notebooks/3.0_results.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nyu5tnRV98NB",
        "outputId": "992602b6-6e60-4719-9937-3667306f4826"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/Colab/password_predict\n"
          ]
        }
      ],
      "source": [
        "%cd /content/drive/MyDrive/Colab/password_predict"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T9EczPdpEjqt",
        "outputId": "63a7162e-c17b-49c6-8218-c1bbd67d840c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " \u001b[0m\u001b[01;34m3_epochs\u001b[0m/                            pass_predict_100_sentence_mapping.pt\n",
            " \u001b[01;34m5_epochs\u001b[0m/                            pass_predict.pt\n",
            "'Copy of train.ipynb'                 pass_predict_sampling.pt\n",
            " \u001b[01;34mdata\u001b[0m/                                test.ipynb\n",
            " data_prep_100.ipynb                  train_100_1m_direct_mapping.ipynb\n",
            " data_prep.ipynb                      train_100_1m_sentence_mapping.ipynb\n",
            " dataset_sampling.ipynb               train_100.ipynb\n",
            " hdf5_conv.ipynb                      train_char_sampling.ipynb\n",
            " \u001b[01;34mlambda_labs\u001b[0m/                         train.ipynb\n",
            " pass_predict_100_direct_mapping.pt   train_sampling.ipynb\n"
          ]
        }
      ],
      "source": [
        "%ls"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yLm8whPPEtM3",
        "outputId": "cac14afa-c237-466a-dc33-e2d6c55d2a35"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "admin@incontakte.ru,123vfif123\n",
            "packadmin@web.de,brandeeanne\n",
            "adminissante@virgilio.it,iwaki123\n",
            "adminisstrator2008@mail.ru,2732865\n",
            "admin@olli.la,leckmich\n",
            "admin@saintpatricks.net,baltimore1\n",
            "alf.admin.real4ever@rambler.ru,Dr011206\n",
            "admin@medisana.com.ua,15091968\n",
            "admin@lawpropertygroup.couk,freeland\n",
            "sosclanadmin@lycos.de,1rnumb\n"
          ]
        }
      ],
      "source": [
        "!grep -iE '.*admin.*@' data/test.csv | head -10"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EqWjPiM-FnQl",
        "outputId": "e9dab3d3-9ec2-47e5-9e96-8722c048dbb8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "testing.trinhh@yahoo.com,oink99\n",
            "testi2112@ya.ru,djlbwf\n",
            "przhottestchicka16@hotmail.com,luvu4\n",
            "sweetest_dew@hotmail.com,1659232hana\n",
            "alessandro_testa@tiscali.it,domenico\n",
            "test171@yandex.ru,2921405\n",
            "test0002@mail.ru,qweqwe\n",
            "applegatestudios@yahoo.com,POOHLINKEDIN\n",
            "steve+test@gmx.net,testtest\n",
            "jackisthegreatest@hotmail.com,poohead12\n"
          ]
        }
      ],
      "source": [
        "!grep -iE '.*test.*@' data/test.csv | head -10"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7J71sIdNZXqo"
      },
      "outputs": [],
      "source": [
        "import csv\n",
        "\n",
        "# Custom char tokenizer\n",
        "def char_tokenizer(text):\n",
        "    return list(text)\n",
        "\n",
        "# Yield tokens from dataset for vocabulary building\n",
        "def yield_tokens(data):\n",
        "    for src, trg in data:\n",
        "        yield char_tokenizer(src)\n",
        "        yield char_tokenizer(trg)\n",
        "\n",
        "# Load data from CSV\n",
        "def load_data_from_csv(filename):\n",
        "    with open(filename, 'r', encoding='utf-8') as file:\n",
        "        csv_reader = csv.reader(file)\n",
        "        data = [(row[0], row[1]) for row in csv_reader]\n",
        "    return data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mqAKLYUNZdri"
      },
      "outputs": [],
      "source": [
        "train_data_raw = load_data_from_csv('data/train.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M-Cd_5dYZlL8",
        "outputId": "4ff9368d-f54f-4a26-cf5f-987b9f4e2dda"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Vocab length: 4131\n"
          ]
        }
      ],
      "source": [
        "from torchtext.vocab import build_vocab_from_iterator\n",
        "\n",
