oxigen 2.2.2

//! This crate provides functions for parallel genetic algorithm execution.

// #![feature(test)]

extern crate historian;
extern crate rand;
extern crate rayon;

pub mod age;
// mod benchmarks;
pub mod crossover;
pub mod genotype;
pub mod mutation_rate;
pub mod niches_beta_rate;
pub mod population_refitness;
pub mod prelude;
pub mod selection;
pub mod selection_rate;
pub mod slope_params;
pub mod stop_criteria;
pub mod survival_pressure;

pub use age::*;
pub use crossover::*;
pub use genotype::Genotype;
pub use mutation_rate::*;
pub use niches_beta_rate::*;
pub use population_refitness::*;
pub use selection::*;
pub use selection_rate::*;
pub use slope_params::*;
use std::fmt::Display;
use std::marker::PhantomData;
pub use stop_criteria::*;
pub use survival_pressure::*;

use historian::Histo;
use rand::distributions::{Standard, Uniform};
use rand::prelude::*;
use rayon::prelude::*;
#[cfg(feature = "global_cache")]
use std::collections::HashMap;
use std::fs::File;
use std::io::prelude::*;
use std::sync::mpsc::channel;

const POPULATION_SEPARATOR: &[u8] = b"\n\n\n\n---------------------------------\n\n\n\n";
const POPULATION_ERR_MSG: &str = "Error writing on population log file";
const PROGRESS_ERR_MSG: &str = "Error writing in progress log file";
const PROGRESS_HEADER: &[u8] = b"Generation\t\
    Solutions\t\
    Last progress\t\
    Progress avg\t\
    Progress std\t\
    Progress max\t\
    Progress min\t\
    Progress p10\t\
    Progress p25\t\
    Progress median\t\
    Progress p75\t\
    Progress p90\t\
    Fitness avg\t\
    Fitness std\t\
    Fitness max\t\
    Fitness min\t\
    Fitness p10\t\
    Fitness p25\t\
    Fitness median\t\
    Fitness p75\t\
    Fitness p90\n";

/// Struct that defines the fitness of each individual and the related information.
#[derive(Copy, Clone, Debug)]
pub struct Fitness {
    /// Age of the individual.
    age: u64,
    /// Actual fitness.
    fitness: f64,
    /// Original fitness of the individual before being unfitnessed by age.
    original_fitness: f64,
    /// Age effect over the original fitness (usually negative).
    age_effect: f64,
    /// Refitness effect over the original fitness (usually negative).
    refitness_effect: f64,
}

/// Struct that defines a pair of individual-fitness
#[derive(Debug)]
pub struct IndWithFitness<T: PartialEq + Send + Sync, Ind: Genotype<T>> {
    /// Individual
    pub ind: Ind,
    /// Fitness (can be not computed yet)
    pub fitness: Option<Fitness>,
    _phantom: PhantomData<T>,
}

impl<T: PartialEq + Send + Sync, Ind: Genotype<T>> IndWithFitness<T, Ind> {
    pub fn new(ind: Ind, fitness: Option<Fitness>) -> IndWithFitness<T, Ind> {
        IndWithFitness {
            ind,
            fitness,
            _phantom: PhantomData,
        }
    }
}

impl<T: PartialEq + Send + Sync, Ind: Genotype<T>> Display for IndWithFitness<T, Ind> {
    fn fmt(&self, f: &mut std::fmt::Formatter) -> Result<(), std::fmt::Error> {
        write!(f, "ind: {}, fitness: {:?}", self.ind, self.fitness)
    }
}

