{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation on the hotpotQA dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from evals.eval_on_hotpot import eval_on_hotpotQA\n",
    "from evals.eval_on_hotpot import answer_with_cognee\n",
    "from evals.eval_on_hotpot import answer_without_cognee\n",
    "from evals.eval_on_hotpot import eval_answers\n",
    "from cognee.base_config import get_base_config\n",
    "from pathlib import Path\n",
    "from tqdm import tqdm\n",
    "import wget\n",
    "import json\n",
    "import statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Getting the answers for the first num_samples questions of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "answer_provider = answer_with_cognee  # For native LLM answers use answer_without_cognee\n",
    "num_samples = 10  # With cognee, it takes ~1m10s per sample\n",
    "\n",
    "base_config = get_base_config()\n",
    "data_root_dir = base_config.data_root_directory\n",
    "\n",
    "if not Path(data_root_dir).exists():\n",
    "    Path(data_root_dir).mkdir()\n",
    "\n",
    "filepath = data_root_dir / Path(\"hotpot_dev_fullwiki_v1.json\")\n",
    "if not filepath.exists():\n",
    "    url = \"http://curtis.ml.cmu.edu/datasets/hotpot/hotpot_dev_fullwiki_v1.json\"\n",
    "    wget.download(url, out=data_root_dir)\n",
    "\n",
    "with open(filepath, \"r\") as file:\n",
    "    dataset = json.load(file)\n",
    "\n",
    "instances = dataset if not num_samples else dataset[:num_samples]\n",
    "answers = []\n",
    "for instance in tqdm(instances, desc=\"Getting answers\"):\n",
    "    answer = await answer_provider(instance)\n",
    "    answers.append(answer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculating the official HotpotQA benchmark metrics: F1 score and EM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from evals.deepeval_metrics import f1_score_metric\n",
    "from evals.deepeval_metrics import em_score_metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f1_metric = f1_score_metric()\n",
    "eval_results = await eval_answers(instances, answers, f1_metric)\n",
    "avg_f1_score = statistics.mean(\n",
    "    [result.metrics_data[0].score for result in eval_results.test_results]\n",
    ")\n",
    "print(\"F1 score: \", avg_f1_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "em_metric = em_score_metric()\n",
    "eval_results = await eval_answers(instances, answers, em_metric)\n",
    "avg_em_score = statistics.mean(\n",
    "    [result.metrics_data[0].score for result in eval_results.test_results]\n",
    ")\n",
    "print(\"EM score: \", avg_em_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculating a custom metric called Correctness\n",
    "##### Correctness is judged by an LLM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from evals.deepeval_metrics import correctness_metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "eval_results = await eval_answers(\n",
    "    instances, answers, correctness_metric\n",
    ")  # note that instantiation is not needed for correctness metric as it is already an instance\n",
    "avg_correctness_score = statistics.mean(\n",
    "    [result.metrics_data[0].score for result in eval_results.test_results]\n",
    ")\n",
    "print(\"Correctness score: \", avg_correctness_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using a metric from Deepeval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from deepeval.metrics import AnswerRelevancyMetric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "relevancy_metric = AnswerRelevancyMetric()\n",
    "eval_results = await eval_answers(\n",
    "    instances, answers, relevancy_metric\n",
    ")  # note that instantiation is not needed for correctness metric as it is already an instance\n",
    "avg_relevancy_score = statistics.mean(\n",
    "    [result.metrics_data[0].score for result in eval_results.test_results]\n",
    ")\n",
    "print(\"Relevancy score: \", avg_relevancy_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Answering and eval in one step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "answer_provider = answer_without_cognee\n",
    "f1_metric = f1_score_metric()\n",
    "f1_score = await eval_on_hotpotQA(\n",
    "    answer_provider, num_samples=10, eval_metric=f1_metric\n",
    ")  # takes ~1m10s per sample\n",
    "print(\"F1 score: \", f1_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
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