benchmarking_hallucination_metrics/benchmark_hallucination_metrics.ipynb

{
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gdq2ZGSlY1wH"
      },
      "source": [
        "# Benchmarking Hallucination Detection Methods\n",
        "\n",
        "In this notebook, we will benchmark hallucination detection methods on subsets of the HaluEval dataset.\n",
        "\n",
        "\n",
        "## Setup\n",
        "\n",
        "The two main requirements to replicate are an OPENAI_API_KEY and a TLM_API_KEY.\n",
        "\n",
        "You can get an OPENAI_API_KEY by signing up for OpenAI at https://platform.openai.com/signup.\n",
        "\n",
        "You can try TLM for free at https://cleanlab.ai/tlm/\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VQqMpRVP3J9P",
        "outputId": "6b39f841-5144-49c2-81f2-835eb1fcdab4"
      },
      "outputs": [],
      "source": [
        "%pip install datasets --quiet # for importing from huggingface and ragasr\n",
        "%pip install ragas==\"v0.1.16\" --quiet # metrics: Faithfulness, etc. Version 0.1.16 is required for HHEM to work\n",
        "%pip install cleanlab-tlm --quiet # metrics: TLM\n",
        "%pip install deepeval --quiet # metrics: G-Eval, Hallucination\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Set your API key\n",
        "import os\n",
        "os.environ[\"CLEANLAB_TLM_API_KEY\"] = \"<API key>\"  # Get your API key from: https://tlm.cleanlab.ai/"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The following requirements are only required if you want to run HHEM-2.0, a hallucination detection model.\n",
        "\n",
        "\n",
        "We advise you have a GPU to run this model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [],
      "source": [
        "%pip install torch torchvision --quiet # for running HHEM2 locally\n",
        "%pip install transformers --quiet # for running HHEM2 locally"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "1EiWmdBI3J9S"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "REQUIRED_CREDS = [\n",
        "    \"OPENAI_API_KEY\",  # https://platform.openai.com/\n",
        "    ]\n",
        "\n",
        "\n",
        "# Try/except in case you're using google colab for creds\n",
        "try:\n",
        "  from google.colab import userdata\n",
        "  for cred in REQUIRED_CREDS:\n",
        "    os.environ[cred] = userdata.get(cred)\n",
        "except ImportError:\n",
        "  pass\n",
        "\n",
        "# Also support dotenv, if preferred\n",
        "try:\n",
        "  from dotenv import load_dotenv\n",
        "  load_dotenv()\n",
        "except ImportError:\n",
        "  pass\n",
        "\n",
        "for cred in REQUIRED_CREDS:\n",
        "    assert cred in os.environ, f\"{cred} not found in environment variables. Please set it before proceeding.\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0lsEwybR3J9S",
        "outputId": "de05d9ea-a768-426a-9773-e4626766fbbe"
      },
      "outputs": [],
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "ds = load_dataset(\"PatronusAI/HaluBench\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "KceNDMMu3J9S"
      },
      "outputs": [],
      "source": [
        "data = ds[\"test\"].to_pandas()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We see the dataset is formatted with context as \"passage\", question, answer, a label as \"FAIL\" (Hallucination) or \"PASS\" (Truthful) and the source dataset:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "uZibFtvP3J9S",
        "outputId": "272d4604-1ad8-472b-c0d9-9df9096295b0"
      },
      "outputs": [
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              "                                             passage  \\\n",
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              "1  As of the census of 2000, there were 218,590 p...   \n",
              "2  Hoping to rebound from the road loss to the Ch...   \n",
              "3  Hoping to rebound from their tough overtime ro...   \n",
              "4  As of the census of 2000, there were 218,590 p...   \n",
              "\n",
              "                                            question  \\\n",
              "0  Which team scored the longest field goal kick ...   \n",
              "1                  How many percent were not  Irish?   \n",
              "2  How many yards was the second longest field go...   \n",
              "3                   How long was the last touchdown?   \n",
              "4  How many in percent from the census weren't Ir...   \n",
              "\n",
              "                                              answer label source_ds  \n",
              "0  ['Rams', 'second', 'Marc Bulger', 'Kevin Curtis']  FAIL      DROP  \n",
              "1                                               87.1  FAIL      DROP  \n",
              "2                                                 42  FAIL      DROP  \n",
              "3                                            18-yard  FAIL      DROP  \n",
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        "data.head()"
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      "execution_count": 7,
      "metadata": {
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        "id": "o5g_bsRU3J9T",
