From 49e386ac4fa46cc5f04e05cb29e250cf1a3354f3 Mon Sep 17 00:00:00 2001 From: Bill Zorn Date: Wed, 29 Jul 2015 01:21:34 -0700 Subject: [PATCH] various improvements, added cbow --- data/cbow.bin | Bin 0 -> 846039 bytes data/cbow.txt | 29488 ++++++++++++++++++++++++++++++++++++++++++++ data/output.txt | 12 +- decode.py | 31 +- encode.py | 34 +- lib/cardlib.py | 71 +- lib/cbow.py | 181 + lib/manalib.py | 34 + lib/transforms.py | 1 + 9 files changed, 29818 insertions(+), 34 deletions(-) create mode 100644 data/cbow.bin create mode 100644 data/cbow.txt create mode 100644 lib/cbow.py diff --git a/data/cbow.bin b/data/cbow.bin new file mode 100644 index 0000000000000000000000000000000000000000..c5ba40c4e16bc9a7b1b6722e07fad62619d1ff7c GIT binary patch literal 846039 zcmXt9c_5YF^R`5?MwSp+k|kv;blrE3l2jxKMNuM>B1uJ?ov0{fEo6z1B8uz2b3{U^ zXrua+5NXw_y}tMN$M5g=yywiEnR905ndg|9&b87qH#L>ynuLSaxPhomN%v%%>0-Zl zd+$HwHE3FJ!v3^zdW*;B=jJ@M9yfT)>-9Uv5o(rgs&3ciw0^zTnDFLj<1MF0Jc~=a 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z+#y^R=|bvtz4>RuK)U|jjE)zM=l2y>Aaptgn|d}wNL2x@?h?yCM!&P_`(-_C8h8_Z zKE?8{C{4TRo1*=w7C)B|VV4RVaP!kbQq!L(*0_zwiaSx4i4uIa6 o3%J|OomJhX*j4UM3~+u(q3K72Z 0 else 1 + +# max length of vocabulary entries +max_w = 50 + + +#### snip! #### + +def read_vector_file(fname): + with open(fname, 'rb') as f: + words = int(f.read(4)) + size = int(f.read(4)) + vocab = [' '] * (words * max_w) + M = [] + for b in range(0,words): + a = 0 + while True: + c = f.read(1) + vocab[b * max_w + a] = c; + if len(c) == 0 or c == ' ': + break + if (a < max_w) and vocab[b * max_w + a] != '\n': + a += 1 + tmp = list(struct.unpack('f'*size,f.read(4 * size))) + length = math.sqrt(sum([tmp[i] * tmp[i] for i in range(0,len(tmp))])) + for i in range(0,len(tmp)): + tmp[i] /= length + M.append(tmp) + return ((''.join(vocab)).split(),M) + +def makevector(vocabulary,vecs,sequence): + words = sequence.split() + indices = [] + for word in words: + if word not in vocabulary: + #print("Missing word in vocabulary: " + word) + continue + #return [0.0]*len(vecs[0]) + indices.append(vocabulary.index(word)) + #res = map(sum,[vecs[i] for i in indices]) + res = None + for v in [vecs[i] for i in indices]: + if res == None: + res = v + else: + res = [x + y for x, y in zip(res,v)] + length = math.sqrt(sum([res[i] * res[i] for i in range(0,len(res))])) + for i in range(0,len(res)): + res[i] /= length + return res + +#### !snip #### + + +try: + import numpy + def cosine_similarity(v1,v2): + A = numpy.array([v1,v2]) + + # from http://stackoverflow.com/questions/17627219/whats-the-fastest-way-in-python-to-calculate-cosine-similarity-given-sparse-mat + + # base similarity matrix (all dot products) + # replace this with A.dot(A.T).todense() for sparse representation + similarity = numpy.dot(A, A.T) + + # squared magnitude of preference vectors (number of occurrences) + square_mag = numpy.diag(similarity) + + # inverse squared magnitude + inv_square_mag = 1 / square_mag + + # if it doesn't occur, set it's inverse magnitude to zero (instead of inf) + inv_square_mag[numpy.isinf(inv_square_mag)] = 0 + + # inverse of the magnitude + inv_mag = numpy.sqrt(inv_square_mag) + + # cosine similarity (elementwise multiply by inverse magnitudes) + cosine = similarity * inv_mag + cosine = cosine.T * inv_mag + + return cosine[0][1] + +except ImportError: + def cosine_similarity(v1,v2): + #compute cosine similarity of v1 to v2: (v1 dot v1)/{||v1||*||v2||) + sumxx, sumxy, sumyy = 0, 0, 0 + for i in range(len(v1)): + x = v1[i]; y = v2[i] + sumxx += x*x + sumyy += y*y + sumxy += x*y + return sumxy/math.sqrt(sumxx*sumyy) + +def cosine_similarity_name(cardvec, v, name): + return (cosine_similarity(cardvec, v), name) + + +class CBOW: + def __init__(self, verbose = True, + vector_fname = os.path.join(datadir, 'cbow.bin'), + card_fname = os.path.join(datadir, 'output.txt')): + self.verbose = verbose + self.cardvecs = [] + + if self.verbose: + print 'Building a cbow model...' + + if self.verbose: + print ' Reading binary vector data from: ' + vector_fname + (vocab, vecs) = read_vector_file(vector_fname) + self.vocab = vocab + self.vecs = vecs + + if self.verbose: + print ' Reading encoded cards from: ' + card_fname + print ' They\'d better be in the same order as the file used to build the vector model!' + with open(card_fname, 'rt') as f: + text = f.read() + for card_src in text.split(utils.cardsep): + if card_src: + card = cardlib.Card(card_src) + name = card.name + self.cardvecs += [(name, makevector(self.vocab, + self.vecs, + card.vectorize()))] + + # self.par = Parallel(n_jobs=segments) + + if self.verbose: + print '... Done.' + print ' vocab size: ' + str(len(self.vocab)) + print ' raw vecs: ' + str(len(self.vecs)) + print ' card vecs: ' + str(len(self.cardvecs)) + + def nearest(self, card, n=5): + if isinstance(card, cardlib.Card): + words = card.vectorize().split('\n\n')[0] + else: + # assume it's a string (that's already a vector) + words = card + + if not words: + return [] + + cardvec = makevector(self.vocab, self.vecs, words) + + comparisons = [cosine_similarity_name(cardvec, v, name) for (name, v) in self.cardvecs] + # comparisons = self.par(delayed(cosine_similarity_name)(cardvec, v, name) + # for (name, v) in self.cardvecs) + + comparisons.sort(reverse = True) + comp_n = comparisons[:n] + + if isinstance(card, cardlib.Card) and card.bside: + comp_n += self.nearest(card.bside) + + return comp_n diff --git a/lib/manalib.py b/lib/manalib.py index 104dd53..688f3f2 100644 --- a/lib/manalib.py +++ b/lib/manalib.py @@ -126,6 +126,18 @@ class Manacost: else: return utils.mana_open_delimiter + ''.join(self.sequence) + utils.mana_close_delimiter + def vectorize(self, delimit = False): + if self.none: + return '' + elif delimit: + ld = '(' + rd = ')' + else: + ld = '' + rd = '' + return ' '.join(map(lambda s: ld + s + rd, self.sequence)) + + class Manatext: '''text representation with embedded mana costs''' @@ -176,3 +188,25 @@ class Manatext: for cost in self.costs: text = text.replace(utils.reserved_mana_marker, cost.encode(randomize = randomize), 1) return text + + def vectorize(self): + text = self.text + special_chars = [utils.reserved_mana_marker, + utils.dash_marker, + utils.bullet_marker, + utils.this_marker, + utils.counter_marker, + utils.choice_open_delimiter, + utils.choice_close_delimiter, + utils.newline, + #utils.x_marker, + utils.tap_marker, + utils.untap_marker, + utils.newline, + ';', ':', '"', ',', '.'] + for char in special_chars: + text = text.replace(char, ' ' + char + ' ') + text = text.replace('/', '/ /') + for cost in self.costs: + text = text.replace(utils.reserved_mana_marker, cost.vectorize(), 1) + return ' '.join(text.split()) diff --git a/lib/transforms.py b/lib/transforms.py index a510d2d..67b7166 100644 --- a/lib/transforms.py +++ b/lib/transforms.py @@ -121,6 +121,7 @@ def text_pass_4b_x(s): s = s.replace(' x ', ' ' + x_marker + ' ') s = s.replace('x:', x_marker + ':') s = s.replace('x~', x_marker + '~') + s = s.replace(u'x\u2014', x_marker + u'\u2014') s = s.replace('x.', x_marker + '.') s = s.replace('x,', x_marker + ',') s = s.replace('x/x', x_marker + '/' + x_marker)