537 lines
22 KiB
Python
537 lines
22 KiB
Python
"""Test the openml loader.
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"""
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import gzip
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import json
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import numpy as np
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import os
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import scipy.sparse
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import sklearn
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from sklearn.datasets import fetch_openml
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from sklearn.datasets.openml import (_open_openml_url,
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_get_data_description_by_id,
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_download_data_arff)
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from sklearn.utils.testing import (assert_warns_message,
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assert_raise_message)
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from sklearn.externals.six import string_types
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from sklearn.externals.six.moves.urllib.error import HTTPError
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from sklearn.datasets.tests.test_common import check_return_X_y
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from functools import partial
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currdir = os.path.dirname(os.path.abspath(__file__))
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# if True, urlopen will be monkey patched to only use local files
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test_offline = True
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test_gzip = True
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def _test_features_list(data_id):
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# XXX Test is intended to verify/ensure correct decoding behavior
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# Not usable with sparse data or datasets that have columns marked as
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# {row_identifier, ignore}
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def decode_column(data_bunch, col_idx):
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col_name = data_bunch.feature_names[col_idx]
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if col_name in data_bunch.categories:
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# XXX: This would be faster with np.take, although it does not
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# handle missing values fast (also not with mode='wrap')
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cat = data_bunch.categories[col_name]
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result = [cat[idx] if 0 <= idx < len(cat) else None for idx in
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data_bunch.data[:, col_idx].astype(int)]
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return np.array(result, dtype='O')
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else:
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# non-nominal attribute
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return data_bunch.data[:, col_idx]
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data_bunch = fetch_openml(data_id=data_id, cache=False, target_column=None)
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# also obtain decoded arff
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data_description = _get_data_description_by_id(data_id, None)
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sparse = data_description['format'].lower() == 'sparse_arff'
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if sparse is True:
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raise ValueError('This test is not intended for sparse data, to keep '
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'code relatively simple')
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data_arff = _download_data_arff(data_description['file_id'],
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sparse, None, False)
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data_downloaded = np.array(data_arff['data'], dtype='O')
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for i in range(len(data_bunch.feature_names)):
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# XXX: Test per column, as this makes it easier to avoid problems with
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# missing values
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np.testing.assert_array_equal(data_downloaded[:, i],
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decode_column(data_bunch, i))
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def _fetch_dataset_from_openml(data_id, data_name, data_version,
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target_column,
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expected_observations, expected_features,
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expected_missing,
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expected_data_dtype, expected_target_dtype,
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expect_sparse, compare_default_target):
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# fetches a dataset in three various ways from OpenML, using the
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# fetch_openml function, and does various checks on the validity of the
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# result. Note that this function can be mocked (by invoking
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# _monkey_patch_webbased_functions before invoking this function)
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data_by_name_id = fetch_openml(name=data_name, version=data_version,
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cache=False)
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assert int(data_by_name_id.details['id']) == data_id
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fetch_openml(name=data_name, cache=False)
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# without specifying the version, there is no guarantee that the data id
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# will be the same
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# fetch with dataset id
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data_by_id = fetch_openml(data_id=data_id, cache=False,
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target_column=target_column)
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assert data_by_id.details['name'] == data_name
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assert data_by_id.data.shape == (expected_observations, expected_features)
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if isinstance(target_column, str):
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# single target, so target is vector
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assert data_by_id.target.shape == (expected_observations, )
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elif isinstance(target_column, list):
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# multi target, so target is array
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assert data_by_id.target.shape == (expected_observations,
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len(target_column))
