example/example_notebook_advanced.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a13d6843ef4301",
   "metadata": {},
   "source": [
    "# Advanced LLM Evaluation with pytest-evals\n",
    "\n",
    "This notebook demonstrates advanced techniques for evaluating LLM performance:\n",
    "1. Running parallel evaluations across multiple models\n",
    "2. Tracking and comparing results across different runs\n",
    "3. Visualizing performance trends and model comparisons\n",
    "4. Statistical analysis of model performance\n",
    "\n",
    "## Setup\n",
    "First, let's load required extensions and configure our environment."
   ]
  },
  {
   "cell_type": "code",
   "id": "cefdc9561d3ab77a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-21T08:33:47.364267Z",
     "start_time": "2025-01-21T08:33:47.278694Z"
    }
   },
   "source": [
    "%load_ext pytest_evals"
   ],
   "outputs": [],
   "execution_count": 1
  },
  {
   "cell_type": "markdown",
   "id": "80ea828ddde1c26a",
   "metadata": {},
   "source": [
    "## Model Implementation\n",
    "\n",
    "Define our classifier that leverages different LLM models to determine if text is computer-related."
   ]
  },
  {
   "cell_type": "code",
   "id": "6e55ed323e3cc980",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-21T08:33:49.412049Z",
     "start_time": "2025-01-21T08:33:49.211224Z"
    }
   },
   "source": [
    "import openai\n",
    "\n",
    "\n",
    "def classify(text: str, model=\"gpt-4o-mini\") -> bool:\n",
    "    \"\"\"Classify text as computer-related or not using specified LLM model.\n",
    "\n",
    "    Args:\n",
    "        text (str): Input text to classify\n",
    "        model (str): Model identifier (e.g., \"gpt-4o\", \"gpt-4o-mini\")\n",
    "\n",
    "    Returns:\n",
    "        bool: True if text is computer-related, False otherwise\n",
    "    \"\"\"\n",
    "    resp = openai.chat.completions.create(\n",
    "        model=model,\n",
    "        messages=[\n",
    "            {\n",
    "                \"role\": \"system\",\n",
    "                \"content\": \"Is this text about a computer-related subject? \"\n",
    "                \"Reply ONLY with either true or false.\",\n",
    "            },\n",
    "            {\"role\": \"user\", \"content\": text},\n",
    "        ],\n",
    "    )\n",
    "    return resp.choices[0].message.content.lower() == \"true\""
   ],
   "outputs": [],
   "execution_count": 2
  },
  {
   "cell_type": "markdown",
   "id": "51c2d42c6a10f861",
   "metadata": {},
   "source": [
    "## Test Data and Results Tracking\n",
    "\n",
    "Set up our test cases and initialize our experiment tracking DataFrame."
   ]
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-21T08:33:50.609927Z",
     "start_time": "2025-01-21T08:33:50.605050Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Define test cases\n",
    "TEST_DATA = [\n",
    "    {\"text\": \"I need to debug this Python code\", \"label\": True},\n",
    "    {\"text\": \"The cat jumped over the lazy dog\", \"label\": False},\n",
    "    {\"text\": \"My monitor keeps flickering\", \"label\": True},\n",
    "    {\"text\": \"The weather is nice today\", \"label\": False},\n",
    "    {\"text\": \"Updating system drivers fixed the issue\", \"label\": True},\n",
    "    {\"text\": \"the new llama can understand bizzare nuanced slang\", \"label\": True},\n",
    "]\n",
    "\n",
    "# Initialize experiment tracking\n",
    "experiments_df = pd.DataFrame()"
   ],
   "id": "58c440f913c3d97",
   "outputs": [],
   "execution_count": 3
  },
  {
   "cell_type": "markdown",
   "id": "959af2e2c4f8670",
   "metadata": {},
   "source": [
    "## Run Model Evaluations\n",
    "\n",
    "Execute evaluations across different models and collect detailed metrics."
