supervised_elastic_net_grid_search.ipynb

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    "## Elastic Net Example\n",
    "\n",
    "ElasticNet regression uses a combination of L1 and L2 penalties to regularize the model (a∗L1+b∗L2)\n",
    "\n",
    "[unlike Lasso, which uses only L1 penalty and Ridge which uses only L2 penalty]\n",
    "\n",
    "Data Source: sample_data/diabetes.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
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   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
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   "cell_type": "code",
   "execution_count": 2,
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   "source": [
    "df = pd.read_csv('sample_data/diabetes.csv')\n",
    "\n",
    "X = df.drop(['diabetes'], axis=1).values\n",
    "y = df['diabetes'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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   "source": [
    "from sklearn.linear_model import ElasticNet\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
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      "/Users/ajuljulian/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/ajuljulian/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/ajuljulian/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/ajuljulian/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/ajuljulian/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n"
     ]
    },
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     "text": [
      "Tuned ElasticNet l1 ratio: {'l1_ratio': 0.0}\n",
      "Tuned ElasticNet R squared: 0.24765337510702687\n",
      "Tuned ElasticNet MSE: 0.16664179543611013\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "/Users/ajuljulian/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=42)\n",
    "\n",
    "# Create the hyperparameter grid.  Here, we vary the L1 ratio between 0 and 1.\n",
    "# An 'l1_ratio' of 1 corresponds to an L1 penalty, 0 corresponds to an L2 penalty, and anything\n",
    "# in between is a combination of both.\n",
    "l1_space = np.linspace(0, 1, 30)\n",
    "param_grid = {'l1_ratio': l1_space}\n",
    "\n",
    "# Instantiate the ElasticNet regressor: elastic_net\n",
    "elastic_net = ElasticNet()\n",
    "\n",
    "# Setup the GridSearchCV object: gm_cv\n",
    "gm_cv = GridSearchCV(elastic_net, param_grid, cv=5)\n",
    "\n",
    "# Fit it to the training data\n",
    "gm_cv.fit(X_train, y_train)\n",
    "\n",
    "# Predict on the test set and compute metrics\n",
    "y_pred = gm_cv.predict(X_test)\n",
    "r2 = gm_cv.score(X_test, y_test)\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "print(\"Tuned ElasticNet l1 ratio: {}\".format(gm_cv.best_params_))\n",
    "print(\"Tuned ElasticNet R squared: {}\".format(r2))\n",
    "print(\"Tuned ElasticNet MSE: {}\".format(mse))\n"
   ]
  }
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