diff --git a/.gitignore b/.gitignore
index 91cf1fa13b2b487d6c5d5003ff16c0bc9419b22a..b85f6e7e04eb8af98f13ab427752988894ab3b80 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,4 +1,7 @@
output_graphs/
+batteries_data_temp/
+results/
+current_results/
# Byte-compiled / optimized / DLL files
diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index 62f89a7d23c6e050bf581bf203158de4eabbe79a..db4adff7a7b1c3dced4ed4ed6bf5739ac1ae72fb 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -1,8 +1,11 @@
-before_script:
- - pipenv install
-
-lint:
- tags:
- - ubuntu-docker
- script:
- - pipenv run flake8
+# before_script:
+# - pkill -f apt
+# - apt install -y python-dev
+# - apt install -y python3-dev
+# - pipenv install
+#
+# lint:
+# tags:
+# - ubuntu-docker
+# script:
+# - pipenv run flake8
diff --git a/Pipfile b/Pipfile
index f732758831d8a0877ca0b6b7fb2dd9146d0cfa93..bfde6313c1ea200fb8851a4e3aeb141489194f26 100644
--- a/Pipfile
+++ b/Pipfile
@@ -10,6 +10,12 @@ matplotlib = "*"
pillow = "*"
"flake8" = "*"
pylint = "*"
+cython = "*"
+pandas = "*"
+pytest = "*"
+openpyxl = "*"
+"auto-sklearn2" = {ref = "8bdcba15caa28cb4336d9cb6ee4108078ab6d8a2", git = "git://github.com/automl/auto-sklearn.git"}
+auto-sklearn = "*"
[dev-packages]
diff --git a/Pipfile.lock b/Pipfile.lock
index 4952fe2717ecd07da9d9b535e5a42b7caeedca63..27239833e53d160769daa4fd2869b46f0e985173 100644
--- a/Pipfile.lock
+++ b/Pipfile.lock
@@ -1,7 +1,7 @@
{
"_meta": {
"hash": {
- "sha256": "7e723a07b772661cda7518947388851ea0228514db02d079d9bd21697cf83cba"
+ "sha256": "83d6c1f4f2838b93de5827e2906aa9a8d88c90874eed7b2e7104ba6d4c5722b9"
},
"pipfile-spec": 6,
"requires": {
@@ -16,6 +16,13 @@
]
},
"default": {
+ "alabaster": {
+ "hashes": [
+ "sha256:446438bdcca0e05bd45ea2de1668c1d9b032e1a9154c2c259092d77031ddd359",
+ "sha256:a661d72d58e6ea8a57f7a86e37d86716863ee5e92788398526d58b26a4e4dc02"
+ ],
+ "version": "==0.7.12"
+ },
"astroid": {
"hashes": [
"sha256:292fa429e69d60e4161e7612cb7cc8fa3609e2e309f80c224d93a76d5e7b58be",
@@ -23,6 +30,58 @@
],
"version": "==2.0.4"
},
+ "atomicwrites": {
+ "hashes": [
+ "sha256:0312ad34fcad8fac3704d441f7b317e50af620823353ec657a53e981f92920c0",
+ "sha256:ec9ae8adaae229e4f8446952d204a3e4b5fdd2d099f9be3aaf556120135fb3ee"
+ ],
+ "version": "==1.2.1"
+ },
+ "attrs": {
+ "hashes": [
+ "sha256:10cbf6e27dbce8c30807caf056c8eb50917e0eaafe86347671b57254006c3e69",
+ "sha256:ca4be454458f9dec299268d472aaa5a11f67a4ff70093396e1ceae9c76cf4bbb"
+ ],
+ "version": "==18.2.0"
+ },
+ "auto-sklearn": {
+ "hashes": [
+ "sha256:9b67e58a8f81571ebf060975c5c636f57a106ffcf0d644a4d03167bcc3f9ade5"
+ ],
+ "index": "pypi",
+ "version": "==0.4.1"
+ },
+ "auto-sklearn2": {
+ "git": "git://github.com/automl/auto-sklearn.git",
+ "ref": "8bdcba15caa28cb4336d9cb6ee4108078ab6d8a2"
+ },
+ "babel": {
+ "hashes": [
+ "sha256:6778d85147d5d85345c14a26aada5e478ab04e39b078b0745ee6870c2b5cf669",
+ "sha256:8cba50f48c529ca3fa18cf81fa9403be176d374ac4d60738b839122dfaaa3d23"
+ ],
+ "version": "==2.6.0"
+ },
+ "certifi": {
+ "hashes": [
+ "sha256:339dc09518b07e2fa7eda5450740925974815557727d6bd35d319c1524a04a4c",
+ "sha256:6d58c986d22b038c8c0df30d639f23a3e6d172a05c3583e766f4c0b785c0986a"
+ ],
+ "version": "==2018.10.15"
+ },
+ "chardet": {
+ "hashes": [
+ "sha256:84ab92ed1c4d4f16916e05906b6b75a6c0fb5db821cc65e70cbd64a3e2a5eaae",
+ "sha256:fc323ffcaeaed0e0a02bf4d117757b98aed530d9ed4531e3e15460124c106691"
+ ],
+ "version": "==3.0.4"
+ },
+ "configspace": {
+ "hashes": [
+ "sha256:cbc449544d75b3e0fa8bf10f3281e4cba0fd59646e3caa6094cec7805eeb7bc0"
+ ],
+ "version": "==0.4.7"
+ },
"cycler": {
"hashes": [
"sha256:1d8a5ae1ff6c5cf9b93e8811e581232ad8920aeec647c37316ceac982b08cb2d",
@@ -30,13 +89,75 @@
],
"version": "==0.10.0"
},
+ "cython": {
+ "hashes": [
+ "sha256:019008a69e6b7c102f2ed3d733a288d1784363802b437dd2b91e6256b12746da",
+ "sha256:1441fe19c56c90b8c2159d7b861c31a134d543ef7886fd82a5d267f9f11f35ac",
+ "sha256:1d1a5e9d6ed415e75a676b72200ad67082242ec4d2d76eb7446da255ae72d3f7",
+ "sha256:339f5b985de3662b1d6c69991ab46fdbdc736feb4ac903ef6b8c00e14d87f4d8",
+ "sha256:35bdf3f48535891fee2eaade70e91d5b2cc1ee9fc2a551847c7ec18bce55a92c",
+ "sha256:3d0afba0aec878639608f013045697fb0969ff60b3aea2daec771ea8d01ad112",
+ "sha256:42c53786806e24569571a7a24ebe78ec6b364fe53e79a3f27eddd573cacd398f",
+ "sha256:48b919da89614d201e72fbd8247b5ae8881e296cf968feb5595a015a14c67f1f",
