active_learning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An experiemnt on using active learning rather than balancing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "import plotly.express as px\n",
    "plt.style.use(['science','ieee', 'no-latex']) # set the style of the plots\n",
    "\n",
    "from util import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train shape: (32561, 13)\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>education</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
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       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>11th</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age         workclass  education      marital-status         occupation  \\\n",
       "0   39         State-gov  Bachelors       Never-married       Adm-clerical   \n",
       "1   50  Self-emp-not-inc  Bachelors  Married-civ-spouse    Exec-managerial   \n",
       "2   38           Private    HS-grad            Divorced  Handlers-cleaners   \n",
       "3   53           Private       11th  Married-civ-spouse  Handlers-cleaners   \n",
       "4   28           Private  Bachelors  Married-civ-spouse     Prof-specialty   \n",
       "\n",
       "    relationship   race     sex  capital-gain  capital-loss  hours-per-week  \\\n",
       "0  Not-in-family  White    Male          2174             0              40   \n",
       "1        Husband  White    Male             0             0              13   \n",
       "2  Not-in-family  White    Male             0             0              40   \n",
       "3        Husband  Black    Male             0             0              40   \n",
       "4           Wife  Black  Female             0             0              40   \n",
       "\n",
       "  native-country target  \n",
       "0  United-States  <=50K  \n",
       "1  United-States  <=50K  \n",
       "2  United-States  <=50K  \n",
       "3  United-States  <=50K  \n",
       "4           Cuba  <=50K  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test shape: (16281, 13)\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>education</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25</td>\n",
       "      <td>Private</td>\n",
       "      <td>11th</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Farming-fishing</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Protective-serv</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>44</td>\n",
       "      <td>Private</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>7688</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  workclass     education      marital-status         occupation  \\\n",
