{ "cells": [ { "cell_type": "markdown", "metadata": { "toc": true }, "source": [ "

Table of Contents

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" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "" ], "text/plain": [ "" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# code for loading the format for the notebook\n", "import os\n", "\n", "# path : store the current path to convert back to it later\n", "path = os.getcwd()\n", "os.chdir(os.path.join('..', '..', 'notebook_format'))\n", "\n", "from formats import load_style\n", "load_style(css_style='custom2.css', plot_style=False)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ethen 2018-08-15 20:33:45 \n", "\n", "CPython 3.6.4\n", "IPython 6.4.0\n", "\n", "numpy 1.14.1\n", "pandas 0.23.0\n", "sklearn 0.19.1\n", "matplotlib 2.2.2\n" ] } ], "source": [ "os.chdir(path)\n", "\n", "# 1. magic for inline plot\n", "# 2. magic to print version\n", "# 3. magic so that the notebook will reload external python modules\n", "# 4. magic to enable retina (high resolution) plots\n", "# https://gist.github.com/minrk/3301035\n", "%matplotlib inline\n", "%load_ext watermark\n", "%load_ext autoreload\n", "%autoreload 2\n", "%config InlineBackend.figure_format='retina'\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "\n", "%watermark -a 'Ethen' -d -t -v -p numpy,pandas,sklearn,matplotlib" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Locality Sensitive Hashing (LSH) - Cosine Distance" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Similarity search is a widely used and important method in many applications. One example is [Shazam](https://www.shazam.com/), the app that let's us identify can song within seconds is leveraging audio fingerprinting and most likely a fast and scalable similarity search method to retrieve the relevant song from a massive database of songs. In this documentation, we'll be introducing **Locality Sensitive Hashing (LSH)**, an approximate nearest neighborhood search technique in the context of recommendation system. Note that, Locality Sensitive Hashing (LSH) is actually a family of algorithm, different distance metric will correspond to a different method. Here we will be focusing on cosine distance." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Content-Based Filtering" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the realm of recommender system, collaborative filtering methods such as matrix factorization are often times workhorse methods of choice. The main idea behind collaborative filtering is that the product you are most likely to buy, is the product that a bunch of people like you also bought. The input to the algorithm is basically a matrix of user IDs and product IDs with values representing who bought what product. Albeit an extremely powerful method, it does suffer from what is known as the cold-start problem.\n", "\n", "Let's say we want to make recommendations to a customer viewing a product details page, who just got there from a Google SERP link. We have no historical record about this customer, so we can't build a matrix of purchases. This is where a different approach, so called content-based filtering/recommendation can be helpful.\n", "\n", "When we say content-based filtering, we are referring to the fact that we're using items or users' meta-data to generate the recommendation. For example, say the product page that this user landed on is \"Nike Pro Hypercool Fitted Men's Compression Shirt\", then we can leverage information such as the item's description, price or other attribute to generate recommendation saying you might also love the item \"Nike Pro Hypercool Printed Men's Tights\".\n", "\n", "The dataset that we'll be working with here is a sample data consisting of outdoor clothing and products from Patagonia. It only consists of two columns, the IDs and the product's text description. To leverage this information, we will transform the raw text into a tfidf-format, and use the tf-idf representation to find similar items for a given item (the most similar items to the item we're interested in is essentially our recommended items)." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "dimension: (500, 2)\n" ] }, { "data": { "text/html": [ "
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iddescription
01Active classic boxers - There's a reason why o...
12Active sport boxer briefs - Skinning up Glory ...
23Active sport briefs - These superbreathable no...
34Alpine guide pants - Skin in, climb ice, switc...
45Alpine wind jkt - On high ridges, steep ice an...
\n", "
" ], "text/plain": [ " id description\n", "0 1 Active classic boxers - There's a reason why o...\n", "1 2 Active sport boxer briefs - Skinning up Glory ...\n", "2 3 Active sport briefs - These superbreathable no...\n", "3 4 Alpine guide pants - Skin in, climb ice, switc...\n", "4 5 Alpine wind jkt - On high ridges, steep ice an..." ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "input_local_path = os.path.join('data', 'sample-data.csv')\n", "df = pd.read_csv(input_local_path)\n", "print('dimension: ', df.shape)\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<500x52262 sparse matrix of type ''\n", "\twith 148989 stored elements in Compressed Sparse Row format>" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.feature_extraction.text import TfidfVectorizer\n", "\n", "\n", "tfidf = TfidfVectorizer(\n", " analyzer='word',\n", " ngram_range=(1, 3),\n", " min_df=0,\n", " stop_words='english')\n", "X_tfidf = tfidf.fit_transform(df['description'])\n", "X_tfidf" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "similar item id: 1\n", "cosine similarity: 1.0000000000000029\n", "item description: Active sport boxer briefs - Skinning up Glory requires enough movement without your boxers deciding to poach their own route. The form-fitting Active Sport Boxer Briefs are made from breathable 93% polyester (71% recycled) fabric that's fast-wicking, dries quickly and has 7% spandex for stretch; the seamless waistband and soft leg edges won't roll or bind. The gusseted, flat-sewn 6\" inseam (size M) is offset to prevent inner-thigh chafe. Fly-free with a smooth front panel. Recyclable through the Common Threads Recycling Program.

