Steady state evolver

This file is part of the TPOT library.

The current version of TPOT was developed at Cedars-Sinai by: - Pedro Henrique Ribeiro (https://github.com/perib, https://www.linkedin.com/in/pedro-ribeiro/) - Anil Saini (anil.saini@cshs.org) - Jose Hernandez (jgh9094@gmail.com) - Jay Moran (jay.moran@cshs.org) - Nicholas Matsumoto (nicholas.matsumoto@cshs.org) - Hyunjun Choi (hyunjun.choi@cshs.org) - Miguel E. Hernandez (miguel.e.hernandez@cshs.org) - Jason Moore (moorejh28@gmail.com)

The original version of TPOT was primarily developed at the University of Pennsylvania by: - Randal S. Olson (rso@randalolson.com) - Weixuan Fu (weixuanf@upenn.edu) - Daniel Angell (dpa34@drexel.edu) - Jason Moore (moorejh28@gmail.com) - and many more generous open-source contributors

TPOT is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

TPOT is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details.

You should have received a copy of the GNU Lesser General Public License along with TPOT. If not, see http://www.gnu.org/licenses/.

SteadyStateEvolver

Source code in tpot2/evolvers/steady_state_evolver.py

class SteadyStateEvolver():
    def __init__(   self,
                    individual_generator ,

                    objective_functions,
                    objective_function_weights,
                    objective_names = None,
                    objective_kwargs = None,
                    bigger_is_better = True,

                    initial_population_size = 50,
                    population_size = 300,
                    max_evaluated_individuals = None,
                    early_stop = None,
                    early_stop_mins = None,
                    early_stop_tol = 0.001,


                    max_time_mins=float("inf"),
                    max_eval_time_mins=10,

                    n_jobs=1,
                    memory_limit="4GB",
                    client=None,

                    crossover_probability=.2,
                    mutate_probability=.7,
                    mutate_then_crossover_probability=.05,
                    crossover_then_mutate_probability=.05,
                    n_parents=2,

                    survival_selector = survival_select_NSGA2,
                    parent_selector = tournament_selection_dominated,

                    budget_range = None,
                    budget_scaling = .5,
                    individuals_until_end_budget = 1,
                    stepwise_steps = 5,

                    verbose = 0,
                    periodic_checkpoint_folder = None,
                    callback = None,

                    rng=None
                    ) -> None:
        """
        Whereas the base_evolver uses a generational approach, the steady state evolver continuously generates individuals as resources become available.

        This evolver will simultaneously evaluated n_jobs individuals. As soon as one individual is evaluated, the current population is updated with survival_selector, 
        a new individual is generated from parents selected with parent_selector, and the new individual is immediately submitted for evaluation.
        In contrast, the base_evolver batches evaluations in generations, and only updates the population and creates new individuals after all individuals in the current generation are evaluated.

        In practice, this means that steady state evolver is more likely to use all cores at all times, allowing for flexibility is duration of evaluations and number of evaluations. However, it 
        may also generate less diverse populations as a result.

        Parameters
        ----------
        individual_generator : generator
            Generator that yields new base individuals. Used to generate initial population.
        objective_functions : list of callables
            list of functions that get applied to the individual and return a float or list of floats
            If an objective function returns multiple values, they are all concatenated in order
            with respect to objective_function_weights and early_stop_tol.
        objective_function_weights : list of floats
            list of weights for each objective function. Sign flips whether bigger is better or not
        objective_names : list of strings, default=None
            Names of the objectives. If None, objective0, objective1, etc. will be used
        objective_kwargs : dict, default=None
            Dictionary of keyword arguments to pass to the objective function
        bigger_is_better : bool, default=True
            If True, the objective function is maximized. If False, the objective function is minimized. Use negative weights to reverse the direction.

        initial_population_size : int, default=50
            Number of random individuals to generate in the initial population. These will all be randomly sampled, all other subsequent individuals will be generated from the population.
        population_size : int, default=50
            Note: This is different from the base_evolver. 
            In steady_state_evolver, the population_size is the number of individuals to keep in the live population. This is the total number of best individuals (as determined by survival_selector) to keep in the population.
            New individuals are generated from this population size.
            In base evolver, this is also the number of individuals to generate in each generation, however, here, we generate individuals as resources become available so there is no concept of a generation.
            It is recommended to use a higher population_size to ensure diversity in the population.
        max_evaluated_individuals : int, default=None
            Maximum number of individuals to evaluate after which training is terminated. If None, will evaluate until time limit is reached.
        early_stop : int, default=None
            If the best individual has not improved in this many evaluations, stop training.
            Note: Also different from base_evolver. In base evolver, this is the number of generations without improvement. Here, it is the number of individuals evaluated without improvement. Naturally, a higher value is recommended.
        early_stop_mins : int, default=None
            If the best individual has not improved in this many minutes, stop training.
                early_stop_tol : float, list of floats, or None, default=0.001
            -list of floats
                list of tolerances for each objective function. If the difference between the best score and the current score is less than the tolerance, the individual is considered to have converged
                If an index of the list is None, that item will not be used for early stopping
            -int
                If an int is given, it will be used as the tolerance for all objectives
        max_time_mins : float, default=float("inf")
            Maximum time to run the optimization. If none or inf, will run until the end of the generations.
        max_eval_time_mins : float, default=10
            Maximum time to evaluate a single individual. If none or inf, there will be no time limit per evaluation.
        n_jobs : int, default=1
            Number of processes to run in parallel.
        memory_limit : str, default=None
            Memory limit for each job. See Dask [LocalCluster documentation](https://distributed.dask.org/en/stable/api.html#distributed.Client) for more information.
        client : dask.distributed.Client, default=None
            A dask client to use for parallelization. If not None, this will override the n_jobs and memory_limit parameters. If None, will create a new client with num_workers=n_jobs and memory_limit=memory_limit.
        crossover_probability : float, default=.2
            Probability of generating a new individual by crossover between two individuals.
        mutate_probability : float, default=.7
            Probability of generating a new individual by crossover between one individuals.
        mutate_then_crossover_probability : float, default=.05
            Probability of generating a new individual by mutating two individuals followed by crossover.
        crossover_then_mutate_probability : float, default=.05
            Probability of generating a new individual by crossover between two individuals followed by a mutation of the resulting individual.
        n_parents : int, default=2
            Number of parents to use for crossover. Must be greater than 1.
        survival_selector : function, default=survival_select_NSGA2
            Function to use to select individuals for survival. Must take a matrix of scores and return selected indexes.
            Used to selected population_size * survival_percentage individuals at the start of each generation to use for mutation and crossover.
        parent_selector : function, default=parent_select_NSGA2
            Function to use to select pairs parents for crossover and individuals for mutation. Must take a matrix of scores and return selected indexes.     

        budget_range : list [start, end], default=None
            This parameter is used for the successive halving algorithm.
            A starting and ending budget to use for the budget scaling. The evolver will interpolate between these values over the generations_until_end_budget.
            Use is dependent on the objective functions. (In TPOTEstimator this corresponds to the percentage of the data to sample.)
        budget_scaling float : [0,1], default=0.5
            A scaling factor to use when determining how fast we move the budget from the start to end budget.
        evaluations_until_end_budget : int, default=1
            The number of evaluations to run before reaching the max budget.
        stepwise_steps : int, default=1
            The number of staircase steps to take when interpolating the budget.
        verbose : int, default=0
            How much information to print during the optimization process. Higher values include the information from lower values.
            0. nothing
            1. progress bar
            2. evaluations progress bar
            3. best individual
            4. warnings
            >=5. full warnings trace
        periodic_checkpoint_folder : str, default=None
            Folder to save the population to periodically. If None, no periodic saving will be done.
            If provided, training will resume from this checkpoint.
        callback : tpot2.CallBackInterface, default=None
            Callback object. Not implemented
        rng : Numpy.Random.Generator, None, default=None
            An object for reproducability of experiments. This value will be passed to numpy.random.default_rng() to create an instnce of the genrator to pass to other classes

            - Numpy.Random.Generator
                Will be used to create and lock in Generator instance with 'numpy.random.default_rng()'. Note this will be the same Generator passed in.
            - None
                Will be used to create Generator for 'numpy.random.default_rng()' where a fresh, unpredictable entropy will be pulled from the OS

        Attributes
        ----------
        population : tpot2.Population
            The population of individuals.
            Use population.population to access the individuals in the current population.
            Use population.evaluated_individuals to access a data frame of all individuals that have been explored.

