{ "cells": [ { "cell_type": "markdown", "id": "3ede0360-00f4-404e-b0d2-4a83cc385654", "metadata": { "id": "3ede0360-00f4-404e-b0d2-4a83cc385654" }, "source": [ "๐Ÿ”— Ensemble Model\n", "---\n", "Weโ€™ll reuse core components built earlier:\n", "\n", "- A fine-tuned LLaMA model\n", "- An XGBoost regression model, stored in Hugging Face\n", "- A ChromaDB vector store, stored on Google Drive and also available on AWS S3\n", "- A GPT-4o mini + RAG pipeline\n", "\n", "We'll run all three models on the same test data, gather their predictions, and train a Linear Regression Ensemble. The ensemble learns how to combine these predictions to output a more accurate final price.\n", "\n", "Once trained, we'll save the ensemble as ensemble_model.pkl, ready for later use.\n", "\n", "- ๐Ÿง‘โ€๐Ÿ’ป Skill Level: Advanced\n", "- โš™๏ธ Hardware: โš ๏ธ GPU required (use Google Colab)\n", "- ๐Ÿ› ๏ธ Requirements: \n", "\n", " - ๐Ÿ”‘ Hugging Face Token and OpenAI Key โ€” must be set in Google Colab secrets or .env files if you are running with your own GPU\n", " - completion of Part 9 of [this series of notebooks](https://github.com/lisekarimi/lexo)\n", "- ๐ŸŽฏ Task: Train and save the Ensemble Model\n", "\n", "---\n", "๐Ÿ“ข Find more LLM notebooks on my [GitHub repository](https://github.com/lisekarimi/lexo)" ] }, { "cell_type": "code", "execution_count": null, "id": "mzYB4XYQeWRQ", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "mzYB4XYQeWRQ", "outputId": "f474ce9b-09fb-4a47-93d7-273fe2d2ba10" }, "outputs": [], "source": [ "# Install required packages in Google Colab\n", "%pip install -q tqdm huggingface_hub numpy sentence-transformers datasets chromadb xgboost peft torch bitsandbytes" ] }, { "cell_type": "code", "execution_count": null, "id": "b3caecd1-8712-4acd-80b5-e8059c16f43f", "metadata": { "id": "b3caecd1-8712-4acd-80b5-e8059c16f43f" }, "outputs": [], "source": [ "# imports\n", "\n", "import os\n", "import re\n", "import zipfile\n", "import chromadb\n", "import joblib\n", "import numpy as np\n", "import pandas as pd\n", "import requests\n", "import torch\n", "from datasets import load_dataset\n", "from google.colab import userdata\n", "from huggingface_hub import HfApi, hf_hub_download, login\n", "from openai import OpenAI\n", "from peft import PeftModel\n", "from sentence_transformers import SentenceTransformer\n", "from sklearn.linear_model import LinearRegression\n", "from sklearn.metrics import r2_score, mean_squared_error\n", "from sklearn.metrics import r2_score\n", "from tqdm import tqdm\n", "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig" ] }, { "cell_type": "code", "execution_count": null, "id": "05d9523f-b6c9-4132-bd2b-6712772b3cd2", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "05d9523f-b6c9-4132-bd2b-6712772b3cd2", "outputId": "7077320e-43e2-4b03-ca7d-e7ea9a3407f8" }, "outputs": [], "source": [ "# Mount Google Drive to access saved ChromaDB and XGBoost model files\n", "\n", "from google.colab import drive\n", "drive.mount(\"/content/drive\")" ] }, { "cell_type": "code", "execution_count": null, "id": "z9735RD_TUHw", "metadata": { "id": "z9735RD_TUHw" }, "outputs": [], "source": [ "# Load from Colab's secure storage\n", "\n", "openai_api_key = userdata.get(\"OPENAI_API_KEY\")\n", "openai = OpenAI(api_key=openai_api_key)\n", "\n", "hf_token = userdata.get(\"HF_TOKEN\")\n", "login(hf_token, add_to_git_credential=True)" ] }, { "cell_type": "code", "execution_count": null, "id": "DtswsfBQxxJF", "metadata": { "id": "DtswsfBQxxJF" }, "outputs": [], "source": [ "# Configuration\n", "\n", "HF_USER = \"lisekarimi\"\n", "ROOT = \"/content/drive/MyDrive/snapr\"\n", "os.makedirs(ROOT, exist_ok=True)\n", "\n", "api = HfApi(token=hf_token)\n", "REPO_NAME = \"smart-deal-finder-models\"\n", "REPO_ID = f\"{HF_USER}/{REPO_NAME}\"" ] }, { "cell_type": "markdown", "id": "qByarIFiTYa1", "metadata": { "id": "qByarIFiTYa1" }, "source": [ "### ๐Ÿ“ฅ Load Test Dataset" ] }, { "cell_type": "code", "execution_count": null, "id": "f9ca3e34", "metadata": {}, "outputs": [], "source": [ "# #If you face NotImplementedError: Loading a dataset cached in a LocalFileSystem is not supported run:\n", "# %pip install -U datasets" ] }, { "cell_type": "code", "execution_count": null, "id": "0eKakxSFTVcA", "metadata": { "id": "0eKakxSFTVcA" }, "outputs": [], "source": [ "DATASET_NAME = f\"{HF_USER}/pricer-data\"\n", "dataset = load_dataset(DATASET_NAME)\n", "test = dataset[\"test\"]" ] }, { "cell_type": "code", "execution_count": null, "id": "cWqvs8JRTggE", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 110 }, "id": "cWqvs8JRTggE", "outputId": "bf7f0113-de82-422a-aaec-54efbb2b9d16" }, "outputs": [], "source": [ "# Format description function (no price in text)\n", "def description(item):\n", " text = item[\"text\"].replace(\n", " \"How much does this cost to the nearest dollar?\\n\\n\", \"\"\n", " )\n", " text = text.split(\"\\n\\nPrice is $\")[0]\n", " return f\"passage: {text}\"\n", "\n", "\n", "description(test[0])" ] }, { "cell_type": "markdown", "id": "alpkYSc2UX0n", "metadata": { "id": "alpkYSc2UX0n" }, "source": [ "### ๐Ÿ“ฅ Load Models and ChromaDB" ] }, { "cell_type": "code", "execution_count": null, "id": "pjPBEgXqmHOA", "metadata": { "id": "pjPBEgXqmHOA" }, "outputs": [], "source": [ "# ChromaDB\n", "\n", "CHROMA_PATH = f\"{ROOT}/chroma\"\n", "COLLECTION_NAME = \"price_items\"\n", "CHROMA_ZIP_URL = \"https://aiprojects-lise-karimi.s3.eu-west-3.amazonaws.com/smart-deal-finder/chroma.zip\"\n", "\n", "# Download and unzip if CHROMA_PATH doesn't exist\n", "if not os.path.exists(CHROMA_PATH):\n", " os.makedirs(CHROMA_PATH, exist_ok=True)\n", " r = requests.get(CHROMA_ZIP_URL)\n", " with open(\"/tmp/chroma.zip\", \"wb\") as f:\n", " f.write(r.content)\n", " with zipfile.ZipFile(\"/tmp/chroma.zip\", \"r\") as zip_ref:\n", " zip_ref.extractall(CHROMA_PATH)\n", "\n", "client = chromadb.PersistentClient(path=CHROMA_PATH)\n", "collection = client.get_or_create_collection(name=COLLECTION_NAME)" ] }, { "cell_type": "code", "execution_count": null, "id": "8fi1BS71XCv1", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 337, "referenced_widgets": [ "c60f1153084a493ea31fac10bf986aef", "6de41ac188dd48aea5d30a90bc52c38c", "d2b4cdcaef6a4c41972f8c96af2814ab", "2180dfb4e6e74df5bf9985c481b6e420", "dcff84c8c3bf4f4bae334e0484207d10", "ca1ed709ecaa4a0e8ac96ffb930e6613", "e5297e7d36334c57aece043f62c79841", "5fdca4e0987a4788983c418941711d7e", "200e8e9b0df84affa177567243bf18d1", "ef6e6dcff8b444bba62c1f76e1127d7c", "7c94357c0d4e444489e8d47d2151437b", "4ffdcd2ec96046ffb5121def27c95c9d", "dd47ad1efe46496cb096a7714cf27c19", "194ad4f8707b4d288e88cdbdfa33605d", "c8a05ae3f5854f24998cc615a8849c88", "b5ec411e72a946f8b4a470de5827c949", "1a0be25b030d43858cee804da65d67a1", "88d1f6a56f9b4a50854aee82c0945cc9", "73e5967ae96942e080f3b05638583bc8", "8434bfa06abf42c98e8ffb0e7b83c9f9", "43d872e632da4d9883ea3d71dc91bdf9", "86729f54df1b4967b2730b48f84a98aa", "83fcebcf2b2c4213835334a998ba91e9", "4ef06b10bfcb418d85534a8b73688eff", "88c466cc89234d8f9f21147882fc5faf", "f87e958c639544c0b925646fc28c4604", "a52988b97dff4759a456398ecea1eaaf", "c1116a13be86401bbbf6e51de0df7d12", "d6ed27ce322748d29ed864808f619ee3", "4a6fbedd3333496081695800cae8bdda", "a7badc083fb34e69bd6f27bc9a805e7b", "a78bcdbac2f74c72938d87c431f23e78", "1d627cf1043642a3815a2902f65b4ded", "3b8cc480ded24f66b03779fd25844670", "0ce0073368c64339b3c1f960861e4b56", "ffc973a4347943ebaa4ead16e04c05f6", "aafba411ee984946a3ec0760580b60b6", "0dd2501d917f48739b2817d598