update docs

Author: xingzhongyu

docs/requirements.txt

@@ -12,3 +12,6 @@ sphinx==6.2.1
 sphinx_rtd_theme==2.0.0
 sphinxcontrib-napoleon==0.7
 sphinx-autodoc-typehints==1.23.0
+nbsphinx==0.9.7
+ipykernel==6.29.5
+wandb==0.21.0

docs/source/conf.py

@@ -32,6 +32,7 @@
     'sphinx.ext.autosummary',
     'sphinx.ext.intersphinx',
     'sphinx.ext.viewcode',
+    'nbsphinx',
     'sphinx_autodoc_typehints',
 ]
 

docs/source/index.rst

@@ -66,6 +66,12 @@ some of the experiments from the original papers.
    api/search
    api/atlas
 
+.. toctree::
+   :maxdepth: 2
+   :caption: Tutorials
+
+   tutorials/tutorial.ipynb
+
 .. note::
    The functions in the :doc:`api/atlas` module are primarily backend APIs for the DANCE 2.0 Platform.
    For interactive usage, please visit the `DANCE 2.0 Platform <http://omicsml.ai:81/dance/>`_.

docs/source/tutorials/tutorial.ipynb

@@ -0,0 +1 @@
+../../../examples/tuning/custom-methods/tutorial.ipynb

examples/tuning/custom-methods/328_138/results/params/0_best.csv

@@ -1,3 +1,3 @@
 id,_runtime,_step,_timestamp,_wandb_runtime,acc,params.0.FilterGenesPercentile.max_val,params.0.FilterGenesPercentile.min_val,params.0.FilterGenesPercentile.mode,params.1.Log1P.base,params.2.FilterGenesRegression.method,params.2.FilterGenesRegression.num_genes,params.3.CellSVD.algorithm,params.3.CellSVD.n_components,test_acc,train_acc
-7jv36906,,,,10,,97,7,var,1.937795425949926,seurat3,122,arpack,613,,
-ddqktqto,13.20279836654663,0.0,1754125620.1302693,11,0.7575757503509521,95,4,sum,9.911407484249043,enclasc,9886,arpack,214,0.717391312122345,0.9656488299369812
+eb4xq6sf,4,0.0,1754371376.674959,4,0.6969696879386902,98,7,cv,4.420786736087686,enclasc,5205,arpack,252,0.6449275612831116,0.9618320465087892
+pfyfb0zj,3,,,3,,96,6,sum,4.969456908680813,scmap,9604,arpack,712,,

examples/tuning/custom-methods/328_138/results/params/1_best.csv

@@ -1,3 +1,3 @@
 id,_runtime,_step,_timestamp,_wandb_runtime,acc,params.0.FilterGenesPercentile.max_val,params.0.FilterGenesPercentile.min_val,params.0.FilterGenesPercentile.mode,params.1.Log1P.base,params.2.FilterGenesRegression.method,params.2.FilterGenesRegression.num_genes,params.3.CellPCA.n_components,params.3.CellPCA.svd_solver,test_acc,train_acc
-hb1f4jun,12.964418649673462,0,1754125700.2164586,11,0.7727272510528564,96,10,rv,2.5186772625549763,scmap,9063,252,arpack,0.6304348111152649,0.9732824563980104
-8aa5pebg,12.543931245803831,0,1754125672.7847412,10,0.7424242496490479,96,1,rv,3.325957103817947,seurat3,4134,794,full,0.6739130616188049,0.9923664331436156
+7mi2ipwh,5,0,1754371402.3761735,5,0.7424242496490479,99,5,rv,7.659831338825212,seurat3,7204,499,full,0.6666666865348816,0.9923664331436156
+kcndo13y,4,0,1754371390.2592788,4,0.6969696879386902,98,3,var,7.13610244329897,enclasc,1518,816,auto,0.6159420013427734,0.9770992398262024

examples/tuning/custom-methods/328_138/results/pipeline/best_test_acc.csv

@@ -1,3 +1,3 @@
 id,_runtime,_step,_timestamp,_wandb_runtime,acc,pipeline.0.filter.gene,pipeline.1.normalize,pipeline.2.filter.gene,pipeline.3.feature.cell,test_acc,train_acc
-z8pa2v0y,13.172678709030151,0,1754125592.6847887,11,0.6515151262283325,FilterGenesPercentile,Log1P,FilterGenesRegression,CellSVD,0.2246376872062683,0.8091602921485901
-pg1eyiyv,13.237998247146606,0,1754125565.5422962,11,0.6515151262283325,FilterGenesPercentile,Log1P,FilterGenesRegression,CellPCA,0.2246376872062683,0.8129770755767822
+g5vpa779,3,0,1754371351.3028655,3,0.6515151262283325,FilterGenesPercentile,Log1P,FilterGenesRegression,CellSVD,0.2246376872062683,0.8091602921485901
+41d81yru,4,0,1754371339.968081,4,0.6515151262283325,FilterGenesPercentile,Log1P,FilterGenesRegression,CellPCA,0.2246376872062683,0.8129770755767822