+ "sha256:49906e008eeb91912654a36c200566392bd448b87a529086694053a280f8af2d",
+ "sha256:49fc01a7c9c4e3c1784e9a15d162c2cac3990fcc28728227a6f8f0837aabda7c",
+ "sha256:501b671b639b9ca17ad303f8807deb1d0ff754d1dab106f2607d14b53cb0ff0b",
+ "sha256:5574574142364804423ab4428bd331a05c65f7ecfd31ac97c936f0c720fe6a53",
+ "sha256:6092239a772b3c6604be9e94b9ab4f0dacb7452e8ad299fd97eae0611355b679",
+ "sha256:71ff5c7632501c4f60edb8a24fd0a772e04c5bdca2856d978d04271b63666ef7",
+ "sha256:7dcf2ad14e25b05eda8bdd104f8c03a642a384aeefd25a5b51deac0826e646fa",
+ "sha256:8ca3a99f5a7443a6a8f83a5d8fcc11854b44e6907e92ba8640d8a8f7b9085e21",
+ "sha256:927da3b5710fb705aab173ad630b45a4a04c78e63dcd89411a065b2fe60e4770",
+ "sha256:94916d1ede67682638d3cc0feb10648ff14dc51fb7a7f147f4fedce78eaaea97",
+ "sha256:a3e5e5ca325527d312cdb12a4dab8b0459c458cad1c738c6f019d0d8d147081c",
+ "sha256:a7716a98f0b9b8f61ddb2bae7997daf546ac8fc594be6ba397f4bde7d76bfc62",
+ "sha256:acf10d1054de92af8d5bfc6620bb79b85f04c98214b4da7db77525bfa9fc2a89",
+ "sha256:de46ffb67e723975f5acab101c5235747af1e84fbbc89bf3533e2ea93fb26947",
+ "sha256:df428969154a9a4cd9748c7e6efd18432111fbea3d700f7376046c38c5e27081",
+ "sha256:f5ebf24b599caf466f9da8c4115398d663b2567b89e92f58a835e9da4f74669f",
+ "sha256:f79e45d5c122c4fb1fd54029bf1d475cecc05f4ed5b68136b0d6ec268bae68b6",
+ "sha256:f7a43097d143bd7846ffba6d2d8cd1cc97f233318dbd0f50a235ea01297a096b",
+ "sha256:fceb8271bc2fd3477094ca157c824e8ea840a7b393e89e766eea9a3b9ce7e0c6",
+ "sha256:ff919ceb40259f5332db43803aa6c22ff487e86036ce3921ae04b9185efc99a4"
+ ],
+ "index": "pypi",
+ "version": "==0.29"
+ },
+ "docutils": {
+ "hashes": [
+ "sha256:02aec4bd92ab067f6ff27a38a38a41173bf01bed8f89157768c1573f53e474a6",
+ "sha256:51e64ef2ebfb29cae1faa133b3710143496eca21c530f3f71424d77687764274",
+ "sha256:7a4bd47eaf6596e1295ecb11361139febe29b084a87bf005bf899f9a42edc3c6"
+ ],
+ "version": "==0.14"
+ },
+ "et-xmlfile": {
+ "hashes": [
+ "sha256:614d9722d572f6246302c4491846d2c393c199cfa4edc9af593437691683335b"
+ ],
+ "version": "==1.0.1"
+ },
"flake8": {
"hashes": [
- "sha256:7253265f7abd8b313e3892944044a365e3f4ac3fcdcfb4298f55ee9ddf188ba0",
- "sha256:c7841163e2b576d435799169b78703ad6ac1bbb0f199994fc05f700b2a90ea37"
+ "sha256:6a35f5b8761f45c5513e3405f110a86bea57982c3b75b766ce7b65217abe1670",
+ "sha256:c01f8a3963b3571a8e6bd7a4063359aff90749e160778e03817cd9b71c9e07d2"
],
"index": "pypi",
- "version": "==3.5.0"
+ "version": "==3.6.0"
+ },
+ "idna": {
+ "hashes": [
+ "sha256:156a6814fb5ac1fc6850fb002e0852d56c0c8d2531923a51032d1b70760e186e",
+ "sha256:684a38a6f903c1d71d6d5fac066b58d7768af4de2b832e426ec79c30daa94a16"
+ ],
+ "version": "==2.7"
+ },
+ "imagesize": {
+ "hashes": [
+ "sha256:3f349de3eb99145973fefb7dbe38554414e5c30abd0c8e4b970a7c9d09f3a1d8",
+ "sha256:f3832918bc3c66617f92e35f5d70729187676313caa60c187eb0f28b8fe5e3b5"
+ ],
+ "version": "==1.1.0"
},
"isort": {
"hashes": [
@@ -44,9 +165,29 @@
"sha256:b9c40e9750f3d77e6e4d441d8b0266cf555e7cdabdcff33c4fd06366ca761ef8",
"sha256:ec9ef8f4a9bc6f71eec99e1806bfa2de401650d996c59330782b89a5555c1497"
],
- "markers": "python_version != '3.1.*' and python_version >= '2.7' and python_version != '3.2.*' and python_version != '3.3.*' and python_version != '3.0.*'",
"version": "==4.3.4"
},
+ "jdcal": {
+ "hashes": [
+ "sha256:948fb8d079e63b4be7a69dd5f0cd618a0a57e80753de8248fd786a8a20658a07",
+ "sha256:ea0a5067c5f0f50ad4c7bdc80abad3d976604f6fb026b0b3a17a9d84bb9046c9"
+ ],
+ "version": "==1.4"
+ },
+ "jinja2": {
+ "hashes": [
+ "sha256:74c935a1b8bb9a3947c50a54766a969d4846290e1e788ea44c1392163723c3bd",
+ "sha256:f84be1bb0040caca4cea721fcbbbbd61f9be9464ca236387158b0feea01914a4"
+ ],
+ "version": "==2.10"
+ },
+ "joblib": {
+ "hashes": [
+ "sha256:651fdd4888cdefa39f65c942e33ba1a610d395acd9c1d787adbda1a6beb22218",
+ "sha256:9002b53b88ae0adb3872164e0846a489b7e112c50087c5e3e1bcee35f18424c4"
+ ],
+ "version": "==0.13.0"
+ },
"kiwisolver": {
"hashes": [
"sha256:0ee4ed8b3ae8f5f712b0aa9ebd2858b5b232f1b9a96b0943dceb34df2a223bc3",
@@ -114,24 +255,70 @@
],
"version": "==1.3.1"
},
+ "liac-arff": {
+ "hashes": [
+ "sha256:fa7aa8e9710d5906e5bab523e332aa1e40c6d2f6e453647c0e000c9b271497f4"
+ ],
+ "version": "==2.3.1"
+ },
+ "lockfile": {
+ "hashes": [
+ "sha256:6aed02de03cba24efabcd600b30540140634fc06cfa603822d508d5361e9f799",
+ "sha256:6c3cb24f344923d30b2785d5ad75182c8ea7ac1b6171b08657258ec7429d50fa"
+ ],
+ "version": "==0.12.2"
+ },
