Simulation Functions
Below are example simulation functions available in libEnsemble. Most of these demonstrate an inexpensive algorithm and do not launch tasks (user applications). To see an example of a simulation function launching tasks, see the Electrostatic Forces tutorial.
Important
See the API for simulation functions here.
six_hump_camel
This module contains various versions that evaluate the six-hump camel function.
- Six-hump camel function is documented here:
- six_hump_camel.six_hump_camel(H, persis_info, sim_specs, libE_info)
Evaluates the six hump camel function for a collection of points given in
H["x"]
. Additionally evaluates the gradient if"grad"
is a field insim_specs["out"]
and pauses forsim_specs["user"]["pause_time"]]
if defined.See also
- six_hump_camel.six_hump_camel_simple(x, _, sim_specs)
Evaluates the six hump camel function for a single point
x
.See also
test_fast_alloc.py # noqa
- six_hump_camel.persistent_six_hump_camel(H, persis_info, sim_specs, libE_info)
Similar to
six_hump_camel
, but runs in persistent mode.
six_hump_camel.py
1"""
2This module contains various versions that evaluate the six-hump camel function.
3
4Six-hump camel function is documented here:
5 https://www.sfu.ca/~ssurjano/camel6.html
6
7"""
8__all__ = [
9 "six_hump_camel",
10 "six_hump_camel_simple",
11 "persistent_six_hump_camel",
12]
13
14import sys
15import time
16
17import numpy as np
18
19from libensemble.message_numbers import EVAL_SIM_TAG, FINISHED_PERSISTENT_SIM_TAG, PERSIS_STOP, STOP_TAG
20from libensemble.tools.persistent_support import PersistentSupport
21
22
23def six_hump_camel(H, persis_info, sim_specs, libE_info):
24 """
25 Evaluates the six hump camel function for a collection of points given in ``H["x"]``.
26 Additionally evaluates the gradient if ``"grad"`` is a field in
27 ``sim_specs["out"]`` and pauses for ``sim_specs["user"]["pause_time"]]`` if
28 defined.
29
30 .. seealso::
31 `test_old_aposmm_with_gradients.py <https://github.com/Libensemble/libensemble/blob/develop/libensemble/tests/regression_tests/test_old_aposmm_with_gradients.py>`_ # noqa
32 """
33
34 batch = len(H["x"])
35 H_o = np.zeros(batch, dtype=sim_specs["out"])
36
37 for i, x in enumerate(H["x"]):
38 H_o["f"][i] = six_hump_camel_func(x)
39
40 if "grad" in H_o.dtype.names:
41 H_o["grad"][i] = six_hump_camel_grad(x)
42
43 if "user" in sim_specs and "pause_time" in sim_specs["user"]:
44 time.sleep(sim_specs["user"]["pause_time"])
45
46 return H_o, persis_info
47
48
49def six_hump_camel_simple(x, _, sim_specs):
50 """
51 Evaluates the six hump camel function for a single point ``x``.
52
53 .. seealso::
54 `test_fast_alloc.py <https://github.com/Libensemble/libensemble/blob/develop/libensemble/tests/regression_tests/test_fast_alloc.py>`_ # noqa
55 """
56
57 H_o = np.zeros(1, dtype=sim_specs["out"])
58
59 H_o["f"] = six_hump_camel_func(x[0][0][:2]) # Ignore more than 2 entries of x
60
61 if sim_specs["user"].get("pause_time"):
62 time.sleep(sim_specs["user"]["pause_time"])
63
64 if sim_specs["user"].get("rand"):
65 H_o["f"] += np.random.normal(0, 1)
66
67 return H_o
68
69
70def persistent_six_hump_camel(H, persis_info, sim_specs, libE_info):
71 """
72 Similar to ``six_hump_camel``, but runs in persistent mode.
73 """
74
75 ps = PersistentSupport(libE_info, EVAL_SIM_TAG)
76
77 # Either start with a work item to process - or just start and wait for data
78 if H.size > 0:
79 tag = None
80 Work = None
81 calc_in = H
82 else:
83 tag, Work, calc_in = ps.recv()
84
85 while tag not in [STOP_TAG, PERSIS_STOP]:
86 # calc_in: This should either be a function (unpack_work ?) or included/unpacked in ps.recv/ps.send_recv.
