driver.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/DhruvaBansal00/ConfidentMT/blob/master/driver.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lr7Pw_tWRMKk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "outputId": "98ad0997-b525-4ac3-83b5-0a27840ea994"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive', force_remount=True)"
      ],
      "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": "bCdkGr5ev6zM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "88c94fe6-646e-4a02-da10-5257c0a8968a"
      },
      "source": [
        "%cd drive/My Drive/ConfidentMachineTranslation/flores\n",
        "!pwd\n",
        "import sys\n",
        "sys.path.append('/content/drive/My Drive/ConfidentMachineTranslation/flores/src/')"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "drive  sample_data\n",
            "/content/drive/My Drive/ConfidentMachineTranslation/flores\n",
            "analysis\t\t  data-bin\t\t     pointWiseComparison15.png\n",
            "backward_models\t\t  DeprecatedColabScripts     Resources\n",
            "checkpoints\t\t  driver.ipynb\t\t     scripts\n",
            "ClassificationDataset\t  Ensembles\t\t     sentencepiece_models\n",
            "classifierComparison.png  language_models\t     src\n",
            "configs\t\t\t  noisychannel\n",
            "data\t\t\t  pointWiseComparison10.png\n",
            "/content/drive/My Drive/ConfidentMachineTranslation/flores\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "woDoqsuoOFS4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 731
        },
        "outputId": "00b75293-872d-4c99-c07c-7ebca92ac207"
      },
      "source": [
        "pip install fairseq sacrebleu sentencepiece fastBPE sacremoses xgboost"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting fairseq\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/67/bf/de299e082e7af010d35162cb9a185dc6c17db71624590f2f379aeb2519ff/fairseq-0.9.0.tar.gz (306kB)\n",
            "\u001b[K     |████████████████████████████████| 307kB 2.8MB/s \n",
            "\u001b[?25hCollecting sacrebleu\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/da/4b/6c7a0b26a48d88f56573d11aa5058808fe0d36ba40951287894f943556b5/sacrebleu-1.4.10-py3-none-any.whl (60kB)\n",
            "\u001b[K     |████████████████████████████████| 61kB 6.2MB/s \n",
            "\u001b[?25hCollecting sentencepiece\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/d4/a4/d0a884c4300004a78cca907a6ff9a5e9fe4f090f5d95ab341c53d28cbc58/sentencepiece-0.1.91-cp36-cp36m-manylinux1_x86_64.whl (1.1MB)\n",
            "\u001b[K     |████████████████████████████████| 1.1MB 7.3MB/s \n",
            "\u001b[?25hCollecting fastBPE\n",
            "  Downloading https://files.pythonhosted.org/packages/e1/37/f97181428a5d151501b90b2cebedf97c81b034ace753606a3cda5ad4e6e2/fastBPE-0.1.0.tar.gz\n",
            "Collecting sacremoses\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/7d/34/09d19aff26edcc8eb2a01bed8e98f13a1537005d31e95233fd48216eed10/sacremoses-0.0.43.tar.gz (883kB)\n",
            "\u001b[K     |████████████████████████████████| 890kB 17.8MB/s \n",
            "\u001b[?25hRequirement already satisfied: xgboost in /usr/local/lib/python3.6/dist-packages (0.90)\n",
            "Requirement already satisfied: cffi in /usr/local/lib/python3.6/dist-packages (from fairseq) (1.14.0)\n",
            "Requirement already satisfied: cython in /usr/local/lib/python3.6/dist-packages (from fairseq) (0.29.20)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from fairseq) (1.18.5)\n",
            "Requirement already satisfied: regex in /usr/local/lib/python3.6/dist-packages (from fairseq) (2019.12.20)\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.6/dist-packages (from fairseq) (1.5.0+cu101)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (from fairseq) (4.41.1)\n",
            "Collecting portalocker\n",
            "  Downloading https://files.pythonhosted.org/packages/53/84/7b3146ec6378d28abc73ab484f09f47dfa008ad6f03f33d90a369f880e25/portalocker-1.7.0-py2.py3-none-any.whl\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from sacremoses) (1.12.0)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.6/dist-packages (from sacremoses) (7.1.2)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.6/dist-packages (from sacremoses) (0.15.1)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from xgboost) (1.4.1)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.6/dist-packages (from cffi->fairseq) (2.20)\n",
            "Requirement already satisfied: future in /usr/local/lib/python3.6/dist-packages (from torch->fairseq) (0.16.0)\n",
            "Building wheels for collected packages: fairseq, fastBPE, sacremoses\n",
            "  Building wheel for fairseq (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for fairseq: filename=fairseq-0.9.0-cp36-cp36m-linux_x86_64.whl size=2021173 sha256=babf6646a039cc412538e554bf23af1492669b140962211449cbf4e71fe51345\n",
