diff --git a/.github/workflows/checks.yaml b/.github/workflows/checks.yaml new file mode 100644 index 0000000..c26010d --- /dev/null +++ b/.github/workflows/checks.yaml @@ -0,0 +1,10 @@ +name: Checks +on: [push, pull_request] + +jobs: + lint: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - uses: actions/setup-python@v5 + - uses: pre-commit/action@v3.0.0 diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..6f89e6a --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,22 @@ +repos: + - repo: https://github.com/psf/black + rev: 22.6.0 + hooks: + - id: black + language_version: python3 + args: ["--line-length", "120"] + exclude: '\.(csv|yml|yaml)$' + # - repo: https://github.com/pycqa/flake8 + # rev: 4.0.1 + # hooks: + # - id: flake8 + # args: ["--max-line-length", "120", "--ignore", "E203"] + # language_version: python3 + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v4.3.0 + hooks: + - id: requirements-txt-fixer + - id: check-yaml + - id: end-of-file-fixer + - id: trailing-whitespace + exclude: '\.(csv|yml|yaml)$' diff --git a/README.md b/README.md index 7ad9e53..50b36ad 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,62 @@ -# sapinet_regularization -Reproduction of simulations from Moyal et al. (202x) _Heterogeneous quantization regularizes spiking neural network activity_. +# Heterogeneous Quantization Regularizes Spiking Neural Network Activity + +Spiking neural network simulations from [Moyal et al.]( +https://www.nature.com/articles/s41598-025-96223-z) (2025), tested on Ubuntu 22.04. + + +Installation +------------ +If you would like to run, modify, or extend this project: + +* Clone the repository: + + git clone https://github.com/cplab/sapinet_regularization + +* Create a conda virtual environment (optional): + + conda create -n python=3.11 + conda activate + +* Install with pip: + + cd sapinet_regularization + pip install -e . + +See ``setup.py`` for dependency information. + + +Simulation +---------- +* To reproduce the results and plots, run the master script from its own directory: + + cd src/projects + python simulation.py -multirun yaml/hq2025.yaml + +* You may modify component, model, and project YAMLs to your liking and run single-use pipelines, e.g.: + + cd src/projects + python simulation.py -experiment yaml/synthetic.yaml + +Plots and diagnostic output will be written to `results/`. + + +Analysis +-------- +To reproduce the statistical analyses: + +* Edit the following R file so that `run_dir` and `meta_dir` point to the run directory +generated in the previous step: + + src/utils/analysis/analysis.r + +* Run `analysis.r` and inspect the results generated under `analysis/`. + +You may also directly inspect the raw synthetic datasets and statistical output +reported in the manuscript. Both are included in this repository under: + + analysis/SciRep-Final + + +Note +---- +Sapicore 0.4 is compatible with both Linux and Windows. diff --git a/analysis/SciRep-Final/csv/full_clf_cond_1.csv b/analysis/SciRep-Final/csv/full_clf_cond_1.csv new file mode 100644 index 0000000..9c21a84 --- /dev/null +++ b/analysis/SciRep-Final/csv/full_clf_cond_1.csv @@ -0,0 +1,16 @@ +model.term,df1,df2,F.ratio,p.value +Layer,1,441,5.805,0.0163940824707428 +Condition,3,441,3298.391,1.40556729801199e-301 +Duplication,3,441,179.135,6.09289163386833e-76 +Session,1,441,555.771,4.11120373447204e-80 +Layer:Condition,3,441,6.572,0.000235805568692485 +Layer:Duplication,3,441,5.944,0.000557259393307905 +Layer:Session,1,441,0.042,0.838225874225619 +Condition:Duplication,9,441,28.307,9.3891107570878e-39 +Condition:Session,3,441,937.419,6.69716854927288e-191 +Duplication:Session,3,441,3.08,0.0273166226018128 +Layer:Condition:Duplication,9,441,1.656,0.0974283388520933 +Layer:Condition:Session,3,441,1.498,0.21432258197009 +Layer:Duplication:Session,3,441,0.047,0.986298344329484 +Condition:Duplication:Session,9,441,7.022,1.65477356379195e-09 +Layer:Condition:Duplication:Session,9,441,2.719,0.00429506307005925 diff --git a/analysis/SciRep-Final/csv/full_clf_cond_2.csv b/analysis/SciRep-Final/csv/full_clf_cond_2.csv new file mode 100644 index 0000000..cd88006 --- /dev/null +++ b/analysis/SciRep-Final/csv/full_clf_cond_2.csv @@ -0,0 +1,49 @@ +contrast,Duplication,Layer,estimate,SE,df,t.ratio,p.value +Homogeneous - Uniform,4,GC,-16.0639880952331,0.860475202517394,440.999999999895,-18.6687403056317,0 +Homogeneous - Scaled,4,GC,-22.2023809523757,0.860475202517393,440.999999999924,-25.8024646002822,0 +Homogeneous - Adaptive,4,GC,-22.0163690476142,0.860475202517394,440.99999999991,-25.5862911368084,0 +Uniform - Scaled,4,GC,-6.13839285714263,0.860475202517395,440.999999999966,-7.13372429465047,2.55417909045264e-12 +Uniform - Adaptive,4,GC,-5.95238095238116,0.860475202517396,440.999999999966,-6.91755083117672,7.5747297323403e-11 +Scaled - Adaptive,4,GC,0.186011904761466,0.860475202517395,440.999999999966,0.216173463473755,0.996428897623739 +Homogeneous - Uniform,8,GC,-26.7745535714287,0.860475202517396,440.999999999966,-31.1160083324859,0 +Homogeneous - Scaled,8,GC,-26.1793154761906,0.860475202517395,440.999999999966,-30.4242532493682,0 +Homogeneous - Adaptive,8,GC,-25.4724702380954,0.860475202517397,440.999999999966,-29.6027940881659,0 +Uniform - Scaled,8,GC,0.595238095238103,0.860475202517395,440.99999999998,0.691755083117656,0.900230925391618 +Uniform - Adaptive,8,GC,1.30208333333337,0.860475202517397,440.99999999998,1.51321424431989,0.430396696763386 +Scaled - Adaptive,8,GC,0.706845238095265,0.860475202517396,440.99999999998,0.821459161202236,0.84431254673782 +Homogeneous - Uniform,16,GC,-28.8690476190483,0.860475202517395,440.999999999966,-33.5501215312067,0 +Homogeneous - Scaled,16,GC,-28.0877976190483,0.860475202517396,440.999999999966,-32.6421929846148,0 +Homogeneous - Adaptive,16,GC,-27.5669642857149,0.860475202517396,440.999999999966,-32.0369072868868,0 +Uniform - Scaled,16,GC,0.781249999999972,0.860475202517396,440.999999999994,0.907928546591879,0.800633047591831 +Uniform - Adaptive,16,GC,1.30208333333337,0.860475202517395,440.999999999994,1.51321424431989,0.430396696763386 +Scaled - Adaptive,16,GC,0.520833333333393,0.860475202517396,440.999999999994,0.605285697728011,0.930375031963111 +Homogeneous - Uniform,32,GC,-28.8839285714293,0.860475202517396,440.999999999966,-33.5674154082847,0 +Homogeneous - Scaled,32,GC,-27.5446428571435,0.860475202517395,440.999999999966,-32.0109664712699,0 +Homogeneous - Adaptive,32,GC,-28.5863095238103,0.860475202517396,440.999999999966,-33.2215378667259,0 +Uniform - Scaled,32,GC,1.33928571428576,0.860475202517397,440.99999999998,1.55644893701476,0.40478545711267 +Uniform - Adaptive,32,GC,0.297619047619017,0.860475202517397,440.99999999998,0.345877541558787,0.985767159729874 +Scaled - Adaptive,32,GC,-1.04166666666675,0.860475202517397,440.99999999998,-1.21057139545597,0.620449130313097 +Homogeneous - Uniform,4,MC,-15.7068452380955,0.860475202517395,440.999999999895,-18.2536872557672,0 +Homogeneous - Scaled,4,MC,-20.9151785714286,0.860475202517394,440.999999999895,-24.3065442330464,0 +Homogeneous - Adaptive,4,MC,-20.1711309523811,0.860475202517395,440.999999999895,-23.4418503791494,0 +Uniform - Scaled,4,MC,-5.20833333333316,0.860475202517394,440.999999999966,-6.05285697727922,1.82240592616623e-08 +Uniform - Adaptive,4,MC,-4.46428571428562,0.860475202517395,440.999999999966,-5.18816312338225,1.93604288611926e-06 +Scaled - Adaptive,4,MC,0.74404761904754,0.860475202517395,440.999999999952,0.864693853896968,0.823056834750585 +Homogeneous - Uniform,8,MC,-22.9464285714286,0.860475202517394,440.999999999966,-26.6671584541854,0 +Homogeneous - Scaled,8,MC,-26.6666666666667,0.860475202517397,440.999999999966,-30.9906277236706,0 +Homogeneous - Adaptive,8,MC,-23.6160714285715,0.860475202517397,440.999999999966,-27.4453829226927,0 +Uniform - Scaled,8,MC,-3.7202380952381,0.860475202517396,440.99999999998,-4.3234692694853,0.000111582040550617 +Uniform - Adaptive,8,MC,-0.669642857142894,0.860475202517395,440.999999999994,-0.778224468507397,0.864309676165084 +Scaled - Adaptive,8,MC,3.05059523809521,0.8604752025174,440.99999999998,3.54524480097789,0.00244348065632305 +Homogeneous - Uniform,16,MC,-26.9568452380952,0.860475202517396,440.999999999966,-31.3278583266904,0 +Homogeneous - Scaled,16,MC,-25.5431547619047,0.860475202517396,440.999999999966,-29.684940004286,0 +Homogeneous - Adaptive,16,MC,-25.766369047619,0.860475202517394,440.999999999966,-29.9443481604552,0 +Uniform - Scaled,16,MC,1.41369047619048,0.860475202517395,440.99999999998,1.64291832240442,0.355621577834319 +Uniform - Adaptive,16,MC,1.19047619047617,0.860475202517394,441.000000000008,1.38351016623528,0.510361743066513 +Scaled - Adaptive,16,MC,-0.223214285714309,0.860475202517394,440.99999999998,-0.259408156169145,0.993876629091368 +Homogeneous - Uniform,32,MC,-25.014880952381,0.860475202517396,440.999999999952,-29.0710073680191,0 +Homogeneous - Scaled,32,MC,-25.2752976190477,0.860475202517397,440.999999999952,-29.3736502168831,0 +Homogeneous - Adaptive,32,MC,-25.5357142857143,0.860475202517395,440.999999999966,-29.6762930657471,0 +Uniform - Scaled,32,MC,-0.260416666666702,0.860475202517398,440.99999999998,-0.30264284886401,0.990364532146554 +Uniform - Adaptive,32,MC,-0.520833333333334,0.860475202517395,440.999999999994,-0.605285697727942,0.930375031963133 +Scaled - Adaptive,32,MC,-0.260416666666632,0.860475202517397,440.99999999998,-0.30264284886393,0.990364532146562 diff --git a/analysis/SciRep-Final/csv/full_clf_cond_3.csv b/analysis/SciRep-Final/csv/full_clf_cond_3.csv new file mode 100644 index 0000000..229042f --- /dev/null +++ b/analysis/SciRep-Final/csv/full_clf_cond_3.csv @@ -0,0 +1,65 @@ +Layer,Session,Condition,Duplication,Accuracy.m,Accuracy.s +GC,Concentration,Homogeneous,4,79.9404761904762,0.947051414443939 +GC,Concentration,Homogeneous,8,81.5997023809524,1.54317700424802 +GC,Concentration,Homogeneous,16,81.3913690476191,1.56257086007124 +GC,Concentration,Homogeneous,32,81.8154761904762,1.09756481634439 +GC,Concentration,Uniform,4,87.0535714285714,4.51921983428477 +GC,Concentration,Uniform,8,95.5357142857143,2.96767489228148 +GC,Concentration,Uniform,16,97.1726190476191,2.45938007890809 +GC,Concentration,Uniform,32,97.6190476190476,1.55869921597137 +GC,Concentration,Scaled,4,87.2767857142857,2.88003689001587 +GC,Concentration,Scaled,8,93.452380952381,2.86351330620455 +GC,Concentration,Scaled,16,96.5773809523809,3.12955207815951 +GC,Concentration,Scaled,32,97.0238095238095,1.68358757425369 +GC,Concentration,Adaptive,4,90.4761904761905,3.88373715873103 +GC,Concentration,Adaptive,8,92.1875,3.56344771548444 +GC,Concentration,Adaptive,16,96.6517857142857,2.75426863392207 +GC,Concentration,Adaptive,32,97.7678571428571,2.54036891943702 +GC,Saturation,Homogeneous,4,56.7559523809524,0.97288722686827 +GC,Saturation,Homogeneous,8,56.6666666666667,0.765589516690297 +GC,Saturation,Homogeneous,16,57.3735119047619,1.09036436872825 +GC,Saturation,Homogeneous,32,56.9940476190476,0.829681014806154 +GC,Saturation,Uniform,4,81.7708333333333,4.33850587414085 +GC,Saturation,Uniform,8,96.2797619047619,3.72066324101346 +GC,Saturation,Uniform,16,99.3303571428571,1.25010122686983 +GC,Saturation,Uniform,32,98.9583333333333,1.64557844220779 +GC,Saturation,Scaled,4,93.8244047619048,4.63191519816374 +GC,Saturation,Scaled,8,97.1726190476191,3.496232179902 +GC,Saturation,Scaled,16,98.3630952380952,1.41397016567312 +GC,Saturation,Scaled,32,96.875,2.00597382858596 +GC,Saturation,Adaptive,4,90.2529761904762,3.81718908880377 +GC,Saturation,Adaptive,8,97.0238095238095,3.45619071988121 +GC,Saturation,Adaptive,16,97.2470238095238,3.29019554165098 +GC,Saturation,Adaptive,32,98.2142857142857,2.22717701593687 +MC,Concentration,Homogeneous,4,83.7574404761905,0.914110702407137 +MC,Concentration,Homogeneous,8,83.3482142857143,1.07036511849017 +MC,Concentration,Homogeneous,16,83.5863095238095,0.937786777215678 +MC,Concentration,Homogeneous,32,83.7946428571429,0.935625352786815 +MC,Concentration,Uniform,4,87.2023809523809,3.14970394174356 +MC,Concentration,Uniform,8,95.2380952380952,2.11048871530456 +MC,Concentration,Uniform,16,97.0982142857143,2.04811090042679 +MC,Concentration,Uniform,32,97.9166666666667,1.31184086909367 +MC,Concentration,Scaled,4,88.9136904761905,3.0507248603621 +MC,Concentration,Scaled,8,95.3125,1.72626377981818 +MC,Concentration,Scaled,16,94.6428571428571,1.59084072566919 +MC,Concentration,Scaled,32,95.3125,2.0232467204642 +MC,Concentration,Adaptive,4,92.6339285714286,3.52775880688384 +MC,Concentration,Adaptive,8,91.5178571428572,3.82794758345148 +MC,Concentration,Adaptive,16,96.9494047619048,1.69668962068324 +MC,Concentration,Adaptive,32,95.3125,2.97512736114452 +MC,Saturation,Homogeneous,4,58.6383928571429,1.15044845377825 +MC,Saturation,Homogeneous,8,58.4821428571429,1.3651601120452 +MC,Saturation,Homogeneous,16,58.4821428571429,1.16425197842141 +MC,Saturation,Homogeneous,32,58.6607142857143,1.25827355871387 +MC,Saturation,Uniform,4,86.6071428571429,3.22905400442986 +MC,Saturation,Uniform,8,92.485119047619,1.62039447280267 +MC,Saturation,Uniform,16,98.8839285714286,1.32861394489794 +MC,Saturation,Uniform,32,94.5684523809524,5.22258739478909 +MC,Saturation,Scaled,4,95.3125,2.34750098018293 +MC,Saturation,Scaled,8,99.8511904761905,0.275541696360882 +MC,Saturation,Scaled,16,98.5119047619048,1.14717156149932 +MC,Saturation,Scaled,32,97.6934523809524,4.20219907682974 +MC,Saturation,Adaptive,4,90.1041666666667,4.17197790740362 +MC,Saturation,Adaptive,8,97.5446428571429,2.61280757873071 +MC,Saturation,Adaptive,16,96.6517857142857,2.89755818861851 +MC,Saturation,Adaptive,32,98.2142857142857,2.71842982366425 diff --git a/analysis/SciRep-Final/csv/full_clf_dup_1.csv b/analysis/SciRep-Final/csv/full_clf_dup_1.csv new file mode 100644 index 0000000..9c21a84 --- /dev/null +++ b/analysis/SciRep-Final/csv/full_clf_dup_1.csv @@ -0,0 +1,16 @@ +model.term,df1,df2,F.ratio,p.value +Layer,1,441,5.805,0.0163940824707428 +Condition,3,441,3298.391,1.40556729801199e-301 +Duplication,3,441,179.135,6.09289163386833e-76 +Session,1,441,555.771,4.11120373447204e-80 +Layer:Condition,3,441,6.572,0.000235805568692485 +Layer:Duplication,3,441,5.944,0.000557259393307905 +Layer:Session,1,441,0.042,0.838225874225619 +Condition:Duplication,9,441,28.307,9.3891107570878e-39 +Condition:Session,3,441,937.419,6.69716854927288e-191 +Duplication:Session,3,441,3.08,0.0273166226018128 +Layer:Condition:Duplication,9,441,1.656,0.0974283388520933 +Layer:Condition:Session,3,441,1.498,0.21432258197009 +Layer:Duplication:Session,3,441,0.047,0.986298344329484 +Condition:Duplication:Session,9,441,7.022,1.65477356379195e-09 +Layer:Condition:Duplication:Session,9,441,2.719,0.00429506307005925 diff --git a/analysis/SciRep-Final/csv/full_clf_dup_2.csv b/analysis/SciRep-Final/csv/full_clf_dup_2.csv new file mode 100644 index 0000000..afbdfa7 --- /dev/null +++ b/analysis/SciRep-Final/csv/full_clf_dup_2.csv @@ -0,0 +1,25 @@ +contrast,Condition,Layer,estimate,SE,df,t.ratio,p.value +Duplication8 - Duplication4,Homogeneous,GC,0.784970238090433,0.860475202517394,440.999999999895,0.912252015855815,0.694108086145561 +Duplication16 - Duplication8,Homogeneous,GC,0.249255952380492,0.860475202517395,440.999999999966,0.28967244105498,0.983673825909224 +Duplication32 - Duplication16,Homogeneous,GC,0.0223214285713724,0.860475202517395,440.999999999966,0.0259408156168465,0.999987446050796 +Duplication8 - Duplication4,Uniform,GC,11.4955357142861,0.860475202517396,440.999999999966,13.35952004271,0 +Duplication16 - Duplication8,Uniform,GC,2.34375000000004,0.860475202517395,440.99999999998,2.72378563977579,0.0187883894856506 +Duplication32 - Duplication16,Uniform,GC,0.0372023809523942,0.860475202517395,440.999999999994,0.0432346926948684,0.999941937723361 +Duplication8 - Duplication4,Scaled,GC,4.76190476190537,0.860475202517396,440.999999999966,5.53404066494188,1.16328894184115e-07 +Duplication16 - Duplication8,Scaled,GC,2.15773809523817,0.860475202517397,440.99999999998,2.50761217630156,0.0343602329101428 +Duplication32 - Duplication16,Scaled,GC,-0.520833333333397,0.860475202517397,440.99999999998,-0.605285697728014,0.879509285876056 +Duplication8 - Duplication4,Adaptive,GC,4.24107142857157,0.860475202517396,440.999999999966,4.9287549672134,3.03696106684903e-06 +Duplication16 - Duplication8,Adaptive,GC,2.34375000000004,0.860475202517395,440.99999999998,2.72378563977579,0.0191034260150131 +Duplication32 - Duplication16,Adaptive,GC,1.04166666666674,0.860475202517397,440.99999999998,1.21057139545597,0.490582187575215 +Duplication8 - Duplication4,Homogeneous,MC,-0.282738095237958,0.860475202517395,440.999999999881,-0.328583664480724,0.976624720760365 +Duplication16 - Duplication8,Homogeneous,MC,0.11904761904772,0.860475202517395,440.999999999966,0.138351016623647,0.998124634689698 +Duplication32 - Duplication16,Homogeneous,MC,0.193452380952311,0.860475202517395,440.999999999952,0.224820402013154,0.99215827990709 +Duplication8 - Duplication4,Uniform,MC,6.95684523809521,0.860475202517397,440.999999999966,8.08488753393745,1.18793863634892e-14 +Duplication16 - Duplication8,Uniform,MC,4.12946428571429,0.860475202517398,440.99999999998,4.79905088912867,8.97547431355505e-06 +Duplication32 - Duplication16,Uniform,MC,-1.74851190476191,0.860475202517398,440.99999999998,-2.03203055665809,0.11214144781483 +Duplication8 - Duplication4,Scaled,MC,5.46875000000015,0.860475202517397,440.999999999966,6.35549982614355,1.04583897098109e-09 +Duplication16 - Duplication8,Scaled,MC,-1.00446428571429,0.860475202517399,440.99999999998,-1.16733670276103,0.519525991055776 +Duplication32 - Duplication16,Scaled,MC,-0.0744047619047302,0.860475202517398,440.99999999998,-0.0864693853896687,0.99953766250479 +Duplication8 - Duplication4,Adaptive,MC,3.16220238095248,0.860475202517396,440.999999999966,3.67494887906261,0.000711872739884711 +Duplication16 - Duplication8,Adaptive,MC,2.26934523809523,0.860475202517395,440.999999999994,2.63731625438602,0.0237694438513918 +Duplication32 - Duplication16,Adaptive,MC,-0.0372023809524071,0.860475202517395,440.999999999994,-0.0432346926948834,0.999941936674904 diff --git a/analysis/SciRep-Final/csv/full_clf_dup_3.csv b/analysis/SciRep-Final/csv/full_clf_dup_3.csv new file mode 100644 index 0000000..229042f --- /dev/null +++ b/analysis/SciRep-Final/csv/full_clf_dup_3.csv @@ -0,0 +1,65 @@ +Layer,Session,Condition,Duplication,Accuracy.m,Accuracy.s +GC,Concentration,Homogeneous,4,79.9404761904762,0.947051414443939 +GC,Concentration,Homogeneous,8,81.5997023809524,1.54317700424802 +GC,Concentration,Homogeneous,16,81.3913690476191,1.56257086007124 +GC,Concentration,Homogeneous,32,81.8154761904762,1.09756481634439 +GC,Concentration,Uniform,4,87.0535714285714,4.51921983428477 +GC,Concentration,Uniform,8,95.5357142857143,2.96767489228148 +GC,Concentration,Uniform,16,97.1726190476191,2.45938007890809 +GC,Concentration,Uniform,32,97.6190476190476,1.55869921597137 +GC,Concentration,Scaled,4,87.2767857142857,2.88003689001587 +GC,Concentration,Scaled,8,93.452380952381,2.86351330620455 +GC,Concentration,Scaled,16,96.5773809523809,3.12955207815951 +GC,Concentration,Scaled,32,97.0238095238095,1.68358757425369 +GC,Concentration,Adaptive,4,90.4761904761905,3.88373715873103 +GC,Concentration,Adaptive,8,92.1875,3.56344771548444 +GC,Concentration,Adaptive,16,96.6517857142857,2.75426863392207 +GC,Concentration,Adaptive,32,97.7678571428571,2.54036891943702 +GC,Saturation,Homogeneous,4,56.7559523809524,0.97288722686827 +GC,Saturation,Homogeneous,8,56.6666666666667,0.765589516690297 +GC,Saturation,Homogeneous,16,57.3735119047619,1.09036436872825 +GC,Saturation,Homogeneous,32,56.9940476190476,0.829681014806154 +GC,Saturation,Uniform,4,81.7708333333333,4.33850587414085 +GC,Saturation,Uniform,8,96.2797619047619,3.72066324101346 +GC,Saturation,Uniform,16,99.3303571428571,1.25010122686983 +GC,Saturation,Uniform,32,98.9583333333333,1.64557844220779 +GC,Saturation,Scaled,4,93.8244047619048,4.63191519816374 +GC,Saturation,Scaled,8,97.1726190476191,3.496232179902 +GC,Saturation,Scaled,16,98.3630952380952,1.41397016567312 +GC,Saturation,Scaled,32,96.875,2.00597382858596 +GC,Saturation,Adaptive,4,90.2529761904762,3.81718908880377 +GC,Saturation,Adaptive,8,97.0238095238095,3.45619071988121 +GC,Saturation,Adaptive,16,97.2470238095238,3.29019554165098 +GC,Saturation,Adaptive,32,98.2142857142857,2.22717701593687 +MC,Concentration,Homogeneous,4,83.7574404761905,0.914110702407137 +MC,Concentration,Homogeneous,8,83.3482142857143,1.07036511849017 +MC,Concentration,Homogeneous,16,83.5863095238095,0.937786777215678 +MC,Concentration,Homogeneous,32,83.7946428571429,0.935625352786815 +MC,Concentration,Uniform,4,87.2023809523809,3.14970394174356 +MC,Concentration,Uniform,8,95.2380952380952,2.11048871530456 +MC,Concentration,Uniform,16,97.0982142857143,2.04811090042679 +MC,Concentration,Uniform,32,97.9166666666667,1.31184086909367 +MC,Concentration,Scaled,4,88.9136904761905,3.0507248603621 +MC,Concentration,Scaled,8,95.3125,1.72626377981818 +MC,Concentration,Scaled,16,94.6428571428571,1.59084072566919 +MC,Concentration,Scaled,32,95.3125,2.0232467204642 +MC,Concentration,Adaptive,4,92.6339285714286,3.52775880688384 +MC,Concentration,Adaptive,8,91.5178571428572,3.82794758345148 +MC,Concentration,Adaptive,16,96.9494047619048,1.69668962068324 +MC,Concentration,Adaptive,32,95.3125,2.97512736114452 +MC,Saturation,Homogeneous,4,58.6383928571429,1.15044845377825 +MC,Saturation,Homogeneous,8,58.4821428571429,1.3651601120452 +MC,Saturation,Homogeneous,16,58.4821428571429,1.16425197842141 +MC,Saturation,Homogeneous,32,58.6607142857143,1.25827355871387 +MC,Saturation,Uniform,4,86.6071428571429,3.22905400442986 +MC,Saturation,Uniform,8,92.485119047619,1.62039447280267 +MC,Saturation,Uniform,16,98.8839285714286,1.32861394489794 +MC,Saturation,Uniform,32,94.5684523809524,5.22258739478909 +MC,Saturation,Scaled,4,95.3125,2.34750098018293 +MC,Saturation,Scaled,8,99.8511904761905,0.275541696360882 +MC,Saturation,Scaled,16,98.5119047619048,1.14717156149932 +MC,Saturation,Scaled,32,97.6934523809524,4.20219907682974 +MC,Saturation,Adaptive,4,90.1041666666667,4.17197790740362 +MC,Saturation,Adaptive,8,97.5446428571429,2.61280757873071 +MC,Saturation,Adaptive,16,96.6517857142857,2.89755818861851 +MC,Saturation,Adaptive,32,98.2142857142857,2.71842982366425 diff --git a/analysis/SciRep-Final/csv/full_util_cond_1.csv b/analysis/SciRep-Final/csv/full_util_cond_1.csv new file mode 100644 index 0000000..1d77633 --- /dev/null +++ b/analysis/SciRep-Final/csv/full_util_cond_1.csv @@ -0,0 +1,16 @@ +model.term,df1,df2,F.ratio,p.value +Layer,1,441,476.183,3.96305556138174e-72 +Condition,3,441,559.527,6.8403554586999e-150 +Duplication,3,441,91.858,3.40888345542054e-46 +Session,1,441,177.808,2.56167620505232e-34 +Layer:Condition,3,441,17.085,1.63219364152819e-10 +Layer:Duplication,3,441,3.314,0.0199558894138793 +Layer:Session,1,441,22.519,2.81648552040241e-06 +Condition:Duplication,9,441,21.523,3.12160379962073e-30 +Condition:Session,3,441,44.078,6.25656723321129e-25 +Duplication:Session,3,441,2.041,0.107374497230754 +Layer:Condition:Duplication,9,441,3.765,0.00013880778846047 +Layer:Condition:Session,3,441,4.683,0.00312120957748287 +Layer:Duplication:Session,3,441,4.212,0.00592960708235977 +Condition:Duplication:Session,9,441,8.261,2.14030550424892e-11 +Layer:Condition:Duplication:Session,9,441,2.721,0.00427463113073923 diff --git a/analysis/SciRep-Final/csv/full_util_cond_2.csv b/analysis/SciRep-Final/csv/full_util_cond_2.csv new file mode 100644 index 0000000..1b0db27 --- /dev/null +++ b/analysis/SciRep-Final/csv/full_util_cond_2.csv @@ -0,0 +1,49 @@ +contrast,Duplication,Layer,estimate,SE,df,t.ratio,p.value +Homogeneous - Uniform,4,GC,10.4663046056266,0.716147151523837,440.999999999881,14.6147402574403,0 +Homogeneous - Scaled,4,GC,2.1892107752952,0.716147151523834,440.999999999881,3.0569286921507,0.0126447092304142 +Homogeneous - Adaptive,4,GC,-4.10713669344144,0.716147151523836,440.999999999867,-5.73504577195088,1.08455520675399e-07 +Uniform - Scaled,4,GC,-8.27709383033139,0.716147151523837,440.99999999998,-11.5578115652896,0 +Uniform - Adaptive,4,GC,-14.573441299068,0.716147151523837,440.999999999966,-20.3497860293911,0 +Scaled - Adaptive,4,GC,-6.29634746873665,0.716147151523837,440.999999999966,-8.79197446410157,0 +Homogeneous - Uniform,8,GC,10.0612189214605,0.716147151523837,440.99999999998,14.0490943796286,0 +Homogeneous - Scaled,8,GC,6.79029673607089,0.716147151523838,440.99999999998,9.48170598964516,0 +Homogeneous - Adaptive,8,GC,2.79702186468859,0.716147151523837,440.99999999998,3.90565243293506,0.000626948524427795 +Uniform - Scaled,8,GC,-3.27092218538965,0.716147151523837,440.999999999994,-4.5673883899834,3.79242805450541e-05 +Uniform - Adaptive,8,GC,-7.26419705677196,0.716147151523836,440.999999999994,-10.1434419466935,0 +Scaled - Adaptive,8,GC,-3.9932748713823,0.716147151523837,440.999999999994,-5.5760535567101,2.56930229980412e-07 +Homogeneous - Uniform,16,GC,10.7824071172446,0.716147151523837,440.99999999998,15.0561334975662,0 +Homogeneous - Scaled,16,GC,8.2959282968895,0.716147151523837,440.99999999998,11.5841112810924,0 +Homogeneous - Adaptive,16,GC,5.08208269086524,0.716147151523837,440.999999999994,7.09642240432214,9.19897491513666e-12 +Uniform - Scaled,16,GC,-2.48647882035513,0.716147151523837,440.999999999994,-3.47202221647372,0.00317330983433284 +Uniform - Adaptive,16,GC,-5.70032442637939,0.716147151523837,441.000000000008,-7.95971109324403,0 +Scaled - Adaptive,16,GC,-3.21384560602426,0.716147151523836,440.999999999994,-4.48768887677031,5.42657447551997e-05 +Homogeneous - Uniform,32,GC,10.2183378331034,0.716147151523838,440.99999999998,14.2684891105977,0 +Homogeneous - Scaled,32,GC,8.71384408520773,0.716147151523837,440.99999999998,12.1676726168165,0 +Homogeneous - Adaptive,32,GC,5.77443565176921,0.716147151523837,440.99999999998,8.06319712294074,0 +Uniform - Scaled,32,GC,-1.5044937478957,0.716147151523838,440.999999999994,-2.1008164937812,0.15441810049639 +Uniform - Adaptive,32,GC,-4.44390218133421,0.716147151523838,440.999999999994,-6.20529198765694,7.52837381323701e-09 +Scaled - Adaptive,32,GC,-2.93940843343851,0.716147151523837,441.000000000008,-4.10447549387575,0.000281196606890188 +Homogeneous - Uniform,4,MC,8.16258212403248,0.716147151523837,441.000000000008,11.3979118769989,0 +Homogeneous - Scaled,4,MC,3.22065977123255,0.716147151523837,441.000000000008,4.49720391176526,5.20084135711141e-05 +Homogeneous - Adaptive,4,MC,1.87992575270353,0.716147151523837,441.000000000008,2.62505512826991,0.0442430494786592 +Uniform - Scaled,4,MC,-4.94192235279993,0.716147151523837,440.999999999994,-6.90070796523365,8.67573790586107e-11 +Uniform - Adaptive,4,MC,-6.28265637132895,0.716147151523837,440.999999999994,-8.77285674872901,0 +Scaled - Adaptive,4,MC,-1.34073401852902,0.716147151523837,440.999999999994,-1.87214878349536,0.241571660339191 +Homogeneous - Uniform,8,MC,9.46135269548082,0.716147151523838,440.999999999994,13.2114645367906,0 +Homogeneous - Scaled,8,MC,7.54442420760383,0.716147151523836,441.000000000008,10.5347402297846,0 +Homogeneous - Adaptive,8,MC,4.93876203115525,0.716147151523838,440.999999999994,6.8962950151326,8.98386920411554e-11 +Uniform - Scaled,8,MC,-1.91692848787699,0.716147151523837,440.999999999994,-2.67672430700604,0.0384817300523131 +Uniform - Adaptive,8,MC,-4.52259066432557,0.716147151523839,440.999999999994,-6.31516952165804,3.93219101724185e-09 +Scaled - Adaptive,8,MC,-2.60566217644858,0.716147151523837,440.999999999994,-3.63844521465202,0.0017390773536069 +Homogeneous - Uniform,16,MC,9.94786557919495,0.716147151523836,441.000000000008,13.8908121857744,0 +Homogeneous - Scaled,16,MC,8.28913141772118,0.716147151523838,441.000000000008,11.5746203836507,0 +Homogeneous - Adaptive,16,MC,6.35743607420086,0.716147151523836,441.000000000008,8.87727621435531,0 +Uniform - Scaled,16,MC,-1.65873416147378,0.716147151523838,440.999999999994,-2.31619180212373,0.095888885872273 +Uniform - Adaptive,16,MC,-3.5904295049941,0.716147151523835,441.000000000008,-5.01353597141913,4.6182196919986e-06 +Scaled - Adaptive,16,MC,-1.93169534352032,0.716147151523838,440.999999999994,-2.69734416929538,0.0363687781931794 +Homogeneous - Uniform,32,MC,10.1695489106578,0.716147151523837,441.000000000008,14.2003621588368,0 +Homogeneous - Scaled,32,MC,8.69077734562083,0.716147151523838,440.999999999994,12.135463119735,0 +Homogeneous - Adaptive,32,MC,6.59267157839497,0.716147151523837,440.999999999994,9.20574991378087,0 +Uniform - Scaled,32,MC,-1.47877156503699,0.716147151523837,441.000000000008,-2.0648990391017,0.166330191874788 +Uniform - Adaptive,32,MC,-3.57687733226285,0.716147151523836,441.000000000008,-4.9946122450559,5.06680184320096e-06 +Scaled - Adaptive,32,MC,-2.09810576722586,0.716147151523837,441.000000000008,-2.92971320595419,0.0186589417750316 diff --git a/analysis/SciRep-Final/csv/full_util_cond_3.csv b/analysis/SciRep-Final/csv/full_util_cond_3.csv new file mode 100644 index 0000000..e77c10a --- /dev/null +++ b/analysis/SciRep-Final/csv/full_util_cond_3.csv @@ -0,0 +1,65 @@ +Layer,Session,Condition,Duplication,Utilization.m,Utilization.s +GC,Concentration,Homogeneous,4,11.6864796009898,3.65121925084762 +GC,Concentration,Homogeneous,8,11.1998716498277,3.23680018042204 +GC,Concentration,Homogeneous,16,11.174778394175,3.07444454223316 +GC,Concentration,Homogeneous,32,11.2567832151365,3.11794669986395 +GC,Concentration,Uniform,4,3.87944976345525,1.32195928596387 +GC,Concentration,Uniform,8,3.94254223610003,0.916368665594993 +GC,Concentration,Uniform,16,3.31306946306976,1.50852385816561 +GC,Concentration,Uniform,32,4.55580992121701,1.5538561092037 +GC,Concentration,Scaled,4,11.0542651205659,3.90230217803539 +GC,Concentration,Scaled,8,6.51756927431206,2.27642365014914 +GC,Concentration,Scaled,16,5.47890989699595,1.98343532988325 +GC,Concentration,Scaled,32,5.26308882642273,1.5503177773135 +GC,Concentration,Adaptive,4,22.7729005776063,6.13162272705228 +GC,Concentration,Adaptive,8,10.2783536612527,4.27289239180837 +GC,Concentration,Adaptive,16,7.20196575104497,1.91802742331239 +GC,Concentration,Adaptive,32,6.40080271823212,1.67592572059699 +GC,Saturation,Homogeneous,4,16.9932849034824,0.840534858512884 +GC,Saturation,Homogeneous,8,16.8270980355891,0.710959864951623 +GC,Saturation,Homogeneous,16,16.7836085338076,0.678076915713321 +GC,Saturation,Homogeneous,32,16.7494453185422,0.763515634912929 +GC,Saturation,Uniform,4,3.86770552976451,0.676724556422294 +GC,Saturation,Uniform,8,3.96198960639566,1.07108240704685 +GC,Saturation,Uniform,16,3.08050323042362,1.10817767696972 +GC,Saturation,Uniform,32,3.01374294625489,1.13587325479142 +GC,Saturation,Scaled,4,13.2470778333167,4.22050449096716 +GC,Saturation,Scaled,8,7.92880693896293,1.76591879513336 +GC,Saturation,Scaled,16,5.88762043720769,1.42171617153094 +GC,Saturation,Scaled,32,5.31545153684057,1.53849565840209 +GC,Saturation,Adaptive,4,14.1211373137496,7.69826756805727 +GC,Saturation,Adaptive,8,12.1545722947869,4.24291898081806 +GC,Saturation,Adaptive,16,10.5922557952072,3.26674987408917 +GC,Saturation,Adaptive,32,10.0565545119082,2.68044730439662 +MC,Concentration,Homogeneous,4,7.82414809733805,0.0744578128373805 +MC,Concentration,Homogeneous,8,7.82414546916327,0.0743982947548655 +MC,Concentration,Homogeneous,16,7.82414809733805,0.0744578128373805 +MC,Concentration,Homogeneous,32,7.82414809733805,0.0744578128373805 +MC,Concentration,Uniform,4,2.24496754813064,0.0306758234989434 +MC,Concentration,Uniform,8,0.96036740702663,0.0211034701249928 +MC,Concentration,Uniform,16,0.737411468950517,0.0104113305386758 +MC,Concentration,Uniform,32,0.591543629548595,0.00640945634255244 +MC,Concentration,Scaled,4,4.50379978381234,0.0782816262618428 +MC,Concentration,Scaled,8,1.76120072223633,0.0627339020612621 +MC,Concentration,Scaled,16,1.20562968353467,0.0617719460056541 +MC,Concentration,Scaled,32,0.933814636349869,0.0709078399862094 +MC,Concentration,Adaptive,4,7.71965082650229,0.416014398846528 +MC,Concentration,Adaptive,8,3.55117731131311,0.230092823604182 +MC,Concentration,Adaptive,16,2.30149117126834,0.198062336200491 +MC,Concentration,Adaptive,32,2.06547644426592,0.131475482261742 +MC,Saturation,Homogeneous,4,13.553840038136,0.0162015835724145 +MC,Saturation,Homogeneous,8,13.553840038136,0.0162015835724145 +MC,Saturation,Homogeneous,16,13.553840038136,0.0162015835724145 +MC,Saturation,Homogeneous,32,13.553840038136,0.0162015835724145 +MC,Saturation,Uniform,4,2.80785633927828,0.0501815084606198 +MC,Saturation,Uniform,8,1.49491270931102,0.0406917472005399 +MC,Saturation,Uniform,16,0.744845508133653,0.0226114068206703 +MC,Saturation,Uniform,32,0.447346684609842,0.0178359723620206 +MC,Saturation,Scaled,4,10.4328688091964,0.274201495349119 +MC,Saturation,Scaled,8,4.52793636985529,0.123755744514424 +MC,Saturation,Scaled,16,3.59409561649704,0.053574453914608 +MC,Saturation,Scaled,32,3.06261880788254,0.0616405754198645 +MC,Saturation,Adaptive,4,9.89848580356453,1.49251094669951 +MC,Saturation,Adaptive,8,7.94928413367566,0.750913344052388 +MC,Saturation,Adaptive,16,6.36162481580401,0.830858087822323 +MC,Saturation,Adaptive,32,6.1271685344182,0.647673309712298 diff --git a/analysis/SciRep-Final/csv/full_util_dup_1.csv b/analysis/SciRep-Final/csv/full_util_dup_1.csv new file mode 100644 index 0000000..1d77633 --- /dev/null +++ b/analysis/SciRep-Final/csv/full_util_dup_1.csv @@ -0,0 +1,16 @@ +model.term,df1,df2,F.ratio,p.value +Layer,1,441,476.183,3.96305556138174e-72 +Condition,3,441,559.527,6.8403554586999e-150 +Duplication,3,441,91.858,3.40888345542054e-46 +Session,1,441,177.808,2.56167620505232e-34 +Layer:Condition,3,441,17.085,1.63219364152819e-10 +Layer:Duplication,3,441,3.314,0.0199558894138793 +Layer:Session,1,441,22.519,2.81648552040241e-06 +Condition:Duplication,9,441,21.523,3.12160379962073e-30 +Condition:Session,3,441,44.078,6.25656723321129e-25 +Duplication:Session,3,441,2.041,0.107374497230754 +Layer:Condition:Duplication,9,441,3.765,0.00013880778846047 +Layer:Condition:Session,3,441,4.683,0.00312120957748287 +Layer:Duplication:Session,3,441,4.212,0.00592960708235977 +Condition:Duplication:Session,9,441,8.261,2.14030550424892e-11 +Layer:Condition:Duplication:Session,9,441,2.721,0.00427463113073923 diff --git a/analysis/SciRep-Final/csv/full_util_dup_2.csv b/analysis/SciRep-Final/csv/full_util_dup_2.csv new file mode 100644 index 0000000..478a304 --- /dev/null +++ b/analysis/SciRep-Final/csv/full_util_dup_2.csv @@ -0,0 +1,25 @@ +contrast,Condition,Layer,estimate,SE,df,t.ratio,p.value +Duplication8 - Duplication4,Homogeneous,GC,-0.326397409528084,0.716147151523836,440.999999999881,-0.455768634747157,0.942257329137423 +Duplication16 - Duplication8,Homogeneous,GC,-0.0342913787170644,0.716147151523837,440.99999999998,-0.0478831461440548,0.99992115455378 +Duplication32 - Duplication16,Homogeneous,GC,0.0239208028480576,0.716147151523836,440.99999999998,0.0334020777673391,0.999973209486967 +Duplication8 - Duplication4,Uniform,GC,0.0786882746379661,0.716147151523837,440.99999999998,0.109877243064545,0.999055078505899 +Duplication16 - Duplication8,Uniform,GC,-0.755479574501149,0.716147151523836,440.999999999994,-1.05492226408165,0.596193623322526 +Duplication32 - Duplication16,Uniform,GC,0.587990086989259,0.716147151523837,441.000000000008,0.821046464735799,0.754590494266851 +Duplication8 - Duplication4,Scaled,GC,-4.92748337030377,0.716147151523836,440.99999999998,-6.88054593224165,4.12245793057764e-11 +Duplication16 - Duplication8,Scaled,GC,-1.53992293953567,0.716147151523836,440.999999999994,-2.15028843759133,0.0854347352696267 +Duplication32 - Duplication16,Scaled,GC,-0.393994985470171,0.716147151523837,441.000000000008,-0.550159257956718,0.905318707133437 +Duplication8 - Duplication4,Adaptive,GC,-7.23055596765812,0.716147151523837,440.99999999998,-10.0964668396331,0 +Duplication16 - Duplication8,Adaptive,GC,-2.31935220489371,0.716147151523836,441.000000000008,-3.23865311753113,0.00336882982517162 +Duplication32 - Duplication16,Adaptive,GC,-0.668432158055922,0.716147151523837,440.999999999994,-0.933372640851275,0.679823379386437 +Duplication8 - Duplication4,Homogeneous,MC,-1.31408729500682e-06,0.716147151523837,441.000000000008,-1.83494033622931e-06,1 +Duplication16 - Duplication8,Homogeneous,MC,1.31408739115213e-06,0.716147151523838,440.999999999994,1.83494047048289e-06,1 +Duplication32 - Duplication16,Homogeneous,MC,3.42781358853017e-15,0.716147151523837,440.999999999994,4.78646543693761e-15,1 +Duplication8 - Duplication4,Uniform,MC,-1.29877188553563,0.716147151523838,440.999999999994,-1.81355449473208,0.178053124724486 +Duplication16 - Duplication8,Uniform,MC,-0.486511569626742,0.716147151523838,440.999999999994,-0.679345814043286,0.840454405749434 +Duplication32 - Duplication16,Uniform,MC,-0.221683331462867,0.716147151523837,440.999999999994,-0.309549973062328,0.980265369831826 +Duplication8 - Duplication4,Scaled,MC,-4.32376575045858,0.716147151523836,440.999999999994,-6.0375381529597,7.27625626417705e-09 +Duplication16 - Duplication8,Scaled,MC,-0.744705896029956,0.716147151523835,440.999999999994,-1.03987831892559,0.606484990422923 +Duplication32 - Duplication16,Scaled,MC,-0.401645927899651,0.716147151523835,440.999999999994,-0.560842736084364,0.900539921824114 +Duplication8 - Duplication4,Adaptive,MC,-3.05883759253901,0.716147151523838,440.999999999994,-4.27124172180303,7.30198358032119e-05 +Duplication16 - Duplication8,Adaptive,MC,-1.41867272895821,0.716147151523839,440.999999999994,-1.98097936428222,0.12470525789491 +Duplication32 - Duplication16,Adaptive,MC,-0.235235504194113,0.716147151523836,441.000000000008,-0.328473699425562,0.976646885186996 diff --git a/analysis/SciRep-Final/csv/full_util_dup_3.csv b/analysis/SciRep-Final/csv/full_util_dup_3.csv new file mode 100644 index 0000000..e1099bf --- /dev/null +++ b/analysis/SciRep-Final/csv/full_util_dup_3.csv @@ -0,0 +1,33 @@ +Duplication,Condition,Layer,Utilization.m,Utilization.s +4,Homogeneous,GC,14.3398822522361,3.7497914265303 +4,Homogeneous,MC,10.688994067737,2.95925806181437 +4,Uniform,GC,3.87357764660988,1.01453697357226 +4,Uniform,MC,2.52641194370446,0.293438181379503 +4,Scaled,GC,12.1506714769413,4.08671060472537 +4,Scaled,MC,7.46833429650439,3.06794871037646 +4,Adaptive,GC,18.4470189456779,8.07231018568796 +4,Adaptive,MC,8.80906831503341,1.54475210031767 +8,Homogeneous,GC,14.0134848427084,3.68364930061934 +8,Homogeneous,MC,10.6889927536496,2.95925872021888 +8,Uniform,GC,3.95226592124784,0.962986887367736 +8,Uniform,MC,1.22764005816883,0.2778084404727 +8,Scaled,GC,7.22318810663749,2.09873586977594 +8,Scaled,MC,3.14456854604581,1.43187666514029 +8,Adaptive,GC,11.2164629780198,4.22611390863683 +8,Adaptive,MC,5.75023072249439,2.33368191759858 +16,Homogeneous,GC,13.9791934639913,3.60758353998199 +16,Homogeneous,MC,10.688994067737,2.95925806181437 +16,Uniform,GC,3.19678634674669,1.28432244577938 +16,Uniform,MC,0.741128488542085,0.0174332257643722 +16,Scaled,GC,5.68326516710182,1.68038141075714 +16,Scaled,MC,2.39986265001586,1.23466270750804 +16,Adaptive,GC,8.89711077312608,3.12441625363055 +16,Adaptive,MC,4.33155799353617,2.17632132743626 +32,Homogeneous,GC,14.0031142668394,3.58523994700867 +32,Homogeneous,MC,10.688994067737,2.95925806181437 +32,Uniform,GC,3.78477643373595,1.537197021302 +32,Uniform,MC,0.519445157079218,0.0755801812662645 +32,Scaled,GC,5.28927018163165,1.49229456709689 +32,Scaled,MC,1.99821672211621,1.10118182510449 +32,Adaptive,GC,8.22867861507016,2.86836490142733 +32,Adaptive,MC,4.09632248934206,2.1454872977278 diff --git a/analysis/SciRep-Final/csv/hh_clf_cond_1.csv b/analysis/SciRep-Final/csv/hh_clf_cond_1.csv new file mode 100644 index 0000000..b0ca417 --- /dev/null +++ b/analysis/SciRep-Final/csv/hh_clf_cond_1.csv @@ -0,0 +1,16 @@ +model.term,df1,df2,F.ratio,p.value +Layer,1,217,9.372,0.00248157314579965 +Condition,1,217,8782.859,1.62094024021444e-177 +Duplication,3,217,135.545,1.72067293338095e-49 +Session,1,217,2477.9,1.104784394652e-120 +Layer:Condition,1,217,23.86,2.0099107277373e-06 +Layer:Duplication,3,217,6.675,0.000248822919398832 +Layer:Session,1,217,3.057,0.0817802288318388 +Condition:Duplication,3,217,116.015,7.45693304645404e-45 +Condition:Session,1,217,2195.971,1.78835066827956e-115 +Duplication:Session,3,217,3.636,0.0136765718931096 +Layer:Condition:Duplication,3,217,2.746,0.0438845293490573 +Layer:Condition:Session,1,217,0.022,0.882725305387403 +Layer:Duplication:Session,3,217,2.702,0.0465168253140417 +Condition:Duplication:Session,3,217,4.534,0.0041765205711525 +Layer:Condition:Duplication:Session,3,217,6.094,0.000533449943942244 diff --git a/analysis/SciRep-Final/csv/hh_clf_cond_2.csv b/analysis/SciRep-Final/csv/hh_clf_cond_2.csv new file mode 100644 index 0000000..de03d67 --- /dev/null +++ b/analysis/SciRep-Final/csv/hh_clf_cond_2.csv @@ -0,0 +1,9 @@ +contrast,Duplication,Layer,estimate,SE,df,t.ratio,p.value +Homogeneous - Uniform,4,GC,-16.0639880952376,0.721377267425607,216.999999999994,-22.2684978036049,5.8125619775264e-58 +Homogeneous - Uniform,8,GC,-26.7745535714286,0.721377267425606,217,-37.1158820501504,6.06962257209351e-96 +Homogeneous - Uniform,16,GC,-28.8690476190477,0.721377267425606,217.000000000004,-40.0193476044418,3.85720719655859e-102 +Homogeneous - Uniform,32,GC,-28.8839285714287,0.721377267425606,217,-40.0399761341349,3.49521017094399e-102 +Homogeneous - Uniform,4,MC,-15.7068452380953,0.721377267425605,217.000000000004,-21.7734130909734,1.70254792741223e-56 +Homogeneous - Uniform,8,MC,-22.9464285714286,0.721377267425603,217.000000000004,-31.8091927866233,1.17814737904576e-83 +Homogeneous - Uniform,16,MC,-26.9568452380952,0.721377267425607,217.000000000004,-37.3685815388897,1.69753438384161e-96 +Homogeneous - Uniform,32,MC,-25.0148809523809,0.721377267425607,217.000000000004,-34.6765584139517,1.85897833583316e-90 diff --git a/analysis/SciRep-Final/csv/hh_clf_cond_3.csv b/analysis/SciRep-Final/csv/hh_clf_cond_3.csv new file mode 100644 index 0000000..f562286 --- /dev/null +++ b/analysis/SciRep-Final/csv/hh_clf_cond_3.csv @@ -0,0 +1,33 @@ +Layer,Session,Condition,Duplication,Accuracy.m,Accuracy.s +GC,Concentration,Homogeneous,4,79.9404761904762,0.947051414443939 +GC,Concentration,Homogeneous,8,81.5997023809524,1.54317700424802 +GC,Concentration,Homogeneous,16,81.3913690476191,1.56257086007124 +GC,Concentration,Homogeneous,32,81.8154761904762,1.09756481634439 +GC,Concentration,Uniform,4,87.0535714285714,4.51921983428477 +GC,Concentration,Uniform,8,95.5357142857143,2.96767489228148 +GC,Concentration,Uniform,16,97.1726190476191,2.45938007890809 +GC,Concentration,Uniform,32,97.6190476190476,1.55869921597137 +GC,Saturation,Homogeneous,4,56.7559523809524,0.97288722686827 +GC,Saturation,Homogeneous,8,56.6666666666667,0.765589516690297 +GC,Saturation,Homogeneous,16,57.3735119047619,1.09036436872825 +GC,Saturation,Homogeneous,32,56.9940476190476,0.829681014806154 +GC,Saturation,Uniform,4,81.7708333333333,4.33850587414085 +GC,Saturation,Uniform,8,96.2797619047619,3.72066324101346 +GC,Saturation,Uniform,16,99.3303571428571,1.25010122686983 +GC,Saturation,Uniform,32,98.9583333333333,1.64557844220779 +MC,Concentration,Homogeneous,4,83.7574404761905,0.914110702407137 +MC,Concentration,Homogeneous,8,83.3482142857143,1.07036511849017 +MC,Concentration,Homogeneous,16,83.5863095238095,0.937786777215678 +MC,Concentration,Homogeneous,32,83.7946428571429,0.935625352786815 +MC,Concentration,Uniform,4,87.2023809523809,3.14970394174356 +MC,Concentration,Uniform,8,95.2380952380952,2.11048871530456 +MC,Concentration,Uniform,16,97.0982142857143,2.04811090042679 +MC,Concentration,Uniform,32,97.9166666666667,1.31184086909367 +MC,Saturation,Homogeneous,4,58.6383928571429,1.15044845377825 +MC,Saturation,Homogeneous,8,58.4821428571429,1.3651601120452 +MC,Saturation,Homogeneous,16,58.4821428571429,1.16425197842141 +MC,Saturation,Homogeneous,32,58.6607142857143,1.25827355871387 +MC,Saturation,Uniform,4,86.6071428571429,3.22905400442986 +MC,Saturation,Uniform,8,92.485119047619,1.62039447280267 +MC,Saturation,Uniform,16,98.8839285714286,1.32861394489794 +MC,Saturation,Uniform,32,94.5684523809524,5.22258739478909 diff --git a/analysis/SciRep-Final/csv/hh_clf_dup_1.csv b/analysis/SciRep-Final/csv/hh_clf_dup_1.csv new file mode 100644 index 0000000..b0ca417 --- /dev/null +++ b/analysis/SciRep-Final/csv/hh_clf_dup_1.csv @@ -0,0 +1,16 @@ +model.term,df1,df2,F.ratio,p.value +Layer,1,217,9.372,0.00248157314579965 +Condition,1,217,8782.859,1.62094024021444e-177 +Duplication,3,217,135.545,1.72067293338095e-49 +Session,1,217,2477.9,1.104784394652e-120 +Layer:Condition,1,217,23.86,2.0099107277373e-06 +Layer:Duplication,3,217,6.675,0.000248822919398832 +Layer:Session,1,217,3.057,0.0817802288318388 +Condition:Duplication,3,217,116.015,7.45693304645404e-45 +Condition:Session,1,217,2195.971,1.78835066827956e-115 +Duplication:Session,3,217,3.636,0.0136765718931096 +Layer:Condition:Duplication,3,217,2.746,0.0438845293490573 +Layer:Condition:Session,1,217,0.022,0.882725305387403 +Layer:Duplication:Session,3,217,2.702,0.0465168253140417 +Condition:Duplication:Session,3,217,4.534,0.0041765205711525 +Layer:Condition:Duplication:Session,3,217,6.094,0.000533449943942244 