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Library for Bagging of Deep Residual Neural Networks

Project description

baggingrnet: Library for Bagging of Deep Residual Neural Networks


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
     pip install ./ 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 import data

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 
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:

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['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  
#Load the prdiction class
#Load the test data 
mbagpre.getInputSample(sim_test, feasList,'gindex')
#Start to make predictions for multiple trained models. 
#Obtain the ensemble predictions from those of multiple models and optional evaluation of the models. 

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_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)
mbagpre.getInputSample(sim_test, feasList,'gindex')

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 PM2.5

This dataset is the real dataset of the 2015 PM2.5 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 PM2.5 dataset is used to test the proposed methods.

from import data
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
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
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
11843 1062A 铁路 承德 117.9664 40.9161 21.27273 362.0 969.876 15.3092365 0.5288071 16.61683 0.4272831
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
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
###### 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']
## 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
    mbag.addTask(name,noutput,sampling_fea, nepoch, nodes, minibatch, isresidual,islog)
4) Initiate the training:

Initiate the parallel programs using 10 cores

5) Prediction using the trained models and optional evaluation of the trained model:
from baggingrnet.model.baggingpre import  ensPrediction
mbagpre.getInputSample(pm25_test, feasList,'gindex')

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:

5) Comparison of ensemble predictions vs. predictions of single models:

Statistics of the performance for the predictions of multiple models and ensemble predictions are made. The following shows R2 and RMSE, barplots and scatter plots.

Performance digits:

## [1] "Ensemble predictions: R2=0.91, RMSE=20.35"

## [1] "Mean performance of predictions of multiple single models: R2=0.86, RMSE=26.07"

## [1] "Ensemble predictions averagely improved the single predictions by 6% for R2, and reduced -5.72ug/m3 for RMSE"

The boxplot shows considerable improvement by bagging (6% in R2 and -5.72 μg/m3), in comparison with single models.

The following shows the scatter plots of observed PM2.5 vs. ensemble predictions/residuals:


For this library and its relevant complete applications, welcome to contact Dr. Lianfa Li. Email: or

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