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        {
          "data": {
            "text/plain": [
              "source_ds\n",
              "halueval        10000\n",
              "DROP             1000\n",
              "pubmedQA         1000\n",
              "FinanceBench     1000\n",
              "covidQA          1000\n",
              "RAGTruth          900\n",
              "Name: count, dtype: int64"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
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      "source": [
        "data.source_ds.value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IuKKUEGQ3J9T"
      },
      "source": [
        "\n",
        "## Instantiating our hallucination metrics\n",
        "\n",
        "We'll instantiate a variety of commonly-used and cutting-edge hallucination metrics to evaluate the performance of our hallucination detection models. \n",
        "\n",
        "These metrics include:\n",
        "\n",
        "\n",
        "### Cleanlab's Trustworthy Language Model (TLM)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "_8XwGILV3J9U"
      },
      "outputs": [],
      "source": [
        "from cleanlab_tlm import TLM\n",
        "\n",
        "tlm = TLM()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vm94-G4y3J9U"
      },
      "source": [
        "Since `tlm.get_trustworthy_score` takes `prompt` and `response`, we'll turn the `context` and `question` into a single prompt:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "bHfUUFWV3J9U"
      },
      "outputs": [],
      "source": [
        "data[\"context_and_question_as_prompt\"] = data.apply(\n",
        "    lambda row: f\"Answer the QUESTION using information **only** from CONTEXT:\\n{row['passage']}\\nQUESTION:\\n{row['question']}\",\n",
        "    axis=1\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PAkoqLrO3J9U"
      },
      "source": [
        "## RAGAS and RAGAS++ instantiation\n",
        "\n",
        "We use both the default instantiation of RAGAS, as well as some improvements we've anecdotally found to be useful. We'll call our improved version `RAGAS++`\n",
        "\n",
        "For our improved RAGAS++ version, we used `gpt-4o-mini` instead of the default `gpt-3.5-turbo-16k` for generation and `gpt-4` for the critic LLM. In addition, we added a full stop character (`.`) to the end of each answer if not already present, and observed that this lowered the error rate of RAGAS code, likely due to its sentence parsing logic that uses the full stop character. To swap the model, we follow the [official RAGAS documentation](https://docs.ragas.io/en/v0.0.22/howtos/customisations/llms.html#evaluating-with-gpt4)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "JY9zwm6k3J9U"
      },
      "outputs": [],
      "source": [
        "from datasets import Dataset\n",
        "from ragas.metrics import (\n",
        "    answer_relevancy,\n",
        "    faithfulness,\n",
        ")\n",
        "\n",
        "from ragas import evaluate"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SF258bBw3J9U"
      },
      "source": [
        "Note that LangChain complains that the ChatOpenAI class is deprecated, but we'll still use this method because its the official RAGAS instructions, and it runs succuesfully."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "QM4TNVhs3J9V"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/var/folders/nr/p0y8_yt54fvbzy49jprx4vzh0000gn/T/ipykernel_31179/1620629814.py:3: LangChainDeprecationWarning: The class `ChatOpenAI` was deprecated in LangChain 0.0.10 and will be removed in 1.0. An updated version of the class exists in the langchain-openai package and should be used instead. To use it run `pip install -U langchain-openai` and import as `from langchain_openai import ChatOpenAI`.\n",
            "  gpt4o_mini = ChatOpenAI(model_name=\"gpt-4o-mini\")\n"
          ]
        }
      ],
      "source": [
        "from langchain.chat_models import ChatOpenAI\n",
        "\n",
        "gpt4o_mini = ChatOpenAI(model_name=\"gpt-4o-mini\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "qseSypoi3J9V"
      },
      "outputs": [],
      "source": [
        "# Add full stop character . if not present for improved RAGAS\n",
        "data[\"answer_ending_in_period\"] = data[\"answer\"].apply(lambda text: text if text.endswith('.') else text + '.')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Oh38pUfS5OXa"
      },
      "source": [
        "For faithfulness, we also use HHEM, an open-source hallucination detection model by Vectara. Running HHEM locally is GPU-intensive, so we'd recommend using a A100 or L4 NVIDIA GPU to run it.\n",
        "\n",
        " This configuration flag allows PyTorch to more flexibly manage GPU memory and can reduce memory issues. It won't have an affect if you're not using GPU.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "7v-giJd5CsB8"
      },
      "outputs": [],
      "source": [
        "!export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "61Y5uBc85G2w",
        "outputId": "bbf98159-194f-41cc-f4a5-a8a2f43e92e1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "cpu\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "You are using a model of type HHEMv2Config to instantiate a model of type HHEMv2. This is not supported for all configurations of models and can yield errors.\n",
