base on Interactively explore unstructured datasets from your dataframe. <p align="center"><a href="https://spotlight.renumics.com"><img src="static/img/spotlight.svg" alt="Gray shape shifter" height="60"/></a></p> <h1 align="center">Renumics Spotlight</h1> <p align="center">Interactively explore unstructured datasets from your dataframe.</p> <p align="center"> <a href="https://github.com/Renumics/spotlight/blob/main/LICENSE"><img src="https://img.shields.io/github/license/renumics/spotlight" height="20"/></a> <a href="https://pypi.org/project/renumics-spotlight/"><img src="https://img.shields.io/pypi/pyversions/renumics-spotlight" height="20"/></a> <a href="https://pypi.org/project/renumics-spotlight/"><img src="https://img.shields.io/pypi/wheel/renumics-spotlight" height="20"/></a> </p> <h3 align="center"> <a href="https://spotlight.renumics.com"><b>Documentation</b></a> &bull; <a href="https://renumics.com/docs/data-centric-ai/playbook"><b>Playbook</b></a> &bull; <a href="https://renumics.com/blog/"><b>Blog</b></a> &bull; <a href="https://renumics.com/api/spotlight/"><b>API Reference</b></a> </h3> <p align="center"><a href="https://spotlight.renumics.com"><img src="static/img/spotlight_video.gif" width="100%"/></a></p> Spotlight helps you to **understand unstructured datasets** fast. You can quickly create **interactive visualizations** and leverage data enrichments (e.g. embeddings, prediction, uncertainties) to **identify critical clusters** in your data. Spotlight supports most unstructured data types including **images, audio, text, videos, time-series and geometric data**. You can start from your existing dataframe: <p align="left"><img src="static/img/dataframe_head_sample.png" width="100%"/></a></p> And start Spotlight with just a few lines of code: ```python from renumics import spotlight spotlight.show(df, dtype={"image": spotlight.Image, "embedding": spotlight.Embedding}) ``` ## 🚀 Start with a use case Machine learning and engineering teams use Spotlight to understand and communicate on complex unstructured data problems. Here are some examples on publicly available datasets along with code snippets (👨‍💻), interactive demos (🕹️) and blog articles (📝): <table> <thead> <tr> <th>Modality</th> <th>Task</th> <th>Description</th> <th>Link</th> </tr> </thead> <tbody> <tr> <td rowspan="3">🖼️ Image</td> <td rowspan="3">[Classification]</td> <td>Find Issues in Any Image Classification Dataset</td> <td><a href="https://www.renumics.com/next/docs/use-cases/image-classification">👨‍💻</a> <a href="https://medium.com/@daniel-klitzke/finding-problematic-data-slices-in-unstructured-data-aeec0a3b9a2a">📝</a> <a href="https://huggingface.co/spaces/renumics/sliceguard-unstructured-data">🕹️</a></td> </tr> <tr> <td>Find data issues in the CIFAR-100 image dataset</td> <td><a href="https://huggingface.co/spaces/renumics/navigate-data-issues">🕹️</a></td> </tr> <tr> <td>Fine-tuning image classification models from Bing image search</td> <td><a href="https://renumics.com/next/docs/use-cases/image-fine-tuning">👨‍💻</a><a href="https://medium.com/@markus.stoll/image-classification-in-2023-8ab7dc552115">📝</a></td> </tr> <tr> <td rowspan="3">🔊 Audio</td> <td rowspan="3">[Classification]</td> <td>Find Issues in Any Audio Classification Dataset</td> <td><a href="https://www.renumics.com/next/docs/use-cases/audio-classification">👨‍💻</a> <a href="https://medium.com/@daniel-klitzke/finding-problematic-data-slices-in-unstructured-data-aeec0a3b9a2a">📝</a><a href="https://huggingface.co/spaces/renumics/whisper-commonvoice-speaker-issues">🕹️</a></td> </tr> <tr> <td>Debug pre-trained gender detection models on the emodb dataset</td> <td><a href="https://medium.com/p/dbfd923a5a79#432e-3559ae606f80">📝</a> <a href="https://huggingface.co/spaces/renumics/emodb-model-debugging">🕹️</a></td> </tr> <tr> <td>Compare gender detection models on the emodb dataset</td> <td><a href="https://medium.com/p/dbfd923a5a79#432e-3559ae606f80">📝</a> <a href="https://huggingface.co/spaces/renumics/emodb-model-comparison">🕹️</a></td> </tr> <tr> <td rowspan="1">📝 Text</td> <td