{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HlK7-onojaYL"
   },
   "source": [
    "# <center>Masakhane - Machine Translation for African Languages (Using JoeyNMT)</center>\n",
    "## <leftalign> Author : Ari Ramkilowan</leftalign>\n",
    "## <leftalign> Language Pair : English - isiZulu</leftalign>\n",
    "## <leftalign> Corpus : JW300 </leftalign>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "rIzB5Yo6nugf"
   },
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "jPmely-suPXt"
   },
   "source": [
    "## Install JoeyNMT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 21825,
     "status": "ok",
     "timestamp": 1574062779366,
     "user": {
      "displayName": "Ari Ramkilowan",
      "photoUrl": "",
      "userId": "10877800210228006715"
     },
     "user_tz": -120
    },
    "id": "K00EyOQ3ubNH",
    "outputId": "f6fbac15-63ed-49ed-f199-7988e61effe1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cloning into 'joeynmt'...\n",
      "remote: Enumerating objects: 2184, done.\u001b[K\n",
      "remote: Total 2184 (delta 0), reused 0 (delta 0), pack-reused 2184\u001b[K\n",
      "Receiving objects: 100% (2184/2184), 2.58 MiB | 4.48 MiB/s, done.\n",
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      "Processing /content/joeynmt\n",
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      "Collecting sacrebleu>=1.3.6\n",
      "  Downloading https://files.pythonhosted.org/packages/0e/e5/93d252182f7cbd4b59bb3ec5797e2ce33cfd6f5aadaf327db170cf4b7887/sacrebleu-1.4.2-py3-none-any.whl\n",
      "Collecting subword-nmt\n",
      "  Downloading https://files.pythonhosted.org/packages/26/08/58267cb3ac00f5f895457777ed9e0d106dbb5e6388fa7923d8663b04b849/subword_nmt-0.3.6-py2.py3-none-any.whl\n",
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      "Collecting pyyaml>=5.1\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/e3/e8/b3212641ee2718d556df0f23f78de8303f068fe29cdaa7a91018849582fe/PyYAML-5.1.2.tar.gz (265kB)\n",
      "\u001b[K     |████████████████████████████████| 266kB 16.2MB/s \n",
      "\u001b[?25hCollecting pylint\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/e9/59/43fc36c5ee316bb9aeb7cf5329cdbdca89e5749c34d5602753827c0aa2dc/pylint-2.4.4-py3-none-any.whl (302kB)\n",
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      "Collecting portalocker\n",
      "  Downloading https://files.pythonhosted.org/packages/91/db/7bc703c0760df726839e0699b7f78a4d8217fdc9c7fcb1b51b39c5a22a4e/portalocker-1.5.2-py2.py3-none-any.whl\n",
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      "Collecting isort<5,>=4.2.5\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/e5/b0/c121fd1fa3419ea9bfd55c7f9c4fedfec5143208d8c7ad3ce3db6c623c21/isort-4.3.21-py2.py3-none-any.whl (42kB)\n",
      "\u001b[K     |████████████████████████████████| 51kB 6.9MB/s \n",
      "\u001b[?25hCollecting mccabe<0.7,>=0.6\n",
      "  Downloading https://files.pythonhosted.org/packages/87/89/479dc97e18549e21354893e4ee4ef36db1d237534982482c3681ee6e7b57/mccabe-0.6.1-py2.py3-none-any.whl\n",
      "Collecting astroid<2.4,>=2.3.0\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ad/ae/86734823047962e7b8c8529186a1ac4a7ca19aaf1aa0c7713c022ef593fd/astroid-2.3.3-py3-none-any.whl (205kB)\n",
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      "\u001b[?25hRequirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.8->tensorflow>=1.14->joeynmt==0.0.1) (2.8.0)\n",
      "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.16.0,>=1.15.0->tensorflow>=1.14->joeynmt==0.0.1) (0.16.0)\n",
      "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.16.0,>=1.15.0->tensorflow>=1.14->joeynmt==0.0.1) (3.1.1)\n",
      "Requirement already satisfied: idna<2.9,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->torchtext->joeynmt==0.0.1) (2.8)\n",
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      "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->torchtext->joeynmt==0.0.1) (3.0.4)\n",
      "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.15.2->seaborn->joeynmt==0.0.1) (2018.9)\n",
      "Collecting typed-ast<1.5,>=1.4.0; implementation_name == \"cpython\" and python_version < \"3.8\"\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/31/d3/9d1802c161626d0278bafb1ffb32f76b9d01e123881bbf9d91e8ccf28e18/typed_ast-1.4.0-cp36-cp36m-manylinux1_x86_64.whl (736kB)\n",
      "\u001b[K     |████████████████████████████████| 737kB 44.2MB/s \n",
      "\u001b[?25hCollecting lazy-object-proxy==1.4.*\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/0b/dd/b1e3407e9e6913cf178e506cd0dee818e58694d9a5cd1984e3f6a8b9a10f/lazy_object_proxy-1.4.3-cp36-cp36m-manylinux1_x86_64.whl (55kB)\n",
      "\u001b[K     |████████████████████████████████| 61kB 7.2MB/s \n",
      "\u001b[?25hBuilding wheels for collected packages: joeynmt, pyyaml\n",
      "  Building wheel for joeynmt (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for joeynmt: filename=joeynmt-0.0.1-cp36-none-any.whl size=71902 sha256=00ffe5c04aa3d9faab1d40630911047ff94093f60c14a19fd8c298fc730a62e2\n",
      "  Stored in directory: /tmp/pip-ephem-wheel-cache-pgpcy60v/wheels/db/01/db/751cc9f3e7f6faec127c43644ba250a3ea7ad200594aeda70a\n",
      "  Building wheel for pyyaml (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for pyyaml: filename=PyYAML-5.1.2-cp36-cp36m-linux_x86_64.whl size=44104 sha256=1af8880955a8fa687c75ed9258d991e926ab037394c1bfdf64543e737eea5149\n",
      "  Stored in directory: /root/.cache/pip/wheels/d9/45/dd/65f0b38450c47cf7e5312883deb97d065e030c5cca0a365030\n",
      "Successfully built joeynmt pyyaml\n",
      "Installing collected packages: portalocker, sacrebleu, subword-nmt, pyyaml, isort, mccabe, typed-ast, lazy-object-proxy, astroid, pylint, joeynmt\n",
      "  Found existing installation: PyYAML 3.13\n",
      "    Uninstalling PyYAML-3.13:\n",
      "      Successfully uninstalled PyYAML-3.13\n",
      "Successfully installed astroid-2.3.3 isort-4.3.21 joeynmt-0.0.1 lazy-object-proxy-1.4.3 mccabe-0.6.1 portalocker-1.5.2 pylint-2.4.4 pyyaml-5.1.2 sacrebleu-1.4.2 subword-nmt-0.3.6 typed-ast-1.4.0\n"
     ]
    }
   ],
   "source": [
    "! git clone https://github.com/joeynmt/joeynmt.git\n",
    "! cd joeynmt; pip3 install ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ZX77ylHfjtY0"
   },
   "source": [
    "\n",
    "## Mount Google Drive\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 128
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 66132,
     "status": "ok",
     "timestamp": 1574062838679,
     "user": {
      "displayName": "Ari Ramkilowan",
      "photoUrl": "",
      "userId": "10877800210228006715"
     },
     "user_tz": -120
    },
    "id": "Gg_9x3FloWuU",
    "outputId": "90265a94-ac0a-4b88-a57c-b9a78b3b4214"
   },
   "outputs": [
    {
     "name": "stdout",
     "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"
     ]
    }
   ],
   "source": [
    "# If running on Google Colab - mount google drive\n",
    "\n",
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "O_-ywGCAjx0G"
   },
   "source": [
    "## What GPU do we have available today ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2648,
     "status": "ok",
     "timestamp": 1574062753188,
     "user": {
      "displayName": "Ari Ramkilowan",
      "photoUrl": "",
      "userId": "10877800210228006715"
     },
     "user_tz": -120
    },
    "id": "bWGP0cQynju4",
    "outputId": "f1d34224-9292-4f47-a295-7ef42f6ea71b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Tesla P100-PCIE-16GB'"
      ]
     },
     "execution_count": 1,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# keep trying until we get a beefier GPU : T4 > V100> P100 > K80\n",
    "\n",
    "import torch\n",
    "import pandas as pd\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\"\n",
    "\n",
    "device_num = torch.cuda.current_device()\n",
    "torch.cuda.get_device_name(device_num)\n",
    "# torch.cuda.is_available()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "mPElaQv_oy5q"
   },
   "source": [
    "## Set your source, target language and experiment tag\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3Dj54KjZ2wal"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "source_language = \"en\"\n",
    "target_language = \"zu\" \n",
    "lc = True  # If True, lowercase the data.\n",
    "seed = 42  # Random seed for shuffling.\n",
    "tag = \"baseline\" # Give a unique name to your folder - this is to ensure you don't rewrite any models you've already submitted\n",
    "vocab_size=4000\n",
    "corpus = \"JW300\"\n",
    "\n",
    "os.environ[\"src\"] = source_language # Sets them in bash as well, since we often use bash scripts\n",
    "os.environ[\"tgt\"] = target_language\n",
    "os.environ[\"tag\"] = tag\n",
    "os.environ[\"vocab_size\"] = str(vocab_size)\n",
    "os.environ[\"corpus\"] = corpus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 5832,
     "status": "ok",
     "timestamp": 1574063825020,
     "user": {
      "displayName": "Ari Ramkilowan",
      "photoUrl": "",
      "userId": "10877800210228006715"
     },
     "user_tz": -120
    },
    "id": "5YcHw7PW3Tvb",
    "outputId": "a838d9ab-772f-4ea1-b990-881c32e7c7f3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/content/drive/My Drive/masakhane/en-zu-baseline/\n"
     ]
    }
   ],
   "source": [
    "# This will save it to a folder in our gdrive instead!\n",
    "# !mkdir -p \"/content/drive/My Drive/masakhane/$src-$trg-$tag\"\n",
    "gdrive_path = f\"/content/drive/My Drive/masakhane/{source_language}-{target_language}-{tag}/\"\n",
    "os.environ[\"gdrive_path\"] = gdrive_path\n",
    "! echo $gdrive_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 7890,
     "status": "ok",
     "timestamp": 1574063828352,
     "user": {
      "displayName": "Ari Ramkilowan",
      "photoUrl": "",
      "userId": "10877800210228006715"
     },
     "user_tz": -120
    },
    "id": "Y99OKT00fnrO",
    "outputId": "37d301bb-e697-4269-ebbb-5d5c31620229"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iwslt14_deen_bpe.yaml\t\t   transformer_reverse.yaml\n",
      "iwslt_deen_bahdanau.yaml\t   transformer_small.yaml\n",
      "iwslt_envi_luong.yaml\t\t   transformer_wmt17_ende.yaml\n",
      "iwslt_envi_xnmt.yaml\t\t   transformer_wmt17_lven.yaml\n",
      "reverse.yaml\t\t\t   wmt_ende_best.yaml\n",
      "small.yaml\t\t\t   wmt_ende_default.yaml\n",
      "transformer_copy.yaml\t\t   wmt_lven_best.yaml\n",
      "transformer_enzu4000const_lr.yaml  wmt_lven_default.yaml\n",
      "transformer_iwslt14_deen_bpe.yaml\n"
     ]
    }
   ],
   "source": [
    "# create path to joeynmt executables scripts, configs etc\n",
    "\n",
    "joey_path = f\"/content/joeynmt\"\n",
    "os.environ[\"joey_path\"] = joey_path\n",
    "! ls $joey_path/configs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "1bM3_Zn34LGu"
   },
   "source": [
    "## Download the global test set.\n",
    "\n",
    "***(This changes from time to time, do this just to make sure you have the most recent version)***\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 617
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 18657,
     "status": "ok",
     "timestamp": 1573570012931,
     "user": {
      "displayName": "Ari Ramkilowan",
      "photoUrl": "",
      "userId": "10877800210228006715"
     },
     "user_tz": -120
    },
    "id": "36X7RKhb4a7P",
    "outputId": "5983e3ae-14c6-4a45-d095-9a9abe89c92c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2019-11-12 14:46:36--  https://raw.githubusercontent.com/juliakreutzer/masakhane/master/jw300_utils/test/test.en-any.en\n",
      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 277791 (271K) [text/plain]\n",
      "Saving to: ‘test.en-any.en’\n",
      "\n",
      "\r",
      "test.en-any.en        0%[                    ]       0  --.-KB/s               \r",
      "test.en-any.en      100%[===================>] 271.28K  --.-KB/s    in 0.02s   \n",
      "\n",
      "2019-11-12 14:46:36 (12.8 MB/s) - ‘test.en-any.en’ saved [277791/277791]\n",
      "\n",
      "--2019-11-12 14:46:39--  https://raw.githubusercontent.com/juliakreutzer/masakhane/master/jw300_utils/test/test.en-zu.en\n",
      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 206207 (201K) [text/plain]\n",
      "Saving to: ‘test.en-zu.en’\n",
      "\n",
      "test.en-zu.en       100%[===================>] 201.37K  --.-KB/s    in 0.02s   \n",
      "\n",
      "2019-11-12 14:46:39 (11.4 MB/s) - ‘test.en-zu.en’ saved [206207/206207]\n",
      "\n",
      "--2019-11-12 14:46:46--  https://raw.githubusercontent.com/juliakreutzer/masakhane/master/jw300_utils/test/test.en-zu.zu\n",
      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 218273 (213K) [text/plain]\n",
      "Saving to: ‘test.en-zu.zu’\n",
      "\n",
      "test.en-zu.zu       100%[===================>] 213.16K  --.-KB/s    in 0.02s   \n",
      "\n",
      "2019-11-12 14:46:47 (10.2 MB/s) - ‘test.en-zu.zu’ saved [218273/218273]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "! wget https://raw.githubusercontent.com/juliakreutzer/masakhane/master/jw300_utils/test/test.en-any.en\n",
    "  \n",
    "! wget https://raw.githubusercontent.com/juliakreutzer/masakhane/master/jw300_utils/test/test.en-$trg.en\n",
    "! mv test.en-$trg.en test.en\n",
    "\n",
    "! wget https://raw.githubusercontent.com/juliakreutzer/masakhane/master/jw300_utils/test/test.en-$trg.$trg \n",
    "! mv test.en-$trg.$trg test.$trg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1549,
     "status": "ok",
     "timestamp": 1573570135440,
     "user": {
      "displayName": "Ari Ramkilowan",
      "photoUrl": "",
      "userId": "10877800210228006715"
     },
     "user_tz": -120
    },
    "id": "DQjxBMQu4AbK",
