Llama Guard Evaluation.ipynb

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 "cells": [
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    },
    "executionInfo": {
     "elapsed": 21591,
     "status": "ok",
     "timestamp": 1727428808744,
     "user": {
      "displayName": "YIPING JIN",
      "userId": "17124138946787548999"
     },
     "user_tz": -120
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    "id": "iFGHdvNV2cQs",
    "outputId": "7ec7f14d-3d3b-4c63-c3d3-04371b2d58d4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mounted at /content/drive\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "_L9402N1S8dG"
   },
   "outputs": [],
   "source": [
    "import pdb  # TODO: remove after debugging\n",
    "import pprint\n",
    "import joblib\n",
    "import time\n",
    "import re\n",
    "import numpy as np\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import json\n",
    "from pathlib import Path\n",
    "import pandas as pd\n",
    "from collections import Counter\n",
    "from nltk.tokenize import word_tokenize\n",
    "from tqdm.notebook import trange\n",
    "\n",
    "from sklearn.metrics import classification_report, accuracy_score\n",
    "\n",
    "import torch\n",
    "\n",
    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
    "\n",
    "ROOT_DIR = Path(\"/content/drive/MyDrive/Colab Notebooks/biases-hatecheck/\")\n",
    "\n",
    "bs = 64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ogdi033DkXdN"
   },
   "outputs": [],
   "source": [
    "ignore_functionalities = [\"F11: Non-hateful use of profanity\",\n",
    "                          \"F22: Abuse targeted at objects\",\n",
    "                          \"F23: Abuse targeted at individuals (not as member of a prot. group)\",\n",
    "                          \"F24: Abuse targeted at nonprotected groups (e.g. professions)\",\n",
    "                          \"F25-29: Spelling variation\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 10,
     "status": "ok",
     "timestamp": 1727428815687,
     "user": {
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     "user_tz": -120
    },
    "id": "i9wsnu4ZnAC3",
    "outputId": "55a59bac-a8a8-4837-c611-1821838179b5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda\n"
     ]
    }
   ],
   "source": [
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "QQabTiUZeWIJ"
   },
   "outputs": [],
   "source": [
    "model = \"llama_guard_8b\" # llama_guard_1b, llama_guard_8b\n",
    "if model == \"llama_guard_1b\":\n",
    "  model_path = ROOT_DIR/\"models/Llama-Guard-3-1B/models--meta-llama--Llama-Guard-3-1B/snapshots/9e4f4b019bb3e964efa227180a64adc015856111\"\n",
    "  model_name = \"Llama-Guard-3-1B\"\n",
    "elif model == \"llama_guard_8b\":\n",
    "  model_path = ROOT_DIR/\"models/Llama-Guard-3-8B/models--meta-llama--Llama-Guard-3-8B/snapshots/38c1124b4a575e0b7714bc73fae0e74554aa080d\"\n",
    "  model_name = \"Llama-Guard-3-8B\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "T_WmbWSHdeYj"
   },
   "source": [
    "## 0. Download Model from HuggingFace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 8822,
     "status": "ok",
     "timestamp": 1727428826478,
     "user": {
      "displayName": "YIPING JIN",
      "userId": "17124138946787548999"
     },
     "user_tz": -120
    },
    "id": "fk6bngILeIbk",
    "outputId": "f6b84ab3-d05e-44b4-8587-4fe795fd8227"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "    _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n",
      "    _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n",
      "    _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n",
      "    _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n",
      "    _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n",
      "\n",
      "    To login, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n",
      "Enter your token (input will not be visible): \n",
      "Add token as git credential? (Y/n) n\n",
      "Token is valid (permission: write).\n",
      "Your token has been saved to /root/.cache/huggingface/token\n",
      "Login successful\n"
     ]
    }
   ],
   "source": [
    "!huggingface-cli login"
   ]
  },
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     "status": "ok",
     "timestamp": 1727361449036,
     "user": {
      "displayName": "YIPING JIN",
      "userId": "17124138946787548999"
     },
     "user_tz": -120
    },
    "id": "_i-D6CYoddMT",
    "outputId": "842257b9-2713-495c-ee11-a7751dc136b4"
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   "outputs": [
    {
     "name": "stderr",
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     "text": [
      "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
      "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
      "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
      "You will be able to reuse this secret in all of your notebooks.\n",
      "Please note that authentication is recommended but still optional to access public models or datasets.\n",
      "  warnings.warn(\n"
     ]
    },
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    {
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    },
    {
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     },
     "metadata": {},
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    },
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'/content/drive/MyDrive/Colab Notebooks/biases-hatecheck/models/Llama-Guard-3-1B/models--meta-llama--Llama-Guard-3-1B/snapshots/9e4f4b019bb3e964efa227180a64adc015856111'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from huggingface_hub import snapshot_download\n",
    "snapshot_download(repo_id=f\"meta-llama/{model_name}\", cache_dir=ROOT_DIR/f\"models/{model_name}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uewwiwxfeStB"
   },
   "source": [
    "## Load model and HelloWorld example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 49,
     "referenced_widgets": [
      "93683ea7f6714ab7a7f8299d042a37a6",
      "30f166ec99824b82b21482788fb71a65",
      "a14ab36c655e479c813fe5f2014f2b9c",
      "fced1cc146df47a99cf650d442d709cd",
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      "c46074892900407c858a89c20244ce70",
      "65b8137f22ad4088ac9ffa80eb7fc426",
      "820a5a4780cf46c29969a252d805476f",
      "997c37903d8c4b03b4d565177d1d7f47"
     ]
    },
    "executionInfo": {
     "elapsed": 192028,
     "status": "ok",
     "timestamp": 1727429026985,
     "user": {
      "displayName": "YIPING JIN",
      "userId": "17124138946787548999"
     },
     "user_tz": -120
    },
    "id": "HKHcNog8TC77",
    "outputId": "58d36527-66f3-4b63-d3dd-ad66e59b0503"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "93683ea7f6714ab7a7f8299d042a37a6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/4 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "chat_model = AutoModelForCausalLM.from_pretrained(model_path).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 267,
     "referenced_widgets": [
      "c5394bea6749488d9e9a86b8d0d017d9",
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      "0bfe7be293224ee0b6957f2c6159730e",
      "c4244aee0f5441c9973fd292c97203b1"
     ]
    },
    "executionInfo": {
     "elapsed": 2801,
     "status": "ok",
     "timestamp": 1727429029775,
     "user": {
      "displayName": "YIPING JIN",
      "userId": "17124138946787548999"
     },
     "user_tz": -120
    },
