Internal change

PiperOrigin-RevId: 449808952

CHANGELOG.md

@@ -1,6 +1,6 @@
 # Changelog
 
-## ???? - ????
+## 0.2.4 - 2022-05-17
 
 ### Features
 

documentation/learners.md

@@ -161,6 +161,21 @@ the gradient of the loss relative to the model output).
     trained with the Random Forest algorithm with a sampling without
     replacement.
 
+#### [honest_fixed_separation](../yggdrasil_decision_forests/learner/decision_tree/decision_tree.proto?q=symbol:honest_fixed_separation)
+
+-   **Type:** Categorical **Default:** false **Possible values:** true, false
+
+-   For honest trees only i.e. honest=true. If true, a new random separation is
+    generated for each tree. If false, the same separation is used for all the
+    trees (e.g., in Gradient Boosted Trees containing multiple trees).
+
+#### [honest_ratio_leaf_examples](../yggdrasil_decision_forests/learner/decision_tree/decision_tree.proto?q=symbol:honest_ratio_leaf_examples)
+
+-   **Type:** Real **Default:** 0.5 **Possible values:** min:0 max:1
+
+-   For honest trees only i.e. honest=true. Ratio of examples used to set the
+    leaf values.
+
 #### [in_split_min_examples_check](../yggdrasil_decision_forests/learner/decision_tree/decision_tree.proto?q=symbol:in_split_min_examples_check)
 
 -   **Type:** Categorical **Default:** true **Possible values:** true, false
@@ -540,6 +555,21 @@ It is probably the most well-known of the Decision Forest training algorithms.
     trained with the Random Forest algorithm with a sampling without
     replacement.
 
+#### [honest_fixed_separation](../yggdrasil_decision_forests/learner/decision_tree/decision_tree.proto?q=symbol:honest_fixed_separation)
+
+-   **Type:** Categorical **Default:** false **Possible values:** true, false
+
+-   For honest trees only i.e. honest=true. If true, a new random separation is
+    generated for each tree. If false, the same separation is used for all the
+    trees (e.g., in Gradient Boosted Trees containing multiple trees).
+
+#### [honest_ratio_leaf_examples](../yggdrasil_decision_forests/learner/decision_tree/decision_tree.proto?q=symbol:honest_ratio_leaf_examples)
+
+-   **Type:** Real **Default:** 0.5 **Possible values:** min:0 max:1
+
+-   For honest trees only i.e. honest=true. Ratio of examples used to set the
+    leaf values.
+
 #### [in_split_min_examples_check](../yggdrasil_decision_forests/learner/decision_tree/decision_tree.proto?q=symbol:in_split_min_examples_check)
 
 -   **Type:** Categorical **Default:** true **Possible values:** true, false
@@ -801,6 +831,21 @@ used to grow the tree while the second is used to prune the tree.
     trained with the Random Forest algorithm with a sampling without
     replacement.
 
+#### [honest_fixed_separation](../yggdrasil_decision_forests/learner/decision_tree/decision_tree.proto?q=symbol:honest_fixed_separation)
+
+-   **Type:** Categorical **Default:** false **Possible values:** true, false
+
+-   For honest trees only i.e. honest=true. If true, a new random separation is
+    generated for each tree. If false, the same separation is used for all the
+    trees (e.g., in Gradient Boosted Trees containing multiple trees).
+
+#### [honest_ratio_leaf_examples](../yggdrasil_decision_forests/learner/decision_tree/decision_tree.proto?q=symbol:honest_ratio_leaf_examples)
+
+-   **Type:** Real **Default:** 0.5 **Possible values:** min:0 max:1
+
+-   For honest trees only i.e. honest=true. Ratio of examples used to set the
+    leaf values.
+
 #### [in_split_min_examples_check](../yggdrasil_decision_forests/learner/decision_tree/decision_tree.proto?q=symbol:in_split_min_examples_check)
 
 -   **Type:** Categorical **Default:** true **Possible values:** true, false

yggdrasil_decision_forests/learner/random_forest/random_forest_test.cc

@@ -273,8 +273,7 @@ TEST_F(RandomForestOnAdult, Base) {
   CHECK_NE(description.find("Label: \"income\""), -1);
 
   // Check the oob predictions.
-  const auto oob_predictions =
-      file::GetContents(oob_prediction_path, file::Defaults()).value();
+  const auto oob_predictions = file::GetContent(oob_prediction_path).value();
   EXPECT_TRUE(absl::StartsWith(oob_predictions, "<=50K,>50K\n"));
   EXPECT_EQ(std::count(oob_predictions.begin(), oob_predictions.end(), '\n'),
             train_dataset_.nrow() + 1 /*the header*/);
@@ -608,8 +607,7 @@ TEST_F(RandomForestOnAbalone, Base) {
   EXPECT_NEAR(metric::RMSE(evaluation_), 2.0825, 0.01);
 
   // Check the oob predictions.
-  const auto oob_predictions =
-      file::GetContents(oob_prediction_path, file::Defaults()).value();
+  const auto oob_predictions = file::GetContent(oob_prediction_path).value();
   EXPECT_TRUE(absl::StartsWith(oob_predictions, "Rings\n"));
   EXPECT_EQ(std::count(oob_predictions.begin(), oob_predictions.end(), '\n'),
             train_dataset_.nrow() + 1 /*the header*/);