[ENH] registry module with lookup and tag register

skpro/distributions/base.py

@@ -20,6 +20,7 @@ class BaseDistribution(BaseObject):
 
     # default tag values - these typically make the "safest" assumption
     _tags = {
+        "object_type": "distribution",  # type of object, e.g., 'distribution'
         "python_version": None,  # PEP 440 python version specifier to limit versions
         "python_dependencies": None,  # string or str list of pkg soft dependencies
         "reserved_params": ["index", "columns"],

skpro/metrics/base.py

@@ -35,6 +35,7 @@ class BaseProbaMetric(BaseObject):
     """
 
     _tags = {
+        "object_type": "metric",  # type of object
         "reserved_params": ["multioutput", "score_average"],
         "scitype:y_pred": "pred_proba",
         "lower_is_better": True,

skpro/registry/__init__.py

@@ -0,0 +1,16 @@
+# -*- coding: utf-8 -*-
+"""Registry and lookup functionality."""
+
+from skpro.registry._lookup import all_objects
+from skpro.registry._tags import (
+    OBJECT_TAG_LIST,
+    OBJECT_TAG_REGISTER,
+    check_tag_is_valid,
+)
+
+__all__ = [
+    "OBJECT_TAG_LIST",
+    "OBJECT_TAG_REGISTER",
+    "all_objects",
+    "check_tag_is_valid",
+]