    "outputId": "9f97c10f-dcb4-455b-cff1-a0bc0f50c671"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded 3571 global test sentences to filter from the training/dev data.\n",
      "There are 0 blank lines in the test set\n"
     ]
    }
   ],
   "source": [
    "# Read the test data to filter from train and dev splits.\n",
    "# Store english portion in set for quick filtering checks.\n",
    "en_test_sents = set()\n",
    "filter_test_sents = \"test.en-any.en\"\n",
    "j = 0\n",
    "blanks = [] # sometimes blank lines creep innto test set - store which lines these are\n",
    "with open(filter_test_sents) as f:\n",
    "  for line in f:\n",
    "    en_test_sents.add(line.strip())\n",
    "    if len(line)<=1:\n",
    "      blanks.append(j)\n",
    "    j += 1\n",
    "print('Loaded {} global test sentences to filter from the training/dev data.'.format(j))\n",
    "print(f'There are {len(blanks)} blank lines in the test set')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "86JzjpzgKFK3"
   },
   "outputs": [],
   "source": [
    "# filter test set\n",
    "\n",
    "source_file = f\"test.{source_language}\"\n",
    "target_file = f\"test.{target_language}\"\n",
    "\n",
    "source = []\n",
    "target = []\n",
    "\n",
    "with open(source_file) as f:\n",
    "  source = f.readlines()\n",
    "            \n",
    "with open(target_file) as f:\n",
    "  target = f.readlines()\n",
    "\n",
    "df = pd.DataFrame(zip(source, target), columns=['source_sentence', 'target_sentence'])\n",
    "\n",
    "# remove trailing newline chars\n",
    "df['source_sentence'] = df['source_sentence'].str.rstrip('\" \\n')\n",
    "df['target_sentence'] = df['target_sentence'].str.rstrip('\" \\n')\n",
    "\n",
    "# remove leading newline chars\n",
    "df['source_sentence'] = df['source_sentence'].str.lstrip('\"')\n",
    "df['target_sentence'] = df['target_sentence'].str.lstrip('\"')\n",
    "\n",
    "# remove rows with really short sentences\n",
    "df = df[~(df['source_sentence'].str.len() <8)] # remove rows wher esource text len <8 characters\n",
    "df = df[~(df['target_sentence'].str.len() <8)] # remove rows wher esource text len <8 characters\n",
    "\n",
    "# save the filtered test set\n",
    "df['source_sentence'].to_csv(f'{source_file}', index=False, header=False, doublequote=False)\n",
    "df['target_sentence'].to_csv(f'{target_file}', index=False, header=False, doublequote=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_BQ_g1o3gUHD"
   },
   "outputs": [],
   "source": [
    "# copy test sets to gdrive\n",
    "! cp test.$src \"$gdrive_path\"\n",
    "! cp test.$trg \"$gdrive_path\"\n",
    "! cp test.$src-any.$src \"$gdrive_path\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LY9LrzQp5BOC"
   },
   "source": [
    "## Import prepared dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1faMfeV45M9A"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "OgtN5iU95BKe"
   },
   "outputs": [],
   "source": [
    "# This csv has extra columns added but no preprocessing done. all preprocessing should be captured in the NMT modelling notebook\n",
    "\n",
    "input_file = f\"{gdrive_path}/{source_language}-{target_language}-{corpus}-new.csv\"\n",
    "df = pd.read_csv(input_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 198
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1280,
     "status": "ok",
     "timestamp": 1573645947746,
     "user": {
      "displayName": "Ari Ramkilowan",
      "photoUrl": "",
      "userId": "10877800210228006715"
     },
     "user_tz": -120
    },
    "id": "7mVDMOgsA2L1",
    "outputId": "8710119c-e3e6-435c-a04f-766879f5e51e"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>source_sentence</th>\n",
       "      <th>target_sentence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Unity in Worship ​ — What Should It Mean for Y...</td>\n",
       "      <td>Ubunye Ekukhulekeleni — Kumelwe Busho Ukuthini...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>As we draw closer and closer to the end of thi...</td>\n",
       "      <td>Njengoba silokhu sisondela ekupheleni kwalesim...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>So it is more difficult , and yet more urgent ...</td>\n",
       "      <td>Ngakho kunzima ngokwengeziwe , futhi kuphuthum...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>The new book United in Worship of the Only Tru...</td>\n",
       "      <td>Incwadi entsha ethi Ubunye Ekukhulekeleni Okuw...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Please send me , postpaid , the 192 - page , h...</td>\n",
       "      <td>Ngicela ningithumele incwadi enoqwembe oluqini...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     source_sentence                                    target_sentence\n",
       "0  Unity in Worship ​ — What Should It Mean for Y...  Ubunye Ekukhulekeleni — Kumelwe Busho Ukuthini...\n",
       "1  As we draw closer and closer to the end of thi...  Njengoba silokhu sisondela ekupheleni kwalesim...\n",
       "2  So it is more difficult , and yet more urgent ...  Ngakho kunzima ngokwengeziwe , futhi kuphuthum...\n",
       "3  The new book United in Worship of the Only Tru...  Incwadi entsha ethi Ubunye Ekukhulekeleni Okuw...\n",
       "4  Please send me , postpaid , the 192 - page , h...  Ngicela ningithumele incwadi enoqwembe oluqini..."
      ]
     },
     "execution_count": 7,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1398,
     "status": "ok",
     "timestamp": 1573645950204,
     "user": {
      "displayName": "Ari Ramkilowan",
      "photoUrl": "",
      "userId": "10877800210228006715"
     },
     "user_tz": -120
    },
    "id": "94Oq1C-YAkKz",
    "outputId": "c654afc8-55ac-4e6f-9532-562c1e4c44d5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " 1062534 samples in original text\n"
     ]
    }
   ],
   "source": [
    "# How many samples\n",
    "size = len(df)\n",
    "print(f\"\\n {size} samples in original text\")\n",
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "EZ2mc6pHXjkr"
   },
   "source": [
    "## Preprocess input data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 181
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 3273,
     "status": "ok",
     "timestamp": 1573645953763,
     "user": {
      "displayName": "Ari Ramkilowan",
      "photoUrl": "",
      "userId": "10877800210228006715"
     },
     "user_tz": -120
    },
    "id": "RNWGwHE-6tHQ",
    "outputId": "4a9117e2-2db8-4fdf-bf85-902d6fca9b38"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1053497 entries, 0 to 1062533\n",
      "Data columns (total 2 columns):\n",
      "source_sentence    1053497 non-null object\n",
      "target_sentence    1053497 non-null object\n",
      "dtypes: object(2)\n",
      "memory usage: 402.1 MB\n",
      "\n",
      " 9037(0.85 %) samples removed by dropping all NaNs\n"
     ]
    }
   ],
   "source": [
    "## Preprocessing - Step 1 : Drop NaNs\n",
    "\n",
    "df_pp = df.dropna()\n",
    "df_pp.info(memory_usage='deep')\n",
    "new_size = len(df_pp)\n",
    "print(f\"\\n {size-new_size}({100*(size-new_size)/size :.2f} %) samples removed by dropping all NaNs\")\n",
    "size = new_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 181
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 4472,
     "status": "ok",
     "timestamp": 1573645956149,
     "user": {
      "displayName": "Ari Ramkilowan",
      "photoUrl": "",
      "userId": "10877800210228006715"
     },
     "user_tz": -120
    },
    "id": "R-CAf05t67va",
    "outputId": "550cbf0a-50c3-4901-f7ae-01fc6c65c043"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 970498 entries, 0 to 1062533\n",
      "Data columns (total 2 columns):\n",
      "source_sentence    970498 non-null object\n",
      "target_sentence    970498 non-null object\n",
      "dtypes: object(2)\n",
      "memory usage: 430.5 MB\n",
      "\n",
      " 82999(7.88 %) samples removed by dropping Source sentence duplicates\n"
     ]
    }
   ],
   "source": [
    "## Preprocessing - Step 2a : Drop all duplicates in Source (en) text\n",
    "\n",
    "df_pp = df_pp.drop_duplicates(subset='source_sentence')\n",
    "df_pp.info(memory_usage='deep')\n",
    "new_size = len(df_pp)\n",
    "print(f\"\\n {size-new_size}({100*(size-new_size)/size :.2f} %) samples removed by dropping Source sentence duplicates\")\n",
    "size = new_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 181
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 4706,
     "status": "ok",
     "timestamp": 1573645958198,
     "user": {
      "displayName": "Ari Ramkilowan",
      "photoUrl": "",
      "userId": "10877800210228006715"
     },
     "user_tz": -120
    },
    "id": "IoPGdLI_n6_L",
    "outputId": "9442c81e-faa1-4933-f7ed-7345723ddc20"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 965453 entries, 0 to 1062533\n",
      "Data columns (total 2 columns):\n",
      "source_sentence    965453 non-null object\n",
      "target_sentence    965453 non-null object\n",
      "dtypes: object(2)\n",
      "memory usage: 465.7 MB\n",
      "\n",
      " 5045(0.52 %) samples removed by dropping Target sentence duplicates\n"
     ]
    }
   ],
   "source": [
    "## Preprocessing - Step 2b : Drop all duplicates in Target (zu) text\n",
    "\n",
    "df_pp = df_pp.drop_duplicates(subset='target_sentence')\n",
    "df_pp.info(memory_usage='deep')\n",
    "new_size = len(df_pp)\n",
    "print(f\"\\n {size-new_size}({100*(size-new_size)/size :.2f} %) samples removed by dropping Target sentence duplicates\")\n",
    "size = new_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "q0vDJborInA_"
   },
   "outputs": [],
   "source": [
    "##  Preprocessing - Step 3 : Remove all numeric entries\n",
    "\n",
    "pattern = r\"([0-9]*\\.?[0-9]*)\"  # catch integers and decimals\n",
    "import re\n",
    "r = re.compile(pattern)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 217
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 18478,
     "status": "ok",
     "timestamp": 1573645975110,
     "user": {
      "displayName": "Ari Ramkilowan",
      "photoUrl": "",
      "userId": "10877800210228006715"
     },
     "user_tz": -120
    },
    "id": "n0OvHiNFL-8z",
    "outputId": "76e915f5-eb6c-4847-a79e-c098538c80a5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 965271 entries, 0 to 1062533\n",
      "Data columns (total 2 columns):\n",
      "source_sentence    965271 non-null object\n",
      "target_sentence    965271 non-null object\n",
      "dtypes: object(2)\n",
      "memory usage: 419.3 MB\n",
      "\n",
      " 182(0.02 %) samples removed by dropping nummeric entries from source text\n",
      "CPU times: user 16.8 s, sys: 116 ms, total: 16.9 s\n",
      "Wall time: 16.9 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "##  Preprocessing - Step 3a : Remove all numeric entries - Source text\n",
    "\n",
    "df_pp['source_sentence'] = df_pp['source_sentence'].str.replace(pattern,\"\")\n",
    "df_pp['source_sentence'] = df_pp['source_sentence'].replace(\"\",np.nan)\n",
    "\n",
    "df_pp = df_pp.dropna()\n",
    "df_pp.info(memory_usage='deep')\n",
    "new_size = len(df_pp)\n",
    "\n",
    "print(f\"\\n {size-new_size}({100*(size-new_size)/size :.2f} %) samples removed by dropping nummeric entries from source text\")\n",
    "size = new_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 217
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 34143,
     "status": "ok",
     "timestamp": 1573645992448,
     "user": {
      "displayName": "Ari Ramkilowan",
      "photoUrl": "",
      "userId": "10877800210228006715"
     },
     "user_tz": -120
    },
    "id": "DlVWfIVJQ_Tn",
    "outputId": "5d81b7fd-f142-4292-9635-46e11eff8d96"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 965271 entries, 0 to 1062533\n",
      "Data columns (total 2 columns):\n",
      "source_sentence    965271 non-null object\n",
      "target_sentence    965271 non-null object\n",
      "dtypes: object(2)\n",
      "memory usage: 380.8 MB\n",
      "\n",
      " 0(0.00 %) samples removed by dropping nummeric entries from target text\n",
      "CPU times: user 17.4 s, sys: 106 ms, total: 17.5 s\n",
      "Wall time: 17.5 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "##  Preprocessing - Step 3b : Remove all numeric entries - Target text\n",
    "\n",
    "df_pp['target_sentence'] = df_pp['target_sentence'].str.replace(r,\"\")\n",
    "df_pp['target_sentence'] = df_pp['target_sentence'].replace(\"\",np.nan)\n",
    "\n",
    "df_pp = df_pp.dropna()\n",
    "df_pp.info(memory_usage='deep')\n",
    "new_size = len(df_pp)\n",
    "\n",
    "print(f\"\\n {size-new_size}({100*(size-new_size)/size :.2f} %) samples removed by dropping nummeric entries from target text\")\n",
    "size = new_size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "4dZI2WM78oCO"
   },
   "source": [
    "#### Preprocessing - Step 4 :Get length of sentences and then drop really short sentences\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 254
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 36713,
     "status": "ok",
     "timestamp": 1573645997009,
     "user": {
      "displayName": "Ari Ramkilowan",
      "photoUrl": "",
      "userId": "10877800210228006715"
     },
     "user_tz": -120
    },
    "id": "zb6yGGDJAZ5H",
    "outputId": "e3a18d55-4e7e-447b-9cd3-22ddbc4ebb94"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 965271 entries, 0 to 1062533\n",
      "Data columns (total 6 columns):\n",
      "source_sentence    965271 non-null object\n",
      "target_sentence    965271 non-null object\n",
      "source_ch_len      965271 non-null int64\n",
      "source_w_len       965271 non-null int64\n",
      "target_ch_len      965271 non-null int64\n",
      "target_w_len       965271 non-null int64\n",
      "dtypes: int64(4), object(2)\n",
