From 286c84133e2b9f8688e8590006824ab3a985b2b9 Mon Sep 17 00:00:00 2001 From: Yigit Sever Date: Fri, 20 Sep 2019 16:47:59 +0300 Subject: Make WMD_matching publish ready --- WMD_matching.py | 331 +++++++++++++++++++++++--------------------------------- 1 file changed, 134 insertions(+), 197 deletions(-) diff --git a/WMD_matching.py b/WMD_matching.py index c65e6e5..3b8b1a9 100644 --- a/WMD_matching.py +++ b/WMD_matching.py @@ -1,21 +1,27 @@ -########################### -# Wasserstein Retrieval # -########################### import argparse +import numpy as np +from mosestokenizer import * +import nltk +import random +from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer +from sklearn.preprocessing import normalize +from Wass_Matcher import Wasserstein_Matcher -parser = argparse.ArgumentParser(description='run matching using wmd and wasserstein distances') -parser.add_argument('source_lang', help='source language short name') -parser.add_argument('target_lang', help='target language short name') -parser.add_argument('source_vector', help='path of the source vector') -parser.add_argument('target_vector', help='path of the target vector') -parser.add_argument('source_defs', help='path of the source definitions') -parser.add_argument('target_defs', help='path of the target definitions') -parser.add_argument('-n', '--instances', help='number of instances in each language to retrieve', default=2000, type=int) +if __name__ == "__main__": -args = parser.parse_args() + parser = argparse.ArgumentParser(description='matching using wmd and wasserstein distance') + parser.add_argument('source_lang', help='source language short name') + parser.add_argument('target_lang', help='target language short name') + parser.add_argument('source_vector', help='path of the source vector') + parser.add_argument('target_vector', help='path of the target vector') + parser.add_argument('source_defs', help='path of the source definitions') + parser.add_argument('target_defs', help='path of the target definitions') + parser.add_argument('-b', '--batch', action='store_true', help='running in batch (store results in csv) or running a single instance (output the results)') + parser.add_argument('mode', choices=['all', 'wmd', 'snk'], default='all', help='which methods to run') + parser.add_argument('-n', '--instances', help='number of instances in each language to retrieve', default=1000, type=int) + args = parser.parse_args() -source_lang = args.source_lang -target_lang = args.target_lang + main(args) def load_embeddings(path, dimension=300): """ @@ -38,27 +44,6 @@ def load_embeddings(path, dimension=300): vectors[" ".join(elems[:-dimension])] = " ".join(elems[-dimension:]) return vectors -####################################################################### -# Vectors Load Here # -####################################################################### - -source_vectors_filename = args.source_vector -target_vectors_filename = args.target_vector -vectors_source = load_embeddings(source_vectors_filename) -vectors_target = load_embeddings(target_vectors_filename) - -####################################################################### -# Corpora Load Here # -####################################################################### - -source_defs_filename = args.source_defs -target_defs_filename = args.target_defs -defs_source = [line.rstrip('\n') for line in open(source_defs_filename, encoding='utf8')] -defs_target = [line.rstrip('\n') for line in open(target_defs_filename, encoding='utf8')] - -import numpy as np -from mosestokenizer import * - def clean_corpus_using_embeddings_vocabulary( embeddings_dictionary, corpus, @@ -86,139 +71,6 @@ def clean_corpus_using_embeddings_vocabulary( tokenize.close() return np.array(clean_corpus), clean_vectors, keys -import nltk -clean_src_corpus, clean_src_vectors, src_keys = clean_corpus_using_embeddings_vocabulary( - set(vectors_source.keys()), - defs_source, - vectors_source, - source_lang, - ) - -clean_target_corpus, clean_target_vectors, target_keys = clean_corpus_using_embeddings_vocabulary( - set(vectors_target.keys()), - defs_target, - vectors_target, - target_lang, - ) - -import random -take = args.instances - -common_keys = set(src_keys).intersection(set(target_keys)) -take = min(len(common_keys), take) # you can't sample more than length -experiment_keys = random.sample(common_keys, take) - -instances = len(experiment_keys) - -clean_src_corpus = list(clean_src_corpus[experiment_keys]) -clean_target_corpus = list(clean_target_corpus[experiment_keys]) - -print(f'{source_lang} - {target_lang} : document sizes: {len(clean_src_corpus)}, {len(clean_target_corpus)}') - -del vectors_source, vectors_target, defs_source, defs_target - -from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer - -vec = CountVectorizer().fit(clean_src_corpus + clean_target_corpus) -common = [word for word in vec.get_feature_names() if word in clean_src_vectors or word in clean_target_vectors] -W_common = [] -for w in common: - if w in clean_src_vectors: - W_common.append(np.array(clean_src_vectors[w])) - else: - W_common.append(np.array(clean_target_vectors[w])) - -print(f'{source_lang} - {target_lang}: the vocabulary size is {len(W_common)}') - -from sklearn.preprocessing import normalize -W_common = np.array(W_common) -W_common = normalize(W_common) -vect = TfidfVectorizer(vocabulary=common, dtype=np.double, norm=None) -vect.fit(clean_src_corpus + clean_target_corpus) -X_train_idf = vect.transform(clean_src_corpus) -X_test_idf = vect.transform(clean_target_corpus) - -vect_tf = CountVectorizer(vocabulary=common, dtype=np.double) -vect_tf.fit(clean_src_corpus + clean_target_corpus) -X_train_tf = vect_tf.transform(clean_src_corpus) -X_test_tf = vect_tf.transform(clean_target_corpus) - -import ot -from lapjv import lapjv -from sklearn.neighbors import KNeighborsClassifier -from sklearn.metrics import euclidean_distances -from sklearn.externals.joblib import Parallel, delayed -from sklearn.utils import check_array -from sklearn.metrics.scorer import check_scoring -from pathos.multiprocessing import ProcessingPool as Pool -from sklearn.metrics import euclidean_distances - -class WassersteinDistances(KNeighborsClassifier): - """ - Implements a nearest neighbors classifier for input distributions using the Wasserstein distance as metric. - Source and target distributions are l_1 normalized before computing the Wasserstein distance. - Wasserstein is parametrized by the distances between the individual points of the distributions. - In this work, we propose to use cross-lingual embeddings for calculating these distances. - - """ - def __init__(self, W_embed, n_neighbors=1, n_jobs=1, verbose=False, sinkhorn= False, sinkhorn_reg=0.1): - """ - Initialization of the class. - Arguments - --------- - W_embed: embeddings of the words, np.array - verbose: True/False - """ - self.sinkhorn = sinkhorn - self.sinkhorn_reg = sinkhorn_reg - self.W_embed = W_embed - self.verbose = verbose - super(WassersteinDistances, self).