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+ "markdown": "\n\n# `ConformalGenerator`\n\nIn this section, we will look at a simple example involving synthetic data, a black-box model and a generic Conformal Counterfactual Generator.\n\n## Black-box Model\n\nWe consider a simple binary classification problem. Let $(X_i, Y_i), \\ i=1,...,n$ denote our feature-label pairs and let $\\mu: \\mathcal{X} \\mapsto \\mathcal{Y}$ denote the mapping from features to labels. For illustration purposes, we will use linearly separable data. \n\n\n\nWhile we could use a linear classifier in this case, let's pretend we need a black-box model for this task and rely on a small Multi-Layer Perceptron (MLP):\n\n\n\nWe can fit this model to data to produce plug-in predictions. \n\n## Conformal Prediction\n\nHere we will instead use a specific case of CP called *split conformal prediction* which can then be summarized as follows:^[In other places split conformal prediction is sometimes referred to as *inductive* conformal prediction.]\n\n1. Partition the training into a proper training set and a separate calibration set: $\\mathcal{D}_n=\\mathcal{D}^{\\text{train}} \\cup \\mathcal{D}^{\\text{cali}}$.\n2. Train the machine learning model on the proper training set: $\\hat\\mu_{i \\in \\mathcal{D}^{\\text{train}}}(X_i,Y_i)$.\n\nThe model $\\hat\\mu_{i \\in \\mathcal{D}^{\\text{train}}}$ can now produce plug-in predictions. \n\n::: callout-note\n\n## Starting Point\n\nNote that this represents the starting point in applications of Algorithmic Recourse: we have some pre-trained classifier $M$ for which we would like to generate plausible Counterfactual Explanations. Next, we turn to the calibration step. \n:::\n\n3. Compute nonconformity scores, $\\mathcal{S}$, using the calibration data $\\mathcal{D}^{\\text{cali}}$ and the fitted model $\\hat\\mu_{i \\in \\mathcal{D}^{\\text{train}}}$. \n4. For a user-specified desired coverage ratio $(1-\\alpha)$ compute the corresponding quantile, $\\hat{q}$, of the empirical distribution of nonconformity scores, $\\mathcal{S}$.\n5. For the given quantile and test sample $X_{\\text{test}}$, form the corresponding conformal prediction set: \n\n$$\nC(X_{\\text{test}})=\\{y:s(X_{\\text{test}},y) \\le \\hat{q}\\}\n$$ {#eq-set}\n\nThis is the default procedure used for classification and regression in [`ConformalPrediction.jl`](https://github.com/pat-alt/ConformalPrediction.jl). \n\nUsing the package, we can apply Split Conformal Prediction as follows:\n\n\n\nTo be clear, all of the calibration steps (3 to 5) are post hoc, and yet none of them involved any changes to the model parameters. These are two important characteristics of Split Conformal Prediction (SCP) that make it particularly useful in the context of Algorithmic Recourse. Firstly, the fact that SCP involves posthoc calibration steps that happen after training, ensures that we need not place any restrictions on the black-box model itself. This stands in contrast to the approach proposed by @schut2021generating in which they essentially restrict the class of models to Bayesian models. Secondly, the fact that the model itself is kept entirely intact ensures that the generated counterfactuals maintain fidelity to the model. Finally, note that we also have not resorted to a surrogate model to learn more about $X \\sim \\mathcal{X}$. Instead, we have used the fitted model itself and a calibration data set to learn about the model's predictive uncertainty. \n\n## Differentiable CP\n\nIn order to use CP in the context of gradient-based counterfactual search, we need it to be differentiable. @stutz2022learning introduce a framework for training differentiable conformal predictors. They introduce a configurable loss function as well as smooth set size penalty.\n\n### Smooth Set Size Penalty\n\nStarting with the former, @stutz2022learning propose the following:\n\n$$\n\\Omega(C_{\\theta}(x;\\tau)) = = \\max (0, \\sum_k C_{\\theta,k}(x;\\tau) - \\kappa)\n$$ {#eq-size-loss}\n\nHere, $C_{\\theta,k}(x;\\tau)$ is loosely defined as the probability that class $k$ is assigned to the conformal prediction set $C$. In the context of Conformal Training, this penalty reduces the **inefficiency** of the conformal predictor. \n\nIn our context, we are not interested in improving the model itself, but rather in producing **plausible** counterfactuals. Provided that our counterfactual $x^\\prime$ is already inside the target domain ($\\mathbb{I}_{y^\\prime = t}=1$), penalizing $\\Omega(C_{\\theta}(x;\\tau))$ corresponds to guiding counterfactuals into regions of the target domain that are characterized by low ambiguity: for $\\kappa=1$ the conformal prediction set includes only the target label $t$ as $\\Omega(C_{\\theta}(x;\\tau))$. Arguably, less ambiguous counterfactuals are more **plausible**. Since the search is guided purely by properties of the model itself and (exchangeable) calibration data, counterfactuals also maintain **high fidelity**.\n\nThe left panel of @fig-losses shows the smooth size penalty in the two-dimensional feature space of our synthetic data.\n\n### Configurable Classification Loss\n\nThe right panel of @fig-losses shows the configurable classification loss in the two-dimensional feature space of our synthetic data.\n\n\n\n## Fidelity and Plausibility\n\nThe main evaluation criteria we are interested in are *fidelity* and *plausibility*. Interestingly, we could also consider using these measures as penalties in the counterfactual search.\n\n### Fidelity\n\nWe propose to define fidelity as follows:\n\n::: {#def-fidelity}\n\n## High-Fidelity Counterfactuals\n\nLet $\\mathcal{X}_{\\theta}|y = p_{\\theta}(X|y)$ denote the class-conditional distribution of $X$ defined by $\\theta$. Then for $x^{\\prime}$ to be considered a high-fidelity counterfactual, we need: $\\mathcal{X}_{\\theta}|t \\approxeq \\mathcal{X}^{\\prime}$ where $t$ denotes the target outcome.\n\n:::\n\nWe can generate samples from $p_{\\theta}(X|y)$ following @grathwohl2020your. In @fig-energy, I have applied the methodology to our synthetic data.\n\n\n\nAs an evaluation metric and penalty, we could use the average distance of the counterfactual $x^{\\prime}$ from these generated samples, for example.\n\n### Plausibility\n\nWe propose to define plausibility as follows:\n\n::: {#def-plausible}\n\n## Plausible Counterfactuals\n\nFormally, let $\\mathcal{X}|t$ denote the conditional distribution of samples in the target class. As before, we have $x^{\\prime}\\sim\\mathcal{X}^{\\prime}$, then for $x^{\\prime}$ to be considered a plausible counterfactual, we need: $\\mathcal{X}|t \\approxeq \\mathcal{X}^{\\prime}$.\n\n:::\n\nAs an evaluation metric and penalty, we could use the average distance of the counterfactual $x^{\\prime}$ from (potentially bootstrapped) training samples in the target class, for example.\n\n## Counterfactual Explanations\n\nNext, let's generate counterfactual explanations for our synthetic data. We first wrap our model in a container that makes it compatible with `CounterfactualExplanations.jl`. Then we draw a random sample, determine its predicted label $\\hat{y}$ and choose the opposite label as our target. \n\n\n\nThe generic Conformal Counterfactual Generator penalises the only the set size only:\n\n$$\nx^\\prime = \\arg \\min_{x^\\prime} \\ell(M(x^\\prime),t) + \\lambda \\mathbb{I}_{y^\\prime = t} \\Omega(C_{\\theta}(x;\\tau)) \n$$ {#eq-solution}\n\n\n\n### Benchmark\n\n\n\nThen we can simply loop over the datasets and eventually concatenate the results like so:\n\n\n\n## Multi-Class\n\n\n\n\n\n\n\n\n\n\n\n\n\n",
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+ "markdown": "---\ntitle: High-Fidelity Counterfactual Explanations through Conformal Prediction\nsubtitle: Research Proposal\nabstract: |\n We propose Conformal Counterfactual Explanations: an effortless and rigorous way to produce realistic and faithful Counterfactual Explanations using Conformal Prediction. To address the need for realistic counterfactuals, existing work has primarily relied on separate generative models to learn the data-generating process. While this is an effective way to produce plausible and model-agnostic counterfactual explanations, it not only introduces a significant engineering overhead but also reallocates the task of creating realistic model explanations from the model itself to the generative model. Recent work has shown that there is no need for any of this when working with probabilistic models that explicitly quantify their own uncertainty. Unfortunately, most models used in practice still do not fulfil that basic requirement, in which case we would like to have a way to quantify predictive uncertainty in a post-hoc fashion.\n---\n\n\n\n## Motivation\n\nCounterfactual Explanations are a powerful, flexible and intuitive way to not only explain black-box models but also enable affected individuals to challenge them through the means of Algorithmic Recourse. \n\n### Counterfactual Explanations or Adversarial Examples?\n\nMost state-of-the-art approaches to generating Counterfactual Explanations (CE) rely on gradient descent in the feature space. The key idea is to perturb inputs $x\\in\\mathcal{X}$ into a black-box model $f: \\mathcal{X} \\mapsto \\mathcal{Y}$ in order to change the model output $f(x)$ to some pre-specified target value $t\\in\\mathcal{Y}$. Formally, this boils down to defining some loss function $\\ell(f(x),t)$ and taking gradient steps in the minimizing direction. The so-generated counterfactuals are considered valid as soon as the predicted label matches the target label. A stripped-down counterfactual explanation is therefore little different from an adversarial example. In @fig-adv, for example, generic counterfactual search as in @wachter2017counterfactual has been applied to MNIST data.\n\n\n\n\n\n{#fig-adv}\n\nThe crucial difference between adversarial examples and counterfactuals is one of intent. While adversarial examples are typically intended to go unnoticed, counterfactuals in the context of Explainable AI are generally sought to be \"plausible\", \"realistic\" or \"feasible\". To fulfil this latter goal, researchers have come up with a myriad of ways. @joshi2019realistic were among the first to suggest that instead of searching counterfactuals in the feature space, we can instead traverse a latent embedding learned by a surrogate generative model. Similarly, @poyiadzi2020face use density ... Finally, @karimi2021algorithmic argues that counterfactuals should comply with the causal model that generates them [CHECK IF WE CAN PHASE THIS LIKE THIS]. Other related approaches include ... All of these different approaches have a common goal: they aim to ensure that the generated counterfactuals comply with the (learned) data-generating process (DGB). \n\n::: {#def-plausible}\n\n## Plausible Counterfactuals\n\nFormally, if $x \\sim \\mathcal{X}$ and for the corresponding counterfactual we have $x^{\\prime}\\sim\\mathcal{X}^{\\prime}$, then for $x^{\\prime}$ to be considered a plausible counterfactual, we need: $\\mathcal{X} \\approxeq \\mathcal{X}^{\\prime}$.\n\n:::\n\nIn the context of Algorithmic Recourse, it makes sense to strive for plausible counterfactuals, since anything else would essentially require individuals to move to out-of-distribution states. But it is worth noting that our ambition to meet this goal, may have implications on our ability to faithfully explain the behaviour of the underlying black-box model (arguably our principal goal). By essentially decoupling the task of learning plausible representations of the data from the model itself, we open ourselves up to vulnerabilities. Using a separate generative model to learn $\\mathcal{X}$, for example, has very serious implications for the generated counterfactuals. @fig-latent compares the results of applying REVISE [@joshi2019realistic] to MNIST data using two different Variational Auto-Encoders: while the counterfactual generated using an expressive (strong) VAE is compelling, the result relying on a less expressive (weak) VAE is not even valid. In this latter case, the decoder step of the VAE fails to yield values in $\\mathcal{X}$ and hence the counterfactual search in the learned latent space is doomed. \n\n{#fig-latent}\n\n> Here it would be nice to have another example where we poison the data going into the generative model to hide biases present in the data (e.g. Boston housing).\n\n- Latent can be manipulated: \n - train biased model\n - train VAE with biased variable removed/attacked (use Boston housing dataset)\n - hypothesis: will generate bias-free explanations\n\n### From Plausible to High-Fidelity Counterfactuals {#sec-fidelity}\n\nIn light of the findings, we propose to generally avoid using surrogate models to learn $\\mathcal{X}$ in the context of Counterfactual Explanations.\n\n::: {#prp-surrogate}\n\n## Avoid Surrogates\n\nSince we are in the business of explaining a black-box model, the task of learning realistic representations of the data should not be reallocated from the model itself to some surrogate model.\n\n:::\n\nIn cases where the use of surrogate models cannot be avoided, we propose to weigh the plausibility of counterfactuals against their fidelity to the black-box model. In the context of Explainable AI, fidelity is defined as describing how an explanation approximates the prediction of the black-box model [@molnar2020interpretable]. Fidelity has become the default metric for evaluating Local Model-Agnostic Models, since they often involve local surrogate models whose predictions need not always match those of the black-box model. \n\nIn the case of Counterfactual Explanations, the concept of fidelity has so far been ignored. This is not altogether surprising, since by construction and design, Counterfactual Explanations work with the predictions of the black-box model directly: as stated above, a counterfactual $x^{\\prime}$ is considered valid if and only if $f(x^{\\prime})=t$, where $t$ denote some target outcome. \n\nDoes fidelity even make sense in the context of CE, and if so, how can we define it? In light of the examples in the previous section, we think it is urgent to introduce a notion of fidelity in this context, that relates to the distributional properties of the generated counterfactuals. In particular, we propose that a high-fidelity counterfactual $x^{\\prime}$ complies with the class-conditional distribution $\\mathcal{X}_{\\theta} = p_{\\theta}(X|y)$ where $\\theta$ denote the black-box model parameters. \n\n::: {#def-fidele}\n\n## High-Fidelity Counterfactuals\n\nLet $\\mathcal{X}_{\\theta}|y = p_{\\theta}(X|y)$ denote the class-conditional distribution of $X$ defined by $\\theta$. Then for $x^{\\prime}$ to be considered a high-fidelity counterfactual, we need: $\\mathcal{X}_{\\theta}|t \\approxeq \\mathcal{X}^{\\prime}$ where $t$ denotes the target outcome.\n\n:::\n\nIn order to assess the fidelity of counterfactuals, we propose the following two-step procedure:\n\n1) Generate samples $X_{\\theta}|y$ and $X^{\\prime}$ from $\\mathcal{X}_{\\theta}|t$ and $\\mathcal{X}^{\\prime}$, respectively.\n2) Compute the Maximum Mean Discrepancy (MMD) between $X_{\\theta}|y$ and $X^{\\prime}$. \n\nIf the computed value is different from zero, we can reject the null-hypothesis of fidelity.\n\n> Two challenges here: 1) implementing the sampling procedure in @grathwohl2020your; 2) it is unclear if MMD is really the right way to measure this. \n\n## Conformal Counterfactual Explanations\n\nIn @sec-fidelity, we have advocated for avoiding surrogate models in the context of Counterfactual Explanations. In this section, we introduce an alternative way to generate high-fidelity Counterfactual Explanations. In particular, we propose Conformal Counterfactual Explanations (CCE), that is Counterfactual Explanations that minimize the predictive uncertainty of conformal models. \n\n### Minimizing Predictive Uncertainty\n\n@schut2021generating demonstrated that the goal of generating realistic (plausible) counterfactuals can also be achieved by seeking counterfactuals that minimize the predictive uncertainty of the underlying black-box model. Similarly, @antoran2020getting ...\n\n- Problem: restricted to Bayesian models.\n- Solution: post-hoc predictive uncertainty quantification. In particular, Conformal Prediction. \n\n### Background on Conformal Prediction\n\n- Distribution-free, model-agnostic and scalable approach to predictive uncertainty quantification.\n- Conformal prediction is instance-based. So is CE. \n- Take any fitted model and turn it into a conformal model using calibration data.\n- Our approach, therefore, relaxes the restriction on the family of black-box models, at the cost of relying on a subset of the data. Arguably, data is often abundant and in most applications practitioners tend to hold out a test data set anyway. \n\n> Does the coverage guarantee carry over to counterfactuals?\n\n### Generating Conformal Counterfactuals\n\nWhile Conformal Prediction has recently grown in popularity, it does introduce a challenge in the context of classification: the predictions of Conformal Classifiers are set-valued and therefore difficult to work with, since they are, for example, non-differentiable. Fortunately, @stutz2022learning introduced carefully designed differentiable loss functions that make it possible to evaluate the performance of conformal predictions in training. We can leverage these recent advances in the context of gradient-based counterfactual search ...\n\n> Challenge: still need to implement these loss functions. \n\n## Experiments\n\n### Research Questions\n\n- Is CP alone enough to ensure realistic counterfactuals?\n- Do counterfactuals improve further as the models get better?\n- Do counterfactuals get more realistic as coverage\n- What happens as we vary coverage and setsize?\n- What happens as we improve the model robustness?\n- What happens as we improve the model's ability to incorporate predictive uncertainty (deep ensemble, laplace)?\n- What happens if we combine with DiCE, ClaPROAR, Gravitational?\n- What about CE robustness to endogenous shifts [@altmeyer2023endogenous]?\n\n- Benchmarking:\n - add PROBE [@pawelczyk2022probabilistically] into the mix.\n - compare travel costs to domain shits.\n\n> Nice to have: What about using Laplace Approximation, then Conformal Prediction? What about using Conformalised Laplace? \n\n## References\n\n",
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-
- const rightElementObserver = visibleItemObserver(rightSideConflictEls);
- const leftElementObserver = visibleItemObserver(leftSideConflictEls);
-
- const hideOverlappedSidebars = () => {
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- }
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- );
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-window.addEventListener("pageshow", (_event) => {
- function getTabSettings() {
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- function setTabSettings(data) {
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- function setTabState(groupName, groupValue) {
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- function findSelectorsToSyncByLanguage() {
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- }
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- function setupSelectorSync() {
- const selectorsToSync = findSelectorsToSyncByLanguage();
- Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => {
- Object.entries(tabSetsByValue).forEach(([value, items]) => {
- items.forEach((item) => {
- item.addEventListener("click", (_event) => {
- setTabState(group, value);
- toggleAll(value, selectorsToSync[group]);
- });
- });
- });
- });
- return selectorsToSync;
- }
-
- const selectorsToSync = setupSelectorSync();
- for (const [group, selectedName] of Object.entries(getTabSettings())) {
- const selectors = selectorsToSync[group];
- // it's possible that stale state gives us empty selections, so we explicitly check here.
- if (selectors) {
- toggleAll(selectedName, selectors);
- }
- }
-});
-
-function throttle(func, wait) {
- let waiting = false;
- return function () {
- if (!waiting) {
- func.apply(this, arguments);
- waiting = true;
- setTimeout(function () {
- waiting = false;
- }, wait);
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- };
-}
-
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-}
-</script>
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s=`scroll${n[0].toUpperCase()+n.slice(1)}`;this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(ht),this._element.classList.add(ct,lt),this._element.style[n]="",j.trigger(this._element,"shown.bs.collapse")}),this._element,!0),this._element.style[n]=`${this._element[s]}px`}hide(){if(this._isTransitioning||!this._isShown())return;if(j.trigger(this._element,"hide.bs.collapse").defaultPrevented)return;const t=this._getDimension();this._element.style[t]=`${this._element.getBoundingClientRect()[t]}px`,u(this._element),this._element.classList.add(ht),this._element.classList.remove(ct,lt);const e=this._triggerArray.length;for(let t=0;t<e;t++){const e=this._triggerArray[t],i=n(e);i&&!this._isShown(i)&&this._addAriaAndCollapsedClass([e],!1)}this._isTransitioning=!0,this._element.style[t]="",this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(ht),this._element.classList.add(ct),j.trigger(this._element,"hidden.bs.collapse")}),this._element,!0)}_isShown(t=this._element){return t.classList.contains(lt)}_getConfig(t){return(t={...rt,...U.getDataAttributes(this._element),...t}).toggle=Boolean(t.toggle),t.parent=r(t.parent),a(ot,t,at),t}_getDimension(){return this._element.classList.contains("collapse-horizontal")?"width":"height"}_initializeChildren(){if(!this._config.parent)return;const t=V.find(ut,this._config.parent);V.find(ft,this._config.parent).filter((e=>!t.includes(e))).forEach((t=>{const e=n(t);e&&this._addAriaAndCollapsedClass([t],this._isShown(e))}))}_addAriaAndCollapsedClass(t,e){t.length&&t.forEach((t=>{e?t.classList.remove(dt):t.classList.add(dt),t.setAttribute("aria-expanded",e)}))}static 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t?(t.nodeName||"").toLowerCase():null}function Wt(t){if(null==t)return window;if("[object Window]"!==t.toString()){var e=t.ownerDocument;return e&&e.defaultView||window}return t}function $t(t){return t instanceof Wt(t).Element||t instanceof Element}function zt(t){return t instanceof Wt(t).HTMLElement||t instanceof HTMLElement}function qt(t){return"undefined"!=typeof ShadowRoot&&(t instanceof Wt(t).ShadowRoot||t instanceof ShadowRoot)}const Ft={name:"applyStyles",enabled:!0,phase:"write",fn:function(t){var e=t.state;Object.keys(e.elements).forEach((function(t){var i=e.styles[t]||{},n=e.attributes[t]||{},s=e.elements[t];zt(s)&&Rt(s)&&(Object.assign(s.style,i),Object.keys(n).forEach((function(t){var e=n[t];!1===e?s.removeAttribute(t):s.setAttribute(t,!0===e?"":e)})))}))},effect:function(t){var e=t.state,i={popper:{position:e.options.strategy,left:"0",top:"0",margin:"0"},arrow:{position:"absolute"},reference:{}};return 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Be={placement:"bottom",modifiers:[],strategy:"absolute"};function Re(){for(var t=arguments.length,e=new Array(t),i=0;i<t;i++)e[i]=arguments[i];return!e.some((function(t){return!(t&&"function"==typeof t.getBoundingClientRect)}))}function We(t){void 0===t&&(t={});var e=t,i=e.defaultModifiers,n=void 0===i?[]:i,s=e.defaultOptions,o=void 0===s?Be:s;return function(t,e,i){void 0===i&&(i=o);var s,r,a={placement:"bottom",orderedModifiers:[],options:Object.assign({},Be,o),modifiersData:{},elements:{reference:t,popper:e},attributes:{},styles:{}},l=[],c=!1,h={state:a,setOptions:function(i){var s="function"==typeof i?i(a.options):i;d(),a.options=Object.assign({},o,a.options,s),a.scrollParents={reference:$t(t)?Ae(t):t.contextElement?Ae(t.contextElement):[],popper:Ae(e)};var r,c,u=function(t){var e=He(t);return Bt.reduce((function(t,i){return t.concat(e.filter((function(t){return t.phase===i})))}),[])}((r=[].concat(n,a.options.modifiers),c=r.reduce((function(t,e){var i=t[e.name];return 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RegExp("ArrowUp|ArrowDown|Escape"),Ge="click.bs.dropdown.data-api",Ze="keydown.bs.dropdown.data-api",Je="show",ti='[data-bs-toggle="dropdown"]',ei=".dropdown-menu",ii=m()?"top-end":"top-start",ni=m()?"top-start":"top-end",si=m()?"bottom-end":"bottom-start",oi=m()?"bottom-start":"bottom-end",ri=m()?"left-start":"right-start",ai=m()?"right-start":"left-start",li={offset:[0,2],boundary:"clippingParents",reference:"toggle",display:"dynamic",popperConfig:null,autoClose:!0},ci={offset:"(array|string|function)",boundary:"(string|element)",reference:"(string|element|object)",display:"string",popperConfig:"(null|object|function)",autoClose:"(boolean|string)"};class hi extends B{constructor(t,e){super(t),this._popper=null,this._config=this._getConfig(e),this._menu=this._getMenuElement(),this._inNavbar=this._detectNavbar()}static get Default(){return li}static get DefaultType(){return ci}static get NAME(){return Ue}toggle(){return this._isShown()?this.hide():this.show()}show(){if(c(this._element)||this._isShown(this._menu))return;const t={relatedTarget:this._element};if(j.trigger(this._element,"show.bs.dropdown",t).defaultPrevented)return;const e=hi.getParentFromElement(this._element);this._inNavbar?U.setDataAttribute(this._menu,"popper","none"):this._createPopper(e),"ontouchstart"in document.documentElement&&!e.closest(".navbar-nav")&&[].concat(...document.body.children).forEach((t=>j.on(t,"mouseover",d))),this._element.focus(),this._element.setAttribute("aria-expanded",!0),this._menu.classList.add(Je),this._element.classList.add(Je),j.trigger(this._element,"shown.bs.dropdown",t)}hide(){if(c(this._element)||!this._isShown(this._menu))return;const t={relatedTarget:this._element};this._completeHide(t)}dispose(){this._popper&&this._popper.destroy(),super.dispose()}update(){this._inNavbar=this._detectNavbar(),this._popper&&this._popper.update()}_completeHide(t){j.trigger(this._element,"hide.bs.dropdown",t).defaultPrevented||("ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>j.off(t,"mouseover",d))),this._popper&&this._popper.destroy(),this._menu.classList.remove(Je),this._element.classList.remove(Je),this._element.setAttribute("aria-expanded","false"),U.removeDataAttribute(this._menu,"popper"),j.trigger(this._element,"hidden.bs.dropdown",t))}_getConfig(t){if(t={...this.constructor.Default,...U.getDataAttributes(this._element),...t},a(Ue,t,this.constructor.DefaultType),"object"==typeof t.reference&&!o(t.reference)&&"function"!=typeof t.reference.getBoundingClientRect)throw new TypeError(`${Ue.toUpperCase()}: Option "reference" provided type "object" without a required "getBoundingClientRect" method.`);return t}_createPopper(t){if(void 0===Fe)throw new TypeError("Bootstrap's dropdowns require Popper (https://popper.js.org)");let e=this._element;"parent"===this._config.reference?e=t:o(this._config.reference)?e=r(this._config.reference):"object"==typeof this._config.reference&&(e=this._config.reference);const i=this._getPopperConfig(),n=i.modifiers.find((t=>"applyStyles"===t.name&&!1===t.enabled));this._popper=qe(e,this._menu,i),n&&U.setDataAttribute(this._menu,"popper","static")}_isShown(t=this._element){return t.classList.contains(Je)}_getMenuElement(){return V.next(this._element,ei)[0]}_getPlacement(){const t=this._element.parentNode;if(t.classList.contains("dropend"))return ri;if(t.classList.contains("dropstart"))return ai;const e="end"===getComputedStyle(this._menu).getPropertyValue("--bs-position").trim();return t.classList.contains("dropup")?e?ni:ii:e?oi:si}_detectNavbar(){return null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return"static"===this._config.display&&(t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,..."function"==typeof this._config.popperConfig?this._config.popperConfig(t):this._config.popperConfig}}_selectMenuItem({key:t,target:e}){const i=V.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter(l);i.length&&v(i,e,t===Ye,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(t&&(2===t.button||"keyup"===t.type&&"Tab"!==t.key))return;const e=V.find(ti);for(let i=0,n=e.length;i<n;i++){const n=hi.getInstance(e[i]);if(!n||!1===n._config.autoClose)continue;if(!n._isShown())continue;const s={relatedTarget:n._element};if(t){const e=t.composedPath(),i=e.includes(n._menu);if(e.includes(n._element)||"inside"===n._config.autoClose&&!i||"outside"===n._config.autoClose&&i)continue;if(n._menu.contains(t.target)&&("keyup"===t.type&&"Tab"===t.key||/input|select|option|textarea|form/i.test(t.target.tagName)))continue;"click"===t.type&&(s.clickEvent=t)}n._completeHide(s)}}static getParentFromElement(t){return n(t)||t.parentNode}static dataApiKeydownHandler(t){if(/input|textarea/i.test(t.target.tagName)?t.key===Ke||t.key!==Ve&&(t.key!==Ye&&t.key!==Xe||t.target.closest(ei)):!Qe.test(t.key))return;const e=this.classList.contains(Je);if(!e&&t.key===Ve)return;if(t.preventDefault(),t.stopPropagation(),c(this))return;const i=this.matches(ti)?this:V.prev(this,ti)[0],n=hi.getOrCreateInstance(i);if(t.key!==Ve)return t.key===Xe||t.key===Ye?(e||n.show(),void n._selectMenuItem(t)):void(e&&t.key!==Ke||hi.clearMenus());n.hide()}}j.on(document,Ze,ti,hi.dataApiKeydownHandler),j.on(document,Ze,ei,hi.dataApiKeydownHandler),j.on(document,Ge,hi.clearMenus),j.on(document,"keyup.bs.dropdown.data-api",hi.clearMenus),j.on(document,Ge,ti,(function(t){t.preventDefault(),hi.getOrCreateInstance(this).toggle()})),g(hi);const di=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",ui=".sticky-top";class fi{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,"paddingRight",(e=>e+t)),this._setElementAttributes(di,"paddingRight",(e=>e+t)),this._setElementAttributes(ui,"marginRight",(e=>e-t))}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t)[e];t.style[e]=`${i(Number.parseFloat(s))}px`}))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,"paddingRight"),this._resetElementAttributes(di,"paddingRight"),this._resetElementAttributes(ui,"marginRight")}_saveInitialAttribute(t,e){const i=t.style[e];i&&U.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=U.getDataAttribute(t,e);void 0===i?t.style.removeProperty(e):(U.removeDataAttribute(t,e),t.style[e]=i)}))}_applyManipulationCallback(t,e){o(t)?e(t):V.find(t,this._element).forEach(e)}isOverflowing(){return this.getWidth()>0}}const pi={className:"modal-backdrop",isVisible:!0,isAnimated:!1,rootElement:"body",clickCallback:null},mi={className:"string",isVisible:"boolean",isAnimated:"boolean",rootElement:"(element|string)",clickCallback:"(function|null)"},gi="show",_i="mousedown.bs.backdrop";class bi{constructor(t){this._config=this._getConfig(t),this._isAppended=!1,this._element=null}show(t){this._config.isVisible?(this._append(),this._config.isAnimated&&u(this._getElement()),this._getElement().classList.add(gi),this._emulateAnimation((()=>{_(t)}))):_(t)}hide(t){this._config.isVisible?(this._getElement().classList.remove(gi),this._emulateAnimation((()=>{this.dispose(),_(t)}))):_(t)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_getConfig(t){return(t={...pi,..."object"==typeof t?t:{}}).rootElement=r(t.rootElement),a("backdrop",t,mi),t}_append(){this._isAppended||(this._config.rootElement.append(this._getElement()),j.on(this._getElement(),_i,(()=>{_(this._config.clickCallback)})),this._isAppended=!0)}dispose(){this._isAppended&&(j.off(this._element,_i),this._element.remove(),this._isAppended=!1)}_emulateAnimation(t){b(t,this._getElement(),this._config.isAnimated)}}const vi={trapElement:null,autofocus:!0},yi={trapElement:"element",autofocus:"boolean"},wi=".bs.focustrap",Ei="backward";class Ai{constructor(t){this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}activate(){const{trapElement:t,autofocus:e}=this._config;this._isActive||(e&&t.focus(),j.off(document,wi),j.on(document,"focusin.bs.focustrap",(t=>this._handleFocusin(t))),j.on(document,"keydown.tab.bs.focustrap",(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,j.off(document,wi))}_handleFocusin(t){const{target:e}=t,{trapElement:i}=this._config;if(e===document||e===i||i.contains(e))return;const n=V.focusableChildren(i);0===n.length?i.focus():this._lastTabNavDirection===Ei?n[n.length-1].focus():n[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?Ei:"forward")}_getConfig(t){return t={...vi,..."object"==typeof t?t:{}},a("focustrap",t,yi),t}}const Ti="modal",Oi="Escape",Ci={backdrop:!0,keyboard:!0,focus:!0},ki={backdrop:"(boolean|string)",keyboard:"boolean",focus:"boolean"},Li="hidden.bs.modal",xi="show.bs.modal",Di="resize.bs.modal",Si="click.dismiss.bs.modal",Ni="keydown.dismiss.bs.modal",Ii="mousedown.dismiss.bs.modal",Pi="modal-open",ji="show",Mi="modal-static";class Hi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._dialog=V.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._ignoreBackdropClick=!1,this._isTransitioning=!1,this._scrollBar=new fi}static get Default(){return Ci}static get NAME(){return Ti}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||j.trigger(this._element,xi,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isAnimated()&&(this._isTransitioning=!0),this._scrollBar.hide(),document.body.classList.add(Pi),this._adjustDialog(),this._setEscapeEvent(),this._setResizeEvent(),j.on(this._dialog,Ii,(()=>{j.one(this._element,"mouseup.dismiss.bs.modal",(t=>{t.target===this._element&&(this._ignoreBackdropClick=!0)}))})),this._showBackdrop((()=>this._showElement(t))))}hide(){if(!this._isShown||this._isTransitioning)return;if(j.trigger(this._element,"hide.bs.modal").defaultPrevented)return;this._isShown=!1;const t=this._isAnimated();t&&(this._isTransitioning=!0),this._setEscapeEvent(),this._setResizeEvent(),this._focustrap.deactivate(),this._element.classList.remove(ji),j.off(this._element,Si),j.off(this._dialog,Ii),this._queueCallback((()=>this._hideModal()),this._element,t)}dispose(){[window,this._dialog].forEach((t=>j.off(t,".bs.modal"))),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new bi({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_getConfig(t){return t={...Ci,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Ti,t,ki),t}_showElement(t){const e=this._isAnimated(),i=V.findOne(".modal-body",this._dialog);this._element.parentNode&&this._element.parentNode.nodeType===Node.ELEMENT_NODE||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0,i&&(i.scrollTop=0),e&&u(this._element),this._element.classList.add(ji),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,j.trigger(this._element,"shown.bs.modal",{relatedTarget:t})}),this._dialog,e)}_setEscapeEvent(){this._isShown?j.on(this._element,Ni,(t=>{this._config.keyboard&&t.key===Oi?