{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.datasets import load_boston\n",
    "from sklearn.model_selection import TimeSeriesSplit as skTimeSeriesSplit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class TimeSeriesSplit():\n",
    "    def __init__(self, n_splits=5):\n",
    "        self.n_splits = n_splits\n",
    "\n",
    "    def split(self, X, y):\n",
    "        indices = np.arange(X.shape[0])\n",
    "        n_folds = self.n_splits + 1\n",
    "        test_size = X.shape[0] // n_folds\n",
    "        test_starts = np.arange(test_size + X.shape[0] % n_folds,\n",
    "                                X.shape[0], test_size)\n",
    "        for test_start in test_starts:\n",
    "            yield (indices[:test_start],\n",
    "                   indices[test_start:test_start + test_size])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = load_boston(return_X_y=True)\n",
    "cv1 = TimeSeriesSplit(n_splits=5)\n",
    "cv2 = skTimeSeriesSplit(n_splits=5)\n",
    "for (train1, test1), (train2, test2) in zip(cv1.split(X, y), cv2.split(X, y)):\n",
    "    assert np.array_equal(train1, train2)\n",
    "    assert np.array_equal(test1, test2)"
   ]
  }
 ],
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