Note
Go to the end to download the full example code
Tasks¶
A tutorial on how to list and download tasks.
# License: BSD 3-Clause
import openml
from openml.tasks import TaskType
import pandas as pd
Tasks are identified by IDs and can be accessed in two different ways:
In a list providing basic information on all tasks available on OpenML. This function will not download the actual tasks, but will instead download meta data that can be used to filter the tasks and retrieve a set of IDs. We can filter this list, for example, we can only list tasks having a special tag or only tasks for a specific target such as supervised classification.
A single task by its ID. It contains all meta information, the target metric, the splits and an iterator which can be used to access the splits in a useful manner.
Listing tasks¶
We will start by simply listing only supervised classification tasks. openml.tasks.list_tasks() returns a dictionary of dictionaries by default, but we request a pandas dataframe instead to have better visualization capabilities and easier access:
tasks = openml.tasks.list_tasks(
task_type=TaskType.SUPERVISED_CLASSIFICATION, output_format="dataframe"
)
print(tasks.columns)
print(f"First 5 of {len(tasks)} tasks:")
print(tasks.head())
Index(['tid', 'ttid', 'did', 'name', 'task_type', 'status',
'estimation_procedure', 'evaluation_measures', 'source_data',
'target_feature', 'MajorityClassSize', 'MaxNominalAttDistinctValues',
'MinorityClassSize', 'NumberOfClasses', 'NumberOfFeatures',
'NumberOfInstances', 'NumberOfInstancesWithMissingValues',
'NumberOfMissingValues', 'NumberOfNumericFeatures',
'NumberOfSymbolicFeatures', 'cost_matrix'],
dtype='object')
First 5 of 4381 tasks:
tid ... cost_matrix
2 2 ... NaN
3 3 ... NaN
4 4 ... NaN
5 5 ... NaN
6 6 ... NaN
[5 rows x 21 columns]
We can filter the list of tasks to only contain datasets with more than 500 samples, but less than 1000 samples:
filtered_tasks = tasks.query("NumberOfInstances > 500 and NumberOfInstances < 1000")
print(list(filtered_tasks.index))
[2, 11, 15, 29, 37, 41, 49, 53, 232, 241, 245, 259, 267, 271, 279, 283, 1766, 1775, 1779, 1793, 1801, 1805, 1813, 1817, 1882, 1891, 1895, 1909, 1917, 1921, 1929, 1933, 1945, 1952, 1956, 1967, 1973, 1977, 1983, 1987, 2079, 2125, 2944, 3022, 3034, 3047, 3049, 3053, 3054, 3055, 3484, 3486, 3492, 3493, 3494, 3512, 3518, 3520, 3521, 3529, 3535, 3549, 3560, 3561, 3583, 3623, 3636, 3640, 3660, 3690, 3691, 3692, 3704, 3706, 3718, 3794, 3803, 3810, 3812, 3813, 3814, 3817, 3833, 3852, 3853, 3857, 3860, 3867, 3877, 3879, 3886, 3913, 3971, 3979, 3992, 3999, 4189, 4191, 4197, 4198, 4199, 4217, 4223, 4225, 4226, 4234, 4240, 4254, 4265, 4266, 4288, 4328, 4341, 4345, 4365, 4395, 4396, 4397, 4409, 4411, 4423, 4499, 4508, 4515, 4517, 4518, 4519, 4522, 4538, 4557, 4558, 4562, 4565, 4572, 4582, 4584, 4591, 4618, 4676, 4684, 4697, 4704, 7286, 7307, 7543, 7548, 7558, 9904, 9905, 9946, 9950, 9971, 9980, 9989, 9990, 10097, 10098, 10101, 12738, 12739, 14954, 14968, 145682, 145800, 145804, 145805, 145825, 145836, 145839, 145848, 145878, 145882, 145914, 145917, 145952, 145959, 145970, 145976, 145978, 146062, 146064, 146065, 146066, 146069, 146092, 146156, 146216, 146219, 146231, 146574, 146576, 146577, 146578, 146583, 146587, 146588, 146593, 146596, 146597, 146600, 146818, 146819, 166859, 166875, 166882, 166884, 166893, 166905, 166906, 166907, 166913, 166915, 166919, 166947, 166953, 166956, 166957, 166958, 166959, 166960, 166967, 166976, 166977, 166978, 166980, 166983, 166988, 166989, 166992, 167016, 167020, 167031, 167037, 167062, 167067, 167068, 167095, 167096, 167100, 167104, 167106, 167151, 167154, 167160, 167163, 167167, 167168, 167171, 167173, 167174, 167175, 167180, 167184, 167187, 167194, 167198, 168300, 168783, 168819, 168820, 168821, 168822, 168823, 168824, 168825, 168907, 189786, 189859, 189899, 