baggingrnet 0.0.2

Library for Bagging of Deep Residual Neural Networks

baggingrnet: Library for Bagging of Deep Residual Neural Networks

Introduction

This package provides The python Library for Bagging of Deep Residual Neural Networks (baggingrnet). Current version just supports the KERAS package of deep learning and will extend to the others in the future. The following functionaity is provoded in this package: * model multBagging: Major class to parallel bagging of autoencoder-based deep residual networks. You can setup its aruments for optimal effects. See the class and its member functions' help for details. resAutoencoder: Major class of the base model of autoencoder-based deep residual network. See the specifics for its details. ensPrediction: Major class to ensemble predictions and optional evaluation for independent test. * util pmetrics: main metrics including rsquare and rmse etc.

• data data: function to access two sample datas to test and demonstrate parallel training and predictions of multiple models by bagging. simData: function to simulate the dataset for a test.

Installation of the package

1. You can directly install this package using the following command for the latest version:

     pip install baggingrnet  
   

2. You can also clone the repository and then install:

    git clone --recursive https://github.com/lspatial/baggingrnet.git
    pip install ./setup.py install 
   

Modeling Framework

The modeling is based on bagging of the encoding-decoding antoencoder based deep residual multilayer percepton (MLP). Residual connections were used from the encoding to decoding layers to improve the learning efficiency and use of bagging is to achieve the stable and improved ensemble predictions, with uncertainty metric (standard deviation).

The relevant paper will be published and will update here once published.

Example 1: Regression of Simulated Data

The dataset is simulated using the following formula:

each covariate defined as: x₁ ∼ U(1, 100),x₂ ∼ U(0, 100),x₃ ∼ U(1, 10),x₄ ∼ U(1, 100),x₅ ∼ U(9, 100),x₆ ∼ U(1, 1009),x₇ ∼ U(5, 300),x₈ U(6 ∼ 200) This example is to illustrate how to use bagging class to train a model and compare the results by the models with and without use of residual connections in the models.

1) Load the dataset:

from baggingrnet.data import data

sim_train=data('sim_train')
sim_train['gindex']=np.array([i for i in range(sim_train.shape[0])])

knitr::kable(py$sim_train[c(1:5),], format = "html")

	x1	x2	x3	x4	x5	x6	x7	x8	y	gindex
9842	69.59893	6.368696	5.950720	97.97698	81.77670	38.12578	38.71023	124.90578	168.7697448	0
2513	88.83580	47.619385	8.107348	23.95389	41.00300	256.75319	203.75759	146.79040	184.8472212	1
9116	65.32664	49.473679	5.982418	75.99401	80.56275	849.48435	204.52137	161.61705	-444.5390646	2
2673	21.72827	64.946680	2.592348	70.32067	42.27824	387.42060	13.15852	88.47877	-166.3553631	3
5607	69.45317	18.811648	5.624373	39.81835	84.80446	333.43811	89.22591	77.25155	-0.5405426	4

###### 2) Set bagging path, list of predictor names, get the bagging class instance and input data:

# Load the major class for parallel bagging training
from baggingrnet.model.bagging import  multBagging  

feasList = ['x'+str(i) for i in range(1,9)] #List of the covariates used in training 
target='y' # Name of the target variable 
bagpath='/tmp/sim_bagging/res' # Path used to 
chkpath(bagpath)
mbag=multBagging(bagpath)
mbag.getInputSample(sim_train, feasList,None,'gindex',target)

3) Define the arguments of a model and append it to the list of modeling duties:

name = str(0) # model name as unique identifier 
nodes = [32,16,8,4] # List of number of nodes for the encoding and coding layers, adjustable optionally; 
minibatch = 512 # Size for mini batch 
isresidual = True # Whether to use residual connections in the model 
nepoch = 200 #Number of epoches 
sampling_fea = False # Whether to bootstrap the predictors/features 
noutput = 1 # Number of the output node 
islog=False # Whether to make the log transformation 
# The following is to add the model's arguments to the list of duties. 
mbag.addTask(name,noutput,sampling_fea, nepoch, nodes, minibatch, isresidual,islog)

4) Initiate the training:

mbag.startMProcess(1)

Here, just one core is used for one model.

