Nice_but_Ugly_Idea-BR41N.IOHackaton2021.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# NICE BUT UGLY IDEA"
      ],
      "metadata": {
        "tags": [],
        "cell_id": "00000-d93bd2dc-fec2-471c-a964-956f9d14a9cf",
        "deepnote_cell_type": "text-cell-h1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## The idea: train more times the last patient"
      ],
      "metadata": {
        "tags": [],
        "cell_id": "00001-1248cf5a-8172-4944-b85c-99a309a8cbc2",
        "deepnote_cell_type": "text-cell-h2"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "b8c97ca5",
        "execution_millis": 5609,
        "execution_start": 1618821538935,
        "cell_id": "00002-590ec702-43ed-4543-a253-383f56eeb37b",
        "deepnote_cell_type": "code"
      },
      "source": [
        "%pylab inline\n",
        "import pandas\n",
        "import sklearn\n",
        "import numpy"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Populating the interactive namespace from numpy and matplotlib\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Creating the arrays to store the data. The data will be stored so the index number corresponds to the patient number"
      ],
      "metadata": {
        "tags": [],
        "cell_id": "00003-8c9ac4d7-4412-4f6c-a58f-903c02e8cd1b",
        "deepnote_cell_type": "markdown"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "cbcf0190",
        "execution_millis": 0,
        "execution_start": 1618821707698,
        "cell_id": "00004-23de40b0-397a-42ec-b0f4-191075f803da",
        "deepnote_cell_type": "code"
      },
      "source": [
        "number_of_patients = 5\n",
        "\n",
        "features_patient = numpy.empty(number_of_patients+1, dtype = object)\n",
        "label_patient    = numpy.empty(number_of_patients+1, dtype = object)"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Loading and reading the Features Matrixes and the Labels.\n",
        "For each one of them we control their form."
      ],
      "metadata": {
        "tags": [],
        "cell_id": "00005-4e246094-6b6d-4d6a-a2d9-6ef7d52fe2af",
        "deepnote_cell_type": "markdown"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "ddd2897e",
        "execution_millis": 178,
        "execution_start": 1618821580484,
        "cell_id": "00006-46797129-a478-46f4-a202-e8f24e7abd42",
        "deepnote_cell_type": "code"
      },
      "source": [
        "# Data directory:\n",
        "data_directory = \"ProcessedData/\"\n",
        "\n",
        "for pat_num in range(1, number_of_patients+1):\n",
        "    file = open(data_directory + \"dataMatrixS\" + str(pat_num) + \".mat.csv\")\n",
        "    features_patient[pat_num] = numpy.loadtxt(file, delimiter=\",\")\n",
        "    print(\"Shape features pat. \" + str(pat_num) + \": \" + str(shape(features_patient[pat_num])))\n"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape features pat. 1: (1200, 152)\n",
            "Shape features pat. 2: (1200, 152)\n",
            "Shape features pat. 3: (1200, 152)\n",
            "Shape features pat. 4: (1200, 152)\n",
            "Shape features pat. 5: (1200, 152)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "fd17ff40",
        "execution_millis": 142,
        "execution_start": 1618821582177,
        "cell_id": "00007-aaa75b2d-9a51-406f-9d57-e994ca60fa1b",
        "deepnote_cell_type": "code"
      },
      "source": [
        "# Data directory:\n",
        "data_directory = \"preprocessed3/\"\n",
        "\n",
        "for pat_num in range(1, number_of_patients+1):\n",
        "    file = open(data_directory + \"targetsVectorS\" + str(pat_num) + \".mat.csv\")\n",
        "    label_patient[pat_num] = numpy.loadtxt(file, delimiter=\",\")\n",
        "    print(\"Shape labels pat. \" + str(pat_num) + \": \" + str(shape(label_patient[pat_num])))"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape labels pat. 1: (1200,)\nShape labels pat. 2: (1200,)\nShape labels pat. 3: (1200,)\nShape labels pat. 4: (1200,)\nShape labels pat. 5: (1200,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Stacking up the Matrix"
      ],
      "metadata": {
        "tags": [],
        "cell_id": "00016-11bb537b-4a3b-4587-8808-cb4b7fbe5c6c",
        "deepnote_cell_type": "markdown"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [],
      "source": [
        "# we are using 5 trials -> 5*16\n",
        "\n",
        "trials = 5\n",
        "epochs = 16"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1200, 152)\n"
          ]
        }
      ],
      "source": [
        "temp_features = features_patient[5][ : (trials*epochs), : ]\n",
        "\n",
        "for i in range(14):\n",
        "    temp_features = vstack((temp_features, features_patient[5][ : (trials*epochs), : ]))\n",
        "print(shape(temp_features))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(4800, 152)\n(6000, 152)\n"
          ]
        }
      ],
      "source": [
        "stacked_features = vstack((features_patient[1], features_patient[2]))\n",
        "\n",
        "for i in range(3, number_of_patients):\n",
        "    stacked_features = vstack((stacked_features, features_patient[i]))\n",