541660", "213ca3afc47945a68e28a6ae005c3b7d", "6355e004d7c34b969b2d2c6ccbc12620", "9359b873cb4a4187b67a1732d78c7534", "32f86a2b9e0547a6bc0a523ca3cfa088", "0f446cb8ee3147438ef1e98e665a2831", "64bb7ebae66d42f2a4d6a3039bf67d4b", "e2b51ee511234ff2bc2cf33227fe2088", "a76d3def06db41fab4ab2f077839d5bc", "fa9598b858c14024aaf15d1417e9683d", "df0bb9a9635643ebb679e115f45dde8e", "527c4d1987334e3e9b2aa0de7d0527a7", "0c6a889a9066484abbcb87b730d7e325", "80a1f4c902154f2184c38ef844a1cca7", "463c3cc65cd343108fe6049e4cde7142", "fb015ce2fd9b48d79db67f80181964b7", "07f46375dc594cd19ac5ab983083b2de", "451ca5f213544cc8b24de6b7d55602fc", "5bb2e645ea7741839e0f88ae484d94d2", "19d1353070f643e08364500e9b1c30e6", "fff94d7934cc4793876903d1c18efbfa", "f0413b6310ef4510bf493e6814fa162d", "a97c1662e64c44ee9f6e5be5617c07ec", "da02a5ab5fe44cc297ab3048509a99e1", "ee8c23aa2ca84b32a02a2500917559d8", "6635ef559f72485e9453f87b3921f954", "d9f2925a563d4d9fa332c15205f44d9b", "73ec7891d53149e7a072a0e310716178", "f7309076b36e4224acc42ade5d09bf37", "cd53294ff44e4955afbfbd4660563b58", "2ae42eb6385f43fab59f2bb56bb8a28e", "fc0f1abaaf054d0d93a27c7ee0f6630d", "c7a61078596c475784307480d26e3661", "5bd26e4ff28f4639b52aab848ada03d1", "ce710ef5ffa14cfe9842c63caebc81e5", "63061726d47940c395a00d5d01556f4f", "2b41598231d14f3ca6354c9543ec4351", "0df33079f63c45d39de21439289aa4a8", "8cc5d2eac9a64d68b72608bb5ae44c89", "dd33a409204c4610a08e44c3e82e00da", "b96b7ac71a6c48a9a6c888f2f34efea5", "794d71dd5b734a3cb5607fc31aaddd18", "0f6e7a2d9b8846178a7492e137d83bba", "657bb839f0ea40eb9873385cecd06fd0", "1ac174e8904943bb9a5e5483e58eef63", "a1c9714fc4ea48af83669481e89c58c7", "763a2d64c8e94ca1b0289264d9f868bc", "ef78cf15ab914b3fa95ff95a86ec7a99", "24350ebdc38a41e689f3e3b09dfc3e35", "b8131b3c4c4c4b20809af9b0e91dd006", "420c50ed8abe49ec9f4f2777e6cd2749", "fc3f2d2c33ee40f8850710c2f4ce331f", "90730ec699e84ddda2af799f8220e7a5", "01f1c4b3b434474dbf2212a05869354d", "912d6c1687324bb9b334bbf98a2b5b30", "dc4106a0020b4b9fa21cd12a44967f2e", "0084e537ffa74ab4a6d5f307b0916d2c", "0edeb9ca771c4ca2a9a678e0e8a91614", "b610d515ddb4405695e6972e45463194", "4af72cb05f284d42bca73fcb88904255", "86f34fb6325e4e878eee0be27946c88b", "bfc26f456f1d440bb80eedac1cb14967", "1f005c3cf7594275a37bb937a3c33db3", "4347e7d3db4d4cc3836a4e69db032f27", "809a4d0270dd4c05817ea224bb78ff5a", "b403a344e84342e4b076e64e829d7354", "6b3cbb0ac0b14e3fa193cd5cb3f8f521", "2d06aaf8d15b456b8fadeb54dd2ea73d", "74842e94dda648d18cd055220a3d2b39", "5a3818bde07841fbb6077bf20b7dec4b", "2a5224c8b3004d249a07297a2111493f", "ff02ac7f08974426b3f70b71e59ed5bb", "0dd550d3e39f42809fa16770231af7e5" ] }, "id": "8fi1BS71XCv1", "outputId": "9256b509-1371-4bb3-bd84-98bb75725ac3" }, "outputs": [], "source": [ "# Embedding Model\n", "\n", "embedding_model = SentenceTransformer(\"intfloat/e5-small-v2\", device=\"cuda\")" ] }, { "cell_type": "code", "execution_count": null, "id": "zmwIbufXUzMo", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 483, "referenced_widgets": [ "36b26207185e4e10a8c60f0f5918aa7c", "96a0c5044c824968a701e20319c8d037", "a567759a6e554c2eb9334559e880a56e", "dcc68ce908ac4cfc9ffdcd0333cce14b", "06d7ac4abde74e1496ef80b9e22cd148", "94ba3ecd90f84291a46462a51ba001b8", "00b94b9d1e4e4e428c7467b4f99e45af", "02219374302849bd93d7aba7f65ee42b", "82223db2023045e88dc3d9652bc6b183", "6e08a796d2c440c58d5e6bb20f39be16", "6ac04725da4f485f8d7efd738de0940a", "230a6add3adb4dbd9f0329412e3f1455", "db5f777e8ec143fe89a2e6296031c9c2", "4f87c3aba240471d96349a037777732e", "74b04b71ce0746a89d146c4044c85890", "582e3cd91e3048639ab48492b5ca4b15", "1d8e31a4418c49e0bfa28b399673f718", "ec17f9e6bbbc4584baf11c0e7c504f84", "19779fea3c8c4a5cbea953b059775110", "3c82450a6db649f59f36668d5521d203", "3447d41776ef406bbfbe3e6c277b82d2", "4195859f8457499ba