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import streamlit as st
import os
import pathlib
import beir
from beir import util
from beir.datasets.data_loader import GenericDataLoader
import pytrec_eval
import pandas as pd
from collections import defaultdict
import json
import copy
def load_jsonl(f):
did2text = defaultdict(list)
sub_did2text = {}
for idx, line in enumerate(f):
inst = json.loads(line)
if "question" in inst:
docid = inst["metadata"][0]["passage_id"] if "doc_id" not in inst else inst["doc_id"]
did2text[docid].append(inst["question"])
elif "text" in inst:
docid = inst["doc_id"] if "doc_id" in inst else inst["did"]
did2text[docid].append(inst["text"])
sub_did2text[inst["did"]] = inst["text"]
elif "query" in inst:
docid = inst["doc_id"] if "doc_id" in inst else inst["did"]
did2text[docid].append(inst["query"])
else:
breakpoint()
raise NotImplementedError("Need to handle this case")
return did2text, sub_did2text
def get_beir(dataset: str):
url = "https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/{}.zip".format(dataset)
out_dir = os.path.join(pathlib.Path(__file__).parent.absolute(), "datasets")
data_path = util.download_and_unzip(url, out_dir)
return GenericDataLoader(data_folder=data_path).load(split="test")
def load_run(f_run):
run = pytrec_eval.parse_run(copy.deepcopy(f_run))
# convert bytes to strings for keys
new_run = defaultdict(dict)
for key, sub_dict in run.items():
new_run[key.decode("utf-8")] = {k.decode("utf-8"): v for k, v in sub_dict.items()}
run_pandas = pd.read_csv(f_run, header=None, index_col=None, sep="\t")
run_pandas.columns = ["qid", "generic", "doc_id", "rank", "score", "model"]
run_pandas.doc_id = run_pandas.doc_id.astype(str)
run_pandas.qid = run_pandas.qid.astype(str)
run_pandas["rank"] = run_pandas["rank"].astype(int)
run_pandas.score = run_pandas.score.astype(float)
# if run_1_alt is not None:
# run_1_alt, run_1_alt_sub = load_jsonl(run_1_alt)
return new_run, run_pandas
with st.sidebar:
dataset_name = st.selectbox("Select a dataset in BEIR", ("scifact", "scidocs","trec-covid", "fever", "fiqa", "nfcorpus", "msmarco", "bioasq", "nq", "hotpotqa", "signal1m", "trec-news", "robust04", "arguana", "quora", "climate-fever", "dbpedia-entity", "webis-touche2020", "cqadupstack"))
metric_name = st.selectbox("Select a metric", ("recall_10", "recall_5"))
# sliderbar of how many Top N to choose
top_n = st.slider("Top N", 1, 100, 3)
x = st.header('Upload a run file')
run1_file = st.file_uploader("Choose a file", key="run1")
y = st.header("Upload a second run file")
run2_file = st.file_uploader("Choose a file", key="run2")
incorrect_only = st.checkbox("Show only incorrect instances", value=False)
one_better_than_two = st.checkbox("Show only instances where run 1 is better than run 2", value=False)
two_better_than_one = st.checkbox("Show only instances where run 2 is better than run 1", value=False)
col1, col2 = st.columns([1, 2], gap="medium")
incorrect = 0
is_better_run1_count = 0
is_better_run2_count = 0
checkboxes = None
with col1:
st.title("Instances")
if run1_file is not None:
print("Running....")
