From 263d9a69faa1543901a4911d33fd85b75bbefd57 Mon Sep 17 00:00:00 2001 From: Tochi-Nwachukwu Date: Mon, 27 Oct 2025 07:53:59 +0100 Subject: [PATCH] Submission for Week 6 Exercise --- .../tochi/product_pricer_finetuning.ipynb | 1683 +++++++++++++++++ 1 file changed, 1683 insertions(+) create mode 100644 week6/community-contributions/tochi/product_pricer_finetuning.ipynb diff --git a/week6/community-contributions/tochi/product_pricer_finetuning.ipynb b/week6/community-contributions/tochi/product_pricer_finetuning.ipynb new file mode 100644 index 0000000..18625e4 --- /dev/null +++ b/week6/community-contributions/tochi/product_pricer_finetuning.ipynb @@ -0,0 +1,1683 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "3plmEok3ExZR" + }, + "source": [ + "# Product Price Prediction: Fine-Tuning GPT-4o-mini to Beat $76 Baseline Error\n", + "\n", + "## Project Description\n", + "\n", + "This project fine-tunes OpenAI's GPT-4o-mini model to predict product prices based solely on product descriptions. The goal is to improve upon the baseline mean absolute error of $76 by training the model to estimate prices from textual product information including specifications, features, and descriptions.\n", + "\n", + "** What I did Differently ** \n", + "- I used a specially curated open source subset of the Amazon Review Dataset to train the model\n", + "\n", + "The dataset used for this project is the **Amazon Product Price Prediction Dataset** curated specifically for LLM fine-tuning by Jai Keshav Sharma (2024). This dataset contains 400,000 Amazon product listings with detailed descriptions and corresponding prices, making it ideal for training language models on price estimation tasks.\n", + "\n", + "### Dataset Citation\n", + "```\n", + "@dataset{sharma2024amazon_price_prediction,\n", + " title={Amazon Product Price Prediction Dataset: Curated for LLM Fine-tuning},\n", + " author={Jai Keshav Sharma},\n", + " year={2024},\n", + " publisher={Hugging Face},\n", + " url={https://huggingface.co/datasets/ksharma9719/Amazon-Reviews-2023-curated_for_price_prediction}\n", + "}\n", + "```\n", + "\n", + "The model is trained to output price estimates in a standardized format, enabling e-commerce applications such as automated pricing, market analysis, and competitive intelligence.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Hst-keo-WWlf" + }, + "source": [ + "Project Description:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "D9gjGvJX8oze" + }, + "outputs": [], + "source": [ + "# imports\n", + "\n", + "import os\n", + "from google.colab import userdata\n", + "import json\n", + "from dotenv import load_dotenv\n", + "from huggingface_hub import login\n", + "from datasets import load_dataset\n", + "import matplotlib.pyplot as plt\n", + "from collections import Counter, defaultdict\n", + "import numpy as np\n", + "import pandas as pd\n", + "from openai import OpenAI\n", + "from typing import Optional\n", + "import re\n", + "from datasets import load_dataset\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "PMh_o7wRF9BK" + }, + "outputs": [], + "source": [ + "hf_token = userdata.get('HF_TOKEN')\n", + "openai_api_key = userdata.get('OPENAI_API_KEY')\n", + "\n", + "login(hf_token, add_to_git_credential=True)\n", + "openai = OpenAI(api_key=openai_api_key)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 113, + "referenced_widgets": [ + "7310470b4a064a44b136f70054041618", + "41482711593a4c28beec209690300478", + "f6d3e158b3d043a884d8c19510a0dfc4", + "b521eab4d01a4993b3be35ea66d12811", + "7807f509779f4f2d891de798ffc1476c", + "5411e5e9e393488dbeb2a255da336180", + "e4f79de41af545be8faad495aeefa2bd", + "2d441fd6a41944fc90190dac589a1131", + "4efc837f649847a1b8a6c651cfcaf7fe", + "02cbae7ae9674de08b58989185c31678", + "e11a725a01ca4092a30e93a9be5d8237", + "59a3948488c54d5b8f62111ac1f4fe6b", + "5c2681fe88f94c8b9040d7c852066c71", + "eb50ffb13e99429f9f97eaab1fd3a518", + "7db75a41217947d8baaad1bae70c3e94", + "d9b2ae0cf0564dbcac954be0a0fd82e9", + "824ac35608b64ee18b2ac4120a141c7a", + "5c846b08710b47a6bb290952faff7beb", + "4e10820a60454823abcbfc98c8f6dc05", + "b4fd2590613542dfa4094a7a0641f939", + "8ede9aef6a6a4f5d837bf205f3cba707", + "28dd05003dcf42c5b2bae63f8cdeb709", + "8a26d6ea6e90444ab201ce610c6f6375", + "17501e8dcca847dc9a741c79363423a3", + "bc6d06024ffd4d359ac58786ef7567d1", + "6907060854534820ab8c58653c9995f5", + "ca274208b30846d6b7938c0124fcdfa3", + "c447f3a3f833421499aba5b8c6e1c3d0", + "c91a04c8314f4850abbaa051eabfb529", + "a9b37dc49c39414897b8472b4da8d384", + "38516c43b95f423a8b16dd98f60201b1", + "ba214b2fd4f647669cfbe5086fb60d63", + "6b376c825b664e248b9b52aaa022afbb" + ] + }, + "id": "6jM-39pw81ub", + "outputId": "a931e235-2b02-42f1-a93d-18f215742957" + }, + "outputs": [], + "source": [ + "# This is the specially curated dataset from ksharrma\n", + "dataset = load_dataset(\n", + " \"ksharma9719/Amazon-Reviews-2023-curated_for_price_prediction\",\n", + " data_files=\"data/train-00000-of-00001.parquet\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "rcvgtV1w_XQ3" + }, + "outputs": [], + "source": [ + "# Access the training data, and dividing it into train and test data\n", + "total_length = len(dataset[\"train\"])\n", + "train_data = dataset[\"train\"].select(range(total_length - 2000))\n", + "test_data = dataset[\"train\"].select(range(total_length - 2000, total_length))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "cIsBDDewCW5T", + "outputId": "3349341e-9d61-4033-f15a-4760f513c9c3" + }, + "outputs": [], + "source": [ + "print(f\"Total entries: {total_length}\")\n", + "print(f\"Training entries: {len(train_data)}\")\n", + "print(f\"Test entries: {len(test_data)}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "UlssOY6oPYwj" + }, + "outputs": [], + "source": [ + "# OpenAI recommends fine-tuning with populations of 50-100 examples\n", + "# But as our examples are very small, I'm suggesting we go with 200 examples (and 1 epoch)\n", + "\n", + "fine_tune_train = train_data.select(range(200))\n", + "fine_tune_validation = train_data.select(range(200,250))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NALlYuKMPgxs" + }, + "source": [ + "## Preparing the data for Fine Tuning Using JSONL" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Nlhgv_nwPe4h" + }, + "outputs": [], + "source": [ + "def messages_for(item):\n", + " system_message = \"You are a price estimation assistant. Respond only with the estimated price in the format: Price is $X.XX\"\n", + "\n", + " # Clean the user prompt more robustly\n", + " user_prompt = item[\"text\"]\n", + " user_prompt = user_prompt.replace(\" to the nearest dollar\", \"\")\n", + " user_prompt = user_prompt.replace(\"\\n\\nPrice is $\", \"\")\n", + "\n", + " # Remove any trailing price information if it exists\n", + " if user_prompt.endswith(str(item[\"price\"])):\n", + " user_prompt = user_prompt.rsplit(str(item[\"price\"]), 1)[0].strip()\n", + "\n", + " return [\n", + " {\"role\": \"system\", \"content\": system_message},\n", + " {\"role\": \"user\", \"content\": user_prompt.strip()},\n", + " {\"role\": \"assistant\", \"content\": f\"Price is ${item['price']:.2f}\"}\n", + " ]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "vwZgVsTHP1Kl", + "outputId": "97bc2bfe-ed65-4465-ffc4-c269eed96108" + }, + "outputs": [], + "source": [ + "messages_for(train_data[0])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "V4wuKvLZQxDx" + }, + "outputs": [], + "source": [ + "# Convert the items into a list of json objects - a \"jsonl\" string\n", + "# Each row represents a message in the form:\n", + "# {\"messages\" : [{\"role\": \"system\", \"content\": \"You estimate prices...