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import copy
import logging
import math
import time
import uuid
from abc import ABC, abstractmethod
from typing import List, Optional
import numpy as np
from autotm.abstract_params import AbstractParams
from autotm.algorithms_for_tuning.genetic_algorithm.statistics_collector import StatisticsCollector
from autotm.algorithms_for_tuning.genetic_algorithm.surrogate import Surrogate, set_surrogate_fitness, \
get_prediction_uncertanty
from autotm.algorithms_for_tuning.individuals import Individual, IndividualBuilder
from autotm.fitness import local_tasks, cluster_tasks
from autotm.schemas import IndividualDTO
logger = logging.getLogger(__name__)
class FitnessEstimator:
def __init__(self, num_fitness_evaluations: Optional[int] = None, statistics_collector: Optional[StatisticsCollector] = None):
self._num_fitness_evaluations = num_fitness_evaluations
self._evaluations_counter = 0
self._statistics_collector = statistics_collector
super().__init__()
@property
def num_fitness_evaluations(self) -> Optional[int]:
return self._num_fitness_evaluations
@property
def evaluations_counter(self) -> int:
return self._evaluations_counter
@abstractmethod
def fit(self, iter_num: int) -> None:
...
@abstractmethod
def log_best_solution(self,
individual: Individual,
wait_for_result_timeout: Optional[float] = None,
alg_args: Optional[str] = None,
is_tmp: bool = False) -> Individual:
...
def estimate(self, iter_num: int, population: List[Individual]) -> List[Individual]:
evaluated = [individual for individual in population if individual.dto.fitness_value is not None]
not_evaluated = [individual for individual in population if individual.dto.fitness_value is None]
evaluations_limit = max(0, self._num_fitness_evaluations - self._evaluations_counter) \
if self._num_fitness_evaluations else len(not_evaluated)
if len(not_evaluated) > evaluations_limit:
not_evaluated = not_evaluated[:evaluations_limit]
self._evaluations_counter += len(not_evaluated)
new_evaluated = self._estimate(iter_num, not_evaluated)
if self._statistics_collector:
for individual in new_evaluated:
self._statistics_collector.log_individual(individual)
return evaluated + new_evaluated
@abstractmethod
def _estimate(self, iter_num: int, population: List[Individual]) -> List[Individual]:
...
class SurrogateEnabledFitnessEstimatorMixin(FitnessEstimator, ABC):
SUPPORTED_CALC_SCHEMES = ["type1", "type2"]
ibuilder: IndividualBuilder
surrogate: Surrogate
calc_scheme: str
speedup: bool
all_params: List[AbstractParams]
all_fitness: List[float]
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
@staticmethod
def surrogate_iteration(iter_num: int) -> bool:
return (iter_num % 2 != 0) if iter_num > 0 else False
def fit(self, iter_num: int) -> None:
surrogate_iteration = self.surrogate_iteration(iter_num)
if (self.calc_scheme == "type1" and not surrogate_iteration) or (self.calc_scheme == "type2"):
self.surrogate.fit(np.array(self.all_params), np.array(self.all_fitness))
def _estimate(self, iter_num: int, population: List[Individual]) -> List[Individual]:
fitness_calc_time_start = time.time()
surrogate_iteration = self.surrogate_iteration(iter_num)
if not self.speedup or not surrogate_iteration or iter_num == -1:
population = super().estimate(iter_num, population)
self.save_params(population)
if self.calc_scheme == "type1" and surrogate_iteration:
population = self.surrogate_calculation(population)
elif self.calc_scheme == "type2" and iter_num != -1:
population = self._calculate_uncertain_res(iter_num, population)
self.save_params(population)
logger.info(f"TIME OF THE SURROGATE-BASED FITNESS FUNCTION: {time.time() - fitness_calc_time_start}")
return population
def surrogate_calculation(self, population: List[Individual]):
x_val = np.array([copy.deepcopy(individ.params.to_vector()) for individ in population])
y_pred = self.surrogate.predict(x_val)
if not self.speedup:
y_val = np.array([individ.fitness_value for individ in population])
def check_val(fval):
return not (fval is None or math.isnan(fval) or math.isinf(fval))
def check_params(p):
return all(check_val(el) for el in p)
clean_params_and_f = []
for i in range(len(y_val)):
if not check_params(x_val[i]) or not check_val(y_val[i]):
logger.warning(
f"Bad params or fitness found. Fitness: {y_val[i]}. Params: {x_val[i]}."
