remove save_trace and load_trace function

pymc/__init__.py

@@ -76,12 +76,7 @@ def __set_compiler_flags():
 
 from pymc import gp, ode, sampling
 from pymc.aesaraf import *
-from pymc.backends import (
-    load_trace,
-    predictions_to_inference_data,
-    save_trace,
-    to_inference_data,
-)
+from pymc.backends import predictions_to_inference_data, to_inference_data
 from pymc.backends.tracetab import *
 from pymc.bart import *
 from pymc.blocking import *

pymc/backends/__init__.py

@@ -61,9 +61,4 @@
 
 """
 from pymc.backends.arviz import predictions_to_inference_data, to_inference_data
-from pymc.backends.ndarray import (
-    NDArray,
-    load_trace,
-    point_list_to_multitrace,
-    save_trace,
-)
+from pymc.backends.ndarray import NDArray, point_list_to_multitrace

pymc/backends/ndarray.py

@@ -32,94 +32,6 @@
 from pymc.model import Model, modelcontext
 
 
-def save_trace(trace: MultiTrace, directory: Optional[str] = None, overwrite=False) -> str:
-    """Save multitrace to file.
-
-    TODO: Also save warnings.
-
-    This is a custom data format for PyMC traces.  Each chain goes inside
-    a directory, and each directory contains a metadata json file, and a
-    numpy compressed file.  See https://docs.scipy.org/doc/numpy/neps/npy-format.html
-    for more information about this format.
-
-    Parameters
-    ----------
-    trace: pm.MultiTrace
-        trace to save to disk
-    directory: str (optional)
-        path to a directory to save the trace
-    overwrite: bool (default False)
-        whether to overwrite an existing directory.
-
-    Returns
-    -------
-    str, path to the directory where the trace was saved
-    """
-    warnings.warn(
-        "The `save_trace` function will soon be removed."
-        "Instead, use `arviz.to_netcdf` to save traces.",
-        FutureWarning,
-    )
-
-    if isinstance(trace, MultiTrace):
-        if directory is None:
-            directory = ".pymc_{}.trace"
-            idx = 1
-            while os.path.exists(directory.format(idx)):
-                idx += 1
-            directory = directory.format(idx)
-
-        if os.path.isdir(directory):
-            if overwrite:
-                shutil.rmtree(directory)
-            else:
-                raise OSError(
-                    "Cautiously refusing to overwrite the already existing {}! Please supply "
-                    "a different directory, or set `overwrite=True`".format(directory)
-                )
-        os.makedirs(directory)
-
-        for chain, ndarray in trace._straces.items():
-            SerializeNDArray(os.path.join(directory, str(chain))).save(ndarray)
-        return directory
-    else:
-        raise TypeError(
-            f"You are attempting to save an InferenceData object but this function "
-            "works only for MultiTrace objects. Use `arviz.to_netcdf` instead"
-        )
-
-
-def load_trace(directory: str, model=None) -> MultiTrace:
-    """Loads a multitrace that has been written to file.
-
-    A the model used for the trace must be passed in, or the command
-    must be run in a model context.
-
-    Parameters
-    ----------
-    directory: str
-        Path to a pymc serialized trace
-    model: pm.Model (optional)
-        Model used to create the trace.  Can also be inferred from context
-
-    Returns
-    -------
-    pm.Multitrace that was saved in the directory
-    """
-    warnings.warn(
-        "The `load_trace` function will soon be removed."
-        "Instead, use `arviz.from_netcdf` to load traces.",
-        FutureWarning,
-    )
-    straces = []
-    for subdir in glob.glob(os.path.join(directory, "*")):
-        if os.path.isdir(subdir):
-            straces.append(SerializeNDArray(subdir).load(model))
-    if not straces:
-        raise TraceDirectoryError("%s is not a PyMC saved chain directory." % directory)
-    return base.MultiTrace(straces)
-
-
 class SerializeNDArray:
     metadata_file = "metadata.json"
     samples_file = "samples.npz"

pymc/tests/test_ndarray_backend.py

@@ -205,78 +205,3 @@ def test_combine_true_squeeze_true(self):
         expected = np.concatenate([self.x, self.y])
         result = base._squeeze_cat([self.x, self.y], True, True)
         npt.assert_equal(result, expected)
-
-
-class TestSaveLoad:
-    @staticmethod
-    def model(rng_seeder=None):
-        with pm.Model(rng_seeder=rng_seeder) as model:
-            x = pm.Normal("x", 0, 1)
-            y = pm.Normal("y", x, 1, observed=2)
-            z = pm.Normal("z", x + y, 1)
-        return model
-
-    @classmethod
-    def setup_class(cls):
-        with TestSaveLoad.model():
-            cls.trace = pm.sample(return_inferencedata=False)
-
-    def test_save_new_model(self, tmpdir_factory):
-        directory = str(tmpdir_factory.mktemp("data"))
-        save_dir = pm.save_trace(self.trace, directory, overwrite=True)
-
-        assert save_dir == directory
-        with pm.Model() as model:
-            w = pm.Normal("w", 0, 1)
-            new_trace = pm.sample(return_inferencedata=False)
-
-        with pytest.raises(OSError):
-            _ = pm.save_trace(new_trace, directory)
-
-        _ = pm.save_trace(new_trace, directory, overwrite=True)
-        with model:
-            new_trace_copy = pm.load_trace(directory)
-
-        assert (new_trace["w"] == new_trace_copy["w"]).all()
-
-    def test_save_and_load(self, tmpdir_factory):
-        directory = str(tmpdir_factory.mktemp("data"))
-        save_dir = pm.save_trace(self.trace, directory, overwrite=True)
-
-        assert save_dir == directory
-
-        trace2 = pm.load_trace(directory, model=TestSaveLoad.model())
-
-        for var in ("x", "z"):
-            assert (self.trace[var] == trace2[var]).all()
-
-        assert self.trace.stat_names == trace2.stat_names
-        for stat in self.trace.stat_names:
-            assert all(self.trace[stat] == trace2[stat]), (
-                "Restored value of statistic %s does not match stored value" % stat
-            )
-
-    def test_bad_load(self, tmpdir_factory):
-        directory = str(tmpdir_factory.mktemp("data"))
-        with pytest.raises(pm.TraceDirectoryError):
-            pm.load_trace(directory, model=TestSaveLoad.model())
-
-    def test_sample_posterior_predictive(self, tmpdir_factory):
-        directory = str(tmpdir_factory.mktemp("data"))
-        save_dir = pm.save_trace(self.trace, directory, overwrite=True)
-
-        assert save_dir == directory
-
-        rng = np.random.RandomState(10)
-
-        with TestSaveLoad.model(rng_seeder=rng):
-            ppc = pm.sample_posterior_predictive(self.trace)
-
-        rng = np.random.RandomState(10)
-
-        with TestSaveLoad.model(rng_seeder=rng):
-            trace2 = pm.load_trace(directory)
-            ppc2 = pm.sample_posterior_predictive(trace2)
-
-        for key, value in ppc.items():
-            assert (value == ppc2[key]).all()