bert_features_process.ipynb

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    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
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      "file_extension": ".py",
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      "pygments_lexer": "ipython3",
      "version": "3.7.3"
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    "colab": {
      "name": "bert_only_process.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "machine_shape": "hm"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5mruz5QNEMY5",
        "colab_type": "text"
      },
      "source": [
        "# <center>BMI 598: Project</center>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mU6hgp3REMY9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# For compatibility with Python 2\n",
        "from __future__ import print_function\n",
        "from past.builtins import xrange\n",
        "\n",
        "from math import sqrt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.model_selection import KFold\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from scipy.stats import pearsonr\n",
        "\n",
        "# To support plots\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "import seaborn as sns\n",
        "\n",
        "sns.set(color_codes=True)\n",
        "\n",
        "# To display all the plots inline\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e6hN_eHPRVC_",
        "colab_type": "code",
        "outputId": "41778eb2-39cd-4a48-e57c-80a1076eb3d8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "965PqYDN-Yx0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "my_drive = '/content/drive/My Drive/'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "fL1ILW2WdkUO"
      },
      "source": [
        "## <center>1. Load the data</center>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A-DEy7iNEMZC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# To increase quality of figures\n",
        "plt.rcParams[\"figure.figsize\"] = (10, 5)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nLDSjKZPEMZE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_data(file):\n",
        "  data = pd.read_csv(file, sep='\\t', header=None, names=[\"Sentence #1\", \"Sentence #2\", \"Similarity\"])\n",
        "  X = data.values[:, :2]\n",
        "  y = data['Similarity'].values\n",
        "  return (X, y)\n",
        "\n",
        "X_train, y_train = get_data(my_drive+'clinicalSTS2019.train.txt')\n",
        "\n",
        "data_train = pd.read_csv(my_drive+'clinicalSTS2019.train.txt', sep='\\t', header=None, names=[\"Sentence #1\", \"Sentence #2\", \"Similarity\"])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kjWDY-kZQIA7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 257
        },
        "outputId": "f098b807-6027-44b8-ea11-23cbf41f4611"
      },
      "source": [
        "print(X_train)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[' Patient arrives, via hospital wheelchair, Gait steady, History obtained from patient, Patient appears comfortable, Patient cooperative, alert, Oriented to person, place and time.'\n",
            "  ' Complex assessment performed, Patient arrives ambulatory, Gait steady, History obtained from, parent, Patient appears comfortable, Patient cooperative, alert, Oriented to person, place and time.']\n",
            " ['oxycodone [ROXICODONE] 5 mg tablet 0.5-1 tablets by mouth every 4 hours as needed.'\n",
            "  'pantoprazole [PROTONIX] 40 mg tablet enteric coated 1 tablet by mouth Bid before meals.']\n",
            " ['The patient has missed 0 hours of work in the past seven days for issues not related to depression.'\n",
            "  ' In the past year the patient has the following number of visits: none in the hospital  none in the er and  one as an outpatient.']\n",
            " ...\n",
            " ['Explained diagnosis and treatment plan; patient/child/care giver expressed understanding of the content.'\n",
            "  ' Explained diagnosis and treatment plan; patient/child/caregiver expressed understanding of the content and agreement with the plan of care.']\n",
            " ['The patient remained focused on group tasks and displayed interest in others and in the group process.'\n",
            "  \" The patient's medication(s) were reconciled comparing the pre- and post-hospitalization lists and then verified in the electronic orders prescribing tool.\"]\n",
            " [' No: new confusion or inability to stay alert and awake; noisy, wheezy or raspy breathing that does not clear with coughing; newly stiff or painful neck; purple or red rash/blotches that stay when pressed by a glass (purpural rash); currently feeling like you are going to collapse every time you stand (sit); muffled voice or inability to open mouth fully or complete inability to swallow'\n",
            "  ' No: new confusion or inability to stay alert and awake; currently feeling like you are going to collapse every time you stand or sit up; purple or red rash/blotches that stay when pressed by a glass (purpural rash); new neck pain and difficulty bending the neck or swelling or puffiness around the eye(s)']]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NNy52l25EMZG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        },
        "outputId": "926455c5-f013-4ed4-db92-2599bf0e9916"
      },
      "source": [
        "data_train"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Sentence #1</th>\n",
              "      <th>Sentence #2</th>\n",
              "      <th>Similarity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Patient arrives, via hospital wheelchair, Gai...</td>\n",
              "      <td>Complex assessment performed, Patient arrives...</td>\n",
              "      <td>3.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>oxycodone [ROXICODONE] 5 mg tablet 0.5-1 table...</td>\n",
              "      <td>pantoprazole [PROTONIX] 40 mg tablet enteric c...</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>The patient has missed 0 hours of work in the ...</td>\n",
              "      <td>In the past year the patient has the followin...</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Given current medication regimen, the followi...</td>\n",
              "      <td>Given current medication regimen, the followi...</td>\n",
              "      <td>3.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>The diagnosis and treatment plan were explaine...</td>\n",
              "      <td>Explained diagnosis and treatment plan; patie...</td>\n",
              "      <td>4.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1649</th>\n",
              "      <td>New wheezing or chest tightness, runny or blo...</td>\n",
              "      <td>F (38 C) or suspected fever; new wheezing or ...</td>\n",
              "      <td>3.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1650</th>\n",
              "      <td>You should not take any medications that cause...</td>\n",
              "      <td>You should not take any medications that caus...</td>\n",
              "      <td>5.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1651</th>\n",
              "      <td>Explained diagnosis and treatment plan; patien...</td>\n",
              "      <td>Explained diagnosis and treatment plan; patie...</td>\n",
              "      <td>4.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1652</th>\n",
              "      <td>The patient remained focused on group tasks an...</td>\n",
              "      <td>The patient's medication(s) were reconciled c...</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1653</th>\n",
              "      <td>No: new confusion or inability to stay alert ...</td>\n",
              "      <td>No: new confusion or inability to stay alert ...</td>\n",
              "      <td>3.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1654 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                            Sentence #1  ... Similarity\n",
              "0      Patient arrives, via hospital wheelchair, Gai...  ...       3.00\n",
              "1     oxycodone [ROXICODONE] 5 mg tablet 0.5-1 table...  ...       1.00\n",
              "2     The patient has missed 0 hours of work in the ...  ...       0.00\n",
              "3      Given current medication regimen, the followi...  ...       3.00\n",
              "4     The diagnosis and treatment plan were explaine...  ...       4.00\n",
              "...                                                 ...  ...        ...\n",
              "1649   New wheezing or chest tightness, runny or blo...  ...       3.00\n",
              "1650  You should not take any medications that cause...  ...       5.00\n",
              "1651  Explained diagnosis and treatment plan; patien...  ...       4.25\n",
              "1652  The patient remained focused on group tasks an...  ...       0.00\n",
              "1653   No: new confusion or inability to stay alert ...  ...       3.00\n",
              "\n",
              "[1654 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d_me6mwNNO1z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train_original = X_train"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yz_65CRQEMZJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        },
        "outputId": "edc8c2ff-062c-4239-f2c2-1bdde265d5d4"
      },
      "source": [
        "sns.distplot(data_train['Similarity'], bins=5, kde=False, rug=True)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1717407550>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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0qlrt7hNVtZrkqdP2Pafp25HDh49lff3kGZY4g9XVHD12fNz2OWPmZfGYk8VjThaTeVk8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\nQf4/8WbzJl0WFG0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WCSQklX1hdAd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_true = y_train"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OsxsVbQBN85L",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        },
        "outputId": "c7897abc-1080-40e9-8c0e-712f0e4cdd5a"
      },
      "source": [
        "#load the training set\n",
        "!cp '/content/drive/My Drive/test.zip' test.zip\n",
        "!unzip test.zip"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  test.zip\n",
            "  inflating: clinicalSTS2019.test.gs.sim.txt  \n",
            "  inflating: clinicalSTS2019.test.txt  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EMy_6kWKQRVJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 257
        },
        "outputId": "6a840517-8693-43ef-8500-878c7fdd8098"
      },
      "source": [
        "X_test_original = pd.read_csv('clinicalSTS2019.test.txt', sep='\\t', header=None, names=[\"Sentence #1\", \"Sentence #2\"])\n",
        "X_test_original = X_test_original.values\n",
        "print(X_train_original)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[' Patient arrives, via hospital wheelchair, Gait steady, History obtained from patient, Patient appears comfortable, Patient cooperative, alert, Oriented to person, place and time.'\n",
            "  ' Complex assessment performed, Patient arrives ambulatory, Gait steady, History obtained from, parent, Patient appears comfortable, Patient cooperative, alert, Oriented to person, place and time.']\n",
            " ['oxycodone [ROXICODONE] 5 mg tablet 0.5-1 tablets by mouth every 4 hours as needed.'\n",
            "  'pantoprazole [PROTONIX] 40 mg tablet enteric coated 1 tablet by mouth Bid before meals.']\n",
            " ['The patient has missed 0 hours of work in the past seven days for issues not related to depression.'\n",
            "  ' In the past year the patient has the following number of visits: none in the hospital  none in the er and  one as an outpatient.']\n",
            " ...\n",
            " ['Explained diagnosis and treatment plan; patient/child/care giver expressed understanding of the content.'\n",
            "  ' Explained diagnosis and treatment plan; patient/child/caregiver expressed understanding of the content and agreement with the plan of care.']\n",
            " ['The patient remained focused on group tasks and displayed interest in others and in the group process.'\n",
            "  \" The patient's medication(s) were reconciled comparing the pre- and post-hospitalization lists and then verified in the electronic orders prescribing tool.\"]\n",
            " [' No: new confusion or inability to stay alert and awake; noisy, wheezy or raspy breathing that does not clear with coughing; newly stiff or painful neck; purple or red rash/blotches that stay when pressed by a glass (purpural rash); currently feeling like you are going to collapse every time you stand (sit); muffled voice or inability to open mouth fully or complete inability to swallow'\n",
            "  ' No: new confusion or inability to stay alert and awake; currently feeling like you are going to collapse every time you stand or sit up; purple or red rash/blotches that stay when pressed by a glass (purpural rash); new neck pain and difficulty bending the neck or swelling or puffiness around the eye(s)']]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vpvc95RfRtjc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "outputId": "0a5ac88d-46d6-454b-e980-c618181e783e"
      },
      "source": [
        "y_test_original = pd.read_csv('clinicalSTS2019.test.gs.sim.txt', sep='\\t', header=None, names=[\"Similarity\"])\n",
        "y_test_original = y_test_original.values\n",
        "y_test_original = y_test_original.flatten()\n",
        "print((y_test_original))"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.5 1.  5.  1.  0.  1.  1.  0.5 1.  1.  2.5 1.  1.  0.  4.  3.5 4.  1.\n",
            " 4.5 1.5 0.5 0.  1.  0.  1.  0.  0.5 2.  1.5 0.  0.  5.  1.  0.5 1.5 1.\n",
            " 0.5 3.  4.  4.  4.  1.  1.5 1.  1.5 1.  3.5 1.  1.  1.  1.  1.  1.  0.5\n",
            " 0.  3.  4.  1.  4.  2.5 1.  0.  0.5 3.5 4.  4.  1.  1.  1.5 0.  1.  0.\n",
            " 0.5 1.  3.  1.  4.5 5.  1.  1.  0.  1.  4.  1.  0.  0.5 1.5 0.  1.  1.\n",
            " 2.  4.  0.  0.  2.5 1.  2.5 4.  2.  3.  4.  3.  0.5 2.5 1.  1.  0.  2.\n",
            " 0.5 1.  5.  1.  0.  4.  3.5 0.  1.  1.  1.  1.5 4.  3.  1.5 0.  1.  0.\n",
            " 1.5 4.5 0.  5.  4.5 0.  1.  2.  4.  0.5 0.5 2.5 0.5 1.  1.  4.5 5.  0.\n",
            " 4.  0.  1.  2.  1.  0.  1.  0.  0.  4.5 1.  4.5 1.  1.5 0.5 1.  4.  1.\n",
            " 1.  0.  0.  2.  1.  1.  0.  0.  1.  1.  1.  2.  5.  0.  0.  4.5 5.  4.\n",
            " 1.  0.  4.5 0.  1.  4.5 3.  4.  1.  1.  1.  0.  1.  2.  1.  4.  1.  1.5\n",
            " 0.5 0.  1.  0.  1.  1.  4.5 1.5 4.  1.  5.  2.  4.  1.  1.  1.  0.  1.5\n",
            " 0.  3.  1.  0.  3.5 1.  1.  1.5 1.  1.5 0.5 0.  0.  1.  1.  1.  0.5 4.\n",
            " 1.  4.  1.  4.  0.  4.  1.5 3.5 0.5 1.  1.  4.5 2.  1.  0.5 0.5 1.5 3.\n",
            " 4.  4.5 2.5 2.  4.  1.  1.  0.5 3.5 1.  4.  1.  1.5 2.5 3.5 2.5 1.  0.\n",
            " 1.5 1.  3.5 1.  1.  0.5 0.  1.5 3.  4.5 1.  5.  1.  0.5 4.  0.5 0.5 1.\n",
            " 4.5 1.  0.  4.  1.5 1.  0.  2.  1.5 1.  2.  2.  1.  1.5 0.5 0.  1.  0.\n",
            " 4.5 3.  3.5 4.  4.  1.  4.  1.  4.  1.  1.  0.5 2.5 4.5 1.  3.  3.5 0.\n",
            " 1.  1.  3.  1.  1.  1.5 4.5 4.  3.  0.5 0.5 4.  3.  1.  1.  5.  1.5 1.\n",
            " 0.5 1.  1.  1.  0.  1.  2.  3.5 1.  0.  0.  0.  5.  1.  1.  3.  0.5 5.\n",
            " 3.5 4.  1.  1.  3.  0.  2.  1.  4.  1.  2.5 0.5 1.  1.  0.  4.  0.5 1.5\n",
            " 4.  1.  0.  1.  3.  0.5 2.  4.  4.  1.  1.  4.5 0.  1.  1.  4.  3.  4.\n",
            " 1.  1.  5.  1.5 0.  4.5 1.  4.  1.  3.  3.5 1.  4.  1.  0.  2.5]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j5XVL7HpQeHi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train60, y_train60 = get_data(\"/content/drive/My Drive/clinicalSTS2019.train_train60.csv\")\n",
        "X_test20, y_test20 = get_data(\"/content/drive/My Drive/clinicalSTS2019.train_test20.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "syIO7Yy_eRLc",
        "colab_type": "text"
      },
      "source": [
        "## 2.Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R8dFigFaiCIj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def accuracy(y_true, y_hat):\n",
        "  return pearsonr(y_true, y_hat)[0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bqZApOf-Fa2J",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# REGRESSION\n",
