[Day 28] ensembling - stacking - iT 邦幫忙::一起幫忙解決難題，拯救 IT 人的一天

第 11 屆 iThome 鐵人賽

DAY 28

AI & Data

跟top kaggler學習如何贏得資料分析競賽系列第 28 篇

[Day 28] ensembling - stacking

11th鐵人賽

madeleine

2019-09-29 16:11:54

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Stacking / 堆疊定義

上2篇把預測結果混合以提升泛化效能(也可以是加強Robustness魯棒性), 本篇 Stacking 開始, 講的是改以先前模型的預測結果來影響新模型

Stacking / 堆疊步驟 (請搭配下列的 example)及注意事項

步驟

Spliting : 分成 training 跟 validation
Train : 舉例分別 fit 以 RandomForestregressor 跟 LeanearRegression 來產生 base model.
Make Prediction : 將預測結果當作是下一階段的 meta learner的 meta model.
train a higher level learner : fit 新的 meta model.

注意事項

面向	注意事項說明
time seris	意思是說當我們分成 training 跟 validation 時, training set 是歷史數據, 但 validation 的是未發生的未來數據, 所以 test 也應是未來數據.
diversity	單模很重要, 同時要看考慮 model 多元性, 瑪博強調 you don't need to worry too much to make all the models really strong, stacking can actually extract the juice from each prediction. 弱沒關係, 總之是三個臭皮匠勝過一個諸葛亮.
diversity may come from	different algorithm 跟 different input feature
performance	要預期會發生跑到一個點, 再增加堆疊也沒有什麼效果, 雖然沒有限制可以堆疊多少個模型, 而是要回頭看數據內容, feature 跟 fit 的 algorithm
meta model	focus 在找到一種將 meta model 組合在一起的方法而不是再去做很深的仔細檢查數據的工作。

Stacking Example : 瑪博手把手教 stacking 的語法示範

Created on Mon Sep 23 23:16:44 2017
@author: Marios Michailidis / 作者是瑪博

This is an example that performs stacking to improve mean squared error
This examples uses 2 bases learners (a linear regression and a random forest) and linear regression (again) as a meta learner to achieve the best score.

The initial train data are split in 2 halves to commence the stacking.

# train is the training data
train=[
       [0.624,0.583],[0.321,0.016],[0.095,0.285],[0.235,0.573],[0.027,0.146],[0.514,0.06],[0.612,0.083],[0.054,0.597],
       [0.344,0.108],[0.671,0.089],[0.486,0.583],[0.829,0.929],[0.451,0.018],[0.448,0.286],[0.26,0.914],[0.893,0.706],
       [0.951,0.487],[0.811,0.075],[0.65,0.505],[0.902,0.24],[0.312,0.617],[0.907,0.844],[0.629,0.194],[0.333,0.559],
       [0.98,0.983],[0.87,0.706],[0.611,0.623],[0.463,0.097],[0.957,0.507],[0.341,0.792],[0.384,0.482],[0.584,0.655],
       [0.446,0.454],[0.314,0.396],[0.061,0.712],[0.951,0.691],[0.71,0.444],[0.238,0.809],[0.943,0.874],[0.325,0.619],
       [0.438,0.146],[0.131,0.055],[0.884,0.083],[0.306,0.641],[0.071,0.32],[0.765,0.402],[0.321,0.584],[0.714,0.444],
       [0.533,0.811],[0.644,0.293],[0.403,0.579],[0.278,0.577],[0.888,0.902],[0.99,0.182],[0.212,0.072],[0.692,0.386],
       [0.919,0.318],[0.082,0.234],[0.99,0.597],[0.867,0.371],[0.158,0.154],[0.304,0.826],[0.088,0.638],[0.382,0.87],
       [0.491,0.75],[0.155,0.731],[0.291,0.494],[0.76,0.304],[0.602,0.904],[0.512,0.713],[0.28,0.626],[0.99,0.566],
       [0.26,0.613],[0.312,0.561],[0.84,0.695],[0.112,0.245],[0.701,0.479],[0.974,0.103],[0.507,0.188],[0.583,0.586],
       [0.965,0.96],[0.112,0.007],[0.018,0.752],[0.063,0.967],[0.456,0.024],[0.214,0.107],[0.086,0.352],[0.892,0.356],
       [0.533,0.533],[0.276,0.241],[0.514,0.363],[0.241,0.765],[0.829,0.821],[0.73,0.54],[0.136,0.635],[0.431,0.248],
       [0.288,0.259],[0.008,0.663],[0.856,0.954],[0.579,0.972]
       ]
# y is the target variable for the train data
y=[19.444,4.992,2.11,13.708,0.805,8.619,10.21,11.443,5.624,11.092,17.783,19.544,7.135,7.411,10.633,19.133,23.157,
   12.794,18.729,14.939,9.564,19.852,10.028,14.571,22.387,18.742,14.089,8.017,23.241,11.613,14.473,13.893,15.127,
   12.062,5.968,19.842,18.636,9.556,21.748,9.319,7.09,2.423,14.594,9.14,6.539,18.883,15.257,18.729,14.114,10.36,
   16.359,14.56,19.991,15.411,3.763,17.696,19.243,1.937,26.017,19.858,2.971,10.657,5.808,11.943,13.442,7.663,
   13.304,17.375,15.677,13.589,8.728,25.399,8.824,14.796,18.179,2.405,19.424,15.835,8.254,19.661,21.516,1.764,
   5.845,8.68,7.262,3.478,7.544,19.942,17.491,4.847,14.444,9.097,18.873,20.894,7.049,7.103,5.038,5.026,20.896,16.037]

