alea/gen_kaggle_cls.py

"""Real Kaggle classification datasets -> decision-model rows.

Eight families, one per dataset (see data/kaggle/manifest.json). Files are
loaded lazily ONCE into a module-level cache (_load); subsampling and the
empirical bucket tables are also computed once at load time.

Row flavors (flagged in code):
  DET  - targets are the record's ground-truth label (0/1 or one-hot).
  SOFT - targets are empirical P(label | bucket) computed over the FULL
         dataset, bucketed by 1-2 strong features (+ Beta(1,1) smoothing).

Text families emit the text itself as state (optionally with a one-line task
prefix); tabular families serialize the record as a compact JSON dict.
"""
import json
import random
from collections import Counter
from pathlib import Path

import pandas as pd

from .genbase import (generate, emit, q_choice, q_noul, q_score, q_regress,
                      validate_row, write_jsonl)

rng = random.Random()

DATA = Path(__file__).resolve().parents[1] / "data" / "kaggle"
_CACHE = {}

CC_FEATS = ["Time"] + [f"V{i}" for i in range(1, 29)] + ["Amount"]
IRIS_FEATS = ["SepalLengthCm", "SepalWidthCm", "PetalLengthCm", "PetalWidthCm"]
IRIS_CLASSES = ["Iris-setosa", "Iris-versicolor", "Iris-virginica"]

# (instruction, polarity) pairs; polarity 1 means "yes" == positive label,
# 0 means the question is phrased on the negative class (target = 1 - p).
_SPAM_Q = [("Is this message spam?", 1),
           ("Is this SMS junk or spam?", 1),
           ("Should this text be filtered out as spam?", 1),
           ("Is this a normal personal message?", 0)]
_FAKE_Q = [("Is this article fake news?", 1),
           ("Is this news story fabricated?", 1),
           ("Was this article made up or intentionally false?", 1),
           ("Is this genuine, trustworthy reporting?", 0)]
_NEWS_Q = ["Which section does this article belong to?",
           "Which news desk should handle this piece?",
           "Pick the most appropriate category for this article.",
           "Under which section should this article be filed?"]
_CHURN_Q = [("Will this customer churn?", 1),
            ("Is this customer likely to cancel their service?", 1),
            ("Will this account be lost to churn?", 1),
            ("Will this customer stay with the company?", 0)]
_HEART_Q = [("Does this patient have heart disease?", 1),
            ("Is heart disease present in this patient?", 1),
            ("Would a cardiologist flag this patient for heart disease?", 1),
            ("Is this patient's heart healthy?", 0)]
_MUSH_Q = [("Is this mushroom poisonous?", 1),
           ("Would eating this mushroom be dangerous?", 1),
           ("Is this mushroom toxic?", 1),
           ("Is this mushroom safe to eat?", 0)]
_FRAUD_Q = [("Is this transaction fraudulent?", 1),
            ("Should this transaction be flagged as fraud?", 1),
            ("Is this a fraudulent card transaction?", 1),
            ("Is this transaction legitimate?", 0)]
_IRIS_Q = ["Which iris species is this?",
           "Identify the species of this iris specimen.",
           "What species does this iris belong to?"]


# ---------------------------------------------------------------------------
# helpers
# ---------------------------------------------------------------------------

def _trunc(s, n):
    """Collapse whitespace and keep the beginning of a long text."""
    s = " ".join(str(s).split())
    if len(s) <= n:
        return s
    return s[:n].rsplit(" ", 1)[0] + "..."


def _clean(v):
    """numpy scalar -> python; round floats; NaN -> None (for json.dumps)."""
    if hasattr(v, "item"):
        v = v.item()
    if isinstance(v, float):
        if v != v:
            return None
        return round(v, 4)
    return v


def _emp_binary(df, keys, y):
    """{key-tuple: P(y==1 | bucket)} with Beta(1,1) smoothing, over full df."""
    g = df.groupby(keys, observed=True)[y].agg(["sum", "count"])
    out = {}
    for k, r in g.iterrows():
        kk = k if isinstance(k, tuple) else (k,)
        out[tuple(str(x) for x in kk)] = float((r["sum"] + 1.0)
                                               / (r["count"] + 2.0))
    return out


def _emp_multi(df, key, label, classes, prior=1.0):
    """{bucket: [P(label==c | bucket) for c in classes]} smoothed."""
    ct = df.groupby(key, observed=True)[label].value_counts().unstack(
        fill_value=0)
    ct = ct.reindex(columns=classes, fill_value=0)
    out = {}
    for k, row in ct.iterrows():
        tot = float(row.sum()) + prior * len(classes)
        out[str(k)] = [float((row[c] + prior) / tot) for c in classes]
    return out


