Note
Go to the end to download the full example code
Tasks: retrieving splits¶
Tasks define a target and a train/test split. Normally, they are the input to the function
openml.runs.run_model_on_task which automatically runs the model on all splits of the task.
However, sometimes it is necessary to manually split a dataset to perform experiments outside of
the functions provided by OpenML. One such example is in the benchmark library
HPOBench which extensively uses data from OpenML,
but not OpenML’s functionality to conduct runs.
# License: BSD 3-Clause
import openml
For this tutorial we will use the famous King+Rook versus King+Pawn on A7 dataset, which has the dataset ID 3 (dataset on OpenML), and for which there exist tasks with all important estimation procedures. It is small enough (less than 5000 samples) to efficiently use it in an example.
We will first start with (task 233), which is a task with a holdout estimation procedure.
task_id = 233
task = openml.tasks.get_task(task_id)
/home/runner/work/openml-python/openml-python/openml/tasks/functions.py:372: FutureWarning: Starting from Version 0.15.0 `download_splits` will default to ``False`` instead of ``True`` and be independent from `download_data`. To disable this message until version 0.15 explicitly set `download_splits` to a bool.
warnings.warn(
/home/runner/work/openml-python/openml-python/openml/datasets/functions.py:437: FutureWarning: Starting from Version 0.15 `download_data`, `download_qualities`, and `download_features_meta_data` will all be ``False`` instead of ``True`` by default to enable lazy loading. To disable this message until version 0.15 explicitly set `download_data`, `download_qualities`, and `download_features_meta_data` to a bool while calling `get_dataset`.
warnings.warn(
Now that we have a task object we can obtain the number of repetitions, folds and samples as defined by the task:
n_repeats, n_folds, n_samples = task.get_split_dimensions()
n_repeats: Number of times the model quality estimation is performedn_folds: Number of folds per repeatn_samples: How many data points to use. This is only relevant for learning curve tasks
A list of all available estimation procedures is available here.
Task 233 is a simple task using the holdout estimation procedure and therefore has only a
single repeat, a single fold and a single sample size:
print(
"Task {}: number of repeats: {}, number of folds: {}, number of samples {}.".format(
task_id,
n_repeats,
n_folds,
n_samples,
)
)
Task 233: number of repeats: 1, number of folds: 1, number of samples 1.
We can now retrieve the train/test split for this combination of repeats, folds and number of samples (indexing is zero-based). Usually, one would loop over all repeats, folds and sample sizes, but we can neglect this here as there is only a single repetition.
train_indices, test_indices = task.get_train_test_split_indices(
repeat=0,
fold=0,
sample=0,
)
print(train_indices.shape, train_indices.dtype)
print(test_indices.shape, test_indices.dtype)
(2142,) int32
(1054,) int32
And then split the data based on this:
X, y = task.get_X_and_y(dataset_format="dataframe")
X_train = X.iloc[train_indices]
y_train = y.iloc[train_indices]
X_test = X.iloc[test_indices]
y_test = y.iloc[test_indices]
print(
"X_train.shape: {}, y_train.shape: {}, X_test.shape: {}, y_test.shape: {}".format(
X_train.shape,
y_train.shape,
X_test.shape,
y_test.shape,
)
)
X_train.shape: (2142, 36), y_train.shape: (2142,), X_test.shape: (1054, 36), y_test.shape: (1054,)
Obviously, we can also retrieve cross-validation versions of the dataset used in task 233:
task_id = 3
task = openml.tasks.get_task(task_id)
X, y = task.get_X_and_y(dataset_format="dataframe")
n_repeats, n_folds, n_samples = task.get_split_dimensions()
print(
"Task {}: number of repeats: {}, number of folds: {}, number of samples {}.".format(
task_id,
n_repeats,
n_folds,
n_samples,
)
)
/home/runner/work/openml-python/openml-python/openml/tasks/functions.py:372: FutureWarning: Starting from Version 0.15.0 `download_splits` will default to ``False`` instead of ``True`` and be independent from `download_data`. To disable this message until version 0.15 explicitly set `download_splits` to a bool.
warnings.warn(
/home/runner/work/openml-python/openml-python/openml/datasets/functions.py:437: FutureWarning: Starting from Version 0.15 `download_data`, `download_qualities`, and `download_features_meta_data` will all be ``False`` instead of ``True`` by default to enable lazy loading. To disable this message until version 0.15 explicitly set `download_data`, `download_qualities`, and `download_features_meta_data` to a bool while calling `get_dataset`.
warnings.warn(
Task 3: number of repeats: 1, number of folds: 10, number of samples 1.
