tabular_tensorflow_NN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "755bc941",
   "metadata": {},
   "outputs": [],
   "source": [
    "import h5py\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import lightgbm\n",
    "import re\n",
    "import umap\n",
    "from tensorflow.keras.layers import Dense\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from tensorflow.keras.optimizers import SGD\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn import metrics\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.experimental import enable_halving_search_cv  # noqa\n",
    "from sklearn.model_selection import HalvingGridSearchCV\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "883405ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_labels_to_excel(filename, sheet_name=2):\n",
    "    df = pd.read_excel(filename, sheet_name=sheet_name)\n",
    "    labels = []\n",
    "    for file in df['file']:\n",
    "        label = re.search(\"squeal|whinnie|softsnort|snort\", file)\n",
    "        if label:\n",
    "            label = label.group(0)\n",
    "            if label == \"squeal\":\n",
    "                labels.append(\"squeal\")\n",
    "            elif label == \"whinnie\":\n",
    "                labels.append(\"whinnie\")\n",
    "            elif label == 'snort':\n",
    "                labels.append(\"snort\")\n",
    "            elif label == 'softsnort':\n",
    "                labels.append(\"softsnort\")\n",
    "    df['label'] = labels\n",
    "    return df\n",
    "\n",
    "def split_dataset(X, y, test_total_ratio, val_train_ratio):\n",
    "    X_train_temp, X_test, y_train_temp, y_test = train_test_split(X, y, test_size=test_total_ratio, random_state=0, shuffle=True)\n",
    "    X_train, X_val, y_train, y_val = train_test_split(X_train_temp, y_train_temp, test_size=val_train_ratio, random_state=0, shuffle=True)\n",
    "    return X_train, y_train, X_val, y_val, X_test, y_test\n",
    "\n",
    "def plot_loss(history):\n",
    "    plt.plot(history.history['loss'], label='loss')\n",
    "    # plt.plot(history.history['val_loss'], label='val_loss')\n",
    "    plt.ylim([0,10])\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Error (Loss)')\n",
    "    plt.legend()\n",
    "    plt.grid(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4b594dbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "directory_file = r\"./Zebras_Assumption_data.xlsx\"\n",
    "df = add_labels_to_excel(directory_file)\n",
    "\n",
    "X = df[df.columns.drop('file').drop('label')]\n",
    "y = df['label']\n",
    "\n",
    "#%% Split into training, testing, and validation(optional) sets\n",
    "# Fractions must add up to 1.0\n",
    "train_frac = 0.70\n",
    "val_frac = 0.00\n",
    "test_frac = 0.30\n",
    "\n",
    "# If we dont need a validation set\n",
    "if val_frac == 0:\n",
    "    val_present = False\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_frac, random_state=0, shuffle=True)\n",
    "# If we need a validation set\n",
    "else:\n",
    "    val_present = True\n",
    "    X_train, y_train, X_val, y_val, X_test, y_test = split_dataset(X, y, test_frac, val_frac/(train_frac + val_frac))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "eba1f857",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[20]\ttraining's multi_logloss: 0.13769\n",
      "[40]\ttraining's multi_logloss: 0.0260983\n",
      "[60]\ttraining's multi_logloss: 0.0044881\n",
      "[80]\ttraining's multi_logloss: 0.000856258\n",
      "[100]\ttraining's multi_logloss: 0.000164422\n",
      "[608 423 542 841 875 710 485 643 737]\n"
     ]
    }
   ],
   "source": [
    "# Feature importance\n",
    "lgbm_model = lightgbm.LGBMClassifier()\n",
    "\n",
    "if val_present == True:\n",
    "    lgbm_model.fit(X_train, y_train, eval_set=[(X_train, y_train), (X_val, y_val)], verbose=20, eval_metric='logloss')\n",
    "else:\n",
    "    lgbm_model.fit(X_train, y_train, eval_set=[(X_train, y_train)], verbose=20, eval_metric='logloss')\n",
    "print(lgbm_model.feature_importances_)\n",
    "feature_importance = lgbm_model.feature_importances_\n",
    "\n",
