Code/FeatureSelection_SelectFromModel.ipynb

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  {
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   "source": [
    "## Feature Selection using sklearn's SelectFromModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "from sklearn.linear_model import LassoCV\n",
    "from sklearn.datasets import load_boston"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# titanic dataset\n",
    "data = pd.read_csv('../Data/data_cleaned.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.616766\n",
       "1    0.383234\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#split data into train and test\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "\n",
    "# Seperate dependent and independent variable\n",
    "x = data.drop(['Survived'], axis = 1)\n",
    "y = data['Survived']\n",
    "\n",
    "train_X, test_X, train_y, test_y = train_test_split(x,y, stratify = y, random_state =34)\n",
    "train_y.value_counts(normalize = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use LassoCV as base model \n",
    "clf = LassoCV()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ram\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:2053: FutureWarning: You should specify a value for 'cv' instead of relying on the default value. The default value will change from 3 to 5 in version 0.22.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Feature Selection\n",
    "# Use threshold as the 'mean'\n",
    "sfm = SelectFromModel(clf, threshold = 'mean')\n",
    "sfm.fit(train_X,train_y)\n",
    "n_features = sfm.transform(train_X).shape[1]\n",
    "n_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2,  4,  5, 23], dtype=int64)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sfm.get_support(indices = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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