зеркало из https://github.com/mozilla/taar.git
Misc cleanups
* added dependency on pytest-flask * Updated logging method names * cleaned up S3 configuration for all recommendation engines All S3 configuration data is now in taar.recommenders.s3config * dropped pinned hashes for packages in requirements.txt
This commit is contained in:
Victor Ng
Родитель
7820aab240
Коммит
bab992b682
40
README.md
40
README.md
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@ -98,3 +98,43 @@ LocaleRecommender:
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EnsembleRecommender:
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* s3://telemetry-parquet/taar/ensemble/ensemble_weight.json
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TAAR breaks out all S3 data load configuration into enviroment
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variables. This ensures that running under test has no chance of
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clobbering the production data in the event that a developer has AWS
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configuration keys installed locally in `~/.aws/`
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Production enviroment variables required for TAAR
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Collaborative Recommender ::
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TAAR_ITEM_MATRIX_BUCKET = "telemetry-public-analysis-2"
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TAAR_ITEM_MATRIX_KEY = "telemetry-ml/addon_recommender/item_matrix.json"
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TAAR_ADDON_MAPPING_BUCKET = "telemetry-public-analysis-2"
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TAAR_ADDON_MAPPING_KEY = "telemetry-ml/addon_recommender/addon_mapping.json"
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Ensemble Recommender ::
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TAAR_ENSEMBLE_BUCKET = "telemetry-parquet"
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TAAR_ENSEMBLE_KEY = "taar/ensemble/ensemble_weight.json"
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Hybrid Recommender ::
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TAAR_WHITELIST_BUCKET = "telemetry-parquet"
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TAAR_WHITELIST_KEY = "telemetry-ml/addon_recommender/only_guids_top_200.json"
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Locale Recommender ::
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TAAR_LOCALE_BUCKET = "telemetry-parquet"
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TAAR_LOCALE_KEY = "taar/locale/top10_dict.json"
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Similarity Recommender ::
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TAAR_SIMILARITY_BUCKET = "telemetry-parquet"
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TAAR_SIMILARITY_DONOR_KEY = "taar/similarity/donors.json"
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TAAR_SIMILARITY_LRCURVES_KEY = "taar/similarity/lr_curves.json"
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@ -1,3 +1 @@
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certifi==2018.10.15 \
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--hash=sha256:339dc09518b07e2fa7eda5450740925974815557727d6bd35d319c1524a04a4c \
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--hash=sha256:6d58c986d22b038c8c0df30d639f23a3e6d172a05c3583e766f4c0b785c0986a
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certifi==2018.10.15
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722
requirements.txt
722
requirements.txt
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@ -1,603 +1,119 @@
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appnope==0.1.0 \
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--hash=sha256:5b26757dc6f79a3b7dc9fab95359328d5747fcb2409d331ea66d0272b90ab2a0 \
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--hash=sha256:8b995ffe925347a2138d7ac0fe77155e4311a0ea6d6da4f5128fe4b3cbe5ed71
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arrow==0.12.1 \
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--hash=sha256:a558d3b7b6ce7ffc74206a86c147052de23d3d4ef0e17c210dd478c53575c4cd
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asn1crypto==0.24.0 \
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--hash=sha256:2f1adbb7546ed199e3c90ef23ec95c5cf3585bac7d11fb7eb562a3fe89c64e87 \
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--hash=sha256:9d5c20441baf0cb60a4ac34cc447c6c189024b6b4c6cd7877034f4965c464e49
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atomicwrites==1.1.5 \
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--hash=sha256:240831ea22da9ab882b551b31d4225591e5e447a68c5e188db5b89ca1d487585 \
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--hash=sha256:a24da68318b08ac9c9c45029f4a10371ab5b20e4226738e150e6e7c571630ae6
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attrs==18.1.0 \
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--hash=sha256:4b90b09eeeb9b88c35bc642cbac057e45a5fd85367b985bd2809c62b7b939265 \
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--hash=sha256:e0d0eb91441a3b53dab4d9b743eafc1ac44476296a2053b6ca3af0b139faf87b
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aws==0.2.5 \
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--hash=sha256:460cd737dee028bcebdb626f0c7acf87753f9e04e3317fda05929625419f2989
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aws-xray-sdk==0.95 \
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--hash=sha256:72791618feb22eaff2e628462b0d58f398ce8c1bacfa989b7679817ab1fad60c \
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--hash=sha256:9e7ba8dd08fd2939376c21423376206bff01d0deaea7d7721c6b35921fed1943
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backcall==0.1.0 \
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--hash=sha256:38ecd85be2c1e78f77fd91700c76e14667dc21e2713b63876c0eb901196e01e4 \
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--hash=sha256:bbbf4b1e5cd2bdb08f915895b51081c041bac22394fdfcfdfbe9f14b77c08bf2
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bcrypt==3.1.4 \
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--hash=sha256:01477981abf74e306e8ee31629a940a5e9138de000c6b0898f7f850461c4a0a5 \
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--hash=sha256:054d6e0acaea429e6da3613fcd12d05ee29a531794d96f6ab959f29a39f33391 \
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--hash=sha256:0872eeecdf9a429c1420158500eedb323a132bc5bf3339475151c52414729e70 \
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--hash=sha256:09a3b8c258b815eadb611bad04ca15ec77d86aa9ce56070e1af0d5932f17642a \
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--hash=sha256:0f317e4ffbdd15c3c0f8ab5fbd86aa9aabc7bea18b5cc5951b456fe39e9f738c \
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--hash=sha256:2788c32673a2ad0062bea850ab73cffc0dba874db10d7a3682b6f2f280553f20 \
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--hash=sha256:321d4d48be25b8d77594d8324c0585c80ae91ac214f62db9098734e5e7fb280f \
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--hash=sha256:346d6f84ff0b493dbc90c6b77136df83e81f903f0b95525ee80e5e6d5e4eef84 \
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--hash=sha256:34dd60b90b0f6de94a89e71fcd19913a30e83091c8468d0923a93a0cccbfbbff \
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--hash=sha256:3b4c23300c4eded8895442c003ae9b14328ae69309ac5867e7530de8bdd7875d \
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--hash=sha256:43d1960e7db14042319c46925892d5fa99b08ff21d57482e6f5328a1aca03588 \
|
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--hash=sha256:49e96267cd9be55a349fd74f9852eb9ae2c427cd7f6455d0f1765d7332292832 \
|
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--hash=sha256:63e06ffdaf4054a89757a3a1ab07f1b922daf911743114a54f7c561b9e1baa58 \
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--hash=sha256:67ed1a374c9155ec0840214ce804616de49c3df9c5bc66740687c1c9b1cd9e8d \
|
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--hash=sha256:6b662a5669186439f4f583636c8d6ea77cf92f7cfe6aae8d22edf16c36840574 \
|
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--hash=sha256:6efd9ca20aefbaf2e7e6817a2c6ed4a50ff6900fafdea1bcb1d0e9471743b144 \
|
||||
--hash=sha256:8569844a5d8e1fdde4d7712a05ab2e6061343ac34af6e7e3d7935b2bd1907bfd \
|
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--hash=sha256:8629ea6a8a59f865add1d6a87464c3c676e60101b8d16ef404d0a031424a8491 \
|
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--hash=sha256:988cac675e25133d01a78f2286189c1f01974470817a33eaf4cfee573cfb72a5 \
|
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--hash=sha256:9a6fedda73aba1568962f7543a1f586051c54febbc74e87769bad6a4b8587c39 \
|
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--hash=sha256:9eced8962ce3b7124fe20fd358cf8c7470706437fa064b9874f849ad4c5866fc \
|
||||
--hash=sha256:a005ed6163490988711ff732386b08effcbf8df62ae93dd1e5bda0714fad8afb \
|
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--hash=sha256:ae35dbcb6b011af6c840893b32399252d81ff57d52c13e12422e16b5fea1d0fb \
|
||||
--hash=sha256:b1e8491c6740f21b37cca77bc64677696a3fb9f32360794d57fa8477b7329eda \
|
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--hash=sha256:c906bdb482162e9ef48eea9f8c0d967acceb5c84f2d25574c7d2a58d04861df1 \
|
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--hash=sha256:cb18ffdc861dbb244f14be32c47ab69604d0aca415bee53485fcea4f8e93d5ef \
|
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--hash=sha256:cc2f24dc1c6c88c56248e93f28d439ee4018338567b0bbb490ea26a381a29b1e \
|
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--hash=sha256:d860c7fff18d49e20339fc6dffc2d485635e36d4b2cccf58f45db815b64100b4 \
|
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--hash=sha256:d86da365dda59010ba0d1ac45aa78390f56bf7f992e65f70b3b081d5e5257b09 \
|
||||
--hash=sha256:e22f0997622e1ceec834fd25947dc2ee2962c2133ea693d61805bc867abaf7ea \
|
||||
--hash=sha256:f2fe545d27a619a552396533cddf70d83cecd880a611cdfdbb87ca6aec52f66b \
|
||||
--hash=sha256:f425e925485b3be48051f913dbe17e08e8c48588fdf44a26b8b14067041c0da6 \
|
||||
--hash=sha256:f7fd3ed3745fe6e81e28dc3b3d76cce31525a91f32a387e1febd6b982caf8cdb \
|
||||
--hash=sha256:f9210820ee4818d84658ed7df16a7f30c9fba7d8b139959950acef91745cc0f7
|
||||
binaryornot==0.4.4 \
|
||||
--hash=sha256:359501dfc9d40632edc9fac890e19542db1a287bbcfa58175b66658392018061 \
|
||||
--hash=sha256:b8b71173c917bddcd2c16070412e369c3ed7f0528926f70cac18a6c97fd563e4
|
||||
boto==2.49.0 \
|
||||
--hash=sha256:147758d41ae7240dc989f0039f27da8ca0d53734be0eb869ef16e3adcfa462e8 \
|
||||
--hash=sha256:ea0d3b40a2d852767be77ca343b58a9e3a4b00d9db440efb8da74b4e58025e5a
|
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boto3==1.7.71 \
|
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--hash=sha256:1fb25a1d8455b97276ef5f1e14255c04f59a985a14ddb69804ddf6c8a3449e08 \
|
||||
--hash=sha256:71ee5169b6957298fb178b294452592cd7c734e5c0d1a67487b56f993085f254
|
||||
botocore==1.10.71 \
|
||||
--hash=sha256:9302ad235db66efa9d11c664b1cb0b259826d82a206446460ea05bcfcc431a4a \
|
||||
--hash=sha256:ffa673c9a53f3ab4eba4ce8cf9d736177ca67509827e716cb5070f0b621fb0a7
|
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cffi==1.11.5 \
|
||||
--hash=sha256:151b7eefd035c56b2b2e1eb9963c90c6302dc15fbd8c1c0a83a163ff2c7d7743 \
|
||||
--hash=sha256:1553d1e99f035ace1c0544050622b7bc963374a00c467edafac50ad7bd276aef \
|
||||
--hash=sha256:1b0493c091a1898f1136e3f4f991a784437fac3673780ff9de3bcf46c80b6b50 \
|
||||
--hash=sha256:2ba8a45822b7aee805ab49abfe7eec16b90587f7f26df20c71dd89e45a97076f \
|
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--hash=sha256:3bb6bd7266598f318063e584378b8e27c67de998a43362e8fce664c54ee52d30 \
|
||||
--hash=sha256:3c85641778460581c42924384f5e68076d724ceac0f267d66c757f7535069c93 \
|
||||
--hash=sha256:3eb6434197633b7748cea30bf0ba9f66727cdce45117a712b29a443943733257 \
|
||||
--hash=sha256:495c5c2d43bf6cebe0178eb3e88f9c4aa48d8934aa6e3cddb865c058da76756b \
|
||||
--hash=sha256:4c91af6e967c2015729d3e69c2e51d92f9898c330d6a851bf8f121236f3defd3 \
|
||||
--hash=sha256:57b2533356cb2d8fac1555815929f7f5f14d68ac77b085d2326b571310f34f6e \
|
||||
--hash=sha256:770f3782b31f50b68627e22f91cb182c48c47c02eb405fd689472aa7b7aa16dc \
|
||||
--hash=sha256:79f9b6f7c46ae1f8ded75f68cf8ad50e5729ed4d590c74840471fc2823457d04 \
|
||||
--hash=sha256:7a33145e04d44ce95bcd71e522b478d282ad0eafaf34fe1ec5bbd73e662f22b6 \
|
||||
--hash=sha256:857959354ae3a6fa3da6651b966d13b0a8bed6bbc87a0de7b38a549db1d2a359 \
|
||||
--hash=sha256:87f37fe5130574ff76c17cab61e7d2538a16f843bb7bca8ebbc4b12de3078596 \
|
||||
--hash=sha256:95d5251e4b5ca00061f9d9f3d6fe537247e145a8524ae9fd30a2f8fbce993b5b \
|
||||
--hash=sha256:9d1d3e63a4afdc29bd76ce6aa9d58c771cd1599fbba8cf5057e7860b203710dd \
|
||||
--hash=sha256:a36c5c154f9d42ec176e6e620cb0dd275744aa1d804786a71ac37dc3661a5e95 \
|
||||
--hash=sha256:a6a5cb8809091ec9ac03edde9304b3ad82ad4466333432b16d78ef40e0cce0d5 \
|
||||
--hash=sha256:ae5e35a2c189d397b91034642cb0eab0e346f776ec2eb44a49a459e6615d6e2e \
|
||||
--hash=sha256:b0f7d4a3df8f06cf49f9f121bead236e328074de6449866515cea4907bbc63d6 \
|
||||
--hash=sha256:b75110fb114fa366b29a027d0c9be3709579602ae111ff61674d28c93606acca \
|
||||
--hash=sha256:ba5e697569f84b13640c9e193170e89c13c6244c24400fc57e88724ef610cd31 \
|
||||
--hash=sha256:be2a9b390f77fd7676d80bc3cdc4f8edb940d8c198ed2d8c0be1319018c778e1 \
|
||||
--hash=sha256:ca1bd81f40adc59011f58159e4aa6445fc585a32bb8ac9badf7a2c1aa23822f2 \
|
||||
--hash=sha256:d5d8555d9bfc3f02385c1c37e9f998e2011f0db4f90e250e5bc0c0a85a813085 \
|
||||
--hash=sha256:e55e22ac0a30023426564b1059b035973ec82186ddddbac867078435801c7801 \
|
||||
--hash=sha256:e90f17980e6ab0f3c2f3730e56d1fe9bcba1891eeea58966e89d352492cc74f4 \
|
||||
--hash=sha256:ecbb7b01409e9b782df5ded849c178a0aa7c906cf8c5a67368047daab282b184 \
|
||||
--hash=sha256:ed01918d545a38998bfa5902c7c00e0fee90e957ce036a4000a88e3fe2264917 \
|
||||
--hash=sha256:edabd457cd23a02965166026fd9bfd196f4324fe6032e866d0f3bd0301cd486f \