__init__(n_neighbors=n_neighbors, n_jobs=n_jobs, metric='precomputed', algorithm='brute') - - def _wmd(self, i, row, X_train): - union_idx = np.union1d(X_train[i].indices, row.indices) - W_minimal = self.W_embed[union_idx] - W_dist = euclidean_distances(W_minimal) - bow_i = X_train[i, union_idx].A.ravel() - bow_j = row[:, union_idx].A.ravel() - if self.sinkhorn: - return ot.sinkhorn2(bow_i, bow_j, W_dist, self.sinkhorn_reg, numItermax=50, method='sinkhorn_stabilized',)[0] - else: - return ot.emd2(bow_i, bow_j, W_dist) - - def _wmd_row(self, row): - X_train = self._fit_X - n_samples_train = X_train.shape[0] - return [self._wmd(i, row, X_train) for i in range(n_samples_train)] - - def _pairwise_wmd(self, X_test, X_train=None): - n_samples_test = X_test.shape[0] - - if X_train is None: - X_train = self._fit_X - pool = Pool(nodes=self.n_jobs) # Parallelization of the calculation of the distances - dist = pool.map(self._wmd_row, X_test) - return np.array(dist) - - def fit(self, X, y): # X_train_idf - X = check_array(X, accept_sparse='csr', copy=True) # check if array is sparse - X = normalize(X, norm='l1', copy=False) - return super(WassersteinDistances, self).fit(X, y) # X_train_idf, np_ones(document collection size) - - def predict(self, X): - X = check_array(X, accept_sparse='csr', copy=True) - X = normalize(X, norm='l1', copy=False) - dist = self._pairwise_wmd(X) - dist = dist * 1000 # for lapjv, small floating point numbers are evil - return super(WassersteinDistances, self).predict(dist) - - def kneighbors(self, X, n_neighbors=1): # X : X_train_idf - X = check_array(X, accept_sparse='csr', copy=True) - X = normalize(X, norm='l1', copy=False) - dist = self._pairwise_wmd(X) - dist = dist * 1000 # for lapjv, small floating point numbers are evil - return lapjv(dist) # and here is the matching part - def mrr_precision_at_k(golden, preds, k_list=[1,]): """ Calculates Mean Reciprocal Error and Hits@1 == Precision@1 @@ -234,32 +86,117 @@ def mrr_precision_at_k(golden, preds, k_list=[1,]): precision_at[k_index] += 1 return my_score/len(golden), (precision_at/len(golden))[0] -print(f'WMD - tfidf: {source_lang} - {target_lang}') -clf = WassersteinDistances(W_embed=W_common, n_neighbors=5, n_jobs=14) -clf.fit(X_train_idf[:instances], np.ones(instances)) -row_ind, col_ind, a = clf.kneighbors(X_test_idf[:instances], n_neighbors=instances) -result = zip(row_ind, col_ind) -hit_one = len([x for x,y in result if x == y]) -print(f'{hit_one} definitions have been mapped correctly') - -import csv -percentage = hit_one / instances * 100 -fields = [f'{source_lang}', f'{target_lang}', f'{instances}', f'{hit_one}', f'{percentage}'] -with open('/home/syigit/multilang_results/wmd_matching_result.csv', 'a') as f: - writer = csv.writer(f) - writer.writerow(fields) - -print(f'Sinkhorn - tfidf: {source_lang} - {target_lang}') -clf = WassersteinDistances(W_embed=W_common, n_neighbors=5, n_jobs=14, sinkhorn=True) -clf.fit(X_train_idf[:instances], np.ones(instances)) -row_ind, col_ind, a = clf.kneighbors(X_test_idf[:instances], n_neighbors=instances) - -result = zip(row_ind, col_ind) -hit_one = len([x for x,y in result if x == y]) -print(f'{hit_one} definitions have been mapped correctly') - -percentage = hit_one / instances * 100 -fields = [f'{source_lang}', f'{target_lang}', f'{instances}', f'{hit_one}', f'{percentage}'] -with open('/home/syigit/multilang_results/sinkhorn_matching_result.csv', 'a') as f: - writer = csv.writer(f) - writer.writerow(fields) +def main(args): + + source_lang = args.source_lang + target_lang = args.target_lang + + source_vectors_filename = args.source_vector + target_vectors_filename = args.target_vector + vectors_source = load_embeddings(source_vectors_filename) + vectors_target = load_embeddings(target_vectors_filename) + + source_defs_filename = args.source_defs + target_defs_filename = args.target_defs + + batch = args.batch + mode = args.mode + defs_source = [line.rstrip('\n') for line in open(source_defs_filename, encoding='utf8')] + defs_target = [line.rstrip('\n') for line in open(target_defs_filename, encoding='utf8')] + + clean_src_corpus, clean_src_vectors, src_keys = clean_corpus_using_embeddings_vocabulary( + set(vectors_source.keys()), + defs_source, + vectors_source, + source_lang, + ) + + clean_target_corpus, clean_target_vectors, target_keys = clean_corpus_using_embeddings_vocabulary( + set(vectors_target.keys()), + defs_target, + vectors_target, + target_lang, + ) + + take = args.instances + + common_keys = set(src_keys).intersection(set(target_keys)) + take = min(len(common_keys), take) # you can't sample more than length + experiment_keys = random.sample(common_keys, take) + + instances = len(experiment_keys) + + clean_src_corpus = list(clean_src_corpus[experiment_keys]) + clean_target_corpus = list(clean_target_corpus[experiment_keys]) + + if (not batch): + print(f'{source_lang} - {target_lang} : document sizes: {len(clean_src_corpus)}, {len(clean_target_corpus)}') + + del vectors_source, vectors_target, defs_source, defs_target + + vec = CountVectorizer().fit(clean_src_corpus + clean_target_corpus) + common = [word for word in vec.get_feature_names() if word in clean_src_vectors or word in clean_target_vectors] + W_common = [] + for w in common: + if w in clean_src_vectors: + W_common.append(np.array(clean_src_vectors[w])) + else: + W_common.append(np.array(clean_target_vectors[w])) + + if (not batch): + print(f'{source_lang} - {target_lang}: the vocabulary size is {len(W_common)}') + + W_common = np.array(W_common) + W_common = normalize(W_common) + vect = TfidfVectorizer(vocabulary=common, dtype=np.double, norm=None) + vect.fit(clean_src_corpus + clean_target_corpus) + X_train_idf = vect.transform(clean_src_corpus) + X_test_idf = vect.transform(clean_target_corpus) + + vect_tf = CountVectorizer(vocabulary=common, dtype=np.double) + vect_tf.fit(clean_src_corpus + clean_target_corpus) + X_train_tf = vect_tf.transform(clean_src_corpus) + X_test_tf = vect_tf.transform(clean_target_corpus) + + if (mode == 'wmd' or mode == 'all'): + if (not batch): + print(f'WMD - tfidf: {source_lang} - {target_lang}') + + clf = WassersteinDistances(W_embed=W_common, n_neighbors=5, n_jobs=14) + clf.fit(X_train_idf[:instances], np.ones(instances)) + row_ind, col_ind, a = clf.kneighbors(X_test_idf[:instances], n_neighbors=instances) + result = zip(row_ind, col_ind) + hit_one = len([x for x,y in result if x == y]) + percentage = hit_one / instances * 100 + + if (not batch): + print(f'{hit_one} definitions have been mapped correctly, {percentage}%') + + if (batch): + import csv + fields = [f'{source_lang}', f'{target_lang}', f'{instances}', f'{hit_one}', f'{percentage}'] + with open('wmd_matching_results.csv', 'a') as f: + writer = csv.writer(f) + writer.writerow(fields) + + if (mode == 'snk' or mode == 'all'): + if (not batch): + print(f'Sinkhorn - tfidf: {source_lang} - {target_lang}') + + clf = WassersteinDistances(W_embed=W_common, n_neighbors=5, n_jobs=14, sinkhorn=True) + clf.fit(X_train_idf[:instances], np.ones(instances)) + row_ind, col_ind, a = clf.kneighbors(X_test_idf[:instances], n_neighbors=instances) + + result = zip(row_ind, col_ind) + hit_one = len([x for x,y in result if x == y]) + + if (not batch): + print(f'{hit_one} definitions have been mapped correctly') + + + if (batch): + percentage = hit_one / instances * 100 + fields = [f'{source_lang}', f'{target_lang}', f'{instances}', f'{hit_one}', f'{percentage}'] + with open('sinkhorn_matching_result.csv', 'a') as f: + writer = csv.writer(f) + writer.writerow(fields) -- cgit v1.2.3-70-g09d2