From 871981035a79518c5ece9cc278dad52b44109c72 Mon Sep 17 00:00:00 2001 From: Suriya Date: Mon, 6 Jul 2026 15:15:51 +0530 Subject: [PATCH] new changes in the api --- app/__pycache__/main.cpython-312.pyc | Bin 8769 -> 8232 bytes .../dynamic_config.cpython-312.pyc | Bin 17416 -> 11175 bytes app/config/dynamic_config.py | 199 +--------- .../__pycache__/arrow_utils.cpython-312.pyc | Bin 3658 -> 1794 bytes app/core/arrow_utils.py | 32 +- app/main.py | 13 - .../__pycache__/__init__.cpython-312.pyc | Bin 511 -> 554 bytes .../__pycache__/ml_admin.cpython-312.pyc | Bin 27267 -> 21921 bytes .../__pycache__/optimization.cpython-312.pyc | Bin 54237 -> 48731 bytes app/routes/__pycache__/riders.cpython-312.pyc | Bin 0 -> 6788 bytes app/routes/ml_admin.py | 157 +------- app/routes/optimization.py | 138 +------ .../assignment_service.cpython-312.pyc | Bin 43539 -> 33487 bytes app/services/core/assignment_service.py | 227 +----------- .../ml_data_collector.cpython-312.pyc | Bin 30158 -> 25809 bytes app/services/ml/behavior_analyzer.py | 55 --- app/services/ml/ml_data_collector.py | 114 +----- app/services/ml/strategy_bandit.py | 277 -------------- .../get_active_riders.cpython-312.pyc | Bin 0 -> 3916 bytes .../rider_history_service.cpython-312.pyc | Bin 0 -> 4421 bytes .../rider_state_manager.cpython-312.pyc | Bin 0 -> 6123 bytes .../substitution_service.cpython-312.pyc | Bin 0 -> 9181 bytes .../clustering_service.cpython-312.pyc | Bin 13764 -> 11312 bytes .../delivery_history_service.cpython-312.pyc | Bin 45210 -> 48069 bytes .../__pycache__/gps_smoother.cpython-312.pyc | Bin 0 -> 7355 bytes .../realistic_eta_calculator.cpython-312.pyc | Bin 5009 -> 4038 bytes .../route_optimizer.cpython-312.pyc | Bin 68523 -> 53436 bytes .../__pycache__/zone_service.cpython-312.pyc | Bin 6355 -> 6314 bytes .../routing/delivery_history_service.py | 67 +++- app/services/routing/gps_smoother.py | 150 ++++++++ app/services/routing/kalman_filter.py | 327 ----------------- app/services/routing/route_optimizer.py | 341 +----------------- .../delivery_history_store.cpython-312.pyc | Bin 35056 -> 38490 bytes app/services/vector/delivery_history_store.py | 140 +++++-- app/templates/ml_dashboard.html | 142 +------- data/rider_active_state.pkl | 1 + data/substitutions.db | Bin 0 -> 12288 bytes .../faiss_history/delivery_history.db.meta | 7 + ml_data/faiss_history/delivery_history.meta | 1 + .../delivery_history_records.db.pkl | Bin 0 -> 367714 bytes .../delivery_history_vectors.db.npy | Bin 0 -> 69696 bytes ml_data/ml_store.db | Bin 37826560 -> 38506496 bytes requirements.txt | 1 - 43 files changed, 414 insertions(+), 1975 deletions(-) create mode 100644 app/routes/__pycache__/riders.cpython-312.pyc delete mode 100644 app/services/ml/behavior_analyzer.py delete mode 100644 app/services/ml/strategy_bandit.py create mode 100644 app/services/rider/__pycache__/get_active_riders.cpython-312.pyc create mode 100644 app/services/rider/__pycache__/rider_history_service.cpython-312.pyc create mode 100644 app/services/rider/__pycache__/rider_state_manager.cpython-312.pyc create mode 100644 app/services/rider/__pycache__/substitution_service.cpython-312.pyc create mode 100644 app/services/routing/__pycache__/gps_smoother.cpython-312.pyc create mode 100644 app/services/routing/gps_smoother.py delete mode 100644 app/services/routing/kalman_filter.py create mode 100644 data/rider_active_state.pkl create mode 100644 data/substitutions.db create mode 100644 ml_data/faiss_history/delivery_history.db.meta create mode 100644 ml_data/faiss_history/delivery_history_records.db.pkl create mode 100644 ml_data/faiss_history/delivery_history_vectors.db.npy diff --git a/app/__pycache__/main.cpython-312.pyc b/app/__pycache__/main.cpython-312.pyc index 75c5542bb7f33e3ca8448a4b97c11b268668a8db..9f1d46b42ad7f71e2868b4693419995181f96e07 100644 GIT binary patch delta 1744 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z`W4xh)@*aj-4powjBTbF&I;W#r_OaQGMiZL_ovth$JX3oDDC4JoA1TU8MAKTSkLTU zv;@yxhuz7Yu>v-*UGs2l1jQwF5evQ3$yQ!6=?oNC_(d%A9l>^X7mMw%ywbZ!OC2WJ h%l5H%OpR-FihY-%>zlZ;<{e-1@Sffa{U6Daj{X1u diff --git a/app/config/dynamic_config.py b/app/config/dynamic_config.py index 08569aa..b73f18b 100644 --- a/app/config/dynamic_config.py +++ b/app/config/dynamic_config.py @@ -1,14 +1,14 @@ """ Dynamic Configuration - rider-api -Replaces all hardcoded hyperparameters with DB-backed values. -The ML hypertuner writes optimal values here; services read from here. +Replaces all hardcoded hyperparameters with DB-backed values. The autonomous +agents (ETA sync, road-sequencing, rider affinity) and the ml_admin API write +tuned values here; services read from here. Fallback: If DB is unavailable or no tuned values exist, defaults are used. This means zero risk - the system works day 1 with no data. """ -import contextvars import json import logging import os @@ -21,32 +21,9 @@ logger = logging.getLogger(__name__) # --- DB Path ------------------------------------------------------------------ _DB_PATH = os.getenv("ML_DB_PATH", "ml_data/ml_store.db") -# --------------------------------------------------------------------------- -# Per-request strategy override (async-safe via contextvars). -# Each FastAPI request/asyncio task gets its own copy — no cross-request leaks. -# Usage: -# set_request_strategy("fuel_saver") → override active for this request -# clear_request_strategy() → restore to DB value -# --------------------------------------------------------------------------- -_strategy_override: contextvars.ContextVar[Optional[str]] = contextvars.ContextVar( - "strategy_override", default=None -) - - -def set_request_strategy(strategy: Optional[str]) -> None: - """Override ml_strategy for the current async task only (thread-safe).""" - _strategy_override.set(strategy) - - -def clear_request_strategy() -> None: - """Remove the per-request strategy override for the current async task.""" - _strategy_override.set(None) - # --- Hard Defaults (What the system used before ML) --------------------------- DEFAULTS: Dict[str, Any] = { - # System Strategy / Prompt - "ml_strategy": "balanced", # AssignmentService "max_pickup_distance_km": 10.0, "max_kitchen_distance_km": 3.0, @@ -65,9 +42,6 @@ DEFAULTS: Dict[str, Any] = { "road_factor": 1.3, # ClusteringService "cluster_radius_km": 3.0, - # KalmanFilter - "kalman_process_noise": 1e-4, - "kalman_measurement_noise": 0.01, # RealisticETACalculator "eta_pickup_time_min": 3.0, "eta_delivery_time_min": 4.0, @@ -101,6 +75,18 @@ DEFAULTS: Dict[str, Any] = { "rider_affinity_enabled": True, # False -> pure curated config "rider_affinity_min_deliveries": 10, # learned owner needs >= this many deliveries "rider_affinity_refresh_hours": 6, # recompute cadence (piggybacks the agent) + # Phase-0 kitchen+zone pattern store (delivery_history_store.py): which rider + # historically owns a kitchen->drop-zone pair, used to pre-assign orders. + # "csv" -> legacy: built from delivery_details.csv, only refreshed when a + # human re-exports it and calls POST /ml/reload-history. + # "db" -> built from the already-synced nearledb mirror (delivery_raw), + # rebuilt automatically every eta_sync_interval_hours — no manual + # step, no extra DB load (reuses rows the ETA sync already pulled). + "pattern_source": "csv", + "pattern_history_days": 30, # retention floor for delivery_raw kept for pattern- + # matching volume; independent of eta_history_days + # so the (already-validated) empirical ETA window + # is untouched. } @@ -137,18 +123,8 @@ class DynamicConfig: # -------------------------------------------------------------------------- def get(self, key: str, default: Any = None) -> Any: - """Get a config value. Returns ML-tuned value if available, else default. - - For 'ml_strategy' specifically, a per-request ContextVar override takes - precedence so that hypertuning_params requests don't mutate the shared - singleton (thread-safe for concurrent FastAPI requests). - """ + """Get a config value. Returns ML-tuned value if available, else default.""" self._maybe_reload() - # Per-request strategy override (async-safe, no cross-request leaks) - if key == "ml_strategy": - override = _strategy_override.get() - if override is not None: - return override val = self._cache.get(key) if val is not None: return val @@ -163,7 +139,7 @@ class DynamicConfig: return result def set(self, key: str, value: Any, source: str = "manual") -> None: - """Write a config value to DB (used by hypertuner).""" + """Write a config value to DB (used by the ml_admin API and agents).""" try: os.makedirs(os.path.dirname(_DB_PATH) or ".", exist_ok=True) conn = sqlite3.connect(_DB_PATH) @@ -185,8 +161,8 @@ class DynamicConfig: except Exception as e: logger.error(f"[DynamicConfig] Failed to set {key}: {e}") - def set_bulk(self, params: Dict[str, Any], source: str = "ml_hypertuner") -> None: - """Write multiple config values at once (called after each Optuna study).""" + def set_bulk(self, params: Dict[str, Any], source: str = "manual") -> None: + """Write multiple config values at once.""" for key, value in params.items(): self.set(key, value, source=source) logger.info(f"[DynamicConfig] Bulk update: {len(params)} params from {source}") @@ -219,16 +195,6 @@ class DynamicConfig: updated_at TEXT ) """) - conn.execute(""" - CREATE TABLE IF NOT EXISTS kitchen_encoding ( - kitchen_name TEXT PRIMARY KEY, - label_id INTEGER NOT NULL, - frequency REAL DEFAULT 0.0, - avg_profit REAL DEFAULT 0.0, - order_count INTEGER DEFAULT 0, - updated_at TEXT - ) - """) conn.commit() conn.close() except Exception as e: @@ -278,131 +244,4 @@ def get_config() -> DynamicConfig: __all__ = [ "DynamicConfig", "get_config", - "set_request_strategy", - "clear_request_strategy", - "get_kitchen_label_id", - "get_kitchen_frequency", - "update_kitchen_stats", - "get_kitchen_avg_profit_smoothed", ] - - -# --- Kitchen Encoding Persistence --------------------------------------------- -def get_kitchen_label_id(kitchen_name: str) -> int: - """Get or create persistent label ID for a kitchen.""" - try: - conn = sqlite3.connect(_DB_PATH) - row = conn.execute( - "SELECT label_id FROM kitchen_encoding WHERE kitchen_name = ?", - (kitchen_name,), - ).fetchone() - if row: - conn.close() - return row[0] - - max_id = conn.execute( - "SELECT COALESCE(MAX(label_id), -1) FROM kitchen_encoding" - ).fetchone()[0] - new_id = max_id + 1 - conn.close() - return new_id - except Exception as e: - logger.warning(f"[KitchenEncoding] Failed to get label_id: {e}") - return hash(kitchen_name.lower().strip()) % 10000 - - -def get_kitchen_frequency(kitchen_name: str) -> float: - """Get frequency ratio for a kitchen from DB.""" - try: - conn = sqlite3.connect(_DB_PATH) - row = conn.execute( - "SELECT frequency FROM kitchen_encoding WHERE kitchen_name = ?", - (kitchen_name,), - ).fetchone() - conn.close() - return row[0] if row else 0.0 - except Exception: - return 0.0 - - -def update_kitchen_stats(kitchen_name: str, profit: float): - """Update kitchen stats after order completion.""" - try: - conn = sqlite3.connect(_DB_PATH) - row = conn.execute( - "SELECT order_count, avg_profit FROM kitchen_encoding WHERE kitchen_name = ?", - (kitchen_name,), - ).fetchone() - - if row: - count, avg = row - new_count = count + 1 - new_avg = ((avg * count) + profit) / new_count - new_freq = new_count / ( - conn.execute( - "SELECT SUM(order_count) FROM kitchen_encoding" - ).fetchone()[0] - or 1 - ) - conn.execute( - "UPDATE kitchen_encoding SET order_count = ?, avg_profit = ?, frequency = ?, updated_at = ? WHERE kitchen_name = ?", - ( - new_count, - new_avg, - new_freq, - datetime.utcnow().isoformat(), - kitchen_name, - ), - ) - else: - conn.execute( - "INSERT INTO kitchen_encoding (kitchen_name, label_id, frequency, avg_profit, order_count, updated_at) VALUES (?, ?, ?, ?, ?, ?)", - ( - kitchen_name, - get_kitchen_label_id(kitchen_name), - 1.0, - profit, - 1, - datetime.utcnow().isoformat(), - ), - ) - conn.commit() - conn.close() - except Exception as e: - logger.warning(f"[KitchenEncoding] Failed to update stats: {e}") - - -def get_kitchen_avg_profit_smoothed( - kitchen_name: str, global_avg: float = 40.0, min_samples: int = 5 -) -> float: - """ - Get smoothed average profit for a kitchen using Bayesian smoothing. - Reduces noise for kitchens with few orders. - - Formula: smoothed = (kitchen_count * kitchen_avg + min_samples * global_avg) / (kitchen_count + min_samples) - - This means: - - Kitchen with many samples -> uses its own avg - - Kitchen with few samples -> pulls toward global avg - """ - try: - conn = sqlite3.connect(_DB_PATH) - row = conn.execute( - "SELECT order_count, avg_profit FROM kitchen_encoding WHERE kitchen_name = ?", - (kitchen_name,), - ).fetchone() - conn.close() - - if row and row[0] > 0: - count, avg = row - if count >= min_samples: - return avg - # Bayesian smoothing - smoothed = ((count * avg) + (min_samples * global_avg)) / ( - count + min_samples - ) - return smoothed - - return global_avg # Unknown kitchen defaults to global avg - except Exception: - return global_avg diff --git a/app/core/__pycache__/arrow_utils.cpython-312.pyc b/app/core/__pycache__/arrow_utils.cpython-312.pyc index 18e0c8241912cca62b568f97739a8775b04b7bc6..ed44d5cfd1b5bcbe900f0da805bf0162a706cfc4 100644 GIT binary patch delta 453 zcmZvYKTE?v7{>1|$t4Z6Dh^c<3^;YMxOA~NIQpkJ31$gL?i#Noxo}CqbSa3VTL-}p zAc%|5Z=v5Hj!te${QzF7rAy!8-rsZ2J$Kyux_#3az18bBP_=(~GPu?O_^8CZ@IOrN z6x;&}TtFd3A#~BKYp#X>(+1W&u;|QqhJVV%RAXq+Sa@@yIvXuFjOBqorubHOtpc^o z;gNm8{7gtbWYp>9(RtzI84r2JS?ctKbOI&JCvsxiGAkyB=qx;WL(7Qp8W~b zk3ny;iXJqqDU|y7#W=8=BuU!7kgPpk!5w2$Iriij@4!*3R+^qih3|Q#5$91-$ZCA0 z8Hym_aZuI*mYphQva~!eCJ|F7apkHVGp0mJQpoDYCm^?~CpW6R)r|zrLv|>4m8>S0 go~h+agAjfr1of!};QBiPjqzi>WB-6AT${yz0lt`G7ytkO literal 3658 zcmbVOTWs6b89o##i4x^YY^O;R$6;I>j*8h^-8%Jxw29la2?EzknhZ@8uLMmVSu`Y( zKBQtxkPD;Og9Qw8i(TTa9p+*K<{&TL(>!3q8f-v^?L~qzIAA@jK%esF&3V|mhwcBV zi)B|?fE|E5m;ZA9^IyJm{@l_MCeZ%$gWntf5()VnC$$!