gendesign/backend/tests/services/forecasting/test_report_assembler.py

"""Unit-тесты §13 сборщика отчёта (#988, 955-A2) — ЧИСТОЕ маппирование, БЕЗ БД.

Сборщик PURE (берёт уже-посчитанные входы, в БД не ходит, §9.x не пересобирает) →
тесты чистые, без фикстур БД:
  • полный сбор (sample analyze dict + sample advisory dict'ы всех под-сервисов) →
    SiteFinderReport со ВСЕМИ восемью секциями заполненными, `as_dict()` JSON-сериализуем,
    confidence посчитана через #990 (структурная rationale присутствует), exec_summary
    headline непустой, advisory True;
  • частичный сбор (ТОЛЬКО analyze dict) → валидный частичный отчёт (market_now заполнен,
    future-секции пусты, confidence 'low');
  • вход КАК dataclass-инстанс, ТАК и как `.as_dict()`-словарь — оба нормализуются
    (`_as_dict_or`), отчёт идентичен;
  • извлечение сигналов качества данных для #990 (deal_count/analog_count/domrf_coverage/
    history_months/confounded) + exec_summary-синтез;
  • graceful: пустой analyze {} → отчёт всё равно валиден (8 секций, JSON-safe).

Детерминированно, без LLM, без сети. DATABASE_URL до импорта app-модулей (зеркало
соседних тестов — на случай side-effect'ов импорта пакета forecasting).
"""

from __future__ import annotations

import json
import os
from dataclasses import dataclass
from typing import Any

os.environ.setdefault("DATABASE_URL", "postgresql+psycopg://test:test@localhost:5432/test")

from app.services.forecasting.report import SiteFinderReport
from app.services.forecasting.report_assembler import (
    _DEGENERATE_SEGMENT_NOTE,
    _all_cells_clamped,
    _analog_count,
    _as_dict_or,
    _build_product_tz,
    _component_confidences,
    _confounded,
    _deal_count,
    _domrf_coverage,
    _history_months,
    _primary_deficit_index,
    _primary_months_of_inventory,
    assemble_report,
)

# Восемь обязательных секций §13 — стабильный контракт `as_dict()`.
_SECTION_KEYS: tuple[str, ...] = (
    "exec_summary",
    "market_now",
    "future_market",
    "product_tz",
    "scenarios",
    "scoring",
    "confidence",
    "meta",
)


# ──────────────────────────────────────────────────────────────────────────────
# Sample-данные: форма `as_dict()` под-сервисов (плоские, JSON-safe). НЕ зовём живые
# сервисы (сборщик их не считает — берёт готовые входы).
# ──────────────────────────────────────────────────────────────────────────────


def _sample_analyze() -> dict[str, Any]:
    """Реалистичный (усечённый) dict вывода analyze_parcel — только релевантные ключи."""
    return {
        "cad_num": "66:41:0000000:1",
        "district": {"district_name": "Верх-Исетский"},
        "competitors": [
            {"obj_id": 1, "comm_name": "ЖК Альфа", "distance_m": 320.0},
            {"obj_id": 2, "comm_name": "ЖК Бета", "distance_m": 540.0},
        ],
        "market_pulse": {
            "competitors_total": 12,
            "competitors_with_price": 9,
            "coverage_pct": 75.0,
            "market_avg_price_per_m2": 145000,
        },
        "market_avg_price_per_m2": 145000,
        "market_data_coverage_pct": 75.0,
        "poi_count": 18,
    }


def _sample_market_metrics() -> dict[str, Any]:
    """Форма MarketMetrics.as_dict() (#950, §9.2) — несёт счётчики качества данных."""
    return {
        "district": "Верх-Исетский",
        "obj_count": 7,
        "n_lots": 240,
        "n_sold": 88,
        "n_available": 152,
        "window_months": 18,
        "premise_kind": "квартира",
        "confidence": "medium",
        "unit_velocity": 8.2,
        "overstock_index": 0.21,
        "absorption_rate": 0.05,
    }


def _sample_supply_layers() -> dict[str, Any]:
    """Форма supply-слоёв (#950, §9.3) — несёт domrf_coverage."""
    return {"open_units": 1200, "hidden_units": 800, "domrf_coverage": 0.55}


