This Repo Contains python implementation of paper Efficient Co-Regularised Least Squares Regression - by Ulf Brefeld, Thomas G¨artner, Tobias Scheffer and Stefan Wrobel
Run the below line
python train.py --dataset <path_to_dataset> --epochs <no of epochs>
--outputDim
is use to specify the number of output dimension
- The model will take last
outputDim
column of dataset as target labels
For comparison, we are implementing RSE as baseline (also known as Ridge regression)
Step1: keep the processed dataset file at ./dataset with last column corresponds to target label
step2:
$ python train.py --dataset ./dataset/pollution.data.txt --epochs 9000 --batch_size 16 --outputDim 1
split of attribute at
9
Loading the datasets...
model...
File name pollution.data.txt
Starting training for 9000 epoch(s)
Training completed for printing loss uncomment 75 and 76 linn in train.
Starting testing for epoch(s)
combine-rms on test set: 51.64
done
Training of Baseline starting
Training of Baseline successful. To print losses uncomment the line 67 and 68
Baseline testing Starting
baseline loss on test: 89.166
- Lisence: MIT
- You may contact me by opening an issue on this repo. Please allow 2-3 days of time to address the issue.