{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mWARNING: Skipping top2vec as it is not installed.\u001b[0m\n",
      "\u001b[33mWARNING: Skipping bertopic as it is not installed.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install \"umap-learn[parametric_umap]\" -Uq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from umap.parametric_umap import ParametricUMAP\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "ParametricUMAP().fit_transform(np.random.random((300000,384)))"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "266722041ed6426a0a88c0d75e9dd39659f44e3a6fea07300cd13bea36eb387d"
  },
  "kernelspec": {
   "display_name": "Python 3.9.7 64-bit ('py3': conda)",
   "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": "3.9.7"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}