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{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/ben/code/hub-recap/.venv/lib/python3.11/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": [
"from datasets import load_dataset\n",
"\n",
"ds = load_dataset(\"cfahlgren1/hub-stats\", \"datasets\")\n",
"ds_df = ds[\"train\"].to_pandas()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"ds = load_dataset(\"cfahlgren1/hub-stats\", \"models\")\n",
"md_df = ds[\"train\"].to_pandas()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Generating train split: 100%|██████████| 309714/309714 [00:00<00:00, 353713.86 examples/s]\n"
]
}
],
"source": [
"ds = load_dataset(\"cfahlgren1/hub-stats\", \"spaces\")\n",
"sp_df = ds[\"train\"].to_pandas()"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'p_99999': 1299, 'p_9999': 491, 'p_999': 125}\n"
]
}
],
"source": [
"dataset_percentiles = {\n",
" \"p_99999\": int(ds_df[\"likes\"].quantile(0.99999)),\n",
" \"p_9999\": int(ds_df[\"likes\"].quantile(0.9999)),\n",
" \"p_999\": int(ds_df[\"likes\"].quantile(0.999)),\n",
"}\n",
"print(dataset_percentiles)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'p_99999': 3698, 'p_9999': 949, 'p_999': 143}\n"
]
}
],
"source": [
"model_percentiles = {\n",
" \"p_99999\": int(md_df[\"likes\"].quantile(0.99999)),\n",
" \"p_9999\": int(md_df[\"likes\"].quantile(0.9999)),\n",
" \"p_999\": int(md_df[\"likes\"].quantile(0.999)),\n",
"}\n",
"print(model_percentiles)"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'p_99999': 6040, 'p_9999': 1552, 'p_999': 326}\n"
]
}
],
"source": [
"space_percentiles = {\n",
" \"p_99999\": int(sp_df[\"likes\"].quantile(0.99999)),\n",
" \"p_9999\": int(sp_df[\"likes\"].quantile(0.9999)),\n",
" \"p_999\": int(sp_df[\"likes\"].quantile(0.999)),\n",
"}\n",
"print(space_percentiles)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"\n",
"with open(\"percentiles.json\", \"w\") as f:\n",
" json.dump(\n",
" {\n",
" \"dataset_percentiles\": dataset_percentiles,\n",
" \"model_percentiles\": model_percentiles,\n",
" \"space_percentiles\": space_percentiles,\n",
" },\n",
" f,\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"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": "3.11.10"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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