def test_replace_missing_vals(self):
M = np.array([('a', 0, 0.0, 0.1),
('b', 1, 1.0, np.nan),
('', -999, np.nan, 0.0),
('d', 1, np.nan, 0.2),
('', -999, 2.0, np.nan)],
dtype=[('str', 'O'), ('int', int), ('float1', float),
('float2', float)])
ctrl = M.copy()
ctrl['float1'] = np.array([0.0, 1.0, -1.0, -1.0, 2.0])
ctrl['float2'] = np.array([0.1, -1.0, 0.0, 0.2, -1.0])
res = replace_missing_vals(M, 'constant', constant=-1.0)
self.assertTrue(np.array_equal(ctrl, res))
ctrl = M.copy()
ctrl['int'] = np.array([100, 1, -999, 1, -999])
ctrl['float1'] = np.array([100, 1.0, np.nan, np.nan, 2.0])
ctrl['float2'] = np.array([0.1, np.nan, 100, 0.2, np.nan])
res = replace_missing_vals(M, 'constant', missing_val=0, constant=100)
self.assertTrue(utils_for_tests.array_equal(ctrl, res))
ctrl = M.copy()
ctrl['int'] = np.array([0, 1, 1, 1, 1])
res = replace_missing_vals(M, 'most_frequent', missing_val=-999)
self.assertTrue(utils_for_tests.array_equal(ctrl, res))
ctrl = M.copy()
ctrl['float1'] = np.array([0.0, 1.0, 1.0, 1.0, 2.0])
ctrl['float2'] = np.array([0.1, 0.1, 0.0, 0.2, 0.1])
res = replace_missing_vals(M, 'mean', missing_val=np.nan)
self.assertTrue(utils_for_tests.array_equal(ctrl, res))
def test_replace_missing_vals(self):
M = np.array([('a', 0, 0.0, 0.1),
('b', 1, 1.0, np.nan),
('', -999, np.nan, 0.0),
('d', 1, np.nan, 0.2),
('', -999, 2.0, np.nan)],
dtype=[('str', 'O'), ('int', int), ('float1', float),
('float2', float)])
ctrl = M.copy()
ctrl['float1'] = np.array([0.0, 1.0, -1.0, -1.0, 2.0])
ctrl['float2'] = np.array([0.1, -1.0, 0.0, 0.2, -1.0])
res = replace_missing_vals(M, 'constant', constant=-1.0)
self.assertTrue(np.array_equal(ctrl, res))
ctrl = M.copy()
ctrl['int'] = np.array([100, 1, -999, 1, -999])
ctrl['float1'] = np.array([100, 1.0, np.nan, np.nan, 2.0])
ctrl['float2'] = np.array([0.1, np.nan, 100, 0.2, np.nan])
res = replace_missing_vals(M, 'constant', missing_val=0, constant=100)
self.assertTrue(utils_for_tests.array_equal(ctrl, res))
ctrl = M.copy()
ctrl['int'] = np.array([0, 1, 1, 1, 1])
res = replace_missing_vals(M, 'most_frequent', missing_val=-999)
self.assertTrue(utils_for_tests.array_equal(ctrl, res))
ctrl = M.copy()
ctrl['float1'] = np.array([0.0, 1.0, 1.0, 1.0, 2.0])
ctrl['float2'] = np.array([0.1, 0.1, 0.0, 0.2, 0.1])
res = replace_missing_vals(M, 'mean', missing_val=np.nan)
self.assertTrue(utils_for_tests.array_equal(ctrl, res))
def test_describe_cols(self):
test_list = [[1, 2],[2, 3],[3, 4],[4, 5],[5, 6],[6, 7]]
test_nd = np.array(test_list)
test_sa = np.array([(1, 2, 'a'), (2, 3, 'b'), (3, 4, 'c'), (4, 5, 'd'),
(5, 6, 'e'), (6, 7, 'f')],
dtype=[('id', int), ('val', float), ('name', 'S1')])
ctrl_list = np.array([('f0', 6, 3.5, 1.707825127659933, 1, 6),
('f1', 6, 4.5, 1.707825127659933, 2, 7)],
dtype=[('Column Name', 'S2'), ('Count', int),