And then perform the aforementioned iteration over all splits:
for repeat_idx in range(n_repeats):
for fold_idx in range(n_folds):
for sample_idx in range(n_samples):
train_indices, test_indices = task.get_train_test_split_indices(
repeat=repeat_idx,
fold=fold_idx,
sample=sample_idx,
)
X_train = X.iloc[train_indices]
y_train = y.iloc[train_indices]
X_test = X.iloc[test_indices]
y_test = y.iloc[test_indices]
print(
"Repeat #{}, fold #{}, samples {}: X_train.shape: {}, "
"y_train.shape {}, X_test.shape {}, y_test.shape {}".format(
repeat_idx,
fold_idx,
sample_idx,
X_train.shape,
y_train.shape,
X_test.shape,
y_test.shape,
)
)
Repeat #0, fold #0, samples 0: X_train.shape: (2876, 36), y_train.shape (2876,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #1, samples 0: X_train.shape: (2876, 36), y_train.shape (2876,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #2, samples 0: X_train.shape: (2876, 36), y_train.shape (2876,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #3, samples 0: X_train.shape: (2876, 36), y_train.shape (2876,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #4, samples 0: X_train.shape: (2876, 36), y_train.shape (2876,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #5, samples 0: X_train.shape: (2876, 36), y_train.shape (2876,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #6, samples 0: X_train.shape: (2877, 36), y_train.shape (2877,), X_test.shape (319, 36), y_test.shape (319,)
Repeat #0, fold #7, samples 0: X_train.shape: (2877, 36), y_train.shape (2877,), X_test.shape (319, 36), y_test.shape (319,)
Repeat #0, fold #8, samples 0: X_train.shape: (2877, 36), y_train.shape (2877,), X_test.shape (319, 36), y_test.shape (319,)
Repeat #0, fold #9, samples 0: X_train.shape: (2877, 36), y_train.shape (2877,), X_test.shape (319, 36), y_test.shape (319,)
And also versions with multiple repeats:
task_id = 1767
task = openml.tasks.get_task(task_id)
X, y = task.get_X_and_y(dataset_format="dataframe")
n_repeats, n_folds, n_samples = task.get_split_dimensions()
print(
"Task {}: number of repeats: {}, number of folds: {}, number of samples {}.".format(
task_id,
n_repeats,
n_folds,
n_samples,
)
)
/home/runner/work/openml-python/openml-python/openml/tasks/functions.py:372: FutureWarning: Starting from Version 0.15.0 `download_splits` will default to ``False`` instead of ``True`` and be independent from `download_data`. To disable this message until version 0.15 explicitly set `download_splits` to a bool.
warnings.warn(
/home/runner/work/openml-python/openml-python/openml/datasets/functions.py:437: FutureWarning: Starting from Version 0.15 `download_data`, `download_qualities`, and `download_features_meta_data` will all be ``False`` instead of ``True`` by default to enable lazy loading. To disable this message until version 0.15 explicitly set `download_data`, `download_qualities`, and `download_features_meta_data` to a bool while calling `get_dataset`.
warnings.warn(
Task 1767: number of repeats: 5, number of folds: 2, number of samples 1.