        "vocab = build_vocab_from_iterator(yield_tokens(train_data_raw), specials=['<sos>', '<eos>', '<unk>'])\n",
        "vocab.set_default_index(vocab[\"<unk>\"])\n",
        "\n",
        "print(f\"Vocab length: {len(vocab)}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FzY9stETaXwv"
      },
      "outputs": [],
      "source": [
        "# save vocab\n",
        "import pickle\n",
        "\n",
        "with open('data/vocab_org.pkl', 'wb') as f:\n",
        "    pickle.dump(vocab, f)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3R0VSZ91GH14"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import pickle\n",
        "\n",
        "class Encoder(nn.Module):\n",
        "    def __init__(self, input_dim, emb_dim, hid_dim, n_layers, dropout):\n",
        "        super().__init__()\n",
        "        self.hid_dim = hid_dim\n",
        "        self.embedding = nn.Embedding(input_dim, emb_dim)\n",
        "        self.rnn = nn.LSTM(emb_dim, hid_dim, n_layers, dropout = dropout)\n",
        "        self.dropout = nn.Dropout(dropout)\n",
        "\n",
        "    def forward(self, src):\n",
        "        embedded = self.dropout(self.embedding(src))\n",
        "        outputs, (hidden, cell) = self.rnn(embedded)\n",
        "        return hidden, cell\n",
        "\n",
        "class Decoder(nn.Module):\n",
        "    def __init__(self, output_dim, emb_dim, hid_dim, n_layers, dropout):\n",
        "        super().__init__()\n",
        "        self.output_dim = output_dim\n",
        "        self.hid_dim = hid_dim\n",
        "        self.embedding = nn.Embedding(output_dim, emb_dim)\n",
        "        self.rnn = nn.LSTM(emb_dim, hid_dim, n_layers, dropout = dropout)\n",
        "        self.fc_out = nn.Linear(hid_dim, output_dim)\n",
        "        self.dropout = nn.Dropout(dropout)\n",
        "\n",
        "    def forward(self, input, hidden, cell):\n",
        "        input = input.unsqueeze(0)\n",
        "        embedded = self.dropout(self.embedding(input))\n",
        "        output, (hidden, cell) = self.rnn(embedded, (hidden, cell))\n",
        "        prediction = self.fc_out(output.squeeze(0))\n",
        "        return prediction, hidden, cell\n",
        "\n",
        "class Seq2Seq(nn.Module):\n",
        "    def __init__(self, encoder, decoder, device):\n",
        "        super().__init__()\n",
        "        self.encoder = encoder\n",
        "        self.decoder = decoder\n",
        "        self.device = device\n",
        "\n",
        "    def forward(self, src, trg, teacher_forcing_ratio=0.5):\n",
        "        trg_len, batch_size = trg.shape\n",
        "        trg_vocab_size = self.decoder.output_dim\n",
        "        outputs = torch.zeros(trg_len, batch_size, trg_vocab_size).to(self.device)\n",
        "        hidden, cell = self.encoder(src)\n",
        "        input = trg[0, :]\n",
        "\n",
        "        for t in range(1, trg_len):\n",
        "            output, hidden, cell = self.decoder(input, hidden, cell)\n",
        "            outputs[t] = output\n",
        "            teacher_force = torch.rand(1) < teacher_forcing_ratio\n",
        "            top1 = output.argmax(1)\n",
        "            input = trg[t] if teacher_force else top1\n",
        "\n",
        "        return outputs\n",
        "\n",
        "# Make sure to use the correct path where your 'vocab.pkl' file is saved\n",
        "vocab_file_path = '/content/drive/MyDrive/Colab/password_predict/data/vocab_org.pkl'\n",
        "\n",
        "# Load the vocabulary object from the file\n",
        "with open(vocab_file_path, 'rb') as f:\n",
        "    vocab = pickle.load(f)\n",
        "\n",
        "INPUT_DIM = len(vocab)\n",
        "OUTPUT_DIM = len(vocab)\n",
        "ENC_EMB_DIM = 256\n",
        "DEC_EMB_DIM = 256\n",
        "HID_DIM = 512\n",
        "N_LAYERS = 2\n",
        "ENC_DROPOUT = 0.5\n",
        "DEC_DROPOUT = 0.5\n",
        "\n",
        "enc = Encoder(INPUT_DIM, ENC_EMB_DIM, HID_DIM, N_LAYERS, ENC_DROPOUT)\n",
        "dec = Decoder(OUTPUT_DIM, DEC_EMB_DIM, HID_DIM, N_LAYERS, DEC_DROPOUT)\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "model = Seq2Seq(enc, dec, device).to(device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-31rs18RILrw",