      "memory usage: 410.3 MB\n",
      "CPU times: user 4.5 s, sys: 28 ms, total: 4.53 s\n",
      "Wall time: 4.53 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# add length columns\n",
    "\n",
    "\n",
    "df_pp['source_ch_len'] = df_pp['source_sentence'].str.len()\n",
    "df_pp['source_w_len'] = [len(text.split()) for text in df_pp['source_sentence']] \n",
    "df_pp['target_ch_len'] = df_pp['target_sentence'].str.len()\n",
    "df_pp['target_w_len'] = [len(text.split()) for text in df_pp['target_sentence']] \n",
    "df_pp.info(memory_usage='deep')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "M6SecEMNBkC7"
   },
   "outputs": [],
   "source": [
    "# # character len distrn - source text - \n",
    "# df_pp['source_ch_len'].value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "A9TFizHyVWKO"
   },
   "outputs": [],
   "source": [
    "# # character len distrn - target text\n",
    "# df_pp['target_ch_len'].value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9ArLyuzIwtLq"
   },
   "outputs": [],
   "source": [
    "## how many rows with source text <=2chars and what do they look like ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "t-e7zGtV-eyq"
   },
   "outputs": [],
   "source": [
    "# # how many single character sentences from source ?\n",
    "# f\"{df_pp['source_ch_len'].value_counts()[1]} single character source sentences\"\n",
    "\n",
    "# df_pp[df_pp['source_ch_len']<=1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "qDe_eeocuBDw"
   },
   "outputs": [],
   "source": [
    "# # how many 2-character sentences from source ?\n",
    "# f\"{df_pp['source_ch_len'].value_counts()[2]} 2-character source sentences\"\n",
    "\n",
    "# df_pp[df_pp['source_ch_len']==2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "agiL0pRVyOEz"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 254
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 31739,
     "status": "ok",
     "timestamp": 1573645998409,
     "user": {
      "displayName": "Ari Ramkilowan",
      "photoUrl": "",
      "userId": "10877800210228006715"
     },
     "user_tz": -120
    },
    "id": "sPTL3nKqyWjv",
    "outputId": "48be4053-8f28-4d89-c6ba-28dca94846e5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 958999 entries, 0 to 1062533\n",
      "Data columns (total 6 columns):\n",
      "source_sentence    958999 non-null object\n",
      "target_sentence    958999 non-null object\n",
      "source_ch_len      958999 non-null int64\n",
      "source_w_len       958999 non-null int64\n",
      "target_ch_len      958999 non-null int64\n",
      "target_w_len       958999 non-null int64\n",
      "dtypes: int64(4), object(2)\n",
      "memory usage: 409.2 MB\n",
      "\n",
      " 6272(0.65 %) samples removed by dropping rows with source sentences <= 8 characters\n"
     ]
    }
   ],
   "source": [
    "##  Preprocessing - Step 4a :  drop everything where the ch_len <=8 in source text\n",
    "\n",
    "df_pp = df_pp[~(df_pp['source_ch_len'] <=8) ]\n",
    "\n",
    "df_pp.info(memory_usage='deep')\n",
    "new_size = len(df_pp)\n",
    "print(f\"\\n {size-new_size}({100*(size-new_size)/size :.2f} %) samples removed by dropping rows with source sentences <= 8 characters\")\n",
    "size = new_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 254
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 30743,
     "status": "ok",
     "timestamp": 1573646000090,
     "user": {
      "displayName": "Ari Ramkilowan",
      "photoUrl": "",
      "userId": "10877800210228006715"
     },
     "user_tz": -120
    },
    "id": "_gnkwkS11rHW",
    "outputId": "02650345-4b60-4d5d-f844-be168f40dd31"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 958286 entries, 0 to 1062533\n",
      "Data columns (total 6 columns):\n",
      "source_sentence    958286 non-null object\n",
      "target_sentence    958286 non-null object\n",
      "source_ch_len      958286 non-null int64\n",
      "source_w_len       958286 non-null int64\n",
      "target_ch_len      958286 non-null int64\n",
      "target_w_len       958286 non-null int64\n",
      "dtypes: int64(4), object(2)\n",
      "memory usage: 409.1 MB\n",
      "\n",
      " 713(0.07 %) samples removed by dropping rows with target sentences <= 8 characters\n"
     ]
    }
   ],
   "source": [
    "##  Preprocessing - Step 4b :  drop everything where the ch_len <=8 in target text\n",
    "\n",
    "df_pp = df_pp[~(df_pp['target_ch_len'] <=8) ]\n",
    "\n",
    "df_pp.info(memory_usage='deep')\n",
    "new_size = len(df_pp)\n",
    "print(f\"\\n {size-new_size}({100*(size-new_size)/size :.2f} %) samples removed by dropping rows with target sentences <= 8 characters\")\n",
    "size = new_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 290
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 3761,
     "status": "ok",
     "timestamp": 1573646003869,
     "user": {
      "displayName": "Ari Ramkilowan",
      "photoUrl": "",
      "userId": "10877800210228006715"
     },
     "user_tz": -120
    },
    "id": "6qhEbRRQUZtd",
    "outputId": "b3ec8540-d93d-4c0b-8b16-d0bce43cd3bd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 958286 entries, 0 to 1062533\n",
      "Data columns (total 6 columns):\n",
      "source_sentence    958286 non-null object\n",
      "target_sentence    958286 non-null object\n",
      "source_ch_len      958286 non-null int64\n",
      "source_w_len       958286 non-null int64\n",
      "target_ch_len      958286 non-null int64\n",
      "target_w_len       958286 non-null int64\n",
      "dtypes: int64(4), object(2)\n",
      "memory usage: 409.1 MB\n",
      "\n",
      " 0(0.00 %) samples removed by dropping rows with extra quotes\n",
      "CPU times: user 2.39 s, sys: 7.98 ms, total: 2.39 s\n",
      "Wall time: 2.41 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "##  Preprocessing - Step 5 :  remove the extra \"\n",
    "df_pp['source_sentence'] = df_pp['source_sentence'].map(lambda x: x.lstrip('\"').rstrip('\"'))\n",
    "df_pp['target_sentence'] = df_pp['target_sentence'].map(lambda x: x.lstrip('\"').rstrip('\"'))\n",
    "\n",
    "\n",
    "df_pp.info(memory_usage='deep')\n",
    "new_size = len(df_pp)\n",
    "print(f\"\\n {size-new_size}({100*(size-new_size)/size :.2f} %) samples removed by dropping rows with extra quotes\")\n",
    "size = new_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 308
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 39916,
     "status": "ok",
     "timestamp": 1573570547663,
     "user": {
      "displayName": "Ari Ramkilowan",
      "photoUrl": "",
      "userId": "10877800210228006715"
     },
     "user_tz": -120
    },
    "id": "tYgL_BspMu8t",
    "outputId": "283eb75f-e44a-4353-a1b1-0fec1e3a0050"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 963871 entries, 0 to 1062533\n",
      "Data columns (total 6 columns):\n",
      "source_sentence    963871 non-null object\n",
      "target_sentence    963871 non-null object\n",
      "source_ch_len      963871 non-null int64\n",
      "source_w_len       963871 non-null int64\n",
      "target_ch_len      963871 non-null int64\n",
      "target_w_len       963871 non-null int64\n",
      "dtypes: int64(4), object(2)\n",
      "memory usage: 409.9 MB\n",
      "\n",
      " 1068(0.11 %) samples removed by dropping rows from test set\n",
      "CPU times: user 3 s, sys: 57 ms, total: 3.05 s\n",
      "Wall time: 3.06 s\n",
      "Compiler : 325 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "##  Preprocessing - Step 6 :  remove text from test set\n",
    "\n",
    "with open(f\"{gdrive_path}/test.en-any.en\") as f:\n",
    "    rows = f.readlines()\n",
    "test_set_en = [row.strip() for row in rows]\n",
    "\n",
    "\n",
    "df_pp = df_pp[~df_pp['source_sentence'].str.strip().isin(test_set_en)]\n",
    "\n",
    "df_pp.info(memory_usage='deep')\n",
    "new_size = len(df_pp)\n",
    "print(f\"\\n {size-new_size}({100*(size-new_size)/size :.2f} %) samples removed by dropping rows from test set\")\n",
    "size = new_size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "RWj_Eh9W5IbD"
   },
   "source": [
    "## create dev df "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 145
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1735,
     "status": "ok",
     "timestamp": 1573646006059,
     "user": {
      "displayName": "Ari Ramkilowan",
      "photoUrl": "",
      "userId": "10877800210228006715"
     },
     "user_tz": -120
    },
    "id": "V30zP0cCNn5Y",
    "outputId": "5101f263-136b-400f-cb5b-d9c8dcfe5436"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 958286 entries, 0 to 958285\n",
      "Data columns (total 2 columns):\n",
      "source_sentence    958286 non-null object\n",
      "target_sentence    958286 non-null object\n",
      "dtypes: object(2)\n",
      "memory usage: 14.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df_dev = df_pp[['source_sentence', 'target_sentence']]\n",
    "# Shuffle the data to remove bias in dev set selection.\n",
    "seed=42\n",
    "df_dev = df_dev.sample(frac=1, random_state=seed).reset_index(drop=True)\n",
    "df_dev.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "rMdFKCgBq-Hp"
   },
   "source": [
    "## Create train test dev sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 154267,
     "status": "ok",
     "timestamp": 1573570705049,
     "user": {
      "displayName": "Ari Ramkilowan",
      "photoUrl": "",
      "userId": "10877800210228006715"
     },
     "user_tz": -120
    },
    "id": "k5ddIOazEJlF",
    "outputId": "a7584841-0de2-483c-a2dd-67a14aa1fe06"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2min 30s, sys: 446 ms, total: 2min 30s\n",
      "Wall time: 2min 31s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# This section does the split between train/dev for the parallel corpora then saves them as separate files\n",
    "# We use 1000 dev test and the given test set.\n",
    "\n",
    "# Do the split between dev/train and create parallel corpora\n",
    "num_dev_patterns = 1000\n",
    "\n",
    "# Optional: lower case the corpora - this will make it easier to generalize, but without proper casing.\n",
    "if lc:  # Julia: making lowercasing optional\n",
    "    df_dev[\"source_sentence\"] = df_dev[\"source_sentence\"].str.lower()\n",
    "    df_dev[\"target_sentence\"] = df_dev[\"target_sentence\"].str.lower()\n",
    "\n",
    "# Julia: test sets are already generated\n",
    "dev = df_dev.tail(num_dev_patterns) # Herman: Error in original\n",
    "stripped = df_dev.drop(df_dev.tail(num_dev_patterns).index)\n",
    "\n",
    "with open(f\"{gdrive_path}/train.\"+source_language, \"w\") as src_file, open(f\"{gdrive_path}/train.\"+target_language, \"w\") as tgt_file:\n",
    "  for index, row in stripped.iterrows():\n",
    "    src_file.write(row[\"source_sentence\"]+\"\\n\")\n",
    "    tgt_file.write(row[\"target_sentence\"]+\"\\n\")\n",
    "    \n",
    "with open(f\"{gdrive_path}/dev.\"+source_language, \"w\") as src_file, open(f\"{gdrive_path}/dev.\"+target_language, \"w\") as tgt_file:\n",
    "  for index, row in dev.iterrows():\n",
    "    src_file.write(row[\"source_sentence\"]+\"\\n\")\n",
    "    tgt_file.write(row[\"target_sentence\"]+\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 672
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 6429,
     "status": "ok",
     "timestamp": 1573647010628,
     "user": {
      "displayName": "Ari Ramkilowan",
      "photoUrl": "",
      "userId": "10877800210228006715"
     },
     "user_tz": -120
    },
    "id": "1tkxvlxFSeJV",
    "outputId": "6b957067-490d-46f6-ef02-afcf77f07360"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000 entries, 956286 to 957285\n",
      "Data columns (total 2 columns):\n",
      "source_sentence    1000 non-null object\n",
      "target_sentence    1000 non-null object\n",
      "dtypes: object(2)\n",
      "memory usage: 15.8+ KB\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>source_sentence</th>\n",
       "      <th>target_sentence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>957281</th>\n",
       "      <td>Shortly thereafter , my wife and I received a ...</td>\n",
       "      <td>Ngokushesha ngemva kwalokho , mina nomkami sat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>957282</th>\n",
       "      <td>Soon I came to realize that learning what the ...</td>\n",
       "      <td>Ngokushesha ngabona ukuthi ukufunda lokho okuf...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>957283</th>\n",
       "      <td>If you have children , how can you help them ?</td>\n",
       "      <td>Uma unabantwana , ungabasiza kanjani ?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>957284</th>\n",
       "      <td>With pleasure and interest , I read the magazi...</td>\n",
       "      <td>Ngenjabulo nesithakazelo ngawufunda lomagazini...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>957285</th>\n",
       "      <td>After investigating all the details of Luke’s ...</td>\n",
       "      <td>Ngemva kokuhlola yonke imininingwane yohambo l...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          source_sentence                                    target_sentence\n",
       "957281  Shortly thereafter , my wife and I received a ...  Ngokushesha ngemva kwalokho , mina nomkami sat...\n",
       "957282  Soon I came to realize that learning what the ...  Ngokushesha ngabona ukuthi ukufunda lokho okuf...\n",
       "957283     If you have children , how can you help them ?             Uma unabantwana , ungabasiza kanjani ?\n",
       "957284  With pleasure and interest , I read the magazi...  Ngenjabulo nesithakazelo ngawufunda lomagazini...\n",
       "957285  After investigating all the details of Luke’s ...  Ngemva kokuhlola yonke imininingwane yohambo l..."
