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Run
create_data.ipynb
: load raw data, do the normalization, and do the datasplit of each data. datalist : ["banknote":https://archive.ics.uci.edu/dataset/267/banknote+authentication 1372 * 5 Classification 1. variance of Wavelet Transformed image (continuous) 2. skewness of Wavelet Transformed image (continuous) 3. curtosis of Wavelet Transformed image (continuous) 4. entropy of image (continuous) 5. class (integer)
"concrete_compression":https://archive.ics.uci.edu/dataset/165/concrete+compressive+strength Regression Number of instances 1030 Number of Attributes 9 Attribute breakdown 8 quantitative input variables, and 1 quantitative output variable
"wine_quality_white": https://archive.ics.uci.edu/dataset/186/wine+quality "wine_quality_red": Regression Number of instances 1599 red , 4898 white Number of Attributes 12 Attribute breakdown 11 quantitative input variables, and 1 quantitative output variable
"california":https://scikit-learn.org/stable/modules/generated/sklearn.datasets.fetch_california_housing.html Regression Samples total 20640 feature 9 + 1
"climate_model_crashes":https://archive.ics.uci.edu/dataset/252/climate+model+simulation+crashes Classification (Discrete Variable Col1, Col2 Mix Type) Column 1: Latin hypercube study ID (study 1 to study 3)
Column 2: simulation ID (run 1 to run 180)
Columns 3-20: values of 18 climate model parameters scaled in the interval [0, 1]
Column 21: simulation outcome (0 = failure, 1 = success)
"connectionist_bench_sonar":https://archive.ics.uci.edu/dataset/151/connectionist+bench+sonar+mines+vs+rocks Classification Instances 208, Features 60
"qsar_biodegradation":https://archive.ics.uci.edu/dataset/254/qsar+biodegradation 1055 instances Classification 41 molecular descriptors and 1 experimental class:
"yeast":https://archive.ics.uci.edu/dataset/110/yeast 1484 * 8+1, Classification
"yacht_hydrodynamics":https://archive.ics.uci.edu/dataset/243/yacht+hydrodynamics Regression, 308 * 6+1 ]
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Run
create_missing.ipynb
Create Missing Masks, include missing rate- Add five fold visualization
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Run
create_visualization.ipynb
Create Missing Mech ScatterPlot, Missing Rate Plot, Missing Distribution Plot -
Run models Under Model folders, each model will create a train and test data
mean.ipynb
fast ,knn.ipynb
fast ,missforest.ipynb
slow operating time imputation from SklearnXBG.ipynb
Missing Imputer from https://github.com/sjtupig/MissingImputerMICE.ipynb
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Run Evaluation RMSE
rmse.ipynb
Downstreammltask.ipynb
if need to normalization? results displayresults_printour.ipynb
and visualization
Todo: pipline finished โ 8 more dataset MIWAE, notMIWAE, OT, GAIN, MCFLOW, TabCSDI, Hyper imputer existing model new: MIDAE VAEI GINN VGAIN<- check data type