From e98c34d48dfbb89abd8dc795d28fba59e56b899a Mon Sep 17 00:00:00 2001
From: Jonas Thamane <166150947+NathiJonas@users.noreply.github.com>
Date: Mon, 9 Mar 2026 00:03:08 +0200
Subject: [PATCH 1/2] Create Jonas Thamane Week 6 PR..ipynb
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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "eb92f125",
+ "metadata": {},
+ "source": [
+ "\n",
+ "## Frontier Model Price Estimation\n",
+ "\n",
+ "**Pipeline:**\n",
+ "1. Load dataset from Hugging Face (`ed-donner/items_lite`)\n",
+ "2. Prepare training data in JSONL format\n",
+ "3. Build a few-shot Claude pricer using those examples\n",
+ "4. Evaluate with the full `Tester` class (scatter plot + error trend chart)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "feef3dbb",
+ "metadata": {},
+ "source": [
+ "## 1. Install Dependencies"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "c53214dd",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "[notice] A new release of pip is available: 24.0 -> 26.0.1\n",
+ "[notice] To update, run: c:\\Users\\Lenovo\\projects\\llm_engineering\\.venv\\Scripts\\python.exe -m pip install --upgrade pip\n"
+ ]
+ }
+ ],
+ "source": [
+ "import sys\n",
+ "!{sys.executable} -m pip install -q anthropic python-dotenv huggingface_hub datasets \\\n",
+ " scikit-learn pandas plotly tqdm"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1f1b6931",
+ "metadata": {},
+ "source": [
+ "## 2. Imports & Environment"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "60f766c0",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Note: Environment variable`HF_TOKEN` is set and is the current active token independently from the token you've just configured.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "HuggingFace token found: hf_akelc...\n",
+ "Anthropic API Key found: sk-ant-api03-me...\n",
+ "\n",
+ "Model : claude-haiku-4-5-20251001\n",
+ "Train : 60 examples (few-shot)\n"
+ ]
+ }
+ ],
+ "source": [
+ "import os\n",
+ "import re\n",
+ "import json\n",
+ "import math\n",
+ "from pathlib import Path\n",
+ "from itertools import accumulate\n",
+ "from concurrent.futures import ThreadPoolExecutor\n",
+ "from dataclasses import dataclass, field\n",
+ "from typing import Optional\n",
+ "\n",
+ "import pandas as pd\n",
+ "import plotly.express as px\n",
+ "import plotly.graph_objects as go\n",
+ "from sklearn.metrics import mean_squared_error, r2_score\n",
+ "from tqdm.notebook import tqdm\n",
+ "from dotenv import load_dotenv\n",
+ "from huggingface_hub import login\n",
+ "import anthropic\n",
+ "\n",
+ "load_dotenv(override=True)\n",
+ "\n",
+ "hf_token = os.getenv(\"HF_TOKEN\", \"\")\n",
+ "if hf_token:\n",
+ " login(hf_token, add_to_git_credential=True)\n",
+ " print(f\"HuggingFace token found: {hf_token[:8]}...\")\n",
+ "else:\n",
+ " print(\"⚠️ HF_TOKEN not set\")\n",
+ "\n",
+ "anthropic_key = os.getenv(\"ANTHROPIC_API_KEY\", \"\")\n",
+ "if anthropic_key:\n",
+ " print(f\"Anthropic API Key found: {anthropic_key[:15]}...\")\n",
+ "else:\n",
+ " print(\"⚠️ ANTHROPIC_API_KEY not set\")\n",
+ "\n",
+ "\n",
+ "LITE_MODE = False\n",
+ "MODEL = \"claude-haiku-4-5-20251001\" \n",
+ "N_TRAIN = 60 \n",
+ "N_VAL = 50 \n",
+ "WORKERS = 5\n",
+ "DEFAULT_SIZE = 200\n",
+ "\n",
+ "print(f\"\\nModel : {MODEL}\")\n",
+ "print(f\"Train : {N_TRAIN} examples (few-shot)\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "30395409",
+ "metadata": {},
+ "source": [
+ "## 3. Load Dataset from Hugging Face\n",
+ "\n",
+ "We use the same `ed-donner/items_lite` dataset as the original notebook.\n",
+ "The `Item` dataclass below replicates the interface of `pricer.items.Item`\n",
+ "so all downstream code works unchanged."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "e969f575",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from huggingface_hub import login\n",
+ "import os\n",
+ "os.environ[\"HF_TOKEN\"] = \"hf_8888888888888888888\"\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "913e1553",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "c:\\Users\\Lenovo\\projects\\llm_engineering\\.venv\\Lib\\site-packages\\huggingface_hub\\file_download.py:129: UserWarning: `huggingface_hub` cache-system uses symlinks by default to efficiently store duplicated files but your machine does not support them in C:\\Users\\Lenovo\\.cache\\huggingface\\hub\\datasets--ed-donner--items_lite. Caching files will still work but in a degraded version that might require more space on your disk. This warning can be disabled by setting the `HF_HUB_DISABLE_SYMLINKS_WARNING` environment variable. For more details, see https://huggingface.co/docs/huggingface_hub/how-to-cache#limitations.\n",
+ "To support symlinks on Windows, you either need to activate Developer Mode or to run Python as an administrator. In order to activate developer mode, see this article: https://docs.microsoft.com/en-us/windows/apps/get-started/enable-your-device-for-development\n",
+ " warnings.warn(message)\n",
+ "Generating train split: 100%|██████████| 20000/20000 [00:00<00:00, 271139.91 examples/s]\n",
+ "Generating validation split: 100%|██████████| 1000/1000 [00:00<00:00, 40156.86 examples/s]\n",
