Use ArviZ-stats

.github/workflows/test.yml

@@ -11,7 +11,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ["3.10", "3.11"]
+        python-version: ["3.11", "3.12", "3.13"]
 
     name: Set up Python ${{ matrix.python-version }}
     steps:

docs/api_reference.rst

@@ -13,4 +13,4 @@ methods in the current release of PyMC-BART.
 =============================
 
 .. automodule:: pymc_bart
-   :members: BART, PGBART, compute_variable_importance, get_variable_inclusion, plot_convergence, plot_ice, plot_pdp, plot_scatter_submodels, plot_variable_importance, plot_variable_inclusion, ContinuousSplitRule, OneHotSplitRule, SubsetSplitRule
+   :members: BART, PGBART, compute_variable_importance, get_variable_inclusion, plot_ice, plot_pdp, plot_scatter_submodels, plot_variable_importance, plot_variable_inclusion, ContinuousSplitRule, OneHotSplitRule, SubsetSplitRule

docs/index.rst

@@ -29,7 +29,7 @@ interpretation of those models and perform variable selection.
 Installation
 ============
 
-PyMC-BART requires a working Python interpreter (3.10+). We recommend installing Python and key numerical libraries using the `Anaconda distribution <https://www.anaconda.com/products/individual#Downloads>`_, which has one-click installers available on all major platforms.
+PyMC-BART requires a working Python interpreter (3.11+). We recommend installing Python and key numerical libraries using the `Anaconda distribution <https://www.anaconda.com/products/individual#Downloads>`_, which has one-click installers available on all major platforms.
 
 Assuming a standard Python environment is installed on your machine, PyMC-BART itself can be installed either using pip or conda-forge.
 

env-dev.yml

@@ -3,8 +3,7 @@ channels:
   - conda-forge
   - defaults
 dependencies:
-  - pymc>=5.16.2,<=5.19.1
-  - arviz>=0.18.0
+  - pymc>=5.16.2,<=5.23.0
   - numba
   - matplotlib
   - numpy
@@ -20,4 +19,5 @@ dependencies:
   - flake8
   - pip
   - pip:
+    - arviz-stats[xarray]>=0.6.0
     - -e .

env.yml

@@ -3,12 +3,12 @@ channels:
   - conda-forge
   - defaults
 dependencies:
-  - pymc>=5.16.2,<=5.19.1
-  - arviz>=0.18.0
+  - pymc>=5.16.2,<=5.23.0
   - numba
   - matplotlib
   - numpy
   - pytensor
   - pip
   - pip:
     - pymc-bart
+    - arviz-stats[xarray]>=0.6.0

pymc_bart/pgbart.py

@@ -346,7 +346,7 @@ def resample(
                 new_particles.append(particles[idx].copy())
             else:
                 new_particles.append(particles[idx])
-                seen.append(idx)
+                seen.append(int(idx))
 
         particles[1:] = new_particles
 

pymc_bart/utils.py

@@ -4,16 +4,16 @@
 import warnings
 from typing import Any, Callable, Optional, Union
 
-import arviz as az
 import matplotlib.pyplot as plt
 import numpy as np
 import numpy.typing as npt
 import pytensor.tensor as pt
+from arviz_base import rcParams
+from arviz_stats.base import array_stats
 from numba import jit
 from pytensor.tensor.variable import Variable
 from scipy.interpolate import griddata
 from scipy.signal import savgol_filter
-from scipy.stats import norm
 
 from .tree import Tree
 
@@ -76,12 +76,12 @@ def _sample_posterior(
 
 
 def plot_convergence(
-    idata: az.InferenceData,
+    idata: Any,
     var_name: Optional[str] = None,
     kind: str = "ecdf",
     figsize: Optional[tuple[float, float]] = None,
     ax=None,
-) -> list[plt.Axes]:
+) -> None:
     """
     Plot convergence diagnostics.
 
@@ -102,39 +102,12 @@ def plot_convergence(
     -------
     list[ax] : matplotlib axes
     """
-    ess_threshold = idata["posterior"]["chain"].size * 100
-    ess = np.atleast_2d(az.ess(idata, method="bulk", var_names=var_name)[var_name].values)
-    rhat = np.atleast_2d(az.rhat(idata, var_names=var_name)[var_name].values)
-
-    if figsize is None:
-        figsize = (10, 3)
-
-    if kind == "ecdf":
-        kind_func: Callable[..., Any] = az.plot_ecdf
-        sharey = True
-    elif kind == "kde":
-        kind_func = az.plot_kde
-        sharey = False
-
-    if ax is None:
-        _, ax = plt.subplots(1, 2, figsize=figsize, sharex="col", sharey=sharey)
-
-    for idx, (essi, rhati) in enumerate(zip(ess, rhat)):
-        kind_func(essi, ax=ax[0], plot_kwargs={"color": f"C{idx}"})
-        kind_func(rhati, ax=ax[1], plot_kwargs={"color": f"C{idx}"})
-
-    ax[0].axvline(ess_threshold, color="0.7", ls="--")
-    # Assume Rhats are N(1, 0.005) iid. Then compute the 0.99 quantile
-    # scaled by the sample size and use it as a threshold.
-    ax[1].axvline(norm(1, 0.005).ppf(0.99 ** (1 / ess.size)), color="0.7", ls="--")
-
-    ax[0].set_xlabel("ESS")
-    ax[1].set_xlabel("R-hat")
-    if kind == "kde":
-        ax[0].set_yticks([])
-        ax[1].set_yticks([])
-
-    return ax
+    warnings.warn(
+        "This function has been deprecated"
+        "Use az.plot_convergence_dist() instead."
+        "https://arviz-plots.readthedocs.io/en/latest/api/generated/arviz_plots.plot_convergence_dist.html",
+        FutureWarning,
+    )
 
