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JustinLovinger/optimal | optimal/algorithms/gaoperators.py | _fitnesses_to_probabilities | def _fitnesses_to_probabilities(fitnesses):
"""Return a list of probabilities proportional to fitnesses."""
# Do not allow negative fitness values
min_fitness = min(fitnesses)
if min_fitness < 0.0:
# Make smallest fitness value 0
fitnesses = map(lambda f: f - min_fitness, fitnesses)
fitness_sum = sum(fitnesses)
# Generate probabilities
# Creates a list of increasing values.
# The greater the gap between two values, the greater the probability.
# Ex. [0.1, 0.23, 0.56, 1.0]
prob_sum = 0.0
probabilities = []
for fitness in fitnesses:
if fitness < 0:
raise ValueError(
"Fitness cannot be negative, fitness = {}.".format(fitness))
prob_sum += (fitness / fitness_sum)
probabilities.append(prob_sum)
probabilities[-1] += 0.0001 # to compensate for rounding errors
return probabilities | python | def _fitnesses_to_probabilities(fitnesses):
"""Return a list of probabilities proportional to fitnesses."""
# Do not allow negative fitness values
min_fitness = min(fitnesses)
if min_fitness < 0.0:
# Make smallest fitness value 0
fitnesses = map(lambda f: f - min_fitness, fitnesses)
fitness_sum = sum(fitnesses)
# Generate probabilities
# Creates a list of increasing values.
# The greater the gap between two values, the greater the probability.
# Ex. [0.1, 0.23, 0.56, 1.0]
prob_sum = 0.0
probabilities = []
for fitness in fitnesses:
if fitness < 0:
raise ValueError(
"Fitness cannot be negative, fitness = {}.".format(fitness))
prob_sum += (fitness / fitness_sum)
probabilities.append(prob_sum)
probabilities[-1] += 0.0001 # to compensate for rounding errors
return probabilities | Return a list of probabilities proportional to fitnesses. | https://github.com/JustinLovinger/optimal/blob/ab48a4961697338cc32d50e3a6b06ac989e39c3f/optimal/algorithms/gaoperators.py#L182-L206 |
JustinLovinger/optimal | optimal/algorithms/gaoperators.py | one_point_crossover | def one_point_crossover(parents):
"""Perform one point crossover on two parent chromosomes.
Select a random position in the chromosome.
Take genes to the left from one parent and the rest from the other parent.
Ex. p1 = xxxxx, p2 = yyyyy, position = 2 (starting at 0), child = xxyyy
"""
# The point that the chromosomes will be crossed at (see Ex. above)
crossover_point = random.randint(1, len(parents[0]) - 1)
return (_one_parent_crossover(parents[0], parents[1], crossover_point),
_one_parent_crossover(parents[1], parents[0], crossover_point)) | python | def one_point_crossover(parents):
"""Perform one point crossover on two parent chromosomes.
Select a random position in the chromosome.
Take genes to the left from one parent and the rest from the other parent.
Ex. p1 = xxxxx, p2 = yyyyy, position = 2 (starting at 0), child = xxyyy
"""
# The point that the chromosomes will be crossed at (see Ex. above)
crossover_point = random.randint(1, len(parents[0]) - 1)
return (_one_parent_crossover(parents[0], parents[1], crossover_point),
_one_parent_crossover(parents[1], parents[0], crossover_point)) | Perform one point crossover on two parent chromosomes.
Select a random position in the chromosome.
Take genes to the left from one parent and the rest from the other parent.
Ex. p1 = xxxxx, p2 = yyyyy, position = 2 (starting at 0), child = xxyyy | https://github.com/JustinLovinger/optimal/blob/ab48a4961697338cc32d50e3a6b06ac989e39c3f/optimal/algorithms/gaoperators.py#L212-L223 |
JustinLovinger/optimal | optimal/algorithms/gaoperators.py | uniform_crossover | def uniform_crossover(parents):
"""Perform uniform crossover on two parent chromosomes.
Randomly take genes from one parent or the other.
Ex. p1 = xxxxx, p2 = yyyyy, child = xyxxy
"""
chromosome_length = len(parents[0])
children = [[], []]
for i in range(chromosome_length):
selected_parent = random.randint(0, 1)
# Take from the selected parent, and add it to child 1
# Take from the other parent, and add it to child 2
children[0].append(parents[selected_parent][i])
children[1].append(parents[1 - selected_parent][i])
return children | python | def uniform_crossover(parents):
"""Perform uniform crossover on two parent chromosomes.
Randomly take genes from one parent or the other.
Ex. p1 = xxxxx, p2 = yyyyy, child = xyxxy
"""
chromosome_length = len(parents[0])
children = [[], []]
for i in range(chromosome_length):
selected_parent = random.randint(0, 1)
# Take from the selected parent, and add it to child 1
# Take from the other parent, and add it to child 2
children[0].append(parents[selected_parent][i])
children[1].append(parents[1 - selected_parent][i])
return children | Perform uniform crossover on two parent chromosomes.
Randomly take genes from one parent or the other.
Ex. p1 = xxxxx, p2 = yyyyy, child = xyxxy | https://github.com/JustinLovinger/optimal/blob/ab48a4961697338cc32d50e3a6b06ac989e39c3f/optimal/algorithms/gaoperators.py#L230-L248 |
JustinLovinger/optimal | optimal/algorithms/gaoperators.py | random_flip_mutate | def random_flip_mutate(population, mutation_chance):
"""Mutate every chromosome in a population, list is modified in place.
Mutation occurs by randomly flipping bits (genes).
"""
for chromosome in population: # For every chromosome in the population
for i in range(len(chromosome)): # For every bit in the chromosome
# If mutation takes place
if random.uniform(0.0, 1.0) <= mutation_chance:
chromosome[i] = 1 - chromosome[i] | python | def random_flip_mutate(population, mutation_chance):
"""Mutate every chromosome in a population, list is modified in place.
Mutation occurs by randomly flipping bits (genes).
"""
for chromosome in population: # For every chromosome in the population
for i in range(len(chromosome)): # For every bit in the chromosome
# If mutation takes place
if random.uniform(0.0, 1.0) <= mutation_chance:
chromosome[i] = 1 - chromosome[i] | Mutate every chromosome in a population, list is modified in place.
Mutation occurs by randomly flipping bits (genes). | https://github.com/JustinLovinger/optimal/blob/ab48a4961697338cc32d50e3a6b06ac989e39c3f/optimal/algorithms/gaoperators.py#L254-L263 |
JustinLovinger/optimal | optimal/optimize.py | _duplicates | def _duplicates(list_):
"""Return dict mapping item -> indices."""
item_indices = {}
for i, item in enumerate(list_):
try:
item_indices[item].append(i)
except KeyError: # First time seen
item_indices[item] = [i]
return item_indices | python | def _duplicates(list_):
"""Return dict mapping item -> indices."""
item_indices = {}
for i, item in enumerate(list_):
try:
item_indices[item].append(i)
except KeyError: # First time seen
item_indices[item] = [i]
return item_indices | Return dict mapping item -> indices. | https://github.com/JustinLovinger/optimal/blob/ab48a4961697338cc32d50e3a6b06ac989e39c3f/optimal/optimize.py#L718-L726 |
JustinLovinger/optimal | optimal/optimize.py | _parse_parameter_locks | def _parse_parameter_locks(optimizer, meta_parameters, parameter_locks):
"""Synchronize meta_parameters and locked_values.
The union of these two sets will have all necessary parameters.
locked_values will have the parameters specified in parameter_locks.
"""
# WARNING: meta_parameters is modified inline
locked_values = {}
if parameter_locks:
for name in parameter_locks:
# Store the current optimzier value
# and remove from our dictionary of paramaters to optimize
locked_values[name] = getattr(optimizer, name)
meta_parameters.pop(name)
return locked_values | python | def _parse_parameter_locks(optimizer, meta_parameters, parameter_locks):
"""Synchronize meta_parameters and locked_values.
The union of these two sets will have all necessary parameters.
locked_values will have the parameters specified in parameter_locks.
"""
# WARNING: meta_parameters is modified inline
locked_values = {}
if parameter_locks:
for name in parameter_locks:
# Store the current optimzier value
# and remove from our dictionary of paramaters to optimize
locked_values[name] = getattr(optimizer, name)
meta_parameters.pop(name)
return locked_values | Synchronize meta_parameters and locked_values.
The union of these two sets will have all necessary parameters.
locked_values will have the parameters specified in parameter_locks. | https://github.com/JustinLovinger/optimal/blob/ab48a4961697338cc32d50e3a6b06ac989e39c3f/optimal/optimize.py#L740-L756 |
JustinLovinger/optimal | optimal/optimize.py | _get_hyperparameter_solution_size | def _get_hyperparameter_solution_size(meta_parameters):
"""Determine size of binary encoding of parameters.
Also adds binary size information for each parameter.
"""
# WARNING: meta_parameters is modified inline
solution_size = 0
for _, parameters in meta_parameters.iteritems():
if parameters['type'] == 'discrete':
# Binary encoding of discrete values -> log_2 N
num_values = len(parameters['values'])
binary_size = helpers.binary_size(num_values)
elif parameters['type'] == 'int':
# Use enough bits to cover range from min to max
# + 1 to include max in range
int_range = parameters['max'] - parameters['min'] + 1
binary_size = helpers.binary_size(int_range)
elif parameters['type'] == 'float':
# Use enough bits to provide fine steps between min and max
float_range = parameters['max'] - parameters['min']
# * 1000 provides 1000 values between each natural number
binary_size = helpers.binary_size(float_range * 1000)
else:
raise ValueError('Parameter type "{}" does not match known values'.
format(parameters['type']))
# Store binary size with parameters for use in decode function
parameters['binary_size'] = binary_size
solution_size += binary_size
return solution_size | python | def _get_hyperparameter_solution_size(meta_parameters):
"""Determine size of binary encoding of parameters.
Also adds binary size information for each parameter.
"""
# WARNING: meta_parameters is modified inline
solution_size = 0
for _, parameters in meta_parameters.iteritems():
if parameters['type'] == 'discrete':
# Binary encoding of discrete values -> log_2 N
num_values = len(parameters['values'])
binary_size = helpers.binary_size(num_values)
elif parameters['type'] == 'int':
# Use enough bits to cover range from min to max
# + 1 to include max in range
int_range = parameters['max'] - parameters['min'] + 1
binary_size = helpers.binary_size(int_range)
elif parameters['type'] == 'float':
# Use enough bits to provide fine steps between min and max
float_range = parameters['max'] - parameters['min']
# * 1000 provides 1000 values between each natural number
binary_size = helpers.binary_size(float_range * 1000)
else:
raise ValueError('Parameter type "{}" does not match known values'.
format(parameters['type']))
# Store binary size with parameters for use in decode function
parameters['binary_size'] = binary_size
solution_size += binary_size
return solution_size | Determine size of binary encoding of parameters.
Also adds binary size information for each parameter. | https://github.com/JustinLovinger/optimal/blob/ab48a4961697338cc32d50e3a6b06ac989e39c3f/optimal/optimize.py#L759-L791 |
JustinLovinger/optimal | optimal/optimize.py | _make_hyperparameter_decode_func | def _make_hyperparameter_decode_func(locked_values, meta_parameters):
"""Create a function that converts the binary solution to parameters."""
# Locked parameters are also returned by decode function, but are not
# based on solution
def decode(solution):
"""Convert solution into dict of hyperparameters."""
# Start with out stationary (locked) paramaters
hyperparameters = copy.deepcopy(locked_values)
# Obtain moving hyperparameters from binary solution
index = 0
for name, parameters in meta_parameters.iteritems():
# Obtain binary for this hyperparameter
binary_size = parameters['binary_size']
binary = solution[index:index + binary_size]
index += binary_size # Just index to start of next hyperparameter
# Decode binary
if parameters['type'] == 'discrete':
i = helpers.binary_to_int(
binary, upper_bound=len(parameters['values']) - 1)
value = parameters['values'][i]
elif parameters['type'] == 'int':
value = helpers.binary_to_int(
binary,
lower_bound=parameters['min'],
upper_bound=parameters['max'])
elif parameters['type'] == 'float':
value = helpers.binary_to_float(
binary,
lower_bound=parameters['min'],
upper_bound=parameters['max'])
else:
raise ValueError(
'Parameter type "{}" does not match known values'.format(
parameters['type']))
# Store value
hyperparameters[name] = value
return hyperparameters
return decode | python | def _make_hyperparameter_decode_func(locked_values, meta_parameters):
"""Create a function that converts the binary solution to parameters."""
# Locked parameters are also returned by decode function, but are not
# based on solution
def decode(solution):
"""Convert solution into dict of hyperparameters."""
# Start with out stationary (locked) paramaters
hyperparameters = copy.deepcopy(locked_values)
# Obtain moving hyperparameters from binary solution
index = 0
for name, parameters in meta_parameters.iteritems():
# Obtain binary for this hyperparameter
binary_size = parameters['binary_size']
binary = solution[index:index + binary_size]
index += binary_size # Just index to start of next hyperparameter
# Decode binary
if parameters['type'] == 'discrete':
i = helpers.binary_to_int(
binary, upper_bound=len(parameters['values']) - 1)
value = parameters['values'][i]
elif parameters['type'] == 'int':
value = helpers.binary_to_int(
binary,
lower_bound=parameters['min'],
upper_bound=parameters['max'])
elif parameters['type'] == 'float':
value = helpers.binary_to_float(
binary,
lower_bound=parameters['min'],
upper_bound=parameters['max'])
else:
raise ValueError(
'Parameter type "{}" does not match known values'.format(
parameters['type']))
# Store value
hyperparameters[name] = value
return hyperparameters
return decode | Create a function that converts the binary solution to parameters. | https://github.com/JustinLovinger/optimal/blob/ab48a4961697338cc32d50e3a6b06ac989e39c3f/optimal/optimize.py#L794-L838 |
JustinLovinger/optimal | optimal/optimize.py | _meta_fitness_func | def _meta_fitness_func(parameters,
_optimizer,
_problems,
_master_fitness_dict,
_runs=20):
"""Test a metaheuristic with parameters encoded in solution.
Our goal is to minimize number of evaluation runs until a solution is found,
while maximizing chance of finding solution to the underlying problem
NOTE: while meta optimization requires a 'known' solution, this solution
can be an estimate to provide the meta optimizer with a sense of progress.
"""
# Create the optimizer with parameters encoded in solution
optimizer = copy.deepcopy(_optimizer)
optimizer._set_hyperparameters(parameters)
optimizer.logging = False
# Preload fitness dictionary from master, and disable clearing dict
# NOTE: master_fitness_dict will be modified inline, and therefore,
# we do not need to take additional steps to update it
if _master_fitness_dict != None: # None means low memory mode
optimizer.clear_cache = False
optimizer._Optimizer__encoded_cache = _master_fitness_dict
# Because metaheuristics are stochastic, we run the optimizer multiple times,
# to obtain an average of performance
all_evaluation_runs = []
solutions_found = []
for _ in range(_runs):
for problem in _problems:
# Get performance for problem
optimizer.optimize(problem)
all_evaluation_runs.append(optimizer.fitness_runs)
if optimizer.solution_found:
solutions_found.append(1.0)
else:
solutions_found.append(0.0)
# Our main goal is to minimize time the optimizer takes
fitness = 1.0 / helpers.avg(all_evaluation_runs)
# Optimizer is heavily penalized for missing solutions
# To avoid 0 fitness
fitness = fitness * helpers.avg(solutions_found)**2 + 1e-19
return fitness | python | def _meta_fitness_func(parameters,
_optimizer,
_problems,
_master_fitness_dict,
_runs=20):
"""Test a metaheuristic with parameters encoded in solution.
Our goal is to minimize number of evaluation runs until a solution is found,
while maximizing chance of finding solution to the underlying problem
NOTE: while meta optimization requires a 'known' solution, this solution
can be an estimate to provide the meta optimizer with a sense of progress.
"""
# Create the optimizer with parameters encoded in solution
optimizer = copy.deepcopy(_optimizer)
optimizer._set_hyperparameters(parameters)
optimizer.logging = False
# Preload fitness dictionary from master, and disable clearing dict
# NOTE: master_fitness_dict will be modified inline, and therefore,
# we do not need to take additional steps to update it
if _master_fitness_dict != None: # None means low memory mode
optimizer.clear_cache = False
optimizer._Optimizer__encoded_cache = _master_fitness_dict
# Because metaheuristics are stochastic, we run the optimizer multiple times,
# to obtain an average of performance
all_evaluation_runs = []
solutions_found = []
for _ in range(_runs):
for problem in _problems:
# Get performance for problem
optimizer.optimize(problem)
all_evaluation_runs.append(optimizer.fitness_runs)
if optimizer.solution_found:
solutions_found.append(1.0)
else:
solutions_found.append(0.0)
# Our main goal is to minimize time the optimizer takes
fitness = 1.0 / helpers.avg(all_evaluation_runs)
# Optimizer is heavily penalized for missing solutions
# To avoid 0 fitness
fitness = fitness * helpers.avg(solutions_found)**2 + 1e-19
return fitness | Test a metaheuristic with parameters encoded in solution.
Our goal is to minimize number of evaluation runs until a solution is found,
while maximizing chance of finding solution to the underlying problem
NOTE: while meta optimization requires a 'known' solution, this solution
can be an estimate to provide the meta optimizer with a sense of progress. | https://github.com/JustinLovinger/optimal/blob/ab48a4961697338cc32d50e3a6b06ac989e39c3f/optimal/optimize.py#L841-L886 |
JustinLovinger/optimal | optimal/optimize.py | Problem.copy | def copy(self,
fitness_function=None,
decode_function=None,
fitness_args=None,
decode_args=None,
fitness_kwargs=None,
decode_kwargs=None):
"""Return a copy of this problem.
