pat-alt

using CounterfactualExplanations.Data: load_mnist
using CSV
using DataFrames
using Flux
using GMT
using Images
using LinearAlgebra
using MLJBase
using MLJModels
using OneHotArrays

pat-alt

using ConformalPrediction
using Distributions
using MLJ
using Plots

# Inputs:
N = 600
xmax = 3.0
d = Uniform(-xmax, xmax)

pat-alt

# Simple
"The `SimpleInductiveClassifier` is the simplest approach to Inductive Conformal Classification. Contrary to the [`NaiveClassifier`](@ref) it computes nonconformity scores using a designated calibration dataset."
mutable struct SimpleInductiveClassifier{Model <: Supervised} <: ConformalSet
    model::Model
    coverage::AbstractFloat
    scores::Union{Nothing,AbstractArray}
    heuristic::Function
    train_ratio::AbstractFloat
end

pat-alt

import CounterfactualExplanations.Generators: ∂ℓ
using LinearAlgebra

# Countefactual loss:
function ∂ℓ(
    generator::AbstractGradientBasedGenerator, 
    counterfactual_state::CounterfactualState) 
  M = counterfactual_state.M
  nn = M.nn
  x′ = counterfactual_state.x′

pat-alt

using Flux
using CounterfactualExplanations, CounterfactualExplanations.Models
import CounterfactualExplanations.Models: logits, probs # import functions in order to extend

# Step 1)
struct TorchNetwork <: Models.AbstractFittedModel
    nn::Any
end

# Step 2)

pat-alt

# Import libraries.
using Flux, Plots, Random, PlotThemes, Statistics, BayesLaplace
theme(:wong)

# Toy data:
xs, y = toy_data_linear(100)
X = hcat(xs...); # bring into tabular format
data = zip(xs,y)

# Build MLP:

pat-alt

# Import libraries.
using Flux, Plots, Random, PlotThemes, Statistics, BayesLaplace
theme(:wong)

# Toy data:
xs, y = toy_data_linear(100)
X = hcat(xs...); # bring into tabular format
data = zip(xs,y)

# Neural network:

pat-alt

# Newton's Method
function arminjo(𝓁, g_t, θ_t, d_t, args, ρ, c=1e-4)
    𝓁(θ_t .+ ρ .* d_t, args...) <= 𝓁(θ_t, args...) .+ c .* ρ .* d_t'g_t
end

function newton(𝓁, θ, ∇𝓁, ∇∇𝓁, args; max_iter=100, τ=1e-5)
    # Intialize:
    converged = false # termination state
    t = 1 # iteration count
    θ_t = θ # initial parameters

pat-alt

# Loss:
function 𝓁(w,w_0,H_0,X,y)
    N = length(y)
    D = size(X)[2]
    μ = sigmoid(w,X)
    Δw = w-w_0
    l = - ∑( y[n] * log(μ[n]) + (1-y[n]) * log(1-μ[n]) for n=1:N) + 1/2 * Δw'H_0*Δw
    return l
end

pat-alt

logit <- function(X, y, beta_0=NULL, tau=1e-9, max_iter=10000) {
  if(!all(X[,1]==1)) {
    X <- cbind(1,X)
  }
  p <- ncol(X)
  n <- nrow(X)
  # Initialization: ----
  if (is.null(beta_0)) {
    beta_latest <- matrix(rep(0, p)) # naive first guess
  }