{ "cells": [ { "cell_type": "markdown", "id": "8745f6ee-a9b0-4f68-9f2c-2e27ea86c2a3", "metadata": {}, "source": [ "# Load Libraries" ] }, { "cell_type": "code", "execution_count": 1, "id": "20508587-c46c-4645-a3d5-845cd55f1512", "metadata": {}, "outputs": [ { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import os\n", "\n", "import pandas as pd\n", "import numpy as np\n", "\n", "import huggingface_hub\n", "import datasets\n", "\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import matplotlib.patches as mpatches\n", "\n", "from datetime import datetime, timezone\n", "\n", "import plotly\n", "import plotly.express as px\n", "import plotly.graph_objects as go\n", "\n", "plotly.offline.init_notebook_mode(connected=True)" ] }, { "cell_type": "code", "execution_count": 2, "id": "a166b52f-d5a4-4422-9564-3bd09c1bb321", "metadata": {}, "outputs": [], "source": [ "# Create the directory for plots\n", "directory = \"./plots\"\n", "if not os.path.exists(directory):\n", " os.makedirs(directory)" ] }, { "cell_type": "code", "execution_count": 3, "id": "ffbf9842-cf52-4989-9de1-91f108b1b146", "metadata": {}, "outputs": [], "source": [ "pd.set_option('display.max_columns', None)" ] }, { "cell_type": "code", "execution_count": 4, "id": "5c4cb1e2-a571-4f8b-98d0-61848c833ac9", "metadata": {}, "outputs": [], "source": [ "# Set colours\n", "colors = [\"#FF9D00\", \"#FFD21E\", \"#FF323D\", \"#32343D\", \"#297373\", \"#CD4631\"]\n", "\n", "ORANGE = \"#FF9D00\"\n", "YELLOW = \"#FFD21E\"\n", "RED = \"#FF323D\"\n", "BLACK = \"#32343D\"\n", "GREEN = \"#297373\"\n", "DARK_ORANGE = \"#CD4631\"" ] }, { "cell_type": "markdown", "id": "d37bd88b-f89d-440d-9541-6b6e589376e9", "metadata": {}, "source": [ "# Data Loading and Preprocessing" ] }, { "cell_type": "markdown", "id": "e1befb4e-f1a3-4c47-a46f-724660e08f31", "metadata": {}, "source": [ "## Load V2" ] }, { "cell_type": "code", "execution_count": 5, "id": "e398b673-20c7-4a83-8230-221829078cb2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(203, 32)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Load the v2 JSONL file\n", "ds = datasets.load_dataset(\"open-llm-leaderboard/contents_v2\", split=\"train\")\n", "data_v2 = ds.to_pandas()\n", "data_v2.shape" ] }, { "cell_type": "code", "execution_count": 6, "id": "464175a6-0034-4c6d-b7c8-51cb69be9db4", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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eval_namePrecisionTypeTWeight typeArchitectureModelfullnameModel shaAverage ⬆️Hub LicenseHub ❤️#Params (B)Available on the hubMergedMoEFlaggeddateChat TemplateIFEval RawIFEvalBBH RawBBHMATH Lvl 5 RawMATH Lvl 5GPQA RawGPQAMUSR RawMUSRMMLU-PRO RawMMLU-PROMaintainer's Highlight
0upstage_SOLAR-10.7B-v1.0_float16float16🟢 pretrained🟢OriginalLlamaForCausalLM<a target=\"_blank\" href=\"https://huggingface.c...upstage/SOLAR-10.7B-v1.0a45090b8e56bdc2b8e32e46b3cd782fc0bea1fa517.072003apache-2.024810TrueTrueTrueFalse2024-06-12T12:27:42ZFalse0.24212624.2126450.50938729.7893580.0211482.1148040.2810404.1387020.43715613.6778650.34001026.667775True
1upstage_SOLAR-10.7B-Instruct-v1.0_float16float16💬 chat models (RLHF, DPO, IFT, ...)💬OriginalLlamaForCausalLM<a target=\"_blank\" href=\"https://huggingface.c...upstage/SOLAR-10.7B-Instruct-v1.0c08c25ed66414a878fe0401a3596d536c083606c19.961989cc-by-nc-4.059210TrueTrueTrueFalse2024-06-12T12:06:58ZTrue0.47366147.3661000.51624931.8724020.0000000.0000000.3087257.8299780.3899376.9421880.31383023.758865True
