Blanco2020_Model_Sleep_TL.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Blanco2020_Model_Sleep_TL.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyM7J14nHFe2NRHrsRWWXxzd",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Div12345/SleepStaging-TransferLearning/blob/main/Blanco2020_Model_Sleep_TL.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L7J9lDSdRTgt"
      },
      "source": [
        "# Blanco et al. 2020 Model\n",
        "[Paper](https://arxiv.org/ftp/arxiv/papers/2103/2103.16215.pdf)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L9sQe2m-bYJE"
      },
      "source": [
        "# Setup"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m5JTtD-_8Dy0",
        "cellView": "form"
      },
      "source": [
        "#@title Dependencies Install\n",
        "!pip install wandb\n",
        "!pip install git+https://github.com/sylvchev/beetl-competition\n",
        "!pip install moabb\n",
        "!pip install braindecode\n",
        "!pip install git+https://github.com/pyRiemann/pyRiemann\n",
        "!pip install matplotlib\n",
        "!pip install https://github.com/ufoym/imbalanced-dataset-sampler/archive/master.zip"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uuMPQYfn8LbD",
        "cellView": "form"
      },
      "source": [
        "#@title Imports\n",
        "from mne import get_config, set_config\n",
        "import os.path as osp\n",
        "import os\n",
        "from beetl.task_datasets import BeetlSleepLeaderboard, BeetlSleepSource\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import mne\n",
        "\n",
        "import braindecode\n",
        "from braindecode import EEGClassifier\n",
        "from braindecode.util import np_to_var, set_random_seeds\n",
        "from braindecode.models import SleepStagerChambon2018\n",
        "from braindecode.datautil.preprocess import preprocess, Preprocessor, zscore\n",
        "from braindecode.samplers.ssl import RelativePositioningSampler\n",
        "#from braindecode.datautil import create_from_X_y\n",
        "from braindecode.datasets import BaseDataset\n",
        "\n",
        "import sklearn\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.metrics import confusion_matrix, classification_report, balanced_accuracy_score, cohen_kappa_score\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib.lines import Line2D\n",
        "\n",
        "import skorch\n",
        "import skorch.dataset\n",
        "from skorch.callbacks import EarlyStopping, Checkpoint, EpochScoring, WandbLogger, TrainEndCheckpoint\n",
        "from skorch.dataset import Dataset\n",
        "from skorch.helper import predefined_split\n",
        "from skorch.classifier import NeuralNetClassifier as NNClassifier\n",
        "\n",
        "import time\n",
        "import math\n",
        "import copy\n",
        "from copy import deepcopy\n",
        "\n",
        "import pickle\n",
        "import torch\n",
        "from torch import nn\n",
        "from torch.utils.data import DataLoader\n",
        "import torch.nn.functional as F\n",
        "\n",
        "from torchsampler import ImbalancedDatasetSampler as IDS\n",
        "\n",
        "import wandb\n",
        "\n",
        "import joblib"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JsEpic6LddRa",
        "outputId": "63440407-60c6-4d04-94bf-fc384c633ae0"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6H7g78UA8OBg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4070091c-8a9c-405c-8c1f-ee16ea3f145b"
      },
      "source": [
        "# Optional wandb for model logging\n",
        "!wandb login\n",
        "# Specific to user - Put it here for convinience"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Paste an API key from your profile and hit enter: \n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v_h42a0Z8SlJ",
        "outputId": "21d2d8ff-521a-42a0-87c9-863eefd148a4"
      },
      "source": [
        "mne.set_log_level(False) # Equivalent to WARNING\n",
        "path = \"/content/drive/MyDrive/mne_data\"\n",
        "set_config(\"MNE_DATA\", path)\n",
        "set_config(\"MNE_DATASETS_BEETLSLEEPLEADERBOARD_PATH\",path)\n",
        "set_config(\"MNE_DATASETS_BEETLSLEEPSOURCE_PATH\",path)\n",
        "get_config()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-4-d9ce88047e42>:4: RuntimeWarning: Setting non-standard config type: \"MNE_DATASETS_BEETLSLEEPLEADERBOARD_PATH\"\n",
            "  set_config(\"MNE_DATASETS_BEETLSLEEPLEADERBOARD_PATH\",path)\n",
            "<ipython-input-4-d9ce88047e42>:5: RuntimeWarning: Setting non-standard config type: \"MNE_DATASETS_BEETLSLEEPSOURCE_PATH\"\n",
            "  set_config(\"MNE_DATASETS_BEETLSLEEPSOURCE_PATH\",path)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'MNE_DATA': '/content/drive/MyDrive/mne_data',\n",
              " 'MNE_DATASETS_BEETLSLEEPLEADERBOARD_PATH': '/content/drive/MyDrive/mne_data',\n",
              " 'MNE_DATASETS_BEETLSLEEPSOURCE_PATH': '/content/drive/MyDrive/mne_data'}"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E_fQU4bJ8TF2",
        "outputId": "7934f309-def9-48a6-cf1b-301d8e06677b"
      },
      "source": [
        "cuda = torch.cuda.is_available()  # check if GPU is available\n",
        "device = 'cuda' if cuda else 'cpu'\n",
        "print(device)\n",
        "if cuda:\n",
        "    torch.backends.cudnn.benchmark = True\n",
        "# Set random seed to be able to reproduce results\n",
        "set_random_seeds(seed=87, cuda=cuda)\n",
        "random_state = 87\n",
        "\n",
        "!nvidia-smi -L\n",
        "# !nvidia-smi -q\n",
        "\n",
        "# gpu = cuda.get_current_device()\n",
        "# print(\"maxThreadsPerBlock = %s\" % str(gpu.MAX_THREADS_PER_BLOCK))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cuda\n",
            "GPU 0: Tesla P100-PCIE-16GB (UUID: GPU-c3e06580-ab8e-2ffd-1dc1-f3ea7be7b9c2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QLNWv8j48Vhd",
        "cellView": "form"
      },
      "source": [
        "#@title Helper Functions\n",
        "\n",
        "def label_count(y_train):\n",
        "  labels= np.unique(y_train)\n",
        "  labelsize=labels.shape[0]\n",
        "  #print('labelsize:',labelsize)\n",
        "  label_count = np.zeros(labelsize).astype(int)\n",
        "  for i in range(labelsize):\n",
        "      # tempy = ys1[ys1==labels[i]]\n",
        "      label_count[i]=y_train[y_train==labels[i]].shape[0]\n",
        "  maxsize = label_count.max()\n",
        "  print(label_count)\n",
        "  return np_to_var(label_count)\n",
        "\n",
        "def label_viz(y):\n",
        "  # Another Nice func with viz\n",
        "  classes_mapping = {0: 'W', 1: 'S1', 2: 'S2', 3: 'S3', 4: 'S4', 5:'REM'}\n",
        "  # This might be a time consuming method though\n",
        "  y_train = pd.Series([y for _, y in train_ds]).map(classes_mapping)\n",
        "  ax = y_train.value_counts().plot(kind='barh')\n",
        "  ax.set_xlabel('Number of training examples');\n",
        "  ax.set_ylabel('Sleep stage');\n",
        "\n",
        "# For trained Skorch model\n",
        "def training_viz(clf):\n",
        "  # For Trained Skorch Classifier\n",
        "  df = pd.DataFrame(clf.history.to_list())\n",
        "  df[['train_mis_clf', 'valid_mis_clf']] = 100 - df[\n",
        "      ['train_bacc', 'valid_bacc']] * 100\n",
        "\n",
        "  # get percent of misclass for better visual comparison to loss\n",
        "  plt.style.use('seaborn-talk')\n",
        "  fig, ax1 = plt.subplots(figsize=(20, 7))\n",
        "  df.loc[:, ['train_loss', 'valid_loss']].plot(\n",
        "      ax=ax1, style=['-', ':'], marker='o', color='tab:blue', legend=False,\n",
        "      fontsize=14)\n",
        "\n",
        "  ax1.tick_params(axis='y', labelcolor='tab:blue', labelsize=14)\n",
        "  ax1.set_ylabel(\"Loss\", color='tab:blue', fontsize=14)\n",
        "\n",
        "  ax2 = ax1.twinx()  # instantiate a second axes that shares the same x-axis\n",
        "\n",
        "  df.loc[:, ['train_mis_clf', 'valid_mis_clf']].plot(\n",
        "      ax=ax2, style=['-', ':'], marker='o', color='tab:red', legend=False)\n",
        "  ax2.tick_params(axis='y', labelcolor='tab:red', labelsize=14)\n",
        "  ax2.set_ylabel('Balanced misclassification rate [%]', color='tab:red',\n",
        "                fontsize=14)\n",
        "  ax2.set_ylim(ax2.get_ylim()[0], 85)  # make some room for legend\n",
        "  ax1.set_xlabel('Epoch', fontsize=14)\n",
        "\n",
        "  # where some data has already been plotted to ax\n",
        "  handles = []\n",
        "  handles.append(\n",
        "      Line2D([0], [0], color='black', linewidth=1, linestyle='-', label='Train'))\n",
        "  handles.append(\n",
        "      Line2D([0], [0], color='black', linewidth=1, linestyle=':', label='Valid'))\n",
        "  plt.legend(handles, [h.get_label() for h in handles], fontsize=14)\n",
        "  plt.tight_layout()\n",
        "\n",
        "# Scaling the data\n",
        "class TrainObject(object):\n",
        "    # Scaling the data\n",
        "    def __init__(self, X, y, nps= False):\n",
        "        assert len(X) == len(y)\n",
        "        # mean = np.mean(X, axis=2, keepdims=True)\n",
        "        # # Here normalise across the window, when channel size is not large enough\n",
        "        # # In motor imagery kit, we put axis = 1, across channel as an example\n",
        "        # std = np.std(X, axis=2, keepdims=True)\n",
        "        # X = (X - mean) / std\n",
        "        X = zscore(X)\n",
        "        # we scale it to 1000 as a better training scale of the shallow CNN\n",
        "        # according to the orignal work of the paper referenced above\n",
        "        if (nps == False):\n",
        "          self.X = np_to_var(X.astype(np.float32)*1e3).to(device)\n",
        "          self.y = np_to_var(y.astype(np.int8)).to(device)\n",
        "        else:\n",
        "          self.X = X.astype(np.float32)*1e3\n",
        "          self.y = y.astype(np.int8)\n",
        "\n",
        "def predict_leaderboard_unlabelled(clf, save_fname, x_test_data = None, emb = False,nps=False):\n",
        "  # Test Data - 6 to 17 - 12 subjects \n",
        "  if x_test_data is None:\n",
        "    _, _, X_test, _ = dsl.get_data(subjects=range(6, 18)) \n",
        "    print(\"Sleep leaderboard - Test Data : There are {} trials with {} electrodes and {} time samples\".format(*X_test.shape))\n",
        "\n",
        "    # print(X_test.shape[0])\n",
        "\n",
        "    x_test_mean = TrainObject(X_test, y = np.zeros(X_test.shape[0]),nps=nps)\n",
        "    if (nps==False):\n",
        "      # means torch must've been the input to the classifier\n",
        "      x_test_data = Dataset(x_test_mean.X,x_test_mean.y)\n",
        "      # Maybe put a tqdm bar?\n",
        "      y_pred = clf.predict(x_test_data)\n",
        "    else:\n",
        "      x_test_data = x_test_mean \n",
        "      y_pred = clf.predict(x_test_data.X)\n",
        "  else:\n",
        "    y_pred = clf.predict(x_test_data)\n",
        "  print(x_test_data.shape)\n",
        "\n",
        "  print(\"Checking if all classes have been predicted\")\n",
        "  print(np.unique(y_pred))\n",
        "  #@markdown Set the save_path for saving the prediction\n",
        "  save_path = \"/content/drive/MyDrive/mne_data/NeuroIPS_Hack_Sleep/predict/\" #@param\n",
        "  np.savetxt(save_path+save_fname+\".txt\",y_pred,delimiter=',',fmt=\"%d\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kHWXsvutVDsq",
        "cellView": "form"
      },
      "source": [
        "#@title Helper Functions for loading the Data\n",
        "def get_trainB():\n",
        "  start = time.time()\n",
        "  dss = BeetlSleepSource()\n",
        "  \n",
        "  start = time.time()\n",
        "  X_train, y_train, _ = dss.get_data()\n",
        "  end = time.time()\n",
        "\n",
        "  print(f\"Train data load time {(end-start)/60} min\")\n",
        "  print(X_train.shape,y_train.shape)\n",
        "  label_count(y_train)\n",
        "\n",
        "  # z-scoring or normalizing\n",
        "  \n",
        "  # start = time.time()\n",
        "  trainX = TrainObject(X_train, y = y_train, nps = False)\n",
        "  end = time.time()\n",
        "  print(f\"Z-scoring time {end-start}\")\n",
        "  \n",
        "  start = time.time()\n",
        "  # trainB = Dataset(trainX)\n",
        "  trainB = Dataset(trainX.X,trainX.y)\n",
        "  end = time.time()\n",
        "  print(f\"Torch dataset time {end-start} sec = {(end-start)/60} min\")\n",
        "  # 10 sec + 7 min\n",
        "  return trainB\n",
        "\n",
        "def get_valB():\n",
        "  # valB\n",
        "  # Labelled Leaderboard Data\n",
        "  start = time.time()\n",
        "  dsl = BeetlSleepLeaderboard()\n",
        "  # dsl.get_data()\n",
        "\n",
        "  # Validation Data - 5? 6? subjects from the test group - Competition says 5, looks like 6\n",
        "\n",
        "  X_target, y_target, _, _ = dsl.get_data(subjects=range(0,6))\n",
        "\n",
        "  end = time.time()\n",
        "  print(f\"leaderboard labelled load time = {(end-start)/60} min\")\n",
        "\n",
        "  print(X_target.shape,y_target.shape)\n",
        "  label_count(y_target)\n",
        "\n",
        "  # z-scoring or normalizing\n",
        "  start = time.time()\n",
        "  valX = TrainObject(X_target, y = y_target, nps = False)\n",