        "outputId": "c78a5b29-4c51-4c1f-b655-ec56605116e3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Vocab length: 4131\n"
          ]
        }
      ],
      "source": [
        "print(f\"Vocab length: {len(vocab)}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "smDiQGdeIvjQ",
        "outputId": "3e49df4e-23cf-430f-ee5a-1237a639cc09"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-rw------- 1 root root 46367098 Jan 10 20:48 /content/drive/MyDrive/Colab/password_predict/5_epochs/pass_predict.pt\n"
          ]
        }
      ],
      "source": [
        "%ls -ltr /content/drive/MyDrive/Colab/password_predict/5_epochs/pass_predict.pt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V8etWPmiISL0",
        "outputId": "38a7f9d1-9539-49ed-c18a-604697de2bba"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<All keys matched successfully>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "model.load_state_dict(torch.load('/content/drive/MyDrive/Colab/password_predict/5_epochs/pass_predict.pt',map_location=torch.device('cpu')))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "trUUDJNxJiZ0"
      },
      "outputs": [],
      "source": [
        "itos = vocab.get_itos()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7v1wx368Ikk9"
      },
      "outputs": [],
      "source": [
        "def predict(model, vocab, device, src_sentence, n=1):\n",
        "    model.eval()\n",
        "\n",
        "    # Convert the source sentence to tokens\n",
        "    src_tokens = [tok for tok in src_sentence]\n",
        "    src_indexes = [vocab[tok] for tok in src_tokens] + [vocab['<eos>']]\n",
        "\n",
        "    # Convert to tensor and add batch dimension\n",
        "    src_tensor = torch.LongTensor(src_indexes).unsqueeze(1).to(device)\n",
        "\n",
        "    # Initialize target sequence with <sos> token\n",
        "    result = []\n",
        "    for i in range(n):\n",
        "        trg_indexes = [vocab['<sos>']]\n",
        "        predicted_sentence = []\n",
        "        # Predict next token\n",
        "        while True:\n",
        "            trg_tensor = torch.LongTensor(trg_indexes).unsqueeze(1).to(device)\n",
        "            with torch.no_grad():\n",
        "                output = model(src_tensor, trg_tensor)\n",
        "                #print(output)\n",
        "                predicted_token = output.argmax(2)[-1, :].item()\n",
        "                # Check for end of sentence\n",
        "                if predicted_token == vocab['<eos>']:\n",
        "                    break\n",
        "\n",
        "                # Append the predicted token to the target sequence\n",
        "                trg_indexes.append(predicted_token)\n",
        "                # Convert the target sequence to words\n",
        "                predicted_sentence.append(itos[predicted_token])\n",
        "        if ''.join(predicted_sentence[1:]) in result:\n",
        "            continue\n",
        "        result.append(''.join(predicted_sentence[1:]))\n",
        "\n",
        "    return result  # Exclude the <sos> token"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "edHQV4kNJJ0k",
        "outputId": "735a298e-15ec-4404-e441-66e915a4aa2d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['maneee',\n",
              " 'mannee',\n",
              " 'moarnen',\n",
              " 'monres',\n",
              " 'moannee',\n",
              " 'manneet',\n",
              " 'maanee',\n",
              " 'moannet',\n",
              " 'maannee',\n",
              " 'moaeee',\n",
              " 'maannae',\n",
              " 'maaeeet',\n",
              " 'monreet',\n",
              " 'maneer',\n",
              " 'maaneete',\n",
              " 'monneet',\n",
              " 'moanee',\n",
              " 'monney',\n",
              " 'monnee',\n",
              " 'maaneet',\n",
              " 'maannet',\n",
              " 'moaenet',\n",
              " 'manner',\n",
              " 'moaree',\n",
              " 'moaneet',\n",
              " 'maanneee',\n",
              " 'maaenlt',\n",
              " 'monees',\n",
              " 'maaens',\n",
              " 'maanne',\n",
              " 'maaeee',\n",
              " 'moaredee',\n",
              " 'maaenet',\n",
              " 'moarnt',\n",