        """

        self.rng = np.random.default_rng(rng)

        self.max_evaluated_individuals = max_evaluated_individuals
        self.individuals_until_end_budget = individuals_until_end_budget

        self.individual_generator = individual_generator
        self.population_size = population_size
        self.objective_functions = objective_functions
        self.objective_function_weights = np.array(objective_function_weights)
        self.bigger_is_better = bigger_is_better
        if not bigger_is_better:
            self.objective_function_weights = np.array(self.objective_function_weights)*-1

        self.population_size_list = None


        self.periodic_checkpoint_folder = periodic_checkpoint_folder
        self.verbose  = verbose
        self.callback = callback
        self.n_jobs = n_jobs

        if max_time_mins is None:
            self.max_time_mins = float("inf")
        else:
            self.max_time_mins = max_time_mins

        #functools requires none for infinite time, doesn't support inf
        if max_eval_time_mins is not None and math.isinf(max_eval_time_mins ):
            self.max_eval_time_mins = None
        else:
            self.max_eval_time_mins = max_eval_time_mins

        self.initial_population_size = initial_population_size
        self.budget_range = budget_range
        self.budget_scaling = budget_scaling
        self.stepwise_steps = stepwise_steps

        self.memory_limit = memory_limit

        self.client = client


        self.survival_selector=survival_selector
        self.parent_selector=parent_selector


        total_var_p = crossover_probability + mutate_probability + mutate_then_crossover_probability + crossover_then_mutate_probability
        self.crossover_probability = crossover_probability / total_var_p
        self.mutate_probability = mutate_probability  / total_var_p
        self.mutate_then_crossover_probability= mutate_then_crossover_probability / total_var_p
        self.crossover_then_mutate_probability= crossover_then_mutate_probability / total_var_p

        self.n_parents = n_parents

        if objective_kwargs is None:
            self.objective_kwargs = {}
        else:
            self.objective_kwargs = objective_kwargs

        ###########


        if self.budget_range is None:
            self.budget_list = None
        else:
            self.budget_list = beta_interpolation(start=self.budget_range[0], end=self.budget_range[1], n=self.generations_until_end_budget, scale=self.budget_scaling, n_steps=self.stepwise_steps)

        if objective_names is None:
            self.objective_names = ["objective"+str(i) for i in range(len(objective_function_weights))]
        else:
            self.objective_names = objective_names

        if self.budget_list is not None:
            if len(self.budget_list) <= self.generation:
                self.budget = self.budget_list[-1]
            else:
                self.budget = self.budget_list[self.generation]
        else:
            self.budget = None


        self.early_stop_tol = early_stop_tol
        self.early_stop_mins = early_stop_mins
        self.early_stop = early_stop

        if isinstance(self.early_stop_tol, float):
            self.early_stop_tol = [self.early_stop_tol for _ in range(len(self.objective_names))]

        self.early_stop_tol = [np.inf if tol is None else tol for tol in self.early_stop_tol]

        self.population = None
        self.population_file = None
        if self.periodic_checkpoint_folder is not None:
            self.population_file = os.path.join(self.periodic_checkpoint_folder, "population.pkl")
            if not os.path.exists(self.periodic_checkpoint_folder):
                os.makedirs(self.periodic_checkpoint_folder)
            if os.path.exists(self.population_file):
                self.population = pickle.load(open(self.population_file, "rb"))


        init_names = self.objective_names
        if self.budget_range is not None:
            init_names = init_names + ["Budget"]
        if self.population is None:
            self.population = tpot2.Population(column_names=init_names)
            initial_population = [next(self.individual_generator) for _ in range(self.initial_population_size)]
            self.population.add_to_population(initial_population, rng=self.rng)


    def optimize(self):
        """
        Creates an initial population and runs the evolutionary algorithm for the given number of generations. 
        If generations is None, will use self.generations.
        """

        #intialize the client
        if self.client is not None: #If user passed in a client manually
           self._client = self.client
        else:

            if self.verbose >= 4:
                silence_logs = 30
            elif self.verbose >=5:
                silence_logs = 40
            else:
                silence_logs = 50
            self._cluster = LocalCluster(n_workers=self.n_jobs, #if no client is passed in and no global client exists, create our own
                    threads_per_worker=1,
                    silence_logs=silence_logs,
                    processes=False,
                    memory_limit=self.memory_limit)
            self._client = Client(self._cluster)


        self.max_queue_size = len(self._client.cluster.workers)

        #set up logging params
        evaluated_count = 0
        generations_without_improvement = np.array([0 for _ in range(len(self.objective_function_weights))])
        timestamp_of_last_improvement = np.array([time.time() for _ in range(len(self.objective_function_weights))])
        best_scores = [-np.inf for _ in range(len(self.objective_function_weights))]
        scheduled_timeout_time = time.time() + self.max_time_mins*60
        budget = None

        submitted_futures = {}
        submitted_inds = set()

        start_time = time.time()

        try:


            if self.verbose >= 1:
                if self.max_evaluated_individuals is not None:
                    pbar = tqdm.tqdm(total=self.max_evaluated_individuals, miniters=1)
                else:
                    pbar = tqdm.tqdm(total=0, miniters=1)
                pbar.set_description("Evaluations")

            #submit initial population
            individuals_to_evaluate = self.get_unevaluated_individuals(self.objective_names, budget=budget,)

            for individual in individuals_to_evaluate:
                if len(submitted_futures) >= self.max_queue_size:
                    break
                future = self._client.submit(tpot2.utils.eval_utils.eval_objective_list, individual,  self.objective_functions, verbose=self.verbose, timeout=self.max_eval_time_mins*60,**self.objective_kwargs)

                submitted_futures[future] = {"individual": individual,
                                            "time": time.time(),
                                            "budget": budget,}
                submitted_inds.add(individual.unique_id())
                self.population.update_column(individual, column_names="Submitted Timestamp", data=time.time())

            done = False
            start_time = time.time()

            enough_parents_evaluated=False
            while not done:

                ###############################
                # Step 1: Check for finished futures
                ###############################

                #wait for at least one future to finish or timeout
                try:
                    next(distributed.as_completed(submitted_futures, timeout=self.max_eval_time_mins*60))
                except dask.distributed.TimeoutError:
                    pass
                except dask.distributed.CancelledError:
                    pass

                #Loop through all futures, collect completed and timeout futures.
                for completed_future in list(submitted_futures.keys()):
                    eval_error = None
                    #get scores and update
                    if completed_future.done(): #if future is done
                        #If the future is done but threw and error, record the error
                        if completed_future.exception() or completed_future.status == "error": #if the future is done and threw an error
                            print("Exception in future")
                            print(completed_future.exception())
                            scores = [np.nan for _ in range(len(self.objective_names))]
                            eval_error = "INVALID"
                        elif completed_future.cancelled(): #if the future is done and was cancelled
                            print("Cancelled future (likely memory related)")
                            scores = [np.nan for _ in range(len(self.objective_names))]
                            eval_error = "INVALID"
                            client.run(gc.collect)
                        else: #if the future is done and did not throw an error, get the scores
                            try:
                                scores = completed_future.result()

                                #check if scores contain "INVALID" or "TIMEOUT"
                                if "INVALID" in scores:
                                    eval_error = "INVALID"
                                    scores = [np.nan]
                                elif "TIMEOUT" in scores:
                                    eval_error = "TIMEOUT"
                                    scores = [np.nan]

                            except Exception as e:
                                print("Exception in future, but not caught by dask")
                                print(e)
                                print(completed_future.exception())
                                print(completed_future)
                                print("status", completed_future.status)
                                print("done", completed_future.done())
                                print("cancelld ", completed_future.cancelled())
                                scores = [np.nan for _ in range(len(self.objective_names))]
                                eval_error = "INVALID"
                        completed_future.release() #release the future
                    else: #if future is not done

                        if self.max_eval_time_mins is not None:
                            #check if the future has been running for too long, cancel the future
                            if time.time() - submitted_futures[completed_future]["time"] > self.max_eval_time_mins*1.25*60:
                                completed_future.cancel()
                                completed_future.release() #release the future
                                if self.verbose >= 4:
                                    print(f'WARNING AN INDIVIDUAL TIMED OUT (Fallback): \n {submitted_futures[completed_future]} \n')

                                scores = [np.nan for _ in range(len(self.objective_names))]
                                eval_error = "TIMEOUT"
                            else:
                                continue #otherwise, continue to next future



                    #update population
                    this_individual = submitted_futures[completed_future]["individual"]
                    this_budget = submitted_futures[completed_future]["budget"]
                    this_time = submitted_futures[completed_future]["time"]

                    if len(scores) < len(self.objective_names):
                        scores = [scores[0] for _ in range(len(self.objective_names))]
                    self.population.update_column(this_individual, column_names=self.objective_names, data=scores)
                    self.population.update_column(this_individual, column_names="Completed Timestamp", data=time.time())
                    self.population.update_column(this_individual, column_names="Eval Error", data=eval_error)
                    if budget is not None:
                        self.population.update_column(this_individual, column_names="Budget", data=this_budget)

                    submitted_futures.pop(completed_future)
                    submitted_inds.add(this_individual.unique_id())
                    if self.verbose >= 1:
                        pbar.update(1)

