LASSO_all_Features.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random\n",
    "from sklearn import linear_model\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def to_list(df_dict, heads):\n",
    "    # {key: [], ...}\n",
    "    out = {}\n",
    "    for _key in list(heads):\n",
    "        out[_key] = [df_dict[_key][key] for key in df_dict[_key].keys()]\n",
    "    return out\n",
    "\n",
    "def encode_input(data_range, data):\n",
    "    return data_range.index(data)\n",
    "\n",
    "def preprocess_data(df_list: dict, need_encoded: list, normalize=True) -> np.ndarray:\n",
    "    output = []\n",
    "    data_length = len(df_list['id'])\n",
    "    data_ranges = {k : list(set(df_list[k])) for k in need_encoded}\n",
    "    for i in range(data_length):\n",
    "        item = []\n",
    "        for key in list(df_list.keys())[1:-1]:\n",
    "            if key in need_encoded:\n",
    "                item.append(encode_input(data_ranges[key], df_list[key][i]))\n",
    "            else:\n",
    "                item.append(df_list[key][i])\n",
    "        output.append(item)\n",
    "    inputs_arr = np.array(output)\n",
    "    targets_arr = np.array(df_list['stroke'])\n",
    "    if normalize:\n",
    "        _range = np.max(inputs_arr, axis=0) - np.min(inputs_arr, axis=0)\n",
    "        inputs_arr = (inputs_arr-np.min(inputs_arr, axis=0)) / _range\n",
    "    return inputs_arr, targets_arr\n",
    "\n",
    "def prepare_data(inputs, targets, seed=1001):\n",
    "    positive_mask = targets == 1\n",
    "    negative_mask = targets == 0\n",
    "    n_minimum = min(np.sum(positive_mask), np.sum(negative_mask))\n",
    "    positive_inputs = inputs[positive_mask][0:n_minimum, :]\n",
    "    positive_targets = targets[positive_mask][0:n_minimum]\n",
    "    negative_inputs = inputs[negative_mask][0:n_minimum, :]\n",
    "    negative_targets = targets[negative_mask][0:n_minimum]\n",
    "    inputs = np.concatenate([positive_inputs, negative_inputs]).tolist()\n",
    "    targets = np.concatenate([positive_targets, negative_targets]).tolist()\n",
    "    np.random.seed(seed)\n",
    "    np.random.shuffle(inputs)\n",
    "    np.random.seed(seed)\n",
    "    np.random.shuffle(targets)\n",
    "    return np.array(inputs), np.array(targets)\n",
    "\n",
    "def metrics(y_pred, y_true):\n",
    "    _confusion_matrix = confusion_matrix(y_pred, y_true)\n",
    "    tp = _confusion_matrix[0,0]\n",
    "    fn = _confusion_matrix[1,0]\n",
    "    fp = _confusion_matrix[0,1]\n",
    "    tn = _confusion_matrix[1,1]\n",
    "    # metrics\n",
    "    precision = tp/(tp+fp)\n",
    "    recall = tp/(tp+fn)\n",
    "    fscore = 2*tp/(2*tp + fp + fn)\n",
    "    accuracy = (tp+tn)/(tp+tn+fp+fn)\n",
    "    miss_rate = fn/(tn+tp)\n",
    "    fall_out_rate = fp/(fp+tn)\n",
    "    # return \n",
    "    return [precision, recall, fscore, accuracy, miss_rate, fall_out_rate]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = './dataset/train_2v.csv'\n",
    "df = pd.read_csv(path)\n",
    "df_clear = df.dropna(axis=0)\n",
    "df_dict = df_clear.to_dict()\n",
    "heads = list(df_dict.keys())\n",
    "df_list = to_list(df_dict, heads)\n",
