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test_dataset.py
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# License: BSD 3-Clause
from time import time
from warnings import filterwarnings, catch_warnings
import numpy as np
import pandas as pd
import pytest
from scipy import sparse
import openml
from openml.testing import TestBase
from openml.exceptions import PyOpenMLError
from openml.datasets import OpenMLDataset, OpenMLDataFeature
class OpenMLDatasetTest(TestBase):
_multiprocess_can_split_ = True
def setUp(self):
super(OpenMLDatasetTest, self).setUp()
openml.config.server = self.production_server
# Load dataset id 2 - dataset 2 is interesting because it contains
# missing values, categorical features etc.
self.dataset = openml.datasets.get_dataset(2, download_data=False)
# titanic as missing values, categories, and string
self.titanic = openml.datasets.get_dataset(40945, download_data=False)
# these datasets have some boolean features
self.pc4 = openml.datasets.get_dataset(1049, download_data=False)
self.jm1 = openml.datasets.get_dataset(1053, download_data=False)
self.iris = openml.datasets.get_dataset(61, download_data=False)
def test_repr(self):
# create a bare-bones dataset as would be returned by
# create_dataset
data = openml.datasets.OpenMLDataset(name="somename",
description="a description")
str(data)
def test_init_string_validation(self):
with pytest.raises(ValueError, match="Invalid symbols in name"):
openml.datasets.OpenMLDataset(name="some name",
description="a description")
with pytest.raises(ValueError, match="Invalid symbols in description"):
openml.datasets.OpenMLDataset(name="somename",
description="a descriptïon")
with pytest.raises(ValueError, match="Invalid symbols in citation"):
openml.datasets.OpenMLDataset(name="somename",
description="a description",
citation="Something by Müller")
def test_get_data_array(self):
# Basic usage
rval, _, categorical, attribute_names = self.dataset.get_data(dataset_format='array')
self.assertIsInstance(rval, np.ndarray)
self.assertEqual(rval.dtype, np.float32)
self.assertEqual((898, 39), rval.shape)
self.assertEqual(len(categorical), 39)
self.assertTrue(all([isinstance(cat, bool) for cat in categorical]))
self.assertEqual(len(attribute_names), 39)
self.assertTrue(all([isinstance(att, str)
for att in attribute_names]))
self.assertIsNone(_)
# check that an error is raised when the dataset contains string
err_msg = "PyOpenML cannot handle string when returning numpy arrays"
with pytest.raises(PyOpenMLError, match=err_msg):
self.titanic.get_data(dataset_format='array')
def test_get_data_pandas(self):
data, _, _, _ = self.titanic.get_data(dataset_format='dataframe')
self.assertTrue(isinstance(data, pd.DataFrame))
self.assertEqual(data.shape[1], len(self.titanic.features))
self.assertEqual(data.shape[0], 1309)
col_dtype = {
'pclass': 'float64',
'survived': 'category',
'name': 'object',
'sex': 'category',
'age': 'float64',
'sibsp': 'float64',
'parch': 'float64',
'ticket': 'object',
'fare': 'float64',
'cabin': 'object',
'embarked': 'category',
'boat': 'object',
'body': 'float64',
'home.dest': 'object'
}
for col_name in data.columns:
self.assertTrue(data[col_name].dtype.name == col_dtype[col_name])
X, y, _, _ = self.titanic.get_data(
dataset_format='dataframe',
target=self.titanic.default_target_attribute)
self.assertTrue(isinstance(X, pd.DataFrame))
self.assertTrue(isinstance(y, pd.Series))
self.assertEqual(X.shape, (1309, 13))
self.assertEqual(y.shape, (1309,))
for col_name in X.columns:
self.assertTrue(X[col_name].dtype.name == col_dtype[col_name])
self.assertTrue(y.dtype.name == col_dtype['survived'])
def test_get_data_boolean_pandas(self):
# test to check that we are converting properly True and False even
# with some inconsistency when dumping the data on openml
data, _, _, _ = self.jm1.get_data()
