Base 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/.

BaseEvolver

Source code in tpot2/evolvers/base_evolver.py

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

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

                    population_size = 50,
                    initial_population_size = None,
                    population_scaling = .5,
                    generations_until_end_population = 1,
                    generations = 50,
                    early_stop = None,
                    early_stop_tol = 0.001,


                    max_time_mins=float("inf"),
                    max_eval_time_mins=5,

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

                    survival_percentage = 1,
                    crossover_probability=.2,
                    mutate_probability=.7,
                    mutate_then_crossover_probability=.05,
                    crossover_then_mutate_probability=.05,

                    mutation_functions = [ind_mutate],
                    crossover_functions = [ind_crossover],

                    mutation_function_weights = None,
                    crossover_function_weights = None,

                    n_parents=2,

                    survival_selector = survival_select_NSGA2,
                    parent_selector = tournament_selection_dominated,

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

                    threshold_evaluation_pruning = None,
                    threshold_evaluation_scaling = .5,
                    min_history_threshold = 20,
                    selection_evaluation_pruning = None,
                    selection_evaluation_scaling = .5,
                    evaluation_early_stop_steps = None,
                    final_score_strategy = "mean",

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

                    ) -> None:
        """
        Uses mutation, crossover, and optimization functions to evolve a population of individuals towards the given objective functions.

        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.
        population_size : int, default=50
            Size of the population
        initial_population_size : int, default=None
            Size of the initial population. If None, population_size will be used.
        population_scaling : int, default=0.5
            Scaling factor to use when determining how fast we move the threshold moves from the start to end percentile.
        generations_until_end_population : int, default=1
            Number of generations until the population size reaches population_size
        generations : int, default=50
            Number of generations to run
        early_stop : int, default=None
            Number of generations without improvement before early stopping. All objectives must have converged within the tolerance for this to be triggered. In general a value of around 5-20 is good.
        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.
        survival_percentage : float, default=1
            Percentage of the population size to utilize for mutation and crossover at the beginning of the generation. The rest are discarded. Individuals are selected with the selector passed into survival_selector. The value of this parameter must be between 0 and 1, inclusive.
            For example, if the population size is 100 and the survival percentage is .5, 50 individuals will be selected with NSGA2 from the existing population. These will be used for mutation and crossover to generate the next 100 individuals for the next generation. The remainder are discarded from the live population. In the next generation, there will now be the 50 parents + the 100 individuals for a total of 150. Surivival percentage is based of the population size parameter and not the existing population size (current population size when using successive halving). Therefore, in the next generation we will still select 50 individuals from the currently existing 150.
        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.
        generations_until_end_budget : int, default=1
            The number of generations to run before reaching the max budget.
        stepwise_steps : int, default=1
            The number of staircase steps to take when interpolating the budget and population size.
        threshold_evaluation_pruning : list [start, end], default=None
            Starting and ending percentile to use as a threshold for the evaluation early stopping. The evolver will interpolate between these values over the evaluation_early_stop_steps.
            Values between 0 and 100.
            At each step of the evaluation, a threshold is calculated based on the previous evaluations. All individuals that are below the performance threshold are not evaluated for further steps.
            For example, if the threshold is set to the 90th percentile of the previous evaluations, all individuals that are below the 90th percentile are not evaluated further. This can save computation by not evaluating all individuals for all steps of cross validation.
        threshold_evaluation_scaling : float [0,inf), default=0.5
            A scaling factor to use when determining how fast we move the threshold moves from the start to end percentile.
            Must be greater than zero. Higher numbers will move the threshold to the end faster.
        min_history_threshold : int, default=0
            The minimum number of previous scores needed before using threshold early stopping.
        selection_evaluation_pruning : list, default=None
            A lower and upper percent of the population size to select each round of CV.
            Values between 0 and 1.
            Selects a percentage of the population to evaluate at each step of the evaluation. 
            For example, one strategy is to evaluate different steps of cross validation one at a time, and only select the best N individuals for subsequent steps. 
            This can save computation by not evaluating all individuals for all steps of cross validation. By default this selection is done with the NSGA2 selector.
        selection_evaluation_scaling : float, default=0.5
            A scaling factor to use when determining how fast we move the threshold moves from the start to end percentile.
            Must be greater than zero. Higher numbers will move the threshold to the end faster.
        evaluation_early_stop_steps : int, default=1
            The number of steps that will be taken from the objective function. (e.g., the number of CV folds to evaluate)
        final_score_strategy : str, default="mean"
            The strategy to use when determining the final score for an individual.
            "mean": The mean of all objective scores
            "last": The score returned by the last call. Currently each objective is evaluated with a clone of the individual.
        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)

        if threshold_evaluation_pruning is not None or selection_evaluation_pruning is not None:
            if evaluation_early_stop_steps is None:
                raise ValueError("evaluation_early_stop_steps must be set when using threshold_evaluation_pruning or selection_evaluation_pruning")

        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.initial_population_size = initial_population_size
        if self.initial_population_size is None:
            self.cur_population_size = population_size
        else:
            self.cur_population_size = initial_population_size

        self.population_scaling = population_scaling
        self.generations_until_end_population = generations_until_end_population

        self.population_size_list = None


        self.periodic_checkpoint_folder = periodic_checkpoint_folder
        self.verbose  = verbose
        self.callback = callback
        self.generations = generations
        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.generation = 0


        self.threshold_evaluation_pruning =threshold_evaluation_pruning
        self.threshold_evaluation_scaling =  max(0.00001,threshold_evaluation_scaling )
        self.min_history_threshold = min_history_threshold

        self.selection_evaluation_pruning = selection_evaluation_pruning
        self.selection_evaluation_scaling =  max(0.00001,selection_evaluation_scaling )
        self.evaluation_early_stop_steps = evaluation_early_stop_steps
        self.final_score_strategy = final_score_strategy

        self.budget_range = budget_range
        self.budget_scaling = budget_scaling
        self.generations_until_end_budget = generations_until_end_budget
        self.stepwise_steps = stepwise_steps

        self.memory_limit = memory_limit

        self.client = client


        self.survival_selector=survival_selector
        self.parent_selector=parent_selector
        self.survival_percentage = survival_percentage

        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.mutation_functions = mutation_functions
        self.crossover_functions = crossover_functions

        if mutation_function_weights is None:
            self.mutation_function_weights = [1 for _ in range(len(mutation_functions))]
        else:
            self.mutation_function_weights = mutation_function_weights

        if mutation_function_weights is None:
            self.crossover_function_weights = [1 for _ in range(len(mutation_functions))]
        else:
            self.crossover_function_weights = crossover_function_weights

        self.n_parents = n_parents

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

        # if objective_kwargs is None:
        #     self.objective_kwargs = [{}] * len(self.objective_functions)
        # elif isinstance(objective_kwargs, dict):
        #     self.objective_kwargs = [objective_kwargs] * len(self.objective_functions)
        # else:
        #     self.objective_kwargs = objective_kwargs

        ###########

        if self.initial_population_size != self.population_size:
            self.population_size_list = beta_interpolation(start=self.cur_population_size, end=self.population_size, scale=self.population_scaling, n=generations_until_end_population, n_steps=self.stepwise_steps)
            self.population_size_list = np.round(self.population_size_list).astype(int)

        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 = 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"))

                if len(self.population.evaluated_individuals)>0 and "Generation" in self.population.evaluated_individuals.columns:
                    self.generation = self.population.evaluated_individuals['Generation'].max() + 1 #TODO check if this is empty?