    "total = 0\n",
    "for i in feature_importance:\n",
    "    total += i\n",
    "average = total / len(feature_importance)\n",
    "\n",
    "feature_importance_series = pd.Series(feature_importance, index=X_train.columns)\n",
    "impt_features_list = feature_importance_series[feature_importance >= average].index\n",
    "X_train = X_train[impt_features_list]\n",
    "X_test = X_test[impt_features_list]\n",
    "if val_present:\n",
    "    X_val = X_val[impt_features_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b5c61ba1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# SCALING\n",
    "X_scaler = StandardScaler()\n",
    "\n",
    "# Fit on Training Data\n",
    "X_scaler.fit(X_train.values)\n",
    "\n",
    "# Transform Training, Validation and Testing data\n",
    "X_train = X_scaler.transform(X_train.values)\n",
    "X_test = X_scaler.transform(X_test.values)\n",
    "\n",
    "lb = LabelEncoder()\n",
    "\n",
    "y_train = lb.fit_transform(y_train)\n",
    "y_test = lb.fit_transform(y_test)\n",
    "\n",
    "if val_present:\n",
    "    X_val = X_scaler.transform(X_val.values)\n",
    "    y_group_val = lb.fit_transform(y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6b32b7b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1000\n",
      "3/3 [==============================] - 0s 3ms/step - loss: 2.2924 - accuracy: 0.0138\n",
      "Epoch 2/1000\n",
      "3/3 [==============================] - 0s 3ms/step - loss: 2.2742 - accuracy: 0.0138\n",
      "Epoch 3/1000\n",
      "3/3 [==============================] - 0s 3ms/step - loss: 2.2564 - accuracy: 0.0138\n",
      "Epoch 4/1000\n",
      "3/3 [==============================] - 0s 4ms/step - loss: 2.2390 - accuracy: 0.0173\n",
      "Epoch 5/1000\n",
      "3/3 [==============================] - 0s 4ms/step - loss: 2.2215 - accuracy: 0.0277\n",
      "Epoch 6/1000\n",
      "3/3 [==============================] - 0s 4ms/step - loss: 2.2037 - accuracy: 0.0311\n",
      "Epoch 7/1000\n",
      "3/3 [==============================] - 0s 4ms/step - loss: 2.1864 - accuracy: 0.0450\n",
      "Epoch 8/1000\n",
      "3/3 [==============================] - 0s 3ms/step - loss: 2.1699 - accuracy: 0.0623\n",
      "Epoch 9/1000\n",
      "3/3 [==============================] - 0s 4ms/step - loss: 2.1525 - accuracy: 0.0761\n",
      "Epoch 10/1000\n",
      "3/3 [==============================] - 0s 3ms/step - loss: 2.1355 - accuracy: 0.0969\n",
      "Epoch 11/1000\n",
      "3/3 [==============================] - 0s 5ms/step - loss: 2.1184 - accuracy: 0.1557\n",
      "Epoch 12/1000\n",
      "3/3 [==============================] - 0s 3ms/step - loss: 2.1013 - accuracy: 0.1730\n",
      "Epoch 13/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 2.0842 - accuracy: 0.2145\n",
      "Epoch 14/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 2.0673 - accuracy: 0.2388\n",
      "Epoch 15/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 2.0501 - accuracy: 0.2837\n",
      "Epoch 16/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 2.0333 - accuracy: 0.3045\n",
      "Epoch 17/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 2.0167 - accuracy: 0.3356\n",
      "Epoch 18/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 1.9995 - accuracy: 0.3599\n",
      "Epoch 19/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 1.9831 - accuracy: 0.3875\n",
      "Epoch 20/1000\n",
      "3/3 [==============================] - 0s 1ms/step - loss: 1.9663 - accuracy: 0.4464\n",
      "Epoch 21/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 1.9496 - accuracy: 0.4740\n",
      "Epoch 22/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 1.9331 - accuracy: 0.5121\n",
      "Epoch 23/1000\n",
      "3/3 [==============================] - 0s 1ms/step - loss: 1.9164 - accuracy: 0.5398\n",
      "Epoch 24/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 1.8997 - accuracy: 0.5571\n",
      "Epoch 25/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 1.8823 - accuracy: 0.5813\n",
      "Epoch 26/1000\n",
      "3/3 [==============================] - 0s 1ms/step - loss: 1.8653 - accuracy: 0.5986\n",