corpus, queries, qrels = get_beir(dataset_name)
evaluator = pytrec_eval.RelevanceEvaluator(
qrels, pytrec_eval.supported_measures)
if run1_file is not None:
run1, run1_pandas = load_run(run1_file)
results1 = evaluator.evaluate(run1) # dict of instance then metrics then values
if run2_file is not None:
run2, run2_pandas = load_run(run2_file)
results2 = evaluator.evaluate(run2)
name_of_columns = ["Overview"] + sorted([str(item) for item in set(run1_pandas.qid.tolist())])
checkboxes = [("Overview", st.checkbox("Overview", key=f"0overview"))]
st.divider()
for idx, item in enumerate(name_of_columns):
is_overview = item == "Overview"
if is_overview:
continue
is_incorrect = False
is_better_run1 = False
is_better_run2 = False
run1_score = results1[str(item)][metric_name] if not is_overview else 1
if run2_file is not None:
run2_score = results2[str(item)][metric_name] if not is_overview else 1
if not is_overview and run1_score == 0 or run2_score == 0:
incorrect += 1
is_incorrect = True
if not is_overview and run1_score > run2_score:
is_better_run1_count += 1
is_better_run1 = True
elif not is_overview and run2_score > run1_score:
is_better_run2_count += 1
is_better_run2 = True
if not incorrect_only or is_incorrect:
if not one_better_than_two or is_better_run1:
if not two_better_than_one or is_better_run2:
check = st.checkbox(str(item), key=f"{idx}check")
st.divider()
checkboxes.append((item, check))
else:
if not is_overview and run1_score == 0:
incorrect += 1
is_incorrect = True
if not incorrect_only or is_incorrect:
check = st.checkbox(str(item), key=f"{idx}check")
st.divider()
checkboxes.append((item, check))
with col2:
if checkboxes is not None:
st.title(f"Information ({len(checkboxes) - 1 if checkboxes else 0}/{len(name_of_columns) - 1})")
else:
st.title(f"Information")
### Only one run file
if run1_file is not None and run2_file is None:
for check_idx, (inst_num, checkbox) in enumerate(checkboxes):
if checkbox:
if inst_num == "Overview":
st.header("Overview")
st.markdown("TODO: Add overview")
else:
st.header(f"Instance Number: {inst_num}")
st.subheader(f"Query")
query_text = queries[str(inst_num)]
st.markdown(query_text)
st.divider()
## Documents
# relevant
relevant_docs = list(qrels[str(inst_num)].keys())
doc_texts = [(doc_id, corpus[doc_id]["title"], corpus[doc_id]["text"]) for doc_id in relevant_docs]
st.subheader("Relevant Documents")
for (docid, title, text) in doc_texts:
st.text_area(f"{docid}: {title}", text)
# top ranked
pred_doc = run1_pandas[run1_pandas.doc_id.isin(relevant_docs)]
rank_pred = pred_doc[pred_doc.qid == str(inst_num)]["rank"].tolist()
st.subheader("Ranked of Documents")
st.markdown(f"Rank: {rank_pred}")
st.divider()
if st.checkbox('Show top ranked documents'):
st.subheader("Top N Ranked Documents")
run1_top_n = run1_pandas[run1_pandas.qid == str(inst_num)][:top_n]
run1_top_n_docs = [corpus[str(doc_id)] for doc_id in run1_top_n.doc_id.tolist()]
for d_idx, doc in enumerate(run1_top_n_docs):
st.text_area(f"{run1_top_n['doc_id'].iloc[d_idx]}: {doc['title']}", doc["text"])
st.divider()
st.subheader("Score")
st.markdown(f"{results1[str(inst_num)][metric_name]}")
break
## Both run files available
elif run1_file is not None and run2_file is not None:
for check_idx, (inst_num, checkbox) in enumerate(checkboxes):
if checkbox:
if inst_num == "Overview":
st.header("Overview")
st.markdown("TODO: Add overview")
else:
st.header(f"Instance Number: {inst_num}")
st.subheader(f"Query")
query_text = queries[str(inst_num)]
st.markdown(query_text)
st.divider()
## Documents
# relevant
relevant_docs = list(qrels[str(inst_num)].keys())
doc_texts = [(doc_id, corpus[doc_id]["title"], corpus[doc_id]["text"]) for doc_id in relevant_docs]
st.subheader("Relevant Documents")
for (docid, title, text) in doc_texts:
st.text_area(f"{docid}: {title}", text)
# top ranked
pred_doc1 = run1_pandas[run1_pandas.doc_id.isin(relevant_docs)]
rank_pred1 = pred_doc1[pred_doc1.qid == str(inst_num)]["rank"].tolist()
pred_doc2 = run2_pandas[run2_pandas.doc_id.isin(relevant_docs)]
rank_pred2 = pred_doc2[pred_doc2.qid == str(inst_num)]["rank"].tolist()
st.subheader("Ranked of Documents")
st.markdown(f"Run 1 Rank: {rank_pred1}")
st.markdown(f"Run 2 Rank: {rank_pred2}")
st.divider()
if st.checkbox('Show top ranked documents for Run 1'):
st.subheader("Top N Ranked Documents")
run1_top_n = run1_pandas[run1_pandas.qid == str(inst_num)][:top_n]
run1_top_n_docs = [corpus[str(doc_id)] for doc_id in run1_top_n.doc_id.tolist()]
for d_idx, doc in enumerate(run1_top_n_docs):
st.text_area(f"{run1_top_n['doc_id'].iloc[d_idx]}: {doc['title']}", doc["text"])
if st.checkbox('Show top ranked documents for Run 2'):
st.subheader("Top N Ranked Documents")
run2_top_n = run2_pandas[run2_pandas.qid == str(inst_num)][:top_n]
run2_top_n_docs = [corpus[str(doc_id)] for doc_id in run2_top_n.doc_id.tolist()]
for d_idx, doc in enumerate(run2_top_n_docs):
st.text_area(f"{run2_top_n['doc_id'].iloc[d_idx]}: {doc['title']}", doc["text"])
st.divider()
st.subheader("Scores")
st.markdown(f"Run 1: {results1[str(inst_num)][metric_name]}")
st.markdown(f"Run 2: {results2[str(inst_num)][metric_name]}")
break
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