Włączanie XGBoost do potoków

Extreme Gradient Boosting with XGBoost

Sergey Fogelson

Head of Data Science, TelevisaUnivision

Przykład potoku scikit-learn z XGBoost

import pandas as pd
import xgboost as xgb
import numpy as np
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.model_selection import cross_val_score

names = ["crime","zone","industry","charles","no","rooms","age", 
        "distance","radial","tax","pupil","aam","lower","med_price"]
data = pd.read_csv("boston_housing.csv",names=names)
X, y = data.iloc[:,:-1], data.iloc[:,-1]

xgb_pipeline = Pipeline[("st_scaler", StandardScaler()),
                        ("xgb_model",xgb.XGBRegressor())]
scores = cross_val_score(xgb_pipeline, X, y, 
                         scoring="neg_mean_squared_error",cv=10)

final_avg_rmse = np.mean(np.sqrt(np.abs(scores)))
print("Final XGB RMSE:", final_avg_rmse)

Final RMSE: 4.02719593323

Dodatkowe komponenty potoków

sklearn_pandas:
- DataFrameMapper - współdziałanie między pandas a scikit-learn
sklearn.impute:
- SimpleImputer - natywne uzupełnianie braków w kolumnach numerycznych i kategorycznych w scikit-learn
sklearn.pipeline:
- FeatureUnion - łączenie wielu potoków cech w jeden potok cech

Czas na ćwiczenia!

Extreme Gradient Boosting with XGBoost