LSTM_CNN.ipynb

{
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      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/simonetome/ApplieAI-Biomedicine-Masciulli-Pietrobon-Tome/blob/main/delivery/CNN_LSTM.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install heartpy\n",
        "!pip install PyWavelets"
      ],
      "metadata": {
        "id": "l1CgC3jGsC2d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1d9abe6e-913e-4aa7-ea09-cd3a778a379e"
      },
      "id": "l1CgC3jGsC2d",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: heartpy in /usr/local/lib/python3.7/dist-packages (1.2.7)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from heartpy) (1.4.1)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from heartpy) (1.19.5)\n",
            "Requirement already satisfied: matplotlib<=3.3.4 in /usr/local/lib/python3.7/dist-packages (from heartpy) (3.2.2)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib<=3.3.4->heartpy) (3.0.6)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib<=3.3.4->heartpy) (1.3.2)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib<=3.3.4->heartpy) (0.11.0)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib<=3.3.4->heartpy) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib<=3.3.4->heartpy) (1.15.0)\n",
            "Requirement already satisfied: PyWavelets in /usr/local/lib/python3.7/dist-packages (1.2.0)\n",
            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.7/dist-packages (from PyWavelets) (1.19.5)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7bfe12d7",
      "metadata": {
        "id": "7bfe12d7"
      },
      "outputs": [],
      "source": [
        "import os \n",
        "import zipfile \n",
        "from scipy.io import loadmat \n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt \n",
        "import numpy as np\n",
        "import io\n",
        "from PIL import Image\n",
        "import tensorflow as tf \n",
        "from tensorflow import keras\n",
        "import sys\n",
        "from scipy.signal import resample, butter, lfilter, iirnotch\n",
        "from tqdm import tqdm\n",
        "import random\n",
        "from sklearn.model_selection import train_test_split\n",
        "import heartpy as hp\n",
        "from tensorflow.keras.callbacks import ModelCheckpoint,EarlyStopping,ReduceLROnPlateau\n",
        "import pywt as pw\n",
        "SEED = 123\n",
        "tf.random.set_seed(SEED)\n",
        "np.random.seed(SEED)\n",
        "random.seed(SEED)\n",
        "os.environ['PYTHONHASHSEED'] = str(SEED)"
      ]
    },
    {
      "cell_type": "code",
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      "id": "5fcdb248",
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        "id": "5fcdb248",
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          "height": 73
        },
        "outputId": "5f1dc8e8-c37b-4f28-af84-f6ea1f6d5a8f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
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            "text/plain": [
              "'\\n#IF run locally\\ndataset_current_folder = \"training_set.zip\" # where the zip is\\ndataset_folder = \"C://Users//simon//Desktop//AppliedAI-project\" # where I want the dataset - avoid the current folder as Git doesn\\'t allow huge uploads\\n\\nwith zipfile.ZipFile(dataset_current_folder, \\'r\\') as zip: # extract the zip file into the desired folder \\n    zip.extractall(dataset_folder)\\n'"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "#IF run locally\n",
        "dataset_current_folder = \"training_set.zip\" # where the zip is\n",
        "dataset_folder = \"C://Users//simon//Desktop//AppliedAI-project\" # where I want the dataset - avoid the current folder as Git doesn't allow huge uploads\n",
        "\n",
        "with zipfile.ZipFile(dataset_current_folder, 'r') as zip: # extract the zip file into the desired folder \n",
        "    zip.extractall(dataset_folder)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#IF local runtime used -> Set os.cwd as google drive volume , mounted locally\n",
        "os.chdir('G:\\Il mio Drive')\n",
        "cwd = os.getcwd()\n",
        "cwd = os.path.join(cwd,'Colab2022/AppliedAI/FinalAssignment')\n",
        "os.chdir(cwd)\n",
        "dataset_current_folder = \"training_set.zip\" # where the zip is\n",
        "dataset_folder = os.path.join(cwd,'training_set_dir')\n"
      ],
      "metadata": {
        "id": "HsPnFitKvg0j",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        },
        "outputId": "ab58b352-eee9-4f23-8e2a-4541abb8a120"
      },
      "id": "HsPnFitKvg0j",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "\"\\n#IF local runtime used -> Set os.cwd as google drive volume , mounted locally\\nos.chdir('G:\\\\Il mio Drive')\\ncwd = os.getcwd()\\ncwd = os.path.join(cwd,'Colab2022/AppliedAI/FinalAssignment')\\nos.chdir(cwd)\\ndataset_folder = os.path.join(cwd,'training_set_dir')\\n\""
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "26e300ba",
      "metadata": {
        "id": "26e300ba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "outputId": "c471cfc8-2ef0-4ec0-a7d7-f4374f318643"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 105/105 [00:00<00:00, 129.71it/s]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
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              "  sigId  ...                                             labels\n",
              "0  S001  ...  [N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, ...\n",
              "1  S002  ...  [N, N, N, N, S, N, S, N, S, N, S, N, S, N, S, ...\n",
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              "\n",
              "[5 rows x 6 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "def load_data(sample_prefix, input_dir):    # everything is returned as a numpy array which is easier to manipulate\n",
        "    label_filepath = os.path.join(input_dir, sample_prefix + '_ann.mat')\n",
        "    peak_filepath = os.path.join(input_dir, sample_prefix + '_rpk.mat')\n",
        "    signal_filepath = os.path.join(input_dir, sample_prefix + '.mat')\n",
        "    if os.path.isfile(label_filepath):\n",
        "        mat_file = loadmat(label_filepath)\n",
        "        label = np.asarray(mat_file['labels'])\n",
        "    if os.path.isfile(peak_filepath):\n",
        "        mat_file = loadmat(peak_filepath)\n",
        "        peaks = np.array(mat_file['rpeaks'],dtype=np.int64)\n",
        "    if os.path.isfile(signal_filepath):\n",
        "        mat_file = loadmat(signal_filepath)\n",
        "        signal = np.asarray(mat_file['ecg'] )\n",
        "\n",
        "    return label, peaks, signal\n",
        "\n",
        "\n",
        "labels = list()             # labels \n",
        "ids = list()                # Id of samples \n",
        "rpeaks = list()             # detected peaks of the signal \n",
        "ecg_signals = list()        # .mat ecg signal \n",
        "frequencies = list()        # sample frequency of the ecg signal \n",
        "\n",
        "\n",
        "for f in os.listdir(dataset_folder):\n",
        "  if f.lower().endswith('.mat'):\n",
        "    id = f[:4]\n",
        "    if id not in ids:\n",
        "      ids.append(id)\n",
        "      sample_prefix = f[:8]\n",
        "      label, peak, signal = load_data(sample_prefix, dataset_folder)\n",
        "      labels.append(label)\n",
        "      rpeaks.append(peak)\n",
        "      ecg_signals.append(signal)\n",
        "      frequencies.append(int(sample_prefix[5:]))\n",
        "cols = [\"sigId\",\"ecg_lead_1\",\"ecg_lead_2\",\"peaks\",\"frequencies\",\"labels\"]\n",
        "\n",
        "# ecg signals is 105 rows [,,,,,]\n",
        "\n",
        "first_lead_signals = []\n",
        "second_lead_signals = []\n",
        "\n",
        "for signal in ecg_signals:\n",
        "    first_lead_signals.append(signal[:,0].tolist())    # converting the array to list as list of array is deprecated \n",
        "    second_lead_signals.append(signal[:,1].tolist())\n",
        "\n",
        "df = pd.DataFrame(data =[ids,first_lead_signals,second_lead_signals,rpeaks,frequencies,labels]).T\n",
        "df.columns = cols\n",
        "\n",
        "# transform peaks \n",
        "for id in tqdm(df.index.tolist()):\n",
        "    peaks = df.iloc[id]['peaks']\n",
        "    p_list = list()\n",
        "    for p in peaks:\n",
        "        p_list.append(p[0])\n",
        "    df.iloc[id]['peaks'] = np.asarray(p_list).astype(np.int64)\n",
        "\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "999717a8",
      "metadata": {
        "id": "999717a8",
        "outputId": "6eafd298-1967-48d2-ad44-1fedc3af3b67",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 40/40 [00:15<00:00,  2.59it/s]\n"
          ]
        }
      ],
      "source": [
        "# because of the curse of dimensionality --> undersampling \n",
        "\n",
        "from scipy.signal import resample\n",
        "\n",
        "ids_128 = df[df['frequencies'] == 128].index.tolist() # 65 signals are sampled using fs = 128\n",
        "ids_250 = df[df['frequencies'] == 250].index.tolist() # 65 signals are sampled using fs = 128\n",
        "sampled_len =  len(df.iloc[ids_250[0]]['ecg_lead_1'])\n",
        "resampled_len = len(df.iloc[ids_128[0]]['ecg_lead_1'])\n",
        "\n",
        "for id in tqdm(ids_250):\n",
        "    row = df.iloc[id]\n",
        "    # first lead\n",
        "    signal = np.asarray(row['ecg_lead_1']).astype(np.float32)\n",
        "    resampled_1 = resample(signal,resampled_len)\n",
        "    \n",
        "    # second lead\n",
        "    signal = np.asarray(row['ecg_lead_2']).astype(np.float32)\n",
        "    resampled_2 = resample(signal,resampled_len)\n",
        "    \n",
        "    df.iloc[id]['ecg_lead_1'] = resampled_1.tolist()\n",
        "    df.iloc[id]['ecg_lead_2'] = resampled_2.tolist()\n",
        "    \n",
        "    for i,p in enumerate(list(df.iloc[id]['peaks'])):\n",
        "        df.iloc[id]['peaks'][i] = int(resampled_len * p/sampled_len)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RJltVza9lkVw"
      },
      "source": [
        "<h2>Bandpass filtering"
      ],
      "id": "RJltVza9lkVw"
    },
    {
      "cell_type": "code",
      "source": [
        "#Preprocessing used in practical lessons\n",
        "from scipy.signal import resample, butter, lfilter, iirnotch\n",
        "\n",
        "def notch_filter(cutoff, fs, q=30):\n",
        "    nyq = 0.5*fs\n",
        "    freq = cutoff/nyq\n",
        "    b, a = iirnotch(freq, q)\n",
        "    return b, a\n",
        "\n",
        "\n",
        "def bandpass_filter(data, filter_order=6, lowcut = .8, highcut = 45, signal_freq=128):\n",
        "    \"\"\"\n",
        "    Method responsible for creating and applying Butterworth filter.\n",
        "    :param deque data: raw data\n",
        "    :param float lowcut: filter lowcut frequency value\n",
        "    :param float highcut: filter highcut frequency value\n",
        "    :param int signal_freq: signal frequency in samples per second (Hz)\n",
        "    :param int filter_order: filter order\n",
        "    :return array: filtered data\n",
        "    \"\"\"\n",
        "    powerline = 60\n",
        "    nyquist_freq = 0.5 * signal_freq\n",
        "    low = lowcut / nyquist_freq\n",
        "    high = highcut / nyquist_freq\n",
        "    b, a = butter(filter_order, [low, high], btype=\"band\")\n",
        "    y = lfilter(b, a, data,zi = None)\n",
        "    return y\n",
        "\n",
        "\n",
        "\n",
        "filtered_df = df.copy(deep= True)\n",
        "\n",
        "ids = filtered_df.index.tolist()\n",
        "\n",
        "for id_ in tqdm(ids):\n",
        "    \n",
        "    row = filtered_df.iloc[id_]\n",
        "    first = row['ecg_lead_1']\n",
        "    second = row['ecg_lead_2']\n",
        "    filtered_first = np.asarray(bandpass_filter(first))\n",
        "    filtered_second =  np.asarray(bandpass_filter(second))\n",
        "\n",
        "    filtered_df.iloc[id_]['ecg_lead_1'] = pd.Series(filtered_first)\n",
        "    filtered_df.iloc[id_]['ecg_lead_2'] = pd.Series(filtered_second)"
      ],
      "metadata": {
        "id": "yyfwxPaQQlid",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "edc4ccda-a154-489b-f030-8eacf7d3964e"
      },
      "id": "yyfwxPaQQlid",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 105/105 [00:04<00:00, 23.06it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b8a6e546",
      "metadata": {
        "id": "b8a6e546",
        "outputId": "2c8424bf-f99e-4161-e896-34fdfecc976f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['N', 'N', 'S', 'N']\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1332x756 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['N', 'N', 'S', 'N']\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1332x756 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# plot signal \n",
        "from scipy.fft import fft, fftfreq\n",
        "\n",
        "def plot_signal(df,id,length):\n",
        "    fig,axs = plt.subplots(2)\n",
        "    fig.set_size_inches(18.5, 10.5)\n",
        "    ax1 = axs[0]\n",
        "    ax2 = axs[1]\n",
        "    \n",
        "    \n",
        "    signal = df.iloc[id]\n",
        "    \n",
        "    first_lead = signal['ecg_lead_1'][:length]\n",
        "    second_lead = signal['ecg_lead_2'][:length]\n",
        "    \n",
        "    peaks_ = signal['peaks']\n",
        "    labels_ = signal['labels']\n",
        "    \n",
        "    x = list(range(length))\n",
        "    \n",
        "    ax1.plot(x,bandpass_filter(first_lead),color='green')\n",
        "    ax2.plot(x,bandpass_filter(second_lead),color='green')\n",
        "\n",
        "\n",
        "    \n",
        "    peaks = list()\n",
        "    p_amplitudes_first = list()\n",
        "    p_amplitudes_second = list()\n",
        "    labels = list()\n",
        "    \n",
        "    for i,p in enumerate(peaks_):\n",
        "        if(p <= length):\n",
        "            peaks.append(p)\n",
        "            p_amplitudes_first.append(first_lead[p])\n",
        "            p_amplitudes_second.append(second_lead[p])\n",
        "            labels.append(labels_[i])\n",
        "        else:\n",
        "            break\n",
        "            \n",
        "    ax1.scatter(peaks,p_amplitudes_first,color='red')\n",
        "    ax2.scatter(peaks,p_amplitudes_second,color='red')\n",
        "    offset = 10\n",
        "    \n",
        "    for i, txt in enumerate(labels):\n",
        "        ax1.annotate(txt, (peaks[i]+offset, p_amplitudes_first[i]),size = 14)\n",
        "        ax2.annotate(txt, (peaks[i]+offset, p_amplitudes_second[i]),size = 14)\n",
        "    \n",
        "    print(labels)\n",
        "    plt.show()\n",
        "\n",
        "plot_signal(df,50,400)\n",
        "plot_signal(filtered_df,50,400)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GbEkpWlulkVx"
      },
      "source": [
        "<h2>Scaling to [0:1] in 10 seconds time window "
      ],
      "id": "GbEkpWlulkVx"
    },
    {
      "cell_type": "code",
      "source": [
        "import heartpy as hp\n",
        "\n",
        "fs = 128\n",
        "time_window = 10\n",
        "samples = fs * time_window \n",
        "\n",
        "for id in tqdm(filtered_df.index.tolist()):\n",
        "    row = filtered_df.loc[id]\n",
        "    first_lead = row['ecg_lead_1']\n",
        "    second_lead = row['ecg_lead_2']\n",
        "    l_ = len(first_lead)\n",
        "    scaled_1 = list()\n",
        "    scaled_2 = list()\n",
        "   \n",
        "    for i in range(int(l_/samples)):\n",
        "        to_scale_1 = first_lead[i*samples:i*samples+samples] \n",
        "        to_scale_2 = second_lead[i*samples:i*samples+samples]\n",
        "        \n",
        "        scaled_1 += list(hp.scale_data(to_scale_1,lower=0,upper=1))\n",
        "        scaled_2 += list(hp.scale_data(to_scale_2,lower=0,upper=1))\n",
        "    \n",
        "    filtered_df.loc[id]['ecg_lead_1'] = scaled_1\n",
        "    filtered_df.loc[id]['ecg_lead_2'] = scaled_2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PUHZVzb7Opz7",
        "outputId": "ce0f8d55-d879-4098-f25e-a14539b5e4ae"
      },
      "id": "PUHZVzb7Opz7",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 105/105 [01:01<00:00,  1.72it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ivJyLPD2lkVy"
      },
      "source": [
        "<h2>Outliers detection and removal using zscore</h2>"
      ],
      "id": "ivJyLPD2lkVy"
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy import stats\n",
        "\n",
        "# Function for the computation of rhythm data \n",
        "def get_2ECG_rhythmfeatures(data):\n",
        "  \n",
        "  #   mean\n",
        "  mean_lead = np.mean(data)\n",
        "\n",
        "  #   median\n",
        "  median_lead = np.median(data)\n",
        "\n",
        "  #   standard deviation\n",
        "  std_lead = np.std(data)\n",
        "\n",
        "  #   variance\n",
        "  var_lead = stats.tvar(data)\n",
        "\n",
        "  #   Skewness\n",
        "  skew_lead = stats.skew(data)\n",
        "\n",
        "  #   Kurtosis\n",
        "  kurt_lead = stats.kurtosis(data)\n",
        "\n",
        "  features = np.hstack([mean_lead,median_lead,std_lead,var_lead,skew_lead,kurt_lead])\n",
        "\n",
        "  return features\n",
        "\n",
        "\n",
        "features_rhythm_lead1 = list()\n",
        "features_rhythm_lead2 = list()\n",
        "\n",
        "for i in range(filtered_df.shape[0]):\n",
        "  tmp_lead1 = get_2ECG_rhythmfeatures(filtered_df['ecg_lead_1'][i])\n",
        "  features_rhythm_lead1.append(tmp_lead1)\n",
        "  tmp_lead2 = get_2ECG_rhythmfeatures(filtered_df['ecg_lead_2'][i])\n",
        "  features_rhythm_lead2.append(tmp_lead2)\n",
        "\n",
        "features_rhythm_lead1 = pd.DataFrame(features_rhythm_lead1, columns = ['mean_lead1', 'median_lead1', 'std_lead1', 'var_lead1', 'skew_lead1','kurt_lead1'])\n",
        "features_rhythm_lead2 = pd.DataFrame(features_rhythm_lead2, columns = ['mean_lead2', 'median_lead2', 'std_lead2', 'var_lead2', 'skew_lead2','kurt_lead2'])\n",
