def init_paths(cls):
if cls.TEMP_PATH is None:
config = six.moves.configparser.RawConfigParser()
path = os.path.join(os.path.expanduser('~'), '.rgfrc')
try:
with codecs.open(path, 'r', 'utf-8') as cfg:
with six.StringIO(cfg.read()) as strIO:
config.readfp(strIO)
except six.moves.configparser.MissingSectionHeaderError:
with codecs.open(path, 'r', 'utf-8') as cfg:
with six.StringIO('[glob]\n' + cfg.read()) as strIO:
config.readfp(strIO)
except Exception:
pass
if SYSTEM in ('Windows', 'Microsoft'):
try:
cls.RGF_PATH = os.path.abspath(
config.get(config.sections()[0], 'rgf_location'))
except Exception:
cls.RGF_PATH = os.path.join(os.path.expanduser('~'),
'rgf.exe')
cls.DEFAULT_RGF_PATH = 'rgf.exe'
else: # Linux, Darwin (macOS), etc.
try:
cls.RGF_PATH = os.path.abspath(
config.get(config.sections()[0], 'rgf_location'))
except Exception:
cls.RGF_PATH = os.path.join(os.path.expanduser('~'), 'rgf')
cls.DEFAULT_RGF_PATH = 'rgf'
try:
cls.FASTRGF_PATH = os.path.abspath(
config.get(config.sections()[0], 'fastrgf_location'))
except Exception:
cls.FASTRGF_PATH = os.path.expanduser('~')
cls.DEFAULT_FASTRGF_PATH = ''
try:
cls.TEMP_PATH = os.path.abspath(
config.get(config.sections()[0], 'temp_location'))
except Exception:
cls.TEMP_PATH = os.path.join(gettempdir(), 'rgf')
if not os.path.isdir(cls.TEMP_PATH):
os.makedirs(cls.TEMP_PATH)
if not os.access(cls.TEMP_PATH, os.W_OK):
raise Exception(
"{0} is not writable directory. Please set "
"config flag 'temp_location' to writable directory".format(
cls.TEMP_PATH))
return cls.DEFAULT_RGF_PATH, cls.RGF_PATH, cls.DEFAULT_FASTRGF_PATH, cls.FASTRGF_PATH, cls.TEMP_PATH
def get_paths():
config = six.moves.configparser.RawConfigParser()
path = os.path.join(os.path.expanduser('~'), '.rgfrc')
try:
with codecs.open(path, 'r', 'utf-8') as cfg:
with six.StringIO(cfg.read()) as strIO:
config.readfp(strIO)
except six.moves.configparser.MissingSectionHeaderError:
with codecs.open(path, 'r', 'utf-8') as cfg:
with six.StringIO('[glob]\n' + cfg.read()) as strIO:
config.readfp(strIO)
except Exception:
pass
if SYSTEM in ('Windows', 'Microsoft'):
try:
rgf_exe = os.path.abspath(
config.get(config.sections()[0], 'exe_location'))
except Exception:
rgf_exe = os.path.join(os.path.expanduser('~'), 'rgf.exe')
try:
temp = os.path.abspath(
config.get(config.sections()[0], 'temp_location'))
except Exception:
temp = os.path.join(os.path.expanduser('~'), 'temp', 'rgf')
def_rgf = 'rgf.exe'
else: # Linux, Darwin (macOS), etc.
