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Decision Tree

This is a Decision Tree implementation with Python which uses information gain to split attributes.

When $Y$ is the target attribute and $X$ is an attribute, the information gain is defined as whera $H$ is the entropy function:

$$\text{InfoGain}(Y, X) = H(Y) - H(Y|X)$$

Installing

$ git clone [email protected]:electricalgorithm/DecisionTree-InformationGain.git
$ cd DecisionTree-InformationGain
$ pip install -r requirements.txt

Usage

$ python decision_tree.py [args...]

Where above [args...] is a placeholder for six command-line arguments: <train input>, <test input>, <max depth>, <train out>, <test out>, <metrics out>. These arguments are described in detail below:

  1. train input: Path to the training input .tsv file.
  2. test input: Path to the test input .tsv file.
  3. max depth: Maximum depth to which the tree should be built.
  4. train out` Path of output .txt file to which the predictions on the training data should be written.
  5. test out: Path of output .txt file to which the predictions on the test data should be written.
  6. metrics out: Path of the output .txt file to which metrics such as train and test error should be written.

Output

The output of the program is written to the files specified by the command-line arguments <train out>, <test out>, and <metrics out>. Furthermore, the tree is also printed to the console with using DecisionTreeLearner::print_tree().

A minimum tree printing node can be explained as:

[X 0/Y 1]
| ATTRIBUTE_A = 0: [X 0/Y 1]
| | ATTRIBUTE_B = 0: [X 0/Y 1]
| | ATTRIBUTE_B = 1: [X 0/Y 1]
| ATTRIBUTE_A = 1: [X 0/Y 1]
| | ATTRIBUTE_C = 0: [X 0/Y 1]

    (X number of samples which equals 0.)
    (Y number of samples which equals 1.)
(...)

Note that print functionality only works for binary classification problems for now.

Further Improvements

  • Add support for printing trees of multi-class classification problems.
  • Add support for continuous attributes.
  • Add support for pruning.
  • Add support for random forests.
  • Add support for cross-validation.


Example Usage

$ python decision_tree.py data/train.tsv data/test.tsv 5 train_out.txt test_out.txt metrics_out.txt

[65 0/135 1]
| F = 0: [42 0/16 1]
| | M2 = 0: [27 0/3 1]
| | | M4 = 0: [22 0/0 1]
| | | M4 = 1: [5 0/3 1]
| | M2 = 1: [15 0/13 1]
| | | M4 = 0: [14 0/7 1]
| | | M4 = 1: [1 0/6 1]
| F = 1: [23 0/119 1]
| | M4 = 0: [21 0/63 1]
| | | M2 = 0: [18 0/26 1]
| | | M2 = 1: [3 0/37 1]
| | M4 = 1: [2 0/56 1]
| | | P1 = 0: [2 0/15 1]
| | | P1 = 1: [0 0/41 1]