ccfd-with-denoising-autoencoder.ipynb

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    "# Introduction\n",
    "##### This Kernel is written based on a paper which utilized 'denoising autoencoder' to detect credit card fraud\n",
    "##### Source : https://arxiv.org/pdf/1908.11553.pdf\n",
    "##### =============================================\n",
    "##### The idea is really simple. To solve the problem of imbalanced dataset of credit card fruad, this paper proposed an oversampling technique(SMOTE) and feed oversampled dataset into denoising autoencoder.\n",
    "##### Then with the dataset represented by denoising autoencoder, they make classifier learn the pattern between normal transaction and abnormal transaction"
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     "text": [
      "/kaggle/input/creditcardfraud/creditcard.csv\n"
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   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
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   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.metrics import precision_recall_curve, average_precision_score\n",
    "from sklearn.metrics import roc_curve, auc, roc_auc_score\n",
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from keras.callbacks import ModelCheckpoint, EarlyStopping\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
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   "source": [
    "## Data Preprocessing\n",
    "##### For dataset preprocessing, drop 'Time' data and normalized the 'Amount' part. \n",
    "##### Other features don't need to do normalization. Then choose the test sample, which account for 20% of total dataset\n"
   ]
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   "source": [
    "df = pd.read_csv('../input/creditcardfraud/creditcard.csv')\n",
    "\n",
    "x = df[df.columns[1:30]].to_numpy()\n",
    "y = df[df.columns[30]].to_numpy()\n",
    "\n",
    "# prepare data\n",
    "df = pd.concat([pd.DataFrame(x)], axis=1)\n",
    "\n",
    "# normalize Amount\n",
    "df[28] = (df[28] - df[28].mean())/df[28].std()\n",
    "\n",
    "# Split the data\n",
    "x = df.to_numpy()\n",
    "x_train_all, x_test, y_train_all, y_test = train_test_split(x,y,stratify=y,test_size=0.2)"
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   "source": [
    "## Oversampling (SMOTE)\n",
    "##### Before oversampling, there are 227451 sample normal train data and 394 sample abnormal data\n",
    "##### After oversampling, there are 227451 sample normal train data and also 227451 sample abnormal data"
   ]
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    "sm = SMOTE()\n",
    "x_train_all, y_train_all = sm.fit_resample(x_train_all,y_train_all)\n",
    "np.unique(y_train_all, return_counts=True)"
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   "source": [
    "# Describe oversampled dataset"
   ]
  },
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   "source": [
    "## Denoising autoencoder\n",
    "##### Paper proposed a 7 layer of autoencoder for dataset denoising process\n",
    "##### After oversampling, they add Gaussian noise to the training dataset, the feed the training dataset in this decnoising autoencoder"
   ]
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   "source": [
    "# Split data for training autoencoder\n",
    "x_train_all, x_valid, y_train_all, y_valid = train_test_split(x_train_all,y_train_all,test_size=0.2)"
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    "x_valid.shape"
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     "text": [
      "2022-06-01 09:06:48.947194: I tensorflow/core/common_runtime/process_util.cc:146] Creating new thread pool with default inter op setting: 2. Tune using inter_op_parallelism_threads for best performance.\n"
     ]
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   "source": [
    "# Model\n",
    "keras.backend.clear_session()\n",
    "\n",
    "denoising_encoder = keras.models.Sequential([\n",
    "    keras.layers.Flatten(input_shape=[29]),\n",
    "    keras.layers.GaussianNoise(0.2),\n",
    "    keras.layers.Dense(22, activation='relu'),\n",
    "    keras.layers.Dense(15, activation='relu'),\n",
    "    keras.layers.Dense(10, activation='relu'),\n",
    "])\n",
    "\n",
    "denoising_decoder = keras.models.Sequential([\n",
    "    keras.layers.Dense(15, activation='relu', input_shape=[10]),\n",
    "    keras.layers.Dense(22, activation='relu'),\n",
    "    keras.layers.Dense(29),\n",
    "])\n",
    "\n",
    "denoising_ae = keras.models.Sequential([denoising_encoder, denoising_decoder])\n",
    "denoising_ae.compile(loss='mean_squared_error', optimizer=keras.optimizers.Adam(), metrics=['mse'])"
   ]
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     "text": [
      "2022-06-01 09:06:49.330733: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)\n"
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     "text": [
      "Epoch 1/40\n",
      "11373/11373 [==============================] - 37s 3ms/step - loss: 0.5692 - mse: 0.5692 - val_loss: 0.3524 - val_mse: 0.3524\n",
      "Epoch 2/40\n",
      "11373/11373 [==============================] - 36s 3ms/step - loss: 0.3007 - mse: 0.3007 - val_loss: 0.2539 - val_mse: 0.2539\n",
      "Epoch 3/40\n",
      "11373/11373 [==============================] - 34s 3ms/step - loss: 0.2551 - mse: 0.2551 - val_loss: 0.2293 - val_mse: 0.2293\n",
      "Epoch 4/40\n",
      "11373/11373 [==============================] - 34s 3ms/step - loss: 0.2353 - mse: 0.2353 - val_loss: 0.2032 - val_mse: 0.2032\n",
      "Epoch 5/40\n",
      "11373/11373 [==============================] - 34s 3ms/step - loss: 0.2218 - mse: 0.2218 - val_loss: 0.1966 - val_mse: 0.1966\n",
      "Epoch 6/40\n",
      "11373/11373 [==============================] - 35s 3ms/step - loss: 0.2124 - mse: 0.2124 - val_loss: 0.1831 - val_mse: 0.1831\n",
      "Epoch 7/40\n",
      "11373/11373 [==============================] - 34s 3ms/step - loss: 0.2030 - mse: 0.2030 - val_loss: 0.1824 - val_mse: 0.1824\n",
      "Epoch 8/40\n",
      "11373/11373 [==============================] - 35s 3ms/step - loss: 0.1977 - mse: 0.1977 - val_loss: 0.1742 - val_mse: 0.1742\n",
      "Epoch 9/40\n",
      "11373/11373 [==============================] - 34s 3ms/step - loss: 0.1937 - mse: 0.1937 - val_loss: 0.1695 - val_mse: 0.1695\n",
      "Epoch 10/40\n",
      "11373/11373 [==============================] - 34s 3ms/step - loss: 0.1902 - mse: 0.1902 - val_loss: 0.1707 - val_mse: 0.1707\n",