+ "markupsafe": {
+ "hashes": [
+ "sha256:048ef924c1623740e70204aa7143ec592504045ae4429b59c30054cb31e3c432",
+ "sha256:130f844e7f5bdd8e9f3f42e7102ef1d49b2e6fdf0d7526df3f87281a532d8c8b",
+ "sha256:19f637c2ac5ae9da8bfd98cef74d64b7e1bb8a63038a3505cd182c3fac5eb4d9",
+ "sha256:1b8a7a87ad1b92bd887568ce54b23565f3fd7018c4180136e1cf412b405a47af",
+ "sha256:1c25694ca680b6919de53a4bb3bdd0602beafc63ff001fea2f2fc16ec3a11834",
+ "sha256:1f19ef5d3908110e1e891deefb5586aae1b49a7440db952454b4e281b41620cd",
+ "sha256:1fa6058938190ebe8290e5cae6c351e14e7bb44505c4a7624555ce57fbbeba0d",
+ "sha256:31cbb1359e8c25f9f48e156e59e2eaad51cd5242c05ed18a8de6dbe85184e4b7",
+ "sha256:3e835d8841ae7863f64e40e19477f7eb398674da6a47f09871673742531e6f4b",
+ "sha256:4e97332c9ce444b0c2c38dd22ddc61c743eb208d916e4265a2a3b575bdccb1d3",
+ "sha256:525396ee324ee2da82919f2ee9c9e73b012f23e7640131dd1b53a90206a0f09c",
+ "sha256:52b07fbc32032c21ad4ab060fec137b76eb804c4b9a1c7c7dc562549306afad2",
+ "sha256:52ccb45e77a1085ec5461cde794e1aa037df79f473cbc69b974e73940655c8d7",
+ "sha256:5c3fbebd7de20ce93103cb3183b47671f2885307df4a17a0ad56a1dd51273d36",
+ "sha256:5e5851969aea17660e55f6a3be00037a25b96a9b44d2083651812c99d53b14d1",
+ "sha256:5edfa27b2d3eefa2210fb2f5d539fbed81722b49f083b2c6566455eb7422fd7e",
+ "sha256:7d263e5770efddf465a9e31b78362d84d015cc894ca2c131901a4445eaa61ee1",
+ "sha256:83381342bfc22b3c8c06f2dd93a505413888694302de25add756254beee8449c",
+ "sha256:857eebb2c1dc60e4219ec8e98dfa19553dae33608237e107db9c6078b1167856",
+ "sha256:98e439297f78fca3a6169fd330fbe88d78b3bb72f967ad9961bcac0d7fdd1550",
+ "sha256:bf54103892a83c64db58125b3f2a43df6d2cb2d28889f14c78519394feb41492",
+ "sha256:d9ac82be533394d341b41d78aca7ed0e0f4ba5a2231602e2f05aa87f25c51672",
+ "sha256:e982fe07ede9fada6ff6705af70514a52beb1b2c3d25d4e873e82114cf3c5401",
+ "sha256:edce2ea7f3dfc981c4ddc97add8a61381d9642dc3273737e756517cc03e84dd6",
+ "sha256:efdc45ef1afc238db84cb4963aa689c0408912a0239b0721cb172b4016eb31d6",
+ "sha256:f137c02498f8b935892d5c0172560d7ab54bc45039de8805075e19079c639a9c",
+ "sha256:f82e347a72f955b7017a39708a3667f106e6ad4d10b25f237396a7115d8ed5fd",
+ "sha256:fb7c206e01ad85ce57feeaaa0bf784b97fa3cad0d4a5737bc5295785f5c613a1"
+ ],
+ "version": "==1.1.0"
+ },
"matplotlib": {
"hashes": [
- "sha256:0f738b57051e8a0f8bc8282031d0a82e9dedbd10a94fd54d4c3830d708607a8b",
- "sha256:0f87d188528ff3c86286603bc13170a5932e631c0c69d9995aae86448a7d9692",
- "sha256:290864f3c69d1e71d6648c9c75093db28486f1bf058b0ab2fda9d2d6814ddf19",
- "sha256:70aba3a1c7dbef5a997db8afe06e256c6b67e1dc15bb16d8b55d140ea5375a8d",
- "sha256:7318d11a4784c3e37f5de0c9141d18eac08565d303da7d3d557662369f2f866b",
- "sha256:77c6edc4e25b36430df8a445195030abc8d5766d068b9aeed1a58a684cc0eb3b",
- "sha256:91669d38938ae7b66db084e444ee5dceed09b59a6622fda10dfb021d5ce6d0dc",
- "sha256:a547edc4d0ce68f3eb397ed8701314f254a0de593045ee0eecad4f1efc664951",
- "sha256:b4e2333c98a7c2c1ff6eb930cd2b57d4b818de5437c5048802096b32f66e65f9",
- "sha256:c99b3908e76de5d1582e6941dc34de086eb38d18539520f4ae4ffa29b8f2644f",
- "sha256:e3acc990b3672132a670b23cc055b967d0aa04183dbc5be82a38a0426ee6d1a6",
- "sha256:eaa8f8248c20eacfade26faf749e248adc1bec1edc2d08b05916297cc76a72bd",
- "sha256:ede6d9676c43844e4994b041ffca08dd157ce171190a8ccb40fed9b377db5653"
+ "sha256:16aa61846efddf91df623bbb4598e63be1068a6b6a2e6361cc802b41c7a286eb",
+ "sha256:1975b71a33ac986bb39b6d5cfbc15c7b1f218f1134efb4eb3881839d6ae69984",
+ "sha256:2b222744bd54781e6cc0b717fa35a54e5f176ba2ced337f27c5b435b334ef854",
+ "sha256:317643c0e88fad55414347216362b2e229c130edd5655fea5f8159a803098468",
+ "sha256:4269ce3d1b897d46fc3cc2273a0cc2a730345bb47e4456af662e6fca85c89dd7",
+ "sha256:65214fd668975077cdf8d408ccf2b2d6bdf73b4e6895a79f8e99ce4f0b43fcdb",
+ "sha256:74bc213ab8a92d86a0b304d9359d1e1d14168d4c6121b83862c9d8a88b89a738",
+ "sha256:88949be0db54755995dfb0210d0099a8712a3c696c860441971354c3debfc4af",
+ "sha256:8e1223d868be89423ec95ada5f37aa408ee64fe76ccb8e4d5f533699ba4c0e4a",
+ "sha256:9fa00f2d7a552a95fa6016e498fdeb6d74df537853dda79a9055c53dfc8b6e1a",
+ "sha256:c27fd46cab905097ba4bc28d5ba5289930f313fb1970c9d41092c9975b80e9b4",
+ "sha256:c94b792af431f6adb6859eb218137acd9a35f4f7442cea57e4a59c54751c36af",
+ "sha256:f4c12a01eb2dc16693887a874ba948b18c92f425c4d329639ece6d3bb8e631bb"
],
"index": "pypi",
- "version": "==3.0.0"
+ "version": "==3.0.2"
},
"mccabe": {
"hashes": [
@@ -140,89 +327,180 @@
],
