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# Copyright 2023 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 unittest import mock
import google.cloud.bigquery as bigquery
import pytest
import bigframes.ml.sql as ml_sql
import bigframes.pandas as bpd
@pytest.fixture(scope="session")
def base_sql_generator() -> ml_sql.BaseSqlGenerator:
return ml_sql.BaseSqlGenerator()
@pytest.fixture(scope="session")
def model_creation_sql_generator() -> ml_sql.ModelCreationSqlGenerator:
return ml_sql.ModelCreationSqlGenerator()
@pytest.fixture(scope="session")
def model_manipulation_sql_generator() -> ml_sql.ModelManipulationSqlGenerator:
return ml_sql.ModelManipulationSqlGenerator(
model_ref=bigquery.ModelReference.from_string(
"my_project_id.my_dataset_id.my_model_id"
)
)
@pytest.fixture(scope="session")
def mock_df():
mock_df = mock.create_autospec(spec=bpd.DataFrame)
mock_df.sql = "input_X_y_sql"
mock_df._to_sql_query.return_value = "input_X_sql", None, None
return mock_df
def test_ml_arima_coefficients(
model_manipulation_sql_generator: ml_sql.ModelManipulationSqlGenerator,
):
sql = model_manipulation_sql_generator.ml_arima_coefficients()
assert (
sql
== """SELECT * FROM ML.ARIMA_COEFFICIENTS(MODEL `my_project_id`.`my_dataset_id`.`my_model_id`)"""
)
def test_options_correct(base_sql_generator: ml_sql.BaseSqlGenerator):
sql = base_sql_generator.options(
model_type="lin_reg", input_label_cols=["col_a"], l1_reg=0.6
)
assert (
sql
== """OPTIONS(
model_type='lin_reg',
input_label_cols=['col_a'],
l1_reg=0.6)"""
)
def test_transform_correct(base_sql_generator: ml_sql.BaseSqlGenerator):
sql = base_sql_generator.transform(
"ML.STANDARD_SCALER(col_a) OVER(col_a) AS scaled_col_a",
"ML.ONE_HOT_ENCODER(col_b) OVER(col_b) AS encoded_col_b",
"ML.LABEL_ENCODER(col_c) OVER(col_c) AS encoded_col_c",
)
assert (
sql
== """TRANSFORM(
ML.STANDARD_SCALER(col_a) OVER(col_a) AS scaled_col_a,
ML.ONE_HOT_ENCODER(col_b) OVER(col_b) AS encoded_col_b,
ML.LABEL_ENCODER(col_c) OVER(col_c) AS encoded_col_c)"""
)
def test_standard_scaler_correct(
base_sql_generator: ml_sql.BaseSqlGenerator,
):
sql = base_sql_generator.ml_standard_scaler("col_a", "scaled_col_a")
assert sql == "ML.STANDARD_SCALER(`col_a`) OVER() AS `scaled_col_a`"
def test_max_abs_scaler_correct(
base_sql_generator: ml_sql.BaseSqlGenerator,
):
sql = base_sql_generator.ml_max_abs_scaler("col_a", "scaled_col_a")
assert sql == "ML.MAX_ABS_SCALER(`col_a`) OVER() AS `scaled_col_a`"
def test_min_max_scaler_correct(
base_sql_generator: ml_sql.BaseSqlGenerator,
):
sql = base_sql_generator.ml_min_max_scaler("col_a", "scaled_col_a")
assert sql == "ML.MIN_MAX_SCALER(`col_a`) OVER() AS `scaled_col_a`"
def test_imputer_correct(
base_sql_generator: ml_sql.BaseSqlGenerator,
):
sql = base_sql_generator.ml_imputer("col_a", "mean", "scaled_col_a")
assert sql == "ML.IMPUTER(`col_a`, 'mean') OVER() AS `scaled_col_a`"
def test_k_bins_discretizer_correct(
base_sql_generator: ml_sql.BaseSqlGenerator,
):
sql = base_sql_generator.ml_bucketize("col_a", [1, 2, 3, 4], "scaled_col_a")
assert sql == "ML.BUCKETIZE(`col_a`, [1, 2, 3, 4], FALSE) AS `scaled_col_a`"
def test_k_bins_discretizer_quantile_correct(
base_sql_generator: ml_sql.BaseSqlGenerator,
):
sql = base_sql_generator.ml_quantile_bucketize("col_a", 5, "scaled_col_a")
assert sql == "ML.QUANTILE_BUCKETIZE(`col_a`, 5) OVER() AS `scaled_col_a`"
def test_one_hot_encoder_correct(
base_sql_generator: ml_sql.BaseSqlGenerator,
):
sql = base_sql_generator.ml_one_hot_encoder(
