Comments (8)
The primary user names are included in GHTorrent.
@EgorBu Can you please attach here the best identity matching results on GitHub?
from identity-matching.
I collected the dataset of full names: /user/internal-datasets/full_names
Random strategy
46% accuracy if we consider identities without a correct name. Otherwise, ~82%.
Biggest number of commits strategy
56% and 99.9%, respectively.
So 99.9% is a very good number and there is no need for digging deeper. We should count commits for each name and sort them, problem solved.
from identity-matching.
I will launch the extraction of identities and dump it here
Update:
Old result found here: /user/egorbu/idmatching/cache/res_name_threshold_5_email_threshold_28_data_aggregated_deduplicated.pkl
- it was launched on initial dataset (not full)
I will launch extraction on full dataset.
from identity-matching.
No, the user names are no longer available. I can take all the logins from GHTorrent, but the names require some GitHub crawling. I will have to run it.
from identity-matching.
I am crawling the full name for each github login on our 11 nodes. ETA 10 days.
from identity-matching.
Fcking GitHub is throttling us with 429. I had to add download delays using the Scrapy's AutoThrottle extension, that will most probably increase ETA.
Fetched ~900k in 10 hours.
from identity-matching.
My experiments showed that the rate-limiting happens if you do more than or equal to 5 requests per second. I set the frequency to 4 and the process has finally stabilized.
from identity-matching.
Progress: 4.6mm through the weekend
from identity-matching.
Related Issues (20)
- Study how the quality depends on the hard identity size limit HOT 1
- Include the external identifiers into the result HOT 2
- Add another output format: Postgres HOT 2
- request external API only once
- Make the project open-source HOT 2
- Assume the output format is parquet when the output path points to a parquet file HOT 1
- Detect the primary name of an identified person HOT 1
- The list of popular names is too large HOT 1
- Bad precision and recall (~60%) on IBM and intel open source stacks HOT 4
- Use more efficient API for GitHub HOT 3
- Consider committer data, not just commit author HOT 3
- Debug the bot detection pipeline HOT 1
- Extract commit date for stats filtering
- panic: json: unsupported value: NaN HOT 13
- Save and load the bot detection model from modelforge HOT 6
- v3.1.0 doesn't seem to finish after running on writeas org HOT 6
- Alter Docker image so it dumps output to a defined folder HOT 4
- Print version at startup HOT 1
- Incremental operation support
- Performance dropped critically HOT 9
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from identity-matching.