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import inspect
import unittest
import os
import numpy as np
from openml import OpenMLDataset
from openml.util import is_string
class OpenMLDatasetTest(unittest.TestCase):
def setUp(self):
# Load dataset id 1
__file__ = inspect.getfile(OpenMLDatasetTest)
self.directory = os.path.dirname(__file__)
self.arff_filename = os.path.join(self.directory, "..", "files",
"datasets", "2", "dataset.arff")
self.pickle_filename = os.path.join(self.directory, "..", "files",
"datasets", "2", "dataset.pkl")
self.dataset = OpenMLDataset(
1, "anneal", 2, "Lorem ipsum.", "arff", None, None, None,
"2014-04-06 23:19:24", None, "Public",
"http://openml.liacs.nl/files/download/2/dataset_2_anneal.ORIG.arff",
"class", None, None, None, None, None, None, None, None, None,
"939966a711925e333bf4aaadeaa71135", data_file=self.arff_filename)
self.sparse_arff_filename = os.path.join(
self.directory, "..", "files", "datasets", "-1", "dataset.arff")
self.sparse_pickle_filename = os.path.join(
self.directory, "..", "files", "datasets", "-1", "dataset.pkl")
self.sparse_dataset = OpenMLDataset(
-1, "dexter", -1, "Lorem ipsum.", "arff", None, None, None, None,
None, "Public",
"http://www.cs.ubc.ca/labs/beta/Projects/autoweka/datasets/dexter.zip",
"class", None, None, None, None, None, None, None, None, None,
None, data_file=self.sparse_arff_filename)
def tearDown(self):
for file_ in [self.pickle_filename, self.sparse_pickle_filename]:
os.remove(file_)
##########################################################################
# Pandas
@unittest.skip("Does not work right now")
def test_get_arff(self):
rval = self.dataset.get_arff()
self.assertIsInstance(rval, tuple)
self.assertIsInstance(rval[0], np.ndarray)
self.assertTrue(hasattr(rval[1], '__dict__'))
self.assertEqual(rval[0].shape, (898, ))
def test_get_data(self):
# Basic usage
rval = self.dataset.get_data()
self.assertIsInstance(rval, np.ndarray)
self.assertEqual(rval.dtype, np.float32)
self.assertEqual((898, 39), rval.shape)
rval, categorical = self.dataset.get_data(
return_categorical_indicator=True)
self.assertEqual(len(categorical), 39)
self.assertTrue(all([isinstance(cat, bool) for cat in categorical]))
rval, attribute_names = self.dataset.get_data(
return_attribute_names=True)
self.assertEqual(len(attribute_names), 39)
self.assertTrue(all([is_string(att) for att in attribute_names]))
def test_get_sparse_dataset(self):
rval = self.sparse_dataset.get_data()
self.assertIsInstance(rval, np.ndarray)
self.assertEqual(rval.dtype, np.float32)
self.assertEqual((2, 20001), rval.shape)
rval, categorical = self.sparse_dataset.get_data(
return_categorical_indicator=True)
self.assertIsInstance(rval, np.ndarray)
self.assertEqual(len(categorical), 20001)
self.assertTrue(all([isinstance(cat, bool) for cat in categorical]))
rval, attribute_names = self.sparse_dataset.get_data(
return_attribute_names=True)
self.assertIsInstance(rval, np.ndarray)
self.assertEqual(len(attribute_names), 20001)
self.assertTrue(all([is_string(att) for att in attribute_names]))
def test_get_data_with_target(self):
X, y = self.dataset.get_data(target="class")
self.assertIsInstance(X, np.ndarray)
self.assertEqual(X.dtype, np.float32)
self.assertIn(y.dtype, [np.int32, np.int64])
self.assertEqual(X.shape, (898, 38))
X, y, attribute_names = self.dataset.get_data(
target="class", return_attribute_names=True)
self.assertEqual(len(attribute_names), 38)
self.assertNotIn("class", attribute_names)
self.assertEqual(y.shape, (898, ))
def test_get_sparse_dataset_with_target(self):
X, y = self.sparse_dataset.get_data(target="class")
self.assertIsInstance(X, np.ndarray)
self.assertEqual(X.dtype, np.float32)
self.assertIsInstance(y, np.ndarray)
self.assertIn(y.dtype, [np.int32, np.int64])
self.assertEqual(X.shape, (2, 20000))
X, y, attribute_names = self.sparse_dataset.get_data(
target="class", return_attribute_names=True)
self.assertIsInstance(X, np.ndarray)
self.assertEqual(len(attribute_names), 20000)
self.assertNotIn("class", attribute_names)
self.assertEqual(y.shape, (2, ))
def test_get_data_with_rowid(self):
self.dataset.row_id_attribute = "condition"
rval, categorical = self.dataset.get_data(
include_row_id=True, return_categorical_indicator=True)
self.assertEqual(rval.dtype, np.float32)
self.assertEqual(rval.shape, (898, 39))
self.assertEqual(len(categorical), 39)
rval, categorical = self.dataset.get_data(
include_row_id=False, return_categorical_indicator=True)
self.assertEqual(rval.dtype, np.float32)
self.assertEqual(rval.shape, (898, 38))
self.assertEqual(len(categorical), 38)
