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initial project setup with README and ignore

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2026-04-08 15:13:42 +05:30
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app/services/ml/behavior_analyzer.py Normal file
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"""
Behavior Analyzer - Production Grade
======================================
Analyzes historical assignment data using the ID3 decision tree to classify
assignment outcomes as 'SUCCESS' or 'RISK'.
Key fixes and upgrades over the original
------------------------------------------
1. BUG FIX: distance_band now uses `total_distance_km` (not `num_orders`).
2. BUG FIX: time_band input is always normalized to uppercase before predict.
3. Rich feature set: distance_band, time_band, load_band, order_density_band.
4. Returns (label, confidence) from the classifier - exposes uncertainty.
5. Trend analysis: tracks rolling success rate over recent N windows.
6. Tree persistence: saves/loads trained tree as JSON to survive restarts.
7. Feature importance proxy: logs which features drove the split.
8. Thread-safe lazy training via a simple lock.
"""
import json
import logging
import os
import sqlite3
import threading
from datetime import datetime
from typing import Any, Dict, List, Optional, Tuple
from app.services.ml.id3_classifier import ID3Classifier, get_behavior_model
logger = logging.getLogger(__name__)
_DB_PATH = os.getenv("ML_DB_PATH", "ml_data/ml_store.db")
_TREE_PATH = os.getenv("ML_TREE_PATH", "ml_data/behavior_tree.json")
# ---------------------------------------------------------------------------
# Band encoders (discrete labels for ID3)
# ---------------------------------------------------------------------------
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"
# ---------------------------------------------------------------------------
# Behavior Analyzer
# ---------------------------------------------------------------------------
class BehaviorAnalyzer:
"""
Trains an ID3 tree on historical assignment logs and predicts whether
a new assignment context is likely to SUCCEED or be at RISK.
Features used
-------------
- distance_band : total route distance bucket
- time_band : time-of-day bucket
- load_band : average load per rider bucket
- order_density_band : orders-per-rider ratio bucket
Target
------
- is_success: "SUCCESS" if unassigned_count == 0, else "RISK"
"""
TARGET = "is_success"
FEATURES = ["distance_band", "time_band", "load_band", "order_density_band"]
def __init__(self):
self._db_path = _DB_PATH
self._tree_path = _TREE_PATH
self.model: ID3Classifier = get_behavior_model(max_depth=5)
self.is_trained: bool = False
self._lock = threading.Lock()
self._training_size: int = 0
self._success_rate: float = 0.0
self._rules: List[str] = []
self._recent_trend: List[float] = []
self._load_tree()
# ------------------------------------------------------------------
# Training
# ------------------------------------------------------------------
def train_on_history(self, limit: int = 2000) -> Dict[str, Any]:
"""Fetch the most recent rows from SQLite and rebuild the tree."""
with self._lock:
try:
rows = self._fetch_rows(limit)
if len(rows) < 10:
logger.warning(f"ID3 BehaviorAnalyzer: only {len(rows)} rows - need >=10.")
return {"status": "insufficient_data", "rows": len(rows)}
training_data, successes = self._preprocess(rows)
if not training_data:
return {"status": "preprocess_failed", "rows": len(rows)}
self.model.train(
data=training_data,
target=self.TARGET,
features=self.FEATURES,
)
self.is_trained = True
self._training_size = len(training_data)
self._success_rate = successes / len(training_data)
self._rules = self.model.get_tree_rules()
self._compute_trend(rows)
self._save_tree()
summary = {
"status": "ok",
"training_rows": self._training_size,
"success_rate": round(self._success_rate, 4),
"n_rules": len(self._rules),
"classes": self.model.classes,
"feature_values": self.model.feature_values,
}
logger.info(
f"ID3 BehaviorAnalyzer trained - rows={self._training_size}, "
f"success_rate={self._success_rate:.1%}, rules={len(self._rules)}"
)
return summary
except Exception as e:
logger.error(f"ID3 BehaviorAnalyzer training failed: {e}", exc_info=True)
return {"status": "error", "message": str(e)}
# ------------------------------------------------------------------
# Prediction
# ------------------------------------------------------------------
def predict(self, distance_km: float, timestamp_or_band: str,
avg_load: float = 4.0, num_orders: int = 5,
num_riders: int = 2) -> Dict[str, Any]:
"""Predict whether an assignment context will SUCCEED or be at RISK."""
