content: """
  This module defines export functions for decision trees.
  """
  
  # Authors: Gilles Louppe <g.louppe@gmail.com>
  #          Peter Prettenhofer <peter.prettenhofer@gmail.com>
  #          Brian Holt <bdholt1@gmail.com>
  #          Noel Dawe <noel@dawe.me>
  #          Satrajit Gosh <satrajit.ghosh@gmail.com>
  #          Trevor Stephens <trev.stephens@gmail.com>
  #          Li Li <aiki.nogard@gmail.com>
  # License: BSD 3 clause
  
  from numbers import Integral
  
  import numpy as np
  
  from ..externals import six
  from ..utils.validation import check_is_fitted
  
  from . import _criterion
  from . import _tree
  
  
  def _color_brew(n):
      """Generate n colors with equally spaced hues.
  
      Parameters
      ----------
      n : int
          The number of colors required.
  
      Returns
      -------
      color_list : list, length n
          List of n tuples of form (R, G, B) being the components of each color.
      """
      color_list = []
  
      # Initialize saturation & value; calculate chroma & value shift
      s, v = 0.75, 0.9
      c = s * v
      m = v - c
  
      for h in np.arange(25, 385, 360. / n).astype(int):
          # Calculate some intermediate values
          h_bar = h / 60.
          x = c * (1 - abs((h_bar % 2) - 1))
          # Initialize RGB with same hue & chroma as our color
          rgb = [(c, x, 0),
                 (x, c, 0),
                 (0, c, x),
                 (0, x, c),
                 (x, 0, c),
                 (c, 0, x),
                 (c, x, 0)]
          r, g, b = rgb[int(h_bar)]
          # Shift the initial RGB values to match value and store
          rgb = [(int(255 * (r + m))),
                 (int(255 * (g + m))),
                 (int(255 * (b + m)))]
          color_list.append(rgb)
  
      return color_list
  
  
  class Sentinel(object):
      def __repr__(self):
          return '"tree.dot"'
  
  
  SENTINEL = Sentinel()
  
  
  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 regressor or 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,
      ...     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 = ['&#35;', '<SUB>', '</SUB>', '&le;', '<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)))
  
              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 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
          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()
  