Python, Huge Iteration Performance Problem

I'm doing an iteration through 3 words, each about 5 million characters long, and I want to find sequences of 20 characters that identifies each word. That is, I want to find all sequences of length 20 in one word that is unique for that word. My problem is that the code I've written takes an extremely long time to run. I've never even completed one word running my program over night.

The function below takes a list containing dictionaries where each dictionary contains each possible word of 20 and its location from one of the 5 million long words.

If anybody has an idea how to optimize this I would be really thankful, I don't have a clue how to continue...

here's a sample of my code:

def findUnique(list):
    # Takes a list with dictionaries and compairs each element in the dictionaries
    # with the others and puts all unique element in new dictionaries and finally
    # puts the new dictionaries in a list.
    # The result is a list with (in this case) 3 dictionaries containing all unique
    # sequence开发者_StackOverflow中文版s and their locations from each string.
    dicList=[]
    listlength=len(list)
    s=0
    valuelist=[]
    for i in list:
        j=i.values()
        valuelist.append(j)
    while s<listlength:
        currdic=list[s]
        dic={}
        for key in currdic:
            currval=currdic[key]
            test=True
            n=0
            while n<listlength:
                if n!=s:
                    if currval in valuelist[n]: #this is where it takes to much time
                        n=listlength
                        test=False
                    else:
                        n+=1
                else:
                    n+=1
            if test:
                dic[key]=currval
        dicList.append(dic)
        s+=1
    return dicList

def slices(seq, length, prefer_last=False):
  unique = {}
  if prefer_last: # this doesn't have to be a parameter, just choose one
    for start in xrange(len(seq) - length + 1):
      unique[seq[start:start+length]] = start
  else: # prefer first
    for start in xrange(len(seq) - length, -1, -1):
      unique[seq[start:start+length]] = start
  return unique

# or find all locations for each slice:
import collections
def slices(seq, length):
  unique = collections.defaultdict(list)
  for start in xrange(len(seq) - length + 1):
    unique[seq[start:start+length]].append(start)
  return unique

This function (currently in my iter_util module) is O(n) (n being the length of each word) and you would use set(slices(..)) (with set operations such as difference) to get slices unique across all words (example below). You could also write the function to return a set, if you don't want to track locations. Memory usage will be high (though still O(n), just a large factor), possibly mitigated (though not by much if length is only 20) with a special "lazy slice" class that stores the base sequence (the string) plus start and stop (or start and length).

Printing unique slices:

a = set(slices("aab", 2)) # {"aa", "ab"}
b = set(slices("abb", 2)) # {"ab", "bb"}
c = set(slices("abc", 2)) # {"ab", "bc"}
all = [a, b, c]
import operator
a_unique = reduce(operator.sub, (x for x in all if x is not a), a)
print a_unique # {"aa"}

Including locations:

a = slices("aab", 2)
b = slices("abb", 2)
c = slices("abc", 2)
all = [a, b, c]
import operator
a_unique = reduce(operator.sub, (set(x) for x in all if x is not a), set(a))
# a_unique is only the keys so far
a_unique = dict((k, a[k]) for k in a_unique)
# now it's a dict of slice -> location(s)
print a_unique # {"aa": 0} or {"aa": [0]}
               # (depending on which slices function used)

In a test script closer to your conditions, using randomly generated words of 5m characters and a slice length of 20, memory usage was so high that my test script quickly hit my 1G main memory limit and started thrashing virtual memory. At that point Python spent very little time on the CPU and I killed it. Reducing either the slice length or word length (since I used completely random words that reduces duplicates and increases memory use) to fit within main memory and it ran under a minute. This situation plus O(n**2) in your original code will take forever, and is why algorithmic time and space complexity are both important.

import operator
import random
import string

def slices(seq, length):
  unique = {}
  for start in xrange(len(seq) - length, -1, -1):
    unique[seq[start:start+length]] = start
  return unique

def sample_with_repeat(population, length, choice=random.choice):
  return "".join(choice(population) for _ in xrange(length))

word_length = 5*1000*1000
words = [sample_with_repeat(string.lowercase, word_length) for _ in xrange(3)]
slice_length = 20
words_slices_sets = [set(slices(x, slice_length)) for x in words]
unique_words_slices = [reduce(operator.sub,
                              (x for x in words_slices_sets if x is not n),
                              n)
                       for n in words_slices_sets]
print [len(x) for x in unique_words_slices]

You say you have a "word" 5 million characters long, but I find it hard to believe this is a word in the usual sense.