|
||||
--hash=sha256:fdf1c1dc5bafc32bc5d08b054f94d659422b05aba244d6be4ddc1c72d9aa70fb
|
||||
chardet==3.0.4 \
|
||||
--hash=sha256:84ab92ed1c4d4f16916e05906b6b75a6c0fb5db821cc65e70cbd64a3e2a5eaae \
|
||||
--hash=sha256:fc323ffcaeaed0e0a02bf4d117757b98aed530d9ed4531e3e15460124c106691
|
||||
click==6.7 \
|
||||
--hash=sha256:29f99fc6125fbc931b758dc053b3114e55c77a6e4c6c3a2674a2dc986016381d \
|
||||
--hash=sha256:f15516df478d5a56180fbf80e68f206010e6d160fc39fa508b65e035fd75130b
|
||||
colander==1.4 \
|
||||
--hash=sha256:3ed2941e006e88c7abe78ee0921f0b91801340acdcd46389380887027108e999 \
|
||||
--hash=sha256:e20e9acf190e5711cf96aa65a5405dac04b6e841028fc361d953a9923dbc4e72
|
||||
colorama==0.3.9 \
|
||||
--hash=sha256:463f8483208e921368c9f306094eb6f725c6ca42b0f97e313cb5d5512459feda \
|
||||
--hash=sha256:48eb22f4f8461b1df5734a074b57042430fb06e1d61bd1e11b078c0fe6d7a1f1
|
||||
cookiecutter==1.6.0 \
|
||||
--hash=sha256:1316a52e1c1f08db0c9efbf7d876dbc01463a74b155a0d83e722be88beda9a3e \
|
||||
--hash=sha256:ed8f54a8fc79b6864020d773ce11539b5f08e4617f353de1f22d23226f6a0d36
|
||||
cookies==2.2.1 \
|
||||
--hash=sha256:15bee753002dff684987b8df8c235288eb8d45f8191ae056254812dfd42c81d3 \
|
||||
--hash=sha256:d6b698788cae4cfa4e62ef8643a9ca332b79bd96cb314294b864ae8d7eb3ee8e
|
||||
coverage==4.5.1 \
|
||||
--hash=sha256:03481e81d558d30d230bc12999e3edffe392d244349a90f4ef9b88425fac74ba \
|
||||
--hash=sha256:0b136648de27201056c1869a6c0d4e23f464750fd9a9ba9750b8336a244429ed \
|
||||
--hash=sha256:104ab3934abaf5be871a583541e8829d6c19ce7bde2923b2751e0d3ca44db60a \
|
||||
--hash=sha256:15b111b6a0f46ee1a485414a52a7ad1d703bdf984e9ed3c288a4414d3871dcbd \
|
||||
--hash=sha256:198626739a79b09fa0a2f06e083ffd12eb55449b5f8bfdbeed1df4910b2ca640 \
|
||||
--hash=sha256:1c383d2ef13ade2acc636556fd544dba6e14fa30755f26812f54300e401f98f2 \
|
||||
--hash=sha256:28b2191e7283f4f3568962e373b47ef7f0392993bb6660d079c62bd50fe9d162 \
|
||||
--hash=sha256:2eb564bbf7816a9d68dd3369a510be3327f1c618d2357fa6b1216994c2e3d508 \
|
||||
--hash=sha256:337ded681dd2ef9ca04ef5d93cfc87e52e09db2594c296b4a0a3662cb1b41249 \
|
||||
--hash=sha256:3a2184c6d797a125dca8367878d3b9a178b6fdd05fdc2d35d758c3006a1cd694 \
|
||||
--hash=sha256:3c79a6f7b95751cdebcd9037e4d06f8d5a9b60e4ed0cd231342aa8ad7124882a \
|
||||
--hash=sha256:3d72c20bd105022d29b14a7d628462ebdc61de2f303322c0212a054352f3b287 \
|
||||
--hash=sha256:3eb42bf89a6be7deb64116dd1cc4b08171734d721e7a7e57ad64cc4ef29ed2f1 \
|
||||
--hash=sha256:4635a184d0bbe537aa185a34193898eee409332a8ccb27eea36f262566585000 \
|
||||
--hash=sha256:56e448f051a201c5ebbaa86a5efd0ca90d327204d8b059ab25ad0f35fbfd79f1 \
|
||||
--hash=sha256:5a13ea7911ff5e1796b6d5e4fbbf6952381a611209b736d48e675c2756f3f74e \
|
||||
--hash=sha256:69bf008a06b76619d3c3f3b1983f5145c75a305a0fea513aca094cae5c40a8f5 \
|
||||
--hash=sha256:6bc583dc18d5979dc0f6cec26a8603129de0304d5ae1f17e57a12834e7235062 \
|
||||
--hash=sha256:701cd6093d63e6b8ad7009d8a92425428bc4d6e7ab8d75efbb665c806c1d79ba \
|
||||
--hash=sha256:7608a3dd5d73cb06c531b8925e0ef8d3de31fed2544a7de6c63960a1e73ea4bc \
|
||||
--hash=sha256:76ecd006d1d8f739430ec50cc872889af1f9c1b6b8f48e29941814b09b0fd3cc \
|
||||
--hash=sha256:7aa36d2b844a3e4a4b356708d79fd2c260281a7390d678a10b91ca595ddc9e99 \
|
||||
--hash=sha256:7d3f553904b0c5c016d1dad058a7554c7ac4c91a789fca496e7d8347ad040653 \
|
||||
--hash=sha256:7e1fe19bd6dce69d9fd159d8e4a80a8f52101380d5d3a4d374b6d3eae0e5de9c \
|
||||
--hash=sha256:8c3cb8c35ec4d9506979b4cf90ee9918bc2e49f84189d9bf5c36c0c1119c6558 \
|
||||
--hash=sha256:9d6dd10d49e01571bf6e147d3b505141ffc093a06756c60b053a859cb2128b1f \
|
||||
--hash=sha256:9e112fcbe0148a6fa4f0a02e8d58e94470fc6cb82a5481618fea901699bf34c4 \
|
||||
--hash=sha256:ac4fef68da01116a5c117eba4dd46f2e06847a497de5ed1d64bb99a5fda1ef91 \
|
||||
--hash=sha256:b8815995e050764c8610dbc82641807d196927c3dbed207f0a079833ffcf588d \
|
||||
--hash=sha256:be6cfcd8053d13f5f5eeb284aa8a814220c3da1b0078fa859011c7fffd86dab9 \
|
||||
--hash=sha256:c1bb572fab8208c400adaf06a8133ac0712179a334c09224fb11393e920abcdd \
|
||||
--hash=sha256:de4418dadaa1c01d497e539210cb6baa015965526ff5afc078c57ca69160108d \
|
||||
--hash=sha256:e05cb4d9aad6233d67e0541caa7e511fa4047ed7750ec2510d466e806e0255d6 \
|
||||
--hash=sha256:e4d96c07229f58cb686120f168276e434660e4358cc9cf3b0464210b04913e77 \
|
||||
--hash=sha256:f3f501f345f24383c0000395b26b726e46758b71393267aeae0bd36f8b3ade80 \
|
||||
--hash=sha256:f8a923a85cb099422ad5a2e345fe877bbc89a8a8b23235824a93488150e45f6e
|
||||
coveralls==1.3.0 \
|
||||
--hash=sha256:32569a43c9dbc13fa8199247580a4ab182ef439f51f65bb7f8316d377a1340e8 \
|
||||
--hash=sha256:664794748d2e5673e347ec476159a9d87f43e0d2d44950e98ed0e27b98da8346
|
||||
cryptography==2.3 \
|
||||
--hash=sha256:21af753934f2f6d1a10fe8f4c0a64315af209ef6adeaee63ca349797d747d687 \
|
||||
--hash=sha256:27bb401a20a838d6d0ea380f08c6ead3ccd8c9d8a0232dc9adcc0e4994576a66 \
|
||||
--hash=sha256:29720c4253263cff9aea64585adbbe85013ba647f6e98367efff9db2d7193ded \
|
||||
--hash=sha256:2a35b7570d8f247889784010aac8b384fd2e4a47b33e15c4a60b45a7c1944120 \
|
||||
--hash=sha256:42c531a6a354407f42ee07fda5c2c0dc822cf6d52744949c182f2b295fbd4183 \
|
||||
--hash=sha256:5eb86f03f9c4f0ac2336ac5431271072ddf7ecc76b338e26366732cfac58aa19 \
|
||||
--hash=sha256:67f7f57eae8dede577f3f7775957f5bec93edd6bdb6ce597bb5b28e1bdf3d4fb \
|
||||
--hash=sha256:6ec84edcbc966ae460560a51a90046503ff0b5b66157a9efc61515c68059f6c8 \
|
||||
--hash=sha256:7ba834564daef87557e7fcd35c3c3183a4147b0b3a57314e53317360b9b201b3 \
|
||||
--hash=sha256:7d7f084cbe1fdb82be5a0545062b59b1ad3637bc5a48612ac2eb428ff31b31ea \
|
||||
--hash=sha256:82409f5150e529d699e5c33fa8fd85e965104db03bc564f5f4b6a9199e591f7c \
|
||||
--hash=sha256:87d092a7c2a44e5f7414ab02fb4145723ebba411425e1a99773531dd4c0e9b8d \
|
||||
--hash=sha256:8c56ef989342e42b9fcaba7c74b446f0cc9bed546dd00034fa7ad66fc00307ef \
|
||||
--hash=sha256:9449f5d4d7c516a6118fa9210c4a00f34384cb1d2028672100ee0c6cce49d7f6 \
|
||||
--hash=sha256:bc2301170986ad82d9349a91eb8884e0e191209c45f5541b16aa7c0cfb135978 \
|
||||
--hash=sha256:c132bab45d4bd0fff1d3fe294d92b0a6eb8404e93337b3127bdec9f21de117e6 \
|
||||
--hash=sha256:c3d945b7b577f07a477700f618f46cbc287af3a9222cd73035c6ef527ef2c363 \
|
||||
--hash=sha256:cee18beb4c807b5c0b178f4fa2fae03cef9d51821a358c6890f8b23465b7e5d2 \
|
||||
--hash=sha256:d01dfc5c2b3495184f683574e03c70022674ca9a7be88589c5aba130d835ea90
|
||||
decorator==4.3.0 \
|
||||
--hash=sha256:2c51dff8ef3c447388fe5e4453d24a2bf128d3a4c32af3fabef1f01c6851ab82 \
|
||||
--hash=sha256:c39efa13fbdeb4506c476c9b3babf6a718da943dab7811c206005a4a956c080c
|
||||
docker==3.4.1 \
|
||||
--hash=sha256:52cf5b1c3c394f9abf897638bfc3336d6b63a0f65969d0d4d2da6d3b1d8032b6 \
|
||||
--hash=sha256:ad077b49660b711d20f50f344f70cfae014d635ef094bf21b0d7df5f0aeedf99
|
||||
docker-pycreds==0.3.0 \
|
||||
--hash=sha256:0a941b290764ea7286bd77f54c0ace43b86a8acd6eb9ead3de9840af52384079 \
|
||||
--hash=sha256:8b0e956c8d206f832b06aa93a710ba2c3bcbacb5a314449c040b0b814355bbff
|
||||
dockerflow==2018.4.0 \
|
||||
--hash=sha256:2ea52a904abfda3430ff4f1effc164863b30d2b69f7ecbf92dd672860b0ec423 \
|
||||
--hash=sha256:388d02c557968e6957140f7b82f669eac70adf5f570bc7705aa749d220a2e535
|
||||
docopt==0.6.2 \
|
||||
--hash=sha256:49b3a825280bd66b3aa83585ef59c4a8c82f2c8a522dbe754a8bc8d08c85c491
|
||||
docutils==0.14 \
|
||||
--hash=sha256:02aec4bd92ab067f6ff27a38a38a41173bf01bed8f89157768c1573f53e474a6 \
|
||||
--hash=sha256:51e64ef2ebfb29cae1faa133b3710143496eca21c530f3f71424d77687764274 \
|
||||
--hash=sha256:7a4bd47eaf6596e1295ecb11361139febe29b084a87bf005bf899f9a42edc3c6
|
||||
Fabric==2.1.3 \
|
||||
--hash=sha256:1ee8d659507c21a191efca119ce25c0e18ee855eea4c9c1d46d41ec9765d42e6 \
|
||||
--hash=sha256:4aeb5bcd9039a1e1225caed4b2ac296bbc347c869bdef7e3717c13ee49dba58a
|
||||
flake8==3.5.0 \
|
||||
--hash=sha256:7253265f7abd8b313e3892944044a365e3f4ac3fcdcfb4298f55ee9ddf188ba0 \
|
||||
--hash=sha256:c7841163e2b576d435799169b78703ad6ac1bbb0f199994fc05f700b2a90ea37
|
||||
Flask==1.0.2 \
|
||||
--hash=sha256:2271c0070dbcb5275fad4a82e29f23ab92682dc45f9dfbc22c02ba9b9322ce48 \
|
||||
--hash=sha256:a080b744b7e345ccfcbc77954861cb05b3c63786e93f2b3875e0913d44b43f05
|
||||
Flask-API==1.0 \
|
||||
--hash=sha256:6f9dc56d55fd82ffb1c5c9fd794cd6c50873ac10cf662e26817c179a655d1e22 \
|
||||
--hash=sha256:fc10a80a13ea6fcf04acc2b1835aea05ec44aa6ae94f2ee85e52cd068567ce35
|
||||
future==0.16.0 \
|
||||
--hash=sha256:e39ced1ab767b5936646cedba8bcce582398233d6a627067d4c6a454c90cfedb
|
||||
idna==2.7 \
|
||||
--hash=sha256:156a6814fb5ac1fc6850fb002e0852d56c0c8d2531923a51032d1b70760e186e \
|
||||
--hash=sha256:684a38a6f903c1d71d6d5fac066b58d7768af4de2b832e426ec79c30daa94a16
|
||||
invoke==1.1.0 \
|
||||
--hash=sha256:1db6cf918e5df10efe4d61101b19763abe1510b6b2fe8c553daba25476de8044 \
|
||||
--hash=sha256:265eead8c89805a2ac5083200842db6da7636ac63fb4fe0d1121b930770f3e2a \
|
||||
--hash=sha256:3e8e2c2e69493227e210a1d19ccc7c44189240385dda4c9b8eb5d98fa0f68a3e
|
||||
iso8601==0.1.12 \
|
||||
--hash=sha256:210e0134677cc0d02f6028087fee1df1e1d76d372ee1db0bf30bf66c5c1c89a3 \
|
||||
--hash=sha256:49c4b20e1f38aa5cf109ddcd39647ac419f928512c869dc01d5c7098eddede82 \
|
||||
--hash=sha256:bbbae5fb4a7abfe71d4688fd64bff70b91bbd74ef6a99d964bab18f7fdf286dd
|
||||
itsdangerous==0.24 \
|
||||
--hash=sha256:cbb3fcf8d3e33df861709ecaf89d9e6629cff0a217bc2848f1b41cd30d360519
|
||||
jedi==0.12.1 \
|
||||
--hash=sha256:b409ed0f6913a701ed474a614a3bb46e6953639033e31f769ca7581da5bd1ec1 \
|
||||
--hash=sha256:c254b135fb39ad76e78d4d8f92765ebc9bf92cbc76f49e97ade1d5f5121e1f6f
|
||||
Jinja2==2.10 \
|
||||
--hash=sha256:74c935a1b8bb9a3947c50a54766a969d4846290e1e788ea44c1392163723c3bd \
|
||||
--hash=sha256:f84be1bb0040caca4cea721fcbbbbd61f9be9464ca236387158b0feea01914a4
|
||||
jinja2-time==0.2.0 \
|
||||
--hash=sha256:d14eaa4d315e7688daa4969f616f226614350c48730bfa1692d2caebd8c90d40 \
|
||||
--hash=sha256:d3eab6605e3ec8b7a0863df09cc1d23714908fa61aa6986a845c20ba488b4efa
|
||||
jmespath==0.9.3 \
|
||||
--hash=sha256:6a81d4c9aa62caf061cb517b4d9ad1dd300374cd4706997aff9cd6aedd61fc64 \
|
||||
--hash=sha256:f11b4461f425740a1d908e9a3f7365c3d2e569f6ca68a2ff8bc5bcd9676edd63
|
||||
jsondiff==1.1.1 \
|
||||
--hash=sha256:2d0437782de9418efa34e694aa59f43d7adb1899bd9a793f063867ddba8f7893
|
||||
jsonpickle==0.9.6 \
|
||||
--hash=sha256:545b3bee0d65e1abb4baa1818edcc9ec239aa9f2ffbfde8084d71c056180054f
|
||||
MarkupSafe==1.0 \
|
||||
--hash=sha256:a6be69091dac236ea9c6bc7d012beab42010fa914c459791d627dad4910eb665
|
||||
mccabe==0.6.1 \
|
||||
--hash=sha256:ab8a6258860da4b6677da4bd2fe5dc2c659cff31b3ee4f7f5d64e79735b80d42 \
|
||||
--hash=sha256:dd8d182285a0fe56bace7f45b5e7d1a6ebcbf524e8f3bd87eb0f125271b8831f
|
||||
mock==2.0.0 \
|
||||
--hash=sha256:5ce3c71c5545b472da17b72268978914d0252980348636840bd34a00b5cc96c1 \
|
||||
--hash=sha256:b158b6df76edd239b8208d481dc46b6afd45a846b7812ff0ce58971cf5bc8bba
|
||||
more-itertools==4.2.0 \
|
||||
--hash=sha256:2b6b9893337bfd9166bee6a62c2b0c9fe7735dcf85948b387ec8cba30e85d8e8 \
|
||||
--hash=sha256:6703844a52d3588f951883005efcf555e49566a48afd4db4e965d69b883980d3 \
|
||||
--hash=sha256:a18d870ef2ffca2b8463c0070ad17b5978056f403fb64e3f15fe62a52db21cc0
|
||||
moto==1.3.3 \
|
||||
--hash=sha256:45d14aca2b06b0083d5e82cfd770ebca0ba77b5070aec6928670240939a78681 \
|
||||
--hash=sha256:ee71b515ba34d64c5f625950fc995594040f793a4a106614ff108ae02c1a2896
|
||||
mozilla-srgutil==0.1.7 \
|
||||
--hash=sha256:b28a8a779500e7700d63eb1cdf4d1c5f83676209df6721103be682441f9ab51a
|
||||
numpy==1.14.3 \
|
||||
--hash=sha256:0074d42e2cc333800bd09996223d40ec52e3b1ec0a5cab05dacc09b662c4c1ae \
|
||||
--hash=sha256:034717bfef517858abc79324820a702dc6cd063effb9baab86533e8a78670689 \
|
||||
--hash=sha256:0db6301324d0568089663ef2701ad90ebac0e975742c97460e89366692bd0563 \
|
||||
--hash=sha256:1864d005b2eb7598063e35c320787d87730d864f40d6410f768fe4ea20672016 \
|
||||
--hash=sha256:46ce8323ca9384814c7645298b8b627b7d04ce97d6948ef02da357b2389d6972 \
|
||||
--hash=sha256:510863d606c932b41d2209e4de6157ab3fdf52001d3e4ad351103176d33c4b8b \
|
||||
--hash=sha256:560e23a12e7599be8e8b67621396c5bc687fd54b48b890adbc71bc5a67333f86 \
|
||||
--hash=sha256:57dc6c22d59054542600fce6fae2d1189b9c50bafc1aab32e55f7efcc84a6c46 \
|
||||
--hash=sha256:760550fdf9d8ec7da9c4402a4afe6e25c0f184ae132011676298a6b636660b45 \
|
||||
--hash=sha256:8670067685051b49d1f2f66e396488064299fefca199c7c80b6ba0c639fedc98 \
|
||||
--hash=sha256:9016692c7d390f9d378fc88b7a799dc9caa7eb938163dda5276d3f3d6f75debf \
|
||||
--hash=sha256:98ff275f1b5907490d26b30b6ff111ecf2de0254f0ab08833d8fe61aa2068a00 \
|
||||
--hash=sha256:9ccf4d5c9139b1e985db915039baa0610a7e4a45090580065f8d8cb801b7422f \
|
||||
--hash=sha256:a8dbab311d4259de5eeaa5b4e83f5f8545e4808f9144e84c0f424a6ee55a7b98 \
|
||||
--hash=sha256:aaef1bea636b6e552bbc5dae0ada87d4f6046359daaa97a05a013b0169620f27 \
|
||||
--hash=sha256:b8987e30d9a0eb6635df9705a75cf8c4a2835590244baecf210163343bc65176 \
|
||||
--hash=sha256:c3fe23df6fe0898e788581753da453f877350058c5982e85a8972feeecb15309 \
|
||||
--hash=sha256:c5eb7254cfc4bd7a4330ad7e1f65b98343836865338c57b0e25c661e41d5cfd9 \
|
||||
--hash=sha256:c80fcf9b38c7f4df666150069b04abbd2fe42ae640703a6e1f128cda83b552b7 \
|
||||
--hash=sha256:e33baf50f2f6b7153ddb973601a11df852697fba4c08b34a5e0f39f66f8120e1 \
|
||||
--hash=sha256:e8578a62a8eaf552b95d62f630bb5dd071243ba1302bbff3e55ac48588508736 \
|
||||
--hash=sha256:f22b3206f1c561dd9110b93d144c6aaa4a9a354e3b07ad36030df3ea92c5bb5b \
|
||||
--hash=sha256:f39afab5769b3aaa786634b94b4a23ef3c150bdda044e8a32a3fc16ddafe803b
|
||||
packaging==17.1 \
|
||||
--hash=sha256:e9215d2d2535d3ae866c3d6efc77d5b24a0192cce0ff20e42896cc0664f889c0 \
|
||||
--hash=sha256:f019b770dd64e585a99714f1fd5e01c7a8f11b45635aa953fd41c689a657375b
|
||||
paramiko==2.4.2 \
|
||||
--hash=sha256:3c16b2bfb4c0d810b24c40155dbfd113c0521e7e6ee593d704e84b4c658a1f3b \
|
||||
--hash=sha256:a8975a7df3560c9f1e2b43dc54ebd40fd00a7017392ca5445ce7df409f900fcb
|
||||
parso==0.3.1 \
|
||||
--hash=sha256:35704a43a3c113cce4de228ddb39aab374b8004f4f2407d070b6a2ca784ce8a2 \
|
||||
--hash=sha256:895c63e93b94ac1e1690f5fdd40b65f07c8171e3e53cbd7793b5b96c0e0a7f24
|
||||
