# Source code for orion.algo.tpe

```# -*- coding: utf-8 -*-
"""
Tree-structured Parzen Estimator Approach
=========================================
"""
import logging

import numpy
from scipy.stats import norm

from orion.algo.base import BaseAlgorithm
from orion.core.utils.points import flatten_dims, regroup_dims

logger = logging.getLogger(__name__)

[docs]def compute_max_ei_point(points, below_likelis, above_likelis):
"""Compute ei among points based on their log likelihood and return the point with max ei.

:param points: list of point with real values.
:param below_likelis: list of log likelihood for each point in the good GMM.
:param above_likelis: list of log likelihood for each point in the bad GMM.
"""
max_ei = -numpy.inf
point_index = 0
for i, (lik_b, lik_a) in enumerate(zip(below_likelis, above_likelis)):
ei = lik_b - lik_a
if ei > max_ei:
max_ei = ei
point_index = i
return points[point_index]

[docs]def ramp_up_weights(total_num, flat_num, equal_weight):

:param total_num: total number of observed trials.
:param flat_num: the number of the most recent trials which
get the full weight where the others will be applied with a linear ramp
from 0 to 1.0. It will only take effect if equal_weight is False.
:param equal_weight: whether all the observed trails share the same weights.
"""
if total_num < flat_num or equal_weight:
return numpy.ones(total_num)

ramp_weights = numpy.linspace(1.0 / total_num, 1.0, num=total_num - flat_num)
flat_weights = numpy.ones(flat_num)
return numpy.concatenate([ramp_weights, flat_weights])

# pylint:disable=assignment-from-no-return
mus, low, high, prior_weight=1.0, equal_weight=False, flat_num=25
):
"""Return the sorted mus, the corresponding sigmas and weights with adaptive kernel estimator.

This adaptive parzen window estimator is based on the original papers and also refer the use of
prior mean in `this implementation
<https://github.com/hyperopt/hyperopt/blob/40bd617a0ca47e09368fb65919464ba0bfa85962/hyperopt/tpe.py#L400-L468>`_.

:param mus: list of real values for observed mus.
:param low: real value for lower bound of points.
:param high: real value for upper bound of points.
:param prior_weight: real value for the weight of the prior mean.
:param equal_weight: bool value indicating if all points with equal weights.
:param flat_num: int value indicating the number of the most recent trials which
get the full weight where the others will be applied with a linear ramp
from 0 to 1.0. It will only take effect if equal_weight is False.
"""
mus = numpy.asarray(mus)

prior_mu = (low + high) * 0.5
prior_sigma = (high - low) * 1.0

size = len(mus)
if size > 1:
order = numpy.argsort(mus)
sorted_mus = mus[order]
prior_mu_pos = numpy.searchsorted(sorted_mus, prior_mu)

weights = ramp_up_weights(size, flat_num, equal_weight)

mixture_mus = numpy.zeros(size + 1)
mixture_mus[:prior_mu_pos] = sorted_mus[:prior_mu_pos]
mixture_mus[prior_mu_pos] = prior_mu
mixture_mus[prior_mu_pos + 1 :] = sorted_mus[prior_mu_pos:]

mixture_weights = numpy.ones(size + 1)
mixture_weights[:prior_mu_pos] = weights[:prior_mu_pos]
mixture_weights[prior_mu_pos] = prior_weight
mixture_weights[prior_mu_pos + 1 :] = weights[prior_mu_pos:]

sigmas = numpy.ones(size + 1)
sigmas = mixture_mus - mixture_mus
sigmas[-1] = mixture_mus[-1] - mixture_mus[-2]
sigmas[1:-1] = numpy.maximum(
(mixture_mus[1:-1] - mixture_mus[0:-2]),
(mixture_mus[2:] - mixture_mus[1:-1]),
)
sigmas = numpy.clip(
sigmas, prior_sigma / max(10, numpy.sqrt(size)), prior_sigma
)

else:
if prior_mu < mus:

mixture_mus = numpy.array([prior_mu, mus])
sigmas = numpy.array([prior_sigma, prior_sigma * 0.5])
mixture_weights = numpy.array([prior_weight, 1.0])
else:
mixture_mus = numpy.array([mus, prior_mu])
sigmas = numpy.array([prior_sigma * 0.5, prior_sigma])
mixture_weights = numpy.array([1.0, prior_weight])

weights = mixture_weights / mixture_weights.sum()

return mixture_mus, sigmas, weights

[docs]class TPE(BaseAlgorithm):
"""Tree-structured Parzen Estimator (TPE) algorithm is one of Sequential Model-Based
Global Optimization (SMBO) algorithms, which will build models to propose new points based
on the historical observed trials.