       "0   25    Private          11th       Never-married  Machine-op-inspct   \n",
       "1   38    Private       HS-grad  Married-civ-spouse    Farming-fishing   \n",
       "2   28  Local-gov    Assoc-acdm  Married-civ-spouse    Protective-serv   \n",
       "3   44    Private  Some-college  Married-civ-spouse  Machine-op-inspct   \n",
       "4   18        NaN  Some-college       Never-married                NaN   \n",
       "\n",
       "  relationship   race     sex  capital-gain  capital-loss  hours-per-week  \\\n",
       "0    Own-child  Black    Male             0             0              40   \n",
       "1      Husband  White    Male             0             0              50   \n",
       "2      Husband  White    Male             0             0              40   \n",
       "3      Husband  Black    Male          7688             0              40   \n",
       "4    Own-child  White  Female             0             0              30   \n",
       "\n",
       "  native-country target  \n",
       "0  United-States  <=50K  \n",
       "1  United-States  <=50K  \n",
       "2  United-States   >50K  \n",
       "3  United-States   >50K  \n",
       "4  United-States  <=50K  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "adult = pd.read_parquet('data/formatted_data.parquet')\n",
    "adult.drop(columns=['fnlwgt', 'education-num'], inplace=True) # dropping these columns\n",
    "train = adult[adult['set'] == 'train'].drop(columns=['set'])\n",
    "test = adult[adult['set'] == 'test'].drop(columns=['set'])\n",
    "print(f'Train shape: {train.shape}')\n",
    "display(train.head())\n",
    "print(f'Test shape: {test.shape}')\n",
    "display(test.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %%capture\n",
    "from modAL.models import ActiveLearner\n",
    "from scipy.sparse import csr_matrix\n",
    "\n",
    "stepSize = 100\n",
    "\n",
    "train_ = dfCategoryLimit(train, 42) # I accidentally used 42 as the max, but it is actually 43. The transformed data is only one column off, so it doesn't matter.\n",
    "XTrain, YTrain, XTest, YTest = XYSplit(train_, test)\n",
    "preprocessor = getPreprocessor().fit(XTrain)\n",
    "XTrainTrans = preprocessor.transform(XTrain) # transform the training data\n",
    "XTestTrans = preprocessor.transform(XTest) # transform the test data\n",
    "\n",
    "startIdx = YTrain.groupby(YTrain).sample(int(stepSize/2)).index.to_numpy() # random sample from each class. Learner will start with this data\n",
    "\n",
    "YTrain = YTrain.to_numpy()\n",
