Comments (2)
@ShaopengLiu1 #19 should be addressed now. I am not closing #19 or #2 until we have a better testing environment spun up, as all tests I have done are locally (very not optimal).
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@ShaopengLiu1 just a note: I added a class that will now compute the absolute ground truth containment indicies. Recall that the last column of StreamingQueryDNADatabase.py
is still an estimate of the containment index (just using un-truncated k-mers). The class to compute the ground truth is at /CMash/CMash/GroundTruth.py
. You can see in this comment how the results by the tests/script_tests/./run_small_tests.sh
correspond quite nicely with the ground truth values.
If you would like to utilize this ground truth class, I strongly suggest you use my personal server (ping me if you forgot the IP address and login info) as it takes quite a bit of time and memory to brute-force calculate all the k-mers and their reverse complements.
To interact with the class, you can do something like:
import CMash.GroundTruth as G
training_database_file = "<snip>/TrainingDatabase.h5"
query_file = "<snip>/taxid_1192839_4_genomic.fna.gz"
g = G.TrueContainment(training_database_file=training_database_file, k_sizes="4-6-1") # this step will take a long time if the k_sizes are realistically large
df = g.return_containment_data_frame(query_file=query_file1, location_of_thresh=-1, coverage_threshold=.1)
Note that the query_file
need not be in the TrainingDatabase.h5
(as its k-mers will still be enumerated if it's not in the training database).
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Related Issues (20)
- Make python 3 compliant
- Testing environment HOT 9
- Improved classification time with KMC HOT 16
- Make a Conda/bioconda release of CMash HOT 3
- Update PyPi release of CMash
- Restructure repo so it's clear which approach is using the streaming, and which is using Bloom filters
- Multiple k-mer sizes bug HOT 2
- Test k-mer frequency distribution idea HOT 2
- StreamingQueryDNA... may be case sensitive to sequences HOT 1
- In post-processing, find correct denominator
- Modularize StreamingQueryDNADatabase.py HOT 3
- Minor refactor of Query.py HOT 1
- Create script to demonstrate how to re-train CMash
- In MakeStreamingDNADatabase.py, don't require output directory to exist
- Gzipping all training files results in a nice reduction: add feature that allows scripts/modules to handle this
- For very large databases, creation of TST is slow and memory intensive HOT 6
- Ground truth computation is too slow for realistic data set sizes HOT 1
- Add KMC as requirement
- Is CMash metric? HOT 4
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