lren-chuv / algorithm-repository Goto Github PK

Seen using latest web-analytics-starter:

0bdc026ca55c        hbpmip/python-histograms:0.3.6        "python /histograms.…"   55 seconds ago       Exited (1) 53 seconds ago                                               

docker logs 0bdc026ca55c                                         2 changed files  master 
INFO:root:Using default number of bins: 20
Traceback (most recent call last):
  File "/histograms.py", line 149, in <module>
    main()
  File "/histograms.py", line 37, in main
    histograms_results = compute_histograms(dep_var, indep_vars, nb_bins)
  File "/histograms.py", line 46, in compute_histograms
    histograms.append(compute_histogram(dep_var, nb_bins=nb_bins))
  File "/histograms.py", line 59, in compute_histogram
    categories, categories_labels = compute_categories(dep_var, nb_bins)
  File "/histograms.py", line 91, in compute_categories
    minimum = min(values)
ValueError: min() arg is an empty sequence

"PARAM_covariables=",
"PARAM_grouping=dataset,gender,agegroup,alzheimerbroadcategory",
"PARAM_meta={\"tiv\":{\"description\":\"Total intra-cranial volume\",\"methodology\":\"lren-nmm-volumes\",\"label\":\"TIV\",\"code\":\"tiv\",\"units\":\"cm3\",\"type\":\"real\"},\"agegroup\":{\"enumerations\":[{\"code\":\"-50y\",\"label\":\"-50y\"},{\"code\":\"50-59y\",\"label\":\"50-59y\"},{\"code\":\"60-69y\",\"label\":\"60-69y\"},{\"code\":\"70-79y\",\"label\":\"70-79y\"},{\"code\":\"+80y\",\"label\":\"+80y\"}],\"description\":\"Age Group\",\"methodology\":\"mip-cde\",\"label\":\"Age Group\",\"code\":\"agegroup\",\"type\":\"polynominal\"},\"alzheimerbroadcategory\":{\"enumerations\":[{\"code\":\"AD\",\"label\":\"Alzheimer's disease\"},{\"code\":\"CN\",\"label\":\"Cognitively Normal\"},{\"code\":\"Other\",\"label\":\"Other\"}],\"description\":\"There will be two broad categories taken into account. Alzheimer's disease (AD) in which the diagnostic is 100% certain and \\\"Other\\\" comprising the rest of Alzheimer's related categories. The \\\"Other\\\" category refers to Alzheime's related diagnosis which origin can be traced to other pathology eg. vascular. In this category MCI diagnosis can also be found. In summary, all Alzheimer's related diagnosis that are not pure.\",\"methodology\":\"mip-cde\",\"label\":\"Alzheimer Broad Category\",\"code\":\"alzheimerbroadcategory\",\"type\":\"polynominal\"},\"dataset\":{\"enumerations\":[{\"code\":\"cde_features_a\",\"label\":\"CHUV\"},{\"code\":\"cde_features_b\",\"label\":\"Brescia\"},{\"code\":\"cde_features_c\",\"label\":\"Lille\"}],\"description\":\"Variable used to differentiate datasets.\",\"methodology\":\"mip-cde\",\"label\":\"Dataset\",\"code\":\"dataset\",\"type\":\"polynominal\"},\"gender\":{\"enumerations\":[{\"code\":\"M\",\"label\":\"Male\"},{\"code\":\"F\",\"label\":\"Female\"}],\"description\":\"Gender of the patient - Sex assigned at birth\",\"methodology\":\"mip-cde\",\"label\":\"Gender\",\"code\":\"gender\",\"length\":1,\"type\":\"binominal\"}}",
"PARAM_query=SELECT \"tiv\",\"dataset\",\"gender\",\"agegroup\",\"alzheimerbroadcategory\" FROM cde_features_a WHERE \"tiv\" IS NOT NULL",
"PARAM_variables=tiv"

While running python-knn/tests/test.sh:

Run the distributed-knn...
WARNING: Dependency conflict: an older version of the 'docker-py' package may be polluting the namespace. If you're experiencing crashes, run the following command to remedy the issue:
pip uninstall docker-py; pip uninstall docker; pip install docker
Starting tests_db_1 ... done
Traceback (most recent call last):
  File "/knn.py", line 28, in <module>
    from sklearn_to_pfa.sklearn_to_pfa import sklearn_to_pfa
  File "/usr/local/lib/python3.6/site-packages/sklearn_to_pfa/sklearn_to_pfa.py", line 23, in <module>
    import titus.prettypfa
  File "/usr/local/lib/python3.6/site-packages/titus/prettypfa.py", line 26, in <module>
    from titus.pfaast import Subs
  File "/usr/local/lib/python3.6/site-packages/titus/pfaast.py", line 25, in <module>
    import titus.lib.core
  File "/usr/local/lib/python3.6/site-packages/titus/lib/core.py", line 25, in <module>
    from titus.signature import Sig
  File "/usr/local/lib/python3.6/site-packages/titus/signature.py", line 23, in <module>
    import titus.P as P
  File "/usr/local/lib/python3.6/site-packages/titus/P.py", line 20, in <module>
    from titus.datatype import Type
  File "/usr/local/lib/python3.6/site-packages/titus/datatype.py", line 23, in <module>
    import avro.io
  File "/usr/local/lib/python3.6/site-packages/avro/io.py", line 200
    bits = (((ord(self.read(1)) & 0xffL)) |
                                      ^