Instead of modeling p(y|x) like other SMBO algorithms, TPE models p(x|y) and p(y),
and p(x|y) is modeled by transforming that generative process, replacing the distributions of
the configuration prior with non-parametric densities.

The TPE defines p(x|y) using two such densities l(x) and g(x) while l(x) is distribution of
good points and g(x) is the distribution of bad points. New point candidates will be sampled
with l(x) and Expected Improvement (EI) optimization scheme will be used to find the most
promising point among the candidates.

- `Algorithms for Hyper-Parameter Optimization
<https://papers.nips.cc/paper/4443-algorithms-for-hyper-parameter-optimization.pdf>`_
- `Making a Science of Model Search: Hyperparameter Optimizationin Hundreds of Dimensions
for Vision Architectures <http://proceedings.mlr.press/v28/bergstra13.pdf>`_

Parameters
----------
space: `orion.algo.space.Space`
Optimisation space with priors for each dimension.
seed: None, int or sequence of int
Seed to sample initial points and candidates points.
Default: ``None``
n_initial_points: int
Number of initial points randomly sampled.
Default: ``20``
n_ei_candidates: int
Number of candidates points sampled for ei compute.
Default: ``24``
gamma: real
Ratio to split the observed trials into good and bad distributions.
Default: ``0.25``
equal_weight: bool
True to set equal weights for observed points.
Default: ``False``
prior_weight: int
The weight given to the prior point of the input space.
Default: ``1.0``
full_weight_num: int
The number of the most recent trials which get the full weight where the others will be
applied with a linear ramp from 0 to 1.0. It will only take effect if equal_weight
is False.

"""

requires_type = None
requires_dist = None
requires_shape = "flattened"

# pylint:disable=too-many-arguments
def __init__(
self,
space,
seed=None,
n_initial_points=20,
n_ei_candidates=24,
gamma=0.25,
equal_weight=False,
prior_weight=1.0,
full_weight_num=25,
):

if n_initial_points < 2:
n_initial_points = 2
logger.warning(
"n_initial_points %s is not valid, set n_initial_points = 2",
str(n_initial_points),
)

if n_ei_candidates < 1:
n_ei_candidates = 1
logger.warning(
"n_ei_candidates %s is not valid, set n_ei_candidates = 1",
str(n_ei_candidates),
)

super(TPE, self).__init__(
space,
seed=seed,
n_initial_points=n_initial_points,
n_ei_candidates=n_ei_candidates,
gamma=gamma,
equal_weight=equal_weight,
prior_weight=prior_weight,
full_weight_num=full_weight_num,
)

@property
def space(self):
"""Return transformed space of TPE"""
return self._space

@space.setter
def space(self, space):
"""Set the space of TPE and initialize it"""
self._space = space
self._initialize()

def _initialize(self):
"""Initialize TPE once the space is transformed"""

for dimension in self.space.values():

if dimension.type != "fidelity" and dimension.prior_name not in [
"uniform",
"reciprocal",
"int_uniform",
"choices",
]:
raise ValueError(
"TPE now only supports uniform, loguniform, uniform discrete "
"and choices as prior."
)

shape = dimension.shape
if shape and len(shape) != 1:
raise ValueError("TPE now only supports 1D shape.")

[docs]    def seed_rng(self, seed):
"""Seed the state of the random number generator.

:param seed: Integer seed for the random number generator.
"""
self.rng = numpy.random.RandomState(seed)

@property
def state_dict(self):
"""Return a state dict that can be used to reset the state of the algorithm."""
_state_dict = super(TPE, self).state_dict

_state_dict["rng_state"] = self.rng.get_state()
_state_dict["seed"] = self.seed
return _state_dict

[docs]    def set_state(self, state_dict):
"""Reset the state of the algorithm based on the given state_dict

:param state_dict: Dictionary representing state of an algorithm
"""
super(TPE, self).set_state(state_dict)

self.seed_rng(state_dict["seed"])
self.rng.set_state(state_dict["rng_state"])

[docs]    def suggest(self, num=1):
"""Suggest a `num` of new sets of parameters. Randomly draw samples
from the import space and return them.

:param num: how many sets to be suggested.

.. note:: New parameters must be compliant with the problem's domain
`orion.algo.space.Space`.
"""
if num > 1:
raise ValueError("TPE should suggest only one point.")