Optionally replace this problems arguments with those passed in.
"""
if fitness_function is None:
fitness_function = self._fitness_function
if decode_function is None:
decode_function = self._decode_function
if fitness_args is None:
fitness_args = self._fitness_args
if decode_args is None:
decode_args = self._decode_args
if fitness_kwargs is None:
fitness_kwargs = self._fitness_kwargs
if decode_kwargs is None:
decode_kwargs = self._decode_kwargs
return Problem(
fitness_function,
decode_function=decode_function,
fitness_args=fitness_args,
decode_args=decode_args,
fitness_kwargs=fitness_kwargs,
decode_kwargs=decode_kwargs) | python | def copy(self,
fitness_function=None,
decode_function=None,
fitness_args=None,
decode_args=None,
fitness_kwargs=None,
decode_kwargs=None):
"""Return a copy of this problem.
Optionally replace this problems arguments with those passed in.
"""
if fitness_function is None:
fitness_function = self._fitness_function
if decode_function is None:
decode_function = self._decode_function
if fitness_args is None:
fitness_args = self._fitness_args
if decode_args is None:
decode_args = self._decode_args
if fitness_kwargs is None:
fitness_kwargs = self._fitness_kwargs
if decode_kwargs is None:
decode_kwargs = self._decode_kwargs
return Problem(
fitness_function,
decode_function=decode_function,
fitness_args=fitness_args,
decode_args=decode_args,
fitness_kwargs=fitness_kwargs,
decode_kwargs=decode_kwargs) | Return a copy of this problem.
Optionally replace this problems arguments with those passed in. | https://github.com/JustinLovinger/optimal/blob/ab48a4961697338cc32d50e3a6b06ac989e39c3f/optimal/optimize.py#L101-L131 |
JustinLovinger/optimal | optimal/optimize.py | Problem.get_fitness | def get_fitness(self, solution):
"""Return fitness for the given solution."""
return self._fitness_function(solution, *self._fitness_args,
**self._fitness_kwargs) | python | def get_fitness(self, solution):
"""Return fitness for the given solution."""
return self._fitness_function(solution, *self._fitness_args,
**self._fitness_kwargs) | Return fitness for the given solution. | https://github.com/JustinLovinger/optimal/blob/ab48a4961697338cc32d50e3a6b06ac989e39c3f/optimal/optimize.py#L133-L136 |
JustinLovinger/optimal | optimal/optimize.py | Problem.decode_solution | def decode_solution(self, encoded_solution):
"""Return solution from an encoded representation."""
return self._decode_function(encoded_solution, *self._decode_args,
**self._decode_kwargs) | python | def decode_solution(self, encoded_solution):
"""Return solution from an encoded representation."""
return self._decode_function(encoded_solution, *self._decode_args,
**self._decode_kwargs) | Return solution from an encoded representation. | https://github.com/JustinLovinger/optimal/blob/ab48a4961697338cc32d50e3a6b06ac989e39c3f/optimal/optimize.py#L138-L141 |
JustinLovinger/optimal | optimal/optimize.py | Optimizer.optimize | def optimize(self, problem, max_iterations=100, max_seconds=float('inf'),
cache_encoded=True, cache_solution=False, clear_cache=True,
logging_func=_print_fitnesses,
n_processes=0):
"""Find the optimal inputs for a given fitness function.
Args:
problem: An instance of Problem. The problem to solve.
max_iterations: The number of iterations to optimize before stopping.
max_seconds: Maximum number of seconds to optimize for, before stopping.
Note that condition is only checked one per iteration,
meaning optimization can take more than max_seconds,
especially if fitnesses take a long time to calculate.
cache_encoded: bool; Whether or not to cache fitness of encoded strings.
Encoded strings are produced directly by the optimizer.
If an encoded string is found in cache, it will not be decoded.
cache_solution: bool; Whether or not to cache fitness of decoded solutions.
Decoded solution is provided by problems decode function.
If problem does not provide a hash solution function,
Various naive hashing methods will be attempted, including:
tuple, tuple(sorted(dict.items)), str.
clear_cache: bool; Whether or not to reset cache after optimization.
Disable if you want to run optimize multiple times on the same problem.
logging_func: func/None; Function taking:
iteration, population, solutions, fitnesses, best_solution, best_fitness
Called after every iteration.
Use for custom logging, or set to None to disable logging.
Note that best_solution and best_fitness are the best of all iterations so far.
n_processes: int; Number of processes to use for multiprocessing.
If <= 0, do not use multiprocessing.
Returns:
object; The best solution, after decoding.
"""
if not isinstance(problem, Problem):
raise TypeError('problem must be an instance of Problem class')
# Prepare pool for multiprocessing
if n_processes > 0:
try:
pool = multiprocessing.Pool(processes=n_processes)
except NameError:
raise ImportError(
'pickle, dill, or multiprocessing library is not available.'
)
else:
pool = None
# Set first, incase optimizer uses _max_iterations in initialization
self.__max_iterations = max_iterations
# Initialize algorithm
self._reset()
best_solution = {'solution': None, 'fitness': None}
population = self.initial_population()
try:
# Begin optimization loop
start = time.clock()
for self.iteration in itertools.count(1): # Infinite sequence of iterations
# Evaluate potential solutions
solutions, fitnesses, finished = self._get_fitnesses(
problem,
population,
cache_encoded=cache_encoded,
cache_solution=cache_solution,
pool=pool)
# If the best fitness from this iteration is better than
# the global best
best_index, best_fitness = max(
enumerate(fitnesses), key=operator.itemgetter(1))
if best_fitness > best_solution['fitness']:
# Store the new best solution
best_solution['fitness'] = best_fitness
best_solution['solution'] = solutions[best_index]
if logging_func:
logging_func(self.iteration, population, solutions,
fitnesses, best_solution['solution'],
best_solution['fitness'])
# Break if solution found
if finished:
self.solution_found = True
break
# Break if out of time
if time.clock() - start >= max_seconds:
break
# Break if out of iterations
if self.iteration >= max_iterations:
break
# Continue optimizing
population = self.next_population(population, fitnesses)
# Store best internally, before returning
self.best_solution = best_solution['solution']
self.best_fitness = best_solution['fitness']
finally:
# Clear caches
if clear_cache:
# Clear caches from memory
self.__encoded_cache = {}
self.__solution_cache = {}
# Reset encoded, and decoded key functions
self._get_encoded_key = self._get_encoded_key_type
self._get_solution_key = self._get_solution_key_type
# Clean up multiprocesses
try:
pool.terminate() # Kill outstanding work
pool.close() # Close child processes
except AttributeError:
# No pool
assert pool is None
return self.best_solution | python | def optimize(self, problem, max_iterations=100, max_seconds=float('inf'),
cache_encoded=True, cache_solution=False, clear_cache=True,
logging_func=_print_fitnesses,
n_processes=0):
"""Find the optimal inputs for a given fitness function.
Args:
problem: An instance of Problem. The problem to solve.
max_iterations: The number of iterations to optimize before stopping.
max_seconds: Maximum number of seconds to optimize for, before stopping.
Note that condition is only checked one per iteration,
meaning optimization can take more than max_seconds,
especially if fitnesses take a long time to calculate.
cache_encoded: bool; Whether or not to cache fitness of encoded strings.
Encoded strings are produced directly by the optimizer.
If an encoded string is found in cache, it will not be decoded.
cache_solution: bool; Whether or not to cache fitness of decoded solutions.
Decoded solution is provided by problems decode function.
If problem does not provide a hash solution function,
Various naive hashing methods will be attempted, including:
tuple, tuple(sorted(dict.items)), str.
clear_cache: bool; Whether or not to reset cache after optimization.
Disable if you want to run optimize multiple times on the same problem.
logging_func: func/None; Function taking:
iteration, population, solutions, fitnesses, best_solution, best_fitness
Called after every iteration.
Use for custom logging, or set to None to disable logging.
Note that best_solution and best_fitness are the best of all iterations so far.
n_processes: int; Number of processes to use for multiprocessing.
If <= 0, do not use multiprocessing.
Returns:
object; The best solution, after decoding.
"""
if not isinstance(problem, Problem):
raise TypeError('problem must be an instance of Problem class')
# Prepare pool for multiprocessing
if n_processes > 0:
try:
pool = multiprocessing.Pool(processes=n_processes)
except NameError:
raise ImportError(
'pickle, dill, or multiprocessing library is not available.'
)
else:
pool = None
# Set first, incase optimizer uses _max_iterations in initialization
self.__max_iterations = max_iterations
# Initialize algorithm
self._reset()
best_solution = {'solution': None, 'fitness': None}
population = self.initial_population()
try:
# Begin optimization loop
start = time.clock()
for self.iteration in itertools.count(1): # Infinite sequence of iterations
# Evaluate potential solutions
solutions, fitnesses, finished = self._get_fitnesses(
problem,
population,
cache_encoded=cache_encoded,
cache_solution=cache_solution,
pool=pool)
# If the best fitness from this iteration is better than
# the global best
best_index, best_fitness = max(
enumerate(fitnesses), key=operator.itemgetter(1))
if best_fitness > best_solution['fitness']:
# Store the new best solution
best_solution['fitness'] = best_fitness
best_solution['solution'] = solutions[best_index]
if logging_func:
logging_func(self.iteration, population, solutions,
fitnesses, best_solution['solution'],
best_solution['fitness'])
# Break if solution found
if finished:
self.solution_found = True
break
# Break if out of time
if time.clock() - start >= max_seconds:
break
# Break if out of iterations
if self.iteration >= max_iterations:
break
# Continue optimizing
population = self.next_population(population, fitnesses)
# Store best internally, before returning
self.best_solution = best_solution['solution']
self.best_fitness = best_solution['fitness']
finally:
# Clear caches
if clear_cache:
# Clear caches from memory
self.__encoded_cache = {}
self.__solution_cache = {}
# Reset encoded, and decoded key functions
self._get_encoded_key = self._get_encoded_key_type
self._get_solution_key = self._get_solution_key_type
# Clean up multiprocesses
try:
pool.terminate() # Kill outstanding work
pool.close() # Close child processes
except AttributeError:
# No pool
assert pool is None
return self.best_solution | Find the optimal inputs for a given fitness function.
Args:
problem: An instance of Problem. The problem to solve.
max_iterations: The number of iterations to optimize before stopping.
max_seconds: Maximum number of seconds to optimize for, before stopping.
Note that condition is only checked one per iteration,
meaning optimization can take more than max_seconds,
especially if fitnesses take a long time to calculate.
cache_encoded: bool; Whether or not to cache fitness of encoded strings.
Encoded strings are produced directly by the optimizer.
If an encoded string is found in cache, it will not be decoded.
cache_solution: bool; Whether or not to cache fitness of decoded solutions.
Decoded solution is provided by problems decode function.
If problem does not provide a hash solution function,
Various naive hashing methods will be attempted, including:
tuple, tuple(sorted(dict.items)), str.
clear_cache: bool; Whether or not to reset cache after optimization.
Disable if you want to run optimize multiple times on the same problem.
logging_func: func/None; Function taking:
iteration, population, solutions, fitnesses, best_solution, best_fitness
Called after every iteration.
Use for custom logging, or set to None to disable logging.
Note that best_solution and best_fitness are the best of all iterations so far.
n_processes: int; Number of processes to use for multiprocessing.
If <= 0, do not use multiprocessing.
Returns:
object; The best solution, after decoding. | https://github.com/JustinLovinger/optimal/blob/ab48a4961697338cc32d50e3a6b06ac989e39c3f/optimal/optimize.py#L198-L319 |
JustinLovinger/optimal | optimal/optimize.py | Optimizer._reset_bookkeeping | def _reset_bookkeeping(self):
"""Reset bookkeeping parameters to initial values.
Call before beginning optimization.
"""
self.iteration = 0
self.fitness_runs = 0
self.best_solution = None
self.best_fitness = None
self.solution_found = False | python | def _reset_bookkeeping(self):
"""Reset bookkeeping parameters to initial values.
Call before beginning optimization.
"""
self.iteration = 0
self.fitness_runs = 0
self.best_solution = None
self.best_fitness = None
self.solution_found = False | Reset bookkeeping parameters to initial values.
Call before beginning optimization. | https://github.com/JustinLovinger/optimal/blob/ab48a4961697338cc32d50e3a6b06ac989e39c3f/optimal/optimize.py#L325-L334 |
JustinLovinger/optimal | optimal/optimize.py | Optimizer._get_fitnesses | def _get_fitnesses(self,
problem,
population,
cache_encoded=True,
cache_solution=False,
pool=None):
"""Get the fitness for every solution in a population.
Args:
problem: Problem; The problem that defines fitness.
population: list; List of potential solutions.
pool: None/multiprocessing.Pool; Pool of processes for parallel
decoding and evaluation.
"""
fitnesses = [None] * len(population)
#############################
# Decoding
#############################
if cache_encoded:
try:
encoded_keys = map(self._get_encoded_key, population)
# Get all fitnesses from encoded_solution cache
to_decode_indices = []
for i, encoded_key in enumerate(encoded_keys):
try:
fitnesses[i] = self.__encoded_cache[encoded_key]
# Note that this fitness will never be better than the current best
# because we have already evaluted it,
# Therefore, we do not need to worry about decoding the solution
except KeyError: # Cache miss
to_decode_indices.append(i)
except UnhashableError: # Cannot hash encoded solution
encoded_keys = None
to_decode_indices = range(len(population))
else:
encoded_keys = None
to_decode_indices = range(len(population))
# Decode all that need to be decoded, and combine back into list the same length
# as population
if encoded_keys is None:
to_decode_keys = None
else:
to_decode_keys = [encoded_keys[i] for i in to_decode_indices]
solutions = [None] * len(population)
for i, solution in zip(to_decode_indices,
self._pmap(
problem.decode_solution,
[population[i] for i in to_decode_indices],
to_decode_keys,
pool)):
solutions[i] = solution
#############################
# Evaluating
#############################
if cache_solution:
try:
# Try to make solutions hashable
# Use user provided hash function if available
if problem.hash_solution:
hash_solution_func = problem.hash_solution
else:
# Otherwise, default to built in "smart" hash function
hash_solution_func = self._get_solution_key
solution_keys = [
hash_solution_func(solution)
# None corresponds to encoded_solutions found in cache
if solution is not None else None for solution in solutions
]
# Get all fitnesses from solution cache
to_eval_indices = []
for i, solution_key in enumerate(solution_keys):
if solution_key is not None: # Otherwise, fitness already found in encoded cache
try:
fitnesses[i] = self.__solution_cache[solution_key]
except KeyError: # Cache miss
to_eval_indices.append(i)
except UnhashableError: # Cannot hash solution
solution_keys = None
to_eval_indices = to_decode_indices[:]
else:
solution_keys = None
to_eval_indices = to_decode_indices[:]
# Evaluate all that need to be evaluated, and combine back into fitnesses list
if solution_keys is None:
if encoded_keys is None:
# No way to detect duplicates
to_eval_keys = None
else:
# Cannot use decoded keys, default to encoded keys
to_eval_keys = [encoded_keys[i] for i in to_eval_indices]
else:
to_eval_keys = [solution_keys[i] for i in to_eval_indices]
finished = False
eval_bookkeeping = {}
for i, fitness_finished in zip(to_eval_indices,
self._pmap(
problem.get_fitness,
[solutions[i] for i in to_eval_indices],
to_eval_keys,
pool,
bookkeeping_dict=eval_bookkeeping)):
# Unpack fitness_finished tuple
try:
fitness, maybe_finished = fitness_finished
if maybe_finished:
finished = True
except TypeError: # Not (fitness, finished) tuple
fitness = fitness_finished
fitnesses[i] = fitness
#############################
# Finishing
#############################
# Bookkeeping
# keep track of how many times fitness is evaluated
self.fitness_runs += len(eval_bookkeeping['key_indices']) # Evaled once for each unique key
# Add evaluated fitnesses to caches (both of them)
if cache_encoded and encoded_keys is not None:
for i in to_decode_indices: # Encoded cache misses
self.__encoded_cache[encoded_keys[i]] = fitnesses[i]
if cache_solution and solution_keys is not None:
for i in to_eval_indices: # Decoded cache misses
self.__solution_cache[solution_keys[i]] = fitnesses[i]
# Return
# assert None not in fitnesses # Un-comment for debugging
return solutions, fitnesses, finished | python | def _get_fitnesses(self,
problem,
population,
cache_encoded=True,
cache_solution=False,
pool=None):
"""Get the fitness for every solution in a population.
Args:
problem: Problem; The problem that defines fitness.
population: list; List of potential solutions.
pool: None/multiprocessing.Pool; Pool of processes for parallel
decoding and evaluation.