\n", + "\n", + "def make_jsonl(items):\n", + " lines = []\n", + " for item in items:\n", + " messages = messages_for(item)\n", + " json_obj = {\"messages\": messages}\n", + " lines.append(json.dumps(json_obj))\n", + " return '\\n'.join(lines)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "0xP5tMiyQ2le", + "outputId": "1cf0b37f-001a-4487-b8c0-e4ce291d63c8" + }, + "outputs": [], + "source": [ + "print(make_jsonl(train_data.select(range(3))))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "t2_q5hiNStKX" + }, + "outputs": [], + "source": [ + "\n", + "def write_jsonl(items, filename):\n", + " with open(filename, \"w\") as f:\n", + " jsonl = make_jsonl(items)\n", + " f.write(jsonl)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "kqhKyb9fS4ny" + }, + "outputs": [], + "source": [ + "write_jsonl(fine_tune_train, \"fine_tune_train.jsonl\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ZKpVJrlwUO3Z" + }, + "outputs": [], + "source": [ + "write_jsonl(fine_tune_validation, \"fine_tune_validation.jsonl\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ES82SYR1Ug9e" + }, + "outputs": [], + "source": [ + "with open(\"fine_tune_train.jsonl\", \"rb\") as f:\n", + " train_file = openai.files.create(file=f, purpose=\"fine-tune\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "-oygMJ7GV_cu", + "outputId": "be56458b-938c-488b-abb6-d48cca56b466" + }, + "outputs": [], + "source": [ + "train_file" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "owDVG0HtWCP-" + }, + "outputs": [], + "source": [ + "with open(\"fine_tune_validation.jsonl\", \"rb\") as f:\n", + " validation_file = openai.files.create(file=f, purpose=\"fine-tune\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "gt4BtFEiWEcx", + "outputId": "2dd875e6-106c-4fe7-a33a-55ce55320ab7" + }, + "outputs": [], + "source": [ + "validation_file" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "j5U9HVFFXSgF" + }, + "outputs": [], + "source": [ + "wandb_integration = {\"type\": \"wandb\", \"wandb\": {\"project\": \"gpt-pricer\"}}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "PZvKoAydXWOk", + "outputId": "5c2c9df0-b63c-45ed-d33c-5e311292d9b2" + }, + "outputs": [], + "source": [ + "openai.fine_tuning.jobs.create(\n", + " training_file=train_file.id,\n", + " validation_file=validation_file.id,\n", + " model=\"gpt-4o-mini-2024-07-18\",\n", + " seed=42,\n", + " hyperparameters={\"n_epochs\": 1},\n", + " integrations = [wandb_integration],\n", + " suffix=\"pricer\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "GgQXC6q5Xx4a", + "outputId": "1c2f7a6e-daeb-4633-c747-8385fe124709" + }, + "outputs": [], + "source": [ + "# job_id = openai.fine_tuning.jobs.list(limit=1).data[0].id\n", + "job_id=\"ftjob-kMWRKdN9t8H0lDAxzHT5kmeB\"\n", + "print(job_id)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "jZskxChzXtO8", + "outputId": "b982bc19-2b68-4838-ae1d-9133065d721f" + }, + "outputs": [], + "source": [ + "openai.fine_tuning.jobs.retrieve(job_id)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "yqwIWxmiYRs3", + "outputId": "f0e02411-06f5-496e-fb0c-6c8bfffa918f" + }, + "outputs": [], + "source": [ + "fine_tuned_model_name = openai.fine_tuning.jobs.retrieve(job_id).fine_tuned_model\n", + "print(fine_tuned_model_name)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "d2dkrZlKYWCI" + }, + "outputs": [], + "source": [ + "# Try this out\n", + "def messages_for(item):\n", + " system_message = \"You estimate prices of items. Reply only with the price, no explanation\"\n", + " user_prompt = item[\"text\"].replace(\" to the nearest dollar\",\"\").replace(\"\\n\\nPrice is $\",\"\")\n", + " return [\n", + " {\"role\": \"system\", \"content\": system_message},\n", + " {\"role\": \"user\", \"content\": user_prompt},\n", + " {\"role\": \"assistant\", \"content\": \"Price is $\"}\n", + " ]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "lwoQbNSeYY1S" + }, + "outputs": [], + "source": [ + "\n", + "def get_price(s):\n", + " s = s.replace('$','').replace(',','')\n", + " match = re.search(r\"[-+]?\\d*\\.