samples = []
if len(self._trials_info) < self.n_initial_points:
new_point = self.space.sample(
1, seed=tuple(self.rng.randint(0, 1000000, size=3))
)
samples.append(new_point)
else:
point = []
below_points, above_points = self.split_trials()

below_points = [flatten_dims(point, self.space) for point in below_points]
above_points = [flatten_dims(point, self.space) for point in above_points]
below_points = list(map(list, zip(*below_points)))
above_points = list(map(list, zip(*above_points)))

idx = 0
for i, dimension in enumerate(self.space.values()):

shape = dimension.shape
if not shape:
shape = (1,)

if dimension.type == "real":
points = self._sample_real_dimension(
dimension,
shape,
below_points[idx : idx + shape],
above_points[idx : idx + shape],
)
elif (
dimension.type == "integer"
and dimension.prior_name == "int_uniform"
):
points = self.sample_one_dimension(
dimension,
shape,
below_points[idx : idx + shape],
above_points[idx : idx + shape],
self._sample_int_point,
)
elif (
dimension.type == "categorical"
and dimension.prior_name == "choices"
):
points = self.sample_one_dimension(
dimension,
shape,
below_points[idx : idx + shape],
above_points[idx : idx + shape],
self._sample_categorical_point,
)
elif dimension.type == "fidelity":
# fidelity dimension
points = [point[i] for point in self.space.sample(num)]
else:
raise NotImplementedError()

if len(points) < shape:
logger.warning(
"TPE failed to sample new point with configuration %s",
self.configuration,
)
return None

idx += shape
point += points

point = regroup_dims(point, self.space)
samples.append(point)

return samples

# pylint:disable=no-self-use
[docs]    def sample_one_dimension(
self, dimension, shape_size, below_points, above_points, sampler
):
"""Sample values for a dimension

:param dimension: Dimension.
:param shape_size: 1D Shape Size of the Real Dimension.
:param below_points: good points with shape (m, n), m=shape_size.
:param above_points: bad points with shape (m, n), m=shape_size.
:param sampler: method to sample one value for upon the dimension.
"""
points = []

for j in range(shape_size):
new_point = sampler(dimension, below_points[j], above_points[j])
if new_point is not None:
points.append(new_point)

return points

def _sample_real_dimension(self, dimension, shape_size, below_points, above_points):
"""Sample values for real dimension"""
if dimension.prior_name == "uniform":
return self.sample_one_dimension(
dimension,
shape_size,
below_points,
above_points,
self._sample_real_point,
)
elif dimension.prior_name == "reciprocal":
return self.sample_one_dimension(
dimension,
shape_size,
below_points,
above_points,
self._sample_loguniform_real_point,
)
else:
raise NotImplementedError()

def _sample_loguniform_real_point(self, dimension, below_points, above_points):
"""Sample one value for real dimension in a loguniform way"""
return self._sample_real_point(
dimension, below_points, above_points, is_log=True
)

def _sample_real_point(self, dimension, below_points, above_points, is_log=False):
"""Sample one value for real dimension based on the observed good and bad points"""
low, high = dimension.interval()
if is_log:
low = numpy.log(low)
high = numpy.log(high)
below_points = numpy.log(below_points)
above_points = numpy.log(above_points)

below_points,
low,
high,
self.prior_weight,
self.equal_weight,
flat_num=self.full_weight_num,
)
above_points,
low,
high,
self.prior_weight,
self.equal_weight,
flat_num=self.full_weight_num,
)

gmm_sampler_below = GMMSampler(
self, below_mus, below_sigmas, low, high, below_weights
)
gmm_sampler_above = GMMSampler(
self, above_mus, above_sigmas, low, high, above_weights
)

candidate_points = gmm_sampler_below.sample(self.n_ei_candidates)
if candidate_points:
lik_blow = gmm_sampler_below.get_loglikelis(candidate_points)
lik_above = gmm_sampler_above.get_loglikelis(candidate_points)
new_point = compute_max_ei_point(candidate_points, lik_blow, lik_above)

if is_log:
new_point = numpy.exp(new_point)

return new_point

return None

def _sample_int_point(self, dimension, below_points, above_points):
"""Sample one value for integer dimension based on the observed good and bad points"""
low, high = dimension.interval()
choices = range(low, high)

below_points = numpy.array(below_points).astype(int) - low
above_points = numpy.array(above_points).astype(int) - low

sampler_below = CategoricalSampler(self, below_points, choices)
candidate_points = sampler_below.sample(self.n_ei_candidates)

if list(candidate_points):
sampler_above = CategoricalSampler(self, above_points, choices)

lik_below = sampler_below.get_loglikelis(candidate_points)
lik_above = sampler_above.get_loglikelis(candidate_points)

new_point = compute_max_ei_point(candidate_points, lik_below, lik_above)
new_point = new_point + low
return new_point

return None

def _sample_categorical_point(self, dimension, below_points, above_points):
"""Sample one value for categorical dimension based on the observed good and bad points"""
choices = dimension.interval()

below_points = [choices.index(point) for point in below_points]
above_points = [choices.index(point) for point in above_points]

sampler_below = CategoricalSampler(self, below_points, choices)
candidate_points = sampler_below.sample(self.n_ei_candidates)

if list(candidate_points):
sampler_above = CategoricalSampler(self, above_points, choices)

lik_below = sampler_below.get_loglikelis(candidate_points)
lik_above = sampler_above.get_loglikelis(candidate_points)

new_point_index = compute_max_ei_point(
candidate_points, lik_below, lik_above
)
new_point = choices[new_point_index]

return new_point
return None

[docs]    def split_trials(self):
"""Split the observed trials into good and bad ones based on the ratio `gamma``"""
sorted_trials = sorted(
self._trials_info.values(), key=lambda x: x["objective"]
)
sorted_points = [list(points) for points, results in sorted_trials]

split_index = int(numpy.ceil(self.gamma * len(sorted_points)))

below = sorted_points[:split_index]
above = sorted_points[split_index:]

return below, above

[docs]    def observe(self, points, results):
"""Observe evaluation `results` corresponding to list of `points` in
space.