(t.preventDefault(),this.hide()):this._config.keyboard||t.key!==Oi||this._triggerBackdropTransition()})):j.off(this._element,Ni)}_setResizeEvent(){this._isShown?j.on(window,Di,(()=>this._adjustDialog())):j.off(window,Di)}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(Pi),this._resetAdjustments(),this._scrollBar.reset(),j.trigger(this._element,Li)}))}_showBackdrop(t){j.on(this._element,Si,(t=>{this._ignoreBackdropClick?this._ignoreBackdropClick=!1:t.target===t.currentTarget&&(!0===this._config.backdrop?this.hide():"static"===this._config.backdrop&&this._triggerBackdropTransition())})),this._backdrop.show(t)}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(j.trigger(this._element,"hidePrevented.bs.modal").defaultPrevented)return;const{classList:t,scrollHeight:e,style:i}=this._element,n=e>document.documentElement.clientHeight;!n&&"hidden"===i.overflowY||t.contains(Mi)||(n||(i.overflowY="hidden"),t.add(Mi),this._queueCallback((()=>{t.remove(Mi),n||this._queueCallback((()=>{i.overflowY=""}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;(!i&&t&&!m()||i&&!t&&m())&&(this._element.style.paddingLeft=`${e}px`),(i&&!t&&!m()||!i&&t&&m())&&(this._element.style.paddingRight=`${e}px`)}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=Hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}j.on(document,"click.bs.modal.data-api",'[data-bs-toggle="modal"]',(function(t){const e=n(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),j.one(e,xi,(t=>{t.defaultPrevented||j.one(e,Li,(()=>{l(this)&&this.focus()}))}));const i=V.findOne(".modal.show");i&&Hi.getInstance(i).hide(),Hi.getOrCreateInstance(e).toggle(this)})),R(Hi),g(Hi);const Bi="offcanvas",Ri={backdrop:!0,keyboard:!0,scroll:!1},Wi={backdrop:"boolean",keyboard:"boolean",scroll:"boolean"},$i="show",zi=".offcanvas.show",qi="hidden.bs.offcanvas";class Fi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get NAME(){return Bi}static get Default(){return Ri}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||j.trigger(this._element,"show.bs.offcanvas",{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._element.style.visibility="visible",this._backdrop.show(),this._config.scroll||(new fi).hide(),this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add($i),this._queueCallback((()=>{this._config.scroll||this._focustrap.activate(),j.trigger(this._element,"shown.bs.offcanvas",{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(j.trigger(this._element,"hide.bs.offcanvas").defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.remove($i),this._backdrop.hide(),this._queueCallback((()=>{this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._element.style.visibility="hidden",this._config.scroll||(new fi).reset(),j.trigger(this._element,qi)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_getConfig(t){return t={...Ri,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Bi,t,Wi),t}_initializeBackDrop(){return new bi({className:"offcanvas-backdrop",isVisible:this._config.backdrop,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:()=>this.hide()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_addEventListeners(){j.on(this._element,"keydown.dismiss.bs.offcanvas",(t=>{this._config.keyboard&&"Escape"===t.key&&this.hide()}))}static jQueryInterface(t){return this.each((function(){const e=Fi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}j.on(document,"click.bs.offcanvas.data-api",'[data-bs-toggle="offcanvas"]',(function(t){const e=n(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),c(this))return;j.one(e,qi,(()=>{l(this)&&this.focus()}));const i=V.findOne(zi);i&&i!==e&&Fi.getInstance(i).hide(),Fi.getOrCreateInstance(e).toggle(this)})),j.on(window,"load.bs.offcanvas.data-api",(()=>V.find(zi).forEach((t=>Fi.getOrCreateInstance(t).show())))),R(Fi),g(Fi);const Ui=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Vi=/^(?:(?:https?|mailto|ftp|tel|file|sms):|[^#&/:?]*(?:[#/?]|$))/i,Ki=/^data:(?:image\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\/(?:mpeg|mp4|ogg|webm)|audio\/(?:mp3|oga|ogg|opus));base64,[\d+/a-z]+=*$/i,Xi=(t,e)=>{const i=t.nodeName.toLowerCase();if(e.includes(i))return!Ui.has(i)||Boolean(Vi.test(t.nodeValue)||Ki.test(t.nodeValue));const n=e.filter((t=>t instanceof RegExp));for(let t=0,e=n.length;t<e;t++)if(n[t].test(i))return!0;return!1};function Yi(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(let t=0,i=s.length;t<i;t++){const i=s[t],n=i.nodeName.toLowerCase();if(!Object.keys(e).includes(n)){i.remove();continue}const o=[].concat(...i.attributes),r=[].concat(e["*"]||[],e[n]||[]);o.forEach((t=>{Xi(t,r)||i.removeAttribute(t.nodeName)}))}return n.body.innerHTML}const Qi="tooltip",Gi=new Set(["sanitize","allowList","sanitizeFn"]),Zi={animation:"boolean",template:"string",title:"(string|element|function)",trigger:"string",delay:"(number|object)",html:"boolean",selector:"(string|boolean)",placement:"(string|function)",offset:"(array|string|function)",container:"(string|element|boolean)",fallbackPlacements:"array",boundary:"(string|element)",customClass:"(string|function)",sanitize:"boolean",sanitizeFn:"(null|function)",allowList:"object",popperConfig:"(null|object|function)"},Ji={AUTO:"auto",TOP:"top",RIGHT:m()?"left":"right",BOTTOM:"bottom",LEFT:m()?"right":"left"},tn={animation:!0,template:'<div class="tooltip" role="tooltip"><div class="tooltip-arrow"></div><div class="tooltip-inner"></div></div>',trigger:"hover focus",title:"",delay:0,html:!1,selector:!1,placement:"top",offset:[0,0],container:!1,fallbackPlacements:["top","right","bottom","left"],boundary:"clippingParents",customClass:"",sanitize:!0,sanitizeFn:null,allowList:{"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},popperConfig:null},en={HIDE:"hide.bs.tooltip",HIDDEN:"hidden.bs.tooltip",SHOW:"show.bs.tooltip",SHOWN:"shown.bs.tooltip",INSERTED:"inserted.bs.tooltip",CLICK:"click.bs.tooltip",FOCUSIN:"focusin.bs.tooltip",FOCUSOUT:"focusout.bs.tooltip",MOUSEENTER:"mouseenter.bs.tooltip",MOUSELEAVE:"mouseleave.bs.tooltip"},nn="fade",sn="show",on="show",rn="out",an=".tooltip-inner",ln=".modal",cn="hide.bs.modal",hn="hover",dn="focus";class un extends B{constructor(t,e){if(void 0===Fe)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t),this._isEnabled=!0,this._timeout=0,this._hoverState="",this._activeTrigger={},this._popper=null,this._config=this._getConfig(e),this.tip=null,this._setListeners()}static get Default(){return tn}static get NAME(){return Qi}static get Event(){return en}static get DefaultType(){return Zi}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(t){if(this._isEnabled)if(t){const e=this._initializeOnDelegatedTarget(t);e._activeTrigger.click=!e._activeTrigger.click,e._isWithActiveTrigger()?e._enter(null,e):e._leave(null,e)}else{if(this.getTipElement().classList.contains(sn))return void this._leave(null,this);this._enter(null,this)}}dispose(){clearTimeout(this._timeout),j.off(this._element.closest(ln),cn,this._hideModalHandler),this.tip&&this.tip.remove(),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this.isWithContent()||!this._isEnabled)return;const t=j.trigger(this._element,this.constructor.Event.SHOW),e=h(this._element),i=null===e?this._element.ownerDocument.documentElement.contains(this._element):e.contains(this._element);if(t.defaultPrevented||!i)return;"tooltip"===this.constructor.NAME&&this.tip&&this.getTitle()!==this.tip.querySelector(an).innerHTML&&(this._disposePopper(),this.tip.remove(),this.tip=null);const n=this.getTipElement(),s=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME);n.setAttribute("id",s),this._element.setAttribute("aria-describedby",s),this._config.animation&&n.classList.add(nn);const o="function"==typeof this._config.placement?this._config.placement.call(this,n,this._element):this._config.placement,r=this._getAttachment(o);this._addAttachmentClass(r);const{container:a}=this._config;H.set(n,this.constructor.DATA_KEY,this),this._element.ownerDocument.documentElement.contains(this.tip)||(a.append(n),j.trigger(this._element,this.constructor.Event.INSERTED)),this._popper?this._popper.update():this._popper=qe(this._element,n,this._getPopperConfig(r)),n.classList.add(sn);const l=this._resolvePossibleFunction(this._config.customClass);l&&n.classList.add(...l.split(" ")),"ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>{j.on(t,"mouseover",d)}));const c=this.tip.classList.contains(nn);this._queueCallback((()=>{const t=this._hoverState;this._hoverState=null,j.trigger(this._element,this.constructor.Event.SHOWN),t===rn&&this._leave(null,this)}),this.tip,c)}hide(){if(!this._popper)return;const 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Dn}show(){j.trigger(this._element,"show.bs.toast").defaultPrevented||(this._clearTimeout(),this._config.animation&&this._element.classList.add("fade"),this._element.classList.remove(Sn),u(this._element),this._element.classList.add(Nn),this._element.classList.add(In),this._queueCallback((()=>{this._element.classList.remove(In),j.trigger(this._element,"shown.bs.toast"),this._maybeScheduleHide()}),this._element,this._config.animation))}hide(){this._element.classList.contains(Nn)&&(j.trigger(this._element,"hide.bs.toast").defaultPrevented||(this._element.classList.add(In),this._queueCallback((()=>{this._element.classList.add(Sn),this._element.classList.remove(In),this._element.classList.remove(Nn),j.trigger(this._element,"hidden.bs.toast")}),this._element,this._config.animation)))}dispose(){this._clearTimeout(),this._element.classList.contains(Nn)&&this._element.classList.remove(Nn),super.dispose()}_getConfig(t){return t={...jn,...U.getDataAttributes(this._element),..."object"==typeof t&&t?t:{}},a(Dn,t,this.constructor.DefaultType),t}_maybeScheduleHide(){this._config.autohide&&(this._hasMouseInteraction||this._hasKeyboardInteraction||(this._timeout=setTimeout((()=>{this.hide()}),this._config.delay)))}_onInteraction(t,e){switch(t.type){case"mouseover":case"mouseout":this._hasMouseInteraction=e;break;case"focusin":case"focusout":this._hasKeyboardInteraction=e}if(e)return void this._clearTimeout();const i=t.relatedTarget;this._element===i||this._element.contains(i)||this._maybeScheduleHide()}_setListeners(){j.on(this._element,"mouseover.bs.toast",(t=>this._onInteraction(t,!0))),j.on(this._element,"mouseout.bs.toast",(t=>this._onInteraction(t,!1))),j.on(this._element,"focusin.bs.toast",(t=>this._onInteraction(t,!0))),j.on(this._element,"focusout.bs.toast",(t=>this._onInteraction(t,!1)))}_clearTimeout(){clearTimeout(this._timeout),this._timeout=null}static jQueryInterface(t){return this.each((function(){const e=Mn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}return R(Mn),g(Mn),{Alert:W,Button:z,Carousel:st,Collapse:pt,Dropdown:hi,Modal:Hi,Offcanvas:Fi,Popover:gn,ScrollSpy:An,Tab:xn,Toast:Mn,Tooltip:un}}));
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) 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-.bi::before,
-[class^="bi-"]::before,
-[class*=" bi-"]::before {
-display: inline-block;
-font-family: bootstrap-icons !important;
-font-style: normal;
-font-weight: normal !important;
-font-variant: normal;
-text-transform: none;
-line-height: 1;
-vertical-align: -.125em;
--webkit-font-smoothing: antialiased;
--moz-osx-font-smoothing: grayscale;
-}
-.bi-123::before { content: "\f67f"; }
-.bi-alarm-fill::before { content: "\f101"; }
-.bi-alarm::before { content: "\f102"; }
-.bi-align-bottom::before { content: "\f103"; }
-.bi-align-center::before { content: "\f104"; }
-.bi-align-end::before { content: "\f105"; }
-.bi-align-middle::before { content: "\f106"; }
-.bi-align-start::before { content: "\f107"; }
-.bi-align-top::before { content: "\f108"; }
-.bi-alt::before { content: "\f109"; }
-.bi-app-indicator::before { content: "\f10a"; }
-.bi-app::before { content: "\f10b"; }
-.bi-archive-fill::before { content: "\f10c"; }
-.bi-archive::before { content: "\f10d"; }
-.bi-arrow-90deg-down::before { content: "\f10e"; }
-.bi-arrow-90deg-left::before { content: "\f10f"; }
-.bi-arrow-90deg-right::before { content: "\f110"; }
-.bi-arrow-90deg-up::before { content: "\f111"; }
-.bi-arrow-bar-down::before { content: "\f112"; }
-.bi-arrow-bar-left::before { content: "\f113"; }
-.bi-arrow-bar-right::before { content: "\f114"; }
-.bi-arrow-bar-up::before { content: "\f115"; }
-.bi-arrow-clockwise::before { content: "\f116"; }
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-.bi-arrow-down-circle-fill::before { content: "\f118"; }
-.bi-arrow-down-circle::before { content: "\f119"; }
-.bi-arrow-down-left-circle-fill::before { content: "\f11a"; }
-.bi-arrow-down-left-circle::before { content: "\f11b"; }
-.bi-arrow-down-left-square-fill::before { content: "\f11c"; }
-.bi-arrow-down-left-square::before { content: "\f11d"; }
-.bi-arrow-down-left::before { content: "\f11e"; }
-.bi-arrow-down-right-circle-fill::before { content: "\f11f"; }
-.bi-arrow-down-right-circle::before { content: "\f120"; }
-.bi-arrow-down-right-square-fill::before { content: "\f121"; }
-.bi-arrow-down-right-square::before { content: "\f122"; }
-.bi-arrow-down-right::before { content: "\f123"; }
-.bi-arrow-down-short::before { content: "\f124"; }
-.bi-arrow-down-square-fill::before { content: "\f125"; }
-.bi-arrow-down-square::before { content: "\f126"; }
-.bi-arrow-down-up::before { content: "\f127"; }
-.bi-arrow-down::before { content: "\f128"; }
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-.bi-arrow-left-square::before { content: "\f12e"; }
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-.bi-arrow-right-circle::before { content: "\f134"; }
-.bi-arrow-right-short::before { content: "\f135"; }
-.bi-arrow-right-square-fill::before { content: "\f136"; }
-.bi-arrow-right-square::before { content: "\f137"; }
-.bi-arrow-right::before { content: "\f138"; }
-.bi-arrow-up-circle-fill::before { content: "\f139"; }
-.bi-arrow-up-circle::before { content: "\f13a"; }
-.bi-arrow-up-left-circle-fill::before { content: "\f13b"; }
-.bi-arrow-up-left-circle::before { content: "\f13c"; }
-.bi-arrow-up-left-square-fill::before { content: "\f13d"; }
-.bi-arrow-up-left-square::before { content: "\f13e"; }
-.bi-arrow-up-left::before { content: "\f13f"; }
-.bi-arrow-up-right-circle-fill::before { content: "\f140"; }
-.bi-arrow-up-right-circle::before { content: "\f141"; }
-.bi-arrow-up-right-square-fill::before { content: "\f142"; }
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-.bi-train-front::before { content: "\f81d"; }
-.bi-train-lightrail-front-fill::before { content: "\f81e"; }
-.bi-train-lightrail-front::before { content: "\f81f"; }
-.bi-truck-front-fill::before { content: "\f820"; }
-.bi-truck-front::before { content: "\f821"; }
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-.bi-ev-station::before { content: "\f83a"; }
-.bi-fuel-pump-diesel-fill::before { content: "\f83b"; }
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-.bi-fuel-pump-fill::before { content: "\f83d"; }
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-.bi-rocket-takeoff-fill::before { content: "\f844"; }
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-.bi-sign-do-not-enter::before { content: "\f852"; }
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-.bi-sign-no-parking-fill::before { content: "\f861"; }
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-.bi-sign-no-right-turn-fill::before { content: "\f863"; }
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-.bi-sign-railroad-fill::before { content: "\f865"; }
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-.bi-building-check::before { content: "\f868"; }
-.bi-building-dash::before { content: "\f869"; }
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-.bi-building-exclamation::before { content: "\f86b"; }
-.bi-building-fill-add::before { content: "\f86c"; }
-.bi-building-fill-check::before { content: "\f86d"; }
-.bi-building-fill-dash::before { content: "\f86e"; }
-.bi-building-fill-down::before { content: "\f86f"; }
-.bi-building-fill-exclamation::before { content: "\f870"; }
-.bi-building-fill-gear::before { content: "\f871"; }
-.bi-building-fill-lock::before { content: "\f872"; }
-.bi-building-fill-slash::before { content: "\f873"; }
-.bi-building-fill-up::before { content: "\f874"; }
-.bi-building-fill-x::before { content: "\f875"; }
-.bi-building-fill::before { content: "\f876"; }
-.bi-building-gear::before { content: "\f877"; }
-.bi-building-lock::before { content: "\f878"; }
-.bi-building-slash::before { content: "\f879"; }
-.bi-building-up::before { content: "\f87a"; }
-.bi-building-x::before { content: "\f87b"; }
-.bi-buildings-fill::before { content: "\f87c"; }
-.bi-buildings::before { content: "\f87d"; }
-.bi-bus-front-fill::before { content: "\f87e"; }
-.bi-bus-front::before { content: "\f87f"; }
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-.bi-globe-central-south-asia::before { content: "\f884"; }
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-.bi-house-add::before { content: "\f887"; }
-.bi-house-check-fill::before { content: "\f888"; }
-.bi-house-check::before { content: "\f889"; }
-.bi-house-dash-fill::before { content: "\f88a"; }
-.bi-house-dash::before { content: "\f88b"; }
-.bi-house-down-fill::before { content: "\f88c"; }
-.bi-house-down::before { content: "\f88d"; }
-.bi-house-exclamation-fill::before { content: "\f88e"; }
-.bi-house-exclamation::before { content: "\f88f"; }
-.bi-house-gear-fill::before { content: "\f890"; }
-.bi-house-gear::before { content: "\f891"; }
-.bi-house-lock-fill::before { content: "\f892"; }
-.bi-house-lock::before { content: "\f893"; }
-.bi-house-slash-fill::before { content: "\f894"; }
-.bi-house-slash::before { content: "\f895"; }
-.bi-house-up-fill::before { content: "\f896"; }
-.bi-house-up::before { content: "\f897"; }
-.bi-house-x-fill::before { content: "\f898"; }
-.bi-house-x::before { content: "\f899"; }
-.bi-person-add::before { content: "\f89a"; }
-.bi-person-down::before { content: "\f89b"; }
-.bi-person-exclamation::before { content: "\f89c"; }
-.bi-person-fill-add::before { content: "\f89d"; }
-.bi-person-fill-check::before { content: "\f89e"; }
-.bi-person-fill-dash::before { content: "\f89f"; }
-.bi-person-fill-down::before { content: "\f8a0"; }
-.bi-person-fill-exclamation::before { content: "\f8a1"; }
-.bi-person-fill-gear::before { content: "\f8a2"; }
-.bi-person-fill-lock::before { content: "\f8a3"; }
-.bi-person-fill-slash::before { content: "\f8a4"; }
-.bi-person-fill-up::before { content: "\f8a5"; }
-.bi-person-fill-x::before { content: "\f8a6"; }
-.bi-person-gear::before { content: "\f8a7"; }
-.bi-person-lock::before { content: "\f8a8"; }
-.bi-person-slash::before { content: "\f8a9"; }
-.bi-person-up::before { content: "\f8aa"; }
-.bi-scooter::before { content: "\f8ab"; }
-.bi-taxi-front-fill::before { content: "\f8ac"; }
-.bi-taxi-front::before { content: "\f8ad"; }
-.bi-amd::before { content: "\f8ae"; }
-.bi-database-add::before { content: "\f8af"; }
-.bi-database-check::before { content: "\f8b0"; }
-.bi-database-dash::before { content: "\f8b1"; }
-.bi-database-down::before { content: "\f8b2"; }
-.bi-database-exclamation::before { content: "\f8b3"; }
-.bi-database-fill-add::before { content: "\f8b4"; }
-.bi-database-fill-check::before { content: "\f8b5"; }
-.bi-database-fill-dash::before { content: "\f8b6"; }
-.bi-database-fill-down::before { content: "\f8b7"; }
-.bi-database-fill-exclamation::before { content: "\f8b8"; }
-.bi-database-fill-gear::before { content: "\f8b9"; }
-.bi-database-fill-lock::before { content: "\f8ba"; }
-.bi-database-fill-slash::before { content: "\f8bb"; }
-.bi-database-fill-up::before { content: "\f8bc"; }
-.bi-database-fill-x::before { content: "\f8bd"; }
-.bi-database-fill::before { content: "\f8be"; }
-.bi-database-gear::before { content: "\f8bf"; }
-.bi-database-lock::before { content: "\f8c0"; }
-.bi-database-slash::before { content: "\f8c1"; }
-.bi-database-up::before { content: "\f8c2"; }
-.bi-database-x::before { content: "\f8c3"; }
-.bi-database::before { content: "\f8c4"; }
-.bi-houses-fill::before { content: "\f8c5"; }
-.bi-houses::before { content: "\f8c6"; }
-.bi-nvidia::before { content: "\f8c7"; }
-.bi-person-vcard-fill::before { content: "\f8c8"; }
-.bi-person-vcard::before { content: "\f8c9"; }
-.bi-sina-weibo::before { content: "\f8ca"; }
-.bi-tencent-qq::before { content: "\f8cb"; }
-.bi-wikipedia::before { content: "\f8cc"; }
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-<section id="conformalgenerator" class="level1">
-<h1><code>ConformalGenerator</code></h1>
-<p>In this section, we will look at a simple example involving synthetic data, a black-box model and a generic Conformal Counterfactual Generator.</p>
-<section id="black-box-model" class="level2">
-<h2 class="anchored" data-anchor-id="black-box-model">Black-box Model</h2>
-<p>We consider a simple binary classification problem. Let <span class="math inline">\((X_i, Y_i), \ i=1,...,n\)</span> denote our feature-label pairs and let <span class="math inline">\(\mu: \mathcal{X} \mapsto \mathcal{Y}\)</span> denote the mapping from features to labels. For illustration purposes, we will use linearly separable data.</p>
-<p>While we could use a linear classifier in this case, let’s pretend we need a black-box model for this task and rely on a small Multi-Layer Perceptron (MLP):</p>
-<p>We can fit this model to data to produce plug-in predictions.</p>
-</section>
-<section id="conformal-prediction" class="level2">
-<h2 class="anchored" data-anchor-id="conformal-prediction">Conformal Prediction</h2>
-<p>Here we will instead use a specific case of CP called <em>split conformal prediction</em> which can then be summarized as follows:<a href="#fn1" class="footnote-ref" id="fnref1" role="doc-noteref"><sup>1</sup></a></p>
-<ol type="1">
-<li>Partition the training into a proper training set and a separate calibration set: <span class="math inline">\(\mathcal{D}_n=\mathcal{D}^{\text{train}} \cup \mathcal{D}^{\text{cali}}\)</span>.</li>
-<li>Train the machine learning model on the proper training set: <span class="math inline">\(\hat\mu_{i \in \mathcal{D}^{\text{train}}}(X_i,Y_i)\)</span>.</li>
-</ol>
-<p>The model <span class="math inline">\(\hat\mu_{i \in \mathcal{D}^{\text{train}}}\)</span> can now produce plug-in predictions.</p>
-<div class="callout callout-style-default callout-note callout-titled">
-<div class="callout-header d-flex align-content-center">
-<div class="callout-icon-container">
-<i class="callout-icon"></i>
-</div>
-<div class="callout-title-container flex-fill">
-Starting Point
-</div>
-</div>
-<div class="callout-body-container callout-body">
-<p>Note that this represents the starting point in applications of Algorithmic Recourse: we have some pre-trained classifier <span class="math inline">\(M\)</span> for which we would like to generate plausible Counterfactual Explanations. Next, we turn to the calibration step.</p>
-</div>
-</div>
-<ol start="3" type="1">
-<li>Compute nonconformity scores, <span class="math inline">\(\mathcal{S}\)</span>, using the calibration data <span class="math inline">\(\mathcal{D}^{\text{cali}}\)</span> and the fitted model <span class="math inline">\(\hat\mu_{i \in \mathcal{D}^{\text{train}}}\)</span>.</li>
-<li>For a user-specified desired coverage ratio <span class="math inline">\((1-\alpha)\)</span> compute the corresponding quantile, <span class="math inline">\(\hat{q}\)</span>, of the empirical distribution of nonconformity scores, <span class="math inline">\(\mathcal{S}\)</span>.</li>
-<li>For the given quantile and test sample <span class="math inline">\(X_{\text{test}}\)</span>, form the corresponding conformal prediction set:</li>
-</ol>
-<p><span id="eq-set"><span class="math display">\[
-C(X_{\text{test}})=\{y:s(X_{\text{test}},y) \le \hat{q}\}
-\tag{1}\]</span></span></p>
-<p>This is the default procedure used for classification and regression in <a href="https://github.com/pat-alt/ConformalPrediction.jl"><code>ConformalPrediction.jl</code></a>.</p>
-<p>Using the package, we can apply Split Conformal Prediction as follows:</p>
-<p>To be clear, all of the calibration steps (3 to 5) are post hoc, and yet none of them involved any changes to the model parameters. These are two important characteristics of Split Conformal Prediction (SCP) that make it particularly useful in the context of Algorithmic Recourse. Firstly, the fact that SCP involves posthoc calibration steps that happen after training, ensures that we need not place any restrictions on the black-box model itself. This stands in contrast to the approach proposed by <span class="citation" data-cites="schut2021generating">Schut et al. (<a href="#ref-schut2021generating" role="doc-biblioref">2021</a>)</span> in which they essentially restrict the class of models to Bayesian models. Secondly, the fact that the model itself is kept entirely intact ensures that the generated counterfactuals maintain fidelity to the model. Finally, note that we also have not resorted to a surrogate model to learn more about <span class="math inline">\(X \sim \mathcal{X}\)</span>. Instead, we have used the fitted model itself and a calibration data set to learn about the model’s predictive uncertainty.</p>
-</section>
-<section id="differentiable-cp" class="level2">
-<h2 class="anchored" data-anchor-id="differentiable-cp">Differentiable CP</h2>
-<p>In order to use CP in the context of gradient-based counterfactual search, we need it to be differentiable. <span class="citation" data-cites="stutz2022learning">Stutz et al. (<a href="#ref-stutz2022learning" role="doc-biblioref">2022</a>)</span> introduce a framework for training differentiable conformal predictors. They introduce a configurable loss function as well as smooth set size penalty.</p>
-<section id="smooth-set-size-penalty" class="level3">
-<h3 class="anchored" data-anchor-id="smooth-set-size-penalty">Smooth Set Size Penalty</h3>
-<p>Starting with the former, <span class="citation" data-cites="stutz2022learning">Stutz et al. (<a href="#ref-stutz2022learning" role="doc-biblioref">2022</a>)</span> propose the following:</p>
-<p><span id="eq-size-loss"><span class="math display">\[
-\Omega(C_{\theta}(x;\tau)) = = \max (0, \sum_k C_{\theta,k}(x;\tau) - \kappa)
-\tag{2}\]</span></span></p>
-<p>Here, <span class="math inline">\(C_{\theta,k}(x;\tau)\)</span> is loosely defined as the probability that class <span class="math inline">\(k\)</span> is assigned to the conformal prediction set <span class="math inline">\(C\)</span>. In the context of Conformal Training, this penalty reduces the <strong>inefficiency</strong> of the conformal predictor.</p>
-<p>In our context, we are not interested in improving the model itself, but rather in producing <strong>plausible</strong> counterfactuals. Provided that our counterfactual <span class="math inline">\(x^\prime\)</span> is already inside the target domain (<span class="math inline">\(\mathbb{I}_{y^\prime = t}=1\)</span>), penalizing <span class="math inline">\(\Omega(C_{\theta}(x;\tau))\)</span> corresponds to guiding counterfactuals into regions of the target domain that are characterized by low ambiguity: for <span class="math inline">\(\kappa=1\)</span> the conformal prediction set includes only the target label <span class="math inline">\(t\)</span> as <span class="math inline">\(\Omega(C_{\theta}(x;\tau))\)</span>. Arguably, less ambiguous counterfactuals are more <strong>plausible</strong>. Since the search is guided purely by properties of the model itself and (exchangeable) calibration data, counterfactuals also maintain <strong>high fidelity</strong>.</p>
-<p>The left panel of <strong>?@fig-losses</strong> shows the smooth size penalty in the two-dimensional feature space of our synthetic data.</p>
-</section>
-<section id="configurable-classification-loss" class="level3">
-<h3 class="anchored" data-anchor-id="configurable-classification-loss">Configurable Classification Loss</h3>
-<p>The right panel of <strong>?@fig-losses</strong> shows the configurable classification loss in the two-dimensional feature space of our synthetic data.</p>
-</section>
-</section>
-<section id="fidelity-and-plausibility" class="level2">
-<h2 class="anchored" data-anchor-id="fidelity-and-plausibility">Fidelity and Plausibility</h2>
-<p>The main evaluation criteria we are interested in are <em>fidelity</em> and <em>plausibility</em>. Interestingly, we could also consider using these measures as penalties in the counterfactual search.</p>
-<section id="fidelity" class="level3">
-<h3 class="anchored" data-anchor-id="fidelity">Fidelity</h3>
-<p>We propose to define fidelity as follows:</p>
-<div id="def-fidelity" class="theorem definition">
-<p><span class="theorem-title"><strong>Definition 1 (High-Fidelity Counterfactuals) </strong></span>Let <span class="math inline">\(\mathcal{X}_{\theta}|y = p_{\theta}(X|y)\)</span> denote the class-conditional distribution of <span class="math inline">\(X\)</span> defined by <span class="math inline">\(\theta\)</span>. Then for <span class="math inline">\(x^{\prime}\)</span> to be considered a high-fidelity counterfactual, we need: <span class="math inline">\(\mathcal{X}_{\theta}|t \approxeq \mathcal{X}^{\prime}\)</span> where <span class="math inline">\(t\)</span> denotes the target outcome.</p>
-</div>
-<p>We can generate samples from <span class="math inline">\(p_{\theta}(X|y)\)</span> following <span class="citation" data-cites="grathwohl2020your">Grathwohl et al. (<a href="#ref-grathwohl2020your" role="doc-biblioref">2020</a>)</span>. In <strong>?@fig-energy</strong>, I have applied the methodology to our synthetic data.</p>
-<p>As an evaluation metric and penalty, we could use the average distance of the counterfactual <span class="math inline">\(x^{\prime}\)</span> from these generated samples, for example.</p>
-</section>
-<section id="plausibility" class="level3">
-<h3 class="anchored" data-anchor-id="plausibility">Plausibility</h3>
-<p>We propose to define plausibility as follows:</p>
-<div id="def-plausible" class="theorem definition">
-<p><span class="theorem-title"><strong>Definition 2 (Plausible Counterfactuals) </strong></span>Formally, let <span class="math inline">\(\mathcal{X}|t\)</span> denote the conditional distribution of samples in the target class. As before, we have <span class="math inline">\(x^{\prime}\sim\mathcal{X}^{\prime}\)</span>, then for <span class="math inline">\(x^{\prime}\)</span> to be considered a plausible counterfactual, we need: <span class="math inline">\(\mathcal{X}|t \approxeq \mathcal{X}^{\prime}\)</span>.</p>
-</div>
-<p>As an evaluation metric and penalty, we could use the average distance of the counterfactual <span class="math inline">\(x^{\prime}\)</span> from (potentially bootstrapped) training samples in the target class, for example.</p>
-</section>
-</section>
-<section id="counterfactual-explanations" class="level2">
-<h2 class="anchored" data-anchor-id="counterfactual-explanations">Counterfactual Explanations</h2>
-<p>Next, let’s generate counterfactual explanations for our synthetic data. We first wrap our model in a container that makes it compatible with <code>CounterfactualExplanations.jl</code>. Then we draw a random sample, determine its predicted label <span class="math inline">\(\hat{y}\)</span> and choose the opposite label as our target.</p>
-<p>The generic Conformal Counterfactual Generator penalises the only the set size only:</p>
-<p><span id="eq-solution"><span class="math display">\[
-x^\prime = \arg \min_{x^\prime} \ell(M(x^\prime),t) + \lambda \mathbb{I}_{y^\prime = t} \Omega(C_{\theta}(x;\tau))
-\tag{3}\]</span></span></p>
-</section>
-<section id="multi-class" class="level2">
-<h2 class="anchored" data-anchor-id="multi-class">Multi-Class</h2>
-</section>
-<section id="benchmark" class="level2 unnumbered">
-
-
-</section>
-</section>
-
-
-<div id="quarto-appendix" class="default"><section class="quarto-appendix-contents" role="doc-bibliography"><h2 class="anchored quarto-appendix-heading">Benchmark</h2><div id="refs" class="references csl-bib-body hanging-indent" role="list">
-<div id="ref-grathwohl2020your" class="csl-entry" role="listitem">
-Grathwohl, Will, Kuan-Chieh Wang, Joern-Henrik Jacobsen, David Duvenaud, Mohammad Norouzi, and Kevin Swersky. 2020. <span>“Your Classifier Is Secretly an Energy Based Model and You Should Treat It Like One.â€</span> In. <a href="https://openreview.net/forum?id=Hkxzx0NtDB">https://openreview.net/forum?id=Hkxzx0NtDB</a>.
-</div>
-<div id="ref-schut2021generating" class="csl-entry" role="listitem">
-Schut, Lisa, Oscar Key, Rory Mc Grath, Luca Costabello, Bogdan Sacaleanu, Yarin Gal, et al. 2021. <span>“Generating <span>Interpretable Counterfactual Explanations By Implicit Minimisation</span> of <span>Epistemic</span> and <span>Aleatoric Uncertainties</span>.â€</span> In <em>International <span>Conference</span> on <span>Artificial Intelligence</span> and <span>Statistics</span></em>, 1756–64. <span>PMLR</span>.
-</div>
-<div id="ref-stutz2022learning" class="csl-entry" role="listitem">
-Stutz, David, Krishnamurthy Dj Dvijotham, Ali Taylan Cemgil, and Arnaud Doucet. 2022. <span>“Learning <span>Optimal</span> <span>Conformal</span> <span>Classifiers</span>.â€</span> In. <a href="https://openreview.net/forum?id=t8O-4LKFVx">https://openreview.net/forum?id=t8O-4LKFVx</a>.
