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docs: replace " : " with ": " #511

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148 changes: 74 additions & 74 deletions bayes_opt/acquisition.py
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4 changes: 2 additions & 2 deletions bayes_opt/bayesian_optimization.py
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Expand Up @@ -302,7 +302,7 @@ def maximize(self, init_points=5, n_iter=25):

Parameters
----------
init_points : int, optional(default=5)
init_points: int, optional(default=5)
Number of iterations before the explorations starts the exploration
for the maximum.

Expand Down Expand Up @@ -335,7 +335,7 @@ def set_bounds(self, new_bounds):

Parameters
----------
new_bounds : dict
new_bounds: dict
A dictionary with the parameter name and its new bounds
"""
self._space.set_bounds(new_bounds)
Expand Down
16 changes: 8 additions & 8 deletions bayes_opt/constraint.py
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Expand Up @@ -17,20 +17,20 @@ class ConstraintModel:

Parameters
----------
fun : None or Callable -> float or np.ndarray
fun: None or Callable -> float or np.ndarray
The constraint function. Should be float-valued or array-valued (if
multiple constraints are present). Needs to take the same parameters
as the optimization target with the same argument names.

lb : float or np.ndarray
lb: float or np.ndarray
The lower bound on the constraints. Should have the same
dimensionality as the return value of the constraint function.

ub : float or np.ndarray
ub: float or np.ndarray
The upper bound on the constraints. Should have the same
dimensionality as the return value of the constraint function.

random_state : np.random.RandomState or int or None, default=None
random_state: np.random.RandomState or int or None, default=None
Random state to use.

Notes
Expand Down Expand Up @@ -82,7 +82,7 @@ def eval(self, **kwargs: dict): # noqa: D417

Parameters
----------
\*\*kwargs : any
\*\*kwargs: any
Function arguments to evaluate the constraint function on.


Expand Down Expand Up @@ -152,7 +152,7 @@ def predict(self, X):

Parameters
----------
X : np.ndarray of shape (n_samples, n_features)
X: np.ndarray of shape (n_samples, n_features)
Parameters for which to predict the probability of constraint
fulfilment.

Expand Down Expand Up @@ -195,7 +195,7 @@ def approx(self, X):

Parameters
----------
X : np.ndarray of shape (n_samples, n_features)
X: np.ndarray of shape (n_samples, n_features)
Parameters for which to estimate the constraint function value.

Returns
Expand All @@ -216,7 +216,7 @@ def allowed(self, constraint_values):

Parameters
----------
constraint_values : np.ndarray of shape (n_samples, n_constraints)
constraint_values: np.ndarray of shape (n_samples, n_constraints)
The values of the constraint function.


Expand Down
26 changes: 13 additions & 13 deletions bayes_opt/domain_reduction.py
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Expand Up @@ -39,16 +39,16 @@ class SequentialDomainReductionTransformer(DomainTransformer):

Parameters
----------
gamma_osc : float, default=0.7
gamma_osc: float, default=0.7
Parameter used to scale (typically dampen) oscillations.

gamma_pan : float, default=1.0
gamma_pan: float, default=1.0
Parameter used to scale (typically unitary) panning.

eta : float, default=0.9
eta: float, default=0.9
Zooming parameter used to shrink the region of interest.

minimum_window : float or np.ndarray or dict, default=0.0
minimum_window: float or np.ndarray or dict, default=0.0
Minimum window size for each parameter. If a float is provided,
the same value is used for all parameters.
"""
Expand All @@ -75,7 +75,7 @@ def initialize(self, target_space: TargetSpace) -> None:

Parameters
----------
target_space : TargetSpace
target_space: TargetSpace
TargetSpace this DomainTransformer operates on.
"""
# Set the original bounds
Expand Down Expand Up @@ -117,7 +117,7 @@ def _update(self, target_space: TargetSpace) -> None:

Parameters
----------
target_space : TargetSpace
target_space: TargetSpace
TargetSpace this DomainTransformer operates on.
"""
# setting the previous
Expand All @@ -144,15 +144,15 @@ def _trim(self, new_bounds: np.ndarray, global_bounds: np.ndarray) -> np.ndarray

Parameters
----------
new_bounds : np.ndarray
new_bounds: np.ndarray
The proposed new_bounds that (may) need adjustment.

global_bounds : np.ndarray
global_bounds: np.ndarray
The maximum allowable bounds for each parameter.

Returns
-------
new_bounds : np.ndarray
new_bounds: np.ndarray
The adjusted bounds after enforcing constraints.
"""
# sort bounds
Expand Down Expand Up @@ -227,7 +227,7 @@ def _window_bounds_compatibility(self, global_bounds: np.ndarray):

Parameters
----------
global_bounds : np.ndarray
global_bounds: np.ndarray
The maximum allowable bounds for each parameter.

Raises
Expand All @@ -246,10 +246,10 @@ def _create_bounds(self, parameters: dict, bounds: np.ndarray) -> dict:

Parameters
----------
parameters : dict
parameters: dict
The parameters for which to create the bounds.

bounds : np.ndarray
bounds: np.ndarray
The bounds for each parameter.
"""
return {param: bounds[i, :] for i, param in enumerate(parameters)}
Expand All @@ -259,7 +259,7 @@ def transform(self, target_space: TargetSpace) -> dict:

Parameters
----------
target_space : TargetSpace
target_space: TargetSpace
TargetSpace this DomainTransformer operates on.