   ]
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-21T08:34:02.226687Z",
     "start_time": "2025-01-21T08:33:52.406707Z"
    }
   },
   "cell_type": "code",
   "source": [
    "%%ipytest_evals --run-eval\n",
    "import pytest\n",
    "from collections import defaultdict\n",
    "\n",
    "@pytest.mark.eval(name=\"computer_classifier\")\n",
    "@pytest.mark.parametrize(\"case\", TEST_DATA)\n",
    "@pytest.mark.parametrize(\"model\", [\"gpt-4o\", \"gpt-4o-mini\", \"gpt-3.5-turbo\"])\n",
    "def test_classifier(case: dict, model, eval_bag):\n",
    "    \"\"\"Test individual classification cases across different models.\n",
    "    \n",
    "    Args:\n",
    "        case (dict): Test case with text and expected label\n",
    "        eval_bag: Container for test results\n",
    "        model: Model identifier\n",
    "    \"\"\"\n",
    "    eval_bag.input_text = case[\"text\"]\n",
    "    eval_bag.label = case[\"label\"]\n",
    "    eval_bag.prediction = classify(case[\"text\"], model)\n",
    "    eval_bag.precision = 1 if eval_bag.prediction == eval_bag.label else 0\n",
    "\n",
    "    print(f\"Model: {model}\")\n",
    "    print(f\"Input: {eval_bag.input_text}\")\n",
    "    print(f\"Expected: {eval_bag.label}, Predicted: {eval_bag.prediction}\\n\")\n",
    "\n",
    "    assert eval_bag.prediction == eval_bag.label"
   ],
   "id": "4c8575999b45c4b0",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "t_4373bcce5bc844c49ab1aee5ce828d18.py::test_classifier[gpt-4o-case0] Model: gpt-4o\n",
      "Input: I need to debug this Python code\n",
      "Expected: True, Predicted: True\n",
      "\n",
      "\u001B[32mPASSED\u001B[0m\n",
      "t_4373bcce5bc844c49ab1aee5ce828d18.py::test_classifier[gpt-4o-case1] Model: gpt-4o\n",
      "Input: The cat jumped over the lazy dog\n",
      "Expected: False, Predicted: False\n",
      "\n",
      "\u001B[32mPASSED\u001B[0m\n",
      "t_4373bcce5bc844c49ab1aee5ce828d18.py::test_classifier[gpt-4o-case2] Model: gpt-4o\n",
      "Input: My monitor keeps flickering\n",
      "Expected: True, Predicted: True\n",
      "\n",
      "\u001B[32mPASSED\u001B[0m\n",
      "t_4373bcce5bc844c49ab1aee5ce828d18.py::test_classifier[gpt-4o-case3] Model: gpt-4o\n",
      "Input: The weather is nice today\n",
      "Expected: False, Predicted: False\n",
      "\n",
      "\u001B[32mPASSED\u001B[0m\n",
      "t_4373bcce5bc844c49ab1aee5ce828d18.py::test_classifier[gpt-4o-case4] Model: gpt-4o\n",
      "Input: Updating system drivers fixed the issue\n",
      "Expected: True, Predicted: True\n",
      "\n",
      "\u001B[32mPASSED\u001B[0m\n",
      "t_4373bcce5bc844c49ab1aee5ce828d18.py::test_classifier[gpt-4o-case5] Model: gpt-4o\n",
      "Input: the new llama can understand bizzare nuanced slang\n",
      "Expected: True, Predicted: True\n",
      "\n",
      "\u001B[32mPASSED\u001B[0m\n",
      "t_4373bcce5bc844c49ab1aee5ce828d18.py::test_classifier[gpt-4o-mini-case0] Model: gpt-4o-mini\n",
      "Input: I need to debug this Python code\n",
      "Expected: True, Predicted: True\n",
      "\n",
      "\u001B[32mPASSED\u001B[0m\n",
      "t_4373bcce5bc844c49ab1aee5ce828d18.py::test_classifier[gpt-4o-mini-case1] Model: gpt-4o-mini\n",
      "Input: The cat jumped over the lazy dog\n",
      "Expected: False, Predicted: False\n",
      "\n",
      "\u001B[32mPASSED\u001B[0m\n",
      "t_4373bcce5bc844c49ab1aee5ce828d18.py::test_classifier[gpt-4o-mini-case2] Model: gpt-4o-mini\n",
      "Input: My monitor keeps flickering\n",
      "Expected: True, Predicted: True\n",
      "\n",
      "\u001B[32mPASSED\u001B[0m\n",
      "t_4373bcce5bc844c49ab1aee5ce828d18.py::test_classifier[gpt-4o-mini-case3] Model: gpt-4o-mini\n",
      "Input: The weather is nice today\n",
      "Expected: False, Predicted: False\n",
      "\n",
      "\u001B[32mPASSED\u001B[0m\n",
      "t_4373bcce5bc844c49ab1aee5ce828d18.py::test_classifier[gpt-4o-mini-case4] Model: gpt-4o-mini\n",
