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()
"data": ["linear", "sigmoid", "poly", "rbf"],
"type": str
}
},
"max_iterations": 500
}
# 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 = HyppopyProject()
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.set_settings(max_iterations=500)
# The custom section can be used freely
project.add_setting("my_var", 10)
# Settings are automatically transformed to member variables of the project class with the section as prefix
if project.max_iterations < 1000 and project.my_var == 10:
print("Project configured!")
# This software is distributed WITHOUT ANY WARRANTY; without
# even the implied warranty of MERCHANTABILITY or FITNESS FOR
# A PARTICULAR PURPOSE.
#
# See LICENSE
import matplotlib.pylab as plt
from hyppopy.SolverPool import SolverPool
from hyppopy.HyppopyProject import HyppopyProject
from hyppopy.VirtualFunction import VirtualFunction
from hyppopy.BlackboxFunction import BlackboxFunction
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",
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
}
},
"max_iterations": 300,
"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.assertTrue(project.hyperparameter["C"]["type"] is float)
self.assertEqual(project.hyperparameter["kernel"]["domain"],
"categorical")
self.assertEqual(project.hyperparameter["kernel"]["data"],
["linear", "sigmoid", "poly", "rbf"])
self.assertTrue(project.hyperparameter["kernel"]["type"] is str)
self.assertEqual(project.max_iterations, 300)
self.assertEqual(project.param1, 1)
self.assertEqual(project.param2, 2)
self.assertEqual(project.function(2, 3), 5)
self.assertTrue(project.get_typeof("C") is float)
self.assertTrue(project.get_typeof("kernel") is str)
project = HyppopyProject()
project.add_hyperparameter(name="C",
domain="uniform",
data=[0.0001, 20],
type=float)
project.add_hyperparameter(name="kernel",
domain="categorical",
data=["linear", "sigmoid", "poly", "rbf"],
type=str)
self.assertEqual(project.hyperparameter["C"]["domain"], "uniform")
self.assertEqual(project.hyperparameter["C"]["data"], [0.0001, 20])
self.assertTrue(project.hyperparameter["C"]["type"] is float)
self.assertEqual(project.hyperparameter["kernel"]["domain"],
"categorical")
self.assertEqual(project.hyperparameter["kernel"]["data"],
["linear", "sigmoid", "poly", "rbf"])
self.assertTrue(project.hyperparameter["kernel"]["type"] is str)
project.set_settings(max_iterations=500)
self.assertEqual(project.max_iterations, 500)
project.add_setting("my_param", 42)
self.assertEqual(project.my_param, 42)
project.add_setting("max_iterations", 200)
self.assertEqual(project.max_iterations, 200)
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()
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)
#
# This software is distributed WITHOUT ANY WARRANTY; without
# even the implied warranty of MERCHANTABILITY or FITNESS FOR
# A PARTICULAR PURPOSE.
#
# See LICENSE
import matplotlib.pylab as plt
from hyppopy.SolverPool import SolverPool
from hyppopy.HyppopyProject import HyppopyProject
from hyppopy.FunctionSimulator import FunctionSimulator
from hyppopy.BlackboxFunction import BlackboxFunction
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)
project.add_setting("max_iterations", 500)
project.add_setting("solver", "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": [],