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b/bias-variance/.gitignore deleted file mode 100644 index 2103f3a0..00000000 --- a/bias-variance/.gitignore +++ /dev/null @@ -1,10 +0,0 @@ -build -.cache -node_modules -dist -yarn.lock -thurs.todo -outline.txt -*.todo -.DS_Store -data diff --git a/bias-variance/Config b/bias-variance/Config deleted file mode 100644 index 5065365c..00000000 --- a/bias-variance/Config +++ /dev/null @@ -1,27 +0,0 @@ -package.Mlu-explain_bias-variance = { - interfaces = (1.0); - - # Use NoOpBuild. See https://w.amazon.com/index.php/BrazilBuildSystem/NoOpBuild - build-system = npm-pretty-much; - build-tools = { - 1.0 = { - NpmPrettyMuch = 1.0; - NodeJS = 12.x; - }; - }; - - # Use runtime-dependencies for when you want to bring in additional - # packages when deploying. - # Use dependencies instead if you intend for these dependencies to - # be exported to other packages that build against you. - dependencies = { - 1.0 = { - }; - }; - - runtime-dependencies = { - 1.0 = { - }; - }; - -}; diff --git a/bias-variance/README.md b/bias-variance/README.md deleted file mode 100644 index 6f949e65..00000000 --- a/bias-variance/README.md +++ /dev/null @@ -1,21 +0,0 @@ -This repo holds code for the MLU-Explain visual essay: A Visual Introduction to the Bias-Variance Tradeoff. - -To view the essay, first clone the repo, then: - -``` -# install libraries -npm i - -# view dev version -npm run start -``` - -To view the full 'build' (i.e. the version we'll want to use in production): - -``` -npm run build -``` - -Then all of the assets will be available in the `dist` directory. 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zg)LJXC!-1Z@|OcQB8bV9+M|wh=>nU={wYT|jS63@zeT~oBd;B@htH6BXU`_LjdBnU zz8!%VU3t(=hy2|!_6RkC6gGfU<2{5516+7CZ1J2go}i8u*96{{BAqxzIoU26I8}k} zv;c Ti8IkMNhUr=nodO5ECOu+zFG@O diff --git a/bias-variance/fonts/generator_config.txt b/bias-variance/fonts/generator_config.txt deleted file mode 100755 index 81e64f6b..00000000 --- a/bias-variance/fonts/generator_config.txt +++ /dev/null @@ -1,5 +0,0 @@ -# Font Squirrel Font-face Generator Configuration File -# Upload this file to the generator to recreate the settings -# you used to create these fonts. - -{"mode":"optimal","formats":["woff","woff2"],"tt_instructor":"default","fix_gasp":"xy","fix_vertical_metrics":"Y","metrics_ascent":"","metrics_descent":"","metrics_linegap":"","add_spaces":"Y","add_hyphens":"Y","fallback":"none","fallback_custom":"100","options_subset":"basic","subset_custom":"","subset_custom_range":"","subset_ot_features_list":"","css_stylesheet":"stylesheet.css","filename_suffix":"-webfont","emsquare":"2048","spacing_adjustment":"0"} \ No newline at end of file diff --git a/bias-variance/fonts/specimen_files/grid_12-825-55-15.css b/bias-variance/fonts/specimen_files/grid_12-825-55-15.css deleted file mode 100755 index 3d6aef78..00000000 --- a/bias-variance/fonts/specimen_files/grid_12-825-55-15.css +++ /dev/null @@ -1,129 +0,0 @@ -/*Notes about grid: -Columns: 12 -Grid Width: 825px -Column Width: 55px -Gutter Width: 15px --------------------------------*/ - - - -.section {margin-bottom: 18px; -} -.section:after {content: ".";display: block;height: 0;clear: both;visibility: hidden;} -.section {*zoom: 1;} - -.section .firstcolumn, -.section .firstcol {margin-left: 0;} - - -/* Border on left hand side of a column. */ -.border { - padding-left: 7px; - margin-left: 7px; - border-left: 1px solid #eee; -} - -/* Border with more whitespace, spans one column. */ -.colborder { - padding-left: 42px; - margin-left: 42px; - border-left: 1px solid #eee; -} - - - -/* The Grid Classes */ -.grid1, .grid1_2cols, .grid1_3cols, .grid1_4cols, .grid2, .grid2_3cols, .grid2_4cols, .grid3, .grid3_2cols, .grid3_4cols, .grid4, .grid4_3cols, .grid5, .grid5_2cols, .grid5_3cols, .grid5_4cols, .grid6, .grid6_4cols, .grid7, .grid7_2cols, .grid7_3cols, .grid7_4cols, .grid8, .grid8_3cols, .grid9, .grid9_2cols, .grid9_4cols, .grid10, .grid10_3cols, .grid10_4cols, .grid11, .grid11_2cols, .grid11_3cols, .grid11_4cols, .grid12 -{margin-left: 15px;float: left;display: inline; overflow: hidden;} - - -.width1, .grid1, .span-1 {width: 55px;} -.width1_2cols,.grid1_2cols {width: 20px;} -.width1_3cols,.grid1_3cols {width: 8px;} -.width1_4cols,.grid1_4cols {width: 2px;} -.input_width1 {width: 49px;} - -.width2, .grid2, .span-2 {width: 125px;} -.width2_3cols,.grid2_3cols {width: 31px;} -.width2_4cols,.grid2_4cols {width: 20px;} -.input_width2 {width: 119px;} - -.width3, .grid3, .span-3 {width: 195px;} -.width3_2cols,.grid3_2cols {width: 90px;} -.width3_4cols,.grid3_4cols {width: 37px;} -.input_width3 {width: 189px;} - -.width4, .grid4, .span-4 {width: 265px;} -.width4_3cols,.grid4_3cols {width: 78px;} -.input_width4 {width: 259px;} - -.width5, .grid5, .span-5 {width: 335px;} -.width5_2cols,.grid5_2cols {width: 160px;} -.width5_3cols,.grid5_3cols {width: 101px;} -.width5_4cols,.grid5_4cols {width: 72px;} -.input_width5 {width: 329px;} - -.width6, .grid6, .span-6 {width: 405px;} -.width6_4cols,.grid6_4cols {width: 90px;} -.input_width6 {width: 399px;} - -.width7, .grid7, .span-7 {width: 475px;} -.width7_2cols,.grid7_2cols {width: 230px;} -.width7_3cols,.grid7_3cols {width: 148px;} -.width7_4cols,.grid7_4cols {width: 107px;} -.input_width7 {width: 469px;} - -.width8, .grid8, .span-8 {width: 545px;} -.width8_3cols,.grid8_3cols {width: 171px;} -.input_width8 {width: 539px;} - -.width9, .grid9, .span-9 {width: 615px;} -.width9_2cols,.grid9_2cols {width: 300px;} -.width9_4cols,.grid9_4cols {width: 142px;} -.input_width9 {width: 609px;} - -.width10, .grid10, .span-10 {width: 685px;} -.width10_3cols,.grid10_3cols {width: 218px;} -.width10_4cols,.grid10_4cols {width: 160px;} -.input_width10 {width: 679px;} - -.width11, .grid11, .span-11 {width: 755px;} -.width11_2cols,.grid11_2cols {width: 370px;} -.width11_3cols,.grid11_3cols {width: 241px;} -.width11_4cols,.grid11_4cols {width: 177px;} -.input_width11 {width: 749px;} - -.width12, .grid12, .span-12 {width: 825px;} -.input_width12 {width: 819px;} - -/* Subdivided grid spaces */ -.emptycols_left1, .prepend-1 {padding-left: 70px;} -.emptycols_right1, .append-1 {padding-right: 70px;} -.emptycols_left2, .prepend-2 {padding-left: 140px;} -.emptycols_right2, .append-2 {padding-right: 140px;} -.emptycols_left3, .prepend-3 {padding-left: 210px;} -.emptycols_right3, .append-3 {padding-right: 210px;} -.emptycols_left4, .prepend-4 {padding-left: 280px;} -.emptycols_right4, .append-4 {padding-right: 280px;} -.emptycols_left5, .prepend-5 {padding-left: 350px;} -.emptycols_right5, .append-5 {padding-right: 350px;} -.emptycols_left6, .prepend-6 {padding-left: 420px;} -.emptycols_right6, .append-6 {padding-right: 420px;} -.emptycols_left7, .prepend-7 {padding-left: 490px;} -.emptycols_right7, .append-7 {padding-right: 490px;} -.emptycols_left8, .prepend-8 {padding-left: 560px;} -.emptycols_right8, .append-8 {padding-right: 560px;} -.emptycols_left9, .prepend-9 {padding-left: 630px;} -.emptycols_right9, .append-9 {padding-right: 630px;} -.emptycols_left10, .prepend-10 {padding-left: 700px;} -.emptycols_right10, .append-10 {padding-right: 700px;} -.emptycols_left11, .prepend-11 {padding-left: 770px;} -.emptycols_right11, .append-11 {padding-right: 770px;} -.pull-1 {margin-left: -70px;} -.push-1 {margin-right: -70px;margin-left: 18px;float: right;} -.pull-2 {margin-left: -140px;} -.push-2 {margin-right: -140px;margin-left: 18px;float: right;} -.pull-3 {margin-left: -210px;} -.push-3 {margin-right: -210px;margin-left: 18px;float: right;} -.pull-4 {margin-left: -280px;} -.push-4 {margin-right: -280px;margin-left: 18px;float: right;} \ No newline at end of file diff --git a/bias-variance/fonts/specimen_files/specimen_stylesheet.css b/bias-variance/fonts/specimen_files/specimen_stylesheet.css deleted file mode 100755 index aecc43c3..00000000 --- a/bias-variance/fonts/specimen_files/specimen_stylesheet.css +++ /dev/null @@ -1,396 +0,0 @@ -@import url('grid_12-825-55-15.css'); - -/* - CSS Reset by Eric Meyer - Released under Public Domain - http://meyerweb.com/eric/tools/css/reset/ -*/ -html, body, div, span, applet, object, iframe, -h1, h2, h3, h4, h5, h6, p, blockquote, pre, -a, abbr, acronym, address, big, cite, code, -del, dfn, em, font, img, ins, kbd, q, s, samp, -small, strike, strong, sub, sup, tt, var, -b, u, i, center, dl, dt, dd, ol, ul, li, -fieldset, form, label, legend, table, -caption, tbody, tfoot, thead, tr, th, td - {margin: 0;padding: 0;border: 0;outline: 0; - font-size: 100%;vertical-align: baseline; - background: transparent;} -body {line-height: 1;} -ol, ul {list-style: none;} -blockquote, q {quotes: none;} -blockquote:before, blockquote:after, -q:before, q:after {content: ''; content: none;} -:focus {outline: 0;} -ins {text-decoration: none;} -del {text-decoration: line-through;} -table {border-collapse: collapse;border-spacing: 0;} - - - - -body { - color: #000; - background-color: #dcdcdc; -} - -a { - text-decoration: none; - color: #1883ba; -} - -h1{ - font-size: 32px; - font-weight: normal; - font-style: normal; - margin-bottom: 18px; -} - -h2{ - font-size: 18px; -} - -#container { - width: 865px; - margin: 0px auto; -} - - -#header { - padding: 20px; - font-size: 36px; - background-color: #000; - color: #fff; -} - -#header span { - color: #666; -} -#main_content { - background-color: #fff; - padding: 60px 20px 20px; -} - - -#footer p { - margin: 0; - padding-top: 10px; - padding-bottom: 50px; - color: #333; - font: 10px Arial, sans-serif; -} - -.tabs { - width: 100%; - height: 31px; - background-color: #444; -} -.tabs li { - float: left; - margin: 0; - overflow: hidden; - background-color: #444; -} -.tabs li a { - display: block; - color: #fff; - text-decoration: none; - font: bold 11px/11px 'Arial'; - text-transform: uppercase; - padding: 10px 15px; - border-right: 1px solid #fff; -} - -.tabs li a:hover { - background-color: #00b3ff; - -} - -.tabs li.active a { - color: #000; - background-color: #fff; -} - - - -div.huge { - - font-size: 300px; - line-height: 1em; - padding: 0; - letter-spacing: -.02em; - overflow: hidden; -} -div.glyph_range { - font-size: 72px; - line-height: 1.1em; -} - -.size10{ font-size: 10px; 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mode 100755 index 878ced33..00000000 --- a/bias-variance/fonts/stylesheet.css +++ /dev/null @@ -1,36 +0,0 @@ -/*! Generated by Font Squirrel (https://www.fontsquirrel.com) on October 7, 2020 */ - - - -@font-face { - font-family: 'amazon_emberregular'; - src: url('amazonember_rg-webfont.woff2') format('woff2'), - url('amazonember_rg-webfont.woff') format('woff'); - font-weight: normal; - font-style: normal; - -} - - - - -@font-face { - font-family: 'amazon_ember_displayheavy'; - src: url('amazonemberdisplay_he-webfont.woff2') format('woff2'), - url('amazonemberdisplay_he-webfont.woff') format('woff'); - font-weight: normal; - font-style: normal; - -} - - - - -@font-face { - font-family: 'amazon_ember_monoregular'; - src: url('amazonembermono_rg-webfont.woff2') format('woff2'), - url('amazonembermono_rg-webfont.woff') format('woff'); - font-weight: normal; - font-style: normal; - -} \ No newline at end of file diff --git a/bias-variance/index.html b/bias-variance/index.html index 95052f5a..d98c7a1d 100644 --- a/bias-variance/index.html +++ b/bias-variance/index.html @@ -13,35 +13,21 @@ property="og:image" content="https://mlu-explain.github.io/assets/bvogimage.png" /> - - - - - - - + + + - -

- -

- The Bias Variance - Tradeoff + The Bias + Variance Tradeoff

- Jared Wilber - & Brent Werness, January 2021 + Jared Wilber & Brent Werness, + January 2021

Prediction errors can be decomposed into two main subcomponents of @@ -113,8 +97,6 @@

the concept.

- -
@@ -123,14 +105,12 @@

-

Using our wildest imagination, we can picture a dataset consisting of features X and labels Y, as on the left. Also imagine that we’d like to generalize this relationship to additional values of X - that we’d like to predict future values based on what we’ve - already seen before. -

+ already seen before.

With our imagination now undoubtedly spent, we can take a very simple approach to modeling the relationship between X and Y by just drawing a line to the general trend of the data. @@ -138,12 +118,10 @@

A Simple Model

-

Our simple model isn’t the best at modeling the relationship - clearly there's information in the data that it's failing to - capture. -

+ capture.

We'll measure the performance of our model by looking at the mean-squared error of its output and the true values (displayed in the bottom barchart). Our model is close to some of the training points, but overall there's definitely room for improvement.

- The error on the training data is important for model tuning, but what we really care about is how it performs on data we haven't seen before, called test data. So let's check that out as well. @@ -159,12 +136,10 @@

Low Complexity & Underfitting

-

Uh-oh, it looks like our earlier suspicions were correct - our model is garbage. The test error is even higher than the train - error! -

+ error!

In this case, we say that our model is underfitting the data: our model is so simple that it fails to adequately capture the @@ -177,16 +152,13 @@

>

-

A Complex Model

-

Our previous model performed poorly because it was too simple. Let's try our luck with something more complex. In fact, let's get as complex as we can - let's train a model that predicts every - point in our training data perfectly. -

+ point in our training data perfectly.

Great! Now our training error is zero. As the old saying goes in Tennessee: Fool me once - shame on you. Fool me twice - er... you can't get fooled again ;). @@ -194,7 +166,6 @@

High Complexity & Overfitting

-

Wait a second... Even though our training error from our model was effectively zero, the error on our test data is high. What gives? @@ -226,12 +197,9 @@

noise.

- -

- Or, mathematically: -

- -

+

+ Or, mathematically:

+

We can’t do much about the irreducible @@ -246,9 +214,7 @@

>Bias represents the difference between the average prediction and the true value:

- -

- +

The term is a tricky one. It refers to the average prediction after the model has been trained over several independent datasets. We can think of the @@ -260,7 +226,6 @@

comes from bias.

-

Variance

@@ -270,8 +235,7 @@

>variance measures how much, on average, predictions vary for a given data point:

- -

+

As you can see in the bottom plot, predictions from overfit (high-complexity) models show a lot more error from variance than from bias. It’s easy to imagine that any unseen data points will @@ -284,13 +248,11 @@

To obtain our best results, we should work to find a happy medium between a model that is so basic it fails to learn meaningful patterns in our data, and one that is so complex it fails to - generalize to unseen data . -

+ generalize to unseen data .

In other words, we don’t want an underfit model, but we don’t want an overfit model either. We want something in between - something with enough complexity to learn learn the generalizable patterns - in our data. -

+ in our data.

By trading some bias for variance (i.e. increasing the complexity of our model), and without going overboard, we can @@ -303,35 +265,29 @@

We just showed, at different levels of complexity, a sample of model realizations alongside their corresponding prediction error - decompositions. -

+ decompositions.

Let’s direct our focus to the error decompositions across model - complexities. -

+ complexities.

For each level of complexity, we’ll aggregate the error decomposition across all data-points, and plot the aggregate - errors at their level of complexity. -

+ errors at their level of complexity.

This aggregation applied to our balanced model (i.e. the middle level of complexity) is shown to the left.

-

The Bias Variance Trade-off

+

The Bias Variance Tradeoff

Repeating this aggregation across our range of model complexities, we can see the relationship between bias and variance in prediction errors manifests itself as a U-shaped curve detailing - the trade off between bias and variance. -

+ the tradeoff between bias and variance.

When a model is too simple (i.e. small values along the x-axis), it ignores useful information, and the error is composed mostly of - that from bias. -

+ that from bias.

When a model is too complex (i.e. large values along the x-axis), it memorizes non-general patterns, and the error is composed - mostly of that from variance. -

+ mostly of that from variance.

The ideal model aims to minimize both bias and variance. It lays in the sweet spot - not too simple, nor too complex. @@ -341,21 +297,18 @@

-


Many models have parameters that change the final learned models, - called hyperparameters Let's look at how these hyperparameters may be - used to control the the bias-variance tradeoff with two examples: - LOESS Regression and K-Nearest Neighbors. + called hyperparameters. Let's look at how these hyperparameters may be + used to control the bias variance tradeoff with two examples: LOESS + Regression and K-Nearest Neighbors.


-
+

- -

LOESS Regression

@@ -364,22 +317,19 @@

LOESS Regression

outcome and some predictor variables. The curve fitting at a given point is weighted by nearby data. This weighting is governed by a smoothing hyperparameter, which represents the proportion of - neighboring data used to calculate each local fit. -

+ neighboring data used to calculate each local fit.

