model_development.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from sklearn.model_selection import train_test_split\n",
    "import seaborn as sns\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import LSTM, Dense, Flatten, Dropout\n",
    "import itertools\n",
    "from sklearn.metrics import precision_recall_curve, average_precision_score\n",
    "from sklearn.metrics import plot_precision_recall_curve\n",
    "from sklearn.metrics import classification_report, confusion_matrix, roc_auc_score\n",
    "import matplotlib.pyplot as pyplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "physical_devices = tf.config.list_physical_devices('GPU')\n",
    "tf.config.experimental.set_memory_growth(physical_devices[0], enable=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# log directory for tensorflow log\n",
    "tf_log_dir = \"C:\\\\Users\\\\cssar\\\\890CA\\\\lstm-mimic\\\\logs\\\\\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "numpy_feature_file = \"C:\\\\Users\\\\cssar\\\\890CA\\\\lstm-mimic\\\\features.npy\"\n",
    "numpy_target_file = \"C:\\\\Users\\\\cssar\\\\890CA\\\\lstm-mimic\\\\targets.npy\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load numpy files\n",
    "X_data = np.load(numpy_feature_file)\n",
    "y_data = np.load(numpy_target_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split the data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_data, y_data, test_size = 0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(4379, 1)"
      ]
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "source": [
    "## Develop LSTM Model\n",
    "\n",
    "LSTM Models requres three-dimensional input with `[Sample, timestamps, features]`. In previous cell, we can see that we have loaded data as required here."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fit and evaluate a model\n",
    "def get_model(trainX, trainy):\n",
    "    n_timesteps, n_features, n_outputs = trainX.shape[1], trainX.shape[2], trainy.shape[1]\n",
    "    model = Sequential()\n",
    "    model.add(LSTM(10, input_shape=(n_timesteps,n_features)))\n",
    "    model.add(Dropout(0.1))\n",
    "    model.add(Dense(n_outputs, activation='sigmoid'))\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": [
     "outputPrepend"
    ]
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      " 0.8890 - val_loss: 0.3502 - val_accuracy: 0.8595\n",
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      "Epoch 419/500\n",
      "4379/4379 - 0s - loss: 0.2552 - accuracy: 0.8986 - val_loss: 0.3598 - val_accuracy: 0.8591\n",
      "Epoch 420/500\n",
      "4379/4379 - 0s - loss: 0.2532 - accuracy: 0.9013 - val_loss: 0.3568 - val_accuracy: 0.8544\n",
      "Epoch 421/500\n",
      "4379/4379 - 0s - loss: 0.2611 - accuracy: 0.8952 - val_loss: 0.3592 - val_accuracy: 0.8563\n",
      "Epoch 422/500\n",
      "4379/4379 - 0s - loss: 0.2570 - accuracy: 0.8972 - val_loss: 0.3565 - val_accuracy: 0.8581\n",
      "Epoch 423/500\n",
      "4379/4379 - 0s - loss: 0.2608 - accuracy: 0.8934 - val_loss: 0.3606 - val_accuracy: 0.8618\n",
      "Epoch 424/500\n",
      "4379/4379 - 0s - loss: 0.2490 - accuracy: 0.8979 - val_loss: 0.3571 - val_accuracy: 0.8623\n",
      "Epoch 425/500\n",
