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MLFunctionsTest.cpp
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MLFunctionsTest.cpp
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/*
* Copyright 2022 HEAVY.AI, Inc.
*
* 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.
*/
#include "../QueryEngine/Execute.h"
#include "../QueryRunner/QueryRunner.h"
#include "QueryEngine/TableFunctions/SystemFunctions/os/ML/MLModel.h"
#include "QueryEngine/TableFunctions/TableFunctionManager.h"
#include "TestHelpers.h"
#include <gtest/gtest.h>
#include <sstream>
#include <string>
#include <vector>
#ifndef BASE_PATH
#define BASE_PATH "./tmp"
#endif
using QR = QueryRunner::QueryRunner;
extern bool g_enable_table_functions;
extern bool g_enable_ml_functions;
using namespace TestHelpers;
namespace {
inline void run_ddl_statement(const std::string& stmt) {
QR::get()->runDDLStatement(stmt);
}
std::shared_ptr<ResultSet> run_multiple_agg(const std::string& query_str,
const ExecutorDeviceType device_type) {
return QR::get()->runSQL(query_str, device_type, false, false);
}
TargetValue run_simple_agg(const std::string& query_str,
const ExecutorDeviceType device_type,
const bool geo_return_geo_tv = true,
const bool allow_loop_joins = true) {
auto rows = QR::get()->runSQL(query_str, device_type, allow_loop_joins);
auto crt_row = rows->getNextRow(true, true);
CHECK_EQ(size_t(1), crt_row.size()) << query_str;
return crt_row[0];
}
} // namespace
bool skip_tests(const ExecutorDeviceType device_type) {
#ifdef HAVE_CUDA
return device_type == ExecutorDeviceType::GPU && !(QR::get()->gpusPresent());
#else
return device_type == ExecutorDeviceType::GPU;
#endif
}
#define SKIP_NO_GPU() \
if (skip_tests(dt)) { \
CHECK(dt == ExecutorDeviceType::GPU); \
LOG(WARNING) << "GPU not available, skipping GPU tests"; \
continue; \
}
const char* iris_schema = R"(
CREATE TABLE ml_iris(
id INT,
sepal_length_cm FLOAT,
sepal_width_cm FLOAT,
petal_length_cm FLOAT,
petal_width_cm FLOAT,
species TEXT);
)";
const char* craigslist_f150s_schema = R"(
CREATE TABLE craigslist_f150s (
region TEXT ENCODING DICT(16),
state TEXT ENCODING DICT(8),
price INTEGER,
year_ SMALLINT,
condition_ TEXT ENCODING DICT(8),
cylinders TEXT ENCODING DICT(8),
fuel TEXT ENCODING DICT(8),
odometer FLOAT,
title_status TEXT ENCODING DICT(8),
transmission TEXT ENCODING DICT(8),
drive TEXT ENCODING DICT(8),
paint_color TEXT ENCODING DICT(8),
posting_date TIMESTAMP(0) ENCODING FIXED(32));
)";
class MLTableFunctionsTest : public ::testing::Test {
public:
void SetUp() override {
ASSERT_NO_THROW(run_ddl_statement("DROP TABLE IF EXISTS ml_iris;"));
ASSERT_NO_THROW(run_ddl_statement(iris_schema));
const std::string import_file{"iris.csv"};
const auto load_str = std::string("COPY ml_iris FROM '") +
"../../Tests/Import/datafiles/ml/" + import_file +
"' WITH (header='true');";
ASSERT_NO_THROW(run_ddl_statement(load_str));
}
void TearDown() override { ASSERT_NO_THROW(run_ddl_statement("DROP TABLE ml_iris;")); }
};
class MLCreateModelTest : public ::testing::Test {
public:
void SetUp() override {
ASSERT_NO_THROW(run_ddl_statement("DROP TABLE IF EXISTS ml_iris;"));
ASSERT_NO_THROW(run_ddl_statement(iris_schema));
const std::string import_file{"iris.csv"};
const auto load_str = std::string("COPY ml_iris FROM '") +
"../../Tests/Import/datafiles/ml/" + import_file +
"' WITH (header='true');";
ASSERT_NO_THROW(run_ddl_statement(load_str));
}
void TearDown() override { ASSERT_NO_THROW(run_ddl_statement("DROP TABLE ml_iris;")); }
};
class MLRegressionFunctionsTest : public testing::TestWithParam<MLModelType> {
public:
void SetUp() override {
ASSERT_NO_THROW(run_ddl_statement("DROP TABLE IF EXISTS ml_iris;"));
ASSERT_NO_THROW(run_ddl_statement(iris_schema));
const std::string import_file{"iris.csv"};
const auto load_str = std::string("COPY ml_iris FROM '") +
