locomp_example.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "902290a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from locomp import *\n",
    "from locomp.MLmodels import *\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d11d16fa",
   "metadata": {},
   "source": [
    "## Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "12cb353a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOCOMP\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "from sklearn import preprocessing\n",
    "\n",
    "def SimuLinear(N,M,N1,SNR, seed=1):\n",
    "    np.random.seed(seed)\n",
    "    X =  np.random.normal(0, 1, (N, M))\n",
    "    np.random.rand(seed*2+1)\n",
    "    X1 =  np.random.normal(0, 1, (N1, M))\n",
    "    M1=int(0.05*M)\n",
    "    beta = np.append(np.append(np.array([SNR]),np.random.normal(5, 1,M1-1)),np.array([0]*(M-M1)))\n",
    "\n",
    "    Y = np.dot(X,beta)+np.random.normal(0, 1,N)\n",
    "    Y1 = np.dot(X1,beta)+np.random.normal(0, 1,N1)\n",
    "\n",
    "    Y = preprocessing.scale(Y)\n",
    "    Y1 = preprocessing.scale(Y1)\n",
    "\n",
    "    return [X,Y,X1,Y1]\n",
    "\n",
    "\n",
    "N=500 \n",
    "M=200\n",
    "B=100\n",
    "\n",
    "n_ratio=int(np.sqrt(N))/N\n",
    "m_ratio=int(np.sqrt(M))/M\n",
    "\n",
    "snr = 5\n",
    "itrial=1\n",
    "\n",
    "fit_func = DecisionTreeReg\n",
    "\n",
    "print('LOCOMP')\n",
    "X,Y,X1,Y1 =SimuLinear(N,M,B,snr,seed = 123*itrial+456)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "41fd5898",
   "metadata": {},
   "outputs": [
    {
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       "       [ 1.00000000e+00,  1.00000000e+00, -9.89421610e-03,\n",
       "         1.66463655e-03],\n",
       "       [ 1.00000000e+00,  1.00000000e+00, -4.44061071e-03,\n",
       "         5.21860955e-03],\n",
       "       [ 1.00000000e+00,  1.00000000e+00, -1.71275693e-03,\n",
       "         1.04466959e-02],\n",
       "       [ 1.00000000e+00,  1.00000000e+00, -3.04353789e-03,\n",
       "         4.20712371e-03],\n",
       "       [ 1.00000000e+00,  1.00000000e+00, -7.00663923e-03,\n",
       "         1.98890068e-03],\n",
       "       [ 1.00000000e+00,  1.00000000e+00, -4.14230524e-03,\n",
       "         4.84102599e-03],\n",
       "       [ 1.00000000e+00,  1.00000000e+00, -8.10809812e-03,\n",
       "         2.87246454e-03],\n",
       "       [ 1.00000000e+00,  1.00000000e+00, -6.62355536e-03,\n",
       "         2.70871099e-03],\n",
       "       [ 1.00000000e+00,  1.00000000e+00, -6.68306198e-03,\n",
       "         3.32619644e-03],\n",
       "       [ 1.00000000e+00,  1.00000000e+00, -2.35517283e-03,\n",
       "         3.95905307e-03],\n",
       "       [ 1.00000000e+00,  1.00000000e+00, -4.19196018e-03,\n",
       "         7.44964184e-03],\n",
       "       [ 1.00000000e+00,  1.00000000e+00, -6.05335000e-03,\n",
       "         1.37477668e-03],\n",
       "       [ 1.00000000e+00,  1.00000000e+00, -6.94583683e-03,\n",
       "         2.11142109e-03],\n",
       "       [ 1.00000000e+00,  1.00000000e+00, -3.04600093e-03,\n",
       "         2.01678783e-03],\n",
       "       [ 1.00000000e+00,  1.00000000e+00, -4.22619197e-03,\n",
       "         4.27576771e-03],\n",
       "       [ 7.23711368e-04,  1.44742274e-03,  1.49763210e-03,\n",
       "         1.18629192e-02],\n",
       "       [ 7.82612987e-02,  1.56522597e-01, -2.04330232e-04,\n",
       "         1.14661367e-02],\n",
       "       [ 1.00000000e+00,  1.00000000e+00, -5.15958316e-03,\n",
       "         1.49191296e-03],\n",
       "       [ 1.00000000e+00,  1.00000000e+00, -5.12389259e-03,\n",
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       "       [ 1.00000000e+00,  1.00000000e+00, -2.05294932e-03,\n",
       "         5.90882469e-03],\n",
       "       [ 1.00000000e+00,  1.00000000e+00, -1.97391403e-03,\n",
       "         6.51247027e-03],\n",
       "       [ 1.00000000e+00,  1.00000000e+00, -3.61141720e-03,\n",
       "         3.37026078e-03],\n",
       "       [ 1.00000000e+00,  1.00000000e+00, -9.30294944e-03,\n",
       "         4.23139154e-03],\n",
       "       [ 1.00000000e+00,  1.00000000e+00, -7.59247169e-03,\n",
       "         2.32351054e-03],\n",
       "       [ 1.00000000e+00,  1.00000000e+00, -3.16909740e-03,\n",
       "         3.65063017e-03],\n",
       "       [ 1.00000000e+00,  1.00000000e+00, -1.66791260e-03,\n",
