diff --git a/.gitignore b/.gitignore index 8cf41ec..9dd30dd 100644 --- a/.gitignore +++ b/.gitignore @@ -3,3 +3,35 @@ **/datasets **/.DS_Store .gitignore +Assignments/assignment2/Herais/saved_params_10000.npy +Assignments/assignment2/Herais/saved_params_15000.npy +Assignments/assignment2/Herais/saved_params_20000.npy +Assignments/assignment2/Herais/saved_params_25000.npy +Assignments/assignment2/Herais/saved_params_30000.npy +Assignments/assignment2/Herais/saved_params_35000.npy +Assignments/assignment2/Herais/saved_params_40000.npy +Assignments/assignment2/Herais/saved_params_5000.npy +Assignments/assignment2/Herais/saved_state_10000.pickle +Assignments/assignment2/Herais/saved_state_15000.pickle +Assignments/assignment2/Herais/saved_state_20000.pickle +Assignments/assignment2/Herais/saved_state_25000.pickle +Assignments/assignment2/Herais/saved_state_30000.pickle +Assignments/assignment2/Herais/saved_state_35000.pickle +Assignments/assignment2/Herais/saved_state_40000.pickle +Assignments/assignment2/Herais/saved_state_5000.pickle +Assignments/assignment2/Herais/saved_state_5000.pickle +Assignments/assignment2/Herais/saved_state_40000.pickle +Assignments/assignment2/Herais/saved_state_35000.pickle +Assignments/assignment2/Herais/saved_state_30000.pickle +Assignments/assignment2/Herais/saved_state_25000.pickle +Assignments/assignment2/Herais/saved_state_20000.pickle +Assignments/assignment2/Herais/saved_state_15000.pickle +Assignments/assignment2/Herais/saved_state_10000.pickle +Assignments/assignment2/Herais/saved_params_5000.npy +Assignments/assignment2/Herais/saved_params_40000.npy +Assignments/assignment2/Herais/saved_params_35000.npy +Assignments/assignment2/Herais/saved_params_30000.npy +Assignments/assignment2/Herais/saved_params_25000.npy +Assignments/assignment2/Herais/saved_params_20000.npy +Assignments/assignment2/Herais/saved_params_15000.npy +Assignments/assignment2/Herais/saved_params_10000.npy diff --git a/Assignments/assignment1/Herais/.ipynb_checkpoints/exploring_word_vectors-checkpoint.ipynb b/Assignments/assignment1/Herais/.ipynb_checkpoints/exploring_word_vectors-checkpoint.ipynb new file mode 100644 index 0000000..74fa297 --- /dev/null +++ b/Assignments/assignment1/Herais/.ipynb_checkpoints/exploring_word_vectors-checkpoint.ipynb @@ -0,0 +1,1260 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# CS224N Assignment 1: Exploring Word Vectors (25 Points)\n", + "### Due 4:30pm, Tue Jan 14 \n", + "\n", + "Welcome to CS224n! \n", + "\n", + "Before you start, make sure you read the README.txt in the same directory as this notebook. You will find many provided codes in the notebook. We highly encourage you to read and understand the provided codes as part of the learning :-)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[nltk_data] Downloading package reuters to\n", + "[nltk_data] C:\\Users\\VSurfacePro3\\AppData\\Roaming\\nltk_data...\n", + "[nltk_data] Package reuters is already up-to-date!\n" + ] + } + ], + "source": [ + "# All Import Statements Defined Here\n", + "# Note: Do not add to this list.\n", + "# ----------------\n", + "\n", + "import sys\n", + "assert sys.version_info[0]==3\n", + "assert sys.version_info[1] >= 5\n", + "\n", + "from gensim.models import KeyedVectors\n", + "from gensim.test.utils import datapath\n", + "import pprint\n", + "import matplotlib.pyplot as plt\n", + "plt.rcParams['figure.figsize'] = [10, 5]\n", + "import nltk\n", + "nltk.download('reuters')\n", + "from nltk.corpus import reuters\n", + "import numpy as np\n", + "import random\n", + "import scipy as sp\n", + "from sklearn.decomposition import TruncatedSVD\n", + "from sklearn.decomposition import PCA\n", + "\n", + "START_TOKEN = ''\n", + "END_TOKEN = ''\n", + "\n", + "np.random.seed(0)\n", + "random.seed(0)\n", + "# ----------------" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Word Vectors\n", + "\n", + "Word Vectors are often used as a fundamental component for downstream NLP tasks, e.g. question answering, text generation, translation, etc., so it is important to build some intuitions as to their strengths and weaknesses. Here, you will explore two types of word vectors: those derived from *co-occurrence matrices*, and those derived via *GloVe*. \n", + "\n", + "**Assignment Notes:** Please make sure to save the notebook as you go along. Submission Instructions are located at the bottom of the notebook.\n", + "\n", + "**Note on Terminology:** The terms \"word vectors\" and \"word embeddings\" are often used interchangeably. The term \"embedding\" refers to the fact that we are encoding aspects of a word's meaning in a lower dimensional space. As [Wikipedia](https://en.wikipedia.org/wiki/Word_embedding) states, \"*conceptually it involves a mathematical embedding from a space with one dimension per word to a continuous vector space with a much lower dimension*\"." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Part 1: Count-Based Word Vectors (10 points)\n", + "\n", + "Most word vector models start from the following idea:\n", + "\n", + "*You shall know a word by the company it keeps ([Firth, J. R. 1957:11](https://en.wikipedia.org/wiki/John_Rupert_Firth))*\n", + "\n", + "Many word vector implementations are driven by the idea that similar words, i.e., (near) synonyms, will be used in similar contexts. As a result, similar words will often be spoken or written along with a shared subset of words, i.e., contexts. By examining these contexts, we can try to develop embeddings for our words. With this intuition in mind, many \"old school\" approaches to constructing word vectors relied on word counts. Here we elaborate upon one of those strategies, *co-occurrence matrices* (for more information, see [here](http://web.stanford.edu/class/cs124/lec/vectorsemantics.video.pdf) or [here](https://medium.com/data-science-group-iitr/word-embedding-2d05d270b285))." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Co-Occurrence\n", + "\n", + "A co-occurrence matrix counts how often things co-occur in some environment. Given some word $w_i$ occurring in the document, we consider the *context window* surrounding $w_i$. Supposing our fixed window size is $n$, then this is the $n$ preceding and $n$ subsequent words in that document, i.e. words $w_{i-n} \\dots w_{i-1}$ and $w_{i+1} \\dots w_{i+n}$. We build a *co-occurrence matrix* $M$, which is a symmetric word-by-word matrix in which $M_{ij}$ is the number of times $w_j$ appears inside $w_i$'s window among all documents.\n", + "\n", + "**Example: Co-Occurrence with Fixed Window of n=1**:\n", + "\n", + "Document 1: \"all that glitters is not gold\"\n", + "\n", + "Document 2: \"all is well that ends well\"\n", + "\n", + "\n", + "| * | `` | all | that | glitters | is | not | gold | well | ends | `` |\n", + "|----------|-------|-----|------|----------|------|------|-------|------|------|-----|\n", + "| `` | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", + "| all | 2 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |\n", + "| that | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 |\n", + "| glitters | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |\n", + "| is | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 |\n", + "| not | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 |\n", + "| gold | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 |\n", + "| well | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 1 |\n", + "| ends | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |\n", + "| `` | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 |\n", + "\n", + "**Note:** In NLP, we often add `` and `` tokens to represent the beginning and end of sentences, paragraphs or documents. In thise case we imagine `` and `` tokens encapsulating each document, e.g., \"`` All that glitters is not gold ``\", and include these tokens in our co-occurrence counts.\n", + "\n", + "The rows (or columns) of this matrix provide one type of word vectors (those based on word-word co-occurrence), but the vectors will be large in general (linear in the number of distinct words in a corpus). Thus, our next step is to run *dimensionality reduction*. In particular, we will run *SVD (Singular Value Decomposition)*, which is a kind of generalized *PCA (Principal Components Analysis)* to select the top $k$ principal components. Here's a visualization of dimensionality reduction with SVD. In this picture our co-occurrence matrix is $A$ with $n$ rows corresponding to $n$ words. We obtain a full matrix decomposition, with the singular values ordered in the diagonal $S$ matrix, and our new, shorter length-$k$ word vectors in $U_k$.\n", + "\n", + "![Picture of an SVD](./imgs/svd.png \"SVD\")\n", + "\n", + "This reduced-dimensionality co-occurrence representation preserves semantic relationships between words, e.g. *doctor* and *hospital* will be closer than *doctor* and *dog*. \n", + "\n", + "**Notes:** If you can barely remember what an eigenvalue is, here's [a slow, friendly introduction to SVD](https://davetang.org/file/Singular_Value_Decomposition_Tutorial.pdf). If you want to learn more thoroughly about PCA or SVD, feel free to check out lectures [7](https://web.stanford.edu/class/cs168/l/l7.pdf), [8](http://theory.stanford.edu/~tim/s15/l/l8.pdf), and [9](https://web.stanford.edu/class/cs168/l/l9.pdf) of CS168. These course notes provide a great high-level treatment of these general purpose algorithms. Though, for the purpose of this class, you only need to know how to extract the k-dimensional embeddings by utilizing pre-programmed implementations of these algorithms from the numpy, scipy, or sklearn python packages. In practice, it is challenging to apply full SVD to large corpora because of the memory needed to perform PCA or SVD. However, if you only want the top $k$ vector components for relatively small $k$ — known as [Truncated SVD](https://en.wikipedia.org/wiki/Singular_value_decomposition#Truncated_SVD) — then there are reasonably scalable techniques to compute those iteratively." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plotting Co-Occurrence Word Embeddings\n", + "\n", + "Here, we will be using the Reuters (business and financial news) corpus. If you haven't run the import cell at the top of this page, please run it now (click it and press SHIFT-RETURN). The corpus consists of 10,788 news documents totaling 1.3 million words. These documents span 90 categories and are split into train and test. For more details, please see https://www.nltk.org/book/ch02.html. We provide a `read_corpus` function below that pulls out only articles from the \"crude\" (i.e. news articles about oil, gas, etc.) category. The function also adds `` and `` tokens to each of the documents, and lowercases words. You do **not** have to perform any other kind of pre-processing." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def read_corpus(category=\"crude\"):\n", + " \"\"\" Read files from the specified Reuter's category.\n", + " Params:\n", + " category (string): category name\n", + " Return:\n", + " list of lists, with words from each of the processed files\n", + " \"\"\"\n", + " files = reuters.fileids(category)\n", + " return [[START_TOKEN] + [w.lower() for w in list(reuters.words(f))] + [END_TOKEN] for f in files]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's have a look what these documents are like…." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[['', 'japan', 'to', 'revise', 'long', '-', 'term', 'energy', 'demand', 'downwards', 'the',\n", + " 'ministry', 'of', 'international', 'trade', 'and', 'industry', '(', 'miti', ')', 'will', 'revise',\n", + " 'its', 'long', '-', 'term', 'energy', 'supply', '/', 'demand', 'outlook', 'by', 'august', 'to',\n", + " 'meet', 'a', 'forecast', 'downtrend', 'in', 'japanese', 'energy', 'demand', ',', 'ministry',\n", + " 'officials', 'said', '.', 'miti', 'is', 'expected', 'to', 'lower', 'the', 'projection', 'for',\n", + " 'primary', 'energy', 'supplies', 'in', 'the', 'year', '2000', 'to', '550', 'mln', 'kilolitres',\n", + " '(', 'kl', ')', 'from', '600', 'mln', ',', 'they', 'said', '.', 'the', 'decision', 'follows',\n", + " 'the', 'emergence', 'of', 'structural', 'changes', 'in', 'japanese', 'industry', 'following',\n", + " 'the', 'rise', 'in', 'the', 'value', 'of', 'the', 'yen', 'and', 'a', 'decline', 'in', 'domestic',\n", + " 'electric', 'power', 'demand', '.', 'miti', 'is', 'planning', 'to', 'work', 'out', 'a', 'revised',\n", + " 'energy', 'supply', '/', 'demand', 'outlook', 'through', 'deliberations', 'of', 'committee',\n", + " 'meetings', 'of', 'the', 'agency', 'of', 'natural', 'resources', 'and', 'energy', ',', 'the',\n", + " 'officials', 'said', '.', 'they', 'said', 'miti', 'will', 'also', 'review', 'the', 'breakdown',\n", + " 'of', 'energy', 'supply', 'sources', ',', 'including', 'oil', ',', 'nuclear', ',', 'coal', 'and',\n", + " 'natural', 'gas', '.', 'nuclear', 'energy', 'provided', 'the', 'bulk', 'of', 'japan', \"'\", 's',\n", + " 'electric', 'power', 'in', 'the', 'fiscal', 'year', 'ended', 'march', '31', ',', 'supplying',\n", + " 'an', 'estimated', '27', 'pct', 'on', 'a', 'kilowatt', '/', 'hour', 'basis', ',', 'followed',\n", + " 'by', 'oil', '(', '23', 'pct', ')', 'and', 'liquefied', 'natural', 'gas', '(', '21', 'pct', '),',\n", + " 'they', 'noted', '.', ''],\n", + " ['', 'energy', '/', 'u', '.', 's', '.', 'petrochemical', 'industry', 'cheap', 'oil',\n", + " 'feedstocks', ',', 'the', 'weakened', 'u', '.', 's', '.', 'dollar', 'and', 'a', 'plant',\n", + " 'utilization', 'rate', 'approaching', '90', 'pct', 'will', 'propel', 'the', 'streamlined', 'u',\n", + " '.', 's', '.', 'petrochemical', 'industry', 'to', 'record', 'profits', 'this', 'year', ',',\n", + " 'with', 'growth', 'expected', 'through', 'at', 'least', '1990', ',', 'major', 'company',\n", + " 'executives', 'predicted', '.', 'this', 'bullish', 'outlook', 'for', 'chemical', 'manufacturing',\n", + " 'and', 'an', 'industrywide', 'move', 'to', 'shed', 'unrelated', 'businesses', 'has', 'prompted',\n", + " 'gaf', 'corp', '&', 'lt', ';', 'gaf', '>,', 'privately', '-', 'held', 'cain', 'chemical', 'inc',\n", + " ',', 'and', 'other', 'firms', 'to', 'aggressively', 'seek', 'acquisitions', 'of', 'petrochemical',\n", + " 'plants', '.', 'oil', 'companies', 'such', 'as', 'ashland', 'oil', 'inc', '&', 'lt', ';', 'ash',\n", + " '>,', 'the', 'kentucky', '-', 'based', 'oil', 'refiner', 'and', 'marketer', ',', 'are', 'also',\n", + " 'shopping', 'for', 'money', '-', 'making', 'petrochemical', 'businesses', 'to', 'buy', '.', '\"',\n", + " 'i', 'see', 'us', 'poised', 'at', 'the', 'threshold', 'of', 'a', 'golden', 'period', ',\"', 'said',\n", + " 'paul', 'oreffice', ',', 'chairman', 'of', 'giant', 'dow', 'chemical', 'co', '&', 'lt', ';',\n", + " 'dow', '>,', 'adding', ',', '\"', 'there', \"'\", 's', 'no', 'major', 'plant', 'capacity', 'being',\n", + " 'added', 'around', 'the', 'world', 'now', '.', 'the', 'whole', 'game', 'is', 'bringing', 'out',\n", + " 'new', 'products', 'and', 'improving', 'the', 'old', 'ones', '.\"', 'analysts', 'say', 'the',\n", + " 'chemical', 'industry', \"'\", 's', 'biggest', 'customers', ',', 'automobile', 'manufacturers',\n", + " 'and', 'home', 'builders', 'that', 'use', 'a', 'lot', 'of', 'paints', 'and', 'plastics', ',',\n", + " 'are', 'expected', 'to', 'buy', 'quantities', 'this', 'year', '.', 'u', '.', 's', '.',\n", + " 'petrochemical', 'plants', 'are', 'currently', 'operating', 'at', 'about', '90', 'pct',\n", + " 'capacity', ',', 'reflecting', 'tighter', 'supply', 'that', 'could', 'hike', 'product', 'prices',\n", + " 'by', '30', 'to', '40', 'pct', 'this', 'year', ',', 'said', 'john', 'dosher', ',', 'managing',\n", + " 'director', 'of', 'pace', 'consultants', 'inc', 'of', 'houston', '.', 'demand', 'for', 'some',\n", + " 'products', 'such', 'as', 'styrene', 'could', 'push', 'profit', 'margins', 'up', 'by', 'as',\n", + " 'much', 'as', '300', 'pct', ',', 'he', 'said', '.', 'oreffice', ',', 'speaking', 'at', 'a',\n", + " 'meeting', 'of', 'chemical', 'engineers', 'in', 'houston', ',', 'said', 'dow', 'would', 'easily',\n", + " 'top', 'the', '741', 'mln', 'dlrs', 'it', 'earned', 'last', 'year', 'and', 'predicted', 'it',\n", + " 'would', 'have', 'the', 'best', 'year', 'in', 'its', 'history', '.', 'in', '1985', ',', 'when',\n", + " 'oil', 'prices', 'were', 'still', 'above', '25', 'dlrs', 'a', 'barrel', 'and', 'chemical',\n", + " 'exports', 'were', 'adversely', 'affected', 'by', 'the', 'strong', 'u', '.', 's', '.', 'dollar',\n", + " ',', 'dow', 'had', 'profits', 'of', '58', 'mln', 'dlrs', '.', '\"', 'i', 'believe', 'the',\n", + " 'entire', 'chemical', 'industry', 'is', 'headed', 'for', 'a', 'record', 'year', 'or', 'close',\n", + " 'to', 'it', ',\"', 'oreffice', 'said', '.', 'gaf', 'chairman', 'samuel', 'heyman', 'estimated',\n", + " 'that', 'the', 'u', '.', 's', '.', 'chemical', 'industry', 'would', 'report', 'a', '20', 'pct',\n", + " 'gain', 'in', 'profits', 'during', '1987', '.', 'last', 'year', ',', 'the', 'domestic',\n", + " 'industry', 'earned', 'a', 'total', 'of', '13', 'billion', 'dlrs', ',', 'a', '54', 'pct', 'leap',\n", + " 'from', '1985', '.', 'the', 'turn', 'in', 'the', 'fortunes', 'of', 'the', 'once', '-', 'sickly',\n", + " 'chemical', 'industry', 'has', 'been', 'brought', 'about', 'by', 'a', 'combination', 'of', 'luck',\n", + " 'and', 'planning', ',', 'said', 'pace', \"'\", 's', 'john', 'dosher', '.', 'dosher', 'said', 'last',\n", + " 'year', \"'\", 's', 'fall', 'in', 'oil', 'prices', 'made', 'feedstocks', 'dramatically', 'cheaper',\n", + " 'and', 'at', 'the', 'same', 'time', 'the', 'american', 'dollar', 'was', 'weakening', 'against',\n", + " 'foreign', 'currencies', '.', 'that', 'helped', 'boost', 'u', '.', 's', '.', 'chemical',\n", + " 'exports', '.', 'also', 'helping', 'to', 'bring', 'supply', 'and', 'demand', 'into', 'balance',\n", + " 'has', 'been', 'the', 'gradual', 'market', 'absorption', 'of', 'the', 'extra', 'chemical',\n", + " 'manufacturing', 'capacity', 'created', 'by', 'middle', 'eastern', 'oil', 'producers', 'in',\n", + " 'the', 'early', '1980s', '.', 'finally', ',', 'virtually', 'all', 'major', 'u', '.', 's', '.',\n", + " 'chemical', 'manufacturers', 'have', 'embarked', 'on', 'an', 'extensive', 'corporate',\n", + " 'restructuring', 'program', 'to', 'mothball', 'inefficient', 'plants', ',', 'trim', 'the',\n", + " 'payroll', 'and', 'eliminate', 'unrelated', 'businesses', '.', 'the', 'restructuring', 'touched',\n", + " 'off', 'a', 'flurry', 'of', 'friendly', 'and', 'hostile', 'takeover', 'attempts', '.', 'gaf', ',',\n", + " 'which', 'made', 'an', 'unsuccessful', 'attempt', 'in', '1985', 'to', 'acquire', 'union',\n", + " 'carbide', 'corp', '&', 'lt', ';', 'uk', '>,', 'recently', 'offered', 'three', 'billion', 'dlrs',\n", + " 'for', 'borg', 'warner', 'corp', '&', 'lt', ';', 'bor', '>,', 'a', 'chicago', 'manufacturer',\n", + " 'of', 'plastics', 'and', 'chemicals', '.', 'another', 'industry', 'powerhouse', ',', 'w', '.',\n", + " 'r', '.', 'grace', '&', 'lt', ';', 'gra', '>', 'has', 'divested', 'its', 'retailing', ',',\n", + " 'restaurant', 'and', 'fertilizer', 'businesses', 'to', 'raise', 'cash', 'for', 'chemical',\n", + " 'acquisitions', '.', 'but', 'some', 'experts', 'worry', 'that', 'the', 'chemical', 'industry',\n", + " 'may', 'be', 'headed', 'for', 'trouble', 'if', 'companies', 'continue', 'turning', 'their',\n", + " 'back', 'on', 'the', 'manufacturing', 'of', 'staple', 'petrochemical', 'commodities', ',', 'such',\n", + " 'as', 'ethylene', ',', 'in', 'favor', 'of', 'more', 'profitable', 'specialty', 'chemicals',\n", + " 'that', 'are', 'custom', '-', 'designed', 'for', 'a', 'small', 'group', 'of', 'buyers', '.', '\"',\n", + " 'companies', 'like', 'dupont', '&', 'lt', ';', 'dd', '>', 'and', 'monsanto', 'co', '&', 'lt', ';',\n", + " 'mtc', '>', 'spent', 'the', 'past', 'two', 'or', 'three', 'years', 'trying', 'to', 'get', 'out',\n", + " 'of', 'the', 'commodity', 'chemical', 'business', 'in', 'reaction', 'to', 'how', 'badly', 'the',\n", + " 'market', 'had', 'deteriorated', ',\"', 'dosher', 'said', '.', '\"', 'but', 'i', 'think', 'they',\n", + " 'will', 'eventually', 'kill', 'the', 'margins', 'on', 'the', 'profitable', 'chemicals', 'in',\n", + " 'the', 'niche', 'market', '.