# TODO this is not yet supported!
#rowid = ["condition", "formability"]
#self.dataset.row_id_attribute = rowid
#rval = self.dataset.get_pandas(include_row_id=False)
def test_get_sparse_dataset_with_rowid(self):
self.sparse_dataset.row_id_attribute = "a_0"
rval, categorical = self.sparse_dataset.get_data(
include_row_id=True, return_categorical_indicator=True)
self.assertIsInstance(rval, np.ndarray)
self.assertEqual(rval.dtype, np.float32)
self.assertEqual(rval.shape, (2, 20001))
self.assertEqual(len(categorical), 20001)
rval, categorical = self.sparse_dataset.get_data(
include_row_id=False, return_categorical_indicator=True)
self.assertIsInstance(rval, np.ndarray)
self.assertEqual(rval.dtype, np.float32)
self.assertEqual(rval.shape, (2, 20000))
self.assertEqual(len(categorical), 20000)
# TODO this is not yet supported!
# rowid = ["condition", "formability"]
#self.dataset.row_id_attribute = rowid
#rval = self.dataset.get_pandas(include_row_id=False)
def test_get_data_with_ignore_attributes(self):
self.dataset.ignore_attributes = "condition"
rval = self.dataset.get_data(include_ignore_attributes=True)
self.assertEqual(rval.dtype, np.float32)
self.assertEqual(rval.shape, (898, 39))
rval, categorical = self.dataset.get_data(
include_ignore_attributes=True, return_categorical_indicator=True)
self.assertEqual(len(categorical), 39)
rval = self.dataset.get_data(include_ignore_attributes=False)
self.assertEqual(rval.dtype, np.float32)
self.assertEqual(rval.shape, (898, 38))
rval, categorical = self.dataset.get_data(
include_ignore_attributes=False, return_categorical_indicator=True)
self.assertEqual(len(categorical), 38)
# TODO test multiple ignore attributes!
def test_get_sparse_dataset_with_ignore_attributes(self):
self.sparse_dataset.ignore_attributes = "a_0"
rval = self.sparse_dataset.get_data(include_ignore_attributes=True)
self.assertIsInstance(rval, np.ndarray)
self.assertEqual(rval.dtype, np.float32)
self.assertEqual(rval.shape, (2, 20001))
rval, categorical = self.sparse_dataset.get_data(
include_ignore_attributes=True, return_categorical_indicator=True)
self.assertIsInstance(rval, np.ndarray)
self.assertEqual(len(categorical), 20001)
rval = self.sparse_dataset.get_data(include_ignore_attributes=False)
self.assertIsInstance(rval, np.ndarray)
self.assertEqual(rval.dtype, np.float32)
self.assertEqual(rval.shape, (2, 20000))
rval, categorical = self.sparse_dataset.get_data(
include_ignore_attributes=False, return_categorical_indicator=True)
self.assertIsInstance(rval, np.ndarray)
self.assertEqual(len(categorical), 20000)
# TODO test multiple ignore attributes!
def test_get_data_rowid_and_ignore_and_target(self):
self.dataset.ignore_attributes = "condition"
self.dataset.row_id_attribute = "hardness"
X, y = self.dataset.get_data(target="class", include_row_id=False,
include_ignore_attributes=False)
self.assertEqual(X.dtype, np.float32)
self.assertIn(y.dtype, [np.int32, np.int64])
self.assertEqual(X.shape, (898, 36))
X, y, categorical = self.dataset.get_data(
target="class", return_categorical_indicator=True)
self.assertEqual(len(categorical), 36)
self.assertListEqual(categorical, [True] * 3 + [False] + [True] * 2 + [
False] + [True] * 23 + [False] * 3 + [True] * 3)
self.assertEqual(y.shape, (898, ))
def test_get_sparse_dataset_rowid_and_ignore_and_target(self):
self.sparse_dataset.ignore_attributes = "a_0"
self.sparse_dataset.row_id_attribute = "a_1"
X, y = self.sparse_dataset.get_data(
target="class", include_row_id=False,
include_ignore_attributes=False)
self.assertIsInstance(X, np.ndarray)
self.assertEqual(X.dtype, np.float32)
self.assertIn(y.dtype, [np.int32, np.int64])
self.assertEqual(X.shape, (2, 19998))
X, y, categorical = self.sparse_dataset.get_data(
target="class", return_categorical_indicator=True)
self.assertIsInstance(X, np.ndarray)
self.assertEqual(len(categorical), 19998)
self.assertListEqual(categorical, [False] * 19998)
self.assertEqual(y.shape, (2, ))