        "  end = time.time()\n",
        "  print(f\"Z-scoring time {end-start} sec\")\n",
        "\n",
        "  \n",
        "  # for braindecode\n",
        "  start = time.time()\n",
        "  valB = Dataset(valX.X,valX.y)\n",
        "  end = time.time()\n",
        "  print(f\"Torch dataset time {end-start} sec = {(end-start)/60} min\")\n",
        "  # 3 sec + 1.5 min\n",
        "  return valB\n",
        "\n",
        "def get_testB():\n",
        "  dsl = BeetlSleepLeaderboard()\n",
        "  _, _, X_test, _ = dsl.get_data(subjects=range(6, 18)) \n",
        "  print(\"Sleep leaderboard - Test Data : There are {} trials with {} electrodes and {} time samples\".format(*X_test.shape))\n",
        "  # print(X_test.shape[0])\n",
        "  # x_test_mean = TrainObject(X_test, y = np.zeros(X_test.shape[0]),nps=nps)\n",
        "\n",
        "  # z-scoring or normalizing\n",
        "  start = time.time()\n",
        "  testX = TrainObject(X_test, y = np.zeros(X_test.shape[0]), nps = False)\n",
        "  end = time.time()\n",
        "  print(f\"Z-scoring time {end-start} sec\")\n",
        "  \n",
        "  # for braindecode\n",
        "  start = time.time()\n",
        "  testB = Dataset(testX.X,testX.y)\n",
        "  end = time.time()\n",
        "  print(f\"Torch dataset time {end-start} sec = {(end-start)/60} min\")\n",
        "  return testB\n",
        "\n",
        "def get_valC():\n",
        "  # Phase 2 Target Data\n",
        "  # Need to download by myself first\n",
        "  target_savebase = '/content/drive/MyDrive/mne_data/MNE-beetlsleeptest-data/sleep_target/'\n",
        "  X_sleep_target = []\n",
        "  y_sleep_target = []\n",
        "  #from s0-s4 in final set\n",
        "  start = time.time()\n",
        "  for subj in range(0, 5):\n",
        "      for session in range(1, 3):\n",
        "          # \"testing_s{}r{}X.npy\", replacing \"leaderboard_s{}r{}X.npy\" before\n",
        "          with open(target_savebase + \"testing_s{}r{}X.npy\".format(subj, session), 'rb') as f:\n",
        "            X_sleep_target.append(pickle.load(f))\n",
        "          with open(target_savebase + \"testing_s{}r{}y.npy\".format(subj, session), 'rb') as g:\n",
        "            y_sleep_target.append(pickle.load(g))\n",
        "  X_sleep_target = np.concatenate(X_sleep_target)\n",
        "  y_sleep_target = np.concatenate(y_sleep_target)\n",
        "\n",
        "\n",
        "  end = time.time()\n",
        "  print(f\"phase 2 labelled load time = {(end-start)/60} min\")\n",
        "\n",
        "  print(\"There are {} trials with {} electrodes and {} time samples\".format(*X_sleep_target.shape))\n",
        "  print(X_sleep_target.shape, y_sleep_target.shape)\n",
        "  label_count(y_sleep_target)\n",
        "\n",
        "  # package in torch dataset with mean normalizing\n",
        "  # z-scoring or normalizing\n",
        "  start = time.time()\n",
        "  valX = TrainObject(X_sleep_target, y = y_sleep_target, nps = False)\n",
        "  end = time.time()\n",
        "  print(f\"Z-scoring time {end-start} sec\")\n",
        "\n",
        "  \n",
        "  # for braindecode\n",
        "  start = time.time()\n",
        "  valC = Dataset(valX.X,valX.y)\n",
        "  end = time.time()\n",
        "  print(f\"Torch dataset time {end-start} sec = {(end-start)/60} min\")\n",
        "  # \n",
        "  return valC\n",
        "\n",
        "def get_testC():\n",
        "  # Phase 2 Test Data - Need to download by myself first\n",
        "  test_savebase = '/content/drive/MyDrive/mne_data/MNE-beetlsleeptest-data/testing/'\n",
        "  X_sleep_test = []\n",
        "  start = time.time()\n",
        "\n",
        "  #starts from s5 in final set\n",
        "  for subj in range(5, 14):\n",
        "      for session in range(1, 3):\n",
        "          # \"testing_s{}r{}X.npy\", replacing \"leaderboard_s{}r{}X.npy\" before\n",
        "          with open(test_savebase + \"testing_s{}r{}X.npy\".format(subj, session), 'rb') as f:\n",
        "              X_sleep_test.append(pickle.load(f))\n",
        "  X_sleep_test = np.concatenate(X_sleep_test)\n",
        "  end = time.time()\n",
        "  print(f\"phase 2 final test set load time = {(end-start)/60} min\")\n",
        "  print (\"There are {} trials with {} electrodes and {} time samples\".format(*X_sleep_test.shape))\n",
        "\n",
        "  # package in torch dataset with mean normalizing\n",
        "  # z-scoring or normalizing\n",
        "  start = time.time()\n",
        "  testX = TrainObject(X_sleep_test, y = np.zeros(X_sleep_test.shape[0]), nps = False)\n",
        "  end = time.time()\n",
        "  print(f\"Z-scoring time {end-start} sec\")\n",
        "  \n",
        "  # for braindecode\n",
        "  start = time.time()\n",
        "  testC = Dataset(testX.X,testX.y)\n",
        "  end = time.time()\n",
        "  print(f\"Torch dataset time {end-start} sec = {(end-start)/60} min\")\n",
        "  return testC"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VFdTDRSBXXXM"
      },
      "source": [
        "from time import time as t\n",
        "def load_obj(path,name):\n",
        "  target = path + name + '.pkl'\n",
        "  with open(target, 'rb') as f:\n",
        "    unpickler = pickle.Unpickler(f)\n",
        "    a = unpickler.load()\n",
        "    return a"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kRK7hmlqXtJB",
        "outputId": "9143d181-759e-44fc-89a8-229bdbdba94c"
      },
      "source": [
        "# Loading the data as Torch datasets with z-scoring\n",
        "trainB = get_trainB()\n",
        "valB = get_valB()\n",
        "testB = get_testB()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train data load time 2.091049063205719 min\n",
            "(90545, 2, 3000) (90545,)\n",
            "[24043  7941 35983  5247  3057 14274]\n",
            "Z-scoring time 142.37633633613586\n",
            "Torch dataset time 0.0035288333892822266 sec = 5.8813889821370444e-05 min\n",
            "leaderboard labelled load time = 0.3546595533688863 min\n",
            "(15442, 2, 3000) (15442,)\n",
            "[6010 1672 5035  704  414 1607]\n",
            "Z-scoring time 1.0841395854949951 sec\n",
            "Torch dataset time 9.655952453613281e-05 sec = 1.6093254089355469e-06 min\n",
            "Sleep leaderboard - Test Data : There are 25748 trials with 2 electrodes and 3000 time samples\n",
            "Z-scoring time 1.6269407272338867 sec\n",
            "Torch dataset time 7.843971252441406e-05 sec = 1.3073285420735677e-06 min\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eV32LyTYm4Sr",
        "outputId": "d0ac10da-276c-4664-caf6-d2cee351441a"
      },
      "source": [
        "print(trainB)\n",
        "print(valB)\n",
        "print(testB)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<skorch.dataset.Dataset object at 0x7f408e16acd0>\n",
            "<skorch.dataset.Dataset object at 0x7f40958a1790>\n",
            "<skorch.dataset.Dataset object at 0x7f41141f1050>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ua33ZwTOf_HG"
      },
      "source": [
        "# Blanco2020 Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XcXLJVhxgWr1"
      },
      "source": [
        "\n",
        "\n",
        "Checkpointing based on val_loss_best, no early stopping"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A1zdYna3d3bR"
      },
      "source": [
        "#@title Blanco2020 Model(Titled SleepEEG here).. Run this Cell.\n",
        "class SleepEEG(nn.Module):\n",
        "    \"\"\"Sleep staging architecture from Blanco et al 2020.\n",
        "\n",
        "    Convolutional neural network for sleep staging described in [Blanco2020]_.\n",
        "    A series of seven convolutional layers with kernel sizes running down from 7 to 3,\n",
        "    in an attempt to extract more general features at the beginning, while more specific\n",
        "    and complex features were extracted in the final stages.\n",
        "\n",
        "    Parameters\n",
        "    ----------\n",
        "    n_channels : int\n",
        "        Number of EEG channels.\n",
        "    sfreq : float\n",
        "        EEG sampling frequency.\n",
        "    n_conv_chans : int\n",
        "        Number of convolutional channels. Set to 20 in [Blanco2020]_.\n",
        "    n_groups : int\n",
        "        Number of groups for the convolution. Set to 2 in [Blanco2020]_ for 2 Channel EEG.\n",
        "        controls the connections between inputs and outputs. n_channels and n_conv_chans must be\n",
        "        divisible by n_groups.\n",
        "    input_size_s : float\n",
        "        Size of the input, in seconds.\n",
        "    n_classes : int\n",
        "        Number of classes.\n",
        "    dropout : float\n",
        "        Dropout rate before the output dense layer.\n",
        "    apply_batch_norm : bool\n",
        "        If True, apply batch normalization after both temporal convolutional\n",
        "        layers.\n",
        "\n",
        "    References\n",
        "    ----------\n",
        "    .. [Blanco2020] Fernandez-Blanco, E., Rivero, D. & Pazos, A. Convolutional\n",
        "        neural networks for sleep stage scoring on a two-channel EEG signal.\n",
        "        Soft Comput 24, 4067–4079 (2020). https://doi.org/10.1007/s00500-019-04174-1\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self, n_channels, sfreq, n_conv_chs=20, time_conv_size_s=0.5,\n",
        "                 max_pool_size_s=0.125, pad_size_s=0.25, input_size_s=30,\n",
        "                 n_classes=5, dropout=0.5, apply_batch_norm=False):\n",
        "        super().__init__()\n",
        "\n",
        "        time_conv_size = 7\n",
        "        max_pool_size = 2\n",
        "        input_size = np.ceil(input_size_s * sfreq).astype(int)\n",
        "\n",
        "        self.n_channels = n_channels\n",
        "\n",
        "        batch_norm = nn.BatchNorm2d if apply_batch_norm else nn.Identity\n",
        "\n",
        "        n_groups = 2\n",
        "        self.feature_extractor = nn.Sequential(\n",
        "            nn.Conv2d(2, n_conv_chs, (1, 7), groups = n_groups, padding=0),\n",
        "            batch_norm(n_conv_chs),\n",
        "            nn.ReLU(),\n",
        "            nn.MaxPool2d((1, max_pool_size)),\n",
        "            nn.Conv2d(n_conv_chs, n_conv_chs, (1, 7), groups =n_conv_chs, padding=0),\n",
        "            batch_norm(n_conv_chs),\n",
        "            nn.ReLU(),\n",
        "            nn.MaxPool2d((1, max_pool_size)),\n",
        "            nn.Conv2d(n_conv_chs, n_conv_chs, (1, 5), groups =n_conv_chs, padding=0),\n",
        "            batch_norm(n_conv_chs),\n",
        "            nn.ReLU(),\n",
        "            nn.MaxPool2d((1, max_pool_size)),\n",
        "            nn.Conv2d(n_conv_chs, n_conv_chs, (1, 5), groups =n_conv_chs, padding=0),\n",
        "            batch_norm(n_conv_chs),\n",
        "            nn.ReLU(),\n",
        "            nn.MaxPool2d((1, max_pool_size)),\n",
        "            nn.Conv2d(n_conv_chs, n_conv_chs, (1, 5), groups =n_conv_chs, padding=0),\n",
        "            batch_norm(n_conv_chs),\n",
        "            nn.ReLU(),\n",
        "            nn.MaxPool2d((1, max_pool_size)),\n",
        "            nn.Conv2d(n_conv_chs, n_conv_chs, (1, 3), groups =n_conv_chs, padding=0),\n",
        "            batch_norm(n_conv_chs),\n",
        "            nn.ReLU(),\n",
        "            nn.MaxPool2d((1, max_pool_size)),\n",
        "            nn.Conv2d(n_conv_chs, n_conv_chs, (1, 3), groups =n_conv_chs, padding=0),\n",
        "            batch_norm(n_conv_chs),\n",
        "            nn.ReLU(),\n",
        "            nn.MaxPool2d((1, max_pool_size))\n",
        "        )\n",
        "        self.len_last_layer = self._len_last_layer(n_channels, input_size)\n",
        "        self.fc = nn.Sequential(\n",
        "            nn.Dropout(dropout),\n",
        "            nn.Linear(self.len_last_layer, n_classes),\n",
        "            nn.Softmax(dim=1)\n",
        "        )\n",
        "\n",
        "    def _len_last_layer(self, n_channels, input_size):\n",
        "        self.feature_extractor.eval()\n",
        "        with torch.no_grad():\n",
        "            out = self.feature_extractor(\n",
        "                torch.Tensor(1,n_channels, 1, input_size)) # batch_size,n_channels,height, width\n",
        "        self.feature_extractor.train()\n",
        "        return len(out.flatten())\n",
        "\n",
        "    def embed(self, x):\n",
        "        if x.ndim == 3:\n",
        "            x = x.unsqueeze(2)\n",
        "\n",
        "        return self.feature_extractor(x).flatten(start_dim=1)\n",
        "\n",
        "    def forward(self, x):\n",
        "        \"\"\"\n",
        "        Forward pass.\n",
        "        Parameters\n",
        "        ----------\n",
        "        x: torch.Tensor\n",
        "            Batch of EEG windows of shape (batch_size, n_channels, n_times).\n",
        "        \"\"\"\n",
        "\n",
        "        return self.fc(self.embed(x))\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OSG377pM5OhZ"
      },
      "source": [
        "# Model training on first group data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z9JIBrTmepcT",
        "outputId": "be4651a9-4b1b-4756-f646-94c1cd82258d"
