Add long roberta and llama baselines for long docs

llama/README.md

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+Code modified from: https://github.com/4AI/LS-LLaMA/tree/main

llama/evaluate_models.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "5d7b1d18",
+   "metadata": {},
+   "source": [
+    "### Evaluate Models\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "80b1b2c7",
+   "metadata": {},
+   "source": [
+    "##### Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "87dc70f1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/anaconda/envs/azureml_py310_sdkv2/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import pickle\n",
+    "import json\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import torch.nn.functional as F\n",
+    "from sklearn import metrics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ed3988f7",
+   "metadata": {},
+   "source": [
+    "##### Evaluation Parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "2a1fc80e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "threshold = 0.5 # currently we don't maximize val f1 to find the threshold... need to grab scores for all the val sets if we do this\n",
+    "num_std = 1.96\n",
+    "num_bootstrap = 1000\n",
+    "line_width = 2\n",
+    "alpha = 0.2\n",
+    "font_size = 16\n",
+    "legend_size = 10\n",
+    "x_size = 10\n",
+    "y_size = 10"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1ac2d927-76e0-48ab-8777-48bc70206d07",
+   "metadata": {},
+   "source": [
+    "##### Initialize Score, Model, and Color Arrays"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "acc793b2-80d0-45ac-9a3a-15dcf8fb53fb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define master lists of labels, scores, names, and colors\n",
+    "all_y_trues, all_y_scores, all_model_names, all_colors = [], [], [], []"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aef4eaf2-fff5-4f83-8ade-2367a2513aa8",
+   "metadata": {},
+   "source": [
+    "##### Load Fine-Tuned Torch LM Results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "26d15d8e-81cc-4cd5-a2dc-765325cb5a55",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # ls llama 3 8b\n",
+    "# with open(\"ls-Meta-Llama-3-8B-msp-v2-mdace-20_raw_labels.pkl\", \"rb\") as f:\n",
+    "#     ls_llama_8b_last_labels = pickle.load(f)\n",
+    "# with open(\"ls-Meta-Llama-3-8B-msp-v2-mdace-20_scores.pkl\", \"rb\") as f:\n",
+    "#     ls_llama_8b_last_scores = pickle.load(f)\n",
+    "\n",
+    "# ls_llama_8b_last_scores_transformed = torch.sigmoid(torch.tensor(ls_llama_8b_last_scores))\n",
+    "# all_model_names.append(\"LS Llama-3 8B (Last)\")\n",
+    "# all_y_trues.append(ls_llama_8b_last_labels)\n",
+    "# all_y_scores.append(ls_llama_8b_last_scores_transformed)\n",
+    "# all_colors.append('#ab20fd')\n",
+    "\n",
+    "# ls unllama 3 8b\n",
+    "with open(\"ls-unllama-Meta-Llama-3-8B-msp-v2-mdace-20_raw_labels.pkl\", \"rb\") as f:\n",
+    "    ls_unllama_8b_max_labels = pickle.load(f)\n",
+    "with open(\"ls-unllama-Meta-Llama-3-8B-msp-v2-mdace-20_raw_scores.pkl\", \"rb\") as f:\n",
+    "    ls_unllama_8b_max_scores = pickle.load(f)\n",
+    "\n",
+    "ls_unllama_8b_max_scores_transformed = torch.sigmoid(torch.tensor(ls_unllama_8b_max_scores)).numpy()\n",
+    "all_model_names.append(\"LS UnLlama-3 8B (Max)\")\n",
+    "all_y_trues.append(ls_unllama_8b_max_labels)\n",
+    "all_y_scores.append(ls_unllama_8b_max_scores_transformed)\n",
+    "\n",
+    "# BELT Max 5 segments\n",
+    "with open(\"./BELT-BASELINE/bioclinicalroberta_belt_mdace20_510_step_128_max_5_labels.pkl\", \"rb\") as f:\n",
+    "    belt_5_max_labels = pickle.load(f)\n",
+    "with open(\"./BELT-BASELINE/bioclinicalroberta_belt_mdace20_510_step_128_max_5_scores.pkl\", \"rb\") as f:\n",
+    "    belt_5_max_scores = pickle.load(f)\n",
+    "\n",
+    "all_model_names.append(\"BELT 128 step 5 seg (Max)\")\n",
+    "all_y_trues.append(belt_5_max_labels)\n",
+    "all_y_scores.append(belt_5_max_scores)\n",
+    "\n",
+    "# BELT Max 128 segments\n",
