Calibrate Jev's Probabilities and Thresholds

"""Calibrate Jev's probabilities for a semantic cache.

Before Tallyboard's support bot writes a new answer, it looks for a cached
question close to the new one and asks Jev whether the cached answer can be
sent as it is. A wrong reuse sends a customer a wrong answer, so the cache
may only reuse when it is very likely right. "Very likely" has to be a
number you can trust, which is what calibration is about.
"""
import json
import os
from concurrent.futures import ThreadPoolExecutor

import numpy as np

import jev

HERE = os.path.dirname(os.path.abspath(__file__))

REUSE_QUESTION = "Can the cached answer be sent, unchanged, as the answer to the new question?"
WORDINGS = {
    "reuse": REUSE_QUESTION,
    "same": "Do the two questions ask the same thing?",
    "verify": ("Is the cached answer a correct and complete answer to the new question, "
               "with nothing in it that would mislead the customer?"),
}


def load_pairs(path="pairs.jsonl"):
    with open(os.path.join(HERE, path), encoding="utf-8") as f:
        return [json.loads(line) for line in f if line.strip()]


def labels(pairs):
    """1 for a reusable pair, 0 otherwise, as a numpy array."""
    return np.array([1 if p["reusable"] else 0 for p in pairs])


# ---------------------------------------------------------------- Step 1
def ask(pair, question=REUSE_QUESTION, post=jev.post):
    """Jev's probability that the pair's cached answer can be reused."""
    # TODO (Step 1): ONE post({"model": jev.MODEL, "state": {...}, "questions": {...}})
    #   state: cached_question, cached_answer and new_question from the pair
    #   questions: {"reuse": {"type": "noul", "instructions": question}}
    # return the noul probability
    raise NotImplementedError("Step 1: write ask()")


def collect(pairs, question=REUSE_QUESTION, post=jev.post):
    """Probabilities for every pair, in the order of `pairs`, as a numpy array."""
    # TODO (Step 1): ask() every pair through ThreadPoolExecutor(16), keep the order,
    # return np.array(...) of the probabilities
    raise NotImplementedError("Step 1: write collect()")


# ---------------------------------------------------------------- Step 2
def reliability(p, y, bins=5):
    """One row per equal-width bin: {"lo", "hi", "n", "mean_p", "rate"}.
    The last bin includes 1.0. Empty bins have n 0 and None for mean_p and rate."""
    raise NotImplementedError("reliability() arrives in Step 2")


def ece(p, y, bins=5):
    """Expected calibration error: the bins' gaps |mean_p - rate|, weighted by their share of pairs."""
    raise NotImplementedError("ece() arrives in Step 2")


def brier(p, y):
    """Mean squared gap between probability and outcome."""
    raise NotImplementedError("brier() arrives in Step 2")


# ---------------------------------------------------------------- Step 3
def logit(p):
    p = np.clip(np.asarray(p, dtype=float), 1e-4, 1 - 1e-4)
    return np.log(p / (1 - p))


def fit_platt(p, y):
    """Platt scaling: fit a logistic regression on logit(p). Returns (a, b)."""
    raise NotImplementedError("fit_platt() arrives in Step 3")


def calibrate(p, a, b):
    """The recalibrated probabilities: sigmoid(a * logit(p) + b)."""
    raise NotImplementedError("calibrate() arrives in Step 3")


# ---------------------------------------------------------------- Step 4
CANDIDATES = [round(i / 100, 2) for i in range(1, 100)]


def precision_recall(p, y, t):
    """(precision, recall) when every pair with p >= t is reused.
    Precision is 1.0 when nothing is reused."""
    raise NotImplementedError("precision_recall() arrives in Step 4")


def threshold_for_precision(p, y, target, min_hits=10):
    """The lowest candidate threshold that reuses at least min_hits pairs with
    precision >= target, or None."""
    raise NotImplementedError("threshold_for_precision() arrives in Step 4")


# ---------------------------------------------------------------- Step 5
def auc(p, y):
    """The chance that a random reusable pair scores above a random non-reusable one (ties count half)."""
    raise NotImplementedError("auc() arrives in Step 5")


def win_rate(p_challenger, p_current, y, n_boot=1000, seed=0):
    """Share of bootstrap resamples where the challenger's AUC beats the current one's.
    Both are scored on the SAME resample each time; resamples with one class are skipped."""
    raise NotImplementedError("win_rate() arrives in Step 5")


def keep_or_switch(win, bar=0.95):
    """Switch wordings only when the challenger wins at least `bar` of the resamples."""
    raise NotImplementedError("keep_or_switch() arrives in Step 5")