2togethercomputer_RedPajama-INCITE-Instruct-3B-...float16🔶 fine-tuned on domain-specific datasets🔶OriginalGPTNeoXForCausalLM<a target=\"_blank\" href=\"https://huggingface.c...togethercomputer/RedPajama-INCITE-Instruct-3B-v10c66778ee09a036886741707733620b91057909a5.877290apache-2.0913TrueTrueTrueFalse2024-06-12T12:07:46ZFalse0.21242621.2426360.3146024.5107860.0060420.6042300.2474830.0000000.3886046.4088540.1109541.217125True
3togethercomputer_RedPajama-INCITE-Chat-3B-v1_f...float16🔶 fine-tuned on domain-specific datasets🔶OriginalGPTNeoXForCausalLM<a target=\"_blank\" href=\"https://huggingface.c...togethercomputer/RedPajama-INCITE-Chat-3B-v1f0e0995eba801096ed04cb87931d96a8316871af4.950649apache-2.01473TrueTrueTrueFalse2024-06-13T17:58:59ZFalse0.16521516.5214960.3216695.1647280.0030210.3021150.2441280.0000000.3684485.0893230.1126991.411052True
4togethercomputer_RedPajama-INCITE-Base-3B-v1_f...float16🟢 pretrained🟢OriginalGPTNeoXForCausalLM<a target=\"_blank\" href=\"https://huggingface.c...togethercomputer/RedPajama-INCITE-Base-3B-v1094fbdd0c911feb485ce55de1952ab2e75277e1e5.645099apache-2.0903TrueTrueTrueFalse2024-06-12T12:28:23ZFalse0.22936322.9362540.3060403.5186080.0090630.9063440.2432890.0000000.3738754.0010420.1111201.235594True
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" ], "text/plain": [ " eval_name Precision \\\n", "0 upstage_SOLAR-10.7B-v1.0_float16 float16 \n", "1 upstage_SOLAR-10.7B-Instruct-v1.0_float16 float16 \n", "2 togethercomputer_RedPajama-INCITE-Instruct-3B-... float16 \n", "3 togethercomputer_RedPajama-INCITE-Chat-3B-v1_f... float16 \n", "4 togethercomputer_RedPajama-INCITE-Base-3B-v1_f... float16 \n", "\n", " Type T Weight type \\\n", "0 🟢 pretrained 🟢 Original \n", "1 💬 chat models (RLHF, DPO, IFT, ...) 💬 Original \n", "2 🔶 fine-tuned on domain-specific datasets 🔶 Original \n", "3 🔶 fine-tuned on domain-specific datasets 🔶 Original \n", "4 🟢 pretrained 🟢 Original \n", "\n", " Architecture Model \\\n", "0 LlamaForCausalLM \n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
eval_namePrecisionTypeTWeight typeArchitectureModelfullnameModel shaAverage ⬆️Hub LicenseHub ❤️#Params (B)Available on the hubMergedMoEFlaggeddateChat TemplateIFEval RawIFEvalBBH RawBBHMATH Lvl 5 RawMATH Lvl 5GPQA RawGPQAMUSR RawMUSRMMLU-PRO RawMMLU-PROMaintainer's Highlight
0upstage_SOLAR-10.7B-v1.0_float16float16🟢 pretrained🟢OriginalLlamaForCausalLM<a target=\"_blank\" href=\"https://huggingface.c...upstage/SOLAR-10.7B-v1.0a45090b8e56bdc2b8e32e46b3cd782fc0bea1fa517.072003apache-2.024810TrueTrueTrueFalse2023-12-12 14:57:41+00:00False0.24212624.2126450.50938729.7893580.0211482.1148040.2810404.1387020.43715613.6778650.3400126.667775True
1upstage_SOLAR-10.7B-Instruct-v1.0_float16float16💬 chat models (RLHF, DPO, IFT, ...)💬OriginalLlamaForCausalLM<a target=\"_blank\" href=\"https://huggingface.c...upstage/SOLAR-10.7B-Instruct-v1.0c08c25ed66414a878fe0401a3596d536c083606c19.961989cc-by-nc-4.059210TrueTrueTrueFalse2023-12-12 12:39:22+00:00True0.47366147.3661000.51624931.8724020.0000000.0000000.3087257.8299780.3899376.9421880.3138323.758865True
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"gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "baxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "text": "Normalized Vs Raw Scores Comparison" }, "width": 600, "xaxis": { "anchor": "y", "autorange": true, "domain": [ 0, 1 ], "range": [ -0.5, 5.5 ], "title": { "text": "Task" }, "type": "category" }, "yaxis": { "anchor": "x", "domain": [ 0, 1 ], "range": [ 0, 100 ], "title": { "text": "Score" }, "type": "linear" } } }, "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot using Plotly Express with text labels\n", "fig = px.bar(mean_scores_df, x='Task', y='Score', color='Type', barmode='group',\n", " labels={'Score': 'Score', 'Task': 'Task'},\n", " title=\"Normalized