\"', 'some', 'top', 'chemical', 'executives', 'share', 'the',\n", + " 'concern', '.', '\"', 'the', 'challenge', 'for', 'our', 'industry', 'is', 'to', 'keep', 'from',\n", + " 'getting', 'carried', 'away', 'and', 'repeating', 'past', 'mistakes', ',\"', 'gaf', \"'\", 's',\n", + " 'heyman', 'cautioned', '.', '\"', 'the', 'shift', 'from', 'commodity', 'chemicals', 'may', 'be',\n", + " 'ill', '-', 'advised', '.', 'specialty', 'businesses', 'do', 'not', 'stay', 'special', 'long',\n", + " '.\"', 'houston', '-', 'based', 'cain', 'chemical', ',', 'created', 'this', 'month', 'by', 'the',\n", + " 'sterling', 'investment', 'banking', 'group', ',', 'believes', 'it', 'can', 'generate', '700',\n", + " 'mln', 'dlrs', 'in', 'annual', 'sales', 'by', 'bucking', 'the', 'industry', 'trend', '.',\n", + " 'chairman', 'gordon', 'cain', ',', 'who', 'previously', 'led', 'a', 'leveraged', 'buyout', 'of',\n", + " 'dupont', \"'\", 's', 'conoco', 'inc', \"'\", 's', 'chemical', 'business', ',', 'has', 'spent', '1',\n", + " '.', '1', 'billion', 'dlrs', 'since', 'january', 'to', 'buy', 'seven', 'petrochemical', 'plants',\n", + " 'along', 'the', 'texas', 'gulf', 'coast', '.', 'the', 'plants', 'produce', 'only', 'basic',\n", + " 'commodity', 'petrochemicals', 'that', 'are', 'the', 'building', 'blocks', 'of', 'specialty',\n", + " 'products', '.', '\"', 'this', 'kind', 'of', 'commodity', 'chemical', 'business', 'will', 'never',\n", + " 'be', 'a', 'glamorous', ',', 'high', '-', 'margin', 'business', ',\"', 'cain', 'said', ',',\n", + " 'adding', 'that', 'demand', 'is', 'expected', 'to', 'grow', 'by', 'about', 'three', 'pct',\n", + " 'annually', '.', 'garo', 'armen', ',', 'an', 'analyst', 'with', 'dean', 'witter', 'reynolds', ',',\n", + " 'said', 'chemical', 'makers', 'have', 'also', 'benefitted', 'by', 'increasing', 'demand', 'for',\n", + " 'plastics', 'as', 'prices', 'become', 'more', 'competitive', 'with', 'aluminum', ',', 'wood',\n", + " 'and', 'steel', 'products', '.', 'armen', 'estimated', 'the', 'upturn', 'in', 'the', 'chemical',\n", + " 'business', 'could', 'last', 'as', 'long', 'as', 'four', 'or', 'five', 'years', ',', 'provided',\n", + " 'the', 'u', '.', 's', '.', 'economy', 'continues', 'its', 'modest', 'rate', 'of', 'growth', '.',\n", + " ''],\n", + " ['', 'turkey', 'calls', 'for', 'dialogue', 'to', 'solve', 'dispute', 'turkey', 'said',\n", + " 'today', 'its', 'disputes', 'with', 'greece', ',', 'including', 'rights', 'on', 'the',\n", + " 'continental', 'shelf', 'in', 'the', 'aegean', 'sea', ',', 'should', 'be', 'solved', 'through',\n", + " 'negotiations', '.', 'a', 'foreign', 'ministry', 'statement', 'said', 'the', 'latest', 'crisis',\n", + " 'between', 'the', 'two', 'nato', 'members', 'stemmed', 'from', 'the', 'continental', 'shelf',\n", + " 'dispute', 'and', 'an', 'agreement', 'on', 'this', 'issue', 'would', 'effect', 'the', 'security',\n", + " ',', 'economy', 'and', 'other', 'rights', 'of', 'both', 'countries', '.', '\"', 'as', 'the',\n", + " 'issue', 'is', 'basicly', 'political', ',', 'a', 'solution', 'can', 'only', 'be', 'found', 'by',\n", + " 'bilateral', 'negotiations', ',\"', 'the', 'statement', 'said', '.', 'greece', 'has', 'repeatedly',\n", + " 'said', 'the', 'issue', 'was', 'legal', 'and', 'could', 'be', 'solved', 'at', 'the',\n", + " 'international', 'court', 'of', 'justice', '.', 'the', 'two', 'countries', 'approached', 'armed',\n", + " 'confrontation', 'last', 'month', 'after', 'greece', 'announced', 'it', 'planned', 'oil',\n", + " 'exploration', 'work', 'in', 'the', 'aegean', 'and', 'turkey', 'said', 'it', 'would', 'also',\n", + " 'search', 'for', 'oil', '.', 'a', 'face', '-', 'off', 'was', 'averted', 'when', 'turkey',\n", + " 'confined', 'its', 'research', 'to', 'territorrial', 'waters', '.', '\"', 'the', 'latest',\n", + " 'crises', 'created', 'an', 'historic', 'opportunity', 'to', 'solve', 'the', 'disputes', 'between',\n", + " 'the', 'two', 'countries', ',\"', 'the', 'foreign', 'ministry', 'statement', 'said', '.', 'turkey',\n", + " \"'\", 's', 'ambassador', 'in', 'athens', ',', 'nazmi', 'akiman', ',', 'was', 'due', 'to', 'meet',\n", + " 'prime', 'minister', 'andreas', 'papandreou', 'today', 'for', 'the', 'greek', 'reply', 'to', 'a',\n", + " 'message', 'sent', 'last', 'week', 'by', 'turkish', 'prime', 'minister', 'turgut', 'ozal', '.',\n", + " 'the', 'contents', 'of', 'the', 'message', 'were', 'not', 'disclosed', '.', '']]\n" + ] + } + ], + "source": [ + "reuters_corpus = read_corpus()\n", + "pprint.pprint(reuters_corpus[:3], compact=True, width=100)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Question 1.1: Implement `distinct_words` [code] (2 points)\n", + "\n", + "Write a method to work out the distinct words (word types) that occur in the corpus. You can do this with `for` loops, but it's more efficient to do it with Python list comprehensions. In particular, [this](https://coderwall.com/p/rcmaea/flatten-a-list-of-lists-in-one-line-in-python) may be useful to flatten a list of lists. If you're not familiar with Python list comprehensions in general, here's [more information](https://python-3-patterns-idioms-test.readthedocs.io/en/latest/Comprehensions.html).\n", + "\n", + "You may find it useful to use [Python sets](https://www.w3schools.com/python/python_sets.asp) to remove duplicate words." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def distinct_words(corpus):\n", + " \"\"\" Determine a list of distinct words for the corpus.\n", + " Params:\n", + " corpus (list of list of strings): corpus of documents\n", + " Return:\n", + " corpus_words (list of strings): list of distinct words across the corpus, sorted (using python 'sorted' function)\n", + " num_corpus_words (integer): number of distinct words across the corpus\n", + " \"\"\"\n", + " corpus_words = []\n", + " num_corpus_words = -1\n", + " \n", + " # ------------------\n", + " # Write your implementation here.\n", + " corpus_words = sorted(list(set([words for rows in corpus for words in rows])))\n", + " num_corpus_words = len(corpus_words)\n", + "\n", + " # ------------------\n", + "\n", + " return corpus_words, num_corpus_words\n", + "\n", + "#import itertools\n", + "#list(itertools.chain(*corpus)) " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--------------------------------------------------------------------------------\n", + "Passed All Tests!\n", + "--------------------------------------------------------------------------------\n" + ] + } + ], + "source": [ + "# ---------------------\n", + "# Run this sanity check\n", + "# Note that this not an exhaustive check for correctness.\n", + "# ---------------------\n", + "\n", + "# Define toy corpus\n", + "test_corpus = [\"{} All that glitters isn't gold {}\".format(START_TOKEN, END_TOKEN).split(\" \"), \"{} All's well that ends well {}\".format(START_TOKEN, END_TOKEN).split(\" \")]\n", + "test_corpus_words, num_corpus_words = distinct_words(test_corpus)\n", + "\n", + "# Correct answers\n", + "ans_test_corpus_words = sorted([START_TOKEN, \"All\", \"ends\", \"that\", \"gold\", \"All's\", \"glitters\", \"isn't\", \"well\", END_TOKEN])\n", + "ans_num_corpus_words = len(ans_test_corpus_words)\n", + "\n", + "# Test correct number of words\n", + "assert(num_corpus_words == ans_num_corpus_words), \"Incorrect number of distinct words. Correct: {}. Yours: {}\".format(ans_num_corpus_words, num_corpus_words)\n", + "\n", + "# Test correct words\n", + "assert (test_corpus_words == ans_test_corpus_words), \"Incorrect corpus_words.\\nCorrect: {}\\nYours: {}\".format(str(ans_test_corpus_words), str(test_corpus_words))\n", + "\n", + "# Print Success\n", + "print (\"-\" * 80)\n", + "print(\"Passed All Tests!\")\n", + "print (\"-\" * 80)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Question 1.2: Implement `compute_co_occurrence_matrix` [code] (3 points)\n", + "\n", + "Write a method that constructs a co-occurrence matrix for a certain window-size $n$ (with a default of 4), considering words $n$ before and $n$ after the word in the center of the window. Here, we start to use `numpy (np)` to represent vectors, matrices, and tensors. If you're not familiar with NumPy, there's a NumPy tutorial in the second half of this cs231n [Python NumPy tutorial](http://cs231n.github.io/python-numpy-tutorial/).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def compute_co_occurrence_matrix(corpus, window_size=4):\n", + " \"\"\" Compute co-occurrence matrix for the given corpus and window_size (default of 4).\n", + " \n", + " Note: Each word in a document should be at the center of a window. Words near edges will have a smaller\n", + " number of co-occurring words.\n", + " \n", + " For example, if we take the document \" All that glitters is not gold \" with window size of 4,\n", + " \"All\" will co-occur with \"\", \"that\", \"glitters\", \"is\", and \"not\".\n", + " \n", + " Params:\n", + " corpus (list of list of strings): corpus of documents\n", + " window_size (int): size of context window\n", + " Return:\n", + " M (a symmetric numpy matrix of shape (number of unique words in the corpus , number of unique words in the corpus)): \n", + " Co-occurence matrix of word counts. \n", + " The ordering of the words in the rows/columns should be the same as the ordering of the words given by the distinct_words function.\n", + " word2Ind (dict): dictionary that maps word to index (i.e. row/column number) for matrix M.\n", + " \"\"\"\n", + " words, num_words = distinct_words(corpus)\n", + " M = None\n", + " word2Ind = {}\n", + " \n", + " # ------------------\n", + " # Write your implementation here.\n", + " M = np.zeros(shape=(num_words, num_words)) # n-by-n matrix filled w/ 0\n", + " for i, word in enumerate(words): # map word to index\n", + " word2Ind[word] = i\n", + " \n", + " for doc in corpus:\n", + " lwords = [None]*window_size + doc + [None]*window_size\n", + " for i in range(len(lwords)):\n", + " if (lwords[i] != None):\n", + " word_ctr = lwords[i]\n", + " words_context = list(filter(None, lwords[i-window_size:i] + lwords[i+1:i+window_size+1]))\n", + " for word_con in words_context:\n", + " if (word_ctr != word_con):\n", + " M[word2Ind[word_ctr], word2Ind[word_con]] += 1\n", + " # ------------------\n", + "\n", + " return M, word2Ind" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--------------------------------------------------------------------------------\n", + "Passed All Tests!\n", + "--------------------------------------------------------------------------------\n" + ] + } + ], + "source": [ + "# ---------------------\n", + "# Run this sanity check\n", + "# Note that this is not an exhaustive check for correctness.\n", + "# ---------------------\n", + "\n", + "# Define toy corpus and get student's co-occurrence matrix\n", + "test_corpus = [\"{} All that glitters isn't gold {}\".format(START_TOKEN, END_TOKEN).split(\" \"), \"{} All's well that ends well {}\".format(START_TOKEN, END_TOKEN).split(\" \")]\n", + "M_test, word2Ind_test = compute_co_occurrence_matrix(test_corpus, window_size=1)\n", + "\n", + "# Correct M and word2Ind\n", + "M_test_ans = np.array( \n", + " [[0., 0., 0., 0., 0., 0., 1., 0., 0., 1.,],\n", + " [0., 0., 1., 1., 0., 0., 0., 0., 0., 0.,],\n", + " [0., 1., 0., 0., 0., 0., 0., 0., 1., 0.,],\n", + " [0., 1., 0., 0., 0., 0., 0., 0., 0., 1.,],\n", + " [0., 0., 0., 0., 0., 0., 0., 0., 1., 1.,],\n", + " [0., 0., 0., 0., 0., 0., 0., 1., 1., 0.,],\n", + " [1., 0., 0., 0., 0., 0., 0., 1., 0., 0.,],\n", + " [0., 0., 0., 0., 0., 1., 1., 0., 0., 0.,],\n", + " [0., 0., 1., 0., 1., 1., 0., 0., 0., 1.,],\n", + " [1., 0., 0., 1., 1., 0., 0., 0., 1., 0.,]]\n", + ")\n", + "ans_test_corpus_words = sorted([START_TOKEN, \"All\", \"ends\", \"that\", \"gold\", \"All's\", \"glitters\", \"isn't\", \"well\", END_TOKEN])\n", + "word2Ind_ans = dict(zip(ans_test_corpus_words, range(len(ans_test_corpus_words))))\n", + "\n", + "# Test correct word2Ind\n", + "assert (word2Ind_ans == word2Ind_test), \"Your word2Ind is incorrect:\\nCorrect: {}\\nYours: {}\".format(word2Ind_ans, word2Ind_test)\n", + "\n", + "# Test correct M shape\n", + "assert (M_test.shape == M_test_ans.shape), \"M matrix has incorrect shape.\\nCorrect: {}\\nYours: {}\".format(M_test.shape, M_test_ans.shape)\n", + "\n", + "# Test correct M values\n", + "for w1 in word2Ind_ans.keys():\n", + " idx1 = word2Ind_ans[w1]\n", + " for w2 in word2Ind_ans.keys():\n", + " idx2 = word2Ind_ans[w2]\n", + " student = M_test[idx1, idx2]\n", + " correct = M_test_ans[idx1, idx2]\n", + " if student != correct:\n", + " print(\"Correct M:\")\n", + " print(M_test_ans)\n", + " print(\"Your M: \")\n", + " print(M_test)\n", + " raise AssertionError(\"Incorrect count at index ({}, {})=({}, {}) in matrix M. Yours has {} but should have {}.\".format(idx1, idx2, w1, w2, student, correct))\n", + "\n", + "# Print Success\n", + "print (\"-\" * 80)\n", + "print(\"Passed All Tests!\")\n", + "print (\"-\" * 80)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Question 1.3: Implement `reduce_to_k_dim` [code] (1 point)\n", + "\n", + "Construct a method that performs dimensionality reduction on the matrix to produce k-dimensional embeddings. Use SVD to take the top k components and produce a new matrix of k-dimensional embeddings. \n", + "\n", + "**Note:** All of numpy, scipy, and scikit-learn (`sklearn`) provide *some* implementation of SVD, but only scipy and sklearn provide an implementation of Truncated SVD, and only sklearn provides an efficient randomized algorithm for calculating large-scale Truncated SVD. So please use [sklearn.decomposition.TruncatedSVD](https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.TruncatedSVD.html)." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def reduce_to_k_dim(M, k=2):\n", + " \"\"\" Reduce a co-occurence count matrix of dimensionality (num_corpus_words, num_corpus_words)\n", + " to a matrix of dimensionality (num_corpus_words, k) using the following SVD function from Scikit-Learn:\n", + " - http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.TruncatedSVD.html\n", + " \n", + " Params:\n", + " M (numpy matrix of shape (number of unique words in the corpus , number of unique words in the corpus)): co-occurence matrix of word counts\n", + " k (int): embedding size of each word after dimension reduction\n", + " Return:\n", + " M_reduced (numpy matrix of shape (number of corpus words, k)): matrix of k-dimensioal word embeddings.\n", + " In terms of the SVD from math class, this actually returns U * S\n", + " \"\"\" \n", + " n_iters = 10 # Use this parameter in your call to `TruncatedSVD`\n", + " M_reduced = None\n", + " print(\"Running Truncated SVD over %i words...\" % (M.shape[0]))\n", + " \n", + " # ------------------\n", + " # Write your implementation here.\n", + " svd = TruncatedSVD(n_components=k, n_iter=n_iters)\n", + " M_reduced = svd.fit_transform(M)\n", + " \n", + " # ------------------\n", + "\n", + " print(\"Done.\")\n", + " return M_reduced" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running Truncated SVD over 10 words...\n", + "Done.\n", + "--------------------------------------------------------------------------------\n", + "Passed All Tests!\n", + "--------------------------------------------------------------------------------\n" + ] + } + ], + "source": [ + "# ---------------------\n", + "# Run this sanity check\n", + "# Note that this is not an exhaustive check for correctness \n", + "# In fact we only check that your M_reduced has the right dimensions.\n", + "# ---------------------\n", + "\n", + "# Define toy corpus and run student code\n", + "test_corpus = [\"{} All that glitters isn't gold {}\".format(START_TOKEN, END_TOKEN).split(\" \"), \"{} All's well that ends well {}\".format(START_TOKEN, END_TOKEN).split(\" \")]\n", + "M_test, word2Ind_test = compute_co_occurrence_matrix(test_corpus, window_size=1)\n", + "M_test_reduced = reduce_to_k_dim(M_test, k=2)\n", + "\n", + "# Test proper dimensions\n", + "assert (M_test_reduced.shape[0] == 10), \"M_reduced has {} rows; should have {}\".format(M_test_reduced.shape[0], 10)\n", + "assert (M_test_reduced.shape[1] == 2), \"M_reduced has {} columns; should have {}\".format(M_test_reduced.shape[1], 2)\n", + "\n", + "# Print Success\n", + "print (\"-\" * 80)\n", + "print(\"Passed All Tests!\")\n", + "print (\"-\" * 80)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Question 1.4: Implement `plot_embeddings` [code] (1 point)\n", + "\n", + "Here you will write a function to plot a set of 2D vectors in 2D space. For graphs, we will use Matplotlib (`plt`).\n", + "\n", + "For this example, you may find it useful to adapt [this code](https://www.pythonmembers.club/2018/05/08/matplotlib-scatter-plot-annotate-set-text-at-label-each-point/). In the future, a good way to make a plot is to look at [the Matplotlib gallery](https://matplotlib.org/gallery/index.html), find a plot that looks somewhat like what you want, and adapt the code they give." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_embeddings(M_reduced, word2Ind, words):\n", + " \"\"\" Plot in a scatterplot the embeddings of the words specified in the list \"words\".\n", + " NOTE: do not plot all the words listed in M_reduced / word2Ind.\n", + " Include a label next to each point.\n", + " \n", + " Params:\n", + " M_reduced (numpy matrix of shape (number of unique words in the corpus , 2)): matrix of 2-dimensioal word embeddings\n", + " word2Ind (dict): dictionary that maps word to indices for matrix M\n", + " words (list of strings): words whose embeddings we want to visualize\n", + " \"\"\"\n", + "\n", + " # ------------------\n", + " # Write your implementation here.\n", + " for word in words:\n", + " wid = word2Ind[word]\n", + " x = M_reduced[wid, 0]\n", + " y = M_reduced[wid, 1]\n", + " plt.scatter(x, y, marker='x', color='red')\n", + " plt.text(x, y, word, fontsize=9 )\n", + " plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--------------------------------------------------------------------------------\n", + "Outputted Plot:\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--------------------------------------------------------------------------------\n" + ] + } + ], + "source": [ + "# ---------------------\n", + "# Run this sanity check\n", + "# Note that this is not an exhaustive check for correctness.\n", + "# The plot produced should look like the \"test solution plot\" depicted below. \n", + "# ---------------------\n", + "\n", + "print (\"-\" * 80)\n", + "print (\"Outputted Plot:\")\n", + "\n", + "M_reduced_plot_test = np.array([[1, 1], [-1, -1], [1, -1], [-1, 1], [0, 0]])\n", + "word2Ind_plot_test = {'test1': 0, 'test2': 1, 'test3': 2, 'test4': 3, 'test5': 4}\n", + "words = ['test1', 'test2', 'test3', 'test4', 'test5']\n", + "plot_embeddings(M_reduced_plot_test, word2Ind_plot_test, words)\n", + "\n", + "print (\"-\" * 80)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Test Plot Solution**\n", + "