      "Input: Updating system drivers fixed the issue\n",
      "Expected: True, Predicted: True\n",
      "\n",
      "\u001B[32mPASSED\u001B[0m\n",
      "t_4373bcce5bc844c49ab1aee5ce828d18.py::test_classifier[gpt-4o-mini-case5] Model: gpt-4o-mini\n",
      "Input: the new llama can understand bizzare nuanced slang\n",
      "Expected: True, Predicted: False\n",
      "\n",
      "\u001B[31mFAILED\u001B[0m\n",
      "t_4373bcce5bc844c49ab1aee5ce828d18.py::test_classifier[gpt-3.5-turbo-case0] Model: gpt-3.5-turbo\n",
      "Input: I need to debug this Python code\n",
      "Expected: True, Predicted: True\n",
      "\n",
      "\u001B[32mPASSED\u001B[0m\n",
      "t_4373bcce5bc844c49ab1aee5ce828d18.py::test_classifier[gpt-3.5-turbo-case1] Model: gpt-3.5-turbo\n",
      "Input: The cat jumped over the lazy dog\n",
      "Expected: False, Predicted: False\n",
      "\n",
      "\u001B[32mPASSED\u001B[0m\n",
      "t_4373bcce5bc844c49ab1aee5ce828d18.py::test_classifier[gpt-3.5-turbo-case2] Model: gpt-3.5-turbo\n",
      "Input: My monitor keeps flickering\n",
      "Expected: True, Predicted: False\n",
      "\n",
      "\u001B[31mFAILED\u001B[0m\n",
      "t_4373bcce5bc844c49ab1aee5ce828d18.py::test_classifier[gpt-3.5-turbo-case3] Model: gpt-3.5-turbo\n",
      "Input: The weather is nice today\n",
      "Expected: False, Predicted: False\n",
      "\n",
      "\u001B[32mPASSED\u001B[0m\n",
      "t_4373bcce5bc844c49ab1aee5ce828d18.py::test_classifier[gpt-3.5-turbo-case4] Model: gpt-3.5-turbo\n",
      "Input: Updating system drivers fixed the issue\n",
      "Expected: True, Predicted: True\n",
      "\n",
      "\u001B[32mPASSED\u001B[0m\n",
      "t_4373bcce5bc844c49ab1aee5ce828d18.py::test_classifier[gpt-3.5-turbo-case5] Model: gpt-3.5-turbo\n",
      "Input: the new llama can understand bizzare nuanced slang\n",
      "Expected: True, Predicted: False\n",
      "\n",
      "\u001B[31mFAILED\u001B[0m\n",
      "\n",
      "============================================= FAILURES =============================================\n",
      "\u001B[31m\u001B[1m________________________________ test_classifier[gpt-4o-mini-case5] ________________________________\u001B[0m\n",
      "\n",
      "case = {'label': True, 'text': 'the new llama can understand bizzare nuanced slang'}\n",
      "model = 'gpt-4o-mini'\n",
      "eval_bag = ResultsBag:\n",
      "{'input_text': 'the new llama can understand bizzare nuanced slang', 'label': True, 'prediction': False, 'precision': 0}\n",
      "\n",
      "    \u001B[0m\u001B[37m@pytest\u001B[39;49;00m.mark.eval(name=\u001B[33m\"\u001B[39;49;00m\u001B[33mcomputer_classifier\u001B[39;49;00m\u001B[33m\"\u001B[39;49;00m)\u001B[90m\u001B[39;49;00m\n",
      "    \u001B[37m@pytest\u001B[39;49;00m.mark.parametrize(\u001B[33m\"\u001B[39;49;00m\u001B[33mcase\u001B[39;49;00m\u001B[33m\"\u001B[39;49;00m, TEST_DATA)\u001B[90m\u001B[39;49;00m\n",
      "    \u001B[37m@pytest\u001B[39;49;00m.mark.parametrize(\u001B[33m\"\u001B[39;49;00m\u001B[33mmodel\u001B[39;49;00m\u001B[33m\"\u001B[39;49;00m, [\u001B[33m\"\u001B[39;49;00m\u001B[33mgpt-4o\u001B[39;49;00m\u001B[33m\"\u001B[39;49;00m, \u001B[33m\"\u001B[39;49;00m\u001B[33mgpt-4o-mini\u001B[39;49;00m\u001B[33m\"\u001B[39;49;00m, \u001B[33m\"\u001B[39;49;00m\u001B[33mgpt-3.5-turbo\u001B[39;49;00m\u001B[33m\"\u001B[39;49;00m])\u001B[90m\u001B[39;49;00m\n",
      "    \u001B[94mdef\u001B[39;49;00m\u001B[90m \u001B[39;49;00m\u001B[92mtest_classifier\u001B[39;49;00m(case: \u001B[96mdict\u001B[39;49;00m, model, eval_bag):\u001B[90m\u001B[39;49;00m\n",
      "    \u001B[90m    \u001B[39;49;00m\u001B[33m\"\"\"Test individual classification cases across different models.\u001B[39;49;00m\n",
      "    \u001B[33m\u001B[39;49;00m\n",
      "    \u001B[33m    Args:\u001B[39;49;00m\n",
      "    \u001B[33m        case (dict): Test case with text and expected label\u001B[39;49;00m\n",
      "    \u001B[33m        eval_bag: Container for test results\u001B[39;49;00m\n",