self.assertTrue(data['defects'].dtype.name == 'category')
self.assertTrue(set(data['defects'].cat.categories) == {True, False})
data, _, _, _ = self.pc4.get_data()
self.assertTrue(data['c'].dtype.name == 'category')
self.assertTrue(set(data['c'].cat.categories) == {True, False})
def test_get_data_no_str_data_for_nparrays(self):
# check that an error is raised when the dataset contains string
err_msg = "PyOpenML cannot handle string when returning numpy arrays"
with pytest.raises(PyOpenMLError, match=err_msg):
self.titanic.get_data(dataset_format='array')
def _check_expected_type(self, dtype, is_cat, col):
if is_cat:
expected_type = 'category'
elif not col.isna().any() and (col.astype('uint8') == col).all():
expected_type = 'uint8'
else:
expected_type = 'float64'
self.assertEqual(dtype.name, expected_type)
def test_get_data_with_rowid(self):
self.dataset.row_id_attribute = "condition"
rval, _, categorical, _ = self.dataset.get_data(include_row_id=True)
self.assertIsInstance(rval, pd.DataFrame)
for (dtype, is_cat, col) in zip(rval.dtypes, categorical, rval):
self._check_expected_type(dtype, is_cat, rval[col])
self.assertEqual(rval.shape, (898, 39))
self.assertEqual(len(categorical), 39)
rval, _, categorical, _ = self.dataset.get_data()
self.assertIsInstance(rval, pd.DataFrame)
for (dtype, is_cat, col) in zip(rval.dtypes, categorical, rval):
self._check_expected_type(dtype, is_cat, rval[col])
self.assertEqual(rval.shape, (898, 38))
self.assertEqual(len(categorical), 38)
def test_get_data_with_target_array(self):
X, y, _, attribute_names = self.dataset.get_data(dataset_format='array', target="class")
self.assertIsInstance(X, np.ndarray)
self.assertEqual(X.dtype, np.float32)
self.assertEqual(X.shape, (898, 38))
self.assertIn(y.dtype, [np.int32, np.int64])
self.assertEqual(y.shape, (898, ))
self.assertEqual(len(attribute_names), 38)
self.assertNotIn("class", attribute_names)
def test_get_data_with_target_pandas(self):
X, y, categorical, attribute_names = self.dataset.get_data(target="class")
self.assertIsInstance(X, pd.DataFrame)
for (dtype, is_cat, col) in zip(X.dtypes, categorical, X):
self._check_expected_type(dtype, is_cat, X[col])
self.assertIsInstance(y, pd.Series)
self.assertEqual(y.dtype.name, 'category')
self.assertEqual(X.shape, (898, 38))
self.assertEqual(len(attribute_names), 38)
self.assertEqual(y.shape, (898, ))
self.assertNotIn("class", attribute_names)
def test_get_data_rowid_and_ignore_and_target(self):
self.dataset.ignore_attribute = ["condition"]
self.dataset.row_id_attribute = ["hardness"]
X, y, categorical, names = self.dataset.get_data(target="class")
self.assertEqual(X.shape, (898, 36))
self.assertEqual(len(categorical), 36)
cats = [True] * 3 + [False, True, True, False] + [True] * 23 + [False] * 3 + [True] * 3
self.assertListEqual(categorical, cats)
self.assertEqual(y.shape, (898, ))
def test_get_data_with_ignore_attributes(self):
self.dataset.ignore_attribute = ["condition"]
rval, _, categorical, _ = self.dataset.get_data(include_ignore_attribute=True)
for (dtype, is_cat, col) in zip(rval.dtypes, categorical, rval):
self._check_expected_type(dtype, is_cat, rval[col])
self.assertEqual(rval.shape, (898, 39))
self.assertEqual(len(categorical), 39)
rval, _, categorical, _ = self.dataset.get_data(include_ignore_attribute=False)
for (dtype, is_cat, col) in zip(rval.dtypes, categorical, rval):
self._check_expected_type(dtype, is_cat, col)
self.assertEqual(rval.shape, (898, 38))
self.assertEqual(len(categorical), 38)
def test_dataset_format_constructor(self):
with catch_warnings():
filterwarnings('error')
self.assertRaises(
DeprecationWarning,
openml.OpenMLDataset,
'Test',
'Test',
format='arff'
)
def test_get_data_with_nonexisting_class(self):