/// Struct that defines a genetic algorithm execution.
pub struct GeneticExecution<T: PartialEq + Send + Sync, Ind: Genotype<T>> {
    /// The number of individuals in the population.
    population_size: usize,
    /// Population with all individuals and their respective fitnesses.
    population: Vec<IndWithFitness<T, Ind>>,
    /// Size of the genotype problem.
    genotype_size: Ind::ProblemSize,
    /// The mutation rate variation along iterations and progress.
    mutation_rate: Box<dyn MutationRate>,
    /// The number of stages in the cup whose individuals are selected to crossover.
    selection_rate: Box<dyn SelectionRate>,
    /// The selection function.
    selection: Box<dyn Selection>,
    /// The age fitness decrease function.
    age: Box<dyn Age>,
    /// The crossover function.
    crossover: Box<dyn Crossover<T, Ind>>,
    /// The function used to recompute the fitness using the full population just
    /// before survival pressure.
    population_refitness: Box<dyn PopulationRefitness<T, Ind>>,
    /// The function used to replace individuals in the population.
    survival_pressure: Box<dyn SurvivalPressure<T, Ind>>,
    /// The stop criterion to finish execution.
    stop_criterion: Box<dyn StopCriterion>,
    /// Cache fitness value of individuals or compute it in each iteration.
    cache_fitness: bool,
    /// Global cache for all evaluated individuals, i.e., if a new individual is
    /// equal to a previous individual its fitness is not recomputed. Only valid
    /// if cache_fitness is true.
    global_cache: bool,
    /// HashMap used for the global cache
    #[cfg(feature = "global_cache")]
    cache_map: HashMap<Ind::GenotypeHash, f64>,
    /// Progress log, writes statistics of the population every certain number of generations.
    progress_log: (u64, Option<File>),
    /// Population log, writes the population and fitnesses every certain number of generations.
    population_log: (u64, Option<File>),
}

impl<T: PartialEq + Send + Sync, Ind: Genotype<T>> Default for GeneticExecution<T, Ind> {
    fn default() -> Self {
        GeneticExecution {
            population_size: 64,
            population: Vec::new(),
            genotype_size: Ind::ProblemSize::default(),
            mutation_rate: Box::new(MutationRates::Constant(0.1)),
            selection_rate: Box::new(SelectionRates::Constant(2)),
            selection: Box::new(SelectionFunctions::Cup),
            age: Box::new(AgeFunctions::None),
            crossover: Box::new(CrossoverFunctions::SingleCrossPoint),
            population_refitness: Box::new(PopulationRefitnessFunctions::None),
            survival_pressure: Box::new(SurvivalPressureFunctions::Worst),
            stop_criterion: Box::new(StopCriteria::SolutionFound),
            cache_fitness: true,
            global_cache: cfg!(feature = "global_cache"),
            #[cfg(feature = "global_cache")]
            cache_map: HashMap::new(),
            progress_log: (0, None),
            population_log: (0, None),
        }
    }
}

impl<T: PartialEq + Send + Sync, Ind: Genotype<T>> GeneticExecution<T, Ind> {
    /// Creates a new default genetic algorithm execution.
    pub fn new() -> Self {
        Self::default()
    }

    /// Sets the population size.
    pub fn population_size(mut self, new_pop_size: usize) -> Self {
        self.population_size = new_pop_size;
        self
    }

    /// Sets the initial population individuals. If lower individuals
    /// than population_size are received, the rest of population will be
    /// generated randomly.
    pub fn population(mut self, new_pop: Vec<IndWithFitness<T, Ind>>) -> Self {
        self.population = new_pop;
        self
    }

    /// Sets the genotype size.
    pub fn genotype_size(mut self, new_size: Ind::ProblemSize) -> Self {
        self.genotype_size = new_size;
        self
    }

    /// Sets the mutation rate.
    pub fn mutation_rate(mut self, new_mut: Box<dyn MutationRate>) -> Self {
        self.mutation_rate = new_mut;
        self
    }

    /// Sets the number of tournament stages whose individuals are selected for crossover.
    pub fn selection_rate(mut self, new_sel_rate: Box<dyn SelectionRate>) -> Self {
        self.selection_rate = new_sel_rate;
        self
    }

    /// Sets the selection function of the genetic algorithm.
    pub fn select_function(mut self, new_sel: Box<dyn Selection>) -> Self {
        self.selection = new_sel;
        self
    }

    /// Sets the age function of the genetic algorithm.
    pub fn age_function(mut self, new_age: Box<dyn Age>) -> Self {
        self.age = new_age;
        self
    }

