mlshell.MetricProducer¶
-
class
mlshell.
MetricProducer
(objects, oid, path_id='path__default', logger_id='logger__default')[source]¶ Bases:
pycnfg.producer.Producer
Factory to produce metric.
Interface: make.
- Parameters
objects (dict) – Dictionary with resulted objects from previous executed producers: {‘section_id__config__id’, object,}.
oid (str) – Unique identifier of produced object.
path_id (str, optional (default='default')) – Project path identifier in objects.
logger_id (str, optional (default='default')) – Logger identifier in objects.
-
objects
¶ Dictionary with resulted objects from previous executed producers: {‘section_id__config__id’, object,}.
- Type
-
logger
¶ Logger.
- Type
Methods
dict_api
(obj[, method])Forwarding api for dictionary object.
dump_cache
(obj[, prefix, cachedir, pkg])Dump intermediate object state to IO.
load_cache
(obj[, prefix, cachedir, pkg])Load intermediate object state from IO.
make
(scorer, score_func[, …])Make scorer from metric callable.
run
(init, steps)Execute configuration steps.
update
(obj, items)Update key(s) for dictionary object.
-
__init__
(objects, oid, path_id='path__default', logger_id='logger__default')[source]¶ Initialize self. See help(type(self)) for accurate signature.
Methods
__init__
(objects, oid[, path_id, logger_id])Initialize self.
dict_api
(obj[, method])Forwarding api for dictionary object.
dump_cache
(obj[, prefix, cachedir, pkg])Dump intermediate object state to IO.
load_cache
(obj[, prefix, cachedir, pkg])Load intermediate object state from IO.
make
(scorer, score_func[, …])Make scorer from metric callable.
run
(init, steps)Execute configuration steps.
update
(obj, items)Update key(s) for dictionary object.
-
classmethod
make
(scorer, score_func, score_func_vector=None, needs_custom_kw_args=False, **kwargs)[source]¶ Make scorer from metric callable.
- Parameters
scorer (
mlshell.Metric
) – Scorer object, will be updated.score_func (callback or str) – Custom function or key from
sklearn.metrics.SCORERS
.score_func_vector (callback, optional (default=None)) – Vectorized score_func returning vector of values for all samples. Mainly for result visualization purpose.
needs_custom_kw_args (bool, optional (default=False)) – If True, before score evaluation extract scorer kwargs from pipeline ‘pass_custom’ step (if existed).
**kwargs (dict) – Additional kwargs to pass in
sklearn.metrics.make_scorer()
(ifscore_func
is not str).
Notes
Extended
sklearn.metrics.make_scorer()
in compliance withmlshell.Metric
.
-
dict_api
(obj, method='update', **kwargs)¶ Forwarding api for dictionary object.
Could be useful to add/pop keys via configuration steps. For example to proceed update: (‘dict_api’, {‘b’:7} )
-
dump_cache
(obj, prefix=None, cachedir=None, pkg='pickle', **kwargs)¶ Dump intermediate object state to IO.
- Parameters
obj (picklable) – Object to dump.
prefix (str, optional (default=None)) – File identifier, added to filename. If None, ‘self.oid’ is used.
cachedir (str, optional(default=None)) – Absolute path to dump dir or relative to ‘project_path’ started with ‘./’. Created, if not exists. If None, “sproject_path/ .temp/objects” is used.
pkg (str, optional (default='pickle')) – Import package and try
pkg
.dump(obj, file, **kwargs).**kwargs (kwargs) – Additional parameters to pass in .dump().
- Returns
obj – Unchanged input for compliance with producer logic.
- Return type
picklable
-
load_cache
(obj, prefix=None, cachedir=None, pkg='pickle', **kwargs)¶ Load intermediate object state from IO.
- Parameters
obj (picklable) – Object template, for producer logic only (ignored).
prefix (str, optional (default=None)) – File identifier. If None, ‘self.oid’ is used.
pkg (str, optional default('pickle')) – Import package and try obj =
pkg
.load(file, **kwargs).cachedir (str, optional(default=None)) – Absolute path to load dir or relative to ‘project_path’ started with ‘./’. If None, ‘project_path/.temp/objects’ is used.
**kwargs (kwargs) – Additional parameters to pass in .load().
- Returns
obj – Loaded cache.
- Return type
picklable object
-
run
(init, steps)¶ Execute configuration steps.
Consecutive call (with decorators):
init = getattr(self, 'method_id')(init, objects=objects, **kwargs)
- Parameters
init (object) – Will be passed as arg in each step and get back as result.
steps (list of tuples) – List of
self
methods to run consecutive with kwargs: (‘method_id’, kwargs, decorators ).
- Returns
configs – List of configurations, prepared for execution: [(‘section_id__config__id’, config), …].
- Return type
list of tuple
Notes
Object identifier
oid
auto added, if produced object hasoid
attribute.