And then again perform the aforementioned iteration over all splits:
for repeat_idx in range(n_repeats):
for fold_idx in range(n_folds):
for sample_idx in range(n_samples):
train_indices, test_indices = task.get_train_test_split_indices(
repeat=repeat_idx,
fold=fold_idx,
sample=sample_idx,
)
X_train = X.iloc[train_indices]
y_train = y.iloc[train_indices]
X_test = X.iloc[test_indices]
y_test = y.iloc[test_indices]
print(
"Repeat #{}, fold #{}, samples {}: X_train.shape: {}, "
"y_train.shape {}, X_test.shape {}, y_test.shape {}".format(
repeat_idx,
fold_idx,
sample_idx,
X_train.shape,
y_train.shape,
X_test.shape,
y_test.shape,
)
)
Repeat #0, fold #0, samples 0: X_train.shape: (1598, 36), y_train.shape (1598,), X_test.shape (1598, 36), y_test.shape (1598,)
Repeat #0, fold #1, samples 0: X_train.shape: (1598, 36), y_train.shape (1598,), X_test.shape (1598, 36), y_test.shape (1598,)
Repeat #1, fold #0, samples 0: X_train.shape: (1598, 36), y_train.shape (1598,), X_test.shape (1598, 36), y_test.shape (1598,)
Repeat #1, fold #1, samples 0: X_train.shape: (1598, 36), y_train.shape (1598,), X_test.shape (1598, 36), y_test.shape (1598,)
Repeat #2, fold #0, samples 0: X_train.shape: (1598, 36), y_train.shape (1598,), X_test.shape (1598, 36), y_test.shape (1598,)
Repeat #2, fold #1, samples 0: X_train.shape: (1598, 36), y_train.shape (1598,), X_test.shape (1598, 36), y_test.shape (1598,)
Repeat #3, fold #0, samples 0: X_train.shape: (1598, 36), y_train.shape (1598,), X_test.shape (1598, 36), y_test.shape (1598,)
Repeat #3, fold #1, samples 0: X_train.shape: (1598, 36), y_train.shape (1598,), X_test.shape (1598, 36), y_test.shape (1598,)
Repeat #4, fold #0, samples 0: X_train.shape: (1598, 36), y_train.shape (1598,), X_test.shape (1598, 36), y_test.shape (1598,)
Repeat #4, fold #1, samples 0: X_train.shape: (1598, 36), y_train.shape (1598,), X_test.shape (1598, 36), y_test.shape (1598,)
And finally a task based on learning curves:
task_id = 1702
task = openml.tasks.get_task(task_id)
X, y = task.get_X_and_y(dataset_format="dataframe")
n_repeats, n_folds, n_samples = task.get_split_dimensions()
print(
"Task {}: number of repeats: {}, number of folds: {}, number of samples {}.".format(
task_id,
n_repeats,
n_folds,
n_samples,
)
)
/home/runner/work/openml-python/openml-python/openml/tasks/functions.py:372: FutureWarning: Starting from Version 0.15.0 `download_splits` will default to ``False`` instead of ``True`` and be independent from `download_data`. To disable this message until version 0.15 explicitly set `download_splits` to a bool.
warnings.warn(
/home/runner/work/openml-python/openml-python/openml/datasets/functions.py:437: FutureWarning: Starting from Version 0.15 `download_data`, `download_qualities`, and `download_features_meta_data` will all be ``False`` instead of ``True`` by default to enable lazy loading. To disable this message until version 0.15 explicitly set `download_data`, `download_qualities`, and `download_features_meta_data` to a bool while calling `get_dataset`.
warnings.warn(
Task 1702: number of repeats: 1, number of folds: 10, number of samples 12.
And then again perform the aforementioned iteration over all splits:
for repeat_idx in range(n_repeats):
for fold_idx in range(n_folds):
for sample_idx in range(n_samples):
train_indices, test_indices = task.get_train_test_split_indices(
repeat=repeat_idx,
fold=fold_idx,
sample=sample_idx,
)
X_train = X.iloc[train_indices]
y_train = y.iloc[train_indices]
X_test = X.iloc[test_indices]
y_test = y.iloc[test_indices]
print(
"Repeat #{}, fold #{}, samples {}: X_train.shape: {}, "
"y_train.shape {}, X_test.shape {}, y_test.shape {}".format(
repeat_idx,
fold_idx,
sample_idx,
X_train.shape,
y_train.shape,
X_test.shape,
y_test.shape,
)
)
Repeat #0, fold #0, samples 0: X_train.shape: (64, 36), y_train.shape (64,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #0, samples 1: X_train.shape: (91, 36), y_train.shape (91,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #0, samples 2: X_train.shape: (128, 36), y_train.shape (128,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #0, samples 3: X_train.shape: (181, 36), y_train.shape (181,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #0, samples 4: X_train.shape: (256, 36), y_train.shape (256,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #0, samples 5: X_train.shape: (362, 36), y_train.shape (362,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #0, samples 6: X_train.shape: (512, 