    /// Sets the crossover function of the genetic algorithm.
    pub fn crossover_function(mut self, new_cross: Box<dyn Crossover<T, Ind>>) -> Self {
        self.crossover = new_cross;
        self
    }

    /// Sets the population refitness function of the genetic algorithm.
    pub fn population_refitness_function(
        mut self,
        new_refit: Box<dyn PopulationRefitness<T, Ind>>,
    ) -> Self {
        self.population_refitness = new_refit;
        self
    }

    /// Sets the survival pressure function of the genetic algorithm.
    pub fn survival_pressure_function(
        mut self,
        new_surv: Box<dyn SurvivalPressure<T, Ind>>,
    ) -> Self {
        self.survival_pressure = new_surv;
        self
    }

    /// Sets the stop criterion of the genetic algorithm.
    pub fn stop_criterion(mut self, new_crit: Box<dyn StopCriterion>) -> Self {
        self.stop_criterion = new_crit;
        self
    }

    /// Sets the cache fitness flag.
    pub fn cache_fitness(mut self, new_cache: bool) -> Self {
        self.cache_fitness = new_cache;
        self
    }

    /// Sets the global cache flag.
    /// Panics: when trying to put it true without `global_cache` feature enabled.
    pub fn global_cache(mut self, new_global_cache: bool) -> Self {
        if cfg!(feature = "global_cache") {
            self.global_cache = new_global_cache;
        } else if new_global_cache {
            panic!("global_cache feature must been enabled to enable global_cache");
        } else {
            self.global_cache = false;
        }
        self
    }

    /// Sets the progress log.
    pub fn progress_log(mut self, generations: u64, log_file: File) -> Self {
        self.progress_log = (generations, Some(log_file));
        self
    }

    /// Sets the progress log.
    pub fn population_log(mut self, generations: u64, log_file: File) -> Self {
        self.population_log = (generations, Some(log_file));
        self
    }

    /// Run the genetic algorithm executiion until the `stop_criterion` is satisfied.
    ///
    /// # Returns
    ///
    /// - A vector with the individuals of the population that are solution of the problem.
    /// - The number of generations run.
    /// - The average progress in the last generations.
    /// - The entire population in the last generation (useful for resuming the execution).
    pub fn run(mut self) -> (Vec<Ind>, u64, f64, Vec<IndWithFitness<T, Ind>>) {
        // Initialize randomly the population
        while self.population.len() < self.population_size {
            self.population.push(IndWithFitness::new(
                Ind::generate(&self.genotype_size),
                None,
            ));
        }
        self.fix();
        self.compute_fitnesses(true);

        if self.progress_log.0 > 0 {
            self.print_progress_header();
        }

        let (generation, progress, solutions) = self.run_loop();

        (solutions, generation, progress, self.population)
    }

    fn run_loop(&mut self) -> (u64, f64, Vec<Ind>) {
        let mut generation: u64 = 0;
        let mut last_progresses: Vec<f64> = Vec::new();
        let mut progress: f64 = std::f64::NAN;
        // A HashSet is not used to not force to implement Hash in Genotype
        let mut solutions: Vec<Ind> = Vec::new();
        let mut mutation_rate;
        let mut selection_rate;
        let mut last_best = 0f64;

        let mut current_fitnesses = self.get_fitnesses();
        self.get_solutions(&mut solutions);
        while !self
            .stop_criterion
            .stop(generation, progress, solutions.len(), &current_fitnesses)
        {
            generation += 1;

            mutation_rate =
                self.mutation_rate
                    .rate(generation, progress, solutions.len(), &current_fitnesses);
            selection_rate =
                self.selection_rate
                    .rate(generation, progress, solutions.len(), &current_fitnesses);

