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    "# Which search terms should I block, and which deserve a chance?\n",
    "\n",
    "**Part one** builds a synthetic month of search terms for one paid-search campaign,\n",
    "runs the rule most people run (\"kill anything that spent money and did not convert\"),\n",
    "and then runs the two-gate rule this post is about: first ask whether the term has\n",
    "anything to do with what you sell, and only then judge performance - and judge it\n",
    "with a confidence interval, not a glance.\n",
    "\n",
    "**Part two**, further down, runs the same two gates on *your* search terms report,\n",
    "on your machine. Nothing is uploaded anywhere.\n",
    "\n",
    "**Synthetic data, labelled as such.** Every number in the article is written by this\n",
    "notebook into `data/worked_example.json` under one fixed seed. Nothing is hand-typed."
   ]
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   "source": [
    "import json\n",
    "import math\n",
    "import re\n",
    "from pathlib import Path\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy.stats import norm\n",
    "\n",
    "for d in (\"figures\", \"data\"):\n",
    "    Path(d).mkdir(exist_ok=True)\n",
    "\n",
    "SEED = 20260904          # one seed; every number below regenerates from it\n",
    "rng = np.random.default_rng(SEED)\n",
    "\n",
    "DAYS = 28                # the month the campaign is watched\n",
    "HORIZON = 90             # the quarter the same terms are followed for\n",
    "N_TERMS = 600            # new search terms that appear during the month\n",
    "BENCHMARK = 0.04         # the campaign's ordinary conversion rate on known keywords\n",
    "CONFIDENCE = 0.95        # the confidence level the two-gate rule judges at\n",
    "# WHERE THE BENCHMARK COMES FROM, stated rather than left implicit. It is an INPUT the shop brings:\n",
    "# the rate its ESTABLISHED keywords convert at, over a lookback window. It is emphatically NOT the\n",
    "# rate of the new search terms in this report -- those blend far lower precisely because most of\n",
    "# them should not be running, and judging a new term against a bar the junk lowered is circular.\n",
    "# The evidence matters for a second reason: the benchmark has its own interval, and the fair-chance\n",
    "# click count inherits it. 93 is exact arithmetic given p; how well you know p is what makes it a\n",
    "# range. These two numbers are ASSUMED for the synthetic shop, and the notebook says so.\n",
    "KNOWN_KEYWORD_CONVERSIONS = 400    # ASSUMED: sales the shop's established keywords produced\n",
    "KNOWN_KEYWORD_CLICKS = 10_000      # ASSUMED: clicks they took over the same window\n",
    "# READER ECONOMICS -- the two numbers the notebook cannot know, stated as assumptions and used\n",
    "# only as arithmetic over the counts it does measure. Without them the comparison stops at \"waste\n",
    "# saved against good terms killed\", which is two units the reader has to price in their head. A\n",
    "# block is also PERMANENT, so the cost of killing a keyword is its forward stream, not just the\n",
    "# conversions that failed to land inside the simulated window.\n",
    "ORDER_VALUE_EUR = 50           # ASSUMED: average value of one sale\n",
    "CONV_PER_KEYWORD_YEAR = 10     # ASSUMED: conversions a converting keyword produces in a year\n",
    "\n",
    "NAIVE_CLICKS = 30        # the \"kill on sight\" rule: this many clicks, zero conversions.\n",
    "                         # Not 5. At five clicks every reader already agrees the block is silly,\n",
    "                         # so a rule set there is a straw man; thirty is where a working media\n",
    "                         # buyer genuinely hesitates, and it is still a third of a fair chance.\n",
    "\n",
    "PAPER, INK, MUTED, LINE = \"#fdfcf9\", \"#1f2124\", \"#565c66\", \"#e6e2da\"\n",
    "GREEN, BLUE, WARM = \"#0a8f5f\", \"#2a78d6\", \"#b06a2c\"\n",
    "FIGDIR = \"figures\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23072563",
   "metadata": {},
   "source": [
    "## 1. A month of search terms\n",
    "\n",
    "A synthetic running-shoe shop. Its campaign bids on a few dozen known keywords, and\n",
    "every day the search engine matches those keywords to queries the shop never bid on.\n",
    "Those are the *search terms*, and this notebook invents 600 of them across a month.\n",
    "\n",
    "Each term has a hidden truth the shop cannot see: whether it is relevant at all, and\n",
    "if so what its real conversion rate is. Clicks arrive with the long tail real search\n",
    "terms have - most terms get a handful of clicks in a month, a few get hundreds.\n",
    "\n",
    "Four hidden classes:\n",
    "\n",
    "- **irrelevant** - matched on a stray word; almost never converts - the band runs up to 0.4%,\n",
    "  four sales in a thousand clicks, which is the occasional visitor who turned out to want shoes\n",
    "  after all. Not zero, and the difference matters: gate one blocks every one of these on day one\n",
    "- **on topic, not shopping** - about the product, but not a purchase: repairs, how-tos,\n",
    "  comparisons (\"how to clean running shoes\", \"running shoes vs walking shoes\"). Someone asking\n",
    "  how to clean their shoes buys a new pair now and then, so one sale in 70 to 200 clicks\n",
    "- **ordinary buyer** - converts at about the campaign's ordinary rate\n",
    "- **buying intent** - converts above the ordinary rate (\"buy running shoes size 9\")\n",
    "\n",
    "The class KEYS below stay `irrelevant` / `poor` / `average` / `good` - they are field names in\n",
    "the emitted JSON and in every downstream binding. The names above are what a reader is shown.\n",
    "\"Wrong intent\" was the earlier display name for `poor` and it was wrong: it reads as *someone\n",
    "who wanted something else*, which is the irrelevant class, and then a 1.5% rate looks absurd.\n",
    "\n",
    "The shop's own description of what it sells is a short list of phrases. Gate one\n",
    "matches against that list, so the list is deliberately imperfect: some good terms\n",
    "use a word the shop never wrote down (\"sneakers\", \"trainers\"), and those are the\n",
    "terms a relevance filter can wrongly kill."
   ]
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     "text": [
      "klass\n",
      "irrelevant    256\n",
      "average       136\n",
      "good          125\n",
      "poor           83\n",
      "Name: terms, dtype: int64\n",
      "month spend on new search terms: EUR 6,631\n",
      "median clicks per term: 6; share with 30 or fewer clicks: 90%\n"
     ]
    }
   ],
   "source": [
    "WHAT_WE_SELL = [\"running shoes\", \"trail running shoes\", \"racing flats\",\n",
    "                \"running socks\", \"marathon shoes\"]\n",
    "\n",
    "PRODUCTS_LISTED = [\"running shoes\", \"trail running shoes\", \"racing flats\",\n",
    "                   \"running socks\", \"marathon shoes\", \"road running shoes\"]\n",
    "PRODUCTS_UNLISTED = [\"running sneakers\", \"jogging shoes\", \"trainers for running\"]\n",
    "# The words the shop never wrote down, DERIVED from the phrases above rather than retyped. Three of\n",
    "# them, not two: an earlier draft named only sneakers and trainers while the full-list arm and the\n",
    "# money scene both counted three, so the two halves of the bundle disagreed about their own\n",
    "# example. Deriving it means the count and the list cannot drift apart again.\n",
    "_STOPWORDS = {\"for\", \"and\", \"the\", \"a\"}    # connectives carry no product meaning\n",
    "MISSING_WORDS = sorted({w for phrase in PRODUCTS_UNLISTED for w in phrase.split()}\n",
    "                       - {w for phrase in WHAT_WE_SELL for w in phrase.split()}\n",
    "                       - _STOPWORDS)\n",
    "\n",
    "BUY_MODS = [\"buy\", \"sale\", \"near me\", \"size 9\", \"size 8\", \"mens\", \"womens\",\n",
    "            \"wide fit\", \"carbon plate\", \"discount\", \"order\", \"cheap\"]\n",
    "MID_MODS = [\"best\", \"review\", \"top rated\", \"comparison\", \"lightweight\",\n",
    "            \"for flat feet\", \"for beginners\", \"cushioned\"]\n",
    "POOR_MODS = [\"how to clean\", \"repair\", \"history of\", \"vs walking shoes\", \"diy\",\n",
    "             \"free\", \"smell\", \"how long do they last\", \"recycling\", \"wash\"]\n",
    "\n",
    "IRRELEVANT = [\"shoe rack\", \"shoe repair near me\", \"running app\", \"running man\",\n",
    "              \"horse running\", \"running late meaning\", \"shoe size chart\",\n",
    "              \"tap shoes\", \"running cost of a car\", \"free running games\",\n",
    "              \"running water pipe\", \"shoe stores hiring\", \"socks knitting pattern\",\n",
    "              \"shoe polish\", \"marathon results\", \"marathon training plan\",\n",
    "              \"flat feet exercises\", \"running injuries\", \"shoe cabinet\",\n",
    "              \"trail map\", \"racing games\", \"sock puppet\", \"running jokes\",\n",
    "              \"shoe cobbler\", \"running shorts\", \"running watch\", \"running belt\",\n",
    "              \"shoe insoles for work boots\", \"trail camera\", \"flats for rent\"]\n",
    "\n",
    "CLASS_SHARE = {\"irrelevant\": 0.45, \"poor\": 0.15, \"average\": 0.22, \"good\": 0.18}\n",
    "SYNONYM_SHARE = 0.12   # share of good/average terms phrased with an unlisted word\n",
    "\n",
    "\n",
    "def make_term(klass, r):\n",
    "    if klass == \"irrelevant\":\n",
    "        base = r.choice(IRRELEVANT)\n",
    "        mod = r.choice([\"\", \"\", \"best\", \"cheap\", \"near me\", \"2026\"])\n",
    "        return f\"{mod} {base}\".strip()\n",
    "    unlisted = r.random() < SYNONYM_SHARE\n",
    "    product = r.choice(PRODUCTS_UNLISTED if unlisted else PRODUCTS_LISTED)\n",
    "    mods = {\"good\": BUY_MODS, \"average\": MID_MODS, \"poor\": POOR_MODS}[klass]\n",
    "    mod = r.choice(mods)\n",
    "    order = r.random() < 0.7\n",
    "    return f\"{mod} {product}\" if order else f\"{product} {mod}\"\n",
    "\n",
    "\n",
    "def true_cvr(klass, r):\n",
    "    return {\"irrelevant\": r.uniform(0.0, 0.004),\n",
    "            \"poor\": r.uniform(0.005, 0.015),\n",
    "            \"average\": r.uniform(0.03, 0.05),\n",
    "            \"good\": r.uniform(0.06, 0.10)}[klass]\n",
    "\n",
    "\n",
    "def build_terms(n, r):\n",
    "    classes = r.choice(list(CLASS_SHARE), size=n, p=list(CLASS_SHARE.values()))\n",
    "    rows, seen = [], set()\n",
    "    for k in classes:\n",
    "        t = make_term(k, r)\n",
    "        while t in seen:                      # every term is distinct\n",
    "            t = make_term(k, r) + \" \" + r.choice([\"online\", \"uk\", \"2026\", \"store\"])\n",
    "        seen.add(t)\n",
    "        rows.append({\"term\": t, \"klass\": k, \"true_cvr\": true_cvr(k, r),\n",
    "                     \"cpc\": float(np.exp(r.normal(np.log(0.80), 0.30))),\n",
    "                     \"clicks_per_day\": float(np.exp(r.normal(-1.0, 1.2))),\n",
    "                     \"ctr\": r.uniform(0.02, 0.08),\n",
    "                     \"first_seen\": int(r.integers(0, 21))})\n",
    "    return pd.DataFrame(rows)\n",
    "\n",
    "\n",
    "def simulate(terms, r, horizon=HORIZON):\n",
    "    \"\"\"Daily clicks, conversions and impressions per term. Zero before first_seen.\"\"\"\n",
    "    n = len(terms)\n",
    "    clicks = np.zeros((n, horizon), dtype=int)\n",
    "    conv = np.zeros((n, horizon), dtype=int)\n",
    "    impr = np.zeros((n, horizon), dtype=int)\n",
    "    for i, row in terms.iterrows():\n",
    "        for d in range(row.first_seen, horizon):\n",
    "            c = r.poisson(row.clicks_per_day)\n",
    "            clicks[i, d] = c\n",
    "            conv[i, d] = r.binomial(c, row.true_cvr)\n",
    "            impr[i, d] = c + r.poisson(row.clicks_per_day * (1 / row.ctr - 1))\n",
    "    return clicks, conv, impr\n",
    "\n",
    "\n",
    "terms = build_terms(N_TERMS, rng)\n",
    "clicks_q, conv_q, impr_q = simulate(terms, rng)          # the quarter\n",
    "clicks, conv, impr = clicks_q[:, :DAYS], conv_q[:, :DAYS], impr_q[:, :DAYS]   # the month\n",
    "cpc_col = terms.cpc.to_numpy()[:, None]\n",
    "terms[\"clicks\"] = clicks.sum(1)\n",
    "terms[\"conversions\"] = conv.sum(1)\n",
    "terms[\"impressions\"] = impr.sum(1)\n",
    "terms[\"cost\"] = (clicks * cpc_col).sum(1)\n",
    "terms[\"clicks_q\"] = clicks_q.sum(1)\n",
    "terms[\"conversions_q\"] = conv_q.sum(1)\n",
    "terms[\"cost_q\"] = (clicks_q * cpc_col).sum(1)\n",
    "print(terms.klass.value_counts().rename(\"terms\"))\n",
    "print(f\"month spend on new search terms: EUR {terms.cost.sum():,.0f}\")\n",
    "print(f\"median clicks per term: {terms.clicks.median():.0f}; \"\n",
    "      f\"share with {NAIVE_CLICKS} or fewer clicks: {(terms.clicks <= NAIVE_CLICKS).mean():.0%}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf1c55de",
   "metadata": {},
   "source": [
    "## 2. The maths of a fair chance\n",
    "\n",
    "A term's conversion rate is a proportion: conversions out of clicks. With a handful\n",
    "of clicks that proportion is barely known, and the honest way to say *how* barely is\n",
    "a confidence interval. The textbook one (observed rate plus or minus two standard\n",
    "errors) breaks exactly where search terms live: at zero conversions it collapses to\n",
    "a zero-width claim that the rate is exactly zero. The Wilson score interval does not,\n",
    "and it is the one this notebook uses throughout.\n",
    "\n",
    "The interval also answers the operational question directly. If a term has zero\n",
    "conversions after $n$ clicks, the *upper* end of its interval is\n",
    "$z^2 / (n + z^2)$. The term can be called worse than the campaign's ordinary rate\n",
    "$p$ only once that upper end drops below $p$, which happens at\n",
    "\n",
    "$$n_{\\min} = \\left\\lfloor \\frac{z^2 (1 - p)}{p} \\right\\rfloor + 1$$\n",
    "\n",
    "clicks. That is the price of a fair chance, and it is steep: the lower your ordinary\n",
    "conversion rate, the more clicks it takes before \"no conversions yet\" means anything."