if not self.is_trained:
return {
"label": "SUCCESS",
"confidence": 0.5,
"features_used": {},
"model_trained": False,
}
KNOWN_BANDS = {
"MORNING_RUSH", "LATE_MORNING", "LUNCH_RUSH",
"AFTERNOON", "EVENING_RUSH", "NIGHT", "LATE_NIGHT", "UNKNOWN"
}
t_band = (
timestamp_or_band.upper()
if timestamp_or_band.upper() in KNOWN_BANDS
else time_band(timestamp_or_band)
)
features_used = {
"distance_band": distance_band(distance_km),
"time_band": t_band,
"load_band": load_band(avg_load),
"order_density_band": order_density_band(num_orders, num_riders),
}
label, confidence = self.model.predict(features_used)
return {
"label": label,
"confidence": round(confidence, 4),
"features_used": features_used,
"model_trained": True,
}
# ------------------------------------------------------------------
# Info / Diagnostics
# ------------------------------------------------------------------
def get_info(self) -> Dict[str, Any]:
return {
"is_trained": self.is_trained,
"training_rows": self._training_size,
"success_rate": round(self._success_rate, 4),
"n_rules": len(self._rules),
"rules": self._rules[:20],
"recent_trend": self._recent_trend,
"feature_names": self.FEATURES,
"feature_values": self.model.feature_values if self.is_trained else {},
"classes": self.model.classes if self.is_trained else [],
}
# ------------------------------------------------------------------
# Internal helpers
# ------------------------------------------------------------------
def _fetch_rows(self, limit: int) -> List[Dict]:
conn = sqlite3.connect(self._db_path)
conn.row_factory = sqlite3.Row
rows = conn.execute(
"SELECT * FROM assignment_ml_log ORDER BY id DESC LIMIT ?", (limit,)
).fetchall()
conn.close()
return [dict(r) for r in rows]
def _preprocess(self, rows: List[Dict]) -> Tuple[List[Dict], int]:
training_data: List[Dict] = []
successes = 0
for r in rows:
try:
dist_km = float(r.get("total_distance_km") or 0.0)
ts = str(r.get("timestamp") or "")
avg_ld = float(r.get("avg_load") or 0.0)
n_orders = int(r.get("num_orders") or 0)
n_riders = int(r.get("num_riders") or 1)
unassigned = int(r.get("unassigned_count") or 0)
label = "SUCCESS" if unassigned == 0 else "RISK"
if label == "SUCCESS":
successes += 1
training_data.append({
"distance_band": distance_band(dist_km),
"time_band": time_band(ts),
"load_band": load_band(avg_ld),
"order_density_band": order_density_band(n_orders, n_riders),
self.TARGET: label,
})
except Exception:
continue
return training_data, successes
def _compute_trend(self, rows: List[Dict], window: int = 50) -> None:
trend = []
for i in range(0, len(rows), window):
chunk = rows[i:i + window]
if not chunk:
break
rate = sum(1 for r in chunk if int(r.get("unassigned_count", 1)) == 0) / len(chunk)
trend.append(round(rate, 4))
self._recent_trend = trend[-20:]
def _save_tree(self) -> None:
try:
os.makedirs(os.path.dirname(self._tree_path) or ".", exist_ok=True)
with open(self._tree_path, "w") as f:
f.write(self.model.to_json())
logger.info(f"ID3 tree persisted -> {self._tree_path}")
except Exception as e:
logger.warning(f"ID3 tree save failed: {e}")
def _load_tree(self) -> None:
try:
if not os.path.exists(self._tree_path):
return
with open(self._tree_path) as f:
self.model = ID3Classifier.from_json(f.read())
self.is_trained = True
self._rules = self.model.get_tree_rules()
logger.info(f"ID3 tree restored - rules={len(self._rules)}")
except Exception as e:
logger.warning(f"ID3 tree load failed (will retrain): {e}")
# ---------------------------------------------------------------------------
# Module-level singleton
# ---------------------------------------------------------------------------
_analyzer: Optional[BehaviorAnalyzer] = None
_analyzer_lock = threading.Lock()
def get_analyzer() -> BehaviorAnalyzer:
global _analyzer
with _analyzer_lock:
if _analyzer is None:
_analyzer = BehaviorAnalyzer()
if not _analyzer.is_trained:
_analyzer.train_on_history()
return _analyzer

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app/services/ml/id3_classifier.py Normal file
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"""
ID3 Classifier - Production Grade
Improvements over v1:
- Chi-squared pruning to prevent overfitting on sparse branches
- Confidence scores on every prediction (Laplace smoothed)
- Gain-ratio variant for high-cardinality features
- Serialization (to_dict / from_dict / to_json / from_json)
- Per-feature importance scores
- Full prediction audit trail via explain()
- min_samples_split and min_info_gain stopping criteria
"""
import math
import json
import logging
from collections import Counter
from typing import Any, Dict, List, Optional, Tuple
logger = logging.getLogger(__name__)
class ID3Classifier:
"""
ID3 decision tree (entropy / information-gain splitting).
All predict* methods work even if the model has never been trained -
they return safe defaults rather than raising.
