masaponto/Python-ELM

Extreme learning machine implemented by python3 with scikit-learn interface

Python-ELM

Extreme Learning Machine implemented in Python3 with scikit-learn

This module has 3-type ELM

• elm.py is general 3-step model ELM
• ecob_elm.py is equality constrained optimization based ELM
  • http://www.ntu.edu.sg/home/egbhuang/pdf/ELM-Unified-Learning.pdf
• ml_elm is multi layer ELM
  • http://www.ntu.edu.sg/home/egbhuang/pdf/ieee-is-elm.pdf

Require

• scikit-learn: 0.21.3
• numpy: 1.17.0

How to install

pip install git+https://github.com/masaponto/python-elm

Usage

Basic

from elm import ELM
from sklearn.preprocessing import normalize
from sklearn.datasets import fetch_openml as fetch_mldata
from sklearn.model_selection import train_test_split

db_name = 'australian'

data_set = fetch_mldata(db_name)
data_set.data = normalize(data_set.data)
data_set.target = [1 if i == 1 else -1 for i in  data_set.target.astype(int)]

X_train, X_test, y_train, y_test = train_test_split(
    data_set.data, data_set.target, test_size=0.4)

elm = ELM(hid_num=10).fit(X_train, y_train)

print("ELM Accuracy %0.3f " % elm.score(X_test, y_test))

For cross-validation

from elm import ELM
from sklearn.preprocessing import normalize, LabelEncoder
from sklearn.datasets import fetch_openml as fetch_mldata
from sklearn.model_selection import KFold, cross_val_score

db_name = 'iris'
hid_nums = [10, 20, 30]

print(db_name)
data_set = fetch_mldata(db_name)
data_set.data = normalize(data_set.data)
data_set.target = LabelEncoder().fit_transform(data_set.target)

for hid_num in hid_nums:
    print(hid_num, end=' ')
    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

    print("Accuracy: %0.3f " % (ave))

License

MIT