air-sent_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# __ULMFiT Sentiment Analysis of US Airlines__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Chapter 1. ULMFiT Intro\n",
    "---\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "[ULMFiT Resource - Howard, Ruder 2018](https://arxiv.org/pdf/1801.06146.pdf)\n",
    "\n",
    "## I. ___The Whats...___\n",
    "---\n",
    "#### _i. What is ULMFiT?_\n",
    "- Universal Language Model Fine-tuning for Text Classification\n",
    "- Uses transfer learning method\n",
    "---\n",
    "#### _ii. What are ULMFiT's benefits?_\n",
    "\n",
    "  - Addresses NLP problems with small datasets and computational resources\n",
    "  - Makes NLP problems easier to solve\n",
    "  - Outperforms state of the art methods on 6 separate text classification tasks\n",
    "  - Reduces error 18-24% on majority of datasets\n",
    "  - matches performance of training from scratch with only 100 labeled samples!!!\n",
    "---\n",
    "#### _iii. What is Transfer Learning?_\n",
    "- A ML problem that stores knowledge gained from solving one problem that directly applies or transfers that knowledge to a related problem\n",
    "- Common approach is by fine tuning original model\n",
    "- Simple models use a single layer of weights (embeddings)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## II. ___The Whys___\n",
    "---\n",
    "\n",
    "#### a. ULMFiT - Why is it significant? & Findings\n",
    "\n",
    "- Controlling how fast the model learns allows for better adaptability\n",
    "- Learns well from a limited number of examples\n",
    "- Performs better than traditional NLP methods\n",
    "- Requires magnitude of 100X less data than current state of the art methods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## III. ___The Hows___\n",
    "---------\n",
    "#### i. Pretrain Language Model on general corpus\n",
    "- Preprocess data, normalize, drop nas, use Average SWD Long Short Term Memory Model\n",
    "\n",
    "#### ii. Fine-tune new dataset\n",
    "- Apply\n",
    "    - Discriminative fine-tuning\n",
    "        - Tunes each layer in neural network with different learning rates\n",
    "        - Stochastic gradient descent accounts for another variable - theta with respect to the layer\n",
    "    - Slanted triangular learning rate\n",
    "        - For adapting params, we want the model to converge quickly\n",
    "        - First linearly increases learning rate\n",
    "        - Then linearly decays\n",
    "\n",
    "#### iii. Text classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# -----------------------------------\n",
    "# import dependencies\n",
    "# -----------------------------------\n",
    "import io\n",
    "import os\n",
    "import re\n",
    "import sys\n",
    "import fastai\n",
    "from fastai import *\n",
    "from fastai.text import *\n",
    "from functools import partial\n",
    "from nltk.corpus import stopwords\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pynvml import *\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "import seaborn as sns\n",
    "import nltk\n",
    "import ssl\n",
    "try:\n",
    "    _create_unverified_https_context = ssl._create_unverified_context\n",
    "except AttributeError:\n",
    "    pass\n",
    "else:\n",
    "    ssl._create_default_https_context = _create_unverified_https_context"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "93.9 µs ± 23.9 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "# setup gpu for faster train times\n",
    "t_gpu = torch.rand(500, 500, 500).cuda()\n",
    "%timeit t_gpu @ t_gpu"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chapter I. Preprocess Data\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# -----------------------------------\n",
    "# a. Load Data\n",
    "# -----------------------------------\n",
    "df = pd.read_csv('resources/Tweets.csv', encoding='ISO-8859-1')\n",
    "# -----------------------------------\n",
    "# b. Visualize Sentiment\n",
    "# -----------------------------------\n",
    "sns.set(style=\"darkgrid\")\n",
    "sns.set(rc={'figure.figsize':(15,7)})\n",
    "sns.countplot(x=\"airline\", hue='airline_sentiment', data=df)\n",
    "plt.title(\"Airline Sentiment For Each Airline\");\n",
    "df = pd.DataFrame({'text': df.text, 'label': df.airline_sentiment})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# -----------------------------------\n",
    "# c. Encode Categories as Int Type\n",
    "# -----------------------------------\n",
    "label_dict = {'negative': 0, 'neutral': 2, 'positive': 4}\n",
    "df.label = df.label \\\n",
    "  .apply(lambda x: label_dict[x])\n",
    "# -----------------------------------\n",
    "# d. Preprocess - Clean\n",
    "#    ==================\n",
    "#    - Replace airline tag to reduce\n",
    "#      error in model\n",
    "#    - Check NA counts\n",
    "# -----------------------------------\n",
    "regex = r\"@(VirginAmerica|united|SouthwestAir|Delta|USAirways|AmericanAir)\"\n",
    "df['text'] = df['text'].apply( \\\n",
    "  lambda x: re.sub(regex, '@airline', x, flags=re.IGNORECASE))\n",
    "df.text = df.text.str.replace(\"[^a-zA-Z]\", \" \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Airline Sentiment Value Counts\n",
