src/experiments.rs

/*!
Experimental setups.
*/

//use numeric_literals::replace_float_literals;
use serde::{Serialize, Deserialize};
use clap::ValueEnum;
use std::collections::HashMap;
use std::hash::{Hash, Hasher};
use std::collections::hash_map::DefaultHasher;

use alg_tools::bisection_tree::*;
use alg_tools::error::DynResult;
use alg_tools::norms::Linfinity;

use crate::ExperimentOverrides;
use crate::kernels::*;
use crate::kernels::SupportProductFirst as Prod;
use crate::pdps::PDPSConfig;
use crate::types::*;
use crate::run::{
    RunnableExperiment,
    ExperimentV2,
    ExperimentBiased,
    Named,
    DefaultAlgorithm,
    AlgorithmConfig,
    ProxTerm
};
//use crate::fb::FBGenericConfig;
use crate::rand_distr::{SerializableNormal, SaltAndPepper};
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};

/// 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,
}

macro_rules! make_float_constant {
    ($name:ident = $value:expr) => {
        #[derive(Debug, Copy, Eq, PartialEq, Clone, Serialize, Deserialize)]
        #[serde(into = "float")]
        struct $name;
        impl Into<float> 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,Deserialize,PartialEq)]
#[serde(bound(
    serialize = "F : Serialize,
                 Loc<F, N> : Serialize",
    deserialize = "F : for<'a> Deserialize<'a>,
                   Loc<F, N> : for<'a> Deserialize<'a>",
))]
struct BallCharacteristic<F : Float, const N : usize> {
    pub center : Loc<F, N>,
    pub radius : F,
}

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

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

//#[replace_float_literals(F::cast_from(literal))]
impl DefaultExperiment {
    /// Convert the experiment shorthand into a runnable experiment configuration.
    pub fn get_experiment(&self, cli : &ExperimentOverrides<float>) -> DynResult<Box<dyn RunnableExperiment<float>>> {
        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 = || {
            let τ0 = 3.0;
            PDPSConfig {
                τ0,
                σ0 : 0.99 / τ0,
                .. Default::default()
            }
        };
        let defaults_2d = HashMap::from([
            (DefaultAlgorithm::PDPS, AlgorithmConfig::PDPS(pdps_2d(), ProxTerm::Wave)),
            (DefaultAlgorithm::RadonPDPS, AlgorithmConfig::PDPS(pdps_2d(), ProxTerm::RadonSquared))
        ]);
        
        //  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();

        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(0.09)),
                    noise_distr : SerializableNormal::new(0.0, cli.variance.unwrap_or(0.2))?,
                    dataterm : DataTerm::L2Squared,
                    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,
                    algorithm_defaults: HashMap::new(),
                }})
            },
            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 : DataTerm::L2Squared,
                    μ_hat : MU_TRUE_1D_BASIC.into(),
                    sensor : BallIndicator { r : SensorWidth1D, exponent : Linfinity },
                    spread : base_spread,
                    kernel : base_spread,
                    kernel_plot_width,
                    noise_seed,
                    algorithm_defaults: HashMap::new(),
                }})
            },
            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 : DataTerm::L2Squared,
                    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,
                    algorithm_defaults: defaults_2d,
                }})
            },
            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 : DataTerm::L2Squared,
                    μ_hat : MU_TRUE_2D_BASIC.into(),
                    sensor : BallIndicator { r : SensorWidth2D, exponent : Linfinity },
                    spread : base_spread,
                    kernel : base_spread,
                    kernel_plot_width,
                    noise_seed,
                    algorithm_defaults: defaults_2d,
                }})
            },
            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 : DataTerm::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,
                    algorithm_defaults: 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 : DataTerm::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,
                    algorithm_defaults: 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 : DataTerm::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,
                    algorithm_defaults: defaults_2d,
                }})
            },
            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 : DataTerm::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,
                    algorithm_defaults: defaults_2d,
                }})
            },
            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 : DataTerm::L2Squared,
                        μ_hat : MU_TRUE_1D_BASIC.into(),
                        sensor : BallIndicator { r : SensorWidth1D, exponent : Linfinity },
                        spread : base_spread,
                        kernel : base_spread,
                        kernel_plot_width,
                        noise_seed,
                        algorithm_defaults: HashMap::new(),
                    },
                }})
            },
        })
    }
}