SKNet 0.0.1

a library used for stacking based on scikit-learn

SKNet

Introduction

SKNet is a new type of neural network that is simple in structure but complex in neuron. Each of its neuron is a traditional estimator such as SVM, RF, etc.

Fetaures

We think that such a network has many applicable scenarios.

• We don't have enough samples to train neural networks.
• We hope to improve the accuracy of the model by means of emsemble.
• We hope to learn some new features.
• We want to save a lot of parameter adjustment time while getting a stable and good model.

Installation

pip install sknet

Example

Computation Graph

Code

from sknet.sequential import Layer,Sequential,SKNeuron

from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import AdaBoostRegressor
from sklearn.ensemble import ExtraTreesRegressor
from sklearn.svm import LinearSVR
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.neighbors import KNeighborsRegressor


from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split


data = load_breast_cancer()
features = data.data
target = data.target

X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)



layer1 = Layer([
    SKNeuron(RandomForestRegressor,params = {"random_state": 0}),
    SKNeuron(GradientBoostingRegressor,params = {"random_state": 0}),
    SKNeuron(AdaBoostRegressor,params = {"random_state": 0}),
    SKNeuron(KNeighborsRegressor),
    SKNeuron(ExtraTreesRegressor,params = {"random_state": 0}),
])

layer2 = Layer([
    SKNeuron(AdaBoostRegressor,params = {"random_state": 0}),
    SKNeuron(LinearSVR,params = {"random_state": 0}),
])

layer3 = Layer([
    SKNeuron(LogisticRegression,params = {"random_state": 0}),
])


model = Sequential([layer1,layer2,layer3],n_splits = 5)
y_pred = model.fit_predict(X_train,y_train, X_test)
print(model.score(y_test,y_pred))


# acc = 0.9736842105263158

Todo

• Two or three level stacking
• multi-processing
• features proxy