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value | code stringlengths 51 19.8k | code_tokens list | docstring stringlengths 3 17.3k | docstring_tokens list | sha stringlengths 40 40 | url stringlengths 87 242 |
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242,000 | SheffieldML/GPyOpt | GPyOpt/acquisitions/LP.py | AcquisitionLP.d_acquisition_function | def d_acquisition_function(self, x):
"""
Returns the gradient of the acquisition function at x.
"""
x = np.atleast_2d(x)
if self.transform=='softplus':
fval = -self.acq.acquisition_function(x)[:,0]
scale = 1./(np.log1p(np.exp(fval))*(1.+np.exp(-fval)))
... | python | def d_acquisition_function(self, x):
x = np.atleast_2d(x)
if self.transform=='softplus':
fval = -self.acq.acquisition_function(x)[:,0]
scale = 1./(np.log1p(np.exp(fval))*(1.+np.exp(-fval)))
elif self.transform=='none':
fval = -self.acq.acquisition_function(x)... | [
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242,001 | SheffieldML/GPyOpt | GPyOpt/acquisitions/LP.py | AcquisitionLP.acquisition_function_withGradients | def acquisition_function_withGradients(self, x):
"""
Returns the acquisition function and its its gradient at x.
"""
aqu_x = self.acquisition_function(x)
aqu_x_grad = self.d_acquisition_function(x)
return aqu_x, aqu_x_grad | python | def acquisition_function_withGradients(self, x):
aqu_x = self.acquisition_function(x)
aqu_x_grad = self.d_acquisition_function(x)
return aqu_x, aqu_x_grad | [
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242,002 | SheffieldML/GPyOpt | GPyOpt/acquisitions/base.py | AcquisitionBase.acquisition_function | def acquisition_function(self,x):
"""
Takes an acquisition and weights it so the domain and cost are taken into account.
"""
f_acqu = self._compute_acq(x)
cost_x, _ = self.cost_withGradients(x)
return -(f_acqu*self.space.indicator_constraints(x))/cost_x | python | def acquisition_function(self,x):
f_acqu = self._compute_acq(x)
cost_x, _ = self.cost_withGradients(x)
return -(f_acqu*self.space.indicator_constraints(x))/cost_x | [
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242,003 | SheffieldML/GPyOpt | GPyOpt/acquisitions/base.py | AcquisitionBase.acquisition_function_withGradients | def acquisition_function_withGradients(self, x):
"""
Takes an acquisition and it gradient and weights it so the domain and cost are taken into account.
"""
f_acqu,df_acqu = self._compute_acq_withGradients(x)
cost_x, cost_grad_x = self.cost_withGradients(x)
f_acq_cost = f_... | python | def acquisition_function_withGradients(self, x):
f_acqu,df_acqu = self._compute_acq_withGradients(x)
cost_x, cost_grad_x = self.cost_withGradients(x)
f_acq_cost = f_acqu/cost_x
df_acq_cost = (df_acqu*cost_x - f_acqu*cost_grad_x)/(cost_x**2)
return -f_acq_cost*self.space.indicator... | [
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242,004 | SheffieldML/GPyOpt | GPyOpt/util/general.py | reshape | def reshape(x,input_dim):
'''
Reshapes x into a matrix with input_dim columns
'''
x = np.array(x)
if x.size ==input_dim:
x = x.reshape((1,input_dim))
return x | python | def reshape(x,input_dim):
'''
Reshapes x into a matrix with input_dim columns
'''
x = np.array(x)
if x.size ==input_dim:
x = x.reshape((1,input_dim))
return x | [
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242,005 | SheffieldML/GPyOpt | GPyOpt/util/general.py | spawn | def spawn(f):
'''
Function for parallel evaluation of the acquisition function
'''
def fun(pipe,x):
pipe.send(f(x))
pipe.close()
return fun | python | def spawn(f):
'''
Function for parallel evaluation of the acquisition function
'''
def fun(pipe,x):
pipe.send(f(x))
pipe.close()
return fun | [
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242,006 | SheffieldML/GPyOpt | GPyOpt/util/general.py | values_to_array | def values_to_array(input_values):
'''
Transforms a values of int, float and tuples to a column vector numpy array
'''
if type(input_values)==tuple:
values = np.array(input_values).reshape(-1,1)
elif type(input_values) == np.ndarray:
values = np.atleast_2d(input_values)
elif type... | python | def values_to_array(input_values):
'''
Transforms a values of int, float and tuples to a column vector numpy array
'''
if type(input_values)==tuple:
values = np.array(input_values).reshape(-1,1)
elif type(input_values) == np.ndarray:
values = np.atleast_2d(input_values)
elif type... | [
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242,007 | SheffieldML/GPyOpt | GPyOpt/util/general.py | merge_values | def merge_values(values1,values2):
'''
Merges two numpy arrays by calculating all possible combinations of rows
'''
array1 = values_to_array(values1)
array2 = values_to_array(values2)
if array1.size == 0:
return array2
if array2.size == 0:
return array1
merged_array = [... | python | def merge_values(values1,values2):
'''
Merges two numpy arrays by calculating all possible combinations of rows
'''
array1 = values_to_array(values1)
array2 = values_to_array(values2)
if array1.size == 0:
return array2
if array2.size == 0:
return array1
merged_array = [... | [
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242,008 | SheffieldML/GPyOpt | GPyOpt/util/general.py | normalize | def normalize(Y, normalization_type='stats'):
"""Normalize the vector Y using statistics or its range.
:param Y: Row or column vector that you want to normalize.
:param normalization_type: String specifying the kind of normalization
to use. Options are 'stats' to use mean and standard deviation,
or... | python | def normalize(Y, normalization_type='stats'):
Y = np.asarray(Y, dtype=float)
if np.max(Y.shape) != Y.size:
raise NotImplementedError('Only 1-dimensional arrays are supported.')
# Only normalize with non null sdev (divide by zero). For only one
# data point both std and ptp return 0.
if nor... | [
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242,009 | SheffieldML/GPyOpt | GPyOpt/experiment_design/grid_design.py | GridDesign.get_samples | def get_samples(self, init_points_count):
"""
This method may return less points than requested.
The total number of generated points is the smallest closest integer of n^d to the selected amount of points.
"""
init_points_count = self._adjust_init_points_count(init_points_count... | python | def get_samples(self, init_points_count):
init_points_count = self._adjust_init_points_count(init_points_count)
samples = np.empty((init_points_count, self.space.dimensionality))
# Use random design to fill non-continuous variables
random_design = RandomDesign(self.space)
random... | [
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242,010 | SheffieldML/GPyOpt | GPyOpt/experiment_design/random_design.py | RandomDesign.get_samples_with_constraints | def get_samples_with_constraints(self, init_points_count):
"""
Draw random samples and only save those that satisfy constraints
Finish when required number of samples is generated
"""
samples = np.empty((0, self.space.dimensionality))
while samples.shape[0] < init_points... | python | def get_samples_with_constraints(self, init_points_count):
samples = np.empty((0, self.space.dimensionality))
while samples.shape[0] < init_points_count:
domain_samples = self.get_samples_without_constraints(init_points_count)
valid_indices = (self.space.indicator_constraints(do... | [
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242,011 | SheffieldML/GPyOpt | GPyOpt/experiment_design/random_design.py | RandomDesign.fill_noncontinous_variables | def fill_noncontinous_variables(self, samples):
"""
Fill sample values to non-continuous variables in place
"""
init_points_count = samples.shape[0]
for (idx, var) in enumerate(self.space.space_expanded):
if isinstance(var, DiscreteVariable) or isinstance(var, Catego... | python | def fill_noncontinous_variables(self, samples):
init_points_count = samples.shape[0]
for (idx, var) in enumerate(self.space.space_expanded):
if isinstance(var, DiscreteVariable) or isinstance(var, CategoricalVariable) :
sample_var = np.atleast_2d(np.random.choice(var.domain,... | [
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242,012 | SheffieldML/GPyOpt | GPyOpt/interface/driver.py | BODriver._get_obj | def _get_obj(self,space):
"""
Imports the acquisition function.