8e61a9f1662931c", "6f80cc0bed9b42e89f9691675ff5a484", "dc39eee63325479a915101649ab04273", "99e1e2c842f845d1b2ded34736b60ad7", "20ff78d4df09401d8aee462b57c57a48", "1aff04abbd0b4f15bd58154b00591264", "9cf024b488154926861d695137268da1", "6470d6b03e3548d783ed252d128ec361", "230a79f3d05d4c9eac73ae6962cb5d2d", "adfe1cc7a6f94b0ab43170c75688374d", "b6d24621c29f4352862f37ae69f2d6ad", "303a6f79669e40d98ed2998f4f5e47cc", "b737799377c948a99edf34c014a105c4", "daf9616a687f406da1d9ee2bd147c850", "cfd34b617326486498a916531bae9a87", "201ea14e31a244a3aa2aeed2c12fb255", "8454d5f263eb45daa0e6f7db6aa3f92d", "f9787ee50d634421ae5f0325126dcb73", "c2200daaf2994ed9ae16587d8d52236c", "93a0ec9723074e9199d1f9db988c30dc", "b1cb687d58ba40f49961d5485a466ef7", "ccc580852d66401993d675c254832379", "7321b89aea1c4746901ef40548bcf056", "8fae24599e174689adbaa52a16270785", "01605d6fb53d4e56aca9a746b2c75566", "45cb352704fe41f1be0f01c61511323d", "881550789f4845dda8561f6b26aff204", "654bf64ec165449993b195209d75f4ba", "9eaa6e09335047d5987c0a6528d5e77e", "39331a837a644795bada1e2b034fe14d", "a339562553394755811bb7077a81843d", "4f58f5fa385f44e0ae09e2019294d597", "431cc587951845d6af39f3e4ab0f2f76", "ac879c2c923b423dabd6d0d60b12266d", "4a3eb0fc1d2d4606a8acb382085a57af", "f82bbcbd14ad48a78ebdfdfb43916bd1", "a634449526034dbdb945c4905f4edeba", "da4133a915ad4d449876a43468203842", "a56f3a61d1ba4011ab6fce4067fa8418", "46727d5afdf7487fb073e7e2d25cc75d", "1480c5a1a0ad4151a12d47bc22685f04", "5720fc31c90344908d9eeb49fe83df1b", "47bd422d424a47e48edd304773162082", "6a68ecb89be34255a0e0fc6db41c1f4d", "12b1b3c7f0914030ad756b676cc97962", "780f5b6ad91142f991a936b55219f61d", "45723f91352b49688469a95e7f47aa9d", "90bbf502500340a1993a957c27ad3d33", "dcbb25b2a082476d905bcf124a849322", "38e6bd6d64b54f9a8cdb4f40eaf41cde", "7544a101cad94a15a2f4eb5639d22525", "501184a0fc424a02a80aecd3f62fb9fd", "1cbf5d28bced46ac8712a4609b5a5867", "9ba6ecc0a422472681d8e61bdb32f87b", "8383700148dd44538ed81ec5a261b7b5", "740e930ff17c4f668818a8c762a5470e", "0d995d8da0464c9ca7b1b444c22de025", "bc8a5e6d27ba402996434f00918c8b0e", "75f225b1a6f845148361b029878b63ea", "c16b051d3cb44607b339770f5f8b6f2e", "7857ed1e0b0f45bdb48269fbad68653d", "e65ffc77bd6740c7924aa5b93297cb89", "3ba97a41b4654dc0bb9bcaaa685b4518", "6504931865a74cb5a80f2ac60da47430", "ff8d5791b13c440d81312a6b96c9592f", "c31bd8cb693b4e248a29f2ded032fb70", "c25b1a42547a422ba7597c99ca4ce249", "5c6338fcad9344e092f5077bf73c4910", "dca176a7a6ea4fe9b025f851976f436a", "611fc076771a4fcca5c46367b711d61a", "0cfde45e26cf4c05b67755c2274f2df8", "5d572d2f46ea484587e085c29318b616", "8ad59c1261844a06b7abecebd7b60377", "82d610cd077c47bd9efd609f2399c861", "07fd58c9d07144a7a0aacab6b8252125", "aa0932b4e66b4f33ba9f5237ea1470da", "0d867615a23a42988bb91b4f0d0cc942", "9c28ff7b0f5c421390ac1ccb899f093f", "0645e7ed6593410eaf9c9c0b25158667", "ca68b1dc60a343f9bd7298a63cadd556", "9597ec6b495c4298b87967ed3e4044db", "d7dad0ae58814124af1e92a078122736", "173575b8b5254537937206759d6b6262", "d1efdc10d36441d88cf7705e846bfbef", "3550d450f95f40eeae0c0d559ae9f4de", "773df79ed7b44f698cce98ca9ed802cc", "24a2b4b88e1d46488eabb9101536beac", "0d61c01e6cdf445b9474f9d759676edd", "9b3505aacd164a19b45aba89eee46378", "bd6fb8b066be46aaa7d457bf89257e54", "f1eca4e5d600407885264d340b4f47a7", "ef1e6a69995845e09781f76a38fced30", "c5f50067867a40b99cb9f312e8adc49f", "4e9cf63dbef041aba2c7f0b9c74466c8", "07baa025b3a14bf89d6f6b438b695bbd", "fb8609ef5b8d4653b25e52f853b7be1f", "2f90ac58752347319d1203b5e8765c0e", "d9815cabf324472a8eab585afabfa47e", "120687e04065424595571941d816a134", "a6d09159931f4d3d91a0647d9fa9d8ba", "833c755f7bf9479abeef0041a82a92ba", "56d848676e644c739e28730af99d69c6", "39654eca8add4c09815cb3e6a45616ec", "02aa70e064744a29af0a68aaca33c741", "7fd83f95cfef4b1dbd881be7