How to correctly apply the same data transformation , used on the training dataset , on real data in a...












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$begingroup$


Let's say I used minmaxscaler while creating my model.
Now, i'm loading that model via Pickle in a Flask app. Upon receiving a request containing a datapoint I would like to apply to it the same transformations that I applied to my training dataset before calling the predict() method. How do I transfer that set of transformations from one file to a webservice?






machine-learning data







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    2












    $begingroup$


    Let's say I used minmaxscaler while creating my model.
    Now, i'm loading that model via Pickle in a Flask app. Upon receiving a request containing a datapoint I would like to apply to it the same transformations that I applied to my training dataset before calling the predict() method. How do I transfer that set of transformations from one file to a webservice?






    machine-learning data







    share|improve this question









    New contributor




    Blenzus is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
    Check out our Code of Conduct.







    $endgroup$















      2












      2








      2


      1



      $begingroup$


      Let's say I used minmaxscaler while creating my model.
      Now, i'm loading that model via Pickle in a Flask app. Upon receiving a request containing a datapoint I would like to apply to it the same transformations that I applied to my training dataset before calling the predict() method. How do I transfer that set of transformations from one file to a webservice?






      machine-learning data







      share|improve this question









      New contributor




      Blenzus is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.







      $endgroup$




      Let's say I used minmaxscaler while creating my model.
      Now, i'm loading that model via Pickle in a Flask app. Upon receiving a request containing a datapoint I would like to apply to it the same transformations that I applied to my training dataset before calling the predict() method. How do I transfer that set of transformations from one file to a webservice?






      machine-learning data




      machine-learning data






      share|improve this question









      New contributor




      Blenzus is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.











      share|improve this question









      New contributor




      Blenzus is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.









      share|improve this question




      share|improve this question








      edited 3 hours ago









      Ethan

      574224




      574224






      New contributor




      Blenzus is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.









      asked 16 hours ago









      BlenzusBlenzus

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      New contributor




      Blenzus is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.





      New contributor





      Blenzus is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.






      Blenzus is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.






















          2 Answers
          2






          active

          oldest

          votes


















          2












          $begingroup$

          Rather than storing and loading many files, create a Scikit-learn transformation pipeline with all of your transformations, and then save that as a pickle or joblib file.



          from sklearn.pipeline import Pipeline
          
from sklearn.externals import joblib
          

          
pipeline = Pipeline([
          
                ('normalization', MinMaxScaler()),
          
                ('classifier', RandomForestClassifier())
          
            ])
          

          
joblib.dump(pipeline, 'transform_predict.joblib')


          You can then just load one transformation pipeline and call fit_transform to transform the input data and get predictions for it:



           pipeline = load('transform_predict.joblib') 
          
 predictions = pipeline.predict(new_data)





          share|improve this answer











          $endgroup$









          • 1




            $begingroup$
            Thanks, this is what i was looking for
            $endgroup$
            – Blenzus
            15 hours ago










          • $begingroup$
            Does this apply to dummy variables?
            $endgroup$
            – Blenzus
            15 hours ago






          • 1




            $begingroup$
            If you're using scikit-learn's OneHotEncoder then yes. Any scikit learn 'transformer' can be used with a pipeline, so anything that implements the TransformerMixin and BaseEstimator: github.com/scikit-learn/scikit-learn/blob/7b136e9/sklearn/… This also means you can create your own custom 'transformers' to add to a pipeline, by implementing these in the same way.
            $endgroup$
            – Dan Carter
            14 hours ago



















          1












          $begingroup$

          You need to save minmaxscaler (along with model). In Flask app, you can :




          1. Load scaler from file

          2. Use this instance of scaler for scaling input values



          #While training



          from sklearn.externals import joblib
          
scaler_filename = "saved_scaler"
          
joblib.dump(scaler, scaler_filename)


          In Flask App



          scaler_filename = "saved_scaler"    
          
scaler = joblib.load(scaler_filename)






          share|improve this answer









          $endgroup$













          • $begingroup$
            Do i need to do this for every normalization library i use? i'll be loading many files into the memory, isn't there way to load something that contains every step of the data transformations?
            $endgroup$
            – Blenzus
            16 hours ago






          • 1




            $begingroup$
            You can save and load all scalers at the same time. Example : stackoverflow.com/questions/33497314/…
            $endgroup$
            – Shamit Verma
            15 hours ago











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          2 Answers
          2






          active

          oldest

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          2 Answers
          2






          active

          oldest

          votes









          active

          oldest

          votes






          active

          oldest

          votes









          2












          $begingroup$

          Rather than storing and loading many files, create a Scikit-learn transformation pipeline with all of your transformations, and then save that as a pickle or joblib file.



