{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import ElasticNetCV, ElasticNet, Ridge\n",
    "from sklearn.model_selection import learning_curve, ShuffleSplit\n",
    "from sklearn.model_selection import GridSearchCV, cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_samples = 500\n",
    "n_features = 500\n",
    "all_inputs = np.random.randn(n_samples, n_features)\n",
    "\n",
    "# Ground truth model: linear combination of the first two variables\n",
    "w_true = np.zeros(n_features)\n",
    "w_true[:2] = 1.\n",
    "y = np.dot(all_inputs, w_true)\n",
    "\n",
    "# Partial observation: the first informative variable cannot be observed\n",
    "X = all_inputs[:, 1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500,)"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9JDXO0EtS4/4P+m0iZ8KcIzMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f22d25bf9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(y);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500, 499)"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "gs_ridge = GridSearchCV(Ridge(), {'alpha': np.logspace(-6, 2, 20)},\n",
    "                        scoring='neg_median_absolute_error', cv=10).fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'alpha': 100.0}"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs_ridge.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.88770978619896734"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs_ridge.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "enet_params = {\n",
    "    'alpha': np.logspace(-6, -1, 6),\n",
    "    'l1_ratio': [0.1, 0.2, 0.3],\n",
    "}\n",
    "gs_enet = GridSearchCV(ElasticNet(max_iter=10000), enet_params,\n",
    "                       scoring='neg_median_absolute_error', cv=10, n_jobs=10)\n",
    "gs_enet.fit(X, y);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.62234474540259543"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs_enet.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'alpha': 0.10000000000000001, 'l1_ratio': 0.3}"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs_enet.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,\n",
    "                        n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5)):\n",
    "    plt.figure()\n",
    "    plt.title(title)\n",
    "    if ylim is not None:\n",
    "        plt.ylim(*ylim)\n",
    "    plt.xlabel(\"Training examples\")\n",
    "    plt.ylabel(\"Score\")\n",
    "    train_sizes, train_scores, test_scores = learning_curve(\n",
    "        estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes,\n",
    "        scoring='neg_median_absolute_error')\n",
    "    train_scores_mean = np.mean(train_scores, axis=1)\n",
    "    train_scores_std = np.std(train_scores, axis=1)\n",
    "    test_scores_mean = np.mean(test_scores, axis=1)\n",
    "    test_scores_std = np.std(test_scores, axis=1)\n",
    "    plt.grid()\n",
    "\n",
    "    plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\n",
    "                     train_scores_mean + train_scores_std, alpha=0.1,\n",
    "                     color=\"r\")\n",
    "    plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\n",
    "                     test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n",
    "    plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\",\n",
    "             label=\"Training score\")\n",
    "    plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\",\n",
    "             label=\"Cross-validation score\")\n",
    "\n",
    "    plt.legend(loc=\"best\")\n",
    "    return plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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etCg8JxuZNT+/YrjEX3frVh4u3buH8cfiR1YKGKljCg6RurCz61VuvrnqlfZt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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f22ad289390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "title = \"Learning Curves (ElasticNet)\"\n",
    "cv = ShuffleSplit(n_splits=50, test_size=0.1, random_state=0)\n",
    "enet = ElasticNet(alpha=1e-1, l1_ratio=0.3, max_iter=10000)\n",
    "plot_learning_curve(enet, title, X, y, ylim=(-2, 0.1), cv=cv, n_jobs=4);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MAP5uZs+H83uZ2TPhaj2AV81sAfA28Hd3fy4b5RURkWrZuqtqBjAjxfxVwKnh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\nEgWHiIhE8v/cs+rOeXTa3QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f231c47eef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "title = \"Learning Curves (Ridge)\"\n",
    "cv = ShuffleSplit(n_splits=50, test_size=0.1, random_state=0)\n",
    "ridge = Ridge(alpha=100)\n",
    "plot_learning_curve(ridge, title, X, y, ylim=(-2, 0.1), cv=cv, n_jobs=4);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}