   ]
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     "text": [
      "z at 95%: 1.9600\n",
      "clicks before a zero-conversion term can be called below 4%: 93\n",
      "rule of three would say: 75\n",
      "at this campaign's average click price that is about EUR 78 per term\n",
      "0 of 20 clicks: 95% interval 0.0% to 16.1%\n",
      "0 of 93 clicks: upper end 3.97%\n"
     ]
    }
   ],
   "source": [
    "def wilson(x, n, z):\n",
    "    \"\"\"Wilson score interval for x successes in n trials. Returns (low, high).\"\"\"\n",
    "    if n <= 0:\n",
    "        return (0.0, 1.0)\n",
    "    x = min(max(x, 0), n)\n",
    "    p = x / n\n",
    "    z2 = z * z\n",
    "    centre = (p + z2 / (2 * n)) / (1 + z2 / n)\n",
    "    half = z * math.sqrt(p * (1 - p) / n + z2 / (4 * n * n)) / (1 + z2 / n)\n",
    "    return (max(0.0, centre - half), min(1.0, centre + half))\n",
    "\n",
    "\n",
    "def z_for(confidence):\n",
    "    return float(norm.ppf(1 - (1 - confidence) / 2))\n",
    "\n",
    "\n",
    "def reread_block_risk(p, benchmark, z, horizons):\n",
    "    \"\"\"Chance a term whose true rate is p is ever blocked when the rule is re-read after every\n",
    "    click on its growing total (block once the Wilson upper bound drops below the benchmark).\n",
    "    Exact: the alive probability mass is carried over conversion counts click by click.\"\"\"\n",
    "    top = max(horizons)\n",
    "    alive = np.zeros(top + 2)\n",
    "    alive[0] = 1.0\n",
    "    blocked, out = 0.0, {}\n",
    "    for n in range(1, top + 1):\n",
    "        nxt = np.zeros(top + 2)\n",
    "        nxt[1:] += alive[:-1] * p\n",
    "        nxt += alive * (1 - p)\n",
    "        alive = nxt\n",
    "        for x in range(n + 1):\n",
    "            if alive[x] > 0 and wilson(x, n, z)[1] < benchmark:\n",
    "                blocked += alive[x]\n",
    "                alive[x] = 0.0\n",
    "        if n in horizons:\n",
    "            out[n] = blocked\n",
    "    return out\n",
    "\n",
    "\n",
    "def n_min(p, z):\n",
    "    \"\"\"Smallest click count at which zero conversions sits confidently below rate p.\"\"\"\n",
    "    return int(math.floor(z * z * (1 - p) / p)) + 1\n",
    "\n",
    "\n",
    "def n_min_by_search(p, z, cap=100_000):\n",
    "    n = 1\n",
    "    while wilson(0, n, z)[1] >= p:\n",
    "        n += 1\n",
    "        if n > cap:\n",
    "            raise RuntimeError(\"no n found\")\n",
    "    return n\n",
    "\n",
    "\n",
    "def rule_of_three_n(p):\n",
    "    \"\"\"The back-of-envelope version: 0 of n has a 95% upper bound of about 3/n.\"\"\"\n",
    "    return int(math.ceil(3 / p))\n",
    "\n",
    "\n",
    "def verdict(x, n, benchmark, z, margin=1.0):\n",
    "    \"\"\"One of: proven, block, undecided. 'accruing' is the same as undecided, said\n",
    "    for a term that has not yet reached the click count where zero conversions could\n",
    "    ever be judged.\"\"\"\n",
    "    low, high = wilson(x, n, z)\n",
    "    if high < benchmark * margin:\n",
    "        return \"block\"\n",
    "    if low > benchmark:\n",
    "        return \"proven\"\n",
    "    return \"accruing\" if n < n_min(benchmark * margin, z) else \"undecided\"\n",
    "\n",
    "\n",
    "Z = z_for(CONFIDENCE)\n",
    "for p_ in (0.005, 0.01, 0.02, 0.04, 0.08):\n",
    "    assert n_min(p_, Z) == n_min_by_search(p_, Z), p_\n",
    "N_MIN = n_min(BENCHMARK, Z)\n",
    "FAIR_CHANCE_EUR = N_MIN * float(terms.cpc.mean())\n",
    "print(f\"z at {CONFIDENCE:.0%}: {Z:.4f}\")\n",
    "print(f\"clicks before a zero-conversion term can be called below {BENCHMARK:.0%}: {N_MIN}\")\n",
    "print(f\"rule of three would say: {rule_of_three_n(BENCHMARK)}\")\n",
    "print(f\"at this campaign's average click price that is about EUR {FAIR_CHANCE_EUR:,.0f} per term\")\n",
    "print(f\"0 of 20 clicks: 95% interval {wilson(0, 20, Z)[0]:.1%} to {wilson(0, 20, Z)[1]:.1%}\")\n",
    "print(f\"0 of {N_MIN} clicks: upper end {wilson(0, N_MIN, Z)[1]:.2%}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf824c8d",
   "metadata": {},
   "source": [
    "## 3. Gate one - does it have anything to do with what you sell?\n",
    "\n",
    "Before any performance question, a free question: does the term contain one of the\n",
    "phrases that describe what you sell? The match is order-free and ignores plurals,\n",
    "so \"womens trail running shoes size 8\" matches \"trail running shoes\" and \"shoe rack\"\n",
    "matches nothing (it has \"shoe\" but not \"running shoe\").\n",
    "\n",
    "This gate is deliberately generous in one direction. A term that matches gets a\n",
    "chance, however weak the match. A term that does not match is blocked at once, and\n",
    "there is no second gate behind that decision - which is why the list has to be\n",
    "generous too. The synthetic shop's list is missing three words - \"sneakers\",\n",
    "\"trainers\" and \"jogging\" - and the count below is what that omission costs."
   ]
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "relevant_by_gate1  False  True \n",
      "klass                          \n",
      "average               15    121\n",
      "good                  12    113\n",
      "irrelevant           256      0\n",
      "poor                   9     74\n",
      "irrelevant terms blocked at gate one: 256 of 256 (passed: 0)\n",
      "good/average terms wrongly blocked because the list lacks their word: 27\n"
     ]
    }
   ],
   "source": [
    "_WORD = re.compile(r\"[a-z0-9]+\")\n",
    "\n",
    "\n",
    "def _stem(tok):\n",
    "    return tok[:-1] if len(tok) > 3 and tok.endswith(\"s\") else tok\n",
    "\n",
    "\n",
    "def normalise(text):\n",
    "    return {_stem(t) for t in _WORD.findall(str(text).lower())}\n",
    "\n",
    "\n",
    "def gate_one(term, sell_list):\n",
    "    \"\"\"True if every word of at least one sell-phrase appears in the term.\"\"\"\n",
    "    toks = normalise(term)\n",
    "    return any(normalise(phrase) <= toks for phrase in sell_list)\n",
    "\n",
    "\n",
    "terms[\"relevant_by_gate1\"] = [gate_one(t, WHAT_WE_SELL) for t in terms.term]\n",
    "g1 = pd.crosstab(terms.klass, terms.relevant_by_gate1)\n",
    "print(g1)\n",
    "irrelevant_blocked = int(((terms.klass == \"irrelevant\") & ~terms.relevant_by_gate1).sum())\n",
    "irrelevant_passed = int(((terms.klass == \"irrelevant\") & terms.relevant_by_gate1).sum())\n",
    "good_killed_by_list = int((terms.klass.isin([\"good\", \"average\"]) & ~terms.relevant_by_gate1).sum())\n",
    "poor_killed_by_list = int(((terms.klass == \"poor\") & ~terms.relevant_by_gate1).sum())\n",
    "print(f\"irrelevant terms blocked at gate one: {irrelevant_blocked} of \"\n",
    "      f\"{(terms.klass == 'irrelevant').sum()} (passed: {irrelevant_passed})\")\n",
    "print(f\"good/average terms wrongly blocked because the list lacks their word: \"\n",
    "      f\"{good_killed_by_list}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38660670",
   "metadata": {},
   "source": [
    "## 4. Three ways to run the month\n",
    "\n",
    "Every morning, each policy looks at what every term has accumulated so far and\n",
    "decides whether to block it from that day on. Spend after a block is saved; a good\n",
    "term blocked is a term whose later conversions are lost.\n",
    "\n",
    "- **Let it run** - block nothing. The ceiling on waste.\n",
    "- **Kill on sight** - block any term with 30 or more clicks and no conversion. The\n",
    "  rule most account managers actually run.\n",
    "- **Two gates** - block on day one what fails gate one; give everything else a fair\n",
    "  chance, and block only once the interval sits confidently below the benchmark.\n",
    "\n",
    "\"Waste\" below is spend on terms that are truly irrelevant or poor. \"False kills\" are\n",
    "good or average terms a policy blocked; \"lost conversions\" is what they would have\n",
    "gone on to convert. The same terms are followed for a quarter as well as a month,\n",
    "because a fair chance takes time and the month alone would hide what gate two does."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "beb81994",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2026-10-24T16:55:42.246912Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- the month (28 days)\n",
      "                      spend   waste  conversions  false_kills  \\\n",
      "let_run              6631.0  3770.0        217.0          0.0   \n",
      "kill_on_sight        6074.0  3273.0        208.0          5.0   \n",
      "two_gates            3351.0   834.0        182.0         27.0   \n",
      "two_gates_full_list  3734.0   872.0        211.0          0.0   \n",
      "\n",
      "                     lost_conversions  bad_blocked  bad_still_running  \n",
      "let_run                           0.0          0.0              339.0  \n",
      "kill_on_sight                     5.0         34.0              305.0  \n",
      "two_gates                        27.0        266.0               73.0  \n",
      "two_gates_full_list               0.0        257.0               82.0  \n",
      "--- the quarter (90 days)\n",
      "                       spend    waste  conversions  false_kills  \\\n",
      "let_run              29683.0  16652.0       1051.0          0.0   \n",
      "kill_on_sight        19773.0   8016.0        914.0         27.0   \n",
      "two_gates            14640.0   2974.0        900.0         27.0   \n",
      "two_gates_full_list  16153.0   3122.0       1009.0          0.0   \n",
      "\n",
      "                     lost_conversions  bad_blocked  bad_still_running  \n",
      "let_run                           0.0          0.0              339.0  \n",
      "kill_on_sight                    98.0        159.0              180.0  \n",
      "two_gates                       107.0        273.0               66.0  \n",
      "two_gates_full_list               0.0        264.0               75.0  \n"
     ]
    }
   ],
   "source": [
    "def run_policy(decide, terms, clicks, conv):\n",
    "    \"\"\"decide(i, cum_clicks, cum_conv, day, term_row) -> True to block from `day` on.\n",
    "    Returns per-term block day (or DAYS if never blocked) and the money/conversion\n",
    "    outcomes.\"\"\"\n",
    "    n = len(terms)\n",
    "    days = clicks.shape[1]\n",
    "    block_day = np.full(n, days)\n",
    "    cum_c = np.zeros(n, dtype=int)\n",
    "    cum_x = np.zeros(n, dtype=int)\n",
    "    for day in range(days):\n",
    "        for i in range(n):\n",
    "            if block_day[i] < days:\n",
    "                continue\n",
    "            if day > 0 and cum_c[i] + cum_x[i] + (day >= terms.first_seen.iat[i]) > 0:\n",
    "                if decide(i, cum_c[i], cum_x[i], day, terms.iloc[i]):\n",
    "                    block_day[i] = day\n",
    "                    continue\n",
    "            cum_c[i] += clicks[i, day]\n",
    "            cum_x[i] += conv[i, day]\n",
    "    live = (np.arange(days)[None, :] < block_day[:, None])\n",
    "    spent = (clicks * live * terms.cpc.to_numpy()[:, None]).sum(1)\n",
    "    got = (conv * live).sum(1)\n",
    "    lost = (conv * ~live).sum(1)\n",
    "    bad = terms.klass.isin([\"irrelevant\", \"poor\"]).to_numpy()\n",
    "    goodish = ~bad\n",
    "    blocked = block_day < days\n",
    "    return {\n",
    "        \"block_day\": block_day,\n",
    "        \"spend\": float(spent.sum()),\n",
    "        \"waste\": float(spent[bad].sum()),\n",
    "        \"conversions\": int(got.sum()),\n",
    "        \"false_kills\": int((blocked & goodish).sum()),\n",
    "        \"lost_conversions\": int(lost[goodish].sum()),\n",
    "        # ALL the sales a policy's blocks cost, irrelevant and poor terms included. The line above\n",
    "        # counts only good and average terms, because those are the blocks that were MISTAKES --\n",
    "        # and that is the right number for \"how often is this rule wrong\". It is the wrong number\n",
    "        # for \"what does this rule cost\", because a correctly-blocked irrelevant term still had a\n",
    "        # few sales in it: the class converts at up to 0.4%, not at nothing. Gate one blocks 256 of\n",
    "        # them on day one, so the two-gate rule's true bill is its false kills PLUS these. Emitted\n",
    "        # beside rather than instead: a rule that is never wrong can still cost something, and the\n",
    "        # honest version of \"the relevance list is unappealable\" needs both halves.\n",
    "        \"lost_conversions_all_classes\": int(lost.sum()),\n",
    "        \"bad_blocked\": int((blocked & bad).sum()),\n",
    "        \"bad_still_running\": int((~blocked & bad).sum()),\n",
    "        \"blocked_total\": int(blocked.sum()),\n",
    "    }\n",
    "\n",
    "\n",
    "def let_run(i, c, x, day, row):\n",
    "    return False\n",
    "\n",
    "\n",
    "def kill_on_sight(k):\n",
    "    def _decide(i, c, x, day, row):\n",
    "        return c >= k and x == 0\n",
    "    return _decide\n",
    "\n",
    "\n",
    "def two_gates(z, benchmark, sell_list, margin=1.0):\n",
    "    relevant = {t: gate_one(t, sell_list) for t in terms.term}\n",
    "\n",
    "    def _decide(i, c, x, day, row):\n",
    "        if not relevant[row.term]:\n",
    "            return True                     # gate one: no path to what we sell\n",
    "        if c == 0:\n",
    "            return False\n",
    "        return verdict(x, c, benchmark, z, margin) == \"block\"\n",
    "    return _decide\n",
    "\n",
    "\n",
    "# A fourth arm, and the one that settles whether the method or the list is at fault. Same two\n",
    "# questions, same interval, same everything -- but gate one is given the words the shop forgot\n",
    "# (\"sneakers\", \"trainers\", \"jogging\"). If the good terms it was killing come back, the interval was\n",
    "# never the problem; an incomplete list was, and a list is free to fix.\n",
    "COMPLETE_LIST = WHAT_WE_SELL + PRODUCTS_UNLISTED\n",
    "\n",
    "POLICIES = {\n",
    "    \"let_run\": let_run,\n",
    "    \"kill_on_sight\": kill_on_sight(NAIVE_CLICKS),\n",
    "    \"two_gates\": two_gates(Z, BENCHMARK, WHAT_WE_SELL),\n",
    "    \"two_gates_full_list\": two_gates(Z, BENCHMARK, COMPLETE_LIST),\n",
    "}\n",
    "results = {name: run_policy(fn, terms, clicks, conv) for name, fn in POLICIES.items()}\n",
    "results_q = {name: run_policy(fn, terms, clicks_q, conv_q) for name, fn in POLICIES.items()}\n",
    "\n",
    "\n",
    "def _summary(res):\n",
    "    return pd.DataFrame({k: {kk: vv for kk, vv in v.items() if kk != \"block_day\"}\n",
