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BigML Tree - Titanic Survival
Raw
README.md

A sunburst visualization of a BigML decision tree built on a dataset describing Titanic passengers and whether they survived.

The model uses BigML's upcoming automatic text processing to discover that passenger name honorifics (like "Mr.") are helpful for predicting survival.

The initial center circle represents the root of the tree. Each outer circle contains the children of the inner circle's nodes. The number of training instances captured by a node determine its arc length (or its size in radians).

Clicking on a node will zoom in to the subtree. After zooming in, selecting the new center point will zoom out one level.

Raw
index.html
<!DOCTYPE html>
<meta charset="utf-8">
<style>
body {
font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
margin: auto;
position: relative;
width: 960px;
background: #fff;
}
#color-controls {
font: 14px sans-serif;
position: absolute;
right: 10px;
top: 10px;
padding: 3px;
}
#color-controls div {
padding: 4px;
}
#hover-info {
font: 14px sans-serif;
position: absolute;
left: 10px;
top: 10px;
}
#summary-info {
font: 14px sans-serif;
position: absolute;
left: 10px;
bottom: 10px;
font: 12px sans-serif;
}
#summary-info div {
padding: 2px;
}
.split-predicate {
font-weight:bold;
border-bottom: 1px solid #DFDFDF;
padding: 7px;
}
.node-info {
margin-top: 10px;
}
.node-info td {
padding: 2px 7px 2px;
}
</style>
<body>
<div id="color-controls">
<form>
<div>
<input type="radio" name="mode" value="prediction" checked \>
<label>Prediction</label>
</div>
<div>
<input type="radio" name="mode" value="confidence"\>
<label id="cnf">Confidence</label>
</div>
<div>
<input type="radio" name="mode" value="split"\>
<label>Split Field</label>
</div>
</form>
</div>
<div id="hover-info"></div>
<div id="summary-info"></div>
</body>
<script src="http://d3js.org/d3.v3.min.js"></script>
<script>
var width = 960,
height = 600,
radius = Math.min(width, height) / 2;
function hover_adjust(d, color) {
return d.hover ? d3.rgb(color).brighter(0.66) : color;
}
var svg = d3.select("body").append("svg")
.attr("width", width)
.attr("height", height)
.append("g")
.attr("transform", "translate(" + width / 2 + "," + (height / 2 + 0) + ")");
function add_missing(d) {
if (d.children) {
var sum = 0;
for (i in d.children) {
sum += d.children[i].count;
d.children[i] = add_missing(d.children[i]);
}
var diff = d.count - sum;
if (diff > 0) {
missing = {"missing": true, "count": diff};
d.children.push(missing);
}
}
return d;
}
var partition = d3.layout.partition().value(function(d) { return d.count; });
var x = d3.scale.linear().range([0, 2 * Math.PI]);
var y = d3.scale.sqrt().range([0, radius]);
var arc = d3.svg.arc()
.startAngle(function(d) { return Math.max(0, Math.min(2 * Math.PI, x(d.x))); })
.endAngle(function(d) { return Math.max(0, Math.min(2 * Math.PI, x(d.x + d.dx))); })
.innerRadius(function(d) { return Math.max(0, y(d.y)); })
.outerRadius(function(d) { return Math.max(0, y(d.y + d.dy)); });
// Interpolate the scales!
function arcTween(d) {
var xd = d3.interpolate(x.domain(), [d.x, d.x + d.dx]),
yd = d3.interpolate(y.domain(), [d.y, 1]),
yr = d3.interpolate(y.range(), [d.y ? 20 : 0, radius]);
return function(d, i) {
return i
? function(t) { return arc(d); }
: function(t) { x.domain(xd(t)); y.domain(yd(t)).range(yr(t)); return arc(d); };
};
}
function find_minmax(node, attr) {
if (node.children) {
minmaxs = node.children.map(function (n) { return find_minmax(n, attr); });
min = Math.min.apply(null, minmaxs.map(function (mm) {return mm.min}));
max = Math.max.apply(null, minmaxs.map(function (mm) {return mm.max}));
return {"min": Math.min(min, node[attr]), "max": Math.max(max, node[attr])};
