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evaluate_candidates_indiv.py
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evaluate_candidates_indiv.py
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__author__ = 'rwechsler'
import cPickle as pickle
import itertools
import random
from annoy import AnnoyIndex
import multiprocessing as mp
import sys
import argparse
import time
import datetime
import numpy as np
def timestamp():
return datetime.datetime.fromtimestamp(time.time()).strftime('%Y-%m-%d %H:%M:%S')
def load_candidate_dump(file_name):
return pickle.load(open(file_name, "rb"))
def load_annoy_tree(model_file_name, vector_dims):
tree = AnnoyIndex(vector_dims)
tree.load(model_file_name)
return tree
def annoy_knn(annoy_tree, vector, true_index, k=100):
neighbours = annoy_tree.get_nns_by_vector(list(vector), k)
if true_index in neighbours:
return True
else:
return False
def test_pair(pair1, pair2, word2vec_model, k=100, show=30):
"""
Only used in interactive mode so far.
:param pair1:
:param pair2:
:param word2vec_model:
:param k:
:param show:
:return:
"""
prefix = pair1[0]
fl1 = pair1[1]
tail1 = pair1[2]
prefix2 = pair2[0]
fl2 = pair2[1]
tail2 = pair2[2]
assert prefix == prefix2
diff = word2vec_model[prefix + fl2 + tail2.lower()] - word2vec_model[tail2]
predicted = word2vec_model[tail1] + diff
true_word = prefix + fl1 + tail1.lower()
neighbours = word2vec_model.most_similar([predicted], topn=k)
print neighbours[:show]
neighbours, _ = zip(*neighbours)
print "Found: ", true_word in neighbours
def candidate_generator(candidates, rank_threshold, sample_size, annoy_tree_file, vector_dims, lock):
for prefix in candidates:
yield (prefix, candidates[prefix], annoy_tree_file, vector_dims, lock, rank_threshold, sample_size)
def mp_wrapper_evaluate_set(argument):
return evaluate_set(*argument)
if __name__ == "__main__":
#### Default Parameters-------------------------------------------####
rank_threshold = 100
sample_set_size = 500
n_processes = 2
####End-Parametes-------------------------------------------------####
parser = argparse.ArgumentParser(description='Evaluate candidates')
parser.add_argument('-d', action="store", dest="vector_dims", type=int, required=True)
parser.add_argument('-t', action="store", dest="annoy_tree_file", required=True)
parser.add_argument('-c', action="store", dest="candidates_index_file", required=True)
parser.add_argument('-o', action="store", dest="result_output_file", required=True)
parser.add_argument('-p', action="store", dest="n_processes", type=int, default=n_processes)
parser.add_argument('-s', action="store", dest="sample_set_size", type=int, default=sample_set_size)
parser.add_argument('-r', action="store", dest="rank_threshold", type=int, default=rank_threshold)
arguments = parser.parse_args(sys.argv[1:])
print timestamp(), "loading candidates"
candidates = load_candidate_dump(arguments.candidates_index_file)
print timestamp(), "load annoy tree"
# global annoy_tree
#annoy_tree = load_annoy_tree(arguments.annoy_tree_file, arguments.vector_dims)
annoy_tree_file = arguments.annoy_tree_file
vector_dims = arguments.vector_dims
manager = mp.Manager()
lock = manager.Lock()
def evaluate_set(prefix, tails, annoy_tree_file, vector_dims, lock, rank_threshold=100, sample_size=1000):
#fname = ''.join(annoy_tree_file)
lock.acquire()
try:
annoy_tree = AnnoyIndex(vector_dims)
annoy_tree.load(annoy_tree_file)
finally:
lock.release()
# annoy_tree = load_annoy_tree(annoy_tree_file, vector_dims)
print mp.current_process().name, id(annoy_tree), prefix.encode('utf-8')
sys.stdout.flush()
counts = dict()
counts[True] = 0
counts[False] = 0
if len(tails) > sample_size:
tails = random.sample(tails, sample_size)
for (comp1, tail1), (comp2, tail2) in itertools.combinations(tails, 2):
diff = np.array(annoy_tree.get_item_vector(comp2))- np.array(annoy_tree.get_item_vector(tail2))
predicted = np.array(annoy_tree.get_item_vector(tail1)) + diff
result = annoy_knn(annoy_tree, predicted, comp1, rank_threshold)
counts[result] += 1
annoy_tree.unload(annoy_tree_file)
return (prefix, float(counts[True]) / (counts[True] + counts[False])) if counts[True] + counts[False] > 0 else (prefix, 0.0)
print timestamp(), "evaluating candidates"
pool = mp.Pool(processes=arguments.n_processes)
params = candidate_generator(candidates, arguments.rank_threshold, arguments.sample_set_size, annoy_tree_file, vector_dims, lock)
results = pool.map(mp_wrapper_evaluate_set, params)
print timestamp(), "pickling"
pickle.dump(results, open(arguments.result_output_file, "wb"))
print timestamp(), "done"