"""
obj_func = self.obj_func
from ..core.task import SingleObjective
return SingleObjective(obj_func, self.config['resources']['cores'], space=space, unfold_args=True) | python | def _get_obj(self,space):
obj_func = self.obj_func
from ..core.task import SingleObjective
return SingleObjective(obj_func, self.config['resources']['cores'], space=space, unfold_args=True) | [
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242,013 | SheffieldML/GPyOpt | GPyOpt/interface/driver.py | BODriver._get_space | def _get_space(self):
"""
Imports the domain.
"""
assert 'space' in self.config, 'The search space is NOT configured!'
space_config = self.config['space']
constraint_config = self.config['constraints']
from ..core.task.space import Design_space
re... | python | def _get_space(self):
assert 'space' in self.config, 'The search space is NOT configured!'
space_config = self.config['space']
constraint_config = self.config['constraints']
from ..core.task.space import Design_space
return Design_space.fromConfig(space_config, constrain... | [
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242,014 | SheffieldML/GPyOpt | GPyOpt/interface/driver.py | BODriver._get_model | def _get_model(self):
"""
Imports the model.
"""
from copy import deepcopy
model_args = deepcopy(self.config['model'])
del model_args['type']
from ..models import select_model
return select_model(self.config['model']['type']).fromConfig(model_arg... | python | def _get_model(self):
from copy import deepcopy
model_args = deepcopy(self.config['model'])
del model_args['type']
from ..models import select_model
return select_model(self.config['model']['type']).fromConfig(model_args) | [
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242,015 | SheffieldML/GPyOpt | GPyOpt/interface/driver.py | BODriver._get_acquisition | def _get_acquisition(self, model, space):
"""
Imports the acquisition
"""
from copy import deepcopy
acqOpt_config = deepcopy(self.config['acquisition']['optimizer'])
acqOpt_name = acqOpt_config['name']
del acqOpt_config['name']
from ..opt... | python | def _get_acquisition(self, model, space):
from copy import deepcopy
acqOpt_config = deepcopy(self.config['acquisition']['optimizer'])
acqOpt_name = acqOpt_config['name']
del acqOpt_config['name']
from ..optimization import AcquisitionOptimizer
acqOpt = Ac... | [
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242,016 | SheffieldML/GPyOpt | GPyOpt/interface/driver.py | BODriver._get_acq_evaluator | def _get_acq_evaluator(self, acq):
"""
Imports the evaluator
"""
from ..core.evaluators import select_evaluator
from copy import deepcopy
eval_args = deepcopy(self.config['acquisition']['evaluator'])
del eval_args['type']
return select_evaluator(self.conf... | python | def _get_acq_evaluator(self, acq):
from ..core.evaluators import select_evaluator
from copy import deepcopy
eval_args = deepcopy(self.config['acquisition']['evaluator'])
del eval_args['type']
return select_evaluator(self.config['acquisition']['evaluator']['type'])(acq, **eval_arg... | [
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242,017 | SheffieldML/GPyOpt | GPyOpt/interface/driver.py | BODriver._check_stop | def _check_stop(self, iters, elapsed_time, converged):
"""
Defines the stopping criterion.
"""
r_c = self.config['resources']
stop = False
if converged==0:
stop=True
if r_c['maximum-iterations'] !='NA' and iters>= r_c['maximum-iterations']:
... | python | def _check_stop(self, iters, elapsed_time, converged):
r_c = self.config['resources']
stop = False
if converged==0:
stop=True
if r_c['maximum-iterations'] !='NA' and iters>= r_c['maximum-iterations']:
stop = True
if r_c['max-run-time'] != 'NA' and... | [
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242,018 | SheffieldML/GPyOpt | GPyOpt/interface/driver.py | BODriver.run | def run(self):
"""
Runs the optimization using the previously loaded elements.
"""
space = self._get_space()
obj_func = self._get_obj(space)
model = self._get_model()
acq = self._get_acquisition(model, space)
acq_eval = self._get_acq_evaluator(acq)
... | python | def run(self):
space = self._get_space()
obj_func = self._get_obj(space)
model = self._get_model()
acq = self._get_acquisition(model, space)
acq_eval = self._get_acq_evaluator(acq)
from ..experiment_design import initial_design
X_init = initial_de... | [
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242,019 | SheffieldML/GPyOpt | GPyOpt/optimization/optimizer.py | choose_optimizer | def choose_optimizer(optimizer_name, bounds):
"""
Selects the type of local optimizer
"""
if optimizer_name == 'lbfgs':
optimizer = OptLbfgs(bounds)
elif optimizer_name == 'DIRECT':
optimizer = OptDirect(bounds)
elif optimizer_name == 'CMA':
... | python | def choose_optimizer(optimizer_name, bounds):
if optimizer_name == 'lbfgs':
optimizer = OptLbfgs(bounds)
elif optimizer_name == 'DIRECT':
optimizer = OptDirect(bounds)
elif optimizer_name == 'CMA':
optimizer = OptCma(bounds)
else:
raise I... | [
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242,020 | SheffieldML/GPyOpt | GPyOpt/util/arguments_manager.py | ArgumentsManager.evaluator_creator | def evaluator_creator(self, evaluator_type, acquisition, batch_size, model_type, model, space, acquisition_optimizer):
"""
Acquisition chooser from the available options. Guide the optimization through sequential or parallel evalutions of the objective.
"""
acquisition_transformation = s... | python | def evaluator_creator(self, evaluator_type, acquisition, batch_size, model_type, model, space, acquisition_optimizer):
acquisition_transformation = self.kwargs.get('acquisition_transformation','none')
if batch_size == 1 or evaluator_type == 'sequential':
return Sequential(acquisition)
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242,021 | SheffieldML/GPyOpt | GPyOpt/acquisitions/LCB.py | AcquisitionLCB._compute_acq | def _compute_acq(self, x):
"""
Computes the GP-Lower Confidence Bound
"""
m, s = self.model.predict(x)
f_acqu = -m + self.exploration_weight * s
return f_acqu | python | def _compute_acq(self, x):
m, s = self.model.predict(x)
f_acqu = -m + self.exploration_weight * s
return f_acqu | [
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242,022 | SheffieldML/GPyOpt | GPyOpt/acquisitions/LCB.py | AcquisitionLCB._compute_acq_withGradients | def _compute_acq_withGradients(self, x):
"""
Computes the GP-Lower Confidence Bound and its derivative
"""
m, s, dmdx, dsdx = self.model.predict_withGradients(x)
f_acqu = -m + self.exploration_weight * s
df_acqu = -dmdx + self.exploration_weight * dsdx
ret... | python | def _compute_acq_withGradients(self, x):
m, s, dmdx, dsdx = self.model.predict_withGradients(x)
f_acqu = -m + self.exploration_weight * s
df_acqu = -dmdx + self.exploration_weight * dsdx
return f_acqu, df_acqu | [
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242,023 | SheffieldML/GPyOpt | GPyOpt/core/task/variables.py | create_variable | def create_variable(descriptor):
"""
Creates a variable from a dictionary descriptor
"""
if descriptor['type'] == 'continuous':
return ContinuousVariable(descriptor['name'], descriptor['domain'], descriptor.get('dimensionality', 1))
elif descriptor['type'] == 'bandit':
return BanditV... | python | def create_variable(descriptor):
if descriptor['type'] == 'continuous':
return ContinuousVariable(descriptor['name'], descriptor['domain'], descriptor.get('dimensionality', 1))
elif descriptor['type'] == 'bandit':
return BanditVariable(descriptor['name'], descriptor['domain'], descriptor.get('di... | [
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242,024 | SheffieldML/GPyOpt | GPyOpt/core/task/variables.py | Variable.expand | def expand(self):
"""
Builds a list of single dimensional variables representing current variable.
