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Stand-alone matplotlib based tree plotting from https://github.com/scikit-learn/scikit-learn/pull/9251
import numpy as np
from numbers import Integral
from sklearn.externals import six
from sklearn.tree.export import _color_brew, _criterion, _tree
def plot_tree(decision_tree, max_depth=None, feature_names=None,
class_names=None, label='all', filled=False,
leaves_parallel=False, impurity=True, node_ids=False,
proportion=False, rotate=False, rounded=False,
special_characters=False, precision=3, ax=None, fontsize=None):
"""Plot a decision tree.
The sample counts that are shown are weighted with any sample_weights that
might be present.
Parameters
----------
decision_tree : decision tree classifier
The decision tree to be exported to GraphViz.
max_depth : int, optional (default=None)
The maximum depth of the representation. If None, the tree is fully
generated.
feature_names : list of strings, optional (default=None)
Names of each of the features.
class_names : list of strings, bool or None, optional (default=None)
Names of each of the target classes in ascending numerical order.
Only relevant for classification and not supported for multi-output.
If ``True``, shows a symbolic representation of the class name.
label : {'all', 'root', 'none'}, optional (default='all')
Whether to show informative labels for impurity, etc.
Options include 'all' to show at every node, 'root' to show only at
the top root node, or 'none' to not show at any node.
filled : bool, optional (default=False)
When set to ``True``, paint nodes to indicate majority class for
classification, extremity of values for regression, or purity of node
for multi-output.
leaves_parallel : bool, optional (default=False)
When set to ``True``, draw all leaf nodes at the bottom of the tree.
impurity : bool, optional (default=True)
When set to ``True``, show the impurity at each node.
node_ids : bool, optional (default=False)
When set to ``True``, show the ID number on each node.
proportion : bool, optional (default=False)
When set to ``True``, change the display of 'values' and/or 'samples'
to be proportions and percentages respectively.
rotate : bool, optional (default=False)
When set to ``True``, orient tree left to right rather than top-down.
rounded : bool, optional (default=False)
When set to ``True``, draw node boxes with rounded corners and use
Helvetica fonts instead of Times-Roman.
special_characters : bool, optional (default=False)
When set to ``False``, ignore special characters for PostScript
compatibility.
precision : int, optional (default=3)
Number of digits of precision for floating point in the values of
impurity, threshold and value attributes of each node.
ax : matplotlib axis, optional (default=None)
Axes to plot to. If None, use current axis.
Examples
--------
>>> from sklearn.datasets import load_iris
>>> clf = tree.DecisionTreeClassifier()
>>> iris = load_iris()
>>> clf = clf.fit(iris.data, iris.target)
>>> plot_tree(clf) # doctest: +SKIP
"""
exporter = _MPLTreeExporter(
max_depth=max_depth, feature_names=feature_names,
class_names=class_names, label=label, filled=filled,
leaves_parallel=leaves_parallel, impurity=impurity, node_ids=node_ids,
proportion=proportion, rotate=rotate, rounded=rounded,
special_characters=special_characters, precision=precision,
fontsize=fontsize)
exporter.export(decision_tree, ax=ax)
class _BaseTreeExporter(object):
def get_color(self, value):
# Find the appropriate color & intensity for a node
if self.colors['bounds'] is None:
# Classification tree
color = list(self.colors['rgb'][np.argmax(value)])
sorted_values = sorted(value, reverse=True)
if len(sorted_values) == 1:
alpha = 0
else:
alpha = ((sorted_values[0] - sorted_values[1])
/ (1 - sorted_values[1]))
else:
# Regression tree or multi-output
color = list(self.colors['rgb'][0])
alpha = ((value - self.colors['bounds'][0]) /
(self.colors['bounds'][1] - self.colors['bounds'][0]))
# unpack numpy scalars
alpha = float(alpha)
# compute the color as alpha against white
color = [int(round(alpha * c + (1 - alpha) * 255, 0)) for c in color]
# Return html color code in #RRGGBB format
hex_codes = [str(i) for i in range(10)]
hex_codes.extend(['a', 'b', 'c', 'd', 'e', 'f'])
color = [hex_codes[c // 16] + hex_codes[c % 16] for c in color]
return '#' + ''.join(color)
def get_fill_color(self, tree, node_id):
# Fetch appropriate color for node
if 'rgb' not in self.colors:
# Initialize colors and bounds if required
self.colors['rgb'] = _color_brew(tree.n_classes[0])
if tree.n_outputs != 1:
# Find max and min impurities for multi-output
self.colors['bounds'] = (np.min(-tree.impurity),
np.max(-tree.impurity))
elif (tree.n_classes[0] == 1 and
len(np.unique(tree.value)) != 1):
# Find max and min values in leaf nodes for regression
self.colors['bounds'] = (np.min(tree.value),
np.max(tree.value))
if tree.n_outputs == 1:
node_val = (tree.value[node_id][0, :] /
tree.weighted_n_node_samples[node_id])
if tree.n_classes[0] == 1:
# Regression
node_val = tree.value[node_id][0, :]
else:
# If multi-output color node by impurity
node_val = -tree.impurity[node_id]
return self.get_color(node_val)
def node_to_str(self, tree, node_id, criterion):
# Generate the node content string
if tree.n_outputs == 1:
value = tree.value[node_id][0, :]
else:
value = tree.value[node_id]
