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# %% [markdown]
# How to list, download and upload benchmark studies.
# In contrast to
# [benchmark suites](https://docs.openml.org/benchmark/#benchmarking-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.
import uuid
from sklearn.ensemble import RandomForestClassifier
import openml
# %% [markdown]
# ##  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
studies = openml.study.list_studies(status="all")
print(studies.head(n=10))
# %% [markdown]
# ## Downloading studies
# This is done based on the study ID.
study = openml.study.get_study(123)
print(study)
# %% [markdown]
# Studies also features a description:
print(study.description)
# %% [markdown]
# Studies are a container for runs:
print(study.runs)
# %% [markdown]
# And we can use the evaluation listing functionality to learn more about
# the evaluations available for the conducted runs:
evaluations = openml.evaluations.list_evaluations(
    function="predictive_accuracy",
    study=study.study_id,
    output_format="dataframe",
print(evaluations.head())
# %% [markdown]
# We'll use the test server for the rest of this tutorial.
openml.config.start_using_configuration_for_example()
# %% [markdown]
# ## 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.
# <div class="admonition warning">
#     <p class="admonition-title">Warning</p>
#         For the rest of this tutorial, we will require the `openml-sklearn` package.
#         Install it with `pip install openml-sklearn`.
# Get sklearn extension to run sklearn models easily on OpenML tasks.
from openml_sklearn import SklearnExtension
extension = SklearnExtension()
# 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
# https://test.openml.org/api/v1/study/1
suite = openml.study.get_suite("OpenML100")
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)
openml.config.stop_using_configuration_for_example()

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