      ]
     },
     "execution_count": 30,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
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       "957284  With pleasure and interest , I read the magazi...  Ngenjabulo nesithakazelo ngawufunda lomagazini...\n",
       "957285  After investigating all the details of Luke’s ...  Ngemva kokuhlola yonke imininingwane yohambo l..."
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       "175"
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       "215"
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     "execution_count": 30,
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   ],
   "source": [
    "# create my own test set\n",
    "\n",
    "num_test_patterns = 1000\n",
    "\n",
    "test = df_dev.tail(2000).iloc[0:num_test_patterns] # Herman\n",
    "test.info()\n",
    "test.tail()\n",
    "\n",
    "# remove data leakage from test set- provided all dataframes used do not have NaNs or duplicates\n",
    "filterd_test = test[~test.isin(df_dev)].dropna()\n",
    "\n",
    "if len(filterd_test)<1:\n",
    "    filterd_test=test\n",
    "else:\n",
    "    pass\n",
    "\n",
    "filterd_test.info()\n",
    "filterd_test.tail()\n",
    "\n",
    "with open(f\"{gdrive_path}/test2.\"+source_language, \"w\") as src_file, open(f\"{gdrive_path}/test2.\"+target_language, \"w\") as tgt_file:\n",
    "  for index, row in test.iterrows():\n",
    "    src_file.write(row[\"source_sentence\"]+\"\\n\")\n",
    "    trg_file.write(row[\"target_sentence\"]+\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 619
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 172216,
     "status": "ok",
     "timestamp": 1573570723006,
     "user": {
      "displayName": "Ari Ramkilowan",
      "photoUrl": "",
      "userId": "10877800210228006715"
     },
     "user_tz": -120
    },
    "id": "3YuVeEAY-Pzk",
    "outputId": "87cf0e39-6cc0-43c0-e001-9d4a5bf53e0d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "in any case , infants laid down either on their back or on their stomach tend to remain in that position \n",
      "□ how can elders help their brothers to serve jehovah with joy ?\n",
      "here are some of the things such parents have in common \n",
      "upon defeating the amalekites , though , he disobeys jehovah by sparing what had been “ devoted to destruction  ”\n",
      "the committees that were set up for korea are reporting good success \n",
      "we can see both aspects ​ — oath and friendship — ​ as the keystone of jehovah’s covenants \n",
      "evidently , people are interested in angels \n",
      "i turned to some boy for love  ”\n",
      "called the song of solomon , it is the bible’s account of a beautiful shulammite girl and her love for a shepherd boy \n",
      "as joakim the wood - carver said : ‘ tomorrow the sun will come up again \n",
      "=================================\n",
      "estienne , however , had the favor and protection of the king \n",
      "he attended a meeting of jehovah’s witnesses and was given a copy of the new world translation \n",
      "mapping paradise , a modern history book , contains details of more than  ancient maps , many showing adam and eve in eden \n",
      "in that way we can take a “ stand against [ satan ] ” and put ourselves on god’s side  ​ —  peter  :  ,  \n",
      "in the first half of the second century bce  , judea was ruled by the syrian seleucids , one of the four dynasties that formed after the breakup of the empire of alexander the great \n",
      "their friends and families would betray them \n",
      "the new military government emphasized national sovereignty , and a bone of contention was the canal zone , which was governed directly by the united states \n",
      "for example , close to , kingdom supporters convened for an international convention in cedar point , ohio , usa  , in september  \n",
      "the earth they rule over will be inhabited , in time , by perfected humans , no doubt thousands of millions of them \n",
      "his universal laws let us plan our lives with confidence \n",
      "=================================\n",
      "\"I am going to make a helper for him , as a complement of him . ”\"\n",
      "\"Moreover , do not call anyone your father on earth , for one is your Father , the heavenly One .\"\n",
      "But the greatest one among you must be your minister .\n",
      "Jesus said : “ You must love your neighbor as yourself . ”\n",
      "Some names in this article have been changed .\n",
      "For day and night your hand was heavy upon me . ”\n",
      "Some of the names in this article have been changed .\n",
      "It does not belong to man who is walking even to direct his step . ”\n",
      "This is the greatest and first commandment . ”\n",
      "Published by Jehovah’s Witnesses but now out of print .\n"
     ]
    }
   ],
   "source": [
    "# Doublecheck the format below. There should be no extra quotation marks or weird characters.\n",
    "! head \"$gdrive_path/train.$src\"\n",
    "! echo \"=================================\"\n",
    "! head \"$gdrive_path/dev.$src\"\n",
    "! echo \"=================================\"\n",
    "! head \"$gdrive_path/test.$src\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 619
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 133934,
     "status": "ok",
     "timestamp": 1573570740478,
     "user": {
      "displayName": "Ari Ramkilowan",
      "photoUrl": "",
      "userId": "10877800210228006715"
     },
     "user_tz": -120
    },
    "id": "Ypai27iMCoJS",
    "outputId": "f548bc63-cd62-4bba-b4f5-83de29902e95"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "noma kunjalo , lapho izinsana zilaliswe ngomhlane noma ngesisu azivamile ukuphenduka \n",
      "□ abadala bangabasiza kanjani abafowabo ukuba bakhonze ujehova ngenjabulo ?\n",
      "nazi ezinye zezinto abazali abanjalo abavumelana ngazo \n",
      "nokho , ngemva kokuba esenqobé ama - amaleki , akamlaleli ujehova ngokusindisa lokho okufanele ngabe ‘ kunikelwe ekubhujisweni  ’\n",
      "amakomiti amiselwa ikorea abika impumelelo enhle \n",
      "singazibona zombili lezici — isifungo nobungane — njengensika yezivumelwano zikajehova \n",
      "ngokusobala , abantu banesithakazelo ezingelosini \n",
      "ngaphendukela komunye umfana ukuze ngithole uthando  ”\n",
      "ibizwa ngokuthi isihlabelelo sezihlabelelo , iwukulandisa kwebhayibheli ngentombi enhle yomshulamiti nangothando lwayo ngomalusi wayo \n",
      "njengoba nje ujoakim umbazi athi : ‘ kusasa ilanga lizophuma futhi \n",
      "=================================\n",
      "nokho , u - estienne wazuza umusa nesivikelo senkosi \n",
      "yaya emhlanganweni wofakazi bakajehova futhi yanikwa ikhophi ye - nguqulo yezwe elisha \n",
      "incwadi yomlando i - mapping paradise , inemininingwane yamabalazwe akudala angaphezu kuka -  , iningi lawo linezithombe zika - adamu no - eva bese - edene \n",
      "ngaleyo ndlela ‘ singamelana nosathane ’ futhi sizibeke ohlangothini lukankulunkulu  —  petru  :  ,  \n",
      "engxenyeni yokuqala yekhulu lesibili leminyaka bce  , ijudiya lalibuswa uzalo lukaseleucus wasesiriya , omunye wemindeni emine yasebukhosini eyaba khona ngemva kokuwa kombuso ka - alexander omkhulu \n",
      "abangane nabomndeni babeyobakhaphela \n",
      "uhulumeni omusha wezempi wagcizelela ukubuswa kwesizwe sonke , futhi into okwakubangiswana ngayo kwakuyindawo yomhubhe , eyayibuswa ngokuqondile iunited states \n",
      "ngokwesibonelo , abantu abasekela umbuso abacela ezinkulungwaneni ezingu -  babuthana emhlanganweni wezizwe ecedar point , e - ohio , e - usa  , ngo - september  \n",
      "ngokuhamba kwesikhathi , umhlaba abawubusayo uyohlalwa abantu abaphelele , ngokungangabazeki izinkulungwane zezigidi zabo \n",
      "imithetho yakhe yendawo yonke isenza sikwazi ukuhlela ukuphila kwethu ngokuqiniseka \n",
      "=================================\n",
      "\"Ngizakumenzela umsizi [ “ umphelelisi , ” NW ] onjengaye . ”\"\n",
      "\"Ngaphezu kwalokho , ningabizi muntu emhlabeni ngokuthi uyihlo , ngoba munye onguYihlo , yiLowo osezulwini .\"\n",
      "Kodwa omkhulu kakhulu phakathi kwenu kumelwe abe isikhonzi senu .\n",
      "UJesu wathi : “ Kumelwe uthande umakhelwane wakho njengoba uzithanda wena . ”\n",
      "Amanye amagama kulesi sihloko aye ashintshwa .\n",
      "Ngokuba imini nobusuku isandla sakho sasinzima phezu kwami . ”\n",
      "Amanye amagama kulesi sihloko ashintshiwe .\n",
      "\"Akukuyo indoda ukuba ihambe , iqondise izinyathelo zayo . ”\"\n",
      "Lona umyalo omkhulu kunayo yonke nowokuqala . ”\n",
      "Yanyatheliswa oFakazi BakaJehova kodwa manje ayisanyatheliswa .\n"
     ]
    }
   ],
   "source": [
    "! head \"$gdrive_path/train.$tgt\"\n",
    "! echo \"=================================\"\n",
    "! head \"$gdrive_path/dev.$tgt\"\n",
    "! echo \"=================================\"\n",
    "! head \"$gdrive_path/test.$tgt\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "exG6Ch3UR_wF"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "bWV-5a34rdsK"
   },
   "source": [
    "## Preprocessing the Data into Subword BPE Tokens\n",
    "\n",
    "- One of the most powerful improvements for agglutinative languages (a feature of most Bantu languages) is using BPE tokenization [ (Sennrich, 2015) ](https://arxiv.org/abs/1508.07909).\n",
    "\n",
    "- It was also shown that by optimizing the umber of BPE codes we significantly improve results for low-resourced languages [(Sennrich, 2019)](https://www.aclweb.org/anthology/P19-1021) [(Martinus, 2019)](https://arxiv.org/abs/1906.05685)\n",
    "\n",
    "- Below we have the scripts for doing BPE tokenization of our data. We use 4000 tokens as recommended by [(Sennrich, 2019)](https://www.aclweb.org/anthology/P19-1021). You do not need to change anything. Simply running the below will be suitable. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 6197,
     "status": "ok",
     "timestamp": 1573647099660,
     "user": {
      "displayName": "Ari Ramkilowan",
      "photoUrl": "",
      "userId": "10877800210228006715"
     },
     "user_tz": -120
    },
    "id": "Bi5fyp1LsLv7",
    "outputId": "bf73dd44-953a-4257-f8cd-cbfdec9177aa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 32.3 ms, sys: 45.2 ms, total: 77.5 ms\n",
      "Wall time: 5.08 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "! subword-nmt learn-joint-bpe-and-vocab --input  \"$gdrive_path\"train.$src  \"$gdrive_path\"train.$tgt -s $vocab_size -o  \"$gdrive_path\"bpe.codes.$vocab_size --write-vocabulary  \"$gdrive_path\"vocab.$src  \"$gdrive_path\"vocab.$tgt\n",
    "\n",
    "# Apply BPE splits to the train, development and test data.\n",
    "! subword-nmt apply-bpe -c \"$gdrive_path\"bpe.codes.$vocab_size --vocabulary \"$gdrive_path\"vocab.$src < \"$gdrive_path\"train.$src > \"$gdrive_path\"train.bpe.$src\n",
    "! subword-nmt apply-bpe -c \"$gdrive_path\"bpe.codes.$vocab_size --vocabulary \"$gdrive_path\"vocab.$tgt < \"$gdrive_path\"train.$tgt > \"$gdrive_path\"train.bpe.$tgt\n",
    "\n",
    "! subword-nmt apply-bpe -c \"$gdrive_path\"bpe.codes.$vocab_size --vocabulary \"$gdrive_path\"vocab.$src < \"$gdrive_path\"dev.$src > \"$gdrive_path\"dev.bpe.$src\n",
    "! subword-nmt apply-bpe -c \"$gdrive_path\"bpe.codes.$vocab_size --vocabulary \"$gdrive_path\"vocab.$tgt < \"$gdrive_path\"dev.$tgt > \"$gdrive_path\"dev.bpe.$tgt\n",
    "\n",
    "! subword-nmt apply-bpe -c \"$gdrive_path\"bpe.codes.$vocab_size --vocabulary \"$gdrive_path\"vocab.$src < \"$gdrive_path\"test2.$src > \"$gdrive_path\"test2.bpe.$src\n",
    "! subword-nmt apply-bpe -c \"$gdrive_path\"bpe.codes.$vocab_size --vocabulary \"$gdrive_path\"vocab.$tgt < \"$gdrive_path\"test2.$tgt > \"$gdrive_path\"test2.bpe.$tgt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "jCmdlJLPMiv7"
   },
   "outputs": [],
   "source": [
    "# Create that vocab using build_vocab\n",
    "! sudo chmod 777 joeynmt/scripts/build_vocab.py\n",
