Notebook to create CI tables in latex

CriaTabelaLatex-CI.ipynb

@@ -0,0 +1,203 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "dQAr2gM1_wFl"
+      },
+      "outputs": [],
+      "source": [
+        "import pandas as pd"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "df = pd.read_parquet('evaluation_results-00000-of-00001.parquet', engine='pyarrow')\n",
+        "df1 = pd.read_parquet('boostrap_confidence_intervals-00000-of-00001.parquet', engine='pyarrow')"
+      ],
+      "metadata": {
+        "id": "Zlng_K58AFsV"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def filtrar_respostas(df, modelo):\n",
+        "  filtro = df[df['id'].str.startswith(modelo)]\n",
+        "  dic = {'C1': \"\", 'C2': \"\", 'C3': \"\", 'C4': \"\", 'C5': \"\"}\n",
+        "  for idx, row in filtro.iterrows():\n",
+        "    for key in dic:\n",
+        "      if key in row['id']:\n",
+        "        print_string = f\"{row['Micro_F1']:.2f} & {row['Weighted_F1']:.2f} & {row['QWK']:.2f} &\"\n",
+        "        dic[key] += print_string\n",
+        "  return dic\n",
+        "\n",
+        "def printar_final(dic, modelo):\n",
+        "  string = f\"{modelo} \"\n",
+        "  for key in dic:\n",
+        "    string += f\"& {dic[key]} \"\n",
+        "  print(string[:-2])\n",
+        "\n",
+        "def filtrar_dfs(perf, boot, modelo, final=\"\"):\n",
+        "  filtro_perf = perf[perf['id'].str.startswith(modelo) & perf['id'].str.endswith(final)]\n",
+        "  filtro_boot = boot[boot['experiment_id'].str.startswith(modelo) & boot['experiment_id'].str.endswith(final)]\n",
+        "  dic = {'C1': {}, 'C2': {}, 'C3': {}, 'C4': {}, 'C5': {}}\n",
+        "  string = \"\"\n",
+        "  for idx, row in filtro_perf.iterrows():\n",
+        "    for key in dic:\n",
+        "      if key in row['id']:\n",
+        "        dic[key]['Micro_F1'] = row['Micro_F1']\n",
+        "        dic[key]['Weighted_F1'] = row['Weighted_F1']\n",
+        "        dic[key]['QWK'] = row['QWK']\n",
+        "  for idx, row in filtro_boot.iterrows():\n",
+        "    for key in dic:\n",
+        "      if key in row['experiment_id']:\n",
+        "          dic[key]['QWK_mean'] = row['QWK_mean']\n",
+        "          dic[key]['QWK_upper_95ci'] = row['QWK_upper_95ci']\n",
+        "          dic[key]['QWK_lower_95ci'] = row['QWK_lower_95ci']\n",
+        "  print(dic)\n",
+        "  string = \"\"\n",
+        "  for key in dic:\n",
+        "    string += f\"{dic[key]['QWK_lower_95ci']:.2f} & {dic[key]['QWK_upper_95ci']:.2f} &\"\n",
+        "  print(f\"{modelo}-{final} & {string[:-2]}\")\n",
+        "\n",
+        "modelos = [(\"jbcs2025_mbert_base\", \"\"), (\"jbcs2025_bertimbau-large\", \"\"), (\"jbcs2025_bertimbau_base-\", \"\"), (\"jbcs2025_phi35-balanced\", \"essay_only\"),\n",
+        "           (\"jbcs2025_phi4-balanced\", \"essay_only\"), (\"jbcs2025_llama31_8b-balanced\", \"essay_only\"), (\"jbcs2025_Phi-3.5-mini-instruct\", \"full_context\"),\n",