      },
      "source": [
        "n_classes = 6\n",
        "sfreq = 100\n",
        "# Extract number of channels and time steps from dataset\n",
        "# n_channels = X_train[0].shape[0] #2\n",
        "n_channels = 2\n",
        "print(n_channels)\n",
        "# input_size_samples = X_train[0].shape[1]\n",
        "input_size_samples = 3000\n",
        "print(input_size_samples)\n",
        "\n",
        "model = SleepEEG(\n",
        "    n_channels,\n",
        "    sfreq,\n",
        "    n_classes=n_classes,\n",
        "    input_size_s=input_size_samples / sfreq\n",
        ")\n",
        "\n",
        "# Send model to GPU\n",
        "if cuda:\n",
        "    model.cuda()\n",
        "print(model)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2\n",
            "3000\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at  /pytorch/c10/core/TensorImpl.h:1156.)\n",
            "  return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SleepEEG(\n",
            "  (feature_extractor): Sequential(\n",
            "    (0): Conv2d(2, 20, kernel_size=(1, 7), stride=(1, 1), groups=2)\n",
            "    (1): Identity()\n",
            "    (2): ReLU()\n",
            "    (3): MaxPool2d(kernel_size=(1, 2), stride=(1, 2), padding=0, dilation=1, ceil_mode=False)\n",
            "    (4): Conv2d(20, 20, kernel_size=(1, 7), stride=(1, 1), groups=20)\n",
            "    (5): Identity()\n",
            "    (6): ReLU()\n",
            "    (7): MaxPool2d(kernel_size=(1, 2), stride=(1, 2), padding=0, dilation=1, ceil_mode=False)\n",
            "    (8): Conv2d(20, 20, kernel_size=(1, 5), stride=(1, 1), groups=20)\n",
            "    (9): Identity()\n",
            "    (10): ReLU()\n",
            "    (11): MaxPool2d(kernel_size=(1, 2), stride=(1, 2), padding=0, dilation=1, ceil_mode=False)\n",
            "    (12): Conv2d(20, 20, kernel_size=(1, 5), stride=(1, 1), groups=20)\n",
            "    (13): Identity()\n",
            "    (14): ReLU()\n",
            "    (15): MaxPool2d(kernel_size=(1, 2), stride=(1, 2), padding=0, dilation=1, ceil_mode=False)\n",
            "    (16): Conv2d(20, 20, kernel_size=(1, 5), stride=(1, 1), groups=20)\n",
            "    (17): Identity()\n",
            "    (18): ReLU()\n",
            "    (19): MaxPool2d(kernel_size=(1, 2), stride=(1, 2), padding=0, dilation=1, ceil_mode=False)\n",
            "    (20): Conv2d(20, 20, kernel_size=(1, 3), stride=(1, 1), groups=20)\n",
            "    (21): Identity()\n",
            "    (22): ReLU()\n",
            "    (23): MaxPool2d(kernel_size=(1, 2), stride=(1, 2), padding=0, dilation=1, ceil_mode=False)\n",
            "    (24): Conv2d(20, 20, kernel_size=(1, 3), stride=(1, 1), groups=20)\n",
            "    (25): Identity()\n",
            "    (26): ReLU()\n",
            "    (27): MaxPool2d(kernel_size=(1, 2), stride=(1, 2), padding=0, dilation=1, ceil_mode=False)\n",
            "  )\n",
            "  (fc): Sequential(\n",
            "    (0): Dropout(p=0.5, inplace=False)\n",
            "    (1): Linear(in_features=400, out_features=6, bias=True)\n",
            "    (2): Softmax(dim=1)\n",
            "  )\n",
            ")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "id": "zU0Qho8bfYQ2",
        "outputId": "b3114fa0-6a5e-4bd8-ffcc-201a6997d3e6"
      },
      "source": [
        "# Create a wandb run\n",
        "wandb_run = wandb.init(name = \"300 epoch train\", project='cnn-fulltrain', entity='sleep_hacking')\n",
        "\n",
        "lr = 5e-3\n",
        "batch_size = 128\n",
        "n_epochs = 300\n",
        "\n",
        "# Log hyperparameters\n",
        "# wandb_run.config.update({\"learning rate\": lr, \"batch size\": batch_size})\n",
        "\n",
        "# Callback WandbLogger logs the \"best trained model\", etc. after every epoch\n",
        "# Not sure of definition of best trained\n",
        "\n",
        "train_bacc = EpochScoring(\n",
        "    scoring='balanced_accuracy', on_train=True, name='train_bacc',\n",
        "    lower_is_better=False)\n",
        "valid_bacc = EpochScoring(\n",
        "    scoring='balanced_accuracy', on_train=False, name='valid_bacc',\n",
        "    lower_is_better=False)\n",
        "callbacks = [('train_bal_acc', train_bacc),\n",
        "             ('valid_bal_acc', valid_bacc),\n",
        "             (\"checkpoint\",Checkpoint(\n",
        "                 monitor = 'valid_bacc_best',\n",
        "                 f_params = None, f_optimizer = None, f_criterion = None,\n",
        "                 f_pickle = \"model_{last_epoch[epoch]}.pkl\",\n",
        "                 dirname = \"/content/drive/MyDrive/mne_data/fulltrain300_checkpoints\")),\n",
        "             (\"wandb\",WandbLogger(wandb_run))\n",
        "             ]\n",
        "\n",
        "def get_labels(x):\n",
        "  return x.y.cpu()\n",
        "\n",
        "arg_dict ={\n",
        "    'iterator_train__batch_size': batch_size,\n",
        "    'iterator_train__sampler': IDS(train,callback_get_label=get_labels),\n",
        "    'iterator_train__shuffle': False,\n",
        "    # 'iterator_val_...'\n",
        "    # 'iterator_train__sampler__callback_get_label':lambda x:x.y.cpu()                                         \n",
        "}\n",
        "\n",
        "clf = EEGClassifier(\n",
        "    model,\n",
        "    criterion=torch.nn.CrossEntropyLoss,\n",
        "    optimizer=torch.optim.SGD,\n",
        "    train_split=predefined_split(valB),  # using valid_set for validation\n",
        "    # train_split=skorch.dataset.CVSplit(10),\n",
        "    # train_split=None,\n",
        "    iterator_train = DataLoader,\n",
        "    # iterator_valid = dl,\n",
        "    optimizer__lr=lr,\n",
        "    batch_size=batch_size,\n",
        "    callbacks=callbacks,\n",
        "    device=device,\n",
        "    **arg_dict\n",
        ")\n",
        "print(clf)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-17-eb74bd14d5eb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     25\u001b[0m                  \u001b[0mf_pickle\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"model_{last_epoch[epoch]}.pkl\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m                  dirname = \"/content/drive/MyDrive/mne_data/fulltrain300_checkpoints\")),\n\u001b[0;32m---> 27\u001b[0;31m              \u001b[0;34m(\u001b[0m\u001b[0;34m\"wandb\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mWandbLogger\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwandb_run\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     28\u001b[0m              ]\n\u001b[1;32m     29\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'wandb_run' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R2q-m4KVf6sw"
      },
      "source": [
        "# Optional loading of model if already trained\n",
        "# load the model\n",
        "# with open('/content/drive/MyDrive/mne_data/fulltrain_CNN_model_13.pkl', 'rb') as f:\n",
        "#     clf = pickle.load(f)\n",
        "\n",
        "# wandb_run = wandb.init(project='fullrun', entity='sleep_hacking')\n",
        "# clf.set_params(callbacks__wandb = WandbLogger(wandb_run))\n",
        "# print(clf)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gYTTP_vY6FlX",
        "outputId": "29b7b868-3077-4d68-f3cb-43df5a5ce0b8"
      },
      "source": [
        "start = time.time()\n",
        "# Model training for a specified number of epochs. `y` is None as it is already\n",
        "# supplied in the dataset.\n",
        "# clf.fit(dl.dataset.X.cpu(), y=dl.dataset.y.cpu(), epochs=n_epochs)\n",
        "clf.fit(train, y=None, epochs=n_epochs)\n",
        "end = time.time()\n",
        "print(f'time taken : {end-start}')\n",
        "# saving\n",
        "with open('/content/drive/MyDrive/mne_data/sleepEEGmodel_fulltrain300.pkl', 'wb') as f:\n",
        "    pickle.dump(model, f)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "  epoch    train_bacc    train_loss    valid_bacc    valid_loss    cp     dur\n",
            "-------  ------------  ------------  ------------  ------------  ----  ------\n",
            "      1        \u001b[36m0.4112\u001b[0m        \u001b[32m1.6161\u001b[0m        \u001b[35m0.3726\u001b[0m        \u001b[31m1.7306\u001b[0m     +  6.6566\n",
            "      2        \u001b[36m0.4218\u001b[0m        \u001b[32m1.6088\u001b[0m        \u001b[35m0.3920\u001b[0m        \u001b[31m1.6755\u001b[0m     +  6.7166\n",
            "      3        \u001b[36m0.4619\u001b[0m        \u001b[32m1.5707\u001b[0m        \u001b[35m0.4565\u001b[0m        \u001b[31m1.5459\u001b[0m     +  7.5454\n",
            "      4        \u001b[36m0.4933\u001b[0m        \u001b[32m1.5409\u001b[0m        0.4397        \u001b[31m1.5359\u001b[0m        6.7412\n",
            "      5        \u001b[36m0.5032\u001b[0m        \u001b[32m1.5318\u001b[0m        \u001b[35m0.4797\u001b[0m        \u001b[31m1.5055\u001b[0m     +  7.2136\n",
            "      6        \u001b[36m0.5076\u001b[0m        \u001b[32m1.5248\u001b[0m        0.4369        \u001b[31m1.4984\u001b[0m        6.6345\n",
            "      7        \u001b[36m0.5098\u001b[0m        \u001b[32m1.5227\u001b[0m        0.4661        \u001b[31m1.4636\u001b[0m        7.2305\n",
            "      8        \u001b[36m0.5137\u001b[0m        \u001b[32m1.5207\u001b[0m        0.4383        1.5252        6.6246\n",
            "      9        \u001b[36m0.5174\u001b[0m        \u001b[32m1.5163\u001b[0m        0.4207        1.4726        6.7199\n",
            "     10        \u001b[36m0.5203\u001b[0m        \u001b[32m1.5143\u001b[0m        0.4305        1.4958        6.6307\n",
            "     11        0.5186        \u001b[32m1.5139\u001b[0m        0.4341        1.4760        6.6710\n",
            "     12        0.5202        \u001b[32m1.5121\u001b[0m        0.4190        1.4790        6.6221\n",
            "     13        \u001b[36m0.5222\u001b[0m        \u001b[32m1.5106\u001b[0m        0.4617        \u001b[31m1.4569\u001b[0m        6.6755\n",
            "     14        \u001b[36m0.5252\u001b[0m        \u001b[32m1.5080\u001b[0m        0.4701        1.4580        6.6261\n",
            "     15        \u001b[36m0.5275\u001b[0m        \u001b[32m1.5054\u001b[0m        0.4521        \u001b[31m1.4469\u001b[0m        6.5898\n",
            "     16        0.5246        1.5100        0.4263        \u001b[31m1.4455\u001b[0m        6.6341\n",
            "     17        0.5243        1.5089        0.4788        1.4673        6.6162\n",
            "     18        \u001b[36m0.5301\u001b[0m        \u001b[32m1.5044\u001b[0m        0.4300        1.4787        6.8002\n",
            "     19        \u001b[36m0.5338\u001b[0m        \u001b[32m1.5004\u001b[0m        0.4670        \u001b[31m1.4435\u001b[0m        6.7814\n",
            "     20        0.5316        1.5022        0.4674        1.4539        6.6591\n",
            "     21        \u001b[36m0.5345\u001b[0m        \u001b[32m1.4984\u001b[0m        0.4702        1.4635        6.7306\n",
            "     22        \u001b[36m0.5355\u001b[0m        1.4989        0.4578        1.4543        6.6560\n",
            "     23        0.5349        1.5007        0.4656        1.5102        6.7132\n",
            "     24        0.5340        1.5011        \u001b[35m0.5054\u001b[0m        \u001b[31m1.4421\u001b[0m     +  6.7007\n",
            "     25        \u001b[36m0.5384\u001b[0m        \u001b[32m1.4964\u001b[0m        0.5029        1.4607        6.6625\n",
            "     26        \u001b[36m0.5394\u001b[0m        1.4973        0.4036        1.4666        7.4179\n",
            "     27        \u001b[36m0.5402\u001b[0m        \u001b[32m1.4956\u001b[0m        0.4868        1.4713        6.6496\n",
            "     28        \u001b[36m0.5404\u001b[0m        \u001b[32m1.4944\u001b[0m        0.4729        1.4475        6.6604\n",
            "     29        \u001b[36m0.5409\u001b[0m        1.4947        0.4711        \u001b[31m1.4373\u001b[0m        6.6360\n",
            "     30        0.5395        1.4954        0.4926        1.4440        6.6925\n",
            "     31        \u001b[36m0.5443\u001b[0m        \u001b[32m1.4904\u001b[0m        0.4754        1.4672        6.6675\n",
            "     32        0.5435        1.4916        \u001b[35m0.5101\u001b[0m        \u001b[31m1.4207\u001b[0m     +  6.6313\n",
            "     33        0.5410        1.4928        0.4874        1.4789        6.6693\n",
            "     34        0.5429        1.4928        0.4733        1.4691        7.3228\n",
            "     35        0.5403        1.4951        0.4732        1.4795        6.6648\n",
            "     36        0.5437        1.4912        \u001b[35m0.5235\u001b[0m        1.4328     +  6.6839\n",
            "     37        0.5442        1.4915        0.4785        1.4270        6.6866\n",
            "     38        \u001b[36m0.5452\u001b[0m        \u001b[32m1.4893\u001b[0m        0.5103        1.4472        7.4076\n",
            "     39        \u001b[36m0.5465\u001b[0m        \u001b[32m1.4883\u001b[0m        \u001b[35m0.5378\u001b[0m        \u001b[31m1.4199\u001b[0m     +  6.6552\n",
            "     40        \u001b[36m0.5470\u001b[0m        \u001b[32m1.4864\u001b[0m        0.5121        \u001b[31m1.4144\u001b[0m        6.6046\n",
            "     41        0.5454        1.4889        0.4451        1.4712        7.2222\n",
            "     42        \u001b[36m0.5471\u001b[0m        1.4880        0.4781        1.4423        6.7546\n",