+ "Generating test split: 100%|██████████| 1000/1000 [00:00<00:00, 63921.97 examples/s]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Loaded 14,000 training items\n",
+ " 3,000 validation items\n",
+ " 3,000 test items\n",
+ "\n",
+ "Sample item:\n",
+ " title : Schlage F59 AND 613 Andover Interior Knob with Deadbolt, Oil Rubbed Bronze (Interior Half Only)\n",
+ " price : $64.30\n"
+ ]
+ }
+ ],
+ "source": [
+ "from datasets import load_dataset\n",
+ "\n",
+ "\n",
+ "@dataclass\n",
+ "class Item:\n",
+ " \"\"\"Mirrors the interface of pricer.items.Item.\"\"\"\n",
+ " title: str\n",
+ " price: float\n",
+ " summary: str = \"\"\n",
+ "\n",
+ " def __post_init__(self):\n",
+ " if not self.summary:\n",
+ " self.summary = self.title\n",
+ "\n",
+ " @classmethod\n",
+ " def from_hub(cls, dataset_name: str):\n",
+ " \"\"\"\n",
+ " Load train / val / test splits from a HuggingFace dataset.\n",
+ " Expects columns: title, price, and optionally: details / description.\n",
+ " \"\"\"\n",
+ " ds = load_dataset(dataset_name)\n",
+ " splits = {}\n",
+ " for split in [\"train\", \"validation\", \"test\"]:\n",
+ " key = split if split in ds else list(ds.keys())[0]\n",
+ " rows = ds[key]\n",
+ " items = []\n",
+ " for row in rows:\n",
+ " title = str(row.get(\"title\", \"\"))\n",
+ " price = float(row.get(\"price\", 0))\n",
+ " details = str(row.get(\"details\", \"\") or row.get(\"description\", \"\") or \"\")\n",
+ " summary = f\"{title}\\n{details}\".strip() if details else title\n",
+ " items.append(cls(title=title, price=price, summary=summary))\n",
+ " splits[split] = items\n",
+ " if split != \"train\": \n",
+ " break\n",
+ "\n",
+ " \n",
+ " all_items = splits.get(\"train\", [])\n",
+ " n = len(all_items)\n",
+ " t_end = int(n * 0.7)\n",
+ " v_end = int(n * 0.85)\n",
+ " return all_items[:t_end], all_items[t_end:v_end], all_items[v_end:]\n",
+ "\n",
+ "\n",
+ "\n",
+ "DATASET = \"ed-donner/items_lite\"\n",
+ "train, val, test = Item.from_hub(DATASET)\n",
+ "print(f\"Loaded {len(train):,} training items\")\n",
+ "print(f\" {len(val):,} validation items\")\n",
+ "print(f\" {len(test):,} test items\")\n",
+ "print(f\"\\nSample item:\")\n",
+ "print(f\" title : {train[0].title}\")\n",
+ "print(f\" price : ${train[0].price:.2f}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d2e351e6",
+ "metadata": {},
+ "source": [
+ "## 4. Subset the Training Data\n",
+ "\n",
+ "Same as the original: 60 training examples, 50 validation examples."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "9f0c014d",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Few-shot training examples : 60\n",
+ "Validation examples : 50\n"
+ ]
+ }
+ ],
+ "source": [
+ "fine_tune_train = train[:N_TRAIN]\n",
+ "fine_tune_validation = val[:N_VAL]\n",
+ "\n",
+ "print(f\"Few-shot training examples : {len(fine_tune_train)}\")\n",
+ "print(f\"Validation examples : {len(fine_tune_validation)}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d0f405c7",
+ "metadata": {},
+ "source": [
+ "# Step 1 — Prepare Data in JSONL Format\n",
+ "\n",
+ "We produce the same JSONL structure as the original OpenAI notebook.\n",
+ "Each line is a JSON object with a `messages` array containing a user turn and an assistant turn."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "296c11a7",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[{'role': 'user',\n",
+ " 'content': 'Estimate the price of this product. Respond with the price only, no explanation.\\n\\nSchlage F59 AND 613 Andover Interior Knob with Deadbolt, Oil Rubbed Bronze (Interior Half Only)'},\n",
+ " {'role': 'assistant', 'content': '$64.30'}]"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "def messages_for(item: Item) -> list[dict]:\n",
+ " \"\"\"Build a (user, assistant) message pair for one item.\"\"\"\n",
+ " user_content = (\n",
+ " f\"Estimate the price of this product. \"\n",
+ " f\"Respond with the price only, no explanation.\\n\\n{item.summary}\"\n",
+ " )\n",
+ " return [\n",
+ " {\"role\": \"user\", \"content\": user_content},\n",
+ " {\"role\": \"assistant\", \"content\": f\"${item.price:.2f}\"},\n",
+ " ]\n",
+ "\n",
+ "\n",
+ "\n",
+ "messages_for(fine_tune_train[0])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "89f7ee8e",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{\"messages\": [{\"role\": \"user\", \"content\": \"Estimate the price of this product. Respond with the price only, no explanation.\\n\\nSchlage F59 AND 613 Andover Interior Knob with Deadbolt, Oil Rubbed Bronze (Interior Half Only)\"}, {\"role\": \"assistant\", \"content\": \"$64.30\"}]}\n",
+ "{\"messages\": [{\"role\": \"user\", \"content\": \"Estimate the price of this product. Respond with the price only, no explanation.\\n\\nKiCA JETFAN 1.0 Mini Electric Air Duster Fan Blower Aluminum, 86,000RPM up to 11m/s Wind Speed for Computer Keyboard Electronics Cleaning, Outdoor Hiking, Camping, Air Cushion Inflation, BBQ\"}, {\"role\": \"assistant\", \"content\": \"$79.00\"}]}\n",
+ "{\"messages\": [{\"role\": \"user\", \"content\": \"Estimate the price of this product. Respond with the price only, no explanation.\\n\\nBose QuietComfort 35 (Series I) Wireless Noise Cancelling Headphones - Silver (Renewed)\"}, {\"role\": \"assistant\", \"content\": \"$240.00\"}]}\n"
+ ]
+ }
+ ],
+ "source": [
+ "def make_jsonl(items: list[Item]) -> str:\n",
+ " \"\"\"\n",
+ " Convert items to JSONL string.\n",
+ " Each line: {\"messages\": [{role, content}, ...]}\n",
+ " \"\"\"\n",
+ " lines = []\n",
+ " for item in items:\n",
+ " obj = {\"messages\": messages_for(item)}\n",
+ " lines.append(json.dumps(obj))\n",
+ " return \"\\n\".join(lines)\n",
+ "\n",
+ "\n",
+ "\n",
+ "print(make_jsonl(train[:3]))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "2f54b459",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Written 60 examples → jsonl/fine_tune_train.jsonl\n",
+ "Written 50 examples → jsonl/fine_tune_validation.jsonl\n"
+ ]
+ }
+ ],
+ "source": [
+ "def write_jsonl(items: list[Item], filename: str):\n",
+ " \"\"\"Write items to a JSONL file.\"\"\"\n",
+ " Path(filename).parent.mkdir(parents=True, exist_ok=True)\n",
+ " with open(filename, \"w\", encoding=\"utf-8\") as f:\n",
+ " f.write(make_jsonl(items))\n",
+ " print(f\"Written {len(items)} examples → {filename}\")\n",
+ "\n",
+ "\n",
+ "write_jsonl(fine_tune_train, \"jsonl/fine_tune_train.jsonl\")\n",
+ "write_jsonl(fine_tune_validation, \"jsonl/fine_tune_validation.jsonl\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "99323e23",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Training JSONL: 60 lines\n",
+ "First line: {\"messages\": [{\"role\": \"user\", \"content\": \"Estimate the price of this product. Respond with the price only, no explanati ...\n"
+ ]
+ }
+ ],
+ "source": [
+ "\n",
+ "with open(\"jsonl/fine_tune_train.jsonl\") as f:\n",
+ " lines = f.readlines()\n",
+ "print(f\"Training JSONL: {len(lines)} lines\")\n",
+ "print(\"First line:\", lines[0][:120], \"...\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "122f2aef",
+ "metadata": {},
+ "source": [
+ "# Step 2 — Build the Few-Shot Claude Pricer\n",
+ "\n",
+ "Since Anthropic does not offer a public fine-tuning API, we achieve the same effect by:\n",
+ "\n",
+ "1. Loading our 60 training examples\n",
+ "2. Injecting them as few-shot examples directly into the **system prompt**\n",
+ "3. Querying Claude with the new product as the final user turn\n",
+ "\n",
+ "This is the recommended Anthropic-native approach for adapting a model to a specific task\n",
+ "without fine-tuning — and for a 60-example dataset it performs comparably."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b47149bd",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "System prompt length: 9,017 characters\n",
+ "\n",
+ "First 400 chars:\n",
+ "You are an expert product pricer. When given a product description, you respond with ONLY the price in the format $X.XX — no explanation, no other text.\n",
+ "\n",
+ "Here are examples of correct pricing:\n",
+ "\n",
+ "Product: Schlage F59 AND 613 Andover Interior Knob with Deadbolt, Oil Rubbed Bronze (Interior Half Only)\n",
+ "Price: $64.30\n",
+ "\n",
+ "Product: KiCA JETFAN 1.0 Mini Electric Air Duster Fan Blower Aluminum, 86,000RPM up to ...\n"
+ ]
+ }
+ ],
+ "source": [
+ "client = anthropic.Anthropic(api_key=anthropic_key)\n",
+ "\n",
+ "\n",
+ "def build_few_shot_system_prompt(examples: list[Item]) -> str:\n",
+ " \"\"\"\n",
+ " Build a system prompt that embeds all training examples as few-shot demonstrations.\n",
+ " This is the Anthropic equivalent of fine-tuning on 60 examples.\n",
+ " \"\"\"\n",
+ " header = (\n",
+ " \"You are an expert product pricer. \"\n",
+ " \"When given a product description, you respond with ONLY the price in the format $X.XX — \"\n",
+ " \"no explanation, no other text.\\n\\n\"\n",
+ " \"Here are examples of correct pricing:\\n\\n\"\n",
+ " )\n",
+ " shots = []\n",
+ " for item in examples:\n",
+ " shots.append(\n",
+ " f\"Product: {item.summary[:300]}\\n\"\n",
+ " f\"Price: ${item.price:.2f}\"\n",
+ " )\n",
+ " return header + \"\\n\\n\".join(shots)\n",
+ "\n",
+ "\n",
+ "\n",
+ "FEW_SHOT_SYSTEM_PROMPT = build_few_shot_system_prompt(fine_tune_train)\n",
+ "\n",
+ "print(f\"System prompt length: {len(FEW_SHOT_SYSTEM_PROMPT):,} characters\")\n",
+ "print(f\"\\nFirst 400 chars:\\n{FEW_SHOT_SYSTEM_PROMPT[:400]}...\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "63df25c8",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "✅ claude_few_shot_pricer defined\n"
+ ]
+ }
+ ],
+ "source": [
+ "def claude_few_shot_pricer(item: Item) -> str:\n",
+ " \"\"\"\n",
+ " Inference function — mirrors gpt_4__1_nano_fine_tuned() from the original.\n",
+ " Uses the pre-built few-shot system prompt + Claude.\n",
+ " \"\"\"\n",
+ " user_message = (\n",
+ " f\"Estimate the price of this product. \"\n",
+ " f\"Respond with the price only, no explanation.\\n\\n{item.summary}\"\n",
+ " )\n",
+ " response = client.messages.create(\n",
+ " model=MODEL,\n",
+ " max_tokens=16, \n",
+ " system=FEW_SHOT_SYSTEM_PROMPT,\n",
+ " messages=[{\"role\": \"user\", \"content\": user_message}]\n",
+ " )\n",
+ " return response.content[0].text.strip()\n",
+ "\n",
+ "\n",
+ "print(\"✅ claude_few_shot_pricer defined\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a114e187",
+ "metadata": {},
+ "source": [
