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I am using three different of the shelf classifiers. It's a three class classification task. I want to calculate the optimal weights (c1weight, c2weight, c3weight) for each classifier (real task more classifiers and also weights for each class).

Maybe simple grid search approach or sklearn ensemble classifier could do that.

vc = VotingClassifier(estimators=[('gbc',GradientBoostingClassifier()),
                   ('rf',RandomForestClassifier()),('svc',SVC(probability=True))],
                   voting='soft',n_jobs=-1)

params = {'weights':[[1,2,3],[2,1,3],[3,2,1]]}
grid_Search = GridSearchCV(param_grid = params, estimator=vc)
grid_Search.fit(X_new,y)
print(grid_Search.best_Score_)

I don't understand how to implement this for the following code.

def get_classification(text, c1weight, c2weight, c3weight):

  prediction1 = classifier1.predict(text)
  if prediction1 = 1:
    class1 =+ 1 * c1weight
  elif prediction1 = 2:
    class2 =+ 1  * c1weight
  else:
    class3 =+ 1  * c1weight


  prediction2 = classifier2.predict(text)
  if prediction2 = 1:
    class1 =+ 1 * c2weight
  elif prediction2 = 2:
    class2 =+ 1  * c2weight
  else:
    class3 =+ 1  * c2weight


  prediction3 = classifier3.predict(text)
  if prediction3 = 1:
    class1 =+ 1 * c3weight
  elif prediction3 = 2:
    class2 =+ 1  * c3weight
  else:
    class3 =+ 1  * c3weight


  if class1 > class2 and class1 > class3:
    return ("class1",class1)
  elif class2 > class1 and class2 > class3:
    return ("class2",class2)
  else:
    return("class3",class3)

c1weight = 0.5
c2weight = 0.7
c3weight = 0.4

for i, row in df_raw.iterrows():
    classification = get_classification(df_raw.at[i, 'text'],c1weight, c2weight, c3weight)
    df_raw[i,'classification'] = classification 
  
score = get_accuracy(df_raw['classification'],df_raw['label'])

SOLVED: This sample code helped me to understand it

def your_function(number):
    print(number)

from sklearn.model_selection import ParameterGrid
param_grid = {'param1': [1, 2, 3]}

grid = ParameterGrid(param_grid)

for params in grid:
    your_function(params['param1'])
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  • $\begingroup$ i had too much paramaters for gridsearch. In this case it would take months to calculate all combinations. Finally i used hyperopt for the hyperparameter optimization. There are some nice basic tutorials out there. This one helped me a lot. You can also find a python notebook there. towardsdatascience.com/… $\endgroup$ – bartman99 Oct 19 '19 at 0:20
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GridSearch finds those optimals weights for you.

You can access these weights through the attribute best_params_ of the GridSearch object, which will return all the optimal parameters (including the weights):

optimal_weights = grid_Search.best_params_
| improve this answer | |
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  • $\begingroup$ in that case my function must return the desired accuracy measure? $\endgroup$ – bartman99 Oct 12 '19 at 0:36
  • $\begingroup$ Yes, because because GridSearchCV uses ParameterGrid in the background but will return you the scores (cross validated) and best parameters : scikit-learn.org/stable/modules/generated/… $\endgroup$ – θ Grunberg Oct 12 '19 at 2:56
  • $\begingroup$ Please don't write the answer in your original post, post it below (and I think you want to use GridSearchCV instead of a simple ParameterGrid) $\endgroup$ – θ Grunberg Oct 12 '19 at 2:58

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