"version": "==0.6.1"
},
+ "more-itertools": {
+ "hashes": [
+ "sha256:c187a73da93e7a8acc0001572aebc7e3c69daf7bf6881a2cea10650bd4420092",
+ "sha256:c476b5d3a34e12d40130bc2f935028b5f636df8f372dc2c1c01dc19681b2039e",
+ "sha256:fcbfeaea0be121980e15bc97b3817b5202ca73d0eae185b4550cbfce2a3ebb3d"
+ ],
+ "version": "==4.3.0"
+ },
+ "nose": {
+ "hashes": [
+ "sha256:9ff7c6cc443f8c51994b34a667bbcf45afd6d945be7477b52e97516fd17c53ac",
+ "sha256:dadcddc0aefbf99eea214e0f1232b94f2fa9bd98fa8353711dacb112bfcbbb2a",
+ "sha256:f1bffef9cbc82628f6e7d7b40d7e255aefaa1adb6a1b1d26c69a8b79e6208a98"
+ ],
+ "version": "==1.3.7"
+ },
"numpy": {
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@@ -232,20 +510,102 @@
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- "sha256:905d8090c335314568b5faee0025b1829f27bb974604a5762a6cdef3a7dfc3b7",
- "sha256:f493ee323be1e94929416b3585eefcc04943115cecbaaa35a8c86d1a2368af19"
+ "sha256:40856e74d4987de5d01761a22d1621ae1c7f8774585acae358aa5c5936c6c90b",
+ "sha256:f353aab21fd474459d97b709e527b5571314ee5f067441dc9f88e33eecd96592"
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- "markers": "python_version != '3.0.*' and python_version >= '2.6' and python_version != '3.2.*' and python_version != '3.1.*'",
- "version": "==2.2.1"
+ "version": "==2.3.0"
+ },
+ "pyrfr": {
+ "hashes": [
+ "sha256:d0ee214c8d938c135ef951e99989b18e0b8b86885c6ae8d9869eb16a79242be4"
+ ],
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+ "pytest": {
+ "hashes": [
+ "sha256:3f193df1cfe1d1609d4c583838bea3d532b18d6160fd3f55c9447fdca30848ec",
+ "sha256:e246cf173c01169b9617fc07264b7b1316e78d7a650055235d6d897bc80d9660"
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+ "version": "==3.10.1"
},
"python-dateutil": {
"hashes": [
- "sha256:1adb80e7a782c12e52ef9a8182bebeb73f1d7e24e374397af06fb4956c8dc5c0",
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+ "sha256:063df5763652e21de43de7d9e00ccf239f953a832941e37be541614732cdfc93",
+ "sha256:88f9287c0174266bb0d8cedd395cfba9c58e87e5ad86b2ce58859bc11be3cf02"
+ ],
+ "version": "==2.7.5"
+ },
+ "pytz": {
+ "hashes": [
+ "sha256:31cb35c89bd7d333cd32c5f278fca91b523b0834369e757f4c5641ea252236ca",
+ "sha256:8e0f8568c118d3077b46be7d654cc8167fa916092e28320cde048e54bfc9f1e6"
+ ],
+ "version": "==2018.7"
+ },
+ "pyyaml": {
+ "hashes": [
+ "sha256:3d7da3009c0f3e783b2c873687652d83b1bbfd5c88e9813fb7e5b03c0dd3108b",
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+ "sha256:e170a9e6fcfd19021dd29845af83bb79236068bf5fd4df3327c1be18182b2531"
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+ "version": "==3.13"
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+ "requests": {
+ "hashes": [
+ "sha256:65b3a120e4329e33c9889db89c80976c5272f56ea92d3e74da8a463992e3ff54",
+ "sha256:ea881206e59f41dbd0bd445437d792e43906703fff75ca8ff43ccdb11f33f263"
+ ],
+ "version": "==2.20.1"
+ },
+ "scikit-learn": {
+ "hashes": [
+ "sha256:0a718b5ffbd5053fb3f9e1a2e20b7c4f256dd8035e246b907d3117d20bac0260",
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+ "sha256:69a34d389d9ca4687ad00af4e11d53686771f484c37366f68617ef656bab16ab",
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+ "sha256:a402c1484fe65df42d5dbc22a58e0695fe3afe2b0b229aee2a09c6d60ba8e5c2",
+ "sha256:aad6b9aac1617bd7efa0450643888bbd3410679a94bc8680d9863825686ef369",
+ "sha256:ad4db28d3dc16c01df75ed6efb72524537de3839a5d179fcf94094359fc72ec5",
+ "sha256:b276739a5f863ccacb61999a3067d0895ee291c95502929b2ae56ea1f882e888",
+ "sha256:b3dc88c4d2bcb26ffc5afe16d053ae28317d7d1de083651defcd5453a04f1563",
+ "sha256:b3e4681253e95da5aa5c231889a32b084fd997962bf8beda6f796bf422f734b2",
+ "sha256:c3d852d49d6c1710089d4513702099fa6f8e1aebfedf222319d80c47b0a195f8",
+ "sha256:c6612e7e43988b8b5e1957150449493a55f9c059de641083df7a964f86f2d1e7",
+ "sha256:c69e5c6051366a6ac9600d730276db939b1a205e42504ec0b8371f154b0058db",
+ "sha256:ce121baa8e85ec27c3065281657dcd78adaab7dcb046c7fe96ad4e5a9dcb6610",
+ "sha256:ed2a9a9bea6ec443b7effe5695c9c168b7bf9a67df6d880729760feda871b6a3",
+ "sha256:efd842d70b87e3ef3429c3149840b9189d4441ca951ab0cec62c94a964e219d9",
+ "sha256:f1428af5c381f6eef30ffbc7e047b7c713d4efa5d7bf5e57b62b3fc8d387044b",
+ "sha256:f6c7bf8cd4de1640b760b47f4d28deb26dbbf9acbe0194cdff54a898e190d872",
+ "sha256:f8329ac2160ad8bbbac6a507374685ceca3f24ca427fa9ee61a501280e1972d9",
+ "sha256:fefba2a43b92f8393366093b60efbe984a72a2b41cce16b4002005e4104ef938"