        "from sklearn.ensemble import GradientBoostingRegressor\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "def train_data_split(X_train, y_train):\n",
        "  return train_test_split(X_train, y_train, test_size=0.2, random_state=598)\n",
        "\n",
        "def predict_train(X_train, y_train, reg):\n",
        "  _X_train, _X_test, _y_train, _y_test = train_data_split(X_train, y_train)\n",
        "  reg.fit(_X_train, _y_train)\n",
        "  y_hat = reg.predict(_X_test)\n",
        "  return y_hat, _y_test\n",
        "\n",
        "def get_optimal_rf_model(X, y):\n",
        "  params = {\n",
        "      'learning_rate': 0.01, \n",
        "      'random_state': 598, \n",
        "      'max_features': 'sqrt'\n",
        "  }\n",
        "  model = GridSearchCV(GradientBoostingRegressor(**params),\n",
        "                            cv=5,\n",
        "                            n_jobs=-1,\n",
        "                            iid=False,\n",
        "                            param_grid={\n",
        "                                'n_estimators':[200, 300, 400, 500],\n",
        "                                'max_depth': [4, 5, 6, 7],\n",
        "                                'min_samples_split': [2, 4, 6, 8],\n",
        "                                'subsample': [0.6, 0.7, 0.8]\n",
        "                            })\n",
        "  model.fit(X, y)\n",
        "  print('Optimal parameters:', model.best_params_)\n",
        "  ml_alg = GradientBoostingRegressor(**params, **model.best_params_)\n",
        "  return ml_alg\n",
        "\n",
        "def plot_accuracy(y, y_hat):\n",
        "  print('Accuracy:', accuracy(y, y_hat))\n",
        "  plt.plot(y, y_hat, 'ro')\n",
        "  plt.xlabel('Actual')\n",
        "  plt.ylabel('Predicted')\n",
        "  plt.title('Predicted vs Actual')\n",
        "  plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rA0iaLtOlj6N",
        "colab_type": "text"
      },
      "source": [
        "## <center>3. BERT based Feature extraction</center>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YVnKo9EJgrdZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 660
        },
        "outputId": "84fa6257-2b20-4b37-891c-96aaeb43fcfd"
      },
      "source": [
        "!pip install transformers"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting transformers\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/70/1a/364556102943cacde1ee00fdcae3b1615b39e52649eddbf54953e5b144c9/transformers-2.2.1-py3-none-any.whl (364kB)\n",
            "\u001b[K     |████████████████████████████████| 368kB 4.8MB/s \n",
            "\u001b[?25hCollecting sentencepiece\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/14/3d/efb655a670b98f62ec32d66954e1109f403db4d937c50d779a75b9763a29/sentencepiece-0.1.83-cp36-cp36m-manylinux1_x86_64.whl (1.0MB)\n",
            "\u001b[K     |████████████████████████████████| 1.0MB 42.9MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from transformers) (1.17.4)\n",
            "Collecting regex\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/8c/db/4b29a0adec5881542cd81cb5d1929b5c0787003c5740b3c921e627d9c2e5/regex-2019.12.9.tar.gz (669kB)\n",
            "\u001b[K     |████████████████████████████████| 675kB 45.5MB/s \n",
            "\u001b[?25hRequirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (from transformers) (4.28.1)\n",
            "Collecting sacremoses\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/1f/8e/ed5364a06a9ba720fddd9820155cc57300d28f5f43a6fd7b7e817177e642/sacremoses-0.0.35.tar.gz (859kB)\n",
            "\u001b[K     |████████████████████████████████| 860kB 38.6MB/s \n",
            "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from transformers) (2.21.0)\n",
            "Requirement already satisfied: boto3 in /usr/local/lib/python3.6/dist-packages (from transformers) (1.10.32)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from sacremoses->transformers) (1.12.0)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.6/dist-packages (from sacremoses->transformers) (7.0)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.6/dist-packages (from sacremoses->transformers) (0.14.0)\n",
            "Requirement already satisfied: idna<2.9,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->transformers) (2.8)\n",
            "Requirement already satisfied: urllib3<1.25,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->transformers) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->transformers) (2019.11.28)\n",
            "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->transformers) (3.0.4)\n",
            "Requirement already satisfied: s3transfer<0.3.0,>=0.2.0 in /usr/local/lib/python3.6/dist-packages (from boto3->transformers) (0.2.1)\n",
            "Requirement already satisfied: botocore<1.14.0,>=1.13.32 in /usr/local/lib/python3.6/dist-packages (from boto3->transformers) (1.13.32)\n",
            "Requirement already satisfied: jmespath<1.0.0,>=0.7.1 in /usr/local/lib/python3.6/dist-packages (from boto3->transformers) (0.9.4)\n",
            "Requirement already satisfied: docutils<0.16,>=0.10 in /usr/local/lib/python3.6/dist-packages (from botocore<1.14.0,>=1.13.32->boto3->transformers) (0.15.2)\n",
            "Requirement already satisfied: python-dateutil<2.8.1,>=2.1; python_version >= \"2.7\" in /usr/local/lib/python3.6/dist-packages (from botocore<1.14.0,>=1.13.32->boto3->transformers) (2.6.1)\n",
            "Building wheels for collected packages: regex, sacremoses\n",
            "  Building wheel for regex (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for regex: filename=regex-2019.12.9-cp36-cp36m-linux_x86_64.whl size=609187 sha256=1cd116df787ba58ef70bb3a7bd0d154679570cfc3d7ae0d57f4e6ec6c0cc815e\n",
            "  Stored in directory: /root/.cache/pip/wheels/0d/fb/b3/a89169557229468c49ca64f6839418f22461f6ee0a74f342b1\n",
            "  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sacremoses: filename=sacremoses-0.0.35-cp36-none-any.whl size=883999 sha256=807198a340dacc91972783c98c67dfc4462ec6a637310785a3e429df903c6eea\n",
            "  Stored in directory: /root/.cache/pip/wheels/63/2a/db/63e2909042c634ef551d0d9ac825b2b0b32dede4a6d87ddc94\n",
            "Successfully built regex sacremoses\n",
            "Installing collected packages: sentencepiece, regex, sacremoses, transformers\n",
            "Successfully installed regex-2019.12.9 sacremoses-0.0.35 sentencepiece-0.1.83 transformers-2.2.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5XXAd7v_Tuxg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 79
        },
        "outputId": "3c28f5e4-5b05-460c-e189-fd4a5cad1fbb"
      },
      "source": [
        "import torch\n",
        "from transformers import *\n",
        "model_class = BertModel\n",
        "tokenizer_class = BertTokenizer.from_pretrained('bert-base-uncased')\n",
        "pretrained_weights = 'bert-base-uncased'"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<p style=\"color: red;\">\n",
              "The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n",
              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n",
              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 231508/231508 [00:00<00:00, 1182861.96B/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-nVpbsMZE7Vg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "4991714e-546d-40c9-8f9a-3944d753afa3"
      },
      "source": [
        "#######################from huggingface documentation \n",
        "\n",
        "vanilla_bert_tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n",
        "vanilla_bert_model = BertModel.from_pretrained('bert-base-uncased')\n",
        "\n"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 313/313 [00:00<00:00, 167344.44B/s]\n",
            "100%|██████████| 440473133/440473133 [00:12<00:00, 36056373.82B/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tzdiHuLbFP5V",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "7260643a-2ebf-4954-9ed0-e5fd2d57246e"
      },
      "source": [
        "input_ids = torch.tensor(vanilla_bert_tokenizer.encode(\"Hello my dog is cute\", add_special_tokens= True)).unsqueeze(0)  # Batch size 1\n",
        "print(input_ids)\n",
        "outputs = vanilla_bert_model(input_ids)\n",
        "last_hidden_states = outputs[0][0].detach().numpy()  # The last hidden-state is the first element of the output tuple\n",
        "\n",
        "print(last_hidden_states)\n",
        "print(len(last_hidden_states))\n",
        "print(last_hidden_states.shape)\n",
        "print(outputs[0])"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([[  101,  7592,  2026,  3899,  2003, 10140,   102]])\n",
            "[[ 0.02520709  0.20342118  0.17750171 ... -0.43001023  0.07378592\n",
            "   0.563757  ]\n",
            " [ 0.03636122  0.56766444  0.9678673  ... -0.2936435  -0.19087738\n",
            "   0.02368227]\n",
            " [-0.02869914  0.29985967  0.6602603  ... -0.5045791  -0.05440554\n",
            "   0.21559508]\n",
            " ...\n",
            " [-0.658215   -0.2710841   0.2554944  ... -0.4243345  -0.21272337\n",
            "   0.94851536]\n",
            " [-0.7283804  -0.5868736  -0.13885021 ...  0.32102168  0.27705067\n",
            "   0.06500375]\n",
            " [ 0.53293115  0.08652045 -0.3042307  ...  0.03785072 -0.52515227\n",
            "  -0.20991923]]\n",
            "7\n",
            "(7, 768)\n",
            "tensor([[[ 0.0252,  0.2034,  0.1775,  ..., -0.4300,  0.0738,  0.5638],\n",
            "         [ 0.0364,  0.5677,  0.9679,  ..., -0.2936, -0.1909,  0.0237],\n",
            "         [-0.0287,  0.2999,  0.6603,  ..., -0.5046, -0.0544,  0.2156],\n",
            "         ...,\n",
            "         [-0.6582, -0.2711,  0.2555,  ..., -0.4243, -0.2127,  0.9485],\n",
            "         [-0.7284, -0.5869, -0.1389,  ...,  0.3210,  0.2771,  0.0650],\n",
            "         [ 0.5329,  0.0865, -0.3042,  ...,  0.0379, -0.5252, -0.2099]]],\n",
            "       grad_fn=<NativeLayerNormBackward>)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qHkWXna8eBFq",
        "colab_type": "text"
      },
      "source": [
        "### one sentence at a time"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Srz2e56Fi9cz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import pairwise"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OKCQlPlFS8pQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def bert_based_features_original(data, local_bert_model, local_tokenizer):\n",
        "  feat = np.zeros((len(data), 20))\n",
        "  cls_vectors = np.zeros((len(data), 768*2))\n",
        "\n",
        "  for i in range(len(data)):\n",
        "\n",
        "    sen_1 = data[i][0]\n",
        "    sen_1_input_ids = torch.tensor([local_tokenizer.encode(sen_1, add_special_tokens=True)])\n",
        "    sen_1_vectors = local_bert_model(sen_1_input_ids)[0][0].detach().numpy()\n",
        "    sen_1_cls_vector = sen_1_vectors[0]\n",
        "    sen_1_cls_vector_reshaped = sen_1_cls_vector.reshape(1, -1)\n",
        "\n",
        "    sen_1_avg_vector = np.average(sen_1_vectors[1:-1], axis=0)\n",
        "    sen_1_max_vector = np.amax(sen_1_vectors[1:-1], axis=0)\n",
        "    sen_1_min_vector = np.amin(sen_1_vectors[1:-1], axis=0)\n",
        "    sen_1_avg_vector_reshaped = sen_1_avg_vector.reshape(1, -1)\n",
        "    sen_1_max_vector_reshaped = sen_1_max_vector.reshape(1, -1)\n",
        "    sen_1_min_vector_reshaped = sen_1_min_vector.reshape(1, -1)\n",
        "\n",
        "\n",
        "\n",
        "    sen_2 = data[i][1]\n",
        "    sen_2_input_ids = torch.tensor([local_tokenizer.encode(sen_2, add_special_tokens=True)])\n",
        "    sen_2_vectors = local_bert_model(sen_2_input_ids)[0][0].detach().numpy()\n",
        "    sen_2_cls_vector = sen_2_vectors[0]\n",
        "    sen_2_cls_vector_reshaped = sen_2_cls_vector.reshape(1, -1)\n",
        "\n",
        "    sen_2_avg_vector = np.average(sen_2_vectors[1:-1], axis=0)\n",
        "    sen_2_max_vector = np.amax(sen_2_vectors[1:-1], axis=0)\n",
        "    sen_2_min_vector = np.amin(sen_2_vectors[1:-1], axis=0)\n",
        "    sen_2_avg_vector_reshaped = sen_2_avg_vector.reshape(1, -1)\n",
        "    sen_2_max_vector_reshaped = sen_2_max_vector.reshape(1, -1)\n",
        "    sen_2_min_vector_reshaped = sen_2_min_vector.reshape(1, -1)\n",
        "\n",
        "\n",
        "\n",
        "    cls_vectors[i] = np.concatenate((sen_1_cls_vector, sen_2_cls_vector))\n",
        "    if i == 0:\n",
        "      print(len(sen_1_cls_vector))\n",
        "      print(len(sen_1_avg_vector))\n",
        "      print(len(sen_1_max_vector))\n",
        "      print(len(sen_1_min_vector))\n",
        "      print(len(sen_1_vectors[1:-1]))\n",
        "      print(len(sen_1))\n",
        "      print(sen_1)\n",
        "    if i % 50 == 0:\n",
        "      print(\"i=\",i)\n",
        "\n",
        "\n",
        "    #calculate the features for max vectors\n",
        "    feat[i, 0] = pairwise.cosine_similarity(sen_1_max_vector_reshaped, sen_2_max_vector_reshaped)[0][0]\n",
        "    euclidean_dist = pairwise.euclidean_distances(sen_1_max_vector_reshaped, sen_2_max_vector_reshaped)[0][0]\n",
        "    feat[i, 1] = euclidean_dist #(1/(0.0000000001+euclidean_dist))\n",
        "    sq_euclidean_dist = euclidean_dist**2\n",
        "    feat[i, 2] = sq_euclidean_dist #(1/(0.0000000001+sq_euclidean_dist))\n",
        "    manhattan_dist = pairwise.manhattan_distances(sen_1_max_vector_reshaped, sen_2_max_vector_reshaped)[0][0]\n",
        "    feat[i, 3] = manhattan_dist #(1/(0.0000000001+manhattan_dist))\n",
        "    pearson_corr, p_val = pearsonr(sen_1_max_vector, sen_2_max_vector)\n",
        "    feat[i, 4] = pearson_corr\n",
        "\n",
        "\n",
        "        #calculate the features for min vectors\n",
        "    feat[i, 5] = pairwise.cosine_similarity(sen_1_min_vector_reshaped, sen_2_min_vector_reshaped)[0][0]\n",
        "    euclidean_dist = pairwise.euclidean_distances(sen_1_min_vector_reshaped, sen_2_min_vector_reshaped)[0][0]\n",
        "    feat[i, 6] = euclidean_dist #(1/(0.0000000001+euclidean_dist))\n",
        "    sq_euclidean_dist = euclidean_dist**2\n",
        "    feat[i, 7] = sq_euclidean_dist #(1/(0.0000000001+sq_euclidean_dist))\n",
        "    manhattan_dist = pairwise.manhattan_distances(sen_1_min_vector_reshaped, sen_2_min_vector_reshaped)[0][0]\n",
        "    feat[i, 8] = manhattan_dist #(1/(0.0000000001+manhattan_dist))\n",
        "    pearson_corr, p_val = pearsonr(sen_1_min_vector, sen_2_min_vector)\n",
        "    feat[i, 9] = pearson_corr\n",
        "\n",
        "\n",
        "        #calculate the features for avg vectors\n",
        "    feat[i, 10] = pairwise.cosine_similarity(sen_1_avg_vector_reshaped, sen_2_avg_vector_reshaped)[0][0]\n",
        "    euclidean_dist = pairwise.euclidean_distances(sen_1_avg_vector_reshaped, sen_2_avg_vector_reshaped)[0][0]\n",
        "    feat[i, 11] = euclidean_dist #(1/(0.0000000001+euclidean_dist))\n",
        "    sq_euclidean_dist = euclidean_dist**2\n",
        "    feat[i, 12] = sq_euclidean_dist #(1/(0.0000000001+sq_euclidean_dist))\n",
        "    manhattan_dist = pairwise.manhattan_distances(sen_1_avg_vector_reshaped, sen_2_avg_vector_reshaped)[0][0]\n",
        "    feat[i, 13] = manhattan_dist #(1/(0.0000000001+manhattan_dist))\n",
        "    pearson_corr, p_val = pearsonr(sen_1_avg_vector, sen_2_avg_vector)\n",
        "    feat[i, 14] = pearson_corr\n",
        "\n",
        "        #calculate the features for cls vectors\n",
        "    feat[i, 15] = pairwise.cosine_similarity(sen_1_cls_vector_reshaped, sen_2_cls_vector_reshaped)[0][0]\n",
        "    euclidean_dist = pairwise.euclidean_distances(sen_1_cls_vector_reshaped, sen_2_cls_vector_reshaped)[0][0]\n",
        "    feat[i, 16] = euclidean_dist #(1/(0.0000000001+euclidean_dist))\n",
        "    sq_euclidean_dist = euclidean_dist**2\n",
        "    feat[i, 17] = sq_euclidean_dist #(1/(0.0000000001+sq_euclidean_dist))\n",
        "    manhattan_dist = pairwise.manhattan_distances(sen_1_cls_vector_reshaped, sen_2_cls_vector_reshaped)[0][0]\n",
        "    feat[i, 18] = manhattan_dist #(1/(0.0000000001+manhattan_dist))\n",
        "    pearson_corr, p_val = pearsonr(sen_1_cls_vector, sen_2_cls_vector)\n",
        "    feat[i, 19] = pearson_corr\n",
        "\n",
