def _disc_no_vals_column(data: np.ndarray, col: int, **_) -> \
_ColumnProperties:
vals, coltype = _TableBuilder._disc_column(data, col)
return _ColumnProperties(valuemap=natural_sorted(set(vals) - {""}),
values=vals,
coltype=coltype,
orig_values=vals)
def test_sort_table(self):
"""
Test if first column of the table is sorted naturally
"""
view = self.widget.view
model = view.model()
self.send_signal(self.widget.Inputs.corpus, self.corpus)
self.send_signal(self.widget.Inputs.words, self.words)
view.horizontalHeader().setSortIndicator(0, Qt.AscendingOrder)
data = [model.data(model.index(i, 0)) for i in range(model.rowCount())]
self.assertListEqual(data, natural_sorted(self.corpus.titles))
view.horizontalHeader().setSortIndicator(0, Qt.DescendingOrder)
data = [model.data(model.index(i, 0)) for i in range(model.rowCount())]
self.assertListEqual(data, natural_sorted(self.corpus.titles)[::-1])
def test_guess_data_type_discrete(self):
# should be DiscreteVariable
valuemap, values, coltype = guess_data_type([1, 2, 1, 2])
self.assertEqual(DiscreteVariable, coltype)
self.assertEqual([1, 2], valuemap)
np.testing.assert_array_equal([1, 2, 1, 2], values)
valuemap, values, coltype = guess_data_type(["1", "2", "1", "2", "a"])
self.assertEqual(DiscreteVariable, coltype)
self.assertEqual(["1", "2", "a"], valuemap)
np.testing.assert_array_equal(['1', '2', '1', '2', 'a'], values)
# just below the threshold for string variable
in_values = list(map(lambda x: str(x) + "a", range(24))) + ["a"] * 76
valuemap, values, coltype = guess_data_type(in_values)
self.assertEqual(DiscreteVariable, coltype)
self.assertEqual(natural_sorted(set(in_values)), valuemap)
np.testing.assert_array_equal(in_values, values)
def guess_data_type(orig_values, namask=None):
"""
Use heuristics to guess data type.
"""
valuemap, values = None, orig_values
is_discrete = is_discrete_values(orig_values)
orig_values = np.asarray(orig_values, dtype=str)
if namask is None:
namask = isnastr(orig_values)
if is_discrete:
valuemap = natural_sorted(is_discrete)
coltype = DiscreteVariable
else:
# try to parse as float
values = np.empty_like(orig_values, dtype=float)
values[namask] = np.nan
try:
np.copyto(values, orig_values, where=~namask, casting="unsafe")
except ValueError:
values = orig_values
coltype = StringVariable
else:
coltype = ContinuousVariable
if coltype is not ContinuousVariable:
# when not continuous variable it can still be time variable even it
# was before recognized as a discrete
tvar = TimeVariable('_')
# introducing new variable prevent overwriting orig_values and values
temp_values = np.empty_like(orig_values, dtype=float)
try:
temp_values[~namask] = [
tvar.parse_exact_iso(i) for i in orig_values[~namask]
]
except ValueError:
pass
else:
valuemap = None
coltype = TimeVariable
values = temp_values
return valuemap, values, coltype
def get_domain(self, domain, data, deduplicate=False):
"""
Create domain (and dataset) from changes made in the widget.