pbr==4.1.0 \
|
||||
--hash=sha256:4f2b11d95917af76e936811be8361b2b19616e5ef3b55956a429ec7864378e0c \
|
||||
--hash=sha256:e0f23b61ec42473723b2fec2f33fb12558ff221ee551962f01dd4de9053c2055
|
||||
pexpect==4.6.0 \
|
||||
--hash=sha256:2a8e88259839571d1251d278476f3eec5db26deb73a70be5ed5dc5435e418aba \
|
||||
--hash=sha256:3fbd41d4caf27fa4a377bfd16fef87271099463e6fa73e92a52f92dfee5d425b
|
||||
pickleshare==0.7.4 \
|
||||
--hash=sha256:84a9257227dfdd6fe1b4be1319096c20eb85ff1e82c7932f36efccfe1b09737b \
|
||||
--hash=sha256:c9a2541f25aeabc070f12f452e1f2a8eae2abd51e1cd19e8430402bdf4c1d8b5
|
||||
pip-api==0.0.1 \
|
||||
--hash=sha256:3cb7b51c541d4c13df43bf254aca371d9feb4669dc6c1cf3cecb9e9360eb3cb6
|
||||
pkginfo==1.4.2 \
|
||||
--hash=sha256:5878d542a4b3f237e359926384f1dde4e099c9f5525d236b1840cf704fa8d474 \
|
||||
--hash=sha256:a39076cb3eb34c333a0dd390b568e9e1e881c7bf2cc0aee12120636816f55aee
|
||||
pluggy==0.6.0 \
|
||||
--hash=sha256:7f8ae7f5bdf75671a718d2daf0a64b7885f74510bcd98b1a0bb420eb9a9d0cff \
|
||||
--hash=sha256:d345c8fe681115900d6da8d048ba67c25df42973bda370783cd58826442dcd7c \
|
||||
--hash=sha256:e160a7fcf25762bb60efc7e171d4497ff1d8d2d75a3d0df7a21b76821ecbf5c5
|
||||
ply==3.11 \
|
||||
--hash=sha256:00c7c1aaa88358b9c765b6d3000c6eec0ba42abca5351b095321aef446081da3 \
|
||||
--hash=sha256:096f9b8350b65ebd2fd1346b12452efe5b9607f7482813ffca50c22722a807ce
|
||||
poyo==0.4.1 \
|
||||
--hash=sha256:103b4ee3e1c7765098fe1cabe43f828db2e2a6079646561a2117e1a809f352d6 \
|
||||
--hash=sha256:230ec11c2f35a23410c1f0e474f09fa4e203686f40ab3adca7b039c845d8c325
|
||||
PrettyTable==0.7.2 \
|
||||
--hash=sha256:2d5460dc9db74a32bcc8f9f67de68b2c4f4d2f01fa3bd518764c69156d9cacd9 \
|
||||
--hash=sha256:853c116513625c738dc3ce1aee148b5b5757a86727e67eff6502c7ca59d43c36 \
|
||||
--hash=sha256:a53da3b43d7a5c229b5e3ca2892ef982c46b7923b51e98f0db49956531211c4f
|
||||
prompt_toolkit==1.0.15 \
|
||||
--hash=sha256:1df952620eccb399c53ebb359cc7d9a8d3a9538cb34c5a1344bdbeb29fbcc381 \
|
||||
--hash=sha256:3f473ae040ddaa52b52f97f6b4a493cfa9f5920c255a12dc56a7d34397a398a4 \
|
||||
--hash=sha256:858588f1983ca497f1cf4ffde01d978a3ea02b01c8a26a8bbc5cd2e66d816917
|
||||
ptyprocess==0.6.0 \
|
||||
--hash=sha256:923f299cc5ad920c68f2bc0bc98b75b9f838b93b599941a6b63ddbc2476394c0 \
|
||||
--hash=sha256:d7cc528d76e76342423ca640335bd3633420dc1366f258cb31d05e865ef5ca1f
|
||||
py==1.5.3 \
|
||||
--hash=sha256:29c9fab495d7528e80ba1e343b958684f4ace687327e6f789a94bf3d1915f881 \
|
||||
--hash=sha256:983f77f3331356039fdd792e9220b7b8ee1aa6bd2b25f567a963ff1de5a64f6a
|
||||
pyaml==17.12.1 \
|
||||
--hash=sha256:66623c52f34d83a2c0fc963e08e8b9d0c13d88404e3b43b1852ef71eda19afa3 \
|
||||
--hash=sha256:f83fc302c52c6b83a15345792693ae0b5bc07ad19f59e318b7617d7123d62990
|
||||
pyasn1==0.4.3 \
|
||||
--hash=sha256:24f21b4fd2dc2b344dee2205fa3930464aa21292216d3d6e39007a2e059e21af \
|
||||
--hash=sha256:2f57960dc7a2820ea5a1782b872d974b639aa3b448ac6628d1ecc5d0fe3986f2 \
|
||||
--hash=sha256:3651774ca1c9726307560792877db747ba5e8a844ea1a41feb7670b319800ab3 \
|
||||
--hash=sha256:602fda674355b4701acd7741b2be5ac188056594bf1eecf690816d944e52905e \
|
||||
--hash=sha256:8fb265066eac1d3bb5015c6988981b009ccefd294008ff7973ed5f64335b0f2d \
|
||||
--hash=sha256:9334cb427609d2b1e195bb1e251f99636f817d7e3e1dffa150cb3365188fb992 \
|
||||
--hash=sha256:9a15cc13ff6bf5ed29ac936ca941400be050dff19630d6cd1df3fb978ef4c5ad \
|
||||
--hash=sha256:a66dcda18dbf6e4663bde70eb30af3fc4fe1acb2d14c4867a861681887a5f9a2 \
|
||||
--hash=sha256:ba77f1e8d7d58abc42bfeddd217b545fdab4c1eeb50fd37c2219810ad56303bf \
|
||||
--hash=sha256:cdc8eb2eaafb56de66786afa6809cd9db2df1b3b595dcb25aa5b9dc61189d40a \
|
||||
--hash=sha256:d01fbba900c80b42af5c3fe1a999acf61e27bf0e452e0f1ef4619065e57622da \
|
||||
--hash=sha256:f281bf11fe204f05859225ec2e9da7a7c140b65deccd8a4eb0bc75d0bd6949e0 \
|
||||
--hash=sha256:fb81622d8f3509f0026b0683fe90fea27be7284d3826a5f2edf97f69151ab0fc
|
||||
pycodestyle==2.3.1 \
|
||||
--hash=sha256:682256a5b318149ca0d2a9185d365d8864a768a28db66a84a2ea946bcc426766 \
|
||||
--hash=sha256:6c4245ade1edfad79c3446fadfc96b0de2759662dc29d07d80a6f27ad1ca6ba9
|
||||
pycparser==2.18 \
|
||||
--hash=sha256:99a8ca03e29851d96616ad0404b4aad7d9ee16f25c9f9708a11faf2810f7b226
|
||||
pyflakes==1.6.0 \
|
||||
--hash=sha256:08bd6a50edf8cffa9fa09a463063c425ecaaf10d1eb0335a7e8b1401aef89e6f \
|
||||
--hash=sha256:8d616a382f243dbf19b54743f280b80198be0bca3a5396f1d2e1fca6223e8805
|
||||
Pygments==2.2.0 \
|
||||
--hash=sha256:78f3f434bcc5d6ee09020f92ba487f95ba50f1e3ef83ae96b9d5ffa1bab25c5d \
|
||||
--hash=sha256:dbae1046def0efb574852fab9e90209b23f556367b5a320c0bcb871c77c3e8cc
|
||||
PyNaCl==1.2.1 \
|
||||
--hash=sha256:04e30e5bdeeb2d5b34107f28cd2f5bbfdc6c616f3be88fc6f53582ff1669eeca \
|
||||
--hash=sha256:0bfa0d94d2be6874e40f896e0a67e290749151e7de767c5aefbad1121cad7512 \
|
||||
--hash=sha256:11aa4e141b2456ce5cecc19c130e970793fa3a2c2e6fbb8ad65b28f35aa9e6b6 \
|
||||
--hash=sha256:13bdc1fe084ff9ac7653ae5a924cae03bf4bb07c6667c9eb5b6eb3c570220776 \
|
||||
--hash=sha256:14339dc233e7a9dda80a3800e64e7ff89d0878ba23360eea24f1af1b13772cac \
|
||||
--hash=sha256:1d33e775fab3f383167afb20b9927aaf4961b953d76eeb271a5703a6d756b65b \
|
||||
--hash=sha256:2a42b2399d0428619e58dac7734838102d35f6dcdee149e0088823629bf99fbb \
|
||||
--hash=sha256:2dce05ac8b3c37b9e2f65eab56c544885607394753e9613fd159d5e2045c2d98 \
|
||||
--hash=sha256:63cfccdc6217edcaa48369191ae4dca0c390af3c74f23c619e954973035948cd \
|
||||
--hash=sha256:6453b0dae593163ffc6db6f9c9c1597d35c650598e2c39c0590d1757207a1ac2 \
|
||||
--hash=sha256:73a5a96fb5fbf2215beee2353a128d382dbca83f5341f0d3c750877a236569ef \
|
||||
--hash=sha256:8abb4ef79161a5f58848b30ab6fb98d8c466da21fdd65558ce1d7afc02c70b5f \
|
||||
--hash=sha256:8ac1167195b32a8755de06efd5b2d2fe76fc864517dab66aaf65662cc59e1988 \
|
||||
--hash=sha256:8f505f42f659012794414fa57c498404e64db78f1d98dfd40e318c569f3c783b \
|
||||
--hash=sha256:9c8a06556918ee8e3ab48c65574f318f5a0a4d31437fc135da7ee9d4f9080415 \
|
||||
--hash=sha256:a1e25fc5650cf64f01c9e435033e53a4aca9de30eb9929d099f3bb078e18f8f2 \
|
||||
--hash=sha256:be71cd5fce04061e1f3d39597f93619c80cdd3558a6c9ba99a546f144a8d8101 \
|
||||
--hash=sha256:c5b1a7a680218dee9da0f1b5e24072c46b3c275d35712bc1d505b85bb03441c0 \
|
||||
--hash=sha256:cb785db1a9468841a1265c9215c60fe5d7af2fb1b209e3316a152704607fc582 \
|
||||
--hash=sha256:cf6877124ae6a0698404e169b3ba534542cfbc43f939d46b927d956daf0a373a \
|
||||
--hash=sha256:d0eb5b2795b7ee2cbcfcadacbe95a13afbda048a262bd369da9904fecb568975 \
|
||||
--hash=sha256:d3a934e2b9f20abac009d5b6951067cfb5486889cb913192b4d8288b216842f1 \
|
||||
--hash=sha256:d795f506bcc9463efb5ebb0f65ed77921dcc9e0a50499dedd89f208445de9ecb \
|
||||
--hash=sha256:d8aaf7e5d6b0e0ef7d6dbf7abeb75085713d0100b4eb1a4e4e857de76d77ac45 \
|
||||
--hash=sha256:de2aaca8386cf4d70f1796352f2346f48ddb0bed61dc43a3ce773ba12e064031 \
|
||||
--hash=sha256:e0d38fa0a75f65f556fb912f2c6790d1fa29b7dd27a1d9cc5591b281321eaaa9 \
|
||||
--hash=sha256:eb2acabbd487a46b38540a819ef67e477a674481f84a82a7ba2234b9ba46f752 \
|
||||
--hash=sha256:eeee629828d0eb4f6d98ac41e9a3a6461d114d1d0aa111a8931c049359298da0 \
|
||||
--hash=sha256:f5836463a3c0cca300295b229b6c7003c415a9d11f8f9288ddbd728e2746524c \
|
||||
--hash=sha256:f5ce9e26d25eb0b2d96f3ef0ad70e1d3ae89b5d60255c462252a3e456a48c053 \
|
||||
--hash=sha256:fabf73d5d0286f9e078774f3435601d2735c94ce9e514ac4fb945701edead7e4
|
||||
pyparsing==2.2.0 \
|
||||
--hash=sha256:0832bcf47acd283788593e7a0f542407bd9550a55a8a8435214a1960e04bcb04 \
|
||||
--hash=sha256:281683241b25fe9b80ec9d66017485f6deff1af5cde372469134b56ca8447a07 \
|
||||
--hash=sha256:8f1e18d3fd36c6795bb7e02a39fd05c611ffc2596c1e0d995d34d67630426c18 \
|
||||
--hash=sha256:9e8143a3e15c13713506886badd96ca4b579a87fbdf49e550dbfc057d6cb218e \
|
||||
--hash=sha256:b8b3117ed9bdf45e14dcc89345ce638ec7e0e29b2b579fa1ecf32ce45ebac8a5 \
|
||||
--hash=sha256:e4d45427c6e20a59bf4f88c639dcc03ce30d193112047f94012102f235853a58 \
|
||||
--hash=sha256:fee43f17a9c4087e7ed1605bd6df994c6173c1e977d7ade7b651292fab2bd010
|
||||
pytest==3.6.0 \
|
||||
--hash=sha256:39555d023af3200d004d09e51b4dd9fdd828baa863cded3fd6ba2f29f757ae2d \
|
||||
--hash=sha256:c76e93f3145a44812955e8d46cdd302d8a45fbfc7bf22be24fe231f9d8d8853a
|
||||
pytest-cov==2.5.1 \
|
||||
--hash=sha256:03aa752cf11db41d281ea1d807d954c4eda35cfa1b21d6971966cc041bbf6e2d \
|
||||
--hash=sha256:890fe5565400902b0c78b5357004aab1c814115894f4f21370e2433256a3eeec
|
||||
python-dateutil==2.6.1 \
|
||||
--hash=sha256:891c38b2a02f5bb1be3e4793866c8df49c7d19baabf9c1bad62547e0b4866aca \
|
||||
--hash=sha256:95511bae634d69bc7329ba55e646499a842bc4ec342ad54a8cdb65645a0aad3c
|
||||
pytz==2018.5 \
|
||||
--hash=sha256:a061aa0a9e06881eb8b3b2b43f05b9439d6583c206d0a6c340ff72a7b6669053 \
|
||||
--hash=sha256:ffb9ef1de172603304d9d2819af6f5ece76f2e85ec10692a524dd876e72bf277
|
||||
PyYAML==3.13 \
|
||||
--hash=sha256:3d7da3009c0f3e783b2c873687652d83b1bbfd5c88e9813fb7e5b03c0dd3108b \
|
||||
--hash=sha256:3ef3092145e9b70e3ddd2c7ad59bdd0252a94dfe3949721633e41344de00a6bf \
|
||||
--hash=sha256:40c71b8e076d0550b2e6380bada1f1cd1017b882f7e16f09a65be98e017f211a \
|
||||
--hash=sha256:558dd60b890ba8fd982e05941927a3911dc409a63dcb8b634feaa0cda69330d3 \
|
||||
--hash=sha256:a7c28b45d9f99102fa092bb213aa12e0aaf9a6a1f5e395d36166639c1f96c3a1 \
|
||||
--hash=sha256:aa7dd4a6a427aed7df6fb7f08a580d68d9b118d90310374716ae90b710280af1 \
|
||||
--hash=sha256:bc558586e6045763782014934bfaf39d48b8ae85a2713117d16c39864085c613 \
|
||||
--hash=sha256:d46d7982b62e0729ad0175a9bc7e10a566fc07b224d2c79fafb5e032727eaa04 \
|
||||
--hash=sha256:d5eef459e30b09f5a098b9cea68bebfeb268697f78d647bd255a085371ac7f3f \
|
||||
--hash=sha256:e01d3203230e1786cd91ccfdc8f8454c8069c91bee3962ad93b87a4b2860f537 \
|
||||
--hash=sha256:e170a9e6fcfd19021dd29845af83bb79236068bf5fd4df3327c1be18182b2531
|
||||
requests==2.20.1 \
|
||||
--hash=sha256:65b3a120e4329e33c9889db89c80976c5272f56ea92d3e74da8a463992e3ff54 \
|
||||
--hash=sha256:ea881206e59f41dbd0bd445437d792e43906703fff75ca8ff43ccdb11f33f263
|
||||
requests-toolbelt==0.8.0 \
|
||||
--hash=sha256:42c9c170abc2cacb78b8ab23ac957945c7716249206f90874651971a4acff237 \
|
||||
--hash=sha256:f6a531936c6fa4c6cfce1b9c10d5c4f498d16528d2a54a22ca00011205a187b5
|
||||
responses==0.9.0 \
|
||||
--hash=sha256:c6082710f4abfb60793899ca5f21e7ceb25aabf321560cc0726f8b59006811c9 \
|
||||
--hash=sha256:f23a29dca18b815d9d64a516b4a0abb1fbdccff6141d988ad8100facb81cf7b3
|
||||
rsa==3.4.2 \
|
||||
--hash=sha256:25df4e10c263fb88b5ace923dd84bf9aa7f5019687b5e55382ffcdb8bede9db5 \
|
||||
--hash=sha256:43f682fea81c452c98d09fc316aae12de6d30c4b5c84226642cf8f8fd1c93abd
|
||||
s3transfer==0.1.13 \
|
||||
--hash=sha256:90dc18e028989c609146e241ea153250be451e05ecc0c2832565231dacdf59c1 \
|
||||
--hash=sha256:c7a9ec356982d5e9ab2d4b46391a7d6a950e2b04c472419f5fdec70cc0ada72f
|
||||
scipy==1.1.0 \
|
||||
--hash=sha256:0611ee97296265af4a21164a5323f8c1b4e8e15c582d3dfa7610825900136bb7 \
|
||||
--hash=sha256:08237eda23fd8e4e54838258b124f1cd141379a5f281b0a234ca99b38918c07a \
|
||||
--hash=sha256:0e645dbfc03f279e1946cf07c9c754c2a1859cb4a41c5f70b25f6b3a586b6dbd \
|
||||
--hash=sha256:0e9bb7efe5f051ea7212555b290e784b82f21ffd0f655405ac4f87e288b730b3 \
|
||||
--hash=sha256:108c16640849e5827e7d51023efb3bd79244098c3f21e4897a1007720cb7ce37 \
|
||||
--hash=sha256:340ef70f5b0f4e2b4b43c8c8061165911bc6b2ad16f8de85d9774545e2c47463 \
|
||||
--hash=sha256:3ad73dfc6f82e494195144bd3a129c7241e761179b7cb5c07b9a0ede99c686f3 \
|
||||
--hash=sha256:3b243c77a822cd034dad53058d7c2abf80062aa6f4a32e9799c95d6391558631 \
|
||||
--hash=sha256:404a00314e85eca9d46b80929571b938e97a143b4f2ddc2b2b3c91a4c4ead9c5 \
|
||||
--hash=sha256:423b3ff76957d29d1cce1bc0d62ebaf9a3fdfaf62344e3fdec14619bb7b5ad3a \
|
||||
--hash=sha256:42d9149a2fff7affdd352d157fa5717033767857c11bd55aa4a519a44343dfef \
|
||||
--hash=sha256:625f25a6b7d795e8830cb70439453c9f163e6870e710ec99eba5722775b318f3 \
|
||||
--hash=sha256:698c6409da58686f2df3d6f815491fd5b4c2de6817a45379517c92366eea208f \
|
||||
--hash=sha256:729f8f8363d32cebcb946de278324ab43d28096f36593be6281ca1ee86ce6559 \
|
||||
--hash=sha256:8190770146a4c8ed5d330d5b5ad1c76251c63349d25c96b3094875b930c44692 \
|
||||
--hash=sha256:878352408424dffaa695ffedf2f9f92844e116686923ed9aa8626fc30d32cfd1 \
|
||||
--hash=sha256:8b984f0821577d889f3c7ca8445564175fb4ac7c7f9659b7c60bef95b2b70e76 \
|
||||
--hash=sha256:8f841bbc21d3dad2111a94c490fb0a591b8612ffea86b8e5571746ae76a3deac \
|
||||
--hash=sha256:c22b27371b3866c92796e5d7907e914f0e58a36d3222c5d436ddd3f0e354227a \
|
||||
--hash=sha256:d0cdd5658b49a722783b8b4f61a6f1f9c75042d0e29a30ccb6cacc9b25f6d9e2 \
|
||||
--hash=sha256:d40dc7f494b06dcee0d303e51a00451b2da6119acbeaccf8369f2d29e28917ac \
|
||||
--hash=sha256:d8491d4784aceb1f100ddb8e31239c54e4afab8d607928a9f7ef2469ec35ae01 \
|
||||
--hash=sha256:dfc5080c38dde3f43d8fbb9c0539a7839683475226cf83e4b24363b227dfe552 \
|
||||
--hash=sha256:e24e22c8d98d3c704bb3410bce9b69e122a8de487ad3dbfe9985d154e5c03a40 \
|
||||
--hash=sha256:e7a01e53163818d56eabddcafdc2090e9daba178aad05516b20c6591c4811020 \
|
||||
--hash=sha256:ee677635393414930541a096fc8e61634304bb0153e4e02b75685b11eba14cae \
|
||||
--hash=sha256:f0521af1b722265d824d6ad055acfe9bd3341765735c44b5a4d0069e189a0f40 \
|
||||
--hash=sha256:f25c281f12c0da726c6ed00535ca5d1622ec755c30a3f8eafef26cf43fede694
|
||||
setuptools_scm==2.1.0 \
|
||||
--hash=sha256:0f386524bb99d959e0d98381d7fe1f0a810e04eace5d2cc6297e701d64de9a7d \
|
||||
--hash=sha256:1261fb48def5ac5e4d04cb6196886cb8c2de5dc066ed2bfee99d4bb21aecb781 \
|
||||
--hash=sha256:95ff5ca2cb1e48a3b92080c90fac35ac015c3f1be185f401f0941b11279fdae8 \
|
||||
--hash=sha256:a767141fecdab1c0b3c8e4c788ac912d7c94a0d6c452d40777ba84f918316379 \
|
||||
--hash=sha256:e2ab256c944e66f063a020a56b4269010d772ce3af757cc703fe56e6fdc2dda1 \
|
||||
--hash=sha256:fda84172bd4dca0b671c1569eef6d4458d7d006c66a5adb41aa7a88462bcb6c0
|
||||
simplegeneric==0.8.1 \
|
||||
--hash=sha256:dc972e06094b9af5b855b3df4a646395e43d1c9d0d39ed345b7393560d0b9173
|
||||
six==1.11.0 \
|
||||
--hash=sha256:70e8a77beed4562e7f14fe23a786b54f6296e34344c23bc42f07b15018ff98e9 \
|
||||
--hash=sha256:832dc0e10feb1aa2c68dcc57dbb658f1c7e65b9b61af69048abc87a2db00a0eb