        "  return feat, cls_vectors"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MGQaTvLfuNMd",
        "colab_type": "text"
      },
      "source": [
        "#### Vanilla bert"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZIpMEHP8Q_o5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 711
        },
        "outputId": "a4f0fe81-3ecc-4fe7-abf0-f980d45a312f"
      },
      "source": [
        "#VANILLA BERT FEATURES\n",
        "features_bert_og, features_bert_cls_og = bert_based_features_original(X_train_original, vanilla_bert_model, vanilla_bert_tokenizer)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "768\n",
            "768\n",
            "768\n",
            "768\n",
            "33\n",
            "179\n",
            " Patient arrives, via hospital wheelchair, Gait steady, History obtained from patient, Patient appears comfortable, Patient cooperative, alert, Oriented to person, place and time.\n",
            "i= 0\n",
            "i= 50\n",
            "i= 100\n",
            "i= 150\n",
            "i= 200\n",
            "i= 250\n",
            "i= 300\n",
            "i= 350\n",
            "i= 400\n",
            "i= 450\n",
            "i= 500\n",
            "i= 550\n",
            "i= 600\n",
            "i= 650\n",
            "i= 700\n",
            "i= 750\n",
            "i= 800\n",
            "i= 850\n",
            "i= 900\n",
            "i= 950\n",
            "i= 1000\n",
            "i= 1050\n",
            "i= 1100\n",
            "i= 1150\n",
            "i= 1200\n",
            "i= 1250\n",
            "i= 1300\n",
            "i= 1350\n",
            "i= 1400\n",
            "i= 1450\n",
            "i= 1500\n",
            "i= 1550\n",
            "i= 1600\n",
            "i= 1650\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "if3TDDkRjuXZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "outputId": "3b13538f-845a-4f0c-9454-71d57ba755f7"
      },
      "source": [
        "y_train_test_hat, y_train_test = predict_train(features_bert_og, y_true, get_optimal_rf_model(features_bert_og[:500], y_true[:500]))\n",
        "plot_accuracy(y_train_test, y_train_test_hat)"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Optimal parameters: {'max_depth': 4, 'min_samples_split': 4, 'n_estimators': 200, 'subsample': 0.6}\n",
            "Accuracy: 0.6952928797176983\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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mqdMWVp/2WQGaiEjBdRLUxNlGlO7WTo3vHuXY6K4Taz9WKhwb3RUp6Ija/bXk8iugb0YH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\nRERExDMK0EREREQ88/8DBRiXf0i5d2kAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1lXLFIpISSgw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_train_test_hat, y_train_test = predict_train(features_bert_cls_og, y_true, get_optimal_rf_model(features_bert_cls_og[:500], y_true[:500]))\n",
        "plot_accuracy(y_train_test, y_train_test_hat)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "giiR-WwBKI7g",
        "colab_type": "text"
      },
      "source": [
        "#### fine tunedBERT on *dataset*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4ys2b0SXKuvI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!cp '/content/drive/My Drive/bert_finetuned_sts_biomedical.zip' ./bert_finetuned_sts_biomedical.zip"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_4-CM_w8Lday",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 271
        },
        "outputId": "b1b20335-6922-41d1-9c69-bfd0fd8c138b"
      },
      "source": [
        "!unzip bert_finetuned_sts_biomedical.zip "
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  bert_finetuned_sts_biomedical.zip\n",
            "   creating: output/\n",
            "   creating: output/training_n2c2_sts_bert-2019-11-28_11-43-46/\n",
            " extracting: output/training_n2c2_sts_bert-2019-11-28_11-43-46/config.json  \n",
            "   creating: output/training_n2c2_sts_bert-2019-11-28_11-43-46/1_Pooling/\n",
            "  inflating: output/training_n2c2_sts_bert-2019-11-28_11-43-46/1_Pooling/config.json  \n",
            "  inflating: output/training_n2c2_sts_bert-2019-11-28_11-43-46/similarity_evaluation_results.csv  \n",
            "  inflating: output/training_n2c2_sts_bert-2019-11-28_11-43-46/modules.json  \n",
            "   creating: output/training_n2c2_sts_bert-2019-11-28_11-43-46/0_BERT/\n",
            "  inflating: output/training_n2c2_sts_bert-2019-11-28_11-43-46/0_BERT/config.json  \n",
            "  inflating: output/training_n2c2_sts_bert-2019-11-28_11-43-46/0_BERT/vocab.txt  \n",
            "  inflating: output/training_n2c2_sts_bert-2019-11-28_11-43-46/0_BERT/pytorch_model.bin  \n",
            "  inflating: output/training_n2c2_sts_bert-2019-11-28_11-43-46/0_BERT/special_tokens_map.json  \n",
            " extracting: output/training_n2c2_sts_bert-2019-11-28_11-43-46/0_BERT/added_tokens.json  \n",
            "  inflating: output/training_n2c2_sts_bert-2019-11-28_11-43-46/0_BERT/sentence_bert_config.json  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m9Yg1mVxN189",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pretrained_weights = 'output/training_n2c2_sts_bert-2019-11-28_11-43-46/0_BERT'\n",
        "\n",
        "# Load pretrained model/tokenizer\n",
        "sts_biomedical_bert_tokenizer = tokenizer_class.from_pretrained(pretrained_weights)\n",
        "sts_biomedical_bert_model = model_class.from_pretrained(pretrained_weights)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b318wP4tOl3l",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "features_bert_sts_biomedical_train60, features_bert_cls_sts_biomedical_train60 = bert_based_features_original(X_train60, sts_biomedical_bert_model, sts_biomedical_bert_tokenizer)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jqAtNNlRRoLk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "features_bert_sts_biomedical_test20, features_bert_cls_sts_biomedical_test20 = bert_based_features_original(X_test20, sts_biomedical_bert_model, sts_biomedical_bert_tokenizer)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ogGS0sF1IzjS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 711
        },
        "outputId": "3b0011bb-38e9-4843-ba85-9222c20e4026"
      },
      "source": [
        "features_bert_sts_biomedical, features_bert_cls_sts_biomedical = bert_based_features_original(X_train_original, sts_biomedical_bert_model, sts_biomedical_bert_tokenizer)"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "768\n",
            "768\n",
            "768\n",
            "768\n",
            "33\n",
            "179\n",
            " Patient arrives, via hospital wheelchair, Gait steady, History obtained from patient, Patient appears comfortable, Patient cooperative, alert, Oriented to person, place and time.\n",
            "i= 0\n",
            "i= 50\n",
            "i= 100\n",
            "i= 150\n",
            "i= 200\n",
            "i= 250\n",
            "i= 300\n",
            "i= 350\n",
            "i= 400\n",
            "i= 450\n",
            "i= 500\n",
            "i= 550\n",
            "i= 600\n",
            "i= 650\n",
            "i= 700\n",
            "i= 750\n",
            "i= 800\n",
            "i= 850\n",
            "i= 900\n",
            "i= 950\n",
            "i= 1000\n",
            "i= 1050\n",
            "i= 1100\n",
            "i= 1150\n",
            "i= 1200\n",
            "i= 1250\n",
            "i= 1300\n",
            "i= 1350\n",
            "i= 1400\n",
            "i= 1450\n",
            "i= 1500\n",
            "i= 1550\n",
            "i= 1600\n",
            "i= 1650\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sbf9QzE3R6AG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "outputId": "e85b82a6-f189-415a-a76a-c3e79ec59717"
      },
      "source": [
        "y_train_test_hat, y_train_test = predict_train(features_bert_sts_biomedical, y_true, get_optimal_rf_model(features_bert_sts_biomedical[:500], y_true[:500]))\n",
        "plot_accuracy(y_train_test, y_train_test_hat)"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Optimal parameters: {'max_depth': 4, 'min_samples_split': 6, 'n_estimators': 400, 'subsample': 0.7}\n",
            "Accuracy: 0.8878744404205204\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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XRdcnH4r+uzJxMrjvEydP5uK8ggKXOOovvRTpPCpXXMX5d26cMaRXueKqtsc/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\nQmCC\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7Up0N-lTOLVE",
        "colab_type": "text"
      },
      "source": [
        "##### test on actual test data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZOwHau28TLpY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "features_bert_sts_biomedical_test, features_bert_cls_sts_biomedical_test = bert_based_features_original(X_test_original, sts_biomedical_bert_model, sts_biomedical_bert_tokenizer)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cJPHoz3WPQ02",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_hat = np.zeros(y_test_original.shape[0])\n",
        "\n",
        "gb_model = GradientBoostingRegressor(n_estimators=100, max_depth=2)\n",
        "gb_model.fit(features_bert_sts_biomedical, y_true)\n",
        "y_hat =gb_model.predict(features_bert_sts_biomedical_test)\n",
        "\n",
        "print('Accuracy:')\n",
        "print(accuracy(y_test_original, y_hat))\n",
        "plt.plot(y_test_original, y_hat, 'ro')\n",
        "plt.xlabel('Actual')\n",
        "plt.ylabel('Predicted')\n",
        "plt.title('Predicted vs Actual')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HiiJrRe7eCUT",
        "colab_type": "text"
      },
      "source": [
        "#### fine tuned **roberta**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Yxcl_P4ierwx",
        "colab": {}
      },
      "source": [
        "!cp '/content/drive/My Drive/roberta_finetuned_sts_biomedical.zip' ./roberta_finetuned_sts_biomedical.zip"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "-QLKUXvgerw9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 271
        },
        "outputId": "d0ca1d95-9696-4033-df1f-541eb4ebc3d5"
      },
      "source": [
        "!unzip roberta_finetuned_sts_biomedical.zip "
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  roberta_finetuned_sts_biomedical.zip\n",
            "   creating: output/training_n2c2_sts_roberta-2019-12-03_08-16-46/\n",
            "   creating: output/training_n2c2_sts_roberta-2019-12-03_08-16-46/0_RoBERTa/\n",
            " extracting: output/training_n2c2_sts_roberta-2019-12-03_08-16-46/0_RoBERTa/added_tokens.json  \n",
            "  inflating: output/training_n2c2_sts_roberta-2019-12-03_08-16-46/0_RoBERTa/merges.txt  \n",
            "  inflating: output/training_n2c2_sts_roberta-2019-12-03_08-16-46/0_RoBERTa/pytorch_model.bin  \n",
            "  inflating: output/training_n2c2_sts_roberta-2019-12-03_08-16-46/0_RoBERTa/sentence_roberta_config.json  \n",
            "  inflating: output/training_n2c2_sts_roberta-2019-12-03_08-16-46/0_RoBERTa/vocab.json  \n",
            "  inflating: output/training_n2c2_sts_roberta-2019-12-03_08-16-46/0_RoBERTa/config.json  \n",
            "  inflating: output/training_n2c2_sts_roberta-2019-12-03_08-16-46/0_RoBERTa/special_tokens_map.json  \n",
            "  inflating: output/training_n2c2_sts_roberta-2019-12-03_08-16-46/similarity_evaluation_results.csv  \n",
            " extracting: output/training_n2c2_sts_roberta-2019-12-03_08-16-46/config.json  \n",
            "  inflating: output/training_n2c2_sts_roberta-2019-12-03_08-16-46/modules.json  \n",
            "   creating: output/training_n2c2_sts_roberta-2019-12-03_08-16-46/1_Pooling/\n",
            "  inflating: output/training_n2c2_sts_roberta-2019-12-03_08-16-46/1_Pooling/config.json  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "73c7gKMYerxA",
        "colab": {}
      },
      "source": [
        "roberta_model_class = RobertaModel\n",
        "roberta_tokenizer_class = RobertaTokenizer\n",
        "pretrained_weights = 'output/training_n2c2_sts_roberta-2019-12-03_08-16-46/0_RoBERTa'\n",
        "# Load pretrained model/tokenizer\n",
        "sts_biomedical_roberta_tokenizer = roberta_tokenizer_class.from_pretrained(pretrained_weights)\n",
        "sts_biomedical_roberta_model = roberta_model_class.from_pretrained(pretrained_weights)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "iivmkrVperxE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 694
        },
        "outputId": "14d2fb52-5d7d-452e-e48a-ca7e3113fd96"
      },
      "source": [
        "features_roberta_sts_biomedical, features_roberta_cls_sts_biomedical = bert_based_features_original(X_train_original, sts_biomedical_roberta_model, sts_biomedical_roberta_tokenizer)"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "768\n",
            "768\n",
            "768\n",
            "768\n",
            "35\n",
            "179\n",
            " Patient arrives, via hospital wheelchair, Gait steady, History obtained from patient, Patient appears comfortable, Patient cooperative, alert, Oriented to person, place and time.\n",
            "i= 0\n",
            "i= 50\n",
            "i= 100\n",
            "i= 150\n",
            "i= 200\n",
            "i= 250\n",
            "i= 300\n",
            "i= 350\n",
            "i= 400\n",
            "i= 450\n",
            "i= 500\n",
            "i= 550\n",
            "i= 600\n",
            "i= 650\n",
            "i= 700\n",
            "i= 750\n",
            "i= 800\n",
            "i= 850\n",
            "i= 900\n",
            "i= 950\n",
            "i= 1000\n",
            "i= 1050\n",
            "i= 1100\n",
            "i= 1150\n",
            "i= 1200\n",
            "i= 1250\n",
            "i= 1350\n",
            "i= 1400\n",
            "i= 1450\n",
            "i= 1500\n",
            "i= 1550\n",
            "i= 1600\n",
            "i= 1650\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "mKMEKhh0erxN",
        "colab": {}
      },
      "source": [
        "features_roberta_sts_biomedical_test, features_roberta_cls_sts_biomedical_test = bert_based_features_original(X_test_original, sts_biomedical_roberta_model, sts_biomedical_roberta_tokenizer)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "GDfLy3G4erxP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "outputId": "669d46aa-7755-44f1-c1a1-bfea1c794d00"
      },
      "source": [
        "y_train_test_hat, y_train_test = predict_train(features_roberta_sts_biomedical, y_true, get_optimal_rf_model(features_roberta_sts_biomedical[:500], y_true[:500]))\n",
        "plot_accuracy(y_train_test, y_train_test_hat)"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Optimal parameters: {'max_depth': 4, 'min_samples_split': 8, 'n_estimators': 400, 'subsample': 0.7}\n",