def _sample_forecasts() -> list[dict[str, Any]]:
    """Per-горизонт DemandSupplyForecast.as_dict() (#952) — деривируем дефицит/конкурентов."""
    return [
        {
            "horizon_months": 6,
            "deficit_index": 0.21,
            "months_of_inventory": 18.5,
            "confidence": "medium",
            "future_competitors": [],
            "confounded": False,
        },
        {
            "horizon_months": 12,
            "deficit_index": 0.34,
            "months_of_inventory": 24.2,
            "confidence": "medium",
            "future_competitors": [{"obj_id": 3, "comm_name": "ЖК Гамма", "relevance_weight": 0.6}],
            "confounded": False,
        },
        {
            "horizon_months": 18,
            "deficit_index": 0.28,
            "months_of_inventory": 31.0,
            "confidence": "low",
            "future_competitors": [],
            "confounded": False,
        },
        {
            "horizon_months": 24,
            "deficit_index": 0.19,
            "months_of_inventory": 40.7,
            "confidence": "low",
            "future_competitors": [],
            "confounded": False,
        },
    ]


def _sample_future_supply() -> dict[str, Any]:
    """Форма FutureSupplyPressure.as_dict() (#950, §9.3)."""
    return {
        "district": "Верх-Исетский",
        "horizon_months": 12,
        "confidence": "low",
        "index": 0.42,
        "breakdown": {"open_units": 1200, "hidden_units": 800},
    }


def _sample_scenarios() -> list[dict[str, Any]]:
    """Три ScenarioForecast.as_dict() (#984) — conservative/base/aggressive."""
    return [
        {
            "scenario": "conservative",
            "advisory": True,
            "rate_path": {12: 18.0},
            "forecasts": [{"horizon_months": 12, "deficit_index": 0.18, "confidence": "medium"}],
        },
        {
            "scenario": "base",
            "advisory": True,
            "rate_path": {12: 16.0},
            "forecasts": [{"horizon_months": 12, "deficit_index": 0.34, "confidence": "medium"}],
        },
        {
            "scenario": "aggressive",
            "advisory": True,
            "rate_path": {12: 13.0},
            "forecasts": [{"horizon_months": 12, "deficit_index": 0.49, "confidence": "medium"}],
        },
    ]


def _sample_overlay() -> dict[str, Any]:
    """Форма build_forecast_overlay (#983) — класс §10.2 / ranked_segments / USP / commercial."""
    return {
        "horizon_months": 12,
        "mode": "demand_supply",
        "advisory": True,
        "ranked_segments": [
            {
                "bucket": "1-Студия",
                "obj_class": "комфорт",
                "deficit_index": 0.34,
                "confidence": "medium",
            },
            {
                "bucket": "2-1-к",
                "obj_class": "комфорт",
                "deficit_index": 0.22,
                "confidence": "medium",
            },
        ],
        "warnings": [],
        "class_reco": {
            "obj_class": "комфорт",
            "mean_deficit_index": 0.28,
            "n_segments": 2,
            "reason": {
                "why": "Класс «комфорт»: сильнейший средний дефицит.",
                "drivers": [{"factor": "deficit_index", "value": 0.28, "direction": "+"}],
                "rejected": [],
                "what_would_change": ["Рост ставки → спрос мягче."],
                "confidence": "medium",
                "advisory": True,
            },
        },
        "usp": [
            {
                "segment": "1-Студия",
                "obj_class": "комфорт",
                "deficit_index": 0.34,
                "usp_text": "Дефицит формата «1-Студия (комфорт)» — стройте его.",
            }
        ],
        "commercial": {"available": False, "caveat": "коммерция: нет достаточных данных"},
    }


def _sample_product_scores() -> dict[str, Any]:
    """Форма ProductScoreCard.as_dict() (#985, 10 скоров + overall)."""
    return {
        "segment": {"obj_class": "комфорт", "room_bucket": "1-к 30-45"},
        "horizon_months": 12,
        "scores": {
            "market_fit": {"key": "market_fit", "value": 0.67, "confidence": "medium"},
            "demand": {"key": "demand", "value": 0.51, "confidence": "medium"},
        },
        "overall": 0.62,
        "advisory": True,
        "confidence": "medium",
    }


def _sample_special_indices() -> dict[str, Any]:
    """Форма SpecialIndices.as_dict() (#986, 6 индексов)."""
    return {
        "segment": {"obj_class": "комфорт"},
        "district": "Верх-Исетский",
        "indices": {
            "launch_window": {"key": "launch_window", "value": 0.6, "label": "12 мес"},
            "product_void": {"key": "product_void", "value": 0.4, "label": "2 белых пятна"},
        },
        "advisory": True,
        "confidence": "medium",
    }


def _full_assemble() -> SiteFinderReport:
    """Полный сбор отчёта из sample analyze dict + sample advisory dict'ов всех секций."""
    return assemble_report(
        _sample_analyze(),
        market_metrics=_sample_market_metrics(),
        supply_layers=_sample_supply_layers(),
        forecasts=_sample_forecasts(),
        future_supply=_sample_future_supply(),
        scenarios=_sample_scenarios(),
        recommendation_overlay=_sample_overlay(),
        product_scores=_sample_product_scores(),
        special_indices=_sample_special_indices(),
        segment={"obj_class": "комфорт", "room_bucket": "1-к 30-45"},
        cad_num="66:41:0000000:1",
        district="Верх-Исетский",
    )


