# copyright: sktime developers, BSD-3-Clause License (see LICENSE file)
"""Registry lookup methods.
This module exports the following methods for registry lookup:
all_estimators(estimator_types, filter_tags)
lookup and filtering of estimators
all_tags(estimator_types)
lookup and filtering of estimator tags
"""
__author__ = ["fkiraly", "mloning", "katiebuc", "miraep8", "xloem"]
# all_estimators is also based on the sklearn utility of the same name
from copy import deepcopy
from operator import itemgetter
from pathlib import Path
import pandas as pd
from skbase.lookup import all_objects
from sktime.registry._base_classes import get_base_class_lookup, get_obj_scitype_list
from sktime.registry._tags import ESTIMATOR_TAG_REGISTER
[文档]def all_estimators(
estimator_types=None,
filter_tags=None,
exclude_estimators=None,
return_names=True,
as_dataframe=False,
return_tags=None,
suppress_import_stdout=True,
):
"""List all estimators or objects in sktime, by scitype or tag.
This function crawls the module and gets all classes that inherit
from sktime's and sklearn's base classes.
Not included are: the base classes themselves, classes defined in test
modules.
Parameters
----------
estimator_types: str, list of str, optional (default=None)
Which kind of estimators should be returned.
if None, no filter is applied and all estimators are returned.
if str or list of str, strings define scitypes specified in search
only estimators that are of (at least) one of the scitypes are returned
possible str values are entries of registry.BASE_CLASS_REGISTER (first col)
for instance 'classifier', 'regressor', 'transformer', 'forecaster'
return_names: bool, optional (default=True)
if True, estimator class name is included in the ``all_estimators``
return in the order: name, estimator class, optional tags, either as
a tuple or as pandas.DataFrame columns
if False, estimator class name is removed from the ``all_estimators`` return.
filter_tags: dict of (str or list of str or re.Pattern), optional (default=None)
For a list of valid tag strings, use the registry.all_tags utility.
``filter_tags`` subsets the returned estimators as follows:
* each key/value pair is statement in "and"/conjunction
* key is tag name to sub-set on
* value str or list of string are tag values
* condition is "key must be equal to value, or in set(value)"
In detail, he return will be filtered to keep exactly the classes
where tags satisfy all the filter conditions specified by ``filter_tags``.
Filter conditions are as follows, for ``tag_name: search_value`` pairs in
the ``filter_tags`` dict, applied to a class ``klass``:
- If ``klass`` does not have a tag with name ``tag_name``, it is excluded.
Otherwise, let ``tag_value`` be the value of the tag with name ``tag_name``.
- If ``search_value`` is a string, and ``tag_value`` is a string,
the filter condition is that ``search_value`` must match the tag value.
- If ``search_value`` is a string, and ``tag_value`` is a list,
the filter condition is that ``search_value`` is contained in ``tag_value``.
- If ``search_value`` is a ``re.Pattern``, and ``tag_value`` is a string,
the filter condition is that ``search_value.fullmatch(tag_value)``
is true, i.e., the regex matches the tag value.
- If ``search_value`` is a ``re.Pattern``, and ``tag_value`` is a list,
the filter condition is that at least one element of ``tag_value``
matches the regex.
- If ``search_value`` is iterable, then the filter condition is that
at least one element of ``search_value`` satisfies the above conditions,
applied to ``tag_value``.
Note: ``re.Pattern`` is supported only from ``scikit-base`` version 0.8.0.
exclude_estimators: str, list of str, optional (default=None)
Names of estimators to exclude.
as_dataframe: bool, optional (default=False)
True: ``all_estimators`` will return a ``pandas.DataFrame`` with named
columns for all of the attributes being returned.
False: ``all_estimators`` will return a list (either a list of
estimators or a list of tuples, see Returns)
return_tags: str or list of str, optional (default=None)
Names of tags to fetch and return each estimator's value of.
For a list of valid tag strings, use the ``registry.all_tags`` utility.
if str or list of str,
the tag values named in return_tags will be fetched for each
estimator and will be appended as either columns or tuple entries.
suppress_import_stdout : bool, optional. Default=True
whether to suppress stdout printout upon import.
Returns
-------
all_estimators will return one of the following:
1. list of estimators, if return_names=False, and return_tags is None
2. list of tuples (optional estimator name, class, ~optional estimator
tags), if return_names=True or return_tags is not None.
3. pandas.DataFrame if as_dataframe = True
if list of estimators:
entries are estimators matching the query,
in alphabetical order of estimator name
if list of tuples:
list of (optional estimator name, estimator, optional estimator
tags) matching the query, in alphabetical order of estimator name,
where
``name`` is the estimator name as string, and is an
optional return
``estimator`` is the actual estimator
``tags`` are the estimator's values for each tag in return_tags
and is an optional return.
if dataframe:
all_estimators will return a pandas.DataFrame.
column names represent the attributes contained in each column.
"estimators" will be the name of the column of estimators, "names"
will be the name of the column of estimator class names and the string(s)
passed in return_tags will serve as column names for all columns of
tags that were optionally requested.