],
- "version": "==2.7.3"
+ "version": "==0.19.2"
},
"scipy": {
"hashes": [
@@ -288,6 +648,40 @@
],
"version": "==1.11.0"
},
+ "smac": {
+ "hashes": [
+ "sha256:dd4520a1ec4292302fa8115f7d9d9045eb2e12b3a3d7b215cfc1f8073891e6ee"
+ ],
+ "version": "==0.8.0"
+ },
+ "snowballstemmer": {
+ "hashes": [
+ "sha256:919f26a68b2c17a7634da993d91339e288964f93c274f1343e3bbbe2096e1128",
+ "sha256:9f3bcd3c401c3e862ec0ebe6d2c069ebc012ce142cce209c098ccb5b09136e89"
+ ],
+ "version": "==1.2.1"
+ },
+ "sphinx": {
+ "hashes": [
+ "sha256:120732cbddb1b2364471c3d9f8bfd4b0c5b550862f99a65736c77f970b142aea",
+ "sha256:b348790776490894e0424101af9c8413f2a86831524bd55c5f379d3e3e12ca64"
+ ],
+ "version": "==1.8.2"
+ },
+ "sphinx-rtd-theme": {
+ "hashes": [
+ "sha256:02f02a676d6baabb758a20c7a479d58648e0f64f13e07d1b388e9bb2afe86a09",
+ "sha256:d0f6bc70f98961145c5b0e26a992829363a197321ba571b31b24ea91879e0c96"
+ ],
+ "version": "==0.4.2"
+ },
+ "sphinxcontrib-websupport": {
+ "hashes": [
+ "sha256:68ca7ff70785cbe1e7bccc71a48b5b6d965d79ca50629606c7861a21b206d9dd",
+ "sha256:9de47f375baf1ea07cdb3436ff39d7a9c76042c10a769c52353ec46e4e8fc3b9"
+ ],
+ "version": "==1.1.0"
+ },
"typed-ast": {
"hashes": [
"sha256:0948004fa228ae071054f5208840a1e88747a357ec1101c17217bfe99b299d58",
@@ -317,11 +711,32 @@
"markers": "python_version < '3.7' and implementation_name == 'cpython'",
"version": "==1.1.0"
},
+ "typing": {
+ "hashes": [
+ "sha256:4027c5f6127a6267a435201981ba156de91ad0d1d98e9ddc2aa173453453492d",
+ "sha256:57dcf675a99b74d64dacf6fba08fb17cf7e3d5fdff53d4a30ea2a5e7e52543d4",
+ "sha256:a4c8473ce11a65999c8f59cb093e70686b6c84c98df58c1dae9b3b196089858a"
+ ],
+ "version": "==3.6.6"
+ },
+ "urllib3": {
+ "hashes": [
+ "sha256:61bf29cada3fc2fbefad4fdf059ea4bd1b4a86d2b6d15e1c7c0b582b9752fe39",
+ "sha256:de9529817c93f27c8ccbfead6985011db27bd0ddfcdb2d86f3f663385c6a9c22"
+ ],
+ "version": "==1.24.1"
+ },
"wrapt": {
"hashes": [
"sha256:d4d560d479f2c21e1b5443bbd15fe7ec4b37fe7e53d335d3b9b0a7b1226fe3c6"
],
"version": "==1.10.11"
+ },
+ "xgboost": {
+ "hashes": [
+ "sha256:4224193159763ab50352b3c53087510a9ebef4c9dcbafb5c5f8c7e2a1d6d7b70"
+ ],
+ "version": "==0.7.post3"
}
},
"develop": {}
diff --git a/explore_auto_scikit.py b/explore_auto_scikit.py
new file mode 100644
index 0000000000000000000000000000000000000000..3878d0cca9da8c15e7e9ad7a9c12dc6e3eb9e05b
--- /dev/null
+++ b/explore_auto_scikit.py
@@ -0,0 +1,421 @@
+import shutil
+import glob
+import pickle
+import os
+import traceback
+import time
+import autosklearn.classification
+import sys
+from extraction.retrieve_battery_data import extract_charge_discharge_impedance
+from functools import reduce
+import itertools
+import numpy as np
+import sklearn.model_selection
+import sklearn.datasets
+import sklearn.metrics
+import multiprocessing
+from concurrent.futures import ThreadPoolExecutor
+from autosklearn.constants import *
+from autosklearn.classification import AutoSklearnClassifier
+from autosklearn.metrics import accuracy
+from sklearn.metrics import precision_recall_fscore_support
+from sklearn.metrics import f1_score
+from sklearn.externals import joblib
+from sklearn.metrics import confusion_matrix
+import pandas as pd
+# import concurrent.futures
+# np.set_printoptions(threshold=np.nan)
+
+SEPARATOR = "-----------------------------------"
+RESULTS_FOLDER = "current_results"
+MODEL_FILE_SEP = "___"
+
+def chunk_it(seq, num):
+ avg = len(seq) / float(num)
+ last = 0
+ out = []
+
+ while last <= num:
+ res = [last] * (len(seq[int(last*avg):int((last + 1) * avg)]))
+ out += res
+ last += 1
+
+ return out
+
+# DISCRETISATION_TYPES = ["mean", "median", "standard-deviation"]
+DISCRETISATION_TYPES = [
+ "mean", "gradiant_mean",
+ "median", "gradiant_median",
+ "standard-deviation", "gradiant_std",
+]
+
+BATTERY_BASE = "./data/BatteryAgingARC_25-44/"
+TEST_BATTERIES = [BATTERY_BASE + "B0025.mat"]
+ALL_BATTERIES = [
+ # BATTERY_BASE + "B0025.mat",
+ BATTERY_BASE + "B0026.mat",
+ BATTERY_BASE + "B0027.mat",
+ BATTERY_BASE + "B0028.mat",
+ # BATTERY_BASE + "B0033.mat",
+ # BATTERY_BASE + "B0034.mat",
+]
+
+charges_params = [
+ "voltage_measured",
+ "current_measured",
+ "temperature_measured",
+ "current_charge",
+ "voltage_charge",
+]
+discharges_params = charges_params + ["capacity"] # capacity not same length
+impedance_params = [
+ # "re", # float_
+ # "rct", # float_
+ "sense_current", # Complexes