      "Epoch 27/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 1.8489 - accuracy: 0.6194\n",
      "Epoch 28/1000\n",
      "3/3 [==============================] - 0s 1ms/step - loss: 1.8320 - accuracy: 0.6228\n",
      "Epoch 29/1000\n",
      "3/3 [==============================] - 0s 1ms/step - loss: 1.8152 - accuracy: 0.6332\n",
      "Epoch 30/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 1.7984 - accuracy: 0.6436\n",
      "Epoch 31/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 1.7810 - accuracy: 0.6644\n",
      "Epoch 32/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 1.7637 - accuracy: 0.6747\n",
      "Epoch 33/1000\n",
      "3/3 [==============================] - 0s 1ms/step - loss: 1.7463 - accuracy: 0.6817\n",
      "Epoch 34/1000\n",
      "3/3 [==============================] - 0s 1ms/step - loss: 1.7287 - accuracy: 0.6886\n",
      "Epoch 35/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 1.7111 - accuracy: 0.6955\n",
      "Epoch 36/1000\n",
      "3/3 [==============================] - 0s 1ms/step - loss: 1.6938 - accuracy: 0.7024\n",
      "Epoch 37/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 1.6766 - accuracy: 0.7163\n",
      "Epoch 38/1000\n",
      "3/3 [==============================] - 0s 1ms/step - loss: 1.6592 - accuracy: 0.7232\n",
      "Epoch 39/1000\n",
      "3/3 [==============================] - 0s 1ms/step - loss: 1.6412 - accuracy: 0.7336\n",
      "Epoch 40/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 1.6238 - accuracy: 0.7509\n",
      "Epoch 41/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 1.6065 - accuracy: 0.7578\n",
      "Epoch 42/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 1.5895 - accuracy: 0.7578\n",
      "Epoch 43/1000\n",
      "3/3 [==============================] - 0s 1ms/step - loss: 1.5725 - accuracy: 0.7612\n",
      "Epoch 44/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 1.5552 - accuracy: 0.7647\n",
      "Epoch 45/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 1.5385 - accuracy: 0.7716\n",
      "Epoch 46/1000\n",
      "3/3 [==============================] - 0s 1ms/step - loss: 1.5217 - accuracy: 0.7751\n",
      "Epoch 47/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 1.5050 - accuracy: 0.7785\n",
      "Epoch 48/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 1.4878 - accuracy: 0.7855\n",
      "Epoch 49/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 1.4713 - accuracy: 0.7889\n",
      "Epoch 50/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 1.4547 - accuracy: 0.7958\n",
      "Epoch 51/1000\n",
      "3/3 [==============================] - 0s 1ms/step - loss: 1.4382 - accuracy: 0.7958\n",
      "Epoch 52/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 1.4219 - accuracy: 0.7993\n",
      "Epoch 53/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 1.4050 - accuracy: 0.8028\n",
      "Epoch 54/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 1.3887 - accuracy: 0.8131\n",
      "Epoch 55/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 1.3723 - accuracy: 0.8166\n",
      "Epoch 56/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 1.3565 - accuracy: 0.8235\n",
      "Epoch 57/1000\n",
      "3/3 [==============================] - 0s 1ms/step - loss: 1.3411 - accuracy: 0.8235\n",
      "Epoch 58/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 1.3260 - accuracy: 0.8235\n",
      "Epoch 59/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 1.3108 - accuracy: 0.8235\n",
      "Epoch 60/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 1.2962 - accuracy: 0.8235\n",
      "Epoch 61/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 1.2813 - accuracy: 0.8235\n",
      "Epoch 62/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 1.2668 - accuracy: 0.8235\n",
      "Epoch 63/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 1.2519 - accuracy: 0.8270\n",
      "Epoch 64/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 1.2373 - accuracy: 0.8270\n",
      "Epoch 65/1000\n",
      "3/3 [==============================] - 0s 3ms/step - loss: 1.2234 - accuracy: 0.8270\n",
