import numbers
import ConfigSpace as CS
import numpy as np
import scipy.stats as ss
import yaml
from ConfigSpace.util import deactivate_inactive_hyperparameters
from scipy.stats import gaussian_kde
from sklearn.utils import check_random_state
from deephyper.core.utils.joblib_utils import Parallel, delayed
from .transformers import (
CategoricalEncoder,
Identity,
LabelEncoder,
LogN,
Normalize,
Pipeline,
StringEncoder,
ToInteger,
)
# helper class to be able to print [1, ..., 4] instead of [1, '...', 4]
class _Ellipsis:
def __repr__(self):
return "..."
def _transpose_list_array(x):
"""Transposes a list matrix"""
n_dims = len(x)
assert n_dims > 0
n_samples = len(x[0])
rows = [None] * n_samples
for i in range(n_samples):
r = [None] * n_dims
for j in range(n_dims):
r[j] = x[j][i]
rows[i] = r
return rows
[docs]def check_dimension(dimension, transform=None):
"""Turn a provided dimension description into a dimension object.
Checks that the provided dimension falls into one of the
supported types. For a list of supported types, look at
the documentation of ``dimension`` below.
If ``dimension`` is already a ``Dimension`` instance, return it.
Args:
dimension (Dimension):
Search space Dimension.
Each search dimension can be defined either as
- a `(lower_bound, upper_bound)` tuple (for `Real` or `Integer`
dimensions),
- a `(lower_bound, upper_bound, "prior")` tuple (for `Real`
dimensions),
- as a list of categories (for `Categorical` dimensions), or
- an instance of a `Dimension` object (`Real`, `Integer` or
`Categorical`).
transform (str): One of
"identity", "normalize", "string", "label", "onehot" optional
- For `Categorical` dimensions, the following transformations are
supported.
- "onehot" (default) one-hot transformation of the original space.
- "label" integer transformation of the original space
- "string" string transformation of the original space.
- "identity" same as the original space.
- For `Real` and `Integer` dimensions, the following transformations
are supported.
- "identity", (default) the transformed space is the same as the
original space.
- "normalize", the transformed space is scaled to be between 0 and 1.
Returns:
dimension (Dimension): Dimension instance.
"""
if isinstance(dimension, Dimension):
return dimension
if not isinstance(dimension, (list, tuple, np.ndarray)):
raise ValueError("Dimension has to be a list or tuple.")
# A `Dimension` described by a single value is assumed to be
# a `Categorical` dimension. This can be used in `BayesSearchCV`
# to define subspaces that fix one value, e.g. to choose the
# model type, see "sklearn-gridsearchcv-replacement.py"
# for examples.
if len(dimension) == 1:
return Categorical(dimension, transform=transform)
if len(dimension) == 2:
if any(
[isinstance(d, (str, bool)) or isinstance(d, np.bool_) for d in dimension]
):
return Categorical(dimension, transform=transform)
elif all([isinstance(dim, numbers.Integral) for dim in dimension]):
return Integer(*dimension, transform=transform)
elif any([isinstance(dim, numbers.Real) for dim in dimension]):
return Real(*dimension, transform=transform)
else:
raise ValueError(
"Invalid dimension {}. Read the documentation for"
" supported types.".format(dimension)
)
if len(dimension) == 3:
if any([isinstance(dim, int) for dim in dimension[:2]]) and dimension[2] in [
"uniform",
"log-uniform",
]:
return Integer(*dimension, transform=transform)
elif any(
[isinstance(dim, (float, int)) for dim in dimension[:2]]
) and dimension[2] in ["uniform", "log-uniform"]:
return Real(*dimension, transform=transform)
else:
return Categorical(dimension, transform=transform)
if len(dimension) == 4:
if (
any([isinstance(dim, int) for dim in dimension[:2]])
and dimension[2] == "log-uniform"
and isinstance(dimension[3], int)
):
return Integer(*dimension, transform=transform)
elif (
any([isinstance(dim, (float, int)) for dim in dimension[:2]])
and dimension[2] == "log-uniform"
and isinstance(dimension[3], int)
):
return Real(*dimension, transform=transform)
if len(dimension) > 3:
return Categorical(dimension, transform=transform)
raise ValueError(
"Invalid dimension {}. Read the documentation for "
"supported types.".format(dimension)
)
[docs]class Dimension:
"""Base class for search space dimensions."""
prior = None
[docs] def rvs(self, n_samples=1, random_state=None):
"""Draw random samples.