      "Epoch 11/40\n",
      "11373/11373 [==============================] - 35s 3ms/step - loss: 0.1873 - mse: 0.1873 - val_loss: 0.1644 - val_mse: 0.1644\n",
      "Epoch 12/40\n",
      "11373/11373 [==============================] - 34s 3ms/step - loss: 0.1848 - mse: 0.1848 - val_loss: 0.1668 - val_mse: 0.1668\n",
      "Epoch 13/40\n",
      "11373/11373 [==============================] - 34s 3ms/step - loss: 0.1818 - mse: 0.1818 - val_loss: 0.1607 - val_mse: 0.1607\n",
      "Epoch 14/40\n",
      "11373/11373 [==============================] - 34s 3ms/step - loss: 0.1793 - mse: 0.1793 - val_loss: 0.1651 - val_mse: 0.1651\n",
      "Epoch 15/40\n",
      "11373/11373 [==============================] - 35s 3ms/step - loss: 0.1760 - mse: 0.1760 - val_loss: 0.1552 - val_mse: 0.1552\n",
      "Epoch 16/40\n",
      "11373/11373 [==============================] - 34s 3ms/step - loss: 0.1734 - mse: 0.1734 - val_loss: 0.1563 - val_mse: 0.1563\n",
      "Epoch 17/40\n",
      "11373/11373 [==============================] - 34s 3ms/step - loss: 0.1724 - mse: 0.1724 - val_loss: 0.1545 - val_mse: 0.1545\n",
      "Epoch 18/40\n",
      "11373/11373 [==============================] - 34s 3ms/step - loss: 0.1718 - mse: 0.1718 - val_loss: 0.1580 - val_mse: 0.1580\n",
      "Epoch 19/40\n",
      "11373/11373 [==============================] - 35s 3ms/step - loss: 0.1717 - mse: 0.1717 - val_loss: 0.1521 - val_mse: 0.1521\n",
      "Epoch 20/40\n",
      "11373/11373 [==============================] - 36s 3ms/step - loss: 0.1710 - mse: 0.1710 - val_loss: 0.1550 - val_mse: 0.1550\n",
      "Epoch 21/40\n",
      "11373/11373 [==============================] - 35s 3ms/step - loss: 0.1702 - mse: 0.1702 - val_loss: 0.1504 - val_mse: 0.1504\n",
      "Epoch 22/40\n",
      "11373/11373 [==============================] - 35s 3ms/step - loss: 0.1707 - mse: 0.1707 - val_loss: 0.1690 - val_mse: 0.1690\n",
      "Epoch 23/40\n",
      "11373/11373 [==============================] - 34s 3ms/step - loss: 0.1693 - mse: 0.1693 - val_loss: 0.1511 - val_mse: 0.1511\n",
      "Epoch 24/40\n",
      "11373/11373 [==============================] - 35s 3ms/step - loss: 0.1682 - mse: 0.1682 - val_loss: 0.1623 - val_mse: 0.1623\n",
      "Epoch 25/40\n",
      "11373/11373 [==============================] - 33s 3ms/step - loss: 0.1675 - mse: 0.1675 - val_loss: 0.1522 - val_mse: 0.1522\n",
      "Epoch 26/40\n",
      "11373/11373 [==============================] - 34s 3ms/step - loss: 0.1675 - mse: 0.1675 - val_loss: 0.1501 - val_mse: 0.1501\n",
      "Epoch 27/40\n",
      "11373/11373 [==============================] - 33s 3ms/step - loss: 0.1665 - mse: 0.1665 - val_loss: 0.1485 - val_mse: 0.1485\n",
      "Epoch 28/40\n",
      "11373/11373 [==============================] - 35s 3ms/step - loss: 0.1667 - mse: 0.1667 - val_loss: 0.1484 - val_mse: 0.1484\n",
      "Epoch 29/40\n",
      "11373/11373 [==============================] - 36s 3ms/step - loss: 0.1660 - mse: 0.1660 - val_loss: 0.1529 - val_mse: 0.1529\n",
      "Epoch 30/40\n",
      "11373/11373 [==============================] - 34s 3ms/step - loss: 0.1661 - mse: 0.1661 - val_loss: 0.1498 - val_mse: 0.1498\n",
      "Epoch 31/40\n",
      "11373/11373 [==============================] - 34s 3ms/step - loss: 0.1651 - mse: 0.1651 - val_loss: 0.1492 - val_mse: 0.1492\n",
      "Epoch 32/40\n",
      "11373/11373 [==============================] - 36s 3ms/step - loss: 0.1645 - mse: 0.1645 - val_loss: 0.1502 - val_mse: 0.1502\n",
      "Epoch 33/40\n",
      "11373/11373 [==============================] - 35s 3ms/step - loss: 0.1637 - mse: 0.1637 - val_loss: 0.1496 - val_mse: 0.1496\n",
      "Epoch 34/40\n",
      "11373/11373 [==============================] - 36s 3ms/step - loss: 0.1626 - mse: 0.1626 - val_loss: 0.1437 - val_mse: 0.1437\n",
      "Epoch 35/40\n",
      "11373/11373 [==============================] - 35s 3ms/step - loss: 0.1618 - mse: 0.1618 - val_loss: 0.1432 - val_mse: 0.1432\n",
      "Epoch 36/40\n",
      "11373/11373 [==============================] - 35s 3ms/step - loss: 0.1620 - mse: 0.1620 - val_loss: 0.1435 - val_mse: 0.1435\n",
      "Epoch 37/40\n",
      "11373/11373 [==============================] - 34s 3ms/step - loss: 0.1613 - mse: 0.1613 - val_loss: 0.1446 - val_mse: 0.1446\n",
      "Epoch 38/40\n",
      "11373/11373 [==============================] - 35s 3ms/step - loss: 0.1600 - mse: 0.1600 - val_loss: 0.1474 - val_mse: 0.1474\n",
      "Epoch 39/40\n",
      "11373/11373 [==============================] - 35s 3ms/step - loss: 0.1588 - mse: 0.1588 - val_loss: 0.1387 - val_mse: 0.1387\n",
      "Epoch 40/40\n",
      "11373/11373 [==============================] - 36s 3ms/step - loss: 0.1563 - mse: 0.1563 - val_loss: 0.1438 - val_mse: 0.1438\n"
     ]
    }
   ],
   "source": [
    "history = denoising_ae.fit(x_train_all, x_train_all, epochs=40, validation_data=(x_valid,x_valid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c3508c13",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-01T09:30:29.435688Z",
     "iopub.status.busy": "2022-06-01T09:30:29.435364Z",
     "iopub.status.idle": "2022-06-01T09:30:29.696236Z",
     "shell.execute_reply": "2022-06-01T09:30:29.695114Z"
    },
    "papermill": {
     "duration": 9.038887,
     "end_time": "2022-06-01T09:30:29.698321",
     "exception": false,
     "start_time": "2022-06-01T09:30:20.659434",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('Model Loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Valid'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0dd029f2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-01T09:30:47.238497Z",
     "iopub.status.busy": "2022-06-01T09:30:47.238161Z",
     "iopub.status.idle": "2022-06-01T09:31:05.126907Z",
     "shell.execute_reply": "2022-06-01T09:31:05.126183Z"
    },
    "papermill": {
     "duration": 26.709845,
     "end_time": "2022-06-01T09:31:05.129691",
     "exception": false,
     "start_time": "2022-06-01T09:30:38.419846",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# making denoised dataset\n",
    "denoising_x_train = denoising_ae.predict(x_train_all)\n",
    "denoising_x_valid = denoising_ae.predict(x_valid)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8982b368",
   "metadata": {
    "papermill": {
     "duration": 8.705719,
     "end_time": "2022-06-01T09:31:22.587290",
     "exception": false,
