from collections import OrderedDict import pytest import pandas as pd import numpy as np from distutils.version import LooseVersion from pandas.util.testing import (assert_series_equal, assert_frame_equal, assert_index_equal) from upsetplot import (from_memberships, from_contents, from_indicators, generate_data) @pytest.mark.parametrize('typ', [set, list, tuple, iter]) def test_from_memberships_no_data(typ): with pytest.raises(ValueError, match='at least one category'): from_memberships([]) with pytest.raises(ValueError, match='at least one category'): from_memberships([[], []]) with pytest.raises(ValueError, match='strings'): from_memberships([[1]]) with pytest.raises(ValueError, match='strings'): from_memberships([[1, 'str']]) with pytest.raises(TypeError): from_memberships([1]) out = from_memberships([typ([]), typ(['hello']), typ(['world']), typ(['hello', 'world']), ]) exp = pd.DataFrame([[False, False, 1], [True, False, 1], [False, True, 1], [True, True, 1]], columns=['hello', 'world', 'ones'] ).set_index(['hello', 'world'])['ones'] assert isinstance(exp.index, pd.MultiIndex) assert_series_equal(exp, out) # test sorting by name out = from_memberships([typ(['hello']), typ(['world'])]) exp = pd.DataFrame([[True, False, 1], [False, True, 1]], columns=['hello', 'world', 'ones'] ).set_index(['hello', 'world'])['ones'] assert_series_equal(exp, out) out = from_memberships([typ(['world']), typ(['hello'])]) exp = pd.DataFrame([[False, True, 1], [True, False, 1]], columns=['hello', 'world', 'ones'] ).set_index(['hello', 'world'])['ones'] assert_series_equal(exp, out) @pytest.mark.parametrize('data,ndim', [ ([1, 2, 3, 4], 1), (np.array([1, 2, 3, 4]), 1), (pd.Series([1, 2, 3, 4], name='foo'), 1), ([[1, 'a'], [2, 'b'], [3, 'c'], [4, 'd']], 2), (pd.DataFrame([[1, 'a'], [2, 'b'], [3, 'c'], [4, 'd']], columns=['foo', 'bar'], index=['q', 'r', 's', 't']), 2), ]) def test_from_memberships_with_data(data, ndim): memberships = [[], ['hello'], ['world'], ['hello', 'world']] out = from_memberships(memberships, data=data) assert out is not data # make sure frame is copied if hasattr(data, 'loc') and np.asarray(data).dtype.kind in 'ifb': # but not deepcopied when possible if LooseVersion(pd.__version__) > LooseVersion('0.35'): assert out.values.base is np.asarray(data).base if ndim == 1: assert isinstance(out, pd.Series) else: assert isinstance(out, pd.DataFrame) assert_frame_equal(pd.DataFrame(out).reset_index(drop=True), pd.DataFrame(data).reset_index(drop=True)) no_data = from_memberships(memberships=memberships) assert_index_equal(out.index, no_data.index) with pytest.raises(ValueError, match='length'): from_memberships(memberships[:-1], data=data) @pytest.mark.parametrize('data', [None, {'attr1': [3, 4, 5, 6, 7, 8], 'attr2': list('qrstuv')}]) @pytest.mark.parametrize('typ', [set, list, tuple, iter]) @pytest.mark.parametrize('id_column', ['id', 'blah']) def test_from_contents_vs_memberships(data, typ, id_column): contents = OrderedDict([('cat1', typ(['aa', 'bb', 'cc'])), ('cat2', typ(['cc', 'dd'])), ('cat3', typ(['ee']))]) # Note that ff is not present in contents data_df = pd.DataFrame(data, index=['aa', 'bb', 'cc', 'dd', 'ee', 'ff']) baseline = from_contents(contents, data=data_df, id_column=id_column) # compare from_contents to from_memberships expected = from_memberships(memberships=[{'cat1'}, {'cat1'}, {'cat1', 'cat2'}, {'cat2'}, {'cat3'}, []], data=data_df) assert_series_equal(baseline[id_column].reset_index(drop=True), pd.Series(['aa', 