      "4379/4379 - 0s - loss: 0.2542 - accuracy: 0.8993 - val_loss: 0.3669 - val_accuracy: 0.8521\n",
      "Epoch 426/500\n",
      "4379/4379 - 0s - loss: 0.2586 - accuracy: 0.8966 - val_loss: 0.3655 - val_accuracy: 0.8581\n",
      "Epoch 427/500\n",
      "4379/4379 - 0s - loss: 0.2495 - accuracy: 0.8993 - val_loss: 0.3608 - val_accuracy: 0.8618\n",
      "Epoch 428/500\n",
      "4379/4379 - 0s - loss: 0.2517 - accuracy: 0.8979 - val_loss: 0.3609 - val_accuracy: 0.8558\n",
      "Epoch 429/500\n",
      "4379/4379 - 0s - loss: 0.2562 - accuracy: 0.8931 - val_loss: 0.3655 - val_accuracy: 0.8516\n",
      "Epoch 430/500\n",
      "4379/4379 - 0s - loss: 0.2661 - accuracy: 0.8931 - val_loss: 0.3554 - val_accuracy: 0.8600\n",
      "Epoch 431/500\n",
      "4379/4379 - 0s - loss: 0.2499 - accuracy: 0.9011 - val_loss: 0.3633 - val_accuracy: 0.8646\n",
      "Epoch 432/500\n",
      "4379/4379 - 0s - loss: 0.2546 - accuracy: 0.8975 - val_loss: 0.3775 - val_accuracy: 0.8549\n",
      "Epoch 433/500\n",
      "4379/4379 - 0s - loss: 0.2614 - accuracy: 0.9007 - val_loss: 0.3644 - val_accuracy: 0.8614\n",
      "Epoch 434/500\n",
      "4379/4379 - 0s - loss: 0.2450 - accuracy: 0.9045 - val_loss: 0.3715 - val_accuracy: 0.8563\n",
      "Epoch 435/500\n",
      "4379/4379 - 0s - loss: 0.2471 - accuracy: 0.9011 - val_loss: 0.3583 - val_accuracy: 0.8623\n",
      "Epoch 436/500\n",
      "4379/4379 - 0s - loss: 0.2538 - accuracy: 0.8991 - val_loss: 0.3600 - val_accuracy: 0.8586\n",
      "Epoch 437/500\n",
      "4379/4379 - 0s - loss: 0.2477 - accuracy: 0.9041 - val_loss: 0.3645 - val_accuracy: 0.8609\n",
      "Epoch 438/500\n",
      "4379/4379 - 0s - loss: 0.2505 - accuracy: 0.9018 - val_loss: 0.3755 - val_accuracy: 0.8540\n",
      "Epoch 439/500\n",
      "4379/4379 - 0s - loss: 0.2562 - accuracy: 0.8954 - val_loss: 0.3619 - val_accuracy: 0.8609\n",
      "Epoch 440/500\n",
      "4379/4379 - 0s - loss: 0.2556 - accuracy: 0.8988 - val_loss: 0.3633 - val_accuracy: 0.8609\n",
      "Epoch 441/500\n",
      "4379/4379 - 0s - loss: 0.2539 - accuracy: 0.8952 - val_loss: 0.3626 - val_accuracy: 0.8586\n",
      "Epoch 442/500\n",
      "4379/4379 - 0s - loss: 0.2500 - accuracy: 0.9025 - val_loss: 0.3647 - val_accuracy: 0.8572\n",
      "Epoch 443/500\n",
      "4379/4379 - 0s - loss: 0.2509 - accuracy: 0.8968 - val_loss: 0.3662 - val_accuracy: 0.8577\n",
      "Epoch 444/500\n",
      "4379/4379 - 0s - loss: 0.2483 - accuracy: 0.9057 - val_loss: 0.3617 - val_accuracy: 0.8600\n",
      "Epoch 445/500\n",
      "4379/4379 - 0s - loss: 0.2491 - accuracy: 0.9000 - val_loss: 0.3632 - val_accuracy: 0.8595\n",
      "Epoch 446/500\n",
      "4379/4379 - 0s - loss: 0.2544 - accuracy: 0.8979 - val_loss: 0.3826 - val_accuracy: 0.8498\n",
      "Epoch 447/500\n",
      "4379/4379 - 0s - loss: 0.2668 - accuracy: 0.8913 - val_loss: 0.3671 - val_accuracy: 0.8567\n",
      "Epoch 448/500\n",
      "4379/4379 - 0s - loss: 0.2527 - accuracy: 0.8986 - val_loss: 0.3809 - val_accuracy: 0.8503\n",
      "Epoch 449/500\n",
      "4379/4379 - 0s - loss: 0.2496 - accuracy: 0.9027 - val_loss: 0.3679 - val_accuracy: 0.8614\n",
      "Epoch 450/500\n",
      "4379/4379 - 0s - loss: 0.2492 - accuracy: 0.9013 - val_loss: 0.3594 - val_accuracy: 0.8623\n",
