id3_dt

Datasets

Currently for ID3 we support following datasets:

• Adult
• Nursery

Running Decision Tree in IBM federated learning on Adult Dataset

This example explains how to run ID3 Decision Trees trained on the Adult Dataset.

The following preprocessing was performed before training:

    * Add column labels as ['1', '2', '3'...'13', 'class'];
    * Feature `fnlwgt` is dropped.

And in AdultDTDataHandler it performs the following preprocessing:

    * Categorize `age` feature into 4 categories,
        i.e., [0, 18] -> 'teen', [18, 40] -> 'adult',
        [40, 80] -> 'old-adult', and [80, 99] -> 'elder'.
    * Categorize `workclass` feature into 3 categories,
        i.e., ["?", "Never-worked", "Private", "Without-pay"] -> 'others',
        ["Federal-gov", " Local-gov"] -> 'gov',
        and ->["Self-emp-inc", "Self-emp-not-inc"] -> 'self'.
    * Categorize `education` feature into 5 categories,
        i.e., ["10th"," 11th", " 12th", " 1st-4th", " 5th-6th", " 7th-8th",
        " 9th"] -> "non_college",
        [" Assoc-acdm", " Assoc-voc"] ->"assoc",
        [" Bachelors", " Some-college"] -> "college",
        [" Doctorate", " HS-grad", " Masters"] -> "grad",
        and [" Preschool", " Prof-school"] ->"others"
    * Categorize `education-num` feature into 3 categories:
        i.e., [0, 5] -> '<5', [5, 10] -> '5-10', and [10, 17] -> '>10'.
    * Categorize `capital-gain` feature into 5 categories,
        i.e., [-1, 1] -> 0, [1, 39999] -> 1,[39999, 49999] ->2,
        [49999, 79999] ->3, and [79999, 99999] ->4.
    * Categorize `capital-loss` feature into 6 categories,
        i.e., [-1, 1] -0, [1, 999] ->1, [999, 1999] ->2, [1999, 2999] ->3,
         [2999, 3999] ->4, [3999, 4499] -> 5.
    * Categorize `hours` feature into 3 categories,
        i.e., [0, 20] -> '<20', [20, 40] -> '20-40', [40, 100] -> '>40'.
    * Categorize `native-coutry` into 5 categories,
        i.e., [' ?',] -> 'others',
        [' Cambodia', ' China', ' Hong', ' India', ' Iran', ' Japan',
        ' Laos', ' Philippines', ' South', ' Taiwan', ' Thailand',
        ' Vietnam'] -> 'asia',
        [' Canada', ' Outlying-US(Guam-USVI-etc)', ' United-States'] -> 'north_america',
        [' Columbia', ' Cuba', ' Dominican-Republic', ' Ecuador',
        ' El-Salvador', ' Guatemala', ' Haiti', ' Honduras', ' Jamaica',
        ' Mexico', ' Nicaragua', ' Peru', ' Puerto-Rico',
        ' Trinadad&Tobago'] -> 'south_america',
        [' England', ' France', ' Germany', ' Greece',
        ' Holand-Netherlands', ' Hungary', ' Ireland', ' Italy', ' Poland',
        ' Portugal', ' Scotland', ' Yugoslavia'] -> 'europe'.
    * Training label column is renamed to `class`

No other preprocessing was performed.

Running Decision Tree in IBM federated learning on Nursery Dataset

This example explains how to run ID3 Decision Trees trained on the Nursery Dataset.

The following preprocessing was performed before training:

* Add column labels as ['1', '2', '3'...'8', 'class'];
* Training label column is renamed to class. No other preprocessing was performed

Steps to run the experiments

• Split data by running:

  python examples/generate_data.py -n <num_parties> -d adult -pp 500
  

• Generate config files by running:

  python examples/generate_configs.py -n 3 -f id3_dt  -d adult -p examples/data/adult/random
  

• In a terminal running an activated IBM FL environment (refer to Quickstart in our website to learn more about how to set up the running environment), start the aggregator by running:

  python -m ibmfl.aggregator.aggregator <agg_config>
  

  Type START and press enter to start accepting connections

• In a terminal running an activated IBM FL environment, start each party by running:

  python -m ibmfl.party.party <party_config>
  

  Type START and press enter to start accepting connections.

  Type REGISTER and press enter to register the party with the aggregator.

• Finally, start training by entering TRAIN in the aggregator terminal.