{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy.stats.mstats import mode\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.impute import SimpleImputer as skSimpleImputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class SimpleImputer():\n",
    "    def __init__(self, strategy='mean', fill_value=None):\n",
    "        self.strategy = strategy\n",
    "        self.fill_value = fill_value  # only used when strategy == 'constant'\n",
    "\n",
    "    def fit(self, X):\n",
    "        mask = np.isnan(X)\n",
    "        masked_X = np.ma.masked_array(X, mask=mask)\n",
    "        if self.strategy == \"mean\":\n",
    "            self.statistics_ = np.array(np.ma.mean(masked_X, axis=0))\n",
    "        elif self.strategy == \"median\":\n",
    "            self.statistics_ = np.array(np.ma.median(masked_X, axis=0))\n",
    "        elif self.strategy == \"most_frequent\":\n",
    "            self.statistics_ = np.array(mode(masked_X, axis=0)[0])\n",
    "        elif self.strategy == \"constant\":\n",
    "            self.statistics_ = np.full(X.shape[1], self.fill_value)\n",
    "        return self\n",
    "\n",
    "    def transform(self, X):\n",
    "        mask = np.isnan(X)\n",
    "        n_missing = np.sum(mask, axis=0)\n",
    "        values = np.repeat(self.statistics_, n_missing)\n",
    "        coordinates = np.where(mask.T)[::-1]\n",
    "        Xt = X.copy()\n",
    "        Xt[coordinates] = values\n",
    "        return Xt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, _ = load_iris(return_X_y=True)\n",
    "rng = np.random.RandomState(0)\n",
    "missing_samples = np.arange(X.shape[0])\n",
    "missing_features = rng.choice(X.shape[1], X.shape[0])\n",
    "X[missing_samples, missing_features] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "est1 = SimpleImputer(strategy=\"mean\").fit(X)\n",
    "est2 = skSimpleImputer(strategy=\"mean\").fit(X)\n",
    "assert np.allclose(est1.statistics_, est2.statistics_)\n",
    "Xt1 = est1.transform(X)\n",
    "Xt2 = est2.transform(X)\n",
    "assert np.allclose(Xt1, Xt2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "est1 = SimpleImputer(strategy=\"median\").fit(X)\n",
    "est2 = skSimpleImputer(strategy=\"median\").fit(X)\n",
    "assert np.allclose(est1.statistics_, est2.statistics_)\n",
    "Xt1 = est1.transform(X)\n",
    "Xt2 = est2.transform(X)\n",
    "assert np.allclose(Xt1, Xt2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "est1 = SimpleImputer(strategy=\"most_frequent\").fit(X)\n",
    "est2 = skSimpleImputer(strategy=\"most_frequent\").fit(X)\n",
    "assert np.allclose(est1.statistics_, est2.statistics_)\n",
    "Xt1 = est1.transform(X)\n",
    "Xt2 = est2.transform(X)\n",
    "assert np.allclose(Xt1, Xt2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "est1 = SimpleImputer(strategy=\"constant\", fill_value=0).fit(X)\n",
    "est2 = skSimpleImputer(strategy=\"constant\", fill_value=0).fit(X)\n",
    "assert np.allclose(est1.statistics_, est2.statistics_)\n",
    "Xt1 = est1.transform(X)\n",
    "Xt2 = est2.transform(X)\n",
    "assert np.allclose(Xt1, Xt2)"
   ]
  }
 ],
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