"""
def __init__(
self,
max_depth: int = 6,
min_samples_split: int = 5,
min_info_gain: float = 0.001,
use_gain_ratio: bool = False,
chi2_pruning: bool = True,
):
self.max_depth = max_depth
self.min_samples_split = min_samples_split
self.min_info_gain = min_info_gain
self.use_gain_ratio = use_gain_ratio
self.chi2_pruning = chi2_pruning
self.tree: Any = None
self.features: List[str] = []
self.target: str = ""
self.classes_: List[str] = []
self.feature_importances_: Dict[str, float] = {}
self.feature_values: Dict[str, List[str]] = {} # unique values seen per feature
self._n_samples: int = 0
self._total_gain: Dict[str, float] = {}
# ------------------------------------------------------------------ train
def train(self, data: List[Dict[str, Any]], target: str, features: List[str]) -> None:
if not data:
logger.warning("ID3: train() called with empty data.")
return
self.target = target
self.features = list(features)
self.classes_ = sorted({str(row.get(target)) for row in data})
self._total_gain = {f: 0.0 for f in features}
self._n_samples = len(data)
# Collect unique values per feature for dashboard display
self.feature_values = {
f: sorted({str(row.get(f)) for row in data if row.get(f) is not None})
for f in features
}
self.tree = self._build_tree(data, list(features), target, depth=0)
if self.chi2_pruning:
self.tree = self._prune(self.tree, data, target)
total_gain = sum(self._total_gain.values()) or 1.0
self.feature_importances_ = {
f: round(v / total_gain, 4) for f, v in self._total_gain.items()
}
logger.info(
f"ID3: trained on {len(data)} samples | "
f"classes={self.classes_} | importances={self.feature_importances_}"
)
# ----------------------------------------------------------- predict API
def predict(self, sample: Dict[str, Any]) -> Tuple[str, float]:
"""Return (label, confidence 0-1). Safe to call before training."""
if self.tree is None:
return "Unknown", 0.0
label, proba = self._classify(self.tree, sample, [])
confidence = proba.get(str(label), 0.0) if isinstance(proba, dict) else 1.0
return str(label), round(confidence, 4)
def predict_proba(self, sample: Dict[str, Any]) -> Dict[str, float]:
"""Full class probability distribution."""
if self.tree is None:
return {}
_, proba = self._classify(self.tree, sample, [])
return proba if isinstance(proba, dict) else {str(proba): 1.0}
def explain(self, sample: Dict[str, Any]) -> Dict[str, Any]:
"""Human-readable decision path for audit / dashboard display."""
if self.tree is None:
return {"prediction": "Unknown", "confidence": 0.0, "decision_path": []}
path: List[str] = []
label, proba = self._classify(self.tree, sample, path)
return {
"prediction": str(label),
"confidence": round(proba.get(str(label), 1.0), 4),
"probabilities": proba,
"decision_path": path,
}
# ---------------------------------------------------------- serialisation
def to_dict(self) -> Dict[str, Any]:
return {
"tree": self.tree,
"features": self.features,
"target": self.target,
"classes": self.classes_,
"feature_importances": self.feature_importances_,
"feature_values": self.feature_values,
"n_samples": self._n_samples,
"params": {
"max_depth": self.max_depth,
"min_samples_split": self.min_samples_split,
"min_info_gain": self.min_info_gain,
"use_gain_ratio": self.use_gain_ratio,
"chi2_pruning": self.chi2_pruning,
},
}
@classmethod
def from_dict(cls, d: Dict[str, Any]) -> "ID3Classifier":
p = d.get("params", {})
obj = cls(
max_depth=p.get("max_depth", 6),
min_samples_split=p.get("min_samples_split", 5),
min_info_gain=p.get("min_info_gain", 0.001),
use_gain_ratio=p.get("use_gain_ratio", False),
chi2_pruning=p.get("chi2_pruning", True),
)
obj.tree = d["tree"]
obj.features = d["features"]
obj.target = d["target"]
obj.classes_ = d["classes"]
obj.feature_importances_ = d.get("feature_importances", {})
obj.feature_values = d.get("feature_values", {})
obj._n_samples = d.get("n_samples", 0)
return obj
def to_json(self) -> str:
return json.dumps(self.to_dict(), indent=2)
@classmethod
def from_json(cls, s: str) -> "ID3Classifier":
return cls.from_dict(json.loads(s))
def summary(self) -> Dict[str, Any]:
return {
"n_samples": self._n_samples,
"n_classes": len(self.classes_),
"classes": self.classes_,
"n_features": len(self.features),
"feature_importances": self.feature_importances_,
"feature_values": self.feature_values,
"trained": self.tree is not None,
}
@property
def classes(self) -> List[str]:
"""Alias for classes_ for compatibility."""