"col_a", "none", 1000000, 0, "encoded_col_a"
)
assert (
sql
== "ML.ONE_HOT_ENCODER(`col_a`, 'none', 1000000, 0) OVER() AS `encoded_col_a`"
)
def test_label_encoder_correct(
base_sql_generator: ml_sql.BaseSqlGenerator,
):
sql = base_sql_generator.ml_label_encoder("col_a", 1000000, 0, "encoded_col_a")
assert sql == "ML.LABEL_ENCODER(`col_a`, 1000000, 0) OVER() AS `encoded_col_a`"
def test_polynomial_expand(
base_sql_generator: ml_sql.BaseSqlGenerator,
):
sql = base_sql_generator.ml_polynomial_expand(["col_a", "col_b"], 2, "poly_exp")
assert sql == "ML.POLYNOMIAL_EXPAND(STRUCT(`col_a`, `col_b`), 2) AS `poly_exp`"
def test_ai_forecast_correct(
base_sql_generator: ml_sql.BaseSqlGenerator,
mock_df: bpd.DataFrame,
):
sql = base_sql_generator.ai_forecast(
source_sql=mock_df.sql,
options={
"model": "TimesFM 2.0",
"data_col": "data1",
"timestamp_col": "time1",
"id_cols": ("id1", "id2"),
"horizon": 10,
"confidence_level": 0.95,
},
)
assert (
sql
== """SELECT * FROM AI.FORECAST((input_X_y_sql),
model => 'TimesFM 2.0',
data_col => 'data1',
timestamp_col => 'time1',
id_cols => ['id1', 'id2'],
horizon => 10,
confidence_level => 0.95)"""
)
def test_create_model_correct(
model_creation_sql_generator: ml_sql.ModelCreationSqlGenerator,
mock_df: bpd.DataFrame,
):
sql = model_creation_sql_generator.create_model(
source_sql=mock_df.sql,
model_ref=bigquery.ModelReference.from_string(
"test-proj._anonXYZ.create_model_correct_sql"
),
options={"option_key1": "option_value1", "option_key2": 2},
)
assert (
sql
== """CREATE OR REPLACE MODEL `test-proj`.`_anonXYZ`.`create_model_correct_sql`
OPTIONS(
option_key1='option_value1',
option_key2=2)
AS input_X_y_sql"""
)
def test_create_model_transform_correct(
model_creation_sql_generator: ml_sql.ModelCreationSqlGenerator,
mock_df: bpd.DataFrame,
):
sql = model_creation_sql_generator.create_model(
source_sql=mock_df.sql,
model_ref=bigquery.ModelReference.from_string(
"test-proj._anonXYZ.create_model_transform"
),
options={"option_key1": "option_value1", "option_key2": 2},
transforms=[
"ML.STANDARD_SCALER(col_a) OVER(col_a) AS scaled_col_a",
"ML.ONE_HOT_ENCODER(col_b) OVER(col_b) AS encoded_col_b",
],
)
assert (
sql
== """CREATE OR REPLACE MODEL `test-proj`.`_anonXYZ`.`create_model_transform`
TRANSFORM(
ML.STANDARD_SCALER(col_a) OVER(col_a) AS scaled_col_a,
ML.ONE_HOT_ENCODER(col_b) OVER(col_b) AS encoded_col_b)
OPTIONS(
option_key1='option_value1',
option_key2=2)
AS input_X_y_sql"""
)
def test_create_llm_remote_model_correct(
model_creation_sql_generator: ml_sql.ModelCreationSqlGenerator,
mock_df: bpd.DataFrame,
):
sql = model_creation_sql_generator.create_llm_remote_model(
source_sql=mock_df.sql,
connection_name="my_project.us.my_connection",
model_ref=bigquery.ModelReference.from_string(
"test-proj._anonXYZ.create_remote_model"
),
options={"option_key1": "option_value1", "option_key2": 2},
)
assert (
sql
== """CREATE OR REPLACE MODEL `test-proj`.`_anonXYZ`.`create_remote_model`
REMOTE WITH CONNECTION `my_project.us.my_connection`
OPTIONS(
option_key1='option_value1',
option_key2=2)
AS input_X_y_sql"""
)
def test_create_remote_model_correct(
model_creation_sql_generator: ml_sql.ModelCreationSqlGenerator,
):
sql = model_creation_sql_generator.create_remote_model(
connection_name="my_project.us.my_connection",
model_ref=bigquery.ModelReference.from_string(
"test-proj._anonXYZ.create_remote_model"
),
options={"option_key1": "option_value1", "option_key2": 2},
)
assert (
sql
== """CREATE OR REPLACE MODEL `test-proj`.`_anonXYZ`.`create_remote_model`
