Merge WMD/SNK matching and retrieval

1 files changed, 0 insertions, 151 deletions

diff --git a/WMD_matching.py b/WMD_matching.py
deleted file mode 100644
index 69ea10e..0000000
--- a/WMD_matching.py
+++ /dev/null
@@ -1,151 +0,0 @@
-import argparse
-import csv
-import random
-
-import numpy as np
-from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
-from sklearn.preprocessing import normalize
-
-from Wasserstein_Distance import (WassersteinMatcher,
-                                  clean_corpus_using_embeddings_vocabulary,
-                                  load_embeddings)
-
-
-def main(args):
-
-    np.seterr(divide="ignore")  # POT has issues with divide by zero errors
-    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
-    runfor = list()
-
-    if mode == "all":
-        runfor.extend(["wmd", "snk"])
-    else:
-        runfor.append(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)
-
-    for metric in runfor:
-        if not batch:
-            print(f"{metric}: {source_lang} - {target_lang}")
-
-        clf = WassersteinMatcher(
-            W_embed=W_common, n_neighbors=5, n_jobs=14, sinkhorn=(metric == "snk")
-        )
-        clf.fit(X_train_idf[:instances], np.ones(instances))
-        p_at_one, percentage = clf.align(X_test_idf[:instances], n_neighbors=instances)
-
-        if not batch:
-            print(f"P @ 1: {p_at_one}\ninstances: {instances}\n{percentage}%")
-        else:
-            fields = [
-                f"{source_lang}",
-                f"{target_lang}",
-                f"{instances}",
-                f"{p_at_one}",
-                f"{percentage}",
-            ]
-            with open(f"{metric}_matching_results.csv", "a") as f:
-                writer = csv.writer(f)
-                writer.writerow(fields)
-
-
-if __name__ == "__main__":
-
-    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()
-
-    main(args)