        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.cur_population_size)]
            self.population.add_to_population(initial_population, self.rng)
            self.population.update_column(self.population.population, column_names="Generation", data=self.generation)


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

        Parameters
        ----------
        generations : int, default=None
            Number of generations to run. If None, will use self.generations.

        """

        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=True,
                    memory_limit=self.memory_limit)
            self._client = Client(self._cluster)



        if generations is None:
            generations = self.generations

        start_time = time.time()

        generations_without_improvement = np.array([0 for _ in range(len(self.objective_function_weights))])
        best_scores = [-np.inf for _ in range(len(self.objective_function_weights))]


        self.scheduled_timeout_time = time.time() + self.max_time_mins*60


        try:
            #for gen in tnrange(generations,desc="Generation", disable=self.verbose<1):
            done = False
            gen = 0
            if self.verbose >= 1:
                if generations is None or np.isinf(generations):
                    pbar = tqdm.tqdm(total=0)
                else:
                    pbar = tqdm.tqdm(total=generations)
                pbar.set_description("Generation")
            while not done:
                # Generation 0 is the initial population
                if self.generation == 0:
                    if self.population_file is not None:
                        pickle.dump(self.population, open(self.population_file, "wb"))
                    self.evaluate_population()
                    if self.population_file is not None:
                        pickle.dump(self.population, open(self.population_file, "wb"))

                    attempts = 2
                    while len(self.population.population) == 0 and attempts > 0:
                        new_initial_population = [next(self.individual_generator) for _ in range(self.cur_population_size)]
                        self.population.add_to_population(new_initial_population, rng=self.rng)
                        attempts -= 1
                        self.evaluate_population()

                    if len(self.population.population) == 0:
                        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.")

                    self.generation += 1
                # Generation 1 is the first generation after the initial population
                else:
                    if time.time() - start_time > self.max_time_mins*60:
                        if self.population.evaluated_individuals[self.objective_names].isnull().all().iloc[0]:
                            raise Exception("No individuals could be evaluated in the initial population as the max_eval_mins time limit was reached before any individuals could be evaluated.")
                        break
                    self.step()

                if self.verbose >= 3:
                    sign = np.sign(self.objective_function_weights)
                    valid_df = self.population.evaluated_individuals[~self.population.evaluated_individuals[self.objective_names].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()
                    print("Generation: ", self.generation)
                    for i, obj in enumerate(self.objective_names):
                        print(f"Best {obj} score: {cur_best_scores[i]}")


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

                        if all(generations_without_improvement>self.early_stop):
                            if self.verbose >= 3:
                                print("Early stop")
                            break

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

                gen += 1
                if self.verbose >= 1:
                    pbar.update(1)

                if generations is not None and gen >= generations:
                    done = True

        except KeyboardInterrupt:
            if self.verbose >= 3:
                print("KeyboardInterrupt")
            self.population.remove_invalid_from_population(column_names=self.objective_names, invalid_value="INVALID")
            self.population.remove_invalid_from_population(column_names=self.objective_names, invalid_value="TIMEOUT")
            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")




        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 step(self,):
        """
        Runs a single generation of the evolutionary algorithm. This includes selecting individuals for survival, generating offspring, and evaluating the offspring.

        """


        if self.population_size_list is not None:
            if self.generation < len(self.population_size_list):
                self.cur_population_size = self.population_size_list[self.generation]
            else:
                self.cur_population_size = self.population_size

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

        if self.survival_selector is not None:
            n_survivors = max(1,int(self.cur_population_size*self.survival_percentage)) #always keep at least one individual
            self.population.survival_select(    selector=self.survival_selector,
                                                weights=self.objective_function_weights,
                                                columns_names=self.objective_names,
                                                n_survivors=n_survivors,
                                                inplace=True,
                                                rng=self.rng,)

        self.generate_offspring()
        self.evaluate_population()

        self.generation += 1

    def generate_offspring(self, ): #your EA Algorithm goes here
        """
        Create population_size new individuals from the current population. 
        This includes selecting parents, applying mutation and crossover, and adding the new individuals to the population.
        """
        parents = self.population.parent_select(selector=self.parent_selector, weights=self.objective_function_weights, columns_names=self.objective_names, k=self.cur_population_size, 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=self.cur_population_size, 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, self.mutation_functions, self.mutation_function_weights, self.crossover_functions, self.crossover_function_weights, add_to_population=True, keep_repeats=False, mutate_until_unique=True, rng=self.rng)

        self.population.update_column(offspring, column_names="Generation", data=self.generation, )

    # Gets a list of unevaluated individuals in the livepopulation, evaluates them, and removes failed attempts
    # TODO This could probably be an independent function?
    def evaluate_population(self,):
        """
        Evaluates the individuals in the population that have not been evaluated yet.
        """
        #Update the sliding scales and thresholds
        # Save population, TODO remove some of these
        if self.population_file is not None: # and time.time() - last_save_time > 60*10:
            pickle.dump(self.population, open(self.population_file, "wb"))
            last_save_time = time.time()


        #Get the current thresholds per step
        self.thresholds = None
        if self.threshold_evaluation_pruning is not None:
            old_data = self.population.evaluated_individuals[self.objective_names]
            old_data = old_data[old_data[self.objective_names].notnull().all(axis=1)]
            if len(old_data) >= self.min_history_threshold:
                self.thresholds = np.array([get_thresholds(old_data[obj_name],
                                                            start=self.threshold_evaluation_pruning[0],
                                                            end=self.threshold_evaluation_pruning[1],
                                                            scale=self.threshold_evaluation_scaling,
                                                            n=self.evaluation_early_stop_steps)
                                        for obj_name in self.objective_names]).T