# This class is using the anneal dataset with labels [1, 2, 3, 4, 5, 'U']. However,
# label 4 does not exist and we test that the features 5 and 'U' are correctly mapped to
# indices 4 and 5, and that nothing is mapped to index 3.
_, y, _, _ = self.dataset.get_data('class', dataset_format='dataframe')
self.assertEqual(list(y.dtype.categories), ['1', '2', '3', '4', '5', 'U'])
_, y, _, _ = self.dataset.get_data('class', dataset_format='array')
self.assertEqual(np.min(y), 0)
self.assertEqual(np.max(y), 5)
# Check that no label is mapped to 3, since it is reserved for label '4'.
self.assertEqual(np.sum(y == 3), 0)
def test_get_data_corrupt_pickle(self):
# Lazy loaded dataset, populate cache.
self.iris.get_data()
# Corrupt pickle file, overwrite as empty.
with open(self.iris.data_pickle_file, "w") as fh:
fh.write("")
# Despite the corrupt file, the data should be loaded from the ARFF file.
# A warning message is written to the python logger.
xy, _, _, _ = self.iris.get_data()
self.assertIsInstance(xy, pd.DataFrame)
self.assertEqual(xy.shape, (150, 5))
class OpenMLDatasetTestOnTestServer(TestBase):
def setUp(self):
super(OpenMLDatasetTestOnTestServer, self).setUp()
# longley, really small dataset
self.dataset = openml.datasets.get_dataset(125, download_data=False)
def test_tagging(self):
tag = "testing_tag_{}_{}".format(self.id(), time())
ds_list = openml.datasets.list_datasets(tag=tag)
self.assertEqual(len(ds_list), 0)
self.dataset.push_tag(tag)
ds_list = openml.datasets.list_datasets(tag=tag)
self.assertEqual(len(ds_list), 1)
self.assertIn(125, ds_list)
self.dataset.remove_tag(tag)
ds_list = openml.datasets.list_datasets(tag=tag)
self.assertEqual(len(ds_list), 0)
class OpenMLDatasetTestSparse(TestBase):
_multiprocess_can_split_ = True
def setUp(self):
super(OpenMLDatasetTestSparse, self).setUp()
openml.config.server = self.production_server
self.sparse_dataset = openml.datasets.get_dataset(4136, download_data=False)
def test_get_sparse_dataset_with_target(self):
X, y, _, attribute_names = self.sparse_dataset.get_data(
dataset_format='array', target="class"
)
self.assertTrue(sparse.issparse(X))
self.assertEqual(X.dtype, np.float32)
self.assertEqual(X.shape, (600, 20000))
self.assertIsInstance(y, np.ndarray)
self.assertIn(y.dtype, [np.int32, np.int64])
self.assertEqual(y.shape, (600, ))
self.assertEqual(len(attribute_names), 20000)
self.assertNotIn("class", attribute_names)
def test_get_sparse_dataset(self):
rval, _, categorical, attribute_names = self.sparse_dataset.get_data(dataset_format='array')
self.assertTrue(sparse.issparse(rval))
self.assertEqual(rval.dtype, np.float32)
self.assertEqual((600, 20001), rval.shape)
self.assertEqual(len(categorical), 20001)
self.assertTrue(all([isinstance(cat, bool) for cat in categorical]))
self.assertEqual(len(attribute_names), 20001)
self.assertTrue(all([isinstance(att, str) for att in attribute_names]))
def test_get_sparse_dataframe(self):
rval, *_ = self.sparse_dataset.get_data()
self.assertTrue(isinstance(rval, pd.SparseDataFrame))