Examples
--------
>>> from sktime.registry import all_estimators
>>> # return a complete list of estimators as pd.Dataframe
>>> all_estimators(as_dataframe=True) # doctest: +SKIP
>>> # return all forecasters by filtering for estimator type
>>> all_estimators("forecaster") # doctest: +SKIP
>>> # return all forecasters which handle missing data in the input by tag filtering
>>> all_estimators("forecaster", filter_tags={"handles-missing-data": True}) # doctest: +SKIP
References
----------
Modified version of ``scikit-learn``'s ``all_estimators``.
""" # noqa: E501
MODULES_TO_IGNORE = (
"tests",
"setup",
"contrib",
"benchmarking",
"utils",
"all",
"plotting",
"_split",
"test_split",
"registry",
)
ROOT = str(Path(__file__).parent.parent) # sktime package root directory
if estimator_types:
clsses = _check_estimator_types(estimator_types)
if not isinstance(estimator_types, list):
estimator_types = [estimator_types]
CLASS_LOOKUP = {x: y for x, y in zip(estimator_types, clsses)}
else:
CLASS_LOOKUP = None
result = all_objects(
object_types=estimator_types,
filter_tags=filter_tags,
exclude_objects=exclude_estimators,
return_names=return_names,
as_dataframe=as_dataframe,
return_tags=return_tags,
suppress_import_stdout=suppress_import_stdout,
package_name="sktime",
path=ROOT,
modules_to_ignore=MODULES_TO_IGNORE,
class_lookup=CLASS_LOOKUP,
)
return result
def _check_list_of_str_or_error(arg_to_check, arg_name):
"""Check that certain arguments are str or list of str.
Parameters
----------
arg_to_check: argument we are testing the type of
arg_name: str,
name of the argument we are testing, will be added to the error if
``arg_to_check`` is not a str or a list of str
Returns
-------
arg_to_check: list of str,
if arg_to_check was originally a str it converts it into a list of str
so that it can be iterated over.
Raises
------
TypeError if arg_to_check is not a str or list of str
"""
# check that return_tags has the right type:
if isinstance(arg_to_check, str):
arg_to_check = [arg_to_check]
if not isinstance(arg_to_check, list) or not all(
isinstance(value, str) for value in arg_to_check
):
raise TypeError(
f"Error in all_estimators! Argument {arg_name} must be either\
a str or list of str"
)
return arg_to_check
def _get_return_tags(estimator, return_tags):
"""Fetch a list of all tags for every_entry of all_estimators.
Parameters
----------
estimator: BaseEstimator, an sktime estimator
return_tags: list of str,
names of tags to get values for the estimator
Returns
-------
tags: a tuple with all the estimators values for all tags in return tags.
a value is None if it is not a valid tag for the estimator provided.
"""
tags = tuple(estimator.get_class_tag(tag) for tag in return_tags)
return tags
def _check_tag_cond(estimator, filter_tags=None, as_dataframe=True):
"""Check whether estimator satisfies filter_tags condition.
Parameters
----------
estimator: BaseEstimator, an sktime estimator
filter_tags: dict of (str or list of str), default=None
subsets the returned estimators as follows:
each key/value pair is statement in "and"/conjunction
key is tag name to sub-set on
value str or list of string are tag values
condition is "key must be equal to value, or in set(value)"
as_dataframe: bool, default=False
if False, return is as described below;
if True, return is converted into a pandas.DataFrame for pretty
display
Returns
-------
cond_sat: bool, whether estimator satisfies condition in filter_tags
"""
if not isinstance(filter_tags, dict):
raise TypeError("filter_tags must be a dict")
cond_sat = True
for key, value in filter_tags.items():
if not isinstance(value, list):
value = [value]
cond_sat = cond_sat and estimator.get_class_tag(key) in set(value)
return cond_sat
def _check_estimator_types(estimator_types):
"""Return list of classes corresponding to type strings."""
estimator_types = deepcopy(estimator_types)
if not isinstance(estimator_types, list):
estimator_types = [estimator_types] # make iterable
def _get_err_msg(estimator_type):
return (
f"Parameter `estimator_type` must be None, a string or a list of "
f"strings. Valid string values are: "
f"{get_obj_scitype_list()}, but found: "
f"{repr(estimator_type)}"
)
BASE_CLASS_LOOKUP = get_base_class_lookup()
for i, estimator_type in enumerate(estimator_types):
if not isinstance(estimator_type, (type, str)):
raise ValueError(
"Please specify `estimator_types` as a list of str or " "types."
)
if isinstance(estimator_type, str):
if estimator_type not in get_obj_scitype_list():
raise ValueError(_get_err_msg(estimator_type))
estimator_type = BASE_CLASS_LOOKUP[estimator_type]
estimator_types[i] = estimator_type
elif isinstance(estimator_type, type):
pass
else:
raise ValueError(_get_err_msg(estimator_type))
return estimator_types