Details:

Fabric: \"4.6-oz 93% polyester (71% recycled)/7% spandex, with moisture-wicking performance. Recyclable through the Common Threads Recycling Program\"

Weight: (60 g 2.1 oz)

Made in Israel.\n", "\n", "similar item id: 2\n", "cosine similarity: 0.4181663992161579\n", "item description: Active sport briefs - These superbreathable no-fly briefs are the minimalist's choice for high-octane endeavors. Made from a blend of fast-wicking, quick-drying 93% polyester (71% recycled) and 7% spandex that has both stretch-mesh (for support) and open mesh (for cooling airflow). Soft edging at the leg openings and a seamless waist won't roll or create friction against layers. With a smooth front panel for opacity. Recyclable through the Common Threads Recycling Program.

Details:

Fabric: \"4.6-oz 93% polyester (71% recycled)/7% spandex, with moisture-wicking performance. Recyclable through the Common Threads Recycling Program\"

Weight: (49 g 1.7 oz)

Made in Israel.\n", "\n", "similar item id: 18\n", "cosine similarity: 0.11546382098627586\n", "item description: Cap 1 boxer briefs - On bivy or belay, the form-fitting Capilene 1 Boxer Briefs stay dry and comfortable. Made from 100% recycled polyester, the underwear excels at moisture-wicking and has Gladiodor natural odor control for the garment. Exposed elastic waistband is brushed for softness; the hem is coverstitched for a smooth glide beneath shorts or pants. Fully-functioning fly and supportive front panel keep you covered. 5 1/2\" inseam (size M). Recyclable through the Common Threads Recycling Program.

Details:

Fabric: 3.7-oz 100% all-recycled polyester with Gladiodor® natural odor control for the garment. Recyclable through the Common Threads Recycling Program

Weight: (72 g 2.5 oz)

Made in USA.\n", "\n", "similar item id: 493\n", "cosine similarity: 0.11303392245400203\n", "item description: Active boxer briefs - A no-fuss travel companion, these skivvies love sink and creek baths, and they dry in a flash. The quick-wicking underwear breathes exceptionally well, keeping you comfortable in any condition. An exposed elastic waistband is brushed for no-chafe softness; the hem is coverstitched for a smooth glide beneath shorts or pants. With a 5 1/2\" inseam (size M) an easy-access fly and a supportive front panel. Made from 100% polyester (54% recycled) with moisture-wicking performance. Recyclable through the Common Threads Recycling Program.

Details:

Fabric: 4-oz 100% polyester (54% recycled) with Gladiodor natural odor control for the garment. Recyclable through the Common Threads Recycling Program

Weight: (72 g 2.5 oz)

Made in USA.\n", "\n", "similar item id: 299\n", "cosine similarity: 0.11247854521091638\n", "item description: Active briefs - Whether you're beating the heat in Bali or skinning up your favorite cirque, these ultrasoft, lightweight briefs provide exceptional stretch and moisture-management for keeping you comfortable and dry. They also glide easily beneath layers. A seamless waistband lies flat and won't roll or bind; newly redesigned single-sided leg binding stays put and is low-profile. With a breathable mini-rib crotch. Solids and prints: 4.6-oz 96% nylon/4% spandex. Stripes: 5.6-oz 93% polyester (58% recycled)/7% spandex.

