gostyle.py

   1 #!/usr/bin/python
   2 """
   3 This file contains several objects we use to process a pattern files for Go game.
   4 We use it to generate input sets for a neural network (libfann), or if we want to perform a PCA analysis to data.
   5 It generates input vectors from pattern files. See `Const.pat_file_regexp' for file format in regexp.
   6
   7 ===== EXAMPLE PAT FILE =====
   8 4632 (border:3 s:5000003 s:6000049 s:700004a)
   9 3497 (atariescape:0 border:0 ldist:4 lldist:2 s:30011a1)
  10 ...
  11 ===== END =====
  12 """
  13 import re
  14 import sys
  15 import cPickle
  16 import random
  17 from itertools import izip,count
  18
  19 class Const:
  20         """Class used to hold global const variables, such as the pat file format."""
  21         pat_file_regexp = '^\s*(\d+)\s*(.+)$'
  22
  23 def print_vector(vector, where=sys.stdout):
  24         """Helper method for printing vector (list of floats)."""
  25         for x in vector:
  26                 print >> where, x,
  27         print >> where
  28
  29 def print_set_to_file( data, filename):
  30         """
  31         Helper method for printing datasets for neural network.
  32         FORMAT of the file
  33         number_of_pairs len_of_input_vector len_of_output_vector
  34         first_input_vector
  35         first_output_vector
  36         second_input_vector
  37         ...
  38         """
  39         def print_set( data, where):
  40                 print >> where,  len(data), len(data[0][0]), len(data[0][1])
  41                 for i,o in data:
  42                         print_vector(i, where)
  43                         print_vector(o, where)
  44         fout = open(filename, 'w')
  45         print_set(data, fout)
  46         fout.close()
  47
  48 def dump_object_to_file(object, filename):
  49         """Helper function to save an object to file using cPickle module."""
  50         f=open(filename,'w')
  51         cPickle.dump(object, f,-1)
  52         f.close()
  53
  54 def load_object_from_file(filename):
  55         """Helper function to recover an object from file using cPickle module."""
  56         f=open(filename,'r')
  57         object = cPickle.load(f)
  58         f.close()
  59         return object
  60
  61 class VectorGenerator(object):
  62         """Abstract class. When called, returns a vector (list of floats). Has output_dim param."""
  63         def __call__(self):
  64                 raise NotImplementedError
  65
  66 class VectorToVector(VectorGenerator):
  67         """Abstract class. When called with a vector (list of floats), returns a vector. Has input_dim and output_dim params."""
  68         def __call__(self, vector):
  69                 raise NotImplementedError
  70
  71 ###
  72 ###             PCA Analysis
  73 ###  Note that you must have `numpy' and `mdp' python modules, otherwise the PCA will
  74 ###  fallback to doing nothing at all (but doing it completely compatibly with the rest of the code :-).
  75 try:
  76         from numpy import array
  77         import mdp
  78         class PCA(VectorToVector):
  79                 """
  80                 Object performing a PCA analysis on either a given vector (see `__call__' function),
  81                 or on a list of vectors (see `process_list_of_vectors' function).
  82                 """
  83                 def __init__(self, list_of_train_vectors, *args, **kwargs):
  84                         self.pca = mdp.nodes.PCANode(*args, **kwargs)
  85                         self.pca.train(array(list_of_train_vectors))
  86                         self.input_dim = self.pca.input_dim
  87                 def __call__(self, vector):
  88                         return list(self.pca(array([vector]))[0])
  89                 def process_list_of_vectors(self, list_of_vectors):
  90                         return [ list(vec) for vec in self.pca(array(list_of_vectors)) ]
  91
  92 except ImportError, e:
  93         print >>sys.stderr, "Warning: %s. PCA will not work."%(str(e))
  94
  95         class PCA(VectorToVector):
  96                 """Default dummy class for PCA, not very useless."""