              " 'maanae',\n",
              " 'moarnr',\n",
              " 'moarne',\n",
              " 'moaeeet',\n",
              " 'maaees',\n",
              " 'manees',\n",
              " 'monreye',\n",
              " 'moanet',\n",
              " 'monneey',\n",
              " 'moares',\n",
              " 'moareen',\n",
              " 'maanes',\n",
              " 'moanne',\n",
              " 'moaene',\n",
              " 'monree']"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "predict(model, vocab, device, \"steve+test@gmx.net\", 100)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q-rmlsovJcf6",
        "outputId": "2de91198-7332-4f2c-a6b5-bc49341b9b4e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['maarn3a',\n",
              " 'mariian',\n",
              " 'marria',\n",
              " 'mar1in3',\n",
              " 'marii3e',\n",
              " 'marn2d',\n",
              " 'marnia',\n",
              " 'maarnaen',\n",
              " 'maanmaa',\n",
              " 'maanna',\n",
              " 'mar1e2',\n",
              " 'maain3',\n",
              " 'maa1n2',\n",
              " 'mar1ea',\n",
              " 'marr2e',\n",
              " 'maanid',\n",
              " 'mar1n2',\n",
              " 'mariinn',\n",
              " 'marni3c',\n",
              " 'maaima',\n",
              " 'marii3',\n",
              " 'maarniac',\n",
              " 'maarin',\n",
              " 'marrna',\n",
              " 'marien',\n",
              " 'marr2a',\n",
              " 'marnin',\n",
              " 'maarim',\n",
              " 'mar1ian',\n",
              " 'marnidc',\n",
              " 'marrti',\n",
              " 'mariin',\n",
              " 'maanne',\n",
              " 'maa1i2',\n",
              " 'maaina',\n",
              " 'marite',\n",
              " 'marnis',\n",
              " 'maa1nm',\n",
              " 'maarn3',\n",
              " 'maainn',\n",
              " 'mar1ee',\n",
              " 'marriinn',\n",
              " 'maarni',\n",
              " 'mariie',\n",
              " 'maa1na',\n",
              " 'maarnaa',\n",
              " 'mar1233',\n",
              " 'marrii',\n",
              " 'maane3',\n",
              " 'maa1im',\n",
              " 'mar1i23',\n",
              " 'maannma',\n",
              " 'maai2a',\n",
              " 'maar2i',\n",
              " 'marr2i',\n",
              " 'marrisn',\n",
              " 'mar12a',\n",
              " 'mariia',\n",
              " 'maannme',\n",
              " 'marnei',\n",
              " 'maainl',\n",
              " 'maania',\n",
              " 'marnee',\n",
              " 'maar2d',\n",
              " 'mar1te3r',\n",
              " 'marnii',\n",
              " 'maanem',\n",
              " 'maar2n0',\n",
              " 'marnie',\n",
              " 'marrie',\n",
              " 'maarnian',\n",
              " 'mariis',\n",
              " 'mar1ia',\n",
              " 'maaime',\n",
              " 'maar23',\n",
              " 'maa1e13',\n",
              " 'maarma',\n",
              " 'maanmi']"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "predict(model, vocab, device, \"admin@saintpatricks.net\", 100)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GmfFh5al-R2p"
      },
      "source": [
        "Below are the stats on common passwords on used dataset\n",
        "\n",
        "```\n",
        "2034268 \"123456\"\n",
        "1214120 \"123456789\"\n",
        " 988396 \"qwerty\"\n",
        " 738200 \"password\"\n",
        " 687856 \"12345\"\n",
        " 537369 \"qwerty123\"\n",
        " 511861 \"1q2w3e\"\n",
        " 438009 \"DEFAULT\"\n",
        " 349405 \"12345678\"\n",
        " 281911 \"111111\"\n",
        " 240898 \"1234567890\"\n",
        " 237525 \"1234567\"\n",
        " 228249 \"123123\"\n",
        " 185868 \"000000\"\n",
        " 166589 \"30media\"\n",
        " 163341 \"qwertyuiop\"\n",
        " 158324 \"10pace\"\n",
        " 149391 \"abc123\"\n",
        " 148217 \"59mile\"\n",
        " 148056 \"59trick\"\n",
        " 147743 \"24crow\"\n",
        " 147397 \"19weed\"\n",
        " 146654 \"66bob\"\n",
        " 142180 \"123321\"\n",
        " 130429 \"1q2w3e4r5t\"\n",
        " 127332 \")ryan\"\n",
        " 124314 \"654321\"\n",
        " 122437 \"password1\"\n",