      "Epoch 451/500\n",
      "4379/4379 - 0s - loss: 0.2615 - accuracy: 0.8920 - val_loss: 0.3784 - val_accuracy: 0.8600\n",
      "Epoch 452/500\n",
      "4379/4379 - 0s - loss: 0.2560 - accuracy: 0.8979 - val_loss: 0.3594 - val_accuracy: 0.8632\n",
      "Epoch 453/500\n",
      "4379/4379 - 0s - loss: 0.2485 - accuracy: 0.9009 - val_loss: 0.3648 - val_accuracy: 0.8595\n",
      "Epoch 454/500\n",
      "4379/4379 - 0s - loss: 0.2512 - accuracy: 0.9002 - val_loss: 0.3691 - val_accuracy: 0.8530\n",
      "Epoch 455/500\n",
      "4379/4379 - 0s - loss: 0.2450 - accuracy: 0.9002 - val_loss: 0.3577 - val_accuracy: 0.8660\n",
      "Epoch 456/500\n",
      "4379/4379 - 0s - loss: 0.2518 - accuracy: 0.8995 - val_loss: 0.3638 - val_accuracy: 0.8521\n",
      "Epoch 457/500\n",
      "4379/4379 - 0s - loss: 0.2527 - accuracy: 0.8984 - val_loss: 0.3634 - val_accuracy: 0.8600\n",
      "Epoch 458/500\n",
      "4379/4379 - 0s - loss: 0.2525 - accuracy: 0.8997 - val_loss: 0.3697 - val_accuracy: 0.8600\n",
      "Epoch 459/500\n",
      "4379/4379 - 0s - loss: 0.2596 - accuracy: 0.8968 - val_loss: 0.3621 - val_accuracy: 0.8614\n",
      "Epoch 460/500\n",
      "4379/4379 - 0s - loss: 0.2498 - accuracy: 0.8972 - val_loss: 0.3627 - val_accuracy: 0.8591\n",
      "Epoch 461/500\n",
      "4379/4379 - 0s - loss: 0.2535 - accuracy: 0.9004 - val_loss: 0.3635 - val_accuracy: 0.8642\n",
      "Epoch 462/500\n",
      "4379/4379 - 0s - loss: 0.2463 - accuracy: 0.9034 - val_loss: 0.3665 - val_accuracy: 0.8618\n",
      "Epoch 463/500\n",
      "4379/4379 - 0s - loss: 0.2483 - accuracy: 0.9025 - val_loss: 0.3589 - val_accuracy: 0.8637\n",
      "Epoch 464/500\n",
      "4379/4379 - 0s - loss: 0.2564 - accuracy: 0.8972 - val_loss: 0.3700 - val_accuracy: 0.8554\n",
      "Epoch 465/500\n",
      "4379/4379 - 0s - loss: 0.2481 - accuracy: 0.9023 - val_loss: 0.3697 - val_accuracy: 0.8581\n",
      "Epoch 466/500\n",
      "4379/4379 - 0s - loss: 0.2481 - accuracy: 0.9002 - val_loss: 0.3622 - val_accuracy: 0.8637\n",
      "Epoch 467/500\n",
      "4379/4379 - 0s - loss: 0.2543 - accuracy: 0.9002 - val_loss: 0.3839 - val_accuracy: 0.8465\n",
      "Epoch 468/500\n",
      "4379/4379 - 0s - loss: 0.2475 - accuracy: 0.9000 - val_loss: 0.3646 - val_accuracy: 0.8609\n",
      "Epoch 469/500\n",
      "4379/4379 - 0s - loss: 0.2509 - accuracy: 0.9025 - val_loss: 0.3644 - val_accuracy: 0.8605\n",
      "Epoch 470/500\n",
      "4379/4379 - 0s - loss: 0.2488 - accuracy: 0.8970 - val_loss: 0.3635 - val_accuracy: 0.8567\n",
      "Epoch 471/500\n",
      "4379/4379 - 0s - loss: 0.2588 - accuracy: 0.8972 - val_loss: 0.3788 - val_accuracy: 0.8544\n",
      "Epoch 472/500\n",
      "4379/4379 - 0s - loss: 0.2609 - accuracy: 0.8956 - val_loss: 0.3573 - val_accuracy: 0.8623\n",
      "Epoch 473/500\n",
      "4379/4379 - 0s - loss: 0.2480 - accuracy: 0.9023 - val_loss: 0.3607 - val_accuracy: 0.8614\n",
      "Epoch 474/500\n",
      "4379/4379 - 0s - loss: 0.2466 - accuracy: 0.9023 - val_loss: 0.3691 - val_accuracy: 0.8614\n",
      "Epoch 475/500\n",
      "4379/4379 - 0s - loss: 0.2503 - accuracy: 0.9020 - val_loss: 0.3695 - val_accuracy: 0.8572\n",
      "Epoch 476/500\n",
      "4379/4379 - 0s - loss: 0.2447 - accuracy: 0.9032 - val_loss: 0.3678 - val_accuracy: 0.8600\n",
