model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "from matplotlib.ticker import (MultipleLocator, AutoMinorLocator)\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from matplotlib.markers import MarkerStyle\n",
    "from sklearn.metrics import ConfusionMatrixDisplay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# seen: train and val. unseen: test.\n",
    "d = pd.read_csv('data.csv')\n",
    "colnum = [7,9,10,11,14,15] #peakmag/duration/rise/fade/b/A_V\n",
    "data_seen, data_test, class_seen, class_test = train_test_split(d[d.columns[colnum]], d['class'], train_size = 0.9, random_state = 12345)\n",
    "data_train, data_val, class_train, class_val = train_test_split(data_seen, class_seen, train_size = 0.9, random_state = 12345)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=20)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=20)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=20)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#set up model\n",
    "model = KNeighborsClassifier(n_neighbors = 20, weights = 'uniform', p = 2)\n",
    "model.fit(data_train, class_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "414\n",
      "0.9366515837104072\n"
     ]
    }
   ],
   "source": [
    "#get number of correct classifications, probabilities\n",
    "probabilities = model.predict_proba(data_val)\n",
    "numcorrect = 0\n",
    "for index,row in enumerate(probabilities):\n",
    "    if row[1] >= 0.5 and class_val.to_numpy()[index] == 1:\n",
    "        numcorrect += 1\n",
    "    elif row[0] >= 0.5 and class_val.to_numpy()[index] == 0:\n",
    "        numcorrect += 1\n",
    "print(numcorrect)\n",
    "print(numcorrect / len(probabilities))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True positives: 355\n",
      "False positives: 25\n",
      "True negative: 58\n",
      "False negatives: 4\n",
      "Score: 0.9343891402714932\n",
      "Precision: 0.9342105263157895\n",
      "Recall: 0.9888579387186629\n"
     ]
    }
   ],
   "source": [
    "# calculate true/false positives/negatives, and keep track of incorrect classifications to do further analysis\n",
    "predictions = model.predict(data_val)\n",
    "tp, tn, fp, fn = 0, 0, 0, 0\n",
    "incorrect = []\n",
    "d = pd.read_csv('data.csv')\n",
    "c = class_val.to_numpy()\n",
    "for idx,p in enumerate(predictions):\n",
    "    if p == 1 and c[idx] == 1:\n",
    "        tp += 1\n",
    "    elif p == 1 and c[idx] == 0:\n",
    "        fp += 1\n",
    "        incorrect.append(d.loc[data_val.index[idx]])\n",
    "    elif p == 0 and c[idx] == 0:\n",
    "        tn += 1\n",
    "    elif p == 0 and c[idx] == 1:\n",
    "        fn += 1\n",
    "        incorrect.append(d.loc[data_val.index[idx]])\n",
    "print(f'True positives: {tp}\\nFalse positives: {fp}\\nTrue negative: {tn}\\nFalse negatives: {fn}')\n",
    "print(f'Score: {model.score(data_val, class_val)}')\n",
    "print(f'Precision: {tp/(tp+fp)}')\n",
    "print(f'Recall: {tp/(tp+fn)}')\n",
    "incorrectdf = pd.DataFrame(incorrect)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True positives: 399\n",
      "False positives: 22\n",
      "True negative: 63\n",
      "False negatives: 7\n",
      "Score: 0.9409368635437881\n",
      "Precision: 0.9477434679334917\n",
      "Recall: 0.9827586206896551\n"
     ]
    }
   ],
   "source": [
    "# calculate true/false positives/negatives\n",
    "predictions = model.predict(data_test)\n",
    "tp, tn, fp, fn = 0, 0, 0, 0\n",
    "c = class_test.to_numpy()\n",
    "for idx,p in enumerate(predictions):\n",
    "    if p == 1 and c[idx] == 1:\n",
    "        tp += 1\n",
    "    elif p == 1 and c[idx] == 0:\n",
    "        fp += 1\n",
    "    elif p == 0 and c[idx] == 0:\n",
    "        tn += 1\n",
    "    elif p == 0 and c[idx] == 1:\n",
    "        fn += 1\n",
    "print(f'True positives: {tp}\\nFalse positives: {fp}\\nTrue negative: {tn}\\nFalse negatives: {fn}')\n",
    "print(f'Score: {model.score(data_test, class_test)}')\n",
    "print(f'Precision: {tp/(tp+fp)}')\n",
    "print(f'Recall: {tp/(tp+fn)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4354    ZTF19aalzdfg\n",
       "4485    ZTF19abhvryy\n",
       "3318    ZTF21abrgbex\n",
       "4059    ZTF18abjrdjs\n",
       "4370    ZTF19aanneuv\n",
       "1637    ZTF20aamttiw\n",
       "3948    ZTF17aaaocpq\n",
       "4740    ZTF20abjapox\n",
       "4350    ZTF19aalfjvu\n",
       "2165    ZTF20acbnwyb\n",
       "4763    ZTF20abpwtmi\n",
       "4250    ZTF18acrhfbk\n",
       "4707    ZTF20aayukfo\n",
       "3965    ZTF18aaeauzu\n",
       "4709    ZTF20aazbbqo\n",
       "4111    ZTF18absdddn\n",
       "4051    ZTF18abfgtcf\n",
       "4035    ZTF18aaylbyr\n",
       "4382    ZTF19aaprdox\n",
       "4344    ZTF19aakswwb\n",
       "4145    ZTF18abujubn\n",
       "4237    ZTF18acgusct\n",
       "4114    ZTF18absgmhg\n",
       "4084    ZTF18abmjija\n",
       "1207    ZTF19acbzgog\n",
       "4008    ZTF18aaqlblz\n",
       "4192    ZTF18abzrsuh\n",
       "4628    ZTF19acrwxfn\n",
       "4046    ZTF18abcohew\n",
       "Name: ZTFID, dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ZTF IDs for incorrect classifications\n",
    "incorrectdf.loc[:,'ZTFID']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find best k value\n",
    "df = pd.DataFrame(columns=['k','accuracy'])\n",
    "for k in range(1,101):\n",
    "    model.set_params(n_neighbors = k)\n",
    "    model.fit(data_train, class_train)\n",
    "    df.loc[k,'k'] = k\n",
    "    df.loc[k,'accuracy'] = model.score(data_val,class_val)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['k'], df['accuracy'])\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.title('Scatterplot of k vs accuracy for k of 1 to 100')\n",
    "plt.savefig('plots/k-accuracy.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ConfusionMatrixDisplay.from_predictions(class_test, \n",
    "                                        model.predict(data_test), normalize='true', \n",
    "                                        display_labels=[\"Variables\", \"Transient\"], \n",
    "                                        cmap=plt.cm.Blues, colorbar=False)\n",
    "plt.title('Confusion Matrix of True Labels vs Predicted Labels')\n",
    "plt.savefig('plots/confusion_matrix.pdf', bbox_inches='tight')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "venv",
   "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": "3.10.7"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "971128a3c59d5cf8c985ac7309fd5f48e0b3677c284928761e0d619fe82322e6"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}