docs: address more technical writers' feedback

bigframes/ml/cluster.py

@@ -175,12 +175,12 @@ def to_gbq(self, model_name: str, replace: bool = False) -> KMeans:
 
         Args:
             model_name (str):
-                the name of the model.
+                The name of the model.
             replace (bool, default False):
                 Determine whether to replace if the model already exists. Default to False.
 
         Returns:
-            KMeans: saved model."""
+            KMeans: Saved model."""
         if not self._bqml_model:
             raise RuntimeError("A model must be fitted before it can be saved")
 

bigframes/ml/decomposition.py

@@ -169,12 +169,12 @@ def to_gbq(self, model_name: str, replace: bool = False) -> PCA:
 
         Args:
             model_name (str):
-                the name of the model.
+                The name of the model.
             replace (bool, default False):
                 Determine whether to replace if the model already exists. Default to False.
 
         Returns:
-            PCA: saved model."""
+            PCA: Saved model."""
         if not self._bqml_model:
             raise RuntimeError("A model must be fitted before it can be saved")
 

bigframes/ml/forecasting.py

@@ -361,7 +361,7 @@ def to_gbq(self, model_name: str, replace: bool = False) -> ARIMAPlus:
 
         Args:
             model_name (str):
-                the name of the model.
+                The name of the model.
             replace (bool, default False):
                 Determine whether to replace if the model already exists. Default to False.
 

bigframes/ml/imported.py

@@ -89,7 +89,7 @@ def to_gbq(self, model_name: str, replace: bool = False) -> TensorFlowModel:
 
         Args:
             model_name (str):
-                the name of the model.
+                The name of the model.
             replace (bool, default False):
                  Default to False.
 
@@ -166,7 +166,7 @@ def to_gbq(self, model_name: str, replace: bool = False) -> ONNXModel:
 
         Args:
             model_name (str):
-                the name of the model.
+                The name of the model.
             replace (bool, default False):
                 Determine whether to replace if the model already exists. Default to False.
 
@@ -282,7 +282,7 @@ def to_gbq(self, model_name: str, replace: bool = False) -> XGBoostModel:
 
         Args:
             model_name (str):
-                the name of the model.
+                The name of the model.
             replace (bool, default False):
                 Determine whether to replace if the model already exists. Default to False.
 

bigframes/ml/linear_model.py

@@ -182,12 +182,12 @@ def to_gbq(self, model_name: str, replace: bool = False) -> LinearRegression:
 
         Args:
             model_name (str):
-                the name of the model.
+                The name of the model.
             replace (bool, default False):
                 Determine whether to replace if the model already exists. Default to False.
 
         Returns:
-            LinearRegression: saved model."""
+            LinearRegression: Saved model."""
         if not self._bqml_model:
             raise RuntimeError("A model must be fitted before it can be saved")
 
@@ -347,12 +347,12 @@ def to_gbq(self, model_name: str, replace: bool = False) -> LogisticRegression:
 
         Args:
             model_name (str):
-                the name of the model.
+                The name of the model.
             replace (bool, default False):
                 Determine whether to replace if the model already exists. Default to False.
 
         Returns:
-            LogisticRegression: saved model."""
+            LogisticRegression: Saved model."""
         if not self._bqml_model:
             raise RuntimeError("A model must be fitted before it can be saved")
 

bigframes/ml/llm.py

@@ -64,8 +64,8 @@ class PaLM2TextGenerator(base.BaseEstimator):
             BQ session to create the model. If None, use the global default session.
         connection_name (str or None):
             Connection to connect with remote service. str of the format <PROJECT_NUMBER/PROJECT_ID>.<LOCATION>.<CONNECTION_ID>.
-            if None, use default connection in session context. BigQuery DataFrame will try to create the connection and attach
-            permission if the connection isn't fully setup.
+            If None, use default connection in session context. BigQuery DataFrame will try to create the connection and attach
+            permission if the connection isn't fully set up.
         max_iterations (Optional[int], Default to 300):
             The number of steps to run when performing supervised tuning.
     """
@@ -191,7 +191,7 @@ def fit(
                 Training labels.
 
         Returns:
-            PaLM2TextGenerator: Fitted Estimator.
+            PaLM2TextGenerator: Fitted estimator.
         """
         X, y = utils.convert_to_dataframe(X, y)
 
@@ -372,12 +372,12 @@ def to_gbq(self, model_name: str, replace: bool = False) -> PaLM2TextGenerator:
 
         Args:
             model_name (str):
-                the name of the model.
+                The name of the model.
             replace (bool, default False):
                 Determine whether to replace if the model already exists. Default to False.
 
