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Sklearn 'Seed' Not Working Properly In a Section of Code [on hold]


Posterior covariance of Normal-Inverse-Wishart not converging properlyLogistic Regression not quite workingWhy is Python's scikit-learn LDA not working correctly and how does it compute LDA via SVD?K-Means Clustering Not Working As ExpcectedEmploying cross_validation to to develop a reasonable linear regression model using scikit learnWhy does sklearn Ridge not accept warm start?Working between sklearn and scipy for convex optimizationPCA principal components in sklearn not matching eigen-vectors of covariance calculated by numpySklearn BaggingRegressor does not work with LightGBMRegressor & MAE objective













0












$begingroup$


I have written an ensemble using Scikit Learn VotingClassifier.



I have set a seed in the cross validation section. However, it does not appear to 'hold'. Meaning, If I re-run the code block I get different results. (I can only assume each run of the code block is dividing the dataset into folds with different constituents instead of 'freezing' the random state.



Here is the code:



#Voting Ensemble of Classification
#Create Submodels
num_folds = 10
seed =7
kfold = KFold(n_splits=num_folds, random_state=seed)
estimators = []
model1 =LogisticRegression()
estimators.append(('LR',model1))
model2 = KNeighborsClassifier()
estimators.append(('KNN',model2))
model3 = GradientBoostingClassifier()
estimators.append(('GBM',model3))
#Create the ensemble
ensemble = VotingClassifier(estimators,voting='soft')
results = cross_val_score(ensemble, X_train, Y_train,cv=kfold)
print(results)


The results printed are the results of the 10 CV fold training. If I run this code block several times I get the following results:



1:



[0.70588235 0.94117647 1. 0.82352941 0.94117647 0.88235294
0.8125 0.875 0.8125 0.9375 ]


2:



[0.76470588 0.94117647 1. 0.82352941 0.94117647 0.88235294
0.8125 0.875 0.8125 0.875 ]


3:



[0.76470588 0.94117647 1. 0.82352941 0.94117647 0.88235294
0.8125 0.875 0.8125 0.875 ]


4:



[0.76470588 0.94117647 1. 0.82352941 1. 0.88235294
0.8125 0.875 0.625 0.875 ]


So it appears my random_state=seed isn't holding.



What is incorrect?



Thanks in advance.










share|cite|improve this question









$endgroup$



put on hold as off-topic by jbowman, Sycorax, Robert Long, Michael Chernick, mdewey 18 hours ago


This question appears to be off-topic. The users who voted to close gave this specific reason:


  • "This question appears to be off-topic because EITHER it is not about statistics, machine learning, data analysis, data mining, or data visualization, OR it focuses on programming, debugging, or performing routine operations within a statistical computing platform. If the latter, you could try the support links we maintain." – Sycorax, Robert Long, Michael Chernick, mdewey
If this question can be reworded to fit the rules in the help center, please edit the question.




















    0












    $begingroup$


    I have written an ensemble using Scikit Learn VotingClassifier.



    I have set a seed in the cross validation section. However, it does not appear to 'hold'. Meaning, If I re-run the code block I get different results. (I can only assume each run of the code block is dividing the dataset into folds with different constituents instead of 'freezing' the random state.



    Here is the code:



    #Voting Ensemble of Classification
    #Create Submodels
    num_folds = 10
    seed =7
    kfold = KFold(n_splits=num_folds, random_state=seed)
    estimators = []
    model1 =LogisticRegression()
    estimators.append(('LR',model1))
    model2 = KNeighborsClassifier()
    estimators.append(('KNN',model2))
    model3 = GradientBoostingClassifier()
    estimators.append(('GBM',model3))
    #Create the ensemble
    ensemble = VotingClassifier(estimators,voting='soft')
    results = cross_val_score(ensemble, X_train, Y_train,cv=kfold)
    print(results)


    The results printed are the results of the 10 CV fold training. If I run this code block several times I get the following results:



    1:



    [0.70588235 0.94117647 1. 0.82352941 0.94117647 0.88235294
    0.8125 0.875 0.8125 0.9375 ]


    2:



    [0.76470588 0.94117647 1. 0.82352941 0.94117647 0.88235294
    0.8125 0.875 0.8125 0.875 ]


    3:



    [0.76470588 0.94117647 1. 0.82352941 0.94117647 0.88235294
    0.8125 0.875 0.8125 0.875 ]


    4:



    [0.76470588 0.94117647 1. 0.82352941 1. 0.88235294
    0.8125 0.875 0.625 0.875 ]


    So it appears my random_state=seed isn't holding.



