in quicker execution



2. using NVIDIA GPU's



Same as above. Which is more understandable since the _smote.py library functions aren't built with parallel programming for CUDA in mind.



I'm pretty frustrated by the lack of results, and a warm PC. What should I do?










share|improve this question









$endgroup$




Problem



fit_resample(X,y)


is taking too long to complete execution for 2million rows.



Dataset specifications



I have a labeled dataset about network features, where the X(features) and Y(labels) are of shape (2M, 24) and (2M,11) respectively.



i.e. there are over 2million rows in the dataset. there are 24 features, and 11 different classes/labels.



Both X and Y are numpy arrays of float dtype.



Motivation for using SVM SMOTE



Due to class imbalance, I realized SVM SMOTE is a good technique balance it, thereby, giving better classification.



Testing with smaller sub-datasets



To test the performance of my classifier, I started small. I made small subdatasets out of the big 2million row dataset.



It took the following code:-



%%time
sm = SVMSMOTE(random_state=42)
X_res, Y_res = sm.fit_resample(X, Y)


1st dataset contains only 7.5k rows. It took about 800ms to run the cell.
2nd dataset contains 115k rows. It took 20min to execute the cell.



Solution Attempts



My system crashes after running continuously for more than 48hrs, running out of memory.



I've tried some ideas, such as



1. splitting it to run on multiple CPU cores using %%px



No improvement in quicker execution



2. using NVIDIA GPU's



Same as above. Which is more understandable since the _smote.py library functions aren't built with parallel programming for CUDA in mind.



I'm pretty frustrated by the lack of results, and a warm PC. What should I do?







python preprocessing numpy sampling smote






share|improve this question













share|improve this question











share|improve this question




share|improve this question










asked 7 hours ago









venom8914venom8914

1312




1312











  • $begingroup$
    Try a linear SVM. It’s less complex. Also reducing your data set will help
    $endgroup$
    – Jon
    2 hours ago
















  • $begingroup$
    Try a linear SVM. It’s less complex. Also reducing your data set will help
    $endgroup$
    – Jon
    2 hours ago















$begingroup$
Try a linear SVM. It’s less complex. Also reducing your data set will help
$endgroup$
– Jon
2 hours ago




$begingroup$
Try a linear SVM. It’s less complex. Also reducing your data set will help
$endgroup$
– Jon
2 hours ago










1 Answer
1






active

oldest

votes


















3












$begingroup$

This is expected and is not related to SMOTE sampling.



The computational complexity of non-linear SVM is on the order of $O(n^2)$ to $O(n^3)$ where $n$ is the number of samples. This means that if it takes 0.8 seconds for 7.5K data points, it should take [3, 48] minutes for 115K, $$[(115/7.5)^2 times 0.8, (115/7.5)^3 times 0.8]s=[3,48]m,$$and from 16 hours to 175 days, 11 days for $O(n^2.5)$, for 2M data points.



You should continue using sample sizes on the order of 100K or less. Also, it is fruitful to track the accuracy (or any other score) as a function of samples for 1K, 10K, 50K, and 100K samples. It is possible that SVM performance plateaus before going beyond 100K, therefore, there is not much to lose by limiting the samples to 100K or less.






share|improve this answer











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    1 Answer
    1






    active

    oldest

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    1 Answer
    1






    active

    oldest

    votes









    active

    oldest

    votes






    active

    oldest

    votes









    3












    $begingroup$

    This is expected and is not related to SMOTE sampling.



    The computational complexity of non-linear SVM is on the order of $O(n^2)$ to $O(n^3)$ where $n$ is the number of samples. This means that if it takes 0.8 seconds for 7.5K data points, it should take [3, 48] minutes for 115K, $$[(115/7.5)^2 times 0.8, (115/7.5)^3 times 0.8]s=[3,48]m,$$and from 16 hours to 175 days, 11 days for $O(n^2.5)$, for 2M data points.



    You should continue using sample sizes on the order of 100K or less. Also, it is fruitful to track the accuracy (or any other score) as a function of samples for 1K, 10K, 50K, and 100K samples. It is possible that SVM performance plateaus before going beyond 100K, therefore, there is not much to lose by limiting the samples to 100K or less.






    share|improve this answer











    $endgroup$

















      3












      $begingroup$

      This is expected and is not related to SMOTE sampling.



      The computational complexity of non-linear SVM is on the order of $O(n^2)$ to $O(n^3)$ where $n$ is the number of samples. This means that if it takes 0.8 seconds for 7.5K data points, it should take [3, 48] minutes for 115K, $$[(115/7.5)^2 times 0.8, (115/7.5)^3 times 0.8]s=[3,48]m,$$and from 16 hours to 175 days, 11 days for $O(n^2.5)$, for 2M data points.



      You should continue using sample sizes on the order of 100K or less. Also, it is fruitful to track the accuracy (or any other score) as a function of samples for 1K, 10K, 50K, and 100K samples. It is possible that SVM performance plateaus before going beyond 100K, therefore, there is not much to lose by limiting the samples to 100K or less.






      share|improve this answer











      $endgroup$















        3












        3








        3





        $begingroup$

        This is expected and is not related to SMOTE sampling.



        The computational complexity of non-linear SVM is on the order of $O(n^2)$ to $O(n^3)$ where $n$ is the number of samples. This means that if it takes 0.8 seconds for 7.5K data points, it should take [3, 48] minutes for 115K, $$[(115/7.5)^2 times 0.8, (115/7.5)^3 times 0.8]s=[3,48]m,$$and from 16 hours to 175 days, 11 days for $O(n^2.5)$, for 2M data points.



        You should continue using sample sizes on the order of 100K or less. Also, it is fruitful to track the accuracy (or any other score) as a function of samples for 1K, 10K, 50K, and 100K samples. It is possible that SVM performance plateaus before going beyond 100K, therefore, there is not much to lose by limiting the samples to 100K or less.






        share|improve this answer











        $endgroup$



        This is expected and is not related to SMOTE sampling.



        The computational complexity of non-linear SVM is on the order of $O(n^2)$ to $O(n^3)$ where $n$ is the number of samples. This means that if it takes 0.8 seconds for 7.5K data points, it should take [3, 48] minutes for 115K, $$[(115/7.5)^2 times 0.8, (115/7.5)^3 times 0.8]s=[3,48]m,$$and from 16 hours to 175 days, 11 days for $O(n^2.5)$, for 2M data points.



        You should continue using sample sizes on the order of 100K or less. Also, it is fruitful to track the accuracy (or any other score) as a function of samples for 1K, 10K, 50K, and 100K samples. It is possible that SVM performance plateaus before going beyond 100K, therefore, there is not much to lose by limiting the samples to 100K or less.







        share|improve this answer














        share|improve this answer



        share|improve this answer








        edited 6 hours ago

























        answered 7 hours ago









        EsmailianEsmailian

        2,631318




        2,631318



























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