Thus, the bias variance tradeoff for LOESS may be controlled for via the smoothness parameter. When the smoothness is small, the amount of data we consider is insufficient for an accurate fit, resulting in large variance. However, if we make the smoothness too high (i.e. over-smoothed), we trade local information for global, resulting in - large bias. -

+ large bias.

Below a LOESS curve is fit to two variables. Randomize the training data to observe the effect different model realizations have on variance, and control the smoothness to observe the tradeoff between under- and over-fitting (and thus, bias and variance).

-
- -
-
- +

K-Nearest Neighbors

@@ -407,8 +354,7 @@

K-Nearest Neighbors

of its K-nearest neighbors. For example, when K = 1, the data point is simply assigned to the class of that single nearest neighbor. If K = 69, the data point is assigned to the majority class of its - 69-nearest neighbors. -

+ 69-nearest neighbors.

We can observe the bias variance tradeoff in KNN directly by playing with the hyperparameter K. When K is small, only a small number of neighbors are considered during the classification vote. The @@ -418,8 +364,7 @@

K-Nearest Neighbors

that deviate sharply from the true decision boundary - go too high and you’ll just obtain a majority vote. This is high bias. On the other hand, for medium values of K, we see smooth the regions that - follow along the true decision boundary. -

+ follow along the true decision boundary.

Explore the trade-off for yourself below! The plot on the left shows the training data. The plot on the right shows decision regions based on the current value of K. Deeper colors reflect more @@ -439,7 +384,6 @@

K-Nearest Neighbors

-

@@ -458,21 +402,11 @@

What About Double Descent?

Fret not, dear reader! As we will detail in our next series of articles, Double Descent actually supports the classical notion of - the bias variance trade off. Stay tuned to learn more! - -

+ the bias variance trade off. Stay tuned to learn more!


- -

It's Finally Over

@@ -480,12 +414,11 @@

It's Finally Over

Exhales deeply. It's finally over. Thanks for reading! We hope that the article is insightful no matter where you are along your machine learning journey, and that you came away with a better - undersatnding of the bias variance tradeoff.

+ understanding of the bias variance tradeoff.

To make things compact, we skipped over some relevant topics (such as regularization), but stay-tuned for more MLU-Explain articles, where we plan to explain those, and other, topics related to machine - learning. -

+ learning.

To learn more about machine learning, check out our self-paced coursesIt's Finally Over


-


References + Open Source

@@ -527,9 +459,8 @@

References + Open Source


(Trevor Hastie; Robert Tibshirani; Jerome Friedman, 2009).
  • - Dive into Deep Learning - (Aston Zhang and Zachary C. Lipton and Mu Li and Alexander J. Smola, - 2020). + Dive into Deep Learning (Aston Zhang + and Zachary C. Lipton and Mu Li and Alexander J. Smola, 2020).
  • @@ -545,7 +476,6 @@

    References + Open Source

    > (statsdirect.com).
  • -
  • D3.js (Mike Bostock, Philippe Rivière) @@ -574,10 +504,7 @@