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assert data_by_id.data.dtype == np.float64
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assert data_by_id.target.dtype == expected_target_dtype
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assert len(data_by_id.feature_names) == expected_features
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for feature in data_by_id.feature_names:
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assert isinstance(feature, string_types)
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# TODO: pass in a list of expected nominal features
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for feature, categories in data_by_id.categories.items():
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feature_idx = data_by_id.feature_names.index(feature)
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values = np.unique(data_by_id.data[:, feature_idx])
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values = values[np.isfinite(values)]
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assert set(values) <= set(range(len(categories)))
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if compare_default_target:
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# check whether the data by id and data by id target are equal
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data_by_id_default = fetch_openml(data_id=data_id, cache=False)
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if data_by_id.data.dtype == np.float64:
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np.testing.assert_allclose(data_by_id.data,
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data_by_id_default.data)
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else:
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assert np.array_equal(data_by_id.data, data_by_id_default.data)
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if data_by_id.target.dtype == np.float64:
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np.testing.assert_allclose(data_by_id.target,
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data_by_id_default.target)
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else:
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assert np.array_equal(data_by_id.target, data_by_id_default.target)
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if expect_sparse:
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assert isinstance(data_by_id.data, scipy.sparse.csr_matrix)
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else:
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assert isinstance(data_by_id.data, np.ndarray)
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# np.isnan doesn't work on CSR matrix
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assert (np.count_nonzero(np.isnan(data_by_id.data)) ==
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expected_missing)
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# test return_X_y option
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fetch_func = partial(fetch_openml, data_id=data_id, cache=False,
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target_column=target_column)
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check_return_X_y(data_by_id, fetch_func)
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return data_by_id
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def _monkey_patch_webbased_functions(context, data_id, gziped_files):
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url_prefix_data_description = "https://openml.org/api/v1/json/data/"
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url_prefix_data_features = "https://openml.org/api/v1/json/data/features/"
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url_prefix_download_data = "https://openml.org/data/v1/"
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url_prefix_data_list = "https://openml.org/api/v1/json/data/list/"
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path_suffix = ''
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read_fn = open
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if gziped_files:
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path_suffix = '.gz'
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read_fn = gzip.open
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def _mock_urlopen_data_description(url):
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assert url.startswith(url_prefix_data_description)
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path = os.path.join(currdir, 'data', 'openml', str(data_id),
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'data_description.json%s' % path_suffix)
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return read_fn(path, 'rb')
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def _mock_urlopen_data_features(url):
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assert url.startswith(url_prefix_data_features)
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path = os.path.join(currdir, 'data', 'openml', str(data_id),
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'data_features.json%s' % path_suffix)
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return read_fn(path, 'rb')
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def _mock_urlopen_download_data(url):
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assert (url.startswith(url_prefix_download_data))
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path = os.path.join(currdir, 'data', 'openml', str(data_id),
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'data.arff%s' % path_suffix)
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return read_fn(path, 'rb')
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def _mock_urlopen_data_list(url):
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# url contains key value pairs of attributes, e.g.,
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# openml.org/api/v1/json/data_name/iris/data_version/1 should
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# ideally become {data_name: 'iris', data_version: '1'}
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assert url.startswith(url_prefix_data_list)
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att_list = url[len(url_prefix_data_list):].split('/')
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key_val_dict = dict(zip(att_list[::2], att_list[1::2]))
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# add defaults, so we can make assumptions about the content
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if 'data_version' not in key_val_dict:
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key_val_dict['data_version'] = None
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if 'status' not in key_val_dict:
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key_val_dict['status'] = "active"
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mock_file = "data_list__%s_%s_%s.json%s" % \
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(key_val_dict['data_name'], key_val_dict['data_version'],
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key_val_dict['status'], path_suffix)
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json_file_path = os.path.join(currdir, 'data', 'openml',
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str(data_id), mock_file)
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# load the file itself, to simulate a http error
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json_data = json.loads(read_fn(json_file_path, 'rb').