                #now we have a list of completed futures

                self.population.remove_invalid_from_population(column_names="Eval Error", invalid_value="INVALID")
                self.population.remove_invalid_from_population(column_names="Eval Error", invalid_value="TIMEOUT")

                #I am not entirely sure if this is necessary. I believe that calling release on the futures should be enough to free up memory. If memory issues persist, this may be a good place to start.
                #client.run(gc.collect) #run garbage collection to free up memory

                ###############################
                # Step 2: Early Stopping
                ###############################
                if self.verbose >= 3:
                    sign = np.sign(self.objective_function_weights)
                    valid_df = self.population.evaluated_individuals[~self.population.evaluated_individuals[["Eval Error"]].isin(["TIMEOUT","INVALID"]).any(axis=1)][self.objective_names]*sign
                    cur_best_scores = valid_df.max(axis=0)*sign
                    cur_best_scores = cur_best_scores.to_numpy()
                    for i, obj in enumerate(self.objective_names):
                        print(f"Best {obj} score: {cur_best_scores[i]}")

                if self.early_stop or self.early_stop_mins:
                    if self.budget is None or self.budget>=self.budget_range[-1]: #self.budget>=1:
                        #get sign of objective_function_weights
                        sign = np.sign(self.objective_function_weights)
                        #get best score for each objective
                        valid_df = self.population.evaluated_individuals[~self.population.evaluated_individuals[["Eval Error"]].isin(["TIMEOUT","INVALID"]).any(axis=1)][self.objective_names]*sign
                        cur_best_scores = valid_df.max(axis=0)
                        cur_best_scores = cur_best_scores.to_numpy()
                        #cur_best_scores =  self.population.get_column(self.population.population, column_names=self.objective_names).max(axis=0)*sign #TODO this assumes the current population is the best

                        improved = ( np.array(cur_best_scores) - np.array(best_scores) >= np.array(self.early_stop_tol) )
                        not_improved = np.logical_not(improved)
                        generations_without_improvement = generations_without_improvement * not_improved + not_improved #set to zero if not improved, else increment

                        timestamp_of_last_improvement = timestamp_of_last_improvement * not_improved + time.time()*improved #set to current time if improved

                        pass
                        #update best score
                        best_scores = [max(best_scores[i], cur_best_scores[i]) for i in range(len(self.objective_names))]

                        if self.early_stop:
                            if all(generations_without_improvement>self.early_stop):
                                if self.verbose >= 3:
                                    print(f"Early stop ({self.early_stop} individuals evaluated without improvement)")
                                break

                        if self.early_stop_mins:
                            if any(time.time() - timestamp_of_last_improvement > self.early_stop_mins*60):
                                if self.verbose >= 3:
                                    print(f"Early stop  ({self.early_stop_mins} seconds passed without improvement)")
                                break

                #if we evaluated enough individuals or time is up, stop
                if self.max_time_mins is not None and time.time() - start_time > self.max_time_mins*60:
                    if self.verbose >= 3:
                        print("Time limit reached")
                    done = True
                    break

                if self.max_evaluated_individuals is not None and len(self.population.evaluated_individuals.dropna(subset=self.objective_names)) >= self.max_evaluated_individuals:
                    print("Evaluated enough individuals")
                    done = True
                    break

                ###############################
                # Step 3: Submit unevaluated individuals from the initial population
                ###############################
                individuals_to_evaluate = self.get_unevaluated_individuals(self.objective_names, budget=budget,)
                individuals_to_evaluate = [ind for ind in individuals_to_evaluate if ind.unique_id() not in submitted_inds]
                for individual in individuals_to_evaluate:
                    if self.max_queue_size > len(submitted_futures):
                        future = self._client.submit(tpot2.utils.eval_utils.eval_objective_list, individual,  self.objective_functions, verbose=self.verbose, timeout=self.max_eval_time_mins*60,**self.objective_kwargs)

                        submitted_futures[future] = {"individual": individual,
                                                    "time": time.time(),
                                                    "budget": budget,}
                        submitted_inds.add(individual.unique_id())

                        self.population.update_column(individual, column_names="Submitted Timestamp", data=time.time())


                ###############################
                # Step 4: Survival Selection
                ###############################
                if self.survival_selector is not None:
                    parents_df = self.population.get_column(self.population.population, column_names=self.objective_names + ["Individual"], to_numpy=False)
                    evaluated = parents_df[~parents_df[self.objective_names].isna().any(axis=1)]
                    if len(evaluated) > self.population_size:
                        unevaluated = parents_df[parents_df[self.objective_names].isna().any(axis=1)]

                        cur_evaluated_population = parents_df["Individual"].to_numpy()
                        if len(cur_evaluated_population) > self.population_size:
                            scores = evaluated[self.objective_names].to_numpy()
                            weighted_scores = scores * self.objective_function_weights
                            new_population_index = np.ravel(self.survival_selector(weighted_scores, k=self.population_size, rng=self.rng)) #TODO make it clear that we are concatenating scores...

                            #set new population
                            try:
                                cur_evaluated_population = np.array(cur_evaluated_population)[new_population_index]
                                cur_evaluated_population = np.concatenate([cur_evaluated_population, unevaluated["Individual"].to_numpy()])
                                self.population.set_population(cur_evaluated_population, rng=self.rng)
                            except Exception as e:
                                print("Exception in survival selection")
                                print(e)
                                print("new_population_index", new_population_index)
                                print("cur_evaluated_population", cur_evaluated_population)
                                print("unevaluated", unevaluated)
                                print("evaluated", evaluated)
                                print("scores", scores)
                                print("weighted_scores", weighted_scores)
                                print("self.objective_function_weights", self.objective_function_weights)
                                print("self.population_size", self.population_size)
                                print("parents_df", parents_df)

                ###############################
                # Step 5: Parent Selection and Variation
                ###############################
                n_individuals_to_submit = self.max_queue_size - len(submitted_futures)
                if n_individuals_to_submit > 0:
                    #count non-nan values in the objective columns
                    if not enough_parents_evaluated:
                        parents_df = self.population.get_column(self.population.population, column_names=self.objective_names, to_numpy=False)
                        scores = parents_df[self.objective_names[0]].to_numpy()
                        #count non-nan values in the objective columns
                        n_evaluated = np.count_nonzero(~np.isnan(scores))
                        if n_evaluated >0 :
                            enough_parents_evaluated=True

                    # parents_df = self.population.get_column(self.population.population, column_names=self.objective_names+ ["Individual"], to_numpy=False)
                    # parents_df = parents_df[~parents_df[self.objective_names].isin(["TIMEOUT","INVALID"]).any(axis=1)]
                    # parents_df = parents_df[~parents_df[self.objective_names].isna().any(axis=1)]

                    # cur_evaluated_population = parents_df["Individual"].to_numpy()
                    # if len(cur_evaluated_population) > 0:
                    #     scores = parents_df[self.objective_names].to_numpy()
                    #     weighted_scores = scores * self.objective_function_weights
                    #     #number of crossover pairs and mutation only parent to generate

                    #     if len(parents_df) < 2:
                    #         var_ops = ["mutate" for _ in range(n_individuals_to_submit)]
                    #     else:
                    #         var_ops = [self.rng.choice(["crossover","mutate_then_crossover","crossover_then_mutate",'mutate'],p=[self.crossover_probability,self.mutate_then_crossover_probability, self.crossover_then_mutate_probability,self.mutate_probability]) for _ in range(n_individuals_to_submit)]

                    #     parents = []
                    #     for op in var_ops:
                    #         if op == "mutate":
                    #             parents.extend(np.array(cur_evaluated_population)[self.parent_selector(weighted_scores, k=1, n_parents=1, rng=self.rng)])
                    #         else:
                    #             parents.extend(np.array(cur_evaluated_population)[self.parent_selector(weighted_scores, k=1, n_parents=2, rng=self.rng)])

                    #     #_offspring = self.population.create_offspring2(parents, var_ops, rng=self.rng, add_to_population=True)
                    #     offspring = self.population.create_offspring2(parents, var_ops, [ind_mutate], None, [ind_crossover], None, add_to_population=True, keep_repeats=False, mutate_until_unique=True, rng=self.rng)

                    if enough_parents_evaluated:

                        parents = self.population.parent_select(selector=self.parent_selector, weights=self.objective_function_weights, columns_names=self.objective_names, k=n_individuals_to_submit, n_parents=2, rng=self.rng)
                        p = np.array([self.crossover_probability, self.mutate_then_crossover_probability, self.crossover_then_mutate_probability, self.mutate_probability])
                        p = p / p.sum()
                        var_op_list = self.rng.choice(["crossover", "mutate_then_crossover", "crossover_then_mutate", "mutate"], size=n_individuals_to_submit, p=p)

                        for i, op in enumerate(var_op_list):
                            if op == "mutate":
                                parents[i] = parents[i][0] #mutations take a single individual

                        offspring = self.population.create_offspring2(parents, var_op_list, [ind_mutate], None, [ind_crossover], None, add_to_population=True, keep_repeats=False, mutate_until_unique=True, rng=self.rng)

                    # If we don't have enough evaluated individuals to use as parents for variation, we create new individuals randomly
                    # This can happen if the individuals in the initial population are invalid
                    elif len(submitted_futures) < self.max_queue_size:

                        initial_population = self.population.evaluated_individuals.iloc[:self.initial_population_size*3]
                        invalid_initial_population = initial_population[initial_population[["Eval Error"]].isin(["TIMEOUT","INVALID"]).any(axis=1)]
                        if len(invalid_initial_population) >= self.initial_population_size*3: #if all individuals in the 3*initial population are invalid
                            raise Exception("No individuals could be evaluated in the initial population. This may indicate a bug in the configuration, included models, or objective functions. Set verbose>=4 to see the errors that caused individuals to fail.")