# Should labels be shown?
labels = (self.label == 'root' and node_id == 0) or self.label == 'all'
characters = self.characters
node_string = characters[-1]
# Write node ID
if self.node_ids:
if labels:
node_string += 'node '
node_string += characters[0] + str(node_id) + characters[4]
# Write decision criteria
if tree.children_left[node_id] != _tree.TREE_LEAF:
# Always write node decision criteria, except for leaves
if self.feature_names is not None:
feature = self.feature_names[tree.feature[node_id]]
else:
feature = "X%s%s%s" % (characters[1],
tree.feature[node_id],
characters[2])
node_string += '%s %s %s%s' % (feature,
characters[3],
round(tree.threshold[node_id],
self.precision),
characters[4])
# Write impurity
if self.impurity:
if isinstance(criterion, _criterion.FriedmanMSE):
criterion = "friedman_mse"
elif not isinstance(criterion, six.string_types):
criterion = "impurity"
if labels:
node_string += '%s = ' % criterion
node_string += (str(round(tree.impurity[node_id], self.precision))
+ characters[4])
# Write node sample count
if labels:
node_string += 'samples = '
if self.proportion:
percent = (100. * tree.n_node_samples[node_id] /
float(tree.n_node_samples[0]))
node_string += (str(round(percent, 1)) + '%' +
characters[4])
else:
node_string += (str(tree.n_node_samples[node_id]) +
characters[4])
# Write node class distribution / regression value
if self.proportion and tree.n_classes[0] != 1:
# For classification this will show the proportion of samples
value = value / tree.weighted_n_node_samples[node_id]
if labels:
node_string += 'value = '
if tree.n_classes[0] == 1:
# Regression
value_text = np.around(value, self.precision)
elif self.proportion:
# Classification
value_text = np.around(value, self.precision)
elif np.all(np.equal(np.mod(value, 1), 0)):
# Classification without floating-point weights
value_text = value.astype(int)
else:
# Classification with floating-point weights
value_text = np.around(value, self.precision)
# Strip whitespace
value_text = str(value_text.astype('S32')).replace("b'", "'")
value_text = value_text.replace("' '", ", ").replace("'", "")
if tree.n_classes[0] == 1 and tree.n_outputs == 1:
value_text = value_text.replace("[", "").replace("]", "")
value_text = value_text.replace("\n ", characters[4])
node_string += value_text + characters[4]
# Write node majority class
if (self.class_names is not None and
tree.n_classes[0] != 1 and
tree.n_outputs == 1):
# Only done for single-output classification trees
if labels:
node_string += 'class = '
if self.class_names is not True:
class_name = self.class_names[np.argmax(value)]
else:
class_name = "y%s%s%s" % (characters[1],
np.argmax(value),
characters[2])
node_string += class_name
# Clean up any trailing newlines
if node_string.endswith(characters[4]):
node_string = node_string[:-len(characters[4])]
return node_string + characters[5]
class _MPLTreeExporter(_BaseTreeExporter):
def __init__(self, max_depth=None, feature_names=None,
class_names=None, label='all', filled=False,
leaves_parallel=False, impurity=True, node_ids=False,
proportion=False, rotate=False, rounded=False,
special_characters=False, precision=3, fontsize=None):
self.max_depth = max_depth
self.feature_names = feature_names
self.class_names = class_names
self.label = label
self.filled = filled
self.leaves_parallel = leaves_parallel
self.impurity = impurity
self.node_ids = node_ids
self.proportion = proportion
self.rotate = rotate
self.rounded = rounded
self.special_characters = special_characters
self.precision = precision
self.fontsize = fontsize
self._scaley = 10
# validate
if isinstance(precision, Integral):
if precision < 0:
raise ValueError("'precision' should be greater or equal to 0."