-</div>
-</div></section><section id="footnotes" class="footnotes footnotes-end-of-document" role="doc-endnotes"><h2 class="anchored quarto-appendix-heading">Footnotes</h2>
-
-<ol>
-<li id="fn1"><p>In other places split conformal prediction is sometimes referred to as <em>inductive</em> conformal prediction.<a href="#fnref1" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
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diff --git a/build/dev/proposal_files/libs/bootstrap/bootstrap-icons.woff b/build/dev/proposal_files/libs/bootstrap/bootstrap-icons.woff
deleted file mode 100644
index 18d21d457558d4dc2e231a8f6ee585fada9c6bab..0000000000000000000000000000000000000000
Binary files a/build/dev/proposal_files/libs/bootstrap/bootstrap-icons.woff and /dev/null differ
diff --git a/build/dev/proposal_files/libs/bootstrap/bootstrap.min.css b/build/dev/proposal_files/libs/bootstrap/bootstrap.min.css
deleted file mode 100644
index d1c139ccab8a2d7d6a2ac5e5fb397d782fbdc6c6..0000000000000000000000000000000000000000
--- a/build/dev/proposal_files/libs/bootstrap/bootstrap.min.css
+++ /dev/null
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[body-start-outset] 50px [body-start] 1.5em [body-content-start] minmax(500px, calc( 800px - 3em )) [body-content-end] 1.5em [body-end body-end-outset page-end-inset page-end] 5fr [screen-end-inset] 1.5em [screen-end]}body.docked.slimcontent .page-columns{display:grid;gap:0;grid-template-columns:[screen-start] 1.5em [screen-start-inset page-start] minmax(50px, 100px) [page-start-inset] 50px [body-start-outset] 50px [body-start] 1.5em [body-content-start] minmax(450px, calc( 750px - 3em )) [body-content-end] 1.5em [body-end] 50px [body-end-outset] minmax(0px, 200px) [page-end-inset] 50px [page-end] 5fr [screen-end-inset] 1.5em [screen-end]}body.docked.listing .page-columns{display:grid;gap:0;grid-template-columns:[screen-start] 1.5em [screen-start-inset page-start] minmax(50px, 100px) [page-start-inset] 50px [body-start-outset] 50px [body-start] 1.5em [body-content-start] minmax(500px, calc( 1000px - 3em )) [body-content-end] 1.5em [body-end] 50px [body-end-outset] minmax(0px, 200px) 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[screen-end]}}@media(max-width: 991.98px){body .page-columns{display:grid;gap:0;grid-template-columns:[screen-start] 1.5em [screen-start-inset page-start page-start-inset body-start-outset] 5fr [body-start] 1.5em [body-content-start] minmax(500px, calc( 800px - 3em )) [body-content-end] 1.5em [body-end] 35px [body-end-outset] minmax(75px, 145px) [page-end-inset] 35px [page-end] 5fr [screen-end-inset] 1.5em [screen-end]}body.fullcontent:not(.floating):not(.docked) .page-columns{display:grid;gap:0;grid-template-columns:[screen-start] 1.5em [screen-start-inset page-start page-start-inset body-start-outset] 5fr [body-start] 1.5em [body-content-start] minmax(500px, calc( 800px - 3em )) [body-content-end] 1.5em [body-end body-end-outset page-end-inset page-end] 5fr [screen-end-inset] 1.5em [screen-end]}body.slimcontent:not(.floating):not(.docked) .page-columns{display:grid;gap:0;grid-template-columns:[screen-start] 1.5em [screen-start-inset page-start page-start-inset body-start-outset] 5fr [body-start] 1.5em [body-content-start] minmax(500px, calc( 800px - 3em )) [body-content-end] 1.5em [body-end] 35px [body-end-outset] minmax(75px, 145px) [page-end-inset] 35px [page-end] 5fr [screen-end-inset] 1.5em [screen-end]}body.listing:not(.floating):not(.docked) .page-columns{display:grid;gap:0;grid-template-columns:[screen-start] 1.5em [screen-start-inset page-start page-start-inset body-start-outset] 5fr [body-start] 1.5em [body-content-start] minmax(500px, calc( 1250px - 3em )) [body-content-end body-end body-end-outset page-end-inset page-end] 5fr [screen-end-inset] 1.5em [screen-end]}body:not(.floating):not(.docked) .page-columns.toc-left{display:grid;gap:0;grid-template-columns:[screen-start] 1.5em [screen-start-inset] 5fr [page-start] 35px [page-start-inset] minmax(0px, 145px) [body-start-outset] 35px [body-start] 1.5em [body-content-start] minmax(450px, calc( 800px - 3em )) [body-content-end] 1.5em [body-end body-end-outset page-end-inset page-end] 5fr [screen-end-inset] 1.5em [screen-end]}body:not(.floating):not(.docked) .page-columns.toc-left .page-columns{display:grid;gap:0;grid-template-columns:[screen-start] 1.5em [screen-start-inset] 5fr [page-start] 35px [page-start-inset] minmax(0px, 145px) [body-start-outset] 35px [body-start] 1.5em [body-content-start] minmax(450px, calc( 800px - 3em )) [body-content-end] 1.5em [body-end body-end-outset page-end-inset page-end] 5fr [screen-end-inset] 1.5em [screen-end]}body.floating .page-columns{display:grid;gap:0;grid-template-columns:[screen-start] 1.5em [screen-start-inset] 5fr [page-start page-start-inset body-start-outset body-start] 1.5em [body-content-start] minmax(500px, calc( 750px - 3em )) [body-content-end] 1.5em [body-end] 50px [body-end-outset] minmax(75px, 150px) [page-end-inset] 25px [page-end] 5fr [screen-end-inset] 1.5em [screen-end]}body.docked .page-columns{display:grid;gap:0;grid-template-columns:[screen-start] 1.5em [screen-start-inset page-start page-start-inset body-start-outset body-start body-content-start] minmax(500px, calc( 750px - 3em )) [body-content-end] 1.5em [body-end] 50px [body-end-outset] minmax(25px, 50px) [page-end-inset] 50px [page-end] 5fr [screen-end-inset] 1.5em [screen-end]}body.docked.fullcontent .page-columns{display:grid;gap:0;grid-template-columns:[screen-start] 1.5em [screen-start-inset page-start page-start-inset body-start-outset body-start body-content-start] minmax(500px, calc( 1000px - 3em )) [body-content-end] 1.5em [body-end body-end-outset page-end-inset page-end] 5fr [screen-end-inset] 1.5em [screen-end]}body.floating.fullcontent .page-columns{display:grid;gap:0;grid-template-columns:[screen-start] 1.5em [screen-start-inset] 5fr [page-start page-start-inset body-start-outset body-start] 1em [body-content-start] minmax(500px, calc( 800px - 3em )) [body-content-end] 1.5em [body-end body-end-outset page-end-inset page-end] 4fr [screen-end-inset] 1.5em [screen-end]}body.docked.slimcontent .page-columns{display:grid;gap:0;grid-template-columns:[screen-start] 1.5em [screen-start-inset page-start page-start-inset body-start-outset body-start body-content-start] minmax(500px, calc( 750px - 3em )) [body-content-end] 1.5em [body-end] 50px [body-end-outset] minmax(25px, 50px) [page-end-inset] 50px [page-end] 5fr [screen-end-inset] 1.5em [screen-end]}body.docked.listing .page-columns{display:grid;gap:0;grid-template-columns:[screen-start] 1.5em [screen-start-inset page-start page-start-inset body-start-outset body-start body-content-start] minmax(500px, calc( 750px - 3em )) [body-content-end] 1.5em [body-end] 50px [body-end-outset] minmax(25px, 50px) [page-end-inset] 50px [page-end] 5fr [screen-end-inset] 1.5em [screen-end]}body.floating.slimcontent .page-columns{display:grid;gap:0;grid-template-columns:[screen-start] 1.5em [screen-start-inset] 5fr [page-start page-start-inset body-start-outset body-start] 1em [body-content-start] minmax(500px, calc( 750px - 3em )) [body-content-end] 1.5em [body-end] 35px [body-end-outset] minmax(75px, 145px) [page-end-inset] 35px [page-end] 4fr [screen-end-inset] 1.5em [screen-end]}body.floating.listing .page-columns{display:grid;gap:0;grid-template-columns:[screen-start] 1.5em [screen-start-inset] 5fr [page-start page-start-inset body-start-outset body-start] 1em [body-content-start] minmax(500px, calc( 750px - 3em )) [body-content-end] 1.5em [body-end] 50px [body-end-outset] minmax(75px, 150px) [page-end-inset] 25px [page-end] 4fr [screen-end-inset] 1.5em [screen-end]}}@media(max-width: 767.98px){body .page-columns,body.fullcontent:not(.floating):not(.docked) .page-columns,body.slimcontent:not(.floating):not(.docked) .page-columns,body.docked .page-columns,body.docked.slimcontent .page-columns,body.docked.fullcontent .page-columns,body.floating .page-columns,body.floating.slimcontent .page-columns,body.floating.fullcontent .page-columns{display:grid;gap:0;grid-template-columns:[screen-start] 1.5em [screen-start-inset page-start page-start-inset body-start-outset body-start body-content-start] minmax(0px, 1fr) [body-content-end body-end body-end-outset page-end-inset page-end screen-end-inset] 1.5em [screen-end]}body:not(.floating):not(.docked) .page-columns.toc-left{display:grid;gap:0;grid-template-columns:[screen-start] 1.5em [screen-start-inset page-start page-start-inset body-start-outset body-start body-content-start] minmax(0px, 1fr) [body-content-end body-end body-end-outset page-end-inset page-end screen-end-inset] 1.5em [screen-end]}body:not(.floating):not(.docked) .page-columns.toc-left .page-columns{display:grid;gap:0;grid-template-columns:[screen-start] 1.5em [screen-start-inset page-start page-start-inset body-start-outset body-start body-content-start] minmax(0px, 1fr) [body-content-end body-end body-end-outset page-end-inset page-end screen-end-inset] 1.5em [screen-end]}nav[role=doc-toc]{display:none}}body,.page-row-navigation{grid-template-rows:[page-top] max-content [contents-top] max-content [contents-bottom] max-content [page-bottom]}.page-rows-contents{grid-template-rows:[content-top] minmax(max-content, 1fr) [content-bottom] minmax(60px, max-content) [page-bottom]}.page-full{grid-column:screen-start/screen-end !important}.page-columns>*{grid-column:body-content-start/body-content-end}.page-columns.column-page>*{grid-column:page-start/page-end}.page-columns.column-page-left>*{grid-column:page-start/body-content-end}.page-columns.column-page-right>*{grid-column:body-content-start/page-end}.page-rows{grid-auto-rows:auto}.header{grid-column:screen-start/screen-end;grid-row:page-top/contents-top}#quarto-content{padding:0;grid-column:screen-start/screen-end;grid-row:contents-top/contents-bottom}body.floating .sidebar.sidebar-navigation{grid-column:page-start/body-start;grid-row:content-top/page-bottom}body.docked .sidebar.sidebar-navigation{grid-column:screen-start/body-start;grid-row:content-top/page-bottom}.sidebar.toc-left{grid-column:page-start/body-start;grid-row:content-top/page-bottom}.sidebar.margin-sidebar{grid-column:body-end/page-end;grid-row:content-top/page-bottom}.page-columns .content{grid-column:body-content-start/body-content-end;grid-row:content-top/content-bottom;align-content:flex-start}.page-columns .page-navigation{grid-column:body-content-start/body-content-end;grid-row:content-bottom/page-bottom}.page-columns .footer{grid-column:screen-start/screen-end;grid-row:contents-bottom/page-bottom}.page-columns .column-body{grid-column:body-content-start/body-content-end}.page-columns .column-body-fullbleed{grid-column:body-start/body-end}.page-columns .column-body-outset{grid-column:body-start-outset/body-end-outset;z-index:998;transform:translate3d(0, 0, 0)}.page-columns .column-body-outset table{background:#fff}.page-columns 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.column-page-inset-right{grid-column:body-content-start/page-end-inset;z-index:998;transform:translate3d(0, 0, 0)}.page-columns .column-page-inset-right figcaption table{background:#fff}.page-columns .column-page-left{grid-column:page-start/body-content-end;z-index:998;transform:translate3d(0, 0, 0)}.page-columns .column-page-left table{background:#fff}.page-columns .column-page-right{grid-column:body-content-start/page-end;z-index:998;transform:translate3d(0, 0, 0)}.page-columns .column-page-right figcaption table{background:#fff}#quarto-content.page-columns #quarto-margin-sidebar,#quarto-content.page-columns #quarto-sidebar{z-index:1}@media(max-width: 991.98px){#quarto-content.page-columns #quarto-margin-sidebar.collapse,#quarto-content.page-columns #quarto-sidebar.collapse{z-index:1055}}#quarto-content.page-columns main.column-page,#quarto-content.page-columns main.column-page-right,#quarto-content.page-columns main.column-page-left{z-index:0}.page-columns 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diff --git a/build/dev/proposal_files/libs/bootstrap/bootstrap.min.js b/build/dev/proposal_files/libs/bootstrap/bootstrap.min.js
deleted file mode 100644
index cc0a25561dfb616832227c9e2b2c1b1bbe69bc05..0000000000000000000000000000000000000000
--- a/build/dev/proposal_files/libs/bootstrap/bootstrap.min.js
+++ /dev/null
@@ -1,7 +0,0 @@
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s=`scroll${n[0].toUpperCase()+n.slice(1)}`;this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(ht),this._element.classList.add(ct,lt),this._element.style[n]="",j.trigger(this._element,"shown.bs.collapse")}),this._element,!0),this._element.style[n]=`${this._element[s]}px`}hide(){if(this._isTransitioning||!this._isShown())return;if(j.trigger(this._element,"hide.bs.collapse").defaultPrevented)return;const t=this._getDimension();this._element.style[t]=`${this._element.getBoundingClientRect()[t]}px`,u(this._element),this._element.classList.add(ht),this._element.classList.remove(ct,lt);const e=this._triggerArray.length;for(let t=0;t<e;t++){const e=this._triggerArray[t],i=n(e);i&&!this._isShown(i)&&this._addAriaAndCollapsedClass([e],!1)}this._isTransitioning=!0,this._element.style[t]="",this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(ht),this._element.classList.add(ct),j.trigger(this._element,"hidden.bs.collapse")}),this._element,!0)}_isShown(t=this._element){return t.classList.contains(lt)}_getConfig(t){return(t={...rt,...U.getDataAttributes(this._element),...t}).toggle=Boolean(t.toggle),t.parent=r(t.parent),a(ot,t,at),t}_getDimension(){return this._element.classList.contains("collapse-horizontal")?"width":"height"}_initializeChildren(){if(!this._config.parent)return;const t=V.find(ut,this._config.parent);V.find(ft,this._config.parent).filter((e=>!t.includes(e))).forEach((t=>{const e=n(t);e&&this._addAriaAndCollapsedClass([t],this._isShown(e))}))}_addAriaAndCollapsedClass(t,e){t.length&&t.forEach((t=>{e?t.classList.remove(dt):t.classList.add(dt),t.setAttribute("aria-expanded",e)}))}static jQueryInterface(t){return this.each((function(){const e={};"string"==typeof t&&/show|hide/.test(t)&&(e.toggle=!1);const i=pt.getOrCreateInstance(this,e);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t]()}}))}}j.on(document,"click.bs.collapse.data-api",ft,(function(t){("A"===t.target.tagName||t.delegateTarget&&"A"===t.delegateTarget.tagName)&&t.preventDefault();const e=i(this);V.find(e).forEach((t=>{pt.getOrCreateInstance(t,{toggle:!1}).toggle()}))})),g(pt);var mt="top",gt="bottom",_t="right",bt="left",vt="auto",yt=[mt,gt,_t,bt],wt="start",Et="end",At="clippingParents",Tt="viewport",Ot="popper",Ct="reference",kt=yt.reduce((function(t,e){return t.concat([e+"-"+wt,e+"-"+Et])}),[]),Lt=[].concat(yt,[vt]).reduce((function(t,e){return t.concat([e,e+"-"+wt,e+"-"+Et])}),[]),xt="beforeRead",Dt="read",St="afterRead",Nt="beforeMain",It="main",Pt="afterMain",jt="beforeWrite",Mt="write",Ht="afterWrite",Bt=[xt,Dt,St,Nt,It,Pt,jt,Mt,Ht];function Rt(t){return t?(t.nodeName||"").toLowerCase():null}function Wt(t){if(null==t)return window;if("[object Window]"!==t.toString()){var e=t.ownerDocument;return e&&e.defaultView||window}return t}function $t(t){return t instanceof Wt(t).Element||t instanceof Element}function zt(t){return t instanceof Wt(t).HTMLElement||t instanceof HTMLElement}function qt(t){return"undefined"!=typeof ShadowRoot&&(t instanceof Wt(t).ShadowRoot||t instanceof ShadowRoot)}const Ft={name:"applyStyles",enabled:!0,phase:"write",fn:function(t){var e=t.state;Object.keys(e.elements).forEach((function(t){var i=e.styles[t]||{},n=e.attributes[t]||{},s=e.elements[t];zt(s)&&Rt(s)&&(Object.assign(s.style,i),Object.keys(n).forEach((function(t){var e=n[t];!1===e?s.removeAttribute(t):s.setAttribute(t,!0===e?"":e)})))}))},effect:function(t){var e=t.state,i={popper:{position:e.options.strategy,left:"0",top:"0",margin:"0"},arrow:{position:"absolute"},reference:{}};return 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e=t.x,i=t.y,n=window.devicePixelRatio||1;return{x:se(se(e*n)/n)||0,y:se(se(i*n)/n)||0}}(r):"function"==typeof h?h(r):r,u=d.x,f=void 0===u?0:u,p=d.y,m=void 0===p?0:p,g=r.hasOwnProperty("x"),_=r.hasOwnProperty("y"),b=bt,v=mt,y=window;if(c){var w=te(i),E="clientHeight",A="clientWidth";w===Wt(i)&&"static"!==Yt(w=Gt(i)).position&&"absolute"===a&&(E="scrollHeight",A="scrollWidth"),w=w,s!==mt&&(s!==bt&&s!==_t||o!==Et)||(v=gt,m-=w[E]-n.height,m*=l?1:-1),s!==bt&&(s!==mt&&s!==gt||o!==Et)||(b=_t,f-=w[A]-n.width,f*=l?1:-1)}var T,O=Object.assign({position:a},c&&he);return l?Object.assign({},O,((T={})[v]=_?"0":"",T[b]=g?"0":"",T.transform=(y.devicePixelRatio||1)<=1?"translate("+f+"px, "+m+"px)":"translate3d("+f+"px, "+m+"px, 0)",T)):Object.assign({},O,((e={})[v]=_?m+"px":"",e[b]=g?f+"px":"",e.transform="",e))}const ue={name:"computeStyles",enabled:!0,phase:"beforeWrite",fn:function(t){var e=t.state,i=t.options,n=i.gpuAcceleration,s=void 0===n||n,o=i.adaptive,r=void 0===o||o,a=i.roundOffsets,l=void 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C=O[s];Object.keys(T).forEach((function(t){var e=[_t,gt].indexOf(t)>=0?1:-1,i=[mt,gt].indexOf(t)>=0?"y":"x";T[t]+=C[i]*e}))}return T}function Le(t,e){void 0===e&&(e={});var i=e,n=i.placement,s=i.boundary,o=i.rootBoundary,r=i.padding,a=i.flipVariations,l=i.allowedAutoPlacements,c=void 0===l?Lt:l,h=ce(n),d=h?a?kt:kt.filter((function(t){return ce(t)===h})):yt,u=d.filter((function(t){return c.indexOf(t)>=0}));0===u.length&&(u=d);var f=u.reduce((function(e,i){return e[i]=ke(t,{placement:i,boundary:s,rootBoundary:o,padding:r})[Ut(i)],e}),{});return Object.keys(f).sort((function(t,e){return f[t]-f[e]}))}const xe={name:"flip",enabled:!0,phase:"main",fn:function(t){var e=t.state,i=t.options,n=t.name;if(!e.modifiersData[n]._skip){for(var s=i.mainAxis,o=void 0===s||s,r=i.altAxis,a=void 0===r||r,l=i.fallbackPlacements,c=i.padding,h=i.boundary,d=i.rootBoundary,u=i.altBoundary,f=i.flipVariations,p=void 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T=e,"break"},M=p?3:1;M>0&&"break"!==j(M);M--);e.placement!==T&&(e.modifiersData[n]._skip=!0,e.placement=T,e.reset=!0)}},requiresIfExists:["offset"],data:{_skip:!1}};function De(t,e,i){return void 0===i&&(i={x:0,y:0}),{top:t.top-e.height-i.y,right:t.right-e.width+i.x,bottom:t.bottom-e.height+i.y,left:t.left-e.width-i.x}}function Se(t){return[mt,_t,gt,bt].some((function(e){return t[e]>=0}))}const Ne={name:"hide",enabled:!0,phase:"main",requiresIfExists:["preventOverflow"],fn:function(t){var e=t.state,i=t.name,n=e.rects.reference,s=e.rects.popper,o=e.modifiersData.preventOverflow,r=ke(e,{elementContext:"reference"}),a=ke(e,{altBoundary:!0}),l=De(r,n),c=De(a,s,o),h=Se(l),d=Se(c);e.modifiersData[i]={referenceClippingOffsets:l,popperEscapeOffsets:c,isReferenceHidden:h,hasPopperEscaped:d},e.attributes.popper=Object.assign({},e.attributes.popper,{"data-popper-reference-hidden":h,"data-popper-escaped":d})}},Ie={name:"offset",enabled:!0,phase:"main",requires:["popperOffsets"],fn:function(t){var e=t.state,i=t.options,n=t.name,s=i.offset,o=void 0===s?[0,0]:s,r=Lt.reduce((function(t,i){return t[i]=function(t,e,i){var n=Ut(t),s=[bt,mt].indexOf(n)>=0?-1:1,o="function"==typeof i?i(Object.assign({},e,{placement:t})):i,r=o[0],a=o[1];return r=r||0,a=(a||0)*s,[bt,_t].indexOf(n)>=0?{x:a,y:r}:{x:r,y:a}}(i,e.rects,o),t}),{}),a=r[e.placement],l=a.x,c=a.y;null!=e.modifiersData.popperOffsets&&(e.modifiersData.popperOffsets.x+=l,e.modifiersData.popperOffsets.y+=c),e.modifiersData[n]=r}},Pe={name:"popperOffsets",enabled:!0,phase:"read",fn:function(t){var e=t.state,i=t.name;e.modifiersData[i]=Ce({reference:e.rects.reference,element:e.rects.popper,strategy:"absolute",placement:e.placement})},data:{}},je={name:"preventOverflow",enabled:!0,phase:"main",fn:function(t){var e=t.state,i=t.options,n=t.name,s=i.mainAxis,o=void 0===s||s,r=i.altAxis,a=void 0!==r&&r,l=i.boundary,c=i.rootBoundary,h=i.altBoundary,d=i.padding,u=i.tether,f=void 0===u||u,p=i.tetherOffset,m=void 0===p?0:p,g=ke(e,{boundary:l,rootBoundary:c,padding:d,altBoundary:h}),_=Ut(e.placement),b=ce(e.placement),v=!b,y=ee(_),w="x"===y?"y":"x",E=e.modifiersData.popperOffsets,A=e.rects.reference,T=e.rects.popper,O="function"==typeof m?m(Object.assign({},e.rects,{placement:e.placement})):m,C={x:0,y:0};if(E){if(o||a){var k="y"===y?mt:bt,L="y"===y?gt:_t,x="y"===y?"height":"width",D=E[y],S=E[y]+g[k],N=E[y]-g[L],I=f?-T[x]/2:0,P=b===wt?A[x]:T[x],j=b===wt?-T[x]:-A[x],M=e.elements.arrow,H=f&&M?Kt(M):{width:0,height:0},B=e.modifiersData["arrow#persistent"]?e.modifiersData["arrow#persistent"].padding:{top:0,right:0,bottom:0,left:0},R=B[k],W=B[L],$=oe(0,A[x],H[x]),z=v?A[x]/2-I-$-R-O:P-$-R-O,q=v?-A[x]/2+I+$+W+O:j+$+W+O,F=e.elements.arrow&&te(e.elements.arrow),U=F?"y"===y?F.clientTop||0:F.clientLeft||0:0,V=e.modifiersData.offset?e.modifiersData.offset[e.placement][y]:0,K=E[y]+z-V-U,X=E[y]+q-V;if(o){var Y=oe(f?ne(S,K):S,D,f?ie(N,X):N);E[y]=Y,C[y]=Y-D}if(a){var Q="x"===y?mt:bt,G="x"===y?gt:_t,Z=E[w],J=Z+g[Q],tt=Z-g[G],et=oe(f?ne(J,K):J,Z,f?ie(tt,X):tt);E[w]=et,C[w]=et-Z}}e.modifiersData[n]=C}},requiresIfExists:["offset"]};function Me(t,e,i){void 0===i&&(i=!1);var n=zt(e);zt(e)&&function(t){var e=t.getBoundingClientRect();e.width,t.offsetWidth,e.height,t.offsetHeight}(e);var s,o,r=Gt(e),a=Vt(t),l={scrollLeft:0,scrollTop:0},c={x:0,y:0};return(n||!n&&!i)&&(("body"!==Rt(e)||we(r))&&(l=(s=e)!==Wt(s)&&zt(s)?{scrollLeft:(o=s).scrollLeft,scrollTop:o.scrollTop}:ve(s)),zt(e)?((c=Vt(e)).x+=e.clientLeft,c.y+=e.clientTop):r&&(c.x=ye(r))),{x:a.left+l.scrollLeft-c.x,y:a.top+l.scrollTop-c.y,width:a.width,height:a.height}}function He(t){var e=new Map,i=new Set,n=[];function s(t){i.add(t.name),[].concat(t.requires||[],t.requiresIfExists||[]).forEach((function(t){if(!i.has(t)){var n=e.get(t);n&&s(n)}})),n.push(t)}return t.forEach((function(t){e.set(t.name,t)})),t.forEach((function(t){i.has(t.name)||s(t)})),n}var Be={placement:"bottom",modifiers:[],strategy:"absolute"};function Re(){for(var t=arguments.length,e=new Array(t),i=0;i<t;i++)e[i]=arguments[i];return!e.some((function(t){return!(t&&"function"==typeof t.getBoundingClientRect)}))}function We(t){void 0===t&&(t={});var e=t,i=e.defaultModifiers,n=void 0===i?[]:i,s=e.defaultOptions,o=void 0===s?Be:s;return function(t,e,i){void 0===i&&(i=o);var s,r,a={placement:"bottom",orderedModifiers:[],options:Object.assign({},Be,o),modifiersData:{},elements:{reference:t,popper:e},attributes:{},styles:{}},l=[],c=!1,h={state:a,setOptions:function(i){var s="function"==typeof i?i(a.options):i;d(),a.options=Object.assign({},o,a.options,s),a.scrollParents={reference:$t(t)?Ae(t):t.contextElement?Ae(t.contextElement):[],popper:Ae(e)};var r,c,u=function(t){var e=He(t);return Bt.reduce((function(t,i){return t.concat(e.filter((function(t){return t.phase===i})))}),[])}((r=[].concat(n,a.options.modifiers),c=r.reduce((function(t,e){var i=t[e.name];return t[e.name]=i?Object.assign({},i,e,{options:Object.assign({},i.options,e.options),data:Object.assign({},i.data,e.data)}):e,t}),{}),Object.keys(c).map((function(t){return c[t]}))));return a.orderedModifiers=u.filter((function(t){return t.enabled})),a.orderedModifiers.forEach((function(t){var e=t.name,i=t.options,n=void 0===i?{}:i,s=t.effect;if("function"==typeof s){var o=s({state:a,name:e,instance:h,options:n});l.push(o||function(){})}})),h.update()},forceUpdate:function(){if(!c){var t=a.elements,e=t.reference,i=t.popper;if(Re(e,i)){a.rects={reference:Me(e,te(i),"fixed"===a.options.strategy),popper:Kt(i)},a.reset=!1,a.placement=a.options.placement,a.orderedModifiers.forEach((function(t){return a.modifiersData[t.name]=Object.assign({},t.data)}));for(var n=0;n<a.orderedModifiers.length;n++)if(!0!==a.reset){var s=a.orderedModifiers[n],o=s.fn,r=s.options,l=void 0===r?{}:r,d=s.name;"function"==typeof o&&(a=o({state:a,options:l,name:d,instance:h})||a)}else a.reset=!1,n=-1}}},update:(s=function(){return new Promise((function(t){h.forceUpdate(),t(a)}))},function(){return r||(r=new Promise((function(t){Promise.resolve().then((function(){r=void 0,t(s())}))}))),r}),destroy:function(){d(),c=!0}};if(!Re(t,e))return h;function d(){l.forEach((function(t){return t()})),l=[]}return h.setOptions(i).then((function(t){!c&&i.onFirstUpdate&&i.onFirstUpdate(t)})),h}}var $e=We(),ze=We({defaultModifiers:[pe,Pe,ue,Ft]}),qe=We({defaultModifiers:[pe,Pe,ue,Ft,Ie,xe,je,le,Ne]});const Fe=Object.freeze({__proto__:null,popperGenerator:We,detectOverflow:ke,createPopperBase:$e,createPopper:qe,createPopperLite:ze,top:mt,bottom:gt,right:_t,left:bt,auto:vt,basePlacements:yt,start:wt,end:Et,clippingParents:At,viewport:Tt,popper:Ot,reference:Ct,variationPlacements:kt,placements:Lt,beforeRead:xt,read:Dt,afterRead:St,beforeMain:Nt,main:It,afterMain:Pt,beforeWrite:jt,write:Mt,afterWrite:Ht,modifierPhases:Bt,applyStyles:Ft,arrow:le,computeStyles:ue,eventListeners:pe,flip:xe,hide:Ne,offset:Ie,popperOffsets:Pe,preventOverflow:je}),Ue="dropdown",Ve="Escape",Ke="Space",Xe="ArrowUp",Ye="ArrowDown",Qe=new RegExp("ArrowUp|ArrowDown|Escape"),Ge="click.bs.dropdown.data-api",Ze="keydown.bs.dropdown.data-api",Je="show",ti='[data-bs-toggle="dropdown"]',ei=".dropdown-menu",ii=m()?"top-end":"top-start",ni=m()?"top-start":"top-end",si=m()?"bottom-end":"bottom-start",oi=m()?"bottom-start":"bottom-end",ri=m()?"left-start":"right-start",ai=m()?"right-start":"left-start",li={offset:[0,2],boundary:"clippingParents",reference:"toggle",display:"dynamic",popperConfig:null,autoClose:!0},ci={offset:"(array|string|function)",boundary:"(string|element)",reference:"(string|element|object)",display:"string",popperConfig:"(null|object|function)",autoClose:"(boolean|string)"};class hi extends B{constructor(t,e){super(t),this._popper=null,this._config=this._getConfig(e),this._menu=this._getMenuElement(),this._inNavbar=this._detectNavbar()}static get Default(){return li}static get DefaultType(){return ci}static get NAME(){return Ue}toggle(){return this._isShown()?this.hide():this.show()}show(){if(c(this._element)||this._isShown(this._menu))return;const t={relatedTarget:this._element};if(j.trigger(this._element,"show.bs.dropdown",t).defaultPrevented)return;const e=hi.getParentFromElement(this._element);this._inNavbar?U.setDataAttribute(this._menu,"popper","none"):this._createPopper(e),"ontouchstart"in document.documentElement&&!e.closest(".navbar-nav")&&[].concat(...document.body.children).forEach((t=>j.on(t,"mouseover",d))),this._element.focus(),this._element.setAttribute("aria-expanded",!0),this._menu.classList.add(Je),this._element.classList.add(Je),j.trigger(this._element,"shown.bs.dropdown",t)}hide(){if(c(this._element)||!this._isShown(this._menu))return;const t={relatedTarget:this._element};this._completeHide(t)}dispose(){this._popper&&this._popper.destroy(),super.dispose()}update(){this._inNavbar=this._detectNavbar(),this._popper&&this._popper.update()}_completeHide(t){j.trigger(this._element,"hide.bs.dropdown",t).defaultPrevented||("ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>j.off(t,"mouseover",d))),this._popper&&this._popper.destroy(),this._menu.classList.remove(Je),this._element.classList.remove(Je),this._element.setAttribute("aria-expanded","false"),U.removeDataAttribute(this._menu,"popper"),j.trigger(this._element,"hidden.bs.dropdown",t))}_getConfig(t){if(t={...this.constructor.Default,...U.getDataAttributes(this._element),...t},a(Ue,t,this.constructor.DefaultType),"object"==typeof t.reference&&!o(t.reference)&&"function"!=typeof t.reference.getBoundingClientRect)throw new TypeError(`${Ue.toUpperCase()}: Option "reference" provided type "object" without a required "getBoundingClientRect" method.`);return t}_createPopper(t){if(void 0===Fe)throw new TypeError("Bootstrap's dropdowns require Popper (https://popper.js.org)");let e=this._element;"parent"===this._config.reference?e=t:o(this._config.reference)?e=r(this._config.reference):"object"==typeof this._config.reference&&(e=this._config.reference);const i=this._getPopperConfig(),n=i.modifiers.find((t=>"applyStyles"===t.name&&!1===t.enabled));this._popper=qe(e,this._menu,i),n&&U.setDataAttribute(this._menu,"popper","static")}_isShown(t=this._element){return t.classList.contains(Je)}_getMenuElement(){return V.next(this._element,ei)[0]}_getPlacement(){const t=this._element.parentNode;if(t.classList.contains("dropend"))return ri;if(t.classList.contains("dropstart"))return ai;const e="end"===getComputedStyle(this._menu).getPropertyValue("--bs-position").trim();return t.classList.contains("dropup")?e?ni:ii:e?oi:si}_detectNavbar(){return null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return"static"===this._config.display&&(t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,..."function"==typeof this._config.popperConfig?this._config.popperConfig(t):this._config.popperConfig}}_selectMenuItem({key:t,target:e}){const i=V.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter(l);i.length&&v(i,e,t===Ye,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(t&&(2===t.button||"keyup"===t.type&&"Tab"!==t.key))return;const e=V.find(ti);for(let i=0,n=e.length;i<n;i++){const n=hi.getInstance(e[i]);if(!n||!1===n._config.autoClose)continue;if(!n._isShown())continue;const s={relatedTarget:n._element};if(t){const e=t.composedPath(),i=e.includes(n._menu);if(e.includes(n._element)||"inside"===n._config.autoClose&&!i||"outside"===n._config.autoClose&&i)continue;if(n._menu.contains(t.target)&&("keyup"===t.type&&"Tab"===t.key||/input|select|option|textarea|form/i.test(t.target.tagName)))continue;"click"===t.type&&(s.clickEvent=t)}n._completeHide(s)}}static getParentFromElement(t){return n(t)||t.parentNode}static dataApiKeydownHandler(t){if(/input|textarea/i.test(t.target.tagName)?t.key===Ke||t.key!==Ve&&(t.key!==Ye&&t.key!==Xe||t.target.closest(ei)):!Qe.test(t.key))return;const e=this.classList.contains(Je);if(!e&&t.key===Ve)return;if(t.preventDefault(),t.stopPropagation(),c(this))return;const i=this.matches(ti)?this:V.prev(this,ti)[0],n=hi.getOrCreateInstance(i);if(t.key!==Ve)return t.key===Xe||t.key===Ye?(e||n.show(),void n._selectMenuItem(t)):void(e&&t.key!==Ke||hi.clearMenus());n.hide()}}j.on(document,Ze,ti,hi.dataApiKeydownHandler),j.on(document,Ze,ei,hi.dataApiKeydownHandler),j.on(document,Ge,hi.clearMenus),j.on(document,"keyup.bs.dropdown.data-api",hi.clearMenus),j.on(document,Ge,ti,(function(t){t.preventDefault(),hi.getOrCreateInstance(this).toggle()})),g(hi);const di=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",ui=".sticky-top";class fi{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,"paddingRight",(e=>e+t)),this._setElementAttributes(di,"paddingRight",(e=>e+t)),this._setElementAttributes(ui,"marginRight",(e=>e-t))}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t)[e];t.style[e]=`${i(Number.parseFloat(s))}px`}))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,"paddingRight"),this._resetElementAttributes(di,"paddingRight"),this._resetElementAttributes(ui,"marginRight")}_saveInitialAttribute(t,e){const i=t.style[e];i&&U.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=U.getDataAttribute(t,e);void 0===i?t.style.removeProperty(e):(U.removeDataAttribute(t,e),t.style[e]=i)}))}_applyManipulationCallback(t,e){o(t)?e(t):V.find(t,this._element).forEach(e)}isOverflowing(){return this.getWidth()>0}}const pi={className:"modal-backdrop",isVisible:!0,isAnimated:!1,rootElement:"body",clickCallback:null},mi={className:"string",isVisible:"boolean",isAnimated:"boolean",rootElement:"(element|string)",clickCallback:"(function|null)"},gi="show",_i="mousedown.bs.backdrop";class bi{constructor(t){this._config=this._getConfig(t),this._isAppended=!1,this._element=null}show(t){this._config.isVisible?(this._append(),this._config.isAnimated&&u(this._getElement()),this._getElement().classList.add(gi),this._emulateAnimation((()=>{_(t)}))):_(t)}hide(t){this._config.isVisible?(this._getElement().classList.remove(gi),this._emulateAnimation((()=>{this.dispose(),_(t)}))):_(t)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_getConfig(t){return(t={...pi,..."object"==typeof t?t:{}}).rootElement=r(t.rootElement),a("backdrop",t,mi),t}_append(){this._isAppended||(this._config.rootElement.append(this._getElement()),j.on(this._getElement(),_i,(()=>{_(this._config.clickCallback)})),this._isAppended=!0)}dispose(){this._isAppended&&(j.off(this._element,_i),this._element.remove(),this._isAppended=!1)}_emulateAnimation(t){b(t,this._getElement(),this._config.isAnimated)}}const vi={trapElement:null,autofocus:!0},yi={trapElement:"element",autofocus:"boolean"},wi=".bs.focustrap",Ei="backward";class Ai{constructor(t){this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}activate(){const{trapElement:t,autofocus:e}=this._config;this._isActive||(e&&t.focus(),j.off(document,wi),j.on(document,"focusin.bs.focustrap",(t=>this._handleFocusin(t))),j.on(document,"keydown.tab.bs.focustrap",(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,j.off(document,wi))}_handleFocusin(t){const{target:e}=t,{trapElement:i}=this._config;if(e===document||e===i||i.contains(e))return;const n=V.focusableChildren(i);0===n.length?i.focus():this._lastTabNavDirection===Ei?n[n.length-1].focus():n[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?Ei:"forward")}_getConfig(t){return t={...vi,..."object"==typeof t?t:{}},a("focustrap",t,yi),t}}const Ti="modal",Oi="Escape",Ci={backdrop:!0,keyboard:!0,focus:!0},ki={backdrop:"(boolean|string)",keyboard:"boolean",focus:"boolean"},Li="hidden.bs.modal",xi="show.bs.modal",Di="resize.bs.modal",Si="click.dismiss.bs.modal",Ni="keydown.dismiss.bs.modal",Ii="mousedown.dismiss.bs.modal",Pi="modal-open",ji="show",Mi="modal-static";class Hi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._dialog=V.