Consider the following data:

   import pandas as pd
    wine = pd.read_csv(r'wine_data.csv', names = ["Cultivator", "Alchol", "Malic_Acid", "Ash", "Alcalinity_of_Ash", "Magnesium", "Total_phenols", "Falvanoids", "Nonflavanoid_phenols", "Proanthocyanins", "Color_intensity", "Hue", "OD280", "Proline"])
    X = wine.drop('Cultivator',axis=1) #input
    y = wine['Cultivator'] #output

y is what i am trying to predict and X is the input and i will be using some sort of mlp classifier. What I want to do is split this data into test, training and validation and then apply K-folds. I'm struggling to see how you do this..

I know that i can obtain validation, test and training by using the following:

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)
X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.25, random_state=1)

But what i want to do now is apply k folds such that for each fold i have 3 sets: validation, testing , training rather than just 2 sets.

i know i can use the below for Kfolds:

kf = KFold(n_splits = 5, shuffle = True, random_state = 2)
X_np=np.array(X)
y_np=np.array(y)

After converting to a numpy array i can then do this:

for train_index, test_index in kf.split(X_np):
    print("TRAIN:", train_index, "TEST:", test_index)
    X_train, X_test = X_np[train_index], X_np[test_index]
    y_train, y_test = y_np[train_index], y_np[test_index]

But how do i get a 'validation_index'. In general the question is how do i use k-folds when i have 3 sets as opposed to just 2?

Also when do i normalize the data; do i normalize when i've split into X_train, X_test as above... or do i do it before?

Any help appreciated.

Consider the following data:

   import pandas as pd
    wine = pd.read_csv(r'wine_data.csv', names = ["Cultivator", "Alchol", "Malic_Acid", "Ash", "Alcalinity_of_Ash", "Magnesium", "Total_phenols", "Falvanoids", "Nonflavanoid_phenols", "Proanthocyanins", "Color_intensity", "Hue", "OD280", "Proline"])
    X = wine.drop('Cultivator',axis=1) #input
    y = wine['Cultivator'] #output

y is what i am trying to predict and X is the input and i will be using some sort of mlp classifier. What I want to do is split this data into test, training and validation and then apply K-folds. I'm struggling to see how you do this..

I know that I can obtain validation, test and training by using the following:

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)
X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.25, random_state=1)

But what I want to do now is apply k folds such that for each fold I have 3 sets: validation, testing , training rather than just 2 sets.

I know I can use the below for Kfolds:

kf = KFold(n_splits = 5, shuffle = True, random_state = 2)
X_np=np.array(X)
y_np=np.array(y)

After converting to a numpy array I can then do this:

for train_index, test_index in kf.split(X_np):
    print("TRAIN:", train_index, "TEST:", test_index)
    X_train, X_test = X_np[train_index], X_np[test_index]
    y_train, y_test = y_np[train_index], y_np[test_index]

But how do I get a 'validation_index'. In general the question is how do I use k-folds when I have 3 sets as opposed to just 2?

Also when do I normalize the data; do I normalize when i've split into X_train, X_test as above... or do I do it before?

Any help appreciated.