interpret support group effects

bambi/interpret/effects.py

@@ -411,6 +411,7 @@ def predictions(
     use_hdi: bool = True,
     prob=None,
     transforms=None,
+    sample_new_groups=False,
 ) -> pd.DataFrame:
     """Compute Conditional Adjusted Predictions
 
@@ -443,6 +444,9 @@ def predictions(
     transforms : dict, optional
         Transformations that are applied to each of the variables being plotted. The keys are the
         name of the variables, and the values are functions to be applied. Defaults to ``None``.
+    sample_new_groups : bool, optional
+        If the model contains group-level effects, and data is passed for unseen groups, whether
+        to sample from the new groups. Defaults to ``False``.
 
     Returns
     -------
@@ -496,11 +500,15 @@ def predictions(
     response_transform = transforms.get(response_name, identity)
 
     if pps:
-        idata = model.predict(idata, data=cap_data, inplace=False, kind="pps")
+        idata = model.predict(
+            idata, data=cap_data, sample_new_groups=sample_new_groups, inplace=False, kind="pps"
+        )
         y_hat = response_transform(idata.posterior_predictive[response.name])
         y_hat_mean = y_hat.mean(("chain", "draw"))
     else:
-        idata = model.predict(idata, data=cap_data, inplace=False)
+        idata = model.predict(
+            idata, data=cap_data, sample_new_groups=sample_new_groups, inplace=False
+        )
         y_hat = response_transform(idata.posterior[response.name_target])
         y_hat_mean = y_hat.mean(("chain", "draw"))
 
@@ -534,6 +542,7 @@ def comparisons(
     use_hdi: bool = True,
     prob: Union[float, None] = None,
     transforms: Union[dict, None] = None,
+    sample_new_groups: bool = False,
 ) -> pd.DataFrame:
     """Compute Conditional Adjusted Comparisons
 
@@ -562,6 +571,9 @@ def comparisons(
     transforms : dict, optional
         Transformations that are applied to each of the variables being plotted. The keys are the
         name of the variables, and the values are functions to be applied. Defaults to ``None``.
+    sample_new_groups : bool, optional
+        If the model contains group-level effects, and data is passed for unseen groups, whether
+        to sample from the new groups. Defaults to ``False``.
 
     Returns
     -------
@@ -631,7 +643,9 @@ def comparisons(
     comparisons_data = create_differences_data(
         conditional_info, contrast_info, conditional_info.user_passed, kind="comparisons"
     )
-    idata = model.predict(idata, data=comparisons_data, inplace=False)
+    idata = model.predict(
+        idata, data=comparisons_data, sample_new_groups=sample_new_groups, inplace=False
+    )
 
     predictive_difference = PredictiveDifferences(
         model,
@@ -663,6 +677,7 @@ def slopes(
     use_hdi: bool = True,
     prob: Union[float, None] = None,
     transforms: Union[dict, None] = None,
+    sample_new_groups: bool = False,
 ) -> pd.DataFrame:
     """Compute Conditional Adjusted Slopes
 
@@ -702,6 +717,9 @@ def slopes(
     transforms : dict, optional
         Transformations that are applied to each of the variables being plotted. The keys are the
         name of the variables, and the values are functions to be applied. Defaults to ``None``.
+    sample_new_groups : bool, optional
+        If the model contains group-level effects, and data is passed for unseen groups, whether
+        to sample from the new groups. Defaults to ``False``.
 
     Returns
     -------
@@ -776,7 +794,9 @@ def slopes(
     slopes_data = create_differences_data(
         conditional_info, wrt_info, conditional_info.user_passed, effect_type
     )
-    idata = model.predict(idata, data=slopes_data, inplace=False)
+    idata = model.predict(
+        idata, data=slopes_data, sample_new_groups=sample_new_groups, inplace=False
+    )
 
     predictive_difference = PredictiveDifferences(
         model, slopes_data, wrt_info, conditional_info, response, use_hdi, effect_type