diff --git a/analysis/SciRep-Final/csv/hh_clf_dup_2.csv b/analysis/SciRep-Final/csv/hh_clf_dup_2.csv new file mode 100644 index 0000000..d946d7c --- /dev/null +++ b/analysis/SciRep-Final/csv/hh_clf_dup_2.csv @@ -0,0 +1,13 @@ +contrast,Condition,Layer,estimate,SE,df,t.ratio,p.value +Duplication8 - Duplication4,Homogeneous,GC,0.784970238095192,0.721377267425607,216.999999999997,1.08815494130627,0.573768055897637 +Duplication16 - Duplication8,Homogeneous,GC,0.24925595238095,0.721377267425606,217,0.345527872357935,0.973031441897109 +Duplication32 - Duplication16,Homogeneous,GC,0.0223214285713437,0.721377267425606,217,0.0309427945393992,0.999978661939374 +Duplication8 - Duplication4,Uniform,GC,11.4955357142862,0.721377267425606,217.000000000004,15.9355391878518,0 +Duplication16 - Duplication8,Uniform,GC,2.34375000000004,0.721377267425605,217.000000000004,3.24899342664932,0.00374617185857606 +Duplication32 - Duplication16,Uniform,GC,0.0372023809523958,0.721377267425606,217.000000000004,0.0515713242325485,0.999901361343554 +Duplication8 - Duplication4,Homogeneous,MC,-0.282738095238067,0.721377267425606,217.000000000004,-0.391942064167174,0.961684572578499 +Duplication16 - Duplication8,Homogeneous,MC,0.119047619047606,0.721377267425606,217,0.165028237544071,0.996833812732069 +Duplication32 - Duplication16,Homogeneous,MC,0.193452380952393,0.721377267425606,217.000000000004,0.268170886009162,0.986902016903146 +Duplication8 - Duplication4,Uniform,MC,6.95684523809522,0.721377267425605,217.000000000007,9.64383763148271,0 +Duplication16 - Duplication8,Uniform,MC,4.12946428571429,0.721377267425604,217.000000000004,5.72441698981062,7.73102302176198e-08 +Duplication32 - Duplication16,Uniform,MC,-1.74851190476191,0.721377267425606,217.000000000004,-2.42385223892882,0.0444542911902797 diff --git a/analysis/SciRep-Final/csv/hh_clf_dup_3.csv b/analysis/SciRep-Final/csv/hh_clf_dup_3.csv new file mode 100644 index 0000000..f562286 --- /dev/null +++ b/analysis/SciRep-Final/csv/hh_clf_dup_3.csv @@ -0,0 +1,33 @@ +Layer,Session,Condition,Duplication,Accuracy.m,Accuracy.s +GC,Concentration,Homogeneous,4,79.9404761904762,0.947051414443939 +GC,Concentration,Homogeneous,8,81.5997023809524,1.54317700424802 +GC,Concentration,Homogeneous,16,81.3913690476191,1.56257086007124 +GC,Concentration,Homogeneous,32,81.8154761904762,1.09756481634439 +GC,Concentration,Uniform,4,87.0535714285714,4.51921983428477 +GC,Concentration,Uniform,8,95.5357142857143,2.96767489228148 +GC,Concentration,Uniform,16,97.1726190476191,2.45938007890809 +GC,Concentration,Uniform,32,97.6190476190476,1.55869921597137 +GC,Saturation,Homogeneous,4,56.7559523809524,0.97288722686827 +GC,Saturation,Homogeneous,8,56.6666666666667,0.765589516690297 +GC,Saturation,Homogeneous,16,57.3735119047619,1.09036436872825 +GC,Saturation,Homogeneous,32,56.9940476190476,0.829681014806154 +GC,Saturation,Uniform,4,81.7708333333333,4.33850587414085 +GC,Saturation,Uniform,8,96.2797619047619,3.72066324101346 +GC,Saturation,Uniform,16,99.3303571428571,1.25010122686983 +GC,Saturation,Uniform,32,98.9583333333333,1.64557844220779 +MC,Concentration,Homogeneous,4,83.7574404761905,0.914110702407137 +MC,Concentration,Homogeneous,8,83.3482142857143,1.07036511849017 +MC,Concentration,Homogeneous,16,83.5863095238095,0.937786777215678 +MC,Concentration,Homogeneous,32,83.7946428571429,0.935625352786815 +MC,Concentration,Uniform,4,87.2023809523809,3.14970394174356 +MC,Concentration,Uniform,8,95.2380952380952,2.11048871530456 +MC,Concentration,Uniform,16,97.0982142857143,2.04811090042679 +MC,Concentration,Uniform,32,97.9166666666667,1.31184086909367 +MC,Saturation,Homogeneous,4,58.6383928571429,1.15044845377825 +MC,Saturation,Homogeneous,8,58.4821428571429,1.3651601120452 +MC,Saturation,Homogeneous,16,58.4821428571429,1.16425197842141 +MC,Saturation,Homogeneous,32,58.6607142857143,1.25827355871387 +MC,Saturation,Uniform,4,86.6071428571429,3.22905400442986 +MC,Saturation,Uniform,8,92.485119047619,1.62039447280267 +MC,Saturation,Uniform,16,98.8839285714286,1.32861394489794 +MC,Saturation,Uniform,32,94.5684523809524,5.22258739478909 diff --git a/analysis/SciRep-Final/csv/hh_util_cond_1.csv b/analysis/SciRep-Final/csv/hh_util_cond_1.csv new file mode 100644 index 0000000..c723366 --- /dev/null +++ b/analysis/SciRep-Final/csv/hh_util_cond_1.csv @@ -0,0 +1,16 @@ +model.term,df1,df2,F.ratio,p.value +Layer,1,217,370.984,7.2740467019666e-49 +Condition,1,217,4267.36,1.09714559296295e-144 +Duplication,3,217,4.266,0.00595691155431508 +Session,1,217,330.909,1.57558229328827e-45 +Layer:Condition,1,217,9.739,0.00204911643130945 +Layer:Duplication,3,217,1.185,0.316501385210002 +Layer:Session,1,217,2.214,0.138248572998543 +Condition:Duplication,3,217,2.407,0.068173022049192 +Condition:Session,1,217,355.523,1.32062802128325e-47 +Duplication:Session,3,217,0.753,0.521798402842639 +Layer:Condition:Duplication,3,217,2.518,0.0590665233243816 +Layer:Condition:Session,1,217,0.578,0.448106898965794 +Layer:Duplication:Session,3,217,0.208,0.890611339459908 +Condition:Duplication:Session,3,217,0.79,0.500701069530802 +Layer:Condition:Duplication:Session,3,217,0.16,0.923459566154531 diff --git a/analysis/SciRep-Final/csv/hh_util_cond_2.csv b/analysis/SciRep-Final/csv/hh_util_cond_2.csv new file mode 100644 index 0000000..6a070b5 --- /dev/null +++ b/analysis/SciRep-Final/csv/hh_util_cond_2.csv @@ -0,0 +1,9 @@ +contrast,Duplication,Layer,estimate,SE,df,t.ratio,p.value +Homogeneous - Uniform,4,GC,10.4663046056262,0.429024556769883,216.999999999994,24.3955839834131,4.08001164339508e-64 +Homogeneous - Uniform,8,GC,10.0612189214606,0.429024556769882,216.999999999994,23.4513823572509,2.05230467626963e-61 +Homogeneous - Uniform,16,GC,10.7824071172446,0.429024556769882,217,25.1323774993795,3.437478460312e-66 +Homogeneous - Uniform,32,GC,10.2183378331034,0.429024556769882,216.999999999997,23.8176059432054,1.81406526641724e-62 +Homogeneous - Uniform,4,MC,8.16258212403256,0.429024556769882,216.99999999999,19.0259088791758,3.894736326223e-48 +Homogeneous - Uniform,8,MC,9.46135269548082,0.429024556769882,216.999999999994,22.0531728223558,2.51607214334279e-57 +Homogeneous - Uniform,16,MC,9.94786557919495,0.429024556769882,216.999999999994,23.1871705761839,1.19355506607275e-60 +Homogeneous - Uniform,32,MC,10.1695489106578,0.429024556769882,216.999999999997,23.7038853608384,3.84626887794387e-62 diff --git a/analysis/SciRep-Final/csv/hh_util_cond_3.csv b/analysis/SciRep-Final/csv/hh_util_cond_3.csv new file mode 100644 index 0000000..ca10587 --- /dev/null +++ b/analysis/SciRep-Final/csv/hh_util_cond_3.csv @@ -0,0 +1,33 @@ +Layer,Session,Condition,Duplication,Utilization.m,Utilization.s +GC,Concentration,Homogeneous,4,11.6864796009898,3.65121925084762 +GC,Concentration,Homogeneous,8,11.1998716498277,3.23680018042204 +GC,Concentration,Homogeneous,16,11.174778394175,3.07444454223316 +GC,Concentration,Homogeneous,32,11.2567832151365,3.11794669986395 +GC,Concentration,Uniform,4,3.87944976345525,1.32195928596387 +GC,Concentration,Uniform,8,3.94254223610003,0.916368665594993 +GC,Concentration,Uniform,16,3.31306946306976,1.50852385816561 +GC,Concentration,Uniform,32,4.55580992121701,1.5538561092037 +GC,Saturation,Homogeneous,4,16.9932849034824,0.840534858512884 +GC,Saturation,Homogeneous,8,16.8270980355891,0.710959864951623 +GC,Saturation,Homogeneous,16,16.7836085338076,0.678076915713321 +GC,Saturation,Homogeneous,32,16.7494453185422,0.763515634912929 +GC,Saturation,Uniform,4,3.86770552976451,0.676724556422294 +GC,Saturation,Uniform,8,3.96198960639566,1.07108240704685 +GC,Saturation,Uniform,16,3.08050323042362,1.10817767696972 +GC,Saturation,Uniform,32,3.01374294625489,1.13587325479142 +MC,Concentration,Homogeneous,4,7.82414809733805,0.0744578128373805 +MC,Concentration,Homogeneous,8,7.82414546916327,0.0743982947548655 +MC,Concentration,Homogeneous,16,7.82414809733805,0.0744578128373805 +MC,Concentration,Homogeneous,32,7.82414809733805,0.0744578128373805 +MC,Concentration,Uniform,4,2.24496754813064,0.0306758234989434 +MC,Concentration,Uniform,8,0.96036740702663,0.0211034701249928 +MC,Concentration,Uniform,16,0.737411468950517,0.0104113305386758 +MC,Concentration,Uniform,32,0.591543629548595,0.00640945634255244 +MC,Saturation,Homogeneous,4,13.553840038136,0.0162015835724145 +MC,Saturation,Homogeneous,8,13.553840038136,0.0162015835724145 +MC,Saturation,Homogeneous,16,13.553840038136,0.0162015835724145 +MC,Saturation,Homogeneous,32,13.553840038136,0.0162015835724145 +MC,Saturation,Uniform,4,2.80785633927828,0.0501815084606198 +MC,Saturation,Uniform,8,1.49491270931102,0.0406917472005399 +MC,Saturation,Uniform,16,0.744845508133653,0.0226114068206703 +MC,Saturation,Uniform,32,0.447346684609842,0.0178359723620206 diff --git a/analysis/SciRep-Final/csv/hh_util_dup_1.csv b/analysis/SciRep-Final/csv/hh_util_dup_1.csv new file mode 100644 index 0000000..c723366 --- /dev/null +++ b/analysis/SciRep-Final/csv/hh_util_dup_1.csv @@ -0,0 +1,16 @@ +model.term,df1,df2,F.ratio,p.value +Layer,1,217,370.984,7.2740467019666e-49 +Condition,1,217,4267.36,1.09714559296295e-144 +Duplication,3,217,4.266,0.00595691155431508 +Session,1,217,330.909,1.57558229328827e-45 +Layer:Condition,1,217,9.739,0.00204911643130945 +Layer:Duplication,3,217,1.185,0.316501385210002 +Layer:Session,1,217,2.214,0.138248572998543 +Condition:Duplication,3,217,2.407,0.068173022049192 +Condition:Session,1,217,355.523,1.32062802128325e-47 +Duplication:Session,3,217,0.753,0.521798402842639 +Layer:Condition:Duplication,3,217,2.518,0.0590665233243816 +Layer:Condition:Session,1,217,0.578,0.448106898965794 +Layer:Duplication:Session,3,217,0.208,0.890611339459908 +Condition:Duplication:Session,3,217,0.79,0.500701069530802 +Layer:Condition:Duplication:Session,3,217,0.16,0.923459566154531 diff --git a/analysis/SciRep-Final/csv/hh_util_dup_2.csv b/analysis/SciRep-Final/csv/hh_util_dup_2.csv new file mode 100644 index 0000000..4532fae --- /dev/null +++ b/analysis/SciRep-Final/csv/hh_util_dup_2.csv @@ -0,0 +1,13 @@ +contrast,Condition,Layer,estimate,SE,df,t.ratio,p.value +Duplication8 - Duplication4,Homogeneous,GC,-0.326397409527693,0.429024556769882,216.999999999994,-0.760789573410746,0.7926903789741 +Duplication16 - Duplication8,Homogeneous,GC,-0.0342913787171008,0.429024556769882,217,-0.0799287084526814,0.99963386672526 +Duplication32 - Duplication16,Homogeneous,GC,0.0239208028480601,0.429024556769882,217,0.0557562556049457,0.999875391148996 +Duplication8 - Duplication4,Uniform,GC,0.0786882746379728,0.429024556769882,217,0.183412052751515,0.995676107799775 +Duplication16 - Duplication8,Uniform,GC,-0.75547957450115,0.429024556769882,217,-1.76092385058129,0.198483619664834 +Duplication32 - Duplication16,Uniform,GC,0.58799008698926,0.429024556769882,217,1.37052781177895,0.390853772216211 +Duplication8 - Duplication4,Homogeneous,MC,-1.31408737577554e-06,0.429024556769882,216.999999999997,-3.06296540615129e-06,1 +Duplication16 - Duplication8,Homogeneous,MC,1.31408738776595e-06,0.429024556769882,216.999999999997,3.06296543409937e-06,1 +Duplication32 - Duplication16,Homogeneous,MC,1.72084568816899e-15,0.429024556769882,217,4.01106570944379e-15,1 +Duplication8 - Duplication4,Uniform,MC,-1.29877188553563,0.429024556769882,216.999999999997,-3.02726700614543,0.00768344812378663 +Duplication16 - Duplication8,Uniform,MC,-0.486511569626742,0.429024556769883,217,-1.13399469086264,0.542646179779179 +Duplication32 - Duplication16,Uniform,MC,-0.221683331462867,0.429024556769883,217,-0.516714784654557,0.919446876614183 diff --git a/analysis/SciRep-Final/csv/hh_util_dup_3.csv b/analysis/SciRep-Final/csv/hh_util_dup_3.csv new file mode 100644 index 0000000..6f64c17 --- /dev/null +++ b/analysis/SciRep-Final/csv/hh_util_dup_3.csv @@ -0,0 +1,17 @@ +Duplication,Condition,Layer,Utilization.m,Utilization.s +4,Homogeneous,GC,14.3398822522361,3.7497914265303 +4,Homogeneous,MC,10.688994067737,2.95925806181437 +4,Uniform,GC,3.87357764660988,1.01453697357226 +4,Uniform,MC,2.52641194370446,0.293438181379503 +8,Homogeneous,GC,14.0134848427084,3.68364930061934 +8,Homogeneous,MC,10.6889927536496,2.95925872021888 +8,Uniform,GC,3.95226592124784,0.962986887367736 +8,Uniform,MC,1.22764005816883,0.2778084404727 +16,Homogeneous,GC,13.9791934639913,3.60758353998199 +16,Homogeneous,MC,10.688994067737,2.95925806181437 +16,Uniform,GC,3.19678634674669,1.28432244577938 +16,Uniform,MC,0.741128488542085,0.0174332257643722 +32,Homogeneous,GC,14.0031142668394,3.58523994700867 +32,Homogeneous,MC,10.688994067737,2.95925806181437 +32,Uniform,GC,3.78477643373595,1.537197021302 +32,Uniform,MC,0.519445157079218,0.0755801812662645 diff --git a/analysis/SciRep-Final/plots/analysis_base.txt b/analysis/SciRep-Final/plots/analysis_base.txt new file mode 100644 index 0000000..0bb6b68 --- /dev/null +++ b/analysis/SciRep-Final/plots/analysis_base.txt @@ -0,0 +1,100 @@ + +Discrimination Accuracy +****************** + model term df1 df2 F.ratio p.value + Duplication 3 1905 0.029 0.9932 + Session 1 1905 487.492 <.0001 + Duplication:Session 3 1905 0.018 0.9966 + + + contrast estimate SE df t.ratio p.value + Duplication4 - Duplication8 -0.1674 1.31 1905 -0.128 0.9992 + Duplication4 - Duplication16 -0.2902 1.31 1905 -0.222 0.9961 + Duplication4 - Duplication32 -0.3621 1.31 1905 -0.277 0.9926 + Duplication8 - Duplication16 -0.1228 1.31 1905 -0.094 0.9997 + Duplication8 - Duplication32 -0.1947 1.31 1905 -0.149 0.9988 + Duplication16 - Duplication32 -0.0719 1.31 1905 -0.055 0.9999 + +Results are averaged over the levels of: Session +Degrees-of-freedom method: kenward-roger +P value adjustment: tukey method for comparing a family of 4 estimates + + Layer Session Condition Duplication Accuracy.m Accuracy.s +1 ET Concentration Homogeneous 4 84.07738 8.878198 +2 ET Concentration Homogeneous 8 84.07738 8.878198 +3 ET Concentration Homogeneous 16 84.07738 8.878198 +4 ET Concentration Homogeneous 32 84.07738 8.878198 +5 ET Saturation Homogeneous 4 72.17262 15.985247 +6 ET Saturation Homogeneous 8 72.17262 15.985247 +7 ET Saturation Homogeneous 16 72.17262 15.985247 +8 ET Saturation Homogeneous 32 72.17262 15.985247 +9 GC Concentration Homogeneous 4 79.94048 19.565738 +10 GC Concentration Homogeneous 8 81.59970 14.614153 +11 GC Concentration Homogeneous 16 81.39137 13.868915 +12 GC Concentration Homogeneous 32 81.81548 13.741943 +13 GC Saturation Homogeneous 4 56.75595 27.731219 +14 GC Saturation Homogeneous 8 56.66667 27.560117 +15 GC Saturation Homogeneous 16 57.37351 28.167748 +16 GC Saturation Homogeneous 32 56.99405 28.073970 +17 MC Concentration Homogeneous 4 83.75744 13.271525 +18 MC Concentration Homogeneous 8 83.34821 12.990006 +19 MC Concentration Homogeneous 16 83.58631 13.144728 +20 MC Concentration Homogeneous 32 83.79464 13.326757 +21 MC Saturation Homogeneous 4 58.63839 29.121654 +22 MC Saturation Homogeneous 8 58.48214 28.902992 +23 MC Saturation Homogeneous 16 58.48214 29.058212 +24 MC Saturation Homogeneous 32 58.66071 29.090513 + + +Mean Utilization +************** + model term df1 df2 F.ratio p.value + Duplication 3 10225 0.068 0.9771 + Session 1 10225 6.794 0.0092 + Duplication:Session 3 10225 0.008 0.9990 + + + contrast estimate SE df t.ratio p.value + Duplication4 - Duplication8 0.3102 1.01 10225 0.307 0.9900 + Duplication4 - Duplication16 0.3752 1.01 10225 0.371 0.9826 + Duplication4 - Duplication32 0.4053 1.01 10225 0.401 0.9782 + Duplication8 - Duplication16 0.0650 1.01 10225 0.064 0.9999 + Duplication8 - Duplication32 0.0951 1.01 10225 0.094 0.9997 + Duplication16 - Duplication32 0.0302 1.01 10225 0.030 1.0000 + +Results are averaged over the levels of: Session +Degrees-of-freedom method: kenward-roger +P value adjustment: tukey method for comparing a family of 4 estimates + + Duplication Utilization.m Utilization.s +1 4 53.12988 36.50223 +2 8 52.81970 36.14323 +3 16 52.75473 36.01335 +4 32 52.72456 36.02838 + + +SD Utilization +************** + model term df1 df2 F.ratio p.value + Duplication 3 10225 0.037 0.9903 + Session 1 10225 1.280 0.2579 + Duplication:Session 3 10225 0.009 0.9988 + + + contrast estimate SE df t.ratio p.value + Duplication4 - Duplication8 0.01580 0.0875 10225 0.181 0.9979 + Duplication4 - Duplication16 0.02741 0.0875 10225 0.313 0.9894 + Duplication4 - Duplication32 0.02273 0.0875 10225 0.260 0.9939 + Duplication8 - Duplication16 0.01161 0.0875 10225 0.133 0.9992 + Duplication8 - Duplication32 0.00693 0.0875 10225 0.079 0.9998 + Duplication16 - Duplication32 -0.00468 0.0875 10225 -0.053 0.9999 + +Results are averaged over the levels of: Session +Degrees-of-freedom method: kenward-roger +P value adjustment: tukey method for comparing a family of 4 estimates + + Duplication Utilization.m Utilization.s +1 4 1.513620 3.203933 +2 8 1.497822 3.119666 +3 16 1.486210 3.092230 +4 32 1.490887 3.105531 diff --git a/analysis/SciRep-Final/plots/analysis_exp.txt b/analysis/SciRep-Final/plots/analysis_exp.txt new file mode 100644 index 0000000..765b0d2 --- /dev/null +++ b/analysis/SciRep-Final/plots/analysis_exp.txt @@ -0,0 +1,1494 @@ + +Figure 3-4 (Het-Hom Utilization by Condition) +************************** +[[1]] + model term df1 df2 F.ratio p.value + Layer 1 217 370.984 <.0001 + Condition 1 217 4267.360 <.0001 + Duplication 3 217 4.266 0.0060 + Session 1 217 330.909 <.0001 + Layer:Condition 1 217 9.739 0.0020 + Layer:Duplication 3 217 1.185 0.3165 + Layer:Session 1 217 2.214 0.1382 + Condition:Duplication 3 217 2.407 0.0682 + Condition:Session 1 217 355.523 <.0001 + Duplication:Session 3 217 0.753 0.5218 + Layer:Condition:Duplication 3 217 2.518 0.0591 + Layer:Condition:Session 1 217 0.578 0.4481 + Layer:Duplication:Session 3 217 0.208 0.8906 + Condition:Duplication:Session 3 217 0.790 0.5007 + Layer:Condition:Duplication:Session 3 217 0.160 0.9235 + + + +[[1]] +Duplication = 4, Layer = GC: + contrast estimate SE df t.ratio p.value + Homogeneous - Uniform 10.47 0.429 217 24.396 <.0001 + +Duplication = 8, Layer = GC: + contrast estimate SE df t.ratio p.value + Homogeneous - Uniform 10.06 0.429 217 23.451 <.0001 + +Duplication = 16, Layer = GC: + contrast estimate SE df t.ratio p.value + Homogeneous - Uniform 10.78 0.429 217 25.132 <.0001 + +Duplication = 32, Layer = GC: + contrast estimate SE df t.ratio p.value + Homogeneous - Uniform 10.22 0.429 217 23.818 <.0001 + +Duplication = 4, Layer = MC: + contrast estimate SE df t.ratio p.value + Homogeneous - Uniform 8.16 0.429 217 19.026 <.0001 + +Duplication = 8, Layer = MC: + contrast estimate SE df t.ratio p.value + Homogeneous - Uniform 9.46 0.429 217 22.053 <.0001 + +Duplication = 16, Layer = MC: + contrast estimate SE df t.ratio p.value + Homogeneous - Uniform 9.95 0.429 217 23.187 <.0001 + +Duplication = 32, Layer = MC: + contrast estimate SE df t.ratio p.value + Homogeneous - Uniform 10.17 0.429 217 23.704 <.0001 + +Results are averaged over the levels of: Session +Degrees-of-freedom method: kenward-roger + + +[[1]] + Layer Session Condition Duplication Utilization.m Utilization.s +1 GC Concentration Homogeneous 4 11.6864796 3.651219251 +2 GC Concentration Homogeneous 8 11.1998716 3.236800180 +3 GC Concentration Homogeneous 16 11.1747784 3.074444542 +4 GC Concentration Homogeneous 32 11.2567832 3.117946700 +5 GC Concentration Uniform 4 3.8794498 1.321959286 +6 GC Concentration Uniform 8 3.9425422 0.916368666 +7 GC Concentration Uniform 16 3.3130695 1.508523858 +8 GC Concentration Uniform 32 4.5558099 1.553856109 +9 GC Saturation Homogeneous 4 16.9932849 0.840534859 +10 GC Saturation Homogeneous 8 16.8270980 0.710959865 +11 GC Saturation Homogeneous 16 16.7836085 0.678076916 +12 GC Saturation Homogeneous 32 16.7494453 0.763515635 +13 GC Saturation Uniform 4 3.8677055 0.676724556 +14 GC Saturation Uniform 8 3.9619896 1.071082407 +15 GC Saturation Uniform 16 3.0805032 1.108177677 +16 GC Saturation Uniform 32 3.0137429 1.135873255 +17 MC Concentration Homogeneous 4 7.8241481 0.074457813 +18 MC Concentration Homogeneous 8 7.8241455 0.074398295 +19 MC Concentration Homogeneous 16 7.8241481 0.074457813 +20 MC Concentration Homogeneous 32 7.8241481 0.074457813 +21 MC Concentration Uniform 4 2.2449675 0.030675823 +22 MC Concentration Uniform 8 0.9603674 0.021103470 +23 MC Concentration Uniform 16 0.7374115 0.010411331 +24 MC Concentration Uniform 32 0.5915436 0.006409456 +25 MC Saturation Homogeneous 4 13.5538400 0.016201584 +26 MC Saturation Homogeneous 8 13.5538400 0.016201584 +27 MC Saturation Homogeneous 16 13.5538400 0.016201584 +28 MC Saturation Homogeneous 32 13.5538400 0.016201584 +29 MC Saturation Uniform 4 2.8078563 0.050181508 +30 MC Saturation Uniform 8 1.4949127 0.040691747 +31 MC Saturation Uniform 16 0.7448455 0.022611407 +32 MC Saturation Uniform 32 0.4473467 0.017835972 + + + +Figure 4C (Het-Hom Utilization by Duplication) +************************** +[[1]] + model term df1 df2 F.ratio p.value + Layer 1 217 370.984 <.0001 + Condition 1 217 4267.360 <.0001 + Duplication 3 217 4.266 0.0060 + Session 1 217 330.909 <.0001 + Layer:Condition 1 217 9.739 0.0020 + Layer:Duplication 3 217 1.185 0.3165 + Layer:Session 1 217 2.214 0.1382 + Condition:Duplication 3 217 2.407 0.0682 + Condition:Session 1 217 355.523 <.0001 + Duplication:Session 3 217 0.753 0.5218 + Layer:Condition:Duplication 3 217 2.518 0.0591 + Layer:Condition:Session 1 217 0.578 0.4481 + Layer:Duplication:Session 3 217 0.208 0.8906 + Condition:Duplication:Session 3 217 0.790 0.5007 + Layer:Condition:Duplication:Session 3 217 0.160 0.9235 + + + +[[1]] +Condition = Homogeneous, Layer = GC: + contrast estimate SE df t.ratio p.value + Duplication8 - Duplication4 -3.26e-01 0.429 217 -0.761 0.7927 + Duplication16 - Duplication8 -3.43e-02 0.429 217 -0.080 0.9996 + Duplication32 - Duplication16 2.39e-02 0.429 217 0.056 0.9999 + +Condition = Uniform, Layer = GC: + contrast estimate SE df t.ratio p.value + Duplication8 - Duplication4 7.87e-02 0.429 217 0.183 0.9957 + Duplication16 - Duplication8 -7.55e-01 0.429 217 -1.761 0.1993 + Duplication32 - Duplication16 5.88e-01 0.429 217 1.371 0.3910 + +Condition = Homogeneous, Layer = MC: + contrast estimate SE df t.ratio p.value + Duplication8 - Duplication4 -1.30e-06 0.429 217 0.000 1.0000 + Duplication16 - Duplication8 1.30e-06 0.429 217 0.000 1.0000 + Duplication32 - Duplication16 0.00e+00 0.429 217 0.000 1.0000 + +Condition = Uniform, Layer = MC: + contrast estimate SE df t.ratio p.value + Duplication8 - Duplication4 -1.30e+00 0.429 217 -3.027 0.0078 + Duplication16 - Duplication8 -4.87e-01 0.429 217 -1.134 0.5426 + Duplication32 - Duplication16 -2.22e-01 0.429 217 -0.517 0.9194 + +Results are averaged over the levels of: Session +Degrees-of-freedom method: kenward-roger +P value adjustment: mvt method for 3 tests + + +[[1]] + Duplication Condition Layer Utilization.m Utilization.s +1 4 Homogeneous GC 14.3398823 3.74979143 +2 4 Homogeneous MC 10.6889941 2.95925806 +3 4 Uniform GC 3.8735776 1.01453697 +4 4 Uniform MC 2.5264119 0.29343818 +5 8 Homogeneous GC 14.0134848 3.68364930 +6 8 Homogeneous MC 10.6889928 2.95925872 +7 8 Uniform GC 3.9522659 0.96298689 +8 8 Uniform MC 1.2276401 0.27780844 +9 16 Homogeneous GC 13.9791935 3.60758354 +10 16 Homogeneous MC 10.6889941 2.95925806 +11 16 Uniform GC 3.1967863 1.28432245 +12 16 Uniform MC 0.7411285 0.01743323 +13 32 Homogeneous GC 14.0031143 3.58523995 +14 32 Homogeneous MC 10.6889941 2.95925806 +15 32 Uniform GC 3.7847764 1.53719702 +16 32 Uniform MC 0.5194452 0.07558018 + + + +Figure 3-4 (Het-Hom Accuracy by Condition) +************************** +[[1]] + model term df1 df2 F.ratio p.value + Layer 1 217 9.372 0.0025 + Condition 1 217 8782.859 <.0001 + Duplication 3 217 135.545 <.0001 + Session 1 217 2477.900 <.0001 + Layer:Condition 1 217 23.860 <.0001 + Layer:Duplication 3 217 6.675 0.0002 + Layer:Session 1 217 3.057 0.0818 + Condition:Duplication 3 217 116.015 <.0001 + Condition:Session 1 217 2195.971 <.0001 + Duplication:Session 3 217 3.636 0.0137 + Layer:Condition:Duplication 3 217 2.746 0.0439 + Layer:Condition:Session 1 217 0.022 0.8827 + Layer:Duplication:Session 3 217 2.702 0.0465 + Condition:Duplication:Session 3 217 4.534 0.0042 + Layer:Condition:Duplication:Session 3 217 6.094 0.0005 + + + +[[1]] +Duplication = 4, Layer = GC: + contrast estimate SE df t.ratio p.value + Homogeneous - Uniform -16.1 0.721 217 -22.268 <.0001 + +Duplication = 8, Layer = GC: + contrast estimate SE df t.ratio p.value + Homogeneous - Uniform -26.8 0.721 217 -37.116 <.0001 + +Duplication = 16, Layer = GC: + contrast estimate SE df t.ratio p.value + Homogeneous - Uniform -28.9 0.721 217 -40.019 <.0001 + +Duplication = 32, Layer = GC: + contrast estimate SE df t.ratio p.value + Homogeneous - Uniform -28.9 0.721 217 -40.040 <.0001 + +Duplication = 4, Layer = MC: + contrast estimate SE df t.ratio p.value + Homogeneous - Uniform -15.7 0.721 217 -21.773 <.0001 + +Duplication = 8, Layer = MC: + contrast estimate SE df t.ratio p.value + Homogeneous - Uniform -22.9 0.721 217 -31.809 <.0001 + +Duplication = 16, Layer = MC: + contrast estimate SE df t.ratio p.value + Homogeneous - Uniform -27.0 0.721 217 -37.369 <.0001 + +Duplication = 32, Layer = MC: + contrast estimate SE df t.ratio p.value + Homogeneous - Uniform -25.0 0.721 217 -34.677 <.0001 + +Results are averaged over the levels of: Session +Degrees-of-freedom method: kenward-roger + + +[[1]] + Layer Session Condition Duplication Accuracy.m Accuracy.s +1 GC Concentration Homogeneous 4 79.94048 0.9470514 +2 GC Concentration Homogeneous 8 81.59970 1.5431770 +3 GC Concentration Homogeneous 16 81.39137 1.5625709 +4 GC Concentration Homogeneous 32 81.81548 1.0975648 +5 GC Concentration Uniform 4 87.05357 4.5192198 +6 GC Concentration Uniform 8 95.53571 2.9676749 +7 GC Concentration Uniform 16 97.17262 2.4593801 +8 GC Concentration Uniform 32 97.61905 1.5586992 +9 GC Saturation Homogeneous 4 56.75595 0.9728872 +10 GC Saturation Homogeneous 8 56.66667 0.7655895 +11 GC Saturation Homogeneous 16 57.37351 1.0903644 +12 GC Saturation Homogeneous 32 56.99405 0.8296810 +13 GC Saturation Uniform 4 81.77083 4.3385059 +14 GC Saturation Uniform 8 96.27976 3.7206632 +15 GC Saturation Uniform 16 99.33036 1.2501012 +16 GC Saturation Uniform 32 98.95833 1.6455784 +17 MC Concentration Homogeneous 4 83.75744 0.9141107 +18 MC Concentration Homogeneous 8 83.34821 1.0703651 +19 MC Concentration Homogeneous 16 83.58631 0.9377868 +20 MC Concentration Homogeneous 32 83.79464 0.9356254 +21 MC Concentration Uniform 4 87.20238 3.1497039 +22 MC Concentration Uniform 8 95.23810 2.1104887 +23 MC Concentration Uniform 16 97.09821 2.0481109 +24 MC Concentration Uniform 32 97.91667 1.3118409 +25 MC Saturation Homogeneous 4 58.63839 1.1504485 +26 MC Saturation Homogeneous 8 58.48214 1.3651601 +27 MC Saturation Homogeneous 16 58.48214 1.1642520 +28 MC Saturation Homogeneous 32 58.66071 1.2582736 +29 MC Saturation Uniform 4 86.60714 3.2290540 +30 MC Saturation Uniform 8 92.48512 1.6203945 +31 MC Saturation Uniform 16 98.88393 1.3286139 +32 MC Saturation Uniform 32 94.56845 5.2225874 + + + +Figure 3-4 (Het-Hom Accuracy by Duplication) +************************** +[[1]] + model term df1 df2 F.ratio p.value + Layer 1 217 9.372 0.0025 + Condition 1 217 8782.859 <.0001 + Duplication 3 217 135.545 <.0001 + Session 1 217 2477.900 <.0001 + Layer:Condition 1 217 23.860 <.0001 + Layer:Duplication 3 217 6.675 0.0002 + Layer:Session 1 217 3.057 0.0818 + Condition:Duplication 3 217 116.015 <.0001 + Condition:Session 1 217 2195.971 <.0001 + Duplication:Session 3 217 3.636 0.0137 + Layer:Condition:Duplication 3 217 2.746 0.0439 + Layer:Condition:Session 1 217 0.022 0.8827 + Layer:Duplication:Session 3 217 2.702 0.0465 + Condition:Duplication:Session 3 217 4.534 0.0042 + Layer:Condition:Duplication:Session 3 217 6.094 0.0005 + + + +[[1]] +Condition = Homogeneous, Layer = GC: + contrast estimate SE df t.ratio p.value + Duplication8 - Duplication4 0.7850 0.721 217 1.088 0.5738 + Duplication16 - Duplication8 0.2493 0.721 217 0.346 0.9730 + Duplication32 - Duplication16 0.0223 0.721 217 0.031 1.0000 + +Condition = Uniform, Layer = GC: + contrast estimate SE df t.ratio p.value + Duplication8 - Duplication4 11.4955 0.721 217 15.936 <.0001 + Duplication16 - Duplication8 2.3438 0.721 217 3.249 0.0038 + Duplication32 - Duplication16 0.0372 0.721 217 0.052 0.9999 + +Condition = Homogeneous, Layer = MC: + contrast estimate SE df t.ratio p.value + Duplication8 - Duplication4 -0.2827 0.721 217 -0.392 0.9617 + Duplication16 - Duplication8 0.1190 0.721 217 0.165 0.9968 + Duplication32 - Duplication16 0.1935 0.721 217 0.268 0.9869 + +Condition = Uniform, Layer = MC: + contrast estimate SE df t.ratio p.value + Duplication8 - Duplication4 6.9568 0.721 217 9.644 <.0001 + Duplication16 - Duplication8 4.1295 0.721 217 5.724 <.0001 + Duplication32 - Duplication16 -1.7485 0.721 217 -2.424 0.0443 + +Results are averaged over the levels of: Session +Degrees-of-freedom method: kenward-roger +P value adjustment: mvt method for 3 tests + + +[[1]] + Layer Session Condition Duplication Accuracy.m Accuracy.s +1 GC Concentration Homogeneous 4 79.94048 0.9470514 +2 GC Concentration Homogeneous 8 81.59970 1.5431770 +3 GC Concentration Homogeneous 16 81.39137 1.5625709 +4 GC Concentration Homogeneous 32 81.81548 1.0975648 +5 GC Concentration Uniform 4 87.05357 4.5192198 +6 GC Concentration Uniform 8 95.53571 2.9676749 +7 GC Concentration Uniform 16 97.17262 2.4593801 +8 GC Concentration Uniform 32 97.61905 1.5586992 +9 GC Saturation Homogeneous 4 56.75595 0.9728872 +10 GC Saturation Homogeneous 8 56.66667 0.7655895 +11 GC Saturation Homogeneous 16 57.37351 1.0903644 +12 GC Saturation Homogeneous 32 56.99405 0.8296810 +13 GC Saturation Uniform 4 81.77083 4.3385059 +14 GC Saturation Uniform 8 96.27976 3.7206632 +15 GC Saturation Uniform 16 99.33036 1.2501012 +16 GC Saturation Uniform 32 98.95833 1.6455784 +17 MC Concentration Homogeneous 4 83.75744 0.9141107 +18 MC Concentration Homogeneous 8 83.34821 1.0703651 +19 MC Concentration Homogeneous 16 83.58631 0.9377868 +20 MC Concentration Homogeneous 32 83.79464 0.9356254 +21 MC Concentration Uniform 4 87.20238 3.1497039 +22 MC Concentration Uniform 8 95.23810 2.1104887 +23 MC Concentration Uniform 16 97.09821 2.0481109 +24 MC Concentration Uniform 32 97.91667 1.3118409 +25 MC Saturation Homogeneous 4 58.63839 1.1504485 +26 MC Saturation Homogeneous 8 58.48214 1.3651601 +27 MC Saturation Homogeneous 16 58.48214 1.1642520 +28 MC Saturation Homogeneous 32 58.66071 1.2582736 +29 MC Saturation Uniform 4 86.60714 3.2290540 +30 MC Saturation Uniform 8 92.48512 1.6203945 +31 MC Saturation Uniform 16 98.88393 1.3286139 +32 MC Saturation Uniform 32 94.56845 5.2225874 + + + +Figure 5 (Combined Utilization by Condition) +************************** +[[1]] + model term df1 df2 F.ratio p.value + Layer 1 441 476.183 <.0001 + Condition 3 441 559.527 <.0001 + Duplication 3 441 91.858 <.0001 + Session 1 441 177.808 <.0001 + Layer:Condition 3 441 17.085 <.0001 + Layer:Duplication 3 441 3.314 0.0200 + Layer:Session 1 441 22.519 <.0001 + Condition:Duplication 9 441 21.523 <.0001 + Condition:Session 3 441 44.078 <.0001 + Duplication:Session 3 441 2.041 0.1074 + Layer:Condition:Duplication 9 441 3.765 0.0001 + Layer:Condition:Session 3 441 4.683 0.0031 + Layer:Duplication:Session 3 441 4.212 0.0059 + Condition:Duplication:Session 9 441 8.261 <.0001 + Layer:Condition:Duplication:Session 9 441 2.721 0.0043 + + + +[[1]] +Duplication = 4, Layer = GC: + contrast estimate SE df t.ratio p.value + Homogeneous - Uniform 10.47 0.716 441 14.615 <.0001 + Homogeneous - Scaled 2.19 0.716 441 3.057 0.0126 + Homogeneous - Adaptive -4.11 0.716 441 -5.735 <.0001 + Uniform - Scaled -8.28 0.716 441 -11.558 <.0001 + Uniform - Adaptive -14.57 0.716 441 -20.350 <.0001 + Scaled - Adaptive -6.30 0.716 441 -8.792 <.0001 + +Duplication = 8, Layer = GC: + contrast estimate SE df t.ratio p.value + Homogeneous - Uniform 10.06 0.716 441 14.049 <.0001 + Homogeneous - Scaled 6.79 0.716 441 9.482 <.0001 + Homogeneous - Adaptive 2.80 0.716 441 3.906 0.0006 + Uniform - Scaled -3.27 0.716 441 -4.567 <.0001 + Uniform - Adaptive -7.26 0.716 441 -10.143 <.0001 + Scaled - Adaptive -3.99 0.716 441 -5.576 <.0001 + +Duplication = 16, Layer = GC: + contrast estimate SE df t.ratio p.value + Homogeneous - Uniform 10.78 0.716 441 15.056 <.0001 + Homogeneous - Scaled 8.30 0.716 441 11.584 <.0001 + Homogeneous - Adaptive 5.08 0.716 441 7.096 <.0001 + Uniform - Scaled -2.49 0.716 441 -3.472 0.0032 + Uniform - Adaptive -5.70 0.716 441 -7.960 <.0001 + Scaled - Adaptive -3.21 0.716 441 -4.488 0.0001 + +Duplication = 32, Layer = GC: + contrast estimate SE df t.ratio p.value + Homogeneous - Uniform 10.22 0.716 441 14.268 <.0001 + Homogeneous - Scaled 8.71 0.716 441 12.168 <.0001 + Homogeneous - Adaptive 5.77 0.716 441 8.063 <.0001 + Uniform - Scaled -1.50 0.716 441 -2.101 0.1544 + Uniform - Adaptive -4.44 0.716 441 -6.205 <.0001 + Scaled - Adaptive -2.94 0.716 441 -4.104 0.0003 + +Duplication = 4, Layer = MC: + contrast estimate SE df t.ratio p.value + Homogeneous - Uniform 8.16 0.716 441 11.398 <.0001 + Homogeneous - Scaled 3.22 0.716 441 4.497 0.0001 + Homogeneous - Adaptive 1.88 0.716 441 2.625 0.0442 + Uniform - Scaled -4.94 0.716 441 -6.901 <.0001 + Uniform - Adaptive -6.28 0.716 441 -8.773 <.0001 + Scaled - Adaptive -1.34 0.716 441 -1.872 0.2416 + +Duplication = 8, Layer = MC: + contrast estimate SE df t.ratio p.value + Homogeneous - Uniform 9.46 0.716 441 13.211 <.0001 + Homogeneous - Scaled 7.54 0.716 441 10.535 <.0001 + Homogeneous - Adaptive 4.94 0.716 441 6.896 <.0001 + Uniform - Scaled -1.92 0.716 441 -2.677 0.0385 + Uniform - Adaptive -4.52 0.716 441 -6.315 <.0001 + Scaled - Adaptive -2.61 0.716 441 -3.638 0.0017 + +Duplication = 16, Layer = MC: + contrast estimate SE df t.ratio p.value + Homogeneous - Uniform 9.95 0.716 441 13.891 <.0001 + Homogeneous - Scaled 8.29 0.716 441 11.575 <.0001 + Homogeneous - Adaptive 6.36 0.716 441 8.877 <.0001 + Uniform - Scaled -1.66 0.716 441 -2.316 0.0959 + Uniform - Adaptive -3.59 0.716 441 -5.014 <.0001 + Scaled - Adaptive -1.93 0.716 441 -2.697 0.0364 + +Duplication = 32, Layer = MC: + contrast estimate SE df t.ratio p.value + Homogeneous - Uniform 10.17 0.716 441 14.200 <.0001 + Homogeneous - Scaled 8.69 0.716 441 12.135 <.0001 + Homogeneous - Adaptive 6.59 0.716 441 9.206 <.0001 + Uniform - Scaled -1.48 0.716 441 -2.065 0.1663 + Uniform - Adaptive -3.58 0.716 441 -4.995 <.0001 + Scaled - Adaptive -2.10 0.716 441 -2.930 0.0187 + +Results are averaged over the levels of: Session +Degrees-of-freedom method: kenward-roger +P value adjustment: tukey method for comparing a family of 4 estimates + + +[[1]] + Layer Session Condition Duplication Utilization.m Utilization.s +1 GC Concentration Homogeneous 4 11.6864796 3.651219251 +2 GC Concentration Homogeneous 8 11.1998716 3.236800180 +3 GC Concentration Homogeneous 16 11.1747784 3.074444542 +4 GC Concentration Homogeneous 32 11.2567832 3.117946700 +5 GC Concentration Uniform 4 3.8794498 1.321959286 +6 GC Concentration Uniform 8 3.9425422 0.916368666 +7 GC Concentration Uniform 16 3.3130695 1.508523858 +8 GC Concentration Uniform 32 4.5558099 1.553856109 +9 GC Concentration Scaled 4 11.0542651 3.902302178 +10 GC Concentration Scaled 8 6.5175693 2.276423650 +11 GC Concentration Scaled 16 5.4789099 1.983435330 +12 GC Concentration Scaled 32 5.2630888 1.550317777 +13 GC Concentration Adaptive 4 22.7729006 6.131622727 +14 GC Concentration Adaptive 8 10.2783537 4.272892392 +15 GC Concentration Adaptive 16 7.2019658 1.918027423 +16 GC Concentration Adaptive 32 6.4008027 1.675925721 +17 GC Saturation Homogeneous 4 16.9932849 0.840534859 +18 GC Saturation Homogeneous 8 16.8270980 0.710959865 +19 GC Saturation Homogeneous 16 16.7836085 0.678076916 +20 GC Saturation Homogeneous 32 16.7494453 0.763515635 +21 GC Saturation Uniform 4 3.8677055 0.676724556 +22 GC Saturation Uniform 8 3.9619896 1.071082407 +23 GC Saturation Uniform 16 3.0805032 1.108177677 +24 GC Saturation Uniform 32 3.0137429 1.135873255 +25 GC Saturation Scaled 4 13.2470778 4.220504491 +26 GC Saturation Scaled 8 7.9288069 1.765918795 +27 GC Saturation Scaled 16 5.8876204 1.421716172 +28 GC Saturation Scaled 32 5.3154515 1.538495658 +29 GC Saturation Adaptive 4 14.1211373 7.698267568 +30 GC Saturation Adaptive 8 12.1545723 4.242918981 +31 GC Saturation Adaptive 16 10.5922558 3.266749874 +32 GC Saturation Adaptive 32 10.0565545 2.680447304 +33 MC Concentration Homogeneous 4 7.8241481 0.074457813 +34 MC Concentration Homogeneous 8 7.8241455 0.074398295 +35 MC Concentration Homogeneous 16 7.8241481 0.074457813 +36 MC Concentration Homogeneous 32 7.8241481 0.074457813 +37 MC Concentration Uniform 4 2.2449675 0.030675823 +38 MC Concentration Uniform 8 0.9603674 0.021103470 +39 MC Concentration Uniform 16 0.7374115 0.010411331 +40 MC Concentration Uniform 32 0.5915436 0.006409456 +41 MC Concentration Scaled 4 4.5037998 0.078281626 +42 MC Concentration Scaled 8 1.7612007 0.062733902 +43 MC Concentration Scaled 16 1.2056297 0.061771946 +44 MC Concentration Scaled 32 0.9338146 0.070907840 +45 MC Concentration Adaptive 4 7.7196508 0.416014399 +46 MC Concentration Adaptive 8 3.5511773 0.230092824 +47 MC Concentration Adaptive 16 2.3014912 0.198062336 +48 MC Concentration Adaptive 32 2.0654764 0.131475482 +49 MC Saturation Homogeneous 4 13.5538400 0.016201584 +50 MC Saturation Homogeneous 8 13.5538400 0.016201584 +51 MC Saturation Homogeneous 16 13.5538400 0.016201584 +52 MC Saturation Homogeneous 32 13.5538400 0.016201584 +53 MC Saturation Uniform 4 2.8078563 0.050181508 +54 MC Saturation Uniform 8 1.4949127 0.040691747 +55 MC Saturation Uniform 16 0.7448455 0.022611407 +56 MC Saturation Uniform 32 0.4473467 0.017835972 +57 MC Saturation Scaled 4 10.4328688 0.274201495 +58 MC Saturation Scaled 8 4.5279364 0.123755745 +59 MC Saturation Scaled 16 3.5940956 0.053574454 +60 MC Saturation Scaled 32 3.0626188 0.061640575 +61 MC Saturation Adaptive 4 9.8984858 1.492510947 +62 MC Saturation Adaptive 8 7.9492841 0.750913344 +63 MC Saturation Adaptive 16 6.3616248 0.830858088 +64 MC Saturation Adaptive 32 6.1271685 0.647673310 + + + +Figure 5 (Combined Utilization by Duplication) +************************** +[[1]] + model term df1 df2 F.ratio p.value + Layer 1 441 476.183 <.0001 + Condition 3 441 559.527 <.0001 + Duplication 3 441 91.858 <.0001 + Session 1 441 177.808 <.0001 + Layer:Condition 3 441 17.085 <.0001 + Layer:Duplication 3 441 3.314 0.0200 + Layer:Session 1 441 22.519 <.0001 + Condition:Duplication 9 441 21.523 <.0001 + Condition:Session 3 441 44.078 <.0001 + Duplication:Session 3 441 2.041 0.1074 + Layer:Condition:Duplication 9 441 3.765 0.0001 + Layer:Condition:Session 3 441 4.683 0.0031 + Layer:Duplication:Session 3 441 4.212 0.0059 + Condition:Duplication:Session 9 441 8.261 <.0001 + Layer:Condition:Duplication:Session 9 441 2.721 0.0043 + + + +[[1]] +Condition = Homogeneous, Layer = GC: + contrast estimate SE df t.ratio p.value + Duplication8 - Duplication4 -0.326397 0.716 441 -0.456 0.9423 + Duplication16 - Duplication8 -0.034291 0.716 441 -0.048 0.9999 + Duplication32 - Duplication16 0.023921 0.716 441 0.033 1.0000 + +Condition = Uniform, Layer = GC: + contrast estimate SE df t.ratio p.value + Duplication8 - Duplication4 0.078688 0.716 441 0.110 0.9991 + Duplication16 - Duplication8 -0.755480 0.716 441 -1.055 0.5961 + Duplication32 - Duplication16 0.587990 0.716 441 0.821 0.7546 + +Condition = Scaled, Layer = GC: + contrast estimate SE df t.ratio p.value + Duplication8 - Duplication4 -4.927483 0.716 441 -6.881 <.0001 + Duplication16 - Duplication8 -1.539923 0.716 441 -2.150 0.0854 + Duplication32 - Duplication16 -0.393995 0.716 441 -0.550 0.9053 + +Condition = Adaptive, Layer = GC: + contrast estimate SE df t.ratio p.value + Duplication8 - Duplication4 -7.230556 0.716 441 -10.096 <.0001 + Duplication16 - Duplication8 -2.319352 0.716 441 -3.239 0.0037 + Duplication32 - Duplication16 -0.668432 0.716 441 -0.933 0.6798 + +Condition = Homogeneous, Layer = MC: + contrast estimate SE df t.ratio p.value + Duplication8 - Duplication4 -0.000001 0.716 441 0.000 1.0000 + Duplication16 - Duplication8 0.000001 0.716 441 0.000 1.0000 + Duplication32 - Duplication16 0.000000 0.716 441 0.000 1.0000 + +Condition = Uniform, Layer = MC: + contrast estimate SE df t.ratio p.value + Duplication8 - Duplication4 -1.298772 0.716 441 -1.814 0.1779 + Duplication16 - Duplication8 -0.486512 0.716 441 -0.679 0.8404 + Duplication32 - Duplication16 -0.221683 0.716 441 -0.310 0.9803 + +Condition = Scaled, Layer = MC: + contrast estimate SE df t.ratio p.value + Duplication8 - Duplication4 -4.323766 0.716 441 -6.038 <.0001 + Duplication16 - Duplication8 -0.744706 0.716 441 -1.040 0.6066 + Duplication32 - Duplication16 -0.401646 0.716 441 -0.561 0.9006 + +Condition = Adaptive, Layer = MC: + contrast estimate SE df t.ratio p.value + Duplication8 - Duplication4 -3.058838 0.716 441 -4.271 0.0001 + Duplication16 - Duplication8 -1.418673 0.716 441 -1.981 0.1250 + Duplication32 - Duplication16 -0.235236 0.716 441 -0.328 0.9766 + +Results are averaged over the levels of: Session +Degrees-of-freedom method: kenward-roger +P value adjustment: mvt method for 3 tests + + +[[1]] + Duplication Condition Layer Utilization.m Utilization.s +1 4 Homogeneous GC 14.3398823 3.74979143 +2 4 Homogeneous MC 10.6889941 2.95925806 +3 4 Uniform GC 3.8735776 1.01453697 +4 4 Uniform MC 2.5264119 0.29343818 +5 4 Scaled GC 12.1506715 4.08671060 +6 4 Scaled MC 7.4683343 3.06794871 +7 4 Adaptive GC 18.4470189 8.07231019 +8 4 Adaptive MC 8.8090683 1.54475210 +9 8 Homogeneous GC 14.0134848 3.68364930 +10 8 Homogeneous MC 10.6889928 2.95925872 +11 8 Uniform GC 3.9522659 0.96298689 +12 8 Uniform MC 1.2276401 0.27780844 +13 8 Scaled GC 7.2231881 2.09873587 +14 8 Scaled MC 3.1445685 1.43187667 +15 8 Adaptive GC 11.2164630 4.22611391 +16 8 Adaptive MC 5.7502307 2.33368192 +17 16 Homogeneous GC 13.9791935 3.60758354 +18 16 Homogeneous MC 10.6889941 2.95925806 +19 16 Uniform GC 3.1967863 1.28432245 +20 16 Uniform MC 0.7411285 0.01743323 +21 16 Scaled GC 5.6832652 1.68038141 +22 16 Scaled MC 2.3998627 1.23466271 +23 16 Adaptive GC 8.8971108 3.12441625 +24 16 Adaptive MC 4.3315580 2.17632133 +25 32 Homogeneous GC 14.0031143 3.58523995 +26 32 Homogeneous MC 10.6889941 2.95925806 +27 32 Uniform GC 3.7847764 1.53719702 +28 32 Uniform MC 0.5194452 0.07558018 +29 32 Scaled GC 5.2892702 1.49229457 +30 32 Scaled MC 1.9982167 1.10118183 +31 32 Adaptive GC 8.2286786 2.86836490 +32 32 Adaptive MC 4.0963225 2.14548730 + + + +Figure 5 (Combined Accuracy by Condition) +************************** +[[1]] + model term df1 df2 F.ratio p.value + Layer 1 441 5.805 0.0164 + Condition 3 441 3298.391 <.0001 + Duplication 3 441 179.135 <.0001 + Session 1 441 555.771 <.0001 + Layer:Condition 3 441 6.572 0.0002 + Layer:Duplication 3 441 5.944 0.0006 + Layer:Session 1 441 0.042 0.8382 + Condition:Duplication 9 441 28.307 <.0001 + Condition:Session 3 441 937.419 <.0001 + Duplication:Session 3 441 3.080 0.0273 + Layer:Condition:Duplication 9 441 1.656 0.0974 + Layer:Condition:Session 3 441 1.498 0.2143 + Layer:Duplication:Session 3 441 0.047 0.9863 + Condition:Duplication:Session 9 441 7.022 <.0001 + Layer:Condition:Duplication:Session 9 441 2.719 0.0043 + + + +[[1]] +Duplication = 4, Layer = GC: + contrast estimate SE df t.ratio p.value + Homogeneous - Uniform -16.064 0.86 441 -18.669 <.0001 + Homogeneous - Scaled -22.202 0.86 441 -25.802 <.0001 + Homogeneous - Adaptive -22.016 0.86 441 -25.586 <.0001 + Uniform - Scaled -6.138 0.86 441 -7.134 <.0001 + Uniform - Adaptive -5.952 0.86 441 -6.918 <.0001 + Scaled - Adaptive 0.186 0.86 441 0.216 0.9964 + +Duplication = 8, Layer = GC: + contrast estimate SE df t.ratio p.value + Homogeneous - Uniform -26.775 0.86 441 -31.116 <.0001 + Homogeneous - Scaled -26.179 0.86 441 -30.424 <.0001 + Homogeneous - Adaptive -25.472 0.86 441 -29.603 <.0001 + Uniform - Scaled 0.595 0.86 441 0.692 0.9002 + Uniform - Adaptive 1.302 0.86 441 1.513 0.4304 + Scaled - Adaptive 0.707 0.86 441 0.821 0.8443 + +Duplication = 16, Layer = GC: + contrast estimate SE df t.ratio p.value + Homogeneous - Uniform -28.869 0.86 441 -33.550 <.0001 + Homogeneous - Scaled -28.088 0.86 441 -32.642 <.0001 + Homogeneous - Adaptive -27.567 0.86 441 -32.037 <.0001 + Uniform - Scaled 0.781 0.86 441 0.908 0.8006 + Uniform - Adaptive 1.302 0.86 441 1.513 0.4304 + Scaled - Adaptive 0.521 0.86 441 0.605 0.9304 + +Duplication = 32, Layer = GC: + contrast estimate SE df t.ratio p.value + Homogeneous - Uniform -28.884 0.86 441 -33.567 <.0001 + Homogeneous - Scaled -27.545 0.86 441 -32.011 <.0001 + Homogeneous - Adaptive -28.586 0.86 441 -33.222 <.0001 + Uniform - Scaled 1.339 0.86 441 1.556 0.4048 + Uniform - Adaptive 0.298 0.86 441 0.346 0.9858 + Scaled - Adaptive -1.042 0.86 441 -1.211 0.6204 + +Duplication = 4, Layer = MC: + contrast estimate SE df t.ratio p.value + Homogeneous - Uniform -15.707 0.86 441 -18.254 <.0001 + Homogeneous - Scaled -20.915 0.86 441 -24.307 <.0001 + Homogeneous - Adaptive -20.171 0.86 441 -23.442 <.0001 + Uniform - Scaled -5.208 0.86 441 -6.053 <.0001 + Uniform - Adaptive -4.464 0.86 441 -5.188 <.0001 + Scaled - Adaptive 0.744 0.86 441 0.865 0.8231 + +Duplication = 8, Layer = MC: + contrast estimate SE df t.ratio p.value + Homogeneous - Uniform -22.946 0.86 441 -26.667 <.0001 + Homogeneous - Scaled -26.667 0.86 441 -30.991 <.0001 + Homogeneous - Adaptive -23.616 0.86 441 -27.445 <.0001 + Uniform - Scaled -3.720 0.86 441 -4.323 0.0001 + Uniform - Adaptive -0.670 0.86 441 -0.778 0.8643 + Scaled - Adaptive 3.051 0.86 441 3.545 0.0024 + +Duplication = 16, Layer = MC: + contrast estimate SE df t.ratio p.value + Homogeneous - Uniform -26.957 0.86 441 -31.328 <.0001 + Homogeneous - Scaled -25.543 0.86 441 -29.685 <.0001 + Homogeneous - Adaptive -25.766 0.86 441 -29.944 <.0001 + Uniform - Scaled 1.414 0.86 441 1.643 0.3556 + Uniform - Adaptive 1.190 0.86 441 1.384 0.5104 + Scaled - Adaptive -0.223 0.86 441 -0.259 0.9939 + +Duplication = 32, Layer = MC: + contrast estimate SE df t.ratio p.value + Homogeneous - Uniform -25.015 0.86 441 -29.071 <.0001 + Homogeneous - Scaled -25.275 0.86 441 -29.374 <.0001 + Homogeneous - Adaptive -25.536 0.86 441 -29.676 <.0001 + Uniform - Scaled -0.260 0.86 441 -0.303 0.9904 + Uniform - Adaptive -0.521 0.86 441 -0.605 0.9304 + Scaled - Adaptive -0.260 0.86 441 -0.303 0.9904 + +Results are averaged over the levels of: Session +Degrees-of-freedom method: kenward-roger +P value adjustment: tukey method for comparing a family of 4 estimates + + +[[1]] + Layer Session Condition Duplication Accuracy.m Accuracy.s +1 GC Concentration Homogeneous 4 79.94048 0.9470514 +2 GC Concentration Homogeneous 8 81.59970 1.5431770 +3 GC Concentration Homogeneous 16 81.39137 1.5625709 +4 GC Concentration Homogeneous 32 81.81548 1.0975648 +5 GC Concentration Uniform 4 87.05357 4.5192198 +6 GC Concentration Uniform 8 95.53571 2.9676749 +7 GC Concentration Uniform 16 97.17262 2.4593801 +8 GC Concentration Uniform 32 97.61905 1.5586992 +9 GC Concentration Scaled 4 87.27679 2.8800369 +10 GC Concentration Scaled 8 93.45238 2.8635133 +11 GC Concentration Scaled 16 96.57738 3.1295521 +12 GC Concentration Scaled 32 97.02381 1.6835876 +13 GC Concentration Adaptive 4 90.47619 3.8837372 +14 GC Concentration Adaptive 8 92.18750 3.5634477 +15 GC Concentration Adaptive 16 96.65179 2.7542686 +16 GC Concentration Adaptive 32 97.76786 2.5403689 +17 GC Saturation Homogeneous 4 56.75595 0.9728872 +18 GC Saturation Homogeneous 8 56.66667 0.7655895 +19 GC Saturation Homogeneous 16 57.37351 1.0903644 +20 GC Saturation Homogeneous 32 56.99405 0.8296810 +21 GC Saturation Uniform 4 81.77083 4.3385059 +22 GC Saturation Uniform 8 96.27976 3.7206632 +23 GC Saturation Uniform 16 99.33036 1.2501012 +24 GC Saturation Uniform 32 98.95833 1.6455784 +25 GC Saturation Scaled 4 93.82440 4.6319152 +26 GC Saturation Scaled 8 97.17262 3.4962322 +27 GC Saturation Scaled 16 98.36310 1.4139702 +28 GC Saturation Scaled 32 96.87500 2.0059738 +29 GC Saturation Adaptive 4 90.25298 3.8171891 +30 GC Saturation Adaptive 8 97.02381 3.4561907 +31 GC Saturation Adaptive 16 97.24702 3.2901955 +32 GC Saturation Adaptive 32 98.21429 2.2271770 +33 MC Concentration Homogeneous 4 83.75744 0.9141107 +34 MC Concentration Homogeneous 8 83.34821 1.0703651 +35 MC Concentration Homogeneous 16 83.58631 0.9377868 +36 MC Concentration Homogeneous 32 83.79464 0.9356254 +37 MC Concentration Uniform 4 87.20238 3.1497039 +38 MC Concentration Uniform 8 95.23810 2.1104887 +39 MC Concentration Uniform 16 97.09821 2.0481109 +40 MC Concentration Uniform 32 97.91667 1.3118409 +41 MC Concentration Scaled 4 88.91369 3.0507249 +42 MC Concentration Scaled 8 95.31250 1.7262638 +43 MC Concentration Scaled 16 94.64286 1.5908407 +44 MC Concentration Scaled 32 95.31250 2.0232467 +45 MC Concentration Adaptive 4 92.63393 3.5277588 +46 MC Concentration Adaptive 8 91.51786 3.8279476 +47 MC Concentration Adaptive 16 96.94940 1.6966896 +48 MC Concentration Adaptive 32 95.31250 2.9751274 +49 MC Saturation Homogeneous 4 58.63839 1.1504485 +50 MC Saturation Homogeneous 8 58.48214 1.3651601 +51 MC Saturation Homogeneous 16 58.48214 1.1642520 +52 MC Saturation Homogeneous 32 58.66071 1.2582736 +53 MC Saturation Uniform 4 86.60714 3.2290540 +54 MC Saturation Uniform 8 92.48512 1.6203945 +55 MC Saturation Uniform 16 98.88393 1.3286139 +56 MC Saturation Uniform 32 94.56845 5.2225874 +57 MC Saturation Scaled 4 95.31250 2.3475010 +58 MC Saturation Scaled 8 99.85119 0.2755417 +59 MC Saturation Scaled 16 98.51190 1.1471716 +60 MC Saturation Scaled 32 97.69345 4.2021991 +61 MC Saturation Adaptive 4 90.10417 4.1719779 +62 MC Saturation Adaptive 8 97.54464 2.6128076 +63 MC Saturation Adaptive 16 96.65179 2.8975582 +64 MC Saturation Adaptive 32 98.21429 2.7184298 + + + +Figure 5 (Combined Accuracy by Duplication) +************************** +[[1]] + model term df1 df2 F.ratio p.value + Layer 1 441 5.805 0.0164 + Condition 3 441 3298.391 <.0001 + Duplication 3 441 179.135 <.0001 + Session 1 441 555.771 <.0001 + Layer:Condition 3 441 6.572 0.0002 + Layer:Duplication 3 441 5.944 0.0006 + Layer:Session 1 441 0.042 0.8382 + Condition:Duplication 9 441 28.307 <.0001 + Condition:Session 3 441 937.419 <.0001 + Duplication:Session 3 441 3.080 0.0273 + Layer:Condition:Duplication 9 441 1.656 0.0974 + Layer:Condition:Session 3 441 1.498 0.2143 + Layer:Duplication:Session 3 441 0.047 0.9863 + Condition:Duplication:Session 9 441 7.022 <.0001 + Layer:Condition:Duplication:Session 9 441 2.719 0.0043 + + + +[[1]] +Condition = Homogeneous, Layer = GC: + contrast estimate SE df t.ratio p.value + Duplication8 - Duplication4 0.7850 0.86 441 0.912 0.6941 + Duplication16 - Duplication8 0.2493 0.86 441 0.290 0.9837 + Duplication32 - Duplication16 0.0223 0.86 441 0.026 1.0000 + +Condition = Uniform, Layer = GC: + contrast estimate SE df t.ratio p.value + Duplication8 - Duplication4 11.4955 0.86 441 13.360 <.0001 + Duplication16 - Duplication8 2.3438 0.86 441 2.724 0.0193 + Duplication32 - Duplication16 0.0372 0.86 441 0.043 0.9999 + +Condition = Scaled, Layer = GC: + contrast estimate SE df t.ratio p.value + Duplication8 - Duplication4 4.7619 0.86 441 5.534 <.0001 + Duplication16 - Duplication8 2.1577 0.86 441 2.508 0.0342 + Duplication32 - Duplication16 -0.5208 0.86 441 -0.605 0.8795 + +Condition = Adaptive, Layer = GC: + contrast estimate SE df t.ratio p.value + Duplication8 - Duplication4 4.2411 0.86 441 4.929 <.0001 + Duplication16 - Duplication8 2.3438 0.86 441 2.724 0.0194 + Duplication32 - Duplication16 1.0417 0.86 441 1.211 0.4907 + +Condition = Homogeneous, Layer = MC: + contrast estimate SE df t.ratio p.value + Duplication8 - Duplication4 -0.2827 0.86 441 -0.329 0.9766 + Duplication16 - Duplication8 0.1190 0.86 441 0.138 0.9981 + Duplication32 - Duplication16 0.1935 0.86 441 0.225 0.9922 + +Condition = Uniform, Layer = MC: + contrast estimate SE df t.ratio p.value + Duplication8 - Duplication4 6.9568 0.86 441 8.085 <.0001 + Duplication16 - Duplication8 4.1295 0.86 441 4.799 <.0001 + Duplication32 - Duplication16 -1.7485 0.86 441 -2.032 0.1125 + +Condition = Scaled, Layer = MC: + contrast estimate SE df t.ratio p.value + Duplication8 - Duplication4 5.4688 0.86 441 6.356 <.0001 + Duplication16 - Duplication8 -1.0045 0.86 441 -1.167 0.5195 + Duplication32 - Duplication16 -0.0744 0.86 441 -0.086 0.9995 + +Condition = Adaptive, Layer = MC: + contrast estimate SE df t.ratio p.value + Duplication8 - Duplication4 3.1622 0.86 441 3.675 0.0007 + Duplication16 - Duplication8 2.2693 0.86 441 2.637 0.0242 + Duplication32 - Duplication16 -0.0372 0.86 441 -0.043 0.9999 + +Results are averaged over the levels of: Session +Degrees-of-freedom method: kenward-roger +P value adjustment: mvt method for 3 tests + + +[[1]] + Layer Session Condition Duplication Accuracy.m Accuracy.s +1 GC Concentration Homogeneous 4 79.94048 0.9470514 +2 GC Concentration Homogeneous 8 81.59970 1.5431770 +3 GC Concentration Homogeneous 16 81.39137 1.5625709 +4 GC Concentration Homogeneous 32 81.81548 1.0975648 +5 GC Concentration Uniform 4 87.05357 4.5192198 +6 GC Concentration Uniform 8 95.53571 2.9676749 +7 GC Concentration Uniform 16 97.17262 2.4593801 +8 GC Concentration Uniform 32 97.61905 1.5586992 +9 GC Concentration Scaled 4 87.27679 2.8800369 +10 GC Concentration Scaled 8 93.45238 2.8635133 +11 GC Concentration Scaled 16 96.57738 3.1295521 +12 GC Concentration Scaled 32 97.02381 1.6835876 +13 GC Concentration Adaptive 4 90.47619 3.8837372 +14 GC Concentration Adaptive 8 92.18750 3.5634477 +15 GC Concentration Adaptive 16 96.65179 2.7542686 +16 GC Concentration Adaptive 32 97.76786 2.5403689 +17 GC Saturation Homogeneous 4 56.75595 0.9728872 +18 GC Saturation Homogeneous 8 56.66667 0.7655895 +19 GC Saturation Homogeneous 16 57.37351 1.0903644 +20 GC Saturation Homogeneous 32 56.99405 0.8296810 +21 GC Saturation Uniform 4 81.77083 4.3385059 +22 GC Saturation Uniform 8 96.27976 3.7206632 +23 GC Saturation Uniform 16 99.33036 1.2501012 +24 GC Saturation Uniform 32 98.95833 1.6455784 +25 GC Saturation Scaled 4 93.82440 4.6319152 +26 GC Saturation Scaled 8 97.17262 3.4962322 +27 GC Saturation Scaled 16 98.36310 1.4139702 +28 GC Saturation Scaled 32 96.87500 2.0059738 +29 GC Saturation Adaptive 4 90.25298 3.8171891 +30 GC Saturation Adaptive 8 97.02381 3.4561907 +31 GC Saturation Adaptive 16 97.24702 3.2901955 +32 GC Saturation Adaptive 32 98.21429 2.2271770 +33 MC Concentration Homogeneous 4 83.75744 0.9141107 +34 MC Concentration Homogeneous 8 83.34821 1.0703651 +35 MC Concentration Homogeneous 16 83.58631 0.9377868 +36 MC Concentration Homogeneous 32 83.79464 0.9356254 +37 MC Concentration Uniform 4 87.20238 3.1497039 +38 MC Concentration Uniform 8 95.23810 2.1104887 +39 MC Concentration Uniform 16 97.09821 2.0481109 +40 MC Concentration Uniform 32 97.91667 1.3118409 +41 MC Concentration Scaled 4 88.91369 3.0507249 +42 MC Concentration Scaled 8 95.31250 1.7262638 +43 MC Concentration Scaled 16 94.64286 1.5908407 +44 MC Concentration Scaled 32 95.31250 2.0232467 +45 MC Concentration Adaptive 4 92.63393 3.5277588 +46 MC Concentration Adaptive 8 91.51786 3.8279476 +47 MC Concentration Adaptive 16 96.94940 1.6966896 +48 MC Concentration Adaptive 32 95.31250 2.9751274 +49 MC Saturation Homogeneous 4 58.63839 1.1504485 +50 MC Saturation Homogeneous 8 58.48214 1.3651601 +51 MC Saturation Homogeneous 16 58.48214 1.1642520 +52 MC Saturation Homogeneous 32 58.66071 1.2582736 +53 MC Saturation Uniform 4 86.60714 3.2290540 +54 MC Saturation Uniform 8 92.48512 1.6203945 +55 MC Saturation Uniform 16 98.88393 1.3286139 +56 MC Saturation Uniform 32 94.56845 5.2225874 +57 MC Saturation Scaled 4 95.31250 2.3475010 +58 MC Saturation Scaled 8 99.85119 0.2755417 +59 MC Saturation Scaled 16 98.51190 1.1471716 +60 MC Saturation Scaled 32 97.69345 4.2021991 +61 MC Saturation Adaptive 4 90.10417 4.1719779 +62 MC Saturation Adaptive 8 97.54464 2.6128076 +63 MC Saturation Adaptive 16 96.65179 2.8975582 +64 MC Saturation Adaptive 32 98.21429 2.7184298 + + + +ET Discrimination Accuracy +************************** + model term df1 df2 F.ratio p.value + Condition 3 217 0.000 1.0000 + Duplication 3 217 0.000 1.0000 + Session 1 217 88.324 <.0001 + Condition:Duplication 9 217 0.000 1.0000 + Condition:Session 3 217 0.000 1.0000 + Duplication:Session 3 217 0.000 1.0000 + Condition:Duplication:Session 9 217 0.000 1.0000 + + +Duplication = 4: + contrast estimate SE df t.ratio p.value + Homogeneous - Uniform 1.41e-13 3.58 217 0.000 1.0000 + Homogeneous - Scaled 1.41e-13 3.58 217 0.000 1.0000 + Homogeneous - Adaptive 1.41e-13 3.58 217 0.000 1.0000 + Uniform - Scaled 0.00e+00 3.58 217 0.000 1.0000 + Uniform - Adaptive 0.00e+00 3.58 217 0.000 1.0000 + Scaled - Adaptive 0.00e+00 3.58 217 0.000 1.0000 + +Duplication = 8: + contrast estimate SE df t.ratio p.value + Homogeneous - Uniform 1.83e-13 3.58 217 0.000 1.0000 + Homogeneous - Scaled 1.83e-13 3.58 217 0.000 1.0000 + Homogeneous - Adaptive 1.83e-13 3.58 217 0.000 1.0000 + Uniform - Scaled 0.00e+00 3.58 217 0.000 1.0000 + Uniform - Adaptive 0.00e+00 3.58 217 0.000 1.0000 + Scaled - Adaptive 0.00e+00 3.58 217 0.000 1.0000 + +Duplication = 16: + contrast estimate SE df t.ratio p.value + Homogeneous - Uniform 1.83e-13 3.58 217 0.000 1.0000 + Homogeneous - Scaled 1.83e-13 3.58 217 0.000 1.0000 + Homogeneous - Adaptive 1.83e-13 3.58 217 0.000 1.0000 + Uniform - Scaled 0.00e+00 3.58 217 0.000 1.0000 + Uniform - Adaptive 0.00e+00 3.58 217 0.000 1.0000 + Scaled - Adaptive 0.00e+00 3.58 217 0.000 1.0000 + +Duplication = 32: + contrast estimate SE df t.ratio p.value + Homogeneous - Uniform 1.83e-13 3.58 217 0.000 1.0000 + Homogeneous - Scaled 1.83e-13 3.58 217 0.000 1.0000 + Homogeneous - Adaptive 1.83e-13 3.58 217 0.000 1.0000 + Uniform - Scaled 0.00e+00 3.58 217 0.000 1.0000 + Uniform - Adaptive 0.00e+00 3.58 217 0.000 1.0000 + Scaled - Adaptive 0.00e+00 3.58 217 0.000 1.0000 + +Results are averaged over the levels of: Session +Degrees-of-freedom method: kenward-roger +P value adjustment: tukey method for comparing a family of 4 estimates + + Session Condition Duplication Accuracy.m Accuracy.s +1 Concentration Homogeneous 4 84.07738 9.431687 +2 Concentration Homogeneous 8 84.07738 9.431687 +3 Concentration Homogeneous 16 84.07738 9.431687 +4 Concentration Homogeneous 32 84.07738 9.431687 +5 Concentration Uniform 4 84.07738 9.431687 +6 Concentration Uniform 8 84.07738 9.431687 +7 Concentration Uniform 16 84.07738 9.431687 +8 Concentration Uniform 32 84.07738 9.431687 +9 Concentration Scaled 4 84.07738 9.431687 +10 Concentration Scaled 8 84.07738 9.431687 +11 Concentration Scaled 16 84.07738 9.431687 +12 Concentration Scaled 32 84.07738 9.431687 +13 Concentration Adaptive 4 84.07738 9.431687 +14 Concentration Adaptive 8 84.07738 9.431687 +15 Concentration Adaptive 16 84.07738 9.431687 +16 Concentration Adaptive 32 84.07738 9.431687 +17 Saturation Homogeneous 4 72.17262 16.981806 +18 Saturation Homogeneous 8 72.17262 16.981806 +19 Saturation Homogeneous 16 72.17262 16.981806 +20 Saturation Homogeneous 32 72.17262 16.981806 +21 Saturation Uniform 4 72.17262 16.981806 +22 Saturation Uniform 8 72.17262 16.981806 +23 Saturation Uniform 16 72.17262 16.981806 +24 Saturation Uniform 32 72.17262 16.981806 +25 Saturation Scaled 4 72.17262 16.981806 +26 Saturation Scaled 8 72.17262 16.981806 +27 Saturation Scaled 16 72.17262 16.981806 +28 Saturation Scaled 32 72.17262 16.981806 +29 Saturation Adaptive 4 72.17262 16.981806 +30 Saturation Adaptive 8 72.17262 16.981806 +31 Saturation Adaptive 16 72.17262 16.981806 +32 Saturation Adaptive 32 72.17262 16.981806 + + +MC Discrimination Accuracy +************************** + model term df1 df2 F.ratio p.value + Condition 3 793 68.501 <.0001 + Duplication 3 793 1.154 0.3265 + Session 1 793 5.356 0.0209 + Condition:Duplication 9 793 0.396 0.9371 + Condition:Session 3 793 21.765 <.0001 + Duplication:Session 3 793 0.023 0.9953 + Condition:Duplication:Session 9 793 0.070 0.9999 + + +Duplication = 4: + contrast estimate SE df t.ratio p.value + Homogeneous - Uniform -15.707 5.27 793 -2.982 0.0156 + Homogeneous - Scaled -20.915 5.27 793 -3.971 0.0005 + Homogeneous - Adaptive -20.171 5.27 793 -3.830 0.0008 + Uniform - Scaled -5.208 7.10 793 -0.733 0.8837 + Uniform - Adaptive -4.464 7.10 793 -0.629 0.9228 + Scaled - Adaptive 0.744 7.10 793 0.105 0.9996 + +Duplication = 8: + contrast estimate SE df t.ratio p.value + Homogeneous - Uniform -22.946 5.27 793 -4.357 0.0001 + Homogeneous - Scaled -26.667 5.27 793 -5.063 <.0001 + Homogeneous - Adaptive -23.616 5.27 793 -4.484 <.0001 + Uniform - Scaled -3.720 7.10 793 -0.524 0.9533 + Uniform - Adaptive -0.670 7.10 793 -0.094 0.9997 + Scaled - Adaptive 3.051 7.10 793 0.430 0.9734 + +Duplication = 16: + contrast estimate SE df t.ratio p.value + Homogeneous - Uniform -26.957 5.27 793 -5.118 <.0001 + Homogeneous - Scaled -25.543 5.27 793 -4.850 <.0001 + Homogeneous - Adaptive -25.766 5.27 793 -4.892 <.0001 + Uniform - Scaled 1.414 7.10 793 0.199 0.9972 + Uniform - Adaptive 1.190 7.10 793 0.168 0.9983 + Scaled - Adaptive -0.223 7.10 793 -0.031 1.0000 + +Duplication = 32: + contrast estimate SE df t.ratio p.value + Homogeneous - Uniform -25.015 5.27 793 -4.750 <.0001 + Homogeneous - Scaled -25.275 5.27 793 -4.799 <.0001 + Homogeneous - Adaptive -25.536 5.27 793 -4.849 <.0001 + Uniform - Scaled -0.260 7.10 793 -0.037 1.0000 + Uniform - Adaptive -0.521 7.10 793 -0.073 0.9999 + Scaled - Adaptive -0.260 7.10 793 -0.037 1.0000 + +Results are averaged over the levels of: Session +Degrees-of-freedom method: kenward-roger +P value adjustment: tukey method for comparing a family of 4 estimates + + Session Condition Duplication Accuracy.m Accuracy.s +1 Concentration Homogeneous 4 83.75744 13.2715249 +2 Concentration Homogeneous 8 83.34821 12.9900065 +3 Concentration Homogeneous 16 83.58631 13.1447278 +4 Concentration Homogeneous 32 83.79464 13.3267574 +5 Concentration Uniform 4 87.20238 3.1497039 +6 Concentration Uniform 8 95.23810 2.1104887 +7 Concentration Uniform 16 97.09821 2.0481109 +8 Concentration Uniform 32 97.91667 1.3118409 +9 Concentration Scaled 4 88.91369 3.0507249 +10 Concentration Scaled 8 95.31250 1.7262638 +11 Concentration Scaled 16 94.64286 1.5908407 +12 Concentration Scaled 32 95.31250 2.0232467 +13 Concentration Adaptive 4 92.63393 3.5277588 +14 Concentration Adaptive 8 91.51786 3.8279476 +15 Concentration Adaptive 16 96.94940 1.6966896 +16 Concentration Adaptive 32 95.31250 2.9751274 +17 Saturation Homogeneous 4 58.63839 29.1216537 +18 Saturation Homogeneous 8 58.48214 28.9029919 +19 Saturation Homogeneous 16 58.48214 29.0582120 +20 Saturation Homogeneous 32 58.66071 29.0905133 +21 Saturation Uniform 4 86.60714 3.2290540 +22 Saturation Uniform 8 92.48512 1.6203945 +23 Saturation Uniform 16 98.88393 1.3286139 +24 Saturation Uniform 32 94.56845 5.2225874 +25 Saturation Scaled 4 95.31250 2.3475010 +26 Saturation Scaled 8 99.85119 0.2755417 +27 Saturation Scaled 16 98.51190 1.1471716 +28 Saturation Scaled 32 97.69345 4.2021991 +29 Saturation Adaptive 4 90.10417 4.1719779 +30 Saturation Adaptive 8 97.54464 2.6128076 +31 Saturation Adaptive 16 96.65179 2.8975582 +32 Saturation Adaptive 32 98.21429 2.7184298 + + +GC Discrimination Accuracy +************************** + model term df1 df2 F.ratio p.value + Condition 3 793 80.102 <.0001 + Duplication 3 793 2.339 0.0722 + Session 1 793 5.145 0.0236 + Condition:Duplication 9 793 0.563 0.8278 + Condition:Session 3 793 19.977 <.0001 + Duplication:Session 3 793 0.036 0.9908 + Condition:Duplication:Session 9 793 0.093 0.9997 + + +Duplication = 4: + contrast estimate SE df t.ratio p.value + Homogeneous - Uniform -16.064 5.28 793 -3.042 0.0130 + Homogeneous - Scaled -22.202 5.28 793 -4.204 0.0002 + Homogeneous - Adaptive -22.016 5.28 793 -4.169 0.0002 + Uniform - Scaled -6.138 7.12 793 -0.862 0.8244 + Uniform - Adaptive -5.952 7.12 793 -0.836 0.8374 + Scaled - Adaptive 0.186 7.12 793 0.026 1.0000 + +Duplication = 8: + contrast estimate SE df t.ratio p.value + Homogeneous - Uniform -26.775 5.28 793 -5.070 <.0001 + Homogeneous - Scaled -26.179 5.28 793 -4.957 <.0001 + Homogeneous - Adaptive -25.472 5.28 793 -4.823 <.0001 + Uniform - Scaled 0.595 7.12 793 0.084 0.9998 + Uniform - Adaptive 1.302 7.12 793 0.183 0.9978 + Scaled - Adaptive 0.707 7.12 793 0.099 0.9996 + +Duplication = 16: + contrast estimate SE df t.ratio p.value + Homogeneous - Uniform -28.869 5.28 793 -5.467 <.0001 + Homogeneous - Scaled -28.088 5.28 793 -5.319 <.0001 + Homogeneous - Adaptive -27.567 5.28 793 -5.220 <.0001 + Uniform - Scaled 0.781 7.12 793 0.110 0.9995 + Uniform - Adaptive 1.302 7.12 793 0.183 0.9978 + Scaled - Adaptive 0.521 7.12 793 0.073 0.9999 + +Duplication = 32: + contrast estimate SE df t.ratio p.value + Homogeneous - Uniform -28.884 5.28 793 -5.469 <.0001 + Homogeneous - Scaled -27.545 5.28 793 -5.216 <.0001 + Homogeneous - Adaptive -28.586 5.28 793 -5.413 <.0001 + Uniform - Scaled 1.339 7.12 793 0.188 0.9976 + Uniform - Adaptive 0.298 7.12 793 0.042 1.0000 + Scaled - Adaptive -1.042 7.12 793 -0.146 0.9989 + +Results are averaged over the levels of: Session +Degrees-of-freedom method: kenward-roger +P value adjustment: tukey method for comparing a family of 4 estimates + + Session Condition Duplication Accuracy.m Accuracy.s +1 Concentration Homogeneous 4 79.94048 19.565738 +2 Concentration Homogeneous 8 81.59970 14.614153 +3 Concentration Homogeneous 16 81.39137 13.868915 +4 Concentration Homogeneous 32 81.81548 13.741943 +5 Concentration Uniform 4 87.05357 4.519220 +6 Concentration Uniform 8 95.53571 2.967675 +7 Concentration Uniform 16 97.17262 2.459380 +8 Concentration Uniform 32 97.61905 1.558699 +9 Concentration Scaled 4 87.27679 2.880037 +10 Concentration Scaled 8 93.45238 2.863513 +11 Concentration Scaled 16 96.57738 3.129552 +12 Concentration Scaled 32 97.02381 1.683588 +13 Concentration Adaptive 4 90.47619 3.883737 +14 Concentration Adaptive 8 92.18750 3.563448 +15 Concentration Adaptive 16 96.65179 2.754269 +16 Concentration Adaptive 32 97.76786 2.540369 +17 Saturation Homogeneous 4 56.75595 27.731219 +18 Saturation Homogeneous 8 56.66667 27.560117 +19 Saturation Homogeneous 16 57.37351 28.167748 +20 Saturation Homogeneous 32 56.99405 28.073970 +21 Saturation Uniform 4 81.77083 4.338506 +22 Saturation Uniform 8 96.27976 3.720663 +23 Saturation Uniform 16 99.33036 1.250101 +24 Saturation Uniform 32 98.95833 1.645578 +25 Saturation Scaled 4 93.82440 4.631915 +26 Saturation Scaled 8 97.17262 3.496232 +27 Saturation Scaled 16 98.36310 1.413970 +28 Saturation Scaled 32 96.87500 2.005974 +29 Saturation Adaptive 4 90.25298 3.817189 +30 Saturation Adaptive 8 97.02381 3.456191 +31 Saturation Adaptive 16 97.24702 3.290196 +32 Saturation Adaptive 32 98.21429 2.227177 + + +MC SD Utilization +***************** + model term df1 df2 F.ratio p.value + Condition 3 793 83.113 <.0001 + Duplication 3 793 4.929 0.0021 + Session 1 793 26.624 <.0001 + Condition:Duplication 9 793 1.556 0.1243 + Condition:Session 3 793 5.349 0.0012 + Duplication:Session 3 793 0.057 0.9822 + Condition:Duplication:Session 9 793 0.171 0.9968 + + +Duplication = 4: + contrast estimate SE df t.ratio p.value + Homogeneous - Uniform 8.16 1.49 793 5.461 <.0001 + Homogeneous - Scaled 3.22 1.49 793 2.155 0.1371 + Homogeneous - Adaptive 1.88 1.49 793 1.258 0.5903 + Uniform - Scaled -4.94 2.02 793 -2.452 0.0685 + Uniform - Adaptive -6.28 2.02 793 -3.117 0.0102 + Scaled - Adaptive -1.34 2.02 793 -0.665 0.9101 + +Duplication = 8: + contrast estimate SE df t.ratio p.value + Homogeneous - Uniform 9.46 1.49 793 6.330 <.0001 + Homogeneous - Scaled 7.54 1.49 793 5.047 <.0001 + Homogeneous - Adaptive 4.94 1.49 793 3.304 0.0055 + Uniform - Scaled -1.92 2.02 793 -0.951 0.7772 + Uniform - Adaptive -4.52 2.02 793 -2.244 0.1125 + Scaled - Adaptive -2.61 2.02 793 -1.293 0.5678 + +Duplication = 16: + contrast estimate SE df t.ratio p.value + Homogeneous - Uniform 9.95 1.49 793 6.655 <.0001 + Homogeneous - Scaled 8.29 1.49 793 5.545 <.0001 + Homogeneous - Adaptive 6.36 1.49 793 4.253 0.0001 + Uniform - Scaled -1.66 2.02 793 -0.823 0.8436 + Uniform - Adaptive -3.59 2.02 793 -1.781 0.2831 + Scaled - Adaptive -1.93 2.02 793 -0.958 0.7731 + +Duplication = 32: + contrast estimate SE df t.ratio p.value + Homogeneous - Uniform 10.17 1.49 793 6.803 <.0001 + Homogeneous - Scaled 8.69 1.49 793 5.814 <.0001 + Homogeneous - Adaptive 6.59 1.49 793 4.410 0.0001 + Uniform - Scaled -1.48 2.02 793 -0.734 0.8835 + Uniform - Adaptive -3.58 2.02 793 -1.775 0.2863 + Scaled - Adaptive -2.10 2.02 793 -1.041 0.7254 + +Results are averaged over the levels of: Session +Degrees-of-freedom method: kenward-roger +P value adjustment: tukey method for comparing a family of 4 estimates + + Condition Duplication Utilization.m Utilization.s +1 Homogeneous 4 10.6889941 7.00657816 +2 Homogeneous 8 10.6889928 7.00658018 +3 Homogeneous 16 10.6889941 7.00657816 +4 Homogeneous 32 10.6889941 7.00657816 +5 Uniform 4 2.5264119 0.29343818 +6 Uniform 8 1.2276401 0.27780844 +7 Uniform 16 0.7411285 0.01743323 +8 Uniform 32 0.5194452 0.07558018 +9 Scaled 4 7.4683343 3.06794871 +10 Scaled 8 3.1445685 1.43187667 +11 Scaled 16 2.3998627 1.23466271 +12 Scaled 32 1.9982167 1.10118183 +13 Adaptive 4 8.8090683 1.54475210 +14 Adaptive 8 5.7502307 2.33368192 +15 Adaptive 16 4.3315580 2.17632133 +16 Adaptive 32 4.0963225 2.14548730 + + +GC SD Utilization +***************** + model term df1 df2 F.ratio p.value + Condition 3 793 27.466 <.0001 + Duplication 3 793 3.480 0.0156 + Session 1 793 1.977 0.1601 + Condition:Duplication 9 793 1.354 0.2053 + Condition:Session 3 793 3.553 0.0141 + Duplication:Session 3 793 0.317 0.8135 + Condition:Duplication:Session 9 793 0.448 0.9090 + + +Duplication = 4: + contrast estimate SE df t.ratio p.value + Homogeneous - Uniform 10.47 2.61 793 4.017 0.0004 + Homogeneous - Scaled 2.19 2.61 793 0.840 0.8353 + Homogeneous - Adaptive -4.11 2.61 793 -1.576 0.3928 + Uniform - Scaled -8.28 3.51 793 -2.356 0.0867 + Uniform - Adaptive -14.57 3.51 793 -4.148 0.0002 + Scaled - Adaptive -6.30 3.51 793 -1.792 0.2779 + +Duplication = 8: + contrast estimate SE df t.ratio p.value + Homogeneous - Uniform 10.06 2.61 793 3.861 0.0007 + Homogeneous - Scaled 6.79 2.61 793 2.606 0.0460 + Homogeneous - Adaptive 2.80 2.61 793 1.073 0.7059 + Uniform - Scaled -3.27 3.51 793 -0.931 0.7882 + Uniform - Adaptive -7.26 3.51 793 -2.068 0.1647 + Scaled - Adaptive -3.99 3.51 793 -1.137 0.6670 + +Duplication = 16: + contrast estimate SE df t.ratio p.value + Homogeneous - Uniform 10.78 2.61 793 4.138 0.0002 + Homogeneous - Scaled 8.30 2.61 793 3.184 0.0082 + Homogeneous - Adaptive 5.08 2.61 793 1.950 0.2080 + Uniform - Scaled -2.49 3.51 793 -0.708 0.8941 + Uniform - Adaptive -5.70 3.51 793 -1.622 0.3665 + Scaled - Adaptive -3.21 3.51 793 -0.915 0.7970 + +Duplication = 32: + contrast estimate SE df t.ratio p.value + Homogeneous - Uniform 10.22 2.61 793 3.922 0.0006 + Homogeneous - Scaled 8.71 2.61 793 3.344 0.0048 + Homogeneous - Adaptive 5.77 2.61 793 2.216 0.1197 + Uniform - Scaled -1.50 3.51 793 -0.428 0.9736 + Uniform - Adaptive -4.44 3.51 793 -1.265 0.5857 + Scaled - Adaptive -2.94 3.51 793 -0.837 0.8370 + +Results are averaged over the levels of: Session +Degrees-of-freedom method: kenward-roger +P value adjustment: tukey method for comparing a family of 4 estimates + + Condition Duplication Utilization.m Utilization.s +1 Homogeneous 4 14.339882 11.8512291 +2 Homogeneous 8 14.013485 11.3295387 +3 Homogeneous 16 13.979193 11.2364542 +4 Homogeneous 32 14.003114 11.2420014 +5 Uniform 4 3.873578 1.0145370 +6 Uniform 8 3.952266 0.9629869 +7 Uniform 16 3.196786 1.2843224 +8 Uniform 32 3.784776 1.5371970 +9 Scaled 4 12.150671 4.0867106 +10 Scaled 8 7.223188 2.0987359 +11 Scaled 16 5.683265 1.6803814 +12 Scaled 32 5.289270 1.4922946 +13 Adaptive 4 18.447019 8.0723102 +14 Adaptive 8 11.216463 4.2261139 +15 Adaptive 16 8.897111 3.1244163 +16 Adaptive 32 8.228679 2.8683649 + + +MC Mean Utilization +***************** + model term df1 df2 F.ratio p.value + Condition 3 793 0.756 0.5192 + Duplication 3 793 0.726 0.5369 + Session 1 793 0.936 0.3336 + Condition:Duplication 9 793 0.282 0.9797 + Condition:Session 3 793 0.350 0.7892 + Duplication:Session 3 793 0.060 0.9810 + Condition:Duplication:Session 9 793 0.024 1.0000 + + +Duplication = 4: + contrast estimate SE df t.ratio p.value + Homogeneous - Uniform -0.892 7.47 793 -0.119 0.9994 + Homogeneous - Scaled -11.090 7.47 793 -1.485 0.4473 + Homogeneous - Adaptive -2.062 7.47 793 -0.276 0.9926 + Uniform - Scaled -10.198 10.07 793 -1.012 0.7422 + Uniform - Adaptive -1.170 10.07 793 -0.116 0.9994 + Scaled - Adaptive 9.028 10.07 793 0.896 0.8068 + +Duplication = 8: + contrast estimate SE df t.ratio p.value + Homogeneous - Uniform -0.129 7.47 793 -0.017 1.0000 + Homogeneous - Scaled -3.089 7.47 793 -0.414 0.9761 + Homogeneous - Adaptive 3.538 7.47 793 0.474 0.9649 + Uniform - Scaled -2.960 10.07 793 -0.294 0.9912 + Uniform - Adaptive 3.667 10.07 793 0.364 0.9835 + Scaled - Adaptive 6.628 10.07 793 0.658 0.9127 + +Duplication = 16: + contrast estimate SE df t.ratio p.value + Homogeneous - Uniform 0.374 7.47 793 0.050 1.0000 + Homogeneous - Scaled -0.620 7.47 793 -0.083 0.9998 + Homogeneous - Adaptive 7.245 7.47 793 0.970 0.7667 + Uniform - Scaled -0.994 10.07 793 -0.099 0.9997 + Uniform - Adaptive 6.871 10.07 793 0.682 0.9039 + Scaled - Adaptive 7.865 10.07 793 0.781 0.8632 + +Duplication = 32: + contrast estimate SE df t.ratio p.value + Homogeneous - Uniform 0.472 7.47 793 0.063 0.9999 + Homogeneous - Scaled 1.403 7.47 793 0.188 0.9976 + Homogeneous - Adaptive 8.052 7.47 793 1.078 0.7032 + Uniform - Scaled 0.931 10.07 793 0.092 0.9997 + Uniform - Adaptive 7.581 10.07 793 0.753 0.8756 + Scaled - Adaptive 6.649 10.07 793 0.660 0.9120 + +Results are averaged over the levels of: Session +Degrees-of-freedom method: kenward-roger +P value adjustment: tukey method for comparing a family of 4 estimates + + Condition Duplication Utilization.m Utilization.s +1 Homogeneous 4 45.86077 31.95208308 +2 Homogeneous 8 45.86077 31.95208308 +3 Homogeneous 16 45.86077 31.95208308 +4 Homogeneous 32 45.86077 31.95208308 +5 Uniform 4 46.75293 0.48099906 +6 Uniform 8 45.98999 0.08629560 +7 Uniform 16 45.48645 0.06888732 +8 Uniform 32 45.38923 0.18675447 +9 Scaled 4 56.95103 4.65358375 +10 Scaled 8 48.95020 2.66617830 +11 Scaled 16 46.48089 2.22861747 +12 Scaled 32 44.45779 1.14594326 +13 Adaptive 4 47.92306 5.65330821 +14 Adaptive 8 42.32265 3.77191388 +15 Adaptive 16 38.61564 2.50669047 +16 Adaptive 32 37.80866 2.14143100 + + +GC Mean Utilization +***************** + model term df1 df2 F.ratio p.value + Condition 3 793 2.083 0.1011 + Duplication 3 793 1.886 0.1304 + Session 1 793 2.487 0.1152 + Condition:Duplication 9 793 0.463 0.8993 + Condition:Session 3 793 2.327 0.0733 + Duplication:Session 3 793 0.108 0.9552 + Condition:Duplication:Session 9 793 0.035 1.0000 + + +Duplication = 4: + contrast estimate SE df t.ratio p.value + Homogeneous - Uniform -14.278 7.55 793 -1.891 0.2328 + Homogeneous - Scaled -13.336 7.55 793 -1.766 0.2906 + Homogeneous - Adaptive -2.787 7.55 793 -0.369 0.9828 + Uniform - Scaled 0.942 10.18 793 0.093 0.9997 + Uniform - Adaptive 11.492 10.18 793 1.129 0.6720 + Scaled - Adaptive 10.549 10.18 793 1.036 0.7283 + +Duplication = 8: + contrast estimate SE df t.ratio p.value + Homogeneous - Uniform -1.631 7.55 793 -0.216 0.9964 + Homogeneous - Scaled -7.810 7.55 793 -1.034 0.7294 + Homogeneous - Adaptive 2.496 7.55 793 0.331 0.9875 + Uniform - Scaled -6.179 10.18 793 -0.607 0.9299 + Uniform - Adaptive 4.128 10.18 793 0.405 0.9775 + Scaled - Adaptive 10.306 10.18 793 1.012 0.7424 + +Duplication = 16: + contrast estimate SE df t.ratio p.value + Homogeneous - Uniform -0.223 7.55 793 -0.030 1.0000 + Homogeneous - Scaled -5.866 7.55 793 -0.777 0.8650 + Homogeneous - Adaptive 6.835 7.55 793 0.905 0.8022 + Uniform - Scaled -5.643 10.18 793 -0.554 0.9454 + Uniform - Adaptive 7.058 10.18 793 0.693 0.8998 + Scaled - Adaptive 12.700 10.18 793 1.247 0.5969 + +Duplication = 32: + contrast estimate SE df t.ratio p.value + Homogeneous - Uniform 0.893 7.55 793 0.118 0.9994 + Homogeneous - Scaled -3.415 7.55 793 -0.452 0.9692 + Homogeneous - Adaptive 8.018 7.55 793 1.062 0.7130 + Uniform - Scaled -4.308 10.18 793 -0.423 0.9745 + Uniform - Adaptive 7.125 10.18 793 0.700 0.8972 + Scaled - Adaptive 11.432 10.18 793 1.123 0.6756 + +Results are averaged over the levels of: Session +Degrees-of-freedom method: kenward-roger +P value adjustment: tukey method for comparing a family of 4 estimates + + Condition Duplication Utilization.m Utilization.s +1 Homogeneous 4 60.39900 32.708396 +2 Homogeneous 8 59.77864 32.347879 +3 Homogeneous 16 59.64869 32.124731 +4 Homogeneous 32 59.58835 32.162099 +5 Uniform 4 74.67739 4.528003 +6 Uniform 8 61.40987 2.039360 +7 Uniform 16 59.87167 1.603123 +8 Uniform 32 58.69538 1.748840 +9 Scaled 4 73.73526 6.029152 +10 Scaled 8 67.58859 5.408473 +11 Scaled 16 65.51448 4.946438 +12 Scaled 32 63.00289 3.073514 +13 Adaptive 4 63.18577 10.325705 +14 Adaptive 8 57.28237 7.331287 +15 Adaptive 16 52.81416 4.961764 +16 Adaptive 32 51.57046 4.654868 diff --git a/analysis/SciRep-Final/plots/clf_Adaptive.svg b/analysis/SciRep-Final/plots/clf_Adaptive.svg new file mode 100644 index 0000000..4a2aa0d --- /dev/null +++ b/analysis/SciRep-Final/plots/clf_Adaptive.svg @@ -0,0 +1,193 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +Concentration + + + + + + + + + +Saturation + + + + + + +4 +8 +16 +32 + + + + +4 +8 +16 +32 +50 +60 +70 +80 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/dev/null +++ b/environment.yml @@ -0,0 +1,789 @@ +name: sapicore +channels: + - pytorch + - nvidia + - defaults +dependencies: + - _libgcc_mutex=0.1=main + - _openmp_mutex=5.1=1_gnu + - alabaster=0.7.16=py311h06a4308_0 + - astroid=2.14.2=py311h06a4308_0 + - asttokens=2.0.5=pyhd3eb1b0_0 + - atomicwrites=1.4.0=py_0 + - autopep8=2.0.4=pyhd3eb1b0_0 + - backcall=0.2.0=pyhd3eb1b0_0 + - binaryornot=0.4.4=pyhd3eb1b0_1 + - blas=1.0=mkl + - brotli=1.0.9=h5eee18b_7 + - brotli-bin=1.0.9=h5eee18b_7 + - brotli-python=1.0.9=py311h6a678d5_7 + - bzip2=1.0.8=h7b6447c_0 + - ca-certificates=2024.3.11=h06a4308_0 + - cffi=1.16.0=py311h5eee18b_0 + - chardet=4.0.0=py311h06a4308_1003 + - charset-normalizer=2.0.4=pyhd3eb1b0_0 + - click=8.1.7=py311h06a4308_0 + - cloudpickle=2.2.1=py311h06a4308_0 + - colorama=0.4.6=py311h06a4308_0 + - comm=0.2.1=py311h06a4308_0 + - cookiecutter=2.6.0=py311h06a4308_0 + - cryptography=41.0.7=py311hdda0065_0 + - cuda-cudart=12.1.105=0 + - cuda-cupti=12.1.105=0 + - cuda-libraries=12.1.0=0 + - cuda-nvrtc=12.1.105=0 + - cuda-nvtx=12.1.105=0 + - cuda-opencl=12.3.101=0 + - cuda-runtime=12.1.0=0 + - cyrus-sasl=2.1.28=h52b45da_1 + - dbus=1.13.18=hb2f20db_0 + - decorator=5.1.1=pyhd3eb1b0_0 + - defusedxml=0.7.1=pyhd3eb1b0_0 + - diff-match-patch=20200713=pyhd3eb1b0_0 + - docstring-to-markdown=0.11=py311h06a4308_0 + - executing=0.8.3=pyhd3eb1b0_0 + - expat=2.6.2=h6a678d5_0 + - ffmpeg=4.3=hf484d3e_0 + - fontconfig=2.14.1=h4c34cd2_2 + - freetype=2.12.1=h4a9f257_0 + - giflib=5.2.1=h5eee18b_3 + - glib=2.78.4=h6a678d5_0 + - glib-tools=2.78.4=h6a678d5_0 + - gmp=6.2.1=h295c915_3 + - gmpy2=2.1.2=py311hc9b5ff0_0 + - gnutls=3.6.15=he1e5248_0 + - gst-plugins-base=1.14.1=h6a678d5_1 + - gstreamer=1.14.1=h5eee18b_1 + - icu=73.1=h6a678d5_0 + - imagesize=1.4.1=py311h06a4308_0 + - importlib_metadata=7.0.1=hd3eb1b0_0 + - inflection=0.5.1=py311h06a4308_0 + - intel-openmp=2023.1.0=hdb19cb5_46306 + - intervaltree=3.1.0=pyhd3eb1b0_0 + - ipykernel=6.28.0=py311h06a4308_0 + - isort=5.13.2=py311h06a4308_0 + - jaraco.classes=3.2.1=pyhd3eb1b0_0 + - jedi=0.18.1=py311h06a4308_1 + - jellyfish=1.0.1=py311hb02cf49_0 + - jpeg=9e=h5eee18b_1 + - jupyter_client=8.6.0=py311h06a4308_0 + - jupyter_core=5.7.2=py311h06a4308_0 + - jupyterlab_pygments=0.2.2=py311h06a4308_0 + - keyring=24.3.1=py311h06a4308_0 + - krb5=1.20.1=h143b758_1 + - lame=3.100=h7b6447c_0 + - lazy-object-proxy=1.10.0=py311h5eee18b_0 + - lcms2=2.12=h3be6417_0 + - ld_impl_linux-64=2.38=h1181459_1 + - lerc=3.0=h295c915_0 + - libbrotlicommon=1.0.9=h5eee18b_7 + - libbrotlidec=1.0.9=h5eee18b_7 + - libbrotlienc=1.0.9=h5eee18b_7 + - libclang=14.0.6=default_hc6dbbc7_1 + - libclang13=14.0.6=default_he11475f_1 + - libcublas=12.1.0.26=0 + - libcufft=11.0.2.4=0 + - libcufile=1.8.1.2=0 + - libcups=2.4.2=h2d74bed_1 + - libcurand=10.3.4.107=0 + - libcusolver=11.4.4.55=0 + - libcusparse=12.0.2.55=0 + - libdeflate=1.17=h5eee18b_1 + - libedit=3.1.20230828=h5eee18b_0 + - libevent=2.1.12=hdbd6064_1 + - libffi=3.4.4=h6a678d5_0 + - libgcc-ng=11.2.0=h1234567_1 + - libglib=2.78.4=hdc74915_0 + - libgomp=11.2.0=h1234567_1 + - libiconv=1.16=h7f8727e_2 + - libidn2=2.3.4=h5eee18b_0 + - libjpeg-turbo=2.0.0=h9bf148f_0 + - libllvm14=14.0.6=hdb19cb5_3 + - libnpp=12.0.2.50=0 + - libnvjitlink=12.1.105=0 + - libnvjpeg=12.1.1.14=0 + - libpng=1.6.39=h5eee18b_0 + - libpq=12.17=hdbd6064_0 + - libsodium=1.0.18=h7b6447c_0 + - libspatialindex=1.9.3=h2531618_0 + - libstdcxx-ng=11.2.0=h1234567_1 + - libtasn1=4.19.0=h5eee18b_0 + - libtiff=4.5.1=h6a678d5_0 + - libunistring=0.9.10=h27cfd23_0 + - libuuid=1.41.5=h5eee18b_0 + - libwebp=1.3.2=h11a3e52_0 + - libwebp-base=1.3.2=h5eee18b_0 + - libxcb=1.15=h7f8727e_0 + - libxkbcommon=1.0.1=h5eee18b_1 + - libxml2=2.10.4=hf1b16e4_1 + - llvm-openmp=14.0.6=h9e868ea_0 + - lz4-c=1.9.4=h6a678d5_0 + - matplotlib-base=3.8.0=py311ha02d727_0 + - matplotlib-inline=0.1.6=py311h06a4308_0 + - mccabe=0.7.0=pyhd3eb1b0_0 + - mkl=2023.1.0=h213fc3f_46344 + - mkl-service=2.4.0=py311h5eee18b_1 + - mkl_fft=1.3.8=py311h5eee18b_0 + - mkl_random=1.2.4=py311hdb19cb5_0 + - mpc=1.1.0=h10f8cd9_1 + - mpfr=4.0.2=hb69a4c5_1 + - mpmath=1.3.0=py311h06a4308_0 + - munkres=1.1.4=py_0 + - mypy_extensions=1.0.0=py311h06a4308_0 + - mysql=5.7.24=h721c034_2 + - nbformat=5.9.2=py311h06a4308_0 + - ncurses=6.4=h6a678d5_0 + - nest-asyncio=1.6.0=py311h06a4308_0 + - nettle=3.7.3=hbbd107a_1 + - nspr=4.35=h6a678d5_0 + - nss=3.89.1=h6a678d5_0 + - numpydoc=1.7.0=py311h06a4308_0 + - openh264=2.1.1=h4ff587b_0 + - openjpeg=2.4.0=h3ad879b_0 + - openssl=3.0.14=h5eee18b_0 + - pandocfilters=1.5.0=pyhd3eb1b0_0 + - parso=0.8.3=pyhd3eb1b0_0 + - pcre2=10.42=hebb0a14_1 + - pexpect=4.8.0=pyhd3eb1b0_3 + - pickleshare=0.7.5=pyhd3eb1b0_1003 + - ply=3.11=py311h06a4308_0 + - psutil=5.9.0=py311h5eee18b_0 + - ptyprocess=0.7.0=pyhd3eb1b0_2 + - pure_eval=0.2.2=pyhd3eb1b0_0 + - pycparser=2.21=pyhd3eb1b0_0 + - pydocstyle=6.3.0=py311h06a4308_0 + - pygments=2.15.1=py311h06a4308_1 + - pylint=2.16.2=py311h06a4308_0 + - pylint-venv=3.0.3=py311h06a4308_0 + - pyls-spyder=0.4.0=pyhd3eb1b0_0 + - pyopenssl=23.2.0=py311h06a4308_0 + - pyqt=5.15.10=py311h6a678d5_0 + - pyqt5-sip=12.13.0=py311h5eee18b_0 + - pyqtwebengine=5.15.10=py311h6a678d5_0 + - pysocks=1.7.1=py311h06a4308_0 + - python=3.11.5=h955ad1f_0 + - python-fastjsonschema=2.16.2=py311h06a4308_0 + - python-lsp-black=2.0.0=py311h06a4308_0 + - python-lsp-jsonrpc=1.1.2=pyhd3eb1b0_0 + - python-lsp-server=1.10.0=py311h06a4308_0 + - python-slugify=5.0.2=pyhd3eb1b0_0 + - python-tzdata=2023.3=pyhd3eb1b0_0 + - pytoolconfig=1.2.6=py311h06a4308_0 + - pytorch-cuda=12.1=ha16c6d3_5 + - pytorch-mutex=1.0=cuda + - pyxdg=0.27=pyhd3eb1b0_0 + - pyzmq=25.1.2=py311h6a678d5_0 + - qdarkstyle=3.2.3=pyhd3eb1b0_0 + - qstylizer=0.2.2=py311h06a4308_0 + - qt-main=5.15.2=h53bd1ea_10 + - qt-webengine=5.15.9=h9ab4d14_7 + - qtawesome=1.2.2=py311h06a4308_0 + - qtpy=2.4.1=py311h06a4308_0 + - readline=8.2=h5eee18b_0 + - rope=1.12.0=py311h06a4308_0 + - rtree=1.0.1=py311h06a4308_0 + - sip=6.7.12=py311h6a678d5_0 + - snowballstemmer=2.2.0=pyhd3eb1b0_0 + - sortedcontainers=2.4.0=pyhd3eb1b0_0 + - soupsieve=2.5=py311h06a4308_0 + - sphinxcontrib-jsmath=1.0.1=pyhd3eb1b0_0 + - sphinxcontrib-serializinghtml=1.1.10=py311h06a4308_0 + - spyder=5.5.1=py311h06a4308_2 + - spyder-kernels=2.5.0=py311h06a4308_0 + - sqlite=3.41.2=h5eee18b_0 + - stack_data=0.2.0=pyhd3eb1b0_0 + - tabulate=0.9.0=py311h06a4308_0 + - tbb=2021.8.0=hdb19cb5_0 + - text-unidecode=1.3=pyhd3eb1b0_0 + - textdistance=4.2.1=pyhd3eb1b0_0 + - three-merge=0.1.1=pyhd3eb1b0_0 + - tinycss2=1.2.1=py311h06a4308_0 + - tk=8.6.12=h1ccaba5_0 + - tomli=2.0.1=py311h06a4308_0 + - torchaudio=2.1.2=py311_cu121 + - torchvision=0.16.2=py311_cu121 + - typing_extensions=4.11.0=py311h06a4308_0 + - ujson=5.10.0=py311h6a678d5_0 + - unidecode=1.2.0=pyhd3eb1b0_0 + - watchdog=4.0.1=py311h06a4308_0 + - whatthepatch=1.0.2=py311h06a4308_0 + - wrapt=1.14.1=py311h5eee18b_0 + - wurlitzer=3.0.2=py311h06a4308_0 + - xz=5.4.5=h5eee18b_0 + - yaml=0.2.5=h7b6447c_0 + - yapf=0.40.2=py311h06a4308_0 + - zeromq=4.3.5=h6a678d5_0 + - zlib=1.2.13=h5eee18b_0 + - zstd=1.5.5=hc292b87_0 + - pip: + - about-time==4.2.1 + - absl-py==2.1.0 + - accumulation-tree==0.6.4 + - addict==2.4.0 + - affine==2.4.0 + - aiocache==0.12.3 + - aiofiles==24.1.0 + - aiohappyeyeballs==2.4.0 + - aiohttp==3.11.8 + - aiosignal==1.3.1 + - alembic==1.14.0 + - alive-progress==3.2.0 + - altair==5.5.0 + - annotated-types==0.7.0 + - anthropic==0.45.1 + - anyio==4.8.0 + - appdirs==1.4.4 + - apscheduler==3.10.4 + - argon2-cffi==23.1.0 + - argon2-cffi-bindings==21.2.0 + - arrow==1.3.0 + - asgiref==3.8.1 + - async-lru==2.0.4 + - async-timeout==5.0.1 + - attrs==23.2.0 + - authlib==1.3.2 + - av==12.3.0 + - babel==2.14.0 + - backoff==2.2.1 + - backports-functools-lru-cache==2.0.0 + - bcrypt==4.2.0 + - beautifulsoup4==4.12.2 + - bidict==0.23.1 + - bitarray==3.0.0 + - black==25.1.0 + - bleach==6.1.0 + - blinker==1.9.0 + - bokeh==3.3.2 + - boto3==1.35.53 + - botocore==1.35.99 + - bottle==0.13.1 + - bottleneck==1.4.2 + - build==1.2.1 + - cachecontrol==0.14.0 + - cachetools==5.3.2 + - certifi==2024.12.14 + - cfgv==3.4.0 + - chroma-hnswlib==0.7.6 + - chromadb==0.6.2 + - cleo==2.1.0 + - cli-helpers==2.3.1 + - click-plugins==1.1.1 + - cligj==0.7.2 + - clip==1.0 + - cmasher==1.7.2 + - colbert-ai==0.2.21 + - colorcet==3.0.1 + - colorclass==2.2.2 + - coloredlogs==15.0.1 + - colorlog==6.8.2 + - colorspacious==1.1.2 + - colour==0.1.5 + - compressed-rtf==1.0.6 + - configobj==5.0.9 + - contourpy==1.3.1 + - copulae==0.7.9 + - coverage==7.6.9 + - cpflows==0.1.2 + - cramjam==2.9.1 + - crashtest==0.4.1 + - ctranslate2==4.5.0 + - cycler==0.12.1 + - dash==2.18.0 + - dash-core-components==2.0.0 + - dash-cytoscape==1.0.2 + - dash-html-components==2.0.0 + - dash-table==5.0.0 + - databricks-sdk==0.39.0 + - dataclasses-json==0.6.7 + - datajoint==0.14.3 + - datasets==3.2.0 + - debugpy==1.8.0 + - deprecated==1.2.15 + - dill==0.3.7 + - distlib==0.3.9 + - distro==1.9.0 + - dnspython==2.7.0 + - docker==7.1.0 + - docstring-parser==0.16 + - docutils==0.20.1 + - docx2txt==0.8 + - duckduckgo-search==7.2.1 + - dulwich==0.21.7 + - durationpy==0.9 + - easygui==0.98.3 + - ebcdic==1.1.1 + - ecdsa==0.19.0 + - eden-kernel==0.3.1350 + - einops==0.8.0 + - email-validator==2.2.0 + - emoji==2.14.1 + - et-xmlfile==1.1.0 + - eval-type-backport==0.2.2 + - events==0.5 + - extract-msg==0.52.0 + - fake-useragent==1.5.1 + - faker==33.1.0 + - fastapi==0.111.0 + - fastapi-cli==0.0.7 + - fastcluster==1.2.6 + - faster-whisper==1.0.3 + - fastgif==2.1 + - fastjsonschema==2.19.1 + - fastparquet==2024.11.0 + - filelock==3.16.1 + - filetype==1.2.0 + - flake8==6.1.0 + - flask==3.1.0 + - flask-cors==5.0.0 + - flatbuffers==25.1.24 + - fonttools==4.55.3 + - fpdf2==2.8.2 + - fqdn==1.5.1 + - frozendict==2.4.4 + - frozenlist==1.4.1 + - fs==2.4.16 + - fsspec==2024.9.0 + - ftfy==6.2.3 + - future==0.18.3 + - futureproof==0.3.1 + - gcp-storage-emulator==2024.8.3 + - gdown==5.1.0 + - git-python==1.0.3 + - gitdb==4.0.11 + - gitpython==3.1.43 + - glcontext==3.0.0 + - google-ai-generativelanguage==0.6.6 + - google-api-core==2.19.1 + - google-api-python-client==2.159.0 + - google-auth==2.26.1 + - google-auth-httplib2==0.2.0 + - google-auth-oauthlib==1.2.0 + - google-cloud-aiplatform==1.62.0 + - google-cloud-bigquery==3.25.0 + - google-cloud-core==2.4.1 + - google-cloud-resource-manager==1.12.5 + - google-cloud-storage==2.19.0 + - google-crc32c==1.5.0 + - google-generativeai==0.7.2 + - google-resumable-media==2.7.2 + - googleapis-common-protos==1.63.2 + - grapheme==0.6.0 + - graphene==3.4.3 + - graphql-core==3.2.5 + - graphql-relay==3.2.0 + - greenlet==3.1.1 + - grpc-google-iam-v1==0.13.1 + - grpcio==1.67.1 + - grpcio-status==1.63.0rc1 + - grpcio-tools==1.62.3 + - gunicorn==23.0.0 + - h11==0.14.0 + - h2==4.1.0 + - h5py==3.12.1 + - holoviews==1.18.1 + - hpack==4.1.0 + - html5lib==1.1 + - htmlmin==0.1.12 + - httpcore==1.0.5 + - httplib2==0.22.0 + - httptools==0.6.4 + - httpx==0.27.0 + - httpx-sse==0.4.0 + - huggingface-hub==0.27.1 + - humanfriendly==10.0 + - hvplot==0.9.1 + - hyperframe==6.1.0 + - hyperopt==0.2.7 + - identify==2.6.3 + - idna==3.10 + - imagehash==4.3.1 + - imageio==2.33.1 + - importlib-metadata==8.4.0 + - importlib-resources==6.5.2 + - iniconfig==2.0.0 + - installer==0.7.0 + - ipympl==0.9.4 + - ipython==8.26.0 + - ipython-genutils==0.2.0 + - ipywidgets==8.1.5 + - isoduration==20.11.0 + - isosurfaces==0.1.2 + - itsdangerous==2.2.0 + - jaraco-classes==3.4.0 + - jeepney==0.8.0 + - jenkspy==0.4.1 + - jinja2==3.1.5 + - jiter==0.7.1 + - jmespath==1.0.1 + - joblib==1.4.2 + - json5==0.9.14 + - jsonpatch==1.33 + - jsonpath-python==1.0.6 + - jsonpickle==4.0.0 + - jsonpointer==2.4 + - jsonschema==4.20.0 + - jsonschema-specifications==2023.12.1 + - jupyter==1.1.1 + - jupyter-console==6.6.3 + - jupyter-core==5.7.0 + - jupyter-events==0.9.0 + - jupyter-lsp==2.2.1 + - jupyter-server==2.12.2 + - jupyter-server-terminals==0.5.1 + - jupyterlab==4.2.1 + - jupyterlab-pygments==0.3.0 + - jupyterlab-server==2.27.2 + - jupyterlab-widgets==3.0.13 + - kiwisolver==1.4.7 + - kubernetes==32.0.0 + - langchain==0.3.7 + - langchain-community==0.3.7 + - langchain-core==0.3.31 + - langchain-text-splitters==0.3.5 + - langdetect==1.0.9 + - langfuse==2.44.0 + - langsmith==0.1.147 + - lark==1.1.9 + - lazy-loader==0.3 + - ldap3==2.9.1 + - lightning==2.4.0 + - lightning-utilities==0.11.7 + - line-profiler==4.1.3 + - linkify-it-py==2.0.2 + - llvmlite==0.43.0 + - lmdb==1.4.1 + - loguru==0.7.2 + - lpips==0.1.4 + - lxml==5.3.0 + - m2r2==0.3.3.post2 + - mako==1.3.5 + - manimgl==1.6.1 + - manimpango==0.4.4 + - mapbox-earcut==1.0.2 + - markdown==3.7 + - markdown-it-py==2.2.0 + - markupsafe==3.0.2 + - marshmallow==3.26.0 + - matplotlib==3.10.1 + - mdit-py-plugins==0.4.2 + - mdurl==0.1.2 + - memory-profiler==0.61.0 + - milvus-lite==2.4.11 + - minio==7.2.12 + - missingno==0.5.2 + - mistune==0.8.4 + - mlflow==2.19.0 + - mlflow-skinny==2.19.0 + - mmh3==5.1.0 + - moderngl==5.11.1 + - moderngl-window==2.4.6 + - monotonic==1.6 + - mordredcommunity==2.0.6 + - more-itertools==10.2.0 + - moto==5.0.27 + - mpld3==0.5.10 + - msgpack==1.0.8 + - msoffcrypto-tool==5.4.2 + - multidict==6.1.0 + - multimethod==1.12 + - multipledispatch==1.0.0 + - multiprocess==0.70.17 + - multitasking==0.0.11 + - mypy==1.13.0 + - myst-parser==4.0.0 + - narwhals==1.14.2 + - natsort==8.4.0 + - nbclient==0.7.4 + - nbconvert==7.14.0 + - networkx==3.4.2 + - nibabel==5.2.1 + - nilearn==0.10.4 + - ninja==1.11.1.3 + - nixio==1.5.4 + - nltk==3.9.1 + - nodeenv==1.9.1 + - notebook==7.2.0 + - notebook-shim==0.2.3 + - numba==0.60.0 + - numexpr==2.10.2 + - numpy==2.2.4 + - nvidia-cublas-cu12==12.4.5.8 + - nvidia-cuda-cupti-cu12==12.4.127 + - nvidia-cuda-nvrtc-cu12==12.4.127 + - nvidia-cuda-runtime-cu12==12.4.127 + - nvidia-cudnn-cu12==9.1.0.70 + - nvidia-cufft-cu12==11.2.1.3 + - nvidia-curand-cu12==10.3.5.147 + - nvidia-cusolver-cu12==11.6.1.9 + - nvidia-cusparse-cu12==12.3.1.170 + - nvidia-nccl-cu12==2.21.5 + - nvidia-nvjitlink-cu12==12.4.127 + - nvidia-nvtx-cu12==12.4.127 + - oauthlib==3.2.2 + - olefile==0.47 + - oletools==0.60.2 + - onnxruntime==1.20.1 + - open-clip-torch==2.7.0 + - open-webui==0.5.7 + - openai==1.55.0 + - opencv-python==4.9.0.80 + - opencv-python-headless==4.10.0.84 + - openpyxl==3.1.5 + - opensearch-py==2.7.1 + - opentelemetry-api==1.27.0 + - opentelemetry-exporter-otlp-proto-common==1.27.0 + - opentelemetry-exporter-otlp-proto-grpc==1.27.0 + - opentelemetry-instrumentation==0.48b0 + - opentelemetry-instrumentation-asgi==0.48b0 + - opentelemetry-instrumentation-fastapi==0.48b0 + - opentelemetry-proto==1.27.0 + - opentelemetry-sdk==1.27.0 + - opentelemetry-semantic-conventions==0.48b0 + - opentelemetry-util-http==0.48b0 + - optuna==4.0.0 + - orjson==3.10.15 + - otumat==0.3.1 + - outcome==1.3.0.post0 + - overrides==7.4.0 + - packaging==23.2 + - pandas==2.2.3 + - pandas-datareader==0.10.0 + - pandas-profiling==3.2.0 + - panel==1.3.6 + - param==2.0.1 + - passlib==1.7.4 + - pathspec==0.12.1 + - patsy==0.5.6 + - pcodedmp==1.2.6 + - pdfminer==20191125 + - pdfminer-six==20240706 + - peewee==3.17.8 + - peewee-migrate==1.12.2 + - pgcli==4.1.0 + - pgi==0.0.11.2 + - pgspecial==2.1.3 + - pgvector==0.3.5 + - phik==0.12.4 + - pi==0.1.2 + - pillow==11.0.0 + - pip==25.0.1 + - pkginfo==1.10.0 + - platformdirs==4.3.6 + - plotly==5.18.0 + - pluggy==1.5.0 + - poetry==1.8.3 + - poetry-core==1.9.0 + - poetry-plugin-export==1.8.0 + - pooch==1.8.0 + - portalocker==2.10.1 + - posthog==3.10.0 + - pre-commit==3.8.0 + - primp==0.11.0 + - prometheus-client==0.19.0 + - prompt-toolkit==3.0.47 + - propcache==0.2.1 + - proto-plus==1.24.0 + - protobuf==4.25.6 + - proxy-tools==0.1.0 + - psycopg==3.2.3 + - psycopg2==2.9.10 + - psycopg2-binary==2.9.9 + - pubchempy==1.0.4 + - py-partiql-parser==0.6.1 + - py-spy==0.3.14 + - py4j==0.10.9.7 + - pyarrow==18.0.0 + - pyasn1==0.5.1 + - pyasn1-modules==0.3.0 + - pycairo==1.27.0 + - pyclipper==1.3.0.post6 + - pycodestyle==2.11.1 + - pycryptodome==3.20.0 + - pyct==0.5.0 + - pydantic==2.9.2 + - pydantic-core==2.23.4 + - pydantic-settings==2.7.1 + - pydeck==0.9.1 + - pydot==3.0.3 + - pydub==0.25.1 + - pyemd==1.0.0 + - pyflakes==3.1.0 + - pyglet==2.0.17 + - pygobject==3.48.2 + - pyjwt==2.10.1 + - pymdown-extensions==10.11.2 + - pymilvus==2.5.0 + - pymongo==4.10.1 + - pymysql==1.1.1 + - pynndescent==0.5.13 + - pyopengl==3.1.7 + - pypandoc==1.13 + - pyparsing==3.1.1 + - pypdf==4.3.1 + - pyperclip==1.9.0 + - pypika==0.48.9 + - pyproject-hooks==1.1.0 + - pyrfume==0.19 + - pyrr==0.10.3 + - pytest==8.3.4 + - pytest-asyncio==0.25.0 + - pytest-cov==4.1.0 + - pytest-django==4.9.0 + - pytest-docker==3.1.1 + - python-dateutil==2.9.0.post0 + - python-dotenv==1.0.1 + - python-engineio==4.11.2 + - python-iso639==2025.1.27 + - python-jose==3.3.0 + - python-json-logger==2.0.7 + - python-magic==0.4.27 + - python-multipart==0.0.18 + - python-oxmsg==0.0.1 + - python-pptx==1.0.0 + - python-socketio==5.11.3 + - pytorch-forecasting==1.1.1 + - pytorch-lightning==2.4.0 + - pytube==15.0.0 + - pytz==2024.2 + - pyudorandom==1.0.0 + - pyvis==0.3.2 + - pyvista==0.43.1 + - pyviz-comms==3.0.0 + - pywavelets==1.7.0 + - pywebview==5.2 + - pyxlsb==1.0.10 + - pyyaml==6.0.2 + - qdrant-client==1.12.2 + - qtconsole==5.5.2 + - quantities==0.13.0 + - rank-bm25==0.2.2 + - rapidfuzz==3.9.2 + - rapidocr-onnxruntime==1.3.24 + - rasterio==1.3.11 + - rdkit==2024.3.6 + - rdkit-pypi==2022.9.5 + - red-black-tree-mod==1.20 + - redis==5.2.1 + - referencing==0.32.0 + - regex==2023.12.25 + - requests==2.32.3 + - requests-oauthlib==1.3.1 + - requests-toolbelt==1.0.0 + - responses==0.25.6 + - retrying==1.3.4 + - rfc3339-validator==0.1.4 + - rfc3986-validator==0.1.1 + - rich==13.9.4 + - rich-toolkit==0.13.2 + - rpds-py==0.16.2 + - rsa==4.9 + - rtfde==0.1.2 + - ruamel-yaml==0.18.6 + - ruamel-yaml-clib==0.2.12 + - s3transfer==0.10.4 + - safetensors==0.5.2 + - sapicore==0.4.0 + - scikit-image==0.22.0 + - scikit-learn==1.6.1 + - scipy==1.14.1 + - scooby==0.9.2 + - screeninfo==0.8.1 + - seaborn==0.13.2 + - secretstorage==3.3.3 + - selenium==4.18.1 + - send2trash==1.8.2 + - sentence-transformers==3.3.1 + - sentencepiece==0.1.99 + - setproctitle==1.3.4 + - setuptools==75.6.0 + - shapely==2.0.5 + - shellingham==1.5.4 + - simple-websocket==1.1.0 + - six==1.17.0 + - skia-pathops==0.8.0.post1 + - smmap==5.0.1 + - sniffio==1.3.0 + - snuggs==1.4.7 + - soundfile==0.12.1 + - sphinx==7.2.6 + - sphinx-rtd-theme==2.0.0 + - sphinxcontrib-applehelp==1.0.8 + - sphinxcontrib-devhelp==1.0.6 + - sphinxcontrib-htmlhelp==2.0.5 + - sphinxcontrib-jquery==4.1 + - sphinxcontrib-qthelp==1.0.7 + - spikeforge==0.1.0 + - sqlalchemy==2.0.32 + - sqlparse==0.5.2 + - starlette==0.37.2 + - statsmodels==0.14.2 + - streamlit==1.40.1 + - subprocess32==3.5.4 + - svgelements==1.9.6 + - sympy==1.13.1 + - tangled-up-in-unicode==0.2.0 + - tdigest==0.5.2.2 + - tenacity==8.2.3 + - tensorboard==2.18.0 + - tensorboard-data-server==0.7.2 + - terminado==0.18.0 + - tf-keras-nightly==2.16.0.dev2024021110 + - threadpoolctl==3.5.0 + - tifffile==2024.1.30 + - tiktoken==0.8.0 + - tokenizers==0.21.0 + - toml==0.10.2 + - tomlkit==0.12.5 + - toolz==1.0.0 + - torch==2.5.1 + - torchmetrics==1.4.2 + - tornado==6.4 + - tqdm==4.67.1 + - traitlets==5.14.3 + - transformers==4.48.1 + - tree-config==1.0.2 + - trio==0.25.0 + - trio-websocket==0.11.1 + - triton==3.1.0 + - trove-classifiers==2024.5.22 + - typer==0.15.1 + - types-python-dateutil==2.8.19.14 + - typing-extensions==4.12.2 + - typing-inspect==0.9.0 + - tzdata==2024.2 + - tzlocal==5.2 + - uc-micro-py==1.0.2 + - umap==0.1.1 + - umap-learn==0.5.6 + - unstructured==0.15.9 + - unstructured-client==0.28.1 + - uri-template==1.3.0 + - uritemplate==4.1.1 + - urllib3==2.3.0 + - uvicorn==0.30.6 + - uvloop==0.21.0 + - validators==0.34.0 + - virtualenv==20.28.0 + - visions==0.7.4 + - voila==0.5.5 + - vtk==9.3.0 + - watchfiles==1.0.4 + - wcwidth==0.2.13 + - webcolors==1.13 + - webencodings==0.5.1 + - websocket-client==1.7.0 + - websockets==12.0 + - werkzeug==3.1.3 + - wheel==0.45.1 + - widgetsnbextension==4.0.13 + - wsproto==1.2.0 + - xgboost==2.1.3 + - xlrd==2.0.1 + - xlsxwriter==3.2.1 + - xmltodict==0.14.2 + - xxhash==3.5.0 + - xyzservices==2023.10.1 + - yarl==1.18.3 + - yfinance==0.2.43 + - youtube-transcript-api==0.6.3 + - zipp==3.21.0 diff --git a/setup.py b/setup.py new file mode 100644 index 0000000..797a1b0 --- /dev/null +++ b/setup.py @@ -0,0 +1,45 @@ +from setuptools import setup, find_packages +from src import __version__ + +with open("README.md") as f: + long_description = f.read() + +setup( + name="sapinet_regularization", + version=__version__, + description="Heterogeneous quantization regularizes spiking neural network activity", + long_description=long_description, + url="https://github.com/cplab/sapinet_regularization", + author="Roy Moyal, Kyrus R. Mama, Matthew Einhorn, Ayon Borthakur, Thomas A. Cleland", + author_email="rm875@cornell.edu, krm74@cornell.edu, me263@cornell.edu, ayon.borthakur@iitg.ac.in, tac29@cornell.edu", + classifiers=[ + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + ], + packages=find_packages(), + install_requires=[ + "sapicore==0.4.0", + "numpy<2", + "scipy", + "pandas", + "torch", + "scikit-learn", + "networkx", + "matplotlib", + "seaborn", + "pyvista", + "psutil", + "PyYAML", + "dill", + "natsort", + "h5py", + "jenkspy", + "tree-config", + "alive_progress", + "importlib-metadata<4.3", + ], + extras_require={ + "dev": ["pytest", "coverage", "flake8", "sphinx<7.0.0", "sphinx-rtd-theme", "m2r2"], + }, +) diff --git a/src/__init__.py b/src/__init__.py new file mode 100644 index 0000000..3dc1f76 --- /dev/null +++ b/src/__init__.py @@ -0,0 +1 @@ +__version__ = "0.1.0" diff --git a/src/models/__init__.py b/src/models/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/models/bulb.py b/src/models/bulb.py new file mode 100644 index 0000000..9a6c4f2 --- /dev/null +++ b/src/models/bulb.py @@ -0,0 +1,476 @@ +""" Core EPL model. """ +from typing import Any + +import logging +import random + +import numpy as np +from scipy import stats + +import torch +from torch import Tensor + +from alive_progress import alive_bar + +from sapicore.engine.neuron.spiking import SpikingNeuron +from sapicore.engine.network import Network +from sapicore.model import Model + +from sapicore.utils.constants import SEED + +from src.models.component.synapse import IICSynapse +from src.utils.math import k_bits + + +__all__ = ("BulbModel", "BulbNetwork") + + +class BulbNetwork(Network): + def __init__(self, **kwargs): + super().__init__(**kwargs) + cfg = self.configuration.get("model", {}) + + self.is_training = False + self.genesis = cfg.get("neurogenesis") + self.elapsed_cycles = 0 + + # connect core EPL network with the random projection method or pick-k-gain-levels method. + if "forward_prop" not in cfg: + self.connect_epl(forward_connectivity_k=cfg.get("forward_k", 1)) + else: + self.connect_epl(forward_connectivity_prop=cfg.get("forward_prop")) + + # connect signal conditioning layer. + if kwargs.get("conditioning", True): + # can be turned off by setting keyword argument `conditioning` to false at instantiation. + self.connect_glom(enable_pg=cfg.get("ntco", False)) + + def connect_epl(self, forward_connectivity_prop: float = None, forward_connectivity_k: int = 1): + """Customizes connectivity of core EPL layers (MC, GC).""" + # redefine the root layer to be MC (otherwise, the gamma oscillator will be auto-detected as root). + self.roots = ["MC"] + + # easy reference to unit quantities. + num_mc = self["MC"].num_units + num_gc = self["GC"].num_units + + hd_factor = num_mc // self["ET"].num_units + gc_mc_ratio = num_gc // num_mc + + if forward_connectivity_prop is not None: + # MC->GC random connection probability. + self["MC--GC"].connect("prop", forward_connectivity_prop) + + else: + # for each sensor-specific subset of MCs, draw connections s.t. 'k' gain levels are represented from each + # set of sensor-specific MCs. Orders of magnitude more efficient than random projection, and guaranteed to + # include all valid MC->GC weight solution candidates. + # uses helper function k_bits, which generates all binary strings of length `hd_factor` that have `k` 1s. + temp = torch.zeros_like(self["MC--GC"].connections) + + # stores all possible draws for a slab of the appropriate dimensions. + draw_list = k_bits(hd_factor, forward_connectivity_k) + + for gc in range(num_gc): + # each slab's draw order should be different from the others. + random.Random(SEED + gc).shuffle(draw_list) + + for slab in range(0, num_mc, hd_factor): + # set connections for this MC->GC slab. + temp[gc, slab : slab + hd_factor] = torch.as_tensor( + [int(x) for x in draw_list[(slab // hd_factor) % len(draw_list)]] + ) + + self["MC--GC"].connections = temp + + # GC->MC connectivity is sparse. + # NOTE in the fully connected post-neurogensis bulb network, GC[i % num_mc] -> MC[i % num_mc]. + self["GC--MC"].connections = torch.eye(num_mc).tile(gc_mc_ratio)[:, :num_gc].to(self.device) + if self.genesis: + enabled_gc = int(self.genesis.get("init") * num_gc) + self["GC--MC"].connections[:, enabled_gc:] = 0 + + def connect_glom(self, enable_pg: bool = False): + """Customizes connectivity of the glomerular signal conditioning layer (ET, PG). + + Warning + ------- + Connecting this layer will change the root node from MC to OSN. + + """ + # redefine the root layer to be the analog OSN layer. + self.roots = ["OSN"] + + # easy reference to unit quantities. + num_et = self["ET"].num_units + num_mc = self["MC"].num_units + + # define connections for all signal conditioning layers except those that should be all-to-all (the default). + self["OSN--ET"].connect("one") + self["ET--ET"].connect("all") + + if enable_pg: + num_pg = self["PG"].num_units + + self["OSN--PG"].connections = torch.eye(num_et).repeat_interleave(num_pg // num_et, 0).to(self.device) + self["PG--ET"].connections = self["OSN--PG"].connections.T + + # since PG->ET is multiplicative, start at 1 to avoid zeroing out ET. + self["PG"].voltage = torch.ones_like(self["PG"].voltage) + + # each ET projects to (num_mc // num_et) MCs, yielding a vertically expanded identity matrix shaped (MCs, ETs). + self["ET--MC"].connections = torch.eye(num_et).repeat_interleave(num_mc // num_et, 0).to(self.device) + + def forward(self, data: torch.tensor) -> dict: + if self.is_training and self.genesis: + # enable a new slab of GCs at the requested rate. + if self.simulation_step % self.genesis.get("freq") == 0: + self.elapsed_cycles += 1 + + num_mc = self["MC"].num_units + num_gc = self["GC"].num_units + + gc_mc_ratio = num_gc // num_mc + enabled_gc = int( + self.genesis.get("init") * num_gc + self.elapsed_cycles * self.genesis.get("rate") * num_gc + ) + + self["GC--MC"].connections = torch.eye(num_mc).tile(gc_mc_ratio)[:, :num_gc].to(self.device) + self["GC--MC"].connections[:, enabled_gc:] = 0 + + return super().forward(data) + + +class BulbModel(Model): + """Core fitting, testing, and diagnostics. Reusable by derivative model classes.""" + + def __init__(self, **kwargs): + # instantiate the base bulb network or a derivative thereof from preexisting YAMLs. + super().__init__(network=BulbNetwork(**kwargs)) + + # dictionary for tracking and reporting convergence rate for each readout layer. + self.converged = {} + + def calibrate( + self, + data: Tensor | list[Tensor], + duration: int | list[int] | Tensor = 1, + rinse: int | list[int] = 0, + target_synapses: list[str] = None, + ): + """Wraps a model.fit() call that toggles off learning for all synapses except those listed in + the `target_synapses` argument. After the model is calibrated, the original learning status is restored. + + Note + ---- + For the bulb network, calibration is only intended to update ET->MC heterogeneous weights and + should not affect weights anywhere else. Thus, call calibrate() with target_synapses=["ET--MC"]. + + """ + # store original learning toggle status for each synapse. + learning_status = {synapse.identifier: synapse.learning for synapse in self.network.get_synapses()} + + # toggle learning off for calibration except for target synapses. + for synapse in self.network.get_synapses(): + self.network[synapse.identifier].set_learning(True if synapse.identifier in target_synapses else False) + + # calibrate the model, updating necessary synapses (in the bulb case, ET->MC, the quantization synapses). + self.fit(data, duration, rinse, verbose=False, disable_learning_after_fit=False, is_training=False) + + # reset learning status to what it was for all synapses. + for key, value in learning_status.items(): + self.network[key].set_learning(value) + + # reset refractory periods and voltages. + for ens in self.network.get_ensembles(): + if isinstance(ens, SpikingNeuron): + ens.refractory_steps = torch.zeros_like(ens.refractory_steps) + ens.voltage = ens.volt_rest + + # reset last spiked logs. + for syn in self.network.get_synapses(): + if isinstance(syn, IICSynapse): + syn.src_last_spiked = torch.zeros_like(syn.src_last_spiked) + syn.dst_last_spiked = torch.zeros_like(syn.dst_last_spiked) + + def fit( + self, + data: Tensor | list[Tensor], + duration: int | list[int] | Tensor = 1, + rinse: int | list[int] = 0, + verbose: bool = True, + disable_learning_after_fit: bool = True, + weight_clamp_threshold: float = None, + is_training=True, + ): + """Fits the model to a training set of `data` vectors, each presented for `duration` simulation steps. + If `disable_learning_after_fit` is True, all synapses' learning switch will be turned off. + + Note + ---- + Automatically toggles off learning when training is over. Turning advanced `diagnostics` on may + increase runtime. + + """ + # flag that we are in training mode (e.g., to enable neurogenesis). + self.network.is_training = is_training + + # wrap 2D tensor data in a list if need be, to accommodate both single- and multi-root cases. + if not isinstance(data, list): + data = [data] + + num_samples = data[0].shape[0] + steps = (sum(rinse) if isinstance(rinse, list) else rinse) + ( + sum(duration) if isinstance(duration, list) else duration + ) + + with alive_bar(total=num_samples * steps, force_tty=True) as bar: + # number of active and inactive synaptic connections, for tracking purposes. + inactive = torch.count_nonzero(~self.network["MC--GC"].connections.bool()) + active = self.network["MC--GC"].weights.numel() - inactive + + # number of differentiated and pruned synaptic connections, for tracking purposes. + diff = 0 + pruned = 0 + + # automatically determine whether GC->MC weights or blocking durations should be logged. + if isinstance(self.network["GC--MC"], IICSynapse): + gc_mc_attr = "blocking_durations" + else: + gc_mc_attr = "weights" + + # log weight and blocking duration evolution for debugging purposes. + mc_gc_evolution = torch.zeros( + num_samples, + self.network["MC--GC"].weights.shape[0], + self.network["MC--GC"].weights.shape[1], + device=self.network.device, + ) + gc_mc_evolution = torch.zeros( + num_samples, + getattr(self.network["GC--MC"], gc_mc_attr).shape[0], + getattr(self.network["GC--MC"], gc_mc_attr).shape[1], + device=self.network.device, + ) + + # iterate over samples. + for i in range(num_samples): + # repeat each sample as many time steps as instructed (sample and hold). + for k in range(duration if isinstance(duration, int) else duration[i]): + self.network.forward([data[j][i, :] for j in range(len(data))]) + bar() + + # "rinse" network with null input if asked. + for _ in range(rinse if isinstance(rinse, int) else rinse[i]): + self.network.forward([torch.zeros_like(data[j][i, :]) for j in range(len(data))]) + bar() + + d_tmp = torch.count_nonzero(self.network["MC--GC"].weights.eq(self.network["MC--GC"].weight_max)) + p_tmp = torch.count_nonzero(self.network["MC--GC"].weights == 0) - inactive + + d_add = d_tmp - diff + p_add = p_tmp - pruned + + diff = d_tmp + pruned = p_tmp + + # if verbose: + # logging.warning( + # f"MC->GC differentiated {(diff / active) * 100:.2f}% (+{d_add}), " + # f"pruned {(pruned / active) * 100:.2f}% (+{p_add})" + # ) + + mc_gc_evolution[i, :, :] = self.network["MC--GC"].weights + gc_mc_evolution[i, :, :] = getattr(self.network["GC--MC"], gc_mc_attr) + + # if asked, learning is turned off after fitting the model to the training set. + if disable_learning_after_fit: + for synapse in self.network.get_synapses(): + synapse.set_learning(False) + + # if applicable, set MC->GC weights under a particular threshold to zero (e.g., undifferentiated weights). + if weight_clamp_threshold is not None: + mc_gc = self.network["MC--GC"] + mc_gc.weights = mc_gc.weights * (mc_gc.weights > weight_clamp_threshold) + + # gc_mc = self.network["GC--MC"] + # gc_mc.weights = gc_mc.weights * (gc_mc.weights < -weight_clamp_threshold) + + # turn off training flag. + self.network.is_training = False + + return mc_gc_evolution, gc_mc_evolution + + def process( + self, + data: Tensor, + repetitions: int | list[int] | Tensor = 1, + rinse: int = 0, + period: int = 50, + conv_warn: int = 95, + ) -> dict[str, Any]: + """Passes a batch of samples to a network, returning population responses of the readout layers, + having been subjected to a custom accumulation method (e.g., the method `sum` to get spike counts + accumulated over each sample's exposure window). + + Parameters + ---------- + data: Tensor + Test samples to be fed to the trained network. + + repetitions: int or list of int or Tensor, optional + Exposure duration for all/each sample, in simulation steps. Defaults to 1. + + rinse: int + How long to present a 0s vector in-between samples. 0 by default to skip. + + period: int + Background inhibitory oscillation period, in simulation steps. Defaults to 50. + + conv_warn: int + Threshold below which to output a ConvergenceWarning, in terms of percentage of samples that + failed to perfectly converge by the end of the testing period. Set to 95% by default. + Warnings refer to a specific readout layer and are based on its spike output. + + Returns + ------- + dict + Dictionary whose keys are readout ensemble identifiers. Its values are 2D tensors logging the + layer's responses to each sample, potentially after accumulation over test cycles. + + """ + # retrieve readout layer list from configuration dictionary. + readout = self.network.configuration.get("processing", {}).get("readout", ["MC", "GC"]) + + # initialize convergence dictionary to gauge performance of inhibitory plasticity rule. + self.converged = {layer: [] for layer in readout} + + # wrap data in a list even if a single tensor was provided, to accommodate the multi-root network case. + if not isinstance(data, list): + data = [data] + + # placeholder for accumulated responses of each readout layer; one response tensor per sample. + # lists are used because dimension of array returned from `accumulator` is unknown (sometimes vector, matrix). + responses: dict[str, Any] = {layer: [] for layer in readout} + steps = (sum(repetitions) if isinstance(repetitions, list) else repetitions) + rinse + + with (alive_bar(total=data[0].shape[0] * steps, force_tty=True) as bar): + # iterate over samples. + for i in range(data[0].shape[0]): + # look up stimulus exposure duration if varies across samples. + duration = repetitions if isinstance(repetitions, int) else repetitions[i] + + # buffer storing responses of each readout layer within a single exposure window. + sample_responses = { + layer: torch.zeros((duration + rinse, self.network[layer].num_units), device=self.network.device) + for layer in readout + } + + # allow network to process each test sample for as many time steps as instructed. + for d in range(duration): + self.network([data[j][i, :] for j in range(len(data))]) + + for j, layer in enumerate(readout): + sample_responses[layer][d, :] = getattr(self.network[layer], self.network[layer].output_field) + + bar() + + # "rinse" network with null input. + for d in range(rinse): + self.network([torch.zeros_like(data[j][i, :]) for j in range(len(data))]) + + for j, layer in enumerate(readout): + sample_responses[layer][d + duration, :] = getattr( + self.network[layer], self.network[layer].output_field + ) + + bar() + + # only execute if convergence warning threshold > 0. + # this is for verifying inhibitory learning rule correctness and incurs overhead. + if conv_warn: + for layer in readout: + layer_resp = sample_responses[layer] + final_diff = sum(layer_resp[-rinse - period : -rinse, :]) - sum( + layer_resp[-rinse - 2 * period : -rinse - period, :] + ) + final_diff = torch.as_tensor(torch.abs(final_diff) > 10**-5) + + converged_prop = 100 - (torch.count_nonzero(final_diff) / layer_resp.shape[1]) * 100 + self.converged[layer].append(converged_prop) + + # apply the accumulator function to each readout population's `sample_responses`. + accumulators = self.network.configuration.get("processing", {}).get("accumulator", {}) + + if accumulators: + for layer in readout: + try: + mode = accumulators.get(layer).get("mode") + take_final = accumulators.get(layer).get("take_final") + except KeyError as e: + print(f"Misconfigured accumulator: {e}") + responses[layer].append(sample_responses[layer]) + + responses[layer].append( + self.accumulator( + data=sample_responses[layer], + rinse=rinse, + period=period, + mode=mode, + take_final=take_final, + ) + ) + else: + responses[layer].append(sample_responses[layer]) + + # cast each accumulated response to a tensor. + for key in responses.keys(): + responses[key] = torch.stack(responses[key]) + + # print convergence summary if applicable. + if conv_warn: + for layer in readout: + mean_conv = torch.mean(torch.as_tensor(self.converged[layer])) + ci = stats.t.interval( + 0.95, + len(self.converged[layer]) - 1, + loc=mean_conv, + scale=stats.sem(torch.as_tensor(self.converged[layer]).cpu()), + ) + + if mean_conv < conv_warn: + logging.warning( + f" {mean_conv:.3f}% of {layer} responses converged " f"(CI = {ci[0]:.2f} - {ci[1]:.2f}). " + ) + + return responses + + @staticmethod + def accumulator(data: Tensor, rinse: int, period: int = 25, mode: str = None, take_final: bool = True): + match mode: + case "sum": + # NOTE plotting data.t() here would easily yield denoising figures by sample (Imam & Cleland, 2020). + return sum(data[-rinse - period : -rinse, :]) if take_final else sum(data[:-rinse, :]) + + case "mean": + return torch.mean(data[-rinse - period : -rinse, :] if take_final else data[:-rinse, :], dim=0) + + case "phase": + z = np.array(torch.argwhere(data.eq(1)).cpu()) + m = [np.where(z[:, 1] == i) for i in range(data.shape[1])] + + # compute mean preferred phase, since we need one value per neuron. + preferred_phases = np.zeros(shape=data.shape[1]) + fired_cells = np.unique(z[:, 1]) + + for cell in range(data.shape[1]): + if cell in fired_cells: + fired_phases = (z[m[cell]][:, 0] % period) + 1 + if not take_final: + preferred_phases[cell] = np.mean(fired_phases) if fired_phases else 0 + else: + preferred_phases[cell] = ( + fired_phases[-1] if any(data[-rinse - period : -rinse, cell]) else 0 + ) + + return torch.as_tensor(preferred_phases) diff --git a/src/models/component/__init__.py b/src/models/component/__init__.py new file mode 100644 index 0000000..ff41d26 --- /dev/null +++ b/src/models/component/__init__.py @@ -0,0 +1 @@ +""" Custom network components. """ diff --git a/src/models/component/neuron.py b/src/models/component/neuron.py new file mode 100644 index 0000000..ffea391 --- /dev/null +++ b/src/models/component/neuron.py @@ -0,0 +1,61 @@ +""" Custom neuron models. """ +import torch +from torch import Tensor + +from sapicore.engine.neuron.analog import AnalogNeuron +from sapicore.engine.neuron.spiking.LIF import LIFNeuron +from sapicore.engine.ensemble import Ensemble + +__all__ = ("MultNeuron", "MultEnsemble") + + +class CyclicLIF(LIFNeuron): + """LIF neuron that clamps its voltage to resting during a prescribed prohibitive window given a cycle length.""" + + def __init__(self, cycle_length: int = 50, prohibitive_start: int = 25, **kwargs): + super().__init__(**kwargs) + + self.cycle_length = cycle_length + self.prohibitive = [prohibitive_start, (prohibitive_start + (cycle_length // 2)) - 1 % cycle_length] + + if self.prohibitive[0] > self.prohibitive[1]: + self.prohibitive[0], self.prohibitive[1] = self.prohibitive[1], self.prohibitive[0] + + def forward(self, data: Tensor) -> dict: + # prevent neuron from emitting a spike event if within prohibitive window. + if self.prohibitive[0] <= self.simulation_step % self.cycle_length <= self.prohibitive[1]: + self.voltage = self.volt_rest + + super().forward(data) + + return self.loggable_state() + + +class CyclicLIFEnsemble(Ensemble, CyclicLIF): + """Ensemble of cyclic LIF neurons.""" + + def __init__(self, **kwargs): + # invoke parent constructor and initialization method(s). + super().__init__(**kwargs) + + +class MultNeuron(AnalogNeuron): + """Analog neuron with multiplicative aggregation of synaptic inputs from different sources.""" + + def __init__(self, **kwargs): + super().__init__(**kwargs) + + # voltage must be initialized to identity, 1 in this case. + self.voltage = torch.ones_like(self.voltage) + + @staticmethod + def aggregate(inputs: list[Tensor], identifiers: list[str] = None) -> Tensor: + return torch.prod(torch.vstack(inputs), dim=0) + + +class MultEnsemble(Ensemble, MultNeuron): + """Ensemble of multiplicative analog neurons.""" + + def __init__(self, **kwargs): + # invoke parent constructor and initialization method(s). + super().__init__(**kwargs) diff --git a/src/models/component/synapse.py b/src/models/component/synapse.py new file mode 100644 index 0000000..8b25f6a --- /dev/null +++ b/src/models/component/synapse.py @@ -0,0 +1,601 @@ +""" Custom synapse models. """ +import torch + +from torch import Tensor +from torch.nn.functional import normalize + +from sapicore.engine.synapse import Synapse +from sapicore.engine.synapse.STDP import STDPSynapse + +from src.utils.math import tensor_linspace, density_from_breaks + +__all__ = ("DecaySTDP", "IICSynapse", "QuantizationSynapse", "ShuntSynapse") + +W_EPSILON = 0.001 # threshold for wiping out IIC synapse weights that approach 0. +K = [] +L = [] +M = [] +N = [] + + +class QuantizationSynapse(Synapse): + """Controls the mapping from an analog source layer to a spiking destination layer. + + Uniform mode: sets quantization weights to be either 1/x or equidistant in the range [`self.limit`, 1]. + The former encodes a uniform prior over the input space; the latter, an exponential prior. + + Adaptive mode: learns the distribution of source activity values over multiple presentations. + Weights are set piecewise, as a function of a smoothing parameter controlling the grain of the approximation. + Within each segment, based on user preference, weights can be either 1/x or linearly spaced between + running extremes identified independently for each feature (e.g., different sensor may have different + unknown response distributions). + + """ + + def __init__(self, **kwargs): + # base synapse initialization. + super().__init__(**kwargs) + + # number of initial features (e.g., number of OSNs/ETs). + self.num_features = self.src_ensemble.num_units + + # infers heterogeneous duplication factor based on number of units in the target layer. + self.duplication_factor = self.dst_ensemble.num_units // self.num_features + + # analog value that would activate one spiking neuron in the target column. + self.limit = self.configuration.get("model").get("limit", 1e-3) + + # current required to fire a spiking neuron in the target column, as parameterized. + self.payload = self.configuration.get("model").get("payload", 1.0) + + # whether homogeneous duplication should be used as a baseline. + self.baseline = self.configuration.get("model").get("baseline", False) + + # whether the naive prior in heterogeneous mode is uniform (1/x weights) or geometric (linspace weights). + self.uniform = self.configuration.get("model").get("uniform", True) + + # adaptive weight density mode. + self.adaptive = self.configuration.get("model").get("adaptive", False) + + # smoothing factor controlling bias-variance with adaptive calibration; no smoothing by default. + self.smoothing = self.configuration.get("model").get("smoothing", 0) + + # store piecewise approximation space pre adjustment for LIF parameterization. + self.space = None + + # tensors for storing running minimums and maximums for each feature. + # start at +-maximal representable float to avoid exceptions on +-inf for flat priors. + self.running_min = (torch.finfo(torch.float).max * torch.ones(self.num_features)).to(self.device) + self.running_max = -torch.finfo(torch.float).max * torch.ones(self.num_features).to(self.device) + + # log calibration observations for adaptive density mechanism. + self.observations = torch.zeros(self.num_features, device=self.device) + + def update_weights(self) -> Tensor: + """Updates quantization weights during calibration procedure.""" + # initialize weight space. + space = torch.zeros((self.duplication_factor, len(self.running_min)), device=self.device) + + # due to normalization, the number of sensors dictates where "flat" distributions would end up. + # thus, we can target an expected median utilization of ~50% by setting a gain factor to sensors*payload. + # the above applies in the uniform, non-adaptive mode (not informed by data). + # in adaptive mode, weights naturally skew to where the data is, so gain correction isn't necessary. + gain = self.payload + + if self.baseline: + # homogeneous duplication mode; all weights are the same. + space = space + self.baseline + + # in baseline mode, all weights are the same, so gain correction isn't necessary. + gain = 1 + + elif not self.adaptive: + # in the non-adaptive case, the value space to be covered by the weights is always [self.limit, 1]. + if self.uniform: + # 1/x weights, corresponding to a uniform prior on the input distribution. + value_space = tensor_linspace( + torch.ones_like(self.running_max), + torch.zeros_like(self.running_max) + (1 / self.limit), + self.duplication_factor, + ) + space = (1 / self.limit) / value_space + + else: + # equidistant weights, corresponding to an exponential prior on the input distribution. + space = tensor_linspace( + 1 / torch.ones_like(self.running_max), + 1 / (torch.zeros_like(self.running_min) + self.limit), + self.duplication_factor + 1, + device=self.device, + )[:-1] + + else: + # generate weights with adaptive densities, corresponding to a nonuniform prior. + # here, sensor running max values bound the range covered by the weights, [self.limit, max_i]. + for i in range(space.shape[1]): + # weight spacing during interpolation can be 1/x or linear, determined by `self.uniform`. + space[:, i] = torch.as_tensor( + density_from_breaks( + self.observations[:, i], + factor=self.duplication_factor, + smoothing=self.smoothing, + inf_replacement=self.limit, + uniform=self.uniform, + ), + device=self.device, + ) + + # sort the results ascending. + space = torch.sort(space, dim=0)[0] + + # adaptive procedure changes weight "center of mass"; gain must be corrected to center the distribution. + if self.adaptive: + correction = torch.median(torch.linspace(self.limit, 1, self.duplication_factor)) / torch.median(1 / space) + gain = gain / correction + + # self.plot_approximation(space, self.observations) + + # update the weights, ensuring they stay on the original hardware device. + self.weights = 0 * self.weights + n = self.duplication_factor + + for i in range(space.shape[1]): + self.weights[n * i : n * (i + 1), i] = gain * torch.as_tensor(space[:, i], device=self.weights.device) + + return self.weights + + @staticmethod + def plot_approximation(space: Tensor, observations: Tensor): + """Plot piecewise approximation next to sorted observation curves for illustration purposes.""" + import matplotlib.pyplot as plt + + plt.clf() + limits = ( + torch.min(torch.min(1 / space), torch.min(observations)), + torch.max(torch.max(1 / space), torch.max(observations)), + ) + + plt.figure(figsize=(25, 20)) + plt.title(f"Values Covered by Adaptive Weights (Step {observations.shape[0]-1})") + [plt.plot(torch.sort(1 / space[:, k]).values.__reversed__()) for k in range(space.shape[1])] + plt.ylim(limits) + plt.show() + + plt.figure(figsize=(25, 20)) + plt.title(f"Calibration Observations (Step {observations.shape[0]-1})") + [plt.plot(torch.sort(observations[1:, k]).values.__reversed__()) for k in range(observations.shape[1])] + plt.ylim(limits) + plt.show() + + plt.close("all") + + def forward(self, data: Tensor) -> dict: + if self.learning: + # log observation for adaptive calibration. + self.observations = torch.vstack((self.observations, torch.as_tensor(data, device=self.device))) + + # data is a 1D sample vector. 0s should be disregarded (filtered). + new_minima = (data < self.running_min) * (data != 0) + new_maxima = (data > self.running_max) * (data != 0) + + # only update weights when necessary. + if any(new_maxima) or any(new_minima): + self.running_min[new_minima] = data[new_minima] + self.running_max[new_maxima] = data[new_maxima] + + self.update_weights() + + return super().forward(data) + + +class DecaySTDP(STDPSynapse): + """STDP synapse with a constant weight decay applied on every N-th step. + + Parameters + ---------- + decay: Tensor + Matrix specifying the amount to subtract from each weight on relevant simulation steps. + + interval: Tensor + Decay will be applied on every simulation step divisible by this number (e.g., on every 5th step). + By default, applies decay on every step (1). Matrix format supported for specifying variable intervals. + + non_causation_penalty: float + Value by which to decrease weights where target spiked but source did not (within the same cycle). + + """ + + _config_props_: tuple[str] = STDPSynapse._config_props_ + ("decay", "interval", "non_causation_penalty") + + def __init__( + self, + decay: Tensor = None, + interval: Tensor = None, + non_causation_penalty: float = 50, + cycle_length: int = 50, + **kwargs, + ): + + # default STDP synapse instantiation. + super().__init__(**kwargs) + + self.decay = decay if decay else torch.zeros_like(self.weights) + self.interval = interval if interval else torch.ones_like(self.weights) + + self.cycle_length = cycle_length + + # optional decay value for synapses where target spiked but source did not. + self.non_causation_penalty = self.configuration.get("model").get("non_causation_penalty", 0) + + def update_weights(self) -> Tensor: + """Overrides weight update implementation with hSTDP rules specified in Imam & Cleland (2020). + + Returns + ------- + Tensor + 2D tensor containing weight deltas for this forward pass (dW). + + """ + # update last spike time to current simulation step if a spike has just arrived at the synapse. + # NOTE a delayed view of the presynaptic spike status is used here, simulating correct "arrival" time. + self.src_last_spiked = self.src_last_spiked + self.delayed_data * (-self.src_last_spiked + self.simulation_step) + + # update last spike time only for postsynaptic units that spiked in this model iteration. + self.dst_last_spiked = self.dst_last_spiked + self.dst_ensemble.spiked * ( + -self.dst_last_spiked + self.simulation_step + ) + + # synapse element pairs that were maxed out or where postsynaptic element never spiked should not be updated. + inclusion = torch.ones_like(self.weights, dtype=torch.int) + + inclusion[self.weights == self.weight_max] = 0 + inclusion[:, self.src_last_spiked == 0] = 0 + inclusion[self.dst_last_spiked == 0, :] = 0 + + # initialize dW matrix, formatted dst.num_units X src.num_units. + delta_weight = torch.zeros(self.matrix_shape, dtype=torch.float, device=self.device) + + # skip computation if inclusion tensor is all 0s (e.g., during rinse periods). + if torch.count_nonzero(inclusion) == 0: + return delta_weight + + # subtract postsynaptic last spike time stamps from presynaptic: + # transpose dst_last_spiked to a column vector, repeat column-wise, then add its negative to src_last_spiked. + dst_tr = self.dst_last_spiked.reshape(self.dst_last_spiked.shape[0], 1) + + # NOTE since we subtract src-dst, negative values warrant LTP. + delta_spike = -dst_tr.repeat(dst_tr.shape[1], self.src_ensemble.num_units) + self.src_last_spiked + + # spike time differences for the potentiation and depression cases (pre < post, pre > post, respectively). + ltp_diffs = (delta_spike < 0.0) * delta_spike + ltd_diffs = (delta_spike > 0.0) * delta_spike + + # add to total delta weight matrix (diffs are in simulation steps, tau are in ms). + delta_weight = delta_weight + (delta_spike < 0.0) * ( + self.alpha_plus * torch.exp(ltp_diffs / (self.tau_plus / self.dt)) + ) + delta_weight = delta_weight + (delta_spike > 0.0) * ( + -self.alpha_minus * torch.exp(-ltd_diffs / (self.tau_minus / self.dt)) + ) + + # only change weights that made it to the inclusion list. + updated_weights = self.weights.add(inclusion * delta_weight) * self.connections + + # weights never truly reach zero, so we need to intentionally zero out < epsilon (defined above). + pruned_connections = torch.abs(self.weights) < W_EPSILON * torch.abs(self.weight_max) + + # if postsynaptic spiked but presynaptic didn't, connection should decay (unless differentiated). + penalty = ((delta_spike < -self.cycle_length) * self.non_causation_penalty) * inclusion + + # set weights to updated values, ensuring pruned connections are zeroed out in the process. + self.weights = (updated_weights - penalty) * ~pruned_connections + 0.0 * pruned_connections + + return delta_weight + + def forward(self, data: Tensor) -> dict: + # apply specified weight decay(s) to cells in accordance with their individual intervals. + # fully differentiated synapses (at w_max) are exempt from decay. + if self.learning: + # decay intervals may vary across synapse elements, so this step must be performed. + is_decay = self.simulation_step % self.interval == 0 + self.weights = self.weights - is_decay * (self.weights < self.weight_max) * self.decay + + # normal STDP synapse operations, including a call to the overridden update_weights. + result = super().forward(data) + + return result + + +class L1Synapse(Synapse): + """L1 normalization synapse meant to connect an analog neuron layer to itself. + + This synapse directly divides the voltage vector of the source ensemble by its sum. + + Warning + ------- + Only usable for self-connections from an ensemble to itself. Efficient but quite un-neuromorphic. + + """ + + def __init__(self, **kwargs): + super().__init__(**kwargs) + + def forward(self, data: Tensor) -> dict: + super().forward(data) + + # no matter what, the output of this layer should be normalized to the range [0, 1]. + self.dst_ensemble.voltage = normalize( + self.dst_ensemble.voltage.reshape(1, self.dst_ensemble.voltage.shape[0]), p=1.0 + ).flatten() + + return self.loggable_state() + + +class ShuntSynapse(Synapse): + """Shunting inhibition with instant, same-cycle action on the destination ensemble voltage. + + This synapse computes the elementwise inverse of the default synapse output, + with inf values replaced by 1 (identity), then multiplies the destination ensemble's voltage by it. + + Warnings + -------- + Designed to project to an ensemble with multiplicative aggregation. + + Instant action is only necessary where lateral shunting inhibition must act before the signal can + propagate to deeper layers. When in doubt, favor a dual compartment design over instant lateral action. + + """ + + def __init__(self, **kwargs): + super().__init__(**kwargs) + + self.output = torch.ones_like(self.output) + + def forward(self, data: Tensor) -> dict: + # standard synapse forward operations. + super().forward(data) + + # emit the inverted sum of incoming conductances (1 / W*x). + self._invert_output() + + # instantly shunt the destination ensemble's activity. + self.dst_ensemble.voltage = self.dst_ensemble.voltage * self.output + + return self.loggable_state() + + def _invert_output(self): + # invert the output (1 / W*x), division by zero handled by converting inf to 1. + self.output = torch.ones_like(self.output) / self.output + self.output[self.output == float("inf")] = 1.0 + + +class MultSynapse(Synapse): + def __init__(self, **kwargs): + super().__init__(**kwargs) + + def forward(self, data: Tensor) -> dict: + # use a list of queues to track transmission delays and "release" input after the fact. + self.delayed_data = self.queue_input(data) + + # enforce weight limits. + self.weights = torch.clamp(self.weights, self.weight_min, self.weight_max) + + # mask non-connections (repeated on every iteration in case mask was updated during simulation). + self.weights = self.weights.multiply_(self.connections) + + def _product_aggregation(w: Tensor, d: Tensor) -> Tensor: + """Multiplies products of nonzero weights and corresponding data values.""" + result = torch.zeros(w.shape[0]) + for k, row in enumerate(w): + result[k] = torch.prod(row[row != 0] * d[torch.argwhere(row != 0).T]) + + return result + + if self.simple_delays: + self.output = _product_aggregation(self.weights, self.delayed_data) + + else: + self.delayed_data = self.delayed_data.reshape(self.matrix_shape) + + # this loop is unavoidable, as N vector multiplications with different delayed_data are needed. + for i in range(self.matrix_shape[0]): + self.output[i] = _product_aggregation(self.weights[i, :], self.delayed_data[i, :]) + + # advance simulation step and return output dictionary. + self.simulation_step += 1 + + return self.loggable_state() + + +class IICSynapse(Synapse): + """Inhibitory plastic synapse for use in the Imam & Cleland (2020) model replication. + + These synapses maintain an integer matrix corresponding to the duration of inhibition that + presynaptic units should exert postsynaptic units, in simulation steps. + + Note + ---- + For future proofing, I avoided conflating the blocking period with the weight matrix. + Weights are reserved for specifying the magnitude and sign of the constant synaptic output, + anticipating modifications depending on, e.g., GC order and simplifying the rebound mechanism. + + """ + + _config_props_: tuple[str] = ( + "weight_max", + "weight_min", + "delay_ms", + "simple_delays", + "learning_rate", + "units_per_column", + "cycle_length", + ) + _loggable_props_: tuple[str] = ("weights", "blocking_durations", "connections", "output") + + def __init__(self, learning_rate: float = 1.0, units_per_column: int = 0, cycle_length: int = 50, **kwargs): + super().