5) Prediction using the trained models and optional evaluation of the trained model:

from baggingrnet.model.baggingpre import  ensPrediction
# Load the test dataset 
sim_test=data('sim_test')
sim_test['gindex']=np.array([i for i in range(sim_test.shape[0])]) # Generate the unique id for merging the predicitons of multiple models 
# Setup the path and target variable  
prepath="/tmp/sim_bagging/res_pre"
chkpath(prepath)
#Load the prdiction class
mbagpre=ensPrediction(bagpath,prepath)
#Load the test data 
mbagpre.getInputSample(sim_test, feasList,'gindex')
#Start to make predictions for multiple trained models. 
mbagpre.startMProcess(1)
#Obtain the ensemble predictions from those of multiple models and optional evaluation of the models. 
mbagpre.aggPredict(isval=True,tfld='y')

The above five steps illustrate the process of loading data, training, testing, and predicting. To compare with the results of residual models, the following code is to get the results for the non-residual models.

mbag.removeTask(name)
bagpath='/tmp/sim_bagging/nores'
chkpath(bagpath)
mbag_nores=multBagging(bagpath)
mbag_nores.getInputSample(sim_train, feasList,None,'gindex','y')
isresidual = False  # This is to set no use of residual connections in the models. 
mbag_nores.addTask(name,noutput,sampling_fea, nepoch, nodes, minibatch, isresidual,islog)
mbag_nores.startMProcess(1) 
prepath="/tmp/sim_bagging/nores_pre"
chkpath(prepath)
mbagpre=ensPrediction(bagpath,prepath)
mbagpre.getInputSample(sim_test, feasList,'gindex')
mbagpre.startMProcess(1)
mbagpre.aggPredict(isval=True,tfld='y')

The comparison of the training/learning curves for residual and non-residual models:

The comparison of the independent test for residual and non-residual models: performance (R2 and RMSE)

## [1] "non residual model   r2: 0.78, rmse: 150.17"

## [1] "residual model   r2: 0.91, rmse: 98.37"

## [1] "Residual model improved R2 by 12.48%, compared with non-residual model"

## [1] "Residual model decreased rmse by -51.8, compared with non-residual model"

The scatter comparison of residual vs. non-residual models for the independent test:

Example 2: Spatiotemporal Estimation of PM_2.5

This dataset is the real dataset of the 2015 PM_2.5 and the relevant covariates for the Beijing-Tianjin-Tangshan area. Due to data security reason, it has been added with small Gaussian noise.

1) Load input data:

Here the PM_2.5 dataset is used to test the proposed methods.

from baggingrnet.data import data
pm25_train=data('pm2.5_train')
pm25_train['gindex']=np.array([i for i in range(pm25_train.shape[0])])