          from sklearn.pipeline import Pipeline
          
from sklearn.externals import joblib
          

          
pipeline = Pipeline([
          
                ('normalization', MinMaxScaler()),
          
                ('classifier', RandomForestClassifier())
          
            ])
          

          
joblib.dump(pipeline, 'transform_predict.joblib')


          You can then just load one transformation pipeline and call fit_transform to transform the input data and get predictions for it:



           pipeline = load('transform_predict.joblib') 
          
 predictions = pipeline.predict(new_data)





          share|improve this answer











          $endgroup$









          • 1




            $begingroup$
            Thanks, this is what i was looking for
            $endgroup$
            – Blenzus
            15 hours ago










          • $begingroup$
            Does this apply to dummy variables?
            $endgroup$
            – Blenzus
            15 hours ago






          • 1




            $begingroup$
            If you're using scikit-learn's OneHotEncoder then yes. Any scikit learn 'transformer' can be used with a pipeline, so anything that implements the TransformerMixin and BaseEstimator: github.com/scikit-learn/scikit-learn/blob/7b136e9/sklearn/… This also means you can create your own custom 'transformers' to add to a pipeline, by implementing these in the same way.
            $endgroup$
            – Dan Carter
            14 hours ago
















          2












          $begingroup$

          Rather than storing and loading many files, create a Scikit-learn transformation pipeline with all of your transformations, and then save that as a pickle or joblib file.



          from sklearn.pipeline import Pipeline
          
from sklearn.externals import joblib
          

          
pipeline = Pipeline([
          
                ('normalization', MinMaxScaler()),
          
                ('classifier', RandomForestClassifier())
          
            ])
          

          
joblib.dump(pipeline, 'transform_predict.joblib')


          You can then just load one transformation pipeline and call fit_transform to transform the input data and get predictions for it:



           pipeline = load('transform_predict.joblib') 
          
 predictions = pipeline.predict(new_data)





          share|improve this answer











          $endgroup$









          • 1




            $begingroup$
            Thanks, this is what i was looking for
            $endgroup$
            – Blenzus
            15 hours ago










          • $begingroup$
            Does this apply to dummy variables?
            $endgroup$
            – Blenzus
            15 hours ago






          • 1




            $begingroup$
            If you're using scikit-learn's OneHotEncoder then yes. Any scikit learn 'transformer' can be used with a pipeline, so anything that implements the TransformerMixin and BaseEstimator: github.com/scikit-learn/scikit-learn/blob/7b136e9/sklearn/… This also means you can create your own custom 'transformers' to add to a pipeline, by implementing these in the same way.
            $endgroup$
            – Dan Carter
            14 hours ago














          2












          2








          2





          $begingroup$

          Rather than storing and loading many files, create a Scikit-learn transformation pipeline with all of your transformations, and then save that as a pickle or joblib file.



          from sklearn.pipeline import Pipeline
          
from sklearn.externals import joblib
          

          
pipeline = Pipeline([
          
                ('normalization', MinMaxScaler()),
          
                ('classifier', RandomForestClassifier())
          
            ])
          

          
joblib.dump(pipeline, 'transform_predict.joblib')


          You can then just load one transformation pipeline and call fit_transform to transform the input data and get predictions for it:



           pipeline = load('transform_predict.joblib') 
          
 predictions = pipeline.predict(new_data)





          share|improve this answer











          $endgroup$



          Rather than storing and loading many files, create a Scikit-learn transformation pipeline with all of your transformations, and then save that as a pickle or joblib file.



          from sklearn.pipeline import Pipeline
          
from sklearn.externals import joblib
          

          
pipeline = Pipeline([
          
                ('normalization', MinMaxScaler()),
          
                ('classifier', RandomForestClassifier())
          
            ])
          

          
joblib.dump(pipeline, 'transform_predict.joblib')


          You can then just load one transformation pipeline and call fit_transform to transform the input data and get predictions for it:



           pipeline = load('transform_predict.joblib') 
          
 predictions = pipeline.predict(new_data)






          share|improve this answer














          share|improve this answer



          share|improve this answer








          edited 15 hours ago

























          answered 15 hours ago









          Dan CarterDan Carter

          7751218




          7751218








          • 1




            $begingroup$
            Thanks, this is what i was looking for
            $endgroup$
            – Blenzus
            15 hours ago










          • $begingroup$
            Does this apply to dummy variables?
            $endgroup$
            – Blenzus
            15 hours ago