} else {
return {"min": node[attr], "max": node[attr]};
}
}
d3.json("titanic-model.json", function(error, root) {
var model = root;
var model_type = model.model_fields[root.objective_field].optype == "categorical" ?
"classification" : "regression";
var minmaxs = {};
if (model_type == "classification") {
minmaxs.confidence = find_minmax(model.root, "confidence");
} else {
// Hacky label switch for regression trees
document.getElementById("cnf").innerHTML = "Expected Error";
minmaxs.expected_error = find_minmax(model.root, "confidence");
minmaxs.output = find_minmax(model.root, "output");
}
model.root = add_missing(model.root);
var scale_pred = model_type == "classification" ?
d3.scale.category10() :
d3.scale.linear().domain([minmaxs.output.min,
minmaxs.output.max])
.range(["#222", "#2ee"]);
var scale_conf = model_type == "classification" ?
d3.scale.linear().domain([minmaxs.confidence.min,
minmaxs.confidence.max])
.range(["#d33", "#3d3"]) :
d3.scale.linear().domain([minmaxs.expected_error.max,
minmaxs.expected_error.min])
.range(["#d33", "#3d3"]);
var scale_split = d3.scale.category20b();
var color_lookup =
{"prediction": function(d) {
if (d.missing) {
return "#FFFFFF";
} else {
return hover_adjust(d, scale_pred(d.output));
}
},
"confidence": function(d) {
if (d.missing) {
return "#FFFFFF";
} else {
return hover_adjust(d, scale_conf(d.confidence));
}
},
"split": function(d) {
if (d.missing) {
return "#FFFFFF";
} else {
return hover_adjust(d, scale_split(d.predicate.field));
}
}
};
var color_fn = color_lookup["prediction"];
var path = svg.selectAll("path")
.data(partition.nodes(model.root))
.enter().append("path")
.attr("d", arc)
.style("fill", color_fn)
.style("stroke", "#fff")
.on("click", click)
.on("mouseover", mouseover)
.on("mouseout", mouseout);
var click_in_progress = false;
function click(d) {
mark_hover(d, false);
click_in_progress = true;
path.transition().duration(750).style("fill", color_fn).attrTween("d", arcTween(d));
setTimeout(function() {click_in_progress = false;}, 750);
}
d3.selectAll("input").on("change", change);
function change() {
color_fn = color_lookup[this.value];
path.transition().duration(250).style("fill", color_fn);
}
function mouseover(d) {
var split = d.predicate;
var split_msg;
if (split.field) {
if (split.term) {
if (split.value == 0 && split.operator == "<=") {
split_msg = model.model_fields[split.field].name +
" does not contain '" + split.term + "'";
} else if (split.value == 0 && split.operator == ">") {
split_msg = model.model_fields[split.field].name +
" contains '" + split.term + "'";
} else {
split_msg = model.model_fields[split.field].name + " contains "
+ "'" + split.term + "'" + " " + split.operator
+ " " + split.value + " time(s)";
}
} else {
split_msg = model.model_fields[split.field].name + " " +
split.operator + " " + split.value;
}
} else {
split_msg = "Tree Root";
}
var conf_msg = {"classification": "Confidence", "regression": "Expected Error"};
var hover = d3.select("#hover-info");
hover.append("div").attr("class", "split-predicate").text(split_msg);
tbody = hover.append("table").attr("class", "node-info").append("tbody");
var output = model_type == "classification" ? d.output : parseFloat(d.output.toFixed(3));
table_add(tbody, "Prediction", output);
table_add(tbody, conf_msg[model_type], parseFloat(d.confidence.toFixed(3)));
table_add(tbody, "Count", d.count);
mark_hover(d, true);
if (!click_in_progress) {
path.style("fill", color_fn);
}
var summ_doc = d3.select("#summary-info");
var summaries = summarize(d);
for (id in summaries) {
if (!summaries.hasOwnProperty(id)) { continue; }
var name = model.model_fields[id].name;
fs = summaries[id];
if (fs.terms) {
occurs = {};