            "     43        \u001b[36m0.5491\u001b[0m        \u001b[32m1.4860\u001b[0m        0.4873        1.4443        6.6758\n",
            "     44        \u001b[36m0.5515\u001b[0m        \u001b[32m1.4839\u001b[0m        0.4851        1.4346        6.6858\n",
            "     45        0.5506        1.4841        0.4549        1.4852        6.6545\n",
            "     46        0.5497        1.4848        0.4832        1.4329        6.6763\n",
            "     47        0.5507        1.4841        0.4434        1.4503        6.6947\n",
            "     48        \u001b[36m0.5519\u001b[0m        1.4840        0.4317        1.4776        6.8927\n",
            "     49        \u001b[36m0.5536\u001b[0m        \u001b[32m1.4808\u001b[0m        0.4671        1.4689        6.6458\n",
            "     50        0.5523        1.4822        0.4983        1.4505        6.6209\n",
            "     51        \u001b[36m0.5547\u001b[0m        \u001b[32m1.4803\u001b[0m        0.4808        1.4155        6.6959\n",
            "     52        0.5537        1.4807        0.5126        1.4385        6.6693\n",
            "     53        \u001b[36m0.5569\u001b[0m        \u001b[32m1.4790\u001b[0m        \u001b[35m0.5378\u001b[0m        1.4303     +  6.6799\n",
            "     54        0.5557        1.4796        0.4319        1.4590        6.7186\n",
            "     55        0.5562        1.4793        0.4671        1.4408        7.3567\n",
            "     56        \u001b[36m0.5576\u001b[0m        \u001b[32m1.4774\u001b[0m        0.4681        1.4488        6.6524\n",
            "     57        \u001b[36m0.5581\u001b[0m        \u001b[32m1.4762\u001b[0m        0.4982        1.4280        6.6888\n",
            "     58        0.5581        1.4767        0.5188        1.4418        6.7467\n",
            "     59        0.5576        1.4764        0.4613        1.4596        6.6516\n",
            "     60        \u001b[36m0.5611\u001b[0m        \u001b[32m1.4756\u001b[0m        0.4156        1.4580        6.7737\n",
            "     61        0.5590        1.4756        0.4920        1.4337        6.6039\n",
            "     62        \u001b[36m0.5624\u001b[0m        \u001b[32m1.4727\u001b[0m        0.4706        1.4312        6.7357\n",
            "     63        0.5623        1.4736        0.4586        1.4684        6.9280\n",
            "     64        0.5612        1.4729        0.4844        1.4294        6.7553\n",
            "     65        \u001b[36m0.5626\u001b[0m        1.4730        0.4893        1.4479        6.7567\n",
            "     66        \u001b[36m0.5633\u001b[0m        \u001b[32m1.4724\u001b[0m        0.4843        1.4335        6.6878\n",
            "     67        0.5622        1.4725        0.5204        1.4205        6.7766\n",
            "     68        \u001b[36m0.5662\u001b[0m        \u001b[32m1.4693\u001b[0m        0.4475        1.4646        6.7707\n",
            "     69        0.5658        1.4694        0.5208        1.4310        6.7326\n",
            "     70        0.5650        1.4714        0.4985        1.4314        6.6645\n",
            "     71        0.5647        1.4710        0.5001        \u001b[31m1.4050\u001b[0m        6.7323\n",
            "     72        0.5643        1.4706        0.4384        1.4548        6.6235\n",
            "     73        0.5634        1.4725        0.5086        1.4554        6.7009\n",
            "     74        0.5643        1.4707        0.4697        1.4372        6.6517\n",
            "     75        0.5656        1.4712        0.5023        1.4140        6.6706\n",
            "     76        0.5647        1.4698        0.5175        1.4165        6.7083\n",
            "     77        \u001b[36m0.5668\u001b[0m        \u001b[32m1.4689\u001b[0m        0.4296        1.4783        6.6419\n",
            "     78        0.5636        1.4710        0.4529        1.4445        6.7151\n",
            "     79        \u001b[36m0.5684\u001b[0m        \u001b[32m1.4667\u001b[0m        0.4848        1.4634        6.6846\n",
            "     80        0.5673        1.4687        0.4709        1.4325        6.7012\n",
            "     81        \u001b[36m0.5688\u001b[0m        1.4670        0.4766        1.4350        6.7183\n",
            "     82        0.5674        1.4681        0.5147        1.4281        6.6483\n",
            "     83        0.5679        1.4678        0.4908        1.4114        6.6333\n",
            "     84        0.5675        1.4677        0.5051        1.4126        6.7993\n",
            "     85        0.5659        1.4704        0.4841        1.4477        6.6548\n",
            "     86        0.5686        \u001b[32m1.4664\u001b[0m        0.4915        1.4112        6.7722\n",
            "     87        0.5671        1.4677        0.4966        1.4353        6.6668\n",
            "     88        0.5665        1.4693        0.4638        1.4511        6.8580\n",
            "     89        0.5687        1.4666        0.4670        1.4617        6.6957\n",
            "     90        0.5673        1.4681        0.5114        1.4060        6.6940\n",
            "     91        \u001b[36m0.5693\u001b[0m        \u001b[32m1.4662\u001b[0m        0.4828        1.4595        6.7114\n",
            "     92        0.5677        1.4675        0.4886        1.4149        6.7548\n",
            "     93        0.5654        1.4693        0.4768        1.4356        6.6851\n",
            "     94        0.5677        1.4678        0.4939        1.4160        6.7852\n",
            "     95        0.5663        1.4686        0.4961        1.4483        6.6773\n",
            "     96        0.5668        1.4688        0.4816        1.4210        6.6403\n",
            "     97        0.5686        1.4674        0.4807        1.4337        6.7072\n",
            "     98        0.5682        1.4673        0.5137        1.4182        6.6680\n",
            "     99        0.5672        1.4679        0.4571        1.4526        6.7696\n",
            "    100        0.5681        1.4681        0.5235        1.4254        6.7013\n",
            "    101        \u001b[36m0.5728\u001b[0m        \u001b[32m1.4627\u001b[0m        \u001b[35m0.5422\u001b[0m        1.4150     +  6.6593\n",
            "    102        0.5677        1.4680        0.4882        1.4278        6.6681\n",
            "    103        \u001b[36m0.5728\u001b[0m        1.4636        0.5267        1.4145        7.3103\n",
            "    104        0.5709        1.4636        0.4643        1.4671        6.8179\n",
            "    105        \u001b[36m0.5736\u001b[0m        1.4628        0.4622        1.4580        6.7782\n",
            "    106        0.5710        1.4644        0.4706        1.4282        6.6888\n",
            "    107        0.5692        1.4661        0.5043        1.4308        6.7973\n",
            "    108        0.5697        1.4653        0.5189        1.4093        6.7338\n",
            "    109        0.5718        1.4646        0.5375        1.4141        6.6437\n",
            "    110        0.5723        1.4628        0.4942        1.4201        6.6645\n",
            "    111        0.5725        1.4636        0.4838        1.4411        6.7069\n",
            "    112        0.5721        1.4639        0.5209        1.4215        6.8714\n",
            "    113        0.5722        1.4637        0.5022        1.4270        6.7279\n",
            "    114        \u001b[36m0.5738\u001b[0m        \u001b[32m1.4627\u001b[0m        0.4700        1.4410        6.9193\n",
            "    115        0.5690        1.4658        0.4680        1.4366        6.7012\n",
            "    116        0.5715        1.4638        0.4933        1.4468        6.6679\n",
            "    117        0.5731        \u001b[32m1.4620\u001b[0m        0.4826        1.4373        6.7345\n",
            "    118        0.5716        1.4636        0.5119        1.4117        6.7756\n",
            "    119        0.5722        1.4632        0.4588        1.4569        6.9220\n",
            "    120        \u001b[36m0.5741\u001b[0m        \u001b[32m1.4607\u001b[0m        0.4640        1.4186        6.7476\n",
            "    121        \u001b[36m0.5743\u001b[0m        1.4615        0.4529        1.4528        6.7208\n",
            "    122        0.5703        1.4659        0.5268        1.4187        6.7982\n",
            "    123        0.5720        1.4630        0.5250        1.4082        6.6801\n",
            "    124        0.5735        1.4630        0.5232        1.4392        6.7743\n",
            "    125        \u001b[36m0.5753\u001b[0m        1.4607        0.4280        1.4398        6.8112\n",
            "    126        0.5739        1.4617        \u001b[35m0.5432\u001b[0m        1.4112     +  6.8156\n",
            "    127        0.5741        1.4620        0.4801        1.4406        6.8585\n",
            "    128        0.5730        1.4631        0.4861        1.4255        7.3396\n",
            "    129        0.5732        1.4626        0.5143        1.4099        6.7579\n",
            "    130        0.5703        1.4644        0.4883        1.4332        6.7538\n",
            "    131        0.5729        1.4632        0.4987        1.4149        6.7321\n",
            "    132        0.5725        1.4632        0.5134        1.4075        6.7232\n",
            "    133        0.5734        1.4623        0.5341        1.4567        6.8238\n",
            "    134        0.5728        1.4629        0.5013        1.4345        6.6763\n",
            "    135        0.5741        1.4621        0.4753        1.4287        6.7802\n",
            "    136        0.5742        1.4619        0.5000        1.4359        6.8360\n",
            "    137        \u001b[36m0.5753\u001b[0m        \u001b[32m1.4593\u001b[0m        0.4749        1.4206        6.7215\n",
            "    138        \u001b[36m0.5757\u001b[0m        1.4611        0.5120        1.4055        6.7842\n",
            "    139        0.5753        1.4607        0.4966        1.4054        6.7551\n",
            "    140        0.5738        1.4611        0.4358        1.4503        6.7843\n",
            "    141        0.5749        1.4607        0.4979        1.4431        6.8081\n",
            "    142        \u001b[36m0.5770\u001b[0m        1.4598        0.4910        1.4193        6.7428\n",
            "    143        0.5747        1.4608        0.4336        1.4557        6.7860\n",
            "    144        0.5763        \u001b[32m1.4586\u001b[0m        0.5008        1.4176        6.7843\n",
            "    145        0.5744        1.4608        0.4969        1.4471        6.7550\n",
            "    146        0.5768        1.4599        0.5200        1.4092        6.8232\n",
            "    147        0.5766        1.4594        0.4854        1.4411        6.7493\n",
            "    148        \u001b[36m0.5778\u001b[0m        \u001b[32m1.4586\u001b[0m        0.4739        1.4501        6.7448\n",
            "    149        0.5757        1.4586        0.4835        1.4092        6.8836\n",
            "    150        \u001b[36m0.5786\u001b[0m        \u001b[32m1.4577\u001b[0m        0.5170        1.4140        6.7994\n",
            "    151        0.5753        1.4611        0.4828        1.4273        6.7576\n",
            "    152        0.5785        \u001b[32m1.4573\u001b[0m        0.5284        1.4134        6.7708\n",
            "    153        \u001b[36m0.5789\u001b[0m        1.4576        0.4618        1.4482        6.7855\n",
            "    154        \u001b[36m0.5790\u001b[0m        \u001b[32m1.4572\u001b[0m        0.5224        1.4199        6.7621\n",
            "    155        \u001b[36m0.5797\u001b[0m        \u001b[32m1.4558\u001b[0m        0.4596        1.4326        6.7933\n",
            "    156        \u001b[36m0.5820\u001b[0m        \u001b[32m1.4541\u001b[0m        0.5278        1.4213        6.8076\n",
            "    157        0.5819        1.4551        0.5183        1.4310        6.7658\n",
            "    158        0.5811        \u001b[32m1.4539\u001b[0m        0.4930        1.4236        6.8132\n",
            "    159        \u001b[36m0.5846\u001b[0m        \u001b[32m1.4520\u001b[0m        0.4616        1.4393        6.7869\n",
            "    160        \u001b[36m0.5855\u001b[0m        \u001b[32m1.4502\u001b[0m        0.5107        1.4196        6.8457\n",
            "    161        0.5814        1.4535        0.4620        1.4302        6.7593\n",
            "    162        0.5849        1.4515        0.4990        1.4333        6.8373\n",
            "    163        \u001b[36m0.5857\u001b[0m        \u001b[32m1.4500\u001b[0m        0.5278        1.4105        6.7284\n",
            "    164        0.5855        \u001b[32m1.4500\u001b[0m        0.4892        1.4112        6.9185\n",
            "    165        \u001b[36m0.5880\u001b[0m        \u001b[32m1.4480\u001b[0m        0.5216        1.4338        6.8067\n",
            "    166        \u001b[36m0.5903\u001b[0m        \u001b[32m1.4460\u001b[0m        \u001b[35m0.5533\u001b[0m        1.4184     +  6.7597\n",
            "    167        \u001b[36m0.5926\u001b[0m        \u001b[32m1.4440\u001b[0m        0.5403        1.4091        6.8000\n",
            "    168        \u001b[36m0.5935\u001b[0m        \u001b[32m1.4429\u001b[0m        0.5075        1.4278        7.4527\n",