    "need_encoded = ['gender', 'ever_married', 'work_type', 'Residence_type', 'smoking_status']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 complete [0.7586206896551724, 0.6586826347305389, 0.7051282051282052, 0.7203647416413373, 0.24050632911392406, 0.21604938271604937]\n",
      "2 complete [0.782051282051282, 0.7134502923976608, 0.746177370030581, 0.7477203647416414, 0.1991869918699187, 0.21518987341772153]\n",
      "3 complete [0.7697368421052632, 0.73125, 0.75, 0.7629179331306991, 0.17131474103585656, 0.20710059171597633]\n",
      "4 complete [0.8, 0.7100591715976331, 0.7523510971786834, 0.7598784194528876, 0.196, 0.1875]\n",
      "5 complete [0.7571428571428571, 0.6625, 0.7066666666666667, 0.7325227963525835, 0.22406639004149378, 0.20118343195266272]\n",
      "6 complete [0.7533333333333333, 0.6932515337423313, 0.7220447284345048, 0.7355623100303952, 0.2066115702479339, 0.22289156626506024]\n",
      "7 complete [0.7243589743589743, 0.6932515337423313, 0.7084639498432602, 0.7173252279635258, 0.211864406779661, 0.25903614457831325]\n",
      "8 complete [0.7368421052631579, 0.7320261437908496, 0.7344262295081967, 0.7537993920972644, 0.16532258064516128, 0.22727272727272727]\n",
      "9 complete [0.8322147651006712, 0.7380952380952381, 0.7823343848580442, 0.790273556231003, 0.16923076923076924, 0.15527950310559005]\n",
      "10 complete [0.8461538461538461, 0.7202380952380952, 0.7781350482315113, 0.790273556231003, 0.18076923076923077, 0.13664596273291926]\n",
      "11 complete [0.7643312101910829, 0.75, 0.7570977917981072, 0.7659574468085106, 0.15873015873015872, 0.21893491124260356]\n",
      "12 complete [0.7959183673469388, 0.7222222222222222, 0.7572815533980582, 0.7720364741641338, 0.17716535433070865, 0.17964071856287425]\n",
      "13 complete [0.8137931034482758, 0.6742857142857143, 0.7375, 0.7446808510638298, 0.23265306122448978, 0.17532467532467533]\n",
      "14 complete [0.7870967741935484, 0.7577639751552795, 0.7721518987341772, 0.7811550151975684, 0.1517509727626459, 0.19642857142857142]\n",
      "15 complete [0.7482993197278912, 0.738255033557047, 0.7432432432432432, 0.7689969604863222, 0.1541501976284585, 0.20555555555555555]\n",
      "16 complete [0.8193548387096774, 0.7341040462427746, 0.774390243902439, 0.7750759878419453, 0.1803921568627451, 0.1794871794871795]\n",
      "17 complete [0.7848101265822784, 0.7045454545454546, 0.7425149700598802, 0.7386018237082067, 0.2139917695473251, 0.2222222222222222]\n",
      "18 complete [0.785234899328859, 0.7597402597402597, 0.7722772277227723, 0.790273556231003, 0.1423076923076923, 0.18285714285714286]\n",
      "19 complete [0.7571428571428571, 0.6794871794871795, 0.7162162162162162, 0.7446808510638298, 0.20408163265306123, 0.19653179190751446]\n",
      "20 complete [0.7423312883435583, 0.7378048780487805, 0.7400611620795107, 0.7416413373860182, 0.1762295081967213, 0.2545454545454545]\n",
      "21 complete [0.7515923566878981, 0.7421383647798742, 0.7468354430379747, 0.756838905775076, 0.1646586345381526, 0.22941176470588234]\n",
      "22 complete [0.75, 0.6851851851851852, 0.7161290322580646, 0.7325227963525835, 0.21161825726141079, 0.2215568862275449]\n",
      "23 complete [0.7928571428571428, 0.7302631578947368, 0.7602739726027398, 0.7872340425531915, 0.1583011583011583, 0.1638418079096045]\n",
