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get ML evaluation metrics everyone can agree on by converting your model output to business outcomes, a.k.a. profits.\u003c/p\u003e\n\n\u003ch3 align=\"center\"\u003e\n 🤖 🪄 📈\n\u003c/h3\u003e\n\n\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"https://i.imgur.com/sCfpF6d.png\"\u003e\n\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n    \u003ca href=\"https://guap.gitbook.io/guap-docs/\"\u003eDocumentation\u003c/a\u003e |\n    \u003ca href=\"https://colab.research.google.com/drive/1XmqitZzUtK-rohKXgKpjqR16npKjx6m9#offline=true\u0026sandboxMode=true\"\u003eColab Demo\u003c/a\u003e |\n    \u003ca href=\"https://twitter.com/guap_ml\"\u003eTwitter\u003c/a\u003e |\n    \u003ca href=\"https://ulyssebottello.com/\"\u003eBlog\u003c/a\u003e |\n    \u003ca href=\"https://github.com/guap-ml/guap/projects/1\"\u003ePublic roadmap\u003c/a\u003e \n    \u003cbr /\u003e\n\u003c/p\u003e\n\n\u003cp align=\"center\"\u003e\n    \u003ca href=\"https://github.com/chetanraj/awesome-github-badges\"\u003e\n        \u003cimg alt=\"Made with love\" src=\"https://img.shields.io/badge/Made%20With-Love-orange.svg\"\u003e\n    \u003c/a\u003e\n\t\u003ca href=\"https://github.com/chetanraj/awesome-github-badges\"\u003e\n        \u003cimg alt=\"py version\" src=\"https://img.shields.io/badge/python-3.6_|_3.7_|_3.8-blue\"\u003e\n    \u003c/a\u003e\n\t    \u003c/a\u003e\n\t\u003ca href=\"https://github.com/chetanraj/awesome-github-badges\"\u003e\n        \u003cimg alt=\"version\" src=\"https://img.shields.io/badge/version-0.1.0-gree\"\u003e\n    \u003c/a\u003e\n\n\u003c/p\u003e\n\n\n## 🧞‍♂️ Why should I use guap?\nOur mission with guap is to align all stakeholders with measurable business outcomes by including the three core teams — business, data science, and IT — throughout the life cycle of the AI models.\n\n* Make collaboration healthier and clearer between tech and non-tech people\n* Make better decisions at every stage of the ML project lifecycle\n\nWant to know more? Read [Why guap exist](https://ulyssebottello.com/why-guap/).\n\n## ✨ Features\nWe're on the journey to make sure every ML use-case that goes to production is a valuable one. And it starts with a simple way to estimate the expected profit/cost of a model based on its confusion matrix.\n\n* **Get the total profit** Based on the test set, guap will give you the total expected profit based on the cost matrix. A great way to have an overview of the model profitability.\n* **Average profit per prediction** Along with the total profit score, guap will give you the average profit/cost per prediction. Perfect if you have costs per prediction, or if you need to estimate the profitability while scaling.\n\nThat's it...for now! Keep up-to-date with release announcements on Twitter [@guap_ml](https://twitter.com/guap_ml)!\n\n## 🪄 Quick tour\nTo immediately use guap, here is how to quickly generate a profit/cost evaluation metric for a given confusion matrix: \n\nInstall `guap` library:\n```\npip install guap\n```\nFlexible integration for any Python script:\n```python\n\u003e\u003e\u003e !pip install guap\n\u003e\u003e\u003e from guap.metrics import guap_metric\n\n# Set the confusion matrix\n\u003e\u003e\u003e y_true = [\"cat\", \"ant\", \"cat\", \"cat\", \"ant\", \"bird\"]\n\u003e\u003e\u003e y_pred = [\"ant\", \"ant\", \"cat\", \"cat\", \"ant\", \"cat\"]\n\n# Set the cost matrix\n\u003e\u003e\u003e cost_matrix = [[200, -1500], [0, 1000]]\n\n# Generate guap scores\n\u003e\u003e\u003e output = guap_metric(y_true, y_pred, cost_matrix)\n[{'sum': -9600, 'average': -3200}]\n```\n\nEasy, right? \n\n## 👀 Demo\nDo you need a step-by-step guide on how to use guap on a very simple ML use case? We got you.\n\nYou can use guap metrics at every step of the ML lifecycle and on unlimited use-cases. But we think an interactive demo is worth thousands and thousands of words.\n\nWe've picked a situation where we evaluate and compare two simple models on a Kaggle dataset, and a use-case deeply rooted with cost savings: credit card fraud detection.\n\n[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1XmqitZzUtK-rohKXgKpjqR16npKjx6m9#offline=true\u0026sandboxMode=true)\n\n## ⌛ Status\n- [x] Alpha: We are demoing guap to users and receiving feedback\n- [ ] Private Beta\n- [ ] Public Beta\n- [ ] Official Launch\n\nWatch \"releases\" of this repo to get notified of major updates, and give the star button a click whilst you're there.\n\n## 🙏 Contributing\nPull requests are welcome. You don't know where to start? let's talk [@guap_ml](https://twitter.com/guap_ml)!\n\n## 💖 License\n[Apache License 2.0](http://www.apache.org/licenses/)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fguap-ml%2Fguap","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fguap-ml%2Fguap","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fguap-ml%2Fguap/lists"}