            "Accuracy: 0.8886079232922197\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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szy/d0XVJH12T9MnSNTm4Zdu5oGIu/YsWR3perdNlU0eOcuAzf8bJyX+ObKTFL+Cqt891\nLn6vTWVqioNbtlG66upI+teJ/kWL/aflIr4ucZvcN87hv3v4fH2xSoXDf/cwpZ+7OJYRtnK5FDhQ\n5GqkazXwGuBgbZTrVcBDxpjbrbXfcNQHERFJWNjk5Timf3qdLvOb+qs/b70tcHoxRBX3NCV8Q/Du\nAfNHR7t+ziSmT9NUENlJ0GWt/RjwsfptY8yPgLdr9aKISLEEJTWXFiygb+ClsX4Y9xLUhC16GihE\nFfc0JXwDnHz0vwe2d7OtUVILJtIUzKoivYiIOBOU1Dyy7pbYRx16CWoCi552cOy5pKlyOuBb9b5d\n+1ySGnFKUzCbSNBlrb0kieOKiEiyglbbnjnwBBOb7oltk2jobRVbL6MinU2VpqNyehzajTjFWaOt\nqKsXRUREZpUImNi2tTl3qFI5dzvKwKuX8ipBoyV+zk2VnjhO/6LFoY/RrnJ6ImUjgvLTSqWunq7d\na1hvj70WWIfXJGoKukREJFGTu3cFtkc92tVtTShv6u/e5qCoHnw0BiZ9fQxcdBEvWAvVKtPPn+DM\ngSdCHdNV7lHoZPaAemOB7XPwG3HyffqAKcdekvDr1z3plb4KukREpK3YV5wFJZmHSD530r9zfEZ4\nWkeDKhVeePzxptthR+1c5B61S2aHlpWY8+aBT4BUWrCgq2P7jTSGDTR7TcKv/4zs10iXiIikVdq3\n6HHVP2/qryVxvlqdPeoTMAoUZtTORe5RUDL7oR33w4svNr2OQUpdTi/C7JHGei5Xq9ZAs5ck/DT9\nDM9dOERERAqr3YddGrjqX89TfCFG7YZWjDGyfuO5gKN/8TAj6zcGbhf01F13sv+OjTx1151M7hsP\n1Y2g86iePj3ntF9d5dSpUPcLY8matZTmzWtq8ws0e5l6TdPPsEa6REQkkIs8o16m1VzlQXWSSO8r\nRHFUCJdz1svITc/nQbTTnWEXN2ThZyQMjXSJiEiiwo52+An60I26BpNfH+nrg76WsYuAqbeXdrHn\ncJBeRm6CXuvywnD7G8dRamFoxRiX3v1JrrxnC5fe/UnfwDELPyNhaKRLREQSNbRijOf37mlKQB+4\n7LLAabXGUZH5o6OcHN8bew2moBGZ1raZqRd8i4dOHzocWV96Gblpdx5++WQDl13mrcSs1U8bHFuZ\nSC5fL+U+VKdLRESyoVz2z0cKOV0WZmXhxLatzSv+gBcef5yJbVubks/9ptVOju91FhgETf01tu2/\nY6PvY9MyHQvtpzBbA9rJPXuaNoqe3LOH+ZdfkVjg1c1xVadLREQy4aXGzAqI6u1zCZt7FLZOV9C0\nWmuJhpPjexMLDFyUffCtGdbX1/PITWtQc+Dfvn/2is2ZaQ4/sD0VK1c7oTpdIiLiy13dqbkFTYuF\nmS4Lvcw/ZJ2usKNFLvbzCxLHVNbEtq1eYFobyfMC3tbcse7LOAQJWqUY5erFolHQJSKSImmqKVQ/\nfiftXT027HYzQVOdAceIcz+/IFFPZfltkeQ38sjMdORV3PNExVFFRGSWTopAuvgg6Wm6LGw+WLk8\ne89Bv/uFDLjq/Pbzg+6SsTsR5VRW0NSrn6iruDMwAFNT/u0ZkqYvMgq6RERSxNW2KGHNHx1tHmlp\naJ9T2O19/AIun/ZeakxVz57l8APbqZ492/NWMnMFbJEGwx0Emq1lH3qp4g5Q6u+n6hN0lfrTEzqE\nuSa9vg5RUp0uEZEUCVtTyFWV7VPffrSj9jgF1Woauu76piruQSqnTnX9mk3uG2di86am0bOJzZtm\nVYKf3DfOxL1f8O5XrXr3u/cLoSvGzxJylShAtWWKtteioH6lL9q1u1b/4tE6otn6WqepOGp6wlUR\nEQmdiO3qgySuZOrGfKuwwtZqCtrPL0iY+x5+YHuolXwT92313Y9x4r6t3ZU7WLXad6TRT2sw1OtK\nShcrMXsRdgQrTeehkS4RkRTpZP+9LGscnehEL9XLSwsW+D5nmA/f0MGnXw5Uu/Y5LL91A0PXXX9+\nxKtcphSQU9V6Hr1UcY/i8XEL+8UjTeehkS6RGGnlkHSj2yKQcSgtWOA7nRQUwDTfKWBVYsw6rbqe\nliAiyPJbN7QtEgv+59FLFfcoHh+3sCNYKo4qUgBpWjEjElbrF4XBN7yRyT27ZxXiHFl3y5zPNbT6\nutBTY1ELW3U97Idv6OAzbPmLHnQSDPUawKfpC0CrTmqiqTiqSM6lacWMZEuoEdK+Pv9Vf319PR3X\nb5udoWtXceaxxzoOVOqjM42FPenvh5bfiywYWXeLbxX41uAzKNAcWn1dpP1JczDkStpH4vwo6BKJ\nSZpWzEh2hB0hXX77e5m4589nPX757e/t+thBXxROfmuc6osvev15/gRnDjwR+oMtzNRY0OjQ0HXX\nd3EWwXoZfQ77Ae8XaA6tWt30Gkh0shZ8KugSiUmaVsxIdoQdIY0jTyXoC0FTraZK5dxITphAwm/U\nbmT9xlltZw48EXug0uvoc9gP+HqgmfRUlqSPgi6RmMSxB5vkX5IjpJ0UH53c9cicQVHQyNLg2MpZ\n920dEYuDRp8laSoZIRKToiz9l2iFLY7aVBiyVoTTrzBkJ/yW1gcKsSoxaGRp8pGH5yxoGYewr61I\nXDTSJRKjrOUbSPLCjpDGsVDDL2+pl1Gg0JXPHS0w0eizJE1Bl4gUTprrp4VN2I5rqqz1i8L+OzZ2\n/VydBG3Tx481VamPayNqyNZqN8kXBV0iUiiu6qf1EtiFGSHNwkINv5GldlqnHKH7axL0+mv0WZKk\noEtECsVF/TQXgd380VHfelDzR0cjef66XoI7v5Gl+aOjTO7ZM3sfwxa9XJN2r39rfzTSJS4p6BIp\nuDRPtcXBxQo2F4HdqW8/Gtwe4SrAXoO71pGlyX3jXoX7ELq9JkGv/+EHtlM9e1a7REhitHpRpMCa\nVsDhdiVZUlysYHMR2IXegLlHZx57rKP2uRx9cKd/JX0f3V6ToNe5cupUYDAs4oKCLpECazcik1d+\nZRGiXsGWp9IEUQeQYR/XyzXp9HVWnS5xRUGXSIEVsViki/ppLgK7WRstz9HeragDyKDHlRcujOya\nBL3+Qa9NFoNhySbldIkUWBZWwGWRi9IEI+tu8d17sXUD5l4tWbOWic2bmhPf+/q7DiCDamUtu+nm\nUK9PmBzEoNf/zIEnnCw+EAmioEukwIpYLHJy33hTEDF9/Jh3m2iTqeMuTXDmwBOB7ZEftzLT/nZN\nLwFR2IAr7KpQv9c/aNq82/y0JBVtAUxeKOgSKbAiFos8/MD22eUKZqY5/MD2TJ335O5dge1R7mF4\naMf9s7f8qVY5tOP+WasSewmIwuh1VWheptNd1ZqT6CnoEim4ohWLdLXqL/aRiEqls/YuVU+fDtXe\na0A0sW2rF0hWKlAuM7Rq9azgsdegqdfp9LDXtH6//SeO079oceTX3kVJEomHEulFRCLmpBRHOeDt\nO6g9Zr0ERBPbtnq5VvWAsVJh8pGHmdi2tel+vSb1B+VuhcnpCntN49iIvFVeRuyKSEGXiBSKi1V/\nLkpxDK1a3VF7t8oLF4ZqD3z95s3jqbvuZP8dG3nqrjt9g492U6WNel0V2kvNsbDX1MW1z1NJkqJR\n0CUihTKy7hbo62tu7OuLdNWfi5GI+ZdfMXtUq1z22iO07Kaboa8lE6Wv32tvUCqV/J/g7Nm5R/xC\nTpX2Wu6jl+sS9rEurr2LkiQSD+V0iUihuFg84CJ36OiDO2cHK5VK5Hk9YV+vsDlxveYe9ZKD2Mt1\nCftYF2VYirgAJi8UdIlI4cS9eKCX/QrDrkxzmdcT5vUKCjb8tN6vNDBAdWpq1v1KAwPhOxlCLyVS\nwj7WVRmWoi2AyQtNL4qIRMxF7lDa8nr8pryCtPbRL+Bq196tXqYnwz626X6lUiw7Hkh2aaRLRCRi\nLnKH0lbY1m/Ka/7oKJN7djdvcN3XN7uP5bJ/XldCKzGDhB1dqt9v6dJBjhw56aBnkhUKukREIuYi\nd6gpyImpHlSnWoOSyX3jTO7Z03Ivn4R7RzXHvN0I7j0XBHq7EdwLqKiouJGurxEiIjmwZM1a3xV/\nYXOH8rIy7eiDO32r/yc1VXpox/3No24AMzNeu4gDGumSVIq7orMUm5t966pz3PYXdmWay61gun29\n0jZVGra6fq/0/iVBFHRJ6mhfMYmTi58vb4Rn9ohK2FIJYXKHXG0F08vr1clU6fN79/DC44+faxu4\n7LJM/r7r/Uva0fRijkzuG5+z8nMWuKjoLMXl4ufLRTkHVyUjenm9gqZK54+ONr1X/fiTH28KuABe\nePzxWdsA9Spsdf1e6P1L2nE20mWM+QrwaqACnAJ+y1r7PVfHz7s8fbvK075ibqaxpBMufr5cFMgs\nLVjgOy0W5XZG0NvrFbSi8eT43qb3qqDnmty9a9am171YdtPNTGze1Jxn5lNdvxd5ev+S6LmcXtxg\nrf0ZgDHmncAm4BqHx8+1PO067+IDy4U8BcJ54uLny0WOUqlU8s0SC9yOx0eoLwUdlHMIer7G53zq\nrjtnvVcFinj1YhZ2I5B8cxZ01QOumpfhjXhJRPL07Spt9Ye6ladA2BUXI4Mufr5c5CgFbbsTdjue\n0F8KQpZz6LWSvq8Y6nSleTcCyT+nOV3GmHuMMT8GPgJEN2YsqatO3YuhFWMMjq08/4ZbLjM4tjJz\ngUqeAmEX6h/ac26O3KNeN00OY2Lb1thzlHrNT4q68n2vz+dnaNXq0PdNi152I5D8c7p60Vp7B4Ax\n5jbgT4G3hX3s8HB0iY5Bli4djP0YcaluvJUnP/s5Kg3bZpQHBnj1xlszd16Hd+3m1LfGz3+TrlQ4\n9a1xqteMsmz1qmQ714Gnly5h6sjRWe0DS5dk7pq0iqP/T3/1Qd8P7RNffZDLbrwh0mNVhy7gRF+J\n6VKJvr4Sg0MXRHpO+3fv8m2f3L2L1/7O+yM5xhMvvujbXn3xxVDnsr/Nl4LGx4d9b9l/4rj/8504\nHur5Fpormfz+D7zf+3KZkRt+ict/831znkfahH0dJDlJXodESkZYa+8zxvy5MWbYWhvqa/+xY6eo\nVMLVuelG1rdrKF11Nctu2zBraqZ01dWZO6+DW7Y1vSEDVKamOLhlG6Wrrk6oV51b9M41vtNYi965\nJnPXpFFcvyt+AWq9PcrjtU6DTR05yoHP/BknJ/85utGuNlNyUZ1Lu/0KQx2jTa5W4+NLV13NwjeN\nMbl717mAaOGbxma9t/QvWuyfy7Ro8azn83uvGloxxvKWx2bx9yTs6yDJcPFZXy6XAgeKnARdxpiF\nwCJr7TO12zcCx2t/JCJ52XU+L9NyLpJ288RVArKTXLss7CXYQa7W5J7dTSPPk3t2M//yK5per05y\n5fLyXuUnLzmpEg9XI10LgC8bYxYAM3jB1o3W2viGriSz8rT6J88fLlFz9WHlIqgfWrXaN5m6lxyl\n1kUGvQr7e9Zu65zGn219yfCkcU9MSQ8nQZe19hCwwsWxJPv0TbGYXH1ouwjq67WlGqfkhlat7rrm\nlN/KwF6F/T3rZOscfcnw1F+HrKetSPS0DZCkjr4pFpeLD+0la9b6FsiMOqhffuuGyAp7+k2J9koj\nUyLuKeiSVNI3RYlXd5tRJyWufMYwQW554ULf2l9Rbp0jUhQpyuoUkTSr7+25913vzvzenkGbUadV\nkvmMy266Gfpavp9HvHWOSFEo6BKROTUVLq1WYytc6kIWV8f6bRztytCKMZbf/p6mYrLLb3+PpiFF\nuqDpRRGZU562NMri6li//KvAIDGDW+eIFIWCLhGZUxZHh4JkdXXsrI2j//BDTP/0p7Pu17+8tcSo\niKSFgi4RmVMWR4eC5GXV3vRzz3XULiLJU9AlInPK6uhQkFxMl1UDVlwGtYtI4hR0icSotYp4FkdU\nQLXTRESioKBLJCZ+VcQPfXELQCaDFdVOS5l588CvYGpCqxxFZG5tgy5jzH2EqBporV0fWY9EciJP\nK/7yJg8jkENjK/33dxxbGfmx8vB6iaTBXGuLDwBP1v78DHgX0Ac8W3vsO4Hn4+ygSFblacVfnkzu\nG2di86Zz12H6+DEmNm/KXM2xk4/+947au9VUow0yXaNNJGltR7qstX9c/7cx5iHgV6y1exra3gz8\n+/i6J5JdeVrxlyeHH9jevO8iwMw0hx/YnqnRm042ou6FRmxFotNJFb0VwL6Wtn8A3hRdd0Tyw6+K\neJZX/OWF3z6C7dqLTiO2IlImywcAABoISURBVNHpJOj6LvBRY8wFALW/PwJ8L46OiWTd0IoxBsdW\nnq8QXi4zOLZSowMSiaANp6PeiDpoZDbqEdv63p7779iY6b09RdrpJOjaCKwEfmaMOYSX4/VmYEMM\n/RLJvMl945wc3wuVitdQqXByfK8+TBJWWrCgo/a0WnbTzbO3/CmXI9+I2sWIrfLGpChCB13W2h9Z\na8eAy4B3AJdba8estQdj651IhrXLhZHkjKy7Bfr6mhv7+rz2rCmV2t+OwNCKMUbWb2za8Hpk/cZI\nR2z1uyJF0VGdLmPMMHAd8Apr7d3GmFcCZWvts3F0TiTLlAuTTnnZBujogzthZqa5cWYmlgT3uCv4\n63dFiiJ00GWMWQ3sBL6NN814N3AF8AHgxlh6J5JhWr3YOVf1oFxsAxT3ueQpUNHvihRFJyNd/xn4\nNWvt3xljTtTa/gF4Y/Tdcqf+xrhfW5tIxPK2X2Hc8lTBP45zaQ3iGBiAqalZ94s6kd4F/a5IUXSS\nSH+Jtfbvav+uV6k/S4a3EmpK3qxWlbwpkXKRC5Mnecrrifpc/BLN/QIugGoGN7zW74oURScB0w+N\nMTdYax9qaPuXwD9F3CdnVPRP4uZiGisv8jRdFvW5+L1XBYm6OKor+l2RIugk6LoT+Jox5q+BC4wx\nn8fL5XpnLD1zIE9v8iJxi3sqPk95PVGfSyfvSVl8vUSKopOSEfuAUeAHwCbgIPBGa+2jMfUtdq6K\n/knn6oUS977r3SqUmAIupuLzVMF//uhoR+1zCfuelNXXS6QoQgddxpgPWGt/aq2921r7v1trP2at\nfdYY87txdjBOeXqTzxPl2qWPi3yrPOX1nHnssY7a5xL0XjV03fXe61UqZfr1EimKTqYX/xD4hE/7\nHwCfiqY7bjXV69HqxdRQrl36uJqKz0teT9Sv11y1xZYuHeTIkZPddTYEV6U8RPJuzqDLGPO/1P7Z\nZ4y5HmgseXwpEN9vugP1N/m437QkPOXapU+e8q1ciOP1SiogzVMpD5GkhZlevLf256V4uVz12/cA\n7wV+K7beSSEp1y59NBXfmTy9Xnkq5SGStDlHuqy1rwYwxnzRWrs+/i5J0alQYvpoKr4zedlqCDTy\nLBKlTnK6PmWMudBa+0y9wRhzIbDYWvuP0XdNikof8OmkqfjOuNxqKM4dNTS1LBKdToKubcA7Wtrm\nAffhlZIQiYw+4CVOeUgMd5VrpZFnkeh0sg3QRdbapxobrLVPApdE2iMRkRj5bamTxZIkrnKt8lTK\nQyRpnYx0PWuMucZa+516gzHmGuCn0XdLRCQeeSlJ4jLXKi+lPESS1knQ9Wngq8aYu4EngcuADwAf\niaNjIiJxyEtiuHKtRLKnk22AvgD8LvArwJ/W/r7TWvvnMfVNRCRyeSlJkqeyFCJF0clIF9baLwNf\njqkvIiKxW7JmLRObN8HM9PnGvv7MBSta5SuSPW2DLmPMbdba+2r/fk/Q/ay1m6LumEge5GGVXD5V\n57idDVrlK5Itc410rcMrCQFwW8B9qniV6kWkgbZPSaejD+6EmZnmxpmZzCXSi0j2tA26rLVva/j3\n9fF3RyQ/XK6S04haeHlJpBeR7JlrejFUor21thJNd0Tyw9WHu0bUOlNasIDq6dO+7VnjoiK9iERn\nrunFacIlO/RF0BeRXHG1pD8vdadcKZVKvm9qpVLJeV96oWBbJLy0fEGZayTr1cCltT+/BewCfhl4\nTe3vh4H3x9lBkaxytaRf02WdqZw61VF7WrmqSC+SdU27UFSrie5CMVdO19P1fxtjfhd4vbX2+VrT\nfmPMt4FvA38WXxeliNLyraQXTUv6Y8y1UpHMYnIZbCtnULIsTbMBndTpehkwH3i+oW1+rV0kMpP7\nxpnYfO+5FWbTx495t8netImL7VO0IXExuQq2NY0pWZem2YBONrzeCnzTGPM+Y8xbjTHvAx6qtYtE\n5tCO+32X9B/acX8yHUo5bUhcTK6mrzWNKVmXpl0oOhnpugs4APwa8ErgOeAzwBdi6JcUmN/Ksnbt\naeZqWkYbEhePq4r0aRolEOlGmmYDQgddtbIQn6v9EZE5aFpG8kA5g5J1adoyK3TQZYwpAXcANwFL\nrbWjxphVwHJr7Zfi6qBIVqUpeVPyx8t9PL+HpJf76G0OEuXPV5pGCUS6lZYtszrJ6foT4L1404kX\n1dqeBT4Ydaek2MoLF3bUnlaalkmpljyoOdtT6vAD25s37QaYmfbaI6ScQZHodJLTtRG42lp71BhT\nLxFxEK+GV1vGmGG8PRwvA84CTwC/Ya090ll3pQiW3XRz0zd4APr6WXbTzcl1qgt5m5bJQxkPyE9x\nVJf1xpQzKBKNTka6+oD6b3P9PWthQ1s7VeBua62x1r4WeBL4WAfHlgIZWjHG0LXXQrn241kuM3Tt\ntZl703e1usyFNBUX7FV1aqqjdhGRqHQSdH0d+JQxZgDO5Xj9B+Cv5nqgtfa4tfaRhqZ9wMUdHFsK\nZHLfOJN79kCltqVnpcLknj2Z+4AfWjHG4NjKpuBxcGxl5oJHUNmANAraKzKLe0iKFEUn04u/g1eT\n62fAS/BGuL4BrO/kgLVNtP8N8JedPG54OP58nqVLB2M/hsztqb/Y4ZurcvQvdnDZjTck06kuHN61\nm1PfGm8KHk99a5zqNaMsW70q2c51aP+J477t0yeOZ+73Zn+b/8vSuVR/4w6e+C+faa5p19fH5b9x\nR6bOI+90LdInyWsSKuiqjWotAX4VWIw3SvWMtXaii2P+N7yA7TOdPOjYsVNUKmH23u5O0isa5Lzp\nk/7XYfrkyUxdo4NbtlFpmbKqTE1xcMs2SlddnVCvutO/aLF/ftqixZm6JuCNBPnVfCstWJCpcyld\ndTXLb3/vrGXwpauuztR55Jk+V9LHxTUpl0uBA0Whgi5rbdUY80/AoLX2MHC4m44YYz4BXAHcWKv7\nJZJbeVq9mKeyAYNveCOTjzzs2541aVkGLyLhdDK9+F3gSuD/6+ZAxpiPAr8A/Iq1Vhmrkn8DA+CX\nnD0w4L4vPUpTccFenXnssY7aRUSi0knQ9Qjw/xhjtgDPcH4FI9baTe0eaIz5eeD38dIpxo0xAAet\ntf+qw/5KAbSb/smUlsTzOdtTLi+jKnkagRSRbOkk6FqJV5drdUt7FWgbdFlrfwBkqwiOJGZk3S1M\nbL53VoLwyLpbkutUN6oBOYhB7eJEboJ6EcmcOYMuY8x84A/wkt+/A3xU04MSp9xMZZVK/gFWxopw\n5k1eiqOKSPaEqdP1WeBG4HFgLfCJWHskkhflgF+voHZxwmUldxGRRmHe/X8ZeIu19i7grcDb4+2S\nFF1uqp83To+GaRcnVFRURJISJuhaYK19DsBa+wzwsni7JEWn6ucSp6BpRE0vikjcwiTS9xtjrud8\nInzrbay1/28cnZNi0uoyiZOmF0UkKWGCrsM0r0481nK7ClwaZaekO5P7xr3k8+PH6F88nM3kc/By\nnio+tXMzlgtVXrjQ94O8vDD+La0kWP/iYf/q+ouHE+iNiBTJnEGXtfYSB/2QHtXzoOrTcvU8KCB7\ngZdfwNWuPaWW3XQzE5s3Ne8j2dfPsptuTq5Tkqvq+iKSLdkaOpBAecqDChpxyNpIxNCKMZbf/p5z\n/e5fPMzy29+TvSA4Z4ZWjDGyfmPTdRlZv1HXRURi10lxVEmxPOVBzR8d9d0bb/7oaAK96U29iruk\ni66LiCRBI105kZfRIdDeeCIikk8a6cqJPOWp5GnUTtIpN4tORCRTFHTlRNPWORn/INHqMolTrhad\niEimKOjKkbzkqeRp1E7Sp92ikzz8/ohIeinoktTJzYbXkkqavhaRpBQ+6KrnduzXh3uq1Eftli4d\n5MiRk0l3R3JE09cikpRCr17MzcbKIhLakjVrKc2b19Sm6WsRcaHQQVeeCoqKSDgqjioiSSn09KJy\nO0SKKS+LTkQkWwo90pWngqIiIiKSboUe6VJpApHwtOhERKQ3hQ66VJpAJBwVFBUR6V2hgy5QaQKR\nMFRQVESkd4UPuiSdNJWVLlp0IiLSu0In0ks6qX5a+mjRiYhI7xR0Seqoflr6qKCoiEjvNL0oqaOp\nrPTRohMRkd4p6JLU0d546aRFJyIivVHQJamzZM1aJjZvgpnp8419/ZrKSlieFjfUz2X6+DH6Fw9n\n+lxEJDsUdEk6VWba3xan8lSnK0/nIiLZokR6SZ1DO+6HarW5sVr12iUReVrckKdzEZFsUdAlqVM9\nfbqjdolfnhY35OlcRCRbFHSJyNzKAW8VQe0ppppjIpKU7L1jSv4NDHTWLvGrVDprT7H5o6MdtYuI\nREVBl6RO+SUv6ahd4pen0aEzjz3WUbuISFQUdEnqVE6d6qhd4penivTK6RKRpBS+ZESeag/lhYqj\npk+eKtLr50tEklLooEv1etLJK456L8w01Obq68vkqEqe5KUi/ZI1a5t+7yG7o3Yiki2Fnl5UvZ4U\n86nTJRKFoRVjjKzfeG5kq3/xMCPrN+qLlojErtAjXcrtSKfDD2yfvSquUuHwA9v1wSiRqI/aiYi4\nVOiRrjytyMoTJdKLiEgeFXqkS7kdEjdtrCwiInWFDrrytCJL0kcLNUREpFGhpxdF4qSFGiIi0qjQ\nQVd9JGL6+DGoVs+NREzuG0+6a4WWl1w7LdQQEZFGhQ66NBKRTnnZGy8vwaOIiESj0EGXRiLSKS97\n4+Vp6xwREeldoRPptR1IOuUlGG5aqKHViyIihVfooEslI9IpT8GwinCmk0p5iEgSCj29OLRijMGx\nlVCuvQzlMoNjK/Xmm7C85HRJOjUtoAEtoBERZ5wEXcaYTxhjDhpjqsaYf+HimGFM7hvn5Pje81vO\nVCqcHN+rN9+E5SWnS9JJC2hEJCmuRrq+AqwCnnZ0vFD05ptOecnpknTSz5eIJMVJTpe19u8BjDEu\nDhea3nzTKU85XcodSp88/XyJSLZkJpF+eHhh5M+5v1w+P7XYqFxm6dLByI8n4fzsF1/Poa8/NKt9\n+Bdfn6nrcnjXbg7ft5XK1BTgBfOH79vK4NAFLFu9KuHe9SZL16FVdeOtPPnZz527LgDlgQFevfHW\nTJ9XlvueZ7ou6ZPkNclM0HXs2CkqlWq0T+oXcNXajxw5Ge2xJLQje/YGtr9s7TrHvenewS3bmj7Y\nASpTUxzcso3SVVcn1KveLV06mOnfj9JVV7Pstg2zRiBLV12d2fPK+jXJK12X9HFxTcrlUuBAUWaC\nrjhomiGdKqdOddSeVpq+Ti+V8hCRJBS6ZMSSNWuhryXu7OtXnS6JhLYBEhGRRq5KRvxXY8yzwKuA\nbxpjfuDiuOG0TllGPIUpHSstWNBRe1qp3piIiDRyEnRZa3/bWvsqa22/tXa5tfbnXRx3Lkcf3Akz\nM82NMzMqGZGwwTe8saP2tDr17Uc7ahcRkXwr9PSicm7SKS/FUfOSmyYiItEodCJ93uSlJpSCYRER\nySMFXTkxuW+cic33npsunT5+zLsN2Qu8SiWo+uTWlUru+9KD0oIFVE+f9m0XEZHiKfT0Yp4c2nG/\nb37aoR33J9OhXvgFXO3aU2pk3S3Q19fc2NfntYuISOEo6MoJvxGVdu0Sv6EVYwxduwrKtV+zcpmh\na1dlb+RRREQioaBLJCaT+8Y5Ob73/M4HlQonx/cyuW882Y6JiEgiih10lQNOP6g9xcoL/bccCGpP\ns7ycy9EHd1I9e7aprXr2rEqSiIgUVPaiiyi12Xsxa5bddLNvdf1lN92cTId6kJdz0SpMERFpVOjV\ni3nae7GeJ5SHkhFN53LiOP2LFmfyXPL08yUiIr0rdNC1ZM1aDn1xS9MUUGnevMzuvZinTXzr5+Ji\nR/i45O3nS0REelPo6cWhFWMMjq1sWl02OLYyN4GLJGtoxRgj6zeeG9nqXzzMyPqN+vkSESmoQo90\nBa0um3/5FfpglEjkafRRRER6U+iRLq0uExEREVcKHXRpdZmIiIi4UuigK2gVmVaXiYiISNQKHXQt\nWbOW0rx5TW1aXSYiIiJxKHQifV7qQYmIiEj6FTrognzUgxIREZH0K/T0ooiIiIgrCrpEREREHCj8\n9GKeTGzbyuTuXV6x13KZoVWrWX7rhqS7JSIiIijoYnLfOEcf3Mn+jCfST2zbyuQjD59vqFTO3Vbg\nJSIikrxCTy9O7hvn0Be3eMVQq1Wmjx/j0Be3MLlvPOmudWxy966O2kVERMStQgddudoGqL5/ZNh2\nERERcarQQZe2ARIRERFXCh10aRsgERERcaXQQdeSNWuhr6+5sa9P2wCJiIhI5AoddHlKc9zOiJY9\nJOdsFxEREacKHXQdfXAnzEw3N85MZzKRfvn6jVBqCRhLJa9dREREElfoOl15SqRv2rz7+DH6Fw9n\ntuaYiIhIHhU66GJgAKam/NszqL55t4iIiKRPoacXaanRNWe7iIiISJeKHXRVq521i4iIiHSp2EGX\niIiIiCPFzunKmfrm3UqkFxERSR8FXTlR37y7vpdkffNuQIGXiIhICmh6MSdytXm3iIhIDhU76Grd\nAmiu9hTLU80xERGRPCp20DUz01l7imnzbhERkXQrdtCVI0vWrKXUss9iad48bd4tIiKSEkqkzwlt\nAyQiIpJuCrpyRNsAiYiIpJemF0VEREQcUNAlIiIi4kChg66XvuY1HbWLiIiIdKvQQddFd35wVoD1\n0te8hovu/GBCPRIREZG8KnwifT3AWrp0kCNHTibcGxEREcmrQo90iYiIiLiioEtERETEAWfTi8aY\nK4GtwDBwDFhvrX3C1fFFREREkuRypOtzwGettVcCnwU+7/DYIiIiIolyEnQZY5YB1wA7ak07gGuM\nMUtdHF9EREQkaa6mFy8EfmKtnQGw1s4YY35aaz8S5gmGhxfG2D3P0qWDsR9DOqfrkj66Jumja5JO\nui7pk+Q1yULJiD6AEydOU6lUYzvI8PBCjh07FdvzS3d0XdJH1yR9dE3SSdclfVxck3K5xKJFC6AW\nvzQqVavxBTJ1tenF/cBwbZSrDy+Z/gpr7VwjXW8G9sTdRxEREZEIXQv8fWODk5Eua+1hY8z3gHXA\nttrf3w0RcAE8itfx54CZ+HopIiIi0rM+4BV48UsTJyNdAMaY/wmvZMQi4AReyQjr5OAiIiIiCXMW\ndImIiIgUmSrSi4iIiDigoEtERETEAQVdIiIiIg4o6BIRERFxQEGXiIiIiAMKukREREQcyMI2QLEy\nxlyJVz9sGK9K/npr7RPJ9qrYjDGfANYClwCvtdZ+P9keiTFmGLgPuAw4CzwB/EbIAscSI2PMV4BX\nAxXgFPBb1trvJdsrATDGfBj4I/Q+lgrGmB8BL9T+AHzQWvuQyz5opAs+B3zWWnsl8Fng8wn3R+Ar\nwCrg6aQ7IudUgbuttcZa+1rgSeBjCfdJPBusta+z1l4NfALYlHSHBIwx1wAr0PtY2rzbWvs/1/44\nDbig4EFXbU/Ia4AdtaYdwDXGmKXJ9UqstX9vrX0m6X7Iedba49baRxqa9gEXJ9QdaWCt/VnDzZfh\njXhJgowxA3hf4v9N0n2RdCn69OKFwE+stTMAtc24f1pr17SJiA9jTBnvw+Qvk+6LeIwx9wBvAUrA\nLyfcHYE/AbZZa39kjEm6L9LsfmNMCW8j6g9Za593efBCj3SJSFf+G17u0GeS7oh4rLV3WGsvAj4E\n/GnS/SkyY8ybgNcD/1fSfZFZrrXWvg54A94XFOfvYUUPup4Bfs4Y0wdQ+/uVtXYRaVFb5HAF8GvW\nWk1jpYy19j7g+trCB0nGauA1wMFa4vargIeMMW9JslMC9bQVa+0UXlC80nUfCh10WWsPA98D1tWa\n1gHf1YoskdmMMR8FfgF4V+1NSxJmjFlojLmw4faNwPHaH0mAtfZj1tpXWmsvsdZeAjwL3GCt/UbC\nXSs0Y8wCY8zLav8uATfhff47VfScLoDfBLYaY/4QOAGsT7g/hWeM+a/AGmA58E1jzDFr7c8n3K1C\nM8b8PPD7wH5gvJanctBa+68S7ZgsAL5sjFkAzOAFWzdaa6vJdkskdUaAnbUZrT7gh8D/5roTpWpV\nv5siIiIicSv09KKIiIiIKwq6RERERBxQ0CUiIiLigIIuEREREQcUdImIiIg4oKBLRKQLxpjrjDHP\nJt0PEckO1ekSkcwyxjwCvA5YPlfBVmPMJcBB4CXW2un4eyci0kwjXSKSSbUg6lqgCrwj2d6IiMxN\nI10iklXrgX3APwAbgC8DGGMuAP4j8G7g5cA/Ab8E7K497vlaRf1fAm4ALrfW3lp77CU0jIYZY24H\n7sLbP+8I8HFr7eddnJyI5I9GukQkq9YD99f+3GCMGam1fwJvj8gxYDFe0FQBVtX+/+XW2oXW2m+F\nOMZh4O3AEHA78GljzDXRnYKIFIlGukQkc4wxbwYuBr5krT1qjHkSuNkY81+A9wArrLU/qd19vPaY\njo9jrf3rhpu7jDHfwJvS/E4v/ReRYlLQJSJZtAH4hrX2aO329lrb/cBLgSejOIgx5q3Ah4Er8WYG\n5uNNV4qIdExBl4hkSi1n618DfcaYiVrzAF7+1iuAF4DLgH9seWjV5+lO4wVSdcsbjjMA7MSbxvyq\ntfZFY8xXgFIU5yEixaOgS0Sy5l3ADPBa4GxD+5fwAqRNwKeMMbcBh4A34k0HHsHL7boU2F97zPeA\nDxpjLgJ+Bvx+w/PNwwvmjgDTtVGvtwDfj+e0RCTvlEgvIlmzAdhsrf2xtXai/gf4DHAL8Ht4U4CP\nAseBjwNla+0Z4CPAXmPM88aYFdbavwX+AngM+B/A1+oHsdaeBH4bL5g7AdwM/KWrkxSR/ClVq34j\n7iIiIiISJY10iYiIiDigoEtERETEAQVdIiIiIg4o6BIRERFxQEGXiIiIiAMKukREREQcUNAlIiIi\n4oCCLhEREREHFHSJiIiIOPD/A5cMlP5jcFU/AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EPa-lpblb7Aa",
        "colab_type": "text"
      },
      "source": [
        "#### fine tuned bio bert"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "aJaOEMURb-oT",
        "colab": {}
      },
      "source": [
        "!cp '/content/drive/My Drive/biobert_finetuned_sts_biomedical_mean_only.zip' ./biobert_finetuned_sts_biomedical_mean_only.zip"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "oOsF5yDMb-oc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 474
        },
        "outputId": "930ab75b-1cd2-4e0b-a42c-9a07735a9493"
      },
      "source": [
        "!unzip biobert_finetuned_sts_biomedical_mean_only.zip "
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  biobert_finetuned_sts_biomedical_mean_only.zip\n",
            "   creating: output/training_n2c2_sts_bert-2019-11-29_05-53-55/\n",
            " extracting: output/training_n2c2_sts_bert-2019-11-29_05-53-55/config.json  \n",
            "   creating: output/training_n2c2_sts_bert-2019-11-29_05-53-55/1_Pooling/\n",
            "  inflating: output/training_n2c2_sts_bert-2019-11-29_05-53-55/1_Pooling/config.json  \n",
            "  inflating: output/training_n2c2_sts_bert-2019-11-29_05-53-55/similarity_evaluation_results.csv  \n",
            "  inflating: output/training_n2c2_sts_bert-2019-11-29_05-53-55/modules.json  \n",
            "   creating: output/training_n2c2_sts_bert-2019-11-29_05-53-55/0_BERT/\n",
            "  inflating: output/training_n2c2_sts_bert-2019-11-29_05-53-55/0_BERT/config.json  \n",
            "  inflating: output/training_n2c2_sts_bert-2019-11-29_05-53-55/0_BERT/vocab.txt  \n",
            "  inflating: output/training_n2c2_sts_bert-2019-11-29_05-53-55/0_BERT/pytorch_model.bin  \n",
            "  inflating: output/training_n2c2_sts_bert-2019-11-29_05-53-55/0_BERT/special_tokens_map.json  \n",
            " extracting: output/training_n2c2_sts_bert-2019-11-29_05-53-55/0_BERT/added_tokens.json  \n",
            "  inflating: output/training_n2c2_sts_bert-2019-11-29_05-53-55/0_BERT/sentence_bert_config.json  \n",
            "   creating: output/training_n2c2_sts_bert-2019-11-29_08-50-55/\n",
            " extracting: output/training_n2c2_sts_bert-2019-11-29_08-50-55/config.json  \n",
            "   creating: output/training_n2c2_sts_bert-2019-11-29_08-50-55/1_Pooling/\n",
            "  inflating: output/training_n2c2_sts_bert-2019-11-29_08-50-55/1_Pooling/config.json  \n",
            "  inflating: output/training_n2c2_sts_bert-2019-11-29_08-50-55/similarity_evaluation_results.csv  \n",