            self.compute_fitnesses(true);
            self.refitness(generation, progress, solutions.len());
            current_fitnesses = self.get_fitnesses();
            let selected = self.selection.select(&current_fitnesses, selection_rate);
            let parents_children = self.cross(&selected);
            // fitnesses must be computed for the new individuals to get solutions
            self.compute_fitnesses(false);
            self.get_solutions(&mut solutions);
            self.mutate(mutation_rate);
            self.fix();
            self.compute_fitnesses(false);
            self.refitness(generation, progress, solutions.len());
            self.age_unfitness();
            // get solutions before kill
            self.get_solutions(&mut solutions);
            self.survival_pressure_kill(&parents_children);

            current_fitnesses = self.get_fitnesses();
            let best = current_fitnesses[0];
            progress = Self::update_progress(last_best, best, &mut last_progresses);
            last_best = best;

            if self.progress_log.0 > 0 && generation % self.progress_log.0 == 0 {
                self.print_progress(generation, progress, &last_progresses, solutions.len());
            }
            if self.population_log.0 > 0 && generation % self.population_log.0 == 0 {
                self.print_population(generation);
            }

            self.update_age();
        }

        (generation, progress, solutions)
    }

    fn print_population(&mut self, generation: u64) {
        if let Some(ref mut f) = self.population_log.1 {
            f.write_all(format!("Generation {}\n", generation).as_bytes())
                .expect(POPULATION_ERR_MSG);
            for (i, ind) in self.population.iter().enumerate() {
                f.write_all(
                    format!(
                        "Individual: {}; fitness: {}, age: {}, original_fitness: {}\n",
                        i,
                        ind.fitness.unwrap().fitness,
                        ind.fitness.unwrap().age,
                        ind.fitness.unwrap().original_fitness
                    )
                    .as_bytes(),
                )
                .expect(POPULATION_ERR_MSG);
                f.write_all(format!("{}\n\n", ind.ind).as_bytes())
                    .expect(POPULATION_ERR_MSG);
            }
            f.write_all(POPULATION_SEPARATOR).expect(POPULATION_ERR_MSG);
        }
    }

    fn print_progress_header(&mut self) {
        if let Some(ref mut f) = self.progress_log.1 {
            f.write_all(PROGRESS_HEADER).expect(PROGRESS_ERR_MSG);
        }
    }

    fn print_progress(
        &mut self,
        generation: u64,
        progress: f64,
        last_progresses: &[f64],
        n_solutions: usize,
    ) {
        let current_fitnesses = self.get_fitnesses();
        if let Some(ref mut f) = self.progress_log.1 {
            let progress_hist = Histo::default();
            for prog in last_progresses.iter() {
                progress_hist.measure(*prog);
            }
            let fit_hist = Histo::default();
            for fit in &current_fitnesses {
                fit_hist.measure(*fit);
            }
            let fitness_avg =
                current_fitnesses.iter().sum::<f64>() / current_fitnesses.len() as f64;
            f.write_all(
                format!(
                    "{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\n",
                    generation,
                    n_solutions,
                    last_progresses[last_progresses.len() - 1],
                    progress,
                    (1_f64 / (last_progresses.len() - 1) as f64)
                        * last_progresses
                            .iter()
                            .fold(0_f64, |acc, x| acc + (x - progress).powi(2))
                            .sqrt(),
                    last_progresses
                        .iter()
                        .max_by(|x, y| x.partial_cmp(&y).unwrap())
                        .unwrap(),
                    last_progresses
                        .iter()
                        .min_by(|x, y| x.partial_cmp(&y).unwrap())
                        .unwrap(),
                    progress_hist.percentile(10_f64),
                    progress_hist.percentile(25_f64),
                    progress_hist.percentile(50_f64),
                    progress_hist.percentile(75_f64),
                    progress_hist.percentile(90_f64),
                    fitness_avg,
                    (1_f64 / (current_fitnesses.len() - 1) as f64)
                        * current_fitnesses
                            .iter()
                            .fold(0_f64, |acc, x| acc + (x - fitness_avg).powi(2))
                            .sqrt(),
                    current_fitnesses
                        .iter()
                        .max_by(|x, y| x.partial_cmp(&y).unwrap())
                        .unwrap(),
                    current_fitnesses
                        .iter()
                        .min_by(|x, y| x.partial_cmp(&y).unwrap())
                        .unwrap(),
                    fit_hist.percentile(10_f64),
                    fit_hist.percentile(25_f64),
                    fit_hist.percentile(50_f64),
                    fit_hist.percentile(75_f64),
                    fit_hist.percentile(90_f64),
                ).as_bytes(),
            ).expect(PROGRESS_ERR_MSG);
        }
    }

