allow init to be None

pymc/sampling.py

@@ -278,19 +278,18 @@ def sample(
     draws : int
         The number of samples to draw. Defaults to 1000. The number of tuned samples are discarded
         by default. See ``discard_tuned_samples``.
-    init : str
-        Initialization method to use for auto-assigned NUTS samplers.
-        See `pm.init_nuts` for a list of all options.
+    init : str or None
+        Initialization method to use for auto-assigned NUTS samplers. See `pm.init_nuts` for a list
+        of all options. If ``None`` no initialization method will be used.
     step : function or iterable of functions
         A step function or collection of functions. If there are variables without step methods,
-        step methods for those variables will be assigned automatically.  By default the NUTS step
-        method will be used, if appropriate to the model; this is a good default for beginning
-        users.
+        step methods for those variables will be assigned automatically. By default the NUTS step
+        method will be used, if appropriate to the model.
     n_init : int
         Number of iterations of initializer. Only works for 'ADVI' init methods.
     initvals : optional, dict, array of dict
-        Dict or list of dicts with initial value strategies to use instead of the defaults from `Model.initial_values`.
-        The keys should be names of transformed random variables.
+        Dict or list of dicts with initial value strategies to use instead of the defaults from
+        `Model.initial_values`. The keys should be names of transformed random variables.
         Initialization methods for NUTS (see ``init`` keyword) can overwrite the default.
     trace : backend or list
         This should be a backend instance, or a list of variables to track.
@@ -317,8 +316,8 @@ def sample(
     model : Model (optional if in ``with`` context)
         Model to sample from. The model needs to have free random variables.
     random_seed : int or list of ints
-        Random seed(s) used by the sampling steps.  A list is accepted if
-        ``cores`` is greater than one.
+        Random seed(s) used by the sampling steps. A list is accepted if ``cores`` is greater than
+        one.
     discard_tuned_samples : bool
         Whether to discard posterior samples of the tune interval.
     compute_convergence_checks : bool, default=True
@@ -330,17 +329,17 @@ def sample(
         is drawn from.
         Sampling can be interrupted by throwing a ``KeyboardInterrupt`` in the callback.
     jitter_max_retries : int
-        Maximum number of repeated attempts (per chain) at creating an initial matrix with uniform jitter
-        that yields a finite probability. This applies to ``jitter+adapt_diag`` and ``jitter+adapt_full``
-        init methods.
+        Maximum number of repeated attempts (per chain) at creating an initial matrix with uniform
+        jitter that yields a finite probability. This applies to ``jitter+adapt_diag`` and
+        ``jitter+adapt_full`` init methods.
     return_inferencedata : bool
-        Whether to return the trace as an :class:`arviz:arviz.InferenceData` (True) object or a `MultiTrace` (False)
-        Defaults to `True`.
+        Whether to return the trace as an :class:`arviz:arviz.InferenceData` (True) object or a
+        `MultiTrace` (False). Defaults to `True`.
     idata_kwargs : dict, optional
         Keyword arguments for :func:`pymc.to_inference_data`
     mp_ctx : multiprocessing.context.BaseContent
-        A multiprocessing context for parallel sampling. See multiprocessing
-        documentation for details.
+        A multiprocessing context for parallel sampling.
+        See multiprocessing documentation for details.
 
     Returns
     -------
@@ -373,7 +372,7 @@ def sample(
           The initial guess for the step size scaled down by :math:`1/n**(1/4)`,
           where n is the dimensionality of the parameter space
 
-    Alternativelly, if you manually declare the ``step_method``\ s, within the ``step``
+    Alternatively, if you manually declare the ``step_method``\ s, within the ``step``
        kwarg, then you can address the ``step_method`` kwargs directly.
        e.g. for a CompoundStep comprising NUTS and BinaryGibbsMetropolis,
        you could send ::
@@ -464,7 +463,7 @@ def sample(
 
     if isinstance(step, list):
         step = CompoundStep(step)
-    elif isinstance(step, NUTS):
+    elif isinstance(step, NUTS) and init is not None:
         if "nuts" in kwargs:
             nuts_kwargs = kwargs.pop("nuts")
             [kwargs.setdefault(k, v) for k, v in nuts_kwargs.items()]
@@ -2272,8 +2271,7 @@ def init_nuts(
     if not isinstance(init, str):
         raise TypeError("init must be a string.")
 
-    if init is not None:
-        init = init.lower()
+    init = init.lower()
 
     if init == "auto":
         init = "jitter+adapt_diag"