       "         6.45208316e-03],\n",
       "       [ 1.00000000e+00,  1.00000000e+00, -4.99080807e-03,\n",
       "         5.29058771e-03],\n",
       "       [ 1.00000000e+00,  1.00000000e+00, -4.23811343e-03,\n",
       "         7.78609424e-03],\n",
       "       [ 1.00000000e+00,  1.00000000e+00, -6.71041115e-03,\n",
       "         4.45775103e-03],\n",
       "       [ 1.00000000e+00,  1.00000000e+00, -2.49901939e-03,\n",
       "         7.04193345e-03],\n",
       "       [ 1.00000000e+00,  1.00000000e+00, -8.45241895e-03,\n",
       "         2.08875992e-03],\n",
       "       [ 1.00000000e+00,  1.00000000e+00, -3.71533583e-03,\n",
       "         6.10250275e-03],\n",
       "       [ 1.00000000e+00,  1.00000000e+00, -5.45710105e-03,\n",
       "         1.50112311e-03],\n",
       "       [ 1.00000000e+00,  1.00000000e+00, -6.36807960e-03,\n",
       "         5.22920050e-03],\n",
       "       [ 1.00000000e+00,  1.00000000e+00, -4.56895190e-03,\n",
       "         3.44853383e-03],\n",
       "       [ 1.00000000e+00,  1.00000000e+00, -4.34703325e-03,\n",
       "         5.67984230e-03],\n",
       "       [ 1.00000000e+00,  1.00000000e+00, -5.10115212e-03,\n",
       "         5.02366379e-03],\n",
       "       [ 1.00000000e+00,  1.00000000e+00, -8.14390243e-03,\n",
       "         1.94808887e-03],\n",
       "       [ 1.00000000e+00,  1.00000000e+00, -3.80798415e-03,\n",
       "         5.38126092e-03],\n",
       "       [ 1.00000000e+00,  1.00000000e+00, -2.00592336e-03,\n",
       "         8.31286945e-03],\n",
       "       [ 1.00000000e+00,  1.00000000e+00, -4.39293604e-03,\n",
       "         6.47583193e-03],\n",
       "       [ 1.00000000e+00,  1.00000000e+00, -2.44522984e-03,\n",
       "         7.03399636e-03],\n",
       "       [ 1.00000000e+00,  1.00000000e+00, -5.89515990e-03,\n",
       "         3.23777680e-03],\n",
       "       [ 1.00000000e+00,  1.00000000e+00, -2.77595936e-03,\n",
       "         1.84614090e-03],\n",
       "       [ 1.00000000e+00,  1.00000000e+00, -1.94514733e-03,\n",
       "         8.22926100e-03],\n",
       "       [ 1.00000000e+00,  1.00000000e+00, -6.33406728e-03,\n",
       "         5.74071111e-03],\n",
       "       [ 1.00000000e+00,  1.00000000e+00, -2.71549716e-03,\n",
       "         5.44332153e-03],\n",
       "       [ 1.00000000e+00,  1.00000000e+00, -5.62595190e-03,\n",
       "         1.79341873e-03],\n",
       "       [ 1.00000000e+00,  1.00000000e+00, -5.89546272e-03,\n",
       "         2.41204203e-03],\n",
       "       [ 4.82508960e-02,  9.65017921e-02,  1.22631184e-05,\n",
       "         8.97330320e-03],\n",
       "       [ 1.00000000e+00,  1.00000000e+00, -5.30289376e-03,\n",
       "         3.18214537e-03],\n",
       "       [ 1.00000000e+00,  1.00000000e+00, -2.57516063e-03,\n",
       "         6.62445117e-03],\n",
       "       [ 1.61733972e-01,  3.23467944e-01, -4.05478949e-04,\n",
       "         8.20058644e-03],\n",
       "       [ 1.00000000e+00,  1.00000000e+00, -4.62282595e-03,\n",
       "         2.96606384e-03],\n",
       "       [ 1.00000000e+00,  1.00000000e+00, -5.16723362e-03,\n",
       "         2.09132358e-03],\n",
       "       [ 1.00000000e+00,  1.00000000e+00, -8.76474111e-03,\n",
       "         1.64135500e-03],\n",
       "       [ 1.00000000e+00,  1.00000000e+00, -7.81173040e-03,\n",
       "         4.34460708e-03]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=LOCOMPReg(X,Y,n_ratio,m_ratio,B,fit_func , selected_features=[],alpha=0.1,bonf=True)\n",
    "x.run_loco()\n",
    "x.loco_ci"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "headed-limitation",
   "metadata": {},
   "source": [
    "## correct variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "recognized-solomon",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.01097093591130105,\n",
       " 0.9999615693734116,\n",
       " 7.266937744584693e-05,\n",
       " 0.00044243574341685913]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.correct_variance(eps=0.00005)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bb404f7",
   "metadata": {},
   "source": [
    "## Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "32cde0ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOCOMP\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "def SimuLinearClass(N,M,N1,SNR,M1=5,seed=1):\n",
