experiments/analysis_prediction.py

import os
import json

import pandas as pd
from datasets import load_dataset

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'
}
tasks = ["nerd", "sentiment", "hate"]
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 task in tasks:
    references[task] = {}
    for s in splits:
        data = load_dataset("tweettemposhift/tweet_temporal_shift", f"{task}_temporal", split=s)
        references[task][s] = [str(i) for i in data['gold_label_binary']]
os.makedirs("experiments/analysis", exist_ok=True)


output = {}
for model_m in model_list:
    flags = []
    for s in splits:
        with open(f"{root_dir}/hate-hate_temporal-{model_m}/{s}.jsonl") as f:
            pred = [json.loads(i)["label"] for i in f.read().split('\n') if len(i)]
            flags += [a == b for a, b in zip(references["hate"][s], pred)]
    count = {}
    for seed_s in range(3):
        flags_rand = []
        for random_r in range(4):
            with open(f"{root_dir}/hate-hate_random{random_r}_seed{seed_s}-{model_m}/test_{random_r + 1}.jsonl") as f:
                pred = [json.loads(i)["label"] for i in f.read().split('\n') if len(i)]
                flags_rand += [a == b for a, b in zip(references["hate"][f"test_{random_r + 1}"], pred)]
        count[f"{model_m}_{seed_s}"] = [not x and y for x, y in zip(flags, flags_rand)]
    output[model_m] = pd.DataFrame(count).sum(1)
df_main = []
for s in splits:
    df_main.append(load_dataset("tweettemposhift/tweet_temporal_shift", "hate_temporal", split=s).to_pandas())
df_main = pd.concat(df_main)
df_main["error_count"] = pd.DataFrame(output).sum(1).values
df_main.sort_values("error_count", ascending=False).to_csv("experiments/analysis/hate.csv")

output = {}
for model_m in model_list:
    flags = []
    for s in splits:
        with open(f"{root_dir}/nerd-nerd_temporal-{model_m}/{s}.jsonl") as f:
            pred = [json.loads(i)["label"] for i in f.read().split('\n') if len(i)]
            flags += [a == b for a, b in zip(references["nerd"][s], pred)]
    count = {}
    for seed_s in range(3):
        flags_rand = []
        for random_r in range(4):
            with open(f"{root_dir}/nerd-nerd_random{random_r}_seed{seed_s}-{model_m}/test_{random_r + 1}.jsonl") as f:
                pred = [json.loads(i)["label"] for i in f.read().split('\n') if len(i)]
                flags_rand += [a == b for a, b in zip(references["nerd"][f"test_{random_r + 1}"], pred)]
        count[f"{model_m}_{seed_s}"] = [not x and y for x, y in zip(flags, flags_rand)]
    output[model_m] = pd.DataFrame(count).sum(1)
df_main = []
for s in splits:
    df_main.append(load_dataset("tweettemposhift/tweet_temporal_shift", "nerd_temporal", split=s).to_pandas())
df_main = pd.concat(df_main)
df_main["error_count"] = pd.DataFrame(output).sum(1).values
df_main.sort_values("error_count", ascending=False).to_csv("experiments/analysis/nerd.csv")


output = {}
for model_m in model_list:
    flags = []
    for s in splits:
        with open(f"{root_dir}/sentiment-sentiment_small_temporal-{model_m}/{s}.jsonl") as f:
            pred = [json.loads(i)["label"] for i in f.read().split('\n') if len(i)]
            flags += [a == b for a, b in zip(references["sentiment"][s], pred)]
    count = {}
    for seed_s in range(3):
        flags_rand = []
        for random_r in range(4):
            with open(f"{root_dir}/sentiment-sentiment_small_random{random_r}_seed{seed_s}-{model_m}/test_{random_r + 1}.jsonl") as f:
                pred = [json.loads(i)["label"] for i in f.read().split('\n') if len(i)]
                flags_rand += [a == b for a, b in zip(references["sentiment"][f"test_{random_r + 1}"], pred)]
        count[f"{model_m}_{seed_s}"] = [not x and y for x, y in zip(flags, flags_rand)]
    output[model_m] = pd.DataFrame(count).sum(1)
df_main = []
for s in splits:
    df_main.append(load_dataset("tweettemposhift/tweet_temporal_shift", "sentiment_small_temporal", split=s).to_pandas())
df_main = pd.concat(df_main)
df_main["error_count"] = pd.DataFrame(output).sum(1).values
df_main.sort_values("error_count", ascending=False).to_csv("experiments/analysis/sentiment.csv")


output = {}
for model_m in model_list:
    flags = []
    for s in splits:
        with open(f"{root_dir}/ner-ner_temporal-{model_m}/{s}.jsonl") as f:
            tmp = [json.loads(i) for i in f.read().split('\n') if len(i)]
        label = [[x for x, y in zip(i["label"], i["prediction"]) if x != -100] for i in tmp]
        pred = [[y for x, y in zip(i["label"], i["prediction"]) if x != -100] for i in tmp]
        flags += [a == b for a, b in zip(label, pred)]
    count = {}
    for seed_s in range(3):
        flags_rand = []
        for random_r in range(4):
            with open(f"{root_dir}/ner-ner_random{random_r}_seed{seed_s}-{model_m}/test_{random_r + 1}.jsonl") as f:
                tmp = [json.loads(i) for i in f.read().split('\n') if len(i)]
            label = [[x for x, y in zip(i["label"], i["prediction"]) if x != -100] for i in tmp]
            pred = [[y for x, y in zip(i["label"], i["prediction"]) if x != -100] for i in tmp]
            flags_rand += [a == b for a, b in zip(label, pred)]
        count[f"{model_m}_{seed_s}"] = [not x and y for x, y in zip(flags, flags_rand)]
    output[model_m] = pd.DataFrame(count).sum(1)
df_main = []
for s in splits:
    df_main.append(load_dataset("tweettemposhift/tweet_temporal_shift", "ner_temporal", split=s).to_pandas())
df_main = pd.concat(df_main)
df_main["error_count"] = pd.DataFrame(output).sum(1).values
df_main.sort_values("error_count", ascending=False).to_csv("experiments/analysis/ner.csv")