     "start_time": "2022-06-01T09:31:13.881571",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Classifier\n",
    "##### Paper proposed a 6 layers autoencoder for dataset denoise proess. After got denoised training dataset from denoised autoencoder, the dataset go into this deep fully connected neural network classifier\n",
    "##### In the end, this model used SoftMax with cross-entropy as the loss function for final classification."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f4fd0364",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-01T09:31:40.054725Z",
     "iopub.status.busy": "2022-06-01T09:31:40.054265Z",
     "iopub.status.idle": "2022-06-01T09:31:40.125167Z",
     "shell.execute_reply": "2022-06-01T09:31:40.124090Z"
    },
    "papermill": {
     "duration": 8.820735,
     "end_time": "2022-06-01T09:31:40.129251",
     "exception": false,
     "start_time": "2022-06-01T09:31:31.308516",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "keras.backend.clear_session()\n",
    "\n",
    "# The classifier with out denoising autoencoder dataset\n",
    "base_classifier = keras.models.Sequential([\n",
    "    keras.layers.Dense(22, activation='relu',input_shape=[29]),\n",
    "    keras.layers.Dense(15, activation='relu'),\n",
    "    keras.layers.Dropout(0.2),\n",
    "    keras.layers.Dense(10, activation='relu'),\n",
    "    keras.layers.Dense(5, activation='relu'),\n",
    "    keras.layers.Dense(1, activation='sigmoid')\n",
    "])\n",
    "\n",
    "base_classifier.compile(loss='binary_crossentropy', optimizer=keras.optimizers.Adam(), metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ae2567d6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-01T09:31:57.618797Z",
     "iopub.status.busy": "2022-06-01T09:31:57.618416Z",
     "iopub.status.idle": "2022-06-01T09:37:16.837038Z",
     "shell.execute_reply": "2022-06-01T09:37:16.835849Z"
    },
    "papermill": {
     "duration": 327.858956,
     "end_time": "2022-06-01T09:37:16.840032",
     "exception": false,
     "start_time": "2022-06-01T09:31:48.981076",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "11373/11373 [==============================] - 33s 3ms/step - loss: 0.0418 - accuracy: 0.9843 - val_loss: 0.0103 - val_accuracy: 0.9977\n",
      "Epoch 2/10\n",
      "11373/11373 [==============================] - 32s 3ms/step - loss: 0.0110 - accuracy: 0.9973 - val_loss: 0.0071 - val_accuracy: 0.9982\n",
      "Epoch 3/10\n",
      "11373/11373 [==============================] - 31s 3ms/step - loss: 0.0089 - accuracy: 0.9979 - val_loss: 0.0053 - val_accuracy: 0.9986\n",
      "Epoch 4/10\n",
      "11373/11373 [==============================] - 32s 3ms/step - loss: 0.0070 - accuracy: 0.9983 - val_loss: 0.0038 - val_accuracy: 0.9992\n",
      "Epoch 5/10\n",
      "11373/11373 [==============================] - 33s 3ms/step - loss: 0.0060 - accuracy: 0.9985 - val_loss: 0.0069 - val_accuracy: 0.9976\n",
      "Epoch 6/10\n",
      "11373/11373 [==============================] - 31s 3ms/step - loss: 0.0057 - accuracy: 0.9987 - val_loss: 0.0038 - val_accuracy: 0.9993\n",
      "Epoch 7/10\n",
      "11373/11373 [==============================] - 32s 3ms/step - loss: 0.0052 - accuracy: 0.9988 - val_loss: 0.0034 - val_accuracy: 0.9993\n",
      "Epoch 8/10\n",
      "11373/11373 [==============================] - 31s 3ms/step - loss: 0.0048 - accuracy: 0.9988 - val_loss: 0.0042 - val_accuracy: 0.9988\n",
      "Epoch 9/10\n",
      "11373/11373 [==============================] - 33s 3ms/step - loss: 0.0044 - accuracy: 0.9990 - val_loss: 0.0036 - val_accuracy: 0.9992\n",
      "Epoch 10/10\n",
      "11373/11373 [==============================] - 31s 3ms/step - loss: 0.0042 - accuracy: 0.9990 - val_loss: 0.0038 - val_accuracy: 0.9992\n"
     ]
    }
   ],
   "source": [
    "clf_base_history = base_classifier.fit(x_train_all, y_train_all, epochs=10, validation_data=(x_valid,y_valid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "79d0ab32",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-01T09:37:38.474526Z",
     "iopub.status.busy": "2022-06-01T09:37:38.474193Z",
     "iopub.status.idle": "2022-06-01T09:37:38.690258Z",
     "shell.execute_reply": "2022-06-01T09:37:38.689543Z"
    },
    "papermill": {
     "duration": 10.967162,
     "end_time": "2022-06-01T09:37:38.692543",
     "exception": false,
     "start_time": "2022-06-01T09:37:27.725381",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(clf_base_history.history['loss'])\n",
    "plt.plot(clf_base_history.history['val_loss'])\n",
    "plt.title('Model Loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Valid'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "73f284a8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-01T09:38:00.154464Z",
     "iopub.status.busy": "2022-06-01T09:38:00.154125Z",
     "iopub.status.idle": "2022-06-01T09:38:00.573379Z",
     "shell.execute_reply": "2022-06-01T09:38:00.572369Z"
    },
    "papermill": {
     "duration": 11.177109,
     "end_time": "2022-06-01T09:38:00.576131",
     "exception": false,
     "start_time": "2022-06-01T09:37:49.399022",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "keras.backend.clear_session()\n",
    "\n",
    "classifier_1 = keras.models.Sequential([\n",
    "    keras.layers.Dense(22, activation='relu',input_shape=[29]),\n",
    "    keras.layers.Dense(15, activation='relu'),\n",
    "    keras.layers.Dropout(0.2),\n",
    "    keras.layers.Dense(10, activation='relu'),\n",
    "    keras.layers.Dense(5, activation='relu'),\n",
    "    keras.layers.Dense(1, activation='sigmoid')\n",
    "])\n",
    "\n",
    "classifier_1.compile(loss='binary_crossentropy', optimizer=keras.optimizers.Adam(), metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fa2c3531",
   "metadata": {
    "_kg_hide-input": true,
    "_kg_hide-output": true,
    "execution": {
     "iopub.execute_input": "2022-06-01T09:38:22.112304Z",
     "iopub.status.busy": "2022-06-01T09:38:22.111951Z",
     "iopub.status.idle": "2022-06-01T09:43:40.828843Z",
     "shell.execute_reply": "2022-06-01T09:43:40.827461Z"
    },
    "papermill": {
     "duration": 329.433412,
     "end_time": "2022-06-01T09:43:40.832028",
     "exception": false,
     "start_time": "2022-06-01T09:38:11.398616",
     "status": "completed"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "11373/11373 [==============================] - 32s 3ms/step - loss: 0.0649 - accuracy: 0.9755 - val_loss: 0.0279 - val_accuracy: 0.9910\n",
      "Epoch 2/10\n",