# ---------------------------------------------------------------------------
# lazy loaders — each file is read ONCE per process
# ---------------------------------------------------------------------------

def _load_sms():
    df = pd.read_csv(DATA / "sms_spam" / "spam.csv", encoding="latin-1",
                     usecols=["v1", "v2"]).dropna(subset=["v2"])
    return [(str(t), float(l == "spam"))
            for l, t in zip(df["v1"], df["v2"])]


def _load_fakenews():
    out = []
    for fn, y in (("Fake.csv", 1.0), ("True.csv", 0.0)):
        df = pd.read_csv(DATA / "fake_news" / fn)
        # ~631 rows have empty body text — skip them
        df = df[df["text"].astype(str).str.strip().str.len() > 0]
        out += [(str(t), str(x), y)
                for t, x in zip(df["title"], df["text"])]
    return out


def _load_news():
    r = random.Random(2024)          # fixed subsample stream (load is once)
    rows = []
    with open(DATA / "news_cat" / "News_Category_Dataset_v3.json",
              encoding="utf-8") as fh:
        for line in fh:
            if r.random() >= 0.13:   # ~27k subsample of 209k
                continue
            d = json.loads(line)
            h = (d.get("headline") or "").strip()
            if not h:
                continue
            cat = d.get("category") or "OTHER"
            if cat == "THE WORLDPOST":   # normalize: merge into WORLDPOST
                cat = "WORLDPOST"
            rows.append((h, (d.get("short_description") or "").strip(), cat))
    # cap the option list: 10 most frequent categories + OTHER bucket
    top = [c for c, _ in Counter(x[2] for x in rows).most_common(10)]
    rows = [(h, s, c if c in top else "OTHER") for h, s, c in rows]
    return {"rows": rows, "options": top + ["OTHER"]}


def _load_telco():
    df = pd.read_csv(DATA / "telco_churn"
                     / "WA_Fn-UseC_-Telco-Customer-Churn.csv")
    # ~11 blank TotalCharges, all tenure==0 new customers -> 0.0
    df["TotalCharges"] = pd.to_numeric(df["TotalCharges"],
                                       errors="coerce").fillna(0.0)
    df["churn"] = (df["Churn"] == "Yes").astype(float)
    df["ten_band"] = pd.cut(df["tenure"], [-1, 12, 36, 60, 73],
                            labels=["0-12", "13-36", "37-60", "61+"])
    feats = [c for c in df.columns
             if c not in ("customerID", "Churn", "churn", "ten_band")]
    return {"rows": df.to_dict("records"), "feats": feats,
            "probs": _emp_binary(df, ["Contract", "ten_band"], "churn"),
            "global": float(df["churn"].mean())}


def _load_heart():
    df = pd.read_csv(DATA / "heart" / "heart.csv")
    df["age_band"] = pd.cut(df["Age"], [0, 45, 55, 65, 120],
                            labels=["<=45", "46-55", "56-65", "66+"])
    feats = [c for c in df.columns
             if c not in ("HeartDisease", "age_band")]
    return {"rows": df.to_dict("records"), "feats": feats,
            "probs": _emp_binary(df, ["ChestPainType", "age_band"],
                                 "HeartDisease"),
            "global": float(df["HeartDisease"].mean())}


def _load_mushroom():
    df = pd.read_csv(DATA / "mushroom" / "mushrooms.csv")
    df["pois"] = (df["class"] == "p").astype(float)
    # veil-type is constant -> drop; class/pois are labels, not features
    feats = [c for c in df.columns
             if c not in ("class", "veil-type", "pois")]
    return {"rows": df.to_dict("records"), "feats": feats,
            "probs": _emp_binary(df, ["gill-size", "gill-color"], "pois"),
            "global": float(df["pois"].mean())}


