chore: fix typos in bigframes.ml

bigframes/dataframe.py

@@ -582,6 +582,7 @@ def __getitem__(
             # Index of column labels can be treated the same as a sequence of column labels.
             pandas.Index,
             bigframes.series.Series,
+            slice,
         ],
     ):  # No return type annotations (like pandas) as type cannot always be determined statically
         # NOTE: This implements the operations described in

bigframes/ml/cluster.py

@@ -59,7 +59,7 @@ def __init__(
         warm_start: bool = False,
     ):
         self.n_clusters = n_clusters
-        # allow the alias to be compatible with sklean
+        # allow the alias to be compatible with sklearn
         self.init = "kmeans++" if init == "k-means++" else init
         self.init_col = init_col
         self.distance_type = distance_type

bigframes/ml/forecasting.py

@@ -211,7 +211,7 @@ def _fit(
         Args:
             X (bigframes.dataframe.DataFrame or bigframes.series.Series,
             or pandas.core.frame.DataFrame or pandas.core.series.Series):
-                A dataframe or series of trainging timestamp.
+                A dataframe or series of training timestamp.
             y (bigframes.dataframe.DataFrame, or bigframes.series.Series,
             or pandas.core.frame.DataFrame, or pandas.core.series.Series):
                 Target values for training.

bigframes/ml/llm.py

@@ -834,7 +834,7 @@ def to_gbq(self, model_name: str, replace: bool = False) -> GeminiTextGenerator:
 class Claude3TextGenerator(base.RetriableRemotePredictor):
     """Claude3 text generator LLM model.
 
-    Go to Google Cloud Console -> Vertex AI -> Model Garden page to enabe the models before use. Must have the Consumer Procurement Entitlement Manager Identity and Access Management (IAM) role to enable the models.
+    Go to Google Cloud Console -> Vertex AI -> Model Garden page to enable the models before use. Must have the Consumer Procurement Entitlement Manager Identity and Access Management (IAM) role to enable the models.
     https://cloud.google.com/vertex-ai/generative-ai/docs/partner-models/use-partner-models#grant-permissions
 
     .. note::

bigframes/ml/model_selection.py

@@ -82,7 +82,7 @@ def train_test_split(
     dfs = list(utils.batch_convert_to_dataframe(*arrays))
 
     def _stratify_split(df: bpd.DataFrame, stratify: bpd.Series) -> List[bpd.DataFrame]:
-        """Split a single DF accoding to the stratify Series."""
+        """Split a single DF according to the stratify Series."""
         stratify = stratify.rename("bigframes_stratify_col")  # avoid name conflicts
         merged_df = df.join(stratify.to_frame(), how="outer")
 

bigframes/ml/preprocessing.py

@@ -434,7 +434,7 @@ def _compile_to_sql(
         if columns is None:
             columns = X.columns
         drop = self.drop if self.drop is not None else "none"
-        # minus one here since BQML's inplimentation always includes index 0, and top_k is on top of that.
+        # minus one here since BQML's implementation always includes index 0, and top_k is on top of that.
         top_k = (
             (self.max_categories - 1)
             if self.max_categories is not None