083d7455", "cdf56625053b417fad2e64a0bed6725b", "4199341c09bd46fab8a3b649d0c8af7a", "334e67f38b8243aa9072f52a32e46080", "492cdb40ffbf444d8e256875663fc598", "655c6e0f21ff4e9db7f35522355d847c", "926c1be6e26f4d9eba332881f975ed38", "47641f7363be4252b9f5e53846bee057", "887f8a2b268541eab71804a44ba1479b", "5b6e78d1727e473ab3b66d6ff042aeca", "acc60a1210104049983341db3010be0a", "c028c37980e14b3ea07b1da6f558651e", "9d1085906e3548078e5e393a86337c3e", "259c86a51f4e4cab9648cc603fc25c7e", "8ce05076e77643a88b062687e2b24493", "8b3b7f947f4d4401bbca47d5720f7450", "9bf9dccee248425da698dbb4526fcad9", "b991477124184bf3b4397762649a6596", "fb139bcae29f49778bf172eb503c0668", "93ba52daa9aa4dee8da91bba6c7d0269", "fefedf36efc94ed287bdeceae698d5b5", "8288b87b06b34ba4b2c7a343d6cea827", "5a85d212a6b5468fbc10e6aeb0ad8bee", "22274b08e14c4c77a6223131779f6f48", "edc0e436da954a33bbea8e80629eb43c", "1d9b1c594680467f9c8a6682d8aeb2e7", "d82f940e0a8a478e8b1ee8f169f798fc", "ab1616f507594b27b898ede4504b4e39", "1fed11f1c7484251a2a7400627ad5f6a" ] }, "id": "zmwIbufXUzMo", "outputId": "2acb6897-4c41-4447-e029-ffcc1b3b4da1" }, "outputs": [], "source": [ "# Fine Tuned Llama Model\n", "\n", "BASE_MODEL = \"meta-llama/Meta-Llama-3.1-8B\"\n", "FINETUNED_MODEL = \"ed-donner/pricer-2024-09-13_13.04.39\"\n", "REVISION = \"e8d637df551603dc86cd7a1598a8f44af4d7ae36\"\n", "\n", "# Quantization config (4-bit)\n", "quant_config = BitsAndBytesConfig(\n", " load_in_4bit=True,\n", " bnb_4bit_use_double_quant=True,\n", " bnb_4bit_compute_dtype=torch.bfloat16,\n", " bnb_4bit_quant_type=\"nf4\",\n", ")\n", "\n", "# Load tokenizer\n", "tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, trust_remote_code=True)\n", "tokenizer.pad_token = tokenizer.eos_token\n", "tokenizer.padding_side = \"right\"\n", "\n", "# Load base model\n", "base_model = AutoModelForCausalLM.from_pretrained(\n", " BASE_MODEL, quantization_config=quant_config, device_map=\"auto\"\n", ")\n", "\n", "# Load fine-tuned model\n", "fine_tuned_model = PeftModel.from_pretrained(\n", " base_model, FINETUNED_MODEL, revision=REVISION\n", ")\n", "\n", "# Align generation config\n", "fine_tuned_model.generation_config.pad_token_id = tokenizer.pad_token_id\n", "\n", "print(f\"Memory footprint: {fine_tuned_model.get_memory_footprint() / 1e6:.1f} MB\")" ] }, { "cell_type": "code", "execution_count": null, "id": "0IHiJNU7a4XC", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 49, "referenced_widgets": [ "0264a3987fbf4040860ffa3fc47940d8", "06d1db35940b469797c39c653741ea36", "84c4d2fdaf734a559ee3eee09f1be295", "fddde0bfed544b18ba39bfaa40eb9e1b", "d40cc525cc28416cad4a45b3631798c9", "e1372af176154902b1f555f30c28c007", "5a1352c5ceb84320b14353b7aa21650d", "522d0ed9e705457e9c72d276e2a26dbd", "4de73aa76f044811990c379737a8e5c0", "9305e96697ab4854ac89a6636991101d", "b00e41d1051340fd904ba719111a907d" ] }, "id": "0IHiJNU7a4XC", "outputId": "c68bc44e-6b15-46c3-c8d9-3f256f368317" }, "outputs": [], "source": [ "# XGBoost Trained Model\n", "\n", "MODEL_FILENAME = \"xgboost_model.pkl\"\n", "model_path = hf_hub_download(repo_id=REPO_ID, filename=MODEL_FILENAME, token=hf_token)\n", "xgb_model = joblib.load(model_path)" ] }, { "cell_type": "markdown", "id": "76BhcPjWa6C5", "metadata": { "id": "76BhcPjWa6C5" }, "source": [ "### ๐Ÿ“Š Model prediction collection" ] }, { "cell_type": "code", "execution_count": null, "id": "LgGmUKJxayZ6", "metadata": { "id": "LgGmUKJxayZ6" }, "outputs": [], "source": [ "def extract_tagged_price(output: str):\n", " \"\"\"Extracts a float price from a string based on 'Price is $' keyword.\"\"\"\n", " try:\n", " contents = output.split(\"Price is $\")[1].replace(\",\", \"\")\n", " match = re.search(r\"[-+]?\\d*\\.