672 create a supported models class property for hssm

docs/tutorials/main_tutorial.ipynb

@@ -415,7 +415,7 @@
    "source": [
     "If we wish to simulate from another model, we can do so by changing the `model` string.\n",
     "\n",
-    "The number of models we can simulate differs from the number of models for which we have likelihoods available (both will increase over time). To get the models for which likelihood functions are supplied out of the box, we should check the `SupportedModels` under `hssm.defaults`."
+    "The number of models we can simulate differs from the number of models for which we have likelihoods available (both will increase over time). To get the models for which likelihood functions are supplied out of the box, we should inspect  `hssm.HSSM.supported_models`."
    ]
   },
   {
@@ -427,59 +427,82 @@
     {
      "data": {
       "text/plain": [
-       "typing.Literal['ddm', 'ddm_sdv', 'full_ddm', 'angle', 'levy', 'ornstein', 'weibull', 'race_no_bias_angle_4', 'ddm_seq2_no_bias', 'lba3', 'lba2']"
+       "('ddm',\n",
+       " 'ddm_sdv',\n",
+       " 'full_ddm',\n",
+       " 'angle',\n",
+       " 'levy',\n",
+       " 'ornstein',\n",
+       " 'weibull',\n",
+       " 'race_no_bias_angle_4',\n",
+       " 'ddm_seq2_no_bias',\n",
+       " 'lba3',\n",
+       " 'lba2')"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "hssm.defaults.SupportedModels"
+    "hssm.HSSM.supported_models"
    ]
   },
   {
    "cell_type": "markdown",
    "id": "43703bd7-f50c-40eb-a130-2070b160a8ec",
    "metadata": {},
    "source": [
-    "If we wish to check more detailed information about a given model, we can use the `default_model_config` under `hssm.default`.\n",
-    "\n",
-    "Let's look at the `ddm`:"
+    "If we wish to check more detailed information about a given supported model, we can use accessors `get_<model_name>_config` under `hssm.default`. For example, we inspect `ddm` model metada below."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
-   "id": "1a526455-37e4-428c-a478-43ef387e496e",
+   "execution_count": 6,
+   "id": "04fd49b7",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "{'response': ['rt', 'response'],\n",
-       " 'list_params': ['v', 'a', 'z', 't', 'theta'],\n",
+       " 'list_params': ['v', 'a', 'z', 't'],\n",
        " 'choices': [-1, 1],\n",
-       " 'description': None,\n",
-       " 'likelihoods': {'approx_differentiable': {'loglik': 'angle.onnx',\n",
+       " 'description': 'The Drift Diffusion Model (DDM)',\n",
+       " 'likelihoods': {'analytical': {'loglik': <function hssm.likelihoods.analytical.logp_ddm(data: numpy.ndarray, v: float, a: float, z: float, t: float, err: float = 1e-15, k_terms: int = 20, epsilon: float = 1e-15) -> numpy.ndarray>,\n",
+       "   'backend': None,\n",
+       "   'bounds': {'v': (-inf, inf),\n",
+       "    'a': (0.0, inf),\n",
+       "    'z': (0.0, 1.0),\n",
+       "    't': (0.0, inf)},\n",
+       "   'default_priors': {'t': {'name': 'HalfNormal', 'sigma': 2.0}},\n",
+       "   'extra_fields': None},\n",
+       "  'approx_differentiable': {'loglik': 'ddm.onnx',\n",
        "   'backend': 'jax',\n",
-       "   'default_priors': {},\n",
+       "   'default_priors': {'t': {'name': 'HalfNormal', 'sigma': 2.0}},\n",
        "   'bounds': {'v': (-3.0, 3.0),\n",
-       "    'a': (0.3, 3.0),\n",
-       "    'z': (0.1, 0.9),\n",
-       "    't': (0.001, 2.0),\n",
-       "    'theta': (-0.1, 1.3)},\n",
+       "    'a': (0.3, 2.5),\n",
+       "    'z': (0.0, 1.0),\n",
+       "    't': (0.0, 2.0)},\n",
+       "   'extra_fields': None},\n",
+       "  'blackbox': {'loglik': <function hssm.likelihoods.blackbox.hddm_to_hssm.<locals>.outer(data: numpy.ndarray, *args, **kwargs)>,\n",
+       "   'backend': None,\n",
+       "   'bounds': {'v': (-inf, inf),\n",
+       "    'a': (0.0, inf),\n",
+       "    'z': (0.0, 1.0),\n",
+       "    't': (0.0, inf)},\n",
+       "   'default_priors': {'t': {'name': 'HalfNormal', 'sigma': 2.0}},\n",
        "   'extra_fields': None}}}"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "hssm.defaults.default_model_config[\"angle\"]"
+    "hssm.defaults.get_ddm_config()"
    ]
   },
   {
@@ -6671,7 +6694,7 @@
        "array([<Axes: title={'center': '94.0% HDI'}>], dtype=object)"
       ]
      },
-     "execution_count": 49,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -6713,7 +6736,7 @@
        "array([<Axes: title={'center': '94.0% HDI'}>], dtype=object)"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -7145,7 +7168,7 @@
        "Lapse distribution: Uniform(lower: 0.0, upper: 20.0)"
       ]
      },
-     "execution_count": 58,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }

src/hssm/hssm.py

@@ -13,7 +13,7 @@
 from inspect import isclass, signature
 from os import PathLike
 from pathlib import Path
-from typing import Any, Callable, Literal, Optional, Union, cast
+from typing import Any, Callable, Literal, Optional, Union, cast, get_args
 
 import arviz as az
 import bambi as bmb
@@ -61,6 +61,34 @@
 _logger = logging.getLogger("hssm")
 
 
+class classproperty:
+    """A decorator that combines the behavior of @property and @classmethod.
+
+    This decorator allows you to define a property that can be accessed on the class
+    itself, rather than on instances of the class. It is useful for defining class-level
+    properties that need to perform some computation or access class-level data.
+
+    This implementation is provided for compatibility with Python versions 3.10 through
+    3.12, as one cannot combine the @property and @classmethod decorators is across all
+    these versions.
+
+    Example
+    -------
+    class MyClass:
+        @classproperty
+        def my_class_property(cls):
+            return "This is a class property"
+
+    print(MyClass.my_class_property)  # Output: This is a class property
+    """
+
+    def __init__(self, fget):
+        self.fget = fget
+
+    def __get__(self, instance, owner):  # noqa: D105
+        return self.fget(owner)
+
+
 class HSSM:
     """The basic Hierarchical Sequential Sampling Model (HSSM) class.
 
@@ -327,6 +355,7 @@ def __init__(
             # Model config is not provided, but at this point was constructed from
             # defaults.
             if model not in typing.get_args(SupportedModels):
+                # TODO: ideally use self.supported_models above but mypy doesn't like it
                 if choices is not None:
                     self.model_config.update_choices(choices)
                 elif model in ssms_model_config:
@@ -480,6 +509,17 @@ def __init__(
         )
         _logger.info("Model initialized successfully.")
 
+    @classproperty
+    def supported_models(cls) -> tuple[SupportedModels, ...]:
+        """Get a tuple of all supported models.
+
+        Returns
+        -------
+        tuple[SupportedModels, ...]
+            A tuple containing all supported model names.
+        """
+        return get_args(SupportedModels)
+
     @classmethod
     def _store_init_args(cls, *args, **kwargs):
         """Store initialization arguments using signature binding."""