Vs Raw Scores Comparison\",\n", " text='Score',\n", " color_discrete_map={'Normalized': \"#FF323D\", 'Raw': \"#FF9D00\"})\n", "\n", "# Update layout and show the plot\n", "fig.update_traces(texttemplate='%{text:.2s}', textposition='outside')\n", "fig.update_layout(\n", " yaxis=dict(range=[0, 100]),\n", " bargap=0.15,\n", " bargroupgap=0.05,\n", " legend_title_text='Score Type',\n", " width=600,\n", " height=350,\n", ")\n", "\n", "with open(\"./plots/normalized_vs_raw.html\", \"w\") as f:\n", " f.write(fig.to_html(full_html=False))\n", "\n", "fig.show()" ] }, { "cell_type": "markdown", "id": "1bd35583-dd65-408f-b353-a1194e718f8a", "metadata": {}, "source": [ "# Analyse individual tasks trends" ] }, { "cell_type": "markdown", "id": "d828671c-f785-4523-bbb7-fc3394fb9cf3", "metadata": {}, "source": [ "## IFEval: Compare Chat and Pretrained Models" ] }, { "cell_type": "code", "execution_count": 13, "id": "1b8e60f1-accf-48ca-b09a-92ffd9f521ce", "metadata": {}, "outputs": [], "source": [ "# Define the color map\n", "color_map = {\n", " '💬 chat models (RLHF, DPO, IFT, ...)': \"#FF323D\",\n", " '🔶 fine-tuned on domain-specific datasets': \"#FF9D00\",\n", " '🟢 pretrained': \"#32343D\"\n", "}" ] }, { "cell_type": "code", "execution_count": 14, "id": "3a591feb-049b-492e-b76e-c9d56347c46d", "metadata": {}, "outputs": [], "source": [ "# Filter the data for relevant types\n", "filtered_data = data_v2[data_v2['Type'].isin(color_map.keys())]\n", "\n", "# Prepare the data for plotting\n", "ifeval_data = filtered_data[['Type', 'IFEval', 'MATH Lvl 5', 'Average ⬆️']]\n", "average_scores = ifeval_data.groupby('Type').mean().reset_index()\n", "average_scores['Color'] = average_scores['Type'].map(color_map)" ] }, { "cell_type": "code", "execution_count": 15, "id": "b7c97adc-335f-4b16-b4d5-2c8a228c8d42", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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TypeIFEvalMATH Lvl 5Average ⬆️Color
0💬 chat models (RLHF, DPO, IFT, ...)47.0141964.72904119.054433#FF323D
1🔶 fine-tuned on domain-specific datasets26.1692632.53129012.222311#FF9D00
2🟢 pretrained21.9588243.23631711.288183#32343D
\n", "
" ], "text/plain": [ " Type IFEval MATH Lvl 5 \\\n", "0 💬 chat models (RLHF, DPO, IFT, ...) 47.014196 4.729041 \n", "1 🔶 fine-tuned on domain-specific datasets 26.169263 2.531290 \n", "2 🟢 pretrained 21.958824 3.236317 \n", "\n", " Average ⬆️ Color \n", "0 19.054433 #FF323D \n", "1 12.222311 #FF9D00 \n", "2 11.288183 #32343D " ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "average_scores" ] }, { "cell_type": "code", "execution_count": 41, "id": "3f992023-7289-454a-b654-5516635f4fd2", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "alignmentgroup": "True", "hovertemplate": "Model Type=%{x}
IFEval Score=%{text}", "legendgroup": "💬 chat models (RLHF, DPO, IFT, ...)", "marker": { "color": "#FF323D", "pattern": { "shape": "" } }, "name": "💬 chat models (RLHF, DPO, IFT, ...)", "offsetgroup": "💬 chat models (RLHF, DPO, IFT, ...)", "orientation": "v", "showlegend": true, "text": [ 47.01419594453827 ], "textposition": "outside", "texttemplate": "%{text:.2s}", "type": "bar", "x": [ "💬 chat models (RLHF, DPO, IFT, ...)" ], "xaxis": "x", "y": [ 47.01419594453827 ], "yaxis": "y" }, { "alignmentgroup": "True", "hovertemplate": "Model Type=%{x}