)
else:
clean_params_and_f.append((x_val[i], y_val[i]))
x_val = clean_params_and_f[0]
y_val = clean_params_and_f[1]
r_2, mse, rmse = self.surrogate.score(x_val, y_val)
logger.info(f"Real values: {list(y_val)}")
logger.info(f"Predicted values: {list(y_pred)}")
logger.info(f"R^2: {r_2}, MSE: {mse}, RMSE: {rmse}")
for ix, individ in enumerate(population):
individ.dto.fitness_value = set_surrogate_fitness(y_pred[ix])
return population
def _calculate_uncertain_res(self, iter_num: int, population: List[Individual], proc:float = 0.3):
if len(population) == 0:
return []
x = np.array([individ.dto.params.to_vector() for individ in population])
certanty = get_prediction_uncertanty(
self.surrogate.surrogate, x, self.surrogate.name
)
recalculate_num = int(np.floor(len(certanty) * proc))
logger.info(f"Certanty values: {certanty}")
certanty, x = (
list(t) for t in zip(*sorted(zip(certanty, x.tolist()), reverse=True))
) # check
calculated = []
for individual in population[:recalculate_num]:
# copy
individual_json = individual.dto.model_dump_json()
individual = self.ibuilder.make_individual(dto=IndividualDTO.model_validate_json(individual_json))
individual.dto.fitness_value = None
calculated.append(individual)
calculated = super().estimate(iter_num, calculated)
self.all_params += [individ.dto.params.to_vector() for individ in calculated]
self.all_fitness += [
individ.dto.fitness_value["avg_coherence_score"] for individ in calculated
]
pred_y = self.surrogate.predict(x[recalculate_num:])
for ix, individual in enumerate(population[recalculate_num:]):
dto = individual.dto
dto = IndividualDTO(
id=str(uuid.uuid4()),
data_path=dto.data_path,
params=dto.params,
dataset=dto.dataset,
fitness_value=set_surrogate_fitness(pred_y[ix]),
exp_id=dto.exp_id,
alg_id=dto.alg_id,
topic_count=dto.topic_count,
tag=dto.tag,
train_option=dto.train_option,
)
calculated.append(self.ibuilder.make_individual(dto=dto))
return calculated
def save_params(self, population):
params_and_f = [
(copy.deepcopy(individ.params.to_vector()), individ.fitness_value)
for individ in population
if individ.fitness_value not in self.all_fitness
]
def check_val(fval):
return not (fval is None or math.isnan(fval) or math.isinf(fval))
def check_params(pp):
return all(check_val(el) for el in pp)
clean_params_and_f = []
for p, f in params_and_f:
if not check_params(p) or not check_val(f):
logger.warning(f"Bad params or fitness found. Fitness: {f}. Params: {p}.")
else:
clean_params_and_f.append((p, f))
pops = [p for p, _ in clean_params_and_f]
fs = [f for _, f in clean_params_and_f]
self.all_params += pops
self.all_fitness += fs
class ComputableFitnessEstimator(FitnessEstimator):
def __init__(self,
ibuilder: IndividualBuilder,
num_fitness_evaluations: Optional[int] = None,
statistics_collector: Optional[StatisticsCollector] = None):
self.ibuilder = ibuilder
super().__init__(num_fitness_evaluations, statistics_collector)
def fit(self, iter_num: int) -> None:
pass
def log_best_solution(self,
individual: Individual,
wait_for_result_timeout: Optional[float] = None,
alg_args: Optional[str] = None,
is_tmp: bool = False) -> Individual:
return local_tasks.log_best_solution(self.ibuilder, individual, wait_for_result_timeout, alg_args, is_tmp)
def _estimate(self, iter_num: int, population: List[Individual]) -> List[Individual]:
return local_tasks.estimate_fitness(self.ibuilder, population)
class DistributedComputableFitnessEstimator(FitnessEstimator):
def __init__(self,
ibuilder: IndividualBuilder,
num_fitness_evaluations: Optional[int] = None,
statistics_collector: Optional[StatisticsCollector] = None):
self.app = cluster_tasks.make_celery_app()
self.ibuilder = ibuilder
super().__init__(num_fitness_evaluations, statistics_collector)
def fit(self, iter_num: int) -> None:
pass
def log_best_solution(self,
individual: Individual,
wait_for_result_timeout: Optional[float] = None,
alg_args: Optional[str] = None,
is_tmp: bool = False) -> Individual:
return cluster_tasks.log_best_solution(self.ibuilder, individual,
wait_for_result_timeout, alg_args, is_tmp, app=self.app)
def _estimate(self, iter_num: int, population: List[Individual]) -> List[Individual]:
return cluster_tasks.parallel_fitness(self.ibuilder, population, app=self.app)
class SurrogateEnabledComputableFitnessEstimator(ComputableFitnessEstimator, SurrogateEnabledFitnessEstimatorMixin):
def __init__(self,
ibuilder: IndividualBuilder,
surrogate: Surrogate,
calc_scheme: str,
speedup: bool = True,
num_fitness_evaluations: Optional[int] = None,
statistics_collector: Optional[StatisticsCollector] = None):
self.ibuilder = ibuilder
self.surrogate = surrogate
self.calc_scheme = calc_scheme
self.speedup = speedup
self.all_params: List[AbstractParams] = []
self.all_fitness: List[float] = []
if calc_scheme not in self.SUPPORTED_CALC_SCHEMES:
raise ValueError(f"Unexpected surrogate scheme! {self.calc_scheme}")
super().__init__(ibuilder, num_fitness_evaluations, statistics_collector)
class DistributedSurrogateEnabledComputableFitnessEstimator(
DistributedComputableFitnessEstimator,
SurrogateEnabledFitnessEstimatorMixin
):
def __init__(self,
ibuilder: IndividualBuilder,
surrogate: Surrogate,
calc_scheme: str,
speedup: bool = True,
num_fitness_evaluations: Optional[int] = None,
statistics_collector: Optional[StatisticsCollector] = None):
self.ibuilder = ibuilder
self.surrogate = surrogate
self.calc_scheme = calc_scheme
self.speedup = speedup
self.all_params: List[AbstractParams] = []
self.all_fitness: List[float] = []
if calc_scheme not in self.SUPPORTED_CALC_SCHEMES:
raise ValueError(f"Unexpected surrogate scheme! {self.calc_scheme}")
super().__init__(ibuilder, num_fitness_evaluations, statistics_collector)