    "id": "2ICZz3z4vMjL",
    "outputId": "2ba3b85c-63bc-4aa8-988f-126d75acdb72"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
      "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
      "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
      "You will be able to reuse this secret in all of your notebooks.\n",
      "Please note that authentication is recommended but still optional to access public models or datasets.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c5394bea6749488d9e9a86b8d0d017d9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "tokenizer_config.json:   0%|          | 0.00/53.2k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d85851d800f04627833d5e996e1befd6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "tokenizer.json:   0%|          | 0.00/9.09M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3508dd34b5f441d286a882b69aedd5c7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "special_tokens_map.json:   0%|          | 0.00/296 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Note: The \"meta-llama/Llama-Guard-3-8B\" tokenizer returns error. We use the tokenizer for 1B model for both cases\n",
    "tokenizer = AutoTokenizer.from_pretrained(f\"meta-llama/Llama-Guard-3-1B\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Q2F0hp5rluRl"
   },
   "outputs": [],
   "source": [
    "def moderate(message):\n",
    "  conversation = [\n",
    "    {\n",
    "        \"role\": \"user\",\n",
    "        \"content\": [\n",
    "            {\n",
    "                \"type\": \"text\",\n",
    "                \"text\": message\n",
    "            },\n",
    "        ],\n",
    "    }\n",
    "  ]\n",
    "\n",
    "  input_ids = tokenizer.apply_chat_template(\n",
    "    conversation,\n",
    "    return_tensors=\"pt\",\n",
    "    excluded_category_keys=[\"S1\", \"S2\", \"S3\", \"S4\", \"S5\", \"S6\", \"S7\", \"S8\", \"S9\", \"S11\", \"S12\", \"S13\"],\n",
    "  ).to(device)\n",
    "\n",
    "  outputs = chat_model.generate(input_ids=input_ids, max_new_tokens=10, pad_token_id=0, return_dict_in_generate=True, output_scores=True)\n",
    "  transition_scores = chat_model.compute_transition_scores(\n",
    "    outputs.sequences, outputs.scores, normalize_logits=True\n",
    "  )\n",
    "\n",
    "  prompt_len = input_ids.shape[-1]\n",
    "  generated_tokens = outputs.sequences[:, prompt_len:]\n",
    "  for tok, score in zip(generated_tokens[0], transition_scores[0]):\n",
    "    #print(f\"| {tok:5d} | {tokenizer.decode(tok):8s} | {score.numpy():.3f} | {np.exp(score.numpy()):.2%}\")\n",
    "    if tokenizer.decode(tok) == \"safe\":\n",
    "      return 1 - np.exp(score.cpu().numpy())\n",
    "    elif tokenizer.decode(tok) == \"unsafe\":\n",
    "      return np.exp(score.cpu().numpy())\n",
    "  return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 725,
     "status": "ok",
     "timestamp": 1727429041273,
     "user": {
      "displayName": "YIPING JIN",
      "userId": "17124138946787548999"
     },
     "user_tz": -120
    },
    "id": "4kyUu-ysoTXF",
    "outputId": "ffc25582-8c20-4358-ea30-30187410f916"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.99667543"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "moderate(\"Trans are against the nature.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GrDDa2KzI8hC"
   },
   "source": [
    "## 1. Load GPT-HateCheckDataset and Predict\n",
    "\n",
    "The prediction files are written to /datasets/GPT-HateCheck/{model}/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "iB06Q6KRXCZ8"
   },
   "outputs": [],
   "source": [
    "dataset_path = ROOT_DIR/\"datasets/GPT-HateCheck/nli_hypothesis_test/output/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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     "base_uri": "https://localhost:8080/",
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      "009514769da5462a922365afa3e54129",
      "aaca7db7027541c9830d64a1a8e25378"
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    },
    "executionInfo": {
     "elapsed": 1305927,
     "status": "ok",
     "timestamp": 1727430385739,
     "user": {
      "displayName": "YIPING JIN",
      "userId": "17124138946787548999"
     },
     "user_tz": -120
    },
    "id": "t2ux2bLbJL5h",
    "outputId": "36ac5887-8231-49ff-a7e7-765c9bea7459"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicting for file: dataset_black people.csv with 749 messages.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d96e5801897847039ddc0b066655bbdc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/749 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicting for file: dataset_Muslims.csv with 628 messages.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "26a1b45b8a9740fda049b94dbd36c372",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/628 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicting for file: dataset_disabled people.csv with 621 messages.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a0d83ab3e9f4419bb7c4f2929ad5d0e6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/621 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicting for file: dataset_trans.csv with 602 messages.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ae3e02c79a66435b81e6d0ff7c28faed",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/602 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicting for file: dataset_women.csv with 584 messages.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bd4cb2d4685e4bbcabbb7bf444a24723",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/584 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicting for file: dataset_gays.csv with 608 messages.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2f528460295941e2bebbc95c8123aa76",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/608 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicting for file: dataset_immigrants.csv with 646 messages.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "415458ed7ddc4beb8aac7fe165a19830",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/646 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for p in dataset_path.glob('**/*.csv'):\n",
    "\n",
    "  if (ROOT_DIR/\"datasets/GPT-HateCheck\"/model/p.name).is_file():\n",
    "    print(f\"[WARNING] File perspective-{p.name} already exists. Skipped.\")\n",
    "    continue\n",
    "  df = pd.read_csv(p)\n",
    "  df = df[df['nli_pass_test']==1]\n",
    "  df = df[~df['functionality'].isin(ignore_functionalities)]\n",
    "  messages = df['message'].tolist()\n",
    "  functionalities = df['functionality'].tolist()\n",
    "  hate_labels = df['hate_label'].tolist()\n",
    "  n_messages = len(messages)\n",
    "  toxicity_scores = []\n",
    "  print(f\"Predicting for file: {p.name} with {n_messages} messages.\")\n",
    "  for i in trange(n_messages):\n",
    "    pred_score = moderate(messages[i])\n",
    "    toxicity_scores.append(pred_score)\n",
    "  df_out = pd.DataFrame({\"message\": messages,\n",
    "                         \"functionality\":  functionalities,\n",
    "                         \"hate_label\": hate_labels,\n",
    "                         \"toxicity_score\": toxicity_scores})\n",
    "  df_out.to_csv(ROOT_DIR/\"datasets/GPT-HateCheck\"/model/p.name, index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EXu8PE39Lo5b"
   },
   "source": [
