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Runtime error
MarkusStoll
commited on
Commit
•
c6a85a5
0
Parent(s):
Duplicate from renumics/cifar10-cleanlab
Browse files- .gitattributes +34 -0
- .gitignore +2 -0
- Dockerfile +18 -0
- README.md +20 -0
- layout.json +137 -0
- prepare.py +47 -0
- requirements.txt +2 -0
- run.py +51 -0
- spotlight-layout.json +1 -0
.gitattributes
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*.7z filter=lfs diff=lfs merge=lfs -text
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*.arrow filter=lfs diff=lfs merge=lfs -text
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*.bin filter=lfs diff=lfs merge=lfs -text
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*.bz2 filter=lfs diff=lfs merge=lfs -text
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*.ckpt filter=lfs diff=lfs merge=lfs -text
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*.ftz filter=lfs diff=lfs merge=lfs -text
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*.h5 filter=lfs diff=lfs merge=lfs -text
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*.joblib filter=lfs diff=lfs merge=lfs -text
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*.lfs.* filter=lfs diff=lfs merge=lfs -text
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*.model filter=lfs diff=lfs merge=lfs -text
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*.npy filter=lfs diff=lfs merge=lfs -text
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*.npz filter=lfs diff=lfs merge=lfs -text
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*.onnx filter=lfs diff=lfs merge=lfs -text
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*.ot filter=lfs diff=lfs merge=lfs -text
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*.parquet filter=lfs diff=lfs merge=lfs -text
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*.pb filter=lfs diff=lfs merge=lfs -text
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*.pickle filter=lfs diff=lfs merge=lfs -text
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*.pkl filter=lfs diff=lfs merge=lfs -text
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*.pt filter=lfs diff=lfs merge=lfs -text
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*.pth filter=lfs diff=lfs merge=lfs -text
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*.rar filter=lfs diff=lfs merge=lfs -text
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*.safetensors filter=lfs diff=lfs merge=lfs -text
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saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.tar.* filter=lfs diff=lfs merge=lfs -text
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*.tflite filter=lfs diff=lfs merge=lfs -text
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*.tgz filter=lfs diff=lfs merge=lfs -text
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*.wasm filter=lfs diff=lfs merge=lfs -text
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*.xz filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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.gitignore
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.venv/
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Dockerfile
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FROM python:3.9
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WORKDIR /code
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ENV HOME=/code
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RUN apt install curl
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RUN pip install pip -U
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RUN pip install renumics-spotlight==1.3.0rc3
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RUN pip install datasets
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COPY prepare.py .
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RUN python prepare.py
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COPY . .
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RUN mkdir -p /code/.cache
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RUN chmod -R 777 /code
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CMD ["python", "run.py"]
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README.md
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---
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title: Cleanlab CIFAR-100 with Spotlight
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emoji: 🧐
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colorFrom: gray
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colorTo: blue
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sdk: docker
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pinned: false
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license: gpl-3.0
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app_file: run.py
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datasets:
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- renumics/cifar100-enriched
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- cifar100
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tags:
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- renumics
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- spotlight
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- EDA
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duplicated_from: renumics/cifar10-cleanlab
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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layout.json
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{
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"orientation": "horizontal",
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"children": [
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{
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"kind": "split",
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"weight": 24.37657642133775,
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"orientation": "vertical",
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"children": [
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{
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"kind": "tab",
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"weight": 23.652554002465973,
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"children": [
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{
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"kind": "widget",
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"name": "Table",
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"type": "table",
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"config": {
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"tableView": "full",
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"visibleColumns": [
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"fine_label_prediction_str",
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"fine_label_str",
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"label_score",
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"near_duplicate_score",
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"outlier_score"
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],
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"sorting": null,
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"orderByRelevance": false
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}
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}
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]
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},
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{
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"kind": "tab",
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"weight": 11.033364782611708,
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"children": [
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{
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"kind": "widget",
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"name": "Issues",
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"type": "IssuesWidget",
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"config": null
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}
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]
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},
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{
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"kind": "tab",
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"weight": 38.67424218071708,
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"children": [
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{
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"kind": "widget",
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"name": "Inspector",
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"type": "inspector",
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"config": {
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"views": [
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{
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"view": "ImageView",
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"columns": [
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"image"
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],
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"name": "image",
