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   "cell_type": "code",
   "execution_count": 1,
   "id": "b1bc6250-d62d-401d-9559-afaa870c0065",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "21f27256-2412-43e3-8e90-a408d0eb2881",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import f1_score\n",
    "\n",
    "class ModelSelectionHelper:\n",
    "\n",
    "    def __init__(self, models, params):\n",
    "        print('init model selection helper notebook')\n",
    "        self.models = models\n",
    "        self.params = params\n",
    "        self.keys = models.keys()\n",
    "        self.grid_searches = {}\n",
    "        \n",
    "    def fit(self, X, y, scoring=None, n_jobs=3, refit=True, cv=5, verbose=1, return_train_score=True):\n",
    "        for key in self.keys:\n",
    "            print('---------------------------------------------------------------------------')\n",
    "            print(key)\n",
    "            \n",
    "            model = self.models[key]\n",
    "            params = self.params[key]\n",
    "            \n",
    "            grid_search = GridSearchCV(model, params, cv=cv, n_jobs=n_jobs,\n",
    "                verbose=verbose, scoring=scoring, refit=refit,\n",
    "                return_train_score=return_train_score)\n",
    "            \n",
    "            grid_search.fit(X, y)\n",
    "            \n",
    "            self.grid_searches[key] = grid_search\n",
    "            \n",
    "            #best_params_ and best_score_ are only available if refit=True\n",
    "            if (refit==True): \n",
    "                print('best_params_: ', grid_search.best_params_)\n",
    "                print('best_score_: ', grid_search.best_score_)\n",
    "                \n",
    "    def get_model_best_estimator(self, key):\n",
    "        return self.grid_searches[key].best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7586a157-bdec-4363-b30c-5e9a7ad13074",
   "metadata": {},
   "outputs": [],
   "source": []
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   "cell_type": "code",
   "execution_count": null,
   "id": "856bb41b-9b4d-4a98-8d55-80811256a220",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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