Examples:
For single dimensional variable, it is returned as is
discrete of (0,2,4) -> discrete of (0,2,4)
For multi dimensional variable, a list of variables is returned, ... | python | def expand(self):
expanded_variables = []
for i in range(self.dimensionality):
one_d_variable = deepcopy(self)
one_d_variable.dimensionality = 1
if self.dimensionality > 1:
one_d_variable.name = '{}_{}'.format(self.name, i+1)
else:
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242,025 | SheffieldML/GPyOpt | GPyOpt/core/task/variables.py | ContinuousVariable.round | def round(self, value_array):
"""
If value falls within bounds, just return it
otherwise return min or max, whichever is closer to the value
Assumes an 1d array with a single element as an input.
"""
min_value = self.domain[0]
max_value = self.domain[1]
... | python | def round(self, value_array):
min_value = self.domain[0]
max_value = self.domain[1]
rounded_value = value_array[0]
if rounded_value < min_value:
rounded_value = min_value
elif rounded_value > max_value:
rounded_value = max_value
return [rounded_v... | [
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242,026 | SheffieldML/GPyOpt | GPyOpt/core/task/variables.py | BanditVariable.round | def round(self, value_array):
"""
Rounds a bandit variable by selecting the closest point in the domain
Closest here is defined by euclidian distance
Assumes an 1d array of the same length as the single variable value
"""
distances = np.linalg.norm(np.array(self.domain) -... | python | def round(self, value_array):
distances = np.linalg.norm(np.array(self.domain) - value_array, axis=1)
idx = np.argmin(distances)
return [self.domain[idx]] | [
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242,027 | SheffieldML/GPyOpt | GPyOpt/core/task/variables.py | DiscreteVariable.round | def round(self, value_array):
"""
Rounds a discrete variable by selecting the closest point in the domain
Assumes an 1d array with a single element as an input.
"""
value = value_array[0]
rounded_value = self.domain[0]
for domain_value in self.domain:
... | python | def round(self, value_array):
value = value_array[0]
rounded_value = self.domain[0]
for domain_value in self.domain:
if np.abs(domain_value - value) < np.abs(rounded_value - value):
rounded_value = domain_value
return [rounded_value] | [
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242,028 | SheffieldML/GPyOpt | GPyOpt/optimization/acquisition_optimizer.py | AcquisitionOptimizer.optimize | def optimize(self, f=None, df=None, f_df=None, duplicate_manager=None):
"""
Optimizes the input function.
:param f: function to optimize.
:param df: gradient of the function to optimize.
:param f_df: returns both the function to optimize and its gradient.
"""
se... | python | def optimize(self, f=None, df=None, f_df=None, duplicate_manager=None):
self.f = f
self.df = df
self.f_df = f_df
## --- Update the optimizer, in case context has beee passed.
self.optimizer = choose_optimizer(self.optimizer_name, self.context_manager.noncontext_bounds)
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242,029 | SheffieldML/GPyOpt | GPyOpt/core/task/objective.py | SingleObjective.evaluate | def evaluate(self, x):
"""
Performs the evaluation of the objective at x.
"""
if self.n_procs == 1:
f_evals, cost_evals = self._eval_func(x)
else:
try:
f_evals, cost_evals = self._syncronous_batch_evaluation(x)
except:
... | python | def evaluate(self, x):
if self.n_procs == 1:
f_evals, cost_evals = self._eval_func(x)
else:
try:
f_evals, cost_evals = self._syncronous_batch_evaluation(x)
except:
if not hasattr(self, 'parallel_error'):
print('Error... | [
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242,030 | SheffieldML/GPyOpt | GPyOpt/core/task/objective.py | SingleObjective._syncronous_batch_evaluation | def _syncronous_batch_evaluation(self,x):
"""
Evaluates the function a x, where x can be a single location or a batch. The evaluation is performed in parallel
according to the number of accessible cores.
"""
from multiprocessing import Process, Pipe
# --- parallel evalua... | python | def _syncronous_batch_evaluation(self,x):
from multiprocessing import Process, Pipe
# --- parallel evaluation of the function
divided_samples = [x[i::self.n_procs] for i in range(self.n_procs)]
pipe = [Pipe() for i in range(self.n_procs)]
proc = [Process(target=spawn(self._eval_... | [
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242,031 | SheffieldML/GPyOpt | GPyOpt/core/evaluators/sequential.py | Sequential.compute_batch | def compute_batch(self, duplicate_manager=None,context_manager=None):
"""
Selects the new location to evaluate the objective.
"""
x, _ = self.acquisition.optimize(duplicate_manager=duplicate_manager)
return x | python | def compute_batch(self, duplicate_manager=None,context_manager=None):
x, _ = self.acquisition.optimize(duplicate_manager=duplicate_manager)
return x | [
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242,032 | SheffieldML/GPyOpt | GPyOpt/acquisitions/LCB_mcmc.py | AcquisitionLCB_MCMC._compute_acq | def _compute_acq(self,x):
"""
Integrated GP-Lower Confidence Bound
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means, stds = self.model.predict(x)
f_acqu = 0
for m,s in zip(means, stds):
f_acqu += -m + self.exploration_weight * s
return f_acqu/(len(means)) | python | def _compute_acq(self,x):
means, stds = self.model.predict(x)
f_acqu = 0
for m,s in zip(means, stds):
f_acqu += -m + self.exploration_weight * s
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242,033 | SheffieldML/GPyOpt | GPyOpt/acquisitions/LCB_mcmc.py | AcquisitionLCB_MCMC._compute_acq_withGradients | def _compute_acq_withGradients(self, x):
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f_acqu = None
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means, stds, dmdxs, dsdxs = self.model.predict_withGradients(x)
f_acqu = None
df_acqu = None
for m, s, dmdx, dsdx in zip(means, stds, dmdxs, dsdxs):
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242,034 | SheffieldML/GPyOpt | GPyOpt/core/task/space.py | Design_space._expand_config_space | def _expand_config_space(self):
"""
Expands the config input space into a list of diccionaries, one for each variable_dic
in which the dimensionality is always one.
Example: It would transform
config_space =[ {'name': 'var_1', 'type': 'continuous', 'domain':(-1,1), 'dimensionali... | python | def _expand_config_space(self):
self.config_space_expanded = []
for variable in self.config_space:
variable_dic = variable.copy()
if 'dimensionality' in variable_dic.keys():
dimensionality = variable_dic['dimensionality']
variable_dic['dimensional... | [
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242,035 | SheffieldML/GPyOpt | GPyOpt/core/task/space.py | Design_space._create_variables_dic | def _create_variables_dic(self):
"""
Returns the variable by passing its name
"""
self.name_to_variable = {}
for variable in self.space_expanded:
self.name_to_variable[variable.name] = variable | python | def _create_variables_dic(self):
self.name_to_variable = {}
for variable in self.space_expanded:
self.name_to_variable[variable.name] = variable | [
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242,036 | SheffieldML/GPyOpt | GPyOpt/core/task/space.py | Design_space._translate_space | def _translate_space(self, space):
"""
Translates a list of dictionaries into internal list of variables
"""
self.space = []
self.dimensionality = 0
self.has_types = d = {t: False for t in self.supported_types}
for i, d in enumerate(space):
descriptor... | python | def _translate_space(self, space):
self.space = []
self.dimensionality = 0
self.has_types = d = {t: False for t in self.supported_types}
for i, d in enumerate(space):
descriptor = deepcopy(d)
descriptor['name'] = descriptor.get('name', 'var_' + str(i))
... | [
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242,037 | SheffieldML/GPyOpt | GPyOpt/core/task/space.py | Design_space._expand_space | def _expand_space(self):
"""
Creates an internal list where the variables with dimensionality larger than one are expanded.