    "                         for k, v in res.items()}).T[\n",
    "        [\"spend\", \"waste\", \"conversions\", \"false_kills\", \"lost_conversions\",\n",
    "         \"bad_blocked\", \"bad_still_running\"]].round(0)\n",
    "\n",
    "\n",
    "print(f\"--- the month ({DAYS} days)\")\n",
    "print(_summary(results))\n",
    "print(f\"--- the quarter ({HORIZON} days)\")\n",
    "print(_summary(results_q))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21fba567",
   "metadata": {},
   "source": [
    "### What the two-gate policy decided, term by term, at the end of the month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0e06c208",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2026-10-24T16:56:02.946333Z"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- end of the month\n",
      "verdict\n",
      "blocked: no match    292\n",
      "accruing             283\n",
      "proven                23\n",
      "undecided              1\n",
      "block                  1\n",
      "Name: count, dtype: int64\n",
      "verdict     accruing  block  blocked: no match  proven  undecided\n",
      "klass                                                            \n",
      "average          118      0                 15       3          0\n",
      "good              92      0                 12      20          1\n",
      "irrelevant         0      0                256       0          0\n",
      "poor              73      1                  9       0          0\n",
      "--- end of the quarter\n",
      "verdict_q\n",
      "blocked: no match    292\n",
      "accruing             213\n",
      "proven                50\n",
      "undecided             37\n",
      "block                  8\n",
      "Name: count, dtype: int64\n",
      "verdict_q   accruing  block  blocked: no match  proven  undecided\n",
      "klass                                                            \n",
      "average           93      0                 15       8         20\n",
      "good              63      0                 12      42          8\n",
      "irrelevant         0      0                256       0          0\n",
      "poor              57      8                  9       0          9\n",
      "terms that reached 93 clicks: 6 of 600 in the month, 116 in the quarter\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   confidence       z  n_min   waste  false_kills  lost_conversions  \\\n",
      "0        0.80  1.2816     40  772.81           29                28   \n",
      "1        0.90  1.6449     65  809.94           27                27   \n",
      "2        0.95  1.9600     93  834.17           27                27   \n",
      "\n",
      "   bad_blocked  \n",
      "0          268  \n",
      "1          266  \n",
      "2          266  \n",
      "   clicks    waste  false_kills  lost_conversions  bad_blocked\n",
      "0       3  1049.21          165               114          240\n",
      "1       5  1443.75          117                94          192\n",
      "2      10  2075.72           49                53          127\n",
      "3      20  2871.40           18                17           66\n",
      "4      30  3272.65            5                 5           34\n",
      "5      50  3598.15            0                 0            6\n"
     ]
    }
   ],
   "source": [
    "Z_TABLE = {c: z_for(c) for c in (0.80, 0.90, 0.95)}\n",
    "\n",
    "\n",
    "def verdicts_at(terms, z, benchmark, sell_list, clicks_col=\"clicks\", conv_col=\"conversions\"):\n",
    "    out = []\n",
    "    for _, row in terms.iterrows():\n",
    "        if not gate_one(row.term, sell_list):\n",
    "            out.append(\"blocked: no match\")\n",
    "        else:\n",
    "            out.append(verdict(row[conv_col], row[clicks_col], benchmark, z))\n",
    "    return pd.Series(out, index=terms.index)\n",
    "\n",
    "\n",
    "terms[\"verdict\"] = verdicts_at(terms, Z, BENCHMARK, WHAT_WE_SELL)\n",
    "terms[\"verdict_q\"] = verdicts_at(terms, Z, BENCHMARK, WHAT_WE_SELL, \"clicks_q\", \"conversions_q\")\n",
    "verdict_counts = terms.verdict.value_counts()\n",
    "verdict_counts_q = terms.verdict_q.value_counts()\n",
    "print(\"--- end of the month\")\n",
    "print(verdict_counts)\n",
    "print(pd.crosstab(terms.klass, terms.verdict))\n",
    "print(\"--- end of the quarter\")\n",
    "print(verdict_counts_q)\n",
    "print(pd.crosstab(terms.klass, terms.verdict_q))\n",
    "reached = int((terms.clicks >= N_MIN).sum())\n",
    "reached_q = int((terms.clicks_q >= N_MIN).sum())\n",
    "print(f\"terms that reached {N_MIN} clicks: {reached} of {N_TERMS} in the month, \"\n",
    "      f\"{reached_q} in the quarter\")\n",
    "\n",
    "# How the confidence level trades false kills against decisiveness, and how the\n",
    "# naive rule's click threshold does the same. Both are computed, not asserted.\n",
    "conf_sweep = []\n",
    "for c, z_ in Z_TABLE.items():\n",
    "    r_ = run_policy(two_gates(z_, BENCHMARK, WHAT_WE_SELL), terms, clicks, conv)\n",
    "    conf_sweep.append({\"confidence\": c, \"z\": round(z_, 4), \"n_min\": n_min(BENCHMARK, z_),\n",
    "                       \"waste\": round(r_[\"waste\"], 2), \"false_kills\": r_[\"false_kills\"],\n",
    "                       \"lost_conversions\": r_[\"lost_conversions\"],\n",
    "                       \"bad_blocked\": r_[\"bad_blocked\"]})\n",
    "naive_sweep = []\n",
    "for k in (3, 5, 10, 20, 30, 50):\n",
    "    r_ = run_policy(kill_on_sight(k), terms, clicks, conv)\n",
    "    naive_sweep.append({\"clicks\": k, \"waste\": round(r_[\"waste\"], 2),\n",
    "                        \"false_kills\": r_[\"false_kills\"],\n",
    "                        \"lost_conversions\": r_[\"lost_conversions\"],\n",
    "                        \"bad_blocked\": r_[\"bad_blocked\"]})\n",
    "print(pd.DataFrame(conf_sweep))\n",
    "print(pd.DataFrame(naive_sweep))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5567244e",
   "metadata": {},
   "source": [
    "## 5. The pictures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f8f89e62",
   "metadata": {
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     "shell.execute_reply": "2026-10-24T16:56:13.228043Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x420 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x420 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x440 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x460 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams.update({\"figure.facecolor\": PAPER, \"axes.facecolor\": PAPER,\n",
    "                     \"axes.edgecolor\": LINE, \"axes.labelcolor\": INK,\n",
    "                     \"xtick.color\": MUTED, \"ytick.color\": MUTED, \"text.color\": INK,\n",
    "                     \"font.size\": 11, \"axes.spines.top\": False,\n",
    "                     \"axes.spines.right\": False})\n",
    "\n",
    "\n",
    "def _save(fig, name):\n",
    "    fig.savefig(f\"{FIGDIR}/{name}\", dpi=150, bbox_inches=\"tight\", facecolor=PAPER)\n",
    "    plt.show()\n",
    "    plt.close(fig)\n",
    "\n",
    "\n",
    "def fig_longtail(terms):\n",
    "    fig, ax = plt.subplots(figsize=(8, 4.2))\n",
    "    c = np.sort(terms.clicks.to_numpy())[::-1]\n",
    "    ax.bar(np.arange(len(c)), np.maximum(c, 0.5), width=1.0, color=BLUE, alpha=0.85)\n",
    "    ax.set_yscale(\"log\")\n",
    "    ax.axhline(N_MIN, color=WARM, lw=1.5, ls=\"--\")\n",
    "    ax.text(len(c) * 0.55, N_MIN * 1.25, f\"{N_MIN} clicks: where a zero-conversion \"\n",
    "            f\"term can first be judged\", color=WARM)\n",
    "    ax.axhline(NAIVE_CLICKS, color=MUTED, lw=1.2, ls=\":\")\n",
    "    ax.text(len(c) * 0.55, NAIVE_CLICKS * 1.25, f\"{NAIVE_CLICKS} clicks: where the \"\n",
    "            f\"kill-on-sight rule acts\", color=MUTED)\n",
    "    ax.set_xlabel(\"search terms, busiest first\")\n",
    "    ax.set_ylabel(\"clicks in the month (log scale)\")\n",
    "    ax.set_title(\"Most search terms never get enough clicks to be judged\")\n",
    "    _save(fig, \"fig_longtail.png\")\n",
    "\n",
    "\n",
    "def fig_fair_chance():\n",
    "    fig, ax = plt.subplots(figsize=(8, 4.2))\n",
    "    n = np.arange(1, 401)\n",
    "    upper = np.array([wilson(0, k, Z)[1] for k in n])\n",
    "    ax.plot(n, upper * 100, color=GREEN, lw=2.2, label=\"upper end of the 95% interval, zero conversions\")\n",
    "    ax.plot(n, np.minimum(3 / n, 1) * 100, color=MUTED, lw=1.2, ls=\":\",\n",
    "            label=\"rule of three (3 / clicks)\")\n",
    "    for p_, col in ((0.02, WARM), (0.04, BLUE), (0.08, INK)):\n",
    "        k = n_min(p_, Z)\n",
    "        ax.axhline(p_ * 100, color=col, lw=1, ls=\"--\", alpha=0.8)\n",
    "        ax.plot([k], [p_ * 100], \"o\", color=col)\n",
    "        ax.annotate(f\"{p_:.0%} benchmark: {k} clicks\", (k, p_ * 100),\n",
    "                    xytext=(k + 12, p_ * 100 + 1.2), color=col, fontsize=10)\n",
    "    ax.set_ylim(0, 22)\n",
    "    ax.set_xlim(0, 400)\n",
    "    ax.set_xlabel(\"clicks with zero conversions\")\n",
    "    ax.set_ylabel(\"highest conversion rate still plausible (%)\")\n",
    "    ax.set_title(\"How many clicks before 'no conversions yet' means anything\")\n",
    "    ax.legend(frameon=False, loc=\"upper right\")\n",
    "    _save(fig, \"fig_fair_chance.png\")\n",
    "\n",
    "\n",
    "def fig_policies(results, results_q):\n",
    "    fig, axes = plt.subplots(1, 2, figsize=(11, 4.4))\n",
    "    for ax, res, title in ((axes[0], results, f\"one month ({DAYS} days)\"),\n",
    "                           (axes[1], results_q, f\"one quarter ({HORIZON} days)\")):\n",
    "        a1, a2 = _policy_panel(ax, res, title)\n",
    "    h1, l1 = a1.get_legend_handles_labels()\n",
    "    h2, l2 = a2.get_legend_handles_labels()\n",
    "    fig.legend(h1 + h2, l1 + l2, frameon=False, loc=\"lower center\", ncol=2,\n",
    "               bbox_to_anchor=(0.5, -0.06))\n",
    "    fig.suptitle(\"Waste caught against good terms killed\", y=1.02)\n",
    "    fig.tight_layout(w_pad=4)\n",
    "    _save(fig, \"fig_policies.png\")\n",
    "\n",
    "\n",
    "def _policy_panel(ax, results, title):\n",
    "    names = {\"let_run\": \"let it run\", \"kill_on_sight\": f\"kill on sight\\n({NAIVE_CLICKS} clicks)\",\n",
    "             \"two_gates\": \"two gates\"}\n",
    "    x = np.arange(3)\n",
    "    waste = [results[k][\"waste\"] for k in names]\n",
    "    lost = [results[k][\"lost_conversions\"] for k in names]\n",
    "    ax.bar(x - 0.18, waste, width=0.36, color=WARM, label=\"wasted spend (EUR)\")\n",
    "    ax2 = ax.twinx()\n",
    "    ax2.bar(x + 0.18, lost, width=0.36, color=BLUE, label=\"conversions lost to false kills\")\n",
    "    ax2.spines[\"right\"].set_visible(True)\n",
    "    ax.set_xticks(x)\n",
    "    ax.set_xticklabels(list(names.values()))\n",
    "    ax.set_ylabel(\"spend on irrelevant or poor terms (EUR)\")\n",
    "    ax2.set_ylabel(\"conversions lost from good terms blocked\")\n",
    "    for i, (w, lo) in enumerate(zip(waste, lost)):\n",
    "        ax.text(i - 0.18, w + 20, f\"EUR {w:,.0f}\", ha=\"center\", color=WARM, fontsize=10)\n",
    "        ax2.text(i + 0.18, lo + 0.4, f\"{lo}\", ha=\"center\", color=BLUE, fontsize=10)\n",
    "    ax.set_ylim(0, max(waste) * 1.25)\n",
    "    ax2.set_ylim(0, max(max(lost), 1) * 1.5)\n",
    "    ax.set_title(title)\n",
    "    return ax, ax2\n",
    "\n",
    "\n",
    "def fig_verdict_map(terms):\n",
    "    \"\"\"Every term at the end of the QUARTER: enough clicks have accrued for gate two\n",
    "    to have said something.\"\"\"\n",
    "    fig, ax = plt.subplots(figsize=(8, 4.6))\n",
    "    cols = {\"blocked: no match\": MUTED, \"block\": WARM, \"proven\": GREEN,\n",
    "            \"undecided\": BLUE, \"accruing\": \"#9db7d8\"}\n",
    "    labels = {\"blocked: no match\": \"blocked at gate one (no match)\",\n",
    "              \"block\": \"blocked at gate two (confidently below)\",\n",
    "              \"proven\": \"proven (confidently above)\",\n",
    "              \"undecided\": \"undecided (interval straddles the benchmark)\",\n",
    "              \"accruing\": \"still accruing (too few clicks to judge)\"}\n",
    "    sub = terms[terms.clicks_q > 0]\n",
    "    for v, col in cols.items():\n",
    "        s = sub[sub.verdict_q == v]\n",
    "        if len(s) == 0:\n",
    "            continue\n",
    "        rate = s.conversions_q / s.clicks_q\n",
    "        ax.scatter(s.clicks_q, rate * 100, s=18, color=col, alpha=0.8, label=labels[v])\n",
    "    ax.axhline(BENCHMARK * 100, color=INK, lw=1, ls=\"--\")\n",
    "    ax.text(1.1, BENCHMARK * 100 + 0.6, f\"campaign benchmark {BENCHMARK:.0%}\", color=INK, fontsize=10)\n",
    "    ax.axvline(N_MIN, color=WARM, lw=1, ls=\":\")\n",
    "    ax.set_xscale(\"log\")\n",
    "    ax.set_xlim(1, max(50, sub.clicks_q.max() * 1.3))\n",
    "    ax.set_ylim(-0.5, 25)\n",
    "    ax.set_xlabel(\"clicks in the quarter (log scale)\")\n",
    "    ax.set_ylabel(\"observed conversion rate (%)\")\n",
    "    ax.set_title(\"Every term at the end of the quarter, and what the two gates say\")\n",
    "    ax.legend(frameon=False, fontsize=9, loc=\"upper center\", bbox_to_anchor=(0.5, -0.16), ncol=2)\n",
    "    _save(fig, \"fig_verdict_map.png\")\n",
    "\n",
    "\n",
    "fig_longtail(terms)\n",
    "fig_fair_chance()\n",
    "fig_policies(results, results_q)\n",
    "fig_verdict_map(terms)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73316a61",
   "metadata": {},
   "source": [
    "## 6. Writing the numbers out\n",
    "\n",
    "Two files. `data/checks.json` pins what the interactive widget must reproduce: the\n",
    "Wilson interval and the verdict for a handful of click/conversion pairs, and the\n",
    "fair-chance click counts for a few benchmarks. `data/worked_example.json` carries\n",
    "every number the article quotes. The prose reads from these; nothing is retyped."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a0432374",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T16:56:13.241532Z",
     "iopub.status.busy": "2026-10-24T16:56:13.240761Z",
     "iopub.status.idle": "2026-10-24T16:56:14.568572Z",
     "shell.execute_reply": "2026-10-24T16:56:14.567863Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the dial from 95% to 80%: {'from': 0.95, 'to': 0.8, 'waste_saved_eur': 61.36, 'extra_good_terms_blocked': 2, 'extra_conversions_lost': 1, 'clicks_needed_drop': 53}\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      " \"let_run\": {\n",
      "  \"spend\": 6631.09,\n",
      "  \"waste\": 3769.62,\n",
      "  \"conversions\": 217,\n",
      "  \"false_kills\": 0,\n",