    "### 1.1 Evaluation and analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "aShYyrMaLU1i"
   },
   "outputs": [],
   "source": [
    "prediction_path = ROOT_DIR/\"datasets/GPT-HateCheck\"/model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 7,
     "status": "ok",
     "timestamp": 1727430393969,
     "user": {
      "displayName": "YIPING JIN",
      "userId": "17124138946787548999"
     },
     "user_tz": -120
    },
    "id": "Hf0CB2s4MPqn",
    "outputId": "19370d3e-63d3-4057-8679-277d1b4cee15"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Target identity: black people\n",
      "toxicity_acc: 0.9065\n",
      "Target identity: Muslims\n",
      "toxicity_acc: 0.9889\n",
      "Target identity: disabled people\n",
      "toxicity_acc: 0.9485\n",
      "Target identity: trans\n",
      "toxicity_acc: 0.9651\n",
      "Target identity: women\n",
      "toxicity_acc: 0.8801\n",
      "Target identity: gays\n",
      "toxicity_acc: 0.9836\n",
      "Target identity: immigrants\n",
      "toxicity_acc: 0.8483\n"
     ]
    }
   ],
   "source": [
    "# Overall accuracy\n",
    "for p in prediction_path.glob('**/*.csv'):\n",
    "  target_identity = re.search('dataset_(.+?).csv', p.name).group(1)\n",
    "  df = pd.read_csv(p)\n",
    "  df = df[~df['functionality'].isin(ignore_functionalities)]\n",
    "  df['toxicity_pred'] = df.apply(lambda x: int(x['toxicity_score']>0.5), axis=1)\n",
    "  toxicity_acc = accuracy_score(y_true=df['hate_label'].tolist(), y_pred=df['toxicity_pred'].tolist())\n",
    "  print(f\"Target identity: {target_identity}\")\n",
    "  print(f\"toxicity_acc: {toxicity_acc:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 619,
     "status": "ok",
     "timestamp": 1727423352781,
     "user": {
      "displayName": "YIPING JIN",
      "userId": "17124138946787548999"
     },
     "user_tz": -120
    },
    "id": "skFK65LoMkiJ",
    "outputId": "4f00d2d3-b642-46ad-e815-1a90a412cab1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Target identity: black people\n",
      "Overall acc: 0.893; Hateful subset acc: 0.944; Non-hateful subset acc: 0.811\n",
      "Target identity: Muslims\n",
      "Overall acc: 0.895; Hateful subset acc: 0.950; Non-hateful subset acc: 0.794\n",
      "Target identity: disabled people\n",
      "Overall acc: 0.839; Hateful subset acc: 0.768; Non-hateful subset acc: 0.970\n",
      "Target identity: trans\n",
      "Overall acc: 0.882; Hateful subset acc: 0.834; Non-hateful subset acc: 0.971\n",
      "Target identity: women\n",
      "Overall acc: 0.829; Hateful subset acc: 0.752; Non-hateful subset acc: 0.995\n",
      "Target identity: gays\n",
      "Overall acc: 0.928; Hateful subset acc: 0.963; Non-hateful subset acc: 0.862\n",
      "Target identity: immigrants\n",
      "Overall acc: 0.870; Hateful subset acc: 0.807; Non-hateful subset acc: 0.987\n"
     ]
    }
   ],
   "source": [
    "# Averaged functionality-level accuracy\n",
    "for p in prediction_path.glob('**/*.csv'):\n",
    "  target_identity = re.search('dataset_(.+?).csv', p.name).group(1)\n",
    "  df = pd.read_csv(p)\n",
    "  df = df[~df['functionality'].isin(ignore_functionalities)]\n",
    "  df['toxicity_pred'] = df.apply(lambda x: int(x['toxicity_score']>0.5), axis=1)\n",
    "  df['is_correct'] = df.apply(lambda x: 1 if x['toxicity_pred']==x['hate_label'] else 0, axis=1)\n",
    "  df_func_acc = df[['functionality', 'hate_label', 'is_correct']].groupby('functionality').mean().reset_index()\n",
    "  overall_acc = df_func_acc['is_correct'].mean()\n",
    "  df_category_acc = df_func_acc[['hate_label', 'is_correct']].groupby('hate_label').mean().reset_index().sort_values('hate_label', ascending=False)\n",
    "  cat_acc = df_category_acc['is_correct'].tolist()\n",
    "  print(f\"Target identity: {target_identity}\")\n",
    "  print(f\"Overall acc: {overall_acc:.3f}; Hateful subset acc: {cat_acc[0]:.3f}; Non-hateful subset acc: {cat_acc[1]:.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 533,
     "status": "ok",
     "timestamp": 1727423366921,
     "user": {
      "displayName": "YIPING JIN",
      "userId": "17124138946787548999"
     },
     "user_tz": -120
    },
    "id": "tmALjfZCNBS0",
    "outputId": "62306a22-0b00-4a72-ecfc-34a98f9751bd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Target identity: black people\n",
      "Overall avg pred: 0.658; Hateful subset avg pred: 0.942; Non-hateful subset avg pred: 0.195\n",
      "Target identity: Muslims\n",
      "Overall avg pred: 0.689; Hateful subset avg pred: 0.953; Non-hateful subset avg pred: 0.199\n",
      "Target identity: disabled people\n",
      "Overall avg pred: 0.504; Hateful subset avg pred: 0.760; Non-hateful subset avg pred: 0.029\n",
      "Target identity: trans\n",
      "Overall avg pred: 0.551; Hateful subset avg pred: 0.829; Non-hateful subset avg pred: 0.035\n",
      "Target identity: women\n",
      "Overall avg pred: 0.511; Hateful subset avg pred: 0.742; Non-hateful subset avg pred: 0.010\n",
      "Target identity: gays\n",
      "Overall avg pred: 0.670; Hateful subset avg pred: 0.960; Non-hateful subset avg pred: 0.130\n",
      "Target identity: immigrants\n",
      "Overall avg pred: 0.529; Hateful subset avg pred: 0.803; Non-hateful subset avg pred: 0.019\n"
     ]
    }
   ],
   "source": [
    "# Averaged functionality-level prediction\n",
    "for p in prediction_path.glob('**/*.csv'):\n",
    "  target_identity = re.search('dataset_(.+?).csv', p.name).group(1)\n",
    "  df = pd.read_csv(p)\n",
    "  df = df[~df['functionality'].isin(ignore_functionalities)]\n",
    "  df_func_acc = df[['functionality', 'hate_label', 'toxicity_score']].groupby('functionality').mean().reset_index()\n",
    "  overall_pred = df_func_acc['toxicity_score'].mean()\n",
    "  df_category_acc = df_func_acc[['hate_label', 'toxicity_score']].groupby('hate_label').mean().reset_index().sort_values('hate_label', ascending=False)\n",
    "  cat_pred = df_category_acc['toxicity_score'].tolist()\n",
    "  print(f\"Target identity: {target_identity}\")\n",
    "  print(f\"Overall avg pred: {overall_pred:.3f}; Hateful subset avg pred: {cat_pred[0]:.3f}; Non-hateful subset avg pred: {cat_pred[1]:.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 303,
     "status": "ok",
     "timestamp": 1727430401811,
     "user": {
      "displayName": "YIPING JIN",
      "userId": "17124138946787548999"
     },
     "user_tz": -120
    },
    "id": "2TL1Mw9ZNSBr",
    "outputId": "80f27ea3-120f-4467-ef9e-406d076abaa2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Target identity: black people\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0      0.965     0.779     0.862       281\n",
      "           1      0.881     0.983     0.929       468\n",
      "\n",
      "    accuracy                          0.907       749\n",
      "   macro avg      0.923     0.881     0.896       749\n",
      "weighted avg      0.913     0.907     0.904       749\n",
      "\n",
      "Target identity: Muslims\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0      0.969     0.995     0.982       190\n",
      "           1      0.998     0.986     0.992       438\n",
      "\n",
      "    accuracy                          0.989       628\n",
      "   macro avg      0.983     0.991     0.987       628\n",
      "weighted avg      0.989     0.989     0.989       628\n",
      "\n",
      "Target identity: disabled people\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0      0.887     0.982     0.932       224\n",
