Metadata-Version: 2.1
Name: rosnet
Version: 0.0.6
Summary: Causal discovery with ML
Home-page: https://github.com/Ho-Jun-Moon/rosnet
Author: papamoon0113
Author-email: papamoon0113@pusan.ac.kr
License: UNKNOWN
Description: # 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
        
        
        
        ```python
        !pip install rosnet
        ```
        
        **🔔 요구 패키지 / Required package**
        
        - numpy  
          
          
        ### 사용법 / Manual
        
        
        
        🇰🇷 **이 패키지의 API는 *scikit-learn, keras* 와 거의 비슷합니다!** 
        
        - 오직 `fit` 과 `predict`, 두 개의 함수만 사용하시면 됩니다.
        
        🔠 **API of this package is just like *scikit-learn* and *keras*!**
        
        - You only need to use two functions: `fit` and `predict`.
        
         
          
        **예시 / Example** 
        
        ```python
        # 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**
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