            "    169        \u001b[36m0.5968\u001b[0m        \u001b[32m1.4397\u001b[0m        0.4787        1.4298        6.8061\n",
            "    170        0.5947        1.4416        0.5129        1.4136        6.7500\n",
            "    171        0.5961        1.4401        0.4806        1.4280        6.8755\n",
            "    172        \u001b[36m0.5992\u001b[0m        \u001b[32m1.4375\u001b[0m        0.4856        1.4191        6.7992\n",
            "    173        0.5960        1.4402        0.5195        1.4109        6.8179\n",
            "    174        0.5966        1.4405        0.5075        1.4310        6.8152\n",
            "    175        0.5972        1.4396        0.4959        1.4153        6.8030\n",
            "    176        0.5992        1.4386        0.5474        \u001b[31m1.3995\u001b[0m        6.7514\n",
            "    177        0.5971        1.4399        0.4745        1.4296        6.8330\n",
            "    178        0.5987        1.4377        0.5450        1.3996        6.8192\n",
            "    179        0.5924        1.4442        0.5106        1.4089        6.7459\n",
            "    180        0.5974        1.4383        0.4865        1.4040        6.7933\n",
            "    181        \u001b[36m0.5993\u001b[0m        \u001b[32m1.4373\u001b[0m        0.5075        1.4116        6.8438\n",
            "    182        0.5982        1.4382        0.4532        1.4355        6.8589\n",
            "    183        \u001b[36m0.5993\u001b[0m        1.4381        0.4905        1.4178        6.8195\n",
            "    184        0.5982        1.4393        0.5082        1.4060        6.7847\n",
            "    185        \u001b[36m0.6009\u001b[0m        \u001b[32m1.4350\u001b[0m        0.4945        1.4385        6.7328\n",
            "    186        0.5989        1.4385        0.5221        1.4072        6.7854\n",
            "    187        0.6005        1.4365        0.5127        1.4173        6.8379\n",
            "    188        0.5977        1.4381        0.4920        1.4118        6.8649\n",
            "    189        0.5972        1.4384        0.5427        1.4146        6.7893\n",
            "    190        \u001b[36m0.6017\u001b[0m        1.4356        0.4887        1.4073        6.8858\n",
            "    191        \u001b[36m0.6022\u001b[0m        \u001b[32m1.4332\u001b[0m        0.4845        1.4040        6.7611\n",
            "    192        0.5995        1.4378        0.4993        1.4071        6.8870\n",
            "    193        0.5997        1.4357        0.5063        1.4194        6.7768\n",
            "    194        0.6014        1.4344        0.5089        1.4161        6.7875\n",
            "    195        0.6001        1.4361        0.5377        1.4072        6.8045\n",
            "    196        0.5994        1.4380        0.5139        1.4041        6.7947\n",
            "    197        0.6011        1.4352        0.4972        1.4325        6.8042\n",
            "    198        0.5975        1.4387        \u001b[35m0.5568\u001b[0m        \u001b[31m1.3797\u001b[0m     +  6.8113\n",
            "    199        0.6009        1.4357        0.5325        1.4167        7.2696\n",
            "    200        0.6012        1.4350        0.5427        1.4049        6.9859\n",
            "    201        \u001b[36m0.6033\u001b[0m        1.4334        0.4984        1.3981        6.8284\n",
            "    202        0.6011        1.4361        0.5090        1.4164        6.8100\n",
            "    203        0.5998        1.4359        0.5033        1.4095        6.7836\n",
            "    204        0.6017        1.4351        0.5004        1.4104        6.8316\n",
            "    205        0.6020        1.4347        0.4930        1.4199        6.8014\n",
            "    206        0.6027        \u001b[32m1.4327\u001b[0m        0.5027        1.4368        6.8385\n",
            "    207        0.6000        1.4363        0.4766        1.4182        6.8131\n",
            "    208        0.6005        1.4374        \u001b[35m0.5600\u001b[0m        1.4072     +  6.7955\n",
            "    209        0.6012        1.4349        0.5073        1.4038        6.8878\n",
            "    210        \u001b[36m0.6037\u001b[0m        \u001b[32m1.4325\u001b[0m        0.5255        1.3999        7.4202\n",
            "    211        \u001b[36m0.6043\u001b[0m        1.4329        0.5029        1.4148        6.8840\n",
            "    212        0.6010        1.4362        0.5243        1.4137        6.8019\n",
            "    213        0.6037        \u001b[32m1.4319\u001b[0m        0.5170        1.4237        6.8781\n",
            "    214        0.6038        1.4327        0.5263        1.4110        6.7446\n",
            "    215        0.6022        1.4351        0.5147        1.4041        6.8100\n",
            "    216        0.6026        1.4346        0.5252        1.4190        6.8709\n",
            "    217        0.6007        1.4345        0.5019        1.4156        6.8706\n",
            "    218        0.6007        1.4364        0.5012        1.4032        6.8420\n",
            "    219        0.6043        1.4320        0.5205        1.3868        6.8766\n",
            "    220        0.6026        1.4342        0.5115        1.4141        6.8471\n",
            "    221        \u001b[36m0.6051\u001b[0m        1.4326        0.5488        1.4222        6.8293\n",
            "    222        0.6051        \u001b[32m1.4318\u001b[0m        0.5153        1.3921        6.9228\n",
            "    223        0.6028        1.4345        0.5336        1.3990        6.8103\n",
            "    224        0.6040        1.4319        0.5265        1.4094        6.8592\n",
            "    225        0.6041        1.4332        0.5236        1.4025        6.9072\n",
            "    226        0.6039        1.4327        0.4992        1.4147        6.9117\n",
            "    227        0.6027        1.4336        0.5021        1.4360        6.8578\n",
            "    228        \u001b[36m0.6056\u001b[0m        \u001b[32m1.4307\u001b[0m        0.5100        1.4083        6.8911\n",
            "    229        0.6040        1.4323        0.5467        1.4004        6.8888\n",
            "    230        0.6042        1.4313        0.4992        1.4062        6.9573\n",
            "    231        \u001b[36m0.6061\u001b[0m        1.4326        0.5293        1.4192        6.8331\n",
            "    232        0.6033        1.4329        0.5441        \u001b[31m1.3744\u001b[0m        6.8034\n",
            "    233        0.6045        1.4325        0.4959        1.4066        6.9022\n",
            "    234        0.6051        1.4318        0.5283        1.4119        6.8639\n",
            "    235        0.6019        1.4348        0.5073        1.4081        6.9243\n",
            "    236        \u001b[36m0.6078\u001b[0m        \u001b[32m1.4293\u001b[0m        \u001b[35m0.5676\u001b[0m        1.3824     +  6.8472\n",
            "    237        0.6056        1.4309        0.4990        1.4124        6.9381\n",
            "    238        0.6050        1.4322        0.5261        1.4080        7.4360\n",
            "    239        0.6060        1.4315        0.4999        1.4274        6.8018\n",
            "    240        0.6073        1.4301        0.5155        1.4007        6.8282\n",
            "    241        \u001b[36m0.6089\u001b[0m        1.4294        0.5372        1.4044        6.8136\n",
            "    242        0.6059        1.4308        0.5050        1.4149        6.8376\n",
            "    243        0.6071        1.4300        0.5356        1.3849        6.8040\n",
            "    244        0.6076        1.4308        0.5054        1.3960        6.8838\n",
            "    245        0.6072        1.4304        0.4786        1.4098        6.9172\n",
            "    246        0.6087        \u001b[32m1.4285\u001b[0m        0.5152        1.3866        6.8542\n",
            "    247        0.6079        1.4291        0.4917        1.4141        6.8711\n",
            "    248        0.6072        1.4296        0.5667        1.4132        6.8766\n",
            "    249        0.6072        1.4314        0.5242        1.4051        6.9180\n",
            "    250        0.6062        1.4295        0.5349        1.3880        6.9664\n",
            "    251        \u001b[36m0.6104\u001b[0m        \u001b[32m1.4269\u001b[0m        0.5526        1.3930        6.8441\n",
            "    252        0.6069        1.4307        0.5374        1.4053        6.8624\n",
            "    253        \u001b[36m0.6105\u001b[0m        1.4273        0.5051        1.4038        6.8482\n",
            "    254        0.6097        \u001b[32m1.4263\u001b[0m        0.5199        1.3906        6.9259\n",
            "    255        0.6077        1.4295        0.5244        1.3824        6.9210\n",
            "    256        0.6092        1.4292        0.5190        1.3867        6.8541\n",
            "    257        0.6096        1.4280        0.5478        1.3902        6.8644\n",
            "    258        0.6073        1.4288        0.5034        1.3969        6.9330\n",
            "    259        0.6082        1.4286        0.5513        1.3928        6.9558\n",
            "    260        \u001b[36m0.6116\u001b[0m        \u001b[32m1.4260\u001b[0m        0.5345        1.4191        6.7272\n",
            "    261        \u001b[36m0.6124\u001b[0m        \u001b[32m1.4249\u001b[0m        0.5298        1.4089        6.8685\n",
            "    262        0.6117        1.4261        0.5555        1.3927        6.9363\n",
            "    263        0.6112        1.4254        0.5376        1.4127        6.9615\n",
            "    264        \u001b[36m0.6137\u001b[0m        \u001b[32m1.4226\u001b[0m        0.4326        1.4520        6.9210\n",
            "    265        0.6095        1.4265        0.5230        1.3865        6.8823\n",
            "    266        0.6087        1.4274        0.5509        1.4127        6.8461\n",
            "    267        0.6101        1.4261        0.5275        1.4116        6.9359\n",
            "    268        0.6113        1.4265        0.5147        1.3941        6.8795\n",
            "    269        0.6109        1.4265        0.5369        1.3989        6.8566\n",
            "    270        0.6131        1.4240        0.5278        1.3933        6.8405\n",
            "    271        0.6112        1.4255        0.5024        1.4035        6.8272\n",
            "    272        0.6122        1.4251        0.5548        1.3747        6.8812\n",
            "    273        \u001b[36m0.6138\u001b[0m        1.4241        0.5077        1.4050        6.8986\n",
            "    274        0.6119        1.4252        0.4741        1.4077        6.8695\n",
            "    275        0.6096        1.4262        0.5375        1.3940        6.9516\n",
            "    276        \u001b[36m0.6153\u001b[0m        \u001b[32m1.4223\u001b[0m        0.5025        1.3899        6.9136\n",
            "    277        0.6108        1.4271        0.5451        1.3906        6.9174\n",
            "    278        0.6106        1.4259        0.5139        1.3954        6.8769\n",
            "    279        0.6147        1.4229        0.5501        1.3992        6.9441\n",
            "    280        \u001b[36m0.6156\u001b[0m        \u001b[32m1.4218\u001b[0m        0.5415        1.4036        6.7348\n",
            "    281        0.6136        1.4229        0.5281        1.3841        6.9086\n",
            "    282        \u001b[36m0.6163\u001b[0m        \u001b[32m1.4209\u001b[0m        0.5199        1.4175        6.8691\n",
            "    283        0.6149        1.4226        0.5346        1.3966        6.8635\n",
            "    284        0.6154        1.4221        0.5555        1.3883        6.9539\n",
            "    285        0.6145        1.4235        0.5028        1.4439        6.7212\n",
            "    286        0.6152        1.4220        0.5579        1.4037        6.7364\n",
            "    287        0.6147        1.4231        0.5362        1.3875        6.9631\n",
            "    288        \u001b[36m0.6166\u001b[0m        \u001b[32m1.4195\u001b[0m        0.5558        1.4024        6.9766\n",
            "    289        0.6164        1.4206        \u001b[35m0.5715\u001b[0m        1.3820     +  6.7160\n",
            "    290        \u001b[36m0.6169\u001b[0m        1.4204        0.5034        1.4128        6.8013\n",
            "    291        0.6154        1.4228        0.5116        1.3931        7.3831\n",
            "    292        \u001b[36m0.6180\u001b[0m        1.4201        0.5357        1.4250        6.9847\n",
            "    293        0.6173        1.4196        0.5008        1.4136        6.9223\n",
            "    294        0.6157        1.4213        0.5470        1.3788        6.8999\n",
            "    295        \u001b[36m0.6181\u001b[0m        1.4202        0.5202        1.3926        6.8881\n",
            "    296        0.6180        \u001b[32m1.4189\u001b[0m        0.5390        1.3875        6.9185\n",
            "    297        \u001b[36m0.6194\u001b[0m        \u001b[32m1.4177\u001b[0m        0.5198        1.3978        6.8671\n",
            "    298        0.6187        1.4185        0.5060        1.4314        6.9593\n",
            "    299        0.6154        1.4217        0.5385        1.4209        7.0162\n",
            "    300        0.6163        1.4208        0.5297        1.4004        6.8964\n",