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"showland": true, "subunitcolor": "white" }, "hoverlabel": { "align": "left" }, "hovermode": "closest", "mapbox": { "style": "light" }, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": { "angularaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "radialaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "scene": { "xaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "yaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "zaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "baxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "text": "Average IFEval Scores by Model Type" }, "xaxis": { "anchor": "y", "autorange": true, "categoryarray": [ "💬 chat models (RLHF, DPO, IFT, ...)", "🔶 fine-tuned on domain-specific datasets", "🟢 pretrained" ], "categoryorder": "array", "domain": [ 0, 1 ], "range": [ -0.5, 2.5 ], "title": { "text": "Model Type" }, "type": "category" }, "yaxis": { "anchor": "x", "domain": [ 0, 1 ], "range": [ 0, 100 ], "title": { "text": "IFEval Score" }, "type": "linear" } } }, "text/html": [ "
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plotly Express scatter plot for Average vs. MATH Level 5 Score\n", "fig = px.scatter(ifeval_data, \n", " x='Average ⬆️', \n", " y='MATH Lvl 5', \n", " color='Type', \n", " color_discrete_map=color_map,\n", " labels={'Average ⬆️': 'Average', 'MATH Lvl 5': 'MATH Level 5 Score'}, \n", " title='Average vs. MATH Scores by Model Type')\n", "fig.update_layout(\n", " yaxis=dict(range=[-5, 100]),\n", " xaxis=dict(range=[0, 100]),\n", " width=600,\n", " height=350,\n", ")\n", "with open(\"./plots/math_vs_avg_all.html\", \"w\") as f:\n", " f.write(fig.to_html(full_html=False))\n", " \n", "fig.show()" ] }, { "cell_type": "markdown", "id": "bd89bddc-1a8a-46f4-97c2-012b9ce952e4", "metadata": {}, "source": [ "## MuSR Through Time" ] }, { "cell_type": "code", "execution_count": 50, "id": "a997ac93-2eac-4542-944a-5613384e2917", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "Date=%{x}
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Ensure the 'date' column is a datetime type\n", "data_v2['date'] = pd.to_datetime(data_v2['date'])\n", "\n", "# Create the Plotly Express scatter plot\n", "fig = px.scatter(data_v2, \n", " x='date', \n", " y='MUSR', \n", " title='MuSR as a Function of the Model Publication Date',\n", " labels={'date': 'Date', 'MUSR': 'MuSR'},\n", " color_discrete_sequence=[colors[0]])\n", "\n", "fig.update_layout(\n", " yaxis=dict(range=[-5, 100]),\n", " width=600,\n", " height=350,\n", ")\n", "\n", "with open(\"./plots/musr_through_time.html\", \"w\") as f:\n", " f.write(fig.to_html(full_html=False))\n", "\n", "fig.show()" ] }, { "cell_type": "markdown", "id": "0b250275-9e46-4d41-8ad0-035cd329917d", "metadata": {}, "source": [ "# Correlations Analysis" ] }, { "cell_type": "markdown", "id": "74cc0d17-deb2-47fd-a4bf-029680ef904b", "metadata": {}, "source": [ "## Heatmaps Per Tasks" ] }, { "cell_type": "code", "execution_count": 20, "id": "7b405942-bd6d-4ad5-8fc0-909825df63b7", "metadata": {}, "outputs": [], "source": [ "# Calculating the correlation matrices\n", "correlation_data = data_v2[tasks_v2]\n", "correlation_matrix = correlation_data.corr()" ] }, { "cell_type": "code", "execution_count": 21, "id": "59029849-558f-4d29-b83f-fd22d3c93140", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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IFEvalBBHMATH Lvl 5GPQAMUSRMMLU-PRO
IFEval1.0000000.6430170.4432730.3945750.3680330.599912
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MATH Lvl 50.4432730.6936551.0000000.6887260.3218170.720156
GPQA0.3945750.8159790.6887261.0000000.4422930.856665
MUSR0.3680330.5813440.3218170.4422931.0000000.523371
MMLU-PRO0.5999120.9547830.7201560.8566650.5233711.000000