87 if Work is not None:
88 persis_info = Work.get("persis_info", persis_info)
89 libE_info = Work.get("libE_info", libE_info)
90
91 # Call standard six_hump_camel sim
92 H_o, persis_info = six_hump_camel(calc_in, persis_info, sim_specs, libE_info)
93
94 tag, Work, calc_in = ps.send_recv(H_o)
95
96 final_return = None
97
98 # Overwrite final point - for testing only
99 if sim_specs["user"].get("replace_final_fields", 0):
100 calc_in = np.ones(1, dtype=[("x", float, (2,))])
101 H_o, persis_info = six_hump_camel(calc_in, persis_info, sim_specs, libE_info)
102 final_return = H_o
103
104 return final_return, persis_info, FINISHED_PERSISTENT_SIM_TAG
105
106
107def six_hump_camel_func(x):
108 """
109 Definition of the six-hump camel
110 """
111 x1 = x[0]
112 x2 = x[1]
113 term1 = (4 - 2.1 * x1**2 + (x1**4) / 3) * x1**2
114 term2 = x1 * x2
115 term3 = (-4 + 4 * x2**2) * x2**2
116
117 return term1 + term2 + term3
118
119
120def six_hump_camel_grad(x):
121 """
122 Definition of the six-hump camel gradient
123 """
124
125 x1 = x[0]
126 x2 = x[1]
127 grad = np.zeros(2)
128
129 grad[0] = 2.0 * (x1**5 - 4.2 * x1**3 + 4.0 * x1 + 0.5 * x2)
130 grad[1] = x1 + 16 * x2**3 - 8 * x2
131
132 return grad
133
134
135if __name__ == "__main__":
136 x = (float(sys.argv[1]), float(sys.argv[2]))
137 result = six_hump_camel_func(x)
138 print(result)
chwirut
- chwirut1.chwirut_eval(H, _, sim_specs)
Evaluates the chwirut objective function at a given set of points in
H["x"]
. If"obj_component"
is a field insim_specs["out"]
, only that component of the objective will be evaluated. Otherwise, all 214 components are evaluated and returned in the"fvec"
field.See also
test_old_aposmm_pounders.py for an example where the entire fvec is computed each call.
See also
test_old_aposmm_one_residual_at_a_time.py for an example where one component of fvec is computed per call
noisy_vector_mapping
This module contains a test noisy function
- noisy_vector_mapping.func_wrapper(H, persis_info, sim_specs, libE_info)
Wraps an objective function
See also
test_persistent_fd_param_finder.py <https://github.com/Libensemble/libensemble/blob/develop/libensemble/tests/regression_tests/test_persistent_fd_param_finder.py>`_ # noqa
- noisy_vector_mapping.noisy_function(x)
noisy_vector_mapping.py
1"""
2This module contains a test noisy function
3"""
4
5import numpy as np
6from numpy import cos, sin
7from numpy.linalg import norm
8
9
10def func_wrapper(H, persis_info, sim_specs, libE_info):
11 """
12 Wraps an objective function
13
14 .. seealso::
15 `test_persistent_fd_param_finder.py` <https://github.com/Libensemble/libensemble/blob/develop/libensemble/tests/regression_tests/test_persistent_fd_param_finder.py>`_ # noqa
16 """
17
18 batch = len(H["x"])
19 H0 = np.zeros(batch, dtype=sim_specs["out"])
20
21 for i, x in enumerate(H["x"]):
22 H0["f_val"][i] = noisy_function(x)[H["f_ind"][i]]
23
24 return H0, persis_info
25
26
27def noisy_function(x):
28 """ """
29 x1 = x[0]