|
||||
spark==0.2.1 \
|
||||
--hash=sha256:df499b57d30178c6d32dbda2af188b9833a261a70ccd262126ed7b415d9e36d1
|
||||
spark_parser==1.8.7 \
|
||||
--hash=sha256:03e115c9c3cf849658ce191b4ad8a512e88c6337141f44b3d87a51a6bf18413a \
|
||||
--hash=sha256:11be196977ce42710d44dbb7adb78b42853875a1f686c64b88315e519a8b1b0d \
|
||||
--hash=sha256:1f37849f320d8f8454456c0ca985deda1413974256945f398e30addaa905d198 \
|
||||
--hash=sha256:4c5e6064afbb3c114749016d585b0e4f9222d4ffa97a1854c9ab70b25783ef48 \
|
||||
--hash=sha256:5426f3ade7ccad74f12e0ffdf34e28a29b0295c245ee5f5de96a5f942025c53c \
|
||||
--hash=sha256:6c1edc94291385d23b56a0eab348aceba72b6d5f363e52e773c7b2c88ec9f8ab \
|
||||
--hash=sha256:76bb0acdfba2c7193e608e9cf995f41799b61b2e3b8e1f13320b7ee66aff7b65 \
|
||||
--hash=sha256:e0769cba3fe78af969b178c571ba297aa8de202a8d48661abf0e46fc8b427e02 \
|
||||
--hash=sha256:e8e456ffa6e83f963f4830884624830bbbea82c9ae6b3b1700f84566550e1ab0
|
||||
thriftpy==0.3.9 \
|
||||
--hash=sha256:309e57d97b5bfa01601393ad4f245451e989d6206a59279e56866b264a99796d
|
||||
tox==3.0.0 \
|
||||
--hash=sha256:96efa09710a3daeeb845561ebbe1497641d9cef2ee0aea30db6969058b2bda2f \
|
||||
--hash=sha256:9ee7de958a43806402a38c0d2aa07fa8553f4d2c20a15b140e9f771c2afeade0
|
||||
tqdm==4.23.4 \
|
||||
--hash=sha256:224291ee0d8c52d91b037fd90806f48c79bcd9994d3b0abc9e44b946a908fccd \
|
||||
--hash=sha256:77b8424d41b31e68f437c6dd9cd567aebc9a860507cb42fbd880a5f822d966fe
|
||||
translationstring==1.3 \
|
||||
--hash=sha256:4ee44cfa58c52ade8910ea0ebc3d2d84bdcad9fa0422405b1801ec9b9a65b72d \
|
||||
--hash=sha256:e26c7bf383413234ed442e0980a2ebe192b95e3745288a8fd2805156d27515b4
|
||||
twine==1.11.0 \
|
||||
--hash=sha256:08eb132bbaec40c6d25b358f546ec1dc96ebd2638a86eea68769d9e67fe2b129 \
|
||||
--hash=sha256:2fd9a4d9ff0bcacf41fdc40c8cb0cfaef1f1859457c9653fd1b92237cc4e9f25
|
||||
uncompyle2==2.0.0 \
|
||||
--hash=sha256:629571965db312d34a714d8e16f25b8d67f85b5bb78abbc950457b1b64b324b1 \
|
||||
--hash=sha256:c1458bb0662c1f7f269c3aa0768ec0c2951789a1fed3e6b3fdfd79dba43eb68d
|
||||
uncompyle6==3.2.0 \
|
||||
--hash=sha256:12b27e7179516d77136629b094e1665fd5c6fc4583eea4a2b81cd98dfb0d046d \
|
||||
--hash=sha256:182a922633e09a688240718c67991da210967f8946755af6e2b2ba6c666c2f1b \
|
||||
--hash=sha256:339329faf77ec6e53b87e70353545c472f253b0f7772ec369ef705e763de9ce6 \
|
||||
--hash=sha256:3985675039554fb0ce3c7cff823a747a93f96a29946c5f39ab3c0f72bad8a2a2 \
|
||||
--hash=sha256:610335002a9c49c1e98a04f615d23602e4d081d3b2992a867a48e5162cb86c0a \
|
||||
--hash=sha256:679c727e3c468a922ed8ee0094448db78448989362aa2ca23ba5d98b3db381f1 \
|
||||
--hash=sha256:83fea296bb01a4e94bf1b0e70ab3eebeff8a1bb4029c0213bc89bdc2c55e7bb1 \
|
||||
--hash=sha256:94a61a046e0754a76c64dc843a3e91d071dfe5dda6691a3b5c8539e4132b6ab1 \
|
||||
--hash=sha256:9b91cecf8ad398cb13b804bbdbc34fb0570a3552777f873eb55c109c28221ded \
|
||||
--hash=sha256:a7c3351e3b8357245faaa3934fb59623b5ecf75f629f6fb5a7d2d02f25ff8021 \
|
||||
--hash=sha256:d4a96c43cec878e438a8abca515d9a05203038adf41c28e9423232dd61b9aeeb \
|
||||
--hash=sha256:ebe473b2e5b64adbeb9a2dc3cb88d1f811ff84cb7289d4f5b942568b558c091f \
|
||||
--hash=sha256:f6ed1d07ac5c7addc23ca6d435fc0c3c9d124e99cb143edffbcdee5c0a564c66
|
||||
urllib3==1.23 \
|
||||
--hash=sha256:a68ac5e15e76e7e5dd2b8f94007233e01effe3e50e8daddf69acfd81cb686baf \
|
||||
--hash=sha256:b5725a0bd4ba422ab0e66e89e030c806576753ea3ee08554382c14e685d117b5
|
||||
virtualenv==16.0.0 \
|
||||
--hash=sha256:2ce32cd126117ce2c539f0134eb89de91a8413a29baac49cbab3eb50e2026669 \
|
||||
--hash=sha256:ca07b4c0b54e14a91af9f34d0919790b016923d157afda5efdde55c96718f752
|
||||
wcwidth==0.1.7 \
|
||||
--hash=sha256:3df37372226d6e63e1b1e1eda15c594bca98a22d33a23832a90998faa96bc65e \
|
||||
--hash=sha256:f4ebe71925af7b40a864553f761ed559b43544f8f71746c2d756c7fe788ade7c
|
||||
websocket-client==0.48.0 \
|
||||
--hash=sha256:18f1170e6a1b5463986739d9fd45c4308b0d025c1b2f9b88788d8f69e8a5eb4a \
|
||||
--hash=sha256:db70953ae4a064698b27ae56dcad84d0ee68b7b43cb40940f537738f38f510c1
|
||||
Werkzeug==0.14.1 \
|
||||
--hash=sha256:c3fd7a7d41976d9f44db327260e263132466836cef6f91512889ed60ad26557c \
|
||||
--hash=sha256:d5da73735293558eb1651ee2fddc4d0dedcfa06538b8813a2e20011583c9e49b
|
||||
whichcraft==0.4.1 \
|
||||
--hash=sha256:9e0d51c9387cb7e9f28b7edb549e6a03da758f7784f991eb4397d7f7808c57fd \
|
||||
--hash=sha256:cd0e10b58960ab877d9f273cd28788730936c3cdaceec2dafad97c7cf3067d46
|
||||
wrapt==1.10.11 \
|
||||
--hash=sha256:d4d560d479f2c21e1b5443bbd15fe7ec4b37fe7e53d335d3b9b0a7b1226fe3c6
|
||||
xdis==3.8.2 \
|
||||
--hash=sha256:1cc6a3be72c1707926cc46f0689d01b5b49cbc0a57080bc756b92159e3cca2d0 \
|
||||
--hash=sha256:4aa484236296738981a4b48f8e2d33836e05bd4364d94c252f8f75f19b407800 \
|
||||
--hash=sha256:4bcd0727c150824b4556c1f0714140ec8d7f7f4d3af5728b084fbcb89bc3a21b \
|
||||
--hash=sha256:52645c3196d9c43d2ebdabff94b005b323795c2b94e418c33b0adcae249e4ae8 \
|
||||
--hash=sha256:52a4d7e13bb76d7610bc3ea57d300fcc47adc09ab78cc1774829f100dc01ed02 \
|
||||
--hash=sha256:6a468d3856632f99f94670abd22471fde325ae7dae92e556b6f670947b81e6a1 \
|
||||
--hash=sha256:8d55ca893c6ed8e66ff9008178a417400034be0d6331c248c600298d0a4371c5 \
|
||||
--hash=sha256:9182db8aa2f9663faeec3f978c096921b0b4690501f200f56f083b8083e6a2bf \
|
||||
--hash=sha256:9ae0fe0bfe9eb046ed112f2a97ee5f7e942d02ff7d49d414ca200dbcb6ccacc9 \
|
||||
--hash=sha256:a05231e0ace28c87aeddcb148cbdf392577c2194fc391850203d7b6a044569cb \
|
||||
--hash=sha256:ceeb62f3c9e730891e3be5be0f5a27b5397abb8bc2bf6bf2a59e6b40017c5cfb \
|
||||
--hash=sha256:d931f8260dd30e7c9053103e5dce8e77febf4497dbb7d1b5c16f0550f4290b5b \
|
||||
--hash=sha256:dd032b73922f0021613c0cf5e8e6d85a592c56a7e68bb27b549d836f1ccc3da8 \
|
||||
--hash=sha256:e8548be5a386f51a6a0295e033984869ed71191879adc3d5aed17cd4a030ad8f \
|
||||
--hash=sha256:f186d5ad93378ec51d9e06dd63e382e7fd26158459a2d9db3de12abbcf4528c7
|
||||
xmltodict==0.11.0 \
|
||||
--hash=sha256:8f8d7d40aa28d83f4109a7e8aa86e67a4df202d9538be40c0cb1d70da527b0df \
|
||||
--hash=sha256:add07d92089ff611badec526912747cf87afd4f9447af6661aca074eeaf32615
|
||||
backports.functools_lru_cache==1.5 \
|
||||
--hash=sha256:9d98697f088eb1b0fa451391f91afb5e3ebde16bbdb272819fd091151fda4f1a \
|
||||
--hash=sha256:f0b0e4eba956de51238e17573b7087e852dfe9854afd2e9c873f73fc0ca0a6dd
|
||||
backports.ssl_match_hostname==3.5.0.1 \
|
||||
--hash=sha256:502ad98707319f4a51fa2ca1c677bd659008d27ded9f6380c79e8932e38dcdf2
|
||||
backports.tempfile==1.0 \
|
||||
--hash=sha256:05aa50940946f05759696156a8c39be118169a0e0f94a49d0bb106503891ff54 \
|
||||
--hash=sha256:1c648c452e8770d759bdc5a5e2431209be70d25484e1be24876cf2168722c762
|
||||
backports.weakref==1.0.post1 \
|
||||
--hash=sha256:81bc9b51c0abc58edc76aefbbc68c62a787918ffe943a37947e162c3f8e19e82 \
|
||||
--hash=sha256:bc4170a29915f8b22c9e7c4939701859650f2eb84184aee80da329ac0b9825c2
|
||||
configparser==3.5.0 \
|
||||
--hash=sha256:5308b47021bc2340965c371f0f058cc6971a04502638d4244225c49d80db273a
|
||||
pathlib2==2.3.2 \
|
||||
--hash=sha256:8eb170f8d0d61825e09a95b38be068299ddeda82f35e96c3301a8a5e7604cb83 \
|
||||
--hash=sha256:d1aa2a11ba7b8f7b21ab852b1fb5afb277e1bb99d5dfc663380b5015c0d80c5a
|
||||
funcsigs==1.0.2 \
|
||||
--hash=sha256:330cc27ccbf7f1e992e69fef78261dc7c6569012cf397db8d3de0234e6c937ca \
|
||||
--hash=sha256:a7bb0f2cf3a3fd1ab2732cb49eba4252c2af4240442415b4abce3b87022a8f50
|
||||
scandir==1.8 \
|
||||
--hash=sha256:01babbf0fea42a135f6e24747cac63225399d97a67a9a7eedc1f0510b63122db \
|
||||
--hash=sha256:0f0059d907817cd3c07f1b658611aabd1af0a4bdc4bb7b211dfd8962d5bd46ba \
|
||||
--hash=sha256:11e3bd756b13db4baca364985575eeef4781ce35ce66e2324811091b39b97cdb \
|
||||
--hash=sha256:49345923704d611458335872925802620fcf895e1c67074dd8ea715e579f2581 \
|
||||
--hash=sha256:7f94d5967d61d1b5e415840b3a8995cb00a90893b9628451745e57a3749546d6 \
|
||||
--hash=sha256:8231e327a3a1c090b4f09ba40cc0b75a85939812d0e8f4c83acd745df3ed6c23 \
|
||||
--hash=sha256:8d5011d3a99042c4d90e8adda0052d4475aae3d57bb927012267a6c59186d870 \
|
||||
--hash=sha256:9f703e6b8eb53211d39c0f10e5c02f86e9a989fd44913b5c992259312d9bd59d \
|
||||
--hash=sha256:b009e15a3d73376a84f8d8fad9b5ab6d9f96cb7606bdb867a4c882f10508e57e \
|
||||
--hash=sha256:b0e0b4e6de8f8aae41a9fb4834127ee125668c363a79c62eb9f9c77de58e7b71 \
|
||||
--hash=sha256:f70d557a271ee9973087dc704daea205c95f021ee149f1605592bb0b1571ad78
|
||||
python-decouple==3.1 \
|
||||
--hash=sha256:1317df14b43efee4337a4aa02914bf004f010cd56d6c4bd894e6474ec8c4fe2d
|
||||
enum34==1.1.6 \
|
||||
--hash=sha256:2d81cbbe0e73112bdfe6ef8576f2238f2ba27dd0d55752a776c41d38b7da2850 \
|
||||
--hash=sha256:644837f692e5f550741432dd3f223bbb9852018674981b1664e5dc339387588a \
|
||||
--hash=sha256:6bd0f6ad48ec2aa117d3d141940d484deccda84d4fcd884f5c3d93c23ecd8c79 \
|
||||
--hash=sha256:8ad8c4783bf61ded74527bffb48ed9b54166685e4230386a9ed9b1279e2df5b1
|
||||
ipaddress==1.0.22 \
|
||||
--hash=sha256:64b28eec5e78e7510698f6d4da08800a5c575caa4a286c93d651c5d3ff7b6794 \
|
||||
--hash=sha256:b146c751ea45cad6188dd6cf2d9b757f6f4f8d6ffb96a023e6f2e26eea02a72c
|
||||
futures==3.2.0; python_version < '3.0' \
|
||||
--hash=sha256:9ec02aa7d674acb8618afb127e27fde7fc68994c0437ad759fa094a574adb265 \
|
||||
--hash=sha256:ec0a6cb848cc212002b9828c3e34c675e0c9ff6741dc445cab6fdd4e1085d1f1
|
||||
appnope==0.1.0
|
||||
arrow==0.12.1
|
||||
asn1crypto==0.24.0
|
||||
atomicwrites==1.1.5
|
||||
attrs==18.1.0
|
||||
aws==0.2.5
|
||||
aws-xray-sdk==0.95
|
||||
backcall==0.1.0
|
||||
bcrypt==3.1.4
|
||||
binaryornot==0.4.4
|
||||
boto==2.49.0
|
||||
boto3==1.7.71
|
||||
botocore==1.10.71
|
||||
cffi==1.11.5
|
||||
chardet==3.0.4
|
||||
click==6.7
|
||||
colander==1.4
|
||||
colorama==0.3.9
|
||||
cookiecutter==1.6.0
|
||||
cookies==2.2.1
|
||||
coverage==4.5.1
|
||||
coveralls==1.3.0
|
||||
cryptography==2.3
|
||||
decorator==4.3.0
|
||||
docker==3.4.1
|
||||
docker-pycreds==0.3.0
|
||||
dockerflow==2018.4.0
|
||||
docopt==0.6.2
|
||||
docutils==0.14
|
||||
Fabric==2.1.3
|
||||
flake8==3.5.0
|
||||
Flask==1.0.2
|
||||
Flask-API==1.0
|
||||
future==0.16.0
|
||||
idna==2.7
|
||||
invoke==1.1.0
|
||||
iso8601==0.1.12
|
||||
itsdangerous==0.24
|
||||
jedi==0.12.1
|
||||
Jinja2==2.10
|
||||
jinja2-time==0.2.0
|
||||
jmespath==0.9.3
|
||||
jsondiff==1.1.1
|
||||
jsonpickle==0.9.6
|
||||
MarkupSafe==1.0
|
||||
mccabe==0.6.1
|
||||
mock==2.0.0
|
||||
more-itertools==4.2.0
|
||||
moto==1.3.3
|
||||
mozilla-srgutil==0.1.7
|
||||
numpy==1.14.3
|
||||
packaging==17.1
|
||||
paramiko==2.4.2
|
||||
parso==0.3.1
|
||||
pbr==4.1.0
|
||||
pexpect==4.6.0
|
||||
pickleshare==0.7.4
|
||||
pip-api==0.0.1
|
||||
pkginfo==1.4.2
|
||||
pluggy==0.6.0
|
||||
ply==3.11
|
||||
poyo==0.4.1
|
||||
PrettyTable==0.7.2
|
||||
prompt_toolkit==1.0.15
|
||||
ptyprocess==0.6.0
|
||||
py==1.5.3
|
||||
pyaml==17.12.1
|
||||
pyasn1==0.4.3
|
||||
pycodestyle==2.3.1
|
||||
pycparser==2.18
|
||||
pyflakes==1.6.0
|
||||
Pygments==2.2.0
|
||||
PyNaCl==1.2.1
|
||||
pyparsing==2.2.0
|
||||
pytest==3.6.0
|
||||
pytest-cov==2.5.1
|
||||
python-dateutil==2.6.1
|
||||
pytz==2018.5
|
||||
PyYAML==3.13
|
||||
requests==2.20.1
|
||||
requests-toolbelt==0.8.0
|
||||
responses==0.9.0
|
||||
rsa==3.4.2
|
||||
s3transfer==0.1.13
|
||||
scipy==1.1.0
|
||||
setuptools_scm==2.1.0
|
||||
simplegeneric==0.8.1
|
||||
six==1.11.0
|
||||
spark==0.2.1
|
||||
spark_parser==1.8.7
|
||||
thriftpy==0.3.9
|
||||
tox==3.0.0
|
||||
tqdm==4.23.4
|
||||
translationstring==1.3
|
||||
twine==1.11.0
|
||||
uncompyle2==2.0.0
|
||||
uncompyle6==3.2.0
|
||||
urllib3==1.23
|
||||
virtualenv==16.0.0
|
||||
wcwidth==0.1.7
|
||||
websocket-client==0.48.0
|
||||
Werkzeug==0.14.1
|
||||
whichcraft==0.4.1
|
||||
wrapt==1.10.11
|
||||
xdis==3.8.2
|
||||
xmltodict==0.11.0
|
||||
backports.functools_lru_cache==1.5
|
||||
backports.ssl_match_hostname==3.5.0.1
|
||||
backports.tempfile==1.0
|
||||
backports.weakref==1.0.post1
|
||||
configparser==3.5.0
|
||||
pathlib2==2.3.2
|
||||
funcsigs==1.0.2
|
||||
scandir==1.8
|
||||
python-decouple==3.1
|
||||
enum34==1.1.6
|
||||
ipaddress==1.0.22
|
||||
futures==3.2.0; python_version < '3.0'
|
||||
pytest-flask==0.14.0
|
||||
|
|
|
|||
|
|
@ -9,11 +9,12 @@ import operator as op
|
|||
|
||||
from .base_recommender import AbstractRecommender
|
||||
|
||||
ITEM_MATRIX_CONFIG = ('telemetry-public-analysis-2', 'telemetry-ml/addon_recommender/item_matrix.json')
|
||||
ADDON_MAPPING_CONFIG = ('telemetry-public-analysis-2', 'telemetry-ml/addon_recommender/addon_mapping.json')
|
||||
from .s3config import TAAR_ITEM_MATRIX_BUCKET
|
||||
from .s3config import TAAR_ITEM_MATRIX_KEY
|
||||
from .s3config import TAAR_ADDON_MAPPING_BUCKET
|
||||
from .s3config import TAAR_ADDON_MAPPING_KEY
|
||||
|
||||
|
||||
# http://garage.pimentech.net/libcommonPython_src_python_libcommon_javastringhashcode/
|
||||
def java_string_hashcode(s):
|
||||
h = 0
|
||||
for c in s:
|
||||
|
|
@ -33,24 +34,19 @@ class CollaborativeRecommender(AbstractRecommender):
|
|||
recommender = CollaborativeRecommender()
|
||||
dists = recommender.recommend(client_info)
|
||||
"""
|
||||
|
||||
def __init__(self, ctx):
|
||||
self._ctx = ctx
|
||||
|
||||
if 'collaborative_addon_mapping' in self._ctx:
|
||||
self._addon_mapping = self._ctx['collaborative_addon_mapping']
|
||||
else:
|
||||
self._addon_mapping = LazyJSONLoader(self._ctx,
|
||||
ADDON_MAPPING_CONFIG[0],
|
||||
ADDON_MAPPING_CONFIG[1])
|
||||
self._addon_mapping = LazyJSONLoader(
|
||||
self._ctx, TAAR_ADDON_MAPPING_BUCKET, TAAR_ADDON_MAPPING_KEY
|
||||
)
|
||||
|
||||
if 'collaborative_item_matrix' in self._ctx:
|
||||
self._raw_item_matrix = self._ctx['collaborative_item_matrix']
|
||||
else:
|
||||
self._raw_item_matrix = LazyJSONLoader(self._ctx,
|
||||
ITEM_MATRIX_CONFIG[0],
|
||||
ITEM_MATRIX_CONFIG[1])
|
||||
self._raw_item_matrix = LazyJSONLoader(
|
||||
self._ctx, TAAR_ITEM_MATRIX_BUCKET, TAAR_ITEM_MATRIX_KEY
|
||||
)
|
||||
|
||||
self.logger = self._ctx[IMozLogging].get_logger('taar')
|
||||
self.logger = self._ctx[IMozLogging].get_logger("taar")
|
||||
|
||||
self.model = None
|
||||
self._build_model()
|
||||
|
|
@ -66,10 +62,18 @@ class CollaborativeRecommender(AbstractRecommender):
|
|||
def _load_json_models(self):