    References + Open Source



  • - - - - - + + diff --git a/bias-variance/js/DoubleDescent.js b/bias-variance/js/DoubleDescent.js deleted file mode 100644 index d63edb6c..00000000 --- a/bias-variance/js/DoubleDescent.js +++ /dev/null @@ -1,232 +0,0 @@ -// import * as d3 from "d3"; -import { select } from "d3-selection"; -import { axisBottom, axisLeft } from "d3-axis"; -import { scaleLinear } from "d3-scale"; -import { line, curveCatmullRom } from "d3-shape"; -import { ddTrainTest, ddUShape } from "./dataFiles"; - -export class DoubleDescent { - constructor(opts) { - // set selections - this.ddContainer = opts.ddContainer; - - // set constant SVG parameters - this.MARGIN = - window.innerWidth <= 600 - ? { TOP: 30, BOTTOM: 40, LEFT: 20, RIGHT: 35 } - : { TOP: 30, BOTTOM: 40, LEFT: 35, RIGHT: 40 }; - this.WIDTH = - window.innerWidth <= 600 - ? window.innerWidth * 0.65 - : window.innerWidth * 0.45; - this.HEIGHT = - window.innerWidth <= 600 - ? window.innerHeight * 0.3 - : window.innerHeight * 0.35; - - // create svg - this.svg = select(`#${this.ddContainer}`) - .append("svg") - .attr("id", `dd-svg`) - .attr("width", this.WIDTH + this.MARGIN.LEFT + this.MARGIN.RIGHT) - .attr("height", this.HEIGHT + this.MARGIN.TOP + this.MARGIN.BOTTOM) - .style("background-color", ""); - // g container for double descent (dd) - this.ddG = this.svg - .append("g") - .attr("id", `dd-g`) - .attr("transform", `translate(${this.MARGIN.LEFT}, ${this.MARGIN.TOP})`); - - // add clip-path to limit U-line height - this.ddG - .append("clipPath") - .attr("id", "dd-clip") - .append("rect") - .attr("x", 0) - .attr("y", 10) - .attr("width", this.WIDTH + this.MARGIN.LEFT + this.MARGIN.RIGHT) - .attr("height", this.HEIGHT + this.MARGIN.TOP + this.MARGIN.BOTTOM); - - // draw axes - this.drawAxes(); - - // draw lines - this.drawDoubleDescentLines(); - - // draw title - // this.addTitle(); - - // add text annotations to chart - this.addAnnotations(); - } - - drawAxes() { - // define scales - this.xScale = scaleLinear().domain([0.5, 21]).range([0, this.WIDTH]); - this.yScale = scaleLinear().domain([0, 34]).range([this.HEIGHT, 0]); - - // define axes - const xAxisGeneratorLoess = axisBottom(this.xScale) - .tickSizeOuter(0) - .ticks(4); - const yAxisGeneratorLoess = axisLeft(this.yScale).tickSizeOuter(0).ticks(4); - - // draw x-axis - const xAxis = this.ddG - .append("g") - .attr("class", "axis") - .attr("id", "dd-x-axis") - .call(xAxisGeneratorLoess) - .attr("transform", `translate(0, ${this.HEIGHT})`); - - // draw y-axis - const yAxis = this.ddG - .append("g") - .attr("class", "axis") - .attr("id", "dd-y-axis") - .call(yAxisGeneratorLoess); - - // add labels to axes - this.svg - .append("text") - .attr("class", "dd-axis-text") - .attr("x", this.WIDTH / 2 + this.MARGIN.RIGHT) - .attr("y", this.HEIGHT + this.MARGIN.TOP + this.MARGIN.BOTTOM / 2) - .text("Model Complexity") - .attr("text-anchor", "middle"); - this.svg - .append("text") - .attr("class", "dd-axis-text") - .attr("y", this.MARGIN.LEFT / 1.4) - .attr("x", -(this.HEIGHT / 2 + this.MARGIN.TOP)) - .attr("dy", ".05em") - .attr("transform", "rotate(-90)") - .attr("text-anchor", "middle") - .text("Error"); - - // #efefef - // add arrow for x-axis - this.svg - .append("defs") - .append("marker") - .attr("id", "arrowhead-right") - .attr("refX", 5) - .attr("refY", 4.7) - .attr("markerWidth", 16) - .attr("markerHeight", 13) - .append("path") - .attr("d", "M 0 0 L 5 5 L 0 10") - .attr("stroke", "black") - .attr("stroke-width", 2) - .attr("fill", "none"); - - // add arrow to x-axis - this.svg - .select("#dd-x-axis path.domain") - .attr("marker-end", "url(#arrowhead-right)"); - - // add arrow for y-axis - this.svg - .append("defs") - .append("marker") - .attr("id", "arrowhead-up") - .attr("refX", 5) - .attr("refY", 4.7) - .attr("markerWidth", 16) - .attr("markerHeight", 23) - .append("path") - .attr("d", "M 10 10 L 5 5 L 0 10") - .attr("stroke", "black") - .attr("stroke-width", 2) - .attr("fill", "none"); - - this.svg - .select("#dd-y-axis path.domain") - .attr("marker-end", "url(#arrowhead-up)"); - } - - drawDoubleDescentLines() { - // init line generator - let lineGen = line() - .x((d) => this.xScale(d.x)) - .curve(curveCatmullRom.alpha(0.8)); - - // draw train/test lines - const stages = ["train", "test"]; - stages.map((stage) => { - // update line generator for given stage - lineGen.y((d) => this.yScale(d[`y_${stage}`])); - - // draw line for given stage - this.ddG - .append("path") - .attr("class", "dd-line") - .attr("id", `dd-line-${stage}`) - .attr("d", lineGen(ddTrainTest)); - }); - - // draw U-shape line - lineGen.y((d) => this.yScale(d.y_u)); - this.ddG - .append("path") - .attr("clip-path", "url(#dd-clip)") - .attr("class", "dd-line") - .attr("id", `dd-line-u`) - .attr("d", lineGen(ddUShape)); - } - - addTitle() { - // add title to plot - this.svg - .append("text") - .attr("id", "dd-title") - .attr("x", this.MARGIN.LEFT) - .attr("y", this.MARGIN.TOP / 1.5) - .attr("text-anchor", "left") - .text("The Double Descent Phenomena") - .style("font-weight", "bold"); - } - - addAnnotations() { - // add text annotations to chart - // train label & circle - this.ddG - .attr("class", "dd-text") - .append("text") - .attr("x", this.xScale(19.25)) - .attr("y", this.yScale(0.8)) - .attr("dx", window.innerWidth <= 600 ? 2.5 : 0) - .text("Train Error"); - this.ddG - .append("circle") - .attr("class", "dd-circle") - .attr("id", "dd-circle-train") - .attr("cx", this.xScale(19)) - .attr("cy", this.yScale(1.2)) - .attr("r", 5); - - // test label - this.ddG - .attr("class", "dd-text") - .append("text") - .attr("x", this.xScale(19.25)) - .attr("y", this.yScale(2.4)) - .attr("dx", window.innerWidth <= 600 ? 2.5 : 0) - .text("Test Error"); - this.ddG - .append("circle") - .attr("class", "dd-circle") - .attr("id", "dd-circle-test") - .attr("cx", this.xScale(19)) - .attr("cy", this.yScale(2.3)) - .attr("r", 5); - - // U label - this.ddG - .attr("class", "dd-text") - .append("text") - .attr("x", this.xScale(9.8)) - .attr("y", this.yScale(30.25)) - .text("Expected Test Error"); - } -} diff --git a/bias-variance/js/ErrorBar.js b/bias-variance/js/ErrorBar.js deleted file mode 100644 index 3451b057..00000000 --- a/bias-variance/js/ErrorBar.js +++ /dev/null @@ -1,816 +0,0 @@ -import { select, selectAll, style } from "d3-selection"; -import { axisBottom, axisLeft } from "d3-axis"; -import { scaleBand, scaleLinear, scaleOrdinal } from "d3-scale"; -import { line, curveCatmullRom } from "d3-shape"; -import { transition } from "d3-transition"; -import { testColorBar, trainColorBar } from "./constants"; -import { errorData } from "./dataFiles"; -import { extent, max } from "d3-array"; -import { easeBack } from "d3-ease"; - -export class ErrorBar { - constructor(opts) { - this.element = opts.element; - this.chartDims = select("#scroll-figure").node().getBoundingClientRect(); - console.log("chartDims", this.chartDims); - this.MARGIN = - window.innerWidth <= 700 - ? { TOP: 20, BOTTOM: 30, LEFT: 30, RIGHT: 9 } - : { TOP: 20, BOTTOM: 40, LEFT: 40, RIGHT: 9 }; - this.WIDTH = this.chartDims.width * 0.8; - this.HEIGHT = window.innerHeight * 0.36; - - // this.HEIGHT = 250; - this.svgMain = select("#svg2") - .append("svg") - .attr("id", "errorBarSvg") - .attr("width", this.WIDTH + this.MARGIN.LEFT + this.MARGIN.RIGHT) - .attr("height", this.HEIGHT + this.MARGIN.TOP + this.MARGIN.BOTTOM); - - this.svg = this.svgMain - .append("g") - .attr("id", "errorchart-g") - .attr("transform", `translate(${this.MARGIN.LEFT}, ${this.MARGIN.TOP})`); - - // add arrow for x-axis - this.svgMain - .append("defs") - .append("marker") - .attr("id", "arrowhead-right") - .attr("refX", 5) - .attr("refY", 4.7) - .attr("markerWidth", 16) - .attr("markerHeight", 13) - .append("path") - .attr("d", "M 0 0 L 5 5 L 0 10") - .attr("stroke", "black") - .attr("stroke-width", 2) - .attr("fill", "none"); - // add arrow for y-axis - this.svgMain - .append("defs") - .append("marker") - .attr("id", "arrowhead-up") - .attr("refX", 5) - .attr("refY", 4.7) - .attr("markerWidth", 16) - .attr("markerHeight", 23) - .append("path") - .attr("d", "M 10 10 L 5 5 L 0 10") - .attr("stroke", "black") - .attr("stroke-width", 2) - .attr("fill", "none"); - - this.colorScale = scaleOrdinal() - .domain(["Train", "Test"]) - .range([trainColorBar, testColorBar]); - - this.xScale = scaleLinear().domain([0, 0.42]).range([0, this.WIDTH]); - this.xAxisGroup = this.svg - .append("g") - .attr("class", "axis") - .attr("id", "error-x-axis") - .attr("transform", `translate(0, ${this.HEIGHT})`); - - this.yScale = scaleBand().range([0, this.HEIGHT]).padding(0.1); - - this.yAxisGroup = this.svg - .append("g") - .attr("class", "axis") - .attr("id", "error-y-axis"); - } - - drawErrorBase() { - // set new axes and scales - this.xScaleError = scaleLinear() - .domain(extent(errorData, (d) => +d.x)) - .range([0, this.WIDTH]); - - this.xAxisGroup - .transition() - .style("opacity", 1) - .call(axisBottom(this.xScaleError).tickSizeOuter(0).ticks(4)) - .selectAll("line.tick") - .style("opacity", 0.2); - - this.yScaleError = scaleLinear() - .domain([-0.05, 0.8]) - .range([this.HEIGHT, 0]); - - this.yAxisGroup - .transition() - .style("opacity", 1) - .call(axisLeft(this.yScaleError).tickSizeOuter(0).ticks(4)); - } - - plotErrorLines(xVal, yVal) { - // resolve yVal - const noise = `noise_${yVal}`; - const error = `error_${yVal}`; - const bias = `bias_${yVal}`; - const variance = `var_${yVal}`; - const errorT = transition().delay(200).duration(1500); - - // update y-axis depending on y-value - if (yVal === "final" || yVal == "point") { - this.yScaleError.domain([-0.05, 1.3]); - this.yAxisGroup - .transition() - .style("opacity", 1) - .call(axisLeft(this.yScaleError).tickSizeOuter(0).ticks(0)); - } else { - this.yScaleError.domain([-0.05, 0.8]); - this.yAxisGroup - .transition() - .style("opacity", 1) - .call(axisLeft(this.yScaleError).tickSizeOuter(0).ticks(4)); - } - - // define line generators - const noiseLine = line() - .x((d) => this.xScaleError(+d[xVal])) - .y((d) => this.yScaleError(+d[noise])) - .curve(curveCatmullRom.alpha(0.8)); - - const errorLine = line() - .x((d) => this.xScaleError(+d[xVal])) - .y((d) => this.yScaleError(+d[error])) - .curve(curveCatmullRom.alpha(0.8)); - - const biasLine = line() - .x((d) => this.xScaleError(+d[xVal])) - .y((d) => this.yScaleError(+d[bias])) - .curve(curveCatmullRom.alpha(0.8)); - - const varLine = line() - .x((d) => this.xScaleError(+d[xVal])) - .y((d) => this.yScaleError(+d[variance])) - .curve(curveCatmullRom.alpha(0.8)); - - // draw noise data - this.noiseLine = this.svg - .selectAll("#noise-line") - .data([errorData], (d) => +d[xVal]); - - this.noiseLine - .enter() - .append("path") - .attr("id", "noise-line") - .merge(this.noiseLine) - .attr("class", "error-line-bottom") - .transition(errorT) - .attr("d", noiseLine); - - // draw error data - this.errorLine = this.svg - .selectAll("#error-line") - .data([errorData], (d) => +d[xVal]); - - this.errorLine - .enter() - .append("path") - .attr("id", "error-line") - .merge(this.errorLine) - .attr("class", "error-line-bottom") - .transition(errorT) - .attr("d", errorLine); - - // draw bias data - this.biasLine = this.svg - .selectAll("#bias-line") - .data([errorData], (d) => +d[xVal]); - - this.biasLine - .enter() - .append("path") - .attr("id", "bias-line") - .merge(this.biasLine) - .attr("class", "error-line-bottom") - .transition(errorT) - .attr("d", biasLine); - - // draw var data - this.varLine = this.svg - .selectAll("#var-line") - .data([errorData], (d) => +d[xVal]); - - this.varLine - .enter() - .append("path") - .attr("id", "var-line") - .merge(this.varLine) - .attr("class", "error-line-bottom") - .transition(errorT) - .attr("d", varLine); - } - - plotComplexCircleOne() { - // add a circle to each complex point - // first, gcreate array for desired circles - const circleData = ["bias", "var", "noise", "error"].map((d) => { - return { - x: errorData[0].x_point, - y: errorData[0][`${d}_point`], - name: d, - }; - }); - - // next, plot circle for each point - this.svg - .selectAll("circle.complexity") - .data(circleData) - .enter() - .append("circle") - .attr("class", "complexity") - .attr("class", (d) => `complex-${d.name}`) - .style("stroke-width", 2) - .attr("stroke", "white") - .attr("cx", (d) => this.xScaleError(+d.x)) - .attr("cy", (d) => this.yScaleError(+d.y)) - .attr("r", 0) - .transition() - .delay(1300) - .attr("r", window.innerWidth <= 600 ? 9.5 : 12) - .transition() - .attr("r", window.innerWidth <= 600 ? 5 : 8); - } - - plotComplexCircleAll() { - // add a circle to each complex point - // first, gcreate array for desired circles - // const circlesData = errorData.filter((d) => d.x !== 0.503); - // console.log("circleData", circlesData); - - const noise = `noise_final`; - const error = `error_final`; - const bias = `bias_final`; - const variance = `var_final`; - - // noise - this.svg - .selectAll("circle.complexity") - .data(errorData) - .enter() - .append("circle") - .attr("class", "complexity") - .attr("class", (d) => `complex-noise`) - .style("stroke-width", 2) - .attr("stroke", "white") - .attr("cx", (d) => this.xScaleError(+d.x)) - .attr("cy", (d) => this.yScaleError(+d[noise])) - .attr("r", 0) - .transition() - .attr("r", window.innerWidth <= 600 ? 9.5 : 12) - .transition() - .attr("r", window.innerWidth <= 600 ? 5 : 8); - - // error - this.svg - .selectAll("circle.complexity") - .data(errorData) - .enter() - .append("circle") - .attr("class", "complexity") - .attr("class", (d) => `complex-error`) - .style("stroke-width", 2) - .attr("stroke", "white") - .attr("cx", (d) => this.xScaleError(+d.x)) - .attr("cy", (d) => this.yScaleError(+d[error])) - .attr("r", 0) - .transition() - .attr("r", window.innerWidth <= 600 ? 9.5 : 12) - .transition() - .attr("r", window.innerWidth <= 600 ? 5 : 8); - - // bias - this.svg - .selectAll("circle.complexity") - .data(errorData) - .enter() - .append("circle") - .attr("class", "complexity") - .attr("class", (d) => `complex-bias`) - .style("stroke-width", 2) - .attr("stroke", "white") - .attr("cx", (d) => this.xScaleError(+d.x)) - .attr("cy", (d) => this.yScaleError(+d[bias])) - .attr("r", 0) - .transition() - .attr("r", window.innerWidth <= 600 ? 9.5 : 12) - .transition() - .attr("r", window.innerWidth <= 600 ? 5 : 8); - - // var - this.svg - .selectAll("circle.complexity") - .data(errorData) - .enter() - .append("circle") - .attr("class", "complexity") - .attr("class", (d) => `complex-var`) - .style("stroke-width", 2) - .attr("stroke", "white") - .attr("cx", (d) => this.xScaleError(+d.x)) - .attr("cy", (d) => this.yScaleError(+d[variance])) - .attr("r", 0) - .transition() - .attr("r", window.innerWidth <= 600 ? 9.5 : 12) - .transition() - .attr("r", window.innerWidth <= 600 ? 5 : 8); - - // next, plot circle for each point - } - - plotReverseDecompositionBar() { - let t = transition().duration(600); - // rescale axis back to what it was - this.xScale.domain([0, 0.42]); - this.xAxisGroup - .transition(t) - .call(axisBottom(this.xScale).tickSizeOuter(0)) - .style("opacity", 1); - // update y-axis in 5 up so it shows earlier - this.yAxisGroup.transition(t).style("opacity", 1); - } - - plotDecompositionBar(data) { - const that = this; - let t = transition().delay(100).duration(600); - // draw same data 3 times, with slightly different errors - // scale y-axis to full - const maxError = max(data, (d) => +d.error); - - // set new band xscale for decomposition values - - this.xScale.domain([0, maxError]); - // this.xScale.domain([0, maxError + maxError * 0.1]); - this.xAxisGroup - .transition(t) - .call(axisBottom(this.xScale).tickSizeOuter(0).ticks(4)); - // update y-axis - this.yScale.domain(["Test"]); - this.yAxisGroup.transition(t).call(axisLeft(this.yScale).tickSizeOuter(0)); - - // join - this.rects = this.svg.selectAll(`rect.errorBar`).data(data, (d) => d.error); - - // exit - this.rects - .exit() - .transition(t) - .attr("height", 0) - .attr("y", this.HEIGHT) - .remove(); - - // update - this.rects - .transition(t) - .attr("x", (d) => this.xScale(0)) - .attr("y", (d) => this.yScale(d.name)) - .attr("width", (d) => this.xScale(maxError)) - .attr("height", this.yScale.bandwidth()) - .attr("id", "decomposition") - .style("opacity", 1); - // hide x-axis group (hide incase scroll up) - this.hideAxes(); - - // DECOMPOSITION - this.decomp = [ - { name: "Test", error: "Bias2", value: 0.33 * maxError }, - { name: "Test", error: "Variance", value: 0.33 * maxError }, - { name: "Test", error: "Noise", value: 0.33 * maxError }, - ]; - - this.biasMap = (d) => { - return d === "Bias2" ? "Bias²" : d; - }; - - // axis for decomp positions - this.decompAxis = scaleBand() - .range([0, this.xScale(maxError)]) - .padding(0) - .domain(["Bias2", "Variance", "Noise"]); - - // save value for error offset - this.biasX = - that.WIDTH / 10 - - this.decompAxis("Bias2") + - this.xScale(0.33 * maxError) / 2; - - const stackColor = scaleOrdinal() - .domain(this.decomp, (d) => d.error) - .range(["coral", "skyblue", "teal"]); - - this.decompLegend = this.svg - .append("g") - .attr("class", "decomp-legend") - .attr("transform", "translate(0,0)"); - - // create g elements to hold decomp rects and labels - this.decompGs = this.decompLegend - .selectAll("g.decompG") - .data(this.decomp) - .enter() - .append("g") - .attr("class", "decompG"); - - // append rectangles to decomp G - const stackedRects = this.decompGs - .append("rect") - .attr("class", "stacked") - .attr("fill", (d) => stackColor(d.error)) - .attr("id", (d) => `rect-${d.error}`) - .attr("x", (d) => this.decompAxis(d.error)) - .attr("y", (d) => this.yScale(d.name)) - .attr("height", this.yScale.bandwidth()) - .attr("width", (d) => this.xScale(+d.value)) - .style("opacity", 0); - - this.decompGs - .append("text") - .attr("class", "decomp-text") - .attr("x", (d) => this.decompAxis(d.error)) - .attr("y", (d) => this.yScale.bandwidth() / 2 + this.MARGIN.TOP) - .attr("dx", (d) => this.xScale(+d.value) / 2) - .text((d) => this.biasMap(d.error)) - .style("opacity", 0); - - // init rectangle borders around text (for labels later) - this.textBox = {}; - selectAll(".decomp-text").each(function (d) { - const textBox = this.getBBox(); - that.textBox[d.error] = textBox; - }); - - // reset text-anchor to middle - // we have to reset it because otherwise the later transition to legend won't work - selectAll(".decomp-text").attr("text-anchor", "middle"); - - // line up transitions - const decompT = transition().delay(600).ease(easeBack).duration(500); - - // transition everything into view - selectAll("rect.stacked").transition(decompT).style("opacity", 0.7); - selectAll(".decomp-text").transition(decompT).style("opacity", 1); - this.rects.transition(decompT).style("opacity", 0); - } - - // scroll up function for decomposition - reverseDecomposition() { - let t = transition().delay(100).duration(600); - - // reposition squares - this.decompGs - .selectAll("rect") - .transition(t) - .attr("x", (d) => this.decompAxis(d.error)) - .attr("y", (d) => this.yScale(d.name)) - .attr("height", this.yScale.bandwidth()) - .attr("width", (d) => this.xScale(+d.value)); - - // reposition text - this.decompGs - .selectAll("text") - .attr("x", (d) => this.decompAxis(d.error)) - .attr("y", (d) => this.yScale.bandwidth() / 2 + this.MARGIN.TOP) - .attr("dx", (d) => this.xScale(+d.value) / 2); - - // translate group back to decomp bar position - this.decompGs - .transition() - .duration(1000) - .attr("transform", "translate(0,0)") - .selectAll(".decomp-text") - .attr("text-anchor", "middle"); - - // remove axes - this.hideAxes(); - - // set up y-axis for bar - this.yScale.domain(["Train", "Test"]); - this.yAxisGroup - .call(axisLeft(this.yScale).tickSizeOuter(0)) - .style("opacity", 1); - } - - hideAxes() { - this.xAxisGroup.transition().style("opacity", 0); - this.yAxisGroup.transition().style("opacity", 0); - } - - positionErrorBarsAsLabels() { - const that = this; - const nodeWidth = (d) => d.getBBox().width; - // const rectOffset = window.innerWidth <= 600 ? 