  97                 def __init__(self, *args, **kwargs):
  98                         pass
  99                 def __call__(self, list_of_vectors):
 100                         return list_of_vectors
 101                 def process_list_of_vectors(self, list_of_vectors):
 102                         return list_of_vectors
 103
 104
 105 class Compose(VectorGenerator):
 106         """
 107         A class used as a composer of different objects, such as InputVectorGenerator and PCA.
 108         Use this if you want to e.g. generate PCA processed vectors.
 109         """
 110         def __init__(self, vector_generator, vector_to_vector):
 111                 if not isinstance(vector_generator, VectorGenerator):
 112                         raise TypeError
 113                 if not isinstance(vector_to_vector, VectorToVector):
 114                         raise TypeError
 115                 self.vector_generator = vector_generator
 116                 self.vector_to_vector = vector_to_vector
 117         #       if vector_generator.output_dim != vector_to_vector.input_dim:
 118         #               raise RuntimeError("Dimensions of Composed object mismatch.")
 119         def __call__(self, *args, **kwargs):
 120                 return self.vector_to_vector(self.vector_generator(*args, **kwargs))
 121
 122 class OccurenceVectorGenerator(VectorGenerator):
 123         """
 124         A class used to generate input vectors based on a relative number of occurences of some input patterns.
 125         The object is initialized with a file of patterns. It takes the topmost `num_features' patterns.
 126         """
 127         def generate_top_pattern_dict(self):
 128                 rexp=re.compile(Const.pat_file_regexp)
 129                 self.top_pattern_dict = {}
 130                 i = 0
 131                 input_file = open(self.filename)
 132                 for line in input_file:
 133                         if i >= self.output_dim:
 134                                 break
 135                         self.top_pattern_dict[rexp.match(line).group(2)] = i
 136                         i += 1
 137                 input_file.close()
 138
 139         def __init__(self, main_pat_file, num_features):
 140                 self.output_dim = num_features
 141                 self.filename = main_pat_file
 142                 self.generate_top_pattern_dict()
 143
 144         def __call__(self, pat_file):
 145                 vector = [0]*len(self.top_pattern_dict)
 146                 rexp=re.compile(Const.pat_file_regexp)
 147                 i = 0
 148                 input_file = open(pat_file)
 149                 for line in input_file:
 150                         match = rexp.match(line)
 151                         if not match:
 152                                 raise IOError("Wrong file format: " + pat_file)
 153                         if match.group(2) in self.top_pattern_dict:
 154                                 index=self.top_pattern_dict[match.group(2)]
 155                                 vector[index] += int(match.group(1))
 156                                 i += 1
 157                         if i >= len(self.top_pattern_dict):
 158                                 break
 159                 input_file.close()
 160                 if len(vector) != self.output_dim:
 161                         raise RuntimeError
 162                 return vector
 163
 164 class Rescale(VectorToVector):
 165         """Class that rescales vectors to a given interval!"""
 166         def __init__(self, a=-1.0, b=1.0):
 167                 if a > b:
 168                         raise RuntimeError
 169                 self.a = a
 170                 self.tot = b - a #abs(a) + abs(b)
 171         def __call__(self, vector):
 172                 if len(vector) == 0:
 173                         raise RuntimeError
 174                 to_zero = 0 - min(vector)
 175                 maximum = max(vector) + to_zero
 176                 return [ self.tot * (x + to_zero) / maximum + self.a for x in vector ]
 177
 178 class InputVectorGenerator(VectorGenerator):
 179         """
 180         First we generate an occurence vector by OccurenceVectorGenerator.
 181         Then, an input vector is generated as a relative number of occurences of the topmost patterns.
 182         The occurences are mapped so that the most frequently used
 183         one is mapped to 1.0 and the rest is mapped relatively on the scale (1.0,-1.0). See `__call__' function.
 184         """
 185         def __init__(self, *args, **kwargs):
 186                 self.gen = Compose(OccurenceVectorGenerator(*args, **kwargs), Rescale(-1.0, 1.0))
 187         def __call__(self, *args, **kwargs):
 188                 return self.gen(*args, **kwargs)
 189
 190 def linear_combination(list_of_vectors, coefs):
 191         if len(list_of_vectors) != len(coefs):
 192                 raise Exception("len(list_of_vectors) != len(coefs)")
 193         if len(list_of_vectors) == 0:
 194                 return
 195         len_vec=len(list_of_vectors[0])
 196         res_vec=[0]*len_vec
 197         for p in xrange(len(list_of_vectors)):
 198                 for i in xrange(len_vec):
 199                         res_vec[i] += coefs[p] * list_of_vectors[p][i]
 200         return res_vec
 201
 202 def get_random_norm_coefs( num ):
 203         coefs=[]
 204         rnd_nums= [ random.random() for i in xrange(num-1) ] + [1]
 205         rnd_nums.sort()
 206         first=0
 207         for next in rnd_nums:
 208                 coefs.append(next-first)
 209                 first=next
 210         return coefs
 211
 212 class Combinator(object):
 213         def __init__(self, num_lincombs = 1, skip_subset_len = [0], max_len = 2):
 214                 self.num_lincombs = num_lincombs
 215                 self.skip_subset_len = skip_subset_len
 216                 self.max_len = max_len
 217         def get_subsets(self, set):
 218                 if len(set) == 0:
 219                         return [[]]
 220                 sub=self.get_subsets(set[1:])
 221                 return sub + filter( lambda x : ( self.max_len==0 or len(x)<=self.max_len ), [ set[:1]+subset  for subset in sub] )
 222         def combine(self, data):
 223                 combinations = []
 224                 for subset in self.get_subsets(range(len(data))):
 225                         if len(subset) in self.skip_subset_len:
 226                                 continue
 227                         input_vectors = [ data[index][0] for index in subset ]
 228                         output_vectors = [ data[index][1] for index in subset ]
 229                         for i in xrange(self.num_lincombs):
 230                                 coefs = get_random_norm_coefs(len(subset))
 231                                 combinations += [(linear_combination(input_vectors, coefs), linear_combination(output_vectors, coefs))]
 232                 return combinations
 233
 234 class PlayerStrategyIdentificator(object):
 235         """Object holding information about default strategies for players."""
 236         def __init__(self, strategy_players):
 237                 self.strategy_players = strategy_players
 238
 239                 self.player_strategy={}
 240                 self.all_players = []
 241                 self.all_strategies = []
 242                 for strategy, players in self.strategy_players.items():
 243                         self.all_strategies += [strategy]
 244                         for player in players:
 245                                 self.all_players += [player]
 246                                 self.player_strategy[player] = strategy
 247
 248         def __call__(self, player_name):
 249                 return self.player_strategy[player_name]
 250
 251 class StrategyOutputVectorGenerator(VectorGenerator):
 252         """
 253         This object generates output vectors for players with strategies specified in `PlayerStrategyIdentificator' object.
 254         It is initialized with a list of strategies `valid_strategies' it shall take into acount.
 255         When called (see `__call__') with a name of a player with a strategy from `valid_strategies' it returns a vector
 256         that corresponds to the strategy like this.
 257         """
 258         def __init__(self, strategy_players, valid_strategies=None):
 259                 self.identificator = PlayerStrategyIdentificator(strategy_players)
 260                 if valid_strategies == None:
 261                         valid_strategies = self.identificator.all_strategies
 262                 index=0
 263                 self.valid_strategy_index={}
 264                 for s in valid_strategies:
 265                         self.valid_strategy_index[s]= index
 266                         index += 1
 267         def __call__(self, player_name):
 268                 try:
 269                         player_strat = self.identificator(player_name)
 270                         player_strat_index = self.valid_strategy_index[player_strat]
 271                         return [ 1.0 if i == player_strat_index else -1.0 for i in xrange(len(self.valid_strategy_index)) ]
 272                 except KeyError:
 273                         return None
 274
 275 class PlanarOutputVectorGenerator(VectorGenerator):
 276         """Class that explicitly returns predefined output vectors for given players."""
 277         def __init__(self, player_vector):
 278                 self.player_vector = player_vector
 279                 self.players = player_vector.keys()
 280         def __call__(self, player_name):
 281                 try:
 282                         return self.player_vector[player_name]
 283                 except KeyError:
 284                         return None
 285
 286 if __name__ == '__main__':
 287         print >>sys.stderr, "This is just a library file..."