            "time taken : 2278.324295282364\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 249
        },
        "id": "nUj7E49hluDx",
        "outputId": "f5c8c834-aaf2-4c2a-def1-1c326e906d2c"
      },
      "source": [
        "# Viz of Training\n",
        "# Wandb may make this unnecessary\n",
        "training_viz(clf)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1440x504 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5PCvI22Pl3R7",
        "outputId": "0b5677ee-5323-418a-d83e-1e70694c1301"
      },
      "source": [
        "# This was done with the last model - not the one with the best valid bacc\n",
        "# Prediction on Test Set\n",
        "y_true = val.y\n",
        "y_pred = clf.predict(val.X)\n",
        "\n",
        "print(confusion_matrix(y_true.cpu(), y_pred))\n",
        "print(classification_report(y_true.cpu(), y_pred))\n",
        "cohen_kappa = cohen_kappa_score(y_true.cpu(), y_pred)\n",
        "print(cohen_kappa)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[4842  863   26   24    1  254]\n",
            " [ 186  850  189   26    5  416]\n",
            " [  56  673 2859  496   66  885]\n",
            " [   3    5  255  355   63   23]\n",
            " [   0    0   33  247  133    1]\n",
            " [  72  708   65    5    0  757]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.94      0.81      0.87      6010\n",
            "           1       0.27      0.51      0.36      1672\n",
            "           2       0.83      0.57      0.68      5035\n",
            "           3       0.31      0.50      0.38       704\n",
            "           4       0.50      0.32      0.39       414\n",
            "           5       0.32      0.47      0.38      1607\n",
            "\n",
            "    accuracy                           0.63     15442\n",
            "   macro avg       0.53      0.53      0.51     15442\n",
            "weighted avg       0.73      0.63      0.66     15442\n",
            "\n",
            "0.5165402314010596\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bvWBY-3gmMeH",
        "outputId": "fd21cb77-9644-4a05-a544-5945658b144d"
      },
      "source": [
        "# Load last checkpointed model\n",
        "with open('/content/drive/MyDrive/mne_data/fulltrain300_checkpoints/model_289.pkl', 'rb') as f:\n",
        "    clf2 = pickle.load(f)\n",
        "\n",
        "# predict on unlabelled\n",
        "save_fname = \"CNN_full_1\"\n",
        "predict_leaderboard_unlabelled(clf2,save_fname)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Sleep leaderboard - Test Data : There are 25748 trials with 2 electrodes and 3000 time samples\n",
            "(25748, 2, 3000)\n",
            "Checking if all classes have been predicted\n",
            "[0 1 2 3 4 5]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R1svcZpKEt4L"
      },
      "source": [
        "wandb.finish()\n",
        "# programatically close session\n",
        "# !kill -9 -1\n",
        "# !kill $(ps aux | awk '{print $2}')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XJW3P64MawmT"
      },
      "source": [
        "Load the last model and do TL with data from second group"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iwI6cKGUa1wH"
      },
      "source": [
        "# 1. Do Full continued training on the second group without freezing any layers"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TrWxyDt_OAkJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cc1c9201-5c62-4b53-90f9-b73756350e8a"
      },
      "source": [
        "def get_labels(x):\n",
        "  return x.y.cpu()\n",
        "# Load last checkpointed model\n",
        "# del clf2\n",
        "with open('/content/drive/MyDrive/mne_data/fulltrain300_checkpoints/model_289.pkl', 'rb') as f:\n",
        "    clf2 = pickle.load(f)\n",
        "\n",
        "# In skorch CV split makes only a single split, doesn't try all the different \n",
        "# combos since training takes too much time otherwise\n",
        "# You can use Sklearn to implement it yourself otherwise\n",
        "# Ref - https://skorch.readthedocs.io/en/stable/user/dataset.html \n",
        "# Ref - https://skorch.readthedocs.io/en/stable/dataset.html \n",
        "clf2.get_params()\n",
        "# Params\n",
        "# Iterator train sampler was custom made giving IDS with train as input\n",
        "# predet_train_split has reference to val\n",
        "# clf2.set_params - deactivate predetermined train_split activate 5CV\n",
        "# clf2.partial_fit() and set warm_start = True\n",
        "\n",
        "# predict on unlabelled\n",
        "# save_fname = \"CNN_full_1\"\n",
        "# predict_leaderboard_unlabelled(clf2,save_fname)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'batch_size': 128,\n",
              " 'callbacks': [('train_bal_acc',\n",
              "   <skorch.callbacks.scoring.EpochScoring at 0x7feeb886df90>),\n",
              "  ('valid_bal_acc', <skorch.callbacks.scoring.EpochScoring at 0x7fedb2302f10>),\n",
              "  ('checkpoint', <skorch.callbacks.training.Checkpoint at 0x7fedb2365c10>),\n",
              "  ('wandb', <skorch.callbacks.logging.WandbLogger at 0x7fedb22b7e10>)],\n",
              " 'callbacks__checkpoint': <skorch.callbacks.training.Checkpoint at 0x7fedb2365c10>,\n",
              " 'callbacks__checkpoint__dirname': '/content/drive/MyDrive/mne_data/fulltrain300_checkpoints',\n",
              " 'callbacks__checkpoint__event_name': 'event_cp',\n",
              " 'callbacks__checkpoint__f_criterion': None,\n",
              " 'callbacks__checkpoint__f_history': 'history.json',\n",
              " 'callbacks__checkpoint__f_optimizer': None,\n",
              " 'callbacks__checkpoint__f_params': None,\n",
              " 'callbacks__checkpoint__f_pickle': 'model_{last_epoch[epoch]}.pkl',\n",
              " 'callbacks__checkpoint__fn_prefix': '',\n",
              " 'callbacks__checkpoint__monitor': 'valid_bacc_best',\n",
              " 'callbacks__checkpoint__sink': <function skorch.utils.noop>,\n",
              " 'callbacks__epoch_timer': <skorch.callbacks.logging.EpochTimer at 0x7feebce3cad0>,\n",
              " 'callbacks__print_log': <skorch.callbacks.logging.PrintLog at 0x7feeb8e2bc50>,\n",
              " 'callbacks__print_log__floatfmt': '.4f',\n",
              " 'callbacks__print_log__keys_ignored': None,\n",
              " 'callbacks__print_log__sink': <function print>,\n",
              " 'callbacks__print_log__stralign': 'right',\n",
              " 'callbacks__print_log__tablefmt': 'simple',\n",
              " 'callbacks__train_bal_acc': <skorch.callbacks.scoring.EpochScoring at 0x7feeb886df90>,\n",
              " 'callbacks__train_bal_acc__lower_is_better': False,\n",
              " 'callbacks__train_bal_acc__name': 'train_bacc',\n",
              " 'callbacks__train_bal_acc__on_train': True,\n",
              " 'callbacks__train_bal_acc__scoring': 'balanced_accuracy',\n",
              " 'callbacks__train_bal_acc__target_extractor': <function skorch.utils.to_numpy>,\n",
              " 'callbacks__train_bal_acc__use_caching': True,\n",
              " 'callbacks__train_loss': <skorch.callbacks.scoring.BatchScoring at 0x7feeb8e2b990>,\n",
              " 'callbacks__train_loss__lower_is_better': True,\n",
              " 'callbacks__train_loss__name': 'train_loss',\n",
              " 'callbacks__train_loss__on_train': True,\n",
              " 'callbacks__train_loss__scoring': <function skorch.utils.train_loss_score>,\n",
              " 'callbacks__train_loss__target_extractor': <function skorch.utils.noop>,\n",
              " 'callbacks__train_loss__use_caching': True,\n",
              " 'callbacks__valid_bal_acc': <skorch.callbacks.scoring.EpochScoring at 0x7fedb2302f10>,\n",
              " 'callbacks__valid_bal_acc__lower_is_better': False,\n",
              " 'callbacks__valid_bal_acc__name': 'valid_bacc',\n",
              " 'callbacks__valid_bal_acc__on_train': False,\n",
              " 'callbacks__valid_bal_acc__scoring': 'balanced_accuracy',\n",
              " 'callbacks__valid_bal_acc__target_extractor': <function skorch.utils.to_numpy>,\n",
              " 'callbacks__valid_bal_acc__use_caching': True,\n",
              " 'callbacks__valid_loss': <skorch.callbacks.scoring.BatchScoring at 0x7feeb8e2b210>,\n",
              " 'callbacks__valid_loss__lower_is_better': True,\n",
              " 'callbacks__valid_loss__name': 'valid_loss',\n",
              " 'callbacks__valid_loss__on_train': False,\n",
              " 'callbacks__valid_loss__scoring': <function skorch.utils.valid_loss_score>,\n",
              " 'callbacks__valid_loss__target_extractor': <function skorch.utils.noop>,\n",
              " 'callbacks__valid_loss__use_caching': True,\n",
              " 'callbacks__wandb': <skorch.callbacks.logging.WandbLogger at 0x7fedb22b7e10>,\n",
              " 'callbacks__wandb__keys_ignored': None,\n",
              " 'callbacks__wandb__save_model': True,\n",
              " 'callbacks__wandb__wandb_run': <wandb.sdk.wandb_run.Run at 0x7fedb22b7ed0>,\n",
              " 'classes': None,\n",
              " 'criterion': torch.nn.modules.loss.CrossEntropyLoss,\n",
              " 'cropped': False,\n",
              " 'dataset': skorch.dataset.Dataset,\n",
              " 'device': 'cuda',\n",
              " 'iterator_train': torch.utils.data.dataloader.DataLoader,\n",
              " 'iterator_train__batch_size': 128,\n",
              " 'iterator_train__sampler': <torchsampler.imbalanced.ImbalancedDatasetSampler at 0x7fedb22c21d0>,\n",
              " 'iterator_train__shuffle': False,\n",
              " 'iterator_valid': torch.utils.data.dataloader.DataLoader,\n",
              " 'lr': 0.01,\n",
              " 'max_epochs': 10,\n",
              " 'module': SleepEEG(\n",
              "   (feature_extractor): Sequential(\n",
              "     (0): Conv2d(2, 20, kernel_size=(1, 7), stride=(1, 1), groups=2)\n",
              "     (1): Identity()\n",
              "     (2): ReLU()\n",
              "     (3): MaxPool2d(kernel_size=(1, 2), stride=(1, 2), padding=0, dilation=1, ceil_mode=False)\n",
              "     (4): Conv2d(20, 20, kernel_size=(1, 7), stride=(1, 1), groups=20)\n",
              "     (5): Identity()\n",
              "     (6): ReLU()\n",
              "     (7): MaxPool2d(kernel_size=(1, 2), stride=(1, 2), padding=0, dilation=1, ceil_mode=False)\n",
              "     (8): Conv2d(20, 20, kernel_size=(1, 5), stride=(1, 1), groups=20)\n",
              "     (9): Identity()\n",
              "     (10): ReLU()\n",
              "     (11): MaxPool2d(kernel_size=(1, 2), stride=(1, 2), padding=0, dilation=1, ceil_mode=False)\n",
              "     (12): Conv2d(20, 20, kernel_size=(1, 5), stride=(1, 1), groups=20)\n",
              "     (13): Identity()\n",
              "     (14): ReLU()\n",
              "     (15): MaxPool2d(kernel_size=(1, 2), stride=(1, 2), padding=0, dilation=1, ceil_mode=False)\n",
              "     (16): Conv2d(20, 20, kernel_size=(1, 5), stride=(1, 1), groups=20)\n",
              "     (17): Identity()\n",
              "     (18): ReLU()\n",
              "     (19): MaxPool2d(kernel_size=(1, 2), stride=(1, 2), padding=0, dilation=1, ceil_mode=False)\n",
              "     (20): Conv2d(20, 20, kernel_size=(1, 3), stride=(1, 1), groups=20)\n",
              "     (21): Identity()\n",
              "     (22): ReLU()\n",
              "     (23): MaxPool2d(kernel_size=(1, 2), stride=(1, 2), padding=0, dilation=1, ceil_mode=False)\n",
              "     (24): Conv2d(20, 20, kernel_size=(1, 3), stride=(1, 1), groups=20)\n",
              "     (25): Identity()\n",
              "     (26): ReLU()\n",
              "     (27): MaxPool2d(kernel_size=(1, 2), stride=(1, 2), padding=0, dilation=1, ceil_mode=False)\n",
              "   )\n",
              "   (fc): Sequential(\n",
              "     (0): Dropout(p=0.5, inplace=False)\n",
              "     (1): Linear(in_features=400, out_features=6, bias=True)\n",
              "     (2): Softmax(dim=1)\n",
              "   )\n",
              " ),\n",
              " 'optimizer': torch.optim.sgd.SGD,\n",
              " 'optimizer__lr': 0.005,\n",
              " 'predict_nonlinearity': 'auto',\n",
              " 'train_split': functools.partial(<function _make_split at 0x7fef1d23ab00>, valid_ds=<skorch.dataset.Dataset object at 0x7feeb8a193d0>),\n",
              " 'verbose': 1,\n",
              " 'warm_start': False}"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "udMTxrapgS9C",
        "outputId": "b47abb9b-fa84-4580-8e7a-f9e7c795e118"
      },
      "source": [
        "# Create an IDS for the val data\n",
        "# wandb_run = wandb.init(name = \"300 epoch continue train\", project='cnn-fulltrain', entity='sleep_hacking')\n",
        "\n",
        "save_path = \"/content/drive/MyDrive/mne_data/fulltrain300_checkpoints\"\n",
        "# cp = Checkpoint(monitor = 'train_bal_acc_best',\n",
        "#                 f_params = None, f_optimizer = None, f_criterion = None,\n",
        "#                 f_pickle = \"model_{last_epoch[epoch]}.pkl\",\n",
        "#                 dirname = save_path)\n",
        "cbs = [('train_bal_acc', train_bacc),\n",
        "            #  (\"wandb\",WandbLogger(wandb_run))\n",
        "             ]\n",
        "\n",
        "clf2.set_params(iterator_train__sampler=IDS(valB,callback_get_label=get_labels),\n",
        "                train_split = None,\n",