      "  \"lost_conversions\": 0,\n",
      "  \"lost_conversions_all_classes\": 0,\n",
      "  \"bad_blocked\": 0,\n",
      "  \"bad_still_running\": 339,\n",
      "  \"blocked_total\": 0\n",
      " },\n",
      " \"kill_on_sight\": {\n",
      "  \"spend\": 6073.83,\n",
      "  \"waste\": 3272.65,\n",
      "  \"conversions\": 208,\n",
      "  \"false_kills\": 5,\n",
      "  \"lost_conversions\": 5,\n",
      "  \"lost_conversions_all_classes\": 9,\n",
      "  \"bad_blocked\": 34,\n",
      "  \"bad_still_running\": 305,\n",
      "  \"blocked_total\": 39\n",
      " },\n",
      " \"two_gates\": {\n",
      "  \"spend\": 3350.69,\n",
      "  \"waste\": 834.17,\n",
      "  \"conversions\": 182,\n",
      "  \"false_kills\": 27,\n",
      "  \"lost_conversions\": 27,\n",
      "  \"lost_conversions_all_classes\": 35,\n",
      "  \"bad_blocked\": 266,\n",
      "  \"bad_still_running\": 73,\n",
      "  \"blocked_total\": 293\n",
      " },\n",
      " \"two_gates_full_list\": {\n",
      "  \"spend\": 3733.68,\n",
      "  \"waste\": 872.21,\n",
      "  \"conversions\": 211,\n",
      "  \"false_kills\": 0,\n",
      "  \"lost_conversions\": 0,\n",
      "  \"lost_conversions_all_classes\": 6,\n",
      "  \"bad_blocked\": 257,\n",
      "  \"bad_still_running\": 82,\n",
      "  \"blocked_total\": 257\n",
      " }\n",
      "}\n",
      "{\n",
      " \"z\": 1.959964,\n",
      " \"n_min\": 93,\n",
      " \"cost_eur\": 78.06,\n",
      " \"rule_of_three_n\": 75,\n",
      " \"interval_0_of_naive\": [\n",
      "  0.0,\n",
      "  0.113513\n",
      " ],\n",
      " \"interval_0_of_n_min\": [\n",
      "  0.0,\n",
      "  0.039668\n",
      " ],\n",
      " \"n_min_table\": [\n",
      "  {\n",
      "   \"benchmark\": 0.01,\n",
      "   \"confidence\": 0.8,\n",
      "   \"n_min\": 163\n",
      "  },\n",
      "  {\n",
      "   \"benchmark\": 0.01,\n",
      "   \"confidence\": 0.9,\n",
      "   \"n_min\": 268\n",
      "  },\n",
      "  {\n",
      "   \"benchmark\": 0.01,\n",
      "   \"confidence\": 0.95,\n",
      "   \"n_min\": 381\n",
      "  },\n",
      "  {\n",
      "   \"benchmark\": 0.02,\n",
      "   \"confidence\": 0.8,\n",
      "   \"n_min\": 81\n",
      "  },\n",
      "  {\n",
      "   \"benchmark\": 0.02,\n",
      "   \"confidence\": 0.9,\n",
      "   \"n_min\": 133\n",
      "  },\n",
      "  {\n",
      "   \"benchmark\": 0.02,\n",
      "   \"confidence\": 0.95,\n",
      "   \"n_min\": 189\n",
      "\n"
     ]
    }
   ],
   "source": [
    "TOLERANCE = 1e-4\n",
    "\n",
    "PINNED = [\n",
    "    {\"label\": \"zero conversions, at the block-on-sight line\", \"clicks\": NAIVE_CLICKS,\n",
    "     \"conversions\": 0,\n",
    "     \"benchmark\": BENCHMARK, \"confidence\": 0.95},\n",
    "    {\"label\": \"zero conversions, well past the fair chance\", \"clicks\": 120,\n",
    "     \"conversions\": 0, \"benchmark\": BENCHMARK, \"confidence\": 0.95},\n",
    "    {\"label\": \"three in forty: looks good, not yet proven\", \"clicks\": 40,\n",
    "     \"conversions\": 3, \"benchmark\": BENCHMARK, \"confidence\": 0.95},\n",
    "    {\"label\": \"nine in sixty: proven\", \"clicks\": 60, \"conversions\": 9,\n",
    "     \"benchmark\": BENCHMARK, \"confidence\": 0.95},\n",
    "    {\"label\": \"zero in forty at a lower bar\", \"clicks\": 40, \"conversions\": 0,\n",
    "     \"benchmark\": BENCHMARK, \"confidence\": 0.80},\n",
    "]\n",
    "\n",
    "\n",
    "def expected_block(spec):\n",
    "    z = z_for(spec[\"confidence\"])\n",
    "    low, high = wilson(spec[\"conversions\"], spec[\"clicks\"], z)\n",
    "    return {\"z\": round(z, 6), \"low\": round(low, 6), \"high\": round(high, 6),\n",
    "            \"verdict\": verdict(spec[\"conversions\"], spec[\"clicks\"], spec[\"benchmark\"], z),\n",
    "            \"n_min\": n_min(spec[\"benchmark\"], z)}\n",
    "\n",
    "\n",
    "def emit_checks(path=\"data/checks.json\"):\n",
    "    sets = [{**s, \"expected\": expected_block(s)} for s in PINNED]\n",
    "    table = [{\"benchmark\": p_, \"confidence\": c, \"n_min\": n_min(p_, z_)}\n",
    "             for p_ in (0.005, 0.01, 0.02, 0.04, 0.08)\n",
    "             for c, z_ in Z_TABLE.items()]\n",
    "    payload = {\n",
    "        \"note\": (\"Pinned outputs of the same Wilson interval and verdict rule the article \"\n",
    "                 \"uses. The widget recomputes each set client-side from the pinned inputs \"\n",
    "                 \"and must reproduce every expected value within the tolerance, or its \"\n",
    "                 \"self-check fails.\"),\n",
    "        \"tolerance\": TOLERANCE,\n",
    "        \"sets\": sets,\n",
    "        \"n_min_table\": table,\n",
    "    }\n",
    "    with open(path, \"w\", encoding=\"utf-8\") as f:\n",
    "        json.dump(payload, f, indent=2)\n",
    "    return payload\n",
    "\n",
    "\n",
    "def emit_worked_example(path=\"data/worked_example.json\"):\n",
    "    r = results\n",
    "    counts = terms.klass.value_counts()\n",
    "    bad = terms.klass.isin([\"irrelevant\", \"poor\"])\n",
    "    payload = {\n",
    "        \"note\": (\"Synthetic data, labelled as such. Generated by this notebook under a \"\n",
    "                 \"single fixed seed, so every figure and every number in the article \"\n",
    "                 \"regenerates identically.\"),\n",
    "        \"data_label\": \"synthetic\",\n",
    "        \"seed\": SEED,\n",
    "        \"campaign\": {\n",
    "            \"days\": DAYS, \"n_terms\": N_TERMS, \"benchmark_cvr\": BENCHMARK,\n",
    "            \"confidence\": CONFIDENCE, \"naive_clicks\": NAIVE_CLICKS,\n",
    "            \"mean_cpc\": round(float(terms.cpc.mean()), 4),\n",
    "            \"spend_eur\": round(float(terms.cost.sum()), 2),\n",
    "            \"spend_on_bad_eur\": round(float(terms.cost[bad].sum()), 2),\n",
    "            \"share_of_spend_on_bad\": round(float(terms.cost[bad].sum() / terms.cost.sum()), 4),\n",
    "            \"conversions\": int(terms.conversions.sum()),\n",
    "            \"clicks\": int(terms.clicks.sum()),\n",
    "            \"class_counts\": {k: int(v) for k, v in counts.items()},\n",
    "            \"what_we_sell\": WHAT_WE_SELL,\n",
    "            # The benchmark's provenance and its own uncertainty. Without this the video shows a 4%\n",
    "            # bar beside 217 sales on this spend and invites the viewer to divide -- which gives\n",
    "            # 2.7%, not 4%, and reads as a contradiction. Both numbers are true and they measure\n",
    "            # different populations; saying which is which is the fix.\n",
    "            \"benchmark_evidence\": {\n",
    "                \"conversions\": KNOWN_KEYWORD_CONVERSIONS,\n",
    "                \"clicks\": KNOWN_KEYWORD_CLICKS,\n",
    "                \"rate\": round(KNOWN_KEYWORD_CONVERSIONS / KNOWN_KEYWORD_CLICKS, 4),\n",
    "                \"interval\": [round(v, 6) for v in\n",
    "                             wilson(KNOWN_KEYWORD_CONVERSIONS, KNOWN_KEYWORD_CLICKS, Z)],\n",
    "            },\n",
    "            # What the 600 NEW terms convert at between them -- the number a viewer would compute\n",
    "            # off the campaign slide, and the reason the benchmark has to be labelled.\n",
    "            \"blended_new_term_cvr\": round(\n",
    "                float(terms.conversions.sum()) / float(terms.clicks.sum()), 4),\n",
    "            # Where the month's sales actually came from. The class bands are an assumption on the\n",
    "            # slide until this row makes them checkable: a viewer who doubts that irrelevant terms\n",
    "            # convert at all can read how few sales they produced.\n",
    "            \"conversions_by_class\": {k: int(v) for k, v in\n",
    "                                     terms.groupby(\"klass\").conversions.sum().items()},\n",
    "            \"clicks_by_class\": {k: int(v) for k, v in\n",
    "                                terms.groupby(\"klass\").clicks.sum().items()},\n",
    "            \"spend_by_class_eur\": {k: round(float(v), 2) for k, v in\n",
    "                                   terms.groupby(\"klass\").cost.sum().items()},\n",
    "        },\n",
    "        \"long_tail\": {\n",
    "            \"median_clicks\": float(terms.clicks.median()),\n",
    "            \"share_at_or_below_naive\": round(float((terms.clicks <= NAIVE_CLICKS).mean()), 4),\n",
    "            \"share_reaching_n_min\": round(float((terms.clicks >= N_MIN).mean()), 4),\n",
    "            \"terms_reaching_n_min\": int((terms.clicks >= N_MIN).sum()),\n",
    "            \"max_clicks\": int(terms.clicks.max()),\n",
    "            # How long the MEDIAN term would take to earn a verdict at its own click pace. Clicks\n",
    "            # accumulate across reviews -- a monthly review reads a running total, it does not\n",
    "            # start again -- so this is the honest answer to \"how long do I wait\", and it is the\n",
    "            # mechanism behind \"not enough data yet is most of your report\".\n",
    "            \"months_to_n_min_median_term\": round(\n",
    "                (N_MIN / (float(terms.clicks.median()) / DAYS)) / 30.44, 1),\n",
    "        },\n",
    "        \"fair_chance\": {\n",
    "            \"z\": round(Z, 6),\n",
    "            \"n_min\": N_MIN,\n",
    "            \"cost_eur\": round(FAIR_CHANCE_EUR, 2),\n",
    "            \"rule_of_three_n\": rule_of_three_n(BENCHMARK),\n",
    "            \"interval_0_of_naive\": [round(v, 6) for v in wilson(0, NAIVE_CLICKS, Z)],\n",
    "            \"interval_0_of_n_min\": [round(v, 6) for v in wilson(0, N_MIN, Z)],\n",
    "            \"n_min_table\": [{\"benchmark\": p_, \"confidence\": c, \"n_min\": n_min(p_, z_)}\n",
    "                            for p_ in (0.01, 0.02, 0.04, 0.08)\n",
    "                            for c, z_ in Z_TABLE.items()],\n",
    "            # THE RANGE ON THE CLICK COUNT ITSELF. 93 is exact arithmetic given a benchmark of 4%,\n",
    "            # so it carries no sampling error of its own -- which is why \"what is the range of the\n",
    "            # 93\" has no answer until you ask where the 4% came from. It INHERITS the benchmark's\n",
    "            # interval: run the same formula at each end and the count runs between the two. A shop\n",
    "            # that knows its rate from 400 sales in 10,000 clicks gets 84 to 102; one guessing from\n",
    "            # 40 in 1,000 gets 68 to 127. As sure as you are of your benchmark, and no surer.\n",
    "            \"n_min_range\": [\n",
    "                n_min(wilson(KNOWN_KEYWORD_CONVERSIONS, KNOWN_KEYWORD_CLICKS, Z)[1], Z),\n",
    "                n_min(wilson(KNOWN_KEYWORD_CONVERSIONS, KNOWN_KEYWORD_CLICKS, Z)[0], Z),\n",
    "            ],\n",
    "        },\n",
    "        # How often the INTERVAL blocks a term it should not have. This is a per-term chance, not a\n",
    "        # share of the blocked terms and not a count: a term whose true rate is exactly the\n",
    "        # benchmark shows nothing across n_min clicks (1 - p)^n_min of the time, and is blocked on\n",
    "        # that first look. It falls away fast above the benchmark. The article and the video both\n",
    "        # shipped this as \"about one blocked term in forty was merely unlucky\", which converts a\n",
    "        # per-term probability into a proportion of a different population -- and the arithmetic\n",
    "        # gives one in forty-five, not forty. Computed here so neither surface has to assert it.\n",
    "        \"false_block_risk\": {\n",
    "            \"clicks\": N_MIN,\n",
    "            \"at_benchmark\": round(float((1 - BENCHMARK) ** N_MIN), 4),\n",
    "            \"one_in_n_at_benchmark\": int(round(1 / (1 - BENCHMARK) ** N_MIN)),\n",
    "            # The one-in-N form as well as the probability, because that is how the article and the\n",
    "            # video both say it out loud, and a prose surface that has to divide 1 by 0.00317 to get\n",
    "            # its sentence is doing arithmetic the notebook should have done. Rounded to two\n",
    "            # significant figures: \"about one in three hundred\" is the claim, not \"one in 315\".\n",
    "            \"by_true_rate\": [{\"true_cvr\": p_,\n",
    "                              \"chance\": round(float((1 - p_) ** N_MIN), 5),\n",
    "                              \"one_in_n\": int(round(1 / (1 - p_) ** N_MIN, -2)) if p_ > 0.05\n",
    "                              else int(round(1 / (1 - p_) ** N_MIN))}\n",
    "                             for p_ in (0.04, 0.05, 0.06, 0.08)],\n",
    "            # The figure above is the FIRST look. The rule is re-read on a growing total, and each\n",
    "            # read is another chance to block a term sitting exactly at the benchmark.\n",
    "            \"reread_at_benchmark\": [{\"clicks\": n_, \"chance\": round(float(c_), 4),\n",
    "                                     \"one_in_n\": int(round(1 / c_))}\n",
    "                                    for n_, c_ in reread_block_risk(\n",
    "                                        BENCHMARK, BENCHMARK, Z, (93, 200, 500, 1000)).items()],\n",
    "        },\n",
    "        # What the synthetic shop actually IS, emitted so the video can show its own assumptions\n",
    "        # rather than asking the viewer to take the campaign on trust. A claim made against a\n",
    "        # dataset nobody has seen is a claim made against nothing.\n",
    "        \"dataset\": {\n",
    "            \"shop\": \"a running-shoe shop buying paid search\",\n",
    "            \"days\": DAYS,\n",
    "            \"n_terms\": N_TERMS,\n",
    "            \"class_share\": CLASS_SHARE,\n",
    "            # what the month actually drew, which is what the article's counts describe\n",
    "            \"class_share_drawn\": {k: round(float((terms.klass == k).mean()), 4) for k in CLASS_SHARE},\n",
    "            \"true_cvr_bands\": {\"irrelevant\": [0.0, 0.004], \"poor\": [0.005, 0.015],\n",
    "                               \"average\": [0.03, 0.05], \"good\": [0.06, 0.10]},\n",
    "            \"synonym_share\": SYNONYM_SHARE,\n",
    "            \"median_clicks\": float(terms.clicks.median()),\n",
    "            \"max_clicks\": int(terms.clicks.max()),\n",
    "            \"mean_cpc_eur\": round(float(terms.cpc.mean()), 4),\n",
    "            \"cost_per_click_eur\": round(float(terms.cost.sum() / terms.clicks.sum()), 4),\n",
    "            \"spend_eur\": round(float(terms.cost.sum()), 2),\n",
    "            \"conversions\": int(terms.conversions.sum()),\n",