    fn update_progress(last_best: f64, best: f64, last_progresses: &mut Vec<f64>) -> f64 {
        last_progresses.push(best - last_best);
        if last_progresses.len() == 16 {
            last_progresses.remove(0);
        }

        last_progresses.par_iter().sum::<f64>() / last_progresses.len() as f64
    }

    fn fix(&mut self) {
        self.population.par_iter_mut().for_each(|indwf| {
            if indwf.ind.fix() {
                indwf.fitness = None
            }
        });
    }

    fn update_age(&mut self) {
        self.population.par_iter_mut().for_each(|indwf| {
            if let Some(mut fit) = indwf.fitness {
                fit.age += 1;
                indwf.fitness = Some(fit);
            }
        });
    }

    fn get_fitnesses(&self) -> Vec<f64> {
        self.population
            .iter()
            .map(|indwf| indwf.fitness.unwrap().fitness)
            .collect::<Vec<f64>>()
    }

    #[cfg(feature = "global_cache")]
    fn compute_fitnesses(&mut self, refresh_on_nocache: bool) {
        if cfg!(feature = "global_cache") && self.cache_fitness && self.global_cache {
            let (sender, receiver) = channel();
            self.population
                .par_iter()
                .enumerate()
                .filter(|(_i, indwf)| indwf.fitness.is_none())
                .for_each_with(sender, |s, (i, indwf)| {
                    let hashed_ind = self.population[i].ind.hash();
                    let new_fit_value = {
                        match self.cache_map.get(&hashed_ind) {
                            Some(val) => *val,
                            None => indwf.ind.fitness(),
                        }
                    };
                    s.send((i, new_fit_value, hashed_ind)).unwrap();
                });
            for (i, new_fit_value, hashed_ind) in receiver {
                // only none fitness individuals are sent to the receiver
                self.population[i].fitness = Some(Fitness {
                    age: 0,
                    fitness: new_fit_value,
                    original_fitness: new_fit_value,
                    age_effect: 0.0,
                    refitness_effect: 0.0,
                });
                // insert in cache if it is not already in it
                self.cache_map.entry(hashed_ind).or_insert(new_fit_value);
            }
        } else {
            self.compute_fitnesses_without_global_cache(refresh_on_nocache);
        }
    }

    #[cfg(not(feature = "global_cache"))]
    fn compute_fitnesses(&mut self, refresh_on_nocache: bool) {
        self.compute_fitnesses_without_global_cache(refresh_on_nocache);
    }

    fn compute_fitnesses_without_global_cache(&mut self, refresh_on_nocache: bool) {
        self.population
            .par_iter_mut()
            .filter(|indwf| {
                if refresh_on_nocache {
                    true
                } else {
                    indwf.fitness.is_none()
                }
            })
            .for_each(|indwf| {
                let new_fit_value = indwf.ind.fitness();
                // this match is only to keep the age
                match indwf.fitness {
                    Some(fit) => {
                        indwf.fitness = Some(Fitness {
                            fitness: new_fit_value,
                            ..fit
                        })
                    }
                    None => {
                        indwf.fitness = Some(Fitness {
                            age: 0,
                            fitness: new_fit_value,
                            original_fitness: new_fit_value,
                            age_effect: 0.0,
                            refitness_effect: 0.0,
                        })
                    }
                }
            });
    }

