Using explicit (predefined) validation set for grid search with sklearn

Question

I have a dataset, which has previously been split into 3 sets: train, validation and test. These sets have to be used as given in order to compare the performance across dif

Answer 1

# Import Libraries
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.model_selection import PredefinedSplit

# Split Data to Train and Validation
X_train, X_val, y_train, y_val = train_test_split(X, y, train_size = 0.8, stratify = y,random_state = 2020)

# Create a list where train data indices are -1 and validation data indices are 0
split_index = [-1 if x in X_train.index else 0 for x in X.index]

# Use the list to create PredefinedSplit
pds = PredefinedSplit(test_fold = split_index)

# Use PredefinedSplit in GridSearchCV
clf = GridSearchCV(estimator = estimator,
                   cv=pds,
                   param_grid=param_grid)

# Fit with all data
clf.fit(X, y)