pre-processing.ipynb

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   "source": [
    "# Preprocessing\n",
    "\n",
    "Before we train our models we have to pre-process our data. Luckily the data does not have to be cleaned, only scaled."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import orchest\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Retrieve the data from the previous step.\n",
    "data = orchest.get_inputs()  # data = [(df_data, df_target)]\n",
    "X, y = data[\"data\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "Scaling the data...\n"
     ]
    }
   ],
   "source": [
    "# Print messages are useful when you are keeping an eye on the logs of\n",
    "# a pipeline step.\n",
    "print(\"Scaling the data...\")\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X.values, y.values, test_size=0.20)\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "orchest.output((X_train, y_train, X_test, y_test), name=\"training_data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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