|
||||
# Download the addon mappings.
|
||||
if self.addon_mapping is None:
|
||||
self.logger.error("Cannot download the addon mapping file {} {}".format(*ADDON_MAPPING_CONFIG))
|
||||
self.logger.error(
|
||||
"Cannot download the addon mapping file {} {}".format(
|
||||
TAAR_ADDON_MAPPING_BUCKET, TAAR_ADDON_MAPPING_KEY
|
||||
)
|
||||
)
|
||||
|
||||
if self.addon_mapping is None:
|
||||
self.logger.error("Cannot download the model file {} {}".format(*ITEM_MATRIX_CONFIG))
|
||||
self.logger.error(
|
||||
"Cannot download the model file {} {}".format(
|
||||
TAAR_ITEM_MATRIX_BUCKET, TAAR_ITEM_MATRIX_KEY
|
||||
)
|
||||
)
|
||||
|
||||
def _build_model(self):
|
||||
if self.raw_item_matrix is None:
|
||||
|
|
@ -77,34 +81,43 @@ class CollaborativeRecommender(AbstractRecommender):
|
|||
|
||||
# Build a dense numpy matrix out of it.
|
||||
num_rows = len(self.raw_item_matrix)
|
||||
num_cols = len(self.raw_item_matrix[0]['features'])
|
||||
num_cols = len(self.raw_item_matrix[0]["features"])
|
||||
|
||||
self.model = np.zeros(shape=(num_rows, num_cols))
|
||||
for index, row in enumerate(self.raw_item_matrix):
|
||||
self.model[index, :] = row['features']
|
||||
self.model[index, :] = row["features"]
|
||||
|
||||
def can_recommend(self, client_data, extra_data={}):
|
||||
# We can't recommend if we don't have our data files.
|
||||
if self.raw_item_matrix is None or self.model is None or self.addon_mapping is None:
|
||||
if (
|
||||
self.raw_item_matrix is None
|
||||
or self.model is None
|
||||
or self.addon_mapping is None
|
||||
):
|
||||
return False
|
||||
|
||||
# We only get meaningful recommendation if a client has at least an
|
||||
# addon installed.
|
||||
if len(client_data.get('installed_addons', [])) > 0:
|
||||
if len(client_data.get("installed_addons", [])) > 0:
|
||||
return True
|
||||
|
||||
return False
|
||||
|
||||
def recommend(self, client_data, limit, extra_data={}):
|
||||
# Addons identifiers are stored as positive hash values within the model.
|
||||
installed_addons_as_hashes =\
|
||||
[positive_hash(addon_id) for addon_id in client_data.get('installed_addons', [])]
|
||||
installed_addons_as_hashes = [
|
||||
positive_hash(addon_id)
|
||||
for addon_id in client_data.get("installed_addons", [])
|
||||
]
|
||||
|
||||
# Build the query vector by setting the position of the queried addons to 1.0
|
||||
# and the other to 0.0.
|
||||
query_vector = np.array([1.0
|
||||
if (entry.get("id") in installed_addons_as_hashes)
|
||||
else 0.0 for entry in self.raw_item_matrix])
|
||||
query_vector = np.array(
|
||||
[
|
||||
1.0 if (entry.get("id") in installed_addons_as_hashes) else 0.0
|
||||
for entry in self.raw_item_matrix
|
||||
]
|
||||
)
|
||||
|
||||
# Build the user factors matrix.
|
||||
user_factors = np.matmul(query_vector, self.model)
|
||||
|
|
@ -119,12 +132,15 @@ class CollaborativeRecommender(AbstractRecommender):
|
|||
# filter out legacy addons from the suggestions.
|
||||
hashed_id = addon.get("id")
|
||||
str_hashed_id = str(hashed_id)
|
||||
if (hashed_id in installed_addons_as_hashes or
|
||||
str_hashed_id not in self.addon_mapping or
|
||||
self.addon_mapping[str_hashed_id].get("isWebextension", False) is False):
|
||||
if (
|
||||
hashed_id in installed_addons_as_hashes
|
||||
or str_hashed_id not in self.addon_mapping
|
||||
or self.addon_mapping[str_hashed_id].get("isWebextension", False)
|
||||
is False
|
||||
):
|
||||
continue
|
||||
|
||||
dist = np.dot(user_factors_transposed, addon.get('features'))
|
||||
dist = np.dot(user_factors_transposed, addon.get("features"))
|
||||
# Read the addon ids from the "addon_mapping" looking it
|
||||
# up by 'id' (which is an hashed value).
|
||||
addon_id = self.addon_mapping[str_hashed_id].get("id")
|
||||
|
|
@ -132,15 +148,14 @@ class CollaborativeRecommender(AbstractRecommender):
|
|||
|
||||
# Sort the suggested addons by their score and return the
|
||||
# sorted list of addon ids.
|
||||
sorted_dists = sorted(distances.items(),
|
||||
key=op.itemgetter(1),
|
||||
reverse=True)
|
||||
sorted_dists = sorted(distances.items(), key=op.itemgetter(1), reverse=True)
|
||||
recommendations = [(s[0], s[1]) for s in sorted_dists[:limit]]
|
||||
|
||||
log_data = (client_data['client_id'],
|
||||
str([r[0] for r in recommendations]))
|
||||
self.logger.info("collaborative_recommender_triggered, "
|
||||
"client_id: [%s], "
|
||||
"guids: [%s]" % log_data)
|
||||
log_data = (client_data["client_id"], str([r[0] for r in recommendations]))
|
||||
self.logger.info(
|
||||
"collaborative_recommender_triggered, "
|
||||
"client_id: [%s], "
|
||||
"guids: [%s]" % log_data
|
||||
)
|
||||
|
||||
return recommendations
|
||||
|
|
|
|||
|
|
@ -7,24 +7,20 @@ import itertools
|
|||
from .base_recommender import AbstractRecommender
|
||||
from .lazys3 import LazyJSONLoader
|
||||
|
||||
|
||||
S3_BUCKET = 'telemetry-parquet'
|
||||
ENSEMBLE_WEIGHTS = 'taar/ensemble/ensemble_weight.json'
|
||||
from .s3config import TAAR_ENSEMBLE_BUCKET
|
||||
from .s3config import TAAR_ENSEMBLE_KEY
|
||||
|
||||
|
||||
class WeightCache:
|
||||
def __init__(self, ctx):
|
||||
self._ctx = ctx
|
||||
|
||||
if 'ensemble_weights' in self._ctx:
|
||||
self._weights = self._ctx['ensemble_weights']
|
||||
else:
|
||||
self._weights = LazyJSONLoader(self._ctx,
|
||||
S3_BUCKET,
|
||||
ENSEMBLE_WEIGHTS)
|
||||
self._weights = LazyJSONLoader(
|
||||
self._ctx, TAAR_ENSEMBLE_BUCKET, TAAR_ENSEMBLE_KEY
|
||||
)
|
||||
|
||||
def getWeights(self):
|
||||
return self._weights.get()[0]['ensemble_weights']
|
||||
return self._weights.get()[0]["ensemble_weights"]
|
||||
|
||||
|
||||
class EnsembleRecommender(AbstractRecommender):
|
||||
|
|
@ -34,12 +30,13 @@ class EnsembleRecommender(AbstractRecommender):
|
|||
factor. The aggregate results are combines and used to recommend
|
||||
addons for users.
|
||||
"""
|
||||
def __init__(self, ctx):
|
||||
self.RECOMMENDER_KEYS = ['collaborative', 'similarity', 'locale']
|
||||
self._ctx = ctx
|
||||
self.logger = self._ctx[IMozLogging].get_logger('taar.ensemble')
|
||||
|
||||
assert 'recommender_factory' in self._ctx
|
||||
def __init__(self, ctx):
|
||||
self.RECOMMENDER_KEYS = ["collaborative", "similarity", "locale"]
|
||||
self._ctx = ctx
|
||||
self.logger = self._ctx[IMozLogging].get_logger("taar.ensemble")
|
||||
|
||||
assert "recommender_factory" in self._ctx
|
||||
|
||||
self._init_from_ctx()
|
||||
|
||||
|
|
@ -47,7 +44,7 @@ class EnsembleRecommender(AbstractRecommender):
|
|||
# Copy the map of the recommenders
|
||||
self._recommender_map = {}
|
||||
|
||||
recommender_factory = self._ctx['recommender_factory']
|
||||
recommender_factory = self._ctx["recommender_factory"]
|
||||
for rkey in self.RECOMMENDER_KEYS:
|
||||
self._recommender_map[rkey] = recommender_factory.create(rkey)
|
||||
|
||||
|
|
@ -56,8 +53,12 @@ class EnsembleRecommender(AbstractRecommender):
|
|||
def can_recommend(self, client_data, extra_data={}):
|
||||
"""The ensemble recommender is always going to be
|
||||
available if at least one recommender is available"""
|
||||
result = sum([self._recommender_map[rkey].can_recommend(client_data)
|
||||
for rkey in self.RECOMMENDER_KEYS])
|
||||
result = sum(
|
||||
[
|
||||
self._recommender_map[rkey].can_recommend(client_data)
|
||||
for rkey in self.RECOMMENDER_KEYS
|
||||
]
|
||||
)
|
||||
self.logger.info("Ensemble can_recommend: {}".format(result))
|
||||
return result
|
||||
|
||||
|
|
@ -76,7 +77,7 @@ class EnsembleRecommender(AbstractRecommender):
|
|||
correct.
|
||||
"""
|
||||
self.logger.info("Ensemble recommend invoked")
|
||||
preinstalled_addon_ids = client_data.get('installed_addons', [])
|
||||
preinstalled_addon_ids = client_data.get("installed_addons", [])
|
||||
|
||||
# Compute an extended limit by adding the length of
|
||||
# the list of any preinstalled addons.
|
||||
|
|
@ -89,9 +90,9 @@ class EnsembleRecommender(AbstractRecommender):
|
|||
recommender = self._recommender_map[rkey]
|
||||
|
||||
if recommender.can_recommend(client_data):
|
||||
raw_results = recommender.recommend(client_data,
|
||||
extended_limit,
|
||||
extra_data)
|
||||
raw_results = recommender.recommend(
|
||||
client_data, extended_limit, extra_data
|
||||
)
|
||||
reweighted_results = []
|
||||
for guid, weight in raw_results:
|
||||
item = (guid, weight * ensemble_weights[rkey])
|
||||
|
|
@ -114,14 +115,20 @@ class EnsembleRecommender(AbstractRecommender):
|
|||
# Sort in reverse order (greatest weight to least)
|
||||
ensemble_suggestions.sort(key=lambda x: -x[1])
|
||||
|
||||
filtered_ensemble_suggestions = [(guid, weight) for (guid, weight)
|
||||
in ensemble_suggestions
|
||||
if guid not in preinstalled_addon_ids]
|
||||
filtered_ensemble_suggestions = [
|
||||
(guid, weight)
|
||||
for (guid, weight) in ensemble_suggestions
|
||||
if guid not in preinstalled_addon_ids
|
||||
]
|
||||
|
||||
results = filtered_ensemble_suggestions[:limit]
|
||||
|
||||
log_data = (client_data['client_id'],
|
||||
str(ensemble_weights),
|
||||
str([r[0] for r in results]))
|
||||
self.logger.info("client_id: [%s], ensemble_weight: [%s], guids: [%s]" % log_data)
|
||||
log_data = (
|
||||
client_data["client_id"],
|
||||
str(ensemble_weights),
|
||||
str([r[0] for r in results]),
|
||||
)
|
||||
self.logger.info(
|
||||
"client_id: [%s], ensemble_weight: [%s], guids: [%s]" % log_data
|
||||
)
|
||||
return results
|
||||
|
|
|
|||
|
|
@ -5,13 +5,10 @@
|
|||
from .base_recommender import AbstractRecommender
|
||||
from .lazys3 import LazyJSONLoader
|
||||
from srgutil.interfaces import IMozLogging
|
||||
import random
|
||||
import operator as op
|
||||
|
||||
S3_BUCKET = "telemetry-parquet"
|
||||
|
||||
ENSEMBLE_WEIGHTS = "taar/ensemble/ensemble_weight.json"
|
||||
CURATED_WHITELIST = "telemetry-ml/addon_recommender/only_guids_top_200.json"
|
||||
import random
|
||||
from .s3config import TAAR_WHITELIST_BUCKET
|
||||
from .s3config import TAAR_WHITELIST_KEY
|
||||
|
||||
|
||||
class CuratedWhitelistCache:
|
||||
|
|
@ -21,10 +18,9 @@ class CuratedWhitelistCache:
|
|||
|
||||
def __init__(self, ctx):
|
||||
self._ctx = ctx
|
||||
if "curated_whitelist_data" in self._ctx:
|
||||
self._data = self._ctx["curated_whitelist_data"]
|
||||
else:
|
||||
self._data = LazyJSONLoader(self._ctx, S3_BUCKET, CURATED_WHITELIST)
|
||||
self._data = LazyJSONLoader(
|
||||
self._ctx, TAAR_WHITELIST_BUCKET, TAAR_WHITELIST_KEY
|
||||
)
|
||||
|
||||
def get_whitelist(self):
|
||||
return self._data.get()[0]
|
||||
|
|
|
|||
|
|
@ -6,9 +6,8 @@ from srgutil.interfaces import IMozLogging
|
|||
from .base_recommender import AbstractRecommender
|
||||
from .lazys3 import LazyJSONLoader
|
||||
|
||||
|
||||
ADDON_LIST_BUCKET = 'telemetry-parquet'
|
||||
ADDON_LIST_KEY = 'taar/locale/top10_dict.json'
|
||||
from .s3config import TAAR_LOCALE_BUCKET
|
||||
from .s3config import TAAR_LOCALE_KEY
|
||||
|
||||
|
||||
class LocaleRecommender(AbstractRecommender):
|
||||
|
|
@ -24,12 +23,9 @@ class LocaleRecommender(AbstractRecommender):
|
|||
def __init__(self, ctx):
|
||||
self._ctx = ctx
|
||||
|
||||
if 'locale_mock_data' in self._ctx:
|
||||
self._top_addons_per_locale = self._ctx['locale_mock_data']
|
||||
else:
|
||||
self._top_addons_per_locale = LazyJSONLoader(self._ctx,
|
||||
ADDON_LIST_BUCKET,
|
||||
ADDON_LIST_KEY)
|
||||
self._top_addons_per_locale = LazyJSONLoader(self._ctx,
|
||||
TAAR_LOCALE_BUCKET,
|
||||
TAAR_LOCALE_KEY)
|
||||
|
||||
self._init_from_ctx()
|
||||
self.logger = self._ctx[IMozLogging].get_logger('taar')
|
||||
|
|
@ -40,7 +36,7 @@ class LocaleRecommender(AbstractRecommender):
|
|||
|
||||
def _init_from_ctx(self):
|
||||
if self.top_addons_per_locale is None:
|
||||
self.logger.error("Cannot download the top per locale file {}".format(ADDON_LIST_KEY))
|
||||
self.logger.error("Cannot download the top per locale file {}".format(TAAR_LOCALE_KEY))
|
||||
|
||||
def can_recommend(self, client_data, extra_data={}):
