python-package/examples/openml_mnist.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from perpetual import PerpetualBooster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "\n",
    "logging.basicConfig()\n",
    "logging.getLogger().setLevel(logging.DEBUG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = pd.read_csv(\"../../resources/fashion_train_flat.csv\", index_col=False, header=None).to_numpy().reshape(63000, -1)\n",
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = pd.read_csv(\"../../resources/fashion_train_y.csv\", index_col=False, header=None).to_numpy().flatten()\n",
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = PerpetualBooster(log_iterations=1)\n",
    "model.fit(X_train, y_train, budget=1.0, timeout=360)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.number_of_trees"
   ]
  }
 ],
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