        " 121947 \"1234\"\n",
        " 121186 \"666666\"\n",
        " 104129 \"qwe123\"\n",
        " 104082 \"7777777\"\n",
        "  95942 \"asdasd\"\n",
        "  95769 \"iloveyou\"\n",
        "  94372 \"123\"\n",
        "  92904 \"1q2w3e4r\"\n",
        "  90866 \"555555\"\n",
        "  83472 \"123qwe\"\n",
        "  82834 \"987654321\"\n",
        "  81854 \"1qaz2wsx\"\n",
        "  81290 \"zxcvbnm\"\n",
        "  79111 \"123456a\"\n",
        "  75744 \"121212\"\n",
        "  69730 \"qazwsx\"\n",
        "  67380 \"112233\"\n",
        "  65360 \"dragon\"\n",
        "  59351 \"monkey\"\n",
        "  58386 \"159753\"\n",
        "  57884 \"777777\"\n",
        "  54620 \"1234qwer\"\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hH4xW-FEMPpO",
        "outputId": "c5f043cb-b0a8-4869-b650-95835ea1ba56"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "seni_seviyorum43@hotmail.de,123456\n",
            "handeus@hotmail.com,123456\n",
            "kapne2009@rambler.ru,123456\n",
            "terue@bk.ru,123456\n",
            "nfokuiodl@yandex.ru,123456\n",
            "analassassin@rocketmail.com,123456\n",
            "tabibi_qi@yahoo.com,123456\n",
            "amangeldi_85.86@inbox.ru,123456\n",
            "kacherra96@yandex.ru,123456\n",
            "wangjinwei@sina.com,123456\n"
          ]
        }
      ],
      "source": [
        "!grep -iE '\\b123456\\b' data/test.csv | head -10"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UPF7PmU3xQlW",
        "outputId": "e442a68b-a70a-41c5-a2df-4c6997a9a997"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "missis.korshunova@bk.ru,123456\n",
            "srinachaus@mail.ru,123456\n",
            "katynaymkina15@mail.ru,123456\n",
            "green_day98@hotmail.com,123456\n",
            "rfb_auto@yahoo.com,123456\n",
            "puftachok@rambler.ru,123456\n",
            "sasha.dochkisinochki@mail.ru,123456\n",
            "kaban_ne@bk.ru,123456\n",
            "hylmi_25@hotmail.com,123456\n",
            "thao.nguyenthanh@ya.ru,123456\n"
          ]
        }
      ],
      "source": [
        "!grep -iE '\\b123456\\b' data/train.csv | head -10"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FBd9hCYvxUif",
        "outputId": "91b95bfb-8817-4384-eedf-b00fbbeaff5f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['hannee',\n",
              " 'handeu',\n",
              " 'haanlae',\n",
              " 'haadne',\n",
              " 'haanne',\n",
              " 'haadlien',\n",
              " 'haarlian',\n",
              " 'hanrei',\n",
              " 'hannlee',\n",
              " 'haannes',\n",
              " 'haarlion',\n",
              " 'haaraa',\n",
              " 'haanea',\n",
              " 'haannaa',\n",
              " 'hannei',\n",
              " 'haadlee',\n",
              " 'haadla',\n",
              " 'handea',\n",
              " 'hannliln',\n",
              " 'haanee',\n",
              " 'haanlua',\n",
              " 'haanae',\n",
              " 'haadee',\n",
              " 'handee',\n",
              " 'haarni',\n",
              " 'haannea',\n",
              " 'haaree',\n",
              " 'haanle',\n",
              " 'haanna',\n",
              " 'hannlio',\n",
              " 'haanlee',\n",
              " 'handeee',\n",
              " 'handlel',\n",
              " 'handeer',\n",
              " 'hanree',\n",
              " 'hannees',\n",
              " 'haanees',\n",
              " 'haadlae',\n",
              " 'haarea',\n",
              " 'haanni',\n",
              " 'haannee',\n",
              " 'haarei',\n",
              " 'haadnuas',\n",
              " 'haadnea',\n",
              " 'hanreus',\n",
              " 'hannlei',\n",
              " 'haanlel',\n",
              " 'haarao',\n",
              " 'haadnee',\n",
              " 'haanlai',\n",
              " 'hannlue',\n",
              " 'hannlul']"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "predict(model, vocab, device, \"handeus@hotmail.com\", 100)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "T6o4vj-zxhhM",
        "outputId": "d2c17a87-9550-45ee-fe54-74562b3aaccc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                            name    password\n",
              "0             arturo100@rubi.net   tequiero1\n",
