{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_regression\n",
    "from algorithms.utils import make_missing_mcar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate random data with MCAR missingness\n",
    "X, y = make_regression(n_samples=500, n_features=20, n_informative=20, random_state=0)\n",
    "X_miss = make_missing_mcar(X, 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the different available imputers: MissForest, DPP-MissForest, Deterministic DPP-Missforest,\\\n",
    "#  MICE, DPP-MICE and Deterministic DPP-MICE\n",
    "\n",
    "from models.imputers import MissForest, DPPMissForest, DeterDPPMissForest\n",
    "from models.imputers import MiceRanger as MICE\n",
    "from models.imputers import DPPMiceRanger as DPPMICE\n",
    "from models.imputers import DeterDPPMiceRanger as DeterDPPMICE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# run the determinisitc DPP MissForest imputer\n",
    "\n",
    "ddpp_mf = DeterDPPMissForest(batch_size=100, max_iter=5, n_estimators=10)\n",
    "\n",
    "X_imputed = ddpp_mf.fit_transform(X_miss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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