1 files changed, 175 insertions, 0 deletions

diff --git a/WMD.py b/WMD.py
new file mode 100644
index 0000000..dd43cd5
--- /dev/null
+++ b/WMD.py
@@ -0,0 +1,175 @@
+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, WassersteinRetriever,
+                                  load_embeddings, process_corpus)
+
+
+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
+    input_mode = args.mode
+    input_paradigm = args.paradigm
+
+    run_method = list()
+    run_paradigm = list()
+
+    if input_paradigm == "all":
+        run_paradigm.extend("matching", "retrieval")
+    else:
+        run_paradigm.append(input_paradigm)
+
+    if input_mode == "all":
+        run_method.extend(["wmd", "snk"])
+    else:
+        run_method.append(input_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 = process_corpus(
+        set(vectors_source.keys()), defs_source, vectors_source, source_lang
+    )
+
+    clean_target_corpus, clean_target_vectors, target_keys = process_corpus(
+        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} "
+            + f" 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 paradigm in run_paradigm:
+        WassersteinDriver = None
+        if paradigm == "matching":
+            WassersteinDriver = WassersteinMatcher
+        else:
+            WassersteinDriver = WassersteinRetriever
+
+        for metric in run_method:
+            if not batch:
+                print(f"{metric}: {source_lang} - {target_lang}")
+
+            clf = WassersteinDriver(
+                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}_{paradigm}_results.csv", "a") as f:
+                    writer = csv.writer(f)
+                    writer.writerow(fields)
+
+
+if __name__ == "__main__":
+
+    parser = argparse.ArgumentParser(
+        description="align dictionaries 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(
+        "paradigm",
+        choices=["all", "retrieval", "matching"],
+        default="all",
+        help="which paradigms to align with",
+    )
+    parser.add_argument(
+        "-n",
+        "--instances",
+        help="number of instances in each language to retrieve",
+        default=1000,
+        type=int,
+    )
+
+    args = parser.parse_args()
+
+    main(args)