This list is the one that is used internally to do the optimization.
"""
## --- Expand the config space
self._expand_config_space()
## ---... | python | def _expand_space(self):
## --- Expand the config space
self._expand_config_space()
## --- Expand the space
self.space_expanded = []
for variable in self.space:
self.space_expanded += variable.expand() | [
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242,038 | SheffieldML/GPyOpt | GPyOpt/core/task/space.py | Design_space.objective_to_model | def objective_to_model(self, x_objective):
''' This function serves as interface between objective input vectors and
model input vectors'''
x_model = []
for k in range(self.objective_dimensionality):
variable = self.space_expanded[k]
new_entry = variable.objecti... | python | def objective_to_model(self, x_objective):
''' This function serves as interface between objective input vectors and
model input vectors'''
x_model = []
for k in range(self.objective_dimensionality):
variable = self.space_expanded[k]
new_entry = variable.objecti... | [
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242,039 | SheffieldML/GPyOpt | GPyOpt/core/task/space.py | Design_space.model_to_objective | def model_to_objective(self, x_model):
''' This function serves as interface between model input vectors and
objective input vectors
'''
idx_model = 0
x_objective = []
for idx_obj in range(self.objective_dimensionality):
variable = self.space_expanded[idx... | python | def model_to_objective(self, x_model):
''' This function serves as interface between model input vectors and
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'''
idx_model = 0
x_objective = []
for idx_obj in range(self.objective_dimensionality):
variable = self.space_expanded[idx... | [
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242,040 | SheffieldML/GPyOpt | GPyOpt/core/task/space.py | Design_space.get_subspace | def get_subspace(self, dims):
'''
Extracts subspace from the reference of a list of variables in the inputs
of the model.
'''
subspace = []
k = 0
for variable in self.space_expanded:
if k in dims:
subspace.append(variable)
k... | python | def get_subspace(self, dims):
'''
Extracts subspace from the reference of a list of variables in the inputs
of the model.
'''
subspace = []
k = 0
for variable in self.space_expanded:
if k in dims:
subspace.append(variable)
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242,041 | SheffieldML/GPyOpt | GPyOpt/core/task/space.py | Design_space.indicator_constraints | def indicator_constraints(self,x):
"""
Returns array of ones and zeros indicating if x is within the constraints
"""
x = np.atleast_2d(x)
I_x = np.ones((x.shape[0],1))
if self.constraints is not None:
for d in self.constraints:
try:
... | python | def indicator_constraints(self,x):
x = np.atleast_2d(x)
I_x = np.ones((x.shape[0],1))
if self.constraints is not None:
for d in self.constraints:
try:
exec('constraint = lambda x:' + d['constraint'], globals())
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242,042 | SheffieldML/GPyOpt | GPyOpt/core/task/space.py | Design_space.input_dim | def input_dim(self):
"""
Extracts the input dimension of the domain.
"""
n_cont = len(self.get_continuous_dims())
n_disc = len(self.get_discrete_dims())
return n_cont + n_disc | python | def input_dim(self):
n_cont = len(self.get_continuous_dims())
n_disc = len(self.get_discrete_dims())
return n_cont + n_disc | [
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242,043 | SheffieldML/GPyOpt | GPyOpt/core/task/space.py | Design_space.round_optimum | def round_optimum(self, x):
"""
Rounds some value x to a feasible value in the design space.
x is expected to be a vector or an array with a single row
"""
x = np.array(x)
if not ((x.ndim == 1) or (x.ndim == 2 and x.shape[0] == 1)):
raise ValueError("Unexpecte... | python | def round_optimum(self, x):
x = np.array(x)
if not ((x.ndim == 1) or (x.ndim == 2 and x.shape[0] == 1)):
raise ValueError("Unexpected dimentionality of x. Got {}, expected (1, N) or (N,)".format(x.ndim))
if x.ndim == 2:
x = x[0]
x_rounded = []
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242,044 | SheffieldML/GPyOpt | GPyOpt/core/task/space.py | Design_space.get_continuous_bounds | def get_continuous_bounds(self):
"""
Extracts the bounds of the continuous variables.
"""
bounds = []
for d in self.space:
if d.type == 'continuous':
bounds.extend([d.domain]*d.dimensionality)
return bounds | python | def get_continuous_bounds(self):
bounds = []
for d in self.space:
if d.type == 'continuous':
bounds.extend([d.domain]*d.dimensionality)
return bounds | [
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242,045 | SheffieldML/GPyOpt | GPyOpt/core/task/space.py | Design_space.get_continuous_dims | def get_continuous_dims(self):
"""
Returns the dimension of the continuous components of the domain.
"""
continuous_dims = []
for i in range(self.dimensionality):
if self.space_expanded[i].type == 'continuous':
continuous_dims += [i]
return con... | python | def get_continuous_dims(self):
continuous_dims = []
for i in range(self.dimensionality):
if self.space_expanded[i].type == 'continuous':
continuous_dims += [i]
return continuous_dims | [
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242,046 | SheffieldML/GPyOpt | GPyOpt/core/task/space.py | Design_space.get_discrete_grid | def get_discrete_grid(self):
"""
Computes a Numpy array with the grid of points that results after crossing the possible outputs of the discrete
variables
"""
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sets_grid = []
for d in self.space:
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sets_grid.extend([d.domain]*d.dimensionality)
return np.array(list(itertools.product(*sets_grid))) | [
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242,047 | SheffieldML/GPyOpt | GPyOpt/core/task/space.py | Design_space.get_discrete_dims | def get_discrete_dims(self):
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Returns the dimension of the discrete components of the domain.
"""
discrete_dims = []
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discrete_dims += [i]
return discrete_dims | python | def get_discrete_dims(self):
discrete_dims = []
for i in range(self.dimensionality):
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discrete_dims += [i]
return discrete_dims | [
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242,048 | SheffieldML/GPyOpt | GPyOpt/core/task/space.py | Design_space.get_bandit | def get_bandit(self):
"""
Extracts the arms of the bandit if any.
"""
arms_bandit = []
for d in self.space:
if d.type == 'bandit':
arms_bandit += tuple(map(tuple, d.domain))
return np.asarray(arms_bandit) | python | def get_bandit(self):
arms_bandit = []
for d in self.space:
if d.type == 'bandit':
arms_bandit += tuple(map(tuple, d.domain))
return np.asarray(arms_bandit) | [
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242,049 | SheffieldML/GPyOpt | GPyOpt/models/rfmodel.py | RFModel.predict | def predict(self, X):
"""
Predictions with the model. Returns posterior means and standard deviations at X.