REMOTE WITH CONNECTION `my_project.us.my_connection`
OPTIONS(
option_key1='option_value1',
option_key2=2)"""
)
def test_create_remote_model_with_params_correct(
model_creation_sql_generator: ml_sql.ModelCreationSqlGenerator,
):
sql = model_creation_sql_generator.create_remote_model(
connection_name="my_project.us.my_connection",
model_ref=bigquery.ModelReference.from_string(
"test-proj._anonXYZ.create_remote_model"
),
input={"column1": "int64"},
output={"result": "array<float64>"},
options={"option_key1": "option_value1", "option_key2": 2},
)
assert (
sql
== """CREATE OR REPLACE MODEL `test-proj`.`_anonXYZ`.`create_remote_model`
INPUT(
`column1` int64)
OUTPUT(
`result` array<float64>)
REMOTE WITH CONNECTION `my_project.us.my_connection`
OPTIONS(
option_key1='option_value1',
option_key2=2)"""
)
def test_create_imported_model_correct(
model_creation_sql_generator: ml_sql.ModelCreationSqlGenerator,
):
sql = model_creation_sql_generator.create_imported_model(
model_ref=bigquery.ModelReference.from_string(
"test-proj._anonXYZ.create_imported_model"
),
options={"option_key1": "option_value1", "option_key2": 2},
)
assert (
sql
== """CREATE OR REPLACE MODEL `test-proj`.`_anonXYZ`.`create_imported_model`
OPTIONS(
option_key1='option_value1',
option_key2=2)"""
)
def test_create_xgboost_imported_model_produces_correct_sql(
model_creation_sql_generator: ml_sql.ModelCreationSqlGenerator,
):
sql = model_creation_sql_generator.create_xgboost_imported_model(
model_ref=bigquery.ModelReference.from_string(
"test-proj._anonXYZ.create_xgboost_imported_model"
),
input={"column1": "int64"},
output={"result": "array<float64>"},
options={"option_key1": "option_value1", "option_key2": 2},
)
assert (
sql
== """CREATE OR REPLACE MODEL `test-proj`.`_anonXYZ`.`create_xgboost_imported_model`
INPUT(
`column1` int64)
OUTPUT(
`result` array<float64>)
OPTIONS(
option_key1='option_value1',
option_key2=2)"""
)
def test_alter_model_correct_sql(
model_manipulation_sql_generator: ml_sql.ModelManipulationSqlGenerator,
):
sql = model_manipulation_sql_generator.alter_model(
options={"option_key1": "option_value1", "option_key2": 2},
)
assert (
sql
== """ALTER MODEL `my_project_id`.`my_dataset_id`.`my_model_id`
SET OPTIONS(
option_key1='option_value1',
option_key2=2)"""
)
def test_ml_predict_correct(
model_manipulation_sql_generator: ml_sql.ModelManipulationSqlGenerator,
mock_df: bpd.DataFrame,
):
sql = model_manipulation_sql_generator.ml_predict(source_sql=mock_df.sql)
assert (
sql
== """SELECT * FROM ML.PREDICT(MODEL `my_project_id`.`my_dataset_id`.`my_model_id`,
(input_X_y_sql))"""
)
def test_ml_llm_evaluate_correct(
model_manipulation_sql_generator: ml_sql.ModelManipulationSqlGenerator,
mock_df: bpd.DataFrame,
):
sql = model_manipulation_sql_generator.ml_llm_evaluate(
source_sql=mock_df.sql, task_type="CLASSIFICATION"
)
assert (
sql
== """SELECT * FROM ML.EVALUATE(MODEL `my_project_id`.`my_dataset_id`.`my_model_id`,
(input_X_y_sql), STRUCT("CLASSIFICATION" AS task_type))"""
)
def test_ml_evaluate_correct(
model_manipulation_sql_generator: ml_sql.ModelManipulationSqlGenerator,
mock_df: bpd.DataFrame,
):
sql = model_manipulation_sql_generator.ml_evaluate(source_sql=mock_df.sql)
assert (
sql
== """SELECT * FROM ML.EVALUATE(MODEL `my_project_id`.`my_dataset_id`.`my_model_id`,
(input_X_y_sql))"""
)
def test_ml_arima_evaluate_correct(
model_manipulation_sql_generator: ml_sql.ModelManipulationSqlGenerator,
):
sql = model_manipulation_sql_generator.ml_arima_evaluate(
show_all_candidate_models=True