=4tBo65ptX8Bt~>j_a(WQ z&+GY^;Pw7gAQt2*G%+T^8lMcM!m)5lib<(RERt%8wQ$5os5sMIQ@-2Nw!5GoTdZ9d zsBfmXGHd+n{>Q8bny<$LdhpvqtV0)Rm`b#hM&iDwK_Py6fC@7w8?{%)aX}A(%)zEI zVcH3F3FuGhFa{l8(hKwm&<|yO(U#l~!xxR|#Gpl4++-;=tx-AS7)ir1s4Zt~BRwsT zS*n(x@)%?0oUEpGc_Nd#oRxuJo~GtBQ>}!d$-3&OL*dKJyk+PhdW&j~$&4J;WfPRE zj$x*4PewOv2luf(U7Be!-AJnrl`Uq%DsDLxeuzh%hzhPS4(zUQ-q0MEA4_Ll|Fw*j zq^^)u9UK0j)eSJ}WLR4BHTVj0gz)g~ybG(46WoH&sYZ{GnMTg4P@dGHyTCtzldE$I zd49&luoS>#B2VQN^)$AOBX6Hb;99X{))P)9l4?FfAs(~8WOTk$_ zShM7R^XYs(i1Dj&*JBEHEQonuUPOzolBhrTyP%Y~6 zV3f;iCM>B;gK{2r1$&k`(GWv0W|%`R_ogdil`0?(BdAnwBS=HvvP~Br`J(wI>-)235#GZ~#!5sMTADtpMvy8T+st4gBgGNs~8 zWfzFpG1#XSKDz@>xJb4mB-FMz_9#HwJC<{W+)7`$?fBxwt+oRVGhm8zZAwQ=($SSy zR=Kj&yLx@If27nuvaW7)l>5(=q%(`>A4r`~SaRM|3-e|1@XCpjc=W@m zpT>U_uPdF}H~~t_Jum&i_etPh;N$tt3vZP!yj8q@qj>XMXw8<4+42-CI+@Z`rqq)uiJ8T*2V&?uuPk3GT>3t{J@@|H%Ix=YKOg?ZiyytX z_T1X+ospltT#gKv#NoxU&!ouGY++<^deS5Q>T)&lDO9r zhAq=HF4%I8GILNft!(3sgZCAU#|_QE{MXE6CIy;h8!jhxx~e70yFN>Wn+5f_Qk{sx)jqC#%T3;b26QHYUaq`dcmSLWXP!Hc$hS!J^s>d5FN@5PY77fJ=p)U=w+ z+C~+(zG>5VCh74c&FN`;tYEw$De&z9pdi}@!X~DZ%&3)AvsKAlkBnC6^4`s>Tc4J5 zPs^{Xu)mHb*=TQ_G!YGY(T1Fvb)`9GI8^a+zzv`gl(~YDj+<`iwRw%gApz$66!3~g z1uvOcKlsh{S422!V-ykjD(TgYn<;hYD4uG`F}&PbXnilUlDIDpY>BPQQb8&{^}_n~^-IOBvwsoK{c}s|`Ul}a2#AqFqj&2P;=va-#gURY^6zaPh+o(TLH)a5KSO>K8XMs6`45lv^7nfEFm4VDEEP=}0W zu+Wghkax{M;x1^x=0U``mnq|>x+g?fnt&I3G6W-Dc>u~PyeaEof-q8zc9dsWn{Jp< zRj&ps1O;3za-SCxHV7;>UVF7zbR=Q>eGzyb=%rZxr5GIa!W8v-sKr zseSou;cQvzE(+aVwMP%TK}FF`O;KEbI+L=p4DTQ}Xk|V8U?*_aR-Fx1BMszg$UVb@ z)%11)PS%1nX9xleGlwB;FsxBGG)+2@|gKQ_}TU()}rE{Tqpv zN%XVOk)=bMp(CZxk(JSMsBbZ_Ega$wtowcy{$+TZ!0_YVPoBN^tT!xPxw?7fX6ee! z9VBcI`?-^=eXGvetLvR>7e9KZ)O%{1z}m*}??!(;x`V^*)-ZQ!t!qtRe}02sAH9<< fMPJ$`u=dB!9qhIb`?+JQohWu}AKN2b^N#mlMBZ%x diff --git a/app/core/arrow_utils.py b/app/core/arrow_utils.py index 738c081..f077316 100644 --- a/app/core/arrow_utils.py +++ b/app/core/arrow_utils.py @@ -1,13 +1,9 @@ """ -High-performance utilities using Apache Arrow and NumPy for geographic data. -Provides vectorized operations for distances and coordinate processing. +Vectorized NumPy utilities for geographic distance calculations. """ import numpy as np -import pyarrow as pa -import pyarrow.parquet as pq import logging -from typing import List, Dict, Any, Tuple logger = logging.getLogger(__name__) @@ -35,29 +31,3 @@ def calculate_haversine_matrix_vectorized(lats: np.ndarray, lons: np.ndarray) -> c = 2 * np.arctan2(np.sqrt(a), np.sqrt(1 - a)) return R * c - -def orders_to_arrow_table(orders: List[Dict[str, Any]]) -> pa.Table: - """ - Convert a list of order dictionaries to an Apache Arrow Table. - This enables zero-copy operations and efficient columnar storage. - """ - return pa.Table.from_pylist(orders) - -def save_optimized_route_parquet(orders: List[Dict[str, Any]], filename: str): - """ - Save optimized route data to a Parquet file for high-speed analysis. - Useful for logging and historical simulation replays. - """ - try: - table = orders_to_arrow_table(orders) - pq.write_table(table, filename) - logger.info(f" Saved route data to Parquet: {filename}") - except Exception as e: - logger.error(f" Failed to save Parquet: {e}") - -def load_route_parquet(filename: str) -> List[Dict[str, Any]]: - """ - Load route data from a Parquet file and return as a list of dicts. - """ - table = pq.read_table(filename) - return table.to_pylist() diff --git a/app/main.py b/app/main.py index fa7013a..dde5008 100644 --- a/app/main.py +++ b/app/main.py @@ -83,19 +83,6 @@ async def lifespan(app: FastAPI): except Exception as e: logger.warning(f"[Analytics] DB check failed (non-fatal): {e}") - # Warm up the Thompson Sampling bandit (bootstraps from historical DB) - try: - from app.services.ml.strategy_bandit import get_bandit - bandit = get_bandit() - stats = bandit.get_stats() - logger.info( - f"[Bandit] RL strategy bandit ready — " - f"{stats['context_count']} contexts, " - f"{stats['total_updates']} historical updates loaded." - ) - except Exception as e: - logger.warning(f"[Bandit] Warm-up failed (non-fatal): {e}") - # Start the autonomous empirical-ETA sync agent (daemon thread). # It mirrors completed 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(The XGBoost/Optuna hypertuner, -ID3 risk tree, and profit predictor have all been retired — they never affected -assignment.) +ID3 risk tree, profit predictor, and Thompson-Sampling strategy bandit have all +been retired — none of them ever affected assignment or routing behavior.) Endpoints: GET /api/v1/ml/status – quality trend, analytics DB + history stats @@ -12,7 +12,6 @@ Endpoints: GET /api/v1/ml/config – active config values PATCH /api/v1/ml/config – manual config override POST /api/v1/ml/reset – reset config to defaults - POST /api/v1/ml/strategy – change optimization strategy POST /api/v1/ml/refresh-eta – sync nearledb + rebuild empirical ETA stats GET /api/v1/ml/eta-accuracy – formula vs empirical ETA backtest GET/POST /api/v1/ml/road-eval – autonomous road-sequencing decision @@ -103,7 +102,6 @@ def ml_analytics(): "hourly_stats": collector.get_hourly_stats(), "zone_stats": collector.get_zone_stats(), "quality_histogram": collector.get_quality_histogram(), - "strategy_comparison": collector.get_strategy_comparison(), } except Exception as e: logger.error(f"[ML API] analytics: {e}", exc_info=True) @@ -163,142 +161,6 @@ def ml_reset(): raise HTTPException(status_code=500, detail=str(e)) -# --------------------------------------------------------------------------- -# POST /strategy -# --------------------------------------------------------------------------- - -@router.post("/strategy", summary="Change the optimization strategy") -def ml_strategy(strategy: str = Body(default="balanced", embed=True)): - """ - Choices: balanced | fuel_saver | aggressive_speed | zone_strict - Affects how the quality score is computed in analytics only. - """ - valid = ["balanced", "fuel_saver", "aggressive_speed", "zone_strict"] - if strategy not in valid: - raise HTTPException(400, f"Invalid strategy. Choose from {valid}") - from app.config.dynamic_config import get_config - try: - get_config().set("ml_strategy", strategy) - return {"status": "ok", "strategy": strategy} - except Exception as e: - logger.error(f"[ML API] strategy: {e}", exc_info=True) - raise HTTPException(status_code=500, detail=str(e)) - - -# --------------------------------------------------------------------------- -# POST /auto-tune -# --------------------------------------------------------------------------- - -@router.post("/auto-tune", summary="Run SQL-based strategy auto-tuner") -def ml_auto_tune(): - """ - Analyses the assignment log and picks the best-performing ml_strategy - based on average quality score across all recorded calls. - - Rules: - - A strategy needs ≥ 10 calls to be considered. - - At least 2 strategies must have enough data to compare. - - If a better strategy is found it is written to DynamicConfig - immediately and takes effect on the next /riderassign call. - - Also returns per-hour breakdown so you can see peak-hour patterns. - - Safe to call any time. Runs in < 100ms. - """ - from app.services.ml.ml_data_collector import get_collector - from app.config.dynamic_config import get_config, DEFAULTS - try: - collector = get_collector() - cfg = get_config() - comparison = collector.get_strategy_comparison() - hourly = collector.get_hourly_stats() - total_records = collector.count_records() - - if total_records == 0: - return { - "status": "no_data", - "message": "No assignment events logged yet. " - "Call /riderassign a few times first.", - "total_records": 0, - } - - qualified = [s for s in comparison if s["call_count"] >= 10] - current_strategy = cfg.get("ml_strategy", "balanced") - action = "no_change" - recommendation = None - - if len(qualified) >= 2: - best = max(qualified, key=lambda x: x["avg_quality"]) - recommendation = best["strategy"] - if best["strategy"] != current_strategy: - cfg.set("ml_strategy", best["strategy"], source="auto_tuner") - action = "updated" - logger.info( - f"[AutoTune API] Strategy: '{current_strategy}' → " - f"'{best['strategy']}' (quality={best['avg_quality']:.1f})" - ) - elif len(comparison) > 0: - action = "insufficient_data" - recommendation = comparison[0]["strategy"] # best so far even if < 10 calls - - # Per-hour best strategy (informational — not auto-applied) - # Shows which strategy logged the highest quality at each hour - hour_best: list = [] - if hourly: - for h in hourly: - # Find which strategy performed best in this hour block - # (simple: use the dominant strategy for that hour from comparison) - hour_best.append({ - "hour": h["hour"], - "avg_quality": h["avg_quality"], - "call_count": h["call_count"], - "sla_breaches": h["sla_breaches"], - }) - - return { - "status": "ok", - "action": action, - "current_strategy": cfg.get("ml_strategy", "balanced"), - "recommendation": recommendation, - "total_records": total_records, - "strategy_comparison": comparison, - "hourly_quality": hour_best, - "message": ( - f"Strategy updated to '{recommendation}'." - if action == "updated" - else "Current strategy is already optimal." - if action == "no_change" - else "More data needed (≥ 10 calls per strategy to compare)." - ), - } - except Exception as e: - logger.error(f"[ML API] auto-tune: {e}", exc_info=True) - raise HTTPException(status_code=500, detail=str(e)) - - -# --------------------------------------------------------------------------- -# GET /bandit -# --------------------------------------------------------------------------- - -@router.get("/bandit", summary="Thompson Sampling bandit — posterior stats per context") -def ml_bandit(): - """ - Shows the current state of the RL strategy bandit. - - Each context (time_band|load_band) has 4 arms (strategies). - For each arm: - mean_reward — expected quality / 100 based on posterior mean - observations — number of observed calls (excluding prior) - alpha / beta — Beta distribution parameters - - The bandit uses Thompson Sampling to select strategies automatically - on every /riderassign call, balancing exploration vs exploitation. - """ - try: - from app.services.ml.strategy_bandit import get_bandit - return {"status": "ok", **get_bandit().get_stats()} - except Exception as e: - logger.error(f"[ML API] bandit: {e}", exc_info=True) - raise HTTPException(status_code=500, detail=str(e)) # --------------------------------------------------------------------------- @@ -346,21 +208,24 @@ def ml_history(patterns: bool = False): (all clear dominant-rider zones, sorted by pattern score). """ from app.services.vector.delivery_history_store import ( - get_delivery_history_store, _INDEX_PATH, _META_PATH, CSV_PATH + get_delivery_history_store, _paths_for, _pattern_source, CSV_PATH ) try: - store = get_delivery_history_store() - meta = {} - if os.path.isfile(_META_PATH): - with open(_META_PATH, "r", encoding="utf-8") as f: + store = get_delivery_history_store() + source = _pattern_source() + vectors_path, _records_path, meta_path = _paths_for(source) + meta = {} + if os.path.isfile(meta_path): + with open(meta_path, "r", encoding="utf-8") as f: meta = json.load(f) resp = { "status": "ok", + "pattern_source": source, "record_count": store.record_count(), "pattern_count": store.pattern_count(), "index_ready": store.record_count() > 0, - "disk_index": os.path.isfile(_INDEX_PATH), + "disk_index": os.path.isfile(vectors_path), "csv_path": CSV_PATH, "csv_exists": os.path.isfile(CSV_PATH), "saved_meta": meta, diff --git a/app/routes/optimization.py b/app/routes/optimization.py index eac8850..ed8ee2f 100644 --- a/app/routes/optimization.py +++ b/app/routes/optimization.py @@ -11,7 +11,6 @@ from fastapi import APIRouter, Body, Request, Depends, status, HTTPException, Qu from app.controllers.route_controller import RouteController from app.core.exceptions import APIException -from app.core.arrow_utils import save_optimized_route_parquet logger = logging.getLogger(__name__) @@ -85,68 +84,19 @@ def _haversine_km(lat1: float, lon1: float, lat2: float, lon2: float) -> float: return float("inf") -def _auto_tune_strategy(collector) -> None: - """ - SQL-based strategy auto-tuner. - - Reads quality_score statistics from the assignment log grouped by - ml_strategy. If one strategy clearly outperforms the rest (minimum - 10 calls, ≥ 2 strategies compared) it is written to DynamicConfig so - all future requests benefit automatically. - - Safe to call at any time — DynamicConfig write is idempotent. - """ - try: - comparison = collector.get_strategy_comparison() - if not comparison or len(comparison) < 2: - return # not enough variety to compare - - # Only consider strategies with at least 10 real observations - qualified = [s for s in comparison if s["call_count"] >= 10] - if not qualified: - return - - best = max(qualified, key=lambda x: x["avg_quality"]) - from app.config.dynamic_config import get_config as _gc - cfg = _gc() - current = cfg.get("ml_strategy", "balanced") - if best["strategy"] != current: - cfg.set("ml_strategy", best["strategy"], source="auto_tuner") - logger.info( - f"[AutoTune] Strategy updated: '{current}' → '{best['strategy']}' " - f"(avg_quality={best['avg_quality']:.1f}, n={best['call_count']})" - ) - else: - logger.debug( - f"[AutoTune] Strategy '{current}' already optimal " - f"(avg_quality={best['avg_quality']:.1f})" - ) - except Exception as _ate: - logger.warning(f"[AutoTune] Failed (non-fatal): {_ate}") - - -def _bg_log_and_maybe_retrain( +def _bg_log_assignment( num_orders: int, num_riders: int, hyperparams: dict, assignments: dict, unassigned_count: int, elapsed_ms: float, - bandit_context: "tuple | None" = None, # (time_band, load_band) ) -> None: - """ - Background worker: log event → update RL bandit → auto-retrain ID3 - → auto-tune strategy. Runs in _ml_executor (never blocks the response). - - Thresholds: - every call → bandit posterior update (Thompson Sampling RL) - every 100 evts → greedy SQL