        "\n",
        "features_rhythm = pd.concat([features_rhythm_lead1, features_rhythm_lead2], axis=1)\n",
        "\n",
        "features = pd.DataFrame(features_rhythm, columns = ['mean_lead1', 'median_lead1', 'std_lead1', 'var_lead1', 'skew_lead1','kurt_lead1',\n",
        "                                                    'mean_lead2', 'median_lead2', 'std_lead2', 'var_lead2', 'skew_lead2','kurt_lead2'])\n",
        "display(features) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "8Ccc6_SuSxUY",
        "outputId": "d5ed0d2e-14e2-4be3-f508-4047875a2c87"
      },
      "id": "8Ccc6_SuSxUY",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-a5d70fdf-7d7d-4267-bd7c-cd40d109b8c2\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean_lead1</th>\n",
              "      <th>median_lead1</th>\n",
              "      <th>std_lead1</th>\n",
              "      <th>var_lead1</th>\n",
              "      <th>skew_lead1</th>\n",
              "      <th>kurt_lead1</th>\n",
              "      <th>mean_lead2</th>\n",
              "      <th>median_lead2</th>\n",
              "      <th>std_lead2</th>\n",
              "      <th>var_lead2</th>\n",
              "      <th>skew_lead2</th>\n",
              "      <th>kurt_lead2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.476086</td>\n",
              "      <td>0.474618</td>\n",
              "      <td>0.128357</td>\n",
              "      <td>0.016476</td>\n",
              "      <td>0.065871</td>\n",
              "      <td>3.853435</td>\n",
              "      <td>0.600828</td>\n",
              "      <td>0.613177</td>\n",
              "      <td>0.119664</td>\n",
              "      <td>0.014319</td>\n",
              "      <td>-1.328497</td>\n",
              "      <td>6.504245</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.309628</td>\n",
              "      <td>0.315643</td>\n",
              "      <td>0.118704</td>\n",
              "      <td>0.014091</td>\n",
              "      <td>2.242173</td>\n",
              "      <td>11.961672</td>\n",
              "      <td>0.662428</td>\n",
              "      <td>0.672944</td>\n",
              "      <td>0.144363</td>\n",
              "      <td>0.020841</td>\n",
              "      <td>-1.038713</td>\n",
              "      <td>3.939288</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.462381</td>\n",
              "      <td>0.466913</td>\n",
              "      <td>0.122431</td>\n",
              "      <td>0.014989</td>\n",
              "      <td>-0.007802</td>\n",
              "      <td>2.618824</td>\n",
              "      <td>0.570732</td>\n",
              "      <td>0.586303</td>\n",
              "      <td>0.134329</td>\n",
              "      <td>0.018044</td>\n",
              "      <td>-0.788964</td>\n",
              "      <td>3.169306</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.399816</td>\n",
              "      <td>0.366260</td>\n",
              "      <td>0.149092</td>\n",
              "      <td>0.022228</td>\n",
              "      <td>0.955791</td>\n",
              "      <td>1.976279</td>\n",
              "      <td>0.658729</td>\n",
              "      <td>0.681179</td>\n",
              "      <td>0.162172</td>\n",
              "      <td>0.026300</td>\n",
              "      <td>-1.171663</td>\n",
              "      <td>1.827310</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.478230</td>\n",
              "      <td>0.488412</td>\n",
              "      <td>0.131123</td>\n",
              "      <td>0.017193</td>\n",
              "      <td>-0.046548</td>\n",
              "      <td>4.308083</td>\n",
              "      <td>0.555436</td>\n",
              "      <td>0.561574</td>\n",
              "      <td>0.148968</td>\n",
              "      <td>0.022191</td>\n",
              "      <td>-0.156231</td>\n",
              "      <td>1.414820</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>100</th>\n",
              "      <td>0.394100</td>\n",
              "      <td>0.385903</td>\n",
              "      <td>0.132414</td>\n",
              "      <td>0.017534</td>\n",
              "      <td>0.582383</td>\n",
              "      <td>1.942821</td>\n",
              "      <td>0.426588</td>\n",
              "      <td>0.419286</td>\n",
              "      <td>0.116018</td>\n",
              "      <td>0.013460</td>\n",
              "      <td>0.446691</td>\n",
              "      <td>2.379165</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>101</th>\n",
              "      <td>0.437926</td>\n",
              "      <td>0.430825</td>\n",
              "      <td>0.127339</td>\n",
              "      <td>0.016215</td>\n",
              "      <td>0.479884</td>\n",
              "      <td>3.274700</td>\n",
              "      <td>0.444411</td>\n",
              "      <td>0.452348</td>\n",
              "      <td>0.126760</td>\n",
              "      <td>0.016068</td>\n",
              "      <td>0.327382</td>\n",
              "      <td>3.613294</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>102</th>\n",
              "      <td>0.345843</td>\n",
              "      <td>0.332378</td>\n",
              "      <td>0.115116</td>\n",
              "      <td>0.013252</td>\n",
              "      <td>1.653328</td>\n",
              "      <td>6.466284</td>\n",
              "      <td>0.403117</td>\n",
              "      <td>0.404505</td>\n",
              "      <td>0.114159</td>\n",
              "      <td>0.013032</td>\n",
              "      <td>0.996624</td>\n",
              "      <td>6.575470</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>103</th>\n",
              "      <td>0.514950</td>\n",
              "      <td>0.522097</td>\n",
              "      <td>0.098893</td>\n",
              "      <td>0.009780</td>\n",
              "      <td>-0.152302</td>\n",
              "      <td>9.197804</td>\n",
              "      <td>0.461116</td>\n",
              "      <td>0.445006</td>\n",
              "      <td>0.129433</td>\n",
              "      <td>0.016753</td>\n",
              "      <td>0.508997</td>\n",
              "      <td>1.575390</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>104</th>\n",
              "      <td>0.537678</td>\n",
              "      <td>0.539089</td>\n",
              "      <td>0.106207</td>\n",
              "      <td>0.011280</td>\n",
              "      <td>-0.579932</td>\n",
              "      <td>6.520011</td>\n",
              "      <td>0.659409</td>\n",
              "      <td>0.655174</td>\n",
              "      <td>0.129539</td>\n",
              "      <td>0.016781</td>\n",
              "      <td>-1.179600</td>\n",
              "      <td>6.316306</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>105 rows × 12 columns</p>\n",
              "</div>\n",
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              "  "
            ],
            "text/plain": [
              "     mean_lead1  median_lead1  std_lead1  ...  var_lead2  skew_lead2  kurt_lead2\n",
              "0      0.476086      0.474618   0.128357  ...   0.014319   -1.328497    6.504245\n",
              "1      0.309628      0.315643   0.118704  ...   0.020841   -1.038713    3.939288\n",
              "2      0.462381      0.466913   0.122431  ...   0.018044   -0.788964    3.169306\n",
              "3      0.399816      0.366260   0.149092  ...   0.026300   -1.171663    1.827310\n",
              "4      0.478230      0.488412   0.131123  ...   0.022191   -0.156231    1.414820\n",
              "..          ...           ...        ...  ...        ...         ...         ...\n",
              "100    0.394100      0.385903   0.132414  ...   0.013460    0.446691    2.379165\n",
              "101    0.437926      0.430825   0.127339  ...   0.016068    0.327382    3.613294\n",
              "102    0.345843      0.332378   0.115116  ...   0.013032    0.996624    6.575470\n",
              "103    0.514950      0.522097   0.098893  ...   0.016753    0.508997    1.575390\n",
              "104    0.537678      0.539089   0.106207  ...   0.016781   -1.179600    6.316306\n",
              "\n",
              "[105 rows x 12 columns]"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize data distribution for each feature\n",
        "for col in features.columns: \n",
        "      features.hist(column = col, bins = 50)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "IodQqzfpSz-L",
        "outputId": "aa210abe-498b-4c11-982a-442e2cf13320"
      },
      "id": "IodQqzfpSz-L",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Cl6RCWeCSVCgLXJIKZYFLUqEscEkqlAUuSYWywCWpUBa4JBXKApekQlngklQoC1ySCmWBS1Kh+irwiDglIr4ZEd+JiE11hZIkLa3nAo+IVcA/As8EHg2cERGPriuYJGlx/RyBPx74TmbenJn3A5PAc+uJJUlaSmRmbxtGvBA4JTNfWj0+C3hCZr5q3noTwET18Fjgm73Hbdw64M6VDtGhUrKWkhPM2oRScsJgZz0qMx82f2HjV+TJzM3A5qb3U4eI2JaZoyudoxOlZC0lJ5i1CaXkhLKy7tPPKZRdwCPmPD6yWiZJWgb9FPhXgWMi4uiIOAA4Hbi8nliSpKX0fAolM/dGxKuAfwdWAe/PzBtqS7YyijjVUyklayk5waxNKCUnlJUV6ONFTEnSyvKTmJJUKAtckgq13xb4Uh/zj4gDI+LS6vmrI2KkWv7QiPh8RMxGxIXztjkhImaqbd4VETHAWaeqMa+tbg9f4azPiIjpav6mI+KkOdvUPq8N5Ry0OX38nCzXRcTzOh1zwLLuqOb72ojYttJZ5zz/m9W/rXM7HXPZZeZ+d6P1oupNwCOBA4DrgEfPW+cVwHuq+6cDl1b31wBPBl4OXDhvm68ATwQC+AzwzAHOOgWMDtC8/i5wRHX/eGBXU/PaYM5Bm9NDgNXV/cOB3bTemLDkmIOStXq8A1g3KPM65/ktwMeAczsdc7lv++sReCcf838u8MHq/hbg5IiIzLw3M78E/GTuyhFxOPCgzLwqW7/NDwGnDWLWBvWT9WuZeVu1/Abg4OoIqIl5rT1nn3mayvrjzNxbLT8I2PeOhKa+5qKJrE3pOStARJwG3ELr70A3Yy6r/bXA1wO3znm8s1rWdp3qL9Ye4KFLjLlziTF70UTWfT5Q/W/pm+s4LVFj1hcA12TmT2lmXpvIuc9AzWlEPCEibgBmgJdXz3cy5qBkhVaZX1GdspqgHj1njYgh4HXAX/Uw5rLaXwtccGZmbgCeUt3OWuE8AETEccDfAC9b6SyLWSDnwM1pZl6dmccBjwNeHxEHrXSmhSyS9cmZ+Vha32z6yoh46oqFbHkL8M7MnF3hHEvaXwu8k4/5/3KdiFgNrAV+sMSYRy4xZi+ayEpm7qr+vAe4hNb//q1o1og4EvgE8GeZedOc9eue1yZyDuSczsm2HZilOm/fwZiDknXuvO6mNe8rPa9PAP42InYArwHeEK0PLQ7e14es5An4pm60Xsi5GTiaX73YcNy8dV7J/30B46Pznt/I0i9injqIWasx11X3H0jr/N7LVzIrcFi1/vPbjFvrvDaRc0Dn9Gh+9ULgUcBttL5Rb8kxByjrGuDQavka4L9pfcvpiv+7qpa/hV+9iNnIvPb1c67kzhv9weBU4Fu0XjV+Y7XsrcBzqvsH0XqF+TtVgTxyzrY7gLtoHSXspHqlGRgFrq/GvJDqk6yDlrX6hzANfJ3WizAXAKtWMivwJuBe4No5t4c3Na915xzQOT2rynItcA1w2mJjDmJWWu/ouK663TAIWeeN8RaqAm9yXnu9+VF6SSrU/noOXJL2exa4JBXKApekQlngklQoC1ySCmWBS1KhLHBJKtT/AvqVOAy+CGAsAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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21NoDJ09PuorRqNtLv9fFsK+jUb3ueinpfT/ISi+zs7NLmdnuN652WEfEFHATcEVmfmqjse12OxcXF4cqeLMWFhaYmZkZy1pbdeDI9T3vP370ENBcL4PWbWKehYUFLrvhsaHm344OT5/h2HKdvYXbX91e+r0uhn0djep110tJ7/tBVnqJiA3DutbZIBGxG7gWuHpQUEuSRq/O2SABfBi4OzN/o/mSJEnr1dmyfgXwVuCiiLi9+nptw3VJktYYuAMrMz8PxBhqkST14W8wSlIBDGtJKoBhLUkFMKwlqQCGtSQVwLCWpAIY1pJUAMNakgpgWEtSAQxrSSqAYS1JBTCsJakAhrUkFcCwlqQCGNaSVADDWpIKYFhLUgHqXIPxIxHxYETcMY6CJEnfr86W9e8ClzRchyRpAwPDOjNvBh4eQy2SpD4iMwcPijgAfDozL9xgzBwwB9BqtQ7Oz89vqqDlE6d63j+9f1/P+zudDlNTU5taayv19NOvzo3mWnnO+l5GtfawP9Nh1+2ltQdOnt7yNBO3U/qA+r00/TrazOtu/XMGve+HXXtYo5x/pZfZ2dmlzGz3GzeysF6r3W7n4uJi3Vqf5MCR63vef/zooZ73LywsMDMzs6m1tlJPP/3q3Giulees72VUaw/7Mx123V4OT5/h2PLTtzzPpO2UPqB+L02/jjbzulv/nEHv+2HXHtYo51/pJSI2DGvPBpGkAhjWklSAOqfufQL4InBBRDwQET/bfFmSpLUG7sDKzDePoxBJUn/uBpGkAhjWklQAw1qSCmBYS1IBDGtJKoBhLUkFMKwlqQCGtSQVwLCWpAIY1pJUAMNakgpgWEtSAQxrSSqAYS1JBTCsJakAhrUkFcCwlqQC1ArriLgkIr4WEfdExJGmi5IkPVmdazDuAn4b+MfAC4E3R8QLmy5MkrSqzpb1y4B7MvPezPweMA+8rtmyJElrRWZuPCDiDcAlmflz1e23Aj+Wme9YN24OmKtuXgB8bfTl9nQO8NCY1mqavWw/O6UPsJftaqWX52fmuf0GDby6eV2ZeRVw1ajmqysiFjOzPe51m2Av289O6QPsZbuq20ud3SAngPPX3H5edZ8kaUzqhPWfAS+IiL8bEc8A3gT8QbNlSZLWGrgbJDPPRMQ7gD8GdgEfycw7G6+svrHvemmQvWw/O6UPsJftqlYvAw8wSpImz99glKQCGNaSVIAdEdYR8YGI+EpE3B4Rn4mI8yZd02ZFxH+IiK9W/VwXEc+ZdE2bERH/NCLujIgnIqLIU6x2yscsRMRHIuLBiLhj0rVsVUScHxE3RsRd1evr8knXtFkRcVZEfCkivlz18v4Nx++EfdYR8ezM/E71/buAF2bm2ydc1qZExKuBP6kO7P46QGa+Z8JlDS0i/j7wBHAl8IuZuTjhkoZSfczC/wZeBTxA96yoN2fmXRMtbBMi4ieBDvB7mXnhpOvZioh4LvDczLwtIs4GloB/UujfSwB7M7MTEbuBzwOXZ+YtvcbviC3rlaCu7AWK/RcoMz+TmWeqm7fQPa+9OJl5d2aO67dYm7BjPmYhM28GHp50HaOQmd/KzNuq7x8F7gb2T7aqzcmuTnVzd/XVN7t2RFgDRMQVEXE/8Bbg3066nhH5F8AfTbqIp6j9wP1rbj9AoaGwU0XEAeAlwK2TrWTzImJXRNwOPAh8NjP79lJMWEfE5yLijh5frwPIzPdm5vnA1cA7Np5tsgb1Uo15L3CGbj/bUp0+pCZExBRwLfDudf+zLkpmPp6ZL6b7P+iXRUTf3VQj+2yQpmXmT9UcejXwh8D7GixnSwb1EhGXAZcCF+c2PqgwxN9JifyYhW2q2r97LXB1Zn5q0vWMQmY+EhE3ApcAPQ8EF7NlvZGIeMGam68DvjqpWrYqIi4B/jXw05n53UnX8xTmxyxsQ9VBuQ8Dd2fmb0y6nq2IiHNXzvaKiD10D2b3za6dcjbItXQ/lvUJ4BvA2zOzyK2giLgHeCbwl9Vdt5R4ZktEvB74LeBc4BHg9sx8zWSrGk5EvBb4IKsfs3DFhEvalIj4BDBD96M4TwLvy8wPT7SoTYqIfwj8KbBM9/0O8MuZ+YeTq2pzIuJFwEfpvr6eBnwyM3+17/idENaStNPtiN0gkrTTGdaSVADDWpIKYFhLUgEMa0kqgGEtSQUwrCWpAP8fzjkwSv2tAOMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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cRTRgP3BxVZ0KnAG8bp3eHw4w1qCuqk9X1f7u5ufpPEd73amqPVU1yCs51yLfogCoqhuB7427jnGrqm9V1S3dfz8E7AE2jbeq8Rv3inqp3wD+adxFaNVtAu5asn03/mIKSDIFPAe4ebyVjN+KvYS8lySfAX5ymV1vrqq/7455M53/8lwx6nrGZZA+SOpIMgFcA7yxqh4cdz3jNvKgrqoXHWp/kguB84Fzag0/qbtfH9Yx36JAB0hyNJ2QvqKqrh13PS0Y97M+zgX+AHhpVX1/nLVobHyLAj0uSYAPA3uq6t3jrqcV475G/T7gOOD6JLcmef+Y6xmLJC9LcjfwQuC6JJ8ad02rpftg8uJbFOwBPraCb1FwxEhyFfA54JQkdyf5zXHXNCZnAq8Czu5mwq1Jzht3UePmS8glqXHjXlFLkvowqCWpcQa1JDXOoJakxhnUktQ4g1qSGmdQS1Lj/h9SSlOSzy5JeAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_outliersAnalysis(abs_zscores):\n",
        "  # number of outliers per subject \n",
        "  bool_zscores = (abs_zscores > 3).sum(axis=1)\n",
        "  bool_zscores.hist()\n",
        "  plt.title(\"Number of outliers per subject\")\n",
        "  plt.show()\n",
        "\n",
        "  # number of outliers per feature \n",
        "  bool_zscores = (abs_zscores > 3).sum(axis=0)\n",
        "  fig = plt.figure()\n",
        "  ax = fig.add_axes([0,0,1,1])\n",
        "\n",
        "  ax.bar(feature_list,bool_zscores)\n",
        "  plt.title(\"Number of outliers per feature\")\n",
        "  plt.xticks(np.arange(len(feature_list)), feature_list,rotation=90)\n",
        "  plt.show()\n",
        "  return"
      ],
      "metadata": {
        "id": "k_30FvRhS0in"
      },
      "id": "k_30FvRhS0in",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Perform z-scoring for outlier removal\n",
        "from scipy.stats import zscore\n",
        "import numpy as np\n",
        "\n",
        "feature_list = features.columns.tolist()\n",
        "print('Train subjects before outlier removal: ' + str(features.shape))\n",
        "\n",
        "# apply zscore\n",
        "zscore_features = features.apply(zscore, axis=0)\n",
        "\n",
        "# select all those values with zscore < -+ 3 and discard outliers \n",
        "abs_zscores = np.abs(zscore_features)\n",
        "filtered_entries = (abs_zscores < 3).all(axis=1)\n",
        "\n",
        "features_or = features[filtered_entries]\n",
        "\n",
        "print('Train subjects after outlier removal: ' + str(features_or.shape))\n",
        "\n",
        "\n",
        "plot_outliersAnalysis(abs_zscores)\n",
        "display(features_or)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "oCj4bYiRVsaQ",
        "outputId": "34e9b72c-87a6-4240-ebf0-3317554a4fba"
      },
      "id": "oCj4bYiRVsaQ",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train subjects before outlier removal: (105, 12)\n",
            "Train subjects after outlier removal: (99, 12)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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OdzOzDDnczcwy5HA3M8tQy+EuaZqkf5N0Q3p8lKQNkrZIulbSga2XaWZmVbTjzP0C4L7S40uByyPiVcBeYHkb1mFmZhW0FO6SjgDOAD6XHgt4K7AmTbIKOLOVdZiZWXWKiOZnltYAnwYOBT4KLAPWp7N2JM0HboyI4+vMuwJYAdDf379weHi4qRp27dnPzqebmrVlr5k3Y9y20dFR+vr6ulhNY1xXNb1aF/Ruba6rmlbqGhoa2hQRA/XapjdbkKR3ArsiYpOkwarzR8RKYCXAwMBADA5WXgQAV65ey2Wbm96MlmxbMjhu28jICM1uUye5rmp6tS7o3dpcVzWdqquVVDwZ+A1JpwMHAS8DrgBmSpoeEc8BRwA7Wi/TzMyqaLrPPSI+HhFHRMQCYDFwa0QsAdYBZ6XJlgJrW67SzMwq6cTn3D8GfETSFuAw4KoOrMPMzCbQls7qiBgBRtLwVuDEdizXzMya4/9QNTPLkMPdzCxDDnczsww53M3MMuRwNzPLkMPdzCxDDnczsww53M3MMuRwNzPLkMPdzCxDDnczsww53M3MMuRwNzPLkMPdzCxDDnczsww53M3MMuRwNzPLkMPdzCxDDnczsww53M3MMuRwNzPLkMPdzCxDDnczsww53M3MMuRwNzPLkMPdzCxDDnczsww53M3MMuRwNzPLkMPdzCxDDnczsww53M3MMuRwNzPLUNPhLmm+pHWS7pV0j6QL0vjZkm6W9ED6Pat95ZqZWSNaOXN/DvijiDgWeCPwAUnHAhcCt0TEMcAt6bGZmXVR0+EeEY9HxB1p+EngPmAesAhYlSZbBZzZapFmZlaNIqL1hUgLgNuA44FHImJmGi9g79jjmnlWACsA+vv7Fw4PDze17l179rPz6ebqbtVr5s0Yt210dJS+vr4uVtMY11VNr9YFvVub66qmlbqGhoY2RcRAvbbpLVUFSOoDvgx8OCKeKPK8EBEhqe6rR0SsBFYCDAwMxODgYFPrv3L1Wi7b3PJmNGXbksFx20ZGRmh2mzrJdVXTq3VB79bmuqrpVF0tfVpG0gEUwb46Ir6SRu+UNDe1zwV2tVaimZlV1cqnZQRcBdwXEZ8pNV0PLE3DS4G1zZdnZmbNaKU/42TgPcBmSXemcX8CXAJcJ2k58DBwdmslmplZVU2He0R8C9A4zac2u1wzM2ud/0PVzCxDDnczsww53M3MMuRwNzPLkMPdzCxDDnczsww53M3MMuRwNzPLkMPdzCxDDnczsww53M3MMuRwNzPLkMPdzCxDDnczsww53M3MMuRwNzPLkMPdzCxDDnczsww53M3MMuRwNzPLkMPdzCxDDnczsww53M3MMuRwNzPLkMPdzCxDDnczsww53M3MMjR9qgswM5tqCy78+pSt++rTDunIcn3mbmaWIYe7mVmGHO5mZhlyuJuZZcgXVM0msHnHfpZN0cW2bZecMSXrtTx05Mxd0mmS7pe0RdKFnViHmZmNr+3hLmka8DfAO4BjgXMkHdvu9ZiZ2fg6ceZ+IrAlIrZGxE+AYWBRB9ZjZmbj6ESf+zxge+nxo8BJtRNJWgGsSA9HJd3f5PrmAD9qct6W6NIJm6esrkm4rmp69fgC77OqerKuoUtbquvI8Rqm7IJqRKwEVra6HEkbI2KgDSW1leuqxnVV16u1ua5qOlVXJ7pldgDzS4+PSOPMzKxLOhHu3wWOkXSUpAOBxcD1HViPmZmNo+3dMhHxnKQPAv8XmAZ8PiLuafd6Slru2ukQ11WN66quV2tzXdV0pC5FRCeWa2ZmU8i3HzAzy5DD3cwsQz0d7pPdxkDSSyVdm9o3SFpQavt4Gn+/pF/vcl0fkXSvpLsk3SLpyFLbTyXdmX7aeqG5gbqWSfphaf3nldqWSnog/Sztcl2Xl2r6gaR9pbZO7q/PS9ol6e5x2iXpr1Pdd0k6odTWkf3VQE1LUi2bJd0u6bWltm1p/J2SNrarpgq1DUraX3q+/qzU1rFbkjRQ1x+Xaro7HVOzU1tH9pmk+ZLWpRy4R9IFdabp7PEVET35Q3Ex9kHgaOBA4HvAsTXT/AHwd2l4MXBtGj42Tf9S4Ki0nGldrGsIODgN//exutLj0SncX8uAz9aZdzawNf2elYZndauumunPp7gI39H9lZb9q8AJwN3jtJ8O3AgIeCOwoQv7a7Ka3jy2LopbfGwotW0D5kzh/hoEbmj1GGh3XTXTvgu4tdP7DJgLnJCGDwV+UOfvsaPHVy+fuTdyG4NFwKo0vAY4VZLS+OGIeCYiHgK2pOV1pa6IWBcR/5Eerqf4rH+ntXLbh18Hbo6IPRGxF7gZOG2K6joHuKZN655QRNwG7JlgkkXAF6OwHpgpaS4d3F+T1RQRt6d1QveOrbF1T7a/xtPRW5JUrKsrx1dEPB4Rd6ThJ4H7KP57v6yjx1cvh3u92xjU7pz/nCYingP2A4c1OG8n6ypbTvHqPOYgSRslrZd0ZptqqlLXb6a3gGskjf2zWU/sr9R9dRRwa2l0p/ZXI8arvZP7q4raYyuAmyRtUnF7j6nwJknfk3SjpOPSuJ7YX5IOpgjJL5dGd3yfqegufj2woaapo8eX7+feQZLOBQaAt5RGHxkROyQdDdwqaXNEPNilkv4ZuCYinpH0+xTvet7apXU3YjGwJiJ+Who3lfurZ0kaogj3U0qjT0n76uXAzZK+n85qu+UOiudrVNLpwNeAY7q4/sm8C/h2RJTP8ju6zyT1UbyYfDginmjXchvRy2fujdzG4D+nkTQdmAHsbnDeTtaFpLcBFwG/ERHPjI2PiB3p91ZghOIVvSt1RcTuUi2fAxY2Om8n6ypZTM1b5g7ur0aMV/uU3mJD0q9QPH+LImL32PjSvtoFfJX2dUU2JCKeiIjRNPwN4ABJc+idW5JMdHy1fZ9JOoAi2FdHxFfqTNLZ46vdFxLaeEFiOsWFhKN4/iLMcTXTfIAXXlC9Lg0fxwsvqG6lfRdUG6nr9RQXkI6pGT8LeGkangM8QJsuLDVY19zS8H8D1sfzF3AeSvXNSsOzu1VXmu7VFBe31I39VVrHAsa/QHgGL7zg9Z1O768GanolxTWkN9eMPwQ4tDR8O3BaO/dVA7X9wtjzRxGSj6R919Ax0Km6UvsMin75Q7qxz9J2fxH4qwmm6ejx1dYnvgMH0ukUV5kfBC5K4z5JcTYMcBDwpXSwfwc4ujTvRWm++4F3dLmubwI7gTvTz/Vp/JuBzeng3gws73JdnwbuSetfB7y6NO/70n7cAry3m3Wlx58ALqmZr9P76xrgceBZin7N5cD7gfendlF88cyDaf0Dnd5fDdT0OWBv6djamMYfnfbT99JzfFE791WDtX2wdHytp/QCVO8Y6FZdaZplFB+yKM/XsX1G0V0WwF2l5+r0bh5fvv2AmVmGernP3czMmuRwNzPLkMPdzCxDDnczsww53M3MMuRwNzPLkMPdzCxD/x98fmMogKZHVQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-9dc3024e-9b7f-4759-b6be-e1357fbdb93f\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean_lead1</th>\n",