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"key": "iW3ihwygEHg4QZv5YzJ8ww"
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},
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{
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"view": "TextLens",
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"columns": [
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"fine_label_str"
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],
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"name": "view",
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"key": "346d7554-5395-44d6-b358-a351901cb02e"
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},
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{
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"view": "TextLens",
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"columns": [
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"fine_label_prediction_str"
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],
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"name": "view",
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"key": "99761cf8-350a-469d-8dbc-0df7b0db4d48"
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},
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{
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"view": "ScalarView",
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"columns": [
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"label_score"
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],
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"name": "view",
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"key": "4ae33ae9-919a-4b10-9216-cd7c9448f9ac"
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},
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{
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"view": "ScalarView",
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"columns": [
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"outlier_score"
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],
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"name": "view",
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"key": "13fb6430-3ffc-422c-92be-243b174b9a15"
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},
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{
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"view": "ScalarView",
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"columns": [
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"near_duplicate_score"
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],
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"name": "view",
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"key": "daf7c0b7-2185-4e50-9eb0-ffab8d1ff906"
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}
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],
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"visibleColumns": 8
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}
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}
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]
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}
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]
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},
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{
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"kind": "tab",
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"weight": 51.915353562320064,
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"children": [
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{
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"kind": "widget",
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"name": "Similarity Map",
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"type": "similaritymap",
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"config": {
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"placeBy": [
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"embedding_reduced"
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],
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"reductionMethod": null,
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"colorBy": "fine_label_str",
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"sizeBy": "is_label_issue",
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"filter": true,
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"umapNNeighbors": 20,
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"umapMetric": "cosine",
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"umapMinDist": 0.15,
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"pcaNormalization": null,
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"umapMenuLocalGlobalBalance": 0.5,
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"umapMenuIsAdvanced": false
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}
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}
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]
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}
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]
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}
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prepare.py
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import pickle
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import datasets
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import os
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import umap
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if __name__ == "__main__":
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cache_file = "dataset_cache.pkl"
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if os.path.exists(cache_file):
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# Load dataset from cache
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with open(cache_file, "rb") as file:
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dataset = pickle.load(file)
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print("Dataset loaded from cache.")
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else:
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# Load dataset using datasets.load_dataset()
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ds = datasets.load_dataset("renumics/cifar100-enriched", split="test")
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print("Dataset loaded using datasets.load_dataset().")
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df = ds.to_pandas()
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df = ds.rename_columns({"fine_label": "labels"}).to_pandas()
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from tabulate import tabulate
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from cleanlab import Datalab
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import pandas as pd
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import numpy as np
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lab = Datalab(data=ds, label_name="fine_label")
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features=np.array([x.tolist() for x in df["embedding"]])
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pred_probs= np.array([x.tolist() for x in df["probabilities"]])
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lab.find_issues(features=features,pred_probs=pred_probs)
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print(tabulate(lab.get_issues().iloc[[0,1,2,3,-3,-2,-1]], headers='keys', tablefmt='psql'))
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df_with_score = pd.concat([df, lab.get_issues()], axis=1)
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df = df_with_score
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# Save dataset to cache
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with open(cache_file, "wb") as file:
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pickle.dump(df, file)
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print("Dataset saved to cache.")
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requirements.txt
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renumics-spotlight
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datasets
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run.py
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import pickle
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from renumics import spotlight
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import os
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import requests
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import pandas as pd
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from renumics import spotlight
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from renumics.spotlight.analysis import DataIssue
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if __name__ == "__main__":
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cache_file = "dataset_cache.pkl"
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if os.path.exists(cache_file):
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# Load dataset from cache
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with open(cache_file, "rb") as file:
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df = pickle.load(file)
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print("Dataset loaded from cache.")