            "  Stored in directory: /root/.cache/pip/wheels/37/3e/1b/0fa30695dcba41e4b0088067fa40f3328d1e8ee78c22cd4766\n",
            "  Building wheel for fastBPE (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for fastBPE: filename=fastBPE-0.1.0-cp36-cp36m-linux_x86_64.whl size=481520 sha256=62da678f43a6b111d7badf9e77142ff5f794588d8ae5dcf8750587bca1f50e7f\n",
            "  Stored in directory: /root/.cache/pip/wheels/f3/0c/9c/fc62058b4d473a5602bcd3d3edfece796f123875379ea82d79\n",
            "  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sacremoses: filename=sacremoses-0.0.43-cp36-none-any.whl size=893260 sha256=9519d3bc5823195c0ec13f97eceb2798e6b9f32697b6bf9f8299cdb07410a58d\n",
            "  Stored in directory: /root/.cache/pip/wheels/29/3c/fd/7ce5c3f0666dab31a50123635e6fb5e19ceb42ce38d4e58f45\n",
            "Successfully built fairseq fastBPE sacremoses\n",
            "Installing collected packages: portalocker, sacrebleu, fairseq, sentencepiece, fastBPE, sacremoses\n",
            "Successfully installed fairseq-0.9.0 fastBPE-0.1.0 portalocker-1.7.0 sacrebleu-1.4.10 sacremoses-0.0.43 sentencepiece-0.1.91\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TcgcnXgn-0F4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "09b7eb26-d4c6-45b8-e6ab-5201e8ff067d"
      },
      "source": [
        "# prints how much GPU RAM is available\n",
        "!ln -sf /opt/bin/nvidia-smi /usr/bin/nvidia-smi\n",
        "!pip install gputil\n",
        "!pip install psutil\n",
        "!pip install humanize\n",
        "import psutil\n",
        "import humanize\n",
        "import os\n",
        "import GPUtil as GPU\n",
        "GPUs = GPU.getGPUs()\n",
        "# XXX: only one GPU on Colab and isn’t guaranteed\n",
        "gpu = GPUs[0]\n",
        "def printm():\n",
        " process = psutil.Process(os.getpid())\n",
        " print(\"Gen RAM Free: \" + humanize.naturalsize( psutil.virtual_memory().available ), \" | Proc size: \" + humanize.naturalsize( process.memory_info().rss))\n",
        " print(\"GPU RAM Free: {0:.0f}MB | Used: {1:.0f}MB | Util {2:3.0f}% | Total {3:.0f}MB\".format(gpu.memoryFree, gpu.memoryUsed, gpu.memoryUtil*100, gpu.memoryTotal))\n",
        "printm()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting gputil\n",
            "  Downloading https://files.pythonhosted.org/packages/ed/0e/5c61eedde9f6c87713e89d794f01e378cfd9565847d4576fa627d758c554/GPUtil-1.4.0.tar.gz\n",
            "Building wheels for collected packages: gputil\n",
            "  Building wheel for gputil (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for gputil: filename=GPUtil-1.4.0-cp36-none-any.whl size=7413 sha256=963331cc138e78f328a1aba23ce07a6003160b1dbbdaf754a4bf1968c2dfdf8a\n",
            "  Stored in directory: /root/.cache/pip/wheels/3d/77/07/80562de4bb0786e5ea186911a2c831fdd0018bda69beab71fd\n",
            "Successfully built gputil\n",
            "Installing collected packages: gputil\n",
            "Successfully installed gputil-1.4.0\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.6/dist-packages (5.4.8)\n",
            "Requirement already satisfied: humanize in /usr/local/lib/python3.6/dist-packages (0.5.1)\n",
            "Gen RAM Free: 12.7 GB  | Proc size: 161.1 MB\n",
            "GPU RAM Free: 11441MB | Used: 0MB | Util   0% | Total 11441MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y74icn97X2Zy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "##Methods for CLI with fairseq - WIP\n",
        "import os\n",
        "import glob\n",
        "from tqdm.notebook import tqdm\n",
        "\n",
        "class FairseqWrapper:\n",
        "\n",
        "    def runFairseqGenerate(self, dataFolder, sourceLang, targetLang, fwModel, beam, lenpen, dataSet, removeBpe, saveFile):\n",
        "        !fairseq-generate $dataFolder --source-lang $sourceLang --target-lang $targetLang \\\n",
        "        --path $fwModel --beam $beam --lenpen $lenpen --gen-subset $dataSet --remove-bpe=$removeBpe --sacrebleu > $saveFile\n",
        "\n",
        "    def runFairseqScore(self, translations, groundTruth, saveFile, metric):\n",
        "        !fairseq-score --sys $translations --ref $groundTruth --$metric > $saveFile\n",
        "    \n",
        "    def runFairseqPreprocessLM(self, srcdict, preprocCommand, bpeTranslations, dest):\n",
        "        !fairseq-preprocess --only-source --srcdict $srcdict --$preprocCommand $bpeTranslations \\\n",
        "        --destdir $dest --workers 20\n",
        "    \n",
        "    def runFairseqPreprocessBinarize(self, srcLang, trgLang, srcdict, preprocCommand, bpeTranslations, destdir):\n",
        "        !fairseq-preprocess --source-lang $srcLang --target-lang $trgLang --srcdict $srcdict\\\n",
        "        --$preprocCommand $bpeTranslations --destdir $destdir --joined-dictionary --workers 20\n",
        "\n",
        "    def runFairseqEvalLM(self, preprocFile, lmModel, batchSize, maxTokens, dataSet, saveFile):\n",