      "           1      0.989     0.929     0.958       397\n",
      "\n",
      "    accuracy                          0.948       621\n",
      "   macro avg      0.938     0.956     0.945       621\n",
      "weighted avg      0.952     0.948     0.949       621\n",
      "\n",
      "Target identity: trans\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0      0.912     1.000     0.954       217\n",
      "           1      1.000     0.945     0.972       385\n",
      "\n",
      "    accuracy                          0.965       602\n",
      "   macro avg      0.956     0.973     0.963       602\n",
      "weighted avg      0.968     0.965     0.965       602\n",
      "\n",
      "Target identity: women\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0      0.723     1.000     0.839       183\n",
      "           1      1.000     0.825     0.904       401\n",
      "\n",
      "    accuracy                          0.880       584\n",
      "   macro avg      0.862     0.913     0.872       584\n",
      "weighted avg      0.913     0.880     0.884       584\n",
      "\n",
      "Target identity: gays\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0      0.973     0.973     0.973       182\n",
      "           1      0.988     0.988     0.988       426\n",
      "\n",
      "    accuracy                          0.984       608\n",
      "   macro avg      0.980     0.980     0.980       608\n",
      "weighted avg      0.984     0.984     0.984       608\n",
      "\n",
      "Target identity: immigrants\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0      0.646     0.994     0.783       178\n",
      "           1      0.997     0.793     0.883       468\n",
      "\n",
      "    accuracy                          0.848       646\n",
      "   macro avg      0.822     0.894     0.833       646\n",
      "weighted avg      0.901     0.848     0.856       646\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for p in prediction_path.glob('**/*.csv'):\n",
    "  target_identity = re.search('dataset_(.+?).csv', p.name).group(1)\n",
    "  df = pd.read_csv(p)\n",
    "  df = df[~df['functionality'].isin(ignore_functionalities)]\n",
    "  df['toxicity_pred'] = df.apply(lambda x: int(x['toxicity_score']>0.5), axis=1)\n",
    "  identity_attack_report = classification_report(y_true=df['hate_label'].tolist(), y_pred=df['toxicity_pred'].tolist(), digits=3)\n",
    "  print(f\"Target identity: {target_identity}\")\n",
    "  print(identity_attack_report)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PFOfzJZ9WcER"
   },
   "source": [
    "## 2. Load HateCheck Dataset and Predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "nXZcvMN6OXV7"
   },
   "outputs": [],
   "source": [
    "dataset_path = ROOT_DIR/\"datasets/HateCheck/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    "executionInfo": {
     "elapsed": 657300,
     "status": "ok",
     "timestamp": 1727424286986,
     "user": {
      "displayName": "YIPING JIN",
      "userId": "17124138946787548999"
     },
     "user_tz": -120
    },
    "id": "3SjKf6q-WjMu",
    "outputId": "48f67507-1da4-4acf-9311-8e2f57bc2fdd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicting for file: immigrants_test_suite_cases.csv with 359 messages.\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicting for file: gay people_test_suite_cases.csv with 439 messages.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0dc2482e226548f9b3a65cc3fcdf9531",
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       "version_minor": 0
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicting for file: black people_test_suite_cases.csv with 378 messages.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicting for file: Muslims_test_suite_cases.csv with 372 messages.\n"
     ]
    },
    {
     "data": {
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicting for file: trans people_test_suite_cases.csv with 359 messages.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "486d7b15f1fb483a9801d878954a4d9b",
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicting for file: women_test_suite_cases.csv with 397 messages.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f561e12c66d048ddacb500bd8bed7dbe",
       "version_major": 2,
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicting for file: disabled people_test_suite_cases.csv with 372 messages.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fe8039852eca49c5b543576658b2f7b7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for p in dataset_path.glob('identities/*.csv'):\n",
    "\n",
    "  if (dataset_path/model/p.name).is_file():\n",
    "    print(f\"[WARNING] File {p.name} already exists. Skipped.\")\n",
    "    continue\n",
    "  df = pd.read_csv(p)\n",
    "  # Remove test cases related to spelling variation\n",
    "  df = df.loc[~df['functionality'].str.startswith('spell_', na=False)]\n",
    "  messages = df['test_case'].tolist()\n",
    "  functionalities = df['functionality'].tolist()\n",
    "  hate_labels = df['label_gold'].tolist()\n",
    "  n_messages = len(messages)\n",
    "  toxicity_scores, identity_attack_scores = [], []\n",
    "  print(f\"Predicting for file: {p.name} with {n_messages} messages.\")\n",
    "\n",
    "  for i in trange(n_messages):\n",
    "    pred_score = moderate(messages[i])\n",
    "    toxicity_scores.append(pred_score)\n",
    "\n",
    "  df_out = pd.DataFrame({\"message\": messages,\n",
    "                         \"functionality\":  functionalities,\n",
    "                         \"hate_label\": hate_labels,\n",
    "                         \"toxicity_score\": toxicity_scores})\n",
    "  df_out.to_csv(dataset_path/model/p.name, index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JX2a4DhKXoq-"
   },
   "source": [
    "### 2.1 Evaluation and analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "nQxtXZdSXi2K"
   },
   "outputs": [],
   "source": [
    "prediction_path = ROOT_DIR/\"datasets/HateCheck\"/model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 591,
     "status": "ok",
     "timestamp": 1718027347177,
     "user": {
      "displayName": "YIPING JIN",
      "userId": "17124138946787548999"
     },
     "user_tz": -120
    },
    "id": "zC7Yr7baYLLO",
    "outputId": "ac8880ef-4f58-47e3-de98-a47e2cdad510"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Target identity: immigrants\n",
      "toxicity_acc: 0.7744\n",
      "Target identity: gay people\n",
      "toxicity_acc: 0.7244\n",
      "Target identity: black people\n",
      "toxicity_acc: 0.7619\n",
      "Target identity: Muslims\n",
      "toxicity_acc: 0.7984\n",
      "Target identity: trans people\n",
      "toxicity_acc: 0.7772\n",
      "Target identity: women\n",
      "toxicity_acc: 0.7380\n",
      "Target identity: disabled people\n",
      "toxicity_acc: 0.7312\n"
     ]
    }
   ],
   "source": [
    "# Overall accuracy\n",
    "for p in prediction_path.glob('**/*.csv'):\n",
    "  target_identity = re.search('(.+?)_test_suite_cases.csv', p.name).group(1)\n",
    "  df = pd.read_csv(p)\n",
    "  df = df[df['functionality'] != 'F25-29: Spelling variation']\n",
    "  df['toxicity_pred'] = df.apply(lambda x: int(x['toxicity_score']>0.5), axis=1)\n",
    "  df['hate_label'] = df.apply(lambda x: ['non-hateful', 'hateful'].index(x['hate_label']), axis=1)\n",
