Mercurial > repos > pointsource_algs / file revision

/*!
Experimental setups.
*/

//use numeric_literals::replace_float_literals;
use crate::kernels::SupportProductFirst as Prod;
use crate::kernels::*;
use crate::run::{DefaultAlgorithm, ExperimentBiased, ExperimentV2, Named, RunnableExperiment};
use crate::types::*;
use crate::{AlgorithmOverrides, ExperimentSetup};
use alg_tools::bisection_tree::*;
use alg_tools::error::DynResult;
use alg_tools::norms::Linfinity;
use clap::{Parser, ValueEnum};
use serde::{Deserialize, Serialize};
use std::collections::hash_map::DefaultHasher;
use std::collections::HashMap;
use std::hash::{Hash, Hasher};
//use crate::fb::InsertionConfig;
use crate::rand_distr::{SaltAndPepper, SerializableNormal};
use crate::regularisation::Regularisation;
use alg_tools::euclidean::Euclidean;
use alg_tools::instance::Instance;
use alg_tools::mapping::Mapping;
use alg_tools::operator_arithmetic::{MappingSum, Weighted};
use itertools::Itertools;
use serde_with::skip_serializing_none;

/// Experiments shorthands, to be used with the command line parser
#[derive(ValueEnum, Debug, Copy, Clone, Eq, PartialEq, Hash, Serialize, Deserialize)]
#[allow(non_camel_case_types)]
pub enum DefaultExperiment {
    /// One dimension, cut gaussian spread, 2-norm-squared data fidelity
    #[clap(name = "1d")]
    Experiment1D,
    /// One dimension, “fast” spread, 2-norm-squared data fidelity
    #[clap(name = "1d_fast")]
    Experiment1DFast,
    /// Two dimensions, cut gaussian spread, 2-norm-squared data fidelity
    #[clap(name = "2d")]
    Experiment2D,
    /// Two dimensions, “fast” spread, 2-norm-squared data fidelity
    #[clap(name = "2d_fast")]
    Experiment2DFast,
    /// One dimension, cut gaussian spread, 1-norm data fidelity
    #[clap(name = "1d_l1")]
    Experiment1D_L1,
    /// One dimension, ‘“fast” spread, 1-norm data fidelity
    #[clap(name = "1d_l1_fast")]
    Experiment1D_L1_Fast,
    /// Two dimensions, cut gaussian spread, 1-norm data fidelity
    #[clap(name = "2d_l1")]
    Experiment2D_L1,
    /// Two dimensions, “fast” spread, 1-norm data fidelity
    #[clap(name = "2d_l1_fast")]
    Experiment2D_L1_Fast,
    /// One dimension, “fast” spread, 2-norm-squared data fidelity with extra TV-regularised bias
    #[clap(name = "1d_tv_fast")]
    Experiment1D_TV_Fast,
    /// Two dimensions, “fast” spread, 2-norm-squared data fidelity with extra TV-regularised bias
    #[clap(name = "2d_tv_fast")]
    Experiment2D_TV_Fast,
}

/// Command line experiment setup overrides
#[skip_serializing_none]
#[derive(Parser, Debug, Serialize, Deserialize, Default, Clone, Hash)]
pub struct DefaultExperimentSetup<F: ClapFloat> {
    /// List of experiments to perform
    #[arg(value_name = "EXPERIMENT")]
    experiments: Vec<DefaultExperiment>,

    #[arg(long)]
    /// Regularisation parameter override.
    ///
    /// Only use if running just a single experiment, as different experiments have different
    /// regularisation parameters.
    alpha: Option<F>,

    #[arg(long)]
    /// Gaussian noise variance override
    variance: Option<F>,

    #[arg(long, value_names = &["MAGNITUDE", "PROBABILITY"])]
    /// Salt and pepper noise override.
    salt_and_pepper: Option<Vec<F>>,

    #[arg(long)]
    /// Noise seed
    noise_seed: Option<u64>,
}

macro_rules! make_float_constant {
    ($name:ident = $value:expr) => {
        #[derive(Debug, Copy, Eq, PartialEq, Clone, Serialize, Deserialize)]
        #[serde(into = "float")]
        struct $name;
        impl Into<f64> for $name {
            #[inline]
            fn into(self) -> float {
                $value
            }
        }
        impl Constant for $name {
            type Type = float;
            fn value(&self) -> float {
                $value
            }
        }
    };
}