      "    \u001B[33m        classifier: Classification function\u001B[39;49;00m\n",
      "    \u001B[33m        model: Model identifier\u001B[39;49;00m\n",
      "    \u001B[33m    \"\"\"\u001B[39;49;00m\u001B[90m\u001B[39;49;00m\n",
      "        eval_bag.input_text = case[\u001B[33m\"\u001B[39;49;00m\u001B[33mtext\u001B[39;49;00m\u001B[33m\"\u001B[39;49;00m]\u001B[90m\u001B[39;49;00m\n",
      "        eval_bag.label = case[\u001B[33m\"\u001B[39;49;00m\u001B[33mlabel\u001B[39;49;00m\u001B[33m\"\u001B[39;49;00m]\u001B[90m\u001B[39;49;00m\n",
      "        eval_bag.prediction = classify(case[\u001B[33m\"\u001B[39;49;00m\u001B[33mtext\u001B[39;49;00m\u001B[33m\"\u001B[39;49;00m], model)\u001B[90m\u001B[39;49;00m\n",
      "        eval_bag.precision = \u001B[94m1\u001B[39;49;00m \u001B[94mif\u001B[39;49;00m eval_bag.prediction == eval_bag.label \u001B[94melse\u001B[39;49;00m \u001B[94m0\u001B[39;49;00m\u001B[90m\u001B[39;49;00m\n",
      "    \u001B[90m\u001B[39;49;00m\n",
      "        \u001B[96mprint\u001B[39;49;00m(\u001B[33mf\u001B[39;49;00m\u001B[33m\"\u001B[39;49;00m\u001B[33mModel: \u001B[39;49;00m\u001B[33m{\u001B[39;49;00mmodel\u001B[33m}\u001B[39;49;00m\u001B[33m\"\u001B[39;49;00m)\u001B[90m\u001B[39;49;00m\n",
      "        \u001B[96mprint\u001B[39;49;00m(\u001B[33mf\u001B[39;49;00m\u001B[33m\"\u001B[39;49;00m\u001B[33mInput: \u001B[39;49;00m\u001B[33m{\u001B[39;49;00meval_bag.input_text\u001B[33m}\u001B[39;49;00m\u001B[33m\"\u001B[39;49;00m)\u001B[90m\u001B[39;49;00m\n",
      "        \u001B[96mprint\u001B[39;49;00m(\u001B[33mf\u001B[39;49;00m\u001B[33m\"\u001B[39;49;00m\u001B[33mExpected: \u001B[39;49;00m\u001B[33m{\u001B[39;49;00meval_bag.label\u001B[33m}\u001B[39;49;00m\u001B[33m, Predicted: \u001B[39;49;00m\u001B[33m{\u001B[39;49;00meval_bag.prediction\u001B[33m}\u001B[39;49;00m\u001B[33m\\n\u001B[39;49;00m\u001B[33m\"\u001B[39;49;00m)\u001B[90m\u001B[39;49;00m\n",
      "    \u001B[90m\u001B[39;49;00m\n",
      ">       \u001B[94massert\u001B[39;49;00m eval_bag.prediction == eval_bag.label\u001B[90m\u001B[39;49;00m\n",
      "\u001B[1m\u001B[31mE       AssertionError: assert False == True\u001B[0m\n",
      "\u001B[1m\u001B[31mE        +  where False = ResultsBag:\\n{'input_text': 'the new llama can understand bizzare nuanced slang', 'label': True, 'prediction': False, 'precision': 0}.prediction\u001B[0m\n",
      "\u001B[1m\u001B[31mE        +  and   True = ResultsBag:\\n{'input_text': 'the new llama can understand bizzare nuanced slang', 'label': True, 'prediction': False, 'precision': 0}.label\u001B[0m\n",
      "\n",
      "\u001B[1m\u001B[31m/var/folders/cn/zpwdtbhd7ylgt032s2t3tpdw0000gn/T/ipykernel_2511/2061077205.py\u001B[0m:25: AssertionError\n",
      "\u001B[31m\u001B[1m_______________________________ test_classifier[gpt-3.5-turbo-case2] _______________________________\u001B[0m\n",
      "\n",
      "case = {'label': True, 'text': 'My monitor keeps flickering'}, model = 'gpt-3.5-turbo'\n",
      "eval_bag = ResultsBag:\n",
      "{'input_text': 'My monitor keeps flickering', 'label': True, 'prediction': False, 'precision': 0}\n",
      "\n",
      "    \u001B[0m\u001B[37m@pytest\u001B[39;49;00m.mark.eval(name=\u001B[33m\"\u001B[39;49;00m\u001B[33mcomputer_classifier\u001B[39;49;00m\u001B[33m\"\u001B[39;49;00m)\u001B[90m\u001B[39;49;00m\n",
      "    \u001B[37m@pytest\u001B[39;49;00m.mark.parametrize(\u001B[33m\"\u001B[39;49;00m\u001B[33mcase\u001B[39;49;00m\u001B[33m\"\u001B[39;49;00m, TEST_DATA)\u001B[90m\u001B[39;49;00m\n",
      "    \u001B[37m@pytest\u001B[39;49;00m.mark.parametrize(\u001B[33m\"\u001B[39;49;00m\u001B[33mmodel\u001B[39;49;00m\u001B[33m\"\u001B[39;49;00m, [\u001B[33m\"\u001B[39;49;00m\u001B[33mgpt-4o\u001B[39;49;00m\u001B[33m\"\u001B[39;49;00m, \u001B[33m\"\u001B[39;49;00m\u001B[33mgpt-4o-mini\u001B[39;49;00m\u001B[33m\"\u001B[39;49;00m, \u001B[33m\"\u001B[39;49;00m\u001B[33mgpt-3.5-turbo\u001B[39;49;00m\u001B[33m\"\u001B[39;49;00m])\u001B[90m\u001B[39;49;00m\n",
