2 files changed, 101 insertions, 234 deletions

diff --git a/WMD_matching.py b/WMD_matching.py
index 2316a10..8a97389 100644
--- a/WMD_matching.py
+++ b/WMD_matching.py
@@ -22,6 +22,7 @@ def main(args):
 
     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')]
 
diff --git a/WMD_retrieval.py b/WMD_retrieval.py
index f99eaa1..b49ba7d 100644
--- a/WMD_retrieval.py
+++ b/WMD_retrieval.py
@@ -1,259 +1,125 @@
-###########################
-#  Wasserstein Retrieval  #
-###########################
 import argparse
-
-parser = argparse.ArgumentParser(description='run retrieval 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)
-
-args = parser.parse_args()
-
-source_lang = args.source_lang
-target_lang = args.target_lang
-
-def load_embeddings(path, dimension=300):
-    """
-    Loads the embeddings from a word2vec formatted file.
-    word2vec format is one line per word and it's associated embedding
-    (dimension x floating numbers) separated by spaces
-    The first line may or may not include the word count and dimension
-    """
-    vectors = {}
-    with open(path, mode='r', encoding='utf8') as fp:
-        first_line = fp.readline().rstrip('\n')
-        if first_line.count(' ') == 1:
-            # includes the "word_count dimension" information
-            (word_count, dimension) = map(int, first_line.split())
-        else: # assume the file only contains vectors
-            fp.seek(0)
-        for line in fp:
-            elems = line.split()
-            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,
-        vectors,
-        language,
-        ):
-    '''
-    Cleans corpus using the dictionary of embeddings.
-    Any word without an associated embedding in the dictionary is ignored.
-    Adds '__target-language' and '__source-language' at the end of the words according to their language.
-    '''
-    clean_corpus, clean_vectors, keys = [], {}, []
-    words_we_want = set(embeddings_dictionary)
-    tokenize = MosesTokenizer(language)
-    for key, doc in enumerate(corpus):
-        clean_doc = []
-        words = tokenize(doc)
-        for word in words:
-            if word in words_we_want:
-                clean_doc.append(word + '__%s' % language)
-                clean_vectors[word + '__%s' % language] = np.array(vectors[word].split()).astype(np.float)
-        if len(clean_doc) > 3 and len(clean_doc) < 25:
-            keys.append(key)
-        clean_corpus.append(' '.join(clean_doc))
-    tokenize.close()
-    return np.array(clean_corpus), clean_vectors, keys
-
-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,
-        )
-
-# Here is the part Wasserstein prunes two corporas to 500 articles each
-# Our dataset does not have that luxury (turns out it's not a luxury but a necessity)
-
 import random
-take = args.instances
+from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
+from sklearn.preprocessing import normalize
+from Wasserstein_Distance import Wasserstein_Retriever
+from Wasserstein_Distance import load_embeddings, clean_corpus_using_embeddings_vocabulary, mrr_precision_at_k
+import csv
+import sys
 
-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)
+def main(args):
 
-instances = len(experiment_keys)
+    np.seterr(divide='ignore') # POT has issues with divide by zero errors
+    source_lang = args.source_lang
+    target_lang = args.target_lang
 
-clean_src_corpus = list(clean_src_corpus[experiment_keys])
-clean_target_corpus = list(clean_target_corpus[experiment_keys])
+    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)
 
-print(f'{source_lang} - {target_lang} : document sizes: {len(clean_src_corpus)}, {len(clean_target_corpus)}')
+    source_defs_filename = args.source_defs
+    target_defs_filename = args.target_defs
 
-del vectors_source, vectors_target, defs_source, defs_target
+    batch = args.batch
+    mode = args.mode
+    runfor = list()
 
-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]))
+    if (mode == 'all'):
+        runfor.extend(['wmd','snk'])
     else:
-        W_common.append(np.array(clean_target_vectors[w]))
+        runfor.append(mode)
 
-print(f'{source_lang} - {target_lang}: the vocabulary size is {len(W_common)}')
+    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')]
 
-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)
+    clean_src_corpus, clean_src_vectors, src_keys = clean_corpus_using_embeddings_vocabulary(
+            set(vectors_source.keys()),
+            defs_source,
+            vectors_source,
+            source_lang,
+            )
 