            "  inflating: output/training_n2c2_sts_bert-2019-11-29_08-50-55/modules.json  \n",
            "   creating: output/training_n2c2_sts_bert-2019-11-29_08-50-55/0_BERT/\n",
            "  inflating: output/training_n2c2_sts_bert-2019-11-29_08-50-55/0_BERT/config.json  \n",
            "  inflating: output/training_n2c2_sts_bert-2019-11-29_08-50-55/0_BERT/vocab.txt  \n",
            "  inflating: output/training_n2c2_sts_bert-2019-11-29_08-50-55/0_BERT/pytorch_model.bin  \n",
            "  inflating: output/training_n2c2_sts_bert-2019-11-29_08-50-55/0_BERT/special_tokens_map.json  \n",
            " extracting: output/training_n2c2_sts_bert-2019-11-29_08-50-55/0_BERT/added_tokens.json  \n",
            "  inflating: output/training_n2c2_sts_bert-2019-11-29_08-50-55/0_BERT/sentence_bert_config.json  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "DVhsxLFsb-oj",
        "colab": {}
      },
      "source": [
        "pretrained_weights = 'output/training_n2c2_sts_bert-2019-11-29_05-53-55/0_BERT'\n",
        "\n",
        "# Load pretrained model/tokenizer\n",
        "sts_biomedical_biobert_tokenizer = tokenizer_class.from_pretrained(pretrained_weights)\n",
        "sts_biomedical_biobert_model = model_class.from_pretrained(pretrained_weights)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "rsyNfZ5Ob-oo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 711
        },
        "outputId": "d2946d8d-90b3-4eb0-bde9-9e6f357f3770"
      },
      "source": [
        "features_biobert_sts_biomedical, features_biobert_cls_sts_biomedical = bert_based_features_original(X_train_original, sts_biomedical_biobert_model, sts_biomedical_biobert_tokenizer)"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "768\n",
            "768\n",
            "768\n",
            "768\n",
            "33\n",
            "179\n",
            " Patient arrives, via hospital wheelchair, Gait steady, History obtained from patient, Patient appears comfortable, Patient cooperative, alert, Oriented to person, place and time.\n",
            "i= 0\n",
            "i= 50\n",
            "i= 100\n",
            "i= 150\n",
            "i= 200\n",
            "i= 250\n",
            "i= 300\n",
            "i= 350\n",
            "i= 400\n",
            "i= 450\n",
            "i= 500\n",
            "i= 550\n",
            "i= 600\n",
            "i= 650\n",
            "i= 700\n",
            "i= 750\n",
            "i= 800\n",
            "i= 850\n",
            "i= 900\n",
            "i= 950\n",
            "i= 1000\n",
            "i= 1050\n",
            "i= 1100\n",
            "i= 1150\n",
            "i= 1200\n",
            "i= 1250\n",
            "i= 1300\n",
            "i= 1350\n",
            "i= 1400\n",
            "i= 1450\n",
            "i= 1500\n",
            "i= 1550\n",
            "i= 1600\n",
            "i= 1650\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vsL69BbkSeHX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "outputId": "aa01ece0-6c8a-4b8f-9880-592f4db06fbd"
      },
      "source": [
        "y_train_test_hat, y_train_test = predict_train(features_biobert_sts_biomedical, y_true, get_optimal_rf_model(features_biobert_sts_biomedical[:500], y_true[:500]))\n",
        "plot_accuracy(y_train_test, y_train_test_hat)"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Optimal parameters: {'max_depth': 4, 'min_samples_split': 2, 'n_estimators': 500, 'subsample': 0.7}\n",
            "Accuracy: 0.9202214653121965\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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OWlmu6nT5bQHk1x5nxaY+K1IWUSbSLwX2dLR9G3hVct0RKY40brwSX9BE8yQn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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_Fr0wtoySfNt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_train_test_hat, y_train_test = predict_train(features_biobert_cls_sts_biomedical, y_true, get_optimal_rf_model(features_biobert_cls_sts_biomedical[:500], y_true[:500]))\n",
        "plot_accuracy(y_train_test, y_train_test_hat)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OmbNLIjvm5_I",
        "colab_type": "text"
      },
      "source": [
        "#### BIO BERT"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jGBxRont-Ens",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        },
        "outputId": "64a5907b-cc62-4998-8ce8-fa1e960b0a5b"
      },
      "source": [
        "!gdown https://drive.google.com/uc?id=1R84voFKHfWV9xjzeLzWBbmY1uOMYpnyD"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1R84voFKHfWV9xjzeLzWBbmY1uOMYpnyD\n",
            "To: /content/biobert_v1.1_pubmed.tar.gz\n",
            "401MB [00:05, 78.1MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CuMp_m-i-PAb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        },
        "outputId": "329da916-40c7-4fc5-8dd6-bf4579fdeb5a"
      },
      "source": [
        "!tar xvzf biobert_v1.1_pubmed.tar.gz"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "biobert_v1.1_pubmed/\n",
            "biobert_v1.1_pubmed/model.ckpt-1000000.data-00000-of-00001\n",
            "biobert_v1.1_pubmed/model.ckpt-1000000.meta\n",
            "biobert_v1.1_pubmed/bert_config.json\n",
            "biobert_v1.1_pubmed/vocab.txt\n",
            "biobert_v1.1_pubmed/model.ckpt-1000000.index\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7UQ3y9DEAFL5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "22b7d711-88d9-46cc-a712-e7127ffd9062"
      },
      "source": [
        "!transformers bert biobert_v1.1_pubmed/model.ckpt-1000000 biobert_v1.1_pubmed/bert_config.json biobert_v1.1_pubmed/pytorch_model.bin"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Building PyTorch model from configuration: {\n",
            "  \"attention_probs_dropout_prob\": 0.1,\n",
            "  \"finetuning_task\": null,\n",
            "  \"hidden_act\": \"gelu\",\n",
            "  \"hidden_dropout_prob\": 0.1,\n",
            "  \"hidden_size\": 768,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"intermediate_size\": 3072,\n",
            "  \"is_decoder\": false,\n",
            "  \"layer_norm_eps\": 1e-12,\n",
            "  \"max_position_embeddings\": 512,\n",
            "  \"num_attention_heads\": 12,\n",
            "  \"num_hidden_layers\": 12,\n",
            "  \"num_labels\": 2,\n",
            "  \"output_attentions\": false,\n",
            "  \"output_hidden_states\": false,\n",
            "  \"output_past\": true,\n",
            "  \"pruned_heads\": {},\n",
            "  \"torchscript\": false,\n",
            "  \"type_vocab_size\": 2,\n",
            "  \"use_bfloat16\": false,\n",
            "  \"vocab_size\": 28996\n",
            "}\n",
            "\n",
            "INFO:transformers.modeling_bert:Converting TensorFlow checkpoint from /content/biobert_v1.1_pubmed/model.ckpt-1000000\n",
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            "INFO:transformers.modeling_bert:Initialize PyTorch weight ['bert', 'encoder', 'layer_8', 'attention', 'self', 'key', 'bias']\n",
            "INFO:transformers.modeling_bert:Initialize PyTorch weight ['bert', 'encoder', 'layer_8', 'attention', 'self', 'key', 'kernel']\n",
            "INFO:transformers.modeling_bert:Initialize PyTorch weight ['bert', 'encoder', 'layer_8', 'attention', 'self', 'query', 'bias']\n",
            "INFO:transformers.modeling_bert:Initialize PyTorch weight ['bert', 'encoder', 'layer_8', 'attention', 'self', 'query', 'kernel']\n",
            "INFO:transformers.modeling_bert:Initialize PyTorch weight ['bert', 'encoder', 'layer_8', 'attention', 'self', 'value', 'bias']\n",
            "INFO:transformers.modeling_bert:Initialize PyTorch weight ['bert', 'encoder', 'layer_8', 'attention', 'self', 'value', 'kernel']\n",
            "INFO:transformers.modeling_bert:Initialize PyTorch weight ['bert', 'encoder', 'layer_8', 'intermediate', 'dense', 'bias']\n",
            "INFO:transformers.modeling_bert:Initialize PyTorch weight ['bert', 'encoder', 'layer_8', 'intermediate', 'dense', 'kernel']\n",
            "INFO:transformers.modeling_bert:Initialize PyTorch weight ['bert', 'encoder', 'layer_8', 'output', 'LayerNorm', 'beta']\n",
            "INFO:transformers.modeling_bert:Initialize PyTorch weight ['bert', 'encoder', 'layer_8', 'output', 'LayerNorm', 'gamma']\n",
            "INFO:transformers.modeling_bert:Initialize PyTorch weight ['bert', 'encoder', 'layer_8', 'output', 'dense', 'bias']\n",
            "INFO:transformers.modeling_bert:Initialize PyTorch weight ['bert', 'encoder', 'layer_8', 'output', 'dense', 'kernel']\n",
            "INFO:transformers.modeling_bert:Initialize PyTorch weight ['bert', 'encoder', 'layer_9', 'attention', 'output', 'LayerNorm', 'beta']\n",
            "INFO:transformers.modeling_bert:Initialize PyTorch weight ['bert', 'encoder', 'layer_9', 'attention', 'output', 'LayerNorm', 'gamma']\n",
            "INFO:transformers.modeling_bert:Initialize PyTorch weight ['bert', 'encoder', 'layer_9', 'attention', 'output', 'dense', 'bias']\n",
            "INFO:transformers.modeling_bert:Initialize PyTorch weight ['bert', 'encoder', 'layer_9', 'attention', 'output', 'dense', 'kernel']\n",
            "INFO:transformers.modeling_bert:Initialize PyTorch weight ['bert', 'encoder', 'layer_9', 'attention', 'self', 'key', 'bias']\n",
            "INFO:transformers.modeling_bert:Initialize PyTorch weight ['bert', 'encoder', 'layer_9', 'attention', 'self', 'key', 'kernel']\n",
            "INFO:transformers.modeling_bert:Initialize PyTorch weight ['bert', 'encoder', 'layer_9', 'attention', 'self', 'query', 'bias']\n",
            "INFO:transformers.modeling_bert:Initialize PyTorch weight ['bert', 'encoder', 'layer_9', 'attention', 'self', 'query', 'kernel']\n",
            "INFO:transformers.modeling_bert:Initialize PyTorch weight ['bert', 'encoder', 'layer_9', 'attention', 'self', 'value', 'bias']\n",
            "INFO:transformers.modeling_bert:Initialize PyTorch weight ['bert', 'encoder', 'layer_9', 'attention', 'self', 'value', 'kernel']\n",
            "INFO:transformers.modeling_bert:Initialize PyTorch weight ['bert', 'encoder', 'layer_9', 'intermediate', 'dense', 'bias']\n",
            "INFO:transformers.modeling_bert:Initialize PyTorch weight ['bert', 'encoder', 'layer_9', 'intermediate', 'dense', 'kernel']\n",
            "INFO:transformers.modeling_bert:Initialize PyTorch weight ['bert', 'encoder', 'layer_9', 'output', 'LayerNorm', 'beta']\n",
            "INFO:transformers.modeling_bert:Initialize PyTorch weight ['bert', 'encoder', 'layer_9', 'output', 'LayerNorm', 'gamma']\n",
            "INFO:transformers.modeling_bert:Initialize PyTorch weight ['bert', 'encoder', 'layer_9', 'output', 'dense', 'bias']\n",
            "INFO:transformers.modeling_bert:Initialize PyTorch weight ['bert', 'encoder', 'layer_9', 'output', 'dense', 'kernel']\n",
            "INFO:transformers.modeling_bert:Initialize PyTorch weight ['bert', 'pooler', 'dense', 'bias']\n",
            "INFO:transformers.modeling_bert:Initialize PyTorch weight ['bert', 'pooler', 'dense', 'kernel']\n",
            "Save PyTorch model to biobert_v1.1_pubmed/pytorch_model.bin\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eFLX6XHie7jh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#after this, renamed bert_config.json to config.json manually\n",
        "!cp biobert_v1.1_pubmed/bert_config.json biobert_v1.1_pubmed/config.json"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DNo3Av0ZePCc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model_class = BertModel\n",
        "tokenizer_class = BertTokenizer.from_pretrained('biobert_v1.1_pubmed/vocab.txt')\n",
        "pretrained_weights = 'biobert_v1.1_pubmed'\n",
        "\n",
        "# Load pretrained model/tokenizer\n",
        "bio_bert_tokenizer = tokenizer_class.from_pretrained(pretrained_weights)\n",
        "bio_bert_model = model_class.from_pretrained(pretrained_weights)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jD92qQKcGcTJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "672c35ba-0625-4f3c-bea7-29e9c1b4b254"
      },
      "source": [
        "input_ids = torch.tensor(bio_bert_tokenizer.encode(\"Hello my dog is cute\", add_special_tokens= True)).unsqueeze(0)  # Batch size 1\n",
        "print(input_ids)\n",
        "outputs = bio_bert_model(input_ids)\n",
        "last_hidden_states = outputs[0][0].detach().numpy()  # The last hidden-state is the first element of the output tuple\n",
        "print(len(outputs))\n",
        "print(last_hidden_states)\n",
        "print(len(last_hidden_states))\n",
        "print(last_hidden_states.shape)\n",
        "print(outputs[0])\n",
        "print(outputs[1])"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([[  101, 19082,  1139,  3676,  1110, 10509,   102]])\n",
            "2\n",
            "[[ 0.30595413  0.6154358  -0.2122898  ... -0.23292513  0.73406136\n",
            "  -0.30510715]\n",
            " [-0.02054498  0.35774222 -0.37772265 ... -0.3032497   0.8740787\n",
            "  -0.3120333 ]\n",
            " [ 0.00812341 -0.03995946 -0.16461788 ... -0.03652666  0.7840643\n",
            "  -0.00675428]\n",
            " ...\n",
            " [ 0.11687239  0.40033427 -0.29609394 ...  0.0957466   0.39718673\n",
            "   0.04781871]\n",
            " [-0.00829414  0.16456313 -0.12430103 ... -0.01243092  0.6628006\n",
            "   0.03179159]\n",
            " [ 0.37802875  0.6493508  -0.95986825 ... -0.16714506  1.3767036\n",
            "  -0.9745663 ]]\n",
            "7\n",
            "(7, 768)\n",
            "tensor([[[ 0.3060,  0.6154, -0.2123,  ..., -0.2329,  0.7341, -0.3051],\n",
            "         [-0.0205,  0.3577, -0.3777,  ..., -0.3032,  0.8741, -0.3120],\n",
            "         [ 0.0081, -0.0400, -0.1646,  ..., -0.0365,  0.7841, -0.0068],\n",
            "         ...,\n",
            "         [ 0.1169,  0.4003, -0.2961,  ...,  0.0957,  0.3972,  0.0478],\n",
            "         [-0.0083,  0.1646, -0.1243,  ..., -0.0124,  0.6628,  0.0318],\n",
            "         [ 0.3780,  0.6494, -0.9599,  ..., -0.1671,  1.3767, -0.9746]]],\n",
            "       grad_fn=<NativeLayerNormBackward>)\n",
            "tensor([[ 1.4617e-01,  8.5892e-02,  9.8272e-01, -1.0000e+00,  1.0000e+00,\n",
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            "         -9.3212e-02,  3.6991e-02,  2.0205e-02, -1.2761e-02,  9.2297e-01,\n",
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            "         -9.9981e-01,  1.0333e-01,  9.7258e-01,  9.9780e-01,  9.9978e-01,\n",
            "          2.9197e-02, -1.5363e-01, -7.3041e-02,  3.2545e-02, -9.9974e-01,\n",
            "         -6.5569e-02,  5.8058e-02,  7.9166e-02, -8.5197e-01, -5.9066e-02,\n",
            "          7.9402e-03,  5.6221e-01, -9.9967e-01,  9.9863e-01, -2.1041e-01,\n",
            "          2.3730e-02, -1.4300e-01, -4.7249e-02, -1.4602e-02,  2.1051e-02,\n",
            "         -9.9995e-01,  1.4819e-01,  1.0000e+00,  9.8658e-01,  9.9728e-01,\n",
            "          7.4635e-01, -9.0610e-01, -7.3428e-02,  1.2253e-01, -9.9923e-01,\n",
            "         -9.9571e-01,  4.7346e-02, -1.3348e-01, -9.9995e-01,  9.8924e-01,\n",
            "         -9.9898e-01,  4.7649e-02, -7.3147e-02,  9.9876e-01,  9.9994e-01,\n",
            "         -1.8736e-01, -9.9984e-01, -9.9995e-01, -9.5899e-01,  1.3081e-01,\n",
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            "          9.8465e-01,  1.0591e-02,  9.4679e-02,  6.6336e-01,  1.0000e+00,\n",
            "          1.8289e-02, -9.9907e-01,  1.9111e-01,  5.7222e-02, -6.8691e-01,\n",
            "         -1.6838e-02,  9.9946e-01, -9.3110e-02,  9.5407e-01,  9.9949e-01,\n",
            "         -9.9983e-01,  9.9240e-01, -9.9994e-01,  8.9914e-01,  9.9977e-01,\n",
            "         -1.0000e+00, -7.4446e-02, -1.0000e+00, -9.1432e-01, -7.7010e-02,\n",
            "          1.5929e-01, -3.1552e-02,  9.1466e-01, -1.0000e+00, -9.9997e-01,\n",
            "          3.5718e-02, -4.2827e-01, -9.9303e-01,  8.4717e-01, -1.8011e-01,\n",
            "         -5.3324e-02,  4.2620e-02, -4.6474e-02, -7.0518e-02, -7.2317e-01,\n",
            "          9.9964e-01, -1.7227e-01,  3.0528e-02, -1.0000e+00,  9.9967e-01,\n",
            "         -3.1969e-01,  5.3460e-02,  1.0000e+00, -1.6690e-01,  3.1510e-02,\n",
            "         -1.3717e-01, -1.0000e+00, -6.6623e-02,  4.9281e-02, -9.8950e-01,\n",