+        "           (\"jbcs2025_phi-4-phi4\", \"full_context\"), (\"jbcs2025_Llama-3.1-8B-llama31\", \"full_context\")]\n",
+        "for (modelo, final) in modelos:\n",
+        "  filtrar_dfs(df, df1, modelo, final=final)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "2_4fdVNNARGi",
+        "outputId": "61516f30-f453-4069-b51a-b1df21126dd8"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "{'C1': {'Micro_F1': 0.5362318840579711, 'Weighted_F1': 0.518137852459147, 'QWK': 0.4505920783993467, 'QWK_mean': 0.4469509804733242, 'QWK_upper_95ci': 0.5646932058882819, 'QWK_lower_95ci': 0.3273452629017379}, 'C2': {'Micro_F1': 0.3623188405797101, 'Weighted_F1': 0.3182603637608693, 'QWK': 0.1449814126394052, 'QWK_mean': 0.1443789019267171, 'QWK_upper_95ci': 0.285562229212569, 'QWK_lower_95ci': 0.0019340927528358}, 'C3': {'Micro_F1': 0.2318840579710145, 'Weighted_F1': 0.1613437300185681, 'QWK': 0.2641316569559441, 'QWK_mean': 0.2618424087718754, 'QWK_upper_95ci': 0.4199083537960727, 'QWK_lower_95ci': 0.1021015072944112}, 'C4': {'Micro_F1': 0.5, 'Weighted_F1': 0.4091229461257213, 'QWK': 0.2817080943275972, 'QWK_mean': 0.2763369862905251, 'QWK_upper_95ci': 0.4236573729369323, 'QWK_lower_95ci': 0.1182640144665461}, 'C5': {'Micro_F1': 0.4057971014492754, 'Weighted_F1': 0.3828592483419307, 'QWK': 0.5735521338377112, 'QWK_mean': 0.5708670849334949, 'QWK_upper_95ci': 0.6715233618747556, 'QWK_lower_95ci': 0.4597349333381409}}\n",
+            "jbcs2025_mbert_base- & 0.33 & 0.56 &0.00 & 0.29 &0.10 & 0.42 &0.12 & 0.42 &0.46 & 0.67\n",
+            "{'C1': {'Micro_F1': 0.7028985507246377, 'Weighted_F1': 0.7092465463174845, 'QWK': 0.7080553295362083, 'QWK_mean': 0.7077306949563342, 'QWK_upper_95ci': 0.7925777763902052, 'QWK_lower_95ci': 0.6143517863619989}, 'C2': {'Micro_F1': 0.3985507246376811, 'Weighted_F1': 0.3825485695613519, 'QWK': 0.4242242542347474, 'QWK_mean': 0.421524005294385, 'QWK_upper_95ci': 0.5651890498796338, 'QWK_lower_95ci': 0.2688782617235809}, 'C3': {'Micro_F1': 0.2898550724637681, 'Weighted_F1': 0.2482589892502322, 'QWK': 0.2693773824650572, 'QWK_mean': 0.2667552963854427, 'QWK_upper_95ci': 0.3967413784980497, 'QWK_lower_95ci': 0.1308675494473104}, 'C4': {'Micro_F1': 0.5434782608695652, 'Weighted_F1': 0.5677143444488495, 'QWK': 0.5718939041414612, 'QWK_mean': 0.5689578701688437, 'QWK_upper_95ci': 0.6680998143802181, 'QWK_lower_95ci': 0.4614869526110953}, 'C5': {'Micro_F1': 0.3623188405797101, 'Weighted_F1': 0.3520852841017614, 'QWK': 0.4785241515279538, 'QWK_mean': 0.4765829843065011, 'QWK_upper_95ci': 0.5987775413884535, 'QWK_lower_95ci': 0.3497986919778412}}\n",
+            "jbcs2025_bertimbau-large- & 0.61 & 0.79 &0.27 & 0.57 &0.13 & 0.40 &0.46 & 0.67 &0.35 & 0.60\n",