        #Get the selectors survival rates per step
        if self.selection_evaluation_pruning is not None:
            lower = self.cur_population_size*self.selection_evaluation_pruning[0]
            upper = self.cur_population_size*self.selection_evaluation_pruning[1]
            #survival_counts = self.cur_population_size*(scipy.special.betainc(1,self.selection_evaluation_scaling,np.linspace(0,1,self.evaluation_early_stop_steps))*(upper-lower)+lower)

            survival_counts = np.array(beta_interpolation(start=lower, end=upper, scale=self.selection_evaluation_scaling, n=self.evaluation_early_stop_steps, n_steps=self.evaluation_early_stop_steps))
            self.survival_counts = survival_counts.astype(int)
        else:
            self.survival_counts = None



        if self.evaluation_early_stop_steps is not None:
            if self.survival_counts is None:
                #TODO if we are not using selection method for each step, we can create single threads that run all steps for an individual. No need to come back each step.
                self.evaluate_population_selection_early_stop(survival_counts=self.survival_counts, thresholds=self.thresholds, budget=self.budget)
            else:
                #parallelize one step at a time. After each step, come together and select the next individuals to run the next step on.
                self.evaluate_population_selection_early_stop(survival_counts=self.survival_counts, thresholds=self.thresholds, budget=self.budget)
        else:
            self.evaluate_population_full(budget=self.budget)


        # Save population, TODO remove some of these
        if self.population_file is not None: # and time.time() - last_save_time > 60*10:
            pickle.dump(self.population, open(self.population_file, "wb"))
            last_save_time = time.time()

    def evaluate_population_full(self, budget=None):
        """
        Evaluates all individuals in the population that have not been evaluated yet.
        This is the normal/default strategy for evaluating individuals without any early stopping of individual evaluation functions. (e.g., no threshold or selection early stopping). Early stopping by generation is still possible.
        """
        individuals_to_evaluate = self.get_unevaluated_individuals(self.objective_names, budget=budget,)

        #print("evaluating this many individuals: ", len(individuals_to_evaluate))

        if len(individuals_to_evaluate) == 0:
            if self.verbose > 3:
                print("No new individuals to evaluate")
            return

        if self.max_eval_time_mins is not None:
            theoretical_timeout = self.max_eval_time_mins * math.ceil(len(individuals_to_evaluate) / self.n_jobs)
            theoretical_timeout = theoretical_timeout*2
        else:
            theoretical_timeout = np.inf
        scheduled_timeout_time_left = self.scheduled_timeout_time - time.time()
        parallel_timeout = min(theoretical_timeout, scheduled_timeout_time_left)
        if parallel_timeout < 0:
            parallel_timeout = 10

        scores, start_times, end_times, eval_errors = tpot2.utils.eval_utils.parallel_eval_objective_list(individuals_to_evaluate, self.objective_functions, verbose=self.verbose, max_eval_time_mins=self.max_eval_time_mins, budget=budget, n_expected_columns=len(self.objective_names), client=self._client, scheduled_timeout_time=self.scheduled_timeout_time, **self.objective_kwargs)

        self.population.update_column(individuals_to_evaluate, column_names=self.objective_names, data=scores)
        if budget is not None:
            self.population.update_column(individuals_to_evaluate, column_names="Budget", data=budget)

        self.population.update_column(individuals_to_evaluate, column_names="Submitted Timestamp", data=start_times)
        self.population.update_column(individuals_to_evaluate, column_names="Completed Timestamp", data=end_times)
        self.population.update_column(individuals_to_evaluate, column_names="Eval Error", data=eval_errors)
        self.population.remove_invalid_from_population(column_names="Eval Error")
        self.population.remove_invalid_from_population(column_names="Eval Error", invalid_value="TIMEOUT")

    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

    def evaluate_population_selection_early_stop(self,survival_counts, thresholds=None, budget=None):
        """
        This function tries to save computation by partially evaluating the individuals and then selecting which individuals to evaluate further based on the results of the partial evaluation. 

        Two strategies are implemented:
            1.  Selection early stopping: Selects a percentage of the population to evaluate at each step of the evaluation. 
                for example, one strategy is to evaluate different steps of cross validation one at a time, and only select the best N individuals for subsequent steps. 
                This can save computation by not evaluating all individuals for all steps of cross validation. By default this selection is done with the NSGA2 selector.
            2.  Threshold early stopping: At each step of the evaluation, a threshold is calculated based on the previous evaluations. All individuals that are below the performance threshold are not evaluated for further steps.
                For example, if the threshold is set to the 90th percentile of the previous evaluations, all individuals that are below the 90th percentile are not evaluated further. This can save computation by not evaluating all individuals for all steps of cross validation.

        Both of these strategies can be used simultaneously. Individuals must pass both the selection and threshold criteria to be evaluated further.

        Parameters
        ----------
        survival_counts : list of ints, default=None
            Number of individuals to select for survival at each step of the evaluation. If None, will not use selection early stopping.
            For example: [10, 5, 2] would select 10 individuals for the first step, 5 for the second, and 2 for the third.
        thresholds : list of floats, default=None
            Thresholds to use for early stopping at each step of the evaluation. If None, will not use threshold early stopping.
        budget : float, default=None
            Budget to use when evaluating individuals. Use is dependent on the objective functions. (In TPOTEstimator this corresponds to the percentage of the data to sample.)
        """

        survival_selector = tpot2.selectors.survival_select_NSGA2

        ################

        objective_function_signs = np.sign(self.objective_function_weights)


        cur_individuals = self.population.population.copy()

        all_step_names = []
        for step in range(self.evaluation_early_stop_steps):
            if budget is None:
                this_step_names = [f"{n}_step_{step}" for n in self.objective_names]
            else:
                this_step_names = [f"{n}_budget_{budget}_step_{step}" for n in self.objective_names]

            all_step_names.append(this_step_names)

            unevaluated_individuals_this_step = self.get_unevaluated_individuals(this_step_names, budget=None, individual_list=cur_individuals)

            if len(unevaluated_individuals_this_step) == 0:
                if self.verbose > 3:
                    print("No new individuals to evaluate")
                continue

            if self.max_eval_time_mins is not None:
                theoretical_timeout = self.max_eval_time_mins * math.ceil(len(unevaluated_individuals_this_step) / self.n_jobs)*60
                theoretical_timeout = theoretical_timeout*2
            else:
                theoretical_timeout = np.inf
            scheduled_timeout_time_left = self.scheduled_timeout_time - time.time()
            parallel_timeout = min(theoretical_timeout, scheduled_timeout_time_left)
            if parallel_timeout < 0:
                parallel_timeout = 10

            scores, start_times, end_times, eval_errors = tpot2.utils.eval_utils.parallel_eval_objective_list(individual_list=unevaluated_individuals_this_step,
                                    objective_list=self.objective_functions,
                                    verbose=self.verbose,
                                    max_eval_time_mins=self.max_eval_time_mins,
                                    step=step,
                                    budget = self.budget,
                                    generation = self.generation,
                                    n_expected_columns=len(self.objective_names),
                                    client=self._client,
                                    scheduled_timeout_time=self.scheduled_timeout_time,
                                    **self.objective_kwargs,
                                    )

            self.population.update_column(unevaluated_individuals_this_step, column_names=this_step_names, data=scores)
            self.population.update_column(unevaluated_individuals_this_step, column_names="Submitted Timestamp", data=start_times)
            self.population.update_column(unevaluated_individuals_this_step, column_names="Completed Timestamp", data=end_times)
            self.population.update_column(unevaluated_individuals_this_step, column_names="Eval Error", data=eval_errors)

