Kfold cross validation in python - python-3.x

Question

What im trying to do;
Get the K-fold cross validated scores of an SVM. The data has all numerical independent variables, and a categorical dependent variable. Im using python3, sklearn and feature engine.
My understanding on the matter;
The independent variable has NA values, all of them are below 5% of the total data points, so i imputed them using the median values from the train set, as the variables are not normally distributed. I also scaled the values of the train and test set using the values from the test set. My train-test split is 80-20.
I understand that it is a good practice to scaled and impute data using only the train set. As this helps avoid over-fit and data leak.
When it comes to Kfold cross validation, the train and test set change.
Question;
Is there a way to ensure that i can re-impute and re-scale the train and test set based on the train set of each fold ?
Any help is appreciated, thank you !
Train-test split using a random seed. Same random seed is used in the K-Fold cross validation.
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 3)
NA value imputation;
from feature_engine import missing_data_imputers as mdi
imputer = mdi.MeanMedianImputer(imputation_method = 'median')
imputer.fit(X_train)
X_train = imputer.transform(X_train)
Variable transformation;
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
scaler.fit(X_train)
X_train_trans = scaler.transform(X_train)
X_test_trans = scaler.transform(X_test)
Below is the SVM;
def svm1(gam, C):
clf1 = svm.SVC(gamma=gam, C=C)
clf1.fit(X_train_trans, y_train)
print('The Trainset Score is {}.'.format(clf1.score(X_train_trans , y_train)))
print('The Testset Score is {}.'.format(clf1.score(X_test_trans , y_test)))
print('')
y_pred1 = clf1.predict(X_test_trans)
print('The confusin matrix is; \n{}'.format(metrics.confusion_matrix(y_test , y_pred1)))
interactive(svm1, gam = G1, C = cc1)
I then merge the train and test set, to get back a transformed dataset;
frames3 = [X_test_trans, X_train_trans ]
X_Final = pd.concat(frames3)
Now i fit the X_Final, which is concated train and test set, to get K-fold cross validated score.
kfold = KFold(n_splits = 10, random_state = 3)
model = svm.SVC(gamma=0.23, C=3.20)
results = cross_val_score(model, PCA_X_Final,y_Final, cv = kfold)
print(results)
print('Accuracy = {}%, Standard Deviation = {}%'.format(round(results.mean(), 4), round(results.std(), 2)))
I would like to know how i can re-scale and re-impute each fold, so that the variables are re-scaled, and NA values re-imputed in each fold using the train set to avoid overfit / dataleak

Answer 1

To impute and scale the data with the parameters derived from each fold in the CV, you first need to establish the engineering steps in a pipeline, and then do CV over the entire pipeline. For example something like this:
set up engineering pipeline:
my_pipe = Pipeline([
# missing data imputation
('imputer_num',
mdi.MeanMedianImputer(imputation_method='mean', variables=['varA', 'varB'])),
# scaler
('scaler', StandardScaler()),
# Gradient Boosted machine (or your SVM instead)
('gbm', GradientBoostingClassifier(random_state=0))
])
then the CV:
param_grid = {
# try different gradient boosted tree model parameters
'gbm__max_depth': [None, 1, 3],
}
# now we set up the grid search with cross-validation
grid_search = GridSearchCV(my_pipe, param_grid,
cv=5, n_jobs=-1, scoring='roc_auc')
More details in this notebook.