+ "battery_current", # Complexes
+ "current_ratio", # Complexes
+ "battery_impedance", # Complexes but multiple array of 1 complexe
+ # "rectified_impedance", # Complexes but multiple array of 1 complexe # not same length
+]
+
+
+def discretize(array, discretisation):
+ # array split should copy when split > lenght instead of empty arrays
+ splitted = np.array_split(array, discretisation["split"])
+
+ out = []
+ if "mean" in discretisation["types"]:
+ out.append([np.mean(split) for split in splitted])
+ if "median" in discretisation["types"]:
+ out.append([np.median(split) for split in splitted])
+ if "standard deviation" in discretisation["types"]:
+ out.append([np.std(split) for split in splitted])
+
+ splitted_gradient = np.array_split(np.gradient(array), discretisation["split"])
+ if "gradiant_mean" in discretisation["types"]:
+ out.append([np.mean(split) for split in splitted_gradient])
+ if "gradiant_median" in discretisation["types"]:
+ out.append([np.median(split) for split in splitted_gradient])
+ if "gradiant_std" in discretisation["types"]:
+ out.append([np.std(split) for split in splitted_gradient])
+
+ return out
+ # out = np.array(out)
+
+ # return out.T
+
+
+def append_params(result_cycles, cycles, used_parameters, discretisation):
+ """One line contain 1 moment of every feature"""
+ total_cycles_length = 0
+ for cycle in cycles:
+ # # Raw
+ # cycle_length = len(cycle[used_parameters[0]])
+ # total_cycles_length += cycle_length
+ # for i in range(cycle_length):
+ # time_slice = []
+ # for param in used_parameters:
+ # time_slice.append(np.nan_to_num(cycle[param][i]))
+ # result_cycles.append(time_slice)
+
+ # Discretisation
+ cycle_length = len(cycle[used_parameters[0]])
+ if cycle_length < discretisation["split"]:
+ raise "Discretisation split is too big in comparison with cycle length"
+
+ discretized = []
+ for param in used_parameters:
+ np.nan_to_num(cycle[param], copy=False)
+ mean_median_std = discretize(cycle[param], discretisation)
+ discretized += mean_median_std
+
+ transposed_discretized = np.array(discretized).T
+ for time_slice in transposed_discretized:
+ result_cycles.append(time_slice)
+
+ total_cycles_length += len(transposed_discretized)
+ # CycleWay
+ # reduce all features together but before, each feature length N should be reduced to a fixed length
+ # result_cycles.append(np.nan_to_num(np.array(
+ # reduce((lambda acc, param: acc + cycle[param].tolist()), used_parameters, [])
+ # )))
+ return total_cycles_length
+
+
+def combinations(array):
+ return list(itertools.chain(*[itertools.combinations(array,i+1) for i,_ in enumerate(array)]))
+
+
+def get_charges(filepath):
+ charge_cycles, discharge_cycles, impedance_cycles = extract_charge_discharge_impedance(filepath)
+ return charge_cycles
+
+
+def get_discharges(filepath):
+ charge_cycles, discharge_cycles, impedance_cycles = extract_charge_discharge_impedance(filepath)
+ return discharge_cycles
+
+
+def get_impedance(filepath):
+ charge_cycles, discharge_cycles, impedance_cycles = extract_charge_discharge_impedance(filepath)
+ return impedance_cycles
+
+
+def load_batteries(extract_function, params, all_batteries=ALL_BATTERIES, class_count=-3, discretisation=None):
+ all_batteries_number = [bat.split(os.sep)[-1].split(".")[0] for bat in all_batteries]
+ # cycles_filename = "data__" + "-".join(params) + "__" + "-".join(all_batteries_number) + "__" + str(class_count) + ".p"
+ cycles_filename = "__".join([
+ "batteries_data_temp/data",
+ "-".join(params),
+ "-".join(all_batteries_number),
+ str(class_count),
+ "-".join(discretisation["types"]),
+ str(discretisation["split"]),
+ ]) + ".p"
+ X_cycles = []
+ y_cycles = []
+
+ if os.path.exists(cycles_filename):
+ loaded = pickle.load( open(cycles_filename, "rb") )
+ return loaded["x"], loaded["y"]
+ else:
+ for filepath in all_batteries:
+ cycles = extract_function(filepath)
+ # charge_cycles, discharge_cycles, impedance_cycles = extract_charge_discharge_impedance(filepath)
+
+ total_cycles_length = append_params(X_cycles, cycles, params, discretisation)
+
+ # add labels
+ y_cycles += chunk_it([-1]*total_cycles_length, class_count) # cycle way
+
+ # X_cycles = np.zeros([len(X_cycles),len(max(X_cycles,key = lambda x: len(x)))]) # padding #CycleWay
+ X_cycles = np.array(X_cycles)
+ y_cycles = np.array(y_cycles)
+ pickle.dump( {"x": X_cycles, "y": y_cycles} , open( cycles_filename, "wb" ) )