"""
X = np.atleast_2d(X)
m = np.empty(shape=(0,1))
s = np.empty(shape=(0,1))
for k in range(X.shape[0]):
preds = []
for pred in sel... | python | def predict(self, X):
X = np.atleast_2d(X)
m = np.empty(shape=(0,1))
s = np.empty(shape=(0,1))
for k in range(X.shape[0]):
preds = []
for pred in self.model.estimators_:
preds.append(pred.predict(X[k,:])[0])
m = np.vstack((m ,np.array(... | [
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242,050 | SheffieldML/GPyOpt | GPyOpt/acquisitions/MPI_mcmc.py | AcquisitionMPI_MCMC._compute_acq | def _compute_acq(self,x):
"""
Integrated Expected Improvement
"""
means, stds = self.model.predict(x)
fmins = self.model.get_fmin()
f_acqu = 0
for m,s,fmin in zip(means, stds, fmins):
_, Phi, _ = get_quantiles(self.jitter, fmin, m, s)
f_acq... | python | def _compute_acq(self,x):
means, stds = self.model.predict(x)
fmins = self.model.get_fmin()
f_acqu = 0
for m,s,fmin in zip(means, stds, fmins):
_, Phi, _ = get_quantiles(self.jitter, fmin, m, s)
f_acqu += Phi
return f_acqu/len(means) | [
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242,051 | SheffieldML/GPyOpt | GPyOpt/acquisitions/MPI_mcmc.py | AcquisitionMPI_MCMC._compute_acq_withGradients | def _compute_acq_withGradients(self, x):
"""
Integrated Expected Improvement and its derivative
"""
means, stds, dmdxs, dsdxs = self.model.predict_withGradients(x)
fmins = self.model.get_fmin()
f_acqu = None
df_acqu = None
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means, stds, dmdxs, dsdxs = self.model.predict_withGradients(x)
fmins = self.model.get_fmin()
f_acqu = None
df_acqu = None
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phi, Phi, u = get_quantiles(self.ji... | [
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242,052 | SheffieldML/GPyOpt | GPyOpt/plotting/plots_bo.py | plot_convergence | def plot_convergence(Xdata,best_Y, filename = None):
'''
Plots to evaluate the convergence of standard Bayesian optimization algorithms
'''
n = Xdata.shape[0]
aux = (Xdata[1:n,:]-Xdata[0:n-1,:])**2
distances = np.sqrt(aux.sum(axis=1))
## Distances between consecutive x's
plt.figure(figs... | python | def plot_convergence(Xdata,best_Y, filename = None):
'''
Plots to evaluate the convergence of standard Bayesian optimization algorithms
'''
n = Xdata.shape[0]
aux = (Xdata[1:n,:]-Xdata[0:n-1,:])**2
distances = np.sqrt(aux.sum(axis=1))
## Distances between consecutive x's
plt.figure(figs... | [
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242,053 | SheffieldML/GPyOpt | GPyOpt/core/evaluators/batch_local_penalization.py | LocalPenalization.compute_batch | def compute_batch(self, duplicate_manager=None, context_manager=None):
"""
Computes the elements of the batch sequentially by penalizing the acquisition.
"""
from ...acquisitions import AcquisitionLP
assert isinstance(self.acquisition, AcquisitionLP)
self.acquisition.upd... | python | def compute_batch(self, duplicate_manager=None, context_manager=None):
from ...acquisitions import AcquisitionLP
assert isinstance(self.acquisition, AcquisitionLP)
self.acquisition.update_batches(None,None,None)
# --- GET first element in the batch
X_batch = self.acquisition.op... | [
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242,054 | SheffieldML/GPyOpt | manual/notebooks_check.py | check_notebooks_for_errors | def check_notebooks_for_errors(notebooks_directory):
''' Evaluates all notebooks in given directory and prints errors, if any '''
print("Checking notebooks in directory {} for errors".format(notebooks_directory))
failed_notebooks_count = 0
for file in os.listdir(notebooks_directory):
if file.en... | python | def check_notebooks_for_errors(notebooks_directory):
''' Evaluates all notebooks in given directory and prints errors, if any '''
print("Checking notebooks in directory {} for errors".format(notebooks_directory))
failed_notebooks_count = 0
for file in os.listdir(notebooks_directory):
if file.en... | [
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242,055 | SheffieldML/GPyOpt | GPyOpt/models/gpmodel.py | GPModel.predict | def predict(self, X, with_noise=True):
"""
Predictions with the model. Returns posterior means and standard deviations at X. Note that this is different in GPy where the variances are given.
Parameters:
X (np.ndarray) - points to run the prediction for.
with_noise (bool)... | python | def predict(self, X, with_noise=True):
m, v = self._predict(X, False, with_noise)
# We can take the square root because v is just a diagonal matrix of variances
return m, np.sqrt(v) | [
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242,056 | SheffieldML/GPyOpt | GPyOpt/models/gpmodel.py | GPModel.predict_covariance | def predict_covariance(self, X, with_noise=True):
"""
Predicts the covariance matric for points in X.
Parameters:
X (np.ndarray) - points to run the prediction for.
with_noise (bool) - whether to add noise to the prediction. Default is True.
"""
_, v = se... | python | def predict_covariance(self, X, with_noise=True):
_, v = self._predict(X, True, with_noise)
return v | [
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242,057 | SheffieldML/GPyOpt | GPyOpt/models/gpmodel.py | GPModel.predict_withGradients | def predict_withGradients(self, X):
"""
Returns the mean, standard deviation, mean gradient and standard deviation gradient at X.
"""
if X.ndim==1: X = X[None,:]
m, v = self.model.predict(X)
v = np.clip(v, 1e-10, np.inf)
dmdx, dvdx = self.model.predictive_gradient... | python | def predict_withGradients(self, X):
if X.ndim==1: X = X[None,:]
m, v = self.model.predict(X)
v = np.clip(v, 1e-10, np.inf)
dmdx, dvdx = self.model.predictive_gradients(X)
dmdx = dmdx[:,:,0]
dsdx = dvdx / (2*np.sqrt(v))
return m, np.sqrt(v), dmdx, dsdx | [
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242,058 | SheffieldML/GPyOpt | GPyOpt/models/gpmodel.py | GPModel_MCMC.predict | def predict(self, X):
"""
Predictions with the model for all the MCMC samples. Returns posterior means and standard deviations at X. Note that this is different in GPy where the variances are given.
"""
if X.ndim==1: X = X[None,:]
ps = self.model.param_array.copy()
means... | python | def predict(self, X):
if X.ndim==1: X = X[None,:]
ps = self.model.param_array.copy()
means = []
stds = []
for s in self.hmc_samples:
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self.model[:] = s
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self.model[self.model._fixes_] = s
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242,059 | SheffieldML/GPyOpt | GPyOpt/models/gpmodel.py | GPModel_MCMC.get_fmin | def get_fmin(self):
"""
Returns the location where the posterior mean is takes its minimal value.
"""
ps = self.model.param_array.copy()
fmins = []
for s in self.hmc_samples:
if self.model._fixes_ is None:
self.model[:] = s
else:
... | python | def get_fmin(self):
ps = self.model.param_array.copy()
fmins = []
for s in self.hmc_samples:
if self.model._fixes_ is None:
self.model[:] = s
else:
self.model[self.model._fixes_] = s
self.model._trigger_params_changed()
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242,060 | SheffieldML/GPyOpt | GPyOpt/models/gpmodel.py | GPModel_MCMC.predict_withGradients | def predict_withGradients(self, X):
"""
Returns the mean, standard deviation, mean gradient and standard deviation gradient at X for all the MCMC samples.
"""
if X.ndim==1: X = X[None,:]
ps = self.model.param_array.copy()
means = []
stds = []
dmdxs = []
... | python | def predict_withGradients(self, X):
if X.ndim==1: X = X[None,:]
ps = self.model.param_array.copy()
means = []
stds = []
dmdxs = []
dsdxs = []
for s in self.hmc_samples:
if self.model._fixes_ is None:
self.model[:] = s
else:
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242,061 | SheffieldML/GPyOpt | GPyOpt/interface/func_loader.py | load_objective | def load_objective(config):
"""
Loads the objective function from a .json file.
"""
assert 'prjpath' in config
assert 'main-file' in config, "The problem file ('main-file') is missing!"
os.chdir(config['prjpath'])
if config['language'].lower()=='python':
assert config['main-fil... | python | def load_objective(config):
assert 'prjpath' in config
assert 'main-file' in config, "The problem file ('main-file') is missing!"
os.chdir(config['prjpath'])
if config['language'].lower()=='python':
assert config['main-file'].endswith('.py'), 'The python problem file has to end with .py!'
