def __init__(self, pypinm, deps=None, import_nm=None, path=None):
store_attr('pypinm,deps')
self.import_nm = ifnone(import_nm, pypinm)
try:
self.ver = str(latest_pypi(pypinm))
except:
raise ValueError(f'package name: {pypinm} not found on pypi.')
self.info = pypi_json(f'{pypinm}/{self.ver}')['info']
self.path = _mkdir(ifnone(path, self.pypinm))
self.meta = {
'package': {
'name': self.pypinm,
'version': self.ver
},
'build': {
'number': 0,
'binary_relocation': False,
'detect_binary_files_with_prefix': False
},
'requirements': {
'host': ['pip', 'python'],
'run': ['python'] + list(L(self.deps))
},
'test': {
'imports': [self.import_nm],
'requires': ['pip']
},
'about': {
'home': self.info['home_page'],
'summary': self.info['summary'],
'license': self.info['license']
},
'extra': {
'recipe-maintainers': ['jph00']
}
}
def tx(xs):
return L(xs).map(list).map(set).reduce(lambda a, b: a | b)
def fit(self: Learner, epochs, lr, cbs, reset_opt=False, wd=None):
"""Default Fit 'self.model' for 'n_cycles' with 'lr' using 'cbs'. Optionally 'reset_opt'.
For run from script with hydra config"""
self.fit(epochs, lr, cbs=L(cbs), reset_opt=reset_opt, wd=wd)
def all_params(self, n=slice(None), with_grad=False):
"List of `param_groups` upto n"
res = L(p for p in self.param_lists[n])
return (L(o for o in res if hasattr(o, "grad") and o.grad is not None)
if with_grad else res)
def xform(xs):
l = L(xs).map(xform_item).filter(lambda x: x and x != "no other")
return (l[0], l[1:])
def __init__(self, x, raw=False):
if raw:
self.t = x
else:
self.t = L(map(tuplify, x))
def part1(xs):
xs = L(xs)
row = walk(list(xs[0]), 0, 127)
seat = walk(list(xs[1]), 0, 7)
return row * 8 + seat
def logic(x, n):
return L(combinations(x.col(),
n)).filter(lambda x: sum(x) == 2020).map(prod)[0]
def _(self, other):
self.logs.append(L(operator, other))
return self
def __init__(self, message):
store_attr()
self.logs = L()
def exec(itr):
return L(f(e) for e in itr)
def __init__(self, default = _NoMatchError,):
store_attr()
self.cases = L()
self.preprocesses = L()