                        n_individuals_to_create = self.max_queue_size - len(submitted_futures)
                        initial_population = [next(self.individual_generator) for _ in range(n_individuals_to_create)]
                        self.population.add_to_population(initial_population, rng=self.rng)




                ###############################
                # Step 6: Add Unevaluated Individuals Generated by Variation
                ###############################
                individuals_to_evaluate = self.get_unevaluated_individuals(self.objective_names, budget=budget,)
                individuals_to_evaluate = [ind for ind in individuals_to_evaluate if ind.unique_id() not in submitted_inds]
                for individual in individuals_to_evaluate:
                    if self.max_queue_size > len(submitted_futures):
                        future = self._client.submit(tpot2.utils.eval_utils.eval_objective_list, individual,  self.objective_functions, verbose=self.verbose, timeout=self.max_eval_time_mins*60,**self.objective_kwargs)

                        submitted_futures[future] = {"individual": individual,
                                                    "time": time.time(),
                                                    "budget": budget,}
                        submitted_inds.add(individual.unique_id())
                        self.population.update_column(individual, column_names="Submitted Timestamp", data=time.time())


                #Checkpointing
                if self.population_file is not None: # and time.time() - last_save_time > 60*10:
                    pickle.dump(self.population, open(self.population_file, "wb"))



        except KeyboardInterrupt:
            if self.verbose >= 3:
                print("KeyboardInterrupt")

        ###############################
        # Step 7: Cleanup
        ###############################

        self.population.remove_invalid_from_population(column_names="Eval Error", invalid_value="INVALID")
        self.population.remove_invalid_from_population(column_names="Eval Error", invalid_value="TIMEOUT")


        #done, cleanup futures
        for future in submitted_futures.keys():
            future.cancel()
            future.release() #release the future

        #I am not entirely sure if this is necessary. I believe that calling release on the futures should be enough to free up memory. If memory issues persist, this may be a good place to start.
        #client.run(gc.collect) #run garbage collection to free up memory

        #checkpoint
        if self.population_file is not None:
            pickle.dump(self.population, open(self.population_file, "wb"))

        if self.client is None: #If we created our own client, close it
            self._client.close()
            self._cluster.close()

        tpot2.utils.get_pareto_frontier(self.population.evaluated_individuals, column_names=self.objective_names, weights=self.objective_function_weights)


    def get_unevaluated_individuals(self, column_names, budget=None, individual_list=None):
        """
        This function is used to get a list of individuals in the current population that have not been evaluated yet.

        Parameters
        ----------
        column_names : list of strings
            Names of the columns to check for unevaluated individuals (generally objective functions).
        budget : float, default=None
            Budget to use when checking for unevaluated individuals. If None, will not check the budget column.
            Finds individuals who have not been evaluated with the given budget on column names.
        individual_list : list of individuals, default=None
            List of individuals to check for unevaluated individuals. If None, will use the current population.
        """
        if individual_list is not None:
            cur_pop = np.array(individual_list)
        else:
            cur_pop = np.array(self.population.population)

        if all([name_step in self.population.evaluated_individuals.columns for name_step in column_names]):
            if budget is not None:
                offspring_scores = self.population.get_column(cur_pop, column_names=column_names+["Budget"], to_numpy=False)
                #Individuals are unevaluated if we have a higher budget OR if any of the objectives are nan
                unevaluated_filter = lambda i: any(offspring_scores.loc[offspring_scores.index[i]][column_names].isna()) or (offspring_scores.loc[offspring_scores.index[i]]["Budget"] < budget)
            else:
                offspring_scores = self.population.get_column(cur_pop, column_names=column_names, to_numpy=False)
                unevaluated_filter = lambda i: any(offspring_scores.loc[offspring_scores.index[i]][column_names].isna())
            unevaluated_individuals_this_step = [i for i in range(len(cur_pop)) if unevaluated_filter(i)]
            return cur_pop[unevaluated_individuals_this_step]

        else: #if column names are not in the evaluated_individuals, then we have not evaluated any individuals yet
            for name_step in column_names:
                self.population.evaluated_individuals[name_step] = np.nan
            return cur_pop

__init__(individual_generator, objective_functions, objective_function_weights, objective_names=None, objective_kwargs=None, bigger_is_better=True, initial_population_size=50, population_size=300, max_evaluated_individuals=None, early_stop=None, early_stop_mins=None, early_stop_tol=0.001, max_time_mins=float('inf'), max_eval_time_mins=10, n_jobs=1, memory_limit='4GB', client=None, crossover_probability=0.2, mutate_probability=0.7, mutate_then_crossover_probability=0.05, crossover_then_mutate_probability=0.05, n_parents=2, survival_selector=survival_select_NSGA2, parent_selector=tournament_selection_dominated, budget_range=None, budget_scaling=0.5, individuals_until_end_budget=1, stepwise_steps=5, verbose=0, periodic_checkpoint_folder=None, callback=None, rng=None)

Whereas the base_evolver uses a generational approach, the steady state evolver continuously generates individuals as resources become available.

This evolver will simultaneously evaluated n_jobs individuals. As soon as one individual is evaluated, the current population is updated with survival_selector, a new individual is generated from parents selected with parent_selector, and the new individual is immediately submitted for evaluation. In contrast, the base_evolver batches evaluations in generations, and only updates the population and creates new individuals after all individuals in the current generation are evaluated.

In practice, this means that steady state evolver is more likely to use all cores at all times, allowing for flexibility is duration of evaluations and number of evaluations. However, it may also generate less diverse populations as a result.

Parameters:

