ml.py

# Copyright 2025 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from __future__ import annotations

from typing import List, Mapping, Optional, Union

import bigframes_vendored.constants
import google.cloud.bigquery
import pandas as pd

from bigframes.bigquery._operations import utils
import bigframes.core.logging.log_adapter as log_adapter
import bigframes.core.sql.ml
import bigframes.dataframe as dataframe
import bigframes.ml.base
import bigframes.session


def _get_model_metadata(
    *,
    bqclient: google.cloud.bigquery.Client,
    model_name: str,
) -> pd.Series:
    model_metadata = bqclient.get_model(model_name)
    model_dict = model_metadata.to_api_repr()
    return pd.Series(model_dict)


@log_adapter.method_logger(custom_base_name="bigquery_ml")
def create_model(
    model_name: str,
    *,
    replace: bool = False,
    if_not_exists: bool = False,
    # TODO(tswast): Also support bigframes.ml transformer classes and/or
    # bigframes.pandas functions?
    transform: Optional[list[str]] = None,
    input_schema: Optional[Mapping[str, str]] = None,
    output_schema: Optional[Mapping[str, str]] = None,
    connection_name: Optional[str] = None,
    options: Optional[Mapping[str, Union[str, int, float, bool, list]]] = None,
    training_data: Optional[Union[pd.DataFrame, dataframe.DataFrame, str]] = None,
    custom_holiday: Optional[Union[pd.DataFrame, dataframe.DataFrame, str]] = None,
    session: Optional[bigframes.session.Session] = None,
) -> pd.Series:
    """
    Creates a BigQuery ML model.

    See the `BigQuery ML CREATE MODEL DDL syntax
    <https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create>`_
    for additional reference.

    Args:
        model_name (str):
            The name of the model in BigQuery.
        replace (bool, default False):
            Whether to replace the model if it already exists.
        if_not_exists (bool, default False):
            Whether to ignore the error if the model already exists.
        transform (list[str], optional):
            A list of SQL transformations for the TRANSFORM clause, which
            specifies the preprocessing steps to apply to the input data.
        input_schema (Mapping[str, str], optional):
            The INPUT clause, which specifies the schema of the input data.
        output_schema (Mapping[str, str], optional):
            The OUTPUT clause, which specifies the schema of the output data.
        connection_name (str, optional):
            The connection to use for the model.
        options (Mapping[str, Union[str, int, float, bool, list]], optional):
            The OPTIONS clause, which specifies the model options.
        training_data (Union[bigframes.pandas.DataFrame, str], optional):
            The query or DataFrame to use for training the model.
        custom_holiday (Union[bigframes.pandas.DataFrame, str], optional):
            The query or DataFrame to use for custom holiday data.
        session (bigframes.session.Session, optional):
            The session to use. If not provided, the default session is used.

    Returns:
        pandas.Series:
            A Series with object dtype containing the model metadata. Reference
            the `BigQuery Model REST API reference
            <https://docs.cloud.google.com/bigquery/docs/reference/rest/v2/models#Model>`_
            for available fields.

    """
    import bigframes.pandas as bpd

    training_data_sql = (
        utils.to_sql(training_data) if training_data is not None else None
    )
    custom_holiday_sql = (
        utils.to_sql(custom_holiday) if custom_holiday is not None else None
    )

    # Determine session from DataFrames if not provided
    if session is None:
        # Try to get session from inputs
        dfs = [
            obj
            for obj in [training_data, custom_holiday]
            if isinstance(obj, dataframe.DataFrame)
        ]
        if dfs:
            session = dfs[0]._session

    sql = bigframes.core.sql.ml.create_model_ddl(
        model_name=model_name,
        replace=replace,
        if_not_exists=if_not_exists,
        transform=transform,
        input_schema=input_schema,
        output_schema=output_schema,
        connection_name=connection_name,
        options=options,
        training_data=training_data_sql,
        custom_holiday=custom_holiday_sql,
    )

    if session is None:
        bpd.read_gbq_query(sql)
        session = bpd.get_global_session()
        assert (
            session is not None
        ), f"Missing connection to BigQuery. Please report how you encountered this error at {bigframes_vendored.constants.FEEDBACK_LINK}."
    else:
        session.read_gbq_query(sql)

    return _get_model_metadata(bqclient=session.bqclient, model_name=model_name)


@log_adapter.method_logger(custom_base_name="bigquery_ml")
def evaluate(
    model: Union[bigframes.ml.base.BaseEstimator, str, pd.Series],
    input_: Optional[Union[pd.DataFrame, dataframe.DataFrame, str]] = None,
    *,
    perform_aggregation: Optional[bool] = None,
    horizon: Optional[int] = None,
    confidence_level: Optional[float] = None,
) -> dataframe.DataFrame:
    """
    Evaluates a BigQuery ML model.