return self.classes_
def get_tree_rules(self) -> List[str]:
"""Extract human-readable if/then rules from the trained tree."""
rules: List[str] = []
if self.tree is None:
return rules
self._extract_rules(self.tree, [], rules)
return rules
def _extract_rules(self, node: Any, conditions: List[str], rules: List[str]) -> None:
"""Recursively walk the tree and collect decision paths as strings."""
if not isinstance(node, dict):
return
if node.get("__leaf__"):
label = node.get("__label__", "?")
proba = node.get("__proba__", {})
conf = proba.get(str(label), 0.0)
prefix = " AND ".join(conditions) if conditions else "(root)"
rules.append(f"{prefix} => {label} ({conf:.0%})")
return
feature = node.get("__feature__", "?")
for val, child in node.get("__branches__", {}).items():
self._extract_rules(child, conditions + [f"{feature}={val}"], rules)
# --------------------------------------------------------- tree building
def _build_tree(
self,
data: List[Dict[str, Any]],
features: List[str],
target: str,
depth: int,
) -> Any:
counts = Counter(str(row.get(target)) for row in data)
# Pure node
if len(counts) == 1:
return self._make_leaf(data, target)
# Stopping criteria
if not features or depth >= self.max_depth or len(data) < self.min_samples_split:
return self._make_leaf(data, target)
best_f, best_gain = self._best_split(data, features, target)
if best_f is None or best_gain < self.min_info_gain:
return self._make_leaf(data, target)
self._total_gain[best_f] = self._total_gain.get(best_f, 0.0) + best_gain
remaining = [f for f in features if f != best_f]
node = {
"__feature__": best_f,
"__gain__": round(best_gain, 6),
"__n__": len(data),
"__branches__": {},
}
for val in {row.get(best_f) for row in data}:
subset = [r for r in data if r.get(best_f) == val]
node["__branches__"][str(val)] = self._build_tree(
subset, remaining, target, depth + 1
)
return node
def _make_leaf(self, data: List[Dict[str, Any]], target: str) -> Dict[str, Any]:
counts = Counter(str(row.get(target)) for row in data)
total = len(data)
k = len(self.classes_) or 1
# Laplace smoothing
proba = {
cls: round((counts.get(cls, 0) + 1) / (total + k), 4)
for cls in self.classes_
}
label = max(proba, key=proba.get)
return {"__leaf__": True, "__label__": label, "__proba__": proba, "__n__": total}
# ---------------------------------------------------------- splitting
def _best_split(
self, data: List[Dict[str, Any]], features: List[str], target: str
) -> Tuple[Optional[str], float]:
base_e = self._entropy(data, target)
best_f, best_gain = None, -1.0
for f in features:
gain = self._info_gain(data, f, target, base_e)
if self.use_gain_ratio:
si = self._split_info(data, f)
gain = gain / si if si > 0 else 0.0
if gain > best_gain:
best_gain = gain
best_f = f
return best_f, best_gain
# ----------------------------------------------------------- pruning
def _prune(self, node: Any, data: List[Dict[str, Any]], target: str) -> Any:
if not isinstance(node, dict) or node.get("__leaf__"):
return node
feature = node["__feature__"]
# Recurse children first
for val in list(node["__branches__"].keys()):
subset = [r for r in data if str(r.get(feature)) == str(val)]
node["__branches__"][val] = self._prune(node["__branches__"][val], subset, target)
# Chi-squared test: if split is not significant, collapse to leaf
if not self._chi2_significant(data, feature, target):
return self._make_leaf(data, target)
return node
def _chi2_significant(
self, data: List[Dict[str, Any]], feature: str, target: str
) -> bool:
classes = self.classes_
feature_vals = list({str(r.get(feature)) for r in data})
if not classes or len(feature_vals) < 2:
return False
total = len(data)
class_totals = Counter(str(r.get(target)) for r in data)
chi2 = 0.0
for val in feature_vals:
subset = [r for r in data if str(r.get(feature)) == val]
n_val = len(subset)
val_counts = Counter(str(r.get(target)) for r in subset)
for cls in classes:
observed = val_counts.get(cls, 0)
expected = (n_val * class_totals.get(cls, 0)) / total
if expected > 0:
chi2 += (observed - expected) ** 2 / expected
df = (len(feature_vals) - 1) * (len(classes) - 1)
if df <= 0:
return False
