File size: 19,822 Bytes
9375c9a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 |
// Copyright (C) 2017 Davis E. King ([email protected])
// License: Boost Software License See LICENSE.txt for the full license.
#include "opaque_types.h"
#include <dlib/python.h>
#include <dlib/global_optimization.h>
#include <dlib/matrix.h>
#include <pybind11/stl.h>
using namespace dlib;
using namespace std;
namespace py = pybind11;
// ----------------------------------------------------------------------------------------
std::vector<bool> list_to_bool_vector(
const py::list& l
)
{
std::vector<bool> result(len(l));
for (long i = 0; i < result.size(); ++i)
{
result[i] = l[i].cast<bool>();
}
return result;
}
matrix<double,0,1> list_to_mat(
const py::list& l
)
{
matrix<double,0,1> result(len(l));
for (long i = 0; i < result.size(); ++i)
result(i) = l[i].cast<double>();
return result;
}
py::list mat_to_list (
const matrix<double,0,1>& m
)
{
py::list l;
for (long i = 0; i < m.size(); ++i)
l.append(m(i));
return l;
}
size_t num_function_arguments(py::object f, size_t expected_num)
{
const auto code_object = f.attr(hasattr(f,"func_code") ? "func_code" : "__code__");
const auto num = code_object.attr("co_argcount").cast<std::size_t>();
if (num < expected_num && (code_object.attr("co_flags").cast<int>() & CO_VARARGS))
return expected_num;
return num;
}
double call_func(py::object f, const matrix<double,0,1>& args)
{
const auto num = num_function_arguments(f, args.size());
DLIB_CASSERT(num == args.size(),
"The function being optimized takes a number of arguments that doesn't agree with the size of the bounds lists you provided to find_max_global()");
DLIB_CASSERT(0 < num && num < 35, "Functions being optimized must take between 1 and 35 scalar arguments.");
#define CALL_WITH_N_ARGS(N) case N: return dlib::gopt_impl::_cwv(f,args,typename make_compile_time_integer_range<N>::type()).cast<double>();
switch (num)
{
CALL_WITH_N_ARGS(1)
CALL_WITH_N_ARGS(2)
CALL_WITH_N_ARGS(3)
CALL_WITH_N_ARGS(4)
CALL_WITH_N_ARGS(5)
CALL_WITH_N_ARGS(6)
CALL_WITH_N_ARGS(7)
CALL_WITH_N_ARGS(8)
CALL_WITH_N_ARGS(9)
CALL_WITH_N_ARGS(10)
CALL_WITH_N_ARGS(11)
CALL_WITH_N_ARGS(12)
CALL_WITH_N_ARGS(13)
CALL_WITH_N_ARGS(14)
CALL_WITH_N_ARGS(15)
CALL_WITH_N_ARGS(16)
CALL_WITH_N_ARGS(17)
CALL_WITH_N_ARGS(18)
CALL_WITH_N_ARGS(19)
CALL_WITH_N_ARGS(20)
CALL_WITH_N_ARGS(21)
CALL_WITH_N_ARGS(22)
CALL_WITH_N_ARGS(23)
CALL_WITH_N_ARGS(24)
CALL_WITH_N_ARGS(25)
CALL_WITH_N_ARGS(26)
CALL_WITH_N_ARGS(27)
CALL_WITH_N_ARGS(28)
CALL_WITH_N_ARGS(29)
CALL_WITH_N_ARGS(30)
CALL_WITH_N_ARGS(31)
CALL_WITH_N_ARGS(32)
CALL_WITH_N_ARGS(33)
CALL_WITH_N_ARGS(34)
CALL_WITH_N_ARGS(35)
default:
DLIB_CASSERT(false, "oops");
break;
}
}
// ----------------------------------------------------------------------------------------
py::tuple py_find_max_global (
py::object f,
py::list bound1,
py::list bound2,
py::list is_integer_variable,
unsigned long num_function_calls,
double solver_epsilon = 0
)
{
DLIB_CASSERT(len(bound1) == len(bound2));
DLIB_CASSERT(len(bound1) == len(is_integer_variable));
auto func = [&](const matrix<double,0,1>& x)
{
return call_func(f, x);
};
auto result = find_max_global(func, list_to_mat(bound1), list_to_mat(bound2),
list_to_bool_vector(is_integer_variable), max_function_calls(num_function_calls),
solver_epsilon);
return py::make_tuple(mat_to_list(result.x),result.y);
}
py::tuple py_find_max_global2 (
py::object f,
py::list bound1,
py::list bound2,
unsigned long num_function_calls,
double solver_epsilon = 0
)
{
DLIB_CASSERT(len(bound1) == len(bound2));
auto func = [&](const matrix<double,0,1>& x)
{
return call_func(f, x);
};
auto result = find_max_global(func, list_to_mat(bound1), list_to_mat(bound2), max_function_calls(num_function_calls), solver_epsilon);
return py::make_tuple(mat_to_list(result.x),result.y);
}
// ----------------------------------------------------------------------------------------
py::tuple py_find_min_global (
py::object f,
py::list bound1,
py::list bound2,
py::list is_integer_variable,
unsigned long num_function_calls,
double solver_epsilon = 0
)
{
DLIB_CASSERT(len(bound1) == len(bound2));
DLIB_CASSERT(len(bound1) == len(is_integer_variable));
auto func = [&](const matrix<double,0,1>& x)
{
return call_func(f, x);
};
auto result = find_min_global(func, list_to_mat(bound1), list_to_mat(bound2),
list_to_bool_vector(is_integer_variable), max_function_calls(num_function_calls),
solver_epsilon);
return py::make_tuple(mat_to_list(result.x),result.y);
}
py::tuple py_find_min_global2 (
py::object f,
py::list bound1,
py::list bound2,
