Convert scikit-learn decision trees to JSON

3 Replies to “Convert scikit-learn decision trees to JSON”

1. here’s my code with your funcion,I can NOT run through it,please help,thanks
   ——————————————————
   # #-*- coding:utf-8 -*-
   # import sys
   # reload(sys)
   # sys.setdefaultencoding(‘utf-8’)
   import numpy as np
   import pandas as pd
   from sklearn.datasets import make_classification
   from sklearn.ensemble import RandomForestClassifier
   from sklearn.tree import DecisionTreeClassifier
   import sklearn
   import numpy
   from IPython.display import display, Image
   import pydotplus
   from sklearn import tree
   from sklearn.tree import _tree
   from sklearn import tree
   import collections
   import drawtree
   import os
   from sklearn.tree._tree import TREE_LEAF

   def treeToJson(decision_tree, feature_names=None):
   from warnings import warn

   js = “”

   def node_to_str(tree, node_id, criterion):
   if not isinstance(criterion, sklearn.tree.tree.six.string_types):
   criterion = “impurity”

   value = tree.value[node_id]
   if tree.n_outputs == 1:
   value = value[0, :]

   jsonValue = ‘, ‘.join([str(x) for x in value])

   if tree.children_left[node_id] == sklearn.tree._tree.TREE_LEAF:
   return ‘”id”: “%s”, “criterion”: “%s”, “impurity”: “%s”, “samples”: “%s”, “value”: [%s]’ \
   % (node_id,
   criterion,
   tree.impurity[node_id],
   tree.n_node_samples[node_id],
   jsonValue)
   else:
   if feature_names is not None:
   feature = feature_names[tree.feature[node_id]]
   else:
   feature = tree.feature[node_id]
   print(“feature=”)
   if “=” in feature:
   ruleType = “=”
   ruleValue = “false”
   else:
   ruleType = “<="
   ruleValue = "%.4f" % tree.threshold[node_id]

   return '"id": "%s", "rule": "%s %s %s", "%s": "%s", "samples": "%s"' \
   % (node_id,
   feature,
   ruleType,
   ruleValue,
   criterion,
   tree.impurity[node_id],
   tree.n_node_samples[node_id])

   def recurse(tree, node_id, criterion, parent=None, depth=0):
   tabs = " " * depth
   js = ""

   left_child = tree.children_left[node_id]
   right_child = tree.children_right[node_id]

   js = js + "\n" + \
   tabs + "{\n" + \
   tabs + " " + node_to_str(tree, node_id, criterion)

   if left_child != sklearn.tree._tree.TREE_LEAF:
   js = js + ",\n" + \
   tabs + ' "left": ' + \
   recurse(tree, \
   left_child, \
   criterion=criterion, \
   parent=node_id, \
   depth=depth + 1) + ",\n" + \
   tabs + ' "right": ' + \
   recurse(tree, \
   right_child, \
   criterion=criterion, \
   parent=node_id,
   depth=depth + 1)

   js = js + tabs + "\n" + \
   tabs + "}"

   return js

   if isinstance(decision_tree, sklearn.tree.tree.Tree):
   js = js + recurse(decision_tree, 0, criterion="impurity")
   else:
   js = js + recurse(decision_tree.tree_, 0, criterion=decision_tree.criterion)

   return j

   def train():
   X, y = make_classification(n_samples=1000,
   n_features=6,
   n_informative=3,
   n_classes=2,
   random_state=0,
   shuffle=False)
   # print"y=",y
   # Creating a dataFrame
   df = pd.DataFrame({'Feature 1':X[:,0],
   'Feature 2':X[:,1],
   'Feature 3':X[:,2],
   'Feature 4':X[:,3],
   'Feature 5':X[:,4],
   'Feature 6':X[:,5],
   'Class':y})
   y_train = df['Class']
   X_train = df.drop('Class',axis = 1)

   dt = DecisionTreeClassifier( random_state=42)
   dt.fit(X_train, y_train)
   return dt,X_train
   #——————上面是生成决策树模型———————————–
   # os.environ["PATH"] += os.pathsep + 'C:\\Anaconda3\\Library\\bin\\graphviz'
   def draw_file(model,dot_file,png_file,X_train):
   dot_data = tree.export_graphviz(model, out_file =dot_file ,
   feature_names=X_train.columns, filled = True
   , rounded = True
   , special_characters = True)

   graph = pydotplus.graph_from_dot_file(dot_file)

   thisIsTheImage = Image(graph.create_png())
   display(thisIsTheImage)
   #print(dt.tree_.feature)

   from subprocess import check_call
   check_call(['dot','-Tpng',dot_file,'-o',png_file])

   # 剪枝函数（这里使用的不是著名的CCP剪枝，而是根据的当前的子树剩余的样本数是否超过阈值，如果小于阈值，就进行剪枝）
   def prune_index(inner_tree, index, threshold):
   if inner_tree.value[index].min() < threshold:
   # turn node into a leaf by "unlinking" its children
   inner_tree.children_left[index] = TREE_LEAF#对左子树进行剪枝操作
   inner_tree.children_right[index] = TREE_LEAF#对右子树进行剪枝操作
   # if there are shildren, visit them as well
   if inner_tree.children_left[index] != TREE_LEAF:
   prune_index(inner_tree, inner_tree.children_left[index], threshold)#对左子树进行递归
   prune_index(inner_tree, inner_tree.children_right[index], threshold)#对右子树进行递归

   #***************************************************************

   if __name__ == '__main__':
   model,X_train=train()
   model_json=treeToJson(model)
   print("model_json=",model_json)

   Reply

2. i had runed it 100000000 times got 000000000000 errors

   Reply

3. —————————- no working at all———————
   dont waste your time here folks

   Reply