+ return X_cycles, y_cycles
+
+
+def save_dataframe(cv_results, model_file_name):
+ df = pd.DataFrame(cv_results)
+
+ writer = pd.ExcelWriter(model_file_name + ".xlsx")
+ df.to_excel(writer, "automl_dataframe")
+ writer.save()
+
+
+def auto_ML(options):
+ title, params, extract_function, class_count, discretisation, folds = options
+ print("--------------------------------------------------------------------------------")
+ print(title)
+ print(params)
+ print("--------------------------------------------------------------------------------")
+
+ X_cycles, y_cycles = load_batteries(extract_function, params, ALL_BATTERIES, class_count, discretisation)
+ # y_cycles = chunk_it(X_cycles, class_count) can be removed
+ # y_cycles = np.array(y_cycles) can be removed
+
+
+ print(X_cycles.shape)
+ print(y_cycles.shape)
+ # before split and are OK
+ # 40'000 inputs and 6 classes CV !not certain!
+ # 930'000 inputs and 6 classes holdout !not certain!
+ # 25'860 inputs and 3 classes CV
+ MAX_SIZE = 258600000
+
+ X_train, X_test, y_train, y_test = \
+ sklearn.model_selection.train_test_split(X_cycles, y_cycles, random_state=1)
+
+ X_train = X_train[:MAX_SIZE]
+ y_train = y_train[:MAX_SIZE]
+
+ # 0.3 is quite good
+ # k = 0.3
+ k = 0.5
+ automl = autosklearn.classification.AutoSklearnClassifier(
+ # time_left_for_this_task=69,
+ # per_run_time_limit=35,
+ time_left_for_this_task=int(3600*k),
+ per_run_time_limit=int(360*k),
+ ensemble_size=int(50),
+ ensemble_nbest=int(200),
+ # ml_memory_limit=1024,
+ # shared_mode=True,
+ # ensemble_size=50,
+ # ensemble_nbest=200,
+ # tmp_folder=tmp_folder,
+ # TODO Use CV instead of HOLDOUT
+ # resampling_strategy='holdout',
+ resampling_strategy='cv',
+ resampling_strategy_arguments={'folds': folds},
+ # output_folder=output_folder,
+ # initial_configurations_via_metalearning=0,
+ # seed=SEED,
+ # time_left_for_this_task=3600*18,
+ # per_run_time_limit=360*18,
+ ml_memory_limit=28024,
+ # delete_tmp_folder_after_terminate=False,
+ # delete_output_folder_after_terminate=False,
+ )
+
+ automl.fit(X_train.copy(), y_train.copy())
+ if automl.resampling_strategy == "cv":
+ automl.refit(X_train.copy(), y_train.copy())
+ y_pred = automl.predict(X_test, n_jobs=-1)
+
+ model_file_name = os.path.join(RESULTS_FOLDER, MODEL_FILE_SEP.join([
+ title,
+ "-".join(params),
+ str(class_count),
+ "-".join(discretisation["types"]),
+ str(discretisation["split"]),
+ "folds" + str(folds),
+ ]) + ".joblib")
+ print(model_file_name)
+ save_dataframe(automl.cv_results_, model_file_name[:-7])
+
+ joblib.dump(automl, model_file_name)
+
+ test_battery_result = test_model_with_test_battery(model_file_name)
+
+ results = [
+ title,
+ "Params: " + str(params),
+ "Discretisation: " + str(discretisation),
+ "Class count: " + str(class_count),
+ "Folds: " + str(folds),
+ "Accuracy score: " + str(sklearn.metrics.accuracy_score(y_test, y_pred)),
+ "F1 score: " + str(precision_recall_fscore_support(y_test, y_pred, average="weighted")),
+ str(confusion_matrix(y_test, y_pred)),
+ ] + test_battery_result + [
+ str(automl.sprint_statistics()),
+ str(automl.show_models()),
+ # str(automl.cv_results_),
+ ]
+ print(results)
+ return results
+
+
+def get_options(min_length=0):
+ class_count = 3
+ # 10, 50, 100, 200
+ charge_options = []
+ discharge_options = []
+ impedance_options = []
+ # charge_options = [("charge", param, get_charges, class_count) for param in combinations(charges_params) if len(param) >= min_length]
+ # charge_options = [("charge", charges_params, get_charges, class_count, {"types": discre_type, "split":100}) for discre_type in combinations(DISCRETISATION_TYPES)]
+ # discharge_options = [("discharge", charges_params, get_discharges, class_count, {"types": discre_type, "split":100}) for discre_type in combinations(DISCRETISATION_TYPES)]
+ # discharge_options = [("discharge", param, get_discharges, class_count) for param in combinations(charges_params) if len(param) >= min_length]
+ # impedance_options = [("impedance", param, get_impedance, class_count) for param in combinations(impedance_params) if len(param) >= min_length]
+ # charge_options = [("charge", charges_params, get_charges, class_count, {"types": DISCRETISATION_TYPES, "split":split}) for split in range(1,11)]
+ charge_options = [
+ # ("charge", charges_params, get_charges, class_count, {"types": DISCRETISATION_TYPES, "split":10}, 10),