    "  toxicity_acc = accuracy_score(y_true=df['hate_label'].tolist(), y_pred=df['toxicity_pred'].tolist())\n",
    "  print(f\"Target identity: {target_identity}\")\n",
    "  print(f\"toxicity_acc: {toxicity_acc:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 259,
     "status": "ok",
     "timestamp": 1718027406644,
     "user": {
      "displayName": "YIPING JIN",
      "userId": "17124138946787548999"
     },
     "user_tz": -120
    },
    "id": "0V90t_-wY2rx",
    "outputId": "820114c5-3dfe-4495-96d3-1d7a81f91b7f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Target identity: immigrants\n",
      "Overall acc: 0.778; Hateful subset acc: 0.917; Non-hateful subset acc: 0.416\n",
      "Target identity: gay people\n",
      "Overall acc: 0.755; Hateful subset acc: 0.917; Non-hateful subset acc: 0.455\n",
      "Target identity: black people\n",
      "Overall acc: 0.764; Hateful subset acc: 0.977; Non-hateful subset acc: 0.304\n",
      "Target identity: Muslims\n",
      "Overall acc: 0.810; Hateful subset acc: 0.961; Non-hateful subset acc: 0.419\n",
      "Target identity: trans people\n",
      "Overall acc: 0.781; Hateful subset acc: 0.871; Non-hateful subset acc: 0.547\n",
      "Target identity: women\n",
      "Overall acc: 0.730; Hateful subset acc: 0.903; Non-hateful subset acc: 0.409\n",
      "Target identity: disabled people\n",
      "Overall acc: 0.738; Hateful subset acc: 0.837; Non-hateful subset acc: 0.481\n"
     ]
    }
   ],
   "source": [
    "# Averaged functionality-level accuracy\n",
    "for p in prediction_path.glob('**/*.csv'):\n",
    "  target_identity = re.search('(.+?)_test_suite_cases.csv', p.name).group(1)\n",
    "  df = pd.read_csv(p)\n",
    "  df = df[df['functionality'] != 'F25-29: Spelling variation']\n",
    "  df['toxicity_pred'] = df.apply(lambda x: int(x['toxicity_score']>0.5), axis=1)\n",
    "  df['hate_label'] = df.apply(lambda x: ['non-hateful', 'hateful'].index(x['hate_label']), axis=1)\n",
    "  df['is_correct'] = df.apply(lambda x: 1 if x['toxicity_pred']==x['hate_label'] else 0, axis=1)\n",
    "  df_func_acc = df[['functionality', 'hate_label', 'is_correct']].groupby('functionality').mean().reset_index()\n",
    "  overall_acc = df_func_acc['is_correct'].mean()\n",
    "  df_category_acc = df_func_acc[['hate_label', 'is_correct']].groupby('hate_label').mean().reset_index().sort_values('hate_label', ascending=False)\n",
    "  cat_acc = df_category_acc['is_correct'].tolist()\n",
    "  print(f\"Target identity: {target_identity}\")\n",
    "  print(f\"Overall acc: {overall_acc:.3f}; Hateful subset acc: {cat_acc[0]:.3f}; Non-hateful subset acc: {cat_acc[1]:.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 382,
     "status": "ok",
     "timestamp": 1718027483970,
     "user": {
      "displayName": "YIPING JIN",
      "userId": "17124138946787548999"
     },
     "user_tz": -120
    },
    "id": "WG2NHoRJY9Fm",
    "outputId": "51c0f45b-72ec-4aa2-b830-efd2453777dd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Target identity: immigrants\n",
      "Overall avg pred: 0.748; Hateful subset avg pred: 0.819; Non-hateful subset avg pred: 0.565\n",
      "Target identity: gay people\n",
      "Overall avg pred: 0.712; Hateful subset avg pred: 0.810; Non-hateful subset avg pred: 0.531\n",
      "Target identity: black people\n",
      "Overall avg pred: 0.772; Hateful subset avg pred: 0.841; Non-hateful subset avg pred: 0.622\n",
      "Target identity: Muslims\n",
      "Overall avg pred: 0.767; Hateful subset avg pred: 0.837; Non-hateful subset avg pred: 0.585\n",
      "Target identity: trans people\n",
      "Overall avg pred: 0.682; Hateful subset avg pred: 0.764; Non-hateful subset avg pred: 0.468\n",
      "Target identity: women\n",
      "Overall avg pred: 0.696; Hateful subset avg pred: 0.771; Non-hateful subset avg pred: 0.555\n",
      "Target identity: disabled people\n",
      "Overall avg pred: 0.653; Hateful subset avg pred: 0.718; Non-hateful subset avg pred: 0.483\n"
     ]
    }
   ],
   "source": [
    "# Averaged functionality-level prediction\n",
    "for p in prediction_path.glob('**/*.csv'):\n",
    "  target_identity = re.search('(.+?)_test_suite_cases.csv', p.name).group(1)\n",
    "  df = pd.read_csv(p)\n",
    "  df = df[df['functionality'] != 'F25-29: Spelling variation']\n",
    "  df['hate_label'] = df.apply(lambda x: ['non-hateful', 'hateful'].index(x['hate_label']), axis=1)\n",
    "  df_func_acc = df[['functionality', 'hate_label', 'toxicity_score']].groupby('functionality').mean().reset_index()\n",
    "  overall_pred = df_func_acc['toxicity_score'].mean()\n",
    "  df_category_acc = df_func_acc[['hate_label', 'toxicity_score']].groupby('hate_label').mean().reset_index().sort_values('hate_label', ascending=False)\n",
    "  cat_pred = df_category_acc['toxicity_score'].tolist()\n",
    "  print(f\"Target identity: {target_identity}\")\n",
    "  print(f\"Overall avg pred: {overall_pred:.3f}; Hateful subset avg pred: {cat_pred[0]:.3f}; Non-hateful subset avg pred: {cat_pred[1]:.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 789,
     "status": "ok",
     "timestamp": 1718027540806,
     "user": {
      "displayName": "YIPING JIN",
      "userId": "17124138946787548999"
     },
     "user_tz": -120
    },
    "id": "hhBaSYp_Y-Ll",
    "outputId": "3cf3eda5-c54d-41ac-9be6-6823414b63fb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Target identity: immigrants\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0      0.687     0.434     0.532       106\n",
      "           1      0.795     0.917     0.851       253\n",
      "\n",
      "    accuracy                          0.774       359\n",
      "   macro avg      0.741     0.675     0.692       359\n",
      "weighted avg      0.763     0.774     0.757       359\n",
      "\n",
      "Target identity: gay people\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0      0.782     0.444     0.566       178\n",
      "           1      0.707     0.916     0.798       261\n",
      "\n",
      "    accuracy                          0.724       439\n",
      "   macro avg      0.745     0.680     0.682       439\n",
      "weighted avg      0.738     0.724     0.704       439\n",
      "\n",
      "Target identity: black people\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0      0.872     0.328     0.477       125\n",
      "           1      0.746     0.976     0.846       253\n",
      "\n",
      "    accuracy                          0.762       378\n",
      "   macro avg      0.809     0.652     0.661       378\n",
      "weighted avg      0.788     0.762     0.724       378\n",
      "\n",
      "Target identity: Muslims\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0      0.810     0.423     0.556       111\n",
      "           1      0.796     0.958     0.870       261\n",
      "\n",
      "    accuracy                          0.798       372\n",
      "   macro avg      0.803     0.691     0.713       372\n",
      "weighted avg      0.800     0.798     0.776       372\n",
      "\n",
      "Target identity: trans people\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0      0.641     0.557     0.596       106\n",
      "           1      0.824     0.870     0.846       253\n",
      "\n",
      "    accuracy                          0.777       359\n",
      "   macro avg      0.733     0.713     0.721       359\n",
      "weighted avg      0.770     0.777     0.772       359\n",
      "\n",