            "/Users/nelson/.local/share/virtualenvs/sandbox-W-dCn-zA/lib/python3.12/site-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "from ragas.metrics import FaithulnesswithHHEM\n",
        "\n",
        "try:\n",
        "  import torch\n",
        "  hhem_device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "  torch.cuda.empty_cache()\n",
        "except:\n",
        "  print(\"No GPU or torch package found, using CPU\")\n",
        "  hhem_device = \"cpu\"\n",
        "\n",
        "\n",
        "print(hhem_device)\n",
        "\n",
        "\n",
        "hhem_batch_size = 5\n",
        "\n",
        "\n",
        "faithfulness_with_hhem = FaithulnesswithHHEM(device=hhem_device, batch_size=hhem_batch_size)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AC220re93J9V"
      },
      "source": [
        "## G-Eval Instantiation\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "AopJ0gXt3J9V"
      },
      "outputs": [],
      "source": [
        "from deepeval.metrics import GEval\n",
        "from deepeval.test_case import LLMTestCaseParams\n",
        "\n",
        "geval_correctness_metric = GEval(\n",
        "    name=\"Correctness\",\n",
        "    criteria=\"Determine whether the output is factually correct given the context.\",\n",
        "    evaluation_params=[LLMTestCaseParams.INPUT, LLMTestCaseParams.ACTUAL_OUTPUT],\n",
        "    model=\"gpt-4o-mini\",\n",
        "    verbose_mode=False\n",
        ")\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Deepeval Hallucination Metric Instantiation\n",
        "\n",
        "The Deepeval [hallucination metric](https://docs.confident-ai.com/docs/metrics-hallucination) compares what percentage of contexts were contradicted by the answer. This metric is not useful in our toy datasets, which have singular contexts. One nuance of this metric is that a high score is bad, as it suggests a likely hallucination."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {},
      "outputs": [],
      "source": [
        "from deepeval.metrics import HallucinationMetric\n",
        "\n",
        "hallucination_metric = HallucinationMetric()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FZt42kFt3J9V"
      },
      "source": [
        "## Self-Eval\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "4dpr2hmO3J9V"
      },
      "outputs": [],
      "source": [
        "CONFIDENCE_PROMPT = \"\"\"Evaluate how confident you are that the given Answer is a good and accurate response to the Question.\n",
        "Please assign a Score using the following 5-point scale:\n",
        "1: You are not confident that the Answer addresses the Question at all, the Answer may be entirely off-topic or irrelevant to the Question.\n",
        "2: You have low confidence that the Answer addresses the Question, there are doubts and uncertainties about the accuracy of the Answer.\n",
        "3: You have moderate confidence that the Answer addresses the Question, the Answer seems reasonably accurate and on-topic, but with room for improvement.\n",
        "4: You have high confidence that the Answer addresses the Question, the Answer provides accurate information that addresses most of the Question.\n",
        "5: You are extremely confident that the Answer addresses the Question, the Answer is highly accurate, relevant, and effectively addresses the Question in its entirety.\n",
        "The output should strictly use the following template: Explanation: [provide a brief reasoning you used to derive the rating Score] and then write 'Score: <rating>' on the last line.\n",
        "\"\"\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Through trial and error, we also found the following score extraction regexes to be useful:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {},
      "outputs": [],
      "source": [
        "import re\n",
        "\n",
        "\n",
        "regex_list = [\n",
        "    re.compile(\n",
        "        r\".*(?:^|\\n)\\s*score:?\\s*\\(?(?P<answer>one|two|three|four|five|[12345])\\)?\",\n",
        "        flags=re.DOTALL | re.IGNORECASE\n",
        "    ),\n",
        "    re.compile(\n",
        "        r\".*\\(?(?P<answer>one|two|three|four|five|[12345])\\)?\",\n",
        "        flags=re.DOTALL | re.IGNORECASE\n",
        "    )\n",
        "]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "Xvoa7jVj3J9V"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "from textwrap import dedent\n",
        "\n",
        "def construct_confidence_prompt(prompt, response):\n",
        "    template = f\"\"\"Question:\n",
        "        {prompt}\n",
        "        Answer: {response}\n",
        "        {CONFIDENCE_PROMPT}\"\"\"\n",
        "    return dedent(template)\n",
        "\n",
        "\n",
        "def parse_self_eval_score(text: str):\n",
        "    try:\n",
        "        score = int(text.split(\"Score:\")[-1].strip())\n",
        "        return (score - 1) / 4  # Normalize to 0-1 range\n",
        "    except:\n",
        "        return np.nan"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Creating a function to run all benchmarks on the dataset:\n",
