def test_dateparts_to_circular(self):
orig_data = pd.DataFrame(data=np.arange(
np.datetime64('2011-07-11'), np.datetime64('2011-07-18')
).reshape(7, 1), columns=["thedate"])
circular_data = date_to_cyclical(orig_data, 'thedate',
new_col_name_prefix='prefix')
intermediate_columns = ["prefix_{}".format(x) for x in
["DAY", "DAY_OF_WEEK", "HOUR", "MINUTE",
"MONTH", "SECOND"]]
expected_columns = ["{}_{}".format(x, y) for y in ["COS", "SIN"] for x
in intermediate_columns]
# no additional columns
np.testing.assert_array_equal(
list(set(circular_data.columns) - set(expected_columns)), list())
# no missing columns
np.testing.assert_array_equal(
list(set(expected_columns) - set(circular_data.columns)), list())
# correct result compared to just splitting the columns
splitted_data = date_to_dateparts(orig_data, 'thedate',
new_col_name_prefix='prefix')
sin_columns = ["{}_{}".format(x, y) for y in ["SIN"] for x in
intermediate_columns]
np.testing.assert_array_equal(circular_data.loc[:, sin_columns], np.sin(
splitted_data.loc[:, intermediate_columns] / (
2.0 * np.pi * np.array([31, 7, 24, 60, 12, 60]))))
cos_columns = ["{}_{}".format(x, y) for y in ["COS"] for x in
intermediate_columns]
np.testing.assert_array_equal(circular_data.loc[:, cos_columns], np.cos(
splitted_data.loc[:, intermediate_columns] / (
2.0 * np.pi * np.array([31, 7, 24, 60, 12, 60]))))
def test_datecyclicalencoding(self):
orig_data = pd.DataFrame(data=np.arange(
np.datetime64('2011-07-11'), np.datetime64('2011-07-18')
).reshape(7, 1), columns=["thedate"])
y = np.repeat(0, 7)
# create splitted to also be able to calculate values
splitted_data = date_to_dateparts(orig_data, 'thedate')
circular_data = DateCyclicalEncoding(['thedate'],
drop=True).fit_transform(orig_data,
y)
intermediate_columns = ["thedate_{}".format(x) for x in
["DAY", "DAY_OF_WEEK", "HOUR", "MINUTE",
"MONTH", "SECOND"]]
expected_columns = ["{}_{}".format(x, y) for y in ["COS", "SIN"] for x
in intermediate_columns]
# no additional columns
np.testing.assert_array_equal(
list(set(circular_data.columns) - set(expected_columns)), list())
# no missing columns
np.testing.assert_array_equal(
list(set(expected_columns) - set(circular_data.columns)), list())
sin_columns = ["{}_{}".format(x, y) for y in ["SIN"] for x in
intermediate_columns]
np.testing.assert_array_equal(circular_data.loc[:, sin_columns], np.sin(
splitted_data.loc[:, intermediate_columns] / (
2.0 * np.pi * np.array([31, 7, 24, 60, 12, 60]))))
cos_columns = ["{}_{}".format(x, y) for y in ["COS"] for x in
intermediate_columns]
np.testing.assert_array_equal(circular_data.loc[:, cos_columns], np.cos(
splitted_data.loc[:, intermediate_columns] / (
2.0 * np.pi * np.array([31, 7, 24, 60, 12, 60]))))
def test_date_to_dateparts(self):
orig_data = pd.DataFrame(
data=np.arange(np.datetime64('2011-07-11'),
np.datetime64('2011-07-18')).reshape(7, 1),
columns=["thedate"])
splitted_data = date_to_dateparts(orig_data,
'thedate',
new_col_name_prefix='prefix')
expected_columns = [
"prefix_{}".format(x) for x in
["DAY", "DAY_OF_WEEK", "HOUR", "MINUTE", "MONTH", "SECOND"]
]
# no additional columns
np.testing.assert_array_equal(
list(set(splitted_data.columns) - set(expected_columns)), list())
# no missing columns
np.testing.assert_array_equal(
list(set(expected_columns) - set(splitted_data.columns)), list())
monday = 0
tuesday = 1
np.testing.assert_array_equal(splitted_data.loc[0, expected_columns],
[11, monday, 0, 0, 7, 0])
np.testing.assert_array_equal(splitted_data.loc[1, expected_columns],
[12, tuesday, 0, 0, 7, 0])