Returns
Expand Down
34 changes: 17 additions & 17 deletions bayes_opt/logger.py
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Expand Up @@ -21,10 +21,10 @@ def _get_default_logger(verbose, is_constrained):

Parameters
----------
verbose : int
verbose: int
Verbosity level of the logger.

is_constrained : bool
is_constrained: bool
Whether the underlying optimizer uses constraints (this requires
an additional column in the output).

Expand All @@ -42,10 +42,10 @@ class ScreenLogger(_Tracker):

Parameters
----------
verbose : int
verbose: int
Verbosity level of the logger.

is_constrained : bool
is_constrained: bool
Whether the logger is associated with a constrained optimization
instance.
"""
Expand Down Expand Up @@ -73,7 +73,7 @@ def verbose(self, v):

Parameters
----------
v : int
v: int
New verbosity level of the logger.
"""
self._verbose = v
Expand All @@ -88,7 +88,7 @@ def _format_number(self, x):

Parameters
----------
x : number
x: number
Value to format.

Returns
Expand All @@ -111,7 +111,7 @@ def _format_bool(self, x):

Parameters
----------
x : boolean
x: boolean
Value to format.

Returns
Expand All @@ -126,7 +126,7 @@ def _format_key(self, key):

Parameters
----------
key : string
key: string
Value to format.

Returns
Expand All @@ -143,7 +143,7 @@ def _step(self, instance, colour=_colour_regular_message):

Parameters
----------
instance : bayesian_optimization.BayesianOptimization
instance: bayesian_optimization.BayesianOptimization
The instance associated with the event.

colour :
Expand All @@ -170,7 +170,7 @@ def _header(self, instance):

Parameters
----------
instance : bayesian_optimization.BayesianOptimization
instance: bayesian_optimization.BayesianOptimization
The instance associated with the header.

Returns
Expand All @@ -195,7 +195,7 @@ def _is_new_max(self, instance):

Parameters
----------
instance : bayesian_optimization.BayesianOptimization
instance: bayesian_optimization.BayesianOptimization
The instance associated with the step.

Returns
Expand All @@ -216,11 +216,11 @@ def update(self, event, instance):

Parameters
----------
event : str
event: str
One of the values associated with `Events.OPTIMIZATION_START`,
`Events.OPTIMIZATION_STEP` or `Events.OPTIMIZATION_END`.

instance : bayesian_optimization.BayesianOptimization
instance: bayesian_optimization.BayesianOptimization
The instance associated with the step.
"""
if event == Events.OPTIMIZATION_START:
Expand Down Expand Up @@ -248,10 +248,10 @@ class JSONLogger(_Tracker):

Parameters
----------
path : str or bytes or os.PathLike
path: str or bytes or os.PathLike
Path to the file to write to.

reset : bool
reset: bool
Whether to overwrite the file if it already exists.

"""
Expand All @@ -269,11 +269,11 @@ def update(self, event, instance):

Parameters
----------
event : str
event: str
One of the values associated with `Events.OPTIMIZATION_START`,
`Events.OPTIMIZATION_STEP` or `Events.OPTIMIZATION_END`.

instance : bayesian_optimization.BayesianOptimization
instance: bayesian_optimization.BayesianOptimization
The instance associated with the step.

"""
Expand Down
4 changes: 2 additions & 2 deletions bayes_opt/observer.py
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Expand Up @@ -24,11 +24,11 @@ def _update_tracker(self, event, instance):

Parameters
----------
event : str
event: str
One of the values associated with `Events.OPTIMIZATION_START`,
`Events.OPTIMIZATION_STEP` or `Events.OPTIMIZATION_END`.

instance : bayesian_optimization.BayesianOptimization
instance: bayesian_optimization.BayesianOptimization
The instance associated with the step.
"""
if event == Events.OPTIMIZATION_STEP:
Expand Down
22 changes: 11 additions & 11 deletions bayes_opt/target_space.py
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Expand Up @@ -22,14 +22,14 @@ class TargetSpace:

Parameters
----------
target_func : function
target_func: function
Function to be maximized.

pbounds : dict
pbounds: dict
Dictionary with parameters names as keys and a tuple with minimum
and maximum values.

random_state : int, RandomState, or None
random_state: int, RandomState, or None
optionally specify a seed for a random number generator

allow_duplicate_points: bool, optional (default=False)
Expand Down Expand Up @@ -219,7 +219,7 @@ def params_to_array(self, params):

Parameters
----------
params : dict
params: dict
a single point, with len(x) == self.dim.

Returns
Expand All @@ -240,7 +240,7 @@ def array_to_params(self, x):

Parameters
----------
x : np.ndarray
x: np.ndarray
a single point, with len(x) == self.dim.

Returns
Expand Down Expand Up @@ -276,13 +276,13 @@ def register(self, params, target, constraint_value=None):

Parameters
----------
params : np.ndarray
params: np.ndarray
a single point, with len(x) == self.dim.

target : float
target: float
target function value

constraint_value : float or None
constraint_value: float or None
Constraint function value

Raises
Expand Down Expand Up @@ -363,12 +363,12 @@ def probe(self, params):

Parameters
----------
params : np.ndarray
params: np.ndarray
a single point, with len(x) == self.dim

Returns
-------
result : float | Tuple(float, float)
result: float | Tuple(float, float)
target function value, or Tuple(target function value, constraint value)

Example
Expand Down Expand Up @@ -504,7 +504,7 @@ def set_bounds(self, new_bounds):

Parameters
----------
new_bounds : dict
new_bounds: dict
A dictionary with the parameter name and its new bounds
"""
for row, key in enumerate(self.keys):
Expand Down
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