      "Target identity: women\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0      0.695     0.419     0.523       136\n",
      "           1      0.749     0.904     0.819       261\n",
      "\n",
      "    accuracy                          0.738       397\n",
      "   macro avg      0.722     0.662     0.671       397\n",
      "weighted avg      0.731     0.738     0.718       397\n",
      "\n",
      "Target identity: disabled people\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0      0.558     0.477     0.515       111\n",
      "           1      0.791     0.839     0.814       261\n",
      "\n",
      "    accuracy                          0.731       372\n",
      "   macro avg      0.674     0.658     0.664       372\n",
      "weighted avg      0.721     0.731     0.725       372\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for p in prediction_path.glob('**/*.csv'):\n",
    "  target_identity = re.search('(.+?)_test_suite_cases.csv', p.name).group(1)\n",
    "  df = pd.read_csv(p)\n",
    "  df = df[df['functionality'] != 'F25-29: Spelling variation']\n",
    "  df['toxicity_pred'] = df.apply(lambda x: int(x['toxicity_score']>0.5), axis=1)\n",
    "  df['hate_label'] = df.apply(lambda x: ['non-hateful', 'hateful'].index(x['hate_label']), axis=1)\n",
    "  identity_attack_report = classification_report(y_true=df['hate_label'].tolist(), y_pred=df['toxicity_pred'].tolist(), digits=3)\n",
    "  print(f\"Target identity: {target_identity}\")\n",
    "  print(identity_attack_report)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cuMET4IWifnZ"
   },
   "source": [
    "### 2.2 Minimum set analysis\n",
    "\n",
    "The examples in HateCheck contains minimum sets where the only difference among examples are the identity terms. Therefore, we can calculate the diviation of predictions among mimimum sets.\n",
    "\n",
    "Thereafter, a naive way to \"debiase\" is to substract the diviation from the prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "y3kO5JJAa6R5"
   },
   "outputs": [],
   "source": [
    "dataset_path = ROOT_DIR/\"datasets/HateCheck/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 2445,
     "status": "ok",
     "timestamp": 1727430437768,
     "user": {
      "displayName": "YIPING JIN",
      "userId": "17124138946787548999"
     },
     "user_tz": -120
    },
    "id": "Umi3_-XNilLG",
    "outputId": "eb51ef22-487a-4d26-8b4b-abe09ba61092"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2968, 13)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(dataset_path/\"test_suite_cases.csv\")\n",
    "df = df.loc[~df['functionality'].str.startswith('spell_', na=False)]\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1086,
     "status": "ok",
     "timestamp": 1727430439917,
     "user": {
      "displayName": "YIPING JIN",
      "userId": "17124138946787548999"
     },
     "user_tz": -120
    },
    "id": "aa7UZ8WYipxE",
    "outputId": "4d9ac005-9556-49e9-adde-386a530600b9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "333 unique templates.\n"
     ]
    }
   ],
   "source": [
    "# Get the list of unique templates that covers all 7 target groups (excluding spelling variation ones)\n",
    "df_temp = df[['case_templ', 'case_id']].groupby(\"case_templ\").count().sort_values(\"case_id\").reset_index()\n",
    "df_temp = df_temp.loc[df_temp['case_id']==7]\n",
    "unique_templates = set(df_temp['case_templ'].tolist())\n",
    "print(f\"{len(unique_templates)} unique templates.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 514,
     "status": "ok",
     "timestamp": 1727430441732,
     "user": {
      "displayName": "YIPING JIN",
      "userId": "17124138946787548999"
     },
     "user_tz": -120
    },
    "id": "aTkZHsJWirUn",
    "outputId": "12278667-5c4d-4b55-a3ac-02d296af79e8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2331, 13)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter the dataset by the templates and sort the result\n",
    "df = df.loc[df['case_templ'].isin(unique_templates)]\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 49,
     "referenced_widgets": [
      "71c86ace40924875a6a9e3ceb0afa32b",
      "0f3df97910894b708082b62d7e62be6d",
      "9cef05897c7a4fa29601b7f06e78b7b5",
      "15116d7df58f4c1ca5ae1408e2f71ce1",
      "22406f3056294c5bad55691d6e99a997",
      "260f81c5eaec4e50a7a0f3dda44c94a0",
      "33811e760bac425eb736d1c4772bfaba",
      "3591b2a38a4745f98a21439bb2ec907a",
      "89591037199f46478077c803abd454fe",
      "fddc37dc0c0c496c84401dd9731082e2",
      "b5ff964d6c714a24ae08aed20b215edc"
     ]
    },
    "executionInfo": {
     "elapsed": 676123,
     "status": "ok",
     "timestamp": 1727431118924,
     "user": {
      "displayName": "YIPING JIN",
      "userId": "17124138946787548999"
     },
     "user_tz": -120
    },
    "id": "eyFdemaOiutE",
    "outputId": "459bc88a-2c34-4cd5-938d-eba2180bd0df"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "71c86ace40924875a6a9e3ceb0afa32b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/2331 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "messages = df['test_case'].tolist()\n",
    "n_messages = len(messages)\n",
    "toxicity_scores = []\n",
    "for i in trange(n_messages):\n",
    "  pred_score = moderate(messages[i])\n",
    "  toxicity_scores.append(pred_score)\n",
    "df['toxicity_score'] = toxicity_scores\n",
    "df.to_csv(dataset_path/model/\"minimum_sets.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "LQu3xQ1Nj0mb"
   },
   "outputs": [],
   "source": [
    "df_median = df[['case_templ', 'toxicity_score']].groupby(\"case_templ\").median().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "gFwHcVi8jkIS"
   },
   "outputs": [],
   "source": [
    "template_avg_score_dict = dict(zip(df_median['case_templ'].tolist(), df_median['toxicity_score'].tolist()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "aZkpT_-OkWTM"
   },
   "outputs": [],
   "source": [
    "df['normalized_toxicity_score'] = df.apply(lambda x: x['toxicity_score']-template_avg_score_dict[x['case_templ']], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "6FvEHgg-kZcj"
   },
   "outputs": [],
   "source": [
    "df_bias = df[['target_ident', 'normalized_toxicity_score']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 966
    },
    "executionInfo": {
     "elapsed": 815,
     "status": "ok",
     "timestamp": 1727431119735,
     "user": {
      "displayName": "YIPING JIN",
      "userId": "17124138946787548999"
     },
     "user_tz": -120
    },
    "id": "Ue4xelbqklO0",
    "outputId": "df0864a3-2726-4b5d-c675-88f1b3b164b7"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-29-ea473cd54cd6>:2: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.barplot(df_bias, x=\"target_ident\", y=\"normalized_toxicity_score\", palette=sns.color_palette(\"Set2\"), errorbar=\"sd\")\n",
      "<ipython-input-29-ea473cd54cd6>:2: UserWarning: The palette list has more values (8) than needed (7), which may not be intended.\n",
      "  sns.barplot(df_bias, x=\"target_ident\", y=\"normalized_toxicity_score\", palette=sns.color_palette(\"Set2\"), errorbar=\"sd\")\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1800x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 3), dpi=300)\n",
    "sns.barplot(df_bias, x=\"target_ident\", y=\"normalized_toxicity_score\", palette=sns.color_palette(\"Set2\"), errorbar=\"sd\")\n",
    "plt.xticks(rotation=30)\n",
    "plt.xlabel(None)\n",