    What is incorrect?



    Thanks in advance.










    share|cite|improve this question









    $endgroup$



    put on hold as off-topic by jbowman, Sycorax, Robert Long, Michael Chernick, mdewey 18 hours ago


    This question appears to be off-topic. The users who voted to close gave this specific reason:


    • "This question appears to be off-topic because EITHER it is not about statistics, machine learning, data analysis, data mining, or data visualization, OR it focuses on programming, debugging, or performing routine operations within a statistical computing platform. If the latter, you could try the support links we maintain." – Sycorax, Robert Long, Michael Chernick, mdewey
    If this question can be reworded to fit the rules in the help center, please edit the question.


















      0












      0








      0





      $begingroup$


      I have written an ensemble using Scikit Learn VotingClassifier.



      I have set a seed in the cross validation section. However, it does not appear to 'hold'. Meaning, If I re-run the code block I get different results. (I can only assume each run of the code block is dividing the dataset into folds with different constituents instead of 'freezing' the random state.



      Here is the code:



      #Voting Ensemble of Classification
      #Create Submodels
      num_folds = 10
      seed =7
      kfold = KFold(n_splits=num_folds, random_state=seed)
      estimators = []
      model1 =LogisticRegression()
      estimators.append(('LR',model1))
      model2 = KNeighborsClassifier()
      estimators.append(('KNN',model2))
      model3 = GradientBoostingClassifier()
      estimators.append(('GBM',model3))
      #Create the ensemble
      ensemble = VotingClassifier(estimators,voting='soft')
      results = cross_val_score(ensemble, X_train, Y_train,cv=kfold)
      print(results)


      The results printed are the results of the 10 CV fold training. If I run this code block several times I get the following results:



      1:



      [0.70588235 0.94117647 1. 0.82352941 0.94117647 0.88235294
      0.8125 0.875 0.8125 0.9375 ]


      2:



      [0.76470588 0.94117647 1. 0.82352941 0.94117647 0.88235294
      0.8125 0.875 0.8125 0.875 ]


      3:



      [0.76470588 0.94117647 1. 0.82352941 0.94117647 0.88235294
      0.8125 0.875 0.8125 0.875 ]


      4:



      [0.76470588 0.94117647 1. 0.82352941 1. 0.88235294
      0.8125 0.875 0.625 0.875 ]


      So it appears my random_state=seed isn't holding.



      What is incorrect?



      Thanks in advance.










      share|cite|improve this question









      $endgroup$




      I have written an ensemble using Scikit Learn VotingClassifier.



      I have set a seed in the cross validation section. However, it does not appear to 'hold'. Meaning, If I re-run the code block I get different results. (I can only assume each run of the code block is dividing the dataset into folds with different constituents instead of 'freezing' the random state.



      Here is the code:



      #Voting Ensemble of Classification
      #Create Submodels
      num_folds = 10
      seed =7
      kfold = KFold(n_splits=num_folds, random_state=seed)
      estimators = []
      model1 =LogisticRegression()
      estimators.append(('LR',model1))
      model2 = KNeighborsClassifier()
      estimators.append(('KNN',model2))
      model3 = GradientBoostingClassifier()
      estimators.append(('GBM',model3))
      #Create the ensemble
      ensemble = VotingClassifier(estimators,voting='soft')
      results = cross_val_score(ensemble, X_train, Y_train,cv=kfold)
      print(results)


      The results printed are the results of the 10 CV fold training. If I run this code block several times I get the following results:



      1:



      [0.70588235 0.94117647 1. 0.82352941 0.94117647 0.88235294
      0.8125 0.875 0.8125 0.9375 ]


      2:



      [0.76470588 0.94117647 1. 0.82352941 0.94117647 0.88235294
      0.8125 0.875 0.8125 0.875 ]


      3:



      [0.76470588 0.94117647 1. 0.82352941 0.94117647 0.88235294
      0.8125 0.875 0.8125 0.875 ]


      4:



      [0.76470588 0.94117647 1. 0.82352941 1. 0.88235294
      0.8125 0.875 0.625 0.875 ]


      So it appears my random_state=seed isn't holding.