Parameters
----------
n_samples : int or None
The number of samples to be drawn.
random_state : int, RandomState instance, or None (default)
Set random state to something other than None for reproducible
results.
"""
rng = check_random_state(random_state)
samples = self._rvs.rvs(size=n_samples, random_state=rng)
return self.inverse_transform(samples)
def set_transformer(self):
raise NotImplementedError
@property
def size(self):
return 1
@property
def transformed_size(self):
return 1
@property
def bounds(self):
raise NotImplementedError
@property
def is_constant(self):
raise NotImplementedError
@property
def transformed_bounds(self):
raise NotImplementedError
@property
def name(self):
return self._name
@name.setter
def name(self, value):
if isinstance(value, str) or value is None:
self._name = value
else:
raise ValueError("Dimension's name must be either string or None.")
def _uniform_inclusive(loc=0.0, scale=1.0):
# like scipy.stats.distributions but inclusive of `high`
# XXX scale + 1. might not actually be a float after scale if
# XXX scale is very large.
return ss.uniform(loc=loc, scale=np.nextafter(scale, scale + 1.0))
def _normal_inclusive(loc=0.0, scale=1.0, lower=-2, upper=2):
assert lower <= upper
a, b = (lower - loc) / scale, (upper - loc) / scale
return ss.truncnorm(a, b, loc=loc, scale=scale)
[docs]class Real(Dimension):
"""Search space dimension that can take on any real value.
Parameters
----------
low : float
Lower bound (inclusive).
high : float
Upper bound (inclusive).
prior : "uniform" or "log-uniform", default="uniform"
Distribution to use when sampling random points for this dimension.
- If `"uniform"`, points are sampled uniformly between the lower
and upper bounds.
- If `"log-uniform"`, points are sampled uniformly between
`log(lower, base)` and `log(upper, base)` where log
has base `base`.
base : int
The logarithmic base to use for a log-uniform prior.
- Default 10, otherwise commonly 2.
transform : "identity", "normalize", optional
The following transformations are supported.
- "identity", (default) the transformed space is the same as the
original space.
- "normalize", the transformed space is scaled to be between
0 and 1.
name : str or None
Name associated with the dimension, e.g., "learning rate".
dtype : str or dtype, default=float
float type which will be used in inverse_transform,
can be float.