	sites	site\_name	city	lon	lat	pm25\_davg	ele	prs	tem	rhu	win	aod	date	haod	shaod	jd	pblh\_re	pre\_re	o3\_re	merra2\_re	month	season	routine	lat2	lon2	latlon	pm25\_avg\_log	siteFactor	stratify	pm25\_davgn	gindex
23123	1010A	昌平镇	北京	116.2300	40.1952	6.80000	57.0	1007.709	20.0859852	0.7609952	17.39427	0.2877372	2015-09-11	0.0002493	0.0000596	254	1153.9781	0.00e+00	304.7577	0.0701715	9	3	1	1615.654	13509.41	4671.888	1.916923	10	10.9	4.869974	0
1339	1014A	南口路	天津	117.1930	39.1730	84.59091	8.5	1021.859	-0.2894622	0.6565141	40.61296	0.2245625	2015-01-16	0.0003819	0.0001312	16	588.0604	2.00e-06	381.0575	0.0570400	1	4	3	1534.524	13734.20	4590.801	4.437827	14	14.1	85.777765	1
11843	1062A	铁路	承德	117.9664	40.9161	21.27273	362.0	969.876	15.3092365	0.5288071	16.61683	0.4272831	2015-05-09	0.0006895	0.0003249	129	619.7123	1.03e-04	372.3307	0.1360869	5	1	1	1674.127	13916.07	4826.725	3.057426	52	52.5	20.691740	2
9373	榆垡	京南榆垡，京南区域点	北京	116.3000	39.5200	12.08696	18.0	1013.116	14.0085974	0.8100768	39.46079	0.5075859	2015-04-12	0.0003755	0.0000713	102	1351.6859	0.00e+00	351.2422	0.0943356	4	1	1	1561.830	13525.69	4596.176	2.492127	91	91.4	10.959723	3
19596	1069A	环境监测监理中心	廊坊	116.7150	39.5571	64.20833	35.0	1005.249	24.4960499	0.8604047	14.01048	1.5149391	2015-08-02	0.0014270	0.0001992	214	1061.6481	8.00e-07	282.9952	0.0722227	8	2	1	1564.764	13622.39	4616.907	4.162133	59	59.8	65.177144	4

###### 2) Set bagging path, list of predictor names, get the bagging class instance and input data:

from baggingrnet.model.bagging import  multBagging
import random as r 
feasList = ['lat', 'lon', 'ele', 'prs', 'tem', 'rhu', 'win', 'pblh_re', 'pre_re', 'o3_re', 'aod', 'merra2_re', 'haod',
         'shaod', 'jd','lat2','lon2','latlon']
target='pm25_avg_log'
bagpath='/tmp/baggingpm25_2/res'
chkpath(bagpath)
mbag=multBagging(bagpath)

## initializing ...

mbag.getInputSample(pm25_train, feasList,None,'gindex',target)

## (29475, 31)

3) Define the arguments of multiple models (here 100 models) and append them to the list of modeling duties:

import random as r 
for i in range(1,81):
    name = str(i)
    nodes = [128 + r.randint(-5,5),128+ r.randint(-5,5),96,64,32,12]
    minibatch = 2560+r.randint(-5,5)
    isresidual = False
    nepoch = 120
    sampling_fea = False
    noutput = 1
    islog=True
    mbag.addTask(name,noutput,sampling_fea, nepoch, nodes, minibatch, isresidual,islog)
    

4) Initiate the training:

Initiate the parallel programs using 10 cores

mbag.startMProcess(10)

5) Prediction using the trained models and optional evaluation of the trained model:

from baggingrnet.model.baggingpre import  ensPrediction
prepath="/tmp/baggingpm25_2p/res"
chkpath(prepath)
mbagpre=ensPrediction(bagpath,prepath)
mbagpre.getInputSample(pm25_test, feasList,'gindex')
mbagpre.startMProcess(10)
mbagpre.aggPredict(isval=True,tfld='pm25_davg')

Finally, the following results were obtaned.

The results are shown as the following:

1) Typical learning curves of non-residual vs. residual models are shown as the following:

2) Mean performance (R2 and RMSE) of the predictions of multiple non-residual vs residual models for the independent dataset :

3) Performance (R2 and RMSE) of the ensembled predictions based on multiple models for the independent dataset:

## [1] "non residual model   r2: 0.88, rmse: 23.55"

## [1] "residual model   r2: 0.91, rmse: 20.35"

## [1] "Residual model improved R2 by 2.97%, compared with non-residual model"

## [1] "Residual model decreased rmse by -3.2, compared with non-residual model"

4) Scatter plots for the ensemble predictions of non-residual vs residual models:

Contact

For this library and its relevant complete applications, welcome to contact Dr. Lianfa Li. Email: [e-mail]; [e-mail]