        "\n",
        "Now that we've instantiated all of our hallucination detection models, we can create a function to run all of them on a given dataset subsection and extract the scores.\n",
        "\n",
        "By creating a function, we can run this on arbitrary slices of the dataset, which is helpful for debugging and testing."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "9ltqxAGG3J9V"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "from deepeval import evaluate as deepeval_evaluate\n",
        "from deepeval.test_case import LLMTestCase\n",
        "from deepeval.dataset import EvaluationDataset\n",
        "\n",
        "from datetime import datetime\n",
        "\n",
        "\n",
        "def score_tlm(data):\n",
        "    tlm_scores = tlm.get_trustworthiness_score(data[\"context_and_question_as_prompt\"].tolist(), data[\"answer\"].tolist())\n",
        "    data[\"tlm_score\"] = tlm_scores\n",
        "    print(f\"Finished computing TLM Scores: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\")\n",
        "    return data\n",
        "\n",
        "def score_default_ragas(data):\n",
        "    ragas_data = {\n",
        "        'question': data[\"question\"].tolist(),\n",
        "        'answer': data[\"answer\"].tolist(),\n",
        "        'contexts': [[ctx] for ctx in data[\"passage\"].tolist()]\n",
        "    }\n",
        "    ragas_dataset = Dataset.from_dict(ragas_data)\n",
        "    default_ragas_scores = evaluate(\n",
        "        ragas_dataset,\n",
        "        metrics=[faithfulness, answer_relevancy]\n",
        "    )\n",
        "    default_ragas_df = default_ragas_scores.to_pandas()\n",
        "    data[\"ragas_faithfulness\"] = default_ragas_df[\"faithfulness\"].tolist()\n",
        "    data[\"ragas_answer_relevancy\"] = default_ragas_df[\"answer_relevancy\"].tolist()\n",
        "    print(f\"Finished computing Default RAGAS Scores: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\")\n",
        "    return data\n",
        "\n",
        "def score_improved_ragas(data):\n",
        "    gpt4o_mini = ChatOpenAI(model_name=\"gpt-4o-mini\")\n",
        "    improved_ragas_data = {\n",
        "        'question': data[\"question\"].tolist(),\n",
        "        'answer': data[\"answer_ending_in_period\"].tolist(),\n",
        "        'contexts': [[ctx] for ctx in data[\"passage\"].tolist()]\n",
        "    }\n",
        "    improved_ragas_scores = evaluate(\n",
        "        Dataset.from_dict(improved_ragas_data),\n",
        "        metrics=[faithfulness, answer_relevancy],\n",
        "        llm=gpt4o_mini\n",
        "    )\n",
        "    improved_ragas_df = improved_ragas_scores.to_pandas()\n",
        "    data[\"ragas++_faithfulness_improved\"] = improved_ragas_df[\"faithfulness\"].tolist()\n",
        "    data[\"ragas++_answer_relevancy_improved\"] = improved_ragas_df[\"answer_relevancy\"].tolist()\n",
        "    print(f\"Finished computing Improved RAGAS Scores: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\")\n",
        "    return data\n",
        "\n",
        "def score_deepeval(data):\n",
        "    test_cases = [\n",
        "        LLMTestCase(\n",
        "            input=row[\"question\"],\n",
        "            actual_output=row[\"answer\"],\n",
        "            context=[row[\"passage\"]],\n",
        "            retrieval_context=[row[\"passage\"]]\n",
        "        ) for _, row in data.iterrows()\n",
        "    ]\n",
        "    dataset = EvaluationDataset(test_cases=test_cases)\n",
        "    geval_results = deepeval_evaluate(dataset, [geval_correctness_metric, hallucination_metric], show_indicator=False, print_results=False)\n",
        "    data[\"geval_correctness\"] = [result.metrics_data[0].score for result in geval_results]\n",
        "    data[\"hallucination_metric\"] = [result.metrics_data[0].score for result in geval_results]\n",
        "    data[\"hallucination_metric_complement\"] = 1 - data[\"hallucination_metric\"]\n",
        "    print(f\"Finished computing G-Eval Scores: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\")\n",
        "    return data\n",
        "\n",
        "def score_self_evaluation(data):\n",
        "    gpt4o_mini = TLM(\"base\", options={\"model\": \"gpt-4o-mini\"})\n",
        "    data[\"self_evaluation_result_raw\"] = gpt4o_mini.prompt([construct_confidence_prompt(row[\"question\"], row[\"answer\"]) for _, row in data.iterrows()])\n",
        "    data[\"self_evaluation_score\"] = data[\"self_evaluation_result_raw\"].apply(lambda res: parse_self_eval_score(res['response']))\n",
        "    print(f\"Finished computing Self-Evaluation Scores: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\")\n",
        "    return data\n",
        "\n",
        "def score_dataset_with_metrics(in_data: pd.DataFrame):\n",
        "    \"\"\"\n",
        "    Given a dataframe with HaluBench format (i.e. columns ['passage', 'answer' 'question', 'label'], score the dataset using multiple metrics.\n",
        "\n",
        "    Return the full dataframe with the scores appended as new columns.\n",
        "    \"\"\"\n",
        "    # We copy the input data to avoid mutating the original dataframe\n",