"""
def __init__(
self,
low,
high,
prior="uniform",
base=10,
transform=None,
name=None,
dtype=float,
loc=None,
scale=None,
):
if high <= low:
raise ValueError(
"the lower bound {} has to be less than the"
" upper bound {}".format(low, high)
)
if prior not in ["uniform", "log-uniform", "normal"]:
raise ValueError(
"prior should be 'normal', 'uniform' or 'log-uniform'"
" got {}".format(prior)
)
self.low = low
self.high = high
self.prior = prior
self.base = base
self.log_base = np.log10(base)
self.name = name
self.dtype = dtype
self.loc = loc
self.scale = scale
self._rvs = None
self.transformer = None
self.transform_ = transform
if isinstance(self.dtype, str) and self.dtype not in [
"float",
"float16",
"float32",
"float64",
]:
raise ValueError(
"dtype must be 'float', 'float16', 'float32'"
"or 'float64'"
" got {}".format(self.dtype)
)
elif isinstance(self.dtype, type) and not np.issubdtype(
self.dtype, np.floating
):
raise ValueError(
"dtype must be a np.floating subtype;" " got {}".format(self.dtype)
)
if transform is None:
transform = "identity"
self.set_transformer(transform)
def __eq__(self, other):
return (
type(self) is type(other)
and np.allclose([self.low], [other.low])
and np.allclose([self.high], [other.high])
and self.prior == other.prior
and self.transform_ == other.transform_
and self.loc == other.loc
and self.scale == other.scale
)
def __repr__(self):
return "Real(low={}, high={}, prior='{}', transform='{}', loc='{}', scale='{}')".format(
self.low, self.high, self.prior, self.transform_, self.loc, self.scale
)
@property
def bounds(self):
return (self.low, self.high)
@property
def is_constant(self):
return self.low == self.high
def __contains__(self, point):
if isinstance(point, list):
point = np.array(point)
if point == np.nan:
return True
else:
return self.low <= point <= self.high
@property
def transformed_bounds(self):
if self.transform_ == "normalize":
return 0.0, 1.0
else:
if self.prior == "uniform":
return self.low, self.high
else:
return (
np.log10(self.low) / self.log_base,
np.log10(self.high) / self.log_base,
)
[docs] def distance(self, a, b):
"""Compute distance between point `a` and `b`.
Parameters
----------
a : float
First point.
b : float
Second point.
"""
if not (a in self and b in self):
raise RuntimeError(
"Can only compute distance for values within "
"the space, not %s and %s." % (a, b)
)
return abs(a - b)
[docs] def update_prior(self, X, y, q=0.9):
"""Fit a Kernel Density Estimator to the data to increase density of samples around regions of interest instead of uniform random-sampling."""
X = np.array(X)
y = np.array(y)
y_ = np.quantile(y, q) # threshold
X_low = X[y <= y_]
# It is possible that fitting the Gaussian Kernel Density Estimator
# triggers an error, for example if all values of X_low are the same.
# In this case, we fall back to uniform sampling or we reuse the last
# fitted self._kde.
try:
kde = gaussian_kde(X_low)
self._kde = kde
except np.linalg.LinAlgError:
pass
[docs] def rvs(self, n_samples=1, random_state=None):
"""Draw random samples.
Parameters
----------
n_samples : int or None
The number of samples to be drawn.
random_state : int, RandomState instance, or None (default)
Set random state to something other than None for reproducible
results.
"""
rng = check_random_state(random_state)
if hasattr(self, "_kde"):
samples = self._kde.resample(n_samples, rng).reshape(-1)
samples = np.clip(samples, self.low, self.high)
else:
samples = self._rvs.rvs(size=n_samples, random_state=rng)
return self.inverse_transform(samples)
[docs]class Integer(Dimension):
"""Search space dimension that can take on integer values.
Parameters
----------
low : int
Lower bound (inclusive).
high : int
Upper bound (inclusive).
prior : "uniform" or "log-uniform", default="uniform"
Distribution to use when sampling random integers for
this dimension.
- If `"uniform"`, integers are sampled uniformly between the lower
and upper bounds.
- If `"log-uniform"`, integers are sampled uniformly between
`log(lower, base)` and `log(upper, base)` where log
has base `base`.
base : int
The logarithmic base to use for a log-uniform prior.
- Default 10, otherwise commonly 2.
transform : "identity", "normalize", optional
The following transformations are supported.
- "identity", (default) the transformed space is the same as the
original space.
- "normalize", the transformed space is scaled to be between
0 and 1.
name : str or None
Name associated with dimension, e.g., "number of trees".
dtype : str or dtype, default=np.int64
integer type which will be used in inverse_transform,
can be int, np.int16, np.uint32, np.int32, np.int64 (default).