30 x2 = x[1]
31 term1 = (4 - 2.1 * x1**2 + (x1**4) / 3) * x1**2
32 term2 = x1 * x2
33 term3 = (-4 + 4 * x2**2) * x2**2
34
35 phi1 = 0.9 * sin(100 * norm(x, 1)) * cos(100 * norm(x, np.inf)) + 0.1 * cos(norm(x, 2))
36 phi1 = phi1 * (4 * phi1**2 - 3)
37
38 phi2 = 0.8 * sin(100 * norm(x, 1)) * cos(100 * norm(x, np.inf)) + 0.2 * cos(norm(x, 2))
39 phi2 = phi2 * (4 * phi2**2 - 3)
40
41 phi3 = 0.7 * sin(100 * norm(x, 1)) * cos(100 * norm(x, np.inf)) + 0.3 * cos(norm(x, 2))
42 phi3 = phi3 * (4 * phi3**2 - 3)
43
44 F = np.zeros(3)
45 F[0] = (1 + 1e-1 * phi1) * term1
46 F[1] = (1 + 1e-2 * phi2) * term2
47 F[2] = (1 + 1e-3 * phi3) * term3
48
49 return F
periodic_func
This module contains a periodic test function
- periodic_func.func_wrapper(H, persis_info, sim_specs, libE_info)
Wraps an objective function
- periodic_func.periodic_func(x)
This function is periodic
borehole
- borehole.borehole(H, persis_info, sim_specs, _)
Wraps the borehole function
- borehole.borehole_func(x)
This evaluates the Borehole function for n-by-8 input matrix x, and returns the flow rate through the Borehole. (Harper and Gupta, 1983) input:
- Parameters:
x (numpy.typing.NDArray) –
x[:,0]: Tu, transmissivity of upper aquifer (m^2/year) x[:,1]: Tl, transmissivity of lower aquifer (m^2/year) x[:,2]: Hu, potentiometric head of upper aquifer (m) x[:,3]: Hl, potentiometric head of lower aquifer (m) x[:,4]: r, radius of influence (m) x[:,5]: rw, radius of borehole (m) x[:,6]: Kw, hydraulic conductivity of borehole (m/year) x[:,7]: L, length of borehole (m)
- Returns:
f – vector of dimension (n, 1): flow rate through the Borehole (m^3/year)
- Return type:
numpy.ndarray
- borehole.gen_borehole_input(n)
Generates and returns n inputs for the Borehole function, according to distributions outlined in Harper and Gupta (1983).
- input:
n: number of input to generate
- output:
matrix of (n, 8), input to borehole_func(x) function
executor_hworld
- executor_hworld.executor_hworld(H, _, sim_specs, info)
Tests launching and polling task and exiting on task finish
executor_hworld.py
1import numpy as np
2
3from libensemble.message_numbers import (
4 MAN_SIGNAL_FINISH,
5 TASK_FAILED,
6 UNSET_TAG,
7 WORKER_DONE,
8 WORKER_KILL_ON_ERR,
9 WORKER_KILL_ON_TIMEOUT,
10)
11
12__all__ = ["executor_hworld"]
13
14# Alt send values through X
15sim_ended_count = 0
16
17
18def custom_polling_loop(exctr, task, timeout_sec=5.0, delay=0.3):
19 import time
20
21 calc_status = UNSET_TAG # Sim func determines status of libensemble calc - returned to worker
22
23 while task.runtime < timeout_sec:
24 time.sleep(delay)
25
26 if exctr.manager_kill_received():
27 exctr.kill(task)
28 calc_status = MAN_SIGNAL_FINISH # Worker will pick this up and close down
29 print(f"Task {task.id} killed by manager on worker {exctr.workerID}")
30 break
31
32 task.poll()
33 if task.finished:
34 break
35 elif task.state == "RUNNING":
36 print(f"Task {task.id} still running on worker {exctr.workerID} ....")