            "          9.6521e-01, -3.4420e-02, -3.7405e-02,  7.2636e-02,  1.1943e-01,\n",
            "         -9.9439e-01,  3.9601e-02, -1.0000e+00,  1.0000e+00, -9.2324e-01,\n",
            "          6.9389e-03,  8.7995e-02, -7.6123e-02, -2.4617e-02,  9.9239e-01,\n",
            "          1.0000e+00, -9.9927e-01,  7.0078e-02,  1.0000e+00, -9.4899e-02,\n",
            "          1.1142e-02, -1.0000e+00, -4.2005e-02, -7.4975e-01,  1.5974e-01,\n",
            "          1.0000e+00, -9.6002e-02, -5.8136e-02,  9.9993e-01, -1.0000e+00,\n",
            "         -9.4607e-01,  4.6559e-02, -5.0276e-02,  4.0457e-02, -1.0000e+00,\n",
            "          1.3581e-01, -9.5138e-01, -2.0009e-01, -1.0000e+00,  9.9147e-01,\n",
            "         -1.0000e+00, -7.0826e-02,  9.9999e-01,  8.6414e-01,  9.9987e-01,\n",
            "         -7.1867e-02, -1.5135e-01,  1.4181e-01, -9.9999e-01,  7.4456e-01,\n",
            "          5.6591e-02,  1.0548e-01, -9.9801e-01, -9.4492e-01, -3.7900e-03,\n",
            "          9.7578e-01, -9.8192e-01,  6.0153e-02, -5.4192e-01, -9.4765e-03,\n",
            "         -5.1914e-02, -1.0090e-01,  8.2624e-02, -8.3932e-01,  2.5575e-01,\n",
            "         -6.2076e-02,  8.3147e-02,  7.7585e-02,  2.1392e-03, -8.2665e-02,\n",
            "          9.9681e-01, -1.0000e+00, -4.1608e-02, -9.9996e-01,  9.9318e-02,\n",
            "          1.3710e-01, -1.1093e-01, -7.8991e-02, -2.3114e-01, -4.7240e-02,\n",
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            "          1.2700e-02, -9.8936e-01,  6.4757e-02,  1.0508e-01,  2.1696e-01,\n",
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            "          9.0851e-01, -1.7850e-02,  1.8447e-02,  1.1141e-01, -2.5611e-01,\n",
            "         -9.9406e-01,  1.0000e+00,  1.0000e+00,  9.9956e-01, -9.9998e-01,\n",
            "          9.8394e-02,  9.1260e-02,  1.0000e+00,  8.3349e-02, -1.8025e-01,\n",
            "          9.9475e-01,  9.9549e-01,  1.3808e-02, -7.0762e-01,  2.9952e-02,\n",
            "          3.3538e-02,  2.1056e-01, -2.2584e-02, -9.6548e-01, -9.9937e-01,\n",
            "          1.8406e-01, -9.9985e-01, -9.9247e-01,  9.8727e-01,  4.5159e-02,\n",
            "          1.0000e+00,  9.9690e-02,  1.5531e-01, -3.7364e-02, -6.0913e-01,\n",
            "         -9.9943e-01, -1.2111e-01, -9.9811e-01,  9.5413e-02, -9.9997e-01,\n",
            "         -1.0000e+00,  1.9049e-02, -1.6023e-02, -9.9656e-01, -2.5591e-02,\n",
            "          9.7433e-02, -9.9790e-01, -8.7929e-01, -9.9975e-01,  3.3817e-01,\n",
            "         -8.6863e-01, -9.6833e-01, -1.8593e-02,  8.8575e-02, -5.7159e-02,\n",
            "         -7.7900e-02, -2.4511e-01, -5.9279e-01,  5.9914e-01,  1.6926e-02,\n",
            "          4.8285e-02,  3.0218e-02, -9.9926e-01, -3.9606e-02, -1.0000e+00,\n",
            "          9.9850e-01, -1.0000e+00,  4.7494e-02,  1.0000e+00,  1.0000e+00,\n",
            "          8.5789e-02, -9.9999e-01,  9.9953e-01,  8.4175e-02,  1.0000e+00,\n",
            "          3.7695e-02, -9.9919e-01, -9.9999e-01,  1.4765e-02,  5.0767e-02,\n",
            "          1.0000e+00,  1.9648e-02,  9.6990e-01,  1.4167e-02, -4.6891e-02,\n",
            "          1.2276e-01, -7.5554e-02, -1.1068e-01, -1.2017e-01,  2.4385e-02,\n",
            "          9.9581e-01, -6.3272e-02,  1.0000e+00]], grad_fn=<TanhBackward>)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BOpsE6yqhkVw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 711
        },
        "outputId": "c6f15a66-7b8a-489b-e7e7-5da2e17d6d60"
      },
      "source": [
        "#BIO BERT FEATURES\n",
        "features_bert_bio, features_bert_cls_bio = bert_based_features_original(X_train_original, bio_bert_model, bio_bert_tokenizer)"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "768\n",
            "768\n",
            "768\n",
            "768\n",
            "33\n",
            "179\n",
            " Patient arrives, via hospital wheelchair, Gait steady, History obtained from patient, Patient appears comfortable, Patient cooperative, alert, Oriented to person, place and time.\n",
            "i= 0\n",
            "i= 50\n",
            "i= 100\n",
            "i= 150\n",
            "i= 200\n",
            "i= 250\n",
            "i= 300\n",
            "i= 350\n",
            "i= 400\n",
            "i= 450\n",
            "i= 500\n",
            "i= 550\n",
            "i= 600\n",
            "i= 650\n",
            "i= 700\n",
            "i= 750\n",
            "i= 800\n",
            "i= 850\n",
            "i= 900\n",
            "i= 950\n",
            "i= 1000\n",
            "i= 1050\n",
            "i= 1100\n",
            "i= 1150\n",
            "i= 1200\n",
            "i= 1250\n",
            "i= 1300\n",
            "i= 1350\n",
            "i= 1400\n",
            "i= 1450\n",
            "i= 1500\n",
            "i= 1550\n",
            "i= 1600\n",
            "i= 1650\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZIywc5y9iy70",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "outputId": "db64ec28-c769-40c2-c935-68d6ab5c3998"
      },
      "source": [
        "y_train_test_hat, y_train_test = predict_train(features_bert_bio, y_true, get_optimal_rf_model(features_bert_bio[:500], y_true[:500]))\n",
        "plot_accuracy(y_train_test, y_train_test_hat)"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Optimal parameters: {'max_depth': 4, 'min_samples_split': 8, 'n_estimators': 300, 'subsample': 0.8}\n",
            "Accuracy: 0.77020093762843\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Cj7qalqz08l7J432WJFjJYy/OMhUpluLKLUAzszvM7N/M7Idm9h0zOy3iNgNm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\nAQK1kch7NW/5AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cQRhFqXdMAiT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_train_test_hat, y_train_test = predict_train(features_bert_cls_bio, y_true, get_optimal_rf_model(features_bert_cls_bio[:500], y_true[:500]))\n",
        "plot_accuracy(y_train_test, y_train_test_hat)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9F9Y0tdXW9fP",
        "colab_type": "text"
      },
      "source": [
        "#### STS-b model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vq4H8-CfW-rn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "outputId": "5e299bf6-2819-48eb-c01e-de81721e0ec7"
      },
      "source": [
        "!wget https://public.ukp.informatik.tu-darmstadt.de/reimers/sentence-transformers/v0.2/bert-base-nli-stsb-mean-tokens.zip"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2019-12-13 01:13:36--  https://public.ukp.informatik.tu-darmstadt.de/reimers/sentence-transformers/v0.2/bert-base-nli-stsb-mean-tokens.zip\n",
            "Resolving public.ukp.informatik.tu-darmstadt.de (public.ukp.informatik.tu-darmstadt.de)... 130.83.167.186\n",
            "Connecting to public.ukp.informatik.tu-darmstadt.de (public.ukp.informatik.tu-darmstadt.de)|130.83.167.186|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 405233603 (386M) [application/zip]\n",
            "Saving to: ‘bert-base-nli-stsb-mean-tokens.zip’\n",
            "\n",
            "bert-base-nli-stsb- 100%[===================>] 386.46M  20.4MB/s    in 22s     \n",
            "\n",
            "2019-12-13 01:13:59 (17.4 MB/s) - ‘bert-base-nli-stsb-mean-tokens.zip’ saved [405233603/405233603]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6DTDJ55WW_So",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "outputId": "58b55f02-151d-493b-b816-c0e2d5a43cf9"
      },
      "source": [
        "!unzip bert-base-nli-stsb-mean-tokens.zip"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  bert-base-nli-stsb-mean-tokens.zip\n",
            "   creating: 0_BERT/\n",
            " extracting: 0_BERT/added_tokens.json  \n",
            "  inflating: 0_BERT/config.json      \n",
            "  inflating: 0_BERT/pytorch_model.bin  \n",
            " extracting: 0_BERT/sentence_bert_config.json  \n",
            "  inflating: 0_BERT/special_tokens_map.json  \n",
            "  inflating: 0_BERT/vocab.txt        \n",
            "   creating: 1_Pooling/\n",
            "  inflating: 1_Pooling/config.json   \n",
            "  inflating: modules.json            \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M-3_BIAsX3I6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pretrained_weights = '0_BERT'\n",
        "\n",
        "# Load pretrained model/tokenizer\n",
        "stsb_bert_tokenizer = tokenizer_class.from_pretrained(pretrained_weights)\n",
        "stsb_bert_model = model_class.from_pretrained(pretrained_weights)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G_DmUajDX2Zo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 711
        },
        "outputId": "5a396ff3-f012-4242-99a9-7e1ea35a97cb"
      },
      "source": [
        "#STSB BERT FEATURES\n",
        "features_bert_stsb, features_bert_cls_stsb = bert_based_features_original(X_train_original, stsb_bert_model, stsb_bert_tokenizer)"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "768\n",
            "768\n",
            "768\n",
            "768\n",
            "33\n",
            "179\n",
            " Patient arrives, via hospital wheelchair, Gait steady, History obtained from patient, Patient appears comfortable, Patient cooperative, alert, Oriented to person, place and time.\n",
            "i= 0\n",
            "i= 50\n",
            "i= 100\n",
            "i= 150\n",
            "i= 200\n",
            "i= 250\n",
            "i= 300\n",
            "i= 350\n",
            "i= 400\n",
            "i= 450\n",
            "i= 500\n",
            "i= 550\n",
            "i= 600\n",
            "i= 650\n",
            "i= 700\n",
            "i= 750\n",
            "i= 800\n",
            "i= 850\n",
            "i= 900\n",
            "i= 950\n",
            "i= 1000\n",
            "i= 1050\n",
            "i= 1100\n",
            "i= 1150\n",
            "i= 1200\n",
            "i= 1250\n",
            "i= 1300\n",
            "i= 1350\n",
            "i= 1400\n",
            "i= 1450\n",
            "i= 1500\n",
            "i= 1550\n",
            "i= 1600\n",
            "i= 1650\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "daDivyzzYDXv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "outputId": "57b55cd7-d59e-43b1-d497-57832b17e51d"
      },
      "source": [
        "y_train_test_hat, y_train_test = predict_train(features_bert_stsb, y_true, get_optimal_rf_model(features_bert_stsb[:500], y_true[:500]))\n",
        "plot_accuracy(y_train_test, y_train_test_hat)"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Optimal parameters: {'max_depth': 5, 'min_samples_split': 2, 'n_estimators': 300, 'subsample': 0.7}\n",
            "Accuracy: 0.7600821885151754\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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TXVsjQgk0odT9V2v9ymZ2CzYq6g1XEud91GPfjM96M1trWpHcNUoQWE/WzvnQ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\niHhGAZqIiIiIZxSgiYiIiHjm/wcT41C5Gaz+EQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cimmkNXpb66H",
        "colab_type": "text"
      },
      "source": [
        "#### STSB + BIOBERT + VANILLA"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LjFDPV21btVo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "features_stsb_bio_og = np.concatenate([features_bert_og, features_bert_bio, features_bert_stsb], axis=1)\n",
        "\n",
        "y_train_test_hat, y_train_test = predict_train(features_stsb_bio_og, y_true, get_optimal_rf_model(features_stsb_bio_og[:500], y_true[:500]))\n",
        "plot_accuracy(y_train_test, y_train_test_hat)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n02rkCO4myaY",
        "colab_type": "text"
      },
      "source": [
        "#### BIO CLINICAL BERT"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EKYJlN1CkQwO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        },
        "outputId": "cf725342-4f02-4d00-b884-2542f1032a10"
      },
      "source": [
        "#CLINICAL BERT\n",
        "\n",
        "!wget -O pretrained_bert_tf.tar.gz https://www.dropbox.com/s/8armk04fu16algz/pretrained_bert_tf.tar.gz?dl=1"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2019-12-13 01:21:04--  https://www.dropbox.com/s/8armk04fu16algz/pretrained_bert_tf.tar.gz?dl=1\n",
            "Resolving www.dropbox.com (www.dropbox.com)... 162.125.1.1, 2620:100:601b:1::a27d:801\n",
            "Connecting to www.dropbox.com (www.dropbox.com)|162.125.1.1|:443... connected.\n",
            "HTTP request sent, awaiting response... 301 Moved Permanently\n",
            "Location: /s/dl/8armk04fu16algz/pretrained_bert_tf.tar.gz [following]\n",
            "--2019-12-13 01:21:04--  https://www.dropbox.com/s/dl/8armk04fu16algz/pretrained_bert_tf.tar.gz\n",
            "Reusing existing connection to www.dropbox.com:443.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://uc7f9f140366d6569332d30152b4.dl.dropboxusercontent.com/cd/0/get/AuLgZ1lmEJIxfMIjzlz97sqK_LUWxMmtN_1u6U_yGydqd9jjnkFJ8o8H-KVkp4MnFPPlypPEhod2aUtFAgC22SSdDEGdQpXSQhtOdkWxtJul6DXEC8XNis-Vjv5qjSkK0zs/file?dl=1# [following]\n",
            "--2019-12-13 01:21:04--  https://uc7f9f140366d6569332d30152b4.dl.dropboxusercontent.com/cd/0/get/AuLgZ1lmEJIxfMIjzlz97sqK_LUWxMmtN_1u6U_yGydqd9jjnkFJ8o8H-KVkp4MnFPPlypPEhod2aUtFAgC22SSdDEGdQpXSQhtOdkWxtJul6DXEC8XNis-Vjv5qjSkK0zs/file?dl=1\n",
            "Resolving uc7f9f140366d6569332d30152b4.dl.dropboxusercontent.com (uc7f9f140366d6569332d30152b4.dl.dropboxusercontent.com)... 162.125.1.6, 2620:100:6016:6::a27d:106\n",
            "Connecting to uc7f9f140366d6569332d30152b4.dl.dropboxusercontent.com (uc7f9f140366d6569332d30152b4.dl.dropboxusercontent.com)|162.125.1.6|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 6418595961 (6.0G) [application/binary]\n",
            "Saving to: ‘pretrained_bert_tf.tar.gz’\n",
            "\n",
            "pretrained_bert_tf. 100%[===================>]   5.98G  38.4MB/s    in 3m 36s  \n",
            "\n",
            "2019-12-13 01:24:41 (28.3 MB/s) - ‘pretrained_bert_tf.tar.gz’ saved [6418595961/6418595961]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mm2UIw33kcDe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        },
        "outputId": "a4cf77c1-8933-4bf6-8eb5-d3e8dcb7e4bc"
      },
      "source": [
        "!tar xvzf pretrained_bert_tf.tar.gz"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "pretrained_bert_tf/\n",
            "pretrained_bert_tf/biobert_pretrain_output_all_notes_150000.tar.gz\n",
            "pretrained_bert_tf/bert_pretrain_output_all_notes_150000.tar.gz\n",
            "pretrained_bert_tf/bert_pretrain_output_disch_100000.tar.gz\n",
            "pretrained_bert_tf/biobert_pretrain_output_disch_100000.tar.gz\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_vL-54UklH4L",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 152
        },
        "outputId": "06540860-321c-4a35-ed09-43e4f5a6ad84"
      },
      "source": [
        "!tar xvzf pretrained_bert_tf/biobert_pretrain_output_all_notes_150000.tar.gz"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "biobert_pretrain_output_all_notes_150000/\n",
            "biobert_pretrain_output_all_notes_150000/model.ckpt-150000.index\n",
            "biobert_pretrain_output_all_notes_150000/model.ckpt-150000.meta\n",
            "biobert_pretrain_output_all_notes_150000/graph.pbtxt\n",
            "biobert_pretrain_output_all_notes_150000/bert_config.json\n",
            "biobert_pretrain_output_all_notes_150000/vocab.txt\n",
            "biobert_pretrain_output_all_notes_150000/pytorch_model.bin\n",
            "biobert_pretrain_output_all_notes_150000/model.ckpt-150000.data-00000-of-00001\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oDdHY6LWk9bB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#!transformers bert biobert_pretrain_output_all_notes_150000/model.ckpt-1000000 biobert_pretrain_output_all_notes_150000/bert_config.json biobert_pretrain_output_all_notes_150000/pytorch_model.bin"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uhP6GMzImGaz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#after this, renamed bert_config.json to config.json manually\n",
        "!cp biobert_pretrain_output_all_notes_150000/bert_config.json biobert_pretrain_output_all_notes_150000/config.json"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "grBT3g3clzss",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model_class = BertModel\n",
        "tokenizer_class = BertTokenizer\n",
