path: root/unscrambler.py

#!/usr/bin/env python3

import logging
from typing import Dict, Mapping

import config
import decorator_utils
import list_utils

cfg = config.add_commandline_args(
    f'Unscramble! ({__file__})', 'A fast word unscrambler.'
)
cfg.add_argument(
    "--unscramble_indexfile",
    help="Path to a file of signature -> word index.",
    metavar="FILENAME",
    default="/usr/share/dict/sparse_index",
)

logger = logging.getLogger(__name__)

letters_bits = 32
letters_mask = 2 ** letters_bits - 1

fprint_bits = 52
fprint_mask = (2 ** fprint_bits - 1) << letters_bits

fprint_feature_bit = {
    'e': 0,
    'i': 2,
    'a': 4,
    'o': 6,
    'r': 8,
    'n': 10,
    't': 12,
    's': 14,
    'l': 16,
    'c': 18,
    'u': 20,
    'p': 22,
    'm': 24,
    'd': 26,
    'h': 28,
    'y': 30,
    'g': 32,
    'b': 34,
    'f': 36,
    'v': 38,
    'k': 40,
    'w': 42,
    'z': 44,
    'x': 46,
    'q': 48,
    'j': 50,
}

letter_sigs = {
    'a': 1789368711,
    'b': 3146859322,
    'c': 43676229,
    'd': 3522623596,
    'e': 3544234957,
    'f': 3448207591,
    'g': 1282648386,
    'h': 3672791226,
    'i': 1582316135,
    'j': 4001984784,
    'k': 831769172,
    'l': 1160692746,
    'm': 2430986565,
    'n': 1873586768,
    'o': 694443915,
    'p': 1602297017,
    'q': 533722196,
    'r': 3754550193,
    's': 1859447115,
    't': 1121373020,
    'u': 2414108708,
    'v': 2693866766,
    'w': 748799881,
    'x': 2627529228,
    'y': 2376066489,
    'z': 802338724,
}


class Unscrambler(object):
    """A class that unscrambles words quickly by computing a signature
    (sig) for the word based on its position independent letter
    population and then using a pregenerated index to look up known
    words the same set of letters.

    Note that each instance of Unscrambler caches its index to speed
    up lookups number 2..N; careless reinstantiation will by slower.

    Sigs are designed to cluster similar words near each other so both
    lookup methods support a "fuzzy match" argument that can be set to
    request similar words that do not match exactly in addition to any
    exact matches.

    """

    def __init__(self):
        # Cached index per instance.
        self.sigs = []
        self.words = []

        if 'unscramble_indexfile' in config.config:
            indexfile = config.config['unscramble_indexfile']
        else:
            indexfile = "/usr/share/dict/sparse_index"

        with open(indexfile, 'r') as rf:
            lines = rf.readlines()
        for line in lines:
            line = line[:-1]
            (fsig, word) = line.split('+')
            fsig = int(fsig, 16)
            self.sigs.append(fsig)
            self.words.append(word)

    # 52 bits
    @staticmethod
    def _compute_word_fingerprint(word: str, population: Mapping[str, int]) -> int:
        fp = 0
        for pair in sorted(population.items(), key=lambda x: x[1], reverse=True):
            letter = pair[0]
            if letter in fprint_feature_bit:
                count = pair[1]
                if count > 3:
                    count = 3
                shift = fprint_feature_bit[letter]
                s = count << shift
                fp |= s
        return fp << letters_bits

    # 32 bits
    @staticmethod
    def _compute_word_letter_sig(
        letter_sigs: Mapping[str, int],
        word: str,
        population: Mapping[str, int],
    ) -> int:
        sig = 0
        for pair in sorted(population.items(), key=lambda x: x[1], reverse=True):
            letter = pair[0]
            if letter not in letter_sigs:
                continue
            s = letter_sigs[letter]
            count = pair[1]
            if count > 1:
                s <<= count
                s |= count
            s &= letters_mask
            sig ^= s
        length = len(word)
        if length > 31:
            length = 31
        sig ^= length << 8
        sig &= letters_mask
        return sig

    # 52 + 32 bits
    @staticmethod
    @decorator_utils.memoized
    def compute_word_sig(word: str) -> int:
        """Given a word, compute its signature for subsequent lookup
        operations.  Signatures are computed based on the letters in
        the word and their frequencies.  We try to cluster "similar"
        words close to each other in the signature space.