        "print(shape(stacked_features))\n",
        "\n",
        "stacked_features = vstack((stacked_features, temp_features))\n",
        "print(shape(stacked_features))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1200,)\n"
          ]
        }
      ],
      "source": [
        "temp_label = label_patient[5][ : (trials*epochs)]\n",
        "\n",
        "for i in range(14):\n",
        "    temp_label = hstack((temp_label, label_patient[5][ : (trials*epochs)]))\n",
        "print(shape(temp_label))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(4800,)\n(6000,)\n"
          ]
        }
      ],
      "source": [
        "stacked_label = hstack((label_patient[1], label_patient[2]))\n",
        "\n",
        "for i in range(3, number_of_patients):\n",
        "    stacked_label = hstack((stacked_label, label_patient[i]))\n",
        "print(shape(stacked_label))\n",
        "\n",
        "stacked_label = hstack((stacked_label, temp_label))\n",
        "print(shape(stacked_label))"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "97def1d3",
        "execution_millis": 346,
        "execution_start": 1618727782860,
        "cell_id": "00049-931a71e6-93cf-4955-9ec6-39d6f5e436e7",
        "deepnote_cell_type": "code"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.decomposition import PCA\n",
        "\n",
        "scaler = StandardScaler()\n",
        "stacked_features = scaler.fit_transform(stacked_features)"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "source": [
        "Freeing memory"
      ],
      "cell_type": "markdown",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [],
      "source": [
        "import gc"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {},
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "20"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "del temp_features\n",
        "del temp_label\n",
        "\n",
        "gc.collect()"
      ]
    },
    {
      "source": [
        "## LDA\n"
      ],
      "cell_type": "markdown",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "### LDA with normal matrixes ###"
      ],
      "metadata": {
        "tags": [],
        "cell_id": "00054-a65a4592-2fa6-4d3c-be5f-b671daf93023",
        "deepnote_cell_type": "markdown"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {},
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearDiscriminantAnalysis()"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
        "LDA = LinearDiscriminantAnalysis()\n",
        "\n",
        "LDA.fit(stacked_features, stacked_label)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Score with the stacked matrixes"
      ],
      "metadata": {
        "tags": [],
        "cell_id": "00056-c85b466a-3372-4e40-8353-df216716f4da",
        "deepnote_cell_type": "markdown"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "6572aa85",
        "execution_start": 1618735144190,
        "execution_millis": 14,
        "cell_id": "00057-fde384c3-f47b-4687-916d-140006cfcc4d",
        "deepnote_cell_type": "code"
      },
      "source": [
        "LDA.score(features_patient[5][ (trials*epochs): , : ], label_patient[5][ (trials*epochs): ])"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7285714285714285"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "source": [
        "## Balancing the data"
      ],
      "cell_type": "markdown",
      "metadata": {}
    },
    {
      "source": [
        "### Reducing the number of zeros ### <br>\n",
        "\n",
        "For each 16 epochs, we remove 14 features rows labelled as 0, so the nuber of 0 and 1 is the same.\n"
      ],
      "cell_type": "markdown",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "227cd34f",
        "execution_millis": 8,
        "execution_start": 1618731073153,
        "cell_id": "00052-d9d08cf6-83b6-482c-ae3c-fa58283df5ff",
        "deepnote_cell_type": "code"
      },
      "source": [
        "reduced_features = []\n",
        "reduced_label = []\n",
        "\n",
        "ratio = shape(stacked_label)[0] / epochs\n",
        "for j in range(int(ratio)):\n",
        "    number_of_zeros = 0\n",
        "    for i in range(epochs):\n",
        "        if stacked_label[i+(j*epochs)] == 1:\n",
        "            reduced_features.append(stacked_features[i+(j*epochs)])\n",
        "            reduced_label.append(stacked_label[i+(j*epochs)])\n",
        "        elif stacked_label[i+(j*epochs)] == 0 and number_of_zeros < 2:\n",
        "            reduced_label.append(stacked_label[i+(j*epochs)])\n",
        "            reduced_features.append(stacked_features[i+(j*epochs)])\n",
        "            number_of_zeros+=1\n",
        "\n",
        "print(len(reduced_features))"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1500\n"
          ]
        }
      ]
    },
    {
      "source": [
        "### Adding more ones ### <br>\n",
        "For each feature row labelled 1, we add 6 almost identical rows, tweaked with some noise, so the number of 0 and 1 is the same. <br>\n",
        "We found that a low value of noise produces the best score, but zero noise actually make it worse."