      "24 complete [0.8266666666666667, 0.6966292134831461, 0.7560975609756098, 0.756838905775076, 0.21686746987951808, 0.17218543046357615]\n",
      "25 complete [0.7924528301886793, 0.7078651685393258, 0.7477744807121661, 0.7416413373860182, 0.21311475409836064, 0.2185430463576159]\n",
      "26 complete [0.7697368421052632, 0.7005988023952096, 0.7335423197492164, 0.7416413373860182, 0.20491803278688525, 0.21604938271604937]\n",
      "27 complete [0.7763975155279503, 0.7621951219512195, 0.7692307692307693, 0.7720364741641338, 0.15354330708661418, 0.21818181818181817]\n",
      "28 complete [0.8, 0.7126436781609196, 0.7537993920972644, 0.7537993920972644, 0.20161290322580644, 0.2]\n",
      "29 complete [0.8129032258064516, 0.7411764705882353, 0.7753846153846153, 0.7781155015197568, 0.171875, 0.18238993710691823]\n",
      "30 complete [0.8802816901408451, 0.7062146892655368, 0.7836990595611285, 0.790273556231003, 0.2, 0.1118421052631579]\n",
      "31 complete [0.7737226277372263, 0.6751592356687898, 0.7210884353741497, 0.7507598784194529, 0.20647773279352227, 0.18023255813953487]\n",
      "32 complete [0.7039473684210527, 0.6903225806451613, 0.6970684039087948, 0.7173252279635258, 0.2033898305084746, 0.25862068965517243]\n",
      "33 complete [0.82, 0.7409638554216867, 0.7784810126582279, 0.7872340425531915, 0.16602316602316602, 0.1656441717791411]\n",
      "34 complete [0.7919463087248322, 0.7329192546583851, 0.7612903225806451, 0.7750759878419453, 0.16862745098039217, 0.18452380952380953]\n",
      "35 complete [0.7096774193548387, 0.7051282051282052, 0.707395498392283, 0.723404255319149, 0.19327731092436976, 0.26011560693641617]\n",
      "36 complete [0.7894736842105263, 0.6976744186046512, 0.7407407407407407, 0.7446808510638298, 0.21224489795918366, 0.20382165605095542]\n",
      "37 complete [0.7682119205298014, 0.7388535031847133, 0.7532467532467533, 0.7689969604863222, 0.16205533596837945, 0.20348837209302326]\n",
      "38 complete [0.8095238095238095, 0.6918604651162791, 0.7460815047021944, 0.7537993920972644, 0.21370967741935484, 0.17834394904458598]\n",
      "39 complete [0.7448275862068966, 0.675, 0.7081967213114754, 0.729483282674772, 0.21666666666666667, 0.21893491124260356]\n",
      "40 complete [0.7763975155279503, 0.7621951219512195, 0.7692307692307693, 0.7720364741641338, 0.15354330708661418, 0.21818181818181817]\n",
      "41 complete [0.8449612403100775, 0.6449704142011834, 0.7315436241610739, 0.756838905775076, 0.24096385542168675, 0.125]\n",
      "42 complete [0.756578947368421, 0.7098765432098766, 0.732484076433121, 0.7446808510638298, 0.19183673469387755, 0.2215568862275449]\n",
      "43 complete [0.8439716312056738, 0.6761363636363636, 0.750788643533123, 0.7598784194528876, 0.228, 0.1437908496732026]\n",
      "44 complete [0.7469879518072289, 0.775, 0.7607361963190185, 0.7629179331306991, 0.14342629482071714, 0.2485207100591716]\n",
      "45 complete [0.785234899328859, 0.7597402597402597, 0.7722772277227723, 0.790273556231003, 0.1423076923076923, 0.18285714285714286]\n",
      "46 complete [0.7468354430379747, 0.7108433734939759, 0.7283950617283951, 0.7325227963525835, 0.1991701244813278, 0.24539877300613497]\n",