              "      <th>median_lead1</th>\n",
              "      <th>std_lead1</th>\n",
              "      <th>var_lead1</th>\n",
              "      <th>skew_lead1</th>\n",
              "      <th>kurt_lead1</th>\n",
              "      <th>mean_lead2</th>\n",
              "      <th>median_lead2</th>\n",
              "      <th>std_lead2</th>\n",
              "      <th>var_lead2</th>\n",
              "      <th>skew_lead2</th>\n",
              "      <th>kurt_lead2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.476086</td>\n",
              "      <td>0.474618</td>\n",
              "      <td>0.128357</td>\n",
              "      <td>0.016476</td>\n",
              "      <td>0.065871</td>\n",
              "      <td>3.853435</td>\n",
              "      <td>0.600828</td>\n",
              "      <td>0.613177</td>\n",
              "      <td>0.119664</td>\n",
              "      <td>0.014319</td>\n",
              "      <td>-1.328497</td>\n",
              "      <td>6.504245</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.309628</td>\n",
              "      <td>0.315643</td>\n",
              "      <td>0.118704</td>\n",
              "      <td>0.014091</td>\n",
              "      <td>2.242173</td>\n",
              "      <td>11.961672</td>\n",
              "      <td>0.662428</td>\n",
              "      <td>0.672944</td>\n",
              "      <td>0.144363</td>\n",
              "      <td>0.020841</td>\n",
              "      <td>-1.038713</td>\n",
              "      <td>3.939288</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.462381</td>\n",
              "      <td>0.466913</td>\n",
              "      <td>0.122431</td>\n",
              "      <td>0.014989</td>\n",
              "      <td>-0.007802</td>\n",
              "      <td>2.618824</td>\n",
              "      <td>0.570732</td>\n",
              "      <td>0.586303</td>\n",
              "      <td>0.134329</td>\n",
              "      <td>0.018044</td>\n",
              "      <td>-0.788964</td>\n",
              "      <td>3.169306</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.399816</td>\n",
              "      <td>0.366260</td>\n",
              "      <td>0.149092</td>\n",
              "      <td>0.022228</td>\n",
              "      <td>0.955791</td>\n",
              "      <td>1.976279</td>\n",
              "      <td>0.658729</td>\n",
              "      <td>0.681179</td>\n",
              "      <td>0.162172</td>\n",
              "      <td>0.026300</td>\n",
              "      <td>-1.171663</td>\n",
              "      <td>1.827310</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.478230</td>\n",
              "      <td>0.488412</td>\n",
              "      <td>0.131123</td>\n",
              "      <td>0.017193</td>\n",
              "      <td>-0.046548</td>\n",
              "      <td>4.308083</td>\n",
              "      <td>0.555436</td>\n",
              "      <td>0.561574</td>\n",
              "      <td>0.148968</td>\n",
              "      <td>0.022191</td>\n",
              "      <td>-0.156231</td>\n",
              "      <td>1.414820</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>100</th>\n",
              "      <td>0.394100</td>\n",
              "      <td>0.385903</td>\n",
              "      <td>0.132414</td>\n",
              "      <td>0.017534</td>\n",
              "      <td>0.582383</td>\n",
              "      <td>1.942821</td>\n",
              "      <td>0.426588</td>\n",
              "      <td>0.419286</td>\n",
              "      <td>0.116018</td>\n",
              "      <td>0.013460</td>\n",
              "      <td>0.446691</td>\n",
              "      <td>2.379165</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>101</th>\n",
              "      <td>0.437926</td>\n",
              "      <td>0.430825</td>\n",
              "      <td>0.127339</td>\n",
              "      <td>0.016215</td>\n",
              "      <td>0.479884</td>\n",
              "      <td>3.274700</td>\n",
              "      <td>0.444411</td>\n",
              "      <td>0.452348</td>\n",
              "      <td>0.126760</td>\n",
              "      <td>0.016068</td>\n",
              "      <td>0.327382</td>\n",
              "      <td>3.613294</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>102</th>\n",
              "      <td>0.345843</td>\n",
              "      <td>0.332378</td>\n",
              "      <td>0.115116</td>\n",
              "      <td>0.013252</td>\n",
              "      <td>1.653328</td>\n",
              "      <td>6.466284</td>\n",
              "      <td>0.403117</td>\n",
              "      <td>0.404505</td>\n",
              "      <td>0.114159</td>\n",
              "      <td>0.013032</td>\n",
              "      <td>0.996624</td>\n",
              "      <td>6.575470</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>103</th>\n",
              "      <td>0.514950</td>\n",
              "      <td>0.522097</td>\n",
              "      <td>0.098893</td>\n",
              "      <td>0.009780</td>\n",
              "      <td>-0.152302</td>\n",
              "      <td>9.197804</td>\n",
              "      <td>0.461116</td>\n",
              "      <td>0.445006</td>\n",
              "      <td>0.129433</td>\n",
              "      <td>0.016753</td>\n",
              "      <td>0.508997</td>\n",
              "      <td>1.575390</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>104</th>\n",
              "      <td>0.537678</td>\n",
              "      <td>0.539089</td>\n",
              "      <td>0.106207</td>\n",
              "      <td>0.011280</td>\n",
              "      <td>-0.579932</td>\n",
              "      <td>6.520011</td>\n",
              "      <td>0.659409</td>\n",
              "      <td>0.655174</td>\n",
              "      <td>0.129539</td>\n",
              "      <td>0.016781</td>\n",
              "      <td>-1.179600</td>\n",
              "      <td>6.316306</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>99 rows × 12 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9dc3024e-9b7f-4759-b6be-e1357fbdb93f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9dc3024e-9b7f-4759-b6be-e1357fbdb93f button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9dc3024e-9b7f-4759-b6be-e1357fbdb93f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "     mean_lead1  median_lead1  std_lead1  ...  var_lead2  skew_lead2  kurt_lead2\n",
              "0      0.476086      0.474618   0.128357  ...   0.014319   -1.328497    6.504245\n",
              "1      0.309628      0.315643   0.118704  ...   0.020841   -1.038713    3.939288\n",
              "2      0.462381      0.466913   0.122431  ...   0.018044   -0.788964    3.169306\n",
              "3      0.399816      0.366260   0.149092  ...   0.026300   -1.171663    1.827310\n",
              "4      0.478230      0.488412   0.131123  ...   0.022191   -0.156231    1.414820\n",
              "..          ...           ...        ...  ...        ...         ...         ...\n",
              "100    0.394100      0.385903   0.132414  ...   0.013460    0.446691    2.379165\n",
              "101    0.437926      0.430825   0.127339  ...   0.016068    0.327382    3.613294\n",
              "102    0.345843      0.332378   0.115116  ...   0.013032    0.996624    6.575470\n",
              "103    0.514950      0.522097   0.098893  ...   0.016753    0.508997    1.575390\n",
              "104    0.537678      0.539089   0.106207  ...   0.016781   -1.179600    6.316306\n",
              "\n",
              "[99 rows x 12 columns]"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Discard outliers\n",
        "filtered_df = filtered_df.loc[features_or.index]\n",
        "filtered_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 640
        },
        "id": "VNtC5w6AS4vl",
        "outputId": "8a3a3186-7b0f-43d5-cd76-c7ae7a3d8e70"
      },
      "id": "VNtC5w6AS4vl",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-f5638d4a-59f5-4355-9623-0fb992728807\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sigId</th>\n",
              "      <th>ecg_lead_1</th>\n",
              "      <th>ecg_lead_2</th>\n",
              "      <th>peaks</th>\n",
              "      <th>frequencies</th>\n",
              "      <th>labels</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>S001</td>\n",
              "      <td>[0.4276998551489322, 0.4297968302978747, 0.436...</td>\n",
              "      <td>[0.532310106303565, 0.5490810754251085, 0.5625...</td>\n",
              "      <td>[29, 110, 191, 272, 353, 433, 514, 595, 676, 7...</td>\n",
              "      <td>128</td>\n",
              "      <td>[N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>S002</td>\n",
              "      <td>[0.30247707914113764, 0.2863248193262056, 0.27...</td>\n",
              "      <td>[0.6568260643541631, 0.6071142459863523, 0.565...</td>\n",
              "      <td>[48, 153, 243, 352, 440, 547, 636, 742, 831, 9...</td>\n",
              "      <td>128</td>\n",
              "      <td>[N, N, N, N, S, N, S, N, S, N, S, N, S, N, S, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>S003</td>\n",
              "      <td>[0.3512323646878618, 0.26321594364695433, 0.19...</td>\n",
              "      <td>[0.5773604271206335, 0.6526769458531666, 0.730...</td>\n",
              "      <td>[91, 209, 326, 394, 537, 653, 745, 872, 984, 1...</td>\n",
              "      <td>128</td>\n",
              "      <td>[N, N, N, S, N, N, S, N, N, S, N, N, N, N, N, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>S004</td>\n",
              "      <td>[0.37546339057539513, 0.30484557586177713, 0.2...</td>\n",
              "      <td>[0.6897625095147278, 0.8119427029099721, 0.919...</td>\n",
              "      <td>[98, 223, 349, 474, 599, 726, 853, 980, 1116, ...</td>\n",
              "      <td>128</td>\n",
              "      <td>[N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>S005</td>\n",
              "      <td>[0.44854563084679466, 0.4171212169476861, 0.40...</td>\n",
              "      <td>[0.5060156800184847, 0.49873767797029817, 0.49...</td>\n",
              "      <td>[27, 127, 225, 324, 423, 523, 623, 722, 822, 9...</td>\n",
              "      <td>128</td>\n",
              "      <td>[N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>100</th>\n",
              "      <td>S114</td>\n",
              "      <td>[0.3867161984104746, 0.2355553526715274, 0.137...</td>\n",
              "      <td>[0.39987440384672607, 0.24801773930726578, 0.0...</td>\n",
              "      <td>[79, 196, 312, 427, 539, 652, 766, 880, 992, 1...</td>\n",
              "      <td>250</td>\n",
              "      <td>[N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>101</th>\n",
              "      <td>S115</td>\n",
              "      <td>[0.5401708356443994, 0.5892531949519814, 0.628...</td>\n",
              "      <td>[0.482446921258743, 0.5604840979852065, 0.6144...</td>\n",
              "      <td>[110, 223, 341, 421, 572, 690, 813, 889, 1041,...</td>\n",
              "      <td>250</td>\n",
              "      <td>[N, N, N, V, N, N, N, V, N, N, N, N, N, N, N, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>102</th>\n",
              "      <td>S116</td>\n",
              "      <td>[0.2932188167995345, 0.24850999229693066, 0.21...</td>\n",
              "      <td>[0.40266123819723953, 0.3845442598460282, 0.36...</td>\n",
              "      <td>[130, 273, 418, 559, 705, 848, 990, 1134, 1276...</td>\n",
              "      <td>250</td>\n",
              "      <td>[N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>103</th>\n",
              "      <td>S117</td>\n",
              "      <td>[0.5088599856075186, 0.510716973774107, 0.4964...</td>\n",
              "      <td>[0.4747395255742389, 0.6609601221105585, 0.677...</td>\n",
              "      <td>[9, 151, 294, 441, 589, 734, 873, 1021, 1160, ...</td>\n",
              "      <td>250</td>\n",
              "      <td>[N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, ...</td>\n",
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              "    <tr>\n",
              "      <th>104</th>\n",
              "      <td>S118</td>\n",
              "      <td>[0.5172109024314657, 0.5738509575206392, 0.654...</td>\n",
              "      <td>[0.6786699453483106, 0.7359087742666479, 0.782...</td>\n",
              "      <td>[52, 137, 221, 307, 388, 470, 555, 636, 719, 8...</td>\n",
              "      <td>250</td>\n",
              "      <td>[N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>99 rows × 6 columns</p>\n",
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              "    .colab-df-convert {\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "    sigId  ...                                             labels\n",
              "0    S001  ...  [N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, ...\n",
              "1    S002  ...  [N, N, N, N, S, N, S, N, S, N, S, N, S, N, S, ...\n",
              "2    S003  ...  [N, N, N, S, N, N, S, N, N, S, N, N, N, N, N, ...\n",
              "3    S004  ...  [N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, ...\n",
              "4    S005  ...  [N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, ...\n",
              "..    ...  ...                                                ...\n",
              "100  S114  ...  [N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, ...\n",
              "101  S115  ...  [N, N, N, V, N, N, N, V, N, N, N, N, N, N, N, ...\n",
              "102  S116  ...  [N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, ...\n",
              "103  S117  ...  [N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, ...\n",
              "104  S118  ...  [N, N, N, N, N, N, N, N, N, N, N, N, N, N, N, ...\n",
              "\n",
              "[99 rows x 6 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UkdEyLT2lkV0"
      },
      "source": [
        "<h2> RR distances analysis"
      ],
      "id": "UkdEyLT2lkV0"
    },
    {
      "cell_type": "code",
      "source": [
        "signal_ids = filtered_df.index.tolist()\n",
        "RR_dict = {}\n",
        "\n",
        "for id_ in tqdm(signal_ids):  \n",
        "  sig_Id = df.iloc[id_]['sigId']\n",
        "  RR_dict[sig_Id] = {}\n",
        "  RR_dict[sig_Id]['RR_distances'] = list()\n",
        "  RR_dict[sig_Id]['S_distances'] = list()\n",
        "  \n",
        "  peaks = df.iloc[id_]['peaks']\n",
        "  labels = df.iloc[id_]['labels']\n",
        "\n",
        "  for i,p in enumerate(peaks[1:-1]):\n",
        "    RR_distance = p-peaks[i]\n",
        "    if(RR_distance < 250): # discard outliers\n",
        "      RR_dict[sig_Id]['RR_distances'].append(RR_distance)\n",
        "      if(labels[i+1] == 'S'):\n",
        "        RR_dict[sig_Id]['S_distances'].append(RR_distance)\n",
        "\n",
        "  RR_dict[sig_Id]['RR_distances'] = np.asarray(RR_dict[sig_Id]['RR_distances'])\n",
        "  RR_dict[sig_Id]['S_distances'] = np.asarray(RR_dict[sig_Id]['S_distances'])\n",
        "\n",
        "  RR_dict[sig_Id]['Avg_RR_distance'] = RR_dict[sig_Id]['RR_distances'].mean()\n",
        "  RR_dict[sig_Id]['AVG_S_distance'] = RR_dict[sig_Id]['S_distances'].mean()\n",
        "\n",
        "\n",
        "RR_df = pd.DataFrame.from_dict(RR_dict,orient=\"index\")\n",
        "RR_df.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 244
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        "id": "NS4Hui7wOrZT",
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      "id": "NS4Hui7wOrZT",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "  0%|          | 0/99 [00:00<?, ?it/s]/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:24: RuntimeWarning: Mean of empty slice.\n",
            "100%|██████████| 99/99 [00:00<00:00, 230.25it/s]\n"
          ]
        },
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              "\n",
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              "      <th></th>\n",
              "      <th>RR_distances</th>\n",
              "      <th>S_distances</th>\n",
              "      <th>Avg_RR_distance</th>\n",
              "      <th>AVG_S_distance</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>S114</th>\n",
              "      <td>[117, 116, 115, 112, 113, 114, 114, 112, 116, ...</td>\n",
              "      <td>[]</td>\n",
              "      <td>114.914745</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
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              "      <td>[73]</td>\n",
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              "      <td>[75]</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>S117</th>\n",
              "      <td>[142, 143, 147, 148, 145, 139, 148, 139, 140, ...</td>\n",
              "      <td>[119, 99, 92, 100, 99, 92, 91, 92, 89, 87, 82,...</td>\n",
              "      <td>106.363048</td>\n",
              "      <td>83.558719</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S118</th>\n",
              "      <td>[85, 84, 86, 81, 82, 85, 81, 83, 83, 82, 84, 8...</td>\n",
              "      <td>[]</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            "text/plain": [
              "                                           RR_distances  ... AVG_S_distance\n",
              "S114  [117, 116, 115, 112, 113, 114, 114, 112, 116, ...  ...            NaN\n",
              "S115  [113, 118, 80, 151, 118, 123, 76, 152, 119, 11...  ...      73.000000\n",
