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Can someone help me understand what is going to be the blind dataset ?

In the index.ipynb it has been said that the blind dataset is going to be same as used by Brendon

When I checked the jypter notebook I found that the blind data is the well NEWBY

But in the magazine publish in Oct 2016 I could find that "SHANKLE" being used as the blind dataset.

Hello.
Why doesn't the code part work for the same logging data?
TypeErrorTraceback (most recent call last)
in ()
12 return labels[row['Lith_Section']-1]
13
---> 14 training_data.loc[:,'FaciesLabels'] = training_data.apply(lambda row: label_facies(row, facies_labels), axis=1)
15 training_data.describe()

5 frames
in label_facies(row, labels)
10
11 def label_facies(row, labels):
---> 12 return labels[row['Lith_Section']-1]
13
14 training_data.loc[:,'FaciesLabels'] = training_data.apply(lambda row: label_facies(row, facies_labels), axis=1)

TypeError: ('list indices must be integers, not float', u'occurred at index 0')

Thank you

first of all, thanks for organizing this contest. It was a great experience. I have been with work for the last few weeks and haven't been noticing the change. I have one question that I don't understand is that PA_team jumps to the top in the stochastic scoring. Is it because you didn't put a highest score from randomly selected seeds?

First, thanks for putting this exercise together. And also please excuse me stumbling through this as I learn pretty much from scratch. But, just wanted to clarify the csv file used in the paper is 'training_data', but the tutorial "facies_classification" notebook uses 'facies_vectors' then renamed to 'training_data', right? The absence of PE data for a couple wells in the tutorial that are not used in the paper (training_data.csv) is the main reason I ask.

Thanks!
Bryan

If something is submitted to the contest, can it still be published in an article in the leading edge?

This may be a bit too meta but:

Making submissions public on Github would make it possible for anyone to take your approach and tweak it slightly and then publish it as their own?

How do other contests such as Kaggle handle this?

Think It might have already been discussed in #4 but just to reconfirm, what is the evaluation metric for this contest ? it's F1 which is 2 * (precision * recall) / (precision + recall) right ?
and not accuracy which would be (sum of the diagonal of confusion matrix) / total number of test data(or blind data)

Hi @kwinkunks considering it's not a traditional format of Machine Learning contests which would be upload the .csv file and it would automatically give the accuracy. I just want to know can we submit 2 or more model(by 1 user) from the time you might have scored their previous model ? Till now I have been building one model waiting for you score to submit my next. I feel like it's one submission per day and a lot of time is left idle in between. Let me know your thoughts.

Hi is there going to be a leader board where we can see the score of our model and the other contributors ?

I did the same job as before but I faced an error as follows.
Here is the message:

git.exe push --progress "origin" master:master

remote: Permission to seg/2016-ml-contest.git denied to ckjeong73.
fatal: unable to access 'https://github.com/seg/2016-ml-contest.git/': The requested URL returned error: 403 git did not exit cleanly (exit code 128) (3500 ms @ 2017-01-27 am 3:32:07)

Is anyone who met the same issue?

I could see 3 files

1. well_data_with_facies.csv
2. validation_data_nofacies.csv
3. nofacies_data.csv
   having the same data which possess info about 2 wells "STUART" , "CRAWFORD" with the same number of rows and the only difference being Facies column being present in "well_data_with_facies.csv". Please do clarify what is the use of these 3 datasets. I am guessing this is the test dataset on which the prediction should be done if the testset data is not blind data. Correct me if I am wrong

HI @kwinkunks, I know it has been one year already, I just happen to take a look at this repo again and found in the utils.py that the score used is "accuracy", not the actual "F1 score", is that right?

Hi guys, can some one tell me how to submit the results to this contest. I'm certainly new to github and everything seems out of my way. I'm not sure if my uploaded file was notified to the admin after I made the pull request. Infact I'm not sure if the pull request I did was correct. I'm not able to see my notebook like how it appears for the others. It looks like a html code for me. If you could post a short video right from forking or cloning to creating a separate folder for the team and uploading and submitting (pull request) the results it would be very helpful for beginners like me. I hope I'm not asking too much.

Thanks
Thanish

I have been messing with things trying to see if a classifier would work well using the marine and non-marine formations and facies separately. This could be a terrible idea, but its also been fun for me learning code. Anyway! It looks like there may be some discrepancies in the data, where a NM facies (1,2,3) shows a M classifier (2) and vice versa that marine facies show a NM classifier. There are not a lot of them (total=~50), but they do exist. And are not present in all the wells but some wells have more than others (eg cross H). Has anyone else noticed this? Or maybe I'm just doing it wrong?? I made a notebook for this here. Also wonder how much it would actually affect predictions bc its a small number, and does the validation data contain the same discrepancies? Any thoughts? Thanks!! @kwinkunks

Can also try this code to check.
td_NM = training_data.loc[training_data['Facies'].isin([1,2,3])]
td_NM.loc[td_NM['NM_M'].isin([2])]

It is usually a bad idea for the validation data to affect the trained model, such as including the validation data when computing the mean for standardization, as this can lead to overly optimistic validation scores.