    "YTest = YTest.to_numpy()\n",
    "\n",
    "learner = ActiveLearner(\n",
    "    estimator=LogisticRegression(C=10.0, solver='saga'),\n",
    "    X_training=XTrainTrans[startIdx],\n",
    "    y_training=YTrain[startIdx],\n",
    ")\n",
    "\n",
    "metrics = []\n",
    "sizeOfDataset = [stepSize] # keep track of the size of the dataset. Alread has stepSize samples\n",
    "\n",
    "# delete the startIdx from the training data\n",
    "XTrainTrans = XTrainTrans.toarray()\n",
    "XTrainTrans = np.delete(XTrainTrans, startIdx, axis=0)\n",
    "XTrainTrans = csr_matrix(XTrainTrans)\n",
    "YTrain = np.delete(YTrain, startIdx, axis=0)\n",
    "\n",
    "while stepSize < XTrainTrans.shape[0]:\n",
    "    # get the next sample to be queried\n",
    "    query_idx, query_sample = learner.query(XTrainTrans, n_instances=stepSize)\n",
    "    # teach the learner the sample\n",
    "    X = XTrainTrans[query_idx]\n",
    "    y = YTrain[query_idx]\n",
    "    # remove the sample from the training set\n",
    "    XTrainTrans = XTrainTrans.toarray()\n",
    "    XTrainTrans = np.delete(XTrainTrans, query_idx, axis=0)\n",
    "    XTrainTrans = csr_matrix(XTrainTrans)\n",
    "    YTrain = np.delete(YTrain, query_idx)\n",
    "    learner.teach(X=X, y=y)\n",
    "    sizeOfDataset.append(learner.X_training.shape[0]) # keep track of the size of the dataset\n",
    "    # save metrics\n",
    "    metrics.append(getMetrics(learner, XTestTrans, YTest))\n",
    "    print(f'Size of Active dataset: {sizeOfDataset[-1]}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "results = pickle.load(open('data/logistic_results.pkl', 'rb'))\n",
    "bestFromSearch = results.testMetrics.iloc[-1] # get the test metrics from the previous logistic regression search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
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QIL311luB5ITUUTRFXMxsnaSjJd0iaVOK4T5QZIbpFyknllnneY6bpP9mMpE0+VHSHyT1MbPk/6VHVrCnKQAAAABk1pw5c6LuZypJPXr0SCn2jBkz1LNnz0AKUNsrKyvT1Vdfrdtvvz2p91911VV65JFHAs0JmfXb3/5WL730UqAx//3vf+uss86Kay/WRHzzzTc69NBDNWPGjEDjStKaNWs0cOBAff311ynH+uqrr/TrX/96h2NvvvmmTj31VG3ZsiXl+Ndcc40++eSTlOMEYffdd/e2/e1vf8tgJom74oordMkllwRyT7Y3depU9e3b17vfazZt2bJFZ511ll555ZW0xF+zZo2OOuqoQP472t6kSZPUs2dPjR8/PtC4QRo3bpyOPvporVy5MuVYGzdu1Gmnnab58+ennhhSxlN0xM3MyiT9yTn3nKSbJA2RlMiO8+Mk/dnMXktHfunknKsl6WNJI6M0Lzez5L4ylroHFVky+GhJeycZ41tFlvn9l5mtCioxZFZl30arXr16hjIBAAAAgHC47bbbvG0nn3xy0nEXLFigww47TEuXLvWe07p1aw0ePFiHHXaYOnXqpObNm6tevXoqKSnR/PnzNWLECL388suaN29e1PfffPPNat++vU477bS48xo/frzuv/9+b3vLli113HHHqXfv3urQoYNatmypevXqqXr16lq9erVWrVqlxYsXa+rUqZo6dapGjhzpzQ/p8eSTT+rxxx/f6fihhx6qY445Roceeqh22WUXFRQUaNOmTVq0aJEmTJigl156Sc65qDGHDx+uCy+88KftlKLq2rWrhgwZot69e6tly5Zq0qSJli5dqgULFmj48OF66aWXtGzZsqjvXbZsmfr376+JEyeqsLAwuQuP4sILL9TkyZN3ONa6dWuddNJJOuqoo9SmTRsVFRVp/fr1+v777/XBBx/o8ccf18yZM6PGe/nll/X2229r0KBBmjFjhs4888ydvuDet29fDR48WIcccoh22WUXNWnSRD/++KNmzZqlN998U4899pg2bNjgzferr75SzZo1A7n+ZPXu3Vt33HFH1LYRI0bo7rvv1nXXXZfhrCp3/fXX67777vO2V69eXQMGDNARRxyhgw46SC1atFDz5s21fv16LVu2TOPHj9dbb72lt956K+rEhblz5+rYY4/VmDFjVLdu3ZTzrVatmnr06KH9999f++67r/bZZx81b95cjRs3VoMGDbRu3TqtWrVKM2fO1MSJEzVs2DBNmTIlaiwz09lnn62uXbuqQ4cOKee2vcsuu0xfffXVDsfq1Kmjo446SoMHD1bXrl1VVFSkJk2aaO3atZozZ45GjRqlp59+2htz+vTpOuKII2IWI2vWrKmDDz5YAwcO1IEHHqiioqKfx4cVK1aopKREkydP1vjx4/Xxxx8nPdvbp6SkRCeccMJOM8w7d+6sY489VkcccYR23313FRUVaePGjVq6dKk+/fRTPffccxo7dmzUmGvXrtVvf/tbvfvuu4HmiiRkci1gXvn1ktRc0lmSHlekILpc0kZJWxTZr3SmpNclXSupc7bzzfeXpF0knSDpTkmvSpqkyN6kayRtk7RekWWRJ0l6QdKVkrpKctnOPYlrZU/TCnr16hVzffzLL7882ykCAAAAQMake0/Txx9/3JxzUeMfcMABScfdsGGD7bvvvt7cd999d3viiSdsy5YtlcbatGmT3Xvvvd79UOvXr2/z5s2LO7dBgwZFjVOnTh178MEHbfPmzQlf79SpU+2OO+6wNm3aVLrH3caNG23Dhg0/v2688cao+dx44407nFfZK5v7MPruc6p8e5pW3DNxv/32s88++yyumNE+p4ULF1rjxo2919GiRQt79dVX44p9zTXXWPXq1b2xBgwYkPDn4NvTtH79+jv9/7/+9a+2adOmmPE2b95s1113nTfH/fbbz7Zu3Wr777//Dse7du1qn376aaX5zpkzx7p27eqN/+ijjyb8GQRt/fr1Vrdu3ZjPoE466ST79ttvs53qz5577rmY+Z511lk2e/bsuGJNnTrV+vbt64118cUXJ5zf+PHjTYrskTpgwAB79NFHk3rOOWbMmJ1+9rZ/9e/fP+GYZv49TaPtwXrKKafE9e9KWVlZ1P/eVq5c+fO/B9Fezjk77bTTEv53/IsvvrA//OEPP+/FHc9+qmb+PU0bNGiww/9v06aNvfTSS3HFfPrpp61OnTreaxw5cmRC15ZvqsKeplkvfvDixYtXoi9RNN1J9+7dY/4CeNVVV2U7RQAAAADImHQVTVetWmWXX365VatWzfsQedKkSUnHv+qqq7x59+vXz0pKShKOOXbsWCsoKIgac/DgwXHFWLlypdWqVWun91erVs0++OCDhHOqaOvWrfbaa6/ZokWL4n6P7x7fcsstKeeTKb57nSpf0XT717HHHltpkbAyxx9/vDf+3nvvbcuWLUso3ogRI6L+nP30euaZZxKK5yuabv9q3ry5ffHFFwnFvfzyy73xhgwZssP/HzRokK1bty7u2MuXL/cWjQ455JCE8kyXSy+9tNLPtVq1ajZo0CD773//m/DPQZAWL15sTZo0iZpjvXr17Nlnn0045pYtW+ycc87xXvf48eMTivfVV1/Zueeea19//XXCuVS0detWu+iii7z3ZdSoUQnH9BVNK77uuuuulPM/66yzvPHr169v77zzTkrxV6xYYbfddlvcRXJf0XT7V8+ePRP+t3nEiBHe3yPOOuusZC4tb1SFoqmzSAECAHKGc65QkZnNP5s+fboKCgrien+Qy7lUFfvtt1/MTcevu+463XXXXRnMCAAAAFkV8N5ScatdW0pm6bdvv5U2bQo+n3jss0/i71m4UFq9Ovhc4tG+vVSnTmLvWb5cKilJ7lpz1NChQ3XrrbdGbZsxY4Zat25daYyNGzdq7dq1Wrx4saZMmaKPP/5Yb7zxhtavXx/1/OrVq+vpp59OaMnb7c2cOVP77LNP1KUf+/Tpo/fee0+1atVKKvaYMWPUu3fvqLHHjRunAw44IOb73377bR199NE7HT/ttNP03HPPJZVTqnz3+JZbbtHQoUMzn1ASfEvepvq8tk2bNjH3xjvssMP03nvvqUaN5HduGzt2rA488MCobXvssYc+//xz7bbbbgnHffnll3XqqadG/QzatGmj2bNnx533yJEjddhhh3nba9SoodGjRye8B/H69evVsWNHLV68OOZ5PXr00OjRoxP+nF999VXvEt9z5sxRu3btEooXtCVLlqhDhw7esbAi55w6duyoXr16qWfPnurevbu6dOmi2rUT2fUtOWeeeaaeffbZnY5Xr15dw4cP18CBA5OKa2Y65phj9Pbbb+/UNmjQIP3vf/9LKm4QzEynnXaaXnzxxZ3azjzzzJhL40Yzb948tW3bNuY5N910U8wl6+MxZswYHXzwwVHbGjdurPfeey/l/cITdc455+jJJ5/0tu+1116aMGGC6tevn3Dsiy66SP/85z93Ol63bl2VlJQkFbOqKykpqfSc0tJSFRcXVzxcZGaVvzkomazQ8uLFi1cQL0WZaZrIKx917tw55jXfeOON2U4RAAAAmSRl51VcnFy+xcXZyzkZJ5+cvXynTUs831tuSf5ac1SsmabpeLVq1cref//9lHKuOEPtp9duu+1mP/zwQ8qfyR//+Meo8U8//fRK3/vII49EfW+iM/+CxExTv1gzTevVq2ffffddyn3EmmX6v//9L6XYv/rVr7yxn3766bjjVDbT9E9/+lPSOd56660xY9erVy/u2WzR+O7hk08+mXTMIL388sveJcrjedWsWdP2228/u+iii+yFF16w77//PvAcv/76a+9svj//+c8px1++fLl3Fv+sWbMCuILkLVu2LOrS2fXr1094KfXKZpp26dIlqeXZKzrssMO8fTz33HMpx09GrJmmtWrVssmTJycde/Hixd6fz3iW885FyY4XyvBM02oCAOS8rVu3xmxP5dujAAAAAICd1apVS4cccogeffRRzZgxQ/369Us61tKlS/XKK69EbbvtttvUrFmzpGP/5Morr1SjRo12Oj5s2DCtWbMm5ntLS0ujHs/HlZzy3WWXXVbprLHKlJSUeGfSDR48WIMGDUop/j333BP1Z1WSHnvssZRi/6Rp06a68sork37/SSedFLP97LPPVodkVl6oJH6sVcYy6eSTT9Zf//pX72zpymzZskWTJk3Sww8/rCFDhqhly5baa6+9dOutt2ru3LmB5Pjwww+rrKxsp+Pt27fXH/7wh5TjFxYW6uKLL47aFmt2YiYUFRVF/Rlat26dxo4dG2hf99xzj2rWrJlSjOnTp+vjjz+O2nbKKackvYJDOp122mnad999k35/y5Yt1bdv36htEydOTDouUkfRFADyAEVTAAAAAMic6tWr66STTtJll12mIUOGqG7duinFe+aZZ6L+Xde2bVudffbZKcX+SaNGjfSrX/1qp+MbNmzwPqz+Sb169aIeX7BgQSC5ITOcczrvvPNSjvPmm29qy5YtUduCKEa1aNFCZ5xxRtS2Tz75RD/88EPKffzmN7/x/lzHo7i4OOb7L7300qRjS/IumT158uSU4gbpiiuu0Kuvvhr3dlmVmTlzpoYOHar27dvriCOO0BdffJF0rC1btkRdlleSbrzxxpSLfD+56KKLVK3aziWWaMv2Zppv6eEJEyYE1kfr1q115JFHphznmWee8bbdcsstKcdPh8svvzzlGIccckjU41Xpv/MwomgKAHkg2r4026NoCgAAAADB2bZtm55//nn98pe/VKtWrfTHP/5RK1euTDre8OHDox4/+eSToz6QT9YRRxwR9fjIkSNjvq9FixZRj//nP/+JOpMLVdMBBxyg9u3bpxzn/fffj3q8VatW3plTiYpW4JeksrIyffjhhynHT3U2rHNOe+21V9S21q1ba++9904p/j6ePaiXLFmSUtygnXDCCZoxY4Yuu+yylL888hMz00cffaRevXppyJAhWrZsWcIxRo0aFXVMrlmzpk444YQAsowoKipS586ddzo+efLklP5NCEKrVq2iHv86wH3vTz311ED+jfL9G9i3b1/vfwvZtNtuu6lr164px+nSpUvU48n8zCM4FE0B5IXp06dr+fLlcb3yUWUzTatXr56hTAAAAAAgXH788Ufddttt6ty5c1LFnE2bNunzzz+P2nbcccelmt4ODjrooKjHK5vV4nvfuHHjdP7552vDhg2ppoYM6NGjRyBxfDMABw8enPRyrRX16tVLu+66a9S2cePGpRS7Ro0agXwWvi8T+P57CSL26tWrU44dtObNm+uBBx7Q/Pnzddddd6VcMN7eiy++qAMOOECTJk1K6H2+2fN9+vRRkyZNAsjs/0W732amKVOmBNpPonwzgBctWhRYH0H8d7RixQpNmzYtatupp56acvx08M0QTVTLli2jHl+1alUg8auaeJ7bT58+PdtpiqlHAPJCQUFBqPdSYXleAAAAAIjP3Llz1aZNm7jOXbt2rVavXq0lS5boyy+/1KhRo/Tqq69q7dq1O527aNEi9e/fX48//rh3llw0U6ZM8S516puFkqyioiLVqFFjp78hZ86cGfN9rVq1UteuXaMWVx977DF98MEHuvTSS3XWWWd5iz3Ivu7du6ccY+3atZo/f37Utv322y/l+Nvr1q1b1JmVqRajWrZsqfr166cUQ5IaNGgQ9fiee+6ZtthVuZhSWFio6667Ttddd52++eYbvfPOO/roo480duzYlJZUXrhwoQ499FB98MEHOvDAA+N6j29PyFT2oPTxFb5mzpyp3r17Jx1327ZtGjt2rCZPnqypU6dq2rRpKi0t1Zo1a7RmzRqtXbtWZpZw3CAnlAQxpsTap7dXr14px0+Hjh07BhLHt3dzVf7vPBW58uyep+gAkAcomgIAAABA8Bo0aKAGDRqoZcuW2n///XXeeefpwQcf1H333afbb79dmzdv3uH8srIynXvuuWrQoIFOPPHEuPr45ptvoh7fbbfd1LBhw5SvoaJmzZrt9NB82bJl2rZtW8xVim666SadfPLJUdsWLFigq6++Wtdee6169eqlAQMGqHfv3urZs2dgS3Yidb7lOhMxb948b6Em6KLpfvvtF3VvyHnz5qUUt2nTpim9/ye+PU2DiO+LvX79+pRjZ8Lee++tvffeW1dccYUk6bvvvtPEiRM1adIkTZo0SRMmTNCPP/4Yd7z169frpJNO0sSJE70zkLfnG1d9Syqnonnz5lGPL168OKl4kyZN0hNPPKGXXnopLcu0BvUz5JzTHnvskXKcb7/9Nurx+vXrR136uCoIagzxfXkjV/47z1c8RQeAPMCepgAAAACQGQ0aNNDNN9+s/v37q1+/fjvNOi0rK9P555+vgw46SLvsskul8RYuXBj1+OLFiwNb6rQy27Zt0+rVq2M+CD7ppJN05pln6plnnvGeU1ZWpjFjxmjMmDGSIvsH7rvvvjrooIPUt29f9enTR82aNQs8f8THN6spEd9//723LYgCyvZ8Rd5YOcSjcePGKb0/2/FzUbt27dSuXTudcsopkiLL106bNk0jR47USy+9pNGjR1c6a3LJkiW64IIL9MYbb8Q8z8y8Bcvzzz9f559/fnIXkaAVK1YkdP6yZct07bXX6qmnnkpqBmm8Nm3aFEic+vXrB7KfqW+54N13373KbjcW9BLPFaXz/qNyPEUHgDzAnqYAAADYgWdvqLSrXTu5973xhhTQQ7yMuPdeaejQ7PTdvn3i77noIqn8QTWC07NnTz311FNRZ5T++OOPuuGGG/TYY49VGqekpCQd6SVsw4YNlc6eefTRR7V161a98MILccXcsmWLJkyYoAkTJujvf/+7nHPq3r27TjvtNA0ZMiSuGWMIThDFvFjLRgZRlN2eL99169ZVOjM6lnR/GSFTX3bIZc45de7cWZ07d9all16q7777TnfeeaeefPLJmM+43nzzTU2aNCnmrOZVq1bttApANiSy1/OHH36ok08+WStXrkxfQuWCKsgF9eUAX3E53YXJVPDfeH6jaAoAeYDleQEAALCDffbJdgaJ6dAh2xkkJuDZVGlXVBR5IXAnnHCCDjnkEI0aNWqntueee0533323iir57BN5sJ5Ovn1Vt1e7dm09//zzOuKII3TzzTdr6dKlCfVhZho/frzGjx+vq6++WqeffrpuvvnmQPaAROWCeDbgm6VWp04d1axZM+X424tVlNm4cWMg+5KiamjXrp0effRRnX/++Tr11FO9++ZK0r333htzxnsujamS9Mknn+joo48ObAZopgT1rNF3v6py0RT5LfX50wCArGN5XgAAAADIjl//+tdRj2/atEmvvPJKpe+v7EuwmZLI7KPzzjtPc+bM0d///vek97Hctm2bnn76aRUXF+uuu+5iOcIc4btP6Zh5xc9E+PTs2VMjR45UixYtvOe89957MX82cmlMLSkp0S9/+cuYBdMGDRpo0KBBuuWWW/Tcc89p1KhRmjVrlpYsWaJVq1Zp/fr12rBhw06vGTNmBHk5GcdsTmQLT9EBIA8w0xQAAAAAsuPwww/3tn322We66KKLYr6/Tp06QaeUEfXq1dOll16qSy+9VDNnztQHH3ygjz/+WJ9++mlCSw5v3bpVN9xwgyZPnqwXXniBB+VVnO/ndcOGDdqyZUugs01Xr16dcB7IfW3atNEDDzygIUOGRG0vKSnR1KlT1aVLl6jtufSzccstt2j58uVR23bffXfdcccd+uUvf5nUNeXKVl1169aNejzRPWGBoPAUHQBynJmxpykAAAAAZEnr1q1Vo0aNqH+XTZ48udL3N2zYMOrxY445Ri+//HKq6cWtdrJ7Ekvq1KmTOnXqpIsvvliSNHPmTI0ePVqfffaZPvvsM82ZM6fSGC+99JI6duyo2267Lek8kH6xlsxcvXq1mjdvHlhfvv1T69evz3OOPPfLX/5S119/vebOnRu1fdasWd6iqW9MlaSxY8dq3333DSTHylT2M7py5Uo9/vjjUdt69uyp4cOHq7CwMOn+c6Xo6NtLOxP7uwLRUDQFgBxXVlZW6TnMNAUAAACA9GnWrFnU2UKlpaWVvncPzx658+bNy6kZU9v7qYj609LFs2fP1ptvvqnHH39cX3/9tfd999xzj8477zy1bt06U6kiQbvuuqu3beHChYEWTRcuXBj1eMuWLQPrA1WTc079+/fXv//976jtscbWOnXqqKCgIOo5ixYtUs+ePQPLMxXDhw/Xxo0bdzreqFEjvfTSSykVTCXpxx9/TOn9meL7N3DRokXatm0bX5BAxrGnKQDkuMr2M5UomgIAAABAOvn2rotnpkz79u2jHv/uu+/i+pJsLthzzz115ZVXatq0aXrnnXfUrl27qOdt3rxZDz/8cIazQyLatGnjXUL5yy+/DLSvSZMmRT3etm3bQPtB1eQbG6XKC4K+986ePTulnIL02WefRT1+2mmnqVWrVinHj2eGf1Ww5557Rj2+bt06TZs2LcPZABRNASDnxbPBPd/KAgAAAID0MDPvA/xatWpV+v7u3btHPb5+/XqNHTs2pdyqoqOOOkqjR4/W7rvvHrX93XffzXBGSESDBg3Upk2bqG2+ImeyfEXYzp07B9oPqqbGjRt72yqbHOAbVz/66KOUcgrSzJkzox4/5phjAok/evToQOKk23777ef9Isbnn3+e4WwAiqYAkPPiKZoy0xQAAAAA0uO7777zrgAUz/KKBQUF2nvvvaO2vf7666mkVmXtsssuuvXWW6O2TZs2zTtzd3vVqkV/rJkvs3OrMt/ypsOHD4/r3sVj3Lhx+v777xPqH/mlpKTE2xaroCpJvXv3jnp85MiR3r1yM813fb4vlCSirKxMn3zyScpxMqFRo0bq2rVr1LYXX3wxs8kAomgKADmPoikAAAAAZE+smUu+ZQcrOv7446Mef/bZZ7Vhw4Zk0qryjjrqqKjHt23bFtdefL5ZvOvXr08pL1TuyCOPjHp8/vz53iVHE/XUU09FPV6tWjUdfvjhgfSBqu3bb7/1tvmW+P7JwIEDVbt27Z2Ob9myRU8++WTKuQVhy5YtUY/XrVs35dhvvvmmFi9enHKcTPHNrh05cmTMfbCBdKBoCgA5jj1NAQAAACB7/vOf/3jbDjnkkLhinH322VGXJ/z+++/1wAMPJJ1bVda8eXNvWzyzFX0zzX744Yekc0J8jj32WNWsWTNq23333Zdy/JKSEj3zzDNR2w477LCYPzvID1u3btXw4cOjtjnn1K1bt5jvb9iwoU488cSobbfffrvWrFmTco6patq0adTjS5YsSTn23/72t5RjZNJZZ53lXaL3T3/6U4azQdhRNAWAHMeepgAAAACQHc8++6zGjx/vbT/hhBPiitOpUyfvzMs777xT06dPTyq/qsy39Grt2rVVUFBQ6ft33XXXqMd9+wQiOAUFBd6ZYa+//nrK+9Jee+213iVUzz333JRiIzWjRo3KyBcTHnnkEW8/++67b1xjxOWXXx71eElJibctk3zLt48cOTKluA8//LBGjRqVUoxM23PPPdW/f/+obS+99JKef/75DGeEMKNoCgA5juV5AQAAACDzRo4cqfPOO8/bfsghh6hLly5xx7vrrrui7tO5Zs0aDRo0KJDZR9GsXr260nMmTpwY+F6hr7zyStTj7du3j+v9nTp1inp8woQJWrlyZbJpIU7XXnutt+2CCy5I+uf1tdde0xNPPBG1rU2bNjr11FOTiotgvP7662rXrp3+9Kc/acWKFWnpY/r06bruuuu87WeddVZccXr06OGdbfrYY4/pzjvvTCq/ymzbti2uZdV9s2UfffTRpJcZnzRpkq6++uqk3pttt912m7ftoosuivkFJSBIFE0BIMdRNAUAAACAzFmxYoWuv/569evXTxs3box6jnNOd999d0Jx9913X1122WVR2+bPn68ePXro008/TThfn08//VQnnniiTjnllErPvfLKK9WxY0c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",
      "text/plain": [
       "<Figure size 1980x1500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "metric = 'accuracy'\n",
    "\n",
    "acc = [m[metric] for m in metrics]\n",
    "plt.plot(np.array(sizeOfDataset[1:]) / len(XTrain), acc)\n",
    "plt.ylabel(metric.capitalize())\n",
    "plt.xlabel('Proportion of Dataset')\n",
    "\n",
    "\n",
    "prevBest = bestFromSearch[metric]\n",
    "plt.axhline(prevBest, color='r', linestyle='--', label='Best from Search')\n",
    "plt.legend()\n",
    "plt.savefig(f'figures/ActiveLearning/active_learning_{metric}.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1980x1500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "metric = 'roc_auc'\n",
    "\n",
    "acc = [m[metric] for m in metrics]\n",
    "plt.plot(np.array(sizeOfDataset[1:]) / len(XTrain), acc)\n",
    "plt.ylabel(metric.capitalize())\n",
    "plt.xlabel('Proportion of Dataset')\n",
    "\n",
    "prevBest = bestFromSearch[metric]\n",
    "plt.axhline(prevBest, color='r', linestyle='--', label='Best from Search')\n",
    "plt.legend()\n",
    "plt.savefig(f'figures/ActiveLearning/active_learning_{metric}.png', bbox_inches='tight')"
   ]
  }
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