\\d+|\\d+\", contents)\n", " return float(match.group()) if match else 0.0\n", " except Exception:\n", " return 0.0" ] }, { "cell_type": "code", "execution_count": null, "id": "ggKf1nSQbAnv", "metadata": { "id": "ggKf1nSQbAnv" }, "outputs": [], "source": [ "def ft_llama_price(description: str):\n", " prompt = (\n", " f\"How much does this cost to the nearest dollar?\\n\\n{description}\\n\\nPrice is $\"\n", " )\n", " inputs = tokenizer(prompt, return_tensors=\"pt\").to(\"cuda\")\n", "\n", " outputs = fine_tuned_model.generate(\n", " **inputs, max_new_tokens=5, num_return_sequences=1\n", " )\n", "\n", " result = tokenizer.decode(outputs[0])\n", " price = extract_tagged_price(result)\n", " return price" ] }, { "cell_type": "code", "execution_count": null, "id": "_cWyYUd4Ub-K", "metadata": { "id": "_cWyYUd4Ub-K" }, "outputs": [], "source": [ "def xgboost_price(description: str):\n", " vector = embedding_model.encode([description], normalize_embeddings=True)[0]\n", " pred = xgb_model.predict([vector])[0]\n", " return round(float(max(0, pred)), 2)" ] }, { "cell_type": "code", "execution_count": null, "id": "3Skod8juXgnN", "metadata": { "id": "3Skod8juXgnN" }, "outputs": [], "source": [ "def gpt4o_price(item):\n", " def get_embedding(text):\n", " return embedding_model.encode([text], normalize_embeddings=True)\n", "\n", " def find_similars(text):\n", " results = collection.query(\n", " query_embeddings=get_embedding(text).astype(float).tolist(), n_results=5\n", " )\n", " docs = results[\"documents\"][0]\n", " prices = [m[\"price\"] for m in results[\"metadatas\"][0]]\n", " return docs, prices\n", "\n", " def format_context(similars, prices):\n", " context = (\n", " \"To provide some context, here are similar products and their prices:\\n\\n\"\n", " )\n", " for sim, price in zip(similars, prices):\n", " context += f\"Product:\\n{sim}\\nPrice is ${price:.2f}\\n\\n\"\n", " return context\n", "\n", " def build_messages(description, similars, prices):\n", " system_message = (\n", " \"You are a pricing expert. \"\n", " \"Given a product description and a few similar products with their prices, \"\n", " \"estimate the most likely price. \"\n", " \"Respond ONLY with a number, no words.\"\n", " )\n", " context = format_context(similars, prices)\n", " user_prompt = (\n", " \"Estimate the price for the following product:\\n\\n\"\n", " + description\n", " + \"\\n\\n\"\n", " + context\n", " )\n", " return [\n", " {\"role\": \"system\", \"content\": system_message},\n", " {\"role\": \"user\", \"content\": user_prompt},\n", " {\"role\": \"assistant\", \"content\": \"Price is $\"},\n", " ]\n", "\n", " docs, prices = find_similars(description(item))\n", " messages = build_messages(description(item), docs, prices)\n", " response = openai.chat.completions.create(\n", " model=\"gpt-4o-mini\", messages=messages, seed=42, max_tokens=5\n", " )\n", " reply = response.choices[0].message.content\n", " return float(\n", " re.search(r\"[-+]?\\d*\\.\\d+|\\d+\", reply.replace(\"$\", \"\").replace(\",\", \"\")).group()\n", " or 0\n", " )" ] }, { "cell_type": "markdown", "id": "98bf0aed", "metadata": {}, "source": [ "### โœ‚๏ธ Split dataset and process" ] }, { "cell_type": "code", "execution_count": null, "id": "8XQK5yrk8On4", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "8XQK5yrk8On4", "outputId": "ec379798-8b73-4e66-a517-a818845c8353" }, "outputs": [], "source": [ "print(\"Splitting entire dataset...