('Mean', float), ('Standard Dev', float),
('Minimum', int), ('Maximum', int)])
ctrl_printout = """
Column Name Count Mean Standard Dev Minimum Maximum
0 f0 6 3.5 1.70782512766 1 6
1 f1 6 4.5 1.70782512766 2 7
""".strip()
with uft.rerout_stdout() as get_stdout:
self.assertTrue(uft.array_equal(ctrl_list,
describe_cols(
test_list)))
self.assertEqual(get_stdout().strip(), ctrl_printout)
self.assertTrue(uft.array_equal(ctrl_list,
describe_cols(
test_nd, verbose=False)))
ctrl_sa = np.array([('id', 6, 3.5, 1.707825127659933, 1, 6),
('val', 6, 4.5, 1.707825127659933, 2, 7),
('name', np.nan, np.nan, np.nan, np.nan, np.nan)],
dtype=[('Column Name', 'S4'), ('Count', float),
('Mean', float), ('Standard Dev', float),
('Minimum', float), ('Maximum', float)])
self.assertTrue(uft.array_equal(ctrl_sa,
describe_cols(
test_sa,
verbose=False)))
def test_array_emitter(self):
db_file = uft.path_of_data('rg_complex_dates.db')
ae = array_emitter.ArrayEmitter(convert_to_unix_time=True)
ae = ae.set_aggregation('bounded', 'SUM')
ae = ae.set_aggregation('no_start', 'SUM')
ae = ae.set_aggregation('no_stop', 'SUM')
ae = ae.set_aggregation('unbounded', 'SUM')
ae = ae.get_rg_from_sql(self.conn_str,
'rg_complex_dates',
feature_col='feature')
res1 = ae.set_interval(datetime(2010, 1, 1), datetime(2010, 6,
30)).emit_M()
res2 = ae.set_interval(datetime(2010, 7, 1), datetime(2010, 12,
31)).emit_M()
res3 = ae.set_interval(datetime(2010, 1, 1), datetime(2010, 12,
31)).emit_M()
ctrl_dtype = [('id', '<i8'), ('bounded_sum', '<f8'),
('no_start_sum', '<f8'), ('no_stop_sum', '<f8'),
('unbounded_sum', '<f8')]
ctrl1_dat = [(0, 1.0, 100.0, 100000.0, 1000000.0),
(1, 0.01, 0.001, 1e-06, 1e-07),
(2, np.nan, np.nan, np.nan, 2e-08)]
ctrl2_dat = [(0, 10.0, 1000.0, 10000.0, 1000000.0),
(1, 0.1, 0.0001, 1e-05, 1e-07),
(2, np.nan, np.nan, np.nan, 2e-08)]
ctrl3_dat = [(0, 11.0, 1100.0, 110000.0, 1000000.0),
(1, 0.11, 0.0011, 1.1e-05, 1e-07),
(2, np.nan, np.nan, np.nan, 2e-08)]
for res, ctrl_dat in zip((res1, res2, res3),
(ctrl1_dat, ctrl2_dat, ctrl3_dat)):
self.assertTrue(
uft.array_equal(res,
np.array(ctrl_dat, dtype=ctrl_dtype),
idx_col='id'))
def test_multiple_aggr(self):
db_file = uft.path_of_data('rg_students.db')
conn_str = 'sqlite:///{}'.format(db_file)
ae = array_emitter.ArrayEmitter()
ae = ae.get_rg_from_sql(conn_str, 'rg_students')
ae = ae.set_default_aggregation(['AVG', 'MIN', 'MAX', 'COUNT'])
ae = ae.set_aggregation('absences', ['MIN', 'MAX'])
ae = ae.set_aggregation('graduated', 'MAX')
ae = ae.set_interval(2005, 2007)
ae = ae.set_label_feature('graduated')
ae = ae.set_label_interval(2009, 2009)
res = ae.emit_M()
ctrl = np.array(
[(0, 2.2, 2.1, 2.3, 2, 3.95, 3.9, 4.0, 2, 7.0, 8.0, 1.0),
(1, 3.45, 3.4, 3.5, 2, np.nan, np.nan, np.nan, np.nan, 0.0, 0.0,
0.0),
(2, 3.4, 3.4, 3.4, 1.0, np.nan, np.nan, np.nan, np.nan, 14.0,
