Feature_masking.ipynb

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   "metadata": {
    "id": "FRXm2FfxzC9A"
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   "source": [
    "# Imports and setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "executionInfo": {
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     "status": "ok",
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import random\n",
    "import re\n",
    "\n",
    "# Data processing.\n",
    "import constants # constants.py\n",
    "import dataset # dataset.py\n",
    "import torch\n",
    "\n",
    "# Model.\n",
    "import models # models.py\n",
    "import torch.nn as nn\n",
    "from transformers import DistilBertModel\n",
    "\n",
    "# Training.\n",
    "import training # training.py\n",
    "import utils # utils.py\n",
    "\n",
    "# If you make a code change that doesn't get picked up by\n",
    "# Jupyter notebook, try reloading like below:\n",
    "# import imp\n",
    "# imp.reload(training)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PL4p_28SzGCv"
   },
   "source": [
    "# Read the data\n",
    "Skip this section if you've already ran the notebook once and have the csvs locally."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "executionInfo": {
     "elapsed": 2809,
     "status": "ok",
     "timestamp": 1621625592336,
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     "user_tz": 420
    },
    "id": "HwNKmWDey-q9"
   },
   "outputs": [],
   "source": [
    "data_df = dataset.read_multiple_datasets([1,2,3], 'Creativity_Combined', shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "executionInfo": {
     "elapsed": 1002,
     "status": "ok",
     "timestamp": 1621625593325,
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     "user_tz": 420
    },
    "id": "wvWVLrupUEnt"
   },
   "outputs": [],
   "source": [
    "test_df = data_df[1000:] # roughly 203 test examples set aside"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                 text  label\n",
      "82  The final idea would be running shoes that wou...  4.225\n"
     ]
    }
   ],
   "source": [
    "print(test_df.head(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                 text  label\n",
      "82  calories or few have a you you to spent alert ...  4.225\n"
     ]
    }
   ],
   "source": [
    "def scramble(text):\n",
    "    words = text.split()\n",
    "    n = len(words)\n",
    "    scrambled = random.sample(words, n)\n",
    "    return(\" \".join(scrambled))\n",
    "\n",
    "scrambled_test_df = test_df.copy()\n",
    "scrambled_test_df['text'] = test_df['text'].apply(scramble)\n",
    "\n",
    "print(scrambled_test_df.head(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# write them to CSV files\n",
    "scrambled_test_df.to_csv('scrambled_ktest.csv', index=False, header=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                 text  label\n",
      "82  and antic andy antic an ancient antic andy ant...  4.225\n"
     ]
    }
   ],
   "source": [
    "def word_rep(word):\n",
    "    if len(word) == 1:\n",
    "        word = \"a\"\n",
    "    if len(word) == 2:\n",
    "        word = \"an\"\n",
    "    if len(word) == 3:\n",
    "        word = \"and\"\n",
    "    if len(word) == 4:\n",
    "        word = \"andy\"\n",
    "    if len(word) == 5:\n",
    "        word = \"antic\"\n",
    "    if len(word) == 6:\n",
    "        word = \"accent\"\n",
    "    if len(word) == 7:\n",
    "        word = \"ancient\"\n",
    "    if len(word) == 8:\n",
    "        word = \"accident\"\n",
    "    if len(word) == 9:\n",
    "        word = \"accidents\"\n",
    "    if len(word) == 10:\n",
    "        word = \"accidental\"\n",
    "    if len(word) >= 11:\n",
    "        word = \"accidentally\"\n",
    "    return(word)\n",
    "\n",
    "def homogenize(text):\n",
    "    words = text.split()\n",
    "    homogenized = [word_rep(word) for word in words]\n",
    "    return(\" \".join(homogenized))\n",
    "\n",
    "hom_test_df = test_df.copy()\n",
    "hom_test_df['text'] = test_df['text'].apply(homogenize)\n",
    "print(hom_test_df.head(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# write them to CSV files\n",
    "hom_test_df.to_csv('hom_ktest.csv', index=False, header=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eXVH4t-UC_wD"
   },
   "source": [
    "## Preprocessing and transform into torchtext Dataset format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "executionInfo": {
     "elapsed": 2679,
     "status": "ok",
     "timestamp": 1621625596002,
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   "outputs": [],
   "source": [
    "# Replace with scrambled_ktest.csv or hom_ktest.csv\n",
    "_, test_dataset = dataset.get_train_test_datasets('ktrain.csv','hom_ktest.csv', add=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NIXP6rB_FY8K"
   },
   "source": [
    "# Test the trained model on held-out dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "id": "-1w-zVceFsx3"
   },
   "outputs": [],
   "source": [
    "# Get a test iterator\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "test_iterator = training.get_iterator(test_dataset, 8, device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 18,
     "status": "ok",
     "timestamp": 1621580211381,
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   "outputs": [],
   "source": [
    "# load the best model saved\n",
    "bert = DistilBertModel.from_pretrained(constants.WEIGHTS_NAME)\n",
    "model = models.BERTLinear(bert, constants.OUTPUT_DIM, 0.2)\n",
    "model.load_state_dict(torch.load(\"linear_best_valid_loss.pt\"))\n",
    "model.to(device)\n",
    "model.eval()\n",
    "# If you change the criterion, make sure it matches with the training criterion in training.py\n",
    "criterion = nn.MSELoss(size_average=False)\n",
    "criterion = criterion.to(device)\n",
    "test_loss, test_corr = training.evaluate(model, test_iterator, criterion)\n",
    "print(test_loss)\n",
    "print(test_corr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "l5g0MO_uYqn7"
   },
   "source": [
    "# Misc other stuff"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IebHhmzl_anV"
   },
   "source": [
    "Link to the trainer class: https://huggingface.co/transformers/main_classes/trainer.html\n",
    "\n",
    "\n",
    "\n",
    "Default training arguments: https://huggingface.co/transformers/main_classes/trainer.html#transformers.TrainingArguments\n",
    "\n",
    "Batch size per device: 8\n",
    "\n",
    "Epoch: 3\n",
    "\n",
    "\n",
    "\n",
    "This should be the model I used to generate my initial results: https://huggingface.co/transformers/model_doc/distilbert.html#distilbertforsequenceclassification\n",
    "\"DistilBert Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks.\""
   ]
  }
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