        "                batch_size = 512, warm_start = True, \n",
        "                callbacks = cbs,\n",
        "                # callbacks__checkpoint = None\n",
        "                # callbacks__wandb = None\n",
        "                )\n",
        "\n",
        "clf2.get_params()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'batch_size': 512,\n",
              " 'callbacks': [('train_bal_acc',\n",
              "   <skorch.callbacks.scoring.EpochScoring at 0x7fef102d8e10>)],\n",
              " 'callbacks__checkpoint': None,\n",
              " 'callbacks__epoch_timer': <skorch.callbacks.logging.EpochTimer at 0x7fedb2315a90>,\n",
              " 'callbacks__print_log': <skorch.callbacks.logging.PrintLog at 0x7fedb22b79d0>,\n",
              " 'callbacks__print_log__floatfmt': '.4f',\n",
              " 'callbacks__print_log__keys_ignored': None,\n",
              " 'callbacks__print_log__sink': <function print>,\n",
              " 'callbacks__print_log__stralign': 'right',\n",
              " 'callbacks__print_log__tablefmt': 'simple',\n",
              " 'callbacks__train_bal_acc': <skorch.callbacks.scoring.EpochScoring at 0x7fef102d8e10>,\n",
              " 'callbacks__train_bal_acc__lower_is_better': False,\n",
              " 'callbacks__train_bal_acc__name': 'train_bacc',\n",
              " 'callbacks__train_bal_acc__on_train': True,\n",
              " 'callbacks__train_bal_acc__scoring': 'balanced_accuracy',\n",
              " 'callbacks__train_bal_acc__target_extractor': <function skorch.utils.to_numpy>,\n",
              " 'callbacks__train_bal_acc__use_caching': True,\n",
              " 'callbacks__train_loss': <skorch.callbacks.scoring.BatchScoring at 0x7fedb2315510>,\n",
              " 'callbacks__train_loss__lower_is_better': True,\n",
              " 'callbacks__train_loss__name': 'train_loss',\n",
              " 'callbacks__train_loss__on_train': True,\n",
              " 'callbacks__train_loss__scoring': <function skorch.utils.train_loss_score>,\n",
              " 'callbacks__train_loss__target_extractor': <function skorch.utils.noop>,\n",
              " 'callbacks__train_loss__use_caching': True,\n",
              " 'callbacks__valid_loss': <skorch.callbacks.scoring.BatchScoring at 0x7fedb2315b90>,\n",
              " 'callbacks__valid_loss__lower_is_better': True,\n",
              " 'callbacks__valid_loss__name': 'valid_loss',\n",
              " 'callbacks__valid_loss__on_train': False,\n",
              " 'callbacks__valid_loss__scoring': <function skorch.utils.valid_loss_score>,\n",
              " 'callbacks__valid_loss__target_extractor': <function skorch.utils.noop>,\n",
              " 'callbacks__valid_loss__use_caching': True,\n",
              " 'callbacks__wandb': <skorch.callbacks.logging.WandbLogger at 0x7fef10e8e410>,\n",
              " 'callbacks__wandb__keys_ignored': None,\n",
              " 'callbacks__wandb__save_model': True,\n",
              " 'callbacks__wandb__wandb_run': <wandb.sdk.wandb_run.Run at 0x7fedb3ffabd0>,\n",
              " 'classes': None,\n",
              " 'criterion': torch.nn.modules.loss.CrossEntropyLoss,\n",
              " 'cropped': False,\n",
              " 'dataset': skorch.dataset.Dataset,\n",
              " 'device': 'cuda',\n",
              " 'iterator_train': torch.utils.data.dataloader.DataLoader,\n",
              " 'iterator_train__batch_size': 128,\n",
              " 'iterator_train__sampler': <torchsampler.imbalanced.ImbalancedDatasetSampler at 0x7fedb23b45d0>,\n",
              " 'iterator_train__shuffle': False,\n",
              " 'iterator_valid': torch.utils.data.dataloader.DataLoader,\n",
              " 'lr': 0.01,\n",
              " 'max_epochs': 10,\n",
              " 'module': SleepEEG(\n",
              "   (feature_extractor): Sequential(\n",
              "     (0): Conv2d(2, 20, kernel_size=(1, 7), stride=(1, 1), groups=2)\n",
              "     (1): Identity()\n",
              "     (2): ReLU()\n",
              "     (3): MaxPool2d(kernel_size=(1, 2), stride=(1, 2), padding=0, dilation=1, ceil_mode=False)\n",
              "     (4): Conv2d(20, 20, kernel_size=(1, 7), stride=(1, 1), groups=20)\n",
              "     (5): Identity()\n",
              "     (6): ReLU()\n",
              "     (7): MaxPool2d(kernel_size=(1, 2), stride=(1, 2), padding=0, dilation=1, ceil_mode=False)\n",
              "     (8): Conv2d(20, 20, kernel_size=(1, 5), stride=(1, 1), groups=20)\n",
              "     (9): Identity()\n",
              "     (10): ReLU()\n",
              "     (11): MaxPool2d(kernel_size=(1, 2), stride=(1, 2), padding=0, dilation=1, ceil_mode=False)\n",
              "     (12): Conv2d(20, 20, kernel_size=(1, 5), stride=(1, 1), groups=20)\n",
              "     (13): Identity()\n",
              "     (14): ReLU()\n",
              "     (15): MaxPool2d(kernel_size=(1, 2), stride=(1, 2), padding=0, dilation=1, ceil_mode=False)\n",
              "     (16): Conv2d(20, 20, kernel_size=(1, 5), stride=(1, 1), groups=20)\n",
              "     (17): Identity()\n",
              "     (18): ReLU()\n",
              "     (19): MaxPool2d(kernel_size=(1, 2), stride=(1, 2), padding=0, dilation=1, ceil_mode=False)\n",
              "     (20): Conv2d(20, 20, kernel_size=(1, 3), stride=(1, 1), groups=20)\n",
              "     (21): Identity()\n",
              "     (22): ReLU()\n",
              "     (23): MaxPool2d(kernel_size=(1, 2), stride=(1, 2), padding=0, dilation=1, ceil_mode=False)\n",
              "     (24): Conv2d(20, 20, kernel_size=(1, 3), stride=(1, 1), groups=20)\n",
              "     (25): Identity()\n",
              "     (26): ReLU()\n",
              "     (27): MaxPool2d(kernel_size=(1, 2), stride=(1, 2), padding=0, dilation=1, ceil_mode=False)\n",
              "   )\n",
              "   (fc): Sequential(\n",
              "     (0): Dropout(p=0.5, inplace=False)\n",
              "     (1): Linear(in_features=400, out_features=6, bias=True)\n",
              "     (2): Softmax(dim=1)\n",
              "   )\n",
              " ),\n",
              " 'optimizer': torch.optim.sgd.SGD,\n",
              " 'optimizer__lr': 0.005,\n",
              " 'predict_nonlinearity': 'auto',\n",
              " 'train_split': None,\n",
              " 'verbose': 1,\n",
              " 'warm_start': True}"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1-F__bdZp4kX",
        "outputId": "ee0fd5c1-8b4e-4ad7-8011-6a2176e58491"
      },
      "source": [
        "n_epochs = 300\n",
        "clf2.partial_fit(valB,y = None, epochs = n_epochs)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  epoch    train_bacc    train_loss    valid_loss     dur\n",
            "-------  ------------  ------------  ------------  ------\n",
            "    291        0.5766        1.4590        1.3820  1.0210\n",
            "    292        0.5784        1.4572        1.3820  1.0198\n",
            "    293        0.5793        1.4538        1.3820  0.9915\n",
            "    294        0.5788        1.4591        1.3820  1.0316\n",
            "    295        0.5796        1.4531        1.3820  0.9896\n",
            "    296        0.5866        1.4502        1.3820  0.9866\n",
            "    297        0.5831        1.4582        1.3820  0.9875\n",
            "    298        0.5918        1.4415        1.3820  1.1068\n",
            "    299        0.5892        1.4451        1.3820  1.0369\n",
            "    300        0.5899        1.4465        1.3820  0.9797\n",
            "    301        0.5853        1.4496        1.3820  0.9924\n",
            "    302        0.5924        1.4449        1.3820  1.0161\n",
            "    303        0.5965        1.4411        1.3820  1.0185\n",
            "    304        0.5898        1.4452        1.3820  1.0154\n",
            "    305        0.5990        1.4389        1.3820  1.0145\n",
            "    306        0.5903        1.4457        1.3820  0.9953\n",
            "    307        0.5954        1.4435        1.3820  1.0128\n",
            "    308        0.5957        1.4413        1.3820  1.0177\n",
            "    309        0.5946        1.4436        1.3820  1.0017\n",
            "    310        0.5948        1.4437        1.3820  0.9998\n",
            "    311        0.5897        1.4487        1.3820  1.1504\n",
            "    312        0.6012        1.4382        1.3820  1.0039\n",
            "    313        0.6004        1.4356        1.3820  1.0162\n",
            "    314        0.6000        1.4412        1.3820  1.0065\n",
            "    315        0.5968        1.4406        1.3820  1.0206\n",
            "    316        0.6040        1.4342        1.3820  0.9871\n",
            "    317        0.6006        1.4400        1.3820  1.0036\n",
            "    318        0.5944        1.4418        1.3820  1.0435\n",
            "    319        0.5940        1.4474        1.3820  1.0108\n",
            "    320        0.6011        1.4394        1.3820  1.0006\n",
            "    321        0.5997        1.4362        1.3820  1.0098\n",
            "    322        0.5991        1.4406        1.3820  1.1169\n",
            "    323        0.6036        1.4347        1.3820  1.0048\n",
            "    324        0.5988        1.4412        1.3820  1.0072\n",
            "    325        0.5973        1.4423        1.3820  1.0102\n",
            "    326        0.5988        1.4413        1.3820  0.9971\n",
            "    327        0.6041        1.4333        1.3820  0.9996\n",
            "    328        0.6003        1.4353        1.3820  1.1540\n",
            "    329        0.5942        1.4400        1.3820  0.9951\n",
            "    330        0.5987        1.4391        1.3820  1.0325\n",
            "    331        0.6066        1.4334        1.3820  1.0166\n",
            "    332        0.6027        1.4352        1.3820  1.0427\n",
            "    333        0.5923        1.4445        1.3820  1.0028\n",
            "    334        0.5943        1.4430        1.3820  1.0118\n",
            "    335        0.6087        1.4292        1.3820  1.1415\n",
            "    336        0.5997        1.4385        1.3820  1.0093\n",
            "    337        0.6015        1.4382        1.3820  1.0374\n",
            "    338        0.6004        1.4358        1.3820  1.0095\n",
            "    339        0.6095        1.4294        1.3820  1.0092\n",
            "    340        0.6008        1.4366        1.3820  1.0116\n",
            "    341        0.6044        1.4341        1.3820  1.1317\n",
            "    342        0.6077        1.4330        1.3820  1.0073\n",
            "    343        0.5978        1.4396        1.3820  1.0388\n",
            "    344        0.6035        1.4360        1.3820  1.0320\n",
            "    345        0.6105        1.4307        1.3820  1.0406\n",
            "    346        0.6065        1.4315        1.3820  1.0094\n",
            "    347        0.6080        1.4317        1.3820  1.0372\n",
            "    348        0.6041        1.4346        1.3820  1.0322\n",
            "    349        0.6034        1.4355        1.3820  1.1747\n",
            "    350        0.6078        1.4308        1.3820  1.0519\n",
            "    351        0.6050        1.4291        1.3820  1.0428\n",
            "    352        0.6030        1.4343        1.3820  1.0289\n",
            "    353        0.6044        1.4311        1.3820  1.0597\n",
            "    354        0.6087        1.4267        1.3820  1.1388\n",
            "    355        0.6050        1.4305        1.3820  1.0248\n",
            "    356        0.6003        1.4386        1.3820  1.0294\n",
            "    357        0.6080        1.4267        1.3820  1.1311\n",
            "    358        \u001b[36m0.6169\u001b[0m        \u001b[32m1.4171\u001b[0m        1.3820  1.0011\n",
            "    359        0.6055        1.4292        1.3820  1.0095\n",
            "    360        0.6040        1.4333        1.3820  1.0248\n",
            "    361        0.6082        1.4330        1.3820  1.0195\n",
            "    362        0.6082        1.4302        1.3820  1.0232\n",
            "    363        0.6088        1.4320        1.3820  1.1703\n",
            "    364        0.6077        1.4290        1.3820  1.0251\n",
            "    365        0.6039        1.4368        1.3820  1.0252\n",
            "    366        0.6100        1.4275        1.3820  1.0352\n",
            "    367        0.6135        1.4245        1.3820  1.0135\n",
            "    368        0.6099        1.4264        1.3820  1.0419\n",
            "    369        \u001b[36m0.6176\u001b[0m        1.4238        1.3820  1.0276\n",
            "    370        0.6079        1.4300        1.3820  1.0143\n",
            "    371        0.6115        1.4261        1.3820  1.0239\n",
            "    372        0.6149        1.4279        1.3820  1.0407\n",
            "    373        0.6086        1.4304        1.3820  1.0127\n",
            "    374        0.6119        1.4272        1.3820  1.1453\n",
            "    375        0.6097        1.4279        1.3820  1.0338\n",
            "    376        0.6060        1.4300        1.3820  1.0230\n",
            "    377        0.6071        1.4308        1.3820  1.0617\n",
            "    378        0.6105        1.4277        1.3820  1.0130\n",
            "    379        0.6107        1.4261        1.3820  1.0191\n",