96.0, np.nan)],
dtype=[('id', '<i8'), ('math_gpa_AVG', '<f8'),
('math_gpa_MIN', '<f8'), ('math_gpa_MAX', '<f8'),
('math_gpa_COUNT', '<i8'), ('english_gpa_AVG', '<f8'),
('english_gpa_MIN', '<f8'), ('english_gpa_MAX', '<f8'),
('english_gpa_COUNT', '<f8'), ('absences_MIN', '<f8'),
('absences_MAX', '<f8'), ('graduated_MAX', '<f8')])
self.assertTrue(uft.array_equal(res, ctrl))
def test_cast_list_of_list_to_sa(self):
L = [[None, None, None],
['a', 5, None],
['ab', 'x', None]]
ctrl = np.array(
[('', '', ''),
('a', '5', ''),
('ab', 'x', '')],
dtype=[('f0', 'S2'),
('f1', 'S1'),
('f2', 'S1')])
conv = utils.cast_list_of_list_to_sa(L)
self.assertTrue(np.array_equal(conv, ctrl))
L = [[None, u'\u05dd\u05d5\u05dc\u05e9', 4.0, 7],
[2, 'hello', np.nan, None],
[4, None, None, 14L]]
ctrl = np.array(
[(-999, u'\u05dd\u05d5\u05dc\u05e9', 4.0, 7),
(2, u'hello', np.nan, -999L),
(4, u'', np.nan, 14L)],
dtype=[('int', int), ('ucode', 'U5'), ('float', float),
('long', long)])
conv = utils.cast_list_of_list_to_sa(
L,
col_names=['int', 'ucode', 'float', 'long'])
self.assertTrue(utils_for_tests.array_equal(ctrl, conv))
def test_from_csv(self):
db_file = uft.path_of_data('rg_complex_dates.csv')
ae = array_emitter.ArrayEmitter()
ae = ae.set_aggregation('bounded', 'SUM')
ae = ae.set_aggregation('no_start', 'SUM')
ae = ae.set_aggregation('no_stop', 'SUM')
ae = ae.set_aggregation('unbounded', 'SUM')
ae = ae.get_rg_from_csv(db_file,
feature_col='feature',
parse_datetimes=['start', 'stop'])
res1 = ae.set_interval(datetime(2010, 1, 1), datetime(2010, 6,
30)).emit_M()
res2 = ae.set_interval(datetime(2010, 7, 1), datetime(2010, 12,
31)).emit_M()
res3 = ae.set_interval(datetime(2010, 1, 1), datetime(2010, 12,
31)).emit_M()
ctrl_dtype = [('id', '<i8'), ('bounded_SUM', '<f8'),
('no_start_SUM', '<f8'), ('no_stop_SUM', '<f8'),
('unbounded_SUM', '<f8')]
ctrl1_dat = [(0, 1.0, 100.0, 100000.0, 1000000.0),
(1, 0.01, 0.001, 1e-06, 1e-07),
(2, np.nan, np.nan, np.nan, 2e-08)]
ctrl2_dat = [(0, 10.0, 1000.0, 10000.0, 1000000.0),
(1, 0.1, 0.0001, 1e-05, 1e-07),
(2, np.nan, np.nan, np.nan, 2e-08)]
ctrl3_dat = [(0, 11.0, 1100.0, 110000.0, 1000000.0),
(1, 0.11, 0.0011, 1.1e-05, 1e-07),
(2, np.nan, np.nan, np.nan, 2e-08)]
for res, ctrl_dat in zip((res1, res2, res3),
(ctrl1_dat, ctrl2_dat, ctrl3_dat)):
self.assertTrue(
uft.array_equal(res, np.array(ctrl_dat, dtype=ctrl_dtype)))
def test_from_csv(self):
db_file = uft.path_of_data('rg_complex_dates.csv')
ae = array_emitter.ArrayEmitter()
ae = ae.set_aggregation('bounded', 'SUM')
ae = ae.set_aggregation('no_start', 'SUM')
ae = ae.set_aggregation('no_stop', 'SUM')
ae = ae.set_aggregation('unbounded', 'SUM')
ae = ae.get_rg_from_csv(db_file, feature_col='feature',
parse_datetimes=['start', 'stop'])
res1 = ae.set_interval(
datetime(2010, 1, 1),