def _load_cc():
    df = pd.read_csv(DATA / "creditcard" / "creditcard.csv")
    df["_amt"] = pd.cut(df["Amount"], [-1, 50, 200, 2000, float("inf")],
                        labels=["<=50", "51-200", "201-2000", "2000+"])
    df["_v14"] = pd.cut(df["V14"], [-float("inf"), -4, -1, float("inf")],
                        labels=["<=-4", "-4..-1", ">-1"])
    # SOFT targets must reflect the TRUE prevalence -> buckets over full df
    probs = _emp_binary(df, ["_amt", "_v14"], "Class")
    fraud = df[df["Class"] == 1].to_dict("records")          # all 492
    legit = df[df["Class"] == 0].sample(16000, random_state=7
                                        ).to_dict("records")
    return {"fraud": fraud, "legit": legit, "probs": probs,
            "global": float(df["Class"].mean())}


def _load_iris():
    df = pd.read_csv(DATA / "iris" / "Iris.csv")
    df["pet_band"] = pd.cut(df["PetalLengthCm"], [0, 2.5, 4.9, 7.0],
                            labels=["<=2.5", "2.5-4.9", ">4.9"])
    return {"rows": df.to_dict("records"), "classes": IRIS_CLASSES,
            "probs": _emp_multi(df, "pet_band", "Species", IRIS_CLASSES),
            "uniform": [1.0 / 3.0] * 3}


_LOADERS = {"sms": _load_sms, "fakenews": _load_fakenews, "news": _load_news,
            "telco": _load_telco, "heart": _load_heart,
            "mushroom": _load_mushroom, "cc": _load_cc, "iris": _load_iris}


def _load(name):
    if name not in _CACHE:
        _CACHE[name] = _LOADERS[name]()
    return _CACHE[name]


# ---------------------------------------------------------------------------
# text families
# ---------------------------------------------------------------------------

def gen_sms_spam():
    rows = _load("sms")
    text, y = rows[rng.randrange(len(rows))]
    state = rng.choice([
        "SMS message: {t}",
        'Incoming text message: "{t}"',
        "Message: {t}",
        "Text received on this phone: {t}",
    ]).format(t=_trunc(text, 400))
    instr, pol = rng.choice(_SPAM_Q)
    qs = [q_noul("spam", instr, y if pol else 1.0 - y)]          # DET
    if rng.random() < 0.35:
        qs.append(q_choice("cls", rng.choice([
            "Classify this message.", "Spam or ham — which is it?"]),
            ["ham", "spam"], [1.0 - y, y]))                      # DET
    return state, qs


def gen_fake_news():
    rows = _load("fakenews")
    title, text, y = rows[rng.randrange(len(rows))]
    state = rng.choice([
        "Title: {h}\n{b}",
        "News article — {h}\n{b}",
        'Headline: "{h}"\nArticle body: {b}',
    ]).format(h=_trunc(title, 160), b=_trunc(text, 600))
    instr, pol = rng.choice(_FAKE_Q)
    qs = [q_noul("fake", instr, y if pol else 1.0 - y)]          # DET
    if rng.random() < 0.30:
        qs.append(q_choice("cls", "Is this article real or fake?",
                           ["real", "fake"], [1.0 - y, y]))      # DET
    return state, qs


def gen_news_cat():
    d = _load("news")
    h, s, cat = d["rows"][rng.randrange(len(d["rows"]))]
    opts = d["options"]
    state = rng.choice([
        "Headline: {h}\nSummary: {s}",
        'Article: "{h}" — {s}',
        "Headline: {h}\n{s}",
    ]).format(h=_trunc(h, 140), s=_trunc(s, 320) if s else "(no summary)")
    t = [0.0] * len(opts)                                        # DET one-hot
    t[opts.index(cat)] = 1.0
    qs = [q_choice("section", rng.choice(_NEWS_Q), opts, t)]
    if rng.random() < 0.30:                                      # DET probe
        c = rng.choice(opts[:-1])   # real categories only (not OTHER)
        qs.append(q_noul("is", f"Is this a {c} article?",
                         float(cat == c)))
    return state, qs


