def test_PoolContent(self):
names = SolverPool.get_solver_names()
self.assertTrue("hyperopt" in names)
self.assertTrue("optunity" in names)
self.assertTrue("optuna" in names)
self.assertTrue("randomsearch" in names)
self.assertTrue("quasirandomsearch" in names)
self.assertTrue("gridsearch" in names)
def compute_deviation(solver_name, vfunc_id, iterations, N, fname):
project = HyppopyProject()
project.add_hyperparameter(name="axis_00", domain="uniform", data=[0, 1], dtype="float")
project.add_hyperparameter(name="axis_01", domain="uniform", data=[0, 1], dtype="float")
project.add_hyperparameter(name="axis_02", domain="uniform", data=[0, 1], dtype="float")
project.add_hyperparameter(name="axis_03", domain="uniform", data=[0, 1], dtype="float")
project.add_hyperparameter(name="axis_04", domain="uniform", data=[0, 1], dtype="float")
vfunc = VirtualFunction()
vfunc.load_default(vfunc_id)
minima = vfunc.minima()
def my_loss_function(data, params):
return vfunc(**params)
blackbox = BlackboxFunction(data=[], blackbox_func=my_loss_function)
results = {}
results["gt"] = []
for mini in minima:
results["gt"].append(np.median(mini[0]))
for iter in iterations:
results[iter] = {"minima": {}, "loss": None}
for i in range(vfunc.dims()):
results[iter]["minima"]["axis_0{}".format(i)] = []
project.add_settings(section="solver", name="max_iterations", value=iter)
project.add_settings(section="custom", name="use_solver", value=solver_name)
solver = SolverPool.get(project=project)
solver.blackbox = blackbox
axis_minima = []
best_losses = []
for i in range(vfunc.dims()):
axis_minima.append([])
for n in range(N):
print("\rSolver={} iteration={} round={}".format(solver, iter, n), end="")
solver.run(print_stats=False)
df, best = solver.get_results()
best_row = df['losses'].idxmin()
best_losses.append(df['losses'][best_row])
for i in range(vfunc.dims()):
tmp = df['axis_0{}'.format(i)][best_row]
axis_minima[i].append(tmp)
for i in range(vfunc.dims()):
results[iter]["minima"]["axis_0{}".format(i)] = [np.mean(axis_minima[i]), np.std(axis_minima[i])]
results[iter]["loss"] = [np.mean(best_losses), np.std(best_losses)]
file = open(fname, 'wb')
pickle.dump(results, file)
file.close()
def test_getGridsearchSolver(self):
config = {
"hyperparameter": {
"value 1": {
"domain": "uniform",
"data": [0, 20],
"type": int,
"frequency": 11
},
"value 2": {
"domain": "normal",
"data": [0, 20.0],
"type": float,
"frequency": 11
},
"value 3": {
"domain": "loguniform",
"data": [1, 10000],
"type": float,
"frequency": 11
},
"categorical": {
"domain": "categorical",
"data": ["a", "b"],
"type": str,
"frequency": 1
}
}
}
res_labels = ['value 1', 'value 2', 'value 3', 'categorical']
res_values = [[0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20],
[
0.0, 5.467452952462635, 8.663855974622837,
9.755510546899107, 9.973002039367397, 10.0,
10.026997960632603, 10.244489453100893,
11.336144025377163, 14.532547047537365, 20.0
],
[
1.0, 2.51188643150958, 6.309573444801933,
15.848931924611136, 39.810717055349734,
100.00000000000004, 251.18864315095806,
630.9573444801938, 1584.8931924611143,
3981.071705534977, 10000.00000000001
], ['a', 'b']]
project = HyppopyProject(config)
solver = SolverPool.get("gridsearch", project)
self.assertTrue(isinstance(solver, GridsearchSolver))
searchspace = solver.convert_searchspace(config["hyperparameter"])
for n in range(len(res_labels)):
self.assertEqual(res_labels[n], searchspace[0][n])
for i in range(3):
self.assertAlmostEqual(res_values[i], searchspace[1][i])
self.assertEqual(res_values[3], searchspace[1][3])
def test_projectNone(self):
solver = SolverPool.get("hyperopt")
solver = SolverPool.get("optunity")
solver = SolverPool.get("optuna")
solver = SolverPool.get("randomsearch")
solver = SolverPool.get("quasirandomsearch")
solver = SolverPool.get("gridsearch")
self.assertRaises(AssertionError, SolverPool.get, "foo")
def test_getOptunitySolver(self):
config = {
"hyperparameter": {
"axis_00": {
"domain": "uniform",
"data": [300, 800],
"type": float
},
"axis_01": {
"domain": "uniform",
"data": [-1, 1],
"type": float
},
"axis_02": {
"domain": "uniform",
"data": [0, 10],
"type": float
}
},
"max_iterations": 100
}
project = HyppopyProject(config)
solver = SolverPool.get("optunity", project)
self.assertTrue(isinstance(solver, OptunitySolver))
vfunc = FunctionSimulator()
vfunc.load_default()
solver.blackbox = vfunc
solver.run(print_stats=False)
df, best = solver.get_results()
self.assertTrue(300 <= best['axis_00'] <= 800)
self.assertTrue(-1 <= best['axis_01'] <= 1)
self.assertTrue(0 <= best['axis_02'] <= 10)
for status in df['status']:
self.assertTrue(status)
for loss in df['losses']:
self.assertTrue(isinstance(loss, float))
# as well as 2 dummy parameter (my_preproc_param, my_dataloader_input) for demonstration purposes.
blackbox = BlackboxFunction(blackbox_func=my_loss_function,
dataloader_func=my_dataloader_function,
preprocess_func=my_preprocess_function,
callback_func=my_callback_function,
my_preproc_param=1,
my_dataloader_input='could/be/a/path')