36), y_train.shape (512,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #0, samples 7: X_train.shape: (724, 36), y_train.shape (724,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #0, samples 8: X_train.shape: (1024, 36), y_train.shape (1024,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #0, samples 9: X_train.shape: (1448, 36), y_train.shape (1448,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #0, samples 10: X_train.shape: (2048, 36), y_train.shape (2048,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #0, samples 11: X_train.shape: (2876, 36), y_train.shape (2876,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #1, samples 0: X_train.shape: (64, 36), y_train.shape (64,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #1, samples 1: X_train.shape: (91, 36), y_train.shape (91,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #1, samples 2: X_train.shape: (128, 36), y_train.shape (128,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #1, samples 3: X_train.shape: (181, 36), y_train.shape (181,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #1, samples 4: X_train.shape: (256, 36), y_train.shape (256,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #1, samples 5: X_train.shape: (362, 36), y_train.shape (362,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #1, samples 6: X_train.shape: (512, 36), y_train.shape (512,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #1, samples 7: X_train.shape: (724, 36), y_train.shape (724,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #1, samples 8: X_train.shape: (1024, 36), y_train.shape (1024,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #1, samples 9: X_train.shape: (1448, 36), y_train.shape (1448,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #1, samples 10: X_train.shape: (2048, 36), y_train.shape (2048,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #1, samples 11: X_train.shape: (2876, 36), y_train.shape (2876,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #2, samples 0: X_train.shape: (64, 36), y_train.shape (64,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #2, samples 1: X_train.shape: (91, 36), y_train.shape (91,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #2, samples 2: X_train.shape: (128, 36), y_train.shape (128,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #2, samples 3: X_train.shape: (181, 36), y_train.shape (181,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #2, samples 4: X_train.shape: (256, 36), y_train.shape (256,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #2, samples 5: X_train.shape: (362, 36), y_train.shape (362,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #2, samples 6: X_train.shape: (512, 36), y_train.shape (512,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #2, samples 7: X_train.shape: (724, 36), y_train.shape (724,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #2, samples 8: X_train.shape: (1024, 36), y_train.shape (1024,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #2, samples 9: X_train.shape: (1448, 36), y_train.shape (1448,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #2, samples 10: X_train.shape: (2048, 36), y_train.shape (2048,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #2, samples 11: X_train.shape: (2876, 36), y_train.shape (2876,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #3, samples 0: X_train.shape: (64, 36), y_train.shape (64,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #3, samples 1: X_train.shape: (91, 36), y_train.shape (91,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #3, samples 2: X_train.shape: (128, 36), y_train.shape (128,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #3, samples 3: X_train.shape: (181, 36), y_train.shape (181,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #3, samples 4: X_train.shape: (256, 36), y_train.shape (256,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #3, samples 5: X_train.shape: (362, 36), y_train.shape (362,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #3, samples 6: X_train.shape: (512, 36), y_train.shape (512,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #3, samples 7: X_train.shape: (724, 36), y_train.shape (724,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #3, samples 8: X_train.shape: (1024, 36), y_train.shape (1024,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #3, samples 9: X_train.shape: (1448, 36), y_train.shape (1448,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #3, samples 