From 483eb2f56657e8e7f419ab1a4fab8dce9ade8609 Mon Sep 17 00:00:00 2001 From: Daniel Baumann Date: Sat, 27 Apr 2024 20:24:20 +0200 Subject: Adding upstream version 14.2.21. Signed-off-by: Daniel Baumann --- src/pybind/mgr/diskprediction_local/__init__.py | 1 + .../mgr/diskprediction_local/models/config.json | 77 ++++++ .../mgr/diskprediction_local/models/svm_1.pkl | Bin 0 -> 281292 bytes .../mgr/diskprediction_local/models/svm_10.pkl | Bin 0 -> 217792 bytes .../mgr/diskprediction_local/models/svm_104.pkl | Bin 0 -> 492492 bytes .../mgr/diskprediction_local/models/svm_105.pkl | Bin 0 -> 217192 bytes .../mgr/diskprediction_local/models/svm_109.pkl | Bin 0 -> 256392 bytes .../mgr/diskprediction_local/models/svm_112.pkl | Bin 0 -> 499492 bytes .../mgr/diskprediction_local/models/svm_114.pkl | Bin 0 -> 276492 bytes .../mgr/diskprediction_local/models/svm_115.pkl | Bin 0 -> 509592 bytes .../mgr/diskprediction_local/models/svm_118.pkl | Bin 0 -> 315192 bytes .../mgr/diskprediction_local/models/svm_119.pkl | Bin 0 -> 485992 bytes .../mgr/diskprediction_local/models/svm_12.pkl | Bin 0 -> 275692 bytes .../mgr/diskprediction_local/models/svm_120.pkl | Bin 0 -> 307592 bytes .../mgr/diskprediction_local/models/svm_123.pkl | Bin 0 -> 246792 bytes .../mgr/diskprediction_local/models/svm_124.pkl | Bin 0 -> 310292 bytes .../mgr/diskprediction_local/models/svm_125.pkl | Bin 0 -> 452492 bytes .../mgr/diskprediction_local/models/svm_128.pkl | Bin 0 -> 550492 bytes .../mgr/diskprediction_local/models/svm_131.pkl | Bin 0 -> 493192 bytes .../mgr/diskprediction_local/models/svm_134.pkl | Bin 0 -> 266692 bytes .../mgr/diskprediction_local/models/svm_138.pkl | Bin 0 -> 488292 bytes .../mgr/diskprediction_local/models/svm_14.pkl | Bin 0 -> 244892 bytes .../mgr/diskprediction_local/models/svm_141.pkl | Bin 0 -> 422368 bytes .../mgr/diskprediction_local/models/svm_145.pkl | Bin 0 -> 359512 bytes .../mgr/diskprediction_local/models/svm_151.pkl | Bin 0 -> 305944 bytes .../mgr/diskprediction_local/models/svm_16.pkl | Bin 0 -> 308192 bytes .../mgr/diskprediction_local/models/svm_161.pkl | Bin 0 -> 305188 bytes .../mgr/diskprediction_local/models/svm_168.pkl | Bin 0 -> 301516 bytes .../mgr/diskprediction_local/models/svm_169.pkl | Bin 0 -> 363400 bytes .../mgr/diskprediction_local/models/svm_174.pkl | Bin 0 -> 323764 bytes .../mgr/diskprediction_local/models/svm_18.pkl | Bin 0 -> 312692 bytes .../mgr/diskprediction_local/models/svm_182.pkl | Bin 0 -> 354652 bytes .../mgr/diskprediction_local/models/svm_185.pkl | Bin 0 -> 317176 bytes .../mgr/diskprediction_local/models/svm_186.pkl | Bin 0 -> 276352 bytes .../mgr/diskprediction_local/models/svm_195.pkl | Bin 0 -> 489544 bytes .../mgr/diskprediction_local/models/svm_201.pkl | Bin 0 -> 307888 bytes .../mgr/diskprediction_local/models/svm_204.pkl | Bin 0 -> 567088 bytes .../mgr/diskprediction_local/models/svm_206.pkl | Bin 0 -> 474856 bytes .../mgr/diskprediction_local/models/svm_208.pkl | Bin 0 -> 283588 bytes .../mgr/diskprediction_local/models/svm_210.pkl | Bin 0 -> 617200 bytes .../mgr/diskprediction_local/models/svm_212.pkl | Bin 0 -> 345148 bytes .../mgr/diskprediction_local/models/svm_213.pkl | Bin 0 -> 357568 bytes .../mgr/diskprediction_local/models/svm_219.pkl | Bin 0 -> 342232 bytes .../mgr/diskprediction_local/models/svm_221.pkl | Bin 0 -> 365128 bytes .../mgr/diskprediction_local/models/svm_222.pkl | Bin 0 -> 314800 bytes .../mgr/diskprediction_local/models/svm_223.pkl | Bin 0 -> 342124 bytes .../mgr/diskprediction_local/models/svm_225.pkl | Bin 0 -> 329812 bytes .../mgr/diskprediction_local/models/svm_227.pkl | Bin 0 -> 296440 bytes .../mgr/diskprediction_local/models/svm_229.pkl | Bin 0 -> 572380 bytes .../mgr/diskprediction_local/models/svm_230.pkl | Bin 0 -> 251188 bytes .../mgr/diskprediction_local/models/svm_234.pkl | Bin 0 -> 277972 bytes .../mgr/diskprediction_local/models/svm_235.pkl | Bin 0 -> 243736 bytes .../mgr/diskprediction_local/models/svm_236.pkl | Bin 0 -> 377872 bytes .../mgr/diskprediction_local/models/svm_239.pkl | Bin 0 -> 571732 bytes .../mgr/diskprediction_local/models/svm_243.pkl | Bin 0 -> 534148 bytes .../mgr/diskprediction_local/models/svm_27.pkl | Bin 0 -> 504592 bytes .../mgr/diskprediction_local/models/svm_3.pkl | Bin 0 -> 557192 bytes .../mgr/diskprediction_local/models/svm_33.pkl | Bin 0 -> 547392 bytes .../mgr/diskprediction_local/models/svm_36.pkl | Bin 0 -> 516692 bytes .../mgr/diskprediction_local/models/svm_44.pkl | Bin 0 -> 546592 bytes .../mgr/diskprediction_local/models/svm_50.pkl | Bin 0 -> 448292 bytes .../mgr/diskprediction_local/models/svm_57.pkl | Bin 0 -> 328292 bytes .../mgr/diskprediction_local/models/svm_59.pkl | Bin 0 -> 494292 bytes .../mgr/diskprediction_local/models/svm_6.pkl | Bin 0 -> 314092 bytes .../mgr/diskprediction_local/models/svm_61.pkl | Bin 0 -> 499492 bytes .../mgr/diskprediction_local/models/svm_62.pkl | Bin 0 -> 483492 bytes .../mgr/diskprediction_local/models/svm_67.pkl | Bin 0 -> 492592 bytes .../mgr/diskprediction_local/models/svm_69.pkl | Bin 0 -> 288292 bytes .../mgr/diskprediction_local/models/svm_71.pkl | Bin 0 -> 228792 bytes .../mgr/diskprediction_local/models/svm_72.pkl | Bin 0 -> 489492 bytes .../mgr/diskprediction_local/models/svm_78.pkl | Bin 0 -> 491392 bytes .../mgr/diskprediction_local/models/svm_79.pkl | Bin 0 -> 284992 bytes .../mgr/diskprediction_local/models/svm_82.pkl | Bin 0 -> 255292 bytes .../mgr/diskprediction_local/models/svm_85.pkl | Bin 0 -> 522092 bytes .../mgr/diskprediction_local/models/svm_88.pkl | Bin 0 -> 502392 bytes .../mgr/diskprediction_local/models/svm_93.pkl | Bin 0 -> 302592 bytes .../mgr/diskprediction_local/models/svm_97.pkl | Bin 0 -> 272392 bytes src/pybind/mgr/diskprediction_local/module.py | 266 +++++++++++++++++++++ src/pybind/mgr/diskprediction_local/predictor.py | 265 ++++++++++++++++++++ 79 files changed, 609 insertions(+) create mode 100644 src/pybind/mgr/diskprediction_local/__init__.py create mode 100644 src/pybind/mgr/diskprediction_local/models/config.json create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_1.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_10.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_104.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_105.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_109.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_112.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_114.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_115.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_118.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_119.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_12.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_120.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_123.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_124.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_125.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_128.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_131.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_134.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_138.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_14.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_141.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_145.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_151.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_16.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_161.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_168.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_169.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_174.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_18.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_182.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_185.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_186.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_195.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_201.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_204.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_206.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_208.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_210.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_212.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_213.