skpro/registry/_lookup.py

@@ -0,0 +1,343 @@
+# -*- coding: utf-8 -*-
+"""Registry lookup methods.
+
+This module exports the following methods for registry lookup:
+
+all_objects(object_types, filter_tags)
+    lookup and filtering of objects
+"""
+# copyright: skpro developers, BSD-3-Clause License (see LICENSE file)
+# based on the sktime module of same name
+
+__author__ = ["fkiraly"]
+# all_objects is based on the sklearn utility all_estimators
+
+
+from copy import deepcopy
+from operator import itemgetter
+from pathlib import Path
+
+import pandas as pd
+from skbase.lookup import all_objects as _all_objects
+
+from skpro.base import BaseEstimator, BaseObject
+from skpro.registry._tags import OBJECT_TAG_REGISTER
+
+VALID_OBJECT_TYPE_STRINGS = set([x[1] for x in OBJECT_TAG_REGISTER])
+
+
+def all_objects(
+    object_types=None,
+    filter_tags=None,
+    exclude_objects=None,
+    return_names=True,
+    as_dataframe=False,
+    return_tags=None,
+    suppress_import_stdout=True,
+):
+    """Get a list of all objects from skpro.
+
+    This function crawls the module and gets all classes that inherit
+    from skpro's and sklearn's base classes.
+
+    Not included are: the base classes themselves, classes defined in test
+    modules.
+
+    Parameters
+    ----------
+    object_types: str, list of str, optional (default=None)
+        Which kind of objects should be returned.
+        if None, no filter is applied and all objects are returned.
+        if str or list of str, strings define scitypes specified in search
+                only objects 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, object class name is included in the all_objects()
+            return in the order: name, object class, optional tags, either as
+            a tuple or as pandas.DataFrame columns
+        if False, object class name is removed from the all_objects()
+            return.
+    filter_tags: dict of (str or list of str), optional (default=None)
+        For a list of valid tag strings, use the registry.all_tags utility.
+        subsets the returned objects 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)"
+    exclude_objects: str, list of str, optional (default=None)
+        Names of objects to exclude.
+    as_dataframe: bool, optional (default=False)
+        if True, all_objects will return a pandas.DataFrame with named
+            columns for all of the attributes being returned.
+        if False, all_objects will return a list (either a list of
+            objects 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 object'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
+            object 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_objects will return one of the following:
+        1. list of objects, if return_names=False, and return_tags is None
+        2. list of tuples (optional object name, class, ~optional object
+                tags), if return_names=True or return_tags is not None.
+        3. pandas.DataFrame if as_dataframe = True
+        if list of objects:
+            entries are objects matching the query,
+            in alphabetical order of object name
+        if list of tuples:
+            list of (optional object name, object, optional object
+            tags) matching the query, in alphabetical order of object name,
+            where
+            ``name`` is the object name as string, and is an
+                optional return
+            ``object`` is the actual object
+            ``tags`` are the object's values for each tag in return_tags
+                and is an optional return.
+        if dataframe:
+            all_objects will return a pandas.DataFrame.
+            column names represent the attributes contained in each column.
+            "objects" will be the name of the column of objects, "names"
+            will be the name of the column of object 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 skpro.registry import all_objects
+    >>> # return a complete list of objects as pd.Dataframe
+    >>> all_objects(as_dataframe=True)
+    >>> # return all probabilistic regressors by filtering for object type
+    >>> all_objects("regressor_proba", as_dataframe=True)
+    >>> # return all regressors which handle missing data in the input by tag filtering
+    >>> all_objects(
+    ...     "regressor_proba",
+    ...     filter_tags={"capability:missing": True},
+    ...     as_dataframe=True
+    ... )
+
+    References
+    ----------
+    Adapted version of sktime's ``all_estimators``,
+    which is an evolution of scikit-learn's ``all_estimators``
+    """
+    MODULES_TO_IGNORE = (
+        "tests",
+        "setup",
+        "contrib",
+        "utils",
+        "all",
+    )
+
+    result = []
+    ROOT = str(Path(__file__).parent.parent)  # skpro package root directory
+
+    if isinstance(filter_tags, str):
+        filter_tags = {filter_tags: True}
+    filter_tags = filter_tags.copy() if filter_tags else None
+
+    if object_types:
+        if filter_tags and "object_type" not in filter_tags.keys():
+            object_tag_filter = {}
+        else:
+            object_tag_filter = {"object_type": object_types}
+        if filter_tags:
+            filter_tags.update(object_tag_filter)
+        else:
+            filter_tags = object_tag_filter
+
+    result = _all_objects(
+        object_types=[BaseObject, BaseEstimator],
+        filter_tags=filter_tags,
+        exclude_objects=exclude_objects,
+        return_names=return_names,
+        as_dataframe=as_dataframe,
+        return_tags=return_tags,
+        suppress_import_stdout=suppress_import_stdout,
+        package_name="skpro",
+        path=ROOT,
+        modules_to_ignore=MODULES_TO_IGNORE,
+    )
+
+    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_objects!  Argument {arg_name} must be either\
+             a str or list of str"
+        )
+    return arg_to_check
+
+
+def _get_return_tags(object, return_tags):
+    """Fetch a list of all tags for every_entry of all_objects.
+
+    Parameters
+    ----------
+    object:  Baseobject, an sktime object
+    return_tags: list of str,
+        names of tags to get values for the object
+
+    Returns
+    -------
+    tags: a tuple with all the objects values for all tags in return tags.
+        a value is None if it is not a valid tag for the object provided.
+    """
+    tags = tuple(object.get_class_tag(tag) for tag in return_tags)
+    return tags
+
+
+def _check_tag_cond(object, filter_tags=None, as_dataframe=True):
+    """Check whether object satisfies filter_tags condition.
+
+    Parameters
+    ----------
+    object: Baseobject, an sktime object
+    filter_tags: dict of (str or list of str), default=None
+        subsets the returned objects 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 object 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 object.get_class_tag(key) in set(value)
+
+    return cond_sat
+
+
+def all_tags(
+    object_types=None,
+    as_dataframe=False,
+):
+    """Get a list of all tags from sktime.
+
+    Retrieves tags directly from `_tags`, offers filtering functionality.
+
+    Parameters
+    ----------
+    object_types: string, list of string, optional (default=None)
+        Which kind of objects should be returned.
+        If None, no filter is applied and all objects are returned.
+    as_dataframe: bool, optional (default=False)
+        if False, return is as described below;
+        if True, return is converted into a pandas.DataFrame for pretty display
+
+    Returns
+    -------
+    tags: list of tuples (a, b, c, d),
+        in alphabetical order by a
+        a : string - name of the tag as used in the _tags dictionary
+        b : string - name of the scitype this tag applies to
+                    must be in _base_classes.BASE_CLASS_SCITYPE_LIST
+        c : string - expected type of the tag value
+            should be one of:
+                "bool" - valid values are True/False
+                "int" - valid values are all integers
+                "str" - valid values are all strings
+                ("str", list_of_string) - any string in list_of_string is valid
+                ("list", list_of_string) - any individual string and sub-list is valid
+        d : string - plain English description of the tag
+    """
+
+    def is_tag_for_type(tag, object_types):
+        tag_types = tag[1]
+        tag_types = _check_list_of_str_or_error(tag_types, "tag_types")
+
+        if isinstance(object_types, str):
+            object_types = [object_types]
+
+        tag_types = set(tag_types)
+        object_types = set(object_types)
+        is_valid_tag_for_type = len(tag_types.intersection(object_types)) > 0
+
+        return is_valid_tag_for_type
+
+    all_tags = OBJECT_TAG_REGISTER
+
+    if object_types:
+        # checking, but not using the return since that is classes, not strings
+        _check_object_types(object_types)
+        all_tags = [tag for tag in all_tags if is_tag_for_type(tag, object_types)]
+
+    all_tags = sorted(all_tags, key=itemgetter(0))
+
+    # convert to pd.DataFrame if as_dataframe=True
+    if as_dataframe:
+        columns = ["name", "scitype", "type", "description"]
+        all_tags = pd.DataFrame(all_tags, columns=columns)
+
+    return all_tags
+
+
+def _check_object_types(object_types):
+    """Return list of classes corresponding to type strings."""
+    object_types = deepcopy(object_types)
+
+    if not isinstance(object_types, list):
+        object_types = [object_types]  # make iterable
+
+    def _get_err_msg(object_type):
+        return (
+            f"Parameter `object_type` must be None, a string or a list of "
+            f"strings. Valid string values are: "
+            f"{tuple(VALID_OBJECT_TYPE_STRINGS)}, but found: "
+            f"{repr(object_type)}"
+        )
+
+    for object_type in object_types:
+        if not isinstance(object_type, (type, str)):
+            raise ValueError(
+                "Please specify `object_types` as a list of str or " "types."
+            )
+        if isinstance(object_type, str):
+            if object_type not in VALID_OBJECT_TYPE_STRINGS:
+                raise ValueError(_get_err_msg(object_type))
+    return object_types