      "    \u001B[94mdef\u001B[39;49;00m\u001B[90m \u001B[39;49;00m\u001B[92mtest_classifier\u001B[39;49;00m(case: \u001B[96mdict\u001B[39;49;00m, model, eval_bag):\u001B[90m\u001B[39;49;00m\n",
      "    \u001B[90m    \u001B[39;49;00m\u001B[33m\"\"\"Test individual classification cases across different models.\u001B[39;49;00m\n",
      "    \u001B[33m\u001B[39;49;00m\n",
      "    \u001B[33m    Args:\u001B[39;49;00m\n",
      "    \u001B[33m        case (dict): Test case with text and expected label\u001B[39;49;00m\n",
      "    \u001B[33m        eval_bag: Container for test results\u001B[39;49;00m\n",
      "    \u001B[33m        classifier: Classification function\u001B[39;49;00m\n",
      "    \u001B[33m        model: Model identifier\u001B[39;49;00m\n",
      "    \u001B[33m    \"\"\"\u001B[39;49;00m\u001B[90m\u001B[39;49;00m\n",
      "        eval_bag.input_text = case[\u001B[33m\"\u001B[39;49;00m\u001B[33mtext\u001B[39;49;00m\u001B[33m\"\u001B[39;49;00m]\u001B[90m\u001B[39;49;00m\n",
      "        eval_bag.label = case[\u001B[33m\"\u001B[39;49;00m\u001B[33mlabel\u001B[39;49;00m\u001B[33m\"\u001B[39;49;00m]\u001B[90m\u001B[39;49;00m\n",
      "        eval_bag.prediction = classify(case[\u001B[33m\"\u001B[39;49;00m\u001B[33mtext\u001B[39;49;00m\u001B[33m\"\u001B[39;49;00m], model)\u001B[90m\u001B[39;49;00m\n",
      "        eval_bag.precision = \u001B[94m1\u001B[39;49;00m \u001B[94mif\u001B[39;49;00m eval_bag.prediction == eval_bag.label \u001B[94melse\u001B[39;49;00m \u001B[94m0\u001B[39;49;00m\u001B[90m\u001B[39;49;00m\n",
      "    \u001B[90m\u001B[39;49;00m\n",
      "        \u001B[96mprint\u001B[39;49;00m(\u001B[33mf\u001B[39;49;00m\u001B[33m\"\u001B[39;49;00m\u001B[33mModel: \u001B[39;49;00m\u001B[33m{\u001B[39;49;00mmodel\u001B[33m}\u001B[39;49;00m\u001B[33m\"\u001B[39;49;00m)\u001B[90m\u001B[39;49;00m\n",
      "        \u001B[96mprint\u001B[39;49;00m(\u001B[33mf\u001B[39;49;00m\u001B[33m\"\u001B[39;49;00m\u001B[33mInput: \u001B[39;49;00m\u001B[33m{\u001B[39;49;00meval_bag.input_text\u001B[33m}\u001B[39;49;00m\u001B[33m\"\u001B[39;49;00m)\u001B[90m\u001B[39;49;00m\n",
      "        \u001B[96mprint\u001B[39;49;00m(\u001B[33mf\u001B[39;49;00m\u001B[33m\"\u001B[39;49;00m\u001B[33mExpected: \u001B[39;49;00m\u001B[33m{\u001B[39;49;00meval_bag.label\u001B[33m}\u001B[39;49;00m\u001B[33m, Predicted: \u001B[39;49;00m\u001B[33m{\u001B[39;49;00meval_bag.prediction\u001B[33m}\u001B[39;49;00m\u001B[33m\\n\u001B[39;49;00m\u001B[33m\"\u001B[39;49;00m)\u001B[90m\u001B[39;49;00m\n",
      "    \u001B[90m\u001B[39;49;00m\n",
      ">       \u001B[94massert\u001B[39;49;00m eval_bag.prediction == eval_bag.label\u001B[90m\u001B[39;49;00m\n",
      "\u001B[1m\u001B[31mE       AssertionError: assert False == True\u001B[0m\n",
      "\u001B[1m\u001B[31mE        +  where False = ResultsBag:\\n{'input_text': 'My monitor keeps flickering', 'label': True, 'prediction': False, 'precision': 0}.prediction\u001B[0m\n",
      "\u001B[1m\u001B[31mE        +  and   True = ResultsBag:\\n{'input_text': 'My monitor keeps flickering', 'label': True, 'prediction': False, 'precision': 0}.label\u001B[0m\n",
      "\n",
      "\u001B[1m\u001B[31m/var/folders/cn/zpwdtbhd7ylgt032s2t3tpdw0000gn/T/ipykernel_2511/2061077205.py\u001B[0m:25: AssertionError\n",
      "\u001B[31m\u001B[1m_______________________________ test_classifier[gpt-3.5-turbo-case5] _______________________________\u001B[0m\n",
      "\n",
      "case = {'label': True, 'text': 'the new llama can understand bizzare nuanced slang'}\n",
      "model = 'gpt-3.5-turbo'\n",
      "eval_bag = ResultsBag:\n",
      "{'input_text': 'the new llama can understand bizzare nuanced slang', 'label': True, 'prediction': False, 'precision': 0}\n",
      "\n",
      "    \u001B[0m\u001B[37m@pytest\u001B[39;49;00m.mark.eval(name=\u001B[33m\"\u001B[39;49;00m\u001B[33mcomputer_classifier\u001B[39;49;00m\u001B[33m\"\u001B[39;49;00m)\u001B[90m\u001B[39;49;00m\n",