unsigned long num_function_calls,
double solver_epsilon = 0
)
{
DLIB_CASSERT(len(bound1) == len(bound2));
auto func = [&](const matrix<double,0,1>& x)
{
return call_func(f, x);
};
auto result = find_min_global(func, list_to_mat(bound1), list_to_mat(bound2), max_function_calls(num_function_calls), solver_epsilon);
return py::make_tuple(mat_to_list(result.x),result.y);
}
// ----------------------------------------------------------------------------------------
function_spec py_function_spec1 (
py::list a,
py::list b
)
{
return function_spec(list_to_mat(a), list_to_mat(b));
}
function_spec py_function_spec2 (
py::list a,
py::list b,
py::list c
)
{
return function_spec(list_to_mat(a), list_to_mat(b), list_to_bool_vector(c));
}
std::shared_ptr<global_function_search> py_global_function_search1 (
py::list functions
)
{
std::vector<function_spec> tmp;
for (const auto& i : functions)
tmp.emplace_back(i.cast<function_spec>());
return std::make_shared<global_function_search>(tmp);
}
std::shared_ptr<global_function_search> py_global_function_search2 (
py::list functions,
py::list initial_function_evals,
double relative_noise_magnitude
)
{
std::vector<function_spec> specs;
for (const auto& i : functions)
specs.emplace_back(i.cast<function_spec>());
std::vector<std::vector<function_evaluation>> func_evals;
for (const auto& i : initial_function_evals)
{
std::vector<function_evaluation> evals;
for (const auto& j : i)
{
evals.emplace_back(j.cast<function_evaluation>());
}
func_evals.emplace_back(std::move(evals));
}
return std::make_shared<global_function_search>(specs, func_evals, relative_noise_magnitude);
}
function_evaluation py_function_evaluation(
const py::list& x,
double y
)
{
return function_evaluation(list_to_mat(x), y);
}
// ----------------------------------------------------------------------------------------
void bind_global_optimization(py::module& m)
{
const char* docstring =
"requires \n\
- len(bound1) == len(bound2) == len(is_integer_variable) \n\
- for all valid i: bound1[i] != bound2[i] \n\
- solver_epsilon >= 0 \n\
- f() is a real valued multi-variate function. It must take scalar real \n\
numbers as its arguments and the number of arguments must be len(bound1). \n\
ensures \n\
- This function performs global optimization on the given f() function. \n\
The goal is to maximize the following objective function: \n\
f(x) \n\
subject to the constraints: \n\
min(bound1[i],bound2[i]) <= x[i] <= max(bound1[i],bound2[i]) \n\
if (is_integer_variable[i]) then x[i] is an integer value (but still \n\
represented with float type). \n\
- find_max_global() runs until it has called f() num_function_calls times. \n\
Then it returns the best x it has found along with the corresponding output \n\
of f(). That is, it returns (best_x_seen,f(best_x_seen)). Here best_x_seen \n\
is a list containing the best arguments to f() this function has found. \n\
- find_max_global() uses a global optimization method based on a combination of \n\
non-parametric global function modeling and quadratic trust region modeling \n\
to efficiently find a global maximizer. It usually does a good job with a \n\
relatively small number of calls to f(). For more information on how it \n\
works read the documentation for dlib's global_function_search object. \n\
However, one notable element is the solver epsilon, which you can adjust. \n\
\n\
The search procedure will only attempt to find a global maximizer to at most \n\
solver_epsilon accuracy. Once a local maximizer is found to that accuracy \n\
the search will focus entirely on finding other maxima elsewhere rather than \n\
on further improving the current local optima found so far. That is, once a \n\
local maxima is identified to about solver_epsilon accuracy, the algorithm \n\
will spend all its time exploring the function to find other local maxima to \n\
investigate. An epsilon of 0 means it will keep solving until it reaches \n\
full floating point precision. Larger values will cause it to switch to pure \n\
global exploration sooner and therefore might be more effective if your \n\
objective function has many local maxima and you don't care about a super \n\
high precision solution. \n\
- Any variables that satisfy the following conditions are optimized on a log-scale: \n\
- The lower bound on the variable is > 0 \n\
- The ratio of the upper bound to lower bound is > 1000 \n\
- The variable is not an integer variable \n\
We do this because it's common to optimize machine learning models that have \n\
parameters with bounds in a range such as [1e-5 to 1e10] (e.g. the SVM C \n\