        "        !fairseq-eval-lm $preprocFile --path $lmModel --batch-size $batchSize --max-tokens $maxTokens \\\n",
        "        --output-word-probs --sample-break-mode eos --gen-subset $dataSet > $saveFile\n",
        "\n",
        "    def deleteFolder(self, folder):\n",
        "        print(\"Deleting contents in folder: \" + str(folder))\n",
        "        for f in tqdm(glob.glob(folder)):\n",
        "            !rm $f\n",
        "    "
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tlOqgoqostAa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dataFolder = \"data-bin/wiki_ne_en_bpe5000/\"\n",
        "fwModel = \"Ensembles/April_04.pt\"\n",
        "bwModel = \"backward_models/April_17.pt\"\n",
        "lmModel = \"language_models/lm/checkpoint_best.pt\"\n",
        "sourceLang = \"ne\"\n",
        "targetLang = \"en\"\n",
        "ensembleFwModels = \":\".join([i for i in glob.glob(\"Ensembles/*\")])"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GizO5tmF_GbV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import translationUtils\n",
        "from translation import Translation\n",
        "import dataUtils\n",
        "import classification\n",
        "import thresholding\n",
        "import numpy as np\n",
        "from tqdm.notebook import tqdm"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vc9enbWYwjGO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "trainTranslations = translationUtils.getTranslationFromDataset(\"valid\", fwModel, bwModel, lmModel, \"ne\", \"en\", FairseqWrapper(), dataFolder, produceGraphs=False, saveTranslations=True)\n",
        "testTranslations = translationUtils.getTranslationFromDataset(\"test\", fwModel, bwModel, lmModel, \"ne\", \"en\", FairseqWrapper(), dataFolder, produceGraphs=False, saveTranslations=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b4pi3Ump9NTf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ensembleTranslations = translationUtils.getTranslationFromDataset(\"test\", ensembleFwModels, bwModel, lmModel, \"ne\", \"en\", FairseqWrapper(), dataFolder, produceGraphs=False, saveTranslations=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UnOpHbWtUiAd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import translationUtils\n",
        "from translation import Translation\n",
        "import dataUtils\n",
        "import classification\n",
        "import thresholding\n",
        "import numpy as np\n",
        "from tqdm.notebook import tqdm\n",
        "import matplotlib.pyplot as plt\n",
        "import random \n",
        "\n",
        "trainTranslations = dataUtils.createObjectsFromFile(\"valid\")\n",
        "testTranslations = dataUtils.createObjectsFromFile(\"test\")"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nun0yPx_9Y3z",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 115,
          "referenced_widgets": [
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        },
        "outputId": "7b889e2e-f4bc-42a8-c90d-c4dafdf66f98"
      },
      "source": [
        "averageLogprobThresholding = thresholding.precisionCurveFromThresholding(testTranslations, np.linspace(-1.5, 0, 25).tolist(), 0, FairseqWrapper())\n",
        "oracleThresholding = thresholding.precisionCurveFromThresholding(testTranslations, np.linspace(0, 60, 25).tolist(), 4, FairseqWrapper())\n",
        "# ensembleThresholding = thresholding.precisionCurveFromThresholding(ensembleTranslations, np.linspace(-1.5, 0, 25).tolist(), 0, FairseqWrapper())"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9f44468d07d744869f4ae294a2ec8490",
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            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=25.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8833f80c2e2441e9a33b2610b3e248f0",
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              "HBox(children=(FloatProgress(value=0.0, max=25.0), HTML(value='')))"
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          },
          "metadata": {
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        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HU-L61J69fGy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
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        "outputId": "1541ab0e-4fc1-447f-b711-fd912242268b"
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      "source": [
        "ncdClassifier = classification.precisionCurveFromClassification(trainTranslations, testTranslations, \"LogisticRegression\", FairseqWrapper(), np.linspace(0.1, 60, 25).tolist(), \n",
        "                                                                [0, 11, 12], [1, 2])"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
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        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
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          ]
        },
        "outputId": "96cc47b6-9d90-4cd5-8796-bf23757e9aa8"