4 : 8; - const rectOffsetX = window.innerWidth <= 600 ? 4 : 8; - const rectOffsetY = window.innerWidth <= 600 ? 4 : 8; - const symbolSpace = - window.innerWidth <= 600 ? rectOffsetX * 1.5 : rectOffsetX * 2; - const errorTransTime = 800; - - // update y-axis - this.yScale.domain(["Test"]); - - // hide label rects behind main rect - selectAll("rect.stacked").style("opacity", 0.7); - selectAll(".decomp-text").style("opacity", 1); - - // position label rects - selectAll("rect.stacked") - .transition() - .attr("x", (d) => this.textBox[d.error].x - rectOffsetX) - .attr("y", (d) => this.textBox[d.error].y - rectOffsetY) - .attr("width", (d) => - window.innerWidth <= 600 - ? this.textBox[d.error].width + 1.5 * rectOffsetX - : this.textBox[d.error].width + 2 * rectOffsetX - ) - .attr("height", (d) => this.textBox[d.error].height + 2 * rectOffsetY); - - let offset = 0; - this.decompGs - .transition() - .duration(errorTransTime) - .attr("transform", function (d) { - // resolve x position for transition - let x = offset; - offset += that.textBox[d.error].width + 2.5 * rectOffsetX + symbolSpace; - let currentxPos = that.decompAxis(d.error) - rectOffsetX * 2; - let xPos = that.WIDTH / 10 - currentxPos; - xPos += x; - xPos = window.innerWidth <= 600 ? xPos - that.MARGIN.LEFT / 2.5 : xPos; - // resolve y position for transition - let currentYPos = that.yScale.bandwidth() / 2 + that.MARGIN.TOP; - // that.yScale.bandwidth() / 2 + this.MARGIN.TOP - const yPos = -currentYPos; - // transition - return `translate(${xPos}, ${yPos})`; - }) - .selectAll(".decomp-text") - .attr("text-anchor", "right"); - - this.decompLegend.attr("transform", function () { - return `translate(${(that.WIDTH - nodeWidth(this)) / 2}, 0)`; - }); - - // init error legend text to get position/bbox - let errorText = this.decompLegend - .append("text") - .attr("id", "error-text") - .attr("x", this.biasX) - .attr("y", this.MARGIN.TOP) - .text("Error") - .style("font-family", "AmazonEmberDisplayHeavy") - .style("opacity", 0); - - // get bbox balues of text - let errorCoords = {}; - select("#error-text").each(function (d, i) { - const textBox = this.getBBox(); - const width = textBox.width + 2 * rectOffsetX; - const height = textBox.height + 2 * rectOffsetY; - errorCoords["x"] = that.biasX - width; - errorCoords["width"] = width; - errorCoords["height"] = height; - errorCoords["y"] = textBox.y; - }); - // translate text to correct position given other elements and bbox - errorText - .transition() - .attr( - "x", - window.innerWidth <= 600 - ? errorCoords.x - rectOffsetX - that.MARGIN.LEFT / 2.5 - : errorCoords.x - rectOffsetX - ) - .attr("y", 0); - // add background rectangle - const errorTextRect = this.decompLegend - .append("rect") - .attr("class", "stacked-error") - .attr("fill", "red") - .attr( - "x", - window.innerWidth <= 600 - ? errorCoords.x - 2 * rectOffsetX - that.MARGIN.LEFT / 2.5 - : errorCoords.x - 2 * rectOffsetX - ) - .attr("y", errorCoords.y - rectOffsetY - that.MARGIN.TOP) - .attr("width", errorCoords.width) - .attr("height", errorCoords.height) - .lower() - .style("opacity", 0); - - // ADD MATH SYMBOLS TO LEGEND - // add '=' sign after error - let errorTextEquals = this.decompLegend - .append("text") - .attr("class", "error-symbol") - .attr( - "x", - window.innerWidth <= 600 - ? errorCoords.x + - errorCoords.width - - rectOffsetX - - that.MARGIN.LEFT / 2.5 - : errorCoords.x + errorCoords.width - rectOffsetX - ) - .attr("y", 0) - .text("=") - .style("font-family", "AmazonEmberDisplayHeavy") - .style("opacity", 0); - - // add '+' signs - let errorTextPlus = this.decompGs - .filter((d, i) => i !== 0) - .append("text") - .attr("class", "error-symbol") - .attr("x", (d) => this.textBox[d.error].x - rectOffsetX - symbolSpace) - .attr("dx", window.innerWidth <= 600 ? -2 : 0) - .attr("y", that.yScale.bandwidth() / 2 + this.MARGIN.TOP) - .text("+") - .style("font-family", "AmazonEmberDisplayHeavy") - .style("opacity", 0); - - // transition elements to visibility - const errorTextT = transition().delay(errorTransTime); - - errorText.transition(errorTextT).style("opacity", 1); - selectAll(".error-symbol").transition(errorTextT).style("opacity", 0.9); - errorTextRect.transition(errorTextT).style("opacity", 0.7); - } - - addTitle(titleText) { - // remove existing title - select("#errorBarTitle").remove(); - - // add new title - this.svg - .append("text") - .attr("id", "errorBarTitle") - .attr("x", this.WIDTH / 2) - .attr("y", 0) - .attr("text-anchor", "middle") - .text(titleText) - .style("font-weight", "bold"); - } - - plotBars(data) { - // set consistent transition - let t = transition().delay(500).duration(600); - - // resolve names for y-axis - const names = extent(data, (d) => d.name).reverse(); - this.yScale.domain(names); - - // update y-axis - this.yAxisGroup.transition(t).call(axisLeft(this.yScale).tickSizeOuter(0)); - - // update x-axis - this.xAxisGroup - .transition(t) - .call(axisBottom(this.xScale).ticks(4).tickSizeOuter(0)) - .selectAll("line.tick") - .style("opacity", 0.1); - - // join rects - this.rects = this.svg.selectAll(`rect.errorBar`).data(data, (d) => d.name); - - // exit rects - this.rects - .exit() - .transition(t) - .attr("width", 0) - .attr("height", 0) - .attr("y", this.HEIGHT) - .remove(); - - // update rects - this.rects - .transition(t) - .attr("x", (d) => this.xScale(0)) - .attr("y", (d) => this.yScale(d.name)) - .attr("width", (d) => this.xScale(+d.error)) - .attr("height", this.yScale.bandwidth()); - - // enter rects - this.rects - .enter() - .append("rect") - .attr("class", "errorBar") - .attr("x", (d) => this.xScale(0)) - .attr("y", (d) => this.yScale(d.name)) - .attr("height", this.yScale.bandwidth()) - .attr("fill", (d) => this.colorScale(d.name)) - .transition(t) - .attr("width", (d) => this.xScale(+d.error)); - } - - repositionSvgNew() { - // set up transition - let trans = transition().duration(1500); - - // determine new height - let newHeight = - window.innerHeight * 0.45 + this.MARGIN.BOTTOM + this.MARGIN.TOP; - - // resize & hide top chart - select("#scatter-svg") - .transition(trans) - .attr("height", 75) - .style("opacity", 0); - - // ensure x-axis lines up with chart re-scaling - this.xAxisGroup - .transition(trans) - .attr( - "transform", - `translate(0, ${newHeight - this.MARGIN.TOP - this.MARGIN.BOTTOM})` - ); - - // remove numbers from x-scale - this.svg.select("#error-x-axis").selectAll(".tick").style("opacity", 0); - - // add arrow to x-axis - this.svg - .select("#error-x-axis path.domain") - .attr("marker-end", "url(#arrowhead-right)"); - - // add 'Model Complexity' label to x-axis - this.svg - .append("text") - .attr("id", "error-x-text") - .attr("x", this.WIDTH / 2) - .attr("y", newHeight - this.MARGIN.TOP - this.MARGIN.BOTTOM / 2) - .text("Model Complexity") - .style("text-anchor", "middle") - .style("font-family", "AmazonEmberDisplayLight"); - - // update y-axis - this.yScaleError - .domain([0, 1]) - .range([newHeight - this.MARGIN.TOP - this.MARGIN.BOTTOM, 0]); - this.yAxisGroup - .transition(trans) - .call(axisLeft(this.yScaleError).tickSizeOuter(0).ticks(4)); - // remove number from y-scale - this.svg.select("#error-y-axis").selectAll(".tick").style("opacity", 0); - - // add arrow to x-axis - this.svg - .select("#error-y-axis path.domain") - .attr("marker-end", "url(#arrowhead-up)"); - // .style("opacity", 0.8); - - // add 'Model Complexity' label to x-axis - this.svg - .append("text") - .attr("transform", "rotate(-90)") - .attr("id", "error-y-text") - .attr("y", 0 - 0.3 * this.MARGIN.LEFT) - .attr("x", 0 - newHeight / 2 + this.MARGIN.TOP) - .text("Test Error") - .style("text-anchor", "middle") - .style("font-family", "AmazonEmberDisplayLight"); - - // make svg larger - this.svgMain.transition(trans).attr("height", newHeight); - } - - upPositionSvg() { - // set up transition - let trans = transition(); - - // make top chart its original height - select("#scatter-svg") - .transition(trans) - .attr("height", window.innerHeight * 0.4 + 15 + 40) - .style("opacity", 1); - - // update y-axis - this.yScaleError.domain([0, 1]).range([this.HEIGHT, 0]); - this.yAxisGroup - .transition(trans) - .call(axisLeft(this.yScaleError).ticks(4).tickSizeOuter(0)); - - // ensure x-axis lines up with chart re-scaling - this.xAxisGroup - .transition(trans) - .attr("transform", `translate(0, ${this.HEIGHT})`); - - // reshow axes numbers - this.svg.select("#error-x-axis").selectAll(".tick").style("opacity", 1); - this.svg.select("#error-y-axis").selectAll(".tick").style("opacity", 1); - // remove axes arrows - this.svg.select("#error-x-axis path.domain").attr("marker-end", "null"); - this.svg.select("#error-y-axis path.domain").attr("marker-end", "null"); - // remove axes text - this.svg.select("#error-x-text").remove(); - this.svg.select("#error-y-text").remove(); - - // redraw svg - this.svgMain - .transition(trans) - .attr("height", this.HEIGHT + this.MARGIN.TOP + this.MARGIN.BOTTOM); - } -} diff --git a/bias-variance/js/KNN.js b/bias-variance/js/KNN.js deleted file mode 100644 index f4fb94ab..00000000 --- a/bias-variance/js/KNN.js +++ /dev/null @@ -1,505 +0,0 @@ -import { select, selectAll, pointer } from "d3-selection"; -import { scaleQuantize, scaleOrdinal } from "d3-scale"; -import { line } from "d3-shape"; -import { transition } from "d3-transition"; -import { curveCatmullRom } from "d3-shape"; -import { range } from "d3-array"; -import { Delaunay } from "d3-delaunay"; -import * as d3hexbin from "d3-hexbin"; - -const dist = (x, y) => (x[0] - y[0]) ** 2 + (x[1] - y[1]) ** 2; -const equation = (x) => x - 0.1 * Math.pow(x, 2) - 1.1 * Math.sin(x) + 4; -const numPoints = 65; - -export class KNN { - constructor(opts) { - const that = this; - // selections - this.fitContainer = opts.fitContainer; - this.predictContainer = opts.predictContainer; - this.sliderContainer = opts.sliderContainer; - - // set k - this.k = opts.k; - - // values for chart components - this.fitPointRadius = 6; - this.hexRadius = 8; - - // set constant SVG parameters - this.MARGIN = { TOP: 0, BOTTOM: 0, LEFT: 0, RIGHT: 0 }; - const containerSize = select(`#${this.fitContainer}`) - .node() - .getBoundingClientRect(); - this.WIDTH = containerSize.width * 0.9; - // this.HEIGHT = containerSize.height * 0.85; - this.HEIGHT = - window.innerWidth <= 600 - ? containerSize.height * 0.7 - : containerSize.height * 0.85; - - this.padding = 40; - - // utils for charts - this.classColorScale = scaleOrdinal() - .domain([0, 1]) - .range(["#94CAE0", "#E57873"]); - this.cellColor = scaleQuantize() - .domain([-0.8, 0.8]) - .range([ - "#94CAE0", - "rgba(148, 202, 224, 0.5)", - "rgba(229, 120, 115, 0.5)", - "#E57873", - ]); - this.xAcc = (d) => +d[0]; - this.yAcc = (d) => +d[1]; - - // reusable hexbin - this.hexbin = d3hexbin - .hexbin() - .radius(this.hexRadius) - .extent([ - [0, 0], - [this.WIDTH, this.HEIGHT], - ]); - - // hexbin data - this.hexCenters = this.hexbin(this.hexbin.centers()); - - // init voronoi - this.delan = Delaunay.from( - this.hexCenters, - (d) => d.x, - (d) => d.y - ); - - // create datasets - function genData(x, y) { - if (Math.random() > 0.9) { - return Math.round(Math.random()); - } - return dBoundary(x, y); - } - - function dBoundary(x, y) { - let nx = (x / that.WIDTH) * 10 + 1.5; - let ny = ((that.HEIGHT - y) / that.HEIGHT) * 10; - - if (ny < equation(nx)) { - return 0; - } else { - return 1; - } - } - - this.trainData = range(numPoints).map(() => { - let x = Math.random() * (this.WIDTH - this.padding) + this.padding / 2; - let y = Math.random() * (this.HEIGHT - this.padding) + this.padding / 2; - return [x, y, genData(x, y)]; - }); - - // add slider to DOM - this.addSlider(); - - this.fitG = this.initChartSvg(this.fitContainer); - this.predictG = this.initChartSvg(this.predictContainer); - - // draw charts - this.drawKnnLeft(); - this.drawKnnRight(); - } - - refreshChartWithNewData() { - const that = this; - // redefine stuff so it stays in scope - const equation = (x) => x - 0.1 * Math.pow(x, 2) - 1.1 * Math.sin(x) + 4; - - // create datasets - function genData(x, y) { - if (Math.random() > 0.9) { - return Math.round(Math.random()); - } - return dBoundary(x, y); - } - - function dBoundary(x, y) { - let nx = (x / that.WIDTH) * 10 + 1.5; - let ny = ((that.HEIGHT - y) / that.HEIGHT) * 10; - return ny < equation(nx) ? 0 : 1; - } - - // regenerate data - this.trainData = range(numPoints).map(() => { - let x = Math.random() * (this.WIDTH - this.padding) + this.padding / 2; - let y = Math.random() * (this.HEIGHT - this.padding) + this.padding / 2; - return [x, y, genData(x, y)]; - }); - - // redraw both charts - this.drawKnnLeft(); - this.drawKnnRight(); - } - - drawKnnLeft() { - const that = this; - // redraw, so remove existing stuff - const removeClasses = [ - ".data-point", - ".highlight-circle", - "mesh-g-fit", - ".overlay", - ".hex-cell", - ".line-g", - ".mesh-g-fit", - ]; - removeClasses.map((d) => { - selectAll(d).remove(); - }); - - // redraw plot - const delan = Delaunay.from( - this.hexCenters, - (d) => d.x, - (d) => d.y - ); - // draw training data circles - const circlesTrain = this.fitG - .selectAll(".data-point") - .data(this.trainData) - .enter() - .append("circle") - .classed("data-point", true) - .attr("r", this.fitPointRadius) - .attr("cx", this.xAcc) - .attr("cy", this.yAcc) - .attr("fill", (d) => this.classColorScale(d[2])) - .style("stroke", "white"); - - // add highlight circle - this.fitG - .append("circle") - .attr("class", "highlight-circle") - .attr("r", this.fitPointRadius) - .style("fill", "black") - .style("stroke", "white"); - // .style("display", "none"); - - // draw hex grid - this.fitG - .append("g") - .attr("class", "mesh-g-fit") - .selectAll("path") - .data(this.hexCenters) - .enter() - .append("path") - .attr("class", "hex-cell-fit") - .attr("d", (d) => `M${d.x},${d.y}${this.hexbin.hexagon()}`) - .attr("fill", "white") - .attr("stroke-width", 0) - .attr("stroke", "white") - .attr("opacity", 0); - - // add the overlay on top of everything to take the mouse events - this.fitG - .append("rect") - .attr("class", "overlay") - .attr("width", that.WIDTH) - .attr("height", that.HEIGHT) - .style("opacity", 0) - .on("mousemove", hexHover) - .on("mouseleave", this.hexLeave); - - function hexHover(event) { - // get the current mouse position - const [mx, my] = pointer(event); - // use the new diagram.find() function to find the Voronoi mouse position - const closestPoint = delan.find(mx, my); - // highlight the point if we found one - const closestHex = that.hexCenters[closestPoint]; - - // get closest data point - const closestData = { x: mx, y: my }; - - that.highlight(closestData, that.k); - - // highlight current cell - const cell = selectAll(".hex-cell").filter( - (d) => d.x === closestHex.x && d.y === closestHex.y - ); - - cell.attr("stroke", "black").attr("stroke-width", 3).raise(); - - // unhover other ones - selectAll(".hex-cell") - .filter((d) => d.x !== closestHex.x || d.y !