      "0    9178\n",
      "2    3099\n",
      "4    2363\n",
      "Name: label, dtype: int64\n",
      "\n",
      "Text NaN Counts\n",
      "text     0\n",
      "label    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# -----------------------------------\n",
    "# e. Check Distribution of Labels to\n",
    "#    Reduce Bias\n",
    "# -----------------------------------\n",
    "print('Airline Sentiment Value Counts\\n' + \n",
    "      f'{df.label.value_counts()}')\n",
    "print('\\nText NaN Counts\\n' + \n",
    "      f'{df.isna().sum()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    3099\n",
       "0    3000\n",
       "4    2363\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# -----------------------------------\n",
    "# f. Create a More Even Distribution\n",
    "#    of Label Types for Model\n",
    "#    ===============================\n",
    "# \n",
    "#      - 3X as more negative labels \n",
    "#        compared to neutral and\n",
    "#        positive\n",
    "# \n",
    "#      - Randomly sample 3,000 negative\n",
    "#        labels to train model with more\n",
    "#        fair representation of data!!!\n",
    "# \n",
    "# -----------------------------------\n",
    "df = pd.concat([ df[ df.label.isin( [2, 4] ) ], \\\n",
    "                 df[ df.label==0].sample(3000) ])\n",
    "df = df.reset_index(drop=True)\n",
    "df.label.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# -----------------------------------\n",
    "# g. Tokenize\n",
    "# -----------------------------------\n",
    "tokenized = df.text.apply(lambda x: x.split())\n",
    "# -----------------------------------\n",
    "# h. Remove Stopwords\n",
    "# -----------------------------------\n",
    "stop_words = stopwords.words('english')\n",
    "tokenized = tokenized.apply(lambda x: \\\n",
    "  [item for item in x if item.lower() not in stop_words] )\n",
    "# -----------------------------------\n",
    "# i. De-tokenize\n",
    "# -----------------------------------\n",
    "detokenized = []\n",
    "for i in range(len(df)):\n",
    "  toke = ' '.join(tokenized[i])\n",
    "  detokenized.append(toke)\n",
    "df.text = detokenized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# -----------------------------------\n",
    "# j. Split into train and validation sets\n",
    "# -----------------------------------\n",
    "trn_data, val_data = train_test_split( df, stratify=df.label, \\\n",
    "                                        test_size=0.4, random_state=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# _Chapter II. Pretrain & ASGD Weight Dropped LSTM_\n",
    "---\n",
    "[ASGD Weight Dropped LSTM](https://arxiv.org/abs/1708.02182)\n",
    "\n",
    "## I. The Whats\n",
    "\n",
    "- What Language Model Should we Use?\n",
    "\n",
    "  - ULMFiT suggests using ASGD Weight Dropped LSTM for our pretrained model in step 1\n",
    "\n",
    "- What is ASGD Weight-Dropped LSTM?\n",
    "\n",
    "  - It's a popular Long Short Term Memory Language Model (type of Recurrent Neural Network) that has a unique set of regularization techniques to improve performance\n",
    "\n",
    "  - Uses DropConnect on hidden-to-hidden weights as form of recurrent regularization\n",
    "\n",
    "- What is a Recurrent Neural Network?\n",
    "\n",
    "  - Networks with loops to allow for persisting information by passing it from one step of the network to the next\n",
    "\n",
    "  - Take the human brain for example\n",
    "  \n",
    "      - We don't make decisions by starting from scratch\n",
    "      \n",
    "      - Use our past experiences and knowledge to pass judgement and carry out our decision making process\n",
    "\n",
    "  - Can be thought of multiple copies of the same network, passing message to each successor\n",
    "\n",
    "  - RNNs are capable of long term dependencies\n",
    "\n",
    "  - However, there's a point where this gap between relevant information and point where more context is needed grows to be too large for RNNs\n",
    "  \n",
    "    - To a point where they are unable to learn and make connections\n",
    "\n",
    "  - This is where Long Shert Term Memory networks come into play!\n",
    "\n",
    "- What is LSTM?\n",
    "\n",
    "  - Special kind of RNN capable of learning long term dependencies\n",
    "\n",
    "  - Introduced by Hochreiter & Schmidhuber (1997)\n",
    "  \n",
    "    - http://www.bioinf.jku.at/publications/older/2604.pdf\n",
    "\n",
    "  - What sets LSTM apart from a regular RNN?\n",
    "  \n",
    "    - RNNs have form of a chain of repeating modules - with a single tanh layer for the structure\n",
    "    \n",
    "    - Whereas the LSTM has 4 neural network layers of interaction.\n",
    "\n",
    "  - Key to LSTM is cell state - horizontal line running through the entire chain of layers\n",
    "\n",
    "  - Has ability to add/remove information to cell state with structures called gates\n",
    "\n",
    "  - Gates regulate information traffic\n",
    "\n",
    "  - Composed of a sigmoid neural net layer and a pointwise multiplication operator\n",
    "\n",
