QReg Repository

Overview

This project implements the Query-Centric Regression, named QReg. QReg is an ensemble method based on various base regression models.

Current QReg supports linear, polynomial, decision tree, xgboost, gboosting regression as its base models.

Dependencies

Python 3.6 or higher, requires scipy, xgboost, numpy, scikit-learn

How to install

pip install qregpy

How to use

Example I

from qregpy import qreg
import numpy as np
# The target fitting function is y=x1+2x2
X = np.array([[1,2],[2,5],[3,7],[4,9],[1,3],[2,4], [3,5], [4,2], [5,1]])
y= np.array([5.2, 12, 17.5, 21.2,7.2, 11,13, 7.8, 6.9])

# train the regression
reg = qreg.QReg(base_models=["linear", "polynomial"], verbose=True).fit(X, y)

# make the prediction for point [3,4]
print(reg.predict([[3,4]]))

Example II

from qregpy import qreg
import pandas as pd

# load the files
df = pd.read_csv("/data/10k.csv")
headerX = ["ss_list_price", "ss_wholesale_cost"]
headerY = "ss_wholesale_cost"

# prepare X and y
X = df[headerX].values
y = df[headerY].values

# train the regression using base models linear regression and XGBoost regression.
reg = qreg.QReg(base_models=["linear","xgboost"], verbose=True).fit(X, y)

# make predictions
reg.predict([[93.35, 53.04], [60.84, 41.96]])