def test_reinterpret_string(self): table = self.data_str domain = table.domain tvars = [] for v in domain.metas: for i, tr in enumerate( [AsContinuous(), AsCategorical(), AsTime(), AsString()]): vtr = apply_reinterpret(v, tr, table_column_data( table, v)).renamed(f"{v.name}_{i}") if isinstance(tr, AsTime): strp = StrpTime("Detect automatically", None, 1, 1) vtr = apply_transform_var(vtr, [strp]) tvars.append(vtr) tdomain = Domain([], metas=tvars) ttable = table.transform(tdomain) assert_array_nanequal( ttable.metas, np.array([ [0.1, 0., np.nan, "0.1", 2010., 0., 1262304000., "2010"], [1.0, 1., np.nan, "1.0", 2020., 1., 1577836800., "2020"], ], dtype=object))
def test_reinterpret_string(self): table = self.data_str domain = table.domain tvars = [] for v in domain.metas: for tr in [AsContinuous(), AsCategorical(), AsTime(), AsString()]: tr = apply_reinterpret(v, tr, table_column_data(table, v)) tvars.append(tr) tdomain = Domain([], metas=tvars) ttable = table.transform(tdomain) assert_array_nanequal( ttable.metas, np.array([ [0.1, 0., np.nan, "0.1", 2010., 0., 1262304000., "2010"], [1.0, 1., np.nan, "1.0", 2020., 1., 1577836800., "2020"], ], dtype=object))
def test_as_time(self): table = self.data domain = table.domain tr = AsTime() dtr = [] for v in domain.variables: vtr = apply_reinterpret(v, tr, table_column_data(table, v)) dtr.append(vtr) ttable = table.transform(Domain(dtr)) assert_array_equal( ttable.X, np.array([ [np.nan, np.nan, 0.25, 180], [np.nan, np.nan, 1.25, 360], [np.nan, np.nan, 0.20, 720], [np.nan, np.nan, 0.00, 000], ], dtype=float))
def test_as_time(self): # this test only test type of format that can be string, continuous and discrete # correctness of time formats is already tested in TimeVariable module d = TimeVariable("_").parse_exact_iso times = ( ["07.02.2022", "18.04.2021"], # date only ["07.02.2022 01:02:03", "18.04.2021 01:02:03"], # datetime ["010203", "010203"], # time ["02-07", "04-18"], ) formats = ["25.11.2021", "25.11.2021 00:00:00", "000000", "11-25"] expected = [ [d("2022-02-07"), d("2021-04-18")], [d("2022-02-07 01:02:03"), d("2021-04-18 01:02:03")], [d("01:02:03"), d("01:02:03")], [d("1900-02-07"), d("1900-04-18")], ] variables = [StringVariable(f"s{i}") for i in range(len(times))] variables += [ DiscreteVariable(f"d{i}", values=t) for i, t in enumerate(times) ] domain = Domain([], metas=variables) metas = [t for t in times] + [list(range(len(x))) for x in times] table = Table(domain, np.empty((len(times[0]), 0)), metas=np.array(metas).transpose()) tr = AsTime() dtr = [] for v, f in zip(domain.metas, chain(formats, formats)): strp = StrpTime(f, *TimeVariable.ADDITIONAL_FORMATS[f]) vtr = apply_transform_var( apply_reinterpret(v, tr, table_column_data(table, v)), [strp]) dtr.append(vtr) ttable = table.transform(Domain([], metas=dtr)) assert_array_equal( ttable.metas, np.array(list(chain(expected, expected)), dtype=float).transpose())
def test_reinterpret(self): var = String("T", ()) for tr in (AsContinuous(), AsCategorical(), AsTime()): t = report_transform(var, [tr]) self.assertIn("→ (", t)