      "11373/11373 [==============================] - 32s 3ms/step - loss: 0.0298 - accuracy: 0.9900 - val_loss: 0.0212 - val_accuracy: 0.9936\n",
      "Epoch 3/10\n",
      "11373/11373 [==============================] - 31s 3ms/step - loss: 0.0236 - accuracy: 0.9926 - val_loss: 0.0164 - val_accuracy: 0.9952\n",
      "Epoch 4/10\n",
      "11373/11373 [==============================] - 32s 3ms/step - loss: 0.0210 - accuracy: 0.9936 - val_loss: 0.0150 - val_accuracy: 0.9961\n",
      "Epoch 5/10\n",
      "11373/11373 [==============================] - 32s 3ms/step - loss: 0.0188 - accuracy: 0.9944 - val_loss: 0.0153 - val_accuracy: 0.9954\n",
      "Epoch 6/10\n",
      "11373/11373 [==============================] - 31s 3ms/step - loss: 0.0177 - accuracy: 0.9947 - val_loss: 0.0118 - val_accuracy: 0.9967\n",
      "Epoch 7/10\n",
      "11373/11373 [==============================] - 31s 3ms/step - loss: 0.0164 - accuracy: 0.9953 - val_loss: 0.0153 - val_accuracy: 0.9951\n",
      "Epoch 8/10\n",
      "11373/11373 [==============================] - 32s 3ms/step - loss: 0.0160 - accuracy: 0.9955 - val_loss: 0.0110 - val_accuracy: 0.9971\n",
      "Epoch 9/10\n",
      "11373/11373 [==============================] - 34s 3ms/step - loss: 0.0154 - accuracy: 0.9957 - val_loss: 0.0119 - val_accuracy: 0.9969\n",
      "Epoch 10/10\n",
      "11373/11373 [==============================] - 32s 3ms/step - loss: 0.0147 - accuracy: 0.9959 - val_loss: 0.0110 - val_accuracy: 0.9972\n"
     ]
    }
   ],
   "source": [
    "clf_1_history = classifier_1.fit(denoising_x_train, y_train_all, epochs=10, validation_data=(denoising_x_valid,y_valid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c5091e31",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-01T09:44:06.305065Z",
     "iopub.status.busy": "2022-06-01T09:44:06.304436Z",
     "iopub.status.idle": "2022-06-01T09:44:06.502384Z",
     "shell.execute_reply": "2022-06-01T09:44:06.501385Z"
    },
    "papermill": {
     "duration": 12.976345,
     "end_time": "2022-06-01T09:44:06.504799",
     "exception": false,
     "start_time": "2022-06-01T09:43:53.528454",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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pcQgRSUHpyXphd283s2uAB4EYcKu7rzWzbwAr3H0p8HPgdjPbANQQhAjAWcA3zKwN6AQWu/ugO10oPZbGaVOKWaZ5mUQkBSUtIADc/X7g/m7b/i3ufjPwwQTPuwe4J5m19Zf5U0v568s7qdrTyPiRuVGXIyLSbwbrIPWQoem/RSRVKSD6aProfErzszT9t4ikHAVEH5kZlRUlLHttN+5ahlREUocCoh9UTi2lur6FV3fui7oUEZF+o4DoB/Mrgou/n3xV3UwikjoUEP1g/MhcJpXk6nRXEUkpCoh+Mn9qKc9srKG9Q8uQikhqUED0k8qKEupb2lmzpTbqUkRE+oUCop+cPiUYh9D03yKSKhQQ/aQkP4uZxxRo+m8RSRkKiH5UWVHCys17aGrtiLoUEZE+U0D0o/kVpbR2dLJi86CbV1BEpNcUEP1oXnkx6WmmbiYRSQkKiH6Ul5XO7IlFuh5CRFKCAqKfzZ9aygtbaqltbIu6FBGRPlFA9LPKilLc4emN6mYSkaFNAdHPTp5QRG5mTN1MIjLkKSD6WWZ6GvMmF/OULpgTkSFOAZEElVNLea26ge21zVGXIiJy1BQQSdA1/bdaESIylCkgkuC4MQUU52VqGVIRGdIUEEmQlmacPqWEZRu0DKmIDF0KiCSZX1HC9rpmNu5qiLoUEZGjooBIksqppYCm/xaRoUsBkSSTSnIZV5SjeZlEZMhSQCSJmVFZUcLTG3fT0alxCBEZehQQSVRZUUptUxtrt2oZUhEZehQQSXT61K7rIdTNJCJDT1IDwswuMLN1ZrbBzG5IsD/LzH4b7n/GzMq77Z9oZvvM7EvJrDNZRo3IZvrofM3LJCJDUtICwsxiwI+BdwEzgUVmNrPbYVcCe9y9ArgZ+Ha3/d8DHkhWjQNh/tRSlm+qoaVdy5CKyNDSo4AwszwzSwvvTzez95lZxhGeNg/Y4O4b3b0VWAIs7HbMQuCX4f27gXPNzML3eT/wOrC2R7/JIFVZUUpzWyfPbd4bdSkiIr3S0xbE40C2mY0DHgI+Btx2hOeMA96Me1wVbkt4jLu3A7VAiZnlA/8MfP1wb2BmV5nZCjNbUV1d3cNfZWCdNqWYWJqpm0lEhpyeBoS5eyPwD8B/u/sHgeOTVxY3Aje7+77DHeTut7j7XHefW1ZWlsRyjl5Bdgazxhdq4j4RGXJ6HBBmdjrwEeDP4bbYEZ6zBZgQ93h8uC3hMWaWDhQCu4HTgO+Y2Sbg88BXzOyaHtY66FROLeX5qlrqm7UMqYgMHT0NiM8DXwbuc/e1ZjYFeOQIz1kOTDOzyWaWCVwKLO12zFLgsvD+xcDDHjjT3cvdvRz4PvDv7v6jHtY66MyvKKGj03lmY03UpYiI9Fh6Tw5y98eAxwDCwepd7n7tEZ7THn7rf5CgtXFrGC7fAFa4+1Lg58DtZrYBqCEIkZQzZ+JIstLTeOq1Xbxj5uioyxER6ZEeBYSZ3QksBjoIWgYFZvYDd/+/h3ueu98P3N9t27/F3W8GPniE17ixJzUOZtkZMU4tL2aZLpgTkSGkp11MM929Dng/wXUJkwnOZJIeml9Rwrod9VTXt0RdiohIj/Q0IDLC6x7eDyx19zZAM9D1wv7pv3W6q4gMET0NiP8FNgF5wONmNgmoS1ZRqeiEcYUUZKerm0lEhoweBYS7/9Ddx7n7heFZRpuBc5JcW0qJpRmnTy3hyQ27tAypiAwJPZ1qo9DMvtd11bKZ/SdBa0J6obKilC17m3ijpjHqUkREjqinXUy3AvXAh8JbHfCLZBWVquaH4xCa/ltEhoKeBsRUd/9aOPHeRnf/OjAlmYWloqlleYwuyOIpDVSLyBDQ04BoMrMzuh6YWSXQlJySUpeZUTm1lKdf202nliEVkUGupwGxGPixmW0K50f6EfDppFWVwuZXlFLT0Mor2+ujLkVE5LB6ehbT8+5+EjALmOXus4G3J7WyFFVZESxDqushRGSw69WKcu5eF15RDfDFJNST8o4pzGFKWZ6m/xaRQa8vS45av1UxzFROLeWZ12tobe+MuhQRkUPqS0BolPUoVVaU0NjawfNVe6MuRUTkkA47m6uZ1ZM4CAzISUpFw8DbppRgBk9t2MWp5cVRlyMiktBhWxDuPsLdCxLcRrh7j6YKl7cqys3khLGFmpdJRAa1vnQxSR/Mryhh1Zt7aGxtj7oUEZGEFBARqZxaSluH8+zrWoZURAYnBURETi0vJjOWxrLX1M0kIoOTAiIiOZkx5kwq4slXdT2EiAxOCogIVU4t5aVtddQ0tEZdiojIWyggIjS/Ipj++2l1M4nIIKSAiNBJ4wvJz0rX9N8iMigpICKUHkvjtMnFLNO8TCIyCCkgIja/opRNuxvZslfLa4jI4KKAiFjX9N+a3VVEBhsFRMRmjB5BaX6mAkJEBh0FRMTMjPlTS1n22m7cNUGuiAweSQ0IM7vAzNaZ2QYzuyHB/iwz+224/xkzKw+3zzOz1eHteTO7KJl1Rq2yooTq+hZe3bkv6lJERPZLWkCYWQz4MfAuYCawyMxmdjvsSmCPu1cANwPfDre/CMx195OBC4D/NbOUnT12/tTgegh1M4nIYJLMFsQ8YIO7b3T3VmAJsLDbMQuBX4b37wbONTNz90Z375rmNJsUX5xoQnEuE4tzeUrTf4vIIJLMgBgHvBn3uCrclvCYMBBqgRIAMzvNzNYCLwCL4wJjPzO7ysxWmNmK6urqJPwKA6eyooRnNu6mrrkt6lJERIBBPEjt7s+4+/HAqcCXzSw7wTG3uPtcd59bVlY28EX2o/fOGktDazvvvPlxHlm3M+pyRESSGhBbgAlxj8eH2xIeE44xFAIH9bO4+8vAPuCEpFU6CMyvKOXez1SSn5XOJ36xnH/63fPUNqk1ISLRSWZALAemmdlkM8sELgWWdjtmKXBZeP9i4GF39/A56QBmNgk4FtiUxFoHhZMnFPHHz53BZxZM5d5VWzj/5sd4+JUdUZclIsNU0gIiHDO4BngQeBm4y93Xmtk3zOx94WE/B0rMbAPwRaDrVNgzgOfNbDVwH/AZdx8Wp/hkZ8S4/oJjue8z8ynKyeSK21bwxbtWU9uo1oSIDCxLlYuz5s6d6ytWrIi6jH7V0t7Bjx7ewH8/+holeZncdNGJnDdzdNRliUgKMbOV7j430b5BO0gtkJUe4x/Pn8EfPltJcV4mn/rVCj6/ZBV7tMCQiAwABUTTXvjNh6F6fdSVHNIJ4wpZes0ZXHfuNP60Zhvn3fw4D67dHnVZIpLiFBC7X4M3noZbzoaVv4RB2uWWmZ7GF86bzh+uqaRsRBafvn0l1/5mlZYrFZGkUUCMPwWuXgbjT4U/Xgt3fRwaa6Ku6pCOH1vI0msq+cI7pvPAi9s4/+bHeOCFbVGXJSIpSAEBUHAMfOz3cN43YN398JMzYNOTUVd1SBmxNK57xzSWXnMGYwqzufrXz/HZO59j976WqEsTkRSigOiSlgaV18En/wrp2XDbe+Bv34COwXt66XHHFHDfZyr50vnTeWjtds67+XH+vEatCRHpHwqI7sbOhk8/DrM/Ck/8J9z6TqjZGHVVh5QRS+Oat0/jT587k3FFOXz2zue4+o6VVNerNSEifaOASCQrHxb+CD54G+zeAD85E55fEnVVhzVjzAju+8x8rr9gBn97eSfn3/wYS5/fqkWIROSoKSAO5/iLYPFTMGYW3PdpuOdT0FwbdVWHlB5L4zMLKvjztWcwsSSPa3+zisV3rGRnfXPUpYnIEKSAOJKiCXD5n+Ccf4EX7wlaE28+G3VVhzVt9AjuWXw6N7zrWB5ZV835Nz/OH1ZvUWtCRHpFAdETaTE4+3r4xAOAw60XwGPfgc6OqCs7pPRYGovPnsr9157J5NI8rluymqtuX8nOOrUmRKRnFBC9MfE0WPwknPAP8MhNwZlOe9888vMiVDEqn7sXz+dfLjyOx9dXc97Nj3Pvc1VqTYjIESkgeiu7ED7wM7joFti+Bn5SCWvvi7qqw4qlGZ86awr3X3cmFaPy+eJdz/PJX65gh1oTInIYCoijddIlsPgJKKmA310Of/gstOyLuqrDmlqWz12fPp2vvmcmT722i/O+9xh3r1RrQkQSU0D0RfEUuOJBOPMfYdWvg/mctq6KuqrDiqUZV54xmQeuO4sZY0bwpd89zyduW8622qaoSxORQUYB0VexDDj33+CyP0JrI/zsPHjqh9DZGXVlhzW5NI/fXnU6X3vvTJ7ZWMP533ucu5a/qdaEiOyngOgvk8+Eq5+CGRfA//