== closestHex.y) - .attr("stroke-width", 0) - .style("stroke", "#161E2D"); - } - // init highlight at random point - this.highlight(this.hexCenters[60], this.k); - this.addDecisionLine(this.fitG); - } - - hexLeave() { - // this.highlight(this.lastDataHovered, this.k); - selectAll(".hex-cell").attr("stroke-width", 0.0); - } - - // callback to highlight a point - highlight(d, k) { - this.k = k; - - select(".highlight-circle") - .style("display", "") - .attr("cx", d.x) - .attr("cy", d.y) - .attr("stroke", "black"); - - selectAll(".neighbor-line").remove(); - - // get location of currently select circle - // let loc = d.slice(0, 2); - let loc = [d.x, d.y]; - // get n-nearest neighbors to circle - let nn = getNNN(this.trainData, loc, this.k); - // edit opacity for polygons - this.fitG.selectAll(".polygon").style("fill", "white"); - // draw lines between points - const points = this.trainData.filter((d, i) => nn.indexOf(i) > -1); - this.fitG - .selectAll(".line") - .data(points) - .enter() - .append("line") - .attr("class", "neighbor-line") - .attr("x1", (d) => loc[0]) - .attr("y1", (d) => loc[1]) - .attr("x2", this.xAcc) - .attr("y2", this.yAcc) - .attr("stroke", "#161E2D") - .style("stroke-width", 2.5) - .style("pointer-events", "none") - .style("opacity", 0.45) - .lower(); - - // track last hovered so can change k easily - this.lastDataHovered = d; - } - - addDecisionLine(gEl) { - const genBoundary = () => { - let step = 1; - let data = []; - for (let x = 1.5; x <= 11.5; x += step) { - data.push({ - x: ((x - 1.5) / 10) * this.WIDTH, - y: this.HEIGHT - (equation(x) / 10) * this.HEIGHT, - }); - } - return data; - }; - const boundaryLine = line() - .x((d) => d.x) - .y((d) => d.y) - .curve(curveCatmullRom.alpha(0.95)); - - gEl - .append("g") - .attr("class", "line-g") - .selectAll(".boundary") - .data([genBoundary()]) - .enter() - .append("path") - .attr("d", boundaryLine) - .attr("class", "boundary") - .attr("fill", "none") - .attr("stroke", "#161E2D") - .attr("stroke-width", 4) - .attr("opacity", 0.5) - .style("stroke-dasharray", "5, 5") - .style("pointer-events", "none"); - - gEl.select("path.boundary").lower(); - } - - drawKnnRight() { - const that = this; - // redraw, so remove existing stuff - const removeClasses = [".mesh-g", ".hex-cell"]; - removeClasses.map((d) => { - this.predictG.selectAll(d).remove(); - }); - - // init voronoi - const delan = Delaunay.from( - this.hexCenters, - (d) => d.x, - (d) => d.y - ); - - // draw hex grid - this.predictG - .append("g") - .attr("class", "mesh-g") - .selectAll("path") - .data(this.hexCenters) - .enter() - .append("path") - .attr("class", "hex-cell") - .attr("d", (d) => `M${d.x},${d.y}${this.hexbin.hexagon()}`) - .attr("stroke-width", 0) - .attr("stroke", "#161E2D"); - - // add the overlay on top of everything to take the mouse events - this.predictG - .append("rect") - .attr("class", "overlay") - .attr("width", this.WIDTH) - .attr("height", this.HEIGHT) - .style("opacity", 0) - .on("mousemove", hexHover) - .on("mouseleave", this.hexLeave); - // lll - function hexHover(event) { - // get the current mouse position - const [mx, my] = pointer(event); - // use the new diagram.find() function to find the Voronoi mouse position - const closestPoint = delan.find(mx, my); - // highlight the point if we found one - const closestHex = that.hexCenters[closestPoint]; - // get closest data point - const closestData = { x: mx, y: my }; - - that.highlight(closestHex, that.k); - - // highlight current cell - const cell = selectAll(".hex-cell").filter( - (d) => d.x === closestHex.x && d.y === closestHex.y - ); - - cell.attr("stroke", "black").attr("stroke-width", 3).raise(); - - // unhover other ones - selectAll(".hex-cell") - .filter((d) => d.x !== closestHex.x || d.y !== closestHex.y) - .attr("stroke-width", 0) - .style("stroke", "#161E2D"); - - select(".highlight-circle") - .style("display", "") - .attr("cx", closestHex.x) - .attr("cy", closestHex.y) - .attr("stroke", "black"); - } - - // draw line - this.addDecisionLine(this.predictG); - this.updateHexGrid(this.k); - } - - updateHexGrid(k) { - const that = this; - - function getKnnClassScores(data, point, k) { - // get nearest neighbors to cell - const nn = getKNN(data, point, k); - // filter data to class scores of nn - const nScores = nn.map((d) => d[2]); - const sum0 = nScores.filter((v) => v === 0).length / k; - const sum1 = nScores.filter((v) => v === 1).length / k; - const cellVal = sum1 - sum0; - return that.cellColor(cellVal); - } - - selectAll(".hex-cell").each(function (d, i) { - const pred = getKnnClassScores(that.trainData, d[0], k); - select(this).attr("fill", pred).attr("stroke", pred); - }); - } - - addSlider() { - const that = this; - // SLIDER - const el = select(`#${this.sliderContainer}`); - - // draw text - el.append("h4").attr("id", `k-text`).html(`K: ${this.k}`); - - // draw slider - el.append("input") - .attr("class", "metric-slider") - .attr("type", "range") - .attr("value", `${this.k}`) - .attr("min", 1) - .attr("max", numPoints) - .attr("step", 2) - .attr("maxlength", "100"); - - selectAll(`.metric-slider`).on("input", function (d) { - // only update text - let val = select(this).property("value"); - // set price to object state - const currValue = Number(val); - this.k = currValue; - select("#k-text").html(`K: ${this.k}`); - // update left chart - that.highlight(that.lastDataHovered, this.k); - // update right chart - that.updateHexGrid(this.k); - }); - } - - // initialize svg and params for chart - initChartSvg(stage) { - const svg = select(`#${stage}`) - .append("svg") - .attr("id", `${stage}-svg`) - .attr("width", this.WIDTH + this.MARGIN.LEFT + this.MARGIN.RIGHT) - .attr("height", this.HEIGHT + this.MARGIN.TOP + this.MARGIN.BOTTOM) - .style("border", "1px solid rgba(0, 0, 0, 0.3)"); - - const g = svg - .append("g") - .attr("id", `${stage}-g`) - .attr("transform", `translate(${this.MARGIN.LEFT}, ${this.MARGIN.TOP})`); - - return g; - } -} - -// Nearest Neighbor Helper Functions -function getNNN(data, loc, k) { - // 1. get the distances between the data (train_data) - // and the current point (loc). - let dists = []; - for (let i = 0; i < data.length; i++) { - dists.push([i, dist(data[i], loc)]); - } - // 2. sort those points to the test data in ascending order - dists.sort((a, b) => a[1] - b[1]); - // 3. Keep the indices of the top k train_data points - let nnn = []; - for (let i = 0; i < k; i++) { - nnn.push(dists[i][0]); - } - return nnn; -} - -function getKNN(data, point, k) { - // 1. get the distances between the data & current point. - let dists = []; - for (let i = 0; i < data.length; i++) { - // now index 2 is the actual data - dists.push([i, dist(data[i], point), data[i]]); - } - // 2. sort those points to the test data in ascending order - dists.sort((a, b) => a[1] - b[1]); - - // 3. Keep the indices of the top n-nearest neighbors - let knn = []; - for (let i = 0; i < k; i++) { - knn.push(dists[i][2]); // to return index, return 0 - } - return knn; -} diff --git a/bias-variance/js/Loess.js b/bias-variance/js/Loess.js deleted file mode 100644 index 634f3a00..00000000 --- a/bias-variance/js/Loess.js +++ /dev/null @@ -1,435 +0,0 @@ -import { select, selectAll } from "d3-selection"; -import { axisBottom, axisLeft } from "d3-axis"; -import { scaleBand, scaleLinear, scaleOrdinal } from "d3-scale"; -import { line } from "d3-shape"; -import { transition } from "d3-transition"; -import { easeBounceInOut, easeExpInOut } from "d3-ease"; -import { regressionLoess } from "d3-regression"; -import { meanSquaredError } from "./meanSquaredError"; -import { randomNormal } from "d3-random"; - -export class Loess { - constructor(opts) { - const that = this; - // selections - this.loessContainer = opts.loessContainer; - this.barContainer = opts.barContainer; - this.sliderContainer = opts.sliderContainer; - - // init bandwidth hyperparameter - this.bandwidth = 0.42; - - // set circle size - this.circleSize = 4.5; - - // set constant SVG parameters - this.MARGIN = { TOP: 30, BOTTOM: 20, LEFT: 35, RIGHT: 10 }; - const containerSize = select(`#${this.loessContainer}`) - .node() - .getBoundingClientRect(); - this.WIDTH = containerSize.width * 0.8; - this.HEIGHT = - window.innerWidth <= 600 - ? containerSize.height * 0.6 - : containerSize.height * 0.7; - - // add slider to DOM - this.addSlider(); - - // draw charts - this.drawLoess(); - this.drawBars(); - - this.generateData(); - - // draw initial line - this.drawLoessLine(this.bandwidth); - } - - genData(train) { - // generate dataset - const n_samples = 30; - - // helper func for rounding (keep in scope) - const roundToTwo = (num) => +(Math.round(num + "e+2") + "e-2"); - - // equation to model - const f = (x) => Math.sin(2.5 * Math.PI * x) / 1.5; - - // want array of objects - let x = Array.from({ length: n_samples }, (x, i) => roundToTwo(i * 0.029)); - x = x.map((val) => val + Math.abs(randomNormal(0, 0.05)())).sort(); - // ensure x-values cover full span of plot - if (train === true) { - x[0] = 0.01; - x[x.length - 1] = 0.99; - } - // define y vals - const y = x.map((val) => f(val) + randomNormal(0, 0.25)()); - - // create dataset of x, y pairs - const data = x.map((val, i) => { - return { x: val, y: y[i] }; - }); - - return data; - } - - generateData() { - this.loessG.selectAll("circle.loess-train").remove(); - - this.loessDataTrain = this.genData(true); - - // draw train circles - this.loessG - .selectAll("circle.loess-train") - .data(this.loessDataTrain) - .enter() - .append("circle") - .attr("class", "loess-train") - .attr("r", this.circleSize) - .attr("cx", (d) => this.xScale(+d.x)) - .attr("cy", (d) => this.yScale(+d.y)); - - this.drawLoessLine(this.bandwidth); - } - - drawLoess() { - // set up svg - this.loessG = this.initChartSvg(this.loessContainer); - - this.xScale = scaleLinear().domain([0, 1]).range([0, this.WIDTH]); - - // define y-scale - this.yScale = scaleLinear().domain([-1.4, 1.4]).range([this.HEIGHT, 0]); - - // define axes - const xAxisGeneratorLoess = axisBottom(this.xScale) - .ticks(4) - .tickSizeOuter(0); - const yAxisGeneratorLoess = axisLeft(this.yScale) - .tickSizeOuter(0) - .tickValues([0]); - - // instantiate line generator - this.lineGenerator = line() - .x((d) => this.xScale(d[0])) - .y((d) => this.yScale(d[1])); - - // add x-axis - this.xAxis = this.loessG - .append("g") - .attr("class", "axis") - .attr("id", "loess-x-axis") - .call(xAxisGeneratorLoess) - .attr("transform", `translate(0, ${this.HEIGHT})`); - - // add y-axis - this.yAxis = this.loessG - .append("g") - .attr("class", "axis") - .attr("id", "loess-y-axis") - .call(yAxisGeneratorLoess); - - // make tick longer for y-axis - select("#loess-y-axis").select("line").attr("x2", this.WIDTH); - select("#loess-y-axis").select("line").lower(); - this.loessDataTest = this.genData(false); - - this.loessG - .selectAll("circle.loess-test") - .data(this.loessDataTest) - .enter() - .append("circle") - .attr("class", "loess-test") - .attr("r", this.circleSize) - .attr("cx", (d) => this.xScale(+d.x)) - .attr("cy", (d) => this.yScale(+d.y)); - - // add legend - this.addLegend(); - } - - drawLoessLine(bandwidth) { - // remove text warning if exits - select("#bar-warning").remove(); - - // remove lines - selectAll("#loess-line").remove(); - selectAll("circle.loess-t").remove(); - - // set-up loess - this.regressionGenerator = regressionLoess() - .x((d) => d.x) - .y((d) => d.y) - .bandwidth(bandwidth); - - // get data from loess model - const modelData = this.regressionGenerator(this.loessDataTrain); - - // draw loess line - this.loesPath = this.loessG - .append("path") - .attr("id", "loess-line") - .datum(modelData) - .attr("d", this.lineGenerator); - - // ensure line under circles - select("path#loess-line").lower(); - - // calculate MSE - const yTrain = this.loessDataTrain.map((d) => d.y); - const yTest = this.loessDataTest.map((d) => d.y); - const yPred = modelData.map((d) => d[1]); - const trainMse = meanSquaredError(yTrain, yPred); - const testMse = meanSquaredError(yTest, yPred); - const mseData = [ - { name: "Train", error: trainMse }, - { name: "Test", error: testMse }, - ]; - this.plotBars(mseData); - // add warning if unfit data - if (trainMse > 0.4) { - this.addBarWarning(); - } - } - - addBarWarning() { - let ttext = this.barG - .append("text") - .attr("id", "bar-warning") - .style("font-size", ".9rem") - .attr("x", 11.5) - .attr("y", 0); - ttext - .append("tspan") - .text("Uh-oh! Sometimes our model doesn't fit the data."); - ttext.append("tspan").text("Try again!").attr("x", 11.5).attr("dy", 15); - } - - drawBars() { - // set up svg - this.barG = this.initChartSvg(this.barContainer); - - // define scales - // x scale - this.xScaleError = scaleLinear().domain([0, 0.25]).range([0, this.WIDTH]); - // y scale - this.yScaleError = scaleBand() - .domain(["Train", "Test"]) - .range([0, this.HEIGHT]) - .padding(0.1); - this.loessBarColor = scaleOrdinal() - .domain(["Test", "Train"]) - .range(["#e57873", "#94CAE0"]); - - // define axes - this.xAxisGeneratorError = axisBottom(this.xScaleError) - .tickSizeOuter(0) - .ticks(4); - - this.yAxisGeneratorError = axisLeft(this.yScaleError) - .tickSizeOuter(0) - .ticks(4); - // add x-axis - this.xAxisError = this.barG - .append("g") - .attr("class", "axis") - .attr("id", "bar-x-axis") - .call(this.xAxisGeneratorError) - .attr("transform", `translate(0, ${this.HEIGHT})`); - - // add y-axis - this.yAxis = this.barG - .append("g") - .attr("class", "axis") - .attr("id", "bar-y-axis") - .call(this.yAxisGeneratorError); - - // instantiate line generator - this.lineGenerator = line() - .x((d) => this.xScale(d[0])) - .y((d) => this.yScale(d[1])); - } - - resizeCharts() { - this.MARGIN = { TOP: 30, BOTTOM: 20, LEFT: 35, RIGHT: 10 }; - const containerSize = select(`#${this.loessContainer}`) - .node() - .getBoundingClientRect(); - this.WIDTH = containerSize.width * 0.8; - // this.HEIGHT = containerSize.height * 0.7; - select("#fit-container-loess-svg") - .transition() - .attr("width", this.WIDTH + this.MARGIN.LEFT + this.MARGIN.RIGHT) - // .attr("height", this.HEIGHT + this.MARGIN.TOP + this.MARGIN.BOTTOM) - .select("#fit-container-loess-g"); - // .attr("transform", `translate(${this.MARGIN.LEFT}, ${this.MARGIN.TOP})`); - - select("#predict-container-loess-svg") - .transition() - .attr("width", this.WIDTH + this.MARGIN.LEFT + this.MARGIN.RIGHT) - // .attr("height", this.HEIGHT + this.MARGIN.TOP + this.MARGIN.BOTTOM) - .select("#predict-container-loess-g"); - // .attr("transform", `translate(${this.MARGIN.LEFT}, ${this.MARGIN.TOP})`); - - // update scales - // errir scales - this.xScaleError.range([0, this.WIDTH]); - this.yScaleError.range([0, this.HEIGHT]); - // loess scales - this.xScale.range([0, this.WIDTH]); - this.yScale.range([this.HEIGHT, 0]); - - // update axes - // error - this.xAxisError - .transition() - .call(axisBottom(this.xScaleError).tickSizeOuter(0).ticks(4)); - // loess - this.xAxis - .transition() - .call(axisBottom(this.xScale).ticks(4).tickSizeOuter(0)); - - // resize bars - this.rects - .transition() - .duration(1000) - .attr("width", (d) => this.xScaleError(+d.error)); - - // redraw circles - this.loessG - .selectAll("circle.loess-train") - .transition() - .ease(easeBounceInOut) - .attr("cx", (d) => this.xScale(+d.x)) - .attr("cy", (d) => this.yScale(+d.y)); - - // redraw circles - this.loessG - .selectAll("circle.loess-test") - .transition() - .ease(easeExpInOut) - .attr("cx", (d) => this.xScale(+d.x)) - .attr("cy", (d) => this.yScale(+d.y)); - - this.drawLoessLine(this.bandwidth); - } - - plotBars(data) { - // need to transition axes - let t = transition().delay(500).duration(600); - - // join - this.rects = this.barG.selectAll(`rect.loessBar`).data(data, (d) => d.name); - - // update - this.rects - .transition() - .duration(65) - .attr("x", (d) => this.xScaleError(0)) - .attr("y", (d) => this.yScaleError(d.name)) - .attr("width", (d) => this.xScaleError(+d.error)) - .attr("height", this.yScaleError.bandwidth()); - - // enter - this.rects - .enter() - .append("rect") - .attr("class", "loessBar") - .attr("x", (d) => this.xScaleError(0)) - .attr("y", (d) => this.yScaleError(d.name)) - .attr("height", this.yScaleError.bandwidth()) - .attr("width", (d) => this.xScaleError(+d.error)) - .attr("fill", (d) => this.loessBarColor(d.name)); - } - - addLegend() { - const that = this; - const legendData = ["Train Data", "Test Data"]; - - const nodeWidth = (d) => d.getBBox().width; - - const legend = this.loessG - .append("g") - .attr("class", "loess-legend") - .attr("transform", "translate(0,0)"); - - const lg = legend.selectAll("g").data(legendData).enter().append("g"); - - const legendCircle = lg - .append("circle") - .attr("r", 4.5) - .attr("id", (d, i) => (i % 2 == 0 ? "loess-train" : "loess-test")) - .attr("cy", 5) - .attr("cx", 1) - .attr("stroke-width", 0); - lg.append("text") - .style("font-size", "14px") - .attr("x", 11.5) - .attr("y", 10) - .text((d) => d); - - let offset = 0; - - lg.attr("transform", function (d) { - let x = offset; - offset += nodeWidth(this) + 10; - return `translate(${x}, ${0})`; - }); - - legend.attr("transform", function () { - return `translate(${ - (that.WIDTH - nodeWidth(this)) / 2 - }, ${-that.MARGIN.TOP / 2})`; - }); - } - - addSlider() { - const that = this; - // SLIDER - const el = select(`#${this.sliderContainer}`); - - // draw text - el.append("h4") - .attr("id", `loess-text`) - .html(`Smoothness: ${this.bandwidth.toFixed(2)}`); - - // draw slider - el.append("input") - .attr("id", `loess-slider`) - .attr("class", "metric-slider") - .attr("type", "range") - .attr("min", 0.01) - .attr("max", 1) - .attr("step", 0.01) - .attr("value", 0.42); - - selectAll(`#loess-slider`).on("input", function (d) { - // only update text - let val = select(this).property("value"); - // set price to object state - const currValue = Number(val); - that.bandwidth = currValue; - select("#loess-text").html(`Smoothness: ${that.bandwidth.toFixed(2)}`); - // update left chart - that.drawLoessLine(that.bandwidth); - }); - } - - // initialize svg and params for chart - initChartSvg(stage) { - const svg = select(`#${stage}`) - .append("svg") - .attr("id", `${stage}-svg`) - .attr("width", this.WIDTH + this.MARGIN.LEFT + this.MARGIN.RIGHT) - .attr("height", this.HEIGHT + this.MARGIN.TOP + this.MARGIN.BOTTOM); - - const g = svg - .append("g") - .attr("id", `${stage}-g`) - .attr("transform", `translate(${this.MARGIN.LEFT}, ${this.MARGIN.TOP})`); - - return g; - } -} diff --git a/bias-variance/js/Scatter.js b/bias-variance/js/Scatter.js deleted file mode 100644 index be585391..00000000 --- a/bias-variance/js/Scatter.js +++ /dev/null @@ -1,577 +0,0 @@ -import { regressionLinear } from "d3-regression"; -import { meanSquaredError } from "./meanSquaredError"; -import { trainData, testData, modelsData } from "./dataFiles"; -import { testColorDot, trainColorDot } from "./constants"; -import { select, selectAll } from "d3-selection"; -import { axisBottom, axisLeft } from "d3-axis"; -import { scaleLinear } from "d3-scale"; -import { line, curveCatmullRom } from "d3-shape"; -import { transition } from "d3-transition"; -import { extent } from "d3-array"; -import { easeBack } from "d3-ease"; - -export class Scatter { - constructor(opts) { - // load in arguments from config object - this.element = opts.element; - this.trainData = trainData; - this.testData = testData; - - this.margin = - window.innerWidth <= 700 - ? { left: 20, right: 1, top: 15, bottom: 30 } - : { left: 40, right: 9, top: 15, bottom: 40 }; - this.chartDims = select("#scroll-figure").node().getBoundingClientRect(); - - // this.innerWidth = - // window.innerWidth <= 700 - // ? window.innerWidth / 3.1 - // : window.innerWidth / 3; - this.innerWidth = this.chartDims.width * 0.85; - this.innerHeight = window.innerHeight * 0.36; - this.radius = window.innerWidth < 700 ? 