"""
fitnesses = [None] * len(population)
#############################
# Decoding
#############################
if cache_encoded:
try:
encoded_keys = map(self._get_encoded_key, population)
# Get all fitnesses from encoded_solution cache
to_decode_indices = []
for i, encoded_key in enumerate(encoded_keys):
try:
fitnesses[i] = self.__encoded_cache[encoded_key]
# Note that this fitness will never be better than the current best
# because we have already evaluted it,
# Therefore, we do not need to worry about decoding the solution
except KeyError: # Cache miss
to_decode_indices.append(i)
except UnhashableError: # Cannot hash encoded solution
encoded_keys = None
to_decode_indices = range(len(population))
else:
encoded_keys = None
to_decode_indices = range(len(population))
# Decode all that need to be decoded, and combine back into list the same length
# as population
if encoded_keys is None:
to_decode_keys = None
else:
to_decode_keys = [encoded_keys[i] for i in to_decode_indices]
solutions = [None] * len(population)
for i, solution in zip(to_decode_indices,
self._pmap(
problem.decode_solution,
[population[i] for i in to_decode_indices],
to_decode_keys,
pool)):
solutions[i] = solution
#############################
# Evaluating
#############################
if cache_solution:
try:
# Try to make solutions hashable
# Use user provided hash function if available
if problem.hash_solution:
hash_solution_func = problem.hash_solution
else:
# Otherwise, default to built in "smart" hash function
hash_solution_func = self._get_solution_key
solution_keys = [
hash_solution_func(solution)
# None corresponds to encoded_solutions found in cache
if solution is not None else None for solution in solutions
]
# Get all fitnesses from solution cache
to_eval_indices = []
for i, solution_key in enumerate(solution_keys):
if solution_key is not None: # Otherwise, fitness already found in encoded cache
try:
fitnesses[i] = self.__solution_cache[solution_key]
except KeyError: # Cache miss
to_eval_indices.append(i)
except UnhashableError: # Cannot hash solution
solution_keys = None
to_eval_indices = to_decode_indices[:]
else:
solution_keys = None
to_eval_indices = to_decode_indices[:]
# Evaluate all that need to be evaluated, and combine back into fitnesses list
if solution_keys is None:
if encoded_keys is None:
# No way to detect duplicates
to_eval_keys = None
else:
# Cannot use decoded keys, default to encoded keys
to_eval_keys = [encoded_keys[i] for i in to_eval_indices]
else:
to_eval_keys = [solution_keys[i] for i in to_eval_indices]
finished = False
eval_bookkeeping = {}
for i, fitness_finished in zip(to_eval_indices,
self._pmap(
problem.get_fitness,
[solutions[i] for i in to_eval_indices],
to_eval_keys,
pool,
bookkeeping_dict=eval_bookkeeping)):
# Unpack fitness_finished tuple
try:
fitness, maybe_finished = fitness_finished
if maybe_finished:
finished = True
except TypeError: # Not (fitness, finished) tuple
fitness = fitness_finished
fitnesses[i] = fitness
#############################
# Finishing
#############################
# Bookkeeping
# keep track of how many times fitness is evaluated
self.fitness_runs += len(eval_bookkeeping['key_indices']) # Evaled once for each unique key
# Add evaluated fitnesses to caches (both of them)
if cache_encoded and encoded_keys is not None:
for i in to_decode_indices: # Encoded cache misses
self.__encoded_cache[encoded_keys[i]] = fitnesses[i]
if cache_solution and solution_keys is not None:
for i in to_eval_indices: # Decoded cache misses
self.__solution_cache[solution_keys[i]] = fitnesses[i]
# Return
# assert None not in fitnesses # Un-comment for debugging
return solutions, fitnesses, finished | Get the fitness for every solution in a population.
Args:
problem: Problem; The problem that defines fitness.
population: list; List of potential solutions.
pool: None/multiprocessing.Pool; Pool of processes for parallel
decoding and evaluation. | https://github.com/JustinLovinger/optimal/blob/ab48a4961697338cc32d50e3a6b06ac989e39c3f/optimal/optimize.py#L336-L475 |
JustinLovinger/optimal | optimal/optimize.py | Optimizer._pmap | def _pmap(self, func, items, keys, pool, bookkeeping_dict=None):
"""Efficiently map func over all items.
Calls func only once for duplicate items.
Item duplicates are detected by corresponding keys.
Unless keys is None.
Serial if pool is None, but still skips duplicates.
"""
if keys is not None: # Otherwise, cannot hash items
# Remove duplicates first (use keys)
# Create mapping (dict) of key to list of indices
key_indices = _duplicates(keys).values()
else: # Cannot hash items
# Assume no duplicates
key_indices = [[i] for i in range(len(items))]
# Use only the first of duplicate indices in decoding
if pool is not None:
# Parallel map
results = pool.map(
functools.partial(_unpickle_run, pickle.dumps(func)),
[items[i[0]] for i in key_indices])
else:
results = map(func, [items[i[0]] for i in key_indices])
# Add bookkeeping
if bookkeeping_dict is not None:
bookkeeping_dict['key_indices'] = key_indices
# Combine duplicates back into list
all_results = [None] * len(items)
for indices, result in zip(key_indices, results):
for j, i in enumerate(indices):
# Avoid duplicate result objects in list,
# in case they are used in functions with side effects
if j > 0:
result = copy.deepcopy(result)
all_results[i] = result
return all_results | python | def _pmap(self, func, items, keys, pool, bookkeeping_dict=None):
"""Efficiently map func over all items.
Calls func only once for duplicate items.
Item duplicates are detected by corresponding keys.
Unless keys is None.
Serial if pool is None, but still skips duplicates.
"""
if keys is not None: # Otherwise, cannot hash items
# Remove duplicates first (use keys)
# Create mapping (dict) of key to list of indices
key_indices = _duplicates(keys).values()
else: # Cannot hash items
# Assume no duplicates
key_indices = [[i] for i in range(len(items))]
# Use only the first of duplicate indices in decoding
if pool is not None:
# Parallel map
results = pool.map(
functools.partial(_unpickle_run, pickle.dumps(func)),
[items[i[0]] for i in key_indices])
else:
results = map(func, [items[i[0]] for i in key_indices])
# Add bookkeeping
if bookkeeping_dict is not None:
bookkeeping_dict['key_indices'] = key_indices
# Combine duplicates back into list
all_results = [None] * len(items)
for indices, result in zip(key_indices, results):
for j, i in enumerate(indices):
# Avoid duplicate result objects in list,
# in case they are used in functions with side effects
if j > 0:
result = copy.deepcopy(result)
all_results[i] = result
return all_results | Efficiently map func over all items.
Calls func only once for duplicate items.
Item duplicates are detected by corresponding keys.
Unless keys is None.
Serial if pool is None, but still skips duplicates. | https://github.com/JustinLovinger/optimal/blob/ab48a4961697338cc32d50e3a6b06ac989e39c3f/optimal/optimize.py#L477-L517 |
JustinLovinger/optimal | optimal/optimize.py | Optimizer._set_hyperparameters | def _set_hyperparameters(self, parameters):
"""Set internal optimization parameters."""
for name, value in parameters.iteritems():
try:
getattr(self, name)
except AttributeError:
raise ValueError(
'Each parameter in parameters must be an attribute. '
'{} is not.'.format(name))
setattr(self, name, value) | python | def _set_hyperparameters(self, parameters):
"""Set internal optimization parameters."""
for name, value in parameters.iteritems():
try:
getattr(self, name)
except AttributeError:
raise ValueError(
'Each parameter in parameters must be an attribute. '
'{} is not.'.format(name))
setattr(self, name, value) | Set internal optimization parameters. | https://github.com/JustinLovinger/optimal/blob/ab48a4961697338cc32d50e3a6b06ac989e39c3f/optimal/optimize.py#L533-L542 |
JustinLovinger/optimal | optimal/optimize.py | Optimizer._get_hyperparameters | def _get_hyperparameters(self):
"""Get internal optimization parameters."""
hyperparameters = {}
for key in self._hyperparameters:
hyperparameters[key] = getattr(self, key)
return hyperparameters | python | def _get_hyperparameters(self):
"""Get internal optimization parameters."""
hyperparameters = {}
for key in self._hyperparameters:
hyperparameters[key] = getattr(self, key)
return hyperparameters | Get internal optimization parameters. | https://github.com/JustinLovinger/optimal/blob/ab48a4961697338cc32d50e3a6b06ac989e39c3f/optimal/optimize.py#L544-L549 |
JustinLovinger/optimal | optimal/optimize.py | Optimizer.optimize_hyperparameters | def optimize_hyperparameters(self,
problems,
parameter_locks=None,
smoothing=20,
max_iterations=100,
_meta_optimizer=None,
_low_memory=True):
"""Optimize hyperparameters for a given problem.
Args:
parameter_locks: a list of strings, each corresponding to a hyperparamter
that should not be optimized.
problems: Either a single problem, or a list of problem instances,
allowing optimization based on multiple similar problems.
smoothing: int; number of runs to average over for each set of hyperparameters.
max_iterations: The number of iterations to optimize before stopping.
_low_memory: disable performance enhancements to save memory
(they use a lot of memory otherwise).
"""
if smoothing <= 0:
raise ValueError('smoothing must be > 0')
# problems supports either one or many problem instances
if isinstance(problems, collections.Iterable):
for problem in problems:
if not isinstance(problem, Problem):
raise TypeError(
'problem must be Problem instance or list of Problem instances'
)
elif isinstance(problems, Problem):
problems = [problems]
else:
raise TypeError(
'problem must be Problem instance or list of Problem instances'
)
# Copy to avoid permanent modification
meta_parameters = copy.deepcopy(self._hyperparameters)
# First, handle parameter locks, since it will modify our
# meta_parameters dict
locked_values = _parse_parameter_locks(self, meta_parameters,
parameter_locks)
# We need to know the size of our chromosome,
# based on the hyperparameters to optimize
solution_size = _get_hyperparameter_solution_size(meta_parameters)
# We also need to create a decode function to transform the binary solution
# into parameters for the metaheuristic
decode = _make_hyperparameter_decode_func(locked_values,
meta_parameters)
# A master fitness dictionary can be stored for use between calls
# to meta_fitness
if _low_memory:
master_fitness_dict = None
else:
master_fitness_dict = {}
additional_parameters = {
'_optimizer': self,
'_problems': problems,
'_runs': smoothing,
'_master_fitness_dict': master_fitness_dict,
}
META_FITNESS = Problem(
_meta_fitness_func,
decode_function=decode,
fitness_kwargs=additional_parameters)
if _meta_optimizer is None:
# Initialize default meta optimizer
# GenAlg is used because it supports both discrete and continous
# attributes
from optimal import GenAlg
# Create metaheuristic with computed decode function and soltuion
# size
_meta_optimizer = GenAlg(solution_size)
else:
# Adjust supplied metaheuristic for this problem
_meta_optimizer._solution_size = solution_size
# Determine the best hyperparameters with a metaheuristic
best_parameters = _meta_optimizer.optimize(
META_FITNESS, max_iterations=max_iterations)
# Set the hyperparameters inline
self._set_hyperparameters(best_parameters)
# And return
return best_parameters | python | def optimize_hyperparameters(self,
problems,
parameter_locks=None,
smoothing=20,
max_iterations=100,
_meta_optimizer=None,
_low_memory=True):
"""Optimize hyperparameters for a given problem.
Args:
parameter_locks: a list of strings, each corresponding to a hyperparamter
that should not be optimized.
problems: Either a single problem, or a list of problem instances,
allowing optimization based on multiple similar problems.
smoothing: int; number of runs to average over for each set of hyperparameters.
max_iterations: The number of iterations to optimize before stopping.
_low_memory: disable performance enhancements to save memory
(they use a lot of memory otherwise).
"""
if smoothing <= 0:
raise ValueError('smoothing must be > 0')
# problems supports either one or many problem instances
if isinstance(problems, collections.Iterable):
for problem in problems:
if not isinstance(problem, Problem):
raise TypeError(
'problem must be Problem instance or list of Problem instances'
)
elif isinstance(problems, Problem):
problems = [problems]
else:
raise TypeError(
'problem must be Problem instance or list of Problem instances'
)
# Copy to avoid permanent modification
meta_parameters = copy.deepcopy(self._hyperparameters)
# First, handle parameter locks, since it will modify our
# meta_parameters dict
locked_values = _parse_parameter_locks(self, meta_parameters,
parameter_locks)
# We need to know the size of our chromosome,
# based on the hyperparameters to optimize
solution_size = _get_hyperparameter_solution_size(meta_parameters)
# We also need to create a decode function to transform the binary solution
# into parameters for the metaheuristic
decode = _make_hyperparameter_decode_func(locked_values,
meta_parameters)
# A master fitness dictionary can be stored for use between calls
# to meta_fitness
if _low_memory:
master_fitness_dict = None
else:
master_fitness_dict = {}
additional_parameters = {
'_optimizer': self,
'_problems': problems,
'_runs': smoothing,
'_master_fitness_dict': master_fitness_dict,
}
META_FITNESS = Problem(
_meta_fitness_func,
decode_function=decode,
fitness_kwargs=additional_parameters)
if _meta_optimizer is None:
# Initialize default meta optimizer
# GenAlg is used because it supports both discrete and continous
# attributes
from optimal import GenAlg
# Create metaheuristic with computed decode function and soltuion
# size
_meta_optimizer = GenAlg(solution_size)
else:
# Adjust supplied metaheuristic for this problem
_meta_optimizer._solution_size = solution_size
# Determine the best hyperparameters with a metaheuristic
best_parameters = _meta_optimizer.optimize(
META_FITNESS, max_iterations=max_iterations)
# Set the hyperparameters inline
self._set_hyperparameters(best_parameters)
# And return
return best_parameters | Optimize hyperparameters for a given problem.
Args:
parameter_locks: a list of strings, each corresponding to a hyperparamter
that should not be optimized.
problems: Either a single problem, or a list of problem instances,
allowing optimization based on multiple similar problems.
smoothing: int; number of runs to average over for each set of hyperparameters.
max_iterations: The number of iterations to optimize before stopping.
_low_memory: disable performance enhancements to save memory
(they use a lot of memory otherwise). | https://github.com/JustinLovinger/optimal/blob/ab48a4961697338cc32d50e3a6b06ac989e39c3f/optimal/optimize.py#L551-L642 |
JustinLovinger/optimal | optimal/benchmark.py | compare | def compare(optimizers, problems, runs=20, all_kwargs={}):
"""Compare a set of optimizers.
Args:
optimizers: list/Optimizer; Either a list of optimizers to compare,
or a single optimizer to test on each problem.
problems: list/Problem; Either a problem instance or a list of problem instances,
one for each optimizer.
all_kwargs: dict/list<dict>; Either the Optimizer.optimize keyword arguments
for all optimizers, or a list of keyword arguments, one for each optimizer.
runs: int; How many times to run each optimizer (smoothness)
Returns:
dict; mapping optimizer identifier to stats.
"""
if not (isinstance(optimizers, collections.Iterable)
or isinstance(problems, collections.Iterable)):
raise TypeError('optimizers or problems must be iterable')
# If optimizers is not a list, repeat into list for each problem
if not isinstance(optimizers, collections.Iterable):
optimizers = [copy.deepcopy(optimizers) for _ in range(len(problems))]
# If problems is not a list, repeat into list for each optimizer
if not isinstance(problems, collections.Iterable):
problems = [copy.deepcopy(problems) for _ in range(len(optimizers))]
# If all_kwargs is not a list, repeat it into a list
if isinstance(all_kwargs, dict):
all_kwargs = [all_kwargs] * len(optimizers)
elif not isinstance(all_kwargs, collections.Iterable):
raise TypeError('all_kwargs must be dict or list of dict')
stats = {}
key_counts = {}
for optimizer, problem, kwargs in zip(optimizers, problems, all_kwargs):
# For nice human readable dictionaries, extract useful names from
# optimizer
class_name = optimizer.__class__.__name__
fitness_func_name = problem._fitness_function.__name__
key_name = '{} {}'.format(class_name, fitness_func_name)
# Keep track of how many optimizers of each class / fitness func
# for better keys in stats dict
try:
key_counts[key_name] += 1
except KeyError:
key_counts[key_name] = 1
# Foo 1, Foo 2, Bar 1, etc.
key = '{} {}'.format(key_name, key_counts[key_name])
print key + ': ',
# Finally, get the actual stats
stats[key] = benchmark(optimizer, problem, runs=runs, **kwargs)
print
return stats | python | def compare(optimizers, problems, runs=20, all_kwargs={}):
"""Compare a set of optimizers.
Args:
optimizers: list/Optimizer; Either a list of optimizers to compare,
or a single optimizer to test on each problem.
problems: list/Problem; Either a problem instance or a list of problem instances,
one for each optimizer.
all_kwargs: dict/list<dict>; Either the Optimizer.optimize keyword arguments
for all optimizers, or a list of keyword arguments, one for each optimizer.
runs: int; How many times to run each optimizer (smoothness)
Returns:
dict; mapping optimizer identifier to stats.
"""
if not (isinstance(optimizers, collections.Iterable)
or isinstance(problems, collections.Iterable)):
raise TypeError('optimizers or problems must be iterable')
# If optimizers is not a list, repeat into list for each problem
if not isinstance(optimizers, collections.Iterable):
optimizers = [copy.deepcopy(optimizers) for _ in range(len(problems))]
# If problems is not a list, repeat into list for each optimizer
if not isinstance(problems, collections.Iterable):
problems = [copy.deepcopy(problems) for _ in range(len(optimizers))]
# If all_kwargs is not a list, repeat it into a list
if isinstance(all_kwargs, dict):
all_kwargs = [all_kwargs] * len(optimizers)
elif not isinstance(all_kwargs, collections.Iterable):
raise TypeError('all_kwargs must be dict or list of dict')
stats = {}
key_counts = {}
for optimizer, problem, kwargs in zip(optimizers, problems, all_kwargs):
# For nice human readable dictionaries, extract useful names from
# optimizer
class_name = optimizer.__class__.__name__
fitness_func_name = problem._fitness_function.__name__
key_name = '{} {}'.format(class_name, fitness_func_name)
# Keep track of how many optimizers of each class / fitness func
# for better keys in stats dict
try:
key_counts[key_name] += 1
except KeyError:
key_counts[key_name] = 1
# Foo 1, Foo 2, Bar 1, etc.
key = '{} {}'.format(key_name, key_counts[key_name])
print key + ': ',
# Finally, get the actual stats
stats[key] = benchmark(optimizer, problem, runs=runs, **kwargs)
print
return stats | Compare a set of optimizers.