bambi/interpret/plotting.py

@@ -86,6 +86,7 @@ def plot_predictions(
     idata: az.InferenceData,
     covariates: Union[str, list],
     target: str = "mean",
+    sample_new_groups: bool = False,
     pps: bool = False,
     use_hdi: bool = True,
     prob=None,
@@ -110,6 +111,9 @@ def plot_predictions(
         Which model parameter to plot. Defaults to 'mean'. Passing a parameter into target only
         works when pps is False as the target may not be available in the posterior predictive
         distribution.
+    sample_new_groups : bool, optional
+        If the model contains group-level effects, and data is passed for unseen groups, whether
+        to sample from the new groups. Defaults to ``False``.
     pps: bool, optional
         Whether to plot the posterior predictive samples. Defaults to ``False``.
     use_hdi : bool, optional
@@ -174,6 +178,7 @@ def plot_predictions(
         use_hdi=use_hdi,
         prob=prob,
         transforms=transforms,
+        sample_new_groups=sample_new_groups,
     )
 
     response_name = get_aliased_name(model.response_component.response_term)
@@ -219,6 +224,7 @@ def plot_comparisons(
     conditional: Union[str, dict, list, None] = None,
     average_by: Union[str, list] = None,
     comparison_type: str = "diff",
+    sample_new_groups: bool = False,
     use_hdi: bool = True,
     prob=None,
     legend: bool = True,
@@ -245,6 +251,9 @@ def plot_comparisons(
         over the other covariates in the model. Defaults to ``None``.
     comparison_type : str, optional
         The type of comparison to plot. Defaults to 'diff'.
+    sample_new_groups : bool, optional
+        If the model contains group-level effects, and data is passed for unseen groups, whether
+        to sample from the new groups. Defaults to ``False``.
     use_hdi : bool, optional
         Whether to compute the highest density interval (defaults to True) or the quantiles.
     prob : float, optional
@@ -332,6 +341,7 @@ def plot_comparisons(
         use_hdi=use_hdi,
         prob=prob,
         transforms=transforms,
+        sample_new_groups=sample_new_groups,
     )
 
     return _plot_differences(
@@ -355,6 +365,7 @@ def plot_slopes(
     average_by: Union[str, list] = None,
     eps: float = 1e-4,
     slope: str = "dydx",
+    sample_new_groups: bool = False,
     use_hdi: bool = True,
     prob=None,
     transforms=None,
@@ -395,6 +406,9 @@ def plot_slopes(
         change in the response.
         'dyex' represents a percent change in 'wrt' is associated with a unit increase
         in the response.
+    sample_new_groups : bool, optional
+        If the model contains group-level effects, and data is passed for unseen groups, whether
+        to sample from the new groups. Defaults to ``False``.
     use_hdi : bool, optional
         Whether to compute the highest density interval (defaults to True) or the quantiles.
     prob : float, optional
@@ -486,6 +500,7 @@ def plot_slopes(
         use_hdi=use_hdi,
         prob=prob,
         transforms=transforms,
+        sample_new_groups=sample_new_groups,
     )
 
     return _plot_differences(

bambi/interpret/utils.py

@@ -1,6 +1,7 @@
 # pylint: disable = too-many-function-args
 # pylint: disable = too-many-nested-blocks
 from dataclasses import dataclass, field
+import re
 from statistics import mode
 from typing import Union
 
@@ -211,17 +212,21 @@ def get_model_covariates(model: Model) -> np.ndarray:
     """
 
     terms = get_model_terms(model)
-    names = []
+    covariates = []
     for term in terms.values():
         if hasattr(term, "components"):
             for component in term.components:
                 # If the component is a function call, use the argument names
                 if isinstance(component, Call):
-                    names.append([arg.name for arg in component.call.args])
+                    covariates.append([arg.name for arg in component.call.args])
                 else:
-                    names.append([component.name])
+                    covariates.append([component.name])
+        elif hasattr(term, "factor"):
+            covariates.append(list(term.var_names))
 
-    return np.unique(names)
+    flatten_covariates = [item for sublist in covariates for item in sublist]
+
+    return np.unique(flatten_covariates)
 
 
 def get_covariates(covariates: dict) -> Covariates:
@@ -330,25 +335,14 @@ def set_default_values(model: Model, data_dict: dict, kind: str) -> dict:
         "slopes",
     ), "kind must be either 'comparisons', 'slopes', or 'predictions'"
 
