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228,200 | 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):
"""
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... | [
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228,201 | 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):
"""
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']:
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228,202 | 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):
"""
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)
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228,203 | 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):
"""
Selects the type of local optimizer
"""
if optimizer_name == 'lbfgs':
optimizer = OptLbfgs(bounds)
elif optimizer_name == 'DIRECT':
optimizer = OptDirect(bounds)
elif optimizer_name == 'CMA':
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228,204 | 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 chooser from the available options. Guide the optimization through sequential or parallel evalutions of the objective.
"""
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228,205 | 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):
"""
Computes the GP-Lower Confidence Bound
"""
m, s = self.model.predict(x)
f_acqu = -m + self.exploration_weight * s
return f_acqu | [
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228,206 | 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):
"""
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
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228,207 | 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):
"""
Creates a variable from a dictionary descriptor
"""
if descriptor['type'] == 'continuous':
return ContinuousVariable(descriptor['name'], descriptor['domain'], descriptor.get('dimensionality', 1))
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228,208 | 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):
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Builds a list of single dimensional variables representing current variable.
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228,209 | SheffieldML/GPyOpt | GPyOpt/core/task/variables.py | ContinuousVariable.round | def round(self, value_array):
"""
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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]
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... | python | 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.
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228,210 | 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):
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Rounds a bandit variable by selecting the closest point in the domain
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Assumes an 1d array of the same length as the single variable value
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228,211 | 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.
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228,212 | 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):
"""
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.
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228,213 | 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):
"""
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)
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228,214 | 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
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"""
from multiprocessing import Process, Pipe
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228,215 | 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):
"""
Selects the new location to evaluate the objective.
"""
x, _ = self.acquisition.optimize(duplicate_manager=duplicate_manager)
return x | [
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228,216 | SheffieldML/GPyOpt | GPyOpt/acquisitions/LCB_mcmc.py | AcquisitionLCB_MCMC._compute_acq | def _compute_acq(self,x):
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"""
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):
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228,217 | SheffieldML/GPyOpt | GPyOpt/acquisitions/LCB_mcmc.py | AcquisitionLCB_MCMC._compute_acq_withGradients | def _compute_acq_withGradients(self, x):
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means, stds, dmdxs, dsdxs = self.model.predict_withGradients(x)
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228,218 | SheffieldML/GPyOpt | GPyOpt/core/task/space.py | Design_space._expand_config_space | def _expand_config_space(self):
"""
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Example: It would transform
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"""
Expands the config input space into a list of diccionaries, one for each variable_dic
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Example: It would transform
config_space =[ {'name': 'var_1', 'type': 'continuous', 'domain':(-1,1), 'dimensionali... | [
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228,219 | 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):
"""
Returns the variable by passing its name
"""
self.name_to_variable = {}
for variable in self.space_expanded:
self.name_to_variable[variable.name] = variable | [
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228,220 | 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):
"""
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):
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228,221 | 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):
"""
Creates an internal list where the variables with dimensionality larger than one are expanded.
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"""
## --- Expand the config space
self._expand_config_space()
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228,222 | 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]
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228,223 | 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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228,224 | 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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228,225 | 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):
"""
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:
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228,226 | 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):
"""
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 | [
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228,227 | 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):
"""
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)):
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228,228 | 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):
"""
Extracts the bounds of the continuous variables.
"""
bounds = []
for d in self.space:
if d.type == 'continuous':
bounds.extend([d.domain]*d.dimensionality)
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228,229 | SheffieldML/GPyOpt | GPyOpt/core/task/space.py | Design_space.get_continuous_dims | def get_continuous_dims(self):
"""
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Returns the dimension of the continuous components of the domain.
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228,230 | 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
"""
sets_grid = []
for d in self.space:
if d.type == 'discrete':
sets_grid.extend([d.doma... | python | 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
"""
sets_grid = []
for d in self.space:
if d.type == 'discrete':
sets_grid.extend([d.doma... | [
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228,231 | SheffieldML/GPyOpt | GPyOpt/core/task/space.py | Design_space.get_discrete_dims | def get_discrete_dims(self):
"""
Returns the dimension of the discrete components of the domain.