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

            #remove invalids:
            invalids = []
            #find indeces of invalids

            for j in range(len(scores)):
                if  any([s=="INVALID" for s in scores[j]]):
                    invalids.append(j)

            for j in range(len(scores)):
                if  any([s=="TIMEOUT" for s in scores[j]]):
                    invalids.append(j)


            #already evaluated
            already_evaluated = list(set(cur_individuals) - set(unevaluated_individuals_this_step))
            #evaluated and valid
            valid_evaluations_this_step = remove_items(unevaluated_individuals_this_step,invalids)
            #update cur_individuals with current individuals with valid scores
            cur_individuals = np.concatenate([already_evaluated, valid_evaluations_this_step])


            #Get average scores

            #array of shape (steps, individuals, objectives)
            offspring_scores = [self.population.get_column(cur_individuals, column_names=step_names) for step_names in all_step_names]
            offspring_scores = np.array(offspring_scores)
            if self.final_score_strategy == 'mean':
                offspring_scores  = offspring_scores.mean(axis=0)
            elif self.final_score_strategy == 'last':
                offspring_scores = offspring_scores[-1]

            #remove individuals with nan scores
            invalids = []
            for i in range(len(offspring_scores)):
                if any(np.isnan(offspring_scores[i])):
                    invalids.append(i)

            cur_individuals = remove_items(cur_individuals,invalids)
            offspring_scores = remove_items(offspring_scores,invalids)

            #if last step, add the final metrics
            if step == self.evaluation_early_stop_steps-1:
                self.population.update_column(cur_individuals, column_names=self.objective_names, data=offspring_scores)
                if budget is not None:
                    self.population.update_column(cur_individuals, column_names="Budget", data=budget)
                return

            #If we have more threads than remaining individuals, we may as well evaluate the extras too
            if self.n_jobs < len(cur_individuals):
                #Remove based on thresholds
                if thresholds is not None:
                    threshold = thresholds[step]
                    invalids = []
                    for i in range(len(offspring_scores)):

                        if all([s*w>t*w for s,t,w in zip(offspring_scores[i],threshold,objective_function_signs)  ]):
                            invalids.append(i)

                    if len(invalids) > 0:

                        max_to_remove = min(len(cur_individuals) - self.n_jobs, len(invalids))

                        if max_to_remove < len(invalids):
                            # invalids = np.random.choice(invalids, max_to_remove, replace=False)
                            invalids = self.rng.choice(invalids, max_to_remove, replace=False)

                        cur_individuals = remove_items(cur_individuals,invalids)
                        offspring_scores = remove_items(offspring_scores,invalids)

                # Remove based on selection
                if survival_counts is not None:
                    if step < self.evaluation_early_stop_steps - 1 and survival_counts[step]>1: #don't do selection for the last loop since they are completed
                        k = survival_counts[step] + len(invalids) #TODO can remove the min if the selections method can ignore k>population size
                        if len(cur_individuals)> 1 and k > self.n_jobs and k < len(cur_individuals):
                            weighted_scores = np.array([s * self.objective_function_weights for s in offspring_scores ])

                            new_population_index = survival_selector(weighted_scores, k=k)
                            cur_individuals = np.array(cur_individuals)[new_population_index]
                            offspring_scores = offspring_scores[new_population_index]

__init__(individual_generator, objective_functions, objective_function_weights, objective_names=None, objective_kwargs=None, bigger_is_better=True, population_size=50, initial_population_size=None, population_scaling=0.5, generations_until_end_population=1, generations=50, early_stop=None, early_stop_tol=0.001, max_time_mins=float('inf'), max_eval_time_mins=5, n_jobs=1, memory_limit='4GB', client=None, survival_percentage=1, crossover_probability=0.2, mutate_probability=0.7, mutate_then_crossover_probability=0.05, crossover_then_mutate_probability=0.05, mutation_functions=[ind_mutate], crossover_functions=[ind_crossover], mutation_function_weights=None, crossover_function_weights=None, n_parents=2, survival_selector=survival_select_NSGA2, parent_selector=tournament_selection_dominated, budget_range=None, budget_scaling=0.5, generations_until_end_budget=1, stepwise_steps=5, threshold_evaluation_pruning=None, threshold_evaluation_scaling=0.5, min_history_threshold=20, selection_evaluation_pruning=None, selection_evaluation_scaling=0.5, evaluation_early_stop_steps=None, final_score_strategy='mean', verbose=0, periodic_checkpoint_folder=None, callback=None, rng=None)

Uses mutation, crossover, and optimization functions to evolve a population of individuals towards the given objective functions.

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
population_size	int	Size of the population	50
initial_population_size	int	Size of the initial population. If None, population_size will be used.	None
population_scaling	int	Scaling factor to use when determining how fast we move the threshold moves from the start to end percentile.	0.5
generations_until_end_population	int	Number of generations until the population size reaches population_size	1
generations	int	Number of generations to run	50
early_stop	int	Number of generations without improvement before early stopping. All objectives must have converged within the tolerance for this to be triggered. In general a value of around 5-20 is good.	None
early_stop_tol	float, list of floats, or None	-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	0.001
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
survival_percentage	float	Percentage of the population size to utilize for mutation and crossover at the beginning of the generation. The rest are discarded. Individuals are selected with the selector passed into survival_selector. The value of this parameter must be between 0 and 1, inclusive. For example, if the population size is 100 and the survival percentage is .5, 50 individuals will be selected with NSGA2 from the existing population. These will be used for mutation and crossover to generate the next 100 individuals for the next generation. The remainder are discarded from the live population. In the next generation, there will now be the 50 parents + the 100 individuals for a total of 150. Surivival percentage is based of the population size parameter and not the existing population size (current population size when using successive halving). Therefore, in the next generation we will still select 50 individuals from the currently existing 150.	1
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
generations_until_end_budget	int	The number of generations to run before reaching the max budget.	1
stepwise_steps	int	The number of staircase steps to take when interpolating the budget and population size.	1
threshold_evaluation_pruning	list[start, end]	Starting and ending percentile to use as a threshold for the evaluation early stopping. The evolver will interpolate between these values over the evaluation_early_stop_steps. Values between 0 and 100. At each step of the evaluation, a threshold is calculated based on the previous evaluations. All individuals that are below the performance threshold are not evaluated for further steps. For example, if the threshold is set to the 90th percentile of the previous evaluations, all individuals that are below the 90th percentile are not evaluated further. This can save computation by not evaluating all individuals for all steps of cross validation.	None
threshold_evaluation_scaling	float [0,inf)	A scaling factor to use when determining how fast we move the threshold moves from the start to end percentile. Must be greater than zero. Higher numbers will move the threshold to the end faster.	0.5
min_history_threshold	int	The minimum number of previous scores needed before using threshold early stopping.	0
selection_evaluation_pruning	list	A lower and upper percent of the population size to select each round of CV. Values between 0 and 1. Selects a percentage of the population to evaluate at each step of the evaluation. For example, one strategy is to evaluate different steps of cross validation one at a time, and only select the best N individuals for subsequent steps. This can save computation by not evaluating all individuals for all steps of cross validation. By default this selection is done with the NSGA2 selector.	None
selection_evaluation_scaling	float	A scaling factor to use when determining how fast we move the threshold moves from the start to end percentile. Must be greater than zero. Higher numbers will move the threshold to the end faster.	0.5
evaluation_early_stop_steps	int	The number of steps that will be taken from the objective function. (e.g., the number of CV folds to evaluate)	1
final_score_strategy	str	The strategy to use when determining the final score for an individual. "mean": The mean of all objective scores "last": The score returned by the last call. Currently each objective is evaluated with a clone of the individual.	"mean"
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/base_evolver.py