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242,062 | SheffieldML/GPyOpt | GPyOpt/acquisitions/EI.py | AcquisitionEI._compute_acq | def _compute_acq(self, x):
"""
Computes the Expected Improvement per unit of cost
"""
m, s = self.model.predict(x)
fmin = self.model.get_fmin()
phi, Phi, u = get_quantiles(self.jitter, fmin, m, s)
f_acqu = s * (u * Phi + phi)
return f_acqu | python | def _compute_acq(self, x):
m, s = self.model.predict(x)
fmin = self.model.get_fmin()
phi, Phi, u = get_quantiles(self.jitter, fmin, m, s)
f_acqu = s * (u * Phi + phi)
return f_acqu | [
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242,063 | SheffieldML/GPyOpt | GPyOpt/interface/config_parser.py | update_config | def update_config(config_new, config_default):
'''
Updates the loaded method configuration with default values.
'''
if any([isinstance(v, dict) for v in list(config_new.values())]):
for k,v in list(config_new.items()):
if isinstance(v,dict) and k in config_default:
u... | python | def update_config(config_new, config_default):
'''
Updates the loaded method configuration with default values.
'''
if any([isinstance(v, dict) for v in list(config_new.values())]):
for k,v in list(config_new.items()):
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242,064 | SheffieldML/GPyOpt | GPyOpt/interface/config_parser.py | parser | def parser(input_file_path='config.json'):
'''
Parser for the .json file containing the configuration of the method.
'''
# --- Read .json file
try:
with open(input_file_path, 'r') as config_file:
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except:
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'''
Parser for the .json file containing the configuration of the method.
'''
# --- Read .json file
try:
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242,065 | SheffieldML/GPyOpt | GPyOpt/util/mcmc_sampler.py | AffineInvariantEnsembleSampler.get_samples | def get_samples(self, n_samples, log_p_function, burn_in_steps=50):
"""
Generates samples.
Parameters:
n_samples - number of samples to generate
log_p_function - a function that returns log density for a specific sample
burn_in_steps - number of burn-in steps... | python | def get_samples(self, n_samples, log_p_function, burn_in_steps=50):
restarts = initial_design('random', self.space, n_samples)
sampler = emcee.EnsembleSampler(n_samples, self.space.input_dim(), log_p_function)
samples, samples_log, _ = sampler.run_mcmc(restarts, burn_in_steps)
# make su... | [
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242,066 | SheffieldML/GPyOpt | GPyOpt/core/bo.py | BO.suggest_next_locations | def suggest_next_locations(self, context = None, pending_X = None, ignored_X = None):
"""
Run a single optimization step and return the next locations to evaluate the objective.
Number of suggested locations equals to batch_size.
:param context: fixes specified variables to a particular... | python | def suggest_next_locations(self, context = None, pending_X = None, ignored_X = None):
self.model_parameters_iterations = None
self.num_acquisitions = 0
self.context = context
self._update_model(self.normalization_type)
suggested_locations = self._compute_next_evaluations(pending... | [
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242,067 | SheffieldML/GPyOpt | GPyOpt/core/bo.py | BO._print_convergence | def _print_convergence(self):
"""
Prints the reason why the optimization stopped.
"""
if self.verbosity:
if (self.num_acquisitions == self.max_iter) and (not self.initial_iter):
print(' ** Maximum number of iterations reached **')
return 1
... | python | def _print_convergence(self):
if self.verbosity:
if (self.num_acquisitions == self.max_iter) and (not self.initial_iter):
print(' ** Maximum number of iterations reached **')
return 1
elif (self._distance_last_evaluations() < self.eps) and (not self.init... | [
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242,068 | SheffieldML/GPyOpt | GPyOpt/core/bo.py | BO.evaluate_objective | def evaluate_objective(self):
"""
Evaluates the objective
"""
self.Y_new, cost_new = self.objective.evaluate(self.suggested_sample)
self.cost.update_cost_model(self.suggested_sample, cost_new)
self.Y = np.vstack((self.Y,self.Y_new)) | python | def evaluate_objective(self):
self.Y_new, cost_new = self.objective.evaluate(self.suggested_sample)
self.cost.update_cost_model(self.suggested_sample, cost_new)
self.Y = np.vstack((self.Y,self.Y_new)) | [
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242,069 | SheffieldML/GPyOpt | GPyOpt/core/bo.py | BO._compute_results | def _compute_results(self):
"""
Computes the optimum and its value.
"""
self.Y_best = best_value(self.Y)
self.x_opt = self.X[np.argmin(self.Y),:]
self.fx_opt = np.min(self.Y) | python | def _compute_results(self):
self.Y_best = best_value(self.Y)
self.x_opt = self.X[np.argmin(self.Y),:]
self.fx_opt = np.min(self.Y) | [
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242,070 | SheffieldML/GPyOpt | GPyOpt/core/bo.py | BO._distance_last_evaluations | def _distance_last_evaluations(self):
"""
Computes the distance between the last two evaluations.
"""
if self.X.shape[0] < 2:
# less than 2 evaluations
return np.inf
return np.sqrt(np.sum((self.X[-1, :] - self.X[-2, :]) ** 2)) | python | def _distance_last_evaluations(self):
if self.X.shape[0] < 2:
# less than 2 evaluations
return np.inf
return np.sqrt(np.sum((self.X[-1, :] - self.X[-2, :]) ** 2)) | [
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242,071 | SheffieldML/GPyOpt | GPyOpt/core/bo.py | BO.save_evaluations | def save_evaluations(self, evaluations_file = None):
"""
Saves evaluations at each iteration of the optimization
:param evaluations_file: name of the file in which the results are saved.
"""
iterations = np.array(range(1, self.Y.shape[0] + 1))[:, None]
results = np.hsta... | python | def save_evaluations(self, evaluations_file = None):
iterations = np.array(range(1, self.Y.shape[0] + 1))[:, None]
results = np.hstack((iterations, self.Y, self.X))
header = ['Iteration', 'Y'] + ['var_' + str(k) for k in range(1, self.X.shape[1] + 1)]
data = [header] + results.tolist()
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242,072 | SheffieldML/GPyOpt | GPyOpt/core/bo.py | BO.save_models | def save_models(self, models_file):
"""
Saves model parameters at each iteration of the optimization
:param models_file: name of the file or a file buffer, in which the results are saved.
"""
if self.model_parameters_iterations is None:
raise ValueError("No iteration... | python | def save_models(self, models_file):
if self.model_parameters_iterations is None:
raise ValueError("No iterations have been carried out yet and hence no iterations of the BO can be saved")
iterations = np.array(range(1,self.model_parameters_iterations.shape[0]+1))[:,None]
results = n... | [
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242,073 | SheffieldML/GPyOpt | GPyOpt/methods/bayesian_optimization.py | BayesianOptimization._init_design_chooser | def _init_design_chooser(self):
"""
Initializes the choice of X and Y based on the selected initial design and number of points selected.
"""
# If objective function was not provided, we require some initial sample data
if self.f is None and (self.X is None or self.Y is None):
... | python | def _init_design_chooser(self):
# If objective function was not provided, we require some initial sample data
if self.f is None and (self.X is None or self.Y is None):
raise InvalidConfigError("Initial data for both X and Y is required when objective function is not provided")
# Cas... | [
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242,074 | coleifer/huey | huey/api.py | crontab | def crontab(minute='*', hour='*', day='*', month='*', day_of_week='*'):
"""
Convert a "crontab"-style set of parameters into a test function that will
return True when the given datetime matches the parameters set forth in
the crontab.
For day-of-week, 0=Sunday and 6=Saturday.