"""
discrete_dims = []
for i in range(self.dimensionality):
if self.space_expanded[i].type == 'discrete':
discrete_dims += [i]
return discrete_dims | python | def get_discrete_dims(self):
"""
Returns the dimension of the discrete components of the domain.
"""
discrete_dims = []
for i in range(self.dimensionality):
if self.space_expanded[i].type == 'discrete':
discrete_dims += [i]
return discrete_dims | [
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228,232 | 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):
"""
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) | [
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228,233 | 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):
"""
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))
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preds = []
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228,234 | 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):
"""
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)
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228,235 | 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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"""
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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228,236 | 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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228,237 | 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):
"""
Computes the elements of the batch sequentially by penalizing the acquisition.
"""
from ...acquisitions import AcquisitionLP
assert isinstance(self.acquisition, AcquisitionLP)
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228,238 | 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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228,239 | 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):
"""
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.
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228,240 | 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):
"""
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.
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228,241 | 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):
"""
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)
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228,242 | SheffieldML/GPyOpt | GPyOpt/models/gpmodel.py | GPModel_MCMC.predict | def predict(self, X):
"""
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"""
if X.ndim==1: X = X[None,:]
ps = self.model.param_array.copy()
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"""
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,:]
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228,243 | 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):
"""
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:
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228,244 | 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):
"""
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 = []
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228,245 | 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):
"""
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'])
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228,246 | 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):
"""
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 | [
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228,247 | 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()):
if isinstance(v,dict) and k in config_default:
u... | [
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228,248 | 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:
config_new = json.load(config_file)
config_file.close()
except:
... | python | 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:
config_new = json.load(config_file)
config_file.close()
except:
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228,249 | 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):
"""
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... | [
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228,250 | 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):
"""
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... | [
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228,251 | 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):
"""
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
... | [
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228,252 | 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):
"""
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)) | [
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228,253 | 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):
"""
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) | [
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228,254 | 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):
"""
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)) | [
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228,255 | 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):
"""
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... | [
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228,256 | 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):
"""
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... | [
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228,257 | 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):
"""
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):
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228,258 | 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='*'):
"""
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.
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228,259 | coleifer/huey | huey/storage.py | BaseStorage.put_if_empty | def put_if_empty(self, key, value):
"""
Atomically write data only if the key is not already set.
:param bytes key: Key to check/set.
:param bytes value: Arbitrary data.
:return: Boolean whether key/value was set.
"""
if self.has_data_for_key(key):
re... | python | def put_if_empty(self, key, value):
"""
Atomically write data only if the key is not already set.
:param bytes key: Key to check/set.
:param bytes value: Arbitrary data.
:return: Boolean whether key/value was set.
"""
if self.has_data_for_key(key):
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228,260 | 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):
"""
Makes an aware datetime.datetime naive in local time zone.
"""
tt = dt.utctimetuple()
ts = calendar.timegm(tt)
local_tt = time.localtime(ts)
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228,261 | coleifer/huey | huey/consumer.py | BaseProcess.sleep_for_interval | def sleep_for_interval(self, start_ts, nseconds):
"""
Sleep for a given interval with respect to the start timestamp.
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if the current tim... | python | def sleep_for_interval(self, start_ts, nseconds):
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228,262 | coleifer/huey | huey/consumer.py | Consumer.start | def start(self):
"""
Start all consumer processes and register signal handlers.
"""
if self.huey.immediate:
raise ConfigurationError(
'Consumer cannot be run with Huey instances where immediate '
'is enabled. Please check your configuration and... | python | def start(self):
"""
Start all consumer processes and register signal handlers.
"""
if self.huey.immediate:
raise ConfigurationError(
'Consumer cannot be run with Huey instances where immediate '
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228,263 | coleifer/huey | huey/consumer.py | Consumer.stop | def stop(self, graceful=False):
"""
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._logger.info('Shutting down gr... | python | def stop(self, graceful=False):
"""
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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228,264 | coleifer/huey | huey/consumer.py | Consumer.run | def run(self):
"""
Run the consumer.