"../../Tests/Import/datafiles/ml/" + import_file +
"' WITH (header='true');";
ASSERT_NO_THROW(run_ddl_statement(load_str));
}
void TearDown() override { ASSERT_NO_THROW(run_ddl_statement("DROP TABLE ml_iris;")); }
};
class MLCategoricalRegressionFunctionsTest : public testing::TestWithParam<MLModelType> {
public:
void SetUp() override {
ASSERT_NO_THROW(run_ddl_statement("DROP TABLE IF EXISTS craigslist_f150s;"));
ASSERT_NO_THROW(run_ddl_statement(craigslist_f150s_schema));
const std::string import_file{"craigslist_f150s.csv.gz"};
const auto load_str = std::string("COPY craigslist_f150s FROM '") +
"../../Tests/Import/datafiles/ml/" + import_file +
"' WITH (header='true');";
ASSERT_NO_THROW(run_ddl_statement(load_str));
}
void TearDown() override {
ASSERT_NO_THROW(run_ddl_statement("DROP TABLE craigslist_f150s;"));
}
};
std::string generate_cursor_query(const std::string& table_name,
const std::string& id_column,
const std::vector<std::string>& feature_columns,
const std::string& filter,
const std::string& numeric_data_type,
const bool add_cursor = true,
const double sample_frac = 0.0,
const bool add_casts = true,
const bool use_alias = true) {
std::ostringstream oss;
if (add_cursor) {
oss << "CURSOR(";
}
oss << "SELECT ";
if (!id_column.empty()) {
oss << id_column << " AS id, ";
}
bool is_first_col = true;
size_t feature_idx = 0;
for (const auto& feature_column : feature_columns) {
if (!is_first_col) {
oss << ", ";
} else {
is_first_col = false;
}
if (add_casts) {
oss << "CAST(";
}
oss << feature_column;
if (add_casts) {
oss << " AS " << numeric_data_type << ")";
}
if (use_alias) {
const std::string alias_name = "var" + std::to_string(feature_idx++);
oss << " AS " << alias_name;
}
}
oss << " FROM " << table_name;
if (!filter.empty() || sample_frac > 0.0) {
oss << " WHERE " << filter;
if (sample_frac > 0.0) {
if (!filter.empty()) {
oss << " AND ";
}
oss << "SAMPLE_RATIO(" << sample_frac << ")";
}
}
if (add_cursor) {
oss << ")";
}
return oss.str();
}
std::string generate_kmeans_query(const std::string& cursor_query,
const int32_t num_clusters,
const int32_t num_iterations,
const std::string& init_type = "",
const bool add_select_star = true) {
std::ostringstream oss;
if (add_select_star) {
oss << "SELECT * FROM ";
}
oss << "TABLE(KMEANS("
<< "data => " << cursor_query << ", num_clusters => " << num_clusters
<< ", num_iterations => " << num_iterations;
if (!init_type.empty()) {
oss << ", init_type => " << init_type;
}
if (add_select_star) {
oss << ")) ORDER BY id ASC";
}
return oss.str();
}
std::string generate_create_model_query(
const std::string& algo_name,
const std::string& model_name,
const std::string& query,
const std::vector<std::pair<std::string, std::string>>& args,
const bool create_or_replace) {
std::ostringstream oss;
oss << "CREATE ";
if (create_or_replace) {
oss << "OR REPLACE ";
}
oss << "MODEL " << model_name << " OF TYPE " << algo_name << " AS " << query;
if (args.size()) {
oss << " WITH (";
bool is_first_arg = true;
for (const auto& arg : args) {
if (!is_first_arg) {
oss << ", ";
}
is_first_arg = false;
oss << arg.first << " = " << arg.second;
}
oss << ");";
} else {
oss << ";";
}
return oss.str();
}
std::string generate_query(const std::string& algo_name,
const std::vector<std::pair<std::string, std::string>>& args,
const std::vector<std::string>& order_by_cols,
const bool make_args_named) {
std::ostringstream oss;
const bool project_data = !order_by_cols.empty();
if (project_data) {
oss << "SELECT * FROM ";
}
oss << "TABLE(" << algo_name << "(";
bool is_first_arg = true;
for (const auto& arg : args) {
if (!is_first_arg) {
oss << ", ";
}
is_first_arg = false;
if (make_args_named) {
oss << arg.first << " => ";
}
oss << arg.second;
}
oss << "))";
if (project_data) {
oss << " ORDER BY ";
is_first_arg = true;
for (const auto& order_by_col : order_by_cols) {
