def __setattr__(self, name, value):
if self._has_terms() and name in self._super_get('_plural'):
# get the total number of arguments
size = np.atleast_1d(flatten(getattr(self, name))).size
# check shapes
if isiterable(value):
value = flatten(value)
if len(value) != size:
raise ValueError('Expected {} to have length {}, but found {} = {}'\
.format(name, size, name, value))
else:
value = [value] * size
# now set each term's sequence of arguments
for term in self._get_terms()[::-1]:
# skip intercept
if term.isintercept:
continue
# how many values does this term get?
n = np.atleast_1d(getattr(term, name)).size
# get the next n values and set them on this term
vals = [value.pop() for _ in range(n)][::-1]
setattr(term, name, vals[0] if n == 1 else vals)
term._validate_arguments()
return
super(MetaTermMixin, self).__setattr__(name, value)
def test_tensor_term_expands_args_to_match_penalties_and_terms():
tensor = te(0, 1, lam=3)
assert len(tensor.lam) == 2
assert len(flatten(tensor.lam)) == 2
tensor = te(0, 1, penalties='auto')
assert len(tensor.lam) == 2
assert len(flatten(tensor.lam)) == 2
tensor = te(0, 1, penalties=['auto', ['auto', 'auto']])
assert len(tensor.lam) == 2
assert len(flatten(tensor.lam)) == 3
def nice_repr(name, param_kvs, line_width=30, line_offset=5, decimals=3, args=None, flatten_attrs=True):
"""
tool to do a nice repr of a class.
Parameters
----------
name : str
class name
param_kvs : dict
dict containing class parameters names as keys,
and the corresponding values as values
line_width : int
desired maximum line width.
default: 30
line_offset : int
desired offset for new lines
default: 5
decimals : int
number of decimal places to keep for float values
default: 3
Returns
-------
out : str
nicely formatted repr of class instance
"""
if not param_kvs and not args :
# if the object has no params it's easy
return '{}()'.format(name)
# sort keys and values
ks = list(param_kvs.keys())
vs = list(param_kvs.values())
idxs = np.argsort(ks)
param_kvs = [(ks[i],vs[i]) for i in idxs]
if args is not None:
param_kvs = [(None, arg) for arg in args] + param_kvs
param_kvs = param_kvs[::-1]
out = ''
current_line = name + '('
while len(param_kvs) > 0:
# flatten sub-term properties, but not `terms`
k, v = param_kvs.pop()
if flatten_attrs and k != 'terms':
v = flatten(v)
# round the floats first
if issubclass(v.__class__, (float, np.ndarray)):
v = round_to_n_decimal_places(v, n=decimals)
v = str(v)
else:
v = repr(v)
# handle args
if k is None:
param = '{},'.format(v)
else:
param = '{}={},'.format(k, v)
# print
if len(current_line + param) <= line_width:
current_line += param
else:
out += current_line + '\n'
current_line = ' '*line_offset + param
if len(current_line) < line_width and len(param_kvs) > 0:
current_line += ' '
out += current_line[:-1] # remove trailing comma
out += ')'
return out