# Critical values at p=0.05
crit_table = {1: 3.841, 2: 5.991, 3: 7.815, 4: 9.488, 5: 11.070, 6: 12.592}
crit = crit_table.get(df, 3.841 * df)
return chi2 > crit
# ---------------------------------------------------------- classify
def _classify(
self, node: Any, row: Dict[str, Any], path: List[str]
) -> Tuple[Any, Any]:
if not isinstance(node, dict):
return node, {str(node): 1.0}
if node.get("__leaf__"):
label = node["__label__"]
proba = node["__proba__"]
path.append(f"predict={label} (p={proba.get(label, 0):.2f})")
return label, proba
feature = node["__feature__"]
value = str(row.get(feature, ""))
path.append(f"{feature}={value}")
branches = node["__branches__"]
if value in branches:
return self._classify(branches[value], row, path)
# Unseen value: weighted vote from all leaf children
all_proba: Counter = Counter()
total_n = 0
for child in branches.values():
if isinstance(child, dict) and child.get("__leaf__"):
n = child.get("__n__", 1)
total_n += n
for cls, p in child.get("__proba__", {}).items():
all_proba[cls] += p * n
if not total_n:
fallback = self.classes_[0] if self.classes_ else "Unknown"
path.append(f"unseen fallback: {fallback}")
return fallback, {fallback: 1.0}
proba = {cls: round(v / total_n, 4) for cls, v in all_proba.items()}
label = max(proba, key=proba.get)
path.append(f"weighted vote: {label}")
return label, proba
# ---------------------------------------------------------- entropy math
def _entropy(self, data: List[Dict[str, Any]], target: str) -> float:
if not data:
return 0.0
counts = Counter(str(row.get(target)) for row in data)
total = len(data)
return -sum((c / total) * math.log2(c / total) for c in counts.values() if c > 0)
def _info_gain(
self,
data: List[Dict[str, Any]],
feature: str,
target: str,
base_entropy: Optional[float] = None,
) -> float:
if base_entropy is None:
base_entropy = self._entropy(data, target)
total = len(data)
buckets: Dict[Any, list] = {}
for row in data:
buckets.setdefault(row.get(feature), []).append(row)
weighted = sum(
(len(sub) / total) * self._entropy(sub, target) for sub in buckets.values()
)
return base_entropy - weighted
def _split_info(self, data: List[Dict[str, Any]], feature: str) -> float:
total = len(data)
counts = Counter(row.get(feature) for row in data)
return -sum((c / total) * math.log2(c / total) for c in counts.values() if c > 0)
# ------------------------------------------------------------------ factory
def get_behavior_model(
max_depth: int = 5,
min_samples_split: int = 8,
min_info_gain: float = 0.005,
use_gain_ratio: bool = True,
chi2_pruning: bool = True,
) -> ID3Classifier:
return ID3Classifier(
max_depth=max_depth,
min_samples_split=min_samples_split,
min_info_gain=min_info_gain,
use_gain_ratio=use_gain_ratio,
chi2_pruning=chi2_pruning,
)

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"""
ML Data Collector - Production Grade
======================================
Logs every assignment call (inputs + outcomes) to SQLite.
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.
"""
import csv
import io
import logging
import os
import sqlite3
import threading
from datetime import datetime, timedelta
from typing import Any, Dict, List, Optional
logger = logging.getLogger(__name__)
_DB_PATH = os.getenv("ML_DB_PATH", "ml_data/ml_store.db")
_WRITE_LOCK = threading.Lock()
def _std(values: List[float]) -> float:
if len(values) < 2:
return 0.0
mean = sum(values) / len(values)
return (sum((v - mean) ** 2 for v in values) / len(values)) ** 0.5
class MLDataCollector:
"""
Event logger for assignment service calls.
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)
quality_score is computed once and FROZEN - never retroactively changed.
"""
def __init__(self):
self._db_path = _DB_PATH
self._ensure_db()
# ------------------------------------------------------------------
# Main logging API
# ------------------------------------------------------------------
def log_assignment_event(
self,
*,
num_orders: int,
num_riders: int,
hyperparams: Dict[str, Any],
assignments: Dict[int, List[Any]],
unassigned_count: int,
elapsed_ms: float,
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.
Call this at the END of AssignmentService.assign_orders() once
outcomes are known.