      "    \u001B[37m@pytest\u001B[39;49;00m.mark.parametrize(\u001B[33m\"\u001B[39;49;00m\u001B[33mcase\u001B[39;49;00m\u001B[33m\"\u001B[39;49;00m, TEST_DATA)\u001B[90m\u001B[39;49;00m\n",
      "    \u001B[37m@pytest\u001B[39;49;00m.mark.parametrize(\u001B[33m\"\u001B[39;49;00m\u001B[33mmodel\u001B[39;49;00m\u001B[33m\"\u001B[39;49;00m, [\u001B[33m\"\u001B[39;49;00m\u001B[33mgpt-4o\u001B[39;49;00m\u001B[33m\"\u001B[39;49;00m, \u001B[33m\"\u001B[39;49;00m\u001B[33mgpt-4o-mini\u001B[39;49;00m\u001B[33m\"\u001B[39;49;00m, \u001B[33m\"\u001B[39;49;00m\u001B[33mgpt-3.5-turbo\u001B[39;49;00m\u001B[33m\"\u001B[39;49;00m])\u001B[90m\u001B[39;49;00m\n",
      "    \u001B[94mdef\u001B[39;49;00m\u001B[90m \u001B[39;49;00m\u001B[92mtest_classifier\u001B[39;49;00m(case: \u001B[96mdict\u001B[39;49;00m, model, eval_bag):\u001B[90m\u001B[39;49;00m\n",
      "    \u001B[90m    \u001B[39;49;00m\u001B[33m\"\"\"Test individual classification cases across different models.\u001B[39;49;00m\n",
      "    \u001B[33m\u001B[39;49;00m\n",
      "    \u001B[33m    Args:\u001B[39;49;00m\n",
      "    \u001B[33m        case (dict): Test case with text and expected label\u001B[39;49;00m\n",
      "    \u001B[33m        eval_bag: Container for test results\u001B[39;49;00m\n",
      "    \u001B[33m        classifier: Classification function\u001B[39;49;00m\n",
      "    \u001B[33m        model: Model identifier\u001B[39;49;00m\n",
      "    \u001B[33m    \"\"\"\u001B[39;49;00m\u001B[90m\u001B[39;49;00m\n",
      "        eval_bag.input_text = case[\u001B[33m\"\u001B[39;49;00m\u001B[33mtext\u001B[39;49;00m\u001B[33m\"\u001B[39;49;00m]\u001B[90m\u001B[39;49;00m\n",
      "        eval_bag.label = case[\u001B[33m\"\u001B[39;49;00m\u001B[33mlabel\u001B[39;49;00m\u001B[33m\"\u001B[39;49;00m]\u001B[90m\u001B[39;49;00m\n",
      "        eval_bag.prediction = classify(case[\u001B[33m\"\u001B[39;49;00m\u001B[33mtext\u001B[39;49;00m\u001B[33m\"\u001B[39;49;00m], model)\u001B[90m\u001B[39;49;00m\n",
      "        eval_bag.precision = \u001B[94m1\u001B[39;49;00m \u001B[94mif\u001B[39;49;00m eval_bag.prediction == eval_bag.label \u001B[94melse\u001B[39;49;00m \u001B[94m0\u001B[39;49;00m\u001B[90m\u001B[39;49;00m\n",
      "    \u001B[90m\u001B[39;49;00m\n",
      "        \u001B[96mprint\u001B[39;49;00m(\u001B[33mf\u001B[39;49;00m\u001B[33m\"\u001B[39;49;00m\u001B[33mModel: \u001B[39;49;00m\u001B[33m{\u001B[39;49;00mmodel\u001B[33m}\u001B[39;49;00m\u001B[33m\"\u001B[39;49;00m)\u001B[90m\u001B[39;49;00m\n",
      "        \u001B[96mprint\u001B[39;49;00m(\u001B[33mf\u001B[39;49;00m\u001B[33m\"\u001B[39;49;00m\u001B[33mInput: \u001B[39;49;00m\u001B[33m{\u001B[39;49;00meval_bag.input_text\u001B[33m}\u001B[39;49;00m\u001B[33m\"\u001B[39;49;00m)\u001B[90m\u001B[39;49;00m\n",
      "        \u001B[96mprint\u001B[39;49;00m(\u001B[33mf\u001B[39;49;00m\u001B[33m\"\u001B[39;49;00m\u001B[33mExpected: \u001B[39;49;00m\u001B[33m{\u001B[39;49;00meval_bag.label\u001B[33m}\u001B[39;49;00m\u001B[33m, Predicted: \u001B[39;49;00m\u001B[33m{\u001B[39;49;00meval_bag.prediction\u001B[33m}\u001B[39;49;00m\u001B[33m\\n\u001B[39;49;00m\u001B[33m\"\u001B[39;49;00m)\u001B[90m\u001B[39;49;00m\n",
      "    \u001B[90m\u001B[39;49;00m\n",
      ">       \u001B[94massert\u001B[39;49;00m eval_bag.prediction == eval_bag.label\u001B[90m\u001B[39;49;00m\n",
      "\u001B[1m\u001B[31mE       AssertionError: assert False == True\u001B[0m\n",
      "\u001B[1m\u001B[31mE        +  where False = ResultsBag:\\n{'input_text': 'the new llama can understand bizzare nuanced slang', 'label': True, 'prediction': False, 'precision': 0}.prediction\u001B[0m\n",
      "\u001B[1m\u001B[31mE        +  and   True = ResultsBag:\\n{'input_text': 'the new llama can understand bizzare nuanced slang', 'label': True, 'prediction': False, 'precision': 0}.label\u001B[0m\n",
      "\n",
      "\u001B[1m\u001B[31m/var/folders/cn/zpwdtbhd7ylgt032s2t3tpdw0000gn/T/ipykernel_2511/2061077205.py\u001B[0m:25: AssertionError\n",
