Source code for datasketch.lsh

from collections import defaultdict


_integration_precision = 0.001
def _integration(f, a, b):
    p = _integration_precision
    area = 0.0
    x = a
    while x < b:
        area += f(x+0.5*p)*p
        x += p
    return area, None

try:
    from scipy.integrate import quad as integrate
except ImportError:
    # For when no scipy installed
    integrate = _integration


def _false_positive_probability(threshold, b, r):
    _probability = lambda s : 1 - (1 - s**float(r))**float(b)
    a, err = integrate(_probability, 0.0, threshold) 
    return a


def _false_negative_probability(threshold, b, r):
    _probability = lambda s : 1 - (1 - (1 - s**float(r))**float(b))
    a, err = integrate(_probability, threshold, 1.0)
    return a


def _optimal_param(threshold, num_perm, false_positive_weight,
        false_negative_weight):
    '''
    Compute the optimal `MinHashLSH` parameter that minimizes the weighted sum
    of probabilities of false positive and false negative.
    '''
    min_error = float("inf")
    opt = (0, 0)
    for b in range(1, num_perm+1):
        max_r = int(num_perm / b)
        for r in range(1, max_r+1):
            fp = _false_positive_probability(threshold, b, r)
            fn = _false_negative_probability(threshold, b, r)
            error = fp*false_positive_weight + fn*false_negative_weight
            if error < min_error:
                min_error = error
                opt = (b, r)
    return opt


[docs]class MinHashLSH(object): ''' The :ref:`minhash_lsh` index. It supports query with `Jaccard similarity`_ threshold. Reference: `Chapter 3, Mining of Massive Datasets <http://www.mmds.org/>`_. Args: threshold (float): The Jaccard similarity threshold between 0.0 and 1.0. The initialized MinHash LSH will be optimized for the threshold by minizing the false positive and false negative. num_perm (int, optional): The number of permutation functions used by the MinHash to be indexed. For weighted MinHash, this is the sample size (`sample_size`). weights (tuple, optional): Used to adjust the relative importance of minizing false positive and false negative when optimizing for the Jaccard similarity threshold. `weights` is a tuple in the format of :code:`(false_positive_weight, false_negative_weight)`. params (tuple, optional): The LSH parameters (i.e., number of bands and size of each bands). This is used to bypass the parameter optimization step in the constructor. `threshold` and `weights` will be ignored if this is given. Note: `weights` must sum to 1.0, and the format is (false positive weight, false negative weight). For example, if minizing false negative (or maintaining high recall) is more important, assign more weight toward false negative: weights=(0.4, 0.6). Try to live with a small difference between weights (i.e. < 0.5). ''' def __init__(self, threshold=0.9, num_perm=128, weights=(0.5,0.5), params=None): if threshold > 1.0 or threshold < 0.0: raise ValueError("threshold must be in [0.0, 1.0]") if num_perm < 2: raise ValueError("Too few permutation functions") if any(w < 0.0 or w > 1.0 for w in weights): raise ValueError("Weight must be in [0.0, 1.0]") if sum(weights) != 1.0: raise ValueError("Weights must sum to 1.0") self.h = num_perm if params is not None: self.b, self.r = params if self.b * self.r > num_perm: raise ValueError("The product of b and r must be less than num_perm") else: false_positive_weight, false_negative_weight = weights self.b, self.r = _optimal_param(threshold, num_perm, false_positive_weight, false_negative_weight) self.hashtables = [defaultdict(list) for _ in range(self.b)] self.hashranges = [(i*self.r, (i+1)*self.r) for i in range(self.b)] self.keys = dict()
[docs] def insert(self, key, minhash): ''' Insert a unique key to the index, together with a MinHash (or weighted MinHash) of the set referenced by the key. Args: key (hashable): The unique identifier of the set. minhash (datasketch.MinHash): The MinHash of the set. ''' if len(minhash) != self.h: raise ValueError("Expecting minhash with length %d, got %d" % (self.h, len(minhash))) if key in self.keys: raise ValueError("The given key already exists") self.keys[key] = [self._H(minhash.hashvalues[start:end]) for start, end in self.hashranges] for H, hashtable in zip(self.keys[key], self.hashtables): hashtable[H].append(key)
[docs] def query(self, minhash): ''' Giving the MinHash of the query set, retrieve the keys that references sets with Jaccard similarities greater than the threshold. Args: minhash (datasketch.MinHash): The MinHash of the query set. Returns: `list` of keys. ''' if len(minhash) != self.h: raise ValueError("Expecting minhash with length %d, got %d" % (self.h, len(minhash))) candidates = set() for (start, end), hashtable in zip(self.hashranges, self.hashtables): H = self._H(minhash.hashvalues[start:end]) if H in hashtable: for key in hashtable[H]: candidates.add(key) return list(candidates)
[docs] def __contains__(self, key): ''' Args: key (hashable): The unique identifier of a set. Returns: bool: True only if the key exists in the index. ''' return key in self.keys
[docs] def remove(self, key): ''' Remove the key from the index. Args: key (hashable): The unique identifier of a set. ''' if key not in self.keys: raise ValueError("The given key does not exist") for H, hashtable in zip(self.keys[key], self.hashtables): hashtable[H].remove(key) if not hashtable[H]: del hashtable[H] self.keys.pop(key)
[docs] def is_empty(self): ''' Returns: bool: Check if the index is empty. ''' return any(len(t) == 0 for t in self.hashtables)
def _H(self, hs): return bytes(hs.byteswap().data) def _query_b(self, minhash, b): if len(minhash) != self.h: raise ValueError("Expecting minhash with length %d, got %d" % (self.h, len(minhash))) if b > len(self.hashtables): raise ValueError("b must be less or equal to the number of hash tables") candidates = set() for (start, end), hashtable in zip(self.hashranges[:b], self.hashtables[:b]): H = self._H(minhash.hashvalues[start:end]) if H in hashtable: for key in hashtable[H]: candidates.add(key) return candidates