+ "# Step 3 — Test the Model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "id": "f73a1d5c",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Product : hansgrohe Croma Select S 7-inch Showerhead Premium Modern 2-Spray Rain, IntenseRain Easy Clean with QuickClean in Brushed Nickel, 26523821\n",
+ "Actual : $253.86\n",
+ "Claude : $249.99\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Smoke test on the first test item\n",
+ "sample = test[0]\n",
+ "prediction = claude_few_shot_pricer(sample)\n",
+ "\n",
+ "print(f\"Product : {sample.title}\")\n",
+ "print(f\"Actual : ${sample.price:.2f}\")\n",
+ "print(f\"Claude : {prediction}\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5de233c3",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Quick test on first 5 items:\n",
+ "\n",
+ "Product Actual Claude\n",
+ "--------------------------------------------------------------------\n",
+ "hansgrohe Croma Select S 7-inch Showerhead P $ 253.86 $149.99\n",
+ "BUMPERS THAT DELIVER - Primered, Front Upper $ 195.99 $189.99\n",
+ "Talent LP12LED PAR 36 DMX RGB LED Mini Light $ 37.80 $89.99\n",
+ "90W 65W AC Charger Fit for Dell Inspiron 550 $ 28.99 $28.99\n",
+ "Case for Xiaomi Mi Note 10 Lite Case Cover,3 $ 10.80 $12.99\n"
+ ]
+ }
+ ],
+ "source": [
+ "# A Quick Test\n",
+ "print(\"Quick test on first 5 items:\\n\")\n",
+ "print(f\"{'Product':<45} {'Actual':>10} {'Claude':>10}\")\n",
+ "print(\"-\" * 68)\n",
+ "for item in test[:5]:\n",
+ " pred = claude_few_shot_pricer(item)\n",
+ " title = item.title[:44]\n",
+ " print(f\"{title:<45} ${item.price:>8.2f} {pred:>10}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bbc94e00",
+ "metadata": {},
+ "source": [
+ "# Step 4 — Full Evaluation with Tester\n",
+ "\n",
+ "The `Tester` class below is identical to the original — threaded evaluation,\n",
+ "colour-coded errors, scatter chart, and running error trend chart."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "id": "301c5283",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "✅ Tester & evaluate defined (rate-limit safe)\n"
+ ]
+ }
+ ],
+ "source": [
+ "SAFE_WORKERS = 1\n",
+ "\n",
+ "GREEN = \"\\033[92m\"\n",
+ "YELLOW = \"\\033[93m\"\n",
+ "RED = \"\\033[91m\"\n",
+ "RESET = \"\\033[0m\"\n",
+ "\n",
+ "COLOR_MAP = {\"red\": RED, \"orange\": YELLOW, \"green\": GREEN}\n",
+ "\n",
+ "\n",
+ "class Tester:\n",
+ "\n",
+ " def __init__(self, predictor, data, title=None, size=DEFAULT_SIZE, workers=SAFE_WORKERS):\n",
+ "\n",
+ " self.predictor = predictor\n",
+ " self.data = data\n",
+ " self.title = title or self.make_title(predictor)\n",
+ "\n",
+ " self.size = size\n",
+ " self.workers = min(workers, SAFE_WORKERS)\n",
+ "\n",
+ " self.titles = []\n",
+ " self.guesses = []\n",
+ " self.truths = []\n",
+ " self.errors = []\n",
+ " self.colors = []\n",
+ "\n",
+ " @staticmethod\n",
+ " def make_title(predictor):\n",
+ "\n",
+ " return (\n",
+ " predictor.__name__\n",
+ " .replace(\"__\", \".\")\n",
+ " .replace(\"_\", \" \")\n",
+ " .title()\n",
+ " .replace(\"Gpt\", \"GPT\")\n",
+ " )\n",
+ "\n",
+ " @staticmethod\n",
+ " def post_process(value):\n",
+ " \"\"\"Extract numeric price safely\"\"\"\n",
+ "\n",
+ " if isinstance(value, str):\n",
+ "\n",
+ " value = value.replace(\"$\", \"\").replace(\",\", \"\")\n",
+ "\n",
+ " match = re.search(r\"[-+]?\\d*\\.\\d+|\\d+\", value)\n",
+ "\n",
+ " return float(match.group()) if match else float(\"nan\")\n",
+ "\n",
+ " return float(value)\n",
+ "\n",
+ " def color_for(self, error, truth):\n",
+ "\n",
+ " if truth == 0:\n",
+ " return \"orange\"\n",
+ "\n",
+ " if error < 40 or error / truth < 0.2:\n",
+ " return \"green\"\n",
+ "\n",
+ " elif error < 80 or error / truth < 0.4:\n",
+ " return \"orange\"\n",
+ "\n",
+ " return \"red\"\n",
+ "\n",
+ " def run_datapoint(self, i):\n",
+ "\n",
+ " datapoint = self.data[i]\n",
+ "\n",
+ " value = self.predictor(datapoint)\n",
+ "\n",
+ " guess = self.post_process(value)\n",
+ "\n",
+ " truth = datapoint.price\n",
+ "\n",
+ " error = abs(guess - truth)\n",
+ "\n",
+ " color = self.color_for(error, truth)\n",
+ "\n",
+ " title = datapoint.title if len(datapoint.title) <= 40 else datapoint.title[:40] + \"...\"\n",
+ "\n",
+ " return title, guess, truth, error, color\n",
+ "\n",
+ " def chart(self, title):\n",
+ "\n",
+ " df = pd.DataFrame({\n",
+ " \"truth\": self.truths,\n",
+ " \"guess\": self.guesses,\n",
+ " \"title\": self.titles,\n",
+ " \"error\": self.errors,\n",
+ " \"color\": self.colors,\n",
+ " })\n",
+ "\n",
+ " df[\"hover\"] = [\n",
+ " f\"{t}\\nGuess=${g:,.2f} Actual=${y:,.2f}\"\n",
+ " for t, g, y in zip(df[\"title\"], df[\"guess\"], df[\"truth\"])\n",
+ " ]\n",
+ "\n",
+ " max_val = float(max(df[\"truth\"].max(), df[\"guess\"].max()))\n",
+ "\n",
+ " fig = px.scatter(\n",
+ " df,\n",
+ " x=\"truth\",\n",
+ " y=\"guess\",\n",
+ " color=\"color\",\n",
+ " color_discrete_map={\n",
+ " \"green\": \"green\",\n",
+ " \"orange\": \"orange\",\n",
+ " \"red\": \"red\"\n",
+ " },\n",
+ " title=title,\n",
+ " labels={\"truth\": \"Actual Price\", \"guess\": \"Predicted Price\"},\n",
+ " width=1000,\n",
+ " height=800\n",
+ " )\n",
+ "\n",
+ " for tr in fig.data:\n",
+ "\n",
+ " mask = df[\"color\"] == tr.name\n",
+ "\n",
+ " tr.customdata = df.loc[mask, [\"hover\"]].to_numpy()\n",
+ "\n",
+ " tr.hovertemplate = \"%{customdata[0]}\"\n",
+ "\n",
+ " tr.marker.update(size=6)\n",
+ "\n",
+ " fig.add_trace(go.Scatter(\n",
+ " x=[0, max_val],\n",
+ " y=[0, max_val],\n",
+ " mode=\"lines\",\n",
+ " line=dict(width=2, dash=\"dash\", color=\"deepskyblue\"),\n",
+ " hoverinfo=\"skip\",\n",
+ " showlegend=False\n",
+ " ))\n",
+ "\n",
+ " fig.update_xaxes(range=[0, max_val])\n",
+ " fig.update_yaxes(range=[0, max_val])\n",
+ "\n",
+ " fig.update_layout(showlegend=False)\n",
+ "\n",
+ " fig.show()\n",
+ "\n",
+ " def error_trend_chart(self):\n",
+ "\n",
+ " n = len(self.errors)\n",
+ "\n",
+ " running_sums = list(accumulate(self.errors))\n",
+ "\n",
+ " x = list(range(1, n + 1))\n",
+ "\n",
+ " running_means = [s / i for s, i in zip(running_sums, x)]\n",
+ "\n",
+ " running_squares = list(accumulate(e * e for e in self.errors))\n",
+ "\n",
+ " running_stds = [\n",
+ " math.sqrt((sq / i) - (m ** 2)) if i > 1 else 0\n",
+ " for i, sq, m in zip(x, running_squares, running_means)\n",
+ " ]\n",
+ "\n",
+ " ci = [\n",
+ " 1.96 * (sd / math.sqrt(i)) if i > 1 else 0\n",
+ " for i, sd in zip(x, running_stds)\n",
+ " ]\n",
+ "\n",
+ " upper = [m + c for m, c in zip(running_means, ci)]\n",
+ "\n",
+ " lower = [m - c for m, c in zip(running_means, ci)]\n",
+ "\n",
+ " fig = go.Figure()\n",
+ "\n",
+ " fig.add_trace(go.Scatter(\n",
+ " x=x + x[::-1],\n",
+ " y=upper + lower[::-1],\n",
+ " fill=\"toself\",\n",
+ " fillcolor=\"rgba(128,128,128,0.2)\",\n",
+ " line=dict(color=\"rgba(255,255,255,0)\"),\n",
+ " hoverinfo=\"skip\"\n",
+ " ))\n",
+ "\n",
+ " fig.add_trace(go.Scatter(\n",
+ " x=x,\n",
+ " y=running_means,\n",
+ " mode=\"lines\",\n",
+ " line=dict(width=3, color=\"firebrick\")\n",
+ " ))\n",
+ "\n",
+ " final_mean = running_means[-1]\n",
+ "\n",
+ " final_ci = ci[-1]\n",
+ "\n",
+ " fig.update_layout(\n",
+ " title=f\"{self.title} Error: ${final_mean:,.2f} ± ${final_ci:,.2f}\",\n",
+ " xaxis_title=\"Number of Datapoints\",\n",
+ " yaxis_title=\"Average Absolute Error ($)\",\n",
+ " width=1000,\n",
+ " height=360,\n",
+ " template=\"plotly_white\",\n",
+ " showlegend=False\n",
+ " )\n",
+ "\n",
+ " fig.show()\n",
+ "\n",
+ " def report(self):\n",
+ "\n",
+ " avg_error = sum(self.errors) / len(self.errors)\n",
+ "\n",
+ " mse = mean_squared_error(self.truths, self.guesses)\n",
+ "\n",
+ " r2 = r2_score(self.truths, self.guesses) * 100\n",
+ "\n",
+ " title = (\n",
+ " f\"{self.title} results
\"\n",
+ " f\"Error: ${avg_error:,.2f} \"\n",
+ " f\"MSE: {mse:,.0f} \"\n",
+ " f\"r²: {r2:.1f}%\"\n",
+ " )\n",
+ "\n",
+ " self.error_trend_chart()\n",
+ "\n",
+ " self.chart(title)\n",
+ "\n",
+ " def run(self):\n",
+ "\n",
+ " for i in tqdm(range(self.size)):\n",
+ "\n",
+ " title, guess, truth, error, color = self.run_datapoint(i)\n",
+ "\n",
+ " self.titles.append(title)\n",
+ " self.guesses.append(guess)\n",
+ " self.truths.append(truth)\n",
+ " self.errors.append(error)\n",
+ " self.colors.append(color)\n",
+ "\n",
+ " print(f\"{COLOR_MAP[color]}${error:.0f} \", end=\"\")\n",
+ "\n",
+ " print(RESET)\n",
+ "\n",
+ " self.report()\n",
+ "\n",
+ "\n",
+ "def evaluate(function, data, size=DEFAULT_SIZE, workers=SAFE_WORKERS):\n",
+ "\n",
+ " Tester(function, data, size=size, workers=workers).run()\n",
+ "\n",
+ "\n",
+ "print(\"✅ Tester & evaluate defined (rate-limit safe)\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "id": "45fb5095",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from tqdm import tqdm\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "df2e7c58",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import time\n",
+ "from anthropic import RateLimitError\n",
+ "\n",
+ "REQUEST_DELAY = 3 \n",
+ "\n",
+ "\n",
+ "def claude_few_shot_pricer(item):\n",
+ " \"\"\"\n",
+ " Claude inference with automatic rate-limit retry and throttling\n",
+ " \"\"\"\n",
+ "\n",
+ " user_message = (\n",
+ " \"Estimate the price of this product. \"\n",
+ " \"Respond with the price only, no explanation.\\n\\n\"\n",
+ " f\"{item.summary}\"\n",
+ " )\n",
+ "\n",
+ " while True:\n",
+ " try:\n",
+ " response = client.messages.create(\n",
+ " model=MODEL,\n",
+ " max_tokens=16,\n",
+ " system=FEW_SHOT_SYSTEM_PROMPT,\n",
+ " messages=[{\"role\": \"user\", \"content\": user_message}]\n",
+ " )\n",
+ "\n",
+ " time.sleep(REQUEST_DELAY) \n",
+ " return response.content[0].text.strip()\n",
+ "\n",
+ " except RateLimitError:\n",
+ " print(\"⚠️ Claude rate limit hit — waiting 8 seconds...\")\n",
+ " time.sleep(8)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0372803a",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
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+ "cell_type": "code",
+ "execution_count": 34,
+ "id": "06741096",
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+ ]
+ },
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+ ]
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Error: $62.51 MSE: 9,900 r²: 63.0%"
+ },
+ "width": 1000,
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+ "source": [
+ "# ── Run the full evaluation ────────────────────────────────────────────────\n",
+ "# This queries Claude for every test item (up to DEFAULT_SIZE=200) in parallel.\n",
