# Otherwise, use default number
try:
train_test_split_folds = int(options["-t"])
except:
train_test_split_folds = default_train_test_split_folds
# We are using Stratified Shuffle Split, the same cross-validation method as
# used in the tester
shuffler = StratifiedShuffleSplit(labels, train_test_split_folds,
random_state = 42)
# Run through all the train-test splits, train, test and measure key metrics
if gridsearchcv == "out":
if len(search_grid)>0:
# Specify CV option
wrapped_algorithm.cv = shuffler
# Fit classifier
clf.fit(features, labels)
# Get the best parameters of cross validation
params = wrapped_algorithm.best_params_
# Convert them to a JSON object with every parameter in a
# one-element array
grid_json = json.dumps(params, ensure_ascii=True)
# Write them to the csv file
with open(gridsearchcv_file_name, 'ab') as csvfile:
writer = csv.writer(csvfile, delimiter=",")
writer.writerow([feature_scaling, feature_selection,
clf_id, grid_json])
# Print clf pipeline on the screen so we see what the script is using.
pprint.pprint(clf)
# Print parameters we found