Name	Type	Description	Default
individual_generator	generator	Generator that yields new base individuals. Used to generate initial population.	required
objective_functions	list of callables	list of functions that get applied to the individual and return a float or list of floats If an objective function returns multiple values, they are all concatenated in order with respect to objective_function_weights and early_stop_tol.	required
objective_function_weights	list of floats	list of weights for each objective function. Sign flips whether bigger is better or not	required
objective_names	list of strings	Names of the objectives. If None, objective0, objective1, etc. will be used	None
objective_kwargs	dict	Dictionary of keyword arguments to pass to the objective function	None
bigger_is_better	bool	If True, the objective function is maximized. If False, the objective function is minimized. Use negative weights to reverse the direction.	True
initial_population_size	int	Number of random individuals to generate in the initial population. These will all be randomly sampled, all other subsequent individuals will be generated from the population.	50
population_size	int	Note: This is different from the base_evolver. In steady_state_evolver, the population_size is the number of individuals to keep in the live population. This is the total number of best individuals (as determined by survival_selector) to keep in the population. New individuals are generated from this population size. In base evolver, this is also the number of individuals to generate in each generation, however, here, we generate individuals as resources become available so there is no concept of a generation. It is recommended to use a higher population_size to ensure diversity in the population.	50
max_evaluated_individuals	int	Maximum number of individuals to evaluate after which training is terminated. If None, will evaluate until time limit is reached.	None
early_stop	int	If the best individual has not improved in this many evaluations, stop training. Note: Also different from base_evolver. In base evolver, this is the number of generations without improvement. Here, it is the number of individuals evaluated without improvement. Naturally, a higher value is recommended.	None
early_stop_mins	int	If the best individual has not improved in this many minutes, stop training. early_stop_tol : float, list of floats, or None, default=0.001 -list of floats list of tolerances for each objective function. If the difference between the best score and the current score is less than the tolerance, the individual is considered to have converged If an index of the list is None, that item will not be used for early stopping -int If an int is given, it will be used as the tolerance for all objectives	None
max_time_mins	float	Maximum time to run the optimization. If none or inf, will run until the end of the generations.	float("inf")
max_eval_time_mins	float	Maximum time to evaluate a single individual. If none or inf, there will be no time limit per evaluation.	10
n_jobs	int	Number of processes to run in parallel.	1
memory_limit	str	Memory limit for each job. See Dask LocalCluster documentation for more information.	None
client	Client	A dask client to use for parallelization. If not None, this will override the n_jobs and memory_limit parameters. If None, will create a new client with num_workers=n_jobs and memory_limit=memory_limit.	None
crossover_probability	float	Probability of generating a new individual by crossover between two individuals.	.2
mutate_probability	float	Probability of generating a new individual by crossover between one individuals.	.7
mutate_then_crossover_probability	float	Probability of generating a new individual by mutating two individuals followed by crossover.	.05
crossover_then_mutate_probability	float	Probability of generating a new individual by crossover between two individuals followed by a mutation of the resulting individual.	.05
n_parents	int	Number of parents to use for crossover. Must be greater than 1.	2
survival_selector	function	Function to use to select individuals for survival. Must take a matrix of scores and return selected indexes. Used to selected population_size * survival_percentage individuals at the start of each generation to use for mutation and crossover.	survival_select_NSGA2
parent_selector	function	Function to use to select pairs parents for crossover and individuals for mutation. Must take a matrix of scores and return selected indexes.	parent_select_NSGA2
budget_range	list[start, end]	This parameter is used for the successive halving algorithm. A starting and ending budget to use for the budget scaling. The evolver will interpolate between these values over the generations_until_end_budget. Use is dependent on the objective functions. (In TPOTEstimator this corresponds to the percentage of the data to sample.)	None
budget_scaling		A scaling factor to use when determining how fast we move the budget from the start to end budget.	0.5
evaluations_until_end_budget	int	The number of evaluations to run before reaching the max budget.	1
stepwise_steps	int	The number of staircase steps to take when interpolating the budget.	1
verbose	int	How much information to print during the optimization process. Higher values include the information from lower values. 0. nothing 1. progress bar 2. evaluations progress bar 3. best individual 4. warnings =5. full warnings trace	0
periodic_checkpoint_folder	str	Folder to save the population to periodically. If None, no periodic saving will be done. If provided, training will resume from this checkpoint.	None
callback	CallBackInterface	Callback object. Not implemented	None
rng	(Generator, None)	An object for reproducability of experiments. This value will be passed to numpy.random.default_rng() to create an instnce of the genrator to pass to other classes Numpy.Random.Generator Will be used to create and lock in Generator instance with 'numpy.random.default_rng()'. Note this will be the same Generator passed in. None Will be used to create Generator for 'numpy.random.default_rng()' where a fresh, unpredictable entropy will be pulled from the OS	None

Attributes:

Name	Type	Description
population	Population	The population of individuals. Use population.population to access the individuals in the current population. Use population.evaluated_individuals to access a data frame of all individuals that have been explored.

Source code in tpot2/evolvers/steady_state_evolver.py

def __init__(   self,
                individual_generator ,

                objective_functions,
                objective_function_weights,
                objective_names = None,
                objective_kwargs = None,
                bigger_is_better = True,

                initial_population_size = 50,
                population_size = 300,
                max_evaluated_individuals = None,
                early_stop = None,
                early_stop_mins = None,
                early_stop_tol = 0.001,


                max_time_mins=float("inf"),
                max_eval_time_mins=10,

                n_jobs=1,
                memory_limit="4GB",
                client=None,

                crossover_probability=.2,
                mutate_probability=.7,
                mutate_then_crossover_probability=.05,
                crossover_then_mutate_probability=.05,
                n_parents=2,

                survival_selector = survival_select_NSGA2,
                parent_selector = tournament_selection_dominated,

                budget_range = None,
                budget_scaling = .5,
                individuals_until_end_budget = 1,
                stepwise_steps = 5,

                verbose = 0,
                periodic_checkpoint_folder = None,
                callback = None,

                rng=None
                ) -> None:
    """
    Whereas the base_evolver uses a generational approach, the steady state evolver continuously generates individuals as resources become available.

    This evolver will simultaneously evaluated n_jobs individuals. As soon as one individual is evaluated, the current population is updated with survival_selector, 
    a new individual is generated from parents selected with parent_selector, and the new individual is immediately submitted for evaluation.
    In contrast, the base_evolver batches evaluations in generations, and only updates the population and creates new individuals after all individuals in the current generation are evaluated.

    In practice, this means that steady state evolver is more likely to use all cores at all times, allowing for flexibility is duration of evaluations and number of evaluations. However, it 
    may also generate less diverse populations as a result.

    Parameters
    ----------
    individual_generator : generator
        Generator that yields new base individuals. Used to generate initial population.
    objective_functions : list of callables
        list of functions that get applied to the individual and return a float or list of floats
        If an objective function returns multiple values, they are all concatenated in order
        with respect to objective_function_weights and early_stop_tol.
    objective_function_weights : list of floats
        list of weights for each objective function. Sign flips whether bigger is better or not
    objective_names : list of strings, default=None
        Names of the objectives. If None, objective0, objective1, etc. will be used
    objective_kwargs : dict, default=None
        Dictionary of keyword arguments to pass to the objective function
    bigger_is_better : bool, default=True
        If True, the objective function is maximized. If False, the objective function is minimized. Use negative weights to reverse the direction.

    initial_population_size : int, default=50
        Number of random individuals to generate in the initial population. These will all be randomly sampled, all other subsequent individuals will be generated from the population.
    population_size : int, default=50
        Note: This is different from the base_evolver. 
        In steady_state_evolver, the population_size is the number of individuals to keep in the live population. This is the total number of best individuals (as determined by survival_selector) to keep in the population.
        New individuals are generated from this population size.
        In base evolver, this is also the number of individuals to generate in each generation, however, here, we generate individuals as resources become available so there is no concept of a generation.
        It is recommended to use a higher population_size to ensure diversity in the population.
    max_evaluated_individuals : int, default=None
        Maximum number of individuals to evaluate after which training is terminated. If None, will evaluate until time limit is reached.
    early_stop : int, default=None
        If the best individual has not improved in this many evaluations, stop training.
        Note: Also different from base_evolver. In base evolver, this is the number of generations without improvement. Here, it is the number of individuals evaluated without improvement. Naturally, a higher value is recommended.
    early_stop_mins : int, default=None
        If the best individual has not improved in this many minutes, stop training.
            early_stop_tol : float, list of floats, or None, default=0.001
        -list of floats
            list of tolerances for each objective function. If the difference between the best score and the current score is less than the tolerance, the individual is considered to have converged
            If an index of the list is None, that item will not be used for early stopping
        -int
            If an int is given, it will be used as the tolerance for all objectives
    max_time_mins : float, default=float("inf")
        Maximum time to run the optimization. If none or inf, will run until the end of the generations.
    max_eval_time_mins : float, default=10
        Maximum time to evaluate a single individual. If none or inf, there will be no time limit per evaluation.
    n_jobs : int, default=1
        Number of processes to run in parallel.
    memory_limit : str, default=None
        Memory limit for each job. See Dask [LocalCluster documentation](https://distributed.dask.org/en/stable/api.html#distributed.Client) for more information.
    client : dask.distributed.Client, default=None
        A dask client to use for parallelization. If not None, this will override the n_jobs and memory_limit parameters. If None, will create a new client with num_workers=n_jobs and memory_limit=memory_limit.
    crossover_probability : float, default=.2
        Probability of generating a new individual by crossover between two individuals.
    mutate_probability : float, default=.7
        Probability of generating a new individual by crossover between one individuals.
    mutate_then_crossover_probability : float, default=.05
        Probability of generating a new individual by mutating two individuals followed by crossover.
    crossover_then_mutate_probability : float, default=.05
        Probability of generating a new individual by crossover between two individuals followed by a mutation of the resulting individual.
    n_parents : int, default=2
        Number of parents to use for crossover. Must be greater than 1.
    survival_selector : function, default=survival_select_NSGA2
        Function to use to select individuals for survival. Must take a matrix of scores and return selected indexes.
        Used to selected population_size * survival_percentage individuals at the start of each generation to use for mutation and crossover.
    parent_selector : function, default=parent_select_NSGA2
        Function to use to select pairs parents for crossover and individuals for mutation. Must take a matrix of scores and return selected indexes.     

    budget_range : list [start, end], default=None
        This parameter is used for the successive halving algorithm.
        A starting and ending budget to use for the budget scaling. The evolver will interpolate between these values over the generations_until_end_budget.
        Use is dependent on the objective functions. (In TPOTEstimator this corresponds to the percentage of the data to sample.)
    budget_scaling float : [0,1], default=0.5
        A scaling factor to use when determining how fast we move the budget from the start to end budget.
    evaluations_until_end_budget : int, default=1
        The number of evaluations to run before reaching the max budget.
    stepwise_steps : int, default=1
        The number of staircase steps to take when interpolating the budget.
    verbose : int, default=0
        How much information to print during the optimization process. Higher values include the information from lower values.
        0. nothing
        1. progress bar
        2. evaluations progress bar
        3. best individual
        4. warnings
        >=5. full warnings trace
    periodic_checkpoint_folder : str, default=None
        Folder to save the population to periodically. If None, no periodic saving will be done.
        If provided, training will resume from this checkpoint.
    callback : tpot2.CallBackInterface, default=None
        Callback object. Not implemented
    rng : Numpy.Random.Generator, None, default=None
        An object for reproducability of experiments. This value will be passed to numpy.random.default_rng() to create an instnce of the genrator to pass to other classes

        - Numpy.Random.Generator
            Will be used to create and lock in Generator instance with 'numpy.random.default_rng()'. Note this will be the same Generator passed in.
        - None
            Will be used to create Generator for 'numpy.random.default_rng()' where a fresh, unpredictable entropy will be pulled from the OS

    Attributes
    ----------
    population : tpot2.Population
        The population of individuals.
        Use population.population to access the individuals in the current population.
        Use population.evaluated_individuals to access a data frame of all individuals that have been explored.