    "! joeynmt/scripts/build_vocab.py \"$gdrive_path\"train.bpe.\"$src\" \"$gdrive_path\"train.bpe.\"$tgt\" --output_path \"$gdrive_path\"vocab.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 346
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 11082,
     "status": "ok",
     "timestamp": 1573647799332,
     "user": {
      "displayName": "Ari Ramkilowan",
      "photoUrl": "",
      "userId": "10877800210228006715"
     },
     "user_tz": -120
    },
    "id": "lm1CNAQvGZGy",
    "outputId": "f4938d62-e78b-42d6-9669-7dc2207f5405"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BPE Zulu Sentences\n",
      "N@@ g@@ oku@@ sh@@ esha ngemva kwalokho , mina nom@@ k@@ ami s@@ athola incwadi ev@@ ela eh@@ hov@@ isi leg@@ at@@ sha le - W@@ at@@ ch T@@ ower S@@ o@@ ci@@ ety isim@@ em@@ ela ukuba siye e@@ S@@ y@@ d@@ ne@@ y \n",
      "N@@ g@@ oku@@ sh@@ esha ngab@@ ona ukuthi ukufunda lokho oku@@ fund@@ iswa i@@ B@@ h@@ ayibheli ku@@ hamb@@ isana nem@@ ith@@ walo yem@@ fanelo ethile , ng@@ abe seng@@ inq@@ uma uku@@ yeka ukul@@ ifunda \n",
      "U@@ m@@ a un@@ abantwana , ung@@ abas@@ iza kanjani ?\n",
      "N@@ gen@@ jabulo nes@@ ith@@ akazelo ng@@ aw@@ u@@ funda lom@@ agazini , futhi ng@@ ifuna ukwazi okwengeziwe ng@@ e@@ B@@ h@@ ayibheli \n",
      "N@@ g@@ em@@ va koku@@ hl@@ ola yonke imin@@ in@@ ing@@ wane yo@@ h@@ ambo luk@@ a@@ L@@ uka ngom@@ kh@@ umbi , it@@ il@@ osi u@@ J@@ ames S@@ m@@ ith l@@ aph@@ etha ngokuthi : “ L@@ okhu kuw@@ ukul@@ andisa kwez@@ enzakalo ezing@@ ok@@ oqobo , ez@@ ibh@@ al@@ we ng@@ othile oway@@ eh@@ il@@ eleke ngokom@@ untu siqu kuzo   \n",
      "Combined BPE Vocab\n",
      "א@@\n",
      "ị@@\n",
      "·\n",
      "(@@\n",
      "̆\n",
      "ṇ@@\n",
      "ז@@\n",
      "{\n",
      "ɓ@@\n",
      "‵\n"
     ]
    }
   ],
   "source": [
    "# Some output\n",
    "! echo \"BPE Zulu Sentences\"\n",
    "! tail -n 5 \"$gdrive_path\"test.bpe.$tgt\n",
    "\n",
    "! echo \"Combined BPE Vocab\"\n",
    "! tail -n 10 \"$gdrive_path\"vocab.txt  # Herman\n",
    "\n",
    "# !cp joeynmt/data/$src$tgt$vocab_size/vocab.txt \"$gdrive_path\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0P_JpDK7v2e8"
   },
   "source": [
    "## Creating the JoeyNMT Config\n",
    "\n",
    "JoeyNMT requires a yaml config. We provide a template below. We've also set a number of defaults with it, that you may play with!\n",
    "\n",
    "- We used Transformer architecture \n",
    "- We set our dropout to reasonably high: 0.3 (recommended in  [(Sennrich, 2019)](https://www.aclweb.org/anthology/P19-1021))\n",
    "\n",
    "Things worth playing with:\n",
    "- The batch size (also recommended to change for low-resourced languages)\n",
    "- The number of epochs (we've set it at 30 just so it runs in about an hour, for testing purposes)\n",
    "- The decoder options (beam_size, alpha)\n",
    "- Evaluation metrics (BLEU versus Crhf4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1356,
     "status": "ok",
     "timestamp": 1574063829726,
     "user": {
      "displayName": "Ari Ramkilowan",
      "photoUrl": "",
      "userId": "10877800210228006715"
     },
     "user_tz": -120
    },
    "id": "4G_GWs_E0N1v",
    "outputId": "9f60a1ef-0133-49c5-cb34-31a36330c652"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'enzu4000baseline'"
      ]
     },
     "execution_count": 16,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name = '%s%s%s%s' % (source_language, target_language, str(vocab_size),tag)\n",
    "name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "o-7TTovtKnF7"
   },
   "outputs": [],
   "source": [
    "# Create this dir before we run for the first time so we store check points\n",
    "# !mkdir -p \"$gdrive_path/pretrained/$src$tgt$vocab_size$tag/\" # Herman"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1419,
     "status": "ok",
     "timestamp": 1574064524153,
     "user": {
      "displayName": "Ari Ramkilowan",
      "photoUrl": "",
      "userId": "10877800210228006715"
     },
     "user_tz": -120
    },
    "id": "nVq7B1IYv2hd",
    "outputId": "4154712d-d2e6-47da-8a34-e0ff6df460f4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3863"
      ]
     },
     "execution_count": 27,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This creates the config file for our JoeyNMT system. It might seem overwhelming so we've provided a couple of useful parameters you'll need to update\n",
    "# (You can of course play with all the parameters if you'd like!)\n",
    "\n",
    "name = '%s%s%s%s' % (source_language, target_language, str(vocab_size),tag)\n",
    "gdrive_path = os.environ[\"gdrive_path\"]\n",
    "\n",
    "# Create the config\n",
    "config = \"\"\"\n",
    "name: \"{name}_transformer\"\n",
    "\n",
    "data:\n",
    "    src: \"{source_language}\"\n",
    "    trg: \"{target_language}\"\n",
    "    train: \"{gdrive_path}train.bpe\"\n",
    "    dev:   \"{gdrive_path}dev.bpe\"\n",
    "    test:  \"{gdrive_path}test.bpe\"\n",
    "    level: \"bpe\"\n",
    "    lowercase: False\n",
    "    max_sent_length: 100\n",
    "    src_vocab: \"{gdrive_path}vocab.txt\"\n",
    "    trg_vocab: \"{gdrive_path}vocab.txt\"\n",
    "\n",
    "testing:\n",
    "    beam_size: 5\n",
    "    alpha: 1.0\n",
    "\n",
    "training:\n",
    "    load_model: \"{gdrive_path}pretrained/{name}/125000.ckpt\" # if uncommented, load a pre-trained model from this checkpoint\n",
    "    random_seed: 42                 # set this seed to make training deterministic\n",
    "    optimizer: \"adam\"               # choices: \"sgd\", \"adam\", \"adadelta\", \"adagrad\", \"rmsprop\", default is SGD\n",
    "    normalization: \"tokens\"\n",
    "    adam_betas: [0.9, 0.999] \n",
    "    # scheduling: \"noam\"           # TODO: try switching from plateau to Noam scheduling\n",
    "    # patience: 5                     # For plateau: decrease learning rate by decrease_factor if validation score has not improved for this many validation rounds.\n",
    "    # learning_rate_factor: 0.5       # factor for Noam scheduler (used with Transformer)\n",
    "    # learning_rate_warmup: 1000      # warmup steps for Noam scheduler (used with Transformer)\n",
    "    decrease_factor: 0.7\n",
    "    loss: \"crossentropy\"\n",
    "    learning_rate: 0.0003\n",
    "    learning_rate_min: 0.000001\n",
    "    weight_decay: 0.0\n",
    "    label_smoothing: 0.1\n",
    "    batch_size: 2048               # mini-batch size as number of sentences (when batch_type is \"sentence\"; default) or total number of tokens (when batch_type is \"token\")\n",
    "    batch_type: \"token\"\n",
    "    eval_batch_size: 1024\n",
    "    eval_batch_type: \"token\"\n",
    "    batch_multiplier: 1\n",
    "    early_stopping_metric: \"ppl\"\n",
    "    epochs: 30                     # TODO: Decrease for when playing around and checking of working. Around 30 is sufficient to check if its working at all\n",
    "    validation_freq: 2500          # validate after this many updates (number of mini-batches), default: 1000\n",
    "    logging_freq: 100              # log the training progress after this many updates, default: 100\n",
    "    eval_metric: \"bleu\"\n",
    "    model_dir: \"models/{name}_transformer\"\n",
    "    overwrite: True               # TODO: Set to True if you want to overwrite possibly existing models. \n",
    "    shuffle: True\n",
    "    use_cuda: True\n",
    "    max_output_length: 100\n",
    "    print_valid_sents: [0, 1, 2, 3]\n",
    "    keep_last_ckpts: 3\n",
    "\n",
    "model:\n",
    "    initializer: \"xavier\"\n",
    "    bias_initializer: \"uniform\"    # initializer for bias terms (xavier, zeros, normal, uniform)\n",
    "    init_gain: 1.0\n",
    "    embed_initializer: \"xavier\"\n",
    "    embed_init_gain: 1.0\n",
    "    tied_embeddings: True\n",
    "    tied_softmax: True\n",
    "    encoder:\n",
    "        type: \"transformer\"\n",
    "        num_layers: 6\n",
    "        num_heads: 4             # TODO: Increase to 8 for larger data.\n",
    "        embeddings:\n",
    "            embedding_dim: 256    # size of embeddings (for Transformer set equal to hidden_size), TODO: Increase to 512 for larger data.\n",
    "            scale: True\n",
    "            dropout: 0.5\n",
    "        # typically ff_size = 4 x hidden_size\n",
    "        hidden_size: 256         # TODO: Increase to 512 for larger data.\n",
    "        ff_size: 1024            # TODO: Increase to 2048 for larger data.\n",
    "        dropout: 0.5\n",
    "    decoder:\n",
    "        type: \"transformer\"\n",
    "        num_layers: 6\n",
    "        num_heads: 8              # TODO: Increase to 8 for larger data.\n",
    "        embeddings:\n",
    "            embedding_dim: 256    # TODO: Increase to 512 for larger data.\n",
    "            scale: True\n",
    "            dropout: 0.2\n",
    "        # typically ff_size = 4 x hidden_size\n",
    "        hidden_size: 256         # TODO: Increase to 512 for larger data.\n",
    "        ff_size: 1024            # TODO: Increase to 2048 for larger data.\n",
    "        dropout: 0.2\n",
    "\"\"\".format(name=name,\n",
    "           gdrive_path=os.environ[\"gdrive_path\"],\n",
    "           source_language=source_language,\n",
    "           target_language=target_language\n",
    "          )\n",
    "\n",
    "with open(\"joeynmt/configs/transformer_{name}.yaml\".format(name=name),'w') as f:\n",
    "    f.write(config)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "nr9b-FiGv2j9"
   },
   "source": [
    "## Train the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 181
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 8735,
     "status": "ok",
     "timestamp": 1574064535664,
     "user": {
      "displayName": "Ari Ramkilowan",
      "photoUrl": "",
      "userId": "10877800210228006715"
     },
     "user_tz": -120
    },
    "id": "NfW_lJhwe6gD",
    "outputId": "3f6ed1f6-9b72-47e0-9dd0-b1bdffd1428c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iwslt14_deen_bpe.yaml\t\t   transformer_iwslt14_deen_bpe.yaml\n",
      "iwslt_deen_bahdanau.yaml\t   transformer_reverse.yaml\n",
      "iwslt_envi_luong.yaml\t\t   transformer_small.yaml\n",
      "iwslt_envi_xnmt.yaml\t\t   transformer_wmt17_ende.yaml\n",
      "reverse.yaml\t\t\t   transformer_wmt17_lven.yaml\n",
      "small.yaml\t\t\t   wmt_ende_best.yaml\n",
      "transformer_copy.yaml\t\t   wmt_ende_default.yaml\n",
      "transformer_enzu4000baseline.yaml  wmt_lven_best.yaml\n",
      "transformer_enzu4000const_lr.yaml  wmt_lven_default.yaml\n"
     ]
    }
   ],
   "source": [
    "! cd joeynmt/configs; ls\n",
    "# copy config to gdrive\n",
    "! cp joeynmt/configs/transformer_$src$tgt$vocab_size$tag.yaml \"$gdrive_path/pretrained/$src$tgt$vocab_size$tag/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 4808207,
     "status": "ok",
     "timestamp": 1573652653699,
     "user": {
      "displayName": "Ari Ramkilowan",
      "photoUrl": "",
      "userId": "10877800210228006715"
     },
     "user_tz": -120
    },
    "id": "WiwUQaqPv2mz",
    "outputId": "f0c6fcac-ce30-4d6e-b0ad-75f1c6c0def8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019-11-18 08:09:23,706 Hello! This is Joey-NMT.\n",
      "2019-11-18 08:09:25,070 Total params: 12175104\n",