\\d+|\\d+\", s)\n", + " return float(match.group()) if match else 0" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "GN59aWE2YmD6", + "outputId": "109afd7f-1b91-4a59-fce3-0fdaed14ea39" + }, + "outputs": [], + "source": [ + "get_price(\"The price is roughly $99.99 because blah blah\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "FDm8z8LXYquQ" + }, + "outputs": [], + "source": [ + "# The function for gpt-4o-mini\n", + "\n", + "def gpt_fine_tuned(item):\n", + " response = openai.chat.completions.create(\n", + " model=fine_tuned_model_name,\n", + " messages=messages_for(item),\n", + " seed=42,\n", + " max_tokens=7\n", + " )\n", + " reply = response.choices[0].message.content\n", + " return get_price(reply)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "OWGEt6ZUYsop", + "outputId": "56a4dade-eafa-4128-fa16-52ef0252bad6" + }, + "outputs": [], + "source": [ + "item = test_data.select([0])[0] # Select returns a dataset, so index [0] to get the item\n", + "print(item[\"price\"])\n", + "print(gpt_fine_tuned(item))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "2Hiov1XUMVB3" + }, + "outputs": [], + "source": [ + "import math\n", + "import matplotlib.pyplot as plt\n", + "\n", + "GREEN = \"\\033[92m\"\n", + "BLUE = \"\\033[94m\"\n", + "YELLOW = \"\\033[93m\"\n", + "RED = \"\\033[91m\"\n", + "RESET = \"\\033[0m\"\n", + "COLOR_MAP = {\"red\":RED, \"orange\": YELLOW, \"green\": GREEN, \"blue\":BLUE}\n", + "\n", + "class Tester:\n", + "\n", + " def __init__(self, predictor, data, title=None, size=250):\n", + " self.predictor = predictor\n", + " self.data = data\n", + " self.title = title or predictor.__name__.replace(\"_\", \" \").title()\n", + " self.size = size\n", + " self.guesses = []\n", + " self.truths = []\n", + " self.errors = []\n", + " self.sles = []\n", + " self.colors = []\n", + "\n", + " def color_for(self, error, truth):\n", + " if error<40 or error/truth < 0.2:\n", + " return \"blue\"\n", + " elif error<80 or error/truth < 0.4:\n", + " return \"orange\"\n", + " else:\n", + " return \"red\"\n", + "\n", + " def run_datapoint(self, i):\n", + " datapoint = self.data[i]\n", + " guess = self.predictor(datapoint)\n", + " truth = datapoint[\"price\"]\n", + " error = abs(guess - truth)\n", + " log_error = math.log(truth+1) - math.log(guess+1)\n", + " sle = log_error ** 2\n", + " color = self.color_for(error, truth)\n", + " title = datapoint[\"text\"] if len(datapoint[\"text\"]) <= 40 else datapoint[\"text\"][:40]+\"...\"\n", + " self.guesses.append(guess)\n", + " self.truths.append(truth)\n", + " self.errors.append(error)\n", + " self.sles.append(sle)\n", + " self.colors.append(color)\n", + " print(f\"{COLOR_MAP[color]}{i+1}: Guess: ${guess:,.2f} Truth: ${truth:,.2f} Error: ${error:,.2f} SLE: {sle:,.2f} Item: {title}{RESET}\")\n", + "\n", + " def chart(self, title):\n", + " max_error = max(self.errors)\n", + " plt.figure(figsize=(12, 8))\n", + " max_val = max(max(self.truths), max(self.guesses))\n", + " plt.plot([0, max_val], [0, max_val], color='deepskyblue', lw=2, alpha=0.6)\n", + " plt.scatter(self.truths, self.guesses, s=3, c=self.colors)\n", + " plt.xlabel('Ground Truth')\n", + " plt.ylabel('Model Estimate')\n", + " plt.xlim(0, max_val)\n", + " plt.ylim(0, max_val)\n", + " plt.title(title)\n", + " plt.show()\n", + "\n", + " def report(self):\n", + " average_error = sum(self.errors) / self.size\n", + " rmsle = math.sqrt(sum(self.sles) / self.size)\n", + " hits = sum(1 for color in self.colors if color==\"blue\")\n", + " title = f\"{self.title} Error=${average_error:,.2f} RMSLE={rmsle:,.2f} Hits={hits/self.size*100:.1f}%\"\n", + " self.chart(title)\n", + "\n", + " def run(self):\n", + " self.error = 0\n", + " for i in range(self.size):\n", + " self.run_datapoint(i)\n", + " self.report()\n", + "\n", + " @classmethod\n", + " def test(cls, function, data):\n", + " cls(function, data).run()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "JGUWcFLrMcKX", + "outputId": 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