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BigML Tree - Pima Diabetes Data
Raw
README.md

A sunburst visualization of a BigML decision tree built on the Pima diabetes 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
diabetes-model.json
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0.716}, "objective_summary": {"categories": [["true", 5], ["false", 6]]}, "output": "false", "children": [{"count": 8, "confidence": 0.40927, "predicate": {"operator": ">", "field": "000004", "value": 28}, "objective_summary": {"categories": [["true", 2], ["false", 6]]}, "output": "false", "children": [{"count": 6, "output": "false", "confidence": 0.60966, "predicate": {"operator": "<=", "field": "000004", "value": 85}, "objective_summary": {"categories": [["false", 6]]}}, {"count": 2, "output": "true", "confidence": 0.34237, "predicate": {"operator": ">", "field": "000004", "value": 85}, "objective_summary": {"categories": [["true", 2]]}}]}, {"count": 3, "output": "true", "confidence": 0.43849, "predicate": {"operator": "<=", "field": "000004", "value": 28}, "objective_summary": {"categories": [["true", 3]]}}]}]}]}]}, {"count": 271, "output": "false", "confidence": 0.87587, "predicate": {"operator": "<=", "field": "000007", "value": 28}, "objective_summary": {"categories": [["true", 23], ["false", 248]]}}]}, {"count": 283, "confidence": 0.55697, "predicate": {"operator": ">", "field": "000001", "value": 127}, "objective_summary": {"categories": [["false", 109], ["true", 174]]}, "output": "true", "children": [{"count": 210, "confidence": 0.65474, "predicate": {"operator": ">", "field": "000005", "value": 29.825}, "objective_summary": {"categories": [["false", 59], ["true", 151]]}, "output": "true", "children": [{"count": 117, "confidence": 0.5077, "predicate": {"operator": "<=", "field": "000001", "value": 157}, "objective_summary": {"categories": [["false", 47], ["true", 70]]}, "output": "true", "children": [{"count": 102, "confidence": 0.45243, "predicate": {"operator": ">", "field": "000002", "value": 61}, "objective_summary": {"categories": [["false", 46], ["true", 56]]}, "output": "true", "children": [{"count": 41, "confidence": 0.53016, "predicate": {"operator": "<=", "field": "000007", "value": 30}, "objective_summary": {"categories": [["true", 13], ["false", 28]]}, "output": "false", "children": [{"count": 8, "output": "false", "confidence": 0.67558, "predicate": {"operator": ">", "field": "000004", "value": 271}, "objective_summary": {"categories": [["false", 8]]}}, {"count": 33, "confidence": 0.43683, "predicate": {"operator": "<=", "field": "000004", "value": 271}, "objective_summary": {"categories": [["true", 13], ["false", 20]]}, "output": "false", "children": [{"count": 25, "confidence": 0.52423, "predicate": {"operator": "<=", "field": "000005", "value": 41.7}, "objective_summary": {"categories": [["true", 7], ["false", 18]]}, "output": "false", "children": [{"count": 13, "output": "false", "confidence": 0.66685, "predicate": {"operator": ">", "field": "000002", "value": 73}, "objective_summary": {"categories": [["true", 1], ["false", 12]]}}, {"count": 12, "confidence": 0.25378, "predicate": {"operator": "<=", "field": "000002", "value": 73}, "objective_summary": {"categories": [["false", 6], ["true", 6]]}, "output": 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"confidence": 0.20654, "predicate": {"operator": "<=", "field": "000001", "value": 129}, "objective_summary": {"categories": [["false", 1]]}}]}, {"count": 2, "output": "false", "confidence": 0.34237, "predicate": {"operator": ">", "field": "000006", "value": 0.617}, "objective_summary": {"categories": [["false", 2]]}}]}, {"count": 3, "output": "false", "confidence": 0.43849, "predicate": {"operator": "<=", "field": "000005", "value": 33.75}, "objective_summary": {"categories": [["false", 3]]}}]}]}]}, {"count": 8, "confidence": 0.40927, "predicate": {"operator": ">", "field": "000005", "value": 41.7}, "objective_summary": {"categories": [["false", 2], ["true", 6]]}, "output": "true", "children": [{"count": 4, "confidence": 0.15004, "predicate": {"operator": ">", "field": "000001", "value": 144}, "objective_summary": {"categories": [["false", 2], ["true", 2]]}, "output": "true", "children": [{"count": 2, "output": "true", "confidence": 0.34237, "predicate": {"operator": "<=", "field": "000006", "value": 0.373}, "objective_summary": {"categories": [["true", 2]]}}, {"count": 2, "output": "false", "confidence": 0.34237, "predicate": {"operator": ">", "field": "000006", "value": 0.373}, "objective_summary": {"categories": [["false", 2]]}}]}, {"count": 4, "output": "true", "confidence": 0.5101, "predicate": {"operator": "<=", "field": "000001", "value": 144}, "objective_summary": {"categories": [["true", 4]]}}]}]}]}, {"count": 61, "confidence": 0.5811, "predicate": {"operator": ">", "field": "000007", "value": 