When set to int, `inverse_transform` returns a list instead of
a numpy array
"""
def __init__(
self,
low,
high,
prior="uniform",
base=10,
transform=None,
name=None,
dtype=np.int64,
loc=None,
scale=None,
):
if high <= low:
raise ValueError(
"the lower bound {} has to be less than the"
" upper bound {}".format(low, high)
)
if prior not in ["uniform", "log-uniform"]:
raise ValueError(
"prior should be 'uniform' or 'log-uniform'" " got {}".format(prior)
)
self.low = low
self.high = high
self.prior = prior
self.base = base
self.log_base = np.log10(base)
self.name = name
self.dtype = dtype
self.transform_ = transform
self._rvs = None
self.transformer = None
self.loc = loc
self.scale = scale
if isinstance(self.dtype, str) and self.dtype not in [
"int",
"int8",
"int16",
"int32",
"int64",
"uint8",
"uint16",
"uint32",
"uint64",
]:
raise ValueError(
"dtype must be 'int', 'int8', 'int16',"
"'int32', 'int64', 'uint8',"
"'uint16', 'uint32', or"
"'uint64', but got {}".format(self.dtype)
)
elif isinstance(self.dtype, type) and self.dtype not in [
int,
np.int8,
np.int16,
np.int32,
np.int64,
np.uint8,
np.uint16,
np.uint32,
np.uint64,
]:
raise ValueError(
"dtype must be 'int', 'np.int8', 'np.int16',"
"'np.int32', 'np.int64', 'np.uint8',"
"'np.uint16', 'np.uint32', or"
"'np.uint64', but got {}".format(self.dtype)
)
if transform is None:
transform = "identity"
self.set_transformer(transform)
def __eq__(self, other):
return (
type(self) is type(other)
and np.allclose([self.low], [other.low])
and np.allclose([self.high], [other.high])
)
def __repr__(self):
return "Integer(low={}, high={}, prior='{}', transform='{}')".format(
self.low, self.high, self.prior, self.transform_
)
@property
def bounds(self):
return (self.low, self.high)
@property
def is_constant(self):
return self.low == self.high
def __contains__(self, point):
if isinstance(point, list):
point = np.array(point)
if point == np.nan:
return True
else:
return self.low <= point <= self.high
@property
def transformed_bounds(self):
if self.transform_ == "normalize":
return 0.0, 1.0
else:
if self.prior == "uniform":
return self.low, self.high
else:
return (
np.log10(self.low) / self.log_base,
np.log10(self.high) / self.log_base,
)
[docs] def distance(self, a, b):
"""Compute distance between point `a` and `b`.
Parameters
----------
a : int
First point.
b : int
Second point.
"""
if not (a in self and b in self):
raise RuntimeError(
"Can only compute distance for values within "
"the space, not %s and %s." % (a, b)
)
return abs(a - b)
[docs]class Categorical(Dimension):
"""Search space dimension that can take on categorical values.
Parameters
----------
categories : list, shape=(n_categories,)
Sequence of possible categories.
prior : list, shape=(categories,), default=None
Prior probabilities for each category. By default all categories
are equally likely.
transform : "onehot", "string", "identity", "label", default="onehot"
- "identity", the transformed space is the same as the original
space.
- "string", the transformed space is a string encoded
representation of the original space.
- "label", the transformed space is a label encoded
representation (integer) of the original space.
- "onehot", the transformed space is a one-hot encoded
representation of the original space.
name : str or None
Name associated with dimension, e.g., "colors".
