Add new create_inits() methods to other stanfit classes

cmdstanpy/stanfit/laplace.py

@@ -1,5 +1,5 @@
 """
-    Container for the result of running a laplace approximation.
+Container for the result of running a laplace approximation.
 """
 
 from typing import (
@@ -52,6 +52,39 @@ def __init__(self, runset: RunSet, mode: CmdStanMLE) -> None:
         config = scan_generic_csv(runset.csv_files[0])
         self._metadata = InferenceMetadata(config)
 
+    def create_inits(
+        self, seed: Optional[int] = None, chains: int = 4
+    ) -> Union[List[Dict[str, np.ndarray]], Dict[str, np.ndarray]]:
+        """
+        Create initial values for the parameters of the model
+        by randomly selecting draws from the Laplace approximation.
+
+        :param seed: Used for random selection, defaults to None
+        :param chains: Number of initial values to return, defaults to 4
+        :return: The initial values for the parameters of the model.
+
+        If ``chains`` is 1, a dictionary is returned, otherwise a list
+        of dictionaries is returned, in the format expected for the
+        ``inits`` argument of :meth:`CmdStanModel.sample`.
+        """
+        self._assemble_draws()
+        rng = np.random.default_rng(seed)
+        idxs = rng.choice(self._draws.shape[0], size=chains, replace=False)
+        if chains == 1:
+            draw = self._draws[idxs[0]]
+            return {
+                name: var.extract_reshape(draw)
+                for name, var in self._metadata.stan_vars.items()
+            }
+        else:
+            return [
+                {
+                    name: var.extract_reshape(self._draws[idx])
+                    for name, var in self._metadata.stan_vars.items()
+                }
+                for idx in idxs
+            ]
+
     def _assemble_draws(self) -> None:
         if self._draws.shape != (0,):
             return

cmdstanpy/stanfit/mcmc.py

@@ -105,6 +105,46 @@ def __init__(
         if not self._is_fixed_param:
             self._check_sampler_diagnostics()
 
+    def create_inits(
+        self, seed: Optional[int] = None, chains: int = 4
+    ) -> Union[List[Dict[str, np.ndarray]], Dict[str, np.ndarray]]:
+        """
+        Create initial values for the parameters of the model by
+        randomly selecting draws from the MCMC samples. If the samples
+        contain draws from multiple chains, each draw will be from
+        a different chain, if possible. Otherwise the chain is randomly
+        selected.
+
+        :param seed: Used for random selection, defaults to None
+        :param chains: Number of initial values to return, defaults to 4
+        :return: The initial values for the parameters of the model.
+
+        If ``chains`` is 1, a dictionary is returned, otherwise a list
+        of dictionaries is returned, in the format expected for the
+        ``inits`` argument of :meth:`CmdStanModel.sample`.
+        """
+        self._assemble_draws()
+        rng = np.random.default_rng(seed)
+        n_draws, n_chains = self._draws.shape[:2]
+        draw_idxs = rng.choice(n_draws, size=chains, replace=False)
+        chain_idxs = rng.choice(
+            n_chains, size=chains, replace=(n_chains <= chains)
+        )
+        if chains == 1:
+            draw = self._draws[draw_idxs[0], chain_idxs[0]]
+            return {
+                name: var.extract_reshape(draw)
+                for name, var in self._metadata.stan_vars.items()
+            }
+        else:
+            return [
+                {
+                    name: var.extract_reshape(self._draws[d, i])
+                    for name, var in self._metadata.stan_vars.items()
+                }
+                for d, i in zip(draw_idxs, chain_idxs)
+            ]
+
     def __repr__(self) -> str:
         repr = 'CmdStanMCMC: model={} chains={}{}'.format(
             self.runset.model,
@@ -685,7 +725,7 @@ def draws_xr(
             )
         if inc_warmup and not self._save_warmup:
             get_logger().warning(
-                "Draws from warmup iterations not available,"
+                'Draws from warmup iterations not available,'
                 ' must run sampler with "save_warmup=True".'
             )
         if vars is None:

cmdstanpy/stanfit/mle.py

@@ -36,6 +36,30 @@ def __init__(self, runset: RunSet) -> None:
         self._save_iterations: bool = optimize_args.save_iterations
         self._set_mle_attrs(runset.csv_files[0])
 
+    def create_inits(
+        self, seed: Optional[int] = None, chains: int = 4
+    ) -> Dict[str, np.ndarray]:
+        """
+        Create initial values for the parameters of the model
+        from the MLE.
+
+        :param seed: Unused. Kept for compatibility with other
+        create_inits methods.
+        :param chains: Unused. Kept for compatibility with other
+        create_inits methods.
+        :return: The initial values for the parameters of the model.
+
+        Returns a dictionary of MLE estimates in the format expected
+        for the ``inits`` argument of :meth:`CmdStanModel.sample`.
+        When running multi-chain sampling, all chains will be initialized
+        at the same points.
+        """
+        # pylint: disable=unused-argument
+
+        return {
+            name: np.array(val) for name, val in self.stan_variables().items()
+        }
+
     def __repr__(self) -> str:
         repr = 'CmdStanMLE: model={}{}'.format(
             self.runset.model, self.runset._args.method_args.compose(0, cmd=[])

cmdstanpy/stanfit/pathfinder.py

@@ -45,7 +45,7 @@ def create_inits(
 
         If ``chains`` is 1, a dictionary is returned, otherwise a list
         of dictionaries is returned, in the format expected for the
-        ``inits`` argument. of :meth:`CmdStanModel.sample`.
+        ``inits`` argument of :meth:`CmdStanModel.sample`.
         """
         self._assemble_draws()
         rng = np.random.default_rng(seed)

cmdstanpy/stanfit/vb.py

@@ -1,7 +1,7 @@
 """Container for the results of running autodiff variational inference"""
 
 from collections import OrderedDict
-from typing import Dict, Optional, Tuple, Union
+from typing import Dict, List, Optional, Tuple, Union
 
 import numpy as np
 import pandas as pd
@@ -30,6 +30,41 @@ def __init__(self, runset: RunSet) -> None:
         self.runset = runset
         self._set_variational_attrs(runset.csv_files[0])
 
+    def create_inits(
+        self, seed: Optional[int] = None, chains: int = 4
+    ) -> Union[List[Dict[str, np.ndarray]], Dict[str, np.ndarray]]:
+        """
+        Create initial values for the parameters of the model
+        by randomly selecting draws from the variational approximation
+        draws.
+
+        :param seed: Used for random selection, defaults to None
+        :param chains: Number of initial values to return, defaults to 4
+        :return: The initial values for the parameters of the model.
+
+        If ``chains`` is 1, a dictionary is returned, otherwise a list
+        of dictionaries is returned, in the format expected for the
+        ``inits`` argument of :meth:`CmdStanModel.sample`.
+        """
+        rng = np.random.default_rng(seed)
+        idxs = rng.choice(
+            self.variational_sample.shape[0], size=chains, replace=False
+        )
+        if chains == 1:
+            draw = self.variational_sample[idxs[0]]
+            return {
+                name: var.extract_reshape(draw)
+                for name, var in self._metadata.stan_vars.items()
+            }
+        else:
+            return [
+                {
+                    name: var.extract_reshape(self.variational_sample[idx])
+                    for name, var in self._metadata.stan_vars.items()
+                }
+                for idx in idxs
+            ]
+
     def __repr__(self) -> str:
         repr = 'CmdStanVB: model={}{}'.format(
             self.runset.model, self.runset._args.method_args.compose(0, cmd=[])