Updated load method in ModelBuilder (#104)

* added predict_posterior

* changed tests

* Update pymc_experimental/model_builder.py

* updated load method

* updated load method

* added required changes

* added required changes

* fixed laod function

* fixed laod functio

* Update pymc_experimental/model_builder.py

* Update pymc_experimental/model_builder.py

* Apply suggestions from code review

* Restructure tests. Fix load().

* Bump isort.

* Skip saving on windows.

* Different approach.

* Different approach.

* Different approach.

* Different approach.

---------

Co-authored-by: Thomas Wiecki <[email protected]>

Author: 5hv5hvnk

.pre-commit-config.yaml

@@ -11,7 +11,7 @@ repos:
         args: [--branch, main]
     -   id: trailing-whitespace
 - repo: https://github.com/PyCQA/isort
-  rev: 5.10.1
+  rev: 5.12.0
   hooks:
     - id: isort
       name: isort

pymc_experimental/model_builder.py

@@ -1,4 +1,4 @@
-#   Copyright 2022 The PyMC Developers
+#   Copyright 2023 The PyMC Developers
 #
 #   Licensed under the Apache License, Version 2.0 (the "License");
 #   you may not use this file except in compliance with the License.
@@ -185,20 +185,19 @@ def load(cls, fname):
         """
 
         filepath = Path(str(fname))
-        data = az.from_netcdf(filepath)
-        idata = data
-        # Since there is an issue with attrs getting saved in netcdf format which will be fixed in future the following part of code is commented
-        # Link of issue -> https://github.com/arviz-devs/arviz/issues/2109
-        # if model.idata.attrs is not None:
-        #     if model.idata.attrs['id'] == self.idata.attrs['id']:
-        #         self = model
-        #         self.idata = data
-        #         return self
-        #     else:
-        #         raise ValueError(
-        #             f"The route '{file}' does not contain an inference data of the same model '{self.__name__}'"
-        #         )
-        return idata
+        idata = az.from_netcdf(filepath)
+        self = cls(
+            dict(zip(idata.attrs["model_config_keys"], idata.attrs["model_config_values"])),
+            dict(zip(idata.attrs["sample_config_keys"], idata.attrs["sample_config_values"])),
+            idata.fit_data.to_dataframe(),
+        )
+        self.idata = idata
+        if self.id() != idata.attrs["id"]:
+            raise ValueError(
+                f"The file '{fname}' does not contain an inference data of the same model or configuration as '{self._model_type}'"
+            )
+
+        return self
 
     def fit(self, data: Dict[str, Union[np.ndarray, pd.DataFrame, pd.Series]] = None):
         """
@@ -238,8 +237,11 @@ def fit(self, data: Dict[str, Union[np.ndarray, pd.DataFrame, pd.Series]] = None
         self.idata.attrs["id"] = self.id()
         self.idata.attrs["model_type"] = self._model_type
         self.idata.attrs["version"] = self.version
-        self.idata.attrs["sample_conifg"] = self.sample_config
-        self.idata.attrs["model_config"] = self.model_config
+        self.idata.attrs["sample_config_keys"] = tuple(self.sample_config.keys())
+        self.idata.attrs["sample_config_values"] = tuple(self.sample_config.values())
+        self.idata.attrs["model_config_keys"] = tuple(self.model_config.keys())
+        self.idata.attrs["model_config_values"] = tuple(self.model_config.values())
+        self.idata.add_groups(fit_data=self.data.to_xarray())
         return self.idata
 
     def predict(
@@ -306,7 +308,7 @@ def predict_posterior(
         >>> model = LinearModel(model_config, sampler_config)
         >>> idata = model.fit(data)
         >>> x_pred = []
-        >>> prediction_data = pd.DataFrame({'input':x_pred})
+        >>> prediction_data = pd.DataFrame({'input': x_pred})
         # samples
         >>> pred_mean = model.predict_posterior(prediction_data)
         """
@@ -355,5 +357,5 @@ def id(self):
         hasher.update(str(self.model_config.values()).encode())
         hasher.update(self.version.encode())
         hasher.update(self._model_type.encode())
-        hasher.update(str(self.sample_config.values()).encode())
+        # hasher.update(str(self.sample_config.values()).encode())
         return hasher.hexdigest()[:16]

pymc_experimental/tests/test_model_builder.py

@@ -1,4 +1,4 @@
-#   Copyright 2022 The PyMC Developers
+#   Copyright 2023 The PyMC Developers
 #
 #   Licensed under the Apache License, Version 2.0 (the "License");
 #   you may not use this file except in compliance with the License.
@@ -13,9 +13,13 @@
 #   limitations under the License.
 