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_219.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_221.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_222.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_223.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_225.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_227.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_229.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_230.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_234.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_235.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_236.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_239.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_243.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_27.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_3.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_33.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_36.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_44.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_50.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_57.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_59.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_6.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_61.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_62.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_67.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_69.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_71.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_72.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_78.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_79.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_82.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_85.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_88.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_93.pkl create mode 100644 src/pybind/mgr/diskprediction_local/models/svm_97.pkl create mode 100644 src/pybind/mgr/diskprediction_local/module.py create mode 100644 src/pybind/mgr/diskprediction_local/predictor.py (limited to 'src/pybind/mgr/diskprediction_local') diff --git a/src/pybind/mgr/diskprediction_local/__init__.py b/src/pybind/mgr/diskprediction_local/__init__.py new file mode 100644 index 00000000..8f210ac9 --- /dev/null +++ b/src/pybind/mgr/diskprediction_local/__init__.py @@ -0,0 +1 @@ +from .module import Module diff --git a/src/pybind/mgr/diskprediction_local/models/config.json b/src/pybind/mgr/diskprediction_local/models/config.json new file mode 100644 index 00000000..9a1485ca --- /dev/null +++ b/src/pybind/mgr/diskprediction_local/models/config.json @@ -0,0 +1,77 @@ +{ +"svm_123.pkl": ["smart_197_raw", "smart_183_raw", "smart_200_raw", "smart_194_raw", "smart_254_raw", "smart_252_raw", "smart_4_raw", "smart_222_raw", "smart_187_raw", "smart_184_raw"], +"svm_105.pkl": ["smart_197_raw", "smart_4_raw", "smart_5_raw", "smart_252_raw", "smart_184_raw", "smart_223_raw", "smart_198_raw", "smart_10_raw", "smart_189_raw", "smart_222_raw"], +"svm_82.pkl":["smart_184_raw", "smart_2_raw", "smart_187_raw", "smart_225_raw", "smart_198_raw", "smart_197_raw", "smart_4_raw", "smart_13_raw", "smart_188_raw", "smart_251_raw"], +"svm_186.pkl":["smart_3_raw", "smart_11_raw", "smart_198_raw", "smart_250_raw", "smart_13_raw", "smart_200_raw", "smart_224_raw", "smart_187_raw", "smart_22_raw", "smart_4_raw", "smart_220_raw"], +"svm_14.pkl":["smart_12_raw", "smart_226_raw", "smart_187_raw", "smart_196_raw", "smart_5_raw", "smart_183_raw", "smart_255_raw", "smart_250_raw", "smart_201_raw", "smart_8_raw"], +"svm_10.pkl":["smart_251_raw", "smart_4_raw", "smart_223_raw", "smart_13_raw", "smart_255_raw", "smart_188_raw", "smart_197_raw", "smart_201_raw", "smart_250_raw", "smart_15_raw"], +"svm_235.pkl":["smart_15_raw", "smart_255_raw", "smart_252_raw", "smart_197_raw", "smart_250_raw", "smart_254_raw", "smart_13_raw", "smart_251_raw", "smart_198_raw", "smart_189_raw", "smart_191_raw"], +"svm_234.pkl":["smart_187_raw", "smart_183_raw", "smart_3_raw", "smart_4_raw", "smart_222_raw", "smart_184_raw", "smart_5_raw", "smart_198_raw