Details:

Fabric: Solids and prints: 4.6-oz 96% nylon/4% spandex. Stripes: 5.6-oz 94% polyester (58% recycled)/7% spandex. All with moisture-wicking performance

Weight: (28 g 1 oz)

Made in Israel.\n", "\n" ] } ], "source": [ "def get_similarity_items(X_tfidf, item_id, topn=5):\n", " \"\"\"\n", " Get the top similar items for a given item id.\n", " The similarity measure here is based on cosine distance.\n", " \"\"\"\n", " query = X_tfidf[item_id]\n", " scores = X_tfidf.dot(query.T).toarray().ravel()\n", " best = np.argpartition(scores, -topn)[-topn:]\n", " return sorted(zip(best, scores[best]), key=lambda x: -x[1])\n", "\n", "\n", "similar_items = get_similarity_items(X_tfidf, item_id=1)\n", "\n", "# an item is always most similar to itself, in real-world\n", "# scenario we might want to filter itself out from the output\n", "for similar_item, similarity in similar_items:\n", " item_description = df.loc[similar_item, 'description']\n", " print('similar item id: ', similar_item)\n", " print('cosine similarity: ', similarity)\n", " print('item description: ', item_description)\n", " print()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Hopefully, we can sort of agree that the result reflects our intuition. Given a item, Active sport boxer briefs, the content-based filtering method based on the item's description in tfidf-representation recommends items such as Active sport briefs, Cap 1 boxer briefs which are all underwear-related products.\n", "\n", "To generate the similar items, the approach we're using here is calculating the cosine distance between our query item and all the other items in our data, then sorting the distance to find the most similar items. This approach might work well for a small dataset, however, as we can imagine this procedure might become a bottleneck to our system as the data points starts to increase, and in near-real time systems where there's a strict latency requirements, this method is most likely not going to cut it." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Getting Started with LSH" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The idea behind LSH is to throw down random lines/vectors. Yes, you are reading it correctly. Then everything that falls below this line has a negative score and will fall into what we'll be referring to as bin 0. On the other hand, everything that's above this line has a positive score and all of those points will be assigned to bin 1. So we're essentially translating the sign of the scores into a binary index. \n", "\n", "\n", "\n", "For example, in the picture above, the orange data point falls above the random vector and we'll label it as a white bin. Then we can use this for nearest neighbor search in the following way: Given a query data point, say it also falls above the random vector, then we will only search for the nearest neighbor amongst the data points that also fall above the random vector, or another way to put it, we will only search amongst the data points that also fall under the same bin.\n", "\n", "If we think about this carefully, the main rationale behind locality sensitive hashing is data points that are located close to each other should be mapped to similar hashes (in same bin/bucket with high probability). i.e. in our made-up example, data points that are close to the orange data point should fall in the same bin, whereas data points that are far away should fall in different bin. However, because of the randomness, it is not likely that all similar items are grouped correctly. Hence, to overcome this limitation, a common practice is to create multiple random vectors.\n", "\n", "e.g. Say we throw down another random vector, this time the orange data point falls under this random vector, thus it falls under a different bin (black bin instead of white).\n", "\n", "\n", "\n", "Since, each bin is represented by a bitwise hash value, which is a number made up of a sequence of 1s and 0s. e.g. for this orange data point, if we consider the white bin as 0 and the black bin as 1, then this data point's bin will be represented by [0, 1].\n", "\n", "Let's use some code to illustrate this process. Our first step is to generate a collection of random vectors from the standard Gaussian distribution." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "def generate_random_vectors(dim, n_vectors):\n", " \"\"\"\n", " generate random projection vectors\n", " the dims comes first in the matrix's shape,\n", " so we can use it for matrix multiplication.\n", " \"\"\"\n", " return np.random.randn(dim, n_vectors)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now generate random vectors of the same dimensionality as our vocabulary size. Each vector can be used to compute one bit in the bin encoding. We generate 16 vectors, leading to a 16-bit encoding of the bin index for each document. Note that 16 is a hyperparamter, we will look into this adjustable parameter in later section." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "vocabulary size: 52262\n", "dimension: (52262, 16)\n" ] }, { "data": { "text/plain": [ "array([[ 1.76405235, 0.40015721, 0.97873798, ..., 0.12167502,\n", " 0.44386323, 0.33367433],\n", " [ 1.49407907, -0.20515826, 0.3130677 , ..., 1.46935877,\n", " 0.15494743, 0.37816252],\n", " [-0.88778575, -1.98079647, -0.34791215, ..., -0.4380743 ,\n", " -1.25279536, 0.77749036],\n", " ...,\n", " [-0.13430852, -2.40513172, -0.15499143, ..., -0.6048063 ,\n", " -0.10716926, -0.25638226],\n", " [ 2.67330814, 0.87243643, -0.12795915, ..., 0.82403241,\n", " 1.4171818 , 0.81563705],\n", " [-0.38178352, 2.22187987, -0.6359808 , ..., 0.50846758,\n", " 1.50555612, 0.22746407]])" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "vocab_size = len(tfidf.get_feature_names())\n", "print('vocabulary size: ', vocab_size)\n", "\n", "np.random.seed(0)\n", "n_vectors = 16\n", "random_vectors = generate_random_vectors(vocab_size, n_vectors)\n", "print('dimension: ', random_vectors.shape)\n", "random_vectors" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we'd like to decide which bin each documents should go. Since 16 random vectors were generated in the previous cell, we have 16 bits to represent the bin index. The first bit is given by the sign of the dot product between the first random vector and the document's TF-IDF vector, and so on." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "dimension: (1, 16)\n" ] }, { "data": { "text/plain": [ "array([[False, False, False, False, False, False, True, True, True,\n", " False, False, True, False, False, False, False]])" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# use one data point's tfidf representation as an example\n", "data_point = X_tfidf[0]\n", "\n", "# True if positive sign; False if negative sign\n", "bin_indices_bits = data_point.dot(random_vectors) >= 0\n", "print('dimension: ', bin_indices_bits.shape)\n", "bin_indices_bits" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "All documents that obtain exactly this vector will be assigned to the same bin. One further preprocessing step we'll perform is to convert this resulting bin into integer representation. This makes it convenient for us to refer to individual bins. \n", "\n", "```\n", "Bin index integer\n", "[0,0,0,0,0,0,0,0,0,0,0,0] => 0\n", "[0,0,0,0,0,0,0,0,0,0,0,1] => 1\n", "[0,0,0,0,0,0,0,0,0,0,1,0] => 2\n", "[0,0,0,0,0,0,0,0,0,0,1,1] => 3\n", "...\n", "[1,1,1,1,1,1,1,1,1,1,0,0] => 65532\n", "[1,1,1,1,1,1,1,1,1,1,0,1] => 65533\n", "[1,1,1,1,1,1,1,1,1,1,1,0] => 65534\n", "[1,1,1,1,1,1,1,1,1,1,1,1] => 65535 (= 2^16-1)\n", "```\n", "\n", "By the [rules of binary number representation](https://en.wikipedia.org/wiki/Binary_number#Decimal), to perform the conversion, we can compute the dot product between the document vector and the vector consisting of powers of 2:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[32768 16384 8192 4096 2048 1024 512 256 128 64 32 16\n", " 8 4 2 1]\n", "[912]\n" ] } ], "source": [ "bin_indices_bits = data_point.dot(random_vectors) >= 0\n", "\n", "# https://wiki.python.org/moin/BitwiseOperators\n", "# x << y is the same as multiplying x by 2 ** y\n", "powers_of_two = 1 << np.arange(n_vectors - 1, -1, step=-1)\n", "print(powers_of_two)\n", "\n", "# final integer representation of individual bins\n", "bin_indices = bin_indices_bits.dot(powers_of_two)\n", "print(bin_indices)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can repeat the identical operation on all documents in our dataset and compute the corresponding bin. We'll again leverage matrix operations so that no explicit loop is needed." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(500, 16)\n" ] }, { "data": { "text/plain": [ "(500,)" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# we can do it for the entire corpus\n", "bin_indices_bits = X_tfidf.dot(random_vectors) >= 0\n", "print(bin_indices_bits.shape)\n", "bin_indices = bin_indices_bits.dot(powers_of_two)\n", "bin_indices.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The resulting array gives us the integer index of the bins for all documents.\n", "\n", "Now we are ready to complete the following function. Given the integer bin indices for the documents, we would curate the list of document IDs that belong to each bin. Since a list is to be maintained for each unique bin index, a dictionary of lists is used." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "from collections import defaultdict\n", "\n", "\n", "def train_lsh(X_tfidf, n_vectors, seed=None): \n", " if seed is not None:\n", " np.random.seed(seed)\n", "\n", " dim = X_tfidf.shape[1]\n", " random_vectors = generate_random_vectors(dim, n_vectors) \n", "\n", " # partition data points into bins,\n", " # and encode bin index bits into integers\n", " bin_indices_bits = X_tfidf.dot(random_vectors) >= 0\n", " powers_of_two = 1 << np.arange(n_vectors - 1, -1, step=-1)\n", " bin_indices = bin_indices_bits.dot(powers_of_two)\n", "\n", " # update `table` so that `table[i]` is the list of document ids with bin index equal to i\n", " table = defaultdict(list)\n", " for idx, bin_index in enumerate(bin_indices):\n", " table[bin_index].append(idx)\n", " \n", " # note that we're storing the bin_indices here\n", " # so we can do some ad-hoc checking with it,\n", " # this isn't actually required\n", " model = {'table': table,\n", " 'random_vectors': random_vectors,\n", " 'bin_indices': bin_indices,\n", " 'bin_indices_bits': bin_indices_bits}\n", " return model\n", "\n", "\n", "# train the model\n", "n_vectors = 16\n", "model = train_lsh(X_tfidf, n_vectors, seed=143)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After generating our LSH model, let's examine the generated bins to get a deeper understanding of them. Recall that during the background section, given a product's tfidf vector representation, we were able to find its similar product using standard cosine similarity. Here, we will look at these similar products' bins to see if the result matches intuition. Remember the idea behind LSH is that similar data points will tend to fall into nearby bins." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "bits 1: [ True False False True True True True False False True True False\n", " False True True False]\n", "bits 2: [ True False False False False False True False False True False False\n", " True True True False]\n", "Number of agreed bins: 11\n" ] } ], "source": [ "# comparison\n", "similar_item_ids = [similar_item for similar_item, _ in similar_items]\n", "bits1 = model['bin_indices_bits'][similar_item_ids[0]]\n", "bits2 = model['bin_indices_bits'][similar_item_ids[1]]\n", "\n", "print('bits 1: ', bits1)\n", "print('bits 2: ', bits2)\n", "print('Number of agreed bins: ', np.sum(bits1 == bits2))" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "bits 1: [ True False False True True True True False False True True False\n", " False True True False]\n", "bits 2: [False True True False False True True False True False False False\n", " False False True True]\n", "Number of agreed bins: 6\n" ] } ], "source": [ "# comparison\n", "bits1 = model['bin_indices_bits'][similar_item_ids[0]]\n", "bits2 = model['bin_indices_bits'][similar_item_ids[4]]\n", "\n", "print('bits 1: ', bits1)\n", "print('bits 2: ', bits2)\n", "print('Number of agreed bins: ', np.sum(bits1 == bits2))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looking at the result above, it does seem like LSH is doing what its intended to do: i.e., similar data points will agree upon more bin indices than dissimilar data points, however, in a high-dimensional space such as text features, we can get unlucky with our selection of only a few random vectors such that dissimilar data points go into the same bin while similar data points fall into different bins. Hence, given a query document, we should consider all documents in the nearby bins and sort them according to their actual distances from the query." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Querying the LSH model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's first implement the logic for searching nearby neighbors, which goes like this:\n", "\n", "\n", "1. Let L be the bit representation of the bin that contains the query documents.\n", "2. Consider all documents in bin L.\n", "3. Consider documents in the bins whose bit representation differs from L by 1 bit.\n", "4. Consider documents in the bins whose bit representation differs from L by 2 bits, and so on ...\n", "\n", "\n", "To obtain candidate bins that differ from the query bin by some number of bits, we use `itertools.combinations`, which produces all possible subsets of a given list. See [this documentation](https://docs.python.org/3/library/itertools.html#itertools.combinations) for details.\n", "\n", "\n", "1. Decide on the search radius r. This will determine the number of different bits between the two vectors.\n", "2. For each subset (n_1, n_2, ..., n_r) of the list [0, 1, 2, ..., n_vectors-1], do the following:\n", " * Flip the bits (n_1, n_2, ..., n_r) of the query bin to produce a new bit vector.\n", " * Fetch the list of documents belonging to the bin indexed by the new bit vector.\n", " * Add those documents to the candidate set." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "from itertools import combinations\n", "\n", "\n", "def search_nearby_bins(query_bin_bits, table, search_radius=3, candidate_set=None):\n", " \"\"\"\n", " For a given query vector and trained LSH model's table\n", " return all candidate neighbors with the specified search radius.