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"""Helper functions for quick command line interfaces with skorch and
from functools import partial
from importlib import import_module
from itertools import chain
import re
import shlex
import sys
from sklearn.base import BaseEstimator
from sklearn.pipeline import FeatureUnion
from sklearn.pipeline import Pipeline
__all__ = ['parse_args']
# matches: bar(),,
P_PARAMS = re.compile(r"(?P<name>^[a-zA-Z][a-zA-Z0-9_\.]*)(?P<params>\(.*\)$)")
P_DEFAULTS = re.compile(
# standard, matches: int (default=123)
# no parens, matches: int, default=123
# no equal, matches: int, default 123
# 'by-default', matches: str (l2 by default)
# 'by-default-double-tick', matches: "l1" or "l2" ("l2" by default)
# 'by-default-single-tick', matches: 'l1' or 'l2' ('l2' by default)
def _param_split(params):
return (p.strip(' ,') for p in shlex.split(params))
def _get_span(s, pattern):
"""Return the span of the first group that matches the pattern."""
i, j = -1, -1
match = pattern.match(s)
if not match:
return i, j
for group_name in pattern.groupindex:
i, j = match.span(group_name)
if (i, j) != (-1, -1):
return i, j
return i, j
def _substitute_default(s, new_value):
"""Replaces the default value in a parameter docstring by a new value.
The docstring must conform to the numpydoc style and have the form
"something (keyname=<value-to-replace>)"
If no matching pattern is found or ``new_value`` is None, return
the input untouched.
>>> _replace_default('int (default=128)', 256)
'int (default=256)'
>>> _replace_default('nonlin (default = ReLU())', nn.Hardtanh(1, 2))
'nonlin (default = Hardtanh(min_val=1, max_val=2))'
if new_value is None:
return s
# BB: ideally, I would like to replace the 'default*' group
# directly but I haven't found a way to do this
i, j = _get_span(s, pattern=P_DEFAULTS)
if (i, j) == (-1, -1):
return s
return '{}{}{}'.format(s[:i], new_value, s[j:])
def _parse_args_kwargs(params):
from fire.parser import DefaultParseValue
args = ()
kwargs = {}
for param in _param_split(params):
if '=' not in param:
args += (DefaultParseValue(param),)
k, v = param.split('=')
kwargs[k.strip()] = DefaultParseValue(v)
return args, kwargs
def _resolve_dotted_name(dotted_name):
"""Returns objects from strings
Deals e.g. with 'torch.nn.Softmax(dim=-1)'.
Modified from palladium:
with added support for instantiated objects.
if not isinstance(dotted_name, str):
return dotted_name
if '.' not in dotted_name:
return dotted_name
args = None
params = None
match = P_PARAMS.match(dotted_name)
if match:
dotted_name ='name')
params ='params')
module, name = dotted_name.rsplit('.', 1)
attr = import_module(module)
attr = getattr(attr, name)
if params:
args, kwargs = _parse_args_kwargs(params[1:-1])
attr = attr(*args, **kwargs)
return attr
def parse_net_kwargs(kwargs):
"""Parse arguments for the estimator.
Resolves dotted names and instantiated classes.
>>> kwargs = {'lr': 0.1, 'module__nonlin': 'torch.nn.Hardtanh(-2, max_val=3)'}
>>> parse_net_kwargs(kwargs)
{'lr': 0.1, 'module__nonlin': Hardtanh(min_val=-2, max_val=3)}
if not kwargs:
return kwargs
resolved = {}
for k, v in kwargs.items():
resolved[k] = _resolve_dotted_name(v)
return resolved
def _yield_preproc_steps(model):
if not isinstance(model, Pipeline):
for key, val in model.get_params().items():
if isinstance(val, BaseEstimator):
if not isinstance(val, (Pipeline, FeatureUnion)):
yield key, val
def _yield_estimators(model):
"""Yield estimator and its prefix from the model.
First, pipeline preprocessing steps are yielded (if there are
any). Next the neural net is yielded. Finally, the module is
yield from _yield_preproc_steps(model)
net_prefixes = []
module_prefixes = []
if isinstance(model, Pipeline):
name = model.steps[-1][0]
net = model.steps[-1][1]
net = model
yield '__'.join(net_prefixes), net
module = net.module
yield '__'.join(module_prefixes), module
def _extract_estimator_cls(estimator):
if isinstance(estimator, partial):
# is partialled
return estimator.func
if not isinstance(estimator, type):
# is instance
return estimator.__class__
return estimator
def _yield_printable_params(param, prefix, defaults):
name, default, descr = param
name = name if not prefix else '__'.join((prefix, name))
default = _substitute_default(default, defaults.get(name))
printable = '--{} : {}'.format(name, default)
yield printable
for line in descr:
yield line
def _get_help_for_params(params, prefix='--', defaults=None, indent=2):
defaults = defaults or {}
for param in params:
first, *rest = tuple(_yield_printable_params(
param, prefix=prefix, defaults=defaults))
yield " " * indent + first
for line in rest:
yield " " * 2 * indent + line
def _get_help_for_estimator(prefix, estimator, defaults=None):
"""Yield help lines for the given estimator and prefix."""
from numpydoc.docscrape import ClassDoc
defaults = defaults or {}
estimator = _extract_estimator_cls(estimator)
yield "<{}> options:".format(estimator.__name__)
doc = ClassDoc(estimator)
yield from _get_help_for_params(
yield '' # add a newline line between estimators
def print_help(model, defaults=None):
"""Print help for the command line arguments of the given model.
model : sklearn.base.BaseEstimator
The basic model, e.g. a ``NeuralNet`` or sklearn ``Pipeline``.
defautls : dict or None (default=None)
Optionally, change the default values to use custom
defaults. Commandline arguments have precedence over defaults.
defaults = defaults or {}
print("This is the help for the model-specific parameters.")
print("To invoke help for the remaining options, run:")
print("python {} -- --help".format(sys.argv[0]))
lines = (_get_help_for_estimator(prefix, estimator, defaults=defaults) for
prefix, estimator in _yield_estimators(model))
def parse_args(kwargs, defaults=None):
"""Apply command line arguments or show help.
Use this in conjunction with the fire library to quickly build
command line interfaces for your scripts.
This function returns another function that must be called with
the estimator (e.g. ``NeuralNet``) to apply the parsed command
line arguments. If the --help option is found, show the
estimator-specific help instead.
Content of
>>> def main(**kwargs):
>>> X, y = get_data()
>>> my_model = get_model()
>>> parsed = parse_args(kwargs)
>>> my_model = parsed(my_model)
>>>, y)
>>> if __name__ == '__main__':
>>> fire.Fire(main)
kwargs : dict
The arguments as parsed by fire.
defautls : dict or None (default=None)
Optionally, change the default values to use custom
defaults. Commandline arguments have precedence over defaults.
print_help_and_exit : callable
If --help is in the arguments, print help and exit.
set_params : callable
If --help is not in the options, apply command line arguments to
the estimator and return it.
import fire
except ImportError:
raise ImportError("Using skorch cli helpers requires the fire library,"
" you can install it with pip: pip install fire.")
import numpydoc.docscrape
except ImportError:
raise ImportError("Using skorch cli helpers requires the numpydoc library,"
" you can install it with pip: pip install numpydoc.")
defaults = defaults or {}
def print_help_and_exit(estimator):
print_help(estimator, defaults=defaults)
def set_params(estimator):
return estimator.set_params(**parse_net_kwargs(kwargs))
if kwargs.get('help'):
return print_help_and_exit
return set_params
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