+            "{'C1': {'Micro_F1': 0.644927536231884, 'Weighted_F1': 0.6413771139990777, 'QWK': 0.6742722265932337, 'QWK_mean': 0.6726698793738349, 'QWK_upper_95ci': 0.7587417074110893, 'QWK_lower_95ci': 0.5786694701512399}, 'C2': {'Micro_F1': 0.3768115942028985, 'Weighted_F1': 0.3822623600358202, 'QWK': 0.4220445459737294, 'QWK_mean': 0.4181918820477945, 'QWK_upper_95ci': 0.5466018786751335, 'QWK_lower_95ci': 0.2775986575464428}, 'C3': {'Micro_F1': 0.3768115942028985, 'Weighted_F1': 0.3338029470113428, 'QWK': 0.3452054794520547, 'QWK_mean': 0.3442546344979946, 'QWK_upper_95ci': 0.4793389519462236, 'QWK_lower_95ci': 0.2084844703358946}, 'C4': {'Micro_F1': 0.644927536231884, 'Weighted_F1': 0.6545879036165807, 'QWK': 0.6258134490238612, 'QWK_mean': 0.623338029229533, 'QWK_upper_95ci': 0.7250524714839471, 'QWK_lower_95ci': 0.5110704244499952}, 'C5': {'Micro_F1': 0.3188405797101449, 'Weighted_F1': 0.2580841303820561, 'QWK': 0.476219483623073, 'QWK_mean': 0.4734979990112671, 'QWK_upper_95ci': 0.5947975929869902, 'QWK_lower_95ci': 0.3401973117894254}}\n",
+            "jbcs2025_bertimbau_base-- & 0.58 & 0.76 &0.28 & 0.55 &0.21 & 0.48 &0.51 & 0.73 &0.34 & 0.59\n",
+            "{'C1': {'Micro_F1': 0.6594202898550725, 'Weighted_F1': 0.6574796332262731, 'QWK': 0.6638104838709677, 'QWK_mean': 0.6616991580966015, 'QWK_upper_95ci': 0.7606224171519745, 'QWK_lower_95ci': 0.5557810707563048}, 'C2': {'Micro_F1': 0.3623188405797101, 'Weighted_F1': 0.3284848932013696, 'QWK': 0.3441810010847668, 'QWK_mean': 0.3409626834563599, 'QWK_upper_95ci': 0.4858406168704411, 'QWK_lower_95ci': 0.1852545870348362}, 'C3': {'Micro_F1': 0.3333333333333333, 'Weighted_F1': 0.3336611749101599, 'QWK': 0.2353562005277044, 'QWK_mean': 0.2335375020354423, 'QWK_upper_95ci': 0.3863527435429713, 'QWK_lower_95ci': 0.0729459707036765}, 'C4': {'Micro_F1': 0.572463768115942, 'Weighted_F1': 0.593693390260896, 'QWK': 0.5590312815338042, 'QWK_mean': 0.5562941450165412, 'QWK_upper_95ci': 0.6581198581878515, 'QWK_lower_95ci': 0.4492714610494979}, 'C5': {'Micro_F1': 0.3550724637681159, 'Weighted_F1': 0.3238493856342826, 'QWK': 0.5186813186813186, 'QWK_mean': 0.5153615140000185, 'QWK_upper_95ci': 0.6379857316052329, 'QWK_lower_95ci': 0.3832796853569664}}\n",
+            "jbcs2025_phi35-balanced-essay_only & 0.56 & 0.76 &0.19 & 0.49 &0.07 & 0.39 &0.45 & 0.66 &0.38 & 0.64\n",
+            "{'C1': {'Micro_F1': 0.6159420289855072, 'Weighted_F1': 0.6184188545576659, 'QWK': 0.6748637903107053, 'QWK_mean': 0.6728228615718327, 'QWK_upper_95ci': 0.7602092291188651, 'QWK_lower_95ci': 0.577372741501964}, 'C2': {'Micro_F1': 0.4565217391304347, 'Weighted_F1': 0.4331935675507009, 'QWK': 0.4118587182355762, 'QWK_mean': 0.408434856024602, 'QWK_upper_95ci': 0.5576720127443971, 'QWK_lower_95ci': 0.2487511341654639}, 'C3': {'Micro_F1': 0.3333333333333333, 'Weighted_F1': 0.2747500757119841, 'QWK': 0.2938151902453355, 'QWK_mean': 0.2922470743680929, 'QWK_upper_95ci': 0.4396449924380227, 'QWK_lower_95ci': 0.1358100563577918}, 'C4': {'Micro_F1': 0.7028985507246377, 'Weighted_F1': 0.6761155293109196, 'QWK': 0.579465541490858, 'QWK_mean': 0.5787732176948, 'QWK_upper_95ci': 0.6877064220183486, 'QWK_lower_95ci': 0.4624229582708449}, 'C5': {'Micro_F1': 0.3115942028985507, 'Weighted_F1': 0.2439300683587842, 'QWK': 0.4574405346962846, 'QWK_mean': 0.4543289424889599, 'QWK_upper_95ci': 0.5775760593701588, 'QWK_lower_95ci': 0.3179854281772712}}\n",