\n", + " \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Question 1.5: Co-Occurrence Plot Analysis [written] (3 points)\n", + "\n", + "Now we will put together all the parts you have written! We will compute the co-occurrence matrix with fixed window of 4 (the default window size), over the Reuters \"crude\" (oil) corpus. Then we will use TruncatedSVD to compute 2-dimensional embeddings of each word. TruncatedSVD returns U\\*S, so we need to normalize the returned vectors, so that all the vectors will appear around the unit circle (therefore closeness is directional closeness). **Note**: The line of code below that does the normalizing uses the NumPy concept of *broadcasting*. If you don't know about broadcasting, check out\n", + "[Computation on Arrays: Broadcasting by Jake VanderPlas](https://jakevdp.github.io/PythonDataScienceHandbook/02.05-computation-on-arrays-broadcasting.html).\n", + "\n", + "Run the below cell to produce the plot. It'll probably take a few seconds to run. What clusters together in 2-dimensional embedding space? What doesn't cluster together that you might think should have? **Note:** \"bpd\" stands for \"barrels per day\" and is a commonly used abbreviation in crude oil topic articles." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running Truncated SVD over 8185 words...\n", + "Done.\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# -----------------------------\n", + "# Run This Cell to Produce Your Plot\n", + "# ------------------------------\n", + "reuters_corpus = read_corpus()\n", + "M_co_occurrence, word2Ind_co_occurrence = compute_co_occurrence_matrix(reuters_corpus)\n", + "M_reduced_co_occurrence = reduce_to_k_dim(M_co_occurrence, k=2)\n", + "\n", + "# Rescale (normalize) the rows to make them each of unit-length\n", + "M_lengths = np.linalg.norm(M_reduced_co_occurrence, axis=1)\n", + "M_normalized = M_reduced_co_occurrence / M_lengths[:, np.newaxis] # broadcasting\n", + "\n", + "words = ['barrels', 'bpd', 'ecuador', 'energy', 'industry', 'kuwait', 'oil', 'output', 'petroleum', 'venezuela']\n", + "\n", + "plot_embeddings(M_normalized, word2Ind_co_occurrence, words)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Ecuador, Venezuela, Kuwait seems to cluster together, all of them are countries. \"bpd\" and \"barrels\" don't cluster, both of them are quatitative words used to describe the amount of oil. Since they are used interchangeably, when one is used, the other is liked not used within the same word window. \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Part 2: Prediction-Based Word Vectors (15 points)\n", + "\n", + "As discussed in class, more recently prediction-based word vectors have demonstrated better performance, such as word2vec and GloVe (which also utilizes the benefit of counts). Here, we shall explore the embeddings produced by GloVe. Please revisit the class notes and lecture slides for more details on the word2vec and GloVe algorithms. If you're feeling adventurous, challenge yourself and try reading [GloVe's original paper](https://nlp.stanford.edu/pubs/glove.pdf).\n", + "\n", + "Then run the following cells to load the GloVe vectors into memory. **Note**: If this is your first time to run these cells, i.e. download the embedding model, it will take about 15 minutes to run. If you've run these cells before, rerunning them will load the model without redownloading it, which will take about 1 to 2 minutes." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "def load_embedding_model():\n", + " \"\"\" Load GloVe Vectors\n", + " Return:\n", + " wv_from_bin: All 400000 embeddings, each lengh 200\n", + " \"\"\"\n", + " import gensim.downloader as api\n", + " wv_from_bin = api.load(\"glove-wiki-gigaword-200\")\n", + " print(\"Loaded vocab size %i\" % len(wv_from_bin.vocab.keys()))\n", + " return wv_from_bin" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded vocab size 400000\n" + ] + } + ], + "source": [ + "# -----------------------------------\n", + "# Run Cell to Load Word Vectors\n", + "# Note: This will take several minutes\n", + "# -----------------------------------\n", + "wv_from_bin = load_embedding_model()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Note: If you are receiving reset by peer error, rerun the cell to restart the download. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Reducing dimensionality of Word Embeddings\n", + "Let's directly compare the GloVe embeddings to those of the co-occurrence matrix. In order to avoid running out of memory, we will work with a sample of 10000 GloVe vectors instead.\n", + "Run the following cells to:\n", + "\n", + "1. Put 10000 Glove vectors into a matrix M\n", + "2. Run reduce_to_k_dim (your Truncated SVD function) to reduce the vectors from 200-dimensional to 2-dimensional." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "def get_matrix_of_vectors(wv_from_bin, required_words=['barrels', 'bpd', 'ecuador', 'energy', 'industry', 'kuwait', 'oil', 'output', 'petroleum', 'venezuela']):\n", + " \"\"\" Put the GloVe vectors into a matrix M.\n", + " Param:\n", + " wv_from_bin: KeyedVectors object; the 400000 GloVe vectors loaded from file\n", + " Return:\n", + " M: numpy matrix shape (num words, 200) containing the vectors\n", + " word2Ind: dictionary mapping each word to its row number in M\n", + " \"\"\"\n", + " import random\n", + " words = list(wv_from_bin.vocab.keys())\n", + " print(\"Shuffling words ...\")\n", + " random.seed(224)\n", + " random.shuffle(words)\n", + " words = words[:10000]\n", + " print(\"Putting %i words into word2Ind and matrix M...\" % len(words))\n", + " word2Ind = {}\n", + " M = []\n", + " curInd = 0\n", + " for w in words:\n", + " try:\n", + " M.append(wv_from_bin.word_vec(w))\n", + " word2Ind[w] = curInd\n", + " curInd += 1\n", + " except KeyError:\n", + " continue\n", + " for w in required_words:\n", + " if w in words:\n", + " continue\n", + " try:\n", + " M.append(wv_from_bin.word_vec(w))\n", + " word2Ind[w] = curInd\n", + " curInd += 1\n", + " except KeyError:\n", + " continue\n", + " M = np.stack(M)\n", + " print(\"Done.\")\n", + " return M, word2Ind" + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shuffling words ...\n", + "Putting 10000 words into word2Ind and matrix M...\n", + "Done.\n", + "Running Truncated SVD over 10010 words...\n", + "Done.\n" + ] + } + ], + "source": [ + "# -----------------------------------------------------------------\n", + "# Run Cell to Reduce 200-Dimensional Word Embeddings to k Dimensions\n", + "# Note: This should be quick to run\n", + "# -----------------------------------------------------------------\n", + "M, word2Ind = get_matrix_of_vectors(wv_from_bin)\n", + "M_reduced = reduce_to_k_dim(M, k=2)\n", + "\n", + "# Rescale (normalize) the rows to make them each of unit-length\n", + "M_lengths = np.linalg.norm(M_reduced, axis=1)\n", + "M_reduced_normalized = M_reduced / M_lengths[:, np.newaxis] # broadcasting" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Note: If you are receiving out of memory issues on your local machine, try closing other applications to free more memory on your device. You may want to try restarting your machine so that you can free up extra memory. Then immediately run the jupyter notebook and see if you can load the word vectors properly. If you still have problems with loading the embeddings onto your local machine after this, please follow the Piazza instructions, as how to run remotely on Stanford Farmshare machines.**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Question 2.1: GloVe Plot Analysis [written] (4 points)\n", + "\n", + "Run the cell below to plot the 2D GloVe embeddings for `['barrels', 'bpd', 'ecuador', 'energy', 'industry', 'kuwait', 'oil', 'output', 'petroleum', 'venezuela']`.\n", + "\n", + "What clusters together in 2-dimensional embedding space? What doesn't cluster together that you might think should have? How is the plot different from the one generated earlier from the co-occurrence matrix? What is a possible reason for causing the difference?" + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "words = ['barrels', 'bpd', 'ecuador', 'energy', 'industry', 'kuwait', 'oil', 'output', 'petroleum', 'venezuela']\n", + "plot_embeddings(M_reduced_normalized, word2Ind, words)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### \"petroleum\", \"venazuela\" and \"ecuador\" cluster togeher.\n", + "\"bpd\" and \"barrels\" do not cluster together, same as their previous relationship in the previous matrix. However, this time they are further apart.\n", + "\n", + "Clustered ones from previous matrix seem to become tighter together, and the non-clustered ones seem to become further apart.\n", + "\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Cosine Similarity\n", + "Now that we have word vectors, we need a way to quantify the similarity between individual words, according to these vectors. One such metric is cosine-similarity. We will be using this to find words that are \"close\" and \"far\" from one another.\n", + "\n", + "We can think of n-dimensional vectors as points in n-dimensional space. If we take this perspective [L1](http://mathworld.wolfram.com/L1-Norm.html) and [L2](http://mathworld.wolfram.com/L2-Norm.html) Distances help quantify the amount of space \"we must travel\" to get between these two points. Another approach is to examine the angle between two vectors. From trigonometry we know that:\n", + "\n", + "\n", + "\n", + "Instead of computing the actual angle, we can leave the similarity in terms of $similarity = cos(\\Theta)$. Formally the [Cosine Similarity](https://en.wikipedia.org/wiki/Cosine_similarity) $s$ between two vectors $p$ and $q$ is defined as:\n", + "\n", + "$$s = \\frac{p \\cdot q}{||p|| ||q||}, \\textrm{ where } s \\in [-1, 1] $$ " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Question 2.2: Words with Multiple Meanings (2 points) [code + written] \n", + "Polysemes and homonyms are words that have more than one meaning (see this [wiki page](https://en.wikipedia.org/wiki/Polysemy) to learn more about the difference between polysemes and homonyms ). Find a word with at least 2 different meanings such that the top-10 most similar words (according to cosine similarity) contain related words from *both* meanings. For example, \"leaves\" has both \"vanishes\" and \"stalks\" in the top 10, and \"scoop\" has both \"handed_waffle_cone\" and \"lowdown\". You will probably need to try several polysemous or homonymic words before you find one. Please state the word you discover and the multiple meanings that occur in the top 10. Why do you think many of the polysemous or homonymic words you tried didn't work (i.e. the top-10 most similar words only contain **one** of the meanings of the words)?\n", + "\n", + "**Note**: You should use the `wv_from_bin.most_similar(word)` function to get the top 10 similar words. This function ranks all other words in the vocabulary with respect to their cosine similarity to the given word. For further assistance please check the __[GenSim documentation](https://radimrehurek.com/gensim/models/keyedvectors.html#gensim.models.keyedvectors.FastTextKeyedVectors.most_similar)__." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['pageant', 'missed', 'she', 'play', 'misses', 'sister', 'beauty',\n", + " 'chance', 'pageants', 'again'], dtype='The word \"miss\" has multiple meanings, its ten most similar words are: 'pageant', 'missed', 'she', 'play', 'misses', 'sister', 'beauty',\n", + " 'chance', 'pageants' and 'again,' While some words are polysemes or homononyms, they have one dominant meaning that appears more frequently than other meanings, as a result the top-10 most similar words all pertain to the dominant meaning." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Question 2.3: Synonyms & Antonyms (2 points) [code + written] \n", + "\n", + "When considering Cosine Similarity, it's often more convenient to think of Cosine Distance, which is simply 1 - Cosine Similarity.\n", + "\n", + "Find three words (w1,w2,w3) where w1 and w2 are synonyms and w1 and w3 are antonyms, but Cosine Distance(w1,w3) < Cosine Distance(w1,w2). For example, w1=\"happy\" is closer to w3=\"sad\" than to w2=\"cheerful\". \n", + "\n", + "Once you have found your example, please give a possible explanation for why this counter-intuitive result may have happened.\n", + "\n", + "You should use the the `wv_from_bin.distance(w1, w2)` function here in order to compute the cosine distance between two words. Please see the __[GenSim documentation](https://radimrehurek.com/gensim/models/keyedvectors.html#gensim.models.keyedvectors.FastTextKeyedVectors.distance)__ for further assistance." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + " # ------------------\n", + " # Write your implementation here.\n", + "\n", + "\n", + " # ------------------" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Write your answer here." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Solving Analogies with Word Vectors\n", + "Word vectors have been shown to *sometimes* exhibit the ability to solve analogies. \n", + "\n", + "As an example, for the analogy \"man : king :: woman : x\" (read: man is to king as woman is to x), what is x?\n", + "\n", + "In the cell below, we show you how to use word vectors to find x. The `most_similar` function finds words that are most similar to the words in the `positive` list and most dissimilar from the words in the `negative` list. The answer to the analogy will be the word ranked most similar (largest numerical value).\n", + "\n", + "**Note:** Further Documentation on the `most_similar` function can be found within the __[GenSim documentation](https://radimrehurek.com/gensim/models/keyedvectors.html#gensim.models.keyedvectors.FastTextKeyedVectors.most_similar)__." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Run this cell to answer the analogy -- man : king :: woman : x\n", + "pprint.pprint(wv_from_bin.most_similar(positive=['woman', 'king'], negative=['man']))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Question 2.4: Finding Analogies [code + written] (2 Points)\n", + "Find an example of analogy that holds according to these vectors (i.e. the intended word is ranked top). In your solution please state the full analogy in the form x:y :: a:b. If you believe the analogy is complicated, explain why the analogy holds in one or two sentences.\n", + "\n", + "**Note**: You may have to try many analogies to find one that works!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + " # ------------------\n", + " # Write your implementation here.\n", + "\n", + "\n", + " # ------------------" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Write your answer here." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Question 2.5: Incorrect Analogy [code + written] (1 point)\n", + "Find an example of analogy that does *not* hold according to these vectors. In your solution, state the intended analogy in the form x:y :: a:b, and state the (incorrect) value of b according to the word vectors." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + " # ------------------\n", + " # Write your implementation here.\n", + "\n", + "\n", + " # ------------------" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Write your answer here." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Question 2.6: Guided Analysis of Bias in Word Vectors [written] (1 point)\n", + "\n", + "It's important to be cognizant of the biases (gender, race, sexual orientation etc.) implicit in our word embeddings. Bias can be dangerous because it can reinforce stereotypes through applications that employ these models.\n", + "\n", + "Run the cell below, to examine (a) which terms are most similar to \"woman\" and \"worker\" and most dissimilar to \"man\", and (b) which terms are most similar to \"man\" and \"worker\" and most dissimilar to \"woman\". Point out the difference between the list of female-associated words and the list of male-associated words, and explain how it is reflecting gender bias." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Run this cell\n", + "# Here `positive` indicates the list of words to be similar to and `negative` indicates the list of words to be\n", + "# most dissimilar from.\n", + "pprint.pprint(wv_from_bin.most_similar(positive=['woman', 'worker'], negative=['man']))\n", + "print()\n", + "pprint.pprint(wv_from_bin.most_similar(positive=['man', 'worker'], negative=['woman']))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Write your answer here." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Question 2.7: Independent Analysis of Bias in Word Vectors [code + written] (1 point)\n", + "\n", + "Use the `most_similar` function to find another case where some bias is exhibited by the vectors. Please briefly explain the example of bias that you discover." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + " # ------------------\n", + " # Write your implementation here.\n", + "\n", + "\n", + " # ------------------" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Write your answer here." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Question 2.8: Thinking About Bias [written] (2 points)\n", + "\n", + "What might be the causes of these biases in the word vectors? You should give least 2 explainations how bias get into the word vectors. How might you be able to investigate/test these causes?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Write your answer here." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Submission Instructions\n", + "\n", + "1. Click the Save button at the top of the Jupyter Notebook.\n", + "2. Select Cell -> All Output -> Clear. This will clear all the outputs from all cells (but will keep the content of all cells). \n", + "2. Select Cell -> Run All. This will run all the cells in order, and will take several minutes.\n", + "3. Once you've rerun everything, select File -> Download as -> PDF via LaTeX (If you have trouble using \"PDF via LaTex\", you can also save the webpage as pdf. Make sure all your solutions especially the coding parts are displayed in the pdf, it's okay if the provided codes get cut off because lines are not wrapped in code cells).\n", + "4. Look at the PDF file and make sure all your solutions are there, displayed correctly. The PDF is the only thing your graders will see!\n", + "5. Submit your PDF on Gradescope." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Assignments/assignment1/Herais/.nbgrader.log b/Assignments/assignment1/Herais/.nbgrader.log new file mode 100644 index 0000000..a70eb65 --- /dev/null +++ b/Assignments/assignment1/Herais/.nbgrader.log @@ -0,0 +1,73 @@ +[NotebookApp | WARNING] No nbgrader_config.py file found (rerun with --debug to see where nbgrader is looking) +[NotebookApp | INFO] Loading the validate_assignment nbgrader 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CS224n! \n", + "\n", + "Before you start, make sure you read the README.txt in the same directory as this notebook. You will find many provided codes in the notebook. We highly encourage you to read and understand the provided codes as part of the learning :-)" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[nltk_data] Downloading package reuters to\n", + "[nltk_data] C:\\Users\\VSurfacePro3\\AppData\\Roaming\\nltk_data...\n", + "[nltk_data] Package reuters is already up-to-date!\n" + ] + } + ], + "source": [ + "# All Import Statements Defined Here\n", + "# Note: Do not add to this list.\n", + "# ----------------\n", + "\n", + "import sys\n", + "assert sys.version_info[0]==3\n", + "assert sys.version_info[1] >= 5\n", + "\n", + "from gensim.models import KeyedVectors\n", + "from gensim.test.utils import datapath\n", + "import pprint\n", + "import matplotlib.pyplot as plt\n", + "plt.rcParams['figure.figsize'] = [10, 5]\n", + "import nltk\n", + "nltk.download('reuters')\n", + "from nltk.corpus import reuters\n", + "import numpy as np\n", + "import random\n", + "import scipy as sp\n", + "from sklearn.decomposition import TruncatedSVD\n", + "from sklearn.decomposition import PCA\n", + "\n", + "START_TOKEN = ''\n", + "END_TOKEN = ''\n", + "\n", + "np.random.seed(0)\n", + "random.seed(0)\n", + "# ----------------" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Word Vectors\n", + "\n", + "Word Vectors are often used as a fundamental component for downstream NLP tasks, e.g. question answering, text generation, translation, etc., so it is important to build some intuitions as to their strengths and weaknesses. Here, you will explore two types of word vectors: those derived from *co-occurrence matrices*, and those derived via *GloVe*. \n", + "\n", + "**Assignment Notes:** Please make sure to save the notebook as you go along. Submission Instructions are located at the bottom of the notebook.\n", + "\n", + "**Note on Terminology:** The terms \"word vectors\" and \"word embeddings\" are often used interchangeably. The term \"embedding\" refers to the fact that we are encoding aspects of a word's meaning in a lower dimensional space. As [Wikipedia](https://en.wikipedia.org/wiki/Word_embedding) states, \"*conceptually it involves a mathematical embedding from a space with one dimension per word to a continuous vector space with a much lower dimension*\"." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Part 1: Count-Based Word Vectors (10 points)\n", + "\n", + "Most word vector models start from the following idea:\n", + "\n", + "*You shall know a word by the company it keeps ([Firth, J. R. 1957:11](https://en.wikipedia.org/wiki/John_Rupert_Firth))*\n", + "\n", + "Many word vector implementations are driven by the idea that similar words, i.e., (near) synonyms, will be used in similar contexts. As a result, similar words will often be spoken or written along with a shared subset of words, i.e., contexts. By examining these contexts, we can try to develop embeddings for our words. With this intuition in mind, many \"old school\" approaches to constructing word vectors relied on word counts. Here we elaborate upon one of those strategies, *co-occurrence matrices* (for more information, see [here](http://web.stanford.edu/class/cs124/lec/vectorsemantics.video.pdf) or [here](https://medium.com/data-science-group-iitr/word-embedding-2d05d270b285))." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Co-Occurrence\n", + "\n", + "A co-occurrence matrix counts how often things co-occur in some environment. Given some word $w_i$ occurring in the document, we consider the *context window* surrounding $w_i$. Supposing our fixed window size is $n$, then this is the $n$ preceding and $n$ subsequent words in that document, i.e. words $w_{i-n} \\dots w_{i-1}$ and $w_{i+1} \\dots w_{i+n}$. We build a *co-occurrence matrix* $M$, which is a symmetric word-by-word matrix in which $M_{ij}$ is the number of times $w_j$ appears inside $w_i$'s window among all documents.\n", + "\n", + "**Example: Co-Occurrence with Fixed Window of n=1**:\n", + "\n", + "Document 1: \"all that glitters is not gold\"\n", + "\n", + "Document 2: \"all is well that ends well\"\n", + "\n", + "\n", + "| * | `` | all | that | glitters | is | not | gold | well | ends | `` |\n", + "|----------|-------|-----|------|----------|------|------|-------|------|------|-----|\n", + "| `` | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", + "| all | 2 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |\n", + "| that | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 |\n", + "| glitters | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |\n", + "| is | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 |\n", + "| not | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 |\n", + "| gold | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 |\n", + "| well | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 1 |\n", + "| ends | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |\n", + "| `` | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 |\n", + "\n", + "**Note:** In NLP, we often add `` and `` tokens to represent the beginning and end of sentences, paragraphs or documents. In thise case we imagine `` and `` tokens encapsulating each document, e.g., \"`` All that glitters is not gold ``\", and include these tokens in our co-occurrence counts.\n", + "\n", + "The rows (or columns) of this matrix provide one type of word vectors (those based on word-word co-occurrence), but the vectors will be large in general (linear in the number of distinct words in a corpus). Thus, our next step is to run *dimensionality reduction*. In particular, we will run *SVD (Singular Value Decomposition)*, which is a kind of generalized *PCA (Principal Components Analysis)* to select the top $k$ principal components. Here's a visualization of dimensionality reduction with SVD. In this picture our co-occurrence matrix is $A$ with $n$ rows corresponding to $n$ words. We obtain a full matrix decomposition, with the singular values ordered in the diagonal $S$ matrix, and our new, shorter length-$k$ word vectors in $U_k$.\n", + "\n", + "![Picture of an SVD](./imgs/svd.png \"SVD\")\n", + "\n", + "This reduced-dimensionality co-occurrence representation preserves semantic relationships between words, e.g. *doctor* and *hospital* will be closer than *doctor* and *dog*. \n", + "\n", + "**Notes:** If you can barely remember what an eigenvalue is, here's [a slow, friendly introduction to SVD](https://davetang.org/file/Singular_Value_Decomposition_Tutorial.pdf). If you want to learn more thoroughly about PCA or SVD, feel free to check out lectures [7](https://web.stanford.edu/class/cs168/l/l7.pdf), [8](http://theory.stanford.edu/~tim/s15/l/l8.pdf), and [9](https://web.stanford.edu/class/cs168/l/l9.pdf) of CS168. These course notes provide a great high-level treatment of these general purpose algorithms. Though, for the purpose of this class, you only need to know how to extract the k-dimensional embeddings by utilizing pre-programmed implementations of these algorithms from the numpy, scipy, or sklearn python packages. In practice, it is challenging to apply full SVD to large corpora because of the memory needed to perform PCA or SVD. However, if you only want the top $k$ vector components for relatively small $k$ — known as [Truncated SVD](https://en.wikipedia.org/wiki/Singular_value_decomposition#Truncated_SVD) — then there are reasonably scalable techniques to compute those iteratively." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plotting Co-Occurrence Word Embeddings\n", + "\n", + "Here, we will be using the Reuters (business and financial news) corpus. If you haven't run the import cell at the top of this page, please run it now (click it and press SHIFT-RETURN). The corpus consists of 10,788 news documents totaling 1.3 million words. These documents span 90 categories and are split into train and test. For more details, please see https://www.nltk.org/book/ch02.html. We provide a `read_corpus` function below that pulls out only articles from the \"crude\" (i.e. news articles about oil, gas, etc.) category. The function also adds `` and `` tokens to each of the documents, and lowercases words. You do **not** have to perform any other kind of pre-processing." + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [], + "source": [ + "def read_corpus(category=\"crude\"):\n", + " \"\"\" Read files from the specified Reuter's category.\n", + " Params:\n", + " category (string): category name\n", + " Return:\n", + " list of lists, with words from each of the processed files\n", + " \"\"\"\n", + " files = reuters.fileids(category)\n", + " return [[START_TOKEN] + [w.lower() for w in list(reuters.words(f))] + [END_TOKEN] for f in files]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's have a look what these documents are like…." + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[['', 'japan', 'to', 'revise', 'long', '-', 'term', 'energy', 'demand', 'downwards', 'the',\n", + " 'ministry', 'of', 'international', 'trade', 'and', 'industry', '(', 'miti', ')', 'will', 'revise',\n", + " 'its', 'long', '-', 'term', 'energy', 'supply', '/', 'demand', 'outlook', 'by', 'august', 'to',\n", + " 'meet', 'a', 'forecast', 'downtrend', 'in', 'japanese', 'energy', 'demand', ',', 'ministry',\n", + " 'officials', 'said', '.', 'miti', 'is', 'expected', 'to', 'lower', 'the', 'projection', 'for',\n", + " 'primary', 'energy', 'supplies', 'in', 'the', 'year', '2000', 'to', '550', 'mln', 'kilolitres',\n", + " '(', 'kl', ')', 'from', '600', 'mln', ',', 'they', 'said', '.', 'the', 'decision', 'follows',\n", + " 'the', 'emergence', 'of', 'structural', 'changes', 'in', 'japanese', 'industry', 'following',\n", + " 'the', 'rise', 'in', 'the', 'value', 'of', 'the', 'yen', 'and', 'a', 'decline', 'in', 'domestic',\n", + " 'electric', 'power', 'demand', '.', 'miti', 'is', 'planning', 'to', 'work', 'out', 'a', 'revised',\n", + " 'energy', 'supply', '/', 'demand', 'outlook', 'through', 'deliberations', 'of', 'committee',\n", + " 'meetings', 'of', 'the', 'agency', 'of', 'natural', 'resources', 'and', 'energy', ',', 'the',\n", + " 'officials', 'said', '.', 'they', 'said', 'miti', 'will', 'also', 'review', 'the', 'breakdown',\n", + " 'of', 'energy', 'supply', 'sources', ',', 'including', 'oil', ',', 'nuclear', ',', 'coal', 'and',\n", + " 'natural', 'gas', '.', 'nuclear', 'energy', 'provided', 'the', 'bulk', 'of', 'japan', \"'\", 's',\n", + " 'electric', 'power', 'in', 'the', 'fiscal', 'year', 'ended', 'march', '31', ',', 'supplying',\n", + " 'an', 'estimated', '27', 'pct', 'on', 'a', 'kilowatt', '/', 'hour', 'basis', ',', 'followed',\n", + " 'by', 'oil', '(', '23', 'pct', ')', 'and', 'liquefied', 'natural', 'gas', '(', '21', 'pct', '),',\n", + " 'they', 'noted', '.', ''],\n", + " ['', 'energy', '/', 'u', '.', 's', '.', 'petrochemical', 'industry', 'cheap', 'oil',\n", + " 'feedstocks', ',', 'the', 'weakened', 'u', '.', 's', '.', 'dollar', 'and', 'a', 'plant',\n", + " 'utilization', 'rate', 'approaching', '90', 'pct', 'will', 'propel', 'the', 'streamlined', 'u',\n", + " '.', 's', '.', 'petrochemical', 'industry', 'to', 'record', 'profits', 'this', 'year', ',',\n", + " 'with', 'growth', 'expected', 'through', 'at', 'least', '1990', ',', 'major', 'company',\n", + " 'executives', 'predicted', '.', 'this', 'bullish', 'outlook', 'for', 'chemical', 'manufacturing',\n", + " 'and', 'an', 'industrywide', 'move', 'to', 'shed', 'unrelated', 'businesses', 'has', 'prompted',\n", + " 'gaf', 'corp', '&', 'lt', ';', 'gaf', '>,', 'privately', '-', 'held', 'cain', 'chemical', 'inc',\n", + " ',', 'and', 'other', 'firms', 'to', 'aggressively', 'seek', 'acquisitions', 'of', 'petrochemical',\n", + " 'plants', '.', 'oil', 'companies', 'such', 'as', 'ashland', 'oil', 'inc', '&', 'lt', ';', 'ash',\n", + " '>,', 'the', 'kentucky', '-', 'based', 'oil', 'refiner', 'and', 'marketer', ',', 'are', 'also',\n", + " 'shopping', 'for', 'money', '-', 'making', 'petrochemical', 'businesses', 'to', 'buy', '.', '\"',\n", + " 'i', 'see', 'us', 'poised', 'at', 'the', 'threshold', 'of', 'a', 'golden', 'period', ',\"', 'said',\n", + " 'paul', 'oreffice', ',', 'chairman', 'of', 'giant', 'dow', 'chemical', 'co', '&', 'lt', ';',\n", + " 'dow', '>,', 'adding', ',', '\"', 'there', \"'\", 's', 'no', 'major', 'plant', 'capacity', 'being',\n", + " 'added', 'around', 'the', 'world', 'now', '.', 'the', 'whole', 'game', 'is', 'bringing', 'out',\n", + " 'new', 'products', 'and', 'improving', 'the', 'old', 'ones', '.\"', 'analysts', 'say', 'the',\n", + " 'chemical', 'industry', \"'\", 's', 'biggest', 'customers', ',', 'automobile', 'manufacturers',\n", + " 'and', 'home', 'builders', 'that', 'use', 'a', 'lot', 'of', 'paints', 'and', 'plastics', ',',\n", + " 'are', 'expected', 'to', 'buy', 'quantities', 'this', 'year', '.', 'u', '.', 's', '.',\n", + " 'petrochemical', 'plants', 'are', 'currently', 'operating', 'at', 'about', '90', 'pct',\n", + " 'capacity', ',', 'reflecting', 'tighter', 'supply', 'that', 'could', 'hike', 'product', 'prices',\n", + " 'by', '30', 'to', '40', 'pct', 'this', 'year', ',', 'said', 'john', 'dosher', ',', 'managing',\n", + " 'director', 'of', 'pace', 'consultants', 'inc', 'of', 'houston', '.', 'demand', 'for', 'some',\n", + " 'products', 'such', 'as', 'styrene', 'could', 'push', 'profit', 'margins', 'up', 'by', 'as',\n", + " 'much', 'as', '300', 'pct', ',', 'he', 'said', '.', 'oreffice', ',', 'speaking', 'at', 'a',\n", + " 'meeting', 'of', 'chemical', 'engineers', 'in', 'houston', ',', 'said', 'dow', 'would', 'easily',\n", + " 'top', 'the', '741', 'mln', 'dlrs', 'it', 'earned', 'last', 'year', 'and', 'predicted', 'it',\n", + " 'would', 'have', 'the', 'best', 'year', 'in', 'its', 'history', '.', 'in', '1985', ',', 'when',\n", + " 'oil', 'prices', 'were', 'still', 'above', '25', 'dlrs', 'a', 'barrel', 'and', 'chemical',\n", + " 'exports', 'were', 'adversely', 'affected', 'by', 'the', 'strong', 'u', '.', 's', '.', 'dollar',\n", + " ',', 'dow', 'had', 'profits', 'of', '58', 'mln', 'dlrs', '.', '\"', 'i', 'believe', 'the',\n", + " 'entire', 'chemical', 'industry', 'is', 'headed', 'for', 'a', 'record', 'year', 'or', 'close',\n", + " 'to', 'it', ',\"', 'oreffice', 'said', '.', 'gaf', 'chairman', 'samuel', 'heyman', 'estimated',\n", + " 'that', 'the', 'u', '.', 's', '.', 'chemical', 'industry', 'would', 'report', 'a', '20', 'pct',\n", + " 'gain', 'in', 'profits', 'during', '1987', '.', 'last', 'year', ',', 'the', 'domestic',\n", + " 'industry', 'earned', 'a', 'total', 'of', '13', 'billion', 'dlrs', ',', 'a', '54', 'pct', 'leap',\n", + " 'from', '1985', '.', 'the', 'turn', 'in', 'the', 'fortunes', 'of', 'the', 'once', '-', 'sickly',\n", + " 'chemical', 'industry', 'has', 'been', 'brought', 'about', 'by', 'a', 'combination', 'of', 'luck',\n", + " 'and', 'planning', ',', 'said', 'pace', \"'\", 's', 'john', 'dosher', '.', 'dosher', 'said', 'last',\n", + " 'year', \"'\", 's', 'fall', 'in', 'oil', 'prices', 'made', 'feedstocks', 'dramatically', 'cheaper',\n", + " 'and', 'at', 'the', 'same', 'time', 'the', 'american', 'dollar', 'was', 'weakening', 'against',\n", + " 'foreign', 'currencies', '.', 'that', 'helped', 'boost', 'u', '.', 's', '.', 'chemical',\n", + " 'exports', '.', 'also', 'helping', 'to', 'bring', 'supply', 'and', 'demand', 'into', 'balance',\n", + " 'has', 'been', 'the', 'gradual', 'market', 'absorption', 'of', 'the', 'extra', 'chemical',\n", + " 'manufacturing', 'capacity', 'created', 'by', 'middle', 'eastern', 'oil', 'producers', 'in',\n", + " 'the', 'early', '1980s', '.', 'finally', ',', 'virtually', 'all', 'major', 'u', '.', 's', '.',\n", + " 'chemical', 'manufacturers', 'have', 'embarked', 'on', 'an', 'extensive', 'corporate',\n", + " 'restructuring', 'program', 'to', 'mothball', 'inefficient', 'plants', ',', 'trim', 'the',\n", + " 'payroll', 'and', 'eliminate', 'unrelated', 'businesses', '.', 'the', 'restructuring', 'touched',\n", + " 'off', 'a', 'flurry', 'of', 'friendly', 'and', 'hostile', 'takeover', 'attempts', '.', 'gaf', ',',\n", + " 'which', 'made', 'an', 'unsuccessful', 'attempt', 'in', '1985', 'to', 'acquire', 'union',\n", + " 'carbide', 'corp', '&', 'lt', ';', 'uk', '>,', 'recently', 'offered', 'three', 'billion', 'dlrs',\n", + " 'for', 'borg', 'warner', 'corp', '&', 'lt', ';', 'bor', '>,', 'a', 'chicago', 'manufacturer',\n", + " 'of', 'plastics', 'and', 'chemicals', '.', 'another', 'industry', 'powerhouse', ',', 'w', '.',\n", + " 'r', '.', 'grace', '&', 'lt', ';', 'gra', '>', 'has', 'divested', 'its', 'retailing', ',',\n", + " 'restaurant', 'and', 'fertilizer', 'businesses', 'to', 'raise', 'cash', 'for', 'chemical',\n", + " 'acquisitions', '.', 'but', 'some', 'experts', 'worry', 'that', 'the', 'chemical', 'industry',\n", + " 'may', 'be', 'headed', 'for', 'trouble', 'if', 'companies', 'continue', 'turning', 'their',\n", + " 'back', 'on', 'the', 'manufacturing', 'of', 'staple', 'petrochemical', 'commodities', ',', 'such',\n", + " 'as', 'ethylene', ',', 'in', 'favor', 'of', 'more', 'profitable', 'specialty', 'chemicals',\n", + " 'that', 'are', 'custom', '-', 'designed', 'for', 'a', 'small', 'group', 'of', 'buyers', '.', '\"',\n", + " 'companies', 'like', 'dupont', '&', 'lt', ';', 'dd', '>', 'and', 'monsanto', 'co', '&', 'lt', ';',\n", + " 'mtc', '>', 'spent', 'the', 'past', 'two', 'or', 'three', 'years', 'trying', 'to', 'get', 'out',\n", + " 'of', 'the', 'commodity', 'chemical', 'business', 'in', 'reaction', 'to', 'how', 'badly', 'the',\n", + " 'market', 'had', 'deteriorated', ',\"', 'dosher', 'said', '.', '\"', 'but', 'i', 'think', 'they',\n", + " 'will', 'eventually', 'kill', 'the', 'margins', 'on', 'the', 'profitable', 'chemicals', 'in',\n", + " 'the', 'niche', 'market', '.