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{"error": 42716.2954, "samples": 506, "value": [22.532806324110698], "label": "RM <= 6.94", "type": "split", "children": [{"error": 17317.3210, "samples": 430, "value": [19.93372093023257], "label": "LSTAT <= 14.40", "type": "split", "children": [{"error": 6632.2175, "samples": 255, "value": [23.349803921568636], "label": "DIS <= 1.38", "type": "split", "children": [{"error": 390.7280, "samples": 5, "value": [45.58], "label": "CRIM <= 10.59", "type": "split", "children": [{"error": 0.0000, "samples": 4, "value": [50.0], "label": "Leaf - 4", "type": "leaf"}, {"error": 0.0000, "samples": 1, "value": [27.9], "label": "Leaf - 5", "type": "leaf"}]}, {"error": 3721.1632, "samples": 250, "value": [22.90520000000001], "label": "RM <= 6.54", "type": "split", "children": [{"error": 1636.0675, "samples": 195, "value": [21.629743589743576], "label": "LSTAT <= 7.57", "type": "split", "children": [{"error": 129.6307, "samples": 43, "value": [23.969767441860473], "label": "TAX <= 222.50", "type": "split", "children": [{"error": 0.0000, "samples": 1, "value": [28.7], "label": "Leaf - 9", "type": "leaf"}, {"error": 106.7229, "samples": 42, "value": [23.85714285714286], "label": "Leaf - 10", "type": "leaf"}]}, {"error": 1204.3720, "samples": 152, "value": [20.967763157894723], "label": "TAX <= 208.00", "type": "split", "children": [{"error": 161.6000, "samples": 5, "value": [26.9], "label": "Leaf - 12", "type": "leaf"}, {"error": 860.8299, "samples": 147, "value": [20.765986394557814], "label": "Leaf - 13", "type": "leaf"}]}]}, {"error": 643.1691, "samples": 55, "value": [27.427272727272726], "label": "TAX <= 269.00", "type": "split", "children": [{"error": 91.4612, "samples": 17, "value": [30.24117647058823], "label": "PTRATIO <= 17.85", "type": "split", "children": [{"error": 26.9890, "samples": 10, "value": [31.71], "label": "Leaf - 16", "type": "leaf"}, {"error": 12.0771, "samples": 7, "value": [28.142857142857142], "label": "Leaf - 17", "type": "leaf"}]}, {"error": 356.8821, "samples": 38, "value": [26.16842105263158], "label": "NOX <= 0.53", "type": "split", "children": [{"error": 232.6986, "samples": 29, "value": [27.006896551724143], "label": "Leaf - 19", "type": "leaf"}, {"error": 38.1000, "samples": 9, "value": [23.466666666666665], "label": "Leaf - 20", "type": "leaf"}]}]}]}]}, {"error": 3373.2512, "samples": 175, "value": [14.955999999999996], "label": "NOX <= 0.61", "type": "split", "children": [{"error": 833.2624, "samples": 68, "value": [18.123529411764697], "label": "CRIM <= 0.55", "type": "split", "children": [{"error": 272.4123, "samples": 39, "value": [19.738461538461536], "label": "AGE <= 60.55", "type": "split", "children": [{"error": 22.5743, "samples": 7, "value": [22.071428571428573], "label": "NOX <= 0.46", "type": "split", "children": [{"error": 0.9800, "samples": 2, "value": [19.6], "label": "Leaf - 25", "type": "leaf"}, {"error": 4.4920, "samples": 5, "value": [23.060000000000002], "label": "Leaf - 26", "type": "leaf"}]}, {"error": 203.4047, "samples": 32, "value": [19.228125], "label": "LSTAT <= 24.69", "type": "split", "children": [{"error": 150.4386, "samples": 28, "value": [19.692857142857147], "label": "Leaf - 28", "type": "leaf"}, {"error": 4.5875, "samples": 4, "value": [15.975000000000001], "label": "Leaf - 29", "type": "leaf"}]}]}, {"error": 322.3524, "samples": 29, "value": [15.951724137931038], "label": "RM <= 6.84", "type": "split", "children": [{"error": 184.2268, "samples": 28, "value": [15.539285714285716], "label": "B <= 26.72", "type": "split", "children": [{"error": 1.1250, "samples": 2, "value": [10.95], "label": "Leaf - 32", "type": "leaf"}, {"error": 137.7385, "samples": 26, "value": [15.892307692307696], "label": "Leaf - 33", "type": "leaf"}]}, {"error": 0.0000, "samples": 1, "value": [27.5], "label": "Leaf - 34", "type": "leaf"}]}]}, {"error": 1424.1422, "samples": 107, "value": [12.942990654205609], "label": "LSTAT <= 19.65", "type": "split", "children": [{"error": 316.3804, "samples": 51, "value": [15.480392156862749], "label": "CRIM <= 12.22", "type": "split", "children": [{"error": 232.6349, "samples": 47, "value": [15.842553191489367], "label": "CRIM <= 5.77", "type": "split", "children": [{"error": 132.1443, "samples": 28, "value": [16.535714285714285], "label": "Leaf - 38", "type": "leaf"}, {"error": 67.2116, "samples": 19, "value": [14.821052631578949], "label": "Leaf - 39", "type": "leaf"}]}, {"error": 5.1475, "samples": 4, "value": [11.225], "label": "CRIM <= 14.17", "type": "split", "children": [{"error": 0.5000, "samples": 2, "value": [12.2], "label": "Leaf - 41", "type": "leaf"}, {"error": 0.8450, "samples": 2, "value": [10.25], "label": "Leaf - 42", "type": "leaf"}]}]}, {"error": 480.3621, "samples": 56, "value": [10.632142857142854], "label": "TAX <= 551.50", "type": "split", "children": [{"error": 23.5290, "samples": 10, "value": [14.41], "label": "DIS <= 1.38", "type": "split", "children": [{"error": 1.2800, "samples": 2, "value": [12.600000000000001], "label": "Leaf - 45", "type": "leaf"}, {"error": 14.0588, "samples": 8, "value": [14.8625], "label": "Leaf - 46", "type": "leaf"}]}, {"error": 283.0846, "samples": 46, "value": [9.81086956521739], "label": "DIS <= 1.41", "type": "split", "children": [{"error": 11.0971, "samples": 7, "value": [12.857142857142858], "label": "Leaf - 48", "type": "leaf"}, {"error": 195.3697, "samples": 39, "value": [9.264102564102567], "label": "Leaf - 49", "type": "leaf"}]}]}]}]}]}, {"error": 6059.4193, "samples": 76, "value": [37.23815789473684], "label": "RM <= 7.44", "type": "split", "children": [{"error": 1899.6122, "samples": 46, "value": [32.11304347826087], "label": "CRIM <= 7.39", "type": "split", "children": [{"error": 864.7674, "samples": 43, "value": [33.348837209302324], "label": "DIS <= 1.89", "type": "split", "children": [{"error": 37.8450, "samples": 2, "value": [45.65], "label": "INDUS <= 18.84", "type": "split", "children": [{"error": 0.0000, "samples": 1, "value": [50.0], "label": "Leaf - 54", "type": "leaf"}, {"error": 0.0000, "samples": 1, "value": [41.3], "label": "Leaf - 55", "type": "leaf"}]}, {"error": 509.5224, "samples": 41, "value": [32.74878048780488], "label": "NOX <= 0.49", "type": "split", "children": [{"error": 135.3867, "samples": 27, "value": [34.15555555555556], "label": "AGE <= 11.95", "type": "split", "children": [{"error": 0.1800, "samples": 2, "value": [29.3], "label": "Leaf - 58", "type": "leaf"}, {"error": 84.2816, "samples": 25, "value": [34.544000000000004], "label": "Leaf - 59", "type": "leaf"}]}, {"error": 217.6521, "samples": 14, "value": [30.03571428571428], "label": "RM <= 7.12", "type": "split", "children": [{"error": 49.6286, "samples": 7, "value": [26.914285714285715], "label": "Leaf - 61", "type": "leaf"}, {"error": 31.6171, "samples": 7, "value": [33.15714285714286], "label": "Leaf - 62", "type": "leaf"}]}]}]}, {"error": 27.9200, "samples": 3, "value": [14.4], "label": "RM <= 7.14", "type": "split", "children": [{"error": 0.0000, "samples": 1, "value": [10.4], "label": "Leaf - 64", "type": "leaf"}, {"error": 3.9200, "samples": 2, "value": [16.4], "label": "CRIM <= 13.93", "type": "split", "children": [{"error": 0.0000, "samples": 1, "value": [17.8], "label": "Leaf - 66", "type": "leaf"}, {"error": 0.0000, "samples": 1, "value": [15.0], "label": "Leaf - 67", "type": "leaf"}]}]}]}, {"error": 1098.8497, "samples": 30, "value": [45.09666666666668], "label": "B <= 361.92", "type": "split", "children": [{"error": 0.0000, "samples": 1, "value": [21.9], "label": "Leaf - 69", "type": "leaf"}, {"error": 542.2097, "samples": 29, "value": [45.896551724137936], "label": "PTRATIO <= 14.80", "type": "split", "children": [{"error": 112.3800, "samples": 14, "value": [48.300000000000004], "label": "RM <= 7.71", "type": "split", "children": [{"error": 37.8475, "samples": 4, "value": [44.725], "label": "CRIM <= 1.00", "type": "split", "children": [{"error": 0.7467, "samples": 3, "value": [42.96666666666667], "label": "Leaf - 73", "type": "leaf"}, {"error": 0.0000, "samples": 1, "value": [50.0], "label": "Leaf - 74", "type": "leaf"}]}, {"error": 2.9610, "samples": 10, "value": [49.730000000000004], "label": "LSTAT <= 3.75", "type": "split", "children": [{"error": 0.0000, "samples": 6, "value": [50.0], "label": "Leaf - 76", "type": "leaf"}, {"error": 1.8675, "samples": 4, "value": [49.325], "label": "Leaf - 77", "type": "leaf"}]}]}, {"error": 273.4773, "samples": 15, "value": [43.653333333333336], "label": "B <= 385.48", "type": "split", "children": [{"error": 16.4920, "samples": 5, "value": [47.160000000000004], "label": "CRIM <= 0.32", "type": "split", "children": [{"error": 1.8467, "samples": 3, "value": [45.833333333333336], "label": "Leaf - 80", "type": "leaf"}, {"error": 1.4450, "samples": 2, "value": [49.15], "label": "Leaf - 81", "type": "leaf"}]}, {"error": 164.7600, "samples": 10, "value": [41.9], "label": "CRIM <= 0.06", "type": "split", "children": [{"error": 19.7067, "samples": 3, "value": [46.46666666666667], "label": "Leaf - 83", "type": "leaf"}, {"error": 55.6771, "samples": 7, "value": [39.94285714285714], "label": "Leaf - 84", "type": "leaf"}]}]}]}]}]}]} |
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import numpy as np | |
from sklearn.tree import _tree | |
def export_json(decision_tree, out_file=None, feature_names=None): | |
"""Export a decision tree in JSON format. | |
This function generates a JSON representation of the decision tree, | |
which is then written into `out_file`. Once exported, graphical renderings | |
can be generated using, for example:: | |
$ dot -Tps tree.dot -o tree.ps (PostScript format) | |
$ dot -Tpng tree.dot -o tree.png (PNG format) | |
Parameters | |
---------- | |
decision_tree : decision tree classifier | |
The decision tree to be exported to JSON. | |
out : file object or string, optional (default=None) | |
Handle or name of the output file. | |
feature_names : list of strings, optional (default=None) | |
Names of each of the features. | |
Returns | |
------- | |
out_file : file object | |
The file object to which the tree was exported. The user is | |