      "\u001B[36m\u001B[1m===================================== short test summary info ======================================\u001B[0m\n",
      "\u001B[31mFAILED\u001B[0m t_4373bcce5bc844c49ab1aee5ce828d18.py::\u001B[1mtest_classifier[gpt-4o-mini-case5]\u001B[0m - AssertionError: assert False == True\n",
      "\u001B[31mFAILED\u001B[0m t_4373bcce5bc844c49ab1aee5ce828d18.py::\u001B[1mtest_classifier[gpt-3.5-turbo-case2]\u001B[0m - AssertionError: assert False == True\n",
      "\u001B[31mFAILED\u001B[0m t_4373bcce5bc844c49ab1aee5ce828d18.py::\u001B[1mtest_classifier[gpt-3.5-turbo-case5]\u001B[0m - AssertionError: assert False == True\n",
      "\u001B[31m=================================== \u001B[31m\u001B[1m3 failed\u001B[0m, \u001B[32m15 passed\u001B[0m\u001B[31m in 9.70s\u001B[0m\u001B[31m ===================================\u001B[0m\n"
     ]
    }
   ],
   "execution_count": 4
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-21T08:34:02.276647Z",
     "start_time": "2025-01-21T08:34:02.250710Z"
    }
   },
   "cell_type": "code",
   "source": [
    "%%ipytest_evals --run-eval-analysis\n",
    "@pytest.mark.eval_analysis(name=\"computer_classifier\")\n",
    "def test_analysis(eval_results):\n",
    "    \"\"\"Analyze results across all models and compute detailed metrics.\n",
    "\n",
    "    Args:\n",
    "        eval_results: Collection of all test results\n",
    "    \"\"\"\n",
    "    # Group results by model\n",
    "    res = defaultdict(list)\n",
    "    for r in eval_results:\n",
    "        res[r.test_params['model']].append(r)\n",
    "\n",
    "    global experiments_df\n",
    "\n",
    "    # Calculate metrics for each model\n",
    "    for model, results in res.items():\n",
    "        tp = sum(1 for r in results if r.result.prediction == r.result.label and r.result.label)\n",
    "        fp = sum(1 for r in results if r.result.prediction != r.result.label and not r.result.label)\n",
    "        tn = sum(1 for r in results if r.result.prediction == r.result.label and not r.result.label)\n",
    "        fn = sum(1 for r in results if r.result.prediction != r.result.label and r.result.label)\n",
    "\n",
    "        # Calculate metrics\n",
    "        precision = tp / (tp + fp) if (tp + fp) > 0 else 0\n",
    "        recall = tp / (tp + fn) if (tp + fn) > 0 else 0\n",
    "        f1 = 2 * (precision * recall) / (precision + recall) if (precision + recall) > 0 else 0\n",
    "        accuracy = (tp + tn) / len(results)\n",
    "\n",
    "        experiments_df = pd.concat([experiments_df, pd.DataFrame([{\n",
    "            'timestamp': pd.Timestamp.now(),\n",
    "            'model': model,\n",
    "            'accuracy': accuracy,\n",
    "            'precision': precision,\n",
    "            'recall': recall,\n",
    "            'f1_score': f1,\n",
    "            'true_positives': tp,\n",
    "            'false_positives': fp,\n",
    "            'true_negatives': tn,\n",
    "            'false_negatives': fn,\n",
    "            'total_samples': len(results),\n",
    "        }])], ignore_index=True)\n",
    "\n",
    "    assert any(model_results['accuracy'].iloc[-1] >= 0.7\n",
    "               for _, model_results in experiments_df.groupby('model'))"
   ],
   "id": "ca31a4d09acc7dcd",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "t_4373bcce5bc844c49ab1aee5ce828d18.py::test_analysis \u001B[32mPASSED\u001B[0m\n",
      "\n",
      "\u001B[32m======================================== \u001B[32m\u001B[1m1 passed\u001B[0m\u001B[32m in 0.01s\u001B[0m\u001B[32m =========================================\u001B[0m\n"
     ]
    }
   ],
   "execution_count": 5
  },
  {
   "cell_type": "markdown",
   "id": "b1d45def36cb41b8",
   "metadata": {},
   "source": [
    "## Analyze Results\n",
    "\n",
    "Examine performance metrics and visualize trends across models and runs."