|
||||
# We can't recommend if we don't have our data files.
|
||||
|
|
|
|||
|
|
@ -21,21 +21,29 @@ from taar.context import default_context
|
|||
from .lazys3 import LazyJSONLoader
|
||||
import random
|
||||
|
||||
from .s3config import TAAR_WHITELIST_BUCKET
|
||||
from .s3config import TAAR_WHITELIST_KEY
|
||||
|
||||
|
||||
# We need to build a default logger for the schema validation as there
|
||||
# is no class to bind to yet.
|
||||
ctx = default_context()
|
||||
schema_logger = ctx[IMozLogging].get_logger('taar.schema_validate')
|
||||
schema_logger = ctx[IMozLogging].get_logger("taar.schema_validate")
|
||||
|
||||
|
||||
TEST_CLIENT_IDS = ['00000000-0000-0000-0000-000000000000',
|
||||
'11111111-1111-1111-1111-111111111111',
|
||||
'22222222-2222-2222-2222-222222222222',
|
||||
'33333333-3333-3333-3333-333333333333']
|
||||
TEST_CLIENT_IDS = [
|
||||
"00000000-0000-0000-0000-000000000000",
|
||||
"11111111-1111-1111-1111-111111111111",
|
||||
"22222222-2222-2222-2222-222222222222",
|
||||
"33333333-3333-3333-3333-333333333333",
|
||||
]
|
||||
|
||||
EMPTY_TEST_CLIENT_IDS = ['00000000-aaaa-0000-0000-000000000000',
|
||||
'11111111-aaaa-1111-1111-111111111111',
|
||||
'22222222-aaaa-2222-2222-222222222222',
|
||||
'33333333-aaaa-3333-3333-333333333333']
|
||||
EMPTY_TEST_CLIENT_IDS = [
|
||||
"00000000-aaaa-0000-0000-000000000000",
|
||||
"11111111-aaaa-1111-1111-111111111111",
|
||||
"22222222-aaaa-2222-2222-222222222222",
|
||||
"33333333-aaaa-3333-3333-333333333333",
|
||||
]
|
||||
|
||||
|
||||
# TODO: rework this function as it seems to add a lot of overhead
|
||||
|
|
@ -45,6 +53,7 @@ def schema_validate(colandar_schema): # noqa: C901
|
|||
Compute the function signature and apply a schema validator on the
|
||||
function.
|
||||
"""
|
||||
|
||||
def real_decorator(func):
|
||||
func_sig = inspect_sig(func)
|
||||
|
||||
|
|
@ -52,7 +61,7 @@ def schema_validate(colandar_schema): # noqa: C901
|
|||
json_arg_names = []
|
||||
for key in func_sig.parameters.keys():
|
||||
json_arg_names.append(key)
|
||||
if key == 'self':
|
||||
if key == "self":
|
||||
continue
|
||||
|
||||
default_val = func_sig.parameters[key].default
|
||||
|
|
@ -63,7 +72,7 @@ def schema_validate(colandar_schema): # noqa: C901
|
|||
|
||||
def wrapper(*w_args, **w_kwargs):
|
||||
|
||||
if json_arg_names[0] == 'self':
|
||||
if json_arg_names[0] == "self":
|
||||
# first arg is 'self', so this is a method.
|
||||
# Strip out self when doing argument validation
|
||||
for i, argval in enumerate(w_args[1:]):
|
||||
|
|
@ -83,16 +92,20 @@ def schema_validate(colandar_schema): # noqa: C901
|
|||
try:
|
||||
schema.deserialize(json_args)
|
||||
except colander.Invalid as e:
|
||||
msg = "Defaulting to empty results. Error deserializing input arguments: " + str(e.asdict().values())
|
||||
msg = (
|
||||
"Defaulting to empty results. Error deserializing input arguments: "
|
||||
+ str(e.asdict().values())
|
||||
)
|
||||
|
||||
# This logger can't use the context logger as the code
|
||||
# is running in a method decorator
|
||||
schema_logger.warn(msg)
|
||||
schema_logger.warning(msg)
|
||||
# Invalid data means TAAR safely returns an empty list
|
||||
return []
|
||||
return func(*w_args, **w_kwargs)
|
||||
|
||||
return wrapper
|
||||
|
||||
return real_decorator
|
||||
|
||||
|
||||
|
|
@ -104,10 +117,11 @@ class RecommenderFactory:
|
|||
the RecommendationManager and eases the implementation of test
|
||||
harnesses.
|
||||
"""
|
||||
|
||||
def __init__(self, ctx):
|
||||
self._ctx = ctx
|
||||
# This map is set in the default context
|
||||
self._recommender_factory_map = self._ctx['recommender_factory_map']
|
||||
self._recommender_factory_map = self._ctx["recommender_factory_map"]
|
||||
|
||||
def get_names(self):
|
||||
return self._recommender_factory_map.keys()
|
||||
|
|
@ -124,24 +138,25 @@ class RecommendationManager:
|
|||
"""Initialize the user profile fetcher and the recommenders.
|
||||
"""
|
||||
self._ctx = ctx
|
||||
self.logger = self._ctx[IMozLogging].get_logger('taar')
|
||||
self.logger = self._ctx[IMozLogging].get_logger("taar")
|
||||
|
||||
assert 'profile_fetcher' in self._ctx
|
||||
assert "profile_fetcher" in self._ctx
|
||||
|
||||
self.profile_fetcher = ctx['profile_fetcher']
|
||||
self.profile_fetcher = ctx["profile_fetcher"]
|
||||
self._recommender_map = {}
|
||||
|
||||
self.logger.info("Initializing recommenders")
|
||||
self._recommender_map[INTERVENTION_A] = EnsembleRecommender(self._ctx.child())
|
||||
|
||||
hybrid_ctx = self._ctx.child()
|
||||
hybrid_ctx['ensemble_recommender'] = self._recommender_map[INTERVENTION_A]
|
||||
hybrid_ctx["ensemble_recommender"] = self._recommender_map[INTERVENTION_A]
|
||||
self._recommender_map[INTERVENTION_B] = HybridRecommender(hybrid_ctx)
|
||||
|
||||
# The whitelist data is only used for test client IDs
|
||||
WHITELIST_S3_BUCKET = 'telemetry-parquet'
|
||||
WHITELIST_S3_KEY = 'telemetry-ml/addon_recommender/only_guids_top_200.json'
|
||||
self._whitelist_data = LazyJSONLoader(self._ctx, WHITELIST_S3_BUCKET, WHITELIST_S3_KEY)
|
||||
|
||||
self._whitelist_data = LazyJSONLoader(
|
||||
self._ctx, TAAR_WHITELIST_BUCKET, TAAR_WHITELIST_KEY
|
||||
)
|
||||
|
||||
@schema_validate(RecommendationManagerQuerySchema)
|
||||
def recommend(self, client_id, limit, extra_data={}):
|
||||
|
|
@ -156,11 +171,11 @@ class RecommendationManager:
|
|||
"""
|
||||
|
||||
# Select recommendation output based on extra_data['branch']
|
||||
branch_selector = extra_data.get('branch', INTERVENTION_CONTROL)
|
||||
method_selector = branch_selector.replace('-', '_')
|
||||
method_name = 'recommend_{}'.format(method_selector)
|
||||
branch_selector = extra_data.get("branch", INTERVENTION_CONTROL)
|
||||
method_selector = branch_selector.replace("-", "_")
|
||||
method_name = "recommend_{}".format(method_selector)
|
||||
self.logger.info("Dispatching to method [{}]".format(method_name))
|
||||
branch_method = getattr(self, 'recommend_%s' % method_selector)
|
||||
branch_method = getattr(self, "recommend_%s" % method_selector)
|
||||
|
||||
if client_id in TEST_CLIENT_IDS:
|
||||
data = self._whitelist_data.get()[0]
|
||||
|
|
@ -175,7 +190,9 @@ class RecommendationManager:
|
|||
|
||||
client_info = self.profile_fetcher.get(client_id)
|
||||
if client_info is None:
|
||||
self.logger.warn("Defaulting to empty results. No client info fetched from dynamo.")
|
||||
self.logger.warning(
|
||||
"Defaulting to empty results. No client info fetched from dynamo."
|
||||
)
|
||||
return []
|
||||
|
||||
return branch_method(client_info, client_id, limit, extra_data)
|
||||
|
|
|
|||
|
|
@ -0,0 +1,28 @@
|
|||
from decouple import config
|
||||
|
||||
TAAR_ENSEMBLE_BUCKET = config("TAAR_ENSEMBLE_BUCKET", default="test_ensemble_bucket")
|
||||
TAAR_ENSEMBLE_KEY = config("TAAR_ENSEMBLE_KEY", default="test_ensemble_key")
|
||||
|
||||
TAAR_WHITELIST_BUCKET = config("TAAR_WHITELIST_BUCKET", default="test_whitelist_bucket")
|
||||
TAAR_WHITELIST_KEY = config("TAAR_WHITELIST_KEY", default="test_whitelist_key")
|
||||
|
||||
TAAR_ITEM_MATRIX_BUCKET = config(
|
||||
"TAAR_ITEM_MATRIX_BUCKET", default="test_matrix_bucket"
|
||||
)
|
||||
TAAR_ITEM_MATRIX_KEY = config("TAAR_ITEM_MATRIX_KEY", default="test_matrix_key")
|
||||
TAAR_ADDON_MAPPING_BUCKET = config(
|
||||
"TAAR_ADDON_MAPPING_BUCKET", default="test_mapping_bucket"
|
||||
)
|
||||
TAAR_ADDON_MAPPING_KEY = config("TAAR_ADDON_MAPPING_KEY", default="test_mapping_key")
|
||||
|
||||
TAAR_LOCALE_BUCKET = config("TAAR_LOCALE_BUCKET", default="test_locale_bucket")
|
||||
TAAR_LOCALE_KEY = config("TAAR_LOCALE_KEY", default="test_locale_key")
|
||||
|
||||
|
||||
TAAR_SIMILARITY_BUCKET = config("telemetry-parquet", default="test_similarity_bucket")
|
||||
TAAR_SIMILARITY_DONOR_KEY = config(
|
||||
"TAAR_SIMILARITY_DONOR_KEY", default="test_similarity_donor_key"
|
||||
)
|
||||
TAAR_SIMILARITY_LRCURVES_KEY = config(
|
||||
"TAAR_SIMILARITY_LRCURVES_KEY", default="test_similarity_lrcurves_key"
|
||||
)
|
||||
|
|
@ -9,6 +9,11 @@ from srgutil.interfaces import IMozLogging
|
|||
import numpy as np
|
||||
from .lazys3 import LazyJSONLoader
|
||||
|
||||
from .s3config import TAAR_SIMILARITY_BUCKET
|
||||
from .s3config import TAAR_SIMILARITY_DONOR_KEY
|
||||
from .s3config import TAAR_SIMILARITY_LRCURVES_KEY
|
||||
|
||||
|
||||
FLOOR_DISTANCE_ADJUSTMENT = 0.001
|
||||
|
||||
CATEGORICAL_FEATURES = ["geo_city", "locale", "os"]
|
||||
|
|
@ -20,11 +25,6 @@ CONTINUOUS_FEATURES = [
|
|||
"unique_tlds",
|
||||
]
|
||||
|
||||
S3_BUCKET = "telemetry-parquet"
|
||||
|
||||
DONOR_LIST_KEY = "taar/similarity/donors.json"
|
||||
LR_CURVES_SIMILARITY_TO_PROBABILITY = "taar/similarity/lr_curves.json"
|
||||
|
||||
|
||||
class SimilarityRecommender(AbstractRecommender):
|
||||
""" A recommender class that returns top N addons based on the
|
||||
|
|
@ -50,13 +50,15 @@ class SimilarityRecommender(AbstractRecommender):
|
|||
if "similarity_donors_pool" in self._ctx:
|
||||
self._donors_pool = self._ctx["similarity_donors_pool"]
|
||||
else:
|
||||
self._donors_pool = LazyJSONLoader(self._ctx, S3_BUCKET, DONOR_LIST_KEY)
|
||||
self._donors_pool = LazyJSONLoader(
|
||||
self._ctx, TAAR_SIMILARITY_BUCKET, TAAR_SIMILARITY_DONOR_KEY
|
||||
)
|
||||
|
||||
if "similarity_lr_curves" in self._ctx:
|
||||
self._lr_curves = self._ctx["similarity_lr_curves"]
|
||||
else:
|
||||
self._lr_curves = LazyJSONLoader(
|
||||
self._ctx, S3_BUCKET, LR_CURVES_SIMILARITY_TO_PROBABILITY
|
||||
self._ctx, TAAR_SIMILARITY_BUCKET, TAAR_SIMILARITY_LRCURVES_KEY
|
||||
)
|
||||
|
||||
self.logger = self._ctx[IMozLogging].get_logger("taar")
|
||||
|
|
@ -75,15 +77,13 @@ class SimilarityRecommender(AbstractRecommender):
|
|||
# Download the addon donors list.
|
||||
if self.donors_pool is None:
|
||||
self.logger.error(
|
||||
"Cannot download the donor list: {}".format(DONOR_LIST_KEY)
|
||||
"Cannot download the donor list: {}".format(TAAR_SIMILARITY_DONOR_KEY)
|
||||
)
|
||||
|
||||
# Download the probability mapping curves from similarity to likelihood of being a good donor.
|
||||
if self.lr_curves is None:
|
||||
self.logger.error(
|
||||
"Cannot download the lr curves: {}".format(
|
||||
LR_CURVES_SIMILARITY_TO_PROBABILITY
|
||||
)
|
||||
"Cannot download the lr curves: {}".format(TAAR_SIMILARITY_LRCURVES_KEY)
|
||||
)
|
||||
self.build_features_caches()
|
||||
|
||||
|
|
|
|||
|
|
@ -10,11 +10,15 @@ import numpy
|
|||
|
||||
from moto import mock_s3
|
||||
import boto3
|
||||
from taar.recommenders.collaborative_recommender import ITEM_MATRIX_CONFIG
|
||||
from taar.recommenders.collaborative_recommender import ADDON_MAPPING_CONFIG
|
||||
from taar.recommenders.collaborative_recommender import (
|
||||
TAAR_ITEM_MATRIX_BUCKET,
|
||||
TAAR_ITEM_MATRIX_KEY,
|
||||
TAAR_ADDON_MAPPING_BUCKET,
|
||||
TAAR_ADDON_MAPPING_KEY,
|
||||
)
|
||||
|
||||
from taar.recommenders.collaborative_recommender import CollaborativeRecommender
|
||||
from taar.recommenders.collaborative_recommender import positive_hash
|
||||
from taar.recommenders.lazys3 import LazyJSONLoader
|
||||
import json
|
||||
|
||||
|
||||
|
|
@ -31,21 +35,15 @@ def install_none_mock_data(ctx):
|
|||
Overload the 'real' addon model and mapping URLs responses so that
|
||||
we always get 404 errors.
|
||||
"""
|
||||
conn = boto3.resource('s3', region_name='us-west-2')
|
||||
conn = boto3.resource("s3", region_name="us-west-2")
|
||||
|
||||
conn.create_bucket(Bucket=ITEM_MATRIX_CONFIG[0])
|
||||
conn.Object(ITEM_MATRIX_CONFIG[0], ITEM_MATRIX_CONFIG[1]).put(Body="")
|
||||
ctx['collaborative_item_matrix'] = LazyJSONLoader(ctx,
|
||||
ITEM_MATRIX_CONFIG[0],
|
||||
ITEM_MATRIX_CONFIG[1])
|
||||
conn.create_bucket(Bucket=TAAR_ITEM_MATRIX_BUCKET)
|
||||
conn.Object(TAAR_ITEM_MATRIX_BUCKET, TAAR_ITEM_MATRIX_KEY).put(Body="")
|
||||
|
||||
# Don't reuse connections with moto. badness happens
|
||||
conn = boto3.resource('s3', region_name='us-west-2')
|
||||
conn.create_bucket(Bucket=ADDON_MAPPING_CONFIG[0])
|
||||
conn.Object(ADDON_MAPPING_CONFIG[0], ADDON_MAPPING_CONFIG[1]).put(Body="")
|
||||
ctx['collaborative_addon_mapping'] = LazyJSONLoader(ctx,
|
||||
ADDON_MAPPING_CONFIG[0],
|
||||
ADDON_MAPPING_CONFIG[1])
|
||||
conn = boto3.resource("s3", region_name="us-west-2")
|
||||
conn.create_bucket(Bucket=TAAR_ADDON_MAPPING_BUCKET)
|
||||
conn.Object(TAAR_ADDON_MAPPING_BUCKET, TAAR_ADDON_MAPPING_KEY).put(Body="")
|
||||
return ctx
|
||||
|
||||
|
||||
|
|
@ -55,37 +53,37 @@ def install_mock_data(ctx):
|
|||
we always the fixture data at the top of this test module.