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read().decode('utf-8'))
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if 'error' in json_data:
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raise HTTPError(url=None, code=412,
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msg='Simulated mock error',
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hdrs=None, fp=None)
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return read_fn(json_file_path, 'rb')
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def _mock_urlopen(url):
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if url.startswith(url_prefix_data_list):
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return _mock_urlopen_data_list(url)
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elif url.startswith(url_prefix_data_features):
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return _mock_urlopen_data_features(url)
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elif url.startswith(url_prefix_download_data):
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return _mock_urlopen_download_data(url)
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elif url.startswith(url_prefix_data_description):
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return _mock_urlopen_data_description(url)
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else:
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raise ValueError('Unknown mocking URL pattern: %s' % url)
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# XXX: Global variable
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if test_offline:
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context.setattr(sklearn.datasets.openml, 'urlopen', _mock_urlopen)
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def test_fetch_openml_iris(monkeypatch):
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# classification dataset with numeric only columns
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data_id = 61
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data_name = 'iris'
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data_version = 1
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target_column = 'class'
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expected_observations = 150
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expected_features = 4
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expected_missing = 0
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_monkey_patch_webbased_functions(monkeypatch, data_id, test_gzip)
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assert_warns_message(
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UserWarning,
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"Multiple active versions of the dataset matching the name"
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" iris exist. Versions may be fundamentally different, "
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"returning version 1.",
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_fetch_dataset_from_openml,
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**{'data_id': data_id, 'data_name': data_name,
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'data_version': data_version,
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'target_column': target_column,
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'expected_observations': expected_observations,
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'expected_features': expected_features,
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'expected_missing': expected_missing,
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'expect_sparse': False,
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'expected_data_dtype': np.float64,
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'expected_target_dtype': object,
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'compare_default_target': True}
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)
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def test_decode_iris():
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data_id = 61
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_test_features_list(data_id)
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def test_fetch_openml_iris_multitarget(monkeypatch):
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# classification dataset with numeric only columns
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data_id = 61
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data_name = 'iris'
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data_version = 1
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target_column = ['sepallength', 'sepalwidth']
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expected_observations = 150
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expected_features = 3
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expected_missing = 0
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_monkey_patch_webbased_functions(monkeypatch, data_id, test_gzip)
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_fetch_dataset_from_openml(data_id, data_name, data_version, target_column,
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expected_observations, expected_features,
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expected_missing,
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object, np.float64, expect_sparse=False,
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compare_default_target=False)
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def test_fetch_openml_anneal(monkeypatch):
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# classification dataset with numeric and categorical columns
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data_id = 2
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data_name = 'anneal'
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data_version = 1
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target_column = 'class'
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# Not all original instances included for space reasons
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expected_observations = 11
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expected_features = 38
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expected_missing = 267
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_monkey_patch_webbased_functions(monkeypatch, data_id, test_gzip)
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_fetch_dataset_from_openml(data_id, data_name, data_version, target_column,
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expected_observations, expected_features,
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expected_missing,
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object, object, expect_sparse=False,
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compare_default_target=True)
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def test_decode_anneal():
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data_id = 2
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_test_features_list(data_id)
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def test_fetch_openml_anneal_multitarget(monkeypatch):
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# classification dataset with numeric and categorical columns
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data_id = 2
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data_name = 'anneal'
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data_version = 1
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target_column = ['class', 'product-type', 'shape']
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# Not all original instances included for space reasons
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expected_observations = 11
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expected_features = 36
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expected_missing = 267
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_monkey_patch_webbased_functions(monkeypatch, data_id, test_gzip)
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_fetch_dataset_from_openml(data_id, data_name, data_version, target_column,
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expected_observations, expected_features,
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expected_missing,
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object, object, expect_sparse=False,
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compare_default_target=False)
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def test_fetch_openml_cpu(monkeypatch):
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# regression dataset with numeric and categorical columns
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data_id = 561
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data_name = 'cpu'
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data_version = 1
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target_column = 'class'
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expected_observations = 209
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expected_features = 7
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expected_missing = 0
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_monkey_patch_webbased_functions(monkeypatch, data_id, test_gzip)
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_fetch_dataset_from_openml(data_id, data_name, data_version, target_column,
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expected_observations, expected_features,
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expected_missing,
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object, np.float64, expect_sparse=False,
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compare_default_target=True)
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def test_decode_cpu():
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data_id = 561
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_test_features_list(data_id)
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def test_fetch_openml_australian(monkeypatch):
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# sparse dataset
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# Australian is the only sparse dataset that is reasonably small
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# as it is inactive, we need to catch the warning. Due to mocking
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# framework, it is not deactivated in our tests