-    terms = get_model_terms(model)
-
-    # Get default values for each variable in the model
-    for term in terms.values():
-        if hasattr(term, "components"):
-            for component in term.components:
-                # If the component is a function call, use the argument names
-                if isinstance(component, Call):
-                    names = [arg.name for arg in component.call.args]
-                else:
-                    names = [component.name]
-                for name in names:
-                    if name not in data_dict:
-                        # For numeric predictors, select the mean.
-                        if component.kind == "numeric":
-                            data_dict[name] = np.mean(model.data[name])
-                        # For categoric predictors, select the most frequent level.
-                        elif component.kind == "categoric":
-                            data_dict[name] = mode(model.data[name])
+    unique_covariates = get_model_covariates(model)
+    for name in unique_covariates:
+        if name not in data_dict:
+            dtype = str(model.data[name].dtype)
+            if re.match(r"float*|int*", dtype):
+                data_dict[name] = np.mean(model.data[name])
+            elif dtype in ("category", "dtype"):
+                data_dict[name] = mode(model.data[name])
 
     if kind in ("comparisons", "slopes"):
         # if value in dict is not a list then convert to a list

tests/test_plots.py

@@ -11,13 +11,23 @@
 
 @pytest.fixture(scope="module")
 def mtcars():
+    "Model with common level effects only"
     data = bmb.load_data('mtcars')
     data["am"] = pd.Categorical(data["am"], categories=[0, 1], ordered=True)
     model = bmb.Model("mpg ~ hp * drat * am", data)
     idata = model.fit(tune=500, draws=500, random_seed=1234)
     return model, idata
 
 
+@pytest.fixture(scope="module")
+def sleep_study():
+    "Model with common and group specific effects"
+    data = bmb.load_data('sleepstudy')
+    model = bmb.Model("Reaction ~ 1 + Days + (Days | Subject)", data)
+    idata = model.fit(tune=500, draws=500, random_seed=1234)
+    return model, idata
+
+
 # Improvement:
 # * Test the actual plots are what we are indeed the desired result.
 # * Test using the dictionary and the list gives the same plot
@@ -224,6 +234,19 @@ def test_multiple_outputs_with_alias(self, pps):
 
         # Test user supplied target argument
         plot_predictions(model, idata, "x", "alpha", pps=False)
+
+
+    def test_group_effects(self, sleep_study):
+        model, idata = sleep_study
+
+        # contains new unseen data
+        plot_predictions(model, idata, ["Days", "Subject"], sample_new_groups=True)
+
+        with pytest.raises(
+            ValueError, match="There are new groups for the factors \('Subject',\) and 'sample_new_groups' is False."
+        ):
+            # default: sample_new_groups=False
+            plot_predictions(model, idata, ["Days", "Subject"])
 
 
 class TestComparison:
@@ -294,6 +317,25 @@ def test_average_by(self, mtcars, average_by):
 
         # unit level with average by
         plot_comparisons(model, idata, "hp", None, average_by)
+
+    def test_group_effects(self, sleep_study):
+        model, idata = sleep_study
+
+        # contains new unseen data
+        plot_comparisons(model, idata, "Days", "Subject", sample_new_groups=True)
+        # user passed values seen in observed data
+        plot_comparisons(
+            model, 
+            idata, 
+            contrast={"Days": [2, 4]},
+            conditional={"Subject": [308, 335, 352, 372]}, 
+        )
+
+        with pytest.raises(
+            ValueError, match="There are new groups for the factors \('Subject',\) and 'sample_new_groups' is False."
+        ):
+            # default: sample_new_groups=False
+            plot_comparisons(model, idata, "Days", "Subject")
 
 
 class TestSlopes:
@@ -374,4 +416,23 @@ def test_average_by(self, mtcars, average_by):
         plot_slopes(model, idata, "hp", ["am", "drat"], average_by)
 
         # unit level with average by
-        plot_slopes(model, idata, "hp", None, average_by)
+        plot_slopes(model, idata, "hp", None, average_by)
+
+    def test_group_effects(self, sleep_study):
+        model, idata = sleep_study
+
+        # contains new unseen data
+        plot_slopes(model, idata, "Days", "Subject", sample_new_groups=True)
+        # user passed values seen in observed data
+        plot_slopes(
+            model,
+            idata,
+            wrt={"Days": 2},
+            conditional={"Subject": 308}
+        )
+
+        with pytest.raises(
+            ValueError, match="There are new groups for the factors \('Subject',\) and 'sample_new_groups' is False."
+        ):
+            # default: sample_new_groups=False
+            plot_slopes(model, idata, "Days", "Subject")