def __init__(self, fname, outfile, n_results):
self.con = Constraint(fname)
self.num_x = self.con.n_dim
self.n_results = n_results
self.outfile = outfile
with open(self.outfile, 'w') as f:
f.write(" ".join(map(str, self.con.example)))
f.write("\n")
self.ineq_cons = {
'type': 'ineq',
'fun': lambda x: self.con.eval_con(x.tolist())
}
bound = [0.0, 1.0]
self.bounds = np.asarray([bound] * self.num_x)
# determining the magnitude of the largest initial point
max_initial = max(self.con.example)
if max_initial == 0.0:
self.alpha = 1.0
else:
self.alpha = 1.0 / max_initial
class Optimizer():
def __init__(self, input_file, N):
self.input_constraints = Constraint(input_file)
#print(self.input_constraints.get_ndim())
#print(self.input_constraints.get_example())
#print(self.input_constraints.get_constraints())
#exam = np.array(list(self.input_constraints.get_example()), dtype=float)
#print(self.input_constraints.apply(exam))
self.x = self.sample(N)
self.get_output()
def sample(self, N):
count = 0
ndim = int(self.input_constraints.get_ndim())
x = np.empty((0, ndim))
while (count < int(N)):
n_samples = int(N) * 10
smpls = lhs(ndim, samples=n_samples)
for i in range(len(smpls)):
if self.input_constraints.apply(smpls[i]) and (count < int(N)):
#print(smpls[i])
x = np.append(x, np.array([smpls[i]]), axis=0)
count += 1
print(count)
print(x.shape)
return x
def get_output(self):
return self.x
def __init__(self, input_file, N):
self.input_constraints = Constraint(input_file)
#print(self.input_constraints.get_ndim())
#print(self.input_constraints.get_example())
#print(self.input_constraints.get_constraints())
#exam = np.array(list(self.input_constraints.get_example()), dtype=float)
#print(self.input_constraints.apply(exam))
self.x = self.sample(N)
self.get_output()
def cal_pop_fitness(self):
# Calculating the fitness value of each solution in the current population.
# The fitness function calculates fitness by adding the no of constraints that
# the population passes.
fitness = []
fname = self.get_filename()
c = Constraint(fname)
for p in self.pop:
f = c.count_passed_constrain(p)
fitness.append(f)
return fitness
def __init__(self,
input_path,
n_results,
beta_max=10**4,
p_beta=1,
p_rw_step=0,
track_correctness=False,
threshold=.999):
self.input_path = input_path
self.constraints = Constraint(input_path)
constraints_funcs = self.constraints.get_functions()
self.n_dim = self.constraints.get_ndim()
self.n_results = n_results
self.beta_max = beta_max
self.p_beta = p_beta
self.p_rw_step = p_rw_step
#call sampling method scmc
i_sample = init_sample(self.n_dim, self.n_results,
self.constraints.bounds,
self.constraints.get_example())
self.i_samples = []
self.i_samples.append(i_sample)
self.histories = []
self.correctnesses = []
print(len(constraints_funcs))
for i in range(1, len(constraints_funcs) + 1):
history, correctness = scmc(
self.n_dim,
self.n_results,
self.i_samples[i - 1],
constraints_funcs[0:i],
beta_max,
self.constraints.bounds,
p_beta,
p_rw_step,
track_correctness=track_correctness,
given_example=self.constraints.get_example(),
threshold=threshold)
self.i_samples.append(history[-1])
self.histories.append(history)
self.correctnesses.append(correctness)
self.results = self.histories[-1][-1]
def main(argv):
if len(sys.argv) < 3:
print(
'Please provide 3 command line arguments (e.g. input_filename, output_filename, n_results'
)
exit(-1)
in_filename = ''
out_filename = ''
niteration = 0
in_filename = sys.argv[1]
out_filename = sys.argv[2]
niteration = int(sys.argv[3])
c = Constraint(in_filename)
v = c.get_example()
print(v)
n = c.get_ndim() #this is the chromosome size for each population
print(n)
# print(c.apply([0.5, 1.0, 0.0, 0.5]))
s = 300 #population size
ga = Genetic(
s, n, in_filename
