fix: address more technical writers feedback (#581)

Author: ashleyxuu

bigframes/_config/compute_options.py

@@ -21,7 +21,7 @@
 @dataclasses.dataclass
 class ComputeOptions:
  """
- Encapsulates configuration for compute options.
+ Encapsulates the configuration for compute options.
 
  **Examples:**
 
@@ -39,7 +39,7 @@ class ComputeOptions:
  Limits the bytes billed for query jobs. Queries that will have
  bytes billed beyond this limit will fail (without incurring a
  charge). If unspecified, this will be set to your project default.
- See `maximum_bytes_billed <https://cloud.google.com/python/docs/reference/bigquery/latest/google.cloud.bigquery.job.QueryJobConfig#google_cloud_bigquery_job_QueryJobConfig_maximum_bytes_billed>`_.
+ See `maximum_bytes_billed`: https://cloud.google.com/python/docs/reference/bigquery/latest/google.cloud.bigquery.job.QueryJobConfig#google_cloud_bigquery_job_QueryJobConfig_maximum_bytes_billed.
  enable_multi_query_execution (bool, Options):
  If enabled, large queries may be factored into multiple smaller queries
  in order to avoid generating queries that are too complex for the query

bigframes/ml/base.py

@@ -193,7 +193,7 @@ def to_gbq(self: _T, model_name: str, replace: bool = False) -> _T:
  model_name (str):
  The name of the model.
  replace (bool, default False):
- Whether to replace if the model already exists. Default to False.
+ Determine whether to replace if the model already exists. Default to False.
 
  Returns:
  Saved transformer."""

bigframes/ml/cluster.py

@@ -177,7 +177,7 @@ def to_gbq(self, model_name: str, replace: bool = False) -> KMeans:
  model_name (str):
  the name of the model.
  replace (bool, default False):
- whether to replace if the model already exists. Default to False.
+ Determine whether to replace if the model already exists. Default to False.
 
  Returns:
  KMeans: saved model."""

bigframes/ml/decomposition.py

@@ -171,7 +171,7 @@ def to_gbq(self, model_name: str, replace: bool = False) -> PCA:
  model_name (str):
  the name of the model.
  replace (bool, default False):
- whether to replace if the model already exists. Default to False.
+ Determine whether to replace if the model already exists. Default to False.
 
  Returns:
  PCA: saved model."""

bigframes/ml/ensemble.py

@@ -192,9 +192,9 @@ def to_gbq(self, model_name: str, replace: bool = False) -> XGBRegressor:
  model_name (str):
  The name of the model.
  replace (bool, default False):
- Whether to replace if the model already exists. Default to False.
+ Determine whether to replace if the model already exists. Default to False.
 
- Returns: saved model."""
+ Returns: Saved model."""
  if not self._bqml_model:
  raise RuntimeError("A model must be fitted before it can be saved")
 
@@ -345,10 +345,10 @@ def to_gbq(self, model_name: str, replace: bool = False) -> XGBClassifier:
  model_name (str):
  The name of the model.
  replace (bool, default False):
- Whether to replace if the model already exists. Default to False.
+ Determine whether to replace if the model already exists. Default to False.
 
  Returns:
- XGBClassifier: saved model."""
+ XGBClassifier: Saved model."""
  if not self._bqml_model:
  raise RuntimeError("A model must be fitted before it can be saved")
 
@@ -508,10 +508,10 @@ def to_gbq(self, model_name: str, replace: bool = False) -> RandomForestRegresso
  model_name (str):
  The name of the model.
  replace (bool, default False):
- Whether to replace if the model already exists. Default to False.
+ Determine whether to replace if the model already exists. Default to False.
 
  Returns:
- RandomForestRegressor: saved model."""
+ RandomForestRegressor: Saved model."""
  if not self._bqml_model:
  raise RuntimeError("A model must be fitted before it can be saved")
 
@@ -671,10 +671,10 @@ def to_gbq(self, model_name: str, replace: bool = False) -> RandomForestClassifi
  model_name (str):
  The name of the model.
  replace (bool, default False):
- Whether to replace if the model already exists. Default to False.
+ Determine whether to replace if the model already exists. Default to False.
 
  Returns:
- RandomForestClassifier: saved model."""
+ RandomForestClassifier: Saved model."""
  if not self._bqml_model:
  raise RuntimeError("A model must be fitted before it can be saved")
 

bigframes/ml/forecasting.py

@@ -363,10 +363,10 @@ def to_gbq(self, model_name: str, replace: bool = False) -> ARIMAPlus:
  model_name (str):
  the name of the model.
  replace (bool, default False):
- whether to replace if the model already exists. Default to False.
+ Determine whether to replace if the model already exists. Default to False.
 
  Returns:
- ARIMAPlus: saved model."""
+ ARIMAPlus: Saved model."""
  if not self._bqml_model:
  raise RuntimeError("A model must be fitted before it can be saved")
 

bigframes/ml/imported.py

@@ -32,7 +32,7 @@ class TensorFlowModel(base.Predictor):
 
  Args:
  model_path (str):
- GCS path that holds the model files.
+ Cloud Storage path that holds the model files.
  session (BigQuery Session):
  BQ session to create the model.
  """
@@ -69,10 +69,10 @@ def predict(self, X: Union[bpd.DataFrame, bpd.Series]) -> bpd.DataFrame:
 
  Args:
  X (bigframes.dataframe.DataFrame):
- Input DataFrame, schema is defined by the model.
+ Input DataFrame. Schema is defined by the model.
 