      "Epoch 477/500\n",
      "4379/4379 - 0s - loss: 0.2485 - accuracy: 0.9027 - val_loss: 0.3718 - val_accuracy: 0.8581\n",
      "Epoch 478/500\n",
      "4379/4379 - 0s - loss: 0.2468 - accuracy: 0.9020 - val_loss: 0.3646 - val_accuracy: 0.8632\n",
      "Epoch 479/500\n",
      "4379/4379 - 0s - loss: 0.2648 - accuracy: 0.8950 - val_loss: 0.3657 - val_accuracy: 0.8540\n",
      "Epoch 480/500\n",
      "4379/4379 - 0s - loss: 0.2499 - accuracy: 0.8970 - val_loss: 0.3664 - val_accuracy: 0.8628\n",
      "Epoch 481/500\n",
      "4379/4379 - 0s - loss: 0.2583 - accuracy: 0.8970 - val_loss: 0.3799 - val_accuracy: 0.8489\n",
      "Epoch 482/500\n",
      "4379/4379 - 0s - loss: 0.2690 - accuracy: 0.8918 - val_loss: 0.3720 - val_accuracy: 0.8554\n",
      "Epoch 483/500\n",
      "4379/4379 - 0s - loss: 0.2500 - accuracy: 0.8977 - val_loss: 0.3694 - val_accuracy: 0.8609\n",
      "Epoch 484/500\n",
      "4379/4379 - 0s - loss: 0.2437 - accuracy: 0.9029 - val_loss: 0.3657 - val_accuracy: 0.8642\n",
      "Epoch 485/500\n",
      "4379/4379 - 0s - loss: 0.2506 - accuracy: 0.9018 - val_loss: 0.3614 - val_accuracy: 0.8618\n",
      "Epoch 486/500\n",
      "4379/4379 - 0s - loss: 0.2411 - accuracy: 0.9068 - val_loss: 0.3657 - val_accuracy: 0.8600\n",
      "Epoch 487/500\n",
      "4379/4379 - 0s - loss: 0.2495 - accuracy: 0.9000 - val_loss: 0.3843 - val_accuracy: 0.8498\n",
      "Epoch 488/500\n",
      "4379/4379 - 0s - loss: 0.2557 - accuracy: 0.8977 - val_loss: 0.3714 - val_accuracy: 0.8632\n",
      "Epoch 489/500\n",
      "4379/4379 - 0s - loss: 0.2464 - accuracy: 0.9027 - val_loss: 0.3667 - val_accuracy: 0.8623\n",
      "Epoch 490/500\n",
      "4379/4379 - 0s - loss: 0.2470 - accuracy: 0.9000 - val_loss: 0.3698 - val_accuracy: 0.8609\n",
      "Epoch 491/500\n",
      "4379/4379 - 0s - loss: 0.2476 - accuracy: 0.9032 - val_loss: 0.3620 - val_accuracy: 0.8618\n",
      "Epoch 492/500\n",
      "4379/4379 - 0s - loss: 0.2402 - accuracy: 0.9061 - val_loss: 0.3733 - val_accuracy: 0.8605\n",
      "Epoch 493/500\n",
      "4379/4379 - 0s - loss: 0.2445 - accuracy: 0.9077 - val_loss: 0.3676 - val_accuracy: 0.8605\n",
      "Epoch 494/500\n",
      "4379/4379 - 0s - loss: 0.2594 - accuracy: 0.8961 - val_loss: 0.3827 - val_accuracy: 0.8512\n",
      "Epoch 495/500\n",
      "4379/4379 - 0s - loss: 0.2573 - accuracy: 0.8979 - val_loss: 0.3837 - val_accuracy: 0.8563\n",
      "Epoch 496/500\n",
      "4379/4379 - 0s - loss: 0.2499 - accuracy: 0.9043 - val_loss: 0.3680 - val_accuracy: 0.8614\n",
      "Epoch 497/500\n",
      "4379/4379 - 0s - loss: 0.2479 - accuracy: 0.9041 - val_loss: 0.3717 - val_accuracy: 0.8600\n",
      "Epoch 498/500\n",
      "4379/4379 - 0s - loss: 0.2484 - accuracy: 0.9020 - val_loss: 0.3690 - val_accuracy: 0.8614\n",
      "Epoch 499/500\n",
      "4379/4379 - 0s - loss: 0.2602 - accuracy: 0.8970 - val_loss: 0.3672 - val_accuracy: 0.8595\n",
      "Epoch 500/500\n",
      "4379/4379 - 0s - loss: 0.2455 - accuracy: 0.9020 - val_loss: 0.3669 - val_accuracy: 0.8628\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x207837e3208>"
      ]
     },
     "metadata": {},
     "execution_count": 17
    }
   ],
   "source": [