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._ignoreBackdropClick=!1,this._isTransitioning=!1,this._scrollBar=new fi}static get Default(){return Ci}static get NAME(){return Ti}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||j.trigger(this._element,xi,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isAnimated()&&(this._isTransitioning=!0),this._scrollBar.hide(),document.body.classList.add(Pi),this._adjustDialog(),this._setEscapeEvent(),this._setResizeEvent(),j.on(this._dialog,Ii,(()=>{j.one(this._element,"mouseup.dismiss.bs.modal",(t=>{t.target===this._element&&(this._ignoreBackdropClick=!0)}))})),this._showBackdrop((()=>this._showElement(t))))}hide(){if(!this._isShown||this._isTransitioning)return;if(j.trigger(this._element,"hide.bs.modal").defaultPrevented)return;this._isShown=!1;const t=this._isAnimated();t&&(this._isTransitioning=!0),this._setEscapeEvent(),this._setResizeEvent(),this._focustrap.deactivate(),this._element.classList.remove(ji),j.off(this._element,Si),j.off(this._dialog,Ii),this._queueCallback((()=>this._hideModal()),this._element,t)}dispose(){[window,this._dialog].forEach((t=>j.off(t,".bs.modal"))),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new bi({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_getConfig(t){return t={...Ci,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Ti,t,ki),t}_showElement(t){const e=this._isAnimated(),i=V.findOne(".modal-body",this._dialog);this._element.parentNode&&this._element.parentNode.nodeType===Node.ELEMENT_NODE||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0,i&&(i.scrollTop=0),e&&u(this._element),this._element.classList.add(ji),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,j.trigger(this._element,"shown.bs.modal",{relatedTarget:t})}),this._dialog,e)}_setEscapeEvent(){this._isShown?j.on(this._element,Ni,(t=>{this._config.keyboard&&t.key===Oi?(t.preventDefault(),this.hide()):this._config.keyboard||t.key!==Oi||this._triggerBackdropTransition()})):j.off(this._element,Ni)}_setResizeEvent(){this._isShown?j.on(window,Di,(()=>this._adjustDialog())):j.off(window,Di)}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(Pi),this._resetAdjustments(),this._scrollBar.reset(),j.trigger(this._element,Li)}))}_showBackdrop(t){j.on(this._element,Si,(t=>{this._ignoreBackdropClick?this._ignoreBackdropClick=!1:t.target===t.currentTarget&&(!0===this._config.backdrop?this.hide():"static"===this._config.backdrop&&this._triggerBackdropTransition())})),this._backdrop.show(t)}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(j.trigger(this._element,"hidePrevented.bs.modal").defaultPrevented)return;const{classList:t,scrollHeight:e,style:i}=this._element,n=e>document.documentElement.clientHeight;!n&&"hidden"===i.overflowY||t.contains(Mi)||(n||(i.overflowY="hidden"),t.add(Mi),this._queueCallback((()=>{t.remove(Mi),n||this._queueCallback((()=>{i.overflowY=""}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;(!i&&t&&!m()||i&&!t&&m())&&(this._element.style.paddingLeft=`${e}px`),(i&&!t&&!m()||!i&&t&&m())&&(this._element.style.paddingRight=`${e}px`)}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=Hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}j.on(document,"click.bs.modal.data-api",'[data-bs-toggle="modal"]',(function(t){const e=n(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),j.one(e,xi,(t=>{t.defaultPrevented||j.one(e,Li,(()=>{l(this)&&this.focus()}))}));const i=V.findOne(".modal.show");i&&Hi.getInstance(i).hide(),Hi.getOrCreateInstance(e).toggle(this)})),R(Hi),g(Hi);const Bi="offcanvas",Ri={backdrop:!0,keyboard:!0,scroll:!1},Wi={backdrop:"boolean",keyboard:"boolean",scroll:"boolean"},$i="show",zi=".offcanvas.show",qi="hidden.bs.offcanvas";class Fi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get NAME(){return Bi}static get Default(){return Ri}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||j.trigger(this._element,"show.bs.offcanvas",{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._element.style.visibility="visible",this._backdrop.show(),this._config.scroll||(new fi).hide(),this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add($i),this._queueCallback((()=>{this._config.scroll||this._focustrap.activate(),j.trigger(this._element,"shown.bs.offcanvas",{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(j.trigger(this._element,"hide.bs.offcanvas").defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.remove($i),this._backdrop.hide(),this._queueCallback((()=>{this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._element.style.visibility="hidden",this._config.scroll||(new fi).reset(),j.trigger(this._element,qi)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_getConfig(t){return t={...Ri,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Bi,t,Wi),t}_initializeBackDrop(){return new bi({className:"offcanvas-backdrop",isVisible:this._config.backdrop,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:()=>this.hide()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_addEventListeners(){j.on(this._element,"keydown.dismiss.bs.offcanvas",(t=>{this._config.keyboard&&"Escape"===t.key&&this.hide()}))}static jQueryInterface(t){return this.each((function(){const e=Fi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}j.on(document,"click.bs.offcanvas.data-api",'[data-bs-toggle="offcanvas"]',(function(t){const e=n(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),c(this))return;j.one(e,qi,(()=>{l(this)&&this.focus()}));const i=V.findOne(zi);i&&i!==e&&Fi.getInstance(i).hide(),Fi.getOrCreateInstance(e).toggle(this)})),j.on(window,"load.bs.offcanvas.data-api",(()=>V.find(zi).forEach((t=>Fi.getOrCreateInstance(t).show())))),R(Fi),g(Fi);const Ui=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Vi=/^(?:(?:https?|mailto|ftp|tel|file|sms):|[^#&/:?]*(?:[#/?]|$))/i,Ki=/^data:(?:image\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\/(?:mpeg|mp4|ogg|webm)|audio\/(?:mp3|oga|ogg|opus));base64,[\d+/a-z]+=*$/i,Xi=(t,e)=>{const i=t.nodeName.toLowerCase();if(e.includes(i))return!Ui.has(i)||Boolean(Vi.test(t.nodeValue)||Ki.test(t.nodeValue));const n=e.filter((t=>t instanceof RegExp));for(let t=0,e=n.length;t<e;t++)if(n[t].test(i))return!0;return!1};function Yi(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(let t=0,i=s.length;t<i;t++){const i=s[t],n=i.nodeName.toLowerCase();if(!Object.keys(e).includes(n)){i.remove();continue}const o=[].concat(...i.attributes),r=[].concat(e["*"]||[],e[n]||[]);o.forEach((t=>{Xi(t,r)||i.removeAttribute(t.nodeName)}))}return n.body.innerHTML}const Qi="tooltip",Gi=new Set(["sanitize","allowList","sanitizeFn"]),Zi={animation:"boolean",template:"string",title:"(string|element|function)",trigger:"string",delay:"(number|object)",html:"boolean",selector:"(string|boolean)",placement:"(string|function)",offset:"(array|string|function)",container:"(string|element|boolean)",fallbackPlacements:"array",boundary:"(string|element)",customClass:"(string|function)",sanitize:"boolean",sanitizeFn:"(null|function)",allowList:"object",popperConfig:"(null|object|function)"},Ji={AUTO:"auto",TOP:"top",RIGHT:m()?"left":"right",BOTTOM:"bottom",LEFT:m()?"right":"left"},tn={animation:!0,template:'<div class="tooltip" role="tooltip"><div class="tooltip-arrow"></div><div class="tooltip-inner"></div></div>',trigger:"hover focus",title:"",delay:0,html:!1,selector:!1,placement:"top",offset:[0,0],container:!1,fallbackPlacements:["top","right","bottom","left"],boundary:"clippingParents",customClass:"",sanitize:!0,sanitizeFn:null,allowList:{"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},popperConfig:null},en={HIDE:"hide.bs.tooltip",HIDDEN:"hidden.bs.tooltip",SHOW:"show.bs.tooltip",SHOWN:"shown.bs.tooltip",INSERTED:"inserted.bs.tooltip",CLICK:"click.bs.tooltip",FOCUSIN:"focusin.bs.tooltip",FOCUSOUT:"focusout.bs.tooltip",MOUSEENTER:"mouseenter.bs.tooltip",MOUSELEAVE:"mouseleave.bs.tooltip"},nn="fade",sn="show",on="show",rn="out",an=".tooltip-inner",ln=".modal",cn="hide.bs.modal",hn="hover",dn="focus";class un extends B{constructor(t,e){if(void 0===Fe)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t),this._isEnabled=!0,this._timeout=0,this._hoverState="",this._activeTrigger={},this._popper=null,this._config=this._getConfig(e),this.tip=null,this._setListeners()}static get Default(){return tn}static get NAME(){return Qi}static get Event(){return en}static get DefaultType(){return Zi}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(t){if(this._isEnabled)if(t){const e=this._initializeOnDelegatedTarget(t);e._activeTrigger.click=!e._activeTrigger.click,e._isWithActiveTrigger()?e._enter(null,e):e._leave(null,e)}else{if(this.getTipElement().classList.contains(sn))return void this._leave(null,this);this._enter(null,this)}}dispose(){clearTimeout(this._timeout),j.off(this._element.closest(ln),cn,this._hideModalHandler),this.tip&&this.tip.remove(),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this.isWithContent()||!this._isEnabled)return;const t=j.trigger(this._element,this.constructor.Event.SHOW),e=h(this._element),i=null===e?this._element.ownerDocument.documentElement.contains(this._element):e.contains(this._element);if(t.defaultPrevented||!i)return;"tooltip"===this.constructor.NAME&&this.tip&&this.getTitle()!==this.tip.querySelector(an).innerHTML&&(this._disposePopper(),this.tip.remove(),this.tip=null);const n=this.getTipElement(),s=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME);n.setAttribute("id",s),this._element.setAttribute("aria-describedby",s),this._config.animation&&n.classList.add(nn);const o="function"==typeof this._config.placement?this._config.placement.call(this,n,this._element):this._config.placement,r=this._getAttachment(o);this._addAttachmentClass(r);const{container:a}=this._config;H.set(n,this.constructor.DATA_KEY,this),this._element.ownerDocument.documentElement.contains(this.tip)||(a.append(n),j.trigger(this._element,this.constructor.Event.INSERTED)),this._popper?this._popper.update():this._popper=qe(this._element,n,this._getPopperConfig(r)),n.classList.add(sn);const l=this._resolvePossibleFunction(this._config.customClass);l&&n.classList.add(...l.split(" ")),"ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>{j.on(t,"mouseover",d)}));const c=this.tip.classList.contains(nn);this._queueCallback((()=>{const t=this._hoverState;this._hoverState=null,j.trigger(this._element,this.constructor.Event.SHOWN),t===rn&&this._leave(null,this)}),this.tip,c)}hide(){if(!this._popper)return;const t=this.getTipElement();if(j.trigger(this._element,this.constructor.Event.HIDE).defaultPrevented)return;t.classList.remove(sn),"ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>j.off(t,"mouseover",d))),this._activeTrigger.click=!1,this._activeTrigger.focus=!1,this._activeTrigger.hover=!1;const e=this.tip.classList.contains(nn);this._queueCallback((()=>{this._isWithActiveTrigger()||(this._hoverState!==on&&t.remove(),this._cleanTipClass(),this._element.removeAttribute("aria-describedby"),j.trigger(this._element,this.constructor.Event.HIDDEN),this._disposePopper())}),this.tip,e),this._hoverState=""}update(){null!==this._popper&&this._popper.update()}isWithContent(){return Boolean(this.getTitle())}getTipElement(){if(this.tip)return this.tip;const t=document.createElement("div");t.innerHTML=this._config.template;const e=t.children[0];return this.setContent(e),e.classList.remove(nn,sn),this.tip=e,this.tip}setContent(t){this._sanitizeAndSetContent(t,this.getTitle(),an)}_sanitizeAndSetContent(t,e,i){const n=V.findOne(i,t);e||!n?this.setElementContent(n,e):n.remove()}setElementContent(t,e){if(null!==t)return o(e)?(e=r(e),void(this._config.html?e.parentNode!==t&&(t.innerHTML="",t.append(e)):t.textContent=e.textContent)):void(this._config.html?(this._config.sanitize&&(e=Yi(e,this._config.allowList,this._config.sanitizeFn)),t.innerHTML=e):t.textContent=e)}getTitle(){const t=this._element.getAttribute("data-bs-original-title")||this._config.title;return this._resolvePossibleFunction(t)}updateAttachment(t){return"right"===t?"end":"left"===t?"start":t}_initializeOnDelegatedTarget(t,e){return e||this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return"function"==typeof t?t.call(this._element):t}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"onChange",enabled:!0,phase:"afterWrite",fn:t=>this._handlePopperPlacementChange(t)}],onFirstUpdate:t=>{t.options.placement!==t.placement&&this._handlePopperPlacementChange(t)}};return{...e,..."function"==typeof this._config.popperConfig?this._config.popperConfig(e):this._config.popperConfig}}_addAttachmentClass(t){this.getTipElement().classList.add(`${this._getBasicClassPrefix()}-${this.updateAttachment(t)}`)}_getAttachment(t){return Ji[t.toUpperCase()]}_setListeners(){this._config.trigger.split(" ").forEach((t=>{if("click"===t)j.on(this._element,this.constructor.Event.CLICK,this._config.selector,(t=>this.toggle(t)));else if("manual"!==t){const e=t===hn?this.constructor.Event.MOUSEENTER:this.constructor.Event.FOCUSIN,i=t===hn?this.constructor.Event.MOUSELEAVE:this.constructor.Event.FOCUSOUT;j.on(this._element,e,this._config.selector,(t=>this._enter(t))),j.on(this._element,i,this._config.selector,(t=>this._leave(t)))}})),this._hideModalHandler=()=>{this._element&&this.hide()},j.on(this._element.closest(ln),cn,this._hideModalHandler),this._config.selector?this._config={...this._config,trigger:"manual",selector:""}:this._fixTitle()}_fixTitle(){const t=this._element.getAttribute("title"),e=typeof this._element.getAttribute("data-bs-original-title");(t||"string"!==e)&&(this._element.setAttribute("data-bs-original-title",t||""),!t||this._element.getAttribute("aria-label")||this._element.textContent||this._element.setAttribute("aria-label",t),this._element.setAttribute("title",""))}_enter(t,e){e=this._initializeOnDelegatedTarget(t,e),t&&(e._activeTrigger["focusin"===t.type?dn:hn]=!0),e.getTipElement().classList.contains(sn)||e._hoverState===on?e._hoverState=on:(clearTimeout(e._timeout),e._hoverState=on,e._config.delay&&e._config.delay.show?e._timeout=setTimeout((()=>{e._hoverState===on&&e.show()}),e._config.delay.show):e.show())}_leave(t,e){e=this._initializeOnDelegatedTarget(t,e),t&&(e._activeTrigger["focusout"===t.type?dn:hn]=e._element.contains(t.relatedTarget)),e._isWithActiveTrigger()||(clearTimeout(e._timeout),e._hoverState=rn,e._config.delay&&e._config.delay.hide?e._timeout=setTimeout((()=>{e._hoverState===rn&&e.hide()}),e._config.delay.hide):e.hide())}_isWithActiveTrigger(){for(const t in this._activeTrigger)if(this._activeTrigger[t])return!0;return!1}_getConfig(t){const e=U.getDataAttributes(this._element);return Object.keys(e).forEach((t=>{Gi.has(t)&&delete e[t]})),(t={...this.constructor.Default,...e,..."object"==typeof t&&t?t:{}}).container=!1===t.container?document.body:r(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),a(Qi,t,this.constructor.DefaultType),t.sanitize&&(t.template=Yi(t.template,t.allowList,t.sanitizeFn)),t}_getDelegateConfig(){const t={};for(const e in this._config)this.constructor.Default[e]!==this._config[e]&&(t[e]=this._config[e]);return t}_cleanTipClass(){const t=this.getTipElement(),e=new RegExp(`(^|\\s)${this._getBasicClassPrefix()}\\S+`,"g"),i=t.getAttribute("class").match(e);null!==i&&i.length>0&&i.map((t=>t.trim())).forEach((e=>t.classList.remove(e)))}_getBasicClassPrefix(){return"bs-tooltip"}_handlePopperPlacementChange(t){const{state:e}=t;e&&(this.tip=e.elements.popper,this._cleanTipClass(),this._addAttachmentClass(this._getAttachment(e.placement)))}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null)}static jQueryInterface(t){return this.each((function(){const e=un.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}g(un);const fn={...un.Default,placement:"right",offset:[0,8],trigger:"click",content:"",template:'<div class="popover" role="tooltip"><div class="popover-arrow"></div><h3 class="popover-header"></h3><div class="popover-body"></div></div>'},pn={...un.DefaultType,content:"(string|element|function)"},mn={HIDE:"hide.bs.popover",HIDDEN:"hidden.bs.popover",SHOW:"show.bs.popover",SHOWN:"shown.bs.popover",INSERTED:"inserted.bs.popover",CLICK:"click.bs.popover",FOCUSIN:"focusin.bs.popover",FOCUSOUT:"focusout.bs.popover",MOUSEENTER:"mouseenter.bs.popover",MOUSELEAVE:"mouseleave.bs.popover"};class gn extends un{static get Default(){return fn}static get NAME(){return"popover"}static get Event(){return mn}static get DefaultType(){return pn}isWithContent(){return this.getTitle()||this._getContent()}setContent(t){this._sanitizeAndSetContent(t,this.getTitle(),".popover-header"),this._sanitizeAndSetContent(t,this._getContent(),".popover-body")}_getContent(){return this._resolvePossibleFunction(this._config.content)}_getBasicClassPrefix(){return"bs-popover"}static jQueryInterface(t){return this.each((function(){const e=gn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}g(gn);const _n="scrollspy",bn={offset:10,method:"auto",target:""},vn={offset:"number",method:"string",target:"(string|element)"},yn="active",wn=".nav-link, .list-group-item, .dropdown-item",En="position";class An extends B{constructor(t,e){super(t),this._scrollElement="BODY"===this._element.tagName?window:this._element,this._config=this._getConfig(e),this._offsets=[],this._targets=[],this._activeTarget=null,this._scrollHeight=0,j.on(this._scrollElement,"scroll.bs.scrollspy",(()=>this._process())),this.refresh(),this._process()}static get Default(){return bn}static get NAME(){return _n}refresh(){const t=this._scrollElement===this._scrollElement.window?"offset":En,e="auto"===this._config.method?t:this._config.method,n=e===En?this._getScrollTop():0;this._offsets=[],this._targets=[],this._scrollHeight=this._getScrollHeight(),V.find(wn,this._config.target).map((t=>{const s=i(t),o=s?V.findOne(s):null;if(o){const t=o.getBoundingClientRect();if(t.width||t.height)return[U[e](o).top+n,s]}return null})).filter((t=>t)).sort(((t,e)=>t[0]-e[0])).forEach((t=>{this._offsets.push(t[0]),this._targets.push(t[1])}))}dispose(){j.off(this._scrollElement,".bs.scrollspy"),super.dispose()}_getConfig(t){return(t={...bn,...U.getDataAttributes(this._element),..."object"==typeof t&&t?t:{}}).target=r(t.target)||document.documentElement,a(_n,t,vn),t}_getScrollTop(){return this._scrollElement===window?this._scrollElement.pageYOffset:this._scrollElement.scrollTop}_getScrollHeight(){return this._scrollElement.scrollHeight||Math.max(document.body.scrollHeight,document.documentElement.scrollHeight)}_getOffsetHeight(){return this._scrollElement===window?window.innerHeight:this._scrollElement.getBoundingClientRect().height}_process(){const t=this._getScrollTop()+this._config.offset,e=this._getScrollHeight(),i=this._config.offset+e-this._getOffsetHeight();if(this._scrollHeight!==e&&this.refresh(),t>=i){const t=this._targets[this._targets.length-1];this._activeTarget!==t&&this._activate(t)}else{if(this._activeTarget&&t<this._offsets[0]&&this._offsets[0]>0)return this._activeTarget=null,void this._clear();for(let e=this._offsets.length;e--;)this._activeTarget!==this._targets[e]&&t>=this._offsets[e]&&(void 0===this._offsets[e+1]||t<this._offsets[e+1])&&this._activate(this._targets[e])}}_activate(t){this._activeTarget=t,this._clear();const e=wn.split(",").map((e=>`${e}[data-bs-target="${t}"],${e}[href="${t}"]`)),i=V.findOne(e.join(","),this._config.target);i.classList.add(yn),i.classList.contains("dropdown-item")?V.findOne(".dropdown-toggle",i.closest(".dropdown")).classList.add(yn):V.parents(i,".nav, .list-group").forEach((t=>{V.prev(t,".nav-link, .list-group-item").forEach((t=>t.classList.add(yn))),V.prev(t,".nav-item").forEach((t=>{V.children(t,".nav-link").forEach((t=>t.classList.add(yn)))}))})),j.trigger(this._scrollElement,"activate.bs.scrollspy",{relatedTarget:t})}_clear(){V.find(wn,this._config.target).filter((t=>t.classList.contains(yn))).forEach((t=>t.classList.remove(yn)))}static jQueryInterface(t){return this.each((function(){const e=An.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}j.on(window,"load.bs.scrollspy.data-api",(()=>{V.find('[data-bs-spy="scroll"]').forEach((t=>new An(t)))})),g(An);const Tn="active",On="fade",Cn="show",kn=".active",Ln=":scope > li > .active";class xn extends B{static get NAME(){return"tab"}show(){if(this._element.parentNode&&this._element.parentNode.nodeType===Node.ELEMENT_NODE&&this._element.classList.contains(Tn))return;let t;const e=n(this._element),i=this._element.closest(".nav, .list-group");if(i){const 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Dn="toast",Sn="hide",Nn="show",In="showing",Pn={animation:"boolean",autohide:"boolean",delay:"number"},jn={animation:!0,autohide:!0,delay:5e3};class Mn extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._timeout=null,this._hasMouseInteraction=!1,this._hasKeyboardInteraction=!1,this._setListeners()}static get DefaultType(){return Pn}static get Default(){return jn}static get NAME(){return Dn}show(){j.trigger(this._element,"show.bs.toast").defaultPrevented||(this._clearTimeout(),this._config.animation&&this._element.classList.add("fade"),this._element.classList.remove(Sn),u(this._element),this._element.classList.add(Nn),this._element.classList.add(In),this._queueCallback((()=>{this._element.classList.remove(In),j.trigger(this._element,"shown.bs.toast"),this._maybeScheduleHide()}),this._element,this._config.animation))}hide(){this._element.classList.contains(Nn)&&(j.trigger(this._element,"hide.bs.toast").defaultPrevented||(this._element.classList.add(In),this._queueCallback((()=>{this._element.classList.add(Sn),this._element.classList.remove(In),this._element.classList.remove(Nn),j.trigger(this._element,"hidden.bs.toast")}),this._element,this._config.animation)))}dispose(){this._clearTimeout(),this._element.classList.contains(Nn)&&this._element.classList.remove(Nn),super.dispose()}_getConfig(t){return t={...jn,...U.getDataAttributes(this._element),..."object"==typeof t&&t?t:{}},a(Dn,t,this.constructor.DefaultType),t}_maybeScheduleHide(){this._config.autohide&&(this._hasMouseInteraction||this._hasKeyboardInteraction||(this._timeout=setTimeout((()=>{this.hide()}),this._config.delay)))}_onInteraction(t,e){switch(t.type){case"mouseover":case"mouseout":this._hasMouseInteraction=e;break;case"focusin":case"focusout":this._hasKeyboardInteraction=e}if(e)return void this._clearTimeout();const i=t.relatedTarget;this._element===i||this._element.contains(i)||this._maybeScheduleHide()}_setListeners(){j.on(this._element,"mouseover.bs.toast",(t=>this._onInteraction(t,!0))),j.on(this._element,"mouseout.bs.toast",(t=>this._onInteraction(t,!1))),j.on(this._element,"focusin.bs.toast",(t=>this._onInteraction(t,!0))),j.on(this._element,"focusout.bs.toast",(t=>this._onInteraction(t,!1)))}_clearTimeout(){clearTimeout(this._timeout),this._timeout=null}static jQueryInterface(t){return this.each((function(){const e=Mn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}return R(Mn),g(Mn),{Alert:W,Button:z,Carousel:st,Collapse:pt,Dropdown:hi,Modal:Hi,Offcanvas:Fi,Popover:gn,ScrollSpy:An,Tab:xn,Toast:Mn,Tooltip:un}}));
-//# sourceMappingURL=bootstrap.bundle.min.js.map
\ No newline at end of file
diff --git a/build/dev/proposal_files/libs/quarto-html/anchor.min.js b/build/dev/proposal_files/libs/quarto-html/anchor.min.js
deleted file mode 100644
index 1c2b86faedde03cb3c3459e4900f7ef29125d94a..0000000000000000000000000000000000000000
--- a/build/dev/proposal_files/libs/quarto-html/anchor.min.js
+++ /dev/null
@@ -1,9 +0,0 @@
-// @license magnet:?xt=urn:btih:d3d9a9a6595521f9666a5e94cc830dab83b65699&dn=expat.txt Expat
-//
-// AnchorJS - v4.3.1 - 2021-04-17
-// https://www.bryanbraun.com/anchorjs/
-// Copyright (c) 2021 Bryan Braun; Licensed MIT
-//
-// @license magnet:?xt=urn:btih:d3d9a9a6595521f9666a5e94cc830dab83b65699&dn=expat.txt Expat
-!function(A,e){"use strict";"function"==typeof define&&define.amd?define([],e):"object"==typeof module&&module.exports?module.exports=e():(A.AnchorJS=e(),A.anchors=new A.AnchorJS)}(this,function(){"use strict";return function(A){function d(A){A.icon=Object.prototype.hasOwnProperty.call(A,"icon")?A.icon:"",A.visible=Object.prototype.hasOwnProperty.call(A,"visible")?A.visible:"hover",A.placement=Object.prototype.hasOwnProperty.call(A,"placement")?A.placement:"right",A.ariaLabel=Object.prototype.hasOwnProperty.call(A,"ariaLabel")?A.ariaLabel:"Anchor",A.class=Object.prototype.hasOwnProperty.call(A,"class")?A.class:"",A.base=Object.prototype.hasOwnProperty.call(A,"base")?A.base:"",A.truncate=Object.prototype.hasOwnProperty.call(A,"truncate")?Math.floor(A.truncate):64,A.titleText=Object.prototype.hasOwnProperty.call(A,"titleText")?A.titleText:""}function w(A){var e;if("string"==typeof A||A instanceof String)e=[].slice.call(document.querySelectorAll(A));else{if(!(Array.isArray(A)||A instanceof NodeList))throw new TypeError("The selector provided to AnchorJS was invalid.");e=[].slice.call(A)}return e}this.options=A||{},this.elements=[],d(this.options),this.isTouchDevice=function(){return Boolean("ontouchstart"in window||window.TouchEvent||window.DocumentTouch&&document instanceof DocumentTouch)},this.add=function(A){var e,t,o,i,n,s,a,c,r,l,h,u,p=[];if(d(this.options),"touch"===(l=this.options.visible)&&(l=this.isTouchDevice()?"always":"hover"),0===(e=w(A=A||"h2, h3, h4, h5, h6")).length)return this;for(null===document.head.querySelector("style.anchorjs")&&((u=document.createElement("style")).className="anchorjs",u.appendChild(document.createTextNode("")),void 0===(A=document.head.querySelector('[rel="stylesheet"],style'))?document.head.appendChild(u):document.head.insertBefore(u,A),u.sheet.insertRule(".anchorjs-link{opacity:0;text-decoration:none;-webkit-font-smoothing:antialiased;-moz-osx-font-smoothing:grayscale}",u.sheet.cssRules.length),u.sheet.insertRule(":hover>.anchorjs-link,.anchorjs-link:focus{opacity:1}",u.sheet.cssRules.length),u.sheet.insertRule("[data-anchorjs-icon]::after{content:attr(data-anchorjs-icon)}",u.sheet.cssRules.length),u.sheet.insertRule('@font-face{font-family:anchorjs-icons;src:url(data:n/a;base64,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) format("truetype")}',u.sheet.cssRules.length)),u=document.querySelectorAll("[id]"),t=[].map.call(u,function(A){return A.id}),i=0;i<e.length;i++)if(this.hasAnchorJSLink(e[i]))p.push(i);else{if(e[i].hasAttribute("id"))o=e[i].getAttribute("id");else if(e[i].hasAttribute("data-anchor-id"))o=e[i].getAttribute("data-anchor-id");else{for(c=a=this.urlify(e[i].textContent),s=0;n=t.indexOf(c=void 0!==n?a+"-"+s:c),s+=1,-1!==n;);n=void 0,t.push(c),e[i].setAttribute("id",c),o=c}(r=document.createElement("a")).className="anchorjs-link "+this.options.class,r.setAttribute("aria-label",this.options.ariaLabel),r.setAttribute("data-anchorjs-icon",this.options.icon),this.options.titleText&&(r.title=this.options.titleText),h=document.querySelector("base")?window.location.pathname+window.location.search:"",h=this.options.base||h,r.href=h+"#"+o,"always"===l&&(r.style.opacity="1"),""===this.options.icon&&(r.style.font="1em/1 anchorjs-icons","left"===this.options.placement&&(r.style.lineHeight="inherit")),"left"===this.options.placement?(r.style.position="absolute",r.style.marginLeft="-1em",r.style.paddingRight=".5em",e[i].insertBefore(r,e[i].firstChild)):(r.style.paddingLeft=".375em",e[i].appendChild(r))}for(i=0;i<p.length;i++)e.splice(p[i]-i,1);return this.elements=this.elements.concat(e),this},this.remove=function(A){for(var e,t,o=w(A),i=0;i<o.length;i++)(t=o[i].querySelector(".anchorjs-link"))&&(-1!==(e=this.elements.indexOf(o[i]))&&this.elements.splice(e,1),o[i].removeChild(t));return this},this.removeAll=function(){this.remove(this.elements)},this.urlify=function(A){var e=document.createElement("textarea");return e.innerHTML=A,A=e.value,this.options.truncate||d(this.options),A.trim().replace(/'/gi,"").replace(/[& +$,:;=?@"#{}|^~[`%!'<>\]./()*\\\n\t\b\v\u00A0]/g,"-").replace(/-{2,}/g,"-").substring(0,this.options.truncate).replace(/^-+|-+$/gm,"").toLowerCase()},this.hasAnchorJSLink=function(A){var e=A.firstChild&&-1<(" "+A.firstChild.className+" ").indexOf(" anchorjs-link "),A=A.lastChild&&-1<(" "+A.lastChild.className+" ").indexOf(" anchorjs-link ");return e||A||!1}}});
-// @license-end
\ No newline at end of file
diff --git a/build/dev/proposal_files/libs/quarto-html/popper.min.js b/build/dev/proposal_files/libs/quarto-html/popper.min.js
deleted file mode 100644
index 2269d66967635e9bde6edb948cd93b0ae1978dfc..0000000000000000000000000000000000000000
--- a/build/dev/proposal_files/libs/quarto-html/popper.min.js
+++ /dev/null
@@ -1,6 +0,0 @@
-/**
- * @popperjs/core v2.11.4 - MIT License
- */
-
-!function(e,t){"object"==typeof exports&&"undefined"!=typeof module?t(exports):"function"==typeof define&&define.amd?define(["exports"],t):t((e="undefined"!=typeof globalThis?globalThis:e||self).Popper={})}(this,(function(e){"use strict";function t(e){if(null==e)return window;if("[object Window]"!==e.toString()){var t=e.ownerDocument;return t&&t.defaultView||window}return e}function n(e){return e instanceof t(e).Element||e instanceof Element}function r(e){return e instanceof t(e).HTMLElement||e instanceof HTMLElement}function o(e){return"undefined"!=typeof ShadowRoot&&(e instanceof t(e).ShadowRoot||e instanceof ShadowRoot)}var i=Math.max,a=Math.min,s=Math.round;function f(e,t){void 0===t&&(t=!1);var n=e.getBoundingClientRect(),o=1,i=1;if(r(e)&&t){var a=e.offsetHeight,f=e.offsetWidth;f>0&&(o=s(n.width)/f||1),a>0&&(i=s(n.height)/a||1)}return{width:n.width/o,height:n.height/i,top:n.top/i,right:n.right/o,bottom:n.bottom/i,left:n.left/o,x:n.left/o,y:n.top/i}}function c(e){var n=t(e);return{scrollLeft:n.pageXOffset,scrollTop:n.pageYOffset}}function p(e){return e?(e.nodeName||"").toLowerCase():null}function u(e){return((n(e)?e.ownerDocument:e.document)||window.document).documentElement}function l(e){return f(u(e)).left+c(e).scrollLeft}function d(e){return t(e).getComputedStyle(e)}function h(e){var t=d(e),n=t.overflow,r=t.overflowX,o=t.overflowY;return/auto|scroll|overlay|hidden/.test(n+o+r)}function m(e,n,o){void 0===o&&(o=!1);var i,a,d=r(n),m=r(n)&&function(e){var t=e.getBoundingClientRect(),n=s(t.width)/e.offsetWidth||1,r=s(t.height)/e.offsetHeight||1;return 1!==n||1!==r}(n),v=u(n),g=f(e,m),y={scrollLeft:0,scrollTop:0},b={x:0,y:0};return(d||!d&&!o)&&(("body"!==p(n)||h(v))&&(y=(i=n)!==t(i)&&r(i)?{scrollLeft:(a=i).scrollLeft,scrollTop:a.scrollTop}:c(i)),r(n)?((b=f(n,!0)).x+=n.clientLeft,b.y+=n.clientTop):v&&(b.x=l(v))),{x:g.left+y.scrollLeft-b.x,y:g.top+y.scrollTop-b.y,width:g.width,height:g.height}}function v(e){var t=f(e),n=e.offsetWidth,r=e.offsetHeight;return Math.abs(t.width-n)<=1&&(n=t.width),Math.abs(t.height-r)<=1&&(r=t.height),{x:e.offsetLeft,y:e.offsetTop,width:n,height:r}}function g(e){return"html"===p(e)?e:e.assignedSlot||e.parentNode||(o(e)?e.host:null)||u(e)}function y(e){return["html","body","#document"].indexOf(p(e))>=0?e.ownerDocument.body:r(e)&&h(e)?e:y(g(e))}function b(e,n){var r;void 0===n&&(n=[]);var o=y(e),i=o===(null==(r=e.ownerDocument)?void 0:r.body),a=t(o),s=i?[a].concat(a.visualViewport||[],h(o)?o:[]):o,f=n.concat(s);return i?f:f.concat(b(g(s)))}function x(e){return["table","td","th"].indexOf(p(e))>=0}function w(e){return r(e)&&"fixed"!==d(e).position?e.offsetParent:null}function O(e){for(var n=t(e),i=w(e);i&&x(i)&&"static"===d(i).position;)i=w(i);return i&&("html"===p(i)||"body"===p(i)&&"static"===d(i).position)?n:i||function(e){var 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-
diff --git a/build/dev/proposal_files/libs/quarto-html/quarto-syntax-highlighting.css b/build/dev/proposal_files/libs/quarto-html/quarto-syntax-highlighting.css
deleted file mode 100644
index 1efa3ee2a03587e817eaf2574765e6053f73dfdd..0000000000000000000000000000000000000000
--- a/build/dev/proposal_files/libs/quarto-html/quarto-syntax-highlighting.css
+++ /dev/null
@@ -1,171 +0,0 @@
-/* quarto syntax highlight colors */
-:root {
- --quarto-hl-ot-color: #003B4F;
- --quarto-hl-at-color: #657422;
- --quarto-hl-ss-color: #20794D;
- --quarto-hl-an-color: #5E5E5E;
- --quarto-hl-fu-color: #4758AB;
- --quarto-hl-st-color: #20794D;
- --quarto-hl-cf-color: #003B4F;
- --quarto-hl-op-color: #5E5E5E;
- --quarto-hl-er-color: #AD0000;
- --quarto-hl-bn-color: #AD0000;
- --quarto-hl-al-color: #AD0000;
- --quarto-hl-va-color: #111111;
- --quarto-hl-bu-color: inherit;
- --quarto-hl-ex-color: inherit;
- --quarto-hl-pp-color: #AD0000;
- --quarto-hl-in-color: #5E5E5E;
- --quarto-hl-vs-color: #20794D;
- --quarto-hl-wa-color: #5E5E5E;
- --quarto-hl-do-color: #5E5E5E;
- --quarto-hl-im-color: #00769E;
- --quarto-hl-ch-color: #20794D;
- --quarto-hl-dt-color: #AD0000;
- --quarto-hl-fl-color: #AD0000;
- --quarto-hl-co-color: #5E5E5E;
- --quarto-hl-cv-color: #5E5E5E;
- --quarto-hl-cn-color: #8f5902;
- --quarto-hl-sc-color: #5E5E5E;
- --quarto-hl-dv-color: #AD0000;
- --quarto-hl-kw-color: #003B4F;
-}
-
-/* other quarto variables */
-:root {
- --quarto-font-monospace: "JuliaMono-Light";
-}
-
-pre > code.sourceCode > span {
- color: #003B4F;
-}
-
-code span {
- color: #003B4F;
-}
-
-code.sourceCode > span {
- color: #003B4F;
-}
-
-div.sourceCode,
-div.sourceCode pre.sourceCode {
- color: #003B4F;
-}
-
-code span.ot {
- color: #003B4F;
-}
-
-code span.at {
- color: #657422;
-}
-
-code span.ss {
- color: #20794D;
-}
-
-code span.an {
- color: #5E5E5E;
-}
-
-code span.fu {
- color: #4758AB;
-}
-
-code span.st {
- color: #20794D;
-}
-
-code span.cf {
- color: #003B4F;
-}
-
-code span.op {
- color: #5E5E5E;
-}
-
-code span.er {
- color: #AD0000;
-}
-
-code span.bn {
- color: #AD0000;
-}
-
-code span.al {
- color: #AD0000;
-}
-
-code span.va {
- color: #111111;
-}
-
-code span.pp {
- color: #AD0000;
-}
-
-code span.in {
- color: #5E5E5E;
-}
-
-code span.vs {
- color: #20794D;
-}
-
-code span.wa {
- color: #5E5E5E;
- font-style: italic;
-}
-
-code span.do {
- color: #5E5E5E;
- font-style: italic;
-}
-
-code span.im {
- color: #00769E;
-}
-
-code span.ch {
- color: #20794D;
-}
-
-code span.dt {
- color: #AD0000;
-}
-
-code span.fl {
- color: #AD0000;
-}
-
-code span.co {
- color: #5E5E5E;
-}
-
-code span.cv {
- color: #5E5E5E;
- font-style: italic;
-}
-
-code span.cn {
- color: #8f5902;
-}
-
-code span.sc {
- color: #5E5E5E;
-}
-
-code span.dv {
- color: #AD0000;
-}
-
-code span.kw {
- color: #003B4F;
-}
-
-.prevent-inlining {
- content: "</";
-}
-
-/*# sourceMappingURL=debc5d5d77c3f9108843748ff7464032.css.map */
diff --git a/build/dev/proposal_files/libs/quarto-html/quarto.js b/build/dev/proposal_files/libs/quarto-html/quarto.js
deleted file mode 100644
index 8846a020523d43436bac714cab4649fbbfe6b50c..0000000000000000000000000000000000000000
--- a/build/dev/proposal_files/libs/quarto-html/quarto.js
+++ /dev/null
@@ -1,840 +0,0 @@
-const sectionChanged = new CustomEvent("quarto-sectionChanged", {
- detail: {},
- bubbles: true,
- cancelable: false,
- composed: false,
-});
-
-window.document.addEventListener("DOMContentLoaded", function (_event) {
- const tocEl = window.document.querySelector('nav.toc-active[role="doc-toc"]');