      "Epoch 66/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 1.2102 - accuracy: 0.8304\n",
      "Epoch 67/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 1.1968 - accuracy: 0.8304\n",
      "Epoch 68/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 1.1832 - accuracy: 0.8339\n",
      "Epoch 69/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 1.1696 - accuracy: 0.8374\n",
      "Epoch 70/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 1.1565 - accuracy: 0.8374\n",
      "Epoch 71/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 1.1433 - accuracy: 0.8374\n",
      "Epoch 72/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 1.1303 - accuracy: 0.8374\n",
      "Epoch 73/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 1.1173 - accuracy: 0.8374\n",
      "Epoch 74/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 1.1050 - accuracy: 0.8374\n",
      "Epoch 75/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 1.0927 - accuracy: 0.8408\n",
      "Epoch 76/1000\n",
      "3/3 [==============================] - 0s 3ms/step - loss: 1.0807 - accuracy: 0.8408\n",
      "Epoch 77/1000\n",
      "3/3 [==============================] - 0s 3ms/step - loss: 1.0685 - accuracy: 0.8408\n",
      "Epoch 78/1000\n",
      "3/3 [==============================] - 0s 3ms/step - loss: 1.0560 - accuracy: 0.8478\n",
      "Epoch 79/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 1.0442 - accuracy: 0.8547\n",
      "Epoch 80/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 1.0328 - accuracy: 0.8547\n",
      "Epoch 81/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 1.0211 - accuracy: 0.8547\n",
      "Epoch 82/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 1.0100 - accuracy: 0.8581\n",
      "Epoch 83/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 0.9994 - accuracy: 0.8581\n",
      "Epoch 84/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 0.9883 - accuracy: 0.8581\n",
      "Epoch 85/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 0.9776 - accuracy: 0.8581\n",
      "Epoch 86/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 0.9674 - accuracy: 0.8581\n",
      "Epoch 87/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 0.9575 - accuracy: 0.8581\n",
      "Epoch 88/1000\n",
      "3/3 [==============================] - 0s 3ms/step - loss: 0.9480 - accuracy: 0.8581\n",
      "Epoch 89/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 0.9382 - accuracy: 0.8581\n",
      "Epoch 90/1000\n",
      "3/3 [==============================] - 0s 1ms/step - loss: 0.9283 - accuracy: 0.8581\n",
      "Epoch 91/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 0.9189 - accuracy: 0.8616\n",
      "Epoch 92/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 0.9092 - accuracy: 0.8651\n",
      "Epoch 93/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 0.9002 - accuracy: 0.8651\n",
      "Epoch 94/1000\n",
      "3/3 [==============================] - 0s 1ms/step - loss: 0.8916 - accuracy: 0.8651\n",
      "Epoch 95/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 0.8824 - accuracy: 0.8651\n",
      "Epoch 96/1000\n",
      "3/3 [==============================] - 0s 3ms/step - loss: 0.8737 - accuracy: 0.8651\n",
      "Epoch 97/1000\n",
      "3/3 [==============================] - 0s 3ms/step - loss: 0.8653 - accuracy: 0.8651\n",
      "Epoch 98/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 0.8569 - accuracy: 0.8651\n",
      "Epoch 99/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 0.8484 - accuracy: 0.8651\n",
      "Epoch 100/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 0.8404 - accuracy: 0.8651\n",
      "Epoch 101/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 0.8324 - accuracy: 0.8651\n",
      "Epoch 102/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 0.8248 - accuracy: 0.8651\n",
      "Epoch 103/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 0.8173 - accuracy: 0.8651\n",
      "Epoch 104/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 0.8097 - accuracy: 0.8651\n",
      "Epoch 105/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 0.8028 - accuracy: 0.8651\n",
