rosnet
🇰🇷 ‘rosnet’ 은 ML을 적용한 causal discovery 패키지입니다. 제 개인 연구를 위해 만들었지만, 다른 사람들도 최대한 사용하기 쉽도록 설계했습니다. 모든 코드는 혼자서 작성했습니다. 다만, 이용자의 편의를 위해 다른 패키지의 API 설계를 따라하긴 했습니다.
🔠 ‘rosnet’ is causal discovery package applied ML . I made it for my personal study. But, it is designed to be used as easy for others as possible. I created all the codes by myself. However, for the user's convenience, I followed the API design of other packages.
목적 / Purpose
🇰🇷 이 패키지의 목적은 다음과 같습니다 :
- ML 알고리즘을 Causal discovery에 적용
- 텐서 기반으로 기존 ML 알고리즘 재설계
🔠 The purpose of this package is as follows :
- Applying ML algorithm to Causal discovery
- Re-engineering existing ML algorithm based on tensor
설치 / Installment
!pip install rosnet
🔔 요구 패키지 / Required package
- numpy
사용법 / Manual
🇰🇷 이 패키지의 API는 scikit-learn, keras 와 거의 비슷합니다!
- 오직
fit과predict, 두 개의 함수만 사용하시면 됩니다.
🔠 API of this package is just like scikit-learn and keras!
- You only need to use two functions:
fitandpredict.
예시 / Example
# Multilayer Perceptron
# **Notice** : I made some ML algorithm as needed, but not all of them.
# If you just want to use ML algorithm itself,
# it is recommened to use other ML packages like scikit-learn, tensorflow ...
from rosnet.neural_network import layers
import rosnet.neural_network as network
X_train = # Your code, numpy.narray expected
y_train = # Your code, numpy.narray expected
def build_model():
model = network.Sequential([
layers.Dense(64, activation='relu', input_shape=(X_train.shape[1], )),
layers.Dense(64, activation='relu'),
layers.Dense(64, activation='relu'),
layers.Dense(64, activation='relu'),
layers.Dense(4)
])
optimizer = network.optimizers.SGD(0.001)
model.compile(loss='mse',
optimizer=optimizer,
metrics=['mae', 'mse'])
return model
model = build_model()
model.fit(X_train, y_train,
epochs=100,
batch_size = 1000,
validation_split = 0.2,
verbose = 0)
개발 기록 / Development log
0.0.1 - 22.03.26
- rosnet.neural_network
- rosnet.neural_network.Sequential add
- rosnet.neural_network.layers add
- rosnet.neural_network.optimizers add
1 maintainer
Metadata
1 maintainer
Release files for rosnet 0.3.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| rosnet-0.3.2.tar.gz | 12.2 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| rosnet-0.3.2-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 31.2 kB
Release files / rosnet-0.3.2.tar.gz
| Download URL | rosnet-0.3.2.tar.gz |
|---|---|
| Size | 12.2 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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| Uploaded via |
twine/3.8.0 pkginfo/1.8.2 readme-renderer/34.0 requests/2.27.1 requests-toolbelt/0.9.1 urllib3/1.26.9 tqdm/4.63.1 importlib-metadata/4.11.3 keyring/23.5.0 rfc3986/2.0.0 colorama/0.4.4 CPython/3.9.5
|
Release files / rosnet-0.3.2-py3-none-any.whl
| Download URL | rosnet-0.3.2-py3-none-any.whl |
|---|---|
| Size | 19.0 kB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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No |
| Uploaded via |
twine/3.8.0 pkginfo/1.8.2 readme-renderer/34.0 requests/2.27.1 requests-toolbelt/0.9.1 urllib3/1.26.9 tqdm/4.63.1 importlib-metadata/4.11.3 keyring/23.5.0 rfc3986/2.0.0 colorama/0.4.4 CPython/3.9.5
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