# ── Полный сбор: восемь секций + JSON-serializability + advisory ──────────────


class TestFullAssemble:
    def test_returns_site_finder_report(self) -> None:
        assert isinstance(_full_assemble(), SiteFinderReport)

    def test_all_eight_sections_present_and_dicts(self) -> None:
        payload = _full_assemble().as_dict()
        for key in _SECTION_KEYS:
            assert key in payload, f"отсутствует секция {key}"
            assert isinstance(payload[key], dict)

    def test_as_dict_is_json_serializable(self) -> None:
        # Главный контракт #987: as_dict() проходит json.dumps без default= (ничего
        # сырого/dataclass'ов в выдаче). Полный round-trip (json.loads == payload) на
        # частичном/пустом отчёте ниже — здесь scenarios.rate_path несёт int-ключи
        # (как ScenarioForecast.as_dict()), которые JSON приводит к строкам, поэтому
        # строгое равенство неинформативно — важна именно сериализуемость.
        payload = _full_assemble().as_dict()
        dumped = json.dumps(payload, ensure_ascii=False)
        assert isinstance(dumped, str)
        assert json.loads(dumped) is not None

    def test_advisory_true_everywhere(self) -> None:
        payload = _full_assemble().as_dict()
        assert payload["advisory"] is True
        assert payload["meta"]["generated_advisory"] is True

    def test_schema_version_present(self) -> None:
        payload = _full_assemble().as_dict()
        assert payload["schema_version"] == payload["meta"]["schema_version"]


# ── market_now ← analyze + §9.2/§9.3 ──────────────────────────────────────────


class TestMarketNow:
    def test_market_metrics_and_supply_passed_through(self) -> None:
        market_now = _full_assemble().as_dict()["market_now"]
        assert market_now["market_metrics"]["unit_velocity"] == 8.2
        assert market_now["supply_layers"]["open_units"] == 1200

    def test_competitors_from_analyze(self) -> None:
        market_now = _full_assemble().as_dict()["market_now"]
        assert len(market_now["competitors"]) == 2
        assert market_now["competitors"][0]["comm_name"] == "ЖК Альфа"

    def test_summary_synthesized(self) -> None:
        market_now = _full_assemble().as_dict()["market_now"]
        assert market_now["summary"] is not None
        assert "абсорбция" in market_now["summary"]


# ── future_market ← forecasts / future_supply / scenarios ─────────────────────


class TestFutureMarket:
    def test_forecasts_by_horizon(self) -> None:
        fm = _full_assemble().as_dict()["future_market"]
        assert len(fm["forecasts_by_horizon"]) == 4
        assert fm["forecasts_by_horizon"][1]["deficit_index"] == 0.34

    def test_months_of_inventory_flows_through(self) -> None:
        # MOI прокидывается per-горизонт автоматически (passthrough as_dict()).
        fm = _full_assemble().as_dict()["future_market"]
        assert fm["forecasts_by_horizon"][1]["months_of_inventory"] == 24.2

    def test_summary_mentions_months_of_inventory(self) -> None:
        # future_market.summary несёт ДИСКРИМИНИРУЮЩИЙ MOI-headline рядом с дефицитом.
        fm = _full_assemble().as_dict()["future_market"]
        assert fm["summary"] is not None
        assert "мес конкурирующего предложения" in fm["summary"]

    def test_future_supply_passed_through(self) -> None:
        fm = _full_assemble().as_dict()["future_market"]
        assert fm["future_supply"]["index"] == 0.42

    def test_future_competitors_from_primary_horizon(self) -> None:
        fm = _full_assemble().as_dict()["future_market"]
        # Будущие конкуренты подняты с горизонта 12 мес (там список непуст).
        assert len(fm["future_competitors"]) == 1
        assert fm["future_competitors"][0]["comm_name"] == "ЖК Гамма"

    def test_scenarios_summary_deficit_spread(self) -> None:
        fm = _full_assemble().as_dict()["future_market"]
        assert fm["scenarios_summary"]["conservative"] == 0.18
        assert fm["scenarios_summary"]["aggressive"] == 0.49


# ── product_tz ← overlay #983 ─────────────────────────────────────────────────


class TestProductTz:
    def test_class_from_overlay(self) -> None:
        pt = _full_assemble().as_dict()["product_tz"]
        assert pt["obj_class"] == "комфорт"

    def test_mix_from_ranked_segments(self) -> None:
        pt = _full_assemble().as_dict()["product_tz"]
        assert len(pt["mix"]) == 2
        assert pt["mix"][0]["bucket"] == "1-Студия"

    def test_usp_and_commercial(self) -> None:
        pt = _full_assemble().as_dict()["product_tz"]
        assert len(pt["usp"]) == 1
        assert pt["commercial"]["available"] is False

    def test_reasons_lifted_from_class_reco(self) -> None:
        pt = _full_assemble().as_dict()["product_tz"]
        assert len(pt["reasons"]) == 1
        assert "комфорт" in pt["reasons"][0]["why"]