If you can provide more information about your input data, a specific solution might be available.

For example, English text (or any other written language) might be sufficiently repetitive that a trie would be useable. In the worst case however, it would run out of memory constructing all 256^20 keys. Knowing your inputs makes all the difference.

edit

I took a look at some genome data to see how this idea stacked up, using a hardcoded [acgt]->[0123] mapping and 4 children per trie node.

1. adenovirus 2: 35,937bp -> 35,899 distinct 20-base sequences using 469,339 trie nodes

2. enterobacteria phage lambda: 48,502bp -> 40,921 distinct 20-base sequences using 529,384 trie nodes.

I didn't get any collisions, either within or between the two data sets, although maybe there is more redundancy and/or overlap in your data. You'd have to try it to see.

If you do get a useful number of collisions, you could try walking the three inputs together, building a single trie, recording the origin of each leaf and pruning collisions from the trie as you go.

If you can't find some way to prune the keys, you could try using a more compact representation. For example you only need 2 bits to store [acgt]/[0123], which might save you space at the cost of slightly more complex code.

I don't think you can just brute force this though - you need to find some way to reduce the scale of the problem, and that depends on your domain knowledge.

Let me build off Roger Pate's answer. If memory is an issue, I'd suggest instead of using the strings as the keys to the dictionary, you could use a hashed value of the string. This would save the cost of the storing the extra copy of the strings as the keys (at worst, 20 times the storage of an individual "word").

import collections
def hashed_slices(seq, length, hasher=None):
  unique = collections.defaultdict(list)
  for start in xrange(len(seq) - length + 1):
    unique[hasher(seq[start:start+length])].append(start)
  return unique

(If you really want to get fancy, you can use a rolling hash, though you'll need to change the function.)

Now, we can combine all the hashes :

unique = []  # Unique words in first string

# create a dictionary of hash values -> word index -> start position
hashed_starts = [hashed_slices(word, 20, hashing_fcn) for word in words]
all_hashed = collections.defaultdict(dict)
for i, hashed in enumerate(hashed_starts) :
   for h, starts in hashed.iteritems() :
     # We only care about the first word
     if h in hashed_starts[0] :
       all_hashed[h][i]=starts

# Now check all hashes
for starts_by_word in all_hashed.itervalues() :
  if len(starts_by_word) == 1 :
    # if there's only one word for the hash, it's obviously valid
    unique.extend(words[0][i:i+20] for i in starts_by_word.values())
  else :
    # we might have a hash collision
    candidates = {}
    for word_idx, starts in starts_by_word.iteritems() :
      candidates[word_idx] = set(words[word_idx][j:j+20] for j in starts)
    # Now go that we have the candidate slices, find the unique ones
    valid = candidates[0]
    for word_idx, candidate_set in candidates.iteritems() :
      if word_idx != 0 :
        valid -= candidate_set
    unique.extend(valid)

(I tried extending it to do all three. It's possible, but the complications would detract from the algorithm.)

Be warned, I haven't tested this. Also, there's probably a lot you can do to simplify the code, but the algorithm makes sense. The hard part is choosing the hash. Too many collisions and you'll won't gain anything. Too few and you'll hit the memory problems. If you are dealing with just DNA base codes, you can hash the 20-character string to a 40-bit number, and still have no collisions. So the slices will take up nearly a fourth of the memory. That would save roughly 250 MB of memory in Roger Pate's answer.

The code is still O(N^2), but the constant should be much lower.

Let's attempt to improve on Roger Pate's excellent answer.

Firstly, let's keep sets instead of dictionaries - they manage uniqueness anyway.

Secondly, since we are likely to run out of memory faster than we run out of CPU time (and patience), we can sacrifice CPU efficiency for the sake of memory efficiency. So perhaps try only the 20s starting with one particular letter. For DNA, this cuts the requirements down by 75%.

seqlen = 20
maxlength = max([len(word) for word in words])
for startletter in letters:
    for letterid in range(maxlength):
        for wordid,word in words:
            if (letterid < len(word)):
                letter = word[letterid]
                if letter is startletter:
                    seq = word[letterid:letterid+seqlen]
                    if seq in seqtrie and not wordid in seqtrie[seq]:
                        seqtrie[seq].append(wordid)

Or, if that's still too much memory, we can go through for each possible starting pair (16 passes instead of 4 for DNA), or every 3 (64 passes) etc.