Use union of default and user provided configs in ModelBuilder

docs/source/guide/benefits/model_deployment.ipynb

@@ -218,7 +218,7 @@
    "source": [
     "We first illustrate the use of `model_config` to define custom priors within the model.\n",
     "\n",
-    "Because there are potentially many variables that can be configured, each model provides a `default_model_config` attribute. This will allow you to see which settings are available by default and easily update only the ones you care about by merging dictionary keys.\n",
+    "Because there are potentially many variables that can be configured, each model provides a `default_model_config` attribute. This will allow you to see which settings are available by default and only define the ones you need to change.\n",
     "\n",
     "We need to create a dummy model to be able to see the configuration dictionary."
    ]
@@ -305,8 +305,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "custom_beta_channel_prior = {'beta_channel': {'sigma': prior_sigma, 'dims': ('channel',)}}\n",
-    "my_model_config = dummy_model.default_model_config | custom_beta_channel_prior"
+    "my_model_config = {'beta_channel': {'sigma': prior_sigma, 'dims': ('channel',)}}"
    ]
   },
   {

docs/source/notebooks/mmm/mmm_example.ipynb

@@ -1009,9 +1009,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "You can use the optional parameter 'model_config' to apply your own priors to the model. Each entry in the 'model_config' contains a key that corresponds to a registered distribution name in our model. The value of the key is a dictionary that describes the input parameters of that specific distribution. If you want to apply your own priors, you can copy the 'model_config' definition below, modify its content, and pass it to `DelayedSaturatedMMM`.\n",
+    "You can use the optional parameter 'model_config' to apply your own priors to the model. Each entry in the 'model_config' contains a key that corresponds to a registered distribution name in our model. The value of the key is a dictionary that describes the input parameters of that specific distribution.\n",
     "\n",
-    "If you're unsure how to define your own priors, you can use the 'default_model_config' property of `DelayedSaturatedMMM` to see the required structure.\n"
+    "If you're unsure how to define your own priors, you can use the 'default_model_config' property of `DelayedSaturatedMMM` to see the required structure."
    ]
   },
   {
@@ -1074,7 +1074,7 @@
     }
    ],
    "source": [
-    "custom_beta_channel_prior = {'beta_channel': {'dist': 'LogNormal',\n",
+    "my_model_config = {'beta_channel': {'dist': 'LogNormal',\n",
     "                                              \"kwargs\":{\"mu\":np.array([2,1]), \"sigma\": prior_sigma},\n",
     "                                              },\n",
     "                            \"likelihood\": {\n",
@@ -1085,9 +1085,7 @@
     "                                    # {'sigma': 5}\n",
     "                                    }\n",
     "                                }\n",
-    "                             }\n",
-    "my_model_config = {**dummy_model.default_model_config, **custom_beta_channel_prior}\n",
-    "my_model_config"
+    "                             }"
    ]
   },
   {
@@ -1110,7 +1108,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sampler_config= {\"progressbar\": True}"
+    "my_sampler_config= {\"progressbar\": True}"
    ]
   },
   {
@@ -1128,7 +1126,7 @@
    "source": [
     "mmm = DelayedSaturatedMMM(\n",
     "    model_config = my_model_config,\n",
-    "    sampler_config = sampler_config,\n",
+    "    sampler_config = my_sampler_config,\n",
     "    date_column=\"date_week\",\n",
     "    channel_columns=[\"x1\", \"x2\"],\n",
     "    control_columns=[\n",
@@ -8988,7 +8986,7 @@
  "metadata": {
   "hide_input": false,
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -9002,7 +9000,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.8.10"
   },
   "toc": {
    "base_numbering": 1,
@@ -9016,6 +9014,35 @@
    "toc_position": {},
    "toc_section_display": true,
    "toc_window_display": false
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
   }
  },
  "nbformat": 4,

pymc_marketing/model_builder.py

@@ -75,11 +75,15 @@ def __init__(
         >>> model = MyModel(model_config, sampler_config)
         """
         if sampler_config is None:
-            sampler_config = self.default_sampler_config
+            sampler_config = {}
         if model_config is None:
-            model_config = self.default_model_config
-        self.sampler_config = sampler_config
-        self.model_config = model_config  # parameters for priors etc.
+            model_config = {}
+        self.sampler_config = (
+            self.default_sampler_config | sampler_config
+        )  # Parameters for fit sampling
+        self.model_config = (
+            self.default_model_config | model_config
+        )  # parameters for priors etc.
         self.model: Optional[pm.Model] = None  # Set by build_model
         self.idata: Optional[
             az.InferenceData

tests/mmm/test_base.py

@@ -68,11 +68,11 @@ def _generate_and_preprocess_model_data(self, X, y):
 