'bb', 'cc', 'dd', 'ee', 'ff'], name=id_column)) assert_frame_equal(baseline.drop([id_column], axis=1), expected) def test_from_contents(typ=set, id_column='id'): contents = OrderedDict([('cat1', {'aa', 'bb', 'cc'}), ('cat2', {'cc', 'dd'}), ('cat3', {'ee'})]) empty_data = pd.DataFrame(index=['aa', 'bb', 'cc', 'dd', 'ee']) baseline = from_contents(contents, data=empty_data, id_column=id_column) # data=None out = from_contents(contents, id_column=id_column) assert_frame_equal(out.sort_values(id_column), baseline) # unordered contents dict out = from_contents({'cat3': contents['cat3'], 'cat2': contents['cat2'], 'cat1': contents['cat1']}, data=empty_data, id_column=id_column) assert_frame_equal(out.reorder_levels(['cat1', 'cat2', 'cat3']), baseline) # empty category out = from_contents({'cat1': contents['cat1'], 'cat2': contents['cat2'], 'cat3': contents['cat3'], 'cat4': []}, data=empty_data, id_column=id_column) assert not out.index.to_frame()['cat4'].any() # cat4 should be all-false assert len(out.index.names) == 4 out.index = out.index.to_frame().set_index(['cat1', 'cat2', 'cat3']).index assert_frame_equal(out, baseline) @pytest.mark.parametrize('id_column', ['id', 'blah']) def test_from_contents_invalid(id_column): contents = OrderedDict([('cat1', {'aa', 'bb', 'cc'}), ('cat2', {'cc', 'dd'}), ('cat3', {'ee'})]) with pytest.raises(ValueError, match='columns overlap'): from_contents(contents, data=pd.DataFrame({'cat1': [1, 2, 3, 4, 5]}), id_column=id_column) with pytest.raises(ValueError, match='duplicate ids'): from_contents({'cat1': ['aa', 'bb'], 'cat2': ['dd', 'dd']}, id_column=id_column) # category named id with pytest.raises(ValueError, match='cannot be named'): from_contents({id_column: {'aa', 'bb', 'cc'}, 'cat2': {'cc', 'dd'}, }, id_column=id_column) # category named id with pytest.raises(ValueError, match='cannot contain'): from_contents(contents, data=pd.DataFrame({id_column: [1, 2, 3, 4, 5]}, index=['aa', 'bb', 'cc', 'dd', 'ee']), id_column=id_column) with pytest.raises(ValueError, match='identifiers in contents'): from_contents({'cat1': ['aa']}, data=pd.DataFrame([[1]]), id_column=id_column) @pytest.mark.parametrize('indicators,data,exc_type,match', [ (["a", "b"], None, ValueError, "data must be provided"), (lambda df: [True, False, True], None, ValueError, "data must be provided"), (["a", "unknown_col"], {"a": [1, 2, 3]}, KeyError, "unknown_col"), (("a",), {"a": [1, 2, 3]}, ValueError, "tuple"), ({"cat1": [0, 1, 1]}, {"a": [1, 2, 3]}, ValueError, "must all be boolean"), (pd.DataFrame({"cat1": [True, False, True]}, index=["a", "b", "c"]), {"A": [1, 2, 3]}, ValueError, "all its values must be present"), ]) def test_from_indicators_invalid(indicators, data, exc_type, match): with pytest.raises(exc_type, match=match): from_indicators(indicators=indicators, data=data) @pytest.mark.parametrize('indicators', [ pd.DataFrame({"cat1": [False, True, False]}), pd.DataFrame({"cat1": [False, True, False]}, dtype="O"), {"cat1": [False, True, False]}, lambda data: {"cat1": {pd.DataFrame(data).index.values[1]: True}}, ]) @pytest.mark.parametrize('data', [ pd.DataFrame({"val1": [3, 4, 5]}), pd.DataFrame({"val1": [3, 4, 5]}, index=["a", "b", "c"]), {"val1": [3, 4, 5]}, ]) def test_from_indicators_equivalence(indicators, data): assert_frame_equal(from_indicators(indicators, data), from_memberships([[], ["cat1"], []], data)) def test_generate_data_warning(): with pytest.warns(DeprecationWarning): generate_data()