    "    np.random.seed(seed)\n",
    "    X =  np.random.normal(0, 1, (N, M))\n",
    "    np.random.rand(seed*2+1)\n",
    "    X1 =  np.random.normal(0, 1, (N1, M))\n",
    "    M1=int(0.05*M)\n",
    "    beta = np.append(np.append(np.array([SNR]),np.random.normal(5, 1,M1-1)),np.array([0]*(M-M1)))\n",
    "    pr = np.dot(X,beta)+np.random.normal(0, 1,N)\n",
    "    pr=(np.exp(pr)/(1+np.exp(pr)))\n",
    "    Y = np.random.binomial(1,pr)\n",
    "\n",
    "    pr1 = np.dot(X1,beta)+np.random.normal(0, 1,N1)\n",
    "    pr1= (np.exp(pr1))/(1+np.exp(pr1))\n",
    "    Y1 = np.random.binomial(1,pr1)\n",
    "    return (X,Y,X1,Y1)\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "N=500 \n",
    "M=200\n",
    "B=100\n",
    "\n",
    "n_ratio=int(np.sqrt(N))/N\n",
    "m_ratio=int(np.sqrt(M))/M\n",
    "\n",
    "snr = 5\n",
    "itrial=1\n",
    "\n",
    "fit_func = DecisionTreeClass\n",
    "\n",
    "print('LOCOMP')\n",
    "X,Y,X1,Y1 =SimuLinearClass(N,M,B,snr,seed = 123*itrial+456)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d8a5f995",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "         6.48510886e-03],\n",
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       "         5.31268172e-03],\n",
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       "         8.11865929e-04],\n",
       "       [ 1.00000000e+00,  1.00000000e+00, -1.53905688e-03,\n",
       "         1.36222401e-03],\n",
       "       [ 1.00000000e+00,  1.00000000e+00, -2.21500301e-03,\n",
       "         3.80220083e-04],\n",
       "       [ 1.00000000e+00,  1.00000000e+00, -2.32607952e-03,\n",
       "         1.10465552e-03],\n",
       "       [ 1.00000000e+00,  1.00000000e+00, -1.01012619e-03,\n",
       "         3.58881716e-03],\n",
       "       [ 2.57223776e-02,  5.14447552e-02,  6.15815699e-05,\n",
       "         2.52252419e-03],\n",
       "       [ 1.00000000e+00,  1.00000000e+00, -2.13294069e-03,\n",
       "         3.21418178e-03],\n",
       "       [ 1.00000000e+00,  1.00000000e+00, -3.74483558e-03,\n",
       "         7.62697469e-04],\n",
       "       [ 1.00000000e+00,  1.00000000e+00, -2.50519279e-03,\n",
       "         1.12733218e-03],\n",
       "       [ 1.00000000e+00,  3.26845595e-02, -2.21744618e-03,\n",
       "        -8.83786372e-05],\n",
       "       [ 1.00000000e+00,  1.00000000e+00, -1.74001743e-03,\n",
       "         1.55607272e-03],\n",
       "       [ 1.00000000e+00,  1.00000000e+00, -4.98435566e-04,\n",
       "         2.04017271e-03],\n",
       "       [ 1.00000000e+00,  4.67431961e-04, -5.22761209e-03,\n",
       "        -7.90959448e-04]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=LOCOMPClass(X,Y,n_ratio,m_ratio,B,fit_func , selected_features=[],alpha=0.1,bonf=True)\n",
    "x.run_loco()\n",
    "x.loco_ci\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "be867940",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'ztest_adjust' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-4-d4eda7b69435>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcorrect_variance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0meps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.00005\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/Documents/Documents - Alice/CIMP/LOCOMP/locomp/_loco_classification.py\u001b[0m in \u001b[0;36mcorrect_variance\u001b[0;34m(self, eps, *args, **kwargs)\u001b[0m\n\u001b[1;32m    113\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mcorrect_variance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0meps\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    114\u001b[0m         \u001b[0mvar\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdiff\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_ratio\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0meps\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 115\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mztest_adjust\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'zz'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvar\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvar\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    116\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'ztest_adjust' is not defined"
     ]
    }
   ],
   "source": [
    "x.correct_variance(eps=0.00005)"
   ]
  }
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