add extraction script

extract_data.py

@@ -0,0 +1,94 @@
+"""Script to generate splits for benchmarking text embedding clustering.
+Based on data from GermEval 2019 Shared Task on Hierarchical Tesk Classification (https://www.inf.uni-hamburg.de/en/inst/ab/lt/resources/data/germeval-2019-hmc.html)."""
+
+import os
+import random
+import sys
+from collections import Counter
+
+import jsonlines
+import numpy as np
+import pandas as pd
+from bs4 import BeautifulSoup
+
+random.seed(42)
+
+# path to "data" folder, can be retrieved from here: https://www.inf.uni-hamburg.de/en/inst/ab/lt/resources/data/germeval-2019-hmc/germeval2019t1-public-data-final.zip
+DATA_PATH = sys.argv[1]
+
+INCLUDE_BODY = (
+    True  # True: combine title and article body (p2p), False: only title (s2s)
+)
+
+NUM_SPLITS = 10
+SPLIT_RANGE = np.array([0.1, 1.0])
+
+
+def get_samples(soup, include_body=INCLUDE_BODY):
+    d1_counter = Counter([d1.string for d1 in soup.find_all("topic", {"d": 1})])
+
+    samples = []
+    for book in soup.find_all("book"):
+        if book.title.string is None or book.body.string is None:
+            continue
+
+        d0_topics = list(set([d.string for d in book.find_all("topic", {"d": 0})]))
+        d1_topics = list(set([d.string for d in book.find_all("topic", {"d": 1})]))
+
+        if len(d0_topics) != 1:
+            continue
+        if len(d1_topics) < 1 or len(d1_topics) > 2:
+            continue
+
+        d0_label = d0_topics[0]
+        d1_label = sorted(d1_topics, key=lambda x: d1_counter[x])[0]
+
+        text = book.title.string
+        if include_body:
+            text += "\n" + book.body.string
+
+        samples.append([text, d0_label, d1_label])
+
+    return pd.DataFrame(samples, columns=["sentences", "d0_label", "d1_label"])
+
+
+def get_split(frame, label="d0_label", split_range=SPLIT_RANGE):
+    samples = random.randint(*(split_range * len(frame)).astype(int))
+    return (
+        frame.sample(samples)[["sentences", label]]
+        .rename(columns={label: "labels"})[["sentences", "labels"]]
+        .to_dict("list")
+    )
+
+
+def write_sets(name, sets):
+    with jsonlines.open(name, "w") as f_out:
+        f_out.write_all(sets)
+
+
+train = open(os.path.join(DATA_PATH, "blurbs_train.txt"), encoding="utf-8").read()
+dev = open(os.path.join(DATA_PATH, "blurbs_dev.txt"), encoding="utf-8").read()
+test = open(os.path.join(DATA_PATH, "blurbs_test.txt"), encoding="utf-8").read()
+
+soup = BeautifulSoup(train + "\n\n" + dev + "\n\n" + test, "html.parser")
+
+samples = get_samples(soup)
+
+sets = []
+# coarse clustering
+for _ in range(NUM_SPLITS):
+    sets.append(get_split(samples))
+
+# fine grained clustering inside top-level category (d0)
+for d0 in samples["d0_label"].unique():
+    sets.append(
+        (samples[samples.d0_label == d0])
+        .rename(columns={"d1_label": "labels"})[["sentences", "labels"]]
+        .to_dict("list")
+    )
+
+# fine grained clustering
+for _ in range(NUM_SPLITS):
+    sets.append(get_split(samples, label="d1_label"))
+
+write_sets("test.jsonl", sets)