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data_id = 292
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data_name = 'Australian'
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data_version = 1
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target_column = 'Y'
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# Not all original instances included for space reasons
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expected_observations = 85
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expected_features = 14
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expected_missing = 0
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_monkey_patch_webbased_functions(monkeypatch, data_id, test_gzip)
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assert_warns_message(
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UserWarning,
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"Version 1 of dataset Australian is inactive,",
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_fetch_dataset_from_openml,
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**{'data_id': data_id, 'data_name': data_name,
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'data_version': data_version,
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'target_column': target_column,
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'expected_observations': expected_observations,
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'expected_features': expected_features,
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'expected_missing': expected_missing,
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'expect_sparse': True,
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'expected_data_dtype': np.float64,
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'expected_target_dtype': object,
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'compare_default_target': False} # numpy specific check
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)
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def test_fetch_openml_miceprotein(monkeypatch):
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# JvR: very important check, as this dataset defined several row ids
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# and ignore attributes. Note that data_features json has 82 attributes,
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# and row id (1), ignore attributes (3) have been removed (and target is
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# stored in data.target)
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data_id = 40966
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data_name = 'MiceProtein'
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data_version = 4
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target_column = 'class'
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# Not all original instances included for space reasons
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expected_observations = 7
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expected_features = 77
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expected_missing = 7
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_monkey_patch_webbased_functions(monkeypatch, data_id, test_gzip)
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_fetch_dataset_from_openml(data_id, data_name, data_version, target_column,
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expected_observations, expected_features,
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expected_missing,
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np.float64, object, expect_sparse=False,
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compare_default_target=True)
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def test_fetch_openml_emotions(monkeypatch):
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# classification dataset with multiple targets (natively)
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data_id = 40589
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data_name = 'emotions'
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data_version = 3
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target_column = ['amazed.suprised', 'happy.pleased', 'relaxing.calm',
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'quiet.still', 'sad.lonely', 'angry.aggresive']
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expected_observations = 13
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expected_features = 72
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expected_missing = 0
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_monkey_patch_webbased_functions(monkeypatch, data_id, test_gzip)
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_fetch_dataset_from_openml(data_id, data_name, data_version, target_column,
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expected_observations, expected_features,
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expected_missing,
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np.float64, object, expect_sparse=False,
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compare_default_target=True)
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def test_decode_emotions():
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data_id = 40589
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_test_features_list(data_id)
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def test_open_openml_url_cache(monkeypatch):
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data_id = 61
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_monkey_patch_webbased_functions(monkeypatch, data_id, test_gzip)
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openml_path = sklearn.datasets.openml._DATA_FILE.format(data_id)
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test_directory = os.path.join(os.path.expanduser('~'), 'scikit_learn_data')
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# first fill the cache
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response1 = _open_openml_url(openml_path, test_directory)
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# assert file exists
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location = os.path.join(test_directory, 'openml.org', openml_path + '.gz')
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assert os.path.isfile(location)
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# redownload, to utilize cache
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response2 = _open_openml_url(openml_path, test_directory)
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assert response1.read() == response2.read()
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def test_fetch_openml_notarget(monkeypatch):
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data_id = 61
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target_column = None
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expected_observations = 150
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expected_features = 5
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_monkey_patch_webbased_functions(monkeypatch, data_id, test_gzip)
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data = fetch_openml(data_id=data_id, target_column=target_column,
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cache=False)
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assert data.data.shape == (expected_observations, expected_features)
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assert data.target is None
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def test_fetch_openml_inactive(monkeypatch):
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# fetch inactive dataset by id
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data_id = 40675
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_monkey_patch_webbased_functions(monkeypatch, data_id, test_gzip)
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glas2 = assert_warns_message(
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UserWarning, "Version 1 of dataset glass2 is inactive,", fetch_openml,
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data_id=data_id, cache=False)
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# fetch inactive dataset by name and version
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assert glas2.data.shape == (163, 9)
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glas2_by_version = assert_warns_message(
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UserWarning, "Version 1 of dataset glass2 is inactive,", fetch_openml,
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data_id=None, name="glass2", version=1, cache=False)
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assert int(glas2_by_version.details['id']) == data_id
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def test_fetch_nonexiting(monkeypatch):
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# there is no active version of glass2
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data_id = 40675
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_monkey_patch_webbased_functions(monkeypatch, data_id, test_gzip)
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# Note that we only want to search by name (not data id)
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assert_raise_message(ValueError, "No active dataset glass2 found",
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fetch_openml, name='glass2', cache=False)
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def test_raises_illegal_multitarget(monkeypatch):
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data_id = 61
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targets = ['sepalwidth', 'class']
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_monkey_patch_webbased_functions(monkeypatch, data_id, test_gzip)
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# Note that we only want to search by name (not data id)
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|
assert_raise_message(ValueError,
|
|
"Can only handle homogeneous multi-target datasets,",
|
|
fetch_openml, data_id=data_id,
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target_column=targets, cache=False)
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|
|
|
|
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def test_warn_ignore_attribute(monkeypatch):
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data_id = 40966
|
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expected_row_id_msg = "target_column={} has flag is_row_identifier."