6 : 10; - - // scale for bottom charts (so don't remake) - this.realizationScale = scaleLinear() - .domain(extent(modelsData, (d) => d.x)) - .range([0, this.innerWidth]); - this.realizationLine = line() - .x((d) => this.realizationScale(d.x)) - .y((d) => this.yScale(d[column])) - .curve(curveCatmullRom.alpha(0.5)); - - // grab errorBar chart so we can line up transitions - this.errorBar = opts.errorBar; - - this.linearRegression = regressionLinear() - .x((d) => d.x) - .y((d) => d.y) - .domain([0, 16]); - - // tracker for loops - this.loopDraw = true; - - this.drawBaseChart(); - } - - loop() { - const that = this; - // loop through async func calls - const args = arguments; - this.loopDraw = true; - if (args.length <= 0) return; - (function chain(i) { - // break when scroll to different stage - if (that.loopDraw !== true) return; - if (i >= args.length || typeof args[i] !== "function") return; - window.setTimeout(function () { - args[i](); - chain(i + 1); - }, 110); - })(0); - } - - drawBaseChart() { - // define x-scale - this.xScale = scaleLinear() - .domain([-0.05, 1.15]) - .range([0, this.innerWidth]); - - // define y-scale - this.yScale = scaleLinear() - .domain([-1.4, 1.4]) - .range([this.innerHeight, 0]); - - // create line generator - this.lineGen = line() - .x((d) => this.xScale(d.x)) - .y((d) => this.yScale(d.y)) - .curve(curveCatmullRom.alpha(0.5)); - - this.lineGenUF = line() - .x((d) => this.xScale(d.x)) - .y((d) => this.yScale(d.y_pred_uf)) - .curve(curveCatmullRom.alpha(0.5)); - - this.lineGenOF = line() - .x((d) => this.xScale(d.x)) - .y((d) => this.yScale(d.y_pred_of)) - .curve(curveCatmullRom.alpha(0.5)); - - // define axes - this.xAxisGenerator = axisBottom(this.xScale).tickSizeOuter(0).ticks(4); - this.yAxisGenerator = axisLeft(this.yScale).tickSizeOuter(0).ticks(4); - - // create svg - this.scatterSvg = select(this.element) - .append("svg") - .attr("id", "scatter-svg") - .attr("width", this.innerWidth + this.margin.left + this.margin.right) - .attr("height", this.innerHeight + this.margin.top + this.margin.bottom); - - // create scatter plot - this.scatterPlot = this.scatterSvg - .append("g") - .attr("transform", `translate(${this.margin.left}, ${this.margin.top})`); - - // add x-axis - this.xAxis = this.scatterPlot - .append("g") - .attr("class", "axis") - .attr("id", "scatter-x-axis") - .call(this.xAxisGenerator) - .attr("transform", `translate(0, ${this.innerHeight})`); - - // add y-axis - this.yAxis = this.scatterPlot - .append("g") - .attr("class", "axis") - .call(this.yAxisGenerator); - - // add circles - this.scatterPlot - .selectAll("circle.train") - .data(this.trainData) - .enter() - .append("circle") - .attr("class", "train") - .attr("r", this.radius) - .attr("cx", (d) => this.xScale(d.x)) - .attr("cy", (d) => this.yScale(d.y)); - - // add labels to axes - this.xLabel = this.scatterSvg - .append("text") - .attr("id", "scatter-x-axis-label") - .attr("x", this.innerWidth / 2 + this.margin.left + this.margin.right) - .attr("y", this.innerHeight + this.margin.top + this.margin.bottom) - .text("X"); - this.yLabel = this.scatterSvg - .append("text") - .attr("id", "scatter-y-axis-label") - .attr("x", 0) - .attr("y", this.innerHeight / 2 + this.margin.top + 4) - .text("Y") - .style("fill", "black"); - } - - drawLineUnderfit() { - // Create a update selection: bind to the new data - this.regressionLine = this.scatterPlot - .selectAll("#regression-line-1") - .data([this.trainData], (d) => d.x); - // Updata the line - this.regressionLine - .enter() - .append("path") - .attr("id", "regression-line-1") - .merge(this.regressionLine) - .transition() - .ease(easeBack) - .duration(1500) - .attr("d", this.lineGenUF); - - // ensure line under circles - selectAll("path#regression-line-1").lower(); - } - - drawLineOverfit() { - this.regressionLine = this.scatterPlot - .selectAll("#regression-line-1") - .data([this.trainData], (d) => d.x); - // Updata the line - this.regressionLine - .enter() - .append("path") - .attr("id", "regression-line-1") - .merge(this.regressionLine) - .transition() - .ease(easeBack) - .duration(1500) - .attr("d", this.lineGenOF); - } - - drawTrainErrorUnderfit() { - const linRegData = this.linearRegression(this.trainData); - const a = linRegData.a; - const b = linRegData.b; - - // draw error bars - let errorData = this.trainData.map((pair) => { - let errorBnds = { - x: pair.x, // keep x at same point - y1: a * pair.x + b, - y2: pair.y, - }; - return errorBnds; - }); - // draw vertical line between pairs of points - let errorLines = this.scatterPlot - .selectAll("line.errorLine") - .data(errorData); - - let errorLinePath = errorLines - .enter() - .merge(errorLines) - .append("line") - .attr("class", "errorLine") - .attr("x1", (d) => this.xScale(d.x)) - .attr("x2", (d) => this.xScale(d.x)) - .attr("y1", (d) => this.yScale(d.y2)) - .attr("y2", (d) => this.yScale(d.y2)); - selectAll("line.errorLine").lower(); - - errorLinePath - .transition() - .duration(1000) - .attr("y1", (d) => this.yScale(d.y1)); - - const trainY = this.trainData.map((d) => d.y); - const trainPred = this.trainData.map((d) => a * d.x + b); - - const trainMSE = meanSquaredError(trainY, trainPred); - this.underfitError = [{ name: "Train", error: trainMSE }]; - this.errorBar.plotBars(this.underfitError); - - // update title of bar - this.errorBar.addTitle("Mean-Squared Error"); - } - - drawTestErrorOverfit() { - const linRegData = this.linearRegression(this.trainData); - const a = linRegData.a; - const b = linRegData.b; - let testErrorData = this.testData.map((pair) => { - let errorBnds = { - x: pair.x, // keep x at same point - y1: a * pair.x + b, - y2: pair.y, - }; - return errorBnds; - }); - - let testErrorLines = this.scatterPlot - .selectAll("line.testErrorLine") - .data(testErrorData); - - testErrorLines - .enter() - .merge(testErrorLines) - .append("line") - .attr("class", "testErrorLine") - .attr("x1", (d) => this.xScale(d.x)) - .attr("x2", (d) => this.xScale(d.x)) - .attr("y1", (d) => this.yScale(d.y2)) - .attr("y2", (d) => this.yScale(d.y2)) - .transition() - .duration(1000) - .attr("y1", (d) => this.yScale(d.y1)); - - selectAll("line.testErrorLine").lower(); - - this.scatterPlot - .selectAll("circle.test") - .data(this.testData) - .enter() - .append("circle") - .attr("class", "test") - .attr("r", 0) - .attr("cx", (d) => this.xScale(d.x)) - .attr("cy", (d) => this.yScale(d.y)) - .transition() - .attr("r", this.radius * 2) - .transition() - .attr("r", this.radius); - - // calculate MSE for barcharts - const testY = this.testData.map((d) => d.y); - const testPred = this.testData.map((d) => a * d.x + b); - const testMSE = meanSquaredError(testY, testPred); - - this.underfitError.push({ name: "Test", error: testMSE }); - this.errorBar.plotBars(this.underfitError); - } - - drawBarsTestOverfit() { - const that = this; - // func to find interpolated point along complex path - // using binary search - function findY(path, x) { - path = path.node(); - const pathLength = path.getTotalLength(); - let start = 0; - let end = pathLength; - let target = (start + end) / 2; - - while (target >= start && target <= pathLength) { - let pos = path.getPointAtLength(target); - - // use threshold bc float point precision - if (Math.abs(pos.x - x) < 0.1) { - return that.yScale.invert(pos.y); - } else if (pos.x > x) { - end = target; - } else { - start = target; - } - target = (start + end) / 2; - } - } - // define error data - let errorData = this.testData.map((pair) => { - let errorBnds = { - x: pair.x, // keep x at same point - y1: findY(that.regressionLine, that.xScale(pair.x)), - y1: findY(that.regressionLine, that.xScale(pair.x)), - y2: pair.y, - }; - return errorBnds; - }); - - // draw error lines - let errorLines = this.scatterPlot - .selectAll("line.errorLineComplex") - .data(errorData); - - let errorLinePath = errorLines - .enter() - .merge(errorLines) - .append("line") - .attr("class", "errorLineComplex") - .attr("x1", (d) => this.xScale(d.x)) - .attr("x2", (d) => this.xScale(d.x)) - .attr("y1", (d) => this.yScale(d.y2)) - .attr("y2", (d) => this.yScale(d.y2)); - - selectAll("line.errorLineComplex").lower(); - - errorLinePath - .transition() - .duration(1000) - .attr("y1", (d) => this.yScale(d.y1)); - - // calculate MSE for barcharts - const testY = this.testData.map((d) => d.y); - const testPred = this.testData.map((d) => - findY(that.regressionLine, that.xScale(d.x)) - ); - const trainY = this.trainData.map((d) => d.y); - const trainPred = this.trainData.map((d) => - findY(that.regressionLine, that.xScale(d.x)) - ); - - const trainMSE = meanSquaredError(trainY, trainPred); - const testMSE = meanSquaredError(testY, testPred); - - // draw bars overfit - this.overfitError.push({ name: "Test", error: testMSE }); - this.errorBar.plotBars(this.overfitError); - } - - drawBarsTrainOverfit() { - this.overfitError = [{ name: "Train", error: 0.001 }]; - this.errorBar.plotBars(this.overfitError); - } - - plotTestBar() { - const testError = this.overfitError.filter((d) => d.name === "Test"); - const maxError = 5; - testError.push({ name: "Test", error: maxError * 1.01 }); - testError.push({ name: "Test", error: maxError * 1.02 }); - this.errorBar.plotDecompositionBar(testError); - this.errorBar.addTitle("Test Error Decomposition"); - } - - modelRealization(opa, column) { - this.realizationLine.y((d) => this.yScale(d[column])); - // Create a update selection: bind to the new data - const modelRealization = this.scatterPlot - .selectAll(`#${column}`) - .data([modelsData]); - - // Updata the line - modelRealization - .enter() - .append("path") - .attr("id", `${column}`) - .attr("class", `model-realization`) - .attr("d", this.realizationLine) - .style("opacity", opa) - .transition() - .style("opacity", opa); - selectAll("path.model-realization").lower(); - } - - drawUnderfitRealizations() { - this.addTitle("Simple Model Realizations"); - // draw associated errors - const that = this; - this.underfitOpa = 0.3; - this.goodFitOpa = 0; - this.overfitOpa = 0; - this.drawLoop = false; - selectAll("[id^='uf-preds']").remove(); - selectAll("[id^='of-preds']").remove(); - selectAll("[id^='good-preds']").remove(); - this.errorBar.drawErrorBase(); - window.setTimeout(function () { - that.drawLoop = true; - }, 120); - this.loop( - () => this.modelRealization(this.underfitOpa, "uf-preds-0"), - () => this.modelRealization(this.underfitOpa, "uf-preds-1"), - () => this.modelRealization(this.underfitOpa, "uf-preds-2"), - () => this.modelRealization(this.underfitOpa, "uf-preds-3"), - () => this.modelRealization(this.underfitOpa, "uf-preds-4"), - () => this.modelRealization(this.underfitOpa, "uf-preds-5"), - () => this.modelRealization(this.underfitOpa, "uf-preds-6"), - () => this.modelRealization(this.underfitOpa, "uf-preds-7"), - () => this.modelRealization(this.underfitOpa, "uf-preds-8"), - () => this.modelRealization(this.underfitOpa, "uf-preds-9"), - () => this.modelRealization(this.underfitOpa, "uf-preds-10"), - () => this.modelRealization(this.underfitOpa, "uf-preds-11"), - () => this.modelRealization(this.underfitOpa, "uf-preds-12") - ); - } - - drawOverfitRealizations() { - const that = this; - this.drawLoop = false; - this.overfitOpa = 0.3; - this.underfitOpa = 0; - this.goodFitOpa = 0; - selectAll("[id^='uf-preds']").remove(); - selectAll("[id^='of-preds']").remove(); - selectAll("[id^='good-preds']").remove(); - this.addTitle("Complex Model Realizations"); - window.setTimeout(function () { - that.drawLoop = true; - }, 120); - this.loop( - () => this.modelRealization(this.overfitOpa, "of-preds-0"), - () => this.modelRealization(this.overfitOpa, "of-preds-1"), - () => this.modelRealization(this.overfitOpa, "of-preds-2"), - () => this.modelRealization(this.overfitOpa, "of-preds-3"), - () => this.modelRealization(this.overfitOpa, "of-preds-4"), - () => this.modelRealization(this.overfitOpa, "of-preds-5"), - () => this.modelRealization(this.overfitOpa, "of-preds-6"), - () => this.modelRealization(this.overfitOpa, "of-preds-7"), - () => this.modelRealization(this.overfitOpa, "of-preds-8"), - () => this.modelRealization(this.overfitOpa, "of-preds-9"), - () => this.modelRealization(this.overfitOpa, "of-preds-10"), - () => this.modelRealization(this.overfitOpa, "of-preds-11"), - () => this.modelRealization(this.overfitOpa, "of-preds-12") - ); - } - - drawGoodRealizations() { - const that = this; - this.drawLoop = false; - this.overfitOpa = 0; - this.underfitOpa = 0; - this.goodFitOpa = 0.3; - selectAll("[id^='uf-preds']").remove(); - selectAll("[id^='of-preds']").remove(); - selectAll("[id^='good-preds']").remove(); - this.addTitle("Balanced Model Realizations"); - window.setTimeout(function () { - that.drawLoop = true; - }, 120); - this.loop( - () => this.modelRealization(this.goodFitOpa, "good-preds-0"), - () => this.modelRealization(this.goodFitOpa, "good-preds-1"), - () => this.modelRealization(this.goodFitOpa, "good-preds-2"), - () => this.modelRealization(this.goodFitOpa, "good-preds-3"), - () => this.modelRealization(this.goodFitOpa, "good-preds-4"), - () => this.modelRealization(this.goodFitOpa, "good-preds-5"), - () => this.modelRealization(this.goodFitOpa, "good-preds-6"), - () => this.modelRealization(this.goodFitOpa, "good-preds-7"), - () => this.modelRealization(this.goodFitOpa, "good-preds-8"), - () => this.modelRealization(this.goodFitOpa, "good-preds-9"), - () => this.modelRealization(this.goodFitOpa, "good-preds-10"), - () => this.modelRealization(this.goodFitOpa, "good-preds-11"), - () => this.modelRealization(this.goodFitOpa, "good-preds-12") - ); - } - - addLegend() { - const that = this; - const legendData = ["Train Data", "Test Data"]; - - const nodeWidth = (d) => d.getBBox().width; - - const legend = this.scatterPlot - .append("g") - .attr("class", "legend") - .attr("transform", "translate(0,0)"); - - const lg = legend.selectAll("g").data(legendData).enter().append("g"); - - const legendCircle = lg - .append("circle") - .attr("class", "scatter-legend-circle") - .attr("r", 6) - .attr("fill", (d, i) => (i % 2 == 0 ? trainColorDot : testColorDot)) - .attr("cy", -5) - .attr("cx", 1) - .attr("stroke-width", 0); - lg.append("text") - .attr("class", "scatter-legend-text") - .attr("x", 11.5) - .attr("y", 0) - .text((d) => d); - - let offset = 0; - - lg.attr("transform", function (d) { - let x = offset; - let offsetValue = window.innerWidth <= 600 ? 3 : 10; - offset += nodeWidth(this) + offsetValue; - return `translate(${x}, ${0})`; - }); - - legend.attr("transform", function () { - return `translate(${(that.innerWidth - nodeWidth(this)) / 2}, 0)`; - }); - } - - removeRealizationLines() { - // remove predrawn lines - this.overfitOpa = 0; - this.underfitOpa = 0; - this.goodFitOpa = 0.3; - selectAll("[id^='uf-preds']").remove(); - selectAll("[id^='of-preds']").remove(); - selectAll("[id^='good-preds']").remove(); - } - - transitionUp5() { - this.removeRealizationLines(); - - // remove lines & symbols from error line charts - selectAll(".error-line-bottom").remove(); - select("rect.stacked-error").remove(); - select("#error-text").remove(); - selectAll(".error-symbol").remove(); - - // update title - this.errorBar.addTitle("Test Error Decomposition"); - - this.errorBar.reverseDecomposition(); - } - - addTitle(title) { - // remove title if shown - select(".scatter-title").remove(); - - // add title - this.scatterSvg - .append("g") - .append("text") - .attr("class", "scatter-title") - .attr("x", this.innerWidth / 2 + this.margin.left) - .attr("y", 20) - .text(title) - .attr("text-anchor", "middle"); - } -} diff --git a/bias-variance/js/annotations.js b/bias-variance/js/annotations.js deleted file mode 100644 index 3b56e3d4..00000000 --- a/bias-variance/js/annotations.js +++ /dev/null @@ -1,105 +0,0 @@ -import { annotate } from "rough-notation"; -const col2 = "rgba(255, 241, 118, .5)"; // "rgba(0,128,128, .48)"; -const biasColor = "skyblue"; -const varianceColor = "rgba(0,128,128, .8)"; -const n1 = document.querySelector("#bias"); -const n2 = document.querySelector("#variance"); -const n3 = document.querySelector("#overfit-highlight"); -const n4 = document.querySelector("#underfit-highlight"); -const n5 = document.querySelector("#underfit-def"); -const n6 = document.querySelector("#overfit-def"); -const n7 = document.querySelector("#bias-def"); -const n8 = document.querySelector("#variance-def"); -const n9 = document.querySelector("#balance-def"); -const n10 = document.querySelector("#decomp-highlight"); -const n11 = document.querySelector("#conclusion"); - -const a1 = annotate(n1, { - type: "highlight", - color: biasColor, - strokeWidth: 1, -}); -const a2 = annotate(n2, { - type: "highlight", - color: varianceColor, - strokeWidth: 0.2, -}); -const a3 = annotate(n3, { - type: "highlight", - padding: [0, 0], - color: col2, - strokeWidth: 1, - iterations: 1, -}); -const a4 = annotate(n4, { - type: "highlight", - padding: [0, 0], - color: col2, - strokeWidth: 2, - iterations: 1, -}); -const a5 = annotate(n5, { - type: "highlight", - padding: [0, 0], - color: col2, - strokeWidth: 2, - iterations: 1, - multiline: true, -}); -const a6 = annotate(n6, { - type: "highlight", - padding: [0, 0], - color: col2, - strokeWidth: 2, - iterations: 1, - multiline: true, -}); -const a7 = annotate(n7, { - type: "highlight", - padding: [0, 0], - color: col2, - strokeWidth: 2, - iterations: 1, - multiline: true, -}); -const a8 = annotate(n8, { - type: "highlight", - padding: [0, 0], - color: col2, - strokeWidth: 2, - iterations: 1, - multiline: true, -}); -const a9 = annotate(n9, { - type: "highlight", - padding: [0, 0], - color: col2, - strokeWidth: 2, - iterations: 1, - multiline: true, -}); -const a10 = annotate(n10, { - type: "highlight", - padding: [0, 0], - color: col2, - strokeWidth: 2, - iterations: 1, - multiline: true, -}); -const a11 = annotate(n11, { - type: "highlight", - padding: [0, 0], - color: col2, - strokeWidth: 2, - iterations: 1, - multiline: true, -}); - -setTimeout(() => { - a1.show(); -}, 750); -setTimeout(() => { - a2.show(); -}, 750); - -export { a3, a4, a5, a6, a7, a8, a9, a10, a11 }; diff --git a/bias-variance/js/constants.js b/bias-variance/js/constants.js deleted file mode 100644 index b9e0773b..00000000 --- a/bias-variance/js/constants.js +++ /dev/null @@ -1,7 +0,0 @@ -const trainColorDot = "#94CAE0"; -const testColorDot = "#E57873"; -const testColorBar = testColorDot; -const trainColorBar = trainColorDot; -const lineColor = "green"; - -export { testColorBar, trainColorBar, trainColorDot, testColorDot, lineColor }; diff --git a/bias-variance/js/dataFiles.js b/bias-variance/js/dataFiles.js