/// Ground-truth measure spike locations and magnitudes for 1D experiments
static MU_TRUE_1D_BASIC: [(float, float); 4] =
    [(0.10, 10.0), (0.30, 2.0), (0.70, 3.0), (0.80, 5.0)];

/// Ground-truth measure spike locations and magnitudes for 2D experiments
static MU_TRUE_2D_BASIC: [([float; 2], float); 4] = [
    ([0.15, 0.15], 10.0),
    ([0.75, 0.45], 2.0),
    ([0.80, 0.50], 4.0),
    ([0.30, 0.70], 5.0),
];

/// The $\{0,1\}$-valued characteristic function of a ball as a [`Mapping`].
#[derive(Debug, Copy, Clone, Serialize, PartialEq)]
struct BallCharacteristic<F: Float, const N: usize> {
    pub center: Loc<N, F>,
    pub radius: F,
}

impl<F: Float, const N: usize> Mapping<Loc<N, F>> for BallCharacteristic<F, N> {
    type Codomain = F;

    fn apply<I: Instance<Loc<N, F>>>(&self, i: I) -> F {
        if self.center.dist2(i) <= self.radius {
            F::ONE
        } else {
            F::ZERO
        }
    }
}

/// Trait for customising the experiments available from the command line
impl ExperimentSetup for DefaultExperimentSetup<f64> {
    type FloatType = f64;

    fn runnables(&self) -> DynResult<Vec<Box<dyn RunnableExperiment<Self::FloatType>>>> {
        self.experiments
            .iter()
            .unique()
            .map(|e| e.get_experiment(self))
            .try_collect()
    }
}

//#[replace_float_literals(F::cast_from(literal))]
impl DefaultExperiment {
    // fn default_list() -> Vec<Self> {
    //     use DefaultExperiment::*;
    //     [
    //         Experiment1D,
    //         Experiment1DFast,
    //         Experiment2D,
    //         Experiment2DFast,
    //         Experiment1D_L1,
    //     ]
    //     .into()
    // }

    /// Convert the experiment shorthand into a runnable experiment configuration.
    fn get_experiment(
        &self,
        cli: &DefaultExperimentSetup<f64>,
    ) -> DynResult<Box<dyn RunnableExperiment<f64>>> {
        let name = "pointsource".to_string() + self.to_possible_value().unwrap().get_name();

        let kernel_plot_width = 0.2;

        const BASE_SEED: u64 = 915373234;

        const N_SENSORS_1D: usize = 100;
        make_float_constant!(SensorWidth1D = 0.4 / (N_SENSORS_1D as float));

        const N_SENSORS_2D: usize = 16;
        make_float_constant!(SensorWidth2D = 0.4 / (N_SENSORS_2D as float));

        //const N_SENSORS_2D_MORE: usize = 32;
        //make_float_constant!(SensorWidth2DMore = 0.4 / (N_SENSORS_2D_MORE as float));

        make_float_constant!(Variance1 = 0.05.powi(2));
        make_float_constant!(CutOff1 = 0.15);
        make_float_constant!(Hat1 = 0.16);
        make_float_constant!(HatBias = 0.05);

        // We use a different step length for PDPS in 2D experiments
        // let pdps_2d = (DefaultAlgorithm::PDPS,
        //     AlgorithmOverrides {
        //         tau0 : Some(3.0),
        //         sigma0 : Some(0.99 / 3.0),
        //         .. Default::default()
        //     }
        // );
        // let radon_pdps_2d = (DefaultAlgorithm::RadonPDPS,
        //     AlgorithmOverrides {
        //         tau0 : Some(3.0),
        //         sigma0 : Some(0.99 / 3.0),
        //         .. Default::default()
        //     }
        // );
        let sliding_fb_cut_gaussian = (DefaultAlgorithm::SlidingFB, AlgorithmOverrides {
            theta0: Some(0.3),
            ..Default::default()
        });
        // let higher_cpos = |alg| (alg,
        //     AlgorithmOverrides {
        //         transport_tolerance_pos : Some(1000.0),
        //         .. Default::default()
        //     }
        // );
        let higher_cpos_merging = |alg| {
            (alg, AlgorithmOverrides {
                transport_tolerance_pos: Some(1000.0),
                merge: Some(true),
                fitness_merging: Some(true),
                ..Default::default()
            })
        };
        let higher_cpos_merging_steptune = |alg| {
            (alg, AlgorithmOverrides {
                transport_tolerance_pos: Some(1000.0),
                theta0: Some(0.3),
                merge: Some(true),
                fitness_merging: Some(true),
                ..Default::default()
            })
        };
        let much_higher_cpos_merging_steptune = |alg| {
            (alg, AlgorithmOverrides {
                transport_tolerance_pos: Some(10000.0),
                sigma0: Some(0.15),
                theta0: Some(0.3),
                merge: Some(true),
                fitness_merging: Some(true),
                ..Default::default()
            })
        };
        //  We add a hash of the experiment name to the configured
        // noise seed to not use the same noise for different experiments.
        let mut h = DefaultHasher::new();
        name.hash(&mut h);
        let noise_seed = cli.noise_seed.unwrap_or(BASE_SEED) + h.finish();