)
assert (
sql
== """SELECT * FROM ML.ARIMA_EVALUATE(MODEL `my_project_id`.`my_dataset_id`.`my_model_id`,
STRUCT(True AS show_all_candidate_models))"""
)
def test_ml_evaluate_no_source_correct(
model_manipulation_sql_generator: ml_sql.ModelManipulationSqlGenerator,
):
sql = model_manipulation_sql_generator.ml_evaluate()
assert (
sql
== """SELECT * FROM ML.EVALUATE(MODEL `my_project_id`.`my_dataset_id`.`my_model_id`)"""
)
def test_ml_centroids_correct(
model_manipulation_sql_generator: ml_sql.ModelManipulationSqlGenerator,
):
sql = model_manipulation_sql_generator.ml_centroids()
assert (
sql
== """SELECT * FROM ML.CENTROIDS(MODEL `my_project_id`.`my_dataset_id`.`my_model_id`)"""
)
def test_ml_forecast_correct_sql(
model_manipulation_sql_generator: ml_sql.ModelManipulationSqlGenerator,
):
sql = model_manipulation_sql_generator.ml_forecast(
struct_options={"option_key1": 1, "option_key2": 2.2},
)
assert (
sql
== """SELECT * FROM ML.FORECAST(MODEL `my_project_id`.`my_dataset_id`.`my_model_id`,
STRUCT(
1 AS `option_key1`,
2.2 AS `option_key2`))"""
)
def test_ml_generate_text_correct(
model_manipulation_sql_generator: ml_sql.ModelManipulationSqlGenerator,
mock_df: bpd.DataFrame,
):
sql = model_manipulation_sql_generator.ml_generate_text(
source_sql=mock_df.sql,
struct_options={"option_key1": 1, "option_key2": 2.2},
)
assert (
sql
== """SELECT * FROM ML.GENERATE_TEXT(MODEL `my_project_id`.`my_dataset_id`.`my_model_id`,
(input_X_y_sql), STRUCT(
1 AS `option_key1`,
2.2 AS `option_key2`))"""
)
def test_ml_generate_embedding_correct(
model_manipulation_sql_generator: ml_sql.ModelManipulationSqlGenerator,
mock_df: bpd.DataFrame,
):
sql = model_manipulation_sql_generator.ml_generate_embedding(
source_sql=mock_df.sql,
struct_options={"option_key1": 1, "option_key2": 2.2},
)
assert (
sql
== """SELECT * FROM ML.GENERATE_EMBEDDING(MODEL `my_project_id`.`my_dataset_id`.`my_model_id`,
(input_X_y_sql), STRUCT(
1 AS `option_key1`,
2.2 AS `option_key2`))"""
)
def test_ml_explain_predict_correct(
model_manipulation_sql_generator: ml_sql.ModelManipulationSqlGenerator,
mock_df: bpd.DataFrame,
):
sql = model_manipulation_sql_generator.ml_explain_predict(
source_sql=mock_df.sql,
struct_options={"option_key1": 1, "option_key2": 2.25},
)
assert (
sql
== """SELECT * FROM ML.EXPLAIN_PREDICT(MODEL `my_project_id`.`my_dataset_id`.`my_model_id`,
(input_X_y_sql), STRUCT(
1 AS `option_key1`,
2.25 AS `option_key2`))"""
)
def test_ml_detect_anomalies_correct_sql(
model_manipulation_sql_generator: ml_sql.ModelManipulationSqlGenerator,
mock_df: bpd.DataFrame,
):
sql = model_manipulation_sql_generator.ml_detect_anomalies(
source_sql=mock_df.sql,
struct_options={"option_key1": 1, "option_key2": 2.2},
)
assert (
sql
== """SELECT * FROM ML.DETECT_ANOMALIES(MODEL `my_project_id`.`my_dataset_id`.`my_model_id`,
STRUCT(
1 AS `option_key1`,
2.2 AS `option_key2`), (input_X_y_sql))"""
)
def test_ml_principal_components_correct(
model_manipulation_sql_generator: ml_sql.ModelManipulationSqlGenerator,
):
sql = model_manipulation_sql_generator.ml_principal_components()
assert (
sql
== """SELECT * FROM ML.PRINCIPAL_COMPONENTS(MODEL `my_project_id`.`my_dataset_id`.`my_model_id`)"""
)
def test_ml_principal_component_info_correct(
model_manipulation_sql_generator: ml_sql.ModelManipulationSqlGenerator,
):
sql = model_manipulation_sql_generator.ml_principal_component_info()
assert (
sql
== """SELECT * FROM ML.PRINCIPAL_COMPONENT_INFO(MODEL `my_project_id`.`my_dataset_id`.`my_model_id`)"""
)