expected to `close()` this object when done with it. | |
Examples | |
-------- | |
>>> from sklearn.datasets import load_iris | |
>>> from sklearn import tree | |
>>> clf = tree.DecisionTreeClassifier() | |
>>> iris = load_iris() | |
>>> clf = clf.fit(iris.data, iris.target) | |
>>> import tempfile | |
>>> out_file = tree.export_json(clf, out_file=tempfile.TemporaryFile()) | |
>>> out_file.close() | |
""" | |
import numpy as np | |
from sklearn.tree import _tree | |
def arr_to_py(arr): | |
arr = arr.ravel() | |
wrapper = float | |
if np.issubdtype(arr.dtype, np.int): | |
wrapper = int | |
return map(wrapper, arr.tolist()) | |
def node_to_str(tree, node_id): | |
node_repr = '"error": %.4f, "samples": %d, "value": %s' \ | |
% (tree.init_error[node_id], | |
tree.n_samples[node_id], | |
arr_to_py(tree.value[node_id])) | |
if tree.children_left[node_id] != _tree.TREE_LEAF: | |
if feature_names is not None: | |
feature = feature_names[tree.feature[node_id]] | |
else: | |
feature = "X[%s]" % tree.feature[node_id] | |
label = '"label": "%s <= %.2f"' % (feature, | |
tree.threshold[node_id]) | |
node_type = '"type": "split"' | |
else: | |
node_type = '"type": "leaf"' | |
label = '"label": "Leaf - %d"' % node_id | |
node_repr = ", ".join((node_repr, label, node_type)) | |
return node_repr | |
def recurse(tree, node_id, parent=None): | |
if node_id == _tree.TREE_LEAF: | |
raise ValueError("Invalid node_id %s" % _tree.TREE_LEAF) | |
left_child = tree.children_left[node_id] | |
right_child = tree.children_right[node_id] | |
# Open node with description | |
out_file.write('{%s' % node_to_str(tree, node_id)) | |
# write children | |
if left_child != _tree.TREE_LEAF: # and right_child != _tree.TREE_LEAF | |
out_file.write(', "children": [') | |
recurse(tree, left_child, node_id) | |
out_file.write(', ') | |
recurse(tree, right_child, node_id) | |
out_file.write(']') | |
# close node | |
out_file.write('}') | |
if out_file is None: | |
out_file = open("tree.json", "w") | |
elif isinstance(out_file, basestring): | |
out_file = open(out_file, "w") | |
if isinstance(decision_tree, _tree.Tree): | |
recurse(decision_tree, 0) | |
else: | |
recurse(decision_tree.tree_, 0) | |
return out_file |
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<!DOCTYPE html> | |
<html> | |
<head> | |
<meta http-equiv="Content-Type" content="text/html;charset=utf-8"/> | |
<script type="text/javascript" src="http://mbostock.github.com/d3/d3.js?2.9.0"></script> | |
<style type="text/css"> | |
body { | |
font-family: "Helvetica Neue", Helvetica; | |
} | |
.hint { | |
font-size: 12px; | |
color: #999; | |
} | |
.node rect { | |
cursor: pointer; | |
fill: #fff; | |
stroke-width: 1.5px; | |
} | |
.node text { | |
font-size: 11px; | |
} | |
path.link { | |
fill: none; | |
stroke: #ccc; | |
} | |
</style> | |
</head> | |
<body> | |
<div id="body"> | |
<div id="footer"> | |
Decision Tree viewer | |
<div class="hint">click to expand or collapse</div> | |
<div id="menu"> | |
<select id="datasets"></select> | |
</div> | |
</div> | |
</div> | |
<script type="text/javascript"> | |
var m = [20, 120, 20, 120], | |
w = 1280 - m[1] - m[3], | |
h = 800 - m[0] - m[2], | |
i = 0, | |
rect_width = 80, | |
rect_height = 20, | |
max_link_width = 20, | |
min_link_width = 1.5, | |
char_to_pxl = 6, | |
root; | |
// Add datasets dropdown | |
d3.select("#datasets") | |