   ]
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-21T08:34:08.110500Z",
     "start_time": "2025-01-21T08:34:07.915693Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Set plotting style\n",
    "sns.set_palette(\"husl\")\n",
    "\n",
    "\n",
    "def plot_performance_comparison():\n",
    "    \"\"\"Create comprehensive performance visualization comparing models.\"\"\"\n",
    "    if len(experiments_df) > 1:\n",
    "        fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(12, 10))\n",
    "\n",
    "        # Plot accuracy trends\n",
    "        sns.lineplot(\n",
    "            data=experiments_df,\n",
    "            x=\"timestamp\",\n",
    "            y=\"accuracy\",\n",
    "            hue=\"model\",\n",
    "            marker=\"o\",\n",
    "            ax=ax1,\n",
    "        )\n",
    "        ax1.set_title(\"Model Accuracy Over Time\")\n",
    "        ax1.axhline(\n",
    "            y=0.7, color=\"r\", linestyle=\"--\", alpha=0.5, label=\"Minimum Threshold\"\n",
    "        )\n",
    "        ax1.set_ylim(0.5, 1.0)\n",
    "\n",
    "        # Plot error metrics\n",
    "        error_data = experiments_df.melt(\n",
    "            id_vars=[\"model\"],\n",
    "            value_vars=[\"false_positives\", \"false_negatives\"],\n",
    "            var_name=\"metric\",\n",
    "            value_name=\"count\",\n",
    "        )\n",
    "        sns.barplot(data=error_data, x=\"model\", y=\"count\", hue=\"metric\", ax=ax2)\n",
    "        ax2.set_title(\"Error Analysis by Model\")\n",
    "\n",
    "        plt.tight_layout()\n",
    "        plt.show()\n",
    "\n",
    "        # Show summary statistics\n",
    "        print(\"\\nPerformance Statistics by Model:\")\n",
    "        display(\n",
    "            experiments_df.groupby(\"model\")[\n",
    "                [\"accuracy\", \"true_positives\", \"false_positives\", \"false_negatives\"]\n",
    "            ].describe()\n",
    "        )\n",
    "\n",
    "\n",
    "plot_performance_comparison()"
   ],
   "id": "d4cc96b376881d9a",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x1000 with 2 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Performance Statistics by Model:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "              accuracy                                                        \\\n",
       "                 count      mean std       min       25%       50%       75%   \n",
       "model                                                                          \n",
       "gpt-3.5-turbo      1.0  0.666667 NaN  0.666667  0.666667  0.666667  0.666667   \n",
       "gpt-4o             1.0  1.000000 NaN  1.000000  1.000000  1.000000  1.000000   \n",
       "gpt-4o-mini        1.0  0.833333 NaN  0.833333  0.833333  0.833333  0.833333   \n",
       "\n",
       "                        true_positives       ... false_positives       \\\n",
       "                    max          count mean  ...             75%  max   \n",
       "model                                        ...                        \n",
       "gpt-3.5-turbo  0.666667            1.0  2.0  ...             0.0  0.0   \n",
       "gpt-4o         1.000000            1.0  4.0  ...             0.0  0.0   \n",
       "gpt-4o-mini    0.833333            1.0  3.0  ...             0.0  0.0   \n",
       "\n",
       "              false_negatives                                    \n",
       "                        count mean std  min  25%  50%  75%  max  \n",
       "model                                                            \n",
       "gpt-3.5-turbo             1.0  2.0 NaN  2.0  2.0  2.0  2.0  2.0  \n",
       "gpt-4o                    1.0  0.0 NaN  0.0  0.0  0.0  0.0  0.0  \n",
       "gpt-4o-mini               1.0  1.0 NaN  1.0  1.0  1.0  1.0  1.0  \n",
       "\n",
       "[3 rows x 32 columns]"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">accuracy</th>\n",
       "      <th colspan=\"2\" halign=\"left\">true_positives</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"2\" halign=\"left\">false_positives</th>\n",
       "      <th colspan=\"8\" halign=\"left\">false_negatives</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>...</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>gpt-3.5-turbo</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gpt-4o</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gpt-4o-mini</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 32 columns</p>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 6
  },
  {
   "cell_type": "markdown",
   "id": "999f79285821072a",
   "metadata": {},
   "source": [
    "## Save Results\n",
    "\n",
    "Optionally save experiment results for future analysis."
   ]
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-21T08:34:10.119140Z",
     "start_time": "2025-01-21T08:34:10.113540Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Save experiment results\n",
    "experiments_df.to_csv(\"experiment_results.csv\", index=False)\n",
    "print(\"Results saved to 'experiment_results.csv'\")"
   ],
   "id": "bc5e706cfa63d8cd",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results saved to 'experiment_results.csv'\n"
     ]
    }
   ],
   "execution_count": 7
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}