        "pretrained_weights = 'biobert_pretrain_output_all_notes_150000'\n",
        "\n",
        "# Load pretrained model/tokenizer\n",
        "bio_clinical_bert_tokenizer = tokenizer_class.from_pretrained(pretrained_weights)\n",
        "bio_clinical_bert_model = model_class.from_pretrained(pretrained_weights)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hNpsMIH6mxPA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#BIO CLINICAL BERT FEATURES\n",
        "features_bert_bio_clinical, features_bert_cls_bio_clinical = bert_based_features_original(X_train_original, bio_clinical_bert_model, bio_clinical_bert_tokenizer)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5MOCRevwnQyu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_train_test_hat, y_train_test = predict_train(features_bert_bio_clinical, y_true, get_optimal_rf_model(features_bert_bio_clinical[:500], y_true[:500]))\n",
        "plot_accuracy(y_train_test, y_train_test_hat)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G8It3dJPv-Ao",
        "colab_type": "text"
      },
      "source": [
        "#### CLINICAL BERT"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "9vM99SPEwUcs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 152
        },
        "outputId": "00e6395c-04f1-425d-ed81-adcdf71ee680"
      },
      "source": [
        "!tar xvzf pretrained_bert_tf/bert_pretrain_output_all_notes_150000.tar.gz"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "bert_pretrain_output_all_notes_150000/\n",
            "bert_pretrain_output_all_notes_150000/model.ckpt-150000.index\n",
            "bert_pretrain_output_all_notes_150000/model.ckpt-150000.meta\n",
            "bert_pretrain_output_all_notes_150000/graph.pbtxt\n",
            "bert_pretrain_output_all_notes_150000/bert_config.json\n",
            "bert_pretrain_output_all_notes_150000/vocab.txt\n",
            "bert_pretrain_output_all_notes_150000/pytorch_model.bin\n",
            "bert_pretrain_output_all_notes_150000/model.ckpt-150000.data-00000-of-00001\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "h0UGFIt7wUc5",
        "colab": {}
      },
      "source": [
        "#!transformers bert biobert_pretrain_output_all_notes_150000/model.ckpt-1000000 biobert_pretrain_output_all_notes_150000/bert_config.json biobert_pretrain_output_all_notes_150000/pytorch_model.bin"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "WL6CpwTswUdA",
        "colab": {}
      },
      "source": [
        "#after this, renamed bert_config.json to config.json manually\n",
        "!cp bert_pretrain_output_all_notes_150000/bert_config.json bert_pretrain_output_all_notes_150000/config.json"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "vnnp7oRnwUdN",
        "colab": {}
      },
      "source": [
        "model_class = BertModel\n",
        "tokenizer_class = BertTokenizer\n",
        "pretrained_weights = 'bert_pretrain_output_all_notes_150000'\n",
        "\n",
        "# Load pretrained model/tokenizer\n",
        "clinical_bert_tokenizer = tokenizer_class.from_pretrained(pretrained_weights)\n",
        "clinical_bert_model = model_class.from_pretrained(pretrained_weights)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "w1flpih8wUdS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 711
        },
        "outputId": "b211b543-fdb1-4d3f-d6e8-670abdba3d07"
      },
      "source": [
        "#CLINICAL BERT FEATURES\n",
        "features_bert_clinical, features_bert_cls_clinical = bert_based_features_original(X_train_original, clinical_bert_model, clinical_bert_tokenizer)"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "768\n",
            "768\n",
            "768\n",
            "768\n",
            "33\n",
            "179\n",
            " Patient arrives, via hospital wheelchair, Gait steady, History obtained from patient, Patient appears comfortable, Patient cooperative, alert, Oriented to person, place and time.\n",
            "i= 0\n",
            "i= 50\n",
            "i= 100\n",
            "i= 150\n",
            "i= 200\n",
            "i= 250\n",
            "i= 300\n",
            "i= 350\n",
            "i= 400\n",
            "i= 450\n",
            "i= 500\n",
            "i= 550\n",
            "i= 600\n",
            "i= 650\n",
            "i= 700\n",
            "i= 750\n",
            "i= 800\n",
            "i= 850\n",
            "i= 900\n",
            "i= 950\n",
            "i= 1000\n",
            "i= 1050\n",
            "i= 1100\n",
            "i= 1150\n",
            "i= 1200\n",
            "i= 1250\n",
            "i= 1300\n",
            "i= 1350\n",
            "i= 1400\n",
            "i= 1450\n",
            "i= 1500\n",
            "i= 1550\n",
            "i= 1600\n",
            "i= 1650\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "iFuKA81CwUdW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "outputId": "8d3f1f69-4d85-49b2-e449-d1fcba32e2d5"
      },
      "source": [
        "y_train_test_hat, y_train_test = predict_train(features_bert_clinical, y_true, get_optimal_rf_model(features_bert_clinical[:500], y_true[:500]))\n",
        "plot_accuracy(y_train_test, y_train_test_hat)"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Optimal parameters: {'max_depth': 7, 'min_samples_split': 6, 'n_estimators': 200, 'subsample': 0.7}\n",
            "Accuracy: 0.7517440407661451\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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OvitJSy1AmwrOap5NNZM23VuBL7j7JHCglnV7C/CxFKooORTKaJtOhZRWl6q4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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CVjc5yqWu4op",
        "colab_type": "text"
      },
      "source": [
        "### Next sentence prediction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q5W-89HMt3t7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def bert_based_features_nsp(data, local_bert_model, local_tokenizer):\n",
        "  cls_vectors = np.zeros((len(data), 768*2))\n",
        "\n",
        "  for i in range(len(data)):\n",
        "\n",
        "    sen_1 = data[i][0] + \" [SEP] \" + data[i][1]\n",
        "    sen_2 = data[i][1] + \" [SEP] \" + data[i][0]\n",
        "\n",
        "    sen_1_input_ids = torch.tensor([local_tokenizer.encode(sen_1, add_special_tokens=True)])\n",
        "    sen_1_vectors = local_bert_model(sen_1_input_ids)[0][0].detach().numpy()\n",
        "    sen_1_cls_vector = sen_1_vectors[0]\n",
        "    sen_1_cls_vector_reshaped = sen_1_cls_vector.reshape(1, -1)\n",
        "\n",
        "    sen_2_input_ids = torch.tensor([local_tokenizer.encode(sen_2, add_special_tokens=True)])\n",
        "    sen_2_vectors = local_bert_model(sen_2_input_ids)[0][0].detach().numpy()\n",
        "    sen_2_cls_vector = sen_2_vectors[0]\n",
        "    sen_2_cls_vector_reshaped = sen_2_cls_vector.reshape(1, -1)\n",
        "\n",
        "    cls_vectors[i] = np.concatenate((sen_1_cls_vector, sen_2_cls_vector))\n",
        "    if i == 0:\n",
        "      print(len(sen_2_cls_vector))\n",
        "      print(len(sen_2_vectors[1:-1]))\n",
        "      print(len(sen_2))\n",
        "      print(sen_2)\n",
        "    if i % 50 == 0:\n",
        "      print(\"i=\",i)\n",
        "\n",
        "  return cls_vectors"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fj9-k1STDOH_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.decomposition import PCA\n",
        "\n",
        "pca = PCA(n_components=20)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2vIH3ce5el3L",
        "colab_type": "text"
      },
      "source": [
        "#### biobert"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A9mLdzM0zxta",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 680
        },
        "outputId": "5a9e6368-10dc-43e1-9a2c-4ab5c60e333d"
      },
      "source": [
        "#BIO BERT FEATURES\n",
        "features_bert_cls_bio_nsp = bert_based_features_nsp(X_train_original, bio_bert_model, bio_bert_tokenizer)"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "768\n",
            "71\n",
            "381\n",
            " Complex assessment performed, Patient arrives ambulatory, Gait steady, History obtained from, parent, Patient appears comfortable, Patient cooperative, alert, Oriented to person, place and time. [SEP]  Patient arrives, via hospital wheelchair, Gait steady, History obtained from patient, Patient appears comfortable, Patient cooperative, alert, Oriented to person, place and time.\n",
            "i= 0\n",
            "i= 50\n",
            "i= 100\n",
            "i= 150\n",
            "i= 200\n",
            "i= 250\n",
            "i= 300\n",
            "i= 350\n",
            "i= 400\n",
            "i= 450\n",
            "i= 500\n",
            "i= 550\n",
            "i= 600\n",
            "i= 650\n",
            "i= 700\n",
            "i= 750\n",
            "i= 800\n",
            "i= 850\n",
            "i= 900\n",
            "i= 950\n",
            "i= 1000\n",
            "i= 1050\n",
            "i= 1100\n",
            "i= 1150\n",
            "i= 1200\n",
            "i= 1250\n",
            "i= 1300\n",
            "i= 1350\n",
            "i= 1400\n",
            "i= 1450\n",
            "i= 1500\n",
            "i= 1550\n",
            "i= 1600\n",
            "i= 1650\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7OUP77slavBe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "features_bert_cls_bio_nsp_scaled = StandardScaler().fit_transform(features_bert_cls_bio_nsp)\n",
        "features_bert_cls_bio_nsp_principal_components = pca.fit_transform(features_bert_cls_bio_nsp_scaled)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "S6BBHwuUKx1V",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "outputId": "5ec98891-1d4a-45f0-8f61-13befd8865ec"
      },
      "source": [
        "y_train_test_hat, y_train_test = predict_train(features_bert_cls_bio_nsp_principal_components, y_true, get_optimal_rf_model(features_bert_cls_bio_nsp_principal_components[1500:], y_true[1500:]))\n",
        "plot_accuracy(y_train_test, y_train_test_hat)"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Optimal parameters: {'max_depth': 4, 'min_samples_split': 2, 'n_estimators': 500, 'subsample': 0.8}\n",
            "Accuracy: 0.7096567125741803\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AdinlT4UQof941qz4a8/MmdMf+zYoPB4XN8xZdguuWnZL0rsR2eFdu3F421YUytW7J44f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\niIjIAgZdRERERBb8f/xr0783Ud68AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hjtKsuNmeqfG",
        "colab_type": "text"
      },
      "source": [
        "#### STSb model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "okypjKC4cfG8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 680
        },
        "outputId": "90ca8bb4-3d5f-4156-b530-58ce552741c5"
      },
      "source": [
        "features_stsb_bert_cls_nsp = bert_based_features_nsp(X_train_original, stsb_bert_model, stsb_bert_tokenizer)"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "768\n",
            "71\n",
            "381\n",
            " Complex assessment performed, Patient arrives ambulatory, Gait steady, History obtained from, parent, Patient appears comfortable, Patient cooperative, alert, Oriented to person, place and time. [SEP]  Patient arrives, via hospital wheelchair, Gait steady, History obtained from patient, Patient appears comfortable, Patient cooperative, alert, Oriented to person, place and time.\n",
            "i= 0\n",
            "i= 50\n",
            "i= 100\n",
            "i= 150\n",
            "i= 200\n",
            "i= 250\n",
            "i= 300\n",
            "i= 350\n",
            "i= 400\n",
            "i= 450\n",
            "i= 500\n",
            "i= 550\n",
            "i= 600\n",
            "i= 650\n",
            "i= 700\n",
            "i= 750\n",
            "i= 800\n",
            "i= 850\n",
            "i= 900\n",
            "i= 950\n",
            "i= 1000\n",
            "i= 1050\n",
            "i= 1100\n",
            "i= 1150\n",
            "i= 1200\n",
            "i= 1250\n",
            "i= 1300\n",
            "i= 1350\n",
            "i= 1400\n",
            "i= 1450\n",
            "i= 1500\n",
            "i= 1550\n",
            "i= 1600\n",
            "i= 1650\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fWNNKGAucxC6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "outputId": "f2bea252-c237-4cda-d27a-8acf6a5420f4"
      },
      "source": [
        "y_train_test_hat, y_train_test = predict_train(features_stsb_bert_cls_nsp, y_true, get_optimal_rf_model(features_stsb_bert_cls_nsp[1500:], y_true[1500:]))\n",
        "plot_accuracy(y_train_test, y_train_test_hat)"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Optimal parameters: {'max_depth': 6, 'min_samples_split': 6, 'n_estimators': 500, 'subsample': 0.8}\n",
            "Accuracy: 0.6577362024482314\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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KeqYAgJYW9Nx4s942auRRq/i97nIhkM1mahqVYonilFJuF0J8QwjRXS6+pJTD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\nSZJUmb+IMn8u1zwHtQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qb8PT7MimeF-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "features_stsb_bert_cls_nsp_scaled = StandardScaler().fit_transform(features_stsb_bert_cls_nsp)\n",
        "features_stsb_bert_cls_nsp_principal_components = pca.fit_transform(features_stsb_bert_cls_nsp_scaled)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_w0sRv6_mgTm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "outputId": "abb5e433-7a5b-4cb6-a8d5-b2eed891b79f"
      },
      "source": [
        "y_train_test_hat, y_train_test = predict_train(features_stsb_bert_cls_nsp_principal_components, y_true, get_optimal_rf_model(features_stsb_bert_cls_nsp_principal_components[1500:], y_true[1500:]))\n",
        "plot_accuracy(y_train_test, y_train_test_hat)"
      ],
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Optimal parameters: {'max_depth': 4, 'min_samples_split': 8, 'n_estimators': 500, 'subsample': 0.6}\n",
            "Accuracy: 0.5755774666385248\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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MXrVkacN+dHhu09bzwlmZ/MgIntu0FZmrrzXSx6TbFisT7k66bbHZ+1klnJXaTT832Wym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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9qCojNKMes2D",
        "colab_type": "text"
      },
      "source": [
        "#### clinical BERT"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iJq6sonqbLSo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Clinical bert model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "9780d300-52cf-4bd2-800e-b58140251cd4",
        "id": "NzUtOgkEbnPp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 680
        }
      },
      "source": [
        "features_clinicalbert_cls_nsp = bert_based_features_nsp(X_train_original, clinical_bert_model, clinical_bert_tokenizer)"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "768\n",
            "71\n",
            "381\n",
            " Complex assessment performed, Patient arrives ambulatory, Gait steady, History obtained from, parent, Patient appears comfortable, Patient cooperative, alert, Oriented to person, place and time. [SEP]  Patient arrives, via hospital wheelchair, Gait steady, History obtained from patient, Patient appears comfortable, Patient cooperative, alert, Oriented to person, place and time.\n",
            "i= 0\n",
            "i= 50\n",
            "i= 100\n",
            "i= 150\n",
            "i= 200\n",
            "i= 250\n",
            "i= 300\n",
            "i= 350\n",
            "i= 400\n",
            "i= 450\n",
            "i= 500\n",
            "i= 550\n",
            "i= 600\n",
            "i= 650\n",
            "i= 700\n",
            "i= 750\n",
            "i= 800\n",
            "i= 850\n",
            "i= 900\n",
            "i= 950\n",
            "i= 1000\n",
            "i= 1050\n",
            "i= 1100\n",
            "i= 1150\n",
            "i= 1200\n",
            "i= 1250\n",
            "i= 1300\n",
            "i= 1350\n",
            "i= 1400\n",
            "i= 1450\n",
            "i= 1500\n",
            "i= 1550\n",
            "i= 1600\n",
            "i= 1650\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "dT-xPfMabnP7",
        "colab": {}
      },
      "source": [
        "features_clinicalbert_cls_nsp_scaled = StandardScaler().fit_transform(features_clinicalbert_cls_nsp)\n",