"""
def __init__(self, categories, prior=None, transform=None, name=None):
self.categories = tuple(categories)
self.name = name
if transform is None:
transform = "onehot"
self.transform_ = transform
self.transformer = None
self._rvs = None
self.prior = prior
if prior is None:
self.prior_ = np.tile(1.0 / len(self.categories), len(self.categories))
else:
self.prior_ = prior
self.set_transformer(transform)
def __eq__(self, other):
return (
type(self) is type(other)
and self.categories == other.categories
and np.allclose(self.prior_, other.prior_)
)
def __repr__(self):
if len(self.categories) > 7:
cats = self.categories[:3] + (_Ellipsis(),) + self.categories[-3:]
else:
cats = self.categories
if self.prior is not None and len(self.prior) > 7:
prior = self.prior[:3] + [_Ellipsis()] + self.prior[-3:]
else:
prior = self.prior
return "Categorical(categories={}, prior={}, transform={})".format(
cats, prior, self.transform_
)
[docs] def rvs(self, n_samples=None, random_state=None):
choices = self._rvs.rvs(size=n_samples, random_state=random_state)
if isinstance(choices, numbers.Integral):
return self.categories[choices]
elif self.transform_ == "normalize" and isinstance(choices, float):
return self.inverse_transform([(choices)])
elif self.transform_ == "normalize":
return self.inverse_transform(list(choices))
else:
return [self.categories[c] for c in choices]
@property
def transformed_size(self):
if self.transform_ == "onehot":
size = len(self.categories)
# when len(categories) == 2, CategoricalEncoder outputs a
# single value
return size if size != 2 else 1
return 1
@property
def bounds(self):
return self.categories
@property
def is_constant(self):
return len(self.categories) <= 1
def __contains__(self, point):
return point in self.categories
@property
def transformed_bounds(self):
if self.transformed_size == 1:
N = len(self.categories)
if self.transform_ == "label":
return 0.0, float(N - 1)
elif self.transform_ == "identity":
return min(self.categories), max(self.categories)
else:
return 0.0, 1.0
else:
return [(0.0, 1.0) for i in range(self.transformed_size)]
[docs] def distance(self, a, b):
"""Compute distance between category `a` and `b`.
As categories have no order the distance between two points is one
if a != b and zero otherwise.
Parameters
----------
a : category
First category.
b : category
Second category.
"""
if not (a in self and b in self):
raise RuntimeError(
"Can only compute distance for values within"
" the space, not {} and {}.".format(a, b)
)
return 1 if a != b else 0
def _sample_dimension(dim, i, n_samples, random_state, out):
"""Wrapper to sample dimension for joblib parallelization."""
out[0][:, i] = dim.rvs(n_samples=n_samples, random_state=random_state)
[docs]class Space:
"""Initialize a search space from given specifications.
Parameters
----------
dimensions : list, shape=(n_dims,)
List of search space dimensions.
Each search dimension can be defined either as
- a `(lower_bound, upper_bound)` tuple (for `Real` or `Integer`
dimensions),
- a `(lower_bound, upper_bound, "prior")` tuple (for `Real`
dimensions),
- as a list of categories (for `Categorical` dimensions), or
- an instance of a `Dimension` object (`Real`, `Integer` or
`Categorical`).
.. note::
The upper and lower bounds are inclusive for `Integer`
dimensions.
"""
def __init__(self, dimensions, model_sdv=None, config_space=None):
# attribute used when a generative model is used to sample
self.model_sdv = model_sdv
# attribute use when a config space is used to sample
assert config_space is None or isinstance(config_space, CS.ConfigurationSpace)
self.config_space = config_space
self.dimensions = [check_dimension(dim) for dim in dimensions]
def __eq__(self, other):
return all([a == b for a, b in zip(self.dimensions, other.dimensions)])
def __repr__(self):
if len(self.dimensions) > 31:
dims = self.dimensions[:15] + [_Ellipsis()] + self.dimensions[-15:]
else:
dims = self.dimensions
return "Space([{}])".format(",\n ".join(map(str, dims)))
def __iter__(self):
return iter(self.dimensions)
@property
def dimension_names(self):
"""
Names of all the dimensions in the search-space.