__init__(**kwargs) + + # state variables specific to inhibitory plastic synapses, over and above Synapse. + self.src_last_spiked = torch.zeros(self.matrix_shape[1], dtype=torch.float, device=self.device) + self.dst_last_spiked = torch.zeros(self.matrix_shape[0], dtype=torch.float, device=self.device) + + # initialize blocking durations matrix. + self.blocking_durations = torch.zeros_like(self.weights) + + # enable or disable learning (weight update method call). + self.learning = True + + # the learning rate parameter eta from Imam & Cleland (2020). + self.learning_rate = learning_rate + + # optionally, specify columnar organization of units. + # if nonzero, nonlocal column-wise operations will be applied to consecutive segments of this size. + self.units_per_column = units_per_column + + # length of reset cycle (e.g., gamma) in simulation steps. Must be known to bound delta B appropriately. + self.cycle_length = cycle_length + + # keep track of which blocking durations were updated. + self.stale = torch.zeros_like(self.blocking_durations).bool() + + def refresh_last_spiked(self): + # update last spike time to current simulation step iff a spike has just arrived at the synapse. + self.src_last_spiked = self.src_last_spiked + self.src_ensemble.spiked * ( + -self.src_last_spiked + self.simulation_step + ) + + # update last spike time only for postsynaptic units that spiked in this model iteration. + self.dst_last_spiked = self.dst_last_spiked + self.dst_ensemble.spiked * ( + -self.dst_last_spiked + self.simulation_step + ) + + def update_blocking(self) -> Tensor: + """Inhibitory plastic synapse blocking duration update rule, called at the end of each oscillatory cycle. + + Delta is the difference between the last spike times of the source and destination units, + in simulation steps, multiplied by the learning rate Eta. + + Warning + ------- + The mechanism assumes targets (MCs) and sources (GCs) only fire during non-overlapping windows within a cycle. + This must be guaranteed by other parameter choices (e.g., refractory period or transmission delays). + It also assumes sources (GCs) fire only once per cycle. + + Returns + ------- + Tensor + 2D tensor containing blocking duration differences. + + """ + # extend last spiking timestamps of source and destination populations to enable pairwise condition checking. + # for instance, check whether GC_i, MC_j both fired using boolean operations on these tensors. + src_row = self.src_last_spiked.reshape(1, self.src_last_spiked.shape[0]) + src_extended = self.connections * src_row.repeat(self.dst_ensemble.num_units, src_row.shape[0]) + + dst_col = self.dst_last_spiked.reshape(self.dst_last_spiked.shape[0], 1) + dst_extended = self.connections * dst_col.repeat(dst_col.shape[1], self.src_ensemble.num_units) + + # check which source and destination neurons fired at all. + src_ever_spiked = self.src_last_spiked != 0 + dst_ever_spiked = self.dst_last_spiked != 0 + + # check which source and destination neurons fired last cycle. + src_last_cycle = src_ever_spiked * ((self.simulation_step - self.src_last_spiked) < self.cycle_length) + src_last_cycle = src_last_cycle.reshape(1, src_last_cycle.shape[0]).repeat(self.dst_ensemble.num_units, 1) + + dst_last_cycle = dst_ever_spiked * ((self.simulation_step - self.dst_last_spiked) < self.cycle_length) + dst_last_cycle = dst_last_cycle.reshape(dst_last_cycle.shape[0], 1).repeat(1, self.src_ensemble.num_units) + + # only update blocking durations for pairs where both fired in this cycle, if their connection is enabled. + to_update = self.connections * (dst_last_cycle * src_last_cycle) + + # compute spike timing differences between destination and source. + # GC - MC is time to beginning of inhibition (from the transmission delayed perspective of MC). + diff_spike = to_update * (src_extended - dst_extended) + # diff_spike = diff_spike * (torch.abs(diff_spike) < self.cycle_length) * (diff_spike > 0) + + # pairs where GC fired but MC didn't in this cycle should be taken to max blocking duration. + # pairs that aren't connected to begin with shouldn't be tampered with (anticipating neurogenesis). + to_max_out = self.connections * src_last_cycle * ~dst_last_cycle + + # desired changes in blocking duration. + # max out blocking periods for co-columnar pairs where source fired last cycle but destination didn't. + # max out skips rinse cycles by checking dst_last_cycle (MCs cannot spike on rinse cycles, GCs might on 1st). + max_out = to_max_out * self.cycle_length + # new_blocking = self.learning_rate * torch.fmod(self.cycle_length - diff_spike, self.cycle_length) + max_out + new_blocking = self.learning_rate * torch.fmod(dst_extended, self.cycle_length) + max_out + + # blocking durations that maxed out previously are not allowed to get back in the game. + # new_blocking[self.blocking_durations == self.cycle_length-1] = self.cycle_length + + # if not rinse cycle, update the blocking durations and ensure they don't go under 0 or above the cycle length. + # durations that have already been learned (!= 0) should not be updated. + if torch.any(dst_last_cycle).int(): + new_blocking = torch.clamp(new_blocking, 0, self.cycle_length) + temp = self.blocking_durations + + self.blocking_durations = (self.stale == 1) * self.blocking_durations + (self.stale == 0) * new_blocking + self.stale = self.stale + torch.as_tensor(temp != new_blocking).bool() + + return self.blocking_durations + + def forward(self, data: Tensor) -> dict: + """IIC synapse forward pass. + + Only updates the blocking duration. The native weights matrix inherited from the base class will serve to + control the magnitude of the constant synaptic output emitted for that duration. + + Additionally, weights will temporarily flip sign when their blocking duration elapses, to simulate rebound. + + """ + # use a list of queues to track transmission delays and "release" input after the fact. + self.delayed_data = self.queue_input(data) + + if not self.simple_delays: + self.delayed_data = self.delayed_data.reshape(self.matrix_shape) + + # enforce weight limits. + self.weights = torch.clamp(self.weights, self.weight_min, self.weight_max) + + # update last spiked times for source and destination ensembles. + self.refresh_last_spiked() + + # learning can be done sensibly at the end of a cycle or on every step, application depending. + if self.learning and self.simulation_step % self.cycle_length == self.cycle_length - 1: + # learning in these synapses amounts to modifying the blocking duration at the end of each cycle. + self.update_blocking() + + # given blocking durations, find out who needs to output right now and what sign the output should be. + spike_elapsed = self.simulation_step - self.src_last_spiked + spike_phase = torch.fmod(self.src_last_spiked, self.cycle_length) + + inclusion = (self.blocking_durations > 0) * (spike_elapsed < self.cycle_length) + block_status = self.blocking_durations + (self.cycle_length - spike_phase) - spike_elapsed + + # if the block status for a synapse is positive (within block), it should send the weight as is (active). + # if the block status is zero (just released), it should send a flipped weight (rebound). + # if the block status is negative (outside window), it should send zero (inactive). + # user should initialize these weights negative, consistent with the Imam-Cleland synapse being inhibitory. + if self.simulation_step > 0: + # computes a mask matrix indicating which GC->MC synapses should be actively blocking (or rebounding). + currently_active = (block_status > 0).int() - (block_status == 0).int() + else: + currently_active = torch.zeros_like(self.weights) + + if self.learning: + # Imam & Cleland (2020) suppressed the effects of these synapses during training. + self.output *= 0.0 + + else: + # emit the weights masked by who's within a blocking or rebound period. + # sum inputs going into each destination unit to obtain the 1D output vector. + self.output = torch.sum(self.weights * self.connections * inclusion * currently_active, dim=1) + + L.append(torch.mean(self.output).item()) + M.append(torch.sum(self.src_ensemble.spiked).item()) + K.append(torch.sum(self.dst_ensemble.spiked).item()) + N.append(torch.mean(self.dst_ensemble.input).item()) + + self.simulation_step += 1 + + return self.loggable_state() diff --git a/src/models/yaml/bulb/bulb.yaml b/src/models/yaml/bulb/bulb.yaml new file mode 100644 index 0000000..24570f8 --- /dev/null +++ b/src/models/yaml/bulb/bulb.yaml @@ -0,0 +1,69 @@ +# Sapinet 2.0 olfactory bulb model. +# Unused components are commented out. + +identifier: sapinet + +# Full path to root directory. If a relative path is provided, assumes the project root. +root: ../models/yaml/bulb + +# Model architecture. +# Traversal order within any single depth level (BFS-inferred) is user determined. +# For example, if OSN->PG, OSN->ET, and PG->ET, then PG and ET are both d=1 away from the root (OSN). +# If PG precedes ET on the ensemble/synapse lists below, traversal order will be [OSN, PG, ET, ...]. +model: + # proportion of MC->GC connections enabled. + forward_prop: 0.25 + + # NEUROGENESIS NOT USED IN MOYAL ET AL. 2025. + # neurogenesis: + # GCs initially enabled, expressed as a proportion of the total number of GCs. + # init: 0 + # rate of genesis, expressed as a proportion of the total number of GCs. + # rate: .125 + # how often to invoke neurogenesis, in simulation steps (note: may be invoked on first step as well). + # set to (training_duration + rinse_duration). + # freq: 300 + + # Paths to ensemble configuration files. Can be absolute or relative to `root`. + ensembles: + # Sensor or input layer. + - ensembles/OSN.yaml + # Oscillators and modulators. + - ensembles/OSC.yaml + # - ensembles/OSCi.yaml + # Conditioning layer. + - ensembles/ET.yaml + # Excitatory layer. + - ensembles/MC.yaml + # Inhibitory layer. + - ensembles/GC.yaml + + # Paths to synapse configuration files. Can be absolute or relative to `root`. + synapses: + - synapses/OSN--ET.yaml + - synapses/ET--ET.yaml + - synapses/ET--MC.yaml + - synapses/OSC--MC.yaml + - synapses/MC--GC.yaml + - synapses/GC--MC.yaml + +processing: + # readout layer specification. + readout: + - ET + - MC + - GC + + # whether and how to accumulate readout layer responses to the test set. + accumulator: + ET: + mode: mean + take_final: true + + MC: + mode: phase + take_final: true + + GC: + mode: phase + take_final: true diff --git a/src/models/yaml/bulb/ensembles/ET.yaml b/src/models/yaml/bulb/ensembles/ET.yaml new file mode 100644 index 0000000..4696f17 --- /dev/null +++ b/src/models/yaml/bulb/ensembles/ET.yaml @@ -0,0 +1,6 @@ +package: snn.models.component.neuron +class: MultEnsemble + +model: + identifier: ET + num_units: 8 diff --git a/src/models/yaml/bulb/ensembles/GC.yaml b/src/models/yaml/bulb/ensembles/GC.yaml new file mode 100644 index 0000000..30e52b4 --- /dev/null +++ b/src/models/yaml/bulb/ensembles/GC.yaml @@ -0,0 +1,32 @@ +package: sapicore.engine.ensemble.spiking +class: LIFEnsemble + +model: + identifier: GC + num_units: 128 + + # background oscillation period (comment out if not applicable). + cycle_length: 50 + + # phase at which to reset refractory period and voltage. + release_phase: 25 + + sweep: + fixed: + volt_thresh: -50 + volt_rest: -60.0 + tau_ref: 50.0 + leak_gl: 1.0 + tau_mem: 4.0 + + # zipped: + # volt_thresh: [-55, -50, -40, -30] + + # random: + # volt_thresh: + # method: uniform + # args: [-55.0, 20.0] + +loggable: +- voltage +- spiked diff --git a/src/models/yaml/bulb/ensembles/MC.yaml b/src/models/yaml/bulb/ensembles/MC.yaml new file mode 100644 index 0000000..a0caa28 --- /dev/null +++ b/src/models/yaml/bulb/ensembles/MC.yaml @@ -0,0 +1,30 @@ +package: sapicore.engine.ensemble.spiking +class: LIFEnsemble + +model: + identifier: MC + num_units: 32 + + # background oscillation period (comment out if not applicable). + cycle_length: 50 + + # phase at which to reset refractory period and voltage. + release_phase: 0 + + sweep: + fixed: + # the wider the gap between rest and thresh, the wider the phase distribution all else equal. + volt_thresh: -55.0 + volt_rest: -60.0 + tau_ref: 50.0 + leak_gl: 1.0 + tau_mem: 4.0 + + # random: + # volt_thresh: + # method: uniform + # args: [-59.0, 40.0] + +loggable: +- voltage +- spiked diff --git a/src/models/yaml/bulb/ensembles/OSC.yaml b/src/models/yaml/bulb/ensembles/OSC.yaml new file mode 100644 index 0000000..ebdb241 --- /dev/null +++ b/src/models/yaml/bulb/ensembles/OSC.yaml @@ -0,0 +1,21 @@ +package: sapicore.engine.ensemble.analog +class: OscillatorEnsemble + +model: + identifier: OSC + num_units: 1 + num_wave_comps: 1 + + sweep: + # this singleton layer produces a 40Hz gamma oscillation. + fixed: + amplitudes: [1.0] + # frequency combinations going into each oscillator unit. + frequencies: [20.0] + # phase shifts for each frequency component, to be multiplied by np.pi (all zeros by default). + phases: [1.0] + # this is, implicitly, a prior on the working range of your sensors/features (i.e., "the lowest k% is noise"). + # baseline shift > amplitude will soft-threshold weak signals by drowning them out of the permissive window. + # baseline shift = amplitude will allow the weakest signal to express itself at the very trough. + # baseline shift < amplitude will make gamma excitatory around the trough. + baseline_shift: 1.0 diff --git a/src/models/yaml/bulb/ensembles/OSCi.yaml b/src/models/yaml/bulb/ensembles/OSCi.yaml new file mode 100644 index 0000000..0792f24 --- /dev/null +++ b/src/models/yaml/bulb/ensembles/OSCi.yaml @@ -0,0 +1,21 @@ +package: sapicore.engine.ensemble.analog +class: OscillatorEnsemble + +model: + identifier: OSCi + num_units: 1 + num_wave_comps: 1 + + sweep: + # this singleton layer produces a 40Hz gamma oscillation. + fixed: + amplitudes: [1.0] + # frequency combinations going into each oscillator unit. + frequencies: [20.0] + # phase shifts for each frequency component, to be multiplied by np.pi (all zeros by default). + phases: [0.0] + # this is, implicitly, a prior on the working range of your sensors/features (i.e., "the lowest k% is noise"). + # baseline shift > amplitude will soft-threshold weak signals by drowning them out of the permissive window. + # baseline shift = amplitude will allow the weakest signal to express itself at the very trough. + # baseline shift < amplitude will make gamma excitatory around the trough. + baseline_shift: 1.0 diff --git a/src/models/yaml/bulb/ensembles/OSN.yaml b/src/models/yaml/bulb/ensembles/OSN.yaml new file mode 100644 index 0000000..fbab5f9 --- /dev/null +++ b/src/models/yaml/bulb/ensembles/OSN.yaml @@ -0,0 +1,6 @@ +package: sapicore.engine.ensemble.analog +class: AnalogEnsemble + +model: + identifier: OSN + num_units: 8 diff --git a/src/models/yaml/bulb/synapses/ET--ET.yaml b/src/models/yaml/bulb/synapses/ET--ET.yaml new file mode 100644 index 0000000..6d9a5c5 --- /dev/null +++ b/src/models/yaml/bulb/synapses/ET--ET.yaml @@ -0,0 +1,28 @@ +package: snn.models.component.synapse +class: ShuntSynapse + +model: + identifier: ET--ET + + source: ET + target: ET + + sweep: + fixed: + # shunting inhibition is expressed by emitting the inverse of the standard output. + # when weights are set to 1, the method will approximate L1 normalization up to negligible numeric error. + weights: 1.0 + weight_max: 10.0 + weight_min: 0.0 + delay_ms: 0.0 + + # setting random weights [0, 1] -> converge to a constant != 1. + # can be utilized for contrast enhancement/suppression. + # random: + # weights: + # method: uniform + # args: [0.1, 0.9] + +loggable: + - weights + - output diff --git a/src/models/yaml/bulb/synapses/ET--MC.yaml b/src/models/yaml/bulb/synapses/ET--MC.yaml new file mode 100644 index 0000000..ff63757 --- /dev/null +++ b/src/models/yaml/bulb/synapses/ET--MC.yaml @@ -0,0 +1,38 @@ +package: snn.models.component.synapse +class: QuantizationSynapse + +model: + identifier: ET--MC + + source: ET + target: MC + + # in baseline mode, when not set to false, all weights are set to this value (homogeneous duplication). + baseline: false + + # in the heterogeneous modes, user may choose equidistant weights or ~ 1/x weights. + # the former corresponds to an exponential prior on the input, the latter to a uniform prior. + uniform: true + + # in the adaptive heterogeneous mode, weight densities are determined based on a calibration set. + adaptive: false + smoothing: -1 # bias-variance lever for weight density interpolation, from 0 (jagged) to 1 (smooth). + + # current required to fire an average LIF unit in the destination ensemble. + payload: 25.0 + + # analog value that would activate exactly one MC per column, given the above payload. + # used to compute an appropriate multiplicative gain factor for the heterogeneous weights. + # if adjusted via multiplication, the OSC->MC weight (gamma amplitude) should be multiplied by the same factor. + limit: .01 + + sweep: + fixed: + weights: 0.0 # overwritten during calibration + weight_max: 10000000.0 + weight_min: 0.0 + delay_ms: 0.0 + +loggable: +- weights +- output diff --git a/src/models/yaml/bulb/synapses/GC--MC.yaml b/src/models/yaml/bulb/synapses/GC--MC.yaml new file mode 100644 index 0000000..c821365 --- /dev/null +++ b/src/models/yaml/bulb/synapses/GC--MC.yaml @@ -0,0 +1,20 @@ +package: snn.models.component.synapse +class: IICSynapse + +model: + identifier: GC--MC + + source: GC + target: MC + + sweep: + fixed: + weights: -0.0 + weight_max: 0.0 + weight_min: -10000000.0 + # delay should be zero for IIC feedback, 24 otherwise. + delay_ms: 0.0 + +loggable: + - weights + - blocking_duration diff --git a/src/models/yaml/bulb/synapses/MC--GC.yaml b/src/models/yaml/bulb/synapses/MC--GC.yaml new file mode 100644 index 0000000..f61f249 --- /dev/null +++ b/src/models/yaml/bulb/synapses/MC--GC.yaml @@ -0,0 +1,32 @@ +package: snn.models.component.synapse +class: DecaySTDP + +model: + identifier: MC--GC + + source: MC + target: GC + + non_causation_penalty: 50 + + sweep: + fixed: + # based roughly on guidelines from Imam & Cleland (2020). + weights: 25.0 + weight_max: 30.0 + weight_min: 0.0 + tau_plus: 4.0 + tau_minus: 4.0 + alpha_plus: 0.3125 + alpha_minus: 1.25 + delay_ms: 25.0 + decay: 0.0 + + # random: + # weights: + # method: uniform + # args: [50.0, 0.0] + +loggable: + - weights + - output diff --git a/src/models/yaml/bulb/synapses/OSC--MC.yaml b/src/models/yaml/bulb/synapses/OSC--MC.yaml new file mode 100644 index 0000000..7320242 --- /dev/null +++ b/src/models/yaml/bulb/synapses/OSC--MC.yaml @@ -0,0 +1,21 @@ +package: sapicore.engine.synapse +class: Synapse + +model: + identifier: OSC--MC + + source: OSC + target: MC + + sweep: + fixed: + # larger weights tighten the permissive window for MCs by pulling up the inhibitory oscillation amplitude. + # for expected behavior, should be set to -1 / (min_resolution * payload). + # 'min_resolution' is the minimal analog value that the spiking layer should be able to represent. + # 'payload' is the current required to fire a spiking unit (e.g., 10). + weights: -20.0 + delay_ms: 0.0 + +loggable: + - weights + - output diff --git a/src/models/yaml/bulb/synapses/OSCi--GC.yaml b/src/models/yaml/bulb/synapses/OSCi--GC.yaml new file mode 100644 index 0000000..c80e32d --- /dev/null +++ b/src/models/yaml/bulb/synapses/OSCi--GC.yaml @@ -0,0 +1,19 @@ +package: sapicore.engine.synapse +class: Synapse + +model: + identifier: OSCi--GC + + source: OSCi + target: GC + + sweep: + fixed: + # larger weights create/tighten a permissive window for GCs. + # optimal values are indicated by nuanced GC RSA matrices. + weights: -0.0 + delay_ms: 0.0 + +loggable: + - weights + - output diff --git a/src/models/yaml/bulb/synapses/OSN--ET.yaml b/src/models/yaml/bulb/synapses/OSN--ET.yaml new file mode 100644 index 0000000..4ba9e09 --- /dev/null +++ b/src/models/yaml/bulb/synapses/OSN--ET.yaml @@ -0,0 +1,17 @@ +package: sapicore.engine.synapse +class: Synapse + +model: + identifier: OSN--ET + + source: OSN + target: ET + + sweep: + fixed: + weights: 1.0 + delay_ms: 0.0 + +loggable: + - weights + - output diff --git a/src/projects/__init__.py b/src/projects/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/projects/simulation.py b/src/projects/simulation.py new file mode 100644 index 0000000..8dc7fdb --- /dev/null +++ b/src/projects/simulation.py @@ -0,0 +1,783 @@ +""" Default simulation pipeline, including similarity, discrimination, and utilization diagnostics. """ +from typing import Type +from numpy.typing import NDArray + +from copy import deepcopy +from datetime import datetime + +import os +import gc +import sys +import random +import zipfile +import warnings + +import numpy as np +import torch +from torch import Tensor +from torch.nn.functional import normalize + +from sklearn.model_selection import StratifiedKFold + +import matplotlib.pyplot as plt +import seaborn as sns + +from sapicore.pipeline import Pipeline +from sapicore.model import Model + +from sapicore.data.sampling import CV, BalancedSampler + +from sapicore.utils.constants import TIME_FORMAT +from sapicore.utils.io import ensure_dir, load_apply_config +from sapicore.utils.seed import fix_random_seed + +# enable running from projects subdirectory in terminal. +src_path = os.path.abspath(os.path.join(os.getcwd(), "..", "..")) +if src_path not in sys.path: + sys.path.insert(0, src_path) + +from src.models.bulb import BulbModel + +from src.utils.data import SNNData +from src.utils.data.io import to_csv, get_files, extract_config +from src.utils.data.loading import synthesize_data, explore_data +from src.utils.orchestration import MultiRun, parse_arguments + +from src.utils.visual.plotting import ( + basic_plots, + training_plots, + line_plots, + cell_recruitment, + cell_responses, + set_plot_theme, +) + +from src.utils.ml.classification import SVM +from src.utils.ml.similarity import representational_analysis +from src.utils.ml.splitting import LimitedStratifiedKFold +from src.utils.math.distance import pairwise_wasserstein_distances + +__all__ = ("Simulation",) + + +class Simulation(Pipeline): + """SNN simulation pipeline with detailed diagnostic output. + + Note + ---- + To reproduce Moyal et al. (2025): + + 1. Run the current file as follows: + python simulation.py -multirun yaml/hq2025.yaml + + 2. Inspect the multirun output plots in `sapinet_regularization/results/`. + + 3. Open src/utils/analysis/analysis.r and change `run_dir` and `meta_dir` to the run output directory above. + 4. Run the R script and inspect the statistical results under sapinet_regularization/analysis/. + + To further experiment with the simulator: + + 1. Modify the configuration YAMLs at the project, model, or component level. + 2. Run the script with -experiment yaml/. + + """ + + def __init__(self, model_class: Type[Model], config: dict | str, data_dir: str, out_dir: str): + """ + Parameters + ---------- + model_class: + Accepts any derivative of :class:`sapicore.model.Model`. + The reference is used to create independent model instances during cross validation. + + config: + Pipeline configuration dictionary or path to an equivalent YAML. + + data_dir: + Directory containing processed external dataset(s), if applicable. + + out_dir: + Directory to write results to. By default, the current timestamp is used to name the run. + """ + # initialize generic pipeline attributes by calling the parent constructor. + super().__init__(config_or_path=config) + + # session and condition names, used when assigning run output to subdirectories. + self.session = self.configuration.get("session") + self.condition = self.configuration.get("condition") + self.variant = self.configuration.get("variant") + self.mode = self.configuration.get("mode", "") + + # data and output directories associated with this pipeline instance. + self.data_dir = data_dir + self.out_dir = out_dir + + # dictionary for recording intermediate readout layer responses ({fold: {layer: response}}). + # aggregated over the stimulus presentation duration (e.g., number of spikes fired). + self.responses = {} + self.readout_layers = [] # list storing references to readout layer objects once instantiated. + self.fold = 0 # tracks cross validation fold for directory navigation purposes. + + # model class reference is specified to enable multiple instantiations (e.g., for different folds). + self.model_class = model_class + + # whether to render plots or just save them to the output directory. + self.render = self.configuration.get("simulation", {}).get("render", False) + self.fig_size = self.configuration.get("fig_size", (25, 20)) + self.image_format = "svg" + + # default colormaps for different use cases. + self.primary_cmap = self.configuration.get("primary_cmap", "viridis") # all uses except below + self.secondary_cmap = self.configuration.get("secondary_cmap", "rocket") # filling matrices/heatmaps + self.class_cmap = self.configuration.get("class_cmap", "Spectral") # differentiating class labels + + # set random number generation seed across libraries for reproducibility. + self.seed = fix_random_seed(self.configuration.get("seed", 314)) + + # shorthand references to frequently used configuration fields, grouped into three dictionaries. + self.syn_params, self.sam_params, self.sim_params = extract_config(self.configuration) + + # default matplot theme. + set_plot_theme() + + def run(self): + """Master script for generic simulation pipeline with diagnostics.""" + key = self.sam_params.get("key") # name of metadata key whose values should be used as class labels. + group_key = self.sam_params.get("group_key") # name of metadata key whose values should be used for grouping. + + # generate or retrieve data based on the given configuration, then transfer to GPU if applicable. + data = self._retrieve_data(self.mode, self.syn_params, self.sam_params, key) + data.buffer.to(self.configuration.get("device")) + + # create output directories. + ensure_dir(os.path.join(self.out_dir, "plots")) + ensure_dir(os.path.join(self.out_dir, "tabular")) + + tr_ind = None + te_ind = None + + # create stratified cross validation folds, potentially limiting training/testing to specific group(s). + if self.sam_params["train_batches"] is not None: + tr_ind = torch.as_tensor(data.metadata["batch"][:] == self.sam_params["train_batches"]) + if self.sam_params["test_batches"] is not None: + te_ind = torch.as_tensor(data.metadata["batch"][:] == self.sam_params["test_batches"]) + + cv = self._stratified_cv( + data, self.sam_params["folds"], self.sam_params["shuffle"], key, tr_mask=tr_ind, te_mask=te_ind + ) + + # simulation logic in a cross validation loop ((test, train) per the few-shot nature of this simulation). + for (i, (test, train)) in enumerate(cv): + self._process_fold(data, train, test, self.sam_params, self.sim_params, key, group_key) + + # group-level plots based on responses accumulated across folds. + if self.configuration.get("simulation", {}).get("group"): + self.fold = False + + for layer in self.responses.keys(): + line_plots(self, self.responses[layer], self.responses[layer].metadata[key][:], log=False) + line_plots(self, self.responses["Raw"], labels=self.responses["Raw"].metadata[key][:], log=True) + + for layer in ["ET", "MC"]: + # NOTE group-level GC channel responses not included due to arbitrary mappings across folds. + cell_responses(self, key, self.responses[layer], binary=False) + + for layer in self.readout_layers: + is_phase = True if layer != "ET" else False # MC/GC responses are phase-coded. + is_util = True if layer != "ET" else False # ET utilization is always 100%. + is_dist = True # whether to plot the spike phase distribution for each readout layer. + + # layer recruitment histogram (% active units). + cell_recruitment( + self, + key, + self.responses[layer], + phase_mode=is_phase, + plot_utilization=is_util, + plot_distribution=is_dist, + dump_to_file=False, + ) + + def _process_fold( + self, data: SNNData, train: NDArray, test: NDArray, samp_cfg: dict, sim_cfg: dict, key: str, group_key: str + ): + """Logic for a single cross validation fold. + + Parameters + ---------- + data: SNNData + Full dataset. + + train: + Training set indices. + + test: NDArray + Test set indices. + + samp_cfg: dict + Configuration dictionary for sampling. + + sim_cfg: dict + Configuration dictionary for simulation. + + key: str + Metadata key for creating class labels. + + group_key: str + Metadata key for grouping. + + """ + self.fold += 1 + + # create fold-specific output subdirectory. + ensure_dir(os.path.join(self.out_dir, "plots", f"F{self.fold}")) + + # prepare training and test samples. + train_set, test_set = self._prepare_data(data, train, test, samp_cfg, sim_cfg, key) + + # load olfactory bulb configuration YAML from disk. + bulb_path = os.path.realpath( + os.path.join(os.path.dirname(__file__), "..", "models", "yaml", "bulb", "bulb.yaml") + ) + bulb_cfg = load_apply_config(bulb_path) + + # instantiate new network and model objects for this CV fold. + model = self.model_class(configuration=bulb_cfg, device=self.configuration.get("device", "cpu")) + print(f"Fold {self.fold}/{samp_cfg['folds']}: instantiating {model.network}") + + # store a zipped configuration snapshot, containing all YAML files in the run directory. + snapshot = list(get_files(os.path.join(os.getcwd(), ".."), "yaml", recursive=True)) + with zipfile.ZipFile(os.path.join(self.out_dir, "config.zip"), "w") as zf: + for file in snapshot: + zf.write(file, compress_type=zipfile.ZIP_DEFLATED, arcname=os.path.basename(file)) + + # reference to the rinse duration, as it is used frequently below. + rinse = sim_cfg["rinse"] + + # attach data accumulator hooks for streaming intermediate results to disk, if applicable. + if sim_cfg["dump"]: + steps = (sim_cfg["train_duration"] + rinse) * train.shape[0] + fold_directory = ensure_dir(os.path.join(self.out_dir, "data", f"F{self.fold}")) + + model.network.add_data_hook(data_dir=fold_directory, steps=steps) + + # adaptive calibration procedure, currently implemented only for BulbModel and its derivatives. + if isinstance(model, BulbModel): + # use training data to calibrate ET->MC quantization weights. + print(f"Calibrating: {len(train)} samples.") + + # present each calibration sample for cycle length, to avoid mistiming later on. + # remember that the entire network is active and keeping count during this step. + cycle_length = int(1000 / model.network["OSC"].frequencies.item()) + model.calibrate(train_set[:], duration=cycle_length, rinse=50, target_synapses=["ET--MC"]) + + # fit model to training data. + print( + f"Training: {len(train)} samples, {rinse}ms rinse + {sim_cfg['train_duration']}ms exposure." + f"\nLabels: {train_set.metadata[key][:]}" + + (f", Groups: {train_set.metadata[group_key][:]}." if key != group_key else ".") + ) + weight_evo, blocking_evo = model.fit( + train_set[:], + rinse=rinse, + duration=sim_cfg["train_duration"], + verbose=sim_cfg["verbose"], + weight_clamp_threshold=29, + ) + + # obtain readout layer list. + self.readout_layers = model.network.configuration.get("processing", {}).get("readout", []) + + # save the trained model and its component diagram. + ensure_dir(os.path.join(self.out_dir, "models")) + model.save(os.path.join(self.out_dir, "models", f"F{self.fold}.pt")) + + # relevant independent variables, to streamline R analysis. + duplication_factor = model.network["MC"].num_units // model.network["ET"].num_units + resolution_threshold = model.network["ET--MC"].limit + smoothing_factor = model.network["ET--MC"].smoothing + + # plot weight and blocking duration evolution. + training_plots( + self, + weight_evo, + train_set.metadata[key][:], + x_range=[25, 30], + title="MC->GC Histograms (Nonzero)", + file_name="evolution_mc_gc", + ) + + # plot rudimentary post-training network diagnostics. + basic_plots(self, model) + + if sim_cfg["test"]: + # pass test samples through the trained network and obtain summarized readout layer responses. + response_data = self._process_test(model, sim_cfg, test_set) + + # total MC/GC spikes across cycles, one line per sample, with x-axis being individual cells, + # sorted most to least active, colored by class. + for layer in response_data.keys(): + line_plots(self, response_data[layer], labels=test_set.metadata[key][:], log=False) + line_plots(self, response_data["Raw"], labels=test_set.metadata[key][:], log=True) + + # raw cell responses, heatmap, preferred phase and binary. + cell_responses(self, key, test_set, binary=False) + + for layer in self.readout_layers: + cell_responses(self, key, response_data[layer], binary=False) + + # cell recruitment histograms, colored by class. + is_phase = True if layer != "ET" else False + is_util = False if layer == "ET" else True + is_dist = True + + cell_recruitment( + self, + key, + response_data[layer], + phase_mode=is_phase, + plot_distribution=is_dist, + plot_utilization=is_util, + duplication_factor=duplication_factor, + resolution_threshold=resolution_threshold, + smoothing_factor=smoothing_factor, + shots=samp_cfg["shots"], + dump_to_file=True, + ) + + # visualize readout layer response manifolds. + for layer in self.readout_layers: + explore_data( + self, + response_data[layer], + key=key, + interactive=sim_cfg["interactive"], + path=os.path.join( + self.out_dir, + "plots", + f"F{self.fold}" if self.fold else "group", + f"projection_{layer}.{self.image_format}", + ), + ) + + # obtain representational similarity and deformation measures. + if sim_cfg["rsa"]: + representational_analysis(self, ["Raw"] + self.readout_layers, response_data, key) + + # estimate mapped manifold separability using leave-one-out CV and grid search on C and gamma. + if sim_cfg["svm"]: + for readout in self.readout_layers: + score, ci, confusion = self._separability_score( + response_data[readout], + key, + render=self.render, + duplication_factor=duplication_factor, + resolution_threshold=resolution_threshold, + smoothing_factor=smoothing_factor, + ) + print(f"{readout} separability: {score:.2f} % (CI = {ci[0]:.2f} - {ci[1]:.2f})\n") + + # release memory. + plt.close("all") + torch.cuda.empty_cache() + gc.collect() + + # fold separator for console output readability. + print("------") + + def _process_test(self, model, sim_params, test_set): + print( + f"Testing: {test_set[:].shape[0]} samples, {sim_params['rinse']}ms rinse + {sim_params['test_duration']}ms exposure." + ) + test_responses = model.process(test_set[:], sim_params["test_duration"], rinse=sim_params["rinse"]) + + # initialize dictionary storing SNNData objects for readout layer responses. + response_data = {} + + for layer in test_responses.keys(): + response_data[layer] = SNNData( + identifier=f"{layer}", buffer=deepcopy(test_responses[layer]), metadata=deepcopy(test_set.metadata) + ) + + if not self.responses: + # if this is the first CV iteration, initialize global response dataset. + self.responses = deepcopy(response_data) + + else: + # append current CV fold readout layer responses to global buffer for aggregation. + for layer in response_data: + self.responses[layer].concatenate(other=response_data[layer]) + + # add raw vectors and their metadata to the global dataset as well. + if "Raw" not in self.responses.keys(): + self.responses["Raw"] = SNNData( + identifier="Raw", buffer=deepcopy(test_set[:]), metadata=deepcopy(test_set.metadata) + ) + else: + self.responses["Raw"].concatenate(test_set) + + # return raw inputs and readout layer responses from this fold for further processing. + response_data["Raw"] = test_set + + return response_data + + def _prepare_data(self, data, train, test, samp_cfg, sim_cfg, key): + # shuffle presentation order within train and test sets (the *indices* already selected for the two sets). + if samp_cfg["shuffle"]: + # avoid variations in order effects on MC->GC learning across runs, for reproducibility. + train = train[random.sample(list(range(len(train))), len(train))] + test = test[random.sample(list(range(len(test))), len(test))] + + # trim data object (buffer and metadata) to the specified train and test indices. + train_set = data.trim(train) + test_set = data.trim(test) + + # test set string identifier, for plotting purposes. + test_set.identifier = "Raw" + + # test set noise settings (false if keys do not exist). + noise_type = sim_cfg.get("noise", {}).get("mode") + noise_add = sim_cfg.get("noise", {}).get("add") + noise_inds = sim_cfg.get("noise", {}).get("inds") + noise_args = sim_cfg.get("noise", {}).get("args") + + # "environmental" noise to add or replace test samples with, if applicable. + if noise_type is not None: + noise_ref = getattr(np.random, noise_type) + test_set[:, noise_inds] = torch.as_tensor( + noise_ref(size=test_set[:, noise_inds].shape, **noise_args), + device=test_set[:].device, + dtype=test_set[:].dtype, + ) + (test_set[:, noise_inds] if noise_add else 0) + + # NOTE the data going into the model is raw; this deep copy is normalized for visualization purposes. + temp = deepcopy(test_set) + temp[:] = normalize(test_set[:], p=1) + + # visually explore synthesized test data if asked (otherwise, saves image to file from default viewpoint). + if sim_cfg["interactive"]: + explore_data(self, temp, key=key, interactive=True) + + return train_set, test_set + + def _retrieve_data(self, mode: str, synthesis_params: dict, sampling_params: dict, key: str): + """Wrapper for data synthesis and/or sampling procedure. + + Note + ---- + This preliminary version supports basic synthetic data generation or loading the UCSD drift set. + In principle, these operations should be relegated to a separate module. + + """ + # generate synthetic dataset based on experiment configuration YAML. + if mode == "Synthetic": + print(f"Synthesizing data with the following properties: {synthesis_params}") + data = synthesize_data( + self, + method=self.configuration.get("synthesis", {}).get("method"), + params=synthesis_params, + sampler=BalancedSampler(replace=True), + duplicate=sampling_params["repetitions"] - 1, + norm=False, + key=key, + group_keys=sampling_params["group_key"], + shots=sampling_params["shots"], + folds=sampling_params["folds"], + ) + else: + raise ValueError(f"Unknown mode: {mode}") + + return data + + def _stratified_cv( + self, + data: SNNData, + folds: int, + shuffle: bool, + key: str | list[str], + tr_mask: Tensor = None, + te_mask: Tensor = None, + ) -> CV: + """Create stratified cross validation folds. + + When `mask` is not None, the indices specified by it should correspond to the group you wish to be smaller. + In few-shot scenarios, mask indices should correspond to the training group and splitting should + be `test, train in cv.split(...)`. + + """ + # sklearn raises ValueError if random_state is set when shuffle is False. + if shuffle: + cv = CV( + data, + LimitedStratifiedKFold( + n_splits=folds, tr_mask=tr_mask, te_mask=te_mask, shuffle=True, random_state=self.seed + ), + label_keys=key, + ) + + else: + cv = CV( + data, + LimitedStratifiedKFold(n_splits=folds, tr_mask=tr_mask, te_mask=te_mask, shuffle=False), + label_keys=key, + ) + + return cv + + def _separability_score( + self, + responses: SNNData, + key: str, + render: bool = True, + duplication_factor: int = None, + resolution_threshold: float = None, + smoothing_factor: float = None, + ): + """Identifies optimal SVM hyperparameters using LOO cross validation on half the test set, + then uses them in LOO CV on the other half to obtain a separability score (ease of classification + of test points having been mapped to the manifold learned by Sapinet). + + In this basic version, test/validation is a single 2-split. + + Parameters + ---------- + responses: SNNData + Sapinet dataset whose buffer contains readout layer responses (to test samples) and whose + metadata is their class labels. + + key: + Metadata key (class name) of interest. + + render: bool + Whether to render the confusion matrix as a seaborn heatmap plot. + + duplication_factor: int + Network's duplication factor, defined as the number of MCs over the number of ETs. + + resolution_threshold: float + Minimal value capable of eliciting MC spike response in one unit. + + smoothing_factor: float + In adaptive weight density mode, smoothing factor applied to piecewise linear interpolation. + + Returns + ------- + score: float + Mean score over cross validation folds + + ci: list of float + Confidence interval, 95% by default. Given as a list containing lower and upper bound. + + confusion: NDArray + Confusion matrix. + + Note + ---- + This measure is stable (not inflated) even when hyperparameter optimization and validation + are both done on the entire test set; the 2-split is to preempt criticism. + + """ + print("Computing optimal SVM hyperparameters for response separability estimation.") + + # identifiers for logging. + svm_header = { + "Session": self.session, + "Condition": self.condition, + "Variant": self.variant, + "Analysis": "SVM", + "Mode": self.mode, + "Layer": responses.identifier, + "Duplication": duplication_factor, + "Resolution": resolution_threshold, + "Smoothing": smoothing_factor, + "Batches": self.configuration.get("sampling", {}).get("included_batches", -1), + "BatchesTr": self.configuration.get("sampling", {}).get("train_batches", -1), + "BatchesTe": self.configuration.get("sampling", {}).get("test_batches", -1), + "Shots": self.configuration.get("sampling", {}).get("shots", -1), + "Fold": self.fold, + } + + # split test set into validation set and remainder. + validation, final_test = list( + StratifiedKFold(n_splits=2, shuffle=True, random_state=self.seed).split( + responses[:].cpu(), responses.metadata[key][:] + ) + )[0] + + optimizer = SVM( + identifiers=svm_header, + data=responses[validation].cpu(), + labels=responses.metadata[key][validation], + cv=CV( + data=responses.trim(validation), + cross_validator=StratifiedKFold( + n_splits=self.configuration.get("sampling", {}).get("folds", 2) // 2, + shuffle=True, + random_state=self.seed, + ), + label_keys=key, + ), + ) + hyper, _ = optimizer.grid_svm(kernel="rbf", seed=self.seed) + + score, ci, confusion = SVM( + identifiers=svm_header, + data=responses[final_test].cpu(), + labels=responses.metadata[key][final_test], + cv=CV( + data=responses.trim(index=final_test), + cross_validator=StratifiedKFold( + n_splits=self.configuration.get("sampling", {}).get("folds", 2) // 2, + shuffle=True, + random_state=self.seed, + ), + label_keys=key, + ), + ).score_svm( + kernel="rbf", + c=hyper["C"], + gamma=hyper["gamma"], + flip=True, + scale=(True if responses.identifier == "ET" else False), + norm=False, + seed=self.seed, + file=os.path.join(self.out_dir, "tabular", f"sep_svm_{responses.identifier}"), + ) + + plt.figure(figsize=self.fig_size) + u_labels = np.unique(responses.metadata[key][:]) + + sns.heatmap(confusion, xticklabels=u_labels, yticklabels=u_labels, annot=True, fmt="g", rasterized=True) + + plt.title(f"{responses.identifier} confusion matrix") + plt.xlabel("Predicted", fontsize=24) + plt.ylabel("Actual", fontsize=24) + + plt.savefig( + os.path.join( + self.out_dir, "plots", f"F{self.fold}", f"confusion_{responses.identifier}.