      "Epoch 106/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 0.7956 - accuracy: 0.8651\n",
      "Epoch 107/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 0.7887 - accuracy: 0.8651\n",
      "Epoch 108/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 0.7819 - accuracy: 0.8651\n",
      "Epoch 109/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 0.7753 - accuracy: 0.8651\n",
      "Epoch 110/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 0.7688 - accuracy: 0.8685\n",
      "Epoch 111/1000\n",
      "3/3 [==============================] - 0s 3ms/step - loss: 0.7624 - accuracy: 0.8685\n",
      "Epoch 112/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 0.7562 - accuracy: 0.8685\n",
      "Epoch 113/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 0.7499 - accuracy: 0.8685\n",
      "Epoch 114/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 0.7436 - accuracy: 0.8685\n",
      "Epoch 115/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 0.7377 - accuracy: 0.8685\n",
      "Epoch 116/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 0.7320 - accuracy: 0.8685\n",
      "Epoch 117/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 0.7264 - accuracy: 0.8720\n",
      "Epoch 118/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 0.7211 - accuracy: 0.8720\n",
      "Epoch 119/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 0.7156 - accuracy: 0.8720\n",
      "Epoch 120/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 0.7104 - accuracy: 0.8720\n",
      "Epoch 121/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 0.7053 - accuracy: 0.8720\n",
      "Epoch 122/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 0.7002 - accuracy: 0.8720\n",
      "Epoch 123/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 0.6952 - accuracy: 0.8720\n",
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    },
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 657/1000\n",
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     ]
    },
    {
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     "output_type": "stream",
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     ]
    },
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     ]
    },
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      "3/3 [==============================] - 0s 3ms/step - loss: 0.3036 - accuracy: 0.9170\n",
      "Epoch 992/1000\n",
      "3/3 [==============================] - 0s 3ms/step - loss: 0.3034 - accuracy: 0.9170\n",
      "Epoch 993/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 0.3034 - accuracy: 0.9170\n",
      "Epoch 994/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 0.3033 - accuracy: 0.9170\n",
      "Epoch 995/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 0.3032 - accuracy: 0.9170\n",
      "Epoch 996/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 0.3032 - accuracy: 0.9170\n",
      "Epoch 997/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 0.3030 - accuracy: 0.9170\n",
      "Epoch 998/1000\n",
      "3/3 [==============================] - 0s 3ms/step - loss: 0.3031 - accuracy: 0.9170\n",
      "Epoch 999/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 0.3029 - accuracy: 0.9170\n",
      "Epoch 1000/1000\n",
      "3/3 [==============================] - 0s 2ms/step - loss: 0.3029 - accuracy: 0.9170\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x15cadffbd00>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nn_clf = tf.keras.Sequential([\n",
    "    tf.keras.layers.Flatten(input_shape=(4,)),\n",
    "    tf.keras.layers.Dense(10,activation=\"relu\"),\n",
    "    tf.keras.layers.Dense(8,activation=\"relu\"),\n",
    "    tf.keras.layers.Dense(10,activation=\"softmax\")\n",
    "])\n",
    "\n",
    "nn_clf.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(), \n",
    "              optimizer=tf.keras.optimizers.SGD(), \n",
    "              metrics=['accuracy'])\n",
    "\n",
    "nn_clf.fit(X_train, y_train, epochs=1000, batch_size=128)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "48e284ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[66  0  0  2]\n",
      " [ 3  0  0  0]\n",
