{ "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\", 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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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=400,\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": 16, "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": 19, "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": 35, "id": "59029849-558f-4d29-b83f-fd22d3c93140", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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IFEval1.000.640.440.390.370.60
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GPQA0.390.820.691.000.440.86
MUSR0.370.580.320.441.000.52
MMLU-PRO0.600.950.720.860.521.00
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" ], "text/plain": [ " IFEval BBH MATH Lvl 5 GPQA MUSR MMLU-PRO\n", "IFEval 1.00 0.64 0.44 0.39 0.37 0.60\n", "BBH 0.64 1.00 0.69 0.82 0.58 0.95\n", "MATH Lvl 5 0.44 0.69 1.00 0.69 0.32 0.72\n", "GPQA 0.39 0.82 0.69 1.00 0.44 0.86\n", "MUSR 0.37 0.58 0.32 0.44 1.00 0.52\n", "MMLU-PRO 0.60 0.95 0.72 0.86 0.52 1.00" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "correlation_matrix.round(2)" ] }, { "cell_type": "code", "execution_count": 39, "id": "f17a8ed0-c930-4ff2-beba-ce9c4ca52513", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "coloraxis": "coloraxis", "hovertemplate": "Benchmark: %{x}
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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": "Correlation Matrix Heatmap" }, "width": 600, "xaxis": { "anchor": "y", "autorange": true, "constrain": "domain", "domain": [ 0.14545454545454545, 0.8545454545454545 ], "range": [ -0.5, 5.5 ], "scaleanchor": "y", "side": "bottom", "tickangle": -45, "tickfont": { "size": 10 }, "title": { "text": "Benchmark" }, "type": "category" }, "yaxis": { "anchor": "x", "autorange": true, "constrain": "domain", "domain": [ 0, 1 ], "range": [ 5.5, -0.5 ], "title": { "text": "Benchmark" }, "type": "category" } } }, "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Assuming 'correlation_matrix' is your DataFrame with correlation data\n", "# Sort the columns and rows alphabetically\n", "correlation_matrix = correlation_matrix.sort_index().sort_index(axis=1)\n", "\n", "fig = px.imshow(correlation_matrix,\n", " text_auto=\".2f\",\n", " labels=dict(x=\"Benchmark\", y=\"Benchmark\", color=\"Correlation\"), # Update axis labels to 'Benchmark'\n", " x=correlation_matrix.columns,\n", " y=correlation_matrix.index,\n", " color_continuous_scale='plasma_r', # Reverse Plasma color scale\n", " title=\"Correlation Matrix Heatmap\",\n", " width=600,\n", " height=500\n", " )\n", "\n", "fig.update(layout_coloraxis_showscale=False)\n", "\n", "# Update layout if necessary\n", "fig.update_xaxes(side=\"bottom\", tickangle=-45, tickfont=dict(size=10)) # Rotate and reduce font size on x-axis\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": 38, "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='plasma_r', 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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" ] }, "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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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Create a Figure for multiple plots\n", "fig = go.Figure()\n", "\n", "# Loop through each task to add a plot\n", "for task, color in zip(tasks_v2, colors):\n", " # Calculate the rolling average\n", " sorted_data = data_v2.sort_values('mean_score')\n", " sorted_data[f'{task}_smooth'] = sorted_data[task].rolling(window=20, min_periods=1).mean()\n", "\n", " # Add a line plot for each task\n", " fig.add_trace(go.Scatter(x=sorted_data['mean_score'], y=sorted_data[f'{task}_smooth'], \n", " mode='lines', name=task, line=dict(color=color)))\n", "\n", "# Update layout with titles and labels\n", "fig.update_layout(\n", " title='Average Curves for Each Evaluation as Function of the Mean',\n", " xaxis_title='Mean Score (All Tasks)',\n", " yaxis_title='Average Score by Task',\n", " legend_title='Task',\n", " template=\"plotly\",\n", " yaxis=dict(range=[-5, 100]),\n", " xaxis=dict(range=[0, 50]),\n", " width=800,\n", " height=600,\n", ")\n", "\n", "# Add grid lines\n", "fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='LightGrey')\n", "fig.update_yaxes(showgrid=True, gridwidth=1, gridcolor='LightGrey')\n", "\n", "# Show the plot\n", "fig.show()" ] }, { "cell_type": "code", "execution_count": 27, "id": "7b68a05e-b70a-417a-9496-097905d0b631", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\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_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 }