        "    data = in_data.copy()\n",
        "    print(f\"Starting to Score Dataset: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\")\n",
        "\n",
        "    data = score_tlm(data)\n",
        "    data = score_default_ragas(data)\n",
        "    data = score_improved_ragas(data)\n",
        "    data = score_deepeval(data)\n",
        "    data = score_self_evaluation(data)\n",
        "\n",
        "    return data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "rp258Da-JkRX"
      },
      "outputs": [],
      "source": [
        "unique_datasets = data.source_ds.unique().tolist()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FyV7PGxUJnjl",
        "outputId": "ac4700fe-08b6-4e1f-99f0-7b3392321444"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['DROP', 'pubmedQA', 'FinanceBench', 'RAGTruth', 'halueval', 'covidQA']"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "unique_datasets"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Running a small batch as an example\n",
        "\n",
        "Here we conduct a test run on a small subsection of the dataset. \n",
        "\n",
        "This is just to illustrate to the notebook viewer how the metrics work."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 797,
          "referenced_widgets": [
            "0ebc3174077b4c7295fc4b7a8827d324",
            "395240d5f727463f8ab169500ae6b50e",
            "b22e99b2785b42ecb15f609a301f2641",
            "d71a674fc6b54c96bd0c45f8e9fdfd09",
            "0512b00c030b4292b542c0a7015e7396",
            "6f57657274cf4cea8c62ef1afe70d6e1",
            "91b6eb8fd4264132ae26eb56c3c23c67",
            "4c4dd7a381304eafbaff84778dacf09e",
            "fa9ddd643d9843ec992501a481173c1f",
            "9b8d426f2ebf4661b97556f1880c3796",
            "e672c5a4dab24961a1c308ada5ca2863",
            "4fd2c05f4108406b99f98fd2f7da8e14",
            "65c05470434444eaab14829cc759b3ed",
            "e14f3cb3aeb54cfd9428904806dcfa9c",
            "12f1332dacfe4aedae1216959a821855",
            "ce4d535bd6754a81aa451fd06ca7e766",
            "9e04d618875046e6a1c6f17cf503971e",
            "650c42bccd2a4607ad01960e500eac60",
            "0cd03a4e08544bad99e258d03a4d284c",
            "b57c21a489734840ba5491f1295bd771",
            "b154e7c6732143e583f13d1ce210f66c",
            "0a02209951404cf5a206fd29fec7b9fd"
          ]
        },
        "id": "xHpf9ikxctRw",
        "outputId": "b6e11be9-0673-448f-bc59-99a2e6b8f92a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Starting to Score Dataset: 2024-09-09 17:25:32\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Querying TLM... 100%|██████████|\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Finished computing TLM Scores: 2024-09-09 17:25:33\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a783ec904f5f4511804020e08acac1f9",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Evaluating:   0%|          | 0/20 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "No statements were generated from the answer.\n",
            "No statements were generated from the answer.\n",
            "No statements were generated from the answer.\n",
            "No statements were generated from the answer.\n",
            "No statements were generated from the answer.\n",
            "No statements were generated from the answer.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Finished computing Default RAGAS Scores: 2024-09-09 17:25:42\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "74c317e5ebd74480bf205ee38c293471",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Evaluating:   0%|          | 0/20 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Finished computing Improved RAGAS Scores: 2024-09-09 17:25:49\n",
            "Event loop is already running. Applying nest_asyncio patch to allow async execution...\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #05f58d; text-decoration-color: #05f58d\">✓</span> Tests finished 🎉! Run <span style=\"color: #008000; text-decoration-color: #008000\">'deepeval login'</span> to view evaluation results on Confident AI. \n",
              "‼️  NOTE: You can also run evaluations on ALL of deepeval's metrics directly on Confident AI instead.\n",
              "</pre>\n"
            ],
            "text/plain": [
              "\u001b[38;2;5;245;141m✓\u001b[0m Tests finished 🎉! Run \u001b[32m'deepeval login'\u001b[0m to view evaluation results on Confident AI. \n",
              "‼️  NOTE: You can also run evaluations on ALL of deepeval's metrics directly on Confident AI instead.\n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Finished computing G-Eval Scores: 2024-09-09 17:25:53\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Querying TLM... 100%|██████████|"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Finished computing Self-Evaluation Scores: 2024-09-09 17:25:54\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "res_dict = {}\n",
        "dataset_name = 'DROP'\n",
        "N_SAMPLE = 10\n",
        "RANDOM_STATE = 42\n",
        "res_dict[dataset_name] = score_dataset_with_metrics(data[data.source_ds == dataset_name].sample(N_SAMPLE, random_state=RANDOM_STATE))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "id": "qzg9TmvCgy7s",