              "1               mahei001@mail.ru   йцувыфячс\n",
              "2  machomiscellanykml4@yandex.ru   iEW843lGo\n",
              "3               angieg@tm.net.my  c_sunstrom\n",
              "4             js152008@yahoo.com      boots5"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-097a57f1-1873-420a-b724-5950706da47a\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "  <thead>\n",
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              "      <th>password</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>arturo100@rubi.net</td>\n",
              "      <td>tequiero1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>mahei001@mail.ru</td>\n",
              "      <td>йцувыфячс</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>machomiscellanykml4@yandex.ru</td>\n",
              "      <td>iEW843lGo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>angieg@tm.net.my</td>\n",
              "      <td>c_sunstrom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>js152008@yahoo.com</td>\n",
              "      <td>boots5</td>\n",
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              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "\n",
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              "      background-color: #E2EBFA;\n",
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              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-097a57f1-1873-420a-b724-5950706da47a button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-097a57f1-1873-420a-b724-5950706da47a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
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              "</svg>\n",
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              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-46b454ee-ea23-4b54-a390-912f5232ad15 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv(\"data/test.csv\", names=['name','password'])\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "So2O_D1P9aQV",
        "outputId": "02ecf46e-3c48-49e3-d6be-6f6c5fe272ba"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting python-Levenshtein\n",
            "  Downloading python_Levenshtein-0.25.0-py3-none-any.whl (9.4 kB)\n",
            "Collecting Levenshtein==0.25.0 (from python-Levenshtein)\n",
            "  Downloading Levenshtein-0.25.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (177 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m177.4/177.4 kB\u001b[0m \u001b[31m5.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting rapidfuzz<4.0.0,>=3.1.0 (from Levenshtein==0.25.0->python-Levenshtein)\n",
            "  Downloading rapidfuzz-3.6.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.4 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.4/3.4 MB\u001b[0m \u001b[31m83.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: rapidfuzz, Levenshtein, python-Levenshtein\n",
            "Successfully installed Levenshtein-0.25.0 python-Levenshtein-0.25.0 rapidfuzz-3.6.1\n"
          ]
        }
      ],
      "source": [
        "!pip install python-Levenshtein"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3Z5IIaDI9YPx",
        "outputId": "674bae15-82dc-419c-b5b1-39504ff5260e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "from Levenshtein import distance as lev\n",
        "lev(\"123456\", \"123456\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IDcRWGwO9iUs",
        "outputId": "c2f94e3d-8582-4459-cb2a-be7a0a43657a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "lev(\"51234\", \"123456\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "device"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KB4v4MmjHcJd",
        "outputId": "199f67ae-6c85-43ec-8edd-9b91dca04afb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "device(type='cuda')"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "J9lqImo90nl5",
        "outputId": "70af5351-d70f-4ec5-ef19-bd8a28b3128d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done   1 tasks      | elapsed:   12.5s\n",
            "[Parallel(n_jobs=-1)]: Done   8 tasks      | elapsed:   24.2s\n",
            "[Parallel(n_jobs=-1)]: Done  17 tasks      | elapsed:   35.6s\n",
            "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:   47.9s\n",