\"', 'some', 'top', 'chemical', 'executives', 'share', 'the',\n", + " 'concern', '.', '\"', 'the', 'challenge', 'for', 'our', 'industry', 'is', 'to', 'keep', 'from',\n", + " 'getting', 'carried', 'away', 'and', 'repeating', 'past', 'mistakes', ',\"', 'gaf', \"'\", 's',\n", + " 'heyman', 'cautioned', '.', '\"', 'the', 'shift', 'from', 'commodity', 'chemicals', 'may', 'be',\n", + " 'ill', '-', 'advised', '.', 'specialty', 'businesses', 'do', 'not', 'stay', 'special', 'long',\n", + " '.\"', 'houston', '-', 'based', 'cain', 'chemical', ',', 'created', 'this', 'month', 'by', 'the',\n", + " 'sterling', 'investment', 'banking', 'group', ',', 'believes', 'it', 'can', 'generate', '700',\n", + " 'mln', 'dlrs', 'in', 'annual', 'sales', 'by', 'bucking', 'the', 'industry', 'trend', '.',\n", + " 'chairman', 'gordon', 'cain', ',', 'who', 'previously', 'led', 'a', 'leveraged', 'buyout', 'of',\n", + " 'dupont', \"'\", 's', 'conoco', 'inc', \"'\", 's', 'chemical', 'business', ',', 'has', 'spent', '1',\n", + " '.', '1', 'billion', 'dlrs', 'since', 'january', 'to', 'buy', 'seven', 'petrochemical', 'plants',\n", + " 'along', 'the', 'texas', 'gulf', 'coast', '.', 'the', 'plants', 'produce', 'only', 'basic',\n", + " 'commodity', 'petrochemicals', 'that', 'are', 'the', 'building', 'blocks', 'of', 'specialty',\n", + " 'products', '.', '\"', 'this', 'kind', 'of', 'commodity', 'chemical', 'business', 'will', 'never',\n", + " 'be', 'a', 'glamorous', ',', 'high', '-', 'margin', 'business', ',\"', 'cain', 'said', ',',\n", + " 'adding', 'that', 'demand', 'is', 'expected', 'to', 'grow', 'by', 'about', 'three', 'pct',\n", + " 'annually', '.', 'garo', 'armen', ',', 'an', 'analyst', 'with', 'dean', 'witter', 'reynolds', ',',\n", + " 'said', 'chemical', 'makers', 'have', 'also', 'benefitted', 'by', 'increasing', 'demand', 'for',\n", + " 'plastics', 'as', 'prices', 'become', 'more', 'competitive', 'with', 'aluminum', ',', 'wood',\n", + " 'and', 'steel', 'products', '.', 'armen', 'estimated', 'the', 'upturn', 'in', 'the', 'chemical',\n", + " 'business', 'could', 'last', 'as', 'long', 'as', 'four', 'or', 'five', 'years', ',', 'provided',\n", + " 'the', 'u', '.', 's', '.', 'economy', 'continues', 'its', 'modest', 'rate', 'of', 'growth', '.',\n", + " ''],\n", + " ['', 'turkey', 'calls', 'for', 'dialogue', 'to', 'solve', 'dispute', 'turkey', 'said',\n", + " 'today', 'its', 'disputes', 'with', 'greece', ',', 'including', 'rights', 'on', 'the',\n", + " 'continental', 'shelf', 'in', 'the', 'aegean', 'sea', ',', 'should', 'be', 'solved', 'through',\n", + " 'negotiations', '.', 'a', 'foreign', 'ministry', 'statement', 'said', 'the', 'latest', 'crisis',\n", + " 'between', 'the', 'two', 'nato', 'members', 'stemmed', 'from', 'the', 'continental', 'shelf',\n", + " 'dispute', 'and', 'an', 'agreement', 'on', 'this', 'issue', 'would', 'effect', 'the', 'security',\n", + " ',', 'economy', 'and', 'other', 'rights', 'of', 'both', 'countries', '.', '\"', 'as', 'the',\n", + " 'issue', 'is', 'basicly', 'political', ',', 'a', 'solution', 'can', 'only', 'be', 'found', 'by',\n", + " 'bilateral', 'negotiations', ',\"', 'the', 'statement', 'said', '.', 'greece', 'has', 'repeatedly',\n", + " 'said', 'the', 'issue', 'was', 'legal', 'and', 'could', 'be', 'solved', 'at', 'the',\n", + " 'international', 'court', 'of', 'justice', '.', 'the', 'two', 'countries', 'approached', 'armed',\n", + " 'confrontation', 'last', 'month', 'after', 'greece', 'announced', 'it', 'planned', 'oil',\n", + " 'exploration', 'work', 'in', 'the', 'aegean', 'and', 'turkey', 'said', 'it', 'would', 'also',\n", + " 'search', 'for', 'oil', '.', 'a', 'face', '-', 'off', 'was', 'averted', 'when', 'turkey',\n", + " 'confined', 'its', 'research', 'to', 'territorrial', 'waters', '.', '\"', 'the', 'latest',\n", + " 'crises', 'created', 'an', 'historic', 'opportunity', 'to', 'solve', 'the', 'disputes', 'between',\n", + " 'the', 'two', 'countries', ',\"', 'the', 'foreign', 'ministry', 'statement', 'said', '.', 'turkey',\n", + " \"'\", 's', 'ambassador', 'in', 'athens', ',', 'nazmi', 'akiman', ',', 'was', 'due', 'to', 'meet',\n", + " 'prime', 'minister', 'andreas', 'papandreou', 'today', 'for', 'the', 'greek', 'reply', 'to', 'a',\n", + " 'message', 'sent', 'last', 'week', 'by', 'turkish', 'prime', 'minister', 'turgut', 'ozal', '.',\n", + " 'the', 'contents', 'of', 'the', 'message', 'were', 'not', 'disclosed', '.', '']]\n" + ] + } + ], + "source": [ + "reuters_corpus = read_corpus()\n", + "pprint.pprint(reuters_corpus[:3], compact=True, width=100)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Question 1.1: Implement `distinct_words` [code] (2 points)\n", + "\n", + "Write a method to work out the distinct words (word types) that occur in the corpus. You can do this with `for` loops, but it's more efficient to do it with Python list comprehensions. In particular, [this](https://coderwall.com/p/rcmaea/flatten-a-list-of-lists-in-one-line-in-python) may be useful to flatten a list of lists. If you're not familiar with Python list comprehensions in general, here's [more information](https://python-3-patterns-idioms-test.readthedocs.io/en/latest/Comprehensions.html).\n", + "\n", + "You may find it useful to use [Python sets](https://www.w3schools.com/python/python_sets.asp) to remove duplicate words." + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [], + "source": [ + "def distinct_words(corpus):\n", + " \"\"\" Determine a list of distinct words for the corpus.\n", + " Params:\n", + " corpus (list of list of strings): corpus of documents\n", + " Return:\n", + " corpus_words (list of strings): list of distinct words across the corpus, sorted (using python 'sorted' function)\n", + " num_corpus_words (integer): number of distinct words across the corpus\n", + " \"\"\"\n", + " corpus_words = []\n", + " num_corpus_words = -1\n", + " \n", + " # ------------------\n", + " # Write your implementation here.\n", + " corpus_words = sorted(list(set([words for rows in corpus for words in rows])))\n", + " num_corpus_words = len(corpus_words)\n", + "\n", + " # ------------------\n", + "\n", + " return corpus_words, num_corpus_words\n", + "\n", + "#import itertools\n", + "#list(itertools.chain(*corpus)) " + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--------------------------------------------------------------------------------\n", + "Passed All Tests!\n", + "--------------------------------------------------------------------------------\n" + ] + } + ], + "source": [ + "# ---------------------\n", + "# Run this sanity check\n", + "# Note that this not an exhaustive check for correctness.\n", + "# ---------------------\n", + "\n", + "# Define toy corpus\n", + "test_corpus = [\"{} All that glitters isn't gold {}\".format(START_TOKEN, END_TOKEN).split(\" \"), \"{} All's well that ends well {}\".format(START_TOKEN, END_TOKEN).split(\" \")]\n", + "test_corpus_words, num_corpus_words = distinct_words(test_corpus)\n", + "\n", + "# Correct answers\n", + "ans_test_corpus_words = sorted([START_TOKEN, \"All\", \"ends\", \"that\", \"gold\", \"All's\", \"glitters\", \"isn't\", \"well\", END_TOKEN])\n", + "ans_num_corpus_words = len(ans_test_corpus_words)\n", + "\n", + "# Test correct number of words\n", + "assert(num_corpus_words == ans_num_corpus_words), \"Incorrect number of distinct words. Correct: {}. Yours: {}\".format(ans_num_corpus_words, num_corpus_words)\n", + "\n", + "# Test correct words\n", + "assert (test_corpus_words == ans_test_corpus_words), \"Incorrect corpus_words.\\nCorrect: {}\\nYours: {}\".format(str(ans_test_corpus_words), str(test_corpus_words))\n", + "\n", + "# Print Success\n", + "print (\"-\" * 80)\n", + "print(\"Passed All Tests!\")\n", + "print (\"-\" * 80)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Question 1.2: Implement `compute_co_occurrence_matrix` [code] (3 points)\n", + "\n", + "Write a method that constructs a co-occurrence matrix for a certain window-size $n$ (with a default of 4), considering words $n$ before and $n$ after the word in the center of the window. Here, we start to use `numpy (np)` to represent vectors, matrices, and tensors. If you're not familiar with NumPy, there's a NumPy tutorial in the second half of this cs231n [Python NumPy tutorial](http://cs231n.github.io/python-numpy-tutorial/).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [], + "source": [ + "def compute_co_occurrence_matrix(corpus, window_size=4):\n", + " \"\"\" Compute co-occurrence matrix for the given corpus and window_size (default of 4).\n", + " \n", + " Note: Each word in a document should be at the center of a window. Words near edges will have a smaller\n", + " number of co-occurring words.\n", + " \n", + " For example, if we take the document \" All that glitters is not gold \" with window size of 4,\n", + " \"All\" will co-occur with \"\", \"that\", \"glitters\", \"is\", and \"not\".\n", + " \n", + " Params:\n", + " corpus (list of list of strings): corpus of documents\n", + " window_size (int): size of context window\n", + " Return:\n", + " M (a symmetric numpy matrix of shape (number of unique words in the corpus , number of unique words in the corpus)): \n", + " Co-occurence matrix of word counts. \n", + " The ordering of the words in the rows/columns should be the same as the ordering of the words given by the distinct_words function.\n", + " word2Ind (dict): dictionary that maps word to index (i.e. row/column number) for matrix M.\n", + " \"\"\"\n", + " words, num_words = distinct_words(corpus)\n", + " M = None\n", + " word2Ind = {}\n", + " \n", + " # ------------------\n", + " # Write your implementation here.\n", + " M = np.zeros(shape=(num_words, num_words)) # n-by-n matrix filled w/ 0\n", + " for i, word in enumerate(words): # map word to index\n", + " word2Ind[word] = i\n", + " \n", + " for doc in corpus:\n", + " lwords = [None]*window_size + doc + [None]*window_size\n", + " for i in range(len(lwords)):\n", + " if (lwords[i] != None):\n", + " word_ctr = lwords[i]\n", + " words_context = list(filter(None, lwords[i-window_size:i] + lwords[i+1:i+window_size+1]))\n", + " for word_con in words_context:\n", + " if (word_ctr != word_con):\n", + " M[word2Ind[word_ctr], word2Ind[word_con]] += 1\n", + " # ------------------\n", + "\n", + " return M, word2Ind" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--------------------------------------------------------------------------------\n", + "Passed All Tests!\n", + "--------------------------------------------------------------------------------\n" + ] + } + ], + "source": [ + "# ---------------------\n", + "# Run this sanity check\n", + "# Note that this is not an exhaustive check for correctness.\n", + "# ---------------------\n", + "\n", + "# Define toy corpus and get student's co-occurrence matrix\n", + "test_corpus = [\"{} All that glitters isn't gold {}\".format(START_TOKEN, END_TOKEN).split(\" \"), \"{} All's well that ends well {}\".format(START_TOKEN, END_TOKEN).split(\" \")]\n", + "M_test, word2Ind_test = compute_co_occurrence_matrix(test_corpus, window_size=1)\n", + "\n", + "# Correct M and word2Ind\n", + "M_test_ans = np.array( \n", + " [[0., 0., 0., 0., 0., 0., 1., 0., 0., 1.,],\n", + " [0., 0., 1., 1., 0., 0., 0., 0., 0., 0.,],\n", + " [0., 1., 0., 0., 0., 0., 0., 0., 1., 0.,],\n", + " [0., 1., 0., 0., 0., 0., 0., 0., 0., 1.,],\n", + " [0., 0., 0., 0., 0., 0., 0., 0., 1., 1.,],\n", + " [0., 0., 0., 0., 0., 0., 0., 1., 1., 0.,],\n", + " [1., 0., 0., 0., 0., 0., 0., 1., 0., 0.,],\n", + " [0., 0., 0., 0., 0., 1., 1., 0., 0., 0.,],\n", + " [0., 0., 1., 0., 1., 1., 0., 0., 0., 1.,],\n", + " [1., 0., 0., 1., 1., 0., 0., 0., 1., 0.,]]\n", + ")\n", + "ans_test_corpus_words = sorted([START_TOKEN, \"All\", \"ends\", \"that\", \"gold\", \"All's\", \"glitters\", \"isn't\", \"well\", END_TOKEN])\n", + "word2Ind_ans = dict(zip(ans_test_corpus_words, range(len(ans_test_corpus_words))))\n", + "\n", + "# Test correct word2Ind\n", + "assert (word2Ind_ans == word2Ind_test), \"Your word2Ind is incorrect:\\nCorrect: {}\\nYours: {}\".format(word2Ind_ans, word2Ind_test)\n", + "\n", + "# Test correct M shape\n", + "assert (M_test.shape == M_test_ans.shape), \"M matrix has incorrect shape.\\nCorrect: {}\\nYours: {}\".format(M_test.shape, M_test_ans.shape)\n", + "\n", + "# Test correct M values\n", + "for w1 in word2Ind_ans.keys():\n", + " idx1 = word2Ind_ans[w1]\n", + " for w2 in word2Ind_ans.keys():\n", + " idx2 = word2Ind_ans[w2]\n", + " student = M_test[idx1, idx2]\n", + " correct = M_test_ans[idx1, idx2]\n", + " if student != correct:\n", + " print(\"Correct M:\")\n", + " print(M_test_ans)\n", + " print(\"Your M: \")\n", + " print(M_test)\n", + " raise AssertionError(\"Incorrect count at index ({}, {})=({}, {}) in matrix M. Yours has {} but should have {}.\".format(idx1, idx2, w1, w2, student, correct))\n", + "\n", + "# Print Success\n", + "print (\"-\" * 80)\n", + "print(\"Passed All Tests!\")\n", + "print (\"-\" * 80)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Question 1.3: Implement `reduce_to_k_dim` [code] (1 point)\n", + "\n", + "Construct a method that performs dimensionality reduction on the matrix to produce k-dimensional embeddings. Use SVD to take the top k components and produce a new matrix of k-dimensional embeddings. \n", + "\n", + "**Note:** All of numpy, scipy, and scikit-learn (`sklearn`) provide *some* implementation of SVD, but only scipy and sklearn provide an implementation of Truncated SVD, and only sklearn provides an efficient randomized algorithm for calculating large-scale Truncated SVD. So please use [sklearn.decomposition.TruncatedSVD](https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.TruncatedSVD.html)." + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [], + "source": [ + "def reduce_to_k_dim(M, k=2):\n", + " \"\"\" Reduce a co-occurence count matrix of dimensionality (num_corpus_words, num_corpus_words)\n", + " to a matrix of dimensionality (num_corpus_words, k) using the following SVD function from Scikit-Learn:\n", + " - http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.TruncatedSVD.html\n", + " \n", + " Params:\n", + " M (numpy matrix of shape (number of unique words in the corpus , number of unique words in the corpus)): co-occurence matrix of word counts\n", + " k (int): embedding size of each word after dimension reduction\n", + " Return:\n", + " M_reduced (numpy matrix of shape (number of corpus words, k)): matrix of k-dimensioal word embeddings.\n", + " In terms of the SVD from math class, this actually returns U * S\n", + " \"\"\" \n", + " n_iters = 10 # Use this parameter in your call to `TruncatedSVD`\n", + " M_reduced = None\n", + " print(\"Running Truncated SVD over %i words...\" % (M.shape[0]))\n", + " \n", + " # ------------------\n", + " # Write your implementation here.\n", + " svd = TruncatedSVD(n_components=k, n_iter=n_iters)\n", + " M_reduced = svd.fit_transform(M)\n", + " \n", + " # ------------------\n", + "\n", + " print(\"Done.\")\n", + " return M_reduced" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running Truncated SVD over 10 words...\n", + "Done.\n", + "--------------------------------------------------------------------------------\n", + "Passed All Tests!\n", + "--------------------------------------------------------------------------------\n" + ] + } + ], + "source": [ + "# ---------------------\n", + "# Run this sanity check\n", + "# Note that this is not an exhaustive check for correctness \n", + "# In fact we only check that your M_reduced has the right dimensions.\n", + "# ---------------------\n", + "\n", + "# Define toy corpus and run student code\n", + "test_corpus = [\"{} All that glitters isn't gold {}\".format(START_TOKEN, END_TOKEN).split(\" \"), \"{} All's well that ends well {}\".format(START_TOKEN, END_TOKEN).split(\" \")]\n", + "M_test, word2Ind_test = compute_co_occurrence_matrix(test_corpus, window_size=1)\n", + "M_test_reduced = reduce_to_k_dim(M_test, k=2)\n", + "\n", + "# Test proper dimensions\n", + "assert (M_test_reduced.shape[0] == 10), \"M_reduced has {} rows; should have {}\".format(M_test_reduced.shape[0], 10)\n", + "assert (M_test_reduced.shape[1] == 2), \"M_reduced has {} columns; should have {}\".format(M_test_reduced.shape[1], 2)\n", + "\n", + "# Print Success\n", + "print (\"-\" * 80)\n", + "print(\"Passed All Tests!\")\n", + "print (\"-\" * 80)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Question 1.4: Implement `plot_embeddings` [code] (1 point)\n", + "\n", + "Here you will write a function to plot a set of 2D vectors in 2D space. For graphs, we will use Matplotlib (`plt`).\n", + "\n", + "For this example, you may find it useful to adapt [this code](https://www.pythonmembers.club/2018/05/08/matplotlib-scatter-plot-annotate-set-text-at-label-each-point/). In the future, a good way to make a plot is to look at [the Matplotlib gallery](https://matplotlib.org/gallery/index.html), find a plot that looks somewhat like what you want, and adapt the code they give." + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_embeddings(M_reduced, word2Ind, words):\n", + " \"\"\" Plot in a scatterplot the embeddings of the words specified in the list \"words\".\n", + " NOTE: do not plot all the words listed in M_reduced / word2Ind.\n", + " Include a label next to each point.\n", + " \n", + " Params:\n", + " M_reduced (numpy matrix of shape (number of unique words in the corpus , 2)): matrix of 2-dimensioal word embeddings\n", + " word2Ind (dict): dictionary that maps word to indices for matrix M\n", + " words (list of strings): words whose embeddings we want to visualize\n", + " \"\"\"\n", + "\n", + " # ------------------\n", + " # Write your implementation here.\n", + " for word in words:\n", + " wid = word2Ind[word]\n", + " x = M_reduced[wid, 0]\n", + " y = M_reduced[wid, 1]\n", + " plt.scatter(x, y, marker='x', color='red')\n", + " plt.text(x, y, word, fontsize=9 )\n", + " plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--------------------------------------------------------------------------------\n", + "Outputted Plot:\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--------------------------------------------------------------------------------\n" + ] + } + ], + "source": [ + "# ---------------------\n", + "# Run this sanity check\n", + "# Note that this is not an exhaustive check for correctness.\n", + "# The plot produced should look like the \"test solution plot\" depicted below. \n", + "# ---------------------\n", + "\n", + "print (\"-\" * 80)\n", + "print (\"Outputted Plot:\")\n", + "\n", + "M_reduced_plot_test = np.array([[1, 1], [-1, -1], [1, -1], [-1, 1], [0, 0]])\n", + "word2Ind_plot_test = {'test1': 0, 'test2': 1, 'test3': 2, 'test4': 3, 'test5': 4}\n", + "words = ['test1', 'test2', 'test3', 'test4', 'test5']\n", + "plot_embeddings(M_reduced_plot_test, word2Ind_plot_test, words)\n", + "\n", + "print (\"-\" * 80)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Test Plot Solution**\n", + "