        "outputId": "538ed981-c06d-49e0-bbbb-7c571cfc38b0"
      },
      "outputs": [
        {
          "data": {
            "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 th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>passage</th>\n",
              "      <th>question</th>\n",
              "      <th>answer</th>\n",
              "      <th>label</th>\n",
              "      <th>source_ds</th>\n",
              "      <th>context_and_question_as_prompt</th>\n",
              "      <th>answer_ending_in_period</th>\n",
              "      <th>tlm_score</th>\n",
              "      <th>ragas_faithfulness</th>\n",
              "      <th>ragas_answer_relevancy</th>\n",
              "      <th>ragas++_faithfulness_improved</th>\n",
              "      <th>ragas++_answer_relevancy_improved</th>\n",
              "      <th>geval_correctness</th>\n",
              "      <th>hallucination_metric</th>\n",
              "      <th>hallucination_metric_complement</th>\n",
              "      <th>self_evaluation_result_raw</th>\n",
              "      <th>self_evaluation_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>521</th>\n",
              "      <td>cd57626b-5eab-4739-ab48-159101318a0b</td>\n",
              "      <td>The Dutch Reformed Church remained the largest...</td>\n",
              "      <td>Which event was a result of the other, the for...</td>\n",
              "      <td>formation, the Restored Reformed Church</td>\n",
              "      <td>PASS</td>\n",
              "      <td>DROP</td>\n",
              "      <td>Answer the QUESTION using information **only**...</td>\n",
              "      <td>formation, the Restored Reformed Church.</td>\n",
              "      <td>0.573012</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.878296</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.878296</td>\n",
              "      <td>0.200477</td>\n",
              "      <td>0.200477</td>\n",
              "      <td>0.799523</td>\n",
              "      <td>{'response': 'Explanation: The Answer states t...</td>\n",
              "      <td>0.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>737</th>\n",
              "      <td>a1699ddb-a829-41df-9ca3-2376edf52f83</td>\n",
              "      <td>On 7-8 October at the Battle of Slobodyshche 1...</td>\n",
              "      <td>How many people in total did the Russians and ...</td>\n",
              "      <td>28700, 2000</td>\n",
              "      <td>PASS</td>\n",
              "      <td>DROP</td>\n",
              "      <td>Answer the QUESTION using information **only**...</td>\n",
              "      <td>28700, 2000.</td>\n",
              "      <td>0.354327</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.901479</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.848822</td>\n",
              "      <td>0.005399</td>\n",
              "      <td>0.005399</td>\n",
              "      <td>0.994601</td>\n",
              "      <td>{'response': 'Explanation: The Answer provides...</td>\n",
              "      <td>0.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>740</th>\n",
              "      <td>ad86ec17-03b4-4b06-a76b-c88c970f862d</td>\n",
              "      <td>The Czech lands were among the first industria...</td>\n",
              "      <td>What caused the value of the crown to decrease?</td>\n",
              "      <td>Munich Agreement, 1938 Munich Agreement</td>\n",
              "      <td>PASS</td>\n",
              "      <td>DROP</td>\n",
              "      <td>Answer the QUESTION using information **only**...</td>\n",
              "      <td>Munich Agreement, 1938 Munich Agreement.</td>\n",
              "      <td>0.916365</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.750391</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.750531</td>\n",
              "      <td>0.201702</td>\n",
              "      <td>0.201702</td>\n",
              "      <td>0.798298</td>\n",
              "      <td>{'response': 'Explanation: The Munich Agreemen...</td>\n",
              "      <td>0.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>660</th>\n",
              "      <td>1025a27b-b313-4d5b-939a-ba3f91551b4f</td>\n",
              "      <td>China is the second largest trading partner of...</td>\n",
              "      <td>How many more million in export in 2015/16 Chi...</td>\n",
              "      <td>2</td>\n",
              "      <td>PASS</td>\n",
              "      <td>DROP</td>\n",
              "      <td>Answer the QUESTION using information **only**...</td>\n",
              "      <td>2.</td>\n",
              "      <td>0.927469</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.814427</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.814427</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>{'response': 'Explanation: The answer states \"...</td>\n",
              "      <td>0.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>411</th>\n",
              "      <td>e03a0296-1748-4e78-b98d-e4140746d2ed</td>\n",
              "      <td>Birth registration of Bahrain is available fro...</td>\n",
              "      <td>How many years after birth registration did de...</td>\n",