- const sidebarEl = window.document.getElementById("quarto-sidebar");
- const leftTocEl = window.document.getElementById("quarto-sidebar-toc-left");
- const marginSidebarEl = window.document.getElementById(
- "quarto-margin-sidebar"
- );
- // function to determine whether the element has a previous sibling that is active
- const prevSiblingIsActiveLink = (el) => {
- const sibling = el.previousElementSibling;
- if (sibling && sibling.tagName === "A") {
- return sibling.classList.contains("active");
- } else {
- return false;
- }
- };
-
- // fire slideEnter for bootstrap tab activations (for htmlwidget resize behavior)
- function fireSlideEnter(e) {
- const event = window.document.createEvent("Event");
- event.initEvent("slideenter", true, true);
- window.document.dispatchEvent(event);
- }
- const tabs = window.document.querySelectorAll('a[data-bs-toggle="tab"]');
- tabs.forEach((tab) => {
- tab.addEventListener("shown.bs.tab", fireSlideEnter);
- });
-
- // fire slideEnter for tabby tab activations (for htmlwidget resize behavior)
- document.addEventListener("tabby", fireSlideEnter, false);
-
- // Track scrolling and mark TOC links as active
- // get table of contents and sidebar (bail if we don't have at least one)
- const tocLinks = tocEl
- ? [...tocEl.querySelectorAll("a[data-scroll-target]")]
- : [];
- const makeActive = (link) => tocLinks[link].classList.add("active");
- const removeActive = (link) => tocLinks[link].classList.remove("active");
- const removeAllActive = () =>
- [...Array(tocLinks.length).keys()].forEach((link) => removeActive(link));
-
- // activate the anchor for a section associated with this TOC entry
- tocLinks.forEach((link) => {
- link.addEventListener("click", () => {
- if (link.href.indexOf("#") !== -1) {
- const anchor = link.href.split("#")[1];
- const heading = window.document.querySelector(
- `[data-anchor-id=${anchor}]`
- );
- if (heading) {
- // Add the class
- heading.classList.add("reveal-anchorjs-link");
-
- // function to show the anchor
- const handleMouseout = () => {
- heading.classList.remove("reveal-anchorjs-link");
- heading.removeEventListener("mouseout", handleMouseout);
- };
-
- // add a function to clear the anchor when the user mouses out of it
- heading.addEventListener("mouseout", handleMouseout);
- }
- }
- });
- });
-
- const sections = tocLinks.map((link) => {
- const target = link.getAttribute("data-scroll-target");
- if (target.startsWith("#")) {
- return window.document.getElementById(decodeURI(`${target.slice(1)}`));
- } else {
- return window.document.querySelector(decodeURI(`${target}`));
- }
- });
-
- const sectionMargin = 200;
- let currentActive = 0;
- // track whether we've initialized state the first time
- let init = false;
-
- const updateActiveLink = () => {
- // The index from bottom to top (e.g. reversed list)
- let sectionIndex = -1;
- if (
- window.innerHeight + window.pageYOffset >=
- window.document.body.offsetHeight
- ) {
- sectionIndex = 0;
- } else {
- sectionIndex = [...sections].reverse().findIndex((section) => {
- if (section) {
- return window.pageYOffset >= section.offsetTop - sectionMargin;
- } else {
- return false;
- }
- });
- }
- if (sectionIndex > -1) {
- const current = sections.length - sectionIndex - 1;
- if (current !== currentActive) {
- removeAllActive();
- currentActive = current;
- makeActive(current);
- if (init) {
- window.dispatchEvent(sectionChanged);
- }
- init = true;
- }
- }
- };
-
- const inHiddenRegion = (top, bottom, hiddenRegions) => {
- for (const region of hiddenRegions) {
- if (top <= region.bottom && bottom >= region.top) {
- return true;
- }
- }
- return false;
- };
-
- const categorySelector = "header.quarto-title-block .quarto-category";
- const activateCategories = (href) => {
- // Find any categories
- // Surround them with a link pointing back to:
- // #category=Authoring
- try {
- const categoryEls = window.document.querySelectorAll(categorySelector);
- for (const categoryEl of categoryEls) {
- const categoryText = categoryEl.textContent;
- if (categoryText) {
- const link = `${href}#category=${encodeURIComponent(categoryText)}`;
- const linkEl = window.document.createElement("a");
- linkEl.setAttribute("href", link);
- for (const child of categoryEl.childNodes) {
- linkEl.append(child);
- }
- categoryEl.appendChild(linkEl);
- }
- }
- } catch {
- // Ignore errors
- }
- };
- function hasTitleCategories() {
- return window.document.querySelector(categorySelector) !== null;
- }
-
- function offsetRelativeUrl(url) {
- const offset = getMeta("quarto:offset");
- return offset ? offset + url : url;
- }
-
- function offsetAbsoluteUrl(url) {
- const offset = getMeta("quarto:offset");
- const baseUrl = new URL(offset, window.location);
-
- const projRelativeUrl = url.replace(baseUrl, "");
- if (projRelativeUrl.startsWith("/")) {
- return projRelativeUrl;
- } else {
- return "/" + projRelativeUrl;
- }
- }
-
- // read a meta tag value
- function getMeta(metaName) {
- const metas = window.document.getElementsByTagName("meta");
- for (let i = 0; i < metas.length; i++) {
- if (metas[i].getAttribute("name") === metaName) {
- return metas[i].getAttribute("content");
- }
- }
- return "";
- }
-
- async function findAndActivateCategories() {
- const currentPagePath = offsetAbsoluteUrl(window.location.href);
- const response = await fetch(offsetRelativeUrl("listings.json"));
- if (response.status == 200) {
- return response.json().then(function (listingPaths) {
- const listingHrefs = [];
- for (const listingPath of listingPaths) {
- const pathWithoutLeadingSlash = listingPath.listing.substring(1);
- for (const item of listingPath.items) {
- if (
- item === currentPagePath ||
- item === currentPagePath + "index.html"
- ) {
- // Resolve this path against the offset to be sure
- // we already are using the correct path to the listing
- // (this adjusts the listing urls to be rooted against
- // whatever root the page is actually running against)
- const relative = offsetRelativeUrl(pathWithoutLeadingSlash);
- const baseUrl = window.location;
- const resolvedPath = new URL(relative, baseUrl);
- listingHrefs.push(resolvedPath.pathname);
- break;
- }
- }
- }
-
- // Look up the tree for a nearby linting and use that if we find one
- const nearestListing = findNearestParentListing(
- offsetAbsoluteUrl(window.location.pathname),
- listingHrefs
- );
- if (nearestListing) {
- activateCategories(nearestListing);
- } else {
- // See if the referrer is a listing page for this item
- const referredRelativePath = offsetAbsoluteUrl(document.referrer);
- const referrerListing = listingHrefs.find((listingHref) => {
- const isListingReferrer =
- listingHref === referredRelativePath ||
- listingHref === referredRelativePath + "index.html";
- return isListingReferrer;
- });
-
- if (referrerListing) {
- // Try to use the referrer if possible
- activateCategories(referrerListing);
- } else if (listingHrefs.length > 0) {
- // Otherwise, just fall back to the first listing
- activateCategories(listingHrefs[0]);
- }
- }
- });
- }
- }
- if (hasTitleCategories()) {
- findAndActivateCategories();
- }
-
- const findNearestParentListing = (href, listingHrefs) => {
- if (!href || !listingHrefs) {
- return undefined;
- }
- // Look up the tree for a nearby linting and use that if we find one
- const relativeParts = href.substring(1).split("/");
- while (relativeParts.length > 0) {
- const path = relativeParts.join("/");
- for (const listingHref of listingHrefs) {
- if (listingHref.startsWith(path)) {
- return listingHref;
- }
- }
- relativeParts.pop();
- }
-
- return undefined;
- };
-
- const manageSidebarVisiblity = (el, placeholderDescriptor) => {
- let isVisible = true;
- let elRect;
-
- return (hiddenRegions) => {
- if (el === null) {
- return;
- }
-
- // Find the last element of the TOC
- const lastChildEl = el.lastElementChild;
-
- if (lastChildEl) {
- // Converts the sidebar to a menu
- const convertToMenu = () => {
- for (const child of el.children) {
- child.style.opacity = 0;
- child.style.overflow = "hidden";
- }
-
- nexttick(() => {
- const toggleContainer = window.document.createElement("div");
- toggleContainer.style.width = "100%";
- toggleContainer.classList.add("zindex-over-content");
- toggleContainer.classList.add("quarto-sidebar-toggle");
- toggleContainer.classList.add("headroom-target"); // Marks this to be managed by headeroom
- toggleContainer.id = placeholderDescriptor.id;
- toggleContainer.style.position = "fixed";
-
- const toggleIcon = window.document.createElement("i");
- toggleIcon.classList.add("quarto-sidebar-toggle-icon");
- toggleIcon.classList.add("bi");
- toggleIcon.classList.add("bi-caret-down-fill");
-
- const toggleTitle = window.document.createElement("div");
- const titleEl = window.document.body.querySelector(
- placeholderDescriptor.titleSelector
- );
- if (titleEl) {
- toggleTitle.append(
- titleEl.textContent || titleEl.innerText,
- toggleIcon
- );
- }
- toggleTitle.classList.add("zindex-over-content");
- toggleTitle.classList.add("quarto-sidebar-toggle-title");
- toggleContainer.append(toggleTitle);
-
- const toggleContents = window.document.createElement("div");
- toggleContents.classList = el.classList;
- toggleContents.classList.add("zindex-over-content");
- toggleContents.classList.add("quarto-sidebar-toggle-contents");
- for (const child of el.children) {
- if (child.id === "toc-title") {
- continue;
- }
-
- const clone = child.cloneNode(true);
- clone.style.opacity = 1;
- clone.style.display = null;
- toggleContents.append(clone);
- }
- toggleContents.style.height = "0px";
- const positionToggle = () => {
- // position the element (top left of parent, same width as parent)
- if (!elRect) {
- elRect = el.getBoundingClientRect();
- }
- toggleContainer.style.left = `${elRect.left}px`;
- toggleContainer.style.top = `${elRect.top}px`;
- toggleContainer.style.width = `${elRect.width}px`;
- };
- positionToggle();
-
- toggleContainer.append(toggleContents);
- el.parentElement.prepend(toggleContainer);
-
- // Process clicks
- let tocShowing = false;
- // Allow the caller to control whether this is dismissed
- // when it is clicked (e.g. sidebar navigation supports
- // opening and closing the nav tree, so don't dismiss on click)
- const clickEl = placeholderDescriptor.dismissOnClick
- ? toggleContainer
- : toggleTitle;
-
- const closeToggle = () => {
- if (tocShowing) {
- toggleContainer.classList.remove("expanded");
- toggleContents.style.height = "0px";
- tocShowing = false;
- }
- };
-
- // Get rid of any expanded toggle if the user scrolls
- window.document.addEventListener(
- "scroll",
- throttle(() => {
- closeToggle();
- }, 50)
- );
-
- // Handle positioning of the toggle
- window.addEventListener(
- "resize",
- throttle(() => {
- elRect = undefined;
- positionToggle();
- }, 50)
- );
-
- window.addEventListener("quarto-hrChanged", () => {
- elRect = undefined;
- });
-
- // Process the click
- clickEl.onclick = () => {
- if (!tocShowing) {
- toggleContainer.classList.add("expanded");
- toggleContents.style.height = null;
- tocShowing = true;
- } else {
- closeToggle();
- }
- };
- });
- };
-
- // Converts a sidebar from a menu back to a sidebar
- const convertToSidebar = () => {
- for (const child of el.children) {
- child.style.opacity = 1;
- child.style.overflow = null;
- }
-
- const placeholderEl = window.document.getElementById(
- placeholderDescriptor.id
- );
- if (placeholderEl) {
- placeholderEl.remove();
- }
-
- el.classList.remove("rollup");
- };
-
- if (isReaderMode()) {
- convertToMenu();
- isVisible = false;
- } else {
- // Find the top and bottom o the element that is being managed
- const elTop = el.offsetTop;
- const elBottom =
- elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight;
-
- if (!isVisible) {
- // If the element is current not visible reveal if there are
- // no conflicts with overlay regions
- if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) {
- convertToSidebar();
- isVisible = true;
- }
- } else {
- // If the element is visible, hide it if it conflicts with overlay regions
- // and insert a placeholder toggle (or if we're in reader mode)
- if (inHiddenRegion(elTop, elBottom, hiddenRegions)) {
- convertToMenu();
- isVisible = false;
- }
- }
- }
- }
- };
- };
-
- // Find any conflicting margin elements and add margins to the
- // top to prevent overlap
- const marginChildren = window.document.querySelectorAll(
- ".column-margin.column-container > * "
- );
-
- const layoutMarginEls = () => {
- let lastBottom = 0;
- for (const marginChild of marginChildren) {
- if (marginChild.offsetParent !== null) {
- // clear the top margin so we recompute it
- marginChild.style.marginTop = null;
- const top = marginChild.getBoundingClientRect().top + window.scrollY;
- if (top < lastBottom) {
- const margin = lastBottom - top;
- marginChild.style.marginTop = `${margin}px`;
- }
- const styles = window.getComputedStyle(marginChild);
- const marginTop = parseFloat(styles["marginTop"]);
- lastBottom =
- top + marginChild.getBoundingClientRect().height + marginTop;
- }
- }
- };
- nexttick(layoutMarginEls);
-
- const tabEls = document.querySelectorAll('a[data-bs-toggle="tab"]');
- for (const tabEl of tabEls) {
- const id = tabEl.getAttribute("data-bs-target");
- if (id) {
- const columnEl = document.querySelector(`${id} .column-margin`);
- if (columnEl)
- tabEl.addEventListener("shown.bs.tab", function (_event) {
- layoutMarginEls();
- });
- }
- }
-
- // Manage the visibility of the toc and the sidebar
- const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, {
- id: "quarto-toc-toggle",
- titleSelector: "#toc-title",
- dismissOnClick: true,
- });
- const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, {
- id: "quarto-sidebarnav-toggle",
- titleSelector: ".title",
- dismissOnClick: false,
- });
- let tocLeftScrollVisibility;
- if (leftTocEl) {
- tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, {
- id: "quarto-lefttoc-toggle",
- titleSelector: "#toc-title",
- dismissOnClick: true,
- });
- }
-
- // Find the first element that uses formatting in special columns
- const conflictingEls = window.document.body.querySelectorAll(
- '[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]'
- );
-
- // Filter all the possibly conflicting elements into ones
- // the do conflict on the left or ride side
- const arrConflictingEls = Array.from(conflictingEls);
- const leftSideConflictEls = arrConflictingEls.filter((el) => {
- if (el.tagName === "ASIDE") {
- return false;
- }
- return Array.from(el.classList).find((className) => {
- return (
- className !== "column-body" &&
- className.startsWith("column-") &&
- !className.endsWith("right") &&
- !className.endsWith("container") &&
- className !== "column-margin"
- );
- });
- });
- const rightSideConflictEls = arrConflictingEls.filter((el) => {
- if (el.tagName === "ASIDE") {
- return true;
- }
-
- const hasMarginCaption = Array.from(el.classList).find((className) => {
- return className == "margin-caption";
- });
- if (hasMarginCaption) {
- return true;
- }
-
- return Array.from(el.classList).find((className) => {
- return (
- className !== "column-body" &&
- !className.endsWith("container") &&
- className.startsWith("column-") &&
- !className.endsWith("left")
- );
- });
- });
-
- const kOverlapPaddingSize = 10;
- function toRegions(els) {
- return els.map((el) => {
- const boundRect = el.getBoundingClientRect();
- const top =
- boundRect.top +
- document.documentElement.scrollTop -
- kOverlapPaddingSize;
- return {
- top,
- bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize,
- };
- });
- }
-
- let hasObserved = false;
- const visibleItemObserver = (els) => {
- let visibleElements = [...els];
- const intersectionObserver = new IntersectionObserver(
- (entries, _observer) => {
- entries.forEach((entry) => {
- if (entry.isIntersecting) {
- if (visibleElements.indexOf(entry.target) === -1) {
- visibleElements.push(entry.target);
- }
- } else {
- visibleElements = visibleElements.filter((visibleEntry) => {
- return visibleEntry !== entry;
- });
- }
- });
-
- if (!hasObserved) {
- hideOverlappedSidebars();
- }
- hasObserved = true;
- },
- {}
- );
- els.forEach((el) => {
- intersectionObserver.observe(el);
- });
-
- return {
- getVisibleEntries: () => {
- return visibleElements;
- },
- };
- };
-
- const rightElementObserver = visibleItemObserver(rightSideConflictEls);
- const leftElementObserver = visibleItemObserver(leftSideConflictEls);
-
- const hideOverlappedSidebars = () => {
- marginScrollVisibility(toRegions(rightElementObserver.getVisibleEntries()));
- sidebarScrollVisiblity(toRegions(leftElementObserver.getVisibleEntries()));
- if (tocLeftScrollVisibility) {
- tocLeftScrollVisibility(
- toRegions(leftElementObserver.getVisibleEntries())
- );
- }
- };
-
- window.quartoToggleReader = () => {
- // Applies a slow class (or removes it)
- // to update the transition speed
- const slowTransition = (slow) => {
- const manageTransition = (id, slow) => {
- const el = document.getElementById(id);
- if (el) {
- if (slow) {
- el.classList.add("slow");
- } else {
- el.classList.remove("slow");
- }
- }
- };
-
- manageTransition("TOC", slow);
- manageTransition("quarto-sidebar", slow);
- };
-
- const readerMode = !isReaderMode();
- setReaderModeValue(readerMode);
-
- // If we're entering reader mode, slow the transition
- if (readerMode) {
- slowTransition(readerMode);
- }
- highlightReaderToggle(readerMode);
-
- // If we're exiting reader mode, restore the non-slow transition
- if (!readerMode) {
- slowTransition(!readerMode);
- }
- };
-
- const highlightReaderToggle = (readerMode) => {
- const els = document.querySelectorAll(".quarto-reader-toggle");
- if (els) {
- els.forEach((el) => {
- if (readerMode) {
- el.classList.add("reader");
- } else {
- el.classList.remove("reader");
- }
- });
- }
- };
-
- const setReaderModeValue = (val) => {
- if (window.location.protocol !== "file:") {
- window.localStorage.setItem("quarto-reader-mode", val);
- } else {
- localReaderMode = val;
- }
- };
-
- const isReaderMode = () => {
- if (window.location.protocol !== "file:") {
- return window.localStorage.getItem("quarto-reader-mode") === "true";
- } else {
- return localReaderMode;
- }
- };
- let localReaderMode = null;
-
- // Walk the TOC and collapse/expand nodes
- // Nodes are expanded if:
- // - they are top level
- // - they have children that are 'active' links
- // - they are directly below an link that is 'active'
- const walk = (el, depth) => {
- // Tick depth when we enter a UL
- if (el.tagName === "UL") {
- depth = depth + 1;
- }
-
- // It this is active link
- let isActiveNode = false;
- if (el.tagName === "A" && el.classList.contains("active")) {
- isActiveNode = true;
- }
-
- // See if there is an active child to this element
- let hasActiveChild = false;
- for (child of el.children) {
- hasActiveChild = walk(child, depth) || hasActiveChild;
- }
-
- // Process the collapse state if this is an UL
- if (el.tagName === "UL") {
- if (depth === 1 || hasActiveChild || prevSiblingIsActiveLink(el)) {
- el.classList.remove("collapse");
- } else {
- el.classList.add("collapse");
- }
-
- // untick depth when we leave a UL
- depth = depth - 1;
- }
- return hasActiveChild || isActiveNode;
- };
-
- // walk the TOC and expand / collapse any items that should be shown
-
- if (tocEl) {
- walk(tocEl, 0);
- updateActiveLink();
- }
-
- // Throttle the scroll event and walk peridiocally
- window.document.addEventListener(
- "scroll",
- throttle(() => {
- if (tocEl) {
- updateActiveLink();
- walk(tocEl, 0);
- }
- if (!isReaderMode()) {
- hideOverlappedSidebars();
- }
- }, 5)
- );
- window.addEventListener(
- "resize",
- throttle(() => {
- if (!isReaderMode()) {
- hideOverlappedSidebars();
- }
- }, 10)
- );
- hideOverlappedSidebars();
- highlightReaderToggle(isReaderMode());
-});
-
-// grouped tabsets
-window.addEventListener("pageshow", (_event) => {
- function getTabSettings() {
- const data = localStorage.getItem("quarto-persistent-tabsets-data");
- if (!data) {
- localStorage.setItem("quarto-persistent-tabsets-data", "{}");
- return {};
- }
- if (data) {
- return JSON.parse(data);
- }
- }
-
- function setTabSettings(data) {
- localStorage.setItem(
- "quarto-persistent-tabsets-data",
- JSON.stringify(data)
- );
- }
-
- function setTabState(groupName, groupValue) {
- const data = getTabSettings();
- data[groupName] = groupValue;
- setTabSettings(data);
- }
-
- function toggleTab(tab, active) {
- const tabPanelId = tab.getAttribute("aria-controls");
- const tabPanel = document.getElementById(tabPanelId);
- if (active) {
- tab.classList.add("active");
- tabPanel.classList.add("active");
- } else {
- tab.classList.remove("active");
- tabPanel.classList.remove("active");
- }
- }
-
- function toggleAll(selectedGroup, selectorsToSync) {
- for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) {
- const active = selectedGroup === thisGroup;
- for (const tab of tabs) {
- toggleTab(tab, active);
- }
- }
- }
-
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- }
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- }, wait);
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diff --git a/build/dev/proposal_files/libs/quarto-html/tippy.css b/build/dev/proposal_files/libs/quarto-html/tippy.css
deleted file mode 100644
index e6ae635cb1f82b176c18afa80dfa029c7a536e70..0000000000000000000000000000000000000000
--- a/build/dev/proposal_files/libs/quarto-html/tippy.css
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/build/dev/proposal_files/libs/quarto-html/tippy.umd.min.js b/build/dev/proposal_files/libs/quarto-html/tippy.umd.min.js
deleted file mode 100644
index ca292be32b252f9a40e231f3a3e696b2506f7c96..0000000000000000000000000000000000000000
--- a/build/dev/proposal_files/libs/quarto-html/tippy.umd.min.js
+++ /dev/null
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diff --git a/build/dev/www/you_may_not_like_it.png b/build/dev/www/you_may_not_like_it.png
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-```{julia}
-using CCE
-using CCE: set_size_penalty, distance_from_energy, distance_from_targets
-using ConformalPrediction
-using CounterfactualExplanations
-using CounterfactualExplanations.Data
-using CounterfactualExplanations.Objectives
-using Distributions
-using Flux
-using JointEnergyModels
-using LinearAlgebra
-using MLJBase
-using MLJFlux
-using Plots
-img_height = 300
-```
-
-# `ConformalGenerator`
-
-In this section, we will look at a simple example involving synthetic data, a black-box model and a generic Conformal Counterfactual Generator.
-
-## Black-box Model
-
-We consider a simple binary classification problem. Let $(X_i, Y_i), \ i=1,...,n$ denote our feature-label pairs and let $\mu: \mathcal{X} \mapsto \mathcal{Y}$ denote the mapping from features to labels. For illustration purposes, we will use linearly separable data.
-
-```{julia}
-counterfactual_data = load_linearly_separable()
-```
-
-While we could use a linear classifier in this case, let's pretend we need a black-box model for this task and rely on a small Multi-Layer Perceptron (MLP):
-
-```{julia}
-builder = MLJFlux.@builder Chain(
- Dense(n_in, 32, relu),
- Dense(32, n_out)
-)
-clf = NeuralNetworkClassifier(builder=builder, epochs=100)
-```
-
-We can fit this model to data to produce plug-in predictions.
-
-## Conformal Prediction
-
-Here we will instead use a specific case of CP called *split conformal prediction* which can then be summarized as follows:^[In other places split conformal prediction is sometimes referred to as *inductive* conformal prediction.]
-
-1. Partition the training into a proper training set and a separate calibration set: $\mathcal{D}_n=\mathcal{D}^{\text{train}} \cup \mathcal{D}^{\text{cali}}$.
-2. Train the machine learning model on the proper training set: $\hat\mu_{i \in \mathcal{D}^{\text{train}}}(X_i,Y_i)$.
-
-The model $\hat\mu_{i \in \mathcal{D}^{\text{train}}}$ can now produce plug-in predictions.
-
-::: callout-note
-
-## Starting Point
-
-Note that this represents the starting point in applications of Algorithmic Recourse: we have some pre-trained classifier $M$ for which we would like to generate plausible Counterfactual Explanations. Next, we turn to the calibration step.
-:::
-
-3. Compute nonconformity scores, $\mathcal{S}$, using the calibration data $\mathcal{D}^{\text{cali}}$ and the fitted model $\hat\mu_{i \in \mathcal{D}^{\text{train}}}$.
-4. For a user-specified desired coverage ratio $(1-\alpha)$ compute the corresponding quantile, $\hat{q}$, of the empirical distribution of nonconformity scores, $\mathcal{S}$.
-5. For the given quantile and test sample $X_{\text{test}}$, form the corresponding conformal prediction set:
-
-$$
-C(X_{\text{test}})=\{y:s(X_{\text{test}},y) \le \hat{q}\}
-$$ {#eq-set}
-
-This is the default procedure used for classification and regression in [`ConformalPrediction.jl`](https://github.com/pat-alt/ConformalPrediction.jl).
-
-Using the package, we can apply Split Conformal Prediction as follows:
-
-```{julia}
-X = table(permutedims(counterfactual_data.X))
-y = counterfactual_data.output_encoder.labels
-conf_model = conformal_model(clf; method=:simple_inductive)
-mach = machine(conf_model, X, y)
-fit!(mach)
-```
-
-To be clear, all of the calibration steps (3 to 5) are post hoc, and yet none of them involved any changes to the model parameters. These are two important characteristics of Split Conformal Prediction (SCP) that make it particularly useful in the context of Algorithmic Recourse. Firstly, the fact that SCP involves posthoc calibration steps that happen after training, ensures that we need not place any restrictions on the black-box model itself. This stands in contrast to the approach proposed by @schut2021generating in which they essentially restrict the class of models to Bayesian models. Secondly, the fact that the model itself is kept entirely intact ensures that the generated counterfactuals maintain fidelity to the model. Finally, note that we also have not resorted to a surrogate model to learn more about $X \sim \mathcal{X}$. Instead, we have used the fitted model itself and a calibration data set to learn about the model's predictive uncertainty.
-
-## Differentiable CP
-
-In order to use CP in the context of gradient-based counterfactual search, we need it to be differentiable. @stutz2022learning introduce a framework for training differentiable conformal predictors. They introduce a configurable loss function as well as smooth set size penalty.
-
-### Smooth Set Size Penalty
-
-Starting with the former, @stutz2022learning propose the following:
-
-$$
-\Omega(C_{\theta}(x;\tau)) = = \max (0, \sum_k C_{\theta,k}(x;\tau) - \kappa)
-$$ {#eq-size-loss}
-
-Here, $C_{\theta,k}(x;\tau)$ is loosely defined as the probability that class $k$ is assigned to the conformal prediction set $C$. In the context of Conformal Training, this penalty reduces the **inefficiency** of the conformal predictor.
-
-In our context, we are not interested in improving the model itself, but rather in producing **plausible** counterfactuals. Provided that our counterfactual $x^\prime$ is already inside the target domain ($\mathbb{I}_{y^\prime = t}=1$), penalizing $\Omega(C_{\theta}(x;\tau))$ corresponds to guiding counterfactuals into regions of the target domain that are characterized by low ambiguity: for $\kappa=1$ the conformal prediction set includes only the target label $t$ as $\Omega(C_{\theta}(x;\tau))$. Arguably, less ambiguous counterfactuals are more **plausible**. Since the search is guided purely by properties of the model itself and (exchangeable) calibration data, counterfactuals also maintain **high fidelity**.
-
-The left panel of @fig-losses shows the smooth size penalty in the two-dimensional feature space of our synthetic data.
-
-### Configurable Classification Loss
-
-The right panel of @fig-losses shows the configurable classification loss in the two-dimensional feature space of our synthetic data.
-
-```{julia}
-#| output: true
-#| echo: false
-#| label: fig-losses
-#| fig-cap: "Illustration of the smooth size loss and the configurable classification loss."
-
-temp = 0.5
-p1 = contourf(mach.model, mach.fitresult, X, y; plot_set_loss=true, zoom=0, temp=temp)
-p2 = contourf(mach.model, mach.fitresult, X, y; plot_classification_loss=true, target=1, zoom=0, temp=temp, clim=nothing, loss_matrix=ones(2,2))
-plot(p1, p2, size=(800,320))
-```
-
-## Fidelity and Plausibility
-
-The main evaluation criteria we are interested in are *fidelity* and *plausibility*. Interestingly, we could also consider using these measures as penalties in the counterfactual search.
-
-### Fidelity
-
-We propose to define fidelity as follows:
-
-::: {#def-fidelity}
-
-## High-Fidelity Counterfactuals
-
-Let $\mathcal{X}_{\theta}|y = p_{\theta}(X|y)$ denote the class-conditional distribution of $X$ defined by $\theta$. Then for $x^{\prime}$ to be considered a high-fidelity counterfactual, we need: $\mathcal{X}_{\theta}|t \approxeq \mathcal{X}^{\prime}$ where $t$ denotes the target outcome.
-
-:::
-
-We can generate samples from $p_{\theta}(X|y)$ following @grathwohl2020your. In @fig-energy, I have applied the methodology to our synthetic data.
-
-```{julia}
-#| fig-cap: "Energy-based conditional samples."
-#| label: fig-energy
-
-niter = 100
-nsamples = 100
-
-plts = []
-for target ∈ counterfactual_data.y_levels
- sampler = CCE.EnergySampler(M, counterfactual_data, target; niter=niter, nsamples=100)
- Xgen = rand(sampler, nsamples)
- plt = plot(M, counterfactual_data; target=target, zoom=-3,cbar=false)
- scatter!(Xgen[1,:],Xgen[2,:],alpha=0.5,color=target,shape=:star,label="X|y=$target")
- push!(plts, plt)
-end
-plot(plts..., layout=(1,length(plts)), size=(img_height*length(plts),img_height))
-```
-
-As an evaluation metric and penalty, we could use the average distance of the counterfactual $x^{\prime}$ from these generated samples, for example.
-
-### Plausibility
-
-We propose to define plausibility as follows:
-
-::: {#def-plausible}
-
-## Plausible Counterfactuals
-
-Formally, let $\mathcal{X}|t$ denote the conditional distribution of samples in the target class. As before, we have $x^{\prime}\sim\mathcal{X}^{\prime}$, then for $x^{\prime}$ to be considered a plausible counterfactual, we need: $\mathcal{X}|t \approxeq \mathcal{X}^{\prime}$.
-
-:::
-
-As an evaluation metric and penalty, we could use the average distance of the counterfactual $x^{\prime}$ from (potentially bootstrapped) training samples in the target class, for example.
-
-## Counterfactual Explanations
-
-Next, let's generate counterfactual explanations for our synthetic data. We first wrap our model in a container that makes it compatible with `CounterfactualExplanations.jl`. Then we draw a random sample, determine its predicted label $\hat{y}$ and choose the opposite label as our target.
-
-```{julia}
-M = CCE.ConformalModel(conf_model, mach.fitresult)
-x = select_factual(counterfactual_data,rand(1:size(counterfactual_data.X,2)))
-y_factual = predict_label(M, counterfactual_data, x)[1]
-target = counterfactual_data.y_levels[counterfactual_data.y_levels .!= y_factual][1]
-```
-
-The generic Conformal Counterfactual Generator penalises the only the set size only:
-
-$$
-x^\prime = \arg \min_{x^\prime} \ell(M(x^\prime),t) + \lambda \mathbb{I}_{y^\prime = t} \Omega(C_{\theta}(x;\tau))
-$$ {#eq-solution}
-
-```{julia}
-#| output: true
-#| echo: false
-#| label: fig-ce
-#| fig-cap: "Comparison of counterfactuals produced using different generators."
-
-λ = 10.0
-
-opt = Descent(0.01)
-ordered_names = [
- "Distance x",
- "Set Size",
- "Distance X_θ|t",
- "Distance X|t"
-]
-loss_fun = Objectives.logitbinarycrossentropy
-generator = GenericGenerator(opt = opt)
-
-# Generators:
-generators = Dict(
- ordered_names[1] => generator,
- ordered_names[2] => deepcopy(generator) |> gen -> @objective(gen, _ + 0.1distance + 10.0set_size_penalty),
- ordered_names[3] => deepcopy(generator) |> gen -> @objective(gen, _ + 0.1distance + 10.0distance_from_energy),
- ordered_names[4] => deepcopy(generator) |> gen -> @objective(gen, _ + 0.1distance + 10.0distance_from_targets),
-)
-
-counterfactuals = Dict([name => generate_counterfactual(x, target, counterfactual_data, M, gen; initialization=:identity, converge_when=:generator_conditions, gradient_tol=1e-3) for (name, gen) in generators])
-
-# Plots:
-plts = []
-for name ∈ ordered_names
- ce = counterfactuals[name]
- plt = plot(ce; title=name, colorbar=false, ticks = false, legend=false, zoom=-0.5)
- if :energy_sampler ∈ collect(keys(ce.params))
- Xgen = ce.params[:energy_sampler].buffer
- scatter!(Xgen[1,:],Xgen[2,:],alpha=0.5,color=target,shape=:star,label="X|y=$target")
- end
- plts = vcat(plts..., plt)
-end
-_n = length(generators)
-plot(plts..., size=(_n * img_height,1.05*img_height), layout=(1,_n))
-```
-
-### Benchmark
-
-```{julia}
-#| output: false
-
-# Data:
-datasets = Dict(
- :linearly_separable => load_linearly_separable(),
- :overlapping => load_overlapping(),
- :moons => load_moons(),
- :circles => load_circles(),
-)
-
-# Models:
-models = Dict(
- :MLP => M,
-)
-```
-
-Then we can simply loop over the datasets and eventually concatenate the results like so:
-
-```{julia}
-#| output: false
-
-measure =
-using CounterfactualExplanations.Evaluation: benchmark
-bmks = []
-for (dataname, dataset) in datasets
- bmk = benchmark(dataset; models=models, generators=generators, suppress_training=true, data_name=dataname)
- push!(bmks, bmk)
-end
-bmk = reduce(vcat, bmks)
-```
-
-## Multi-Class
-
-```{julia}
-counterfactual_data = load_multi_class()
-```
-
-```{julia}
-X = table(permutedims(counterfactual_data.X))
-y = counterfactual_data.output_encoder.labels
-conf_model = conformal_model(clf; method=:simple_inductive)
-mach = machine(conf_model, X, y)
-fit!(mach)
-```
-
-```{julia}
-#| output: true
-#| echo: false
-#| label: fig-losses-multi
-#| fig-cap: "Illustration of the smooth size loss and the configurable classification loss."
-
-temp = 0.5
-p1 = contourf(mach.model, mach.fitresult, X, y; plot_set_loss=true, zoom=0, temp=temp)
-p2 = contourf(mach.model, mach.fitresult, X, y; plot_classification_loss=true, target=1, zoom=0, temp=temp, clim=nothing, loss_matrix=ones(4,4))
-plot(p1, p2, size=(800,320))
-```
-
-```{julia}
-#| fig-cap: "Energy-based conditional samples."