try:
rgf_exe = os.path.abspath(
config.get(config.sections()[0], 'exe_location'))
except Exception:
rgf_exe = os.path.join(os.path.expanduser('~'), 'rgf')
try:
temp = os.path.abspath(
config.get(config.sections()[0], 'temp_location'))
except Exception:
temp = os.path.join('/tmp', 'rgf')
def_rgf = 'rgf'
try:
fastrgf_path = os.path.abspath(
config.get(config.sections()[0], 'fastrgf_location'))
except Exception:
fastrgf_path = os.path.expanduser('~')
def_fastrgf = ''
return def_rgf, rgf_exe, def_fastrgf, fastrgf_path, temp
def export_decision_path(decision_tree,
out_file=None,
feature_names=None,
label='all',
special_characters=False,
node_ids=False,
rounded=True,
proportion=False,
impurity=True,
class_names=None):
def recurse(the_tree, node_id):
if node_id == _tree.TREE_LEAF:
raise ValueError("Invalid node_id %s" % _tree.TREE_LEAF)
left_child = the_tree.children_left[node_id]
right_child = the_tree.children_right[node_id]
if left_child != _tree.TREE_LEAF:
child2parent[left_child] = (LEFT, node_id)
child2parent[right_child] = (RIGHT, node_id)
recurse(the_tree, left_child)
recurse(the_tree, right_child)
else:
leafs.append(node_id)
def node_to_str(tree, node, criterion):
node_id = node[1]
node_pos = node[0]
# 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 = (label == 'root' and node_id == 0) or label == 'all'
# PostScript compatibility for special characters
if special_characters:
characters = ['#', '<SUB>', '</SUB>', '≤', '<br/>', '>']
node_string = '<'
else:
characters = ['#', '[', ']', '<=', '\\n', '"', '>']
node_string = '"'
# Write node ID
if 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 feature_names is not None:
feature = 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] if node_pos == 1 else characters[6],
round(tree.threshold[node_id], 4), characters[4])
# Write impurity
if 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], 4)) +
characters[4])
# Write node sample count
if labels:
node_string += 'samples = '
if 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 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, 4)
elif proportion:
# Classification
value_text = np.around(value, 2)
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, 4)
# 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 (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 class_names is not True:
class_name = 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[-2:] == '\\n':
node_string = node_string[:-2]
if node_string[-5:] == '<br/>':
node_string = node_string[:-5]
return node_string + characters[5]
# open out file
return_string = False
own_file = False
if isinstance(out_file, six.string_types):
if six.PY3:
out_file = open(out_file, "w", encoding="utf-8")
else:
out_file = open(out_file, "wb")
own_file = True
if out_file is None:
return_string = True
out_file = six.StringIO()
out_file.write('digraph Decision_path {\n')
out_file.write('rankdir = LR;\n')
out_file.write('node [shape=box];\n')
child2parent = {}
leafs = []
recurse(decision_tree.tree_, 0)
idx = 0
for leaf in leafs:
path = []
cur_node = (0, leaf)
while True:
path.append(cur_node)
if cur_node[1] == 0:
break
cur_node = child2parent[cur_node[1]]
path.reverse()
for node in path:
out_file.write('f%dt%d [label=%s];\n' %
(idx, node[1],
node_to_str(decision_tree.tree_, node,
decision_tree.criterion)))
if node[1] != 0:
out_file.write('f%dt%d -> f%dt%d;\n' %
(idx, child2parent[node[1]][1], idx, node[1]))
idx += 1
out_file.write('}')
if return_string:
return out_file.getvalue()
if own_file:
out_file.close()
def export_graphviz(decision_tree, out_file=SENTINEL, 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,
marked_nodes=[]):
"""Export a decision tree in DOT format.
This function generates a GraphViz 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)
The sample counts that are shown are weighted with any sample_weights that
might be present.
Read more in the :ref:`User Guide <tree>`.
Parameters
----------
decision_tree : decision tree classifier
The decision tree to be exported to GraphViz.
out_file : file object or string, optional (default='tree.dot')
Handle or name of the output file. If ``None``, the result is
returned as a string. This will the default from version 0.20.
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.
Returns
-------
dot_data : string
String representation of the input tree in GraphViz dot format.
Only returned if ``out_file`` is None.