) #initializes ga type object with populaton size, chromosome size, input file name
p = ga.get_population()
print(p)
print(ga.cal_pop_fitness())
temp_solution = ga.eval_ga(niteration)
# print(temp_solution)
# write result to the output
with open(out_filename, 'w') as filehandle:
for item in temp_solution:
s = " ".join(str(e) for e in item)
# print(s)
l = s
# l = item
print(l)
filehandle.write('%s\n' % l)
filehandle.close()
def __eq__(self, other):
if type(other) == str and other == '__builtins__': return False
r = self - other
if r.dtype != object: return all(r)
if r.size == 1: return asscalar(r) == 0
# TODO: rework it
return ooarray(
[Constraint(elem, lb=0.0, ub=0.0) for elem in r.tolist()])
def get_constraints(input_file):
"""reads the input file and creates an instance of the Constraint class
"""
# create a new instance of the Constraint class by reading input_file
# catch several errors: file not found, first two lines (number of
# dimensions and starting vector) not formatted correctly, or syntax
# error in the constraints
try:
space = Constraint(input_file)
except FileNotFoundError:
sys.exit("Error: input file not found")
except ValueError:
sys.exit("Error: input file formatted improperly")
except SyntaxError:
sys.exit("Error: syntax error in constraints")
# get the example point and make sure it has the correct dimensionality
example = space.get_example()
if len(example) < space.get_ndim():
sys.exit("Error: example point does not have enough dimensions")
# check and make sure the example point actually satisfies all of the
# constraints. This additionally serves to make sure the constraints
# are specified correctly
try:
check_example = space.apply(example)
except IndexError:
sys.exit("Error: invalid constraints")
except NameError:
sys.exit("Error: invalid constraints")
# if space.apply(example) returned false, then throw an error
if not check_example:
sys.exit("Error: example point is invalid")
return space
def __init__(self, input_file_name):
"""
Construct a Generator object and set up the log files.
:param string input_file_name: Name of the constraints file
"""
# Setup class logger
setup_logger('log_generator.log')
self.log = logging.getLogger('log_generator.log')
# Set up the constraints
self.input_file_name = input_file_name
try:
self.constraints = Constraint(self.input_file_name)
except FileNotFoundError:
self.log.critical(
f'Constraints file not found: {self.input_file_name}')
else:
self.log.info(f'Setup Generator for {self.input_file_name}')
# Set and queue to hold and explore the required points
self.point_set = set()
self.points_queue = deque()
class Sampler:
def __init__(self, filename, max_points, max_time):
self.constraint = Constraint(filename)
self.n_dim = self.constraint.n_dim
self.example = self.constraint.example
self.max_points = max_points
self.max_time = max_time
def test_inside(self, x):
if np.any(np.less(x, 0.)):
return False
if np.any(np.greater(x, 1.)):
return False
return self.constraint.apply(x)
def line_search(self, x, direction):
tiny = 1e-6
low, high = 0., np.sqrt(self.n_dim) + tiny
for _ in range(20):
middle = 0.5 * (low + high)
inside = self.test_inside(x + middle * direction)
if inside:
low = middle
else:
high = middle
return low
def random_direction(self):
vector = np.random.normal(size=self.n_dim)
return vector / np.linalg.norm(vector)
def hit_and_run(self):
assert self.test_inside(self.example)
points = [np.array(self.example)]
start_time = time.time()
while len(points) < self.max_points and time.time(
) - start_time < self.max_time:
direction = self.random_direction()
factor = self.line_search(points[-1], direction)
if factor > 0.:
new_point = points[-1] + np.random.uniform(
) * factor * direction
if self.test_inside(new_point):