deleted file mode 100644 index d1015f81..00000000 --- a/bias-variance/js/dataFiles.js +++ /dev/null @@ -1,777 +0,0 @@ -export const trainData = [ - { x: 0.005, y: 0.564, y_pred_uf: 0.424, y_pred_of: 0.564 }, - { x: 0.114, y: 0.576, y_pred_uf: 0.341, y_pred_of: 0.576 }, - { x: 0.218, y: 0.634, y_pred_uf: 0.261, y_pred_of: 0.634 }, - { x: 0.323, y: 0.569, y_pred_uf: 0.181, y_pred_of: 0.569 }, - { x: 0.448, y: -0.593, y_pred_uf: 0.087, y_pred_of: -0.593 }, - { x: 0.528, y: -0.414, y_pred_uf: 0.025, y_pred_of: -0.414 }, - { x: 0.63, y: -0.849, y_pred_uf: -0.053, y_pred_of: -0.849 }, - { x: 0.749, y: -0.106, y_pred_uf: -0.143, y_pred_of: -0.106 }, - { x: 0.839, y: -0.191, y_pred_uf: -0.212, y_pred_of: -0.191 }, - { x: 1.05, y: 0.35, y_pred_uf: -0.373, y_pred_of: 0.35 }, -]; -export const testData = [ - { x: 0.028, y: 0.24 }, - { x: 0.161, y: 0.27 }, - { x: 0.25, y: 1.098 }, - { x: 0.367, y: -0.032 }, - { x: 0.441, y: -0.273 }, - { x: 0.503, y: -1.252 }, - { x: 0.603, y: -0.546 }, - { x: 0.759, y: -0.98 }, - { x: 0.844, y: 0.227 }, - { x: 0.957, y: 1.154 }, -]; - -export const ddTrainTest = [ - { x: 1, y_train: 14, y_test: 23 }, - { x: 3, y_train: 6, y_test: 17 }, - { x: 4, y_train: 4, y_test: 15.5 }, - { x: 5, y_train: 3, y_test: 15 }, - { x: 6, y_train: 2.5, y_test: 15 }, - { x: 7, y_train: 2, y_test: 16 }, - { x: 8, y_train: 1.8, y_test: 18.5 }, - { x: 9, y_train: 1.8, y_test: 24 }, - { x: 11, y_train: 1.6, y_test: 6 }, - { x: 13, y_train: 1.4, y_test: 3 }, - { x: 15, y_train: 1.1, y_test: 2 }, - { x: 17, y_train: 1.1, y_test: 1.82 }, - { x: 19, y_train: 1.1, y_test: 1.82 }, -]; - -export const ddUShape = [ - { x: 1, y_u: 23 }, - { x: 3, y_u: 17 }, - { x: 4, y_u: 15.5 }, - { x: 5, y_u: 15 }, - { x: 6, y_u: 15 }, - { x: 7, y_u: 16 }, - { x: 8, y_u: 18.5 }, - { x: 9, y_u: 25.5 }, - { x: 10, y_u: 35 }, -]; - -export const errorData = [ - { - x: 0.028, - error_uf: 0.108, - error_of: 7.631, - error_good: 0.171, - bias_uf: 0.013, - bias_of: 0.315, - bias_good: 0.019, - var_uf: 0.022, - var_of: 7.318, - var_good: 0.107, - noise_uf: 0.057, - noise_of: 0.057, - noise_good: 0.057, - noise_final: 0.03, - error_final: 1.09, - bias_final: 0.95, - var_final: 0.11, - noise_point: 0.03, - error_point: 0.55, - bias_point: 0.29, - var_point: 0.27, - x_point: 0.48, - }, - { - x: 0.161, - error_uf: 0.242, - error_of: 0.655, - error_good: 0.085, - bias_uf: 0.19, - bias_of: 0.016, - bias_good: 0.007, - var_uf: 0.014, - var_of: 0.593, - var_good: 0.027, - noise_uf: 0.049, - noise_of: 0.049, - noise_good: 0.049, - noise_final: 0.03, - error_final: 0.81, - bias_final: 0.65, - var_final: 0.13, - noise_point: 0.03, - error_point: 0.55, - bias_point: 0.29, - var_point: 0.27, - x_point: 0.48, - }, - { - x: 0.25, - error_uf: 0.239, - error_of: 0.154, - error_good: 0.081, - bias_uf: 0.208, - bias_of: 0.0, - bias_good: 0.002, - var_uf: 0.01, - var_of: 0.092, - var_good: 0.021, - noise_uf: 0.061, - noise_of: 0.061, - noise_good: 0.061, - noise_final: 0.03, - error_final: 0.68, - bias_final: 0.5, - var_final: 0.15, - noise_point: 0.03, - error_point: 0.55, - bias_point: 0.29, - var_point: 0.27, - x_point: 0.48, - }, - { - x: 0.367, - error_uf: 0.064, - error_of: 0.102, - error_good: 0.075, - bias_uf: 0.004, - bias_of: 0.0, - bias_good: 0.007, - var_uf: 0.007, - var_of: 0.046, - var_good: 0.016, - noise_uf: 0.056, - noise_of: 0.056, - noise_good: 0.056, - noise_final: 0.03, - error_final: 0.58, - bias_final: 0.35, - var_final: 0.2, - noise_point: 0.03, - error_point: 0.55, - bias_point: 0.29, - var_point: 0.27, - x_point: 0.48, - }, - { - x: 0.441, - error_uf: 0.167, - error_of: 0.113, - error_good: 0.084, - bias_uf: 0.083, - bias_of: 0.001, - bias_good: 0.001, - var_uf: 0.007, - var_of: 0.052, - var_good: 0.023, - noise_uf: 0.061, - noise_of: 0.061, - noise_good: 0.061, - noise_final: 0.03, - error_final: 0.55, - bias_final: 0.3, - var_final: 0.24, - noise_point: 0.03, - error_point: 0.55, - bias_point: 0.29, - var_point: 0.27, - x_point: 0.48, - }, - { - x: 0.503, - error_uf: 0.391, - error_of: 0.133, - error_good: 0.106, - bias_uf: 0.282, - bias_of: 0.0, - bias_good: 0.001, - var_uf: 0.007, - var_of: 0.057, - var_good: 0.027, - noise_uf: 0.076, - noise_of: 0.076, - noise_good: 0.076, - noise_final: 0.03, - error_final: 0.54, - bias_final: 0.24, - var_final: 0.28, - noise_point: 0.03, - error_point: 0.55, - bias_point: 0.29, - var_point: 0.27, - x_point: 0.48, - }, - { - x: 0.603, - error_uf: 0.562, - error_of: 0.128, - error_good: 0.092, - bias_uf: 0.451, - bias_of: 0.004, - bias_good: 0.004, - var_uf: 0.009, - var_of: 0.065, - var_good: 0.028, - noise_uf: 0.054, - noise_of: 0.054, - noise_good: 0.054, - noise_final: 0.03, - error_final: 0.546, - bias_final: 0.2, - var_final: 0.34, - noise_point: 0.03, - error_point: 0.55, - bias_point: 0.29, - var_point: 0.27, - x_point: 0.48, - }, - { - x: 0.759, - error_uf: 0.137, - error_of: 0.175, - error_good: 0.107, - bias_uf: 0.022, - bias_of: 0.015, - bias_good: 0.007, - var_uf: 0.017, - var_of: 0.103, - var_good: 0.038, - noise_uf: 0.069, - noise_of: 0.069, - noise_good: 0.069, - noise_final: 0.03, - error_final: 0.68, - bias_final: 0.15, - var_final: 0.5, - noise_point: 0.03, - error_point: 0.55, - bias_point: 0.29, - var_point: 0.27, - x_point: 0.48, - }, - { - x: 0.844, - error_uf: 0.177, - error_of: 0.262, - error_good: 0.092, - bias_uf: 0.108, - bias_of: 0.0, - bias_good: 0.014, - var_uf: 0.022, - var_of: 0.221, - var_good: 0.035, - noise_uf: 0.04, - noise_of: 0.04, - noise_good: 0.04, - noise_final: 0.03, - error_final: 0.81, - bias_final: 0.13, - var_final: 0.65, - noise_point: 0.03, - error_point: 0.55, - bias_point: 0.29, - var_point: 0.27, - x_point: 0.48, - }, - { - x: 0.957, - error_uf: 0.713, - error_of: 6.517, - error_good: 0.216, - bias_uf: 0.609, - bias_of: 0.213, - bias_good: 0.012, - var_uf: 0.032, - var_of: 6.239, - var_good: 0.155, - noise_uf: 0.051, - noise_of: 0.051, - noise_good: 0.051, - noise_final: 0.03, - error_final: 1.09, - bias_final: 0.11, - var_final: 0.95, - noise_point: 0.03, - error_point: 0.55, - bias_point: 0.29, - var_point: 0.27, - x_point: 0.48, - }, -]; -export const modelsData = [ - { - x: 0.013, - "uf-preds-0": 0.418, - "uf-preds-1": 0.614, - "uf-preds-2": 0.28, - "uf-preds-3": 0.435, - "uf-preds-4": 0.303, - "uf-preds-5": 0.183, - "uf-preds-6": 0.248, - "uf-preds-7": -0.007, - "uf-preds-8": 0.168, - "uf-preds-9": 0.109, - "uf-preds-10": 0.273, - "uf-preds-11": 0.296, - "uf-preds-12": 0.414, - "uf-preds-13": 0.059, - "uf-preds-14": 0.571, - "of-preds-0": 0.534, - "of-preds-1": -4.423, - "of-preds-2": -0.019, - "of-preds-3": -2.004, - "of-preds-4": -1.84, - "of-preds-5": 10.122, - "of-preds-6": -8.253, - "of-preds-7": 0.826, - "of-preds-8": -7.947, - "of-preds-9": -0.013, - "of-preds-10": -0.86, - "of-preds-11": -0.565, - "of-preds-12": -2.681, - "of-preds-13": 0.323, - "of-preds-14": -0.943, - "good-preds-0": 0.523, - "good-preds-1": 0.02, - "good-preds-2": -0.127, - "good-preds-3": -0.042, - "good-preds-4": -0.116, - "good-preds-5": -0.417, - "good-preds-6": -1.14, - "good-preds-7": 0.254, - "good-preds-8": -1.017, - "good-preds-9": 0.015, - "good-preds-10": 0.133, - "good-preds-11": 0.058, - "good-preds-12": 0.614, - "good-preds-13": 0.082, - "good-preds-14": 0.07, - }, - { - x: 0.125, - "uf-preds-0": 0.333, - "uf-preds-1": 0.474, - "uf-preds-2": 0.196, - "uf-preds-3": 0.339, - "uf-preds-4": 0.242, - "uf-preds-5": 0.168, - "uf-preds-6": 0.223, - "uf-preds-7": -0.018, - "uf-preds-8": 0.134, - "uf-preds-9": 0.056, - "uf-preds-10": 0.218, - "uf-preds-11": 0.266, - "uf-preds-12": 0.335, - "uf-preds-13": 0.063, - "uf-preds-14": 0.45, - "of-preds-0": 0.582, - "of-preds-1": 2.216, - "of-preds-2": 0.653, - "of-preds-3": 0.959, - "of-preds-4": 0.518, - "of-preds-5": -1.566, - "of-preds-6": 0.825, - "of-preds-7": -0.044, - "of-preds-8": 0.755, - "of-preds-9": 0.475, - "of-preds-10": -0.56, - "of-preds-11": 0.788, - "of-preds-12": 0.782, - "of-preds-13": 0.047, - "of-preds-14": 2.575, - "good-preds-0": 0.823, - "good-preds-1": 0.904, - "good-preds-2": 0.813, - "good-preds-3": 0.61, - "good-preds-4": 0.63, - "good-preds-5": 0.727, - "good-preds-6": 0.6, - "good-preds-7": 0.534, - "good-preds-8": 0.598, - "good-preds-9": 0.63, - "good-preds-10": 0.77, - 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-// chart selections -const chart1Container = "#scroll-viz"; -const chart2Container = "#svg2"; - -// get current width to track mobile -const screenWidth = window.innerWidth; -const mobileBreak = 600; - -// using d3 for convenience -let main = select("main"); -let scrolly = main.select("#scrolly"); -let figure = scrolly.select("figure"); -let article = scrolly.select("article"); -let step = article.selectAll(".step"); -let scatterChart = select(chart1Container); -let errorChart = select(chart1Container); - -const stepHPercentage = 1; -const offsetTriggerFromTop = screenWidth > mobileBreak ? 0.5 : 0.5; - -// ensure refresh goes to top of page -window.onbeforeunload = () => window.scrollTo(0, 0); - -// initialize the scrollama -let scroller = scrollama(); - -const errorBar = new ErrorBar(chart2Container); -const reglin = new Scatter({ - element: chart1Container, - errorBar: errorBar, -}); - -// KNN Stuff -const KnnModel = new KNN({ - fitContainer: "fit-container", - predictContainer: "predict-container", - sliderContainer: "slider-container", - k: 3, -}); - -// button handler for knn -select("#button-knn").on("click", function () { - KnnModel.refreshChartWithNewData(); -}); - -// LOESS Stuff -const LOESSModel = new Loess({ - loessContainer: "fit-container-loess", - barContainer: "predict-container-loess", - sliderContainer: "slider-container-loess", - k: 3, -}); - -select("#button-loess").on("click", function () { - LOESSModel.generateData(); -}); - -// draw double descent chart -const ddModel = new DoubleDescent({ - ddContainer: "dd-container", -}); - -// generic window resize listener event -function handleResize() { - // 1. update height of step elements - // important: height of each step - let stepHPercentageResponsive = - window.innerWidth <= 900 ? 1.8 : stepHPercentage; - let stepH = Math.floor(window.innerHeight * stepHPercentageResponsive); - step.style("height", stepH + "px"); - - let figureHeight = window.innerHeight / 1.1; - let figureWidth = window.innerWidth / 1.1; - let responsiveWidth = window.innerWidth / 2.4; - let responsiveHeight = window.innerHeight * 0.3; - let figureMarginTop = (window.innerHeight - figureHeight) / 2; - - figure - .style("height", `${figureHeight}px`) - .style("width", `${figureWidth}px`) - .style("top", `${figureMarginTop}px`); - - scatterChart - .attr("height", `${responsiveHeight}px`) - .attr("width", `${responsiveWidth}px`); - - errorChart - .attr("height", `${responsiveHeight}px`) - .attr("width", `${responsiveWidth}px`); - - // update knnSize - // KnnModel.resizeCharts(); - LOESSModel.resizeCharts(); - - // 3. tell scrollama to update new element dimensions - scroller.resize(); -} - -// remove lines drawn from simulations all in one go -const removeModelRealizations = () => { - selectAll("[id^='uf-preds']").remove(); - selectAll("[id^='of-preds']").remove(); - selectAll("[id^='good-preds']").remove(); -}; - -// define event dict for each step in scrolly -const stepEvents = { - down: { - 0: () => { - reglin.drawLineUnderfit(); - }, - 1: () => { - reglin.drawTrainErrorUnderfit(); - select("#errorBarSvg") - .selectAll(".axes") - .transition() - .delay(600) - .attr("visibility", "visible"); - }, - 2: () => { - reglin.addLegend(); - reglin.drawTestErrorOverfit(); - setTimeout(() => { - a4.show(); - a5.show(); - }, 750); - }, - 3: () => { - // remove the underfit error lines - const removeClasses = [".errorLine", ".testErrorLine"]; - removeClasses.map((d) => { - selectAll(d).remove(); - }); - // draw the overfit line - reglin.drawLineOverfit(); - // update bar - reglin.drawBarsTrainOverfit(); - }, - 4: () => { - reglin.drawBarsTestOverfit(); - setTimeout(() => { - a3.show(); - a6.show(); - }, 750); - }, - 5: () => { - // remove error lines from prev step - removeModelRealizations(); - selectAll(".errorLineComplex").remove(); - reglin.plotTestBar(); - // remove circles (make sure to add back in) - selectAll("circle.test").transition().attr("r", 0); - select("#regression-line-1").attr("visibility", "hidden"); - selectAll(".legend").attr("visibility", "hidden"); - setTimeout(() => { - a10.show(); - }, 750); - }, - 6: () => { - removeModelRealizations(); - reglin.errorBar.addTitle(""); - reglin.errorBar.positionErrorBarsAsLabels(); - reglin.drawUnderfitRealizations(); - reglin.errorBar.plotErrorLines("x", "uf"); - setTimeout(() => { - a7.show(); - }, 750); - }, - 7: () => { - removeModelRealizations(); - reglin.drawOverfitRealizations(); - reglin.errorBar.plotErrorLines("x", "of"); - setTimeout(() => { - a8.show(); - }, 750); - }, - 8: () => { - removeModelRealizations(); - reglin.drawGoodRealizations(); - reglin.errorBar.plotErrorLines("x", "good"); - setTimeout(() => { - a9.show(); - }, 750); - }, - 9: () => { - removeModelRealizations(); - reglin.errorBar.repositionSvgNew(); - reglin.errorBar.plotErrorLines("x_point", "point"); - reglin.errorBar.plotComplexCircleOne(); - }, - 10: () => { - removeModelRealizations(); - reglin.errorBar.plotErrorLines("x", "final"); - reglin.errorBar.plotComplexCircleAll(); - setTimeout(() => { - a11.show(); - }, 750); - }, - }, - - up: { - 0: () => { - removeModelRealizations(); - selectAll("rect.errorBar").transition().attr("width", 0); - selectAll(".errorBar").transition().delay(500).remove(); - selectAll(".errorLine").remove(); - select("#errorBarSvg") - .selectAll(".axes") - .transition() - .delay(600) - .attr("visibility", "hidden"); - // remove barchart title - select("#errorBarTitle").remove(); - selectAll(".legend").remove(); - }, - 1: () => { - removeModelRealizations(); - reglin.drawTrainErrorUnderfit(); - selectAll(".testErrorLine").remove(); - selectAll("circle.test").transition().attr("r", 0).transition().remove(); - selectAll(".legend").remove(); - }, - 2: () => { - removeModelRealizations(); - selectAll(".testErrorLine").remove(); - selectAll(".errorLineComplex").remove(); - reglin.drawLineUnderfit(); - reglin.addLegend(); - reglin.drawTestErrorOverfit(); - }, - 3: () => { - removeModelRealizations(); - selectAll(".errorLineComplex").remove(); - // draw the overfit line - reglin.drawLineOverfit(); - // update bar - reglin.drawBarsTrainOverfit(); - }, - 4: () => { - removeModelRealizations(); - selectAll("rect.stacked").remove(); - selectAll(".decomp-text").remove(); - selectAll("circle.test").transition().attr("r", 10); - select("#regression-line-1").attr("visibility", "visible"); - selectAll(".legend").attr("visibility", "visible"); - reglin.errorBar.plotReverseDecompositionBar(); - reglin.errorBar.addTitle("Mean-Squared Error"); - reglin.drawBarsTestOverfit(); - }, - 5: () => { - // reset title - reglin.addTitle(""); - // redraw model lines - removeModelRealizations(); - reglin.transitionUp5(); - }, - 6: () => { - removeModelRealizations(); - reglin.drawUnderfitRealizations(); - reglin.errorBar.plotErrorLines("x", "uf"); - }, - 7: () => { - removeModelRealizations(); - reglin.drawOverfitRealizations(); - reglin.errorBar.plotErrorLines("x", "of"); - }, - 8: () => { - reglin.errorBar.upPositionSvg(); - reglin.errorBar.plotErrorLines("x", "good"); - selectAll("[class^='complex-']").transition().attr("r", 0).remove(); - }, - 9: () => { - selectAll("[class^='complex-']").transition().attr("r", 0).remove(); - reglin.errorBar.plotErrorLines("x_point", "point"); - reglin.errorBar.plotComplexCircleOne(); - }, - 10: () => {}, // placeholder to stop error throw - }, -}; - -// scrollama event handlers -function handleStepEnter(response) { - // update chart based on step - stepEvents[response.direction][response.index](); -} - -function setupStickyfill() { - selectAll(".sticky").each(function () { - Stickyfill.add(this); - }); -} - -function init() { - setupStickyfill(); - - // 1. force a resize on load to ensure proper dimensions are sent to scrollama - handleResize(); - - // 2. setup the scroller passing options - // this will also initialize trigger observations - // 3. bind scrollama event handlers (this can be chained like below) - scroller - .setup({ - step: "#scrolly article .step", - offset: offsetTriggerFromTop, - // debug: true, - }) - .onStepEnter(handleStepEnter); - - // setup resize event - window.addEventListener("resize", handleResize); -} - -// kick things off -init(); diff --git a/bias-variance/js/katexCalls.js b/bias-variance/js/katexCalls.js deleted file mode 100644 index 63368ac7..00000000 --- a/bias-variance/js/katexCalls.js +++ /dev/null @@ -1,44 +0,0 @@ -import katex from "katex"; - -katex.render( - `\\mathrm{Error}(x) = \\Big( \\mathbf{E}\\big[\\widehat{f}(x)\\big] - f(x) \\Big)^2 + \\mathbf{E}\\Big[\\Big(\\widehat{f}(x) - \\mathbf{E}\\big[\\widehat{f}(x)\\big]\\Big)^2\\Big] + \\mathrm{Noise} `, - document.querySelector(".katex-bv-equation"), - { - throwOnError: false, - } -); - -katex.render( - `\\mathrm{Bias}^2(x) = \\Big( \\mathbf{E}\\big[\\widehat{f}(x)\\big] - f(x) \\Big)^2`, - document.querySelector(".katex-bias"), - { - throwOnError: false, - } -); - -katex.render( - `\\mathrm{Variance}(x) = \\mathbf{E}\\Big[\\Big(\\widehat{f}(x) - \\mathbf{E}\\big[\\widehat{f}(x)\\big]\\Big)^2\\Big]`, - document.querySelector(".katex-var"), - { - throwOnError: false, - } -); - -katex.render( - `\\mathrm{Error} = \\mathrm{Bias}^2 + \\mathrm{Variance} + \\mathrm{Noise}`, - document.querySelector(".katex-bv-text"), - { - throwOnError: false, - } -); - -katex.render(`\\thinspace \\mathrm{Noise}`, document.querySelector(".katex-noise"), { - throwOnError: false, -}); -katex.render( - `\\thinspace E[\\widehat{f}(x)]`, - document.querySelector(".katex-bias-inline"), - { - throwOnError: false, - } -); diff --git a/bias-variance/js/meanSquaredError.js b/bias-variance/js/meanSquaredError.js deleted file mode 100644 index b1919c04..00000000 --- a/bias-variance/js/meanSquaredError.js +++ /dev/null @@ -1,15 +0,0 @@ -// calculate MSE for two equal-dimension arrays - -export const meanSquaredError = (actual, pred) => { - // helper funcs - const sqr = a => a * a; - const add = (a, b) => a + b; - const difference = (a, b) => a.map((x, i) => x - b[i]); - - // calculate MSE - const squaredError = difference(actual, pred).map(sqr); - const sumSquaredError = squaredError.reduce(add, 0); - const mse = sumSquaredError / actual.length; - - return mse -} \ No newline at end of file diff --git a/bias-variance/package-lock.json b/bias-variance/package-lock.json deleted file mode 100644 index 24589b66..00000000 --- a/bias-variance/package-lock.json +++ /dev/null @@ -1,7545 +0,0 @@ -{ - 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.col-align-r > .vlist-t { - text-align: right; -} -.katex .svg-align { - text-align: left; -} -.katex svg { - display: block; - position: absolute; - width: 100%; - height: inherit; - fill: currentColor; - stroke: currentColor; - fill-rule: nonzero; - fill-opacity: 1; - stroke-width: 1; - stroke-linecap: butt; - stroke-linejoin: miter; - stroke-miterlimit: 4; - stroke-dasharray: none; - stroke-dashoffset: 0; - stroke-opacity: 1; -} -.katex svg path { - stroke: none; -} -.katex img { - border-style: none; - min-width: 0; - min-height: 0; - max-width: none; - max-height: none; -} -.katex .stretchy { - width: 100%; - display: block; - position: relative; - overflow: hidden; -} -.katex .stretchy:after, -.katex .stretchy:before { - content: ""; -} -.katex .hide-tail { - width: 100%; - position: relative; - overflow: hidden; -} -.katex .halfarrow-left { - position: absolute; - left: 0; - width: 50.2%; - overflow: hidden; -} -.katex .halfarrow-right { - position: absolute; - right: 0; - width: 50.2%; - overflow: hidden; -} -.katex .brace-left { - position: absolute; - left: 0; - width: 25.1%; - overflow: hidden; -} -.katex .brace-center { - position: absolute; - left: 25%; - width: 50%; - overflow: hidden; -} -.katex .brace-right { - position: absolute; - right: 0; - width: 25.1%; - overflow: hidden; -} -.katex .x-arrow-pad { - padding: 0 0.5em; -} -.katex .mover, -.katex .munder, -.katex .x-arrow { - text-align: center; -} -.katex .boxpad { - padding: 0 0.3em; -} -.katex .fbox, -.katex .fcolorbox { - box-sizing: border-box; - border: 0.04em solid; -} -.katex .cancel-pad { - padding: 0 0.2em; -} -.katex .cancel-lap { - margin-left: -0.2em; - margin-right: -0.2em; -} -.katex .sout { - border-bottom-style: solid; - border-bottom-width: 0.08em; -} -.katex-display { - display: block; - margin: 1em 0; - text-align: center; -} -.katex-display > .katex { - display: block; - text-align: center; - white-space: nowrap; -} -.katex-display > .katex > .katex-html { - display: block; - position: relative; -} -.katex-display > .katex > .katex-html > .tag { - position: absolute; - right: 0; -} -.katex-display.leqno > .katex > .katex-html > .tag { - left: 0; - right: auto; -} -.katex-display.fleqn > .katex { - text-align: left; - padding-left: 2em; -} diff --git a/bias-variance/css/styles.scss b/bias-variance/css/styles.scss deleted file mode 100644 index d3ad681c..00000000 --- a/bias-variance/css/styles.scss +++ /dev/null @@ -1,927 +0,0 @@ -/* ====================== */ -/* FONTS */ -/* ====================== */ - -@font-face { - font-family: AmazonEmberDisplayHeavy; - src: url("../fonts/AmazonEmberDisplay_He.otf") format("opentype"); -} - -@font-face { - font-family: AmazonEmber; - src: url("../fonts/AmazonEmber_Rg.otf") format("opentype"); -} - -@font-face { - font-family: AmazonEmberMono; - src: url("../fonts/AmazonEmberMono_Rg.otf") format("opentype"); -} - -@font-face { - font-family: AmazonEmberDisplayLight; - src: url("../fonts/AmazonEmberDisplay_Lt.otf") format("opentype"); -} - -* { - font-family: AmazonEmber; -} - -/* ====================== */ -/* RESPONSIVE VARS */ -/* ====================== */ - -html { - font-size: 16px; -} - -body { - max-width: 2500px; - margin: 0 auto !important; - float: none !important; -} - -// Breakpoints -$bp-small: 48em; // 768px -$bp-medium: 64em; // 1024px -$bp-large: 85.375em; // 1366px -$bp-xlarge: 120em; // 1920px -$bp-xxlarge: 160em; // 2560px - -// Media Queries -$mq-small: "(min-width: #{$bp-small})"; -$mq-medium: "(min-width: #{$bp-medium})"; -$mq-large: "(min-width: #{$bp-large})"; -$mq-xlarge: "(min-width: #{$bp-xlarge})"; -$mq-xxlarge: "(min-width: #{$bp-xxlarge})"; -$mq-retina: "(-webkit-min-device-pixel-ratio: 2), (min-resolution: 192dpi)"; - -// colors -$squid-ink: #232f3e; -$test-color: #e57873; -$train-color: #94cae0; -$bg-color: white; -$ai_color: #354848; - -// Font-Size -$base-font-size: 1rem; - -// Line-Height -$base-line-height: 1.5; -$header-line-height: 1.25; -/* ====================== */ -/* Variables */ -/* ====================== */ -/* circle.test, rect.errorB */ - -/* ====================== */ -/* LAYOUT STYLES */ -/* ====================== */ - -#intro { - text-align: center; - max-width: 90%; - padding-bottom: 5rem; - margin: auto; -} - -#intro__hed { - font-size: 4rem; - margin-top: 5px; - margin-bottom: 0; - letter-spacing: 1.5px; - font-family: AmazonEmberDisplayHeavy; - text-transform: uppercase; - max-width: 70%; - margin: auto; -} - -#ai { - color: #7bacc9; - font-family: AmazonEmberDisplayHeavy; -} - -.logo { - color: $squid-ink; - width: 10rem; -} - -.bolder { - font-family: AmazonEmberDisplayHeavy; -} - -#intro-icon { - display: flex; - text-transform: uppercase; - font-weight: bold; - color: $ai_color; - flex-direction: row; - padding-top: 1rem; - justify-content: center; -} - -#intro-icon h2 { - padding-left: 2%; - font-family: AmazonEmberDisplayHeavy; - font-size: 1.2rem; - margin-top: 10px; - margin-bottom: 10px; -} - -#intro__dek { - text-align: left; - font-size: 17px; - margin-top: 1rem; - max-width: 60%; - margin: auto; - margin-top: 2rem; -} - -#intro__author, -#intro__date { - font-size: 1.1rem; - margin: 0px; - margin-top: 15px; - font-family: AmazonEmberDisplayLight; - padding-bottom: 0px; - margin-bottom: 0px; -} - -#bias, -#variance { - font-family: AmazonEmberDisplayHeavy; -} - -a { - color: $squid-ink; - text-decoration: underline; -} - -a:hover { - background-color: #999; - color: white; - text-decoration: none; -} - -/* ====================== */ -/* SCROLLY-TELLING STYLES */ -/* ====================== */ - -#scrolly { - position: relative; - display: -webkit-box; - display: -ms-flexbox; - display: flex; - padding: 1rem; - padding: 0 2%; - - @media #{$mq-large} { - padding: 0 4%; - } - - @media #{$mq-xlarge} { - padding: 0 10%; - } -} - -#scrolly > * { - -webkit-box-flex: 1; - -ms-flex: 1; - flex: 1; -} - -article { - position: relative; - padding: 0 1rem; - max-width: 40rem; -} - -figure { - position: -webkit-sticky; - position: sticky; - width: 100%; - margin: 0; - -webkit-transform: translate3d(0, 0, 0); - -moz-transform: translate3d(0, 0, 0); - transform: translate3d(0, 0, 0); - z-index: -999; - vertical-align: center; -} - -figure p { - text-align: center; - padding: 1rem; - position: absolute; - top: 50%; - left: 50%; - -moz-transform: translate(-50%, -50%); - -webkit-transform: translate(-50%, -50%); - transform: translate(-50%, -50%); - font-size: 8rem; - font-weight: 900; -} - -.step { - margin: 0 auto 2rem auto; - color: $squid-ink; -} - -.step:last-child { - margin-bottom: 0; -} - -.step p { - padding: 1rem; - padding-left: 15%; - text-align: left; - max-width: 750px; - line-height: 1.55em; - font-size: 1rem; - letter-spacing: 0.1px; - color: $squid-ink; - padding-top: 0px; -} - -.step ul { - text-align: center; - padding: 1rem; - font-size: 1.2rem; - padding-left: 25%; - text-align: left; - max-width: 750px; - line-height: 1.55em; - font-size: 16px; - letter-spacing: 0.1px; - color: $squid-ink; - padding-top: 0px; -} - -.step p.step-head { - margin-bottom: 5px; - font-family: AmazonEmberDisplayHeavy; - font-size: 1.1rem; -} - -/* ====================== */ -/* svg animate */ -/* ====================== */ - -#eyes { - fill: $squid-ink; - animation: eye 4s linear infinite 0s; -} - -@keyframes eye { - 0% { - fill: $squid-ink; - } - 5%, - 20% { - fill: $squid-ink; - } - 15%, - 30% { - fill: white; - } - 40% { - fill: $squid-ink; - } -} - -/* ====================== */ -/* SCATTER PLOT */ -/* ====================== */ - -#regression-line-1 { - fill: none; - opacity: 0.85; - stroke: darkorange; - stroke-width: 5.5px; -} - -.model-realization { - fill: none; - opacity: 0.85; - stroke: darkorange; - stroke-width: 5px; -} - -.axis line { - stroke: #ddd; -} -.axis .baseline line { - stroke: #555; -} -.axis .domain { - opacity: 0.2; -} - -#svg2 .domain { - opacity: 0.2; -} - -#errorBarSvg .axes .line { - opacity: 0; -} - -.errorLine, -.testErrorLine, -.errorLineComplex { - fill: none; - stroke: $squid-ink; - stroke-width: 3.5px; - pointer-events: none; - stroke-dasharray: 3, 3; - fill: none; - z-index: -1; -} - -#svg-scatter { - -moz-transition: transform 2s ease-in-out 0s; - -webkit-transition: transform 2s ease-in-out 0s; - -o-transition: transform 2s ease-in-out 0s; - transform: scale(1); -} - -circle.train { - fill: $train-color; - stroke: $bg-color; - stroke-width: 2; -} - -circle.test { - fill: $test-color; - stroke: $bg-color; - stroke-width: 2; -} - -/* ====================== */ -/* BAR PLOT */ -/* ====================== */ -.decomp-text { - font-family: AmazonEmberDisplayHeavy; - font-size: 1rem; -} - -#noise-line, -#error-line, -#bias-line, -#var-line { - fill: none; - opacity: 0.65; - stroke-width: 8px; -} - -path#noise-line { - stroke: coral; -} -path#error-line { - stroke: red; -} -path#bias-line { - stroke: skyblue; -} -path#var-line { - stroke: teal; -} - -circle.complex-noise { - fill: coral; -} -circle.complex-error { - fill: red; -} -circle.complex-bias { - fill: skyblue; -} -circle.complex-var { - fill: teal; -} - -/* ====================== */ -/* KNN */ -/* ====================== */ -.models { - margin: 0 auto; - padding: 0; - margin-top: 0; - // background-color: pink; - - padding: 0 2%; - - @media #{$mq-large} { - padding: 0 4%; - } - - @media #{$mq-xlarge} { - padding: 0 10%; - } -} - -.model-header { - font-family: AmazonEmberDisplayHeavy; - max-width: 60%; - margin: 0 auto; - padding-bottom: 0.5rem; - text-align: left; - font-weight: bold; - font-size: 1.3rem; -} - -.model-text { - max-width: 60%; - margin: 0 auto; - text-align: left; - font-size: 16px; - line-height: 1.5em; -} - -.model-text-wrapper { - padding-top: 5px; - margin-top: 0px; -} - -.model-wrapper { - display: grid; - grid-template-columns: repeat(2, 1fr); - grid-row-gap: 10px; - grid-column-gap: 10px; - grid-template-rows: minmax(300px, 55%); -} - -#k-text, -#loess-text { - padding-bottom: 3px; - margin: 0; - font-size: 0.9rem; - font-family: AmazonEmberDisplayHeavy; -} - -.model-controls { - padding-top: 2em; - padding-bottom: 1em; - display: grid; - justify-content: center; - grid-template-columns: repeat(2, 15%); - grid-row-gap: 0.5em; - grid-column-gap: 0.5em; - grid-template-rows: minmax(40px, 3%); -} -/* ====================== */ -/* LOESS */ -/* ====================== */ -path#loess-line { - fill: none; - stroke: darkorange; - stroke-width: 2px; -} - -circle.loess-train { - fill: $train-color; - stroke: $bg-color; - stroke-width: 1.5; -} -circle.loess-test { - fill: $test-color; - stroke: $bg-color; - stroke-width: 1.5; - opacity: 0.8; -} - -.loess-legend { - #loess-train { - fill: $train-color; - } - #loess-test { - fill: $test-color; - } -} - -/* ====================== */ -/* Outro */ -/* ====================== */ -#outro { - margin: 0 auto; - font-family: AmazonEmberDisplayLight; - // background-color: #eceff1; - - h1 { - text-decoration: underline; - } - - p { - font-size: 1rem; - font-family: AmazonEmberDisplayLight; - } - - ul { - margin: auto; - max-width: 60%; - list-style-position: outside; - } - li { - text-align: left; - font-size: 1rem; - padding-left: 0px; - } -} - -/* ====================== */ -/* Body Centering */ -/* ====================== */ -body { - text-align: center; - font-size: $base-font-size; - line-height: $base-line-height; - margin: 0px; - - // responsive font sizes - @media #{$mq-medium} { - font-size: $base-font-size * 1; - line-height: $base-line-height * 1; - } - - @media #{$mq-large} { - font-size: $base-font-size * 1; - } - - @media #{$mq-xlarge} { - font-size: $base-font-size * 1.2; - } - - @media #{$mq-xxlarge} { - font-size: $base-font-size * 1.5; - } -} - -// label font-size -.decomp-text, -#error-text { - font-size: 0.75rem; - @media #{$mq-medium} { - font-size: 0.9rem; - } - @media #{$mq-large} { - font-size: 0.9rem; - } - - @media #{$mq-xlarge} { - font-size: $base-font-size; - } - - @media #{$mq-xxlarge} { - font-size: $base-font-size; - } -} - -/* ====================== */ -/* Math Equations */ -/* ====================== */ -.katex { - font-size: 1.1rem; -} - -/* ====================== */ -/* SLIDER STYLES */ -/* ====================== */ - -.slider-container { - width: 100%; /* Width of the outside container */ -} - -/* The slider itself */ -.metric-slider { - -webkit-appearance: none; /* Override default CSS styles */ - appearance: none; - width: 100%; /* Full-width */ - height: 1em; /* Specified height */ - background: #999; /* Grey background */ - outline: none; /* Remove outline */ - opacity: 0.7; /* Set transparency (for mouse-over effects on hover) */ - -webkit-transition: 0.2s; /* 0.2 seconds transition on hover */ - transition: opacity 0.2s; -} - -/* Mouse-over effects */ -.metric-slider:hover { - opacity: 1; /* Fully shown on mouse-over */ -} - -/* The slider handle (use -webkit- (Chrome, Opera, Safari, Edge) and -moz- (Firefox) to override default look) */ -.metric-slider::-webkit-slider-thumb { - -webkit-appearance: none; /* Override default look */ - appearance: none; - width: 1.5em; /* Set a specific slider handle width */ - height: 1.5em; /* Slider handle height */ - background: $squid-ink; - border: 1px solid white; - cursor: pointer; /* Cursor on hover */ -} - -.metric-slider::-moz-range-thumb { - width: 1.5em; /* Set a specific slider handle width */ - height: 1.5em; /* Slider handle height */ - background: $squid-ink; - border: 1px solid white; - cursor: pointer; /* Cursor on hover */ -} - -.data-button { - display: table; - padding: 0; - margin: 0; - cursor: pointer; - max-width: 90%; - text-align: center; - font-size: 0.85em; - border: 1px solid #e0e0e0; - text-decoration: none; - color: $squid-ink; - outline: none; - border-radius: 2px; - font-family: AmazonEmberDisplayHeavy; -} - -#button-knn:hover, -#button-loess:hover { - // color: #fff; - background: #e0e0e0; -} - -/* ====================== */ -/* DOUBLE DESCENT */ -/* ====================== */ -.dd-text { - font-size: 0.7rem; - font-family: AmazonEmberDisplayLight; -} - -.dd-line { - fill: none; - stroke-width: 4; -} - -#dd-line-u { - stroke: black; - stroke-dasharray: 8, 8; - stroke-width: 2; -} -#dd-line-train { - stroke: $train-color; -} -#dd-line-test { - stroke: $test-color; -} - -.dd-circle { - stroke: $bg-color; -} - -#dd-circle-train { - fill: $train-color; -} - -#dd-circle-test { - fill: $test-color; -} - -#dd-x-axis, -#dd-y-axis { - .tick { - opacity: 0; - } -} - -.dd-axis-text { - font-size: 0.85rem; -} - -hr { - max-width: 5%; - margin: 2rem auto; -} - -body { - background-color: white; -} - -@media (max-width: 1100px) { - .model-wrapper { - display: grid; - grid-template-columns: repeat(2, 1fr); - grid-row-gap: 10px; - grid-column-gap: 10px; - grid-template-rows: minmax(300px, 55%); - } -} - -@media (max-width: 900px) { - #scrolly { - padding: 0rem; - padding-right: 0.5rem; - } - #intro__author, - #intro__date { - font-size: 1.5rem; - padding-top: 2rem; - padding-bottom: 2rem; - } - - #intro__dek { - font-size: 1.2rem; - max-width: 100%; - } - - .model-wrapper { - display: grid; - grid-template-columns: repeat(1, 1fr); - grid-row-gap: 1px; - grid-template-rows: minmax(250px, 45%); - } - - .model-text, - .model-header, - #outro ul { - max-width: 80%; - } - - // new - article { - position: relative; - padding: 0rem; - max-width: 45%; - } - - figure { - position: -webkit-sticky; - position: sticky; - width: 55%; - margin: 0; - -webkit-transform: translate3d(0, 0, 0); - -moz-transform: translate3d(0, 0, 0); - transform: translate3d(0, 0, 0); - z-index: -999; - vertical-align: center; - } -} - -@media (max-width: 600px) { - .katex { - font-size: 0.8rem; - } - - .model-wrapper { - display: grid; - grid-template-columns: repeat(1, 1fr); - grid-row-gap: 1px; - grid-template-rows: minmax(200px, 40%); - } - - .model-controls { - grid-template-columns: 40% 55%; - font-size: 1rem; - align-items: center; - } - - .data-button { - padding: 0.5rem 1rem; - // font-size: 1rem; - } - - #k-text, - #loess-text { - font-size: 1rem; - font-family: AmazonEmberDisplayHeavy; - } - - .model-text { - max-width: 80%; - font-size: 0.9rem; - } - .model-header { - max-width: 80%; - } - #intro__hed { - max-width: 100%; - font-size: 3.3rem; - } - - #intro-icon { - padding-top: 4rem; - padding-bottom: 2rem; - } - - #intro__dek { - font-size: 0.9rem; - } - - #intro__author, - #intro__date { - font-size: 1.2rem; - padding-top: 3rem; - } - - #regression-line-1 { - stroke-width: 3.5px; - } - .errorLine, - .errorLineComplex, - .testErrorLine { - stroke-width: 2px; - opacity: 0.5; - } - circle.train, - circle.test { - stroke-width: 1.5px; - r: 4.5; - } - - .model-realization { - stroke-width: 3px; - } - - .step p { - text-align: center; - padding: 0rem; - font-size: 0.9rem; - margin: 0rem; - padding-left: 0.5%; - text-align: left; - max-width: 100%; - line-height: 1.55em; - // font-size: 16px; - letter-spacing: 0.1px; - color: $squid-ink; - padding-top: 0px; - } - - .model-text p { - font-size: 0.9rem; - } - - .axis { - color: $squid-ink; - font-size: 0.5rem; - } - - .models { - padding-top: 0px; - max-width: 100%; - } - - #scatter-x-axis-label, - #scatter-y-axis-label { - display: none; - } - .scatter-legend-text { - font-size: 0.7rem; - } - .scatter-legend-circle { - r: 4.2; - } - #errorBarTitle { - font-size: 0.75rem; - } - .scatter-title { - font-size: 0.7rem; - } - .decomp-text, - #error-text, - .error-symbol { - font-size: 0.55rem; - } - - #bias-line, - #noise-line, - #error-line, - #var-line { - stroke-width: 5px; - } - - #error-x-text, - #error-y-text { - font-size: 0.85rem; - } - - #dd-title { - font-size: 0.9rem; - } - .dd-text { - font-size: 0.6rem; - } -} - -// one of these should work -article { - z-index: 999999; -} - -#intro-icon > a { - display: flex; - text-decoration: none; - padding-top: 1rem; - padding-bottom: 1rem; -} - -#intro-icon > a:hover { - background-color: white; -} diff 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