+ "# Expect it to take 1-3 minutes depending on rate limits.\n",
+ "\n",
+ "evaluate(claude_few_shot_pricer, test, size=50)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a0994f77",
+ "metadata": {},
+ "source": [
+ "## Bonus — Zero-Shot Baseline\n",
+ "\n",
+ "Compare performance against a plain Claude with no training examples."
+ ]
+ },
+ {
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+ "execution_count": 35,
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+ "#19d3f3",
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+ "#FF97FF",
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+ ],
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+ "geo": {
+ "bgcolor": "white",
+ "lakecolor": "white",
+ "landcolor": "#E5ECF6",
+ "showlakes": true,
+ "showland": true,
+ "subunitcolor": "white"
+ },
+ "hoverlabel": {
+ "align": "left"
+ },
+ "hovermode": "closest",
+ "mapbox": {
+ "style": "light"
+ },
+ "paper_bgcolor": "white",
+ "plot_bgcolor": "#E5ECF6",
+ "polar": {
+ "angularaxis": {
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": ""
+ },
+ "bgcolor": "#E5ECF6",
+ "radialaxis": {
+ "gridcolor": "white",
+ "linecolor": "white",
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+ }
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+ "scene": {
+ "xaxis": {
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+ "zerolinecolor": "white"
+ },
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+ "backgroundcolor": "#E5ECF6",
+ "gridcolor": "white",
+ "gridwidth": 2,
+ "linecolor": "white",
+ "showbackground": true,
+ "ticks": "",
+ "zerolinecolor": "white"
+ },
+ "zaxis": {
+ "backgroundcolor": "#E5ECF6",
+ "gridcolor": "white",
+ "gridwidth": 2,
+ "linecolor": "white",
+ "showbackground": true,
+ "ticks": "",
+ "zerolinecolor": "white"
+ }
+ },
+ "shapedefaults": {
+ "line": {
+ "color": "#2a3f5f"
+ }
+ },
+ "ternary": {
+ "aaxis": {
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": ""
+ },
+ "baxis": {
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": ""
+ },
+ "bgcolor": "#E5ECF6",
+ "caxis": {
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": ""
+ }
+ },
+ "title": {
+ "x": 0.05
+ },
+ "xaxis": {
+ "automargin": true,
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": "",
+ "title": {
+ "standoff": 15
+ },
+ "zerolinecolor": "white",
+ "zerolinewidth": 2
+ },
+ "yaxis": {
+ "automargin": true,
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": "",
+ "title": {
+ "standoff": 15
+ },
+ "zerolinecolor": "white",
+ "zerolinewidth": 2
+ }
+ }
+ },
+ "title": {
+ "text": "Claude Zero Shot results
Error: $81.43 MSE: 16,869 r²: 36.9%"
+ },
+ "width": 1000,
+ "xaxis": {
+ "anchor": "y",
+ "domain": [
+ 0,
+ 1
+ ],
+ "range": [
+ 0,
+ 899.99
+ ],
+ "title": {
+ "text": "Actual Price"
+ }
+ },
+ "yaxis": {
+ "anchor": "x",
+ "domain": [
+ 0,
+ 1
+ ],
+ "range": [
+ 0,
+ 899.99
+ ],
+ "title": {
+ "text": "Predicted Price"
+ }
+ }
+ }
+ }
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "def claude_zero_shot(item: Item) -> str:\n",
+ " \"\"\"Claude with no few-shot examples — baseline comparison.\"\"\"\n",
+ " response = client.messages.create(\n",
+ " model=MODEL,\n",
+ " max_tokens=16,\n",
+ " system=\"You are an expert product pricer. Respond with ONLY the price in the format $X.XX.\",\n",
+ " messages=[{\n",
+ " \"role\": \"user\",\n",
+ " \"content\": f\"Estimate the price of this product:\\n\\n{item.summary}\"\n",
+ " }]\n",
+ " )\n",
+ " return response.content[0].text.strip()\n",
+ "\n",
+ "\n",
+ "# Evaluate zero-shot baseline (smaller sample for speed)\n",
+ "evaluate(claude_zero_shot, test, size=50)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "76c2e49e",
+ "metadata": {},
+ "source": [
+ "## Summary\n",
+ "\n",
+ "| Component | Original (OpenAI) | This notebook (Anthropic) |\n",
+ "|---|---|---|\n",
+ "| LLM | GPT-4.1-nano (fine-tuned) | Claude Haiku (few-shot) |\n",
+ "| Training data | 60 examples → JSONL → upload → training job | 60 examples → JSONL → injected into system prompt |\n",
+ "| Inference | Fine-tuned model endpoint | Claude API with few-shot system prompt |\n",
+ "| Evaluation | `Tester` class + Plotly charts | Identical `Tester` class + Plotly charts |\n",
+ "| Dataset | `ed-donner/items_lite` (HuggingFace) | Same |\n",
+ "\n",
+ "### Key takeaways\n",
+ "- Few-shot prompting with 60 examples is a strong substitute for fine-tuning on small datasets\n",
+ "- The JSONL preparation pipeline is identical and future-proof — if Anthropic releases a fine-tuning API, the same files can be uploaded directly\n",
+ "- Swap `claude-haiku-4-5-20251001` → `claude-sonnet-4-6` for higher accuracy at higher cost\n",
+ "- Swap `MODEL` and `N_TRAIN` at the top of the notebook to experiment"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": ".venv (3.11.9)",
+ "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",
+ "version": "3.11.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
From 20049453c37908684b139e2eb820ec7b6a929017 Mon Sep 17 00:00:00 2001
From: Jonas Thamane <166150947+NathiJonas@users.noreply.github.com>
Date: Mon, 9 Mar 2026 19:43:23 +0200
Subject: [PATCH 2/2] Update Jonas Thamane Week 6 PR..ipynb
Fixed the outputs issue.
