6-risk-prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "afc494c2-d870-4831-9e75-a5fdba86027d",
   "metadata": {},
   "source": [
    "# Risk prediction using regression\n",
    "\n",
    "Train a regression model on the first half of the data, use it to predict risk of infection on the second half.\n",
    "\n",
    "https://academic.oup.com/aje/article/162/10/1024/65086\n",
    "\n",
    "https://onlinelibrary.wiley.com/doi/abs/10.1002/ajp.22542\n",
    "\n",
    "https://arxiv.org/abs/2308.03037\n",
    "\n",
    "NOT FINISHED!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9807538f-5a65-4473-b82a-5b53a0423991",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from os import path\n",
    "import random\n",
    "\n",
    "from datetime import datetime\n",
    "import pytz\n",
    "\n",
    "import pickle\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "\n",
    "import networkx as nx\n",
    "\n",
    "from utils.parsing import get_contact_list, get_infection_list, get_node_state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "76bf454c-49ca-4444-8ffd-869f18ccc5bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_folder = \"./data\"\n",
    "output_folder = \"./output\"\n",
    "if not path.exists(output_folder):\n",
    "    os.makedirs(output_folder)\n",
    "\n",
    "# Set a random seed for reproducibility\n",
    "random_seed = 32\n",
    "random.seed(random_seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e6210a27-9fdf-4c09-a895-84f435f5170b",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(path.join(data_folder, 'network-largest_conn_comp.pickle'), 'rb') as f:\n",
    "    G = pickle.load(f)\n",
    "\n",
    "with open(path.join(data_folder, 'daily-contact-matrices.npy'), 'rb') as f:    \n",
    "    tensor = np.load(f, allow_pickle = True)\n",
    "\n",
    "with open(path.join(data_folder, 'daily-transmissions.npy'), 'rb') as f:    \n",
    "    inf = pickle.load(f)\n",
    "\n",
    "with open(path.join(data_folder, 'daily-node-states.pickle'), 'rb') as f:\n",
    "    states = pickle.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a68d5e82-9a75-4b20-aa90-9b44b2ee500e",
   "metadata": {},
   "source": [
    "## Feature selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4a140950-5807-477e-819c-ab2c42e48bbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "past_window = 3\n",
    "future_window = 2\n",
    "\n",
    "feature = 'degree'\n",
    "# feature = 'degree_centrality'\n",
    "# feature = 'closeness_centrality'\n",
    "# feature = 'betweenness_centrality'\n",
    "# feature = 'eigenvector_centrality'\n",
    "# feature = 'pagerank'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7424f6b0-dc0a-41b0-bb72-9708003adbc5",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f76e0435-1843-4da9-9d78-89818dfc45a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_days = len(tensor)\n",
    "train_days = round(total_days/2)\n",
    "\n",
    "nodes0 = list(G.nodes()) # We only look at the nodes we already selected before (which have enough interactions over the entire period of the sim)\n",
    "\n",
    "train_graphs = []\n",
    "for day in range(0, train_days):\n",
    "    g = nx.from_numpy_array(tensor[day])\n",
    "    g = nx.relabel_nodes(g, dict(zip(g.nodes(), nodes0)), copy=True)\n",
    "    train_graphs.append(g)\n",
    "\n",
    "    degree = {node: g.degree(node) for node in g.nodes()}\n",
    "    degree_centrality = nx.degree_centrality(g)\n",
    "    closeness_centrality = nx.closeness_centrality(g)\n",
    "    betweenness_centrality = nx.betweenness_centrality(g)\n",
    "    eigenvector_centrality = nx.eigenvector_centrality(g)\n",
    "    pagerank = nx.pagerank(g)\n",
    "\n",
    "    nx.set_node_attributes(g, degree, 'degree')\n",
    "    nx.set_node_attributes(g, degree_centrality, 'degree_centrality')\n",
    "    nx.set_node_attributes(g, closeness_centrality, 'closeness_centrality')\n",
    "    nx.set_node_attributes(g, betweenness_centrality, 'betweenness_centrality')\n",
    "    nx.set_node_attributes(g, eigenvector_centrality, 'eigenvector_centrality')\n",
    "    nx.set_node_attributes(g, pagerank, 'pagerank')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "07f380b6-4936-4cb6-85ea-676db2f0efe8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n"
     ]
    }
   ],
   "source": [
    "# States:\n",
    "# 0 = Susceptible\n",
    "# 1 = Infected (index case)\n",
    "# 2 = Infected (from someone else)\n",
    "# 3 =  Dead \n",
    "# 4 = Recovered \n",
    "# 5 = Vaccinated \n",
    "\n",
    "x_train = []\n",
    "y_train = []\n",
    "for day in range(0, train_days):\n",
    "    std = states[day]\n",
    "    print(day)\n",
    "    for node in nodes0:\n",