points.append(new_point)
return points
def __init__(self):
def f(X, **kwargs):
return -(X[1] + 47)*np.sin(np.sqrt(np.abs(X[0]/2 + (X[1]+47)))) + \
-X[0]*np.sin(np.sqrt(np.abs(X[0] - (X[1]+ 47))))
def bound_constraint(X, **kwargs):
return np.all(np.logical_or(X < -512, X > 512))
constraints = [
Constraint(bound_constraint, **{})
]
super().__init__(f, constraints,
n_dimensions_in = 2,
n_dimensions_out = 1,
**{})
def constraint_from_string(string : str, l_deprel:str = None) -> Constraint:
""" string format 'cas=N' or 'num=@' or 'gen=subj.gen' or 'num = verb.num'
@ will be replaced with the lval
over-write rule: num/3 means is_bare will be yes no matter what it was before, no error"""
if EQUALS in string: separator = EQUALS
elif OVERWRITE in string: separator = OVERWRITE
else: raise Exception('Invalid constraint %s, no separator' % string)
is_none_ok = (NONE_NOT_OK not in string)
string = string.replace(NONE_NOT_OK, EQUALS)
is_overwrite = (separator == OVERWRITE)
(lstr, rstr) = string.split(separator)
lstr = lstr.strip()
rstr = rstr.strip()
if rstr[-1] == '!': # mandatory condition
score = float('Inf')
rstr = rstr[:-1]
elif '?' in rstr:
(rstr, score) = rstr.split('?')
score = float(score) if score else 0.5
else:
score = DEFAULT_ERROR_SCORE
# do lexp
lstr = lstr.split('.') # split by period -- ie, subj.gen
lexp = [l_deprel] if l_deprel else []
lexp = lexp + lstr
# for item in lstr:
# lexp.append(item)
# do rexp
rexpr = []
if rstr == '@':
rexpr = lstr
elif '.' in rstr:
rstr = rstr.split('.')
for item in rstr:
rexpr.append(item)
else: # plain string
rexpr = rstr
if is_overwrite:
return OverwriteConstraint(lexp, rexpr)
else:
return Constraint(lexp, rexpr, score, is_none_ok)
def sampler(input_file, output_file, n_result):
ct = Constraint(input_file)
ndim = ct.get_ndim()
current_results = []
while len(current_results) < n_result:
res = np.random.random(ndim)
res = (res / sum(res) * sum(ct.get_example())).round(5)
if not ct.apply(res):
continue
current_results.append(res)
print('example:', ct.get_example())
print('res:', res)
with open(output_file, 'a') as f:
for l, el in enumerate(current_results):
string = ' '.join(map(str, el))
# for item in string:
f.write(string)
f.write('\n')
return current_results
class SCMC():
"""Use sequentially constrained Monte Carlo method to sample given constrained domains uniformly
Methods
-------
write_ouput:
write the final sample in given path
get_correctness:
return the correctness of the final state
get_history:
return a List, the history all samples in the sequential diffusion process
plot_results:
scatter plot the results along two arbitrary axes
params
------
comp1, int: first axis (h)
comp2, int: second axis (v)
n_iter, int: if want history instead of final state
print_constraints:
print the original constraints
"""
def __init__(self,
input_path,
n_results,
beta_max=10**4,
p_beta=1,
p_rw_step=0,
track_correctness=False,
threshold=.999):
self.input_path = input_path
self.constraints = Constraint(input_path)
constraints_funcs = self.constraints.get_functions()
self.n_dim = self.constraints.get_ndim()
self.n_results = n_results
self.beta_max = beta_max
self.p_beta = p_beta
self.p_rw_step = p_rw_step
#call sampling method scmc
i_sample = init_sample(self.n_dim, self.n_results,
self.constraints.bounds,
self.constraints.get_example())
self.i_samples = []
self.i_samples.append(i_sample)
self.histories = []
self.correctnesses = []
print(len(constraints_funcs))
for i in range(1, len(constraints_funcs) + 1):
history, correctness = scmc(
self.n_dim,
self.n_results,
self.i_samples[i - 1],
constraints_funcs[0:i],
beta_max,
self.constraints.bounds,
p_beta,
p_rw_step,
track_correctness=track_correctness,
given_example=self.constraints.get_example(),
