Convert a decision tree to a table
I'm looking for a way to convert a decision tree trained using scikit sklearn into a decision table.
I would like to know how to parse the decision tree structure to find the decisions made at each step.
Then I would like ideas on how to structure this table.
Do you know a way or have a idea to do it?
machine-learning datatable scikit-learn decision-tree converters
edited Nov 20 '18 at 23:36
zx485
14.7k133048
asked Nov 20 '18 at 18:22
Siegfried T. NguyenSiegfried T. Nguyen
2
add a comment |
I'm looking for a way to convert a decision tree trained using scikit sklearn into a decision table.
I would like to know how to parse the decision tree structure to find the decisions made at each step.
Then I would like ideas on how to structure this table.
Do you know a way or have a idea to do it?
machine-learning datatable scikit-learn decision-tree converters
edited Nov 20 '18 at 23:36
zx485
14.7k133048
asked Nov 20 '18 at 18:22
Siegfried T. NguyenSiegfried T. Nguyen
2
See this example
– Vivek Kumar
Nov 21 '18 at 10:07
add a comment |
I'm looking for a way to convert a decision tree trained using scikit sklearn into a decision table.
I would like to know how to parse the decision tree structure to find the decisions made at each step.
Then I would like ideas on how to structure this table.
Do you know a way or have a idea to do it?
machine-learning datatable scikit-learn decision-tree converters
edited Nov 20 '18 at 23:36
zx485
14.7k133048
asked Nov 20 '18 at 18:22
Siegfried T. NguyenSiegfried T. Nguyen
2
I'm looking for a way to convert a decision tree trained using scikit sklearn into a decision table.
I would like to know how to parse the decision tree structure to find the decisions made at each step.
Then I would like ideas on how to structure this table.
Do you know a way or have a idea to do it?
machine-learning datatable scikit-learn decision-tree converters
machine-learning datatable scikit-learn decision-tree converters
edited Nov 20 '18 at 23:36
zx485
14.7k133048
asked Nov 20 '18 at 18:22
Siegfried T. NguyenSiegfried T. Nguyen
2
edited Nov 20 '18 at 23:36
zx485
14.7k133048
asked Nov 20 '18 at 18:22
Siegfried T. NguyenSiegfried T. Nguyen
2
edited Nov 20 '18 at 23:36
zx485
14.7k133048
edited Nov 20 '18 at 23:36
zx485
14.7k133048
edited Nov 20 '18 at 23:36
zx485
14.7k133048
14.7k133048
asked Nov 20 '18 at 18:22
Siegfried T. NguyenSiegfried T. Nguyen
2
asked Nov 20 '18 at 18:22
Siegfried T. NguyenSiegfried T. Nguyen
2
asked Nov 20 '18 at 18:22
Siegfried T. NguyenSiegfried T. Nguyen
2
2
See this example
– Vivek Kumar
Nov 21 '18 at 10:07
add a comment |
See this example
– Vivek Kumar
Nov 21 '18 at 10:07
See this example
– Vivek Kumar
Nov 21 '18 at 10:07
See this example
– Vivek Kumar
Nov 21 '18 at 10:07
add a comment |
1 Answer
1
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oldest
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Here is a sample code to convert a decision tree into a "python" code. You can easily adapt it to make a table.
All you need to do is create a global variable that is a table that is the size of the number of leaves times the number of features (or feature categories) and fill it recursively
def tree_to_code(tree, feature_names, classes_names):
tree_ = tree.tree_
feature_name = [
feature_names[i] if i != _tree.TREE_UNDEFINED else "undefined!"
for i in tree_.feature
]
print( "def tree(" + ", ".join(feature_names) + "):" )
def recurse(node, depth):
indent = " " * depth
if tree_.feature[node] != _tree.TREE_UNDEFINED:
name = feature_name[node]
threshold = tree_.threshold[node]
print( indent + "if " + name + " <= " + str(threshold)+ ":" )
recurse(tree_.children_left[node], depth + 1)
print( indent + "else: # if " + name + "<=" + str(threshold) )
recurse(tree_.children_right[node], depth + 1)
else:
impurity = tree.tree_.impurity[node]
dico, label = cast_value_to_dico( tree_.value[node], classes_names )
print( indent + "# impurity=" + str(impurity) + " count_max=" + str(dico[label]) )
print( indent + "return " + str(label) )
recurse(0, 1)
answered Nov 20 '18 at 19:54
Gabriel MGabriel M
1,16741223
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
Here is a sample code to convert a decision tree into a "python" code. You can easily adapt it to make a table.