    fn age_unfitness(&mut self) {
        let age_function = &self.age;
        self.population.par_iter_mut().for_each(|indwf| {
            if let Some(fit) = indwf.fitness {
                let age_exceed: i64 = fit.age as i64 - age_function.age_threshold() as i64;
                if age_exceed >= 0 {
                    let new_fit = age_function.age_unfitness(age_exceed as u64, fit.fitness);
                    indwf.fitness = Some(Fitness {
                        fitness: new_fit,
                        age: fit.age,
                        original_fitness: fit.original_fitness,
                        age_effect: fit.age_effect + (new_fit - fit.fitness),
                        refitness_effect: fit.refitness_effect,
                    });
                }
            }
        });
    }

    fn get_solutions(&self, solutions: &mut Vec<Ind>) {
        for indwf in &self.population {
            if indwf
                .ind
                .is_solution(indwf.fitness.unwrap().original_fitness)
                && Self::not_found_yet_solution(&solutions, &indwf.ind)
            {
                solutions.push(indwf.ind.clone());
            }
        }
    }

    #[allow(clippy::never_loop)]
    fn not_found_yet_solution(solutions: &[Ind], other: &Ind) -> bool {
        for ind in solutions {
            if other.distance(ind) == 0.0 {
                return false;
            }
        }

        true
    }

    fn cross(&mut self, selected: &[usize]) -> Vec<Reproduction> {
        let reprs_number = (selected.len() + 1) / 2;
        let mut reprs = Vec::with_capacity(reprs_number);
        let (sender, receiver) = channel();

        std::ops::Range {
            start: 0,
            end: reprs_number,
        }
        .into_par_iter()
        .for_each_with(sender, |s, i| {
            let ind1 = i * 2;
            let mut ind2 = ind1 + 1;
            // If the number of selected individuals is odd, the last crossover is done
            // using a random one among the selected individuals
            if ind2 >= selected.len() {
                ind2 = SmallRng::from_entropy().sample(Uniform::from(0..selected.len()));
            }
            let (crossed1, crossed2) = self.crossover.cross(
                &self.population[selected[ind1]].ind,
                &self.population[selected[ind2]].ind,
            );
            s.send((selected[ind1], selected[ind2], crossed1, crossed2))
                .unwrap();
        });
        for (parent1, parent2, child1, child2) in receiver {
            self.population.push(IndWithFitness {
                ind: child1,
                fitness: None,
                _phantom: PhantomData,
            });
            self.population.push(IndWithFitness {
                ind: child2,
                fitness: None,
                _phantom: PhantomData,
            });
            reprs.push(Reproduction {
                parents: (parent1, parent2),
                children: (self.population.len() - 2, self.population.len() - 1),
            });
        }
        reprs
    }

    fn mutate(&mut self, mutation_rate: f64) {
        self.population.par_iter_mut().for_each(|indwf| {
            let mut rgen = SmallRng::from_entropy();
            for gen in 0..indwf.ind.iter().len() {
                let random: f64 = rgen.sample(Standard);
                if random < mutation_rate {
                    indwf.ind.mutate(&mut rgen, gen);
                    indwf.fitness = None;
                }
            }
        });
    }

    fn refitness(&mut self, generation: u64, progress: f64, n_solutions: usize) {
        let (sender, receiver) = channel();

        self.population
            .par_iter()
            .enumerate()
            .for_each_with(sender, |s, (i, indwf)| {
                if let Some(fit) = indwf.fitness {
                    let new_fit = self.population_refitness.population_refitness(
                        i,
                        &self.population,
                        generation,
                        progress,
                        n_solutions,
                    );
                    if (new_fit - fit.fitness).abs() > 0.000_001 {
                        s.send((
                            i,
                            Some(Fitness {
                                fitness: new_fit,
                                age: fit.age,
                                original_fitness: fit.original_fitness,
                                age_effect: fit.age_effect,
                                refitness_effect: new_fit - fit.fitness,
                            }),
                        ))
                        .unwrap()
                    }
                }
            });
        for (i, fit) in receiver {
            self.population[i].fitness = fit;
        }
    }

    fn survival_pressure_kill(&mut self, parents_children: &[Reproduction]) {
        self.survival_pressure.kill(
            self.population_size,
            &mut self.population,
            &parents_children,
        );
    }
}