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expected_ignore_msg = "target_column={} has flag is_ignore."
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|
_monkey_patch_webbased_functions(monkeypatch, data_id, test_gzip)
|
|
# single column test
|
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assert_warns_message(UserWarning, expected_row_id_msg.format('MouseID'),
|
|
fetch_openml, data_id=data_id,
|
|
target_column='MouseID',
|
|
cache=False)
|
|
assert_warns_message(UserWarning, expected_ignore_msg.format('Genotype'),
|
|
fetch_openml, data_id=data_id,
|
|
target_column='Genotype',
|
|
cache=False)
|
|
# multi column test
|
|
assert_warns_message(UserWarning, expected_row_id_msg.format('MouseID'),
|
|
fetch_openml, data_id=data_id,
|
|
target_column=['MouseID', 'class'],
|
|
cache=False)
|
|
assert_warns_message(UserWarning, expected_ignore_msg.format('Genotype'),
|
|
fetch_openml, data_id=data_id,
|
|
target_column=['Genotype', 'class'],
|
|
cache=False)
|
|
|
|
|
|
def test_string_attribute(monkeypatch):
|
|
data_id = 40945
|
|
_monkey_patch_webbased_functions(monkeypatch, data_id, test_gzip)
|
|
# single column test
|
|
assert_raise_message(ValueError,
|
|
'STRING attributes are not yet supported',
|
|
fetch_openml, data_id=data_id, cache=False)
|
|
|
|
|
|
def test_illegal_column(monkeypatch):
|
|
data_id = 61
|
|
_monkey_patch_webbased_functions(monkeypatch, data_id, test_gzip)
|
|
assert_raise_message(KeyError, "Could not find target_column=",
|
|
fetch_openml, data_id=data_id,
|
|
target_column='undefined', cache=False)
|
|
|
|
assert_raise_message(KeyError, "Could not find target_column=",
|
|
fetch_openml, data_id=data_id,
|
|
target_column=['undefined', 'class'],
|
|
cache=False)
|
|
|
|
|
|
def test_fetch_openml_raises_missing_values_target(monkeypatch):
|
|
data_id = 2
|
|
_monkey_patch_webbased_functions(monkeypatch, data_id, test_gzip)
|
|
assert_raise_message(ValueError, "Target column ",
|
|
fetch_openml, data_id=data_id, target_column='family')
|
|
|
|
|
|
def test_fetch_openml_raises_illegal_argument():
|
|
assert_raise_message(ValueError, "Dataset data_id=",
|
|
fetch_openml, data_id=-1, name="name")
|
|
|
|
assert_raise_message(ValueError, "Dataset data_id=",
|
|
fetch_openml, data_id=-1, name=None,
|
|
version="version")
|
|
|
|
assert_raise_message(ValueError, "Dataset data_id=",
|
|
fetch_openml, data_id=-1, name="name",
|
|
version="version")
|
|
|
|
assert_raise_message(ValueError, "Neither name nor data_id are provided. "
|
|
"Please provide name or data_id.", fetch_openml)
|