" Got {} instead.".format(precision))
else:
raise ValueError("'precision' should be an integer. Got {}"
" instead.".format(type(precision)))
# The depth of each node for plotting with 'leaf' option
self.ranks = {'leaves': []}
# The colors to render each node with
self.colors = {'bounds': None}
self.characters = ['#', '[', ']', '<=', '\n', '', '']
self.bbox_args = dict(fc='w')
if self.rounded:
self.bbox_args['boxstyle'] = "round"
self.arrow_args = dict(arrowstyle="<-")
def _make_tree(self, node_id, et):
# traverses _tree.Tree recursively, builds intermediate
# "_reingold_tilford.Tree" object
name = self.node_to_str(et, node_id, criterion='entropy')
if (et.children_left[node_id] != et.children_right[node_id]):
children = [self._make_tree(et.children_left[node_id], et),
self._make_tree(et.children_right[node_id], et)]
else:
return Tree(name, node_id)
return Tree(name, node_id, *children)
def export(self, decision_tree, ax=None):
import matplotlib.pyplot as plt
from matplotlib.text import Annotation
if ax is None:
ax = plt.gca()
ax.set_axis_off()
my_tree = self._make_tree(0, decision_tree.tree_)
dt = buchheim(my_tree)
self._scalex = 1
self.recurse(dt, decision_tree.tree_, ax)
anns = [ann for ann in ax.get_children()
if isinstance(ann, Annotation)]
# get all the annotated points
xys = [ann.xyann for ann in anns]
mins = np.min(xys, axis=0)
maxs = np.max(xys, axis=0)
ax.set_xlim(mins[0], maxs[0])
ax.set_ylim(maxs[1], mins[1])
if self.fontsize is None:
# get figure to data transform
inv = ax.transData.inverted()
renderer = ax.figure.canvas.get_renderer()
# update sizes of all bboxes
for ann in anns:
ann.update_bbox_position_size(renderer)
# get max box width
widths = [inv.get_matrix()[0, 0]
* ann.get_bbox_patch().get_window_extent().width
for ann in anns]
# get minimum max size to not be too big.
max_width = max(max(widths), 1)
# adjust fontsize to avoid overlap
# width should be around 1 in data coordinates
size = anns[0].get_fontsize() / max_width
for ann in anns:
ann.set_fontsize(size)
def recurse(self, node, tree, ax, depth=0):
kwargs = dict(bbox=self.bbox_args, ha='center', va='center',
zorder=100 - 10 * depth)
if self.fontsize is not None:
kwargs['fontsize'] = self.fontsize
xy = (node.x * self._scalex, node.y * self._scaley)
if self.max_depth is None or depth <= self.max_depth:
if self.filled:
kwargs['bbox']['fc'] = self.get_fill_color(tree,
node.tree.node_id)
if node.parent is None:
# root
ax.annotate(node.tree.node, xy, **kwargs)
else:
xy_parent = (node.parent.x * self._scalex,
node.parent.y * self._scaley)
kwargs["arrowprops"] = self.arrow_args
ax.annotate(node.tree.node, xy_parent, xy, **kwargs)
for child in node.children:
self.recurse(child, tree, ax, depth=depth + 1)
else:
xy_parent = (node.parent.x * self._scalex, node.parent.y *
self._scaley)
kwargs["arrowprops"] = self.arrow_args
kwargs['bbox']['fc'] = 'grey'
ax.annotate("\n (...) \n", xy_parent, xy, **kwargs)
class DrawTree(object):
def __init__(self, tree, parent=None, depth=0, number=1):
self.x = -1.