    See the `BigQuery ML EVALUATE function syntax
    <https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-evaluate>`_
    for additional reference.

    Args:
        model (bigframes.ml.base.BaseEstimator or str):
            The model to evaluate.
        input_ (Union[bigframes.pandas.DataFrame, str], optional):
            The DataFrame or query to use for evaluation. If not provided, the
            evaluation data from training is used.
        perform_aggregation (bool, optional):
            A BOOL value that indicates the level of evaluation for forecasting
            accuracy. If you specify TRUE, then the forecasting accuracy is on
            the time series level. If you specify FALSE, the forecasting
            accuracy is on the timestamp level. The default value is TRUE.
        horizon (int, optional):
            An INT64 value that specifies the number of forecasted time points
            against which the evaluation metrics are computed. The default value
            is the horizon value specified in the CREATE MODEL statement for the
            time series model, or 1000 if unspecified. When evaluating multiple
            time series at the same time, this parameter applies to each time
            series.
        confidence_level (float, optional):
            A FLOAT64 value that specifies the percentage of the future values
            that fall in the prediction interval. The default value is 0.95. The
            valid input range is ``[0, 1)``.

    Returns:
        bigframes.pandas.DataFrame:
            The evaluation results.
    """
    import bigframes.pandas as bpd

    model_name, session = utils.get_model_name_and_session(model, input_)
    table_sql = utils.to_sql(input_) if input_ is not None else None

    sql = bigframes.core.sql.ml.evaluate(
        model_name=model_name,
        table=table_sql,
        perform_aggregation=perform_aggregation,
        horizon=horizon,
        confidence_level=confidence_level,
    )

    if session is None:
        return bpd.read_gbq_query(sql)
    else:
        return session.read_gbq_query(sql)


@log_adapter.method_logger(custom_base_name="bigquery_ml")
def predict(
    model: Union[bigframes.ml.base.BaseEstimator, str, pd.Series],
    input_: Union[pd.DataFrame, dataframe.DataFrame, str],
    *,
    threshold: Optional[float] = None,
    keep_original_columns: Optional[bool] = None,
    trial_id: Optional[int] = None,
) -> dataframe.DataFrame:
    """
    Runs prediction on a BigQuery ML model.

    See the `BigQuery ML PREDICT function syntax
    <https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-predict>`_
    for additional reference.

    Args:
        model (bigframes.ml.base.BaseEstimator or str):
            The model to use for prediction.
        input_ (Union[bigframes.pandas.DataFrame, str]):
            The DataFrame or query to use for prediction.
        threshold (float, optional):
            The threshold to use for classification models.
        keep_original_columns (bool, optional):
            Whether to keep the original columns in the output.
        trial_id (int, optional):
            An INT64 value that identifies the hyperparameter tuning trial that
            you want the function to evaluate. The function uses the optimal
            trial by default. Only specify this argument if you ran
            hyperparameter tuning when creating the model.

    Returns:
        bigframes.pandas.DataFrame:
            The prediction results.
    """
    import bigframes.pandas as bpd

    model_name, session = utils.get_model_name_and_session(model, input_)
    table_sql = utils.to_sql(input_)

    sql = bigframes.core.sql.ml.predict(
        model_name=model_name,
        table=table_sql,
        threshold=threshold,
        keep_original_columns=keep_original_columns,
        trial_id=trial_id,
    )

    if session is None:
        return bpd.read_gbq_query(sql)
    else:
        return session.read_gbq_query(sql)


@log_adapter.method_logger(custom_base_name="bigquery_ml")
def explain_predict(
    model: Union[bigframes.ml.base.BaseEstimator, str, pd.Series],
    input_: Union[pd.DataFrame, dataframe.DataFrame, str],
    *,
    top_k_features: Optional[int] = None,
    threshold: Optional[float] = None,
    integrated_gradients_num_steps: Optional[int] = None,
    approx_feature_contrib: Optional[bool] = None,
) -> dataframe.DataFrame:
    """
    Runs explainable prediction on a BigQuery ML model.

    See the `BigQuery ML EXPLAIN_PREDICT function syntax
    <https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-explain-predict>`_
    for additional reference.