if (!is_first_arg) {
oss << ", ";
}
is_first_arg = false;
oss << order_by_col;
}
oss << " ASC NULLS LAST;";
}
return oss.str();
}
std::string generate_unsupervised_classifier_precision_query(
const std::string& algo_name,
const std::vector<std::pair<std::string, std::string>>& args,
const std::string& data_table_name,
const std::string& data_table_id_col,
const std::string& data_table_class_col,
const bool make_args_named = true) {
const auto classifier_query = generate_query(algo_name, args, {}, make_args_named);
std::ostringstream oss;
oss << "SELECT CAST(SUM(n) AS DOUBLE) / SUM(total_in_class) "
<< "FROM(SELECT class, cluster_id, n, total_in_class, perc_in_cluster "
<< "FROM(SELECT class, cluster_id, n, total_in_class, perc_in_cluster, "
<< "ROW_NUMBER() OVER (PARTITION BY class ORDER BY perc_in_cluster DESC) AS "
"cluster_rank "
<< "FROM(SELECT class, cluster_id, COUNT(*) as n, SUM(COUNT(*)) OVER (PARTITION BY "
"class) "
<< "AS total_in_class, CAST(COUNT(*) AS DOUBLE) / SUM(COUNT(*)) OVER (PARTITION BY "
"class) "
<< "AS perc_in_cluster "
<< "FROM(SELECT " << data_table_class_col << " AS class, cluster_id "
<< "FROM " << data_table_name << ", " << classifier_query << " classifier_query "
<< "WHERE " << data_table_name << "." << data_table_id_col
<< " = classifier_query.id) "
<< "GROUP BY class, cluster_id)) "
<< "WHERE cluster_rank = 1);";
return oss.str();
}
double calc_classification_accuracy(const std::string& data_table_name,
const std::string& classification_query) {
std::ostringstream oss;
return 0;
}
std::vector<std::string> get_supported_ml_frameworks() {
const std::string query =
"SELECT ml_framework FROM "
"TABLE(supported_ml_frameworks()) WHERE is_available = TRUE ORDER BY ml_framework "
"DESC;";
const auto rows = run_multiple_agg(query, ExecutorDeviceType::CPU);
std::vector<std::string> supported_ml_frameworks;
for (size_t row_idx = 0; row_idx < rows->rowCount(); ++row_idx) {
auto crt_row = rows->getNextRow(true, true);
supported_ml_frameworks.emplace_back(
std::string("'") +
boost::get<std::string>(TestHelpers::v<NullableString>(crt_row[0])) +
std::string("'"));
}
return supported_ml_frameworks;
}
TEST_F(MLTableFunctionsTest, SupportedMLFrameworks) {
const std::vector<std::string> expected_ml_frameworks = {"onedal", "mlpack"};
std::vector<bool> expected_is_available;
std::vector<bool> expected_is_default;
bool found_ml_framework = false;
#ifdef HAVE_ONEDAL
expected_is_available.emplace_back(true);
expected_is_default.emplace_back(!found_ml_framework);
found_ml_framework = true;
#else
expected_is_available.emplace_back(false);
expected_is_default.emplace_back(false);
#endif
#ifdef HAVE_MLPACK
expected_is_available.emplace_back(true);
expected_is_default.emplace_back(!found_ml_framework);
found_ml_framework = true;
#else
expected_is_available.emplace_back(false);
expected_is_default.emplace_back(false);
#endif
for (auto dt : {ExecutorDeviceType::CPU, ExecutorDeviceType::GPU}) {
const std::string query =
"SELECT ml_framework, is_available, is_default FROM "
"TABLE(supported_ml_frameworks()) ORDER BY ml_framework DESC;";
const auto rows = run_multiple_agg(query, dt);
const size_t num_rows = rows->rowCount();
EXPECT_EQ(num_rows, size_t(2));
EXPECT_EQ(rows->colCount(), size_t(3));
for (size_t row_idx = 0; row_idx < num_rows; ++row_idx) {
auto crt_row = rows->getNextRow(true, true);
const auto ml_framework =
boost::get<std::string>(TestHelpers::v<NullableString>(crt_row[0]));
EXPECT_EQ(ml_framework, expected_ml_frameworks[row_idx]);
EXPECT_EQ(TestHelpers::v<int64_t>(crt_row[1]),
expected_is_available[row_idx] ? 1 : 0);
EXPECT_EQ(TestHelpers::v<int64_t>(crt_row[2]),
expected_is_default[row_idx] ? 1 : 0);
}
}
}
TEST_F(MLTableFunctionsTest, KMeansMissingArgs) {
const auto cursor_query = generate_cursor_query(
"ml_iris",
"id",