Args:
optimizers: list/Optimizer; Either a list of optimizers to compare,
or a single optimizer to test on each problem.
problems: list/Problem; Either a problem instance or a list of problem instances,
one for each optimizer.
all_kwargs: dict/list<dict>; Either the Optimizer.optimize keyword arguments
for all optimizers, or a list of keyword arguments, one for each optimizer.
runs: int; How many times to run each optimizer (smoothness)
Returns:
dict; mapping optimizer identifier to stats. | https://github.com/JustinLovinger/optimal/blob/ab48a4961697338cc32d50e3a6b06ac989e39c3f/optimal/benchmark.py#L37-L96 |
JustinLovinger/optimal | optimal/benchmark.py | benchmark | def benchmark(optimizer, problem, runs=20, **kwargs):
"""Run an optimizer through a problem multiple times.
Args:
optimizer: Optimizer; The optimizer to benchmark.
problem: Problem; The problem to benchmark on.
runs: int > 0; Number of times that optimize is called on problem.
Returns:
dict; A dictionary of various statistics.
"""
stats = {'runs': []}
# Disable logging, to avoid spamming the user
# TODO: Maybe we shouldn't disable by default?
kwargs = copy.copy(kwargs)
kwargs['logging_func'] = None
# Determine effectiveness of metaheuristic over many runs
# The stochastic nature of metaheuristics make this necessary
# for an accurate evaluation
for _ in range(runs):
optimizer.optimize(problem, **kwargs)
# Convert bool to number for mean and standard deviation calculations
if optimizer.solution_found:
finished_num = 1.0
else:
finished_num = 0.0
stats_ = {
'fitness': optimizer.best_fitness,
'fitness_runs': optimizer.fitness_runs,
'solution_found': finished_num
}
stats['runs'].append(stats_)
# Little progress 'bar'
print '.',
# Mean gives a good overall idea of the metaheuristics effectiveness
# Standard deviation (SD) shows consistency of performance
_add_mean_sd_to_stats(stats)
return stats | python | def benchmark(optimizer, problem, runs=20, **kwargs):
"""Run an optimizer through a problem multiple times.
Args:
optimizer: Optimizer; The optimizer to benchmark.
problem: Problem; The problem to benchmark on.
runs: int > 0; Number of times that optimize is called on problem.
Returns:
dict; A dictionary of various statistics.
"""
stats = {'runs': []}
# Disable logging, to avoid spamming the user
# TODO: Maybe we shouldn't disable by default?
kwargs = copy.copy(kwargs)
kwargs['logging_func'] = None
# Determine effectiveness of metaheuristic over many runs
# The stochastic nature of metaheuristics make this necessary
# for an accurate evaluation
for _ in range(runs):
optimizer.optimize(problem, **kwargs)
# Convert bool to number for mean and standard deviation calculations
if optimizer.solution_found:
finished_num = 1.0
else:
finished_num = 0.0
stats_ = {
'fitness': optimizer.best_fitness,
'fitness_runs': optimizer.fitness_runs,
'solution_found': finished_num
}
stats['runs'].append(stats_)
# Little progress 'bar'
print '.',
# Mean gives a good overall idea of the metaheuristics effectiveness
# Standard deviation (SD) shows consistency of performance
_add_mean_sd_to_stats(stats)
return stats | Run an optimizer through a problem multiple times.
Args:
optimizer: Optimizer; The optimizer to benchmark.
problem: Problem; The problem to benchmark on.
runs: int > 0; Number of times that optimize is called on problem.
Returns:
dict; A dictionary of various statistics. | https://github.com/JustinLovinger/optimal/blob/ab48a4961697338cc32d50e3a6b06ac989e39c3f/optimal/benchmark.py#L99-L143 |
JustinLovinger/optimal | optimal/benchmark.py | aggregate | def aggregate(all_stats):
"""Combine stats for multiple optimizers to obtain one mean and sd.
Useful for combining stats for the same optimizer class and multiple problems.
Args:
all_stats: dict; output from compare.
"""
aggregate_stats = {'means': [], 'standard_deviations': []}
for optimizer_key in all_stats:
# runs is the mean, for add_mean_sd function
mean_stats = copy.deepcopy(all_stats[optimizer_key]['mean'])
mean_stats['name'] = optimizer_key
aggregate_stats['means'].append(mean_stats)
# also keep track of standard deviations
sd_stats = copy.deepcopy(
all_stats[optimizer_key]['standard_deviation'])
sd_stats['name'] = optimizer_key
aggregate_stats['standard_deviations'].append(sd_stats)
_add_mean_sd_to_stats(aggregate_stats, 'means')
return aggregate_stats | python | def aggregate(all_stats):
"""Combine stats for multiple optimizers to obtain one mean and sd.
Useful for combining stats for the same optimizer class and multiple problems.
Args:
all_stats: dict; output from compare.
"""
aggregate_stats = {'means': [], 'standard_deviations': []}
for optimizer_key in all_stats:
# runs is the mean, for add_mean_sd function
mean_stats = copy.deepcopy(all_stats[optimizer_key]['mean'])
mean_stats['name'] = optimizer_key
aggregate_stats['means'].append(mean_stats)
# also keep track of standard deviations
sd_stats = copy.deepcopy(
all_stats[optimizer_key]['standard_deviation'])
sd_stats['name'] = optimizer_key
aggregate_stats['standard_deviations'].append(sd_stats)
_add_mean_sd_to_stats(aggregate_stats, 'means')
return aggregate_stats | Combine stats for multiple optimizers to obtain one mean and sd.
Useful for combining stats for the same optimizer class and multiple problems.
Args:
all_stats: dict; output from compare. | https://github.com/JustinLovinger/optimal/blob/ab48a4961697338cc32d50e3a6b06ac989e39c3f/optimal/benchmark.py#L146-L169 |
JustinLovinger/optimal | optimal/benchmark.py | _mean_of_runs | def _mean_of_runs(stats, key='runs'):
"""Obtain the mean of stats.
Args:
stats: dict; A set of stats, structured as above.
key: str; Optional key to determine where list of runs is found in stats
"""
num_runs = len(stats[key])
first = stats[key][0]
mean = {}
for stat_key in first:
# Skip non numberic attributes
if isinstance(first[stat_key], numbers.Number):
mean[stat_key] = sum(run[stat_key]
for run in stats[key]) / float(num_runs)
return mean | python | def _mean_of_runs(stats, key='runs'):
"""Obtain the mean of stats.
Args:
stats: dict; A set of stats, structured as above.
key: str; Optional key to determine where list of runs is found in stats
"""
num_runs = len(stats[key])
first = stats[key][0]
mean = {}
for stat_key in first:
# Skip non numberic attributes
if isinstance(first[stat_key], numbers.Number):
mean[stat_key] = sum(run[stat_key]
for run in stats[key]) / float(num_runs)
return mean | Obtain the mean of stats.
Args:
stats: dict; A set of stats, structured as above.
key: str; Optional key to determine where list of runs is found in stats | https://github.com/JustinLovinger/optimal/blob/ab48a4961697338cc32d50e3a6b06ac989e39c3f/optimal/benchmark.py#L180-L198 |
JustinLovinger/optimal | optimal/benchmark.py | _sd_of_runs | def _sd_of_runs(stats, mean, key='runs'):
"""Obtain the standard deviation of stats.
Args:
stats: dict; A set of stats, structured as above.
mean: dict; Mean for each key in stats.
key: str; Optional key to determine where list of runs is found in stats
"""
num_runs = len(stats[key])
first = stats[key][0]
standard_deviation = {}
for stat_key in first:
# Skip non numberic attributes
if isinstance(first[stat_key], numbers.Number):
standard_deviation[stat_key] = math.sqrt(
sum((run[stat_key] - mean[stat_key])**2
for run in stats[key]) / float(num_runs))
return standard_deviation | python | def _sd_of_runs(stats, mean, key='runs'):
"""Obtain the standard deviation of stats.
Args:
stats: dict; A set of stats, structured as above.
mean: dict; Mean for each key in stats.
key: str; Optional key to determine where list of runs is found in stats
"""
num_runs = len(stats[key])
first = stats[key][0]
standard_deviation = {}
for stat_key in first:
# Skip non numberic attributes
if isinstance(first[stat_key], numbers.Number):
standard_deviation[stat_key] = math.sqrt(
sum((run[stat_key] - mean[stat_key])**2
for run in stats[key]) / float(num_runs))
return standard_deviation | Obtain the standard deviation of stats.
Args:
stats: dict; A set of stats, structured as above.
mean: dict; Mean for each key in stats.
key: str; Optional key to determine where list of runs is found in stats | https://github.com/JustinLovinger/optimal/blob/ab48a4961697338cc32d50e3a6b06ac989e39c3f/optimal/benchmark.py#L201-L221 |
JustinLovinger/optimal | optimal/algorithms/pbil.py | _sample | def _sample(probability_vec):
"""Return random binary string, with given probabilities."""
return map(int,
numpy.random.random(probability_vec.size) <= probability_vec) | python | def _sample(probability_vec):
"""Return random binary string, with given probabilities."""
return map(int,
numpy.random.random(probability_vec.size) <= probability_vec) | Return random binary string, with given probabilities. | https://github.com/JustinLovinger/optimal/blob/ab48a4961697338cc32d50e3a6b06ac989e39c3f/optimal/algorithms/pbil.py#L126-L129 |
JustinLovinger/optimal | optimal/algorithms/pbil.py | _adjust_probability_vec_best | def _adjust_probability_vec_best(population, fitnesses, probability_vec,
adjust_rate):
"""Shift probabilities towards the best solution."""
best_solution = max(zip(fitnesses, population))[1]
# Shift probabilities towards best solution
return _adjust(probability_vec, best_solution, adjust_rate) | python | def _adjust_probability_vec_best(population, fitnesses, probability_vec,
adjust_rate):
"""Shift probabilities towards the best solution."""
best_solution = max(zip(fitnesses, population))[1]
# Shift probabilities towards best solution
return _adjust(probability_vec, best_solution, adjust_rate) | Shift probabilities towards the best solution. | https://github.com/JustinLovinger/optimal/blob/ab48a4961697338cc32d50e3a6b06ac989e39c3f/optimal/algorithms/pbil.py#L132-L138 |
JustinLovinger/optimal | optimal/algorithms/pbil.py | _mutate_probability_vec | def _mutate_probability_vec(probability_vec, mutation_chance, mutation_adjust_rate):
"""Randomly adjust probabilities.
WARNING: Modifies probability_vec argument.
"""
bits_to_mutate = numpy.random.random(probability_vec.size) <= mutation_chance
probability_vec[bits_to_mutate] = _adjust(
probability_vec[bits_to_mutate],
numpy.random.random(numpy.sum(bits_to_mutate)), mutation_adjust_rate) | python | def _mutate_probability_vec(probability_vec, mutation_chance, mutation_adjust_rate):
"""Randomly adjust probabilities.
WARNING: Modifies probability_vec argument.
"""
bits_to_mutate = numpy.random.random(probability_vec.size) <= mutation_chance
probability_vec[bits_to_mutate] = _adjust(
probability_vec[bits_to_mutate],
numpy.random.random(numpy.sum(bits_to_mutate)), mutation_adjust_rate) | Randomly adjust probabilities.
WARNING: Modifies probability_vec argument. | https://github.com/JustinLovinger/optimal/blob/ab48a4961697338cc32d50e3a6b06ac989e39c3f/optimal/algorithms/pbil.py#L141-L149 |
JustinLovinger/optimal | optimal/algorithms/pbil.py | PBIL.next_population | def next_population(self, population, fitnesses):
"""Make a new population after each optimization iteration.
Args:
population: The population current population of solutions.
fitnesses: The fitness associated with each solution in the population
Returns:
list; a list of solutions.
"""
# Update probability vector
self._probability_vec = _adjust_probability_vec_best(
population, fitnesses, self._probability_vec, self._adjust_rate)
# Mutate probability vector
_mutate_probability_vec(self._probability_vec, self._mutation_chance,
self._mutation_adjust_rate)
# Return new samples
return [
_sample(self._probability_vec)
for _ in range(self._population_size)
] | python | def next_population(self, population, fitnesses):
"""Make a new population after each optimization iteration.
Args:
population: The population current population of solutions.
fitnesses: The fitness associated with each solution in the population
Returns:
list; a list of solutions.
"""
# Update probability vector
self._probability_vec = _adjust_probability_vec_best(
population, fitnesses, self._probability_vec, self._adjust_rate)
# Mutate probability vector
_mutate_probability_vec(self._probability_vec, self._mutation_chance,
self._mutation_adjust_rate)
# Return new samples
return [
_sample(self._probability_vec)
for _ in range(self._population_size)
] | Make a new population after each optimization iteration.
Args:
population: The population current population of solutions.
fitnesses: The fitness associated with each solution in the population
Returns:
list; a list of solutions. | https://github.com/JustinLovinger/optimal/blob/ab48a4961697338cc32d50e3a6b06ac989e39c3f/optimal/algorithms/pbil.py#L102-L123 |
JustinLovinger/optimal | optimal/examples/benchmark_gaoperators.py | benchmark_multi | def benchmark_multi(optimizer):
"""Benchmark an optimizer configuration on multiple functions."""
# Get our benchmark stats
all_stats = benchmark.compare(optimizer, PROBLEMS, runs=100)
return benchmark.aggregate(all_stats) | python | def benchmark_multi(optimizer):
"""Benchmark an optimizer configuration on multiple functions."""
# Get our benchmark stats
all_stats = benchmark.compare(optimizer, PROBLEMS, runs=100)
return benchmark.aggregate(all_stats) | Benchmark an optimizer configuration on multiple functions. | https://github.com/JustinLovinger/optimal/blob/ab48a4961697338cc32d50e3a6b06ac989e39c3f/optimal/examples/benchmark_gaoperators.py#L49-L53 |
JustinLovinger/optimal | optimal/algorithms/crossentropy.py | _sample | def _sample(probabilities, population_size):
"""Return a random population, drawn with regard to a set of probabilities"""
population = []
for _ in range(population_size):
solution = []
for probability in probabilities:
# probability of 1.0: always 1
# probability of 0.0: always 0
if random.uniform(0.0, 1.0) < probability:
solution.append(1)
else:
solution.append(0)
population.append(solution)
return population | python | def _sample(probabilities, population_size):
"""Return a random population, drawn with regard to a set of probabilities"""
population = []
for _ in range(population_size):
solution = []
for probability in probabilities:
# probability of 1.0: always 1
# probability of 0.0: always 0
if random.uniform(0.0, 1.0) < probability:
solution.append(1)
else:
solution.append(0)
population.append(solution)
return population | Return a random population, drawn with regard to a set of probabilities | https://github.com/JustinLovinger/optimal/blob/ab48a4961697338cc32d50e3a6b06ac989e39c3f/optimal/algorithms/crossentropy.py#L112-L125 |
JustinLovinger/optimal | optimal/algorithms/crossentropy.py | _chance | def _chance(solution, pdf):
"""Return the chance of obtaining a solution from a pdf.
The probability of many independant weighted "coin flips" (one for each bit)
"""
# 1.0 - abs(bit - p) gives probability of bit given p
return _prod([1.0 - abs(bit - p) for bit, p in zip(solution, pdf)]) | python | def _chance(solution, pdf):
"""Return the chance of obtaining a solution from a pdf.
The probability of many independant weighted "coin flips" (one for each bit)
"""
# 1.0 - abs(bit - p) gives probability of bit given p
return _prod([1.0 - abs(bit - p) for bit, p in zip(solution, pdf)]) | Return the chance of obtaining a solution from a pdf.
The probability of many independant weighted "coin flips" (one for each bit) | https://github.com/JustinLovinger/optimal/blob/ab48a4961697338cc32d50e3a6b06ac989e39c3f/optimal/algorithms/crossentropy.py#L132-L138 |
JustinLovinger/optimal | optimal/algorithms/crossentropy.py | _pdf_value | def _pdf_value(pdf, population, fitnesses, fitness_threshold):
"""Give the value of a pdf.
This represents the likelihood of a pdf generating solutions
that exceed the threshold.
"""
# Add the chance of obtaining a solution from the pdf
# when the fitness for that solution exceeds a threshold
value = 0.0
for solution, fitness in zip(population, fitnesses):
if fitness >= fitness_threshold:
# 1.0 + chance to avoid issues with chance of 0
value += math.log(1.0 + _chance(solution, pdf))
# The official equation states that value is now divided by len(fitnesses)
# however, this is unnecessary when we are only obtaining the best pdf,
# because every solution is of the same size
return value | python | def _pdf_value(pdf, population, fitnesses, fitness_threshold):
"""Give the value of a pdf.
This represents the likelihood of a pdf generating solutions
that exceed the threshold.
"""
# Add the chance of obtaining a solution from the pdf
# when the fitness for that solution exceeds a threshold
value = 0.0
for solution, fitness in zip(population, fitnesses):
if fitness >= fitness_threshold:
# 1.0 + chance to avoid issues with chance of 0
value += math.log(1.0 + _chance(solution, pdf))
# The official equation states that value is now divided by len(fitnesses)
# however, this is unnecessary when we are only obtaining the best pdf,
# because every solution is of the same size
return value | Give the value of a pdf.
This represents the likelihood of a pdf generating solutions
that exceed the threshold. | https://github.com/JustinLovinger/optimal/blob/ab48a4961697338cc32d50e3a6b06ac989e39c3f/optimal/algorithms/crossentropy.py#L141-L158 |
JustinLovinger/optimal | optimal/algorithms/crossentropy.py | _update_pdf | def _update_pdf(population, fitnesses, pdfs, quantile):
"""Find a better pdf, based on fitnesses."""
# First we determine a fitness threshold based on a quantile of fitnesses
fitness_threshold = _get_quantile_cutoff(fitnesses, quantile)
# Then check all of our possible pdfs with a stochastic program
return _best_pdf(pdfs, population, fitnesses, fitness_threshold) | python | def _update_pdf(population, fitnesses, pdfs, quantile):
"""Find a better pdf, based on fitnesses."""