    Args:
        model (bigframes.ml.base.BaseEstimator or str):
            The model to use for prediction.
        input_ (Union[bigframes.pandas.DataFrame, str]):
            The DataFrame or query to use for prediction.
        top_k_features (int, optional):
            The number of top features to return.
        threshold (float, optional):
            The threshold for binary classification models.
        integrated_gradients_num_steps (int, optional):
            an INT64 value that specifies the number of steps to sample between
            the example being explained and its baseline. This value is used to
            approximate the integral in integrated gradients attribution
            methods. Increasing the value improves the precision of feature
            attributions, but can be slower and more computationally expensive.
        approx_feature_contrib (bool, optional):
            A BOOL value that indicates whether to use an approximate feature
            contribution method in the XGBoost model explanation.

    Returns:
        bigframes.pandas.DataFrame:
            The prediction results with explanations.
    """
    import bigframes.pandas as bpd

    model_name, session = utils.get_model_name_and_session(model, input_)
    table_sql = utils.to_sql(input_)

    sql = bigframes.core.sql.ml.explain_predict(
        model_name=model_name,
        table=table_sql,
        top_k_features=top_k_features,
        threshold=threshold,
        integrated_gradients_num_steps=integrated_gradients_num_steps,
        approx_feature_contrib=approx_feature_contrib,
    )

    if session is None:
        return bpd.read_gbq_query(sql)
    else:
        return session.read_gbq_query(sql)


@log_adapter.method_logger(custom_base_name="bigquery_ml")
def global_explain(
    model: Union[bigframes.ml.base.BaseEstimator, str, pd.Series],
    *,
    class_level_explain: Optional[bool] = None,
) -> dataframe.DataFrame:
    """
    Gets global explanations for a BigQuery ML model.

    See the `BigQuery ML GLOBAL_EXPLAIN function syntax
    <https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-global-explain>`_
    for additional reference.

    Args:
        model (bigframes.ml.base.BaseEstimator or str):
            The model to get explanations from.
        class_level_explain (bool, optional):
            Whether to return class-level explanations.

    Returns:
        bigframes.pandas.DataFrame:
            The global explanation results.
    """
    import bigframes.pandas as bpd

    model_name, session = utils.get_model_name_and_session(model)
    sql = bigframes.core.sql.ml.global_explain(
        model_name=model_name,
        class_level_explain=class_level_explain,
    )

    if session is None:
        return bpd.read_gbq_query(sql)
    else:
        return session.read_gbq_query(sql)


@log_adapter.method_logger(custom_base_name="bigquery_ml")
def transform(
    model: Union[bigframes.ml.base.BaseEstimator, str, pd.Series],
    input_: Union[pd.DataFrame, dataframe.DataFrame, str],
) -> dataframe.DataFrame:
    """
    Transforms input data using a BigQuery ML model.

    See the `BigQuery ML TRANSFORM function syntax
    <https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-transform>`_
    for additional reference.

    Args:
        model (bigframes.ml.base.BaseEstimator or str):
            The model to use for transformation.
        input_ (Union[bigframes.pandas.DataFrame, str]):
            The DataFrame or query to use for transformation.

    Returns:
        bigframes.pandas.DataFrame:
            The transformed data.
    """
    import bigframes.pandas as bpd

    model_name, session = utils.get_model_name_and_session(model, input_)
    table_sql = utils.to_sql(input_)

    sql = bigframes.core.sql.ml.transform(
        model_name=model_name,
        table=table_sql,
    )

    if session is None:
        return bpd.read_gbq_query(sql)
    else:
        return session.read_gbq_query(sql)


@log_adapter.method_logger(custom_base_name="bigquery_ml")
def generate_text(
    model: Union[bigframes.ml.base.BaseEstimator, str, pd.Series],
    input_: Union[pd.DataFrame, dataframe.DataFrame, str],
    *,
    temperature: Optional[float] = None,
    max_output_tokens: Optional[int] = None,
    top_k: Optional[int] = None,
    top_p: Optional[float] = None,
    flatten_json_output: Optional[bool] = None,
    stop_sequences: Optional[List[str]] = None,
    ground_with_google_search: Optional[bool] = None,
    request_type: Optional[str] = None,
) -> dataframe.DataFrame:
    """
    Generates text using a BigQuery ML model.

    See the `BigQuery ML GENERATE_TEXT function syntax
    <https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-generate-text>`_
    for additional reference.