          • 1




            $begingroup$
            If you're using scikit-learn's OneHotEncoder then yes. Any scikit learn 'transformer' can be used with a pipeline, so anything that implements the TransformerMixin and BaseEstimator: github.com/scikit-learn/scikit-learn/blob/7b136e9/sklearn/… This also means you can create your own custom 'transformers' to add to a pipeline, by implementing these in the same way.
            $endgroup$
            – Dan Carter
            14 hours ago














          • 1




            $begingroup$
            Thanks, this is what i was looking for
            $endgroup$
            – Blenzus
            15 hours ago










          • $begingroup$
            Does this apply to dummy variables?
            $endgroup$
            – Blenzus
            15 hours ago






          • 1




            $begingroup$
            If you're using scikit-learn's OneHotEncoder then yes. Any scikit learn 'transformer' can be used with a pipeline, so anything that implements the TransformerMixin and BaseEstimator: github.com/scikit-learn/scikit-learn/blob/7b136e9/sklearn/… This also means you can create your own custom 'transformers' to add to a pipeline, by implementing these in the same way.
            $endgroup$
            – Dan Carter
            14 hours ago








          1




          1




          $begingroup$
          Thanks, this is what i was looking for
          $endgroup$
          – Blenzus
          15 hours ago




          $begingroup$
          Thanks, this is what i was looking for
          $endgroup$
          – Blenzus
          15 hours ago












          $begingroup$
          Does this apply to dummy variables?
          $endgroup$
          – Blenzus
          15 hours ago




          $begingroup$
          Does this apply to dummy variables?
          $endgroup$
          – Blenzus
          15 hours ago




          1




          1




          $begingroup$
          If you're using scikit-learn's OneHotEncoder then yes. Any scikit learn 'transformer' can be used with a pipeline, so anything that implements the TransformerMixin and BaseEstimator: github.com/scikit-learn/scikit-learn/blob/7b136e9/sklearn/… This also means you can create your own custom 'transformers' to add to a pipeline, by implementing these in the same way.
          $endgroup$
          – Dan Carter
          14 hours ago




          $begingroup$
          If you're using scikit-learn's OneHotEncoder then yes. Any scikit learn 'transformer' can be used with a pipeline, so anything that implements the TransformerMixin and BaseEstimator: github.com/scikit-learn/scikit-learn/blob/7b136e9/sklearn/… This also means you can create your own custom 'transformers' to add to a pipeline, by implementing these in the same way.
          $endgroup$
          – Dan Carter
          14 hours ago











          1












          $begingroup$

          You need to save minmaxscaler (along with model). In Flask app, you can :




          1. Load scaler from file

          2. Use this instance of scaler for scaling input values



          #While training



          from sklearn.externals import joblib
          
scaler_filename = "saved_scaler"
          
joblib.dump(scaler, scaler_filename)


          In Flask App



          scaler_filename = "saved_scaler"    
          
scaler = joblib.load(scaler_filename)






          share|improve this answer









          $endgroup$













          • $begingroup$
            Do i need to do this for every normalization library i use? i'll be loading many files into the memory, isn't there way to load something that contains every step of the data transformations?
            $endgroup$
            – Blenzus
            16 hours ago






          • 1




            $begingroup$
            You can save and load all scalers at the same time. Example : stackoverflow.com/questions/33497314/…
            $endgroup$
            – Shamit Verma
            15 hours ago
















          1












          $begingroup$

          You need to save minmaxscaler (along with model). In Flask app, you can :




          1. Load scaler from file

          2. Use this instance of scaler for scaling input values



          #While training



          from sklearn.externals import joblib
          
scaler_filename = "saved_scaler"
          
joblib.dump(scaler, scaler_filename)


          In Flask App



          scaler_filename = "saved_scaler"    
          
scaler = joblib.load(scaler_filename)






          share|improve this answer









          $endgroup$













          • $begingroup$
            Do i need to do this for every normalization library i use? i'll be loading many files into the memory, isn't there way to load something that contains every step of the data transformations?
            $endgroup$
            – Blenzus
            16 hours ago






          • 1




            $begingroup$
            You can save and load all scalers at the same time. Example : stackoverflow.com/questions/33497314/…
            $endgroup$
            – Shamit Verma
            15 hours ago














          1












          1








          1





          $begingroup$

          You need to save minmaxscaler (along with model). In Flask app, you can :




          1. Load scaler from file

          2. Use this instance of scaler for scaling input values



          #While training



          from sklearn.externals import joblib
          
scaler_filename = "saved_scaler"
          
joblib.dump(scaler, scaler_filename)


          In Flask App



          scaler_filename = "saved_scaler"    
          
scaler = joblib.load(scaler_filename)






          share|improve this answer









          $endgroup$



          You need to save minmaxscaler (along with model). In Flask app, you can :