sq3HER1A3uaS/S0oxPVE7mL58/k5ljC7j+njVc9ovlVO1pjLo0ERkEtKJcf3OH534Ff7khmNNp4Y/h2AujruqIOjudO57ZzLceeIXmtg5OnlDEghmjWDCjjBPGFpKWZlGXKCJJcLgV5RQQyVK9Hu65Ara/AKd+Es7/JmTkRF3VEb1Z08jvVlbx2LqdrNlSizuU5mdy1vQyFswYxVnTSinKzYy6TBHpJwqIqLS3BDPCPv0jKDsOLv45jD4+6qp6bNe+Fh5fX82j66p5/NVq9ja2kWYwe+JIFoSBcfzYArUuRIYwBUTUNvwV7rs6mMfp/G/CvE+BDa0P1Y5OZ/Wbe3ls3U4eXV/NmqpgTqrS/CzOnl7GghllnDWtjMLcjIgrFZHeUEAMBvuqg2slXn0Qpr0zGJvIL4u6qqPW1bp4ZF01j6+vprYpaF3MmTiSBTOC1sXMY9S6EBnsFBCDhTs8+1N46F+DK7Iv+h+oeEfUVfVZV+vi0XU7eXRdNS9sCVoXZSMOtC7OrFDrQmQwUkAMNjvWwt1XQvXLcPo1wXUU6VlRV9VvqutbeGx9NY+u28kTr+6itqmNWJoxZ2JwZtTZ08s4fmwBNsS62URSkQJiMGprgoe+Cst/CmNOhA/cCmXTo66q37V3dIati2oeXb+TF7fUATBqf+tiFGdMK6UwR60LkSgoIAazV+4PxibamuCCf4c5lwfrY6eonfXNPLaumkfXV/PE+mrqmtuJpRmnTBzJ2TOC7qiZx6h1ITJQFBCDXd02+P1i2PgojD4Rzv0qTDt/yJ3p1FvtHZ2sihu7WLv1QOtiwYwyzpxWxrzJxYwuyI64UpHUpYAYCjo7gxXrHrkJ9rwOE94WjE2UV0Zd2YDZWdfMo+ureSy87qK+uR2ASSW5nFpezLzJxZw2uZiJxblqYYj0k8gCwswuAH4AxICfufu3uu3PAn4FnALsBi5x901mdh7wLSATaAX+yd0fPtx7DfmA6NLRBqtuD1asq98GU88NgmLsyVFXNqDaOzp5aVsdz75ewzOv17BiUw17GtuAoIUxb3Lx/tv0USN0Oq3IUYokIMwsBqwHzgOqgOXAInd/Ke6YzwCz3H2xmV0KXOTul5jZbGCHu281sxOAB9193OHeL2UCoktbU3BK7JPfg6Y9MHMhnPOvKTmQ3ROdnc6G6n08+3rN/tv2cMGjwpwMTi0fybzJxZxaXswJ4wrJiKXuOI5If4oqIE4HbnT3d4aPvwzg7v8Rd8yD4TFPm1k6sB0o87iiLOhL2A0c4+4th3q/lAuILs11wVQdT/8Y2hrhpA/Dgn+GoolRVxYpd6dqTxPPvF7D8tdreHZTDa/vagAgJyPGnElFzCsvYd7kYmZPLCI7IxZxxSKD0+ECIj2J7zsOiF+wuQo47VDHuHu7mdUCJcCuuGM+ADyXKBzM7CrgKoCJE1P0AzO7AM75Csy7Cp74Hiz/GbxwF8y9As780pC+GrsvzIwJxblMKM7l4lPGA8EZUstf38PyTUG31Pf/th53yIgZs8YXcWp5MIZxSvlICrJ1Wq3IkSSzBXExcIG7fzJ8/DHgNHe/Ju6YF8NjqsLHr4XH7AofHw8sBc5399cO934p24LorrYKHvt2sIJdeja87WqY/znIKYq6skGntqmNlZtr9rcy1lTV0t7pmMFxYwr2j2GcWl5M2YjUuVBRpDeGZBeTmY0HHgY+4e5PHen9hk1AdNm1ITjjae29kF0EZ3we5n0aMnOjrmzQamrtYNWbe3j29RqWb6ph5eY9NLcFK/9NKc3bHxbzJhczfmSOzpSSYSGqgEgnGKQ+F9hCMEj9YXdfG3fMZ4ET4wap/8HdP2RmRcBjwNfd/d6evN+wC4gu29bAw/8HXn0I8kfDWf8Ecy6DdK3ZcCSt7Z28uLU2GMMIQ6MuPLV2bGE2p4YtjBmjRzBuZA6jRmQT09lSkmKiPM31QuD7BKe53uruN5nZN4AV7r7UzLKB24HZQA1wqbtvNLN/Bb4MvBr3cue7+85DvdewDYgum58O1p54YxkUTQrGLU78IKRpcLanOjuddTvq949hPPt6DdX1B4a+0tOMMYXZjCvKCW4jg59j4+5rMFyGGl0oN1y4w4a/wd++DtvXBIsUvf1f4dh3p/xV2cng7rxR08jGXQ1s3dvElj1NbNnbtP/+9rpmOrv98ynJyzw4OOLCY1xRDkW5Geq6kkFFATHcdHbCy3+Ah78JuzfAuFOCi+2mLIi6spTS1tHJjrrmg4NjbxNVew7c7xrj6JKbGXtLqyM+REaNyCJd13DIAFJADFcd7fD8b+DRb0FdFUw+OwiK8Qn/X5B+5u7UNLSydW8zW/Y2hsER3A8CpZmahtaDnhNLM8YUZB8UHl1hMrE4l3FFOWSmK0Ck/ygghru2Zlj5C3j8u9C4C459D5zzLzB6ZtSVDXuNre1ha6OrJdIYhEjYKtle10xHXD9WmsHYohwmleQysTiPSSW5TAqvB5lUkssIXd8hvaSAkEBLPfz9J7Dsh8H9WZfAghugeHLUlckhtHd0sqO+haqaRt6Iu23eHfzs3gIpzstkYhgWk4pzmVhyIETKRmRp/EPeQgEhB2usgSdvhmdvgc724LTYs6+HEWOirkx6qa65jTd2x4dGA5t3B/e31TYdNIiekxFjYnEuE0ty94dI8DOP8SNzNH/VMKWAkMTqtsHj/xee+yWkZcBpn4bK6yC3OOrKpB+0tndStaeRzTWNvBkGSFeIvFHTeNAA+qG6riaWBAGSn5XMWXkkSgoIObyajcFA9pq7IGsEzPk4jJkFpdOCW9aIqCuUfubu7KxvCUOjYX8LZHNNI2/sbtg/tXqXkrzM/XNf5Welk5cZIzczRm5WOrmZMXIyYuRlpZOTGSMvM9wW3s8Jj1ULZXBSQEjP7FgLD98E6/8C3nFg+4hjoKQCSqeHt/B+wfiUXh51OOvquuoa6+jqutqyt4mGlnYaWjpoaus48gvFyYylhaERC0MjCJL9QZPR7X5W3P7Mg+/nZcUoys0kLzOmcZU+UkBI77S3Bqva7XoVdq0PrqXYtT64NdceOC49JwyOMDBKwhZHSQVk5UdXvwyIzk6nub0jCIvWDhrb2vffb2htD7a1dtDY2k7jIbY1tgTP238/3N7e/QrEQ0hPMwpzMijMzaAoJ4Oi3EyKcjIoyMmgKG5bYW4GhTkHHhdkp+t6k1BU033LUJWeCWUzgls8d2jYFYbGq2GAvApbV8NLfwCPuyisYFwYFtO6tTrG6aruFJGWZuE3+/7/GGlt79wfNI2tB4dOQ2s7DS3t1Da1sbexjb1NbdQ2tlHb1MbO+mbW76intrGN+pb2w77HiOx0ivYHR2ZcyISPuwdPeOxwmk5FASE9ZxasP5Ff9ta1sttbgrGMrlbHrleDEFnzW2ipO3BcRm5cd9W0AyFSUtH/M9G6Q3tzsDpfWyO0NgY/25qgrSH82QStDQeO2f+z8cD+Y2bByR+BgrH9W99Qs/MVePPvMP2CpJ/xlpmeRmZ6GoW5R39dR1tHJ3VNQXB0hcjeplb2hmFy4Gcre5va2FrbFB7TdtC1J91lpaftD5Gi3AyK8zIZmZdJcW74My+DkbmZwfbwZ+4Q7QpTF5Mklzvs29mt1REGyN43gLj//wonxLU6pkHOyG4f3o2H+LBPtC+8Ty///7Y0yMgLwiojB9LSgy42S4Np58Psj8H0d0JsmFyQ1lwLL94Lq+6ALeG/r1gmnHQpzL82+O+UYtydfS3t+wPkQEslCJe6uMd7GtrY09ga3g4dLJnpafsDZGRuxsGB0vU4LlCK8zIHrKWiMQgZnNqawlbH+mB9i65xjt0boHVfgicYZOYFH9wZOcEHeUZO0Crp+kDPyA1vOQd+ZuYl3pfZ/djc4IO/+ze9mo3BB+SqX8O+7ZA3Ck7+cHC2V8nUAflTDSh32PwUPHd70HXY3gRlxwbhOGl+8LdY/eug1TjjwuDU6IndF4scfjo7nfrmdmoaW6lpaGVPQys1jd1+dgVKuG1vt7PF4uVkxMLWyVtbJAcCJmjBjBqRTXHe0U3xr4CQocUd6rcFrYf4D/D0rGjHLzraYcNf4blfHTjTa1JlEBTHvW/oL9ZUuwWevzMIwj2vQ1YBnPCBIBjGzTn4b7+vOrjQcvlPoWkPTHgbVF4L09+lM9t6ob2jk9qmIDRqGtqCYEkYMG3sCbclGlu58MQx/PdHTjmqGhQQIv2tfjusvhNW3R60MLIK4cSLg7AYe3LU1fVcewusuz9oFWz4G+BQfmYQCse998ih19oQPHfZj6D2jaB7sPLaYBqXdC3jmgyt7Z3sbWyNa6m0UZKfydumlBzV6ykgRJJlf3fMr8LumObgIsM5Hw8CI2dk1BUmtv2F4IN9zW+DFkDBuKDb7OQPQ/GU3r9eRzu89Ht46gfBWiT5o+G0xTD3Cq2XPsgpIEQGQtNeeOF3QVhsXwPp2TBzYfBtvPyM6E/vbdoDL9wdtHq2PR8MNh/7bpj9UZhyTv+sPugOGx8NJoR87WHIzIdTLoe3XQ2F4/v++tLvFBAiA23r6uCDeM3voKU2+FY++2PBN/SBnBSxsxNefzRoLbz8J+hogdEnwpyPBUvSJnPerW1rgqB48d4gHE+4OOh+Gn188t5Tek0BIRKV1kZ4eWlwRtDmJ8FiwWmycz4OFedBLEmXIu3ZFIyRrL4Tat+E7CKY9aGgtXDMScl5z0PZ+wY8/d/BpJBtjcHvXXltMNYRdatKFBAig8KuDUGrYvWd0LAT8scELYrZH+2f02XbmuDlPwbv8frjgMHUc4LXn/FuyMju+3v0RWMNrPh5sCZJ4y4YOzs4Rfa49/VP95YcFQWEyGDS0QavPhSMVbz6UDBFSfmZ4emy7w1O6+0pd9j6XNCF9MI9QXdW0cSgO+ukRVA0IXm/x9FqawqWwl32X8EZYCPL4fRrgqvVh/qpwkOQAkJksKrbeuB02T2bILsQTvxQEBbHzDr08xp2BdOzr7oDdq6NGxD/KEw6Y2hci9DZAa/8GZ76PmxZCbklMO/TMO9TWpNkACkgRAa7zs5gjOK5X8FLS4PB5GNOPjCYnF0YnEr62sNBmKx7ADrbYOyc4Jjj/2Honk7qDpuXBQPa6/8SXBQ5+6Nw+meD1sVg1NEODdXBioz5o4MJLocoBYTIUNJYc+B02R0vBtOqV5wbfMuu3xZ80551Kcz+SOqdEbTz5aDrac1dwZXqM98fDGiPnZ3893YPpnip3wH7dgTTquzbGVwUuW9nuC28NezioHm+coqDs9PyRx/8c8SYYKxpxOjg5yDsQlNAiAxF7rB1VRAUr/w5+JCc/dFgNtUh/I21R+q2wt//B1b8AlrrYfLZQVBMPbf3Zz51tAeD4vs/6LeHH/Q7u23bGU7w2E1aRvCBnz8q/MAfFXzY548KBtf37QyCuz4Mla6A6Uwwz1JWwVtDJH90sChXV4iMGB0cN0BneCkgRGRoaq6FlbcFYVG/LbiGo/JaOP6i4Kr1rm/23T/o47/1N1STcFbf7MLwg3903Id13Id/17bsot6P6XR2Bhcm7tse1rIjQYiEP9ub3vr89JyDA2PEMQmCZUwwVtPHIFFAiMjQ1t4SdLst+y+ofiWYhr0zwYJAaenBbLsjRnf78O/2OH9U784WSxb3YL2U/YERHyjdfsavq9Klq3Vz/PvhnTcdVQmRrShnZhcAPwBiwM/c/Vvd9mcBvwJOAXYDl7j7JjMrAe4GTgVuc/drklmniAxy6VlB99pJHw5ODd78JOSWvvXDP6d4aJzB1cUsaMlkF0LZ9MMf29oQBkbYGokPjyRNY5K0gDCzGPBj4DygClhuZkvd/aW4w64E9rh7hZldCnwbuARoBr4KnBDeRESCD/8ZFwS34SYzL5iy5WgmUzxKyYzaecAGd9/o7q3AEmBht2MWAr8M798NnGtm5u4N7v4kQVCIiEgEkhkQ44A34x5XhdsSHuPu7UAtcHSTmouISL8aQp11b2VmV5nZCjNbUV1dHXU5IiIpJZkBsQWInwhmfLgt4TFmlg4UEgxW94i73+Luc919bllZWR/LFRGReMkMiOXANDObbGaZwKXA0m7HLAUuC+9fDDzsqXLerYjIEJe0s5jcvd3MrgEeJDjN9VZ3X2tm3wBWuPtS4OfA7Wa2AaghCBEAzGwTUABkmtn7gfO7nQElIiJJlNTrINz9fuD+btv+Le5+M/DBQzy3PJm1iYjI4Q3pQWoREUmelJlqw8yqgc19eIlSYFc/lTPU6W9xMP09DtDf4mCp8PeY5O4Jz/JJmYDoKzNbcaj5SIYb/S0Opr/HAfpbHCzV/x7qYhIRkYQUECIikpAC4oBboi5gENHf4mD6exygv8XBUvrvoTEIERFJSC0IERFJSAEhIiIJDfuAMLMLzGydmW0wsxuiridKZjbBzB4xs5fMbK2ZXRd1TVEzs5iZrTKzP0VdS9TMrMjM7jazV8zsZTM7PeqaomRmXwj/nbxoZr8xs+yoa+pvwzog4la9excwE1hkZjOjrSpS7cA/uvtM4G3