# ── #1959 honesty-guard: вся сетка прижата к ±1.0 → нет категоричных вердиктов ──


class TestDegenerateHonestyGuard:
    def test_all_cells_clamped_detector(self) -> None:
        # Все измеримые |di| ≥ порога → degenerate.
        assert _all_cells_clamped([-1.0, -1.0, 1.0]) is True
        assert _all_cells_clamped([-1.0, -0.9999999]) is True
        # Хотя бы одна различается → НЕ degenerate.
        assert _all_cells_clamped([-1.0, 0.3]) is False
        assert _all_cells_clamped([0.0]) is False
        # None-ячейки / пусто / мусор → False (нечего защищать).
        assert _all_cells_clamped([None, None]) is False
        assert _all_cells_clamped([]) is False
        assert _all_cells_clamped([True, "x"]) is False

    def test_degenerate_grid_suppresses_verdicts(self) -> None:
        # Вся сетка −1.0 → вместо «строить»/«избегать» — честная плашка.
        product_tz = {
            "ranked_segments": [
                {"bucket": "1-Студия", "obj_class": "стандарт", "deficit_index": -1.0},
                {"bucket": "2-1к", "obj_class": "комфорт", "deficit_index": -1.0},
            ]
        }
        pt = _build_product_tz(product_tz).as_dict()
        assert all(m["signal"] == _DEGENERATE_SEGMENT_NOTE for m in pt["mix"])
        assert all(m["signal"] not in {"строить", "избегать", "баланс"} for m in pt["mix"])

    def test_differentiated_grid_keeps_categorical_verdicts(self) -> None:
        # Ячейки различаются → категоричные вердикты честны (фикс #1959 сработал).
        product_tz = {
            "ranked_segments": [
                {"bucket": "1-Студия", "obj_class": "комфорт", "deficit_index": 0.73},
                {"bucket": "2-3к", "obj_class": "комфорт", "deficit_index": -0.14},
                {"bucket": "3-Студия", "obj_class": "стандарт", "deficit_index": -1.0},
            ]
        }
        pt = _build_product_tz(product_tz).as_dict()
        signals = {m["bucket"]: m["signal"] for m in pt["mix"]}
        assert signals["1-Студия"] == "строить"
        assert signals["2-3к"] == "избегать"
        assert signals["3-Студия"] == "избегать"
        assert _DEGENERATE_SEGMENT_NOTE not in signals.values()


# ── scenarios ← #984 by_scenario ──────────────────────────────────────────────


class TestScenarios:
    def test_by_scenario_keyed(self) -> None:
        sc = _full_assemble().as_dict()["scenarios"]
        assert set(sc["by_scenario"].keys()) == {"conservative", "base", "aggressive"}

    def test_summary_present(self) -> None:
        sc = _full_assemble().as_dict()["scenarios"]
        assert sc["summary"] is not None


# ── scoring ← #985 + #986 ─────────────────────────────────────────────────────


class TestScoring:
    def test_product_scores_and_overall(self) -> None:
        scoring = _full_assemble().as_dict()["scoring"]
        assert scoring["product_scores"]["overall"] == 0.62
        assert scoring["overall"] == 0.62

    def test_special_indices_passed_through(self) -> None:
        scoring = _full_assemble().as_dict()["scoring"]
        assert scoring["special_indices"]["indices"]["launch_window"]["value"] == 0.6


# ── confidence ← compute_report_confidence (#990) ─────────────────────────────


class TestConfidence:
    def test_level_computed(self) -> None:
        conf = _full_assemble().as_dict()["confidence"]
        assert conf["level"] in ("high", "medium", "low")

    def test_advisory_never_high(self) -> None:
        # advisory=True → #990 cap 'medium' (НИКОГДА 'high').
        conf = _full_assemble().as_dict()["confidence"]
        assert conf["level"] != "high"

    def test_structural_rationale_present(self) -> None:
        conf = _full_assemble().as_dict()["confidence"]
        assert isinstance(conf["rationale"], str) and conf["rationale"]

    def test_factors_carry_advisory_capped(self) -> None:
        conf = _full_assemble().as_dict()["confidence"]
        assert "advisory_capped" in conf["factors"]

    def test_data_quality_factors_extracted(self) -> None:
        # deal_count (n_sold=88) / analog_count (obj_count=7) / domrf_coverage (0.55)
        # извлечены и попали в факторы #990.
        conf = _full_assemble().as_dict()["confidence"]
        factors = conf["factors"]
        assert "deal_count" in factors
        assert "analog_count" in factors
        assert "domrf_coverage" in factors