         Returns:
-            PaLM2TextGenerator: saved model."""
+            PaLM2TextGenerator: Saved model."""
 
         new_model = self._bqml_model.copy(model_name, replace)
         return new_model.session.read_gbq_model(model_name)
@@ -390,16 +390,16 @@ class PaLM2TextEmbeddingGenerator(base.BaseEstimator):
     Args:
         model_name (str, Default to "textembedding-gecko"):
             The model for text embedding. “textembedding-gecko” returns model embeddings for text inputs.
-            "textembedding-gecko-multilingual" returns model embeddings for text inputs which support over 100 languages
+            "textembedding-gecko-multilingual" returns model embeddings for text inputs which support over 100 languages.
             Default to "textembedding-gecko".
         version (str or None):
             Model version. Accepted values are "001", "002", "003", "latest" etc. Will use the default version if unset.
             See https://cloud.google.com/vertex-ai/docs/generative-ai/learn/model-versioning for details.
         session (bigframes.Session or None):
             BQ session to create the model. If None, use the global default session.
         connection_name (str or None):
-            connection to connect with remote service. str of the format <PROJECT_NUMBER/PROJECT_ID>.<LOCATION>.<CONNECTION_ID>.
-            if None, use default connection in session context.
+            Connection to connect with remote service. str of the format <PROJECT_NUMBER/PROJECT_ID>.<LOCATION>.<CONNECTION_ID>.
+            If None, use default connection in session context.
     """
 
     def __init__(
@@ -539,12 +539,12 @@ def to_gbq(
 
         Args:
             model_name (str):
-                the name of the model.
+                The name of the model.
             replace (bool, default False):
                 Determine whether to replace if the model already exists. Default to False.
 
         Returns:
-            PaLM2TextEmbeddingGenerator: saved model."""
+            PaLM2TextEmbeddingGenerator: Saved model."""
 
         new_model = self._bqml_model.copy(model_name, replace)
         return new_model.session.read_gbq_model(model_name)
@@ -565,8 +565,8 @@ class GeminiTextGenerator(base.BaseEstimator):
             BQ session to create the model. If None, use the global default session.
         connection_name (str or None):
             Connection to connect with remote service. str of the format <PROJECT_NUMBER/PROJECT_ID>.<LOCATION>.<CONNECTION_ID>.
-            if None, use default connection in session context. BigQuery DataFrame will try to create the connection and attach
-            permission if the connection isn't fully setup.
+            If None, use default connection in session context. BigQuery DataFrame will try to create the connection and attach
+            permission if the connection isn't fully set up.
     """
 
     def __init__(
@@ -719,12 +719,12 @@ def to_gbq(self, model_name: str, replace: bool = False) -> GeminiTextGenerator:
 
         Args:
             model_name (str):
-                the name of the model.
+                The name of the model.
             replace (bool, default False):
                 Determine whether to replace if the model already exists. Default to False.
 
         Returns:
-            GeminiTextGenerator: saved model."""
+            GeminiTextGenerator: Saved model."""
 
         new_model = self._bqml_model.copy(model_name, replace)
         return new_model.session.read_gbq_model(model_name)

bigframes/ml/pipeline.py

@@ -126,12 +126,12 @@ def to_gbq(self, model_name: str, replace: bool = False) -> Pipeline:
 
         Args:
             model_name (str):
-                the name of the model(pipeline).
+                The name of the model(pipeline).
             replace (bool, default False):
-                whether to replace if the model(pipeline) already exists. Default to False.
+                Whether to replace if the model(pipeline) already exists. Default to False.
 
         Returns:
-            Pipeline: saved model(pipeline)."""
+            Pipeline: Saved model(pipeline)."""
         if not self._estimator._bqml_model:
             raise RuntimeError("A model must be fitted before it can be saved")
 

bigframes/ml/remote.py

@@ -30,12 +30,12 @@
 
 @log_adapter.class_logger
 class VertexAIModel(base.BaseEstimator):
-    """Remote model from a Vertex AI https endpoint. User must specify https endpoint, input schema and output schema.
-    How to deploy a model in Vertex AI https://cloud.google.com/bigquery/docs/bigquery-ml-remote-model-tutorial#Deploy-Model-on-Vertex-AI.
+    """Remote model from a Vertex AI HTTPS endpoint. User must specify HTTPS endpoint, input schema and output schema.
+    For more information, see Deploy model on Vertex AI: https://cloud.google.com/bigquery/docs/bigquery-ml-remote-model-tutorial#Deploy-Model-on-Vertex-AI.
 
