googleapis · GarrettWu · Dec 11, 2025 · Dec 10, 2025 · Dec 10, 2025
@@ -248,6 +248,13 @@ def fit(
    ) -> _T:
        return self._fit(X, y)

+    def fit_predict(
+        self: _T,
+        X: utils.ArrayType,
+        y: Optional[utils.ArrayType] = None,
+    ) -> _T:
+        return self.fit(X).predict(X)
+

 class RetriableRemotePredictor(BaseEstimator):
    def _predict_and_retry(

@@ -1736,7 +1736,7 @@
   "provenance": []
  },
  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "venv (3.10.14)",
   "language": "python",
   "name": "python3"
  },
@@ -1750,7 +1750,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.10.14"
  }
 },
 "nbformat": 4,

@@ -115,6 +115,26 @@ def predict(
        """
        raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)

+    def fit_predict(
+        self,
+        X,
+        y=None,
+    ):
+        """Compute cluster centers and predict cluster index for each sample.
+
+        Convenience method; equivalent to calling fit(X) followed by predict(X).
+
+        Args:
+            X (bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series):
+                DataFrame of shape (n_samples, n_features). Training data.
+            y (default None):
+                Not used, present here for API consistency by convention.
+
+        Returns:
+            bigframes.dataframe.DataFrame: DataFrame of shape (n_samples, n_input_columns + n_prediction_columns). Returns predicted labels.
+        """
+        raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
+
    def score(
        self,
        X,

@@ -94,3 +94,23 @@ def predict(self, X):
        Returns:
            bigframes.dataframe.DataFrame: Predicted DataFrames."""
        raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
+
+    def fit_predict(
+        self,
+        X,
+        y=None,
+    ):
+        """Fit the model with X and generate a predicted rating for every user-item row combination for a matrix factorization model. on X.
+
+        Convenience method; equivalent to calling fit(X) followed by predict(X).
+
+        Args:
+            X (bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series):
+                DataFrame of shape (n_samples, n_features). Training data.
+            y (default None):
+                Not used, present here for API consistency by convention.
+
+        Returns:
+            bigframes.dataframe.DataFrame: DataFrame of shape (n_samples, n_input_columns + n_prediction_columns). Returns predicted labels.
+        """
+        raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
@@ -101,6 +101,26 @@ def predict(self, X):
            bigframes.dataframe.DataFrame: Predicted DataFrames."""
        raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)

+    def fit_predict(
+        self,
+        X,
+        y=None,
+    ):
+        """Fit the model with X and apply the dimensionality reduction on X.
+
+        Convenience method; equivalent to calling fit(X) followed by predict(X).
+
+        Args:
+            X (bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series):
+                DataFrame of shape (n_samples, n_features). Training data.
+            y (default None):
+                Not used, present here for API consistency by convention.
+
+        Returns:
+            bigframes.dataframe.DataFrame: DataFrame of shape (n_samples, n_input_columns + n_prediction_columns). Returns predicted labels.
+        """
+        raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
+
    @property
    def components_(self):
        """Principal axes in feature space, representing the directions of maximum variance in the data.