    """

    self.rng = np.random.default_rng(rng)

    self.max_evaluated_individuals = max_evaluated_individuals
    self.individuals_until_end_budget = individuals_until_end_budget

    self.individual_generator = individual_generator
    self.population_size = population_size
    self.objective_functions = objective_functions
    self.objective_function_weights = np.array(objective_function_weights)
    self.bigger_is_better = bigger_is_better
    if not bigger_is_better:
        self.objective_function_weights = np.array(self.objective_function_weights)*-1

    self.population_size_list = None


    self.periodic_checkpoint_folder = periodic_checkpoint_folder
    self.verbose  = verbose
    self.callback = callback
    self.n_jobs = n_jobs

    if max_time_mins is None:
        self.max_time_mins = float("inf")
    else:
        self.max_time_mins = max_time_mins

    #functools requires none for infinite time, doesn't support inf
    if max_eval_time_mins is not None and math.isinf(max_eval_time_mins ):
        self.max_eval_time_mins = None
    else:
        self.max_eval_time_mins = max_eval_time_mins

    self.initial_population_size = initial_population_size
    self.budget_range = budget_range
    self.budget_scaling = budget_scaling
    self.stepwise_steps = stepwise_steps

    self.memory_limit = memory_limit

    self.client = client


    self.survival_selector=survival_selector
    self.parent_selector=parent_selector


    total_var_p = crossover_probability + mutate_probability + mutate_then_crossover_probability + crossover_then_mutate_probability
    self.crossover_probability = crossover_probability / total_var_p
    self.mutate_probability = mutate_probability  / total_var_p
    self.mutate_then_crossover_probability= mutate_then_crossover_probability / total_var_p
    self.crossover_then_mutate_probability= crossover_then_mutate_probability / total_var_p

    self.n_parents = n_parents

    if objective_kwargs is None:
        self.objective_kwargs = {}
    else:
        self.objective_kwargs = objective_kwargs

    ###########


    if self.budget_range is None:
        self.budget_list = None
    else:
        self.budget_list = beta_interpolation(start=self.budget_range[0], end=self.budget_range[1], n=self.generations_until_end_budget, scale=self.budget_scaling, n_steps=self.stepwise_steps)

    if objective_names is None:
        self.objective_names = ["objective"+str(i) for i in range(len(objective_function_weights))]
    else:
        self.objective_names = objective_names

    if self.budget_list is not None:
        if len(self.budget_list) <= self.generation:
            self.budget = self.budget_list[-1]
        else:
            self.budget = self.budget_list[self.generation]
    else:
        self.budget = None


    self.early_stop_tol = early_stop_tol
    self.early_stop_mins = early_stop_mins
    self.early_stop = early_stop

    if isinstance(self.early_stop_tol, float):
        self.early_stop_tol = [self.early_stop_tol for _ in range(len(self.objective_names))]

    self.early_stop_tol = [np.inf if tol is None else tol for tol in self.early_stop_tol]

    self.population = None
    self.population_file = None
    if self.periodic_checkpoint_folder is not None:
        self.population_file = os.path.join(self.periodic_checkpoint_folder, "population.pkl")
        if not os.path.exists(self.periodic_checkpoint_folder):
            os.makedirs(self.periodic_checkpoint_folder)
        if os.path.exists(self.population_file):
            self.population = pickle.load(open(self.population_file, "rb"))


    init_names = self.objective_names
    if self.budget_range is not None:
        init_names = init_names + ["Budget"]
    if self.population is None:
        self.population = tpot2.Population(column_names=init_names)
        initial_population = [next(self.individual_generator) for _ in range(self.initial_population_size)]
        self.population.add_to_population(initial_population, rng=self.rng)

get_unevaluated_individuals(column_names, budget=None, individual_list=None)

This function is used to get a list of individuals in the current population that have not been evaluated yet.

Parameters:

Name	Type	Description	Default
column_names	list of strings	Names of the columns to check for unevaluated individuals (generally objective functions).	required
budget	float	Budget to use when checking for unevaluated individuals. If None, will not check the budget column. Finds individuals who have not been evaluated with the given budget on column names.	None
individual_list	list of individuals	List of individuals to check for unevaluated individuals. If None, will use the current population.	None

Source code in tpot2/evolvers/steady_state_evolver.py

def get_unevaluated_individuals(self, column_names, budget=None, individual_list=None):
    """
    This function is used to get a list of individuals in the current population that have not been evaluated yet.

    Parameters
    ----------
    column_names : list of strings
        Names of the columns to check for unevaluated individuals (generally objective functions).
    budget : float, default=None
        Budget to use when checking for unevaluated individuals. If None, will not check the budget column.
        Finds individuals who have not been evaluated with the given budget on column names.
    individual_list : list of individuals, default=None
        List of individuals to check for unevaluated individuals. If None, will use the current population.
    """
    if individual_list is not None:
        cur_pop = np.array(individual_list)
    else:
        cur_pop = np.array(self.population.population)

    if all([name_step in self.population.evaluated_individuals.columns for name_step in column_names]):
        if budget is not None:
            offspring_scores = self.population.get_column(cur_pop, column_names=column_names+["Budget"], to_numpy=False)
            #Individuals are unevaluated if we have a higher budget OR if any of the objectives are nan
            unevaluated_filter = lambda i: any(offspring_scores.loc[offspring_scores.index[i]][column_names].isna()) or (offspring_scores.loc[offspring_scores.index[i]]["Budget"] < budget)
        else:
            offspring_scores = self.population.get_column(cur_pop, column_names=column_names, to_numpy=False)
            unevaluated_filter = lambda i: any(offspring_scores.loc[offspring_scores.index[i]][column_names].isna())
        unevaluated_individuals_this_step = [i for i in range(len(cur_pop)) if unevaluated_filter(i)]
        return cur_pop[unevaluated_individuals_this_step]

    else: #if column names are not in the evaluated_individuals, then we have not evaluated any individuals yet
        for name_step in column_names:
            self.population.evaluated_individuals[name_step] = np.nan
        return cur_pop

optimize()

Creates an initial population and runs the evolutionary algorithm for the given number of generations. If generations is None, will use self.generations.

Source code in tpot2/evolvers/steady_state_evolver.py

def optimize(self):
    """
    Creates an initial population and runs the evolutionary algorithm for the given number of generations. 
    If generations is None, will use self.generations.
    """

    #intialize the client
    if self.client is not None: #If user passed in a client manually
       self._client = self.client
    else:

        if self.verbose >= 4:
            silence_logs = 30
        elif self.verbose >=5:
            silence_logs = 40
        else:
            silence_logs = 50
        self._cluster = LocalCluster(n_workers=self.n_jobs, #if no client is passed in and no global client exists, create our own
                threads_per_worker=1,
                silence_logs=silence_logs,
                processes=False,
                memory_limit=self.memory_limit)
        self._client = Client(self._cluster)


    self.max_queue_size = len(self._client.cluster.workers)

    #set up logging params
    evaluated_count = 0
    generations_without_improvement = np.array([0 for _ in range(len(self.objective_function_weights))])
    timestamp_of_last_improvement = np.array([time.time() for _ in range(len(self.objective_function_weights))])
    best_scores = [-np.inf for _ in range(len(self.objective_function_weights))]
    scheduled_timeout_time = time.time() + self.max_time_mins*60
    budget = None

    submitted_futures = {}
    submitted_inds = set()

    start_time = time.time()

    try:


        if self.verbose >= 1:
            if self.max_evaluated_individuals is not None:
                pbar = tqdm.tqdm(total=self.max_evaluated_individuals, miniters=1)
            else:
                pbar = tqdm.tqdm(total=0, miniters=1)
            pbar.set_description("Evaluations")

