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From: yoshi-code-bot <70984784+yoshi-code-bot@users.noreply.github.com>
Date: Wed, 2 Oct 2024 09:34:51 -0700
Subject: [PATCH] chore: update types from Discovery (#1414)
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* chore: update types from Discovery

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---
 src/types.d.ts | 24 +++++++++++++++++++++---
 1 file changed, 21 insertions(+), 3 deletions(-)

diff --git a/src/types.d.ts b/src/types.d.ts
index f1e2ac73..f2de0b87 100644
--- a/src/types.d.ts
+++ b/src/types.d.ts
@@ -13,7 +13,7 @@
 // limitations under the License.
 
 /**
- * Discovery Revision: 20240905
+ * Discovery Revision: 20240919
  */
 
 /**
@@ -3400,7 +3400,8 @@ declare namespace bigquery {
       | 'RANDOM_FOREST_CLASSIFIER'
       | 'TENSORFLOW_LITE'
       | 'ONNX'
-      | 'TRANSFORM_ONLY';
+      | 'TRANSFORM_ONLY'
+      | 'CONTRIBUTION_ANALYSIS';
     /**
      * Output only. Training type of the job.
      */
@@ -3503,7 +3504,8 @@ declare namespace bigquery {
       | 'RANDOM_FOREST_CLASSIFIER'
       | 'TENSORFLOW_LITE'
       | 'ONNX'
-      | 'TRANSFORM_ONLY';
+      | 'TRANSFORM_ONLY'
+      | 'CONTRIBUTION_ANALYSIS';
     /**
      * Output only. For single-objective [hyperparameter tuning](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-hp-tuning-overview) models, it only contains the best trial. For multi-objective [hyperparameter tuning](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-hp-tuning-overview) models, it contains all Pareto optimal trials sorted by trial_id.
      */
@@ -5511,6 +5513,10 @@ declare namespace bigquery {
      * Subsample ratio of columns when constructing each tree for boosted tree models.
      */
     colsampleBytree?: number;
+    /**
+     * The contribution metric. Applies to contribution analysis models. Allowed formats supported are for summable and summable ratio contribution metrics. These include expressions such as "SUM(x)" or "SUM(x)/SUM(y)", where x and y are column names from the base table.
+     */
+    contributionMetric?: string;
     /**
      * Type of normalization algorithm for boosted tree models using dart booster.
      */
@@ -5550,6 +5556,10 @@ declare namespace bigquery {
      * If true, perform decompose time series and save the results.
      */
     decomposeTimeSeries?: boolean;
+    /**
+     * Optional. Names of the columns to slice on. Applies to contribution analysis models.
+     */
+    dimensionIdColumns?: Array<string>;
     /**
      * Distance type for clustering models.
      */
@@ -5771,6 +5781,10 @@ declare namespace bigquery {
      * Number of integral steps for the integrated gradients explain method.
      */
     integratedGradientsNumSteps?: string;
+    /**
+     * Name of the column used to determine the rows corresponding to control and test. Applies to contribution analysis models.
+     */
+    isTestColumn?: string;
     /**
      * Item column specified for matrix factorization models.
      */
@@ -5834,6 +5848,10 @@ declare namespace bigquery {
      * Maximum depth of a tree for boosted tree models.
      */
     maxTreeDepth?: string;
+    /**
+     * The apriori support minimum. Applies to contribution analysis models.
+     */
+    minAprioriSupport?: number;
     /**
      * When early_stop is true, stops training when accuracy improvement is less than 'min_relative_progress'. Used only for iterative training algorithms.
      */