.on("change", function() { | |
if (this.value !== '-') { | |
d3.json(this.value + ".json", load_dataset); | |
} | |
}) | |
.selectAll("option") | |
.data([ | |
"-", | |
"iris", | |
"boston", | |
]) | |
.enter().append("option") | |
.attr("value", String) | |
.text(String); | |
var tree = d3.layout.tree() | |
.size([h, w]); | |
var diagonal = d3.svg.diagonal() | |
.projection(function(d) { return [d.x, d.y]; }); | |
var vis = d3.select("#body").append("svg:svg") | |
.attr("width", w + m[1] + m[3]) | |
.attr("height", h + m[0] + m[2] + 1000) | |
.append("svg:g") | |
.attr("transform", "translate(" + m[3] + "," + m[0] + ")"); | |
// global scale for link width | |
var link_stoke_scale = d3.scale.linear(); | |
var color_map = d3.scale.category10(); | |
// stroke style of link - either color or function | |
var stroke_callback = "#ccc"; | |
function load_dataset(json) { | |
root = json; | |
root.x0 = 0; | |
root.y0 = 0; | |
var n_samples = root.samples; | |
var n_labels = root.value.length; | |
if (n_labels >= 2) { | |
stroke_callback = mix_colors; | |
} else if (n_labels === 1) { | |
stroke_callback = mean_interpolation(root); | |
} | |
link_stoke_scale = d3.scale.linear() | |
.domain([0, n_samples]) | |
.range([min_link_width, max_link_width]); | |
function toggleAll(d) { | |
if (d && d.children) { | |
d.children.forEach(toggleAll); | |
toggle(d); | |
} | |
} | |
// Initialize the display to show a few nodes. | |
root.children.forEach(toggleAll); | |
update(root); | |
} | |
function update(source) { | |
var duration = d3.event && d3.event.altKey ? 5000 : 500; | |
// Compute the new tree layout. | |
var nodes = tree.nodes(root).reverse(); | |
// Normalize for fixed-depth. | |
nodes.forEach(function(d) { d.y = d.depth * 180; }); | |
// Update the nodes… | |
var node = vis.selectAll("g.node") | |
.data(nodes, function(d) { return d.id || (d.id = ++i); }); | |
// Enter any new nodes at the parent's previous position. | |
var nodeEnter = node.enter().append("svg:g") | |
.attr("class", "node") | |
.attr("transform", function(d) { return "translate(" + source.x0 + "," + source.y0 + ")"; }) | |
.on("click", function(d) { toggle(d); update(d); }); | |
nodeEnter.append("svg:rect") | |
.attr("x", function(d) { | |
var label = node_label(d); | |
var text_len = label.length * char_to_pxl; | |
var width = d3.max([rect_width, text_len]) | |
return -width / 2; | |
}) | |
.attr("width", 1e-6) | |
.attr("height", 1e-6) | |
.attr("rx", function(d) { return d.type === "split" ? 2 : 0;}) | |
.attr("ry", function(d) { return d.type === "split" ? 2 : 0;}) | |
.style("stroke", function(d) { return d.type === "split" ? "steelblue" : "olivedrab";}) | |
.style("fill", function(d) { return d._children ? "lightsteelblue" : "#fff"; }); | |
nodeEnter.append("svg:text") | |
.attr("dy", "12px") | |
.attr("text-anchor", "middle") | |
.text(node_label) | |
.style("fill-opacity", 1e-6); | |
// Transition nodes to their new position. | |
var nodeUpdate = node.transition() | |
.duration(duration) | |
.attr("transform", function(d) { return "translate(" + d.x + "," + d.y + ")"; }); | |
nodeUpdate.select("rect") | |
.attr("width", function(d) { | |
var label = node_label(d); | |
var text_len = label.length * char_to_pxl; | |
var width = d3.max([rect_width, text_len]) | |
return width; | |
}) | |
.attr("height", rect_height) | |