# First we determine a fitness threshold based on a quantile of fitnesses
fitness_threshold = _get_quantile_cutoff(fitnesses, quantile)
# Then check all of our possible pdfs with a stochastic program
return _best_pdf(pdfs, population, fitnesses, fitness_threshold) | Find a better pdf, based on fitnesses. | https://github.com/JustinLovinger/optimal/blob/ab48a4961697338cc32d50e3a6b06ac989e39c3f/optimal/algorithms/crossentropy.py#L175-L181 |
JustinLovinger/optimal | optimal/helpers.py | binary_to_float | def binary_to_float(binary_list, lower_bound, upper_bound):
"""Return a floating point number between lower and upper bounds, from binary.
Args:
binary_list: list<int>; List of 0s and 1s.
The number of bits in this list determine the number of possible
values between lower and upper bound.
Increase the size of binary_list for more precise floating points.
lower_bound: Minimum value for output, inclusive.
A binary list of 0s will have this value.
upper_bound: Maximum value for output, inclusive.
A binary list of 1s will have this value.
Returns:
float; A floating point number.
"""
# Edge case for empty binary_list
if binary_list == []:
# With 0 bits, only one value can be represented,
# and we default to lower_bound
return lower_bound
# A little bit of math gets us a floating point
# number between upper and lower bound
# We look at the relative position of
# the integer corresponding to our binary list
# between the upper and lower bound,
# and offset that by lower bound
return ((
# Range between lower and upper bound
float(upper_bound - lower_bound)
# Divided by the maximum possible integer
/ (2**len(binary_list) - 1)
# Times the integer represented by the given binary
* binary_to_int(binary_list))
# Plus the lower bound
+ lower_bound) | python | def binary_to_float(binary_list, lower_bound, upper_bound):
"""Return a floating point number between lower and upper bounds, from binary.
Args:
binary_list: list<int>; List of 0s and 1s.
The number of bits in this list determine the number of possible
values between lower and upper bound.
Increase the size of binary_list for more precise floating points.
lower_bound: Minimum value for output, inclusive.
A binary list of 0s will have this value.
upper_bound: Maximum value for output, inclusive.
A binary list of 1s will have this value.
Returns:
float; A floating point number.
"""
# Edge case for empty binary_list
if binary_list == []:
# With 0 bits, only one value can be represented,
# and we default to lower_bound
return lower_bound
# A little bit of math gets us a floating point
# number between upper and lower bound
# We look at the relative position of
# the integer corresponding to our binary list
# between the upper and lower bound,
# and offset that by lower bound
return ((
# Range between lower and upper bound
float(upper_bound - lower_bound)
# Divided by the maximum possible integer
/ (2**len(binary_list) - 1)
# Times the integer represented by the given binary
* binary_to_int(binary_list))
# Plus the lower bound
+ lower_bound) | Return a floating point number between lower and upper bounds, from binary.
Args:
binary_list: list<int>; List of 0s and 1s.
The number of bits in this list determine the number of possible
values between lower and upper bound.
Increase the size of binary_list for more precise floating points.
lower_bound: Minimum value for output, inclusive.
A binary list of 0s will have this value.
upper_bound: Maximum value for output, inclusive.
A binary list of 1s will have this value.
Returns:
float; A floating point number. | https://github.com/JustinLovinger/optimal/blob/ab48a4961697338cc32d50e3a6b06ac989e39c3f/optimal/helpers.py#L34-L71 |
JustinLovinger/optimal | optimal/helpers.py | binary_to_int | def binary_to_int(binary_list, lower_bound=0, upper_bound=None):
"""Return the base 10 integer corresponding to a binary list.
The maximum value is determined by the number of bits in binary_list,
and upper_bound. The greater allowed by the two.
Args:
binary_list: list<int>; List of 0s and 1s.
lower_bound: Minimum value for output, inclusive.
A binary list of 0s will have this value.
upper_bound: Maximum value for output, inclusive.
If greater than this bound, we "bounce back".
Ex. w/ upper_bound = 2: [0, 1, 2, 2, 1, 0]
Ex.
raw_integer = 11, upper_bound = 10, return = 10
raw_integer = 12, upper_bound = 10, return = 9
Returns:
int; Integer value of the binary input.
"""
# Edge case for empty binary_list
if binary_list == []:
# With 0 bits, only one value can be represented,
# and we default to lower_bound
return lower_bound
else:
# The builtin int construction can take a base argument,
# but it requires a string,
# so we convert our binary list to a string
integer = int(''.join([str(bit) for bit in binary_list]), 2)
# Trim if over upper_bound
if (upper_bound is not None) and integer + lower_bound > upper_bound:
# Bounce back. Ex. w/ upper_bound = 2: [0, 1, 2, 2, 1, 0]
return upper_bound - (integer % (upper_bound - lower_bound + 1))
else:
# Not over upper_bound
return integer + lower_bound | python | def binary_to_int(binary_list, lower_bound=0, upper_bound=None):
"""Return the base 10 integer corresponding to a binary list.
The maximum value is determined by the number of bits in binary_list,
and upper_bound. The greater allowed by the two.
Args:
binary_list: list<int>; List of 0s and 1s.
lower_bound: Minimum value for output, inclusive.
A binary list of 0s will have this value.
upper_bound: Maximum value for output, inclusive.
If greater than this bound, we "bounce back".
Ex. w/ upper_bound = 2: [0, 1, 2, 2, 1, 0]
Ex.
raw_integer = 11, upper_bound = 10, return = 10
raw_integer = 12, upper_bound = 10, return = 9
Returns:
int; Integer value of the binary input.
"""
# Edge case for empty binary_list
if binary_list == []:
# With 0 bits, only one value can be represented,
# and we default to lower_bound
return lower_bound
else:
# The builtin int construction can take a base argument,
# but it requires a string,
# so we convert our binary list to a string
integer = int(''.join([str(bit) for bit in binary_list]), 2)
# Trim if over upper_bound
if (upper_bound is not None) and integer + lower_bound > upper_bound:
# Bounce back. Ex. w/ upper_bound = 2: [0, 1, 2, 2, 1, 0]
return upper_bound - (integer % (upper_bound - lower_bound + 1))
else:
# Not over upper_bound
return integer + lower_bound | Return the base 10 integer corresponding to a binary list.
The maximum value is determined by the number of bits in binary_list,
and upper_bound. The greater allowed by the two.
Args:
binary_list: list<int>; List of 0s and 1s.
lower_bound: Minimum value for output, inclusive.
A binary list of 0s will have this value.
upper_bound: Maximum value for output, inclusive.
If greater than this bound, we "bounce back".
Ex. w/ upper_bound = 2: [0, 1, 2, 2, 1, 0]
Ex.
raw_integer = 11, upper_bound = 10, return = 10
raw_integer = 12, upper_bound = 10, return = 9
Returns:
int; Integer value of the binary input. | https://github.com/JustinLovinger/optimal/blob/ab48a4961697338cc32d50e3a6b06ac989e39c3f/optimal/helpers.py#L74-L111 |
JustinLovinger/optimal | optimal/algorithms/baseline.py | _int_to_binary | def _int_to_binary(integer, size=None):
"""Return bit list representation of integer.
If size is given, binary string is padded with 0s, or clipped to the size.
"""
binary_list = map(int, format(integer, 'b'))
if size is None:
return binary_list
else:
if len(binary_list) > size:
# Too long, take only last n
return binary_list[len(binary_list)-size:]
elif size > len(binary_list):
# Too short, pad
return [0]*(size-len(binary_list)) + binary_list
else:
# Just right
return binary_list | python | def _int_to_binary(integer, size=None):
"""Return bit list representation of integer.
If size is given, binary string is padded with 0s, or clipped to the size.
"""
binary_list = map(int, format(integer, 'b'))
if size is None:
return binary_list
else:
if len(binary_list) > size:
# Too long, take only last n
return binary_list[len(binary_list)-size:]
elif size > len(binary_list):
# Too short, pad
return [0]*(size-len(binary_list)) + binary_list
else:
# Just right
return binary_list | Return bit list representation of integer.
If size is given, binary string is padded with 0s, or clipped to the size. | https://github.com/JustinLovinger/optimal/blob/ab48a4961697338cc32d50e3a6b06ac989e39c3f/optimal/algorithms/baseline.py#L164-L182 |
JustinLovinger/optimal | optimal/algorithms/baseline.py | _RandomOptimizer.next_population | def next_population(self, population, fitnesses):
"""Make a new population after each optimization iteration.
Args:
population: The population current population of solutions.
fitnesses: The fitness associated with each solution in the population
Returns:
list; a list of solutions.
"""
return common.make_population(self._population_size,
self._generate_solution) | python | def next_population(self, population, fitnesses):
"""Make a new population after each optimization iteration.
Args:
population: The population current population of solutions.
fitnesses: The fitness associated with each solution in the population
Returns:
list; a list of solutions.
"""
return common.make_population(self._population_size,
self._generate_solution) | Make a new population after each optimization iteration.
Args:
population: The population current population of solutions.
fitnesses: The fitness associated with each solution in the population
Returns:
list; a list of solutions. | https://github.com/JustinLovinger/optimal/blob/ab48a4961697338cc32d50e3a6b06ac989e39c3f/optimal/algorithms/baseline.py#L63-L73 |
JustinLovinger/optimal | optimal/algorithms/baseline.py | RandomReal._generate_solution | def _generate_solution(self):
"""Return a single random solution."""
return common.random_real_solution(
self._solution_size, self._lower_bounds, self._upper_bounds) | python | def _generate_solution(self):
"""Return a single random solution."""
return common.random_real_solution(
self._solution_size, self._lower_bounds, self._upper_bounds) | Return a single random solution. | https://github.com/JustinLovinger/optimal/blob/ab48a4961697338cc32d50e3a6b06ac989e39c3f/optimal/algorithms/baseline.py#L108-L111 |
JustinLovinger/optimal | optimal/algorithms/baseline.py | ExhaustiveBinary.next_population | def next_population(self, population, fitnesses):
"""Make a new population after each optimization iteration.
Args:
population: The population current population of solutions.
fitnesses: The fitness associated with each solution in the population
Returns:
list; a list of solutions.
"""
return [self._next_solution() for _ in range(self._population_size)] | python | def next_population(self, population, fitnesses):
"""Make a new population after each optimization iteration.
Args:
population: The population current population of solutions.
fitnesses: The fitness associated with each solution in the population
Returns:
list; a list of solutions.
"""
return [self._next_solution() for _ in range(self._population_size)] | Make a new population after each optimization iteration.
Args:
population: The population current population of solutions.
fitnesses: The fitness associated with each solution in the population
Returns:
list; a list of solutions. | https://github.com/JustinLovinger/optimal/blob/ab48a4961697338cc32d50e3a6b06ac989e39c3f/optimal/algorithms/baseline.py#L147-L156 |
cloudsigma/cgroupspy | cgroupspy/trees.py | BaseTree._build_tree | def _build_tree(self):
"""
Build a full or a partial tree, depending on the groups/sub-groups specified.
"""
groups = self._groups or self.get_children_paths(self.root_path)
for group in groups:
node = Node(name=group, parent=self.root)
self.root.children.append(node)
self._init_sub_groups(node) | python | def _build_tree(self):
"""
Build a full or a partial tree, depending on the groups/sub-groups specified.
"""
groups = self._groups or self.get_children_paths(self.root_path)
for group in groups:
node = Node(name=group, parent=self.root)
self.root.children.append(node)
self._init_sub_groups(node) | Build a full or a partial tree, depending on the groups/sub-groups specified. | https://github.com/cloudsigma/cgroupspy/blob/e705ac4ccdfe33d8ecc700e9a35a9556084449ca/cgroupspy/trees.py#L71-L80 |
cloudsigma/cgroupspy | cgroupspy/trees.py | BaseTree._init_sub_groups | def _init_sub_groups(self, parent):
"""
Initialise sub-groups, and create any that do not already exist.
"""
if self._sub_groups:
for sub_group in self._sub_groups:
for component in split_path_components(sub_group):
fp = os.path.join(parent.full_path, component)
if os.path.exists(fp):
node = Node(name=component, parent=parent)
parent.children.append(node)
else:
node = parent.create_cgroup(component)
parent = node
self._init_children(node)
else:
self._init_children(parent) | python | def _init_sub_groups(self, parent):
"""
Initialise sub-groups, and create any that do not already exist.
"""
if self._sub_groups:
for sub_group in self._sub_groups:
for component in split_path_components(sub_group):
fp = os.path.join(parent.full_path, component)
if os.path.exists(fp):
node = Node(name=component, parent=parent)
parent.children.append(node)
else:
node = parent.create_cgroup(component)
parent = node
self._init_children(node)
else:
self._init_children(parent) | Initialise sub-groups, and create any that do not already exist. | https://github.com/cloudsigma/cgroupspy/blob/e705ac4ccdfe33d8ecc700e9a35a9556084449ca/cgroupspy/trees.py#L82-L99 |
cloudsigma/cgroupspy | cgroupspy/trees.py | BaseTree._init_children | def _init_children(self, parent):
"""
Initialise each node's children - essentially build the tree.
"""
for dir_name in self.get_children_paths(parent.full_path):
child = Node(name=dir_name, parent=parent)
parent.children.append(child)
self._init_children(child) | python | def _init_children(self, parent):
"""
Initialise each node's children - essentially build the tree.
"""
for dir_name in self.get_children_paths(parent.full_path):
child = Node(name=dir_name, parent=parent)
parent.children.append(child)
self._init_children(child) | Initialise each node's children - essentially build the tree. | https://github.com/cloudsigma/cgroupspy/blob/e705ac4ccdfe33d8ecc700e9a35a9556084449ca/cgroupspy/trees.py#L101-L109 |
cloudsigma/cgroupspy | cgroupspy/nodes.py | Node.full_path | def full_path(self):
"""Absolute system path to the node"""
if self.parent:
return os.path.join(self.parent.full_path, self.name)
return self.name | python | def full_path(self):
"""Absolute system path to the node"""
if self.parent:
return os.path.join(self.parent.full_path, self.name)
return self.name | Absolute system path to the node | https://github.com/cloudsigma/cgroupspy/blob/e705ac4ccdfe33d8ecc700e9a35a9556084449ca/cgroupspy/nodes.py#L87-L92 |
cloudsigma/cgroupspy | cgroupspy/nodes.py | Node.path | def path(self):
"""Node's relative path from the root node"""
if self.parent:
try:
parent_path = self.parent.path.encode()
except AttributeError:
parent_path = self.parent.path
return os.path.join(parent_path, self.name)
return b"/" | python | def path(self):
"""Node's relative path from the root node"""
if self.parent:
try:
parent_path = self.parent.path.encode()
except AttributeError:
parent_path = self.parent.path
return os.path.join(parent_path, self.name)
return b"/" | Node's relative path from the root node | https://github.com/cloudsigma/cgroupspy/blob/e705ac4ccdfe33d8ecc700e9a35a9556084449ca/cgroupspy/nodes.py#L95-L104 |
cloudsigma/cgroupspy | cgroupspy/nodes.py | Node._get_node_type | def _get_node_type(self):
"""Returns the current node's type"""
if self.parent is None:
return self.NODE_ROOT
elif self.parent.node_type == self.NODE_ROOT:
return self.NODE_CONTROLLER_ROOT
elif b".slice" in self.name or b'.partition' in self.name:
return self.NODE_SLICE
elif b".scope" in self.name:
return self.NODE_SCOPE
else:
return self.NODE_CGROUP | python | def _get_node_type(self):
"""Returns the current node's type"""
if self.parent is None:
return self.NODE_ROOT
elif self.parent.node_type == self.NODE_ROOT:
return self.NODE_CONTROLLER_ROOT
elif b".slice" in self.name or b'.partition' in self.name:
return self.NODE_SLICE
elif b".scope" in self.name:
return self.NODE_SCOPE
else:
return self.NODE_CGROUP | Returns the current node's type | https://github.com/cloudsigma/cgroupspy/blob/e705ac4ccdfe33d8ecc700e9a35a9556084449ca/cgroupspy/nodes.py#L106-L118 |
cloudsigma/cgroupspy | cgroupspy/nodes.py | Node._get_controller_type | def _get_controller_type(self):
"""Returns the current node's controller type"""
if self.node_type == self.NODE_CONTROLLER_ROOT and self.name in self.CONTROLLERS:
return self.name
elif self.parent:
return self.parent.controller_type
else:
return None | python | def _get_controller_type(self):
"""Returns the current node's controller type"""
if self.node_type == self.NODE_CONTROLLER_ROOT and self.name in self.CONTROLLERS:
return self.name
elif self.parent:
return self.parent.controller_type
else:
return None | Returns the current node's controller type | https://github.com/cloudsigma/cgroupspy/blob/e705ac4ccdfe33d8ecc700e9a35a9556084449ca/cgroupspy/nodes.py#L120-L128 |
cloudsigma/cgroupspy | cgroupspy/nodes.py | Node.create_cgroup | def create_cgroup(self, name):
"""
Create a cgroup by name and attach it under this node.
"""
node = Node(name, parent=self)
if node in self.children:
raise RuntimeError('Node {} already exists under {}'.format(name, self.path))
name = name.encode()
fp = os.path.join(self.full_path, name)
os.mkdir(fp)
self.children.append(node)
return node | python | def create_cgroup(self, name):
"""
Create a cgroup by name and attach it under this node.
"""
node = Node(name, parent=self)
if node in self.children:
raise RuntimeError('Node {} already exists under {}'.format(name, self.path))
name = name.encode()
fp = os.path.join(self.full_path, name)
os.mkdir(fp)
self.children.append(node)
return node | Create a cgroup by name and attach it under this node. | https://github.com/cloudsigma/cgroupspy/blob/e705ac4ccdfe33d8ecc700e9a35a9556084449ca/cgroupspy/nodes.py#L137-L149 |
cloudsigma/cgroupspy | cgroupspy/nodes.py | Node.delete_cgroup | def delete_cgroup(self, name):
"""
Delete a cgroup by name and detach it from this node.