      ],
      "cell_type": "markdown",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "4805843d",
        "execution_millis": 5379,
        "execution_start": 1618734778500,
        "cell_id": "00053-b5139c9d-0dd5-46d2-93c5-0da502ed4172",
        "deepnote_cell_type": "code"
      },
      "source": [
        "\n",
        "enlarged_features = stacked_features.copy()\n",
        "enlarged_label = stacked_label.copy()\n",
        "\n",
        "rows = shape(stacked_label)[0]\n",
        "w = -1\n",
        "for row in range(rows):\n",
        "    if stacked_label[row] == 1:\n",
        "        for i in range(6):\n",
        "            w += 1\n",
        "            enlarged_label = hstack((enlarged_label, 1))\n",
        "            enlarged_features = vstack((enlarged_features, stacked_features[row]))\n",
        "            for feature in range(152):\n",
        "                #noise = 0\n",
        "                noise = numpy.random.normal(0, .01)\n",
        "                enlarged_features[rows + w][feature] += noise\n",
        "\n",
        "print(len(enlarged_features))"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10500\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### LDA with the enlarged matrixes ###\n"
      ],
      "metadata": {
        "tags": [],
        "cell_id": "00058-dea2b3bf-993f-4edb-8ca5-1c29347032b7",
        "deepnote_cell_type": "markdown"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "66aa251b",
        "execution_millis": 373,
        "execution_start": 1618734786280,
        "cell_id": "00059-88b0e1b6-2954-4165-9541-fbb3637c395c",
        "deepnote_cell_type": "code"
      },
      "source": [
        "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
        "LDA = LinearDiscriminantAnalysis()\n",
        "\n",
        "LDA.fit(enlarged_features, enlarged_label)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearDiscriminantAnalysis()"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Score on the whole dataset"
      ],
      "metadata": {
        "tags": [],
        "cell_id": "00061-d559ac03-0937-45b3-9cb0-c050546d0977",
        "deepnote_cell_type": "markdown"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "6e077953",
        "execution_millis": 14,
        "execution_start": 1618734789652,
        "cell_id": "00060-37e54156-62e0-4aea-bd9f-f1c41c4979a2",
        "deepnote_cell_type": "code"
      },
      "source": [
        "LDA.score(stacked_features, stacked_label)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8481666666666666"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "source": [
        "Score on the last patient"
      ],
      "cell_type": "markdown",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "6572aa85",
        "execution_millis": 11,
        "execution_start": 1618734793244,
        "cell_id": "00062-d73328e3-4477-427b-aa18-e69c0f821b45",
        "deepnote_cell_type": "code"
      },
      "source": [
        "LDA.score(features_patient[5][(trials*epochs) : , : ], label_patient[5][(trials*epochs) : ])"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8285714285714286"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### LDA with the reduced matrixes ###"
      ],
      "metadata": {
        "tags": [],
        "cell_id": "00063-0c6797ab-9607-4f13-b284-95b1c59f7a0b",
        "deepnote_cell_type": "markdown"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "9c0f0859",
        "execution_start": 1618732549302,
        "execution_millis": 61,
        "cell_id": "00064-507c13b8-95c0-441b-8273-c94c388c6b3c",
        "deepnote_cell_type": "code"
      },
      "source": [
        "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
        "LDA = LinearDiscriminantAnalysis()\n",
        "\n",
        "LDA.fit(reduced_features, reduced_label)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearDiscriminantAnalysis()"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "source": [
        "Score on the whole dataset"
      ],
      "cell_type": "markdown",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "6e077953",
        "execution_millis": 13,
        "execution_start": 1618732563457,
        "cell_id": "00065-44225c9b-321f-438f-87a6-24ea99fead97",
        "deepnote_cell_type": "code"
      },
      "source": [
        "LDA.score(stacked_features, stacked_label)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7956666666666666"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "** Score on the last patient **"
      ],
      "metadata": {
        "tags": [],
        "cell_id": "00066-6010e7fd-f6c8-4aec-8d0a-0f620653b417",
        "deepnote_cell_type": "markdown"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "6572aa85",
        "execution_start": 1618732569304,
        "execution_millis": 12,
        "cell_id": "00067-a3a58bd9-83fc-4755-9266-9a364aefe570",
        "deepnote_cell_type": "code"
      },
      "source": [