.. versionadded:: 0.18
Examples
--------
>>> from sklearn.datasets import load_iris
>>> from sklearn import tree
>>> clf = tree.DecisionTreeClassifier()
>>> iris = load_iris()
>>> clf = clf.fit(iris.data, iris.target)
>>> tree.export_graphviz(clf,
... out_file='tree.dot') # doctest: +SKIP
"""
def get_color(value):
# Find the appropriate color & intensity for a node
if colors['bounds'] is None:
# Classification tree
color = list(colors['rgb'][np.argmax(value)])
sorted_values = sorted(value, reverse=True)
if len(sorted_values) == 1:
alpha = 0
else:
alpha = int(np.round(255 * (sorted_values[0] -
sorted_values[1]) /
(1 - sorted_values[1]), 0))
else:
# Regression tree or multi-output
color = list(colors['rgb'][0])
alpha = int(np.round(255 * ((value - colors['bounds'][0]) /
(colors['bounds'][1] -
colors['bounds'][0])), 0))
# Return html color code in #RRGGBBAA format
color.append(alpha)
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 node_to_str(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 = (label == 'root' and node_id == 0) or label == 'all'
# PostScript compatibility for special characters
if special_characters:
characters = ['#', '<SUB>', '</SUB>', '≤', '<br/>', '>']
node_string = '<'
else:
characters = ['#', '[', ']', '<=', '\\n', '"']
node_string = '"'
# Write node ID
if 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 feature_names is not None:
feature = 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],
precision),
characters[4])
# Write impurity
if 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], precision)) +
characters[4])
# Write node sample count
if labels:
node_string += 'samples = '
if 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 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, precision)
elif proportion:
# Classification
value_text = np.around(value, 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, 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 (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 class_names is not True:
class_name = 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[-2:] == '\\n':
node_string = node_string[:-2]
if node_string[-5:] == '<br/>':
node_string = node_string[:-5]
return node_string + characters[5]
def recurse(tree, node_id, criterion, parent=None, depth=0):
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]
# Add node with description
if max_depth is None or depth <= max_depth:
# Collect ranks for 'leaf' option in plot_options
if left_child == _tree.TREE_LEAF:
ranks['leaves'].append(str(node_id))
elif str(depth) not in ranks:
ranks[str(depth)] = [str(node_id)]
else:
ranks[str(depth)].append(str(node_id))
out_file.write('%d [label=%s'
% (node_id,
node_to_str(tree, node_id, criterion)))
# Mark nodes (on a decision path)
if node_id in marked_nodes:
out_file.write(', color=red')
if filled:
# Fetch appropriate color for node
if 'rgb' not in colors:
# Initialize colors and bounds if required
colors['rgb'] = _color_brew(tree.n_classes[0])
if tree.n_outputs != 1:
# Find max and min impurities for multi-output
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
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]
out_file.write(', fillcolor="%s"' % get_color(node_val))
out_file.write('] ;\n')
if parent is not None:
# Add edge to parent
out_file.write('%d -> %d' % (parent, node_id))
if parent == 0:
# Draw True/False labels if parent is root node
angles = np.array([45, -45]) * ((rotate - .5) * -2)
out_file.write(' [labeldistance=2.5, labelangle=')
if node_id == 1:
out_file.write('%d, headlabel="True"]' % angles[0])
else:
out_file.write('%d, headlabel="False"]' % angles[1])
out_file.write(' ;\n')
if left_child != _tree.TREE_LEAF:
recurse(tree, left_child, criterion=criterion, parent=node_id,
depth=depth + 1)
recurse(tree, right_child, criterion=criterion, parent=node_id,
depth=depth + 1)
else:
ranks['leaves'].append(str(node_id))
out_file.write('%d [label="(...)"' % node_id)
if filled:
# color cropped nodes grey
out_file.write(', fillcolor="#C0C0C0"')