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timeout = self._stop_flag_timeout
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try:
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self._log... | python | def run(self):
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Run the consumer.
"""
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228,265 | 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):
"""
Check the health of the worker processes. Workers that have died will
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"""
self._logger.debug('Checking worker health.')
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228,266 | 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):
"""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 | [
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228,267 | 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):
""" 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 | [
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228,268 | 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):
"""
Algorithm to find the 2 high tides for the day, the first of which is smaller and occurs
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"""
last_prediction = None
first_high_tide = None
second_high_tide = None
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228,269 | 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):
"""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,
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228,270 | 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):
"""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... | [
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228,271 | 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):
"""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 | [
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228,272 | 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):
"""Builds an AudioPlayer Directive's audioItem and updates current_stream"""
audio_item = {'stream': {}}
stream = audio_item['stream']
# existing stream
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228,273 | 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):
"""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
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228,274 | 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():
"""
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, '... | [
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228,275 | 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'):
"""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`:
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228,276 | 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):
"""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
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228,277 | 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):
"""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
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@ask.session_ended
def session_ended():
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228,278 | 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={}):
"""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... | [
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228,279 | 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):
"""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 | [
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228,280 | 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):
"""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
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def eval_element():
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228,281 | 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={}):
"""Decorator routes an Connections.Response to the wrapped function.
Request is sent when Alexa completes the purchase flow.
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228,282 | 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):
"""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... | [
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228,283 | 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):
"""
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... | [
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228,284 | johnwheeler/flask-ask | flask_ask/core.py | Ask._map_player_request_to_func | def _map_player_request_to_func(self, player_request_type):
"""Provides appropriate parameters to the on_playback functions."""
# calbacks for on_playback requests are optional
view_func = self._intent_view_funcs.get(player_request_type, lambda: None)
argspec = inspect.getargspec(view_f... | python | def _map_player_request_to_func(self, player_request_type):
"""Provides appropriate parameters to the on_playback functions."""
# calbacks for on_playback requests are optional
view_func = self._intent_view_funcs.get(player_request_type, lambda: None)
argspec = inspect.getargspec(view_f... | [
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228,285 | johnwheeler/flask-ask | flask_ask/core.py | Ask._map_purchase_request_to_func | def _map_purchase_request_to_func(self, purchase_request_type):
"""Provides appropriate parameters to the on_purchase functions."""
if purchase_request_type in self._intent_view_funcs:
view_func = self._intent_view_funcs[purchase_request_type]
else:
raise NotImpl... | python | def _map_purchase_request_to_func(self, purchase_request_type):
"""Provides appropriate parameters to the on_purchase functions."""
if purchase_request_type in self._intent_view_funcs:
view_func = self._intent_view_funcs[purchase_request_type]
else:
raise NotImpl... | [
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228,286 | johnwheeler/flask-ask | flask_ask/cache.py | push_stream | def push_stream(cache, user_id, stream):
"""
Push a stream onto the stream stack in cache.
:param cache: werkzeug BasicCache-like object
:param user_id: id of user, used as key in cache
:param stream: stream object to push onto stack
:return: True on successful update,
False if fa... | python | def push_stream(cache, user_id, stream):
"""
Push a stream onto the stream stack in cache.
:param cache: werkzeug BasicCache-like object
:param user_id: id of user, used as key in cache
:param stream: stream object to push onto stack
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228,287 | johnwheeler/flask-ask | flask_ask/cache.py | pop_stream | def pop_stream(cache, user_id):
"""
Pop an item off the stack in the cache. If stack
is empty after pop, it deletes the stack.
:param cache: werkzeug BasicCache-like object
:param user_id: id of user, used as key in cache
:return: top item from stack, otherwise None
"""
stack = cache.g... | python | def pop_stream(cache, user_id):
"""
Pop an item off the stack in the cache. If stack
is empty after pop, it deletes the stack.