+    "with open(\"./BELT-BASELINE/bioclinicalroberta_belt_mdace20_510_step_448_max_128_labels.pkl\", \"rb\") as f:\n",
+    "    belt_128_max_labels = pickle.load(f)\n",
+    "with open(\"./BELT-BASELINE/bioclinicalroberta_belt_mdace20_510_step_448_max_128_scores.pkl\", \"rb\") as f:\n",
+    "    belt_128_max_scores = pickle.load(f)\n",
+    "\n",
+    "all_model_names.append(\"BELT 448 step 128 seg (Max)\")\n",
+    "all_y_trues.append(belt_128_max_labels)\n",
+    "all_y_scores.append(belt_128_max_scores)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f21cd42",
+   "metadata": {},
+   "source": [
+    "##### Print Performance for all Metrics for all Models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "2665e9ce-07c4-4196-9fe7-7f911123b8f9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def print_mean_ci_of_metric_list(metric_list, metric_name, num_std):\n",
+    "    mean_metric = np.mean(metric_list)\n",
+    "    std_metric = np.std(metric_list)\n",
+    "    metric_low = np.maximum(mean_metric - std_metric * num_std, 0)\n",
+    "    metric_high = np.minimum(mean_metric + std_metric * num_std, 1)\n",
+    "\n",
+    "    print(\n",
+    "        f\"{metric_name}: {round(mean_metric, 3)} ([{round(metric_low, 3)} - {round(metric_high, 3)}] 95% CI)\"\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "4d6ff4a8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Results for LS UnLlama-3 8B (Max)\n",
+      "\n",
+      "Micro Average Precision: 0.277 ([0.256 - 0.299] 95% CI)\n",
+      "Micro ROC AUC: 0.828 ([0.818 - 0.839] 95% CI)\n",
+      "\n",
+      "Results for BELT 128 step 5 seg (Max)\n",
+      "\n",
+      "Micro Average Precision: 0.707 ([0.698 - 0.716] 95% CI)\n",
+      "Micro ROC AUC: 0.942 ([0.94 - 0.944] 95% CI)\n",
+      "\n",
+      "Results for BELT 448 step 128 seg (Max)\n",
+      "\n",
+      "Micro Average Precision: 0.804 ([0.797 - 0.812] 95% CI)\n",
+      "Micro ROC AUC: 0.971 ([0.969 - 0.972] 95% CI)\n"
+     ]
+    }
+   ],
+   "source": [
+    "model2metric_df = {}\n",
+    "for y_trues, y_scores, name in zip(\n",
+    "    all_y_trues, all_y_scores, all_model_names\n",
+    "):\n",
+    "    \n",
+    "    micro_aps, macro_aps, micro_roc_aucs, macro_roc_aucs = [], [], [], []\n",
+    "    for i in range(num_bootstrap):\n",
+    "        \n",
+    "        # Sample N records with replacement where N is the total number of records\n",
+    "        sample_indices = np.random.choice(len(y_trues), len(y_trues))\n",
+    "        sample_labels = np.array(y_trues)[sample_indices]\n",
+    "        sample_scores = np.array(y_scores)[sample_indices]\n",
+    "        \n",
+    "        micro_ap = metrics.average_precision_score(y_true=sample_labels, y_score=sample_scores, average='micro')\n",
+    "        micro_aps.append(micro_ap)\n",
+    "\n",
+    "        # macro_ap = metrics.average_precision_score(y_true=sample_labels, y_score=sample_scores, average='macro')\n",
+    "        # macro_aps.append(macro_ap)\n",
+    "\n",
+    "        micro_roc_auc = metrics.roc_auc_score(y_true=sample_labels, y_score=sample_scores, average='micro')\n",
+    "        micro_roc_aucs.append(micro_roc_auc)\n",
+    "\n",
+    "        # macro_roc_auc = metrics.roc_auc_score(y_true=sample_labels, y_score=sample_scores, average='macro')\n",
+    "        # macro_roc_aucs.append(macro_roc_auc)\n",
+    "            \n",
+    "    metric_df = pd.DataFrame({\n",
+    "        \"micro_aps\": micro_aps,\n",
+    "        \"micro_roc_aucs\": micro_roc_aucs,\n",
+    "    })\n",
+    "    model2metric_df[name] = metric_df\n",
+    "\n",
+    "    print(f\"\\nResults for {name}\\n\")\n",
+    "    print_mean_ci_of_metric_list(micro_aps, metric_name=\"Micro Average Precision\", num_std=num_std)\n",
+    "    print_mean_ci_of_metric_list(micro_roc_aucs, metric_name=\"Micro ROC AUC\", num_std=num_std)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.10 - SDK v2",
+   "language": "python",
+   "name": "python310-sdkv2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}