# Last step, is we use our SolverPool which automatically returns the correct solver.
# There are multiple ways to get the desired solver from the solver pool.
# 1. solver = SolverPool.get('hyperopt')
# solver.project = project
# 2. solver = SolverPool.get('hyperopt', project)
# 3. The SolverPool will look for the field 'use_solver' in the project instance, if
# it is present it will be used to specify the solver so that in this case it is enough
# to pass the project instance.
solver = SolverPool.get(project=project)
# Give the solver your blackbox and run it. After execution we can get the result
# via get_result() which returns a pandas dataframe containing the complete history
# The dict best contains the best parameter set.
solver.blackbox = blackbox
#solver.start_viewer()
solver.run()
df, best = solver.get_results()
print("\n")
print("*" * 100)
print("Best Parameter Set:\n{}".format(best))
print("*" * 100)
"type": float,
"frequency": 10
}
},
"max_iterations": 500,
"solver": "optunity"
}
project = HyppopyProject(config=config)
# The user defined loss function
def my_loss_function_params(params):
x = params['x']
y = params['y']
return x**2 + y**3
solver = MPISolverWrapper(solver=SolverPool.get(project=project))
solver.blackbox = my_loss_function_params
solver.run()
df, best = solver.get_results()
if solver.is_master() is True:
print("\n")
print("*" * 100)
print("Best Parameter Set:\n{}".format(best))
print("*" * 100)
def compute_deviation(solver_name, vfunc_id, iterations, N, fname):
project = HyppopyProject()
project.add_hyperparameter(name="axis_00",
domain="uniform",
data=[0, 1],
type=float)
project.add_hyperparameter(name="axis_01",
domain="uniform",
data=[0, 1],
type=float)
project.add_hyperparameter(name="axis_02",
domain="uniform",
data=[0, 1],
type=float)
project.add_hyperparameter(name="axis_03",
domain="uniform",
data=[0, 1],
type=float)
project.add_hyperparameter(name="axis_04",
domain="uniform",
data=[0, 1],
type=float)
vfunc = FunctionSimulator()
vfunc.load_default(vfunc_id)
minima = vfunc.minima()
def my_loss_function(data, params):
return vfunc(**params)
blackbox = BlackboxFunction(data=[], blackbox_func=my_loss_function)
results = {}
results["gt"] = []
for mini in minima:
results["gt"].append(np.median(mini[0]))
for iter in iterations:
results[iter] = {
"minima": {},
"distance": {},
"duration": None,
"set_difference": None,
"loss": None,
"loss_history": {}
}
for i in range(vfunc.dims()):
results[iter]["minima"]["axis_0{}".format(i)] = []
results[iter]["distance"]["axis_0{}".format(i)] = []
project.add_setting("max_iterations", iter)
project.add_setting("solver", solver_name)
solver = SolverPool.get(project=project)
solver.blackbox = blackbox
axis_minima = []
best_losses = []
best_sets_diff = []
for i in range(vfunc.dims()):
axis_minima.append([])
loss_history = []
durations = []
for n in range(N):
print("\rSolver={} iteration={} round={}".format(solver, iter, n),
end="")
start = time.time()
solver.run(print_stats=False)
end = time.time()
durations.append(end - start)
df, best = solver.get_results()
loss_history.append(np.flip(np.sort(df['losses'].values)))
best_row = df['losses'].idxmin()
best_losses.append(df['losses'][best_row])
best_sets_diff.append(
abs(df['axis_00'][best_row] - best['axis_00']) +
abs(df['axis_01'][best_row] - best['axis_01']) +
abs(df['axis_02'][best_row] - best['axis_02']) +
abs(df['axis_03'][best_row] - best['axis_03']) +
abs(df['axis_04'][best_row] - best['axis_04']))
for i in range(vfunc.dims()):
tmp = df['axis_0{}'.format(i)][best_row]
axis_minima[i].append(tmp)
results[iter]["loss_history"] = loss_history
for i in range(vfunc.dims()):
results[iter]["minima"]["axis_0{}".format(i)] = [
np.mean(axis_minima[i]),
np.std(axis_minima[i])
]
dist = np.sqrt((axis_minima[i] - results["gt"][i])**2)
results[iter]["distance"]["axis_0{}".format(i)] = [
np.mean(dist), np.std(dist)
]
results[iter]["loss"] = [np.mean(best_losses), np.std(best_losses)]
results[iter]["set_difference"] = sum(best_sets_diff)
results[iter]["duration"] = np.mean(durations)
file = open(fname, 'wb')
pickle.dump(results, file)
file.close()