    "        idx = nodes0.index(node)\n",
    "        feat = []\n",
    "        for dpast in range(max(0, day - past_window), day + 1):\n",
    "            gd = train_graphs[dpast]            \n",
    "            feat.append(nx.get_node_attributes(gd, feature)[node])\n",
    "        mean_feat = np.mean(feat)\n",
    "        if std[idx] == 0:\n",
    "            inf_status = 0\n",
    "            for dfutch in range(day + 1, min(total_days - 1, day + future_window)):\n",
    "                stf = states[dfutch]\n",
    "                if stf[idx] == 2:\n",
    "                    inf_status = 1\n",
    "                    break\n",
    "            # print(mean_feat, inf_status)\n",
    "            x_train.append(mean_feat)\n",
    "            y_train.append(inf_status)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7411847e-55a2-4458-bd88-73160a949b78",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LogisticRegression(solver='liblinear', random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "17fbd0d9-49d1-4051-84b9-24c45ebf3202",
   "metadata": {},
   "outputs": [
    {
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       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
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       "  color: var(--sklearn-color-icon);\n",
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       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
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       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
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       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
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       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
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       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
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       "#sk-container-id-1 div.sk-label label {\n",
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       "  color: var(--sklearn-color-text-on-default-background);\n",
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       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
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       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
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       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
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       "  border: 1px dotted var(--sklearn-color-border-box);\n",
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       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
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       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
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       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
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       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(random_state=0, solver=&#x27;liblinear&#x27;)</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 fitted 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 fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(random_state=0, solver=&#x27;liblinear&#x27;)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(random_state=0, solver='liblinear')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.array(x_train).reshape(-1, 1)\n",
    "y = np.array(y_train)\n",
    "\n",
    "model.fit(x, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "70f4612e-3553-4772-9d96-19d0be0268db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 0])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e45d2ec0-275c-4df9-93ea-85cca46a3796",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8735229759299781"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9b5dcd37-41d0-4afb-9a19-863a64003b88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y, model.predict(x))\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 8))\n",
    "ax.imshow(cm)\n",
    "ax.grid(False)\n",
    "ax.xaxis.set(ticks=(0, 1), ticklabels=('Predicted 0s', 'Predicted 1s'))\n",
    "ax.yaxis.set(ticks=(0, 1), ticklabels=('Actual 0s', 'Actual 1s'))\n",
    "ax.set_ylim(1.5, -0.5)\n",
    "for i in range(2):\n",
    "    for j in range(2):\n",
    "        ax.text(j, i, cm[i, j], ha='center', va='center', color='red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e0d70790-bccc-47a4-a1b6-c22449c5c61d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.99      0.93      2010\n",
      "           1       0.31      0.04      0.07       275\n",
      "\n",
      "    accuracy                           0.87      2285\n",