"""
index = 0
names = []
for dim in self.dimensions:
if dim.name is None:
names.append("X_%d" % index)
else:
names.append(dim.name)
index += 1
return names
@property
def is_real(self):
"""
Returns true if all dimensions are Real
"""
return all([isinstance(dim, Real) for dim in self.dimensions])
[docs] @classmethod
def from_yaml(cls, yml_path, namespace=None):
"""Create Space from yaml configuration file
Parameters
----------
yml_path : str
Full path to yaml configuration file, example YaML below:
Space:
- Integer:
low: -5
high: 5
- Categorical:
categories:
- a
- b
- Real:
low: 1.0
high: 5.0
prior: log-uniform
namespace : str, default=None
Namespace within configuration file to use, will use first
namespace if not provided
Returns
-------
space : Space
Instantiated Space object
"""
with open(yml_path, "rb") as f:
config = yaml.safe_load(f)
dimension_classes = {
"real": Real,
"integer": Integer,
"categorical": Categorical,
}
# Extract space options for configuration file
if isinstance(config, dict):
if namespace is None:
options = next(iter(config.values()))
else:
options = config[namespace]
elif isinstance(config, list):
options = config
else:
raise TypeError("YaML does not specify a list or dictionary")
# Populate list with Dimension objects
dimensions = []
for option in options:
key = next(iter(option.keys()))
# Make configuration case insensitive
dimension_class = key.lower()
values = {k.lower(): v for k, v in option[key].items()}
if dimension_class in dimension_classes:
# Instantiate Dimension subclass and add it to the list
dimension = dimension_classes[dimension_class](**values)
dimensions.append(dimension)
space = cls(dimensions=dimensions)
return space
[docs] def rvs(self, n_samples=1, random_state=None, n_jobs=1):
"""Draw random samples.
The samples are in the original space. They need to be transformed
before being passed to a model or minimizer by `space.transform()`.
Parameters
----------
n_samples : int, default=1
Number of samples to be drawn from the space.
random_state : int, RandomState instance, or None (default)
Set random state to something other than None for reproducible
results.
Returns
-------
points : list of lists, shape=(n_points, n_dims)
Points sampled from the space.
"""
rng = check_random_state(random_state)
if self.config_space:
req_points = []
hps_names = self.config_space.get_hyperparameter_names()
if self.model_sdv is None:
confs = self.config_space.sample_configuration(n_samples)
if n_samples == 1:
confs = [confs]
else:
confs = self.model_sdv.sample(n_samples)
sdv_names = confs.columns
new_hps_names = list(set(hps_names) - set(sdv_names))
# randomly sample the new hyperparameters
for name in new_hps_names:
hp = self.config_space.get_hyperparameter(name)
rvs = []
for i in range(n_samples):
v = hp._sample(rng)
rv = hp._transform(v)
rvs.append(rv)
confs[name] = rvs
# reoder the column names
confs = confs[hps_names]
confs = confs.to_dict("records")
for idx, conf in enumerate(confs):
cf = deactivate_inactive_hyperparameters(conf, self.config_space)
confs[idx] = cf.get_dictionary()
for idx, conf in enumerate(confs):
point = []
for i, hps_name in enumerate(hps_names):
# If the parameter is inactive due to some conditions then we attribute the
# lower bound value to break symmetries and enforce the same representation.
if hps_name in conf.keys():
val = conf[hps_name]
else:
val = self.dimensions[i].bounds[0]
point.append(val)
req_points.append(point)
return req_points
else:
if self.model_sdv is None:
# Regular sampling without transfer learning from flat search space
# Joblib parallel optimization
# Draw
columns = np.zeros((n_samples, len(self.dimensions)), dtype="O")
random_states = rng.randint(
low=0, high=2**31, size=len(self.dimensions)
)
Parallel(n_jobs=n_jobs, verbose=0, require="sharedmem")(
delayed(_sample_dimension)(
dim,
i,
n_samples,
np.random.RandomState(random_states[i]),
[columns],
)
for i, dim in enumerate(self.dimensions)
)
return columns.tolist()
else:
confs = self.model_sdv.sample(n_samples) # sample from SDV
columns = []
for dim in self.dimensions:
if dim.name in confs.columns:
columns.append(confs[dim.name].values.tolist())
else:
columns.append(dim.rvs(n_samples=n_samples, random_state=rng))
# Transpose
return _transpose_list_array(columns)
@property
def n_dims(self):
"""The dimensionality of the original space."""
