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{
"cells": [
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'prob': {'file': 'prob.json'}}"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import json\n",
"import numpy as np\n",
"import plotly.graph_objects as go\n",
"RED_FULL=\"rgba(255, 0, 0, 1)\"\n",
"\n",
"# Define the function 1 - (1 - x^8)^14\n",
"def func1(x):\n",
" return 1 - np.power(1 - np.power(x, 8), 14)\n",
"\n",
"# Define the function 1 - (1 - x^20)^450\n",
"def func2(x):\n",
" return 1 - np.power(1 - np.power(x, 20), 450)\n",
"\n",
"# Generate x values from 0 to 1\n",
"x = np.linspace(0, 1, 1000)\n",
"\n",
"# Calculate y values for each function\n",
"y1 = func1(x)\n",
"y2 = func2(x)\n",
"\n",
"# Create traces\n",
"trace1 = go.Scatter(x=x, y=y1, mode='lines', name='FineWeb: 1-(1-s^8)^14')\n",
"trace2 = go.Scatter(x=x, y=y2, mode='lines', name='RefinedWeb: 1-(1-s^20)^450')\n",
"vertical_line = go.Scatter(x=[0.75, 0.75], y=[0, 1], mode='lines', line=dict(color='red', dash='dash'), name='Threshold')\n",
"\n",
"# Define layout\n",
"layout = {\n",
" 'title': {\n",
" 'text': 'MinHash parameters',\n",
" },\n",
" 'xaxis': {\n",
" 'title': {\n",
" 'text': 'Document similarity (s)',\n",
" },\n",
" },\n",
" 'yaxis': {\n",
" 'title': {\n",
" 'text': 'Matched as dups probability',\n",
" },\n",
" },\n",
"}\n",
"\n",
"\n",
"def normalize_run_name(run_name):\n",
" return run_name.replace(\"/\", \"_\")\n",
"\n",
"\n",
"def save_for_plot(dir_name, df, views, xlabel=\"Dataset\", ylabel=\"Matched as dups probability\", plot_name=\"plot name\", custom_layout={}, ranges={}, x_column=None, default_metric=None):\n",
" import os\n",
" files = {}\n",
" os.makedirs(f\"data/plots/{dir_name}\", exist_ok=True)\n",
" for view in views:\n",
" data = {}\n",
" for run_name in df[\"runname\"].unique():\n",
" run_name_only=df[df[\"runname\"]==run_name]\n",
" data[run_name] = {\n",
" \"x\": run_name_only[x_column].tolist() if x_column else [run_name],\n",
" \"y\": run_name_only[view].tolist(),\n",
" \"label\": run_name,\n",
" }\n",
" file_name = f\"{normalize_run_name(view)}.json\"\n",
" files[view] = {\"file\": f\"{file_name}\"}\n",
" with open(f\"data/plots/{dir_name}/{file_name}\", \"w\") as f:\n",
" json.dump({\n",
" \"data\": data,\n",
" \"layout\": {\n",
" \"title\": {\n",
" \"text\": plot_name,\n",
" },\n",
" \"xaxis\": {\n",
" \"title\": {\n",
" \"text\": xlabel,\n",
" },\n",
" },\n",
" \"yaxis\": {\n",
" # \"range\": ranges.get(view, None),\n",
" \"title\": {\n",
" \"text\": ylabel,\n",
" },\n",
" },\n",
" \"shapes\": [\n",
" {\n",
" \"type\": \"line\",\n",
" \"x0\": 0.75,\n",
" \"y0\": 0.0,\n",
" \"x1\": 0.75,\n",
" \"y1\": 1.2,\n",
" \"xref\": \"x\",\n",
" \"yref\": \"y\",\n",
" \"line\": {\n",
" \"color\": RED_FULL,\n",
" \"width\": 1,\n",
" \"dash\": \"dashdot\"\n",
" },\n",
" \"showarrow\": False\n",
" }\n",
" ],\n",
" **custom_layout,\n",
" },\n",
" }, f)\n",
" with open(f\"data/plots/{dir_name}/index.json\", \"w\") as f:\n",
" json.dump({\n",
" \"files\": files,\n",
" \"settings\": {\n",
" \"defaultMetric\": default_metric,\n",
" \"slider\": None,\n",
" \"autoSetXRange\": False,\n",
" }\n",
" }, f)\n",
" return files\n",
"\n",
"import pandas as pd\n",
"df = pd.DataFrame({\n",
" \"runname\": [\"FineWeb: 1-(1-s^8)^14\"]*len(x) + [\"RefinedWeb: 1-(1-s^20)^450\"]*len(x),\n",
" \"similarity\": x.tolist()+x.tolist(),\n",
" \"prob\": y1.tolist()+y2.tolist(),\n",
" \"view\": [\"normal\"]*2*len(x)\n",
"})\n",
"\n",
"custom_layout = {\n",
" \"legend\": {\n",
" \"orientation\": \"v\",\n",
" \"xanchor\": \"left\",\n",
" \"yanchor\": \"top\",\n",
" \"x\": 0,\n",
" \"y\": 1,\n",
" },\n",
"}\n",
"\n",
"save_for_plot(\"minhash_params\", df, [\"prob\"], xlabel=\"Document similarity (s)\", plot_name=\"MinHash parameters\", custom_layout=custom_layout, ranges={}, x_column=\"similarity\", default_metric=\"prob\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "datatrove",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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