not_occurs = {};
occurances = {};
for (term in fs.terms) {
ts = fs.terms[term];
ts.toString = function() { return JSON.stringify(this)};
var to;
if (occurances[ts]) {
to = occurances[ts];
} else {
to = {};
}
to[term] = true;
occurances[ts] = to;
}
for (ts in occurances) {
msg = name;
tsp = JSON.parse(ts);
if (tsp.max == 0) {
msg += " does not contain [ ";
} else {
msg += " contains [ ";
}
for (term in occurances[ts]) {
msg += term + " ";
}
msg += "]"
if (tsp.max != 0 && !(tsp.min == 0 && !isNum(tsp.max))) {
if (isNum(tsp.min)) {
msg += " more than " + tsp.min;
}
if (isNum(tsp.min) && isNum(tsp.max)) {
msg += " but";
}
if (isNum(tsp.max)) {
msg += " no more than " + tsp.max;
}
msg += " time(s)";
}
summ_doc.append("div").text(msg);
}
} else {
var msg = name;
if (isNum(fs.min)) {
msg = parseFloat(fs.min.toFixed(3)) + " < " + msg;
}
if (isNum(fs.max)) {
msg += " <= " + parseFloat(fs.max.toFixed(3));
}
if (fs.eq) {
msg += " = " + fs.eq;
} else if (fs.not_eq) {
msg += " !=";
var first = true;
for (category in fs.not_eq) {
if (first) {
first = false;
} else {
msg += "|";
}
if (!fs.not_eq.hasOwnProperty(category)) { continue; }
msg += " " + category;
}
}
summ_doc.append("div").text(msg);
}
}
}
function mouseout(d) {
d3.select("#hover-info").html("");
d3.select("#summary-info").html("");
mark_hover(d, false);
if (!click_in_progress) {
path.style("fill", color_fn);
}
}
function mark_hover (d, val) {
if (d.parent) { mark_hover(d.parent, val); };
d.hover = val;
}
});
d3.select(self.frameElement).style("height", height + "px");
function isNum(n) {
return !isNaN(parseFloat(n)) && isFinite(n);
}
function summarize (node) {
var pred = node.predicate;
if (node.parent) {
var summary = summarize(node.parent);
switch(pred.operator) {
case "<=":
if (pred.term) {
if (summary[pred.field]) {
if (summary[pred.field].terms[pred.term]) {
var old_max = summary[pred.field].terms[pred.term].max;
max = isNum(old_max) ? Math.min(pred.value, old_max) : pred.value;
summary[pred.field].terms[pred.term].max = max;
} else {
summary[pred.field].terms[pred.term] = {"max": pred.value};
}
} else {
summary[pred.field] = {};
terms = {};
terms[pred.term] = {"max": pred.value};
summary[pred.field].terms = terms;
}
} else {
if (summary[pred.field]) {
var old_max = summary[pred.field].max;
max = isNum(old_max) ? Math.min(pred.value, old_max) : pred.value;
summary[pred.field].max = max;
} else {
terms = {};
terms[pred.term] = {"max": pred.value};
summary[pred.field] = {"max": pred.value};
}
}
break;
case ">":
if (pred.term) {
if (summary[pred.field]) {
if (summary[pred.field].terms[pred.term]) {
var old_min = summary[pred.field].terms[pred.term].min;
min = isNum(old_min) ? Math.max(pred.value, old_min) : pred.value;
summary[pred.field].terms[pred.term].min = min;
} else {
summary[pred.field].terms[pred.term] = {"min": pred.value};
}
} else {
summary[pred.field] = {};
terms = {};
terms[pred.term] = {"min": pred.value};
summary[pred.field].terms = terms;
}
} else {
if (summary[pred.field]) {
var old_min = summary[pred.field].min;
min = isNum(old_min) ? Math.max(pred.value, old_min) : pred.value;
summary[pred.field].min = min;
} else {
summary[pred.field] = {"min": pred.value};
}
}
break;
case "=":
summary[pred.field] = {"eq": pred.value};
break;
case "!=":
if (!summary[pred.field]) {
summary[pred.field] = {};
}
if (!summary[pred.field].not_eq) {
summary[pred.field].not_eq = {};
}
summary[pred.field].not_eq[pred.value] = true;
break;
}
return summary;
} else {
return {};
}
}
function table_add (table, field, val) {
var row = table.append("tr");
row.append("td").text(field);
row.append("td").text(val);
return row;
}
</script>
Raw
titanic-model.json
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