Acceptable input... | python | def crontab(minute='*', hour='*', day='*', month='*', day_of_week='*'):
validation = (
('m', month, range(1, 13)),
('d', day, range(1, 32)),
('w', day_of_week, range(8)), # 0-6, but also 7 for Sunday.
('H', hour, range(24)),
('M', minute, range(60))
)
cron_settings = ... | [
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242,075 | coleifer/huey | huey/storage.py | BaseStorage.put_if_empty | def put_if_empty(self, key, value):
"""
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:param bytes key: Key to check/set.
:param bytes value: Arbitrary data.
:return: Boolean whether key/value was set.
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242,076 | coleifer/huey | huey/utils.py | make_naive | def make_naive(dt):
"""
Makes an aware datetime.datetime naive in local time zone.
"""
tt = dt.utctimetuple()
ts = calendar.timegm(tt)
local_tt = time.localtime(ts)
return datetime.datetime(*local_tt[:6]) | python | def make_naive(dt):
tt = dt.utctimetuple()
ts = calendar.timegm(tt)
local_tt = time.localtime(ts)
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242,077 | coleifer/huey | huey/consumer.py | BaseProcess.sleep_for_interval | def sleep_for_interval(self, start_ts, nseconds):
"""
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sleep_time = nseconds - (time.time() - start_ts)
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return
self._logger.debug('Sleeping for %s', sleep_time)
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242,078 | coleifer/huey | huey/consumer.py | Consumer.start | def start(self):
"""
Start all consumer processes and register signal handlers.
"""
if self.huey.immediate:
raise ConfigurationError(
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if self.huey.immediate:
raise ConfigurationError(
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# Log startup message.
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242,079 | coleifer/huey | huey/consumer.py | Consumer.stop | def stop(self, graceful=False):
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Set the stop-flag.
If `graceful=True`, this method blocks until the workers to finish
executing any tasks they might be currently working on.
"""
self.stop_flag.set()
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self.stop_flag.set()
if graceful:
self._logger.info('Shutting down gracefully...')
try:
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worker_process.join()
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242,080 | coleifer/huey | huey/consumer.py | Consumer.run | def run(self):
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Run the consumer.
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242,081 | coleifer/huey | huey/consumer.py | Consumer.check_worker_health | def check_worker_health(self):
"""
Check the health of the worker processes. Workers that have died will
be replaced with new workers.
"""
self._logger.debug('Checking worker health.')
workers = []
restart_occurred = False
for i, (worker, worker_t) in enum... | python | def check_worker_health(self):
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242,082 | coleifer/huey | huey/contrib/djhuey/__init__.py | close_db | def close_db(fn):
"""Decorator to be used with tasks that may operate on the database."""
@wraps(fn)
def inner(*args, **kwargs):
try:
return fn(*args, **kwargs)
finally:
if not HUEY.immediate:
close_old_connections()
return inner | python | def close_db(fn):
@wraps(fn)
def inner(*args, **kwargs):
try:
return fn(*args, **kwargs)
finally:
if not HUEY.immediate:
close_old_connections()
return inner | [
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242,083 | johnwheeler/flask-ask | samples/purchase/model.py | Product.list | def list(self):
""" return list of purchasable and not entitled products"""
mylist = []
for prod in self.product_list:
if self.purchasable(prod) and not self.entitled(prod):
mylist.append(prod)
return mylist | python | def list(self):
mylist = []
for prod in self.product_list:
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mylist.append(prod)
return mylist | [
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242,084 | johnwheeler/flask-ask | samples/tidepooler/tidepooler.py | _find_tide_info | def _find_tide_info(predictions):
"""
Algorithm to find the 2 high tides for the day, the first of which is smaller and occurs
mid-day, the second of which is larger and typically in the evening.
"""
last_prediction = None
first_high_tide = None
second_high_tide = None
low_tide = None... | python | def _find_tide_info(predictions):
last_prediction = None
first_high_tide = None
second_high_tide = None
low_tide = None
first_tide_done = False
for prediction in predictions:
if last_prediction is None:
last_prediction = prediction
continue
if last_predict... | [
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242,085 | johnwheeler/flask-ask | flask_ask/models.py | audio.enqueue | def enqueue(self, stream_url, offset=0, opaque_token=None):
"""Adds stream to the queue. Does not impact the currently playing stream."""
directive = self._play_directive('ENQUEUE')
audio_item = self._audio_item(stream_url=stream_url,
offset=offset,
... | python | def enqueue(self, stream_url, offset=0, opaque_token=None):
directive = self._play_directive('ENQUEUE')
audio_item = self._audio_item(stream_url=stream_url,
offset=offset,
push_buffer=False,
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242,086 | johnwheeler/flask-ask | flask_ask/models.py | audio.play_next | def play_next(self, stream_url=None, offset=0, opaque_token=None):
"""Replace all streams in the queue but does not impact the currently playing stream."""
directive = self._play_directive('REPLACE_ENQUEUED')
directive['audioItem'] = self._audio_item(stream_url=stream_url, offset=offset, opaque... | python | def play_next(self, stream_url=None, offset=0, opaque_token=None):
directive = self._play_directive('REPLACE_ENQUEUED')
directive['audioItem'] = self._audio_item(stream_url=stream_url, offset=offset, opaque_token=opaque_token)
self._response['directives'].append(directive)
return self | [
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242,087 | johnwheeler/flask-ask | flask_ask/models.py | audio.resume | def resume(self):
"""Sends Play Directive to resume playback at the paused offset"""
directive = self._play_directive('REPLACE_ALL')
directive['audioItem'] = self._audio_item()
self._response['directives'].append(directive)
return self | python | def resume(self):
directive = self._play_directive('REPLACE_ALL')
directive['audioItem'] = self._audio_item()
self._response['directives'].append(directive)
return self | [
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242,088 | johnwheeler/flask-ask | flask_ask/models.py | audio._audio_item | def _audio_item(self, stream_url=None, offset=0, push_buffer=True, opaque_token=None):
"""Builds an AudioPlayer Directive's audioItem and updates current_stream"""
audio_item = {'stream': {}}
stream = audio_item['stream']
# existing stream
if not stream_url:
# stream... | python | def _audio_item(self, stream_url=None, offset=0, push_buffer=True, opaque_token=None):
audio_item = {'stream': {}}
stream = audio_item['stream']
# existing stream
if not stream_url:
# stream.update(current_stream.__dict__)
stream['url'] = current_stream.url
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242,089 | johnwheeler/flask-ask | flask_ask/models.py | audio.clear_queue | def clear_queue(self, stop=False):
"""Clears queued streams and optionally stops current stream.
Keyword Arguments:
stop {bool} set True to stop current current stream and clear queued streams.
set False to clear queued streams and allow current stream to finish
... | python | def clear_queue(self, stop=False):
directive = {}
directive['type'] = 'AudioPlayer.ClearQueue'
if stop:
directive['clearBehavior'] = 'CLEAR_ALL'
else:
directive['clearBehavior'] = 'CLEAR_ENQUEUED'
self._response['directives'].append(directive)
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] | fe407646ae404a8c90b363c86d5c4c201b6a5580 | https://github.com/johnwheeler/flask-ask/blob/fe407646ae404a8c90b363c86d5c4c201b6a5580/flask_ask/models.py#L425-L442 |
242,090 | johnwheeler/flask-ask | flask_ask/core.py | find_ask | def find_ask():
"""
Find our instance of Ask, navigating Local's and possible blueprints.
Note: This only supports returning a reference to the first instance
of Ask found.
