docs: correct the params rendering for ml.remote and ml.ensemble modules

bigframes/ml/remote.py

@@ -47,10 +47,10 @@ class VertexAIModel(base.BaseEstimator):
     Args:
         endpoint (str):
             Vertex AI https endpoint.
-        input ({column_name: column_type}):
-            Input schema. Supported types are "bool", "string", "int64", "float64", "array<bool>", "array<string>", "array<int64>", "array<float64>".
-        output ({column_name: column_type}):
-            Output label schema. Supported the same types as the input.
+        input (Mapping):
+            Input schema: `{column_name: column_type}`. Supported types are "bool", "string", "int64", "float64", "array<bool>", "array<string>", "array<int64>", "array<float64>".
+        output (Mapping):
+            Output label schema: `{column_name: column_type}`. Supported the same types as the input.
         session (bigframes.Session or None):
             BQ session to create the model. If None, use the global default session.
         connection_name (str or None):

docs/templates/toc.yml

@@ -108,12 +108,6 @@
       - name: PaLM2TextEmbeddingGenerator
         uid: bigframes.ml.llm.PaLM2TextEmbeddingGenerator
       name: llm
-    - items:
-      - name: Overview
-        uid: bigframes.ml.remote
-      - name: VertexAIModel
-        uid: bigframes.ml.remote.VertexAIModel
-      name: remote
     - items:
       - name: metrics
         uid: bigframes.ml.metrics
@@ -144,6 +138,12 @@
       - name: OneHotEncoder
         uid: bigframes.ml.preprocessing.OneHotEncoder
       name: preprocessing
+    - items:
+      - name: Overview
+        uid: bigframes.ml.remote
+      - name: VertexAIModel
+        uid: bigframes.ml.remote.VertexAIModel
+      name: remote
     name: bigframes.ml
   name: BigQuery DataFrames
   status: beta

third_party/bigframes_vendored/sklearn/ensemble/_forest.py

@@ -47,16 +47,16 @@ def fit(self, X, y):
         """Build a forest of trees from the training set (X, y).
 
         Args:
-            X:
+            X (bigframes.dataframe.DataFrame or bigframes.series.Series):
                 Series or DataFrame of shape (n_samples, n_features). Training data.
 
-            y:
+            y (bigframes.dataframe.DataFrame or bigframes.series.Series):
                 Series or DataFrame of shape (n_samples,) or (n_samples, n_targets).
                 Target values. Will be cast to X's dtype if necessary.
 
 
         Returns:
-            Fitted Estimator.
+            ForestModel: Fitted Estimator.
         """
         raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
 
@@ -73,12 +73,12 @@ def predict(self, X):
         mean predicted regression targets of the trees in the forest.
 
         Args:
-            X:
+            X (bigframes.dataframe.DataFrame or bigframes.series.Series):
                 Series or DataFrame of shape (n_samples, n_features). The data matrix for
                 which we want to get the predictions.
 
         Returns:
-            The predicted values.
+            bigframes.dataframe.DataFrame: The predicted values.
         """
         raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
 
@@ -91,38 +91,38 @@ class RandomForestRegressor(ForestRegressor):
     to improve the predictive accuracy and control over-fitting.
 