     Args:
         endpoint (str):
-            Vertex AI https endpoint.
+            Vertex AI HTTPS endpoint.
         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):
@@ -44,8 +44,8 @@ class VertexAIModel(base.BaseEstimator):
             BQ session to create the model. If None, use the global default session.
         connection_name (str or None):
             Connection to connect with remote service. str of the format <PROJECT_NUMBER/PROJECT_ID>.<LOCATION>.<CONNECTION_ID>.
-            if None, use default connection in session context. BigQuery DataFrame will try to create the connection and attach
-            permission if the connection isn't fully setup.
+            If None, use default connection in session context. BigQuery DataFrame will try to create the connection and attach
+            permission if the connection isn't fully set up.
     """
 
     def __init__(

third_party/bigframes_vendored/sklearn/cluster/_kmeans.py

@@ -75,7 +75,7 @@ def fit(
                 Not used, present here for API consistency by convention.
 
         Returns:
-            KMeans: Fitted Estimator.
+            KMeans: Fitted estimator.
         """
         raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
 

third_party/bigframes_vendored/sklearn/ensemble/_forest.py

@@ -56,7 +56,7 @@ def fit(self, X, y):
 
 
         Returns:
-            ForestModel: Fitted Estimator.
+            ForestModel: Fitted estimator.
         """
         raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
 

third_party/bigframes_vendored/sklearn/linear_model/_base.py

@@ -109,6 +109,6 @@ def fit(
                 Target values. Will be cast to X's dtype if necessary.
 
         Returns:
-            LinearRegression: Fitted Estimator.
+            LinearRegression: Fitted estimator.
         """
         raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)

third_party/bigframes_vendored/sklearn/linear_model/_logistic.py

@@ -79,6 +79,6 @@ def fit(
 
 
         Returns:
-            LogisticRegression: Fitted Estimator.
+            LogisticRegression: Fitted estimator.
         """
         raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)

third_party/bigframes_vendored/sklearn/metrics/pairwise.py

@@ -21,7 +21,7 @@ def paired_cosine_distances(X, Y) -> bpd.DataFrame:
             Input data. X and Y are mapped by indexes, must have the same index.
 
     Returns:
-        bigframes.dataframe.DataFrame: DataFrame with columns of X, Y and cosine_distance
+        bigframes.dataframe.DataFrame: DataFrame with columns of X, Y and cosine_distance.
     """
     raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
 
@@ -36,7 +36,7 @@ def paired_manhattan_distance(X, Y) -> bpd.DataFrame:
             Input data. X and Y are mapped by indexes, must have the same index.
 
     Returns:
-        bigframes.dataframe.DataFrame: DataFrame with columns of X, Y and manhattan_distance
+        bigframes.dataframe.DataFrame: DataFrame with columns of X, Y and manhattan_distance.
     """
     raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
 
@@ -51,6 +51,6 @@ def paired_euclidean_distances(X, Y) -> bpd.DataFrame:
             Input data. X and Y are mapped by indexes, must have the same index.
 
     Returns:
-        bigframes.dataframe.DataFrame: DataFrame with columns of X, Y and euclidean_distance
+        bigframes.dataframe.DataFrame: DataFrame with columns of X, Y and euclidean_distance.
     """
     raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)

third_party/bigframes_vendored/sklearn/pipeline.py

@@ -25,9 +25,8 @@ class Pipeline(BaseEstimator, metaclass=ABCMeta):
     The final estimator only needs to implement `fit`.
 
     The purpose of the pipeline is to assemble several steps that can be
-    cross-validated together while setting different parameters. This
-    simplifies code and allows for deploying an estimator and peprocessing
-    together, e.g. with `Pipeline.to_gbq(...).`
+    cross-validated together while setting different parameters. This simplifies code and allows for
+    deploying an estimator and preprocessing together, e.g. with `Pipeline.to_gbq(...).`
     """
 
     def fit(

third_party/bigframes_vendored/sklearn/preprocessing/_encoder.py

@@ -84,6 +84,6 @@ def transform(self, X):
                 The DataFrame or Series to be transformed.
 
         Returns:
-            bigframes.dataframe.DataFrame: The result is categorized as index: number, value: number.
-                Where index is the position of the dict that seeing the category, and value is 0 or 1."""
+            bigframes.dataframe.DataFrame: The result is categorized as index: number, value: number,
+                where index is the position of the dict seeing the category, and value is 0 or 1."""
         raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)

third_party/bigframes_vendored/xgboost/sklearn.py

@@ -38,7 +38,7 @@ def fit(self, X, y):
                 Target values. Will be cast to X's dtype if necessary.
 
         Returns:
-            XGBModel: Fitted Estimator.
+            XGBModel: Fitted estimator.
         """
         raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)