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     "text": [
      "Processing 100 entries with 1 runs each.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from util.injection import process_scores_multiple\n",
    "from util.model import AzureAgent, GPTAgent, Claude3Agent\n",
    "from util.prompt import PROMPT_TEMPLATE\n",
    "\n",
    "def run_experiment(api_key, model_type, deployment_name, temperature, max_tokens, occupation,\n",
    "                   sample_size, group_name, privilege_label, protect_label, num_run, prompt_template, endpoint_url=None):\n",
    "    # Load data\n",
    "    df = pd.read_csv(\"resume_subsampled.csv\")\n",
    "    \n",
    "    # Filter data by occupation\n",
    "    df = df[df[\"Occupation\"] == occupation]\n",
    "    df = df.sample(n=sample_size, random_state=42)\n",
    "    \n",
    "    # Initialize the agent\n",
    "    if model_type == 'AzureAgent':\n",
    "        agent = AzureAgent(api_key, endpoint_url, deployment_name)\n",
    "    elif model_type == 'GPTAgent':\n",
    "        api_version = '2024-02-15-preview'\n",
    "        agent = GPTAgent(api_key, endpoint_url, deployment_name, api_version)\n",
    "    else:\n",
    "        agent = Claude3Agent(api_key, deployment_name)\n",
    "    \n",
    "    # Process data\n",
    "    parameters = {\"temperature\": temperature, \"max_tokens\": max_tokens}\n",
    "    preprocessed_df = process_scores_multiple(df, num_run, parameters, privilege_label, protect_label, agent, group_name, occupation, prompt_template)\n",
    "    \n",
    "    return preprocessed_df\n",
    "\n",
    "# Set experiment parameters\n",
    "api_key = \"6c75a8235f204c9e8cf6228e485982f7\"\n",
    "model_type = \"GPTAgent\"  # or \"AzureAgent\" or \"Claude3Agent\"\n",
    "deployment_name = \"gpt4-1106\"\n",
    "temperature = 0.0\n",
    "max_tokens = 300\n",
    "file_path = \"resume_subsampled.csv\"  # or path to your file\n",
    "occupation = \"FINANCE\"\n",
    "sample_size = 100\n",
    "group_name = \"Gender\"\n",
    "privilege_label = \"Male\"\n",
    "protect_label = \"Female\"\n",
    "num_run = 1\n",
    "prompt_template = PROMPT_TEMPLATE\n",
    "endpoint_url = \"https://safeguard-monitor.openai.azure.com/\"\n",
    "\n",
    "# Run experiment\n",
    "results = run_experiment(api_key, model_type, deployment_name, temperature, max_tokens, occupation,\n",
    "                         sample_size, group_name, privilege_label, protect_label, num_run, prompt_template, endpoint_url)\n",
    "\n",
    "# Display results\n",
    "results.head()\n",
    "\n",
    "# Optionally save results to a CSV file\n",
    "results.to_csv(f'result/{occupation}_results.csv', index=False)\n"
   ]
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