    def test_confounded_window_factor_surfaces_when_any_forecast_confounded(self) -> None:
        # #1222 end-to-end: хотя бы один forecast.confounded=True → confounded_window
        # фактор присутствует и тянет уровень в 'low' (#990: confounded fact-level low,
        # weakest-link → итог 'low'; advisory cap ниже 'low' не двигает).
        forecasts = _sample_forecasts()
        forecasts[2]["confounded"] = True  # 18-мес forecast confounded
        report = assemble_report(
            _sample_analyze(),
            market_metrics=_sample_market_metrics(),
            supply_layers=_sample_supply_layers(),
            forecasts=forecasts,
            future_supply=_sample_future_supply(),
            scenarios=_sample_scenarios(),
            recommendation_overlay=_sample_overlay(),
            product_scores=_sample_product_scores(),
            special_indices=_sample_special_indices(),
            cad_num="66:41:0000000:1",
            district="Верх-Исетский",
        ).as_dict()
        factors = report["confidence"]["factors"]
        assert "confounded_window" in factors, "шок-окно перманентно мёртв — #1222 регрессия"
        assert factors["confounded_window"]["value"] is True
        assert factors["confounded_window"]["level"] == "low"
        # Weakest-link MIN → итоговый уровень тоже 'low' (был бы 'medium' без шока).
        assert report["confidence"]["level"] == "low"

    def test_confounded_window_factor_absent_when_no_forecast_confounded(self) -> None:
        # Зеркальный кейс: все forecast'ы confounded=False → factor отсутствует
        # (confidence_engine добавляет confounded factor ТОЛЬКО при True, line 450 —
        # чистое окно не тянет искусственно вверх, иначе тонкие отчёты получили бы
        # фантомный 'high'-вклад). Гарантия, что previous test НЕ false-positive
        # (factor не «всегда low»), достигается тем, что предыдущий тест проверяет
        # уровень: «low» доступен только если confounded factor реально добавлен.
        conf = _full_assemble().as_dict()["confidence"]
        factors = conf["factors"]
        assert "confounded_window" not in factors


# ── _component_confidences — per-horizon forecast collapse (#1958) ────────────


class TestComponentConfidencesForecastCollapse:
    """#1958: forecasts отдаёт confidence НА КАЖДЫЙ горизонт; раньше каждый горизонт
    эмитился отдельной парой → «Прогноз спрос/предложение» дублировался ×N в факторах.
    Теперь сворачиваем в ОДНУ пару weakest-link'ом (MIN ранга по горизонтам)."""

    def test_four_horizons_collapse_to_one_forecast_pair(self) -> None:
        forecasts = [
            {"horizon_months": 6, "confidence": "high"},
            {"horizon_months": 12, "confidence": "high"},
            {"horizon_months": 18, "confidence": "high"},
            {"horizon_months": 24, "confidence": "high"},
        ]
        out = _component_confidences(None, None, forecasts, None, None)
        forecast_pairs = [p for p in out if p[0] == "forecasts"]
        assert len(forecast_pairs) == 1, "per-горизонт forecast confidences не свёрнуты (#1958)"
        assert forecast_pairs[0] == ("forecasts", "high")

    def test_weakest_link_min_across_horizons(self) -> None:
        # Худший горизонт (low) тянет общий forecast-фактор вниз.
        forecasts = [
            {"horizon_months": 6, "confidence": "high"},
            {"horizon_months": 12, "confidence": "medium"},
            {"horizon_months": 18, "confidence": "low"},
            {"horizon_months": 24, "confidence": "high"},
        ]
        out = _component_confidences(None, None, forecasts, None, None)
        forecast_pairs = [p for p in out if p[0] == "forecasts"]
        assert forecast_pairs == [("forecasts", "low")]

    def test_no_forecast_pair_when_all_confidences_missing(self) -> None:
        forecasts = [
            {"horizon_months": 6},  # нет confidence
            {"horizon_months": 12, "confidence": "garbage"},  # не whitelisted
        ]
        out = _component_confidences(None, None, forecasts, None, None)
        assert [p for p in out if p[0] == "forecasts"] == []

    def test_other_service_pairs_intact(self) -> None:
        # Свёртка forecasts не должна задевать остальные per-service факторы.
        out = _component_confidences(
            {"confidence": "medium"},  # market_metrics
            {"confidence": "high"},  # future_supply
            [{"horizon_months": 12, "confidence": "low"}],
            {"confidence": "high"},  # product_scores
            {"confidence": "medium"},  # special_indices
        )
        names = [p[0] for p in out]
        assert names.count("forecasts") == 1
        assert ("market_metrics", "medium") in out
        assert ("future_supply", "high") in out
        assert ("product_scores", "high") in out
        assert ("special_indices", "medium") in out