{"petal_length_cm", "petal_width_cm", "sepal_length_cm", "sepal_width_cm"},
"",
"float");
for (auto dt : {ExecutorDeviceType::CPU, ExecutorDeviceType::GPU}) {
SKIP_NO_GPU();
// Missing args
{
for (bool make_args_named : {false, true}) {
if (make_args_named) {
EXPECT_NO_THROW(run_multiple_agg(
generate_query("KMEANS",
{{"data", cursor_query}, {"num_clusters", "3"}},
{"id"},
make_args_named),
dt));
} else {
EXPECT_ANY_THROW(run_multiple_agg(
generate_query("KMEANS",
{{"data", cursor_query}, {"num_clusters", "3"}},
{"id"},
make_args_named),
dt));
}
EXPECT_ANY_THROW(run_multiple_agg(
generate_query("KMEANS",
{{"data", cursor_query}, {"num_iterations", "10"}},
{"id"},
make_args_named),
dt));
if (make_args_named) {
EXPECT_NO_THROW(run_multiple_agg(generate_query("KMEANS",
{
{"data", cursor_query},
{"num_clusters", "3"},
{"init_type", "'DEFAULT'"},
},
{"id"},
make_args_named),
dt));
} else {
EXPECT_ANY_THROW(run_multiple_agg(generate_query("KMEANS",
{
{"data", cursor_query},
{"num_clusters", "3"},
{"init_type", "'DEFAULT'"},
},
{"id"},
make_args_named),
dt));
}
EXPECT_ANY_THROW(run_multiple_agg(
generate_query("KMEANS",
{{"num_clusters", "3"}, {"num_iterations", "10"}},
{"id"},
make_args_named),
dt));
}
}
}
}
TEST_F(MLTableFunctionsTest, KMeansInvalidType) {
for (auto dt : {ExecutorDeviceType::CPU, ExecutorDeviceType::GPU}) {
SKIP_NO_GPU();
for (auto numeric_data_type : {"int", "bigint"}) {
const auto cursor_query = generate_cursor_query(
"ml_iris",
"id",
{"petal_length_cm", "petal_width_cm", "sepal_length_cm", "sepal_width_cm"},
"",
numeric_data_type);
EXPECT_NO_THROW(run_multiple_agg(
generate_query(
"KMEANS",
{{"data", cursor_query}, {"num_clusters", "3"}, {"num_iterations", "10"}},
{"id"},
true /* make_args_named */),
dt));
}
for (auto numeric_data_type : {"text"}) {
// We do not auto-cast from TEXT to numeric types, so this will fail
const auto cursor_query = generate_cursor_query(
"ml_iris",
"id",
{"petal_length_cm", "petal_width_cm", "sepal_length_cm", "sepal_width_cm"},
"",
numeric_data_type);
EXPECT_ANY_THROW(run_multiple_agg(
generate_query(
"KMEANS",
{{"data", cursor_query}, {"num_clusters", "3"}, {"num_iterations", "10"}},
{"id"},
true /* make_args_named */),
dt));
}
}
}
TEST_F(MLTableFunctionsTest, KMeansInvalidArgs) {
const auto numeric_data_type{"float"};
const auto cursor_query = generate_cursor_query(
"ml_iris",
"id",
{"petal_length_cm", "petal_width_cm", "sepal_length_cm", "sepal_width_cm"},
"",
numeric_data_type);
for (auto dt : {ExecutorDeviceType::CPU, ExecutorDeviceType::GPU}) {
SKIP_NO_GPU();
// Should throw if num clusters <= 0
{
EXPECT_THROW(run_multiple_agg(generate_query("KMEANS",
{{"data", cursor_query},
{"num_clusters", "0"},
{"num_iterations", "10"}},
{"id"},
true /* make_args_named */),
dt),
UserTableFunctionError);
EXPECT_THROW(run_multiple_agg(generate_query("KMEANS",
{{"data", cursor_query},
{"num_clusters", "-1"},
{"num_iterations", "10"}},
{"id"},
true /* make_args_named */),
dt),
UserTableFunctionError);
// There are only 150 observations/rows in the iris dataset
// We should throw if we ask for more clusters than that (here 300)
EXPECT_THROW(run_multiple_agg(generate_query("KMEANS",
{{"data", cursor_query},
{"num_clusters", "300"},
{"num_iterations", "10"}},
{"id"},
true /* make_args_named */),
dt),
UserTableFunctionError);
}
// Should throw if num_iterations <= 0
{
EXPECT_THROW(run_multiple_agg(generate_query("KMEANS",
{{"data", cursor_query},
{"num_clusters", "3"},
{"num_iterations", "0"}},
{"id"},
true /* make_args_named */),
dt),
UserTableFunctionError);
EXPECT_THROW(run_multiple_agg(generate_query("KMEANS",
{{"data", cursor_query},
{"num_clusters", "3"},
{"num_iterations", "-1"}},
{"id"},
true /* make_args_named */),
dt),
UserTableFunctionError);
}
// Should throw if kmeans init type is invalid
{
EXPECT_THROW(run_multiple_agg(generate_query("KMEANS",