 
+import sys
+import tempfile
+
 import numpy as np
 import pandas as pd
 import pymc as pm
+import pytest
 
 from pymc_experimental.model_builder import ModelBuilder
 
@@ -77,93 +81,35 @@ def create_sample_input(cls):
 
 
 def test_fit():
-    with pm.Model() as model:
-        x = np.linspace(start=0, stop=1, num=100)
-        y = 5 * x + 3
-        x = pm.MutableData("x", x)
-        y_data = pm.MutableData("y_data", y)
-
-        a_loc = 7
-        a_scale = 3
-        b_loc = 5
-        b_scale = 3
-        obs_error = 2
-
-        a = pm.Normal("a", a_loc, sigma=a_scale)
-        b = pm.Normal("b", b_loc, sigma=b_scale)
-        obs_error = pm.HalfNormal("σ_model_fmc", obs_error)
-
-        y_model = pm.Normal("y_model", a + b * x, obs_error, observed=y_data)
-
-        idata = pm.sample(tune=100, draws=200, chains=1, cores=1, target_accept=0.5)
-        idata.extend(pm.sample_prior_predictive())
-        idata.extend(pm.sample_posterior_predictive(idata))
-
     data, model_config, sampler_config = test_ModelBuilder.create_sample_input()
-    model_2 = test_ModelBuilder(model_config, sampler_config, data)
-    model_2.idata = model_2.fit()
-    assert str(model_2.idata.groups) == str(idata.groups)
-
+    model = test_ModelBuilder(model_config, sampler_config, data)
+    model.fit()
+    assert model.idata is not None
+    assert "posterior" in model.idata.groups()
 
-def test_predict():
     x_pred = np.random.uniform(low=0, high=1, size=100)
     prediction_data = pd.DataFrame({"input": x_pred})
-    data, model_config, sampler_config = test_ModelBuilder.create_sample_input()
-    model_2 = test_ModelBuilder(model_config, sampler_config, data)
-    model_2.idata = model_2.fit()
-    model_2.predict(prediction_data)
-    with pm.Model() as model:
-        x = np.linspace(start=0, stop=1, num=100)
-        y = 5 * x + 3
-        x = pm.MutableData("x", x)
-        y_data = pm.MutableData("y_data", y)
-        a_loc = 7
-        a_scale = 3
-        b_loc = 5
-        b_scale = 3
-        obs_error = 2
-
-        a = pm.Normal("a", a_loc, sigma=a_scale)
-        b = pm.Normal("b", b_loc, sigma=b_scale)
-        obs_error = pm.HalfNormal("σ_model_fmc", obs_error)
-
-        y_model = pm.Normal("y_model", a + b * x, obs_error, observed=y_data)
+    pred = model.predict(prediction_data)
+    assert "y_model" in pred.keys()
+    post_pred = model.predict_posterior(prediction_data)
+    assert "y_model" in post_pred.keys()
 
-        idata = pm.sample(tune=10, draws=20, chains=3, cores=1)
-        idata.extend(pm.sample_prior_predictive())
-        idata.extend(pm.sample_posterior_predictive(idata))
-        y_test = pm.sample_posterior_predictive(idata)
-
-        assert str(model_2.idata.groups) == str(idata.groups)
 
+@pytest.mark.skipif(
+    sys.platform == "win32", reason="Permissions for temp files not granted on windows CI."
+)
+def test_save_load():
+    data, model_config, sampler_config = test_ModelBuilder.create_sample_input()
+    model = test_ModelBuilder(model_config, sampler_config, data)
+    model.fit()
+    temp = tempfile.NamedTemporaryFile(mode="w", encoding="utf-8", delete=False)
+    model.save(temp.name)
+    model2 = test_ModelBuilder.load(temp.name)
+    assert model.idata.groups() == model2.idata.groups()
 
-def test_predict_posterior():
     x_pred = np.random.uniform(low=0, high=1, size=100)
     prediction_data = pd.DataFrame({"input": x_pred})
-    data, model_config, sampler_config = test_ModelBuilder.create_sample_input()
-    model_2 = test_ModelBuilder(model_config, sampler_config, data)
-    model_2.idata = model_2.fit()
-    model_2.predict_posterior(prediction_data)
-    with pm.Model() as model:
-        x = np.linspace(start=0, stop=1, num=100)
-        y = 5 * x + 3
-        x = pm.MutableData("x", x)
-        y_data = pm.MutableData("y_data", y)
-        a_loc = 7
-        a_scale = 3
-        b_loc = 5
-        b_scale = 3
-        obs_error = 2
-
-        a = pm.Normal("a", a_loc, sigma=a_scale)
-        b = pm.Normal("b", b_loc, sigma=b_scale)
-        obs_error = pm.HalfNormal("σ_model_fmc", obs_error)
-
-        y_model = pm.Normal("y_model", a + b * x, obs_error, observed=y_data)
-
-        idata = pm.sample(tune=10, draws=20, chains=3, cores=1)
-        idata.extend(pm.sample_prior_predictive())
-        idata.extend(pm.sample_posterior_predictive(idata))
-        y_test = pm.sample_posterior_predictive(idata)
-
-        assert str(model_2.idata.groups) == str(idata.groups)
+    pred1 = model.predict(prediction_data)
+    pred2 = model2.predict(prediction_data)
+    assert pred1["y_model"].shape == pred2["y_model"].shape
+    temp.close()