def __init__(   self,
                individual_generator ,

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

                population_size = 50,
                initial_population_size = None,
                population_scaling = .5,
                generations_until_end_population = 1,
                generations = 50,
                early_stop = None,
                early_stop_tol = 0.001,


                max_time_mins=float("inf"),
                max_eval_time_mins=5,

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

                survival_percentage = 1,
                crossover_probability=.2,
                mutate_probability=.7,
                mutate_then_crossover_probability=.05,
                crossover_then_mutate_probability=.05,

                mutation_functions = [ind_mutate],
                crossover_functions = [ind_crossover],

                mutation_function_weights = None,
                crossover_function_weights = None,

                n_parents=2,

                survival_selector = survival_select_NSGA2,
                parent_selector = tournament_selection_dominated,

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

                threshold_evaluation_pruning = None,
                threshold_evaluation_scaling = .5,
                min_history_threshold = 20,
                selection_evaluation_pruning = None,
                selection_evaluation_scaling = .5,
                evaluation_early_stop_steps = None,
                final_score_strategy = "mean",

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

                ) -> None:
    """
    Uses mutation, crossover, and optimization functions to evolve a population of individuals towards the given objective functions.

    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.
    population_size : int, default=50
        Size of the population
    initial_population_size : int, default=None
        Size of the initial population. If None, population_size will be used.
    population_scaling : int, default=0.5
        Scaling factor to use when determining how fast we move the threshold moves from the start to end percentile.
    generations_until_end_population : int, default=1
        Number of generations until the population size reaches population_size
    generations : int, default=50
        Number of generations to run
    early_stop : int, default=None
        Number of generations without improvement before early stopping. All objectives must have converged within the tolerance for this to be triggered. In general a value of around 5-20 is good.
    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.
    survival_percentage : float, default=1
        Percentage of the population size to utilize for mutation and crossover at the beginning of the generation. The rest are discarded. Individuals are selected with the selector passed into survival_selector. The value of this parameter must be between 0 and 1, inclusive.
        For example, if the population size is 100 and the survival percentage is .5, 50 individuals will be selected with NSGA2 from the existing population. These will be used for mutation and crossover to generate the next 100 individuals for the next generation. The remainder are discarded from the live population. In the next generation, there will now be the 50 parents + the 100 individuals for a total of 150. Surivival percentage is based of the population size parameter and not the existing population size (current population size when using successive halving). Therefore, in the next generation we will still select 50 individuals from the currently existing 150.
    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.
    generations_until_end_budget : int, default=1
        The number of generations to run before reaching the max budget.
    stepwise_steps : int, default=1
        The number of staircase steps to take when interpolating the budget and population size.
    threshold_evaluation_pruning : list [start, end], default=None
        Starting and ending percentile to use as a threshold for the evaluation early stopping. The evolver will interpolate between these values over the evaluation_early_stop_steps.
        Values between 0 and 100.
        At each step of the evaluation, a threshold is calculated based on the previous evaluations. All individuals that are below the performance threshold are not evaluated for further steps.
        For example, if the threshold is set to the 90th percentile of the previous evaluations, all individuals that are below the 90th percentile are not evaluated further. This can save computation by not evaluating all individuals for all steps of cross validation.
    threshold_evaluation_scaling : float [0,inf), default=0.5
        A scaling factor to use when determining how fast we move the threshold moves from the start to end percentile.
        Must be greater than zero. Higher numbers will move the threshold to the end faster.
    min_history_threshold : int, default=0
        The minimum number of previous scores needed before using threshold early stopping.
    selection_evaluation_pruning : list, default=None
        A lower and upper percent of the population size to select each round of CV.
        Values between 0 and 1.
        Selects a percentage of the population to evaluate at each step of the evaluation. 
        For example, one strategy is to evaluate different steps of cross validation one at a time, and only select the best N individuals for subsequent steps. 
        This can save computation by not evaluating all individuals for all steps of cross validation. By default this selection is done with the NSGA2 selector.
    selection_evaluation_scaling : float, default=0.5
        A scaling factor to use when determining how fast we move the threshold moves from the start to end percentile.
        Must be greater than zero. Higher numbers will move the threshold to the end faster.
    evaluation_early_stop_steps : int, default=1
        The number of steps that will be taken from the objective function. (e.g., the number of CV folds to evaluate)
    final_score_strategy : str, default="mean"
        The strategy to use when determining the final score for an individual.
        "mean": The mean of all objective scores
        "last": The score returned by the last call. Currently each objective is evaluated with a clone of the individual.
    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)

    if threshold_evaluation_pruning is not None or selection_evaluation_pruning is not None:
        if evaluation_early_stop_steps is None:
            raise ValueError("evaluation_early_stop_steps must be set when using threshold_evaluation_pruning or selection_evaluation_pruning")

    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.initial_population_size = initial_population_size
    if self.initial_population_size is None:
        self.cur_population_size = population_size
    else:
        self.cur_population_size = initial_population_size

    self.population_scaling = population_scaling
    self.generations_until_end_population = generations_until_end_population

    self.population_size_list = None


    self.periodic_checkpoint_folder = periodic_checkpoint_folder
    self.verbose  = verbose
    self.callback = callback
    self.generations = generations
    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.generation = 0


    self.threshold_evaluation_pruning =threshold_evaluation_pruning
    self.threshold_evaluation_scaling =  max(0.00001,threshold_evaluation_scaling )
    self.min_history_threshold = min_history_threshold

    self.selection_evaluation_pruning = selection_evaluation_pruning
    self.selection_evaluation_scaling =  max(0.00001,selection_evaluation_scaling )
    self.evaluation_early_stop_steps = evaluation_early_stop_steps
    self.final_score_strategy = final_score_strategy

    self.budget_range = budget_range
    self.budget_scaling = budget_scaling
    self.generations_until_end_budget = generations_until_end_budget
    self.stepwise_steps = stepwise_steps

    self.memory_limit = memory_limit

    self.client = client


    self.survival_selector=survival_selector
    self.parent_selector=parent_selector
    self.survival_percentage = survival_percentage

