GH-16063 UpliftDRF - Add error in the explain method (#16064)

Add error in exmplain for UpliftDRF models

h2o-py/h2o/explanation/_explain.py

@@ -1930,6 +1930,17 @@ def _get_xy(model):
     return x, y
 
 
+def _get_treatment(model):
+    # type: (h2o.model.ModelBase) ->  str
+    """
+    Get treatment column.
+    :param model: H2O Model
+    :returns: treatment column name
+    """
+    treat = model.actual_params.get("treatment_column")
+    return treat
+
+
 def _consolidate_varimps(model):
     # type (h2o.model.ModelBase) -> Dict
     """
@@ -3037,6 +3048,8 @@ def _process_models_input(
         models_with_varimp = [model for model in models if _has_varimp(model)]
     tree_models_to_show = _get_tree_models(models, 1 if is_aml else float("inf"))
     y = _get_xy(models_to_show[0])[1]
+    if any(_get_treatment(x) is not None for x in models_to_show):
+        raise ValueError("Uplift models currently cannot be used with explain function.")
     classification = frame[y].isfactor()[0]
     multinomial_classification = classification and frame[y].nlevels()[0] > 2
     targets = [None]

h2o-py/tests/testdir_algos/uplift/pyunit_uplift_rf_explain.py

@@ -0,0 +1,53 @@
+import sys, os
+
+sys.path.insert(1, os.path.join("..", "..", ".."))
+import h2o
+from tests import pyunit_utils
+from h2o.estimators import H2OUpliftRandomForestEstimator
+
+
+def uplift_random_forest_explain():
+    print("Uplift Distributed Random Forest explain test")
+    seed = 12345
+
+    treatment_column = "treatment"
+    response_column = "outcome"
+    x_names = ["feature_"+str(x) for x in range(1, 3)]
+
+    train_h2o = h2o.upload_file(pyunit_utils.locate("smalldata/uplift/upliftml_train.csv"))
+    train_h2o[treatment_column] = train_h2o[treatment_column].asfactor()
+    train_h2o[response_column] = train_h2o[response_column].asfactor()
+
+    valid_h2o = h2o.upload_file(pyunit_utils.locate("smalldata/uplift/upliftml_test.csv"))
+    valid_h2o[treatment_column] = valid_h2o[treatment_column].asfactor()
+    valid_h2o[response_column] = valid_h2o[response_column].asfactor()
+
+    ntrees = 2
+    max_depth = 2
+    min_rows = 10
+    sample_rate = 0.8
+
+    uplift_model = H2OUpliftRandomForestEstimator(
+        ntrees=ntrees,
+        max_depth=max_depth,
+        treatment_column=treatment_column,
+        min_rows=min_rows,
+        seed=seed,
+        sample_rate=sample_rate,
+        score_each_iteration=True
+    )
+
+    uplift_model.train(y=response_column, x=x_names, training_frame=train_h2o, validation_frame=valid_h2o)
+    print(uplift_model)
+
+    # should throw error
+    try:
+        uplift_model.explain(valid_h2o)
+    except ValueError:
+        assert True, "The explain function should fail with UpliftDRF."
+
+
+if __name__ == "__main__":
+    pyunit_utils.standalone_test(uplift_random_forest_explain)
+else:
+    uplift_random_forest_explain()

h2o-r/h2o-package/R/explain.R

@@ -242,7 +242,10 @@ case_insensitive_match_arg <- function(arg, choices) {
       .self
     },
     get_model = function(model_id) {
-      return(memoised_models$get_model(model_id))
+      model <- memoised_models$get_model(model_id)
+      if (!is.null(model@allparameters$treatment_column))
+        stop("Uplift models have not supported in explain yet.")
+      return(model)
     }
   )
 )
@@ -314,7 +317,6 @@ case_insensitive_match_arg <- function(arg, choices) {
         object$model_ids <- head(object$model_ids, n = min(top_n_from_AutoML, length(object$model_ids)))
       }
     }
-
     return(object)
   }
 

h2o-r/tests/testdir_algos/uplift/runit_uplit_rf_explain.R

@@ -0,0 +1,39 @@
+setwd(normalizePath(dirname(R.utils::commandArgs(asValues = TRUE)$"f")))
+source("../../../scripts/h2o-r-test-setup.R")
+
+
+test.uplift <- function() {
+    ntrees <- 2
+    max_depth <- 2
+    min_rows <- 10
+    sample_rate <- 0.8
+    seed <- 42
+    set.seed(seed)
+    x <- c("feature_1", "feature_2", "feature_3")
+    y <- "outcome"
+    treatment_col <- "treatment"
+
+    # Test data preparation for each implementation
+    train <- h2o.importFile(path=locate("smalldata/uplift/upliftml_train.csv"), 
+                            col.types=list(by.col.name=c(treatment_col, y), types=c("factor", "factor")))
+    test <- h2o.importFile(path=locate("smalldata/uplift/upliftml_test.csv"), 
+                           col.types=list(by.col.name=c(treatment_col, y), types=c("factor", "factor")))
+
+    model <- h2o.upliftRandomForest(
+        x = x,
+        y = y,
+        training_frame = train,
+        validation_frame = test,
+        treatment_column = treatment_col,
+        ntrees = ntrees,
+        max_depth = max_depth,
+        min_rows = min_rows,
+        sample_rate = sample_rate,
+        score_each_iteration=TRUE,
+        seed = seed)
+
+    print(model)
+    expect_error(h2o.explain(model, test))
+}
+
+doTest("Uplift Random Forest Test: Test H2O RF uplift", test.uplift)