Merge branch 'main' into arima_fix_huanc

third_party/bigframes_vendored/sklearn/decomposition/_pca.py

@@ -20,6 +20,30 @@
 class PCA(BaseEstimator, metaclass=ABCMeta):
     """Principal component analysis (PCA).
 
+    **Examples:**
+
+        >>> import bigframes.pandas as bpd
+        >>> from bigframes.ml.decomposition import PCA
+        >>> bpd.options.display.progress_bar = None
+        >>> X = bpd.DataFrame({"feat0": [-1, -2, -3, 1, 2, 3], "feat1": [-1, -1, -2, 1, 1, 2]})
+        >>> pca = PCA(n_components=2).fit(X)
+        >>> pca.predict(X) # doctest:+SKIP
+            principal_component_1  principal_component_2
+        0              -0.755243               0.157628
+        1               -1.05405              -0.141179
+        2              -1.809292               0.016449
+        3               0.755243              -0.157628
+        4                1.05405               0.141179
+        5               1.809292              -0.016449
+        <BLANKLINE>
+        [6 rows x 2 columns]
+        >>> pca.explained_variance_ratio_ # doctest:+SKIP
+            principal_component_id  explained_variance_ratio
+        0                       1                   0.00901
+        1                       0                   0.99099
+        <BLANKLINE>
+        [2 rows x 2 columns]
+
     Args:
         n_components (int, float or None, default None):
             Number of components to keep. If n_components is not set, all

third_party/bigframes_vendored/sklearn/impute/_base.py

@@ -18,6 +18,22 @@ class SimpleImputer(_BaseImputer):
     Replace missing values using a descriptive statistic (e.g. mean, median, or
     most frequent) along each column.
 
+    **Examples:**
+
+        >>> import bigframes.pandas as bpd
+        >>> from bigframes.ml.impute import SimpleImputer
+        >>> bpd.options.display.progress_bar = None
+        >>> X_train = bpd.DataFrame({"feat0": [7.0, 4.0, 10.0], "feat1": [2.0, None, 5.0], "feat2": [3.0, 6.0, 9.0]})
+        >>> imp_mean = SimpleImputer().fit(X_train)
+        >>> X_test = bpd.DataFrame({"feat0": [None, 4.0, 10.0], "feat1": [2.0, None, None], "feat2": [3.0, 6.0, 9.0]})
+        >>> imp_mean.transform(X_test)
+           imputer_feat0  imputer_feat1  imputer_feat2
+        0            7.0            2.0            3.0
+        1            4.0            3.5            6.0
+        2           10.0            3.5            9.0
+        <BLANKLINE>
+        [3 rows x 3 columns]
+
     Args:
         strategy ({'mean', 'median', 'most_frequent'}, default='mean'):
             The imputation strategy. 'mean': replace missing values using the mean along