\n", " \n", " Example\n", " -------\n", " model = train_lsh(X_tfidf, n_vectors=16, seed=143)\n", " query = model['bin_index_bits'][0] # vector for the first document\n", " candidates = search_nearby_bins(query, model['table'])\n", " \"\"\"\n", " if candidate_set is None:\n", " candidate_set = set()\n", "\n", " n_vectors = query_bin_bits.shape[0]\n", " powers_of_two = 1 << np.arange(n_vectors - 1, -1, step=-1)\n", "\n", " for different_bits in combinations(range(n_vectors), search_radius):\n", " # flip the bits (n_1, n_2, ..., n_r) of the query bin to produce a new bit vector\n", " index = list(different_bits)\n", " alternate_bits = query_bin_bits.copy()\n", " alternate_bits[index] = np.logical_not(alternate_bits[index])\n", "\n", " # convert the new bit vector to an integer index\n", " nearby_bin = alternate_bits.dot(powers_of_two)\n", "\n", " # fetch the list of documents belonging to\n", " # the bin indexed by the new bit vector,\n", " # then add those documents to candidate_set;\n", " # make sure that the bin exists in the table\n", " if nearby_bin in table:\n", " candidate_set.update(table[nearby_bin])\n", "\n", " return candidate_set" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The next code chunk uses this searching for nearby bins logic to search for similar documents and return a dataframe that contains the most similar data points according to LSH and their corresponding distances." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "from sklearn.metrics.pairwise import pairwise_distances\n", "\n", "\n", "def get_nearest_neighbors(X_tfidf, query_vector, model, max_search_radius=3):\n", " table = model['table']\n", " random_vectors = model['random_vectors']\n", "\n", " # compute bin index for the query vector, in bit representation.\n", " bin_index_bits = np.ravel(query_vector.dot(random_vectors) >= 0)\n", "\n", " # search nearby bins and collect candidates\n", " candidate_set = set()\n", " for search_radius in range(max_search_radius + 1):\n", " candidate_set = search_nearby_bins(bin_index_bits, table, search_radius, candidate_set)\n", "\n", " # sort candidates by their true distances from the query\n", " candidate_list = list(candidate_set)\n", " candidates = X_tfidf[candidate_list]\n", " distance = pairwise_distances(candidates, query_vector, metric='cosine').flatten()\n", " \n", " distance_col = 'distance'\n", " nearest_neighbors = pd.DataFrame({\n", " 'id': candidate_list, distance_col: distance\n", " }).sort_values(distance_col).reset_index(drop=True)\n", " return nearest_neighbors" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "original similar items:\n", "[(1, 1.0000000000000029), (2, 0.4181663992161579), (18, 0.11546382098627586), (493, 0.11303392245400203), (299, 0.11247854521091638)]\n", "dimension: (67, 2)\n" ] }, { "data": { "text/html": [ "
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iddistance
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" ], "text/plain": [ " id distance\n", "0 1 2.220446e-16\n", "1 2 5.818336e-01\n", "2 317 9.008780e-01\n", "3 213 9.117783e-01\n", "4 272 9.173818e-01" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print('original similar items:\\n' + str(similar_items))\n", "\n", "item_id = 1\n", "query_vector = X_tfidf[item_id]\n", "nearest_neighbors = get_nearest_neighbors(X_tfidf, query_vector, model, max_search_radius=5)\n", "print('dimension: ', nearest_neighbors.shape)\n", "nearest_neighbors.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can observe from the result above that when we use a max_search_radius of 5, our LSH-based similarity search wasn't capable of retrieving the actual most similar items to our target data point, this is expected as we have mentioned LSH is an approximate nearest neighborhood search method. However, if we were to increase the radius parameter to 10, we can in fact retrieve all the actual most similar items." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "dimension: (455, 2)\n" ] }, { "data": { "text/html": [ "
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iddistance
012.220446e-16
125.818336e-01
2188.845362e-01
34938.869661e-01
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" ], "text/plain": [ " id distance\n", "0 1 2.220446e-16\n", "1 2 5.818336e-01\n", "2 18 8.845362e-01\n", "3 493 8.869661e-01\n", "4 299 8.875215e-01" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nearest_neighbors = get_nearest_neighbors(X_tfidf, query_vector, model, max_search_radius=10)\n", "print('dimension: ', nearest_neighbors.shape)\n", "nearest_neighbors.head()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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iddistancedescription
012.220446e-16Active classic boxers - There's a reason why o...
125.818336e-01Active sport boxer briefs - Skinning up Glory ...
2188.845362e-01Cap 1 bottoms - Spring skiing is as transient ...
34938.869661e-01'73 logo t-shirt - Patagonia's timeless '73 Lo...
42998.875215e-01Active boy shorts - We've worn these versatile...
\n", "