---
.../Jonas Thamane Week 6 PR..ipynb | 6082 +----------------
1 file changed, 25 insertions(+), 6057 deletions(-)
diff --git a/community-contributions/Jonas Thamane Week 6 PR..ipynb b/community-contributions/Jonas Thamane Week 6 PR..ipynb
index 9d61c00031..d0678120aa 100644
--- a/community-contributions/Jonas Thamane Week 6 PR..ipynb
+++ b/community-contributions/Jonas Thamane Week 6 PR..ipynb
@@ -25,20 +25,10 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"id": "c53214dd",
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "\n",
- "[notice] A new release of pip is available: 24.0 -> 26.0.1\n",
- "[notice] To update, run: c:\\Users\\Lenovo\\projects\\llm_engineering\\.venv\\Scripts\\python.exe -m pip install --upgrade pip\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"import sys\n",
"!{sys.executable} -m pip install -q anthropic python-dotenv huggingface_hub datasets \\\n",
@@ -58,26 +48,7 @@
"execution_count": null,
"id": "60f766c0",
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Note: Environment variable`HF_TOKEN` is set and is the current active token independently from the token you've just configured.\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "HuggingFace token found: hf_akelc...\n",
- "Anthropic API Key found: sk-ant-api03-me...\n",
- "\n",
- "Model : claude-haiku-4-5-20251001\n",
- "Train : 60 examples (few-shot)\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"import os\n",
"import re\n",
@@ -139,14 +110,14 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": null,
"id": "e969f575",
"metadata": {},
"outputs": [],
"source": [
"from huggingface_hub import login\n",
"import os\n",
- "os.environ[\"HF_TOKEN\"] = \"hf_8888888888888888888\"\n",
+ "os.environ[\"HF_TOKEN\"] = \"hf_9999989998989898\"\n",
"\n"
]
},
@@ -155,33 +126,7 @@
"execution_count": null,
"id": "913e1553",
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\Users\\Lenovo\\projects\\llm_engineering\\.venv\\Lib\\site-packages\\huggingface_hub\\file_download.py:129: UserWarning: `huggingface_hub` cache-system uses symlinks by default to efficiently store duplicated files but your machine does not support them in C:\\Users\\Lenovo\\.cache\\huggingface\\hub\\datasets--ed-donner--items_lite. Caching files will still work but in a degraded version that might require more space on your disk. This warning can be disabled by setting the `HF_HUB_DISABLE_SYMLINKS_WARNING` environment variable. For more details, see https://huggingface.co/docs/huggingface_hub/how-to-cache#limitations.\n",
- "To support symlinks on Windows, you either need to activate Developer Mode or to run Python as an administrator. In order to activate developer mode, see this article: https://docs.microsoft.com/en-us/windows/apps/get-started/enable-your-device-for-development\n",
- " warnings.warn(message)\n",
- "Generating train split: 100%|██████████| 20000/20000 [00:00<00:00, 271139.91 examples/s]\n",
- "Generating validation split: 100%|██████████| 1000/1000 [00:00<00:00, 40156.86 examples/s]\n",
- "Generating test split: 100%|██████████| 1000/1000 [00:00<00:00, 63921.97 examples/s]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Loaded 14,000 training items\n",
- " 3,000 validation items\n",
- " 3,000 test items\n",
- "\n",
- "Sample item:\n",
- " title : Schlage F59 AND 613 Andover Interior Knob with Deadbolt, Oil Rubbed Bronze (Interior Half Only)\n",
- " price : $64.30\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from datasets import load_dataset\n",
"\n",
@@ -238,31 +183,12 @@
"print(f\" price : ${train[0].price:.2f}\")"
]
},
- {
- "cell_type": "markdown",
- "id": "d2e351e6",
- "metadata": {},
- "source": [
- "## 4. Subset the Training Data\n",
- "\n",
- "Same as the original: 60 training examples, 50 validation examples."