    "model = get_model(X_train, y_train)\n",
    "model.compile(\n",
    "    optimizer = tf.keras.optimizers.Adam(lr=0.001),\n",
    "    loss= 'binary_crossentropy',\n",
    "    metrics = ['accuracy']\n",
    ")\n",
    "\n",
    "tensorboard_callbacks = tf.keras.callbacks.TensorBoard(\n",
    "    log_dir=tf_log_dir\n",
    ")\n",
    "\n",
    "verbose, epochs, batch_size = 2, 500, 128\n",
    "model.fit(X_train, y_train,\n",
    "    epochs=epochs, \n",
    "    batch_size=batch_size, \n",
    "    verbose=verbose, \n",
    "    validation_data = (X_test, y_test),\n",
    "    callbacks = [tensorboard_callbacks]\n",
    ") \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred =  model.predict(X_test)\n",
    "y_pred = (y_pred > 0.5) * 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(2157, 1)"
      ]
     },
     "metadata": {},
     "execution_count": 11
    }
   ],
   "source": [
    "mask = (y_pred > 0.5) *1\n",
    "mask.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "cf_matrix = confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "              precision    recall  f1-score   support\n\n         0.0       0.85      0.92      0.88      1320\n         1.0       0.85      0.75      0.80       837\n\n    accuracy                           0.85      2157\n   macro avg       0.85      0.83      0.84      2157\nweighted avg       0.85      0.85      0.85      2157\n\n===============================================================================\n0.8330455088519605\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))\n",
    "print('===============================================================================')\n",
    "print(roc_auc_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(array([0.38803894, 0.85286104, 1.        ]),\n",
       " array([1.       , 0.7479092, 0.       ]),\n",
       " array([0, 1]))"
      ]
     },
     "metadata": {},
     "execution_count": 14
    }
   ],
   "source": [
    "precision_recall_curve(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm_plot_labels = ['Live','died']\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                        normalize=False,\n",
    "                        title='Confusion matrix',\n",
    "                        cmap=pyplot.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    pyplot.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    pyplot.title(title)\n",
    "    pyplot.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    pyplot.xticks(tick_marks, classes, rotation=45)\n",
    "    pyplot.yticks(tick_marks, classes)\n",
    "\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        pyplot.text(j, i, cm[i, j],\n",
    "            horizontalalignment=\"center\",\n",
    "            color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    pyplot.tight_layout()\n",
    "    pyplot.ylabel('True label')\n",
    "    pyplot.xlabel('Predicted label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Confusion matrix, without normalization\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 2 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plot_confusion_matrix(cm=cf_matrix, classes=cm_plot_labels, title='Confusion Matrix')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}