", "smart_200_raw", "smart_8_raw", "smart_10_raw"], +"svm_119.pkl":["smart_254_raw", "smart_8_raw", "smart_183_raw", "smart_184_raw", "smart_195_raw", "smart_252_raw", "smart_191_raw", "smart_10_raw", "smart_200_raw", "smart_197_raw"], +"svm_227.pkl":["smart_254_raw", "smart_189_raw", "smart_225_raw", "smart_224_raw", "smart_197_raw", "smart_223_raw", "smart_4_raw", "smart_183_raw", "smart_11_raw", "smart_184_raw", "smart_13_raw"], +"svm_18.pkl":["smart_197_raw", "smart_3_raw", "smart_220_raw", "smart_193_raw", "smart_10_raw", "smart_187_raw", "smart_188_raw", "smart_225_raw", "smart_194_raw", "smart_13_raw"], +"svm_78.pkl":["smart_10_raw", "smart_183_raw", "smart_191_raw", "smart_13_raw", "smart_198_raw", "smart_22_raw", "smart_195_raw", "smart_12_raw", "smart_224_raw", "smart_200_raw"], +"svm_239.pkl":["smart_3_raw", "smart_254_raw", "smart_199_raw", "smart_225_raw", "smart_187_raw", "smart_195_raw", "smart_197_raw", "smart_2_raw", "smart_193_raw", "smart_220_raw", "smart_183_raw"], +"svm_174.pkl":["smart_183_raw", "smart_196_raw", "smart_225_raw", "smart_189_raw", "smart_4_raw", "smart_3_raw", "smart_9_raw", "smart_198_raw", "smart_15_raw", "smart_5_raw", "smart_194_raw"], +"svm_104.pkl":["smart_12_raw", "smart_198_raw", "smart_197_raw", "smart_4_raw", "smart_240_raw", "smart_187_raw", "smart_225_raw", "smart_8_raw", "smart_3_raw", "smart_2_raw"], +"svm_12.pkl":["smart_222_raw", "smart_251_raw", "smart_194_raw", "smart_9_raw", "smart_184_raw", "smart_191_raw", "smart_187_raw", "smart_255_raw", "smart_4_raw", "smart_11_raw"], +"svm_97.pkl":["smart_15_raw", "smart_197_raw", "smart_190_raw", "smart_199_raw", "smart_200_raw", "smart_12_raw", "smart_191_raw", "smart_254_raw", "smart_194_raw", "smart_201_raw"], +"svm_118.pkl":["smart_11_raw", "smart_225_raw", "smart_196_raw", "smart_197_raw", "smart_198_raw", "smart_200_raw", "smart_3_raw", "smart_10_raw", "smart_191_raw", "smart_22_raw"], +"svm_185.pkl":["smart_191_raw", "smart_254_raw", "smart_3_raw", "smart_190_raw", "smart_15_raw", "smart_22_raw", "smart_2_raw", "smart_198_raw", "smart_13_raw", "smart_226_raw", "smart_225_raw"], +"svm_206.pkl":["smart_183_raw", "smart_192_raw", "smart_197_raw", "smart_255_raw", "smart_187_raw", "smart_254_raw", "smart_198_raw", "smart_13_raw", "smart_226_raw", "smart_240_raw", "smart_8_raw"], +"svm_225.pkl":["smart_224_raw", "smart_11_raw", "smart_5_raw", "smart_4_raw", "smart_225_raw", "smart_197_raw", "smart_15_raw", "smart_183_raw", "smart_193_raw", "smart_190_raw", "smart_187_raw"], +"svm_169.pkl":["smart_252_raw", "smart_183_raw", "smart_254_raw", "smart_11_raw", "smart_193_raw", "smart_22_raw", "smart_226_raw", "smart_189_raw", "smart_225_raw", "smart_198_raw", "smart_200_raw"], +"svm_79.pkl":["smart_184_raw", "smart_196_raw", "smart_4_raw", "smart_226_raw", "smart_199_raw", "smart_187_raw", "smart_193_raw", "smart_188_raw", "smart_12_raw", "smart_250_raw"], +"svm_69.pkl":["smart_187_raw", "smart_9_raw", "smart_200_raw", "smart_11_raw", "smart_252_raw", "smart_189_raw", "smart_4_raw", "smart_188_raw", "smart_255_raw", "smart_201_raw"], +"svm_201.pkl":["smart_224_raw", "smart_8_raw", "smart_250_raw", "smart_2_raw", "smart_198_raw", "smart_15_raw", "smart_193_raw", "smart_223_raw", "smart_3_raw", "smart_11_raw", "smart_191_raw"], +"svm_114.pkl":["smart_226_raw", "smart_188_raw", "smart_2_raw", "smart_11_raw", "smart_4_raw", "smart_193_raw", "smart_184_raw", "smart_194_raw", "smart_198_raw", "smart_13_raw"], +"svm_219.pkl":["smart_12_raw", "smart_22_raw", "smart_8_raw", "smart_191_raw", "smart_197_raw", "smart_254_raw", "smart_15_raw", "smart_193_raw", "smart_199_raw", "smart_225_raw", "smart_192_raw"], +"svm_168.pkl":["smart_255_raw", "smart_191_raw", "smart_193_raw", "smart_220_raw", "smart_5_raw", "smart_3_raw", "smart_222_raw", "smart_223_raw", "smart_197_raw", "smart_196_raw", "smart_22_raw"], +"svm_243.pkl":["smart_11_raw", "smart_255_raw", "smart_10_raw", "smart_189_raw", "smart_225_raw", "smart_240_raw", "smart_222_raw", "smart_197_raw", "smart_183_raw", "smart_198_raw", "smart_12_raw"], +"svm_195.pkl":["smart_183_raw", "smart_5_raw", "smart_11_raw", "smart_197_raw", "smart_15_raw", "smart_9_raw", "smart_4