AZ4f53wPgOuDlqIsYJH4A/MXdjwVOYhj/XcxsHHAtMNfdTyCYb+7Swz9r6BnWAUHPVr0bNtx9m7s/F96vJ/gA6L7I07BhZuOBdwM/i7qWqJlZIXAWwQSbuHuru++NtKjopQM54VIFucDWiOvpd8M9IHqy6t2wZGblwGzgmYhLidL3geuBzojrGAwmA9XAL8Iut5+ZWV7URUXF3bcA3wXeALYBte7+ULRV9b/hHhCSgJnlA/cAn3f3uqjriYKZvQfY6e4ro65lkEgH5gD/4+6zgQZg2I7ZmdlIgt6GycBYIM/MPhptVf1vuAdET1a9G1bMLIMgHH7t7vdGXU+EKoH3heuSLAHebmZ3RFtSpKqAKnfvalHeTRAYw9U7gNfdvdrd24B7gfkR19TvhntA9GTVu2HDzIygj/lld/9e1PVEyd2/7O7jw3VJLiVY7TDlviH2lLtvB940sxnhpnOB4byA1xvA28wsN/x3cy4pOGif1AWDBrtDrXoXcVlRqgQ+BrxgZqvDbV8JF34S+Rzw6/DL1EbgExHXExl3f8bM7gaeIzj7bxUpOO2GptoQEZGEhnsXk4iIHIICQkREElJAiIhIQgoIERFJSAEhIiIJKSBEesHMOsxsddyt364mNrNyM3uxv15PpK+G9XUQIkehyd1PjroIkYGgFoRIPzCzTWb2HTN7wcyeNbOKcHu5mT1sZmvM7G9mNjHcPtrM7jOz58Nb1zQNMTP7abjOwENmlhPZLyXDngJCpHdyunUxXRK3r9bdTwR+RDATLMB/Ab9091nAr4Efhtt/CDzm7icRzGnUdQX/NODH7n48sBf4QFJ/G5HD0JXUIr1gZvvcPT/B9k3A2919Yzjh4XZ3LzGzXcAx7t4Wbt/m7qVmVg2Md/eWuNcoB/6fu08LH/8zkOHu3xyAX03kLdSCEOk/foj7vdESd78DjRNKhBQQIv3nkrifT4f3l3FgKcqPAE+E9/8GXA37170uHKgiRXpK305EeicnbqZbCNZo7jrVdaSZrSFoBSwKt32OYBW2fyJYka1rBtTrgFvM7EqClsLVBCuTiQwaGoMQ6QfhGMRcd98VdS0i/UVdTCIikpBaECIikpBaECIikpACQkREElJAiIhIQgoIERFJSAEhIiIJ/X+vXpVVHX9+0QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(clf_1_history.history['loss'])\n",
    "plt.plot(clf_1_history.history['val_loss'])\n",
    "plt.title('Model Loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Valid'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cc156c32",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-01T09:44:32.153165Z",
     "iopub.status.busy": "2022-06-01T09:44:32.152844Z",
     "iopub.status.idle": "2022-06-01T09:44:32.469327Z",
     "shell.execute_reply": "2022-06-01T09:44:32.468352Z"
    },
    "papermill": {
     "duration": 13.101136,
     "end_time": "2022-06-01T09:44:32.471695",
     "exception": false,
     "start_time": "2022-06-01T09:44:19.370559",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# one-hot encodig label for softmax\n",
    "num = np.unique(y_train_all, axis=0)\n",
    "num = num.shape[0]\n",
    "\n",
    "encoding_train = np.eye(num)[y_train_all]\n",
    "encoding_valid = np.eye(num)[y_valid]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c64fc956",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-01T09:44:58.323779Z",
     "iopub.status.busy": "2022-06-01T09:44:58.323443Z",
     "iopub.status.idle": "2022-06-01T09:44:58.393004Z",
     "shell.execute_reply": "2022-06-01T09:44:58.392225Z"
    },
    "papermill": {
     "duration": 12.83944,
     "end_time": "2022-06-01T09:44:58.395728",
     "exception": false,
     "start_time": "2022-06-01T09:44:45.556288",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Model\n",
    "keras.backend.clear_session()\n",
    "\n",
    "classifier_2 = keras.models.Sequential([\n",
    "    keras.layers.Dense(22, activation='relu',input_shape=[29]),\n",
    "    keras.layers.Dense(15, activation='relu'),\n",
    "    keras.layers.Dropout(0.2),\n",
    "    keras.layers.Dense(10, activation='relu'),\n",
    "    keras.layers.Dense(5, activation='relu'),\n",
    "    keras.layers.Dense(2, activation='softmax')\n",
    "])\n",
    "\n",
    "classifier_2.compile(loss='categorical_crossentropy', optimizer=keras.optimizers.Adam(), metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "79428425",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-01T09:45:23.900208Z",
     "iopub.status.busy": "2022-06-01T09:45:23.899881Z",
     "iopub.status.idle": "2022-06-01T09:50:31.923538Z",
     "shell.execute_reply": "2022-06-01T09:50:31.922921Z"
    },
    "papermill": {
     "duration": 320.767264,
     "end_time": "2022-06-01T09:50:31.926671",
     "exception": false,
     "start_time": "2022-06-01T09:45:11.159407",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "11373/11373 [==============================] - 31s 3ms/step - loss: 0.0701 - accuracy: 0.9754 - val_loss: 0.0291 - val_accuracy: 0.9908\n",
      "Epoch 2/10\n",
      "11373/11373 [==============================] - 31s 3ms/step - loss: 0.0302 - accuracy: 0.9902 - val_loss: 0.0188 - val_accuracy: 0.9939\n",
      "Epoch 3/10\n",
      "11373/11373 [==============================] - 31s 3ms/step - loss: 0.0247 - accuracy: 0.9923 - val_loss: 0.0172 - val_accuracy: 0.9949\n",
      "Epoch 4/10\n",
      "11373/11373 [==============================] - 32s 3ms/step - loss: 0.0218 - accuracy: 0.9933 - val_loss: 0.0151 - val_accuracy: 0.9955\n",
      "Epoch 5/10\n",
      "11373/11373 [==============================] - 31s 3ms/step - loss: 0.0196 - accuracy: 0.9941 - val_loss: 0.0163 - val_accuracy: 0.9951\n",
      "Epoch 6/10\n",
      "11373/11373 [==============================] - 30s 3ms/step - loss: 0.0188 - accuracy: 0.9944 - val_loss: 0.0139 - val_accuracy: 0.9958\n",
      "Epoch 7/10\n",
      "11373/11373 [==============================] - 30s 3ms/step - loss: 0.0175 - accuracy: 0.9947 - val_loss: 0.0160 - val_accuracy: 0.9955\n",
      "Epoch 8/10\n",
      "11373/11373 [==============================] - 29s 3ms/step - loss: 0.0165 - accuracy: 0.9951 - val_loss: 0.0116 - val_accuracy: 0.9969\n",
      "Epoch 9/10\n",
      "11373/11373 [==============================] - 32s 3ms/step - loss: 0.0157 - accuracy: 0.9955 - val_loss: 0.0115 - val_accuracy: 0.9967\n",
      "Epoch 10/10\n",
      "11373/11373 [==============================] - 30s 3ms/step - loss: 0.0151 - accuracy: 0.9957 - val_loss: 0.0109 - val_accuracy: 0.9971\n"
     ]
    }
   ],
   "source": [
    "clf_2_history = classifier_2.fit(denoising_x_train, encoding_train, epochs=10, validation_data=(denoising_x_valid,encoding_valid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7262cd8b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-01T09:51:01.287451Z",
     "iopub.status.busy": "2022-06-01T09:51:01.287137Z",
     "iopub.status.idle": "2022-06-01T09:51:01.493174Z",
     "shell.execute_reply": "2022-06-01T09:51:01.492045Z"
    },
    "papermill": {
     "duration": 14.812514,
     "end_time": "2022-06-01T09:51:01.495515",
     "exception": false,
     "start_time": "2022-06-01T09:50:46.683001",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(clf_2_history.history['loss'])\n",
    "plt.plot(clf_2_history.history['val_loss'])\n",
    "plt.title('Model Loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Valid'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9bf8bf12",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-01T09:51:31.135309Z",
     "iopub.status.busy": "2022-06-01T09:51:31.134588Z",
