def categorical_to_int(data, levels, NA_action, origin=None):
assert isinstance(levels, tuple)
# In this function, missing values are always mapped to -1
if isinstance(data, pd.Categorical):
data_levels_tuple = tuple(data.levels)
if not data_levels_tuple == levels:
raise PatsyError("mismatching levels: expected %r, got %r"
% (levels, data_levels_tuple), origin)
# pd.Categorical also uses -1 to indicate NA, and we don't try to
# second-guess its NA detection, so we can just pass it back.
return data.labels
elif hasattr(data, 'dtype') and hasattr(data, 'astype') and \
np.issubdtype(data.dtype, np.bool_):
return data.astype('int')
if isinstance(data, _CategoricalBox):
if data.levels is not None and tuple(data.levels) != levels:
raise PatsyError("mismatching levels: expected %r, got %r"
% (levels, tuple(data.levels)), origin)
data = data.data
if hasattr(data, "shape") and len(data.shape) > 1:
raise PatsyError("categorical data must be 1-dimensional",
origin)
if not iterable(data) or isinstance(data, basestring):
raise PatsyError("categorical data must be an iterable container")
try:
level_to_int = dict(zip(levels, xrange(len(levels))))
except TypeError:
raise PatsyError("Error interpreting categorical data: "
"all items must be hashable", origin)
out = np.empty(len(data), dtype=int)
for i, value in enumerate(data):
if NA_action.is_categorical_NA(value):
out[i] = -1
else:
try:
out[i] = level_to_int[value]
except KeyError:
SHOW_LEVELS = 4
level_strs = []
if len(levels) <= SHOW_LEVELS:
level_strs += [repr(level) for level in levels]
else:
level_strs += [repr(level)
for level in levels[:SHOW_LEVELS//2]]
level_strs.append("...")
level_strs += [repr(level)
for level in levels[-SHOW_LEVELS//2:]]
level_str = "[%s]" % (", ".join(level_strs))
raise PatsyError("Error converting data to categorical: "
"observation with value %r does not match "
"any of the expected levels (expected: %s)"
% (value, level_str), origin)
except TypeError:
raise PatsyError("Error converting data to categorical: "
"encountered unhashable value %r"
% (value,), origin)
if isinstance(data, pd.Series):
out = pd.Series(out, index=data.index)
return out
def _categorical_shape_fix(data):
# helper function
# data should not be a _CategoricalBox or pandas Categorical or anything
# -- it should be an actual iterable of data, but which might have the
# wrong shape.
if hasattr(data, "ndim") and data.ndim > 1:
raise PatsyError("categorical data cannot be >1-dimensional")
# coerce scalars into 1d, which is consistent with what we do for numeric
# factors. (See statsmodels/statsmodels#1881)
if (not iterable(data)
or isinstance(data, (six.text_type, six.binary_type))):
data = [data]
return data
def _categorical_shape_fix(data):
# helper function
# data should not be a _CategoricalBox or pandas Categorical or anything
# -- it should be an actual iterable of data, but which might have the
# wrong shape.
if hasattr(data, "ndim") and data.ndim > 1:
raise PatsyError("categorical data cannot be >1-dimensional")
# coerce scalars into 1d, which is consistent with what we do for numeric
# factors. (See statsmodels/statsmodels#1881)
if (not iterable(data) or isinstance(data,
(six.text_type, six.binary_type))):
data = [data]
return data