_raw", "smart_220_raw", "smart_12_raw", "smart_192_raw", "smart_240_raw"], +"svm_222.pkl":["smart_10_raw", "smart_13_raw", "smart_188_raw", "smart_15_raw", "smart_192_raw", "smart_224_raw", "smart_225_raw", "smart_187_raw", "smart_222_raw", "smart_220_raw", "smart_252_raw"], +"svm_62.pkl":["smart_196_raw", "smart_251_raw", "smart_187_raw", "smart_224_raw", "smart_11_raw", "smart_12_raw", "smart_8_raw", "smart_199_raw", "smart_220_raw", "smart_195_raw"], +"svm_151.pkl":["smart_187_raw", "smart_223_raw", "smart_200_raw", "smart_189_raw", "smart_251_raw", "smart_255_raw", "smart_222_raw", "smart_192_raw", "smart_12_raw", "smart_183_raw", "smart_22_raw"], +"svm_125.pkl":["smart_9_raw", "smart_252_raw", "smart_197_raw", "smart_251_raw", "smart_11_raw", "smart_12_raw", "smart_188_raw", "smart_240_raw", "smart_10_raw", "smart_223_raw"], +"svm_124.pkl":["smart_193_raw", "smart_187_raw", "smart_183_raw", "smart_11_raw", "smart_10_raw", "smart_8_raw", "smart_194_raw", "smart_189_raw", "smart_222_raw", "smart_191_raw"], +"svm_67.pkl":["smart_2_raw", "smart_8_raw", "smart_225_raw", "smart_240_raw", "smart_13_raw", "smart_5_raw", "smart_187_raw", "smart_198_raw", "smart_199_raw", "smart_3_raw"], +"svm_115.pkl":["smart_222_raw", "smart_193_raw", "smart_223_raw", "smart_195_raw", "smart_252_raw", "smart_189_raw", "smart_199_raw", "smart_187_raw", "smart_15_raw", "smart_184_raw"], +"svm_1.pkl":["smart_201_raw", "smart_8_raw", "smart_200_raw", "smart_252_raw", "smart_251_raw", "smart_187_raw", "smart_9_raw", "smart_188_raw", "smart_15_raw", "smart_184_raw"], +"svm_112.pkl":["smart_220_raw", "smart_197_raw", "smart_10_raw", "smart_188_raw", "smart_12_raw", "smart_4_raw", "smart_196_raw", "smart_3_raw", "smart_240_raw", "smart_225_raw"], +"svm_138.pkl":["smart_183_raw", "smart_10_raw", "smart_191_raw", "smart_195_raw", "smart_223_raw", "smart_189_raw", "smart_187_raw", "smart_255_raw", "smart_226_raw", "smart_8_raw"], +"svm_229.pkl":["smart_224_raw", "smart_8_raw", "smart_192_raw", "smart_220_raw", 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= TIME_DAYS * 7 + + +class Module(MgrModule): + MODULE_OPTIONS = [ + { + 'name': 'sleep_interval', + 'default': str(600), + }, + { + 'name': 'predict_interval', + 'default': str(86400), + }, + ] + + COMMANDS = [] + + def __init__(self, *args, **kwargs): + super(Module, self).__init__(*args, **kwargs) + # options + for opt in self.MODULE_OPTIONS: + setattr(self, opt['name'], opt['default']) + # other + self._run = True + self._event = Event() + + def config_notify(self): + for opt in self.MODULE_OPTIONS: + setattr(self, + opt['name'], + self.get_module_option(opt['name'])) + self.log.debug(' %s = %s', opt['name'], getattr(self, opt['name'])) + if self.get_ceph_option('device_failure_prediction_mode') == 'local': + self._event.set() + + def refresh_config(self): + for opt in self.MODULE_OPTIONS: + setattr(self, + opt['name'], + self.get_module_option(opt['name'])) + self.log.debug(' %s = %s', opt['name'], getattr(self, opt['name'])) + + def handle_command(self, _, cmd): + self.log.debug('handle_command cmd: %s', cmd) + raise NotImplementedError(cmd['prefix']) + + def self_test(self): + ret, out, err = self.predict_all_devices() + assert ret == 0 + return 0, 'self test succeed', '' + + def serve(self): + self.log.info('Starting diskprediction local module') + self.config_notify() + last_predicted = None + ls = self.get_store('last_predicted') + if ls: + try: + last_predicted = datetime.datetime.strptime(ls, TIME_FORMAT) + except ValueError: + pass + self.log.debug('Last predicted %s', last_predicted) + + while self._run: + self.refresh_config() + mode = self.get_ceph_option('device_failure_prediction_mode') + if mode == 'local': + now = datetime.datetime.utcnow() + if not last_predicted: + next_predicted = now + else: + predicted_frequency = int(self.predict_interval) or 86400 + seconds = (last_predicted - datetime.datetime.utcfromtimestamp(0)).total_seconds() + seconds -= seconds % predicted_frequency + seconds += predicted_frequency + next_predicted = datetime.datetime.utcfromtimestamp(seconds) + if last_predicted: + self.log.debug('Last scrape %s, next scrape due %s', + last_predicted.strftime(TIME_FORMAT), + next_predicted.strftime(TIME_FORMAT)) + else: + self.log.debug('Last scrape never, next scrape due %s', + next_predicted.strftime(TIME_FORMAT)) + if now >= next_predicted: + self.predict_all_devices() + last_predicted = now + self.set_store('last_predicted', last_predicted.strftime(TIME_FORMAT)) + + sleep_interval = int(self.sleep_interval) or 60 + self.log.debug('Sleeping for %d seconds', sleep_interval) + self._event.wait(sleep_interval) + self._event.clear() + + def shutdown(self): + self.log.info('Stopping') + self._run = False + self._event.set() + + @staticmethod + def _convert_timestamp(predicted_timestamp, life_expectancy_day): + """ + :param predicted_timestamp: unit is nanoseconds + :param life_expectancy_day: unit is seconds + :return: + date format '%Y-%m-%d' ex. 2018-01-01 + """ + return datetime.datetime.fromtimestamp( + predicted_timestamp / (1000 ** 3) + life_expectancy_day).strftime('%Y-%m-%d') + + def _predict_life_expentancy(self, devid): + predicted_result = '' + from .predictor import get_diskfailurepredictor_path, DiskFailurePredictor + health_data = {} + predict_datas = [] + try: + r, outb, outs = self.remote('devicehealth', 'show_device_metrics', devid=devid, sample='') + if r != 0: + self.log.error('failed to get device %s health', devid) + health_data = {} + else: + health_data = json.loads(outb) + except Exception as e: + self.log.error('failed to get device %s health data due to %s', devid, str(e)) + + obj_predictor = DiskFailurePredictor() + obj_predictor.initialize("{}/models".format(get_diskfailurepredictor_path())) + + if len(health_data) >= 6: + o_keys = sorted(health_data.keys(), reverse=True) + for o_key in o_keys: + dev_smart = {} + s_val = health_data[o_key] + ata_smart = s_val.get('ata_smart_attributes', {}) + for attr in ata_smart.get('table', []): + if attr.get('raw', {}).get('string'): + if str(attr.get('raw', {}).get('string', '0')).isdigit(): + dev_smart['smart_%s_raw' % attr.get('id')] = \ + int(attr.get('raw', {}).get('string', '0')) + else: + if str(attr.get('raw', {}).get('string', '0')).split(' ')[0].isdigit(): + dev_smart['smart_%s_raw' % attr.get('id')] = \ + int(attr.get('raw', {}).get('string', + '0').split(' ')[0]) + else: + dev_smart['smart_%s_raw' % attr.get('id')] = \ + attr.get('raw', {}).get('value', 0) + if s_val.get('power_on_time', {}).get('hours') is not None: + dev_smart['smart_9_raw'] = int(s_val['power_on_time']['hours']) + if dev_smart: + predict_datas.append(dev_smart) + if len(predict_datas) >= 12: + break + else: + self.log.error('unable to predict device due to health data records less than 6 days') + + if len(predict_datas) >= 6: + predicted_result = obj_predictor.predict(predict_datas) + return predicted_result + + def predict_life_expectancy(self, devid): + result = self._predict_life_expentancy(devid) + if result.lower() == 'good': + return 0, '>6w', '' + elif result.lower() == 'warning': + return 0, '>=2w and <=6w', '' + elif result.lower() == 'bad': + return 0, '<2w', '' + else: + return 0, 'unknown', '' + + def _reset_device_life_expectancy(self, device_id): + result = CommandResult('') + self.send_command(result, 'mon', '', json.dumps({ + 'prefix': 'device rm-life-expectancy', + 'devid': device_id + }), '') + ret, _, outs = result.wait() + if ret != 0: + self.log.error( + 'failed to reset device life expectancy, %s' % outs) + return ret + + def _set_device_life_expectancy(self, device_id, from_date, to_date=None): + result = CommandResult('') + + if to_date is None: + self.send_command(result, 'mon', '', json.dumps({ + 'prefix': 'device set-life-expectancy', + 'devid': device_id, + 'from': from_date + }), '') + else: + self.send_command(result, 'mon', '', json.dumps({ + 'prefix': 'device set-life-expectancy', + 'devid': device_id, + 'from': from_date, + 'to': to_date + }), '') + ret, _, outs = result.wait() + if ret != 0: + self.log.error( + 'failed to set device life expectancy, %s' % outs) + return ret + + def predict_all_devices(self): + devices = self.get('devices').get('devices', []) + for devInfo in devices: + if not devInfo.get('daemons'): + continue + if not devInfo.get('devid'): + continue + result = self._predict_life_expentancy(devInfo['devid']) + if result == 'unknown': + self._reset_device_life_expectancy(devInfo['devid']) + continue + predicted = int(time.time() * (1000 ** 3)) + + if result.lower() == 'good': + life_expectancy_day_min = (TIME_WEEK * 6) + TIME_DAYS + life_expectancy_day_max = None + elif result.lower() == 'warning': + life_expectancy_day_min = (TIME_WEEK * 2) + life_expectancy_day_max = (TIME_WEEK * 6) + elif result.lower() == 'bad': + life_expectancy_day_min = 0 + life_expectancy_day_max = (TIME_WEEK * 2) - TIME_DAYS + else: + predicted = None + life_expectancy_day_min = None + life_expectancy_day_max = None + + if predicted and devInfo['devid'] and life_expectancy_day_min: + from_date = None + to_date = None + try: + if life_expectancy_day_min: + from_date = self._convert_timestamp(predicted, life_expectancy_day_min) + + if life_expectancy_day_max: + to_date = self._convert_timestamp(predicted, life_expectancy_day_max) + + self._set_device_life_expectancy(devInfo['devid'], from_date, to_date) + self._logger.info( + 'succeed to set device {} life expectancy from: {}, to: {}'.format( + devInfo['devid'], from_date, to_date)) + except Exception as e: + self._logger.error( + 'failed to set device {} life expectancy from: {}, to: {}, {}'.format( + devInfo['devid'], from_date, to_date, str(e))) + else: + self._reset_device_life_expectancy(devInfo['devid']) + return 0, 'succeed to predicted all devices', '' diff --git a/src/pybind/mgr/diskprediction_local/predictor.py b/src/pybind/mgr/diskprediction_local/predictor.py new file mode 100644 index 00000000..bf9b0d7d --- /dev/null +++ b/src/pybind/mgr/diskprediction_local/predictor.py @@ -0,0 +1,265 @@ +"""Sample code for disk failure prediction. + +This sample code is a community version for anyone who is interested in Machine +Learning and care about disk failure. + +This class provides a disk failure prediction module. Given models dirpath to +initialize a predictor instance and then use 6 days data to predict. Predict +function will return a string to indicate disk failure status: "Good", +"Warning", "Bad", or "Unknown". + +An example code is as follows: + +>>> model = disk_failure_predictor.DiskFailurePredictor() +>>> status = model.initialize("./models") +>>> if status: +>>> model.predict(disk_days) +'Bad' + + +Provided by ProphetStor Data Services Inc. +http://www.prophetstor.com/ + +""" + +from __future__ import print_function +import os +import json +import pickle + + +def get_diskfailurepredictor_path(): + path = os.path.abspath(__file__) + dir_path = os.path.dirname(path) + return dir_path + + +class DiskFailurePredictor(object): + """Disk failure prediction + + This class implements a disk failure prediction module. + """ + + CONFIG_FILE = "config.json" + EXCLUDED_ATTRS = ['smart_9_raw', 'smart_241_raw', 'smart_242_raw'] + + def __init__(self): + """ + This function may throw exception due to wrong file operation. + """ + + self.model_dirpath = "" + self.model_context = {} + + def initialize(self, model_dirpath): + """ + Initialize all models. + + Args: None + + Returns: + Error message. If all goes well, return an empty string. + + Raises: + """ + + config_path = os.path.join(model_dirpath, self.CONFIG_FILE) + if not os.path.isfile(config_path): + return "Missing config file: " + config_path + else: + with open(config_path) as f_conf: + self.model_context = json.load(f_conf) + + for model_name in self.model_context: + model_path = os.path.join(model_dirpath, model_name) + + if not os.path.isfile(model_path): + return "Missing model file: " + model_path + + self.model_dirpath = model_dirpath + + def __preprocess(self, disk_days): + """ + Preprocess disk attributes. + + Args: + disk_days: Refer to function predict(...). + + Returns: + new_disk_days: Processed disk days. + """ + + req_attrs = [] + new_disk_days = [] + + attr_list = set.intersection(*[set(disk_day.keys()) + for disk_day in disk_days]) + for attr in attr_list: + if (attr.startswith('smart_') and attr.endswith('_raw')) and \ + attr not in self.EXCLUDED_ATTRS: + req_attrs.append(attr) + + for disk_day in disk_days: + new_disk_day = {} + for