|
||||
"""
|
||||
|
||||
addon_space = [{"id": "addon1.id", "name": "addon1.name", "isWebextension": True},
|
||||
{"id": "addon2.id", "name": "addon2.name", "isWebextension": True},
|
||||
{"id": "addon3.id", "name": "addon3.name", "isWebextension": True},
|
||||
{"id": "addon4.id", "name": "addon4.name", "isWebextension": True},
|
||||
{"id": "addon5.id", "name": "addon5.name", "isWebextension": True}]
|
||||
addon_space = [
|
||||
{"id": "addon1.id", "name": "addon1.name", "isWebextension": True},
|
||||
{"id": "addon2.id", "name": "addon2.name", "isWebextension": True},
|
||||
{"id": "addon3.id", "name": "addon3.name", "isWebextension": True},
|
||||
{"id": "addon4.id", "name": "addon4.name", "isWebextension": True},
|
||||
{"id": "addon5.id", "name": "addon5.name", "isWebextension": True},
|
||||
]
|
||||
|
||||
fake_addon_matrix = []
|
||||
for i, addon in enumerate(addon_space):
|
||||
row = {"id": positive_hash(addon['id']), "features": [0, 0.2, 0.0, 0.1, 0.15]}
|
||||
row['features'][i] = 1.0
|
||||
row = {"id": positive_hash(addon["id"]), "features": [0, 0.2, 0.0, 0.1, 0.15]}
|
||||
row["features"][i] = 1.0
|
||||
fake_addon_matrix.append(row)
|
||||
|
||||
fake_mapping = {}
|
||||
for addon in addon_space:
|
||||
java_hash = positive_hash(addon['id'])
|
||||
java_hash = positive_hash(addon["id"])
|
||||
fake_mapping[str(java_hash)] = addon
|
||||
|
||||
conn = boto3.resource('s3', region_name='us-west-2')
|
||||
conn.create_bucket(Bucket=ITEM_MATRIX_CONFIG[0])
|
||||
conn.Object(ITEM_MATRIX_CONFIG[0], ITEM_MATRIX_CONFIG[1]).put(Body=json.dumps(fake_addon_matrix))
|
||||
conn = boto3.resource("s3", region_name="us-west-2")
|
||||
conn.create_bucket(Bucket=TAAR_ITEM_MATRIX_BUCKET)
|
||||
conn.Object(TAAR_ITEM_MATRIX_BUCKET, TAAR_ITEM_MATRIX_KEY).put(
|
||||
Body=json.dumps(fake_addon_matrix)
|
||||
)
|
||||
|
||||
conn = boto3.resource('s3', region_name='us-west-2')
|
||||
conn.create_bucket(Bucket=ADDON_MAPPING_CONFIG[0])
|
||||
conn.Object(ADDON_MAPPING_CONFIG[0], ADDON_MAPPING_CONFIG[1]).put(Body=json.dumps(fake_mapping))
|
||||
conn = boto3.resource("s3", region_name="us-west-2")
|
||||
conn.create_bucket(Bucket=TAAR_ADDON_MAPPING_BUCKET)
|
||||
conn.Object(TAAR_ADDON_MAPPING_BUCKET, TAAR_ADDON_MAPPING_KEY).put(
|
||||
Body=json.dumps(fake_mapping)
|
||||
)
|
||||
|
||||
ctx['collaborative_addon_mapping'] = LazyJSONLoader(ctx,
|
||||
ADDON_MAPPING_CONFIG[0],
|
||||
ADDON_MAPPING_CONFIG[1])
|
||||
ctx['collaborative_item_matrix'] = LazyJSONLoader(ctx,
|
||||
ITEM_MATRIX_CONFIG[0],
|
||||
ITEM_MATRIX_CONFIG[1])
|
||||
return ctx
|
||||
|
||||
|
||||
|
|
@ -106,8 +104,9 @@ def test_can_recommend(test_ctx):
|
|||
|
||||
# For some reason, moto doesn't like to play nice with this call
|
||||
# Check that we can recommend if we the user has at least an addon.
|
||||
assert r.can_recommend({"installed_addons": ["uBlock0@raymondhill.net"],
|
||||
"client_id": "test-client"})
|
||||
assert r.can_recommend(
|
||||
{"installed_addons": ["uBlock0@raymondhill.net"], "client_id": "test-client"}
|
||||
)
|
||||
|
||||
|
||||
@mock_s3
|
||||
|
|
@ -141,8 +140,10 @@ def test_best_recommendation(test_ctx):
|
|||
r = CollaborativeRecommender(ctx)
|
||||
|
||||
# An non-empty set of addons should give a list of recommendations
|
||||
fixture_client_data = {"installed_addons": ["addon4.id"],
|
||||
"client_id": "test_client"}
|
||||
fixture_client_data = {
|
||||
"installed_addons": ["addon4.id"],
|
||||
"client_id": "test_client",
|
||||
}
|
||||
assert r.can_recommend(fixture_client_data)
|
||||
recommendations = r.recommend(fixture_client_data, 1)
|
||||
|
||||
|
|
@ -154,9 +155,9 @@ def test_best_recommendation(test_ctx):
|
|||
result = recommendations[0]
|
||||
assert type(result) is tuple
|
||||
assert len(result) == 2
|
||||
assert result[0] == 'addon2.id'
|
||||
assert result[0] == "addon2.id"
|
||||
assert type(result[1]) is numpy.float64
|
||||
assert numpy.isclose(result[1], numpy.float64('0.3225'))
|
||||
assert numpy.isclose(result[1], numpy.float64("0.3225"))
|
||||
|
||||
|
||||
@mock_s3
|
||||
|
|
@ -168,8 +169,10 @@ def test_recommendation_weights(test_ctx):
|
|||
r = CollaborativeRecommender(ctx)
|
||||
|
||||
# An non-empty set of addons should give a list of recommendations
|
||||
fixture_client_data = {"installed_addons": ["addon4.id"],
|
||||
"client_id": "test_client"}
|
||||
fixture_client_data = {
|
||||
"installed_addons": ["addon4.id"],
|
||||
"client_id": "test_client",
|
||||
}
|
||||
assert r.can_recommend(fixture_client_data)
|
||||
recommendations = r.recommend(fixture_client_data, 2)
|
||||
assert isinstance(recommendations, list)
|
||||
|
|
@ -180,15 +183,15 @@ def test_recommendation_weights(test_ctx):
|
|||
result = recommendations[0]
|
||||
assert type(result) is tuple
|
||||
assert len(result) == 2
|
||||
assert result[0] == 'addon2.id'
|
||||
assert result[0] == "addon2.id"
|
||||
assert type(result[1]) is numpy.float64
|
||||
assert numpy.isclose(result[1], numpy.float64('0.3225'))
|
||||
assert numpy.isclose(result[1], numpy.float64("0.3225"))
|
||||
|
||||
# Verify that addon2 - the most heavy weighted addon was
|
||||
# recommended
|
||||
result = recommendations[1]
|
||||
assert type(result) is tuple
|
||||
assert len(result) == 2
|
||||
assert result[0] == 'addon5.id'
|
||||
assert result[0] == "addon5.id"
|
||||
assert type(result[1]) is numpy.float64
|
||||
assert numpy.isclose(result[1], numpy.float64('0.29'))
|
||||
assert numpy.isclose(result[1], numpy.float64("0.29"))
|
||||
|
|
|
|||
|
|
@ -3,30 +3,24 @@
|
|||
# file, You can obtain one at http://mozilla.org/MPL/2.0/.
|
||||
|
||||
from taar.recommenders.ensemble_recommender import WeightCache, EnsembleRecommender
|
||||
from taar.recommenders.s3config import (
|
||||
TAAR_ENSEMBLE_BUCKET,
|
||||
TAAR_ENSEMBLE_KEY,
|
||||
)
|
||||
from moto import mock_s3
|
||||
import boto3
|
||||
import json
|
||||
from taar.recommenders.lazys3 import LazyJSONLoader
|
||||
from .mocks import MockRecommenderFactory
|
||||
|
||||
EXPECTED = {'collaborative': 1000,
|
||||
'similarity': 100,
|
||||
'locale': 10}
|
||||
EXPECTED = {"collaborative": 1000, "similarity": 100, "locale": 10}
|
||||
|
||||
|
||||
def install_mock_ensemble_data(ctx):
|
||||
DATA = {'ensemble_weights': EXPECTED}
|
||||
DATA = {"ensemble_weights": EXPECTED}
|
||||
|
||||
S3_BUCKET = 'telemetry-parquet'
|
||||
ENSEMBLE_WEIGHTS = 'taar/ensemble/ensemble_weight.json'
|
||||
|
||||
conn = boto3.resource('s3', region_name='us-west-2')
|
||||
conn.create_bucket(Bucket=S3_BUCKET)
|
||||
conn.Object(S3_BUCKET, ENSEMBLE_WEIGHTS).put(Body=json.dumps(DATA))
|
||||
|
||||
ctx['ensemble_weights'] = LazyJSONLoader(ctx,
|
||||
S3_BUCKET,
|
||||
ENSEMBLE_WEIGHTS)
|
||||
conn = boto3.resource("s3", region_name="us-west-2")
|
||||
conn.create_bucket(Bucket=TAAR_ENSEMBLE_BUCKET)
|
||||
conn.Object(TAAR_ENSEMBLE_BUCKET, TAAR_ENSEMBLE_KEY).put(Body=json.dumps(DATA))
|
||||
|
||||
return ctx
|
||||
|
||||
|
|
@ -43,20 +37,24 @@ def test_weight_cache(test_ctx):
|
|||
def test_recommendations(test_ctx):
|
||||
ctx = install_mock_ensemble_data(test_ctx)
|
||||
|
||||
EXPECTED_RESULTS = [('ghi', 3430.0),
|
||||
('def', 3320.0),
|
||||
('ijk', 3200.0),
|
||||
('hij', 3100.0),
|
||||
('lmn', 420.0)]
|
||||
EXPECTED_RESULTS = [
|
||||
("ghi", 3430.0),
|
||||
("def", 3320.0),
|
||||
("ijk", 3200.0),
|
||||
("hij", 3100.0),
|
||||
("lmn", 420.0),
|
||||
]
|
||||
|
||||
factory = MockRecommenderFactory()
|
||||
ctx['recommender_factory'] = factory
|
||||
ctx["recommender_factory"] = factory
|
||||
|
||||
ctx['recommender_map'] = {'collaborative': factory.create('collaborative'),
|
||||
'similarity': factory.create('similarity'),
|
||||
'locale': factory.create('locale')}
|
||||
ctx["recommender_map"] = {
|
||||
"collaborative": factory.create("collaborative"),
|
||||
"similarity": factory.create("similarity"),
|
||||
"locale": factory.create("locale"),
|
||||
}
|
||||
r = EnsembleRecommender(ctx.child())
|
||||
client = {'client_id': '12345'} # Anything will work here
|
||||
client = {"client_id": "12345"} # Anything will work here
|
||||
|
||||
recommendation_list = r.recommend(client, 5)
|
||||
assert isinstance(recommendation_list, list)
|
||||
|
|
@ -67,25 +65,28 @@ def test_recommendations(test_ctx):
|
|||
def test_preinstalled_guids(test_ctx):
|
||||
ctx = install_mock_ensemble_data(test_ctx)
|
||||
|
||||
EXPECTED_RESULTS = [('ghi', 3430.0),
|
||||
('ijk', 3200.0),
|
||||
('lmn', 420.0),
|
||||
('klm', 409.99999999999994),
|
||||
('abc', 23.0)]
|
||||
EXPECTED_RESULTS = [
|
||||
("ghi", 3430.0),
|
||||
("ijk", 3200.0),
|
||||
("lmn", 420.0),
|
||||
("klm", 409.99999999999994),
|
||||
("abc", 23.0),
|
||||
]
|
||||
|
||||
factory = MockRecommenderFactory()
|
||||
ctx['recommender_factory'] = factory
|
||||
ctx["recommender_factory"] = factory
|
||||
|
||||
ctx['recommender_map'] = {'collaborative': factory.create('collaborative'),
|
||||
'similarity': factory.create('similarity'),
|
||||
'locale': factory.create('locale')}
|
||||
ctx["recommender_map"] = {
|
||||
"collaborative": factory.create("collaborative"),
|
||||
"similarity": factory.create("similarity"),
|
||||
"locale": factory.create("locale"),
|
||||
}
|
||||
r = EnsembleRecommender(ctx.child())
|
||||
|
||||
# 'hij' should be excluded from the suggestions list
|
||||
# The other two addon GUIDs 'def' and 'jkl' will never be
|
||||
# recommended anyway and should have no impact on results
|
||||
client = {'client_id': '12345',
|
||||
'installed_addons': ['def', 'hij', 'jkl']}
|
||||
client = {"client_id": "12345", "installed_addons": ["def", "hij", "jkl"]}
|
||||
|
||||
recommendation_list = r.recommend(client, 5)
|
||||
print(recommendation_list)
|
||||
|
|
|
|||
|
|
@ -10,11 +10,9 @@ from taar.recommenders.hybrid_recommender import CuratedRecommender
|
|||
from taar.recommenders.hybrid_recommender import HybridRecommender
|
||||
from taar.recommenders.ensemble_recommender import EnsembleRecommender
|
||||
|
||||
from taar.recommenders.hybrid_recommender import S3_BUCKET
|
||||
from taar.recommenders.hybrid_recommender import CURATED_WHITELIST
|
||||
from taar.recommenders.s3config import TAAR_WHITELIST_BUCKET, TAAR_WHITELIST_KEY
|
||||
|
||||
# from taar.recommenders.hybrid_recommender import ENSEMBLE_WEIGHTS
|
||||
from taar.recommenders.lazys3 import LazyJSONLoader
|
||||
from .test_ensemblerecommender import install_mock_ensemble_data
|
||||
from .mocks import MockRecommenderFactory
|
||||
|
||||
|
|
@ -27,9 +25,8 @@ def install_no_curated_data(ctx):
|
|||
ctx = ctx.child()
|
||||
conn = boto3.resource("s3", region_name="us-west-2")
|
||||
|
||||
conn.create_bucket(Bucket=S3_BUCKET)
|
||||
conn.Object(S3_BUCKET, CURATED_WHITELIST).put(Body="")
|
||||
ctx["curated_whitelist_data"] = LazyJSONLoader(ctx, S3_BUCKET, CURATED_WHITELIST)
|
||||
conn.create_bucket(Bucket=TAAR_WHITELIST_BUCKET)
|
||||
conn.Object(TAAR_WHITELIST_BUCKET, TAAR_WHITELIST_KEY).put(Body="")
|
||||
|
||||
return ctx
|
||||
|
||||
|
|
@ -42,9 +39,10 @@ def install_mock_curated_data(ctx):
|
|||
ctx = ctx.child()
|
||||
conn = boto3.resource("s3", region_name="us-west-2")
|
||||
|
||||
conn.create_bucket(Bucket=S3_BUCKET)
|
||||
conn.Object(S3_BUCKET, CURATED_WHITELIST).put(Body=json.dumps(mock_data))
|
||||
ctx["curated_whitelist_data"] = LazyJSONLoader(ctx, S3_BUCKET, CURATED_WHITELIST)
|
||||
conn.create_bucket(Bucket=TAAR_WHITELIST_BUCKET)
|
||||
conn.Object(TAAR_WHITELIST_BUCKET, TAAR_WHITELIST_KEY).put(
|
||||
Body=json.dumps(mock_data)
|
||||
)
|
||||
|
||||
return ctx
|
||||
|
||||
|
|
|
|||
|
|
@ -9,30 +9,28 @@ import json
|
|||
|
||||
|
||||
from taar.recommenders import LocaleRecommender
|
||||
from taar.recommenders.lazys3 import LazyJSONLoader
|
||||
from taar.recommenders.locale_recommender import ADDON_LIST_BUCKET, ADDON_LIST_KEY
|
||||
from taar.recommenders.s3config import TAAR_LOCALE_KEY, TAAR_LOCALE_BUCKET
|
||||
|
||||
|
||||
FAKE_LOCALE_DATA = {
|
||||
"te-ST": [
|
||||
"{1e6b8bce-7dc8-481c-9f19-123e41332b72}", "some-other@nice-addon.com",
|
||||
"{66d1eed2-a390-47cd-8215-016e9fa9cc55}", "{5f1594c3-0d4c-49dd-9182-4fbbb25131a7}"
|
||||
"{1e6b8bce-7dc8-481c-9f19-123e41332b72}",
|
||||
"some-other@nice-addon.com",
|
||||
"{66d1eed2-a390-47cd-8215-016e9fa9cc55}",
|
||||
"{5f1594c3-0d4c-49dd-9182-4fbbb25131a7}",
|
||||
],
|
||||
"en": [
|
||||
"some-uuid@test-addon.com", "other-addon@some-id.it"
|
||||
]
|
||||
"en": ["some-uuid@test-addon.com", "other-addon@some-id.it"],
|
||||
}
|
||||
|
||||
|
||||
def install_mock_data(ctx):
|
||||
ctx = ctx.child()
|
||||
conn = boto3.resource('s3', region_name='us-west-2')
|
||||
conn = boto3.resource("s3", region_name="us-west-2")
|
||||
|
||||
conn.create_bucket(Bucket=ADDON_LIST_BUCKET)
|
||||
conn.Object(ADDON_LIST_BUCKET, ADDON_LIST_KEY).put(Body=json.dumps(FAKE_LOCALE_DATA))
|
||||
ctx['locale_mock_data'] = LazyJSONLoader(ctx,
|
||||
ADDON_LIST_BUCKET,
|
||||
ADDON_LIST_KEY)
|
||||
conn.create_bucket(Bucket=TAAR_LOCALE_BUCKET)
|
||||
conn.Object(TAAR_LOCALE_BUCKET, TAAR_LOCALE_KEY).put(
|
||||
Body=json.dumps(FAKE_LOCALE_DATA)
|
||||
)
|
||||
|
||||
return ctx
|
||||
|
||||
|
|
|
|||
|
|
@ -8,11 +8,17 @@ from moto import mock_s3
|
|||
from taar.recommenders import RecommendationManager
|
||||
from taar.recommenders.recommendation_manager import TEST_CLIENT_IDS
|
||||
from taar.recommenders.recommendation_manager import EMPTY_TEST_CLIENT_IDS
|
||||
from taar.recommenders.lazys3 import LazyJSONLoader
|
||||
from taar.schema import INTERVENTION_A
|
||||
from taar.schema import INTERVENTION_B
|
||||
from taar.schema import INTERVENTION_CONTROL
|
||||
from taar.recommenders.base_recommender import AbstractRecommender
|
||||
|
||||
from taar.recommenders.ensemble_recommender import (
|
||||
TAAR_ENSEMBLE_BUCKET,
|
||||
TAAR_ENSEMBLE_KEY,
|
||||
)
|
||||
|
||||
|
||||
from .mocks import MockRecommenderFactory
|
||||
from .test_hybrid_recommender import install_mock_curated_data
|
||||
|
||||
|
|
@ -20,6 +26,7 @@ from .test_hybrid_recommender import install_mock_curated_data
|
|||
class StubRecommender(AbstractRecommender):
|
||||
""" A shared, stub recommender that can be used for testing.
