def _process_algorithm(algorithm):
"""Identify the algorithm from the user-supplied string.
Args:
algorithm (str): Package and name of the algorithm. It should be of the format
{pkg}_{name}.
Returns:
origin (str): Name of the package.
algo_name (str): Name of the algorithm.
"""
current_dir_path = Path(__file__).resolve().parent
with open(current_dir_path / "algo_dict.json") as j:
algos = json.load(j)
origin, algo_name = algorithm.split("_", 1)
try:
assert algo_name in algos[
origin], "Invalid algorithm requested: {}".format(algorithm)
except (AssertionError, KeyError):
proposals = propose_algorithms(algorithm, algos)
raise NotImplementedError(
f"{algorithm} is not a valid choice. Did you mean one of {proposals}?"
)
return origin, algo_name
def _internal_minimize(
criterion,
criterion_kwargs,
params,
internal_params,
constraints,
algorithm,
algo_options,
gradient,
gradient_options,
general_options,
database,
queue,
fitness_factor,
):
"""Create the internal criterion function and minimize it.
Args:
criterion (function):
Python function that takes a pandas DataFrame with parameters as the first
argument and returns a scalar floating point value.
criterion_kwargs (dict):
additional keyword arguments for criterion
params (pd.DataFrame):
See :ref:`params`.
internal_params (DataFrame):
See :ref:`params`.
constraints (list):
list with constraint dictionaries. See for details.
algorithm (str):
specifies the optimization algorithm. See :ref:`list_of_algorithms`.
algo_options (dict):
algorithm specific configurations for the optimization
gradient (callable or None):
Gradient function.
gradient_options (dict):
Options for the gradient function.
general_options (dict):
additional configurations for the optimization
database (sqlalchemy.sql.schema.MetaData). The engine that connects to the
database can be accessed via ``database.bind``.
queue (Queue):
queue to which the fitness evaluations and params DataFrames are supplied.
fitness_factor (float):
multiplicative factor for the fitness displayed in the dashboard.
Set to -1 for maximizations to plot the fitness that is being maximized.
"""
logging_decorator = functools.partial(
log_evaluation,
database=database,
tables=["params_history", "criterion_history", "comparison_plot"],
)
exception_decorator = functools.partial(
handle_exceptions,
database=database,
params=params,
constraints=constraints,
start_params=internal_params,
general_options=general_options,
)
internal_criterion = create_internal_criterion(
criterion=criterion,
params=params,
constraints=constraints,
criterion_kwargs=criterion_kwargs,
logging_decorator=logging_decorator,
exception_decorator=exception_decorator,
queue=queue,
fitness_factor=fitness_factor,
)
internal_gradient = create_internal_gradient(
gradient=gradient,
gradient_options=gradient_options,
criterion=criterion,
params=params,
internal_params=internal_params,
constraints=constraints,
criterion_kwargs=criterion_kwargs,
database=database,
exception_decorator=exception_decorator,
fitness_factor=fitness_factor,
algorithm=algorithm,
general_options=general_options,
)
current_dir_path = Path(__file__).resolve().parent
with open(current_dir_path / "algo_dict.json") as j:
algos = json.load(j)
origin, algo_name = algorithm.split("_", 1)
try:
assert algo_name in algos[
origin], "Invalid algorithm requested: {}".format(algorithm)
except (AssertionError, KeyError):
proposals = propose_algorithms(algorithm, algos)
raise NotImplementedError(
f"{algorithm} is not a valid choice. Did you mean one of {proposals}?"