# ── exec_summary — синтез ─────────────────────────────────────────────────────


class TestExecSummary:
    def test_headline_non_empty(self) -> None:
        es = _full_assemble().as_dict()["exec_summary"]
        assert isinstance(es["headline"], str) and es["headline"]

    def test_headline_mentions_class(self) -> None:
        es = _full_assemble().as_dict()["exec_summary"]
        assert "комфорт" in es["headline"]

    def test_verdict_and_key_numbers(self) -> None:
        es = _full_assemble().as_dict()["exec_summary"]
        assert es["verdict"] is not None
        assert es["key_numbers"]["deficit_index"] == 0.34
        assert es["key_numbers"]["months_of_inventory"] == 24.2
        assert es["key_numbers"]["overall_score"] == 0.62

    def test_verdict_mentions_months_of_inventory(self) -> None:
        # ДИСКРИМИНИРУЮЩИЙ MOI-headline присутствует в вердикте exec_summary.
        es = _full_assemble().as_dict()["exec_summary"]
        assert "мес конкурирующего предложения" in es["verdict"]

    def test_overall_confidence_matches_section(self) -> None:
        payload = _full_assemble().as_dict()
        assert payload["exec_summary"]["overall_confidence"] == payload["confidence"]["level"]


# ── meta ──────────────────────────────────────────────────────────────────────


class TestMeta:
    def test_context_fields(self) -> None:
        meta = _full_assemble().as_dict()["meta"]
        assert meta["cad_num"] == "66:41:0000000:1"
        assert meta["district"] == "Верх-Исетский"
        assert meta["segment"]["obj_class"] == "комфорт"
        assert meta["horizons"] == [6, 12, 18, 24]


# ── Частичный сбор: ТОЛЬКО analyze dict → валидный частичный отчёт ─────────────


class TestPartialAssemble:
    def _partial(self) -> SiteFinderReport:
        return assemble_report(_sample_analyze(), cad_num="66:41:0000000:1")

    def test_valid_partial_report(self) -> None:
        report = self._partial()
        assert isinstance(report, SiteFinderReport)
        payload = report.as_dict()
        # Все восемь секций присутствуют (контракт стабилен), JSON-safe.
        for key in _SECTION_KEYS:
            assert key in payload
        assert json.loads(json.dumps(payload, ensure_ascii=False)) == payload

    def test_market_now_filled_from_analyze(self) -> None:
        payload = self._partial().as_dict()
        # competitors из analyze есть; market_metrics/supply_layers None (не переданы).
        assert len(payload["market_now"]["competitors"]) == 2
        assert payload["market_now"]["market_metrics"] is None
        assert payload["market_now"]["supply_layers"] is None

    def test_future_sections_empty(self) -> None:
        payload = self._partial().as_dict()
        assert payload["future_market"]["forecasts_by_horizon"] == []
        assert payload["future_market"]["future_supply"] is None
        assert payload["scenarios"]["by_scenario"] == {}
        assert payload["scoring"]["product_scores"] is None
        assert payload["product_tz"]["obj_class"] is None

    def test_confidence_capped_advisory_on_partial(self) -> None:
        # Без forecasts/metrics #990 видит из analyze только analog_count (12 →
        # high) + domrf_coverage (75% → 0.75 → high); None-сигналы (deal_count/
        # history) #990 в факторы НЕ добавляет (None ≠ low-фактор). Weakest-link →
        # high, но advisory-cap опускает до 'medium' (НИКОГДА не 'high').
        payload = self._partial().as_dict()
        assert payload["confidence"]["level"] == "medium"
        assert payload["confidence"]["level"] != "high"
        assert payload["confidence"]["rationale"]

    def test_exec_summary_still_has_headline(self) -> None:
        payload = self._partial().as_dict()
        assert payload["exec_summary"]["headline"]

    def test_advisory_true(self) -> None:
        assert self._partial().as_dict()["advisory"] is True


# ── Graceful: пустой analyze {} → отчёт всё равно валиден ──────────────────────


class TestGracefulEmpty:
    def test_empty_analyze_valid_report(self) -> None:
        report = assemble_report({})
        payload = report.as_dict()
        for key in _SECTION_KEYS:
            assert key in payload
        assert json.loads(json.dumps(payload, ensure_ascii=False)) == payload
        assert payload["advisory"] is True
        # Всё тонко → confidence 'low', headline честный «недостаточно данных».
        assert payload["confidence"]["level"] == "low"
        assert payload["exec_summary"]["headline"]

    def test_all_none_inputs(self) -> None:
        # Явные None всех под-сервисов — не должно бросать, отчёт валиден.
        report = assemble_report(
            {},
            market_metrics=None,
            supply_layers=None,
            forecasts=None,
            future_supply=None,
            scenarios=None,
            recommendation_overlay=None,
            product_scores=None,
            special_indices=None,
        )
        assert isinstance(report, SiteFinderReport)