            "[Parallel(n_jobs=-1)]: Done  37 tasks      | elapsed:  1.1min\n",
            "[Parallel(n_jobs=-1)]: Done  48 tasks      | elapsed:  1.4min\n",
            "[Parallel(n_jobs=-1)]: Done  61 tasks      | elapsed:  1.7min\n",
            "[Parallel(n_jobs=-1)]: Done  74 tasks      | elapsed:  2.0min\n",
            "[Parallel(n_jobs=-1)]: Done  89 tasks      | elapsed:  2.4min\n",
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          ]
        },
        {
          "output_type": "execute_result",
          "data": {
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              "                            name  password best_pred_pass  avg_edit_dist  \\\n",
              "3104677       cheyen1@hotmail.fr    cheyen         cheyen       2.333333   \n",
              "25405791        111erik@yahoo.de   111erik        1111rik       2.500000   \n",
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              "23293912   niknak_niknak@aol.com    niknak         niknak       2.800000   \n",
              "8455995   mariya27092000@mail.ru    mariya         marnya       2.800000   \n",
              "19209736     corradoant@yahoo.ie   corrado        corredo       2.888889   \n",
              "2485136       ipas@icanet.net.mx      ipas          ipasa       2.900000   \n",
              "27504328       nan632@rambler.ru    nan632         nan636       2.906977   \n",
              "7948035        dssdsdds@inbox.ru  dssdsdds       dssssdds       2.916667   \n",
              "8202643          ali-sel@mail.ru    alisel         alisel       2.929825   \n",
              "\n",
              "          min_distance  \n",
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              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"temp_df\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"dssdsdds@inbox.ru\",\n          \"111erik@yahoo.de\",\n          \"corradoant@yahoo.ie\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"password\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"dssdsdds\",\n          \"111erik\",\n          \"corrado\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"best_pred_pass\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"dssssdds\",\n          \"1111rik\",\n          \"corredo\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"avg_edit_dist\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.20225497022193917,\n        \"min\": 2.3333333333333335,\n        \"max\": 2.9298245614035086,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          2.9166666666666665,\n          2.5,\n          2.9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"min_distance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          1,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "from tqdm.notebook import tqdm\n",
        "import pandas as pd\n",
        "from joblib import Parallel, delayed\n",
        "\n",
        "# Assuming the predict and lev functions are defined elsewhere\n",
        "\n",
        "tqdm.pandas()\n",
        "\n",
        "def avg_and_min_edit_dist(username, actual_pass):\n",
        "    pred_passes = predict(model, vocab, device, username, 100)\n",
        "    if not pred_passes:\n",
        "        return (0, None)  # Return 0 and None if pred_passes is empty\n",
        "\n",
        "    # Calculate edit distances between actual_pass and each predicted pass\n",
        "    edit_distances = [lev(str(actual_pass), str(pred_pass)) for pred_pass in pred_passes]\n",
        "    avg_edit_dist = sum(edit_distances) / len(pred_passes)\n",
        "    min_distance = min(edit_distances)\n",
        "\n",
        "    # Return the predicted pass with the minimum edit distance\n",
        "    best_pred_pass = pred_passes[edit_distances.index(min(edit_distances))]\n",
        "    return (best_pred_pass, avg_edit_dist, min_distance)\n",
        "\n",
        "# Sample a subset of the dataframe\n",
        "temp_df = df.sample(n=10000)\n",
        "\n",
        "# Apply the function and create two new columns for best predicted password and average edit distance\n",
        "# temp_df[['best_pred_pass', 'avg_edit_dist']] = temp_df.progress_apply(lambda x: pd.Series(avg_and_min_edit_dist(x['name'], x['password'])), axis=1)\n",
        "\n",
