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Commit ca47835

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tswastgoogle-labs-jules[bot]gemini-code-assist[bot]
authored
feat: add support for hparam_range and hparam_candidates to bigframes.bigquery.create_model (#16640)
Fixes internal issue b/501171054 🦕 --------- Co-authored-by: google-labs-jules[bot] <161369871+google-labs-jules[bot]@users.noreply.github.com> Co-authored-by: tswast <247555+tswast@users.noreply.github.com> Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
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5 files changed

+88-16Lines changed: 88 additions & 16 deletions

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‎packages/bigframes/bigframes/bigquery/__init__.py‎

Copy file name to clipboardExpand all lines: packages/bigframes/bigframes/bigquery/__init__.py
+9-1Lines changed: 9 additions & 1 deletion
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@@ -87,7 +87,11 @@
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to_json,
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to_json_string,
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)
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from bigframes.bigquery._operations.mathematical import rand
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from bigframes.bigquery._operations.mathematical import (
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hparam_candidates,
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hparam_range,
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rand,
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)
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from bigframes.bigquery._operations.search import create_vector_index, vector_search
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from bigframes.bigquery._operations.sql import sql_scalar
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from bigframes.bigquery._operations.struct import struct
@@ -130,6 +134,8 @@
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to_json,
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to_json_string,
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# mathematical ops
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hparam_candidates,
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hparam_range,
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rand,
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# search ops
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create_vector_index,
@@ -187,6 +193,8 @@
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"to_json",
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"to_json_string",
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# mathematical ops
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"hparam_candidates",
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"hparam_range",
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"rand",
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# search ops
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"create_vector_index",
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‎packages/bigframes/bigframes/bigquery/_operations/mathematical.py‎

Copy file name to clipboardExpand all lines: packages/bigframes/bigframes/bigquery/_operations/mathematical.py
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@@ -14,6 +14,8 @@
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from __future__ import annotations
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from typing import Sequence
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import bigframes.core.col
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import bigframes.core.expression
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from bigframes import dtypes
@@ -51,3 +53,71 @@ def rand() -> bigframes.core.col.Expression:
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is_deterministic=False,
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)
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return bigframes.core.col.Expression(bigframes.core.expression.OpExpression(op, ()))
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def hparam_range(min: float, max: float) -> bigframes.core.col.Expression:
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"""
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Defines the minimum and maximum bounds of the search space of continuous
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values for a hyperparameter.
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**Examples:**
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>>> import bigframes.pandas as bpd
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>>> import bigframes.bigquery as bbq
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>>> # Specify a range of values for a hyperparameter.
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>>> learn_rate = bbq.hparam_range(0.0001, 1.0)
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Args:
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min (float or int):
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The minimum bound of the search space.
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max (float or int):
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The maximum bound of the search space.
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Returns:
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bigframes.pandas.api.typing.Expression:
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An expression that can be used in model options.
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"""
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min_expr = bigframes.core.expression.const(min)
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max_expr = bigframes.core.expression.const(max)
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op = ops.SqlScalarOp(
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_output_type=dtypes.FLOAT_DTYPE,
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sql_template="HPARAM_RANGE({0}, {1})",
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is_deterministic=True,
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)
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return bigframes.core.col.Expression(
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bigframes.core.expression.OpExpression(op, (min_expr, max_expr))
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)
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def hparam_candidates(
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candidates: Sequence[float | str],
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) -> bigframes.core.col.Expression:
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"""
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Specifies the set of discrete values for the hyperparameter.
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**Examples:**
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>>> import bigframes.pandas as bpd
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>>> import bigframes.bigquery as bbq
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>>> # Specify a set of values for a hyperparameter.
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>>> optimizer = bbq.hparam_candidates(['ADAGRAD', 'SGD', 'FTRL'])
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Args:
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candidates (Sequence[float | str]):
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The set of discrete values for the hyperparameter.
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Returns:
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bigframes.pandas.api.typing.Expression:
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An expression that can be used in model options.
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"""
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candidates_expr = bigframes.core.expression.const(tuple(candidates))
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op = ops.SqlScalarOp(
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_output_type=dtypes.STRING_DTYPE,
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sql_template="HPARAM_CANDIDATES({0})",
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is_deterministic=True,
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)
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return bigframes.core.col.Expression(
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bigframes.core.expression.OpExpression(op, (candidates_expr,))
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)
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‎packages/bigframes/tests/unit/core/sql/snapshots/test_ml/test_create_model_expression_option/create_model_expression_option.sql‎

Copy file name to clipboardExpand all lines: packages/bigframes/tests/unit/core/sql/snapshots/test_ml/test_create_model_expression_option/create_model_expression_option.sql
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‎packages/bigframes/tests/unit/core/sql/snapshots/test_ml/test_create_model_hparam_tuning/create_model_hparam_tuning.sql‎

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@@ -0,0 +1,3 @@
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CREATE MODEL `my_model`
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OPTIONS(model_type = 'LINEAR_REG', learn_rate = HPARAM_RANGE(0.0001, 1.0), optimizer = HPARAM_CANDIDATES(['ADAGRAD', 'SGD']))
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AS SELECT * FROM t
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‎packages/bigframes/tests/unit/core/sql/test_ml.py‎

Copy file name to clipboardExpand all lines: packages/bigframes/tests/unit/core/sql/test_ml.py
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@@ -14,6 +14,7 @@
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import pytest
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import bigframes.bigquery as bbq
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import bigframes.core.col as col
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import bigframes.core.expression as ex
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import bigframes.core.sql.ml
@@ -101,24 +102,17 @@ def test_create_model_list_option(snapshot):
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snapshot.assert_match(sql, "create_model_list_option.sql")
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def test_create_model_expression_option(snapshot):
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# An expression that calls a function on a literal value
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# e.g. 0.1 * 10
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literal_expr = ex.ScalarConstantExpression(0.1, dtypes.FLOAT_DTYPE)
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multiplier_expr = ex.ScalarConstantExpression(10, dtypes.INT_DTYPE)
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math_expr = col.Expression(
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ex.OpExpression(op=numeric_ops.mul_op, inputs=(literal_expr, multiplier_expr))
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)
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def test_create_model_hparam_tuning(snapshot):
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sql = bigframes.core.sql.ml.create_model_ddl(
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model_name="my_model",
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options={
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"l2_reg": math_expr,
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"booster_type": "gbtree",
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"model_type": "LINEAR_REG",
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"learn_rate": bbq.hparam_range(0.0001, 1.0),
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"optimizer": bbq.hparam_candidates(["ADAGRAD", "SGD"]),
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},
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training_data="SELECT * FROM t",
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)
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snapshot.assert_match(sql, "create_model_expression_option.sql")
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snapshot.assert_match(sql, "create_model_hparam_tuning.sql")
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def test_evaluate_model_basic(snapshot):

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