{ "cells": [ { "cell_type": "markdown", "id": "40d49349-faaa-420c-9b65-0bdc9edfabce", "metadata": {}, "source": [ "# The Price is Right\n", "\n", "Today we build a more complex solution for estimating prices of goods.\n", "\n", "1. Day 2.0 notebook: create a RAG database with our 400,000 training data\n", "2. Day 2.1 notebook: visualize in 2D\n", "3. Day 2.2 notebook: visualize in 3D\n", "4. Day 2.3 notebook: build and test a RAG pipeline with GPT-4o-mini\n", "5. Day 2.4 notebook: (a) bring back our Random Forest pricer (b) Create a Ensemble pricer that allows contributions from all the pricers\n", "\n", "Phew! That's a lot to get through in one day!\n", "\n", "## PLEASE NOTE:\n", "\n", "We already have a very powerful product estimator with our proprietary, fine-tuned LLM. Most people would be very satisfied with that! The main reason we're adding these extra steps is to deepen your expertise with RAG and with Agentic workflows.\n", "\n", "## Finishing off with Random Forests & Ensemble" ] }, { "cell_type": "code", "execution_count": null, "id": "fbcdfea8-7241-46d7-a771-c0381a3e7063", "metadata": {}, "outputs": [], "source": [ "# imports\n", "\n", "import os\n", "import re\n", "import math\n", "import json\n", "from tqdm import tqdm\n", "import random\n", "from dotenv import load_dotenv\n", "from huggingface_hub import login\n", "import numpy as np\n", "import pickle\n", "from openai import OpenAI\n", "from sentence_transformers import SentenceTransformer\n", "from datasets import load_dataset\n", "import chromadb\n", "from items import Item\n", "from testing import Tester\n", "import pandas as pd\n", "import numpy as np\n", "from sklearn.ensemble import RandomForestRegressor\n", "from sklearn.linear_model import LinearRegression\n", "from sklearn.metrics import mean_squared_error, r2_score\n", "import joblib\n" ] }, { "cell_type": "code", "execution_count": null, "id": "e6e88bd1-f89c-4b98-92fa-aa4bc1575bca", "metadata": {}, "outputs": [], "source": [ "# CONSTANTS\n", "\n", "QUESTION = \"How much does this cost to the nearest dollar?\\n\\n\"\n", "DB = \"products_vectorstore\"" ] }, { "cell_type": "code", "execution_count": null, "id": "98666e73-938e-469d-8987-e6e55ba5e034", "metadata": {}, "outputs": [], "source": [ "# environment\n", "\n", "load_dotenv(override=True)\n", "os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY', 'your-key-if-not-using-env')\n", "os.environ['HF_TOKEN'] = os.getenv('HF_TOKEN', 'your-key-if-not-using-env')" ] }, { "cell_type": "code", "execution_count": null, "id": "dc696493-0b6f-48aa-9fa8-b1ae0ecaf3cd", "metadata": {}, "outputs": [], "source": [ "# Load in the test pickle file:\n", "\n", "with open('test.pkl', 'rb') as file:\n", " test = pickle.load(file)" ] }, { "cell_type": "code", "execution_count": null, "id": "d26a1104-cd11-4361-ab25-85fb576e0582", "metadata": {}, "outputs": [], "source": [ "client = chromadb.PersistentClient(path=DB)\n", "collection = client.get_or_create_collection('products')" ] }, { "cell_type": "code", "execution_count": null, "id": "e00b82a9-a8dc-46f1-8ea9-2f07cbc8e60d", "metadata": {}, "outputs": [], "source": [ "result = collection.get(include=['embeddings', 'documents', 'metadatas'])\n", "vectors = np.array(result['embeddings'])\n", "documents = result['documents']\n", "prices = [metadata['price'] for metadata in result['metadatas']]" ] }, { "cell_type": "markdown", "id": "bf6492cb-b11a-4ad5-859b-a71a78ffb949", "metadata": {}, "source": [ "# Random Forest\n", "\n", "We will now train a Random Forest model.\n", "\n", "Can you spot the difference from what we did in Week 6? In week 6 we used the word2vec model to form vectors; this time we'll use the vectors we already have in Chroma, from the SentenceTransformer model." ] }, { "cell_type": "code", "execution_count": null, "id": "48894777-101f-4fe5-998c-47079407f340", "metadata": {}, "outputs": [], "source": [ "# This next line takes an hour on my M1 Mac!