    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.mutation_functions = mutation_functions
    self.crossover_functions = crossover_functions

    if mutation_function_weights is None:
        self.mutation_function_weights = [1 for _ in range(len(mutation_functions))]
    else:
        self.mutation_function_weights = mutation_function_weights

    if mutation_function_weights is None:
        self.crossover_function_weights = [1 for _ in range(len(mutation_functions))]
    else:
        self.crossover_function_weights = crossover_function_weights

    self.n_parents = n_parents

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

    # if objective_kwargs is None:
    #     self.objective_kwargs = [{}] * len(self.objective_functions)
    # elif isinstance(objective_kwargs, dict):
    #     self.objective_kwargs = [objective_kwargs] * len(self.objective_functions)
    # else:
    #     self.objective_kwargs = objective_kwargs

    ###########

    if self.initial_population_size != self.population_size:
        self.population_size_list = beta_interpolation(start=self.cur_population_size, end=self.population_size, scale=self.population_scaling, n=generations_until_end_population, n_steps=self.stepwise_steps)
        self.population_size_list = np.round(self.population_size_list).astype(int)

    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 = 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"))

            if len(self.population.evaluated_individuals)>0 and "Generation" in self.population.evaluated_individuals.columns:
                self.generation = self.population.evaluated_individuals['Generation'].max() + 1 #TODO check if this is empty?

    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.cur_population_size)]
        self.population.add_to_population(initial_population, self.rng)
        self.population.update_column(self.population.population, column_names="Generation", data=self.generation)

evaluate_population()

Evaluates the individuals in the population that have not been evaluated yet.

Source code in tpot2/evolvers/base_evolver.py

def evaluate_population(self,):
    """
    Evaluates the individuals in the population that have not been evaluated yet.
    """
    #Update the sliding scales and thresholds
    # Save population, TODO remove some of these
    if self.population_file is not None: # and time.time() - last_save_time > 60*10:
        pickle.dump(self.population, open(self.population_file, "wb"))
        last_save_time = time.time()


    #Get the current thresholds per step
    self.thresholds = None
    if self.threshold_evaluation_pruning is not None:
        old_data = self.population.evaluated_individuals[self.objective_names]
        old_data = old_data[old_data[self.objective_names].notnull().all(axis=1)]
        if len(old_data) >= self.min_history_threshold:
            self.thresholds = np.array([get_thresholds(old_data[obj_name],
                                                        start=self.threshold_evaluation_pruning[0],
                                                        end=self.threshold_evaluation_pruning[1],
                                                        scale=self.threshold_evaluation_scaling,
                                                        n=self.evaluation_early_stop_steps)
                                    for obj_name in self.objective_names]).T

    #Get the selectors survival rates per step
    if self.selection_evaluation_pruning is not None:
        lower = self.cur_population_size*self.selection_evaluation_pruning[0]
        upper = self.cur_population_size*self.selection_evaluation_pruning[1]
        #survival_counts = self.cur_population_size*(scipy.special.betainc(1,self.selection_evaluation_scaling,np.linspace(0,1,self.evaluation_early_stop_steps))*(upper-lower)+lower)

        survival_counts = np.array(beta_interpolation(start=lower, end=upper, scale=self.selection_evaluation_scaling, n=self.evaluation_early_stop_steps, n_steps=self.evaluation_early_stop_steps))
        self.survival_counts = survival_counts.astype(int)
    else:
        self.survival_counts = None



    if self.evaluation_early_stop_steps is not None:
        if self.survival_counts is None:
            #TODO if we are not using selection method for each step, we can create single threads that run all steps for an individual. No need to come back each step.
            self.evaluate_population_selection_early_stop(survival_counts=self.survival_counts, thresholds=self.thresholds, budget=self.budget)
        else:
            #parallelize one step at a time. After each step, come together and select the next individuals to run the next step on.
            self.evaluate_population_selection_early_stop(survival_counts=self.survival_counts, thresholds=self.thresholds, budget=self.budget)
    else:
        self.evaluate_population_full(budget=self.budget)


    # Save population, TODO remove some of these
    if self.population_file is not None: # and time.time() - last_save_time > 60*10:
        pickle.dump(self.population, open(self.population_file, "wb"))
        last_save_time = time.time()

evaluate_population_full(budget=None)

Evaluates all individuals in the population that have not been evaluated yet. This is the normal/default strategy for evaluating individuals without any early stopping of individual evaluation functions. (e.g., no threshold or selection early stopping). Early stopping by generation is still possible.

Source code in tpot2/evolvers/base_evolver.py

def evaluate_population_full(self, budget=None):
    """
    Evaluates all individuals in the population that have not been evaluated yet.
    This is the normal/default strategy for evaluating individuals without any early stopping of individual evaluation functions. (e.g., no threshold or selection early stopping). Early stopping by generation is still possible.
    """
    individuals_to_evaluate = self.get_unevaluated_individuals(self.objective_names, budget=budget,)

    #print("evaluating this many individuals: ", len(individuals_to_evaluate))

    if len(individuals_to_evaluate) == 0:
        if self.verbose > 3:
            print("No new individuals to evaluate")
        return

    if self.max_eval_time_mins is not None:
        theoretical_timeout = self.max_eval_time_mins * math.ceil(len(individuals_to_evaluate) / self.n_jobs)
        theoretical_timeout = theoretical_timeout*2
    else:
        theoretical_timeout = np.inf
    scheduled_timeout_time_left = self.scheduled_timeout_time - time.time()
    parallel_timeout = min(theoretical_timeout, scheduled_timeout_time_left)
    if parallel_timeout < 0:
        parallel_timeout = 10

    scores, start_times, end_times, eval_errors = tpot2.utils.eval_utils.parallel_eval_objective_list(individuals_to_evaluate, self.objective_functions, verbose=self.verbose, max_eval_time_mins=self.max_eval_time_mins, budget=budget, n_expected_columns=len(self.objective_names), client=self._client, scheduled_timeout_time=self.scheduled_timeout_time, **self.objective_kwargs)

    self.population.update_column(individuals_to_evaluate, column_names=self.objective_names, data=scores)
    if budget is not None:
        self.population.update_column(individuals_to_evaluate, column_names="Budget", data=budget)

    self.population.update_column(individuals_to_evaluate, column_names="Submitted Timestamp", data=start_times)
    self.population.update_column(individuals_to_evaluate, column_names="Completed Timestamp", data=end_times)
    self.population.update_column(individuals_to_evaluate, column_names="Eval Error", data=eval_errors)
    self.population.remove_invalid_from_population(column_names="Eval Error")
    self.population.remove_invalid_from_population(column_names="Eval Error", invalid_value="TIMEOUT")

evaluate_population_selection_early_stop(survival_counts, thresholds=None, budget=None)

This function tries to save computation by partially evaluating the individuals and then selecting which individuals to evaluate further based on the results of the partial evaluation.