Returns
-------
Args:
domain (Domain): original domain
data (Table): original data
deduplicate (bool): if True, variable names are deduplicated and
the result contains an additional list with names of renamed
variables
Returns:
(new_domain, [attribute_columns, class_var_columns, meta_columns])
or
(new_domain, [attribute_columns, class_var_columns, meta_columns], renamed)
"""
# Allow type-checking with type() instead of isinstance() for exact comparison
# pylint: disable=unidiomatic-typecheck
variables = self.model().variables
places = [[], [], []] # attributes, class_vars, metas
cols = [[], [], []] # Xcols, Ycols, Mcols
def numbers_are_round(var, col_data):
if type(var) == ContinuousVariable:
data = np.asarray(col_data.data) # Works for dense and sparse
data = data[~np.isnan(data)]
return (data == data.astype(int)).all()
return False
# Exit early with original domain if the user didn't actually change anything
if all((name == orig_var.name and tpe == type(orig_var)
and place == orig_plc)
for (name, tpe, place, _, _), (orig_var, orig_plc) in zip(
variables,
chain(((at, Place.feature) for at in domain.attributes), (
(cl, Place.class_var) for cl in domain.class_vars), (
(mt, Place.meta) for mt in domain.metas)))):
if deduplicate:
return domain, [data.X, data.Y, data.metas], []
else:
return domain, [data.X, data.Y, data.metas]
relevant_names = [var[0] for var in variables if var[2] != Place.skip]
if deduplicate:
renamed_iter = iter(get_unique_names_duplicates(relevant_names))
else:
renamed_iter = iter(relevant_names)
renamed = []
for (name, tpe, place, _, may_be_numeric), (orig_var, orig_plc) in \
zip(variables,
chain([(at, Place.feature) for at in domain.attributes],
[(cl, Place.class_var)
for cl in domain.class_vars],
[(mt, Place.meta) for mt in domain.metas])):
if place == Place.skip:
continue
new_name = next(renamed_iter)
if new_name != name and name not in renamed:
renamed.append(name)
col_data = self._get_column(data, orig_var, orig_plc)
is_sparse = sp.issparse(col_data)
if new_name == orig_var.name and tpe == type(orig_var):
var = orig_var
elif tpe == type(orig_var):
var = orig_var.copy(name=new_name)
elif tpe == DiscreteVariable:
values = natural_sorted(
list(
str(i) for i in unique(col_data)
if not self._is_missing(i)))
round_numbers = numbers_are_round(orig_var, col_data)
col_data = [
np.nan if self._is_missing(x) else values.index(str(x))
for x in self._iter_vals(col_data)
]
if round_numbers:
values = [str(int(float(v))) for v in values]
var = tpe(new_name, values)
col_data = self._to_column(col_data, is_sparse)
elif tpe == StringVariable:
var = tpe.make(new_name)
if type(orig_var) in [DiscreteVariable, TimeVariable]:
col_data = [
orig_var.repr_val(x) if not np.isnan(x) else ""
for x in self._iter_vals(col_data)
]
elif type(orig_var) == ContinuousVariable:
round_numbers = numbers_are_round(orig_var, col_data)
col_data = [
'' if np.isnan(x) else
str(int(x)) if round_numbers else orig_var.repr_val(x)
for x in self._iter_vals(col_data)
]
# don't obey sparsity for StringVariable since they are
# in metas which are transformed to dense below
col_data = self._to_column(col_data, False, dtype=object)
elif tpe == ContinuousVariable and type(
orig_var) == DiscreteVariable:
var = tpe.make(new_name)
if may_be_numeric:
col_data = [
np.nan if self._is_missing(x) else float(
orig_var.values[int(x)])
for x in self._iter_vals(col_data)
]
col_data = self._to_column(col_data, is_sparse)
else:
var = tpe(new_name)
places[place].append(var)
cols[place].append(col_data)
# merge columns for X, Y and metas
feats = cols[Place.feature]
X = self._merge(feats) if feats else np.empty((len(data), 0))
Y = self._merge(cols[Place.class_var], force_dense=True)
m = self._merge(cols[Place.meta], force_dense=True)
domain = Domain(*places)
if deduplicate:
return domain, [X, Y, m], renamed
else:
return domain, [X, Y, m]
def test_natural_sorted_numbers(self):
data = [1, 20, 2, 12]
res = [1, 2, 12, 20]
self.assertListEqual(res, natural_sorted(data))
def test_natural_sorted_numbers_str(self):
data = ["1", "20", "2", "12"]
res = ["1", "2", "12", "20"]
self.assertListEqual(res, natural_sorted(data))
def test_natural_sorted_text(self):
data = ["b", "aa", "c", "dd"]
res = ["aa", "b", "c", "dd"]
self.assertListEqual(res, natural_sorted(data))
def test_natural_sorted(self):
data = ["something1", "something20", "something2", "something12"]
res = ["something1", "something2", "something12", "something20"]
self.assertListEqual(res, natural_sorted(data))