        #submit initial population
        individuals_to_evaluate = self.get_unevaluated_individuals(self.objective_names, budget=budget,)

        for individual in individuals_to_evaluate:
            if len(submitted_futures) >= self.max_queue_size:
                break
            future = self._client.submit(tpot2.utils.eval_utils.eval_objective_list, individual,  self.objective_functions, verbose=self.verbose, timeout=self.max_eval_time_mins*60,**self.objective_kwargs)

            submitted_futures[future] = {"individual": individual,
                                        "time": time.time(),
                                        "budget": budget,}
            submitted_inds.add(individual.unique_id())
            self.population.update_column(individual, column_names="Submitted Timestamp", data=time.time())

        done = False
        start_time = time.time()

        enough_parents_evaluated=False
        while not done:

            ###############################
            # Step 1: Check for finished futures
            ###############################

            #wait for at least one future to finish or timeout
            try:
                next(distributed.as_completed(submitted_futures, timeout=self.max_eval_time_mins*60))
            except dask.distributed.TimeoutError:
                pass
            except dask.distributed.CancelledError:
                pass

            #Loop through all futures, collect completed and timeout futures.
            for completed_future in list(submitted_futures.keys()):
                eval_error = None
                #get scores and update
                if completed_future.done(): #if future is done
                    #If the future is done but threw and error, record the error
                    if completed_future.exception() or completed_future.status == "error": #if the future is done and threw an error
                        print("Exception in future")
                        print(completed_future.exception())
                        scores = [np.nan for _ in range(len(self.objective_names))]
                        eval_error = "INVALID"
                    elif completed_future.cancelled(): #if the future is done and was cancelled
                        print("Cancelled future (likely memory related)")
                        scores = [np.nan for _ in range(len(self.objective_names))]
                        eval_error = "INVALID"
                        client.run(gc.collect)
                    else: #if the future is done and did not throw an error, get the scores
                        try:
                            scores = completed_future.result()

                            #check if scores contain "INVALID" or "TIMEOUT"
                            if "INVALID" in scores:
                                eval_error = "INVALID"
                                scores = [np.nan]
                            elif "TIMEOUT" in scores:
                                eval_error = "TIMEOUT"
                                scores = [np.nan]

                        except Exception as e:
                            print("Exception in future, but not caught by dask")
                            print(e)
                            print(completed_future.exception())
                            print(completed_future)
                            print("status", completed_future.status)
                            print("done", completed_future.done())
                            print("cancelld ", completed_future.cancelled())
                            scores = [np.nan for _ in range(len(self.objective_names))]
                            eval_error = "INVALID"
                    completed_future.release() #release the future
                else: #if future is not done

                    if self.max_eval_time_mins is not None:
                        #check if the future has been running for too long, cancel the future
                        if time.time() - submitted_futures[completed_future]["time"] > self.max_eval_time_mins*1.25*60:
                            completed_future.cancel()
                            completed_future.release() #release the future
                            if self.verbose >= 4:
                                print(f'WARNING AN INDIVIDUAL TIMED OUT (Fallback): \n {submitted_futures[completed_future]} \n')

                            scores = [np.nan for _ in range(len(self.objective_names))]
                            eval_error = "TIMEOUT"
                        else:
                            continue #otherwise, continue to next future



                #update population
                this_individual = submitted_futures[completed_future]["individual"]
                this_budget = submitted_futures[completed_future]["budget"]
                this_time = submitted_futures[completed_future]["time"]

                if len(scores) < len(self.objective_names):
                    scores = [scores[0] for _ in range(len(self.objective_names))]
                self.population.update_column(this_individual, column_names=self.objective_names, data=scores)
                self.population.update_column(this_individual, column_names="Completed Timestamp", data=time.time())
                self.population.update_column(this_individual, column_names="Eval Error", data=eval_error)
                if budget is not None:
                    self.population.update_column(this_individual, column_names="Budget", data=this_budget)

                submitted_futures.pop(completed_future)
                submitted_inds.add(this_individual.unique_id())
                if self.verbose >= 1:
                    pbar.update(1)

            #now we have a list of completed futures

            self.population.remove_invalid_from_population(column_names="Eval Error", invalid_value="INVALID")
            self.population.remove_invalid_from_population(column_names="Eval Error", invalid_value="TIMEOUT")

            #I am not entirely sure if this is necessary. I believe that calling release on the futures should be enough to free up memory. If memory issues persist, this may be a good place to start.
            #client.run(gc.collect) #run garbage collection to free up memory

            ###############################
            # Step 2: Early Stopping
            ###############################
            if self.verbose >= 3:
                sign = np.sign(self.objective_function_weights)
                valid_df = self.population.evaluated_individuals[~self.population.evaluated_individuals[["Eval Error"]].isin(["TIMEOUT","INVALID"]).any(axis=1)][self.objective_names]*sign
                cur_best_scores = valid_df.max(axis=0)*sign
                cur_best_scores = cur_best_scores.to_numpy()
                for i, obj in enumerate(self.objective_names):
                    print(f"Best {obj} score: {cur_best_scores[i]}")

            if self.early_stop or self.early_stop_mins:
                if self.budget is None or self.budget>=self.budget_range[-1]: #self.budget>=1:
                    #get sign of objective_function_weights
                    sign = np.sign(self.objective_function_weights)
                    #get best score for each objective
                    valid_df = self.population.evaluated_individuals[~self.population.evaluated_individuals[["Eval Error"]].isin(["TIMEOUT","INVALID"]).any(axis=1)][self.objective_names]*sign
                    cur_best_scores = valid_df.max(axis=0)
                    cur_best_scores = cur_best_scores.to_numpy()
                    #cur_best_scores =  self.population.get_column(self.population.population, column_names=self.objective_names).max(axis=0)*sign #TODO this assumes the current population is the best

                    improved = ( np.array(cur_best_scores) - np.array(best_scores) >= np.array(self.early_stop_tol) )
                    not_improved = np.logical_not(improved)
                    generations_without_improvement = generations_without_improvement * not_improved + not_improved #set to zero if not improved, else increment

                    timestamp_of_last_improvement = timestamp_of_last_improvement * not_improved + time.time()*improved #set to current time if improved

                    pass
                    #update best score
                    best_scores = [max(best_scores[i], cur_best_scores[i]) for i in range(len(self.objective_names))]

                    if self.early_stop:
                        if all(generations_without_improvement>self.early_stop):
                            if self.verbose >= 3:
                                print(f"Early stop ({self.early_stop} individuals evaluated without improvement)")
                            break

                    if self.early_stop_mins:
                        if any(time.time() - timestamp_of_last_improvement > self.early_stop_mins*60):
                            if self.verbose >= 3:
                                print(f"Early stop  ({self.early_stop_mins} seconds passed without improvement)")
                            break

            #if we evaluated enough individuals or time is up, stop
            if self.max_time_mins is not None and time.time() - start_time > self.max_time_mins*60:
                if self.verbose >= 3:
                    print("Time limit reached")
                done = True
                break

            if self.max_evaluated_individuals is not None and len(self.population.evaluated_individuals.dropna(subset=self.objective_names)) >= self.max_evaluated_individuals:
                print("Evaluated enough individuals")
                done = True
                break

            ###############################
            # Step 3: Submit unevaluated individuals from the initial population
            ###############################
            individuals_to_evaluate = self.get_unevaluated_individuals(self.objective_names, budget=budget,)
            individuals_to_evaluate = [ind for ind in individuals_to_evaluate if ind.unique_id() not in submitted_inds]
            for individual in individuals_to_evaluate:
                if self.max_queue_size > len(submitted_futures):
                    future = self._client.submit(tpot2.utils.eval_utils.eval_objective_list, individual,  self.objective_functions, verbose=self.verbose, timeout=self.max_eval_time_mins*60,**self.objective_kwargs)

                    submitted_futures[future] = {"individual": individual,
                                                "time": time.time(),
                                                "budget": budget,}
                    submitted_inds.add(individual.unique_id())

                    self.population.update_column(individual, column_names="Submitted Timestamp", data=time.time())


            ###############################
            # Step 4: Survival Selection
            ###############################
            if self.survival_selector is not None:
                parents_df = self.population.get_column(self.population.population, column_names=self.objective_names + ["Individual"], to_numpy=False)
                evaluated = parents_df[~parents_df[self.objective_names].isna().any(axis=1)]
                if len(evaluated) > self.population_size:
                    unevaluated = parents_df[parents_df[self.objective_names].isna().any(axis=1)]

                    cur_evaluated_population = parents_df["Individual"].to_numpy()
                    if len(cur_evaluated_population) > self.population_size:
                        scores = evaluated[self.objective_names].to_numpy()
                        weighted_scores = scores * self.objective_function_weights
                        new_population_index = np.ravel(self.survival_selector(weighted_scores, k=self.population_size, rng=self.rng)) #TODO make it clear that we are concatenating scores...