" ], "text/plain": [ " id distance description\n", "0 1 2.220446e-16 Active classic boxers - There's a reason why o...\n", "1 2 5.818336e-01 Active sport boxer briefs - Skinning up Glory ...\n", "2 18 8.845362e-01 Cap 1 bottoms - Spring skiing is as transient ...\n", "3 493 8.869661e-01 '73 logo t-shirt - Patagonia's timeless '73 Lo...\n", "4 299 8.875215e-01 Active boy shorts - We've worn these versatile..." ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# we can perform a join with the original table to get the description\n", "# for sanity checking purpose\n", "nearest_neighbors.head().merge(df, on='id', how='inner')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Experimenting with LSH" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the following sections we have implemented a few experiments so we can gain some intuition as to how LSH behaves in different situations. This will help us understand the effect of searching nearby bins and the performance of LSH versus computing nearest neighbors using a brute force search.\n", "\n", "The first experiment that we'll be looking at is the **effect of nearby bin search**.\n", "\n", "How does nearby bin search affect the three variables that are tied to the search radius:\n", "\n", "- Number of candidate documents considered\n", "- Query time\n", "- Distance of approximate neighbors from the query\n", "\n", "Let's run LSH multiple times, each with different radius for nearby bin search. We will keep track of the three variables that were discussed above." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "import time\n", "\n", "query_time_history = []\n", "num_candidates_history = []\n", "max_distance_history = []\n", "min_distance_history = []\n", "average_distance_history = []\n", "\n", "topn = 5\n", "query_vector = X_tfidf[1]\n", "for max_search_radius in range(12):\n", " start = time.time() \n", " nearest_neighbors = get_nearest_neighbors(X_tfidf, query_vector, model, max_search_radius)\n", " end = time.time()\n", " query_time = end - start\n", "\n", " # the closest neighbor is the query point itself, thus\n", " # we'll exclude it from the calculation\n", " distances = nearest_neighbors['distance'].iloc[1:].head(topn)\n", " max_distance = distances.max()\n", " min_distance = distances.min()\n", " average_distance = distances.mean()\n", " num_candidates = nearest_neighbors.shape[0]\n", "\n", " query_time_history.append(query_time)\n", " num_candidates_history.append(num_candidates) \n", " max_distance_history.append(max_distance)\n", " min_distance_history.append(min_distance)\n", " average_distance_history.append(average_distance)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "image/png": { "height": 416, "width": 562 } }, "output_type": "display_data" } ], "source": [ "# change default style figure and font size\n", "plt.rcParams['figure.figsize'] = 8, 6\n", "plt.rcParams['font.size'] = 12\n", "\n", "\n", "linewidth = 4\n", "plt.figure()\n", "plt.plot(num_candidates_history, linewidth=4)\n", "plt.xlabel('Search radius')\n", "plt.ylabel('# of documents searched')\n", "plt.tight_layout()\n", "\n", "plt.figure()\n", "plt.plot(query_time_history, linewidth=linewidth)\n", "plt.xlabel('Search radius')\n", "plt.ylabel('Query time (seconds)')\n", "plt.tight_layout()\n", "\n", "plt.figure()\n", "plt.plot(average_distance_history, linewidth=linewidth, label='Average of 10 neighbors')\n", "plt.plot(max_distance_history, linewidth=linewidth, label='Farthest of 10 neighbors')\n", "plt.plot(min_distance_history, linewidth=linewidth, label='Closest of 10 neighbors')\n", "plt.xlabel('Search radius')\n", "plt.ylabel('Cosine distance of neighbors')\n", "plt.legend(loc='best')\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Some observations:\n", "\n", "- As we increase the search radius, we find more neighbors that are a smaller distance away.\n", "- With increased search radius comes a greater number documents that have to be searched. Query time is higher as a consequence.\n", "- With sufficiently high search radius, the results of LSH begin to resemble the results of brute-force search.\n", "\n", "The next experiment that we'll be conducting is one that we are probably most interested in: **Quality metrics for neighbors**. Since LSH is an approximate nearest neighborhood method, often times, we would like to have a metric such as precision or recall to measure the quality of the method. For example, with precision, we would be able to answer questions such as: How many of the 10 neighbors given by LSH are among the true 10 nearest neighbors?\n", "\n", "Our previous experiment is limited by the fact that it was run with a single query (data point). We should repeat the analysis for the entire data to make sure it generalizes. As Iterating over all data points would take a long time, we will cheat here and randomly choose a small subset." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 / 10\n", "2 / 10\n", "3 / 10\n", "4 / 10\n", "5 / 10\n", "6 / 10\n", "7 / 10\n", "8 / 10\n", "9 / 10\n", "10 / 10\n" ] }, { "data": { "text/plain": [ "{0: [0.1, 0.2, 0.1, 0.1, 0.2, 0.1, 0.1, 0.1, 0.1, 0.2],\n", " 1: [0.1, 0.2, 0.1, 0.1, 0.2, 0.1, 0.1, 0.1, 0.1, 0.4],\n", " 2: [0.1, 0.2, 0.1, 0.1, 0.3, 0.1, 0.1, 0.2, 0.2, 0.4],\n", " 3: [0.1, 0.2, 0.1, 0.2, 0.3, 0.1, 0.1, 0.2, 0.2, 0.4],\n", " 4: [0.1, 0.2, 0.1, 0.2, 0.5, 0.1, 0.1, 0.4, 0.2, 0.4],\n", " 5: [0.2, 0.4, 0.2, 0.2, 0.5, 0.3, 0.1, 0.4, 0.3, 0.5],\n", " 6: [0.3, 0.5, 0.3, 0.4, 0.5, 0.4, 0.5, 0.8, 0.5, 0.8],\n", " 7: [0.3, 0.8, 0.5, 0.7, 0.9, 0.6, 0.9, 0.8, 0.8, 1.0],\n", " 8: [0.7, 0.9, 0.8, 0.8, 1.0, 0.7, 1.0, 0.8, 0.9, 1.0],\n", " 9: [0.9, 1.0, 0.8, 0.9, 1.0, 0.8, 1.0, 0.8, 0.9, 1.0],\n", " 10: [1.0, 1.0, 1.0, 0.9, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "topn = 10\n", "max_radius = 11\n", "num_queries = 10\n", "np.random.seed(0)\n", "\n", "# key = radius, value = list of precision for different queries\n", "precision_history = {i: [] for i in range(max_radius)}\n", "\n", "random_query_ids = np.random.choice(X_tfidf.shape[0], num_queries, replace=False)\n", "for i, query_id in enumerate(random_query_ids):\n", " print('%s / %s' % (i + 1, num_queries))\n", "\n", " # get the set of true nearest neighbors\n", " similar_items = get_similarity_items(X_tfidf, item_id=query_id, topn=topn)\n", " ground_truth = set(similar_item for similar_item, _ in similar_items)\n", "\n", " # compute precision metric for each radius\n", " query_vector = X_tfidf[query_id]\n", " for radius in range(max_radius):\n", " nearest_neighbors = get_nearest_neighbors(X_tfidf, query_vector, model, max_search_radius=radius)\n", " candidate_set = set(nearest_neighbors['id'].head(topn))\n", "\n", " # precision = (# of neighbors both in result and ground_truth)\n", " precision = len(candidate_set & ground_truth) / topn\n", " precision_history[radius].append(precision)\n", "\n", "precision_history" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "image/png": { "height": 416, "width": 561 } }, "output_type": "display_data" } ], "source": [ "mean_precision = [np.mean(precision_history[i]) for i in range(len(precision_history))]\n", "\n", "plt.figure()\n", "plt.plot(mean_precision, linewidth=4, label='Precison@' + str(topn))\n", "plt.xlabel('Search radius')\n", "plt.ylabel('Precision')\n", "plt.legend()\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this article, we saw that LSH performs an efficient neighbor search by randomly partitioning all reference data points into different bins, when it comes to the similarity search stage, it will only consider searching within data points that fall within the same bin. Then a radius parameter gives the end-user full control over the trade-off between the speed of the search versus the quality of the nearest neighbors.\n", "\n", "There are many other applications of LSH, here we are using it with text-based features, the same idea can also be applied to images as well. And for those interested, the following link contains a blog post of its use-case at Uber, where they are using the technique to find similar trips based on their spatial properties, a method for identifying fraudulent drivers. [Blog: Detecting Abuse at Scale: Locality Sensitive Hashing at Uber Engineering](https://eng.uber.com/lsh/)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Reference\n", "\n", "- [Coursera: Washington Clustering & Retrieval](https://www.coursera.org/learn/ml-clustering-and-retrieval)\n", "- [Jupyter Notebook: Locality Sensitive Hashing](http://nbviewer.jupyter.org/github/smarthi/UnivOfWashington-Machine-Learning/blob/master/Clustering_IR/week2/1_nearest-neighbors-lsh-implementation_blank.ipynb)\n", "- [Blog: Locality Sensitive Hashing for Similar Item Search](https://towardsdatascience.com/locality-sensitive-hashing-for-music-search-f2f1940ace23)\n", "- [Blog: A Simple Content-Based Recommendation Engine in Python](http://blog.untrod.com/2016/06/simple-similar-products-recommendation-engine-in-python.html)\n", "- [Stackoverflow: How to understand Locality Sensitive Hashing?](https://stackoverflow.com/questions/12952729/how-to-understand-locality-sensitive-hashing)" ] } ], "metadata": { "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": "3.6.4" }, "toc": { "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": true, "toc_position": { "height": "calc(100% - 180px)", "left": "10px", "top": "150px", "width": "241px" }, "toc_section_display": true, "toc_window_display": true }, "varInspector": { "cols": { "lenName": 16, "lenType": 16, "lenVar": 40 }, "kernels_config": { "python": { "delete_cmd_postfix": "", "delete_cmd_prefix": "del ", "library": "var_list.py", "varRefreshCmd": "print(var_dic_list())" }, "r": { "delete_cmd_postfix": ") ", "delete_cmd_prefix": "rm(", "library": "var_list.r", "varRefreshCmd": "cat(var_dic_list()) " } }, "types_to_exclude": [ "module", "function", "builtin_function_or_method", "instance", "_Feature" ], "window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }