# 2. Approximate Retrieval

Quickly find nearest neighbors in (very) high dimensions.

Examples:

• Image search and image completion
• Song search

## Distance functions

• $d : S \times S \rightarrow \mathbb{R}$ is a distance function iff
• $\forall s, t \in S : d(s, t) \ge 0$
• $\forall s : d(s, s) = 0$
• $\forall s, t \in S : d(s, t) = d(t, s)$
• $\forall s, t, r \in S: d(s, t) + d(t, r) \ge d(s, r)$ (triangle inequality)
• if $\forall s, t \in S: d(s, t) = 0 \implies s = t$, then d is a "stronger" function called a metric
• We make use of this by representing objects as vectors
• images become feature vectors (see Computer Vision course)
• documents become bag-of-words or tf-idf representations
• Many types of distances
• $\ell_p$, such as the Euclidean distance ($\ell_2 = \| x' - x \|_2 = \sqrt{\sum_{i = 1}^{D}{(x'_i - x_i)^2}}$)
• cosine distance (used a lot in text search), $d_{\text{cosine}}(x, x') = \arccos \frac{x^T x'}{\|x\|_2 \|x'\|_2} = \theta$
• edit distance (expensive)
• Jaccard-distance (for sets)

## Curse of dimensionality

In very large dimensions, the minimum distance between any two points gets very close to the maximum distance between any points.

$\lim_{D \rightarrow \infty} P[d_{max} \le (1 + \epsilon)d_{min}] = 1$

## Approximate retrieval

### Input

A data set $S$ and a distance function $d$.

### Problem 1: Nearest neighbor

Given $q$, find $s* = \text{argmin}_{s \in S} d(q, s)$

### Problem 2: Near-duplicate detection

Find all $s$, $s'$ in $S$, with distance at most $\epsilon$.

• Use (word) shingling and Jaccard distance as a similarity measure.
• Can even hash shingles to save space
• Jaccard similarity: $JSim(A, B) = \frac{|A \cap B|}{|A \cup B|}$
• Jaccard distance: $d(A, B) = 1 - JSim(A, B)$


In [1]:

def jaccard_sim(a, b):
"""The Jaccard similarity of the two sets a and b."""
return len(a & b) * 1.0 / len(a | b)

def jaccard_distance(a, b):
return 1 - jaccard_sim(a, b)

x = {1, 5, 6, 10}
y = {2, 5, 6, 20}
print("Similarity: %.2f" % jaccard_sim(x, y))
print("Distance:   %.2f" % jaccard_distance(x, y))




Similarity: 0.33
Distance:   0.67


• Scale remains problematic; we can't just do a double loop over all $N$ elements...
• Hashing works well for exact duplicates, can it work with near duplicates?
• Yes, we have locality sensitive hashing (LSH)

## Min-hashing

Reorder shingle matrix rows with random permutation $\pi$

$\operatorname{hash}(C) =$ minimum row number in which permuted column contains a one (C represents a column, i.e. a document in shingle form)

$\operatorname{hash}(C) = h_\pi(C) = \min_{i:C(i)=1}\pi(i)$ (hence the min in min-hashing)

Turns out that the probability of two documents sharing a hash is equal to their Jaccard similarity: $P[h(C_1) = h(C_2)] = Sim(C_1, C_2)$ (trivial but interesting proof; see slides).

This means we can use many hash functions, see how often they clash for a pair of documents, and we have a decent estimate for the documents' Jaccard similarity.

An alternative to min-hashing is sim-hashing (see Information Retrieval)



In [44]:

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline




In [45]:

shingle_matrix = np.array([
[1, 0, 1, 0],
[1, 0, 0, 1],
[0, 1, 0, 1],
[0, 1, 0, 1],
[0, 1, 0, 1],
[1, 0, 1, 0],
[1, 0, 1, 0]
])


• Want to find all duplicates with > 90% similarity
• Apply min-hash to all documents and look for candidate pairs (documents hashed to same bucket)
• we find 90% of 90%-duplicates (since the probability of a single hash function colliding is 90% by default, since Pr = Sim)
• we miss 10% of 90%-duplicates
• $P(\text{miss with 1 function }) = 1 - s$ ($s$ = similarity)
• $P(\text{miss with k functions}) = (1 - s)^k$
• multiple hash functions $\implies$ exponentially fewer misses!


In [46]:

def min_hash(matrix, permutation):
signature = [None for col in matrix[0]]
for index, row in enumerate(permutation):
for col, byte in enumerate(signature):
if byte is None and matrix[row][col] == 1:
signature[col] = index

return signature

def array_sim(a1, a2):
in_common = len(a1[a1 == a2])
total = len(a1)
#     print("In common: %d" % in_common)
#     print("Total:     %d" % total)
return in_common * 1.0 / total

def col_col_sim(matrix, c1_index, c2_index):
tr = matrix.T
c1 = tr[c1_index]
c2 = tr[c2_index]
return array_sim(c1, c2)

def sig_sig_sim(matrix, c1_index, c2_index, hash_fns):
sig_matrix = np.array([min_hash(matrix, hf) for hf in hash_fns])
tr = sig_matrix.T
c1 = tr[c1_index]
c2 = tr[c2_index]
return array_sim(c1, c2)




In [47]:

# Same permutations as in slides, just 0-indexed and reversed
p1 = [0, 4, 1, 6, 5, 3, 2]
p2 = [2, 1, 3, 0, 6, 4, 5]
p3 = [4, 5, 0, 1, 6, 3, 2]
print(min_hash(shingle_matrix, p1))
print(min_hash(shingle_matrix, p2))
print(min_hash(shingle_matrix, p3))




[0, 1, 0, 1]
[1, 0, 3, 0]
[1, 0, 1, 0]




In [48]:

hash_fns = [p1, p2, p3]
pairs = [(0, 2), (1, 3), (0, 1), (2, 3)]
for c1, c2 in pairs:
print("Columns %d and %d:" % (c1 + 1, c2 + 1))
print("Col/col similarity: %.2f" % col_col_sim(shingle_matrix, c1, c2))
print("Sig/sig similarity: %.2f" % sig_sig_sim(shingle_matrix, c1, c2, hash_fns))




Columns 1 and 3:
Col/col similarity: 0.86
Sig/sig similarity: 0.67
Columns 2 and 4:
Col/col similarity: 0.86
Sig/sig similarity: 1.00
Columns 1 and 2:
Col/col similarity: 0.00
Sig/sig similarity: 0.00
Columns 3 and 4:
Col/col similarity: 0.00
Sig/sig similarity: 0.00



## Scaling up min-hash

While the above method works well, it's quite problematic to actually permute a huge dataset

We therefore want to implement the permutations as linear hash funtions (see your local linear algebra course for more info about why this works!).

$$\pi(r) = a \cdot r + b \operatorname{mod} n =: h_{a,b}(r), \> a, b \> \text{random}$$

This is huge! It allows us to iteratively process any shingle matrix (row-wise), without having to actually move (huge) data around (necessary for performing actual permutations).

Moreover, $h_{a,b}$ is efficient to store.

Note: on slide 17, try to really figure out the prof's corrections, if time allows it.

## Increasing the accuracy

• More hash functions, any collision $\implies$ similar leads to fewer false negatives, but more false positives


In [56]:

index = np.arange(0, 1, 0.01)
curves = {'k = %d' % k: 1 - (1 - index) ** k for k in range(1, 20, 2)}
fr = pd.DataFrame(data=curves, index=pd.Index(index))




In [57]:

ax = fr.plot()
plt.xlabel("Similarity")
plt.ylabel("P(hit)")




Out[57]:

<matplotlib.text.Text at 0x10d206908>


• At this point, we would still need to do pairwise comparison of columns in the signature matrix and see whether they are close enough. Even though we would typically use far fewer hash functions than we would have shingles, we're still talking about $\mathcal{O}(N^2)$ operations.
• Let's hash some more!
• Hash entire columns (i.e. sets of many min-hashes of the same document) and bucketize; only evaluate similarity of the documents which share a bucket.
• Split matrix rows into $b$ bands of $r$ rows. So each band contains a few ($r$) hashes for all documents.
• We do a bucketization in each band.
• (Just use a linear hash function to hash the partial signature for every band, for every document.)
• $h_{a_i, b}(s) = \sum\limits_{i=1}^{r}a_is_i + b \operatorname{mod} n$, with $a_1, \dots, a_r$ and $b$ random
• Each band is independent (i.e. its own hash table)
• If there's a collision between two documents in at least one band, treat the documents as candidates. Why is this banding useful?

## Analysis of partitioning

• So why do the banding at all?
• $C_i = [B_{i, 1}, \dots, B_{i, b}]$, a document represented as a bunch of bands (each containing $r$ signatures, so $r$ numbers, essentially)
• $P\big(h(C_{1,j}) = h(C_{2, j})\big) = s^r$ (pow(similarity, band length)), probability of collision in fixed band $j$; (basic probability theory, since every band has $r$ elements, and the probability of an individual collision is equal to our target similarity, as explained earlier in this course)
• $P(\text{no collision on band j}) = 1 - s^r$
• $P(\text{no collision on any band}) = (1 - s^r)^b$ (basic probability theory)
• $P(C_1 \> \text{and} \> C_2 \> \text{are candidate pair}) = P(\text{collision on any band}) = 1 - (1 - s^r)^b$
• This is a versatile function; mess around with $r$ and $b$ to produce your optimal curve! (see slides for pretty graphs)
• Prefer false positives, and perform more detailed checks when necessary.
• Key insight: use a hash function to hash similar items to same bucket.
• Use multiple hash functions in clever ways (e.g. using banding) to boost the gap between similar and non-similar pairs.
• See formal definition and $(d_1, d_2, p_1, p_2)$-sensitivity
• How to generalize what we did above for Jaccard distance?
• AND/OR construction
• r-way AND $\implies$ decrease false positives (want ALL match) $(d_1, d_2, p_1, p_2) \rightarrow (d_1, d_2, p_1^r, p_2^r)$
• b-way OR $\implies$ decrease false negatives (want ANY match) $(d1, d2, p_1, p_2) \rightarrow (d_1, d_2, 1 - (1 - p_1)^b, 1 - (1 - p_2)^b)$
• Combine in either order. For our Jaccard-similarity example above, this meant r-way AND first (want identical match between all band components, to consider bands as candidate pairs), then a b-way OR (we cared about at least one band matching between two documents)
• Can do even more complex combindations.
• Total number of hash functions is the product of all $r$ and $b$ values. A $(4, 4)$ OR-AND followed by a $(4, 4)$ AND-OR uses $4 \cdot 4 \cdot 4 \cdot 4$ hash functions (think bands within bands).

TODO: Numeric example from slides maybe.

## LSH for other hash functions

• Can use with cosine distance by fixing a hyperplane (by its unit-length normal $w \in \mathbb{R}^d$) and just seeing on which side of it a feature vector is.
• Namely: $h_w(v) = \operatorname{sign}(w^Tv)$
• But more on this in a future lecture (Active Learning)


In [ ]: