expectation-reflection 0.0.1

Expectation Reflection for classification

Expectation Reflection

The current version of Expectation Reflection is applied for binary classication. Its extension for regression tasks is in development.

History

• Danh-Tai Hoang, Juyong Song, Vipul Periwal, and Junghyo Jo, Network inference in stochastic systems from neurons to currencies: Improved performance at small sample size, Physical Review E, 99, 023311 (2019)

• Danh-Tai Hoang, Junghyo Jo, and Vipul Periwal, Data-driven inference of hidden nodes in networks, Physical Review E, 99, 042114 (2019)

Installation

From PyPi

pip install expectation-reflection

From Repository

git clone https://github.com/danhtaihoang/expectation-reflection.git

Usage

• Import expectation_reflection package into python script:

from expectation_reflection import classication as ER

• Train the model with (X_train, y_train) to get the value of intercept b and interaction weights w from features X_train to target y_train. In the current version, the target needs to be formatted in form of [0,1].

b,w = ER.fit(X_train, y_train)
print('intercept:', b)
print('interaction weights:', b)

• Using the trained b and w, we can predict outputs y_pred and their probability p_pred of new inputs X_test:

y_pred,p_pred = model.predict(X_test,b,w)
print('predicted output:',y_pred)
print('predicted probability:',y_pred)