    "            \"cost_per_conversion_eur\": round(float(terms.cost.sum()) / int(terms.conversions.sum()), 2),\n",
    "        },\n",
    "        # At the block-on-sight line, how often a genuinely good term shows nothing anyway. With the\n",
    "        # line at five clicks this was so obvious it needed no number; at thirty it is the whole\n",
    "        # claim, so it is computed rather than asserted: the chance of zero conversions in\n",
    "        # NAIVE_CLICKS clicks at the good class's average true rate, and the one-in-N form the\n",
    "        # narration says out loud.\n",
    "        \"coin_flip\": {\n",
    "            \"clicks\": NAIVE_CLICKS,\n",
    "            \"good_mean_cvr\": round(float(terms.loc[terms.klass == \"good\", \"true_cvr\"].mean()), 4),\n",
    "            \"p_zero_if_good\": round(\n",
    "                float((1 - terms.loc[terms.klass == \"good\", \"true_cvr\"].mean()) ** NAIVE_CLICKS), 4),\n",
    "            \"one_in_n_if_good\": int(round(\n",
    "                1 / float((1 - terms.loc[terms.klass == \"good\", \"true_cvr\"].mean()) ** NAIVE_CLICKS))),\n",
    "        },\n",
    "        # The two relevant, genuinely-good terms that gathered the MOST clicks and still converted\n",
    "        # nobody by the end of the month. These are the rows the reflex slide should show, and the\n",
    "        # click counts have to be real: at five clicks nobody actually blocks, so a slide arguing\n",
    "        # against a five-click block argues against nobody. The interesting row is the one where\n",
    "        # blocking feels defensible and is still not supported.\n",
    "        \"reflex_examples\": [\n",
    "            {\"term\": r.term, \"clicks\": int(r.clicks), \"conversions\": int(r.conversions),\n",
    "             \"true_rate\": round(float(r.true_cvr), 4)}\n",
    "            for _, r in (terms[(terms.conversions == 0)\n",
    "                               & (terms.relevant_by_gate1)\n",
    "                               & (terms.klass.isin([\"good\", \"average\"]))]\n",
    "                         .sort_values(\"clicks\", ascending=False).head(2).iterrows())\n",
    "        ],\n",
    "        \"gate_one\": {\n",
    "            \"irrelevant_blocked\": irrelevant_blocked,\n",
    "            \"irrelevant_total\": int((terms.klass == \"irrelevant\").sum()),\n",
    "            \"irrelevant_passed\": irrelevant_passed,\n",
    "            \"good_or_average_killed_by_list_gap\": good_killed_by_list,\n",
    "            \"poor_killed_by_list_gap\": poor_killed_by_list,\n",
    "            \"missing_words\": MISSING_WORDS,\n",
    "            # How the casualties split across the three words. The article and the caveat scene\n",
    "            # both said \"two missing words\" while naming three elsewhere; counting them settles it\n",
    "            # and stops the two surfaces disagreeing again.\n",
    "            \"killed_by_word\": {\n",
    "                w: int((terms.klass.isin([\"good\", \"average\"])\n",
    "                        & ~terms.relevant_by_gate1\n",
    "                        & terms.term.str.contains(w, case=False, regex=False)).sum())\n",
    "                for w in MISSING_WORDS},\n",
    "            # The single most expensive casualty, named. \"27 good terms\" is a count a viewer\n",
    "            # cannot feel; one term with its real clicks, sales and spend is the same fact with a\n",
    "            # face on it -- and this one is the account's busiest term of all.\n",
    "            \"busiest_unlisted_term\": (\n",
    "                lambda d: {\"term\": d.term, \"clicks\": int(d.clicks),\n",
    "                           \"conversions\": int(d.conversions), \"cost_eur\": round(float(d.cost), 2)}\n",
    "            )(terms[terms.klass.isin([\"good\", \"average\"]) & ~terms.relevant_by_gate1]\n",
    "              .sort_values(\"clicks\", ascending=False).iloc[0]),\n",
    "        },\n",
    "        \"policies\": {k: {kk: (round(vv, 2) if isinstance(vv, float) else vv)\n",
    "                         for kk, vv in v.items() if kk != \"block_day\"}\n",
    "                     for k, v in r.items()},\n",
    "        # What a conversion has to be worth before a rule stops paying for itself. Blocking buys\n",
    "        # saved waste and costs conversions, so the trade turns on a number the notebook does not\n",
    "        # know: the value of a conversion. Below the break-even, block; above it, let it run. Stated\n",
    "        # rather than implied, because the article recommends a rule and the reader's break-even may\n",
    "        # sit the other side of it.\n",
    "        # What the killed keywords were worth, on the reader's two assumptions. A blocked keyword\n",
    "        # stays blocked, so this is a yearly stream against a one-off saving -- both horizons are\n",
    "        # named wherever the figure is shown, because comparing them as if they were the same\n",
    "        # period is the easiest way to make either rule look better than it is.\n",
    "        \"reader_economics\": {\n",
    "            \"assumed_order_value_eur\": ORDER_VALUE_EUR,\n",
    "            # Every term clicks at a steady daily rate, so a block keeps saving spend AND keeps losing\n",
    "            # sales at the same pace for as long as it stands: compare one quarter with one quarter.\n",
    "            # (An earlier version set a quarter's saving against a year of lost sales.) Spend saved\n",
    "            # counts every blocked term, good ones included; sales lost count every class.\n",
    "            \"quarter_trade\": {\n",
    "                k: {\"spend_saved_eur\": round(results_q[\"let_run\"][\"spend\"] - v[\"spend\"], 2),\n",
    "                    \"conversions_lost\": int(results_q[\"let_run\"][\"conversions\"] - v[\"conversions\"]),\n",
    "                    \"sales_lost_at_order_value_eur\": int(results_q[\"let_run\"][\"conversions\"] - v[\"conversions\"])\n",
    "                    * ORDER_VALUE_EUR,\n",
    "                    \"break_even_eur\": round((results_q[\"let_run\"][\"spend\"] - v[\"spend\"])\n",
    "                                            / max(results_q[\"let_run\"][\"conversions\"] - v[\"conversions\"], 1), 2)}\n",
    "                for k, v in results_q.items() if k != \"let_run\"},\n",
    "        },\n",
    "        \"break_even_conversion_value_eur\": {\n",
    "            # all spend saved (a blocked good term saves its own spend too) over all conversions lost\n",
    "            k: round((r[\"let_run\"][\"spend\"] - v[\"spend\"])\n",
    "                     / max(r[\"let_run\"][\"conversions\"] - v[\"conversions\"], 1), 2)\n",
    "            for k, v in r.items() if k != \"let_run\"\n",
    "        },\n",
    "        \"quarter\": {\n",
    "            \"days\": HORIZON,\n",
    "            \"spend_eur\": round(float(terms.cost_q.sum()), 2),\n",
    "            \"terms_reaching_n_min\": reached_q,\n",
    "            \"policies\": {k: {kk: (round(vv, 2) if isinstance(vv, float) else vv)\n",
    "                             for kk, vv in v.items() if kk != \"block_day\"}\n",
    "                         for k, v in results_q.items()},\n",
    "            \"verdicts\": {k: int(v) for k, v in verdict_counts_q.items()},\n",
    "            \"verdicts_by_class\": {k: {kk: int(vv) for kk, vv in row.items()}\n",
    "                                  for k, row in pd.crosstab(terms.klass, terms.verdict_q).iterrows()},\n",
    "        },\n",
    "        \"verdicts_end_of_month\": {k: int(v) for k, v in verdict_counts.items()},\n",
    "        \"verdicts_by_class\": {k: {kk: int(vv) for kk, vv in row.items()}\n",
    "                              for k, row in pd.crosstab(terms.klass, terms.verdict).iterrows()},\n",
    "        \"sweeps\": {\"confidence\": conf_sweep, \"kill_on_sight_clicks\": naive_sweep},\n",
    "        # What the confidence dial actually buys, COMPUTED. An earlier draft of the\n",
    "        # article subtracted two rows of the sweep by hand and typed the difference\n",
    "        # into prose, which is the one thing the grounding rule forbids outright:\n",
    "        # a derived number is a function's output or it is not a number.\n",
    "        \"confidence_dial\": dial,\n",
    "    }\n",
    "    with open(path, \"w\", encoding=\"utf-8\") as f:\n",
    "        json.dump(payload, f, indent=2)\n",
    "    return payload\n",
    "\n",
    "\n",
    "def confidence_dial(sweep, loose=0.80, strict=0.95):\n",
    "    \"\"\"What dropping from `strict` confidence to `loose` buys, and costs, on this campaign.\"\"\"\n",
    "    lo = next(r for r in sweep if r[\"confidence\"] == loose)\n",
    "    hi = next(r for r in sweep if r[\"confidence\"] == strict)\n",
    "    return {\"from\": strict, \"to\": loose,\n",
    "            \"waste_saved_eur\": round(hi[\"waste\"] - lo[\"waste\"], 2),\n",
    "            \"extra_good_terms_blocked\": lo[\"false_kills\"] - hi[\"false_kills\"],\n",
    "            \"extra_conversions_lost\": lo[\"lost_conversions\"] - hi[\"lost_conversions\"],\n",
    "            \"clicks_needed_drop\": hi[\"n_min\"] - lo[\"n_min\"]}\n",
    "\n",
    "\n",
    "dial = confidence_dial(conf_sweep)\n",
    "print(\"the dial from 95% to 80%:\", dial)\n",
    "checks = emit_checks()\n",
    "worked = emit_worked_example()\n",
    "print(json.dumps(worked[\"policies\"], indent=1))\n",
    "print(json.dumps(worked[\"fair_chance\"], indent=1)[:600])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "151cb19d",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "# Part two: your own search terms\n",
    "\n",
    "Everything below runs on your machine. Your file is read from disk, judged in this\n",
    "process, and nothing is sent anywhere.\n",
    "\n",
    "You need three things: a **search terms report** exported from your ad platform, a\n",
    "short list of **what you sell** (the phrases gate one matches against), and the\n",
    "**benchmark** conversion rate the terms are judged against - which the notebook\n",
    "works out from the file itself if you do not supply one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7c0ea87a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T16:56:14.704998Z",
     "iopub.status.busy": "2026-10-24T16:56:14.704667Z",
     "iopub.status.idle": "2026-10-24T16:56:14.820394Z",
     "shell.execute_reply": "2026-10-24T16:56:14.819429Z"
    }
   },
   "outputs": [],
   "source": [
    "# ---- export reader (shared across every Stochastic Strata notebook; do not edit here) ----------\n",
    "# One reader for the file you bring. Ad platforms and analytics tools do not hand over clean tables:\n",
    "# a Google Ads download opens with a title line and a date-range line and closes with \"Total:\" rows,\n",
    "# a GA4 download opens with \"#\" lines and writes dates as 20261019, a European download separates\n",
    "# with semicolons and writes 1.234,56, and Excel turns numbers into \"1,234.56\" or \"€12.30\". This\n",
    "# reader finds the real header row, sets the title, comment and total rows aside, turns formatted\n",
    "# numbers back into numbers, and reads dates in the platforms' own forms. Where a file is genuinely\n",
    "# ambiguous - 06/07/2026 could be the 6th of July or the 7th of June - it stops and asks rather than\n",
    "# guessing, because a guessed date silently moves a day to the wrong weekday.\n",
    "#\n",
    "#     table, notes = read_export(\"my_export.csv\", columns={\"date\": [\"day\", \"date\"], ...})\n",
    "#     for note in notes: print(note)\n",
    "#\n",
    "# `columns` maps the names this notebook uses to the names your file might use (case and spacing do\n",
    "# not matter, and a currency suffix such as \"(EUR)\" is ignored). `date_columns` are read as dates,\n",
    "# `id_columns` are kept as text. Everything else that reads as numbers becomes numbers; a share\n",
    "# written \"49.41%\" becomes 0.4941. A share the platform shows only as \"< 10%\" or \"> 90%\" is read as\n",
    "# 0.10 or 0.90 and the direction is kept in a column ending \"_bound\", because it is a limit, not a\n",
    "# measurement. Every row set aside, and why, comes back in `notes`.\n",
    "import csv as _csv\n",
    "import io as _io\n",
    "import re as _re\n",
    "from pathlib import Path as _Path\n",
    "\n",
    "import numpy as _np\n",
    "import pandas as _pd\n",
    "\n",
    "\n",
    "class ExportReadError(ValueError):\n",
    "    \"\"\"The file cannot be read without guessing; the message says what to change.\"\"\"\n",
    "\n",
    "\n",
    "_TOTAL_ROW = _re.compile(r\"^\\s*(grand\\s+)?totals?\\b\", _re.I)\n",
    "_CURRENCY = _re.compile(r\"[€$£¥₹]|\\b(EUR|USD|GBP|CHF|SEK|NOK|DKK|PLN|AUD|CAD)\\b\", _re.I)\n",
    "_EMPTY = {\"\", \"--\", \"-\", \"—\", \"–\", \"n/a\", \"na\", \"nan\", \"none\", \"null\", \"(not set)\"}\n",
    "_MONTHS = {m: i for i, m in enumerate(\n",
    "    [\"jan\", \"feb\", \"mar\", \"apr\", \"may\", \"jun\", \"jul\", \"aug\", \"sep\", \"oct\", \"nov\", \"dec\"], 1)}\n",
    "\n",
    "\n",
    "def _norm(name):\n",
    "    name = _re.sub(r\"\\((?:[A-Z]{3}|[€$£])\\)\", \"\", str(name))\n",
    "    return _re.sub(r\"[\\s_\\-./]+\", \" \", name).strip().lower()\n",
    "\n",
    "\n",
    "def _decode(raw):\n",
    "    if raw[:2] in (b\"\\xff\\xfe\", b\"\\xfe\\xff\"):\n",
    "        return raw.decode(\"utf-16\")\n",
    "    for encoding in (\"utf-8-sig\", \"cp1252\", \"latin-1\"):\n",
    "        try:\n",
    "            return raw.decode(encoding)\n",
    "        except UnicodeDecodeError:\n",
    "            continue\n",
    "    return raw.decode(\"utf-8\", errors=\"replace\")\n",
    "\n",
    "\n",
    "def _rows_from(source):\n",
    "    \"\"\"Every row of the file as a list of strings, whatever the container.\"\"\"\n",
    "    if isinstance(source, _pd.DataFrame):\n",
    "        return [list(map(str, source.columns))] + source.astype(str).values.tolist(), \",\"\n",
    "    path = _Path(source)\n",
    "    if not path.exists():\n",
    "        raise ExportReadError(f\"There is no file at {path}. Check the name and the folder.\")\n",
    "    if path.suffix.lower() in (\".xlsx\", \".xlsm\", \".xls\"):\n",
    "        sheet = _pd.read_excel(path, header=None, dtype=str).fillna(\"\")\n",
    "        return sheet.values.tolist(), \",\"\n",
    "    text = _decode(path.read_bytes())\n",
    "    lines = [ln for ln in text.splitlines() if not ln.lstrip().startswith(\"#\")]\n",
    "    counts = {d: sum(ln.count(d) for ln in lines[:60]) for d in (\",\", \";\", \"\\t\")}\n",
    "    delimiter = max(counts, key=counts.get) if any(counts.values()) else \",\"\n",
