Benchmark studies¶

How to list, download and upload benchmark studies. In contrast to benchmark suites which hold a list of tasks, studies hold a list of runs. As runs contain all information on flows and tasks, all required information about a study can be retrieved.

In [ ]:

import uuid

from sklearn.ensemble import RandomForestClassifier

import openml

import uuid from sklearn.ensemble import RandomForestClassifier import openml

Listing studies¶

• Use the output_format parameter to select output type
• Default gives dict, but we'll use dataframe to obtain an easier-to-work-with data structure

In [ ]:

studies = openml.study.list_studies(output_format="dataframe", status="all")
print(studies.head(n=10))

studies = openml.study.list_studies(output_format="dataframe", status="all") print(studies.head(n=10))

Downloading studies¶

This is done based on the study ID.

In [ ]:

study = openml.study.get_study(123)
print(study)

study = openml.study.get_study(123) print(study)

Studies also features a description:

In [ ]:

print(study.description)

print(study.description)

Studies are a container for runs:

In [ ]:

print(study.runs)

print(study.runs)

And we can use the evaluation listing functionality to learn more about the evaluations available for the conducted runs:

In [ ]:

evaluations = openml.evaluations.list_evaluations(
    function="predictive_accuracy",
    output_format="dataframe",
    study=study.study_id,
)
print(evaluations.head())

evaluations = openml.evaluations.list_evaluations( function="predictive_accuracy", output_format="dataframe", study=study.study_id, ) print(evaluations.head())

We'll use the test server for the rest of this tutorial.

.. warning:: .. include:: ../../test_server_usage_warning.txt

In [ ]:

openml.config.start_using_configuration_for_example()

openml.config.start_using_configuration_for_example()

Uploading studies¶

Creating a study is as simple as creating any kind of other OpenML entity. In this examples we'll create a few runs for the OpenML-100 benchmark suite which is available on the OpenML test server.

In [ ]:

# Model to be used
clf = RandomForestClassifier()

# We'll create a study with one run on 3 datasets present in the suite
tasks = [115, 259, 307]

# To verify
suite = openml.study.get_suite(1)
print(all([t_id in suite.tasks for t_id in tasks]))

run_ids = []
for task_id in tasks:
    task = openml.tasks.get_task(task_id)
    run = openml.runs.run_model_on_task(clf, task)
    run.publish()
    run_ids.append(run.run_id)

# The study needs a machine-readable and unique alias. To obtain this,
# we simply generate a random uuid.
alias = uuid.uuid4().hex

new_study = openml.study.create_study(
    name="Test-Study",
    description="Test study for the Python tutorial on studies",
    run_ids=run_ids,
    alias=alias,
    benchmark_suite=suite.study_id,
)
new_study.publish()
print(new_study)

# Model to be used clf = RandomForestClassifier() # We'll create a study with one run on 3 datasets present in the suite tasks = [115, 259, 307] # To verify suite = openml.study.get_suite(1) print(all([t_id in suite.tasks for t_id in tasks])) run_ids = [] for task_id in tasks: task = openml.tasks.get_task(task_id) run = openml.runs.run_model_on_task(clf, task) run.publish() run_ids.append(run.run_id) # The study needs a machine-readable and unique alias. To obtain this, # we simply generate a random uuid. alias = uuid.uuid4().hex new_study = openml.study.create_study( name="Test-Study", description="Test study for the Python tutorial on studies", run_ids=run_ids, alias=alias, benchmark_suite=suite.study_id, ) new_study.publish() print(new_study)

In [ ]:

openml.config.stop_using_configuration_for_example()
# License: BSD 3-Clause

openml.config.stop_using_configuration_for_example() # License: BSD 3-Clause