MInor docstring fix

blackjax/adaptation/mclmc_adaptation.py

@@ -11,26 +11,25 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-"""Algorithms to adapt the MCLMC kernel parameters, namely step size and L.
-
-"""
+"""Algorithms to adapt the MCLMC kernel parameters, namely step size and L."""
 
 from typing import NamedTuple
 
 import jax
 import jax.numpy as jnp
 from jax.flatten_util import ravel_pytree
 
-from blackjax.diagnostics import effective_sample_size  # type: ignore
+from blackjax.diagnostics import effective_sample_size
 from blackjax.util import pytree_size
 
 
 class MCLMCAdaptationState(NamedTuple):
     """Represents the tunable parameters for MCLMC adaptation.
 
-    Attributes:
-        L (float): The momentum decoherent rate for the MCLMC algorithm.
-        step_size (float): The step size used for the MCLMC algorithm.
+    L
+        The momentum decoherent rate for the MCLMC algorithm.
+    step_size
+        The step size used for the MCLMC algorithm.
     """
 
     L: float
@@ -52,25 +51,39 @@ def mclmc_find_L_and_step_size(
     """
     Finds the optimal value of the parameters for the MCLMC algorithm.
 
-    Args:
-        mclmc_kernel (callable): The kernel function used for the MCMC algorithm.
-        num_steps (int): The number of MCMC steps that will subsequently be run, after tuning.
-        state (MCMCState): The initial state of the MCMC algorithm.
-        rng_key (jax.random.PRNGKey): The random number generator key.
-        frac_tune1 (float): The fraction of tuning for the first step of the adaptation.
-        frac_tune2 (float): The fraction of tuning for the second step of the adaptation.
-        frac_tune3 (float): The fraction of tuning for the third step of the adaptation.
-        desired_energy_var (float): The desired energy variance for the MCMC algorithm.
-        trust_in_estimate (float): The trust in the estimate of optimal stepsize.
-        num_effective_samples (int): The number of effective samples for the MCMC algorithm.
-
-    Returns:
-        tuple: A tuple containing the final state of the MCMC algorithm and the final hyperparameters.
-
-    Raises:
-        None
-
-    Examples:
+    Parameters
+    ----------
+    mclmc_kernel
+        The kernel function used for the MCMC algorithm.
+    num_steps
+        The number of MCMC steps that will subsequently be run, after tuning.
+    state
+        The initial state of the MCMC algorithm.
+    rng_key
+        The random number generator key.
+    frac_tune1
+        The fraction of tuning for the first step of the adaptation.
+    frac_tune2
+        The fraction of tuning for the second step of the adaptation.
+    frac_tune3
+        The fraction of tuning for the third step of the adaptation.
+    desired_energy_va
+        The desired energy variance for the MCMC algorithm.
+    trust_in_estimate
+        The trust in the estimate of optimal stepsize.
+    num_effective_samples
+        The number of effective samples for the MCMC algorithm.
+
+    Returns
+    -------
+    A tuple containing the final state of the MCMC algorithm and the final hyperparameters.
+
+
+    Examples
+    -------
+
+    .. code::
+
         # Define the kernel function
         def kernel(x):
             return x ** 2
@@ -265,7 +278,8 @@ def adaptation_L(state, params, num_steps, key):
 
 
 def handle_nans(previous_state, next_state, step_size, step_size_max, kinetic_change):
-    """if there are nans, let's reduce the stepsize, and not update the state. The function returns the old state in this case."""
+    """if there are nans, let's reduce the stepsize, and not update the state. The
+    function returns the old state in this case."""
 
     reduced_step_size = 0.8
     p, unravel_fn = ravel_pytree(next_state.position)

blackjax/mcmc/mclmc.py

@@ -31,13 +31,13 @@ class MCLMCInfo(NamedTuple):
     """
     Additional information on the MCLMC transition.
 