        >>> train = Unscrambler.compute_word_sig('train')
        >>> train
        23178969883741

        >>> retain = Unscrambler.compute_word_sig('retrain')
        >>> retain
        24282502197479

        >>> retain - train
        1103532313738

        """
        population = list_utils.population_counts(word)
        fprint = Unscrambler._compute_word_fingerprint(word, population)
        letter_sig = Unscrambler._compute_word_letter_sig(letter_sigs, word, population)
        assert fprint & letter_sig == 0
        sig = fprint | letter_sig
        return sig

    @staticmethod
    def repopulate(
        letter_sigs: Dict[str, int],
        dictfile: str = '/usr/share/dict/words',
        indexfile: str = '/usr/share/dict/sparse_index',
    ) -> None:
        """Before calling this method, change letter_sigs from the default above
        unless you want to populate the same exact files.

        """
        words_by_sigs = {}
        seen = set()
        with open(dictfile, "r") as f:
            for word in f:
                word = word.replace('\n', '')
                word = word.lower()
                sig = Unscrambler.compute_word_sig(letter_sigs, word)
                logger.debug("%s => 0x%x" % (word, sig))
                if word in seen:
                    continue
                seen.add(word)
                if sig in words_by_sigs:
                    words_by_sigs[sig] += ",%s" % word
                else:
                    words_by_sigs[sig] = word
        with open(indexfile, 'w') as f:
            for sig in sorted(words_by_sigs.keys()):
                word = words_by_sigs[sig]
                print(f'0x{sig:x}+{word}', file=f)

    def lookup(
        self, word: str, *, include_fuzzy_matches: bool = False
    ) -> Dict[str, bool]:
        """Looks up a potentially scrambled word optionally including near
        "fuzzy" matches.

        >>> u = Unscrambler()
        >>> u.lookup('eanycleocipd', include_fuzzy_matches=False)
        {'encyclopedia': True}

        """
        sig = Unscrambler.compute_word_sig(word)
        return self.lookup_by_sig(sig, include_fuzzy_matches=include_fuzzy_matches)

    def lookup_by_sig(
        self, sig: int, *, include_fuzzy_matches: bool = False
    ) -> Dict[str, bool]:
        """Looks up a word that has already been translated into a signature by
        a previous call to Unscrambler.compute_word_sig.  Optionally returns
        near "fuzzy" matches.

        >>> sig = Unscrambler.compute_word_sig('sunepsapetuargiarin')
        >>> sig
        18491949645300288339

        >>> u = Unscrambler()
        >>> u.lookup_by_sig(sig, include_fuzzy_matches=True)
        {'pupigerous': False, 'pupigenous': False, 'unpurposing': False, 'superpurgation': False, 'unsupporting': False, 'superseptuaginarian': True, 'purpurogallin': False, 'scuppaug': False, 'purpurigenous': False, 'purpurogenous': False, 'proppage': False}

        """
        ret = {}
        (exact, location) = list_utils.binary_search(self.sigs, sig)
        start = location - 5
        if start < 0:
            start = 0
        end = location + 6
        if end > len(self.words):
            end = len(self.words)

        for x in range(start, end):
            word = self.words[x]
            fsig = self.sigs[x]
            if include_fuzzy_matches is True or (fsig == sig):
                ret[word] = fsig == sig
        return ret


#
# To repopulate, change letter_sigs and then call Unscrambler.repopulate.
# See notes above.  See also ~/bin/unscramble.py --populate_destructively.
#


if __name__ == "__main__":
    import doctest

    doctest.testmod()