Raises OSError if the cgroup is not empty.
"""
name = name.encode()
fp = os.path.join(self.full_path, name)
if os.path.exists(fp):
os.rmdir(fp)
node = Node(name, parent=self)
try:
self.children.remove(node)
except ValueError:
return | python | def delete_cgroup(self, name):
"""
Delete a cgroup by name and detach it from this node.
Raises OSError if the cgroup is not empty.
"""
name = name.encode()
fp = os.path.join(self.full_path, name)
if os.path.exists(fp):
os.rmdir(fp)
node = Node(name, parent=self)
try:
self.children.remove(node)
except ValueError:
return | Delete a cgroup by name and detach it from this node.
Raises OSError if the cgroup is not empty. | https://github.com/cloudsigma/cgroupspy/blob/e705ac4ccdfe33d8ecc700e9a35a9556084449ca/cgroupspy/nodes.py#L151-L164 |
cloudsigma/cgroupspy | cgroupspy/nodes.py | Node.delete_empty_children | def delete_empty_children(self):
"""
Walk through the children of this node and delete any that are empty.
"""
for child in self.children:
child.delete_empty_children()
try:
if os.path.exists(child.full_path):
os.rmdir(child.full_path)
except OSError: pass
else: self.children.remove(child) | python | def delete_empty_children(self):
"""
Walk through the children of this node and delete any that are empty.
"""
for child in self.children:
child.delete_empty_children()
try:
if os.path.exists(child.full_path):
os.rmdir(child.full_path)
except OSError: pass
else: self.children.remove(child) | Walk through the children of this node and delete any that are empty. | https://github.com/cloudsigma/cgroupspy/blob/e705ac4ccdfe33d8ecc700e9a35a9556084449ca/cgroupspy/nodes.py#L166-L176 |
cloudsigma/cgroupspy | cgroupspy/nodes.py | NodeControlGroup.add_node | def add_node(self, node):
"""
A a Node object to the group. Only one node per cgroup is supported
"""
if self.controllers.get(node.controller_type, None):
raise RuntimeError("Cannot add node {} to the node group. A node for {} group is already assigned".format(
node,
node.controller_type
))
self.nodes.append(node)
if node.controller:
self.controllers[node.controller_type] = node.controller
setattr(self, node.controller_type, node.controller) | python | def add_node(self, node):
"""
A a Node object to the group. Only one node per cgroup is supported
"""
if self.controllers.get(node.controller_type, None):
raise RuntimeError("Cannot add node {} to the node group. A node for {} group is already assigned".format(
node,
node.controller_type
))
self.nodes.append(node)
if node.controller:
self.controllers[node.controller_type] = node.controller
setattr(self, node.controller_type, node.controller) | A a Node object to the group. Only one node per cgroup is supported | https://github.com/cloudsigma/cgroupspy/blob/e705ac4ccdfe33d8ecc700e9a35a9556084449ca/cgroupspy/nodes.py#L219-L231 |
cloudsigma/cgroupspy | cgroupspy/nodes.py | NodeControlGroup.group_tasks | def group_tasks(self):
"""All tasks in the hierarchy, affected by this group."""
tasks = set()
for node in walk_tree(self):
for ctrl in node.controllers.values():
tasks.update(ctrl.tasks)
return tasks | python | def group_tasks(self):
"""All tasks in the hierarchy, affected by this group."""
tasks = set()
for node in walk_tree(self):
for ctrl in node.controllers.values():
tasks.update(ctrl.tasks)
return tasks | All tasks in the hierarchy, affected by this group. | https://github.com/cloudsigma/cgroupspy/blob/e705ac4ccdfe33d8ecc700e9a35a9556084449ca/cgroupspy/nodes.py#L241-L247 |
cloudsigma/cgroupspy | cgroupspy/nodes.py | NodeControlGroup.tasks | def tasks(self):
"""Tasks in this exact group"""
tasks = set()
for ctrl in self.controllers.values():
tasks.update(ctrl.tasks)
return tasks | python | def tasks(self):
"""Tasks in this exact group"""
tasks = set()
for ctrl in self.controllers.values():
tasks.update(ctrl.tasks)
return tasks | Tasks in this exact group | https://github.com/cloudsigma/cgroupspy/blob/e705ac4ccdfe33d8ecc700e9a35a9556084449ca/cgroupspy/nodes.py#L250-L255 |
cloudsigma/cgroupspy | cgroupspy/controllers.py | Controller.filepath | def filepath(self, filename):
"""The full path to a file"""
return os.path.join(self.node.full_path, filename) | python | def filepath(self, filename):
"""The full path to a file"""
return os.path.join(self.node.full_path, filename) | The full path to a file | https://github.com/cloudsigma/cgroupspy/blob/e705ac4ccdfe33d8ecc700e9a35a9556084449ca/cgroupspy/controllers.py#L48-L51 |
cloudsigma/cgroupspy | cgroupspy/controllers.py | Controller.get_property | def get_property(self, filename):
"""Opens the file and reads the value"""
with open(self.filepath(filename)) as f:
return f.read().strip() | python | def get_property(self, filename):
"""Opens the file and reads the value"""
with open(self.filepath(filename)) as f:
return f.read().strip() | Opens the file and reads the value | https://github.com/cloudsigma/cgroupspy/blob/e705ac4ccdfe33d8ecc700e9a35a9556084449ca/cgroupspy/controllers.py#L53-L57 |
cloudsigma/cgroupspy | cgroupspy/controllers.py | Controller.set_property | def set_property(self, filename, value):
"""Opens the file and writes the value"""
with open(self.filepath(filename), "w") as f:
return f.write(str(value)) | python | def set_property(self, filename, value):
"""Opens the file and writes the value"""
with open(self.filepath(filename), "w") as f:
return f.write(str(value)) | Opens the file and writes the value | https://github.com/cloudsigma/cgroupspy/blob/e705ac4ccdfe33d8ecc700e9a35a9556084449ca/cgroupspy/controllers.py#L59-L63 |
cloudsigma/cgroupspy | cgroupspy/utils.py | walk_tree | def walk_tree(root):
"""Pre-order depth-first"""
yield root
for child in root.children:
for el in walk_tree(child):
yield el | python | def walk_tree(root):
"""Pre-order depth-first"""
yield root
for child in root.children:
for el in walk_tree(child):
yield el | Pre-order depth-first | https://github.com/cloudsigma/cgroupspy/blob/e705ac4ccdfe33d8ecc700e9a35a9556084449ca/cgroupspy/utils.py#L32-L38 |
cloudsigma/cgroupspy | cgroupspy/utils.py | walk_up_tree | def walk_up_tree(root):
"""Post-order depth-first"""
for child in root.children:
for el in walk_up_tree(child):
yield el
yield root | python | def walk_up_tree(root):
"""Post-order depth-first"""
for child in root.children:
for el in walk_up_tree(child):
yield el
yield root | Post-order depth-first | https://github.com/cloudsigma/cgroupspy/blob/e705ac4ccdfe33d8ecc700e9a35a9556084449ca/cgroupspy/utils.py#L41-L47 |
cloudsigma/cgroupspy | cgroupspy/utils.py | get_device_major_minor | def get_device_major_minor(dev_path):
"""
Returns the device (major, minor) tuple for simplicity
:param dev_path: Path to the device
:return: (device major, device minor)
:rtype: (int, int)
"""
stat = os.lstat(dev_path)
return os.major(stat.st_rdev), os.minor(stat.st_rdev) | python | def get_device_major_minor(dev_path):
"""
Returns the device (major, minor) tuple for simplicity
:param dev_path: Path to the device
:return: (device major, device minor)
:rtype: (int, int)
"""
stat = os.lstat(dev_path)
return os.major(stat.st_rdev), os.minor(stat.st_rdev) | Returns the device (major, minor) tuple for simplicity
:param dev_path: Path to the device
:return: (device major, device minor)
:rtype: (int, int) | https://github.com/cloudsigma/cgroupspy/blob/e705ac4ccdfe33d8ecc700e9a35a9556084449ca/cgroupspy/utils.py#L50-L58 |
globality-corp/openapi | openapi/base.py | SchemaAware.validate | def validate(self):
"""
Validate that this instance matches its schema.
"""
schema = Schema(self.__class__.SCHEMA)
resolver = RefResolver.from_schema(
schema,
store=REGISTRY,
)
validate(self, schema, resolver=resolver) | python | def validate(self):
"""
Validate that this instance matches its schema.
"""
schema = Schema(self.__class__.SCHEMA)
resolver = RefResolver.from_schema(
schema,
store=REGISTRY,
)
validate(self, schema, resolver=resolver) | Validate that this instance matches its schema. | https://github.com/globality-corp/openapi/blob/ee1de8468abeb800e3ad0134952726cdce6b2459/openapi/base.py#L48-L58 |
globality-corp/openapi | openapi/base.py | SchemaAware.dumps | def dumps(self):
"""
Dump this instance as YAML.
"""
with closing(StringIO()) as fileobj:
self.dump(fileobj)
return fileobj.getvalue() | python | def dumps(self):
"""
Dump this instance as YAML.
"""
with closing(StringIO()) as fileobj:
self.dump(fileobj)
return fileobj.getvalue() | Dump this instance as YAML. | https://github.com/globality-corp/openapi/blob/ee1de8468abeb800e3ad0134952726cdce6b2459/openapi/base.py#L67-L74 |
globality-corp/openapi | openapi/base.py | SchemaAware.loads | def loads(cls, s):
"""
Load an instance of this class from YAML.
"""
with closing(StringIO(s)) as fileobj:
return cls.load(fileobj) | python | def loads(cls, s):
"""
Load an instance of this class from YAML.
"""
with closing(StringIO(s)) as fileobj:
return cls.load(fileobj) | Load an instance of this class from YAML. | https://github.com/globality-corp/openapi/blob/ee1de8468abeb800e3ad0134952726cdce6b2459/openapi/base.py#L85-L91 |
globality-corp/openapi | openapi/base.py | SchemaAwareDict.property_schema | def property_schema(self, key):
"""
Lookup the schema for a specific property.
"""
schema = self.__class__.SCHEMA
# first try plain properties
plain_schema = schema.get("properties", {}).get(key)
if plain_schema is not None:
return plain_schema
# then try pattern properties
pattern_properties = schema.get("patternProperties", {})
for pattern, pattern_schema in pattern_properties.items():
if match(pattern, key):
return pattern_schema
# finally try additional properties (defaults to true per JSON Schema)
return schema.get("additionalProperties", True) | python | def property_schema(self, key):
"""
Lookup the schema for a specific property.
"""
schema = self.__class__.SCHEMA
# first try plain properties
plain_schema = schema.get("properties", {}).get(key)
if plain_schema is not None:
return plain_schema
# then try pattern properties
pattern_properties = schema.get("patternProperties", {})
for pattern, pattern_schema in pattern_properties.items():
if match(pattern, key):
return pattern_schema
# finally try additional properties (defaults to true per JSON Schema)
return schema.get("additionalProperties", True) | Lookup the schema for a specific property. | https://github.com/globality-corp/openapi/blob/ee1de8468abeb800e3ad0134952726cdce6b2459/openapi/base.py#L141-L158 |
globality-corp/openapi | openapi/model.py | make | def make(class_name, base, schema):
"""
Create a new schema aware type.
"""
return type(class_name, (base,), dict(SCHEMA=schema)) | python | def make(class_name, base, schema):
"""
Create a new schema aware type.
"""
return type(class_name, (base,), dict(SCHEMA=schema)) | Create a new schema aware type. | https://github.com/globality-corp/openapi/blob/ee1de8468abeb800e3ad0134952726cdce6b2459/openapi/model.py#L11-L15 |
globality-corp/openapi | openapi/model.py | make_definition | def make_definition(name, base, schema):
"""
Create a new definition.
"""
class_name = make_class_name(name)
cls = register(make(class_name, base, schema))
globals()[class_name] = cls | python | def make_definition(name, base, schema):
"""
Create a new definition.
"""
class_name = make_class_name(name)
cls = register(make(class_name, base, schema))
globals()[class_name] = cls | Create a new definition. | https://github.com/globality-corp/openapi/blob/ee1de8468abeb800e3ad0134952726cdce6b2459/openapi/model.py#L18-L25 |
globality-corp/openapi | openapi/registry.py | register | def register(cls):
"""
Register a class.
"""
definition_name = make_definition_name(cls.__name__)
REGISTRY[definition_name] = cls
return cls | python | def register(cls):
"""
Register a class.
"""
definition_name = make_definition_name(cls.__name__)
REGISTRY[definition_name] = cls
return cls | Register a class. | https://github.com/globality-corp/openapi/blob/ee1de8468abeb800e3ad0134952726cdce6b2459/openapi/registry.py#L12-L19 |
globality-corp/openapi | openapi/registry.py | lookup | def lookup(schema):
"""
Lookup a class by property schema.
"""
if not isinstance(schema, dict) or "$ref" not in schema:
return None
ref = schema["$ref"]
return REGISTRY.get(ref) | python | def lookup(schema):
"""
Lookup a class by property schema.
"""
if not isinstance(schema, dict) or "$ref" not in schema:
return None
ref = schema["$ref"]
return REGISTRY.get(ref) | Lookup a class by property schema. | https://github.com/globality-corp/openapi/blob/ee1de8468abeb800e3ad0134952726cdce6b2459/openapi/registry.py#L22-L32 |
thecynic/pylutron | pylutron/__init__.py | LutronConnection.connect | def connect(self):
"""Connects to the lutron controller."""
if self._connected or self.is_alive():
raise ConnectionExistsError("Already connected")
# After starting the thread we wait for it to post us
# an event signifying that connection is established. This
# ensures that the caller only resumes when we are fully connected.
self.start()
with self._lock:
self._connect_cond.wait_for(lambda: self._connected) | python | def connect(self):
"""Connects to the lutron controller."""
if self._connected or self.is_alive():
raise ConnectionExistsError("Already connected")
# After starting the thread we wait for it to post us
# an event signifying that connection is established. This
# ensures that the caller only resumes when we are fully connected.
self.start()
with self._lock:
self._connect_cond.wait_for(lambda: self._connected) | Connects to the lutron controller. | https://github.com/thecynic/pylutron/blob/4d9222c96ef7ac7ac458031c058ad93ec31cebbf/pylutron/__init__.py#L63-L72 |
thecynic/pylutron | pylutron/__init__.py | LutronConnection._send_locked | def _send_locked(self, cmd):
"""Sends the specified command to the lutron controller.
Assumes self._lock is held.
"""
_LOGGER.debug("Sending: %s" % cmd)
try:
self._telnet.write(cmd.encode('ascii') + b'\r\n')
except BrokenPipeError:
self._disconnect_locked() | python | def _send_locked(self, cmd):
"""Sends the specified command to the lutron controller.
Assumes self._lock is held.
"""
_LOGGER.debug("Sending: %s" % cmd)
try:
self._telnet.write(cmd.encode('ascii') + b'\r\n')
except BrokenPipeError:
self._disconnect_locked() | Sends the specified command to the lutron controller.
Assumes self._lock is held. | https://github.com/thecynic/pylutron/blob/4d9222c96ef7ac7ac458031c058ad93ec31cebbf/pylutron/__init__.py#L74-L83 |
thecynic/pylutron | pylutron/__init__.py | LutronConnection._do_login_locked | def _do_login_locked(self):
"""Executes the login procedure (telnet) as well as setting up some
connection defaults like turning off the prompt, etc."""
self._telnet = telnetlib.Telnet(self._host)
self._telnet.read_until(LutronConnection.USER_PROMPT)
self._telnet.write(self._user + b'\r\n')
self._telnet.read_until(LutronConnection.PW_PROMPT)
self._telnet.write(self._password + b'\r\n')
self._telnet.read_until(LutronConnection.PROMPT)
self._send_locked("#MONITORING,12,2")
self._send_locked("#MONITORING,255,2")
self._send_locked("#MONITORING,3,1")
self._send_locked("#MONITORING,4,1")
self._send_locked("#MONITORING,5,1")
self._send_locked("#MONITORING,6,1")
self._send_locked("#MONITORING,8,1") | python | def _do_login_locked(self):
"""Executes the login procedure (telnet) as well as setting up some
connection defaults like turning off the prompt, etc."""
self._telnet = telnetlib.Telnet(self._host)
self._telnet.read_until(LutronConnection.USER_PROMPT)
self._telnet.write(self._user + b'\r\n')
self._telnet.read_until(LutronConnection.PW_PROMPT)
self._telnet.write(self._password + b'\r\n')
self._telnet.read_until(LutronConnection.PROMPT)
self._send_locked("#MONITORING,12,2")
self._send_locked("#MONITORING,255,2")
self._send_locked("#MONITORING,3,1")
self._send_locked("#MONITORING,4,1")
self._send_locked("#MONITORING,5,1")
self._send_locked("#MONITORING,6,1")
self._send_locked("#MONITORING,8,1") | Executes the login procedure (telnet) as well as setting up some
connection defaults like turning off the prompt, etc. | https://github.com/thecynic/pylutron/blob/4d9222c96ef7ac7ac458031c058ad93ec31cebbf/pylutron/__init__.py#L93-L109 |
thecynic/pylutron | pylutron/__init__.py | LutronConnection._disconnect_locked | def _disconnect_locked(self):
"""Closes the current connection. Assume self._lock is held."""
self._connected = False
self._connect_cond.notify_all()
self._telnet = None
_LOGGER.warning("Disconnected") | python | def _disconnect_locked(self):
"""Closes the current connection. Assume self._lock is held."""
self._connected = False
self._connect_cond.notify_all()
self._telnet = None
_LOGGER.warning("Disconnected") | Closes the current connection. Assume self._lock is held. | https://github.com/thecynic/pylutron/blob/4d9222c96ef7ac7ac458031c058ad93ec31cebbf/pylutron/__init__.py#L111-L116 |
thecynic/pylutron | pylutron/__init__.py | LutronConnection._maybe_reconnect | def _maybe_reconnect(self):
"""Reconnects to the controller if we have been previously disconnected."""
with self._lock:
if not self._connected:
_LOGGER.info("Connecting")
self._do_login_locked()
self._connected = True
self._connect_cond.notify_all()
_LOGGER.info("Connected") | python | def _maybe_reconnect(self):
"""Reconnects to the controller if we have been previously disconnected."""
with self._lock:
if not self._connected:
_LOGGER.info("Connecting")
self._do_login_locked()
self._connected = True
self._connect_cond.notify_all()
_LOGGER.info("Connected") | Reconnects to the controller if we have been previously disconnected. | https://github.com/thecynic/pylutron/blob/4d9222c96ef7ac7ac458031c058ad93ec31cebbf/pylutron/__init__.py#L118-L126 |
thecynic/pylutron | pylutron/__init__.py | LutronConnection.run | def run(self):
"""Main thread function to maintain connection and receive remote status."""