              "S116  [143, 145, 141, 146, 143, 142, 144, 142, 143, ...  ...      75.000000\n",
              "S117  [142, 143, 147, 148, 145, 139, 148, 139, 140, ...  ...      83.558719\n",
              "S118  [85, 84, 86, 81, 82, 85, 81, 83, 83, 82, 84, 8...  ...            NaN\n",
              "\n",
              "[5 rows x 4 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import heartpy as hp \n",
        "\n",
        "classes = np.array([\"N\",\"S\",\"V\"])\n",
        "\n",
        "patch_length = 350 \n",
        "\n",
        "def convert_to_one_hot(label):\n",
        "    return np.array(classes == label,dtype=np.float32)\n",
        "\n",
        "def create_patch_dataset(df):\n",
        "    \n",
        "    dataset_dict = {}\n",
        "    ids = df['sigId']\n",
        "   \n",
        "    for id in tqdm(ids):\n",
        "        row = df[df['sigId'] == id]\n",
        "        sigId = row['sigId'].values[0]\n",
        "        peaks = row['peaks'].values[0]\n",
        "        first_lead_signal = row['ecg_lead_1'].values[0]\n",
        "        second_lead_signal = row['ecg_lead_2'].values[0]\n",
        "        labels = row['labels'].values[0]\n",
        "        avg_RR = RR_df.loc[sigId]['Avg_RR_distance'] # T for the signal \n",
        "\n",
        "        for i,peak in enumerate(peaks):\n",
        "            \n",
        "            stringIdx = str(sigId)+'_'+str(i)    \n",
        "            dataset_dict[stringIdx] = {}\n",
        "            dataset_dict[stringIdx][\"first_lead\"] = list()\n",
        "            dataset_dict[stringIdx][\"second_lead\"] = list()\n",
        "            size = list(range(int(peak-(avg_RR)),int(peak+avg_RR)))\n",
        "\n",
        "            for s in size:\n",
        "                if(s < 0 or s >= len(first_lead_signal)):\n",
        "                    dataset_dict[stringIdx][\"first_lead\"].append(0.5)\n",
        "                    dataset_dict[stringIdx][\"second_lead\"].append(0.5)\n",
        "                else:\n",
        "                    dataset_dict[stringIdx][\"first_lead\"].append(first_lead_signal[s])\n",
        "                    dataset_dict[stringIdx][\"second_lead\"].append(second_lead_signal[s])\n",
        "            \n",
        "            first_lead = dataset_dict[stringIdx][\"first_lead\"][:]\n",
        "            second_lead = dataset_dict[stringIdx][\"second_lead\"][:]\n",
        "            \n",
        "            first_lead = resample(first_lead,patch_length)\n",
        "            second_lead = resample(second_lead,patch_length)\n",
        "            \n",
        "            dataset_dict[stringIdx][\"first_lead\"] = first_lead[:]\n",
        "            dataset_dict[stringIdx][\"second_lead\"] = second_lead[:]\n",
        "            \n",
        "            dataset_dict[stringIdx][\"stringLabel\"] = labels[i]\n",
        "            dataset_dict[stringIdx][\"label\"] = convert_to_one_hot(labels[i])\n",
        "\n",
        "    dataset_df = pd.DataFrame.from_dict(dataset_dict,orient=\"index\")\n",
        "    return dataset_df"
      ],
      "metadata": {
        "id": "cdZlQwFMe7IV"
      },
      "id": "cdZlQwFMe7IV",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p47oXLNolkV1"
      },
      "source": [
        "<h2> Split dataset by patient & creation of patches"
      ],
      "id": "p47oXLNolkV1"
    },
    {
      "cell_type": "code",
      "source": [
        "training_df, test_df_ = train_test_split(filtered_df,test_size = 0.2,random_state=SEED)\n",
        "validation_df, test_df = train_test_split(test_df_,test_size = 0.5,random_state=SEED)\n",
        "\n",
        "training_patches_df_ = create_patch_dataset(training_df)\n",
        "validation_patches_df_ = create_patch_dataset(validation_df)\n",
        "test_patches_df = create_patch_dataset(test_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DXU_KOOr_wNW",
        "outputId": "dcd013f7-de35-42e8-8f8c-89dcdc40c12b"
      },
      "id": "DXU_KOOr_wNW",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 79/79 [00:59<00:00,  1.33it/s]\n",
            "100%|██████████| 10/10 [00:08<00:00,  1.18it/s]\n",
            "100%|██████████| 10/10 [00:07<00:00,  1.33it/s]\n"
          ]
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "validation_patches_df_[(validation_patches_df_['stringLabel']=='S')]"
      ],
      "metadata": {
        "id": "tkQWUJ9YmSrT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
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      "id": "tkQWUJ9YmSrT",
      "execution_count": null,
      "outputs": [
        {
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              "      <th>S005_39</th>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S064_3059</th>\n",
              "      <td>[0.3693840785186533, 0.37151584791021114, 0.37...</td>\n",
              "      <td>[0.5394217494213223, 0.543052483404949, 0.5260...</td>\n",
              "      <td>S</td>\n",
              "      <td>[0.0, 1.0, 0.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S064_3081</th>\n",
              "      <td>[0.4230572239043309, 0.4366958716495918, 0.411...</td>\n",
              "      <td>[0.4510251392245645, 0.42712265057551313, 0.44...</td>\n",
              "      <td>S</td>\n",
              "      <td>[0.0, 1.0, 0.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S064_3082</th>\n",
              "      <td>[0.44638189159873387, 0.4401345348253622, 0.42...</td>\n",
              "      <td>[0.3790647327767259, 0.342817164539148, 0.3337...</td>\n",
              "      <td>S</td>\n",
              "      <td>[0.0, 1.0, 0.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S064_3102</th>\n",
              "      <td>[0.45226238011228737, 0.44918793016293523, 0.4...</td>\n",
              "      <td>[0.4116255847162849, 0.4188745340184911, 0.458...</td>\n",
              "      <td>S</td>\n",
              "      <td>[0.0, 1.0, 0.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S064_3103</th>\n",
              "      <td>[0.4763961481950001, 0.4797152486900815, 0.479...</td>\n",
              "      <td>[0.3590849918562143, 0.34488954080847883, 0.36...</td>\n",
              "      <td>S</td>\n",
              "      <td>[0.0, 1.0, 0.0]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1032 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2d823dd7-9cbb-4f17-8c19-ed80f8bcf109')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2d823dd7-9cbb-4f17-8c19-ed80f8bcf109 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2d823dd7-9cbb-4f17-8c19-ed80f8bcf109');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                                  first_lead  ...            label\n",
              "S005_39    [0.4463474336263962, 0.4557493391500907, 0.461...  ...  [0.0, 1.0, 0.0]\n",
              "S005_42    [0.4692449475206276, 0.44556545258078634, 0.48...  ...  [0.0, 1.0, 0.0]\n",
              "S005_56    [0.4621633346927982, 0.4272114754631793, 0.500...  ...  [0.0, 1.0, 0.0]\n",
              "S005_119   [0.48021660373305003, 0.5181339555858254, 0.46...  ...  [0.0, 1.0, 0.0]\n",
              "S005_278   [0.4906965792794459, 0.4863384727437226, 0.473...  ...  [0.0, 1.0, 0.0]\n",
              "...                                                      ...  ...              ...\n",
              "S064_3059  [0.3693840785186533, 0.37151584791021114, 0.37...  ...  [0.0, 1.0, 0.0]\n",
              "S064_3081  [0.4230572239043309, 0.4366958716495918, 0.411...  ...  [0.0, 1.0, 0.0]\n",
              "S064_3082  [0.44638189159873387, 0.4401345348253622, 0.42...  ...  [0.0, 1.0, 0.0]\n",
              "S064_3102  [0.45226238011228737, 0.44918793016293523, 0.4...  ...  [0.0, 1.0, 0.0]\n",
              "S064_3103  [0.4763961481950001, 0.4797152486900815, 0.479...  ...  [0.0, 1.0, 0.0]\n",
              "\n",
              "[1032 rows x 4 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "    signal = validation_patches_df_.loc[\"S064_3059\"]\n",
        "    length = len(signal['first_lead'])\n",
        "    fig,axs = plt.subplots(2)\n",
        "    fig.set_size_inches(18.5, 10.5)\n",
        "    ax1 = axs[0]\n",
        "    ax2 = axs[1]\n",
        "  \n",
        "    \n",
        "    first_lead = signal['first_lead'][:length]\n",
        "    second_lead = signal['second_lead'][:length]\n",
        "    \n",
        "    x = list(range(length))\n",
        "    \n",
        "    ax1.plot(x,first_lead)\n",
        "    ax2.plot(x,second_lead)\n",
        "    \n",
        "    plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 619
        },
        "id": "tlZvxtbv8ePE",
        "outputId": "68ec4cef-3c24-40c2-b5f9-a47396720294"
      },
      "id": "tlZvxtbv8ePE",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1332x756 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "validation_patches_df_[(validation_patches_df_['stringLabel']=='V')]"
      ],
      "metadata": {
        "id": "P2OtuXiuD6ie",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "ee5e75a6-3342-4088-8253-37f0a03f0692"
      },
      "id": "P2OtuXiuD6ie",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-1ae6e2de-8930-4f68-9a65-975e53bf1829\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>first_lead</th>\n",
              "      <th>second_lead</th>\n",
              "      <th>stringLabel</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>S028_22</th>\n",
              "      <td>[0.5067651556531894, 0.5138383442651376, 0.522...</td>\n",
              "      <td>[0.40793282010527804, 0.41054385921702913, 0.4...</td>\n",
              "      <td>V</td>\n",
              "      <td>[0.0, 0.0, 1.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S028_117</th>\n",
              "      <td>[0.5442432844786468, 0.550514148720906, 0.5761...</td>\n",
              "      <td>[0.4253274360313075, 0.42934110996140745, 0.42...</td>\n",
              "      <td>V</td>\n",
              "      <td>[0.0, 0.0, 1.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S028_267</th>\n",
              "      <td>[0.6271705672202565, 0.6327518889381447, 0.625...</td>\n",
              "      <td>[0.6596580631343364, 0.6580953250805055, 0.658...</td>\n",
              "      <td>V</td>\n",
              "      <td>[0.0, 0.0, 1.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S028_313</th>\n",
              "      <td>[0.5651618361532128, 0.5746011639294005, 0.567...</td>\n",
              "      <td>[0.4498977398274356, 0.45669140321091484, 0.44...</td>\n",
              "      <td>V</td>\n",
              "      <td>[0.0, 0.0, 1.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S028_334</th>\n",
              "      <td>[0.5294385589017899, 0.5433975763042678, 0.536...</td>\n",
              "      <td>[0.5426718443165235, 0.5440166426426345, 0.551...</td>\n",
              "      <td>V</td>\n",
              "      <td>[0.0, 0.0, 1.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S064_3008</th>\n",
              "      <td>[0.5006293496851453, 0.49226923428529706, 0.50...</td>\n",
              "      <td>[0.3332966872143271, 0.3185728376004523, 0.332...</td>\n",
              "      <td>V</td>\n",
              "      <td>[0.0, 0.0, 1.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S064_3013</th>\n",
              "      <td>[0.503653923599582, 0.5165183964353227, 0.5204...</td>\n",
              "      <td>[0.2968394072316792, 0.27981802403945183, 0.26...</td>\n",
              "      <td>V</td>\n",
              "      <td>[0.0, 0.0, 1.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S064_3091</th>\n",
              "      <td>[0.47970845507487564, 0.46900455145114267, 0.4...</td>\n",
              "      <td>[0.39239041579390294, 0.37801700705836605, 0.4...</td>\n",
              "      <td>V</td>\n",
              "      <td>[0.0, 0.0, 1.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S064_3097</th>\n",
              "      <td>[0.4829838960577496, 0.4731752236148085, 0.475...</td>\n",
              "      <td>[0.33914512911601646, 0.3197216305338219, 0.35...</td>\n",
              "      <td>V</td>\n",
              "      <td>[0.0, 0.0, 1.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S064_3106</th>\n",
              "      <td>[0.489207518518916, 0.47993425908531484, 0.485...</td>\n",
              "      <td>[0.4037899409437056, 0.40116040951974125, 0.42...</td>\n",
              "      <td>V</td>\n",
              "      <td>[0.0, 0.0, 1.0]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1703 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1ae6e2de-8930-4f68-9a65-975e53bf1829')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1ae6e2de-8930-4f68-9a65-975e53bf1829 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-1ae6e2de-8930-4f68-9a65-975e53bf1829');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                                  first_lead  ...            label\n",
              "S028_22    [0.5067651556531894, 0.5138383442651376, 0.522...  ...  [0.0, 0.0, 1.0]\n",
              "S028_117   [0.5442432844786468, 0.550514148720906, 0.5761...  ...  [0.0, 0.0, 1.0]\n",
              "S028_267   [0.6271705672202565, 0.6327518889381447, 0.625...  ...  [0.0, 0.0, 1.0]\n",
              "S028_313   [0.5651618361532128, 0.5746011639294005, 0.567...  ...  [0.0, 0.0, 1.0]\n",
              "S028_334   [0.5294385589017899, 0.5433975763042678, 0.536...  ...  [0.0, 0.0, 1.0]\n",
              "...                                                      ...  ...              ...\n",
              "S064_3008  [0.5006293496851453, 0.49226923428529706, 0.50...  ...  [0.0, 0.0, 1.0]\n",
              "S064_3013  [0.503653923599582, 0.5165183964353227, 0.5204...  ...  [0.0, 0.0, 1.0]\n",
              "S064_3091  [0.47970845507487564, 0.46900455145114267, 0.4...  ...  [0.0, 0.0, 1.0]\n",
              "S064_3097  [0.4829838960577496, 0.4731752236148085, 0.475...  ...  [0.0, 0.0, 1.0]\n",
              "S064_3106  [0.489207518518916, 0.47993425908531484, 0.485...  ...  [0.0, 0.0, 1.0]\n",
              "\n",
              "[1703 rows x 4 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "    signal = validation_patches_df_.loc[\"S064_3008\"]\n",
        "    length = len(signal['first_lead'])\n",
        "    fig,axs = plt.subplots(2)\n",
        "    fig.set_size_inches(18.5, 10.5)\n",
        "    ax1 = axs[0]\n",
        "    ax2 = axs[1]\n",
        "  \n",
        "    \n",
        "    first_lead = signal['first_lead'][:length]\n",
        "    second_lead = signal['second_lead'][:length]\n",
        "    \n",
        "    x = list(range(length))\n",
        "    \n",
        "    ax1.plot(x,first_lead)\n",
        "    ax2.plot(x,second_lead)\n",
        "    \n",
        "    plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 619
        },
        "id": "HyQNLqpGEC5V",
        "outputId": "9814b05d-29b1-4d2a-f2c3-e190a4e83ad6"
      },
      "id": "HyQNLqpGEC5V",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1332x756 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1l2ufSgJlkV1"
      },
      "source": [
        "<h2> Drop and resampling "
      ],
      "id": "1l2ufSgJlkV1"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7b4fd24d",
      "metadata": {
        "id": "7b4fd24d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "outputId": "b009ce44-27c9-49e1-a940-edaf559b55ce"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "How many training indx: 168940\n",
            "Balancing training...\n",
            "pre drop: 181313\n",
            "post drop:27358\n",
            "20924\n",
            "1032\n",
            "1703\n",
            "Balancing validation\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "training_patches_df = training_patches_df_.copy(deep=True)\n",
        "validation_patches_df = validation_patches_df_.copy(deep=True)\n",
        "\n",
        "training_df.head()\n",
        "\n",
        "training_patients = training_df['sigId']\n",
        "validation_patients = validation_df['sigId']\n",
        "\n",
        "# calculate percentage of heartbeats to drop \n",
        "\n",
        "num_n = len(training_patches_df[(training_patches_df['stringLabel']=='N')])\n",
        "num_s = len(training_patches_df[(training_patches_df['stringLabel']=='S')])\n",
        "num_v = len(training_patches_df[(training_patches_df['stringLabel']=='V')])\n",
        "to_drop = num_n - max(num_s,num_v) * 2\n",
        "perc_to_drop_train = to_drop/(num_n)\n",
        "\n",
        "\n",
        "num_n = len(validation_patches_df[(validation_patches_df['stringLabel']=='N')])\n",
        "num_s = len(validation_patches_df[(validation_patches_df['stringLabel']=='S')])\n",
        "num_v = len(validation_patches_df[(validation_patches_df['stringLabel']=='V')])\n",
        "to_drop = 0\n",
        "perc_to_drop_valid = 0\n",
        "\n",
        "\n",
        "train_indexes = training_patches_df.index[training_patches_df['stringLabel'] == 'N'].tolist()\n",
        "valid_indexes = validation_patches_df.index[validation_patches_df['stringLabel'] == 'N'].tolist()\n",
        "\n",
        "print(f\"How many training indx: {len(train_indexes)}\")\n",
        "\n",
        "train_to_drop = list()\n",
        "valid_to_drop = list()\n",
        "lengths = list()\n",
        "dict_train = {}\n",
        "valid_train = {}\n",
        "\n",
        "print(\"Balancing training...\")\n",
        "\n",
        "for p in training_patients:\n",
        "    dict_train[p] = list()\n",
        "\n",
        "for hb in train_indexes:\n",
        "    dict_train[hb.split('_')[0]].append(hb)\n",
        "\n",
        "for p in dict_train:\n",
        "   random.shuffle(dict_train[p])\n",
        "\n",
        "for k in dict_train:\n",
        "    train_to_drop += dict_train[k][:int(len(dict_train[k]) * perc_to_drop_train)]\n",
        "\n",
        "print(f\"pre drop: {len(training_patches_df.index)}\")\n",
        "training_patches_df = training_patches_df.drop(train_to_drop)\n",
        "print(f\"post drop:{len(training_patches_df.index)}\")\n",
        "print(num_n)\n",
        "print(num_s)\n",
        "print(num_v)\n",
        "print(\"Balancing validation\")\n",
        "\n",