# ---------------------------------------------------------------------------
# tabular families
# ---------------------------------------------------------------------------

def gen_telco():
    d = _load("telco")
    row = d["rows"][rng.randrange(len(d["rows"]))]
    rec = {k: _clean(row[k]) for k in d["feats"]}
    state = "Customer record: " + json.dumps(rec, ensure_ascii=False)
    if rng.random() < 0.5:
        p_churn = float(row["churn"])                            # DET
    else:
        # SOFT: empirical P(churn | Contract x tenure-band), full dataset
        p_churn = d["probs"].get(
            (str(row["Contract"]), str(row["ten_band"])), d["global"])
    instr, pol = rng.choice(_CHURN_Q)
    qs = [q_noul("churn", instr, p_churn if pol else 1.0 - p_churn)]
    if rng.random() < 0.4:                       # exact numeric threshold probe
        if rng.random() < 0.5:
            thr = rng.choice([30, 50, 70, 90])
            qs.append(q_noul(
                "mc", f"Is the monthly charge over ${thr}?",
                float(row["MonthlyCharges"] > thr)))
        else:
            thr = rng.choice([12, 24, 48])
            qs.append(q_noul(
                "ten", f"Has this customer subscribed for over {thr} months?",
                float(row["tenure"] > thr)))
    if rng.random() < 0.2:
        qs.append(q_choice("cls", "What will this customer do?",
                           ["stay", "churn"],
                           [1.0 - p_churn, p_churn]))
    return state, qs


def gen_heart():
    d = _load("heart")
    row = d["rows"][rng.randrange(len(d["rows"]))]
    rec = {k: _clean(row[k]) for k in d["feats"]}
    state = "Patient record: " + json.dumps(rec, ensure_ascii=False)
    if rng.random() < 0.5:
        p_hd = float(row["HeartDisease"])                        # DET
    else:
        # SOFT: empirical P(heart disease | ChestPainType x age-band)
        p_hd = d["probs"].get(
            (str(row["ChestPainType"]), str(row["age_band"])),
            d["global"])
    instr, pol = rng.choice(_HEART_Q)
    qs = [q_noul("hd", instr, p_hd if pol else 1.0 - p_hd)]
    if rng.random() < 0.4:                       # exact numeric threshold probe
        probe = rng.random()
        if probe < 0.34:
            thr = rng.choice([130, 140, 160])
            qs.append(q_noul(
                "bp", f"Is resting blood pressure above {thr} mm Hg?",
                float(row["RestingBP"] > thr)))
        elif probe < 0.67:
            thr = rng.choice([200, 240, 300])
            qs.append(q_noul(
                "chol", f"Is serum cholesterol over {thr} mg/dl?",
                float(row["Cholesterol"] > thr)))
        else:
            thr = rng.choice([120, 140, 160])
            qs.append(q_noul(
                "hr", f"Is the maximum heart rate under {thr} bpm?",
                float(row["MaxHR"] < thr)))
    if rng.random() < 0.2:
        qs.append(q_choice("cls", "What is the cardiac assessment?",
                           ["no disease", "heart disease"],
                           [1.0 - p_hd, p_hd]))
    return state, qs


def gen_mushroom():
    d = _load("mushroom")
    row = d["rows"][rng.randrange(len(d["rows"]))]
    rec = {k: _clean(row[k]) for k in d["feats"]}
    state = ("Mushroom specimen (single-letter feature codes): "
             + json.dumps(rec, ensure_ascii=False))
    if rng.random() < 0.5:
        p_pois = float(row["pois"])                              # DET
    else:
        # SOFT: empirical P(poisonous | gill-size x gill-color)
        p_pois = d["probs"].get(
            (str(row["gill-size"]), str(row["gill-color"])),
            d["global"])
    instr, pol = rng.choice(_MUSH_Q)
    qs = [q_noul("pois", instr, p_pois if pol else 1.0 - p_pois)]
    if rng.random() < 0.4:                       # exact feature-existence probe
        probe = rng.random()
        if probe < 0.34:
            qs.append(q_noul(
                "bruise", "Does this mushroom have bruises (code 't')?",
                float(row["bruises"] == "t")))
        elif probe < 0.67:
            qs.append(q_noul(
                "odor", "Is this mushroom odorless (odor code 'n')?",
                float(row["odor"] == "n")))
        else:
            qs.append(q_noul(
                "gill", "Is the gill size broad (code 'b')?",
                float(row["gill-size"] == "b")))
    if rng.random() < 0.2:
        qs.append(q_choice("cls", "Classify this mushroom.",
                           ["edible", "poisonous"],
                           [1.0 - p_pois, p_pois]))
    return state, qs