\")\n", "np.random.seed(42)\n", "all_indices = list(range(len(test)))\n", "np.random.shuffle(all_indices)\n", "\n", "train_split_size = int(0.8 * len(all_indices))\n", "train_indices = all_indices[:train_split_size] # 80% of total\n", "test_indices = all_indices[train_split_size:] # 20% of total\n", "\n", "train_indices = train_indices[:250] # First 250 from training split\n", "test_indices = test_indices[:50] # First 50 from testing split" ] }, { "cell_type": "code", "execution_count": null, "id": "XN7P5fkkXfgP", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "XN7P5fkkXfgP", "outputId": "69f9d265-a402-48ab-a91e-8c6032ea4118" }, "outputs": [], "source": [ "# Process subset of TRAINING data\n", "ft_llama_preds_train = []\n", "gpt4omini_preds_train = []\n", "xgboost_preds_train = []\n", "true_prices_train = []\n", "\n", "for i in tqdm(train_indices):\n", " item = test[i]\n", " text = description(item)\n", " true_prices_train.append(item[\"price\"])\n", " ft_llama_preds_train.append(ft_llama_price(text))\n", " gpt4omini_preds_train.append(gpt4o_price(item))\n", " xgboost_preds_train.append(xgboost_price(text))" ] }, { "cell_type": "code", "execution_count": null, "id": "1_6_atEgHnFR", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "1_6_atEgHnFR", "outputId": "956e4dcb-2300-44ab-a66b-9b1254216762" }, "outputs": [], "source": [ "print(\"True Prices:\", true_prices_train)\n", "print(\"FT-LLaMA Predictions:\", ft_llama_preds_train)\n", "print(\"GPT-4o-mini Predictions:\", gpt4omini_preds_train)\n", "print(\"XGBoost Predictions:\", xgboost_preds_train)" ] }, { "cell_type": "markdown", "id": "ygJsuvtLtOdR", "metadata": { "id": "ygJsuvtLtOdR" }, "source": [ "Example :\n", "- True Prices: [245.0, 24.99, 302.4, 737.0, ...]\n", "- FT-LLaMA Predictions: [99.0, 53.0, 550.0, 852.0, ...]\n", "- GPT-4o-mini Predictions: [179.99, 97.0, 348.0, 769.0, ...]\n", "- XGBoost Predictions: [220.19, 59.85, 254.29, 335.76, 165.04, ...]" ] }, { "cell_type": "code", "execution_count": null, "id": "tYWMhTrXcA7x", "metadata": { "id": "tYWMhTrXcA7x" }, "outputs": [], "source": [ "# Create features for TRAINING data\n", "maxes_train = [\n", " max(a, b, c)\n", " for a, b, c in zip(ft_llama_preds_train, gpt4omini_preds_train, xgboost_preds_train)\n", "]\n", "means_train = [\n", " np.mean([a, b, c])\n", " for a, b, c in zip(ft_llama_preds_train, gpt4omini_preds_train, xgboost_preds_train)\n", "]\n", "\n", "# Create TRAINING dataframe\n", "X_train = pd.DataFrame(\n", " {\n", " \"FT_LLaMA\": ft_llama_preds_train,\n", " \"GPT4oMini\": gpt4omini_preds_train,\n", " \"XGBoost\": xgboost_preds_train,\n", " \"Max\": maxes_train,\n", " \"Mean\": means_train,\n", " }\n", ")\n", "\n", "y_train = pd.Series(true_prices_train)" ] }, { "cell_type": "markdown", "id": "e1682cf0", "metadata": {}, "source": [ "### ๐Ÿ‹๏ธTrain the Ensemble Model" ] }, { "cell_type": "code", "execution_count": null, "id": "-WsFABEicOyo", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "-WsFABEicOyo", "outputId": "42ae6421-fb4e-4ae6-ab54-b075e311b94d" }, "outputs": [], "source": [ "np.random.seed(42)\n", "lr = LinearRegression()\n", "lr.fit(X_train, y_train)\n", "\n", "# Print feature coefficients\n", "feature_columns = X_train.columns.tolist()\n", "for feature, coef in zip(feature_columns, lr.coef_):\n", " print(f\"{feature}: {coef:.2f}\")\n", "print(f\"Intercept={lr.intercept_:.2f}\")" ] }, { "cell_type": "markdown", "id": "GnYPOslHFgGx", "metadata": { "id": "GnYPOslHFgGx" }, "source": [ "- FT_LLaMA: 0.52\n", "- GPT4oMini: 0.17\n", "- XGBoost: -0.31\n", "- Max: 0.45\n", "- Mean: 0.13\n", "- Intercept=-6.06\n", "\n", "---\n", "FT_LLaMA is the most influential model in the ensemble.\n", "\n", "Max prediction also has strong positive impact.\n", "\n", "GPT4oMini and Mean contribute less, but still add value.\n", "\n", "XGBoost has a negative coefficient, acting as a counterbalance.