        let default_merge_radius = 0.01;

        use DefaultExperiment::*;
        Ok(match self {
            Experiment1D => {
                let base_spread = Gaussian { variance: Variance1 };
                let spread_cutoff = BallIndicator { r: CutOff1, exponent: Linfinity };
                Box::new(Named {
                    name,
                    data: ExperimentV2 {
                        domain: [[0.0, 1.0]].into(),
                        sensor_count: [N_SENSORS_1D],
                        regularisation: Regularisation::NonnegRadon(cli.alpha.unwrap_or(0.08)),
                        noise_distr: SerializableNormal::new(0.0, cli.variance.unwrap_or(0.2))?,
                        dataterm: DataTermType::L222,
                        μ_hat: MU_TRUE_1D_BASIC.into(),
                        sensor: BallIndicator { r: SensorWidth1D, exponent: Linfinity },
                        spread: Prod(spread_cutoff, base_spread),
                        kernel: Prod(AutoConvolution(spread_cutoff), base_spread),
                        kernel_plot_width,
                        noise_seed,
                        default_merge_radius,
                        algorithm_overrides: HashMap::from([
                            sliding_fb_cut_gaussian,
                            higher_cpos_merging(DefaultAlgorithm::RadonFB),
                            higher_cpos_merging(DefaultAlgorithm::RadonSlidingFB),
                        ]),
                    },
                })
            }
            Experiment1DFast => {
                let base_spread = HatConv { radius: Hat1 };
                Box::new(Named {
                    name,
                    data: ExperimentV2 {
                        domain: [[0.0, 1.0]].into(),
                        sensor_count: [N_SENSORS_1D],
                        regularisation: Regularisation::NonnegRadon(cli.alpha.unwrap_or(0.06)),
                        noise_distr: SerializableNormal::new(0.0, cli.variance.unwrap_or(0.2))?,
                        dataterm: DataTermType::L222,
                        μ_hat: MU_TRUE_1D_BASIC.into(),
                        sensor: BallIndicator { r: SensorWidth1D, exponent: Linfinity },
                        spread: base_spread,
                        kernel: base_spread,
                        kernel_plot_width,
                        noise_seed,
                        default_merge_radius,
                        algorithm_overrides: HashMap::from([
                            higher_cpos_merging(DefaultAlgorithm::RadonFB),
                            higher_cpos_merging(DefaultAlgorithm::RadonSlidingFB),
                        ]),
                    },
                })
            }
            Experiment2D => {
                let base_spread = Gaussian { variance: Variance1 };
                let spread_cutoff = BallIndicator { r: CutOff1, exponent: Linfinity };
                Box::new(Named {
                    name,
                    data: ExperimentV2 {
                        domain: [[0.0, 1.0]; 2].into(),
                        sensor_count: [N_SENSORS_2D; 2],
                        regularisation: Regularisation::NonnegRadon(cli.alpha.unwrap_or(0.19)),
                        noise_distr: SerializableNormal::new(0.0, cli.variance.unwrap_or(0.25))?,
                        dataterm: DataTermType::L222,
                        μ_hat: MU_TRUE_2D_BASIC.into(),
                        sensor: BallIndicator { r: SensorWidth2D, exponent: Linfinity },
                        spread: Prod(spread_cutoff, base_spread),
                        kernel: Prod(AutoConvolution(spread_cutoff), base_spread),
                        kernel_plot_width,
                        noise_seed,
                        default_merge_radius,
                        algorithm_overrides: HashMap::from([
                            sliding_fb_cut_gaussian,
                            higher_cpos_merging(DefaultAlgorithm::RadonFB),
                            higher_cpos_merging(DefaultAlgorithm::RadonSlidingFB),
                        ]),
                    },
                })
            }
            Experiment2DFast => {
                let base_spread = HatConv { radius: Hat1 };
                Box::new(Named {
                    name,
                    data: ExperimentV2 {
                        domain: [[0.0, 1.0]; 2].into(),
                        sensor_count: [N_SENSORS_2D; 2],
                        regularisation: Regularisation::NonnegRadon(cli.alpha.unwrap_or(0.12)),
                        noise_distr: SerializableNormal::new(0.0, cli.variance.unwrap_or(0.15))?, //0.25
                        dataterm: DataTermType::L222,
                        μ_hat: MU_TRUE_2D_BASIC.into(),
                        sensor: BallIndicator { r: SensorWidth2D, exponent: Linfinity },