self.assertEqual((600, 20001), rval.shape)
def test_get_sparse_dataset_with_rowid(self):
self.sparse_dataset.row_id_attribute = ["V256"]
rval, _, categorical, _ = self.sparse_dataset.get_data(
dataset_format='array', include_row_id=True
)
self.assertTrue(sparse.issparse(rval))
self.assertEqual(rval.dtype, np.float32)
self.assertEqual(rval.shape, (600, 20001))
self.assertEqual(len(categorical), 20001)
rval, _, categorical, _ = self.sparse_dataset.get_data(
dataset_format='array', include_row_id=False
)
self.assertTrue(sparse.issparse(rval))
self.assertEqual(rval.dtype, np.float32)
self.assertEqual(rval.shape, (600, 20000))
self.assertEqual(len(categorical), 20000)
def test_get_sparse_dataset_with_ignore_attributes(self):
self.sparse_dataset.ignore_attribute = ["V256"]
rval, _, categorical, _ = self.sparse_dataset.get_data(
dataset_format='array', include_ignore_attribute=True
)
self.assertTrue(sparse.issparse(rval))
self.assertEqual(rval.dtype, np.float32)
self.assertEqual(rval.shape, (600, 20001))
self.assertEqual(len(categorical), 20001)
rval, _, categorical, _ = self.sparse_dataset.get_data(
dataset_format='array', include_ignore_attribute=False
)
self.assertTrue(sparse.issparse(rval))
self.assertEqual(rval.dtype, np.float32)
self.assertEqual(rval.shape, (600, 20000))
self.assertEqual(len(categorical), 20000)
def test_get_sparse_dataset_rowid_and_ignore_and_target(self):
# TODO: re-add row_id and ignore attributes
self.sparse_dataset.ignore_attribute = ["V256"]
self.sparse_dataset.row_id_attribute = ["V512"]
X, y, categorical, _ = self.sparse_dataset.get_data(
dataset_format='array',
target="class",
include_row_id=False,
include_ignore_attribute=False,
)
self.assertTrue(sparse.issparse(X))
self.assertEqual(X.dtype, np.float32)
self.assertIn(y.dtype, [np.int32, np.int64])
self.assertEqual(X.shape, (600, 19998))
self.assertEqual(len(categorical), 19998)
self.assertListEqual(categorical, [False] * 19998)
self.assertEqual(y.shape, (600, ))
def test_get_sparse_categorical_data_id_395(self):
dataset = openml.datasets.get_dataset(395, download_data=True)
feature = dataset.features[3758]
self.assertTrue(isinstance(dataset, OpenMLDataset))
self.assertTrue(isinstance(feature, OpenMLDataFeature))
self.assertEqual(dataset.name, 're1.wc')
self.assertEqual(feature.name, 'CLASS_LABEL')
self.assertEqual(feature.data_type, 'nominal')
self.assertEqual(len(feature.nominal_values), 25)
class OpenMLDatasetQualityTest(TestBase):
def test__check_qualities(self):
qualities = [{'oml:name': 'a', 'oml:value': '0.5'}]
qualities = openml.datasets.dataset._check_qualities(qualities)
self.assertEqual(qualities['a'], 0.5)
qualities = [{'oml:name': 'a', 'oml:value': 'null'}]
qualities = openml.datasets.dataset._check_qualities(qualities)
self.assertNotEqual(qualities['a'], qualities['a'])
qualities = [{'oml:name': 'a', 'oml:value': None}]
qualities = openml.datasets.dataset._check_qualities(qualities)
self.assertNotEqual(qualities['a'], qualities['a'])