Add prediction type to return the mean, variance, and (if implemented…

…) mode

Additionally, this change avoids unnecessary sampling if the prediction type doesn't need it.

lightgbmlss/distributions/distribution_utils.py

@@ -364,6 +364,7 @@ def predict_dist(self,
             - "quantile" calculates the quantiles from the predicted distribution.
             - "parameters" returns the predicted distributional parameters.
             - "expectiles" returns the predicted expectiles.
+            - "properties" returns the mean, variance, and (if implemented) mode.
         n_samples : int
             Number of samples to draw from the predicted distribution.
         quantiles : List[float]
@@ -402,18 +403,35 @@ def predict_dist(self,
         dist_params_predt = pd.DataFrame(dist_params_predt)
         dist_params_predt.columns = self.param_dict.keys()
 
-        # Draw samples from predicted response distribution
-        pred_samples_df = self.draw_samples(predt_params=dist_params_predt,
-                                            n_samples=n_samples,
-                                            seed=seed)
-
         if pred_type == "parameters":
             return dist_params_predt
 
         elif pred_type == "expectiles":
             return dist_params_predt
+
+        elif pred_type == "properties":
+            if self.tau is None:
+                pred_params = torch.tensor(dist_params_predt.values)
+                dist_kwargs = {arg_name: param for arg_name, param in zip(self.distribution_arg_names, pred_params.T)}
+                dist_pred = self.distribution(**dist_kwargs)
+                pred_props = pd.DataFrame({"mean": dist_pred.mean.detach().numpy(),
+                                           "variance": dist_pred.variance.detach().numpy()})
+                try:
+                    dist_pred.mode
+                except NotImplementedError:
+                    pass
+                else:
+                    pred_props["mode"] = dist_pred.mode.detach().numpy()
+                return pred_props
+            else:
+                raise ValueError("Invalid prediction type.")
+
+        # Draw samples from predicted response distribution
+        pred_samples_df = self.draw_samples(predt_params=dist_params_predt,
+                                            n_samples=n_samples,
+                                            seed=seed)
 
-        elif pred_type == "samples":
+        if pred_type == "samples":
             return pred_samples_df
 
         elif pred_type == "quantiles":

lightgbmlss/model.py

@@ -439,6 +439,7 @@ def predict(self,
             - "quantile" calculates the quantiles from the predicted distribution.
             - "parameters" returns the predicted distributional parameters.
             - "expectiles" returns the predicted expectiles.
+            - "properties" returns the mean, variance, and (if implemented) mode.
         n_samples : int
             Number of samples to draw from the predicted distribution.
         quantiles : List[float]