                        spread: base_spread,
                        kernel: base_spread,
                        kernel_plot_width,
                        noise_seed,
                        default_merge_radius,
                        algorithm_overrides: HashMap::from([
                            higher_cpos_merging(DefaultAlgorithm::RadonFB),
                            higher_cpos_merging(DefaultAlgorithm::RadonSlidingFB),
                        ]),
                    },
                })
            }
            Experiment1D_L1 => {
                let base_spread = Gaussian { variance: Variance1 };
                let spread_cutoff = BallIndicator { r: CutOff1, exponent: Linfinity };
                Box::new(Named {
                    name,
                    data: ExperimentV2 {
                        domain: [[0.0, 1.0]].into(),
                        sensor_count: [N_SENSORS_1D],
                        regularisation: Regularisation::NonnegRadon(cli.alpha.unwrap_or(0.1)),
                        noise_distr: SaltAndPepper::new(
                            cli.salt_and_pepper.as_ref().map_or(0.6, |v| v[0]),
                            cli.salt_and_pepper.as_ref().map_or(0.4, |v| v[1]),
                        )?,
                        dataterm: DataTermType::L1,
                        μ_hat: MU_TRUE_1D_BASIC.into(),
                        sensor: BallIndicator { r: SensorWidth1D, exponent: Linfinity },
                        spread: Prod(spread_cutoff, base_spread),
                        kernel: Prod(AutoConvolution(spread_cutoff), base_spread),
                        kernel_plot_width,
                        noise_seed,
                        default_merge_radius,
                        algorithm_overrides: HashMap::new(),
                    },
                })
            }
            Experiment1D_L1_Fast => {
                let base_spread = HatConv { radius: Hat1 };
                Box::new(Named {
                    name,
                    data: ExperimentV2 {
                        domain: [[0.0, 1.0]].into(),
                        sensor_count: [N_SENSORS_1D],
                        regularisation: Regularisation::NonnegRadon(cli.alpha.unwrap_or(0.12)),
                        noise_distr: SaltAndPepper::new(
                            cli.salt_and_pepper.as_ref().map_or(0.6, |v| v[0]),
                            cli.salt_and_pepper.as_ref().map_or(0.4, |v| v[1]),
                        )?,
                        dataterm: DataTermType::L1,
                        μ_hat: MU_TRUE_1D_BASIC.into(),
                        sensor: BallIndicator { r: SensorWidth1D, exponent: Linfinity },
                        spread: base_spread,
                        kernel: base_spread,
                        kernel_plot_width,
                        noise_seed,
                        default_merge_radius,
                        algorithm_overrides: HashMap::new(),
                    },
                })
            }
            Experiment2D_L1 => {
                let base_spread = Gaussian { variance: Variance1 };
                let spread_cutoff = BallIndicator { r: CutOff1, exponent: Linfinity };
                Box::new(Named {
                    name,
                    data: ExperimentV2 {
                        domain: [[0.0, 1.0]; 2].into(),
                        sensor_count: [N_SENSORS_2D; 2],
                        regularisation: Regularisation::NonnegRadon(cli.alpha.unwrap_or(0.35)),
                        noise_distr: SaltAndPepper::new(
                            cli.salt_and_pepper.as_ref().map_or(0.8, |v| v[0]),
                            cli.salt_and_pepper.as_ref().map_or(0.2, |v| v[1]),
                        )?,
                        dataterm: DataTermType::L1,
                        μ_hat: MU_TRUE_2D_BASIC.into(),
                        sensor: BallIndicator { r: SensorWidth2D, exponent: Linfinity },
                        spread: Prod(spread_cutoff, base_spread),
                        kernel: Prod(AutoConvolution(spread_cutoff), base_spread),
                        kernel_plot_width,
                        noise_seed,
                        default_merge_radius,
                        algorithm_overrides: HashMap::from([]),
                    },
                })
            }
            Experiment2D_L1_Fast => {
                let base_spread = HatConv { radius: Hat1 };
                Box::new(Named {
                    name,
                    data: ExperimentV2 {
                        domain: [[0.0, 1.0]; 2].into(),
                        sensor_count: [N_SENSORS_2D; 2],
                        regularisation: Regularisation::NonnegRadon(cli.alpha.unwrap_or(0.40)),