        "for p in validation_patients:\n",
        "    valid_train[p] = list()\n",
        "    \n",
        "for hb in valid_indexes:\n",
        "    valid_train[hb.split('_')[0]].append(hb)\n",
        "\n",
        "for p in valid_train:\n",
        "    random.shuffle(valid_train[p])\n",
        "\n",
        "for k in valid_train:\n",
        "    valid_to_drop += valid_train[k][:int(perc_to_drop_valid * len(valid_train[k]))]\n",
        "\n",
        "validation_balanced_patches_df = validation_patches_df.drop(valid_to_drop)\n",
        "\n",
        "\n",
        "training_labels = training_patches_df['stringLabel'].values\n",
        "validation_labels = validation_balanced_patches_df['stringLabel'].values\n",
        "\n",
        "classes = [\"N\",\"S\",\"V\"]\n",
        "\n",
        "training_counts = [0]*3\n",
        "validation_counts = [0]*3\n",
        "\n",
        "for l in training_labels:\n",
        "    training_counts[classes.index(l)] += 1\n",
        "for l in validation_labels:\n",
        "    validation_counts[classes.index(l)] += 1\n",
        "\n",
        "fig1, (ax1,ax2) = plt.subplots(1,2)\n",
        "\n",
        "ax1.pie(training_counts, explode=None, labels=classes, autopct='%1.1f%%',\n",
        "        shadow=False, startangle=90)\n",
        "ax1.axis('equal')  \n",
        "ax2.pie(validation_counts, explode=None, labels=classes, autopct='%1.1f%%',\n",
        "        shadow=False, startangle=90)\n",
        "ax2.axis('equal')  \n",
        "\n",
        "fig1.set_facecolor(\"white\")\n",
        "\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# resample v and s\n",
        "from sklearn.utils import shuffle\n",
        "\n",
        "\n",
        "ids_S = training_patches_df.index[training_patches_df['stringLabel']=='S']\n",
        "ids_V = training_patches_df.index[training_patches_df['stringLabel']=='V']\n",
        "ids_N = training_patches_df.index[training_patches_df['stringLabel']=='N']\n",
        "\n",
        "to_add = {}\n",
        "\n",
        "to_resample_s = len(ids_N) - len(ids_S)\n",
        "to_resample_v = len(ids_N) - len(ids_V)\n",
        "\n",
        "for i in tqdm(range(to_resample_s)):\n",
        "    id = random.choice(ids_S)\n",
        "    row = training_patches_df.loc[id]\n",
        "    idx = id + '_' + str(i)\n",
        "    to_add[idx] = {}\n",
        "    to_add[idx]['first_lead'] = row['first_lead']\n",
        "    to_add[idx]['second_lead'] = row['second_lead']\n",
        "    to_add[idx]['stringLabel'] = row['stringLabel']\n",
        "    to_add[idx]['label'] = row['label']\n",
        "\n",
        "\n",
        "\n",
        "for i in tqdm(range(to_resample_v)):\n",
        "    id = random.choice(ids_V)\n",
        "    row = training_patches_df.loc[id]\n",
        "    idx = id + '_' + str(i)\n",
        "    to_add[idx] = {}\n",
        "    to_add[idx]['first_lead'] = row['first_lead']\n",
        "    to_add[idx]['second_lead'] = row['second_lead']\n",
        "    to_add[idx]['stringLabel'] = row['stringLabel']\n",
        "    to_add[idx]['label'] = row['label']\n",
        "\n",
        "\n",
        "to_add_df = pd.DataFrame.from_dict(to_add,orient=\"index\")\n",
        "training_patches_df = pd.concat([training_patches_df,to_add_df],axis=0) \n",
        "training_patches_df = shuffle(training_patches_df)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pVLGB1qgKsDu",
        "outputId": "1f1b1729-a7c4-4714-e59a-4ea506aa767f"
      },
      "id": "pVLGB1qgKsDu",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7512/7512 [00:01<00:00, 5265.87it/s]\n",
            "100%|██████████| 10085/10085 [00:01<00:00, 5517.66it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "training_labels = training_patches_df['stringLabel'].values\n",
        "validation_labels = validation_balanced_patches_df['stringLabel'].values\n",
        "\n",
        "classes = [\"N\",\"S\",\"V\"]\n",
        "\n",
        "training_counts = [0]*3\n",
        "validation_counts = [0]*3\n",
        "\n",
        "for l in training_labels:\n",
        "    training_counts[classes.index(l)] += 1\n",
        "for l in validation_labels:\n",
        "    validation_counts[classes.index(l)] += 1\n",
        "\n",
        "fig1, (ax1,ax2) = plt.subplots(1,2)\n",
        "\n",
        "ax1.pie(training_counts, explode=None, labels=classes, autopct='%1.1f%%',\n",
        "        shadow=False, startangle=90)\n",
        "ax1.axis('equal')  \n",
        "ax2.pie(validation_counts, explode=None, labels=classes, autopct='%1.1f%%',\n",
        "        shadow=False, startangle=90)\n",
        "ax2.axis('equal')  \n",
        "\n",
        "fig1.set_facecolor(\"white\")\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 247
        },
        "id": "BkE1OjD-K5lE",
        "outputId": "466834c8-2342-4260-8cd7-99c97d6d4008"
      },
      "id": "BkE1OjD-K5lE",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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t+v1quxvdEzbWFfuf86w46KHfW3bOeddOfqX/czWNCpd83MD/xTzCvytOPvkhGL8U1vLOUnW6GQLqFJk8eTLTpk2jvr7pvco+/fkgmwuDH4snIser61+l1nnq/6dXXXUVixcvZsOGDTgcDgYMGKBSdeqZubag1U+A8Lkaqd39yzGjFqp+qKLqB//rLp9TjsfuofjDYnY/vpvdU48uW1m5qBLFqWBp7w/j6y4ZyOc/lTLwvXruHWymzqnw9zUuBucc3U795VUuFtwUy2uXWXl7nT+Zn1rq5Mwr7+TD0g5hfa2v/Sef3WXh72YIKHRTU1MZN24c06ZNO+VxlXYnf1sog+tbm6rGKl7fcOp3OvHx8YwcOZKJEye2ylZudb2LF7/fqXUZYac3Wxnw1+kYYo8GY+qFqaRe6G9x5kzMoe+bfen+VHf/x9TuALir3NRtriPnzhz0Fj16nZ576w6w8rdxbPtDPGP7mnj8RycPDTNj/VWPhMkADreCw+3/854qH+vc3fnSPCbsr9Xl8XH/l7802X0WrIAf/02ZMqXJUQwvfp9H7eH3EaJVmZU/i22V2055zIQJE9i8eXOrDN2XFuVR7ZDv7ZMp+bSErPFZ8N9u/EuS+9Cl/Ojb9w0lXgpsPsb0NB1z3sPDLdz6dSPPLXfyx0Fm7l6dxe6z7lWt7k0FNczeEN4H/ifuGT8Ju/3ocK/MzEwcjpPv77Sz1MaX6wsCr0xENJ/i4+V1L/P+pe+f9Jirr7467K0GLeSV1jFzrXxvn4xtkw1jopGYzjHYd/gzY1LR3iNf9ykK9y1sZMbVMced2z/LwOo7/Q/KZjnOYZUSTzxQPucFdHoDKRf+FkNcSljrf2lRHqPPaE+M2dD0wQEI24y05xbsJILWDRZhsLZ0LUsLQ7f4dLR4ev521Z50RyNHvgPbRht5U/IofKuQhh0Onnhr05Gv1zlha5mPC2bU0/m1OlYXernyM8eRh2kANRmDuX1WFYnnTqBmxaekXHAH8Wdeim19+BdhKqltZNryvU0fGKAWtXSba3l+BUt2lYfj0iLCvLr+VYZlD8OgD0+rINL8vK+KZfmBTQ5qK7KuzyLr+izAP36344JaPr72aKAmWXVUPHh0LY8LZtTz0iVWzsn2fw85U3txzqLemLt0xhCTgOJ2gk4HOp3/zyp4d+lebhnSmaRYU9MHt1BYWrovLmz9DxiE3+6a3czbO0/rMlTz5k+yqWRLnBHfkYRG/4iAv/7YyLd5p+4H98Znc03VHync+BMJZ/sfniUOvJqyL6dSvfg9Evq3fHGbQNgaPWEbQhbyVcbWH6hi7FurQnnJqNEaVxlrjj6pffjiN61/0fNtxbWMeT00uxxEk8yUBhxZTwR07keeVPoXbGr6QPyTH35veoZFYRqL21IJFiOrHrmIeEtoOwRC3tL9aNWBUF9SRLgdVTvYXK7+wiFqe/MndefoR7tBST2bH7gGC0/EPRoxgQv+lci+XBf6B6YhDd1Ku5MFW0tDeUkRJT7f+bnWJYTVvop6vttSonUZUeX3Nc2bFKWg4/2Mh5hRnBvmilrug5X7Q76TcEhD9/N1BTLdt41auH8hNY01WpcRNu8v3yejcVrgrKTuDNrfvOUb/517D0/t6x3migKzv9LBDzsDXxHtREIWuj6fwqdrDobqciLKuHwuZu+erXUZYeH0ePl2c+tdRSwcJtU1b5nLLR1uYtLuIWGuJjjTV4Z2T7WQ9RD/tKuMwuqWryeqeFyUfvoQiscNPh+xvYaRPOImKhb8HVepf+dOU0o27cb8Gb352MHUzuI8Khf+478XUkgefiOxPc/F66ilfPYz+Jx2kkfcQmzPoQCUffUUqZf8AWNCu+BerIp8Lh/7ntuH4lFQvAqJAxPJvCaTwmmFNO5vRFEULFkWcu7MwWA9dtiWY6+D4ulHwyLj6gwSByQ2uZWKKSWwYTJf5n3JHafd0epWE1u8o0xmVrbAaYldGL55SZPHFeVczpW7R6tQUXBW7K5kT7mdbiHa2idkoRvwAzSDicwbnkVvjkHxeij95EFiug4g9aLfobfEAlC1+D3qNswjacj1x5xqSu9E+9teQ6c34LFXUTL9HmK6D6Z++xLiz7qc2J5DKftyKrE9h+LYvQZzZteoClwAnUlH54c6Y7AaUDwKe5/dS8IZCbS/sT2GGH/IlnxWQtV/qki/Iv2Yc605VrpN7YbOoMNd42b347tJ6J9wZCuVxAGJ7H9lP4kDEo/ZSiVQhfZClhctZ0Ru61o396v1hVqXEFV+52h6Zxhb5mAuOXBjUDs/qGne5hLuHdUjJNcKSfdCQZUj4MkQOp3uSAtW8XnA5wWd7kjgKoqC4nFxZBL3r+hNVnT/HZT/62N0BiOK24ni9aDT61F8XurWzSFx8NiAatSSTqc70oJVvP7WLjqOBK6iKCgu5UT/POgtenQG/xcU96+OOcVWKsH6PK91PVCrsDtlok8L9IjvyIX5p95W3Znai8sP/T/qPdEzoWZBCB+ihqSlu2j7oaAeMig+LyUfTMZTXULC2WOwZPu3U66Y/xoNe9dhSutAyoW/PeG5zuI8Khf8HY+tjLQr7kOnNxDX93wqvv0b9s3fk3z+7dRtmE/caReiN1kDL1JDik9hz//twVXmIvWiVGK7+X8hFf6rkLpf6rBmW8m6IeuE5zr2OCiaVoS70k3upFx0Bh3JQ5IpeLuAqp+qyBqXdcxWKsFaWbwSh9tBrCk26GtFgm82FuGRJ2jNNsllQMfJ/7288dlcb59CUaPlpMdEorxDdewuq6N7RvC7YhumtmRnyZN4Y3E++ytPvvhNU3Q6PQlnjSbhrNHY1n6DOas7hrhkYnsOIXHgVbhK81Ea7Zgzux13rjEhjYSzxxDbYyjVP04j/rQL0ZtjiDvtAhL6X4beGo9t5eckj7iJqkVvUb91McbENIyJwbfq/ldu7g7KnaFfoUin0x1ZTq9yYSUxnWMwJhpJPDuRtEvTaNjXgNfhJabT8QuImFJNtLuoHQkDEiidWUryuckYrAaShyaTekEqhlgDZXPLyLgmg5IPS6hZUYMp1YQpNbBuBp/i4+yMs+mU2CnYlx0Rnv9uZ6ve+6w54mM8uOOb7qPtHJfDYztXnjR0FUsi/88wlZXVp95rLVKlxpkZ0jX47smgmzZury9kCznrrfFYO/ajYe/RDRB1egNxfc7DsWvlKc81pXVAZ4rBVX5s33Ltis9IOncc9duXYMk9jXZj7qNm+achqVdthjgDcX3isG85utqbTq8jaXAStnW2U55rzbait+pxFh07d/3XW6nE9owl93e5lH0T3BCZVSWtY0ai3elhY0G11mVEjd95Y9ErJx4yqhjMPBUfWZMfWmr+L6HpYgg6dDccqMYRxJbqXkctvkZ/iPjcThr3b8TULgd3tf+pu6IoNOSvwZR6/MBpd00pis9/b09tGZ6qQoxJGUe/XlWEt64Sa8d+KJ7Di2Yc7v+NDh6bB2+9/zX6XD7s2+yYs8w4D/nDU1EUbBttmNsfv1Gfq9zl7wMGXBUunCVOTGlHW7DOUifuKjfxfeLxuXxH+nx9ruDGWq8uWR3U+ZFi1Z5K3F7pWmiO3NgsRueduDWsoGN6xkO8XxTenR/CLb/MHpJ3PUH36S7fHdyKS157FRXzXwXFB4qP2N4jiOk2kEOfPITP6QAUTBldaHfJ3QA48tfgKs0necTNOAu3U756FhgM6HR6Ui++C0Ps0bcuNUs/Ivm8WwCI63M+5bOfxrZ6FkkjbgqqZjV5aj0UvleI4lNAgaRBSSScmcC+Z/fhbfSCAtYOVrJvywbAttFGw74GMq/NpH5XPRXzK/wP0/SQfUs2xoSj/8sPfXWIzLH+3VuThyRz4PUDVMyvIOOajBPW0lz51flUNFSQFpMW1HW0tlQeoDXbb0nC6DvxXnH/yf0jT+7uo3JF4fHzvkquOSu4mXNBL3hzzZsr2Hiw9c5Eaom2uuDNiTw34jmu6HqF1mUE5YK//RjUs4rWoqkFb7Ji0lmQtwWT9/h3kFs73MQV+eHfakctEwZ14Llr+wV1jaC6F2yNbn6RTSfFCawqju5+3YIqhwRuM91uSDth4BbnXMZvomDyQ0uE4vlVUKG7ak+lrKAvTija+3U3Fci7t+ZIs6Ry3Y7j+3JtmYO5+MBNUTP5obn2ltdTYQ9uIfWgQ1eIEylzlLG/dr/WZQRsa5G8g2uO28xZWDyNx3zOmRJ9kx9aYm2Qrd2gQndPub3pg0Sbtd+2X+sSAra1WEK3KSnmJMbtXHbM57zx7RlfH32TH1piW/Gph2c2JajQLaiSPi9xcoV10btmQV5pndYlRLybrR2IddUf+btiSeRu3SNssoVmYZhIlV8W3PdGwKHr8yltfqaOOLVCe3SGbqXdSYU9esZyayHBFM+NeUe3LlIMZp6Of5Tvy6NrQalA5JcF9w4/4NAtsTXKwHFxSkV1oZ8SrYbdQf5QtQU3xnYhvtH/NltBx4yMh5gW5ZMfmqugyhHUAIKAQ/egDKcRTYjWlm6prbHpg9qwWGMst/xqWv7i3Lt5Yl/rmPzQHG6vQnEQ7/IDDl3pzxVNKbJHZ0u3vC64IUGt3fj4biQ5/GtSbOtwI3fuPlfjitQXTP4F3tKV0BVNaPA0UNEQ3DRxLUjonpzVYOHWfP/eZyU5l0bFzg/hUFkfeJ9/4C3dagld0bRoHMEgoXtyYxN6kmYvw5Y5iFEHbsarhHRv26hhawx8+6aA/8WC6dMQbUdJffRtW14e5Iyj1sqsN3PH3o04U3oy+tBdrXbyQ3MEs2dewKEbzHKOou1o9ETfQ6lKGS52Qlcl9SLNqzC+/n4KW/Hkh+awNZx4RbXmCDh03d7g1lwVbYPbF3276Mr39vGMOiO3F+3jj7qHW/3kh+YIpnsh4PV0PTJGVzRDNIauL7jVTlul0cl9+dA2gu/Ko3uN5FCpa9SgpeuS1oBoBs9JFraOZJK5x1J8OvSVw/hXYduY/NAcwayd1jYfPYZYnNHLjB7LeazqAGcmHr95ZlsW5Br5mpCW7rHKaq18lNc6NhoNFZMh8OgMuHshmJu2Jg92yud3jdMxFewH4OODML/3SF7V13IoCseohprJENiuwlqSJaJFU8zGwNu6AYeuUd+6FiduqUvSqngx/lOSS4/fpXjMzh+50BzLtL4j+cC+i0Zv2x2CZNQFvQ2f6tr4t7ZohmAanQGf2VZbuh1jGvm+xxzeqb/3hIF7WIzLwR83zWdOpZNLUk5TscLIEo0t3aTY43dWFuLXzFp0L1hMbSt0LXofr3Zdz2UVM9AXVDf7vOzqg7xcfZB1nQbwQlIsO+sOhLHKyGM2RF+ApcZG3y8Koa5g8i/g0G2fZGVjwLeNLnd1OMBkz3QshbsCvsY5B9bzuU7P7L4X8oa3jCpn29iDKzM2U+sSWiwlLvp+UQh1pcUHPjkk4NDtkBob8E2jxbCUWl5L+ZL04h9Ccj294uO6bf/hUmsSb/cZzqe2HVE5pKolOiRE3zCjVOleEE1on2QN+NyA28gdW3HoZljcfNPjez523RuywP21hMZaHtg4n69tOs5Lbr3rkJr0JjJiM7Quo8WkpSua0j4pJuBzA2/pprS+0DXofDzf5RfG1kxHX1Ae9vt1Lt/DP8v3sLzbUF6MgX310bn+7Mlkx2ej10Vf3396QtteV0A0LZiWbsCh29pauje1L+ZRwwfEFm9R/d7D96xiiN7IZ6eN4i3nQercrWO7mNz4XK1LCEiXtDitSxARzGTQBdWnG3AzJCclBkMrGNDYP9HO8m4f80z1/cRWqB+4hxl9Hm7Z8j3ziw4xLuUMDLroXzYvNyE6Q7erhK44hcxEK/ogsi+ocbpZiYE3sbWWZPLwWY8f+VqZTG7RAq3LOSKlvpLHN8znc4eVgUk9tC4nKNHa0m0XbyFFho2Jk+idlRDU+UF1uEVrF8P/dd7B+uSHGVrwHjp3ZO6A0at0B+9vWswrpk7kROGwK4jeli5A76xErUsQEapv++C+N4IK3c5R9jbsqswytnR8hTtKn8IYJduDX7xrGXPytnBP4unEGAN/YqqFaBwudlifIH+wROvVN1vD0B3cJTWom6ulZ1wDP/SYxWu2+0goW6d1OS1m8TQyafMC5hffU4cAABRfSURBVJXZ+E3K6eiCWlhOHUmWJHqkRG/3SL/cJK1LEBHqzA7JQZ0fVOgO6x7ZCxrHGXxM67GShcbJdC2YjU6J7jWAM2pLeHbDAj52J9MvsavW5ZzSoKxBUTlc7LAhXdtpXYKIQJmJlqDG6EKQoZueYAm6Uzlc7uu4h01pf+Wign+gc9ZpXU5I9SvczMebl/CMtTsZ1sgMh6HZQ7UuIShZSVYZxSCOM6BTStDXCLopEmmt3QvbVbOhy1v8qexxTLV7tS4nbHQoXLnjB+buzefOpDMw6yNrFtWQ9kO0LiFoQ7tF5i80oZ0LegY/wzLo0B3eIzJCN9fqZEGPuUxruJfUkmVal6OaWKedezfNZ061i1EpfbUuB/APFYvmh2iHndstMr63RWTQ6eCC3ulBXyfoFaYHd0nFbNBrtmeaSa/wctf1XFE5A31BlSY1RILcqoO8WnWQnzsP5IUEC7vsBzWrZUh29Ldywd/S1elkzzThd3p2EhkJwc9NCLqlG2s2clbH4J7mBerO3AK2ZD3FlYWvoG9ou4H7a4P2r+WLrat4PK43KWZtnsAPbR/d/bmHpcaZObtj8H14onUY2Ts0izeF5PHyCJW7GAYl21jddTqPVTyEtWqnqveOBgbFy7iti5h38CA3J5+h6pY5ep2ewe0Hq3a/cLvyzGytSxAR4sJICt2L+qgzYyrd7OarHov43H0vWcX/VuWe0SyxoZaHNs7nK7uBYcm9Vbln//T+JFlazxjXMf3at/n9AAXkJMdwZojGbockdPu0T+TsMHYx6HQKz3bdwuqEBxlQMANdG97oMRBdy/J5e+Mi/qnPpXNceFtu43qNC+v11ZYWb4m4ETpCfdcNyEWnC80v35CNXr9laKdQXeoY49uXsjX3b9xY/ByG+kNhuUdbcd6elczevp77E/qSYIoP+fVTralc0umSkF9Xa1f1ly6Gtkyvg3EDQzcaJ2ShO/qM9rQL4Yr7ZyTUs7T7ZzxfPYW48k0hu25bZ/K5ue2X75lbXM7YlDNCOmvs2h7XRuXuv0259LQsYs3Rv9SmCMyw7mnkJIdu3ZOQ/cRZjAauPyf43wYJRg8f91jCt0ymY+FcdMh4nXBoZy9n6ob5zGyI5eyk7kFfT6/Tc33P60NQWeSJsxi5bkD0rpgmgnPDwI4hvV5IJ8ffNLgjwTxzeKRzHhtTH2V4wTvo3PWhK0ycVJ+S7Xyw6Qf+ZupC+5jAB36PyBlBdnzrfRt+5/CuQX1vi+jULs7MxX1DO1AgpKHbITWW83u2/Ad3dHoFv3T6O5NKn8BoKwhlSaKZLtu1hG/zt/OHxNOJMbR8APj4XuPDUFXk6NgulstOz9K6DKGyicO7YDaGduGmkC8D1ZIHal1jG/l3j9n80z6ZxENrQl2KaCGru4G7Ni/g24p6Rqec3uzzcuNzGZ4zPIyVRYZJ53XTugShoqQYE7ed2znk1w156F7QM4NO7U69o0SMwcs73Vez2PRnehTMivolF1ubrJoiXtiwgI887TgtsUuTx9/Q+4aQDaeJZP07JDOws8xQayt+O7wL8ZbQTywKeejq9Truu7jnSb9+T8d9bE6fyqWFr6Nz1ob69iKE+hds5LPNS3kypgdplhMvWJ8Rm9HquxZ+7Z4Lo3dhdtF8iVYjtw/rHJZrh2WV6SvPzOaMnGNnb5zfrpr1Xd5hStmjmGv2hOO2Igx0KFyzfTHz9u1h4gmWkPxj/z9iNUbvBqUtdV7PdIbLZIlW7/ZhXUi0hmf4Y1hCV6fT8fBo/7TT9lYXc3vMZ0bDZNqVLAnH7YQK4px1/HnTfL6p8TAy2b+EZI+UHlzV/SqNK1PfXy7vTRvoTWmz0uLN3Dmi6W61QIVtJZRzu6Xx3rk1XLTjr+gLKsJ1G6GyDpX7eb1yP6u6DMJ89pSo3pInUKfnJHH9gFy+WFeoyf1ta7/BvnkR6MCU3pm00ZNpLNxOzU/TURQfelMM7cZMxpRy4iF8HlsZxf/6A0nDbiRp8LV4HbWUz34Gn9NO8ohbiO3pXyWu7KunSL3kDxgT2tZi7g9c2itsrVwIU0v3sIuHDUHval1b5Qi/oeZ0BuQO07oMzTxwaW8SwvCQpSmeugps6+eSddurZP/2TfD5qN+xlKpFb5J2xf1k3/EGcX3Pp3bl5ye9RvXifxHTdcCRv9dvX0L8WZeTdesr2NbNAcCxew3mzK5tLnDPzE1iXAgmeZ1KeJsp7brB8MlhvYXQgCkWLn9e6yo0lZ5g4b5LTv7AOKx8XhSPC8XnRfE4McSngk6Hz+Xwf9lZ7//cCTh2rcKYnIUp7egsK53BiOJ2ong96PR6FJ+XunVzSBw8VpWXEykMeh3PXHNG2Efi6BQlzOviuxvhzcFQvT+stwmVRo/CedPrcXrB44Pr+hh5YqSV385pYF2JF0WBnu30zLg6hnjzsf9zfi7yMmluAwAKMPV8C9f0MVFe7+OazxuoaVR4+kILV/f2v3W5aqaDt8ZYyU6IsrfoF/0VRkzRugrNKYrChPdWs3qvugvo29bNoWbpR+iMZqxdziL9Nw/QWLCV8tnPoDOa0VtiybrlZfSWY4du+lwNHPr8MTLHP43t59noTDEkDb4Wn7Oeim//htdRQ/L5t+OuOIjeEkv8GaNUfV1a++3wLjx+Rfi3vAp/6ALsXgwfj4UoWEdBURTq3RBv1uH2KgyfXs/fL7PSN91AosUfsvctbCQjTsdfhluOOdfhVjAbwKjXUVLn48y36ymeEs+ba12kxui4to+J0Z84+On2OObmuVlf4mXqBVH25D+jL0xaAsbI2ghTKwVVDi7/+zLsTo8q9/M22in/+lnSr3oIvSWO8jnPE9trGI5dK0kafB2W7F7UrvkKT1UR7S7/0zHnVv8wDXP7nsT1GUHN8k+OhO7/Xr/im+dJv/ZRqhe/h6/RTuKga7Dk9FHl9Wmla1oc8/40nFhz+LuM1Glidb8Iht2ryq2CpdPpjrRg3T5we0EHRwJXURQa3AonegMSa9IdWfC60cORJ9wmvQ6HG5weMOjB41N4bY2LB4dZTnCVCGaOh+s/kMD9lQ6psTx+hXqB1Lh/E8akTAyxSegMRmJ7DsVZuB132T4s2b0AiOszAmfRjuPOdZbkUf3TdArfmoht3bfYVn+Bbf3cY46pXfEZSeeOo377Eiy5p9FuzH3ULP9UldemFbNRz+sTzlIlcCGMoxeOc9FfoWg97I/8nXq9PoUB79azu8rH3QPNDM71/zPdMaeBBfke+qbrefnSE7dQ1xR6mPhtIwdqfHx0TQxGvY4bzzBx4+wG3l3v4oVRVt5c6+KWfiZiTVE27ujK1yFdo37MCDZ+YEcWbTvE4p1lYb+XMTEdV3EePncjOqOFxgObMWf1wJG3AndVEabUHBr2bcLU7viHQVk3vXjkz4dbuokDfnPkc+6qIrx1lVg79sNVtg+d0Qw6UDyusL8uLT18eW9Oz1FvtxN1uhcOs5fB2yPAXqraLYNR06hwzecO3rjcyukZ/vVUvT6Fe75rZGC2gTvOOnmLb0e5l9u+aWDpHXFYjUfDtbpBYdwsB1+Pj+XP3zdS3agwZaiZoR3UfxLeIoMmwei/aV1FxCqvc/KbN5ZTamsM+71qln1C/c5l6PR6zJndaHfZn2jYu5aaZZ+ATofeGk+70ZMxJWfhyF+DqzSf5BE3H3uNE3QvlH/zPMnn3YIpNQdvfQ3ls5/G53SQNOIm4nq1zpEqo/pk8K/bBqp6T3VDF2D/CvjwSvCp0wcWrCeXOIk1wf3nHu0KWHrAw4srXMy78dRrTFz4QT0vXmzlnOyjC2Dft7CRK3sZya/0YTbAdX1NXPuFg4U3x4XtNQQt5xy44zvpVmjCL4U1jHtnFY1uWUskGmQlWvnu3hGkhHDzheZQ/7F552Fw4eOq37a5yut91DT6fw81uBX+vddDr3Z6dlf5f5AUReHbPA+9047/p9tX7cPj8597oMbHzgofnZOPtnLzK70U2nxc0NmIw62g1/n7fRvcKrywQMWkwjjpx22OfrnJvDC2n9ZliGaIMRl455YBqgcuqNmn+2vDJ0PBz5A3X5Pbn0qJXeG2bxx4feBTYNxpJsb0NDJiugObU0FR4MwsPW+N8W/f8W2em3XFXp4caWX5QQ/Pr3Bh0vv3VXpzjJW02KPh/OgPTp650N9innCGiatnNvD8ChdPXhChD9R0ehj7HiTJrgnNdVX/HHaU1PH2EllfJFIZ9DremHAWZ3YI32a6p6J+98JhjbXw7kiokm/OiHXBI3DBQ1pXEXV8PoXffbhOlQdrouWeuuo0bhnaWbP7azcq35oEt30LKeFbWEIEYcjdErgB0ut1/OPGsxnU5cSzwoR2Jp3XVdPABS1DF/xvW2+fJ8EbaYbcDZc9q3UVUS3GbGD67QMZ0EkWPY8UV/fP5uHLe2tdhsahCxK8kUYCN2TiLEZm3DFQs75DcdT1A3J5ZVz/iNjhRPvQBQneSCGBG3IJVhMfThx03KL+Qj23DOnEi9f1Qx8h2zlr9yDtRGoLYcYVUL1P60raHgncsKp1uPntB2tZd6Ba61LalEnndeWR0ZG1bkRkhS5I8GpBAlcVjW4vk2du4vtt0TEjM9pNHtWDyaMib9p65IUuQF0pfHkHHFypdSWtm94IF/0fDPtT08eKkPD5FJ77bgfvLZNGRbhYjHpeGNuPq8/K0bqUE4rM0AXwemDxVFj5htaVtE7xWXD9dOh0rtaVtElfrC3gsW+24vLKlOFQykiw8PYtAzi7Y+SOGonc0D1sx1z45m6Q7dpDp/MIuO59iM/QupI2bVNBDX/6bCMHqxxal9IqDOqSyj9uPIuMhMheozryQxegai98cSuUbtG6kiing+F/hgsfA72h6cNF2NmdHh7/ZitfbyzSupSoZdDr+N2Irtx/SU+MhsgYkHUq0RG64N/2Z8EU2Pix1pVEJ2syXPsu9LxU60rECXy9sZC/frONOpV2oGgtuqbH8dL1Z0Z0d8L/ip7QPWzjx/D9w+C0aV1J9MgdBGP/BSmdtK5EnMLBSgcPzNrMmn3q7rkWjfQ6mDisC/df2gurKbretUVf6ALUHYJFj8KWL7WuJLLFpMKoqXD2rUf3DhIRb86mIp5dsINDNqfWpUSkrulxvDi2H+d0js61LaIzdA/btxTm3w8VeVpXEmF0cPYtMOoJiI3Ob8y2rt7p4fXF+by/Yh9ub/T+iIZSuzgz947qwY2DOkZF3+3JRHfogn9o2frp8NPz4KjQuhrtdTkfLn4SsvtrXYkIgd1ldp5bsKNNLxNpNemZOKwLd13QjQSrSetyghb9oXuYsw6Wvwar/gmeBq2rUV/GaXDxE9DjYq0rEWGwtaiWf/64m++3ldJKfmKbZDboufqsbP58cU/aJ8VoXU7ItJ7QPayuFNa9D+s/iJoNMIPSeQQMvBP6XAn66H3LJZon/1Ad//xxN3N/KcHra10/uoelxJq4aXAnbj23U8SPuQ1E6wvdw7we2DkX1k6Lim3fW8SSCGfe4A/b9F5aVyM0cLDSwSc/H+Cr9YVU2FvHFund0uOYOLwLY8/OjboRCS3RekP318p2wrppsHlmdA81yzwDBk6EfuPBHMG7BwvVuL0+/rP9ELM3FvFTXlnUPXRLtBoZfUZ7rj4rh8FdUiNivdtwaxuhe5jTDlu+gC1fQeHP4I2CFkJ8JnQf5R/21XGI1tWICFZd7+L7baX8lFfGit2V2CN0okWi1ciovpmMPr09I3qmYTG23lbtibSt0P01Vz0cWAl7foS9P0HZNq0r8jPF+bep73oBdB0JmX21rkhEIbfXx/oD1fyUV86SXeXsLLVp9gAu3mLknM4pDOnajiFd23FGThKGCFlQXAttN3T/V90hf/ge/qgrVue+OgNknwXdRvqDNncQGM3q3FtEBZ1Ox3333cfLL78MwEsvvYTdbmfq1KnNvkZdo5vtxTa2FtvYVlzL9mIbu8vseEL8MC451kT39Hi6Z/g/zumc2uZD9n9J6J5MQw1U7/d/1Bw4+ufq/VBTAD53869lTYaUzv5puCmdj34kd4LkjmCI/rGHInysVivt27dn7dq1pKWlBRS6J9Lo9lJc08Ahm5NDtkYO2RoptTVSZnNS5/Tg8nhxenz4fhXMcRYjybEmkmLMpMSaSI41kRxrplNqLN0z4mkXbwny1bZ+Rq0LiFgxyRDT/8STDHw+f0vYWQde938/XKD4/AGqN4LB7P+Iz/BfS4gAGY1GJk2axKuvvsozzzwTsutaTQa6psfTNT0+ZNcUTZPQDYRe799MUwiV3H333fTr148HH3xQ61JEkGQ0vRBRIDExkVtvvZXXX39d61JEkCR0w+CZZ57htNNOo1+/fvTv3581a9ZoXZJoBSZPnsy0adOor6/XuhQRBOleCLFVq1Yxb948NmzYgMVioaKiApcrCsYDi4iXmprKuHHjmDZtGhMnTtS6HBEgaemGWElJCWlpaVgs/qe4aWlpZGdna1yVaC2mTJlCRYWsphfNZMhYiNntdoYPH47D4WDUqFGMHz+e888/X+uyhBARQlq6IRYfH8/69et59913SU9PZ/z48cyYMUPrsoQQEUJaumE2a9YsPvjgA+bOnat1KUKICCAt3RDLy8sjPz//yN83bdpEp06yIaQQwk9GL4SY3W7nnnvuoaamBqPRSPfu3Xn33Xe1LksIESGke0EIIVQk3QtCCKEiCV0hhFCRhK4QQqhIQlcIIVQkoSuEECqS0BVCCBVJ6AohhIokdIUQQkUSukIIoSIJXSGEUJGErhBCqEhCVwghVCShK4QQKpLQFUIIFUnoCiGEiiR0hRBCRRK6QgihIgldIYRQkYSuEEKoSEJXCCFUJKErhBAqktAVQggVSegKIYSKJHSFEEJFErpCCKEiCV0hhFCRhK4QQqhIQlcIIVQkoSuEECqS0BVCCBVJ6AohhIokdIUQQkUSukIIoSIJXSGEUJGErhBCqOj/A0TNmcNwo63VAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<h2>Create input"
      ],
      "metadata": {
        "id": "oSls57EwCw-o"
      },
      "id": "oSls57EwCw-o"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "61c04340",
      "metadata": {
