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import os
import json
import numpy as np
import pandas as pd
from datasets import load_dataset
os.makedirs("experiments/analysis", exist_ok=True)
root_dir = "experiments/prediction_files"
id_to_label = {
'0': 'arts_&_culture',
'1': 'business_&_entrepreneurs',
'2': 'celebrity_&_pop_culture',
'3': 'diaries_&_daily_life',
'4': 'family',
'5': 'fashion_&_style',
'6': 'film_tv_&_video',
'7': 'fitness_&_health',
'8': 'food_&_dining',
'9': 'gaming',
'10': 'learning_&_educational',
'11': 'music',
'12': 'news_&_social_concern',
'13': 'other_hobbies',
'14': 'relationships',
'15': 'science_&_technology',
'16': 'sports',
'17': 'travel_&_adventure',
'18': 'youth_&_student_life'
}
splits = ["test_1", "test_2", "test_3", "test_4"]
model_list = [
"roberta-base",
"bertweet-base",
"bernice",
"roberta-large",
"bertweet-large",
"twitter-roberta-base-2019-90m",
"twitter-roberta-base-dec2020",
"twitter-roberta-base-2021-124m",
"twitter-roberta-base-2022-154m",
"twitter-roberta-large-2022-154m"
]
references = {}
for s in splits:
data = load_dataset("tweettemposhift/tweet_temporal_shift", f"topic_temporal", split=s)
references[s] = [{id_to_label[str(n)] for n, k in enumerate(i) if k == 1} for i in data['gold_label_list']]
count = {}
pred_tmp = {}
for model_m in model_list:
flags = []
pred_all = []
for s in splits:
with open(f"{root_dir}/topic-topic_temporal-{model_m}/{s}.jsonl") as f:
pred = [set(json.loads(i)["label"]) for i in f.read().split('\n') if len(i)]
flags += [len(a.intersection(b)) > 0 for a, b in zip(references[s], pred)]
pred_all += pred
for seed_s in range(3):
flags_rand = []
for random_r in range(4):
with open(f"{root_dir}/topic-topic_random{random_r}_seed{seed_s}-{model_m}/test_{random_r + 1}.jsonl") as f:
pred = [set(json.loads(i)["label"]) for i in f.read().split('\n') if len(i)]
label = references[f"test_{random_r + 1}"]
flags_rand += [len(a.intersection(b)) > 0 for a, b in zip(label, pred)]
tmp_flag = [not x and y for x, y in zip(flags, flags_rand)]
count[f"{model_m}_{seed_s}"] = tmp_flag
pred_tmp[f"{model_m}_{seed_s}"] = [list(x) if y else [] for x, y in zip(pred_all, tmp_flag)]
df_tmp = pd.DataFrame([[dict(zip(*np.unique(i, return_counts=True))) for i in pd.DataFrame(pred_tmp).sum(1).values]], index=["errors"]).T
df_tmp["error_count"] = pd.DataFrame(count).sum(1).values
gold_label = []
text = []
for s in splits:
gold_label += load_dataset("tweettemposhift/tweet_temporal_shift", "topic_temporal", split=s)['gold_label_list']
text += load_dataset("tweettemposhift/tweet_temporal_shift", "topic_temporal", split=s)['text']
df_tmp["true_label"] = [", ".join([id_to_label[str(n)] for n, k in enumerate(i) if k == 1]) for i in gold_label]
df_tmp["text"] = text
df_tmp.sort_values("error_count", ascending=False).to_csv("experiments/analysis/topic.csv")
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