    "    return [row for row in _csv.reader(_io.StringIO(\"\\n\".join(lines)), delimiter=delimiter)], delimiter\n",
    "\n",
    "\n",
    "def _find_header(rows, wanted):\n",
    "    \"\"\"The first row that names the columns: the one matching most expected names, else the first\n",
    "    row as wide as the data below it that is mostly words rather than numbers.\"\"\"\n",
    "    widths = [len([c for c in r if str(c).strip()]) for r in rows]\n",
    "    if wanted:\n",
    "        best, best_hits = None, 0\n",
    "        for i, row in enumerate(rows[:40]):\n",
    "            names = {_norm(c) for c in row if str(c).strip()}\n",
    "            hits = sum(1 for options in wanted.values() if names & set(options))\n",
    "            if hits > best_hits:\n",
    "                best, best_hits = i, hits\n",
    "        if best is not None:\n",
    "            return best\n",
    "    body = [w for w in widths if w > 1]\n",
    "    width = max(set(body), key=body.count) if body else 1\n",
    "    for i, row in enumerate(rows[:40]):\n",
    "        cells = [str(c).strip() for c in row if str(c).strip()]\n",
    "        if len(cells) >= max(2, width - 1) and sum(_looks_number(c) for c in cells) <= len(cells) / 2:\n",
    "            return i\n",
    "    return 0\n",
    "\n",
    "\n",
    "def _looks_number(text):\n",
    "    if _re.fullmatch(r\"\\d{1,4}[./\\-]\\d{1,2}[./\\-]\\d{1,4}\", str(text).strip()):\n",
    "        return False  # a date such as 22.09.2026, not a number with separators\n",
    "    return bool(_re.fullmatch(r\"[<>]?\\s*[-+(]?[€$£]?\\s*[\\d.,\\s']+\\s*%?\\)?\", str(text).strip()))\n",
    "\n",
    "\n",
    "def _decimal_comma(values):\n",
    "    \"\"\"True when these values write 1.234,56 (a comma followed by one or two final digits, or dots\n",
    "    grouping thousands with a comma after them), False when they write 1,234.56, None when they do\n",
    "    not say (whole numbers only, or a lone '1.340').\"\"\"\n",
    "    comma_votes = dot_votes = 0\n",
    "    for v in values:\n",
    "        s = _re.sub(r\"[^\\d.,]\", \"\", v)\n",
    "        if \",\" in s and \".\" in s:\n",
    "            comma_votes += s.rfind(\",\") > s.rfind(\".\")\n",
    "            dot_votes += s.rfind(\".\") > s.rfind(\",\")\n",
    "        elif _re.search(r\",\\d{1,2}$\", s):\n",
    "            comma_votes += 1\n",
    "        elif _re.search(r\"\\.\\d{1,2}$\", s) or _re.search(r\"\\.\\d{4,}$\", s):\n",
    "            dot_votes += 1\n",
    "    if comma_votes == dot_votes:\n",
    "        return None\n",
    "    return comma_votes > dot_votes\n",
    "\n",
    "\n",
    "def _to_number(text, comma_decimal):\n",
    "    s = str(text).strip().replace(\" \", \" \")\n",
    "    if s.lower() in _EMPTY:\n",
    "        return _np.nan, None, True\n",
    "    bound = None\n",
    "    if s[:1] in \"<>\":\n",
    "        bound, s = s[0], s[1:].strip()\n",
    "    negative = s.startswith(\"(\") and s.endswith(\")\")\n",
    "    s = _CURRENCY.sub(\"\", s).strip(\"() \").replace(\" \", \"\").replace(\"'\", \"\")\n",
    "    percent = s.endswith(\"%\")\n",
    "    s = s.rstrip(\"%\")\n",
    "    if comma_decimal:\n",
    "        s = s.replace(\".\", \"\").replace(\",\", \".\")\n",
    "    else:\n",
    "        s = s.replace(\",\", \"\")\n",
    "    try:\n",
    "        value = float(s)\n",
    "    except ValueError:\n",
    "        return _np.nan, None, False\n",
    "    if negative:\n",
    "        value = -value\n",
    "    return (value / 100.0 if percent else value), bound, True\n",
    "\n",
    "\n",
    "def _parse_dates(values, column, dayfirst):\n",
    "    text = [str(v).strip() for v in values]\n",
    "    present = [t for t in text if t.lower() not in _EMPTY]\n",
    "    if not present:\n",
    "        return _pd.Series([_pd.NaT] * len(text)), []\n",
    "    notes = []\n",
    "    if all(_re.fullmatch(r\"\\d{8}\", t) for t in present):\n",
    "        return _pd.to_datetime(_pd.Series(text), format=\"%Y%m%d\", errors=\"coerce\"), notes\n",
    "    if all(_re.fullmatch(r\"\\d{10}(\\.\\d+)?|\\d{13}|\\d{16}\", t) for t in present):\n",
    "        digits = len(present[0].split(\".\")[0])\n",
    "        unit = {10: \"s\", 13: \"ms\", 16: \"us\"}[digits]\n",
    "        notes.append(f\"'{column}' holds epoch times in {dict(s='seconds', ms='milliseconds', us='microseconds')[unit]}; read as such.\")\n",
    "        return _pd.to_datetime(_pd.to_numeric(_pd.Series(text), errors=\"coerce\"), unit=unit), notes\n",
    "    cleaned = [_re.sub(r\"\\s+(UTC|GMT|Z)$\", \"\", t) for t in text]\n",
    "    slash = [t for t in cleaned if _re.fullmatch(r\"\\d{1,2}[/.\\-]\\d{1,2}[/.\\-]\\d{2,4}(\\s.*)?\", t)]\n",
    "    if slash and len(slash) >= len(present) * 0.9:\n",
    "        firsts = [int(_re.split(r\"[/.\\-]\", t)[0]) for t in slash]\n",
    "        seconds = [int(_re.split(r\"[/.\\-]\", t)[1]) for t in slash]\n",
    "        dotted = all(\".\" in t.split(\" \")[0] for t in slash)\n",
    "        if dayfirst is None:\n",
    "            if max(firsts) > 12 and max(seconds) <= 12:\n",
    "                dayfirst = True\n",
    "            elif max(seconds) > 12 and max(firsts) <= 12:\n",
    "                dayfirst = False\n",
    "            elif dotted:\n",
    "                dayfirst = True\n",
    "            else:\n",
    "                raise ExportReadError(\n",
    "                    f\"The dates in '{column}' (for example {slash[0]}) could be day-first or month-first, \"\n",
    "                    \"and nothing in the file says which. Re-run with dayfirst=True for 6 July written \"\n",
    "                    \"06/07, or dayfirst=False for June 7 written 06/07 - or export the dates as \"\n",
    "                    \"2026-07-06.\")\n",
    "        return _pd.to_datetime(_pd.Series(cleaned), dayfirst=dayfirst, errors=\"coerce\"), notes\n",
    "    parsed = _pd.to_datetime(_pd.Series(cleaned), errors=\"coerce\", format=\"mixed\")\n",
    "    return parsed, notes\n",
    "\n",
    "\n",
    "def read_export(source, columns=None, date_columns=(), id_columns=(), dayfirst=None,\n",
    "                max_unreadable_share=0.02):\n",
    "    \"\"\"Read a downloaded report into a table with this notebook's column names. Returns\n",
    "    (table, notes); raises ExportReadError with a plain-words fix when it cannot read without\n",
    "    guessing.\"\"\"\n",
    "    columns = columns or {}\n",
    "    # Each name's candidates in the caller's order of preference, the name itself first.\n",
    "    wanted = {name: list(dict.fromkeys([_norm(name)] + [_norm(a) for a in aliases]))\n",
    "              for name, aliases in columns.items()}\n",
    "    rows, delimiter = _rows_from(source)\n",
    "    rows = [r for r in rows if any(str(c).strip() for c in r)]\n",
    "    if not rows:\n",
    "        raise ExportReadError(\"The file is empty.\")\n",
    "    notes = []\n",
    "    h = _find_header(rows, wanted)\n",
    "    if h:\n",
    "        notes.append(f\"set aside {h} line(s) above the header (a report title or date range).\")\n",
    "    header = [str(c).strip() for c in rows[h]]\n",
    "    body, totals = [], 0\n",
    "    for r in rows[h + 1:]:\n",
    "        first = next((str(c) for c in r if str(c).strip()), \"\")\n",
    "        if _TOTAL_ROW.match(first):\n",
    "            totals += 1\n",
    "            continue\n",
    "        if [str(c).strip() for c in r] == header:\n",
    "            continue\n",
    "        body.append([str(c) for c in r] + [\"\"] * (len(header) - len(r)))\n",
    "    if totals:\n",
    "        notes.append(f\"set aside {totals} total row(s).\")\n",
    "    frame = _pd.DataFrame([r[:len(header)] for r in body], columns=header)\n",
    "    rename = {}\n",
    "    for name, options in wanted.items():\n",
    "        by_norm = {}\n",
    "        for c in frame.columns:\n",
    "            by_norm.setdefault(_norm(c), c)\n",
    "        match = next((by_norm[o] for o in options if o in by_norm and by_norm[o] not in rename), None)\n",
    "        if match is not None:\n",
    "            rename[match] = name\n",
    "    frame = frame.rename(columns=rename)\n",
    "    missing = [name for name in columns if name not in frame.columns]\n",
    "    if missing:\n",
    "        seen = \", \".join(f\"'{c}'\" for c in header[:12])\n",
    "        raise ExportReadError(\n",
    "            f\"Could not find the column(s) {', '.join(missing)}. The file's columns are {seen}. \"\n",
    "            \"Rename the column in the file, or add its name to the list this cell accepts.\")\n",
    "    dates = set(date_columns)\n",
    "    # The file's own convention, for a column that cannot tell by itself: a clicks column of whole\n",
    "    # numbers says nothing, but its \"1.340\" means one thousand three hundred and forty when the\n",
    "    # file's other columns write decimals with a comma.\n",
    "    every = [str(v) for c in frame.columns if c not in date_columns for v in frame[c].tolist()\n",
    "             if _looks_number(v)]\n",
    "    file_comma = _decimal_comma(every)\n",
    "    if file_comma is None:\n",
    "        file_comma = delimiter == \";\"\n",
    "    ids = set(id_columns) | {c for c in frame.columns if _re.search(r\"(^|[ _])(id|pseudo id)$\", _norm(c))}\n",
    "    for col in frame.columns:\n",
    "        values = frame[col].tolist()\n",
    "        if col in dates:\n",
    "            parsed, extra = _parse_dates(values, col, dayfirst)\n",
    "            notes += extra\n",
    "            bad = int(parsed.isna().sum() - sum(str(v).strip().lower() in _EMPTY for v in values))\n",
    "            if bad > max(1, max_unreadable_share * len(values)):\n",
    "                example = next(v for v, p in zip(values, parsed) if _pd.isna(p) and str(v).strip())\n",
    "                raise ExportReadError(f\"{bad} value(s) in '{col}' are not dates I can read, for example '{example}'.\")\n",
    "            frame[col] = parsed.values\n",
    "            continue\n",
    "        if col in ids:\n",
    "            continue\n",
    "        present = [v for v in values if str(v).strip().lower() not in _EMPTY]\n",
    "        if not present or sum(_looks_number(v) for v in present) < 0.5 * len(present):\n",
    "            continue\n",
    "        comma = _decimal_comma(present)\n",
    "        if comma is None:\n",
    "            comma = file_comma\n",
    "        parsed = [_to_number(v, comma) for v in values]\n",
    "        unreadable = [v for v, (_, _, ok) in zip(values, parsed) if not ok]\n",
    "        if unreadable and col not in columns:\n",
    "            notes.append(f\"'{col}' mixes numbers with labels such as '{unreadable[0]}'; kept as text.\")\n",
    "            continue\n",
    "        if unreadable:\n",
    "            notes.append(f\"{len(unreadable)} value(s) in '{col}' could not be read as numbers, for example \"\n",
    "                         f\"'{unreadable[0]}'; they are left empty.\")\n",
    "            if len(unreadable) > max(1, max_unreadable_share * len(values)):\n",
    "                raise ExportReadError(\n",
    "                    f\"{len(unreadable)} of {len(values)} values in '{col}' are not numbers I can read, for \"\n",
    "                    f\"example '{unreadable[0]}'. Fix them in the file, or export the column unformatted.\")\n",
    "        frame[col] = [p[0] for p in parsed]\n",
    "        bounds = [p[1] for p in parsed]\n",
    "        if any(bounds):\n",
    "            # An object column, so a day with no limit stays None: a newer pandas would infer a string\n",
    "            # column here and turn None into NaN, which is truthy and reads as a limit.\n",
    "            frame[f\"{col}_bound\"] = _pd.Series(bounds, index=frame.index, dtype=object)\n",
    "            notes.append(f\"'{col}' has values the platform shows only as a limit (for example \"\n",
    "                         f\"'{next(v for v, b in zip(values, bounds) if b)}'); kept as the limit, with the \"\n",
    "                         f\"direction in '{col}_bound'.\")\n",
    "    if dates:\n",
    "        undated = frame[list(dates)].isna().all(axis=1)\n",
    "        if undated.any():\n",
    "            frame = frame[~undated].reset_index(drop=True)\n",
    "            notes.append(f\"set aside {int(undated.sum())} row(s) with no date (GA4 writes its grand total \"\n",
    "                         \"this way, under the header).\")\n",
    "    return frame, notes\n",
    "# ---- end of export reader -------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a1024996",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T16:56:14.845432Z",
     "iopub.status.busy": "2026-10-24T16:56:14.844124Z",
     "iopub.status.idle": "2026-10-24T16:56:14.900273Z",
     "shell.execute_reply": "2026-10-24T16:56:14.899574Z"
    }
   },
   "outputs": [],
   "source": [
    "class DataProblem(Exception):\n",
    "    \"\"\"Raised with a message written for a person, not a stack trace.\"\"\"\n",
    "\n",
    "\n",
    "ALIASES = {\n",
    "    \"search term\": \"term\", \"search_term\": \"term\", \"query\": \"term\", \"search query\": \"term\",\n",
    "    \"keyword text\": \"term\", \"term\": \"term\",\n",
    "    \"impr.\": \"impressions\", \"impr\": \"impressions\", \"impressions\": \"impressions\",\n",
    "    \"clicks\": \"clicks\", \"click\": \"clicks\",\n",
    "    \"cost\": \"cost\", \"spend\": \"cost\", \"amount spent\": \"cost\", \"cost (eur)\": \"cost\",\n",
    "    \"cost (usd)\": \"cost\", \"cost (gbp)\": \"cost\",\n",
    "    \"conversions\": \"conversions\", \"conv.\": \"conversions\", \"conv\": \"conversions\",\n",
    "    \"results\": \"conversions\", \"leads\": \"conversions\",\n",
    "    \"search_term_view.search_term\": \"term\", \"metrics.impressions\": \"impressions\",\n",
    "    \"metrics.clicks\": \"clicks\", \"metrics.conversions\": \"conversions\",\n",
    "    \"metrics.cost_micros\": \"cost_micros\", \"cost_micros\": \"cost_micros\",\n",
    "}\n",
    "NEEDED = (\"term\", \"clicks\", \"conversions\")\n",
    "\n",
    "\n",
    "def read_search_terms(path):\n",
    "    # Load a search terms export and hand back one clean row per term. The shared reader\n",
    "    # handles the title lines, Total rows, .xlsx, UTF-16 tab-separated \"Excel\" CSVs, comma\n",
    "    # decimals and thousands separators.\n",
    "    columns = {\"term\": [k for k, v in ALIASES.items() if v == \"term\"],\n",
    "               \"clicks\": [k for k, v in ALIASES.items() if v == \"clicks\"]}\n",
    "    try:\n",
    "        raw, notes = read_export(path, columns=columns)\n",