self.y = depth
self.tree = tree
self.children = [DrawTree(c, self, depth + 1, i + 1)
for i, c
in enumerate(tree.children)]
self.parent = parent
self.thread = None
self.mod = 0
self.ancestor = self
self.change = self.shift = 0
self._lmost_sibling = None
# this is the number of the node in its group of siblings 1..n
self.number = number
def left(self):
return self.thread or len(self.children) and self.children[0]
def right(self):
return self.thread or len(self.children) and self.children[-1]
def lbrother(self):
n = None
if self.parent:
for node in self.parent.children:
if node == self:
return n
else:
n = node
return n
def get_lmost_sibling(self):
if not self._lmost_sibling and self.parent and self != \
self.parent.children[0]:
self._lmost_sibling = self.parent.children[0]
return self._lmost_sibling
lmost_sibling = property(get_lmost_sibling)
def __str__(self):
return "%s: x=%s mod=%s" % (self.tree, self.x, self.mod)
def __repr__(self):
return self.__str__()
def buchheim(tree):
dt = firstwalk(DrawTree(tree))
min = second_walk(dt)
if min < 0:
third_walk(dt, -min)
return dt
def third_walk(tree, n):
tree.x += n
for c in tree.children:
third_walk(c, n)
def firstwalk(v, distance=1.):
if len(v.children) == 0:
if v.lmost_sibling:
v.x = v.lbrother().x + distance
else:
v.x = 0.
else:
default_ancestor = v.children[0]
for w in v.children:
firstwalk(w)
default_ancestor = apportion(w, default_ancestor, distance)
# print("finished v =", v.tree, "children")
execute_shifts(v)
midpoint = (v.children[0].x + v.children[-1].x) / 2
w = v.lbrother()
if w:
v.x = w.x + distance
v.mod = v.x - midpoint
else:
v.x = midpoint
return v
def apportion(v, default_ancestor, distance):
w = v.lbrother()
if w is not None:
# in buchheim notation:
# i == inner; o == outer; r == right; l == left; r = +; l = -
vir = vor = v
vil = w
vol = v.lmost_sibling
sir = sor = v.mod
sil = vil.mod
sol = vol.mod
while vil.right() and vir.left():
vil = vil.right()
vir = vir.left()
vol = vol.left()
vor = vor.right()
vor.ancestor = v
shift = (vil.x + sil) - (vir.x + sir) + distance
if shift > 0:
move_subtree(ancestor(vil, v, default_ancestor), v, shift)
sir = sir + shift
sor = sor + shift
sil += vil.mod
sir += vir.mod
sol += vol.mod
sor += vor.mod
if vil.right() and not vor.right():
vor.thread = vil.right()
vor.mod += sil - sor
else:
if vir.left() and not vol.left():
vol.thread = vir.left()
vol.mod += sir - sol
default_ancestor = v
return default_ancestor
def move_subtree(wl, wr, shift):
subtrees = wr.number - wl.number
# print(wl.tree, "is conflicted with", wr.tree, 'moving', subtrees,
# 'shift', shift)
# print wl, wr, wr.number, wl.number, shift, subtrees, shift/subtrees
wr.change -= shift / subtrees
wr.shift += shift
wl.change += shift / subtrees
wr.x += shift
wr.mod += shift
def execute_shifts(v):
shift = change = 0
for w in v.children[::-1]:
# print("shift:", w, shift, w.change)
w.x += shift
w.mod += shift
change += w.change
shift += w.shift + change
def ancestor(vil, v, default_ancestor):
# the relevant text is at the bottom of page 7 of
# "Improving Walker's Algorithm to Run in Linear Time" by Buchheim et al,
# (2002)
# http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.16.8757&rep=rep1&type=pdf
if vil.ancestor in v.parent.children:
return vil.ancestor
else:
return default_ancestor
def second_walk(v, m=0, depth=0, min=None):
v.x += m
v.y = depth
if min is None or v.x < min:
min = v.x
for w in v.children:
min = second_walk(w, m + v.mod, depth + 1, min)
return min
class Tree(object):
def __init__(self, node="", node_id=-1, *children):
self.node = node
self.width = len(node)
self.node_id = node_id
if children:
self.children = children
else:
self.children = []
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