\n", "\n", "\n", "Overall: FT_LLaMA leads, max adds value, XGBoost corrects for overestimationโ€”resulting in a balanced ensemble." ] }, { "cell_type": "markdown", "id": "wyx39HEL9niI", "metadata": { "id": "wyx39HEL9niI" }, "source": [ "### ๐Ÿ”ฎ Prediction" ] }, { "cell_type": "code", "execution_count": null, "id": "W3F0nNBXlrUJ", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "W3F0nNBXlrUJ", "outputId": "1dbd9702-50cf-4d80-b8ab-9b2000dd3b10" }, "outputs": [], "source": [ "# Process subset of TEST data\n", "ft_llama_preds_test = []\n", "gpt4omini_preds_test = []\n", "xgboost_preds_test = []\n", "true_prices_test = []\n", "\n", "print(\"Processing TEST data (50 items)...\")\n", "for i in tqdm(test_indices):\n", " item = test[i]\n", " text = description(item)\n", " true_prices_test.append(item[\"price\"])\n", " ft_llama_preds_test.append(ft_llama_price(text))\n", " gpt4omini_preds_test.append(gpt4o_price(item))\n", " xgboost_preds_test.append(xgboost_price(text))\n", "\n", "# Create features for TEST data\n", "maxes_test = [\n", " max(a, b, c)\n", " for a, b, c in zip(ft_llama_preds_test, gpt4omini_preds_test, xgboost_preds_test)\n", "]\n", "means_test = [\n", " np.mean([a, b, c])\n", " for a, b, c in zip(ft_llama_preds_test, gpt4omini_preds_test, xgboost_preds_test)\n", "]\n", "\n", "# Create TEST dataframe\n", "X_test = pd.DataFrame(\n", " {\n", " \"FT_LLaMA\": ft_llama_preds_test,\n", " \"GPT4oMini\": gpt4omini_preds_test,\n", " \"XGBoost\": xgboost_preds_test,\n", " \"Max\": maxes_test,\n", " \"Mean\": means_test,\n", " }\n", ")\n", "\n", "y_test = pd.Series(true_prices_test)" ] }, { "cell_type": "markdown", "id": "mVn6AAGq96wm", "metadata": { "id": "mVn6AAGq96wm" }, "source": [ "### ๐Ÿงช Evaluation" ] }, { "cell_type": "code", "execution_count": null, "id": "y25l8rR791wG", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "y25l8rR791wG", "outputId": "0a02a620-eb0d-46a6-8f54-1046c2394ab3" }, "outputs": [], "source": [ "# Evaluate on the test set\n", "print(\"Evaluating model...\")\n", "y_pred = lr.predict(X_test)\n", "r2 = r2_score(y_test, y_pred)\n", "print(f\"Rยฒ score: {r2:.4f}\")\n", "\n", "# Calculate RMSE\n", "rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n", "print(f\"RMSE: {rmse:.2f}\")\n", "\n", "# Calculate MAPE\n", "mape = np.mean(np.abs((y_test - y_pred) / y_test)) * 100\n", "print(f\"MAPE: {mape:.2f}%\")" ] }, { "cell_type": "markdown", "id": "vHJLe6LNEBrB", "metadata": { "id": "vHJLe6LNEBrB" }, "source": [ "Evaluating model...\n", "- Rยฒ score: 0.7376\n", "- RMSE: 127.62\n", "- MAPE: 29.70%\n", "\n", "---\n", "\n", "- Rยฒ = 0.74: This is a solid Rยฒ value, indicating our model explains about 74% of the variance in the price data\n", "Generally, an Rยฒ above 0.7 is considered good for price prediction tasks\n", "- RMSE = 127.6: Average error; good if prices are in the thousands.\n", "- MAPE = 29.7%: This means our predictions are off by roughly 30% on average. Typical for price prediction, but thereโ€™s room for improvement.\n" ] }, { "cell_type": "markdown", "id": "C6cEJ57WApkG", "metadata": { "id": "C6cEJ57WApkG" }, "source": [ "### ๐Ÿš€ Push to HF" ] }, { "cell_type": "code", "execution_count": null, "id": "_n7n_MnscS4r", "metadata": { "id": "_n7n_MnscS4r" }, "outputs": [], "source": [ "# Serialize Ensemble model locally for Hugging Face upload\n", "\n", "MODEL_DIR = os.path.join(ROOT, \"models\")\n", "MODEL_FILENAME = \"ensemble_model.pkl\"\n", "LOCAL_MODEL = os.path.join(MODEL_DIR, MODEL_FILENAME)\n", "\n", "os.makedirs(MODEL_DIR, exist_ok=True)\n", "joblib.dump(lr, LOCAL_MODEL)\n", "\n", "# Create the model repo if it doesn't exist\n", "api.create_repo(repo_id=REPO_ID, repo_type=\"model\", private=True, exist_ok=True)\n", "\n", "# Upload the saved model\n", "api.upload_file(\n", " path_or_fileobj=LOCAL_MODEL,\n", " path_in_repo=MODEL_FILENAME,\n", " repo_id=REPO_ID,\n", " repo_type=\"model\",\n", ")" ] } ], "metadata": { "accelerator": 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