tabulate_sample.py

#!/usr/bin/env python
# -*- coding: utf-8 -*-

import numpy as np
from tabulate import tabulate
from sklearn.preprocessing import normalize
from sklearn.datasets import fetch_mldata
from sklearn.model_selection import cross_val_score, KFold
from elm import ELM


def main():
    """
    This script is for sample of tabulate.
    ELM is from https://github.com/masaponto/Python-ELM

    The output is as follows

    australian
    |   # of N_h |   Accuracy(%) |
    |-----------:|--------------:|
    |         10 |      0.718551 |
    |         20 |      0.763623 |
    |         30 |      0.775507 |

    iris
    |   # of N_h |   Accuracy(%) |
    |-----------:|--------------:|
    |         10 |      0.893333 |
    |         20 |      0.897333 |
    |         30 |      0.9      |


    """

    headder = ["# of N_h", "Accuracy(%)"]

    db_names = ['australian', 'iris']
    hid_nums = [10, 20, 30]

    for db_name in db_names:
        aves = []
        print(db_name)
        data_set = fetch_mldata(db_name)
        data_set.data = normalize(data_set.data)

        for hid_num in hid_nums:

            e = ELM(hid_num)
            ave = 0
            for i in range(10):
                cv = KFold(n_splits=5, shuffle=True)
                scores = cross_val_score(e, data_set.data, data_set.target, cv=cv, scoring='accuracy', n_jobs=-1)
                ave += scores.mean()
            ave /= 10
            aves.append(ave)

        table = [[hid_num, ave] for hid_num, ave in zip(hid_nums, aves)]

        print(tabulate(table, headder, tablefmt="pipe"))
        print()


if __name__ == "__main__":
    main()