alo.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import itertools\n",
    "import functools\n",
    "import multiprocessing\n",
    "import tqdm\n",
    "import scipy.linalg\n",
    "import sklearn.linear_model\n",
    "\n",
    "import alo\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def risk_outsample(beta, beta_truth, k, sigma):\n",
    "    return np.sum((beta - beta_truth) ** 2, axis=-1) / k + sigma ** 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "n, p, k, d, sigma = 800, 400, 160, 2, 0.5\n",
    "(X, C, y), beta = alo.generate_data(n, p, k, d, sigma, seed=43)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "beta_sol, lambda_sol = alo.solve_standard(X, C, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "100%|██████████| 800/800 [01:15<00:00, 10.53it/s]\n"
     ]
    }
   ],
   "source": [
    "beta_loo, lambda_sol = alo.solve_loo(X, C, y, progress=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "risk_loo = np.square(np.expand_dims(y, -1) - (beta_loo @ np.expand_dims(X, -1)).squeeze(-1)).mean(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "risk_alo, df = alo.alo_classo_risk(X, C, y, beta_sol)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "ax.loglog(lambda_sol, risk_outsample(beta_sol, beta, k, sigma), lambda_sol, risk_loo, lambda_sol, risk_alo)\n",
    "ax.legend(['oos', 'loo', 'alo'])\n",
    "fig.suptitle('c-lasso')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#fig.savefig(f'c_lasso_alo_n{n}_p{p}.pdf', )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}