        "id": "61c04340"
      },
      "outputs": [],
      "source": [
        "def create_input(df,step):\n",
        "    \n",
        "    x = list()\n",
        "    y = list()\n",
        "    \n",
        "    for id in tqdm(df.index.tolist()):\n",
        "        row = df.loc[id]\n",
        "        x.append(np.transpose(np.asarray([row['first_lead'],row['second_lead']]).astype(np.float32)))\n",
        "        if step == 1:\n",
        "          y.append(row['stringLabel']!='N')\n",
        "        elif step == 2:\n",
        "          y.append(row['stringLabel']!='S')\n",
        "        else:\n",
        "          if row['stringLabel'] == 'N':\n",
        "            y.append(0)\n",
        "          elif row['stringLabel'] == 'S':\n",
        "            y.append(1)\n",
        "          else:\n",
        "            y.append(2)\n",
        "\n",
        "    x = np.asarray(x).astype(np.float32)\n",
        "    y = np.asarray(y).astype(np.float32)\n",
        "    \n",
        "\n",
        "    return x,y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ca8d6b36",
      "metadata": {
        "id": "ca8d6b36",
        "outputId": "c720fcdc-deef-4398-c9b8-cc316d1ff694",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 44955/44955 [00:09<00:00, 4654.34it/s]\n",
            "100%|██████████| 23659/23659 [00:05<00:00, 4574.06it/s]\n",
            "100%|██████████| 24866/24866 [00:05<00:00, 4671.01it/s]\n"
          ]
        }
      ],
      "source": [
        "x_train,y_train = create_input(training_patches_df, 1)\n",
        "x_valid,y_valid = create_input(validation_balanced_patches_df, 1)\n",
        "x_test,y_test = create_input(test_patches_df,1)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.utils.class_weight import compute_class_weight\n",
        "timesteps = 350 #patch size\n",
        "channels_num = 2\n",
        "\n",
        "input = keras.layers.Input(shape=(timesteps, channels_num))\n",
        "\n",
        "conv1 = keras.layers.Conv1D(filters=32,kernel_size=25,activation=\"relu\")(input)\n",
        "maxPool1 = keras.layers.MaxPool1D(pool_size=2)(conv1)\n",
        "dropout1 = keras.layers.Dropout(0.05)(maxPool1)\n",
        "conv2 = keras.layers.Conv1D(filters=64,kernel_size=12,activation=\"relu\")(dropout1)\n",
        "maxPool2 = keras.layers.MaxPool1D(pool_size=2)(conv2)\n",
        "dropout2 = keras.layers.Dropout(0.1)(maxPool2)\n",
        "conv3 = keras.layers.Conv1D(filters=128,kernel_size=9,activation=\"relu\")(dropout2)\n",
        "maxPool3 = keras.layers.MaxPool1D(pool_size=2)(conv3)\n",
        "dropout3 = keras.layers.Dropout(0.15)(maxPool3)\n",
        "lstm1 = keras.layers.LSTM(64, return_sequences=True)(dropout3)\n",
        "lstm2 = keras.layers.LSTM(64, return_sequences=False)(lstm1)\n",
        "output = keras.layers.Dense(1, activation=tf.nn.sigmoid)(lstm2)\n",
        "model_NvsRest = keras.models.Model(inputs=input, outputs=output)\n",
        "\n",
        "model_NvsRest.summary()\n",
        "\n",
        "callback = keras.callbacks.EarlyStopping(monitor='val_loss', patience=10,mode=\"auto\", restore_best_weights=True)\n",
        "\n",
        "batch_size = 128\n",
        "learning_rate = 1e-5\n",
        "model_NvsRest.compile(\n",
        "    optimizer = keras.optimizers.Adam(learning_rate=learning_rate),\n",
        "    loss = 'binary_crossentropy',\n",
        "    metrics = ['accuracy'],\n",
        "    \n",
        ")\n",
        "\n",
        "class_weights = compute_class_weight(\n",
        "                                            class_weight = 'balanced', \n",
        "                                            classes = y_train, \n",
        "                                            y = y_train\n",
        "                                        )\n",
        "d_class_weights = dict(enumerate(class_weights))\n",
        "\n",
        "history = model_NvsRest.fit(\n",
        "    x = x_train,\n",
        "    y = y_train, \n",
        "    batch_size = batch_size,\n",
        "    steps_per_epoch=len(x_train)/batch_size,\n",
        "    validation_data=(x_valid, y_valid),\n",
        "    epochs = 100,\n",
        "    shuffle = True,\n",
        "    callbacks=[callback],\n",
        "    class_weight = d_class_weights\n",
        ").history\n"
      ],
      "metadata": {
        "id": "0g-fXU9Xv3cW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2e4740da-f341-43fe-8af6-0b7ada271fe0"
      },
      "id": "0g-fXU9Xv3cW",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 350, 2)]          0         \n",
            "                                                                 \n",
            " conv1d (Conv1D)             (None, 326, 32)           1632      \n",
            "                                                                 \n",
            " max_pooling1d (MaxPooling1D  (None, 163, 32)          0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 163, 32)           0         \n",
            "                                                                 \n",
            " conv1d_1 (Conv1D)           (None, 152, 64)           24640     \n",
            "                                                                 \n",
            " max_pooling1d_1 (MaxPooling  (None, 76, 64)           0         \n",
            " 1D)                                                             \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 76, 64)            0         \n",
            "                                                                 \n",
            " conv1d_2 (Conv1D)           (None, 68, 128)           73856     \n",
            "                                                                 \n",
            " max_pooling1d_2 (MaxPooling  (None, 34, 128)          0         \n",
            " 1D)                                                             \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 34, 128)           0         \n",
            "                                                                 \n",
            " lstm (LSTM)                 (None, 34, 64)            49408     \n",
            "                                                                 \n",
            " lstm_1 (LSTM)               (None, 64)                33024     \n",
            "                                                                 \n",
            " dense (Dense)               (None, 1)                 65        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 182,625\n",
            "Trainable params: 182,625\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/100\n",
            "351/351 [==============================] - 23s 42ms/step - loss: 0.6911 - accuracy: 0.6565 - val_loss: 0.7070 - val_accuracy: 0.2725\n",
            "Epoch 2/100\n",
            "351/351 [==============================] - 12s 35ms/step - loss: 0.6717 - accuracy: 0.6559 - val_loss: 0.5280 - val_accuracy: 0.8153\n",
            "Epoch 3/100\n",
            "351/351 [==============================] - 12s 35ms/step - loss: 0.5688 - accuracy: 0.7347 - val_loss: 0.3381 - val_accuracy: 0.8864\n",
            "Epoch 4/100\n",
            "351/351 [==============================] - 12s 35ms/step - loss: 0.4739 - accuracy: 0.7951 - val_loss: 0.3137 - val_accuracy: 0.8916\n",
            "Epoch 5/100\n",
            "351/351 [==============================] - 12s 35ms/step - loss: 0.4357 - accuracy: 0.8140 - val_loss: 0.2946 - val_accuracy: 0.8929\n",
            "Epoch 6/100\n",
            "351/351 [==============================] - 12s 35ms/step - loss: 0.4119 - accuracy: 0.8258 - val_loss: 0.2602 - val_accuracy: 0.9054\n",
            "Epoch 7/100\n",
            "351/351 [==============================] - 12s 35ms/step - loss: 0.3952 - accuracy: 0.8372 - val_loss: 0.2358 - val_accuracy: 0.9185\n",
            "Epoch 8/100\n",
            "351/351 [==============================] - 12s 35ms/step - loss: 0.3818 - accuracy: 0.8425 - val_loss: 0.2226 - val_accuracy: 0.9222\n",
            "Epoch 9/100\n",
            "351/351 [==============================] - 12s 35ms/step - loss: 0.3703 - accuracy: 0.8499 - val_loss: 0.2120 - val_accuracy: 0.9211\n",
            "Epoch 10/100\n",
            "351/351 [==============================] - 12s 35ms/step - loss: 0.3598 - accuracy: 0.8561 - val_loss: 0.2063 - val_accuracy: 0.9259\n",
            "Epoch 11/100\n",
            "351/351 [==============================] - 12s 35ms/step - loss: 0.3543 - accuracy: 0.8588 - val_loss: 0.1969 - val_accuracy: 0.9224\n",
            "Epoch 12/100\n",
            "351/351 [==============================] - 12s 35ms/step - loss: 0.3435 - accuracy: 0.8645 - val_loss: 0.1951 - val_accuracy: 0.9191\n",
            "Epoch 13/100\n",
            "351/351 [==============================] - 12s 35ms/step - loss: 0.3345 - accuracy: 0.8690 - val_loss: 0.1792 - val_accuracy: 0.9281\n",
            "Epoch 14/100\n",
            "351/351 [==============================] - 12s 35ms/step - loss: 0.3259 - accuracy: 0.8731 - val_loss: 0.1749 - val_accuracy: 0.9311\n",
            "Epoch 15/100\n",
            "351/351 [==============================] - 12s 34ms/step - loss: 0.3202 - accuracy: 0.8767 - val_loss: 0.1708 - val_accuracy: 0.9291\n",
            "Epoch 16/100\n",
            "351/351 [==============================] - 12s 34ms/step - loss: 0.3135 - accuracy: 0.8792 - val_loss: 0.1663 - val_accuracy: 0.9314\n",
            "Epoch 17/100\n",
            "351/351 [==============================] - 12s 34ms/step - loss: 0.3049 - accuracy: 0.8841 - val_loss: 0.1705 - val_accuracy: 0.9323\n",
            "Epoch 18/100\n",
            "351/351 [==============================] - 12s 34ms/step - loss: 0.2983 - accuracy: 0.8877 - val_loss: 0.1628 - val_accuracy: 0.9381\n",
            "Epoch 19/100\n",
            "351/351 [==============================] - 12s 34ms/step - loss: 0.2947 - accuracy: 0.8888 - val_loss: 0.1609 - val_accuracy: 0.9388\n",
            "Epoch 20/100\n",
            "351/351 [==============================] - 12s 34ms/step - loss: 0.2893 - accuracy: 0.8920 - val_loss: 0.1528 - val_accuracy: 0.9415\n",
            "Epoch 21/100\n",
            "351/351 [==============================] - 12s 34ms/step - loss: 0.2858 - accuracy: 0.8934 - val_loss: 0.1612 - val_accuracy: 0.9380\n",
            "Epoch 22/100\n",
            "351/351 [==============================] - 12s 34ms/step - loss: 0.2809 - accuracy: 0.8957 - val_loss: 0.1570 - val_accuracy: 0.9412\n",
            "Epoch 23/100\n",
            "351/351 [==============================] - 12s 34ms/step - loss: 0.2761 - accuracy: 0.8977 - val_loss: 0.1494 - val_accuracy: 0.9442\n",
            "Epoch 24/100\n",
            "351/351 [==============================] - 12s 34ms/step - loss: 0.2724 - accuracy: 0.9005 - val_loss: 0.1387 - val_accuracy: 0.9483\n",
            "Epoch 25/100\n",
            "351/351 [==============================] - 12s 34ms/step - loss: 0.2693 - accuracy: 0.9004 - val_loss: 0.1604 - val_accuracy: 0.9372\n",
            "Epoch 26/100\n",
            "351/351 [==============================] - 12s 34ms/step - loss: 0.2667 - accuracy: 0.9028 - val_loss: 0.1703 - val_accuracy: 0.9301\n",
            "Epoch 27/100\n",
            "351/351 [==============================] - 12s 34ms/step - loss: 0.2620 - accuracy: 0.9053 - val_loss: 0.1690 - val_accuracy: 0.9319\n",
            "Epoch 28/100\n",
            "351/351 [==============================] - 12s 34ms/step - loss: 0.2573 - accuracy: 0.9057 - val_loss: 0.1758 - val_accuracy: 0.9294\n",
            "Epoch 29/100\n",
            "351/351 [==============================] - 12s 34ms/step - loss: 0.2550 - accuracy: 0.9074 - val_loss: 0.1675 - val_accuracy: 0.9355\n",
            "Epoch 30/100\n",
            "351/351 [==============================] - 13s 36ms/step - loss: 0.2521 - accuracy: 0.9092 - val_loss: 0.1606 - val_accuracy: 0.9369\n",
            "Epoch 31/100\n",
            "351/351 [==============================] - 12s 34ms/step - loss: 0.2494 - accuracy: 0.9095 - val_loss: 0.1745 - val_accuracy: 0.9309\n",
            "Epoch 32/100\n",
            "351/351 [==============================] - 12s 34ms/step - loss: 0.2456 - accuracy: 0.9116 - val_loss: 0.1769 - val_accuracy: 0.9273\n",
            "Epoch 33/100\n",
            "351/351 [==============================] - 12s 34ms/step - loss: 0.2433 - accuracy: 0.9128 - val_loss: 0.1466 - val_accuracy: 0.9445\n",
            "Epoch 34/100\n",
            "351/351 [==============================] - 12s 34ms/step - loss: 0.2408 - accuracy: 0.9142 - val_loss: 0.1545 - val_accuracy: 0.9393\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns \n",
        "def plot_confusion_matrix(y_true, y_pred):\n",
        "  \"\"\"\n",
        "    :param ndarray y_true: contains the ground truth labels in the shape (n_samples,)\n",
        "    :param ndarray y_pred: contains the predicted labels in the shape (n_samples,)\n",
        "  \"\"\"\n",
        "  con_mat = tf.math.confusion_matrix(labels=np.squeeze(y_true), predictions=np.rint(np.squeeze(y_pred))).numpy()\n",
        "  con_mat_norm = np.around(con_mat.astype('float') / con_mat.sum(axis=1)[:, np.newaxis], decimals=2)\n",
        "  con_mat_df = pd.DataFrame(con_mat_norm,\n",
        "                  index = classes, \n",
        "                  columns = classes)\n",
        "  figure = plt.figure(figsize=(8, 8))\n",
        "  sns.heatmap(con_mat_df, annot=True,cmap=plt.cm.Blues)\n",
        "  plt.tight_layout()\n",
        "  plt.ylabel('True label')\n",
        "  plt.xlabel('Predicted label')\n",
        "  plt.show()"
      ],
      "metadata": {
        "id": "bBM8Q4e29S73"
      },
      "id": "bBM8Q4e29S73",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "424b06c0",
      "metadata": {
        "id": "424b06c0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 598
        },
        "outputId": "7e505c13-761f-4054-e3c9-0b4952bd7017"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Making predictions ...\r"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ys+TkpUlenmSbJL9MsufUMgDAWrfa5KS19qskL+hgLADADEys6z0nVbVjVX2xqq6tqmuq6gtVtWMXgwMA1j0zua3zySTHJblXknsnOT7Jp0Y5KABg5aq6+8yGmRQnd2utfaK1tmTq869JNhj1wACAddOq3q1zj6mvX66qNyT5dCbftfOcJCd1MDYA4C6M+zwnq2qI/UEmi5Hl/wb+17RtLckbRzUoAGDdtap36+zQ5UAAgJkZ8+BkRpOwpar2SLJ7pvWatNY+PqpBAQDrrtUWJ1X1liT7Z7I4OSnJE5N8J4niBABmwTo/z0mSZyZ5bJKrWmt/meRBSTYd6agAgHXWTG7r3NJaW1ZVS6pqkyTXJFkw4nEBACsx5sHJjIqTxVW1WZIPZ/IJnhuTnDbSUQEA66yZvFvnr6a+fqiqvpJkk9ba2aMdFgCwMuvsPCdVtdeqtrXWfjiaIQEA67JVJSfvWcW2luSAtTyWFVz49VWdHlhTmz/t/bM9BBhLt3zp0NkewthY1SRsj+lyIADAzMzkUdshG/frAwAGZkYzxAIA/THuDbGSEwCgV2YyfX0leUGSHVtrR1bVdkm2bq2dMfLRAQB/YGK8g5MZJScfSPLwJM+bWv5dkmNHNiIAYJ02k56Th7XW9qqqs5KktXZ9Va034nEBACshOUluq6o5mZzbJFW1ZZJlIx0VALDOmkly8r4kn0uyVVW9I5NvKX7TSEcFAKzUuD+tM5N36/xbVf0gyWOTVJKntdbOH/nIAIB10kye1tkuyc1Jvjh9XWvt0lEODAC4a+PeczKT2zonZrLfpJJskGSHJBckuf8IxwUArKNmclvnAdOXp95W/FcjGxEAsEpj3nLyx88Q21r7YZKHjWAsAAAz6jn5m2mLE0n2SvLLkY0IAFiliTGPTmbSc7LxtO9LMtmD8pnRDAcAWNetsjiZmnxt49baazoaDwCwGuP+1t6VXl9VzW2tLU3yyA7HAwCs41aVnJyRyf6SH1XVCUmOT3LT8o2ttc+OeGwAwF0Y85aTGfWcbJDkuiQH5I75TloSxQkAsNatqjjZaupJnXNyR1GyXBvpqACAddaqipM5Se6eFYuS5RQnADBL1uVHia9srR3Z2UgAALLq4mS8yzIAGKgxD05W+aj0YzsbBQDAlJUmJ621X3c5EABgZibW4eQEAKBzM5nnBADokXF/WkdyAgD0iuQEAAZmzIMTyQkA0C+SEwAYGE/rAAB0SHICAANTYz6Ju+QEAOgVyQkADIyeEwCADklOAGBgJCcAAB2SnADAwNSYTxErOQEAekVxAgD0its6ADAwGmIBADokOQGAgRnzfljJCQDQL5ITABiYiTGPTiQnAECvSE4AYGA8rQMA0CHJCQAMzJi3nEhOAIB+UZwAwMBMpDr7rE5VHVRVF1TVRVX1hlXs94yqalX10NVfHwDAGqiqOUmOTfLEJLsneV5V7X4X+22c5FVJTp/JcRUnADAwVd19VmOfJBe11i5urd2a5NNJDr6L/d6W5F1J/msm16c4AQBWqqoWVtXiaZ+F0zZvk+SyacuXT62b/vu9kixorZ0403N6WgcABqbLeU5aa4uSLFqT31bVRJL/neSFf8zvJCcAwJq6IsmCacvbTq1bbuMkeyT5ZlX9Ism+SU5YXVOs5AQABqZH79Y5M8nOVbVDJouS5yZ5/vKNrbXfJtli+XJVfTPJa1pri1d1UMkJALBGWmtLkhya5OQk5yc5rrV2blUdWVVPXdPjSk4AYGD6E5wkrbWTkpx0p3VHrGTf/WdyTMkJANArihMAoFfc1gGAgelRQ+xISE4AgF6RnADAwIx5cCI5AQD6RXICAAMz7snCuF8fADAwkhMAGJga86YTyQkA0CuSEwAYmPHOTSQnAEDPSE4AYGDMEAsA0CHJCQAMzHjnJpITAKBnJCcAMDBj3nIiOQEA+kVyAgADY4ZYAIAOSU4AYGDGPVkY9+sDAAZGcQIA9IrbOgAwMBpiAQA6JDkBgIEZ79xEcgIA9IzkBAAGRs8JAECHJCcAMDDjniyM+/UBAAMjOQGAgdFzAgDQIckJAAzMeOcmkhMAoGckJwAwMGPeciI5AQD6RXICAAMzMeZdJ5ITAKBXJCcAMDB6TgAAOiQ5AYCBKT0nAADdUZwAAL3itg4ADIyGWACADklOAGBgTMIGANAhyQkADIyeEwCADklOAGBgJCcAAB2SnADAwJi+HgCgQ5ITABiYifEOTiQnAEC/SE4AYGD0nAAAdEhyAgADY54TAIAOSU4AYGD0nAAAdEhyAgADY54TAIAOjbQ4qapnzWQdAMByo05O3jjDdQDADFWH/8yGkfScVNUTkzwpyTZV9b5pmzZJsmQU5wQAxsOoGmJ/mWRxkqcm+cG09b9LctiIzgkA64Rxn4RtJMVJa+3HSX5cVZ9srd2WJFW1eZIFrbXrR3FORuOM076TY495V5YtW5YnPfXped5fvHiF7WeftTjHHnNULv75hXnT296VRx/w+CTJRT/7ad571Ntz8003ZWJiIi944UvymAMPmo1LgF46cK/tcvTC/TJnovLRr56Xo//9hytsX7Dl3fPhwx6XTTdaP3MmKm/+2Gk5efF/Zu6ciXzwlQdkz/tumblzKv/29Qty9PE/WMlZYJhG/SjxKVX11Knz/CDJNVX1vdaa9GQAli5dmvcd/c4c9b5F2XKr+fmrv3xeHr7f/rnPDve9fZ+t5t8rr3vz23P8Jz+6wm/X32CDvOGId2Tb7bbPr669Ji974XOz976PyN033qTjq4D+mZiovPdlj86T3/SFXHHdjfnOMc/Ol06/JD+97I7/7/b65+ydz3z7onz4y+dk1wWb5/Nv/bPs+uKP5xn/baesP28iex/6qWy4/tyc9YHn57hv/SyXXvO7WbwiujbmwcnIG2I3ba3dkOTpST7eWntYkseO+JysJT8975xss+12ufc222bevHl5zIEH5XunfmOFfba+9za57867pGrF/5QWbHefbLvd9kmSLbbcKpttfo/85nqhGSTJ3rvMz8+v/G1+cfUNuW3Jshx/6oV5yr47rrBPay2b3G29JMmmG62fK3990+3r77bBvMyZqGy43tzcumRZfnfzrZ1fA4zSqJOTuVV1ryTPTnL4iM/FWvara6/OllvNv315y63m5/xzf/JHH+en5/4kS267LffedsHaHB4M1r3vuVEuv/aOpOOKX92Yfe43f4V93vHJM/LFtx2cl/3ZA3O3DebmyYd/IUny2e/+PE/Zd4dc8okX5W7rz83rPvydXH/j7zsdP7NvYsybTkadnByZ5OQkP2+tnVlVOya5cGU7V9XCqlpcVYv/7aMfGfHQ6MJ1v7o2f/93f5vXvvnITEyY8w9m6tmP3iX/+rXzs9MLP5pD3vql/NOrD0xVsvcuW2XpspYd/+JfstuLP55XHbJn7jPf7VLGy0iTk9ba8UmOn7Z8cZJnrGL/RUkWJcnl1/++jXJsrN4WW87PtddcffvytddcnS223GrGv7/pphvzt3/z8rzopa/I7ns8aBRDhEH65XU3ZdstN759eZst7p4rrrtphX3+x4G75eC3fDFJcvpPr8oG683JFptsmGc/epd89QeXZsnSZbn2t7fktPOvzEN23iq/uPqGTq+B2TXeucnoZ4jdpaq+VlXnTC0/sKreNMpzsvbsutv9c8Vl/5krf3l5brvttnzjlK/kEfvtP6Pf3nbbbXnL6/86j3/Sn93+BA8wafHPrs5O994028/fOPPmTuRZj9o5J55+yQr7XHbtjdn/QdsmSe637ebZYN7cXPvbW3L5tTdm/wdOrr/b+nOzz/22zgWX6+divFRrowsoqupbSV6b5B9baw+eWndOa22P1f1WctIPp3/v2zn2mKOybNnSPPEpT8sL/nJh/mXRsbnfrrvnEY96TH563jl5y+v/Ojf+7obMW2/93OOeW+SfP/W5nPLlL+Xdbz8i99nxjid7Xvfmt2WnXXadxashSXb+7x+e7SGQ5AkP3T7vfsnko8QfO+W8HHXcD/LmF+yTH154TU484xfZdcHm+cArDshGG85Lay2H/8v38rWzLstGG8zLor9+bHZdsHmqKp/4j/NzzGfPmu3LIcktXzq0s0Dj+z//TWd/R+573806D2pGXZyc2Vrbu6rOmlac/Ki1tufqfqs4gdFQnMBoKE7WnlE/rfOrqrpvkpYkVfXMJFeO+JwAMNZm6503XRl1cfLyTDa47lpVVyS5JMkLRnxOAGDARv20zsVJHldVG2Wy+fbmJM9N8p+jPC8AjLMxn+ZkNE/rVNUmVfXGqnp/VR2YyaLkfyS5KJMTsgEA3KVRJSefSHJ9ktOSvCSTs8NWkkNaaz8a0TkBYJ0w5sHJyIqTHVtrD0iSqvpIJptgt2ut/deIzgcAjIlRFSe3Lf/SWltaVZcrTABgLRnz6GRUxcmDqmr5XMqVZMOp5UrSWmteBAEA3KWRFCettTmjOC4AMP5GPc8JALCWjfskbN5hDwD0iuQEAAbGJGwAAB2SnADAwIx5cCI5AQD6RXICAEMz5tGJ5AQA6BXJCQAMjHlOAAA6JDkBgIExzwkAQIckJwAwMGMenEhOAIB+kZwAwNCMeXQiOQEAekVyAgADY54TAICVqKqDquqCqrqoqt5wF9v/pqrOq6qzq+prVbX96o6pOAGAganq7rPqcdScJMcmeWKS3ZM8r6p2v9NuZyV5aGvtgUn+PclRq7s+xQkAsKb2SXJRa+3i1tqtST6d5ODpO7TWvtFau3lq8ftJtl3dQRUnAMBKVdXCqlo87bNw2uZtklw2bfnyqXUr8+IkX17dOTXEAsDAdNkO21pblGTRn3qcqvrzJA9N8ujV7as4AQDW1BVJFkxb3nZq3Qqq6nFJDk/y6Nba71d3UMUJAAxNf54kPjPJzlW1QyaLkucmef70HarqwUn+MclBrbVrZnJQPScAwBpprS1JcmiSk5Ocn+S41tq5VXVkVT11ard3J7l7kuOr6kdVdcLqjis5AYCB6dMkbK21k5KcdKd1R0z7/rg/9piSEwCgVyQnADAwq5scbegkJwBAr0hOAGBgxjw4kZwAAP0iOQGAoRnz6ERyAgD0iuQEAAamT/OcjILkBADoFckJAAyMeU4AADokOQGAgRnz4ERyAgD0i+QEAIZmzKMTyQkA0CuKEwCgV9zWAYCBMQkbAECHJCcAMDAmYQMA6JDkBAAGZsyDE8kJANAvkhMAGJoxj04kJwBAr0hOAGBgzHMCANAhyQkADIx5TgAAOiQ5AYCBGfPgRHICAPSL5AQAhmbMoxPJCQDQK5ITABgY85wAAHRIcgIAA2OeEwCADilOAIBecVsHAAZmzO/qSE4AgH6RnADAwGiIBQDokOQEAAZnvKMTyQkA0CuSEwAYGD0nAAAdkpwAwMCMeXAiOQEA+kVyAgADo+cEAKBDkhMAGJga864TyQkA0CuSEwAYmvEOTiQnAEC/SE4AYGDGPDiRnAAA/SI5AYCBMc8JAECHFCcAQK+4rQMAA2MSNgCADklOAGBoxjs4kZwAAP0iOQGAgRnz4ERyAgD0i+QEAAbGJGwAAB2SnADAwJjnBACgQ5ITABgYPScAAB1SnAAAvaI4AQB6Rc8JAAyMnhMAgA5JTgBgYMxzAgDQIckJAAyMnhMAgA4pTgCAXnFbBwAGZszv6khOAIB+kZwAwNCMeXQiOQEAekVyAgADYxI2AIAOSU4AYGBMwgYA0CHJCQAMzJgHJ5ITAKBfJCcAMDRjHp1ITgCAXpGcAMDAmOcEAKBDkhMAGBjznAAAdKhaa7M9BsZAVS1srS2a7XHAuPFni3WR5IS1ZeFsDwDGlD9brHMUJwBAryhOAIBeUZywtrgnDqPhzxbrHA2xAECvSE4AgF5RnAAAvaI4YY1VVauq90xbfk1VvXUWhwSDUVVLq+pHVXVOVX2xqjZbg2PsWVVPGsX4YDYpTvhT/D7J06tqi9keCAzQLa21PVtreyT5dZKXr8Ex9kyiOGHsKE74UyzJ5JMEh832QGDgTkuyTZJU1X2r6itV9YOq+nZV7Tq1/llTKcuPq+rUqlovyZFJnjOVwDxnFscPa5UX//GnOjbJ2VV11GwPBIaoquYkeWySf5patSjJS1trF5L5IOIAAARTSURBVFbVw5J8IMkBSY5I8oTW2hVVtVlr7daqOiLJQ1trh87K4GFEFCf8SVprN1TVx5O8Msktsz0eGJANq+pHmUxMzk9ySlXdPckjkhxfd7x2dv2p//1uko9W1XFJPtv1YKFLbuuwNrw3yYuTbDTbA4EBuaW1tmeS7ZNUJntOJpL8ZqoXZflntyRprb00yZuSLEjyg6q652wNHEZNccKfrLX26yTHZbJAAf4IrbWbM5k8vjrJzUkuqapnJUlNetDU9/u21k5vrR2R5NpMFim/S7Lx7IwcRkdxwtryniSe2oE10Fo7K8nZSZ6X5AVJXlxVP05ybpKDp3Z7d1X9pKrOSfK9JD9O8o0ku2uIZdyYvh4A6BXJCQDQK4oTAKBXFCcAQK8oTgCAXlGcAAC9ojiBEbvT22ePr6q7/QnH+mhVPXPq+0eqavdV7Lt/VT1iDc7xi7t6mePK1t9pnxv/yHO9tape88eOERhvihMYvelvn701yUunb6yqNXqNRGvtf7bWzlvFLvtncip0gEFRnEC3vp1kp6lU49tVdUKS86pqTlW9u6rOrKqzq+p/JbfPEPr+qrqgqv4jyVbLD1RV36yqh059P6iqfjj1xtqvVdV9MlkEHTaV2uxXVVtW1WemznFmVT1y6rf3rKqvVtW5VfWRTE6lvkpV9fmpt+aeW1UL77TtmKn1X6uqLafW3eWbdgHuihf/QUemEpInJvnK1Kq9kuzRWrtk6i/437bW9q6q9ZN8t6q+muTBSe6XZPck85Ocl+Sf73TcLZN8OMmjpo51j9bar6vqQ0lubK0dPbXfJ5Mc01r7TlVtl+TkJLsleUuS77TWjqyqJ2dmryF40dQ5NkxyZlV9prV2XSbfr7S4tXbY1Btz35Lk0Kz8TbsAf0BxAqO3/O2zyWRy8k+ZvN1yRmvtkqn1j0/ywOX9JEk2TbJzkkcl+VRrbWmSX1bV1+/i+PsmOXX5sabedXRXHpfJqc6XL28y9RbcRyV5+tRvT6yq62dwTa+sqkOmvi+YGut1SZYl+X9T6/81yWdX86ZdgD+gOIHRW/722dtN/SV90/RVSV7RWjv5Tvs9aS2OYyLJvq21/7qLscxYVe2fyULn4a21m6vqm0k2WMnuLdPetPvHDhhYN+k5gX44OcnLqmpeklTVLlW1UZJTkzxnqiflXkkecxe//X6SR1XVDlO/vcfU+ju/sfarSV6xfKGqlhcLpyZ5/tS6JybZfDVj3TTJ9VOFya6ZTG6Wm0iyPP15fiZvF92QlbxpF+CuKE6gHz6SyX6SH069dfYfM5lsfi7JhVPbPp7ktDv/sLV2bZKFmbyF8uPccVvli0kOWd4Qm+SVSR461XB7Xu54aujvMlncnJvJ2zuXrmasX0kyt6rOT/IPmSyOlrspyT5T13BAkiOn1q/sTbsAf8BbiQGAXpGcAAC9ojgBAHpFcQIA9IriBADoFcUJANArihMAoFcUJwBAr/x/Qf4cOUQhjmkAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "classes = ['N', 'Rest']\n",
        "print(\"Making predictions ...\",end=\"\\r\")\n",
        "y_pred= model_NvsRest.predict(x_test)\n",
        "\n",
        "plot_confusion_matrix(y_test, y_pred)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history[\"loss\"], label=\"Training Loss\")\n",
        "plt.plot(history[\"val_loss\"], label=\"Validation Loss\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "Sdh6LFFkIQV8",
        "outputId": "c096a1bf-f160-43cc-cc04-f537599869e7"
      },
      "id": "Sdh6LFFkIQV8",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "classes = np.array([\"S\",\"V\"])\n",
        "training_patches_df_red = training_patches_df[training_patches_df['stringLabel']!='N']\n",
        "validation_patches_df_red = validation_balanced_patches_df[validation_balanced_patches_df['stringLabel']!='N']\n",
        "test_patches_df_red = test_patches_df[test_patches_df['stringLabel']!='N']"
      ],
      "metadata": {
        "id": "87KZoJN4H-71"
      },
      "id": "87KZoJN4H-71",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train,y_train = create_input(training_patches_df_red, step=2)\n",
        "x_valid,y_valid = create_input(validation_patches_df_red, step=2)\n",
        "x_test,y_test = create_input(test_patches_df_red,step=2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J3Fq7OdLIX8z",
        "outputId": "8b9ae7df-bc08-4347-d3d3-90227c00f211"
      },
      "id": "J3Fq7OdLIX8z",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 29970/29970 [00:06<00:00, 4367.57it/s]\n",
            "100%|██████████| 2735/2735 [00:00<00:00, 4478.25it/s]\n",
            "100%|██████████| 1635/1635 [00:00<00:00, 4439.51it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.utils.class_weight import compute_class_weight\n",
        "timesteps = 350 #patch size\n",
        "channels_num = 2\n",
        "\n",
        "input = keras.layers.Input(shape=(timesteps, channels_num))\n",
        "\n",
        "conv1 = keras.layers.Conv1D(filters=32,kernel_size=25,activation=\"relu\")(input)\n",
        "maxPool1 = keras.layers.MaxPool1D(pool_size=2)(conv1)\n",
        "dropout1 = keras.layers.Dropout(0.05)(maxPool1)\n",
        "conv2 = keras.layers.Conv1D(filters=64,kernel_size=12,activation=\"relu\")(dropout1)\n",
        "maxPool2 = keras.layers.MaxPool1D(pool_size=2)(conv2)\n",
        "dropout2 = keras.layers.Dropout(0.1)(maxPool2)\n",
        "conv3 = keras.layers.Conv1D(filters=128,kernel_size=9,activation=\"relu\")(dropout2)\n",
        "maxPool3 = keras.layers.MaxPool1D(pool_size=2)(conv3)\n",
        "dropout3 = keras.layers.Dropout(0.15)(maxPool3)\n",
        "lstm1 = keras.layers.LSTM(64, return_sequences=True)(dropout3)\n",
        "lstm2 = keras.layers.LSTM(64, return_sequences=False)(lstm1)\n",
        "output = keras.layers.Dense(1, activation=tf.nn.sigmoid)(lstm2)\n",
        "model_SvsV = keras.models.Model(inputs=input, outputs=output)\n",
        "\n",
        "model_SvsV.summary()\n",
        "\n",
        "callback = keras.callbacks.EarlyStopping(monitor='val_loss', patience=10,mode=\"auto\", restore_best_weights=True)\n",
        "\n",
        "batch_size = 128\n",
        "learning_rate = 1e-5\n",
        "model_SvsV.compile(\n",
        "    optimizer = keras.optimizers.Adam(learning_rate=learning_rate),\n",
        "    loss = 'binary_crossentropy',\n",
        "    metrics = ['accuracy'],\n",
        "    \n",
        ")\n",
        "\n",
        "class_weights = compute_class_weight(\n",
        "                                            class_weight = 'balanced', \n",
        "                                            classes = y_train, \n",
        "                                            y = y_train\n",
        "                                        )\n",
        "d_class_weights = dict(enumerate(class_weights))\n",
        "\n",
        "history_2 = model_SvsV.fit(\n",
        "    x = x_train,\n",
        "    y = y_train, \n",
        "    batch_size = batch_size,\n",
        "    steps_per_epoch=len(x_train)/batch_size,\n",
        "    validation_data=(x_valid, y_valid),\n",
        "    epochs = 100,\n",
        "    shuffle = True,\n",
        "    callbacks=[callback],\n",
        "    class_weight = d_class_weights\n",
        ").history\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iVIuMQyGIdhE",
        "outputId": "8bf4ac6e-ed71-4a3e-f0f9-fbd9ade0ed6c"
      },
      "id": "iVIuMQyGIdhE",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_2 (InputLayer)        [(None, 350, 2)]          0         \n",
            "                                                                 \n",
            " conv1d_3 (Conv1D)           (None, 326, 32)           1632      \n",
            "                                                                 \n",
            " max_pooling1d_3 (MaxPooling  (None, 163, 32)          0         \n",
            " 1D)                                                             \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 163, 32)           0         \n",
            "                                                                 \n",
            " conv1d_4 (Conv1D)           (None, 152, 64)           24640     \n",
            "                                                                 \n",
            " max_pooling1d_4 (MaxPooling  (None, 76, 64)           0         \n",
            " 1D)                                                             \n",
            "                                                                 \n",
            " dropout_4 (Dropout)         (None, 76, 64)            0         \n",
            "                                                                 \n",
            " conv1d_5 (Conv1D)           (None, 68, 128)           73856     \n",
            "                                                                 \n",
            " max_pooling1d_5 (MaxPooling  (None, 34, 128)          0         \n",
            " 1D)                                                             \n",
            "                                                                 \n",
            " dropout_5 (Dropout)         (None, 34, 128)           0         \n",
            "                                                                 \n",
            " lstm_2 (LSTM)               (None, 34, 64)            49408     \n",
            "                                                                 \n",
            " lstm_3 (LSTM)               (None, 64)                33024     \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 1)                 65        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 182,625\n",
            "Trainable params: 182,625\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/100\n",
            "234/234 [==============================] - 16s 42ms/step - loss: 0.6913 - accuracy: 0.5007 - val_loss: 0.6858 - val_accuracy: 0.6227\n",
            "Epoch 2/100\n",
            "234/234 [==============================] - 7s 31ms/step - loss: 0.6812 - accuracy: 0.5650 - val_loss: 0.6580 - val_accuracy: 0.7020\n",
            "Epoch 3/100\n",
            "234/234 [==============================] - 7s 31ms/step - loss: 0.6015 - accuracy: 0.6780 - val_loss: 0.5964 - val_accuracy: 0.5583\n",
            "Epoch 4/100\n",
            "234/234 [==============================] - 7s 31ms/step - loss: 0.5303 - accuracy: 0.7318 - val_loss: 0.5282 - val_accuracy: 0.7152\n",
            "Epoch 5/100\n",
            "234/234 [==============================] - 7s 31ms/step - loss: 0.4764 - accuracy: 0.7766 - val_loss: 0.4543 - val_accuracy: 0.8168\n",
            "Epoch 6/100\n",
            "234/234 [==============================] - 7s 31ms/step - loss: 0.4202 - accuracy: 0.8160 - val_loss: 0.3868 - val_accuracy: 0.8651\n",
            "Epoch 7/100\n",
            "234/234 [==============================] - 7s 31ms/step - loss: 0.3849 - accuracy: 0.8367 - val_loss: 0.3592 - val_accuracy: 0.8592\n",
            "Epoch 8/100\n",
            "234/234 [==============================] - 7s 31ms/step - loss: 0.3628 - accuracy: 0.8450 - val_loss: 0.3473 - val_accuracy: 0.8559\n",
            "Epoch 9/100\n",
            "234/234 [==============================] - 7s 31ms/step - loss: 0.3443 - accuracy: 0.8529 - val_loss: 0.3224 - val_accuracy: 0.8731\n",
            "Epoch 10/100\n",
            "234/234 [==============================] - 7s 31ms/step - loss: 0.3326 - accuracy: 0.8595 - val_loss: 0.3215 - val_accuracy: 0.8695\n",
            "Epoch 11/100\n",
            "234/234 [==============================] - 7s 31ms/step - loss: 0.3210 - accuracy: 0.8650 - val_loss: 0.3270 - val_accuracy: 0.8578\n",
            "Epoch 12/100\n",
            "234/234 [==============================] - 7s 31ms/step - loss: 0.3118 - accuracy: 0.8689 - val_loss: 0.3203 - val_accuracy: 0.8570\n",
            "Epoch 13/100\n",
            "234/234 [==============================] - 7s 30ms/step - loss: 0.2984 - accuracy: 0.8744 - val_loss: 0.3271 - val_accuracy: 0.8483\n",
            "Epoch 14/100\n",
            "234/234 [==============================] - 7s 30ms/step - loss: 0.2872 - accuracy: 0.8795 - val_loss: 0.3323 - val_accuracy: 0.8410\n",
            "Epoch 15/100\n",
            "234/234 [==============================] - 7s 31ms/step - loss: 0.2800 - accuracy: 0.8827 - val_loss: 0.2995 - val_accuracy: 0.8629\n",
            "Epoch 16/100\n",
            "234/234 [==============================] - 7s 30ms/step - loss: 0.2705 - accuracy: 0.8876 - val_loss: 0.3361 - val_accuracy: 0.8358\n",
            "Epoch 17/100\n",
            "234/234 [==============================] - 7s 31ms/step - loss: 0.2649 - accuracy: 0.8905 - val_loss: 0.2958 - val_accuracy: 0.8600\n",
            "Epoch 18/100\n",
            "234/234 [==============================] - 7s 30ms/step - loss: 0.2570 - accuracy: 0.8926 - val_loss: 0.3163 - val_accuracy: 0.8410\n",
            "Epoch 19/100\n",
            "234/234 [==============================] - 7s 30ms/step - loss: 0.2467 - accuracy: 0.8994 - val_loss: 0.2901 - val_accuracy: 0.8567\n",
            "Epoch 20/100\n",
            "234/234 [==============================] - 7s 30ms/step - loss: 0.2437 - accuracy: 0.8980 - val_loss: 0.3120 - val_accuracy: 0.8384\n",
            "Epoch 21/100\n",
            "234/234 [==============================] - 7s 30ms/step - loss: 0.2350 - accuracy: 0.9048 - val_loss: 0.3050 - val_accuracy: 0.8428\n",
            "Epoch 22/100\n",
            "234/234 [==============================] - 7s 30ms/step - loss: 0.2363 - accuracy: 0.9042 - val_loss: 0.2875 - val_accuracy: 0.8508\n",
            "Epoch 23/100\n",
            "234/234 [==============================] - 7s 30ms/step - loss: 0.2288 - accuracy: 0.9094 - val_loss: 0.2726 - val_accuracy: 0.8611\n",
            "Epoch 24/100\n",
            "234/234 [==============================] - 7s 30ms/step - loss: 0.2225 - accuracy: 0.9115 - val_loss: 0.3000 - val_accuracy: 0.8446\n",
            "Epoch 25/100\n",
            "234/234 [==============================] - 7s 31ms/step - loss: 0.2183 - accuracy: 0.9124 - val_loss: 0.3149 - val_accuracy: 0.8303\n",
            "Epoch 26/100\n",
            "234/234 [==============================] - 7s 30ms/step - loss: 0.2135 - accuracy: 0.9168 - val_loss: 0.2760 - val_accuracy: 0.8508\n",
            "Epoch 27/100\n",
            "234/234 [==============================] - 7s 30ms/step - loss: 0.2129 - accuracy: 0.9160 - val_loss: 0.2815 - val_accuracy: 0.8479\n",
            "Epoch 28/100\n",
            "234/234 [==============================] - 7s 31ms/step - loss: 0.2099 - accuracy: 0.9184 - val_loss: 0.2719 - val_accuracy: 0.8570\n",
            "Epoch 29/100\n",
            "234/234 [==============================] - 7s 31ms/step - loss: 0.2045 - accuracy: 0.9205 - val_loss: 0.2630 - val_accuracy: 0.8654\n",
            "Epoch 30/100\n",
            "234/234 [==============================] - 7s 31ms/step - loss: 0.2024 - accuracy: 0.9209 - val_loss: 0.2607 - val_accuracy: 0.8647\n",
            "Epoch 31/100\n",
            "234/234 [==============================] - 7s 31ms/step - loss: 0.1983 - accuracy: 0.9241 - val_loss: 0.2675 - val_accuracy: 0.8592\n",
            "Epoch 32/100\n",
            "234/234 [==============================] - 7s 31ms/step - loss: 0.1954 - accuracy: 0.9260 - val_loss: 0.2718 - val_accuracy: 0.8527\n",
            "Epoch 33/100\n",
            "234/234 [==============================] - 7s 31ms/step - loss: 0.1953 - accuracy: 0.9264 - val_loss: 0.2718 - val_accuracy: 0.8534\n",
            "Epoch 34/100\n",
            "234/234 [==============================] - 7s 30ms/step - loss: 0.1920 - accuracy: 0.9271 - val_loss: 0.2793 - val_accuracy: 0.8472\n",
            "Epoch 35/100\n",
            "234/234 [==============================] - 7s 31ms/step - loss: 0.1880 - accuracy: 0.9299 - val_loss: 0.2670 - val_accuracy: 0.8548\n",
            "Epoch 36/100\n",
            "234/234 [==============================] - 7s 31ms/step - loss: 0.1838 - accuracy: 0.9323 - val_loss: 0.2432 - val_accuracy: 0.8804\n",
            "Epoch 37/100\n",
            "234/234 [==============================] - 7s 31ms/step - loss: 0.1832 - accuracy: 0.9324 - val_loss: 0.2603 - val_accuracy: 0.8607\n",
            "Epoch 38/100\n",
            "234/234 [==============================] - 7s 31ms/step - loss: 0.1808 - accuracy: 0.9329 - val_loss: 0.2557 - val_accuracy: 0.8662\n",
            "Epoch 39/100\n",
            "234/234 [==============================] - 7s 31ms/step - loss: 0.1797 - accuracy: 0.9342 - val_loss: 0.2866 - val_accuracy: 0.8417\n",
            "Epoch 40/100\n",
            "234/234 [==============================] - 7s 31ms/step - loss: 0.1774 - accuracy: 0.9360 - val_loss: 0.2623 - val_accuracy: 0.8578\n",
            "Epoch 41/100\n",
            "234/234 [==============================] - 7s 31ms/step - loss: 0.1745 - accuracy: 0.9371 - val_loss: 0.2375 - val_accuracy: 0.8859\n",
            "Epoch 42/100\n",
            "234/234 [==============================] - 7s 31ms/step - loss: 0.1724 - accuracy: 0.9364 - val_loss: 0.2786 - val_accuracy: 0.8490\n",
            "Epoch 43/100\n",
            "234/234 [==============================] - 7s 31ms/step - loss: 0.1697 - accuracy: 0.9387 - val_loss: 0.2482 - val_accuracy: 0.8728\n",
            "Epoch 44/100\n",
            "234/234 [==============================] - 7s 31ms/step - loss: 0.1639 - accuracy: 0.9417 - val_loss: 0.2758 - val_accuracy: 0.8516\n",
            "Epoch 45/100\n",
            "234/234 [==============================] - 7s 31ms/step - loss: 0.1654 - accuracy: 0.9395 - val_loss: 0.2714 - val_accuracy: 0.8537\n",
            "Epoch 46/100\n",