+            "jbcs2025_phi4-balanced-essay_only & 0.58 & 0.76 &0.25 & 0.56 &0.14 & 0.44 &0.46 & 0.69 &0.32 & 0.58\n",
+            "{'C1': {'Micro_F1': 0.6884057971014492, 'Weighted_F1': 0.6710623362797277, 'QWK': 0.6308698236576183, 'QWK_mean': 0.6295464847933743, 'QWK_upper_95ci': 0.7354045431661362, 'QWK_lower_95ci': 0.5147318788303388}, 'C2': {'Micro_F1': 0.3478260869565217, 'Weighted_F1': 0.3205156603404325, 'QWK': 0.3259325044404972, 'QWK_mean': 0.3241019129105552, 'QWK_upper_95ci': 0.4686752350465797, 'QWK_lower_95ci': 0.1789150070543026}, 'C3': {'Micro_F1': 0.4202898550724637, 'Weighted_F1': 0.3911901539778777, 'QWK': 0.3788697141351601, 'QWK_mean': 0.3764592992419188, 'QWK_upper_95ci': 0.5044785095617992, 'QWK_lower_95ci': 0.2482348708257247}, 'C4': {'Micro_F1': 0.6521739130434783, 'Weighted_F1': 0.6518531929244537, 'QWK': 0.5181762168823167, 'QWK_mean': 0.5159162816272473, 'QWK_upper_95ci': 0.6375067485990894, 'QWK_lower_95ci': 0.3896899614645441}, 'C5': {'Micro_F1': 0.2681159420289855, 'Weighted_F1': 0.2546847901101209, 'QWK': 0.3773307964120685, 'QWK_mean': 0.3747412056981141, 'QWK_upper_95ci': 0.5091521322586843, 'QWK_lower_95ci': 0.23148683225562}}\n",
+            "jbcs2025_llama31_8b-balanced-essay_only & 0.51 & 0.74 &0.18 & 0.47 &0.25 & 0.50 &0.39 & 0.64 &0.23 & 0.51\n",
+            "{'C1': {'Micro_F1': 0.217391304347826, 'Weighted_F1': 0.1687997907859967, 'QWK': 0.2897608544230323, 'QWK_mean': 0.2879013220800692, 'QWK_upper_95ci': 0.4180318752745349, 'QWK_lower_95ci': 0.1492478126231326}, 'C2': {'Micro_F1': 0.217391304347826, 'Weighted_F1': 0.1687997907859967, 'QWK': 0.2897608544230323, 'QWK_mean': 0.2879013220800692, 'QWK_upper_95ci': 0.4180318752745349, 'QWK_lower_95ci': 0.1492478126231326}, 'C3': {'Micro_F1': 0.5217391304347826, 'Weighted_F1': 0.5157532058528155, 'QWK': 0.68215073742887, 'QWK_mean': 0.6784054100778548, 'QWK_upper_95ci': 0.7665213161461424, 'QWK_lower_95ci': 0.5789753729855492}, 'C4': {'Micro_F1': 0.6594202898550725, 'Weighted_F1': 0.6261409273457467, 'QWK': 0.4689490445859872, 'QWK_mean': 0.4684319359888135, 'QWK_upper_95ci': 0.5818181818181818, 'QWK_lower_95ci': 0.354099864489924}, 'C5': {'Micro_F1': 0.3333333333333333, 'Weighted_F1': 0.3008136591917735, 'QWK': 0.4890643389564535, 'QWK_mean': 0.4866040546746337, 'QWK_upper_95ci': 0.6122719854533736, 'QWK_lower_95ci': 0.3491336242147614}}\n",
+            "jbcs2025_Phi-3.5-mini-instruct-full_context & 0.15 & 0.42 &0.15 & 0.42 &0.58 & 0.77 &0.35 & 0.58 &0.35 & 0.61\n",