"""
try:
now = datetime.utcnow()
hour = now.hour
day_of_week = now.weekday()
is_peak = int(hour in (7, 8, 9, 12, 13, 18, 19, 20))
rider_loads = [len(orders) for orders in assignments.values() if orders]
riders_used = len(rider_loads)
total_assigned = sum(rider_loads)
avg_load = total_assigned / riders_used if riders_used else 0.0
load_std = _std(rider_loads) if rider_loads else 0.0
all_orders = [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,
unassigned_count=unassigned_count,
load_std=load_std,
riders_used=riders_used,
num_riders=num_riders,
total_distance_km=total_distance_km,
max_orders_per_rider=max_opr,
ml_strategy=ml_strategy,
)
row = {
"timestamp": now.isoformat(),
"hour": hour,
"day_of_week": day_of_week,
"is_peak": is_peak,
"zone_id": zone_id,
"city_id": city_id,
"weather_code": weather_code,
"num_orders": num_orders,
"num_riders": num_riders,
"max_pickup_distance_km": hyperparams.get("max_pickup_distance_km", 10.0),
"max_kitchen_distance_km": hyperparams.get("max_kitchen_distance_km", 3.0),
"max_orders_per_rider": max_opr,
"ideal_load": hyperparams.get("ideal_load", 6),
"workload_balance_threshold": hyperparams.get("workload_balance_threshold", 0.7),
"workload_penalty_weight": hyperparams.get("workload_penalty_weight", 100.0),
"distance_penalty_weight": hyperparams.get("distance_penalty_weight", 2.0),
"cluster_radius_km": hyperparams.get("cluster_radius_km", 3.0),
"search_time_limit_seconds": hyperparams.get("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,
"avg_load": round(avg_load, 3),
"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),
}
with _WRITE_LOCK:
self._insert(row)
logger.info(
f"[MLCollector] zone={zone_id} orders={num_orders} "
f"assigned={total_assigned} unassigned={unassigned_count} "
f"quality={quality_score:.1f} elapsed={elapsed_ms:.0f}ms"
)
except Exception as e:
logger.warning(f"[MLCollector] Logging failed (non-fatal): {e}")
# ------------------------------------------------------------------
# Data retrieval for training
# ------------------------------------------------------------------
def get_training_data(
self,
min_records: int = 30,
strategy_filter: Optional[str] = None,
since_hours: Optional[int] = None,
) -> Optional[List[Dict[str, Any]]]:
"""
Return logged rows for model training.
quality_score is returned AS-IS (frozen at log time - no re-scoring).
"""
try:
conn = sqlite3.connect(self._db_path)
conn.row_factory = sqlite3.Row
query = "SELECT * FROM assignment_ml_log"
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 >= ?")
params.append(cutoff)
if clauses:
query += " WHERE " + " AND ".join(clauses)
query += " ORDER BY id ASC"
rows = conn.execute(query, params).fetchall()
conn.close()
if len(rows) < min_records:
logger.info(f"[MLCollector] {len(rows)} records < {min_records} minimum.")
return None
return [dict(r) for r in rows]
except Exception as e:
logger.error(f"[MLCollector] get_training_data failed: {e}")
return None
# ------------------------------------------------------------------
# Analytics API
# ------------------------------------------------------------------
def get_recent_quality_trend(self, last_n: int = 50) -> Dict[str, Any]:
"""Recent quality scores + series for sparkline charts."""
try:
conn = sqlite3.connect(self._db_path)
rows = conn.execute(
"SELECT quality_score, timestamp, unassigned_count, elapsed_ms "
"FROM assignment_ml_log ORDER BY id DESC LIMIT ?", (last_n,)
).fetchall()
conn.close()
if not rows:
return {"avg_quality": 0.0, "sample_size": 0, "history": []}
scores = [r[0] for r in rows]
return {
"avg_quality": round(sum(scores) / len(scores), 2),
"min_quality": round(min(scores), 2),
"max_quality": round(max(scores), 2),
"sample_size": len(scores),
"history": list(reversed(scores)),
"timestamps": list(reversed([r[1] for r in rows])),
"unassigned_series": list(reversed([r[2] for r in rows])),
"latency_series": list(reversed([r[3] for r in rows])),
}
except Exception:
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."""
try:
conn = sqlite3.connect(self._db_path)
cutoff = (datetime.utcnow() - timedelta(days=last_days)).isoformat()
rows = conn.execute(
"""
SELECT hour,
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
FROM assignment_ml_log WHERE timestamp >= ?
GROUP BY hour ORDER BY hour
""", (cutoff,)
).fetchall()
conn.close()
return [
{
"hour": r[0],
"call_count": r[1],
"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
]
except Exception as e:
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:
conn = sqlite3.connect(self._db_path)
rows = conn.execute("SELECT quality_score FROM assignment_ml_log").fetchall()
conn.close()
scores = [r[0] for r in rows if r[0] is not None]
if not scores:
return []
bin_width = 100.0 / bins
return [
{
"range": f"{i*bin_width:.0f}-{(i+1)*bin_width:.0f}",
"count": sum(1 for s in scores if i*bin_width <= s < (i+1)*bin_width)
}
for i in range(bins)
]
except Exception as e:
logger.error(f"[MLCollector] get_quality_histogram: {e}")
return []
def get_zone_stats(self) -> List[Dict[str, Any]]:
"""Quality and SLA 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
"""
).fetchall()
conn.close()
return [
{
"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),
}
for r in rows
]
except Exception as e:
logger.error(f"[MLCollector] get_zone_stats: {e}")
return []
def count_records(self) -> int:
try:
conn = sqlite3.connect(self._db_path)
count = conn.execute("SELECT COUNT(*) FROM assignment_ml_log").fetchone()[0]
conn.close()
return count
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:
conn = sqlite3.connect(self._db_path)
conn.row_factory = sqlite3.Row
rows = conn.execute("SELECT * FROM assignment_ml_log ORDER BY id ASC").fetchall()
conn.close()
if not rows:
return ""
buf = io.StringIO()
writer = csv.DictWriter(buf, fieldnames=rows[0].keys())
writer.writeheader()
writer.writerows([dict(r) for r in rows])
return buf.getvalue()
except Exception as e:
logger.error(f"[MLCollector] export_csv failed: {e}")
return ""
def purge_old_records(self, keep_days: int = 90) -> int:
"""Delete records older than keep_days. Returns count deleted."""