      What is incorrect?



      Thanks in advance.







      python scikit-learn ensemble






      share|cite|improve this question













      share|cite|improve this question











      share|cite|improve this question




      share|cite|improve this question










      asked yesterday









      Windstorm1981Windstorm1981

      1415




      1415




      put on hold as off-topic by jbowman, Sycorax, Robert Long, Michael Chernick, mdewey 18 hours ago


      This question appears to be off-topic. The users who voted to close gave this specific reason:


      • "This question appears to be off-topic because EITHER it is not about statistics, machine learning, data analysis, data mining, or data visualization, OR it focuses on programming, debugging, or performing routine operations within a statistical computing platform. If the latter, you could try the support links we maintain." – Sycorax, Robert Long, Michael Chernick, mdewey
      If this question can be reworded to fit the rules in the help center, please edit the question.







      put on hold as off-topic by jbowman, Sycorax, Robert Long, Michael Chernick, mdewey 18 hours ago


      This question appears to be off-topic. The users who voted to close gave this specific reason:


      • "This question appears to be off-topic because EITHER it is not about statistics, machine learning, data analysis, data mining, or data visualization, OR it focuses on programming, debugging, or performing routine operations within a statistical computing platform. If the latter, you could try the support links we maintain." – Sycorax, Robert Long, Michael Chernick, mdewey
      If this question can be reworded to fit the rules in the help center, please edit the question.




















          1 Answer
          1






          active

          oldest

          votes


















          2












          $begingroup$

          Random seed of models (LogisticRegression, GradientBoostingClassifier) needs to be fixed too, so that their random behavior becomes reproducible. Here is a working example that produces the same result over multiple runs:



          import sklearn
          from sklearn.model_selection import KFold, cross_val_score
          from sklearn.linear_model import LogisticRegression
          from sklearn.neighbors import KNeighborsClassifier
          from sklearn.ensemble import GradientBoostingClassifier, VotingClassifier
          import numpy as np

          #Voting Ensemble of Classification
          #Create Submodels
          num_folds = 10
          seed =7

          # Data
          np.random.seed(seed)
          feature_1 = np.random.normal(0, 2, 10000)
          feature_2 = np.random.normal(5, 6, 10000)
          X_train = np.vstack([feature_1, feature_2]).T
          Y_train = np.random.randint(0, 2, 10000).T

          kfold = KFold(n_splits=num_folds, random_state=seed)
          estimators = []
          model1 =LogisticRegression(random_state=seed)
          estimators.append(('LR',model1))
          model2 = KNeighborsClassifier()
          estimators.append(('KNN',model2))
          model3 = GradientBoostingClassifier(random_state=seed)
          estimators.append(('GBM',model3))
          #Create the ensemble
          ensemble = VotingClassifier(estimators,voting='soft')
          results = cross_val_score(ensemble, X_train, Y_train,cv=kfold)
          print('sklearn version', sklearn.__version__)
          print(results)


          Output:



          sklearn version 0.19.1
          [0.502 0.496 0.483 0.513 0.515 0.508 0.517 0.499 0.515 0.504]





          share|cite|improve this answer











          $endgroup$












          • $begingroup$
            Thanks for your quick reply. Not sure I follow completely. random_state=seed fixes my cross validation. I note your line np.random.seed(seed). Intuitively it suggests to me it is ensuring repeatable generation of toy data. I already have a data set. How does that apply to 'fixing seed of models'?
            $endgroup$
            – Windstorm1981
            yesterday











          • $begingroup$
            @Windstorm1981 My bad. Updated.
            $endgroup$
            – Esmailian
            yesterday