          1. Load scaler from file

          2. Use this instance of scaler for scaling input values



          #While training



          from sklearn.externals import joblib
          
scaler_filename = "saved_scaler"
          
joblib.dump(scaler, scaler_filename)


          In Flask App



          scaler_filename = "saved_scaler"    
          
scaler = joblib.load(scaler_filename)







          share|improve this answer












          share|improve this answer



          share|improve this answer










          answered 16 hours ago









          Shamit VermaShamit Verma

          1,009210




          1,009210












          • $begingroup$
            Do i need to do this for every normalization library i use? i'll be loading many files into the memory, isn't there way to load something that contains every step of the data transformations?
            $endgroup$
            – Blenzus
            16 hours ago






          • 1




            $begingroup$
            You can save and load all scalers at the same time. Example : stackoverflow.com/questions/33497314/…
            $endgroup$
            – Shamit Verma
            15 hours ago


















          • $begingroup$
            Do i need to do this for every normalization library i use? i'll be loading many files into the memory, isn't there way to load something that contains every step of the data transformations?
            $endgroup$
            – Blenzus
            16 hours ago






          • 1




            $begingroup$
            You can save and load all scalers at the same time. Example : stackoverflow.com/questions/33497314/…
            $endgroup$
            – Shamit Verma
            15 hours ago
















          $begingroup$
          Do i need to do this for every normalization library i use? i'll be loading many files into the memory, isn't there way to load something that contains every step of the data transformations?
          $endgroup$
          – Blenzus
          16 hours ago




          $begingroup$
          Do i need to do this for every normalization library i use? i'll be loading many files into the memory, isn't there way to load something that contains every step of the data transformations?
          $endgroup$
          – Blenzus
          16 hours ago




          1




          1




          $begingroup$
          You can save and load all scalers at the same time. Example : stackoverflow.com/questions/33497314/…
          $endgroup$
          – Shamit Verma
          15 hours ago




          $begingroup$
          You can save and load all scalers at the same time. Example : stackoverflow.com/questions/33497314/…
          $endgroup$
          – Shamit Verma
          15 hours ago










          Blenzus is a new contributor. Be nice, and check out our Code of Conduct.










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          Blenzus is a new contributor. Be nice, and check out our Code of Conduct.
















