start porting tests in ArviZ

pymc3/__init__.py

@@ -40,7 +40,12 @@ def __set_compiler_flags():
 
 from pymc3 import gp, ode, sampling
 from pymc3.aesaraf import *
-from pymc3.backends import load_trace, save_trace
+from pymc3.backends import (
+    load_trace,
+    predictions_to_inference_data,
+    save_trace,
+    to_inference_data,
+)
 from pymc3.backends.tracetab import *
 from pymc3.blocking import *
 from pymc3.data import *

pymc3/backends/__init__.py

@@ -60,6 +60,7 @@
 Saved backends can be loaded using `arviz.from_netcdf`
 
 """
+from pymc3.backends.arviz import predictions_to_inference_data, to_inference_data
 from pymc3.backends.ndarray import (
     NDArray,
     load_trace,

pymc3/backends/arviz.py

@@ -17,15 +17,14 @@
 import numpy as np
 import xarray as xr
 
-from aesara.gof.graph import ancestors
+from aesara.graph.basic import ancestors
 from aesara.tensor.var import TensorVariable
 from arviz import InferenceData, concat, rcParams
 from arviz.data.base import CoordSpec, DimSpec, dict_to_dataset, requires
 
 import pymc3
 
 from pymc3.model import modelcontext
-from pymc3.sampling import _DefaultTrace
 from pymc3.util import get_default_varnames
 
 if TYPE_CHECKING:
@@ -42,6 +41,61 @@
 Var = Any  # pylint: disable=invalid-name
 
 
+class _DefaultTrace:
+    """
+    Utility for collecting samples into a dictionary.
+
+    Name comes from its similarity to ``defaultdict``:
+    entries are lazily created.
+
+    Parameters
+    ----------
+    samples : int
+        The number of samples that will be collected, per variable,
+        into the trace.
+
+    Attributes
+    ----------
+    trace_dict : Dict[str, np.ndarray]
+        A dictionary constituting a trace.  Should be extracted
+        after a procedure has filled the `_DefaultTrace` using the
+        `insert()` method
+    """
+
+    trace_dict: Dict[str, np.ndarray] = {}
+    _len: Optional[int] = None
+
+    def __init__(self, samples: int):
+        self._len = samples
+        self.trace_dict = {}
+
+    def insert(self, k: str, v, idx: int):
+        """
+        Insert `v` as the value of the `idx`th sample for the variable `k`.
+
+        Parameters
+        ----------
+        k: str
+            Name of the variable.
+        v: anything that can go into a numpy array (including a numpy array)
+            The value of the `idx`th sample from variable `k`
+        ids: int
+            The index of the sample we are inserting into the trace.
+        """
+        value_shape = np.shape(v)
+
+        # initialize if necessary
+        if k not in self.trace_dict:
+            array_shape = (self._len,) + value_shape
+            self.trace_dict[k] = np.empty(array_shape, dtype=np.array(v).dtype)
+
+        # do the actual insertion
+        if value_shape == ():
+            self.trace_dict[k][idx] = v
+        else:
+            self.trace_dict[k][idx, :] = v
+
+
 class InferenceDataConverter:  # pylint: disable=too-many-instance-attributes
     """Encapsulate InferenceData specific logic."""
 

pymc3/sampling.py

@@ -27,7 +27,6 @@
 
 import aesara
 import aesara.gradient as tg
-import arviz
 import numpy as np
 import packaging
 import xarray
@@ -38,6 +37,7 @@
 import pymc3 as pm
 
 from pymc3.aesaraf import inputvars
+from pymc3.backends.arviz import _DefaultTrace
 from pymc3.backends.base import BaseTrace, MultiTrace
 from pymc3.backends.ndarray import NDArray
 from pymc3.blocking import DictToArrayBijection
@@ -344,7 +344,7 @@ def sample(
         Whether to return the trace as an :class:`arviz:arviz.InferenceData` (True) object or a `MultiTrace` (False)
         Defaults to `False`, but we'll switch to `True` in an upcoming release.
     idata_kwargs : dict, optional
-        Keyword arguments for :func:`arviz:arviz.from_pymc3`
+        Keyword arguments for :func:`pymc3.to_inference_data`
     mp_ctx : multiprocessing.context.BaseContent
         A multiprocessing context for parallel sampling. See multiprocessing
         documentation for details.
@@ -639,7 +639,7 @@ def sample(
         ikwargs = dict(model=model, save_warmup=not discard_tuned_samples, log_likelihood=False)
         if idata_kwargs:
             ikwargs.update(idata_kwargs)
-        idata = arviz.from_pymc3(trace, **ikwargs)
+        idata = pm.to_inference_data(trace, **ikwargs)
 
     if compute_convergence_checks:
         if draws - tune < 100:
@@ -1546,61 +1546,6 @@ def stop_tuning(step):
     return step
 
 
-class _DefaultTrace:
-    """
-    Utility for collecting samples into a dictionary.
-
-    Name comes from its similarity to ``defaultdict``:
-    entries are lazily created.
-
-    Parameters
-    ----------
-    samples : int
-        The number of samples that will be collected, per variable,
-        into the trace.
-
-    Attributes
-    ----------
-    trace_dict : Dict[str, np.ndarray]
-        A dictionary constituting a trace.  Should be extracted
-        after a procedure has filled the `_DefaultTrace` using the
-        `insert()` method
-    """
-
-    trace_dict: Dict[str, np.ndarray] = {}
-    _len: Optional[int] = None
-
-    def __init__(self, samples: int):
-        self._len = samples
-        self.trace_dict = {}
-
-    def insert(self, k: str, v, idx: int):
-        """
-        Insert `v` as the value of the `idx`th sample for the variable `k`.
-
-        Parameters
-        ----------
-        k: str
-            Name of the variable.
-        v: anything that can go into a numpy array (including a numpy array)
-            The value of the `idx`th sample from variable `k`
-        ids: int
-            The index of the sample we are inserting into the trace.
-        """
-        value_shape = np.shape(v)
-
-        # initialize if necessary
-        if k not in self.trace_dict:
-            array_shape = (self._len,) + value_shape
-            self.trace_dict[k] = np.empty(array_shape, dtype=np.array(v).dtype)
-
-        # do the actual insertion
-        if value_shape == ():
-            self.trace_dict[k][idx] = v
-        else:
-            self.trace_dict[k][idx, :] = v
-
-
 def sample_posterior_predictive(
     trace,
     samples: Optional[int] = None,