try:
cutoff = (datetime.utcnow() - timedelta(days=keep_days)).isoformat()
conn = sqlite3.connect(self._db_path)
cursor = conn.execute(
"DELETE FROM assignment_ml_log WHERE timestamp < ?", (cutoff,)
)
deleted = cursor.rowcount
conn.commit()
conn.close()
logger.info(f"[MLCollector] Purged {deleted} records older than {keep_days} days.")
return deleted
except Exception as e:
logger.error(f"[MLCollector] purge failed: {e}")
return 0
# ------------------------------------------------------------------
# Quality Score Formula (frozen at log time - do not change behavior)
# ------------------------------------------------------------------
@staticmethod
def _compute_quality_score(
num_orders: int, unassigned_count: int, load_std: float,
riders_used: int, num_riders: int, total_distance_km: float,
max_orders_per_rider: int, ml_strategy: str = "balanced",
) -> float:
if num_orders == 0:
return 0.0
assigned_ratio = 1.0 - (unassigned_count / num_orders)
max_std = max(1.0, max_orders_per_rider / 2.0)
balance_ratio = max(0.0, 1.0 - (load_std / max_std))
max_dist = max(1.0, float((num_orders - unassigned_count) * 8.0))
distance_ratio = max(0.0, 1.0 - (total_distance_km / max_dist))
weights = {
"aggressive_speed": (80.0, 20.0, 0.0),
"fuel_saver": (30.0, 70.0, 0.0),
"zone_strict": (40.0, 30.0, 30.0),
"balanced": (50.0, 25.0, 25.0),
}
w_comp, w_dist, w_bal = weights.get(ml_strategy, (50.0, 25.0, 25.0))
return min(
assigned_ratio * w_comp + distance_ratio * w_dist + balance_ratio * w_bal,
100.0,
)
@staticmethod
def _get_km(order: Any) -> float:
try:
return float(order.get("kms") or order.get("calculationDistanceKm") or 0.0)
except Exception:
return 0.0
# ------------------------------------------------------------------
# DB Bootstrap
# ------------------------------------------------------------------
def _ensure_db(self) -> None:
try:
os.makedirs(os.path.dirname(self._db_path) or ".", exist_ok=True)
conn = sqlite3.connect(self._db_path)
conn.execute("""
CREATE TABLE IF NOT EXISTS assignment_ml_log (
id INTEGER PRIMARY KEY AUTOINCREMENT,
timestamp TEXT NOT NULL,
hour INTEGER,
day_of_week INTEGER,
is_peak INTEGER DEFAULT 0,
zone_id TEXT DEFAULT 'default',
city_id TEXT DEFAULT 'default',
weather_code TEXT DEFAULT 'CLEAR',
num_orders INTEGER,
num_riders INTEGER,
max_pickup_distance_km REAL,
max_kitchen_distance_km REAL,
max_orders_per_rider INTEGER,
ideal_load INTEGER,
workload_balance_threshold REAL,
workload_penalty_weight REAL,
distance_penalty_weight REAL,
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,
avg_load REAL,
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
)
""")
migrations = [
"ALTER TABLE assignment_ml_log ADD COLUMN is_peak INTEGER DEFAULT 0",
"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:
try:
conn.execute(ddl)
except Exception:
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)
conn.commit()
conn.close()
except Exception as e:
logger.error(f"[MLCollector] DB init failed: {e}")
def _insert(self, row: Dict[str, Any]) -> None:
os.makedirs(os.path.dirname(self._db_path) or ".", exist_ok=True)
conn = sqlite3.connect(self._db_path)
cols = ", ".join(row.keys())
placeholders = ", ".join(["?"] * len(row))
conn.execute(
f"INSERT INTO assignment_ml_log ({cols}) VALUES ({placeholders})",
list(row.values()),
)
conn.commit()
conn.close()
# ---------------------------------------------------------------------------
# Module-level singleton
# ---------------------------------------------------------------------------
_collector: Optional[MLDataCollector] = None
def get_collector() -> MLDataCollector:
global _collector
if _collector is None:
_collector = MLDataCollector()
return _collector

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app/services/ml/ml_hypertuner.py Normal file
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"""
ML Hypertuner - Production Grade
===================================
XGBoost surrogate model + Optuna TPE Bayesian optimization.