.style("fill", function(d) { return d._children ? "lightsteelblue" : "#fff"; }); | |
nodeUpdate.select("text") | |
.style("fill-opacity", 1); | |
// Transition exiting nodes to the parent's new position. | |
var nodeExit = node.exit().transition() | |
.duration(duration) | |
.attr("transform", function(d) { return "translate(" + source.x + "," + source.y + ")"; }) | |
.remove(); | |
nodeExit.select("rect") | |
.attr("width", 1e-6) | |
.attr("height", 1e-6); | |
nodeExit.select("text") | |
.style("fill-opacity", 1e-6); | |
// Update the links | |
var link = vis.selectAll("path.link") | |
.data(tree.links(nodes), function(d) { return d.target.id; }); | |
// Enter any new links at the parent's previous position. | |
link.enter().insert("svg:path", "g") | |
.attr("class", "link") | |
.attr("d", function(d) { | |
var o = {x: source.x0, y: source.y0}; | |
return diagonal({source: o, target: o}); | |
}) | |
.transition() | |
.duration(duration) | |
.attr("d", diagonal) | |
.style("stroke-width", function(d) {return link_stoke_scale(d.target.samples);}) | |
.style("stroke", stroke_callback); | |
// Transition links to their new position. | |
link.transition() | |
.duration(duration) | |
.attr("d", diagonal) | |
.style("stroke-width", function(d) {return link_stoke_scale(d.target.samples);}) | |
.style("stroke", stroke_callback); | |
// Transition exiting nodes to the parent's new position. | |
link.exit().transition() | |
.duration(duration) | |
.attr("d", function(d) { | |
var o = {x: source.x, y: source.y}; | |
return diagonal({source: o, target: o}); | |
}) | |
.remove(); | |
// Stash the old positions for transition. | |
nodes.forEach(function(d) { | |
d.x0 = d.x; | |
d.y0 = d.y; | |
}); | |
} | |
// Toggle children. | |
function toggle(d) { | |
if (d.children) { | |
d._children = d.children; | |
d.children = null; | |
} else { | |
d.children = d._children; | |
d._children = null; | |
} | |
} | |
// Node labels | |
function node_label(d) { | |
if (d.type === "leaf") { | |
// leaf | |
var formatter = d3.format(".2f"); | |
var vals = []; | |
d.value.forEach(function(v) { | |
vals.push(formatter(v)); | |
}); | |
return "[" + vals.join(", ") + "]"; | |
} else { | |
// split node | |
return d.label; | |
} | |
} | |
/** | |
* Mixes colors according to the relative frequency of classes. | |
*/ | |
function mix_colors(d) { | |
var value = d.target.value; | |
var sum = d3.sum(value); | |
var col = d3.rgb(0, 0, 0); | |
value.forEach(function(val, i) { | |
var label_color = d3.rgb(color_map(i)); | |
var mix_coef = val / sum; | |
col.r += mix_coef * label_color.r; | |
col.g += mix_coef * label_color.g; | |
col.b += mix_coef * label_color.b; | |
}); | |
return col; | |
} | |
/** | |
* A linear interpolator for value[0]. | |
* | |
* Useful for link coloring in regression trees. | |
*/ | |
function mean_interpolation(root) { | |
var max = 1e-9, | |
min = 1e9; | |
function recurse(node) { | |
if (node.value[0] > max) { | |
max = node.value[0]; | |
} | |
if (node.value[0] < min) { | |
min = node.value[0]; | |
} | |
if (node.children) { | |
node.children.forEach(recurse); | |
} | |
} | |
recurse(root); | |
var scale = d3.scale.linear().domain([min, max]) | |
.range(["#2166AC","#B2182B"]); | |
function interpolator(d) { | |
return scale(d.target.value[0]); | |
} | |
return interpolator; | |
} | |
</script> | |
</body> | |
</html> |
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