datetime(2010, 6, 30)).emit_M()
res2 = ae.set_interval(
datetime(2010, 7, 1),
datetime(2010, 12, 31)).emit_M()
res3 = ae.set_interval(
datetime(2010, 1, 1),
datetime(2010, 12, 31)).emit_M()
ctrl_dtype = [('id', '<i8'), ('bounded', '<f8'),
('no_start', '<f8'), ('no_stop', '<f8'),
('unbounded', '<f8')]
ctrl1_dat = [(0, 1.0, 100.0, 100000.0, 1000000.0),
(1, 0.01, 0.001, 1e-06, 1e-07),
(2, np.nan, np.nan, np.nan, 2e-08)]
ctrl2_dat = [(0, 10.0, 1000.0, 10000.0, 1000000.0),
(1, 0.1, 0.0001, 1e-05, 1e-07),
(2, np.nan, np.nan, np.nan, 2e-08)]
ctrl3_dat = [(0, 11.0, 1100.0, 110000.0, 1000000.0),
(1, 0.11, 0.0011, 1.1e-05, 1e-07),
(2, np.nan, np.nan, np.nan, 2e-08)]
for res, ctrl_dat in zip((res1, res2, res3), (ctrl1_dat, ctrl2_dat,
ctrl3_dat)):
self.assertTrue(uft.array_equal(
res,
np.array(ctrl_dat, dtype=ctrl_dtype)))
def test_array_emitter(self):
db_file = uft.path_of_data('rg_complex_dates.db')
ae = array_emitter.ArrayEmitter(convert_to_unix_time=True)
ae = ae.set_aggregation('bounded', 'SUM')
ae = ae.set_aggregation('no_start', 'SUM')
ae = ae.set_aggregation('no_stop', 'SUM')
ae = ae.set_aggregation('unbounded', 'SUM')
ae = ae.get_rg_from_sql(self.conn_str, 'rg_complex_dates',
feature_col='feature')
res1 = ae.set_interval(
datetime(2010, 1, 1),
datetime(2010, 6, 30)).emit_M()
res2 = ae.set_interval(
datetime(2010, 7, 1),
datetime(2010, 12, 31)).emit_M()
res3 = ae.set_interval(
datetime(2010, 1, 1),
datetime(2010, 12, 31)).emit_M()
ctrl_dtype = [('id', '<i8'), ('bounded_sum', '<f8'),
('no_start_sum', '<f8'), ('no_stop_sum', '<f8'),
('unbounded_sum', '<f8')]
ctrl1_dat = [(0, 1.0, 100.0, 100000.0, 1000000.0),
(1, 0.01, 0.001, 1e-06, 1e-07),
(2, np.nan, np.nan, np.nan, 2e-08)]
ctrl2_dat = [(0, 10.0, 1000.0, 10000.0, 1000000.0),
(1, 0.1, 0.0001, 1e-05, 1e-07),
(2, np.nan, np.nan, np.nan, 2e-08)]
ctrl3_dat = [(0, 11.0, 1100.0, 110000.0, 1000000.0),
(1, 0.11, 0.0011, 1.1e-05, 1e-07),
(2, np.nan, np.nan, np.nan, 2e-08)]
for res, ctrl_dat in zip((res1, res2, res3), (ctrl1_dat, ctrl2_dat,
ctrl3_dat)):
self.assertTrue(uft.array_equal(
res,
np.array(ctrl_dat, dtype=ctrl_dtype), idx_col='id'))
def test_cast_list_of_list_to_sa2(self):
L = [[None, None, None],
['a', 5, None],
['ab', 'x', None]]
ctrl = np.array(
[('', '', ''),
('a', '5', ''),
('ab', 'x', '')],
dtype=[('f0', 'S2'),
('f1', 'S1'),
('f2', 'S1')])
conv = utils.cast_list_of_list_to_sa(L)
self.assertTrue(np.array_equal(conv, ctrl))
L = [[None, u'\u05dd\u05d5\u05dc\u05e9', 4.0, 7],
[2, 'hello', np.nan, None],
[4, None, None, 14L]]
ctrl = np.array(
[(-999, u'\u05dd\u05d5\u05dc\u05e9', 4.0, 7),
(2, u'hello', np.nan, -999L),
(4, u'', np.nan, 14L)],
dtype=[('int', int), ('ucode', 'U5'), ('float', float),
('long', long)])
conv = utils.cast_list_of_list_to_sa(
L,
col_names=['int', 'ucode', 'float', 'long'])