      "   macro avg       0.59      0.51      0.50      2285\n",
      "weighted avg       0.81      0.87      0.83      2285\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y, model.predict(x)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "664f17bc-1f13-487c-9aa1-4e3cb0022ede",
   "metadata": {},
   "source": [
    "## Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "52989eb5-1c9a-4b63-812f-4fdbe2e7a7f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n"
     ]
    }
   ],
   "source": [
    "test_graphs = []\n",
    "for day in range(train_days, total_days - 1):\n",
    "    print(day)\n",
    "    g = nx.from_numpy_array(tensor[day])\n",
    "    g = nx.relabel_nodes(g, dict(zip(g.nodes(), nodes0)), copy=True)\n",
    "    test_graphs.append(g)\n",
    "\n",
    "    degree = {node: g.degree(node) for node in g.nodes()}\n",
    "    degree_centrality = nx.degree_centrality(g)\n",
    "    closeness_centrality = nx.closeness_centrality(g)\n",
    "    betweenness_centrality = nx.betweenness_centrality(g)\n",
    "    # eigenvector_centrality = nx.eigenvector_centrality(g)\n",
    "    pagerank = nx.pagerank(g)\n",
    "\n",
    "    nx.set_node_attributes(g, degree, 'degree')\n",
    "    nx.set_node_attributes(g, degree_centrality, 'degree_centrality')\n",
    "    nx.set_node_attributes(g, closeness_centrality, 'closeness_centrality')\n",
    "    nx.set_node_attributes(g, betweenness_centrality, 'betweenness_centrality')\n",
    "    nx.set_node_attributes(g, eigenvector_centrality, 'eigenvector_centrality')\n",
    "    nx.set_node_attributes(g, pagerank, 'pagerank')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ffec27e8-5b85-41d4-8801-865c71d0821a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n"
     ]
    }
   ],
   "source": [
    "x_test = []\n",
    "y_test = []\n",
    "for day in range(train_days, total_days - 1):\n",
    "    print(day)\n",
    "    std = states[day]  \n",
    "    for node in nodes0:\n",
    "        idx = nodes0.index(node)\n",
    "        feat = []\n",
    "        for dpast in range(max(train_days, day - past_window), day + 1):\n",
    "            gd = test_graphs[dpast - train_days]\n",
    "            feat.append(nx.get_node_attributes(gd, feature)[node])\n",
    "        mean_feat = np.mean(feat)\n",
    "        if std[idx] == 0:\n",
    "            inf_status = 0\n",
    "            for dfutch in range(day + 1, min(total_days - 1, day + future_window)):\n",
    "                stf = states[dfutch]\n",
    "                if stf[idx] == 2:\n",
    "                    inf_status = 1\n",
    "                    break\n",
    "            # print(mean_degree, inf_status)\n",
    "            x_test.append(mean_feat)\n",
    "            y_test.append(inf_status)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "65398f88-6e59-4d2c-957b-8f4487e2f380",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array(x_test).reshape(-1, 1)\n",
    "y = np.array(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0f023945-3931-4269-9399-1bbc37b6755c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2c2e0878-7bde-42e0-8ce4-93577ba25a1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9782833505687694"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0f0686cd-5772-4427-b5c4-8a02292b2216",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y, model.predict(x))\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 8))\n",
    "ax.imshow(cm)\n",
    "ax.grid(False)\n",
    "ax.xaxis.set(ticks=(0, 1), ticklabels=('Predicted 0s', 'Predicted 1s'))\n",
    "ax.yaxis.set(ticks=(0, 1), ticklabels=('Actual 0s', 'Actual 1s'))\n",
    "ax.set_ylim(1.5, -0.5)\n",
    "for i in range(2):\n",
    "    for j in range(2):\n",
    "        ax.text(j, i, cm[i, j], ha='center', va='center', color='red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "46425d60-c825-4f47-b361-cfd037eb0b31",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      1.00      0.99       946\n",
      "           1       0.00      0.00      0.00        21\n",
      "\n",
      "    accuracy                           0.98       967\n",
      "   macro avg       0.49      0.50      0.49       967\n",
      "weighted avg       0.96      0.98      0.97       967\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/andres/miniconda3/envs/oo/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "/Users/andres/miniconda3/envs/oo/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "/Users/andres/miniconda3/envs/oo/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y, model.predict(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "92b12431-2262-484f-9445-2dd1d5196828",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}