{{"data", cursor_query},
{"num_clusters", "3"},
{"num_iterations", "10"},
{"init_type", "'foo_bar'"}},
{"id"},
true /* make_args_named */),
dt),
UserTableFunctionError);
}
}
}
TEST_F(MLTableFunctionsTest, KMeansNumClusters) {
for (auto numeric_data_type : {"float", "double"}) {
const auto cursor_query = generate_cursor_query(
"ml_iris",
"id",
{"petal_length_cm", "petal_width_cm", "sepal_length_cm", "sepal_width_cm"},
"",
numeric_data_type);
const auto supported_ml_frameworks = get_supported_ml_frameworks();
for (auto& ml_framework : supported_ml_frameworks) {
for (auto dt : {ExecutorDeviceType::CPU, ExecutorDeviceType::GPU}) {
SKIP_NO_GPU();
const auto num_obs = TestHelpers::v<int64_t>(
run_simple_agg("SELECT COUNT(DISTINCT id) FROM ml_iris;", dt));
for (int32_t num_clusters = 1; num_clusters < 10; ++num_clusters) {
std::ostringstream oss;
oss << "SELECT COUNT(DISTINCT id), COUNT(DISTINCT cluster_id) FROM "
"TABLE(KMEANS("
<< "data => " << cursor_query << ", num_clusters => " << num_clusters
<< ", num_iterations => 10, init_type => 'DEFAULT', "
<< "preferred_ml_framework => " << ml_framework << "));";
const std::string query = oss.str();
const auto rows = run_multiple_agg(query, dt);
EXPECT_EQ(rows->rowCount(), size_t(1));
EXPECT_EQ(rows->colCount(), size_t(2));
auto crt_row = rows->getNextRow(true, true);
EXPECT_EQ(TestHelpers::v<int64_t>(crt_row[0]), num_obs);
EXPECT_EQ(TestHelpers::v<int64_t>(crt_row[1]), num_clusters);
}
}
}
}
}
TEST_F(MLTableFunctionsTest, KMeansPrecision) {
const std::string data_table_name{"ml_iris"};
const std::string id_col{"id"};
const std::vector<std::string> feature_cols{
{"petal_length_cm", "petal_width_cm", "sepal_length_cm", "sepal_width_cm"}};
const std::string class_col{"species"};
const auto supported_ml_frameworks = get_supported_ml_frameworks();
for (auto& ml_framework : supported_ml_frameworks) {
for (auto numeric_data_type : {"DOUBLE"}) {
for (auto dt : {ExecutorDeviceType::CPU, ExecutorDeviceType::GPU}) {
SKIP_NO_GPU();
const size_t num_classes = TestHelpers::v<int64_t>(run_simple_agg(
"SELECT COUNT(DISTINCT " + class_col + ") FROM " + data_table_name + ";",
dt));
const auto cursor_query = generate_cursor_query(
data_table_name, id_col, feature_cols, "", numeric_data_type);
const auto precision_query = generate_unsupervised_classifier_precision_query(
"KMEANS",
{{"data", cursor_query},
{"num_clusters", std::to_string(num_classes)},
{"num_iterations", "10"},
{"init_type", "'DEFAULT'"},
{"preferred_ml_framework", ml_framework}},
data_table_name,
id_col,
class_col,
true);
EXPECT_GE(TestHelpers::v<double>(run_simple_agg(precision_query, dt)), 0.8);
}
}
}
}
// Hitting issues with the dbscan preflight require checks
// causing a crash at launch time, so disabling until can troubleshoot
TEST_F(MLTableFunctionsTest, DBSCANInvalidArgs) {
const std::string data_table_name{"ml_iris"};
const std::string id_col{"id"};
const std::vector<std::string> feature_cols{
{"petal_length_cm", "petal_width_cm", "sepal_length_cm", "sepal_width_cm"}};
const std::string class_col{"species"};
const auto numeric_data_type{"float"};
const auto cursor_query =
generate_cursor_query(data_table_name, id_col, feature_cols, "", numeric_data_type);
for (auto dt : {ExecutorDeviceType::CPU, ExecutorDeviceType::GPU}) {
SKIP_NO_GPU();
// Should throw if epsilon <= 0
EXPECT_THROW(run_multiple_agg(generate_query("DBSCAN",
{{"data", cursor_query},
{"epsilon", "-1.0"},
{"min_observations", "10"}},
{"id"},
true /* make_args_named */),
dt),
UserTableFunctionError);
EXPECT_THROW(run_multiple_agg(generate_query("DBSCAN",
{{"data", cursor_query},
{"epsilon", "0.0"},
{"min_observations", "10"}},
{"id"},
true /* make_args_named */),
dt),
UserTableFunctionError);
// Should throw if min_observations <= 0
EXPECT_THROW(run_multiple_agg(generate_query("DBSCAN",
{{"data", cursor_query},
