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BigML Tree - Concrete Data
Raw
README.md

A sunburst visualization of a BigML decision tree built on the concrete compressive strength dataset.

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
concrete-model.json
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Raw
index.html
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<input type="radio" name="mode" value="prediction" checked \>
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<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 + 10) + ")");
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("concrete-model.json", function(error, root) {
var model = root.model;
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");
}
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) { return hover_adjust(d, scale_pred(d.output)); },
"confidence": function(d) { return hover_adjust(d, scale_conf(d.confidence)); },
"split": function(d) { return hover_adjust(d, scale_split(d.predicate.field)); } };
var color_fn = color_lookup["prediction"];
var path = svg.selectAll("path")
.data(partition.nodes(root.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 = split.field ? model.model_fields[split.field].name + " " +
split.operator + " " + split.value
: "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;
var msg = name;
fs = summaries[id];
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;
}
msg = msg;
}
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 (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 {
summary[pred.field] = {"max": pred.value};
}
break;
case ">":
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>
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