      },
      "source": [
        "comparisonPoints = []\n",
        "featureSubsets = [[0], #just baseline Forward Model score [P(T|S)]\n",
        "                  [0, 11, 12], #Similar to noisy channel decoding: P(y|x), p(x|y), p(y) \n",
        "                  [0, 11, 12, 19, 20], #Like above + sentence length features\n",
        "                  [0, 11, 12, 16, 17, 18], #Like above + ngram features\n",
        "                  [0, 5, 6, 11, 12, 19, 20], #Like above + sentence length features + Rare words\n",
        "                  [0, 11, 12, 13, 14, 19, 20], #Like above + sentence length features + end of sentence identifiers\n",
        "                  [0, 5, 6, 11, 12, 16, 17, 18], #Like above + ngram features + Rare words\n",
        "                  [0, 11, 12, 13, 14, 16, 17, 18], #Like above + ngram features + end of sentence identifiers\n",
        "                  [0, 5, 6, 11, 12, 13, 14, 19, 20], #Like above + sentence length features + Rare words + end of sentence identifiers\n",
        "                  [0, 5, 6, 11, 12, 13, 14, 16, 17, 18] #Like above + ngram features + Rare words + end of sentence identifiers\n",
        "                  ]\n",
        "for subset in featureSubsets:\n",
        "    currPoints = classification.precisionCurveFromClassification(trainTranslations, testTranslations, \"LogisticRegression\", FairseqWrapper(), [15], subset, [i for i in range(1, len(subset))])\n",
        "    comparisonPoints.append((currPoints[0][0], currPoints[1][0]))\n"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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            "\n"
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          "name": "stdout"
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            "\n"
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            "\n"
          ],
          "name": "stdout"
        },
        {
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            "\n"
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TcvEVAG4evyL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "95817ffe-3a77-42b1-e273-b1324f5b7a2b"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import random \n",
        "\n",
        "plt.plot(averageLogprobThresholding[0], averageLogprobThresholding[1], label = \"Average Logprob Baseline\", color=(random.random(), random.random(), random.random()))\n",
        "plt.plot(oracleThresholding[0], oracleThresholding[1], label = \"Oracle\", color=(random.random(), random.random(), random.random()))\n",
        "\n",
        "for index, point in enumerate(comparisonPoints):\n",
        "    plt.scatter([point[0]], [point[1]], label = str(index), color=(random.random(), random.random(), random.random()), s=20)\n",
        "\n",
        "plt.legend(loc=\"upper right\")\n",
        "plt.savefig(\"pointWiseComparison10.png\")\n",
        "plt.show()\n"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8pGuuoEImtLs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        },
        "outputId": "a5590852-5011-4bbd-a72b-4c414b1d82d7"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import random \n",
        "from sklearn.metrics import auc\n",
        "\n",
        "plt.plot(averageLogprobThresholding[0], averageLogprobThresholding[1], label = \"Average Logprob Baseline\", color=(random.random(), random.random(), random.random()))\n",
        "plt.plot(oracleThresholding[0], oracleThresholding[1], label = \"Oracle\", color=(random.random(), random.random(), random.random()))\n",
        "plt.plot(ncdClassifier[0], ncdClassifier[1], label = \"NCD feature classifier\", color=(random.random(), random.random(), random.random()))\n",
        "# plt.plot(ensembleThresholding[0], ensembleThresholding[1], label = \"Ensemble Average Baseline\", color=(random.random(), random.random(), random.random()))\n",
        "\n",
        "print(\"[averageLogprobThresholding] AUC for included fraction: {}\".format(auc(averageLogprobThresholding[0], averageLogprobThresholding[1])))\n",
        "print(\"[oracleThresholding] AUC for included fraction: {}\".format(auc(oracleThresholding[0], oracleThresholding[1])))\n",
        "print(\"[ncdClassifier] AUC for included fraction: {}\".format(auc(ncdClassifier[0], ncdClassifier[1])))\n",
        "# print(\"[ensembleAverageThresholding] AUC for included fraction: {}\".format(auc(ensembleThresholding[0], ensembleThresholding[1])))\n",
        "\n",
        "\n",
        "plt.legend(loc=\"upper left\")\n",
        "plt.savefig(\"classifierComparison.png\")\n",
        "plt.show()\n"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[averageLogprobThresholding] AUC for included fraction: 12.273183421516753\n",
            "[oracleThresholding] AUC for included fraction: 16.294950617283952\n",
            "[ncdClassifier] AUC for included fraction: 12.87656790123457\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}