Darren L

Hi, I am using AdaRank for training my data. I am trying several metrics to see the best result produced and I noticed that by using P@K and MAP, the output shows the P@k and MAP always 1.0. I am just wondering, are those metrics not supported to be used with AdaRank algorithm? I look forward to your response. Cheers

Hi, I am using my model to rank a ranking problem using -score option. I noticed the output consists of 3 columns, the first column is the query ID and the third column is the relevance scoring. I am just wondering, is the second column the document ID? For example, with this data 2 qid:12 1:0.4 2:0.7 #docid:0 3 qid:12 1:0.3 2:0.23 #docid:1 1 qid:12 1:0.45 2:0.52 #docid:2 With the result of: 12 0 0.787 12 1 0.43 12 2 0.752 does it mean the result with second column 0 (i.e. first row) represents my...

Hi Stephen, Thank you for your response. I have checked that out and their GitHub page, unfortunately, they are not using the same algorithm as I am (i.e. AdaRank). I am using AdaRank model right now and I can see the model created is the features weight. From what I can think of now, I will just export the weight into my application and use it to calculate the relevance scoring. Could you point me to the formula how AdaRank would use these weights to generate relevance scores? Cheers!

Thank you for your response. For production purpose, how would you suggest to deploy/use the RankLib's model in our system? Note that we are working in Python environment. Cheers!

Hi, I would like to used my model produced by RankLib to rank my new data. I know that I need to use -rank option to rank it after loading the saved model. However, I am curious about the format of the dataset for the actual ranking. Will it just like the training/testing dataset without the relevance ranking? i.e. qid:123 1:0.5 2:0.6 3:0.7 ... #docID:1512 or should I append random number for relevance ranking so it have the same format as the training dataset? i.e. 1 qid:123 1:0.5 2:0.6 3:0.7 ......

Hi, I would like to used my model produced by RankLib to rank my new data. I know that I need to use -rank option to rank it after loading the saved model. However, I am curious about the format of the dataset for the actual ranking. Will it just like the training/testing dataset without the relevance ranking? i.e. qid:123 1:0.5 2:0.6 3:0.7 ... #docID:1512 or should I append random number for relevance ranking so it have the same format as the training dataset? i.e. 1 qid:123 1:0.5 2:0.6 3:0.7 ......

Hi Stephen, Thank you for your response. I have solved this issue since I got the login account to the machine I worked at. Previously I was just using Jupyter endpoint environment. By using console in the machine, I could take a look at the actual error message and could compare it with RankLib source code. Turns out, the minimum number of relevance ranking for List-Wise is 1, not 0, which initially I thought it is. Cheers!

Hi, I am using RankLib for my data (shape: 218279 rows × 1504 columns) using python and getting error code 1 with none output. I am just wondering is there any documentation regarding error codes on RankLib? I am using Jupyter iPython for my project and run the process using subprocess.run. In case you are wondering, below is my code to train. train_data = 'learning_to_rank_data/training.txt' test_data = '' validate_data = '' metric2t = 'NDCG@2' model_dest = 'learning_to_rank_data/model.txt' try:...