docs: add more preprocessing models into the docs menu. (#97)

* docs: add more preprocessing models into the docs menu. * Trigger kokoro rebuild

Author: ashleyxuu

docs/templates/toc.yml

@@ -121,10 +121,18 @@
  - items:
  - name: Overview
  uid: bigframes.ml.preprocessing
- - name: OneHotEncoder
- uid: bigframes.ml.preprocessing.OneHotEncoder
+ - name: KBinsDiscretizer
+ uid: bigframes.ml.preprocessing.KBinsDiscretizer
+ - name: LabelEncoder
+ uid: bigframes.ml.preprocessing.LabelEncoder
+ - name: MaxAbsScaler
+ uid: bigframes.ml.preprocessing.MaxAbsScaler
+ - name: MinMaxScaler
+ uid: bigframes.ml.preprocessing.MinMaxScaler
  - name: StandardScaler
  uid: bigframes.ml.preprocessing.StandardScaler
+ - name: OneHotEncoder
+ uid: bigframes.ml.preprocessing.OneHotEncoder
  name: preprocessing
  name: bigframes.ml
  name: BigQuery DataFrames

third_party/bigframes_vendored/sklearn/preprocessing/_data.py

@@ -29,7 +29,7 @@ class StandardScaler(BaseEstimator, TransformerMixin):
  individual features do not more or less look like standard normally
  distributed data (e.g. Gaussian with 0 mean and unit variance).
 
- Examples:
+ **Examples:**
 
  .. code-block::
 

third_party/bigframes_vendored/sklearn/preprocessing/_encoder.py

@@ -17,6 +17,21 @@ class OneHotEncoder(BaseEstimator):
  Note that this method deviates from Scikit-Learn; instead of producing sparse
  binary columns, the encoding is a single column of `STRUCT<index INT64, value DOUBLE>`.
 
+ **Examples:**
+
+ Given a dataset with two features, we let the encoder find the unique
+ values per feature and transform the data to a binary one-hot encoding.
+
+ .. code-block::
+
+ from bigframes.ml.preprocessing import OneHotEncoder
+ import bigframes.pandas as bpd
+
+ enc = OneHotEncoder()
+ X = bpd.DataFrame({"a": ["Male", "Female", "Female"], "b": ["1", "3", "2"]})
+ enc.fit(X)
+ print(enc.transform(bpd.DataFrame({"a": ["Female", "Male"], "b": ["1", "4"]})))
+
  Args:
  drop (Optional[Literal["most_frequent"]], default None):
  Specifies a methodology to use to drop one of the categories per feature.
@@ -37,21 +52,6 @@ class OneHotEncoder(BaseEstimator):
  when considering infrequent categories. If there are infrequent categories,
  max_categories includes the category representing the infrequent categories along with the frequent categories.
  Default None, set limit to 1,000,000.
-
- Examples:
-
- Given a dataset with two features, we let the encoder find the unique
- values per feature and transform the data to a binary one-hot encoding.
-
- .. code-block::
-
- from bigframes.ml.preprocessing import OneHotEncoder
- import bigframes.pandas as bpd
-
- enc = OneHotEncoder()
- X = bpd.DataFrame({"a": ["Male", "Female", "Female"], "b": ["1", "3", "2"]})
- enc.fit(X)
- print(enc.transform(bpd.DataFrame({"a": ["Female", "Male"], "b": ["1", "4"]})))
  """
 
  def fit(self, X, y=None):