{"epsilon", "0.5"},
{"min_observations", "-10"}},
{"id"},
true /* make_args_named */),
dt),
UserTableFunctionError);
EXPECT_THROW(run_multiple_agg(generate_query("DBSCAN",
{{"data", cursor_query},
{"epsilon", "0.5"},
{"min_observations", "0"}},
{"id"},
true /* make_args_named */),
dt),
UserTableFunctionError);
}
}
TEST_F(MLTableFunctionsTest, DBSCAN) {
const std::string data_table_name{"ml_iris"};
const std::string id_col{"id"};
const std::vector<std::string> feature_cols{
{"petal_length_cm", "petal_width_cm", "sepal_length_cm", "sepal_width_cm"}};
const std::string class_col{"species"};
const auto supported_ml_frameworks = get_supported_ml_frameworks();
for (auto& ml_framework : supported_ml_frameworks) {
for (auto numeric_data_type : {"DOUBLE"}) {
const auto cursor_query = generate_cursor_query(
"ml_iris",
"id",
{"petal_length_cm", "petal_width_cm", "sepal_length_cm", "sepal_width_cm"},
"",
numeric_data_type);
for (auto dt : {ExecutorDeviceType::CPU, ExecutorDeviceType::GPU}) {
SKIP_NO_GPU();
const auto num_obs = TestHelpers::v<int64_t>(run_simple_agg(
"SELECT COUNT(DISTINCT id) FROM " + data_table_name + ";", dt));
const auto dbscan_partial_query =
generate_query("DBSCAN",
{{"data", cursor_query},
{"epsilon", "0.5"},
{"min_observations", "10"},
{"preferred_ml_framework", ml_framework}},
{},
true /* make_args_named */);
const auto query =
"SELECT COUNT(DISTINCT id), COUNT(DISTINCT cluster_id) "
" FROM " +
dbscan_partial_query + ";";
const auto rows = run_multiple_agg(query, dt);
EXPECT_EQ(rows->rowCount(), size_t(1));
EXPECT_EQ(rows->colCount(), size_t(2));
auto crt_row = rows->getNextRow(true, true);
EXPECT_EQ(TestHelpers::v<int64_t>(crt_row[0]), num_obs);
EXPECT_GT(TestHelpers::v<int64_t>(crt_row[1]), 1);
}
}
}
}
TEST_F(MLTableFunctionsTest, PCA) {
const std::string data_table_name{"ml_iris"};
const std::string id_col{"id"};
const std::string model_name{"PCA_MODEL"};
const std::string quoted_model_name{"'" + model_name + "'"};
const std::vector<std::string> feature_cols{
{"petal_length_cm", "petal_width_cm", "sepal_length_cm", "sepal_width_cm"}};
const auto supported_ml_frameworks = get_supported_ml_frameworks();
for (auto& ml_framework : supported_ml_frameworks) {
if (ml_framework != "'onedal'") {
continue;
}
for (auto numeric_data_type : {"DOUBLE"}) {
const auto cursor_query = generate_cursor_query(
"ml_iris",
"",
{"petal_length_cm", "petal_width_cm", "sepal_length_cm", "sepal_width_cm"},
"",
numeric_data_type);
for (auto dt : {ExecutorDeviceType::CPU, ExecutorDeviceType::GPU}) {
SKIP_NO_GPU();
const auto pca_partial_query =
generate_query("PCA_FIT",
{{"model_name", quoted_model_name},
{"data", cursor_query},
{"preferred_ml_framework", ml_framework}},
{},
true /* make_args_named */);
const auto query = "SELECT * FROM " + pca_partial_query + ";";
const auto fit_rows = run_multiple_agg(query, dt);
EXPECT_EQ(fit_rows->rowCount(), size_t(1));
EXPECT_EQ(fit_rows->colCount(), size_t(1));
auto model_row = fit_rows->getNextRow(true, true);
EXPECT_EQ(boost::get<std::string>(TestHelpers::v<NullableString>(model_row[0])),
model_name);
const auto model = g_ml_models.getModel(model_name);
const auto pca_model = std::dynamic_pointer_cast<PcaModel>(model);
const auto model_type = pca_model->getModelType();
EXPECT_EQ(model_type, MLModelType::PCA);
const auto num_features = pca_model->getNumFeatures();
constexpr int64_t expected_num_features = 4;
EXPECT_EQ(num_features, expected_num_features);
const auto& eigenvalues = pca_model->getEigenvalues();
const auto& eigenvectors = pca_model->getEigenvectors();
const std::vector<double> expected_eigenvalues{
2.91082, 0.921221, 0.147353, 0.0206075};
const std::vector<std::vector<double>> expected_eigenvectors{
{0.581254, 0.565611, 0.522371, -0.263355},
{0.0210948, 0.065416, 0.372318, 0.925557},
{-0.140893, -0.633801, 0.721017, -0.242033},