     "iopub.status.idle": "2022-06-01T09:51:31.142106Z",
     "shell.execute_reply": "2022-06-01T09:51:31.141065Z"
    },
    "papermill": {
     "duration": 15.015753,
     "end_time": "2022-06-01T09:51:31.144366",
     "exception": false,
     "start_time": "2022-06-01T09:51:16.128613",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1]), array([56864,    98]))"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(y_test, return_counts=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "496efa22",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-01T09:52:00.544008Z",
     "iopub.status.busy": "2022-06-01T09:52:00.543065Z",
     "iopub.status.idle": "2022-06-01T09:52:02.776151Z",
     "shell.execute_reply": "2022-06-01T09:52:02.775125Z"
    },
    "papermill": {
     "duration": 17.009776,
     "end_time": "2022-06-01T09:52:02.779087",
     "exception": false,
     "start_time": "2022-06-01T09:51:45.769311",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "denoising_x_test = denoising_ae.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3cfb59ef",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-01T09:52:32.252698Z",
     "iopub.status.busy": "2022-06-01T09:52:32.252398Z",
     "iopub.status.idle": "2022-06-01T09:52:34.393657Z",
     "shell.execute_reply": "2022-06-01T09:52:34.392342Z"
    },
    "papermill": {
     "duration": 16.927029,
     "end_time": "2022-06-01T09:52:34.396201",
     "exception": false,
     "start_time": "2022-06-01T09:52:17.469172",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "result = classifier_1.predict(denoising_x_test)\n",
    "result = np.where(result > 0.5, 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3e9a2958",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-01T09:53:03.757899Z",
     "iopub.status.busy": "2022-06-01T09:53:03.757257Z",
     "iopub.status.idle": "2022-06-01T09:53:03.773647Z",
     "shell.execute_reply": "2022-06-01T09:53:03.772650Z"
    },
    "papermill": {
     "duration": 14.769563,
     "end_time": "2022-06-01T09:53:03.776041",
     "exception": false,
     "start_time": "2022-06-01T09:52:49.006478",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "precision, recall, thresholds = precision_recall_curve(y_test, result)\n",
    "average_precision = average_precision_score(y_test, result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ab97b509",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-01T09:53:33.240504Z",
     "iopub.status.busy": "2022-06-01T09:53:33.240188Z",
     "iopub.status.idle": "2022-06-01T09:53:33.442832Z",
     "shell.execute_reply": "2022-06-01T09:53:33.441698Z"
    },
    "papermill": {
     "duration": 15.039204,
     "end_time": "2022-06-01T09:53:33.445263",
     "exception": false,
     "start_time": "2022-06-01T09:53:18.406059",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Precision-Recall curve: Average Precision = 0.20')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.step(recall, precision, color='k', alpha=0.7, where='post')\n",
    "plt.fill_between(recall,precision, step='post', alpha=0.3, color='k')\n",
    "\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "\n",
    "plt.title('Precision-Recall curve: Average Precision = {0:0.2f}'.format(average_precision))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "381410fb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-01T09:54:02.916547Z",
     "iopub.status.busy": "2022-06-01T09:54:02.916194Z",
     "iopub.status.idle": "2022-06-01T09:54:02.924937Z",
     "shell.execute_reply": "2022-06-01T09:54:02.924058Z"
    },
    "papermill": {
     "duration": 14.743176,
     "end_time": "2022-06-01T09:54:02.927159",
     "exception": false,
     "start_time": "2022-06-01T09:53:48.183983",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "fpr, tpr, thresholds = roc_curve(y_test, result)\n",
    "areaUnderROC = auc(fpr,tpr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "8aa24ef3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-01T09:54:32.521649Z",
     "iopub.status.busy": "2022-06-01T09:54:32.521039Z",
     "iopub.status.idle": "2022-06-01T09:54:32.725299Z",
     "shell.execute_reply": "2022-06-01T09:54:32.724378Z"
    },
    "papermill": {
     "duration": 14.989106,
     "end_time": "2022-06-01T09:54:32.727362",
     "exception": false,
     "start_time": "2022-06-01T09:54:17.738256",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(fpr, tpr, color='r', lw=2, label='ROC curve')\n",
    "plt.plot([0,1],[0,1],color='k', lw=2, linestyle='--')\n",
    "plt.xlim([0.0,1.0])\n",
    "plt.ylim([0.0,1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operationg characteristic: Area under the curve = {0:0.2f}'.format(areaUnderROC))\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5de3a890",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-01T09:55:02.287772Z",
     "iopub.status.busy": "2022-06-01T09:55:02.287113Z",
     "iopub.status.idle": "2022-06-01T09:55:04.689992Z",
     "shell.execute_reply": "2022-06-01T09:55:04.689169Z"
    },
    "papermill": {
     "duration": 17.26699,
     "end_time": "2022-06-01T09:55:04.692682",
     "exception": false,
     "start_time": "2022-06-01T09:54:47.425692",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "result2 = classifier_2.predict(denoising_x_test)\n",
    "result2 = result2.argmax(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "70591ea8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-01T09:55:34.155896Z",
     "iopub.status.busy": "2022-06-01T09:55:34.155273Z",
     "iopub.status.idle": "2022-06-01T09:55:34.170638Z",
     "shell.execute_reply": "2022-06-01T09:55:34.169511Z"
    },
    "papermill": {
     "duration": 14.688805,
     "end_time": "2022-06-01T09:55:34.173147",
     "exception": false,
     "start_time": "2022-06-01T09:55:19.484342",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "precision, recall, thresholds = precision_recall_curve(y_test, result2)\n",
    "average_precision = average_precision_score(y_test, result2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "dbdcd149",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-01T09:56:03.519853Z",
     "iopub.status.busy": "2022-06-01T09:56:03.519479Z",
     "iopub.status.idle": "2022-06-01T09:56:03.719877Z",
     "shell.execute_reply": "2022-06-01T09:56:03.718921Z"
    },
    "papermill": {
     "duration": 14.895342,
     "end_time": "2022-06-01T09:56:03.722177",
     "exception": false,