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" ], "text/plain": [ " IFEval BBH MATH Lvl 5 GPQA MUSR MMLU-PRO\n", "IFEval 1.000000 0.643017 0.443273 0.394575 0.368033 0.599912\n", "BBH 0.643017 1.000000 0.693655 0.815979 0.581344 0.954783\n", "MATH Lvl 5 0.443273 0.693655 1.000000 0.688726 0.321817 0.720156\n", "GPQA 0.394575 0.815979 0.688726 1.000000 0.442293 0.856665\n", "MUSR 0.368033 0.581344 0.321817 0.442293 1.000000 0.523371\n", "MMLU-PRO 0.599912 0.954783 0.720156 0.856665 0.523371 1.000000" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "correlation_matrix" ] }, { "cell_type": "code", "execution_count": 28, "id": "f17a8ed0-c930-4ff2-beba-ce9c4ca52513", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "coloraxis": "coloraxis", "hovertemplate": "Variable: %{x}
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"title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "text": "Correlation Matrix Heatmap (Normalized Tasks)" }, "width": 600, "xaxis": { "anchor": "y", "autorange": true, "constrain": "domain", "domain": [ 0.10199004975124376, 0.8980099502487562 ], "range": [ -0.5, 5.5 ], "scaleanchor": "y", "side": "bottom", "title": { "text": "Variable" }, "type": "category" }, "yaxis": { "anchor": "x", "autorange": true, "constrain": "domain", "domain": [ 0, 1 ], "range": [ 5.5, -0.5 ], "title": { "text": "Variable" }, "type": "category" } } }, "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Use px.imshow to create a heatmap\n", "fig = px.imshow(correlation_matrix,\n", " text_auto=True, # Automatically add text on each cell\n", " labels=dict(x=\"Variable\", y=\"Variable\", color=\"Correlation\"),\n", " x=correlation_matrix.columns,\n", " y=correlation_matrix.index,\n", " color_continuous_scale=[(0, \"#FFD21E\"), (1, \"#FF323D\")], # Color scale from 0 to +1\n", " title=\"Correlation Matrix Heatmap (Normalized Tasks)\",\n", " width=600,\n", " height=500,\n", " )\n", "\n", "# Update layout if necessary\n", "fig.update_xaxes(side=\"bottom\") # To ensure x-axis labels are on the bottom\n", "fig.update_yaxes(autorange=\"reversed\") # To reverse the y-axis to match traditional matrix layout\n", "\n", "# Save the plot as HTML if needed\n", "with open(\"./plots/correlation_heatmap.html\", \"w\") as f:\n", " f.write(fig.to_html(full_html=False))\n", "\n", "fig.show()" ] }, { "cell_type": "markdown", "id": "6db7711d-7423-491e-9c7d-21e14f7cb52d", "metadata": {}, "source": [ "## Heatmaps Per Model Type" ] }, { "cell_type": "code", "execution_count": 23, "id": "ab7b4f7b-2482-4bbd-a852-848c98f6e770", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Suppress missing font warning\n", "import warnings\n", "warnings.filterwarnings(\"ignore\", category=UserWarning, message=\".*Glyph.*missing from font.*\")\n", "\n", "model_types = data_v2['Type'].unique()\n", "num_types = len(model_types)\n", "rows = (num_types + 1) // 2 # Calculate the number of rows needed\n", "\n", "fig, axes = plt.subplots(rows, 2, figsize=(20, rows * 8))\n", "\n", "for i, model_type in enumerate(model_types):\n", " row = i // 2\n", " col = i % 2\n", " model_data = data_v2[data_v2['Type'] == model_type]\n", " correlation_matrix_model = model_data[tasks_v2].corr()\n", " \n", " sns.heatmap(correlation_matrix_model, annot=True, fmt=\".2f\", cmap='coolwarm', vmin=-1, vmax=1, ax=axes[row, col])\n", " axes[row, col].set_title(f'Correlation Matrix Heatmap ({model_type})')\n", "\n", "# If the number of plots is odd, remove the last empty subplot\n", "if num_types % 2 != 0:\n", " fig.delaxes(axes[-1, -1])\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "2c8dc1c6-f3f3-4a46-b249-9da373ef74ce", "metadata": {}, "source": [ "# Outlier Detection" ] }, { "cell_type": "code", "execution_count": 24, "id": "fad7f30a-2a0f-41bf-92c9-8c250fb9ceed", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 16.766858\n", "1 19.628255\n", "2 5.663939\n", "3 4.748119\n", "4 5.432974\n", "Name: mean_score, dtype: float64" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Calculate the mean score across all tasks for each model\n", "data_v2['mean_score'] = data_v2[tasks_v2].mean(axis=1)\n", "data_v2['mean_score'].head()" ] }, { "cell_type": "code", "execution_count": 25, "id": "8101ca2e-495b-4168-a2f9-6f7145312a8c", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "Task=IFEval