All you need to do is create a global variable that is a table that is the size of the number of leaves times the number of features (or feature categories) and fill it recursively
def tree_to_code(tree, feature_names, classes_names):
tree_ = tree.tree_
feature_name = [
feature_names[i] if i != _tree.TREE_UNDEFINED else "undefined!"
for i in tree_.feature
]
print( "def tree(" + ", ".join(feature_names) + "):" )
def recurse(node, depth):
indent = " " * depth
if tree_.feature[node] != _tree.TREE_UNDEFINED:
name = feature_name[node]
threshold = tree_.threshold[node]
print( indent + "if " + name + " <= " + str(threshold)+ ":" )
recurse(tree_.children_left[node], depth + 1)
print( indent + "else: # if " + name + "<=" + str(threshold) )
recurse(tree_.children_right[node], depth + 1)
else:
impurity = tree.tree_.impurity[node]
dico, label = cast_value_to_dico( tree_.value[node], classes_names )
print( indent + "# impurity=" + str(impurity) + " count_max=" + str(dico[label]) )
print( indent + "return " + str(label) )
recurse(0, 1)
answered Nov 20 '18 at 19:54
Gabriel MGabriel M
1,16741223
add a comment |
Here is a sample code to convert a decision tree into a "python" code. You can easily adapt it to make a table.
All you need to do is create a global variable that is a table that is the size of the number of leaves times the number of features (or feature categories) and fill it recursively
def tree_to_code(tree, feature_names, classes_names):
tree_ = tree.tree_
feature_name = [
feature_names[i] if i != _tree.TREE_UNDEFINED else "undefined!"
for i in tree_.feature
]
print( "def tree(" + ", ".join(feature_names) + "):" )
def recurse(node, depth):
indent = " " * depth
if tree_.feature[node] != _tree.TREE_UNDEFINED:
name = feature_name[node]
threshold = tree_.threshold[node]
print( indent + "if " + name + " <= " + str(threshold)+ ":" )
recurse(tree_.children_left[node], depth + 1)
print( indent + "else: # if " + name + "<=" + str(threshold) )
recurse(tree_.children_right[node], depth + 1)
else:
impurity = tree.tree_.impurity[node]
dico, label = cast_value_to_dico( tree_.value[node], classes_names )
print( indent + "# impurity=" + str(impurity) + " count_max=" + str(dico[label]) )
print( indent + "return " + str(label) )
recurse(0, 1)
answered Nov 20 '18 at 19:54
Gabriel MGabriel M
1,16741223
add a comment |
Here is a sample code to convert a decision tree into a "python" code. You can easily adapt it to make a table.
All you need to do is create a global variable that is a table that is the size of the number of leaves times the number of features (or feature categories) and fill it recursively
def tree_to_code(tree, feature_names, classes_names):
tree_ = tree.tree_
feature_name = [
feature_names[i] if i != _tree.TREE_UNDEFINED else "undefined!"
for i in tree_.feature
]
print( "def tree(" + ", ".join(feature_names) + "):" )
def recurse(node, depth):
indent = " " * depth
if tree_.feature[node] != _tree.TREE_UNDEFINED:
name = feature_name[node]
threshold = tree_.threshold[node]
print( indent + "if " + name + " <= " + str(threshold)+ ":" )
recurse(tree_.children_left[node], depth + 1)
print( indent + "else: # if " + name + "<=" + str(threshold) )
recurse(tree_.children_right[node], depth + 1)
else:
impurity = tree.tree_.impurity[node]
dico, label = cast_value_to_dico( tree_.value[node], classes_names )
print( indent + "# impurity=" + str(impurity) + " count_max=" + str(dico[label]) )
print( indent + "return " + str(label) )
recurse(0, 1)
answered Nov 20 '18 at 19:54
Gabriel MGabriel M
1,16741223
Here is a sample code to convert a decision tree into a "python" code. You can easily adapt it to make a table.
All you need to do is create a global variable that is a table that is the size of the number of leaves times the number of features (or feature categories) and fill it recursively
def tree_to_code(tree, feature_names, classes_names):
tree_ = tree.tree_
feature_name = [
feature_names[i] if i != _tree.TREE_UNDEFINED else "undefined!"
for i in tree_.feature
]
print( "def tree(" + ", ".join(feature_names) + "):" )
def recurse(node, depth):
indent = " " * depth
if tree_.feature[node] != _tree.TREE_UNDEFINED:
name = feature_name[node]
threshold = tree_.threshold[node]
print( indent + "if " + name + " <= " + str(threshold)+ ":" )
recurse(tree_.children_left[node], depth + 1)
print( indent + "else: # if " + name + "<=" + str(threshold) )
recurse(tree_.children_right[node], depth + 1)
else:
impurity = tree.tree_.impurity[node]
dico, label = cast_value_to_dico( tree_.value[node], classes_names )
print( indent + "# impurity=" + str(impurity) + " count_max=" + str(dico[label]) )
print( indent + "return " + str(label) )
recurse(0, 1)
answered Nov 20 '18 at 19:54
Gabriel MGabriel M
1,16741223
answered Nov 20 '18 at 19:54
Gabriel MGabriel M
1,16741223
answered Nov 20 '18 at 19:54
Gabriel MGabriel M
1,16741223
answered Nov 20 '18 at 19:54
Gabriel MGabriel M
1,16741223
1,16741223
add a comment |
add a comment |
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See this example
– Vivek Kumar
Nov 21 '18 at 10:07