-    Attributes
-    ----------
-    transformed_position :
+    transformed_position
         The value of the samples after a transformation. This is typically a projection onto a lower dimensional subspace.
-    logdensity :
+    logdensity
         The log-density of the distribution at the current step of the MCLMC chain.
-    energy_change :
+    kinetic_change
+        The difference in kinetic energy between the current and previous step.
+    energy_change
         The difference in energy between the current and previous step.
     """
 
@@ -68,9 +68,9 @@ def build_kernel(logdensity_fn, integrator, transform):
     transform
         Value of the difference in energy above which we consider that the transition is divergent.
     L
-      the momentum decoherence rate
+        the momentum decoherence rate.
     step_size
-      step size of the integrator
+        step size of the integrator.
 
     Returns
     -------
@@ -136,8 +136,8 @@ class mclmc:
 
     .. code::
 
-       step = jax.jit(mclmc.step)
-       new_state, info = step(rng_key, state)
+        step = jax.jit(mclmc.step)
+        new_state, info = step(rng_key, state)
 
     Parameters
     ----------
@@ -146,11 +146,11 @@ class mclmc:
     transform
         A function to perform on the samples drawn from the target distribution
     L
-      the momentum decoherence rate
+        the momentum decoherence rate
     step_size
-      step size of the integrator
+        step size of the integrator
     integrator
-      an integrator. We recommend using the default here.
+        an integrator. We recommend using the default here.
 
     Returns
     -------
@@ -185,13 +185,13 @@ def partially_refresh_momentum(momentum, rng_key, step_size, L):
 
     Parameters
     ----------
-    rng_key:
+    rng_key
         The pseudo-random number generator key used to generate random numbers.
-    momentum:
+    momentum
         PyTree that the structure the output should to match.
-    step_size:
+    step_size
         Step size
-    L:
+    L
         controls rate of momentum change
 
     Returns

blackjax/smc/base.py

@@ -24,17 +24,18 @@ class SMCState(NamedTuple):
 
     Particles must be a ArrayTree, each leave represents a variable from the posterior,
     being an array of size `(n_particles, ...)`.
+
     Examples (three particles):
-        - Single univariate posterior:
-            [ Array([[1.], [1.2], [3.4]]) ]
-        - Single bivariate  posterior:
-            [Array([[1,2], [3,4], [5,6]])]
-        - Two variables, each univariate:
-            [ Array([[1.], [1.2], [3.4]]),
-            Array([[50.], [51], [55]]) ]
-        - Two variables, first one bivariate, second one 4-variate:
-            [ Array([[1., 2.], [1.2, 0.5], [3.4, 50]]),
-            Array([[50., 51., 52., 51], [51., 52., 52. ,54.], [55., 60, 60, 70]])]
+    - Single univariate posterior:
+        [ Array([[1.], [1.2], [3.4]]) ]
+    - Single bivariate  posterior:
+        [ Array([[1,2], [3,4], [5,6]]) ]
+    - Two variables, each univariate:
+        [ Array([[1.], [1.2], [3.4]]),
+        Array([[50.], [51], [55]]) ]
+    - Two variables, first one bivariate, second one 4-variate:
+        [ Array([[1., 2.], [1.2, 0.5], [3.4, 50]]),
+        Array([[50., 51., 52., 51], [51., 52., 52. ,54.], [55., 60, 60, 70]]) ]
     """
 
     particles: ArrayTree

blackjax/smc/tuning/from_particles.py

@@ -33,7 +33,8 @@ def mass_matrix_from_particles(particles) -> Array:
     Implements tuning from section 3.1 from https://arxiv.org/pdf/1808.07730.pdf
     Computing a mass matrix to be used in HMC from particles.
     Given the particles covariance matrix, set all non-diagonal elements as zero,
-     take the inverse, and keep the diagonal.
+    take the inverse, and keep the diagonal.
+
     Returns
     -------
     A mass Matrix

requirements-doc.txt

@@ -8,7 +8,7 @@ jax>=0.4.16
 jaxlib>=0.4.16
 jaxopt
 jupytext
-myst_nb>=1.0.0rc0
+myst_nb>=1.0.0
 numba
 numpyro
 optax