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Hall Of Fame””Slayer Wins 'Best Metal' Grammy Award””Slayer Guitarist Jeff Hanneman Dies””Bullet-For My Valentine booed at Metal Hammer Golden Gods Awards””Unholy Aliance””The End Of Slayer?””Slayer: We Could Thrash Out Two More Albums If We're Fast Enough...””'The Unholy Alliance: Chapter III' UK Dates Added”originalet”Megadeth And Slayer To Co-Headline 'Canadian Carnage' Trek”originalet”World Painted Blood””Release “World Painted Blood” by Slayer””Metallica Heading To Cinemas””Slayer, Megadeth To Join Forces For 'European Carnage' Tour - Dec. 18, 2010”originalet”Slayer's Hanneman Contracts Acute Infection; Band To Bring In Guest Guitarist””Cannibal Corpse's Pat O'Brien Will Step In As Slayer's Guest Guitarist”originalet”Slayer’s Jeff Hanneman Dead at 49””Dave Lombardo Says He Made Only $67,000 In 2011 While Touring With Slayer””Slayer: We Do Not Agree With Dave Lombardo's Substance Or Timeline Of Events””Slayer Welcomes Drummer Paul Bostaph Back To The Fold””Slayer Hope to Unveil Never-Before-Heard Jeff Hanneman Material on Next Album””Slayer Debut New Song 'Implode' During Surprise Golden Gods Appearance””Release group Repentless by Slayer””Repentless - Slayer - Credits””Slayer””Metal Storm Awards 2015””Slayer - to release comic book "Repentless #1"””Slayer To Release 'Repentless' 6.66" Vinyl Box Set””BREAKING NEWS: Slayer Announce Farewell Tour””Slayer Recruit Lamb of God, Anthrax, Behemoth + Testament for Final Tour””Slayer lägger ner efter 37 år””Slayer Announces Second North American Leg Of 'Final' Tour””Final World Tour””Slayer Announces Final European Tour With Lamb of God, Anthrax And Obituary””Slayer To Tour Europe With Lamb of God, Anthrax And Obituary””Slayer To Play 'Last French Show Ever' At Next Year's Hellfst””Slayer's Final World Tour Will Extend Into 2019””Death Angel's Rob Cavestany On Slayer's 'Farewell' Tour: 'Some Of Us Could See This Coming'””Testament Has No Plans To Retire Anytime Soon, Says Chuck Billy””Anthrax's Scott Ian On Slayer's 'Farewell' Tour Plans: 'I Was Surprised And I Wasn't Surprised'””Slayer””Slayer's Morbid Schlock””Review/Rock; For Slayer, the Mania Is the Message””Slayer - Biography””Slayer - Reign In Blood”originalet”Dave Lombardo””An exclusive oral history of Slayer”originalet”Exclusive! Interview With Slayer Guitarist Jeff Hanneman”originalet”Thinking Out Loud: Slayer's Kerry King on hair metal, Satan and being polite””Slayer Lyrics””Slayer - Biography””Most influential artists for extreme metal music””Slayer - Reign in Blood””Slayer guitarist Jeff Hanneman dies aged 49””Slatanic Slaughter: A Tribute to Slayer””Gateway to Hell: A Tribute to Slayer””Covered In Blood””Slayer: The Origins of Thrash in San Francisco, CA.””Why They Rule - #6 Slayer”originalet”Guitar World's 100 Greatest Heavy Metal Guitarists Of All Time”originalet”The fans have spoken: Slayer comes out on top in readers' polls”originalet”Tribute to Jeff Hanneman (1964-2013)””Lamb Of God Frontman: We Sound Like A Slayer Rip-Off””BEHEMOTH Frontman Pays Tribute To SLAYER's JEFF HANNEMAN””Slayer, Hatebreed Doing Double Duty On This Year's Ozzfest””System of a Down””Lacuna Coil’s Andrea Ferro Talks Influences, Skateboarding, Band Origins + More””Slayer - Reign in Blood””Into The Lungs of Hell””Slayer rules - en utställning om fans””Slayer and Their Fans Slashed Through a No-Holds-Barred Night at Gas Monkey””Home””Slayer””Gold & Platinum - The Big 4 Live from Sofia, Bulgaria””Exclusive! Interview With Slayer Guitarist Kerry King””2008-02-23: Wiltern, Los Angeles, CA, USA””Slayer's Kerry King To Perform With Megadeth Tonight! - Oct. 21, 2010”originalet”Dave Lombardo - Biography”Slayer Case DismissedArkiveradUltimate Classic Rock: Slayer guitarist Jeff Hanneman dead at 49.”Slayer: "We could never do any thing like Some Kind Of Monster..."””Cannibal Corpse'S Pat O'Brien Will Step In As Slayer'S Guest Guitarist | The Official Slayer Site”originalet”Slayer Wins 'Best Metal' Grammy Award””Slayer Guitarist Jeff Hanneman Dies””Kerrang! Awards 2006 Blog: Kerrang! Hall Of Fame””Kerrang! Awards 2013: Kerrang! Legend”originalet”Metallica, Slayer, Iron Maien Among Winners At Metal Hammer Awards””Metal Hammer Golden Gods Awards””Bullet For My Valentine Booed At Metal Hammer Golden Gods Awards””Metal Storm Awards 2006””Metal Storm Awards 2015””Slayer's Concert History””Slayer - Relationships””Slayer - Releases”Slayers officiella webbplatsSlayer på MusicBrainzOfficiell webbplatsSlayerSlayerr1373445760000 0001 1540 47353068615-5086262726cb13906545x(data)6033143kn20030215029

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          SlayerMusikgrupper bildade 1981Amerikanska thrash metal-grupper amerikansktthrash metalHuntington Park1981gitarristernaJeff HannemanKerry KingbasistenTom ArayatrummisenDave LombardoGary HoltPaul Bostaphcertifierats med guldMetallicaAnthraxMegadeththrash metal1986extrema metalgenrerdeath metal22 januari2018USAKanadaEuropaHuntington ParkKalifornien1981gitarristernaJeff HannemanKerry KingbassångDave Lombardoheavy metalbrittiskaIron MaidenJudas PriestthrashMetallicaRattSteeler1982demoundergroundMetal Blade1983high schoolGene HoglanDeathStrapping Young Ladkassettvinyl1984LondonMarquee Club1984Eldorado StudiosTrack Record StudiosLos Angelesmastrade1985CDCombat Recordsextreme metalVenomExodusStudio 54 club1985VHSCombat RecordsBelgienUFORick RubinDef Jam Recordship hoprockakterThe Black CrowesRun–D.M.C.LL Cool JBeastie BoysthrashbpmAngel of DeathRaining BloodAndy WallaceHowie Weinberg1986bildskivaBillboard 200RIAAMetal HammerKerrang!Angela Gossowdeath metalArch Enemythrash metalOverk...
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