            "    380        0.6082        1.4292        1.3820  1.0240\n",
            "    381        0.6114        1.4258        1.3820  1.0130\n",
            "    382        0.6058        1.4294        1.3820  1.0328\n",
            "    383        0.6074        1.4317        1.3820  1.0370\n",
            "    384        0.6026        1.4357        1.3820  1.0276\n",
            "    385        0.6103        1.4262        1.3820  1.0612\n",
            "    386        0.6009        1.4341        1.3820  1.0497\n",
            "    387        0.6102        1.4266        1.3820  1.0397\n",
            "    388        0.6155        1.4236        1.3820  1.0261\n",
            "    389        0.6128        1.4281        1.3820  1.0307\n",
            "    390        0.6103        1.4284        1.3820  1.0501\n",
            "    391        0.6141        1.4244        1.3820  1.0458\n",
            "    392        \u001b[36m0.6232\u001b[0m        \u001b[32m1.4160\u001b[0m        1.3820  1.0381\n",
            "    393        0.6073        1.4295        1.3820  1.0268\n",
            "    394        0.6117        1.4289        1.3820  1.0639\n",
            "    395        0.6093        1.4294        1.3820  1.0213\n",
            "    396        0.6146        1.4227        1.3820  1.1459\n",
            "    397        0.6059        1.4304        1.3820  1.0198\n",
            "    398        0.6076        1.4296        1.3820  1.1394\n",
            "    399        0.6099        1.4272        1.3820  1.0216\n",
            "    400        0.6113        1.4247        1.3820  1.0411\n",
            "    401        0.6089        1.4289        1.3820  1.0574\n",
            "    402        0.6159        1.4225        1.3820  1.1877\n",
            "    403        0.6124        1.4280        1.3820  1.0614\n",
            "    404        0.6145        1.4260        1.3820  1.0328\n",
            "    405        0.6144        1.4276        1.3820  1.0295\n",
            "    406        0.6147        1.4249        1.3820  1.0397\n",
            "    407        0.6043        1.4310        1.3820  1.1765\n",
            "    408        0.6095        1.4294        1.3820  1.0357\n",
            "    409        0.6146        1.4199        1.3820  1.0578\n",
            "    410        0.6222        1.4195        1.3820  1.0268\n",
            "    411        0.6137        1.4268        1.3820  1.0215\n",
            "    412        0.6072        1.4255        1.3820  1.0451\n",
            "    413        0.6165        1.4220        1.3820  1.0405\n",
            "    414        0.6179        1.4213        1.3820  1.0432\n",
            "    415        0.6159        1.4235        1.3820  1.0199\n",
            "    416        0.6146        1.4231        1.3820  1.0339\n",
            "    417        0.6168        1.4205        1.3820  1.0342\n",
            "    418        0.6128        1.4283        1.3820  1.0555\n",
            "    419        0.6164        1.4224        1.3820  1.0525\n",
            "    420        0.6192        1.4180        1.3820  1.0312\n",
            "    421        0.6091        1.4301        1.3820  1.0352\n",
            "    422        0.6138        1.4197        1.3820  1.1748\n",
            "    423        0.6125        1.4249        1.3820  1.0524\n",
            "    424        0.6159        1.4211        1.3820  1.0437\n",
            "    425        0.6132        1.4256        1.3820  1.0263\n",
            "    426        0.6076        1.4268        1.3820  1.0471\n",
            "    427        0.6208        1.4176        1.3820  1.0495\n",
            "    428        0.6181        1.4205        1.3820  1.0555\n",
            "    429        0.6101        1.4278        1.3820  1.0227\n",
            "    430        \u001b[36m0.6235\u001b[0m        \u001b[32m1.4139\u001b[0m        1.3820  1.0594\n",
            "    431        0.6100        1.4281        1.3820  1.0441\n",
            "    432        0.6180        1.4207        1.3820  1.0263\n",
            "    433        0.6147        1.4237        1.3820  1.0283\n",
            "    434        0.6092        1.4285        1.3820  1.0587\n",
            "    435        0.6086        1.4310        1.3820  1.1488\n",
            "    436        0.6189        1.4216        1.3820  1.0345\n",
            "    437        0.6128        1.4243        1.3820  1.0228\n",
            "    438        0.6094        1.4278        1.3820  1.0397\n",
            "    439        0.6142        1.4239        1.3820  1.0409\n",
            "    440        0.6154        1.4218        1.3820  1.1949\n",
            "    441        0.6098        1.4272        1.3820  1.0578\n",
            "    442        0.6188        1.4197        1.3820  1.1826\n",
            "    443        0.6134        1.4265        1.3820  1.0526\n",
            "    444        0.6183        1.4218        1.3820  1.0415\n",
            "    445        0.6135        1.4246        1.3820  1.0349\n",
            "    446        0.6198        1.4187        1.3820  1.0319\n",
            "    447        0.6149        1.4238        1.3820  1.0445\n",
            "    448        0.6143        1.4236        1.3820  1.0371\n",
            "    449        0.6152        1.4224        1.3820  1.0303\n",
            "    450        0.6147        1.4249        1.3820  1.0440\n",
            "    451        0.6072        1.4305        1.3820  1.0417\n",
            "    452        0.6189        1.4173        1.3820  1.0556\n",
            "    453        0.6154        1.4227        1.3820  1.0676\n",
            "    454        0.6135        1.4257        1.3820  1.0380\n",
            "    455        0.6155        1.4230        1.3820  1.0571\n",
            "    456        0.6114        1.4255        1.3820  1.0331\n",
            "    457        0.6165        1.4255        1.3820  1.0681\n",
            "    458        0.6166        1.4220        1.3820  1.0272\n",
            "    459        0.6158        1.4197        1.3820  1.0214\n",
            "    460        0.6119        1.4257        1.3820  1.0136\n",
            "    461        0.6092        1.4281        1.3820  1.0532\n",
            "    462        0.6103        1.4252        1.3820  1.0175\n",
            "    463        0.6182        1.4190        1.3820  1.0424\n",
            "    464        0.6151        1.4217        1.3820  1.0499\n",
            "    465        0.6182        1.4211        1.3820  1.0280\n",
            "    466        0.6096        1.4306        1.3820  1.0502\n",
            "    467        0.6163        1.4194        1.3820  1.0496\n",
            "    468        0.6181        1.4196        1.3820  1.0197\n",
            "    469        0.6203        1.4191        1.3820  1.0341\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<class 'braindecode.classifier.EEGClassifier'>[initialized](\n",
              "  module_=SleepEEG(\n",
              "    (feature_extractor): Sequential(\n",
              "      (0): Conv2d(2, 20, kernel_size=(1, 7), stride=(1, 1), groups=2)\n",
              "      (1): Identity()\n",
              "      (2): ReLU()\n",
              "      (3): MaxPool2d(kernel_size=(1, 2), stride=(1, 2), padding=0, dilation=1, ceil_mode=False)\n",
              "      (4): Conv2d(20, 20, kernel_size=(1, 7), stride=(1, 1), groups=20)\n",
              "      (5): Identity()\n",
              "      (6): ReLU()\n",
              "      (7): MaxPool2d(kernel_size=(1, 2), stride=(1, 2), padding=0, dilation=1, ceil_mode=False)\n",
              "      (8): Conv2d(20, 20, kernel_size=(1, 5), stride=(1, 1), groups=20)\n",
              "      (9): Identity()\n",
              "      (10): ReLU()\n",
              "      (11): MaxPool2d(kernel_size=(1, 2), stride=(1, 2), padding=0, dilation=1, ceil_mode=False)\n",
              "      (12): Conv2d(20, 20, kernel_size=(1, 5), stride=(1, 1), groups=20)\n",
              "      (13): Identity()\n",
              "      (14): ReLU()\n",
              "      (15): MaxPool2d(kernel_size=(1, 2), stride=(1, 2), padding=0, dilation=1, ceil_mode=False)\n",
              "      (16): Conv2d(20, 20, kernel_size=(1, 5), stride=(1, 1), groups=20)\n",
              "      (17): Identity()\n",
              "      (18): ReLU()\n",
              "      (19): MaxPool2d(kernel_size=(1, 2), stride=(1, 2), padding=0, dilation=1, ceil_mode=False)\n",
              "      (20): Conv2d(20, 20, kernel_size=(1, 3), stride=(1, 1), groups=20)\n",
              "      (21): Identity()\n",
              "      (22): ReLU()\n",
              "      (23): MaxPool2d(kernel_size=(1, 2), stride=(1, 2), padding=0, dilation=1, ceil_mode=False)\n",
              "      (24): Conv2d(20, 20, kernel_size=(1, 3), stride=(1, 1), groups=20)\n",
              "      (25): Identity()\n",
              "      (26): ReLU()\n",
              "      (27): MaxPool2d(kernel_size=(1, 2), stride=(1, 2), padding=0, dilation=1, ceil_mode=False)\n",
              "    )\n",
              "    (fc): Sequential(\n",
              "      (0): Dropout(p=0.5, inplace=False)\n",
              "      (1): Linear(in_features=400, out_features=6, bias=True)\n",
              "      (2): Softmax(dim=1)\n",
              "    )\n",
              "  ),\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QT7I8fpjrTlJ",
        "outputId": "34d3950f-c001-49e2-9002-65bcb0b42796"
      },
      "source": [
        "!wandb login"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mdiv12345\u001b[0m (use `wandb login --relogin` to force relogin)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nw_K855qvWba"
      },
      "source": [
        "# Save this model once\n",
        "with open('/content/drive/MyDrive/mne_data/sleepEEGmodel_continue_fulltrain300.pkl', 'wb') as f:\n",
        "    pickle.dump(clf2, f)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oZibuYepyd__",
        "outputId": "15b7f680-59ab-4d49-ed52-247ee5e045b5"
      },
      "source": [
        "save_fname = \"CNN_full_continue_1\"\n",
        "# predict_leaderboard_unlabelled(clf2,save_fname)\n",
        "y_pred = clf2.predict(testB.X)\n",
        "print(\"Checking if all classes have been predicted\")\n",
        "print(np.unique(y_pred))\n",
        "# save_fname = \"LogReg_newton-cg_1500\"\n",
        "np.savetxt(\"/content/drive/MyDrive/mne_data/predict/\"+save_fname+\".txt\",y_pred,delimiter=',',fmt=\"%d\")\n",
        "\n",
        "# testB is loaded\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Checking if all classes have been predicted\n",
            "[0 1 2 3 4 5]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cangvJq0y_Zb",
        "outputId": "e3307712-2722-4b2c-a85b-9a68c8a0f008"
      },
      "source": [
        "testB.y.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([25748])"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 462,
          "referenced_widgets": [
            "1b2cc77d606b43008b0f89b5eefe4c38",
            "e7b899c98516402a9d8b3cb56ed8d309",
            "53c772f32eed42d9a11aa0586bf862da",
            "48215ce1eaad4b07b8ba1e389283355a",
            "abdab663ed9b4fcbb68ed1bbc25316f6",
            "b6f6052e270342dab855a5226fe53d42",
            "5c3d94eddda04261aed59df7f03daa5b",
            "6da004f3772a4f5eb862becb45d7d8b1"
          ]
        },
        "id": "hZvfLH51yn2j",
        "outputId": "781116b9-df90-425f-8dd3-eb4301a45859"
      },
      "source": [
        "wandb.finish()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<br/>Waiting for W&B process to finish, PID 1640<br/>Program ended successfully."
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1b2cc77d606b43008b0f89b5eefe4c38",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "VBox(children=(Label(value=' 0.00MB of 0.00MB uploaded (0.00MB deduped)\\r'), FloatProgress(value=1.0, max=1.0)…"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "Find user logs for this run at: <code>/content/wandb/run-20210914_095646-1gnk24xc/logs/debug.log</code>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "Find internal logs for this run at: <code>/content/wandb/run-20210914_095646-1gnk24xc/logs/debug-internal.log</code>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<h3>Run summary:</h3><br/><style>\n",
              "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
              "    </style><table class=\"wandb\">\n",
              "<tr><td>_runtime</td><td>899</td></tr><tr><td>_timestamp</td><td>1631614308</td></tr><tr><td>_step</td><td>0</td></tr></table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<h3>Run history:</h3><br/><style>\n",
              "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
              "    </style><table class=\"wandb\">\n",
              "<tr><td>_runtime</td><td>▁</td></tr><tr><td>_timestamp</td><td>▁</td></tr><tr><td>_step</td><td>▁</td></tr></table><br/>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                    <br/>Synced <strong style=\"color:#cdcd00\">300 epoch continue train</strong>: <a href=\"https://wandb.ai/sleep_hacking/cnn-fulltrain/runs/1gnk24xc\" target=\"_blank\">https://wandb.ai/sleep_hacking/cnn-fulltrain/runs/1gnk24xc</a><br/>\n",
              "                "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AEugtLEPRB3k"
      },
      "source": [
        "# End"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K_JZwmp2TBKW"
      },
      "source": [
        "The model didn't show much scope for improvement, so moved on to the AttnSleep model"
      ]
    }
  ]
}