# test is the test data
test=[
      [0.463,0.496],[0.45,0.365],[0.131,0.283],[0.015,0.827],[0.076,0.302],[0.092,0.356],[0.765,0.039],[0.94,0.767],[0.413,0.343],
      [0.484,0.155],[0.464,0.695],[0.574,0.767],[0.81,0.848],[0.888,0.317],[0.802,0.776],[0.197,0.417],[0.076,0.9],[0.071,0.248],
      [0.377,0.356],[0.523,0.538],[0.282,0.151],[0.299,0.342],[0.171,0.879],[0.125,0.123],[0.38,0.554],[0.138,0.919],[0.984,0.361],
      [0.07,0.95],[0.674,0.511],[0.514,0.808],[0.808,0.83],[0.573,0.622],[0.719,0.961],[0.479,0.144],[0.158,0.708],[0.365,0.306],
      [0.704,0.963],[0.959,0.614],[0.36,0.8],[0.937,0.178],[0.412,0.69],[0.145,0.122],[0.386,0.832],[0.419,0.622],[0.908,0.44],
      [0.139,0.227],[0.57,0.852],[0.322,0.763],[0.407,0.94],[0.972,0.735],[0.027,0.671],[0.875,0.533],[0.117,0.829],[0.837,0.725],
      [0.963,0.674],[0.065,0.641],[0.271,0.693],[0.845,0.423],[0.332,0.341],[0.548,0.883],[0.979,0.094],[0.806,0.249],[0.924,0.513],
      [0.564,0.971],[0.768,0.098],[0.258,0.096],[0.365,0.811],[0.241,0.83],[0.636,0.481],[0.583,0.037],[0.408,0.535],[0.147,0.737],
      [0.027,0.452],[0.871,0.599],[0.774,0.614],[0.563,0.268],[0.573,0.424],[0.902,0.863],[0.274,0.253],[0.312,0.135],[0.435,0.416],
      [0.973,0.094],[0.541,0.022],[0.501,0.773],[0.18,0.936],[0.253,0.042],[0.354,0.242],[0.268,0.671],[0.253,0.382],[0.488,0.956],
      [0.081,0.715],[0.786,0.647],[0.813,0.999],[0.967,0.846],[0.3,0.26],[0.06,0.658],[0.366,0.988],[0.397,0.978],[0.535,0.935]
      ]


# ytest is the target variable for the test data
ytest=[15.828,13.165,2.805,6.464,6.338,7.868,11.866,19.99,12.631,7.707,12.534,14.495,19.236,19.504,17.825,10.311,7.744,1.643,12.255,
       17.601,4.874,10.789,9.428,2.327,15.502,8.762,21.087,8.585,19.251,13.568,19.096,13.478,18.991,7.625,7.578,10.835,18.129,19.543,
       11.518,15.077,11.418,2.585,12.003,11.192,22.111,2.727,15.859,10.681,13.344,20.361,5.576,23.465,8.48,18.288,20.473,5.871,9.61,
       19.963,10.884,15.108,15.787,13.071,23.744,16.452,12.334,4.794,11.67,9.674,18.095,9.58,15.796,7.597,8.211,24.461,16.518,9.306,
       16.248,20.532,4.874,5.217,14.104,15.786,8.479,13.659,9.66,4.149,6.156,9.4,10.784,14.966,6.84,16.894,19.311,21.661,5.455,
       6.063,13.329,13.911,15.998]

Stacking / 堆疊步驟

from sklearn.metrics import mean_squared_error # the metric to test 
from sklearn.ensemble import RandomForestRegressor #import model
from sklearn.linear_model import LinearRegression #import model
import numpy as np #import numpy for stats
from sklearn.model_selection import train_test_split # split the training data

# train is the training data
# y is the target variable for the train data
# test is the test data
# ytest is the target variable for the test data

#split train data in 2 parts, training and  valdiation.
training,valid,ytraining,yvalid = train_test_split(
        train,y,test_size=0.5,random_state=2)
#specify models
model1=RandomForestRegressor(random_state=2)
model2=LinearRegression()
#fit models
model1.fit(training,ytraining)
model2.fit(training,ytraining)
#make predictions for validation
preds1=model1.predict(valid)
preds2=model2.predict(valid)
#make predictions for test data
test_preds1=model1.predict(test)
test_preds2=model2.predict(test)
#Form a new dataset for valid and test via stacking the predictions
stacked_predictions=np.column_stack((preds1,preds2))
stacked_test_predictions=np.column_stack((test_preds1,test_preds2))
#specify meta model
meta_model=LinearRegression()
#fit meta model on stacked predictions
meta_model.fit(stacked_predictions,yvalid)
#make predictions on the stacked predictions of the test data
final_predictions=meta_model.predict(stacked_test_predictions)

print ("mean squared error of model 1 ", mean_squared_error(ytest,test_preds1)) 
print ("mean squared error of model 2 ", mean_squared_error(ytest,test_preds2)) 
print ("mean squared error of meta model ", mean_squared_error(ytest,final_predictions)) 

"""
Prints
('mean squared error of model 1 ', 3.2368156824242451)
('mean squared error of model 2 ', 4.5111357598518351)
('mean squared error of meta model ', 2.6495691512424853)
"""