I had obtained the index of training set and testing set with code below.
df = pandas.read_pickle(filepath + filename)
kf = KFold(n_splits = n_splits, shuffle = shuffle, random_state =
randomState)
result = next(kf.split(df), None)
#train can be accessed with result[0]
#test can be accessed with result[1]
I wonder if there is any faster way to separate them into 2 dataframe respectively with the row indexes I retrieved.
You need DataFrame.iloc for select rows by positions:
Sample:
np.random.seed(100)
df = pd.DataFrame(np.random.random((10,5)), columns=list('ABCDE'))
df.index = df.index * 10
print (df)
A B C D E
0 0.543405 0.278369 0.424518 0.844776 0.004719
10 0.121569 0.670749 0.825853 0.136707 0.575093
20 0.891322 0.209202 0.185328 0.108377 0.219697
30 0.978624 0.811683 0.171941 0.816225 0.274074
40 0.431704 0.940030 0.817649 0.336112 0.175410
50 0.372832 0.005689 0.252426 0.795663 0.015255
60 0.598843 0.603805 0.105148 0.381943 0.036476
70 0.890412 0.980921 0.059942 0.890546 0.576901
80 0.742480 0.630184 0.581842 0.020439 0.210027
90 0.544685 0.769115 0.250695 0.285896 0.852395
from sklearn.model_selection import KFold
#added some parameters
kf = KFold(n_splits = 5, shuffle = True, random_state = 2)
result = next(kf.split(df), None)
print (result)
(array([0, 2, 3, 5, 6, 7, 8, 9]), array([1, 4]))
train = df.iloc[result[0]]
test = df.iloc[result[1]]
print (train)
A B C D E
0 0.543405 0.278369 0.424518 0.844776 0.004719
20 0.891322 0.209202 0.185328 0.108377 0.219697
30 0.978624 0.811683 0.171941 0.816225 0.274074
50 0.372832 0.005689 0.252426 0.795663 0.015255
60 0.598843 0.603805 0.105148 0.381943 0.036476
70 0.890412 0.980921 0.059942 0.890546 0.576901
80 0.742480 0.630184 0.581842 0.020439 0.210027
90 0.544685 0.769115 0.250695 0.285896 0.852395
print (test)
A B C D E
10 0.121569 0.670749 0.825853 0.136707 0.575093
40 0.431704 0.940030 0.817649 0.336112 0.175410