        "results = Parallel(n_jobs=-1, verbose=10)(delayed(avg_and_min_edit_dist)(row['name'], row['password']) for index, row in temp_df.iterrows())\n",
        "temp_df[['best_pred_pass', 'avg_edit_dist', 'min_distance']] = pd.DataFrame(results, index=temp_df.index)\n",
        "\n",
        "temp_df.sort_values('avg_edit_dist').head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "h6uxxiit34wM"
      },
      "outputs": [],
      "source": [
        "# save temp_df to a csv\n",
        "temp_df.to_csv(\"data/org_test_results.csv\", index=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8fjnzmDnJebP"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "\n",
        "temp_df = pd.read_csv(\"data/org_test_results.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lev(str(123.0),str(23.2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "thLdzu43GTLP",
        "outputId": "1cb5313a-6ee7-45be-d34d-37644460e85c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yNL4dS_522IE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "7b40fa5f-ba03-47f9-8b29-e6dc6266aeba"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                 name password best_pred_pass  avg_edit_dist  \\\n",
              "22192965        jinkies_0@hotmail.com  jinkies        jinkies       3.246154   \n",
              "2782697   leonid-lebedev00@rambler.ru   leonid         leonid       3.622222   \n",
              "3104677            cheyen1@hotmail.fr   cheyen         cheyen       2.333333   \n",
              "23293912        niknak_niknak@aol.com   niknak         niknak       2.800000   \n",
              "8202643               ali-sel@mail.ru   alisel         alisel       2.929825   \n",
              "27738652      nataliewest21@gmail.com  natalie        natalie       3.036585   \n",
              "3080859           holopp190@pisem.net   holopp         holope       4.484536   \n",
              "5738463             woppahead@mail.ru   123456        1234556       4.132653   \n",
              "30651967      arielbeasley1@yahoo.com   ariel1         aaiel1       3.962963   \n",
              "19209736          corradoant@yahoo.ie  corrado        corredo       2.888889   \n",
              "\n",
              "          min_distance  \n",
              "22192965             0  \n",
              "2782697              0  \n",
              "3104677              0  \n",
              "23293912             0  \n",
              "8202643              0  \n",
              "27738652             0  \n",
              "3080859              1  \n",
              "5738463              1  \n",
              "30651967             1  \n",
              "19209736             1  "
            ],
            "text/html": [
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-63450fd1-446f-4dbb-92a8-1594196aa11f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"temp_df\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"arielbeasley1@yahoo.com\",\n          \"leonid-lebedev00@rambler.ru\",\n          \"nataliewest21@gmail.com\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"password\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"ariel1\",\n          \"leonid\",\n          \"natalie\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"best_pred_pass\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"aaiel1\",\n          \"leonid\",\n          \"natalie\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"avg_edit_dist\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6817824930203347,\n        \"min\": 2.3333333333333335,\n        \"max\": 4.484536082474227,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          3.962962962962963,\n          3.6222222222222222,\n          3.0365853658536586\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"min_distance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "temp_df.sort_values('min_distance').head(10)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "temp_df.sort_values('min_distance').to_csv(\"data/org_test_results_sorted.csv\", index=False)"
      ],
      "metadata": {
        "id": "tM9pl8f2EAPs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "2aRb_uVrEKe1"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "A100",
      "machine_shape": "hm",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}