threshold=threshold)
self.i_samples.append(history[-1])
self.histories.append(history)
self.correctnesses.append(correctness)
self.results = self.histories[-1][-1]
# self.history, self.correctness = scmc(self.n_dim, self.n_results, i_sample ,constraints_funcs, beta_max, self.constraints.bounds,
# p_beta,p_rw_step,
# track_correctness=track_correctness,
# given_example = self.constraints.get_example())
# self.results = self.history[-1]
# self.history, self.correctness = scmc(self.n_dim, self.n_results, constraints_funcs, beta_max, self.constraints.bounds,
# p_beta,p_rw_step,
# track_correctness=track_correctness,
# given_example = self.constraints.get_example())
# self.results = self.history[-1]
# self.write_ouput(output_path)
def write_output(self, output_path):
"""
save the sample to a output_path
"""
np.savetxt(output_path, self.results)
def get_correctness(self):
"""
get correctness
"""
correctness = [self.constraints.apply(x) for x in self.results]
correctness = sum(correctness) / self.n_results
return correctness
def get_history(self):
return self.history
def get_results(self):
"""
get the sample
"""
return self.results
def plot_results(self, comp1, comp2, n_iter=None):
"""
Plot the sample in x[comp1],x[comp2]
"""
if n_iter is None:
sample = self.results
else:
sample = self.history[n_iter - 1]
plt.figure()
plt.scatter(sample[:, comp1], sample[:, comp2])
plt.xlim(0, 1)
plt.ylim(0, 1)
def plot_all_axis(self, n_iter=None):
n_dim = self.n_dim
for i in range(n_dim):
for j in range(i + 1, n_dim):
self.plot_results(i, j, n_iter)
plt.xlabel('x_{}'.format(i))
plt.ylabel('x_{}'.format(j))
def print_constraints(self):
print(self.constraints.get_exprs_string())
def _get_type_constraint(deprel:str, type_name : str, error_score = float('inf')) -> Constraint:
if deprel:
return Constraint([deprel, Monomial.CATEGORY], type_name, error_score)
else:
return Constraint([Monomial.CATEGORY], type_name, error_score)
if dists_to_resampled[new_min_dist_inds] > min_dist_history[-1]:
samples[vector_ind_to_replace] = resampled
return samples, min_dist_history
def parse_inputs(input_args):
input_fname_path, output_filename, n_results = sys.argv[1:]
try:
n_results = int(n_results)
if n_results < 2:
raise ValueError("n_results must be an integer larger than 1")
except:
raise ValueError("n_results must be an integer larger than 1")
if not os.path.isfile(input_fname_path):
raise OSError("Input file not found")
return input_fname_path, output_filename, n_results
if __name__ == "__main__":
input_fname, output_fname, n_results = parse_inputs(sys.argv[1:])
cons = Constraint(input_fname)
samples = generate_random_samples(cons, n_results=n_results)
samples, _ = spread_out_samples(cons,
samples,
resample_attempts=max(1000, n_results))
np.savetxt(output_fname, samples, fmt='%f', delimiter=' ')
def validate_output_file(self, constraints_file_name, points_file_name,
expected):
"""
Validates output file against constraint file to see if required number of points are available.
:param string constraints_file_name: path to a file that contains the constraints
:param string points_file_name: path to a file that contains the vectors to be validated
:param int expected: Number of points that are expected in this file
"""
self.log.info(
f'Running validation: {constraints_file_name} <-> {points_file_name}'
)
# Return if constraints or points files are not found.
try:
constraints = Constraint(constraints_file_name)
except FileNotFoundError:
self.log.critical(
f'Constraints file {constraints_file_name} not found. Skipping validation...'
)
return
try:
points_file = open(points_file_name, 'r')
except FileNotFoundError:
self.log.critical(
f'Points file {points_file_name} not found. Skipping validation...'