      " [ 9  1  5  0]\n",
      " [ 0  0  0 38]]\n"
     ]
    }
   ],
   "source": [
    "y_pred_proba = nn_clf.predict(X_test)\n",
    "\n",
    "y_pred = []\n",
    "for proba in y_pred_proba:\n",
    "    highest_group = proba.argmax()\n",
    "    y_pred.append(highest_group)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3cd00779",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0.5, 'snort'),\n",
       " Text(0, 1.5, 'softsnort'),\n",
       " Text(0, 2.5, 'squeal'),\n",
       " Text(0, 3.5, 'whinnie')]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Va3WWLlnCuGOPYoeddmXE5svN+1r1Ghoa+OkF53PphCuor6/niMMOYdTue7DRsGFZh9blXBbNqq0s8trLpWgNXdJGklZLfx8l6WRJa1Y6kKZrFNuWR5Lo1Wt1AJYuXcrSpUshn//ene6ZGU8zePAGrDd4MN26d2f0vvsx+YFJWYeVCZdFs2ori7zW0EtpcrkFaJA0DLgcGApc1wmxPFritlxqaGjgK4eNYZ89d2a7z+3IiM1Wvdo5wLy5cxk4aOCy9QH19cydOzfDiLLjsmhWbWUhlb50pVISemNELAXGAP8TEd8DBlUqAEkDJW0D9JS0laSt02UU0KvIseMkTZM07co/TahUSB1SW1vL1Tfcxh33PcBzz8zg3/96KdN4shLEctvy2iOgs7ksmlVbWeS1hl5KG/oSSV8Gjga+mG7rVsEY9ga+BqwH/Irmxor3gR+1d2BEjAfGA8xf2LD8X0wGevfuw9Yjt+WxRx5ko2HDsw6ny9XXD+SNOW8sW583dy4DBgzIMKLsuCyaVVtZdEaellQLTANei4j9JfUDbgCGADOBsRExv71zlFJDPwbYAbggIl6RNBS4ZkUCLxQRE4E9gRMiYo+I2D1dDoyIWyt1nc40/513+OCD9wFYtGgRU//xKBsM2TDjqLKx6YjNmDVrJrNnv8qSxYu595672W33PbIOKxMui2bVVha1NSp5KcN3gOcL1k8HJkXEcGBSut6uojX0iHgOOBlA0lpA74j4eTlRlnCNRknfAK6t5Hm7yltvvcl/nXMGDY2NRGMje+41mp13HZV1WJmoq6vjjDPP4ZvjjqexsYGDxhzMsFXwmwq4LApVW1lUuilF0nrAfsAFwCnp5gOBUenvE4HJwA/bPU9E+y0VkiYDB5Ak/yeBN4G/R8Qp7RxWNklnAx+RfMVY0LQ9It4p5fi8NLlkrWf32qxDMMu9HnUr1g9tn4v/UXK+ufdbn/sGMK5g0/i0uXgZSTcDPwN6A6elTS7vRsSaBfvMj4i12rtWKW3ofSPifUnHA1dExI8lPV3qmynDsenPbxdsC2DVbLsws9wqp4ZeeK+vjXPtD8yLiOlpZ5AOKyWh10kaBIwFzlyRi7UnIoZ21rnNzCqpwi0uOwEHSNoX6AH0kXQNMFfSoIiYk+bgecVOVMpN0fOB+4B/RcRUSRsCFe+TJ6lb+tDSzelyoqRK9qYxM6sIlfFfMRFxRkSsFxFDgMOBv0XEUcAdJL0LSX/eXuxcpdwUvQm4qWD9ZeDgolGW72KS7pB/TNe/km47vhOuZWbWYWX2XumonwM3SjoOmAUcWuyAogldUg/gOGBTkq8DAETEsW0e1DHbRkTh45V/k/RUha9hZrbCOut5oYiYTNKbhYh4m6RLd8lKaXK5GhhI8gDQ30keAPqgnIuUqEHSRk0radNOQydcx8xshdRIJS9dqZSbosMi4lBJB0bEREnXkbSpV9r3gQckvUzytOgGJA81mZnlSl5HLSjp0f/057uSRgBvkDyKWlERMUnScGBjkoT+QkR8XOnrmJmtqLyOQ1NKQh+fPiF6Nsld1zWAczopnm1IPizqgC0kERFXddK1zMw6JKf5vKReLpelv/6dTnzIR9LVwEYkT6M2tZ0H4IRuZrlSm9OM3mZCl9Tuo/0R8esKxzIS+GwUG4vAzCxjK2OTS+8uiyLxDElvmjldfF0zs7LkdAa6thN6RJzXlYEA6wDPSfonsOxmaEQc0MVxmJm1a6WroUv6BfByRFzSYvv3gIER0e4wjh1wboXPZ2bWKXKaz9ttctkfGNHK9t8AT1NkXN4OmAZ8lI6N/mlgE+AvFb6GmdkK66JH/8vW3pOiERGNrWxspHPmtJ8C9JC0LsnsHMcAV3bCdczMVkhe5xRtL6EvTB/0+YR020edEIsiYiHwJeB3ETGGZPwYM7NcURlLV2qvyeUc4C+SfgJMT7eNBM4AvtsJsUjSDsCRJIOBAXj6HTPLna4eo6VU7fVy+Yukg0jGWDkp3fwMcHBEzOiEWL5D8mFxW0Q8mw7O9UAnXMfMbIXkNJ+3/6RoRDxD8wDrna2+sItiRLws6cEuuraZWcny2m2xlOFzu8oZJW4zM8tUbY1KXrpSKYNzdSpJ+wD7AutK+m3BS32ApdlEZWbWtpxW0LNP6MA7JH3QD6D55iskk2h8r+STLFhc4bBWToPqehTfaRXxwSLXB5r07eXpeSspr00u7T0p+juS0Q5bFREnVyiGiyNia0l7R8TECp3TzKzT5KmtulB7NfRpXRRDd0lHA9tL+lLLFyPi1i6Kw8ysJCtdDb0La8snkPQ9XxP4YsswACd0M8uVnD75X7wNXVJ/knFbPgssa6CNiD0qEUBEPAQ8JGlaRFxeiXOamXWmlXEslybXAs8DQ4HzgJnA1E6I5WpJJ0u6OV1OkuQ7OWaWOzUqfenSuErYZ+205rwkIv4eEccCn+uEWP5IMqfoH9Nla+DiTriOmdkKkUpfulIp3RaXpD/nSNoPeB1YrxNi2TYitihY/5ukpzrhOmZmK6SSY7lI6kEy2uxqJDn55oj4saR+wA3AEJKWkbERMb/duEq43k8k9QVOBU4DLqOM/uFlaJC0UdNKOpZLQzv7m5lloqaMpQQfA3ukFdotgdGSPgecDkyKiOEkQ4qfXuxERWvoEXFX+ut7wO6lxdchpwEPSHo5XR9CMia6mVmuVPKmaEQE8GG62i1dAjgQGJVunwhMpsjEQqX0crmCVh4wStvSK2ltkhmShpC8kR1JPkTMzHKlnBYXSeOAcQWbxkfE+Bb71JI8KT8M+ENE/ENSfUTMAYiIOZIGFLtWKW3odxX83gMYQ9KOXmlnR8RNkvoAewG/Irkpun0nXMvMrMPKqaCnyXt8kX0agC0lrQncJqm16T+LKqXJ5ZbCdUnXA//bkYsV0dRevh9wSUTcLuncTriOmdkK6awJLiLiXUmTgdHAXEmD0tr5IGBe0bg6cM3hwPodOK6Y1yRdCowF7pG0GvkdMsHMVmGV7LYoqX9aM0dST+DzwAvAHTTPR3E0cHuxc5XShv4Bn2xDf4MiDfMdNJbkU+mi9FNqEMlsSWZmuVLhB4YGARPTdvQa4MaIuEvSo8CNko4DZgGHFjtRKU0uvVc02lKkE0TfWrA+B5jTFdc2MytHbQWbXCLiaWCrVra/DexZzrmKNmlImlTKNjOzVUVeH/1vbzz0HkAvYB1JawFNofUBPtUFsZmZ5dJKN3wu8A3guyTJezrNCf194A+dG5aZWX7ldLDFdsdD/w3wG0knRcTvujAmM7Ncy2kFvaRugY1NXWoAJK0l6VudF5KZWb7VSCUvXRpXCft8PSLebVpJR/v6eqdFZGaWc7U1pS9dqZRH/2skKR1ApmnMge6dG5aZWX7VkM82l1IS+n0kndsvIXnA6ATg3k6NaiX05xuv5b47byUiGH3Alzho7FFZh5SJc8/+EVOmTKZfv7W5+bY7sw4nc2MP+AI9e61ObU0NtXW1TLjqxqxDyszDD07hwp9fQGNDI2MOPpTjvj6u+EE5ldc29FIS+g9JRgr7JklPl/uBCZ0Z1Mpm5sv/4r47b+W/J1xDt7punH3qt9l2h11Yd/AGWYfW5b544BgO+/KRnH1m0aGbVxm/ueRPrLnmWlmHkamGhgZ+esH5XDrhCurr6znisEMYtfsebDRsWNahdUhee7kUbeGJiMaIuCQiDomIg4FnAfd6KfDqzJfZeNPN6dGjJ7V1dYzYahsemfK3rMPKxDYjt6Vv375Zh2E588yMpxk8eAPWGzyYbt27M3rf/Zj8wMr7fGJeb4qWUkNH0pbAl4HDgFcoeER/RUnaur3XI+LxSl2rs2yw4TAmjv8977/3Lt1XW41pjz7E8E0+m3VYlgcSp544DkkcMOZQDvhS0eE4qtK8uXMZOGjgsvUB9fXMePrpDCNaMZWc4KKS2ntS9NPA4SSJ/G2Sue0UEZWetehX7bwWwB4Vvl7FrT9kQw496hjO/N4J9OjZi6HDPk1tbW3WYVkO/PGyq1mn/wDmv/M2p5z4ddYfMpQttx6ZdVhdLpafIye3T1uWIq+ht1dDfwF4EPhiRPwLQFLF5xJdkQ+IwplAfnLR7zj8q8dVLK5y7b3/GPbefwwAV176W9bpX59ZLJYf6/RPJplZq9/a7DJqT55/dsYqmdDr6wfyxpw3lq3PmzuXAQOKTsCTW3kd17u9uA4mGSr3AUkTJO0JndtXR9IISWMlfbVpaW//iBgfESMjYmSWyRzg3fnvADDvjTk88ve/sdvn98k0HsveRx8tZOGCBct+n/rYI2y40fCMo8rGpiM2Y9asmcye/SpLFi/m3nvuZrfdc//lu02SSl66UnuP/t9GMhXS6sBBwPeAekkXA7dFxP2VDETSj0kmRP0scA+wD/AQcFUlr9NZLjjzVN5//z3qauv41iln0LtPn6xDysTpPziF6VOn8u6789l7z9044dsnMeZLh2QdVibmv/02Z/7gOwA0LG3g86P3Zfsdd844qmzU1dVxxpnn8M1xx9PY2MBBYw5m2LCV98Mtpy0uKH1eqLSdpX4kg6wfFhEV/XiVNAPYAngiIraQVA9cFhFfLOX4f7/5UelvpIoN6tsj6xBy44NFS7MOITf69uqWdQi50qNuxXLyNdNnl5xvjtpmvS7L/2U1BUXEOxFxaaWTeeqjiGgElqYTRc8DNuyE65iZrZCVbjz0DExLBwGbQDJc74fAPzONyMysFXntoZObhB4RTSM4XiLpXqBPOjWTmVmurIy9XLqUEkdJOiciZgLvStou67jMzFrKay+X3CR04I/ADiQPMgF8gGdGMrMcUhlLV8pNkwuwfURsLekJSMZdl+Rhes0sd9yGXtySdKz1pnHX+wON2YZkZra82pwm9Dw1ufwWuA0YIOkCkoeKfpptSGZmy3OTSxERca2k6UDTEAMHRcTzGYdlZracSlbQJQ0meSJ+IEmrxPiI+E36IOcNwBBgJjA2nQK0TbmpoUtaH1gI3AncASxIt5mZ5UoNKnkpwVLg1Ij4DPA54NuSPgucDkyKiOHApHS9XbmpoQN3k7SfC+gBDAX+D9g0y6DMzFqqZA09IuYAc9LfP5D0PLAucCDJ+FYAE4HJJDPItSk3CT0iNitcTye++EZG4ZiZtamcmYgKh/lOjY+I8W3sOwTYCvgHUJ8meyJijqSi4w3nJqG3FBGPS9o26zjMzFoqsSkFSIb5BlpN4IUkrQHcAnw3It7vSNfI3CR0SacUrNYAWwNvZhSOmVmbKt1rUVI3kmR+bUQ0TfE5V9KgtHY+iGTAwnbl5qYo0LtgWY2kTf3ATCMyM2uFVPpS/FwScDnwfET8uuClO4Cj09+PBm4vdq7c1NAj4rysYzAzK4Uq28N8J+ArwAxJT6bbfgT8HLhR0nHALJK5KNqVm4Qu6Y72Xo+IA7oqFjOz9lRynPOIeIi2n0Has5xz5SahA6+QdKy/Jl3/Mkln+vuyCsjMrDXl9HLpSnlK6FtFxK4F63dKmhIRP8osIjOzVlS4yaVi8nRTtL+kZVPOpb/3zzAeM7NWeQq64r4LTJb0MskTo0P5ZGd8M7NcyGsNPU8JvQ8wgiSRHwDsCLyVaURmZq3IaRN6rhL62RFxk6TewF7Ar4CLge1LOXjdtXp2Zmy2Eurbq1vWIeTGkVdNzzqEXLnl2G1W6HiPh15cQ/pzP+CSiLgd8IxFZpY7eR0PPU8J/TVJlwJjgXskrUa+4jMzS+Q0o+cpYY4l6XM+OiLeBfoB3880IjOzVqiM/7pSbtrQI2IhcGvB+rIxgs3M8iSnTej5SehmZiuLnOZzJ3Qzs3J1ZKzyruCEbmZWppzmcyd0M7Ny5TSfO6GbmZUtpxndCd3MrEwey8XMrEq4Dd3MrEo4oZuZVQk3uZiZVQnX0M3MqkRO87kTuplZ2XKa0Z3QzczKVJPTNhcndDOzMuUznedrPHQzs5VDBSe4kPQnSfMkPVOwrZ+kv0p6Kf25VilhOaGbmZWpwhNcXAmMbrHtdGBSRAwHJqXrRTmhm5mVSSp9KSYipgDvtNh8IDAx/X0icFApcTmhm5mVqZwWF0njJE0rWMaVcIn6dNa2ptnbBpQSl2+KVsjDD07hwp9fQGNDI2MOPpTjvl7Kv1l1clk0W5XLolut+K99N6ZbraiVeHTmfG54Yg5D+vXkGzuuT7faGhoimPDILP711sKswy1LORNcRMR4YHznRdPMCb0CGhoa+OkF53PphCuor6/niMMOYdTue7DRsGFZh9blXBbNVvWyWNIQnPuXF1m0tJFawU/234THZ7/P4Vt/ihufnMMTs99n6/X68JVt1+PHf3kx63DL0gW9FudKGhQRcyQNAuaVcpCbXCrgmRlPM3jwBqw3eDDdundn9L77MfmBSVmHlQmXRTOXBSxa2ghAbY2ok4CACHp2qwWgV/da5i9ckmGEHVPBTi5tuQM4Ov39aOD2Ug5yDb0C5s2dy8BBA5etD6ivZ8bTT2cYUXZcFs1cFlAj+MUBn2Fgn9W49/k3eenNhfzpH7M5e+/hHL3tekhw5l3/l3WY5atgDV3S9cAoYB1Js4EfAz8HbpR0HDALOLSUc+UmoStplDoS2DAizpe0PjAwIv6ZcWhFBbHctrxOItvZXBbNXBbQGHDa7c/Tq3stP9xzIwav2YO9Nu7Plf94lcf+8y47Dl2Lb+2yAefd+1LWoZalkqMtRsSX23hpz3LPlacmlz8COwBNb+4D4A/tHVB49/jyCV1yz6FV9fUDeWPOG8vW582dy4ABJd2Urjoui2Yui2YLFzfwzJwP2Gq9vowavjaP/eddAB55ZT7D1lk92+A6oJLdFispTwl9+4j4NrAIICLmA93bOyAixkfEyIgYmWXvgU1HbMasWTOZPftVlixezL333M1uu++RWTxZclk0W9XLok+POnp1T9rKu9eKzT/Vm9feW8T8hYvZdOAaAGw2qDdz3l+UZZgdkteEnpsmF2CJpFpIvqdK6g80ZhtSaerq6jjjzHP45rjjaWxs4KAxBzNs2PCsw8qEy6LZql4Wa/Xsxom7DqFWSVPTI6/MZ/qr77Fg8VKO3X4wtTVicUNwycOzsg61bHmd4EIRy7fzZUHSkcBhwNYkT0YdApwVETeVcvyipa00WJoZAEdeNT3rEHLllmO3WaGMPOudj0vON+v3W63Lsn9uaugRca2k6SQ3AgQcFBHPZxyWmdly8lk/z0FCl9QnIt6X1I+k8/z1Ba/1i4iWYxyYmWUqr52VMk/owHXA/sB0kvZztfi5YXahmZktL6/dTzNP6BGxf/pzaNaxmJmVIp/pPAcJvZCkdYENKIgrHVrSzCw3clpBz09Cl3QhSS+X54CGdHMATuhmlit57baYm4ROMoD7xhHxcdaBmJm1K5/5PFcJ/WWgG+CEbma5ltN8nquEvhB4UtIkCpJ6RJycXUhmZsuryWkjep4S+h3pYmaWb/nM5/lJ6BExsfheZmbZy2k+z09Cl7QTcC7N3RYFRET4wSIzy5WctrjkJ6EDlwPfI3litKHIvmZmmXG3xeLei4i/ZB2EmVkxrqEX94CkXwK38sleLo9nF5KZ2fKc0IvbPv05smBbAKvOFC9mtlJwk0sREbF71jGYmZXCNfQ2SDoqIq6RdEprr0fEr7s6JjOz9uQ0n2ef0IGmKb97ZxqFmVmpcprRM0/oEXFp+vO8rGMxMyuFH/0vQlJ/4OvAED45HvqxWcVkZtaaSqdzSaOB3wC1wGUR8fOOnCc3CR24HXgQ+F/8YJGZ5