      "47 complete [0.7633587786259542, 0.6622516556291391, 0.7092198581560284, 0.7507598784194529, 0.20647773279352227, 0.17415730337078653]\n",
      "48 complete [0.7935483870967742, 0.7409638554216867, 0.7663551401869159, 0.7720364741641338, 0.16929133858267717, 0.19631901840490798]\n",
      "49 complete [0.8629032258064516, 0.6604938271604939, 0.7482517482517482, 0.7811550151975684, 0.2140077821011673, 0.10179640718562874]\n",
      "50 complete [0.7142857142857143, 0.7333333333333333, 0.7236842105263158, 0.7446808510638298, 0.16326530612244897, 0.24581005586592178]\n",
      "51 complete [0.7870967741935484, 0.7577639751552795, 0.7721518987341772, 0.7811550151975684, 0.1517509727626459, 0.19642857142857142]\n",
      "52 complete [0.7847222222222222, 0.6975308641975309, 0.738562091503268, 0.756838905775076, 0.19678714859437751, 0.18562874251497005]\n",
      "53 complete [0.7651006711409396, 0.7125, 0.7378640776699029, 0.7537993920972644, 0.18548387096774194, 0.20710059171597633]\n",
      "54 complete [0.8164556962025317, 0.7371428571428571, 0.7747747747747747, 0.7720364741641338, 0.18110236220472442, 0.18831168831168832]\n",
      "55 complete [0.8639455782312925, 0.7514792899408284, 0.8037974683544303, 0.8115501519756839, 0.15730337078651685, 0.125]\n",
      "56 complete [0.8258064516129032, 0.6956521739130435, 0.7551622418879056, 0.7477203647416414, 0.22764227642276422, 0.18620689655172415]\n",
      "57 complete [0.8111888111888111, 0.725, 0.7656765676567657, 0.78419452887538, 0.17054263565891473, 0.15976331360946747]\n",
      "58 complete [0.8193548387096774, 0.774390243902439, 0.7962382445141066, 0.8024316109422492, 0.14015151515151514, 0.1696969696969697]\n",
      "59 complete [0.7744360902255639, 0.6242424242424243, 0.6912751677852349, 0.7203647416413373, 0.2616033755274262, 0.18292682926829268]\n",
      "60 complete [0.8269230769230769, 0.7413793103448276, 0.7818181818181819, 0.7811550151975684, 0.17509727626459143, 0.17419354838709677]\n",
      "61 complete [0.7672955974842768, 0.7530864197530864, 0.7601246105919003, 0.7659574468085106, 0.15873015873015872, 0.2215568862275449]\n",
      "62 complete [0.774390243902439, 0.7604790419161677, 0.7673716012084593, 0.7659574468085106, 0.15873015873015872, 0.22839506172839505]\n",
      "63 complete [0.74375, 0.7083333333333334, 0.725609756097561, 0.7264437689969605, 0.20502092050209206, 0.2546583850931677]\n",
      "64 complete [0.8152866242038217, 0.735632183908046, 0.7734138972809668, 0.7720364741641338, 0.18110236220472442, 0.1870967741935484]\n",
      "65 complete [0.8243243243243243, 0.7218934911242604, 0.7697160883280757, 0.7781155015197568, 0.18359375, 0.1625]\n",
      "66 complete [0.7763975155279503, 0.7621951219512195, 0.7692307692307693, 0.7720364741641338, 0.15354330708661418, 0.21818181818181817]\n",
      "67 complete [0.756578947368421, 0.7615894039735099, 0.759075907590759, 0.7781155015197568, 0.140625, 0.20786516853932585]\n",
      "68 complete [0.7816901408450704, 0.6981132075471698, 0.7375415282392026, 0.7598784194528876, 0.192, 0.18235294117647058]\n",
      "69 complete [0.7682119205298014, 0.6946107784431138, 0.7295597484276729, 0.7386018237082067, 0.20987654320987653, 0.21604938271604937]\n",