        "LDA.score(features_patient[5][(trials*epochs) : , : ], label_patient[5][(trials*epochs) : ])"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7598214285714285"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "source": [
        "## SVM ##"
      ],
      "cell_type": "markdown",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "fc2386af",
        "execution_millis": 259029,
        "execution_start": 1618731745823,
        "cell_id": "00069-2c031be3-c338-459f-8022-13a70a9a7a4a",
        "deepnote_cell_type": "code"
      },
      "source": [
        "from sklearn import svm, model_selection\n",
        "\n",
        "model = svm.SVC(kernel='linear')\n",
        "gammas = np.logspace(-5, 0.5, 20)\n",
        "scores = []\n",
        "\n",
        "for gamma in gammas:\n",
        "    model.gamma = gamma\n",
        "    s = model_selection.cross_val_score(model, reduced_features, reduced_label,\n",
        "                                        cv=10, scoring='accuracy')\n",
        "    scores.append(s.mean())"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "e8f16f99",
        "execution_millis": 585,
        "execution_start": 1618732028147,
        "cell_id": "00070-5dc69ead-02ca-4bd5-8729-6c0a58cb6c9e",
        "deepnote_cell_type": "code"
      },
      "source": [
        "print ('Best gamma:', gammas[np.argmax(scores)])\n",
        "print ('Best score:', scores[np.argmax(scores)])\n",
        "\n",
        "semilogx(gammas, scores)\n",
        "plt.xlabel('gamma')\n",
        "plt.ylabel('Score (roc_auc)')"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best gamma: 1e-05\nBest score: 0.8\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Score (roc_auc)')"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "bb6ef0f",
        "execution_millis": 1554,
        "execution_start": 1618732115606,
        "cell_id": "00072-202503c5-9670-4618-83e5-12b9e924acfa",
        "deepnote_cell_type": "code"
      },
      "source": [
        "model = svm.SVC(kernel= 'linear')\n",
        "model.fit(reduced_features, reduced_label)\n"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(kernel='linear')"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "caae7fd4",
        "execution_millis": 225,
        "execution_start": 1618732121405,
        "cell_id": "00073-885e2730-0eb4-4898-85c9-431fbcf3debf",
        "deepnote_cell_type": "code"
      },
      "source": [
        "model.score(stacked_features, stacked_label)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7985"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "ae290393",
        "execution_millis": 53,
        "execution_start": 1618732123830,
        "cell_id": "00074-73446135-9f17-480a-809c-7513ab91e5c1",
        "deepnote_cell_type": "code"
      },
      "source": [
        "model.score(features_patient[5][(epochs*trials) : , : ], label_patient[5][(epochs*trials) : ])"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5285714285714286"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "5dce9539",
        "execution_millis": 47,
        "execution_start": 1618732133850,
        "cell_id": "00075-94502661-ecf9-4544-b20f-83e4fc59048f",
        "deepnote_cell_type": "code"
      },
      "source": [
        "predicted_labels = model.predict(features_patient[5][(epochs*trials) : , : ])\n",
        "\n",
        "print(\"Shape: \" + str(shape(predicted_labels)))\n",
        "print(predicted_labels)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape: (1120,)\n[0. 0. 0. ... 1. 1. 0.]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "1d99a85",
        "execution_millis": 11,
        "execution_start": 1618731639932,
        "cell_id": "00077-0df53dbd-877b-413b-8f12-2d63fb3506f5",
        "deepnote_cell_type": "code"
      },
      "source": [
        "for r in label_patient[5].nonzero():\n",
        "    print(str(r))"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[   4    5   16   26   36   44   48   61   70   72   92   94  100  110\n  114  121  129  135  152  154  162  172  189  190  202  203  209  212\n  226  234  244  247  265  269  274  278  293  302  305  309  321  326\n  344  349  356  366  372  380  384  395  401  409  423  430  435  439\n  454  463  466  479  488  492  497  506  518  522  528  535  550  558\n  564  575  580  589  592  604  608  616  625  626  643  655  662  669\n  672  673  688  699  705  717  728  731  744  750  755  763  769  774\n  795  798  802  808  820  826  832  845  860  861  876  878  882  889\n  903  909  914  927  929  937  955  957  963  975  983  984  994  995\n 1008 1021 1025 1031 1049 1055 1058 1071 1078 1080 1092 1096 1113 1116\n 1121 1135 1140 1151 1160 1164 1170 1171 1194 1198]\n"
          ]
        }
      ]
    }
  ],
  "nbformat": 4,
  "nbformat_minor": 2,
  "metadata": {
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      "display_name": "Python 3.7.10 64-bit ('myenv': conda)"
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}