                        noise_distr: SaltAndPepper::new(
                            cli.salt_and_pepper.as_ref().map_or(0.8, |v| v[0]),
                            cli.salt_and_pepper.as_ref().map_or(0.2, |v| v[1]),
                        )?,
                        dataterm: DataTermType::L1,
                        μ_hat: MU_TRUE_2D_BASIC.into(),
                        sensor: BallIndicator { r: SensorWidth2D, exponent: Linfinity },
                        spread: base_spread,
                        kernel: base_spread,
                        kernel_plot_width,
                        noise_seed,
                        default_merge_radius,
                        algorithm_overrides: HashMap::from([]),
                    },
                })
            }
            Experiment1D_TV_Fast => {
                let base_spread = HatConv { radius: HatBias };
                Box::new(Named {
                    name,
                    data: ExperimentBiased {
                        λ: 0.02,
                        bias: MappingSum::new([
                            Weighted::new(1.0, BallCharacteristic {
                                center: 0.3.into(),
                                radius: 0.2,
                            }),
                            Weighted::new(0.5, BallCharacteristic {
                                center: 0.6.into(),
                                radius: 0.3,
                            }),
                        ]),
                        base: ExperimentV2 {
                            domain: [[0.0, 1.0]].into(),
                            sensor_count: [N_SENSORS_1D],
                            regularisation: Regularisation::NonnegRadon(cli.alpha.unwrap_or(0.2)),
                            noise_distr: SerializableNormal::new(0.0, cli.variance.unwrap_or(0.1))?,
                            dataterm: DataTermType::L222,
                            μ_hat: MU_TRUE_1D_BASIC.into(),
                            sensor: BallIndicator { r: SensorWidth1D, exponent: Linfinity },
                            spread: base_spread,
                            kernel: base_spread,
                            kernel_plot_width,
                            noise_seed,
                            default_merge_radius,
                            algorithm_overrides: HashMap::from([
                                higher_cpos_merging_steptune(DefaultAlgorithm::RadonForwardPDPS),
                                higher_cpos_merging_steptune(DefaultAlgorithm::RadonSlidingPDPS),
                            ]),
                        },
                    },
                })
            }
            Experiment2D_TV_Fast => {
                let base_spread = HatConv { radius: Hat1 };
                Box::new(Named {
                    name,
                    data: ExperimentBiased {
                        λ: 0.005,
                        bias: MappingSum::new([
                            Weighted::new(1.0, BallCharacteristic {
                                center: [0.3, 0.3].into(),
                                radius: 0.2,
                            }),
                            Weighted::new(0.5, BallCharacteristic {
                                center: [0.6, 0.6].into(),
                                radius: 0.3,
                            }),
                        ]),
                        base: ExperimentV2 {
                            domain: [[0.0, 1.0]; 2].into(),
                            sensor_count: [N_SENSORS_2D; 2],
                            regularisation: Regularisation::NonnegRadon(cli.alpha.unwrap_or(0.06)),
                            noise_distr: SerializableNormal::new(
                                0.0,
                                cli.variance.unwrap_or(0.15),
                            )?, //0.25
                            dataterm: DataTermType::L222,
                            μ_hat: MU_TRUE_2D_BASIC.into(),
                            sensor: BallIndicator { r: SensorWidth2D, exponent: Linfinity },
                            spread: base_spread,
                            kernel: base_spread,
                            kernel_plot_width,
                            noise_seed,
                            default_merge_radius,
                            algorithm_overrides: HashMap::from([
                                much_higher_cpos_merging_steptune(
                                    DefaultAlgorithm::RadonForwardPDPS,
                                ),
                                much_higher_cpos_merging_steptune(
                                    DefaultAlgorithm::RadonSlidingPDPS,
                                ),
                            ]),
                        },
                    },
                })
            }
        })
    }
}