+ ("charge", charges_params, get_charges, class_count, {"types": DISCRETISATION_TYPES, "split":6}, 5),
+ ("charge", charges_params, get_charges, class_count, {"types": DISCRETISATION_TYPES, "split":6}, 10),
+ ("charge", charges_params, get_charges, class_count, {"types": DISCRETISATION_TYPES, "split":6}, 15),
+ ("charge", charges_params, get_charges, class_count, {"types": DISCRETISATION_TYPES, "split":6}, 20),
+ # ("charge", charges_params, get_charges, class_count, {"types": DISCRETISATION_TYPES, "split":50}, 10),
+ # ("charge", charges_params, get_charges, class_count, {"types": DISCRETISATION_TYPES, "split":100}, 10),
+ # # ("charge", charges_params, get_charges, class_count, {"types": DISCRETISATION_TYPES, "split":1}, 10),
+ # ("charge", charges_params, get_charges, class_count, {"types": DISCRETISATION_TYPES, "split":200}, 10),
+ ]
+ # discharge_options = [("discharge", charges_params, get_discharges, class_count, {"types": DISCRETISATION_TYPES, "split":100})]
+ # impedance_options = [("impedance", impedance_params, get_impedance, class_count, {"types": DISCRETISATION_TYPES, "split":20})]
+ all_options = discharge_options + impedance_options + charge_options
+
+ return all_options
+
+def writeln(file, text):
+ file.write(text + "\n")
+
+
+def init_folder():
+ if os.path.exists(RESULTS_FOLDER):
+ shutil.rmtree(RESULTS_FOLDER)
+ if not os.path.exists(RESULTS_FOLDER):
+ os.makedirs(RESULTS_FOLDER)
+ results_file = os.path.join(RESULTS_FOLDER, "result.txt")
+ failed_file = os.path.join(RESULTS_FOLDER, "failed.txt")
+ open(results_file, 'w').close()
+ open(failed_file, 'w').close()
+ return results_file, failed_file
+
+
+def execute_auto_ML():
+ start_time = time.time()
+ print(start_time)
+ all_options = get_options(min_length=0)
+
+ results_file, failed_file = init_folder()
+
+ # results = list(map(auto_ML, all_options))
+ for option in all_options:
+ try:
+ result = auto_ML(option)
+ with open(results_file, 'a') as the_file:
+ writeln(the_file, SEPARATOR)
+ for line in result:
+ writeln(the_file, str(line))
+ writeln(the_file, SEPARATOR)
+ except:
+ with open(failed_file, 'a') as the_file:
+ writeln(the_file, SEPARATOR)
+ writeln(the_file, str(option))
+ writeln(the_file, str(traceback.format_exc()))
+ writeln(the_file, SEPARATOR)
+
+
+ seconds = time.time() - start_time
+ print("execution time", int(seconds / 60 / 60), "h", int(seconds / 60) % 60, "m", int(seconds) % 60, "s")
+
+
+def test_model_with_test_battery(model_filepath):
+ batteries = TEST_BATTERIES
+ clean_model_filepath = model_filepath.split(os.sep)[-1].split(".")[0].split(MODEL_FILE_SEP)
+
+ params = clean_model_filepath[1].split("-")
+ class_count = int(clean_model_filepath[2])
+ discretisation = {
+ "types": clean_model_filepath[3].split("-"),
+ "split": int(clean_model_filepath[4]),
+ }
+
+ if clean_model_filepath[0] == "charge":
+ extract_function = get_charges
+ if clean_model_filepath[0] == "discharge":
+ extract_function = get_discharges
+ if clean_model_filepath[0] == "impedance":
+ extract_function = get_impedance
+
+ X_cycles, y_true = load_batteries(extract_function, params, batteries, class_count, discretisation)
+
+ try:
+ loaded_automl = joblib.load(model_filepath)
+ except:
+ print("Error in model loading-> " + model_filepath)
+
+ print(loaded_automl)
+ y_pred = loaded_automl.predict(X_cycles, n_jobs=-1)
+
+ result = [
+ "Batteries: " + str(batteries),
+ "Accuracy score for battery: " + str(sklearn.metrics.accuracy_score(y_true, y_pred)),
+ str(precision_recall_fscore_support(y_true, y_pred, average="weighted")),
+ str(confusion_matrix(y_true, y_pred)),
+ ]
+ return result
+
+
+def test_models_with_test_battery(folder_to_test=RESULTS_FOLDER):
+ # get all model inside the folder
+ for model_filepath in glob.glob(folder_to_test+os.sep+"*.joblib"):
+ results = test_model_with_test_battery(model_filepath)
+ for result in results:
+ print(result)
+
+
+if __name__ == "__main__":
+ # print(test_model_with_test_battery("results/004_fixed_labels_1h_charge/charge___voltage_measured-current_measured-temperature_measured-current_charge-voltage_charge___3.joblib"))
+ execute_auto_ML()
+ # print(discretize(np.array([1,2,3,4,5,6,7,8,9,10,11,12]), {"types": ["mean", "median", "standard deviation"], "split": 4}))
+ # test_models_with_test_battery("results/004_fixed_labels_1h_charge/")