notebooks/transforming.ipynb

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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import statements\n",
    "import polars as pl\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import preprocessing.transform_data as td\n",
    "import preprocessing.feature_engineering as fe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_raw = pl.read_parquet(\"./data/2022_data_ST4000DM000.parquet\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "check for hard drives with inconsistent time series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fe.search_faulty_drives(df_raw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_raw = td.filter_out_inconsistent_drives(df_raw)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "train-test-split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Train, Test = td.custom_train_test_split(df_raw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Train = pd.read_pickle(\"train_list.obj\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Test = pd.read_pickle(\"test_list.obj\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "create data chunks based on train-test-split and save them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1. chunk out of 10 processed and saved!\n",
      "2. chunk out of 10 processed and saved!\n",
      "3. chunk out of 10 processed and saved!\n",
      "4. chunk out of 10 processed and saved!\n",
      "5. chunk out of 10 processed and saved!\n",
      "6. chunk out of 10 processed and saved!\n",
      "7. chunk out of 10 processed and saved!\n",
      "8. chunk out of 10 processed and saved!\n",
      "9. chunk out of 10 processed and saved!\n",
      "10. chunk out of 10 processed and saved!\n"
     ]
    }
   ],
   "source": [
    "td.create_chunks(df_raw,Train, name=\"Train_30\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1. chunk out of 4 processed and saved!\n",
      "2. chunk out of 4 processed and saved!\n",
      "3. chunk out of 4 processed and saved!\n",
      "4. chunk out of 4 processed and saved!\n"
     ]
    }
   ],
   "source": [
    "td.create_chunks(df_raw,Test, name=\"Test_30\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "import the chunks into a dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = td.combine_chunks(\"Train_30\", 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = td.combine_chunks(\"Test_30\", 4)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "create target for X_train, X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = td.create_y(X_train)\n",
    "y_train.to_pickle(\"data/y_train_30.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test = td.create_y(X_test)\n",
    "y_test.to_pickle(\"data/y_test_30.pkl\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### transforming\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import statements\n",
    "from sktime.transformations.panel.rocket import Rocket, MiniRocketMultivariate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#list of features to be transformed\n",
    "col_list = td.feature_list(target=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "MiniRocketMultivariate()"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "minirocket_multi = MiniRocketMultivariate()\n",
    "minirocket_multi.fit(X_train[col_list])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_transform = minirocket_multi.transform(X_train[col_list])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_transform.to_pickle(\"data/X_train_30_transform.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_transform = minirocket_multi.transform(X_test[col_list])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_transform.to_pickle(\"data/X_test_30_transform.pkl\")"
   ]
  }
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