      "2019-11-18 08:09:25,071 Trainable parameters: ['decoder.layer_norm.bias', 'decoder.layer_norm.weight', 'decoder.layers.0.dec_layer_norm.bias', 'decoder.layers.0.dec_layer_norm.weight', 'decoder.layers.0.feed_forward.layer_norm.bias', 'decoder.layers.0.feed_forward.layer_norm.weight', 'decoder.layers.0.feed_forward.pwff_layer.0.bias', 'decoder.layers.0.feed_forward.pwff_layer.0.weight', 'decoder.layers.0.feed_forward.pwff_layer.3.bias', 'decoder.layers.0.feed_forward.pwff_layer.3.weight', 'decoder.layers.0.src_trg_att.k_layer.bias', 'decoder.layers.0.src_trg_att.k_layer.weight', 'decoder.layers.0.src_trg_att.output_layer.bias', 'decoder.layers.0.src_trg_att.output_layer.weight', 'decoder.layers.0.src_trg_att.q_layer.bias', 'decoder.layers.0.src_trg_att.q_layer.weight', 'decoder.layers.0.src_trg_att.v_layer.bias', 'decoder.layers.0.src_trg_att.v_layer.weight', 'decoder.layers.0.trg_trg_att.k_layer.bias', 'decoder.layers.0.trg_trg_att.k_layer.weight', 'decoder.layers.0.trg_trg_att.output_layer.bias', 'decoder.layers.0.trg_trg_att.output_layer.weight', 'decoder.layers.0.trg_trg_att.q_layer.bias', 'decoder.layers.0.trg_trg_att.q_layer.weight', 'decoder.layers.0.trg_trg_att.v_layer.bias', 'decoder.layers.0.trg_trg_att.v_layer.weight', 'decoder.layers.0.x_layer_norm.bias', 'decoder.layers.0.x_layer_norm.weight', 'decoder.layers.1.dec_layer_norm.bias', 'decoder.layers.1.dec_layer_norm.weight', 'decoder.layers.1.feed_forward.layer_norm.bias', 'decoder.layers.1.feed_forward.layer_norm.weight', 'decoder.layers.1.feed_forward.pwff_layer.0.bias', 'decoder.layers.1.feed_forward.pwff_layer.0.weight', 'decoder.layers.1.feed_forward.pwff_layer.3.bias', 'decoder.layers.1.feed_forward.pwff_layer.3.weight', 'decoder.layers.1.src_trg_att.k_layer.bias', 'decoder.layers.1.src_trg_att.k_layer.weight', 'decoder.layers.1.src_trg_att.output_layer.bias', 'decoder.layers.1.src_trg_att.output_layer.weight', 'decoder.layers.1.src_trg_att.q_layer.bias', 'decoder.layers.1.src_trg_att.q_layer.weight', 'decoder.layers.1.src_trg_att.v_layer.bias', 'decoder.layers.1.src_trg_att.v_layer.weight', 'decoder.layers.1.trg_trg_att.k_layer.bias', 'decoder.layers.1.trg_trg_att.k_layer.weight', 'decoder.layers.1.trg_trg_att.output_layer.bias', 'decoder.layers.1.trg_trg_att.output_layer.weight', 'decoder.layers.1.trg_trg_att.q_layer.bias', 'decoder.layers.1.trg_trg_att.q_layer.weight', 'decoder.layers.1.trg_trg_att.v_layer.bias', 'decoder.layers.1.trg_trg_att.v_layer.weight', 'decoder.layers.1.x_layer_norm.bias', 'decoder.layers.1.x_layer_norm.weight', 'decoder.layers.2.dec_layer_norm.bias', 'decoder.layers.2.dec_layer_norm.weight', 'decoder.layers.2.feed_forward.layer_norm.bias', 'decoder.layers.2.feed_forward.layer_norm.weight', 'decoder.layers.2.feed_forward.pwff_layer.0.bias', 'decoder.layers.2.feed_forward.pwff_layer.0.weight', 'decoder.layers.2.feed_forward.pwff_layer.3.bias', 'decoder.layers.2.feed_forward.pwff_layer.3.weight', 'decoder.layers.2.src_trg_att.k_layer.bias', 'decoder.layers.2.src_trg_att.k_layer.weight', 'decoder.layers.2.src_trg_att.output_layer.bias', 'decoder.layers.2.src_trg_att.output_layer.weight', 'decoder.layers.2.src_trg_att.q_layer.bias', 'decoder.layers.2.src_trg_att.q_layer.weight', 'decoder.layers.2.src_trg_att.v_layer.bias', 'decoder.layers.2.src_trg_att.v_layer.weight', 'decoder.layers.2.trg_trg_att.k_layer.bias', 'decoder.layers.2.trg_trg_att.k_layer.weight', 'decoder.layers.2.trg_trg_att.output_layer.bias', 'decoder.layers.2.trg_trg_att.output_layer.weight', 'decoder.layers.2.trg_trg_att.q_layer.bias', 'decoder.layers.2.trg_trg_att.q_layer.weight', 'decoder.layers.2.trg_trg_att.v_layer.bias', 'decoder.layers.2.trg_trg_att.v_layer.weight', 'decoder.layers.2.x_layer_norm.bias', 'decoder.layers.2.x_layer_norm.weight', 'decoder.layers.3.dec_layer_norm.bias', 'decoder.layers.3.dec_layer_norm.weight', 'decoder.layers.3.feed_forward.layer_norm.bias', 'decoder.layers.3.feed_forward.layer_norm.weight', 'decoder.layers.3.feed_forward.pwff_layer.0.bias', 'decoder.layers.3.feed_forward.pwff_layer.0.weight', 'decoder.layers.3.feed_forward.pwff_layer.3.bias', 'decoder.layers.3.feed_forward.pwff_layer.3.weight', 'decoder.layers.3.src_trg_att.k_layer.bias', 'decoder.layers.3.src_trg_att.k_layer.weight', 'decoder.layers.3.src_trg_att.output_layer.bias', 'decoder.layers.3.src_trg_att.output_layer.weight', 'decoder.layers.3.src_trg_att.q_layer.bias', 'decoder.layers.3.src_trg_att.q_layer.weight', 'decoder.layers.3.src_trg_att.v_layer.bias', 'decoder.layers.3.src_trg_att.v_layer.weight', 'decoder.layers.3.trg_trg_att.k_layer.bias', 'decoder.layers.3.trg_trg_att.k_layer.weight', 'decoder.layers.3.trg_trg_att.output_layer.bias', 'decoder.layers.3.trg_trg_att.output_layer.weight', 'decoder.layers.3.trg_trg_att.q_layer.bias', 'decoder.layers.3.trg_trg_att.q_layer.weight', 'decoder.layers.3.trg_trg_att.v_layer.bias', 'decoder.layers.3.trg_trg_att.v_layer.weight', 'decoder.layers.3.x_layer_norm.bias', 'decoder.layers.3.x_layer_norm.weight', 'decoder.layers.4.dec_layer_norm.bias', 'decoder.layers.4.dec_layer_norm.weight', 'decoder.layers.4.feed_forward.layer_norm.bias', 'decoder.layers.4.feed_forward.layer_norm.weight', 'decoder.layers.4.feed_forward.pwff_layer.0.bias', 'decoder.layers.4.feed_forward.pwff_layer.0.weight', 'decoder.layers.4.feed_forward.pwff_layer.3.bias', 'decoder.layers.4.feed_forward.pwff_layer.3.weight', 'decoder.layers.4.src_trg_att.k_layer.bias', 'decoder.layers.4.src_trg_att.k_layer.weight', 'decoder.layers.4.src_trg_att.output_layer.bias', 'decoder.layers.4.src_trg_att.output_layer.weight', 'decoder.layers.4.src_trg_att.q_layer.bias', 'decoder.layers.4.src_trg_att.q_layer.weight', 'decoder.layers.4.src_trg_att.v_layer.bias', 'decoder.layers.4.src_trg_att.v_layer.weight', 'decoder.layers.4.trg_trg_att.k_layer.bias', 'decoder.layers.4.trg_trg_att.k_layer.weight', 'decoder.layers.4.trg_trg_att.output_layer.bias', 'decoder.layers.4.trg_trg_att.output_layer.weight', 'decoder.layers.4.trg_trg_att.q_layer.bias', 'decoder.layers.4.trg_trg_att.q_layer.weight', 'decoder.layers.4.trg_trg_att.v_layer.bias', 'decoder.layers.4.trg_trg_att.v_layer.weight', 'decoder.layers.4.x_layer_norm.bias', 'decoder.layers.4.x_layer_norm.weight', 'decoder.layers.5.dec_layer_norm.bias', 'decoder.layers.5.dec_layer_norm.weight', 'decoder.layers.5.feed_forward.layer_norm.bias', 'decoder.layers.5.feed_forward.layer_norm.weight', 'decoder.layers.5.feed_forward.pwff_layer.0.bias', 'decoder.layers.5.feed_forward.pwff_layer.0.weight', 'decoder.layers.5.feed_forward.pwff_layer.3.bias', 'decoder.layers.5.feed_forward.pwff_layer.3.weight', 'decoder.layers.5.src_trg_att.k_layer.bias', 'decoder.layers.5.src_trg_att.k_layer.weight', 'decoder.layers.5.src_trg_att.output_layer.bias', 'decoder.layers.5.src_trg_att.output_layer.weight', 'decoder.layers.5.src_trg_att.q_layer.bias', 'decoder.layers.5.src_trg_att.q_layer.weight', 'decoder.layers.5.src_trg_att.v_layer.bias', 'decoder.layers.5.src_trg_att.v_layer.weight', 'decoder.layers.5.trg_trg_att.k_layer.bias', 'decoder.layers.5.trg_trg_att.k_layer.weight', 'decoder.layers.5.trg_trg_att.output_layer.bias', 'decoder.layers.5.trg_trg_att.output_layer.weight', 'decoder.layers.5.trg_trg_att.q_layer.bias', 'decoder.layers.5.trg_trg_att.q_layer.weight', 'decoder.layers.5.trg_trg_att.v_layer.bias', 'decoder.layers.5.trg_trg_att.v_layer.weight', 'decoder.layers.5.x_layer_norm.bias', 'decoder.layers.5.x_layer_norm.weight', 'encoder.layer_norm.bias', 'encoder.layer_norm.weight', 'encoder.layers.0.feed_forward.layer_norm.bias', 'encoder.layers.0.feed_forward.layer_norm.weight', 'encoder.layers.0.feed_forward.pwff_layer.0.bias', 'encoder.layers.0.feed_forward.pwff_layer.0.weight', 'encoder.layers.0.feed_forward.pwff_layer.3.bias', 'encoder.layers.0.feed_forward.pwff_layer.3.weight', 'encoder.layers.0.layer_norm.bias', 'encoder.layers.0.layer_norm.weight', 'encoder.layers.0.src_src_att.k_layer.bias', 'encoder.layers.0.src_src_att.k_layer.weight', 'encoder.layers.0.src_src_att.output_layer.bias', 'encoder.layers.0.src_src_att.output_layer.weight', 'encoder.layers.0.src_src_att.q_layer.bias', 'encoder.layers.0.src_src_att.q_layer.weight', 'encoder.layers.0.src_src_att.v_layer.bias', 'encoder.layers.0.src_src_att.v_layer.weight', 'encoder.layers.1.feed_forward.layer_norm.bias', 'encoder.layers.1.feed_forward.layer_norm.weight', 'encoder.layers.1.feed_forward.pwff_layer.0.bias', 'encoder.layers.1.feed_forward.pwff_layer.0.weight', 'encoder.layers.1.feed_forward.pwff_layer.3.bias', 'encoder.layers.1.feed_forward.pwff_layer.3.weight', 'encoder.layers.1.layer_norm.bias', 'encoder.layers.1.layer_norm.weight', 'encoder.layers.1.src_src_att.k_layer.bias', 'encoder.layers.1.src_src_att.k_layer.weight', 'encoder.layers.1.src_src_att.output_layer.bias', 'encoder.layers.1.src_src_att.output_layer.weight', 'encoder.layers.1.src_src_att.q_layer.bias', 'encoder.layers.1.src_src_att.q_layer.weight', 'encoder.layers.1.src_src_att.v_layer.bias', 'encoder.layers.1.src_src_att.v_layer.weight', 'encoder.layers.2.feed_forward.layer_norm.bias', 'encoder.layers.2.feed_forward.layer_norm.weight', 'encoder.layers.2.feed_forward.pwff_layer.0.bias', 'encoder.layers.2.feed_forward.pwff_layer.0.weight', 'encoder.layers.2.feed_forward.pwff_layer.3.bias', 'encoder.layers.2.feed_forward.pwff_layer.3.weight', 'encoder.layers.2.layer_norm.bias', 'encoder.layers.2.layer_norm.weight', 'encoder.layers.2.src_src_att.k_layer.bias', 'encoder.layers.2.src_src_att.k_layer.weight', 'encoder.layers.2.src_src_att.output_layer.bias', 'encoder.layers.2.src_src_att.output_layer.weight', 'encoder.layers.2.src_src_att.q_layer.bias', 'encoder.layers.2.src_src_att.q_layer.weight', 'encoder.layers.2.src_src_att.v_layer.bias', 'encoder.layers.2.src_src_att.v_layer.weight', 'encoder.layers.3.feed_forward.layer_norm.bias', 'encoder.layers.3.feed_forward.layer_norm.weight', 'encoder.layers.3.feed_forward.pwff_layer.0.bias', 'encoder.layers.3.feed_forward.pwff_layer.0.weight', 'encoder.layers.3.feed_forward.pwff_layer.3.bias', 'encoder.layers.3.feed_forward.pwff_layer.3.weight', 'encoder.layers.3.layer_norm.bias', 'encoder.layers.3.layer_norm.weight', 'encoder.layers.3.src_src_att.k_layer.bias', 'encoder.layers.3.src_src_att.k_layer.weight', 'encoder.layers.3.src_src_att.output_layer.bias', 'encoder.layers.3.src_src_att.output_layer.weight', 'encoder.layers.3.src_src_att.q_layer.bias', 'encoder.layers.3.src_src_att.q_layer.weight', 'encoder.layers.3.src_src_att.v_layer.bias', 'encoder.layers.3.src_src_att.v_layer.weight', 'encoder.layers.4.feed_forward.layer_norm.bias', 'encoder.layers.4.feed_forward.layer_norm.weight', 'encoder.layers.4.feed_forward.pwff_layer.0.bias', 'encoder.layers.4.feed_forward.pwff_layer.0.weight', 'encoder.layers.4.feed_forward.pwff_layer.3.bias', 'encoder.layers.4.feed_forward.pwff_layer.3.weight', 'encoder.layers.4.layer_norm.bias', 'encoder.layers.4.layer_norm.weight', 'encoder.layers.4.src_src_att.k_layer.bias', 'encoder.layers.4.src_src_att.k_layer.weight', 'encoder.layers.4.src_src_att.output_layer.bias', 'encoder.layers.4.src_src_att.output_layer.weight', 'encoder.layers.4.src_src_att.q_layer.bias', 'encoder.layers.4.src_src_att.q_layer.weight', 'encoder.layers.4.src_src_att.v_layer.bias', 'encoder.layers.4.src_src_att.v_layer.weight', 'encoder.layers.5.feed_forward.layer_norm.bias', 'encoder.layers.5.feed_forward.layer_norm.weight', 'encoder.layers.5.feed_forward.pwff_layer.0.bias', 'encoder.layers.5.feed_forward.pwff_layer.0.weight', 'encoder.layers.5.feed_forward.pwff_layer.3.bias', 'encoder.layers.5.feed_forward.pwff_layer.3.weight', 'encoder.layers.5.layer_norm.bias', 'encoder.layers.5.layer_norm.weight', 'encoder.layers.5.src_src_att.k_layer.bias', 'encoder.layers.5.src_src_att.k_layer.weight', 'encoder.layers.5.src_src_att.output_layer.bias', 'encoder.layers.5.src_src_att.output_layer.weight', 'encoder.layers.5.src_src_att.q_layer.bias', 'encoder.layers.5.src_src_att.q_layer.weight', 'encoder.layers.5.src_src_att.v_layer.bias', 'encoder.layers.5.src_src_att.v_layer.weight', 'src_embed.lut.weight']\n",
      "2019-11-18 08:09:28,442 Loading model from /content/drive/My Drive/masakhane/en-zu-baseline/pretrained/enzu4000baseline/125000.ckpt\n",
      "2019-11-18 08:09:28,856 cfg.name                           : enzu4000baseline_transformer\n",
      "2019-11-18 08:09:28,856 cfg.data.src                       : en\n",
      "2019-11-18 08:09:28,856 cfg.data.trg                       : zu\n",
      "2019-11-18 08:09:28,856 cfg.data.train                     : /content/drive/My Drive/masakhane/en-zu-baseline/train.bpe\n",
      "2019-11-18 08:09:28,856 cfg.data.dev                       : /content/drive/My Drive/masakhane/en-zu-baseline/dev.bpe\n",
      "2019-11-18 08:09:28,856 cfg.data.test                      : /content/drive/My Drive/masakhane/en-zu-baseline/test.bpe\n",