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"" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "text": "Outlier Detection: Mean Score vs. Task Score" }, "width": 800, "xaxis": { "anchor": "y", "domain": [ 0, 1 ], "range": [ 0, 50 ], "title": { "text": "Mean Score (All Tasks)" }, "type": "linear" }, "yaxis": { "anchor": "x", "domain": [ 0, 1 ], "range": [ -5, 100 ], "title": { "text": "Score by Task" }, "type": "linear" } } }, "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Create a large figure to accommodate all plots\n", "fig = px.scatter(data_frame=data_v2, x='mean_score', y=tasks_v2,\n", " labels={\"value\": \"Score by Task\", \"variable\": \"Task\"},\n", " title='Outlier Detection: Mean Score vs. Task Score',\n", " color_discrete_sequence=colors,\n", " opacity=0.7)\n", "\n", "# Update axes labels\n", "fig.update_xaxes(title_text='Mean Score (All Tasks)')\n", "fig.update_yaxes(title_text='Score by Task')\n", "\n", "# Add grid lines for better readability\n", "fig.update_layout(grid=dict(columns=1, rows=1),\n", " yaxis=dict(range=[-5, 100]),\n", " xaxis=dict(range=[0, 50]),\n", " width=800,\n", " height=600,\n", ")\n", "\n", "# Show the plot\n", "fig.show()" ] }, { "cell_type": "code", "execution_count": 26, "id": "3f29d621-d130-4ca4-96a2-7d9a396097c1", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, 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 eval_namemean_scoreIFEvalBBHMATH Lvl 5GPQAMUSRMMLU-PRO
124Qwen_Qwen2-72B-Instruct_bfloat1642.48630879.89168757.48300935.12084616.33109617.16796948.923242
66meta-llama_Meta-Llama-3-70B-Instruct_bfloat1636.18340280.99077150.18513323.3383694.92170010.92057346.743868
67meta-llama_Meta-Llama-3-70B_bfloat1626.36547116.03190648.70981316.54078519.68680116.01119841.212323
62microsoft_Orca-2-13b_bfloat1618.13681631.27933927.3080190.9818734.02684625.78776019.437057
\n" ], "text/plain": [ "" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Define a function to highlight the maximum value in each column\n", "def highlight_max(data, color='lightgreen'):\n", " attr = f'background-color: {color}'\n", " if data.ndim == 1:\n", " is_max = data == data.max()\n", " return [attr if v else '' for v in is_max]\n", " else:\n", " is_max = data == data.max().max()\n", " return pd.DataFrame(np.where(is_max, attr, ''),\n", " index=data.index, columns=data.columns)\n", "\n", "# Find the best model for each task based on the maximum score for that task\n", "best_models = data_v2.loc[data_v2[tasks_v2].idxmax()].sort_values(by=\"mean_score\", \n", " ascending = False)\n", "best_models_unique = best_models.drop_duplicates(subset=['eval_name'])\n", "\n", "styled_best_models = best_models_unique[['eval_name', 'mean_score'] + tasks_v2].style.apply(highlight_max, subset=tasks_v2)\n", "\n", "styled_best_models" ] }, { "cell_type": "code", "execution_count": null, "id": "f3f9d9e3-b734-40b3-8687-c362988a651b", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "b69f1e0c-ebe1-4f73-8398-adedc566c51a", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Leaderboard EDA", "language": "python", "name": "leaderboard_eda" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.1" } }, "nbformat": 4, "nbformat_minor": 5 }