9669 lines
290 KiB
Plaintext
9669 lines
290 KiB
Plaintext
{
|
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"cells": [
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "3ede0360-00f4-404e-b0d2-4a83cc385654",
|
||
"metadata": {
|
||
"id": "3ede0360-00f4-404e-b0d2-4a83cc385654"
|
||
},
|
||
"source": [
|
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"🔗 Ensemble Model\n",
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"---\n",
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"We’ll reuse core components built earlier:\n",
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"\n",
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"- A fine-tuned LLaMA model\n",
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"- An XGBoost regression model, stored in Hugging Face\n",
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"- A ChromaDB vector store, stored on Google Drive and also available on AWS S3\n",
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"- A GPT-4o mini + RAG pipeline\n",
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"\n",
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"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",
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"\n",
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"Once trained, we'll save the ensemble as ensemble_model.pkl, ready for later use.\n",
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"\n",
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"- 🧑💻 Skill Level: Advanced\n",
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"- ⚙️ Hardware: ⚠️ GPU required (use Google Colab)\n",
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"- 🛠️ Requirements: \n",
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"\n",
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" - 🔑 Hugging Face Token and OpenAI Key — must be set in Google Colab secrets or .env files if you are running with your own GPU\n",
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" - completion of Part 9 of [this series of notebooks](https://github.com/lisekarimi/lexo)\n",
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"- 🎯 Task: Train and save the Ensemble Model\n",
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"\n",
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"---\n",
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"📢 Find more LLM notebooks on my [GitHub repository](https://github.com/lisekarimi/lexo)"
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]
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},
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{
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||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "mzYB4XYQeWRQ",
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "mzYB4XYQeWRQ",
|
||
"outputId": "f474ce9b-09fb-4a47-93d7-273fe2d2ba10"
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||
},
|
||
"outputs": [],
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||
"source": [
|
||
"# Install required packages in Google Colab\n",
|
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"%pip install -q tqdm huggingface_hub numpy sentence-transformers datasets chromadb xgboost peft torch bitsandbytes"
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]
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||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "b3caecd1-8712-4acd-80b5-e8059c16f43f",
|
||
"metadata": {
|
||
"id": "b3caecd1-8712-4acd-80b5-e8059c16f43f"
|
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},
|
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"outputs": [],
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"source": [
|
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"# imports\n",
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"\n",
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"import os\n",
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"import re\n",
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"import zipfile\n",
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"import chromadb\n",
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"import joblib\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import requests\n",
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"import torch\n",
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"from datasets import load_dataset\n",
|
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"from google.colab import userdata\n",
|
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"from huggingface_hub import HfApi, hf_hub_download, login\n",
|
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"from openai import OpenAI\n",
|
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"from peft import PeftModel\n",
|
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"from sentence_transformers import SentenceTransformer\n",
|
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"from sklearn.linear_model import LinearRegression\n",
|
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"from sklearn.metrics import r2_score, mean_squared_error\n",
|
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"from sklearn.metrics import r2_score\n",
|
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"from tqdm import tqdm\n",
|
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"from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig"
|
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]
|
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},
|
||
{
|
||
"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",
|
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"\n",
|
||
"from google.colab import drive\n",
|
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"drive.mount(\"/content/drive\")"
|
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]
|
||
},
|
||
{
|
||
"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",
|
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"\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",
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"HF_USER = \"lisekarimi\"\n",
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"ROOT = \"/content/drive/MyDrive/snapr\"\n",
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"os.makedirs(ROOT, exist_ok=True)\n",
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"\n",
|
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"api = HfApi(token=hf_token)\n",
|
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"REPO_NAME = \"smart-deal-finder-models\"\n",
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"REPO_ID = f\"{HF_USER}/{REPO_NAME}\""
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "qByarIFiTYa1",
|
||
"metadata": {
|
||
"id": "qByarIFiTYa1"
|
||
},
|
||
"source": [
|
||
"### 📥 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",
|
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"dataset = load_dataset(DATASET_NAME)\n",
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||
"test = dataset[\"test\"]"
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]
|
||
},
|
||
{
|
||
"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",
|
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" \"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",
|
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"\n",
|
||
"\n",
|
||
"description(test[0])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "alpkYSc2UX0n",
|
||
"metadata": {
|
||
"id": "alpkYSc2UX0n"
|
||
},
|
||
"source": [
|
||
"### 📥 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": [
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"0084e537ffa74ab4a6d5f307b0916d2c",
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"0edeb9ca771c4ca2a9a678e0e8a91614",
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"86f34fb6325e4e878eee0be27946c88b",
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||
"bfc26f456f1d440bb80eedac1cb14967",
|
||
"1f005c3cf7594275a37bb937a3c33db3",
|
||
"4347e7d3db4d4cc3836a4e69db032f27",
|
||
"809a4d0270dd4c05817ea224bb78ff5a",
|
||
"b403a344e84342e4b076e64e829d7354",
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||
"6b3cbb0ac0b14e3fa193cd5cb3f8f521",
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||
"2d06aaf8d15b456b8fadeb54dd2ea73d",
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||
"74842e94dda648d18cd055220a3d2b39",
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||
"5a3818bde07841fbb6077bf20b7dec4b",
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||
"2a5224c8b3004d249a07297a2111493f",
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]
|
||
},
|
||
"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": [
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||
"36b26207185e4e10a8c60f0f5918aa7c",
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||
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||
"dcc68ce908ac4cfc9ffdcd0333cce14b",
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||
"06d7ac4abde74e1496ef80b9e22cd148",
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||
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|
||
"00b94b9d1e4e4e428c7467b4f99e45af",
|
||
"02219374302849bd93d7aba7f65ee42b",
|
||
"82223db2023045e88dc3d9652bc6b183",
|
||
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"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": [
|
||
"### 📊 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": [
|
||
"### ✂️ 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": [
|
||
"### 🏋️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 overestimation—resulting in a balanced ensemble."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "wyx39HEL9niI",
|
||
"metadata": {
|
||
"id": "wyx39HEL9niI"
|
||
},
|
||
"source": [
|
||
"### 🔮 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": [
|
||
"### 🧪 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\"R² 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",
|
||
"- R² score: 0.7376\n",
|
||
"- RMSE: 127.62\n",
|
||
"- MAPE: 29.70%\n",
|
||
"\n",
|
||
"---\n",
|
||
"\n",
|
||
"- R² = 0.74: This is a solid R² value, indicating our model explains about 74% of the variance in the price data\n",
|
||
"Generally, an R² 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 there’s room for improvement.\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "C6cEJ57WApkG",
|
||
"metadata": {
|
||
"id": "C6cEJ57WApkG"
|
||
},
|
||
"source": [
|
||
"### 🚀 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": "GPU",
|
||
"colab": {
|
||
"gpuType": "T4",
|
||
"provenance": []
|
||
},
|
||
"kernelspec": {
|
||
"display_name": "Python 3 (ipykernel)",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
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"version": "3.11.11"
|
||
},
|
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"widgets": {
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"application/vnd.jupyter.widget-state+json": {
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"model_module": "@jupyter-widgets/base",
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"_model_name": "LayoutModel",
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"_view_count": null,
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|
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|
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|
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|
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|
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|
||
"flex_flow": null,
|
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"grid_area": null,
|
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"grid_auto_columns": null,
|
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"grid_auto_flow": null,
|
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|
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|
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"grid_gap": null,
|
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"grid_row": null,
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