            "234/234 [==============================] - 7s 31ms/step - loss: 0.1648 - accuracy: 0.9409 - val_loss: 0.2620 - val_accuracy: 0.8581\n",
            "Epoch 47/100\n",
            "234/234 [==============================] - 7s 31ms/step - loss: 0.1623 - accuracy: 0.9409 - val_loss: 0.2752 - val_accuracy: 0.8483\n",
            "Epoch 48/100\n",
            "234/234 [==============================] - 7s 31ms/step - loss: 0.1610 - accuracy: 0.9429 - val_loss: 0.2592 - val_accuracy: 0.8625\n",
            "Epoch 49/100\n",
            "234/234 [==============================] - 7s 31ms/step - loss: 0.1573 - accuracy: 0.9441 - val_loss: 0.2401 - val_accuracy: 0.8859\n",
            "Epoch 50/100\n",
            "234/234 [==============================] - 7s 31ms/step - loss: 0.1597 - accuracy: 0.9434 - val_loss: 0.2429 - val_accuracy: 0.8786\n",
            "Epoch 51/100\n",
            "234/234 [==============================] - 7s 31ms/step - loss: 0.1559 - accuracy: 0.9463 - val_loss: 0.2432 - val_accuracy: 0.8808\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Making predictions ...\",end=\"\\r\")\n",
        "y_pred= model_SvsV.predict(x_test)\n",
        "classes = ['S', 'V']\n",
        "plot_confusion_matrix(y_test, y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 598
        },
        "id": "aI6l0VJTMLx8",
        "outputId": "ad4c36dd-b14a-49fe-8121-c3b6de7ddde0"
      },
      "id": "aI6l0VJTMLx8",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Making predictions ...\r"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history_2[\"loss\"], label=\"Training Loss\")\n",
        "plt.plot(history_2[\"val_loss\"], label=\"Validation Loss\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "10vRiPCILFzh",
        "outputId": "f31b8280-eb48-4139-a30a-d75d4f6dd95e"
      },
      "id": "10vRiPCILFzh",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "352d223e",
      "metadata": {
        "id": "352d223e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 807
        },
        "outputId": "c7948760-0f7c-4ce0-d7d5-5a795f9b4f9c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Making predictions on full validation set...\r"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 23659/23659 [00:05<00:00, 4489.17it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.98      0.96      0.97     20924\n",
            "         1.0       0.53      0.62      0.57      1032\n",
            "         2.0       0.76      0.82      0.79      1703\n",
            "\n",
            "    accuracy                           0.94     23659\n",
            "   macro avg       0.76      0.80      0.78     23659\n",
            "weighted avg       0.94      0.94      0.94     23659\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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JiEnAPcAPUkpzmjqva04kSSqYPG3CllIaD4xf7tjpDX5OwAmlW7OYnEiSpFwxOZEkqWCq8hOctAiTE0mSlCsmJ5IkFUye1py0BJMTSZKUKyYnkiQVTIUHJyYnkiQpX0xOJEkqmKCyoxOTE0mSlCsmJ5IkFYz7nEiSJGXI5ESSpIJxnxNJkqQMmZxIklQwFR6cmJxIkqR8sTmRJEm54rSOJEkFU1Xh8zomJ5IkKVdMTiRJKpgKD05MTiRJUr6YnEiSVDBuwiZJkpQhkxNJkgqmwoMTkxNJkpQvJieSJBWM+5xIkiRlyOREkqSCqezcxOREkiTljMmJJEkF4z4nkiRJGTI5kSSpYKoqOzgxOZEkSfliciJJUsG45kSSJClDJieSJBVMhQcnJieSJClfbE4kSVKurHRaJyIuBtLKHk8pHdsiFUmSpCZV+oLYptacPJZZFZIkSSUrbU5SSlc1vB8RHVNKH7R8SZIkqSmtfhO2iNghIiYBL5TubxERY1u8MkmS1Co156PEFwJ7ArcApJSejogvtGhVkiRppSp9zUmzPq2TUpqy3KHaFqhFkiSpWcnJlIjYEUgR0RY4Dni+ZcuSJEkrU9m5SfOSk+8A3wP6A28BW5buS5IkfeZWmZyklGYDh2ZQiyRJaoaq1r7mJCLWi4hbI2JWRMyMiJsjYr0sipMkSa1Pc6Z1/gxcB/QD1gKuB65pyaIkSdLKRWR3K4fmNCcdU0p/SCktKd3+CLRv6cIkSVLr1NR36/Qo/Xh7RJwMXEv9d+18FRifQW2SJGkFKn2fk6YWxD5OfTOy9E/gyAaPJeCUlipKkiS1Xk19t866WRYiSZKap8KDk2ZtwkZEbAoMpcFak5TS1S1VlCRJar1W2ZxExE+A4dQ3J+OBvYB/ATYnkiSVQavf5wQ4ENgVmJ5S+i9gC6Bri1YlSZJareZM6yxMKdVFxJKI6ALMBAa2cF2t1pMTH+T3l5xLXV0tu35xX/Y75L8aPT7pmSf4/SXn8sarkzn+1LPYYefdlj32h3EX8cQj/wLgwK8fwU677JFp7Sq/bdfpxrHD16OqCm779wz+9Oi0T4zZZYOe/NcOg0gJJs9+nzPGv8T6vdfkhF3XY8121dSlxB8emcrdL80uwxUoD5569EF+P/Zc6urq2HWvfdn34G83evz//vpH7rr9Ztq0aUOXrt357kmn07umX3mKbaUqPDhpVnPyWER0Ay6n/hM8C4CHWrSqVqq2tpYrLjqb0385lh69azj5qG8wbIedGbjOxxvy9urTl+/98Gfccv0fGj338Yfv57WXX+DccX9m8aLF/OTE0Wy17Y50XLNT1pehMqkKOH7Eepxww3PMmr+IcYduwb9eeYc33lm4bMyAbu05dNsBHHXtMyz4qJZuHdoC8OHiWs6642WmzvuQnmu244pDt2DiG3NZ8JFfQN7a1NXW8ruLz+HUcy6hZ68aTjn6mwzb4QsMWPvjv4fWWX8jzr7kQNZo3547b/0rf7z8Io4/9RdlrFqVZpXTOimlo1JK81JKlwG7A98qTe/oMzb5hefo238gNWsNoG3btuy0yx48+uC9jcb06bsW6wwe8on5xqlvvMbGm29FmzbVtO/QgbXXHcJTjz6YXfEqu437dmbavA95+92PWFKXuOuFWfy/wT0ajdl7sxpufGr6sqZj3sLFAEyd9yFT530IwJz3FzF34eJljYtal8kvPkfftQZS028A1W3bsuPwPXj0wX82GrPplsNYo3395yOGbLwp78yaUY5SW7WIyOxWDittTiJi6+VvQA+guvTzSkXENhHRt8H9b5a+k+eiBpu7aTnvzJ5Jr941y+737F3DO7NnNeu5aw8ewlOPPsRHHy7kvXfn8uzTjzF7pn9htCa9OrVj5vxFy+7PWrCI3p3XaDRmYPcODOzenku+uhmXHrI5267T7RPn2bhvJ9pWBdNKzYpal3dmz6Rnw7+HevXhndkzVzr+7ttvZsttd8yiNLUiTU3rnNfEYwkY0cTjvwV2A4iILwBnA8cAWwLjqF9k+wkRMRoYDXD62b/mwEMPa+Il1NCWw3bglRcn8eNjD6NL1+5sMHQzqtq0KXdZypk2VcGA7h049vpn6dOpHRd/dTO+ffWTy5KUnmu25ccjN+CsCS+Rylyr8u++f4zn1Zee56fnjSt3KaowTW3Ctst/cN42KaV3Sj9/FRiXUroBuCEinmriNcdR37zw76kLWt3fjT169WF2g3h0zqwZ9OjVu9nPP+DQwzng0MMBuPDM/2GtAYM+8xqVX7MXLKJP53bL7vfu1I5Z8z9qNGbW/EVMmj6f2rrE2+99xJS5CxnQrQMvzFhAx3ZtOGffoVz+wBtMentB1uUrJ3r06sOchn8PzZ5Jj159PjHumSce4cY/X8lPzxtH23btPvG4WlZzPmpbZC11fW0iYmnjsytwd4PHmrXxW2u0/kZDeXvaFGa8PY3FixfzwD13ss2OOzfrubW1tcx/dx4Ar7/yMm+8Opkthm3fkuUqZ16YPp8B3TrQr8saVFcFu27UmwdefafRmPtfmcNWA+p3AujavpqB3Tvw1rsfUl0VnLnPRkyYNJN/vjynHOUrJwZvWP/30My3p7Fk8WIevPdOhu3whUZjXpv8ApdfeBY/HHM+Xbs7U6/PXks1CtcA/4yI2cBC4H6AiFgfeLeFXrPw2rSp5ohjfsjPf3Q0dXW1jNhrFAPXGcy1v7+UwRsOZZsdd2byC8/xy5+cxPsL3uOxh+7nL1f9lguvvJ7a2iWc9v0jAOiw5poce8oZtGljH9ia1Ca48J5XOfeATagKGP/sTF6fs5DDdhzEi9MX8MCr7zDx9Xlss3Y3rv7WVtSlxNj7Xue9D5ew+8a92aJ/F7q0r2bkJvX/Sv7FhMlMnvV+ma9KWWvTpprDjv4BZ55yDHV1teyy5z4MXGcwf/nfyxi8wcYM23Fn/jjuIj5cuJDzzzgZgF59avjRGReUufLWpdK/+C9SapnZk4jYHugH3JlSer90bAOgU0rpiVU9vzVO6+iz8b3rni53CSqoiw/cvNwlqMC2GNQ5s47h2JteyOx35EX7bpR5J9Sc7esDOBRYL6U0JiIGAX1TShObel5K6eEVHHtptSuVJElA/b5Glaw5a07GAjsAh5TuzwcuabGKJElSq9acRQnbpZS2jognAVJKcyPCpdmSJJWJyQksjog21O9tQkT0BupatCpJktRqNSc5uQi4EegTEWdSv4HaqS1alSRJWqlK/7TOKpuTlNKfIuJx6vcrCWDflNLzLV6ZJElqlZrzaZ1BwAfArQ2PpZTebMnCJEnSilX6mpPmTOvcRv16kwDaA+sCLwKbtGBdkiSplWrOtM5mDe+XvpH4qBarSJIkNanCl5x8+u/WKe3uul0L1CJJktSsNScnNLhbBWwNvNViFUmSpCZVVXh00pw1J50b/LyE+jUoN7RMOZIkqbVrsjkpbb7WOaV0Ukb1SJKkVfjUazIKZqXXFxHVKaVaYKcM65EkSa1cU8nJROrXlzwVEbcA1wPvL30wpfS3Fq5NkiStQIUvOWnWmpP2wBxgBB/vd5IAmxNJkvSZa6o56VP6pM6zfNyULJVatCpJktRqNdWctAE60bgpWcrmRJKkMmnNHyV+O6U0JrNKJEmSaLo5qey2TJKkgqrw4KTJj0rvmlkVkiRJJStNTlJK72RZiCRJap6qVpycSJIkZa45+5xIkqQcqfRP65icSJKkXDE5kSSpYCo8ODE5kSRJ+WJyIklSwfhpHUmSpAyZnEiSVDBR4Zu4m5xIkqRcMTmRJKlgXHMiSZKUIZMTSZIKxuREkiQpQyYnkiQVTFT4FrEmJ5IkKVdsTiRJUq44rSNJUsG4IFaSJClDNieSJBVMRHa3VdcSIyPixYiYHBEnNzHugIhIETFsVee0OZEkSaslItoAlwB7AUOBQyJi6ArGdQaOAx5pznltTiRJKpiqiMxuq7AtMDml9GpKaRFwLTBqBePOAM4BPmzW9X2aPwxJktS6RMToiHiswW10g4f7A1Ma3J9aOtbw+VsDA1NKtzX3Nf20jiRJBZPlp3VSSuOAcavz3IioAs4Hvv1pnmdyIkmSVtc0YGCD+wNKx5bqDGwK3BsRrwPbA7esalGsyYkkSQWTo93rHwWGRMS61DclBwNfW/pgSuldoNfS+xFxL3BSSumxpk5qciJJklZLSmkJcDQwAXgeuC6l9FxEjImIfVb3vCYnkiQVTBX5iU5SSuOB8csdO30lY4c355wmJ5IkKVdMTiRJKpgcrTlpESYnkiQpV0xOJEkqGL+VWJIkKUMmJ5IkFUwzvvOm0ExOJElSrpicSJJUMBUenJicSJKkfLE5kSRJueK0jiRJBeOCWEmSpAyZnEiSVDAVHpyYnEiSpHwxOZEkqWAqPVmo9OuTJEkFY3IiSVLBRIUvOjE5kSRJuWJyIklSwVR2bmJyIkmScsbkRJKkgnGHWEmSpAyZnEiSVDCVnZuYnEiSpJwxOZEkqWAqfMmJyYkkScoXkxNJkgrGHWIlSZIyZHIiSVLBVHqyUOnXJ0mSCsbmRJIk5YrTOpIkFYwLYiVJkjJkciJJUsFUdm5iciJJknImt8lJu2r7Jq2eO47ZsdwlqKBqvn51uUtQgc3/y7cyey3XnEiSJGUot8mJJElasUpPFir9+iRJUsGYnEiSVDCuOZEkScqQyYkkSQVT2bmJyYkkScoZkxNJkgqmwpecmJxIkqR8MTmRJKlgqip81YnJiSRJyhWTE0mSCsY1J5IkSRkyOZEkqWDCNSeSJEnZsTmRJEm54rSOJEkF44JYSZKkDJmcSJJUMG7CJkmSlCGTE0mSCsY1J5IkSRkyOZEkqWBMTiRJkjJkciJJUsG4fb0kSVKGTE4kSSqYqsoOTkxOJElSvpicSJJUMK45kSRJypDJiSRJBeM+J5IkSRkyOZEkqWBccyJJkpQhkxNJkgrGfU4kSZIyZHMiSZJyxWkdSZIKxgWxkiRJGTI5kSSpYNyETZIkKUMmJ5IkFUyFBycmJ5IkKV9MTiRJKpiqCl90YnIiSZJyxeREkqSCqezcxOREkiTljMmJJElFU+HRicmJJEnKFZMTSZIKxu/WkSRJypDJiSRJBVPh25yYnEiSpHwxOZEkqWAqPDgxOZEkSfliciJJUtFUeHRiciJJknLF5kSSJOWK0zqSJBWMm7BJkiRlyOREkqSCcRM2SZKkDJmcSJJUMBUenJicSJKkfDE5kSSpaCo8OjE5kSRJuWJyIklSwbjPiSRJUoZsTiRJKpiI7G6rriVGRsSLETE5Ik5eweMnRMSkiHgmIu6KiLVXdU6bE0mStFoiog1wCbAXMBQ4JCKGLjfsSWBYSmlz4K/AL1d1XpsTSZIKJjK8rcK2wOSU0qsppUXAtcCohgNSSveklD4o3X0YGLCqk9qcSJKklYqI0RHxWIPb6AYP9wemNLg/tXRsZQ4Hbl/Va/ppHUmSiibDD+uklMYB4/7T80TE14FhwM6rGmtzIkmSVtc0YGCD+wNKxxqJiN2AHwM7p5Q+WtVJbU4kSSqYHO1z8igwJCLWpb4pORj4WsMBEbEV8FtgZEppZnNO6poTSZK0WlJKS4CjgQnA88B1KaXnImJMROxTGvYroBNwfUQ8FRG3rOq8JieSJBVMc/YfyUpKaTwwfrljpzf4ebdPe06TE0mSlCs2J5IkKVec1pEkqWByNKvTIkxOJElSrpicSJJUNBUenZicSJKkXDE5kSSpYHK0CVuLMDmRJEm5YnKSM48/8gBXXPwrauvq2ONL+3LgoYc1evzZpx/niovP5fVXX+YHp/+CnYbvvuyxWTPe5uJfjmH2zBlEwOnn/IaafmtlfQkqowf+dT+/OvtM6mrr2PeAAznsiNGNHl+0aBGnnfIjnp/0HF27deOcc89nrf4DePjBB7jowvNYvHgxbdu25fsn/pBtt9u+TFehcthti7X45be3paoquPrulzn/5mcbPT6g55r89ns70bVjO9pUBT/58xPc+dQ0dtmsHz/72udoV13FoiV1nPrHx7jvuelluorWI0+bsLUEm5Mcqa2t5bcXns2Y8y6lZ+8aTjzyULbdaWcGrTN42Zjeffpx3Ck/46Zrr/7E8y846zQO+voRbIaVUoQAAA0mSURBVLXN9iz84AOqqir83atGamtrOfvnY7j08iup6VvDoV89iJ13GcHgwesvG3PT3/5K5y5duOX2O7lj/G38+vzzOOe8C+jWvTsX/uZS+vSpYfLLL3HUkUdw5933lfFqlKWqCM47bHtGnXkn0+Z8wD9/8SVue2wKL057d9mYH+6/OX976A1+9/cX2bB/V244eTc2PeYG5sz/iK/88i6mz13IxgO7cdP/7M6G372+jFejSvCZT+tExCURsdNnfd7W4OXnn6Vf/4H0XWsAbdu25fMj9uSRf93baExNv7VYd/AGRFXj/3Rvvv4KtbW1bLVN/b92O3TsyBrtO2RVunLg2X8/w8BBgxgwcCBt27Zjz72+yL1339VozL1338WXR+0LwG577MnERx4ipcRGGw+lT58aAAavP4SPPvyIRYsWZX4NKo9h6/fi1Rnv8frMBSyureOGB19j720GNhqTSHTp0BaArh3bMX3uBwA88/o7TJ+7EIDnp8yjfbs2tKt2xUBLiwxv5dAS76CXgHMj4vWI+GXp2wjVDHNmz6RX6RcEQK/eNcyZPatZz31rypus2akzZ516IscdfjC/v/QCamtrW6pU5dDMmTOo6dtv2f2amr7MmjljuTEz6VsaU11dTadOnZk3b16jMf/4+wQ2GjqUdu3atXzRyoV+PToybc77y+5Pm/MB/bqv2WjMWdc/zVc/vx4vjD2Qv568Kyf9/pFPnGfUdmvz9GtzWLSkrsVrVmX7zJuTlNKvU0o7ADsDc4ArI+KFiPhJRGzQ1HMjYnREPBYRj/3lD1d+1qVVtNraJUx65kkOO+p4zv/tH5n+1lTuumOVX/woNfLK5Je56PzzOPX0n5W7FOXMQTuty5/+OZmNjvorB559F5cf/flG6x42GtCNMV/7HMdd/nD5imxNKjw6abHsLaX0RkrpnJTSVsAhwL7Uf51yU88Zl1IallIa9tVvHNbU0IrUs1cfZjf4l+7sWTPo2at3857bu4Z119+AvmsNoE11Ndv/v1149aUXWqpU5VCfPjXMmP72svszZkynd4Mkrn5MH6aXxixZsoQFC+bTrVu3+vHTp3PCcUdzxlnnMHDQoOwKV9m9/c4H9O/5cVLSv2dH3p77fqMx39xlCH976HUAJr48izXatqFn5/YArNWjI9ecOJwjx97PazPmZ1W2KliLNScRUR0RX46IPwG3Ay8C+7fU61WCIRttwltT32T629NYvHgx9989ge12Gt7s576/YD7vznsHgGeeeJSB66zXgtUqbzbZdDPefPMNpk2dyuLFi5hw+3iG7zKi0ZiddxnBrTffBMA/7pzANtttT0Qw/733OOaoIzn2+yey5dZbl6N8ldHjr8xmcN8urN27E23bVHHAjuty22NTG42ZMnsBwzetnxLcsH9X2rdtw+z3PqRrx7b89eRd+ck1T/Dwi82bhtZ/LjL8X1muL6X02Z4wYnfqk5IvAhOBa4GbU0rvN/nE5bw4/YPPtrCCeOzh+7ni4nOpq6tjty+O4ivfOII//W4s6280lO12Gs7Lzz/HWaedwIL579Gu3Rp069GTS666AYAnH32YK8eeDykxeMON+d5Jp9G2bdsyX1H2BvZsvQuB77/vn5x7zlnU1dYxar8DOOLI7zD2NxcxdJNNGb7LCD766CNOPeWHvPj883Tp2pWzf3U+AwYO5PLfXsqVV4xj0KC1l53r0nG/o0fPnmW8muzVfP2Tn4JrLfbYsj/nfGsbqqqq+MO9L3Pujf/mxwdtyZOvzmH841PYsH9XfnPkjqzZvpqU4LQ/Pc7dz7zFD/bfnBNHbcor0z9OTEad+Xdmv/dhGa+mPOb/5VuZ/SZ/4e3sfkdu1K9j5h1KSzQndwN/Bm5IKc1d3fO01uZE/7nW3JzoP9OamxP957JsTrL8Hblh3+ybk898n5OU0ohVj5IkSVoxN2GTJKlgKn2LTXfKkSRJuWJyIklS0VR4dGJyIkmScsXmRJIk5YrTOpIkFUy5NkfLismJJEnKFZMTSZIKJio7ODE5kSRJ+WJyIklSwVR4cGJyIkmS8sXkRJKkoqnw6MTkRJIk5YrJiSRJBeM+J5IkSRkyOZEkqWDc50SSJClDJieSJBVMhQcnJieSJClfTE4kSSqaCo9OTE4kSVKumJxIklQw7nMiSZKUIZMTSZIKxn1OJEmSMmRzIkmScsVpHUmSCqbCZ3VMTiRJUr6YnEiSVDAuiJUkScqQyYkkSYVT2dGJyYkkScoVkxNJkgrGNSeSJEkZMjmRJKlgKjw4MTmRJEn5YnIiSVLBuOZEkiQpQyYnkiQVTFT4qhOTE0mSlCsmJ5IkFU1lBycmJ5IkKV9MTiRJKpgKD05MTiRJUr6YnEiSVDDucyJJkpQhmxNJkpQrTutIklQwbsImSZKUIZMTSZKKprKDE5MTSZKULyYnkiQVTIUHJyYnkiQpX0xOJEkqGDdhkyRJypDJiSRJBeM+J5IkSRkyOZEkqWBccyJJkpQhmxNJkpQrNieSJClXXHMiSVLBuOZEkiQpQyYnkiQVjPucSJIkZcjkRJKkgnHNiSRJUoZsTiRJUq44rSNJUsFU+KyOyYkkScoXkxNJkoqmwqMTkxNJkpQrJieSJBWMm7BJkiRlyOREkqSCcRM2SZKkDJmcSJJUMBUenJicSJKkfDE5kSSpaCo8OjE5kSRJuWJyIklSwbjPiSRJUoZMTiRJKhj3OZEkScpQpJTKXYNWQ0SMTimNK3cdKh7fO1pdvneUFZOT4hpd7gJUWL53tLp87ygTNieSJClXbE4kSVKu2JwUl/O+Wl2+d7S6fO8oEy6IlSRJuWJyIkmScsXmRJIk5YrNSYFERIqI8xrcPykiflrGklQwEfHjiHguIp6JiKciYrty16R8i4h7ImLP5Y59PyIuLVdNqnw2J8XyEbB/RPQqdyEqnojYAdgb2DqltDmwGzClvFWpAK4BDl7u2MGl41KLsDkpliXUr5Y/vtyFqJD6AbNTSh8BpJRmp5TeKnNNyr+/Al+KiHYAEbEOsBZwfxlrUoWzOSmeS4BDI6JruQtR4dwJDIyIlyJibETsXO6ClH8ppXeAicBepUMHA9clP+qpFmRzUjAppfeAq4Fjy12LiiWltAD4HPVbkM8C/hIR3y5rUSqKhlM7TumoxbnPSYFExIKUUqeI6AE8Afye+v+GPy1vZSqiiDgQ+FZK6cvlrkX5FhGdgFeBkcC1KaUNylySKpzJSQGVYtbrgMPLXYuKIyI2jIghDQ5tCbxRrnpUHKXU7R7gSkxNlAGbk+I6D/BTO/o0OgFXRcSkiHgGGAr8tLwlqUCuAbbA5kQZcFpHkiTlismJJEnKFZsTSZKUKzYnkiQpV2xOJElSrticSJKkXLE5kVpYRNSWvgH42Yi4PiI6/gfn+t/S5mlExBURMbSJscMjYsfVeI3XV/Tlkis7vtyYBZ/ytX4aESd92holVTabE6nlLUwpbZlS2hRYBHyn4YMRUb06J00pHZFSmtTEkOHAp25OJKncbE6kbN0PrF9KNe6PiFuASRHRJiJ+FRGPRsQzEXEkQNT7TUS8GBH/APosPVFE3BsRw0o/j4yIJyLi6Yi4q/TNsd8Bji+lNp+PiN4RcUPpNR6NiJ1Kz+0ZEXdGxHMRcQUQq7qIiLgpIh4vPWf0co9dUDp+V0T0Lh0bHBF3lJ5zf0Rs9Fn8YUqqTKv1LzZJn14pIdkLuKN0aGtg05TSa6Vf8O+mlLaJiDWAByLiTmArYEPqd3OtASZRv4V4w/P2Bi4HvlA6V4+U0jsRcRmwIKV0bmncn4ELUkr/iohBwARgY+AnwL9SSmMi4ks072sRDiu9Rgfg0Yi4IaU0B1gTeCyldHxEnF4699HAOOA7KaWXI2I7YCwwYjX+GCW1AjYnUsvrEBFPlX6+H/gd9dMtE1NKr5WO7wFsvnQ9CdAVGAJ8AbgmpVQLvBURd6/g/NsD9y09V+m7l1ZkN2BoxLJgpEvpC92+AOxfeu5tETG3Gdd0bETsV/p5YKnWOUAd8JfS8T8Cfyu9xo7A9Q1ee41mvIakVsrmRGp5C1NKWzY8UPol/X7DQ8AxKaUJy4374mdYRxWwfUrpwxXU0mwRMZz6RmeHlNIHEXEv0H4lw1Ppdect/2cgSSvjmhMpHyYA342ItgARsUFErAncB3y1tCalH7DLCp77MPCFiFi39NwepePzgc4Nxt0JHLP0TkQsbRbuA75WOrYX0H0VtXYF5pYak42oT26WqgKWpj9fo3666D3gtYg4qPQaERFbrOI1JLViNidSPlxB/XqSJyLiWeC31CebNwIvlx67Gnho+SemlGYBo6mfQnmaj6dVbgX2W7ogFjgWGFZacDuJjz819DPqm5vnqJ/eeXMVtd4BVEfE88DZ1DdHS70PbFu6hhHAmNLxQ4HDS/U9B4xqxp+JpFbKbyWWJEm5YnIiSZJyxeZEkiTlis2JJEnKFZsTSZKUKzYnkiQpV2xOJElSrticSJKkXPn/2/gD/b6GaOoAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "from sklearn.metrics import classification_report\n",
        "print(\"Making predictions on full validation set...\",end=\"\\r\")\n",
        "x_valid_full,y_valid_full = create_input(validation_patches_df_, 3)\n",
        "\n",
        "pred_NvsRest = model_NvsRest.predict(x_valid_full)\n",
        "pred_fin = np.rint(np.squeeze(pred_NvsRest))\n",
        "\n",
        "x_valid_full_SvsV = x_valid_full[pred_fin == 1]\n",
        "\n",
        "y_pred = model_SvsV.predict(x_valid_full_SvsV)\n",
        "pred_SvsV = np.rint(np.squeeze(y_pred))\n",
        "\n",
        "k = 0;\n",
        "for i in range(len(pred_fin)):\n",
        "  if pred_fin[i] == 1:\n",
        "    if pred_SvsV[k] == 0:\n",
        "      pred_fin[i] = 1\n",
        "    else:\n",
        "      pred_fin[i] = 2\n",
        "    k += 1;\n",
        "\n",
        "classes = [\"N\",\"S\",\"V\"]\n",
        "print(classification_report(y_valid_full, pred_fin))    \n",
        "conf = plot_confusion_matrix(y_valid_full, pred_fin)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<h2> Save and load model"
      ],
      "metadata": {
        "id": "NlQBI2sGFaJ0"
      },
      "id": "NlQBI2sGFaJ0"
    },
    {
      "cell_type": "code",
      "source": [
        "# convert the history.history dict to a pandas DataFrame:     \n",
        "hist_df = pd.DataFrame(history) \n",
        "net_name = 'OneVsAll_RVsRest'\n",
        "# save to csv: \n",
        "hist_csv_file = net_name +'_history.csv'\n",
        "with open(os.path.join(cwd,hist_csv_file), mode='w') as f:\n",
        "    hist_df.to_csv(f)\n",
        "model_NvsRest.save_weights(os.path.join(cwd,net_name +'_weights.h5'))\n",
        "model_NvsRest.save(os.path.join(cwd,net_name))"
      ],
      "metadata": {
        "id": "5hZvAhkiclSW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b1714248-c226-436f-e55b-a8629187963d"
      },
      "id": "5hZvAhkiclSW",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as lstm_cell_layer_call_fn, lstm_cell_layer_call_and_return_conditional_losses, lstm_cell_1_layer_call_fn, lstm_cell_1_layer_call_and_return_conditional_losses, lstm_cell_layer_call_fn while saving (showing 5 of 10). These functions will not be directly callable after loading.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Assets written to: /content/drive/My Drive/FinalAssignment/OneVsAll_RVsRest/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Assets written to: /content/drive/My Drive/FinalAssignment/OneVsAll_RVsRest/assets\n",
            "WARNING:absl:<keras.layers.recurrent.LSTMCell object at 0x7f076e6f3710> has the same name 'LSTMCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.LSTMCell'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
            "WARNING:absl:<keras.layers.recurrent.LSTMCell object at 0x7f076e6f8b50> has the same name 'LSTMCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.LSTMCell'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# convert the history.history dict to a pandas DataFrame:     \n",
        "hist_df = pd.DataFrame(history) \n",
        "net_name = 'OneVsAll_SVsV'\n",
        "# save to csv: \n",
        "hist_csv_file = net_name +'_history.csv'\n",
        "with open(os.path.join(cwd,hist_csv_file), mode='w') as f:\n",
        "    hist_df.to_csv(f)\n",
        "model_SvsV.save_weights(os.path.join(cwd,net_name +'_weights.h5'))\n",
        "model_SvsV.save(os.path.join(cwd,net_name))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9wUIhh6wMbyB",
        "outputId": "3d1cded0-1f4d-46ee-f953-7c271ac85fd7"
      },
      "id": "9wUIhh6wMbyB",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as lstm_cell_2_layer_call_fn, lstm_cell_2_layer_call_and_return_conditional_losses, lstm_cell_3_layer_call_fn, lstm_cell_3_layer_call_and_return_conditional_losses, lstm_cell_2_layer_call_fn while saving (showing 5 of 10). These functions will not be directly callable after loading.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Assets written to: /content/drive/My Drive/FinalAssignment/OneVsAll_SVsV/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Assets written to: /content/drive/My Drive/FinalAssignment/OneVsAll_SVsV/assets\n",
            "WARNING:absl:<keras.layers.recurrent.LSTMCell object at 0x7f07bc427090> has the same name 'LSTMCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.LSTMCell'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
            "WARNING:absl:<keras.layers.recurrent.LSTMCell object at 0x7f07bc427c50> has the same name 'LSTMCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.LSTMCell'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "tf-gpu",
      "language": "python",
      "name": "tf-gpu"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.7"
    },
    "colab": {
      "name": "LSTM_CNN.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 5
}