Upload create_data_retrieval.py

create_data_retrieval.py

@@ -0,0 +1,182 @@
+from collections import Counter
+import json
+import re
+
+import datasets
+import pandas as pd
+from huggingface_hub import create_repo, upload_file, hf_hub_download
+from huggingface_hub.utils._errors import HfHubHTTPError
+
+########################
+# Cleanup queries data #
+########################
+
+# load dataset
+dl_path = hf_hub_download(
+    repo_id="antoinelb7/alloprof",
+    filename="data/alloprof.csv",
+    repo_type="dataset",
+    revision="0faa90fee1ad1a6e3e461d7be49abf71488e6687"
+    )
+alloprof_queries = pd.read_csv(dl_path)
+
+# remove non-queries
+alloprof_queries = alloprof_queries[alloprof_queries["is_query"]]
+
+# remove nans in text
+alloprof_queries = alloprof_queries[~alloprof_queries["text"].isna()]
+
+# most data flagged as language "en" are actually french. We je remove english ones
+# by matching specifig words
+alloprof_queries = alloprof_queries[
+    ~(
+        (alloprof_queries["text"].str.lower().str.startswith("hi"))
+        | (alloprof_queries["text"].str.lower().str.startswith("hello"))
+        | (alloprof_queries["text"].str.lower().str.startswith("how"))
+        | (alloprof_queries["text"].str.lower().str.startswith("i "))
+    )
+]
+
+# only keep queries with french relevant documents
+alloprof_queries = alloprof_queries[
+    (~alloprof_queries["relevant"].isna()) & (alloprof_queries["relevant"].str.endswith("-fr"))
+]
+
+# remove queries with url in text because question relies on picture
+alloprof_queries = alloprof_queries[~alloprof_queries["text"].str.contains("https://www.alloprof.qc.ca")]
+
+
+# split multiple relevant docs and remove -fr suffix on id
+def parse_relevant_ids(row):
+    row = row.split(";")
+    row = [r[:-3] for r in row if r.endswith("-fr")]
+    return row
+
+
+alloprof_queries["relevant"] = alloprof_queries["relevant"].apply(parse_relevant_ids)
+
+
+# Parse the answer
+def parse_answer(row):
+    try:
+        row = json.loads(row)
+        text = []
+        for i in row:
+            if type(i["insert"]) is not dict:
+                text.append(i["insert"])
+        text = "".join(text)
+    except:
+        text = row
+    return text.replace(" ", " ").replace("\u200b", "").replace("\xa0", "")
+
+
+alloprof_queries["answer"] = alloprof_queries["answer"].apply(parse_answer)
+
+# only keep useful columns
+alloprof_queries = alloprof_queries[["id", "text", "answer", "relevant", "subject"]]
+
+# remove duplicate queries (same text)
+alloprof_queries = alloprof_queries.drop_duplicates(subset=["text"], keep="first")
+
+##########################
+# Cleanup documents data #
+##########################
+
+# load dataset
+dl_path = hf_hub_download(
+    repo_id="antoinelb7/alloprof",
+    filename="data/pages/page-content-fr.json",
+    repo_type="dataset",
+    revision="0faa90fee1ad1a6e3e461d7be49abf71488e6687"
+    )
+alloprof_docs = pd.read_json(dl_path)
+
+# Remove Nans in data
+alloprof_docs = alloprof_docs[~alloprof_docs["data"].isna()]
+
+# parse dataset
+def parse_row(row):
+    return [row["file"]["uuid"], row["file"]["title"], row["file"]["topic"]]
+
+
+def get_text(row):
+    text = []
+    for s in row["file"]["sections"]:
+        for m in s["modules"]:
+            if m["type"] == "blocSpecial":
+                if m["subtype"] in ["definition", "exemple"]:
+                    for sm in m["submodules"]:
+                        if sm["type"] == "text":
+                            text.append(sm["text"])
+            elif m["type"] == "text":
+                text.append(m["text"])
+    text = " ".join(text)
+    text = re.sub("<[^<]+?>", "", text)
+    text = text.replace("&nbsp;", " ").replace("\u200b", "")
+    text = re.sub("\s{2,}", " ", text)
+
+    return text
+
+
+parsed_df = alloprof_docs["data"].apply(parse_row)
+alloprof_docs[["uuid", "title", "topic"]] = parsed_df.tolist()
+alloprof_docs["text"] = alloprof_docs["data"].apply(get_text)
+
+# remove unnecessary columns
+alloprof_docs = alloprof_docs[["uuid", "title", "topic", "text"]]
+
+################
+# Post Process #
+################
+
+# check that all relevant docs mentioned in queries are in docs dataset
+relevants = alloprof_queries["relevant"].tolist()
+relevants = {i for j in relevants for i in j}  # flatten list and get uniques
+assert relevants.issubset(
+    alloprof_docs["uuid"].tolist()
+), "Some relevant document of queries are not present in the corpus"
+
+# convert to Dataset
+alloprof_queries = datasets.Dataset.from_pandas(alloprof_queries)
+alloprof_docs = datasets.Dataset.from_pandas(alloprof_docs)
+
+# identify duplicate documents
+# (duplicates are actually error documents,
+# such as "fiche en construction", " ", ...
+duplicate_docs = Counter(alloprof_docs["text"])
+duplicate_docs = {k:v for k,v in duplicate_docs.items() if v > 1}
+
+# for each text that is in duplicate...
+for dup_text in duplicate_docs:
+    # ...get the ids of docs that have that text
+    duplicate_ids = [d["uuid"] for d in alloprof_docs if d["text"] == dup_text]
+    # ...delete all the documents that have these ids from the corpus dataset
+    alloprof_docs = alloprof_docs.filter(lambda x: x["uuid"] not in duplicate_ids)
+    # ...delete them from the relevant documents in queries
+    alloprof_queries = alloprof_queries.map(lambda x: {"relevant": [i for i in x["relevant"] if i not in duplicate_ids]})
+
+# remove the queries that have no remaining relevant documents
+alloprof_queries = alloprof_queries.filter(lambda x: len(x["relevant"]) > 0)
+
+# split queries into train-test
+alloprof_queries = alloprof_queries.train_test_split(test_size=.2)
+
+####################
+# Upload to HF Hub #
+####################
+
+# create HF repo
+repo_id = "lyon-nlp/alloprof"
+try:
+    create_repo(repo_id, repo_type="dataset")
+except HfHubHTTPError as e:
+    print("HF repo already exist")
+
+# save datasets as json
+alloprof_queries["train"].to_pandas().to_json("queries-train.json", orient="records")
+alloprof_queries["test"].to_pandas().to_json("queries-test.json", orient="records")
+alloprof_docs.to_pandas().to_json("documents.json", orient="records")
+
+upload_file(path_or_fileobj="queries-train.json", path_in_repo="queries-train.json", repo_id=repo_id, repo_type="dataset")
+upload_file(path_or_fileobj="queries-test.json", path_in_repo="queries-test.json", repo_id=repo_id, repo_type="dataset")
+upload_file(path_or_fileobj="documents.json", path_in_repo="documents.json", repo_id=repo_id, repo_type="dataset")