189900, 189932, 189937, 189941, 190136, 190138, 190139, 190140, 190143, 190146, 233090, 233094, 233109, 233115, 233171, 233206, 359953, 359954, 359955, 360857, 360865, 360868, 360869, 360951, 360953, 360964, 361107, 361109, 361146, 361147, 361148, 361149, 361150, 361151, 361152, 361153, 361154, 361155, 361156, 361157, 361158, 361159, 361160, 361161, 361163, 361164, 361165, 361166, 361167, 361168, 361169, 361170, 361171, 361172, 361173, 361174, 361175, 361176, 361183, 361185, 361190, 361305, 361338, 361340, 361412, 361415, 361424, 361426, 361432, 361433, 361434, 361436, 361437, 361440, 361442, 361443, 361457, 361463, 361483, 361486, 361492, 361495, 361498, 361499, 361502, 361504, 361506, 361507, 361512, 361517, 361522, 361529, 361531, 361540, 361557, 361572, 361624, 361650, 361660, 361983, 361984, 361985, 361986, 361987, 361988, 361989]
# Number of tasks
print(len(filtered_tasks))
375
Then, we can further restrict the tasks to all have the same resampling strategy:
filtered_tasks = filtered_tasks.query('estimation_procedure == "10-fold Crossvalidation"')
print(list(filtered_tasks.index))
[2, 11, 15, 29, 37, 41, 49, 53, 2079, 3022, 3484, 3486, 3492, 3493, 3494, 3512, 3518, 3520, 3521, 3529, 3535, 3549, 3560, 3561, 3583, 3623, 3636, 3640, 3660, 3690, 3691, 3692, 3704, 3706, 3718, 3794, 3803, 3810, 3812, 3813, 3814, 3817, 3833, 3852, 3853, 3857, 3860, 3867, 3877, 3879, 3886, 3913, 3971, 3979, 3992, 3999, 7286, 7307, 7548, 7558, 9904, 9905, 9946, 9950, 9971, 9980, 9989, 9990, 10097, 10098, 10101, 14954, 14968, 145682, 145800, 145804, 145805, 145825, 145836, 145839, 145848, 145878, 145882, 145914, 145917, 145952, 145959, 145970, 145976, 145978, 146062, 146064, 146065, 146066, 146069, 146092, 146156, 146216, 146219, 146231, 146818, 146819, 168300, 168907, 189932, 189937, 189941, 190136, 190138, 190139, 190140, 190143, 190146, 233171, 359953, 359954, 359955, 360857, 360865, 360868, 360869, 360951, 360953, 360964, 361107, 361109, 361146, 361147, 361148, 361149, 361150, 361151, 361152, 361153, 361154, 361155, 361156, 361157, 361158, 361159, 361160, 361161, 361163, 361164, 361165, 361166, 361167, 361168, 361169, 361170, 361171, 361172, 361173, 361174, 361175, 361176, 361183, 361185, 361190, 361305, 361338, 361340, 361983, 361984, 361985, 361987, 361988]
# Number of tasks
print(len(filtered_tasks))
167
Resampling strategies can be found on the OpenML Website.
Similar to listing tasks by task type, we can list tasks by tags:
tasks = openml.tasks.list_tasks(tag="OpenML100", output_format="dataframe")
print(f"First 5 of {len(tasks)} tasks:")
print(tasks.head())
First 5 of 91 tasks:
tid ... NumberOfSymbolicFeatures
3 3 ... 37
6 6 ... 1
11 11 ... 1
12 12 ... 1
14 14 ... 1
[5 rows x 19 columns]
Furthermore, we can list tasks based on the dataset id:
tasks = openml.tasks.list_tasks(data_id=1471, output_format="dataframe")
print(f"First 5 of {len(tasks)} tasks:")
print(tasks.head())
First 5 of 25 tasks:
tid ... number_samples
9983 9983 ... NaN
14951 14951 ... NaN
56483 56483 ... NaN
56484 56484 ... NaN
56485 56485 ... NaN
[5 rows x 23 columns]
In addition, a size limit and an offset can be applied both separately and simultaneously:
tasks = openml.tasks.list_tasks(size=10, offset=50, output_format="dataframe")
print(tasks)
tid ... number_samples
59 59 ... NaN
60 60 ... NaN
62 62 ... 9
63 63 ... 12
64 64 ... 1
65 65 ... 7
66 66 ... 5
67 67 ... 6
68 68 ... 5
69 69 ... 4
[10 rows x 21 columns]
OpenML 100 is a curated list of 100 tasks to start using OpenML. They are all supervised classification tasks with more than 500 instances and less than 50000 instances per task. To make things easier, the tasks do not contain highly unbalanced data and sparse data. However, the tasks include missing values and categorical features. You can find out more about the OpenML 100 on the OpenML benchmarking page.
