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()
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")
project.add_settings(section="solver", name="max_iterations", value=500)
project.add_settings(section="custom", name="use_solver", value="randomsearch")
plt.ion()
fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(12, 8), sharey=True)
plot_data = {
"iterations": [],
"loss": [],
"axis_00": [],
"axis_01": [],
"axis_02": [],
"axis_03": [],
"axis_04": []
}
"kernel": {
"domain": "categorical",
"data": ["linear", "sigmoid", "poly", "rbf"],
"type": "str"
}
},
"settings": {
"solver": {
"max_iterations": 500
},
"custom": {}
}}
# When creating a HyppopyProject instance we
# pass the config dictionary to the constructor.
project = HyppopyProject(config=config)
# When building the project programmatically we can also use the methods
# add_hyperparameter and add_settings
project.clear()
project.add_hyperparameter(name="C", domain="uniform", data=[0.0001, 20], dtype="float")
project.add_hyperparameter(name="kernel", domain="categorical", data=["linear", "sigmoid"], dtype="str")
project.add_settings(section="solver", name="max_iterations", value=500)
# The custom section can be used freely
project.add_settings(section="custom", name="my_var", value=10)
# Settings are automatically transformed to member variables of the project class with the section as prefix
if project.solver_max_iterations < 1000 and project.custom_my_var == 10:
print("Project configured!")
def test_project_creation(self):
config = {
"hyperparameter": {
"C": {
"domain": "uniform",
"data": [0.0001, 20],
"type": "float"
},
"kernel": {
"domain": "categorical",
"data": ["linear", "sigmoid", "poly", "rbf"],
"type": "str"
}
},
"settings": {
"solver": {
"max_iterations": 300
},
"custom": {
"param1": 1,
"param2": 2,
"function": foo
}
}}
project = HyppopyProject()
project.set_config(config)
self.assertEqual(project.hyperparameter["C"]["domain"], "uniform")
self.assertEqual(project.hyperparameter["C"]["data"], [0.0001, 20])
self.assertEqual(project.hyperparameter["C"]["type"], "float")
self.assertEqual(project.hyperparameter["kernel"]["domain"], "categorical")
self.assertEqual(project.hyperparameter["kernel"]["data"], ["linear", "sigmoid", "poly", "rbf"])
self.assertEqual(project.hyperparameter["kernel"]["type"], "str")
self.assertEqual(project.solver_max_iterations, 300)
self.assertEqual(project.custom_param1, 1)
self.assertEqual(project.custom_param2, 2)
self.assertEqual(project.custom_function(2, 3), 5)
self.assertTrue(project.get_typeof("C") is float)
self.assertTrue(project.get_typeof("kernel") is str)
project.clear()
self.assertTrue(len(project.hyperparameter) == 0)
self.assertTrue(len(project.settings) == 0)
self.assertTrue("solver_max_iterations" not in project.__dict__.keys())
self.assertTrue("custom_param1" not in project.__dict__.keys())
self.assertTrue("custom_param2" not in project.__dict__.keys())
self.assertTrue("custom_function" not in project.__dict__.keys())
project.add_hyperparameter(name="C", domain="uniform", data=[0.0001, 20], dtype="float")
project.add_hyperparameter(name="kernel", domain="categorical", data=["linear", "sigmoid", "poly", "rbf"], dtype="str")
self.assertEqual(project.hyperparameter["C"]["domain"], "uniform")
self.assertEqual(project.hyperparameter["C"]["data"], [0.0001, 20])
self.assertEqual(project.hyperparameter["C"]["type"], "float")
self.assertEqual(project.hyperparameter["kernel"]["domain"], "categorical")
self.assertEqual(project.hyperparameter["kernel"]["data"], ["linear", "sigmoid", "poly", "rbf"])
self.assertEqual(project.hyperparameter["kernel"]["type"], "str")
project.add_settings("solver", "max_iterations", 500)
self.assertEqual(project.solver_max_iterations, 500)
project.add_settings("solver", "max_iterations", 200)
self.assertEqual(project.solver_max_iterations, 200)