          • 1




            $begingroup$
            ha! Clear now. So fixing the cv fixes the data splits. Fixing the models fixes how the models handle the (fixed) data splits?
            $endgroup$
            – Windstorm1981
            yesterday






          • 1




            $begingroup$
            @Windstorm1981 Exactly!
            $endgroup$
            – Esmailian
            yesterday

















          1 Answer
          1






          active

          oldest

          votes








          1 Answer
          1






          active

          oldest

          votes









          active

          oldest

          votes






          active

          oldest

          votes









          2












          $begingroup$

          Random seed of models (LogisticRegression, GradientBoostingClassifier) needs to be fixed too, so that their random behavior becomes reproducible. Here is a working example that produces the same result over multiple runs:



          import sklearn
          from sklearn.model_selection import KFold, cross_val_score
          from sklearn.linear_model import LogisticRegression
          from sklearn.neighbors import KNeighborsClassifier
          from sklearn.ensemble import GradientBoostingClassifier, VotingClassifier
          import numpy as np

          #Voting Ensemble of Classification
          #Create Submodels
          num_folds = 10
          seed =7

          # Data
          np.random.seed(seed)
          feature_1 = np.random.normal(0, 2, 10000)
          feature_2 = np.random.normal(5, 6, 10000)
          X_train = np.vstack([feature_1, feature_2]).T
          Y_train = np.random.randint(0, 2, 10000).T

          kfold = KFold(n_splits=num_folds, random_state=seed)
          estimators = []
          model1 =LogisticRegression(random_state=seed)
          estimators.append(('LR',model1))
          model2 = KNeighborsClassifier()
          estimators.append(('KNN',model2))
          model3 = GradientBoostingClassifier(random_state=seed)
          estimators.append(('GBM',model3))
          #Create the ensemble
          ensemble = VotingClassifier(estimators,voting='soft')
          results = cross_val_score(ensemble, X_train, Y_train,cv=kfold)
          print('sklearn version', sklearn.__version__)
          print(results)


          Output:



          sklearn version 0.19.1
          [0.502 0.496 0.483 0.513 0.515 0.508 0.517 0.499 0.515 0.504]





          share|cite|improve this answer











          $endgroup$












          • $begingroup$
            Thanks for your quick reply. Not sure I follow completely. random_state=seed fixes my cross validation. I note your line np.random.seed(seed). Intuitively it suggests to me it is ensuring repeatable generation of toy data. I already have a data set. How does that apply to 'fixing seed of models'?
            $endgroup$
            – Windstorm1981
            yesterday











          • $begingroup$
            @Windstorm1981 My bad. Updated.
            $endgroup$
            – Esmailian
            yesterday






          • 1




            $begingroup$
            ha! Clear now. So fixing the cv fixes the data splits. Fixing the models fixes how the models handle the (fixed) data splits?
            $endgroup$
            – Windstorm1981
            yesterday






          • 1




            $begingroup$
            @Windstorm1981 Exactly!
            $endgroup$
            – Esmailian
            yesterday















          2












          $begingroup$

          Random seed of models (LogisticRegression, GradientBoostingClassifier) needs to be fixed too, so that their random behavior becomes reproducible. Here is a working example that produces the same result over multiple runs:



          import sklearn
          from sklearn.model_selection import KFold, cross_val_score
          from sklearn.linear_model import LogisticRegression
          from sklearn.neighbors import KNeighborsClassifier
          from sklearn.ensemble import GradientBoostingClassifier, VotingClassifier
          import numpy as np

          #Voting Ensemble of Classification
          #Create Submodels
          num_folds = 10
          seed =7

          # Data
          np.random.seed(seed)
          feature_1 = np.random.normal(0, 2, 10000)
          feature_2 = np.random.normal(5, 6, 10000)
          X_train = np.vstack([feature_1, feature_2]).T
          Y_train = np.random.randint(0, 2, 10000).T

          kfold = KFold(n_splits=num_folds, random_state=seed)
          estimators = []
          model1 =LogisticRegression(random_state=seed)
          estimators.append(('LR',model1))
          model2 = KNeighborsClassifier()
          estimators.append(('KNN',model2))
          model3 = GradientBoostingClassifier(random_state=seed)
          estimators.append(('GBM',model3))
          #Create the ensemble
          ensemble = VotingClassifier(estimators,voting='soft')
          results = cross_val_score(ensemble, X_train, Y_train,cv=kfold)
          print('sklearn version', sklearn.__version__)
          print(results)