Two strategies are implemented: 1. Selection early stopping: Selects a percentage of the population to evaluate at each step of the evaluation. for example, one strategy is to evaluate different steps of cross validation one at a time, and only select the best N individuals for subsequent steps. This can save computation by not evaluating all individuals for all steps of cross validation. By default this selection is done with the NSGA2 selector. 2. Threshold early stopping: At each step of the evaluation, a threshold is calculated based on the previous evaluations. All individuals that are below the performance threshold are not evaluated for further steps. For example, if the threshold is set to the 90th percentile of the previous evaluations, all individuals that are below the 90th percentile are not evaluated further. This can save computation by not evaluating all individuals for all steps of cross validation.

Both of these strategies can be used simultaneously. Individuals must pass both the selection and threshold criteria to be evaluated further.

Parameters:

Name	Type	Description	Default
survival_counts	list of ints	Number of individuals to select for survival at each step of the evaluation. If None, will not use selection early stopping. For example: [10, 5, 2] would select 10 individuals for the first step, 5 for the second, and 2 for the third.	None
thresholds	list of floats	Thresholds to use for early stopping at each step of the evaluation. If None, will not use threshold early stopping.	None
budget	float	Budget to use when evaluating individuals. Use is dependent on the objective functions. (In TPOTEstimator this corresponds to the percentage of the data to sample.)	None

Source code in tpot2/evolvers/base_evolver.py

def evaluate_population_selection_early_stop(self,survival_counts, thresholds=None, budget=None):
    """
    This function tries to save computation by partially evaluating the individuals and then selecting which individuals to evaluate further based on the results of the partial evaluation. 

    Two strategies are implemented:
        1.  Selection early stopping: Selects a percentage of the population to evaluate at each step of the evaluation. 
            for example, one strategy is to evaluate different steps of cross validation one at a time, and only select the best N individuals for subsequent steps. 
            This can save computation by not evaluating all individuals for all steps of cross validation. By default this selection is done with the NSGA2 selector.
        2.  Threshold early stopping: At each step of the evaluation, a threshold is calculated based on the previous evaluations. All individuals that are below the performance threshold are not evaluated for further steps.
            For example, if the threshold is set to the 90th percentile of the previous evaluations, all individuals that are below the 90th percentile are not evaluated further. This can save computation by not evaluating all individuals for all steps of cross validation.

    Both of these strategies can be used simultaneously. Individuals must pass both the selection and threshold criteria to be evaluated further.

    Parameters
    ----------
    survival_counts : list of ints, default=None
        Number of individuals to select for survival at each step of the evaluation. If None, will not use selection early stopping.
        For example: [10, 5, 2] would select 10 individuals for the first step, 5 for the second, and 2 for the third.
    thresholds : list of floats, default=None
        Thresholds to use for early stopping at each step of the evaluation. If None, will not use threshold early stopping.
    budget : float, default=None
        Budget to use when evaluating individuals. Use is dependent on the objective functions. (In TPOTEstimator this corresponds to the percentage of the data to sample.)
    """

    survival_selector = tpot2.selectors.survival_select_NSGA2

    ################

    objective_function_signs = np.sign(self.objective_function_weights)


    cur_individuals = self.population.population.copy()

    all_step_names = []
    for step in range(self.evaluation_early_stop_steps):
        if budget is None:
            this_step_names = [f"{n}_step_{step}" for n in self.objective_names]
        else:
            this_step_names = [f"{n}_budget_{budget}_step_{step}" for n in self.objective_names]

        all_step_names.append(this_step_names)

        unevaluated_individuals_this_step = self.get_unevaluated_individuals(this_step_names, budget=None, individual_list=cur_individuals)

        if len(unevaluated_individuals_this_step) == 0:
            if self.verbose > 3:
                print("No new individuals to evaluate")
            continue

        if self.max_eval_time_mins is not None:
            theoretical_timeout = self.max_eval_time_mins * math.ceil(len(unevaluated_individuals_this_step) / self.n_jobs)*60
            theoretical_timeout = theoretical_timeout*2
        else:
            theoretical_timeout = np.inf
        scheduled_timeout_time_left = self.scheduled_timeout_time - time.time()
        parallel_timeout = min(theoretical_timeout, scheduled_timeout_time_left)
        if parallel_timeout < 0:
            parallel_timeout = 10

        scores, start_times, end_times, eval_errors = tpot2.utils.eval_utils.parallel_eval_objective_list(individual_list=unevaluated_individuals_this_step,
                                objective_list=self.objective_functions,
                                verbose=self.verbose,
                                max_eval_time_mins=self.max_eval_time_mins,
                                step=step,
                                budget = self.budget,
                                generation = self.generation,
                                n_expected_columns=len(self.objective_names),
                                client=self._client,
                                scheduled_timeout_time=self.scheduled_timeout_time,
                                **self.objective_kwargs,
                                )

        self.population.update_column(unevaluated_individuals_this_step, column_names=this_step_names, data=scores)
        self.population.update_column(unevaluated_individuals_this_step, column_names="Submitted Timestamp", data=start_times)
        self.population.update_column(unevaluated_individuals_this_step, column_names="Completed Timestamp", data=end_times)
        self.population.update_column(unevaluated_individuals_this_step, column_names="Eval Error", data=eval_errors)

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

        #remove invalids:
        invalids = []
        #find indeces of invalids

        for j in range(len(scores)):
            if  any([s=="INVALID" for s in scores[j]]):
                invalids.append(j)

        for j in range(len(scores)):
            if  any([s=="TIMEOUT" for s in scores[j]]):
                invalids.append(j)


        #already evaluated
        already_evaluated = list(set(cur_individuals) - set(unevaluated_individuals_this_step))
        #evaluated and valid
        valid_evaluations_this_step = remove_items(unevaluated_individuals_this_step,invalids)
        #update cur_individuals with current individuals with valid scores
        cur_individuals = np.concatenate([already_evaluated, valid_evaluations_this_step])


        #Get average scores

        #array of shape (steps, individuals, objectives)
        offspring_scores = [self.population.get_column(cur_individuals, column_names=step_names) for step_names in all_step_names]
        offspring_scores = np.array(offspring_scores)
        if self.final_score_strategy == 'mean':
            offspring_scores  = offspring_scores.mean(axis=0)
        elif self.final_score_strategy == 'last':
            offspring_scores = offspring_scores[-1]

        #remove individuals with nan scores
        invalids = []
        for i in range(len(offspring_scores)):
            if any(np.isnan(offspring_scores[i])):
                invalids.append(i)

        cur_individuals = remove_items(cur_individuals,invalids)
        offspring_scores = remove_items(offspring_scores,invalids)