    "    except ExportReadError as problem:\n",
    "        raise DataProblem(str(problem)) from None\n",
    "    for note in notes:\n",
    "        print(\"  \" + note)\n",
    "    lowered = {str(c).strip().lower(): c for c in raw.columns}\n",
    "    found = {}\n",
    "    for src, dst in ALIASES.items():\n",
    "        if src in lowered and dst not in found and dst not in (\"term\", \"clicks\"):\n",
    "            found[dst] = lowered[src]\n",
    "    if \"conversions\" not in found:\n",
    "        if \"all conv.\" in lowered:\n",
    "            raise DataProblem(\"The file has 'All conv.' but no 'Conversions' column. Judge a term on the \"\n",
    "                              \"conversion you pay for: add the Conversions column and export again.\")\n",
    "        raise DataProblem(f\"I could not find a conversions column. The file has: {', '.join(map(str, raw.columns))}.\")\n",
    "    out = pd.DataFrame({\"term\": raw[\"term\"].astype(str).str.strip(), \"clicks\": raw[\"clicks\"]})\n",
    "    out[\"conversions\"] = pd.to_numeric(raw[found[\"conversions\"]], errors=\"coerce\")\n",
    "    out[\"impressions\"] = pd.to_numeric(raw[found[\"impressions\"]], errors=\"coerce\") if \"impressions\" in found else np.nan\n",
    "    if \"cost\" in found:\n",
    "        out[\"cost\"] = pd.to_numeric(raw[found[\"cost\"]], errors=\"coerce\")\n",
    "    elif \"cost_micros\" in found:\n",
    "        out[\"cost\"] = pd.to_numeric(raw[found[\"cost_micros\"]], errors=\"coerce\") / 1e6\n",
    "    else:\n",
    "        out[\"cost\"] = np.nan\n",
    "    out = out.dropna(subset=[\"clicks\", \"conversions\"])\n",
    "    if len(out) == 0:\n",
    "        raise DataProblem(\"No usable rows: every row was a total or had unreadable numbers.\")\n",
    "    # The same term appears once per campaign, ad group and day: sum it up.\n",
    "    out = (out.groupby(\"term\", as_index=False)\n",
    "              .agg(impressions=(\"impressions\", \"sum\"), clicks=(\"clicks\", \"sum\"),\n",
    "                   conversions=(\"conversions\", \"sum\"), cost=(\"cost\", \"sum\")))\n",
    "    if (out.conversions % 1 != 0).any():\n",
    "        print(\"  conversions are fractional (credit shared across ads by data-driven attribution); \"\n",
    "              \"they are kept as they are, never rounded down\")\n",
    "    bad = out[out.clicks < out.conversions]\n",
    "    if len(bad):\n",
    "        raise DataProblem(f\"{len(bad)} term(s) have more conversions than clicks, for example \"\n",
    "                          f\"{list(bad.term)[:3]}. That happens when a conversion action counts every \"\n",
    "                          \"conversion rather than one per click, or a view-through column was exported \"\n",
    "                          \"as conversions; export a conversion counted once per click.\")\n",
    "    return out.sort_values(\"clicks\", ascending=False).reset_index(drop=True)\n",
    "\n",
    "\n",
    "MIN_TERMS = 20                # fewer than this and the benchmark is a guess\n",
    "MIN_BENCHMARK_CONVERSIONS = 30   # the benchmark's own interval is wide below this\n",
    "\n",
    "\n",
    "def check_inputs(df, sell_list, benchmark):\n",
    "    problems, notes = [], []\n",
    "    if not sell_list or all(not str(s).strip() for s in sell_list):\n",
    "        problems.append(\"WHAT_YOU_SELL is empty. Gate one needs at least one phrase \"\n",
    "                        \"describing what you sell, or it would block everything.\")\n",
    "    if len(df) < MIN_TERMS:\n",
    "        notes.append(f\"Only {len(df)} distinct terms. The gates still work term by term, \"\n",
    "                     \"but a benchmark worked out from so few terms is a guess - set \"\n",
    "                     \"BENCHMARK_CVR from the campaign's known keywords instead.\")\n",
    "    total_c, total_x = float(df.clicks.sum()), float(df.conversions.sum())\n",
    "    if total_x == 0 and benchmark is None:\n",
    "        problems.append(\"The file has zero conversions in total, so no benchmark can be \"\n",
    "                        \"worked out from it and gate two cannot run. Gate one still can: \"\n",
    "                        \"set BENCHMARK_CVR to your campaign's ordinary rate to judge \"\n",
    "                        \"performance too, or run gate one only.\")\n",
    "    if benchmark is None and 0 < total_x < MIN_BENCHMARK_CONVERSIONS:\n",
    "        notes.append(f\"The benchmark rests on only {total_x:,.1f} conversions, so it is itself \"\n",
    "                     \"uncertain; verdicts near the line should be read as undecided.\")\n",
    "    if sell_list:\n",
    "        m = df.term.map(lambda t: gate_one(t, sell_list))\n",
    "        if m.all() and len(df) > 5:\n",
    "            notes.append(\"Every term matches your list. That usually means a phrase is too \"\n",
    "                         \"short (a single common word); gate one is doing nothing.\")\n",
    "        for phrase in sell_list:\n",
    "            if len(str(phrase).split()) == 1:\n",
    "                hits = [term for term in df.term if gate_one(term, [phrase])][:3]\n",
    "                if hits:\n",
    "                    notes.append(f\"'{phrase}' is a single word, so it lets through every term containing it, \"\n",
    "                                 f\"for example {hits}. Check those are terms you want to keep.\")\n",
    "        if not m.any():\n",
    "            notes.append(\"No term matches your list at all. Check the phrases are the words \"\n",
    "                         \"people actually type, not your internal product names.\")\n",
    "    if df.clicks.max() < n_min(benchmark or max(total_x / max(total_c, 1), 0.001), Z):\n",
    "        notes.append(\"No term has reached the click count where zero conversions could be \"\n",
    "                     \"judged, so gate two will call nothing 'block' yet. That is the honest \"\n",
    "                     \"state, not a fault.\")\n",
    "    return problems, notes\n",
    "\n",
    "\n",
    "def judge_your_terms(df, sell_list, benchmark=None, confidence=0.95):\n",
    "    problems, notes = check_inputs(df, sell_list, benchmark)\n",
    "    if problems:\n",
    "        return {\"ran\": False, \"problems\": problems, \"notes\": notes}\n",
    "    z = z_for(confidence)\n",
    "    total_c, total_x = float(df.clicks.sum()), float(df.conversions.sum())\n",
    "    if benchmark is None:\n",
    "        benchmark = total_x / total_c if total_c else 0.0\n",
    "        b_low, b_high = wilson(total_x, total_c, z)\n",
    "        notes.append(f\"Benchmark worked out from the file: {benchmark:.2%} \"\n",
    "                     f\"(95% interval {b_low:.2%} to {b_high:.2%}, from {total_x:,.1f} conversions \"\n",
    "                     f\"in {total_c:,.0f} clicks).\")\n",
    "    rows = []\n",
    "    for _, r in df.iterrows():\n",
    "        relevant = gate_one(r.term, sell_list)\n",
    "        low, high = wilson(float(r.conversions), float(r.clicks), z)\n",
    "        v = \"blocked: no match\" if not relevant else verdict(\n",
    "            float(r.conversions), float(r.clicks), benchmark, z)\n",
    "        rows.append({\"term\": r.term, \"clicks\": int(r.clicks), \"conversions\": round(float(r.conversions), 2),\n",
    "                     \"cost\": (None if pd.isna(r.cost) else round(float(r.cost), 2)),\n",
    "                     \"rate\": (r.conversions / r.clicks if r.clicks else None),\n",
    "                     \"low\": low, \"high\": high, \"gate_one\": relevant, \"verdict\": v})\n",
    "    out = pd.DataFrame(rows)\n",
    "    return {\"ran\": True, \"problems\": [], \"notes\": notes, \"benchmark\": benchmark,\n",
    "            \"n_min\": n_min(benchmark, z), \"verdicts\": out}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6273523",
   "metadata": {},
   "source": [
    "### Getting the export"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4f9587d2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T16:56:14.956069Z",
     "iopub.status.busy": "2026-10-24T16:56:14.955542Z",
     "iopub.status.idle": "2026-10-24T16:56:14.965403Z",
     "shell.execute_reply": "2026-10-24T16:56:14.964747Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GOOGLE ADS (the two-minute route)\n",
      "  Campaigns > pick the campaign > Insights and reports > Search terms.\n",
      "  Date range: at least the last 90 days, ideally the terms' whole life - the total\n",
      "  keeps growing and the rule reads it; end it as long before today as your\n",
      "  conversions take to arrive.\n",
      "  Columns: Search term, Impr., Clicks, Cost, Conversions (Match type and Ad group\n",
      "  are fine to leave in - they are summed away).   Download > CSV.\n",
      "  The export opens with a title line and a date line above the header, and ends\n",
      "  with \"Total\" rows. The loader skips both.\n",
      "\n",
      "GOOGLE ADS SCRIPTS / API (when you want it automated)\n",
      "  SELECT search_term_view.search_term, metrics.impressions, metrics.clicks,\n",
      "         metrics.cost_micros, metrics.conversions\n",
      "  FROM search_term_view\n",
      "  WHERE segments.date BETWEEN '<YYYY-MM-DD>' AND '<YYYY-MM-DD>'\n",
      "    AND campaign.id = <YOUR_CAMPAIGN_ID>\n",
      "  (cost_micros is cost times a million - divide before saving as 'cost')\n",
      "\n",
      "MICROSOFT ADVERTISING\n",
      "  Reports > Search terms report, same columns, same date range. Download as CSV.\n",
      "\n",
      "WHAT TO WATCH FOR\n",
      "  - One row per term per campaign/ad group/day is fine: the loader sums them.\n",
      "  - Export CONVERSIONS, not 'all conversions' or view-through - a term should be\n",
      "    judged on the conversion you actually pay for.\n",
      "  - The 'what you sell' list is the words people TYPE, not your product names.\n",
      "  - The benchmark should be the campaign's ordinary conversion rate on its known\n",
      "    keywords. If you do not set one, it is worked out from the file's totals,\n",
      "    which mixes the new terms in - read the note it prints.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "EXPORT_RECIPE = \"\"\"\\\n",
    "GOOGLE ADS (the two-minute route)\n",
    "  Campaigns > pick the campaign > Insights and reports > Search terms.\n",
    "  Date range: at least the last 90 days, ideally the terms' whole life - the total\n",
    "  keeps growing and the rule reads it; end it as long before today as your\n",
    "  conversions take to arrive.\n",
    "  Columns: Search term, Impr., Clicks, Cost, Conversions (Match type and Ad group\n",
    "  are fine to leave in - they are summed away).   Download > CSV.\n",
    "  The export opens with a title line and a date line above the header, and ends\n",
    "  with \"Total\" rows. The loader skips both.\n",
    "\n",
    "GOOGLE ADS SCRIPTS / API (when you want it automated)\n",
    "  SELECT search_term_view.search_term, metrics.impressions, metrics.clicks,\n",
    "         metrics.cost_micros, metrics.conversions\n",
    "  FROM search_term_view\n",
    "  WHERE segments.date BETWEEN '<YYYY-MM-DD>' AND '<YYYY-MM-DD>'\n",
    "    AND campaign.id = <YOUR_CAMPAIGN_ID>\n",
    "  (cost_micros is cost times a million - divide before saving as 'cost')\n",
    "\n",
    "MICROSOFT ADVERTISING\n",
    "  Reports > Search terms report, same columns, same date range. Download as CSV.\n",
    "\n",
    "WHAT TO WATCH FOR\n",
    "  - One row per term per campaign/ad group/day is fine: the loader sums them.\n",
    "  - Export CONVERSIONS, not 'all conversions' or view-through - a term should be\n",
    "    judged on the conversion you actually pay for.\n",
    "  - The 'what you sell' list is the words people TYPE, not your product names.\n",
    "  - The benchmark should be the campaign's ordinary conversion rate on its known\n",
    "    keywords. If you do not set one, it is worked out from the file's totals,\n",
    "    which mixes the new terms in - read the note it prints.\n",
    "\"\"\"\n",
    "print(EXPORT_RECIPE)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ade6abe",
   "metadata": {},
   "source": [
    "### Point it at your file\n",
    "\n",
    "The four lines in the box are the only ones to change. Everything else runs as is.\n",
    "Verdicts are printed in five groups and written to `your_verdicts.csv` next to the\n",
    "notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "261380be",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T16:56:14.973660Z",
     "iopub.status.busy": "2026-10-24T16:56:14.973301Z",
     "iopub.status.idle": "2026-10-24T16:56:15.005422Z",
     "shell.execute_reply": "2026-10-24T16:56:15.004600Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "STOP: Fill in WHAT_YOU_SELL with the phrases people type when they want what you sell. It is deliberately not pre-filled: a default list would judge your terms against somebody else's shop.\n"
     ]
    }
   ],
   "source": [
    "# ---------------------------------------------------------------\n",
    "YOUR_FILE      = \"search_terms_report.csv\"          # <- your export\n",
    "WHAT_YOU_SELL  = [\"<phrase people type>\", \"<another phrase>\"]   # <- what you sell\n",
    "BENCHMARK_CVR  = None     # <- e.g. 0.04 for 4%; None = work it out from the file\n",
    "CONFIDENCE_LVL = 0.95     # <- how sure before blocking: 0.80 / 0.90 / 0.95\n",
    "# ---------------------------------------------------------------\n",
    "# Set this to True ONLY to watch the demonstration first. It judges invented data,\n",
    "# and every line it prints says so.\n",
    "RUN_THE_DEMO_INSTEAD = False\n",
    "\n",
    "\n",
    "def _demo_export():\n",
    "    \"\"\"The synthetic campaign, written in the shape a Google Ads export has.\"\"\"\n",
    "    d = terms[[\"term\", \"impressions\", \"clicks\", \"cost\", \"conversions\"]].copy()\n",
    "    d.columns = [\"Search term\", \"Impr.\", \"Clicks\", \"Cost\", \"Conversions\"]\n",
    "    d[\"Cost\"] = d[\"Cost\"].round(2)\n",
    "    path = Path(\"demo_search_terms.csv\")\n",
    "    with open(path, \"w\", encoding=\"utf-8\", newline=\"\") as f:\n",
    "        f.write(\"Search terms report\\n\")\n",
    "        f.write(\"1 Aug 2026 - 28 Aug 2026\\n\")\n",
    "        d.to_csv(f, index=False)\n",
    "        f.write(f\"Total: Search terms,{int(d['Impr.'].sum())},{int(d.Clicks.sum())},\"\n",
    "                f\"{d.Cost.sum():.2f},{int(d.Conversions.sum())}\\n\")\n",
    "    return path\n",
    "\n",
    "\n",
    "# This cell REFUSES rather than falling back: a mistyped filename must not quietly\n",