      "70 complete [0.7792207792207793, 0.7361963190184049, 0.7570977917981072, 0.7659574468085106, 0.17063492063492064, 0.20481927710843373]\n",
      "71 complete [0.7986111111111112, 0.7055214723926381, 0.749185667752443, 0.7659574468085106, 0.19047619047619047, 0.1746987951807229]\n",
      "72 complete [0.7835820895522388, 0.6481481481481481, 0.7094594594594594, 0.7386018237082067, 0.2345679012345679, 0.17365269461077845]\n",
      "73 complete [0.7931034482758621, 0.7055214723926381, 0.7467532467532467, 0.7629179331306991, 0.19123505976095617, 0.18072289156626506]\n",
      "74 complete [0.7931034482758621, 0.688622754491018, 0.7371794871794872, 0.7507598784194529, 0.21052631578947367, 0.18518518518518517]\n",
      "75 complete [0.7297297297297297, 0.7152317880794702, 0.7224080267558528, 0.7477203647416414, 0.17479674796747968, 0.2247191011235955]\n",
      "76 complete [0.7635135135135135, 0.6766467065868264, 0.7174603174603175, 0.729483282674772, 0.225, 0.21604938271604937]\n",
      "77 complete [0.7368421052631579, 0.7320261437908496, 0.7344262295081967, 0.7537993920972644, 0.16532258064516128, 0.22727272727272727]\n",
      "78 complete [0.7898089171974523, 0.7654320987654321, 0.7774294670846394, 0.78419452887538, 0.14728682170542637, 0.19760479041916168]\n",
      "79 complete [0.7891566265060241, 0.7797619047619048, 0.7844311377245509, 0.7811550151975684, 0.14396887159533073, 0.21739130434782608]\n",
      "80 complete [0.743421052631579, 0.7290322580645161, 0.7361563517915309, 0.7537993920972644, 0.1693548387096774, 0.22413793103448276]\n",
      "81 complete [0.754601226993865, 0.75, 0.7522935779816514, 0.7537993920972644, 0.16532258064516128, 0.24242424242424243]\n",
      "82 complete [0.7919463087248322, 0.7329192546583851, 0.7612903225806451, 0.7750759878419453, 0.16862745098039217, 0.18452380952380953]\n",
      "83 complete [0.7518248175182481, 0.6518987341772152, 0.6983050847457627, 0.729483282674772, 0.22916666666666666, 0.19883040935672514]\n",
      "84 complete [0.7933333333333333, 0.7212121212121212, 0.7555555555555555, 0.7659574468085106, 0.18253968253968253, 0.18902439024390244]\n",
      "85 complete [0.7368421052631579, 0.7320261437908496, 0.7344262295081967, 0.7537993920972644, 0.16532258064516128, 0.22727272727272727]\n",
      "86 complete [0.7785234899328859, 0.6904761904761905, 0.7318611987381703, 0.7416413373860182, 0.21311475409836064, 0.20496894409937888]\n",
      "87 complete [0.7866666666666666, 0.7108433734939759, 0.7468354430379747, 0.756838905775076, 0.1927710843373494, 0.19631901840490798]\n",
      "88 complete [0.8066666666666666, 0.7333333333333333, 0.7682539682539683, 0.7781155015197568, 0.171875, 0.17682926829268292]\n",
      "89 complete [0.8, 0.7126436781609196, 0.7537993920972644, 0.7537993920972644, 0.20161290322580644, 0.2]\n",
      "90 complete [0.8076923076923077, 0.7368421052631579, 0.7706422018348624, 0.7720364741641338, 0.17716535433070865, 0.189873417721519]\n",
      "91 complete [0.7450980392156863, 0.7080745341614907, 0.7261146496815286, 0.7386018237082067, 0.1934156378600823, 0.23214285714285715]\n",
      "92 complete [0.7414965986394558, 0.6646341463414634, 0.7009646302250804, 0.7173252279635258, 0.2330508474576271, 0.23030303030303031]\n",