A simple random sampler though does not take anything into account.
"""
super(TPE, self).observe(points, results)

[docs]class GMMSampler:
"""Gaussian Mixture Model Sampler for TPE algorithm

Parameters
----------
tpe: `TPE` algorithm
The tpe algorithm object which this sampler will be part of.
mus: list
mus for each Gaussian components in the GMM.
Default: ``None``
sigmas: list
sigmas for each Gaussian components in the GMM.
low: real
Lower bound of the sampled points.
high: real
Upper bound of the sampled points.
weights: list
Weights for each Gaussian components in the GMM
Default: ``None``

"""

def __init__(self, tpe, mus, sigmas, low, high, weights=None):
self.tpe = tpe

self.mus = mus
self.sigmas = sigmas
self.low = low
self.high = high
self.weights = weights if weights is not None else len(mus) * [1.0 / len(mus)]

self.pdfs = []
self._build_mixture()

def _build_mixture(self):
"""Build the Gaussian components in the GMM"""
for mu, sigma in zip(self.mus, self.sigmas):
self.pdfs.append(norm(mu, sigma))

[docs]    def sample(self, num=1):
"""Sample required number of points"""
point = []
for _ in range(num):
pdf = numpy.argmax(self.tpe.rng.multinomial(1, self.weights))
new_points = self.pdfs[pdf].rvs(size=5)
for pt in new_points:
if self.low <= pt < self.high:
point.append(pt)
break

return point

[docs]    def get_loglikelis(self, points):
"""Return the log likelihood for the points"""
points = numpy.array(points)
weight_likelis = [
numpy.log(self.weights[i] * pdf.pdf(points))
for i, pdf in enumerate(self.pdfs)
]
weight_likelis = numpy.array(weight_likelis)
# (num_weights, num_points) => (num_points, num_weights)
weight_likelis = weight_likelis.transpose()

# log-sum-exp trick
max_likeli = numpy.nanmax(weight_likelis, axis=1)
point_likeli = max_likeli + numpy.log(
numpy.nansum(numpy.exp(weight_likelis - max_likeli[:, None]), axis=1)
)

return point_likeli

[docs]class CategoricalSampler:
"""Categorical Sampler for discrete integer and categorical choices

Parameters
----------
tpe: `TPE` algorithm
The tpe algorithm object which this sampler will be part of.
observations: list
Observed values in the dimension
choices: list
Candidate values for the dimension

"""

def __init__(self, tpe, observations, choices):
self.tpe = tpe
self.obs = observations
self.choices = choices

self._build_multinomial_weights()

def _build_multinomial_weights(self):
"""Build weights for categorical distribution based on observations"""
weights_obs = ramp_up_weights(
len(self.obs), self.tpe.full_weight_num, self.tpe.equal_weight
)
counts_obs = numpy.bincount(
self.obs, minlength=len(self.choices), weights=weights_obs
)
counts_obs = counts_obs + self.tpe.prior_weight
self.weights = counts_obs / counts_obs.sum()

[docs]    def sample(self, num=1):
"""Sample required number of points"""
samples = self.tpe.rng.multinomial(n=1, pvals=self.weights, size=num)

assert samples.shape == (num,) + (len(self.weights),)

samples_index = samples.argmax(-1)
assert samples_index.shape == (num,)

return samples_index

[docs]    def get_loglikelis(self, points):
"""Return the log likelihood for the points"""
return numpy.log(numpy.asarray(self.weights)[points])
```
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