     "start_time": "2022-06-01T09:55:48.826835",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Precision-Recall curve: Average Precision = 0.24')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.step(recall, precision, color='k', alpha=0.7, where='post')\n",
    "plt.fill_between(recall,precision, step='post', alpha=0.3, color='k')\n",
    "\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "\n",
    "plt.title('Precision-Recall curve: Average Precision = {0:0.2f}'.format(average_precision))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "82063c99",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-01T09:56:33.018438Z",
     "iopub.status.busy": "2022-06-01T09:56:33.018115Z",
     "iopub.status.idle": "2022-06-01T09:56:33.028266Z",
     "shell.execute_reply": "2022-06-01T09:56:33.026995Z"
    },
    "papermill": {
     "duration": 14.601846,
     "end_time": "2022-06-01T09:56:33.030654",
     "exception": false,
     "start_time": "2022-06-01T09:56:18.428808",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "fpr, tpr, thresholds = roc_curve(y_test, result2)\n",
    "areaUnderROC = auc(fpr,tpr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ab664cdd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-01T09:57:02.386635Z",
     "iopub.status.busy": "2022-06-01T09:57:02.385945Z",
     "iopub.status.idle": "2022-06-01T09:57:02.586485Z",
     "shell.execute_reply": "2022-06-01T09:57:02.585588Z"
    },
    "papermill": {
     "duration": 14.826731,
     "end_time": "2022-06-01T09:57:02.588523",
     "exception": false,
     "start_time": "2022-06-01T09:56:47.761792",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(fpr, tpr, color='r', lw=2, label='ROC curve')\n",
    "plt.plot([0,1],[0,1],color='k', lw=2, linestyle='--')\n",
    "plt.xlim([0.0,1.0])\n",
    "plt.ylim([0.0,1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operationg characteristic: Area under the curve = {0:0.2f}'.format(areaUnderROC))\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a9cad36e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-01T09:57:32.098813Z",
     "iopub.status.busy": "2022-06-01T09:57:32.098244Z",
     "iopub.status.idle": "2022-06-01T09:57:34.172270Z",
     "shell.execute_reply": "2022-06-01T09:57:34.171136Z"
    },
    "papermill": {
     "duration": 16.799518,
     "end_time": "2022-06-01T09:57:34.174774",
     "exception": false,
     "start_time": "2022-06-01T09:57:17.375256",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "base_result = base_classifier.predict(x_test)\n",
    "base_result = np.where(base_result > 0.5, 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "4bf7d3e6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-01T09:58:03.524252Z",
     "iopub.status.busy": "2022-06-01T09:58:03.523907Z",
     "iopub.status.idle": "2022-06-01T09:58:03.539126Z",
     "shell.execute_reply": "2022-06-01T09:58:03.538055Z"
    },
    "papermill": {
     "duration": 14.646274,
     "end_time": "2022-06-01T09:58:03.541759",
     "exception": false,
     "start_time": "2022-06-01T09:57:48.895485",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "precision, recall, thresholds = precision_recall_curve(y_test, base_result)\n",
    "average_precision = average_precision_score(y_test, base_result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "8e3f48e0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-01T09:58:32.918199Z",
     "iopub.status.busy": "2022-06-01T09:58:32.917905Z",
     "iopub.status.idle": "2022-06-01T09:58:33.109272Z",
     "shell.execute_reply": "2022-06-01T09:58:33.108402Z"
    },
    "papermill": {
     "duration": 14.852185,
     "end_time": "2022-06-01T09:58:33.111991",
     "exception": false,
     "start_time": "2022-06-01T09:58:18.259806",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Precision-Recall curve: Average Precision = 0.52')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.step(recall, precision, color='k', alpha=0.7, where='post')\n",
    "plt.fill_between(recall,precision, step='post', alpha=0.3, color='k')\n",
    "\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "\n",
    "plt.title('Precision-Recall curve: Average Precision = {0:0.2f}'.format(average_precision))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "89e7adb0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-01T09:59:02.617939Z",
     "iopub.status.busy": "2022-06-01T09:59:02.617207Z",
     "iopub.status.idle": "2022-06-01T09:59:02.626154Z",
     "shell.execute_reply": "2022-06-01T09:59:02.625344Z"
    },
    "papermill": {
     "duration": 14.84865,
     "end_time": "2022-06-01T09:59:02.628349",
     "exception": false,
     "start_time": "2022-06-01T09:58:47.779699",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "fpr, tpr, thresholds = roc_curve(y_test, base_result)\n",
    "areaUnderROC = auc(fpr,tpr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "2e77c89d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-01T09:59:32.126474Z",
     "iopub.status.busy": "2022-06-01T09:59:32.126138Z",
     "iopub.status.idle": "2022-06-01T09:59:32.349312Z",
     "shell.execute_reply": "2022-06-01T09:59:32.348364Z"
    },
    "papermill": {
     "duration": 14.875326,
     "end_time": "2022-06-01T09:59:32.351420",
     "exception": false,
     "start_time": "2022-06-01T09:59:17.476094",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(fpr, tpr, color='r', lw=2, label='ROC curve')\n",
    "plt.plot([0,1],[0,1],color='k', lw=2, linestyle='--')\n",
    "plt.xlim([0.0,1.0])\n",
    "plt.ylim([0.0,1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operationg characteristic: Area under the curve = {0:0.2f}'.format(areaUnderROC))\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be74aaf1",
   "metadata": {
    "papermill": {
     "duration": 14.68091,
     "end_time": "2022-06-01T10:00:01.768583",
     "exception": false,
     "start_time": "2022-06-01T09:59:47.087673",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0203bfd1",
   "metadata": {
    "papermill": {
     "duration": 14.855345,
     "end_time": "2022-06-01T10:00:31.341281",
     "exception": false,
     "start_time": "2022-06-01T10:00:16.485936",
     "status": "completed"
    },
    "tags": []
   },
   "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": "3.7.12"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 3265.383516,
   "end_time": "2022-06-01T10:00:49.371725",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2022-06-01T09:06:23.988209",
   "version": "2.3.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}