10: X_train.shape: (2048, 36), y_train.shape (2048,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #3, samples 11: X_train.shape: (2876, 36), y_train.shape (2876,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #4, samples 0: X_train.shape: (64, 36), y_train.shape (64,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #4, samples 1: X_train.shape: (91, 36), y_train.shape (91,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #4, samples 2: X_train.shape: (128, 36), y_train.shape (128,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #4, samples 3: X_train.shape: (181, 36), y_train.shape (181,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #4, samples 4: X_train.shape: (256, 36), y_train.shape (256,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #4, samples 5: X_train.shape: (362, 36), y_train.shape (362,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #4, samples 6: X_train.shape: (512, 36), y_train.shape (512,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #4, samples 7: X_train.shape: (724, 36), y_train.shape (724,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #4, samples 8: X_train.shape: (1024, 36), y_train.shape (1024,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #4, samples 9: X_train.shape: (1448, 36), y_train.shape (1448,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #4, samples 10: X_train.shape: (2048, 36), y_train.shape (2048,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #4, samples 11: X_train.shape: (2876, 36), y_train.shape (2876,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #5, samples 0: X_train.shape: (64, 36), y_train.shape (64,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #5, samples 1: X_train.shape: (91, 36), y_train.shape (91,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #5, samples 2: X_train.shape: (128, 36), y_train.shape (128,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #5, samples 3: X_train.shape: (181, 36), y_train.shape (181,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #5, samples 4: X_train.shape: (256, 36), y_train.shape (256,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #5, samples 5: X_train.shape: (362, 36), y_train.shape (362,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #5, samples 6: X_train.shape: (512, 36), y_train.shape (512,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #5, samples 7: X_train.shape: (724, 36), y_train.shape (724,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #5, samples 8: X_train.shape: (1024, 36), y_train.shape (1024,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #5, samples 9: X_train.shape: (1448, 36), y_train.shape (1448,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #5, samples 10: X_train.shape: (2048, 36), y_train.shape (2048,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #5, samples 11: X_train.shape: (2876, 36), y_train.shape (2876,), X_test.shape (320, 36), y_test.shape (320,)
Repeat #0, fold #6, samples 0: X_train.shape: (64, 36), y_train.shape (64,), X_test.shape (319, 36), y_test.shape (319,)
Repeat #0, fold #6, samples 1: X_train.shape: (91, 36), y_train.shape (91,), X_test.shape (319, 36), y_test.shape (319,)
Repeat #0, fold #6, samples 2: X_train.shape: (128, 36), y_train.shape (128,), X_test.shape (319, 36), y_test.shape (319,)
Repeat #0, fold #6, samples 3: X_train.shape: (181, 36), y_train.shape (181,), X_test.shape (319, 36), y_test.shape (319,)
Repeat #0, fold #6, samples 4: X_train.shape: (256, 36), y_train.shape (256,), X_test.shape (319, 36), y_test.shape (319,)
Repeat #0, fold #6, samples 5: X_train.shape: (362, 36), y_train.shape (362,), X_test.shape (319, 36), y_test.shape (319,)
Repeat #0, fold #6, samples 6: X_train.shape: (512, 36), y_train.shape (512,), X_test.shape (319, 36), y_test.shape (319,)
Repeat #0, fold #6, samples 7: X_train.shape: (724, 36), y_train.shape (724,), X_test.shape (319, 36), y_test.shape (319,)
Repeat #0, fold #6, samples 8: X_train.shape: (1024, 36), y_train.shape (1024,), X_test.shape (319, 36), y_test.shape (319,)
Repeat #0, fold #6, samples 9: X_train.shape: (1448, 36), y_train.shape (1448,), X_test.shape (319, 36), y_test.shape (319,)
Repeat #0, fold #6, samples 10: X_train.shape: (2048, 36), y_train.shape (2048,), X_test.shape (319, 36), y_test.shape (319,)
Repeat #0, fold #6, samples 11: X_train.shape: (2877, 36), y_train.shape (2877,), X_test.shape (319, 36), y_test.shape (319,)