      "2019-11-18 08:09:28,856 cfg.data.level                     : bpe\n",
      "2019-11-18 08:09:28,856 cfg.data.lowercase                 : False\n",
      "2019-11-18 08:09:28,856 cfg.data.max_sent_length           : 100\n",
      "2019-11-18 08:09:28,856 cfg.data.src_vocab                 : /content/drive/My Drive/masakhane/en-zu-baseline/vocab.txt\n",
      "2019-11-18 08:09:28,857 cfg.data.trg_vocab                 : /content/drive/My Drive/masakhane/en-zu-baseline/vocab.txt\n",
      "2019-11-18 08:09:28,857 cfg.testing.beam_size              : 5\n",
      "2019-11-18 08:09:28,857 cfg.testing.alpha                  : 1.0\n",
      "2019-11-18 08:09:28,857 cfg.training.load_model            : /content/drive/My Drive/masakhane/en-zu-baseline/pretrained/enzu4000baseline/125000.ckpt\n",
      "2019-11-18 08:09:28,857 cfg.training.random_seed           : 42\n",
      "2019-11-18 08:09:28,857 cfg.training.optimizer             : adam\n",
      "2019-11-18 08:09:28,857 cfg.training.normalization         : tokens\n",
      "2019-11-18 08:09:28,857 cfg.training.adam_betas            : [0.9, 0.999]\n",
      "2019-11-18 08:09:28,857 cfg.training.decrease_factor       : 0.7\n",
      "2019-11-18 08:09:28,858 cfg.training.loss                  : crossentropy\n",
      "2019-11-18 08:09:28,858 cfg.training.learning_rate         : 0.0003\n",
      "2019-11-18 08:09:28,858 cfg.training.learning_rate_min     : 1e-06\n",
      "2019-11-18 08:09:28,858 cfg.training.weight_decay          : 0.0\n",
      "2019-11-18 08:09:28,858 cfg.training.label_smoothing       : 0.1\n",
      "2019-11-18 08:09:28,858 cfg.training.batch_size            : 2048\n",
      "2019-11-18 08:09:28,858 cfg.training.batch_type            : token\n",
      "2019-11-18 08:09:28,858 cfg.training.eval_batch_size       : 1024\n",
      "2019-11-18 08:09:28,858 cfg.training.eval_batch_type       : token\n",
      "2019-11-18 08:09:28,858 cfg.training.batch_multiplier      : 1\n",
      "2019-11-18 08:09:28,858 cfg.training.early_stopping_metric : ppl\n",
      "2019-11-18 08:09:28,859 cfg.training.epochs                : 30\n",
      "2019-11-18 08:09:28,859 cfg.training.validation_freq       : 2500\n",
      "2019-11-18 08:09:28,859 cfg.training.logging_freq          : 100\n",
      "2019-11-18 08:09:28,859 cfg.training.eval_metric           : bleu\n",
      "2019-11-18 08:09:28,859 cfg.training.model_dir             : models/enzu4000baseline_transformer\n",
      "2019-11-18 08:09:28,859 cfg.training.overwrite             : True\n",
      "2019-11-18 08:09:28,859 cfg.training.shuffle               : True\n",
      "2019-11-18 08:09:28,859 cfg.training.use_cuda              : True\n",
      "2019-11-18 08:09:28,859 cfg.training.max_output_length     : 100\n",
      "2019-11-18 08:09:28,859 cfg.training.print_valid_sents     : [0, 1, 2, 3]\n",
      "2019-11-18 08:09:28,860 cfg.training.keep_last_ckpts       : 3\n",
      "2019-11-18 08:09:28,860 cfg.model.initializer              : xavier\n",
      "2019-11-18 08:09:28,860 cfg.model.bias_initializer         : uniform\n",
      "2019-11-18 08:09:28,860 cfg.model.init_gain                : 1.0\n",
      "2019-11-18 08:09:28,860 cfg.model.embed_initializer        : xavier\n",
      "2019-11-18 08:09:28,860 cfg.model.embed_init_gain          : 1.0\n",
      "2019-11-18 08:09:28,860 cfg.model.tied_embeddings          : True\n",
      "2019-11-18 08:09:28,860 cfg.model.tied_softmax             : True\n",
      "2019-11-18 08:09:28,860 cfg.model.encoder.type             : transformer\n",
      "2019-11-18 08:09:28,861 cfg.model.encoder.num_layers       : 6\n",
      "2019-11-18 08:09:28,861 cfg.model.encoder.num_heads        : 4\n",
      "2019-11-18 08:09:28,861 cfg.model.encoder.embeddings.embedding_dim : 256\n",
      "2019-11-18 08:09:28,861 cfg.model.encoder.embeddings.scale : True\n",
      "2019-11-18 08:09:28,861 cfg.model.encoder.embeddings.dropout : 0.5\n",
      "2019-11-18 08:09:28,861 cfg.model.encoder.hidden_size      : 256\n",
      "2019-11-18 08:09:28,861 cfg.model.encoder.ff_size          : 1024\n",
      "2019-11-18 08:09:28,861 cfg.model.encoder.dropout          : 0.5\n",
      "2019-11-18 08:09:28,862 cfg.model.decoder.type             : transformer\n",
      "2019-11-18 08:09:28,862 cfg.model.decoder.num_layers       : 6\n",
      "2019-11-18 08:09:28,862 cfg.model.decoder.num_heads        : 8\n",
      "2019-11-18 08:09:28,862 cfg.model.decoder.embeddings.embedding_dim : 256\n",
      "2019-11-18 08:09:28,862 cfg.model.decoder.embeddings.scale : True\n",
      "2019-11-18 08:09:28,862 cfg.model.decoder.embeddings.dropout : 0.2\n",
      "2019-11-18 08:09:28,862 cfg.model.decoder.hidden_size      : 256\n",
      "2019-11-18 08:09:28,862 cfg.model.decoder.ff_size          : 1024\n",
      "2019-11-18 08:09:28,862 cfg.model.decoder.dropout          : 0.2\n",
      "2019-11-18 08:09:28,862 Data set sizes: \n",
      "\ttrain 960914,\n",
      "\tvalid 1000,\n",
      "\ttest 2711\n",
      "2019-11-18 08:09:28,863 First training example:\n",
      "\t[SRC] in any case , in@@ fan@@ ts la@@ id down e@@ ither on their back or on their st@@ om@@ ach t@@ end to remain in that position\n",
      "\t[TRG] noma kunjalo , lapho izin@@ s@@ ana zil@@ alis@@ we ngom@@ hl@@ ane noma nges@@ is@@ u az@@ iv@@ amile ukuph@@ end@@ uka\n",
      "2019-11-18 08:09:28,863 First 10 words (src): (0) <unk> (1) <pad> (2) <s> (3) </s> (4) , (5) the (6) to (7) of (8) a (9) and\n",
      "2019-11-18 08:09:28,863 First 10 words (trg): (0) <unk> (1) <pad> (2) <s> (3) </s> (4) , (5) the (6) to (7) of (8) a (9) and\n",
      "2019-11-18 08:09:28,863 Number of Src words (types): 4355\n",
      "2019-11-18 08:09:28,863 Number of Trg words (types): 4355\n",
      "2019-11-18 08:09:28,863 Model(\n",
      "\tencoder=TransformerEncoder(num_layers=6, num_heads=4),\n",
      "\tdecoder=TransformerDecoder(num_layers=6, num_heads=8),\n",
      "\tsrc_embed=Embeddings(embedding_dim=256, vocab_size=4355),\n",
      "\ttrg_embed=Embeddings(embedding_dim=256, vocab_size=4355))\n",
      "2019-11-18 08:09:28,867 EPOCH 1\n",
      "2019-11-18 08:09:42,572 Epoch   1 Step:   125100 Batch Loss:     1.880358 Tokens per Sec:     9710, Lr: 0.000147\n",
      "2019-11-18 08:09:55,273 Epoch   1 Step:   125200 Batch Loss:     1.454566 Tokens per Sec:    10484, Lr: 0.000147\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Train the model\n",
    "# You can press Ctrl-C to stop. And then run the next cell to save your checkpoints! \n",
    "! cd joeynmt; python3 -m joeynmt train configs/transformer_$src$tgt$vocab_size$tag.yaml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 8406,
     "status": "ok",
     "timestamp": 1573652834023,
     "user": {
      "displayName": "Ari Ramkilowan",
      "photoUrl": "",
      "userId": "10877800210228006715"
     },
     "user_tz": -120
    },
    "id": "UPMK6siVv2pt",
    "outputId": "e0b92905-7ab8-4d73-f256-2f5186d51dc4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cp: cannot create symbolic link '/content/drive/My Drive/masakhane/en-zu-const_lr/pretrained/enzu4000const_lr2/best.ckpt': Function not implemented\n"
     ]
    }
   ],
   "source": [
    "# Copy the created models from the notebook storage to google drive for persistant storage \n",
    "# !mkdir \"$gdrive_path\"\"pretrained/$src$trg$vocab_size$tag\"\"2/\"\n",
    "!cp  -r joeynmt/models/${src}${trg}${vocab_size}${tag}_transformer/* \"$gdrive_path\"\"pretrained/$src$trg$vocab_size$tag/\"\n",
    "!cp  joeynmt/models/${src}${trg}${vocab_size}${tag}_transformer/best.ckpt \"$gdrive_path\"\"pretrained/$src$trg$vocab_size$tag/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "WZ5M6bGFRypy"
   },
   "outputs": [],
   "source": [
    "# copy across the config file\n",
    "!cp  joeynmt/configs/transformer_${src}${trg}${vocab_size}${tag}.yaml \"$gdrive_path\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3Ms5xKfbv2tc"
   },
   "source": [
    "## Test our model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 72
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 47109,
     "status": "ok",
     "timestamp": 1573653059703,
     "user": {
      "displayName": "Ari Ramkilowan",
      "photoUrl": "",
      "userId": "10877800210228006715"
     },
     "user_tz": -120
    },
    "id": "iB9UiELxnuD8",
    "outputId": "989548d7-0360-401c-c580-ae66b920b62b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019-11-13 13:50:15,142 Hello! This is Joey-NMT.\n",
      "2019-11-13 13:50:45,623  dev bleu:  11.87 [Beam search decoding with beam size = 5 and alpha = 1.0]\n",
      "2019-11-13 13:50:57,050 test bleu:   5.00 [Beam search decoding with beam size = 5 and alpha = 1.0]\n"
     ]
    }
   ],
   "source": [
    "# ! cd joeynmt; python3 -m joeynmt test \"$gdrive_path\"\"pretrained/$src$trg$vocab_size$tag/config.yaml\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 72
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 6428237,
     "status": "ok",
     "timestamp": 1573631067307,
     "user": {
      "displayName": "Ari Ramkilowan",
      "photoUrl": "",
      "userId": "10877800210228006715"
     },
     "user_tz": -120
    },
    "id": "8i7TIQb-jvi9",
    "outputId": "32fe975b-03b7-417c-d3d0-ecac7efaf7be"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019-11-13 07:43:32,728 Hello! This is Joey-NMT.\n",
      "2019-11-13 07:44:03,502  dev bleu:  13.64 [Beam search decoding with beam size = 5 and alpha = 1.0]\n",
      "2019-11-13 07:44:24,289 test bleu:   4.87 [Beam search decoding with beam size = 5 and alpha = 1.0]\n"
     ]
    }
   ],
   "source": [
    "# ! cd joeynmt; python3 -m joeynmt test \"$gdrive_path\"\"transformer_${src}${trg}${vocab_size}${tag}.yaml\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 326
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 125171,
     "status": "ok",
     "timestamp": 1573481353557,
     "user": {
      "displayName": "Ari Ramkilowan",
      "photoUrl": "",
      "userId": "10877800210228006715"
     },
     "user_tz": -120
    },
    "id": "i0VGYuzwSG4j",
    "outputId": "5328c3ec-7f6b-42e3-ef41-ccae7921648e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019-11-11 14:07:11,216 Hello! This is Joey-NMT.\n",
      "\n",
      "Please enter a source sentence (pre-processed): \n",
      "Hello and welcome, my name is Ari\n",
      "JoeyNMT: futhi igama lami liwusizo\n",
      "\n",
      "Please enter a source sentence (pre-processed): \n",
      "Thank you for contacting customer service\n",
      "JoeyNMT: uqondisa inkonzo\n",
      "\n",
      "Please enter a source sentence (pre-processed): \n",
      "How can I be of assistance ?\n",
      "JoeyNMT: ingabe kungenzeka yini ukuba khona ?\n",
      "\n",
      "Please enter a source sentence (pre-processed): \n",
      "\n",
      "Bye.\n"
     ]
    }
   ],
   "source": [
    "# Translate mode is mopre interactive but almsot the same as running in test mode\n",
    "! cd joeynmt; python3 -m joeynmt translate \"$gdrive_path\"\"transformer_${src}${trg}${vocab_size}${tag}.yaml\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 6430388,
     "status": "ok",
     "timestamp": 1573631073955,
     "user": {
      "displayName": "Ari Ramkilowan",
      "photoUrl": "",
      "userId": "10877800210228006715"
     },
     "user_tz": -120
    },
    "id": "Pc2xgodBv2ys",
    "outputId": "86b32d53-277c-4d0c-b565-8a57c7c3c653"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Steps: 233000\tLoss: 59206.16406\tPPL: 9.66595\tbleu: 12.25790\tLR: 0.00030000\t\n"
     ]
    }
   ],
   "source": [
    "# Output our validation accuracy\n",
    "! cat \"$gdrive_path/pretrained/${src}${trg}${vocab_size}${tag}/validations.txt\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 145
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 3379,
     "status": "ok",
     "timestamp": 1573631363009,
     "user": {
      "displayName": "Ari Ramkilowan",
      "photoUrl": "",
      "userId": "10877800210228006715"
     },
     "user_tz": -120
    },
    "id": "FyCycJkwvHHo",
