model.fit doesn't remove prior samples (#741)

* type hint only

* more informative errors

* check for attr

* remove type ignore

* check for attr

* check for attr

* reduce indentation

* new error names

pymc_marketing/clv/models/basic.py

@@ -58,7 +58,7 @@ def _validate_cols(
                     raise ValueError(f"Column {required_col} has duplicate entries")
 
     def __repr__(self):
-        if self.model is None:
+        if not hasattr(self, "model"):
             return self._model_type
         else:
             return f"{self._model_type}\n{self.model.str_repr()}"

pymc_marketing/mmm/base.py

@@ -267,14 +267,16 @@ def get_target_transformer(self) -> Pipeline:
     def prior(self) -> Dataset:
         if self.idata is None or "prior" not in self.idata:
             raise RuntimeError(
-                "The model hasn't been fit yet, call .sample_prior_predictive() with extend_idata=True first"
+                "The model hasn't been sampled yet, call .sample_prior_predictive() first"
             )
         return self.idata["prior"]
 
     @property
-    def prior_predictive(self) -> az.InferenceData:
+    def prior_predictive(self) -> Dataset:
         if self.idata is None or "prior_predictive" not in self.idata:
-            raise RuntimeError("The model hasn't been fit yet, call .fit() first")
+            raise RuntimeError(
+                "The model hasn't been sampled yet, call .sample_prior_predictive() first"
+            )
         return self.idata["prior_predictive"]
 
     @property
@@ -286,7 +288,9 @@ def fit_result(self) -> Dataset:
     @property
     def posterior_predictive(self) -> Dataset:
         if self.idata is None or "posterior_predictive" not in self.idata:
-            raise RuntimeError("The model hasn't been fit yet, call .fit() first")
+            raise RuntimeError(
+                "The model hasn't been fit yet, call .sample_posterior_predictive() first"
+            )
         return self.idata["posterior_predictive"]
 
     def plot_prior_predictive(

pymc_marketing/mmm/delayed_saturated_mmm.py

@@ -1829,7 +1829,7 @@ def add_lift_test_measurements(
             model.add_lift_test_measurements(df_lift_test)
 
         """
-        if self.model is None:
+        if not hasattr(self, "model"):
             raise RuntimeError(
                 "The model has not been built yet. Please, build the model first."
             )

pymc_marketing/model_builder.py

@@ -86,7 +86,7 @@ def __init__(
         self.model_config = (
             self.default_model_config | model_config
         )  # parameters for priors etc.
-        self.model: pm.Model | None = None  # Set by build_model
+        self.model: pm.Model
         self.idata: az.InferenceData | None = None  # idata is generated during fitting
         self.is_fitted_ = False
 
@@ -458,19 +458,22 @@ def fit(
         if self.X is None or self.y is None:
             raise ValueError("X and y must be set before calling build_model!")
 
-        if self.model is None:
+        if not hasattr(self, "model"):
             self.build_model(self.X, self.y)
 
         sampler_config = self.sampler_config.copy()
         sampler_config["progressbar"] = progressbar
         sampler_config["random_seed"] = random_seed
         sampler_config.update(**kwargs)
 
-        sampler_config.update(**kwargs)
-        if self.model is not None:
-            with self.model:
-                sampler_args = {**self.sampler_config, **kwargs}
-                self.idata = pm.sample(**sampler_args)
+        sampler_args = {**self.sampler_config, **kwargs}
+        with self.model:
+            idata = pm.sample(**sampler_args)
+
+        if self.idata:
+            self.idata.extend(idata, join="right")
+        else:
+            self.idata = idata
 
         X_df = pd.DataFrame(X, columns=X.columns)
         combined_data = pd.concat([X_df, y_df], axis=1)
@@ -537,7 +540,7 @@ def sample_prior_predictive(
         X_pred,
         y_pred=None,
         samples: int | None = None,
-        extend_idata: bool = False,
+        extend_idata: bool = True,
         combined: bool = True,
         **kwargs,
     ):
@@ -552,7 +555,7 @@ def sample_prior_predictive(
             Number of samples from the prior parameter distributions to generate.
             If not set, uses sampler_config['draws'] if that is available, otherwise defaults to 500.
         extend_idata : Boolean determining whether the predictions should be added to inference data object.
-            Defaults to False.
+            Defaults to True.
         combined: Combine chain and draw dims into sample. Won't work if a dim named sample already exists.
             Defaults to True.
         **kwargs: Additional arguments to pass to pymc.sample_prior_predictive
@@ -567,21 +570,19 @@ def sample_prior_predictive(
         if samples is None:
             samples = self.sampler_config.get("draws", 500)
 