out_file.write('] ;\n' % node_id)
if parent is not None:
# Add edge to parent
out_file.write('%d -> %d ;\n' % (parent, node_id))
check_is_fitted(decision_tree, 'tree_')
own_file = False
return_string = False
try:
if out_file == SENTINEL:
warnings.warn("out_file can be set to None starting from 0.18. "
"This will be the default in 0.20.",
DeprecationWarning)
out_file = "tree.dot"
if isinstance(out_file, six.string_types):
if six.PY3:
out_file = open(out_file, "w", encoding="utf-8")
else:
out_file = open(out_file, "wb")
own_file = True
if out_file is None:
return_string = True
out_file = six.StringIO()
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)))
# Check length of feature_names before getting into the tree node
# Raise error if length of feature_names does not match
# n_features_ in the decision_tree
if feature_names is not None:
if len(feature_names) != decision_tree.n_features_:
raise ValueError("Length of feature_names, %d "
"does not match number of features, %d"
% (len(feature_names),
decision_tree.n_features_))
# The depth of each node for plotting with 'leaf' option
ranks = {'leaves': []}
# The colors to render each node with
colors = {'bounds': None}
out_file.write('digraph Tree {\n')
# Specify node aesthetics
out_file.write('node [shape=box')
rounded_filled = []
if filled:
rounded_filled.append('filled')
if rounded:
rounded_filled.append('rounded')
if len(rounded_filled) > 0:
out_file.write(', style="%s", color="black"'
% ", ".join(rounded_filled))
if rounded:
out_file.write(', fontname=helvetica')
out_file.write('] ;\n')
# Specify graph & edge aesthetics
if leaves_parallel:
out_file.write('graph [ranksep=equally, splines=polyline] ;\n')
if rounded:
out_file.write('edge [fontname=helvetica] ;\n')
if rotate:
out_file.write('rankdir=LR ;\n')
# Now recurse the tree and add node & edge attributes
if isinstance(decision_tree, _tree.Tree):
recurse(decision_tree, 0, criterion="impurity")
else:
recurse(decision_tree.tree_, 0, criterion=decision_tree.criterion)
# If required, draw leaf nodes at same depth as each other
if leaves_parallel:
for rank in sorted(ranks):
out_file.write("{rank=same ; " +
"; ".join(r for r in ranks[rank]) + "} ;\n")
out_file.write("}")
if return_string:
return out_file.getvalue()
finally:
if own_file:
out_file.close()
def export_graphviz(decision_tree,
out_file=None,
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):
"""Export a decision tree in DOT format.
This function generates a GraphViz 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)
The sample counts that are shown are weighted with any sample_weights that
might be present.
Read more in the :ref:`User Guide <tree>`.
Parameters
----------
decision_tree : decision tree classifier
The decision tree to be exported to GraphViz.
out_file : file object or string, optional (default=None)
Handle or name of the output file. If ``None``, the result is
returned as a string.
.. versionchanged:: 0.20
Default of out_file changed from "tree.dot" to None.
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.
Returns
-------
dot_data : string
String representation of the input tree in GraphViz dot format.
Only returned if ``out_file`` is None.
.. versionadded:: 0.18
Examples
--------
>>> from sklearn.datasets import load_iris
>>> from sklearn import tree
>>> clf = tree.DecisionTreeClassifier()
>>> iris = load_iris()
>>> clf = clf.fit(iris.data, iris.target)
>>> tree.export_graphviz(clf) # doctest: +ELLIPSIS
'digraph Tree {...
"""
check_is_fitted(decision_tree, 'tree_')
own_file = False
return_string = False
try:
if isinstance(out_file, six.string_types):
if six.PY3:
out_file = open(out_file, "w", encoding="utf-8")
else:
out_file = open(out_file, "wb")
own_file = True
if out_file is None:
return_string = True
out_file = six.StringIO()
exporter = _DOTTreeExporter(out_file=out_file,
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)
exporter.export(decision_tree)
if return_string:
return exporter.out_file.getvalue()
finally:
if own_file:
out_file.close()