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chore: address comments from technical writers for legal review (#555)
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bigframes/_config/bigquery_options.py

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@@ -57,7 +57,7 @@ def application_name(self) -> Optional[str]:
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"""The application name to amend to the user-agent sent to Google APIs.
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The application name to amend to the user agent sent to Google APIs.
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The recommended format is ``"appplication-name/major.minor.patch_version"``
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The recommended format is ``"application-name/major.minor.patch_version"``
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or ``"(gpn:PartnerName;)"`` for official Google partners.
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"""
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return self._application_name

bigframes/ml/compose.py

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@@ -13,7 +13,7 @@
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# limitations under the License.
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"""Build composite transformers on heterogeneous data. This module is styled
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after Scikit-Learn's compose module:
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after scikit-Learn's compose module:
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https://scikit-learn.org/stable/modules/classes.html#module-sklearn.compose."""
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from __future__ import annotations

bigframes/ml/imported.py

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@@ -34,7 +34,7 @@ class TensorFlowModel(base.Predictor):
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model_path (str):
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GCS path that holds the model files.
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session (BigQuery Session):
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BQ session to create the model
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BQ session to create the model.
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"""
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def __init__(
@@ -113,7 +113,7 @@ class ONNXModel(base.Predictor):
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model_path (str):
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Cloud Storage path that holds the model files.
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session (BigQuery Session):
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BQ session to create the model
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BQ session to create the model.
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"""
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def __init__(
@@ -207,7 +207,7 @@ class XGBoostModel(base.Predictor):
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and feature_types are both specified in the model file. Supported types
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are "bool", "string", "int64", "float64", "array<bool>", "array<string>", "array<int64>", "array<float64>".
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session (BigQuery Session):
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BQ session to create the model
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BQ session to create the model.
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"""
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def __init__(

third_party/bigframes_vendored/pandas/core/groupby/__init__.py

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Original file line numberDiff line numberDiff line change
@@ -78,7 +78,7 @@ def median(
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Include only float, int, boolean columns.
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exact (bool, default False):
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Calculate the exact median instead of an approximation. Note:
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``exact=True`` not yet supported.
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``exact=True`` is not supported.
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Returns:
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pandas.Series or pandas.DataFrame: Median of groups.
@@ -178,7 +178,7 @@ def sum(
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Include only float, int, boolean columns.
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min_count (int, default 0):
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The required number of valid values to perform the operation. If fewer
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than ``min_count`` non-NA values are present the result will be NA.
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than ``min_count`` and non-NA values are present, the result will be NA.
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Returns:
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Series or DataFrame: Computed sum of values within each group.
@@ -194,7 +194,7 @@ def prod(self, numeric_only: bool = False, min_count: int = 0):
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Include only float, int, boolean columns.
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min_count (int, default 0):
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The required number of valid values to perform the operation. If fewer
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than ``min_count`` non-NA values are present the result will be NA.
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than ``min_count`` and non-NA values are present, the result will be NA.
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Returns:
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Series or DataFrame: Computed prod of values within each group.
@@ -214,7 +214,7 @@ def min(
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Include only float, int, boolean columns.
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min_count (int, default 0):
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The required number of valid values to perform the operation. If fewer
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than ``min_count`` non-NA values are present the result will be NA.
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than ``min_count`` and non-NA values are present, the result will be NA.
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Returns:
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Series or DataFrame: Computed min of values within each group.
@@ -234,7 +234,7 @@ def max(
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Include only float, int, boolean columns.
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min_count (int, default 0):
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The required number of valid values to perform the operation. If fewer
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than ``min_count`` non-NA values are present the result will be NA.
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than ``min_count`` and non-NA values are present, the result will be NA.
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Returns:
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Series or DataFrame: Computed max of values within each group.

third_party/bigframes_vendored/sklearn/compose/_column_transformer.py

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@@ -19,9 +19,9 @@ class ColumnTransformer(_BaseComposition):
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"""Applies transformers to columns of BigQuery DataFrames.
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This estimator allows different columns or column subsets of the input
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to be transformed separately and the features generated by each transformer
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to be transformed separately, and the features generated by each transformer
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will be concatenated to form a single feature space.
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This is useful for heterogeneous or columnar data, to combine several
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This is useful for heterogeneous or columnar data to combine several
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feature extraction mechanisms or transformations into a single transformer.
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Args:

third_party/bigframes_vendored/sklearn/decomposition/_pca.py

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@@ -22,8 +22,8 @@ class PCA(BaseEstimator, metaclass=ABCMeta):
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Args:
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n_components (int, float or None, default None):
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Number of components to keep.
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If n_components is not set all components are kept. n_components = min(n_samples, n_features).
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Number of components to keep. If n_components is not set all
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components are kept, n_components = min(n_samples, n_features).
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If 0 < n_components < 1, select the number of components such that the amount of variance that needs to be explained is greater than the percentage specified by n_components.
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svd_solver ("full", "randomized" or "auto", default "auto"):
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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.

third_party/bigframes_vendored/sklearn/linear_model/_logistic.py

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Original file line numberDiff line numberDiff line change
@@ -38,7 +38,7 @@ class LogisticRegression(LinearClassifierMixin, BaseEstimator):
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automatically adjust weights inversely proportional to class
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frequencies in the input data as
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``n_samples / (n_classes * np.bincount(y))``. Dict isn't
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supported now.
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supported.
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l1_reg (float or None, default None):
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The amount of L1 regularization applied. Default to None. Can't be set in "normal_equation" mode. If unset, value 0 is used.
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l2_reg (float, default 0.0):

third_party/bigframes_vendored/sklearn/preprocessing/_discretization.py

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@@ -18,7 +18,7 @@ class KBinsDiscretizer(TransformerMixin, BaseEstimator):
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strategy ({'uniform', 'quantile'}, default='quantile'):
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Strategy used to define the widths of the bins. 'uniform': All bins
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in each feature have identical widths. 'quantile': All bins in each
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feature have the same number of points. Only `uniform` is supported now.
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feature have the same number of points. Only `uniform` is supported.
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"""
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def fit(self, X, y=None):

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