Team Ai
Datasetpublic

OpenScientificCodeRegistry/Database

Open Scientific Code Registry (OSCR): the authors' scripts The code published by the authors of open-access neuroscience papers, as found and verified by Open Scientific Code Registry (OSCR). Each file is here exactly as it is at the source, at the verified commit, under the license of its repository. 421,275 unique files (3,782 MB of text) from 8,849 repositories, in 24 Parquet block(s). Only files whose repository's license allows redistribution, confirmed by the repository's… See the full description on the dataset page: https://huggingface.co/datasets/OpenScientificCodeRegistry/Database.

sourceHugging Faceotherupdated 2d agoView on Hugging Face
0likes5.6kdownloads
github.com__avantishri__shap.json399 linesDownload Raw Back to b0
1{2 "format": "oscr-script-manifest/1",3 "repository": "github.com/avantishri/shap",4 "url": "https://github.com/AvantiShri/shap/blob/master/shap/explainers/deep/deep_tf.py",5 "host": "github.com",6 "commit": "29d2ffab405619340419fc848de6b53e2ef0f00c",7 "license": "MIT",8 "license_confirmed_by": "license file LICENSE",9 "redistribution": "yes",10 "files": [11  {12   "path": "LICENSE",13   "sha256": "cf5c26305c632e5451b6eed7e0964a36c4cec1102f0c6467db063b3483a19127",14   "language": "License",15   "lines": 21,16   "truncated": false,17   "block": 3,18   "row": 805419  },20  {21   "path": "README.md",22   "sha256": "d7c7c2ebcc04febe39578326e8d5b822a886a6339e24bd6b1f69d7602d2db613",23   "language": "Text",24   "lines": 330,25   "truncated": false,26   "block": 4,27   "row": 272828  },29  {30   "path": "docs/_build/html/_static/doctools.js",31   "sha256": "ba5080dd83bfbc09c8440ecc3e163b7352073f7428a92facb9dfcd04ba29188b",32   "language": "JavaScript",33   "lines": 313,34   "truncated": false,35   "block": 3,36   "row": 692137  },38  {39   "path": "docs/_build/html/_static/jquery-1.11.1.js",40   "sha256": "cd3c6430971469db591628b33e747328f6cd70c60bdb56b6e92e3dca4b54d998",41   "language": "JavaScript",42   "lines": 7205,43   "truncated": true,44   "block": 3,45   "row": 697646  },47  {48   "path": "docs/_build/html/_static/jquery.js",49   "sha256": "87083882cc6015984eb0411a99d3981817f5dc5c90ba24f0940420c5548d82de",50   "language": "JavaScript",51   "lines": 4,52   "truncated": false,53   "block": 3,54   "row": 696455  },56  {57   "path": "docs/_build/html/_static/searchtools.js",58   "sha256": "e8d039d87d8f443d02abb76df25715de53a329a7a35cb58d4767c08a574fbe00",59   "language": "JavaScript",60   "lines": 761,61   "truncated": false,62   "block": 3,63   "row": 694964  },65  {66   "path": "docs/_build/html/_static/underscore-1.3.1.js",67   "sha256": "f808f0aa32fbe90fb9c9c846917faff3fdd4e236c284b76c02dd33753dc90177",68   "language": "JavaScript",69   "lines": 999,70   "truncated": false,71   "block": 1,72   "row": 152173  },74  {75   "path": "docs/_build/html/_static/underscore.js",76   "sha256": "42d8fad13bc28fc726775196ec9ab953febf9bde175c5845128361c953fa17f4",77   "language": "JavaScript",78   "lines": 31,79   "truncated": false,80   "block": 3,81   "row": 693082  },83  {84   "path": "docs/_build/html/_static/websupport.js",85   "sha256": "3e61ad44de4625bdd7aa3b1ac8ddad355c919de8a23bd16fb36053901ff23cb9",86   "language": "JavaScript",87   "lines": 808,88   "truncated": false,89   "block": 3,90   "row": 694891  },92  {93   "path": "docs/_build/html/searchindex.js",94   "sha256": "b6a7ce5b9dfb0083c38f80f10b0e3ad765822702fa1df2456edb188ba518bdd6",95   "language": "JavaScript",96   "lines": 1,97   "truncated": false,98   "block": 3,99   "row": 6886100  },101  {102   "path": "docs/conf.py",103   "sha256": "4881c9d870fe145359d2614c98cb526de43177b8c42a611317aded1a2395da3e",104   "language": "Python",105   "lines": 345,106   "truncated": false,107   "block": 3,108   "row": 25296109  },110  {111   "path": "javascript/color-set.js",112   "sha256": "d810db500a5918cd5339d35942fa89694402d2d5b302e9de7fb8a348cada39e7",113   "language": "JavaScript",114   "lines": 13,115   "truncated": false,116   "block": 3,117   "row": 6737118  },119  {120   "path": "javascript/random-explanation.js",121   "sha256": "778c7a344fb05071205923d049996eb20e4ce6384edb403b16e65cfc6110a2ad",122   "language": "JavaScript",123   "lines": 25,124   "truncated": false,125   "block": 3,126   "row": 6771127  },128  {129   "path": "javascript/test.js",130   "sha256": "6a2469463841fdf4ea5fb60cdf37dff1f42be685f5d76ba45af8e7db2d804827",131   "language": "JavaScript",132   "lines": 4,133   "truncated": false,134   "block": 3,135   "row": 6695136  },137  {138   "path": "javascript/webpack.config.js",139   "sha256": "06f5ee47cd91d893554965cbc179ad330ba296d3e9782fbbc00e19832be8cd0e",140   "language": "JavaScript",141   "lines": 31,142   "truncated": false,143   "block": 3,144   "row": 6780145  },146  {147   "path": "notebooks/deep_explainer/Front Page DeepExplainer MNIST Example.ipynb",148   "sha256": "8a971e397d9937bef5f31ea4251ccf38c47bf6e80e6445bd8bdb88704b54e452",149   "language": "Jupyter",150   "lines": 92,151   "truncated": false,152   "block": 3,153   "row": 7204154  },155  {156   "path": "notebooks/deep_explainer/Keras LSTM for IMDB Sentiment Classification.ipynb",157   "sha256": "cbabff2f329b2b5499cdf210d084e72c4589ac16516e400f8d1d17eb41eeb731",158   "language": "Jupyter",159   "lines": 94,160   "truncated": false,161   "block": 3,162   "row": 7243163  },164  {165   "path": "notebooks/deep_explainer/PyTorch Deep Explainer Genomics example With Hypothetical Importance Scores.ipynb",166   "sha256": "30181e7e8163f9190d11f6467874f729111b7a7a4899ea38ed7fc2874f78c5a5",167   "language": "Jupyter",168   "lines": 250,169   "truncated": false,170   "block": 3,171   "row": 7617172  },173  {174   "path": "notebooks/deep_explainer/PyTorch Deep Explainer MNIST example.ipynb",175   "sha256": "5945623dd9e87bc06f1f02ea46986678097c5553bb47773a7aedafa51eca08a5",176   "language": "Jupyter",177   "lines": 118,178   "truncated": false,179   "block": 3,180   "row": 7253181  },182  {183   "path": "notebooks/deep_explainer/Tensorflow DeepExplainer Genomics Example With Hypothetical Importance Scores.ipynb",184   "sha256": "b7ba99ea0aa0182eaeea8f5aef6977e54996783ff0aa09e9948e81d494e7e5db",185   "language": "Jupyter",186   "lines": 199,187   "truncated": false,188   "block": 3,189   "row": 7528190  },191  {192   "path": "notebooks/gradient_explainer/Explain an Intermediate Layer of VGG16 on ImageNet (PyTorch).ipynb",193   "sha256": "be930f4e052904e8a1c8184059ac3b92f6b23cdd553d8c2d4799799bf61218f5",194   "language": "Jupyter",195   "lines": 71,196   "truncated": false,197   "block": 3,198   "row": 7194199  },200  {201   "path": "notebooks/gradient_explainer/Explain an Intermediate Layer of VGG16 on ImageNet.ipynb",202   "sha256": "721e1e19787fd585adae2b5beeee20ee390fe1aa9710e4c74d7374d330dc5626",203   "language": "Jupyter",204   "lines": 58,205   "truncated": false,206   "block": 3,207   "row": 7189208  },209  {210   "path": "notebooks/kernel_explainer/Census income classification with Keras.ipynb",211   "sha256": "0a6c4db6f5d398395036b1d06c06345e5f49bf200f015db5e5d961c6b8a09449",212   "language": "Jupyter",213   "lines": 91,214   "truncated": false,215   "block": 3,216   "row": 7197217  },218  {219   "path": "notebooks/kernel_explainer/Census income classification with scikit-learn.ipynb",220   "sha256": "1ef34b1bdbb2ac884f4c89bef19a64cb2946a10e51e0ee61fe87f91796965e40",221   "language": "Jupyter",222   "lines": 104,223   "truncated": false,224   "block": 3,225   "row": 7261226  },227  {228   "path": "notebooks/kernel_explainer/Diabetes regression.ipynb",229   "sha256": "7430b9be9e11fa4c76b05912986c44673f6b9cb82e1bc7312f26cb8865880951",230   "language": "Jupyter",231   "lines": 120,232   "truncated": false,233   "block": 3,234   "row": 7215235  },236  {237   "path": "notebooks/kernel_explainer/ImageNet VGG16 Model with Keras.ipynb",238   "sha256": "c3b7b605d20e89348cc642d3b0964a9b17044f0ffad3f517af1c75ce7e80331e",239   "language": "Jupyter",240   "lines": 92,241   "truncated": false,242   "block": 3,243   "row": 7205244  },245  {246   "path": "notebooks/kernel_explainer/Iris classification with scikit-learn.ipynb",247   "sha256": "4472f70c403fa0f296d757422bccb09962ba2133eb40606ba16d4bee7a597d7f",248   "language": "Jupyter",249   "lines": 132,250   "truncated": false,251   "block": 3,252   "row": 7314253  },254  {255   "path": "notebooks/kernel_explainer/Simple Kernel SHAP.ipynb",256   "sha256": "cc773ff2d7420d891f7c34430d245f8d8d2cb7bede1ec013c26aaf1e648ba9ee",257   "language": "Jupyter",258   "lines": 71,259   "truncated": false,260   "block": 3,261   "row": 7117262  },263  {264   "path": "notebooks/linear_explainer/Sentiment Analysis with Logistic Regression.ipynb",265   "sha256": "842e7072dc522064ba7c57c9b18ae911b4a39132516cc35cc58a765318f63136",266   "language": "Jupyter",267   "lines": 91,268   "truncated": false,269   "block": 3,270   "row": 7208271  },272  {273   "path": "notebooks/plots/dependence_plot.ipynb",274   "sha256": "96e7db49e88cde5ced274e154a8e2e70e83f01ebe2e81df7e970d418c4de597c",275   "language": "Jupyter",276   "lines": 111,277   "truncated": false,278   "block": 3,279   "row": 7305280  },281  {282   "path": "notebooks/tree_explainer/Basic SHAP Interaction Value Example in XGBoost.ipynb",283   "sha256": "edb2559daabe09ff1872e026153fa5b35b98a79fb570d3a1e8ddbd95fe8ff68e",284   "language": "Jupyter",285   "lines": 196,286   "truncated": false,287   "block": 3,288   "row": 7402289  },290  {291   "path": "notebooks/tree_explainer/Catboost tutorial.ipynb",292   "sha256": "a45d4c914db09267345cb9813aa9ade6901213b17107fb35b943424e44f6ba1d",293   "language": "Jupyter",294   "lines": 127,295   "truncated": false,296   "block": 3,297   "row": 7336298  },299  {300   "path": "notebooks/tree_explainer/Census income classification with LightGBM.ipynb",301   "sha256": "933b1ad5d67fecdd470c0d8bb41976cb9e3798ef5754cc44bedf4900a88f0dbd",302   "language": "Jupyter",303   "lines": 122,304   "truncated": false,305   "block": 3,306   "row": 7388307  },308  {309   "path": "notebooks/tree_explainer/Census income classification with XGBoost.ipynb",310   "sha256": "d11424c2f278b69903836a5f5e53e6026a1f9a2427e68bb0b09ccb9d19a8eb6f",311   "language": "Jupyter",312   "lines": 208,313   "truncated": false,314   "block": 3,315   "row": 7530316  },317  {318   "path": "notebooks/tree_explainer/Explaining the Loss of a Model.ipynb",319   "sha256": "ec35d156eff916ff71e9422f9b10600110ee8c497ae2bb8192da85c4ae1281e2",320   "language": "Jupyter",321   "lines": 35,322   "truncated": false,323   "block": 3,324   "row": 7058325  },326  {327   "path": "notebooks/tree_explainer/Fitting a Linear Simulation with XGBoost.ipynb",328   "sha256": "a1732002113cde7d447fef1a24c09e28a1303451809ce7edc132a3d250da0dc0",329   "language": "Jupyter",330   "lines": 176,331   "truncated": false,332   "block": 3,333   "row": 7398334  },335  {336   "path": "notebooks/tree_explainer/Force Plot Colors.ipynb",337   "sha256": "da6714343a96f0d3dfe36e2d68b3dfff1c85ae4040a2f7ec64b065e89c04f999",338   "language": "Jupyter",339   "lines": 44,340   "truncated": false,341   "block": 3,342   "row": 7128343  },344  {345   "path": "notebooks/tree_explainer/Front page example (XGBoost).ipynb",346   "sha256": "e6ec2d87bacd360e8197539a8d035c2b5ba9f73a0442fc58e04fd165e1d2836f",347   "language": "Jupyter",348   "lines": 38,349   "truncated": false,350   "block": 3,351   "row": 7050352  },353  {354   "path": "notebooks/tree_explainer/League of Legends Win Prediction with XGBoost.ipynb",355   "sha256": "8d628b181504bf1ed6d9f94d478274c98431d05939fcd714015232f90ee2670d",356   "language": "Jupyter",357   "lines": 185,358   "truncated": false,359   "block": 3,360   "row": 7562361  },362  {363   "path": "notebooks/tree_explainer/NHANES I Survival Model.ipynb",364   "sha256": "00390cd30931030269b9eff7d67ed12012d23054f750f454b872a6ede75e42db",365   "language": "Jupyter",366   "lines": 274,367   "truncated": false,368   "block": 3,369   "row": 7580370  },371  {372   "path": "notebooks/tree_explainer/Perfomance Comparison.ipynb",373   "sha256": "9a226838aaef4651c0ac33865061487e19c75784e65e8dd72fcff72656b75936",374   "language": "Jupyter",375   "lines": 225,376   "truncated": false,377   "block": 3,378   "row": 7452379  },380  {381   "path": "notebooks/tree_explainer/Python Version of Tree SHAP.ipynb",382   "sha256": "af2f011781c456e26f9cb5e1db951bc93ce242a02b70423ff22c7eee54c382d4",383   "language": "Jupyter",384   "lines": 377,385   "truncated": false,386   "block": 3,387   "row": 7671388  },389  {390   "path": "notebooks/tree_explainer/Scatter Density vs. Violin Plot Comparison.ipynb",391   "sha256": "8054d0b16c89381d52081e64315074627aeffbf1e31174f8e5b70e11403c8967",392   "language": "Jupyter",393   "lines": 79,394   "truncated": false,395   "block": 3,396   "row": 7365397  }398 ]399}