-#| label: fig-energy-multi
-
-niter = 100
-nsamples = 100
-
-plts = []
-for target ∈ counterfactual_data.y_levels
- sampler = CCE.EnergySampler(M, counterfactual_data, target; niter=niter, nsamples=100)
- Xgen = rand(sampler, nsamples)
- plt = plot(M, counterfactual_data; target=target, zoom=-3,cbar=false)
- scatter!(Xgen[1,:],Xgen[2,:],alpha=0.5,color=target,shape=:star,label="X|y=$target")
- push!(plts, plt)
-end
-plot(plts..., layout=(1,length(plts)), size=(img_height*length(plts),img_height))
-```
-
-```{julia}
-M = CCE.ConformalModel(conf_model, mach.fitresult; likelihood=:classification_multi)
-x = select_factual(counterfactual_data,rand(1:size(counterfactual_data.X,2)))
-y_factual = predict_label(M, counterfactual_data, x)[1]
-target = counterfactual_data.y_levels[counterfactual_data.y_levels .!= y_factual][1]
-```
-
-
-```{julia}
-counterfactuals = Dict([name => generate_counterfactual(x, target, counterfactual_data, M, gen; initialization=:identity, converge_when=:generator_conditions, gradient_tol=1e-3) for (name, gen) in generators])
-
-# Plots:
-plts = []
-for name ∈ ordered_names
- ce = counterfactuals[name]
- plt = plot(ce; title=name, colorbar=false, ticks = false, legend=false, zoom=-0.5)
- if :energy_sampler ∈ collect(keys(ce.params))
- Xgen = ce.params[:energy_sampler].buffer
- scatter!(Xgen[1,:],Xgen[2,:],alpha=0.5,color=target,shape=:star,label="X|y=$target")
- end
- plts = vcat(plts..., plt)
-end
-_n = length(generators)
-plot(plts..., size=(_n * img_height,1.05*img_height), layout=(1,_n))
-```
-
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+++ b/docs/index.html
@@ -6,10 +6,10 @@
<meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
-<meta name="author" content="Norah Jones">
-<meta name="dcterms.date" content="2023-02-14">
+<meta name="author" content="Patrick Altmeyer">
+<meta name="dcterms.date" content="2023-03-20">
-<title>notebooks</title>
+<title>Conformal Counterfactual Explanations</title>
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+<link href="./notebooks/proposal.html" rel="next">
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- <button type="button" class="quarto-btn-toggle btn" aria-label="Show secondary navigation">
- <i class="bi bi-chevron-right"></i>
+ <nav class="quarto-secondary-nav">
+ <div class="container-fluid d-flex">
+ <button type="button" class="quarto-btn-toggle btn" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar,#quarto-sidebar-glass" aria-controls="quarto-sidebar" aria-expanded="false" aria-label="Toggle sidebar navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
+ <i class="bi bi-layout-text-sidebar-reverse"></i>
+ </button>
+ <nav class="quarto-page-breadcrumbs" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="./index.html">Preface</a></li></ol></nav>
+ <a class="flex-grow-1" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar,#quarto-sidebar-glass" aria-controls="quarto-sidebar" aria-expanded="false" aria-label="Toggle sidebar navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
+ </a>
+ <button type="button" class="btn quarto-search-button" aria-label="Search" onclick="window.quartoOpenSearch();">
+ <i class="bi bi-search"></i>
</button>
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<div id="quarto-content" class="quarto-container page-columns page-rows-contents page-layout-article">
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- <nav id="quarto-sidebar" class="sidebar collapse sidebar-navigation floating overflow-auto">
+ <nav id="quarto-sidebar" class="sidebar collapse collapse-horizontal sidebar-navigation floating overflow-auto">
<div class="pt-lg-2 mt-2 text-left sidebar-header">
<div class="sidebar-title mb-0 py-0">
- <a href="./">notebooks</a>
+ <a href="./">Conformal Counterfactual Explanations</a>
</div>
</div>
- <div class="mt-2 flex-shrink-0 align-items-center">
+ <div class="mt-2 flex-shrink-0 align-items-center">
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</div>
- </div>
+ </div>
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@@ -101,22 +106,23 @@ ul.task-list li input[type="checkbox"] {
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<div class="sidebar-item-container">
- <a href="./intro.html" class="sidebar-item-text sidebar-link"><span class="chapter-number">1</span> <span class="chapter-title">Introduction</span></a>
+ <a href="./notebooks/proposal.html" class="sidebar-item-text sidebar-link"><span class="chapter-number">1</span> <span class="chapter-title">High-Fidelity Counterfactual Explanations through Conformal Prediction</span></a>
</div>
</li>
<li class="sidebar-item">
<div class="sidebar-item-container">
- <a href="./summary.html" class="sidebar-item-text sidebar-link"><span class="chapter-number">2</span> <span class="chapter-title">Summary</span></a>
+ <a href="./notebooks/intro.html" class="sidebar-item-text sidebar-link"><span class="chapter-number">2</span> <span class="chapter-title"><code>ConformalGenerator</code></span></a>
</div>
</li>
<li class="sidebar-item">
<div class="sidebar-item-container">
- <a href="./references.html" class="sidebar-item-text sidebar-link">References</a>
+ <a href="./notebooks/references.html" class="sidebar-item-text sidebar-link">References</a>
</div>
</li>
</ul>
</div>
</nav>
+<div id="quarto-sidebar-glass" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar,#quarto-sidebar-glass"></div>
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<div id="quarto-margin-sidebar" class="sidebar margin-sidebar">
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@@ -132,7 +138,8 @@ ul.task-list li input[type="checkbox"] {
<header id="title-block-header" class="quarto-title-block default">
<div class="quarto-title">
-<h1 class="title d-none d-lg-block">notebooks</h1>
+<h1 class="title">Conformal Counterfactual Explanations</h1>
+<p class="subtitle lead">Online Companion</p>
</div>
@@ -142,14 +149,14 @@ ul.task-list li input[type="checkbox"] {
<div>
<div class="quarto-title-meta-heading">Author</div>
<div class="quarto-title-meta-contents">
- <p>Norah Jones </p>
+ <p>Patrick Altmeyer </p>
</div>
</div>
<div>
<div class="quarto-title-meta-heading">Published</div>
<div class="quarto-title-meta-contents">
- <p class="date">February 14, 2023</p>
+ <p class="date">March 20, 2023</p>
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@@ -161,8 +168,6 @@ ul.task-list li input[type="checkbox"] {
<section id="preface" class="level1 unnumbered">
<h1 class="unnumbered">Preface</h1>
-<p>This is a Quarto book.</p>
-<p>To learn more about Quarto books visit <a href="https://quarto.org/docs/books" class="uri">https://quarto.org/docs/books</a>.</p>
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+ <a href="./notebooks/proposal.html" class="pagination-link">
+ <span class="nav-page-text"><span class="chapter-number">1</span> <span class="chapter-title">High-Fidelity Counterfactual Explanations through Conformal Prediction</span></span> <i class="bi bi-arrow-right-short"></i>
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diff --git a/build/dev/proposal.html b/docs/notebooks/proposal.html
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- <h2 id="toc-title">Table of contents</h2>
-
- <ul>
- <li><a href="#motivation" id="toc-motivation" class="nav-link active" data-scroll-target="#motivation">Motivation</a>
- <ul class="collapse">
- <li><a href="#counterfactual-explanations-or-adversarial-examples" id="toc-counterfactual-explanations-or-adversarial-examples" class="nav-link" data-scroll-target="#counterfactual-explanations-or-adversarial-examples">Counterfactual Explanations or Adversarial Examples?</a></li>
- <li><a href="#sec-fidelity" id="toc-sec-fidelity" class="nav-link" data-scroll-target="#sec-fidelity">From Plausible to High-Fidelity Counterfactuals</a></li>
- </ul></li>
- <li><a href="#conformal-counterfactual-explanations" id="toc-conformal-counterfactual-explanations" class="nav-link" data-scroll-target="#conformal-counterfactual-explanations">Conformal Counterfactual Explanations</a>
- <ul class="collapse">
- <li><a href="#minimizing-predictive-uncertainty" id="toc-minimizing-predictive-uncertainty" class="nav-link" data-scroll-target="#minimizing-predictive-uncertainty">Minimizing Predictive Uncertainty</a></li>
- <li><a href="#background-on-conformal-prediction" id="toc-background-on-conformal-prediction" class="nav-link" data-scroll-target="#background-on-conformal-prediction">Background on Conformal Prediction</a></li>
- <li><a href="#generating-conformal-counterfactuals" id="toc-generating-conformal-counterfactuals" class="nav-link" data-scroll-target="#generating-conformal-counterfactuals">Generating Conformal Counterfactuals</a></li>
- </ul></li>
- <li><a href="#experiments" id="toc-experiments" class="nav-link" data-scroll-target="#experiments">Experiments</a>
- <ul class="collapse">
- <li><a href="#research-questions" id="toc-research-questions" class="nav-link" data-scroll-target="#research-questions">Research Questions</a></li>
- </ul></li>
- <li><a href="#references" id="toc-references" class="nav-link" data-scroll-target="#references">References</a></li>
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+ Conformal Counterfactual Explanations
+ </a>
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+ <a href="/notebooks/proposal.html" class="sidebar-item-text sidebar-link active"><span class='chapter-number'>1</span>Â <span class='chapter-title'>High-Fidelity Counterfactual Explanations through Conformal Prediction</span></a>
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-<h1 class="title">High-Fidelity Counterfactual Explanations through Conformal Prediction</h1>
+<h1 class="title"><span class="chapter-number">1</span>Â <span class="chapter-title">High-Fidelity Counterfactual Explanations through Conformal Prediction</span></h1>
<p class="subtitle lead">Research Proposal</p>
</div>
@@ -109,32 +163,53 @@ div.csl-indent {
</div>
</header>
-
-<section id="motivation" class="level2">
-<h2 class="anchored" data-anchor-id="motivation">Motivation</h2>
+<nav id="TOC" role="doc-toc">
+ <h2 id="toc-title">Table of contents</h2>
+
+ <ul>
+ <li><a href="#motivation" id="toc-motivation"><span class="header-section-number">1.1</span> Motivation</a>
+ <ul>
+ <li><a href="#counterfactual-explanations-or-adversarial-examples" id="toc-counterfactual-explanations-or-adversarial-examples"><span class="header-section-number">1.1.1</span> Counterfactual Explanations or Adversarial Examples?</a></li>
+ <li><a href="#sec-fidelity" id="toc-sec-fidelity"><span class="header-section-number">1.1.2</span> From Plausible to High-Fidelity Counterfactuals</a></li>
+ </ul></li>
+ <li><a href="#conformal-counterfactual-explanations" id="toc-conformal-counterfactual-explanations"><span class="header-section-number">1.2</span> Conformal Counterfactual Explanations</a>
+ <ul>
+ <li><a href="#minimizing-predictive-uncertainty" id="toc-minimizing-predictive-uncertainty"><span class="header-section-number">1.2.1</span> Minimizing Predictive Uncertainty</a></li>
+ <li><a href="#background-on-conformal-prediction" id="toc-background-on-conformal-prediction"><span class="header-section-number">1.2.2</span> Background on Conformal Prediction</a></li>
+ <li><a href="#generating-conformal-counterfactuals" id="toc-generating-conformal-counterfactuals"><span class="header-section-number">1.2.3</span> Generating Conformal Counterfactuals</a></li>
+ </ul></li>
+ <li><a href="#experiments" id="toc-experiments"><span class="header-section-number">1.3</span> Experiments</a>
+ <ul>
+ <li><a href="#research-questions" id="toc-research-questions"><span class="header-section-number">1.3.1</span> Research Questions</a></li>
+ </ul></li>
+ <li><a href="#references" id="toc-references"><span class="header-section-number">1.4</span> References</a></li>
+ </ul>
+</nav>
+<section id="motivation" class="level2" data-number="1.1">
+<h2 data-number="1.1"><span class="header-section-number">1.1</span> Motivation</h2>
<p>Counterfactual Explanations are a powerful, flexible and intuitive way to not only explain black-box models but also enable affected individuals to challenge them through the means of Algorithmic Recourse.</p>
-<section id="counterfactual-explanations-or-adversarial-examples" class="level3">
-<h3 class="anchored" data-anchor-id="counterfactual-explanations-or-adversarial-examples">Counterfactual Explanations or Adversarial Examples?</h3>
-<p>Most state-of-the-art approaches to generating Counterfactual Explanations (CE) rely on gradient descent in the feature space. The key idea is to perturb inputs <span class="math inline">\(x\in\mathcal{X}\)</span> into a black-box model <span class="math inline">\(f: \mathcal{X} \mapsto \mathcal{Y}\)</span> in order to change the model output <span class="math inline">\(f(x)\)</span> to some pre-specified target value <span class="math inline">\(t\in\mathcal{Y}\)</span>. Formally, this boils down to defining some loss function <span class="math inline">\(\ell(f(x),t)\)</span> and taking gradient steps in the minimizing direction. The so-generated counterfactuals are considered valid as soon as the predicted label matches the target label. A stripped-down counterfactual explanation is therefore little different from an adversarial example. In <a href="#fig-adv">Figure 1</a>, for example, generic counterfactual search as in <span class="citation" data-cites="wachter2017counterfactual">Wachter, Mittelstadt, and Russell (<a href="#ref-wachter2017counterfactual" role="doc-biblioref">2017</a>)</span> has been applied to MNIST data.</p>
-<div id="fig-adv" class="quarto-figure quarto-figure-center anchored">
-<figure class="figure">
-<p><img src="www/you_may_not_like_it.png" class="img-fluid figure-img"></p>
-<p></p><figcaption class="figure-caption">Figure 1: You may not like it, but this is what stripped-down counterfactuals look like. Here we have used <span class="citation" data-cites="wachter2017counterfactual">Wachter, Mittelstadt, and Russell (<a href="#ref-wachter2017counterfactual" role="doc-biblioref">2017</a>)</span> to generate multiple counterfactuals for turning an 8 (eight) into a 3 (three).</figcaption><p></p>
+<section id="counterfactual-explanations-or-adversarial-examples" class="level3" data-number="1.1.1">
+<h3 data-number="1.1.1"><span class="header-section-number">1.1.1</span> Counterfactual Explanations or Adversarial Examples?</h3>
+<p>Most state-of-the-art approaches to generating Counterfactual Explanations (CE) rely on gradient descent in the feature space. The key idea is to perturb inputs <span class="math inline">\(x\in\mathcal{X}\)</span> into a black-box model <span class="math inline">\(f: \mathcal{X} \mapsto \mathcal{Y}\)</span> in order to change the model output <span class="math inline">\(f(x)\)</span> to some pre-specified target value <span class="math inline">\(t\in\mathcal{Y}\)</span>. Formally, this boils down to defining some loss function <span class="math inline">\(\ell(f(x),t)\)</span> and taking gradient steps in the minimizing direction. The so-generated counterfactuals are considered valid as soon as the predicted label matches the target label. A stripped-down counterfactual explanation is therefore little different from an adversarial example. In <a href="#fig-adv">Figure <span class="quarto-unresolved-ref">fig-adv</span></a>, for example, generic counterfactual search as in <span class="citation" data-cites="wachter2017counterfactual">Wachter, Mittelstadt, and Russell (<a href="#ref-wachter2017counterfactual" role="doc-biblioref">2017</a>)</span> has been applied to MNIST data.</p>
+<div id="fig-adv" class="quarto-figure quarto-figure-center">
+<figure>
+<p><img src="www/you_may_not_like_it.png" class="img-fluid" /></p>
+<p><figcaption>Figure 1.1: You may not like it, but this is what stripped-down counterfactuals look like. Here we have used <span class="citation" data-cites="wachter2017counterfactual">Wachter, Mittelstadt, and Russell (<a href="#ref-wachter2017counterfactual" role="doc-biblioref">2017</a>)</span> to generate multiple counterfactuals for turning an 8 (eight) into a 3 (three).</figcaption></p>
</figure>
</div>
<p>The crucial difference between adversarial examples and counterfactuals is one of intent. While adversarial examples are typically intended to go unnoticed, counterfactuals in the context of Explainable AI are generally sought to be “plausibleâ€, “realistic†or “feasibleâ€. To fulfil this latter goal, researchers have come up with a myriad of ways. <span class="citation" data-cites="joshi2019realistic">Joshi et al. (<a href="#ref-joshi2019realistic" role="doc-biblioref">2019</a>)</span> were among the first to suggest that instead of searching counterfactuals in the feature space, we can instead traverse a latent embedding learned by a surrogate generative model. Similarly, <span class="citation" data-cites="poyiadzi2020face">Poyiadzi et al. (<a href="#ref-poyiadzi2020face" role="doc-biblioref">2020</a>)</span> use density … Finally, <span class="citation" data-cites="karimi2021algorithmic">Karimi, Schölkopf, and Valera (<a href="#ref-karimi2021algorithmic" role="doc-biblioref">2021</a>)</span> argues that counterfactuals should comply with the causal model that generates them [CHECK IF WE CAN PHASE THIS LIKE THIS]. Other related approaches include … All of these different approaches have a common goal: they aim to ensure that the generated counterfactuals comply with the (learned) data-generating process (DGB).</p>
<div id="def-plausible" class="theorem definition">
-<p><span class="theorem-title"><strong>Definition 1 (Plausible Counterfactuals) </strong></span>Formally, if <span class="math inline">\(x \sim \mathcal{X}\)</span> and for the corresponding counterfactual we have <span class="math inline">\(x^{\prime}\sim\mathcal{X}^{\prime}\)</span>, then for <span class="math inline">\(x^{\prime}\)</span> to be considered a plausible counterfactual, we need: <span class="math inline">\(\mathcal{X} \approxeq \mathcal{X}^{\prime}\)</span>.</p>
+<p><span class="theorem-title"><strong>Definition 1.1 (Plausible Counterfactuals) </strong></span>Formally, if <span class="math inline">\(x \sim \mathcal{X}\)</span> and for the corresponding counterfactual we have <span class="math inline">\(x^{\prime}\sim\mathcal{X}^{\prime}\)</span>, then for <span class="math inline">\(x^{\prime}\)</span> to be considered a plausible counterfactual, we need: <span class="math inline">\(\mathcal{X} \approxeq \mathcal{X}^{\prime}\)</span>.</p>
</div>
-<p>In the context of Algorithmic Recourse, it makes sense to strive for plausible counterfactuals, since anything else would essentially require individuals to move to out-of-distribution states. But it is worth noting that our ambition to meet this goal, may have implications on our ability to faithfully explain the behaviour of the underlying black-box model (arguably our principal goal). By essentially decoupling the task of learning plausible representations of the data from the model itself, we open ourselves up to vulnerabilities. Using a separate generative model to learn <span class="math inline">\(\mathcal{X}\)</span>, for example, has very serious implications for the generated counterfactuals. <a href="#fig-latent">Figure 2</a> compares the results of applying REVISE <span class="citation" data-cites="joshi2019realistic">(<a href="#ref-joshi2019realistic" role="doc-biblioref">Joshi et al. 2019</a>)</span> to MNIST data using two different Variational Auto-Encoders: while the counterfactual generated using an expressive (strong) VAE is compelling, the result relying on a less expressive (weak) VAE is not even valid. In this latter case, the decoder step of the VAE fails to yield values in <span class="math inline">\(\mathcal{X}\)</span> and hence the counterfactual search in the learned latent space is doomed.</p>
-<div id="fig-latent" class="quarto-figure quarto-figure-center anchored">
-<figure class="figure">
-<p><img src="www/mnist_9to4_latent.png" class="img-fluid figure-img"></p>
-<p></p><figcaption class="figure-caption">Figure 2: Counterfactual explanations for MNIST using a Latent Space generator: turning a nine (9) into a four (4).</figcaption><p></p>
+<p>In the context of Algorithmic Recourse, it makes sense to strive for plausible counterfactuals, since anything else would essentially require individuals to move to out-of-distribution states. But it is worth noting that our ambition to meet this goal, may have implications on our ability to faithfully explain the behaviour of the underlying black-box model (arguably our principal goal). By essentially decoupling the task of learning plausible representations of the data from the model itself, we open ourselves up to vulnerabilities. Using a separate generative model to learn <span class="math inline">\(\mathcal{X}\)</span>, for example, has very serious implications for the generated counterfactuals. <a href="#fig-latent">Figure <span class="quarto-unresolved-ref">fig-latent</span></a> compares the results of applying REVISE <span class="citation" data-cites="joshi2019realistic">(<a href="#ref-joshi2019realistic" role="doc-biblioref">Joshi et al. 2019</a>)</span> to MNIST data using two different Variational Auto-Encoders: while the counterfactual generated using an expressive (strong) VAE is compelling, the result relying on a less expressive (weak) VAE is not even valid. In this latter case, the decoder step of the VAE fails to yield values in <span class="math inline">\(\mathcal{X}\)</span> and hence the counterfactual search in the learned latent space is doomed.</p>
+<div id="fig-latent" class="quarto-figure quarto-figure-center">
+<figure>
+<p><img src="www/mnist_9to4_latent.png" class="img-fluid" /></p>
+<p><figcaption>Figure 1.2: Counterfactual explanations for MNIST using a Latent Space generator: turning a nine (9) into a four (4).</figcaption></p>
</figure>
</div>
-<blockquote class="blockquote">
-<p>Here it would be nice to have another example where we poison the data going into the generative model to hide biases present in the data (e.g. Boston housing).</p>
+<blockquote>
+<p>Here it would be nice to have another example where we poison the data going into the generative model to hide biases present in the data (e.g. Boston housing).</p>
</blockquote>
<ul>
<li>Latent can be manipulated:
@@ -145,17 +220,17 @@ div.csl-indent {
</ul></li>
</ul>
</section>
-<section id="sec-fidelity" class="level3">
-<h3 class="anchored" data-anchor-id="sec-fidelity">From Plausible to High-Fidelity Counterfactuals</h3>
+<section id="sec-fidelity" class="level3" data-number="1.1.2">
+<h3 data-number="1.1.2"><span class="header-section-number">1.1.2</span> From Plausible to High-Fidelity Counterfactuals</h3>
<p>In light of the findings, we propose to generally avoid using surrogate models to learn <span class="math inline">\(\mathcal{X}\)</span> in the context of Counterfactual Explanations.</p>
<div id="prp-surrogate" class="theorem proposition">
-<p><span class="theorem-title"><strong>Proposition 1 (Avoid Surrogates) </strong></span>Since we are in the business of explaining a black-box model, the task of learning realistic representations of the data should not be reallocated from the model itself to some surrogate model.</p>
+<p><span class="theorem-title"><strong>Proposition 1.1 (Avoid Surrogates) </strong></span>Since we are in the business of explaining a black-box model, the task of learning realistic representations of the data should not be reallocated from the model itself to some surrogate model.</p>
</div>
<p>In cases where the use of surrogate models cannot be avoided, we propose to weigh the plausibility of counterfactuals against their fidelity to the black-box model. In the context of Explainable AI, fidelity is defined as describing how an explanation approximates the prediction of the black-box model <span class="citation" data-cites="molnar2020interpretable">(<a href="#ref-molnar2020interpretable" role="doc-biblioref">Molnar 2020</a>)</span>. Fidelity has become the default metric for evaluating Local Model-Agnostic Models, since they often involve local surrogate models whose predictions need not always match those of the black-box model.</p>
<p>In the case of Counterfactual Explanations, the concept of fidelity has so far been ignored. This is not altogether surprising, since by construction and design, Counterfactual Explanations work with the predictions of the black-box model directly: as stated above, a counterfactual <span class="math inline">\(x^{\prime}\)</span> is considered valid if and only if <span class="math inline">\(f(x^{\prime})=t\)</span>, where <span class="math inline">\(t\)</span> denote some target outcome.</p>
<p>Does fidelity even make sense in the context of CE, and if so, how can we define it? In light of the examples in the previous section, we think it is urgent to introduce a notion of fidelity in this context, that relates to the distributional properties of the generated counterfactuals. In particular, we propose that a high-fidelity counterfactual <span class="math inline">\(x^{\prime}\)</span> complies with the class-conditional distribution <span class="math inline">\(\mathcal{X}_{\theta} = p_{\theta}(X|y)\)</span> where <span class="math inline">\(\theta\)</span> denote the black-box model parameters.</p>
<div id="def-fidele" class="theorem definition">
-<p><span class="theorem-title"><strong>Definition 2 (High-Fidelity Counterfactuals) </strong></span>Let <span class="math inline">\(\mathcal{X}_{\theta}|y = p_{\theta}(X|y)\)</span> denote the class-conditional distribution of <span class="math inline">\(X\)</span> defined by <span class="math inline">\(\theta\)</span>. Then for <span class="math inline">\(x^{\prime}\)</span> to be considered a high-fidelity counterfactual, we need: <span class="math inline">\(\mathcal{X}_{\theta}|t \approxeq \mathcal{X}^{\prime}\)</span> where <span class="math inline">\(t\)</span> denotes the target outcome.</p>
+<p><span class="theorem-title"><strong>Definition 1.2 (High-Fidelity Counterfactuals) </strong></span>Let <span class="math inline">\(\mathcal{X}_{\theta}|y = p_{\theta}(X|y)\)</span> denote the class-conditional distribution of <span class="math inline">\(X\)</span> defined by <span class="math inline">\(\theta\)</span>. Then for <span class="math inline">\(x^{\prime}\)</span> to be considered a high-fidelity counterfactual, we need: <span class="math inline">\(\mathcal{X}_{\theta}|t \approxeq \mathcal{X}^{\prime}\)</span> where <span class="math inline">\(t\)</span> denotes the target outcome.</p>
</div>
<p>In order to assess the fidelity of counterfactuals, we propose the following two-step procedure:</p>
<ol type="1">
@@ -163,46 +238,46 @@ div.csl-indent {
<li>Compute the Maximum Mean Discrepancy (MMD) between <span class="math inline">\(X_{\theta}|y\)</span> and <span class="math inline">\(X^{\prime}\)</span>.</li>
</ol>
<p>If the computed value is different from zero, we can reject the null-hypothesis of fidelity.</p>
-<blockquote class="blockquote">
+<blockquote>
<p>Two challenges here: 1) implementing the sampling procedure in <span class="citation" data-cites="grathwohl2020your">Grathwohl et al. (<a href="#ref-grathwohl2020your" role="doc-biblioref">2020</a>)</span>; 2) it is unclear if MMD is really the right way to measure this.</p>
</blockquote>
</section>
</section>
-<section id="conformal-counterfactual-explanations" class="level2">
-<h2 class="anchored" data-anchor-id="conformal-counterfactual-explanations">Conformal Counterfactual Explanations</h2>
-<p>In <a href="#sec-fidelity">Section 1.2</a>, we have advocated for avoiding surrogate models in the context of Counterfactual Explanations. In this section, we introduce an alternative way to generate high-fidelity Counterfactual Explanations. In particular, we propose Conformal Counterfactual Explanations (CCE), that is Counterfactual Explanations that minimize the predictive uncertainty of conformal models.</p>
-<section id="minimizing-predictive-uncertainty" class="level3">
-<h3 class="anchored" data-anchor-id="minimizing-predictive-uncertainty">Minimizing Predictive Uncertainty</h3>
+<section id="conformal-counterfactual-explanations" class="level2" data-number="1.2">
+<h2 data-number="1.2"><span class="header-section-number">1.2</span> Conformal Counterfactual Explanations</h2>
+<p>In <a href="#sec-fidelity"><span class="quarto-unresolved-ref">sec-fidelity</span></a>, we have advocated for avoiding surrogate models in the context of Counterfactual Explanations. In this section, we introduce an alternative way to generate high-fidelity Counterfactual Explanations. In particular, we propose Conformal Counterfactual Explanations (CCE), that is Counterfactual Explanations that minimize the predictive uncertainty of conformal models.</p>
+<section id="minimizing-predictive-uncertainty" class="level3" data-number="1.2.1">
+<h3 data-number="1.2.1"><span class="header-section-number">1.2.1</span> Minimizing Predictive Uncertainty</h3>
<p><span class="citation" data-cites="schut2021generating">Schut et al. (<a href="#ref-schut2021generating" role="doc-biblioref">2021</a>)</span> demonstrated that the goal of generating realistic (plausible) counterfactuals can also be achieved by seeking counterfactuals that minimize the predictive uncertainty of the underlying black-box model. Similarly, <span class="citation" data-cites="antoran2020getting">Antorán et al. (<a href="#ref-antoran2020getting" role="doc-biblioref">2020</a>)</span> …</p>
<ul>
<li>Problem: restricted to Bayesian models.</li>
<li>Solution: post-hoc predictive uncertainty quantification. In particular, Conformal Prediction.</li>
</ul>
</section>
-<section id="background-on-conformal-prediction" class="level3">
-<h3 class="anchored" data-anchor-id="background-on-conformal-prediction">Background on Conformal Prediction</h3>
+<section id="background-on-conformal-prediction" class="level3" data-number="1.2.2">
+<h3 data-number="1.2.2"><span class="header-section-number">1.2.2</span> Background on Conformal Prediction</h3>
<ul>
<li>Distribution-free, model-agnostic and scalable approach to predictive uncertainty quantification.</li>
<li>Conformal prediction is instance-based. So is CE.</li>
<li>Take any fitted model and turn it into a conformal model using calibration data.</li>
<li>Our approach, therefore, relaxes the restriction on the family of black-box models, at the cost of relying on a subset of the data. Arguably, data is often abundant and in most applications practitioners tend to hold out a test data set anyway.</li>
</ul>
-<blockquote class="blockquote">
+<blockquote>
<p>Does the coverage guarantee carry over to counterfactuals?</p>
</blockquote>
</section>
-<section id="generating-conformal-counterfactuals" class="level3">
-<h3 class="anchored" data-anchor-id="generating-conformal-counterfactuals">Generating Conformal Counterfactuals</h3>
+<section id="generating-conformal-counterfactuals" class="level3" data-number="1.2.3">
+<h3 data-number="1.2.3"><span class="header-section-number">1.2.3</span> Generating Conformal Counterfactuals</h3>
<p>While Conformal Prediction has recently grown in popularity, it does introduce a challenge in the context of classification: the predictions of Conformal Classifiers are set-valued and therefore difficult to work with, since they are, for example, non-differentiable. Fortunately, <span class="citation" data-cites="stutz2022learning">Stutz et al. (<a href="#ref-stutz2022learning" role="doc-biblioref">2022</a>)</span> introduced carefully designed differentiable loss functions that make it possible to evaluate the performance of conformal predictions in training. We can leverage these recent advances in the context of gradient-based counterfactual search …</p>
-<blockquote class="blockquote">
+<blockquote>
<p>Challenge: still need to implement these loss functions.</p>
</blockquote>
</section>
</section>
-<section id="experiments" class="level2">
-<h2 class="anchored" data-anchor-id="experiments">Experiments</h2>
-<section id="research-questions" class="level3">
-<h3 class="anchored" data-anchor-id="research-questions">Research Questions</h3>
+<section id="experiments" class="level2" data-number="1.3">
+<h2 data-number="1.3"><span class="header-section-number">1.3</span> Experiments</h2>
+<section id="research-questions" class="level3" data-number="1.3.1">
+<h3 data-number="1.3.1"><span class="header-section-number">1.3.1</span> Research Questions</h3>
<ul>
<li><p>Is CP alone enough to ensure realistic counterfactuals?</p></li>
<li><p>Do counterfactuals improve further as the models get better?</p></li>
@@ -218,53 +293,58 @@ div.csl-indent {
<li>compare travel costs to domain shits.</li>
</ul></li>
</ul>
-<blockquote class="blockquote">
+<blockquote>
<p>Nice to have: What about using Laplace Approximation, then Conformal Prediction? What about using Conformalised Laplace?</p>
</blockquote>
</section>
</section>
-<section id="references" class="level2 unnumbered">
-
-
-</section>
-
-<div id="quarto-appendix" class="default"><section class="quarto-appendix-contents" role="doc-bibliography"><h2 class="anchored quarto-appendix-heading">References</h2><div id="refs" class="references csl-bib-body hanging-indent" role="doc-bibliography">
-<div id="ref-altmeyer2023endogenous" class="csl-entry" role="doc-biblioentry">
+<section id="references" class="level2" data-number="1.4">
+<h2 data-number="1.4"><span class="header-section-number">1.4</span> References</h2>
+<div id="quarto-navigation-envelope" class="hidden">
+<p><span class="hidden" data-render-id="quarto-int-sidebar-title">Conformal Counterfactual Explanations</span> <span class="hidden" data-render-id="quarto-int-navbar-title">Conformal Counterfactual Explanations</span> <span class="hidden" data-render-id="quarto-int-next"><span class="chapter-number">2</span>Â <span class="chapter-title"><code>ConformalGenerator</code></span></span> <span class="hidden" data-render-id="quarto-int-prev">Preface</span> <span class="hidden" data-render-id="quarto-int-sidebar:/index.html">Preface</span> <span class="hidden" data-render-id="quarto-int-sidebar:/notebooks/proposal.html"><span class="chapter-number">1</span>Â <span class="chapter-title">High-Fidelity Counterfactual Explanations through Conformal Prediction</span></span> <span class="hidden" data-render-id="quarto-int-sidebar:/notebooks/intro.html"><span class="chapter-number">2</span>Â <span class="chapter-title"><code>ConformalGenerator</code></span></span> <span class="hidden" data-render-id="quarto-int-sidebar:/notebooks/references.html">References</span></p>
+</div>
+<div id="quarto-meta-markdown" class="hidden">
+<p><span class="hidden" data-render-id="quarto-metatitle">Conformal Counterfactual Explanations - <span class="chapter-number">1</span>Â <span class="chapter-title">High-Fidelity Counterfactual Explanations through Conformal Prediction</span></span> <span class="hidden" data-render-id="quarto-twittercardtitle">Conformal Counterfactual Explanations - <span class="chapter-number">1</span>Â <span class="chapter-title">High-Fidelity Counterfactual Explanations through Conformal Prediction</span></span> <span class="hidden" data-render-id="quarto-ogcardtitle">Conformal Counterfactual Explanations - <span class="chapter-number">1</span>Â <span class="chapter-title">High-Fidelity Counterfactual Explanations through Conformal Prediction</span></span> <span class="hidden" data-render-id="quarto-metasitename">Conformal Counterfactual Explanations</span> <span class="hidden" data-render-id="quarto-twittercarddesc">We propose Conformal Counterfactual Explanations: an effortless and rigorous way to produce realistic and faithful Counterfactual Explanations using Conformal Prediction. To address the need for realistic counterfactuals, existing work has primarily relied on separate generative models to learn the data-generating process. While this is an effective way to produce plausible and model-agnostic counterfactual explanations, it not only introduces a significant engineering overhead but also reallocates the task of creating realistic model explanations from the model itself to the generative model. Recent work has shown that there is no need for any of this when working with probabilistic models that explicitly quantify their own uncertainty. Unfortunately, most models used in practice still do not fulfil that basic requirement, in which case we would like to have a way to quantify predictive uncertainty in a post-hoc fashion.</span> <span class="hidden" data-render-id="quarto-ogcardddesc">We propose Conformal Counterfactual Explanations: an effortless and rigorous way to produce realistic and faithful Counterfactual Explanations using Conformal Prediction. To address the need for realistic counterfactuals, existing work has primarily relied on separate generative models to learn the data-generating process. While this is an effective way to produce plausible and model-agnostic counterfactual explanations, it not only introduces a significant engineering overhead but also reallocates the task of creating realistic model explanations from the model itself to the generative model. Recent work has shown that there is no need for any of this when working with probabilistic models that explicitly quantify their own uncertainty. Unfortunately, most models used in practice still do not fulfil that basic requirement, in which case we would like to have a way to quantify predictive uncertainty in a post-hoc fashion.</span></p>
+</div>
+<div id="refs" class="references csl-bib-body hanging-indent" role="list">
+<div id="ref-altmeyer2023endogenous" class="csl-entry" role="listitem">
Altmeyer, Patrick, Giovan Angela, Aleksander Buszydlik, Karol Dobiczek, Arie van Deursen, and Cynthia Liem. 2023. <span>“Endogenous <span>Macrodynamics</span> in <span>Algorithmic</span> <span>Recourse</span>.â€</span> In <em>First <span>IEEE</span> <span>Conference</span> on <span>Secure</span> and <span>Trustworthy</span> <span>Machine</span> <span>Learning</span></em>.
</div>
-<div id="ref-antoran2020getting" class="csl-entry" role="doc-biblioentry">
+<div id="ref-antoran2020getting" class="csl-entry" role="listitem">
Antorán, Javier, Umang Bhatt, Tameem Adel, Adrian Weller, and José Miguel Hernández-Lobato. 2020. <span>“Getting a Clue: <span>A</span> Method for Explaining Uncertainty Estimates.â€</span> <a href="https://arxiv.org/abs/2006.06848">https://arxiv.org/abs/2006.06848</a>.
</div>
-<div id="ref-grathwohl2020your" class="csl-entry" role="doc-biblioentry">
+<div id="ref-grathwohl2020your" class="csl-entry" role="listitem">
Grathwohl, Will, Kuan-Chieh Wang, Joern-Henrik Jacobsen, David Duvenaud, Mohammad Norouzi, and Kevin Swersky. 2020. <span>“Your Classifier Is Secretly an Energy Based Model and You Should Treat It Like One.â€</span> In. <a href="https://openreview.net/forum?id=Hkxzx0NtDB">https://openreview.net/forum?id=Hkxzx0NtDB</a>.
</div>
-<div id="ref-joshi2019realistic" class="csl-entry" role="doc-biblioentry">
+<div id="ref-joshi2019realistic" class="csl-entry" role="listitem">
Joshi, Shalmali, Oluwasanmi Koyejo, Warut Vijitbenjaronk, Been Kim, and Joydeep Ghosh. 2019. <span>“Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systems.â€</span> <a href="https://arxiv.org/abs/1907.09615">https://arxiv.org/abs/1907.09615</a>.
</div>
-<div id="ref-karimi2021algorithmic" class="csl-entry" role="doc-biblioentry">
+<div id="ref-karimi2021algorithmic" class="csl-entry" role="listitem">
Karimi, Amir-Hossein, Bernhard Schölkopf, and Isabel Valera. 2021. <span>“Algorithmic Recourse: From Counterfactual Explanations to Interventions.â€</span> In <em>Proceedings of the 2021 <span>ACM Conference</span> on <span>Fairness</span>, <span>Accountability</span>, and <span>Transparency</span></em>, 353–62.
</div>
-<div id="ref-molnar2020interpretable" class="csl-entry" role="doc-biblioentry">
+<div id="ref-molnar2020interpretable" class="csl-entry" role="listitem">
Molnar, Christoph. 2020. <em>Interpretable Machine Learning</em>. <span>Lulu. com</span>.
</div>
-<div id="ref-pawelczyk2022probabilistically" class="csl-entry" role="doc-biblioentry">
+<div id="ref-pawelczyk2022probabilistically" class="csl-entry" role="listitem">
Pawelczyk, Martin, Teresa Datta, Johannes van-den-Heuvel, Gjergji Kasneci, and Himabindu Lakkaraju. 2022. <span>“Probabilistically <span>Robust</span> <span>Recourse</span>: <span>Navigating</span> the <span>Trade</span>-Offs Between <span>Costs</span> and <span>Robustness</span> in <span>Algorithmic</span> <span>Recourse</span>.â€</span> <em>arXiv Preprint arXiv:2203.06768</em>.
</div>
-<div id="ref-poyiadzi2020face" class="csl-entry" role="doc-biblioentry">
+<div id="ref-poyiadzi2020face" class="csl-entry" role="listitem">
Poyiadzi, Rafael, Kacper Sokol, Raul Santos-Rodriguez, Tijl De Bie, and Peter Flach. 2020. <span>“<span>FACE</span>: <span>Feasible</span> and Actionable Counterfactual Explanations.â€</span> In <em>Proceedings of the <span>AAAI</span>/<span>ACM Conference</span> on <span>AI</span>, <span>Ethics</span>, and <span>Society</span></em>, 344–50.
</div>
-<div id="ref-schut2021generating" class="csl-entry" role="doc-biblioentry">
+<div id="ref-schut2021generating" class="csl-entry" role="listitem">
Schut, Lisa, Oscar Key, Rory Mc Grath, Luca Costabello, Bogdan Sacaleanu, Yarin Gal, et al. 2021. <span>“Generating <span>Interpretable Counterfactual Explanations By Implicit Minimisation</span> of <span>Epistemic</span> and <span>Aleatoric Uncertainties</span>.â€</span> In <em>International <span>Conference</span> on <span>Artificial Intelligence</span> and <span>Statistics</span></em>, 1756–64. <span>PMLR</span>.
</div>
-<div id="ref-stutz2022learning" class="csl-entry" role="doc-biblioentry">
+<div id="ref-stutz2022learning" class="csl-entry" role="listitem">
Stutz, David, Krishnamurthy Dj Dvijotham, Ali Taylan Cemgil, and Arnaud Doucet. 2022. <span>“Learning <span>Optimal</span> <span>Conformal</span> <span>Classifiers</span>.â€</span> In. <a href="https://openreview.net/forum?id=t8O-4LKFVx">https://openreview.net/forum?id=t8O-4LKFVx</a>.
</div>
-<div id="ref-wachter2017counterfactual" class="csl-entry" role="doc-biblioentry">
+<div id="ref-wachter2017counterfactual" class="csl-entry" role="listitem">
Wachter, Sandra, Brent Mittelstadt, and Chris Russell. 2017. <span>“Counterfactual Explanations Without Opening the Black Box: <span>Automated</span> Decisions and the <span>GDPR</span>.â€</span> <em>Harv. JL & Tech.</em> 31: 841.
</div>
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diff --git a/docs/site_libs/quarto-html/quarto.js b/docs/site_libs/quarto-html/quarto.js
index 8846a020523d43436bac714cab4649fbbfe6b50c..e81be0399f77351d30579679aaa90b7fb451d703 100644
--- a/docs/site_libs/quarto-html/quarto.js
+++ b/docs/site_libs/quarto-html/quarto.js
@@ -614,7 +614,6 @@ window.document.addEventListener("DOMContentLoaded", function (_event) {
manageTransition("TOC", slow);
manageTransition("quarto-sidebar", slow);
};
-
const readerMode = !isReaderMode();
setReaderModeValue(readerMode);
@@ -623,6 +622,7 @@ window.document.addEventListener("DOMContentLoaded", function (_event) {
slowTransition(readerMode);
}
highlightReaderToggle(readerMode);
+ hideOverlappedSidebars();
// If we're exiting reader mode, restore the non-slow transition
if (!readerMode) {
diff --git a/docs/site_libs/quarto-nav/quarto-nav.js b/docs/site_libs/quarto-nav/quarto-nav.js
index 1377c79aa1e06038e033f283de55f2ac3fdee37f..c83857ec57b8df609c433c0d995e136ad1ee42fe 100644
--- a/docs/site_libs/quarto-nav/quarto-nav.js
+++ b/docs/site_libs/quarto-nav/quarto-nav.js
@@ -8,6 +8,47 @@ const headroomChanged = new CustomEvent("quarto-hrChanged", {
window.document.addEventListener("DOMContentLoaded", function () {
let init = false;
+ // Manage the back to top button, if one is present.