parameter) and it's much more appropriate to optimize these kinds of \n\
variables on a log scale. So we transform them by applying log() to \n\
them and then undo the transform via exp() before invoking the function \n\
being optimized. Therefore, this transformation is invisible to the user \n\
supplied functions. In most cases, it improves the efficiency of the \n\
optimizer.";
/*!
requires
- len(bound1) == len(bound2) == len(is_integer_variable)
- for all valid i: bound1[i] != bound2[i]
- solver_epsilon >= 0
- f() is a real valued multi-variate function. It must take scalar real
numbers as its arguments and the number of arguments must be len(bound1).
ensures
- This function performs global optimization on the given f() function.
The goal is to maximize the following objective function:
f(x)
subject to the constraints:
min(bound1[i],bound2[i]) <= x[i] <= max(bound1[i],bound2[i])
if (is_integer_variable[i]) then x[i] is an integer value (but still
represented with float type).
- find_max_global() runs until it has called f() num_function_calls times.
Then it returns the best x it has found along with the corresponding output
of f(). That is, it returns (best_x_seen,f(best_x_seen)). Here best_x_seen
is a list containing the best arguments to f() this function has found.
- find_max_global() uses a global optimization method based on a combination of
non-parametric global function modeling and quadratic trust region modeling
to efficiently find a global maximizer. It usually does a good job with a
relatively small number of calls to f(). For more information on how it
works read the documentation for dlib's global_function_search object.
However, one notable element is the solver epsilon, which you can adjust.
The search procedure will only attempt to find a global maximizer to at most
solver_epsilon accuracy. Once a local maximizer is found to that accuracy
the search will focus entirely on finding other maxima elsewhere rather than
on further improving the current local optima found so far. That is, once a
local maxima is identified to about solver_epsilon accuracy, the algorithm
will spend all its time exploring the function to find other local maxima to
investigate. An epsilon of 0 means it will keep solving until it reaches
full floating point precision. Larger values will cause it to switch to pure
global exploration sooner and therefore might be more effective if your
objective function has many local maxima and you don't care about a super
high precision solution.
- Any variables that satisfy the following conditions are optimized on a log-scale:
- The lower bound on the variable is > 0
- The ratio of the upper bound to lower bound is > 1000
- The variable is not an integer variable
We do this because it's common to optimize machine learning models that have
parameters with bounds in a range such as [1e-5 to 1e10] (e.g. the SVM C
parameter) and it's much more appropriate to optimize these kinds of
variables on a log scale. So we transform them by applying log() to
them and then undo the transform via exp() before invoking the function
being optimized. Therefore, this transformation is invisible to the user
supplied functions. In most cases, it improves the efficiency of the
optimizer.
!*/
m.def("find_max_global", &py_find_max_global, docstring, py::arg("f"),
py::arg("bound1"), py::arg("bound2"), py::arg("is_integer_variable"),
py::arg("num_function_calls"), py::arg("solver_epsilon")=0);
m.def("find_max_global", &py_find_max_global2,
"This function simply calls the other version of find_max_global() with is_integer_variable set to False for all variables.",
py::arg("f"), py::arg("bound1"), py::arg("bound2"), py::arg("num_function_calls"),
py::arg("solver_epsilon")=0);
m.def("find_min_global", &py_find_min_global,
"This function is just like find_max_global(), except it performs minimization rather than maximization.",
py::arg("f"), py::arg("bound1"), py::arg("bound2"), py::arg("is_integer_variable"),
py::arg("num_function_calls"), py::arg("solver_epsilon")=0);
m.def("find_min_global", &py_find_min_global2,
"This function simply calls the other version of find_min_global() with is_integer_variable set to False for all variables.",
py::arg("f"), py::arg("bound1"), py::arg("bound2"), py::arg("num_function_calls"),
py::arg("solver_epsilon")=0);
// -------------------------------------------------
// -------------------------------------------------
py::class_<function_evaluation> (m, "function_evaluation", R"RAW(
This object records the output of a real valued function in response to
some input.
