feat: add ml PCA model params (#474)

bigframes/ml/decomposition.py

@@ -17,7 +17,7 @@
 
 from __future__ import annotations
 
-from typing import List, Optional, Union
+from typing import List, Literal, Optional, Union
 
 import bigframes_vendored.sklearn.decomposition._pca
 from google.cloud import bigquery
@@ -35,21 +35,29 @@ class PCA(
 ):
     __doc__ = bigframes_vendored.sklearn.decomposition._pca.PCA.__doc__
 
-    def __init__(self, n_components: int = 3):
+    def __init__(
+        self,
+        n_components: int = 3,
+        *,
+        svd_solver: Literal["full", "randomized", "auto"] = "auto",
+    ):
         self.n_components = n_components
+        self.svd_solver = svd_solver
         self._bqml_model: Optional[core.BqmlModel] = None
         self._bqml_model_factory = globals.bqml_model_factory()
 
     @classmethod
     def _from_bq(cls, session: bigframes.Session, model: bigquery.Model) -> PCA:
         assert model.model_type == "PCA"
 
-        kwargs = {}
+        kwargs: dict = {}
 
         # See https://cloud.google.com/bigquery/docs/reference/rest/v2/models#trainingrun
         last_fitting = model.training_runs[-1]["trainingOptions"]
         if "numPrincipalComponents" in last_fitting:
             kwargs["n_components"] = int(last_fitting["numPrincipalComponents"])
+        if "pcaSolver" in last_fitting:
+            kwargs["svd_solver"] = str(last_fitting["pcaSolver"])
 
         new_pca = cls(**kwargs)
         new_pca._bqml_model = core.BqmlModel(session, model)
@@ -69,6 +77,7 @@ def _fit(
             options={
                 "model_type": "PCA",
                 "num_principal_components": self.n_components,
+                "pca_solver": self.svd_solver,
             },
         )
         return self

tests/system/large/ml/test_decomposition.py

@@ -84,3 +84,74 @@ def test_decomposition_configure_fit_score_predict(
         in reloaded_model._bqml_model.model_name
     )
     assert reloaded_model.n_components == 3
+
+
+def test_decomposition_configure_fit_score_predict_params(
+    session, penguins_df_default_index, dataset_id
+):
+    model = decomposition.PCA(n_components=5, svd_solver="randomized")
+    model.fit(penguins_df_default_index)
+
+    new_penguins = session.read_pandas(
+        pd.DataFrame(
+            {
+                "tag_number": [1633, 1672, 1690],
+                "species": [
+                    "Adelie Penguin (Pygoscelis adeliae)",
+                    "Gentoo penguin (Pygoscelis papua)",
+                    "Adelie Penguin (Pygoscelis adeliae)",
+                ],
+                "island": ["Dream", "Biscoe", "Torgersen"],
+                "culmen_length_mm": [37.8, 46.5, 41.1],
+                "culmen_depth_mm": [18.1, 14.8, 18.6],
+                "flipper_length_mm": [193.0, 217.0, 189.0],
+                "body_mass_g": [3750.0, 5200.0, 3325.0],
+                "sex": ["MALE", "FEMALE", "MALE"],
+            }
+        ).set_index("tag_number")
+    )
+
+    # Check score to ensure the model was fitted
+    score_result = model.score(new_penguins).to_pandas()
+    score_expected = pd.DataFrame(
+        {
+            "total_explained_variance_ratio": [0.932897],
+        },
+        dtype="Float64",
+    )
+    score_expected = score_expected.reindex(index=score_expected.index.astype("Int64"))
+
+    pd.testing.assert_frame_equal(
+        score_result, score_expected, check_exact=False, rtol=0.1
+    )
+
+    result = model.predict(new_penguins).to_pandas()
+    expected = pd.DataFrame(
+        {
+            "principal_component_1": [-1.459, 2.258, -1.685],
+            "principal_component_2": [-1.120, -1.351, -0.874],
+            "principal_component_3": [-0.646, 0.443, -0.704],
+            "principal_component_4": [-0.539, 0.234, -0.571],
+            "principal_component_5": [-0.876, 0.122, 0.609],
+        },
+        dtype="Float64",
+        index=pd.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"),
+    )
+
+    tests.system.utils.assert_pandas_df_equal_pca(
+        result,
+        expected,
+        check_exact=False,
+        rtol=0.1,
+    )
+
+    # save, load, check n_components to ensure configuration was kept
+    reloaded_model = model.to_gbq(
+        f"{dataset_id}.temp_configured_pca_model", replace=True
+    )
+    assert (
+        f"{dataset_id}.temp_configured_pca_model"
+        in reloaded_model._bqml_model.model_name
+    )
+    assert reloaded_model.n_components == 5
+    assert reloaded_model.svd_solver == "RANDOMIZED"

third_party/bigframes_vendored/sklearn/decomposition/_pca.py

@@ -32,9 +32,11 @@ class PCA(BaseEstimator, metaclass=ABCMeta):
     truncated SVD.
 
     Args:
-         n_components (Optional[int], default 3):
+        n_components (Optional[int], default 3):
             Number of components to keep. if n_components is not set all components
             are kept.
+        svd_solver ("full", "randomized" or "auto", default "auto"):
+            The solver to use to calculate the principal components. Details: https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-pca#pca_solver.
 
     """