- ]
- },
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": null,
"id": "9f0c014d",
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Few-shot training examples : 60\n",
- "Validation examples : 50\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"fine_tune_train = train[:N_TRAIN]\n",
"fine_tune_validation = val[:N_VAL]\n",
@@ -287,20 +213,7 @@
"execution_count": null,
"id": "296c11a7",
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[{'role': 'user',\n",
- " 'content': 'Estimate the price of this product. Respond with the price only, no explanation.\\n\\nSchlage F59 AND 613 Andover Interior Knob with Deadbolt, Oil Rubbed Bronze (Interior Half Only)'},\n",
- " {'role': 'assistant', 'content': '$64.30'}]"
- ]
- },
- "execution_count": 14,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"def messages_for(item: Item) -> list[dict]:\n",
" \"\"\"Build a (user, assistant) message pair for one item.\"\"\"\n",
@@ -323,17 +236,7 @@
"execution_count": null,
"id": "89f7ee8e",
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "{\"messages\": [{\"role\": \"user\", \"content\": \"Estimate the price of this product. Respond with the price only, no explanation.\\n\\nSchlage F59 AND 613 Andover Interior Knob with Deadbolt, Oil Rubbed Bronze (Interior Half Only)\"}, {\"role\": \"assistant\", \"content\": \"$64.30\"}]}\n",
- "{\"messages\": [{\"role\": \"user\", \"content\": \"Estimate the price of this product. Respond with the price only, no explanation.\\n\\nKiCA JETFAN 1.0 Mini Electric Air Duster Fan Blower Aluminum, 86,000RPM up to 11m/s Wind Speed for Computer Keyboard Electronics Cleaning, Outdoor Hiking, Camping, Air Cushion Inflation, BBQ\"}, {\"role\": \"assistant\", \"content\": \"$79.00\"}]}\n",
- "{\"messages\": [{\"role\": \"user\", \"content\": \"Estimate the price of this product. Respond with the price only, no explanation.\\n\\nBose QuietComfort 35 (Series I) Wireless Noise Cancelling Headphones - Silver (Renewed)\"}, {\"role\": \"assistant\", \"content\": \"$240.00\"}]}\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"def make_jsonl(items: list[Item]) -> str:\n",
" \"\"\"\n",
@@ -353,19 +256,10 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": null,
"id": "2f54b459",
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Written 60 examples → jsonl/fine_tune_train.jsonl\n",
- "Written 50 examples → jsonl/fine_tune_validation.jsonl\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"def write_jsonl(items: list[Item], filename: str):\n",
" \"\"\"Write items to a JSONL file.\"\"\"\n",
@@ -384,16 +278,7 @@
"execution_count": null,
"id": "99323e23",
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Training JSONL: 60 lines\n",
- "First line: {\"messages\": [{\"role\": \"user\", \"content\": \"Estimate the price of this product. Respond with the price only, no explanati ...\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"\n",
"with open(\"jsonl/fine_tune_train.jsonl\") as f:\n",
@@ -424,25 +309,7 @@
"execution_count": null,
"id": "b47149bd",
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "System prompt length: 9,017 characters\n",
- "\n",
- "First 400 chars:\n",
- "You are an expert product pricer. When given a product description, you respond with ONLY the price in the format $X.XX — no explanation, no other text.\n",
- "\n",
- "Here are examples of correct pricing:\n",
- "\n",
- "Product: Schlage F59 AND 613 Andover Interior Knob with Deadbolt, Oil Rubbed Bronze (Interior Half Only)\n",
- "Price: $64.30\n",
- "\n",
- "Product: KiCA JETFAN 1.0 Mini Electric Air Duster Fan Blower Aluminum, 86,000RPM up to ...\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"client = anthropic.Anthropic(api_key=anthropic_key)\n",
"\n",
@@ -479,15 +346,7 @@
"execution_count": null,
"id": "63df25c8",
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "✅ claude_few_shot_pricer defined\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"def claude_few_shot_pricer(item: Item) -> str:\n",
" \"\"\"\n",
@@ -520,20 +379,10 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": null,
"id": "f73a1d5c",
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Product : hansgrohe Croma Select S 7-inch Showerhead Premium Modern 2-Spray Rain, IntenseRain Easy Clean with QuickClean in Brushed Nickel, 26523821\n",
- "Actual : $253.86\n",
- "Claude : $249.99\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# Smoke test on the first test item\n",
"sample = test[0]\n",
@@ -549,23 +398,7 @@
"execution_count": null,
"id": "5de233c3",
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Quick test on first 5 items:\n",
- "\n",
- "Product Actual Claude\n",
- "--------------------------------------------------------------------\n",
- "hansgrohe Croma Select S 7-inch Showerhead P $ 253.86 $149.99\n",
- "BUMPERS THAT DELIVER - Primered, Front Upper $ 195.99 $189.99\n",
- "Talent LP12LED PAR 36 DMX RGB LED Mini Light $ 37.80 $89.99\n",
- "90W 65W AC Charger Fit for Dell Inspiron 550 $ 28.99 $28.99\n",
- "Case for Xiaomi Mi Note 10 Lite Case Cover,3 $ 10.80 $12.99\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# A Quick Test\n",
"print(\"Quick test on first 5 items:\\n\")\n",
@@ -577,31 +410,12 @@
" print(f\"{title:<45} ${item.price:>8.2f} {pred:>10}\")"
]
},
- {
- "cell_type": "markdown",
- "id": "bbc94e00",
- "metadata": {},
- "source": [
- "# Step 4 — Full Evaluation with Tester\n",
- "\n",
- "The `Tester` class below is identical to the original — threaded evaluation,\n",
- "colour-coded errors, scatter chart, and running error trend chart."
- ]
- },
{
"cell_type": "code",
- "execution_count": 33,
+ "execution_count": null,
"id": "301c5283",
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "✅ Tester & evaluate defined (rate-limit safe)\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"SAFE_WORKERS = 1\n",
"\n",
@@ -853,7 +667,7 @@
},
{
"cell_type": "code",
- "execution_count": 30,
+ "execution_count": null,
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Error: $62.51 MSE: 9,900 r²: 63.0%"
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"source": [
"# ── Run the full evaluation ────────────────────────────────────────────────\n",
"# This queries Claude for every test item (up to DEFAULT_SIZE=200) in parallel.\n",
@@ -3842,2940 +738,12 @@
"evaluate(claude_few_shot_pricer, test, size=50)"
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"cell_type": "code",
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"source": [
"def claude_zero_shot(item: Item) -> str:\n",
" \"\"\"Claude with no few-shot examples — baseline comparison.\"\"\"\n",