Seen on CLM Vertex, with research + CLM datasets:

            "PARAM_variables=rs610932_a",
            "CHRONOS_JOB_NAME=python_histograms_bab479f1_1b1b_4e03_b381_dfa4f6893111",
            "PARAM_grouping=dataset,gender,agegroup,alzheimerbroadcategory",
            "OUT_JDBC_JAR_PATH=/usr/lib/R/libraries/postgresql-9.4-1201.jdbc41.jar",
            "OUT_JDBC_PASSWORD=aDB/neuroinfo",
            "PARAM_covariables=",
            "mesos_task_id=ct:1507726483217:2:python_histograms_bab479f1_1b1b_4e03_b381_dfa4f6893111:",
            "CHRONOS_RESOURCE_DISK=256.0",
            "DOCKER_IMAGE=hbpmip/python-histograms:4cb93ea",
            "MESOS_SANDBOX=/mnt/mesos/sandbox",
            "OUT_JDBC_DRIVER=org.postgresql.Driver",
            "PARAM_query=select rs610932_a,dataset,gender,agegroup,alzheimerbroadcategory from mip_local_features where rs610932_a is not null and dataset is not null and gender is not null and agegroup is not null and alzheimerbroadcategory is not null ",
            "CHRONOS_RESOURCE_CPU=0.5",
            "IN_JDBC_URL=jdbc:postgresql://hos49130.intranet.chuv:31433/research",
            "OUT_JDBC_URL=jdbc:postgresql://hos49130.intranet.chuv:31433/woken",
            "IN_JDBC_USER=research",
            "NODE=hos49130.intranet.chuv",
            "JOB_ID=bab479f1-1b1b-4e03-b381-dfa4f6893111",
            "OUT_JDBC_USER=woken",
            "PARAM_meta={\"agegroup\":{\"enumerations\":[{\"code\":\"-50y\",\"label\":\"-50y\"},{\"code\":\"50-59y\",\"label\":\"50-59y\"},{\"code\":\"60-69y\",\"label\":\"60-69y\"},{\"code\":\"70-79y\",\"label\":\"70-79y\"},{\"code\":\"+80y\",\"label\":\"+80y\"}],\"description\":\"Age Group\",\"methodology\":\"mip-cde\",\"label\":\"Age Group\",\"code\":\"agegroup\",\"type\":\"polynominal\"},\"alzheimerbroadcategory\":{\"enumerations\":[{\"code\":\"AD\",\"label\":\"Alzheimer's disease\"},{\"code\":\"CN\",\"label\":\"Cognitively Normal\"},{\"code\":\"Other\",\"label\":\"Other\"}],\"description\":\"There will be two broad categories taken into account. Alzheimer's disease (AD) in which the diagnostic is 100% certain and \\\"Other\\\" comprising the rest of Alzheimer's related categories. The \\\"Other\\\" category refers to Alzheime's related diagnosis which origin can be traced to other pathology eg. vascular. In this category MCI diagnosis can also be found. In summary, all Alzheimer's related diagnosis that are not pure.\",\"methodology\":\"mip-cde\",\"label\":\"Alzheimer Broad Category\",\"code\":\"alzheimerbroadcategory\",\"type\":\"polynominal\"},\"dataset\":{\"enumerations\":[{\"code\":\"edsd\",\"label\":\"EDSD\"},{\"code\":\"adni\",\"label\":\"ADNI\"},{\"code\":\"ppmi\",\"label\":\"PPMI\"}],\"description\":\"Variable used to differentiate datasets.\",\"label\":\"Dataset\",\"code\":\"dataset\",\"type\":\"polynominal\"},\"rs610932_a\":{\"sql_type\":\"int\",\"enumerations\":[{\"code\":0,\"label\":0},{\"code\":1,\"label\":1},{\"code\":2,\"label\":2}],\"description\":\"\",\"methodology\":\"lren-nmm-volumes\",\"label\":\"rs610932_A\",\"code\":\"rs610932_a\",\"type\":\"polynominal\"},\"gender\":{\"enumerations\":[{\"code\":\"M\",\"label\":\"Male\"},{\"code\":\"F\",\"label\":\"Female\"}],\"description\":\"Gender of the patient - Sex assigned at birth\",\"methodology\":\"mip-cde\",\"label\":\"Gender\",\"code\":\"gender\",\"length\":1,\"type\":\"binominal\"}}",
            "HOST=hos49130.intranet.chuv",
            "IN_JDBC_JAR_PATH=/usr/lib/R/libraries/postgresql-9.4-1201.jdbc41.jar",
            "CHRONOS_RESOURCE_MEM=512.0",
            "MESOS_CONTAINER_NAME=mesos-3987976c-26a3-415d-8263-f8db921fb1b4",
            "IN_JDBC_PASSWORD=r5s3a6c9d8p2v",
            "[email protected]",
            "IN_JDBC_DRIVER=org.postgresql.Driver",
            "PATH=/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin",
            "LANG=C.UTF-8",
            "LC_ALL=C.UTF-8",
            "COMPUTE_IN=/data/in",
            "COMPUTE_OUT=/data/out",
            "MODEL=histograms",
            "FUNCTION=python-histograms",
            "CODE=histo",
            "NAME=Histograms"

