BigML Tree - Concrete Data (Icicle)

README.md

An icicle mapping of a BigML decision tree built on the concrete compressive strength dataset.

The top row represents the root of the tree. Each lower row contains the children of the upper row's nodes. The number of training instances captured by a node determine its width.

concrete-model.json

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index.html

<!DOCTYPE html>
<meta charset="utf-8">
<style>

body {
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  margin: auto;
  position: relative;
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<body>
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    </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;

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");

var partition = d3.layout.partition().value(function(d) { return d.count; });

var x = d3.scale.linear().range([20, width - 30])
var y = d3.scale.linear().range([120, height - 90])

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("svg:rect")
      .attr("x", function(d) { return x(d.x); })
      .attr("y", function(d) { return y(d.y); })
      .attr("width", function(d) { return x(d.dx) - 21; })
      .attr("height", function(d) { return 38; })
      .style("fill", color_fn)
      .on("mouseover", mouseover)
      .on("mouseout", mouseout);

  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);
    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);
    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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