self.assertTrue(uft.array_equal(ctrl, conv))
def test_convert_to_sa(self):
# already a structured array
sa = np.array([(1, 1.0, 'a', datetime(2015, 01, 01)),
(2, 2.0, 'b', datetime(2016, 01, 01))],
dtype=[('int', int), ('float', float), ('str', 'S1'),
('date', 'M8[s]')])
self.assertTrue(np.array_equal(sa, utils.convert_to_sa(sa)))
# homogeneous array no col names provided
nd = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
ctrl = np.array([(1, 2, 3), (4, 5, 6), (7, 8, 9)],
dtype=[('f0', int), ('f1', int), ('f2', int)])
self.assertTrue(np.array_equal(ctrl, utils.convert_to_sa(nd)))
# homogeneous array with col names provided
nd = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
ctrl = np.array([(1, 2, 3), (4, 5, 6), (7, 8, 9)],
dtype=[('i0', int), ('i1', int), ('i2', int)])
self.assertTrue(np.array_equal(ctrl, utils.convert_to_sa(
nd,
col_names=['i0', 'i1', 'i2'])))
# list of lists no col name provided
lol = [[1, 1, None],
['abc', 2, 3.4]]
ctrl = np.array([('1', 1, np.nan),
('abc', 2, 3.4)],
dtype=[('f0', 'S3'), ('f1', int), ('f2', float)])
res = utils.convert_to_sa(lol)
self.assertTrue(utils_for_tests.array_equal(ctrl, res))
# list of lists with col name provided
lol = [['hello', 1.2, datetime(2012, 1, 1), None],
[1.3, np.nan, None, '2013-01-01'],
[1.4, 1.5, '2014-01-01', 'NO_SUCH_RECORD']]
ctrl = np.array([('hello', 1.2, datetime(2012, 1, 1), utils.NOT_A_TIME),
('1.3', np.nan, utils.NOT_A_TIME, datetime(2013, 1, 1)),
('1.4', 1.5, datetime(2014, 1, 1), utils.NOT_A_TIME)],
dtype=[('i0', 'S5'), ('i1', float), ('i2', 'M8[us]'),
('i3', 'M8[us]')])
res = utils.convert_to_sa(lol, col_names = ['i0', 'i1', 'i2', 'i3'])
self.assertTrue(utils_for_tests.array_equal(ctrl, res))
def test_convert_to_sa(self):
# already a structured array
sa = np.array([(1, 1.0, 'a', datetime(2015, 01, 01)),
(2, 2.0, 'b', datetime(2016, 01, 01))],
dtype=[('int', int), ('float', float), ('str', 'O'),
('date', 'M8[s]')])
self.assertTrue(np.array_equal(sa, utils.convert_to_sa(sa)))
# homogeneous array no col names provided
nd = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
ctrl = np.array([(1, 2, 3), (4, 5, 6), (7, 8, 9)],
dtype=[('f0', int), ('f1', int), ('f2', int)])
self.assertTrue(np.array_equal(ctrl, utils.convert_to_sa(nd)))
# homogeneous array with col names provided
nd = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
ctrl = np.array([(1, 2, 3), (4, 5, 6), (7, 8, 9)],
dtype=[('i0', int), ('i1', int), ('i2', int)])
self.assertTrue(np.array_equal(ctrl, utils.convert_to_sa(
nd,
col_names=['i0', 'i1', 'i2'])))
# list of lists no col name provided
lol = [[1, 1, None],
['abc', 2, 3.4]]
ctrl = np.array([('1', 1, np.nan),
('abc', 2, 3.4)],
dtype=[('f0', 'S3'), ('f1', int), ('f2', float)])
res = utils.convert_to_sa(lol)
self.assertTrue(uft.array_equal(ctrl, res))