-        if self.model is None:
+        if not hasattr(self, "model"):
             self.build_model(X_pred, y_pred)
 
         self._data_setter(X_pred, y_pred)
-        if self.model is not None:
-            with self.model:  # sample with new input data
-                prior_pred: az.InferenceData = pm.sample_prior_predictive(
-                    samples, **kwargs
-                )
-                self.set_idata_attrs(prior_pred)
-                if extend_idata:
-                    if self.idata is not None:
-                        self.idata.extend(prior_pred, join="right")
-                    else:
-                        self.idata = prior_pred
+        with self.model:  # sample with new input data
+            prior_pred: az.InferenceData = pm.sample_prior_predictive(samples, **kwargs)
+            self.set_idata_attrs(prior_pred)
+
+        if extend_idata:
+            if self.idata is not None:
+                self.idata.extend(prior_pred, join="right")
+            else:
+                self.idata = prior_pred
 
         prior_predictive_samples = az.extract(
             prior_pred, "prior_predictive", combined=combined
@@ -590,7 +591,11 @@ def sample_prior_predictive(
         return prior_predictive_samples
 
     def sample_posterior_predictive(
-        self, X_pred, extend_idata: bool = True, combined: bool = True, **kwargs
+        self,
+        X_pred,
+        extend_idata: bool = True,
+        combined: bool = True,
+        **sample_posterior_predictive_kwargs,
     ):
         """
         Sample from the model's posterior predictive distribution.
@@ -603,7 +608,7 @@ def sample_posterior_predictive(
             Defaults to True.
         combined: Combine chain and draw dims into sample. Won't work if a dim named sample already exists.
             Defaults to True.
-        **kwargs: Additional arguments to pass to pymc.sample_posterior_predictive
+        **sample_posterior_predictive_kwargs: Additional arguments to pass to pymc.sample_posterior_predictive
 
         Returns
         -------
@@ -612,16 +617,21 @@ def sample_posterior_predictive(
         """
         self._data_setter(X_pred)
 
-        with self.model:  # type: ignore
-            post_pred = pm.sample_posterior_predictive(self.idata, **kwargs)
-            if extend_idata:
-                self.idata.extend(post_pred, join="right")  # type: ignore
+        with self.model:
+            post_pred = pm.sample_posterior_predictive(
+                self.idata, **sample_posterior_predictive_kwargs
+            )
+
+        if extend_idata:
+            self.idata.extend(post_pred, join="right")  # type: ignore
 
-        posterior_predictive_samples = az.extract(
-            post_pred, "posterior_predictive", combined=combined
+        variable_name = (
+            "predictions"
+            if sample_posterior_predictive_kwargs.get("predictions")
+            else "posterior_predictive"
         )
 
-        return posterior_predictive_samples
+        return az.extract(post_pred, variable_name, combined=combined)
 
     def get_params(self, deep=True):
         """

tests/conftest.py

@@ -77,7 +77,7 @@ def set_model_fit(model: CLVModel, fit: InferenceData | Dataset):
         assert "posterior" in fit.groups()
     else:
         fit = InferenceData(posterior=fit)
-    if model.model is None:
+    if not hasattr(model, "model"):
         model.build_model()
     model.idata = fit
     model.idata.add_groups(fit_data=model.data.to_xarray())

tests/mmm/test_base.py

@@ -270,7 +270,9 @@ def test_calling_prior_predictive_before_fit_raises_error(test_mmm, toy_X, toy_y
     test_mmm.idata = None
     with pytest.raises(
         RuntimeError,
-        match=re.escape("The model hasn't been fit yet, call .fit() first"),
+        match=re.escape(
+            "The model hasn't been sampled yet, call .sample_prior_predictive() first"
+        ),
     ):
         test_mmm.prior_predictive
 
@@ -297,7 +299,7 @@ def test_calling_prior_before_sample_prior_predictive_raises_error(
     with pytest.raises(
         RuntimeError,
         match=re.escape(
-            "The model hasn't been fit yet, call .sample_prior_predictive() with extend_idata=True first"
+            "The model hasn't been sampled yet, call .sample_prior_predictive() first",
         ),
     ):
         test_mmm.prior

tests/model_builder/test_model_builder.py → tests/test_model_builder.py

@@ -135,9 +135,9 @@ def _save_input_params(self, idata):
     def output_var(self):
         return "output"
 