              "      <td>15</td>\n",
              "      <td>FAIL</td>\n",
              "      <td>DROP</td>\n",
              "      <td>Answer the QUESTION using information **only**...</td>\n",
              "      <td>15.</td>\n",
              "      <td>0.045738</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.864775</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.864775</td>\n",
              "      <td>0.100694</td>\n",
              "      <td>0.100694</td>\n",
              "      <td>0.899306</td>\n",
              "      <td>{'response': 'Explanation: The answer states t...</td>\n",
              "      <td>0.50</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                       id  \\\n",
              "521  cd57626b-5eab-4739-ab48-159101318a0b   \n",
              "737  a1699ddb-a829-41df-9ca3-2376edf52f83   \n",
              "740  ad86ec17-03b4-4b06-a76b-c88c970f862d   \n",
              "660  1025a27b-b313-4d5b-939a-ba3f91551b4f   \n",
              "411  e03a0296-1748-4e78-b98d-e4140746d2ed   \n",
              "\n",
              "                                               passage  \\\n",
              "521  The Dutch Reformed Church remained the largest...   \n",
              "737  On 7-8 October at the Battle of Slobodyshche 1...   \n",
              "740  The Czech lands were among the first industria...   \n",
              "660  China is the second largest trading partner of...   \n",
              "411  Birth registration of Bahrain is available fro...   \n",
              "\n",
              "                                              question  \\\n",
              "521  Which event was a result of the other, the for...   \n",
              "737  How many people in total did the Russians and ...   \n",
              "740    What caused the value of the crown to decrease?   \n",
              "660  How many more million in export in 2015/16 Chi...   \n",
              "411  How many years after birth registration did de...   \n",
              "\n",
              "                                      answer label source_ds  \\\n",
              "521  formation, the Restored Reformed Church  PASS      DROP   \n",
              "737                              28700, 2000  PASS      DROP   \n",
              "740  Munich Agreement, 1938 Munich Agreement  PASS      DROP   \n",
              "660                                        2  PASS      DROP   \n",
              "411                                       15  FAIL      DROP   \n",
              "\n",
              "                        context_and_question_as_prompt  \\\n",
              "521  Answer the QUESTION using information **only**...   \n",
              "737  Answer the QUESTION using information **only**...   \n",
              "740  Answer the QUESTION using information **only**...   \n",
              "660  Answer the QUESTION using information **only**...   \n",
              "411  Answer the QUESTION using information **only**...   \n",
              "\n",
              "                      answer_ending_in_period  tlm_score  ragas_faithfulness  \\\n",
              "521  formation, the Restored Reformed Church.   0.573012                 NaN   \n",
              "737                              28700, 2000.   0.354327                 0.5   \n",
              "740  Munich Agreement, 1938 Munich Agreement.   0.916365                 1.0   \n",
              "660                                        2.   0.927469                 NaN   \n",
              "411                                       15.   0.045738                 NaN   \n",
              "\n",
              "     ragas_answer_relevancy  ragas++_faithfulness_improved  \\\n",
              "521                0.878296                            1.0   \n",
              "737                0.901479                            0.5   \n",
              "740                0.750391                            1.0   \n",
              "660                0.814427                            1.0   \n",
              "411                0.864775                            0.0   \n",
              "\n",
              "     ragas++_answer_relevancy_improved  geval_correctness  \\\n",
              "521                           0.878296           0.200477   \n",
              "737                           0.848822           0.005399   \n",
              "740                           0.750531           0.201702   \n",
              "660                           0.814427           0.000000   \n",
              "411                           0.864775           0.100694   \n",
              "\n",
              "     hallucination_metric  hallucination_metric_complement  \\\n",
              "521              0.200477                         0.799523   \n",
              "737              0.005399                         0.994601   \n",
              "740              0.201702                         0.798298   \n",
              "660              0.000000                         1.000000   \n",
              "411              0.100694                         0.899306   \n",
              "\n",
              "                            self_evaluation_result_raw  self_evaluation_score  \n",