:param cache: werkzeug BasicCache-like object
:param user_id: id of user, used as key in cache
:return: top item from stack, otherwise None
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228,288 | johnwheeler/flask-ask | flask_ask/cache.py | top_stream | def top_stream(cache, user_id):
"""
Peek at the top of the stack in the cache.
:param cache: werkzeug BasicCache-like object
:param user_id: id of user, used as key in cache
:return: top item in user's cached stack, otherwise None
"""
if not user_id:
return None
stack = ca... | python | def top_stream(cache, user_id):
"""
Peek at the top of the stack in the cache.
:param cache: werkzeug BasicCache-like object
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:return: top item in user's cached stack, otherwise None
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:param cache: werkzeug BasicCache-like object
:param user_id: id of user, used as key in cache
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228,289 | ecederstrand/exchangelib | exchangelib/transport.py | wrap | def wrap(content, version, account=None):
"""
Generate the necessary boilerplate XML for a raw SOAP request. The XML is specific to the server version.
ExchangeImpersonation allows to act as the user we want to impersonate.
"""
envelope = create_element('s:Envelope', nsmap=ns_translation)
header... | python | def wrap(content, version, account=None):
"""
Generate the necessary boilerplate XML for a raw SOAP request. The XML is specific to the server version.
ExchangeImpersonation allows to act as the user we want to impersonate.
"""
envelope = create_element('s:Envelope', nsmap=ns_translation)
header... | [
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228,290 | ecederstrand/exchangelib | exchangelib/autodiscover.py | discover | def discover(email, credentials):
"""
Performs the autodiscover dance and returns the primary SMTP address of the account and a Protocol on success. The
autodiscover and EWS server might not be the same, so we use a different Protocol to do the autodiscover request,
and return a hopefully-cached Protoco... | python | def discover(email, credentials):
"""
Performs the autodiscover dance and returns the primary SMTP address of the account and a Protocol on success. The
autodiscover and EWS server might not be the same, so we use a different Protocol to do the autodiscover request,
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228,291 | ecederstrand/exchangelib | exchangelib/properties.py | TimeZone.to_server_timezone | def to_server_timezone(self, timezones, for_year):
"""Returns the Microsoft timezone ID corresponding to this timezone. There may not be a match at all, and there
may be multiple matches. If so, we return a random timezone ID.
:param timezones: A list of server timezones, as returned by
... | python | def to_server_timezone(self, timezones, for_year):
"""Returns the Microsoft timezone ID corresponding to this timezone. There may not be a match at all, and there
may be multiple matches. If so, we return a random timezone ID.
:param timezones: A list of server timezones, as returned by
... | [
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228,292 | ecederstrand/exchangelib | exchangelib/protocol.py | BaseProtocol.decrease_poolsize | def decrease_poolsize(self):
"""Decreases the session pool size in response to error messages from the server requesting to rate-limit
requests. We decrease by one session per call.
"""
# Take a single session from the pool and discard it. We need to protect this with a lock while we are... | python | def decrease_poolsize(self):
"""Decreases the session pool size in response to error messages from the server requesting to rate-limit
requests. We decrease by one session per call.
"""
# Take a single session from the pool and discard it. We need to protect this with a lock while we are... | [
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228,293 | ecederstrand/exchangelib | exchangelib/util.py | is_iterable | def is_iterable(value, generators_allowed=False):
"""
Checks if value is a list-like object. Don't match generators and generator-like objects here by default, because
callers don't necessarily guarantee that they only iterate the value once. Take care to not match string types and
bytes.
:param va... | python | def is_iterable(value, generators_allowed=False):
"""
Checks if value is a list-like object. Don't match generators and generator-like objects here by default, because
callers don't necessarily guarantee that they only iterate the value once. Take care to not match string types and
bytes.
:param va... | [
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228,294 | ecederstrand/exchangelib | exchangelib/util.py | chunkify | def chunkify(iterable, chunksize):
"""
Splits an iterable into chunks of size ``chunksize``. The last chunk may be smaller than ``chunksize``.