def gen_creditcard():
    d = _load("cc")
    # oversample fraud so generated rows keep ~10-30% positives
    pool = d["fraud"] if rng.random() < rng.uniform(0.10, 0.30) else d["legit"]
    row = pool[rng.randrange(len(pool))]
    rec = {k: _clean(row[k]) for k in CC_FEATS}
    state = ("Card transaction (PCA-anonymized features V1-V28): "
             + json.dumps(rec, ensure_ascii=False))
    if rng.random() < 0.5:
        p_fraud = float(row["Class"])                            # DET
    else:
        # SOFT: empirical P(fraud | Amount-band x V14-band), full dataset
        p_fraud = d["probs"].get(
            (str(row["_amt"]), str(row["_v14"])), d["global"])
    instr, pol = rng.choice(_FRAUD_Q)
    qs = [q_noul("fraud", instr, p_fraud if pol else 1.0 - p_fraud)]
    if rng.random() < 0.4:                       # exact numeric threshold probe
        thr = rng.choice([50, 100, 500, 1000])
        qs.append(q_noul(
            "amt", f"Is the transaction amount over ${thr}?",
            float(row["Amount"] > thr)))
    if rng.random() < 0.15:                      # feature probe on top predictor
        qs.append(q_noul(
            "v14", "Is V14 below -1 (anomalous component)?",
            float(row["V14"] < -1)))
    if rng.random() < 0.2:
        qs.append(q_choice("cls", "How should this transaction be classified?",
                           ["legitimate", "fraud"],
                           [1.0 - p_fraud, p_fraud]))
    return state, qs


def gen_iris():
    d = _load("iris")
    row = d["rows"][rng.randrange(len(d["rows"]))]
    rec = {k: _clean(row[k]) for k in IRIS_FEATS}
    state = ("Iris specimen measurements (cm): "
             + json.dumps(rec, ensure_ascii=False))
    species = row["Species"]
    classes = d["classes"]
    if rng.random() < 0.5:
        t = [1.0 if c == species else 0.0 for c in classes]      # DET one-hot
    else:
        # SOFT: empirical P(species | petal-length band)
        t = d["probs"].get(str(row["pet_band"]), d["uniform"])
    qs = [q_choice("species", rng.choice(_IRIS_Q), classes, t)]
    if rng.random() < 0.4:
        if rng.random() < 0.5:                   # exact numeric threshold probe
            thr = rng.choice([3.0, 4.5, 5.0])
            qs.append(q_noul(
                "pl", f"Is the petal longer than {thr} cm?",
                float(row["PetalLengthCm"] > thr)))
        else:                                    # DET species probe
            c = rng.choice(classes)
            qs.append(q_noul("issp", f"Is this specimen {c}?",
                             float(species == c)))
    return state, qs


FAMILIES = {"sms_spam": gen_sms_spam, "fake_news": gen_fake_news,
            "news_cat": gen_news_cat, "telco_churn": gen_telco,
            "heart": gen_heart, "mushroom": gen_mushroom,
            "creditcard": gen_creditcard, "iris": gen_iris}
WEIGHTS = {"sms_spam": 0.15, "fake_news": 0.13, "news_cat": 0.14,
           "telco_churn": 0.15, "heart": 0.12, "mushroom": 0.12,
           "creditcard": 0.14, "iris": 0.05}


def make_rows(n, seed):
    return generate(FAMILIES, WEIGHTS, n, seed, rng)


if __name__ == "__main__":
    rows = make_rows(10, 0)
    for r in rows[:3]:
        validate_row(r)
        print(r["id"], "|", r["state"][:90])
    print(f"{len(rows)} rows OK")