-    def _data_setter(self, X: pd.Series, y: pd.Series = None):
+    def _data_setter(self, X: pd.DataFrame, y: pd.Series = None):
         with self.model:
-            pm.set_data({"x": X.values})
+            pm.set_data({"x": X["input"].values})
             if y is not None:
                 y = y.values if isinstance(y, pd.Series) else y
                 pm.set_data({"y_data": y})
@@ -195,8 +195,8 @@ def test_save_load(fitted_model_instance):
     assert fitted_model_instance.id == test_builder2.id
     x_pred = rng.uniform(low=0, high=1, size=100)
     prediction_data = pd.DataFrame({"input": x_pred})
-    pred1 = fitted_model_instance.predict(prediction_data["input"])
-    pred2 = test_builder2.predict(prediction_data["input"])
+    pred1 = fitted_model_instance.predict(prediction_data)
+    pred2 = test_builder2.predict(prediction_data)
     assert pred1.shape == pred2.shape
     temp.close()
 
@@ -230,9 +230,9 @@ def test_fit(fitted_model_instance):
     assert fitted_model_instance.idata.posterior.dims["draw"] == 100
 
     prediction_data = pd.DataFrame({"input": rng.uniform(low=0, high=1, size=100)})
-    fitted_model_instance.predict(prediction_data["input"])
+    fitted_model_instance.predict(prediction_data)
     post_pred = fitted_model_instance.sample_posterior_predictive(
-        prediction_data["input"], extend_idata=True, combined=True
+        prediction_data, extend_idata=True, combined=True
     )
     assert (
         post_pred[fitted_model_instance.output_var].shape[0]
@@ -256,7 +256,7 @@ def test_predict(fitted_model_instance):
     rng = np.random.default_rng(42)
     x_pred = rng.uniform(low=0, high=1, size=100)
     prediction_data = pd.DataFrame({"input": x_pred})
-    pred = fitted_model_instance.predict(prediction_data["input"])
+    pred = fitted_model_instance.predict(prediction_data)
     # Perform elementwise comparison using numpy
     assert type(pred) == np.ndarray
     assert len(pred) > 0
@@ -269,7 +269,7 @@ def test_sample_posterior_predictive(fitted_model_instance, combined):
     x_pred = rng.uniform(low=0, high=1, size=n_pred)
     prediction_data = pd.DataFrame({"input": x_pred})
     pred = fitted_model_instance.sample_posterior_predictive(
-        prediction_data["input"], combined=combined, extend_idata=True
+        prediction_data, combined=combined, extend_idata=True
     )
     chains = fitted_model_instance.idata.sample_stats.dims["chain"]
     draws = fitted_model_instance.idata.sample_stats.dims["draw"]
@@ -313,7 +313,7 @@ def test_sample_xxx_predictive_keeps_second(
     method_name = f"sample_{name}"
     method = getattr(fitted_model_instance, method_name)
 
-    X_pred = toy_X["input"]
+    X_pred = toy_X
 
     kwargs = {
         "X_pred": X_pred,
@@ -329,3 +329,26 @@ def test_sample_xxx_predictive_keeps_second(
 
     sample = getattr(fitted_model_instance.idata, name)
     xr.testing.assert_allclose(sample, second_sample)
+
+
+def test_prediction_kwarg(fitted_model_instance, toy_X):
+    result = fitted_model_instance.sample_posterior_predictive(
+        toy_X,
+        extend_idata=True,
+        predictions=True,
+    )
+    assert "predictions" in fitted_model_instance.idata
+    assert "predictions_constant_data" in fitted_model_instance.idata
+
+    assert isinstance(result, xr.Dataset)
+
+
+def test_fit_after_prior_keeps_prior(toy_X, toy_y):
+    model = ModelBuilderTest()
+    model.sample_prior_predictive(toy_X)
+    assert "prior" in model.idata
+    assert "prior_predictive" in model.idata
+
+    model.fit(X=toy_X, y=toy_y, chains=1, draws=100, tune=100)
+    assert "prior" in model.idata
+    assert "prior_predictive" in model.idata