# ── Вход КАК dataclass, ТАК и as_dict-словарь → нормализуется ──────────────────


@dataclass(frozen=True)
class _FakeMetrics:
    """Минимальный stand-in под-сервиса с `as_dict()` (как реальный MarketMetrics)."""

    unit_velocity: float
    obj_count: int
    n_sold: int

    def as_dict(self) -> dict[str, Any]:
        return {
            "unit_velocity": self.unit_velocity,
            "obj_count": self.obj_count,
            "n_sold": self.n_sold,
            "confidence": "medium",
            "window_months": 18,
        }


class TestInputNormalization:
    def test_dataclass_instance_accepted(self) -> None:
        # Передаём ОБЪЕКТ (не as_dict) — сборщик нормализует через _as_dict_or.
        report = assemble_report(_sample_analyze(), market_metrics=_FakeMetrics(8.2, 7, 88))
        market_now = report.as_dict()["market_now"]
        assert market_now["market_metrics"]["unit_velocity"] == 8.2

    def test_dataclass_and_dict_give_same_market_metrics(self) -> None:
        from_obj = assemble_report(
            _sample_analyze(), market_metrics=_FakeMetrics(8.2, 7, 88)
        ).as_dict()
        from_dict = assemble_report(
            _sample_analyze(), market_metrics=_FakeMetrics(8.2, 7, 88).as_dict()
        ).as_dict()
        assert from_obj["market_now"]["market_metrics"] == from_dict["market_now"]["market_metrics"]


# ── Pure-хелперы извлечения сигналов (юнит, без БД) ────────────────────────────


class TestSignalExtractionHelpers:
    def test_as_dict_or_normalizes(self) -> None:
        assert _as_dict_or(None) is None
        assert _as_dict_or({"a": 1}) == {"a": 1}
        assert _as_dict_or(_FakeMetrics(1.0, 2, 3))["obj_count"] == 2
        assert _as_dict_or(42) is None  # мусор → None (graceful)

    def test_deal_count_is_windowed_not_cumulative(self) -> None:
        # #1964: deal_count = unit_velocity × window_months (продажи ЗА ОКНО), НЕ
        # кумулятивный n_sold. confidence_engine помечает фактор «за N мес» и гейтит
        # порогами окна — кумулятивный n_sold (≫50) сделал бы гейт бессмысленным и
        # подпись лживой.
        assert _deal_count({}, {"unit_velocity": 8.2, "window_months": 18}) == round(8.2 * 18)
        # n_sold ИГНОРИРУЕТСЯ (кумулятив, не оконный) — даже когда присутствует.
        assert _deal_count({}, {"n_sold": 99000, "unit_velocity": 5.0, "window_months": 6}) == 30
        # нет unit_velocity / window / market_metrics → None (нет оконного сигнала)
        assert _deal_count({}, {"n_sold": 88}) is None
        assert _deal_count({}, {"unit_velocity": 8.2}) is None
        assert _deal_count({}, {"unit_velocity": 8.2, "window_months": 0}) is None
        assert _deal_count({}, None) is None

    def test_analog_count_prefers_obj_count(self) -> None:
        assert _analog_count({"market_pulse": {"competitors_total": 12}}, {"obj_count": 7}) == 7

    def test_analog_count_fallback_to_analyze(self) -> None:
        assert _analog_count({"market_pulse": {"competitors_total": 12}}, None) == 12
        assert _analog_count({"competitors": [{}, {}, {}]}, None) == 3
        assert _analog_count({}, None) is None

    def test_domrf_coverage_fraction_and_percent(self) -> None:
        # supply_layers доля (0.55) → как есть; analyze проценты (75.0) → /100.
        assert _domrf_coverage({}, {"domrf_coverage": 0.55}) == 0.55
        assert _domrf_coverage({"market_data_coverage_pct": 75.0}, None) == 0.75
        assert _domrf_coverage({}, None) is None

    def test_domrf_coverage_sub_one_percent_not_read_as_fraction(self) -> None:
        # BUG #3: настоящий sub-1% процент (0.8% покрытия) — percent-ветка делит на 100
        # → 0.008, НЕ 0.8 (что было бы 80% и инфлировало бы confidence в exactly
        # near-zero кейсе, который §15 призван флагать).
        assert _domrf_coverage({"market_data_coverage_pct": 0.8}, None) == 0.008
        # Каноничный sparse-сигнал проекта ~2.5%.
        assert _domrf_coverage({"market_data_coverage_pct": 2.5}, None) == 0.025

