{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bbf8df52",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install fABBA\n",
    "# !git clone https://github.com/nla-group/ABBA.git"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "552aad3a",
   "metadata": {},
   "source": [
    "## Run ABBA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "11fb01c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [07:51<00:00,  4.71s/it]\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from tqdm import tqdm\n",
    "from ABBA.ABBA import ABBA\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "np.random.seed(1)\n",
    "\n",
    "length = 5000\n",
    "mu, sigma = 0, 1 # mean and standard deviation\n",
    "data_df = np.zeros((100, length))\n",
    "runtime = np.zeros((100, 2)) # symbols: 10, 100, 1000\n",
    "accuracy = np.zeros((100, 2)) # symbols: 10, 100, 1000\n",
    "k_symbols = [100, 1000]\n",
    "TOL = 0.5\n",
    "\n",
    "for i in tqdm(range(100)):\n",
    "    ts = np.random.normal(mu, sigma, length)\n",
    "    data_df[i] = ts\n",
    "    \n",
    "    st = time.time()\n",
    "    abba = ABBA(min_k=k_symbols[0], max_k=k_symbols[0], tol=TOL, verbose=0, scl=1)\n",
    "    string, centers = abba.transform(ts)\n",
    "    reconstructed_ts = abba.inverse_transform(string, centers, ts[0])\n",
    "    runtime[i, 0] = time.time() - st\n",
    "    accuracy[i, 0] = mean_squared_error(ts, reconstructed_ts, squared=False)\n",
    "    \n",
    "    st = time.time()\n",
    "    abba = ABBA(min_k=k_symbols[1], max_k=k_symbols[1], tol=TOL, verbose=0, scl=1)\n",
    "    string, centers = abba.transform(ts)\n",
    "    reconstructed_ts = abba.inverse_transform(string, centers, ts[0])\n",
    "    runtime[i, 1] = time.time() - st\n",
    "    accuracy[i, 1] = mean_squared_error(ts, reconstructed_ts, squared=False)\n",
    "\n",
    "    \n",
    "pd.DataFrame(data_df).to_csv(\"ts_test.csv\", index=False)\n",
    "pd.DataFrame(runtime).to_csv(\"runtime.csv\", index=False)\n",
    "pd.DataFrame(accuracy).to_csv(\"accuracy.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "123a6112",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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