\n", + " \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Question 1.5: Co-Occurrence Plot Analysis [written] (3 points)\n", + "\n", + "Now we will put together all the parts you have written! We will compute the co-occurrence matrix with fixed window of 4 (the default window size), over the Reuters \"crude\" (oil) corpus. Then we will use TruncatedSVD to compute 2-dimensional embeddings of each word. TruncatedSVD returns U\\*S, so we need to normalize the returned vectors, so that all the vectors will appear around the unit circle (therefore closeness is directional closeness). **Note**: The line of code below that does the normalizing uses the NumPy concept of *broadcasting*. If you don't know about broadcasting, check out\n", + "[Computation on Arrays: Broadcasting by Jake VanderPlas](https://jakevdp.github.io/PythonDataScienceHandbook/02.05-computation-on-arrays-broadcasting.html).\n", + "\n", + "Run the below cell to produce the plot. It'll probably take a few seconds to run. What clusters together in 2-dimensional embedding space? What doesn't cluster together that you might think should have? **Note:** \"bpd\" stands for \"barrels per day\" and is a commonly used abbreviation in crude oil topic articles." + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running Truncated SVD over 8185 words...\n", + "Done.\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# -----------------------------\n", + "# Run This Cell to Produce Your Plot\n", + "# ------------------------------\n", + "reuters_corpus = read_corpus()\n", + "M_co_occurrence, word2Ind_co_occurrence = compute_co_occurrence_matrix(reuters_corpus)\n", + "M_reduced_co_occurrence = reduce_to_k_dim(M_co_occurrence, k=2)\n", + "\n", + "# Rescale (normalize) the rows to make them each of unit-length\n", + "M_lengths = np.linalg.norm(M_reduced_co_occurrence, axis=1)\n", + "M_normalized = M_reduced_co_occurrence / M_lengths[:, np.newaxis] # broadcasting\n", + "\n", + "words = ['barrels', 'bpd', 'ecuador', 'energy', 'industry', 'kuwait', 'oil', 'output', 'petroleum', 'venezuela']\n", + "\n", + "plot_embeddings(M_normalized, word2Ind_co_occurrence, words)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Ecuador, Venezuela, Kuwait seems to cluster together, all of them are countries. \"bpd\" and \"barrels\" don't cluster, both of them are quatitative words used to describe the amount of oil. Since they are used interchangeably, when one is used, the other is liked not used within the same word window. \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Part 2: Prediction-Based Word Vectors (15 points)\n", + "\n", + "As discussed in class, more recently prediction-based word vectors have demonstrated better performance, such as word2vec and GloVe (which also utilizes the benefit of counts). Here, we shall explore the embeddings produced by GloVe. Please revisit the class notes and lecture slides for more details on the word2vec and GloVe algorithms. If you're feeling adventurous, challenge yourself and try reading [GloVe's original paper](https://nlp.stanford.edu/pubs/glove.pdf).\n", + "\n", + "Then run the following cells to load the GloVe vectors into memory. **Note**: If this is your first time to run these cells, i.e. download the embedding model, it will take about 15 minutes to run. If you've run these cells before, rerunning them will load the model without redownloading it, which will take about 1 to 2 minutes." + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [], + "source": [ + "def load_embedding_model():\n", + " \"\"\" Load GloVe Vectors\n", + " Return:\n", + " wv_from_bin: All 400000 embeddings, each lengh 200\n", + " \"\"\"\n", + " import gensim.downloader as api\n", + " wv_from_bin = api.load(\"glove-wiki-gigaword-200\")\n", + " print(\"Loaded vocab size %i\" % len(wv_from_bin.vocab.keys()))\n", + " return wv_from_bin" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded vocab size 400000\n" + ] + } + ], + "source": [ + "# -----------------------------------\n", + "# Run Cell to Load Word Vectors\n", + "# Note: This will take several minutes\n", + "# -----------------------------------\n", + "wv_from_bin = load_embedding_model()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Note: If you are receiving reset by peer error, rerun the cell to restart the download. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Reducing dimensionality of Word Embeddings\n", + "Let's directly compare the GloVe embeddings to those of the co-occurrence matrix. In order to avoid running out of memory, we will work with a sample of 10000 GloVe vectors instead.\n", + "Run the following cells to:\n", + "\n", + "1. Put 10000 Glove vectors into a matrix M\n", + "2. Run reduce_to_k_dim (your Truncated SVD function) to reduce the vectors from 200-dimensional to 2-dimensional." + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [], + "source": [ + "def get_matrix_of_vectors(wv_from_bin, required_words=['barrels', 'bpd', 'ecuador', 'energy', 'industry', 'kuwait', 'oil', 'output', 'petroleum', 'venezuela']):\n", + " \"\"\" Put the GloVe vectors into a matrix M.\n", + " Param:\n", + " wv_from_bin: KeyedVectors object; the 400000 GloVe vectors loaded from file\n", + " Return:\n", + " M: numpy matrix shape (num words, 200) containing the vectors\n", + " word2Ind: dictionary mapping each word to its row number in M\n", + " \"\"\"\n", + " import random\n", + " words = list(wv_from_bin.vocab.keys())\n", + " print(\"Shuffling words ...\")\n", + " random.seed(224)\n", + " random.shuffle(words)\n", + " words = words[:10000]\n", + " print(\"Putting %i words into word2Ind and matrix M...\" % len(words))\n", + " word2Ind = {}\n", + " M = []\n", + " curInd = 0\n", + " for w in words:\n", + " try:\n", + " M.append(wv_from_bin.word_vec(w))\n", + " word2Ind[w] = curInd\n", + " curInd += 1\n", + " except KeyError:\n", + " continue\n", + " for w in required_words:\n", + " if w in words:\n", + " continue\n", + " try:\n", + " M.append(wv_from_bin.word_vec(w))\n", + " word2Ind[w] = curInd\n", + " curInd += 1\n", + " except KeyError:\n", + " continue\n", + " M = np.stack(M)\n", + " print(\"Done.\")\n", + " return M, word2Ind" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shuffling words ...\n", + "Putting 10000 words into word2Ind and matrix M...\n", + "Done.\n", + "Running Truncated SVD over 10010 words...\n", + "Done.\n" + ] + } + ], + "source": [ + "# -----------------------------------------------------------------\n", + "# Run Cell to Reduce 200-Dimensional Word Embeddings to k Dimensions\n", + "# Note: This should be quick to run\n", + "# -----------------------------------------------------------------\n", + "M, word2Ind = get_matrix_of_vectors(wv_from_bin)\n", + "M_reduced = reduce_to_k_dim(M, k=2)\n", + "\n", + "# Rescale (normalize) the rows to make them each of unit-length\n", + "M_lengths = np.linalg.norm(M_reduced, axis=1)\n", + "M_reduced_normalized = M_reduced / M_lengths[:, np.newaxis] # broadcasting" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Note: If you are receiving out of memory issues on your local machine, try closing other applications to free more memory on your device. You may want to try restarting your machine so that you can free up extra memory. Then immediately run the jupyter notebook and see if you can load the word vectors properly. If you still have problems with loading the embeddings onto your local machine after this, please follow the Piazza instructions, as how to run remotely on Stanford Farmshare machines.**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Question 2.1: GloVe Plot Analysis [written] (4 points)\n", + "\n", + "Run the cell below to plot the 2D GloVe embeddings for `['barrels', 'bpd', 'ecuador', 'energy', 'industry', 'kuwait', 'oil', 'output', 'petroleum', 'venezuela']`.\n", + "\n", + "What clusters together in 2-dimensional embedding space? What doesn't cluster together that you might think should have? How is the plot different from the one generated earlier from the co-occurrence matrix? What is a possible reason for causing the difference?" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "words = ['barrels', 'bpd', 'ecuador', 'energy', 'industry', 'kuwait', 'oil', 'output', 'petroleum', 'venezuela']\n", + "plot_embeddings(M_reduced_normalized, word2Ind, words)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### \"petroleum\", \"venazuela\" and \"ecuador\" cluster togeher.\n", + "\"bpd\" and \"barrels\" do not cluster together, same as their previous relationship in the previous matrix. However, this time they are further apart.\n", + "\n", + "Clustered ones from previous matrix seem to become tighter together, and the non-clustered ones seem to become further apart.\n", + "\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Cosine Similarity\n", + "Now that we have word vectors, we need a way to quantify the similarity between individual words, according to these vectors. One such metric is cosine-similarity. We will be using this to find words that are \"close\" and \"far\" from one another.\n", + "\n", + "We can think of n-dimensional vectors as points in n-dimensional space. If we take this perspective [L1](http://mathworld.wolfram.com/L1-Norm.html) and [L2](http://mathworld.wolfram.com/L2-Norm.html) Distances help quantify the amount of space \"we must travel\" to get between these two points. Another approach is to examine the angle between two vectors. From trigonometry we know that:\n", + "\n", + "\n", + "\n", + "Instead of computing the actual angle, we can leave the similarity in terms of $similarity = cos(\\Theta)$. Formally the [Cosine Similarity](https://en.wikipedia.org/wiki/Cosine_similarity) $s$ between two vectors $p$ and $q$ is defined as:\n", + "\n", + "$$s = \\frac{p \\cdot q}{||p|| ||q||}, \\textrm{ where } s \\in [-1, 1] $$ " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Question 2.2: Words with Multiple Meanings (2 points) [code + written] \n", + "Polysemes and homonyms are words that have more than one meaning (see this [wiki page](https://en.wikipedia.org/wiki/Polysemy) to learn more about the difference between polysemes and homonyms ). Find a word with at least 2 different meanings such that the top-10 most similar words (according to cosine similarity) contain related words from *both* meanings. For example, \"leaves\" has both \"vanishes\" and \"stalks\" in the top 10, and \"scoop\" has both \"handed_waffle_cone\" and \"lowdown\". You will probably need to try several polysemous or homonymic words before you find one. Please state the word you discover and the multiple meanings that occur in the top 10. Why do you think many of the polysemous or homonymic words you tried didn't work (i.e. the top-10 most similar words only contain **one** of the meanings of the words)?\n", + "\n", + "**Note**: You should use the `wv_from_bin.most_similar(word)` function to get the top 10 similar words. This function ranks all other words in the vocabulary with respect to their cosine similarity to the given word. For further assistance please check the __[GenSim documentation](https://radimrehurek.com/gensim/models/keyedvectors.html#gensim.models.keyedvectors.FastTextKeyedVectors.most_similar)__." + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": {}, + "outputs": [ + { + "ename": "SyntaxError", + "evalue": "invalid syntax (, line 5)", + "output_type": "error", + "traceback": [ + "\u001b[1;36m File \u001b[1;32m\"\"\u001b[1;36m, line \u001b[1;32m5\u001b[0m\n\u001b[1;33m wv_from_bin.most_similar(word):\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n" + ] + } + ], + "source": [ + " # ------------------\n", + " # Write your implementation here.\n", + "\n", + " word = 'miss'\n", + " wv_from_bin.most_similar(word):\n", + "# li = []\n", + "# for i in wv_from_bin.most_similar(word): \n", + "# li.append(i[0])\n", + "# np.array(li)\n", + " # ------------------" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### The word \"miss\" has multiple meanings, its ten most similar words are: 'pageant', 'missed', 'she', 'play', 'misses', 'sister', 'beauty', 'chance', 'pageants' and 'again,' While some words are polysemes or homononyms, they have one dominant meaning that appears more frequently than other meanings, as a result the top-10 most similar words all pertain to the dominant meaning." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Question 2.3: Synonyms & Antonyms (2 points) [code + written] \n", + "\n", + "When considering Cosine Similarity, it's often more convenient to think of Cosine Distance, which is simply 1 - Cosine Similarity.\n", + "\n", + "Find three words (w1,w2,w3) where w1 and w2 are synonyms and w1 and w3 are antonyms, but Cosine Distance(w1,w3) < Cosine Distance(w1,w2). For example, w1=\"happy\" is closer to w3=\"sad\" than to w2=\"cheerful\". \n", + "\n", + "Once you have found your example, please give a possible explanation for why this counter-intuitive result may have happened.\n", + "\n", + "You should use the the `wv_from_bin.distance(w1, w2)` function here in order to compute the cosine distance between two words. Please see the __[GenSim documentation](https://radimrehurek.com/gensim/models/keyedvectors.html#gensim.models.keyedvectors.FastTextKeyedVectors.distance)__ for further assistance." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + " # ------------------\n", + " # Write your implementation here.\n", + " w1 = 'short' # synonyms\n", + " w2 = 'tiny' # synonyms \n", + " w3 = 'tall' #antonyms\n", + " wv_from_bin.most_similar(w1)\n", + " d_w1_w3 = wv_from_bin.distance(w1, w3)\n", + " d_w1_w2 = wv_from_bin.distance(w1, w2)\n", + " print(d_w1_w3, ' < ', d_w1_w2)\n", + " # Cosine Distance(w1,w3) < Cosine Distance(w1,w2)\n", + " # ------------------" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### The three words w1, w2 and w3 are \"short,\" 'tiny\" and \"tall\" respectively. Certain phrases are constructed by antonyms to illustrate concepts, resulting in more frequent co-occurances of the antonyms. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Solving Analogies with Word Vectors\n", + "Word vectors have been shown to *sometimes* exhibit the ability to solve analogies. \n", + "\n", + "As an example, for the analogy \"man : king :: woman : x\" (read: man is to king as woman is to x), what is x?\n", + "\n", + "In the cell below, we show you how to use word vectors to find x. The `most_similar` function finds words that are most similar to the words in the `positive` list and most dissimilar from the words in the `negative` list. The answer to the analogy will be the word ranked most similar (largest numerical value).\n", + "\n", + "**Note:** Further Documentation on the `most_similar` function can be found within the __[GenSim documentation](https://radimrehurek.com/gensim/models/keyedvectors.html#gensim.models.keyedvectors.FastTextKeyedVectors.most_similar)__." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Run this cell to answer the analogy -- man : king :: woman : x\n", + "pprint.pprint(wv_from_bin.most_similar(positive=['woman', 'king'], negative=['man']))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Question 2.4: Finding Analogies [code + written] (2 Points)\n", + "Find an example of analogy that holds according to these vectors (i.e. the intended word is ranked top). In your solution please state the full analogy in the form x:y :: a:b. If you believe the analogy is complicated, explain why the analogy holds in one or two sentences.\n", + "\n", + "**Note**: You may have to try many analogies to find one that works!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + " # ------------------\n", + " # Write your implementation here.\n", + " # x:y :: a:b\n", + " x = 'girl'\n", + " y = 'princess'\n", + " a = 'boy'\n", + " # b_espected = 'prince'\n", + " pprint.pprint(wv_from_bin.most_similar(positive=[a, y], negative=[x]))\n", + " # ------------------" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### girl:princess :: boy:prince" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Question 2.5: Incorrect Analogy [code + written] (1 point)\n", + "Find an example of analogy that does *not* hold according to these vectors. In your solution, state the intended analogy in the form x:y :: a:b, and state the (incorrect) value of b according to the word vectors." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + " # ------------------\n", + " # Write your implementation here.\n", + " x = 'say'\n", + " y = 'said'\n", + " a = 'wait'\n", + " # b_espected = 'waited'\n", + " pprint.pprint(wv_from_bin.most_similar(positive=[a, y], negative=[x]))\n", + " # ------------------" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### say:said :: wait:waited, waiting." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Question 2.6: Guided Analysis of Bias in Word Vectors [written] (1 point)\n", + "\n", + "It's important to be cognizant of the biases (gender, race, sexual orientation etc.) implicit in our word embeddings. Bias can be dangerous because it can reinforce stereotypes through applications that employ these models.