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label_issue_rows = df[df["is_label_issue"]].sort_values("label_score").index.tolist()
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label_issue = DataIssue(severity="medium", title="label-issue", rows=label_issue_rows, description="Label issue found by cleanlab")
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outlier_issue_row = df[df["outlier_score"]<0.6].sort_values("outlier_score").index.tolist()
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outlier_issue = DataIssue(severity="medium", title="outlier-issue", rows=outlier_issue_row, description="Outlier score < 0.6")
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near_duplicate_issue_row = df[df["is_near_duplicate_issue"]].sort_values("near_duplicate_score").index.tolist()
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near_duplicate_issue = DataIssue(severity="medium", title="near-duplicate-issue", rows=near_duplicate_issue_row, description="Near duplicate issue found by cleanlab")
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df = df.drop(["full_image"], axis=1)
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while True:
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dtypes = {
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"image": spotlight.Image,
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33 |
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"embedding": spotlight.Embedding,
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"embedding_reduced": spotlight.Embedding,
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"probabilities": spotlight.Embedding,
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}
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view = spotlight.show(
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df,
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dtype=dtypes,
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issues=[label_issue,outlier_issue,near_duplicate_issue],
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42 |
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layout="layout.json",
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43 |
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port=7860,
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44 |
+
host="0.0.0.0",
|
45 |
+
allow_filebrowsing=False,
|
46 |
+
)
|
47 |
+
|
48 |
+
view.close()
|
49 |
+
|
50 |
+
else:
|
51 |
+
print(f"Dataset {cache_file} not found. Please run prepare.py first.")
|
spotlight-layout.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"orientation":"vertical","children":[{"kind":"split","weight":55.52608311229001,"orientation":"horizontal","children":[{"kind":"tab","weight":41.16700201207244,"children":[{"kind":"widget","name":"Table","type":"table","config":{"tableView":"full","visibleColumns":["coarse_label_str","fine_label_prediction_str","fine_label_str","image","__idx__"],"sorting":[["__idx__","DESC"]],"orderByRelevance":false,"columnWidths":{"coarse_label":92,"coarse_label_str":165,"embedding_reduced":128,"fine_label":92,"fine_label_prediction":92,"fine_label_prediction_error":92,"fine_label_prediction_str":244,"fine_label_str":194,"full_image":256,"image":200,"split":256,"__idx__":92,"__last_edited_at__":192,"__last_edited_by__":256}}}]},{"kind":"tab","weight":33.66487769877306,"children":[{"kind":"widget","name":"Similarity Map","type":"similaritymap","config":{"placeBy":null,"reductionMethod":null,"colorBy":"fine_label_str","sizeBy":null,"filter":false,"umapNNeighbors":20,"umapMetric":null,"umapMinDist":0.15,"pcaNormalization":null,"umapMenuLocalGlobalBalance":null,"umapMenuIsAdvanced":false}},{"kind":"widget","name":"Scatter Plot","type":"scatterplot","config":{"xAxisColumn":null,"yAxisColumn":null,"colorBy":null,"sizeBy":null,"filter":false}}]},{"kind":"tab","weight":25.168120289154498,"children":[{"kind":"widget","name":"Histogram","type":"histogram","config":{"columnKey":"fine_label_str","stackByColumnKey":"fine_label_prediction_error","filter":false}}]}]},{"kind":"tab","weight":44.47391688770999,"children":[{"kind":"widget","name":"Inspector","type":"inspector","config":{"views":[{"view":"ImageView","key":"es3NP2fbYeG5TzTQqCzZKL","name":"image","columns":["image"]},{"view":"TextLens","columns":["fine_label_str"],"name":"view","key":"7262001a-1270-467c-bfe8-6b6883adbc7d"},{"view":"TextLens","columns":["fine_label_prediction_str"],"name":"view","key":"f72a8ae9-17c4-4097-b454-79a6f60865db"},{"view":"ScalarView","columns":["fine_label_prediction_error"],"name":"view","key":"be4d6afe-9093-4581-b363-df11d3c6d34a"}],"visibleColumns":4}}]}]}
|