    Args:
        model (bigframes.ml.base.BaseEstimator or str):
            The model to use for text generation.
        input_ (Union[bigframes.pandas.DataFrame, str]):
            The DataFrame or query to use for text generation.
        temperature (float, optional):
            A FLOAT64 value that is used for sampling promiscuity. The value
            must be in the range ``[0.0, 1.0]``. A lower temperature works well
            for prompts that expect a more deterministic and less open-ended
            or creative response, while a higher temperature can lead to more
            diverse or creative results. A temperature of ``0`` is
            deterministic, meaning that the highest probability response is
            always selected.
        max_output_tokens (int, optional):
            An INT64 value that sets the maximum number of tokens in the
            generated text.
        top_k (int, optional):
            An INT64 value that changes how the model selects tokens for
            output. A ``top_k`` of ``1`` means the next selected token is the
            most probable among all tokens in the model's vocabulary. A
            ``top_k`` of ``3`` means that the next token is selected from
            among the three most probable tokens by using temperature. The
            default value is ``40``.
        top_p (float, optional):
            A FLOAT64 value that changes how the model selects tokens for
            output. Tokens are selected from most probable to least probable
            until the sum of their probabilities equals the ``top_p`` value.
            For example, if tokens A, B, and C have a probability of 0.3, 0.2,
            and 0.1 and the ``top_p`` value is ``0.5``, then the model will
            select either A or B as the next token by using temperature. The
            default value is ``0.95``.
        flatten_json_output (bool, optional):
            A BOOL value that determines the content of the generated JSON column.
        stop_sequences (List[str], optional):
            An ARRAY<STRING> value that contains the stop sequences for the model.
        ground_with_google_search (bool, optional):
            A BOOL value that determines whether to ground the model with Google Search.
        request_type (str, optional):
            A STRING value that contains the request type for the model.

    Returns:
        bigframes.pandas.DataFrame:
            The generated text.
    """
    import bigframes.pandas as bpd

    model_name, session = utils.get_model_name_and_session(model, input_)
    table_sql = utils.to_sql(input_)

    sql = bigframes.core.sql.ml.generate_text(
        model_name=model_name,
        table=table_sql,
        temperature=temperature,
        max_output_tokens=max_output_tokens,
        top_k=top_k,
        top_p=top_p,
        flatten_json_output=flatten_json_output,
        stop_sequences=stop_sequences,
        ground_with_google_search=ground_with_google_search,
        request_type=request_type,
    )

    if session is None:
        return bpd.read_gbq_query(sql)
    else:
        return session.read_gbq_query(sql)


@log_adapter.method_logger(custom_base_name="bigquery_ml")
def generate_embedding(
    model: Union[bigframes.ml.base.BaseEstimator, str, pd.Series],
    input_: Union[pd.DataFrame, dataframe.DataFrame, str],
    *,
    flatten_json_output: Optional[bool] = None,
    task_type: Optional[str] = None,
    output_dimensionality: Optional[int] = None,
) -> dataframe.DataFrame:
    """
    Generates text embedding using a BigQuery ML model.

    See the `BigQuery ML GENERATE_EMBEDDING function syntax
    <https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-generate-embedding>`_
    for additional reference.

    Args:
        model (bigframes.ml.base.BaseEstimator or str):
            The model to use for text embedding.
        input_ (Union[bigframes.pandas.DataFrame, str]):
            The DataFrame or query to use for text embedding.
        flatten_json_output (bool, optional):
            A BOOL value that determines the content of the generated JSON column.
        task_type (str, optional):
            A STRING value that specifies the intended downstream application task.
            Supported values are:
            - `RETRIEVAL_QUERY`
            - `RETRIEVAL_DOCUMENT`
            - `SEMANTIC_SIMILARITY`
            - `CLASSIFICATION`
            - `CLUSTERING`
            - `QUESTION_ANSWERING`
            - `FACT_VERIFICATION`
            - `CODE_RETRIEVAL_QUERY`
        output_dimensionality (int, optional):
            An INT64 value that specifies the size of the output embedding.

    Returns:
        bigframes.pandas.DataFrame:
            The generated text embedding.
    """
    import bigframes.pandas as bpd

    model_name, session = utils.get_model_name_and_session(model, input_)
    table_sql = utils.to_sql(input_)

    sql = bigframes.core.sql.ml.generate_embedding(
        model_name=model_name,
        table=table_sql,
        flatten_json_output=flatten_json_output,
        task_type=task_type,
        output_dimensionality=output_dimensionality,
    )

    if session is None:
        return bpd.read_gbq_query(sql)
    else:
        return session.read_gbq_query(sql)