# list of lists with col name provided
lol = [['hello', 1.2, datetime(2012, 1, 1), None],
[1.3, np.nan, None, '2013-01-01'],
[1.4, 1.5, '2014-01-01', 'NO_SUCH_RECORD']]
ctrl = np.array([('hello', 1.2, datetime(2012, 1, 1), utils.NOT_A_TIME),
('1.3', np.nan, utils.NOT_A_TIME, datetime(2013, 1, 1)),
('1.4', 1.5, datetime(2014, 1, 1), utils.NOT_A_TIME)],
dtype=[('i0', 'S5'), ('i1', float), ('i2', 'M8[us]'),
('i3', 'M8[us]')])
res = utils.convert_to_sa(lol, col_names = ['i0', 'i1', 'i2', 'i3'])
self.assertTrue(uft.array_equal(ctrl, res))
def test_get_top_features(self):
M, labels = uft.generate_test_matrix(1000, 15, random_state=0)
M = utils.cast_np_sa_to_nd(M)
M_train, M_test, labels_train, labels_test = train_test_split(
M,
labels)
clf = RandomForestClassifier(random_state=0)
clf.fit(M_train, labels_train)
ctrl_feat_importances = clf.feature_importances_
ctrl_col_names = ['f{}'.format(i) for i in xrange(15)]
ctrl_feat_ranks = np.argsort(ctrl_feat_importances)[::-1][:10]
ctrl = utils.convert_to_sa(
zip(ctrl_col_names, ctrl_feat_importances),
col_names=('feat_name', 'score'))[ctrl_feat_ranks]
res = dsp.get_top_features(clf, M, verbose=False)
self.assertTrue(uft.array_equal(ctrl, res))
res = dsp.get_top_features(clf, col_names=['f{}'.format(i) for i in xrange(15)], verbose=False)
self.assertTrue(uft.array_equal(ctrl, res))
def test_describe_cols(self):
test_list = [[1, 2],[2, 3],[3, 4],[4, 5],[5, 6],[6, 7]]
test_nd = np.array(test_list)
test_sa = np.array([(1, 2, 'a'), (2, 3, 'b'), (3, 4, 'c'), (4, 5, 'd'),
(5, 6, 'e'), (6, 7, 'f')],
dtype=[('id', int), ('val', float), ('name', 'S1')])
ctrl_list = np.array([('f0', 6, 3.5, 1.707825127659933, 1, 6),
('f1', 6, 4.5, 1.707825127659933, 2, 7)],
dtype=[('Column Name', 'S2'), ('Count', int),
('Mean', float), ('Standard Dev', float),
('Minimum', int), ('Maximum', int)])
self.assertTrue(utils_for_tests.array_equal(ctrl_list,
describe_cols(test_list)))
self.assertTrue(utils_for_tests.array_equal(ctrl_list,
describe_cols(test_nd)))
ctrl_sa = np.array([('id', 6, 3.5, 1.707825127659933, 1, 6),
('val', 6, 4.5, 1.707825127659933, 2, 7),
('name', np.nan, np.nan, np.nan, np.nan, np.nan)],
dtype=[('Column Name', 'S4'), ('Count', float),
('Mean', float), ('Standard Dev', float),
('Minimum', float), ('Maximum', float)])
self.assertTrue(utils_for_tests.array_equal(ctrl_sa,
describe_cols(test_sa)))
def test_describe_cols(self):
test_list = [[1, 2],[2, 3],[3, 4],[4, 5],[5, 6],[6, 7]]
test_nd = np.array(test_list)
test_sa = np.array([(1, 2, 'a'), (2, 3, 'b'), (3, 4, 'c'), (4, 5, 'd'),
(5, 6, 'e'), (6, 7, 'f')],
dtype=[('id', int), ('val', float), ('name', 'S1')])
ctrl_list = np.array([('f0', 6, 3.5, 1.707825127659933, 1, 6),
('f1', 6, 4.5, 1.707825127659933, 2, 7)],
dtype=[('Column Name', 'S2'), ('Count', int),