)
return
# Counters for number of points that were generated, passed, failed,
generated = 0
passed = 0
failed = 0
for itr, line in enumerate(points_file):
x = [float(_) for _ in line.strip().split(" ")]
# Log how many points have been checked.
if itr != 0 and (itr % pow(10, floor(log10(itr + 1))) == 0):
self.log.debug(f'Checked {itr}')
# Count number of points generated/passed/failed
generated += 1
try:
if constraints.apply(x):
passed += 1
else:
self.log.warning(f'Point outside constraints: {x}')
failed += 1
except:
self.log.error(f'{sys.exc_info()[1]}, point: {x}')
points_file.close()
# Points file summary
self.log.info(f'{generated} generated, {expected} expected')
self.log.info(f'{passed} passed, {failed} failed')
if generated != expected:
self.log.warning(f'Unexpected number of points')
if failed > 0:
self.log.warning(f'{failed} points failed')
if passed == expected or passed == generated:
self.log.info(f'All points are valid')
self.log.info(
f'Validated {constraints_file_name} <-> {points_file_name}')
self.log.info(f'-- Validation complete -- ')
return
def __init__(self, filename, max_points, max_time):
self.constraint = Constraint(filename)
self.n_dim = self.constraint.n_dim
self.example = self.constraint.example
self.max_points = max_points
self.max_time = max_time
class Solution():
def __init__(self, fname, outfile, n_results):
self.con = Constraint(fname)
self.num_x = self.con.n_dim
self.n_results = n_results
self.outfile = outfile
with open(self.outfile, 'w') as f:
f.write(" ".join(map(str, self.con.example)))
f.write("\n")
self.ineq_cons = {
'type': 'ineq',
'fun': lambda x: self.con.eval_con(x.tolist())
}
bound = [0.0, 1.0]
self.bounds = np.asarray([bound] * self.num_x)
# determining the magnitude of the largest initial point
max_initial = max(self.con.example)
if max_initial == 0.0:
self.alpha = 1.0
else:
self.alpha = 1.0 / max_initial
def solve(self):
x0_all = np.random.random_sample(
(self.n_results * 2, self.num_x)) / self.alpha
output = []
cnt = 0
for i in range(len(x0_all)):
x0 = x0_all[i, :]
if self.con.apply(x0.tolist()):
x_feasible = x0
else:
try:
res = self.optimize_feasible(x0)
x_feasible = res.x
if res.status != 0 or any(x_feasible < 0.0) or any(
x_feasible > 1.0) or list(x_feasible) in output:
continue
except:
continue
# if self.con.apply(x_feasible.tolist()):
# print "True"
with open(self.outfile, 'a') as f:
f.write(" ".join(map(str, x_feasible)))
f.write("\n")
output.append(list(x_feasible))
cnt += 1
if cnt == self.n_results:
break
return output
def optimize_feasible(self, x0):
res = optimize.minimize(self.f,
x0,
method='SLSQP',
constraints=[self.ineq_cons],
options={
'ftol': 1e-8,
'disp': False
},
bounds=self.bounds)
return res
def f(self, x):
constr = self.con.eval_con(x.tolist())
constr[np.where(constr > 0)] = 0
return np.sum(np.square(constr))
def constraint(self, x):
constr = self.con.eval_con(x.tolist())
return constr
def get_constrain(self):
fname = self.get_filename()
c = Constraint(fname)
self.no_of_constrain = c.get_no_of_constrain()
return self.no_of_constrain
class Generator:
"""
Generator class to generate output_file with required number of points that satisfy constraints in input_file.
"""
def __init__(self, input_file_name):
"""
Construct a Generator object and set up the log files.
:param string input_file_name: Name of the constraints file
"""
# Setup class logger
setup_logger('log_generator.log')
self.log = logging.getLogger('log_generator.log')
# Set up the constraints
self.input_file_name = input_file_name
try:
self.constraints = Constraint(self.input_file_name)
except FileNotFoundError:
self.log.critical(
f'Constraints file not found: {self.input_file_name}')
else:
self.log.info(f'Setup Generator for {self.input_file_name}')
# Set and queue to hold and explore the required points
self.point_set = set()
self.points_queue = deque()
# Generate_valid_points uses a form of breadth first search to generate n points satisfying the constraints defined in input_file_name.
def generate_valid_points(self, n_points, min_step_size=1E-30):
"""
Generate required number of points that satisfy constraints.