rowspan="1">[Classification]</td> <td>Find Issues in Any Text Classification Dataset</td> <td><a href="https://www.renumics.com/next/docs/use-cases/text-classification">👨‍💻</a> <a href="https://medium.com/@daniel-klitzke/finding-problematic-data-slices-in-unstructured-data-aeec0a3b9a2a">📝</a></td> </tr> <tr> <td rowspan="2">📈🖼️ Mixed</td> <td rowspan="2">[EDA]</td> <td>Explore results from the Formula1 Montreal 2023 GP</td> <td><a href="https://huggingface.co/spaces/renumics/f1_montreal_gp">🕹️</a></td> </tr> <tr> <td>Explore a crash simulation dataset</td> <td><a href="https://huggingface.co/spaces/renumics/crash-simulation-demo">🕹️</a></td> </tr> </tbody> </table> ## ⏱️ Quickstart Get started by installing Spotlight and loading your first dataset. #### What you'll need - [Python](https://www.python.org/downloads/) version 3.8-3.12 #### Install Spotlight via [pip](https://packaging.python.org/en/latest/key_projects/#pip) ```bash pip install renumics-spotlight ``` > We recommend installing Spotlight and everything you need to work on your data in a separate [virtual environment](https://docs.python.org/3/tutorial/venv.html). #### Load a dataset and start exploring ```python import pandas as pd from renumics import spotlight df = pd.read_csv("https://renumics.com/data/mnist/mnist-tiny.csv") spotlight.show(df, dtype={"image": spotlight.Image}) ``` `pd.read_csv` loads a sample csv file as a pandas [DataFrame](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html). `spotlight.show` opens up spotlight in the browser with the pandas dataframe ready for you to explore. The `dtype` argument specifies custom column types for the browser viewer. #### Load a [Hugging Face](https://huggingface.co/) audio dataset with embeddings and a pre-defined layout ```python import datasets from renumics import spotlight ds = datasets.load_dataset('renumics/emodb-enriched', split='all') layout= spotlight.layouts.debug_classification(label='gender', prediction='m1_gender_prediction', embedding='m1_embedding', features=['age', 'emotion']) spotlight.show(ds, layout=layout) ``` Here, the data types are discovered automatically from the dataset and we use a pre-defined layout for model debugging. Custom layouts can be built programmatically or via the UI. > The `datasets[audio]` package can be installed via pip. #### Usage Tracking We have added crash report and performance collection. We do NOT collect user data other than an anonymized Machine Id obtained by py-machineid, and only log our own actions. We do NOT collect folder names, dataset names, or row data of any kind only aggregate performance statistics like total time of a table_load, crash data, etc. Collecting Spotlight crashes will help us improve stability. To opt out of the crash report collection define an environment variable called `SPOTLIGHT_OPT_OUT` and set it to true. e.G.`export SPOTLIGHT_OPT_OUT=true` ## We are very happy to hear your feedback - Open an issue on [Github](https://github.com/renumics) - Have a [coffee talk](https://calendly.com/stefan-suwelack/dcai-intro-30-min) with us - Join our channel on [Discord](https://discord.gg/VAQdFCU5YD) ## Learn more about unstructured data workflows - 🤗 [Huggingface](https://huggingface.co/renumics) example spaces and datasets - 🏀 [Playbook](https://renumics.com/docs/data-centric-ai/playbook) for data-centric AI workflows - 🍰 [Sliceguard](https://github.com/Renumics/sliceguard) library for automatic slice detection ## Contribute We are currently participating in the running [Hacktoberfest 2023](https://hacktoberfest.com/). If you would like to contribute to Spotlight, the easiest way is to have a look at our [Contribution Docs](https://renumics.com/docs/development) and the [CONTRIBUTING.md](./CONTRIBUTING.md). We are also equally happy about non-code contributions -- whether it's reporting bugs, suggesting features, contributing design ideas, or offering feedback, every non-code contribution is highly valued and helps make our project better for everyone. ", Assign "at most 3 tags" to the expected json: {"id":"3397","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"