)
bounds = tuple(
params.query("_internal_free")[["lower", "upper"]].to_numpy().T)
if database:
update_scalar_field(database, "optimization_status", "running")
if origin in ["nlopt", "pygmo"]:
results = minimize_pygmo_np(
internal_criterion,
internal_params,
bounds,
origin,
algo_name,
algo_options,
internal_gradient,
)
elif origin == "scipy":
results = minimize_scipy_np(
internal_criterion,
internal_params,
bounds=bounds,
algo_name=algo_name,
algo_options=algo_options,
gradient=internal_gradient,
)
elif origin == "tao":
crit_val = general_options["start_criterion_value"]
len_criterion_value = 1 if np.isscalar(crit_val) else len(crit_val)
results = minimize_pounders_np(
internal_criterion,
internal_params,
bounds,
n_errors=len_criterion_value,
**algo_options,
)
else:
raise NotImplementedError("Invalid algorithm requested.")
if database:
update_scalar_field(database, "optimization_status", results["status"])
params = reparametrize_from_internal(
internal=results["x"],
fixed_values=params["_internal_fixed_value"].to_numpy(),
pre_replacements=params["_pre_replacements"].to_numpy().astype(int),
processed_constraints=constraints,
post_replacements=params["_post_replacements"].to_numpy().astype(int),
processed_params=params,
)
return results, params
def _minimize(
criterion,
criterion_args,
criterion_kwargs,
params,
internal_params,
constraints,
algorithm,
algo_options,
general_options,
queue,
):
"""
Create the internal criterion function and minimize it.
Args:
criterion (function):
Python function that takes a pandas Series with parameters as the first
argument and returns a scalar floating point value.
criterion_args (list or tuple):
additional positional arguments for criterion
criterion_kwargs (dict):
additional keyword arguments for criterion
params (pd.DataFrame):
See :ref:`params`.
internal_params (DataFrame):
See :ref:`params`.
constraints (list):
list with constraint dictionaries. See for details.
algorithm (str):
specifies the optimization algorithm. See :ref:`list_of_algorithms`.
algo_options (dict):
algorithm specific configurations for the optimization
general_options (dict):
additional configurations for the optimization
queue (Queue):
queue to which originally the parameters DataFrame is supplied and to which
the updated parameter Series will be supplied later.
"""
internal_criterion = _create_internal_criterion(
criterion=criterion,
params=params,
internal_params=internal_params,
constraints=constraints,
criterion_args=criterion_args,
criterion_kwargs=criterion_kwargs,
queue=queue,
)
with open(os.path.join(os.path.dirname(__file__), "algo_dict.json")) as j:
algos = json.load(j)
origin, algo_name = algorithm.split("_", 1)
try:
assert algo_name in algos[
origin], "Invalid algorithm requested: {}".format(algorithm)
except (AssertionError, KeyError):
proposals = propose_algorithms(algorithm, algos)
raise NotImplementedError(
f"{algorithm} is not a valid choice. Did you mean one of {proposals}?"
)
if origin in ["nlopt", "pygmo"]:
prob = _create_problem(internal_criterion, internal_params)
algo = _create_algorithm(algo_name, algo_options, origin)
pop = _create_population(prob, algo_options, internal_params)
evolved = algo.evolve(pop)
result = _process_results(evolved, params, internal_params,
constraints, origin)
elif origin == "scipy":
bounds = _get_scipy_bounds(internal_params)
x0 = _x_from_params(params, constraints)
minimized = scipy_minimize(
internal_criterion,
x0,
method=algo_name,
bounds=bounds,
options=algo_options,
)
result = _process_results(minimized, params, internal_params,
constraints, origin)
else:
raise ValueError("Invalid algorithm requested.")
return result
def _single_optimize(
direction,
criterion,
criterion_kwargs,
params,
algorithm,
constraints,
algo_options,
derivative,
derivative_kwargs,
criterion_and_derivative,
criterion_and_derivative_kwargs,
numdiff_options,
logging,
log_options,
error_handling,
error_penalty,
cache_size,
):
"""Minimize or maximize *criterion* using *algorithm* subject to *constraints*.
See the docstring of ``optimize`` for an explanation of all arguments.
Returns:
dict: The optimization result.