        #if last step, add the final metrics
        if step == self.evaluation_early_stop_steps-1:
            self.population.update_column(cur_individuals, column_names=self.objective_names, data=offspring_scores)
            if budget is not None:
                self.population.update_column(cur_individuals, column_names="Budget", data=budget)
            return

        #If we have more threads than remaining individuals, we may as well evaluate the extras too
        if self.n_jobs < len(cur_individuals):
            #Remove based on thresholds
            if thresholds is not None:
                threshold = thresholds[step]
                invalids = []
                for i in range(len(offspring_scores)):

                    if all([s*w>t*w for s,t,w in zip(offspring_scores[i],threshold,objective_function_signs)  ]):
                        invalids.append(i)

                if len(invalids) > 0:

                    max_to_remove = min(len(cur_individuals) - self.n_jobs, len(invalids))

                    if max_to_remove < len(invalids):
                        # invalids = np.random.choice(invalids, max_to_remove, replace=False)
                        invalids = self.rng.choice(invalids, max_to_remove, replace=False)

                    cur_individuals = remove_items(cur_individuals,invalids)
                    offspring_scores = remove_items(offspring_scores,invalids)

            # Remove based on selection
            if survival_counts is not None:
                if step < self.evaluation_early_stop_steps - 1 and survival_counts[step]>1: #don't do selection for the last loop since they are completed
                    k = survival_counts[step] + len(invalids) #TODO can remove the min if the selections method can ignore k>population size
                    if len(cur_individuals)> 1 and k > self.n_jobs and k < len(cur_individuals):
                        weighted_scores = np.array([s * self.objective_function_weights for s in offspring_scores ])

                        new_population_index = survival_selector(weighted_scores, k=k)
                        cur_individuals = np.array(cur_individuals)[new_population_index]
                        offspring_scores = offspring_scores[new_population_index]

generate_offspring()

Create population_size new individuals from the current population. This includes selecting parents, applying mutation and crossover, and adding the new individuals to the population.

Source code in tpot2/evolvers/base_evolver.py

def generate_offspring(self, ): #your EA Algorithm goes here
    """
    Create population_size new individuals from the current population. 
    This includes selecting parents, applying mutation and crossover, and adding the new individuals to the population.
    """
    parents = self.population.parent_select(selector=self.parent_selector, weights=self.objective_function_weights, columns_names=self.objective_names, k=self.cur_population_size, 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=self.cur_population_size, 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, self.mutation_functions, self.mutation_function_weights, self.crossover_functions, self.crossover_function_weights, add_to_population=True, keep_repeats=False, mutate_until_unique=True, rng=self.rng)

    self.population.update_column(offspring, column_names="Generation", data=self.generation, )

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/base_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(generations=None)

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

Parameters:

Name	Type	Description	Default
generations	int	Number of generations to run. If None, will use self.generations.	None

Source code in tpot2/evolvers/base_evolver.py

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

    Parameters
    ----------
    generations : int, default=None
        Number of generations to run. If None, will use self.generations.

    """

    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=True,
                memory_limit=self.memory_limit)
        self._client = Client(self._cluster)



    if generations is None:
        generations = self.generations

    start_time = time.time()

    generations_without_improvement = np.array([0 for _ in range(len(self.objective_function_weights))])
    best_scores = [-np.inf for _ in range(len(self.objective_function_weights))]


    self.scheduled_timeout_time = time.time() + self.max_time_mins*60


    try:
        #for gen in tnrange(generations,desc="Generation", disable=self.verbose<1):
        done = False
        gen = 0
        if self.verbose >= 1:
            if generations is None or np.isinf(generations):
                pbar = tqdm.tqdm(total=0)
            else:
                pbar = tqdm.tqdm(total=generations)
            pbar.set_description("Generation")
        while not done:
            # Generation 0 is the initial population
            if self.generation == 0:
                if self.population_file is not None:
                    pickle.dump(self.population, open(self.population_file, "wb"))
                self.evaluate_population()
                if self.population_file is not None:
                    pickle.dump(self.population, open(self.population_file, "wb"))

                attempts = 2
                while len(self.population.population) == 0 and attempts > 0:
                    new_initial_population = [next(self.individual_generator) for _ in range(self.cur_population_size)]
                    self.population.add_to_population(new_initial_population, rng=self.rng)
                    attempts -= 1
                    self.evaluate_population()

                if len(self.population.population) == 0:
                    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.")

                self.generation += 1
            # Generation 1 is the first generation after the initial population
            else:
                if time.time() - start_time > self.max_time_mins*60:
                    if self.population.evaluated_individuals[self.objective_names].isnull().all().iloc[0]:
                        raise Exception("No individuals could be evaluated in the initial population as the max_eval_mins time limit was reached before any individuals could be evaluated.")
                    break
                self.step()

            if self.verbose >= 3:
                sign = np.sign(self.objective_function_weights)
                valid_df = self.population.evaluated_individuals[~self.population.evaluated_individuals[self.objective_names].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()
                print("Generation: ", self.generation)
                for i, obj in enumerate(self.objective_names):
                    print(f"Best {obj} score: {cur_best_scores[i]}")


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

                    if all(generations_without_improvement>self.early_stop):
                        if self.verbose >= 3:
                            print("Early stop")
                        break

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

            gen += 1
            if self.verbose >= 1:
                pbar.update(1)

            if generations is not None and gen >= generations:
                done = True

    except KeyboardInterrupt:
        if self.verbose >= 3:
            print("KeyboardInterrupt")
        self.population.remove_invalid_from_population(column_names=self.objective_names, invalid_value="INVALID")
        self.population.remove_invalid_from_population(column_names=self.objective_names, invalid_value="TIMEOUT")
        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")




    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)

step()

Runs a single generation of the evolutionary algorithm. This includes selecting individuals for survival, generating offspring, and evaluating the offspring.

Source code in tpot2/evolvers/base_evolver.py

def step(self,):
    """
    Runs a single generation of the evolutionary algorithm. This includes selecting individuals for survival, generating offspring, and evaluating the offspring.

    """


    if self.population_size_list is not None:
        if self.generation < len(self.population_size_list):
            self.cur_population_size = self.population_size_list[self.generation]
        else:
            self.cur_population_size = self.population_size

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

    if self.survival_selector is not None:
        n_survivors = max(1,int(self.cur_population_size*self.survival_percentage)) #always keep at least one individual
        self.population.survival_select(    selector=self.survival_selector,
                                            weights=self.objective_function_weights,
                                            columns_names=self.objective_names,
                                            n_survivors=n_survivors,
                                            inplace=True,
                                            rng=self.rng,)

    self.generate_offspring()
    self.evaluate_population()

    self.generation += 1

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/base_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/base_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)