\n", + "\n", + "Run the cell below, to examine (a) which terms are most similar to \"woman\" and \"worker\" and most dissimilar to \"man\", and (b) which terms are most similar to \"man\" and \"worker\" and most dissimilar to \"woman\". Point out the difference between the list of female-associated words and the list of male-associated words, and explain how it is reflecting gender bias." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Run this cell\n", + "# Here `positive` indicates the list of words to be similar to and `negative` indicates the list of words to be\n", + "# most dissimilar from.\n", + "pprint.pprint(wv_from_bin.most_similar(positive=['woman', 'worker'], negative=['man']))\n", + "print()\n", + "pprint.pprint(wv_from_bin.most_similar(positive=['man', 'worker'], negative=['woman']))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### woman worker and man worker seem to differ in physical states and occupations. For physical state, Woman is close ot pregnant, while man is not. For occupations, woman is close to mother, teacher, child, homemaker and nurses, while man is close to mechanic and factory." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Question 2.7: Independent Analysis of Bias in Word Vectors [code + written] (1 point)\n", + "\n", + "Use the `most_similar` function to find another case where some bias is exhibited by the vectors. Please briefly explain the example of bias that you discover." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + " # ------------------\n", + " # Write your implementation here.\n", + "pprint.pprint(wv_from_bin.most_similar(positive=['british', 'food'], negative=['french']))\n", + "print()\n", + "pprint.pprint(wv_from_bin.most_similar(positive=['french', 'food'], negative=['british']))\n", + " # ------------------" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### \"British\" \"food\" has very little food varieties from similar words analysis, where as \"French\" \"food\" has vast varieties, such as \"vegetables,\" \"bread\" and \"seafood\" from similar words analysis, suggesting that the French culture places greater emphasis on eating. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Question 2.8: Thinking About Bias [written] (2 points)\n", + "\n", + "What might be the causes of these biases in the word vectors? You should give least 2 explainations how bias get into the word vectors. How might you be able to investigate/test these causes?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Real word literatures are baised with stereotypes, using these literatures as training corpus preserves such bias in the learned models. While some word pairs theoretically share similar characteristics, a pair receives more attention than the other, for example, French food is more talked about than British food, while both countries have a wide variety of food, more literature is produced for French food, leading to more variety of words to co-occur with French food. To test for bias between word vectors, we can compare the word lists generated by running the most_similar function on different vectors, then compare the differences between these vectors." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Submission Instructions\n", + "\n", + "1. Click the Save button at the top of the Jupyter Notebook.\n", + "2. Select Cell -> All Output -> Clear. This will clear all the outputs from all cells (but will keep the content of all cells). \n", + "2. Select Cell -> Run All. This will run all the cells in order, and will take several minutes.\n", + "3. Once you've rerun everything, select File -> Download as -> PDF via LaTeX (If you have trouble using \"PDF via LaTex\", you can also save the webpage as pdf. Make sure all your solutions especially the coding parts are displayed in the pdf, it's okay if the provided codes get cut off because lines are not wrapped in code cells).\n", + "4. Look at the PDF file and make sure all your solutions are there, displayed correctly. The PDF is the only thing your graders will see!\n", + "5. Submit your PDF on Gradescope." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Assignments/assignment1/Herais/imgs/inner_product.png b/Assignments/assignment1/Herais/imgs/inner_product.png new file mode 100644 index 0000000..09ad7df Binary files /dev/null and b/Assignments/assignment1/Herais/imgs/inner_product.png differ diff --git a/Assignments/assignment1/Herais/imgs/svd.png b/Assignments/assignment1/Herais/imgs/svd.png new file mode 100644 index 0000000..a33b316 Binary files /dev/null and b/Assignments/assignment1/Herais/imgs/svd.png differ diff --git a/Assignments/assignment1/Herais/imgs/test_plot.png b/Assignments/assignment1/Herais/imgs/test_plot.png new file mode 100644 index 0000000..c2f5f00 Binary files /dev/null and b/Assignments/assignment1/Herais/imgs/test_plot.png differ diff --git a/Assignments/assignment2/Herais/Ans Written.mht b/Assignments/assignment2/Herais/Ans Written.mht new file mode 100644 index 0000000..65d270c --- /dev/null +++ b/Assignments/assignment2/Herais/Ans Written.mht @@ -0,0 +1,56418 @@ +MIME-Version: 1.0 +Content-Type: multipart/related; boundary="----=_NextPart_01D61F15.5BC4D990" + +This document is a Single File Web Page, also known as a Web Archive file. 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numpy + - python=3.7 + - scikit-learn diff --git a/Assignments/assignment2/Herais/get_datasets.sh b/Assignments/assignment2/Herais/get_datasets.sh new file mode 100644 index 0000000..2809f50 --- /dev/null +++ b/Assignments/assignment2/Herais/get_datasets.sh @@ -0,0 +1,15 @@ +#!/bin/bash + +DATASETS_DIR="utils/datasets" +mkdir -p $DATASETS_DIR + +cd $DATASETS_DIR + +# Get Stanford Sentiment Treebank +if hash wget 2>/dev/null; then + wget http://nlp.stanford.edu/~socherr/stanfordSentimentTreebank.zip +else + curl -L http://nlp.stanford.edu/~socherr/stanfordSentimentTreebank.zip -o stanfordSentimentTreebank.zip +fi +unzip stanfordSentimentTreebank.zip +rm stanfordSentimentTreebank.zip diff --git a/Assignments/assignment2/Herais/run.py b/Assignments/assignment2/Herais/run.py new file mode 100644 index 0000000..005b293 --- /dev/null +++ b/Assignments/assignment2/Herais/run.py @@ -0,0 +1,75 @@ +#!/usr/bin/env python + +import random +import numpy as np +from utils.treebank import StanfordSentiment +import matplotlib +matplotlib.use('agg') +import matplotlib.pyplot as plt +import time + +from word2vec import * +from sgd import * + +# Check Python Version +import sys +assert sys.version_info[0] == 3 +assert sys.version_info[1] >= 5 + +# Reset the random seed to make sure that everyone gets the same results +random.seed(314) +dataset = StanfordSentiment() +tokens = dataset.tokens() +nWords = len(tokens) + +# We are going to train 10-dimensional vectors for this assignment +dimVectors = 10 + +# Context size +C = 5 + +# Reset the random seed to make sure that everyone gets the same results +random.seed(31415) +np.random.seed(9265) + +startTime=time.time() +wordVectors = np.concatenate( + ((np.random.rand(nWords, dimVectors) - 0.5) / + dimVectors, np.zeros((nWords, dimVectors))), + axis=0) +wordVectors = sgd( + lambda vec: word2vec_sgd_wrapper(skipgram, tokens, vec, dataset, C, + negSamplingLossAndGradient), + wordVectors, 0.3, 40000, None, True, PRINT_EVERY=10) +# Note that normalization is not called here. This is not a bug, +# normalizing during training loses the notion of length. + +print("sanity check: cost at convergence should be around or below 10") +print("training took %d seconds" % (time.time() - startTime)) + +# concatenate the input and output word vectors +wordVectors = np.concatenate( + (wordVectors[:nWords,:], wordVectors[nWords:,:]), + axis=0) + +visualizeWords = [ + "great", "cool", "brilliant", "wonderful", "well", "amazing", + "worth", "sweet", "enjoyable", "boring", "bad", "dumb", + "annoying", "female", "male", "queen", "king", "man", "woman", "rain", "snow", + "hail", "coffee", "tea"] + +visualizeIdx = [tokens[word] for word in visualizeWords] +visualizeVecs = wordVectors[visualizeIdx, :] +temp = (visualizeVecs - np.mean(visualizeVecs, axis=0)) +covariance = 1.0 / len(visualizeIdx) * temp.T.dot(temp) +U,S,V = np.linalg.svd(covariance) +coord = temp.dot(U[:,0:2]) + +for i in range(len(visualizeWords)): + plt.text(coord[i,0], coord[i,1], visualizeWords[i], + bbox=dict(facecolor='green', alpha=0.1)) + +plt.xlim((np.min(coord[:,0]), np.max(coord[:,0]))) +plt.ylim((np.min(coord[:,1]), np.max(coord[:,1]))) + +plt.savefig('word_vectors.png') diff --git a/Assignments/assignment2/Herais/sgd.py b/Assignments/assignment2/Herais/sgd.py new file mode 100644 index 0000000..095b773 --- /dev/null +++ b/Assignments/assignment2/Herais/sgd.py @@ -0,0 +1,132 @@ +#!/usr/bin/env python + +# Save parameters every a few SGD iterations as fail-safe +SAVE_PARAMS_EVERY = 5000 + +import pickle +import glob +import random +import numpy as np +import os.path as op + +def load_saved_params(): + """ + A helper function that loads previously saved parameters and resets + iteration start. + """ + st = 0 + for f in glob.glob("saved_params_*.npy"): + iter = int(op.splitext(op.basename(f))[0].split("_")[2]) + if (iter > st): + st = iter + + if st > 0: + params_file = "saved_params_%d.npy" % st + state_file = "saved_state_%d.pickle" % st + params = np.load(params_file) + with open(state_file, "rb") as f: + state = pickle.load(f) + return st, params, state + else: + return st, None, None + + +def save_params(iter, params): + params_file = "saved_params_%d.npy" % iter + np.save(params_file, params) + with open("saved_state_%d.pickle" % iter, "wb") as f: + pickle.dump(random.getstate(), f) + + +def sgd(f, x0, step, iterations, postprocessing=None, useSaved=False, + PRINT_EVERY=10): + """ Stochastic Gradient Descent + + Implement the stochastic gradient descent method in this function. + + Arguments: + f -- the function to optimize, it should take a single + argument and yield two outputs, a loss and the gradient + with respect to the arguments + x0 -- the initial point to start SGD from + step -- the step size for SGD + iterations -- total iterations to run SGD for + postprocessing -- postprocessing function for the parameters + if necessary. In the case of word2vec we will need to + normalize the word vectors to have unit length. + PRINT_EVERY -- specifies how many iterations to output loss + + Return: + x -- the parameter value after SGD finishes + """ + + # Anneal learning rate every several iterations + ANNEAL_EVERY = 20000 + + if useSaved: + start_iter, oldx, state = load_saved_params() + if start_iter > 0: + x0 = oldx + step *= 0.5 ** (start_iter / ANNEAL_EVERY) + + if state: + random.setstate(state) + else: + start_iter = 0 + + x = x0 + + if not postprocessing: + postprocessing = lambda x: x + + exploss = None + + for iter in range(start_iter + 1, iterations + 1): + # You might want to print the progress every few iterations. + + loss = None + ### YOUR CODE HERE (~2 lines) + loss, x_gradient = f(x) + x -= step * x_gradient + ### END YOUR CODE + + x = postprocessing(x) + if iter % PRINT_EVERY == 50: + if not exploss: + exploss = loss + else: + exploss = .95 * exploss + .05 * loss + print("iter %d: %f" % (iter, exploss)) + + if iter % SAVE_PARAMS_EVERY == 0 and useSaved: + save_params(iter, x) + + if iter % ANNEAL_EVERY == 0: + step *= 0.5 + + return x + + +def sanity_check(): + quad = lambda x: (np.sum(x ** 2), x * 2) + + print("Running sanity checks...") + t1 = sgd(quad, 0.5, 0.01, 1000, PRINT_EVERY=100) + print("test 1 result:", t1) + assert abs(t1) <= 1e-6 + + t2 = sgd(quad, 0.0, 0.01, 1000, PRINT_EVERY=100) + print("test 2 result:", t2) + assert abs(t2) <= 1e-6 + + t3 = sgd(quad, -1.5, 0.01, 1000, PRINT_EVERY=100) + print("test 3 result:", t3) + assert abs(t3) <= 1e-6 + + print("-" * 40) + print("ALL TESTS PASSED") + print("-" * 40) + + +if __name__ == "__main__": + sanity_check() diff --git a/Assignments/assignment2/Herais/utils/__init__.py b/Assignments/assignment2/Herais/utils/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/Assignments/assignment2/Herais/utils/gradcheck.py b/Assignments/assignment2/Herais/utils/gradcheck.py new file mode 100644 index 0000000..0272dc2 --- /dev/null +++ b/Assignments/assignment2/Herais/utils/gradcheck.py @@ -0,0 +1,211 @@ +#!/usr/bin/env python + +import numpy as np +import random + + +# First implement a gradient checker by filling in the following functions +def gradcheck_naive(f, x, gradientText): + """ Gradient check for a function f. + Arguments: + f -- a function that takes a single argument and outputs the + loss and its gradients + x -- the point (numpy array) to check the gradient at + gradientText -- a string detailing some context about the gradient computation + + Notes: + Note that gradient checking is a sanity test that only checks whether the + gradient and loss values produced by your implementation are consistent with + each other. Gradient check passing on its own doesn’t guarantee that you + have the correct gradients. It will pass, for example, if both the loss and + gradient values produced by your implementation are 0s (as is the case when + you have not implemented anything). Here is a detailed explanation of what + gradient check is doing if you would like some further clarification: + http://ufldl.stanford.edu/tutorial/supervised/DebuggingGradientChecking/. + """ + rndstate = random.getstate() + random.setstate(rndstate) + fx, grad = f(x) # Evaluate function value at original point + h = 1e-4 # Do not change this! + + # Iterate over all indexes ix in x to check the gradient. + it = np.nditer(x, flags=['multi_index'], op_flags=['readwrite']) + while not it.finished: + ix = it.multi_index + + x[ix] += h # increment by h + random.setstate(rndstate) + fxh, _ = f(x) # evalute f(x + h) + x[ix] -= 2 * h # restore to previous value (very important!) + random.setstate(rndstate) + fxnh, _ = f(x) + x[ix] += h + numgrad = (fxh - fxnh) / 2 / h + + # Compare gradients + reldiff = abs(numgrad - grad[ix]) / max(1, abs(numgrad), abs(grad[ix])) + if reldiff > 1e-5: + print("Gradient check failed for %s." % gradientText) + print("First gradient error found at index %s in the vector of gradients" % str(ix)) + print("Your gradient: %f \t Numerical gradient: %f" % ( + grad[ix], numgrad)) + return + + it.iternext() # Step to next dimension + + print("Gradient check passed!. Read the docstring of the `gradcheck_naive`" + " method in utils.gradcheck.py to understand what the gradient check does.") + + +def grad_tests_softmax(skipgram, dummy_tokens, dummy_vectors, dataset): + print ("======Skip-Gram with naiveSoftmaxLossAndGradient Test Cases======") + + # first test + output_loss, output_gradCenterVecs, output_gradOutsideVectors = \ + skipgram("c", 3, ["a", "b", "e", "d", "b", "c"], + dummy_tokens, dummy_vectors[:5,:], dummy_vectors[5:,:], dataset) + + assert np.allclose(output_loss, 11.16610900153398), \ + "Your loss does not match expected loss." + expected_gradCenterVecs = [[ 0., 0., 0. ], + [ 0., 0., 0. ], + [-1.26947339, -1.36873189, 2.45158957], + [ 0., 0., 0. ], + [ 0., 0., 0. ]] + expected_gradOutsideVectors = [[-0.41045956, 0.18834851, 1.43272264], + [ 0.38202831, -0.17530219, -1.33348241], + [ 0.07009355, -0.03216399, -0.24466386], + [ 0.09472154, -0.04346509, -0.33062865], + [-0.13638384, 0.06258276, 0.47605228]] + + assert np.allclose(output_gradCenterVecs, expected_gradCenterVecs), \ + "Your gradCenterVecs do not match expected gradCenterVecs." + assert np.allclose(output_gradOutsideVectors, expected_gradOutsideVectors), \ + "Your gradOutsideVectors do not match expected gradOutsideVectors." + print("The first test passed!") + + # second test + output_loss, output_gradCenterVecs, output_gradOutsideVectors = \ + skipgram("b", 3, ["a", "b", "e", "d", "b", "c"], + dummy_tokens, dummy_vectors[:5,:], dummy_vectors[5:,:], dataset) + assert np.allclose(output_loss, 9.87714910003414), \ + "Your loss does not match expected loss." + expected_gradCenterVecs = [[ 0., 0., 0. ], + [-0.14586705, -1.34158321, -0.29291951], + [ 0., 0., 0. ], + [ 0., 0., 0. ], + [ 0., 0., 0. ]] + expected_gradOutsideVectors = [[-0.30342672, 0.19808298, 0.19587419], + [-0.41359958, 0.27000601, 0.26699522], + [-0.08192272, 0.05348078, 0.05288442], + [ 0.6981188, -0.4557458, -0.45066387], + [ 0.10083022, -0.06582396, -0.06508997]] + + assert np.allclose(output_gradCenterVecs, expected_gradCenterVecs), \ + "Your gradCenterVecs do not match expected gradCenterVecs." + assert np.allclose(output_gradOutsideVectors, expected_gradOutsideVectors), \ + "Your gradOutsideVectors do not match expected gradOutsideVectors." + print("The second test passed!") + + # third test + output_loss, output_gradCenterVecs, output_gradOutsideVectors = \ + skipgram("a", 3, ["a", "b", "e", "d", "b", "c"], + dummy_tokens, dummy_vectors[:5,:], dummy_vectors[5:,:], dataset) + + assert np.allclose(output_loss, 10.810758628593335), \ + "Your loss does not match expected loss." + expected_gradCenterVecs = [[-1.1790274, -1.35861865, 1.53590492], + [ 0., 0., 0. ], + [ 0., 0., 0. ], + [ 0., 0., 0. ], + [ 0., 0., 0. ]] + expected_gradOutsideVectors = [[-7.96035953e-01, -1.79609012e-02, 2.07761330e-01], + [ 1.40175316e+00, 3.16276545e-02, -3.65850437e-01], + [-1.99691259e-01, -4.50561933e-03, 5.21184016e-02], + [ 2.02560028e-02, 4.57034715e-04, -5.28671357e-03], + [-4.26281954e-01, -9.61816867e-03, 1.11257419e-01]] + + assert np.allclose(output_gradCenterVecs, expected_gradCenterVecs), \ + "Your gradCenterVecs do not match expected gradCenterVecs." + assert np.allclose(output_gradOutsideVectors, expected_gradOutsideVectors), \ + "Your gradOutsideVectors do not match expected gradOutsideVectors." + print("The third test passed!") + + print("All 3 tests passed!") + + +def grad_tests_negsamp(skipgram, dummy_tokens, dummy_vectors, dataset, negSamplingLossAndGradient): + print ("======Skip-Gram with negSamplingLossAndGradient======") + + # first test + output_loss, output_gradCenterVecs, output_gradOutsideVectors = \ + skipgram("c", 1, ["a", "b"], dummy_tokens, dummy_vectors[:5,:], + dummy_vectors[5:,:], dataset, negSamplingLossAndGradient) + + assert np.allclose(output_loss, 16.15119285363322), \ + "Your loss does not match expected loss." + expected_gradCenterVecs = [[ 0., 0., 0. ], + [ 0., 0., 0. ], + [-4.54650789, -1.85942252, 0.76397441], + [ 0., 0., 0. ], + [ 0., 0., 0. ]] + expected_gradOutsideVectors = [[-0.69148188, 0.31730185, 2.41364029], + [-0.22716495, 0.10423969, 0.79292674], + [-0.45528438, 0.20891737, 1.58918512], + [-0.31602611, 0.14501561, 1.10309954], + [-0.80620296, 0.36994417, 2.81407799]] + + assert np.allclose(output_gradCenterVecs, expected_gradCenterVecs), \ + "Your gradCenterVecs do not match expected gradCenterVecs." + assert