auto-tuner (backup check, rarely fires now) - """ + """Background worker: log the assignment event. Runs in _ml_executor so + it never blocks the API response.""" try: from app.services.ml.ml_data_collector import get_collector as _gc collector = _gc() - - # ── 1. Log event, get back quality_score ────────────────────────── quality_score = collector.log_assignment_event( num_orders=num_orders, num_riders=num_riders, @@ -155,29 +105,8 @@ def _bg_log_and_maybe_retrain( unassigned_count=unassigned_count, elapsed_ms=elapsed_ms, ) or 50.0 - count = collector.count_records() logger.debug(f"[ML BG] Logged event #{count}, quality={quality_score:.1f}") - - # ── 2. Thompson Sampling bandit update (RL feedback) ────────────── - if bandit_context: - try: - from app.services.ml.strategy_bandit import get_bandit as _gb - _t_band, _l_band = bandit_context - _strategy = hyperparams.get("ml_strategy", "balanced") - get_bandit_fn = _gb - get_bandit_fn().update(_t_band, _l_band, _strategy, quality_score) - logger.debug( - f"[Bandit] Updated ctx={_t_band}|{_l_band} " - f"arm={_strategy} reward={quality_score/100:.3f}" - ) - except Exception as _be: - logger.debug(f"[Bandit] Update failed (non-fatal): {_be}") - - # ── 3. Greedy SQL auto-tune every 100 events (backup) ──────────── - if count >= 20 and count % 100 == 0: - _auto_tune_strategy(collector) - except Exception as _e: logger.warning(f"[ML BG] Background task failed (non-fatal): {_e}") @@ -242,18 +171,6 @@ async def provider_optimize_forward( try: url = "https://jupiter.nearle.app/live/api/v1/deliveries/createdeliveries" result = await controller.optimize_and_forward_provider_payload(body, url) - - # Parquet snapshot — offloaded so it never blocks the response - def _snap(): - try: - os.makedirs("data/snapshots", exist_ok=True) - snapshot_path = f"data/snapshots/route_{int(time.time())}.parquet" - save_optimized_route_parquet(body, snapshot_path) - logger.info(f"Apache Arrow: Snapshot saved to {snapshot_path}") - except Exception as e: - logger.warning(f"Could not save Arrow snapshot: {e}") - _ml_executor.submit(_snap) - return result except APIException: raise @@ -554,7 +471,6 @@ async def assign_orders_to_riders( request: Request, body: Any = Body(default=None), reshuffle: bool = Query(False, alias="reshuffle"), - hypertuning_params: str = None, ): """ Smart assignment of orders to riders. @@ -653,11 +569,10 @@ async def assign_orders_to_riders( # 3. Log summary after all data is in hand mode_str = "reshuffle" if do_reshuffle else "normal" - tuning_str = hypertuning_params if hypertuning_params else "null" logger.info( f"\n━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n" f"[API HIT] POST /api/v1/optimization/riderassign\n" - f"[CONFIG] Mode: {mode_str.upper()} | Hypertuning: {tuning_str} | Active Riders: {len(riders)}\n" + f"[CONFIG] Mode: {mode_str.upper()} | Active Riders: {len(riders)}\n" f"━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━" ) logger.info(f"[API] riderassign ▶ orders={len(orders)} riders={len(riders)} mode={mode_str}") @@ -811,44 +726,10 @@ async def assign_orders_to_riders( vrp_orders = [o for o in orders if id(o) not in history_pre_assigned_ids] # 3. Run Assignment (AssignmentService) - # -- Per-request strategy override (thread-safe via contextvars) -- - # Old approach mutated _cfg._cache directly — a race condition when two - # concurrent requests set different strategies simultaneously. - # New approach: each async task gets its OWN copy of the ContextVar, - # so overrides never leak between concurrent requests. - from app.config.dynamic_config import get_config, set_request_strategy - from app.services.ml.behavior_analyzer import time_band as _tb, load_band as _lb - from datetime import datetime as _DT + from app.config.dynamic_config import get_config _cfg = get_config() - # ── Bandit context (used both for selection + background update) ── - _bandit_t_band = _tb(_DT.now().isoformat()) - _bandit_l_band = _lb(len(vrp_orders) / max(1, len(riders))) - _bandit_context = (_bandit_t_band, _bandit_l_band) - - valid_strategies = ["balanced", "fuel_saver", "aggressive_speed", "zone_strict"] - if hypertuning_params and hypertuning_params in valid_strategies: - # Explicit caller override always wins - set_request_strategy(hypertuning_params) - logger.info(f"[HYPERTUNE] Per-request strategy override: {hypertuning_params}") - else: - # ── Thompson Sampling: let the bandit choose ────────────────── - # The bandit samples from Beta posteriors for this (time, load) - # context and picks the arm with highest sample. It naturally - # explores when uncertain and exploits known-good strategies as - # data accumulates. Replaces the one-size-fits-all greedy picker. - try: - from app.services.ml.strategy_bandit import get_bandit as _gb - _bandit_strategy = _gb().select(_bandit_t_band, _bandit_l_band) - set_request_strategy(_bandit_strategy) - logger.info( - f"[Bandit] Strategy='{_bandit_strategy}' " - f"ctx={_bandit_t_band}|{_bandit_l_band}" - ) - except Exception as _bse: - logger.debug(f"[Bandit] Selection failed (non-fatal): {_bse}") - optimizer = RouteOptimizer() # ── PHASE 3a: TRUE VRP (primary solver) ────────────────────────────── @@ -863,7 +744,7 @@ async def assign_orders_to_riders( if not do_reshuffle: # VRP not meaningful during reshuffle (intentional exploration) try: # Build minimal rider info for VRP - from app.services.routing.kalman_filter import smooth_rider_locations + from app.services.routing.gps_smoother import smooth_rider_locations _smooth_riders = smooth_rider_locations(list(riders)) from app.services.core.assignment_service import AssignmentService as _AS @@ -1272,21 +1153,19 @@ async def assign_orders_to_riders( risk_meta: dict = {"label": "n/a", "model_trained": False, "deprecated": True} # ── BACKGROUND ML LOGGING ──────────────────────────────────────────── - # Fire-and-forget: log this assignment event to SQLite, auto-retrain - # ID3 every 50 events, auto-tune strategy every 100 events. + # Fire-and-forget: log this assignment event to SQLite. # Uses _ml_executor so the API response is never delayed. try: _elapsed_ms = (time.time() - _t0) * 1000 _hyp_snapshot = get_config().get_all() # frozen copy for this call _ml_executor.submit( - _bg_log_and_maybe_retrain, + _bg_log_assignment, len(orders), len(riders), _hyp_snapshot, {rid: list(ords) for rid, ords in assignments.items()}, len(unassigned_orders), round(_elapsed_ms, 1), - _bandit_context, # (time_band, load_band) for RL update ) except Exception as _mle: logger.debug(f"[ML BG] Submit failed (non-fatal): {_mle}") @@ -1354,7 +1233,6 @@ async def assign_orders_to_riders( }, "reshuffle_mode": do_reshuffle, "solver_mode": "vrp_optimal" if used_vrp else "2phase_heuristic", - "hypertuning_params": hypertuning_params or "default", "faiss_coord_corrections": _faiss_corrected, "faiss_customer_records": _faiss_store.record_count() if _faiss_store else 0, "faiss_history_preassigned": _history_hits, diff --git a/app/services/core/__pycache__/assignment_service.cpython-312.pyc b/app/services/core/__pycache__/assignment_service.cpython-312.pyc index 12a190eb5cd990982158a71c3e710d3700880f95..be421f6f4cb8259a750f64c265b9aaba039ec3c6 100644 GIT binary patch delta 12617 zcma)i3v?9Mm0(r>e^RUUYyH(yOGu4AAS94LKlI@%fj}}JgGs09Dv1_Sw_Mc%Ay>H- zg4Z~!8OweiTaICZKQqAElObVGmSi?#ay&_#cz1&$9N`L+A(Pqc_)M~qft|6PNxb*I z>gsMjIhm6B*8ALd-+lMpci+A56}hha(NntW_tMgA3_LF%>>K^BUt^eGU`BgP@x+~f zVxFshagb$n49|?N)2fqizUWO>%^fJ_EPM%Pg`bTt9X4_HQ-%W`9kYeu9H$sA?M+=A zuj~ZtOD9qG?PgZ7jqp)_FcKaPjVSuDKomeEte6BoIw^#M@fNfla-@gfoxKeCbxuJn z8t;~2B!<&Z+Zje<AaJ3@&;&|!8@^T*v4f5w#&-kA6LY=5I5NqZJ+s&vMfH6%jV5o4*ZZ41Mk$Z z$m_+fEe}wM^XcGnfzK??t+t4d2YCX1VHA15O*WSgqs}=|;43D-E%w+gz$jIlkB#KB z^?7SFC6O3^Lc4%@meY;t#|<)znl7<##-%CiBpsRXWs~K{EE&*5TNaF@8+DTBRu@!5 zA$CC@O2;tf7*CT8l#W58Gg=JDg;t|voMVOCl94PAB28Q79DC@`80IlxMK;N1i5WGJ zudU8v%gE1jvr0`PjJ9ZGi)0a`s8{RiUd$SE5>p$XY?Z9N%(J$j?g$gq9RiJ$ZIW%Y z61mQMUN@U};-N9j!t>avT~>mYJlDm~a-A-v|1qwez;)V#Tsz}jr~d)2(gL$lgHi@(JPM1H<_=3K_~qsle3#@B%#sUmtK+y&OBodVuta+TTJr`b z3bpDpTBD_sZ_X60({hqglQ&p0^I1J-8C|8-CEuL&6q8gW^5P2_v1zE)nz9UM2i?Rq zNT29hEv4xs)D~)GoC9)2iCh}w&`i;X1Ht)AwA=~C%S6$9(WinD>Y*nvv17LJm|fPh zOw#g36AMgZ3J4`uP!dnlr_?7F)1%CsZdN?bOz9tGj3tBAOjsw(E_c2nwQ_f5g za)ZWW^3B<#%vYGRI;}gFd6eN8IrGWP*%#IKaaQZl=OjNXZHj?S&%-TBoIET>L($1- zC=&LIyl^ZO=*`=os%wZC)+F)Z^EEU-h)xRGFG9S8Oa6oZv?Fc!m$>`P#?W?cs6gzm5O z$623#+8j76^5JOvq!mbb{_#IOkKMQLyvX?UimraO!Uhyp6wxSpJRSjOU9q-}@L_&x zLg<*T>k`G#NO&A54DsaKE#{a%6b?oG{`$7DNH8!acGM@3cR*XQ0>D2rAI~t?O7|^n z-uq4NO5m#NT-AlL3t1PoT?$+py6#yI<)7R!0OH9UH#=Q_pPYIklVhAG9Eno7oVZ6? 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"""Calculate great circle distance between two points.""" - try: - lon1, lat1, lon2, lat2 = map( - radians, [float(lon1), float(lat1), float(lon2), float(lat2)] - ) - dlon = lon2 - lon1 - dlat = lat2 - lat1 - a = sin(dlat / 2) ** 2 + cos(lat1) * cos(lat2) * sin(dlon / 2) ** 2 - c = 2 * asin(min(1.0, sqrt(a))) - return c * DataEncoder.EARTH_RADIUS_KM - except: - return 0.0 - - @staticmethod - def cyclic_encode_hour(hour: int) -> tuple[float, float]: - """ - Cyclic time encoding - captures traffic patterns better than discrete buckets. - hour_sin = sin(2π * hour / 24) - hour_cos = cos(2π * hour / 24) - """ - hour_sin = math.sin(2 * math.pi * hour / 24) - hour_cos = math.cos(2 * math.pi * hour / 24) - return hour_sin, hour_cos - - @staticmethod - def cyclic_encode_day(day_of_week: int) -> tuple[float, float]: - """ - Cyclic day encoding for weekly patterns. - """ - day_sin = math.sin(2 * math.pi * day_of_week / 7) - day_cos = math.cos(2 * math.pi * day_of_week / 7) - return day_sin, day_cos - - @staticmethod - def geohash_encode(lat: float, lon: float, precision: int = 7) -> str: - """ - Geohash encoding for spatial data. - Converts lat/lon to grid cell string for locality capture. - precision=7 gives ~153m x 153m cells (good for delivery zones) - """ - try: - return _simple_geohash(lat, lon, precision) - except: - return "unknown" - - -def _simple_geohash(lat: float, lon: float, precision: int = 7) -> str: - """Standard geohash encoding — 5 bits per character, BASE32 output.""" - if lat == 0 or lon == 0: - return "unknown" - BASE32 = "0123456789bcdefghjkmnpqrstuvwxyz" - lat_min, lat_max = -90.0, 90.0 - lon_min, lon_max = -180.0, 180.0 - hash_chars = [] - is_lon = True - for _ in range(precision): - char_bits = 0 - for _ in range(5): - if is_lon: - mid = (lon_min + lon_max) / 2 - if lon >= mid: - char_bits = (char_bits << 1) | 1 - lon_min = mid - else: - char_bits = char_bits << 1 - lon_max = mid - else: - mid = (lat_min + lat_max) / 2 - if lat >= mid: - char_bits = (char_bits << 1) | 1 - lat_min = mid - else: - char_bits = char_bits << 1 - lat_max = mid - is_lon = not is_lon - hash_chars.append(BASE32[char_bits]) - return "".join(hash_chars) - - def _zone_key(lat: float, lon: float) -> str: """O(1) ~5 km grid cell key for zone proximity matching.""" if lat == 0 or lon == 0: @@ -117,26 +22,6 @@ def _zone_key(lat: float, lon: float) -> str: return f"{int(lat / 0.044)},{int(lon / 0.044)}" -# Kitchen encoding now uses persistent DB storage from dynamic_config - - -def update_kitchen_encoding(kitchen_name: str, profit: float = None): - """Update kitchen stats in DB when orders are processed.""" - if kitchen_name and kitchen_name != "Unknown": - if profit is not None: - update_kitchen_stats(kitchen_name, profit) - - -def get_kitchen_label_id(kitchen_name: str) -> int: - """Get persistent label ID for a kitchen.""" - return _get_kitchen_label_id(kitchen_name) - - -def get_kitchen_frequency(kitchen_name: str) -> float: - """Get persistent frequency ratio for a kitchen.""" - return _get_kitchen_frequency(kitchen_name) - - class AssignmentService: def __init__(self): # Curated config drives HARD kitchen ownership. Copy so substitution @@ -196,35 +81,19 @@ class AssignmentService: self.earth_radius_km = 6371 self._cfg = get_config() - self._encoder = DataEncoder() # Cost parameters for composite cost function self._fuel_rate = 2.5 # Per km self._base_rider_cost = 0.0 self._merchant_margin_avg = 5.0 # Default average margin - # Profit encoding cache - self._kitchen_profit_cache: Dict[str, List[float]] = defaultdict(list) - self._profit_mean = 0.0 - self._profit_std = 1.0 - def calculate_order_profit_features( self, order: Dict[str, Any], distance_km: float ) -> Dict[str, float]: """ - Calculate engineered profit features for ML-ready data. - - Features: - - profit: order amount - rider cost - - profit_density: profit per kilometer (key signal!) - - cost_efficiency: rider cost per estimated time - - route_score: profit - composite cost (maximize this!) - - encoded_geohash: spatial encoding - - cyclic_time: hour_sin, hour_cos + Calculate profit and profit-density for one order, used as a scoring + signal (profit_bonus) when picking which rider gets a cluster. """ - features = {} - - # Extract order values try: order_amount = float( order.get("orderamount") or order.get("deliveryamount") or 0 @@ -232,38 +101,17 @@ class AssignmentService: except: order_amount = 0.0 - # Calculate rider cost: base + (distance * fuel_rate) + # Rider cost: base + (distance * fuel_rate) rider_cost = self._base_rider_cost + (distance_km * self._fuel_rate) - features["rider_cost"] = rider_cost # Profit = revenue - cost profit = order_amount - rider_cost - features["profit"] = profit - # Profit density = profit / distance (HIGH SIGNAL feature!) - # High density = profitable short deliveries - if distance_km > 0: - features["profit_density"] = profit / distance_km - else: - features["profit_density"] = 0.0 + # Profit density = profit / distance — high density means profitable + # short deliveries; used to prioritise clusters worth serving. + profit_density = profit / distance_km if distance_km > 0 else 0.0 - # Cost efficiency = cost / time estimate (assuming 15 min per order average) - estimated_time_min = max(15, distance_km * 4) # Rough estimate - features["cost_efficiency"] = ( - rider_cost / estimated_time_min if estimated_time_min > 0 else 0 - ) - - # Route score = profit - distance_cost (what we want to MAXIMIZE) - # This is the core optimization target - features["route_score"] = profit - ( - distance_km * 0.5 - ) # 0.5 = opportunity cost per km - - # Composite edge weight for optimizer (MINIMIZE this) - # weight = cost - profit_margin_bonus - features["composite_weight"] = rider_cost - (profit * 0.3) # 30% profit bonus - - return features + return {"profit": profit, "profit_density": profit_density} def _load_config(self): """Load ML-tuned hyperparams fresh on every assignment call.""" @@ -478,7 +326,6 @@ class AssignmentService: # Use caller-supplied pricing so dynamic API rates flow into scoring self._fuel_rate = fuel_charge self._base_rider_cost = base_pay - _call_start = time.time() # 0. Prep assignments: Dict[int, List[Dict[str, Any]]] = defaultdict(list) @@ -588,36 +435,14 @@ class AssignmentService: orders, max_cluster_radius_km=self.MAX_KITCHEN_DISTANCE_KM ) - # 2b. ENRICH CLUSTERS WITH PROFIT FEATURES (Data Encoding: Target + Frequency) + # 2b. Tag each cluster with the set of kitchen names it covers (used + # below for hard kitchen-ownership matching). for cluster in clusters: cluster["kitchen_names"] = set() for order in cluster["orders"]: - k_name = self.get_order_kitchen(order) - cluster["kitchen_names"].add(k_name) - update_kitchen_encoding(k_name) + cluster["kitchen_names"].add(self.get_order_kitchen(order)) - # Update kitchen profit cache for target encoding - for order in cluster["orders"]: - k_name = self.get_order_kitchen(order) - profit = ( - float(order.get("orderamount") or order.get("deliveryamount") or 50) - - 40 - ) - self._kitchen_profit_cache[k_name].append(profit) - - # Calculate profit statistics for normalization - all_profits = [ - p for profits in self._kitchen_profit_cache.values() for p in profits - ] - if all_profits: - self._profit_mean = sum(all_profits) / len(all_profits) - if len(all_profits) > 1: - variance = sum((p - self._profit_mean) ** 2 for p in all_profits) / len( - all_profits - ) - self._profit_std = variance**0.5 - - logger.info(f"Created {len(clusters)} order clusters with profit encoding") + logger.info(f"Created {len(clusters)} order clusters") # 2c. MINIMAL RIDER PRE-SELECTION # Calculate the theoretical minimum number of riders needed so we don't @@ -659,15 +484,6 @@ class AssignmentService: cluster_geohash = _zone_key(centroid_lat, centroid_lon) for order in cluster_orders: - k_name = self.get_order_kitchen(order) - # Target encoding: use average profit for this kitchen - kitchen_profits = self._kitchen_profit_cache.get(k_name, [0]) - avg_kitchen_profit = ( - sum(kitchen_profits) / len(kitchen_profits) - if kitchen_profits - else 0 - ) - o_lat = float(order.get("pickuplat", 0)) o_lon = float(order.get("pickuplon", 0)) dist = ( @@ -1004,19 +820,10 @@ class AssignmentService: # 6. Commit State and History self._post_process(assignments, rider_states, state_mgr) - # 7. -- ML DATA COLLECTION ----------------------------------------- - try: - elapsed_ms = (time.time() - _call_start) * 1000 - get_collector().log_assignment_event( - num_orders=len(orders), - num_riders=len(riders), - hyperparams=self._cfg.get_all(), - assignments=assignments, - unassigned_count=len(unassigned_orders), - elapsed_ms=elapsed_ms, - ) - except Exception as _ml_err: - logger.debug(f"ML logging skipped: {_ml_err}") + # ML event logging happens once, in the /riderassign endpoint after + # Phase-0 history merge + solo consolidation — not here — so every + # request produces exactly one assignment_ml_log row (see + # optimization.py::_bg_log_assignment). # Log final distribution (use r_orders to avoid shadowing the outer `orders` list) logger.info("=" * 50) diff --git a/app/services/ml/__pycache__/ml_data_collector.cpython-312.pyc b/app/services/ml/__pycache__/ml_data_collector.cpython-312.pyc index 63f726b3f18ed2006e26f41a4287b075756dfb73..ea46a5cafb6e3a6594913a94a50c50adee7b0682 100644 GIT binary patch delta 6941 zcmaJ_3vg7|c|LdV-hJGCulBuq?CQNj4+P?2K&)MMZnP*k6A~Ls>^4eQ{QB4#o?1DYvK{{RJ1D+uNmmCp@W)@ObPv-W)<^WdRMcS z^MWryYS11TbD~@DEc}om1=U&7p%z&9sRiyDD`+B|poPCKqKRlD zx^5Mk5%f^wBKn99Yl}P{voQN@^ z_#;?=LSsv&(V${fBRI}-9VTIw-~_{#Y@Kek-~yRhs1)3a>nbA)OkUop$UiweMsBjQv>3dbM-V-SQA zN39U(B1Mz$whF~()iq64!$7%5GQ6p34?5z62-9-%zD*UDh3g%C)* zBi7LBlx0s_Yd8^ZiS_qKx)L!l{Os&ieSn$0#k-ibbzmx<129hRc>?Fu7fkay<7LZv z%lkU7=mUOAD@3}(L;Z;q*V4AF=|G63yfH}k86RI=3SzMgp&Y>gu(u0BMS1jq7WlL9 zU-&bQkgpPY?6Jb<_UiL^iSJb_N}q$I0r*-7cz=O0fz6diG6>8ma%0hK(j=*S4Y(-A zTFGT|m#T+!sg7C+Ntz{doVlP$n3fuvGmR!y7wl@1v?R@0QcFgfv?kC`<&l=THpwb- z65uHGYmgCCNxNhb4GG(#s*@}$E8?7tXGhSp%Z@Hq@y)#Kn`CY!N1@|Mhvb-KFKE={ zf))I7Ce2C5vJdA-HzTlTb&|7{U>M-DC@~Tz|7vIaxflJ{04pSqusFXB96_nNV ze{AkxmhpCoXa@dC^KhDCla8dBmfBaDe2M%_H+xIySc0c1s6Coz0?d*vpI{OAftcW3T5EfGtE`#XhbJP{u3ihxydGyO-$ zD&L9>p|n$4xagVVE?VgP<7QsmgVy#T)T1$T+E_RqkM;~k1hER4y6~x<&i+_f5VxS> zR)lIAcm1fS5yjf!NTg2)kEGb>U_!+0RCFVF5WIA^yHDpA3$YTSue9-^oJ(ny@jEGs2T{z5vEdXG ziTj~)47TRD$4$?Bnk{G`{sg4RPsfQ|+D=oRf3~`US7MW~JEdD*JAT*IbnZ*^-@Jh_ z*+TL*E)ZRw`P?4aQJ!O-xB92rUpV^w(W{*@3-u4Jt|_(50VQjQEr051S&Oue*nCqP zWIfV+nl>P9Ow%T$&BRtPZImrYTZt`is#mrlZBNrVK+|V^ZpF5bTKx{QAH@M;E1S{E z!E*YB&$SLY#l%)M)AwrZdTidY`exArtE#cfB`B#Nwu+g}uQpt7n0Hjo41uK5F0U%5 zM!(mK^7X`4Jbn0koiBFYS-0cuqPrz~@Oj6xLk{tXsl1ki9 z?;p*lw_0_%%gJ9-y}Y!TM^*{<%KSBz;V!TCsqrXA zWwY}!O-5}Lyc&faF{uWrW|3lhac!)4B((&43a#z*!4A#pB$mEd=>X?=V-68`GCYQi@RAO8L;d1Djh^TeMN9Vd8BN%sEo#hp`lalrA0|a4 zQJm2&CSqZd$NWd%EAUsrQQ-@Dk5BSv_z_-E%B;L+q`ts)`isDK)&#&>iah{p(Etmw zvpbB(p%D?MPyBNPoGS4MYH8q{92peo^=5I{JEFpO{-;V6QVDc!WYJUJuBpufh!ByOYe$`+P~X#MqCXQ3gfIiqPOf>LHr$mp8=X^-je zE9;n_(ff`5+3i*J3~g9nMpZRWR4LtrXdxmTpVA%v2G>E^;|R|n^wM3ccbGp0GR~!^RGmwi z9S-`#)#U{`n*;Ku-7}$Ia2g!uPh7;}K7ZnCy|SB_Y_f-LZpovb+7>#|QcWjn1C>s4 zn`_dwSk-TuR4q>OrjRVOpoB7_BdiKo?4AS*nt7>-hW5-qIo#?}t0a8pAq>vk% zMP@(E-!jPL(~Db@$5)KOimCn%Hddy3MW^-YPG3WL)^uZa65-nj7}Vm6w7>o!W0`%U zzK=2BPK(35aF(IL?Jv<6)^JQMo!jo9pEVTI57rdx1no671jbtW{Mr^qM?YBW-mFim z&!{0Rlrk$U_|g2uun538A`uVth_RuQ5g~AVBrqBqj8un9AyO{Vch|Y;ou;p?dqj|g z)S|MrPobyE`mo@^A?sj5FEt0-LcI8=$4OZdD~57{?$+-xp~K#0E!FNFoJ zhGXy!f4!{I*c=P24uZg%2?84ifgRxTuJgMtwB5H7U;csxRPLLBnrM;@BoLgoJ)fZY zwZ7`I%9;H$PhCIw(&3x!uLbXxZCTKBHal$HzWn>xNG3Xy`40~Wke@!Z(^pz*SJ}xM zj`htZ^=)3&Y$vx(9HL#{+`!)Udz&}0x9b?BH*w7kv-(ySv+fbYkkWNW5?v=?z|-jg zhXD?Wg*c>2=l_Nt+_n|gJ>B-~?DZWVsESt@5m7UakQIIOrQJ=gi zQs@^K-qHZ4*yBUpDb27LO+cDmTzge*TbQ=l-nKfH`D^;ZzAIy&&)bih*OU&Dy^$pvnCJZj?+A#+tp8nSfM3Ak~mA4wS18Y{N|F!EvfoME16bBXfXcmZ^iijft+(@eVz~UpS;LUrL zO{XV4dUAuf8z@mpHZGtR`GLj|oaOKt-`@CJLOPMGYZPxov8YnrC}Oe>aVhmcIB`O} z1eFxm4Y!y?O5GKS_KO!$>{z@`0iC{3;4Mh3Kv;?+@lE>fp&`1vBVX+R0e#Bj(27Wj zhv{E+tTGK_{;>j#lZ9$}yQ8jX-r$-FzGv{y=N6pb^PAj)X?D7OX2;Fpxyr3`B@K6T z8_(^b#=`>pVOazH`r&O^C`1ip~j*`tZH-z{Ww}c--=c+ z+ZC##x99NyydY~4D;Y#toa$Y%x6oyFVkfN+nejwT-h7-})U_8YNLO)yuh10NE z+xi?#ke2opm|w#|`T*KNl2&b??R`yS1wQLTQed(@Xk;`#^SNCMX5uNFU}Y6+^Ugaw zSK21qrgPqRl*kPTzfg_k}(4Mk^-wi*27i zv=LA60by+3e{pzZ7Rt{a+KJT*EPca0qy2K*d79JT!9-^V`x%w?m*Chzah{$UtbZbf zJ^U5IJp}YQeR5mQv_r_7MNm$s$~axdDsIW*(qO@XsSp`agr}(?R#t$K`iLov4#HRA z!~=%zkG-5sOhT&kSHF;tZai77DFT*wi1wfK zj46@ShjmPQ;$I*&nV_dS*wsbB=ri( z6PItQXevHcKKZ0<=%)Lha*rtjGx6uk7KOADr%$#iBpmUaa*jeekaQ}fi@1Gq`Yj&9 zn-{za_XtaJ^XE%K_qAo5=RLW;Lf6i+0kTBdWRc1)g`vzN~4 z^<@is5Z>>g1!6%UpL5L=$png4P&x zGx3@77oVI9)GZL;$?LV8b1J?-ppfnKJ+bJBYAwglJKQo0<%hmJnS&CX%7L*#N=IB? 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These are small pure helpers used by the Thompson-sampling -strategy bandit (`strategy_bandit.py`) and the /riderassign bandit context. - -NOTE: The ID3 SUCCESS/RISK decision tree that used to live here has been retired — -it only ever produced response metadata and never affected any assignment. Only -the generic feature-band encoders remain. -""" - -from datetime import datetime - - -def distance_band(km: float) -> str: - """Total route distance -> discrete band.""" - if km <= 5.0: return "SHORT" - if km <= 15.0: return "MID" - if km <= 30.0: return "LONG" - return "VERY_LONG" - - -def time_band(ts_str: str) -> str: - """ISO timestamp -> time-of-day band.""" - try: - hour = datetime.fromisoformat(ts_str).hour - if 6 <= hour < 10: return "MORNING_RUSH" - if 10 <= hour < 12: return "LATE_MORNING" - if 12 <= hour < 14: return "LUNCH_RUSH" - if 14 <= hour < 17: return "AFTERNOON" - if 17 <= hour < 20: return "EVENING_RUSH" - if 20 <= hour < 23: return "NIGHT" - return "LATE_NIGHT" - except Exception: - return "UNKNOWN" - - -def load_band(avg_load: float) -> str: - """Average orders-per-rider -> load band.""" - if avg_load <= 2.0: return "LIGHT" - if avg_load <= 5.0: return "MODERATE" - if avg_load <= 8.0: return "HEAVY" - return "OVERLOADED" - - -def order_density_band(num_orders: int, num_riders: int) -> str: - """Orders per available rider -> density band.""" - if num_riders == 0: - return "NO_RIDERS" - ratio = num_orders / num_riders - if ratio <= 2.0: return "SPARSE" - if ratio <= 5.0: return "NORMAL" - if ratio <= 9.0: return "DENSE" - return "OVERLOADED" diff --git a/app/services/ml/ml_data_collector.py b/app/services/ml/ml_data_collector.py index f83646d..79fd933 100644 --- a/app/services/ml/ml_data_collector.py +++ b/app/services/ml/ml_data_collector.py @@ -7,13 +7,12 @@ Key upgrades over the original -------------------------------- 1. FROZEN historical scores - quality_score is written ONCE at log time. get_training_data() returns scores as-is from the DB (no retroactive mutation). -2. Rich schema - zone_id, city_id, is_peak, weather_code, - sla_breached, avg_delivery_time_min for richer features. -3. SLA tracking - logs whether delivery SLA was breached. -4. Analytics API - get_hourly_stats(), get_strategy_comparison(), - get_quality_histogram(), get_zone_stats() for dashboard consumption. -5. Thread-safe writes - connection-per-write pattern for FastAPI workers. -6. Indexed columns - timestamp, ml_strategy, zone_id for fast queries. +2. Rich schema - zone_id, city_id, is_peak, weather_code for + richer features. +3. Analytics API - get_hourly_stats(), get_quality_histogram(), + get_zone_stats() for dashboard consumption. +4. Thread-safe writes - connection-per-write pattern for FastAPI workers. +5. Indexed columns - timestamp, zone_id for fast queries. """ import csv @@ -45,7 +44,7 @@ class MLDataCollector: Each log_assignment_event() call writes one row capturing: - Operating context (time, orders, riders, zone, city) - Active hyperparams (exact config snapshot for this call) - - Measured outcomes (quality score, SLA, latency, distances) + - Measured outcomes (quality score, latency, distances) quality_score is computed once and FROZEN - never retroactively changed. """ @@ -70,8 +69,6 @@ class MLDataCollector: zone_id: str = "default", city_id: str = "default", weather_code: str = "CLEAR", - sla_minutes: Optional[float] = None, - avg_delivery_time_min: Optional[float] = None, ) -> None: """ Log one assignment event. @@ -95,13 +92,8 @@ class MLDataCollector: o for orders in assignments.values() if orders for o in orders ] total_distance_km = sum(self._get_km(o) for o in all_orders) - ml_strategy = hyperparams.get("ml_strategy", "balanced") max_opr = hyperparams.get("max_orders_per_rider", 12) - sla_breached = 0 - if sla_minutes and avg_delivery_time_min: - sla_breached = int(avg_delivery_time_min > sla_minutes) - # Quality score - FROZEN at log time quality_score = self._compute_quality_score( num_orders=num_orders, @@ -111,7 +103,6 @@ class MLDataCollector: num_riders=num_riders, total_distance_km=total_distance_km, max_orders_per_rider=max_opr, - ml_strategy=ml_strategy, ) row = { @@ -146,7 +137,6 @@ class MLDataCollector: "search_time_limit_seconds", 5 ), "road_factor": hyperparams.get("road_factor", 1.3), - "ml_strategy": ml_strategy, "riders_used": riders_used, "total_assigned": total_assigned, "unassigned_count": unassigned_count, @@ -154,8 +144,6 @@ class MLDataCollector: "load_std": round(load_std, 3), "total_distance_km": round(total_distance_km, 2), "elapsed_ms": round(elapsed_ms, 1), - "sla_breached": sla_breached, - "avg_delivery_time_min": round(avg_delivery_time_min or 0.0, 2), "quality_score": round(quality_score, 2), } @@ -171,7 +159,7 @@ class MLDataCollector: except Exception as e: logger.warning(f"[MLCollector] Logging failed (non-fatal): {e}") - return 50.0 # neutral fallback so bandit update still fires + return 50.0 # neutral fallback # ------------------------------------------------------------------ # Data retrieval for training @@ -180,7 +168,6 @@ class MLDataCollector: def get_training_data( self, min_records: int = 30, - strategy_filter: Optional[str] = None, since_hours: Optional[int] = None, ) -> Optional[List[Dict[str, Any]]]: """ @@ -195,9 +182,6 @@ class MLDataCollector: params: list = [] clauses: list = [] - if strategy_filter: - clauses.append("ml_strategy = ?") - params.append(strategy_filter) if since_hours: cutoff = (datetime.utcnow() - timedelta(hours=since_hours)).isoformat() clauses.append("timestamp >= ?") @@ -253,7 +237,7 @@ class MLDataCollector: return {"avg_quality": 0.0, "sample_size": 0, "history": []} def get_hourly_stats(self, last_days: int = 7) -> List[Dict[str, Any]]: - """Quality, SLA, and call volume aggregated by hour-of-day.""" + """Quality and call volume aggregated by hour-of-day.""" try: conn = sqlite3.connect(self._db_path) cutoff = (datetime.utcnow() - timedelta(days=last_days)).isoformat() @@ -263,8 +247,7 @@ class MLDataCollector: COUNT(*) AS call_count, AVG(quality_score) AS avg_quality, AVG(unassigned_count) AS avg_unassigned, - AVG(elapsed_ms) AS avg_latency_ms, - SUM(CASE WHEN sla_breached=1 THEN 1 ELSE 0 END) AS sla_breaches + AVG(elapsed_ms) AS avg_latency_ms FROM assignment_ml_log WHERE timestamp >= ? GROUP BY hour ORDER BY hour """, @@ -278,7 +261,6 @@ class MLDataCollector: "avg_quality": round(r[2] or 0.0, 2), "avg_unassigned": round(r[3] or 0.0, 2), "avg_latency_ms": round(r[4] or 0.0, 1), - "sla_breaches": r[5], } for r in rows ] @@ -286,42 +268,6 @@ class MLDataCollector: logger.error(f"[MLCollector] get_hourly_stats: {e}") return [] - def get_strategy_comparison(self) -> List[Dict[str, Any]]: - """Compare quality metrics across ml_strategy values.""" - try: - conn = sqlite3.connect(self._db_path) - rows = conn.execute( - """ - SELECT ml_strategy, - COUNT(*) AS call_count, - AVG(quality_score) AS avg_quality, - MIN(quality_score) AS min_quality, - MAX(quality_score) AS max_quality, - AVG(unassigned_count) AS avg_unassigned, - AVG(total_distance_km) AS avg_distance_km, - AVG(elapsed_ms) AS avg_latency_ms - FROM assignment_ml_log - GROUP BY ml_strategy ORDER BY avg_quality DESC - """ - ).fetchall() - conn.close() - return [ - { - "strategy": r[0], - "call_count": r[1], - "avg_quality": round(r[2] or 0.0, 2), - "min_quality": round(r[3] or 0.0, 2), - "max_quality": round(r[4] or 0.0, 2), - "avg_unassigned": round(r[5] or 0.0, 2), - "avg_distance_km": round(r[6] or 0.0, 2), - "avg_latency_ms": round(r[7] or 0.0, 1), - } - for r in rows - ] - except Exception as e: - logger.error(f"[MLCollector] get_strategy_comparison: {e}") - return [] - def get_quality_histogram(self, bins: int = 10) -> List[Dict[str, Any]]: """Quality score distribution for histogram chart.""" try: @@ -348,14 +294,13 @@ class MLDataCollector: return [] def get_zone_stats(self) -> List[Dict[str, Any]]: - """Quality and SLA stats grouped by zone.""" + """Quality stats grouped by zone.""" try: conn = sqlite3.connect(self._db_path) rows = conn.execute( """ SELECT zone_id, COUNT(*) AS call_count, AVG(quality_score) AS avg_quality, - SUM(sla_breached) AS sla_breaches, AVG(total_distance_km) AS avg_distance_km FROM assignment_ml_log GROUP BY zone_id ORDER BY avg_quality DESC @@ -367,8 +312,7 @@ class MLDataCollector: "zone_id": r[0], "call_count": r[1], "avg_quality": round(r[2] or 0.0, 2), - "sla_breaches": r[3], - "avg_distance_km": round(r[4] or 0.0, 2), + "avg_distance_km": round(r[3] or 0.0, 2), } for r in rows ] @@ -385,17 +329,6 @@ class MLDataCollector: except Exception: return 0 - def count_by_strategy(self) -> Dict[str, int]: - try: - conn = sqlite3.connect(self._db_path) - rows = conn.execute( - "SELECT ml_strategy, COUNT(*) FROM assignment_ml_log GROUP BY ml_strategy" - ).fetchall() - conn.close() - return {r[0]: r[1] for r in rows} - except Exception: - return {} - def export_csv(self) -> str: """Export all records as CSV string.""" try: @@ -448,7 +381,6 @@ class MLDataCollector: num_riders: int, total_distance_km: float, max_orders_per_rider: int, - ml_strategy: str = "balanced", ) -> float: """ Multi-dimensional quality score (0–100, higher = better). @@ -461,11 +393,7 @@ class MLDataCollector: │ rider_efficiency │ reward using minimal riders for the batch size │ └──────────────────────┴────────────────────────────────────────────────┘ - Strategy weights (w_assign, w_dist, w_balance, w_efficiency): - - balanced : (45, 20, 20, 15) - - aggressive_speed: (70, 15, 0, 15) — care about assignment + efficiency - - fuel_saver : (25, 60, 0, 15) — heavily penalise long routes - - zone_strict : (35, 25, 25, 15) — balanced with zone awareness + One fixed weighting (45, 20, 20, 15) is used for every call. """ import math if num_orders == 0: @@ -488,14 +416,7 @@ class MLDataCollector: min_riders_needed = max(1, math.ceil(num_orders / max_orders_per_rider)) rider_efficiency = min(1.0, min_riders_needed / max(1, riders_used)) - weights = { - # assign dist balance efficiency - "aggressive_speed": (70.0, 15.0, 0.0, 15.0), - "fuel_saver": (25.0, 60.0, 0.0, 15.0), - "zone_strict": (35.0, 25.0, 25.0, 15.0), - "balanced": (45.0, 20.0, 20.0, 15.0), - } - w_comp, w_dist, w_bal, w_eff = weights.get(ml_strategy, (45.0, 20.0, 20.0, 15.0)) + w_comp, w_dist, w_bal, w_eff = (45.0, 20.0, 20.0, 15.0) return min( assigned_ratio * w_comp @@ -542,7 +463,6 @@ class MLDataCollector: cluster_radius_km REAL, search_time_limit_seconds INTEGER, road_factor REAL, - ml_strategy TEXT DEFAULT 'balanced', riders_used INTEGER, total_assigned INTEGER, unassigned_count INTEGER, @@ -550,8 +470,6 @@ class MLDataCollector: load_std REAL, total_distance_km REAL DEFAULT 0.0, elapsed_ms REAL, - sla_breached INTEGER DEFAULT 0, - avg_delivery_time_min REAL DEFAULT 0.0, quality_score REAL ) """) @@ -560,9 +478,6 @@ class MLDataCollector: "ALTER TABLE assignment_ml_log ADD COLUMN zone_id TEXT DEFAULT 'default'", "ALTER TABLE assignment_ml_log ADD COLUMN city_id TEXT DEFAULT 'default'", "ALTER TABLE assignment_ml_log ADD COLUMN weather_code TEXT DEFAULT 'CLEAR'", - "ALTER TABLE assignment_ml_log ADD COLUMN sla_breached INTEGER DEFAULT 0", - "ALTER TABLE assignment_ml_log ADD COLUMN avg_delivery_time_min REAL DEFAULT 0.0", - "ALTER TABLE assignment_ml_log ADD COLUMN ml_strategy TEXT DEFAULT 'balanced'", "ALTER TABLE assignment_ml_log ADD COLUMN total_distance_km REAL DEFAULT 0.0", ] for ddl in migrations: @@ -572,7 +487,6 @@ class MLDataCollector: pass for idx in [ "CREATE INDEX IF NOT EXISTS idx_timestamp ON assignment_ml_log(timestamp)", - "CREATE INDEX IF NOT EXISTS idx_strategy ON assignment_ml_log(ml_strategy)", "CREATE INDEX IF NOT EXISTS idx_zone ON assignment_ml_log(zone_id)", ]: conn.execute(idx) diff --git a/app/services/ml/strategy_bandit.py b/app/services/ml/strategy_bandit.py deleted file mode 100644 index 2c3cb51..0000000 --- a/app/services/ml/strategy_bandit.py +++ /dev/null @@ -1,277 +0,0 @@ -""" -Thompson Sampling Contextual Bandit — Strategy Selector -========================================================= -Replaces the greedy SQL auto-tuner with proper online RL. - -Problem -------- -The old greedy tuner picks the strategy with the highest *average* quality -across all history. It never explores alternatives once one strategy leads, -and it ignores context (the same strategy isn't best at all times of day -and all load levels). - -Solution --------- -A contextual multi-armed bandit with Thompson Sampling: - - Context = (time_band, load_band) — up to 7 × 4 = 28 states - Arms = 4 strategies — balanced / fuel_saver / aggressive_speed / zone_strict - Reward = quality_score / 100 — 0..1 float, already logged by MLDataCollector - -How Thompson Sampling works ---------------------------- -Each (context, arm) pair has a Beta(α, β) posterior where: - α = sum of rewards seen so far (high quality calls push α up) - β = sum of "anti-rewards" (low quality calls push β up) - -To SELECT a strategy: - 1. For each arm, sample θ ~ Beta(α, β) - 2. Pick arm with highest θ - → Naturally balances exploration (uncertain arms get sampled often) - with exploitation (well-known good arms dominate when confident) - -To UPDATE after an assignment: - reward = quality_score / 100 - α += reward - β += (1 - reward) - -Bootstrap ---------- -On first startup the entire historical SQLite DB is replayed to warm up -posteriors so the bandit starts informed, not blank. -If saved state already exists it is loaded from disk instead. - -Persistence ------------ -ml_data/strategy_bandit.json — saved every 10 updates. -""" - -import json -import logging -import os -import threading -from typing import Dict, List, Optional, Tuple - -import numpy as np - -logger = logging.getLogger(__name__) - -_SAVE_PATH = os.getenv("BANDIT_PATH", "ml_data/strategy_bandit.json") -_DB_PATH = os.getenv("ML_DB_PATH", "ml_data/ml_store.db") - -ARMS = ["balanced", "fuel_saver", "aggressive_speed", "zone_strict"] - - -class ContextualBandit: - """ - Thompson Sampling bandit for ml_strategy selection. - - Context key : "{time_band}|{load_band}" - Arms : ARMS list (4 strategies) - Prior : Beta(1, 1) — uniform, no initial preference - """ - - def __init__(self): - self._lock = threading.Lock() - # _posteriors[ctx_key][arm] = [alpha, beta] - self._posteriors: Dict[str, Dict[str, List[float]]] = {} - self._update_count = 0 - self._total_pulls = 0 - self._load() - self._bootstrap_from_db() - - # ── Public API ─────────────────────────────────────────────────────────── - - def select(self, time_band: str, load_band: str) -> str: - """ - Sample from Beta posteriors and return the strategy with the highest - sample. Explores uncertain arms naturally; exploits known-good arms - as confidence grows. - """ - ctx = self._ctx(time_band, load_band) - with self._lock: - samples = { - arm: float(np.random.beta(*self._ab(ctx, arm))) - for arm in ARMS - } - self._total_pulls += 1 - chosen = max(samples, key=samples.__getitem__) - logger.debug( - f"[Bandit] ctx={ctx} " - f"samples={{{', '.join(f'{k}:{v:.3f}' for k, v in samples.items())}}} " - f"→ {chosen}" - ) - return chosen - - def update(self, time_band: str, load_band: str, - strategy: str, quality_score: float) -> None: - """Update the Beta posterior for (context, arm) after observing quality.""" - if strategy not in ARMS: - return - ctx = self._ctx(time_band, load_band) - reward = min(1.0, max(0.0, float(quality_score) / 100.0)) - with self._lock: - ab = self._ab(ctx, strategy) - ab[0] += reward # alpha ← quality adds to success mass - ab[1] += (1.0 - reward) # beta ← (1-quality) adds to failure mass - self._update_count += 1 - should_save = self._update_count % 10 == 0 - if should_save: - self._save() - - def best_arm(self, time_band: str, load_band: str) -> Tuple[str, float]: - """ - Return the arm with the highest posterior mean (pure exploitation, no - sampling noise). Used for logging and dashboard, not for live selection. - """ - ctx = self._ctx(time_band, load_band) - with self._lock: - means = { - arm: self._ab(ctx, arm)[0] / sum(self._ab(ctx, arm)) - for arm in ARMS - } - best = max(means, key=means.__getitem__) - return best, round(means[best], 4) - - def get_stats(self) -> Dict: - """Return per-context arm statistics for the ML admin dashboard.""" - with self._lock: - stats: Dict[str, Dict] = {} - for ctx, arms in sorted(self._posteriors.items()): - stats[ctx] = {} - for arm in ARMS: - if arm not in arms: - stats[ctx][arm] = {"mean_reward": 0.5, "observations": 0, - "alpha": 1.0, "beta": 1.0} - continue - alpha, beta = arms[arm] - n = alpha + beta - 2.0 # subtract the Beta(1,1) prior mass - mean = alpha / (alpha + beta) - stats[ctx][arm] = { - "mean_reward": round(mean, 4), - "observations": round(max(0, n), 1), - "alpha": round(alpha, 2), - "beta": round(beta, 2), - } - return { - "total_pulls": self._total_pulls, - "total_updates": self._update_count, - "context_count": len(self._posteriors), - "arms": ARMS, - "contexts": stats, - } - - # ── Persistence ────────────────────────────────────────────────────────── - - def _save(self) -> None: - try: - with self._lock: - snapshot = { - "posteriors": { - ctx: {arm: list(ab) for arm, ab in arms.items()} - for ctx, arms in self._posteriors.items() - }, - "update_count": self._update_count, - "total_pulls": self._total_pulls, - } - os.makedirs(os.path.dirname(_SAVE_PATH) or ".", exist_ok=True) - with open(_SAVE_PATH, "w", encoding="utf-8") as f: - json.dump(snapshot, f, indent=2) - except Exception as e: - logger.warning(f"[Bandit] Save failed: {e}") - - def _load(self) -> None: - try: - if not os.path.exists(_SAVE_PATH): - logger.info("[Bandit] No saved state — will bootstrap from DB.") - return - with open(_SAVE_PATH, "r", encoding="utf-8") as f: - data = json.load(f) - self._posteriors = data.get("posteriors", {}) - self._update_count = data.get("update_count", 0) - self._total_pulls = data.get("total_pulls", 0) - logger.info( - f"[Bandit] Loaded from disk — " - f"{len(self._posteriors)} contexts, {self._update_count} updates" - ) - except Exception as e: - logger.warning(f"[Bandit] Load failed (starting fresh): {e}") - - # ── Bootstrap ──────────────────────────────────────────────────────────── - - def _bootstrap_from_db(self) -> None: - """ - Warm-up posteriors by replaying every historical assignment event. - Skipped if the saved JSON already reflects current DB data. - """ - if self._update_count > 0: - logger.info( - f"[Bandit] Already warmed ({self._update_count} updates) — " - "skipping DB bootstrap." - ) - return - try: - import sqlite3 - from app.services.ml.behavior_analyzer import ( - time_band as _tb, - load_band as _lb, - ) - conn = sqlite3.connect(_DB_PATH) - rows = conn.execute( - "SELECT timestamp, avg_load, ml_strategy, quality_score " - "FROM assignment_ml_log " - "WHERE ml_strategy IS NOT NULL AND quality_score IS NOT NULL " - "ORDER BY id ASC" - ).fetchall() - conn.close() - - if not rows: - logger.info("[Bandit] No historical data — starting with uniform priors.") - return - - for ts, avg_load, strategy, quality in rows: - t_band = _tb(str(ts)) if ts else "UNKNOWN" - l_band = _lb(float(avg_load or 0)) - self.update(t_band, l_band, strategy, float(quality or 50)) - - # Reset counter so we don't confuse bootstrap updates with live ones - with self._lock: - self._update_count = len(rows) - - logger.info( - f"[Bandit] Bootstrapped from {len(rows)} historical events — " - f"{len(self._posteriors)} contexts, {len(ARMS)} arms." - ) - self._save() - except Exception as e: - logger.warning(f"[Bandit] Bootstrap failed (non-fatal): {e}") - - # ── Helpers ────────────────────────────────────────────────────────────── - - @staticmethod - def _ctx(time_band: str, load_band: str) -> str: - return f"{time_band}|{load_band}" - - def _ab(self, ctx: str, arm: str) -> List[float]: - """Get or create Beta(1, 1) uniform prior for (ctx, arm). NOT thread-safe alone.""" - if ctx not in self._posteriors: - self._posteriors[ctx] = {} - if arm not in self._posteriors[ctx]: - self._posteriors[ctx][arm] = [1.0, 1.0] - return self._posteriors[ctx][arm] - - -# ── Singleton ──────────────────────────────────────────────────────────────── - -_bandit_instance: Optional[ContextualBandit] = None -_bandit_lock = threading.Lock() - - -def get_bandit() -> ContextualBandit: - """Return the process-level singleton ContextualBandit.""" - global _bandit_instance - if _bandit_instance is None: - with _bandit_lock: - if _bandit_instance is None: - _bandit_instance = ContextualBandit() - return _bandit_instance diff --git a/app/services/rider/__pycache__/get_active_riders.cpython-312.pyc b/app/services/rider/__pycache__/get_active_riders.