('Mean', float), ('Standard Dev', float),
('Minimum', int), ('Maximum', int)])
self.assertTrue(uft.array_equal(ctrl_list,
describe_cols(test_list)))
self.assertTrue(uft.array_equal(ctrl_list,
describe_cols(test_nd)))
ctrl_sa = np.array([('id', 6, 3.5, 1.707825127659933, 1, 6),
('val', 6, 4.5, 1.707825127659933, 2, 7),
('name', np.nan, np.nan, np.nan, np.nan, np.nan)],
dtype=[('Column Name', 'S4'), ('Count', float),
('Mean', float), ('Standard Dev', float),
('Minimum', float), ('Maximum', float)])
self.assertTrue(uft.array_equal(ctrl_sa,
describe_cols(test_sa)))
def test_basic(self):
db_file = uft.path_of_data('rg_students.db')
conn_str = 'sqlite:///{}'.format(db_file)
ae = array_emitter.ArrayEmitter()
ae = ae.get_rg_from_sql(conn_str, 'rg_students')
ae = ae.set_aggregation('absences', 'MAX')
ae = ae.set_interval(2005, 2007)
res = ae.emit_M()
ctrl = np.array([(0, 2.2, 3.95, 8.0),
(1, 3.45, np.nan, 0.0),
(2, 3.4, np.nan, 96.0)],
dtype=[('id', '<i8'), ('math_gpa', '<f8'),
('english_gpa', '<f8'),
('absences', '<f8')])
self.assertTrue(uft.array_equal(res, ctrl))
def test_get_top_features(self):
M, labels = uft.generate_test_matrix(1000, 15, random_state=0)
M = utils.cast_np_sa_to_nd(M)
M_train, M_test, labels_train, labels_test = train_test_split(
M,
labels)
clf = RandomForestClassifier(random_state=0)
clf.fit(M_train, labels_train)
res = dsp.get_top_features(clf, M, verbose=False)
ctrl = utils.convert_to_sa(
[('f5', 0.0773838526068),
('f13', 0.0769596713039),
('f8', 0.0751584839431),
('f6', 0.0730815879102),
('f11', 0.0684456133071),
('f9', 0.0666747414603),
('f10', 0.0659621889608),
('f7', 0.0657988099065),
('f2', 0.0634000069218),
('f0', 0.0632912268319)],
col_names=('feat_name', 'score'))
self.assertTrue(uft.array_equal(ctrl, res))
res = dsp.get_top_features(clf, col_names=['f{}'.format(i) for i in xrange(15)], verbose=False)
self.assertTrue(uft.array_equal(ctrl, res))
def test_table(self):
data = np.array(['a', 'b', 'a', 'b', 'b', 'b', 'b', 'a', 'c', 'c',
'b', 'c', 'a'], dtype='O')
ctrl_sa = np.array(
[('a', 4), ('b', 6), ('c', 3)],
dtype=[('col_name', 'S1'), ('count', int)])
ctrl_printout = """
col_name count
0 a 4
1 b 6
2 c 3
""".strip()
with uft.rerout_stdout() as get_stdout:
self.assertTrue(uft.array_equal(ctrl_sa,
table(data)))
self.assertEqual(get_stdout().strip(), ctrl_printout)
def test_get_top_features(self):
M, labels = uft.generate_test_matrix(1000, 15, random_state=0)
M = utils.cast_np_sa_to_nd(M)
M_train, M_test, labels_train, labels_test = train_test_split(
M, labels)
clf = RandomForestClassifier(random_state=0)
clf.fit(M_train, labels_train)
res = comm.get_top_features(clf, M, verbose=False)
ctrl = utils.convert_to_sa([('f5', 0.0773838526068),
('f13', 0.0769596713039),
('f8', 0.0751584839431),
('f6', 0.0730815879102),
('f11', 0.0684456133071),