              "521  {'response': 'Explanation: The Answer states t...                   0.50  \n",
              "737  {'response': 'Explanation: The Answer provides...                   0.50  \n",
              "740  {'response': 'Explanation: The Munich Agreemen...                   0.25  \n",
              "660  {'response': 'Explanation: The answer states \"...                   0.25  \n",
              "411  {'response': 'Explanation: The answer states t...                   0.50  "
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "res_dict[dataset_name].head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n",
        "## Running the entire benchmark suite\n",
        "\n",
        "The below code will run all benchmark metrics on all datasets in HaluBench. This may take a while to complete!\n",
        "\n",
        "To run this code, please change `SHOULD_RUN` from `False` to `True`.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
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          ]
        },
        "id": "u9ILruoQJugi",
        "outputId": "e4ea11e1-387e-4d57-ed02-238d48250a26"
      },
      "outputs": [],
      "source": [
        "SHOULD_RUN = False\n",
        "\n",
        "if SHOULD_RUN:\n",
        "  res_dict = {}\n",
        "  for dataset_name in unique_datasets:\n",
        "    print(dataset_name)\n",
        "    try:\n",
        "      res_dict[dataset_name] = score_dataset_with_metrics(data[data.source_ds == dataset_name])\n",
        "      res_dict[dataset_name].to_csv(f\"results_{dataset_name}.csv\")\n",
        "    except:\n",
        "      print(f\"Error with dataset {dataset_name}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The above code may take a long time to run. Once you do have results, you can plot them with the following code.\n",
        "\n",
        "We'll use our small example to illustrate:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "JSaO0CyV_Unz"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import roc_curve, roc_auc_score, auc\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "IMPUTATION_VALUE = .5\n",
        "\n",
        "def calculate_and_plot_auroc(result_df, dataset_name):\n",
        "    result = result_df\n",
        "    score_list = [\n",
        "        (\"TLM\", result[\"tlm_score\"]),\n",
        "        (\"RAGAS Faithfulness\", result[\"ragas_faithfulness\"]),\n",
        "        (\"RAGAS Answer Relevancy\", result[\"ragas_answer_relevancy\"]),\n",
        "        # Uncomment this if you computed Faithfulness with HHEM\n",
        "        # (\"RAGAS Faithfulness HHEM\", result[\"ragas_faithfulness_hhem\"]),\n",
        "        (\"RAGAS++ Faithfulness\", result[\"ragas++_faithfulness_improved\"]),\n",
        "        (\"RAGAS++ Answer Relevancy\", result[\"ragas++_answer_relevancy_improved\"]),\n",
        "        (\"G-eval Correctness\", result[\"geval_correctness\"]),\n",
        "        (\"Hallucination Score\", result[\"hallucination_metric_complement\"]),\n",
        "        (\"Self-Evaluation\", result[\"self_evaluation_score\"])\n",
        "    ]\n",
        "    result[\"is_correct\"] = result.label == \"PASS\"\n",
        "    for name, score in score_list:\n",
        "      fpr, tpr, _ = roc_curve(result.is_correct, score.fillna(IMPUTATION_VALUE))\n",
        "      auroc = auc(fpr, tpr)\n",
        "      # We'll use a dotted line to indicate that the metric returned some null inputs\n",
        "      # This is useful to indicate you may want to investigate the metric further\n",
        "      if any(score.isnull()):\n",
        "        plt.plot(fpr, tpr, label=f'{name} (auroc = {auroc:.3f})', linestyle='dotted')\n",
        "      else:\n",
        "        plt.plot(fpr, tpr, label=f'{name} (auroc = {auroc:.3f})')\n",
        "\n",
        "\n",
        "\n",
        "    plt.legend(bbox_to_anchor=(1, 1))\n",
        "    plt.title(f\"ROC Curve: {dataset_name}\")\n",
        "    plt.xlabel(\"False Positive Rate\")\n",
        "    plt.ylabel(\"True Positive Rate\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Plot an example sample of the results\n",
        "\n",
        "We can now use our function to plot the results. \n",
        "\n",
        "\n",
        "We'll plot the **toy sample** results we ran earlier simply to illustrate how the plotting function works.\n",
        "\n",
        "\n",
        "### *Why are some of the lines dotted?*\n",
        "\n",
        "We use a *dotted* line to represent when a metric failed to compute for at least one row in the dataset and was imputed to the default value"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "s-8kGvaf-Wd1",
        "outputId": "6e36ad57-ef01-4f67-8dd6-f14b877762a3"
      },
      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "calculate_and_plot_auroc(res_dict[dataset_name], dataset_name+\" (Sample)\")"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
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        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.12.5"
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            "_view_module_version": "1.5.0",
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            "box_style": "",
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