          Output:



          sklearn version 0.19.1
          [0.502 0.496 0.483 0.513 0.515 0.508 0.517 0.499 0.515 0.504]





          share|cite|improve this answer











          $endgroup$












          • $begingroup$
            Thanks for your quick reply. Not sure I follow completely. random_state=seed fixes my cross validation. I note your line np.random.seed(seed). Intuitively it suggests to me it is ensuring repeatable generation of toy data. I already have a data set. How does that apply to 'fixing seed of models'?
            $endgroup$
            – Windstorm1981
            yesterday











          • $begingroup$
            @Windstorm1981 My bad. Updated.
            $endgroup$
            – Esmailian
            yesterday






          • 1




            $begingroup$
            ha! Clear now. So fixing the cv fixes the data splits. Fixing the models fixes how the models handle the (fixed) data splits?
            $endgroup$
            – Windstorm1981
            yesterday






          • 1




            $begingroup$
            @Windstorm1981 Exactly!
            $endgroup$
            – Esmailian
            yesterday













          2












          2








          2





          $begingroup$

          Random seed of models (LogisticRegression, GradientBoostingClassifier) needs to be fixed too, so that their random behavior becomes reproducible. Here is a working example that produces the same result over multiple runs:



          import sklearn
          from sklearn.model_selection import KFold, cross_val_score
          from sklearn.linear_model import LogisticRegression
          from sklearn.neighbors import KNeighborsClassifier
          from sklearn.ensemble import GradientBoostingClassifier, VotingClassifier
          import numpy as np

          #Voting Ensemble of Classification
          #Create Submodels
          num_folds = 10
          seed =7

          # Data
          np.random.seed(seed)
          feature_1 = np.random.normal(0, 2, 10000)
          feature_2 = np.random.normal(5, 6, 10000)
          X_train = np.vstack([feature_1, feature_2]).T
          Y_train = np.random.randint(0, 2, 10000).T

          kfold = KFold(n_splits=num_folds, random_state=seed)
          estimators = []
          model1 =LogisticRegression(random_state=seed)
          estimators.append(('LR',model1))
          model2 = KNeighborsClassifier()
          estimators.append(('KNN',model2))
          model3 = GradientBoostingClassifier(random_state=seed)
          estimators.append(('GBM',model3))
          #Create the ensemble
          ensemble = VotingClassifier(estimators,voting='soft')
          results = cross_val_score(ensemble, X_train, Y_train,cv=kfold)
          print('sklearn version', sklearn.__version__)
          print(results)


          Output:



          sklearn version 0.19.1
          [0.502 0.496 0.483 0.513 0.515 0.508 0.517 0.499 0.515 0.504]





          share|cite|improve this answer











          $endgroup$



          Random seed of models (LogisticRegression, GradientBoostingClassifier) needs to be fixed too, so that their random behavior becomes reproducible. Here is a working example that produces the same result over multiple runs:



          import sklearn
          from sklearn.model_selection import KFold, cross_val_score
          from sklearn.linear_model import LogisticRegression
          from sklearn.neighbors import KNeighborsClassifier
          from sklearn.ensemble import GradientBoostingClassifier, VotingClassifier
          import numpy as np

          #Voting Ensemble of Classification
          #Create Submodels
          num_folds = 10
          seed =7

          # Data
          np.random.seed(seed)
          feature_1 = np.random.normal(0, 2, 10000)
          feature_2 = np.random.normal(5, 6, 10000)
          X_train = np.vstack([feature_1, feature_2]).T
          Y_train = np.random.randint(0, 2, 10000).T

          kfold = KFold(n_splits=num_folds, random_state=seed)
          estimators = []
          model1 =LogisticRegression(random_state=seed)
          estimators.append(('LR',model1))
          model2 = KNeighborsClassifier()
          estimators.append(('KNN',model2))
          model3 = GradientBoostingClassifier(random_state=seed)
          estimators.append(('GBM',model3))
          #Create the ensemble
          ensemble = VotingClassifier(estimators,voting='soft')
          results = cross_val_score(ensemble, X_train, Y_train,cv=kfold)
          print('sklearn version', sklearn.__version__)
          print(results)


          Output:



          sklearn version 0.19.1
          [0.502 0.496 0.483 0.513 0.515 0.508 0.517 0.499 0.515 0.504]






          share|cite|improve this answer














          share|cite|improve this answer



          share|cite|improve this answer








          edited yesterday

























          answered yesterday









          EsmailianEsmailian

          35115




          35115











          • $begingroup$
            Thanks for your quick reply. Not sure I follow completely. random_state=seed fixes my cross validation. I note your line np.random.seed(seed). Intuitively it suggests to me it is ensuring repeatable generation of toy data. I already have a data set. How does that apply to 'fixing seed of models'?
            $endgroup$
            – Windstorm1981
            yesterday











          • $begingroup$
            @Windstorm1981 My bad. Updated.
            $endgroup$
            – Esmailian
            yesterday






          • 1




            $begingroup$
            ha! Clear now. So fixing the cv fixes the data splits. Fixing the models fixes how the models handle the (fixed) data splits?
            $endgroup$
            – Windstorm1981
            yesterday






          • 1




            $begingroup$
            @Windstorm1981 Exactly!
            $endgroup$
            – Esmailian
            yesterday
















          • $begingroup$
            Thanks for your quick reply. Not sure I follow completely. random_state=seed fixes my cross validation. I note your line np.random.seed(seed). Intuitively it suggests to me it is ensuring repeatable generation of toy data. I already have a data set. How does that apply to 'fixing seed of models'?
            $endgroup$
            – Windstorm1981
            yesterday











          • $begingroup$
            @Windstorm1981 My bad. Updated.
            $endgroup$
            – Esmailian
            yesterday






          • 1




            $begingroup$
            ha! Clear now. So fixing the cv fixes the data splits. Fixing the models fixes how the models handle the (fixed) data splits?
            $endgroup$
            – Windstorm1981
            yesterday






          • 1




            $begingroup$
            @Windstorm1981 Exactly!
            $endgroup$
            – Esmailian
            yesterday















          $begingroup$
          Thanks for your quick reply. Not sure I follow completely. random_state=seed fixes my cross validation. I note your line np.random.seed(seed). Intuitively it suggests to me it is ensuring repeatable generation of toy data. I already have a data set. How does that apply to 'fixing seed of models'?
          $endgroup$
          – Windstorm1981
          yesterday





          $begingroup$
          Thanks for your quick reply. Not sure I follow completely. random_state=seed fixes my cross validation. I note your line np.random.seed(seed). Intuitively it suggests to me it is ensuring repeatable generation of toy data. I already have a data set. How does that apply to 'fixing seed of models'?
          $endgroup$
          – Windstorm1981
          yesterday













          $begingroup$
          @Windstorm1981 My bad. Updated.
          $endgroup$
          – Esmailian
          yesterday




          $begingroup$
          @Windstorm1981 My bad. Updated.
          $endgroup$
          – Esmailian
          yesterday




          1




          1




          $begingroup$
          ha! Clear now. So fixing the cv fixes the data splits. Fixing the models fixes how the models handle the (fixed) data splits?
          $endgroup$
          – Windstorm1981
          yesterday




          $begingroup$
          ha! Clear now. So fixing the cv fixes the data splits. Fixing the models fixes how the models handle the (fixed) data splits?
          $endgroup$
          – Windstorm1981
          yesterday




          1




          1




          $begingroup$
          @Windstorm1981 Exactly!
          $endgroup$
          – Esmailian
          yesterday




          $begingroup$
          @Windstorm1981 Exactly!
          $endgroup$
          – Esmailian
          yesterday



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