Repeat #0, fold #7, samples 0: X_train.shape: (64, 36), y_train.shape (64,), X_test.shape (319, 36), y_test.shape (319,)
Repeat #0, fold #7, samples 1: X_train.shape: (91, 36), y_train.shape (91,), X_test.shape (319, 36), y_test.shape (319,)
Repeat #0, fold #7, samples 2: X_train.shape: (128, 36), y_train.shape (128,), X_test.shape (319, 36), y_test.shape (319,)
Repeat #0, fold #7, samples 3: X_train.shape: (181, 36), y_train.shape (181,), X_test.shape (319, 36), y_test.shape (319,)
Repeat #0, fold #7, samples 4: X_train.shape: (256, 36), y_train.shape (256,), X_test.shape (319, 36), y_test.shape (319,)
Repeat #0, fold #7, samples 5: X_train.shape: (362, 36), y_train.shape (362,), X_test.shape (319, 36), y_test.shape (319,)
Repeat #0, fold #7, samples 6: X_train.shape: (512, 36), y_train.shape (512,), X_test.shape (319, 36), y_test.shape (319,)
Repeat #0, fold #7, samples 7: X_train.shape: (724, 36), y_train.shape (724,), X_test.shape (319, 36), y_test.shape (319,)
Repeat #0, fold #7, samples 8: X_train.shape: (1024, 36), y_train.shape (1024,), X_test.shape (319, 36), y_test.shape (319,)
Repeat #0, fold #7, samples 9: X_train.shape: (1448, 36), y_train.shape (1448,), X_test.shape (319, 36), y_test.shape (319,)
Repeat #0, fold #7, samples 10: X_train.shape: (2048, 36), y_train.shape (2048,), X_test.shape (319, 36), y_test.shape (319,)
Repeat #0, fold #7, samples 11: X_train.shape: (2877, 36), y_train.shape (2877,), X_test.shape (319, 36), y_test.shape (319,)
Repeat #0, fold #8, samples 0: X_train.shape: (64, 36), y_train.shape (64,), X_test.shape (319, 36), y_test.shape (319,)
Repeat #0, fold #8, samples 1: X_train.shape: (91, 36), y_train.shape (91,), X_test.shape (319, 36), y_test.shape (319,)
Repeat #0, fold #8, samples 2: X_train.shape: (128, 36), y_train.shape (128,), X_test.shape (319, 36), y_test.shape (319,)
Repeat #0, fold #8, samples 3: X_train.shape: (181, 36), y_train.shape (181,), X_test.shape (319, 36), y_test.shape (319,)
Repeat #0, fold #8, samples 4: X_train.shape: (256, 36), y_train.shape (256,), X_test.shape (319, 36), y_test.shape (319,)
Repeat #0, fold #8, samples 5: X_train.shape: (362, 36), y_train.shape (362,), X_test.shape (319, 36), y_test.shape (319,)
Repeat #0, fold #8, samples 6: X_train.shape: (512, 36), y_train.shape (512,), X_test.shape (319, 36), y_test.shape (319,)
Repeat #0, fold #8, samples 7: X_train.shape: (724, 36), y_train.shape (724,), X_test.shape (319, 36), y_test.shape (319,)
Repeat #0, fold #8, samples 8: X_train.shape: (1024, 36), y_train.shape (1024,), X_test.shape (319, 36), y_test.shape (319,)
Repeat #0, fold #8, samples 9: X_train.shape: (1448, 36), y_train.shape (1448,), X_test.shape (319, 36), y_test.shape (319,)
Repeat #0, fold #8, samples 10: X_train.shape: (2048, 36), y_train.shape (2048,), X_test.shape (319, 36), y_test.shape (319,)
Repeat #0, fold #8, samples 11: X_train.shape: (2877, 36), y_train.shape (2877,), X_test.shape (319, 36), y_test.shape (319,)
Repeat #0, fold #9, samples 0: X_train.shape: (64, 36), y_train.shape (64,), X_test.shape (319, 36), y_test.shape (319,)
Repeat #0, fold #9, samples 1: X_train.shape: (91, 36), y_train.shape (91,), X_test.shape (319, 36), y_test.shape (319,)
Repeat #0, fold #9, samples 2: X_train.shape: (128, 36), y_train.shape (128,), X_test.shape (319, 36), y_test.shape (319,)
Repeat #0, fold #9, samples 3: X_train.shape: (181, 36), y_train.shape (181,), X_test.shape (319, 36), y_test.shape (319,)
Repeat #0, fold #9, samples 4: X_train.shape: (256, 36), y_train.shape (256,), X_test.shape (319, 36), y_test.shape (319,)
Repeat #0, fold #9, samples 5: X_train.shape: (362, 36), y_train.shape (362,), X_test.shape (319, 36), y_test.shape (319,)
Repeat #0, fold #9, samples 6: X_train.shape: (512, 36), y_train.shape (512,), X_test.shape (319, 36), y_test.shape (319,)
Repeat #0, fold #9, samples 7: X_train.shape: (724, 36), y_train.shape (724,), X_test.shape (319, 36), y_test.shape (319,)
Repeat #0, fold #9, samples 8: X_train.shape: (1024, 36), y_train.shape (1024,), X_test.shape (319, 36), y_test.shape (319,)
Repeat #0, fold #9, samples 9: X_train.shape: (1448, 36), y_train.shape (1448,), X_test.shape (319, 36), y_test.shape (319,)
Repeat #0, fold #9, samples 10: X_train.shape: (2048, 36), y_train.shape (2048,), X_test.shape (319, 36), y_test.shape (319,)
Repeat #0, fold #9, samples 11: X_train.shape: (2877, 36), y_train.shape (2877,), X_test.shape (319, 36), y_test.shape (319,)
Total running time of the script: ( 0 minutes 4.882 seconds)