return len(self.dimensions)
@property
def transformed_n_dims(self):
"""The dimensionality of the warped space."""
return sum([dim.transformed_size for dim in self.dimensions])
@property
def bounds(self):
"""The dimension bounds, in the original space."""
b = []
for dim in self.dimensions:
if dim.size == 1:
b.append(dim.bounds)
else:
b.extend(dim.bounds)
return b
def __contains__(self, point):
"""Check that `point` is within the bounds of the space."""
for component, dim in zip(point, self.dimensions):
if component not in dim:
return False
return True
def __getitem__(self, dimension_names):
"""
Lookup and return the search-space dimension with the given name.
This allows for dict-like lookup of dimensions, for example:
`space['foo']` returns the dimension named 'foo' if it exists,
otherwise `None` is returned.
It also allows for lookup of a list of dimension-names, for example:
`space[['foo', 'bar']]` returns the two dimensions named
'foo' and 'bar' if they exist.
Parameters
----------
dimension_names : str or list(str)
Name of a single search-space dimension (str).
List of names for search-space dimensions (list(str)).
Returns
-------
dims tuple (index, Dimension), list(tuple(index, Dimension)), \
(None, None)
A single search-space dimension with the given name,
or a list of search-space dimensions with the given names.
"""
def _get(dimension_name):
"""Helper-function for getting a single dimension."""
index = 0
# Get the index of the search-space dimension using its name.
for dim in self.dimensions:
if dimension_name == dim.name:
return (index, dim)
elif dimension_name == index:
return (index, dim)
index += 1
return (None, None)
if isinstance(dimension_names, (str, int)):
# Get a single search-space dimension.
dims = _get(dimension_name=dimension_names)
elif isinstance(dimension_names, (list, tuple)):
# Get a list of search-space dimensions.
# Note that we do not check whether the names are really strings.
dims = [_get(dimension_name=name) for name in dimension_names]
else:
msg = (
"Dimension name should be either string or"
"list of strings, but got {}."
)
raise ValueError(msg.format(type(dimension_names)))
return dims
@property
def transformed_bounds(self):
"""The dimension bounds, in the warped space."""
b = []
for dim in self.dimensions:
if dim.transformed_size == 1:
b.append(dim.transformed_bounds)
else:
b.extend(dim.transformed_bounds)
return b
@property
def is_categorical(self):
"""Space contains exclusively categorical dimensions"""
return all([isinstance(dim, Categorical) for dim in self.dimensions])
@property
def is_partly_categorical(self):
"""Space contains any categorical dimensions"""
return any([isinstance(dim, Categorical) for dim in self.dimensions])
@property
def n_constant_dimensions(self):
"""Returns the number of constant dimensions which have zero degree of
freedom, e.g. an Integer dimensions with (0., 0.) as bounds.
"""
n = 0
for dim in self.dimensions:
if dim.is_constant:
n += 1
return n
[docs] def distance(self, point_a, point_b):
"""Compute distance between two points in this space.
Parameters
----------
point_a : array
First point.
point_b : array
Second point.
"""
distance = 0.0
for a, b, dim in zip(point_a, point_b, self.dimensions):
distance += dim.distance(a, b)
return distance
[docs] def update_prior(self, X, y, q=0.9):
"""Update the prior of the dimensions. Instead of doing random-sampling, a kernel density estimation is fit on the region of interest and
sampling is performed from this distribution."""
y = np.array(y)
for i, dim in enumerate(self.dimensions):
Xi = [x[i] for x in X]
if hasattr(dim, "update_prior"):
dim.update_prior(Xi, y, q=q)