    def test_domrf_coverage_fraction_source_unchanged(self) -> None:
        # supply_layers — уже доля: 0.025 остаётся 0.025 (НЕ делим на 100).
        assert _domrf_coverage({}, {"domrf_coverage": 0.025}) == 0.025

    def test_history_months_from_window(self) -> None:
        assert _history_months({"window_months": 18}, []) == 18
        assert _history_months(None, []) is None

    def test_confounded_any_horizon(self) -> None:
        assert _confounded([{"confounded": False}, {"confounded": True}]) is True
        assert _confounded([{"is_confounded_window": True}]) is True
        assert _confounded([{"confounded": False}]) is False
        assert _confounded([]) is False

    def test_confounded_missing_key_is_false_not_keyerror(self) -> None:
        # #1222: forecast БЕЗ confounded-ключа НЕ должен ронять (.get() default → None
        # ≠ True → False). Раньше .get() уже стоял, но это контракт-тест: гарантирует,
        # что defensive-чтение НЕ деградирует в KeyError при произвольных формах.
        assert _confounded([{}, {"horizon_months": 12}]) is False
        assert _confounded([{"confounded": "no"}]) is False  # truthy-but-not-True → False
        assert _confounded([{"confounded": 1}]) is False  # «is True» строго, не truthy

    def test_confounded_reads_real_demand_supply_forecast_as_dict(self) -> None:
        # #1222 regression: DemandSupplyForecast.as_dict() ОБЯЗАН нести ключ `confounded`,
        # иначе шок-окно §15 (#990) перманентно мёртв. Строим РЕАЛЬНЫЙ frozen-dataclass
        # БЕЗ БД, сериализуем через его собственный `as_dict()` и убеждаемся, что
        # `_confounded` видит флаг (контракт-тест: ловит дрейф ключа продьюсера).
        from app.services.forecasting.demand_supply_forecast import DemandSupplyForecast

        clean = DemandSupplyForecast(
            segment={"obj_class": "комфорт"},
            horizon_months=12,
            base_pace_units_per_mo=8.0,
            demand_norm_coefficient=1.0,
            macro_coefficient=1.0,
            projected_demand_units=100.0,
            open_units=300,
            hidden_release_units=80.0,
            future_online_units=20.0,
            projected_supply_units=400.0,
            balance_units=-300.0,
            balance_ratio=0.25,
            deficit_index=-0.5,
            months_of_inventory=48.0,
            rate_future=18.0,
            rate_sensitivity_phrase=None,
            future_competitors=[],
            advisory=True,
            confidence="medium",
            confounded=False,
        ).as_dict()
        shock = DemandSupplyForecast(
            segment={"obj_class": "комфорт"},
            horizon_months=24,
            base_pace_units_per_mo=8.0,
            demand_norm_coefficient=1.0,
            macro_coefficient=1.0,
            projected_demand_units=200.0,
            open_units=300,
            hidden_release_units=80.0,
            future_online_units=20.0,
            projected_supply_units=400.0,
            balance_units=-200.0,
            balance_ratio=0.5,
            deficit_index=-0.3,
            months_of_inventory=24.0,
            rate_future=18.0,
            rate_sensitivity_phrase=None,
            future_competitors=[],
            advisory=True,
            confidence="medium",
            confounded=True,
        ).as_dict()
        assert "confounded" in clean and "confounded" in shock  # контракт ключа
        assert _confounded([clean]) is False
        assert _confounded([clean, shock]) is True

    def test_primary_deficit_prefers_12mo(self) -> None:
        forecasts = [
            {"horizon_months": 6, "deficit_index": 0.21},
            {"horizon_months": 12, "deficit_index": 0.34},
        ]
        assert _primary_deficit_index(forecasts) == 0.34

    def test_primary_deficit_fallback_first(self) -> None:
        forecasts = [{"horizon_months": 6, "deficit_index": 0.21}]
        assert _primary_deficit_index(forecasts) == 0.21
        assert _primary_deficit_index([]) is None

    def test_primary_moi_prefers_12mo(self) -> None:
        forecasts = [
            {"horizon_months": 6, "months_of_inventory": 18.5},
            {"horizon_months": 12, "months_of_inventory": 24.2},
        ]
        assert _primary_months_of_inventory(forecasts) == 24.2

    def test_primary_moi_fallback_first(self) -> None:
        forecasts = [{"horizon_months": 6, "months_of_inventory": 18.5}]
        assert _primary_months_of_inventory(forecasts) == 18.5
        assert _primary_months_of_inventory([]) is None

    def test_primary_moi_none_when_absent(self) -> None:
        # forecast без MOI-ключа → None (graceful, не KeyError).
        assert _primary_months_of_inventory([{"horizon_months": 12, "deficit_index": -1.0}]) is None