:param int n_points: Required number of points.
:param float min_step_size: Optional minimum step size (default = 1E-30) for exploration.
"""
# Return if constraint file is unavailable
try:
self.constraints
except NameError:
self.log.critical(
f'Constraints file not found {self.input_file_name} not found. Terminating generation...'
)
return
self.log.info(
f'Points required: {n_points}, min_step_size: {min_step_size}')
self.log.info(
f'Constraints file: {self.input_file_name}, Dimension: {self.constraints.get_ndim()}, Starting point: {self.constraints.get_example()}'
)
# Cast as a tuple since immutability is required to put into a set.
self.point_set.add(tuple(self.constraints.get_example()))
# Initial step size for exploration.
step_size = 1.0
# Run while more points are required.
while len(self.point_set) < n_points:
# Add all valid points to the queue for exploration.
for _ in self.point_set:
self.points_queue.append(_)
# Counter to count points found at a given step size.
n_new_points = 0
# Run while the queue exists and more points are required.
while self.points_queue and len(self.point_set) < n_points:
# Explore outward from each point in the queue and add the found valid points to the queue and set.
current_point = self.points_queue.popleft()
n_new_points += self.explore_point(current_point, step_size,
n_points, self.constraints)
# End inner while loop: Done with finding points at given step size
self.log.debug(
f'Found: {n_new_points} with step size: {step_size}, total in set: {len(self.point_set)}'
)
# Decrease step_size and search again.
step_size /= 2
if step_size < min_step_size:
self.log.warning(
f'Step size is too small: {step_size}, breaking search')
break
# End outer while loop: Done with finding required number of points.
self.log.info(f'Points found: {len(self.point_set)}')
self.log.info(f'-- Generation complete -- ')
return
def explore_point(self, current_point, step_size, n_points, constraints):
"""
Explore outward from the current point in all dimensions with a given step size.
Points that satisfy constraints are added to the queue and the point_set.
The function returns the number of valid points found from current point.
:param tuple current_point: Starting point of exploration
:param float step_size: extent of exploration
:param int n_points: required number of points
:param Constraint constraints: set of constraints to satisfy
"""
found_points = 0
for i_dim in range(len(current_point)):
for direction in [-1, +1]:
# Update the coordinates, use round() to remove floating point errors, and cast as tuple for hash-abilty.
next_point = list(current_point)
round_to_digit = max(
self.n_digits_after_decimal(next_point[i_dim]),
self.n_digits_after_decimal(step_size))
next_point[i_dim] = round(
next_point[i_dim] + (direction * step_size),
round_to_digit)
next_point = tuple(next_point)
try:
# If next_point is within the unit hypercube, meets constraints, and is not already explored, add to queue and set.
if self.unit_cube_check(next_point) and constraints.apply(
next_point) and next_point not in self.point_set:
self.points_queue.append(next_point)
self.point_set.add(next_point)
found_points += 1
# ZeroDivisionError exception is logged as a warning and the script continues.
except ZeroDivisionError:
self.log.warning(
f'{sys.exc_info()[1]}, point: {next_point}')
# Unexpected exceptions are logged as errors and the script continues.
except:
self.log.error(f'{sys.exc_info()}, point: {next_point}')
# Return if the number of required points are found.
if len(self.point_set) == n_points:
return found_points
return found_points
def write_to_file(self, output_file_name):
"""
Setup output file and write coordinates to it.
:param string output_file_name: Name of the file to write the generated points
"""
# If constraint file was not found, point_set is empty so there is nothing to write.
if len(self.point_set) == 0:
self.log.error(
f'No points generated for {self.input_file_name}. Terminating writing to {output_file_name}...'