{0.801154, -0.523547, -0.261995, 0.124135}};
EXPECT_EQ(eigenvalues.size(), size_t(expected_num_features));
for (int64_t feature_idx = 0; feature_idx < expected_num_features;
++feature_idx) {
EXPECT_NEAR(eigenvalues[feature_idx], expected_eigenvalues[feature_idx], 1e-2);
EXPECT_EQ(eigenvectors[feature_idx].size(), size_t(expected_num_features));
for (int64_t ev_idx = 0; ev_idx < expected_num_features; ++ev_idx) {
EXPECT_NEAR(eigenvectors[feature_idx][ev_idx],
expected_eigenvectors[feature_idx][ev_idx],
1e-2);
}
}
const std::string pca_project_substr(
"PCA_PROJECT(" + quoted_model_name +
", petal_length_cm, petal_width_cm, sepal_length_cm, sepal_width_cm");
const std::string pca_project_query(
"SELECT petal_length_cm, petal_width_cm, sepal_length_cm, sepal_width_cm, " +
pca_project_substr + ", 1) AS pca_1, " + pca_project_substr +
", 2) AS pca_2, " + pca_project_substr + ", 3) AS pca_3, " +
pca_project_substr + ", 4) AS pca_4 FROM ml_iris;");
const auto pca_project_rows = run_multiple_agg(pca_project_query, dt);
EXPECT_EQ(pca_project_rows->colCount(), size_t(8));
const auto num_rows = pca_project_rows->rowCount();
EXPECT_EQ(num_rows,
static_cast<size_t>(TestHelpers::v<int64_t>(
run_simple_agg("SELECT COUNT(*) FROM ml_iris", dt))));
const auto& col_means = pca_model->getColumnMeans();
const auto& col_std_devs = pca_model->getColumnStdDevs();
for (size_t row_idx = 0; row_idx < num_rows; ++row_idx) {
auto crt_row = pca_project_rows->getNextRow(true, true);
std::vector<double> scaled_input_features;
for (size_t input_feature_idx = 0; input_feature_idx < 4; ++input_feature_idx) {
scaled_input_features.emplace_back(
(TestHelpers::v<float>(crt_row[input_feature_idx]) -
col_means[input_feature_idx]) /
col_std_devs[input_feature_idx]);
}
for (size_t pca_component_idx = 0; pca_component_idx < 4; ++pca_component_idx) {
double expected_pca_project = 0.0;
for (size_t input_feature_idx = 0; input_feature_idx < 4;
++input_feature_idx) {
expected_pca_project += scaled_input_features[input_feature_idx] *
eigenvectors[pca_component_idx][input_feature_idx];
}
// Offset by 4 as pca features are stored after actual features
EXPECT_NEAR(TestHelpers::v<double>(crt_row[pca_component_idx + 4]),
expected_pca_project,
1e-2);
}
}
}
}
}
}
TEST_P(MLRegressionFunctionsTest, REG_MODEL_FIT_NO_ROWS) {
const auto model_type = GetParam();
const auto model_type_str = get_ml_model_type_str(model_type);
const std::string model_name{model_type_str + "_MODEL"};
const std::string quoted_model_name{"'" + model_type_str + "_MODEL'"};
const std::string model_fit_func{model_type_str + "_FIT"};
const std::string data_table_name{"ml_iris"};
const std::string id_col{""}; // No id column allowed for Fit calls
const std::string label_column{"petal_length_cm"};
const auto supported_ml_frameworks = get_supported_ml_frameworks();
const bool make_args_named{true};
for (auto& ml_framework : supported_ml_frameworks) {
if (model_type != MLModelType::LINEAR_REG && ml_framework == "'mlpack'") {
continue;
}
for (bool use_create_syntax : {false, true}) {
for (auto dt : {ExecutorDeviceType::CPU, ExecutorDeviceType::GPU}) {
SKIP_NO_GPU();
for (std::string numeric_data_type : {"DOUBLE"}) {
const auto cursor_query = generate_cursor_query(
data_table_name,
id_col,
{"petal_length_cm", "petal_width_cm", "sepal_length_cm", "sepal_width_cm"},
"SAMPLE_RATIO(0.0)",
numeric_data_type,
!use_create_syntax);
const auto fit_query =
use_create_syntax
? generate_create_model_query(
model_type_str,
model_name,
cursor_query,
{{"preferred_ml_framework", ml_framework}},
true)
: generate_query(model_fit_func,
{{"model_name", quoted_model_name},
{"data", cursor_query},
{"preferred_ml_framework", ml_framework}},
{"model_name"},
make_args_named);
if (!use_create_syntax) {
EXPECT_ANY_THROW(run_multiple_agg(fit_query, dt));
} else {
