# License: BSD 3-Clause

import unittest

from typing import List

from random import randint, shuffle

from openml.exceptions import OpenMLServerException

from openml.testing import TestBase

from openml.datasets import (

get_dataset,

list_datasets,

)

from openml.tasks import TaskType, create_task, get_task

class OpenMLTaskTest(TestBase):

"""

A helper class. The methods of the test case

are only executed in subclasses of the test case.

"""

__test__ = False

@classmethod

def setUpClass(cls):

if cls is OpenMLTaskTest:

raise unittest.SkipTest("Skip OpenMLTaskTest tests," " it's a base class")

super(OpenMLTaskTest, cls).setUpClass()

def setUp(self, n_levels: int = 1):

super(OpenMLTaskTest, self).setUp()

def test_download_task(self):

return get_task(self.task_id)

def test_upload_task(self):

# We don't know if the task in question already exists, so we try a few times. Checking

# beforehand would not be an option because a concurrent unit test could potentially

# create the same task and make this unit test fail (i.e. getting a dataset and creating

# a task for it is not atomic).

compatible_datasets = self._get_compatible_rand_dataset()

for i in range(100):

try:

dataset_id = compatible_datasets[i % len(compatible_datasets)]

# TODO consider implementing on the diff task types.

task = create_task(

task_type=self.task_type,

dataset_id=dataset_id,

target_name=self._get_random_feature(dataset_id),

estimation_procedure_id=self.estimation_procedure,

)

task.publish()

TestBase._mark_entity_for_removal("task", task.id)

TestBase.logger.info(

"collected from {}: {}".format(__file__.split("/")[-1], task.id)

)

# success

break

except OpenMLServerException as e:

# Error code for 'task already exists'

# Should be 533 according to the docs

# (# https://www.openml.org/api_docs#!/task/post_task)

if e.code == 614:

continue

else:

raise e

else:

raise ValueError(

"Could not create a valid task for task type ID {}".format(self.task_type)

)

def _get_compatible_rand_dataset(self) -> List:

compatible_datasets = []

active_datasets = list_datasets(status="active")

# depending on the task type, find either datasets

# with only symbolic features or datasets with only

# numerical features.

if self.task_type == TaskType.SUPERVISED_REGRESSION:

# regression task

for dataset_id, dataset_info in active_datasets.items():

if "NumberOfSymbolicFeatures" in dataset_info:

if dataset_info["NumberOfSymbolicFeatures"] == 0:

compatible_datasets.append(dataset_id)

elif self.task_type == TaskType.CLUSTERING:

# clustering task

compatible_datasets = list(active_datasets.keys())

else:

for dataset_id, dataset_info in active_datasets.items():

# extra checks because of:

# https://github.com/openml/OpenML/issues/959

if "NumberOfNumericFeatures" in dataset_info:

if dataset_info["NumberOfNumericFeatures"] == 0:

compatible_datasets.append(dataset_id)

# in-place shuffling

shuffle(compatible_datasets)

return compatible_datasets

# random_dataset_pos = randint(0, len(compatible_datasets) - 1)

#

# return compatible_datasets[random_dataset_pos]

def _get_random_feature(self, dataset_id: int) -> str:

random_dataset = get_dataset(dataset_id)

# necessary loop to overcome string and date type

# features.

while True:

random_feature_index = randint(0, len(random_dataset.features) - 1)

random_feature = random_dataset.features[random_feature_index]

if self.task_type == TaskType.SUPERVISED_REGRESSION:

if random_feature.data_type == "numeric":

break

else:

if random_feature.data_type == "nominal":

break

return random_feature.name