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View Code? Open in Web Editor NEWA Python implementation of Online Sequential Extreme Machine Learning (OS-ELM) for online machine learning
License: Apache License 2.0
A Python implementation of Online Sequential Extreme Machine Learning (OS-ELM) for online machine learning
License: Apache License 2.0
This library needs testing regarding behavior to ensure the models are working fine in some given scenarios. A BDD testing approach is suggested to cover that with some functional tests.
This library could have a new feature: a module optimization
with methods to try different hyper-parameters settings to get the best configuration of a model for a given dataset X, y.
ELMClassifier inherits from ELMRegressor, so GenELMClassifier should not be needed
Otherwise, with large input (e.g. +10k rows) it would involve expensive algebraic operations
Let's say the model was trained with classes 1-N, and then a batch of data comes to a fit() call with a new class N+1. The model should incorporate this new class, without losing information of the previous ones
They should be updated to make them work. In particular, the sequential learning example may need some fixes to work as expected
A good option would be a simple Jupyter notebook, where it could show the main features to let the user understand how to completely use this library
A BatchNormalization layer could be a good idea to avoid overflows. Also, Dropout could be something useful (to be checked)
We need that to quickly inspect signatures and examples
All the calls to pinv2
function could raise errors if a singular matrix is operated. We could either identify these cases or just try/catch them to raise a proper error / warning
e.g. replace "sigmoid" with this one: https://docs.scipy.org/doc/scipy/reference/generated/scipy.special.expit.html
Hello, i would like to learn, how to make to run os-elm classification with iris database.
Is it possible ?
If I try to install that package via pip.
pip install pyoselm
I get the following error:
ERROR: Command errored out with exit status 1:
command: ...\python.exe' -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"....\\pyoselm\\setup.py'"'"'; __file__='
"'"'....\\pyoselm\\setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"
'"'exec'"'"'))' egg_info --egg-base '....\pyoselm\
Complete output (5 lines):
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "....\setup.py", line 18, in <module>
install_requires=open("requirements.txt").read().split()
FileNotFoundError: [Errno 2] No such file or directory: 'requirements.txt'
----------------------------------------
ERROR: Command errored out with exit status 1: python setup.py egg_info Check the logs for full command output.
ImportError Traceback (most recent call last)
F:\TEMP\ipykernel_234664\765891537.py in <cell line: 1>()
----> 1 from pyoselm import OSELMRegressor, OSELMClassifier
2 from sklearn.datasets import load_digits, make_regression
3 from sklearn.model_selection import train_test_split
4
5 print("Regression task")
d:\env\conda\envs\money\lib\site-packages\pyoselm_init_.py in
1 from pyoselm.elm import *
2 from pyoselm.layer import *
----> 3 from pyoselm.oselm import *
4
5 version = "1.0.1"
d:\env\conda\envs\money\lib\site-packages\pyoselm\oselm.py in
10
11 import numpy as np
---> 12 from scipy.linalg import pinv2
13 from scipy.sparse import eye
14 from scipy.special import softmax
ImportError: cannot import name 'pinv2' from 'scipy.linalg' (d:\env\conda\envs\money\lib\site-packages\scipy\linalg_init_.py)
To allow better compatibility with sklearn API
In order to increase the learning capacity of a model, it would be convenient to think in architectures of multiple layers.
Check this paper: https://www.hindawi.com/journals/mpe/2017/4670187/
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