    "# judge the demonstration's invented terms and print them as if they were yours.\n",
    "stop = None\n",
    "if RUN_THE_DEMO_INSTEAD:\n",
    "    frame = read_search_terms(_demo_export())\n",
    "    sell, bench, conf = WHAT_WE_SELL, BENCHMARK, CONFIDENCE\n",
    "    tag = \"DEMO (invented data) | \"\n",
    "else:\n",
    "    tag = \"\"\n",
    "    sell, bench, conf = WHAT_YOU_SELL, BENCHMARK_CVR, CONFIDENCE_LVL\n",
    "    if any(s.startswith(\"<\") for s in WHAT_YOU_SELL):\n",
    "        stop = (\"Fill in WHAT_YOU_SELL with the phrases people type when they want what \"\n",
    "                \"you sell. It is deliberately not pre-filled: a default list would judge \"\n",
    "                \"your terms against somebody else's shop.\")\n",
    "    elif not Path(YOUR_FILE).exists():\n",
    "        stop = (f\"No file called {YOUR_FILE!r} in this folder. Check the name and that it \"\n",
    "                \"uploaded. Nothing has been judged - set RUN_THE_DEMO_INSTEAD = True to \"\n",
    "                \"watch the demonstration first.\")\n",
    "    else:\n",
    "        frame = read_search_terms(YOUR_FILE)\n",
    "\n",
    "if stop:\n",
    "    print(\"STOP:\", stop)\n",
    "else:\n",
    "    res = judge_your_terms(frame, sell, bench, conf)\n",
    "    for p_ in res[\"problems\"]:\n",
    "        print(tag + \"STOP:\", p_)\n",
    "    for n_ in res[\"notes\"]:\n",
    "        print(tag + \"NOTE:\", n_)\n",
    "    if res[\"ran\"]:\n",
    "        v = res[\"verdicts\"]\n",
    "        # The demo writes its OWN file. One shared filename means watching the\n",
    "        # demonstration AFTER a real run silently replaces your verdicts with 600 invented\n",
    "        # rows - in the file you would then work through in your account. That is the\n",
    "        # spend-money-on-fabricated-data shape the refusal above is written against,\n",
    "        # arriving through a different door. Found by an adversarial review, not a check.\n",
    "        out_file = \"demo_verdicts.csv\" if RUN_THE_DEMO_INSTEAD else \"your_verdicts.csv\"\n",
    "        print()\n",
    "        print(tag + f\"benchmark used: {res['benchmark']:.2%}; a zero-conversion term can be \"\n",
    "                    f\"called below it after {res['n_min']} clicks\")\n",
    "        groups = [(\"blocked: no match\", \"BLOCK NOW - nothing to do with what you sell\"),\n",
    "                  (\"block\", \"BLOCK - had a fair chance, confidently below the benchmark\"),\n",
    "                  (\"proven\", \"KEEP - confidently above the benchmark (consider adding as a keyword)\"),\n",
    "                  (\"undecided\", \"KEEP WATCHING - interval straddles the benchmark\"),\n",
    "                  (\"accruing\", \"KEEP WATCHING - too few clicks to judge yet\")]\n",
    "        for key, title in groups:\n",
    "            s = v[v.verdict == key]\n",
    "            print(f\"\\n{tag}{title}: {len(s)} term(s)\")\n",
    "            for _, r in s.head(12).iterrows():\n",
    "                rate = \"-\" if r.rate is None else f\"{r.rate:.1%}\"\n",
    "                print(f\"{tag}  {r.term[:44]:44s} {r.clicks:5d} clicks {r.conversions:3d} conv \"\n",
    "                      f\"rate {rate:>6s}  plausible {r.low:.1%}-{r.high:.1%}\")\n",
    "            if len(s) > 12:\n",
    "                print(f\"{tag}  ... and {len(s) - 12} more in {out_file}\")\n",
    "        v.to_csv(out_file, index=False)\n",
    "        print(f\"\\n{tag}written to {out_file}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76499f4c",
   "metadata": {},
   "source": [
    "### Watching it fail properly\n",
    "\n",
    "The loader and the checks are exercised here on purpose: a second export built from\n",
    "the synthetic campaign must reproduce the notebook's own verdicts exactly, and the\n",
    "broken files must stop with a sentence rather than a wrong number."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "33f392b0",
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    "execution": {
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     "iopub.status.idle": "2026-10-24T16:56:16.295641Z",
     "shell.execute_reply": "2026-10-24T16:56:16.294449Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  set aside 2 line(s) above the header (a report title or date range).\n",
      "  set aside 1 total row(s).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "round trip through the export loader reproduces every verdict: PASS\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a second campaign (250 terms, a different seed) judged through the your-data path: PASS\n",
      "  verdicts: {'blocked: no match': np.int64(136), 'accruing': np.int64(105), 'proven': np.int64(7), 'undecided': np.int64(2)}\n",
      "--- wrong column names\n",
      "    STOP: Could not find the column(s) clicks. The file's columns are 'term', 'impressions', 'sessions', 'orders', 'cost'. Rename the column in the file, or add its name to the list this cell accepts.\n",
      "\n",
      "--- only eight terms, no benchmark given\n",
      "    NOTE: Only 8 distinct terms. The gates still work term by term, but a benchmark worked out from so few terms is a guess - set BENCHMARK_CVR from the campaign's known keywords instead.\n",
      "    NOTE: Benchmark worked out from the file: 4.01% (95% interval 3.06% to 5.23%, from 51.0 conversions in 1,272 clicks).\n",
      "\n",
      "--- zero conversions in the file, no benchmark given\n",
      "    STOP: The file has zero conversions in total, so no benchmark can be worked out from it and gate two cannot run. Gate one still can: set BENCHMARK_CVR to your campaign's ordinary rate to judge performance too, or run gate one only.\n",
      "    NOTE: No term has reached the click count where zero conversions could be judged, so gate two will call nothing 'block' yet. That is the honest state, not a fault.\n",
      "\n",
      "--- empty what-you-sell list\n",
      "    STOP: WHAT_YOU_SELL is empty. Gate one needs at least one phrase describing what you sell, or it would block everything.\n",
      "    NOTE: Only 8 distinct terms. The gates still work term by term, but a benchmark worked out from so few terms is a guess - set BENCHMARK_CVR from the campaign's known keywords instead.\n",
      "\n",
      "--- a list of single common words, so everything matches\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    NOTE: Every term matches your list. That usually means a phrase is too short (a single common word); gate one is doing nothing.\n",
      "    NOTE: 'shoes' is a single word, so it lets through every term containing it, for example ['recycling marathon shoes', 'running shoes womens', 'near me shoe cabinet']. Check those are terms you want to keep.\n",
      "    NOTE: 'running' is a single word, so it lets through every term containing it, for example ['carbon plate trainers for running', 'running shoes womens', 'for flat feet road running shoes store']. Check those are terms you want to keep.\n",
      "    NOTE: 'socks' is a single word, so it lets through every term containing it, for example ['cheap socks knitting pattern uk', 'running socks comparison', 'lightweight running socks 2026']. Check those are terms you want to keep.\n",
      "    NOTE: 'flats' is a single word, so it lets through every term containing it, for example ['for flat feet road running shoes store', 'racing flats best 2026', 'how long do they last racing flats']. Check those are terms you want to keep.\n",
      "    NOTE: 'marathon' is a single word, so it lets through every term containing it, for example ['recycling marathon shoes', 'near me marathon shoes store', 'marathon shoes diy']. Check those are terms you want to keep.\n",
      "    NOTE: 'trail' is a single word, so it lets through every term containing it, for example ['trail running shoes lightweight store', 'trail running shoes wash', 'trail camera 2026']. Check those are terms you want to keep.\n",
      "    NOTE: 'racing' is a single word, so it lets through every term containing it, for example ['2026 racing games', 'racing flats best 2026', 'how long do they last racing flats']. Check those are terms you want to keep.\n",
      "\n",
      "--- clicks and conversions swapped\n",
      "    STOP: 562 term(s) have more conversions than clicks, for example ['2026 flat feet exercises 2026', '2026 flats for rent', '2026 free running games']. That happens when a conversion action counts every conversion rather than one per click, or a view-through column was exported as conversions; export a conversion counted once per click.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 1. The round trip: the demonstration export, in Google's file shape, judged by the\n",
    "#    part-two path, must agree with part one's verdicts term for term.\n",
    "_rt = read_search_terms(_demo_export())\n",
    "_res = judge_your_terms(_rt, WHAT_WE_SELL, BENCHMARK, CONFIDENCE)\n",
    "_merged = terms[[\"term\", \"verdict\"]].merge(_res[\"verdicts\"][[\"term\", \"verdict\"]],\n",
    "                                            on=\"term\", suffixes=(\"_part_one\", \"_part_two\"))\n",
    "_agree = (_merged.verdict_part_one == _merged.verdict_part_two).all()\n",
    "print(\"round trip through the export loader reproduces every verdict:\",\n",
    "      \"PASS\" if _agree and len(_merged) == N_TERMS else \"FAIL\")\n",
    "assert _agree and len(_merged) == N_TERMS\n",
    "\n",
    "# 2. A SECOND campaign, built from a different seed, run end to end through the part-two\n",
    "#    path. The round trip above proves the loader does not mangle this campaign; this\n",
    "#    proves the rule works on data it was not written against. Its verdicts are checked\n",
    "#    against a direct computation on the same rows, so the arm can actually fail.\n",
    "_rng2 = np.random.default_rng(SEED + 1)\n",
    "_t2 = build_terms(250, _rng2)\n",
    "_c2, _x2, _i2 = simulate(_t2, _rng2, horizon=DAYS)\n",
    "_t2[\"clicks\"], _t2[\"conversions\"], _t2[\"impressions\"] = _c2.sum(1), _x2.sum(1), _i2.sum(1)\n",
    "_t2[\"cost\"] = (_c2 * _t2.cpc.to_numpy()[:, None]).sum(1)\n",
    "_second = _t2[[\"term\", \"impressions\", \"clicks\", \"cost\", \"conversions\"]].copy()\n",
    "_second.columns = [\"Search term\", \"Impr.\", \"Clicks\", \"Cost\", \"Conversions\"]\n",
    "_second.to_csv(\"_second_campaign.csv\", index=False)\n",
    "_r2 = judge_your_terms(read_search_terms(\"_second_campaign.csv\"), WHAT_WE_SELL, BENCHMARK,\n",
    "                       CONFIDENCE)\n",
    "_direct = verdicts_at(_t2, Z, BENCHMARK, WHAT_WE_SELL)\n",
    "_check2 = _t2[[\"term\"]].assign(direct=_direct).merge(\n",
    "    _r2[\"verdicts\"][[\"term\", \"verdict\"]], on=\"term\")\n",
    "_ok2 = bool((_check2.direct == _check2.verdict).all()) and len(_check2) == len(_t2)\n",
    "print(f\"a second campaign ({len(_t2)} terms, a different seed) judged through the \"\n",
    "      f\"your-data path: {'PASS' if _ok2 else 'FAIL'}\")\n",
    "print(f\"  verdicts: {dict(_r2['verdicts'].verdict.value_counts())}\")\n",
    "assert _ok2\n",
    "Path(\"_second_campaign.csv\").unlink(missing_ok=True)\n",
    "\n",
    "# 3. Broken inputs. Each must STOP or NOTE in plain words.\n",
    "_cases = {}\n",
    "_wrong = _rt.rename(columns={\"clicks\": \"sessions\", \"conversions\": \"orders\"})\n",
    "_wrong.to_csv(\"_broken_columns.csv\", index=False)\n",
    "_cases[\"wrong column names\"] = (\"_broken_columns.csv\", WHAT_WE_SELL, BENCHMARK)\n",
    "_rt.head(8).to_csv(\"_tiny.csv\", index=False)\n",
    "_cases[\"only eight terms, no benchmark given\"] = (\"_tiny.csv\", WHAT_WE_SELL, None)\n",
    "_zero = _rt.copy()\n",
    "_zero[\"conversions\"] = 0\n",
    "_zero.to_csv(\"_zero_conv.csv\", index=False)\n",
    "_cases[\"zero conversions in the file, no benchmark given\"] = (\"_zero_conv.csv\", WHAT_WE_SELL, None)\n",
    "_cases[\"empty what-you-sell list\"] = (\"_tiny.csv\", [], BENCHMARK)\n",
    "_rt.to_csv(\"_full.csv\", index=False)\n",
    "_cases[\"a list of single common words, so everything matches\"] = (\n",
    "    \"_full.csv\", [\"shoes\", \"running\", \"socks\", \"flats\", \"marathon\", \"trail\", \"racing\"], BENCHMARK)\n",
    "_swap = _rt.rename(columns={\"clicks\": \"conversions\", \"conversions\": \"clicks\"})\n",
    "_swap.to_csv(\"_swapped.csv\", index=False)\n",
    "_cases[\"clicks and conversions swapped\"] = (\"_swapped.csv\", WHAT_WE_SELL, BENCHMARK)\n",
    "\n",
    "for name, (path, sell, bench) in _cases.items():\n",
    "    print(f\"--- {name}\")\n",
    "    try:\n",
    "        r_ = judge_your_terms(read_search_terms(path), sell, bench)\n",
    "        for p_ in r_[\"problems\"]:\n",
    "            print(\"    STOP:\", p_)\n",
    "        for n_ in r_[\"notes\"]:\n",
    "            print(\"    NOTE:\", n_)\n",
    "        if r_[\"ran\"] and not r_[\"notes\"]:\n",
    "            print(\"    ran with no notes\")\n",
    "    except DataProblem as e:\n",
    "        print(\"    STOP:\", e)\n",
    "    print()\n",
    "for f_ in (\"_broken_columns.csv\", \"_tiny.csv\", \"_zero_conv.csv\", \"_swapped.csv\", \"_full.csv\"):\n",
    "    Path(f_).unlink(missing_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1329edf",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## What this method claims, and what it does not\n",
    "\n",
    "**It claims:** a term with no path to what you sell is not worth a data-gathering\n",
    "budget, and a term that has had a fair chance and sits confidently below your\n",
    "ordinary conversion rate is worth blocking - with \"fair chance\" and \"confidently\"\n",
    "both defined by a confidence interval rather than by patience.\n",
    "\n",
    "**It does not claim** that the interval knows your intent. A term that never converts\n",
    "directly may still be doing a job - a research query that starts journeys your\n",
    "other keywords finish. Judging every term on direct conversions punishes those, and\n",
    "the notebook cannot tell them apart from genuinely poor terms. Set them aside by\n",
    "name if you know them.\n",
    "\n",
    "**It does not claim** that gate one is right. It is only as good as the list you\n",
    "give it, and its mistakes are silent: a good term blocked for a missing word never\n",
    "gathers the data that would have saved it. Keep the list generous, and read the\n",
    "\"blocked: no match\" group before applying it.\n",
    "\n",
    "*Synthetic data throughout part one, generated in this notebook under a fixed seed.\n",
    "Part two runs on your data, locally.*"
   ]
  }
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