woken version: 9750bdc
woken-validation version: 9750bdc
knn version: 0.2.1 or naive-bayes version: 0.2.0

When running RapidMiner algorithms on some co-variables with no sufficient data i guess (e.g. : leftcerebralwhitematter), RapidMiner complains that: does not have sufficient capabilities for handling an example set with only one label

http://frontend/experiment/?variables=alzheimerbroadcategory&coVariables=minimentalstate

An error has occurred while running your experiment K-nearest neighbors with k=5. Here's the message:

Invalid JSON:
java.lang.Exception: com.opendatagroup.hadrian.errors.PFASemanticException: PFA semantic error at JSON line:col 1:12319 (PFA field "fcns -> toArray -> do -> 0"): array constructed with "new" has wrong type for item 0: {"type":"enum","symbols":["_28","_27","_21","_30","_29","_18","_26","_9","_24","_13","_22","_25","_16","_12","_20","_23","_19","_17","_4","_15","_11","_8"],"name":"Enum_minimentalstate"} rather than "double"

INFO:root:variable: rs17125944_c
INFO:root:groups: ['dataset', 'gender', 'agegroup', 'alzheimerbroadcategory']
INFO:root:columns: ['rs17125944_c', 'dataset', 'gender', 'agegroup', 'alzheimerbroadcategory']
Traceback (most recent call last):
File "/main.py", line 273, in
main()
File "/main.py", line 31, in main
json.dumps(generate_descriptive_stats(var, groups, data, data_columns),
File "/main.py", line 45, in generate_descriptive_stats
output.append(generate_histogram(data, data_columns, var))
File "/main.py", line 78, in generate_histogram
var_categories)
File "/main.py", line 105, in histo_nominal
sums[v.rstrip()] += 1
KeyError: '0'

docker inspect 8ca8c5b6026a
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"ShmSize": 67108864,
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"OUT_JDBC_USER=woken",
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"PARAM_meta={"rs17125944_c":{"sql_type":"int","enumerations":[{"code":0,"label":0},{"code":1,"label":1},{"code":2,"label":2}],"description":"","methodology":"lren-nmm-volumes","label":"rs17125944_C","code":"rs17125944_c","type":"polynominal"},"agegroup":{"enumerations":[{"code":"-50y","label":"-50y"},{"code":"50-59y","label":"50-59y"},{"code":"60-69y","label":"60-69y"},{"code":"70-79y","label":"70-79y"},{"code":"+80y","label":"+80y"}],"description":"Age Group","methodology":"mip-cde","label":"Age Group","code":"agegroup","type":"polynominal"},"alzheimerbroadcategory":{"enumerations":[{"code":"AD","label":"Alzheimer's disease"},{"code":"CN","label":"Cognitively Normal"},{"code":"Other","label":"Other"}],"description":"There will be two broad categories taken into account. Alzheimer's disease (AD) in which the diagnostic is 100% certain and \"Other\" comprising the rest of Alzheimer's related categories. The \"Other\" category refers to Alzheime's related diagnosis which origin can be traced to other pathology eg. vascular. In this category MCI diagnosis can also be found. In summary, all Alzheimer's related diagnosis that are not pure.","methodology":"mip-cde","label":"Alzheimer Broad Category","code":"alzheimerbroadcategory","type":"polynominal"},"dataset":{"enumerations":[{"code":"edsd","label":"EDSD"},{"code":"adni","label":"ADNI"},{"code":"ppmi","label":"PPMI"}],"description":"Variable used to differentiate datasets.","label":"Dataset","code":"dataset","type":"polynominal"},"gender":{"enumerations":[{"code":"M","label":"Male"},{"code":"F","label":"Female"}],"description":"Gender of the patient - Sex assigned at birth","methodology":"mip-cde","label":"Gender","code":"gender","length":1,"type":"binominal"}}",
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]

woken version: 9750bdc
woken-validation version: 9750bdc
knn version: 0.2.1 or naive-bayes version: 0.2.0

When using nominal variables with "integers" as categories (e.g. apoe4), RapidMiner complains (see woken logs) that : "numerical label not supported"

Use the following template to list all Docker images created in this project, add also for each algorithm a description and a list of its capabilities (predictive, distributed)

https://github.com/LREN-CHUV/java-base-docker-images/blob/master/README.md