-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)
+    clean_target_corpus, clean_target_vectors, target_keys = clean_corpus_using_embeddings_vocabulary(
+            set(vectors_target.keys()),
+            defs_target,
+            vectors_target,
+            target_lang,
+            )
 
-import ot
-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
+    take = args.instances
 
-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.
+    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)
 
-    """
-    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')
+    instances = len(experiment_keys)
 
-    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)
+    clean_src_corpus = list(clean_src_corpus[experiment_keys])
+    clean_target_corpus = list(clean_target_corpus[experiment_keys])
 
-    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)]
+    if (not batch):
+        print(f'{source_lang} - {target_lang} : document sizes: {len(clean_src_corpus)}, {len(clean_target_corpus)}')
 
-    def _pairwise_wmd(self, X_test, X_train=None):
-        n_samples_test = X_test.shape[0]
+    del vectors_source, vectors_target, defs_source, defs_target
 
-        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 = check_array(X, accept_sparse='csr', copy=True)
-        X = normalize(X, norm='l1', copy=False)
-        return super(WassersteinDistances, self).fit(X, y)
-
-    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)
-        return super(WassersteinDistances, self).predict(dist)
-
-    def kneighbors(self, X, n_neighbors=1):
-        X = check_array(X, accept_sparse='csr', copy=True)
-        X = normalize(X, norm='l1', copy=False)
-        dist = self._pairwise_wmd(X)
-        return super(WassersteinDistances, self).kneighbors(dist, n_neighbors)
+    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)
+
+    for metric in runfor:
+        if (not batch):
+            print(f'{metric} - tfidf: {source_lang} - {target_lang}')
+
+        clf = WassersteinDistances(W_embed=W_common, n_neighbors=5, n_jobs=14, sinkhorn=(metric == 'snk'))
+        clf.fit(X_train_idf[:instances], np.ones(instances))
+        dist, preds = clf.kneighbors(X_test_idf[:instances], n_neighbors=instances)
+        mrr, p_at_1 = mrr_precision_at_k(list(range(len(preds))), preds)
+        percentage = p_at_1 * 100
+
+        if (not batch):
+            print(f'MRR: {mrr} | Precision @ 1: {p_at_1}')
+        else:
+            fields = [f'{source_lang}', f'{target_lang}', f'{instances}', f'{mrr}', f'{p_at_1}', f'{percentage}']
+            with open(f'{metric}_retrieval_result.csv', 'a') as f:
+                writer = csv.writer(f)
+                writer.writerow(fields)
 
-def mrr_precision_at_k(golden, preds, k_list=[1,]):
-    """
-    Calculates Mean Reciprocal Error and Hits@1 == Precision@1
-    """
-    my_score = 0
-    precision_at = np.zeros(len(k_list))
-    for key, elem in enumerate(golden):
-        if elem in preds[key]:
-            location = np.where(preds[key]==elem)[0][0]
-            my_score += 1/(1+ location)
-        for k_index, k_value in enumerate(k_list):
-            if location < k_value:
-                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))
-dist, preds = clf.kneighbors(X_test_idf[:instances], n_neighbors=instances)
-mrr, p_at_1 = mrr_precision_at_k(list(range(len(preds))), preds)
-print(f'MRR: {mrr} | Precision @ 1: {p_at_1}')
+if __name__ == "__main__":
 
-import csv
-percentage = p_at_1 * 100
-fields = [f'{source_lang}', f'{target_lang}', f'{instances}', f'{mrr}', f'{p_at_1}', f'{percentage}']
-with open('/home/syigit/multilang_results/wmd_retrieval_result.csv', 'a') as f:
-    writer = csv.writer(f)
-    writer.writerow(fields)
+    parser = argparse.ArgumentParser(description='run retrieval using wmd or snk')
+    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=2000, type=int)
 
-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))
-dist, preds = clf.kneighbors(X_test_idf[:instances], n_neighbors=instances)
-mrr, p_at_1 = mrr_precision_at_k(list(range(len(preds))), preds)
-print(f'MRR: {mrr} | Precision @ 1: {p_at_1}')
+    args = parser.parse_args()
 
-percentage = p_at_1 * 100
-fields = [f'{source_lang}', f'{target_lang}', f'{instances}', f'{mrr}', f'{p_at_1}', f'{percentage}']
-with open('/home/syigit/multilang_results/sinkhorn_retrieval_result.csv', 'a') as f:
-    writer = csv.writer(f)
-    writer.writerow(fields)
+    main(args)