('f9', 0.0666747414603),
('f10', 0.0659621889608),
('f7', 0.0657988099065),
('f2', 0.0634000069218),
('f0', 0.0632912268319)],
col_names=('feat_name', 'score'))
self.assertTrue(uft.array_equal(ctrl, res))
def test_join(self):
# test basic inner join
a1 = np.array([(0, 'Lisa', 2),
(1, 'Bill', 1),
(2, 'Fred', 2),
(3, 'Samantha', 2),
(4, 'Augustine', 1),
(5, 'William', 0)], dtype=[('id', int),
('name', 'O'),
('dept_id', int)])
a2 = np.array([(0, 'accts receivable'),
(1, 'accts payable'),
(2, 'shipping')], dtype=[('id', int),
('name', 'S16')])
ctrl = pd.DataFrame(a1).merge(
pd.DataFrame(a2),
left_on='dept_id',
right_on='id').to_records(index=False)
res = utils.join(a1, a2, 'inner', 'dept_id', 'id')
self.assertTrue(uft.array_equal(ctrl, res, idx_col='id_x'))
# test column naming rules
a1 = np.array([(0, 'a', 1, 2, 3)], dtype=[('idx0', int),
('name', 'O'),
('a1_idx1', int),
('idx2', int),
('idx3', int)])
a2 = np.array([(0, 'b', 1, 2, 3)], dtype=[('idx0', int),
('name', 'O'),
('a2_idx1', int),
('idx2', int),
('idx3', int)])
pd1 = pd.DataFrame(a1)
pd2 = pd.DataFrame(a2)
ctrl = pd1.merge(
pd2,
left_on=['idx0', 'a1_idx1', 'idx2'],
right_on=['idx0', 'a2_idx1', 'idx2'],
suffixes=['_left', '_right']).to_records(index=False)
res = utils.join(
a1,
a2,
'inner',
left_on=['idx0', 'a1_idx1', 'idx2'],
right_on=['idx0', 'a2_idx1', 'idx2'],
suffixes=['_left', '_right'])
self.assertTrue(uft.array_equal(ctrl, res, idx_col='idx0'))
# outer joins
a1 = np.array(
[(0, 'a1_0', 0),
(1, 'a1_1', 1),
(1, 'a1_2', 2),
(2, 'a1_3', 3),
(3, 'a1_4', 4)],
dtype=[('key', int), ('label', 'O'), ('idx', int)])
a2 = np.array(
[(0, 'a2_0', 0),
(1, 'a2_1', 1),
(2, 'a2_2', 2),
(2, 'a2_3', 3),
(4, 'a2_4', 4)],
dtype=[('key', int), ('label', 'O'), ('idx', int)])
#for how in ('inner', 'left', 'right', 'outer'):
merged_dtype = [('key', int), ('label_x', 'O'), ('idx_x', int),
('label_y', 'O'), ('idx_y', int)]
merge_algos = ('inner', 'left', 'right', 'outer')
merged_data = [[(0, 'a1_0', 0, 'a2_0', 0),
(1, 'a1_1', 1, 'a2_1', 1),
(1, 'a1_2', 2, 'a2_1', 1),
(2, 'a1_3', 3, 'a2_2', 2),
(2, 'a1_3', 3, 'a2_3', 3)],
[(0, 'a1_0', 0, 'a2_0', 0),
(1, 'a1_1', 1, 'a2_1', 1),
(1, 'a1_2', 2, 'a2_1', 1),
(2, 'a1_3', 3, 'a2_2', 2),
(2, 'a1_3', 3, 'a2_3', 3),
(3, 'a1_4', 4, '', -999)],
[(0, 'a1_0', 0, 'a2_0', 0),
(1, 'a1_1', 1, 'a2_1', 1),
(1, 'a1_2', 2, 'a2_1', 1),
(2, 'a1_3', 3, 'a2_2', 2),
(2, 'a1_3', 3, 'a2_3', 3),
(4, '', -999, 'a2_4', 4)],
[(0, 'a1_0', 0, 'a2_0', 0),
(1, 'a1_1', 1, 'a2_1', 1),
(1, 'a1_2', 2, 'a2_1', 1),
(2, 'a1_3', 3, 'a2_2', 2),
(2, 'a1_3', 3, 'a2_3', 3),
(4, '', -999, 'a2_4', 4),
(3, 'a1_4', 4, '', -999)]]
for how, data in zip(merge_algos, merged_data):
res = utils.join(
a1,
a2,
how,
left_on='key',
right_on='key')
ctrl = np.array(data, dtype=merged_dtype)
self.assertTrue(uft.array_equal(ctrl, res))