    "  - Sigmoid layer outputs number 0-1 describing information permeability\n",
    "\n",
    "  - 0 means:\n",
    "\n",
    "  ![](https://media.giphy.com/media/njYrp176NQsHS/giphy.gif)\n",
    "\n",
    "\n",
    "  - 1 means:\n",
    "\n",
    "  ![](https://media.giphy.com/media/lHQuX9d5DBhug/giphy.gif)\n",
    "\n",
    "\n",
    "- What is ASGD Weight Dropped LSTM?\n",
    "\n",
    "  - RNN recurrent connections are prone to overfitting\n",
    "\n",
    "  - Dropouts have been very successful in feed forward and convulational neural networks\n",
    "\n",
    "  - However, applying dropouts similarly to RNN's hidden state is ineffective, disrupts long term retention ability\n",
    "\n",
    "  - To counter this problem, DropConnect is proposed\n",
    "\n",
    "  - Typically in Dropout, randomly selected subset of activations is set to zero within each layer\n",
    "\n",
    "  - DropConnect, instead of activations, randomly selected subset of weights within the network is set to zero\n",
    "\n",
    "  - Each unit receives input from random subset of units in previous layer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chapter III. Finetune\n",
    "----\n",
    "[Hyper Parameter Fine Tuning - Learning Rate, Momentum, Weight Decay](https://arxiv.org/pdf/1803.09820.pdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CUDA not avail\n"
     ]
    },
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       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from universal import *\n",
    "mdl = ULMFiT(max_lr=4e-2)\n",
    "mdl.finetune('label', 'text', trn_data, val_data, drop_mult=0.7, cyc_len=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chapter IV. Classify & Predict\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CUDA Not Avail...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.941172</td>\n",
       "      <td>0.881104</td>\n",
       "      <td>0.594978</td>\n",
       "      <td>00:51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.946719</td>\n",
       "      <td>0.841319</td>\n",
       "      <td>0.615362</td>\n",
       "      <td>00:50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.942540</td>\n",
       "      <td>0.876486</td>\n",
       "      <td>0.593205</td>\n",
       "      <td>00:52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.930938</td>\n",
       "      <td>0.856369</td>\n",
       "      <td>0.595273</td>\n",
       "      <td>00:53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.933795</td>\n",
       "      <td>0.888923</td>\n",
       "      <td>0.576957</td>\n",
       "      <td>00:53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.919428</td>\n",
       "      <td>0.865953</td>\n",
       "      <td>0.599409</td>\n",
       "      <td>00:53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.887331</td>\n",
       "      <td>0.820996</td>\n",
       "      <td>0.617725</td>\n",
       "      <td>00:53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.893611</td>\n",
       "      <td>0.798951</td>\n",
       "      <td>0.637814</td>\n",
       "      <td>00:53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.850820</td>\n",
       "      <td>0.824974</td>\n",
       "      <td>0.619202</td>\n",
       "      <td>00:53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.835078</td>\n",
       "      <td>0.798895</td>\n",
       "      <td>0.629542</td>\n",
       "      <td>00:53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learner = mdl.classify(cyc_len=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Category tensor(2), tensor(2), tensor([0.1921, 0.1075, 0.7004]))"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# testing positive category\n",
    "learner.predict('I love airline, they really care about their customers!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Category tensor(0), tensor(0), tensor([0.5144, 0.1797, 0.3059]))"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# testing negative category\n",
    "learner.predict('i hate airline, i will never return to them!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Category tensor(0), tensor(0), tensor([0.5612, 0.1234, 0.3155]))"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# resting neutral category\n",
    "learner.predict('my experience with airline was very average')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chapter V: Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- With only 8462 data points, I trained with a max learning rate of 4e-2 and received an accuracy of around 62%\n",
    "\n",
    "- The model could've used a bit more training, validation loss was getting close to training, but it was still under training\n",
    "\n",
    "    - This means it is underfitting\n",
    "    \n",
    "    - However, if there were more cycles it looks likely they would converge\n",
    "\n",
    "- Although the model is good at identifying the positive and negative sentiment, the neutral remains challenging\n",
    "\n",
    "- Overall, the ULMFiT model works really well with limited dataset, and performed very well in comparison to other models given the constraint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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