Finally, it is also possible to list all tasks on OpenML with:
tasks = openml.tasks.list_tasks(output_format="dataframe")
print(len(tasks))
47257
Exercise¶
Search for the tasks on the ‘eeg-eye-state’ dataset.
tasks.query('name=="eeg-eye-state"')
Downloading tasks¶
We provide two functions to download tasks, one which downloads only a single task by its ID, and one which takes a list of IDs and downloads all of these tasks:
task_id = 31
task = openml.tasks.get_task(task_id)
/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/sphinx_gallery/gen_rst.py:722: 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.
exec(self.code, self.fake_main.__dict__)
/home/runner/work/openml-python/openml-python/openml/tasks/functions.py:442: 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`.
dataset = get_dataset(task.dataset_id, *dataset_args, **get_dataset_kwargs)
Properties of the task are stored as member variables:
print(task)
OpenML Classification Task
==========================
Task Type Description: https://www.openml.org/tt/TaskType.SUPERVISED_CLASSIFICATION
Task ID..............: 31
Task URL.............: https://www.openml.org/t/31
Estimation Procedure.: crossvalidation
Target Feature.......: class
# of Classes.........: 2
Cost Matrix..........: Available
And:
ids = [2, 1891, 31, 9983]
tasks = openml.tasks.get_tasks(ids)
print(tasks[0])
/home/runner/work/openml-python/openml-python/examples/30_extended/tasks_tutorial.py:135: 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.
tasks = openml.tasks.get_tasks(ids)
OpenML Classification Task
==========================
Task Type Description: https://www.openml.org/tt/TaskType.SUPERVISED_CLASSIFICATION
Task ID..............: 2
Task URL.............: https://www.openml.org/t/2
Estimation Procedure.: crossvalidation
Evaluation Measure...: predictive_accuracy
Target Feature.......: class
# of Classes.........: 6
Cost Matrix..........: Available
Creating tasks¶
You can also create new tasks. Take the following into account:
You can only create tasks on active datasets
For now, only the following tasks are supported: classification, regression, clustering, and learning curve analysis.
For now, tasks can only be created on a single dataset.
The exact same task must not already exist.
Creating a task requires the following input:
task_type: The task type ID, required (see below). Required.
dataset_id: The dataset ID. Required.
target_name: The name of the attribute you aim to predict. Optional.
estimation_procedure_id : The ID of the estimation procedure used to create train-test splits. Optional.
evaluation_measure: The name of the evaluation measure. Optional.
Any additional inputs for specific tasks
It is best to leave the evaluation measure open if there is no strong prerequisite for a specific measure. OpenML will always compute all appropriate measures and you can filter or sort results on your favourite measure afterwards. Only add an evaluation measure if necessary (e.g. when other measure make no sense), since it will create a new task, which scatters results across tasks.
We’ll use the test server for the rest of this tutorial.
Warning
This example uploads data. For that reason, this example connects to the test server at test.openml.org. This prevents the main server from crowding with example datasets, tasks, runs, and so on. The use of this test server can affect behaviour and performance of the OpenML-Python API.
openml.config.start_using_configuration_for_example()
/home/runner/work/openml-python/openml-python/examples/30_extended/tasks_tutorial.py:171: UserWarning: Switching to the test server https://test.openml.org/api/v1/xml to not upload results to the live server. Using the test server may result in reduced performance of the API!
openml.config.start_using_configuration_for_example()
Example¶
Let’s create a classification task on a dataset. In this example we will do this on the Iris dataset (ID=128 (on test server)). We’ll use 10-fold cross-validation (ID=1), and predictive accuracy as the predefined measure (this can also be left open). If a task with these parameters exists, we will get an appropriate exception. If such a task doesn’t exist, a task will be created and the corresponding task_id will be returned.
try:
my_task = openml.tasks.create_task(
task_type=TaskType.SUPERVISED_CLASSIFICATION,
dataset_id=128,
target_name="class",
evaluation_measure="predictive_accuracy",
estimation_procedure_id=1,
)
my_task.publish()
except openml.exceptions.OpenMLServerException as e:
# Error code for 'task already exists'
if e.code == 614:
# Lookup task
tasks = openml.tasks.list_tasks(data_id=128, output_format="dataframe")
tasks = tasks.query(
'task_type == "Supervised Classification" '
'and estimation_procedure == "10-fold Crossvalidation" '
'and evaluation_measures == "predictive_accuracy"'
)
task_id = tasks.loc[:, "tid"].values[0]
print("Task already exists. Task ID is", task_id)
# reverting to prod server
openml.config.stop_using_configuration_for_example()
/home/runner/work/openml-python/openml-python/openml/tasks/functions.py:285: RuntimeWarning: Could not create task type id for 10 due to error 10 is not a valid TaskType
procs = _get_estimation_procedure_list()
/home/runner/work/openml-python/openml-python/openml/tasks/functions.py:285: RuntimeWarning: Could not create task type id for 11 due to error 11 is not a valid TaskType
procs = _get_estimation_procedure_list()
/home/runner/work/openml-python/openml-python/openml/tasks/functions.py:234: RuntimeWarning: Could not create task type id for 10 due to error 10 is not a valid TaskType
return __list_tasks(api_call=api_call, output_format=output_format)
Task already exists. Task ID is 1364
Total running time of the script: (2 minutes 19.648 seconds)