  Returns:
- bigframes.dataframe.DataFrame: Output DataFrame, schema is defined by the model."""
+ bigframes.dataframe.DataFrame: Output DataFrame. Schema is defined by the model."""
 
  if not self._bqml_model:
  if self.model_path is None:
@@ -91,10 +91,10 @@ def to_gbq(self, model_name: str, replace: bool = False) -> TensorFlowModel:
  model_name (str):
  the name of the model.
  replace (bool, default False):
- whether to replace if the model already exists. Default to False.
+ Default to False.
 
  Returns:
- TensorFlowModel: saved model."""
+ TensorFlowModel: Saved model."""
  if not self._bqml_model:
  if self.model_path is None:
  raise ValueError("Model GCS path must be provided.")
@@ -146,7 +146,7 @@ def predict(self, X: Union[bpd.DataFrame, bpd.Series]) -> bpd.DataFrame:
 
  Args:
  X (bigframes.dataframe.DataFrame or bigframes.series.Series):
- Input DataFrame or Series, schema is defined by the model.
+ Input DataFrame or Series. Schema is defined by the model.
 
  Returns:
  bigframes.dataframe.DataFrame: Output DataFrame, schema is defined by the model."""
@@ -168,10 +168,10 @@ def to_gbq(self, model_name: str, replace: bool = False) -> ONNXModel:
  model_name (str):
  the name of the model.
  replace (bool, default False):
- whether to replace if the model already exists. Default to False.
+ Determine whether to replace if the model already exists. Default to False.
 
  Returns:
- ONNXModel: saved model."""
+ ONNXModel: Saved model."""
  if not self._bqml_model:
  if self.model_path is None:
  raise ValueError("Model GCS path must be provided.")
@@ -262,10 +262,10 @@ def predict(self, X: Union[bpd.DataFrame, bpd.Series]) -> bpd.DataFrame:
 
  Args:
  X (bigframes.dataframe.DataFrame or bigframes.series.Series):
- Input DataFrame or Series, schema is defined by the model.
+ Input DataFrame or Series. Schema is defined by the model.
 
  Returns:
- bigframes.dataframe.DataFrame: Output DataFrame, schema is defined by the model."""
+ bigframes.dataframe.DataFrame: Output DataFrame. Schema is defined by the model."""
 
  if not self._bqml_model:
  if self.model_path is None:
@@ -284,10 +284,10 @@ def to_gbq(self, model_name: str, replace: bool = False) -> XGBoostModel:
  model_name (str):
  the name of the model.
  replace (bool, default False):
- whether to replace if the model already exists. Default to False.
+ Determine whether to replace if the model already exists. Default to False.
 
  Returns:
- XGBoostModel: saved model."""
+ XGBoostModel: Saved model."""
  if not self._bqml_model:
  if self.model_path is None:
  raise ValueError("Model GCS path must be provided.")

bigframes/ml/linear_model.py

@@ -184,7 +184,7 @@ def to_gbq(self, model_name: str, replace: bool = False) -> LinearRegression:
  model_name (str):
  the name of the model.
  replace (bool, default False):
- whether to replace if the model already exists. Default to False.
+ Determine whether to replace if the model already exists. Default to False.
 
  Returns:
  LinearRegression: saved model."""
@@ -349,7 +349,7 @@ def to_gbq(self, model_name: str, replace: bool = False) -> LogisticRegression:
  model_name (str):
  the name of the model.
  replace (bool, default False):
- whether to replace if the model already exists. Default to False.
+ Determine whether to replace if the model already exists. Default to False.
 
  Returns:
  LogisticRegression: saved model."""

bigframes/ml/llm.py

@@ -248,7 +248,7 @@ def to_gbq(self, model_name: str, replace: bool = False) -> PaLM2TextGenerator:
  model_name (str):
  the name of the model.
  replace (bool, default False):
- whether to replace if the model already exists. Default to False.
+ Determine whether to replace if the model already exists. Default to False.
 
  Returns:
  PaLM2TextGenerator: saved model."""
@@ -415,7 +415,7 @@ def to_gbq(
  model_name (str):
  the name of the model.
  replace (bool, default False):
- whether to replace if the model already exists. Default to False.
+ Determine whether to replace if the model already exists. Default to False.
 
  Returns:
  PaLM2TextEmbeddingGenerator: saved model."""
@@ -595,7 +595,7 @@ def to_gbq(self, model_name: str, replace: bool = False) -> GeminiTextGenerator:
  model_name (str):
  the name of the model.
  replace (bool, default False):
- whether to replace if the model already exists. Default to False.
+ Determine whether to replace if the model already exists. Default to False.
 
  Returns:
  GeminiTextGenerator: saved model."""

third_party/bigframes_vendored/pandas/core/config_init.py

@@ -13,7 +13,7 @@
 from __future__ import annotations
 
 display_options_doc = """
-Encapsulates configuration for displaying objects.
+Encapsulates the configuration for displaying objects.
 
 **Examples:**
 
@@ -79,7 +79,7 @@
 """
 
 sampling_options_doc = """
-Encapsulates configuration for data sampling.
+Encapsulates the configuration for data sampling.
 
 Attributes:
  max_download_size (int, default 500):