    "outputId": "1548c29b-99be-49a4-a70c-228fc3be0c8f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bpe.codes.4000\t     test.bpe.en     train.en\n",
      "dev.bpe.en\t     test.bpe.zu     train.zu\n",
      "dev.bpe.zu\t     test.en\t     transformer_enzu4000const_lr.yaml\n",
      "dev.en\t\t     test.en-any.en  vocab.en\n",
      "dev.zu\t\t     test.zu\t     vocab.txt\n",
      "en-zu-JW300-new.csv  train.bpe.en    vocab.zu\n",
      "pretrained\t     train.bpe.zu\n"
     ]
    }
   ],
   "source": [
    "! ls \"$gdrive_path\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "97oMTrIPuJMp"
   },
   "source": [
    "## Check vocab coverage in test set\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "AjZ57knl2QHY"
   },
   "source": [
    "### Vocab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "GxwRPgOuwsJf"
   },
   "outputs": [],
   "source": [
    "# get BPE vocab\n",
    "filename =  f\"{gdrive_path}/vocab.txt\"\n",
    "with open(filename) as f:\n",
    "    vocab_tokens = [tok.strip() for tok in f]\n",
    "\n",
    "vocab_tokens_set = set(vocab_tokens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 653,
     "status": "ok",
     "timestamp": 1573635495524,
     "user": {
      "displayName": "Ari Ramkilowan",
      "photoUrl": "",
      "userId": "10877800210228006715"
     },
     "user_tz": -120
    },
    "id": "sTqrs_zlxW6N",
    "outputId": "872ee5e1-7476-40ec-f83e-80a1be83c369"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'There are 4351 tokens in the combined vocab, 4351 are unique'"
      ]
     },
     "execution_count": 104,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f'There are {len(vocab_tokens)} tokens in the combined vocab, {len(vocab_tokens_set)} are unique'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "aDX4QfRG2UIl"
   },
   "source": [
    "### Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Aq3U2WKrwyxf"
   },
   "outputs": [],
   "source": [
    "# get training BPE vocab\n",
    "en_filename =  f\"{gdrive_path}/train.bpe.en\"\n",
    "with open(en_filename) as f:\n",
    "    en_train_tokens = [tok for line in f for tok in line.split()]\n",
    "\n",
    "zu_filename =  f\"{gdrive_path}/train.bpe.zu\"\n",
    "with open(zu_filename) as f:\n",
    "    zu_train_tokens = [tok for line in f for tok in line.split()]\n",
    "\n",
    "en_train_tokens_set = set(en_train_tokens)\n",
    "zu_train_tokens_set = set(zu_train_tokens)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 72
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1377,
     "status": "ok",
     "timestamp": 1573633826102,
     "user": {
      "displayName": "Ari Ramkilowan",
      "photoUrl": "",
      "userId": "10877800210228006715"
     },
     "user_tz": -120
    },
    "id": "a671NcImvX6b",
    "outputId": "aa84b801-9b27-4ca5-a2fd-a527fc0ce57b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 24094443 english tokens in the training set vocab, 3112 are unique \n",
      "There are 24223704 zulu tokens in the training set vocab, 4188 are unique\n",
      "Thats a uniqueness ratio of 1.29E-04 for english and 1.73E-04 for zulu\n"
     ]
    }
   ],
   "source": [
    "print(f'There are {len(en_train_tokens)} english tokens in the training set vocab, {len(en_train_tokens_set)} are unique ')\n",
    "print(f'There are {len(zu_train_tokens)} zulu tokens in the training set vocab, {len(zu_train_tokens_set)} are unique')\n",
    "print(f'Thats a uniqueness ratio of {len(en_train_tokens_set)/len(en_train_tokens) :.2E} for english and {len(zu_train_tokens_set)/len(zu_train_tokens) :.2E} for zulu')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "I_zcE-Jh2YBL"
   },
   "source": [
    "### Dev"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "aCLNLgaLwDpo"
   },
   "outputs": [],
   "source": [
    "# get dev BPE vocab\n",
    "en_filename =  f\"{gdrive_path}/dev.bpe.en\"\n",
    "with open(en_filename) as f:\n",
    "    en_dev_tokens = [tok for line in f for tok in line.split()]\n",
    "\n",
    "zu_filename =  f\"{gdrive_path}/dev.bpe.zu\"\n",
    "with open(zu_filename) as f:\n",
    "    zu_dev_tokens = [tok for line in f for tok in line.split()]\n",
    "\n",
    "en_dev_tokens_set = set(en_dev_tokens)\n",
    "zu_dev_tokens_set = set(zu_dev_tokens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 72
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1091,
     "status": "ok",
     "timestamp": 1573633710140,
     "user": {
      "displayName": "Ari Ramkilowan",
      "photoUrl": "",
      "userId": "10877800210228006715"
     },
     "user_tz": -120
    },
    "id": "u0qL8IvJyxsk",
    "outputId": "361c42c8-5f32-4ae0-a4d9-f00c36f2df43"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 24651 english tokens in the dev set vocab, 2104 are unique \n",
      "There are 25098 zulu tokens in the dev set vocab, 2418 are unique\n",
      "Thats a uniqueness ratio of 0.09 for english and 0.10 for zulu\n"
     ]
    }
   ],
   "source": [
    "print(f'There are {len(en_dev_tokens)} english tokens in the dev set vocab, {len(en_dev_tokens_set)} are unique ')\n",
    "print(f'There are {len(zu_dev_tokens)} zulu tokens in the dev set vocab, {len(zu_dev_tokens_set)} are unique')\n",
    "print(f'Thats a uniqueness ratio of {len(en_dev_tokens_set)/len(en_dev_tokens) :.2f} for english and {len(zu_dev_tokens_set)/len(zu_dev_tokens) :.2f} for zulu')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "bxnIRo862bRK"
   },
   "source": [
    "### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Cp4f5Yfby3sD"
   },
   "outputs": [],
   "source": [
    "# get test BPE vocab\n",
    "en_filename =  f\"{gdrive_path}/test.bpe.en\"\n",
    "with open(en_filename) as f:\n",
    "    en_test_tokens = [tok for line in f for tok in line.split()]\n",
    "\n",
    "\n",
    "zu_filename =  f\"{gdrive_path}/test.bpe.zu\"\n",
    "with open(zu_filename) as f:\n",
    "    zu_test_tokens = [tok for line in f for tok in line.split()]\n",
    "\n",
    "\n",
    "en_test_tokens_set = set(en_test_tokens)\n",
    "zu_test_tokens_set = set(zu_test_tokens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 72
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1108,
     "status": "ok",
     "timestamp": 1573633992400,
     "user": {
      "displayName": "Ari Ramkilowan",
      "photoUrl": "",
      "userId": "10877800210228006715"
     },
     "user_tz": -120
    },
    "id": "VEtywXli18Iy",
    "outputId": "95c99a91-448f-40ef-a272-b12e080f0be2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 66255 english tokens in the test set vocab, 2072 are unique \n",
      "There are 67851 zulu tokens in the test set vocab, 2336 are unique\n",
      "Thats a uniqueness ratio of 3.13E-02 for english and 3.44E-02 for zulu\n"
     ]
    }
   ],
   "source": [
    "print(f'There are {len(en_test_tokens)} english tokens in the test set vocab, {len(en_test_tokens_set)} are unique ')\n",
    "print(f'There are {len(zu_test_tokens)} zulu tokens in the test set vocab, {len(zu_test_tokens_set)} are unique')\n",
    "print(f'Thats a uniqueness ratio of {len(en_test_tokens_set)/len(en_test_tokens) :.2E} for english and {len(zu_test_tokens_set)/len(zu_test_tokens) :.2E} for zulu')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "mfArSFfh47q7"
   },
   "source": [
    "### Vocab Differences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "rbQc9ekZ5en6"
   },
   "outputs": [],
   "source": [
    "test_en_not_in_vocab = vocab_tokens_set.difference(en_test_tokens_set)\n",
    "test_en_IS_in_vocab = vocab_tokens_set.intersection(en_test_tokens_set)\n",
    "\n",
    "test_zu_not_in_vocab = vocab_tokens_set.difference(zu_test_tokens_set)\n",
    "test_zu_IS_in_vocab = vocab_tokens_set.intersection(zu_test_tokens_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1304,
     "status": "ok",
     "timestamp": 1573635836880,
     "user": {
      "displayName": "Ari Ramkilowan",
      "photoUrl": "",
      "userId": "10877800210228006715"
     },
     "user_tz": -120
    },
    "id": "gpnMX2Zh6d0p",
    "outputId": "b233229f-8702-4ddd-b81d-75b7885c5971"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 2003 tokens from vocab in the en test set  -> 46.035% Coverage\n",
      "There are 2269 tokens from vocab in the en test set  -> 52.149% Coverage\n"
     ]
    }
   ],
   "source": [
    "print(f'There are {len(test_en_IS_in_vocab)} tokens from vocab in the en test set  -> {100 *len(test_en_IS_in_vocab)/len(vocab_tokens_set) :.3f}% Coverage')\n",
    "print(f'There are {len(test_zu_IS_in_vocab)} tokens from vocab in the en test set  -> {100 *len(test_zu_IS_in_vocab)/len(vocab_tokens_set) :.3f}% Coverage')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1653,
     "status": "ok",
     "timestamp": 1573635837243,
     "user": {
      "displayName": "Ari Ramkilowan",
      "photoUrl": "",
      "userId": "10877800210228006715"
     },
     "user_tz": -120
    },
    "id": "ctzCXdkwABlT",
    "outputId": "35ee1b5b-2248-4234-d022-99db920d2459"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 2348 tokens from vocab in the en test set  -> 53.965% Missing\n",
      "There are 2082 tokens from vocab in the en test set  -> 47.851% Missing\n"
     ]
    }
   ],
   "source": [
    "print(f'There are {len(test_en_not_in_vocab)} tokens from vocab in the en test set  -> {100 *len(test_en_not_in_vocab)/len(vocab_tokens_set) :.3f}% Missing')\n",
    "print(f'There are {len(test_zu_not_in_vocab)} tokens from vocab in the en test set  -> {100 *len(test_zu_not_in_vocab)/len(vocab_tokens_set) :.3f}% Missing')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "oSvbkDa4CEkj"
   },
   "outputs": [],
   "source": [
    "# Overall coverage: Remember that our vocab is combined eng/zulu so we're expecting ~50% each\n",
    "global_coverage =list(test_en_IS_in_vocab.union(test_zu_IS_in_vocab))\n",
    "global_diff = list(test_en_not_in_vocab.difference(test_zu_IS_in_vocab))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 947,
     "status": "ok",
     "timestamp": 1573639300558,
     "user": {
      "displayName": "Ari Ramkilowan",
      "photoUrl": "",
      "userId": "10877800210228006715"
     },
     "user_tz": -120
    },
    "id": "DanG8GelMxvt",
    "outputId": "a32a9ee2-7b37-4f4d-ea72-e3c044f02bed"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "84.78510687198346"
      ]
     },
     "execution_count": 181,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "100*len(global_coverage)/len(vocab_tokens_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1144,
     "status": "ok",
     "timestamp": 1573639311218,
     "user": {
      "displayName": "Ari Ramkilowan",
      "photoUrl": "",
      "userId": "10877800210228006715"
     },
     "user_tz": -120
    },
    "id": "JgQuTrYnNi1S",
    "outputId": "8a9453a8-93fb-46b5-b55f-1f6419e4e4fc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15.214893128016548"
      ]
     },
     "execution_count": 182,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "100*len(global_diff)/len(vocab_tokens_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "TbznnpA26ZaP"
   },
   "source": [
    "### What are some of the missing tokens ?\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1572,
     "status": "ok",
     "timestamp": 1573639329783,
     "user": {
      "displayName": "Ari Ramkilowan",
      "photoUrl": "",
      "userId": "10877800210228006715"
     },
     "user_tz": -120
    },
    "id": "ZxbzLw68MQhe",
    "outputId": "9aafeddc-721e-4ca0-c1da-f8a8924c3ace"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ᾶ@@', 'ẹ', 'ã@@', 'isambulo', 'elapha', 'juda', 'ì', 'temple', 'ū@@', 'ḥ@@']"
      ]
     },
     "execution_count": 183,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "global_diff[0:10]"
   ]
  }
 ],
 "metadata": {
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    "jPmely-suPXt",
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    "mPElaQv_oy5q",
    "1bM3_Zn34LGu"
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   "name": "ari_en_zu_JW300.ipynb",
   "provenance": [],
   "toc_visible": true
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