{self.image_format}" + ) + ) + + if render: + plt.show() + + return score, ci, confusion + + def dump_utilization( + self, df, duplication_factor, labels, resolution_threshold, responses, shots, smoothing_factor + ): + # pairwise earthmover distance between class-specific utilization histograms. + # measures potential to confound systematic concentration differences with essential analyte properties. + util_by_class = np.array([df[labels == k] for k in np.unique(labels)], dtype="object") + em_distances = pairwise_wasserstein_distances(util_by_class) + + upper_triangular_indices = np.triu_indices(em_distances.shape[0], k=1) + em_filtered = em_distances[upper_triangular_indices] + + emd_header = [ + "Session", + "Condition", + "Variant", + "Analysis", + "Mode", + "Layer", + "Duplication", + "Resolution", + "Smoothing", + "Shots", + "Fold", + "Class1", + "Class2", + "EMD", + ] + + # base directory for CSV output. + out_base = os.path.join(self.out_dir, "tabular") + + for z in range(len(upper_triangular_indices[0])): + emd_content = [ + self.session, + self.condition, + self.variant, + "Utilization", + self.mode, + responses.identifier, + duplication_factor, + resolution_threshold, + smoothing_factor, + shots, + self.fold, + upper_triangular_indices[0][z], + upper_triangular_indices[1][z], + em_filtered[z], + ] + to_csv(os.path.join(out_base, f"emd_{responses.identifier}"), header=emd_header, content=emd_content) + + util_header = [ + "Session", + "Condition", + "Variant", + "Analysis", + "Mode", + "Layer", + "Duplication", + "Resolution", + "Smoothing", + "Shots", + "Fold", + "Class", + "Sample", + "Utilization", + ] + + for cls in range(util_by_class.shape[0]): + for sample in range(len(util_by_class[cls])): + util_content = [ + self.session, + self.condition, + self.variant, + "Utilization", + self.mode, + responses.identifier, + duplication_factor, + resolution_threshold, + smoothing_factor, + shots, + self.fold, + cls + 1, + sample + 1, + util_by_class[cls][sample].item(), + ] + to_csv(os.path.join(out_base, f"util_{responses.identifier}"), header=util_header, content=util_content) + + +if __name__ == "__main__": + args = parse_arguments() + + # timestamp for individual run directory. + stamp = datetime.now().strftime(TIME_FORMAT) + + # create data and output directories if nonexistent. + ensure_dir(args.data) + ensure_dir(args.output) + + run_dir = ensure_dir(os.path.join(os.path.realpath(args.output), stamp)) if args.output else None + dat_dir = ensure_dir(os.path.realpath(args.data)) if args.data else None + + # global plotting settings. + plt.rcParams.update({"figure.max_open_warning": 0}) + + # suppress irrelevant dependency-invoked warnings encountered during development. + warnings.filterwarnings("ignore", category=RuntimeWarning) + + if not args.multirun: + # run a single simulation from the given pipeline configuration dictionary. + Simulation(model_class=BulbModel, config=args.experiment, data_dir=dat_dir, out_dir=run_dir).run() + else: + # run multiple simulations from the given multi-run and pipeline configuration dictionaries. + MultiRun( + config=args.multirun, + out_dir=run_dir, + child_kwargs={"model_class": BulbModel, "config": args.experiment, "data_dir": dat_dir, "out_dir": run_dir}, + ).run() diff --git a/src/projects/yaml/hq2025.yaml b/src/projects/yaml/hq2025.yaml new file mode 100644 index 0000000..f34891b --- /dev/null +++ b/src/projects/yaml/hq2025.yaml @@ -0,0 +1,158 @@ +# multi-run experiment configuration file. +identifier: "HQ-SciRep25" + +pipeline: Simulation +package: src.projects.simulation + +config: "synthetic.yaml" + +# if CPU parallelization is enabled, use this many processes. +workers: 1 + +grid: + baseline: + # subdirectory names for each level in this condition (dataset_gain_duplication). + names: ["con_b1", "con_b2", "con_b3", "con_b4", "con_b5", "con_b6", "con_b7", "con_b8", "con_b9", "con_b10", + "sat_b1", "sat_b2", "sat_b3", "sat_b4", "sat_b5", "sat_b6", "sat_b7", "sat_b8", "sat_b9", "sat_b10"] + + # YAML editing instructions; multiple entries are varied together. + "../models/yaml/bulb/bulb.yaml": + ["model", "forward_prop"]: [.5, .5, .5, .5, .5, .5, .5, .5, .5, .5, + .5, .5, .5, .5, .5, .5, .5, .5, .5, .5] + # in accordance with duplication factor, varying 4/8/16/32. + "../models/yaml/bulb/ensembles/MC.yaml": + [ "model", "num_units" ]: [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, + 8, 8, 8, 8, 8, 8, 8, 8, 8, 8] + "../models/yaml/bulb/ensembles/GC.yaml": + [ "model", "num_units" ]: [32, 32, 32, 32, 32, 32, 32, 32, 32, 32, + 32, 32, 32, 32, 32, 32, 32, 32, 32, 32] + [ "model", "sweep", "fixed", "volt_thresh" ]: [ -50, -50, -50, -50, -50, -50, -50, -50, -50, -50, + -50, -50, -50, -50, -50, -50, -50, -50, -50, -50 ] + + "../models/yaml/bulb/synapses/ET--MC.yaml": + ["model", "baseline"]: [25, 28, 32, 37, 45, 56, 73, 109, 208, 2500, + 25, 28, 32, 37, 45, 56, 73, 109, 208, 2500] + ["model", "uniform"]: True + ["model", "adaptive"]: False + ["model", "smoothing"]: [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, + -1, -1, -1, -1, -1, -1, -1, -1, -1, -1] + "../projects/yaml/synthetic.yaml": + ["synthesis", "physical", "depth"]: [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, + 3, 3, 3, 3, 3, 3, 3, 3, 3, 3] + ["condition"]: "Baseline" + + ["simulation", "duplication"]: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] + ["synthesis", "physical", "sigmoid", "loc"]: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + .5, .5, .5, .5, .5, .5, .5, .5, .5, .5] + ["synthesis", "physical", "sigmoid", "scale"]: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 8, 8, 8, 8, 8, 8, 8, 8, 8, 8] + ["synthesis", "physical", "noise", "scale"]: [[.01, .01, .01, .01, .01, .02, .05, .1], + [.01, .01, .01, .01, .01, .02, .05, .1], + [.01, .01, .01, .01, .01, .02, .05, .1], + [.01, .01, .01, .01, .01, .02, .05, .1], + [.01, .01, .01, .01, .01, .02, .05, .1], + [.01, .01, .01, .01, .01, .02, .05, .1], + [.01, .01, .01, .01, .01, .02, .05, .1], + [.01, .01, .01, .01, .01, .02, .05, .1], + [.01, .01, .01, .01, .01, .02, .05, .1], + [.01, .01, .01, .01, .01, .02, .05, .1], + .02, .02, .02, .02, .02, .02, .02, .02, .02, .02] + + ["session"]: ["Concentration", "Concentration", "Concentration", "Concentration", "Concentration", + "Concentration", "Concentration", "Concentration", "Concentration", "Concentration", + "Saturation", "Saturation", "Saturation", "Saturation", "Saturation", "Saturation", + "Saturation", "Saturation", "Saturation", "Saturation"] + ["variant"]: ["con_b1", "con_b2", "con_b3", "con_b4", "con_b5", "con_b6", "con_b7", "con_b8", "con_b9", "con_b10", + "sat_b1", "sat_b2", "sat_b3", "sat_b4", "sat_b5", "sat_b6", "sat_b7", "sat_b8", "sat_b9", "sat_b10"] + + uniform: + # subdirectory names for each level in this condition. + names: ["con_4", "con_8", "con_16", "con_32", "sat_4", "sat_8", "sat_16", "sat_32"] + # MC->GC connection probability. + "../models/yaml/bulb/bulb.yaml": + ["model", "forward_prop"]: [.25, .125, .0625, .03125, .25, .125, .0625, .03125] + # duplication factor, varying 4/8/16/32. + "../models/yaml/bulb/ensembles/MC.yaml": + ["model", "num_units"]: [32, 64, 128, 256, 32, 64, 128, 256] + "../models/yaml/bulb/ensembles/GC.yaml": + ["model", "num_units"]: [128, 256, 512, 1024, 128, 256, 512, 1024] + [ "model", "sweep", "fixed", "volt_thresh" ]: [ -50, -50, -50, -50, -50, -50, -50, -50 ] + "../models/yaml/bulb/synapses/ET--MC.yaml": + ["model", "baseline"]: False + ["model", "uniform"]: True + ["model", "adaptive"]: False + ["model", "smoothing"]: [-1, -1, -1, -1, -1, -1, -1, -1] + "../projects/yaml/synthetic.yaml": + ["simulation", "duplication"]: [4, 8, 16, 32, 4, 8, 16, 32] + ["synthesis", "physical", "sigmoid", "loc"]: [0, 0, 0, 0, .5, .5, .5, .5] + ["synthesis", "physical", "sigmoid", "scale"]: [0, 0, 0, 0, 8, 8, 8, 8] + ["synthesis", "physical", "noise", "scale"]: [[.01, .01, .01, .01, .01, .02, .05, .1], + [.01, .01, .01, .01, .01, .02, .05, .1], + [.01, .01, .01, .01, .01, .02, .05, .1], + [.01, .01, .01, .01, .01, .02, .05, .1], + .02, .02, .02, .02] + ["condition"]: "Uniform" + ["session"]: ["Concentration", "Concentration", "Concentration", "Concentration", + "Saturation", "Saturation", "Saturation", "Saturation"] + ["variant"]: ["con_4", "con_8", "con_16", "con_32", "sat_4", "sat_8", "sat_16", "sat_32"] + + adaptive: + # subdirectory names for each level in this condition. + names: ["con_4_s0", "con_8_s0", "con_16_s0", "con_32_s0", + "con_4_s25", "con_8_s25", "con_16_s25", "con_32_s25", + "con_4_s50", "con_8_s50", "con_16_s50", "con_32_s50", + "sat_4_s0", "sat_8_s0", "sat_16_s0", "sat_32_s0", + "sat_4_s25", "sat_8_s25", "sat_16_s25", "sat_32_s25", + "sat_4_s50", "sat_8_s50", "sat_16_s50", "sat_32_s50"] + # MC->GC connection probability, optimally (#GC/#MC) / (#MC/#ET) to center GC utilization histogram. + "../models/yaml/bulb/bulb.yaml": + ["model", "forward_prop"]: [.25, .125, .0625, .03125, .25, .125, .0625, .03125, .25, .125, .0625, .03125, + .25, .125, .0625, .03125, .25, .125, .0625, .03125, .25, .125, .0625, .03125] + # duplication factor, varying 8/16/32/64. + "../models/yaml/bulb/ensembles/MC.yaml": + ["model", "num_units"]: [32, 64, 128, 256, 32, 64, 128, 256, 32, 64, 128, 256, + 32, 64, 128, 256, 32, 64, 128, 256, 32, 64, 128, 256] + "../models/yaml/bulb/ensembles/GC.yaml": + ["model", "num_units"]: [64, 128, 256, 512, 64, 128, 256, 512, 64, 128, 256, 512, + 128, 256, 512, 1024, 128, 256, 512, 1024, 128, 256, 512, 1024] + [ "model", "sweep", "fixed", "volt_thresh" ]: [ -50, -50, -50, -50, -50, -50, -50, -50, -50, -50, -50, -50, + -50, -50, -50, -50, -50, -50, -50, -50, -50, -50, -50, -50 ] + "../models/yaml/bulb/synapses/ET--MC.yaml": + ["model", "baseline"]: False + ["model", "uniform"]: True + ["model", "adaptive"]: True + ["model", "sweep", "fixed", "weights" ]: 0.0 + ["model", "smoothing"]: [0, 0, 0, 0, 0.25, 0.25, 0.25, 0.25, 0.5, 0.5, 0.5, 0.5, + 0, 0, 0, 0, 0.25, 0.25, 0.25, 0.25, 0.5, 0.5, 0.5, 0.5] + "../projects/yaml/synthetic.yaml": + ["simulation", "duplication"]: [4, 8, 16, 32, 4, 8, 16, 32, 4, 8, 16, 32, + 4, 8, 16, 32, 4, 8, 16, 32, 4, 8, 16, 32] + ["synthesis", "physical", "sigmoid", "loc"]: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + .5, .5, .5, .5, .5, .5, .5, .5, .5, .5, .5, .5,] + ["synthesis", "physical", "sigmoid", "scale"]: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8] + ["synthesis", "physical", "noise", "scale"]: [[.01, .01, .01, .01, .01, .02, .05, .1], + [.01, .01, .01, .01, .01, .02, .05, .1], + [.01, .01, .01, .01, .01, .02, .05, .1], + [.01, .01, .01, .01, .01, .02, .05, .1], + [.01, .01, .01, .01, .01, .02, .05, .1], + [.01, .01, .01, .01, .01, .02, .05, .1], + [.01, .01, .01, .01, .01, .02, .05, .1], + [.01, .01, .01, .01, .01, .02, .05, .1], + [.01, .01, .01, .01, .01, .02, .05, .1], + [.01, .01, .01, .01, .01, .02, .05, .1], + [.01, .01, .01, .01, .01, .02, .05, .1], + [.01, .01, .01, .01, .01, .02, .05, .1], + .02, .02, .02, .02, .02, .02, .02, .02, .02, .02, .02, .02] + ["condition"]: "Adaptive" + ["session"]: ["Concentration", "Concentration", "Concentration", "Concentration", "Concentration", "Concentration", + "Concentration", "Concentration", "Concentration", "Concentration", "Concentration", "Concentration", + "Saturation", "Saturation", "Saturation", "Saturation", "Saturation", "Saturation", + "Saturation", "Saturation", "Saturation", "Saturation", "Saturation", "Saturation"] + ["variant"]: ["con_4_s0", "con_8_s0", "con_16_s0", "con_32_s0", + "con_4_s25", "con_8_s25", "con_16_s25", "con_32_s25", + "con_4_s50", "con_8_s50", "con_16_s50", "con_32_s50", + "sat_4_s0", "sat_8_s0", "sat_16_s0", "sat_32_s0", + "sat_4_s25", "sat_8_s25", "sat_16_s25", "sat_32_s25", + "sat_4_s50", "sat_8_s50", "sat_16_s50", "sat_32_s50"] diff --git a/src/projects/yaml/synthetic.yaml b/src/projects/yaml/synthetic.yaml new file mode 100644 index 0000000..4fc3586 --- /dev/null +++ b/src/projects/yaml/synthetic.yaml @@ -0,0 +1,162 @@ +identifier: SciRep24 + +# specify one of two currently supported modes for the full simulation pipeline. +# "Synthetic" to generate artificial data; "Drift" for UCSD drift dataset (default). +mode: Synthetic + +# session and condition identifiers for tabular output and multirunner. +condition: Uniform +session: Concentration + +device: cpu # hardware device, "cpu" or "cuda". +seed: 314 # random number generation seed for reproducibility. + +# matplotlib/seaborn colormaps for different use cases. +primary_cmap: viridis # for generic plots. +secondary_cmap: rocket # for heatmap values. +class_cmap: Spectral # for labeled point cloud projections. + +# data synthesis parameters. +synthesis: + # switch between data synthesis methods ("sklearn" or "physical"). + method: physical + + # if using scikit's make_classification procedure. + sklearn: + classes: 6 + samples: 320 + + features: 4 + informative: 4 + redundant: 0 + + separation: 2.0 + clusters_per_class: 1 + flipped_label_prop: 0.0 + + # if using our hierarchical discrimination problem generation procedure. + physical: + # seed vector defining initial affinities (coordinates on the line), sorted descending. + # seed_vector: [1, .99, .98, .97, .96, .95, .94, .93] + seed_vector: [1.0, .95, .8, .75, .4, .35, .2, .15] + # number of samples per class. + n_samples: 512 + # hierarchy depth, i.e. the number of 2-splits to perform. + # used for swapping affinities recursively when deriving additional sensors. + depth: 3 + # sensors to include; must be <= len(affinities) and a multiple of 2 for balancing purposes. + sensors: 8 + # target sparsity of the final affinity matrix, in terms of its Hoyer measure ranging [0, 1]. + sparsity: .4 + # scipy distribution parameters for signal and noise components (loc/scale can have different meanings; see docs). + signal: + distribution: expon + loc: [1, 2, 4, 8, 16, 32, 64, 128] + scale: [.1, .1, 1, 1, 1, 1, 1, 1] + # loc: [1,1,1,1,1,1,1,1] + # scale: [.01,.01,.01,.01,.1,.1,.1,.1] + # loc: [2,2,4,8,16,32,140,280] + # scale: [1,1,2,2,4,4,4,4] + # loc: [2,2,4,8,16,32,64,128] + # scale: [1,1,2,2,4,4,4,4] + contaminants: + loc: 0 + scale: 0 + noise: + distribution: norm + proportion: 1 + loc: 0 + scale: + - 0.01 + - 0.01 + - 0.01 + - 0.01 + - 0.01 + - 0.02 + - 0.05 + - 0.1 + sigmoid: + loc: 0 + scale: 0 + +sampling: + # metadata key whose values should be treated as class labels. + # if using with a real dataset, should correspond to an existing metadata key; in synthesis mode, can be anything. + key: Analyte + + # metadata key whose values should be used for grouping and experimental design. + group_key: Analyte + + # sampling and cross validation configuration. + folds: 8 + shots: 2 + + # for synthetic data only, how many times to repeat the sampled subset. + repetitions: 0 + + # whether to shuffle samples before training/testing. + shuffle: true + +simulation: + # train, test, and rinse (empty inter-trial intervals) durations in simulation steps. + train_duration: 250 + test_duration: 250 + rinse: 50 + + # whether to run a test set or stop after training. + test: true + + # whether to apply noise to the test set. + # noise: + # mode: uniform + # inds: [1, 7] + # args: + # low: .2 + # high: .8 + + # whether to perform classification and/or representational similarity analysis on data and responses. + svm: true + rsa: true + + # whether 3D data and response visualization plots should be interactive (true) or static (false). + interactive: false + + # whether to render individual fold plots and/or group-level plots, respectively. + # note that group plot SVGs can get prohibitively large, and should only be used with small/toy runs. + render: false + group: true + fig_size: (16, 12) + + # whether to track weight evolution during training (differentiation, pruning). + verbose: true + + # whether to dump intermediate data to file. + dump: false + + # automatically edited by multi-run script; for logging purposes only. + duplication: 4 + + tensorboard: + - spiked: + kind: raster + format: image + + - weights: + kind: heatmap + format: image + step: 150 + + - blocking_durations: + kind: heatmap + format: image + step: 150 + + - voltage: + kind: trace + format: scalar + + - input: + kind: trace + format: scalar + +variant: con_4 diff --git a/src/utils/__init__.py b/src/utils/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/utils/analysis/__init__.py b/src/utils/analysis/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/utils/analysis/analysis.r b/src/utils/analysis/analysis.r new file mode 100644 index 0000000..dabb5d0 --- /dev/null +++ b/src/utils/analysis/analysis.r @@ -0,0 +1,588 @@ +# -------------- IMPORTS -------------- # +suppressPackageStartupMessages({ + library(docstring) + library(data.table) + library(ggplot2) + library(lme4) + library(emmeans) + library(multcomp) + library(doBy) + library(dplyr) +}) + +# --------- INITIALIZATION --------- # +rm(list=ls(all=TRUE)) # clean up session variables. + +source(file.path("snn", "utils", "analysis", "descriptive.r")) # load descriptive statistics utility functions. +source(file.path("snn", "utils", "analysis", "plotting.r")) # load plotting utility functions. + +# --------- GLOBAL VARIABLES --------- # +# scan available run output directories. +AVAILABLE_RUNS <- sort(list.dirs(file.path("results"), recursive=FALSE, full.names=FALSE)) + +run_dirs <- "SciRep-FF-Final" +run_names <- run_dirs + +meta_dir <- "SciRep-FF-Final" + +# select smoothing level to represent the adaptive condition. +# .5 will be relabeled to "Scaled", -1 is "Uniform" (no adaptation). +adaptive_level <- .25 +smoothing_levels <- c(adaptive_level, .5, -1) + +# --------- UTILITY FUNCTIONS --------- # +read_data <- function(run, pattern="*.csv") { + #' @title Reads tabluar Sapicore simulation output + #' + #' @description Processes tabular data files residing in the `run` directory being processed. + #' Run can be specified by directory name or by index, with 0 representing the most recent timestamped run. + #' + #' @param run integer or string: a single run directory to process. + #' @param pattern string: filename pattern to include in the search (e.g., "*.csv"). + #' + #' @return data data.frame: a dataframe containing the data read from the directory. + #' If multiple files exist in the directory, they will be concatenated. + # current run data path; if provided with integers, starts from most recent. + if (is.numeric(run)) { + run_dir <- AVAILABLE_RUNS[length(AVAILABLE_RUNS)-run] + } else { + run_dir <- run + } + run_path <- file.path("results", run_dir) + + if (!dir.exists(run_path)) { + stop(sprintf("Run directory does not exist (%s)", run_path)) + } + message(sprintf("Processing run directory: %s", run_path)) + + # read files and concatenate into a single master frame. + files <- file.path(run_path, list.files(path=run_path, pattern=pattern, recursive=TRUE)) + frame <- data.frame(rbindlist(lapply(files, read.csv, header=TRUE), fill=TRUE)) + + return(frame) +} + +# --------- ANALYSIS PIPELINE --------- # +process_runs <- function(runs, pattern, between_vars, within_vars) { + # process the specified runs sequentially. + d <- NULL + + # read data from current directory and bind it to the rest. + for (i in seq_along(runs)) { + temp <- read_data(runs[i], pattern=pattern) + temp$RunID <- i + + d <- rbind(d, temp) + } + + # ensure that the IVs and DVs are factors. + factorvars <- vapply(d[, c(between_vars, within_vars), drop=FALSE], FUN=is.factor, FUN.VALUE=logical(1)) + + if (!all(factorvars)) { + nonfactorvars <- names(factorvars)[!factorvars] + message("Automatically converting the following non-factors to factors: ", + paste(nonfactorvars, collapse = ", ")) + d[nonfactorvars] <- lapply(d[nonfactorvars], factor) + } + + return(d) +} + +group_plot <- function(d, dep_var, within_vars, between_vars, id_var=NULL, runs=0, run_names=NULL, meta_dir=NULL, + formula=NULL, title="", xlab="", ylab="", x_var=NULL, fill_var=NULL, facet_var=NULL, + group_var=NULL, pattern="*.csv", target_file="", yrange=NULL, line=F, palette="viridis", + all_base=NULL) { + #' @title Group-level analysis and plotting + #' + #' @description Performs a statistical analysis of aggregated tabular simulation data from the specified runs, + #' then saves the resulting plots in the `output` subdirectory. + #' + #' @param dep_var string: name of dependent variable (what is being measured). + #' @param within_vars vector[string]: name(s) of within-participant independent variables. + #' @param between_vars vector[string]: name(s) of between-participant independent variables. + #' @param id_var string: name of variable identifying individual entries, e.g. participant or trial ID. + #' + #' @param runs vector[integer or string]: optional, lists run directories to be read, expressed in terms of + #' timestamp recency or directory names. By default, processes most recent timestamped run directory. + #' @param run_names vector[string]: optional, name for analysis output directories corresponding to each run. + #' + #' @param formula string: formula by which to aggregate the tabular data, e.g. Accuracy ~ Batch*Shots*Trial. + #' Variables that are not to be plotted should not be included in the formula s.t. they are averaged over. + #' + #' @param title string: plot title. + #' @param xlab string: x-axis label. + #' @param ylab string: y-axis label. + #' + #' @param x_var string: name of variable to be plotted in x-axis. + #' @param fill_var string: name of variable whose levels will be used to color the bars and dots. + #' @param facet_var string: name of variable whose levels will be plotted as facets (distinct sub-panels). + #' + #' @examples + #' group_plot(dep_var="Speed", within_vars="Car", between_vars="Team", id_var="Subject", runs=0, + #' run_names="test", formula="Speed~Car*Team*Subject", + #' title="Speed by Car X Team", xlab="Car", ylab="Speed", + #' x_var="Car", fill_var="Car", facet_var="Team") + + # generate run-wise or group-level plots, depending on `meta_dir`. + for (i in seq_along(runs)) { + # set output path for this run. + if (!is.null(meta_dir)) { + df <- d + output_path <- file.path("analysis", meta_dir) + } else { + df <- d[d$RunID==i,] + if (is.null(names)) { + output_path <- file.path("results", runs[i]) + } else { + output_path <- file.path("results", run_names[i]) + } + } + + # create output directory for this run. + dir.create(output_path, showWarnings=FALSE, recursive=TRUE) + + # aggregate over levels of the unplotted variables (to avoid element duplication in plot). + df <- aggregate(formula(formula), df, mean) + + # generate plot. + func = ifelse(line, line_plot, bar_plot) + ggsave(filename = file.path(output_path, target_file), width=24, height=12, units="in", device="svg", dpi=600, + plot = func(df, dep_var, within_vars, between_vars, id_var, x_var=x_var, fill_var=fill_var, + facet_var=facet_var, group_var=group_var, title=title, xlab=xlab, ylab=ylab, yrange=yrange, + palette=palette, all_base=all_base)) + } +} + +# --------- Regularization Analysis --------- # +regularization <- function(cond_inclusion, layer_inclusion, in_pattern, out_fid, agg_fun, + line=F, incl_class=F, contr_dup=T, x_var="Duplication") { + if(incl_class) { + frml <- "Utilization ~ Analysis*Session*Condition*Variant*Smoothing*Layer*Class*Duplication*Resolution*Fold" + fill_var <- "Class" + within_vars <- c("Duplication", "Resolution", "Smoothing", "Layer", "Class") + } else { + frml <- "Utilization ~ Analysis*Session*Condition*Variant*Smoothing*Layer*Duplication*Resolution*Fold" + fill_var <- "Condition" + within_vars <- c("Duplication", "Resolution", "Smoothing", "Layer") + } + if (line) { + within_vars <- append(within_vars, "Fold") + } + between_vars <- c("Session", "Condition") + + id_var <- "Analysis" + dep_var <- "Utilization" + + if (length(cond_inclusion) > 1) { + facet_var <- "Session" + } else if (length(layer_inclusion) > 1) { + facet_var <- "Layer" + } else { + facet_var <- NULL + } + + # patch for final GLM case, combining homogeneous and uniform for duplication-wise comparisons. + emm_options(pbkrtest.limit=15000) + emm_options(lmerTest.limit=15000) + + # analyze and plot utilization data. + util <- process_runs(run_dirs, in_pattern, between_vars, within_vars) + util <- util[util$Smoothing %in% smoothing_levels,] + + util$Condition <- factor(util$Condition, levels=c("Baseline", "Homogeneous", "Uniform", "Scaled", "Adaptive")) + util$Condition[util$Smoothing==.5] <- factor("Scaled") + util$Condition[util$Condition=="Baseline"] <- factor("Homogeneous") + + util <- util[util$Layer %in% layer_inclusion,] + util <- util[util$Condition %in% cond_inclusion,] + + util$Variant <- factor(util$Variant) + util$HomWeight <- util$Variant + + if ("Homogeneous" %in% cond_inclusion) { + gain_levels <- list("25"=c("con_b1", "sat_b1"), "28"=c("con_b2", "sat_b2"), "32"=c("con_b3", "sat_b3"), + "37"=c("con_b4", "sat_b4"), "45"=c("con_b5", "sat_b5"), "56"=c("con_b6", "sat_b6"), + "73"=c("con_b7", "sat_b7"), "109"=c("con_b8", "sat_b8"), "208"=c("con_b9", "sat_b9"), + "2500"=c("con_b10", "sat_b10")) + levels(util$HomWeight) <- gain_levels + + util$HomWeight <- factor(util$HomWeight, levels=c("25", "28", "32", "37", "45", "56", "73", "109", "208", "2500", "0")) + util$HomWeight[is.na(util$HomWeight)] <- 0 + } + util <- util[util$Duplication != 1, ] + frml2 <- paste(frml, "*HomWeight", sep="") + + # if (!line) { + # ignore homogeneous duplication factors when aggregating. + # frml <- gsub("\\*Variant", "", frml) + # } + + # compute function (sd) WITHIN levels of HomWeight first. + std <- aggregate(formula(frml2), util, agg_fun) + + if (!line) { + frml <- gsub("\\*Variant", "", frml) + std <- aggregate(formula(frml), std, mean) + } else { + std <- aggregate(formula(frml2), std, mean) + within_vars <- append(within_vars, "HomWeight") + } + + # if (!line) { + # aggregate over homogeneous duplication factors (don't exist in the original formula). + # this computes std ACROSS levels of HomWeight, potentially inflating it for homogeneous. + # frml <- gsub("\\*Variant", "", frml) + # std <- aggregate(formula(frml), util, agg_fun) + #} else { + # retain homogeneous duplication factor levels. + # this computes std WITHIN levels of HomWeight, the correct thing to do. + # ONLY THEN should we average over those levels if we don't care about them. + # std <- aggregate(formula(frml), util, agg_fun) + # within_vars <- append(within_vars, "HomWeight") + # } + + contr_type <- ifelse(contr_dup, "consec", "pairwise") + + if (!is.null(meta_dir)) { + if(length(cond_inclusion) == 1) { + std_glm <- lmer(Utilization ~ Duplication*Session + (1|Fold), std) + std_omnibus <- joint_tests(std_glm, lmer.df="kenward-roger") + std_contr <- contrast(emmeans(std_glm, ~ Duplication), contr_type) + descriptive <- summaryBy(Utilization ~ Duplication, data = std, + FUN = function(x) {c(m = mean(x), s = sd(x))}) + } else { + if (length(layer_inclusion) == 1) { + std_glm <- lmer(Utilization ~ Condition*Duplication*Session + (1|Fold), std) + std_omnibus <- joint_tests(std_glm, lmer.df="kenward-roger") + if (contr_dup) { + std_contr <- contrast(emmeans(std_glm, ~ Duplication|Condition), contr_type) + descriptive <- summaryBy(Utilization ~ Duplication|Condition, data = std, + FUN = function(x) {c(m = mean(x), s = sd(x))}) + } else { + std_contr <- contrast(emmeans(std_glm, ~ Condition|Duplication), contr_type) + descriptive <- summaryBy(Utilization ~ Condition|Duplication, data = std, + FUN = function(x) {c(m = mean(x), s = sd(x))}) + } + } else { + std_glm <- lmer(Utilization ~ Layer*Condition*Duplication*Session + (1|Fold), std) + std_omnibus <- joint_tests(std_glm, lmer.df="kenward-roger") + if (contr_dup) { + std_contr <- contrast(emmeans(std_glm, ~ Duplication|Condition|Layer), contr_type) + descriptive <- summaryBy(Utilization ~ Duplication|Condition|Layer, data = std, + FUN = function(x) {c(m = mean(x), s = sd(x))}) + } else { + std_contr <- contrast(emmeans(std_glm, ~ Condition|Duplication|Layer), contr_type) + descriptive <- summaryBy(Utilization ~ Layer|Session|Condition|Duplication, data = std, + FUN = function(x) {c(m = mean(x), s = sd(x))}) + } + } + } + } + + yrng <- c(0, max(std$Utilization)) + # yrng <- NULL + group_plot(d=std, dep_var=dep_var, within_vars=within_vars, between_vars=between_vars, id_var=id_var, + runs=run_dirs, run_names=run_names, meta_dir=meta_dir, formula=ifelse(!line, frml, frml2), + title=sprintf("%s Utilization", out_fid), xlab=x_var, ylab=dep_var, yrange=yrng, + x_var=x_var, fill_var=fill_var, facet_var=facet_var, group_var=group_var, pattern="util_(.*).csv", + target_file=sprintf("util_%s.svg", out_fid), line=line, + palette=ifelse(incl_class, "Spectral", "viridis")) + + return(list(std_omnibus, std_contr, descriptive)) +} + +# --------- Discrimination Analysis --------- # +discrimination <- function(cond_inclusion, layer_inclusion, in_pattern, out_fid, line=F, contr_dup=T, x_var="Duplication") { + id_var <- "Analysis" + dep_var <- "Accuracy" + + if (line) { + within_vars <- c("Shots", "Duplication", "Resolution", "Layer", "Fold") + } else { + within_vars <- c("Shots", "Duplication", "Resolution", "Layer") + } + between_vars <- c("Session", "Condition") + + fill_var <- ifelse(length(cond_inclusion)==1, "Layer", "Condition") + facet_var <- "Session" + + frml <- "Accuracy ~ Analysis*Session*Condition*Variant*Shots*Layer*Duplication*Resolution*Fold*RunID" + + # analyze and plot discrimination performance data. + clf <- process_runs(run_dirs, in_pattern, between_vars, within_vars) + clf <- clf[clf$Smoothing %in% smoothing_levels,] + + clf$Condition <- factor(clf$Condition, levels=c("Baseline", "Homogeneous", "Uniform", "Scaled", "Adaptive")) + clf$Condition[clf$Smoothing==.5] <- factor("Scaled") + clf$Condition[clf$Condition=="Baseline"] <- factor("Homogeneous") + + gain_levels <- list("25"=c("con_b1", "sat_b1"), "28"=c("con_b2", "sat_b2"), "32"=c("con_b3", "sat_b3"), + "37"=c("con_b4", "sat_b4"), "45"=c("con_b5", "sat_b5"), "56"=c("con_b6", "sat_b6"), + "73"=c("con_b7", "sat_b7"), "109"=c("con_b8", "sat_b8"), "208"=c("con_b9", "sat_b9"), + "2500"=c("con_b10", "sat_b10")) + + clf <- clf[clf$Layer %in% layer_inclusion,] + + all_base <- clf[clf$Variant %in% unlist(gain_levels, use.names=F),] + all_base <- aggregate(Accuracy ~ Variant*Fold, all_base, mean) + + clf <- clf[clf$Condition %in% cond_inclusion,] + + clf$HomWeight <- factor(clf$Variant) + if ("Homogeneous" %in% cond_inclusion) { + levels(clf$HomWeight) <- gain_levels + + clf$HomWeight <- factor(clf$HomWeight, levels=c("25", "28", "32", "37", "45", "56", "73", "109", "208", "2500", "0")) + clf$HomWeight[is.na(clf$HomWeight)] <- 0 + } + clf <- clf[clf$Duplication != 1, ] + frml2 <- paste(frml, "*HomWeight", sep="") + + if (!line) { + # aggregate over homogeneous duplication factors (don't exist in the original formula). + frml <- gsub("\\*Variant", "", frml) + clf <- aggregate(formula(frml), clf, mean) + } else { + # retain homogeneous duplication factor levels. + clf <- aggregate(formula(frml2), clf, mean) + within_vars <- append(within_vars, "HomWeight") + } + + contr_type <- ifelse(contr_dup, "consec", "pairwise") + + if (!is.null(meta_dir)) { + if(length(cond_inclusion) == 1) { + clf_glm <- lmer(Accuracy ~ Duplication*Session + (1|Fold), clf) + clf_omnibus <- joint_tests(clf_glm, lmer.df="kenward-roger") + clf_contr <- contrast(emmeans(clf_glm, ~ Duplication), contr_type) + } else { + if (length(layer_inclusion) == 1) { + clf_glm <- lmer(Accuracy ~ Condition*Duplication*Session + (1|Fold), clf) + clf_omnibus <- joint_tests(clf_glm, lmer.df="kenward-roger") + if (contr_dup) { + clf_contr <- contrast(emmeans(clf_glm, ~ Duplication|Condition), contr_type) + } else { + clf_contr <- contrast(emmeans(clf_glm, ~ Condition|Duplication), contr_type) + } + } else { + clf_glm <- lmer(Accuracy ~ Layer*Condition*Duplication*Session + (1|Fold), clf) + clf_omnibus <- joint_tests(clf_glm, lmer.df="kenward-roger") + if (contr_dup) { + clf_contr <- contrast(emmeans(clf_glm, ~ Duplication|Condition|Layer), contr_type) + } else { + clf_contr <- contrast(emmeans(clf_glm, ~ Condition|Duplication|Layer), contr_type) + } + } + } + } + + yrng <- ifelse(("Homogeneous" %in% cond_inclusion) & length(cond_inclusion) == 1, c(0, 109), c(50, 109)) + # yrng <- NULL + d <- group_plot(d=clf, dep_var=dep_var, + within_vars=within_vars, between_vars=between_vars, id_var=id_var, + runs=run_dirs, run_names=run_names, meta_dir=meta_dir, + formula=ifelse(!line, frml, frml2), + title=sprintf("%s Classification Accuracy", out_fid), + xlab=x_var, ylab=dep_var, yrange=yrng, + x_var=x_var, fill_var=fill_var, facet_var=facet_var, pattern="sep_svm(.*)_group.csv", + target_file=sprintf("clf_%s.svg", out_fid), line=line, all_base=NULL) + + if (length(layer_inclusion) == 1) { + descriptive <- summaryBy(Accuracy ~ Session|Condition|Duplication, data = clf, + FUN = function(x) {c(m = mean(x), s = sd(x))}) + } else { + descriptive <- summaryBy(Accuracy ~ Layer|Session|Condition|Duplication, data = clf, + FUN = function(x) {c(m = mean(x), s = sd(x))}) + } + + return(list(clf_omnibus, clf_contr, descriptive)) +} + +# --------- Regularization Within Condition (Class-specific line plots) --------- # +util_base_m <- regularization(c("Homogeneous"), c("MC", "GC"), "util_(.*).csv", "Base_M", mean, T, T, F, "HomWeight") +util_base_sd <- regularization(c("Homogeneous"), c("MC", "GC"), "util_(.*).csv", "Base_SD", sd, T, T, F, "HomWeight") + +util_uniform_m <- regularization(c("Uniform"), c("MC", "GC"), "util_(.*).csv", "Uniform_M", mean, T, T, F) +util_uniform_sd <- regularization(c("Uniform"), c("MC", "GC"), "util_(.*).csv", "Uniform_SD", sd, T, T, F) + +util_scaled_m <- regularization(c("Scaled"), c("MC", "GC"), "util_(.*).csv", "Scaled_M", mean, T, T, F) +util_scaled_sd <- regularization(c("Scaled"), c("MC", "GC"), "util_(.*).csv", "Scaled_SD", sd, T, T, F) + +util_adaptive_m <- regularization(c("Adaptive"), c("MC", "GC"), "util_(.*).csv", "Adaptive_M", mean, T, T, F) +util_adaptive_sd <- regularization(c("Adaptive"), c("MC", "GC"), "util_(.*).csv", "Adaptive_SD", sd, T, T, F) + +# --------- Regularization Across Conditions (Line and bar plots) --------- # +util_mc_m <- regularization(c("Adaptive", "Scaled", "Uniform", "Homogeneous"), c("MC"), "util_MC.csv", "MC_bar_M", mean, F, F, F) +util_gc_m <- regularization(c("Adaptive", "Scaled", "Uniform", "Homogeneous"), c("GC"), "util_GC.csv", "GC_bar_M", mean, F, F, F) + +util_mc_m <- regularization(c("Adaptive", "Scaled", "Uniform", "Homogeneous"), c("MC"), "util_MC.csv", "MC_line_M", mean, T, F, F) +util_gc_m <- regularization(c("Adaptive", "Scaled", "Uniform", "Homogeneous"), c("GC"), "util_GC.csv", "GC_line_M", mean, T, F, F) + +util_mc_sd <- regularization(c("Adaptive", "Scaled", "Uniform", "Homogeneous"), c("MC"), "util_MC.csv", "MC_bar_SD", sd, F, F, F) +util_gc_sd <- regularization(c("Adaptive", "Scaled", "Uniform", "Homogeneous"), c("GC"), "util_GC.csv", "GC_bar_SD", sd, F, F, F) + +util_mc_sd <- regularization(c("Adaptive", "Scaled", "Uniform", "Homogeneous"), c("MC"), "util_MC.csv", "MC_line_SD", sd, T, F, F) +util_gc_sd <- regularization(c("Adaptive", "Scaled", "Uniform", "Homogeneous"), c("GC"), "util_GC.csv", "GC_line_SD", sd, T, F, F) + +util_comb <- regularization(c("Uniform", "Homogeneous"), c("MC", "GC"), "util_(.*).csv", "MCGC_line_SD", sd, F, F, F) +util_comb_dup <- regularization(c("Uniform", "Homogeneous"), c("MC", "GC"), "util_(.*).csv", "MCGC_line_SD", sd, F, F, T) +util_full <- regularization(c("Adaptive", "Scaled", "Uniform", "Homogeneous"), c("MC", "GC"), "util_(.*).csv", "MCGC_line_SD", sd, F, F, F) +util_full_dup <- regularization(c("Adaptive", "Scaled", "Uniform", "Homogeneous"), c("MC", "GC"), "util_(.*).csv", "MCGC_line_SD", sd, F, F, T) + + +# --------- Intra-Condition Classification --------- # +clf_base <- discrimination(c("Homogeneous"), c("ET", "MC", "GC"), "sep_svm(.*)_group.csv", "Base", T, F, "HomWeight") +clf_uniform <- discrimination(c("Uniform"), c("ET", "MC", "GC"), "sep_svm(.*)_group.csv", "Uniform", T,F) +clf_scaled <- discrimination(c("Scaled"), c("ET", "MC", "GC"), "sep_svm(.*)_group.csv", "Scaled", T, F) +clf_adaptive <- discrimination(c("Adaptive"), c("ET", "MC", "GC"), "sep_svm(.*)_group.csv", "Adaptive", T, F) + +clf_et <- discrimination(c("Adaptive", "Scaled", "Uniform", "Homogeneous"), c("ET"), "sep_svm(.*)_group.csv", "ET_bar", F, F) +clf_mc <- discrimination(c("Adaptive", "Scaled", "Uniform", "Homogeneous"), c("MC"), "sep_svm(.*)_group.csv", "MC_bar", F, F) +clf_gc <- discrimination(c("Adaptive", "Scaled", "Uniform", "Homogeneous"), c("GC"), "sep_svm(.*)_group.csv", "GC_bar", F, F) + +clf_mc <- discrimination(c("Adaptive", "Scaled", "Uniform", "Homogeneous"), c("MC"), "sep_svm(.*)_group.csv", "MC_line", T, F) +clf_gc <- discrimination(c("Adaptive", "Scaled", "Uniform", "Homogeneous"), c("GC"), "sep_svm(.*)_group.csv", "GC_line", T, F) + +clf_comb <- discrimination(c("Uniform", "Homogeneous"), c("MC", "GC"), "sep_svm(.*)_group.csv", "MCGC_bar", F, F) +clf_comb_dup <- discrimination(c("Uniform", "Homogeneous"), c("MC", "GC"), "sep_svm(.*)_group.csv", "MCGC_bar", F, T) +clf_full <- discrimination(c("Adaptive", "Scaled", "Uniform", "Homogeneous"), c("MC", "GC"), "sep_svm(.*)_group.csv", "MCGC_bar", F, F) +clf_full_dup <- discrimination(c("Adaptive", "Scaled", "Uniform", "Homogeneous"), c("MC", "GC"), "sep_svm(.*)_group.csv", "MCGC_bar", F, T) + +# --------- Log Results --------- # +if (!dir.exists(file.path("analysis", meta_dir, "csv"))) { + dir.create(file.path("analysis", meta_dir, "csv")) +} + +# dump console output to this file # +if (!is.null(meta_dir)) { + sink(file=file.path("analysis", meta_dir, "analysis_base.txt")) + + cat(paste("\nDiscrimination Accuracy\n******************\n")) + for (item in clf_base) { + print(item) + cat("\n") + } + + cat(paste("\nMean Utilization\n**************\n")) + for (item in util_base_m) { + print(item) + cat("\n") + } + + cat(paste("\nSD Utilization\n**************\n")) + for (item in util_base_sd) { + print(item) + cat("\n") + } + + sink(NULL) + sink(file=file.path("analysis", meta_dir, "analysis_exp.txt")) + + cat(paste("\nFigure 3-4 (Het-Hom Utilization by Condition)\n**************************\n")) + for (i in seq_along(util_comb)) { + print(util_comb[i]) + cat("\n") + write.table(util_comb[i], file.path("analysis", meta_dir, "csv", paste("hh_util_cond_", i, ".csv", sep="")), + row.names=FALSE, quote=FALSE, sep=',') + } + + cat(paste("\nFigure 4C (Het-Hom Utilization by Duplication)\n**************************\n")) + for (i in seq_along(util_comb_dup)) { + print(util_comb_dup[i]) + cat("\n") + write.table(util_comb_dup[i], file.path("analysis", meta_dir, "csv", paste("hh_util_dup_", i, ".csv", sep="")), + row.names=FALSE, quote=FALSE, sep=',') + } + + cat(paste("\nFigure 3-4 (Het-Hom Accuracy by Condition)\n**************************\n")) + for (i in seq_along(clf_comb)) { + print(clf_comb[i]) + cat("\n") + write.table(clf_comb[i], file.path("analysis", meta_dir, "csv", paste("hh_clf_cond_", i, ".csv", sep="")), + row.names=FALSE, quote=FALSE, sep=',') + } + + cat(paste("\nFigure 3-4 (Het-Hom Accuracy by Duplication)\n**************************\n")) + for (i in seq_along(clf_comb_dup)) { + print(clf_comb_dup[i]) + cat("\n") + write.table(clf_comb_dup[i], file.path("analysis", meta_dir, "csv", paste("hh_clf_dup_", i, ".csv", sep="")), + row.names=FALSE, quote=FALSE, sep=',') + } + + cat(paste("\nFigure 5 (Combined Utilization by Condition)\n**************************\n")) + for (i in seq_along(util_full)) { + print(util_full[i]) + cat("\n") + write.table(util_full[i], file.path("analysis", meta_dir, "csv", paste("full_util_cond_", i, ".csv", sep="")), + row.names=FALSE, quote=FALSE, sep=',') + } + + cat(paste("\nFigure 5 (Combined Utilization by Duplication)\n**************************\n")) + for (i in seq_along(util_full_dup)) { + print(util_full_dup[i]) + cat("\n") + write.table(util_full_dup[i], file.path("analysis", meta_dir, "csv", paste("full_util_dup_", i, ".csv", sep="")), + row.names=FALSE, quote=FALSE, sep=',') + } + + cat(paste("\nFigure 5 (Combined Accuracy by Condition)\n**************************\n")) + for (i in seq_along(clf_full)) { + print(clf_full[i]) + cat("\n") + write.table(clf_full[i], file.path("analysis", meta_dir, "csv", paste("full_clf_cond_", i, ".csv", sep="")), + row.names=FALSE, quote=FALSE, sep=',') + } + + cat(paste("\nFigure 5 (Combined Accuracy by Duplication)\n**************************\n")) + for (i in seq_along(clf_full_dup)) { + print(clf_full_dup[i]) + cat("\n") + write.table(clf_full_dup[i], file.path("analysis", meta_dir, "csv", paste("full_clf_dup_", i, ".csv", sep="")), + row.names=FALSE, quote=FALSE, sep=',') + } + + cat(paste("\nET Discrimination Accuracy\n**************************\n")) + for (item in clf_et) { + print(item) + cat("\n") + } + + cat(paste("\nMC Discrimination Accuracy\n**************************\n")) + for (item in clf_mc) { + print(item) + cat("\n") + } + + cat(paste("\nGC Discrimination Accuracy\n**************************\n")) + for (item in clf_gc) { + print(item) + cat("\n") + } + + cat(paste("\nMC SD Utilization\n*****************\n")) + for (item in util_mc_sd) { + print(item) + cat("\n") + } + + cat(paste("\nGC SD Utilization\n*****************\n")) + for (item in util_gc_sd) { + print(item) + cat("\n") + } + + cat(paste("\nMC Mean Utilization\n*****************\n")) + for (item in util_mc_m) { + print(item) + cat("\n") + } + + cat(paste("\nGC Mean Utilization\n*****************\n")) + for (item in util_gc_m) { + print(item) + cat("\n") + } + + sink(NULL) +} diff --git a/src/utils/analysis/descriptive.r b/src/utils/analysis/descriptive.r new file mode 100644 index 0000000..f658323 --- /dev/null +++ b/src/utils/analysis/descriptive.r @@ -0,0 +1,153 @@ +# Descriptive Statistics # + +# -------------- IMPORTS -------------- # +suppressPackageStartupMessages({ + library(docstring) + library(plyr) +}) + +# --------- UTILITY FUNCTIONS --------- # +z_score <- function(x, data) { + #' @title Computes a standard Z score. + return ((x-mean(data)) / sd(data)) +} + +normDataWithin <- function(data=NULL, idvar, measurevar, betweenvars=NULL, + na.rm=FALSE, .drop=TRUE) { + #' @title Normalizes the data within specified groups in a data frame. + #' + #' @description Normalizes each participant (identified by idvar) so that they have the same mean, + #' within each group specified by betweenvars. + #' + #' @param data: a data frame. + #' @param idvar: the name of a column that identifies each subject (or matched subjects). + #' @param measurevar: the name of a column that contains the variable to be summariezed. + #' @param betweenvars: a vector containing names of columns that are between-subjects variables. + #' @param na.rm: a boolean that indicates whether to ignore NA's. + #' + #' @return Normalized data frame. + # Measure var on left, idvar + between vars on right of formula. + data.subjMean <- ddply( + data, c(idvar, betweenvars), .drop=.drop, .fun = function(xx, col, na.rm) { + c(subjMean = mean(xx[,col], na.rm=na.rm)) + }, measurevar, na.rm + ) + + # Put the subject means with original data + data <- merge(data, data.subjMean) + + # Get the normalized data in a new column + measureNormedVar <- paste(measurevar, "_norm") + data[[measureNormedVar]] <- data[[measurevar]] - data[["subjMean"]] + mean(data[[measurevar]]) + + # Remove subject mean column. + data$subjMean <- NULL + + return(data) +} + +summarySE <- function(data=NULL, measurevar, groupvars=NULL, na.rm=FALSE, + conf.interval=.95, .drop=TRUE) { + #' @title Summarizes data. + #' @description Computes count, mean, standard deviation, standard error of the mean, and + #' confidence interval (default 95%). + #' + #' @param data: a data frame. + #' @param measurevar: the name of a column that contains the variable to be summariezed + #' @param groupvars: a vector containing names of columns that contain grouping variables + #' @param na.rm: a boolean that indicates whether to ignore NA's + #' @param conf.interval: the percent range of the confidence interval (default is 95%) + # New version of length which can handle NA's: if na.rm==T, don't count them + length2 <- function (x, na.rm=FALSE) { + if (na.rm) sum(!is.na(x)) + else length(x) + } + + # This does the summary. For each group's data frame, return a vector with + # N, mean, and sd + summary <- ddply(data, groupvars, .drop=.drop, + .fun = function(xx, col) { + c(N = length2(xx[[col]], na.rm=na.rm), + mean = mean(xx[[col]], na.rm=na.rm), + sd = sd(xx[[col]], na.rm=na.rm)) + }, + measurevar + ) + + summary <- rename(summary, c("mean" = measurevar)) # rename "mean" column. + summary$se <- summary$sd / sqrt(summary$N) # Calculate standard error of the mean. + + # Confidence interval multiplier for standard error + # Calculate t-statistic for confidence interval: + # e.g., if conf.interval is .95, use .975 (above/below), and use df=N-1 + ciMult <- qt(conf.interval/2 + .5, summary$N-1) + summary$ci <- summary$se * ciMult + + return(summary) +} + +summarySEwithin <- function(data=NULL, measurevar, betweenvars=NULL, withinvars=NULL, + idvar=NULL, na.rm=FALSE, conf.interval=.95, .drop=TRUE) { + #' @title Summarizes data, handling within-participant factors by removing inter-subject variability. + #' + #' @description Computes descriptive statistics while correctly handling within-participants variables (Morey, 2008). + #' Statistics include count, un-normed mean, normed mean (with same between-group mean), standard deviation, + #' standard error of the mean, and confidence interval. + #' + #' @param data: a data frame. + #' @param measurevar: the name of a column that contains the variable to be summarized. + #' @param betweenvars: a vector containing names of columns that are between-subjects variables. + #' @param withinvars: a vector containing names of columns that are within-subjects variables. + #' @param idvar: the name of a column that identifies each subject (or matched subjects) + #' @param na.rm: a boolean that indicates whether to ignore NA's + #' @param conf.interval: the percent range of the confidence interval (default is 95%) + + # convert data table to frame if necessary. + data <- data.frame(data) + + # Ensure that the betweenvars and withinvars are factors + factorvars <- vapply(data[, c(betweenvars, withinvars), drop=FALSE], + FUN=is.factor, FUN.VALUE=logical(1)) + + if (!all(factorvars)) { + nonfactorvars <- names(factorvars)[!factorvars] + message("Automatically converting the following non-factors to factors: ", + paste(nonfactorvars, collapse = ", ")) + data[nonfactorvars] <- lapply(data[nonfactorvars], factor) + } + + # Get the means from the un-normed data + datac <- summarySE(data, measurevar, groupvars=c(betweenvars, withinvars), + na.rm=na.rm, conf.interval=conf.interval, .drop=.drop) + + # Drop all the unused columns (these will be calculated with normed data) + datac$sd <- NULL + datac$se <- NULL + datac$ci <- NULL + + # Norm each subject's data + ndata <- normDataWithin(data, idvar, measurevar, betweenvars, na.rm, .drop=.drop) + + # This is the name of the new column + measurevar_n <- paste(measurevar, "_norm") + + # Collapse the normed data - now we can treat between and within vars the same + ndatac <- summarySE(ndata, measurevar_n, groupvars=c(betweenvars, withinvars), + na.rm=na.rm, conf.interval=conf.interval, .drop=.drop) + + # Apply correction from Morey (2008) to the standard error and confidence interval + # Get the product of the number of conditions of within-S variables + nWithinGroups <- prod(vapply(ndatac[,withinvars, drop=FALSE], FUN=nlevels, + FUN.VALUE=numeric(1))) + + correctionFactor <- sqrt( nWithinGroups / (nWithinGroups-1) ) + + # Apply the correction factor + ndatac$sd <- ndatac$sd * correctionFactor + ndatac$se <- ndatac$se * correctionFactor + ndatac$ci <- ndatac$ci * correctionFactor + + + # Combine the un-normed means with the normed results + merge(datac, ndatac) +} diff --git a/src/utils/analysis/plotting.r b/src/utils/analysis/plotting.r new file mode 100644 index 0000000..f55e2f3 --- /dev/null +++ b/src/utils/analysis/plotting.r @@ -0,0 +1,274 @@ +# -------------- IMPORTS -------------- # +suppressPackageStartupMessages({ + library(ggplot2) + library(ggbeeswarm) + library(ggpp) + library(vioplot) + library(scales) + library(see) + library(plotrix) +}) + +# ------------- HELPERS ------------- # +position_nudgedodge <- function(x = 0, y = 0, width = 0.75) { + ggproto(NULL, PositionNudgedodge, + x = x, + y = y, + width = width + ) +} + +PositionNudgedodge <- ggproto("PositionNudgedodge", PositionDodge, + x = 0, + y = 0, + width = 0.3, + + setup_params = function(self, data) { + l <- ggproto_parent(PositionDodge,self)$setup_params(data) + append(l, list(x = self$x, y = self$y)) + }, + + compute_layer = function(self, data, params, layout) { + d <- ggproto_parent(PositionNudge,self)$compute_layer(data,params,layout) + d <- ggproto_parent(PositionDodge,self)$compute_layer(d,params,layout) + d + } +) + +#' Nudge any other positioning function from ggplot2 +#' @param x Nudge in X direction +#' @param y Nudge in Y direction +#' @param position Any positioning operator from ggplot2 like \link{position_jitter}[ggplot2] +#' @return A combination positioning operator which first runs the original positioning, +#' then adds a nudge on top of that. +#' @export +position_nudge_any <- function(x = 0, y = 0, position) { + ggproto(NULL, PositionNudgeAny, + nudge = ggplot2::position_nudge(x, y), + position = position + ) +} + +#' Internal class doing the actual nudging on top of the other operation +#' @keywords internal +PositionNudgeAny <- ggplot2::ggproto("PositionNudgeAny", ggplot2::Position, + nudge = NULL, + nudge_params = NULL, + position = NULL, + position_params = NULL, + + setup_params = function(self, data) { + list(nudge = self$nudge, + nudge_params = self$nudge$setup_params(data), + position = self$position, + position_params = self$position$setup_params(data)) + }, + + setup_data = function(self, data, params) { + data <- params$position$setup_data(data, params$position_params) + params$nudge$setup_data(data, params$nudge_params) + }, + + compute_layer = function(self, data, params, layout) { + data <- params$position$compute_layer(data, params$position_params, layout) + params$nudge$compute_layer(data, params$nudge_params, layout) + } +) + +pretty_lim <- function(x, n=5) { + r2 <- ifelse(x<0, 0, x) + pr <- pretty(r2,n) + r_out <- range(pr) + r_out +} + + +pretty_unexpanded <- function(x, n=5) { + if(x[1]<=0){ + r2 <- x + c(-x[1],x[1]) + }else{ + r2 <- x + c((x[2]-x[1])*0.04545455,-(x[2]-x[1])*0.04545455) + } + pout <- pretty(r2,n) + pout +} + +# --------- PLOTTING FUNCTIONS --------- # +publication_theme <- theme_bw() + + theme( + plot.title = element_text(size = 32, color="black", face = "bold"), + axis.title = element_text(size = 24, color="black"), + # axis.line = element_line(linewidth = 1), + axis.text = element_text(size = 32, color="black"), + axis.title.x = element_text(margin=margin(10,0,0,0)), + axis.title.y = element_text(margin=margin(0,10,0,0)), + strip.text = element_text(size = 24), + strip.background = element_blank(), + # legend.position = c(.99, .99), + legend.justification = c("right", "top"), + legend.box.just = "right", + legend.margin = margin(6, 6, 6, 6), + legend.text = element_text(size = 14), + legend.title = element_text(size = 18) + ) + +theme2 <- theme(panel.background = element_rect(fill = 'white'), + axis.text = element_text(size = 16, color="black"), + plot.title = element_text(size = 16, face = "bold"), + axis.title = element_text(size = 16, color = "black"), + axis.title.x = element_text(margin=margin(10,0,0,0)), + axis.title.y = element_text(margin=margin(0,10,0,0)), + strip.text = element_text(size = 12), + legend.justification = "right") + +line_plot <- function(df, dep_var, within_vars, between_vars, id_var, + x_var=NULL, fill_var=NULL, facet_var=NULL, group_var=NULL, + title="", xlab="", ylab="", yrange=NULL, palette="viridis", all_base=NULL) { + #' @title Multiline plot with datapoints and shaded CI ribbons. + #' @description Plots the requested variables after computing full descriptive statistics. + if (!is.null(within_vars)) { + descriptives <- summarySEwithin(df, measurevar=dep_var, withinvars=within_vars, + betweenvars=between_vars, idvar=id_var) + } else { + descriptives <- summarySE(df, measurevar=dep_var, groupvars=between_vars) + } + + p <- ggplot(descriptives, aes(x=.data[[x_var]], y=.data[[dep_var]], fill=.data[[fill_var]], group=.data[[fill_var]])) + + if (!is.null(facet_var)) { + p <- p + facet_wrap(~.data[[facet_var]]) + } + + p <- p + stat_summary(fun = mean, geom = "line", aes(color=.data[[fill_var]], group=.data[[fill_var]]), linewidth=1.25) + p <- p + stat_summary(fun = mean, geom = "point", aes(group=.data[[fill_var]])) + p <- p + stat_summary(geom="ribbon", fun.data = mean_se, aes(group=.data[[fill_var]]), alpha=.1) + + if (palette == "viridis") { + p <- p + scale_fill_viridis_d() + scale_color_viridis_d() + } else { + p <- p + scale_fill_brewer(palette=palette) + scale_color_brewer(palette=palette) + } + p <- p + theme(plot.title = element_text(hjust = 0.5)) + publication_theme + + if (!is.null(yrange)) { + p <- p + coord_cartesian(ylim=c(yrange[1], yrange[2])) + } else { + p <- p + coord_cartesian(ylim=c(max(min(df[dep_var]-descriptives$ci), 0), max(df[dep_var]+descriptives$ci))) + } + + p <- p + scale_y_continuous(breaks = breaks_pretty(5)) + +} + +bar_plot <- function(df, dep_var, within_vars, between_vars, id_var, + x_var=NULL, fill_var=NULL, facet_var=NULL, group_var=NULL, + title="", xlab="", ylab="", yrange=NULL, beeswarm=T, palette="viridis", + all_base=NULL) { + #' @title Bar plot with dots and CI. + #' @description Plots the requested variables after computing full descriptive statistics. + #' Levels of 'x_var' will be plotted as bars, levels of 'facet_var' as facets. + #' Title and axis labels may also be provided. + if (!is.null(within_vars)) { + descriptives <- summarySEwithin(df, measurevar=dep_var, withinvars=within_vars, + betweenvars=between_vars, idvar=id_var) + } else { + descriptives <- summarySE(df, measurevar=dep_var, groupvars=between_vars) + } + + # determine how each variable will be plotted. + vars <- c(within_vars, between_vars) + + x_var <- ifelse(is.null(x_var), vars[1], x_var) + fill_var <- ifelse(is.null(fill_var), vars[ifelse(length(var)>1, 2, 1)], fill_var) + # facet_var <- ifelse(is.null(facet_var), vars[ifelse(length(vars)>2, 3, 2)], facet_var) + + # plotting control. + bar_width <- 1.0 + bar_pad <- bar_width/2.0 + + violin_width <- bar_width/1.5 + line_width <- 0.3 + err_width <- 0.02 + + if (x_var != fill_var) { + swarm_nudge_x <- -bar_width/(2 * length(unique(df[[fill_var]]))) + } else { + swarm_nudge_x <- -bar_width/1.5 + } + ci_nudge_x <- swarm_nudge_x + err_width/1.5 + + # compute external baseline while respecting levels of facet variable. + if (!is.null(all_base)) { + all_base$Session <- ifelse(grepl("con", all_base$Variant), "Concentration", "Saturation") + all_base <- aggregate(formula(paste(dep_var, "~ Session")), all_base, function(x) c(mean = mean(x), se = std.error(x))) + all_base <- do.call(data.frame, all_base) + + descriptives$base_m <- ifelse(descriptives$Session=="Concentration", + all_base[all_base$Session=="Concentration",]$Accuracy.mean, + all_base[all_base$Session=="Saturation",]$Accuracy.mean) + + descriptives$base_se <- ifelse(descriptives$Session=="Concentration", + all_base[all_base$Session=="Concentration",]$Accuracy.se, + all_base[all_base$Session=="Saturation",]$Accuracy.se) + } + + # construct bar plot with data dots, SE wicks, and 95% CI ranges. + p <- ggplot(descriptives, aes(x=.data[[x_var]], y=.data[[dep_var]], fill=.data[[fill_var]])) + + p <- p + geom_bar(position=position_dodge2(preserve="total", padding=bar_pad), + stat="identity", width=bar_width, alpha=1) + + if (!is.null(facet_var)) { + p <- p + facet_wrap(~.data[[facet_var]]) + } + + # p <- p + geom_violin(data=df, aes(x=.data[[x_var]], y=.data[[dep_var]]), width=.6, adjust=2, + # position = position_dodge(width=bar_width), alpha=.3) + + # custom baseline mean and SE ribbon. + if (!is.null(all_base)) { + p <- p + geom_hline(aes(yintercept = .data[["base_m"]]), alpha=1, color="darkgreen") + p <- p + geom_rect(aes(xmin=-Inf, xmax=Inf, ymin=.data[["base_m"]]-.data[["base_se"]], ymax=.data[["base_m"]]+.data[["base_se"]]), + alpha=.01, color=NA, fill="darkgreen") + } + + p <- p + geom_linerange(aes(ymin = .data[[dep_var]]-ci, ymax = .data[[dep_var]]+ci), linewidth=line_width, alpha=.7, + position = position_nudge_any(x=ci_nudge_x, y=0, position_dodge(width=bar_width)), color = "red") + + p <- p + ggtitle(title) + labs(x=xlab, y=ylab) + p <- p + geom_errorbar(aes(ymin=.data[[dep_var]]-se, ymax=.data[[dep_var]]+se), width=err_width, + linewidth=line_width, position = position_dodge(width=bar_width), color='black') + + if (!beeswarm) { + dot_position <- position_nudge(x = -.48) + } else { + dot_position <- position_nudge_any(x=swarm_nudge_x, y=0, position_beeswarm(dodge.width=bar_width, side=-1L, cex=.75)) + } + + p <- p + geom_point( + data=df, aes(x=.data[[x_var]], y=.data[[dep_var]], color=.data[[fill_var]]), position=dot_position, size=.75) + + # set a reasonable y axis range (DV). + if (!is.null(yrange)) { + p <- p + coord_cartesian(ylim=c(yrange[1], yrange[2])) + } else { + p <- p + coord_cartesian(ylim=c(max(min(df[dep_var]-descriptives$ci), 0), max(df[dep_var]+descriptives$ci))) + } + + p <- p + scale_y_continuous(breaks = breaks_pretty(5)) + p <- p + guides(fill=guide_legend(title=fill_var), color="none", override.aes = list(shape = NA)) + + # p <- p + scale_fill_manual(values=c("#42be71", "#2a788e", "#FFA500", "#453781"), name=xlab) + + # scale_color_manual(values=c("#42be71", "#2a788e", "#FFA500", "#453781"), name=xlab) + + # theme(plot.title = element_text(hjust = 0.5)) + theme + + if (palette == "viridis") { + p <- p + scale_fill_viridis_d() + scale_color_viridis_d() + + } else { + p <- p + scale_color_brewer(palette=palette) + } + p <- p + theme(plot.title = element_text(hjust = 0.5)) + publication_theme + + return(p) +} diff --git a/src/utils/data/__init__.py b/src/utils/data/__init__.py new file mode 100644 index 0000000..61341d4 --- /dev/null +++ b/src/utils/data/__init__.py @@ -0,0 +1,103 @@ +""" Enhancements to base Sapicore Data class. """ +from __future__ import annotations +from typing import Any + +import os + +import numpy as np +import pandas as pd + +import torch + +from sapicore.data import Data, Metadata, AxisDescriptor +from sapicore.utils.io import ensure_dir + + +class SNNData(Data): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + def load(self, indices: Any = None): + """Load data and labels to memory from the .pt files saved by :meth:`~data.Data._standardize`. + Overwrites `buffer` and `descriptors`. If specified, selects only the rows at `indices`. + + Parameters + ---------- + indices: slice, optional + Specific indices to load. + + """ + # the labels file has been created by _standardize upon first obtaining the data. + labels = torch.load(os.path.join(self.root, "labels.pt")) + if indices is not None: + labels = labels[indices] + + # by default, assume that all labels describe the 0th axis (rows). + self.metadata = Metadata(*[AxisDescriptor(name=col, labels=labels[col].to_list(), axis=0) for col in labels]) + + # load the data into the buffer (synthetic sets are typically small and don't require lazy loading). + self.buffer = torch.load(os.path.join(self.root, "data.pt")) + + if indices is not None: + self.buffer = self.buffer[indices] + + return self + + def save(self): + """Dump the contents of the `buffer` and `metadata` table to disk at `destination`. + This implementation saves the tensors as .pt files. + + """ + torch.save(self.buffer, os.path.join(ensure_dir(self.root), "data.pt")) + torch.save(self.metadata.to_dataframe(), os.path.join(ensure_dir(self.root), "labels.pt")) + + def concatenate(self, other: SNNData): + """Concatenates another dataset to this one, covering buffer and metadata labels alike.""" + # append other dataset's buffer to this one by vertically stacking. + self.buffer = torch.cat((self[:], other[:])) + + # append values of shared metadata keys if found. + for key in other.metadata.to_dataframe(): + if key in self.metadata.to_dataframe(): + self.metadata[key].labels = np.concatenate((self.metadata[key][:], other.metadata[key][:])) + + def select(self, conditions: str | list[str], axis: int = 0) -> slice | list: + """Selects sample indices by descriptor values based on a pandas logical statement applied to one or more + :class:`AxisDescriptor` objects, aggregated into a dataframe using :meth:`aggregate_descriptors`. + + Importantly, these filtering operations can be performed prior to loading any data to memory, as they only + depend on :class:`AxisDescriptor` objects (labels) attached to this dataset. + + Note + ---- + Applying selection criteria with pd.eval syntax is simple: + + * "age > 21" would execute `pd.eval("self.table.age > 21", target=df)`. + * "lobe.isin['F', 'P']" would execute `pd.eval("self.table.lobe.isin(['F', 'P']", target=df)`. + + Where `df` is the table attribute of the :class:`~AxisDescriptor` the condition is applied to. + + Warning + ------- + This method is general and may eventually be moved to the base :class:`~data.Data` class. + + """ + # wrap a single string condition in a list if necessary. + if isinstance(conditions, str): + conditions = [conditions] + + # aggregate relevant axis descriptors into a pandas dataframe (table). + df = self.metadata.to_dataframe(axis=axis) + + # evaluate the expressions and filter the dataframe. + for expression in conditions: + parsed = "& ".join([("df." + expr).replace(". ", ".") for expr in expression.split("&")]) + parsed = "| ".join([("df." + expr).replace(". ", ".") for expr in parsed.split("|")]) + + parsed = parsed.replace("df.df.", "df.") + parsed = parsed.replace("df.(", "(df.") + + df = df[pd.eval(parsed, target=df).to_list()] + + # return indices where expressions evaluated to true. + return df.index.to_list() diff --git a/src/utils/data/io.py b/src/utils/data/io.py new file mode 100644 index 0000000..faf3ac0 --- /dev/null +++ b/src/utils/data/io.py @@ -0,0 +1,96 @@ +""" File I/O utilities. """ +import csv +import glob +import os + + +def extract_config(configuration) -> (dict, dict, dict): + """Configuration parameter extraction for simulation pipeline.""" + if configuration.get("synthesis", {}).get("method") == "sklearn": + # point cloud data is synthesized with scikit-learn. + synthesis_params = { + # sklearn.make_classification keyword arguments, loaded directly from pipeline configuration file. + "n_classes": configuration.get("synthesis", {}).get("sklearn", {}).get("classes"), + "n_samples": configuration.get("synthesis", {}).get("sklearn", {}).get("samples"), + "n_features": configuration.get("synthesis", {}).get("sklearn", {}).get("features"), + "n_redundant": configuration.get("synthesis", {}).get("sklearn", {}).get("redundant"), + "n_informative": configuration.get("synthesis", {}).get("sklearn", {}).get("informative"), + "class_sep": configuration.get("synthesis", {}).get("sklearn", {}).get("separation"), + "n_clusters_per_class": configuration.get("synthesis", {}).get("sklearn", {}).get("clusters_per_class"), + "flip_y": configuration.get("synthesis", {}).get("sklearn", {}).get("flipped_label_prop"), + } + elif configuration.get("synthesis", {}).get("method") == "physical": + # data with nontrivial, olfactory receptor-like geometry is synthesized directly. + synthesis_params = { + # sklearn.make_classification keyword arguments, loaded directly from pipeline configuration file. + "seed_vector": configuration.get("synthesis", {}).get("physical", {}).get("seed_vector"), + "n_samples": configuration.get("synthesis", {}).get("physical", {}).get("n_samples"), + "depth": configuration.get("synthesis", {}).get("physical", {}).get("depth"), + "sensors": configuration.get("synthesis", {}).get("physical", {}).get("sensors"), + "sparsity": configuration.get("synthesis", {}).get("physical", {}).get("sparsity"), + "signal": configuration.get("synthesis", {}).get("physical", {}).get("signal"), + "contaminants": configuration.get("synthesis", {}).get("physical", {}).get("contaminants"), + "noise": configuration.get("synthesis", {}).get("physical", {}).get("noise"), + "sigmoid": configuration.get("synthesis", {}).get("physical", {}).get("sigmoid"), + } + else: + synthesis_params = {} + + # for easier referencing of important configuration parameters throughout the script. + sampling_params = { + "repetitions": configuration.get("sampling", {}).get("repetitions", 0), + "folds": configuration.get("sampling", {}).get("folds"), + "shots": configuration.get("sampling", {}).get("shots", 1), + "shuffle": configuration.get("sampling", {}).get("shuffle"), + "batches": configuration.get("sampling", {}).get("included_batches", [1]), + "train_batches": configuration.get("sampling", {}).get("train_batches", None), + "test_batches": configuration.get("sampling", {}).get("test_batches", None), + "key": configuration.get("sampling", {}).get("key", "Category"), + "group_key": configuration.get("sampling", {}).get("group_key", ""), + } + simulation_params = { + "dump": configuration.get("simulation", {}).get("dump"), + "test": configuration.get("simulation", {}).get("test"), + "noise": configuration.get("simulation", {}).get("noise", {}), + "rsa": configuration.get("simulation", {}).get("rsa"), + "svm": configuration.get("simulation", {}).get("svm"), + "hetdup": configuration.get("simulation", {}).get("hetdup"), + "render": configuration.get("simulation", {}).get("render"), + "verbose": configuration.get("simulation", {}).get("verbose"), + "tensorboard": configuration.get("simulation", {}).get("tensorboard"), + "interactive": configuration.get("simulation", {}).get("interactive"), + "train_duration": configuration.get("simulation", {}).get("train_duration"), + "test_duration": configuration.get("simulation", {}).get("test_duration"), + "rinse": configuration.get("simulation", {}).get("rinse"), + } + + return synthesis_params, sampling_params, simulation_params + + +def get_files(path, extension, recursive=False): + """A generator of filepaths for each file into path with the target extension.""" + if not recursive: + for file_path in glob.iglob(path + "/*." + extension): + yield file_path + else: + for root, dirs, files in os.walk(path): + for file_path in glob.iglob(root + "/*." + extension): + yield file_path + + +def to_csv(file: str, header: list, content: list): + """Saves header and content to a CSV. + + Note + ---- + Opens the file in append mode by default. Caller should ensure the correct file is added to. + + """ + with open(file + ".csv", "a") as f: + writer = csv.writer(f, delimiter=",") + + if os.path.getsize(file + ".csv") == 0: + writer.writerow(header) + + params = [str(p) for p in content] + writer.writerow(params) diff --git a/src/utils/data/loading.py b/src/utils/data/loading.py new file mode 100644 index 0000000..816a570 --- /dev/null +++ b/src/utils/data/loading.py @@ -0,0 +1,117 @@ +from typing import Callable + +import os +import numpy as np + +import torch +from torch.nn.functional import normalize + +import matplotlib.pyplot as plt + +from sklearn.decomposition import PCA +from sklearn.datasets import make_classification + +from sapicore.data.external.drift import DriftDataset +from sapicore.data.sampling import BalancedSampler + +from src.utils.data import SNNData +from src.utils.data.synthesis import PhysicalSynthesizer, Synthetic + +from src.utils.visual import Explorer +from src.utils.visual.embedding import SklearnEmbedder + + +def load_drift( + pipeline, filter_conditions: list[str], sampler: Callable | BalancedSampler, shots: int = 1, folds: int = 2 +) -> DriftDataset: + """1: Ethanol; 2: Ethylene; 3:Ammonia; 4: Acetaldehyde; 5: Acetone; 6: Toluene.""" + drift = DriftDataset(identifier="UCSD drift", root=os.path.join(pipeline.data_dir, "drift")).load() + # reps = self.configuration.get("simulation", {}).get("hetdup") + + # take the first feature (`DR`) from the first 8 sensors (there are 16 features X 8 non-redundant sensors). + total_features = drift[:].shape[1] + drift_subset = drift.sample(lambda: torch.arange(0, total_features // 2, 8), axis=1) + + # since the full set has 13910 entries, sample 2 of each batch X analyte combination (train and test). + # sampling can be stratified with `n` representing a fraction of the total dataset. + # here, users can specify a logical expression to filter classes or batches. + key = pipeline.configuration.get("sampling", {}).get("key") + group_key = pipeline.configuration.get("sampling", {}).get("group_key") + + drift_subset = drift_subset.trim(drift.select(filter_conditions)) + drift_subset = drift_subset.sample(method=sampler, group_keys=[key, group_key], n=(shots * folds)) + + # move data tensor to GPU if necessary. + drift_subset.buffer = drift_subset.buffer.to(pipeline.configuration.get("device", "cpu")) + + return drift_subset + + +def synthesize_data( + pipeline, + method: str, + params: dict, + sampler: Callable | BalancedSampler, + duplicate: int = 0, + norm: bool = False, + key: str = "Category", + group_keys: str | list[str] = None, + shots: int = 1, + folds: int = 2, +) -> SNNData: + if method == "sklearn": + data = Synthetic(algorithm=make_classification).generate(key=key, **params) + + elif method == "physical": + data = PhysicalSynthesizer(**params).generate(key=key, n=params["n_samples"]) + + else: + raise ValueError(f"Invalid data synthesis setting: {method}.") + + data = data.sample(method=sampler, group_keys=group_keys, n=shots * folds) + + # optional, duplicate initially generated datapoints and their respective labels. + temp_data = data[:] + temp_labels = data.metadata[key].labels + + for _ in range(duplicate): + data.buffer = torch.vstack((data[:], temp_data)) + data.metadata[key].labels = np.hstack((data.metadata[key][:], temp_labels)) + + # shift data to positive range by adding a constant. + min_value = torch.min(data[:]) + if min_value < 0: + data[:] = data[:] - min_value + + # optional, L1-normalize the data. + if norm: + data[:] = normalize(data[:], p=1.0) + + # ensure data buffer tensor is on the correct device. + data.buffer = data.buffer.to(pipeline.configuration.get("device", "cpu")) + + return data + + +def explore_data( + pipeline, + data: SNNData, + proj: str = "diamond", + key: str = "Category", + interactive: bool = True, + path: str = "", + point_size: int = 10, +): + emb = PCA(n_components=3, random_state=pipeline.seed) + cmap = plt.get_cmap(pipeline.class_cmap, pipeline.configuration.get("synthesis", {}).get("classes")) + + explorer = Explorer(data=data, embedder=SklearnEmbedder(data=data, embedder=emb), fit=True, cmap=cmap) + explorer.vista3d( + key=key, + style="points", + point_size=point_size, + render_points_as_spheres=True, + projection=proj, + interactive=interactive, + path=path, + ) diff --git a/src/utils/data/synthesis.py b/src/utils/data/synthesis.py new file mode 100644 index 0000000..ce5d096 --- /dev/null +++ b/src/utils/data/synthesis.py @@ -0,0 +1,342 @@ +""" Data synthesis. """ +from typing import Any, Callable +from importlib import import_module + +import torch +from torch import Tensor + +import numpy as np +from numpy.linalg import norm + +import matplotlib.pyplot as plt + +from scipy.stats import rv_continuous +from scipy.stats.distributions import expon, uniform + +from sapicore.data import Metadata, AxisDescriptor +from src.utils.data import SNNData + + +class Synthetic: + """Wraps a given data synthesis method and returns an enhanced Sapicore dataset with labels.""" + + def __init__(self, algorithm: Callable): + self.algorithm = algorithm + + def generate(self, key: str, *args, **kwargs) -> SNNData: + """Default data and label generation method. Applies the given algorithm (Callable) under + the assumption that it returns samples and labels.""" + samples, labels = self.algorithm(*args, **kwargs) + return SNNData(buffer=torch.as_tensor(samples), metadata=Metadata(AxisDescriptor(name=key, labels=labels))) + + +class RandomVariable: + def __init__(self, distribution: rv_continuous = expon, **kwargs): + self.distribution = distribution + self.kws = kwargs + + def generate(self, size: int = 1, normalize: bool = False): + values = self.distribution.rvs(size=size, **self.kws) + return values / norm(values, 1) if normalize else values + + +class SourceMixture: + """Generates 'n' samples of length 'sources' given a scipy distribution and a set of design matrices. + + Samples are mixtures of sources whose quantities are expressed in arbitrary units. When a SourceMixture + object is called with an integer 'n', it generates n samples in accordance with the design matrices + given at initialization. The resulting matrix is meant to be multiplied by a transposed affinity matrix + to obtain sensor responses. + + Experimental designs are expressed in terms of a condition X source X concentration (loc) matrix, + a condition X source X spread (scale) matrix, and a scipy distribution common to all sources. + In scipy, 'loc' determines characteristics like mean, peak, or lower boundary, depending on the + specific distribution, while 'scale' determines standard deviation or range. + + Meant to be wrapped by utils.data.Synthetic to generate an SNNData set. + + """ + + def __init__(self, locations: Tensor, scales: Tensor, distribution: rv_continuous = expon, n_samples: int = 1): + # scipy distribution to use throughout. + self.distribution = distribution + + # experimental design matrices, condition X source. + self.locations = locations + self.scales = scales + + # easy reference to numeric descriptors. + self.n_conditions = locations.shape[0] + self.n_sources = locations.shape[1] + self.n_samples = n_samples + + if self.locations.shape != self.scales.shape: + raise ValueError("Inconsistent source mixture design matrices.") + + def __call__(self, n: int = 1): + """Generates 'n' samples per condition label present in the self.design dictionary.""" + samples = torch.zeros((self.n_conditions * self.n_samples, self.n_sources)) + labels = [] + + for c in range(self.n_conditions): + labels.extend([c + 1] * self.n_samples) + for s in range(self.n_sources): + rvs = RandomVariable(self.distribution, loc=self.locations[c, s], scale=self.scales[c, s]) + samples[c * self.n_samples : (c + 1) * self.n_samples, s] = torch.as_tensor( + rvs.generate(size=self.n_samples) + ) + + return samples, labels + + +class PhysicalSynthesizer(Synthetic): + def __init__( + self, + seed_vector: Tensor, + depth: int, + sparsity: float, + sensors: int, + signal: dict[str, Any], + contaminants: dict[str, Any], + noise: dict[str, Any], + sigmoid: dict[str, Any], + **kwargs, + ): + # affinity and concentration configuration. + self.seed_vector = torch.as_tensor(seed_vector) + self.depth = depth + self.sparsity = sparsity + self.sensors = sensors + + self.signal = signal if signal else {} + self.contaminants = contaminants if contaminants else {} + self.noise = noise if noise else {} + self.sigmoid = sigmoid if sigmoid else {} + + # store computed affinity matrix. + self.affinities = torch.zeros((self.sensors, len(self.seed_vector))) + + super().__init__(algorithm=self.make_labeled_samples) + + @staticmethod + def sharpen(array: Tensor, exp: float = 1, c: float = 0): + """Sharpens an N-dimensional array by exponentiation-normalization, with additive modulation.""" + z = np.clip(array + c, 0, None) + l1 = norm(z**exp, 1) + + return (z**exp) / (l1 if l1 != 0 else 1) + + @staticmethod + def hoyer_measure(vector: Tensor) -> float: + """Computes the Hoyer sparsity measure for the given vector or matrix, ranging between 0 and 1. + + Parameters + ---------- + vector: NDArray + Affinity tensor to compute the Hoyer sparsity for. + + Returns + ------- + HoyerSparsity: float + Hoyer measure of the tensor. + + """ + sqrt_n = np.sqrt(vector.shape[vector.dim() - 1]) + return (1 / (sqrt_n - 1)) * ( + sqrt_n - np.mean(norm(vector, 1, axis=vector.ndim - 1) / norm(vector, 2, axis=vector.dim() - 1)) + ) + + @staticmethod + def add_noise(matrix: Tensor, distribution: rv_continuous, proportion: float = 1, **kwargs): + """Adds noise to 'proportion' of the 'matrix' rows, drawn from scipy 'distribution' with **kwargs. + The noised indices are randomly selected. + + """ + n_replace = int(proportion * matrix.shape[matrix.dim() - 1]) + inds = np.random.choice(list(range(matrix.shape[matrix.dim() - 1])), n_replace, replace=False) + + for row in range(len(matrix)): + matrix[row, inds] += torch.as_tensor(distribution.rvs(size=n_replace, **kwargs)) + + return matrix + + def tune_contrast( + self, matrix: Tensor, target: float = 0.5, eps: float = 0.0001, c_delta: float = 0.00001 + ) -> Tensor: + """Sharpens a matrix row-wise to the desired sparsity. + + Parameters + ---------- + matrix: Tensor + The matrix to sharpen. + + target: float + Desired Hoyer sparsity level to arrive at, ranging between 0 and 1 (defaults to 0.5). + + eps: float + Error tolerance (defaults to .001). + + c_delta: float + Value by which to change the additive constant with each iteration. + + """ + target = np.clip(target, 0, 1) + temp = matrix + + def status(m: Tensor): + if self.hoyer_measure(m) < target - eps: + return -1 + elif self.hoyer_measure(m) > target + eps: + return 1 + else: + return 0 + + # initial exponent and constant to start the search from. + exp = 2 + const = 0 + + overshot = False + while exp > 1 and not overshot: + # record current status. + init_status = status(temp) + + # increment exponent if flat, decrement if sharp. + exp -= init_status * 0.1 + + # sharpen the matrix and check if gone past target sparsity in this iteration. + temp = self.sharpen(matrix, exp=exp) + overshot = init_status != status(temp) + matrix = temp + + # fine tune with additive component. + overshot = False + while not overshot and status(temp) != 0: + # record current status and increment additive component. + init_status = status(temp) + + # positive constants flatten, negatives sharpen. + const += init_status * c_delta + + # sharpen the matrix and check if gone past target sparsity in this iteration. + temp = self.sharpen(matrix, exp=1, c=const) + overshot = init_status != status(temp) + matrix = temp + + return matrix + + def contrast_plot(self, vector: Tensor, cmap: str = "coolwarm"): + """Visualize contrast curves for this synthetic data generation scenario.""" + vector /= norm(vector, 1) + h = self.hoyer_measure(vector) + + targets = [h + i for i in np.arange(-0.3, 0.3, 0.01)] + colors = [plt.get_cmap(cmap)(i) for i in np.linspace(0, 1, len(targets))] + + print(f"Initial Hoyer: {h}") + plt.plot(vector, "-o", color="black", linewidth=8) + + for k, t in enumerate(targets): + optimized = self.tune_contrast(vector, target=t) + plt.plot(optimized, "-o", color=colors[k], alpha=0.7) + + plt.show() + + def make_affinities(self, vector: Tensor, depth: int): + """Generates varied sensor affinities from a seed vector while retaining relative distances. + + Given a base vector and a depth parameter, recursively swap elements starting at the deepest level, + saving the intermediate results as the affinities of different potential sensors. + + The goal is to ensure that each source has one sensor that prefers it to every other source, while + respecting the original distances induced by the base vector. Meaning, if molecules A and B are very + similar to each other and different to C and D, sensors with high affinity for the former will have + low affinity for the latter. Within the group of AB sensors, some will prefer A and others B, preserving + the distance between them in the base vector. + + Note + ---- + This function generates N sensors for N sources, where N is len(base). + + """ + + def _swap(mat, pivot: int): + if pivot > 0: + mat[pivot:, :pivot], mat[pivot:, pivot:] = mat[pivot:, pivot:], mat[pivot:, :pivot].copy() + + # recursive calls for each quadrant. + r = np.vstack( + ( + np.hstack((_swap(mat[:pivot, :pivot], pivot // 2), _swap(mat[:pivot, pivot:], pivot // 2))), + np.hstack((_swap(mat[pivot:, :pivot], pivot // 2), _swap(mat[pivot:, pivot:], pivot // 2))), + ) + ) + return r + else: + return mat + + self.affinities = _swap(np.array([vector] * (2**depth)), pivot=len(vector) // 2) + + while self.affinities.shape[0] < self.sensors: + self.affinities = np.vstack((self.affinities, self.affinities)) + + @staticmethod + def sigmoid_sum(concentrations, affinities, translation, scale): + result = torch.zeros((concentrations.shape[0], affinities.shape[0])) + for i, x in enumerate(concentrations): + for j, sensor in enumerate(affinities): + result[i, j] = torch.sum((1 + torch.e ** (-scale * (x * affinities[j, :] - translation))) ** -1) + + return result + + def make_labeled_samples(self, n: int) -> (Tensor, Tensor): + # sharpen or flatten the seed affinity vector to match specified target. + tuned = self.tune_contrast(self.seed_vector, target=self.sparsity) + n_diag = len(self.seed_vector) + + # generate affinities. + self.make_affinities(tuned, self.depth) + + # if signal distribution parameters were specified as constants. + if all([not isinstance(x, list) for x in [self.signal["loc"], self.signal["scale"]]]): + # generate balanced analyte mixture samples (concentration on diagonal, 0 otherwise). + location_matrix = np.eye(n_diag) * self.signal["loc"] + (1 - np.eye(n_diag)) * self.contaminants["loc"] + + # identity matrix for pure samples; 1s for "noise" amounts centered at 0 for off-diagonal sources. + scale_matrix = np.eye(n_diag) * self.signal["scale"] + (1 - np.eye(n_diag)) * self.contaminants["scale"] + + # if signal distribution parameters were specified as lists. + else: + # NOTE for now, contaminant concentrations and variances are assumed constant across analytes. + location_matrix = np.eye(n_diag) + (1 - np.eye(n_diag)) * self.contaminants["loc"] + scale_matrix = np.eye(n_diag) + (1 - np.eye(n_diag)) * self.contaminants["scale"] + + np.fill_diagonal(location_matrix, self.signal["loc"]) + np.fill_diagonal(scale_matrix, self.signal["scale"]) + + # create samples and labels from the above source mixture parameters, then generate affinities. + signal_distribution = getattr(import_module("scipy.stats"), self.signal["distribution"]) + noise_distribution = getattr(import_module("scipy.stats"), self.noise["distribution"]) + + samples, labels = SourceMixture(location_matrix, scale_matrix, signal_distribution, n_samples=n)() + + # drop some sensors to replicate the weak secondary sources problem. + self.affinities = self.affinities[: self.sensors, :] + + # samples passed through sensors. + if not self.sigmoid or (self.sigmoid.get("loc") == 0 and self.sigmoid.get("scale") == 0): + samples = torch.matmul(samples[:], torch.as_tensor(self.affinities.transpose())) + else: + samples = self.sigmoid_sum( + samples[:], torch.as_tensor(self.affinities), self.sigmoid["loc"], self.sigmoid["scale"] + ) + + if self.noise.get("proportion", 0) > 0: + samples = self.add_noise( + matrix=samples, + distribution=noise_distribution, + proportion=self.noise["proportion"], + loc=self.noise["loc"], + scale=self.noise["scale"], + ) + + return samples, labels diff --git a/src/utils/math/__init__.py b/src/utils/math/__init__.py new file mode 100644 index 0000000..3e0d0fd --- /dev/null +++ b/src/utils/math/__init__.py @@ -0,0 +1,220 @@ +""" Mathematical and computational utility functions. """ +import itertools + +import numpy as np + +import torch +from torch import Tensor + +from jenkspy import jenks_breaks +from scipy.stats import gaussian_kde + +import matplotlib.pyplot as plt + + +def count_duplicates(tensor1, tensor2, tol=1e-5): + """ + Compute the percentage of duplicated 'rows' between two nD tensors. + A row is defined as a slice along the first dimension. + Handles NaN values and performs 'close' comparison for floating-point values. + + :param tensor1: First nD tensor. + :param tensor2: Second nD tensor. + :param tol: Tolerance for float comparisons. + :return: Percentage of slices in tensor1 that have duplicates in tensor2. + """ + # Ensure both tensors have the same shape except the first dimension + assert tensor1.shape[1:] == tensor2.shape[1:], "Tensors must have the same shape except for the first dimension." + + def rows_are_close(row1, row2, tol): + """Helper function to compare two rows, handling NaNs and float precision.""" + nan_mask1 = torch.isnan(row1) + nan_mask2 = torch.isnan(row2) + + # Check that NaN positions are the same in both rows + if not torch.equal(nan_mask1, nan_mask2): + return False + + # Check that non-NaN values are close within tolerance + return torch.allclose(row1[~nan_mask1], row2[~nan_mask2], atol=tol) + + # Count the number of duplicate rows + duplicated_count = 0 + for row1 in tensor1: + for row2 in tensor2: + if rows_are_close(row1, row2, tol): + duplicated_count += 1 + break # Move to the next row in tensor1 once a match is found + + # Calculate the percentage of duplicated rows in tensor1 + percentage = (duplicated_count / len(tensor1)) * 100 + return percentage + + +def map_words_to_integers(arr): + unique_words = np.unique(arr) # Get unique words + word_to_int = {word: idx + 1 for idx, word in enumerate(unique_words)} # Create mapping + mapped_array = np.vectorize(word_to_int.get)(arr) # Map words to integers + + return mapped_array + + +def density_from_breaks( + arr: Tensor, + factor: int, + smoothing: float = 0, + inf_replacement: float = 1e-3, + uniform: bool = True, + kde: bool = False, +) -> Tensor: + """Computes desired densities from observations, knowing the duplication factor. + + Smoothing should be used to flatten the prior, reflecting decreased confidence in the sample. + + Parameters + ---------- + arr: Tensor + Array of observations. + + factor: int + Duplication factor. + + smoothing: float + Smoothing factor between 0 and 1. Higher values flatten the prior and + reflect decreased confidence in the sample. + + inf_replacement: float + If 0s exist in observations, replace them with this value to prevent division by infinity. + Defaults to 1e+3, but may be set to False, None, or zero to use the maximal numeric value instead. + + uniform: bool + Whether interpolated weights should follow a 1/x distribution, corresponding to a uniform prior. + If set to False, weights are linearly spaced, corresponding to an exponential prior. + + kde: bool + Whether to use KDE method rather than Jenks natural breaks (latter is default). + + """ + if not uniform: + arr = 1.0 / arr + + # force not to cover the subthreshold value range [0, 1/limit] if some observations are 0. + arr[arr < inf_replacement] = inf_replacement + + # add a 1s observation to ensure that, across sensors, the value range covered would be locked to [1/limit, 1]. + # stabilizes utilization with automated parameter selection and makes comparisons easier. + arr = torch.cat((arr, torch.ones(1, device=arr.device))) + + # fix smoothing value if too small given duplication factor. + smoothing_corrected = int(max([smoothing * factor, 1])) + + # find out what the maximal value in the array is. + max_numeric = torch.max(arr[arr < torch.inf]) + + # take care of potential inf values, e.g., if input is 1/x and there were 0s in the original observation vector. + if inf_replacement: + arr[arr == torch.inf] = inf_replacement + else: + # if inf_replacement was set to False, None, or zero, use maximal numeric value. + if max_numeric: + arr[arr == torch.inf] = max_numeric + + # if we only have identical observations (which can happen with many 0s, or just a single observation). + if len(torch.unique(arr)) == 1: + # return a default linspace from that value to some cap. + return torch.linspace(arr[0], inf_replacement if inf_replacement else max_numeric, factor) + + sm = factor // smoothing_corrected + if sm == 1: + # the regularized case, completely smooth (linear coverage), but scaled to the observed range of each sensor. + n_classes = 2 + else: + n_classes = min(len(arr), factor) if (len(arr) < sm or sm < 1) else sm + n_classes = len(torch.unique(arr)) if len(torch.unique(arr)) < n_classes else n_classes + + breaks = torch.as_tensor(jenks_breaks(torch.unique(arr), n_classes), device=arr.device)[1:] + + # gradient method with KDE. + if kde: + kde = gaussian_kde(arr, bw_method=smoothing + (0.01 if smoothing == 0 else 0), weights=None) + interp_range = np.arange(inf_replacement, 1, (1 - inf_replacement) / (factor * 100)) + + # find inflection points. + grad = np.gradient(kde.evaluate(interp_range)) + breaks = np.hstack((inf_replacement, interp_range[np.where(np.diff(np.sign(grad)))[0]], 1)) + + # prune if smoothing factor yields excessive number of bisection points. + excess = len(breaks) - factor + if excess > 0: + segment_widths = torch.as_tensor( + [breaks[i + 1] - breaks[i] for i in range(len(breaks) - 1)], device=arr.device + ) + ranked_segments = torch.argsort(segment_widths, descending=False) + breaks = np.delete(breaks, ranked_segments[-excess:]) + + # number of classes now guaranteed to be < factor. + n_classes = len(breaks) + + # back to a tensor. + breaks = torch.as_tensor(breaks, device=arr.device) + + initial_allocation = factor // (n_classes - 1) + difference = factor - initial_allocation * (n_classes - 1) + + # allocate initial densities equally among variable-width segments. + densities = torch.zeros(n_classes - 1, device=arr.device) + initial_allocation + + # compute segment widths and rank them for intelligent (de)allocation of the difference from duplication factor. + segment_widths = torch.as_tensor([breaks[i + 1] - breaks[i] for i in range(n_classes - 1)], device=arr.device) + + sign = -1 if difference < 0 else 1 + ranked_segments = torch.argsort(segment_widths, descending=sign < 0) + + for i in range(int(difference)): + densities[ranked_segments[i % (n_classes - 1)]] += sign + + if sum(densities) != factor: + raise ValueError( + f"Densities ({densities}) didn't sum to duplication factor ({factor}). " f"Check implementation." + ) + + # linspace with the densities found. + weights = [] + for i in range(n_classes - 1): + segment = torch.linspace(breaks[i], breaks[i + 1], int(densities[i]) + 1) + if uniform: + # [:-1] or [1:] prevent double sampling at middle segment borders. + # [:-1] retains 1/limit and discards 1 (to reverse this, use [1:]). + segment = 1 / segment[1:] + weights.extend(segment) + + weights = torch.as_tensor(weights, device=arr.device) + + # plt.vlines(1 / weights, ymin=.999, ymax=1.001, color="crimson") + # plt.scatter(arr, [1] * len(arr), color="black", s=.5) + # plt.xlim((inf_replacement, 1)) + # plt.show() + + return weights + + +def tensor_linspace(start: Tensor, end: Tensor, steps: int, device: str = "cpu") -> Tensor: + """Pytorch's version of linspace does not support matrices.""" + result = torch.zeros((steps, start.shape[start.dim() - 1]), device=device) + for i in range(start.shape[start.dim() - 1]): + result[:, i] = torch.linspace(start[i], end[i], steps) + + return result + + +def k_bits(n, k): + """Generate all binary integers of length 'n' with exactly 'k' 1-bits.""" + result = [] + + for bits in itertools.combinations(range(n), k): + s = ["0"] * n + for bit in bits: + s[bit] = "1" + result.append("".join(s)) + + return result diff --git a/src/utils/math/distance.py b/src/utils/math/distance.py new file mode 100644 index 