                        #set new population
                        try:
                            cur_evaluated_population = np.array(cur_evaluated_population)[new_population_index]
                            cur_evaluated_population = np.concatenate([cur_evaluated_population, unevaluated["Individual"].to_numpy()])
                            self.population.set_population(cur_evaluated_population, rng=self.rng)
                        except Exception as e:
                            print("Exception in survival selection")
                            print(e)
                            print("new_population_index", new_population_index)
                            print("cur_evaluated_population", cur_evaluated_population)
                            print("unevaluated", unevaluated)
                            print("evaluated", evaluated)
                            print("scores", scores)
                            print("weighted_scores", weighted_scores)
                            print("self.objective_function_weights", self.objective_function_weights)
                            print("self.population_size", self.population_size)
                            print("parents_df", parents_df)

            ###############################
            # Step 5: Parent Selection and Variation
            ###############################
            n_individuals_to_submit = self.max_queue_size - len(submitted_futures)
            if n_individuals_to_submit > 0:
                #count non-nan values in the objective columns
                if not enough_parents_evaluated:
                    parents_df = self.population.get_column(self.population.population, column_names=self.objective_names, to_numpy=False)
                    scores = parents_df[self.objective_names[0]].to_numpy()
                    #count non-nan values in the objective columns
                    n_evaluated = np.count_nonzero(~np.isnan(scores))
                    if n_evaluated >0 :
                        enough_parents_evaluated=True

                # parents_df = self.population.get_column(self.population.population, column_names=self.objective_names+ ["Individual"], to_numpy=False)
                # parents_df = parents_df[~parents_df[self.objective_names].isin(["TIMEOUT","INVALID"]).any(axis=1)]
                # parents_df = parents_df[~parents_df[self.objective_names].isna().any(axis=1)]

                # cur_evaluated_population = parents_df["Individual"].to_numpy()
                # if len(cur_evaluated_population) > 0:
                #     scores = parents_df[self.objective_names].to_numpy()
                #     weighted_scores = scores * self.objective_function_weights
                #     #number of crossover pairs and mutation only parent to generate

                #     if len(parents_df) < 2:
                #         var_ops = ["mutate" for _ in range(n_individuals_to_submit)]
                #     else:
                #         var_ops = [self.rng.choice(["crossover","mutate_then_crossover","crossover_then_mutate",'mutate'],p=[self.crossover_probability,self.mutate_then_crossover_probability, self.crossover_then_mutate_probability,self.mutate_probability]) for _ in range(n_individuals_to_submit)]

                #     parents = []
                #     for op in var_ops:
                #         if op == "mutate":
                #             parents.extend(np.array(cur_evaluated_population)[self.parent_selector(weighted_scores, k=1, n_parents=1, rng=self.rng)])
                #         else:
                #             parents.extend(np.array(cur_evaluated_population)[self.parent_selector(weighted_scores, k=1, n_parents=2, rng=self.rng)])

                #     #_offspring = self.population.create_offspring2(parents, var_ops, rng=self.rng, add_to_population=True)
                #     offspring = self.population.create_offspring2(parents, var_ops, [ind_mutate], None, [ind_crossover], None, add_to_population=True, keep_repeats=False, mutate_until_unique=True, rng=self.rng)

                if enough_parents_evaluated:

                    parents = self.population.parent_select(selector=self.parent_selector, weights=self.objective_function_weights, columns_names=self.objective_names, k=n_individuals_to_submit, n_parents=2, rng=self.rng)
                    p = np.array([self.crossover_probability, self.mutate_then_crossover_probability, self.crossover_then_mutate_probability, self.mutate_probability])
                    p = p / p.sum()
                    var_op_list = self.rng.choice(["crossover", "mutate_then_crossover", "crossover_then_mutate", "mutate"], size=n_individuals_to_submit, p=p)

                    for i, op in enumerate(var_op_list):
                        if op == "mutate":
                            parents[i] = parents[i][0] #mutations take a single individual

                    offspring = self.population.create_offspring2(parents, var_op_list, [ind_mutate], None, [ind_crossover], None, add_to_population=True, keep_repeats=False, mutate_until_unique=True, rng=self.rng)

                # If we don't have enough evaluated individuals to use as parents for variation, we create new individuals randomly
                # This can happen if the individuals in the initial population are invalid
                elif len(submitted_futures) < self.max_queue_size:

                    initial_population = self.population.evaluated_individuals.iloc[:self.initial_population_size*3]
                    invalid_initial_population = initial_population[initial_population[["Eval Error"]].isin(["TIMEOUT","INVALID"]).any(axis=1)]
                    if len(invalid_initial_population) >= self.initial_population_size*3: #if all individuals in the 3*initial population are invalid
                        raise Exception("No individuals could be evaluated in the initial population. This may indicate a bug in the configuration, included models, or objective functions. Set verbose>=4 to see the errors that caused individuals to fail.")

                    n_individuals_to_create = self.max_queue_size - len(submitted_futures)
                    initial_population = [next(self.individual_generator) for _ in range(n_individuals_to_create)]
                    self.population.add_to_population(initial_population, rng=self.rng)




            ###############################
            # Step 6: Add Unevaluated Individuals Generated by Variation
            ###############################
            individuals_to_evaluate = self.get_unevaluated_individuals(self.objective_names, budget=budget,)
            individuals_to_evaluate = [ind for ind in individuals_to_evaluate if ind.unique_id() not in submitted_inds]
            for individual in individuals_to_evaluate:
                if self.max_queue_size > len(submitted_futures):
                    future = self._client.submit(tpot2.utils.eval_utils.eval_objective_list, individual,  self.objective_functions, verbose=self.verbose, timeout=self.max_eval_time_mins*60,**self.objective_kwargs)

                    submitted_futures[future] = {"individual": individual,
                                                "time": time.time(),
                                                "budget": budget,}
                    submitted_inds.add(individual.unique_id())
                    self.population.update_column(individual, column_names="Submitted Timestamp", data=time.time())


            #Checkpointing
            if self.population_file is not None: # and time.time() - last_save_time > 60*10:
                pickle.dump(self.population, open(self.population_file, "wb"))



    except KeyboardInterrupt:
        if self.verbose >= 3:
            print("KeyboardInterrupt")

    ###############################
    # Step 7: Cleanup
    ###############################

    self.population.remove_invalid_from_population(column_names="Eval Error", invalid_value="INVALID")
    self.population.remove_invalid_from_population(column_names="Eval Error", invalid_value="TIMEOUT")


    #done, cleanup futures
    for future in submitted_futures.keys():
        future.cancel()
        future.release() #release the future

    #I am not entirely sure if this is necessary. I believe that calling release on the futures should be enough to free up memory. If memory issues persist, this may be a good place to start.
    #client.run(gc.collect) #run garbage collection to free up memory

    #checkpoint
    if self.population_file is not None:
        pickle.dump(self.population, open(self.population_file, "wb"))

    if self.client is None: #If we created our own client, close it
        self._client.close()
        self._cluster.close()

    tpot2.utils.get_pareto_frontier(self.population.evaluated_individuals, column_names=self.objective_names, weights=self.objective_function_weights)

ind_crossover(ind1, ind2, rng)

Calls the ind1.crossover(ind2, rng=rng)

Parameters:

Name	Type	Description	Default
ind1	BaseIndividual		required
ind2	BaseIndividual		required
rng	int or Generator	A numpy random generator to use for reproducibility	required

Source code in tpot2/evolvers/steady_state_evolver.py

def ind_crossover(ind1, ind2, rng):
    """
    Calls the ind1.crossover(ind2, rng=rng)
    Parameters
    ----------
    ind1 : tpot2.BaseIndividual
    ind2 : tpot2.BaseIndividual
    rng : int or numpy.random.Generator
        A numpy random generator to use for reproducibility
    """
    rng = np.random.default_rng(rng)
    return ind1.crossover(ind2, rng=rng)

ind_mutate(ind, rng)

Calls the ind.mutate method on the individual

Parameters:

Name	Type	Description	Default
ind	BaseIndividual	The individual to mutate	required
rng	int or Generator	A numpy random generator to use for reproducibility	required

Source code in tpot2/evolvers/steady_state_evolver.py

def ind_mutate(ind, rng):
    """
    Calls the ind.mutate method on the individual

    Parameters
    ----------
    ind : tpot2.BaseIndividual
        The individual to mutate
    rng : int or numpy.random.Generator
        A numpy random generator to use for reproducibility
    """
    rng = np.random.default_rng(rng)
    return ind.mutate(rng=rng)