"""
# store all arguments in a dictionary to save them in the database later
problem_data = {
"direction": direction,
# "criterion"-criterion,
"criterion_kwargs": criterion_kwargs,
"algorithm": algorithm,
"constraints": constraints,
"algo_options": algo_options,
# "derivative"-derivative,
"derivative_kwargs": derivative_kwargs,
# "criterion_and_derivative"-criterion_and_derivative,
"criterion_and_derivative_kwargs": criterion_and_derivative_kwargs,
"numdiff_options": numdiff_options,
"logging": logging,
"log_options": log_options,
"error_handling": error_handling,
"error_penalty": error_penalty,
"cache_size": int(cache_size),
}
# partial the kwargs into corresponding functions
criterion = functools.partial(criterion, **criterion_kwargs)
if derivative is not None:
derivative = functools.partial(derivative, **derivative_kwargs)
if criterion_and_derivative is not None:
criterion_and_derivative = functools.partial(
criterion_and_derivative, **criterion_and_derivative_kwargs)
# process params and constraints
params = process_bounds(params)
for col in ["value", "lower_bound", "upper_bound"]:
params[col] = params[col].astype(float)
_check_params(params)
processed_constraints, processed_params = process_constraints(
constraints, params)
# name and group column are needed in the dashboard but could lead to problems
# if present anywhere else
params_with_name_and_group = _add_name_and_group_columns_to_params(params)
problem_data["params"] = params_with_name_and_group
# get internal parameters and bounds
x = reparametrize_to_internal(
params["value"].to_numpy(),
processed_params["_internal_free"].to_numpy(),
processed_constraints,
)
free = processed_params.query("_internal_free")
lower_bounds = free["_internal_lower"].to_numpy()
upper_bounds = free["_internal_upper"].to_numpy()
# process algorithm and algo_options
if isinstance(algorithm, str):
algo_name = algorithm
else:
algo_name = getattr(algorithm, "name", "your algorithm")
if isinstance(algorithm, str):
try:
algorithm = AVAILABLE_ALGORITHMS[algorithm]
except KeyError:
proposed = propose_algorithms(algorithm,
list(AVAILABLE_ALGORITHMS))
raise ValueError(
f"Invalid algorithm: {algorithm}. Did you mean {proposed}?")
algo_options = _adjust_options_to_algorithms(algo_options, lower_bounds,
upper_bounds, algorithm,
algo_name)
# get partialed reparametrize from internal
pre_replacements = processed_params["_pre_replacements"].to_numpy()
post_replacements = processed_params["_post_replacements"].to_numpy()
fixed_values = processed_params["_internal_fixed_value"].to_numpy()
partialed_reparametrize_from_internal = functools.partial(
reparametrize_from_internal,
fixed_values=fixed_values,
pre_replacements=pre_replacements,
processed_constraints=processed_constraints,
post_replacements=post_replacements,
)
# get convert derivative
pre_replace_jac = pre_replace_jacobian(pre_replacements=pre_replacements,
dim_in=len(x))
post_replace_jac = post_replace_jacobian(
post_replacements=post_replacements)
convert_derivative = functools.partial(
convert_external_derivative_to_internal,
fixed_values=fixed_values,
pre_replacements=pre_replacements,
processed_constraints=processed_constraints,
pre_replace_jac=pre_replace_jac,
post_replace_jac=post_replace_jac,
)
# do first function evaluation
first_eval = {
"internal_params": x,
"external_params": params,
"output": criterion(params),
}
# fill numdiff_options with defaults
numdiff_options = _fill_numdiff_options_with_defaults(
numdiff_options, lower_bounds, upper_bounds)
# create and initialize the database
if not logging:
database = False
else:
database = _create_and_initialize_database(logging, log_options,
first_eval, problem_data)
# set default error penalty
error_penalty = _fill_error_penalty_with_defaults(error_penalty,
first_eval, direction)
# create cache
x_hash = hash_array(x)
cache = {x_hash: {"criterion": first_eval["output"]}}
# partial the internal_criterion_and_derivative_template
internal_criterion_and_derivative = functools.partial(
internal_criterion_and_derivative_template,
direction=direction,
criterion=criterion,
params=params,
reparametrize_from_internal=partialed_reparametrize_from_internal,
convert_derivative=convert_derivative,
derivative=derivative,
criterion_and_derivative=criterion_and_derivative,
numdiff_options=numdiff_options,
database=database,
database_path=logging,
log_options=log_options,
error_handling=error_handling,
error_penalty=error_penalty,
first_criterion_evaluation=first_eval,
cache=cache,
cache_size=cache_size,
)
res = algorithm(internal_criterion_and_derivative, x, **algo_options)
p = params.copy()
p["value"] = partialed_reparametrize_from_internal(res["solution_x"])
res["solution_params"] = p
if "solution_criterion" not in res:
res["solution_criterion"] = criterion(p)