0000000..ccdb122 --- /dev/null +++ b/src/utils/math/distance.py @@ -0,0 +1,38 @@ +""" Distance utility functions. """ +import numpy as np +from scipy.stats import wasserstein_distance + + +def pairwise_wasserstein_distances(arr): + """ + Compute pairwise Wasserstein distances between histograms represented by rows in arr, which may be + of variable length. + + Parameters: + - arr: numpy array with dtype=object and shape (n_classes,). + + Returns: + - distance_matrix: numpy array of shape (n_classes, n_classes) + where distance_matrix[i, j] is the Wasserstein distance between histograms + of class i and class j. + """ + n_classes = len(arr) + + # initialize distance matrix. + distance_matrix = np.zeros((n_classes, n_classes)) + + # compute pairwise Wasserstein distances. + for i in range(n_classes): + for j in range(i + 1, n_classes): + # extract histograms for class i and class j. + hist_i = arr[i] + hist_j = arr[j] + + # compute Wasserstein distance between hist_i and hist_j. + distance = wasserstein_distance(hist_i.flatten(), hist_j.flatten()) + + # store the computed distance in both directions. + distance_matrix[i, j] = distance + distance_matrix[j, i] = distance + + return distance_matrix diff --git a/src/utils/math/filters.py b/src/utils/math/filters.py new file mode 100644 index 0000000..d86267c --- /dev/null +++ b/src/utils/math/filters.py @@ -0,0 +1,84 @@ +from numpy.typing import NDArray + +import numpy as np +from scipy import ndimage + + +def cluster_size_thresholding(array, min_size: int = None, percentile: float = None): + """ + Zero out islands in the array that are smaller than min_size or not in the top percentile_threshold. + + Parameters + ---------- + array: NDArray + 2D array containing data. + + min_size: int + Minimum size of islands to retain. + + percentile: float, optional + Percentile threshold to retain top islands by size. + + Returns + ------- + NDArray: 2D array with small islands zeroed out. + + """ + if min_size is None and percentile is None: + raise ValueError("Either min_size or percentile_threshold must be provided.") + + # Create a copy of the array to avoid modifying the original + array_copy = np.copy(array) + + # Define the structure for 8-connectivity + structure = np.array([[1, 1, 1], [1, 1, 1], [1, 1, 1]]) + + # Label connected components using 8-connectivity + labeled_array, num_features = ndimage.label(array_copy, structure=structure) + + # Calculate the size of each island + island_sizes = [] + for i in range(1, num_features + 1): + island_sizes.append((i, np.sum(labeled_array == i))) + + # Determine the size threshold if percentile_threshold is provided + if percentile is not None: + if not (0 < percentile <= 100): + raise ValueError("percentile_threshold must be between 0 and 100.") + + sizes = [size for _, size in island_sizes] + size_threshold = np.percentile(sizes, percentile) + else: + size_threshold = 0 # Default to zero if percentile_threshold is not provided + + # Iterate over the labeled components + for i, size in island_sizes: + # Zero out the component if it's smaller than the size threshold + if size < max(min_size or 0, size_threshold): + array_copy[labeled_array == i] = 0 + + return array_copy, size_threshold + + +def gaussian(arr: NDArray, sigma: float): + """Gaussian filtering, allowing intensity to leak into NaN regions. + + See Also + -------- + https://stackoverflow.com/a/36307291/7128154 + + """ + gauss = arr.copy() + gauss[np.isnan(gauss)] = 0 + gauss = ndimage.gaussian_filter(gauss, sigma=sigma, mode="constant", cval=0) + + norm = np.ones(shape=arr.shape) + norm[np.isnan(arr)] = 0 + norm = ndimage.gaussian_filter(norm, sigma=sigma, mode="constant", cval=0) + + # avoid RuntimeWarning: invalid value encountered in true_divide + norm = np.where(norm == 0, 1, norm) + gauss = gauss / norm + gauss[np.isnan(arr)] = np.nan + + return gauss diff --git a/src/utils/ml/__init__.py b/src/utils/ml/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/utils/ml/classification.py b/src/utils/ml/classification.py new file mode 100644 index 0000000..7cf9edb --- /dev/null +++ b/src/utils/ml/classification.py @@ -0,0 +1,255 @@ +""" Classification utilities. """ +import csv +import os +from copy import deepcopy + +import numpy as np +from numpy.typing import NDArray + +import torch + +from torch import Tensor +from torch.nn.functional import normalize + +from scipy import stats + +from sklearn.svm import SVC +from sklearn.metrics import confusion_matrix, matthews_corrcoef +from sklearn.model_selection import GridSearchCV, LeaveOneOut + +from sapicore.data.sampling import CV +from sapicore.utils.io import ensure_dir + +from src.utils.data import SNNData +from src.utils.ml.splitting import LimitedStratifiedKFold + + +class SVM: + def __init__(self, identifiers: dict, data: Tensor, labels: Tensor | NDArray, cv: CV = None): + self.identifiers = identifiers + + self.data = data + self.labels = labels + + self.cv = LeaveOneOut() if cv is None else cv + + # fold tracker. + self.fold = 0 + + def score_svm( + self, + kernel: str = "rbf", + c: float = 10**5, + gamma="scale", + seed=96, + ci_alpha=0.95, + flip=False, + scale=False, + norm=False, + file=None, + ): + """Shorthand for fitting, testing, and scoring with a single SVM. + + Returns mean accuracy across folds, a confidence interval, and a confusion matrix. + If `file` is not None, saves results to CSV. + + """ + svm = SVC(kernel=kernel, C=c, gamma=gamma, random_state=seed, probability=True, class_weight="balanced") + + scores = np.zeros(self.data.shape[0]) + mcc = np.zeros(self.data.shape[0]) + # extended = [] + + classes = len(np.unique(self.labels)) + confusion = np.zeros(shape=(classes, classes)) + + for i, (train, test) in enumerate(self.cv): + if flip: + train, test = test, train + + temp = deepcopy(self.data) + + # scale then normalize a copy of data based on training set for fairness w.r.t. sapinet. + if norm: + temp[:] = normalize(temp[:].cpu(), p=1) + + if scale: + # NOTE scaling factor(s) for the entire set are computed based on the training set. + max_values, _ = torch.max(torch.abs(temp[train]), dim=0) + temp[:] = torch.nan_to_num(temp[:] / max_values) + + # fit the model. + svm.fit(temp[train], self.labels[train]) + + # compute various scores (accuracy, MCC, F1, precision, recall). + scores[i] = 100.0 * svm.score(temp[test], self.labels[test]) + mcc[i] = matthews_corrcoef(y_pred=svm.predict(temp[test]), y_true=self.labels[test]) + # extended.append(precision_recall_fscore_support(y_pred=svm.predict(temp[test]), y_true=self.labels[test])) + + # compute confusion matrix and add to accumulated count across folds. + conf_mat = confusion_matrix( + y_true=self.labels[test], y_pred=svm.predict(temp[test]), labels=np.unique(self.labels) + ) + confusion += conf_mat + + # advance fold/iteration counts and append current fold data to file(s), if applicable. + self.fold += 1 + + if file: + ensure_dir(os.path.dirname(file)) + self.save( + file + "_detail", + results={"Accuracy": scores[i], "MCC": mcc[i]}, + confusion=None, + extended=None, + ) + + # scores was initialized to max possible length (LOO), but now we can trim to how many folds there were. + scores = scores[: self.fold] + mcc = mcc[: self.fold] + + acc_mean = np.mean(scores) + mcc_mean = np.mean(mcc) + + acc_ci = stats.t.interval(ci_alpha, len(scores) - 1, loc=np.mean(scores), scale=stats.sem(scores)) + mcc_ci = stats.t.interval(ci_alpha, len(mcc) - 1, loc=np.mean(mcc), scale=stats.sem(mcc)) + + self.fold = 0 + if file: + self.save( + file + "_group", + results={ + "Accuracy": acc_mean, + "Acc_CI0": acc_ci[0], + "Acc_CI1": acc_ci[1], + "MCC": mcc_mean, + "MCC_CI0": mcc_ci[0], + "MCC_CI1": mcc_ci[1], + }, + confusion=confusion, + ) + + return acc_mean, acc_ci, confusion + + def grid_svm(self, kernel: str = "rbf", seed: int = 96) -> (dict[str, float], float): + """Cross validated grid search optimization of C and gamma for a full dataset.""" + c_range = np.logspace(-7, 9, 17) + gamma_range = np.logspace(-8, 10, 19) + + param_grid = dict(gamma=gamma_range, C=c_range) + grid = GridSearchCV( + SVC(kernel=kernel, class_weight="balanced", random_state=seed), + param_grid=param_grid, + cv=self.cv.cross_validator, + n_jobs=-1, + verbose=0, + ) + + grid.fit(self.data, self.labels) + # print(f"Optimal: {grid.best_params_}") + + return grid.best_params_, grid.best_score_ + + def save(self, file, results, confusion=None, extended=None): + """Saves classification results and parameters to `file`. + + Confusion matrices and extended metrics (e.g., F1, precision, recall) are optional arguments. + + Note + ---- + Opens the file in append mode by default. Caller should ensure the correct file is added to. + + """ + with open(file + ".csv", "a") as f: + writer = csv.writer(f, delimiter=",") + + # header should include all identifiers deemed relevant by the caller. + # those may include experiment, session, fold, model ID. + if os.path.getsize(file + ".csv") == 0: + header = list(self.identifiers) + ["InternalFold"] + list(results) + writer.writerow(header) + + params = [str(p) for p in self.identifiers.values()] + [self.fold] + [str(p) for p in results.values()] + writer.writerow(params) + + if extended is not None: + extended = np.array(extended) + + with open(file + "_ext.csv", "a") as f: + writer = csv.writer(f, delimiter=",") + + if os.path.getsize(file + "_ext.csv") == 0: + header = list(self.identifiers) + [ + "InternalFold", + "Class", + "Precision", + "Recall", + "FScore", + "Support", + ] + writer.writerow(header) + + for cls in range(len(extended[0])): + params = [str(p) for p in self.identifiers.values()] + [self.fold, cls + 1] + params.extend(extended[:, cls]) + + writer.writerow(params) + + if confusion is not None: + np.savez(file + "_conf.npz", confusion) + + +def baseline_svm(self, data: SNNData, shots: int, key: str | list[str], file: str = None): + """Fit a baseline SVM to the original dataset, assumed normalized.""" + # identifiers for logging. + svm_id = { + "Session": self.session, + "Condition": self.condition, + "Variant": self.variant, + "Analysis": "SVM", + "Mode": self.mode, + "Layer": "RBF", + "Batches": self.configuration.get("sampling", {}).get("included_batches", -1), + "BatchesTr": self.configuration.get("sampling", {}).get("train_batches", -1), + "BatchesTe": self.configuration.get("sampling", {}).get("test_batches", -1), + "Shots": self.configuration.get("sampling", {}).get("shots", -1), + } + + optimizer = SVM( + identifiers=svm_id, + data=data[: data[:].shape[0] // 2].cpu(), + labels=data.metadata[key][: data[:].shape[0] // 2], + cv=CV( + data=data.trim(index=slice(0, data[:].shape[0] // 2)), + cross_validator=LimitedStratifiedKFold( + n_splits=self.configuration.get("sampling", {}).get("folds", 2) // 2, + ), + label_keys=key, + ), + ) + hyper, _ = optimizer.grid_svm(kernel="rbf", seed=self.seed) + + score, ci, confusion = SVM( + identifiers=svm_id, + data=data[data[:].shape[0] // 2 :].cpu(), + labels=data.metadata[key][data[:].shape[0] // 2 :], + cv=CV( + data=data.trim(index=slice(data[:].shape[0] // 2, data[:].shape[0])), + cross_validator=LimitedStratifiedKFold( + n_splits=self.configuration.get("sampling", {}).get("folds", 2) // 2 + ), + label_keys=key, + ), + ).score_svm( + kernel="rbf", + c=hyper["C"], + gamma=hyper["gamma"], + flip=True, + scale=True, + norm=True, + seed=self.seed, + file=file, + ) + + print(f"{shots}-shot SVM baseline: {score:.2f} % " f"(CI = {ci[0]:.2f} - {ci[1]:.2f})\n") + return score, ci, confusion diff --git a/src/utils/ml/similarity.py b/src/utils/ml/similarity.py new file mode 100644 index 0000000..7f0ed46 --- /dev/null +++ b/src/utils/ml/similarity.py @@ -0,0 +1,157 @@ +""" Representational similarity analysis utilities. """ +from numpy.typing import NDArray + +import os +import numpy as np + +import torch +from torch import Tensor + +from scipy.spatial.distance import cdist + +import matplotlib.pyplot as plt +import seaborn as sns + +from src.utils.math import map_words_to_integers +from src.utils.data import SNNData + + +class RSA: + def __init__( + self, + data: Tensor, + labels: Tensor | NDArray, + primary_cmap: str = "viridis", + secondary_cmap: str = "rocket", + class_cmap: str = "Spectral", + identifier: str = "", + ): + self.data = data + self.labels = labels + + self.primary_cmap = primary_cmap + self.secondary_cmap = secondary_cmap + self.class_cmap = class_cmap + + self.identifier = identifier + + def pairwise_matrix(self, render: bool = False, save_path: str = "", **kwargs) -> Tensor: + """Pairwise distances.""" + # sort rows by class if asked. + indices = np.argsort(self.labels) + + # compute pairwise distances. + matrix = cdist(self.data[indices], self.data[indices]) + + if save_path or render: + plt.clf() + + colormap = plt.get_cmap(self.class_cmap) + colors = [colormap(i) for i in np.linspace(0, 1, len(np.unique(self.labels)))] + + sns.clustermap( + matrix, + annot=False, + cmap=plt.get_cmap(self.secondary_cmap), + row_colors=[colors[int(i) - 1] for i in map_words_to_integers(self.labels[indices])], + xticklabels=False, + yticklabels=False, + rasterized=True, + figsize=(25, 25), + **kwargs, + ) + + plt.grid(False) + plt.title(f"{self.identifier} pairwise distances", loc="left") + + if save_path: + plt.savefig(save_path) + + if render: + plt.show() + + return torch.as_tensor(matrix) + + def distance_matrix(self, render: bool = False, save_path: str = "", **kwargs) -> (Tensor, float): + """RSA Euclidean distance matrix, rendering optional. Also returns variance within classes as % of total.""" + dim = len(np.unique(self.labels)) + matrix = torch.zeros((dim, dim), device="cpu") + + for i in range(dim): + for j in range(i, dim): + matrix[i, j] = np.mean( + cdist( + self.data[np.where(self.labels == i + 1)[0], :], self.data[np.where(self.labels == j + 1)[0], :] + ) + ) + + variance_within = torch.sum(matrix.diag()) / torch.sum(matrix) + + if save_path or render: + # mirror upper triangle to lower and plot resulting distance matrix. + matrix[np.tril_indices(dim, -1)] = matrix.T[np.tril_indices(dim, -1)] + + plt.clf() + sns.heatmap(matrix, cmap=self.secondary_cmap, rasterized=True, **kwargs) + + plt.title(f"{self.identifier} dissimilarity by class") + + if save_path: + plt.savefig(save_path) + + if render: + plt.show() + + return matrix, variance_within + + +def representational_analysis( + pipeline, layers: list[str], response_data: dict[str, SNNData], key: str = "", deformation: list = None +): + def pairwise(rsa_object, render=pipeline.render): + # depict the pairwise distances and their deltas as information flows from layer to layer. + return rsa_object.pairwise_matrix( + render=render, + save_path=os.path.join( + pipeline.out_dir, + "plots" + (os.sep + f"F{pipeline.fold}" if pipeline.fold else ""), + f"pairwise_{rsa_object.identifier}.{pipeline.image_format}", + ), + ) + + def deformation_map(m1, m2, title, file, render=pipeline.render): + plt.clf() + + plt.figure(figsize=pipeline.fig_size) + sns.heatmap(m2 - m1, cmap="coolwarm", center=0, rasterized=True) + + plt.title(title) + plt.savefig( + os.path.join(pipeline.out_dir, "plots" + (os.sep + f"F{pipeline.fold}" if pipeline.fold else ""), file) + ) + + if render: + plt.show() + + rsa_results = {} + pairwise_results = {} + + for layer in layers: + rsa_results[layer] = RSA( + response_data[layer][:].cpu(), + response_data[layer].metadata[key][:], + primary_cmap=pipeline.primary_cmap, + secondary_cmap=pipeline.secondary_cmap, + class_cmap=pipeline.class_cmap, + identifier=layer, + ) + pairwise_results[layer] = pairwise(rsa_results[layer]) + + if deformation: + for pair in deformation: + deformation_map( + pairwise_results[pair[0]], + pairwise_results[pair[1]], + title=f"{pair[0]} to {pair[1]} deformation map, sorted by class", + file=f"deformation_{pair[0]}_{pair[1]}.svg", + ) diff --git a/src/utils/ml/splitting.py b/src/utils/ml/splitting.py new file mode 100644 index 0000000..a3ea07a --- /dev/null +++ b/src/utils/ml/splitting.py @@ -0,0 +1,127 @@ +""" Custom cross validation splitters. """ +import warnings +import numpy as np + +from sklearn.model_selection import StratifiedKFold +from sklearn.utils.multiclass import type_of_target +from sklearn.utils.validation import check_random_state, column_or_1d, indexable + +__all__ = ("LimitedStratifiedKFold",) + + +class LimitedStratifiedKFold(StratifiedKFold): + """A StratifiedKFold variant that allows for limiting train/test set selection to a subset of the indices + (e.g., based on group identity) while retaining full flexibility w.r.t. sampling and number of folds. + + The `tr_mask` parameter is for the segment intended to be smaller (in few-shot scenarios, that would be the + training set, hence the naming convention). The `te_mask` is for ensuring only specific indices will be tested, + as opposed to everything that isn't in the training fold. + + If neither mask is supplied, behaves like StratifiedKFold. + + Example + ------- + >>> data = np.array([0.1, 0.2, 2.2, 2.4, 2.3, 4.55, 5.8, 8.8, 9, 10, 11, 12, 13, 14]) + >>> labels = np.array(["a", "b", "b", "b", "a", "a", "a", "a", "b", "b", "b", "a", "b", "a"]) + >>> batches = np.array([1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3]) + + >>> cv_obj = LimitedStratifiedKFold(n_splits=3, tr_mask=(groups == 1), te_mask=(groups != 3), shuffle=True) + + >>> for te, tra in cv_obj.split(X, y, groups=groups): + >>> print(f"Train: {tra}, Test: {te}") + + """ + + def __init__(self, n_splits=5, *, tr_mask=None, te_mask=None, shuffle=False, random_state=None): + super().__init__(n_splits=n_splits, shuffle=shuffle, random_state=random_state) + + self.tr_mask = tr_mask + self.te_mask = te_mask + + def split(self, X, y=None, groups=None): + X, y, groups = indexable(X, y, groups) + indices = np.arange(len(X)) + + for test_index in self._iter_test_masks(X, y, groups): + if self.te_mask is None: + retain = np.logical_not(test_index) + else: + retain = np.logical_not(test_index) * np.array(self.te_mask) + + train_index = indices[retain] + test_index = indices[test_index] + yield train_index, test_index + + def _make_test_folds(self, X, y=None): + """Overrides original implementation.""" + rng = check_random_state(self.random_state) + + y = np.asarray(y) + if self.tr_mask is not None: + y = y[self.tr_mask] + + type_of_target_y = type_of_target(y) + allowed_target_types = ("binary", "multiclass") + if type_of_target_y not in allowed_target_types: + raise ValueError( + "Supported target types are: {}. Got {!r} instead.".format(allowed_target_types, type_of_target_y) + ) + + y = column_or_1d(y) + + _, y_idx, y_inv = np.unique(y, return_index=True, return_inverse=True) + _, class_perm = np.unique(y_idx, return_inverse=True) + y_encoded = class_perm[y_inv] + + n_classes = len(y_idx) + y_counts = np.bincount(y_encoded) + min_groups = np.min(y_counts) + if np.all(self.n_splits > y_counts): + raise ValueError( + "n_splits=%d cannot be greater than the" " number of members in each class." % self.n_splits + ) + if self.n_splits > min_groups: + warnings.warn( + "The least populated class in y has only %d" + " members, which is less than n_splits=%d." % (min_groups, self.n_splits), + UserWarning, + ) + + y_order = np.sort(y_encoded) + allocation = np.asarray( + [ + # get every n_splits-th element starting at i. + np.bincount(y_order[i :: self.n_splits], minlength=n_classes) + for i in range(self.n_splits) + ] + ) + + test_folds = np.empty(len(y), dtype="i") + for k in range(n_classes): + folds_for_class = np.arange(self.n_splits).repeat(allocation[:, k]) + if self.shuffle: + rng.shuffle(folds_for_class) + test_folds[y_encoded == k] = folds_for_class + + if self.tr_mask is None: + return test_folds + + corrected = np.zeros_like(self.tr_mask) - 1 + j = 0 + for i, index in enumerate(self.tr_mask): + if self.tr_mask[i]: + corrected[i] = test_folds[j] + j += 1 + + return corrected.reshape(corrected.shape[0]) + + +if __name__ == "__main__": + X = np.array([0.1, 0.2, 2.2, 2.4, 2.3, 4.55, 5.8, 8.8, 9, 10, 11, 12, 13, 14]) + y = np.array(["a", "b", "b", "b", "a", "a", "a", "a", "b", "b", "b", "a", "b", "a"]) + groups = np.array([1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3]) + + cv = LimitedStratifiedKFold(n_splits=3, tr_mask=(groups == 1), te_mask=(groups != 3), shuffle=True) + + for test, train in cv.split(X, y, groups=groups): + print(f"Train: {train}, Test: {test}") diff --git a/src/utils/orchestration/__init__.py b/src/utils/orchestration/__init__.py new file mode 100644 index 0000000..db321ed --- /dev/null +++ b/src/utils/orchestration/__init__.py @@ -0,0 +1,195 @@ +""" Multirun orchestration utility classes. """ +import importlib +import os +from argparse import ArgumentParser +from pathlib import Path +from typing import Any + +from ruamel.yaml import YAML +from sapicore.pipeline import Pipeline +from sapicore.utils.io import ensure_dir + +__all__ = ("ConfigEditor", "MultiRun", "parse_arguments") + + +class ConfigEditor: + """Load or edit a specific nested value in a YAML file or a dictionary.""" + + def __init__(self, path: str = None, content: dict = None): + self.path = path + self.content = self.load() if (path and content is None) else content + + def load(self): + """Returns content of YAML at `path`.""" + return YAML().load(Path(self.path)) + + def edit(self, destination: list, entry: Any): + """Edits YAML in `path`, replacing the nested value in `destination` with `entry`.""" + self._touch(self.content, destination, entry) + if self.path is not None: + YAML().dump(self.content, Path(self.path)) + + @staticmethod + def _touch(d: dict, lst: list, entry: Any = None) -> dict: + """Returns the dictionary `d` after replacing the value at `lst` with `entry`.""" + if len(lst) == 1: + if entry is not None: + d[lst[0]] = entry + return d + return ConfigEditor._touch(d[lst[0]], lst[1:], entry) + + +class MultiRun(Pipeline): + """Chains parameterized runs of the same simulation pipeline. + + The configuration dictionary specifies which YAMLs to touch, what parameters to change, and in what order. + + When a key is identified as a path to a YAML file, the value is treated as a set of editing instructions; + otherwise, it is treated as the name of an organization level containing such instructions. + + Thus, the format supports nested experimental designs, where additional levels of nesting can be + encountered after a YAML has been touched. + + Warning + ------- + This version modifies YAML files on disk as necessary. For replication purposes, all configuration YAMLs + in the project directory tree are zipped and saved alongside the results of each branch. + + """ + + def __init__(self, config: dict | str = None, out_dir: str = None, child_kwargs: dict = None): + # base pipeline constructor. + super().__init__(config_or_path=config) + + # pipeline class to chain multiple instances of. + self.pipeline = self._pipeline_import() + self.arguments = child_kwargs if child_kwargs is not None else {} + + self.run_dir = out_dir + + # where we currently are in the traversal. + self.pointer = self.configuration.get("grid") + + # for tracking concurrent processes. + self.futures = [] + + def _pipeline_import(self) -> [Pipeline]: + """Imports the pipeline object to be run with each parameter configuration.""" + module = importlib.import_module(self.configuration.get("package")) + return getattr(module, self.configuration.get("pipeline")) + + @staticmethod + def _scan_levels(instructions: dict) -> int: + """Scans a dictionary for its number of levels, validating its format along the way. + + Note + ---- + List entries must have the same number of values ("zipped" traversal). + + """ + levels = 1 + for yaml_file, order in instructions.items(): + for destination, value in order.items(): + if isinstance(value, list): + if len(value) != levels and levels > 1: + raise ValueError(f"Misconfigured experimental design at {destination}: {value} != {levels}") + levels = len(value) + return levels + + @staticmethod + def _parse_level(level: dict) -> (dict, dict, list[str]): + """Splits a given dictionary into its instructions component and inner conditions component.""" + instructions = {k: v for k, v in level.items() if os.path.exists(k)} + inner_conditions = {k: v for k, v in level.items() if k != "names" and not os.path.exists(k)} + names = level.get("names", "") + + return instructions, inner_conditions, names + + def _process_entries(self, nested_dict: dict, path: str): + """Recursively processes nested dictionary entries describing tasks contingent on configuration editing jobs. + + Each level of the dictionary contains (1) path keys, (2) condition keys. Path keys specify YAML files, + and their values are a set of editing instructions. Editing instructions may map multiple values to the + same YAML field, meaning they should be looped over (the number of values should match across entries, + as in "zipped" traversal). + + If the dictionary contains condition keys, a recursive call to this method is made with a + sub-dictionary containing them and an updated output directory. If it only contains path keys, + the child pipeline's run() method is invoked. + + Thus, the leaves of this nested structure are executed with every requested parameter combination above it, + reflecting the nested experimental design specified by the multirun YAML. + + """ + instructions, inner_dict, level_names = self._parse_level(nested_dict) + + for k in range(self._scan_levels(instructions)): + if not instructions: + # this is a container condition, with no jobs to perform. + if inner_dict: + for key in inner_dict.keys(): + # in this case, subdirectory names are the sub-condition (key) names. + self._process_entries(inner_dict[key], ensure_dir(os.path.join(path, key))) + else: + for yaml_path, order in instructions.items(): + if os.path.exists(yaml_path): + editor = ConfigEditor(path=yaml_path) + + # edit YAML values as necessary. + for destination, value in order.items(): + if not isinstance(value, list): + editor.edit(destination, value) + else: + # if multiple entries are list-formatted, parameters are zipped. + editor.edit(destination, value[k]) + + # having finished editing the parent branch's parameters, call on child branches. + if inner_dict: + for key in inner_dict.keys(): + self._process_entries(inner_dict[key], ensure_dir(os.path.join(path, key))) + else: + # execute the pipeline with the current parameterization. + task = self.pipeline(**self.arguments) + task.out_dir = ensure_dir(os.path.join(path, level_names[k])) + + task.run() + + def run(self): + self._process_entries(self.configuration.get("grid"), self.run_dir) + + +def parse_arguments(): + parser = ArgumentParser() + parser.add_argument( + "-experiment", + action="store", + dest="experiment", + required=False, + default="yaml/synthetic.yaml", + help="Path to an experiment configuration YAML.", + ) + parser.add_argument( + "-multirun", + action="store", + dest="multirun", + required=False, + default=False, + help="Path to a multirun configuration YAML.", + ) + parser.add_argument( + "-data", + action="store", + dest="data", + required=False, + default="../../datasets", + help="Destination path for fetched or generated datasets.", + ) + parser.add_argument( + "-output", + action="store", + dest="output", + required=False, + default="../../results", + help="Destination path for trained model files (PT) and intermediate simulation output.", + ) + return parser.parse_args() diff --git a/src/utils/visual/__init__.py b/src/utils/visual/__init__.py new file mode 100644 index 0000000..359bd1e --- /dev/null +++ b/src/utils/visual/__init__.py @@ -0,0 +1,156 @@ +import os +import re +import psutil +import warnings + +import numpy as np +from scipy.signal import savgol_filter + +from matplotlib import pyplot as plt +from matplotlib.colors import Colormap + +import pyvista as pv + +from snn.utils.data import SNNData +from snn.utils.visual.embedding import Embedder, SklearnEmbedder + +ALPHA = 0.5 + + +class Explorer: + def __init__( + self, data: SNNData, embedder: Embedder | SklearnEmbedder = None, fit: bool = False, cmap: str | Colormap = None + ): + self.data = data + + # transfer to CPU if necessary. + self.data.buffer = self.data.buffer.to("cpu") + + # an embedding method is completely optional. + if embedder is not None: + self.embedder = embedder + self.projection = self.fit() if fit else None + + else: + # data will not be projected to a lower dimension, and calling fit() will do nothing. + self.embedder = None + self.projection = self.data + + self.cmap = plt.get_cmap("viridis") if cmap is None else cmap + + # check for jupyter context. + self.is_notebook = self.is_notebook() + + def fit(self, *args, **kwargs) -> SNNData: + """Project data to lower dimension using the given embedder.""" + if self.embedder is not None: + self.projection = self.embedder(*args, **kwargs) + + return self.projection + + def scatter2d(self, key: str): + fig = plt.figure() + fig.add_subplot() + + color = self.projection.metadata[key][:] if key else None + plt.scatter(self.projection[:, 0], self.projection[:, 1], c=color) + + def scatter3d(self, key: str): + fig = plt.figure() + ax = fig.add_subplot(projection="3d") + + color = self.projection.metadata[key][:] if key else None + ax.scatter(self.projection[:, 0], self.projection[:, 1], self.projection[:, 2], c=color) + + def spline3d(self, key: str): + fig = plt.figure() + ax = fig.add_subplot(projection="3d") + + color = self.projection.metadata[key][:] if key else None + for cls in set(self.projection.metadata[key]): + d = self.projection.trim(self.projection.select(conditions=[f"{key}=={cls}"])) + + x_fine = savgol_filter(d[:, 0], 3, 2, mode="wrap") + y_fine = savgol_filter(d[:, 1], 3, 2, mode="wrap") + z_fine = savgol_filter(d[:, 2], 3, 2, mode="wrap") + + ax.plot(x_fine, y_fine, z_fine, alpha=ALPHA) + + ax.scatter(self.projection[:, 0], self.projection[:, 1], self.projection[:, 2], c=color) + + def surf3d(self, key: str): + fig = plt.figure() + ax = fig.add_subplot(projection="3d") + + for cls in set(self.projection.metadata[key]): + d = self.projection.trim(self.projection.select(conditions=[f"{key}=={cls}"])) + ax.plot_trisurf(d[:, 0], d[:, 1], d[:, 2], alpha=ALPHA, antialiased=True) + + plt.show() + + @staticmethod + def is_notebook(): + return any(re.search("jupyter", x) for x in psutil.Process().parent().cmdline()) + + def vista3d(self, key: str, projection: str = None, interactive: bool = True, path: str = "", **kwargs): + # suppress warnings. + warnings.filterwarnings("ignore", category=UserWarning) + + # create a connected point cloud representation by computing class-specific surfaces. + wrap = "'" if isinstance(self.projection.metadata[key][0], str) else "" + surfaces = [self.projection.select(f"{key}=={wrap}{i}{wrap}") for i in set(self.projection.metadata[key][:])] + vtk_format = [] + + title = kwargs.pop("title", "") + + for surf_indices in surfaces: + # FIX np.ndarray has no index_select in projection.trim when axis != None. + vtk_format.append(len(self.projection.trim(surf_indices)[:])) + vtk_format.extend(surf_indices) + + cloud = pv.PolyData(np.array(self.projection[:]), vtk_format) + + # add labels and colors. + cloud[key] = self.projection.metadata[key][:] + _, colors = np.unique(self.projection.metadata[key][:], return_inverse=True) + + if projection == "sphere": + surf = pv.Sphere(radius=200, theta_resolution=400, phi_resolution=400) + surf = surf.interpolate(cloud, radius=1, strategy="closest_point", pass_point_data=True) + elif projection == "torus": + surf = pv.ParametricTorus(200, 20) + surf = surf.interpolate(cloud, radius=1, strategy="closest_point", pass_point_data=True) + elif projection == "diamond": + surf = cloud.delaunay_3d() + surf.interpolate(cloud, radius=1, strategy="closest_point", pass_point_data=True) + else: + surf = cloud + + # plot class surfaces. + plotter = pv.Plotter(notebook=self.is_notebook, off_screen=not interactive, window_size=(1920, 1080)) + plotter.add_mesh( + cloud, + scalars=colors, + cmap=self.cmap, + scalar_bar_args={"title": key, "n_labels": len(set(cloud[key])), "fmt": "%.1f"}, + **kwargs, + ) + + plotter.add_title(title) + plotter.add_mesh(surf, cmap=self.cmap, opacity=0.1) + plotter.background_color = "white" + + image = plotter.show(jupyter_backend="ipygany", interactive=interactive, return_img=True) + + if not self.is_notebook: + if not interactive: + # show a static plot; redundant for notebooks. + plt.imshow(image) + + plt.axis("off") + plt.title(title) + + plt.show() + + if path and not interactive: + plt.imsave(path, image) diff --git a/src/utils/visual/embedding.py b/src/utils/visual/embedding.py new file mode 100644 index 0000000..75cb4f8 --- /dev/null +++ b/src/utils/visual/embedding.py @@ -0,0 +1,33 @@ +""" Plot and explore structural properties of your data. """ +from typing import Any + +from sklearn.base import BaseEstimator +from snn.utils.data import SNNData + +__all__ = ("Embedder", "SklearnEmbedder") + + +class Embedder: + """Abstract interface for dimensionality reduction operations.""" + + def __init__(self, data: SNNData, algorithm: Any, **kwargs): + self.data = data + self.algorithm = algorithm + + for key, value in kwargs.items(): + setattr(self, key, value) + + def __call__(self, *args, **kwargs): + return self.transform(*args, **kwargs) + + def transform(self, *args, **kwargs) -> SNNData: + """Transform the data from original space to `dimension` by applying `algorithm`.""" + raise NotImplementedError + + +class SklearnEmbedder(Embedder): + def __init__(self, data: SNNData, embedder: BaseEstimator): + super().__init__(data=data, algorithm=embedder) + + def transform(self) -> SNNData: + return SNNData(buffer=self.algorithm.fit_transform(self.data[:].cpu()), metadata=self.data.metadata) diff --git a/src/utils/visual/plotting.py b/src/utils/visual/plotting.py new file mode 100644 index 0000000..7d52fdd --- /dev/null +++ b/src/utils/visual/plotting.py @@ -0,0 +1,410 @@ +""" Plotting utility functions. """ +import os +import numpy as np + +import torch +from torch import Tensor + +import matplotlib +import seaborn as sns + +import matplotlib.pyplot as plt +from matplotlib.colors import LogNorm + +from sapicore.utils.constants import SYNAPSE_SPLITTERS + +from src.utils.data import SNNData +from src.utils.math import map_words_to_integers + + +def attribute_plots( + pipeline, model, attributes: dict[str, str | list[str]], active_only: bool = True, plot_type: str = "hist", **kwargs +): + """Plots select model component attributes. + + Parameters + ---------- + pipeline: Simulation + Configured simulation pipeline instance. + + model: + Accepts all Sapicore Model derivatives. + + attributes: dict + Dictionary specifying which attributes should be plotted for each named component (key). + Formatted, e.g., {"MC->GC": ["weight"], "GC->MC": ["blocking_duration"]}. + + active_only: bool + When true, if component is a synapse, only includes elements marked active in the `connections` matrix. + + plot_type: str + Only "hist" and "heatmap" currently accepted. + + Note + ---- + This method and others like it will eventually be relegated to utils.visual. + + """ + for comp, attributes in attributes.items(): + if not isinstance(attributes, list): + # cast if user supplied a single attribute not wrapped in a list. + attributes = [attributes] + + for attr in attributes: + # retrieve current attribute from component. + elements = getattr(model.network[comp], attr) + + # if component is a synapse and asked to filter inactive connections. + if any([s in comp for s in SYNAPSE_SPLITTERS]) and active_only: + elements = elements[torch.where(model.network[comp].connections == 1)] + + match plot_type: + case "hist": + df = elements.flatten().cpu() + plt.figure(figsize=pipeline.fig_size) + sns.histplot(data=df, rasterized=True) + + case "heatmap": + d = elements if not active_only else elements.reshape(1, elements.shape[0]) + d = d[np.argsort(kwargs["labels"])] if "labels" in kwargs else d + + plt.figure(figsize=pipeline.fig_size) + sns.heatmap( + d.cpu(), + cmap=pipeline.primary_cmap, + rasterized=True, + norm=LogNorm() if kwargs.get("log_norm") else None, + ) + plt.grid(False) + + case _: + raise RuntimeError(f"Unknown plot type requested ({plot_type}).") + + plt.title(f"{comp} {attr.replace('_', ' ')}") + plt.savefig( + os.path.join( + pipeline.out_dir, + "plots", + f"F{pipeline.fold}" if pipeline.fold else "group", + f"{attr}_{comp}_{plot_type}.{pipeline.image_format}", + ) + ) + + if pipeline.render: + plt.show() + + plt.clf() + plt.close("all") + + +def basic_plots(pipeline, model): + # weight and blocking duration histograms. + attribute_plots( + pipeline, + model, + attributes={"MC->GC": "weights", "GC->MC": "blocking_durations"}, + active_only=True, + plot_type="hist", + ) + # weight heatmaps. + attribute_plots( + pipeline, + model, + attributes={"ET->MC": "weights", "MC->GC": "weights", "GC->MC": "blocking_durations"}, + active_only=False, + plot_type="heatmap", + log_norm=False, + ) + + # GC order plot. + order_plots(pipeline, synapse=model.network["MC->GC"], threshold=25) + + +def line_plots(pipeline, responses, labels, sort=True, log=False): + """Class-colored sorted line plots depicting population responses as accumulated.""" + sorted_responses = np.flip(np.sort(responses[:].cpu()), axis=1) if sort else np.array(responses[:].cpu()) + + if not pipeline.render: + matplotlib.use("Agg") + + fig, ax = plt.subplots(figsize=pipeline.fig_size) + ax.plot(sorted_responses.transpose(), "-", label=labels, alpha=1.0, linewidth=1.0) + + if log: + ax.set_yscale("log") + + colormap = plt.get_cmap(pipeline.class_cmap) + colors = [colormap(i) for i in np.linspace(0, 1, len(np.unique(labels)))] + + for y, z in enumerate(ax.lines): + z.set_color(colors[map_words_to_integers(labels)[y] - 1]) + + plt.title(f"Sorted {responses.identifier}") + plt.savefig( + os.path.join( + pipeline.out_dir, + "plots" + (os.sep + f"F{pipeline.fold}" if pipeline.fold else ""), + f"sorted_line_{responses.identifier}{'_log' if log else ''}.{pipeline.image_format}", + ) + ) + + if pipeline.render: + plt.show() + + plt.clf() + plt.close("all") + + +def cell_recruitment( + pipeline, + key: str, + responses: SNNData, + plot_utilization: bool = True, + plot_distribution: bool = True, + phase_mode: bool = True, + duplication_factor: int = None, + resolution_threshold: float = None, + smoothing_factor: float = None, + shots: int = -1, + dump_to_file: bool = True, +): + """Computes proportion of readout layer cells (whose responses were passed to this method) + that have fired and the distribution of their responses. + + Optionally: + Renders/saves histograms, colored by class and stacked. + Dumps relevant utilization data to CSVs, for further processing. + + Note + ---- + This manner of plotting emphasizes variability, however small. + + To better communicate utilization% uniformity (where it exists), use: + plt.bar(height=torch.count_nonzero(responses[sorted_indices], dim=1), x=np.arange(responses[:].shape[0])) + + """ + sorted_indices = np.argsort(responses.metadata[key][:]) + labels = responses.metadata[key][sorted_indices] + + colors = [plt.get_cmap(pipeline.class_cmap)(i) for i in np.linspace(0, 1, len(np.unique(labels)))] + df = (torch.count_nonzero(responses[sorted_indices], dim=1) / responses[:].shape[1] * 100.0).reshape( + responses[:].shape[0], 1 + ) + + if plot_utilization: + # process and save utilization data for later R analysis. + if dump_to_file: + pipeline.dump_utilization( + df.cpu(), + duplication_factor, + labels, + resolution_threshold, + responses, + shots, + smoothing_factor, + ) + + plt.figure(figsize=pipeline.fig_size) + plot_histogram( + pipeline, + "Cell Recruitment (%)", + "recruitment", + responses.identifier, + colors, + df.cpu(), + labels, + bins=50, + range=(0, 100), + ) + if pipeline.render: + plt.show() + plt.clf() + + if plot_distribution: + plt.figure(figsize=pipeline.fig_size) + plot_histogram( + pipeline, + "Distribution", + "dist", + responses.identifier, + colors, + responses[sorted_indices].cpu(), + labels, + bins=50, + range=(0, 50) if phase_mode else None, + ) + if pipeline.render: + plt.show() + plt.clf() + + plt.close("all") + + +def training_plots( + pipeline, + evolution: Tensor, + labels: list, + x_range: list, + nonzero: bool = True, + title: str = "", + file_name: str = "", +): + """Helper function for training diagnostics.""" + num_samples = len(labels) + num_classes = len(np.unique(labels)) + spc = num_samples // num_classes + word_ids = map_words_to_integers(labels) + + colormap = plt.get_cmap(pipeline.primary_cmap) + colors = [colormap(i) for i in np.linspace(0, 1, num_classes)] + + # create shot X analyte panel. + fig = plt.figure(figsize=pipeline.fig_size, dpi=600) + fig.suptitle(title) + + panels = fig.subfigures(nrows=spc, ncols=1) + if spc == 1: + panels = [panels] + + for row, sub in enumerate(panels): + axes = sub.subplots(nrows=1, ncols=num_classes) + for col, ax in enumerate(axes): + sample = evolution[row * num_classes + col] + analyte = word_ids[row * num_classes + col] + rng = x_range[1] - x_range[0] + + ax.set_title(f"Sample {(row * num_classes + col) + 1}\nAnalyte {analyte}") + ax.hist( + sample[sample != 0].cpu() if nonzero else sample.flatten().cpu(), + color=colors[analyte - 1], + bins=50 if rng >= 50 else rng, + range=(x_range[0], x_range[1]), + density=True, + ) + ax.set_ylim(0, 1) + + if file_name: + plt.savefig( + os.path.join( + pipeline.out_dir, + "plots" + (os.sep + f"F{pipeline.fold}" if pipeline.fold else ""), + f"{file_name}.{pipeline.image_format}", + ) + ) + + if pipeline.render: + plt.show() + plt.clf() + plt.close("all") + + +def plot_histogram( + pipeline, title: str, prefix: str, identifier: str, colors: list, df: Tensor, labels: Tensor | list, **kwargs +): + # n_classes = len(np.unique(labels)) + # if df.shape[1] == 1: + # df = df.reshape(df.shape[0]//n_classes, n_classes) + + plt.clf() + plt.hist( + df.T.cpu(), + histtype="bar", + color=[colors[int(i) - 1] for i in map_words_to_integers(labels)], + stacked=True, + fill=True, + rasterized=True, + **kwargs, + ) + plt.title(f"{identifier} {title}") + plt.savefig( + os.path.join( + pipeline.out_dir, + "plots" + (os.sep + f"F{pipeline.fold}" if pipeline.fold else ""), + f"{prefix}_{identifier}.{pipeline.image_format}", + ) + ) + plt.close("all") + + +def cell_responses(pipeline, key: str, responses: SNNData, binary: bool = False): + """Plots raw cell responses as a heatmap.""" + sorted_indices = np.argsort(responses.metadata[key][:]) + + if binary: + df = responses[sorted_indices].bool().int().cpu() + else: + df = responses[sorted_indices].cpu() + + plt.clf() + plt.figure(figsize=pipeline.fig_size) + sns.heatmap(df, cmap="rocket", rasterized=True) + plt.title(f"{responses.identifier}, sorted by class") + plt.savefig( + os.path.join( + pipeline.out_dir, + "plots" + (os.sep + f"F{pipeline.fold}" if pipeline.fold else ""), + f"responses_{responses.identifier}.{pipeline.image_format}", + ) + ) + + if pipeline.render: + plt.show() + plt.close("all") + + +def order_plots(pipeline, synapse, threshold: float = 0.0): + """Compute and plot postsynaptic cell orders, defined by number of incoming weights > `threshold` + (between 0 and 1). The threshold is expressed as a percentile. + + """ + plt.clf() + plt.figure(figsize=pipeline.fig_size) + + orders = torch.count_nonzero(synapse.weights * (synapse.weights > threshold), dim=1) + sns.histplot(orders.cpu(), bins=len(torch.unique(orders)), rasterized=True) + + plt.title(f"Cell orders (number of differentiated {synapse.identifier} weights > {threshold})") + plt.savefig( + os.path.join( + pipeline.out_dir, + "plots", + f"F{pipeline.fold}", + f"weights_th_{threshold}_{synapse.identifier}.{pipeline.image_format}", + ) + ) + + if pipeline.render: + plt.show() + plt.close("all") + + +def set_plot_theme(width: float = 12, height: float = 6, font: float = 15): + """Universal plot theme reproducing Moyal et al. (2025).""" + + plt.rcParams["figure.figsize"] = (width, height) + plt.rcParams["font.size"] = font + + # plt.rcParams['font.family'] = 'T' + plt.rcParams["axes.labelsize"] = plt.rcParams["font.size"] + plt.rcParams["axes.titlesize"] = 1.5 * plt.rcParams["font.size"] + plt.rcParams["legend.fontsize"] = plt.rcParams["font.size"] + plt.rcParams["xtick.labelsize"] = plt.rcParams["font.size"] + plt.rcParams["ytick.labelsize"] = plt.rcParams["font.size"] + + plt.rcParams["savefig.dpi"] = 300 + plt.rcParams["xtick.major.size"] = 3 + plt.rcParams["xtick.minor.size"] = 3 + plt.rcParams["xtick.major.width"] = 1 + plt.rcParams["xtick.minor.width"] = 1 + plt.rcParams["ytick.major.size"] = 3 + plt.rcParams["ytick.minor.size"] = 3 + plt.rcParams["ytick.major.width"] = 1 + plt.rcParams["ytick.minor.width"] = 1 + plt.rcParams["legend.frameon"] = False + plt.rcParams["legend.loc"] = "center left" + plt.rcParams["axes.linewidth"] = 3.0 + + plt.gca().spines["right"].set_color("none") + plt.gca().spines["top"].set_color("none") + plt.gca().xaxis.set_ticks_position("bottom") + plt.gca().yaxis.set_ticks_position("left")