         @property
         def default_model_config(self):
-            pass
+            return {}
 
         @property
         def default_sampler_config(self):
-            pass
+            return {}
 
         @property
         def output_var(self):

tests/model_builder/test_model_builder.py

@@ -57,7 +57,7 @@ def fitted_model_instance(toy_X, toy_y):
         "b": {"loc": 0, "scale": 10},
         "obs_error": 2,
     }
-    model = test_ModelBuilder(
+    model = ModelBuilderTest(
         model_config=model_config,
         sampler_config=sampler_config,
         test_parameter="test_paramter",
@@ -79,15 +79,15 @@ def not_fitted_model_instance(toy_X, toy_y):
         "b": {"loc": 0, "scale": 10},
         "obs_error": 2,
     }
-    model = test_ModelBuilder(
+    model = ModelBuilderTest(
         model_config=model_config,
         sampler_config=sampler_config,
         test_parameter="test_paramter",
     )
     return model
 
 
-class test_ModelBuilder(ModelBuilder):
+class ModelBuilderTest(ModelBuilder):
     def __init__(self, model_config=None, sampler_config=None, test_parameter=None):
         self.test_parameter = test_parameter
         super().__init__(model_config=model_config, sampler_config=sampler_config)
@@ -159,14 +159,28 @@ def default_sampler_config(self) -> Dict:
         }
 
 
+def test_model_and_sampler_config():
+    default = ModelBuilderTest()
+    assert default.model_config == default.default_model_config
+    assert default.sampler_config == default.default_sampler_config
+
+    nondefault = ModelBuilderTest(
+        model_config={"obs_error": 3}, sampler_config={"draws": 42}
+    )
+    assert nondefault.model_config != nondefault.default_model_config
+    assert nondefault.sampler_config != nondefault.default_sampler_config
+    assert nondefault.model_config == default.model_config | {"obs_error": 3}
+    assert nondefault.sampler_config == default.sampler_config | {"draws": 42}
+
+
 def test_save_input_params(fitted_model_instance):
     assert fitted_model_instance.idata.attrs["test_paramter"] == '"test_paramter"'
 
 
 def test_save_load(fitted_model_instance):
     temp = tempfile.NamedTemporaryFile(mode="w", encoding="utf-8", delete=False)
     fitted_model_instance.save(temp.name)
-    test_builder2 = test_ModelBuilder.load(temp.name)
+    test_builder2 = ModelBuilderTest.load(temp.name)
     assert fitted_model_instance.idata.groups() == test_builder2.idata.groups()
     assert fitted_model_instance.id == test_builder2.id
     x_pred = np.random.uniform(low=0, high=1, size=100)
@@ -184,14 +198,14 @@ def test_initial_build_and_fit(fitted_model_instance, check_idata=True) -> Model
 
 
 def test_save_without_fit_raises_runtime_error():
-    model_builder = test_ModelBuilder()
+    model_builder = ModelBuilderTest()
     with pytest.raises(RuntimeError):
         model_builder.save("saved_model")
 
 
 def test_empty_sampler_config_fit(toy_X, toy_y):
     sampler_config = {}
-    model_builder = test_ModelBuilder(sampler_config=sampler_config)
+    model_builder = ModelBuilderTest(sampler_config=sampler_config)
     model_builder.idata = model_builder.fit(
         X=toy_X, y=toy_y, chains=1, draws=100, tune=100
     )
@@ -215,7 +229,7 @@ def test_fit(fitted_model_instance):
 
 
 def test_fit_no_y(toy_X):
-    model_builder = test_ModelBuilder()
+    model_builder = ModelBuilderTest()
     model_builder.idata = model_builder.fit(X=toy_X, chains=1, draws=100, tune=100)
     assert model_builder.model is not None
     assert model_builder.idata is not None
@@ -260,14 +274,14 @@ def test_model_config_formatting():
             ],
         },
     }
-    model_builder = test_ModelBuilder()
+    model_builder = ModelBuilderTest()
     converted_model_config = model_builder._model_config_formatting(model_config)
     np.testing.assert_equal(converted_model_config["a"]["dims"], ("x",))
     np.testing.assert_equal(converted_model_config["a"]["loc"], np.array([0, 0]))
 
 
 def test_id():
-    model_builder = test_ModelBuilder()
+    model_builder = ModelBuilderTest()
     expected_id = hashlib.sha256(
         str(model_builder.model_config.values()).encode()
         + model_builder.version.encode()