    "plt.ylabel(\"Normalized Prediction\")\n",
    "plt.savefig(dataset_path/f\"{model}_target_identity_bias.png\", dpi=300, bbox_inches='tight', pad_inches=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 308
    },
    "executionInfo": {
     "elapsed": 15,
     "status": "ok",
     "timestamp": 1727431119735,
     "user": {
      "displayName": "YIPING JIN",
      "userId": "17124138946787548999"
     },
     "user_tz": -120
    },
    "id": "e1JP46DglFu9",
    "outputId": "dff6e515-94de-443d-a360-b21b374e8c9c"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "summary": "{\n  \"name\": \"df_bias\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"target_ident\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Muslims\",\n          \"black people\",\n          \"trans people\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"normalized_toxicity_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.03654348080983726,\n        \"min\": -0.07910573231923329,\n        \"max\": 0.033187366283691684,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0.024535080692073604,\n          0.033187366283691684,\n          -0.004126794882364817\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
       "type": "dataframe",
       "variable_name": "df_bias"
      },
      "text/html": [
       "\n",
       "  <div id=\"df-b0a392dd-f94a-4253-85b0-6b7d5b1e6e06\" class=\"colab-df-container\">\n",
       "    <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>target_ident</th>\n",
       "      <th>normalized_toxicity_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Muslims</td>\n",
       "      <td>0.024535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>black people</td>\n",
       "      <td>0.033187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>disabled people</td>\n",
       "      <td>-0.002953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>gay people</td>\n",
       "      <td>0.004714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>immigrants</td>\n",
       "      <td>-0.014624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>trans people</td>\n",
       "      <td>-0.004127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>women</td>\n",
       "      <td>-0.079106</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "    <div class=\"colab-df-buttons\">\n",
       "\n",
       "  <div class=\"colab-df-container\">\n",
       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b0a392dd-f94a-4253-85b0-6b7d5b1e6e06')\"\n",
       "            title=\"Convert this dataframe to an interactive table.\"\n",
       "            style=\"display:none;\">\n",
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      ],
      "text/plain": [
       "      target_ident  normalized_toxicity_score\n",
       "0          Muslims                   0.024535\n",
       "1     black people                   0.033187\n",
       "2  disabled people                  -0.002953\n",
       "3       gay people                   0.004714\n",
       "4       immigrants                  -0.014624\n",
       "5     trans people                  -0.004127\n",
       "6            women                  -0.079106"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bias = df_bias.groupby(\"target_ident\").mean().reset_index()\n",
    "df_bias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "0juSBvpYkqZw"
   },
   "outputs": [],
   "source": [
    "df_bias.to_csv(dataset_path/f\"{model}_identity_bias.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QYlFkKsssfPA"
   },
   "source": [
    "## 3. Debias predictions on GPT-HateCheck using the identity bias from §2.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "oyUAcPbfqb7o"
   },
   "outputs": [],
   "source": [
    "prediction_path = ROOT_DIR/\"datasets/GPT-HateCheck\"/model\n",
    "bias_df_path = ROOT_DIR/f\"datasets/HateCheck/{model}_identity_bias.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "xJey4WejT0Dr"
   },
   "outputs": [],
   "source": [
    "ignore_functionalities = [\"F11: Non-hateful use of profanity\",\n",
    "                          \"F22: Abuse targeted at objects\",\n",
    "                          \"F23: Abuse targeted at individuals (not as member of a prot. group)\",\n",
    "                          \"F24: Abuse targeted at nonprotected groups (e.g. professions)\",\n",
    "                          \"F25-29: Spelling variation\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "WgpPhaCKsnbM"
   },
   "outputs": [],
   "source": [
    "df_bias = pd.read_csv(bias_df_path)\n",
    "bias_dict = dict(zip(df_bias['target_ident'].tolist(), df_bias['normalized_toxicity_score'].tolist()))\n",
    "bias_dict['trans'] = bias_dict['trans people']\n",
    "bias_dict['gays'] = bias_dict['gay people']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 595,
     "status": "ok",
     "timestamp": 1727431120320,
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      "displayName": "YIPING JIN",
      "userId": "17124138946787548999"
     },
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    },
    "id": "FD6AM172sqBD",
    "outputId": "cc59290e-5c0d-4b0c-9022-3d9de70aad3a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Target identity: black people\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0      0.965     0.779     0.862       281\n",
      "           1      0.881     0.983     0.929       468\n",
      "\n",
      "    accuracy                          0.907       749\n",
      "   macro avg      0.923     0.881     0.896       749\n",
      "weighted avg      0.913     0.907     0.904       749\n",
      "\n",
      "Target identity: Muslims\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0      0.969     0.995     0.982       190\n",
      "           1      0.998     0.986     0.992       438\n",
      "\n",
      "    accuracy                          0.989       628\n",
      "   macro avg      0.983     0.991     0.987       628\n",
      "weighted avg      0.989     0.989     0.989       628\n",
      "\n",
      "Target identity: disabled people\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0      0.887     0.982     0.932       224\n",
      "           1      0.989     0.929     0.958       397\n",
      "\n",
      "    accuracy                          0.948       621\n",
      "   macro avg      0.938     0.956     0.945       621\n",
      "weighted avg      0.952     0.948     0.949       621\n",
      "\n",
      "Target identity: trans\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0      0.912     1.000     0.954       217\n",
      "           1      1.000     0.945     0.972       385\n",
      "\n",
      "    accuracy                          0.965       602\n",
      "   macro avg      0.956     0.973     0.963       602\n",
      "weighted avg      0.968     0.965     0.965       602\n",
      "\n",
      "Target identity: women\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0      0.723     1.000     0.839       183\n",
      "           1      1.000     0.825     0.904       401\n",
      "\n",
      "    accuracy                          0.880       584\n",
      "   macro avg      0.862     0.913     0.872       584\n",
      "weighted avg      0.913     0.880     0.884       584\n",
      "\n",
      "Target identity: gays\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0      0.973     0.973     0.973       182\n",
      "           1      0.988     0.988     0.988       426\n",
