"""
evaluate.py - measure what each component of SentimentDetector actually adds.

    pip install datasets scikit-learn
    python evaluate.py            # hand-built suite + TweetEval irony/sentiment
    python evaluate.py --n 1000   # TweetEval sentiment sample size

Thresholds were NOT tuned on the TweetEval test sets. Paste your own measured numbers
into README.md - never numbers you have not run.
"""
import argparse

import numpy as np
from sklearn.metrics import accuracy_score, f1_score

from detector import SARCASM_THRESHOLD, SentimentDetector, label_from_score

CASES = [
    ("Oh great, another Monday morning meeting. Just what I needed 🙄", "negative"),
    ("I absolutely love waiting 3 hours at the DMV.", "negative"),
    ("Thanks a lot for telling me at the last minute 🙃", "negative"),
    ("Yeah right, because that always works /s", "negative"),
    ("Wow, what a brilliant idea. Nobody has ever thought of that.", "negative"),
    ("Great, my laptop died right before the deadline.", "negative"),
    ("Nothing beats debugging at 3 AM, truly living the dream 🙃", "negative"),
    ("Perfect. Just perfect. My flight got cancelled.", "negative"),
    ("Sure, because 'customer service' is exactly what this was.", "negative"),
    ("I can't believe how terrible this update is 😡", "negative"),
    ("Ugh, the wifi is down again.", "negative"),
    ("Just got my acceptance letter!!! 🎉😭", "positive"),
    ("I'm so proud of my little brother 🥰", "positive"),
    ("Best pizza I've had in years 🍕😍", "positive"),
    ("Not bad at all, honestly impressed.", "positive"),
    ("lol I'm dead 💀 that was hilarious", "positive"),
    ("So happy for you!! Congrats 🎊", "positive"),
    ("I actually enjoyed the workshop, learned a lot!", "positive"),
    ("Wow, you finished the whole project in one night? Amazing work!", "positive"),
    ("I hate how much I love this song 😍", "positive"),
    ("The package arrived on Tuesday.", "neutral"),
    ("It's a meeting at 3 PM in room 204.", "neutral"),
]


def run_suite(det):
    print(f"\n== Hand-built suite ({len(CASES)} cases; small and written by the author - indicative only) ==")
    configs = {"baseline (text only)": (False, False), "+ emoji": (False, True), "+ emoji + sarcasm": (True, True)}
    y = [c[1] for c in CASES]
    for name, (sarc, emo) in configs.items():
        pred = [label_from_score(det.analyze(t, sarcasm_aware=sarc, emoji_aware=emo).mean) for t, _ in CASES]
        print(f"{name:24s} accuracy = {accuracy_score(y, pred):.2%}")
        if sarc and emo:
            for (t, gold), p in zip(CASES, pred):
                if gold != p:
                    print(f"   miss: {t!r}  gold={gold} pred={p}")


def run_tweeteval(det, n):
    from datasets import load_dataset  # hub id: cardiffnlp/tweet_eval

    print("\n== TweetEval irony (test) - F1 on the irony class ==")
    ds = load_dataset("cardiffnlp/tweet_eval", "irony", split="test")
    texts, y = ds["text"], np.array(ds["label"])
    base = det.irony_probs(texts) >= 0.5
    fused = np.array([max(r.sarcasm for r in det.analyze(t).sentences) >= SARCASM_THRESHOLD for t in texts])
    print(f"irony model alone : F1 = {f1_score(y, base):.3f}")
    print(f"fused sarcasm     : F1 = {f1_score(y, fused):.3f}")

    print(f"\n== TweetEval sentiment (test sample, n={n}) - macro-F1 ==")
    ds = load_dataset("cardiffnlp/tweet_eval", "sentiment", split="test").shuffle(seed=0).select(range(n))
    texts, y = ds["text"], np.array(ds["label"])  # 0 neg, 1 neu, 2 pos
    base = det.sentiment_probs(texts).argmax(1)
    ids = {"negative": 0, "neutral": 1, "mixed": 1, "positive": 2}
    full = np.array([ids[label_from_score(det.analyze(t).mean)] for t in texts])
    print(f"baseline model    : macro-F1 = {f1_score(y, base, average='macro'):.3f}")
    print(f"full pipeline     : macro-F1 = {f1_score(y, full, average='macro'):.3f}")


if __name__ == "__main__":
    ap = argparse.ArgumentParser()
    ap.add_argument("--n", type=int, default=1000)
    ap.add_argument("--skip-tweeteval", action="store_true")
    a = ap.parse_args()
    det = SentimentDetector()
    run_suite(det)
    if not a.skip_tweeteval:
        run_tweeteval(det, a.n)