        "features_clinicalbert_cls_nsp_principal_components = pca.fit_transform(features_clinicalbert_cls_nsp_scaled)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "63e1722e-ca68-4804-f936-0d92ff2db828",
        "id": "geoihFVqbnP_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        }
      },
      "source": [
        "y_train_test_hat, y_train_test = predict_train(features_clinicalbert_cls_nsp_principal_components, y_true, get_optimal_rf_model(features_clinicalbert_cls_nsp_principal_components[1500:], y_true[1500:]))\n",
        "plot_accuracy(y_train_test, y_train_test_hat)"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Optimal parameters: {'max_depth': 5, 'min_samples_split': 4, 'n_estimators': 500, 'subsample': 0.7}\n",
            "Accuracy: 0.6403145219543447\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DTTHKumg2rptB2yhcvIgCLk78TuC+aGa6VGMig77P1fsVx2dglEGsK3vSEHAB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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qB6SqqqCSLI3",
        "colab_type": "text"
      },
      "source": [
        "#### finetuned BioBERT"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lRVzGJG3SNVX",
        "colab_type": "code",
        "outputId": "54cf5665-e706-4eb1-b4d7-5d126dfde588",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 663
        }
      },
      "source": [
        "#NSP finetuned BIO BERT FEATURES\n",
        "features_biobert_cls_sts_biomedical_nsp = bert_based_features_nsp(X_train_original,  sts_biomedical_biobert_model, sts_biomedical_biobert_tokenizer)"
      ],
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "768\n",
            "71\n",
            "381\n",
            " Complex assessment performed, Patient arrives ambulatory, Gait steady, History obtained from, parent, Patient appears comfortable, Patient cooperative, alert, Oriented to person, place and time. [SEP]  Patient arrives, via hospital wheelchair, Gait steady, History obtained from patient, Patient appears comfortable, Patient cooperative, alert, Oriented to person, place and time.\n",
            "i= 0\n",
            "i= 50\n",
            "i= 150\n",
            "i= 200\n",
            "i= 250\n",
            "i= 300\n",
            "i= 350\n",
            "i= 400\n",
            "i= 450\n",
            "i= 500\n",
            "i= 550\n",
            "i= 600\n",
            "i= 650\n",
            "i= 700\n",
            "i= 750\n",
            "i= 800\n",
            "i= 850\n",
            "i= 900\n",
            "i= 950\n",
            "i= 1000\n",
            "i= 1050\n",
            "i= 1100\n",
            "i= 1150\n",
            "i= 1200\n",
            "i= 1250\n",
            "i= 1300\n",
            "i= 1350\n",
            "i= 1400\n",
            "i= 1450\n",
            "i= 1500\n",
            "i= 1550\n",
            "i= 1600\n",
            "i= 1650\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "7P5JDFJ6SN0E",
        "colab": {}
      },
      "source": [
        "#PCA\n",
        "features_biobert_cls_sts_biomedical_nsp_scaled = StandardScaler().fit_transform(features_biobert_cls_sts_biomedical_nsp)\n",
        "features_biobert_cls_sts_biomedical_nsp_principal_components = pca.fit_transform(features_biobert_cls_sts_biomedical_nsp_scaled)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aedaiTZonxeE",
        "colab_type": "text"
      },
      "source": [
        "### combining one-sentence and NSP"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iB9UtZ7MnZp4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "features_biobert_onesentence_nsp = np.concatenate([features_biobert_sts_biomedical, features_biobert_cls_sts_biomedical_nsp_principal_components], axis = 1)\n",
        "\n",
        "y_train_test_hat, y_train_test = predict_train(features_biobert_onesentence_nsp, y_true, get_optimal_rf_model(features_biobert_onesentence_nsp[1500:], y_true[1500:]))\n",
        "plot_accuracy(y_train_test, y_train_test_hat)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gO6yUlGjuV_X",
        "colab_type": "text"
      },
      "source": [
        "## <center>4. Preliminary error analysis</center>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XKAiEd9M0eMy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "print(len(y_hat))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cIUI7Ho1zz9Z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for i, y_elem in enumerate(y_true):\n",
        "  if abs(y_elem - y_hat[i]) > 2:\n",
        "    print(X_train_original[i], y_elem, y_hat[i])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "105szZ2AtIhv",
        "colab_type": "text"
      },
      "source": [
        "## 5.caching features"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "twkP5UiGiI6n",
        "colab_type": "text"
      },
      "source": [
        "### one sentence features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cM49AwFvnU7Z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# CACHING ALL THE FEATURES"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VBsO8cOyvTPA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pickle\n",
        "def cache_features(name, features, cls):\n",
        "  f = open('/content/drive/My Drive/'+name+'_features.pkl', 'wb')\n",
        "  pickle.dump(features, f)\n",
        "  f.close()\n",
        "  f2 = open('/content/drive/My Drive/'+name+'_cls_features.pkl', 'wb')\n",
        "  pickle.dump(cls, f2)\n",
        "  f2.close()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dbrWNqZ6v9Rn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cache_features('bert_vanilla', features_bert_og, features_bert_cls_og)\n",
        "cache_features('biobert', features_bert_bio, features_bert_cls_bio)\n",
        "cache_features('bert_nli_stsb', features_bert_stsb, features_bert_cls_stsb)\n",
        "cache_features('bioclinicalbert', features_bert_bio_clinical, features_bert_cls_bio_clinical)\n",
        "cache_features('clinicalbert', features_bert_clinical, features_bert_cls_clinical)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "16aYsoxAW5rI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cache_features('roberta_stsbiomedical', features_roberta_sts_biomedical, features_roberta_cls_sts_biomedical)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zjt14_PNZziW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cache_features('bert_stsbiomedical', features_bert_sts_biomedical, features_bert_cls_sts_biomedical)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lPAdRCYWd1vD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cache_features('biobert_stsbiomedical', features_biobert_sts_biomedical, features_biobert_cls_sts_biomedical)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6-7uUYzreOk7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#TEST data features\n",
        "\n",
        "#VANILLA\n",
        "features_bert_og_test, features_bert_cls_og_test = bert_based_features_original(X_test_original, vanilla_bert_model, vanilla_bert_tokenizer)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vhfitMqEk93T",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# STS B finetuned\n",
        "features_bert_sts_biomedical_test, features_bert_cls_sts_biomedical_test = bert_based_features_original(X_test_original, sts_biomedical_bert_model, sts_biomedical_bert_tokenizer)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YOUOez34lAxB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# roberta finetuned\n",
        "features_roberta_sts_biomedical_test, features_roberta_cls_sts_biomedical_test = bert_based_features_original(X_test_original, sts_biomedical_roberta_model, sts_biomedical_roberta_tokenizer)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XZK20WnSlDc0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# biobert finetuned (mean only)\n",
        "features_biobert_sts_biomedical_test, features_biobert_cls_sts_biomedical_test = bert_based_features_original(X_test_original, sts_biomedical_biobert_model, sts_biomedical_biobert_tokenizer)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m6GisXtGlF43",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#BIO BERT FEATURES\n",
        "features_bert_bio_test, features_bert_cls_bio_test = bert_based_features_original(X_test_original, bio_bert_model, bio_bert_tokenizer)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YA56ezQclIjl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#STS B\n",
        "features_bert_stsb_test, features_bert_cls_stsb_test = bert_based_features_original(X_test_original, stsb_bert_model, stsb_bert_tokenizer)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EuqNm5eLlK8J",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#BIO CLINICAL BERT FEATURES\n",
        "features_bert_bio_clinical_test, features_bert_cls_bio_clinical_test = bert_based_features_original(X_test_original, bio_clinical_bert_model, bio_clinical_bert_tokenizer)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-ACek_LxlNH5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#CLINICAL BERT FEATURES\n",
        "features_bert_clinical_test, features_bert_cls_clinical_test = bert_based_features_original(X_test_original, clinical_bert_model, clinical_bert_tokenizer)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_xcYL3oFlOQJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cache_features('bert_vanilla_test', features_bert_og_test, features_bert_cls_og_test)\n",
        "cache_features('biobert_test', features_bert_bio_test, features_bert_cls_bio_test)\n",
        "cache_features('bert_nli_stsb_test', features_bert_stsb_test, features_bert_cls_stsb_test)\n",
        "cache_features('bioclinicalbert_test', features_bert_bio_clinical_test, features_bert_cls_bio_clinical_test)\n",
        "cache_features('clinicalbert_test', features_bert_clinical_test, features_bert_cls_clinical_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_osje3P3nHND",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cache_features('roberta_stsbiomedical_test', features_roberta_sts_biomedical_test, features_roberta_cls_sts_biomedical_test)\n",
        "cache_features('bert_stsbiomedical_test', features_bert_sts_biomedical_test, features_bert_cls_sts_biomedical_test)\n",
        "cache_features('biobert_stsbiomedical_test', features_biobert_sts_biomedical_test, features_biobert_cls_sts_biomedical_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kHFSVmzGiL10",
        "colab_type": "text"
      },
      "source": [
        "### NSP features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F2Y3p21tiASP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pickle\n",
        "def cache_nsp_features(name, features):\n",
        "  f = open('/content/drive/My Drive/'+name+'_NSP_features.pkl', 'wb')\n",
        "  pickle.dump(features, f)\n",
        "  f.close()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hLn3xo_eq9pu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cache_nsp_features('biobert', features_bert_cls_bio_nsp_principal_components)\n",
        "cache_nsp_features('bert_nli_stsb', features_stsb_bert_cls_nsp_principal_components)\n",
        "cache_nsp_features('clinicalbert', features_clinicalbert_cls_nsp_principal_components)\n",
        "cache_nsp_features('biobert_stsbiomedical', features_biobert_cls_sts_biomedical_nsp_principal_components)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bGXy_E-ZibGB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "outputId": "9b2bdf0f-6e98-4c18-8ee8-63e9dc36baec"
      },
      "source": [
        "#BIO BERT FEATURES\n",
        "features_bert_cls_bio_nsp_test = bert_based_features_nsp(X_test_original, bio_bert_model, bio_bert_tokenizer)"
      ],
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "768\n",
            "24\n",
            "125\n",
            "male who presents for evaluation of Med Management. [SEP] Requested to see patient for evaluation of: pressure ulcer consult.\n",
            "i= 0\n",
            "i= 50\n",
            "i= 100\n",
            "i= 150\n",
            "i= 200\n",
            "i= 250\n",
            "i= 300\n",
            "i= 350\n",
            "i= 400\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tlc3vtVRqsRq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "outputId": "a0e502b5-2cae-412d-9cab-89bd040d81db"
      },
      "source": [
        "# STSb model\n",
        "features_stsb_bert_cls_nsp_test = bert_based_features_nsp(X_test_original, stsb_bert_model, stsb_bert_tokenizer)"
      ],
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "768\n",
            "23\n",
            "125\n",
            "male who presents for evaluation of Med Management. [SEP] Requested to see patient for evaluation of: pressure ulcer consult.\n",
            "i= 0\n",
            "i= 50\n",
            "i= 100\n",
            "i= 150\n",
            "i= 200\n",
            "i= 250\n",
            "i= 300\n",
            "i= 350\n",
            "i= 400\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "8e316a08-2a16-4549-d0c9-21e992c7fa9a",
        "id": "YL0W2T2Gqxpn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        }
      },
      "source": [
        "# clincal BERT\n",
        "features_clinicalbert_cls_nsp_test = bert_based_features_nsp(X_test_original, clinical_bert_model, clinical_bert_tokenizer)"
      ],
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "768\n",
            "24\n",
            "125\n",
            "male who presents for evaluation of Med Management. [SEP] Requested to see patient for evaluation of: pressure ulcer consult.\n",
            "i= 0\n",
            "i= 50\n",
            "i= 100\n",
            "i= 150\n",
            "i= 200\n",
            "i= 250\n",
            "i= 300\n",
            "i= 350\n",
            "i= 400\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "8fb81fb8-9952-40d6-b6a3-7f69beabffd7",
        "id": "5_semMCbqxpz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        }
      },
      "source": [
        "#finetuned BIO BERT FEATURES\n",
        "features_biobert_cls_sts_biomedical_nsp_test = bert_based_features_nsp(X_test_original,  sts_biomedical_biobert_model, sts_biomedical_biobert_tokenizer)"
      ],
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "768\n",
            "23\n",
            "125\n",
            "male who presents for evaluation of Med Management. [SEP] Requested to see patient for evaluation of: pressure ulcer consult.\n",
            "i= 0\n",
            "i= 50\n",
            "i= 100\n",
            "i= 150\n",
            "i= 200\n",
            "i= 250\n",
            "i= 300\n",
            "i= 350\n",
            "i= 400\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1gt3gD9zt0SK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#PCA\n",
        "\n",
        "features_bert_cls_bio_nsp_test_scaled = StandardScaler().fit_transform(features_bert_cls_bio_nsp_test)\n",
        "features_bert_cls_bio_nsp_test_principal_components = pca.fit_transform(features_bert_cls_bio_nsp_test_scaled)\n",
        "\n",
        "features_stsb_bert_cls_nsp_test_scaled = StandardScaler().fit_transform(features_stsb_bert_cls_nsp_test)\n",
        "features_stsb_bert_cls_nsp_test_principal_components = pca.fit_transform(features_stsb_bert_cls_nsp_test_scaled)\n",
        "\n",
        "features_clinicalbert_cls_nsp_test_scaled = StandardScaler().fit_transform(features_clinicalbert_cls_nsp_test)\n",
        "features_clinicalbert_cls_nsp_test_principal_components = pca.fit_transform(features_clinicalbert_cls_nsp_test_scaled)\n",
        "\n",
        "features_biobert_cls_sts_biomedical_nsp_test_scaled = StandardScaler().fit_transform(features_biobert_cls_sts_biomedical_nsp_test)\n",
        "features_biobert_cls_sts_biomedical_nsp_test_principal_components = pca.fit_transform(features_biobert_cls_sts_biomedical_nsp_test_scaled)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hc38_4PVr7PH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cache_nsp_features('biobert_test', features_bert_cls_bio_nsp_test_principal_components)\n",
        "cache_nsp_features('bert_nli_stsb_test', features_stsb_bert_cls_nsp_test_principal_components)\n",
        "cache_nsp_features('clinicalbert_test', features_clinicalbert_cls_nsp_test_principal_components)\n",
        "cache_nsp_features('biobert_stsbiomedical_test', features_biobert_cls_sts_biomedical_nsp_test_principal_components)"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}