np.allclose(output_gradOutsideVectors, expected_gradOutsideVectors), \ + "Your gradOutsideVectors do not match expected gradOutsideVectors." + print("The first test passed!") + + # second test + output_loss, output_gradCenterVecs, output_gradOutsideVectors = \ + skipgram("c", 2, ["a", "b", "c", "a"], dummy_tokens, dummy_vectors[:5,:], + dummy_vectors[5:,:], dataset, negSamplingLossAndGradient) + assert np.allclose(output_loss, 28.653567707668795), \ + "Your loss does not match expected loss." + expected_gradCenterVecs = [ [ 0., 0., 0. ], + [ 0., 0., 0. ], + [-6.42994865, -2.16396482, -1.89240934], + [ 0., 0., 0. ], + [ 0., 0., 0. ]] + expected_gradOutsideVectors = [ [-0.80413277, 0.36899421, 2.80685192], + [-0.9277269, 0.42570813, 3.23826131], + [-0.7511534, 0.34468345, 2.62192569], + [-0.94807832, 0.43504684, 3.30929863], + [-1.12868414, 0.51792184, 3.93970919]] + + assert np.allclose(output_gradCenterVecs, expected_gradCenterVecs), \ + "Your gradCenterVecs do not match expected gradCenterVecs." + assert np.allclose(output_gradOutsideVectors, expected_gradOutsideVectors), \ + "Your gradOutsideVectors do not match expected gradOutsideVectors." + print("The second test passed!") + + # third test + output_loss, output_gradCenterVecs, output_gradOutsideVectors = \ + skipgram("a", 3, ["a", "b", "e", "d", "b", "c"], + dummy_tokens, dummy_vectors[:5,:], + dummy_vectors[5:,:], dataset, negSamplingLossAndGradient) + assert np.allclose(output_loss, 60.648705494891914), \ + "Your loss does not match expected loss." + expected_gradCenterVecs = [ [-17.89425315, -7.36940626, -1.23364121], + [ 0., 0., 0. ], + [ 0., 0., 0. ], + [ 0., 0., 0. ], + [ 0., 0., 0. ]] + expected_gradOutsideVectors = [[-6.4780819, -0.14616449, 1.69074639], + [-0.86337952, -0.01948037, 0.22533766], + [-9.59525734, -0.21649709, 2.5043133 ], + [-6.02261515, -0.13588783, 1.57187189], + [-9.69010072, -0.21863704, 2.52906694]] + + assert np.allclose(output_gradCenterVecs, expected_gradCenterVecs), \ + "Your gradCenterVecs do not match expected gradCenterVecs." + assert np.allclose(output_gradOutsideVectors, expected_gradOutsideVectors), \ + "Your gradOutsideVectors do not match expected gradOutsideVectors." + print("The third test passed!") + + print("All 3 tests passed!") diff --git a/Assignments/assignment2/Herais/utils/treebank.py b/Assignments/assignment2/Herais/utils/treebank.py new file mode 100644 index 0000000..9b9ddee --- /dev/null +++ b/Assignments/assignment2/Herais/utils/treebank.py @@ -0,0 +1,248 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +import pickle +import numpy as np +import os +import random + +class StanfordSentiment: + def __init__(self, path=None, tablesize = 1000000): + if not path: + path = "utils/datasets/stanfordSentimentTreebank" + + self.path = path + self.tablesize = tablesize + + def tokens(self): + if hasattr(self, "_tokens") and self._tokens: + return self._tokens + + tokens = dict() + tokenfreq = dict() + wordcount = 0 + revtokens = [] + idx = 0 + + for sentence in self.sentences(): + for w in sentence: + wordcount += 1 + if not w in tokens: + tokens[w] = idx + revtokens += [w] + tokenfreq[w] = 1 + idx += 1 + else: + tokenfreq[w] += 1 + + tokens["UNK"] = idx + revtokens += ["UNK"] + tokenfreq["UNK"] = 1 + wordcount += 1 + + self._tokens = tokens + self._tokenfreq = tokenfreq + self._wordcount = wordcount + self._revtokens = revtokens + return self._tokens + + def sentences(self): + if hasattr(self, "_sentences") and self._sentences: + return self._sentences + + sentences = [] + with open(self.path + "/datasetSentences.txt", "r") as f: + first = True + for line in f: + if first: + first = False + continue + + splitted = line.strip().split()[1:] + # Deal with some peculiar encoding issues with this file + sentences += [[w.lower() for w in splitted]] + + self._sentences = sentences + self._sentlengths = np.array([len(s) for s in sentences]) + self._cumsentlen = np.cumsum(self._sentlengths) + + return self._sentences + + def numSentences(self): + if hasattr(self, "_numSentences") and self._numSentences: + return self._numSentences + else: + self._numSentences = len(self.sentences()) + return self._numSentences + + def allSentences(self): + if hasattr(self, "_allsentences") and self._allsentences: + return self._allsentences + + sentences = self.sentences() + rejectProb = self.rejectProb() + tokens = self.tokens() + allsentences = [[w for w in s + if 0 >= rejectProb[tokens[w]] or random.random() >= rejectProb[tokens[w]]] + for s in sentences * 30] + + allsentences = [s for s in allsentences if len(s) > 1] + + self._allsentences = allsentences + + return self._allsentences + + def getRandomContext(self, C=5): + allsent = self.allSentences() + sentID = random.randint(0, len(allsent) - 1) + sent = allsent[sentID] + wordID = random.randint(0, len(sent) - 1) + + context = sent[max(0, wordID - C):wordID] + if wordID+1 < len(sent): + context += sent[wordID+1:min(len(sent), wordID + C + 1)] + + centerword = sent[wordID] + context = [w for w in context if w != centerword] + + if len(context) > 0: + return centerword, context + else: + return self.getRandomContext(C) + + def sent_labels(self): + if hasattr(self, "_sent_labels") and self._sent_labels: + return self._sent_labels + + dictionary = dict() + phrases = 0 + with open(self.path + "/dictionary.txt", "r") as f: + for line in f: + line = line.strip() + if not line: continue + splitted = line.split("|") + dictionary[splitted[0].lower()] = int(splitted[1]) + phrases += 1 + + labels = [0.0] * phrases + with open(self.path + "/sentiment_labels.txt", "r") as f: + first = True + for line in f: + if first: + first = False + continue + + line = line.strip() + if not line: continue + splitted = line.split("|") + labels[int(splitted[0])] = float(splitted[1]) + + sent_labels = [0.0] * self.numSentences() + sentences = self.sentences() + for i in range(self.numSentences()): + sentence = sentences[i] + full_sent = " ".join(sentence).replace('-lrb-', '(').replace('-rrb-', ')') + sent_labels[i] = labels[dictionary[full_sent]] + + self._sent_labels = sent_labels + return self._sent_labels + + def dataset_split(self): + if hasattr(self, "_split") and self._split: + return self._split + + split = [[] for i in range(3)] + with open(self.path + "/datasetSplit.txt", "r") as f: + first = True + for line in f: + if first: + first = False + continue + + splitted = line.strip().split(",") + split[int(splitted[1]) - 1] += [int(splitted[0]) - 1] + + self._split = split + return self._split + + def getRandomTrainSentence(self): + split = self.dataset_split() + sentId = split[0][random.randint(0, len(split[0]) - 1)] + return self.sentences()[sentId], self.categorify(self.sent_labels()[sentId]) + + def categorify(self, label): + if label <= 0.2: + return 0 + elif label <= 0.4: + return 1 + elif label <= 0.6: + return 2 + elif label <= 0.8: + return 3 + else: + return 4 + + def getDevSentences(self): + return self.getSplitSentences(2) + + def getTestSentences(self): + return self.getSplitSentences(1) + + def getTrainSentences(self): + return self.getSplitSentences(0) + + def getSplitSentences(self, split=0): + ds_split = self.dataset_split() + return [(self.sentences()[i], self.categorify(self.sent_labels()[i])) for i in ds_split[split]] + + def sampleTable(self): + if hasattr(self, '_sampleTable') and self._sampleTable is not None: + return self._sampleTable + + nTokens = len(self.tokens()) + samplingFreq = np.zeros((nTokens,)) + self.allSentences() + i = 0 + for w in range(nTokens): + w = self._revtokens[i] + if w in self._tokenfreq: + freq = 1.0 * self._tokenfreq[w] + # Reweigh + freq = freq ** 0.75 + else: + freq = 0.0 + samplingFreq[i] = freq + i += 1 + + samplingFreq /= np.sum(samplingFreq) + samplingFreq = np.cumsum(samplingFreq) * self.tablesize + + self._sampleTable = [0] * self.tablesize + + j = 0 + for i in range(self.tablesize): + while i > samplingFreq[j]: + j += 1 + self._sampleTable[i] = j + + return self._sampleTable + + def rejectProb(self): + if hasattr(self, '_rejectProb') and self._rejectProb is not None: + return self._rejectProb + + threshold = 1e-5 * self._wordcount + + nTokens = len(self.tokens()) + rejectProb = np.zeros((nTokens,)) + for i in range(nTokens): + w = self._revtokens[i] + freq = 1.0 * self._tokenfreq[w] + # Reweigh + rejectProb[i] = max(0, 1 - np.sqrt(threshold / freq)) + + self._rejectProb = rejectProb + return self._rejectProb + + def sampleTokenIdx(self): + return self.sampleTable()[random.randint(0, self.tablesize - 1)] \ No newline at end of file diff --git a/Assignments/assignment2/Herais/utils/utils.py b/Assignments/assignment2/Herais/utils/utils.py new file mode 100644 index 0000000..8561cb8 --- /dev/null +++ b/Assignments/assignment2/Herais/utils/utils.py @@ -0,0 +1,43 @@ +#!/usr/bin/env python + +import numpy as np + +def normalizeRows(x): + """ Row normalization function + + Implement a function that normalizes each row of a matrix to have + unit length. + """ + N = x.shape[0] + x /= np.sqrt(np.sum(x**2, axis=1)).reshape((N,1)) + 1e-30 + return x + +def softmax(x): + """Compute the softmax function for each row of the input x. + It is crucial that this function is optimized for speed because + it will be used frequently in later code. + + Arguments: + x -- A D dimensional vector or N x D dimensional numpy matrix. + Return: + x -- You are allowed to modify x in-place + """ + orig_shape = x.shape + + if len(x.shape) > 1: + # Matrix + tmp = np.max(x, axis=1) + x -= tmp.reshape((x.shape[0], 1)) + x = np.exp(x) + tmp = np.sum(x, axis=1) + x /= tmp.reshape((x.shape[0], 1)) + else: + # Vector + tmp = np.max(x) + x -= tmp + x = np.exp(x) + tmp = np.sum(x) + x /= tmp + + assert x.shape == orig_shape + return x \ No newline at end of file diff --git a/Assignments/assignment2/Herais/word2vec.py b/Assignments/assignment2/Herais/word2vec.py new file mode 100644 index 0000000..bc540ee --- /dev/null +++ b/Assignments/assignment2/Herais/word2vec.py @@ -0,0 +1,243 @@ +#!/usr/bin/env python + +import numpy as np +import random + +from utils.gradcheck import gradcheck_naive, grad_tests_softmax, grad_tests_negsamp +from utils.utils import normalizeRows, softmax + + +def sigmoid(x): + """ + Compute the sigmoid function for the input here. + Arguments: + x -- A scalar or numpy array. + Return: + s -- sigmoid(x) + """ + + ### YOUR CODE HERE (~1 Line) + s = 1 / (1 + np.exp(-x)) + ### END YOUR CODE + + return s + + +def naiveSoftmaxLossAndGradient( + centerWordVec, + outsideWordIdx, + outsideVectors, + dataset +): + """ Naive Softmax loss & gradient function for word2vec models + + Implement the naive softmax loss and gradients between a center word's + embedding and an outside word's embedding. This will be the building block + for our word2vec models. + + Arguments: + centerWordVec -- numpy ndarray, center word's embedding + in shape (word vector length, ) + (v_c in the pdf handout) + outsideWordIdx -- integer, the index of the outside word + (o of u_o in the pdf handout) + outsideVectors -- outside vectors is + in shape (num words in vocab, word vector length) + for all words in vocab (U in the pdf handout) + dataset -- needed for negative sampling, unused here. + + Return: + loss -- naive softmax loss + gradCenterVec -- the gradient with respect to the center word vector + in shape (word vector length, ) + (dJ / dv_c in the pdf handout) + gradOutsideVecs -- the gradient with respect to all the outside word vectors + in shape (num words in vocab, word vector length) + (dJ / dU) + """ + + ### YOUR CODE HERE (~6-8 Lines) + y_hat = softmax(np.dot(outsideVectors, centerWordVec)) + loss = -np.log(y_hat[outsideWordIdx]) + delta_y_hat = y_hat.copy() + delta_y_hat[outsideWordIdx] -= 1 # y_hat - y + gradCenterVec = np.dot(delta_y_hat, outsideVectors) # dJ / dv_c + gradOutsideVecs = np.dot(delta_y_hat.reshape(-1, 1), centerWordVec.reshape(1, -1)) # dJ / dU + ### Please use the provided softmax function (imported earlier in this file) + ### This numerically stable implementation helps you avoid issues pertaining + ### to integer overflow. + + ### END YOUR CODE + + return loss, gradCenterVec, gradOutsideVecs + + +def getNegativeSamples(outsideWordIdx, dataset, K): + """ Samples K indexes which are not the outsideWordIdx """ + + negSampleWordIndices = [None] * K + for k in range(K): + newidx = dataset.sampleTokenIdx() + while newidx == outsideWordIdx: + newidx = dataset.sampleTokenIdx() + negSampleWordIndices[k] = newidx + return negSampleWordIndices + + +def negSamplingLossAndGradient( + centerWordVec, + outsideWordIdx, + outsideVectors, + dataset, + K=10 +): + """ Negative sampling loss function for word2vec models + + Implement the negative sampling loss and gradients for a centerWordVec + and a outsideWordIdx word vector as a building block for word2vec + models. K is the number of negative samples to take. + + Note: The same word may be negatively sampled multiple times. For + example if an outside word is sampled twice, you shall have to + double count the gradient with respect to this word. Thrice if + it was sampled three times, and so forth. + + Arguments/Return Specifications: same as naiveSoftmaxLossAndGradient + """ + + # Negative sampling of words is done for you. Do not modify this if you + # wish to match the autograder and receive points! + negSampleWordIndices = getNegativeSamples(outsideWordIdx, dataset, K) + indices = [outsideWordIdx] + negSampleWordIndices + + ### YOUR CODE HERE (~10 Lines) + sigmoid_uo_vc = sigmoid(np.dot(outsideVectors[outsideWordIdx], centerWordVec)) + loss = -np.log(sigmoid_uo_vc) + gradCenterVec = (sigmoid_uo_vc - 1) * outsideVectors[outsideWordIdx] + gradOutsideVecs = np.zeros(shape=outsideVectors.shape) + gradOutsideVecs[outsideWordIdx] = -(1 - sigmoid_uo_vc) * centerWordVec + for i in negSampleWordIndices: + sigmoid_n_uk_vc = sigmoid(np.dot(-outsideVectors[i], centerWordVec)) + loss += -np.log(sigmoid_n_uk_vc) + gradCenterVec += (1 - sigmoid_n_uk_vc) * outsideVectors[i] + gradOutsideVecs[i] += (1 - sigmoid_n_uk_vc) * centerWordVec + ### Please use your implementation of sigmoid in here. + + ### END YOUR CODE + + return loss, gradCenterVec, gradOutsideVecs + + +def skipgram(currentCenterWord, windowSize, outsideWords, word2Ind, + centerWordVectors, outsideVectors, dataset, + word2vecLossAndGradient=naiveSoftmaxLossAndGradient): + """ Skip-gram model in word2vec + + Implement the skip-gram model in this function. + + Arguments: + currentCenterWord -- a string of the current center word + windowSize -- integer, context window size + outsideWords -- list of no more than 2*windowSize strings, the outside words + word2Ind -- a dictionary that maps words to their indices in + the word vector list + centerWordVectors -- center word vectors (as rows) is in shape + (num words in vocab, word vector length) + for all words in vocab (V in pdf handout) + outsideVectors -- outside vectors is in shape + (num words in vocab, word vector length) + for all words in vocab (U in the pdf handout) + word2vecLossAndGradient -- the loss and gradient function for + a prediction vector given the outsideWordIdx + word vectors, could be one of the two + loss functions you implemented above. + + Return: + loss -- the loss function value for the skip-gram model + (J in the pdf handout) + gradCenterVec -- the gradient with respect to the center word vector + in shape (word vector length, ) + (dJ / dv_c in the pdf handout) + gradOutsideVecs -- the gradient with respect to all the outside word vectors + in shape (num words in vocab, word vector length) + (dJ / dU) + """ + + ### YOUR CODE HERE (~8 Lines) + loss = 0.0 + gradCenterVecs = np.zeros(centerWordVectors.shape) + gradOutsideVectors = np.zeros(outsideVectors.shape) + for outsideWord in outsideWords: + loss_tmp, gradCenterVec_tmp, gradOutsideVecs_tmp = word2vecLossAndGradient( + centerWordVectors[word2Ind[currentCenterWord]], word2Ind[outsideWord], outsideVectors, dataset) + loss += loss_tmp + gradCenterVecs[word2Ind[currentCenterWord]] += gradCenterVec_tmp + gradOutsideVectors += gradOutsideVecs_tmp + ### END YOUR CODE + + return loss, gradCenterVecs, gradOutsideVectors + + +############################################# +# Testing functions below. DO NOT MODIFY! # +############################################# + +def word2vec_sgd_wrapper(word2vecModel, word2Ind, wordVectors, dataset, + windowSize, + word2vecLossAndGradient=naiveSoftmaxLossAndGradient): + batchsize = 50 + loss = 0.0 + grad = np.zeros(wordVectors.shape) + N = wordVectors.shape[0] + centerWordVectors = wordVectors[:int(N/2),:] + outsideVectors = wordVectors[int(N/2):,:] + for i in range(batchsize): + windowSize1 = random.randint(1, windowSize) + centerWord, context = dataset.getRandomContext(windowSize1) + + c, gin, gout = word2vecModel( + centerWord, windowSize1, context, word2Ind, centerWordVectors, + outsideVectors, dataset, word2vecLossAndGradient + ) + loss += c / batchsize + grad[:int(N/2), :] += gin / batchsize + grad[int(N/2):, :] += gout / batchsize + + return loss, grad + + +def test_word2vec(): + """ Test the two word2vec implementations, before running on Stanford Sentiment Treebank """ + dataset = type('dummy', (), {})() + def dummySampleTokenIdx(): + return random.randint(0, 4) + + def getRandomContext(C): + tokens = ["a", "b", "c", "d", "e"] + return tokens[random.randint(0,4)], \ + [tokens[random.randint(0,4)] for i in range(2*C)] + dataset.sampleTokenIdx = dummySampleTokenIdx + dataset.getRandomContext = getRandomContext + + random.seed(31415) + np.random.seed(9265) + dummy_vectors = normalizeRows(np.random.randn(10,3)) + dummy_tokens = dict([("a",0), ("b",1), ("c",2),("d",3),("e",4)]) + + print("==== Gradient check for skip-gram with naiveSoftmaxLossAndGradient ====") + gradcheck_naive(lambda vec: word2vec_sgd_wrapper( + skipgram, dummy_tokens, vec, dataset, 5, naiveSoftmaxLossAndGradient), + dummy_vectors, "naiveSoftmaxLossAndGradient Gradient") + grad_tests_softmax(skipgram, dummy_tokens, dummy_vectors, dataset) + + print("==== Gradient check for skip-gram with negSamplingLossAndGradient ====") + gradcheck_naive(lambda vec: word2vec_sgd_wrapper( + skipgram, dummy_tokens, vec, dataset, 5, negSamplingLossAndGradient), + dummy_vectors, "negSamplingLossAndGradient Gradient") + + grad_tests_negsamp(skipgram, dummy_tokens, dummy_vectors, dataset, negSamplingLossAndGradient) + + +if __name__ == "__main__": + test_word2vec() + diff --git a/Assignments/assignment2/Herais/word_vectors.png b/Assignments/assignment2/Herais/word_vectors.png new file mode 100644 index 0000000..e4442f6 Binary files /dev/null and b/Assignments/assignment2/Herais/word_vectors.png differ