"""
if hasattr(current_app, 'ask'):
return getattr(current_app, 'ask')
else:
if hasattr(current_app, '... | python | def find_ask():
if hasattr(current_app, 'ask'):
return getattr(current_app, 'ask')
else:
if hasattr(current_app, 'blueprints'):
blueprints = getattr(current_app, 'blueprints')
for blueprint_name in blueprints:
if hasattr(blueprints[blueprint_name], 'ask'):... | [
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Note: This only supports returning a reference to the first instance
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242,091 | johnwheeler/flask-ask | flask_ask/core.py | Ask.init_app | def init_app(self, app, path='templates.yaml'):
"""Initializes Ask app by setting configuration variables, loading templates, and maps Ask route to a flask view.
The Ask instance is given the following configuration variables by calling on Flask's configuration:
`ASK_APPLICATION_ID`:
... | python | def init_app(self, app, path='templates.yaml'):
if self._route is None:
raise TypeError("route is a required argument when app is not None")
self.app = app
app.ask = self
app.add_url_rule(self._route, view_func=self._flask_view_func, methods=['POST'])
app.j... | [
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The Ask instance is given the following configuration variables by calling on Flask's configuration:
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242,092 | johnwheeler/flask-ask | flask_ask/core.py | Ask.launch | def launch(self, f):
"""Decorator maps a view function as the endpoint for an Alexa LaunchRequest and starts the skill.
@ask.launch
def launched():
return question('Welcome to Foo')
The wrapped function is registered as the launch view function and renders the response
... | python | def launch(self, f):
self._launch_view_func = f
@wraps(f)
def wrapper(*args, **kw):
self._flask_view_func(*args, **kw)
return f | [
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def launched():
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The wrapped function is registered as the launch view function and renders the response
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242,093 | johnwheeler/flask-ask | flask_ask/core.py | Ask.session_ended | def session_ended(self, f):
"""Decorator routes Alexa SessionEndedRequest to the wrapped view function to end the skill.
@ask.session_ended
def session_ended():
return "{}", 200
The wrapped function is registered as the session_ended view function
and renders the re... | python | def session_ended(self, f):
self._session_ended_view_func = f
@wraps(f)
def wrapper(*args, **kw):
self._flask_view_func(*args, **kw)
return f | [
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@ask.session_ended
def session_ended():
return "{}", 200
The wrapped function is registered as the session_ended view function
and renders the response for requests to the end of the s... | [
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] | fe407646ae404a8c90b363c86d5c4c201b6a5580 | https://github.com/johnwheeler/flask-ask/blob/fe407646ae404a8c90b363c86d5c4c201b6a5580/flask_ask/core.py#L214-L232 |
242,094 | johnwheeler/flask-ask | flask_ask/core.py | Ask.intent | def intent(self, intent_name, mapping={}, convert={}, default={}):
"""Decorator routes an Alexa IntentRequest and provides the slot parameters to the wrapped function.
Functions decorated as an intent are registered as the view function for the Intent's URL,
and provide the backend responses to... | python | def intent(self, intent_name, mapping={}, convert={}, default={}):
def decorator(f):
self._intent_view_funcs[intent_name] = f
self._intent_mappings[intent_name] = mapping
self._intent_converts[intent_name] = convert
self._intent_defaults[intent_name] = default
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Functions decorated as an intent are registered as the view function for the Intent's URL,
and provide the backend responses to give your Skill its functionality.
@ask.intent('WeatherIntent', mapp... | [
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242,095 | johnwheeler/flask-ask | flask_ask/core.py | Ask.default_intent | def default_intent(self, f):
"""Decorator routes any Alexa IntentRequest that is not matched by any existing @ask.intent routing."""
self._default_intent_view_func = f
@wraps(f)
def wrapper(*args, **kw):
self._flask_view_func(*args, **kw)
return f | python | def default_intent(self, f):
self._default_intent_view_func = f
@wraps(f)
def wrapper(*args, **kw):
self._flask_view_func(*args, **kw)
return f | [
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] | fe407646ae404a8c90b363c86d5c4c201b6a5580 | https://github.com/johnwheeler/flask-ask/blob/fe407646ae404a8c90b363c86d5c4c201b6a5580/flask_ask/core.py#L270-L277 |
242,096 | johnwheeler/flask-ask | flask_ask/core.py | Ask.display_element_selected | def display_element_selected(self, f):
"""Decorator routes Alexa Display.ElementSelected request to the wrapped view function.
@ask.display_element_selected
def eval_element():
return "", 200
The wrapped function is registered as the display_element_selected view function
... | python | def display_element_selected(self, f):
self._display_element_selected_func = f
@wraps(f)
def wrapper(*args, **kw):
self._flask_view_func(*args, **kw)
return f | [
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@ask.display_element_selected
def eval_element():
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The wrapped function is registered as the display_element_selected view function
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] | fe407646ae404a8c90b363c86d5c4c201b6a5580 | https://github.com/johnwheeler/flask-ask/blob/fe407646ae404a8c90b363c86d5c4c201b6a5580/flask_ask/core.py#L279-L297 |
242,097 | johnwheeler/flask-ask | flask_ask/core.py | Ask.on_purchase_completed | def on_purchase_completed(self, mapping={'payload': 'payload','name':'name','status':'status','token':'token'}, convert={}, default={}):
"""Decorator routes an Connections.Response to the wrapped function.
Request is sent when Alexa completes the purchase flow.
See https://developer.amazon.co... | python | def on_purchase_completed(self, mapping={'payload': 'payload','name':'name','status':'status','token':'token'}, convert={}, default={}):
def decorator(f):
self._intent_view_funcs['Connections.Response'] = f
self._intent_mappings['Connections.Response'] = mapping
self._intent_... | [
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Request is sent when Alexa completes the purchase flow.
See https://developer.amazon.com/docs/in-skill-purchase/add-isps-to-a-skill.html#handle-results
The wrapped view function may accept parameters from the Request.
... | [
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] | fe407646ae404a8c90b363c86d5c4c201b6a5580 | https://github.com/johnwheeler/flask-ask/blob/fe407646ae404a8c90b363c86d5c4c201b6a5580/flask_ask/core.py#L300-L328 |
242,098 | johnwheeler/flask-ask | flask_ask/core.py | Ask.run_aws_lambda | def run_aws_lambda(self, event):
"""Invoke the Flask Ask application from an AWS Lambda function handler.
Use this method to service AWS Lambda requests from a custom Alexa
skill. This method will invoke your Flask application providing a
WSGI-compatible environment that wraps the origi... | python | def run_aws_lambda(self, event):
# We are guaranteed to be called by AWS as a Lambda function does not
# expose a public facing interface.
self.app.config['ASK_VERIFY_REQUESTS'] = False
# Convert an environment variable to a WSGI "bytes-as-unicode" string
enc, esc = sys.getfiles... | [
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Use this method to service AWS Lambda requests from a custom Alexa
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provided to the AWS ... | [
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] | fe407646ae404a8c90b363c86d5c4c201b6a5580 | https://github.com/johnwheeler/flask-ask/blob/fe407646ae404a8c90b363c86d5c4c201b6a5580/flask_ask/core.py#L585-L683 |
242,099 | johnwheeler/flask-ask | flask_ask/core.py | Ask._parse_timestamp | def _parse_timestamp(timestamp):
"""
Parse a given timestamp value, raising ValueError if None or Flasey
"""
if timestamp:
try:
return aniso8601.parse_datetime(timestamp)
except AttributeError:
# raised by aniso8601 if raw_timestamp... | python | def _parse_timestamp(timestamp):
if timestamp:
try:
return aniso8601.parse_datetime(timestamp)
except AttributeError:
# raised by aniso8601 if raw_timestamp is not valid string
# in ISO8601 format
try:
re... | [
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] | fe407646ae404a8c90b363c86d5c4c201b6a5580 | https://github.com/johnwheeler/flask-ask/blob/fe407646ae404a8c90b363c86d5c4c201b6a5580/flask_ask/core.py#L724-L740 |
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