"""
from .queryset import QuerySet
if hasattr(iterable, '__getitem__') and not isinstance(iterable, QuerySet):
# tuple, list. QuerySet has __getitem_... | python | def chunkify(iterable, chunksize):
"""
Splits an iterable into chunks of size ``chunksize``. The last chunk may be smaller than ``chunksize``.
"""
from .queryset import QuerySet
if hasattr(iterable, '__getitem__') and not isinstance(iterable, QuerySet):
# tuple, list. QuerySet has __getitem_... | [
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228,295 | ecederstrand/exchangelib | exchangelib/util.py | peek | def peek(iterable):
"""
Checks if an iterable is empty and returns status and the rewinded iterable
"""
from .queryset import QuerySet
if isinstance(iterable, QuerySet):
# QuerySet has __len__ but that evaluates the entire query greedily. We don't want that here. Instead, peek()
# sh... | python | def peek(iterable):
"""
Checks if an iterable is empty and returns status and the rewinded iterable
"""
from .queryset import QuerySet
if isinstance(iterable, QuerySet):
# QuerySet has __len__ but that evaluates the entire query greedily. We don't want that here. Instead, peek()
# sh... | [
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228,296 | ecederstrand/exchangelib | exchangelib/util.py | xml_to_str | def xml_to_str(tree, encoding=None, xml_declaration=False):
"""Serialize an XML tree. Returns unicode if 'encoding' is None. Otherwise, we return encoded 'bytes'."""
if xml_declaration and not encoding:
raise ValueError("'xml_declaration' is not supported when 'encoding' is None")
if encoding:
... | python | def xml_to_str(tree, encoding=None, xml_declaration=False):
"""Serialize an XML tree. Returns unicode if 'encoding' is None. Otherwise, we return encoded 'bytes'."""
if xml_declaration and not encoding:
raise ValueError("'xml_declaration' is not supported when 'encoding' is None")
if encoding:
... | [
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228,297 | ecederstrand/exchangelib | exchangelib/util.py | is_xml | def is_xml(text):
"""
Helper function. Lightweight test if response is an XML doc
"""
# BOM_UTF8 is an UTF-8 byte order mark which may precede the XML from an Exchange server
bom_len = len(BOM_UTF8)
if text[:bom_len] == BOM_UTF8:
return text[bom_len:bom_len + 5] == b'<?xml'
return te... | python | def is_xml(text):
"""
Helper function. Lightweight test if response is an XML doc
"""
# BOM_UTF8 is an UTF-8 byte order mark which may precede the XML from an Exchange server
bom_len = len(BOM_UTF8)
if text[:bom_len] == BOM_UTF8:
return text[bom_len:bom_len + 5] == b'<?xml'
return te... | [
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228,298 | ecederstrand/exchangelib | exchangelib/services.py | GetItem.call | def call(self, items, additional_fields, shape):
"""
Returns all items in an account that correspond to a list of ID's, in stable order.
:param items: a list of (id, changekey) tuples or Item objects
:param additional_fields: the extra fields that should be returned with the item, as Fi... | python | def call(self, items, additional_fields, shape):
"""
Returns all items in an account that correspond to a list of ID's, in stable order.
:param items: a list of (id, changekey) tuples or Item objects
:param additional_fields: the extra fields that should be returned with the item, as Fi... | [
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228,299 | ecederstrand/exchangelib | exchangelib/services.py | FindFolder.call | def call(self, additional_fields, restriction, shape, depth, max_items, offset):
"""
Find subfolders of a folder.
:param additional_fields: the extra fields that should be returned with the folder, as FieldPath objects
:param shape: The set of attributes to return
:param depth: ... | python | def call(self, additional_fields, restriction, shape, depth, max_items, offset):
"""
Find subfolders of a folder.
:param additional_fields: the extra fields that should be returned with the folder, as FieldPath objects
:param shape: The set of attributes to return
:param depth: ... | [
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