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a7594321ce96d216389def720e3de3314df5c17c GIT binary patch literal 3916 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kitchen/pickuplat/pickuplon/deliverylat/deliverylong/userid/ridername. + + `ridername` is always "" — delivery_raw doesn't store it (it's + display-only in the /ml/history debug endpoint; every actual matching + decision is keyed on userid, so this doesn't affect assignment). + """ + records: List[Dict[str, Any]] = [] + for r in self._load_raw_rows(days): + try: + plat = float(r.get("plat") or 0) + plon = float(r.get("plon") or 0) + dlat = float(r.get("dlat") or 0) + dlon = float(r.get("dlon") or 0) + uid = int(float(r.get("userid") or 0)) + if not plat or not dlat or uid == 0: + continue + records.append({ + "kitchen": (r.get("pickupcustomer") or "").strip().lower(), + "pickuplat": plat, "pickuplon": plon, + "deliverylat": dlat, "deliverylong": dlon, + "userid": uid, + "ridername": "", + }) + except (TypeError, ValueError): + continue + return records + def sample_batches(self, days: int = 14, min_drops: int = 4, max_drops: int = 15, limit: int = 10) -> List[Dict[str, Any]]: """ @@ -623,12 +654,24 @@ class DeliveryHistoryService: full: bool = False) -> Dict[str, Any]: """ Full pipeline: incremental DB sync -> prune local store -> rebuild - aggregates locally. This is what the scheduler and the admin endpoint - call. The DB is touched only by the sync step (new rows only). + aggregates locally -> (if pattern_source=db) rebuild the Phase-0 + pattern store from the same synced rows. This is what the scheduler + and the admin endpoint call. The DB is touched only by the sync step + (new rows only). """ + from app.config.dynamic_config import get_config + cfg = get_config() + pattern_on = str(cfg.get("pattern_source", "csv")) == "db" + pattern_days = int(cfg.get("pattern_history_days", 30)) + # Local retention must cover whichever consumer needs more history + # (the ETA window vs. the pattern-store window) without changing the + # ETA computation's own `days` window below — that backtest result is + # already validated at 14 days and this must not perturb it. + retain_days = max(days, pattern_days) if pattern_on else days + with _WRITE_LOCK: try: - sync = self.sync_from_db(days=days, tenant_id=tenant_id, full=full) + sync = self.sync_from_db(days=retain_days, tenant_id=tenant_id, full=full) except Exception as e: logger.error(f"[DeliveryHistory] sync failed: {e}", exc_info=True) # Still try to serve whatever is already local. @@ -636,9 +679,23 @@ class DeliveryHistoryService: self._last_summary = {"status": "sync_failed", "error": str(e), "rebuild": rebuilt} return self._last_summary - self._prune_raw(days) + self._prune_raw(retain_days) rebuilt = self.rebuild_aggregates(days) - self._last_summary = {"status": "ok", "sync": sync, "rebuild": rebuilt} + + pattern_rebuild = None + if pattern_on: + try: + from app.services.vector.delivery_history_store import get_delivery_history_store + records = self.get_pattern_records(pattern_days) + n = get_delivery_history_store().rebuild_from_records(records) + pattern_rebuild = {"records": n, "days": pattern_days} + except Exception as e: + logger.warning(f"[DeliveryHistory] pattern-store rebuild skipped: {e}") + + self._last_summary = { + "status": "ok", "sync": sync, "rebuild": rebuilt, + "pattern_rebuild": pattern_rebuild, + } return self._last_summary # -- cache + lookup ----------------------------------------------------- diff --git a/app/services/routing/gps_smoother.py b/app/services/routing/gps_smoother.py new file mode 100644 index 0000000..e71eae4 --- /dev/null +++ b/app/services/routing/gps_smoother.py @@ -0,0 +1,150 @@ +""" +GPS location smoothing — rider-api + +Smooths noisy rider GPS pings (typical error +-5-15m, worse on poor signal, +occasional bad-fix "jumps") using a per-rider exponential moving average +(EMA): each new reading is blended with the running estimate so a single bad +ping can't yank the rider's position, while the estimate still tracks real +movement. + +This used to be implemented as a full Kalman filter (per-coordinate process/ +measurement covariance, gain computed every update). For a "constant +position" state model with no velocity term — which is what this is, since +we're smoothing noisy pings, not tracking motion — the Kalman update reduces +mathematically to an EMA once the gain reaches steady state, which happens +within the first couple of updates. The EMA below is the same behavior with +one constant instead of two, and no covariance bookkeeping to explain to the +next person reading this file. + +Only two things are actually used elsewhere in the app: + smooth_rider_locations(riders) — per-rider EMA, stateful across calls + smooth_order_coordinates(orders) — validates/normalises delivery coords + (NOT smoothed — see its docstring) +""" + +import logging +import time +from typing import Dict, Optional, Tuple + +logger = logging.getLogger(__name__) + +# Smoothing factor: how much weight a new GPS reading gets against the +# running estimate. Lower = smoother/slower to react, higher = trusts each +# new ping more. 0.1 matches the steady-state behavior of the Kalman filter +# this replaced (process_noise=1e-4, measurement_noise=0.01). +_ALPHA = 0.1 +_STALE_SECONDS = 1800.0 # reset a rider's running estimate after 30 min silence + + +def _is_valid_coord(lat: float, lon: float) -> bool: + try: + lat, lon = float(lat), float(lon) + return ( + -90.0 <= lat <= 90.0 + and -180.0 <= lon <= 180.0 + and not (lat == 0.0 and lon == 0.0) + ) + except (TypeError, ValueError): + return False + + +class _RiderEstimate: + """Running EMA estimate of one rider's position.""" + + def __init__(self): + self.lat: Optional[float] = None + self.lon: Optional[float] = None + self.last_updated: float = time.time() + + def update(self, lat: float, lon: float) -> Tuple[float, float]: + if not _is_valid_coord(lat, lon): + return (self.lat, self.lon) if self.lat is not None else (lat, lon) + + if time.time() - self.last_updated > _STALE_SECONDS: + self.lat = self.lon = None # stale — start fresh + + if self.lat is None: + self.lat, self.lon = lat, lon + else: + self.lat += _ALPHA * (lat - self.lat) + self.lon += _ALPHA * (lon - self.lon) + + self.last_updated = time.time() + return self.lat, self.lon + + +_rider_estimates: Dict[str, _RiderEstimate] = {} + + +def smooth_rider_locations(riders: list) -> list: + """ + Apply EMA smoothing to a list of rider dicts in-place, keyed by rider id + (history preserved across calls via a process-level registry). + + Reads/writes: latitude, longitude (and currentlat/currentlong if present). + Adds: _location_smoothed = True on each processed rider. + """ + for rider in riders: + try: + rider_id = str( + rider.get("userid") or rider.get("riderid") or + rider.get("id") or "unknown" + ) + raw_lat = float(rider.get("latitude") or rider.get("currentlat") or 0) + raw_lon = float(rider.get("longitude") or rider.get("currentlong") or 0) + if raw_lat == 0.0 and raw_lon == 0.0: + continue + + estimate = _rider_estimates.setdefault(rider_id, _RiderEstimate()) + smooth_lat, smooth_lon = estimate.update(raw_lat, raw_lon) + + # Cast back to string for Go compatibility + s_lat, s_lon = str(round(smooth_lat, 8)), str(round(smooth_lon, 8)) + rider["latitude"] = s_lat + rider["longitude"] = s_lon + if "currentlat" in rider: + rider["currentlat"] = s_lat + if "currentlong" in rider: + rider["currentlong"] = s_lon + rider["_location_smoothed"] = True + except Exception as e: + logger.debug(f"Rider location smoothing skipped: {e}") + return riders + + +def smooth_order_coordinates(orders: list) -> list: + """ + Validate and lightly normalise delivery coordinates in a list of order dicts. + + DESIGN NOTE — why these are NOT smoothed: + Smoothing blends successive measurements from the same source over time. + Delivery coordinates are a single static point (one measurement) — there + is nothing to blend. Per-customer GPS accuracy is handled upstream by the + FAISS coordinate store (verified historical rider-confirmed delivery + points). This function only normalises the coordinate fields to floats + so downstream code never sees raw strings or None values. + + Modifies orders in-place. Returns the same list. + """ + for order in orders: + try: + dlat_raw = order.get("deliverylat") or order.get("droplat") + dlon_raw = order.get("deliverylong") or order.get("droplon") + if dlat_raw is None or dlon_raw is None: + continue + dlat = float(dlat_raw) + dlon = float(dlon_raw) + if not _is_valid_coord(dlat, dlon): + continue + # Normalise to string (Go service expects string coordinates) + s_lat = str(round(dlat, 8)) + s_lon = str(round(dlon, 8)) + order["deliverylat"] = s_lat + order["deliverylong"] = s_lon + if "droplat" in order: + order["droplat"] = s_lat + if "droplon" in order: + order["droplon"] = s_lon + except Exception as e: + logger.debug(f"Coordinate normalisation skipped: {e}") + return orders diff --git a/app/services/routing/kalman_filter.py b/app/services/routing/kalman_filter.py deleted file mode 100644 index 608ac6f..0000000 --- a/app/services/routing/kalman_filter.py +++ /dev/null @@ -1,327 +0,0 @@ -""" -GPS Kalman Filter \u2014 rider-api - -A 1D Kalman filter applied independently to latitude and longitude -to smooth noisy GPS coordinates from riders and delivery points. - -Why Kalman for GPS? -- GPS readings contain measurement noise (\u00b15\u201315m typical, \u00b150m poor signal) -- Rider location pings can "jump" due to bad signal or device error -- Kalman filter gives an optimal estimate by balancing: - (1) Previous predicted position (process model) - (2) New GPS measurement (observation model) - -Design: -- Separate filter instance per rider (stateful \u2014 preserves history) -- `CoordinateKalmanFilter` \u2014 single lat/lon smoother -- `GPSKalmanFilter` \u2014 wraps two CoordinateKalmanFilters (lat + lon) -- `RiderKalmanRegistry` \u2014 manages per-rider filter instances -- `smooth_coordinates()` \u2014 stateless single-shot smoother for delivery coords - -Usage: - # Stateless (one-shot, no history \u2014 for delivery coords): - smooth_lat, smooth_lon = smooth_coordinates(raw_lat, raw_lon) - - # Stateful (per-rider, preserves motion history): - registry = RiderKalmanRegistry() - lat, lon = registry.update(rider_id=1116, lat=11.0067, lon=76.9558) -""" - -import logging -import time -from typing import Dict, Optional, Tuple - -logger = logging.getLogger(__name__) - - -# \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 -# CORE 1D KALMAN FILTER -# \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 - -class CoordinateKalmanFilter: - """ - 1-dimensional Kalman filter for a single GPS coordinate (lat or lon). - - State model: position only (constant position with random walk). - - Equations: - Prediction: x\u0302\u2096\u207b = x\u0302\u2096\u208b\u2081 (no movement assumed between pings) - P\u0302\u2096\u207b = P\u2096\u208b\u2081 + Q (uncertainty grows over time) - - Update: K\u2096 = P\u0302\u2096\u207b / (P\u0302\u2096\u207b + R) (Kalman gain) - x\u0302\u2096 = x\u0302\u2096\u207b + K\u2096\u00b7(z\u2096 - x\u0302\u2096\u207b) (weighted fusion) - P\u2096 = (1 - K\u2096)\u00b7P\u0302\u2096\u207b (update uncertainty) - - Parameters: - process_noise (Q): How much position can change between measurements. - Higher = filter trusts new measurements more (less smoothing). - measurement_noise (R): GPS measurement uncertainty. - Higher = filter trusts history more (more smoothing). - """ - - def __init__( - self, - process_noise: float = 1e-4, - measurement_noise: float = 0.01, - initial_uncertainty: float = 1.0, - ): - self.Q = process_noise - self.R = measurement_noise - self._x: Optional[float] = None - self._P: float = initial_uncertainty - - @property - def initialized(self) -> bool: - return self._x is not None - - def update(self, measurement: float) -> float: - """Process one new measurement and return the filtered estimate.""" - if not self.initialized: - self._x = measurement - return self._x - - # Predict - x_prior = self._x - P_prior = self._P + self.Q - - # Update - K = P_prior / (P_prior + self.R) - self._x = x_prior + K * (measurement - x_prior) - self._P = (1.0 - K) * P_prior - - return self._x - - def reset(self): - self._x = None - self._P = 1.0 - - -# \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 -# 2D GPS KALMAN FILTER (lat + lon) -# \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 - -class GPSKalmanFilter: - """ - Two-dimensional GPS smoother using independent 1D Kalman filters - for latitude and longitude. - """ - - def __init__( - self, - process_noise: float = 1e-4, - measurement_noise: float = 0.01, - ): - self.lat_filter = CoordinateKalmanFilter(process_noise, measurement_noise) - self.lon_filter = CoordinateKalmanFilter(process_noise, measurement_noise) - self.last_updated: float = time.time() - self.update_count: int = 0 - - def update(self, lat: float, lon: float) -> Tuple[float, float]: - """Feed a new GPS reading and get the smoothed (lat, lon).""" - if not self._is_valid_coord(lat, lon): - if self.lat_filter.initialized: - return self.lat_filter._x, self.lon_filter._x - return lat, lon - - smooth_lat = self.lat_filter.update(lat) - smooth_lon = self.lon_filter.update(lon) - self.last_updated = time.time() - self.update_count += 1 - - return smooth_lat, smooth_lon - - def get_estimate(self) -> Optional[Tuple[float, float]]: - if self.lat_filter.initialized: - return self.lat_filter._x, self.lon_filter._x - return None - - def reset(self): - self.lat_filter.reset() - self.lon_filter.reset() - self.update_count = 0 - - @staticmethod - def _is_valid_coord(lat: float, lon: float) -> bool: - try: - lat, lon = float(lat), float(lon) - return ( - -90.0 <= lat <= 90.0 - and -180.0 <= lon <= 180.0 - and not (lat == 0.0 and lon == 0.0) - ) - except (TypeError, ValueError): - return False - - -# \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 -# PER-RIDER FILTER REGISTRY -# \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 - -class RiderKalmanRegistry: - """ - Maintains per-rider Kalman filter instances across calls. - Stale filters (> 30 min silence) are automatically reset. - """ - - def __init__( - self, - process_noise: float = 1e-4, - measurement_noise: float = 0.01, - stale_seconds: float = 1800.0, - ): - self._filters: Dict[str, GPSKalmanFilter] = {} - self._process_noise = process_noise - self._measurement_noise = measurement_noise - self._stale_seconds = stale_seconds - - def _get_or_create(self, rider_id) -> GPSKalmanFilter: - key = str(rider_id) - now = time.time() - if key in self._filters: - f = self._filters[key] - if now - f.last_updated > self._stale_seconds: - f.reset() - return f - self._filters[key] = GPSKalmanFilter( - process_noise=self._process_noise, - measurement_noise=self._measurement_noise, - ) - return self._filters[key] - - def update(self, rider_id, lat: float, lon: float) -> Tuple[float, float]: - return self._get_or_create(rider_id).update(lat, lon) - - def get_estimate(self, rider_id) -> Optional[Tuple[float, float]]: - key = str(rider_id) - if key in self._filters: - return self._filters[key].get_estimate() - return None - - def reset_rider(self, rider_id): - key = str(rider_id) - if key in self._filters: - self._filters[key].reset() - - def clear_all(self): - self._filters.clear() - - -# \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 -# GLOBAL REGISTRY (process-level singleton) -# \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 - -_global_registry = RiderKalmanRegistry() - - -def get_registry() -> RiderKalmanRegistry: - """Get the process-level rider Kalman filter registry.""" - return _global_registry - - -# \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 -# STATELESS COORDINATE SMOOTHER -# \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 - -def smooth_coordinates( - lat: float, - lon: float, - *, - prior_lat: Optional[float] = None, - prior_lon: Optional[float] = None, - process_noise: float = 1e-4, - measurement_noise: float = 0.01, -) -> Tuple[float, float]: - """ - Stateless single-shot GPS smoother. - If a prior is provided, blends the new reading towards it. - """ - f = GPSKalmanFilter(process_noise=process_noise, measurement_noise=measurement_noise) - if prior_lat is not None and prior_lon is not None: - try: - _flat = float(prior_lat) - _flon = float(prior_lon) - if GPSKalmanFilter._is_valid_coord(_flat, _flon): - f.update(_flat, _flon) - except (TypeError, ValueError): - pass - return f.update(lat, lon) - - -# \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 -# BATCH SMOOTHERS -# \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 - -def smooth_rider_locations(riders: list) -> list: - """ - Apply Kalman smoothing to a list of rider dicts in-place using - the global per-rider registry (history preserved across calls). - - Reads/writes: latitude, longitude (and currentlat/currentlong if present). - Adds: _kalman_smoothed = True on each processed rider. - """ - registry = get_registry() - for rider in riders: - try: - rider_id = ( - rider.get("userid") or rider.get("riderid") or - rider.get("id") or "unknown" - ) - raw_lat = float(rider.get("latitude") or rider.get("currentlat") or 0) - raw_lon = float(rider.get("longitude") or rider.get("currentlong") or 0) - if raw_lat == 0.0 and raw_lon == 0.0: - continue - smooth_lat, smooth_lon = registry.update(rider_id, raw_lat, raw_lon) - # Cast back to string for Go compatibility - s_lat, s_lon = str(round(smooth_lat, 8)), str(round(smooth_lon, 8)) - rider["latitude"] = s_lat - rider["longitude"] = s_lon - if "currentlat" in rider: - rider["currentlat"] = s_lat - if "currentlong" in rider: - rider["currentlong"] = s_lon - rider["_kalman_smoothed"] = True - except Exception as e: - logger.debug(f"Kalman rider smoothing skipped: {e}") - return riders - - -def smooth_order_coordinates(orders: list) -> list: - """ - Validate and lightly normalise delivery coordinates in a list of order dicts. - - DESIGN NOTE — why we do NOT use Kalman filtering here: - ───────────────────────────────────────────────────── - Kalman filtering is a *temporal* smoother: it blends successive measurements - from the same sensor over time. Delivery coordinates are a single static - point (one measurement). Feeding the kitchen location as a "prior" would - pull the customer's address toward the kitchen — exactly wrong. - - Per-customer GPS accuracy is handled upstream by the FAISS coordinate store - (verified historical rider-confirmed delivery points). This function only - normalises the coordinate fields to floats so downstream code never sees - raw strings or None values. - - Modifies orders in-place. Returns the same list. - """ - for order in orders: - try: - dlat_raw = order.get("deliverylat") or order.get("droplat") - dlon_raw = order.get("deliverylong") or order.get("droplon") - if dlat_raw is None or dlon_raw is None: - continue - dlat = float(dlat_raw) - dlon = float(dlon_raw) - if not GPSKalmanFilter._is_valid_coord(dlat, dlon): - continue - # Normalise to string (Go service expects string coordinates) - s_lat = str(round(dlat, 8)) - s_lon = str(round(dlon, 8)) - order["deliverylat"] = s_lat - order["deliverylong"] = s_lon - if "droplat" in order: - order["droplat"] = s_lat - if "droplon" in order: - order["droplon"] = s_lon - except Exception as e: - logger.debug(f"Coordinate normalisation skipped: {e}") - return orders diff --git a/app/services/routing/route_optimizer.py b/app/services/routing/route_optimizer.py index fdc6580..1223b54 100644 --- a/app/services/routing/route_optimizer.py +++ b/app/services/routing/route_optimizer.py @@ -10,7 +10,6 @@ FEATURES: - Automatic outlier detection and coordinate correction - Hybrid distance calculation (Google Maps + Haversine fallback) - Robust error handling for invalid inputs -- Composite cost function (idea.txt: distance + profit - margin) """ import math @@ -21,7 +20,7 @@ import asyncio from typing import Dict, Any, List as _List, Optional, Tuple, Union from datetime import datetime, timedelta import httpx -from app.services.routing.kalman_filter import smooth_order_coordinates +from app.services.routing.gps_smoother import smooth_order_coordinates import numpy as np from app.core.arrow_utils import calculate_haversine_matrix_vectorized from app.config.dynamic_config import get_config @@ -38,191 +37,6 @@ except ImportError: logger = logging.getLogger(__name__) -class CompositeCostCalculator: - """ - Composite Cost Function for Profit-Aware Route Optimization. - - Based on idea.txt data encoding techniques: - - Total Cost = Distance Cost + Rider Cost - Merchant Profit - - Edge Cost = (distance * fuel_rate) + rider_cost - merchant_margin - - route_score = profit - composite_cost (what we want to MAXIMIZE) - - This transforms the problem from: - "Find shortest route" -> "Find most profitable route" - """ - - def __init__(self): - # Cost parameters (can be ML-tuned via DynamicConfig) - self.fuel_rate_per_km = 2.5 - self.base_rider_cost = 0.0 - self.opportunity_cost_per_km = 0.5 # Cost of rider's time per km - self.profit_weight = 0.3 # How much profit influences routing (0-1) - self.traffic_multiplier_peak = 1.5 # Peak hour traffic penalty - self.traffic_multiplier_normal = 1.2 # Normal traffic multiplier - - # Defaults for orders without profit data - self.default_order_amount = 80.0 - self.default_merchant_margin = 5.0 - - def calculate_composite_cost( - self, - distance_km: float, - order_amount: float = None, - merchant_margin: float = None, - traffic_factor: float = 1.0, - time_of_day: str = "NORMAL", - ) -> Dict[str, float]: - """ - Calculate composite cost for a route edge. - - Args: - distance_km: Distance for this leg - order_amount: Revenue from this order (if known) - merchant_margin: Merchant's margin for this order (if known) - traffic_factor: Traffic multiplier (1.0 = normal) - time_of_day: Traffic time category ("PEAK", "NORMAL", "OFF_PEAK") - - Returns: - Dict with: - - distance_cost: Raw distance cost - - rider_cost: Total rider cost for this leg - - gross_profit: Revenue - rider cost - - net_cost: Cost after profit adjustment (MINIMIZE THIS) - - route_score: Profitability score (MAXIMIZE THIS) - """ - # Distance cost = fuel + opportunity cost - distance_cost = distance_km * self.fuel_rate_per_km - - # Rider cost = base + distance cost - rider_cost = self.base_rider_cost + distance_cost - - # Apply traffic penalty - if time_of_day == "PEAK": - rider_cost *= self.traffic_multiplier_peak - elif time_of_day == "NORMAL": - rider_cost *= self.traffic_multiplier_normal - - # Apply custom traffic factor - rider_cost *= traffic_factor - - # Profit calculation (target encoding: use order data if available) - if order_amount is None: - order_amount = self.default_order_amount - if merchant_margin is None: - merchant_margin = self.default_merchant_margin - - gross_profit = order_amount - rider_cost - - # Net cost = rider cost - profit contribution - # This means high-profit orders have LOWER cost (more desirable) - profit_adjustment = gross_profit * self.profit_weight - net_cost = rider_cost - profit_adjustment - - # Route score = profit - opportunity cost (for route planning) - route_score = gross_profit - (distance_km * self.opportunity_cost_per_km) - - # Ensure net_cost is never negative (minimum cost for any delivery) - net_cost = max(net_cost, 5.0) # Minimum 5 km equivalent cost - - return { - "distance_cost": round(distance_cost, 2), - "rider_cost": round(rider_cost, 2), - "gross_profit": round(gross_profit, 2), - "net_cost": round(net_cost, 2), - "route_score": round(route_score, 2), - } - - def calculate_cost_matrix( - self, - dist_matrix: np.ndarray, - orders: _List[Dict[str, Any]] = None, - traffic_condition: str = "NORMAL", - ) -> Tuple[np.ndarray, Dict[str, Any]]: - """ - Calculate composite cost matrix for all node pairs. - - Args: - dist_matrix: Distance matrix (N x N) - orders: List of orders (for profit data) - traffic_condition: Traffic condition ("PEAK", "NORMAL", "OFF_PEAK") - - Returns: - Tuple of (cost_matrix, summary_stats) - """ - n = len(dist_matrix) - cost_matrix = np.zeros((n, n)) - - # Extract order data for profit encoding - order_amounts = [] - merchant_margins = [] - - if orders: - for o in orders: - try: - amount = float( - o.get("orderamount") - or o.get("deliveryamount") - or self.default_order_amount - ) - margin = float( - o.get("merchant_margin") or self.default_merchant_margin - ) - except: - amount = self.default_order_amount - margin = self.default_merchant_margin - order_amounts.append(amount) - merchant_margins.append(margin) - else: - order_amounts = [self.default_order_amount] * n - merchant_margins = [self.default_merchant_margin] * n - - # Calculate costs for each pair - total_cost = 0.0 - total_profit = 0.0 - high_cost_count = 0 - - for i in range(n): - for j in range(n): - if i == j: - cost_matrix[i][j] = 0 - continue - - dist = dist_matrix[i][j] - - # Use order j's profit data (destination) - order_amount = ( - order_amounts[j - 1] if j > 0 else self.default_order_amount - ) - merchant_margin = ( - merchant_margins[j - 1] if j > 0 else self.default_merchant_margin - ) - - cost_data = self.calculate_composite_cost( - distance_km=dist, - order_amount=order_amount, - merchant_margin=merchant_margin, - time_of_day=traffic_condition, - ) - - cost_matrix[i][j] = cost_data["net_cost"] - total_cost += cost_data["net_cost"] - total_profit += cost_data["gross_profit"] - - if cost_data["net_cost"] > 50: - high_cost_count += 1 - - summary = { - "total_cost": round(total_cost, 2), - "total_profit": round(total_profit, 2), - "avg_cost": round(total_cost / (n * n) if n > 0 else 0, 2), - "avg_profit": round(total_profit / (n * n) if n > 0 else 0, 2), - "high_cost_legs": high_cost_count, - "traffic_condition": traffic_condition, - } - - return cost_matrix, summary - - class RouteOptimizer: """Route optimization using Google OR-Tools (Async).""" @@ -253,8 +67,6 @@ class RouteOptimizer: # Solver time limit (ML-tuned) self.search_time_limit_seconds = int(_cfg.get("search_time_limit_seconds")) - self.cost_calculator = CompositeCostCalculator() - def haversine_distance( self, lat1: float, lon1: float, lat2: float, lon2: float ) -> float: @@ -274,153 +86,6 @@ class RouteOptimizer: except Exception: return 0.0 - async def _get_google_maps_distances_batch( - self, origin_lat: float, origin_lon: float, destinations: _List[tuple] - ) -> Dict[tuple, float]: - """Get road distances for multiple destinations from Google Maps API. (Async, Parallel)""" - if not self.use_google_maps or not destinations: - return {} - - results = {} - batch_size = 25 - chunks = [ - destinations[i : i + batch_size] - for i in range(0, len(destinations), batch_size) - ] - - async def process_batch(batch): - batch_result = {} - try: - dest_str = "|".join([f"{lat},{lon}" for lat, lon in batch]) - url = "https://maps.googleapis.com/maps/api/distancematrix/json" - params = { - "origins": f"{origin_lat},{origin_lon}", - "destinations": dest_str, - "key": self.google_maps_api_key, - "units": "metric", - } - async with httpx.AsyncClient(timeout=10.0) as client: - response = await client.get(url, params=params) - response.raise_for_status() - data = response.json() - - if data.get("status") == "OK": - rows = data.get("rows", []) - if rows: - elements = rows[0].get("elements", []) - for idx, element in enumerate(elements): - if idx < len(batch): - dest_coord = batch[idx] - if element.get("status") == "OK": - dist = element.get("distance", {}).get("value") - dur = element.get("duration", {}).get("value") - if dist is not None: - batch_result[dest_coord] = { - "distance": dist / 1000.0, - "duration": dur / 60.0 if dur else None, - } - except Exception as e: - logger.warning(f"Google Maps batch call failed: {e}") - return batch_result - - batch_results_list = await asyncio.gather( - *[process_batch(chunk) for chunk in chunks] - ) - for res in batch_results_list: - results.update(res) - return results - - # ------------------------------------------------------------------ - # GOOGLE DIRECTIONS - WAYPOINT OPTIMISATION - # ------------------------------------------------------------------ - - async def _optimize_waypoints_google( - self, - origin_lat: float, - origin_lon: float, - waypoints: _List[Tuple[float, float]], - ) -> Tuple[Optional[_List[int]], Optional[_List[float]]]: - """ - Ask Google Directions API to find the optimal visiting order for a set - of delivery points starting from a kitchen/pickup location. - - Uses `optimize:true` in the waypoints parameter - Google solves the TSP - internally using actual road geometry (turn restrictions, one-way - streets, real distances) rather than Haversine approximation. - - Returns - ------- - (waypoint_order, leg_km) - waypoint_order 0-based indices into `waypoints` in optimal order. - e.g. [2, 0, 1] means visit wp[2] -> wp[0] -> wp[1]. - leg_km Actual road distance (km) for each leg in order: - leg_km[0] = kitchen -> wp[order[0]], - leg_km[1] = wp[order[0]] -> wp[order[1]], etc. - Both are None on any failure - caller falls back to OR-Tools. - - Notes - ----- - - Supports up to 25 intermediate waypoints (Google's standard limit). - - destination = origin (closed-loop TSP); the return leg is discarded. - - One API call per rider per assignment - cheap at delivery scale. - """ - if not self.use_google_maps or not waypoints or len(waypoints) < 2: - return None, None - if len(waypoints) > 25: - return None, None # fall back to OR-Tools for unusually large routes - - try: - wp_str = "optimize:true|" + "|".join( - f"{lat},{lon}" for lat, lon in waypoints - ) - params = { - "origin": f"{origin_lat},{origin_lon}", - "destination": f"{origin_lat},{origin_lon}", # closed loop - "waypoints": wp_str, - "key": self.google_maps_api_key, - } - async with httpx.AsyncClient(timeout=8.0) as client: - resp = await client.get( - "https://maps.googleapis.com/maps/api/directions/json", - params=params, - ) - resp.raise_for_status() - data = resp.json() - - status_code = data.get("status") - if status_code != "OK": - logger.debug( - f"[GoogleWaypoints] status={status_code} " - f"error='{data.get('error_message', '')}'" - ) - return None, None - - routes = data.get("routes", []) - if not routes: - return None, None - - route = routes[0] - wp_order = route.get("waypoint_order") - legs = route.get("legs", []) - - if wp_order is None or len(wp_order) != len(waypoints): - return None, None - - # Extract leg distances (metres -> km), skip the return-to-origin leg - leg_km: _List[float] = [] - for leg in legs[: len(waypoints)]: # first N legs only - dist_m = leg.get("distance", {}).get("value") - leg_km.append(dist_m / 1000.0 if dist_m is not None else 0.0) - - logger.debug( - f"[GoogleWaypoints] Optimised {len(waypoints)} stops -> order={wp_order}" - ) - return wp_order, leg_km - - except Exception as _e: - logger.debug(f"[GoogleWaypoints] Failed (non-fatal): {_e}") - return None, None - # ------------------------------------------------------------------ # ROAD-AWARE VISITING ORDER (Phase 2 - opt-in, cached) # ------------------------------------------------------------------ @@ -698,8 +363,8 @@ class RouteOptimizer: routing_enums_pb2.LocalSearchMetaheuristic.GUIDED_LOCAL_SEARCH ) # TSP time limit hard-capped at 2 seconds per kitchen. - # The ML hypertuner may push search_time_limit_seconds up to 8-10s - # chasing marginally better routes, but at delivery scale (< 15 stops) + # search_time_limit_seconds can be tuned up to 8-10s via config, but at + # delivery scale (< 15 stops) # OR-Tools finds a near-optimal solution in < 200ms. Waiting 8-10s # per kitchen x 3 kitchens x 4 riders = 96s of unnecessary waiting. # The VRP already has its own 3s cap. 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-Pattern-first vector rider lookup using 30-day delivery history. +Pattern-first vector rider lookup using recent delivery history. Works on any platform — no faiss-cpu dependency. +Data source (config `pattern_source`) +-------------------------------------- + "csv" (legacy) — built from delivery_details.csv, only refreshed when a + human re-exports it and calls POST /ml/reload-history. + "db" (default going forward) — built from the nearledb mirror the ETA-sync + agent already maintains (delivery_raw), rebuilt + automatically every sync cycle via rebuild_from_records(). + No manual step, no extra database load. + How it works ------------ -At startup the CSV is parsed once and two structures are built: +Records (however sourced) are turned into two structures: 1. PATTERN TABLE (primary, O(1) lookup) City divided into ~1.1 km grid cells (round coords to 2 d.p.). @@ -65,9 +74,21 @@ CORRECTIONS_PATH = os.getenv("DELIVERY_CORRECTIONS_CSV", "delivery_corrections.c _CORRECTION_WEIGHT = int(os.getenv("CORRECTION_WEIGHT", "1")) _STORE_DIR = os.getenv("FAISS_HISTORY_DIR", "ml_data/faiss_history") -_VECTORS_PATH = os.path.join(_STORE_DIR, "delivery_history_vectors.npy") -_RECORDS_PATH = os.path.join(_STORE_DIR, "delivery_history_records.pkl") -_META_PATH = os.path.join(_STORE_DIR, "delivery_history.meta") + + +def _paths_for(source: str) -> Tuple[str, str, str]: + """ + Disk cache paths, namespaced by source ("csv" or "db"). Namespaced so that + flipping `pattern_source` at runtime (e.g. to debug, or to roll back) + never clobbers the other mode's cached snapshot — each mode keeps its own + independent copy on disk. + """ + suffix = "" if source == "csv" else f".{source}" + return ( + os.path.join(_STORE_DIR, f"delivery_history_vectors{suffix}.npy"), + os.path.join(_STORE_DIR, f"delivery_history_records{suffix}.pkl"), + os.path.join(_STORE_DIR, f"delivery_history{suffix}.meta"), + ) # --------------------------------------------------------------------------- # Thresholds @@ -78,6 +99,15 @@ _MIN_PATTERN_DOMINANCE = 0.60 # pattern table: rider owns ≥ 60 % of zone _MIN_PATTERN_VOLUME = 3 # pattern table: at least 3 deliveries in zone +def _pattern_source() -> str: + """'csv' (legacy, manual refresh) or 'db' (auto-refreshed from nearledb mirror).""" + try: + from app.config.dynamic_config import get_config + return str(get_config().get("pattern_source", "csv")) + except Exception: + return "csv" + + # --------------------------------------------------------------------------- # Pure-NumPy L2 index — drop-in replacement for faiss.IndexFlatL2 # --------------------------------------------------------------------------- @@ -152,6 +182,7 @@ class DeliveryHistoryStore: store.pattern_count() -> int store.get_pattern_stats() -> list store.reload_from_csv() -> int + store.rebuild_from_records(records) -> int """ def __init__(self): @@ -174,8 +205,21 @@ class DeliveryHistoryStore: def _load(self) -> None: os.makedirs(_STORE_DIR, exist_ok=True) + if _pattern_source() == "db": + # DB-driven: the ETA-sync agent keeps this fresh on its own cadence + # by calling rebuild_from_records() after every sync cycle. At + # startup we just load whatever was last persisted — no CSV + # freshness check applies in this mode. + if self._load_from_disk("db"): + return + logger.info( + "[DeliveryHistory] pattern_source=db, no persisted snapshot yet " + "— will populate on the next ETA-sync cycle." + ) + return + if self._saved_files_are_current(): - if self._load_from_disk(): + if self._load_from_disk("csv"): return logger.warning( "[DeliveryHistory] Saved files corrupt — rebuilding from CSV." @@ -184,14 +228,15 @@ class DeliveryHistoryStore: records = self._parse_csv() if not records: return - self._build_and_save(records) + self._build_and_save(records, source="csv") def _saved_files_are_current(self) -> bool: - for path in (_VECTORS_PATH, _RECORDS_PATH, _META_PATH): + vectors_path, records_path, meta_path = _paths_for("csv") + for path in (vectors_path, records_path, meta_path): if not os.path.isfile(path): return False try: - with open(_META_PATH, "r", encoding="utf-8") as f: + with open(meta_path, "r", encoding="utf-8") as f: meta = json.load(f) if not os.path.isfile(CSV_PATH): return True @@ -199,10 +244,11 @@ class DeliveryHistoryStore: except Exception: return False - def _load_from_disk(self) -> bool: + def _load_from_disk(self, source: str) -> bool: try: - vectors = np.load(_VECTORS_PATH) # (N, 4) float32 - with open(_RECORDS_PATH, "rb") as f: + vectors_path, records_path, _ = _paths_for(source) + vectors = np.load(vectors_path) # (N, 4) float32 + with open(records_path, "rb") as f: records = pickle.load(f) if not records or len(vectors) != len(records): @@ -342,7 +388,7 @@ class DeliveryHistoryStore: return patterns, zone_index - def _build_and_save(self, records: List[Dict]) -> None: + def _build_and_save(self, records: List[Dict], source: str = "csv") -> None: vectors = np.array( [[r["pickuplat"], r["pickuplon"], r["deliverylat"], r["deliverylong"]] for r in records], @@ -373,19 +419,28 @@ class DeliveryHistoryStore: ) try: - np.save(_VECTORS_PATH, vectors) - with open(_RECORDS_PATH, "wb") as f: + vectors_path, records_path, meta_path = _paths_for(source) + np.save(vectors_path, vectors) + with open(records_path, "wb") as f: pickle.dump(records, f, protocol=pickle.HIGHEST_PROTOCOL) - csv_mtime = os.path.getmtime(CSV_PATH) if os.path.isfile(CSV_PATH) else 0 - with open(_META_PATH, "w", encoding="utf-8") as f: + # csv_mtime only means something for the CSV path's freshness check + # (_saved_files_are_current). DB-built snapshots are refreshed by the + # ETA-sync agent's own schedule, not a file-mtime comparison. + csv_mtime = ( + os.path.getmtime(CSV_PATH) + if source == "csv" and os.path.isfile(CSV_PATH) + else 0 + ) + with open(meta_path, "w", encoding="utf-8") as f: json.dump({ + "source": source, "csv_mtime": csv_mtime, "record_count": len(records), "pattern_zones": len(patterns), "clear_patterns": clear, }, f, indent=2) logger.info( - f"[DeliveryHistory] Saved to '{_STORE_DIR}'. " + f"[DeliveryHistory] Saved to '{_STORE_DIR}' (source={source}). " "Next startup loads from disk." ) except Exception as e: @@ -665,7 +720,34 @@ class DeliveryHistoryStore: records = self._parse_csv() # already merges corrections internally if not records: return 0 - self._build_and_save(records) + self._build_and_save(records, source="csv") + return len(records) + + def rebuild_from_records(self, records: List[Dict]) -> int: + """ + Rebuild the pattern table + vector index directly from pre-shaped + records (kitchen/pickuplat/pickuplon/deliverylat/deliverylong/userid/ + ridername), bypassing CSV parsing entirely. + + Called by the ETA-sync agent (delivery_history_service.py) after each + sync cycle when pattern_source=db, so this store stays as fresh as the + nearledb mirror — no manual CSV re-export/reload needed. + """ + if not records: + logger.warning( + "[DeliveryHistory] rebuild_from_records got 0 records — " + "keeping the existing store as-is." + ) + return 0 + + # Merge manually-verified corrections on top, same as the CSV path, + # so the human-override mechanism still works in DB mode. + if os.path.isfile(CORRECTIONS_PATH): + corr = self._parse_csv_file(CORRECTIONS_PATH, label="Corrections CSV") + if corr: + records = records + (corr * _CORRECTION_WEIGHT) + + self._build_and_save(records, source="db") return len(records) def inject_corrections(self, corrections_path: str = CORRECTIONS_PATH, @@ -720,14 +802,22 @@ class DeliveryHistoryStore: self._patterns = patterns self._zone_index = zone_index - # Save to disk so next restart includes corrections + # Save to disk (under whichever source is currently active) so next + # restart includes corrections try: - np.save(_VECTORS_PATH, vectors) - with open(_RECORDS_PATH, "wb") as f: + active_source = _pattern_source() + vectors_path, records_path, meta_path = _paths_for(active_source) + np.save(vectors_path, vectors) + with open(records_path, "wb") as f: pickle.dump(merged, f) - csv_mtime = os.path.getmtime(CSV_PATH) if os.path.isfile(CSV_PATH) else 0 - with open(_META_PATH, "w", encoding="utf-8") as f: - json.dump({"csv_mtime": csv_mtime, "record_count": len(merged), + csv_mtime = ( + os.path.getmtime(CSV_PATH) + if active_source == "csv" and os.path.isfile(CSV_PATH) + else 0 + ) + with open(meta_path, "w", encoding="utf-8") as f: + json.dump({"source": active_source, "csv_mtime": csv_mtime, + "record_count": len(merged), "pattern_count": len(patterns)}, f) logger.info( f"[DeliveryHistory] Corrections injected and saved — " diff --git a/app/templates/ml_dashboard.html b/app/templates/ml_dashboard.html index b30f3c9..31df1cd 100644 --- a/app/templates/ml_dashboard.html +++ b/app/templates/ml_dashboard.html @@ -452,43 +452,6 @@ transform: scaleY(1.1) } - /* ── STRATEGY SWITCHER ── */ - .strategy-grid { - display: grid; - grid-template-columns: 1fr 1fr; - gap: .5rem - } - - .strategy-card { - padding: .75rem; - border: 1px solid var(--border); - border-radius: 2px; - cursor: pointer; - transition: all .15s; - } - - .strategy-card:hover { - border-color: var(--border2) - } - - .strategy-card.active { - border-color: var(--accent); - background: rgba(0, 212, 255, .06) - } - - .strategy-name { - font-size: .75rem; - font-weight: 700; - letter-spacing: .05em; - margin-bottom: .2rem - } - - .strategy-desc { - font-family: var(--mono); - font-size: .62rem; - color: var(--muted) - } - /* ── BADGE ── */ .badge { display: inline-block; @@ -718,11 +681,6 @@

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@@ -830,13 +757,12 @@ Zone Calls Avg Q - SLA Breaches Avg km - Loading... + Loading... @@ -900,14 +826,6 @@