CODEWITHSUNDEEP
python
Chaitanya Patil
Chaitanya Patil

Reputation: 333

cross val score returns NAN in sklearn

I am getting nan score from cross_val_score. May I please know how to handle it.. Struggling since a day.

from sklearn.model_selection import KFold, cross_val_score
from sklearn.model_selection import train_test_split
import hyperopt
from hyperopt import tpe
from hyperopt import STATUS_OK
from hyperopt import Trials
from hyperopt import hp
from hyperopt import fmin
from sklearn.linear_model import LogisticRegression
df = pd.read_csv('creditcard.csv')
df.head()

def hyperopt_train_test(params):
    cv = StratifiedShuffleSplit(n_splits = 1, test_size = .25, random_state = 0 ) 
    clf =LogisticRegression(**params)

    return cross_val_score(clf,  df.loc[:,:'Amount'], df['Class'],cv = cv,scoring='f1').mean()

space = {
    'C' : hp.uniform('C', 0.05, 10),
    'penalty': hp.choice('penalty',['l2','l1']),
    'max_iter':hp.choice('max_iter',[200,300,400,500])
}

def f(params,scores=[]):
    acc = hyperopt_train_test(params)
    print(acc, "Accuracy")
    scores.append(acc)
    return {'scores':scores,'loss': 1-acc, 'status': STATUS_OK, 'scores':scores}

trials = Trials()
trials
best = fmin(f, space, algo=tpe.suggest, max_evals=10, trials=trials)
print('best:',best)
hyperopt.space_eval(space,best)

DataSet can be downloaded here

Upvotes: 1

Views: 2694

Answers (2)

Aditya Nathireddy
Aditya Nathireddy

Reputation: 134

cross_val_score method has this weird problem, if there is any issue with the underlying datatypes or parameters and encounters error internally instead of printing the exact error traceback it gives nans in the output.

To understand your specific problem, use this parameter "error_score"

cross_val_score(xgboost.XGBClassifier(), X.values, y.values, cv=kfold, error_score="raise")

This will print the exact error which you need to fix. In my case, I had to convert my string variables to floats.

Upvotes: 3

Keshav Rawat
Keshav Rawat

Reputation: 1

In cross_val_score, try using df.loc[:,:'Amount'].values, df['Class'].values

Upvotes: 0

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