+ let lastScrollTop = window.pageYOffset || document.documentElement.scrollTop;
+ const scrollDownBuffer = 5;
+ const scrollUpBuffer = 35;
+ const btn = document.getElementById("quarto-back-to-top");
+ const hideBackToTop = () => {
+ btn.style.display = "none";
+ };
+ const showBackToTop = () => {
+ btn.style.display = "inline-block";
+ };
+ if (btn) {
+ window.document.addEventListener(
+ "scroll",
+ function () {
+ const currentScrollTop =
+ window.pageYOffset || document.documentElement.scrollTop;
+
+ // Shows and hides the button 'intelligently' as the user scrolls
+ if (currentScrollTop - scrollDownBuffer > lastScrollTop) {
+ hideBackToTop();
+ lastScrollTop = currentScrollTop <= 0 ? 0 : currentScrollTop;
+ } else if (currentScrollTop < lastScrollTop - scrollUpBuffer) {
+ showBackToTop();
+ lastScrollTop = currentScrollTop <= 0 ? 0 : currentScrollTop;
+ }
+
+ // Show the button at the bottom, hides it at the top
+ if (currentScrollTop <= 0) {
+ hideBackToTop();
+ } else if (
+ window.innerHeight + currentScrollTop >=
+ document.body.offsetHeight
+ ) {
+ showBackToTop();
+ }
+ },
+ false
+ );
+ }
+
function throttle(func, wait) {
var timeout;
return function () {
diff --git a/docs/site_libs/quarto-search/quarto-search.js b/docs/site_libs/quarto-search/quarto-search.js
index 6fd4b5b90b816f481440e916c3d316de48ae717e..4f77b17f83cdbd09e2f952ce050f5dedc0651377 100644
--- a/docs/site_libs/quarto-search/quarto-search.js
+++ b/docs/site_libs/quarto-search/quarto-search.js
@@ -45,7 +45,7 @@ window.document.addEventListener("DOMContentLoaded", function (_event) {
// highlight matches on the page
if (query !== null && mainEl) {
// perform any highlighting
- highlight(query, mainEl);
+ highlight(escapeRegExp(query), mainEl);
// fix up the URL to remove the q query param
const replacementUrl = new URL(window.location);
@@ -80,23 +80,20 @@ window.document.addEventListener("DOMContentLoaded", function (_event) {
// the media query since we generate different HTML for sidebar overlays than we do
// for sidebar input UI)
const detachedMediaQuery =
- quartoSearchOptions.type === "overlay"
- ? "all"
- : quartoSearchOptions.location === "navbar"
- ? "(max-width: 991px)"
- : "none";
+ quartoSearchOptions.type === "overlay" ? "all" : "(max-width: 991px)";
// If configured, include the analytics client to send insights
const plugins = configurePlugins(quartoSearchOptions);
let lastState = null;
- const { setIsOpen } = autocomplete({
+ const { setIsOpen, setQuery, setCollections } = autocomplete({
container: searchEl,
detachedMediaQuery: detachedMediaQuery,
defaultActiveItemId: 0,
panelContainer: "#quarto-search-results",
panelPlacement: quartoSearchOptions["panel-placement"],
debug: false,
+ openOnFocus: true,
plugins,
classNames: {
form: "d-flex",
@@ -299,9 +296,15 @@ window.document.addEventListener("DOMContentLoaded", function (_event) {
},
templates: {
noResults({ createElement }) {
+ const hasQuery = lastState.query;
+
return createElement(
"div",
- { class: "quarto-search-no-results" },
+ {
+ class: `quarto-search-no-results${
+ hasQuery ? "" : " no-query"
+ }`,
+ },
language["search-no-results-text"]
);
},
@@ -361,6 +364,12 @@ window.document.addEventListener("DOMContentLoaded", function (_event) {
},
});
+ window.quartoOpenSearch = () => {
+ setIsOpen(false);
+ setIsOpen(true);
+ focusSearchInput();
+ };
+
// Remove the labeleledby attribute since it is pointing
// to a non-existent label
if (quartoSearchOptions.type === "overlay") {
@@ -976,6 +985,10 @@ function clearHighlight(searchterm, el) {
}
}
+function escapeRegExp(string) {
+ return string.replace(/[.*+?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string
+}
+
// highlight matches
function highlight(term, el) {
const termRegex = new RegExp(term, "ig");
diff --git a/docs/summary.html b/docs/summary.html
deleted file mode 100644
index f2652ceb2dadd12f78fd3e7df185e49d77b3ad48..0000000000000000000000000000000000000000
--- a/docs/summary.html
+++ /dev/null
@@ -1,383 +0,0 @@
-<!DOCTYPE html>
-<html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head>
-
-<meta charset="utf-8">
-<meta name="generator" content="quarto-99.9.9">
-
-<meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
-
-
-<title>notebooks - 2 Summary</title>
-<style>
-code{white-space: pre-wrap;}
-span.smallcaps{font-variant: small-caps;}
-div.columns{display: flex; gap: min(4vw, 1.5em);}
-div.column{flex: auto; overflow-x: auto;}
-div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;}
-ul.task-list{list-style: none;}
-ul.task-list li input[type="checkbox"] {
- width: 0.8em;
- margin: 0 0.8em 0.2em -1.6em;
- vertical-align: middle;
-}
-</style>
-
-
-<script src="site_libs/quarto-nav/quarto-nav.js"></script>
-<script src="site_libs/quarto-nav/headroom.min.js"></script>
-<script src="site_libs/clipboard/clipboard.min.js"></script>
-<script src="site_libs/quarto-search/autocomplete.umd.js"></script>
-<script src="site_libs/quarto-search/fuse.min.js"></script>
-<script src="site_libs/quarto-search/quarto-search.js"></script>
-<meta name="quarto:offset" content="./">
-<link href="./references.html" rel="next">
-<link href="./intro.html" rel="prev">
-<script src="site_libs/quarto-html/quarto.js"></script>
-<script src="site_libs/quarto-html/popper.min.js"></script>
-<script src="site_libs/quarto-html/tippy.umd.min.js"></script>
-<script src="site_libs/quarto-html/anchor.min.js"></script>
-<link href="site_libs/quarto-html/tippy.css" rel="stylesheet">
-<link href="site_libs/quarto-html/quarto-syntax-highlighting.css" rel="stylesheet" id="quarto-text-highlighting-styles">
-<script src="site_libs/bootstrap/bootstrap.min.js"></script>
-<link href="site_libs/bootstrap/bootstrap-icons.css" rel="stylesheet">
-<link href="site_libs/bootstrap/bootstrap.min.css" rel="stylesheet" id="quarto-bootstrap" data-mode="light">
-<script id="quarto-search-options" type="application/json">{
- "location": "sidebar",
- "copy-button": false,
- "collapse-after": 3,
- "panel-placement": "start",
- "type": "textbox",
- "limit": 20,
- "language": {
- "search-no-results-text": "No results",
- "search-matching-documents-text": "matching documents",
- "search-copy-link-title": "Copy link to search",
- "search-hide-matches-text": "Hide additional matches",
- "search-more-match-text": "more match in this document",
- "search-more-matches-text": "more matches in this document",
- "search-clear-button-title": "Clear",
- "search-detached-cancel-button-title": "Cancel",
- "search-submit-button-title": "Submit"
- }
-}</script>
-
-
-</head>
-
-<body class="nav-sidebar floating">
-
-<div id="quarto-search-results"></div>
- <header id="quarto-header" class="headroom fixed-top">
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diff --git a/notebooks/Project.toml b/notebooks/Project.toml
new file mode 100644
index 0000000000000000000000000000000000000000..7de2a71557207ad46fb5353699faa82897259c1f
--- /dev/null
+++ b/notebooks/Project.toml
@@ -0,0 +1,13 @@
+[deps]
+CCE = "0232c203-4013-4b0d-ad96-43e3e11ac3bf"
+ConformalPrediction = "98bfc277-1877-43dc-819b-a3e38c30242f"
+CounterfactualExplanations = "2f13d31b-18db-44c1-bc43-ebaf2cff0be0"
+Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
+Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
+Images = "916415d5-f1e6-5110-898d-aaa5f9f070e0"
+JointEnergyModels = "48c56d24-211d-4463-bbc0-7a701b291131"
+MLDatasets = "eb30cadb-4394-5ae3-aed4-317e484a6458"
+MLJBase = "a7f614a8-145f-11e9-1d2a-a57a1082229d"
+MLJFlux = "094fc8d1-fd35-5302-93ea-dabda2abf845"
+Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
+Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
diff --git a/notebooks/_quarto.yml b/notebooks/_quarto.yml
deleted file mode 100644
index 9415920a175e96bce1a392e1f2931df95dfc8af6..0000000000000000000000000000000000000000
--- a/notebooks/_quarto.yml
+++ /dev/null
@@ -1,33 +0,0 @@
-project:
- type: book
- output-dir: ../docs
-
-book:
- title: "Conformal Counterfactual Explanations"
- subtitle: "Online Companion"
- author: "Patrick Altmeyer"
- date: today
- chapters:
- - index.qmd
- - intro.qmd
- - summary.qmd
- - references.qmd
-
-bibliography: ../bib.bib
-
-format:
- html:
- theme: cosmo
- pdf:
- documentclass: scrreprt
-
-execute:
- echo: false
- eval: true
- output: false
- freeze: auto
-
-jupyter: julia-1.8
-
-
-
diff --git a/notebooks/conformal.qmd b/notebooks/conformal.qmd
deleted file mode 100644
index f592ac3d69214798adddeeb02c169f186ee2e69d..0000000000000000000000000000000000000000
--- a/notebooks/conformal.qmd
+++ /dev/null
@@ -1,189 +0,0 @@
-```{julia}
-using CCE
-using ConformalPrediction
-using CounterfactualExplanations
-using CounterfactualExplanations.Data
-using CounterfactualExplanations.Objectives
-using Flux
-using LinearAlgebra
-using MLJBase
-using MLJFlux
-using Plots
-```
-
-# `ConformalGenerator`
-
-In this section, we will look at a simple example involving synthetic data, a black-box model and a generic Conformal Counterfactual Generator.
-
-## Black-box Model
-
-We consider a simple binary classification problem. Let $(X_i, Y_i), \ i=1,...,n$ denote our feature-label pairs and let $\mu: \mathcal{X} \mapsto \mathcal{Y}$ denote the mapping from features to labels. For illustration purposes, we will use linearly separable data.
-
-```{julia}
-counterfactual_data = load_linearly_separable()
-```
-
-While we could use a linear classifier in this case, let's pretend we need a black-box model for this task and rely on a small Multi-Layer Perceptron (MLP):
-
-```{julia}
-builder = MLJFlux.@builder Chain(
- Dense(n_in, 32, relu),
- Dense(32, n_out)
-)
-clf = NeuralNetworkClassifier(builder=builder, epochs=100)
-```
-
-We can fit this model to data to produce plug-in predictions.
-
-### Split Conformal Classification
-
-Here we will instead use a specific case of CP called *split conformal prediction* which can then be summarized as follows:^[In other places split conformal prediction is sometimes referred to as *inductive* conformal prediction.]
-
-1. Partition the training into a proper training set and a separate calibration set: $\mathcal{D}_n=\mathcal{D}^{\text{train}} \cup \mathcal{D}^{\text{cali}}$.
-2. Train the machine learning model on the proper training set: $\hat\mu_{i \in \mathcal{D}^{\text{train}}}(X_i,Y_i)$.
-
-The model $\hat\mu_{i \in \mathcal{D}^{\text{train}}}$ can now produce plug-in predictions.
-
-::: callout-note
-
-## Starting Point
-
-Note that this represents the starting point in applications of Algorithmic Recourse: we have some pre-trained classifier $M$ for which we would like to generate plausible Counterfactual Explanations. Next, we turn to the calibration step.
-:::
-
-3. Compute nonconformity scores, $\mathcal{S}$, using the calibration data $\mathcal{D}^{\text{cali}}$ and the fitted model $\hat\mu_{i \in \mathcal{D}^{\text{train}}}$.
-4. For a user-specified desired coverage ratio $(1-\alpha)$ compute the corresponding quantile, $\hat{q}$, of the empirical distribution of nonconformity scores, $\mathcal{S}$.
-5. For the given quantile and test sample $X_{\text{test}}$, form the corresponding conformal prediction set:
-
-$$
-C(X_{\text{test}})=\{y:s(X_{\text{test}},y) \le \hat{q}\}
-$$ {#eq-set}
-
-This is the default procedure used for classification and regression in [`ConformalPrediction.jl`](https://github.com/pat-alt/ConformalPrediction.jl).
-
-Using the package, we can apply Split Conformal Prediction as follows:
-
-```{julia}
-X = table(permutedims(counterfactual_data.X))
-y = counterfactual_data.output_encoder.labels
-conf_model = conformal_model(clf; method=:simple_inductive)
-mach = machine(conf_model, X, y)
-fit!(mach)
-```
-
-To be clear, all of the calibration steps (3 to 5) are post hoc, and yet none of them involved any changes to the model parameters. These are two important characteristics of Split Conformal Prediction (SCP) that make it particularly useful in the context of Algorithmic Recourse. Firstly, the fact that SCP involves posthoc calibration steps that happen after training, ensures that we need not place any restrictions on the black-box model itself. This stands in contrast to the approach proposed by @schut2021generating in which they essentially restrict the class of models to Bayesian models. Secondly, the fact that the model itself is kept entirely intact ensures that the generated counterfactuals maintain fidelity to the model. Finally, note that we also have not resorted to a surrogate model to learn more about $X \sim \mathcal{X}$. Instead, we have used the fitted model itself and a calibration data set to learn about the model's predictive uncertainty.
-
-## Counterfactual Explanation
-
-We first wrap our model in a container that makes it compatible with `CounterfactualExplanations.jl`. Then we draw a random sample, determine its predicted label $\hat{y}$ and choose the opposite label as our target.
-
-```{julia}
-M = CCE.ConformalModel(conf_model, mach.fitresult)
-x = select_factual(counterfactual_data,rand(1:size(counterfactual_data.X,2)))
-y_factual = predict_label(M, counterfactual_data, x)[1]
-target = counterfactual_data.y_levels[counterfactual_data.y_levels .!= y_factual][1]
-```
-
-The generic Conformal Counterfactual Generator works with the following counterfactual search objective,
-
-$$
-x^\prime = \arg \min_{x^\prime} \ell(M(x^\prime),t) + \lambda \mathbb{I}_{y^\prime = t} \Omega(C_{\theta}(x;\tau))
-$$ {#eq-solution}
-
-where
-
-$$
-\Omega(C_{\theta}(x;\tau)) = = \max (0, \sum_k C_{\theta,k}(x;\tau) - \kappa)
-$$ {#eq-size-loss}
-
-is a smooth size penalty for conformal classifiers introduced by @stutz2022learning. Here, $C_{\theta,k}(x;\tau)$ is loosely defined as the probability that class $k$ is assigned to the conformal prediction set $C$. In the context of Conformal Training, this penalty reduces the **inefficiency** of the conformal predictor. In our context, we are not interested in improving the model itself, but rather in producing **plausible** counterfactuals. Provided that our counterfactual $x^\prime$ is already inside the target domain ($\mathbb{I}_{y^\prime = t}=1$), penalizing $\Omega(C_{\theta}(x;\tau))$ corresponds to guiding counterfactuals into regions of the target domain that are characterized by low ambiguity: for $\kappa=1$ the conformal prediction set includes only the target label $t$ as $\Omega(C_{\theta}(x;\tau))$. Arguably, less ambiguous counterfactuals are more **plausible**. Since the search is guided purely by properties of the model itself and (exchangeable) calibration data, counterfactuals also maintain **high fidelity**.
-
-```{julia}
-#| output: true
-#| echo: false
-#| label: fig-losses
-#| fig-cap: "Illustration of the smooth size loss and the configurable classification loss."
-temp = 0.5
-p1 = contourf(mach.model, mach.fitresult, X, y; plot_set_loss=true, zoom=0, temp=temp)
-p2 = contourf(mach.model, mach.fitresult, X, y; plot_classification_loss=true, target=target, zoom=0, temp=temp, clim=nothing, loss_matrix=ones(2,2))
-plot(p1, p2, size=(800,320))
-```
-
-### Penalizing Size
-
-```{julia}
-#| output: true
-#| echo: false
-#| label: fig-ce
-#| fig-cap: "Comparison of counterfactuals produced using different generators."
-
-opt = Descent(0.01)
-ordered_names = [
- "Generic (γ=0.5)",
- "Conformal (λ₂=1)",
- "Conformal (λ₂=10)"
-]
-loss_fun = Objectives.logitbinarycrossentropy
-generator = GenericGenerator(opt = opt)
-
-# Generators:
-generators = Dict(
- ordered_names[1] => generator,
- ordered_names[2] => deepcopy(generator) |> gen -> @objective(gen, _ + 0.1distance_l2 + 1.0set_size_penalty),
- ordered_names[3] => deepcopy(generator) |> gen -> @objective(gen, _ + 0.1distance_l2 + 10.0set_size_penalty),
-)
-
-counterfactuals = Dict([name => generate_counterfactual(x, target, counterfactual_data, M, gen; initialization=:identity, converge_when=:generator_conditions, gradient_tol=1e-3) for (name, gen) in generators])
-
-# Plots:
-plts = []
-for name ∈ ordered_names
- ce = counterfactuals[name]
- plt = plot(ce; title=name, colorbar=false, ticks = false, legend=false, zoom=0)
- plts = vcat(plts..., plt)
-end
-_n = length(generators)
-img_size = 300
-plot(plts..., size=(_n * img_size,1.05*img_size), layout=(1,_n))
-```
-
-
-### Configurable Classification Loss
-
-```{julia}
-#| output: true
-#| echo: false
-#| label: fig-ce
-#| fig-cap: "Comparison of counterfactuals produced using different generators."
-
-opt = Descent(0.01)
-temp = 0.75
-ordered_names = [
- "Generic (γ=0.5)",
- "Generic (γ=0.9)",
- "Conformal (λ₂=1)",
- "Conformal (λ₂=10)"
-]
-loss_fun = Objectives.logitbinarycrossentropy
-
-# Generators:
-generators = Dict(
- ordered_names[1] => GenericGenerator(opt = opt, decision_threshold=0.5),
- ordered_names[2] => GenericGenerator(opt = opt, decision_threshold=0.9),
- ordered_names[3] => CCE.ConformalGenerator(opt=opt, λ=[0.1,1], temp=temp),
- ordered_names[4] => CCE.ConformalGenerator(opt=opt, λ=[0.1,10], temp=temp),
-)
-
-counterfactuals = Dict([name => generate_counterfactual(x, target, counterfactual_data, M, gen; initialization=:identity) for (name, gen) in generators])
-
-# Plots:
-plts = []
-for name ∈ ordered_names
- ce = counterfactuals[name]
- plt = plot(ce; title=name, colorbar=false, ticks = false, legend=false, zoom=0)
- plts = vcat(plts..., plt)
-end
-_n = length(generators)
-img_size = 300
-plot(plts..., size=(_n * img_size,1.05*img_size), layout=(1,_n))
-```
\ No newline at end of file
diff --git a/notebooks/cover.png b/notebooks/cover.png
deleted file mode 100644
index e1f5bc61d11049afe5b5dc2a67a3a32f406be964..0000000000000000000000000000000000000000
Binary files a/notebooks/cover.png and /dev/null differ
diff --git a/notebooks/index.qmd b/notebooks/index.qmd
deleted file mode 100644
index 2062215669dae5803b1dfb01405c1f89a01c8782..0000000000000000000000000000000000000000
--- a/notebooks/index.qmd
+++ /dev/null
@@ -1,5 +0,0 @@
-# Preface {.unnumbered}
-
-This is a Quarto book.
-
-To learn more about Quarto books visit <https://quarto.org/docs/books>.
diff --git a/notebooks/intro.qmd b/notebooks/intro.qmd
index efcf1721e9d4e8678e9bb1eb039bc768e33d1e90..3d0bf1005165a996d2ee810c3c5f7e17cacc490f 100644
--- a/notebooks/intro.qmd
+++ b/notebooks/intro.qmd
@@ -1,5 +1,319 @@
-# Introduction
+```{julia}
+include("notebooks/setup.jl")
+eval(setup_notebooks)
+```
-This is a book created from markdown and executable code.
+# `ConformalGenerator`
+
+In this section, we will look at a simple example involving synthetic data, a black-box model and a generic Conformal Counterfactual Generator.
+
+## Black-box Model
+
+We consider a simple binary classification problem. Let $(X_i, Y_i), \ i=1,...,n$ denote our feature-label pairs and let $\mu: \mathcal{X} \mapsto \mathcal{Y}$ denote the mapping from features to labels. For illustration purposes, we will use linearly separable data.
+
+```{julia}
+counterfactual_data = load_linearly_separable()
+```
+
+While we could use a linear classifier in this case, let's pretend we need a black-box model for this task and rely on a small Multi-Layer Perceptron (MLP):
+
+```{julia}
+builder = MLJFlux.@builder Chain(
+ Dense(n_in, 32, relu),
+ Dense(32, n_out)
+)
+clf = NeuralNetworkClassifier(builder=builder, epochs=100)
+```
+
+We can fit this model to data to produce plug-in predictions.
+
+## Conformal Prediction
+
+Here we will instead use a specific case of CP called *split conformal prediction* which can then be summarized as follows:^[In other places split conformal prediction is sometimes referred to as *inductive* conformal prediction.]
+
+1. Partition the training into a proper training set and a separate calibration set: $\mathcal{D}_n=\mathcal{D}^{\text{train}} \cup \mathcal{D}^{\text{cali}}$.
+2. Train the machine learning model on the proper training set: $\hat\mu_{i \in \mathcal{D}^{\text{train}}}(X_i,Y_i)$.
+
+The model $\hat\mu_{i \in \mathcal{D}^{\text{train}}}$ can now produce plug-in predictions.
+
+::: callout-note
+
+## Starting Point
+
+Note that this represents the starting point in applications of Algorithmic Recourse: we have some pre-trained classifier $M$ for which we would like to generate plausible Counterfactual Explanations. Next, we turn to the calibration step.
+:::
+
+3. Compute nonconformity scores, $\mathcal{S}$, using the calibration data $\mathcal{D}^{\text{cali}}$ and the fitted model $\hat\mu_{i \in \mathcal{D}^{\text{train}}}$.
+4. For a user-specified desired coverage ratio $(1-\alpha)$ compute the corresponding quantile, $\hat{q}$, of the empirical distribution of nonconformity scores, $\mathcal{S}$.
+5. For the given quantile and test sample $X_{\text{test}}$, form the corresponding conformal prediction set:
+
+$$
+C(X_{\text{test}})=\{y:s(X_{\text{test}},y) \le \hat{q}\}
+$$ {#eq-set}
+
+This is the default procedure used for classification and regression in [`ConformalPrediction.jl`](https://github.com/pat-alt/ConformalPrediction.jl).
+
+Using the package, we can apply Split Conformal Prediction as follows:
+
+```{julia}
+X = table(permutedims(counterfactual_data.X))
+y = counterfactual_data.output_encoder.labels
+conf_model = conformal_model(clf; method=:simple_inductive)
+mach = machine(conf_model, X, y)
+fit!(mach)
+```
+
+To be clear, all of the calibration steps (3 to 5) are post hoc, and yet none of them involved any changes to the model parameters. These are two important characteristics of Split Conformal Prediction (SCP) that make it particularly useful in the context of Algorithmic Recourse. Firstly, the fact that SCP involves posthoc calibration steps that happen after training, ensures that we need not place any restrictions on the black-box model itself. This stands in contrast to the approach proposed by @schut2021generating in which they essentially restrict the class of models to Bayesian models. Secondly, the fact that the model itself is kept entirely intact ensures that the generated counterfactuals maintain fidelity to the model. Finally, note that we also have not resorted to a surrogate model to learn more about $X \sim \mathcal{X}$. Instead, we have used the fitted model itself and a calibration data set to learn about the model's predictive uncertainty.
+
+## Differentiable CP
+
+In order to use CP in the context of gradient-based counterfactual search, we need it to be differentiable. @stutz2022learning introduce a framework for training differentiable conformal predictors. They introduce a configurable loss function as well as smooth set size penalty.
+
+### Smooth Set Size Penalty
+
+Starting with the former, @stutz2022learning propose the following:
+
+$$
+\Omega(C_{\theta}(x;\tau)) = = \max (0, \sum_k C_{\theta,k}(x;\tau) - \kappa)
+$$ {#eq-size-loss}
+
+Here, $C_{\theta,k}(x;\tau)$ is loosely defined as the probability that class $k$ is assigned to the conformal prediction set $C$. In the context of Conformal Training, this penalty reduces the **inefficiency** of the conformal predictor.
+
+In our context, we are not interested in improving the model itself, but rather in producing **plausible** counterfactuals. Provided that our counterfactual $x^\prime$ is already inside the target domain ($\mathbb{I}_{y^\prime = t}=1$), penalizing $\Omega(C_{\theta}(x;\tau))$ corresponds to guiding counterfactuals into regions of the target domain that are characterized by low ambiguity: for $\kappa=1$ the conformal prediction set includes only the target label $t$ as $\Omega(C_{\theta}(x;\tau))$. Arguably, less ambiguous counterfactuals are more **plausible**. Since the search is guided purely by properties of the model itself and (exchangeable) calibration data, counterfactuals also maintain **high fidelity**.
+
+The left panel of @fig-losses shows the smooth size penalty in the two-dimensional feature space of our synthetic data.
+
+### Configurable Classification Loss
+
+The right panel of @fig-losses shows the configurable classification loss in the two-dimensional feature space of our synthetic data.
+
+```{julia}
+#| output: true
+#| echo: false
+#| label: fig-losses
+#| fig-cap: "Illustration of the smooth size loss and the configurable classification loss."
+
+temp = 0.5
+p1 = contourf(mach.model, mach.fitresult, X, y; plot_set_loss=true, zoom=0, temp=temp)
+p2 = contourf(mach.model, mach.fitresult, X, y; plot_classification_loss=true, target=1, zoom=0, temp=temp, clim=nothing, loss_matrix=ones(2,2))
+plot(p1, p2, size=(800,320))
+```
+
+## Fidelity and Plausibility
+
+The main evaluation criteria we are interested in are *fidelity* and *plausibility*. Interestingly, we could also consider using these measures as penalties in the counterfactual search.
+
+### Fidelity
+
+We propose to define fidelity as follows:
+
+::: {#def-fidelity}
+
+## High-Fidelity Counterfactuals
+
+Let $\mathcal{X}_{\theta}|y = p_{\theta}(X|y)$ denote the class-conditional distribution of $X$ defined by $\theta$. Then for $x^{\prime}$ to be considered a high-fidelity counterfactual, we need: $\mathcal{X}_{\theta}|t \approxeq \mathcal{X}^{\prime}$ where $t$ denotes the target outcome.
+
+:::
+
+We can generate samples from $p_{\theta}(X|y)$ following @grathwohl2020your. In @fig-energy, I have applied the methodology to our synthetic data.
+
+```{julia}
+#| fig-cap: "Energy-based conditional samples."
+#| label: fig-energy
+
+M = CCE.ConformalModel(conf_model, mach.fitresult)
+
+niter = 100
+nsamples = 100
+
+plts = []
+for target ∈ counterfactual_data.y_levels
+ sampler = CCE.EnergySampler(M, counterfactual_data, target; niter=niter, nsamples=100)
+ Xgen = rand(sampler, nsamples)
+ plt = plot(M, counterfactual_data; target=target, zoom=-3,cbar=false)
+ scatter!(Xgen[1,:],Xgen[2,:],alpha=0.5,color=target,shape=:star,label="X|y=$target")
+ push!(plts, plt)
+end
+plot(plts..., layout=(1,length(plts)), size=(img_height*length(plts),img_height))
+```
+
+As an evaluation metric and penalty, we could use the average distance of the counterfactual $x^{\prime}$ from these generated samples, for example.
+
+### Plausibility
+
+We propose to define plausibility as follows:
+
+::: {#def-plausible}
+
+## Plausible Counterfactuals
+
+Formally, let $\mathcal{X}|t$ denote the conditional distribution of samples in the target class. As before, we have $x^{\prime}\sim\mathcal{X}^{\prime}$, then for $x^{\prime}$ to be considered a plausible counterfactual, we need: $\mathcal{X}|t \approxeq \mathcal{X}^{\prime}$.
+
+:::
+
+As an evaluation metric and penalty, we could use the average distance of the counterfactual $x^{\prime}$ from (potentially bootstrapped) training samples in the target class, for example.
+
+## Counterfactual Explanations
+
+Next, let's generate counterfactual explanations for our synthetic data. We first wrap our model in a container that makes it compatible with `CounterfactualExplanations.jl`. Then we draw a random sample, determine its predicted label $\hat{y}$ and choose the opposite label as our target.
+
+```{julia}
+x = select_factual(counterfactual_data,rand(1:size(counterfactual_data.X,2)))
+y_factual = predict_label(M, counterfactual_data, x)[1]
+target = counterfactual_data.y_levels[counterfactual_data.y_levels .!= y_factual][1]
+```
+
+The generic Conformal Counterfactual Generator penalises the only the set size only:
+
+$$
+x^\prime = \arg \min_{x^\prime} \ell(M(x^\prime),t) + \lambda \mathbb{I}_{y^\prime = t} \Omega(C_{\theta}(x;\tau))
+$$ {#eq-solution}
+
+```{julia}
+#| output: true
+#| echo: false
+#| label: fig-ce
+#| fig-cap: "Comparison of counterfactuals produced using different generators."
+
+λ = 10.0
+
+opt = Descent(0.01)
+ordered_names = [
+ "Distance x",
+ "Set Size",
+ "Distance X_θ|t",
+ "Distance X|t"
+]
+loss_fun = Objectives.logitbinarycrossentropy
+generator = GenericGenerator(opt = opt)
+
+# Generators:
+generators = Dict(
+ ordered_names[1] => generator,
+ ordered_names[2] => deepcopy(generator) |> gen -> @objective(gen, _ + 0.1distance + 10.0set_size_penalty),
+ ordered_names[3] => deepcopy(generator) |> gen -> @objective(gen, _ + 0.1distance + 10.0distance_from_energy),
+ ordered_names[4] => deepcopy(generator) |> gen -> @objective(gen, _ + 0.1distance + 10.0distance_from_targets),
+)
+
+counterfactuals = Dict([name => generate_counterfactual(x, target, counterfactual_data, M, gen; initialization=:identity, converge_when=:generator_conditions, gradient_tol=1e-3) for (name, gen) in generators])
+
+# Plots:
+plts = []
+for name ∈ ordered_names
+ ce = counterfactuals[name]
+ plt = plot(ce; title=name, colorbar=false, ticks = false, legend=false, zoom=-0.5)
+ if :energy_sampler ∈ collect(keys(ce.params))
+ Xgen = ce.params[:energy_sampler].buffer
+ scatter!(Xgen[1,:],Xgen[2,:],alpha=0.5,color=target,shape=:star,label="X|y=$target")
+ end
+ plts = vcat(plts..., plt)
+end
+_n = length(generators)
+plot(plts..., size=(_n * img_height,1.05*img_height), layout=(1,_n))
+```
+
+### Benchmark
+
+```{julia}
+#| output: false
+
+# Data:
+datasets = Dict(
+ :linearly_separable => load_linearly_separable(),
+ :overlapping => load_overlapping(),
+ :moons => load_moons(),
+ :circles => load_circles(),
+)
+
+# Models:
+models = Dict(
+ :MLP => M,
+)
+```
+
+Then we can simply loop over the datasets and eventually concatenate the results like so:
+
+```{julia}
+#| output: false
+
+measure =
+using CounterfactualExplanations.Evaluation: benchmark
+bmks = []
+for (dataname, dataset) in datasets
+ bmk = benchmark(dataset; models=models, generators=generators, suppress_training=true, data_name=dataname)
+ push!(bmks, bmk)
+end
+bmk = reduce(vcat, bmks)
+```
+
+## Multi-Class
+
+```{julia}
+counterfactual_data = load_multi_class()
+```
+
+```{julia}
+X = table(permutedims(counterfactual_data.X))
+y = counterfactual_data.output_encoder.labels
+conf_model = conformal_model(clf; method=:simple_inductive)
+mach = machine(conf_model, X, y)
+fit!(mach)
+```
+
+```{julia}
+#| output: true
+#| echo: false
+#| label: fig-losses-multi
+#| fig-cap: "Illustration of the smooth size loss and the configurable classification loss."
+
+temp = 0.5
+p1 = contourf(mach.model, mach.fitresult, X, y; plot_set_loss=true, zoom=0, temp=temp)
+p2 = contourf(mach.model, mach.fitresult, X, y; plot_classification_loss=true, target=1, zoom=0, temp=temp, clim=nothing, loss_matrix=ones(4,4))
+plot(p1, p2, size=(800,320))
+```
+
+```{julia}
+#| fig-cap: "Energy-based conditional samples."
+#| label: fig-energy-multi
+
+niter = 100
+nsamples = 100
+M = CCE.ConformalModel(conf_model, mach.fitresult; likelihood=:classification_multi)
+
+plts = []
+for target ∈ counterfactual_data.y_levels
+ sampler = CCE.EnergySampler(M, counterfactual_data, target; niter=niter, nsamples=100)
+ Xgen = rand(sampler, nsamples)
+ plt = plot(M, counterfactual_data; target=target, zoom=-0.5,cbar=false)
+ scatter!(Xgen[1,:],Xgen[2,:],alpha=0.5,color=target,shape=:star,label="X|y=$target")
+ push!(plts, plt)
+end
+plot(plts..., layout=(1,length(plts)), size=(img_height*length(plts),img_height))
+```
+
+```{julia}
+
+x = select_factual(counterfactual_data,rand(1:size(counterfactual_data.X,2)))
+y_factual = predict_label(M, counterfactual_data, x)[1]
+target = counterfactual_data.y_levels[counterfactual_data.y_levels .!= y_factual][1]
+```
+
+
+```{julia}
+counterfactuals = Dict([name => generate_counterfactual(x, target, counterfactual_data, M, gen; initialization=:identity, converge_when=:generator_conditions, gradient_tol=1e-3) for (name, gen) in generators])
+
+# Plots:
+plts = []
+for name ∈ ordered_names
+ ce = counterfactuals[name]
+ plt = plot(ce; title=name, colorbar=false, ticks = false, legend=false, zoom=-0.5)
+ if :energy_sampler ∈ collect(keys(ce.params))
+ Xgen = ce.params[:energy_sampler].buffer
+ scatter!(Xgen[1,:],Xgen[2,:],alpha=0.5,color=target,shape=:star,label="X|y=$target")
+ end
+ plts = vcat(plts..., plt)
+end
+_n = length(generators)
+plot(plts..., size=(_n * img_height,1.05*img_height), layout=(1,_n))
+```
-See @knuth84 for additional discussion of literate programming.