_LOGGER.info("Started")
while True:
self._maybe_reconnect()
line = ''
try:
# If someone is sending a command, we can lose our connection so grab a
# copy beforehand. We don't need the lock because if the connection is
# open, we are the only ones that will read from telnet (the reconnect
# code runs synchronously in this loop).
t = self._telnet
if t is not None:
line = t.read_until(b"\n")
except EOFError:
try:
self._lock.acquire()
self._disconnect_locked()
continue
finally:
self._lock.release()
self._recv_cb(line.decode('ascii').rstrip()) | python | def run(self):
"""Main thread function to maintain connection and receive remote status."""
_LOGGER.info("Started")
while True:
self._maybe_reconnect()
line = ''
try:
# If someone is sending a command, we can lose our connection so grab a
# copy beforehand. We don't need the lock because if the connection is
# open, we are the only ones that will read from telnet (the reconnect
# code runs synchronously in this loop).
t = self._telnet
if t is not None:
line = t.read_until(b"\n")
except EOFError:
try:
self._lock.acquire()
self._disconnect_locked()
continue
finally:
self._lock.release()
self._recv_cb(line.decode('ascii').rstrip()) | Main thread function to maintain connection and receive remote status. | https://github.com/thecynic/pylutron/blob/4d9222c96ef7ac7ac458031c058ad93ec31cebbf/pylutron/__init__.py#L128-L149 |
thecynic/pylutron | pylutron/__init__.py | LutronXmlDbParser.parse | def parse(self):
"""Main entrypoint into the parser. It interprets and creates all the
relevant Lutron objects and stuffs them into the appropriate hierarchy."""
import xml.etree.ElementTree as ET
root = ET.fromstring(self._xml_db_str)
# The structure is something like this:
# <Areas>
# <Area ...>
# <DeviceGroups ...>
# <Scenes ...>
# <ShadeGroups ...>
# <Outputs ...>
# <Areas ...>
# <Area ...>
# First area is useless, it's the top-level project area that defines the
# "house". It contains the real nested Areas tree, which is the one we want.
top_area = root.find('Areas').find('Area')
self.project_name = top_area.get('Name')
areas = top_area.find('Areas')
for area_xml in areas.getiterator('Area'):
area = self._parse_area(area_xml)
self.areas.append(area)
return True | python | def parse(self):
"""Main entrypoint into the parser. It interprets and creates all the
relevant Lutron objects and stuffs them into the appropriate hierarchy."""
import xml.etree.ElementTree as ET
root = ET.fromstring(self._xml_db_str)
# The structure is something like this:
# <Areas>
# <Area ...>
# <DeviceGroups ...>
# <Scenes ...>
# <ShadeGroups ...>
# <Outputs ...>
# <Areas ...>
# <Area ...>
# First area is useless, it's the top-level project area that defines the
# "house". It contains the real nested Areas tree, which is the one we want.
top_area = root.find('Areas').find('Area')
self.project_name = top_area.get('Name')
areas = top_area.find('Areas')
for area_xml in areas.getiterator('Area'):
area = self._parse_area(area_xml)
self.areas.append(area)
return True | Main entrypoint into the parser. It interprets and creates all the
relevant Lutron objects and stuffs them into the appropriate hierarchy. | https://github.com/thecynic/pylutron/blob/4d9222c96ef7ac7ac458031c058ad93ec31cebbf/pylutron/__init__.py#L166-L190 |
thecynic/pylutron | pylutron/__init__.py | LutronXmlDbParser._parse_area | def _parse_area(self, area_xml):
"""Parses an Area tag, which is effectively a room, depending on how the
Lutron controller programming was done."""
area = Area(self._lutron,
name=area_xml.get('Name'),
integration_id=int(area_xml.get('IntegrationID')),
occupancy_group_id=area_xml.get('OccupancyGroupAssignedToID'))
for output_xml in area_xml.find('Outputs'):
output = self._parse_output(output_xml)
area.add_output(output)
# device group in our case means keypad
# device_group.get('Name') is the location of the keypad
for device_group in area_xml.find('DeviceGroups'):
if device_group.tag == 'DeviceGroup':
devs = device_group.find('Devices')
elif device_group.tag == 'Device':
devs = [device_group]
else:
_LOGGER.info("Unknown tag in DeviceGroups child %s" % devs)
devs = []
for device_xml in devs:
if device_xml.tag != 'Device':
continue
if device_xml.get('DeviceType') in (
'SEETOUCH_KEYPAD',
'SEETOUCH_TABLETOP_KEYPAD',
'PICO_KEYPAD',
'HYBRID_SEETOUCH_KEYPAD',
'MAIN_REPEATER'):
keypad = self._parse_keypad(device_xml)
area.add_keypad(keypad)
elif device_xml.get('DeviceType') == 'MOTION_SENSOR':
motion_sensor = self._parse_motion_sensor(device_xml)
area.add_sensor(motion_sensor)
#elif device_xml.get('DeviceType') == 'VISOR_CONTROL_RECEIVER':
return area | python | def _parse_area(self, area_xml):
"""Parses an Area tag, which is effectively a room, depending on how the
Lutron controller programming was done."""
area = Area(self._lutron,
name=area_xml.get('Name'),
integration_id=int(area_xml.get('IntegrationID')),
occupancy_group_id=area_xml.get('OccupancyGroupAssignedToID'))
for output_xml in area_xml.find('Outputs'):
output = self._parse_output(output_xml)
area.add_output(output)
# device group in our case means keypad
# device_group.get('Name') is the location of the keypad
for device_group in area_xml.find('DeviceGroups'):
if device_group.tag == 'DeviceGroup':
devs = device_group.find('Devices')
elif device_group.tag == 'Device':
devs = [device_group]
else:
_LOGGER.info("Unknown tag in DeviceGroups child %s" % devs)
devs = []
for device_xml in devs:
if device_xml.tag != 'Device':
continue
if device_xml.get('DeviceType') in (
'SEETOUCH_KEYPAD',
'SEETOUCH_TABLETOP_KEYPAD',
'PICO_KEYPAD',
'HYBRID_SEETOUCH_KEYPAD',
'MAIN_REPEATER'):
keypad = self._parse_keypad(device_xml)
area.add_keypad(keypad)
elif device_xml.get('DeviceType') == 'MOTION_SENSOR':
motion_sensor = self._parse_motion_sensor(device_xml)
area.add_sensor(motion_sensor)
#elif device_xml.get('DeviceType') == 'VISOR_CONTROL_RECEIVER':
return area | Parses an Area tag, which is effectively a room, depending on how the
Lutron controller programming was done. | https://github.com/thecynic/pylutron/blob/4d9222c96ef7ac7ac458031c058ad93ec31cebbf/pylutron/__init__.py#L192-L227 |
thecynic/pylutron | pylutron/__init__.py | LutronXmlDbParser._parse_output | def _parse_output(self, output_xml):
"""Parses an output, which is generally a switch controlling a set of
lights/outlets, etc."""
output = Output(self._lutron,
name=output_xml.get('Name'),
watts=int(output_xml.get('Wattage')),
output_type=output_xml.get('OutputType'),
integration_id=int(output_xml.get('IntegrationID')))
return output | python | def _parse_output(self, output_xml):
"""Parses an output, which is generally a switch controlling a set of
lights/outlets, etc."""
output = Output(self._lutron,
name=output_xml.get('Name'),
watts=int(output_xml.get('Wattage')),
output_type=output_xml.get('OutputType'),
integration_id=int(output_xml.get('IntegrationID')))
return output | Parses an output, which is generally a switch controlling a set of
lights/outlets, etc. | https://github.com/thecynic/pylutron/blob/4d9222c96ef7ac7ac458031c058ad93ec31cebbf/pylutron/__init__.py#L229-L237 |
thecynic/pylutron | pylutron/__init__.py | LutronXmlDbParser._parse_keypad | def _parse_keypad(self, keypad_xml):
"""Parses a keypad device (the Visor receiver is technically a keypad too)."""
keypad = Keypad(self._lutron,
name=keypad_xml.get('Name'),
integration_id=int(keypad_xml.get('IntegrationID')))
components = keypad_xml.find('Components')
if not components:
return keypad
for comp in components:
if comp.tag != 'Component':
continue
comp_type = comp.get('ComponentType')
if comp_type == 'BUTTON':
button = self._parse_button(keypad, comp)
keypad.add_button(button)
elif comp_type == 'LED':
led = self._parse_led(keypad, comp)
keypad.add_led(led)
return keypad | python | def _parse_keypad(self, keypad_xml):
"""Parses a keypad device (the Visor receiver is technically a keypad too)."""
keypad = Keypad(self._lutron,
name=keypad_xml.get('Name'),
integration_id=int(keypad_xml.get('IntegrationID')))
components = keypad_xml.find('Components')
if not components:
return keypad
for comp in components:
if comp.tag != 'Component':
continue
comp_type = comp.get('ComponentType')
if comp_type == 'BUTTON':
button = self._parse_button(keypad, comp)
keypad.add_button(button)
elif comp_type == 'LED':
led = self._parse_led(keypad, comp)
keypad.add_led(led)
return keypad | Parses a keypad device (the Visor receiver is technically a keypad too). | https://github.com/thecynic/pylutron/blob/4d9222c96ef7ac7ac458031c058ad93ec31cebbf/pylutron/__init__.py#L239-L257 |
thecynic/pylutron | pylutron/__init__.py | LutronXmlDbParser._parse_button | def _parse_button(self, keypad, component_xml):
"""Parses a button device that part of a keypad."""
button_xml = component_xml.find('Button')
name = button_xml.get('Engraving')
button_type = button_xml.get('ButtonType')
direction = button_xml.get('Direction')
# Hybrid keypads have dimmer buttons which have no engravings.
if button_type == 'SingleSceneRaiseLower':
name = 'Dimmer ' + direction
if not name:
name = "Unknown Button"
button = Button(self._lutron, keypad,
name=name,
num=int(component_xml.get('ComponentNumber')),
button_type=button_type,
direction=direction)
return button | python | def _parse_button(self, keypad, component_xml):
"""Parses a button device that part of a keypad."""
button_xml = component_xml.find('Button')
name = button_xml.get('Engraving')
button_type = button_xml.get('ButtonType')
direction = button_xml.get('Direction')
# Hybrid keypads have dimmer buttons which have no engravings.
if button_type == 'SingleSceneRaiseLower':
name = 'Dimmer ' + direction
if not name:
name = "Unknown Button"
button = Button(self._lutron, keypad,
name=name,
num=int(component_xml.get('ComponentNumber')),
button_type=button_type,
direction=direction)
return button | Parses a button device that part of a keypad. | https://github.com/thecynic/pylutron/blob/4d9222c96ef7ac7ac458031c058ad93ec31cebbf/pylutron/__init__.py#L259-L275 |
thecynic/pylutron | pylutron/__init__.py | LutronXmlDbParser._parse_led | def _parse_led(self, keypad, component_xml):
"""Parses an LED device that part of a keypad."""
component_num = int(component_xml.get('ComponentNumber'))
led_num = component_num - 80
led = Led(self._lutron, keypad,
name=('LED %d' % led_num),
led_num=led_num,
component_num=component_num)
return led | python | def _parse_led(self, keypad, component_xml):
"""Parses an LED device that part of a keypad."""
component_num = int(component_xml.get('ComponentNumber'))
led_num = component_num - 80
led = Led(self._lutron, keypad,
name=('LED %d' % led_num),
led_num=led_num,
component_num=component_num)
return led | Parses an LED device that part of a keypad. | https://github.com/thecynic/pylutron/blob/4d9222c96ef7ac7ac458031c058ad93ec31cebbf/pylutron/__init__.py#L277-L285 |
thecynic/pylutron | pylutron/__init__.py | LutronXmlDbParser._parse_motion_sensor | def _parse_motion_sensor(self, sensor_xml):
"""Parses a motion sensor object.
TODO: We don't actually do anything with these yet. There's a lot of info
that needs to be managed to do this right. We'd have to manage the occupancy
groups, what's assigned to them, and when they go (un)occupied. We'll handle
this later.
"""
return MotionSensor(self._lutron,
name=sensor_xml.get('Name'),
integration_id=int(sensor_xml.get('IntegrationID'))) | python | def _parse_motion_sensor(self, sensor_xml):
"""Parses a motion sensor object.
TODO: We don't actually do anything with these yet. There's a lot of info
that needs to be managed to do this right. We'd have to manage the occupancy
groups, what's assigned to them, and when they go (un)occupied. We'll handle
this later.
"""
return MotionSensor(self._lutron,
name=sensor_xml.get('Name'),
integration_id=int(sensor_xml.get('IntegrationID'))) | Parses a motion sensor object.
TODO: We don't actually do anything with these yet. There's a lot of info
that needs to be managed to do this right. We'd have to manage the occupancy
groups, what's assigned to them, and when they go (un)occupied. We'll handle
this later. | https://github.com/thecynic/pylutron/blob/4d9222c96ef7ac7ac458031c058ad93ec31cebbf/pylutron/__init__.py#L287-L297 |
thecynic/pylutron | pylutron/__init__.py | Lutron.subscribe | def subscribe(self, obj, handler):
"""Subscribes to status updates of the requested object.
DEPRECATED
The handler will be invoked when the controller sends a notification
regarding changed state. The user can then further query the object for the
state itself."""
if not isinstance(obj, LutronEntity):
raise InvalidSubscription("Subscription target not a LutronEntity")
_LOGGER.warning("DEPRECATED: Subscribing via Lutron.subscribe is obsolete. "
"Please use LutronEntity.subscribe")
if obj not in self._legacy_subscribers:
self._legacy_subscribers[obj] = handler
obj.subscribe(self._dispatch_legacy_subscriber, None) | python | def subscribe(self, obj, handler):
"""Subscribes to status updates of the requested object.
DEPRECATED
The handler will be invoked when the controller sends a notification
regarding changed state. The user can then further query the object for the
state itself."""
if not isinstance(obj, LutronEntity):
raise InvalidSubscription("Subscription target not a LutronEntity")
_LOGGER.warning("DEPRECATED: Subscribing via Lutron.subscribe is obsolete. "
"Please use LutronEntity.subscribe")
if obj not in self._legacy_subscribers:
self._legacy_subscribers[obj] = handler
obj.subscribe(self._dispatch_legacy_subscriber, None) | Subscribes to status updates of the requested object.