VkFM7qkWuAPwF7AbGCqpDsi4rlyz5WnhN4rIn6YdRBmZsVUuNvidsC/IuJlAEn/DziQZLKfsuQpod8lad+IuKcjB/eoy/42haRxETE+6zjywGXRLA9lccux22R5+WXyUBaV0LNb6flG0jhgXMGm8S3KYF3g1YL12TQ/l1MWRURHjqsYSR+QPEAEsAbJU6JL0/WIiD6ZBNYBkqZFxMjie1Y/l0Uzl0Uzl8XyJB0K7B0Rx6frXwG2i4iTyj1X5jX0iOgNIOlqkjb0ByPi+WyjMjPrMrOBwQXr6wGvd+RENRUJpzKuAAYCv5X0b0k3S/pO1kGZmXWyqcBwSUMldQcOp4OT/WReQ28SEX+T9HdgW2B34ARgBElXnpXFSt82WEEui2Yui2YuixYiYqmkE4H7SLot/ikinu3IuTJvQ2+SziW6OvAoSdPLQxExL9uozMxWHnlqcnkaWExSK98cGCGpZ7YhmZmtPHJTQ28iaQ3gGOA0YGBErJZxSGZmK4Xc1NAlnSjpBuBJ4CDgT8A+WcbUGSRtKWnfrOPoKEm7SHpW0pOSduiK9yLpa5I+1dnXyRtJkyXlvoufpA/b2H6CpK+uwHnvkbRmhwNbBeXmpijQE/g1MD0ilhbbeWUkqQ7YkmQSjw49QJUDRwIXRcQVkr5GJ7+X9LHorwHP0MGuXJaNiLhkBY9faSs+mYkIL20sJDdp7waeIkkohwEzgfOAx4EZwCbpvv2AP5PcC3gM2Dzdfi7Jnf37geuAWcCbJN9EDsv6PbbzPvcEnkjf45+A1YDjgXeAV4DrW74XYLf09yfTY3sDo4DJwM3AC8C1NDf1LXeNdPtM4BzgIeAo4EPg/9Lz9sxJ+YxO389DwG+Buwr+vU8rOPYZYEj6+1HAP9P3cSlQm26/GJgGPAucV3DsZGBkDv4+fgCcnP7+38DfCv79rkn/fS5Iy+cxoL5lWaTv5cL0/b8I7JJu/xrJtJP3Ai8Bvyi47kxgnfbKzssnl9w0ueTUaOD1iNgiIkaQ/NEBvBURW5P8j3hauu084ImI2Bz4EXBVwXm2AQ6MiCNIEtUNEbFlRNzQJe+iuNbe55UkHzibkXyT+2ZEXEbSP/b7EfFlln8vpwHfjogtgV2Aj9LzbwV8F/gssCGwk6QerV2jIKZFEbFzRFxDkuyOTK/zEV2vtfKZAHyR5H0OLHYCSZ8h+SDYKS2fBpJvOwBnRvL05ObAbpI2r/xbWCFTSN4nJN/I1pDUDdiZpEfa6sBjEbFFuu/X2zhPXURsR/K38OOC7VuSlM1mwGGSCh+yKVZ2VsAJvX0zgM9LulDSLhHxXrr91vTndJLhfiH5474akj71wNqS+qav3ZFRIirVJ94nyXt6JSJeTF+fCOxawnkeBn4t6WRgzWhuOvtnRMyOiEaSGtYQYOMi18jLhx0sXz5DSWJ/KZLq4zUlnGNPkg/2qZKeTNc3TF8bK+lxkm8rm5J88OXJdGAbSb1JhuZ4lCSx70KS0BcDdxXsO6SN87T2/w3ApIh4LyIWkQxItUGL49orOyuQpzb03ImIFyVtA+wL/EzS/elLH6c/G2guw9YG62nqQrSg86JccS3fJ0nzUEfO83NJd6fneUzS59OXPi7YranMig1ulJsya6N82uoetpRPVpR6pD8FTIyIMwp3ljSU5JvNthExX9KVBcfkQkQskTSTpPfZIyTNirsDGwHPA0vSDzb45P8TLbX2/03h9raOb7XsbHmuobcj7VmxMP3afxGwdTu7TyH9GihpFEmzzPut7PcBOZtur5X3uSMwRNKwdJevAH9v5dBPvBdJG0XEjIi4kKSZZJN2LvtCiddY7jpdrY3yGSppo3SXLxfsPpP070TS1iS1eYBJwCGSBqSv9ZO0AdCH5MPrPUn15Ldn1xSSD54pJLXyE4AnCxJ5Z2qr7KwF19DbtxnwS0mNwBKSNt6b29j3XOAKSU8DC4Gj29jvAeD09Kvjz3LSjt7a++wL3JT2zJkKtNZj4RPvBdhZ0u4ktazngL8AO7R2wYhYJOmYEq4BSVv7JZI+AnbIoPmqtfJZB7hb0lskN0ZHpPveAnw1LZOpJDcAiYjnJJ0F3C+pJj3PtyPiMUlPkNwQfZmk2SqPHgTOBB6NiAWSFqXbOl1bZQf8pyuuvzLJ3YNFZiub9BvZaRGxf8ah2CrOTS5mZlXCNXQzsyrhGrqZWZVwQjczqxJO6GZmVcIJ3cysSjihm5lVCSd0M7Mq4YRuZlYlnNDNzKqEE7qZWZVwQjczqxJO6GZmVcIJ3cysSjihm5lVCSd0M7Mq4YRuFSGpQdKTkp6RdJOkXitwrislHZL+fpmkNidNljRK0o4duMZMSeu0sn0NSZdK+rekZyVNkbR9+tqH5V7HrCs5oVulfBQRW0bECJJZ4E8ofFFSbUdOGhHHR8Rz7ewyimSOz0q5DHgHGB4RmwJfI5luziz3nNCtMzwIDEtrzw9Iug6YIalW0i8lTZX0tKRvACjxe0nPSbobGNB0IkmTJY1Mfx8t6XFJT0maJGkIyQfH99JvB7tI6i/plvQaUyXtlB67tqT7JT0h6VKSmeQ/IZ30eXvgrIhoBIiIlyPi7hb7rZFe/3FJMyQdmG5fXdLdaXzPSDos3f7z9L09LemiCpe12TKeJNoqKp3weR/g3nTTdsCIiHhF0jjgvYjYVtJqwMOS7ge2AjYmmYy5nmSC6T+1OG9/YAKwa3qufhHxjqRLgA8j4qJ0v+uA/46IhyStD9wHfAb4MfBQRJwvaT9gXCvhb0oyk31Dkbe5CBgTEe+nzTaPSboDGA28HhH7pbH0ldQPGANsEhEhac2SCtKsA5zQrVJ6pjPdQ1JDv5ykKeSfEfFKuv0LwOZN7eNAX2A4sCtwfZpIX5f0t1bO/zlgStO5IuKdNuL4PPBZaVkFvI+k3uk1vpQee7ek+R17m0BSu/+ppF2BRmBdkg+iGcBFki4E7oqIB9MPuEXAZem3j7tW4Lpm7XJCt0r5KCK2LNyQJtUFhZuAkyLivhb77QsUm9xWJewDSTPiDhHxUSuxFDv+WWALSTVNTS5tOBLoD2wTEUskzQR6RMSLkrYB9gV+Jun+9BvBdsCewOHAicAeJbwPs7K5Dd260n3ANyV1A5D0aUmrA1OAw9M29kHA7q0c+yiwm6Sh6bH90u0fAL0L9rufJGmS7rdl+usUkkSMpH2AtVpeICL+DUwDzlP6CSBpeFMbeYG+wLw0me8ObJDu+ylgYURcA1wEbC1pDaBvRNwDfBfYErNO4hq6daXLgCHA42nCfBM4CLiNpNY6A3gR+HvLAyPizbQN/lZJNcA8YC/gTuDmNOmeBJwM/EHS0yR/31NIbpyeB1wv6fH0/LPaiPF44FfAvyQtBN4Gvt9in2uBOyVNA54EXki3bwb8UlIjsAT4JsmHze2SepB8y/heKQVl1hGKKOVbrJmZ5Z2bXMzMqoQTuplZlXBCNzOrEk7oZmZVwgndzKxKOKGbmVUJJ3QzsyrhhG5mViX+P0oluXNkRkaVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import seaborn as sns\n",
    "\n",
    "cf_matrix = confusion_matrix(y_test, y_pred)\n",
    "ax = sns.heatmap(cf_matrix, annot=True, cmap='Blues')\n",
    "ax.set_title('Confusion Matrix\\n')\n",
    "ax.set_xlabel('\\nPredicted Class')\n",
    "ax.set_ylabel('Actual Class\\n')\n",
    "## Ticket labels - List must be in alphabetical order\n",
    "ax.xaxis.set_ticklabels(['snort','softsnort', 'squeal', 'whinnie'])\n",
    "ax.yaxis.set_ticklabels(['snort','softsnort', 'squeal', 'whinnie'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1564b1b1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}