\n", "\n", "rf_model = RandomForestRegressor(n_estimators=100, random_state=42, n_jobs=-1)\n", "rf_model.fit(vectors, prices)" ] }, { "cell_type": "code", "execution_count": null, "id": "62eb7ddf-e1da-481e-84c6-1256547566bd", "metadata": {}, "outputs": [], "source": [ "# Save the model to a file\n", "\n", "joblib.dump(rf_model, 'random_forest_model.pkl')" ] }, { "cell_type": "code", "execution_count": null, "id": "d281dc5e-761e-4a5e-86b3-29d9c0a33d4a", "metadata": {}, "outputs": [], "source": [ "# Load it back in again\n", "\n", "rf_model = joblib.load('random_forest_model.pkl')" ] }, { "cell_type": "code", "execution_count": null, "id": "5d438dec-8e5b-4e60-bb6f-c3f82e522dd9", "metadata": {}, "outputs": [], "source": [ "from agents.specialist_agent import SpecialistAgent\n", "from agents.frontier_agent import FrontierAgent\n", "from agents.random_forest_agent import RandomForestAgent" ] }, { "cell_type": "code", "execution_count": null, "id": "afc39369-b97b-4a90-b17e-b20ef501d3c9", "metadata": {}, "outputs": [], "source": [ "specialist = SpecialistAgent()\n", "frontier = FrontierAgent(collection)\n", "random_forest = RandomForestAgent()" ] }, { "cell_type": "code", "execution_count": null, "id": "8e2d0d0a-8bb8-4b39-b046-322828c39244", "metadata": {}, "outputs": [], "source": [ "def description(item):\n", " return item.prompt.split(\"to the nearest dollar?\\n\\n\")[1].split(\"\\n\\nPrice is $\")[0]" ] }, { "cell_type": "code", "execution_count": null, "id": "bfe0434f-b29e-4cc0-bad9-b07624665727", "metadata": {}, "outputs": [], "source": [ "def rf(item):\n", " return random_forest.price(description(item))" ] }, { "cell_type": "code", "execution_count": null, "id": "cdf233ec-264f-4b34-9f2b-27c39692137b", "metadata": {}, "outputs": [], "source": [ "Tester.test(rf, test)" ] }, { "cell_type": "code", "execution_count": null, "id": "9f759bd2-7a7e-4c1a-80a0-e12470feca89", "metadata": {}, "outputs": [], "source": [ "product = \"Quadcast HyperX condenser mic for high quality audio for podcasting\"" ] }, { "cell_type": "code", "execution_count": null, "id": "e44dbd25-fb95-4b6b-bbbb-8da5fc817105", "metadata": {}, "outputs": [], "source": [ "print(specialist.price(product))\n", "print(frontier.price(product))\n", "print(random_forest.price(product))" ] }, { "cell_type": "code", "execution_count": null, "id": "1779b353-e2bb-4fc7-be7c-93057e4d688a", "metadata": {}, "outputs": [], "source": [ "specialists = []\n", "frontiers = []\n", "random_forests = []\n", "prices = []\n", "for item in tqdm(test[1000:1250]):\n", " text = description(item)\n", " specialists.append(specialist.price(text))\n", " frontiers.append(frontier.price(text))\n", " random_forests.append(random_forest.price(text))\n", " prices.append(item.price)" ] }, { "cell_type": "code", "execution_count": null, "id": "f0bca725-4e34-405b-8d90-41d67086a25d", "metadata": {}, "outputs": [], "source": [ "mins = [min(s,f,r) for s,f,r in zip(specialists, frontiers, random_forests)]\n", "maxes = [max(s,f,r) for s,f,r in zip(specialists, frontiers, random_forests)]\n", "\n", "X = pd.DataFrame({\n", " 'Specialist': specialists,\n", " 'Frontier': frontiers,\n", " 'RandomForest': random_forests,\n", " 'Min': mins,\n", " 'Max': maxes,\n", "})\n", "\n", "# Convert y to a Series\n", "y = pd.Series(prices)" ] }, { "cell_type": "code", "execution_count": null, "id": "1be5be8a-3e7f-42a2-be54-0c7e380f7cc4", "metadata": {}, "outputs": [], "source": [ "# Train a Linear Regression\n", "np.random.seed(42)\n", "\n", "lr = LinearRegression()\n", "lr.fit(X, y)\n", "\n", "feature_columns = X.columns.tolist()\n", "\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": "code", "execution_count": null, "id": "0bdf6e68-28a3-4ed2-b17e-de0ede923d34", "metadata": {}, "outputs": [], "source": [ "joblib.dump(lr, 'ensemble_model.pkl')" ] }, { "cell_type": "code", "execution_count": null, "id": "e762441a-9470-4dd7-8a8f-ec0430e908c7", "metadata": {}, "outputs": [], "source": [ "from agents.ensemble_agent import EnsembleAgent\n", "ensemble = EnsembleAgent(collection)" ] }, { "cell_type": "code", "execution_count": null, "id": "1a29f03c-8010-43b7-ae7d-1bc85ca6e8e2", "metadata": {}, "outputs": [], "source": [ "ensemble.price(product)" ] }, { "cell_type": "code", "execution_count": null, "id": "e6a5e226-a508-43d5-aa42-cefbde72ffdf", "metadata": {}, "outputs": [], "source": [ "def ensemble_pricer(item):\n", " return max(0,ensemble.price(description(item)))" ] }, { "cell_type": "code", "execution_count": null, "id": "8397b1ef-2ea3-4af8-bb34-36594e0600cc", "metadata": {}, "outputs": [], "source": [ "Tester.test(ensemble_pricer, test)" ] }, { "cell_type": "markdown", "id": "347c5350-d4b5-42ae-96f6-ec94f6ab41d7", "metadata": {}, "source": [ "# WHAT A DAY!\n", "\n", "We got so much done - a Fronter RAG pipeline, a Random Forest model using transformer-based encodings, and an Ensemble model.\n", "\n", "You can do better, for sure!\n", "\n", "Tweak this, and try adding components into the ensemble, to beat my performance." ] }, { "cell_type": "code", "execution_count": null, "id": "85009065-851e-44a2-b39f-4c116f7fbd22", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "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", "version": "3.11.11" } }, "nbformat": 4, "nbformat_minor": 5 }