attr in req_attrs: + if float(disk_day[attr]) >= 0.0: + new_disk_day[attr] = disk_day[attr] + + new_disk_days.append(new_disk_day) + + return new_disk_days + + @staticmethod + def __get_diff_attrs(disk_days): + """ + Get 5 days differential attributes. + + Args: + disk_days: Refer to function predict(...). + + Returns: + attr_list: All S.M.A.R.T. attributes used in given disk. Here we + use intersection set of all disk days. + + diff_disk_days: A list struct comprises 5 dictionaries, each + dictionary contains differential attributes. + + Raises: + Exceptions of wrong list/dict operations. + """ + + all_attrs = [set(disk_day.keys()) for disk_day in disk_days] + attr_list = list(set.intersection(*all_attrs)) + attr_list = disk_days[0].keys() + prev_days = disk_days[:-1] + curr_days = disk_days[1:] + diff_disk_days = [] + + for prev, cur in zip(prev_days, curr_days): + diff_disk_days.append({attr:(int(cur[attr]) - int(prev[attr])) + for attr in attr_list}) + + return attr_list, diff_disk_days + + def __get_best_models(self, attr_list): + """ + Find the best model from model list according to given attribute list. + + Args: + attr_list: All S.M.A.R.T. attributes used in given disk. + + Returns: + modelpath: The best model for the given attribute list. + model_attrlist: 'Ordered' attribute list of the returned model. + Must be aware that SMART attributes is in order. + + Raises: + """ + + models = self.model_context.keys() + + scores = [] + for model_name in models: + scores.append(sum(attr in attr_list + for attr in self.model_context[model_name])) + max_score = max(scores) + + # Skip if too few matched attributes. + if max_score < 3: + print("Too few matched attributes") + return None + + best_models = {} + best_model_indices = [idx for idx, score in enumerate(scores) + if score > max_score - 2] + for model_idx in best_model_indices: + model_name = list(models)[model_idx] + model_path = os.path.join(self.model_dirpath, model_name) + model_attrlist = self.model_context[model_name] + best_models[model_path] = model_attrlist + + return best_models + # return os.path.join(self.model_dirpath, model_name), model_attrlist + + @staticmethod + def __get_ordered_attrs(disk_days, model_attrlist): + """ + Return ordered attributes of given disk days. + + Args: + disk_days: Unordered disk days. + model_attrlist: Model's ordered attribute list. + + Returns: + ordered_attrs: Ordered disk days. + + Raises: None + """ + + ordered_attrs = [] + + for one_day in disk_days: + one_day_attrs = [] + + for attr in model_attrlist: + if attr in one_day: + one_day_attrs.append(one_day[attr]) + else: + one_day_attrs.append(0) + + ordered_attrs.append(one_day_attrs) + + return ordered_attrs + + def predict(self, disk_days): + """ + Predict using given 6-days disk S.M.A.R.T. attributes. + + Args: + disk_days: A list struct comprises 6 dictionaries. These + dictionaries store 'consecutive' days of disk SMART + attributes. + Returns: + A string indicates prediction result. One of following four strings + will be returned according to disk failure status: + (1) Good : Disk is health + (2) Warning : Disk has some symptoms but may not fail immediately + (3) Bad : Disk is in danger and data backup is highly recommended + (4) Unknown : Not enough data for prediction. + + Raises: + Pickle exceptions + """ + + all_pred = [] + + proc_disk_days = self.__preprocess(disk_days) + attr_list, diff_data = DiskFailurePredictor.__get_diff_attrs(proc_disk_days) + modellist = self.__get_best_models(attr_list) + if modellist is None: + return "Unknown" + + for modelpath in modellist: + model_attrlist = modellist[modelpath] + ordered_data = DiskFailurePredictor.__get_ordered_attrs( + diff_data, model_attrlist) + + try: + with open(modelpath, 'rb') as f_model: + clf = pickle.load(f_model) + + except UnicodeDecodeError: + # Compatibility for python3 + with open(modelpath, 'rb') as f_model: + clf = pickle.load(f_model, encoding='latin1') + + pred = clf.predict(ordered_data) + + all_pred.append(1 if any(pred) else 0) + + score = 2 ** sum(all_pred) - len(modellist) + if score > 10: + return "Bad" + if score > 4: + return "Warning" + return "Good" -- cgit v1.2.3