     Args:
-        num_parallel_tree: Optional[int]
+        num_parallel_tree (Optional[int]):
             Number of parallel trees constructed during each iteration. Default to 100. Minimum value is 2.
-        tree_method: Optional[str]
+        tree_method (Optional[str]):
             Specify which tree method to use. Default to "auto". If this parameter is set to
             default, XGBoost will choose the most conservative option available. Possible values: ""exact", "approx",
             "hist".
-        min_child_weight : Optional[float]
+        min_child_weight (Optional[float]):
             Minimum sum of instance weight(hessian) needed in a child. Default to 1.
-        colsample_bytree : Optional[float]
+        colsample_bytree (Optional[float]):
             Subsample ratio of columns when constructing each tree. Default to 1.0. The value should be between 0 and 1.
-        colsample_bylevel : Optional[float]
+        colsample_bylevel (Optional[float]):
             Subsample ratio of columns for each level. Default to 1.0. The value should be between 0 and 1.
-        colsample_bynode : Optional[float]
+        colsample_bynode (Optional[float]):
             Subsample ratio of columns for each split. Default to 0.8. The value should be between 0 and 1.
-        gamma : Optional[float]
+        gamma (Optional[float]):
             (min_split_loss) Minimum loss reduction required to make a further partition on a
             leaf node of the tree. Default to 0.0.
-        max_depth :  Optional[int]
+        max_depth (Optional[int]):
             Maximum tree depth for base learners. Default to 15. The value should be greater than 0 and less than 1.
-        subsample : Optional[float]
+        subsample (Optional[float]:
             Subsample ratio of the training instance. Default to 0.8. The value should be greater than 0 and less than 1.
-        reg_alpha : Optional[float]
+        reg_alpha (Optional[float]):
             L1 regularization term on weights (xgb's alpha). Default to 0.0.
-        reg_lambda : Optional[float]
+        reg_lambda (Optional[float]):
             L2 regularization term on weights (xgb's lambda). Default to 1.0.
-        early_stop: Optional[bool]
+        early_stop (Optional[bool]):
             Whether training should stop after the first iteration. Default to True.
-        min_rel_progress: Optional[float]
+        min_rel_progress (Optional[float]):
             Minimum relative loss improvement necessary to continue training when early_stop is set to True. Default to 0.01.
-        enable_global_explain: Optional[bool]
+        enable_global_explain (Optional[bool]):
             Whether to compute global explanations using explainable AI to evaluate global feature importance to the model. Default to False.
-        xgboost_version: Optional[str]
+        xgboost_version (Optional[str]):
             Specifies the Xgboost version for model training.  Default to "0.9". Possible values: "0.9", "1.1".
     """
 
@@ -144,7 +144,7 @@ def predict(self, X):
                 which we want to get the predictions.
 
         Returns:
-            The predicted values.
+            bigframes.dataframe.DataFrame: The predicted values.
         """
         raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
 
@@ -158,37 +158,37 @@ class RandomForestClassifier(ForestClassifier):
     improve the predictive accuracy and control over-fitting.
 
     Args:
-        num_parallel_tree: Optional[int]
+        num_parallel_tree (Optional[int]):
             Number of parallel trees constructed during each iteration. Default to 100. Minimum value is 2.
-        tree_method: Optional[str]
+        tree_method (Optional[str]):
             Specify which tree method to use. Default to "auto". If this parameter is set to
             default, XGBoost will choose the most conservative option available. Possible values: ""exact", "approx",
             "hist".
-        min_child_weight : Optional[float]
+        min_child_weight (Optional[float]):
             Minimum sum of instance weight(hessian) needed in a child. Default to 1.
-        colsample_bytree : Optional[float]
+        colsample_bytree (Optional[float]):
             Subsample ratio of columns when constructing each tree. Default to 1.0. The value should be between 0 and 1.
-        colsample_bylevel : Optional[float]
+        colsample_bylevel (Optional[float]):
             Subsample ratio of columns for each level. Default to 1.0. The value should be between 0 and 1.
-        colsample_bynode : Optional[float]
+        colsample_bynode (Optional[float]):
             Subsample ratio of columns for each split. Default to 0.8. The value should be between 0 and 1.
-        gamma : Optional[float]
+        gamma (Optional[float]):
             (min_split_loss) Minimum loss reduction required to make a further partition on a
             leaf node of the tree. Default to 0.0.
-        max_depth :  Optional[int]
+        max_depth (Optional[int]):
             Maximum tree depth for base learners. Default to 15. The value should be greater than 0 and less than 1.
-        subsample : Optional[float]
+        subsample (Optional[float]):
             Subsample ratio of the training instance. Default to 0.8. The value should be greater than 0 and less than 1.
-        reg_alpha : Optional[float]
+        reg_alpha (Optional[float]):
             L1 regularization term on weights (xgb's alpha). Default to 0.0.
-        reg_lambda : Optional[float]
+        reg_lambda (Optional[float]):
             L2 regularization term on weights (xgb's lambda). Default to 1.0.
-        early_stop: Optional[bool]
+        early_stop (Optional[bool]):
             Whether training should stop after the first iteration. Default to True.
-        min_rel_progress: Optional[float]
+        min_rel_progress (Optional[float]):
             Minimum relative loss improvement necessary to continue training when early_stop is set to True. Default to 0.01.
-        enable_global_explain: Optional[bool]
+        enable_global_explain (Optional[bool]):
             Whether to compute global explanations using explainable AI to evaluate global feature importance to the model. Default to False.
-        xgboost_version: Optional[str]
+        xgboost_version (Optional[str]):
             Specifies the Xgboost version for model training.  Default to "0.9". Possible values: "0.9", "1.1".ß
     """