diff --git a/notebooks/proposal.html b/notebooks/proposal.html
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+ <a href="/notebooks/proposal.html" class="sidebar-item-text sidebar-link active"><span class='chapter-number'>1</span>Â <span class='chapter-title'>High-Fidelity Counterfactual Explanations through Conformal Prediction</span></a>
+ </div>
+</li>
+ <li class="sidebar-item">
+ <div class="sidebar-item-container">
+ <a href="/notebooks/intro.html" class="sidebar-item-text sidebar-link"><span class='chapter-number'>2</span>Â <span class='chapter-title'>`ConformalGenerator`</span></a>
+ </div>
+</li>
+ <li class="sidebar-item">
+ <div class="sidebar-item-container">
+ <a href="/notebooks/references.html" class="sidebar-item-text sidebar-link">References</a>
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+</li>
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+<div class="quarto-title">
+<h1 class="title"><span class="chapter-number">1</span>Â <span class="chapter-title">High-Fidelity Counterfactual Explanations through Conformal Prediction</span></h1>
+<p class="subtitle lead">Research Proposal</p>
+</div>
+
+
+
+<div class="quarto-title-meta">
+
+
+
+
+ </div>
+
+<div>
+ <div class="abstract">
+ <div class="abstract-title">Abstract</div>
+ <p>We propose Conformal Counterfactual Explanations: an effortless and rigorous way to produce realistic and faithful Counterfactual Explanations using Conformal Prediction. To address the need for realistic counterfactuals, existing work has primarily relied on separate generative models to learn the data-generating process. While this is an effective way to produce plausible and model-agnostic counterfactual explanations, it not only introduces a significant engineering overhead but also reallocates the task of creating realistic model explanations from the model itself to the generative model. Recent work has shown that there is no need for any of this when working with probabilistic models that explicitly quantify their own uncertainty. Unfortunately, most models used in practice still do not fulfil that basic requirement, in which case we would like to have a way to quantify predictive uncertainty in a post-hoc fashion.</p>
+ </div>
+</div>
+
+</header>
+<nav id="TOC" role="doc-toc">
+ <h2 id="toc-title">Table of contents</h2>
+
+ <ul>
+ <li><a href="#motivation" id="toc-motivation"><span class="header-section-number">1.1</span> Motivation</a>
+ <ul>
+ <li><a href="#counterfactual-explanations-or-adversarial-examples" id="toc-counterfactual-explanations-or-adversarial-examples"><span class="header-section-number">1.1.1</span> Counterfactual Explanations or Adversarial Examples?</a></li>
+ <li><a href="#sec-fidelity" id="toc-sec-fidelity"><span class="header-section-number">1.1.2</span> From Plausible to High-Fidelity Counterfactuals</a></li>
+ </ul></li>
+ <li><a href="#conformal-counterfactual-explanations" id="toc-conformal-counterfactual-explanations"><span class="header-section-number">1.2</span> Conformal Counterfactual Explanations</a>
+ <ul>
+ <li><a href="#minimizing-predictive-uncertainty" id="toc-minimizing-predictive-uncertainty"><span class="header-section-number">1.2.1</span> Minimizing Predictive Uncertainty</a></li>
+ <li><a href="#background-on-conformal-prediction" id="toc-background-on-conformal-prediction"><span class="header-section-number">1.2.2</span> Background on Conformal Prediction</a></li>
+ <li><a href="#generating-conformal-counterfactuals" id="toc-generating-conformal-counterfactuals"><span class="header-section-number">1.2.3</span> Generating Conformal Counterfactuals</a></li>
+ </ul></li>
+ <li><a href="#experiments" id="toc-experiments"><span class="header-section-number">1.3</span> Experiments</a>
+ <ul>
+ <li><a href="#research-questions" id="toc-research-questions"><span class="header-section-number">1.3.1</span> Research Questions</a></li>
+ </ul></li>
+ <li><a href="#references" id="toc-references"><span class="header-section-number">1.4</span> References</a></li>
+ </ul>
+</nav>
+<section id="motivation" class="level2" data-number="1.1">
+<h2 data-number="1.1"><span class="header-section-number">1.1</span> Motivation</h2>
+<p>Counterfactual Explanations are a powerful, flexible and intuitive way to not only explain black-box models but also enable affected individuals to challenge them through the means of Algorithmic Recourse.</p>
+<section id="counterfactual-explanations-or-adversarial-examples" class="level3" data-number="1.1.1">
+<h3 data-number="1.1.1"><span class="header-section-number">1.1.1</span> Counterfactual Explanations or Adversarial Examples?</h3>
+<p>Most state-of-the-art approaches to generating Counterfactual Explanations (CE) rely on gradient descent in the feature space. The key idea is to perturb inputs <span class="math inline">\(x\in\mathcal{X}\)</span> into a black-box model <span class="math inline">\(f: \mathcal{X} \mapsto \mathcal{Y}\)</span> in order to change the model output <span class="math inline">\(f(x)\)</span> to some pre-specified target value <span class="math inline">\(t\in\mathcal{Y}\)</span>. Formally, this boils down to defining some loss function <span class="math inline">\(\ell(f(x),t)\)</span> and taking gradient steps in the minimizing direction. The so-generated counterfactuals are considered valid as soon as the predicted label matches the target label. A stripped-down counterfactual explanation is therefore little different from an adversarial example. In <a href="#fig-adv">Figure <span class="quarto-unresolved-ref">fig-adv</span></a>, for example, generic counterfactual search as in <span class="citation" data-cites="wachter2017counterfactual">Wachter, Mittelstadt, and Russell (<a href="#ref-wachter2017counterfactual" role="doc-biblioref">2017</a>)</span> has been applied to MNIST data.</p>
+<div id="fig-adv" class="quarto-figure quarto-figure-center">
+<figure>
+<p><img src="www/you_may_not_like_it.png" class="img-fluid" /></p>
+<p><figcaption>Figure 1.1: You may not like it, but this is what stripped-down counterfactuals look like. Here we have used <span class="citation" data-cites="wachter2017counterfactual">Wachter, Mittelstadt, and Russell (<a href="#ref-wachter2017counterfactual" role="doc-biblioref">2017</a>)</span> to generate multiple counterfactuals for turning an 8 (eight) into a 3 (three).</figcaption></p>
+</figure>
+</div>
+<p>The crucial difference between adversarial examples and counterfactuals is one of intent. While adversarial examples are typically intended to go unnoticed, counterfactuals in the context of Explainable AI are generally sought to be “plausibleâ€, “realistic†or “feasibleâ€. To fulfil this latter goal, researchers have come up with a myriad of ways. <span class="citation" data-cites="joshi2019realistic">Joshi et al. (<a href="#ref-joshi2019realistic" role="doc-biblioref">2019</a>)</span> were among the first to suggest that instead of searching counterfactuals in the feature space, we can instead traverse a latent embedding learned by a surrogate generative model. Similarly, <span class="citation" data-cites="poyiadzi2020face">Poyiadzi et al. (<a href="#ref-poyiadzi2020face" role="doc-biblioref">2020</a>)</span> use density … Finally, <span class="citation" data-cites="karimi2021algorithmic">Karimi, Schölkopf, and Valera (<a href="#ref-karimi2021algorithmic" role="doc-biblioref">2021</a>)</span> argues that counterfactuals should comply with the causal model that generates them [CHECK IF WE CAN PHASE THIS LIKE THIS]. Other related approaches include … All of these different approaches have a common goal: they aim to ensure that the generated counterfactuals comply with the (learned) data-generating process (DGB).</p>
+<div id="def-plausible" class="theorem definition">
+<p><span class="theorem-title"><strong>Definition 1.1 (Plausible Counterfactuals) </strong></span>Formally, if <span class="math inline">\(x \sim \mathcal{X}\)</span> and for the corresponding counterfactual we have <span class="math inline">\(x^{\prime}\sim\mathcal{X}^{\prime}\)</span>, then for <span class="math inline">\(x^{\prime}\)</span> to be considered a plausible counterfactual, we need: <span class="math inline">\(\mathcal{X} \approxeq \mathcal{X}^{\prime}\)</span>.</p>
+</div>
+<p>In the context of Algorithmic Recourse, it makes sense to strive for plausible counterfactuals, since anything else would essentially require individuals to move to out-of-distribution states. But it is worth noting that our ambition to meet this goal, may have implications on our ability to faithfully explain the behaviour of the underlying black-box model (arguably our principal goal). By essentially decoupling the task of learning plausible representations of the data from the model itself, we open ourselves up to vulnerabilities. Using a separate generative model to learn <span class="math inline">\(\mathcal{X}\)</span>, for example, has very serious implications for the generated counterfactuals. <a href="#fig-latent">Figure <span class="quarto-unresolved-ref">fig-latent</span></a> compares the results of applying REVISE <span class="citation" data-cites="joshi2019realistic">(<a href="#ref-joshi2019realistic" role="doc-biblioref">Joshi et al. 2019</a>)</span> to MNIST data using two different Variational Auto-Encoders: while the counterfactual generated using an expressive (strong) VAE is compelling, the result relying on a less expressive (weak) VAE is not even valid. In this latter case, the decoder step of the VAE fails to yield values in <span class="math inline">\(\mathcal{X}\)</span> and hence the counterfactual search in the learned latent space is doomed.</p>
+<div id="fig-latent" class="quarto-figure quarto-figure-center">
+<figure>
+<p><img src="www/mnist_9to4_latent.png" class="img-fluid" /></p>
+<p><figcaption>Figure 1.2: Counterfactual explanations for MNIST using a Latent Space generator: turning a nine (9) into a four (4).</figcaption></p>
+</figure>
+</div>
+<blockquote>
+<p>Here it would be nice to have another example where we poison the data going into the generative model to hide biases present in the data (e.g. Boston housing).</p>
+</blockquote>
+<ul>
+<li>Latent can be manipulated:
+<ul>
+<li>train biased model</li>
+<li>train VAE with biased variable removed/attacked (use Boston housing dataset)</li>
+<li>hypothesis: will generate bias-free explanations</li>
+</ul></li>
+</ul>
+</section>
+<section id="sec-fidelity" class="level3" data-number="1.1.2">
+<h3 data-number="1.1.2"><span class="header-section-number">1.1.2</span> From Plausible to High-Fidelity Counterfactuals</h3>
+<p>In light of the findings, we propose to generally avoid using surrogate models to learn <span class="math inline">\(\mathcal{X}\)</span> in the context of Counterfactual Explanations.</p>
+<div id="prp-surrogate" class="theorem proposition">
+<p><span class="theorem-title"><strong>Proposition 1.1 (Avoid Surrogates) </strong></span>Since we are in the business of explaining a black-box model, the task of learning realistic representations of the data should not be reallocated from the model itself to some surrogate model.</p>
+</div>
+<p>In cases where the use of surrogate models cannot be avoided, we propose to weigh the plausibility of counterfactuals against their fidelity to the black-box model. In the context of Explainable AI, fidelity is defined as describing how an explanation approximates the prediction of the black-box model <span class="citation" data-cites="molnar2020interpretable">(<a href="#ref-molnar2020interpretable" role="doc-biblioref">Molnar 2020</a>)</span>. Fidelity has become the default metric for evaluating Local Model-Agnostic Models, since they often involve local surrogate models whose predictions need not always match those of the black-box model.</p>
+<p>In the case of Counterfactual Explanations, the concept of fidelity has so far been ignored. This is not altogether surprising, since by construction and design, Counterfactual Explanations work with the predictions of the black-box model directly: as stated above, a counterfactual <span class="math inline">\(x^{\prime}\)</span> is considered valid if and only if <span class="math inline">\(f(x^{\prime})=t\)</span>, where <span class="math inline">\(t\)</span> denote some target outcome.</p>
+<p>Does fidelity even make sense in the context of CE, and if so, how can we define it? In light of the examples in the previous section, we think it is urgent to introduce a notion of fidelity in this context, that relates to the distributional properties of the generated counterfactuals. In particular, we propose that a high-fidelity counterfactual <span class="math inline">\(x^{\prime}\)</span> complies with the class-conditional distribution <span class="math inline">\(\mathcal{X}_{\theta} = p_{\theta}(X|y)\)</span> where <span class="math inline">\(\theta\)</span> denote the black-box model parameters.</p>
+<div id="def-fidele" class="theorem definition">
+<p><span class="theorem-title"><strong>Definition 1.2 (High-Fidelity Counterfactuals) </strong></span>Let <span class="math inline">\(\mathcal{X}_{\theta}|y = p_{\theta}(X|y)\)</span> denote the class-conditional distribution of <span class="math inline">\(X\)</span> defined by <span class="math inline">\(\theta\)</span>. Then for <span class="math inline">\(x^{\prime}\)</span> to be considered a high-fidelity counterfactual, we need: <span class="math inline">\(\mathcal{X}_{\theta}|t \approxeq \mathcal{X}^{\prime}\)</span> where <span class="math inline">\(t\)</span> denotes the target outcome.</p>
+</div>
+<p>In order to assess the fidelity of counterfactuals, we propose the following two-step procedure:</p>
+<ol type="1">
+<li>Generate samples <span class="math inline">\(X_{\theta}|y\)</span> and <span class="math inline">\(X^{\prime}\)</span> from <span class="math inline">\(\mathcal{X}_{\theta}|t\)</span> and <span class="math inline">\(\mathcal{X}^{\prime}\)</span>, respectively.</li>
+<li>Compute the Maximum Mean Discrepancy (MMD) between <span class="math inline">\(X_{\theta}|y\)</span> and <span class="math inline">\(X^{\prime}\)</span>.</li>
+</ol>
+<p>If the computed value is different from zero, we can reject the null-hypothesis of fidelity.</p>
+<blockquote>
+<p>Two challenges here: 1) implementing the sampling procedure in <span class="citation" data-cites="grathwohl2020your">Grathwohl et al. (<a href="#ref-grathwohl2020your" role="doc-biblioref">2020</a>)</span>; 2) it is unclear if MMD is really the right way to measure this.</p>
+</blockquote>
+</section>
+</section>
+<section id="conformal-counterfactual-explanations" class="level2" data-number="1.2">
+<h2 data-number="1.2"><span class="header-section-number">1.2</span> Conformal Counterfactual Explanations</h2>
+<p>In <a href="#sec-fidelity"><span class="quarto-unresolved-ref">sec-fidelity</span></a>, we have advocated for avoiding surrogate models in the context of Counterfactual Explanations. In this section, we introduce an alternative way to generate high-fidelity Counterfactual Explanations. In particular, we propose Conformal Counterfactual Explanations (CCE), that is Counterfactual Explanations that minimize the predictive uncertainty of conformal models.</p>
+<section id="minimizing-predictive-uncertainty" class="level3" data-number="1.2.1">
+<h3 data-number="1.2.1"><span class="header-section-number">1.2.1</span> Minimizing Predictive Uncertainty</h3>
+<p><span class="citation" data-cites="schut2021generating">Schut et al. (<a href="#ref-schut2021generating" role="doc-biblioref">2021</a>)</span> demonstrated that the goal of generating realistic (plausible) counterfactuals can also be achieved by seeking counterfactuals that minimize the predictive uncertainty of the underlying black-box model. Similarly, <span class="citation" data-cites="antoran2020getting">Antorán et al. (<a href="#ref-antoran2020getting" role="doc-biblioref">2020</a>)</span> …</p>
+<ul>
+<li>Problem: restricted to Bayesian models.</li>
+<li>Solution: post-hoc predictive uncertainty quantification. In particular, Conformal Prediction.</li>
+</ul>
+</section>
+<section id="background-on-conformal-prediction" class="level3" data-number="1.2.2">
+<h3 data-number="1.2.2"><span class="header-section-number">1.2.2</span> Background on Conformal Prediction</h3>
+<ul>
+<li>Distribution-free, model-agnostic and scalable approach to predictive uncertainty quantification.</li>
+<li>Conformal prediction is instance-based. So is CE.</li>
+<li>Take any fitted model and turn it into a conformal model using calibration data.</li>
+<li>Our approach, therefore, relaxes the restriction on the family of black-box models, at the cost of relying on a subset of the data. Arguably, data is often abundant and in most applications practitioners tend to hold out a test data set anyway.</li>
+</ul>
+<blockquote>
+<p>Does the coverage guarantee carry over to counterfactuals?</p>
+</blockquote>
+</section>
+<section id="generating-conformal-counterfactuals" class="level3" data-number="1.2.3">
+<h3 data-number="1.2.3"><span class="header-section-number">1.2.3</span> Generating Conformal Counterfactuals</h3>
+<p>While Conformal Prediction has recently grown in popularity, it does introduce a challenge in the context of classification: the predictions of Conformal Classifiers are set-valued and therefore difficult to work with, since they are, for example, non-differentiable. Fortunately, <span class="citation" data-cites="stutz2022learning">Stutz et al. (<a href="#ref-stutz2022learning" role="doc-biblioref">2022</a>)</span> introduced carefully designed differentiable loss functions that make it possible to evaluate the performance of conformal predictions in training. We can leverage these recent advances in the context of gradient-based counterfactual search …</p>
+<blockquote>
+<p>Challenge: still need to implement these loss functions.</p>
+</blockquote>
+</section>
+</section>
+<section id="experiments" class="level2" data-number="1.3">
+<h2 data-number="1.3"><span class="header-section-number">1.3</span> Experiments</h2>
+<section id="research-questions" class="level3" data-number="1.3.1">
+<h3 data-number="1.3.1"><span class="header-section-number">1.3.1</span> Research Questions</h3>
+<ul>
+<li><p>Is CP alone enough to ensure realistic counterfactuals?</p></li>
+<li><p>Do counterfactuals improve further as the models get better?</p></li>
+<li><p>Do counterfactuals get more realistic as coverage</p></li>
+<li><p>What happens as we vary coverage and setsize?</p></li>
+<li><p>What happens as we improve the model robustness?</p></li>
+<li><p>What happens as we improve the model’s ability to incorporate predictive uncertainty (deep ensemble, laplace)?</p></li>
+<li><p>What happens if we combine with DiCE, ClaPROAR, Gravitational?</p></li>
+<li><p>What about CE robustness to endogenous shifts <span class="citation" data-cites="altmeyer2023endogenous">(<a href="#ref-altmeyer2023endogenous" role="doc-biblioref">Altmeyer et al. 2023</a>)</span>?</p></li>
+<li><p>Benchmarking:</p>
+<ul>
+<li>add PROBE <span class="citation" data-cites="pawelczyk2022probabilistically">(<a href="#ref-pawelczyk2022probabilistically" role="doc-biblioref">Pawelczyk et al. 2022</a>)</span> into the mix.</li>
+<li>compare travel costs to domain shits.</li>
+</ul></li>
+</ul>
+<blockquote>
+<p>Nice to have: What about using Laplace Approximation, then Conformal Prediction? What about using Conformalised Laplace?</p>
+</blockquote>
+</section>
+</section>
+<section id="references" class="level2" data-number="1.4">
+<h2 data-number="1.4"><span class="header-section-number">1.4</span> References</h2>
+<div id="quarto-navigation-envelope" class="hidden">
+<p><span class="hidden" data-render-id="quarto-int-sidebar-title">Conformal Counterfactual Explanations</span> <span class="hidden" data-render-id="quarto-int-navbar-title">Conformal Counterfactual Explanations</span> <span class="hidden" data-render-id="quarto-int-next"><span class="chapter-number">2</span>Â <span class="chapter-title"><code>ConformalGenerator</code></span></span> <span class="hidden" data-render-id="quarto-int-prev">Preface</span> <span class="hidden" data-render-id="quarto-int-sidebar:/index.html">Preface</span> <span class="hidden" data-render-id="quarto-int-sidebar:/notebooks/proposal.html"><span class="chapter-number">1</span>Â <span class="chapter-title">High-Fidelity Counterfactual Explanations through Conformal Prediction</span></span> <span class="hidden" data-render-id="quarto-int-sidebar:/notebooks/intro.html"><span class="chapter-number">2</span>Â <span class="chapter-title"><code>ConformalGenerator</code></span></span> <span class="hidden" data-render-id="quarto-int-sidebar:/notebooks/references.html">References</span></p>
+</div>
+<div id="quarto-meta-markdown" class="hidden">
+<p><span class="hidden" data-render-id="quarto-metatitle">Conformal Counterfactual Explanations - <span class="chapter-number">1</span>Â <span class="chapter-title">High-Fidelity Counterfactual Explanations through Conformal Prediction</span></span> <span class="hidden" data-render-id="quarto-twittercardtitle">Conformal Counterfactual Explanations - <span class="chapter-number">1</span>Â <span class="chapter-title">High-Fidelity Counterfactual Explanations through Conformal Prediction</span></span> <span class="hidden" data-render-id="quarto-ogcardtitle">Conformal Counterfactual Explanations - <span class="chapter-number">1</span>Â <span class="chapter-title">High-Fidelity Counterfactual Explanations through Conformal Prediction</span></span> <span class="hidden" data-render-id="quarto-metasitename">Conformal Counterfactual Explanations</span> <span class="hidden" data-render-id="quarto-twittercarddesc">We propose Conformal Counterfactual Explanations: an effortless and rigorous way to produce realistic and faithful Counterfactual Explanations using Conformal Prediction. To address the need for realistic counterfactuals, existing work has primarily relied on separate generative models to learn the data-generating process. While this is an effective way to produce plausible and model-agnostic counterfactual explanations, it not only introduces a significant engineering overhead but also reallocates the task of creating realistic model explanations from the model itself to the generative model. Recent work has shown that there is no need for any of this when working with probabilistic models that explicitly quantify their own uncertainty. Unfortunately, most models used in practice still do not fulfil that basic requirement, in which case we would like to have a way to quantify predictive uncertainty in a post-hoc fashion.</span> <span class="hidden" data-render-id="quarto-ogcardddesc">We propose Conformal Counterfactual Explanations: an effortless and rigorous way to produce realistic and faithful Counterfactual Explanations using Conformal Prediction. To address the need for realistic counterfactuals, existing work has primarily relied on separate generative models to learn the data-generating process. While this is an effective way to produce plausible and model-agnostic counterfactual explanations, it not only introduces a significant engineering overhead but also reallocates the task of creating realistic model explanations from the model itself to the generative model. Recent work has shown that there is no need for any of this when working with probabilistic models that explicitly quantify their own uncertainty. Unfortunately, most models used in practice still do not fulfil that basic requirement, in which case we would like to have a way to quantify predictive uncertainty in a post-hoc fashion.</span></p>
+</div>
+<div id="refs" class="references csl-bib-body hanging-indent" role="list">
+<div id="ref-altmeyer2023endogenous" class="csl-entry" role="listitem">
+Altmeyer, Patrick, Giovan Angela, Aleksander Buszydlik, Karol Dobiczek, Arie van Deursen, and Cynthia Liem. 2023. <span>“Endogenous <span>Macrodynamics</span> in <span>Algorithmic</span> <span>Recourse</span>.â€</span> In <em>First <span>IEEE</span> <span>Conference</span> on <span>Secure</span> and <span>Trustworthy</span> <span>Machine</span> <span>Learning</span></em>.
+</div>
+<div id="ref-antoran2020getting" class="csl-entry" role="listitem">
+Antorán, Javier, Umang Bhatt, Tameem Adel, Adrian Weller, and José Miguel Hernández-Lobato. 2020. <span>“Getting a Clue: <span>A</span> Method for Explaining Uncertainty Estimates.â€</span> <a href="https://arxiv.org/abs/2006.06848">https://arxiv.org/abs/2006.06848</a>.
+</div>
+<div id="ref-grathwohl2020your" class="csl-entry" role="listitem">
+Grathwohl, Will, Kuan-Chieh Wang, Joern-Henrik Jacobsen, David Duvenaud, Mohammad Norouzi, and Kevin Swersky. 2020. <span>“Your Classifier Is Secretly an Energy Based Model and You Should Treat It Like One.â€</span> In. <a href="https://openreview.net/forum?id=Hkxzx0NtDB">https://openreview.net/forum?id=Hkxzx0NtDB</a>.
+</div>
+<div id="ref-joshi2019realistic" class="csl-entry" role="listitem">
+Joshi, Shalmali, Oluwasanmi Koyejo, Warut Vijitbenjaronk, Been Kim, and Joydeep Ghosh. 2019. <span>“Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systems.â€</span> <a href="https://arxiv.org/abs/1907.09615">https://arxiv.org/abs/1907.09615</a>.
+</div>
+<div id="ref-karimi2021algorithmic" class="csl-entry" role="listitem">
+Karimi, Amir-Hossein, Bernhard Schölkopf, and Isabel Valera. 2021. <span>“Algorithmic Recourse: From Counterfactual Explanations to Interventions.â€</span> In <em>Proceedings of the 2021 <span>ACM Conference</span> on <span>Fairness</span>, <span>Accountability</span>, and <span>Transparency</span></em>, 353–62.
+</div>
+<div id="ref-molnar2020interpretable" class="csl-entry" role="listitem">
+Molnar, Christoph. 2020. <em>Interpretable Machine Learning</em>. <span>Lulu. com</span>.
+</div>
+<div id="ref-pawelczyk2022probabilistically" class="csl-entry" role="listitem">
+Pawelczyk, Martin, Teresa Datta, Johannes van-den-Heuvel, Gjergji Kasneci, and Himabindu Lakkaraju. 2022. <span>“Probabilistically <span>Robust</span> <span>Recourse</span>: <span>Navigating</span> the <span>Trade</span>-Offs Between <span>Costs</span> and <span>Robustness</span> in <span>Algorithmic</span> <span>Recourse</span>.â€</span> <em>arXiv Preprint arXiv:2203.06768</em>.
+</div>
+<div id="ref-poyiadzi2020face" class="csl-entry" role="listitem">
+Poyiadzi, Rafael, Kacper Sokol, Raul Santos-Rodriguez, Tijl De Bie, and Peter Flach. 2020. <span>“<span>FACE</span>: <span>Feasible</span> and Actionable Counterfactual Explanations.â€</span> In <em>Proceedings of the <span>AAAI</span>/<span>ACM Conference</span> on <span>AI</span>, <span>Ethics</span>, and <span>Society</span></em>, 344–50.
+</div>
+<div id="ref-schut2021generating" class="csl-entry" role="listitem">
+Schut, Lisa, Oscar Key, Rory Mc Grath, Luca Costabello, Bogdan Sacaleanu, Yarin Gal, et al. 2021. <span>“Generating <span>Interpretable Counterfactual Explanations By Implicit Minimisation</span> of <span>Epistemic</span> and <span>Aleatoric Uncertainties</span>.â€</span> In <em>International <span>Conference</span> on <span>Artificial Intelligence</span> and <span>Statistics</span></em>, 1756–64. <span>PMLR</span>.
+</div>
+<div id="ref-stutz2022learning" class="csl-entry" role="listitem">
+Stutz, David, Krishnamurthy Dj Dvijotham, Ali Taylan Cemgil, and Arnaud Doucet. 2022. <span>“Learning <span>Optimal</span> <span>Conformal</span> <span>Classifiers</span>.â€</span> In. <a href="https://openreview.net/forum?id=t8O-4LKFVx">https://openreview.net/forum?id=t8O-4LKFVx</a>.
+</div>
+<div id="ref-wachter2017counterfactual" class="csl-entry" role="listitem">
+Wachter, Sandra, Brent Mittelstadt, and Chris Russell. 2017. <span>“Counterfactual Explanations Without Opening the Black Box: <span>Automated</span> Decisions and the <span>GDPR</span>.â€</span> <em>Harv. JL & Tech.</em> 31: 841.
+</div>
+</div>
+</section>
+
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+ "---\n",
+ "title: High-Fidelity Counterfactual Explanations through Conformal Prediction\n",
+ "subtitle: Research Proposal\n",
+ "abstract: |\n",
+ " We propose Conformal Counterfactual Explanations: an effortless and rigorous way to produce realistic and faithful Counterfactual Explanations using Conformal Prediction. To address the need for realistic counterfactuals, existing work has primarily relied on separate generative models to learn the data-generating process. While this is an effective way to produce plausible and model-agnostic counterfactual explanations, it not only introduces a significant engineering overhead but also reallocates the task of creating realistic model explanations from the model itself to the generative model. Recent work has shown that there is no need for any of this when working with probabilistic models that explicitly quantify their own uncertainty. Unfortunately, most models used in practice still do not fulfil that basic requirement, in which case we would like to have a way to quantify predictive uncertainty in a post-hoc fashion.\n",
+ "---"
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+ "include(\"notebooks/setup.jl\")\n",
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+ "source": [
+ "## Motivation\n",
+ "\n",
+ "Counterfactual Explanations are a powerful, flexible and intuitive way to not only explain black-box models but also enable affected individuals to challenge them through the means of Algorithmic Recourse. \n",
+ "\n",
+ "### Counterfactual Explanations or Adversarial Examples?\n",
+ "\n",
+ "Most state-of-the-art approaches to generating Counterfactual Explanations (CE) rely on gradient descent in the feature space. The key idea is to perturb inputs $x\\in\\mathcal{X}$ into a black-box model $f: \\mathcal{X} \\mapsto \\mathcal{Y}$ in order to change the model output $f(x)$ to some pre-specified target value $t\\in\\mathcal{Y}$. Formally, this boils down to defining some loss function $\\ell(f(x),t)$ and taking gradient steps in the minimizing direction. The so-generated counterfactuals are considered valid as soon as the predicted label matches the target label. A stripped-down counterfactual explanation is therefore little different from an adversarial example. In @fig-adv, for example, generic counterfactual search as in @wachter2017counterfactual has been applied to MNIST data.\n"
+ ],
+ "id": "a244bee7"
+ },
+ {
+ "cell_type": "code",
+ "metadata": {},
+ "source": [
+ "# Data:\n",
+ "counterfactual_data = load_mnist()\n",
+ "X, y = CounterfactualExplanations.DataPreprocessing.unpack_data(counterfactual_data)\n",
+ "input_dim, n_obs = size(counterfactual_data.X)\n",
+ "M = load_mnist_mlp()\n",
+ "# Target:\n",
+ "factual_label = 8\n",
+ "x = reshape(X[:,rand(findall(predict_label(M, counterfactual_data).==factual_label))],input_dim,1)\n",
+ "target = 3\n",
+ "factual = predict_label(M, counterfactual_data, x)[1]\n",
+ "# Search:\n",
+ "n_ce = 3\n",
+ "generator = GenericGenerator()\n",
+ "ces = generate_counterfactual(x, target, counterfactual_data, M, generator; num_counterfactuals=n_ce)"
+ ],
+ "id": "687cba09",
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {},
+ "source": [
+ "image_size = 200\n",
+ "p1 = plot(\n",
+ " convert2image(MNIST, reshape(x,28,28)),\n",
+ " axis=nothing, \n",
+ " size=(image_size, image_size),\n",
+ " title=\"Factual\"\n",
+ ")\n",
+ "plts = [p1]\n",
+ "\n",
+ "counterfactuals = CounterfactualExplanations.counterfactual(ces)\n",
+ "phat = target_probs(ces)\n",
+ "for x in zip(eachslice(counterfactuals; dims=3), eachslice(phat; dims=3))\n",
+ " ce, _phat = (x[1],x[2])\n",
+ " _title = \"p(y=$(target)|x′)=$(round(_phat[1]; digits=3))\"\n",
+ " plt = plot(\n",
+ " convert2image(MNIST, reshape(ce,28,28)),\n",
+ " axis=nothing, \n",
+ " size=(image_size, image_size),\n",
+ " title=_title\n",
+ " )\n",
+ " plts = [plts..., plt]\n",
+ "end\n",
+ "plt = plot(plts...; size=(image_size * (n_ce + 1),image_size), layout=(1,(n_ce + 1)))\n",
+ "savefig(plt, joinpath(www_path, \"you_may_not_like_it.png\"))"
+ ],
+ "id": "fda7c987",
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "{#fig-adv}\n",
+ "\n",
+ "The crucial difference between adversarial examples and counterfactuals is one of intent. While adversarial examples are typically intended to go unnoticed, counterfactuals in the context of Explainable AI are generally sought to be \"plausible\", \"realistic\" or \"feasible\". To fulfil this latter goal, researchers have come up with a myriad of ways. @joshi2019realistic were among the first to suggest that instead of searching counterfactuals in the feature space, we can instead traverse a latent embedding learned by a surrogate generative model. Similarly, @poyiadzi2020face use density ... Finally, @karimi2021algorithmic argues that counterfactuals should comply with the causal model that generates them [CHECK IF WE CAN PHASE THIS LIKE THIS]. Other related approaches include ... All of these different approaches have a common goal: they aim to ensure that the generated counterfactuals comply with the (learned) data-generating process (DGB). \n",
+ "\n",
+ "::: {#def-plausible}\n",
+ "\n",
+ "## Plausible Counterfactuals\n",
+ "\n",
+ "Formally, if $x \\sim \\mathcal{X}$ and for the corresponding counterfactual we have $x^{\\prime}\\sim\\mathcal{X}^{\\prime}$, then for $x^{\\prime}$ to be considered a plausible counterfactual, we need: $\\mathcal{X} \\approxeq \\mathcal{X}^{\\prime}$.\n",
+ "\n",
+ ":::\n",
+ "\n",
+ "In the context of Algorithmic Recourse, it makes sense to strive for plausible counterfactuals, since anything else would essentially require individuals to move to out-of-distribution states. But it is worth noting that our ambition to meet this goal, may have implications on our ability to faithfully explain the behaviour of the underlying black-box model (arguably our principal goal). By essentially decoupling the task of learning plausible representations of the data from the model itself, we open ourselves up to vulnerabilities. Using a separate generative model to learn $\\mathcal{X}$, for example, has very serious implications for the generated counterfactuals. @fig-latent compares the results of applying REVISE [@joshi2019realistic] to MNIST data using two different Variational Auto-Encoders: while the counterfactual generated using an expressive (strong) VAE is compelling, the result relying on a less expressive (weak) VAE is not even valid. In this latter case, the decoder step of the VAE fails to yield values in $\\mathcal{X}$ and hence the counterfactual search in the learned latent space is doomed. \n",
+ "\n",
+ "{#fig-latent}\n",
+ "\n",
+ "> Here it would be nice to have another example where we poison the data going into the generative model to hide biases present in the data (e.g. Boston housing).\n",
+ "\n",
+ "- Latent can be manipulated: \n",
+ " - train biased model\n",
+ " - train VAE with biased variable removed/attacked (use Boston housing dataset)\n",
+ " - hypothesis: will generate bias-free explanations\n",
+ "\n",
+ "### From Plausible to High-Fidelity Counterfactuals {#sec-fidelity}\n",
+ "\n",
+ "In light of the findings, we propose to generally avoid using surrogate models to learn $\\mathcal{X}$ in the context of Counterfactual Explanations.\n",
+ "\n",
+ "::: {#prp-surrogate}\n",
+ "\n",
+ "## Avoid Surrogates\n",
+ "\n",
+ "Since we are in the business of explaining a black-box model, the task of learning realistic representations of the data should not be reallocated from the model itself to some surrogate model.\n",
+ "\n",
+ ":::\n",
+ "\n",
+ "In cases where the use of surrogate models cannot be avoided, we propose to weigh the plausibility of counterfactuals against their fidelity to the black-box model. In the context of Explainable AI, fidelity is defined as describing how an explanation approximates the prediction of the black-box model [@molnar2020interpretable]. Fidelity has become the default metric for evaluating Local Model-Agnostic Models, since they often involve local surrogate models whose predictions need not always match those of the black-box model. \n",
+ "\n",
+ "In the case of Counterfactual Explanations, the concept of fidelity has so far been ignored. This is not altogether surprising, since by construction and design, Counterfactual Explanations work with the predictions of the black-box model directly: as stated above, a counterfactual $x^{\\prime}$ is considered valid if and only if $f(x^{\\prime})=t$, where $t$ denote some target outcome. \n",
+ "\n",
+ "Does fidelity even make sense in the context of CE, and if so, how can we define it? In light of the examples in the previous section, we think it is urgent to introduce a notion of fidelity in this context, that relates to the distributional properties of the generated counterfactuals. In particular, we propose that a high-fidelity counterfactual $x^{\\prime}$ complies with the class-conditional distribution $\\mathcal{X}_{\\theta} = p_{\\theta}(X|y)$ where $\\theta$ denote the black-box model parameters. \n",
+ "\n",
+ "::: {#def-fidele}\n",
+ "\n",
+ "## High-Fidelity Counterfactuals\n",
+ "\n",
+ "Let $\\mathcal{X}_{\\theta}|y = p_{\\theta}(X|y)$ denote the class-conditional distribution of $X$ defined by $\\theta$. Then for $x^{\\prime}$ to be considered a high-fidelity counterfactual, we need: $\\mathcal{X}_{\\theta}|t \\approxeq \\mathcal{X}^{\\prime}$ where $t$ denotes the target outcome.\n",
+ "\n",
+ ":::\n",
+ "\n",
+ "In order to assess the fidelity of counterfactuals, we propose the following two-step procedure:\n",
+ "\n",
+ "1) Generate samples $X_{\\theta}|y$ and $X^{\\prime}$ from $\\mathcal{X}_{\\theta}|t$ and $\\mathcal{X}^{\\prime}$, respectively.\n",
+ "2) Compute the Maximum Mean Discrepancy (MMD) between $X_{\\theta}|y$ and $X^{\\prime}$. \n",
+ "\n",
+ "If the computed value is different from zero, we can reject the null-hypothesis of fidelity.\n",
+ "\n",
+ "> Two challenges here: 1) implementing the sampling procedure in @grathwohl2020your; 2) it is unclear if MMD is really the right way to measure this. \n",
+ "\n",
+ "## Conformal Counterfactual Explanations\n",
+ "\n",
+ "In @sec-fidelity, we have advocated for avoiding surrogate models in the context of Counterfactual Explanations. In this section, we introduce an alternative way to generate high-fidelity Counterfactual Explanations. In particular, we propose Conformal Counterfactual Explanations (CCE), that is Counterfactual Explanations that minimize the predictive uncertainty of conformal models. \n",
+ "\n",
+ "### Minimizing Predictive Uncertainty\n",
+ "\n",
+ "@schut2021generating demonstrated that the goal of generating realistic (plausible) counterfactuals can also be achieved by seeking counterfactuals that minimize the predictive uncertainty of the underlying black-box model. Similarly, @antoran2020getting ...\n",
+ "\n",
+ "- Problem: restricted to Bayesian models.\n",
+ "- Solution: post-hoc predictive uncertainty quantification. In particular, Conformal Prediction. \n",
+ "\n",
+ "### Background on Conformal Prediction\n",
+ "\n",
+ "- Distribution-free, model-agnostic and scalable approach to predictive uncertainty quantification.\n",
+ "- Conformal prediction is instance-based. So is CE. \n",
+ "- Take any fitted model and turn it into a conformal model using calibration data.\n",
+ "- Our approach, therefore, relaxes the restriction on the family of black-box models, at the cost of relying on a subset of the data. Arguably, data is often abundant and in most applications practitioners tend to hold out a test data set anyway. \n",
+ "\n",
+ "> Does the coverage guarantee carry over to counterfactuals?\n",
+ "\n",
+ "### Generating Conformal Counterfactuals\n",
+ "\n",
+ "While Conformal Prediction has recently grown in popularity, it does introduce a challenge in the context of classification: the predictions of Conformal Classifiers are set-valued and therefore difficult to work with, since they are, for example, non-differentiable. Fortunately, @stutz2022learning introduced carefully designed differentiable loss functions that make it possible to evaluate the performance of conformal predictions in training. We can leverage these recent advances in the context of gradient-based counterfactual search ...\n",
+ "\n",
+ "> Challenge: still need to implement these loss functions. \n",
+ "\n",
+ "## Experiments\n",
+ "\n",
+ "### Research Questions\n",
+ "\n",
+ "- Is CP alone enough to ensure realistic counterfactuals?\n",
+ "- Do counterfactuals improve further as the models get better?\n",
+ "- Do counterfactuals get more realistic as coverage\n",
+ "- What happens as we vary coverage and setsize?\n",
+ "- What happens as we improve the model robustness?\n",
+ "- What happens as we improve the model's ability to incorporate predictive uncertainty (deep ensemble, laplace)?\n",
+ "- What happens if we combine with DiCE, ClaPROAR, Gravitational?\n",
+ "- What about CE robustness to endogenous shifts [@altmeyer2023endogenous]?\n",
+ "\n",
+ "- Benchmarking:\n",
+ " - add PROBE [@pawelczyk2022probabilistically] into the mix.\n",
+ " - compare travel costs to domain shits.\n",
+ "\n",
+ "> Nice to have: What about using Laplace Approximation, then Conformal Prediction? What about using Conformalised Laplace? \n",
+ "\n",
+ "## References\n"
+ ],
+ "id": "cb684712"
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "name": "julia-(4-threads)-1.8",
+ "language": "julia",
+ "display_name": "Julia (4 threads) 1.8.3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/dev/proposal.qmd b/notebooks/proposal.qmd
similarity index 98%
rename from dev/proposal.qmd
rename to notebooks/proposal.qmd
index 08bdb0e73a427b874d46f3b7a2790ef8cb22bcec..4c4aa5c74ac5198834b6c296e5502792031ca1d2 100644
--- a/dev/proposal.qmd
+++ b/notebooks/proposal.qmd
@@ -6,14 +6,8 @@ abstract: |
---
```{julia}
-using CounterfactualExplanations
-using CounterfactualExplanations.Data: load_mnist
-using CounterfactualExplanations.Models: load_mnist_mlp
-using Images
-using MLDatasets
-using MLDatasets: convert2image
-using Plots
-www_path = "dev/www"
+include("notebooks/setup.jl")
+eval(setup_notebooks)
```
## Motivation
diff --git a/notebooks/references.bib b/notebooks/references.bib
deleted file mode 100644
index 0220dbdf2e2eedcc3ce546914ffbb890db023244..0000000000000000000000000000000000000000
--- a/notebooks/references.bib
+++ /dev/null
@@ -1,19 +0,0 @@
-@article{knuth84,
- author = {Knuth, Donald E.},
- title = {Literate Programming},
- year = {1984},
- issue_date = {May 1984},
- publisher = {Oxford University Press, Inc.},
- address = {USA},
- volume = {27},
- number = {2},
- issn = {0010-4620},
- url = {https://doi.org/10.1093/comjnl/27.2.97},
- doi = {10.1093/comjnl/27.2.97},
- journal = {Comput. J.},
- month = may,
- pages = {97–111},
- numpages = {15}
-}
-
-
diff --git a/notebooks/setup.jl b/notebooks/setup.jl
new file mode 100644
index 0000000000000000000000000000000000000000..00f4e67b8c6a9878ab52e0eedf07fc273b067916
--- /dev/null
+++ b/notebooks/setup.jl
@@ -0,0 +1,31 @@
+setup_notebooks = quote
+
+ using Pkg
+ Pkg.activate("notebooks")
+
+ using CCE
+ using CCE: set_size_penalty, distance_from_energy, distance_from_targets
+ using ConformalPrediction
+ using CounterfactualExplanations
+ using CounterfactualExplanations.Data
+ using CounterfactualExplanations.Models: load_mnist_mlp
+ using CounterfactualExplanations.Objectives
+ using Distributions
+ using Flux
+ using Images
+ using JointEnergyModels
+ using LinearAlgebra
+ using MLDatasets
+ using MLDatasets: convert2image
+ using MLJBase
+ using MLJFlux
+ using Plots
+ using Random
+
+ # Setup:
+ theme(:wong)
+ Random.seed!(2023)
+ www_path = "notebooks/www"
+ img_height = 300
+
+end;
\ No newline at end of file
diff --git a/notebooks/summary.qmd b/notebooks/summary.qmd
deleted file mode 100644
index b450ab7d0e39389fba4c34f043b5a4145dd9d674..0000000000000000000000000000000000000000
--- a/notebooks/summary.qmd
+++ /dev/null
@@ -1,3 +0,0 @@
-# Summary
-
-In summary, this book has no content whatsoever.
diff --git a/build/dev/www/mnist_9to4_latent.png b/notebooks/www/mnist_9to4_latent.png
similarity index 100%
rename from build/dev/www/mnist_9to4_latent.png
rename to notebooks/www/mnist_9to4_latent.png
diff --git a/notebooks/www/you_may_not_like_it.png b/notebooks/www/you_may_not_like_it.png
new file mode 100644
index 0000000000000000000000000000000000000000..78cc0733619723f5a0b54693a978ae2ce5123cbf
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