      "\n",
      "    accuracy                          0.984       608\n",
      "   macro avg      0.980     0.980     0.980       608\n",
      "weighted avg      0.984     0.984     0.984       608\n",
      "\n",
      "Target identity: immigrants\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0      0.646     0.994     0.783       178\n",
      "           1      0.997     0.793     0.883       468\n",
      "\n",
      "    accuracy                          0.848       646\n",
      "   macro avg      0.822     0.894     0.833       646\n",
      "weighted avg      0.901     0.848     0.856       646\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Before debiasing\n",
    "for p in prediction_path.glob('**/*.csv'):\n",
    "  target_identity = re.search('dataset_(.+?).csv', p.name).group(1)\n",
    "  df = pd.read_csv(p)\n",
    "  df = df[~df['functionality'].isin(ignore_functionalities)]\n",
    "  df['toxicity_pred'] = df.apply(lambda x: int(x['toxicity_score']>0.5), axis=1)\n",
    "  toxicity_report = classification_report(y_true=df['hate_label'].tolist(), y_pred=df['toxicity_pred'].tolist(), digits=3)\n",
    "  print(f\"Target identity: {target_identity}\")\n",
    "  print(toxicity_report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 20,
     "status": "ok",
     "timestamp": 1727431120321,
     "user": {
      "displayName": "YIPING JIN",
      "userId": "17124138946787548999"
     },
     "user_tz": -120
    },
    "id": "nQ6ing-o6X0p",
    "outputId": "a3c0f3d9-200b-499b-f961-9c0e9d76595e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Target identity: black people\n",
      "Overall avg pred: 0.650; Hateful subset avg pred: 0.947; Non-hateful subset avg pred: 0.167\n",
      "Target identity: Muslims\n",
      "Overall avg pred: 0.624; Hateful subset avg pred: 0.959; Non-hateful subset avg pred: 0.000\n",
      "Target identity: disabled people\n",
      "Overall avg pred: 0.611; Hateful subset avg pred: 0.927; Non-hateful subset avg pred: 0.025\n",
      "Target identity: trans\n",
      "Overall avg pred: 0.615; Hateful subset avg pred: 0.942; Non-hateful subset avg pred: 0.009\n",
      "Target identity: women\n",
      "Overall avg pred: 0.639; Hateful subset avg pred: 0.896; Non-hateful subset avg pred: 0.083\n",
      "Target identity: gays\n",
      "Overall avg pred: 0.647; Hateful subset avg pred: 0.979; Non-hateful subset avg pred: 0.032\n",
      "Target identity: immigrants\n",
      "Overall avg pred: 0.543; Hateful subset avg pred: 0.803; Non-hateful subset avg pred: 0.060\n"
     ]
    }
   ],
   "source": [
    "# Averaged functionality-level prediction after debiasing\n",
    "for p in prediction_path.glob('**/*.csv'):\n",
    "  target_identity = re.search('dataset_(.+?).csv', p.name).group(1)\n",
    "  df = pd.read_csv(p)\n",
    "  df = df[~df['functionality'].isin(ignore_functionalities)]\n",
    "  df['toxicity_score'] = df.apply(lambda x: x['toxicity_score']-bias_dict[target_identity], axis=1)\n",
    "  df_func_acc = df[['functionality', 'hate_label', 'toxicity_score']].groupby('functionality').mean().reset_index()\n",
    "  overall_pred = df_func_acc['toxicity_score'].mean()\n",
    "  df_category_acc = df_func_acc[['hate_label', 'toxicity_score']].groupby('hate_label').mean().reset_index().sort_values('hate_label', ascending=False)\n",
    "  cat_pred = df_category_acc['toxicity_score'].tolist()\n",
    "  print(f\"Target identity: {target_identity}\")\n",
    "  print(f\"Overall avg pred: {overall_pred:.3f}; Hateful subset avg pred: {cat_pred[0]:.3f}; Non-hateful subset avg pred: {cat_pred[1]:.3f}\")"
   ]
  },
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    "executionInfo": {
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      "Target identity: black people\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0      0.965     0.779     0.862       281\n",
      "           1      0.881     0.983     0.929       468\n",
      "\n",
      "    accuracy                          0.907       749\n",
      "   macro avg      0.923     0.881     0.896       749\n",
      "weighted avg      0.913     0.907     0.904       749\n",
      "\n",
      "Target identity: Muslims\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0      0.969     0.995     0.982       190\n",
      "           1      0.998     0.986     0.992       438\n",
      "\n",
      "    accuracy                          0.989       628\n",
      "   macro avg      0.983     0.991     0.987       628\n",
      "weighted avg      0.989     0.989     0.989       628\n",
      "\n",
      "Target identity: disabled people\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0      0.887     0.982     0.932       224\n",
      "           1      0.989     0.929     0.958       397\n",
      "\n",
      "    accuracy                          0.948       621\n",
      "   macro avg      0.938     0.956     0.945       621\n",
      "weighted avg      0.952     0.948     0.949       621\n",
      "\n",
      "Target identity: trans\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0      0.912     1.000     0.954       217\n",
      "           1      1.000     0.945     0.972       385\n",
      "\n",
      "    accuracy                          0.965       602\n",
      "   macro avg      0.956     0.973     0.963       602\n",
      "weighted avg      0.968     0.965     0.965       602\n",
      "\n",
      "Target identity: women\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0      0.744     1.000     0.853       183\n",
      "           1      1.000     0.843     0.915       401\n",
      "\n",
      "    accuracy                          0.892       584\n",
      "   macro avg      0.872     0.921     0.884       584\n",
      "weighted avg      0.920     0.892     0.895       584\n",
      "\n",
      "Target identity: gays\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0      0.973     0.973     0.973       182\n",
      "           1      0.988     0.988     0.988       426\n",
      "\n",
      "    accuracy                          0.984       608\n",
      "   macro avg      0.980     0.980     0.980       608\n",
      "weighted avg      0.984     0.984     0.984       608\n",
      "\n",
      "Target identity: immigrants\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0      0.656     0.994     0.790       178\n",
      "           1      0.997     0.801     0.889       468\n",
      "\n",
      "    accuracy                          0.854       646\n",
      "   macro avg      0.826     0.898     0.839       646\n",
      "weighted avg      0.903     0.854     0.861       646\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# After debiasing\n",
    "for p in prediction_path.glob('**/*.csv'):\n",
    "  target_identity = re.search('dataset_(.+?).csv', p.name).group(1)\n",
    "  df = pd.read_csv(p)\n",
    "  df = df[~df['functionality'].isin(ignore_functionalities)]\n",
    "  df['toxicity_score'] = df.apply(lambda x: x['toxicity_score']-bias_dict[target_identity], axis=1)\n",
    "  df['toxicity_pred'] = df.apply(lambda x: int(x['toxicity_score']>0.5), axis=1)\n",
    "  toxicity_report = classification_report(y_true=df['hate_label'].tolist(), y_pred=df['toxicity_pred'].tolist(), digits=3)\n",
    "  print(f\"Target identity: {target_identity}\")\n",
    "  print(toxicity_report)"
   ]
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