+            "{'C1': {'Micro_F1': 0.6376811594202898, 'Weighted_F1': 0.6354643426463825, 'QWK': 0.642614752634399, 'QWK_mean': 0.6417455296636442, 'QWK_upper_95ci': 0.7221995154900485, 'QWK_lower_95ci': 0.5534959702303135}, 'C2': {'Micro_F1': 0.4420289855072464, 'Weighted_F1': 0.3727858293075684, 'QWK': 0.4587224505042959, 'QWK_mean': 0.4544687530156993, 'QWK_upper_95ci': 0.5738800252215724, 'QWK_lower_95ci': 0.325822690895961}, 'C3': {'Micro_F1': 0.4565217391304347, 'Weighted_F1': 0.4145905872632278, 'QWK': 0.6404105501849862, 'QWK_mean': 0.6368017049735983, 'QWK_upper_95ci': 0.7283785107281081, 'QWK_lower_95ci': 0.5336049056396244}, 'C4': {'Micro_F1': 0.7318840579710145, 'Weighted_F1': 0.6872463768115942, 'QWK': 0.5601593625498008, 'QWK_mean': 0.5622525366238156, 'QWK_upper_95ci': 0.6667808258369575, 'QWK_lower_95ci': 0.4570995751571452}, 'C5': {'Micro_F1': 0.3623188405797101, 'Weighted_F1': 0.3672380004905022, 'QWK': 0.5073723420766724, 'QWK_mean': 0.5024746726275736, 'QWK_upper_95ci': 0.6317146430085705, 'QWK_lower_95ci': 0.3566272536367714}}\n",
+            "jbcs2025_phi-4-phi4-full_context & 0.55 & 0.72 &0.33 & 0.57 &0.53 & 0.73 &0.46 & 0.67 &0.36 & 0.63\n",
+            "{'C1': {'Micro_F1': 0.6811594202898551, 'Weighted_F1': 0.655233980808711, 'QWK': 0.6525276002324231, 'QWK_mean': 0.6513882888286967, 'QWK_upper_95ci': 0.745725728701958, 'QWK_lower_95ci': 0.5488248441997104}, 'C2': {'Micro_F1': 0.3768115942028985, 'Weighted_F1': 0.3372856483605482, 'QWK': 0.3204971475142625, 'QWK_mean': 0.3171723261367537, 'QWK_upper_95ci': 0.4365708703636233, 'QWK_lower_95ci': 0.1914638191605268}, 'C3': {'Micro_F1': 0.3550724637681159, 'Weighted_F1': 0.3410931961351028, 'QWK': 0.3373259954677889, 'QWK_mean': 0.3340102348845638, 'QWK_upper_95ci': 0.4823077722195361, 'QWK_lower_95ci': 0.1776691149012335}, 'C4': {'Micro_F1': 0.5869565217391305, 'Weighted_F1': 0.5764511923932214, 'QWK': 0.4831460674157303, 'QWK_mean': 0.4792630651774341, 'QWK_upper_95ci': 0.6020372960585834, 'QWK_lower_95ci': 0.3507696538121665}, 'C5': {'Micro_F1': 0.2391304347826087, 'Weighted_F1': 0.2215770027405685, 'QWK': 0.1835841058368659, 'QWK_mean': 0.1818097905579081, 'QWK_upper_95ci': 0.3405974422544362, 'QWK_lower_95ci': 0.012417733580868}}\n",
+            "jbcs2025_Llama-3.1-8B-llama31-full_context & 0.55 & 0.75 &0.19 & 0.44 &0.18 & 0.48 &0.35 & 0.60 &0.01 & 0.34\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "modelos = ['jbcs2025_bertimbau_base-']"
+      ],
+      "metadata": {
+        "id": "dhyTkpR2ARwp"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "zyaRKx5oChUn"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from torch import nn\n",
+        "import torch"
+      ],
+      "metadata": {
+        "id": "cB-6u1yyChfR"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "m = nn.Sigmoid()\n",
+        "input = torch.randn(2)\n",
+        "print(input)\n",
+        "output = m(torch.tensor(-100))\n",
+        "output"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "IJ4ySLhpClfe",
+        "outputId": "b48b901d-fdec-4515-830c-8a1069d2b807"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "tensor([ 1.5602, -1.1152])\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "tensor(0.)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 6
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "kvuSSIcCCuOa"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}