30}, "objective_summary": {"categories": [["false", 18], ["true", 43]]}, "output": "true", "children": [{"count": 33, "confidence": 0.6908, "predicate": {"operator": ">", "field": "000006", "value": 0.4295}, "objective_summary": {"categories": [["false", 5], ["true", 28]]}, "output": "true", "children": [{"count": 23, "output": "true", "confidence": 0.79008, "predicate": {"operator": "<=", "field": "000005", "value": 38.6}, "objective_summary": {"categories": 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"predicate": {"operator": "<=", "field": "000006", "value": 0.4295}, "objective_summary": {"categories": [["false", 13], ["true", 15]]}, "output": "true", "children": [{"count": 23, "confidence": 0.36811, "predicate": {"operator": "<=", "field": "000005", "value": 45.55}, "objective_summary": {"categories": [["true", 10], ["false", 13]]}, "output": "false", "children": [{"count": 16, "confidence": 0.33178, "predicate": {"operator": ">", "field": "000005", "value": 32.45}, "objective_summary": {"categories": [["false", 7], ["true", 9]]}, "output": "true", "children": [{"count": 3, "output": "true", "confidence": 0.43849, "predicate": {"operator": "<=", "field": "000005", "value": 33.05}, "objective_summary": {"categories": [["true", 3]]}}, {"count": 13, "confidence": 0.29143, "predicate": {"operator": ">", "field": "000005", "value": 33.05}, "objective_summary": {"categories": [["true", 6], ["false", 7]]}, "output": "false", "children": [{"count": 10, "confidence": 0.31267, "predicate": {"operator": ">", "field": "000005", "value": 34.15}, "objective_summary": {"categories": [["false", 4], ["true", 6]]}, "output": "true", "children": [{"count": 2, "output": "false", "confidence": 0.34237, "predicate": {"operator": "<=", "field": "000001", "value": 130}, "objective_summary": {"categories": [["false", 2]]}}, {"count": 8, "confidence": 0.40927, "predicate": {"operator": ">", "field": "000001", "value": 130}, "objective_summary": {"categories": [["false", 2], ["true", 6]]}, "output": "true", "children": [{"count": 3, "confidence": 0.20765, "predicate": {"operator": ">", "field": "000005", "value": 37.25}, "objective_summary": {"categories": [["true", 1], ["false", 2]]}, "output": "false", "children": [{"count": 2, "output": "false", "confidence": 0.34237, "predicate": {"operator": "<=", "field": "000006", "value": 0.2505}, "objective_summary": {"categories": [["false", 2]]}}, {"count": 1, "output": "true", "confidence": 0.20654, "predicate": {"operator": ">", "field": "000006", "value": 0.2505}, "objective_summary": {"categories": [["true", 1]]}}]}, {"count": 5, "output": "true", "confidence": 0.56551, "predicate": {"operator": "<=", "field": "000005", "value": 37.25}, "objective_summary": {"categories": [["true", 5]]}}]}]}, {"count": 3, "output": "false", "confidence": 0.43849, "predicate": {"operator": "<=", "field": "000005", "value": 34.15}, "objective_summary": {"categories": [["false", 3]]}}]}]}, {"count": 7, "confidence": 0.48687, "predicate": {"operator": "<=", "field": "000005", "value": 32.45}, "objective_summary": {"categories": [["true", 1], ["false", 6]]}, "output": "false", "children": [{"count": 1, "output": "true", "confidence": 0.20654, "predicate": {"operator": "<=", "field": "000006", "value": 0.146}, "objective_summary": {"categories": [["true", 1]]}}, {"count": 6, "output": "false", "confidence": 0.60966, "predicate": {"operator": ">", "field": "000006", "value": 0.146}, "objective_summary": {"categories": [["false", 6]]}}]}]}, {"count": 5, "output": "true", "confidence": 0.56551, "predicate": {"operator": ">", "field": "000005", "value": 45.55}, "objective_summary": {"categories": [["true", 5]]}}]}]}]}, {"count": 15, "output": "true", "confidence": 0.70183, "predicate": {"operator": "<=", "field": "000002", "value": 61}, "objective_summary": {"categories": [["false", 1], ["true", 14]]}}]}, {"count": 93, "confidence": 0.78788, "predicate": {"operator": ">", "field": "000001", "value": 157}, "objective_summary": {"categories": [["false", 12], ["true", 81]]}, "output": "true", "children": [{"count": 3, "confidence": 0.20765, "predicate": {"operator": ">", "field": "000004", "value": 629}, "objective_summary": {"categories": [["true", 1], ["false", 2]]}, "output": "false", "children": [{"count": 2, "output": "false", "confidence": 0.34237, "predicate": {"operator": "<=", "field": "000007", "value": 45}, "objective_summary": {"categories": [["false", 2]]}}, {"count": 1, "output": "true", "confidence": 0.20654, 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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 + 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("diabetes-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>
@rajthakur609
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Can i Create a tree using this json..

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