      "93 complete [0.7843137254901961, 0.7547169811320755, 0.7692307692307693, 0.7811550151975684, 0.1517509727626459, 0.19411764705882353]\n",
      "94 complete [0.7935483870967742, 0.7068965517241379, 0.7477203647416414, 0.7477203647416414, 0.2073170731707317, 0.2064516129032258]\n",
      "95 complete [0.78, 0.7358490566037735, 0.7572815533980582, 0.7720364741641338, 0.16535433070866143, 0.19411764705882353]\n",
      "96 complete [0.7843137254901961, 0.7058823529411765, 0.7430340557275542, 0.7477203647416414, 0.2032520325203252, 0.20754716981132076]\n",
      "97 complete [0.8103448275862069, 0.7621621621621621, 0.7855153203342619, 0.7659574468085106, 0.1746031746031746, 0.22916666666666666]\n",
      "98 complete [0.7792207792207793, 0.7228915662650602, 0.75, 0.756838905775076, 0.18473895582329317, 0.2085889570552147]\n",
      "99 complete [0.8050314465408805, 0.7441860465116279, 0.7734138972809668, 0.7720364741641338, 0.1732283464566929, 0.19745222929936307]\n",
      "100 complete [0.7651006711409396, 0.6785714285714286, 0.7192429022082019, 0.729483282674772, 0.225, 0.21739130434782608]\n"
     ]
    }
   ],
   "source": [
    "# Lasso\n",
    "n_repeat = 100\n",
    "_metrics = []\n",
    "\n",
    "for i in range(n_repeat):\n",
    "    # generate data\n",
    "    seed = random.randint(1, 1000)\n",
    "    inputs, targets = preprocess_data(df_list, need_encoded)\n",
    "    inputs, targets = prepare_data(inputs, targets, seed)\n",
    "    n_samples = inputs.shape[0]\n",
    "    tr_inputs = inputs[0:int(n_samples*0.7), :]\n",
    "    tr_targets = targets[0:int(n_samples*0.7)]\n",
    "    te_inputs = inputs[int(n_samples*0.7):, :]\n",
    "    te_targets = targets[int(n_samples*0.7):]\n",
    "    # train clf\n",
    "    lasso_model = linear_model.LassoCV()\n",
    "    lasso_model.fit(tr_inputs, tr_targets)\n",
    "    # evaluate clf\n",
    "    te_pred = lasso_model.predict(te_inputs)\n",
    "    te_pred[te_pred>0.5] = 1\n",
    "    te_pred[te_pred<0.5] = 0\n",
    "    run_metrics = metrics(te_pred, te_targets)\n",
    "    print(i+1, \"complete\", run_metrics)\n",
    "    _metrics.append(run_metrics)\n",
    "\n",
    "run_metrics = np.array(_metrics)\n",
    "mean_metrics = np.mean(run_metrics, axis=0)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "========= MEAN OF 100 Experiments ========\n",
      "\n",
      "precision 0.7823766367859758\n",
      "recall 0.7173082447332974\n",
      "fscore 0.7477451750393221\n",
      "accuracy 0.7587537993920974\n",
      "miss_rate 0.18680683950967653\n",
      "fall_out_rate 0.19938307274013833\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n\\n\\n========= MEAN OF 100 Experiments ========\\n\")\n",
    "# 100 experiments => run_metrics\n",
    "for i, key in enumerate([\"precision\", \"recall\", \"fscore\", \"accuracy\", \"miss_rate\", \"fall_out_rate\"]): \n",
    "    print(key, mean_metrics[i])\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "f07d5c506dc792c1d17042ce6d63d3539913070c7203ee1d707a2b2ce1ee992d"
  },
  "kernelspec": {
   "display_name": "Python 3.8.5 64-bit ('pytorch': conda)",
   "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": "3.8.5"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}