 
 def plot_ice(
@@ -408,7 +381,7 @@ def identity(x):
                 if var in var_discrete:
                     _, idx_uni = np.unique(new_x, return_index=True)
                     y_means = p_di.mean(0)[idx_uni]
-                    hdi = az.hdi(p_di)[idx_uni]
+                    hdi = array_stats.hdi(p_di, prob=rcParams["stats.ci_prob"], axis=0)[idx_uni]
                     axes[count].errorbar(
                         new_x[idx_uni],
                         y_means,
@@ -418,11 +391,13 @@ def identity(x):
                     )
                     axes[count].set_xticks(new_x[idx_uni])
                 else:
-                    az.plot_hdi(
+                    _plot_hdi(
                         new_x,
                         p_di,
                         smooth=smooth,
-                        fill_kwargs={"alpha": alpha, "color": color},
+                        alpha=alpha,
+                        color=color,
+                        smooth_kwargs=smooth_kwargs,
                         ax=axes[count],
                     )
                     if smooth:
@@ -659,7 +634,7 @@ def _create_pdp_data(
 def _smooth_mean(
     new_x: npt.NDArray,
     p_di: npt.NDArray,
-    kind: str = "pdp",
+    kind: str = "neutral",
     smooth_kwargs: Optional[dict[str, Any]] = None,
 ) -> tuple[np.ndarray, np.ndarray]:
     """
@@ -688,7 +663,10 @@ def _smooth_mean(
     smooth_kwargs.setdefault("polyorder", 2)
     x_data = np.linspace(np.nanmin(new_x), np.nanmax(new_x), 200)
     x_data[0] = (x_data[0] + x_data[1]) / 2
-    if kind == "pdp":
+
+    if kind == "neutral":
+        interp = griddata(new_x, p_di, x_data)
+    elif kind == "pdp":
         interp = griddata(new_x, p_di.mean(0), x_data)
     else:
         interp = griddata(new_x, p_di.T, x_data)
@@ -800,7 +778,7 @@ def plot_variable_inclusion(idata, X, labels=None, figsize=None, plot_kwargs=Non
 
 
 def compute_variable_importance(  # noqa: PLR0915 PLR0912
-    idata: az.InferenceData,
+    idata: Any,
     bartrv: Variable,
     X: npt.NDArray,
     method: str = "VI",
@@ -904,7 +882,7 @@ def compute_variable_importance(  # noqa: PLR0915 PLR0912
                 [pearsonr2(predicted_all[j], predicted_subset[j]) for j in range(samples)]
             )
             r2_mean[idx] = np.mean(r_2)
-            r2_hdi[idx] = az.hdi(r_2)
+            r2_hdi[idx] = array_stats.hdi(r_2, prob=rcParams["stats.ci_prob"])
             preds[idx] = predicted_subset.squeeze()
 
     if method in ["backward", "backward_VI"]:
@@ -954,7 +932,7 @@ def compute_variable_importance(  # noqa: PLR0915 PLR0912
 
             # Save values for plotting later
             r2_mean[i_var - init] = max_r_2
-            r2_hdi[i_var - init] = az.hdi(r_2_without_least_important_vars)
+            r2_hdi[i_var - init] = array_stats.hdi(r_2_without_least_important_vars)
             preds[i_var - init] = least_important_samples.squeeze()
 
             # extend current list of least important variable
@@ -1079,7 +1057,7 @@ def plot_variable_importance(
     )
     ax.fill_between(
         [-0.5, n_vars - 0.5],
-        *az.hdi(r_2_ref),
+        *array_stats.hdi(r_2_ref, prob=rcParams["stats.ci_prob"]),
         alpha=0.1,
         color=plot_kwargs.get("color_ref", "grey"),
     )
@@ -1229,3 +1207,22 @@ def pearsonr2(A, B):
     am = A - np.mean(A)
     bm = B - np.mean(B)
     return (am @ bm) ** 2 / (np.sum(am**2) * np.sum(bm**2))
+
+
+def _plot_hdi(x, y, smooth, color, alpha, smooth_kwargs, ax):
+    x = np.asarray(x)
+    y = np.asarray(y)
+    hdi_prob = rcParams["stats.ci_prob"]
+    hdi_data = array_stats.hdi(y, hdi_prob, axis=0)
+    if smooth:
+        if isinstance(x[0], np.datetime64):
+            raise TypeError("Cannot deal with x as type datetime. Recommend setting smooth=False.")
+
+        x_data, y_data = _smooth_mean(x, hdi_data, smooth_kwargs=smooth_kwargs)
+    else:
+        idx = np.argsort(x)
+        x_data = x[idx]
+        y_data = hdi_data[idx]
+
+    ax.fill_between(x_data, y_data[:, 0], y_data[:, 1], color=color, alpha=alpha)
+    return ax

requirements.txt

@@ -1,5 +1,5 @@
-pymc>=5.16.2, <=5.23.0
-arviz>=0.18.0
+pymc>=5.16.2,<=5.23.0
+arviz-stats[xarray]>=0.6.0
 numba
 matplotlib
-numpy
+numpy>=2.0

setup.py

@@ -29,9 +29,9 @@
     "Development Status :: 5 - Production/Stable",
     "Programming Language :: Python",
     "Programming Language :: Python :: 3",
-    "Programming Language :: Python :: 3.9",
-    "Programming Language :: Python :: 3.10",
     "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: 3.12",
+    "Programming Language :: Python :: 3.13",
     "License :: OSI Approved :: Apache Software License",
     "Intended Audience :: Science/Research",
     "Topic :: Scientific/Engineering",