DEPRECATED
The handler will be invoked when the controller sends a notification
regarding changed state. The user can then further query the object for the
state itself. | https://github.com/thecynic/pylutron/blob/4d9222c96ef7ac7ac458031c058ad93ec31cebbf/pylutron/__init__.py#L330-L344 |
thecynic/pylutron | pylutron/__init__.py | Lutron.register_id | def register_id(self, cmd_type, obj):
"""Registers an object (through its integration id) to receive update
notifications. This is the core mechanism how Output and Keypad objects get
notified when the controller sends status updates."""
ids = self._ids.setdefault(cmd_type, {})
if obj.id in ids:
raise IntegrationIdExistsError
self._ids[cmd_type][obj.id] = obj | python | def register_id(self, cmd_type, obj):
"""Registers an object (through its integration id) to receive update
notifications. This is the core mechanism how Output and Keypad objects get
notified when the controller sends status updates."""
ids = self._ids.setdefault(cmd_type, {})
if obj.id in ids:
raise IntegrationIdExistsError
self._ids[cmd_type][obj.id] = obj | Registers an object (through its integration id) to receive update
notifications. This is the core mechanism how Output and Keypad objects get
notified when the controller sends status updates. | https://github.com/thecynic/pylutron/blob/4d9222c96ef7ac7ac458031c058ad93ec31cebbf/pylutron/__init__.py#L346-L353 |
thecynic/pylutron | pylutron/__init__.py | Lutron._dispatch_legacy_subscriber | def _dispatch_legacy_subscriber(self, obj, *args, **kwargs):
"""This dispatches the registered callback for 'obj'. This is only used
for legacy subscribers since new users should register with the target
object directly."""
if obj in self._legacy_subscribers:
self._legacy_subscribers[obj](obj) | python | def _dispatch_legacy_subscriber(self, obj, *args, **kwargs):
"""This dispatches the registered callback for 'obj'. This is only used
for legacy subscribers since new users should register with the target
object directly."""
if obj in self._legacy_subscribers:
self._legacy_subscribers[obj](obj) | This dispatches the registered callback for 'obj'. This is only used
for legacy subscribers since new users should register with the target
object directly. | https://github.com/thecynic/pylutron/blob/4d9222c96ef7ac7ac458031c058ad93ec31cebbf/pylutron/__init__.py#L355-L360 |
thecynic/pylutron | pylutron/__init__.py | Lutron._recv | def _recv(self, line):
"""Invoked by the connection manager to process incoming data."""
if line == '':
return
# Only handle query response messages, which are also sent on remote status
# updates (e.g. user manually pressed a keypad button)
if line[0] != Lutron.OP_RESPONSE:
_LOGGER.debug("ignoring %s" % line)
return
parts = line[1:].split(',')
cmd_type = parts[0]
integration_id = int(parts[1])
args = parts[2:]
if cmd_type not in self._ids:
_LOGGER.info("Unknown cmd %s (%s)" % (cmd_type, line))
return
ids = self._ids[cmd_type]
if integration_id not in ids:
_LOGGER.warning("Unknown id %d (%s)" % (integration_id, line))
return
obj = ids[integration_id]
handled = obj.handle_update(args) | python | def _recv(self, line):
"""Invoked by the connection manager to process incoming data."""
if line == '':
return
# Only handle query response messages, which are also sent on remote status
# updates (e.g. user manually pressed a keypad button)
if line[0] != Lutron.OP_RESPONSE:
_LOGGER.debug("ignoring %s" % line)
return
parts = line[1:].split(',')
cmd_type = parts[0]
integration_id = int(parts[1])
args = parts[2:]
if cmd_type not in self._ids:
_LOGGER.info("Unknown cmd %s (%s)" % (cmd_type, line))
return
ids = self._ids[cmd_type]
if integration_id not in ids:
_LOGGER.warning("Unknown id %d (%s)" % (integration_id, line))
return
obj = ids[integration_id]
handled = obj.handle_update(args) | Invoked by the connection manager to process incoming data. | https://github.com/thecynic/pylutron/blob/4d9222c96ef7ac7ac458031c058ad93ec31cebbf/pylutron/__init__.py#L362-L383 |
thecynic/pylutron | pylutron/__init__.py | Lutron.send | def send(self, op, cmd, integration_id, *args):
"""Formats and sends the requested command to the Lutron controller."""
out_cmd = ",".join(
(cmd, str(integration_id)) + tuple((str(x) for x in args)))
self._conn.send(op + out_cmd) | python | def send(self, op, cmd, integration_id, *args):
"""Formats and sends the requested command to the Lutron controller."""
out_cmd = ",".join(
(cmd, str(integration_id)) + tuple((str(x) for x in args)))
self._conn.send(op + out_cmd) | Formats and sends the requested command to the Lutron controller. | https://github.com/thecynic/pylutron/blob/4d9222c96ef7ac7ac458031c058ad93ec31cebbf/pylutron/__init__.py#L389-L393 |
thecynic/pylutron | pylutron/__init__.py | Lutron.load_xml_db | def load_xml_db(self):
"""Load the Lutron database from the server."""
import urllib.request
xmlfile = urllib.request.urlopen('http://' + self._host + '/DbXmlInfo.xml')
xml_db = xmlfile.read()
xmlfile.close()
_LOGGER.info("Loaded xml db")
parser = LutronXmlDbParser(lutron=self, xml_db_str=xml_db)
assert(parser.parse()) # throw our own exception
self._areas = parser.areas
self._name = parser.project_name
_LOGGER.info('Found Lutron project: %s, %d areas' % (
self._name, len(self.areas)))
return True | python | def load_xml_db(self):
"""Load the Lutron database from the server."""
import urllib.request
xmlfile = urllib.request.urlopen('http://' + self._host + '/DbXmlInfo.xml')
xml_db = xmlfile.read()
xmlfile.close()
_LOGGER.info("Loaded xml db")
parser = LutronXmlDbParser(lutron=self, xml_db_str=xml_db)
assert(parser.parse()) # throw our own exception
self._areas = parser.areas
self._name = parser.project_name
_LOGGER.info('Found Lutron project: %s, %d areas' % (
self._name, len(self.areas)))
return True | Load the Lutron database from the server. | https://github.com/thecynic/pylutron/blob/4d9222c96ef7ac7ac458031c058ad93ec31cebbf/pylutron/__init__.py#L395-L412 |
thecynic/pylutron | pylutron/__init__.py | _RequestHelper.request | def request(self, action):
"""Request an action to be performed, in case one."""
ev = threading.Event()
first = False
with self.__lock:
if len(self.__events) == 0:
first = True
self.__events.append(ev)
if first:
action()
return ev | python | def request(self, action):
"""Request an action to be performed, in case one."""
ev = threading.Event()
first = False
with self.__lock:
if len(self.__events) == 0:
first = True
self.__events.append(ev)
if first:
action()
return ev | Request an action to be performed, in case one. | https://github.com/thecynic/pylutron/blob/4d9222c96ef7ac7ac458031c058ad93ec31cebbf/pylutron/__init__.py#L441-L451 |
thecynic/pylutron | pylutron/__init__.py | LutronEntity._dispatch_event | def _dispatch_event(self, event: LutronEvent, params: Dict):
"""Dispatches the specified event to all the subscribers."""
for handler, context in self._subscribers:
handler(self, context, event, params) | python | def _dispatch_event(self, event: LutronEvent, params: Dict):
"""Dispatches the specified event to all the subscribers."""
for handler, context in self._subscribers:
handler(self, context, event, params) | Dispatches the specified event to all the subscribers. | https://github.com/thecynic/pylutron/blob/4d9222c96ef7ac7ac458031c058ad93ec31cebbf/pylutron/__init__.py#L485-L488 |
thecynic/pylutron | pylutron/__init__.py | LutronEntity.subscribe | def subscribe(self, handler: LutronEventHandler, context):
"""Subscribes to events from this entity.
handler: A callable object that takes the following arguments (in order)
obj: the LutrongEntity object that generated the event
context: user-supplied (to subscribe()) context object
event: the LutronEvent that was generated.
params: a dict of event-specific parameters
context: User-supplied, opaque object that will be passed to handler.
"""
self._subscribers.append((handler, context)) | python | def subscribe(self, handler: LutronEventHandler, context):
"""Subscribes to events from this entity.
handler: A callable object that takes the following arguments (in order)
obj: the LutrongEntity object that generated the event
context: user-supplied (to subscribe()) context object
event: the LutronEvent that was generated.
params: a dict of event-specific parameters
context: User-supplied, opaque object that will be passed to handler.
"""
self._subscribers.append((handler, context)) | Subscribes to events from this entity.
handler: A callable object that takes the following arguments (in order)
obj: the LutrongEntity object that generated the event
context: user-supplied (to subscribe()) context object
event: the LutronEvent that was generated.
params: a dict of event-specific parameters
context: User-supplied, opaque object that will be passed to handler. | https://github.com/thecynic/pylutron/blob/4d9222c96ef7ac7ac458031c058ad93ec31cebbf/pylutron/__init__.py#L490-L501 |
thecynic/pylutron | pylutron/__init__.py | Output.handle_update | def handle_update(self, args):
"""Handles an event update for this object, e.g. dimmer level change."""
_LOGGER.debug("handle_update %d -- %s" % (self._integration_id, args))
state = int(args[0])
if state != Output._ACTION_ZONE_LEVEL:
return False
level = float(args[1])
_LOGGER.debug("Updating %d(%s): s=%d l=%f" % (
self._integration_id, self._name, state, level))
self._level = level
self._query_waiters.notify()
self._dispatch_event(Output.Event.LEVEL_CHANGED, {'level': self._level})
return True | python | def handle_update(self, args):
"""Handles an event update for this object, e.g. dimmer level change."""
_LOGGER.debug("handle_update %d -- %s" % (self._integration_id, args))
state = int(args[0])
if state != Output._ACTION_ZONE_LEVEL:
return False
level = float(args[1])
_LOGGER.debug("Updating %d(%s): s=%d l=%f" % (
self._integration_id, self._name, state, level))
self._level = level
self._query_waiters.notify()
self._dispatch_event(Output.Event.LEVEL_CHANGED, {'level': self._level})
return True | Handles an event update for this object, e.g. dimmer level change. | https://github.com/thecynic/pylutron/blob/4d9222c96ef7ac7ac458031c058ad93ec31cebbf/pylutron/__init__.py#L555-L567 |
thecynic/pylutron | pylutron/__init__.py | Output.__do_query_level | def __do_query_level(self):
"""Helper to perform the actual query the current dimmer level of the
output. For pure on/off loads the result is either 0.0 or 100.0."""
self._lutron.send(Lutron.OP_QUERY, Output._CMD_TYPE, self._integration_id,
Output._ACTION_ZONE_LEVEL) | python | def __do_query_level(self):
"""Helper to perform the actual query the current dimmer level of the
output. For pure on/off loads the result is either 0.0 or 100.0."""
self._lutron.send(Lutron.OP_QUERY, Output._CMD_TYPE, self._integration_id,
Output._ACTION_ZONE_LEVEL) | Helper to perform the actual query the current dimmer level of the
output. For pure on/off loads the result is either 0.0 or 100.0. | https://github.com/thecynic/pylutron/blob/4d9222c96ef7ac7ac458031c058ad93ec31cebbf/pylutron/__init__.py#L569-L573 |
thecynic/pylutron | pylutron/__init__.py | Output.level | def level(self):
"""Returns the current output level by querying the remote controller."""
ev = self._query_waiters.request(self.__do_query_level)
ev.wait(1.0)
return self._level | python | def level(self):
"""Returns the current output level by querying the remote controller."""
ev = self._query_waiters.request(self.__do_query_level)
ev.wait(1.0)
return self._level | Returns the current output level by querying the remote controller. | https://github.com/thecynic/pylutron/blob/4d9222c96ef7ac7ac458031c058ad93ec31cebbf/pylutron/__init__.py#L580-L584 |
thecynic/pylutron | pylutron/__init__.py | Output.level | def level(self, new_level):
"""Sets the new output level."""
if self._level == new_level:
return
self._lutron.send(Lutron.OP_EXECUTE, Output._CMD_TYPE, self._integration_id,
Output._ACTION_ZONE_LEVEL, "%.2f" % new_level)
self._level = new_level | python | def level(self, new_level):
"""Sets the new output level."""
if self._level == new_level:
return
self._lutron.send(Lutron.OP_EXECUTE, Output._CMD_TYPE, self._integration_id,
Output._ACTION_ZONE_LEVEL, "%.2f" % new_level)
self._level = new_level | Sets the new output level. | https://github.com/thecynic/pylutron/blob/4d9222c96ef7ac7ac458031c058ad93ec31cebbf/pylutron/__init__.py#L587-L593 |
thecynic/pylutron | pylutron/__init__.py | KeypadComponent.handle_update | def handle_update(self, action, params):
"""Handle the specified action on this component."""
_LOGGER.debug('Keypad: "%s" Handling "%s" Action: %s Params: %s"' % (
self._keypad.name, self.name, action, params))
return False | python | def handle_update(self, action, params):
"""Handle the specified action on this component."""
_LOGGER.debug('Keypad: "%s" Handling "%s" Action: %s Params: %s"' % (
self._keypad.name, self.name, action, params))
return False | Handle the specified action on this component. | https://github.com/thecynic/pylutron/blob/4d9222c96ef7ac7ac458031c058ad93ec31cebbf/pylutron/__init__.py#L640-L644 |
thecynic/pylutron | pylutron/__init__.py | Button.press | def press(self):
"""Triggers a simulated button press to the Keypad."""
self._lutron.send(Lutron.OP_EXECUTE, Keypad._CMD_TYPE, self._keypad.id,
self.component_number, Button._ACTION_PRESS) | python | def press(self):
"""Triggers a simulated button press to the Keypad."""
self._lutron.send(Lutron.OP_EXECUTE, Keypad._CMD_TYPE, self._keypad.id,
self.component_number, Button._ACTION_PRESS) | Triggers a simulated button press to the Keypad. | https://github.com/thecynic/pylutron/blob/4d9222c96ef7ac7ac458031c058ad93ec31cebbf/pylutron/__init__.py#L687-L690 |
thecynic/pylutron | pylutron/__init__.py | Button.handle_update | def handle_update(self, action, params):
"""Handle the specified action on this component."""
_LOGGER.debug('Keypad: "%s" %s Action: %s Params: %s"' % (
self._keypad.name, self, action, params))
ev_map = {
Button._ACTION_PRESS: Button.Event.PRESSED,
Button._ACTION_RELEASE: Button.Event.RELEASED
}
if action not in ev_map:
_LOGGER.debug("Unknown action %d for button %d in keypad %d" % (
action, self.number, self.keypad.name))
return False
self._dispatch_event(ev_map[action], {})
return True | python | def handle_update(self, action, params):
"""Handle the specified action on this component."""
_LOGGER.debug('Keypad: "%s" %s Action: %s Params: %s"' % (
self._keypad.name, self, action, params))
ev_map = {
Button._ACTION_PRESS: Button.Event.PRESSED,
Button._ACTION_RELEASE: Button.Event.RELEASED
}
if action not in ev_map:
_LOGGER.debug("Unknown action %d for button %d in keypad %d" % (
action, self.number, self.keypad.name))
return False
self._dispatch_event(ev_map[action], {})
return True | Handle the specified action on this component. | https://github.com/thecynic/pylutron/blob/4d9222c96ef7ac7ac458031c058ad93ec31cebbf/pylutron/__init__.py#L692-L705 |
thecynic/pylutron | pylutron/__init__.py | Led.__do_query_state | def __do_query_state(self):
"""Helper to perform the actual query for the current LED state."""
self._lutron.send(Lutron.OP_QUERY, Keypad._CMD_TYPE, self._keypad.id,
self.component_number, Led._ACTION_LED_STATE) | python | def __do_query_state(self):
"""Helper to perform the actual query for the current LED state."""
self._lutron.send(Lutron.OP_QUERY, Keypad._CMD_TYPE, self._keypad.id,
self.component_number, Led._ACTION_LED_STATE) | Helper to perform the actual query for the current LED state. | https://github.com/thecynic/pylutron/blob/4d9222c96ef7ac7ac458031c058ad93ec31cebbf/pylutron/__init__.py#L738-L741 |
thecynic/pylutron | pylutron/__init__.py | Led.state | def state(self):
"""Returns the current LED state by querying the remote controller."""
ev = self._query_waiters.request(self.__do_query_state)
ev.wait(1.0)
return self._state | python | def state(self):
"""Returns the current LED state by querying the remote controller."""
ev = self._query_waiters.request(self.__do_query_state)
ev.wait(1.0)
return self._state | Returns the current LED state by querying the remote controller. | https://github.com/thecynic/pylutron/blob/4d9222c96ef7ac7ac458031c058ad93ec31cebbf/pylutron/__init__.py#L749-L753 |
thecynic/pylutron | pylutron/__init__.py | Led.state | def state(self, new_state: bool):
"""Sets the new led state.
new_state: bool
"""
self._lutron.send(Lutron.OP_EXECUTE, Keypad._CMD_TYPE, self._keypad.id,
self.component_number, Led._ACTION_LED_STATE,
int(new_state))
self._state = new_state | python | def state(self, new_state: bool):
"""Sets the new led state.
new_state: bool
"""
self._lutron.send(Lutron.OP_EXECUTE, Keypad._CMD_TYPE, self._keypad.id,
self.component_number, Led._ACTION_LED_STATE,
int(new_state))
self._state = new_state | Sets the new led state.
new_state: bool | https://github.com/thecynic/pylutron/blob/4d9222c96ef7ac7ac458031c058ad93ec31cebbf/pylutron/__init__.py#L756-L764 |
thecynic/pylutron | pylutron/__init__.py | Led.handle_update | def handle_update(self, action, params):
"""Handle the specified action on this component."""
_LOGGER.debug('Keypad: "%s" %s Action: %s Params: %s"' % (
self._keypad.name, self, action, params))
if action != Led._ACTION_LED_STATE:
_LOGGER.debug("Unknown action %d for led %d in keypad %d" % (
action, self.number, self.keypad.name))
return False
elif len(params) < 1:
_LOGGER.debug("Unknown params %s (action %d on led %d in keypad %d)" % (
params, action, self.number, self.keypad.name))
return False
self._state = bool(params[0])
self._query_waiters.notify()
self._dispatch_event(Led.Event.STATE_CHANGED, {'state': self._state})
return True | python | def handle_update(self, action, params):
"""Handle the specified action on this component."""
_LOGGER.debug('Keypad: "%s" %s Action: %s Params: %s"' % (
self._keypad.name, self, action, params))
if action != Led._ACTION_LED_STATE:
_LOGGER.debug("Unknown action %d for led %d in keypad %d" % (
action, self.number, self.keypad.name))
return False
elif len(params) < 1:
_LOGGER.debug("Unknown params %s (action %d on led %d in keypad %d)" % (
params, action, self.number, self.keypad.name))
return False
self._state = bool(params[0])
self._query_waiters.notify()
self._dispatch_event(Led.Event.STATE_CHANGED, {'state': self._state})
return True | Handle the specified action on this component. | https://github.com/thecynic/pylutron/blob/4d9222c96ef7ac7ac458031c058ad93ec31cebbf/pylutron/__init__.py#L766-L781 |
thecynic/pylutron | pylutron/__init__.py | Keypad.add_button | def add_button(self, button):
"""Adds a button that's part of this keypad. We'll use this to
dispatch button events."""
self._buttons.append(button)
self._components[button.component_number] = button | python | def add_button(self, button):
"""Adds a button that's part of this keypad. We'll use this to
dispatch button events."""
self._buttons.append(button)
self._components[button.component_number] = button | Adds a button that's part of this keypad. We'll use this to
dispatch button events. | https://github.com/thecynic/pylutron/blob/4d9222c96ef7ac7ac458031c058ad93ec31cebbf/pylutron/__init__.py#L802-L806 |
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