Init

.gitignore

+.envrc
+.ipynb_checkpoints
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README.md

 # analytics-tutorial-cmlkit
 
-Tutorial for [`cmlkit`](https://github.com/sirmarcel/cmlkit).
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+Tutorial for [`cmlkit`](https://github.com/sirmarcel/cmlkit).
+
+In addition to the prerequisites in `setup.py`, this tutorial requires [`qmmlpack`](https://gitlab.com/qmml/qmmlpack/-/tree/development) on the DEVELOPMENT branch.
+
+It also requires the environment variables `CML_PLUGINS=cscribe` and `CML_DATASET_PATH=data/` to be set.
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assets/.gitignore

+# Ignore everything in this directory
+*
+# Except this file
+!.gitignore
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data/.gitignore

+# Ignore everything in this directory
+*
+# Except this file
+!.gitignore
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metainfo.json

+{
+  "authors": [
+    "Langer, Marcel"
+  ],
+  "email": "langer@fhi-berlin.mpg.de",
+  "title": "cmlkit tutorial",
+  "description": "In this tutorial we will get to know cmlkit, a python package for specifying, evaluating, and optimising machine learning models, and use it to compete in the Nomad 2018 Kaggle challenge.",
+  "url": "https://gitlab.mpcdf.mpg.de/nomad-lab/analytics-tutorial-template",
+  "link": "https://analytics-toolkit.nomad-coe.eu/hub/user-redirect/notebooks/tutorials/Welcome.ipynb",
+  "created": "",
+  "updated": "",
+  "flags":{
+    "isPublic": false,
+    "featured": false,
+    "top_of_list": false
+  },
+  "labels": {
+    "application_keyword": [
+      "Formation energy prediction"
+    ],
+    "application_section": [
+      "Materials property prediction"
+    ],
+    "application_system": [
+      "Group-III oxidess"
+    ],
+    "category": [
+      "Tutorial"
+    ],
+    "data_analytics_method": [
+      "Kernel ridge regression",
+      "SOAP",
+      "MBTR",
+      "Symmetry Functions",
+    ],
+    "platform": [
+      "jupyter"
+    ]
+
+  }
+}
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run_nmd18_hpo/status.txt

+### Status of run nmd18_hpo at 2020-02-24 12:28:46 ###
+ nmd18_hpo: Done. Have a good day! Runtime: 1918.8/inf. Active evaluations: 0.
+ Counted trials: 400/400.
+ Best 3: 0.0240 0.0244 0.0256. Live: 41/T: 400 (378)/E: 306 (287).
+ {'QMMLException': 22}

run_nmd18_hpo/suggestion-0.yml

+model:
+  per: cell
+  regression:
+    krr:
+      kernel:
+        kernel_atomic:
+          kernelf:
+            gaussian:
+              ls: 32.0
+          norm: false
+      nl: 0.00048828125
+  representation:
+    ds_soap:
+      cutoff: 8
+      elems: [8, 13, 31, 49]
+      l_max: 8
+      n_max: 3
+      rbf: gto
+      sigma: 0.25

run_nmd18_hpo/suggestion-1.yml

+model:
+  per: cell
+  regression:
+    krr:
+      kernel:
+        kernel_atomic:
+          kernelf:
+            gaussian:
+              ls: 8.0
+          norm: false
+      nl: 0.03125
+  representation:
+    ds_soap:
+      cutoff: 7
+      elems: [8, 13, 31, 49]
+      l_max: 7
+      n_max: 5
+      rbf: gto
+      sigma: 0.25

run_nmd18_hpo/suggestion-2.yml

+model:
+  per: cell
+  regression:
+    krr:
+      kernel:
+        kernel_atomic:
+          kernelf:
+            gaussian:
+              ls: 64.0
+          norm: false
+      nl: 0.03125
+  representation:
+    ds_soap:
+      cutoff: 7
+      elems: [8, 13, 31, 49]
+      l_max: 6
+      n_max: 5
+      rbf: gto
+      sigma: 0.5

run_nmd18_hpo/suggestion-3.yml

+model:
+  per: cell
+  regression:
+    krr:
+      kernel:
+        kernel_atomic:
+          kernelf:
+            gaussian:
+              ls: 2048.0
+          norm: false
+      nl: 3.814697265625e-06
+  representation:
+    ds_soap:
+      cutoff: 3
+      elems: [8, 13, 31, 49]
+      l_max: 7
+      n_max: 6
+      rbf: gto
+      sigma: 1.0

run_nmd18_hpo/suggestion-4.yml

+model:
+  per: cell
+  regression:
+    krr:
+      kernel:
+        kernel_atomic:
+          kernelf:
+            gaussian:
+              ls: 512.0
+          norm: false
+      nl: 0.0078125
+  representation:
+    ds_soap:
+      cutoff: 10
+      elems: [8, 13, 31, 49]
+      l_max: 5
+      n_max: 6
+      rbf: gto
+      sigma: 0.5

setup.py

+import json
+from setuptools import setup, find_packages
+
+with open('metainfo.json') as file:
+    metainfo = json.load(file)
+
+setup(
+    name='tutorial_cmlkit',
+    version='0.1',
+    author=', '.join(metainfo['authors']),
+    author_email=metainfo['email'],
+    url=metainfo['url'],
+    description=metainfo['title'],
+    long_description=metainfo['description'],
+    packages=find_packages(),
+    install_requires=['cmlkit', 'cscribe'],
+)