)
return
self.log.info(f'Writing: {self.input_file_name} -> {output_file_name}')
# Warn if file already exists and will be overwritten.
if path.exists(output_file_name):
self.log.warning(
f'{output_file_name} already exists, overwriting file')
# Open file for writing and write set of points to it.
# Use an iterator to remove the new line character from the last line.
with open(output_file_name, "w") as output_file:
itr = 0
new_line = '\n'
for point in self.point_set:
itr += 1
if itr == len(self.point_set): new_line = ''
output_file.write(" ".join(map(str, point)) + new_line)
self.log.info(f'Written {itr} lines.')
self.log.info(f'-- Writing complete -- ')
return
def unit_cube_check(self, x):
"""
Check if the given vector is within the unit hypercube.
:param tuple x: Object that is used to represent the vector
"""
for _ in x:
if _ > 1 or _ < 0:
return False
return True
def n_digits_after_decimal(self, x):
"""
Return the number of digits after the decimal in x.
:param float x: input number x
"""
return len(str(x).split(".")[-1])
from constraints import Constraint
from exploration import Explore
# read inputs
inputs = sys.argv[-3:]
input_file = inputs[0]
output_file = inputs[1]
n_results = int(inputs[2])
# Algorithm parameters
inflation = 4
crowding_distance = 0.0005
mutation_period = 4
# Defining main objects for exploration
c = Constraint(input_file)
e = Explore(c, n_results, inflation, output_file)
# Run and return run time
start = time.time()
e.select(crowding_distance=crowding_distance,
algorithm='hybrid',
mutation_period=mutation_period)
# e.deflate()
e.write2file()
end = time.time()
print('Time', end - start)
# Plotting sampling distribution just for visualization
e.plotting(index=(0, 1), plot_training=True)
def main():
# Obtain input arguments
parser = argparse.ArgumentParser(description='Process inputs to sampler')
parser.add_argument('input_file', default=None, type=str)
parser.add_argument('output_file', default="output.txt", type=str)
parser.add_argument('N', default=1000, type=int)
parser.add_argument('--plot_bool', action="store_true")
# Parse input arguments
args = parser.parse_args()
input_file = args.input_file
output_file = args.output_file
N = args.N
plot_bool = args.plot_bool
np.random.seed(0)
# Load file and the corresponding constraint functions and valid example
if not path.isfile(input_file) and input_file:
raise ValueError('Input file does not exist')
elif input_file:
constraint = Constraint(input_file)
x0 = np.array(constraint.get_example())
constraints = constraint.eval_constraints
constraint_bool = constraint.apply
constraint_bools = constraint.apply_list
else:
# Used for local testing
constraints = func_constraints
constraint_bool = func_constraints_bool
x0 = x0
# Obtain bounds of input space from which initial sample guesses will
# be uniformly drawn
bounds = get_bounds(x0, constraint_bools)
# Find valid uniformly distributed samples with constrained SMC
N_star = int(N * 1.1)
run = RunSMC(N=N_star,
bounds=bounds,
type="constrained_smc",
tau_T=1e7,
constraints=constraints)
x, x0 = run.get_x(), run.get_x0()
# Evaluate validity. uniqueness, and spread of results
# Subsample result
acc, x_valid = get_accuracy(x, constraint_bool)
idx_x, idx_x_valid = np.arange(x.shape[0]), np.arange(x_valid.shape[0])
x = x_valid[np.random.choice(idx_x_valid, N, replace=False)] if x_valid.shape[0] >= N else \
x[np.random.choice(idx_x, N, replace=False)]
std = np.std(x, axis=0)
uniq_nums = np.unique(x, axis=0).shape[0]
print('accuracy: {acc} std: {std} unique candidates: {uniq}'.format(
acc=acc, std=std, uniq=uniq_nums))
# Save output and plot if 2D and plot_bool
np.savetxt(output_file, x, delimiter=' ')
if x.shape[1] == 2 and plot_bool:
plt.plot(x0[:, 0], x0[:, 1], 'o', x[:, 0], x[:, 1], '*')
plt.show()