EXPECT_ANY_THROW(run_ddl_statement(fit_query));
}
}
}
}
}
}
TEST_P(MLRegressionFunctionsTest, REG_MODEL_FIT) {
const auto model_type = GetParam();
const auto model_type_str = get_ml_model_type_str(model_type);
const std::string model_name{model_type_str + "_MODEL"};
const std::string quoted_model_name{"'" + model_type_str + "_MODEL'"};
const std::string model_fit_func{model_type_str + "_FIT"};
const std::string data_table_name{"ml_iris"};
const std::string id_col{""}; // No id column allowed for Fit calls
const std::string label_column{"petal_length_cm"};
const auto supported_ml_frameworks = get_supported_ml_frameworks();
const bool make_args_named{true};
for (auto& ml_framework : supported_ml_frameworks) {
if (model_type != MLModelType::LINEAR_REG && ml_framework == "'mlpack'") {
continue;
}
for (bool use_create_syntax : {false, true}) {
for (auto dt : {ExecutorDeviceType::CPU, ExecutorDeviceType::GPU}) {
SKIP_NO_GPU();
for (std::string numeric_data_type : {"DOUBLE"}) {
// Synthetic data, also test nulls
{
const std::string query =
"SELECT CAST(CASE WHEN MOD(generate_series, 10) = 0 THEN NULL "
"ELSE generate_series * 0.001 * 2.0 + 10.0 END AS " +
numeric_data_type +
") AS var0, "
"CAST(generate_series * 0.001 AS " +
numeric_data_type + ") AS var1 FROM TABLE(generate_series(0, 99999))";
const std::string cursor_query = "CURSOR(" + query + ")";
const auto fit_query =
use_create_syntax
? generate_create_model_query(
model_type_str,
model_name,
query,
{{"preferred_ml_framework", ml_framework}},
true)
: generate_query(model_fit_func,
{{"model_name", quoted_model_name},
{"data", cursor_query},
{"preferred_ml_framework", ml_framework}},
{"model_name"},
make_args_named);
if (!use_create_syntax) {
const auto fit_rows = run_multiple_agg(fit_query, dt);
EXPECT_EQ(fit_rows->rowCount(), 1UL);
EXPECT_EQ(fit_rows->colCount(), 1UL);
auto model_row = fit_rows->getNextRow(true, true);
EXPECT_EQ(
boost::get<std::string>(TestHelpers::v<NullableString>(model_row[0])),
model_name);
} else {
run_ddl_statement(fit_query);
}
if (model_type == MLModelType::LINEAR_REG) {
const auto coef_query = generate_query("LINEAR_REG_COEFS",
{{"model_name", quoted_model_name}},
{"coef_idx, sub_coef_idx"},
make_args_named);
const auto coef_rows = run_multiple_agg(coef_query, dt);
constexpr double allowed_epsilon{0.1};
const std::vector<double> expected_coefs = {10.0, 2.0};
const int64_t num_rows = coef_rows->rowCount();
EXPECT_EQ(num_rows, 2L);
EXPECT_EQ(coef_rows->colCount(), size_t(5));
for (int64_t row_idx = 0; row_idx < num_rows; ++row_idx) {
auto crt_row = coef_rows->getNextRow(true, true);
EXPECT_EQ(TestHelpers::v<int64_t>(crt_row[0]), row_idx);
if (use_create_syntax) {
const auto feature =
boost::get<std::string>(TestHelpers::v<NullableString>(crt_row[1]));
const std::string expected_feature =
row_idx == 0 ? "intercept" : "var" + std::to_string(row_idx);
EXPECT_EQ(feature, expected_feature);
}
EXPECT_EQ(TestHelpers::v<int64_t>(crt_row[2]), 1L);
const double actual_coef = TestHelpers::v<double>(crt_row[4]);
EXPECT_GE(actual_coef, expected_coefs[row_idx] - allowed_epsilon);
EXPECT_LE(actual_coef, expected_coefs[row_idx] + allowed_epsilon);
}
} else if (model_type == MLModelType::RANDOM_FOREST_REG) {
const auto var_importance_query =
generate_query("RANDOM_FOREST_REG_VAR_IMPORTANCE",
{{"model_name", quoted_model_name}},
{"feature_id, sub_feature_id"},
make_args_named);
const auto var_importance_rows = run_multiple_agg(var_importance_query, dt);
constexpr double allowed_epsilon_frac{0.02};
const std::vector<double> expected_var_importances = {4996.12};
const int64_t num_rows = var_importance_rows->rowCount();
EXPECT_EQ(num_rows, 1L);
EXPECT_EQ(var_importance_rows->colCount(), size_t(5));
for (int64_t row_idx = 0; row_idx < num_rows; ++row_idx) {
auto crt_row = var_importance_rows->getNextRow(true, true);
// Feature id