37
38 if task.stdout_exists():
39 if "Error" in task.read_stdout():
40 print(
41 "Found (deliberate) Error in output file - cancelling " f"task {task.id} on worker {exctr.workerID}"
42 )
43 exctr.kill(task)
44 calc_status = WORKER_KILL_ON_ERR
45 break
46
47 # After exiting loop
48 if task.finished:
49 print(f"Task {task.id} done on worker {exctr.workerID}")
50 # Fill in calc_status if not already
51 if calc_status == UNSET_TAG:
52 if task.state == "FINISHED": # Means finished successfully
53 calc_status = WORKER_DONE
54 elif task.state == "FAILED":
55 calc_status = TASK_FAILED
56
57 else:
58 # assert task.state == 'RUNNING', "task.state expected to be RUNNING. Returned: " + str(task.state)
59 print(f"Task {task.id} timed out - killing on worker {exctr.workerID}")
60 exctr.kill(task)
61 if task.finished:
62 print(f"Task {task.id} done on worker {exctr.workerID}")
63 calc_status = WORKER_KILL_ON_TIMEOUT
64
65 return task, calc_status
66
67
68def executor_hworld(H, _, sim_specs, info):
69 """Tests launching and polling task and exiting on task finish"""
70 exctr = info["executor"]
71 cores = sim_specs["user"]["cores"]
72 ELAPSED_TIMEOUT = "elapsed_timeout" in sim_specs["user"]
73
74 wait = False
75 args_for_sim = "sleep 1"
76 calc_status = UNSET_TAG
77
78 batch = len(H["x"])
79 H_o = np.zeros(batch, dtype=sim_specs["out"])
80
81 if "six_hump_camel" not in exctr.default_app("sim").full_path:
82 global sim_ended_count
83 sim_ended_count += 1
84 print("sim_ended_count", sim_ended_count, flush=True)
85
86 if ELAPSED_TIMEOUT:
87 args_for_sim = "sleep 60" # Manager kill - if signal received else completes
88 timeout = 65.0
89
90 else:
91 timeout = 6.0
92 launch_shc = False
93
94 if sim_ended_count == 1:
95 args_for_sim = "sleep 1" # Should finish
96 elif sim_ended_count == 2:
97 args_for_sim = "sleep 1 Error" # Worker kill on error
98 elif sim_ended_count == 3:
99 wait = True
100 args_for_sim = "sleep 1" # Should finish
101 launch_shc = True
102 elif sim_ended_count == 4:
103 args_for_sim = "sleep 8" # Worker kill on timeout
104 timeout = 1.0
105 elif sim_ended_count == 5:
106 args_for_sim = "sleep 2 Fail" # Manager kill - if signal received else completes
107
108 task = exctr.submit(calc_type="sim", num_procs=cores, app_args=args_for_sim, hyperthreads=True)
109
110 if wait:
111 task.wait()
112 if not task.finished:
113 calc_status = UNSET_TAG
114 if task.state == "FINISHED":
115 calc_status = WORKER_DONE
116 elif task.state == "FAILED":
117 calc_status = TASK_FAILED
118
119 else:
120 if sim_ended_count >= 2:
121 calc_status = exctr.polling_loop(task, timeout=timeout, delay=0.3, poll_manager=True)
122 if sim_ended_count == 2 and task.stdout_exists() and "Error" in task.read_stdout():
123 calc_status = WORKER_KILL_ON_ERR
124 else:
125 task, calc_status = custom_polling_loop(exctr, task, timeout)
126
127 else:
128 launch_shc = True
129 calc_status = UNSET_TAG
130
131 # Comparing six_hump_camel output, directly called vs. submitted as app
132 for i, x in enumerate(H["x"]):
133 H_o["f"][i] = six_hump_camel_func(x)
134 if launch_shc:
135 # Test launching a named app.
136 app_args = " ".join(str(val) for val in list(x[:]))
137 task = exctr.submit(app_name="six_hump_camel", num_procs=1, app_args=app_args)
138 task.wait()
139 output = np.float64(task.read_stdout())
140 assert np.isclose(H_o["f"][i], output)
141 calc_status = WORKER_DONE
142
143 # This is just for testing at calling script level - status of each task
144 H_o["cstat"] = calc_status
145
146 return H_o, calc_status
147
148
149def six_hump_camel_func(x):
150 """
151 Definition of the six-hump camel
152 """
153 x1 = x[0]
154 x2 = x[1]
155 term1 = (4 - 2.1 * x1**2 + (x1**4) / 3) * x1**2
156 term2 = x1 * x2
157 term3 = (-4 + 4 * x2**2) * x2**2
158
159 return term1 + term2 + term3