In particular, if you have a function F(x) then the function_evaluation is
simply a struct that records x and the scalar value F(x). )RAW")
.def(py::init<matrix<double,0,1>,double>(), py::arg("x"), py::arg("y"))
.def(py::init<>(&py_function_evaluation), py::arg("x"), py::arg("y"))
.def_readonly("x", &function_evaluation::x)
.def_readonly("y", &function_evaluation::y);
py::class_<function_spec> (m, "function_spec", "See: http://dlib.net/dlib/global_optimization/global_function_search_abstract.h.html")
.def(py::init<matrix<double,0,1>,matrix<double,0,1>>(), py::arg("bound1"), py::arg("bound2") )
.def(py::init<matrix<double,0,1>,matrix<double,0,1>,std::vector<bool>>(), py::arg("bound1"), py::arg("bound2"), py::arg("is_integer") )
.def(py::init<>(&py_function_spec1), py::arg("bound1"), py::arg("bound2"))
.def(py::init<>(&py_function_spec2), py::arg("bound1"), py::arg("bound2"), py::arg("is_integer"))
.def_readonly("lower", &function_spec::lower)
.def_readonly("upper", &function_spec::upper)
.def_readonly("is_integer_variable", &function_spec::is_integer_variable);
py::class_<function_evaluation_request> (m, "function_evaluation_request", "See: http://dlib.net/dlib/global_optimization/global_function_search_abstract.h.html")
.def_property_readonly("function_idx", &function_evaluation_request::function_idx)
.def_property_readonly("x", &function_evaluation_request::x)
.def_property_readonly("has_been_evaluated", &function_evaluation_request::has_been_evaluated)
.def("set", &function_evaluation_request::set);
py::class_<global_function_search, std::shared_ptr<global_function_search>> (m, "global_function_search", "See: http://dlib.net/dlib/global_optimization/global_function_search_abstract.h.html")
.def(py::init<function_spec>(), py::arg("function"))
.def(py::init<>(&py_global_function_search1), py::arg("functions"))
.def(py::init<>(&py_global_function_search2), py::arg("functions"), py::arg("initial_function_evals"), py::arg("relative_noise_magnitude"))
.def("set_seed", &global_function_search::set_seed, py::arg("seed"))
.def("num_functions", &global_function_search::num_functions)
.def("get_function_evaluations", [](const global_function_search& self) {
std::vector<function_spec> specs;
std::vector<std::vector<function_evaluation>> function_evals;
self.get_function_evaluations(specs,function_evals);
py::list py_specs, py_func_evals;
for (const auto& s : specs)
py_specs.append(s);
for (const auto& i : function_evals)
{
py::list tmp;
for (const auto& j : i)
tmp.append(j);
py_func_evals.append(tmp);
}
return py::make_tuple(py_specs,py_func_evals);})
.def("get_best_function_eval", [](const global_function_search& self) {
matrix<double,0,1> x; double y; size_t idx; self.get_best_function_eval(x,y,idx); return py::make_tuple(x,y,idx);})
.def("get_next_x", &global_function_search::get_next_x)
.def("get_pure_random_search_probability", &global_function_search::get_pure_random_search_probability)
.def("set_pure_random_search_probability", &global_function_search::set_pure_random_search_probability, py::arg("prob"))
.def("get_solver_epsilon", &global_function_search::get_solver_epsilon)
.def("set_solver_epsilon", &global_function_search::set_solver_epsilon, py::arg("eps"))
.def("get_relative_noise_magnitude", &global_function_search::get_relative_noise_magnitude)
.def("set_relative_noise_magnitude", &global_function_search::set_relative_noise_magnitude, py::arg("value"))
.def("get_monte_carlo_upper_bound_sample_num", &global_function_search::get_monte_carlo_upper_bound_sample_num)
.def("set_monte_carlo_upper_bound_sample_num", &global_function_search::set_monte_carlo_upper_bound_sample_num, py::arg("num"))
;
}
|