|
||||
"""
|
||||
|
||||
def __init__(self, can_recommend, stub_recommendations):
|
||||
self._can_recommend = can_recommend
|
||||
self._recommendations = stub_recommendations
|
||||
|
|
@ -36,25 +43,18 @@ def install_mocks(ctx):
|
|||
|
||||
class MockProfileFetcher:
|
||||
def get(self, client_id):
|
||||
return {'client_id': client_id}
|
||||
return {"client_id": client_id}
|
||||
|
||||
ctx['profile_fetcher'] = MockProfileFetcher()
|
||||
ctx['recommender_factory'] = MockRecommenderFactory()
|
||||
ctx["profile_fetcher"] = MockProfileFetcher()
|
||||
ctx["recommender_factory"] = MockRecommenderFactory()
|
||||
|
||||
DATA = {'ensemble_weights': {'collaborative': 1000,
|
||||
'similarity': 100,
|
||||
'locale': 10}}
|
||||
DATA = {
|
||||
"ensemble_weights": {"collaborative": 1000, "similarity": 100, "locale": 10}
|
||||
}
|
||||
|
||||
S3_BUCKET = 'telemetry-parquet'
|
||||
ENSEMBLE_WEIGHTS = 'taar/ensemble/ensemble_weight.json'
|
||||
|
||||
conn = boto3.resource('s3', region_name='us-west-2')
|
||||
conn.create_bucket(Bucket=S3_BUCKET)
|
||||
conn.Object(S3_BUCKET, ENSEMBLE_WEIGHTS).put(Body=json.dumps(DATA))
|
||||
|
||||
ctx['ensemble_weights'] = LazyJSONLoader(ctx,
|
||||
S3_BUCKET,
|
||||
ENSEMBLE_WEIGHTS)
|
||||
conn = boto3.resource("s3", region_name="us-west-2")
|
||||
conn.create_bucket(Bucket=TAAR_ENSEMBLE_BUCKET)
|
||||
conn.Object(TAAR_ENSEMBLE_BUCKET, TAAR_ENSEMBLE_KEY).put(Body=json.dumps(DATA))
|
||||
|
||||
return ctx
|
||||
|
||||
|
|
@ -70,21 +70,23 @@ def test_none_profile_returns_empty_list(test_ctx):
|
|||
def test_intervention_a(test_ctx):
|
||||
ctx = install_mocks(test_ctx)
|
||||
|
||||
EXPECTED_RESULTS = [('ghi', 3430.0),
|
||||
('def', 3320.0),
|
||||
('ijk', 3200.0),
|
||||
('hij', 3100.0),
|
||||
('lmn', 420.0),
|
||||
('klm', 409.99999999999994),
|
||||
('jkl', 400.0),
|
||||
('abc', 23.0),
|
||||
('fgh', 22.0),
|
||||
('efg', 21.0)]
|
||||
EXPECTED_RESULTS = [
|
||||
("ghi", 3430.0),
|
||||
("def", 3320.0),
|
||||
("ijk", 3200.0),
|
||||
("hij", 3100.0),
|
||||
("lmn", 420.0),
|
||||
("klm", 409.99999999999994),
|
||||
("jkl", 400.0),
|
||||
("abc", 23.0),
|
||||
("fgh", 22.0),
|
||||
("efg", 21.0),
|
||||
]
|
||||
|
||||
manager = RecommendationManager(ctx.child())
|
||||
recommendation_list = manager.recommend('some_ignored_id',
|
||||
10,
|
||||
extra_data={'branch': INTERVENTION_A})
|
||||
recommendation_list = manager.recommend(
|
||||
"some_ignored_id", 10, extra_data={"branch": INTERVENTION_A}
|
||||
)
|
||||
|
||||
assert isinstance(recommendation_list, list)
|
||||
assert recommendation_list == EXPECTED_RESULTS
|
||||
|
|
@ -100,9 +102,9 @@ def test_intervention_b(test_ctx):
|
|||
ctx = install_mock_curated_data(ctx)
|
||||
|
||||
manager = RecommendationManager(ctx.child())
|
||||
recommendation_list = manager.recommend('some_ignored_id',
|
||||
4,
|
||||
extra_data={'branch': INTERVENTION_B})
|
||||
recommendation_list = manager.recommend(
|
||||
"some_ignored_id", 4, extra_data={"branch": INTERVENTION_B}
|
||||
)
|
||||
|
||||
assert isinstance(recommendation_list, list)
|
||||
assert len(recommendation_list) == 4
|
||||
|
|
@ -114,9 +116,9 @@ def test_intervention_control(test_ctx):
|
|||
ctx = install_mock_curated_data(ctx)
|
||||
|
||||
manager = RecommendationManager(ctx.child())
|
||||
recommendation_list = manager.recommend('some_ignored_id',
|
||||
10,
|
||||
extra_data={'branch': INTERVENTION_CONTROL})
|
||||
recommendation_list = manager.recommend(
|
||||
"some_ignored_id", 10, extra_data={"branch": INTERVENTION_CONTROL}
|
||||
)
|
||||
|
||||
assert len(recommendation_list) == 0
|
||||
|
||||
|
|
@ -127,15 +129,15 @@ def test_fixed_client_id_valid(test_ctx):
|
|||
ctx = install_mock_curated_data(ctx)
|
||||
|
||||
manager = RecommendationManager(ctx.child())
|
||||
recommendation_list = manager.recommend(TEST_CLIENT_IDS[0],
|
||||
10,
|
||||
extra_data={'branch': INTERVENTION_A})
|
||||
recommendation_list = manager.recommend(
|
||||
TEST_CLIENT_IDS[0], 10, extra_data={"branch": INTERVENTION_A}
|
||||
)
|
||||
|
||||
assert len(recommendation_list) == 10
|
||||
|
||||
recommendation_list = manager.recommend(TEST_CLIENT_IDS[0],
|
||||
10,
|
||||
extra_data={'branch': INTERVENTION_B})
|
||||
recommendation_list = manager.recommend(
|
||||
TEST_CLIENT_IDS[0], 10, extra_data={"branch": INTERVENTION_B}
|
||||
)
|
||||
|
||||
assert len(recommendation_list) == 10
|
||||
|
||||
|
|
@ -148,15 +150,15 @@ def test_intervention_names(test_ctx):
|
|||
ctx = install_mock_curated_data(ctx)
|
||||
|
||||
manager = RecommendationManager(ctx.child())
|
||||
recommendation_list = manager.recommend(TEST_CLIENT_IDS[0],
|
||||
10,
|
||||
extra_data={'branch': 'intervention-a'})
|
||||
recommendation_list = manager.recommend(
|
||||
TEST_CLIENT_IDS[0], 10, extra_data={"branch": "intervention-a"}
|
||||
)
|
||||
|
||||
assert len(recommendation_list) == 10
|
||||
|
||||
recommendation_list = manager.recommend(TEST_CLIENT_IDS[0],
|
||||
10,
|
||||
extra_data={'branch': 'intervention-b'})
|
||||
recommendation_list = manager.recommend(
|
||||
TEST_CLIENT_IDS[0], 10, extra_data={"branch": "intervention-b"}
|
||||
)
|
||||
|
||||
assert len(recommendation_list) == 10
|
||||
|
||||
|
|
@ -167,14 +169,14 @@ def test_fixed_client_id_empty_list(test_ctx):
|
|||
ctx = install_mock_curated_data(ctx)
|
||||
|
||||
manager = RecommendationManager(ctx.child())
|
||||
recommendation_list = manager.recommend(EMPTY_TEST_CLIENT_IDS[0],
|
||||
10,
|
||||
extra_data={'branch': INTERVENTION_A})
|
||||
recommendation_list = manager.recommend(
|
||||
EMPTY_TEST_CLIENT_IDS[0], 10, extra_data={"branch": INTERVENTION_A}
|
||||
)
|
||||
|
||||
assert len(recommendation_list) == 0
|
||||
|
||||
recommendation_list = manager.recommend(EMPTY_TEST_CLIENT_IDS[0],
|
||||
10,
|
||||
extra_data={'branch': INTERVENTION_B})
|
||||
recommendation_list = manager.recommend(
|
||||
EMPTY_TEST_CLIENT_IDS[0], 10, extra_data={"branch": INTERVENTION_B}
|
||||
)
|
||||
|
||||
assert len(recommendation_list) == 0
|
||||
|
|
|
|||
|
|
@ -12,14 +12,21 @@ from taar.recommenders.lazys3 import LazyJSONLoader
|
|||
import boto3
|
||||
from moto import mock_s3
|
||||
|
||||
from taar.recommenders.similarity_recommender import S3_BUCKET
|
||||
from taar.recommenders.similarity_recommender import \
|
||||
CATEGORICAL_FEATURES, CONTINUOUS_FEATURES, DONOR_LIST_KEY, LR_CURVES_SIMILARITY_TO_PROBABILITY, \
|
||||
SimilarityRecommender
|
||||
from taar.recommenders.similarity_recommender import (
|
||||
CATEGORICAL_FEATURES,
|
||||
CONTINUOUS_FEATURES,
|
||||
SimilarityRecommender,
|
||||
)
|
||||
|
||||
from .similarity_data import CONTINUOUS_FEATURE_FIXTURE_DATA
|
||||
from .similarity_data import CATEGORICAL_FEATURE_FIXTURE_DATA
|
||||
|
||||
from taar.recommenders.s3config import (
|
||||
TAAR_SIMILARITY_BUCKET,
|
||||
TAAR_SIMILARITY_DONOR_KEY,
|
||||
TAAR_SIMILARITY_LRCURVES_KEY,
|
||||
)
|
||||
|
||||
|
||||
def generate_fake_lr_curves(num_elements, ceiling=10.0):
|
||||
"""
|
||||
|
|
@ -53,46 +60,53 @@ def generate_a_fake_taar_client():
|
|||
"bookmark_count": 7,
|
||||
"tab_open_count": 4,
|
||||
"total_uri": 222,
|
||||
"unique_tlds": 21
|
||||
"unique_tlds": 21,
|
||||
}
|
||||
|
||||
|
||||
def install_no_data(ctx):
|
||||
ctx = ctx.child()
|
||||
conn = boto3.resource('s3', region_name='us-west-2')
|
||||
conn = boto3.resource("s3", region_name="us-west-2")
|
||||
|
||||
conn.create_bucket(Bucket=S3_BUCKET)
|
||||
conn.Object(S3_BUCKET, DONOR_LIST_KEY).put(Body="")
|
||||
conn.create_bucket(Bucket=TAAR_SIMILARITY_BUCKET)
|
||||
conn.Object(TAAR_SIMILARITY_BUCKET, TAAR_SIMILARITY_DONOR_KEY).put(Body="")
|
||||
|
||||
conn.Object(S3_BUCKET, LR_CURVES_SIMILARITY_TO_PROBABILITY).put(Body="")
|
||||
conn.Object(TAAR_SIMILARITY_BUCKET, TAAR_SIMILARITY_LRCURVES_KEY).put(Body="")
|
||||
|
||||
ctx['similarity_donors_pool'] = LazyJSONLoader(ctx,
|
||||
S3_BUCKET,
|
||||
DONOR_LIST_KEY)
|
||||
ctx["similarity_donors_pool"] = LazyJSONLoader(
|
||||
ctx, TAAR_SIMILARITY_BUCKET, TAAR_SIMILARITY_DONOR_KEY
|
||||
)
|
||||
|
||||
ctx['similarity_lr_curves'] = LazyJSONLoader(ctx,
|
||||
S3_BUCKET,
|
||||
LR_CURVES_SIMILARITY_TO_PROBABILITY)
|
||||
ctx["similarity_lr_curves"] = LazyJSONLoader(
|
||||
ctx, TAAR_SIMILARITY_BUCKET, TAAR_SIMILARITY_LRCURVES_KEY
|
||||
)
|
||||
|
||||
return ctx
|
||||
|
||||
|
||||
def install_categorical_data(ctx):
|
||||
ctx = ctx.child()
|
||||
conn = boto3.resource('s3', region_name='us-west-2')
|
||||
conn = boto3.resource("s3", region_name="us-west-2")
|
||||
|
||||
conn.create_bucket(Bucket=S3_BUCKET)
|
||||
conn.Object(S3_BUCKET, DONOR_LIST_KEY).put(Body=json.dumps(CATEGORICAL_FEATURE_FIXTURE_DATA))
|
||||
try:
|
||||
conn.create_bucket(Bucket=TAAR_SIMILARITY_BUCKET)
|
||||
except Exception:
|
||||
pass
|
||||
conn.Object(TAAR_SIMILARITY_BUCKET, TAAR_SIMILARITY_DONOR_KEY).put(
|
||||
Body=json.dumps(CATEGORICAL_FEATURE_FIXTURE_DATA)
|
||||
)
|
||||
|
||||
conn.Object(S3_BUCKET, LR_CURVES_SIMILARITY_TO_PROBABILITY).put(Body=json.dumps(generate_fake_lr_curves(1000)))
|
||||
conn.Object(TAAR_SIMILARITY_BUCKET, TAAR_SIMILARITY_LRCURVES_KEY).put(
|
||||
Body=json.dumps(generate_fake_lr_curves(1000))
|
||||
)
|
||||
|
||||
ctx['similarity_donors_pool'] = LazyJSONLoader(ctx,
|
||||
S3_BUCKET,
|
||||
DONOR_LIST_KEY)
|
||||
ctx["similarity_donors_pool"] = LazyJSONLoader(
|
||||
ctx, TAAR_SIMILARITY_BUCKET, TAAR_SIMILARITY_DONOR_KEY
|
||||
)
|
||||
|
||||
ctx['similarity_lr_curves'] = LazyJSONLoader(ctx,
|
||||
S3_BUCKET,
|
||||
LR_CURVES_SIMILARITY_TO_PROBABILITY)
|
||||
ctx["similarity_lr_curves"] = LazyJSONLoader(
|
||||
ctx, TAAR_SIMILARITY_BUCKET, TAAR_SIMILARITY_LRCURVES_KEY
|
||||
)
|
||||
|
||||
return ctx
|
||||
|
||||
|
|
@ -102,20 +116,23 @@ def install_continuous_data(ctx):
|
|||
cts_data = json.dumps(CONTINUOUS_FEATURE_FIXTURE_DATA)
|
||||
lrs_data = json.dumps(generate_fake_lr_curves(1000))
|
||||
|
||||
conn = boto3.resource('s3', region_name='us-west-2')
|
||||
conn = boto3.resource("s3", region_name="us-west-2")
|
||||
|
||||
conn.create_bucket(Bucket=S3_BUCKET)
|
||||
conn.Object(S3_BUCKET, DONOR_LIST_KEY).put(Body=cts_data)
|
||||
try:
|
||||
conn.create_bucket(Bucket=TAAR_SIMILARITY_BUCKET)
|
||||
except Exception:
|
||||
pass
|
||||
conn.Object(TAAR_SIMILARITY_BUCKET, TAAR_SIMILARITY_DONOR_KEY).put(Body=cts_data)
|
||||
|
||||
conn.Object(S3_BUCKET, LR_CURVES_SIMILARITY_TO_PROBABILITY).put(Body=lrs_data)
|
||||
conn.Object(TAAR_SIMILARITY_BUCKET, TAAR_SIMILARITY_LRCURVES_KEY).put(Body=lrs_data)
|
||||
|
||||
ctx['similarity_donors_pool'] = LazyJSONLoader(ctx,
|
||||
S3_BUCKET,
|
||||
DONOR_LIST_KEY)
|
||||
ctx["similarity_donors_pool"] = LazyJSONLoader(
|
||||
ctx, TAAR_SIMILARITY_BUCKET, TAAR_SIMILARITY_DONOR_KEY
|
||||
)
|
||||
|
||||
ctx['similarity_lr_curves'] = LazyJSONLoader(ctx,
|
||||
S3_BUCKET,
|
||||
LR_CURVES_SIMILARITY_TO_PROBABILITY)
|
||||
ctx["similarity_lr_curves"] = LazyJSONLoader(
|
||||
ctx, TAAR_SIMILARITY_BUCKET, TAAR_SIMILARITY_LRCURVES_KEY
|
||||
)
|
||||
|
||||
return ctx
|
||||
|
||||
|
|
@ -209,7 +226,7 @@ def test_compute_clients_dist(test_ctx):
|
|||
"bookmark_count": 1,
|
||||
"tab_open_count": 1,
|
||||
"total_uri": 1,
|
||||
"unique_tlds": 1
|
||||
"unique_tlds": 1,
|
||||
},
|
||||
{
|
||||
"client_id": "test-client-003",
|
||||
|
|
@ -221,7 +238,7 @@ def test_compute_clients_dist(test_ctx):
|
|||
"bookmark_count": 10,
|
||||
"tab_open_count": 1,
|
||||
"total_uri": 1,
|
||||
"unique_tlds": 1
|
||||
"unique_tlds": 1,
|
||||
},
|
||||
{
|
||||
"client_id": "test-client-004",
|
||||
|
|
@ -233,8 +250,8 @@ def test_compute_clients_dist(test_ctx):
|
|||
"bookmark_count": 10,
|
||||
"tab_open_count": 10,
|
||||
"total_uri": 100,
|
||||
"unique_tlds": 10
|
||||
}
|
||||
"unique_tlds": 10,
|
||||
},
|
||||
]
|
||||
per_client_test = []
|
||||
|
||||
|
|
@ -260,27 +277,39 @@ def test_distance_functions(test_ctx):
|
|||
assert len(recs) > 0
|
||||
|
||||
# Make it a generally poor match for the donors.
|
||||
test_client.update({'total_uri': 10, 'bookmark_count': 2, 'subsession_length': 10})
|
||||
test_client.update({"total_uri": 10, "bookmark_count": 2, "subsession_length": 10})
|
||||
|
||||
all_client_values_zero = test_client
|
||||
# Make all categorical variables non-matching with any donor.
|
||||
all_client_values_zero.update({key: 'zero' for key in test_client.keys() if key in CATEGORICAL_FEATURES})
|
||||
all_client_values_zero.update(
|
||||
{key: "zero" for key in test_client.keys() if key in CATEGORICAL_FEATURES}
|
||||
)
|
||||
recs = r.recommend(all_client_values_zero, 10)
|
||||
assert len(recs) == 0
|
||||
|
||||
# Make all continuous variables equal to zero.
|
||||
all_client_values_zero.update({key: 0 for key in test_client.keys() if key in CONTINUOUS_FEATURES})
|
||||
all_client_values_zero.update(
|
||||
{key: 0 for key in test_client.keys() if key in CONTINUOUS_FEATURES}
|
||||
)
|
||||
recs = r.recommend(all_client_values_zero, 10)
|
||||
assert len(recs) == 0
|
||||
|
||||
# Make all categorical variables non-matching with any donor.
|
||||
all_client_values_high = test_client
|
||||
all_client_values_high.update({key: 'one billion' for key in test_client.keys() if key in CATEGORICAL_FEATURES})
|
||||
all_client_values_high.update(
|
||||
{
|
||||
key: "one billion"
|
||||
for key in test_client.keys()
|
||||
if key in CATEGORICAL_FEATURES
|
||||
}
|
||||
)
|
||||
recs = r.recommend(all_client_values_high, 10)
|
||||
assert len(recs) == 0
|
||||
|
||||
# Make all continuous variables equal to a very high numerical value.
|
||||
all_client_values_high.update({key: 1e60 for key in test_client.keys() if key in CONTINUOUS_FEATURES})
|
||||
all_client_values_high.update(
|
||||
{key: 1e60 for key in test_client.keys() if key in CONTINUOUS_FEATURES}
|
||||
)
|
||||
recs = r.recommend(all_client_values_high, 10)
|
||||
assert len(recs) == 0
|
||||
|
||||
|
|
@ -300,7 +329,7 @@ def test_weights_continuous(test_ctx):
|
|||
# In the ensemble method recommendations should be a sorted list of tuples
|
||||
# containing [(guid, weight), (guid, weight)... (guid, weight)].
|
||||
recommendation_list = r.recommend(generate_a_fake_taar_client(), 2)
|
||||
with open('/tmp/similarity_recommender.json', 'w') as fout:
|
||||
with open("/tmp/similarity_recommender.json", "w") as fout:
|
||||
fout.write(json.dumps(recommendation_list))
|
||||
|
||||
# Make sure the structure of the recommendations is correct and
|
||||
|
|
@ -326,14 +355,14 @@ def test_weights_continuous(test_ctx):
|
|||
|
||||
@mock_s3
|
||||
def test_weights_categorical(test_ctx):
|
||||
'''
|
||||
"""
|
||||
This should get :
|
||||
["{test-guid-1}", "{test-guid-2}", "{test-guid-3}", "{test-guid-4}"],
|
||||
["{test-guid-9}", "{test-guid-10}", "{test-guid-11}", "{test-guid-12}"]
|
||||
from the first two entries in the sample data where the geo_city
|
||||
data
|
||||
|
||||
'''
|
||||
"""
|
||||
# Create a new instance of a SimilarityRecommender.
|
||||
cat_ctx = install_categorical_data(test_ctx)
|
||||
cts_ctx = install_continuous_data(test_ctx)
|
||||
|
|
|
|||
Загрузка…
Ссылка в новой задаче