# in the long run we can get some of those from the database if logging was used.
optional_entries = [
"solution_derivative",
"solution_hessian",
"n_criterion_evaluations",
"n_derivative_evaluations",
"n_iterations",
"success",
"reached_convergence_criterion",
"message",
]
for entry in optional_entries:
res[entry] = res.get(entry, f"Not reported by {algo_name}")
if logging:
_log_final_status(res, database, logging, log_options)
return res
def _internal_minimize(
criterion,
criterion_kwargs,
params,
internal_params,
constraints,
algorithm,
algo_options,
general_options,
queue,
fitness_factor,
):
"""Create the internal criterion function and minimize it.
Args:
criterion (function):
Python function that takes a pandas DataFrame with parameters as the first
argument and returns a scalar floating point value.
criterion_kwargs (dict):
additional keyword arguments for criterion
params (pd.DataFrame):
See :ref:`params`.
internal_params (DataFrame):
See :ref:`params`.
constraints (list):
list with constraint dictionaries. See for details.
algorithm (str):
specifies the optimization algorithm. See :ref:`list_of_algorithms`.
algo_options (dict):
algorithm specific configurations for the optimization
general_options (dict):
additional configurations for the optimization
queue (Queue):
queue to which the fitness evaluations and params DataFrames are supplied.
fitness_factor (float):
multiplicative factor for the fitness displayed in the dashboard.
Set to -1 for maximizations to plot the fitness that is being maximized.
"""
internal_criterion = create_internal_criterion(
criterion=criterion,
params=params,
constraints=constraints,
criterion_kwargs=criterion_kwargs,
queue=queue,
fitness_factor=fitness_factor,
)
current_dir_path = Path(__file__).resolve().parent
with open(current_dir_path / "algo_dict.json") as j:
algos = json.load(j)
origin, algo_name = algorithm.split("_", 1)
try:
assert algo_name in algos[
origin], "Invalid algorithm requested: {}".format(algorithm)
except (AssertionError, KeyError):
proposals = propose_algorithms(algorithm, algos)
raise NotImplementedError(
f"{algorithm} is not a valid choice. Did you mean one of {proposals}?"
)
if origin == "pygmo" and algorithm != "pygmo_simulated_annealing":
assert (
"popsize" in algo_options
), f"For genetic optimizers like {algo_name}, popsize is mandatory."
assert (
"gen" in algo_options
), f"For genetic optimizers like {algo_name}, gen is mandatory."
if origin in ["nlopt", "pygmo"]:
prob = _create_problem(internal_criterion, params)
algo = _create_algorithm(algo_name, algo_options, origin)
pop = _create_population(prob, algo_options, internal_params)
evolved = algo.evolve(pop)
result = _process_results(evolved, params, internal_params,
constraints, origin)
elif origin == "scipy":
bounds = _get_scipy_bounds(params)
minimized = scipy_minimize(
internal_criterion,
internal_params,
method=algo_name,
bounds=bounds,
options=algo_options,
)
result = _process_results(minimized, params, internal_params,
constraints, origin)
elif origin == "tao":
len_output = algo_options.pop("len_output", None)
if len_output is None:
len_output = len(criterion(params))
bounds = (params.query("_internal_free")[["lower", "upper"
]].to_numpy().T.tolist())
minimized = minimize_pounders(internal_criterion, internal_params,
len_output, bounds, **algo_options)
result = _process_results(minimized, params, internal_params,
constraints, origin)
else:
raise ValueError("Invalid algorithm requested.")
return result