If there are already lots of boreholes drilled with log data but no core facies classification, and the goal is simply to classify these wells, I could imagine potentially including all of the log data when standardizing the data. If the goal is instead to make something which can be applied to the existing wells, and also any future boreholes that are drilled, then I think it would be unwise.

Should we make this against the rules of the contest, or is it permissible in this case?

Hi @kwinkunks I think I did see somewhere which explains about what each feature in the provided dataset means. But I am not sure though whether I really read the feature description. But in case if I'm missing something can you provide me the link for it or if it's not present now can you add it as data description in the repo ? Thanks

Which score will be used to score an individual prediction?

accuracy(confusion_matrix)
accuracy_adjacent(confusion_matrix, adjacent_facies)

skm.metrics.f1_score(Y_true, Y_pred, sample="weighted")
accuracy*accuracy_adjacent?

The prediction was done on which testing dataset to reach an accuracy of 0.43 ? I mean, was the prediction done on the blind data set with SHANKLE well or NEWBY well or any other data?

Hi,
It is really cool competition. Unfortunately, we could not join this competition. I wish we could. But, I have checked the most of repos and I have realised that "True labels", considering that file 'blind_stuart_crawford_core_facies.csv', have 890 data when the predicted submissions of teams have only 830 data. Is it a mistake or update in the repo? How can we decide our test accuracy ourselves? Thank you for your time,
Vural

So which one exactly is going to be the test set ?
is it the blind set(the Shankle well) or anything else ?

Hi @kwinkunks, first of all sorry of opening this issue which is more of a knowing Github than this contest. I'm logging back to this contest after few weeks. I'm lost where I have left it. I don't want to start over again the few of the analysis and the questions I raised before as all are closed now. But I am unable to see the closed issues. If I could view them and also few of the issues that were raised by other members and which closed now. I can get started easily.

The contest outlines that the same well as in the publication will be used to judge the performance of the
proposed solutions. This can lead to overfitting by using the prediction capability for
the proposed well as a loss function.

Should another performance measure be used to compensate for overfitting?

Is it mandatory that only the five wire line log and the two geologic constraining features be used for modelling ? I would like to explore more and work on feature engineering. Please do confirm

Uhm maybe a dumb question, but I see some people using "facies_vectors.csv" and others "training_data.csv". What is "the" training set or is it all up to us?

Hi everyone,

As everyone has seen, the random seed can have a significant effect on the prediction scores. This is due to the fact that most of us are using algorithms with a random component (e.g., random forest, extra trees...).
The effect is probably enhanced by the fact that the dataset we are working on is small and non stationary.

Matt has been solving the problem by testing a series of random seeds and taking the best. This avoids discarding a model just because of a "bad" random seed. However, this might favor the most unstable models. A very stable model will yield scores in a small range when testing several random seeds, while an unstable model will yield a wide range of scores when testing several random seeds. Thus, it is likely that an unstable model can get a very high score given enough random seeds are tested. But it does not mean the model will be good at predicting new test data.

A possible solution would be to test 10 (or an other number) random seeds and to take the median score as the prediction score. It would require us to directly include that in our scripts to avoid further work for Matt. We could just make 10 predictions, using 10 random seeds and export them in a single csv file.

What do you guys (and especially Matt) think about that?

In the README, there's a link to http://library.seg.org/toc/leedff/35/10 which says "The Leading Edge, volume 35, issue 10 was not found."

Hi @kwinkunks

Enjoyable contest, thanks for the efforts you're putting in! Just wondering whether scores for all submissions are put into the leader board? Or only the top scores for each team? I haven't seen a score for my second submission and I wanted to ascertain how far off the mark it might be!!

Cheers,
George

Hi all- Has anyone tried generating a PE for the wells that do not have it (Alexander D and Kimzey A)?
I have made some attempts using from sklearn.svm import SVR and playing around with the different models. Do you think this would lead to a valid answer? Are there better regression techniques to use?

I started a repo for PE regression that includes a notebook I have been playing with. To call it sloppy is probably an understatement, but its there as a work in progress.

New to GitHub and I cant figure out how to submit my notebook despite looking at "Help with how to make submission #27".

How do i make my own folder in the SEG/2016-ml-contest --- 'create new file' ??
then I have to make the pull request?

I have the notebook uploaded onto my git page...

Any help?
Thanks

Thanks for organizing this ML contest. It was fun and a very useful learning experience. I blame the data for my poor result :) Looking forward to the MLevent at the EAGE-Paris this year.

Is there interest for a geo-ML linkedin group for connecting and further discussions? or does it already exist?

Also, are there any plans to have a follow-up on this? I thought being able to see other people's notebook was helpful on one hand but also leads to drags down the plurality of methods. Maybe for a next one I would suggest keep the top5 hidden, or make sharing optional till the results.