Key upgrades over the original
--------------------------------
1. Persistent Optuna study - stores trial history in SQLite so every
retrain warm-starts from the previous study (progressively smarter).
2. Multi-objective optimization - optimizes quality score AND latency
simultaneously using Pareto-front search (NSGA-II sampler).
3. Segment-aware training - trains separate surrogates for peak vs
off-peak hours (very different operating regimes).
4. Lag features - rolling_avg_quality_5 and quality_delta_10
added to the feature matrix for trend-awareness.
5. SHAP feature importance - uses TreeExplainer when available;
falls back to XGBoost fscore.
6. Warm-start incremental fit - adds trees on top of existing model
instead of cold retraining every time.
7. Staleness detection - warns if model is older than 24h.
8. Richer audit reports - JSON report includes Pareto frontier,
segment stats, improvement proof, and top-10 trial params.
"""
import json
import logging
import os
from datetime import datetime, timedelta
from typing import Any, Dict, List, Optional, Tuple
import numpy as np
from sklearn.model_selection import KFold
from sklearn.metrics import r2_score, mean_absolute_error
logger = logging.getLogger(__name__)
try:
import xgboost as xgb
XGB_AVAILABLE = True
except ImportError:
XGB_AVAILABLE = False
logger.warning("[Hypertuner] xgboost not installed.")
try:
import optuna
optuna.logging.set_verbosity(optuna.logging.WARNING)
OPTUNA_AVAILABLE = True
except ImportError:
OPTUNA_AVAILABLE = False
logger.warning("[Hypertuner] optuna not installed.")
try:
import shap
SHAP_AVAILABLE = True
except ImportError:
SHAP_AVAILABLE = False
# ---------------------------------------------------------------------------
# Feature columns
# ---------------------------------------------------------------------------
BASE_FEATURE_COLS = [
"hour", "day_of_week", "is_peak",
"num_orders", "num_riders",
"max_pickup_distance_km", "max_kitchen_distance_km",
"max_orders_per_rider", "ideal_load",
"workload_balance_threshold", "workload_penalty_weight",
"distance_penalty_weight", "cluster_radius_km",
"search_time_limit_seconds", "road_factor",
]
LAG_FEATURE_COLS = [
"rolling_avg_quality_5", # rolling mean of last 5 quality scores
"quality_delta_10", # quality[i] - quality[i-10]
]
ALL_FEATURE_COLS = BASE_FEATURE_COLS + LAG_FEATURE_COLS
LABEL_COL = "quality_score"
SEARCH_SPACE = {
"max_pickup_distance_km": ("float", 4.0, 15.0),
"max_kitchen_distance_km": ("float", 1.0, 8.0),
"max_orders_per_rider": ("int", 6, 20),
"ideal_load": ("int", 2, 10),
"workload_balance_threshold": ("float", 0.3, 0.95),
"workload_penalty_weight": ("float", 20.0, 200.0),
"distance_penalty_weight": ("float", 0.5, 10.0),
"cluster_radius_km": ("float", 1.0, 8.0),
"search_time_limit_seconds": ("int", 2, 15),
"road_factor": ("float", 1.1, 1.6),
}
_STUDY_DB_PATH = os.getenv("ML_DB_PATH", "ml_data/ml_store.db")
_REPORT_DIR = "ml_data/reports"
_MAX_MODEL_AGE_H = 24
# ---------------------------------------------------------------------------
# MLHypertuner
# ---------------------------------------------------------------------------
class MLHypertuner:
"""XGBoost surrogate + Optuna TPE / NSGA-II hyperparameter optimizer."""
def __init__(self):
self._model: Optional[Any] = None
self._peak_model: Optional[Any] = None
self._offpeak_model: Optional[Any] = None
self._model_trained_at: Optional[datetime] = None
self._training_rows: int = 0
self._latest_validation: Optional[Dict] = None
self._latest_baseline: Optional[Dict] = None
self._feature_importance: Optional[Dict[str, float]] = None
self._top_trials: List[Dict] = []
self._pareto_frontier: List[Dict] = []
self._load_latest_report()
# ------------------------------------------------------------------
# Main entry point
# ------------------------------------------------------------------
def run(
self,
n_trials: int = 150,
min_training_records: int = 30,
context_override: Optional[Dict] = None,
multi_objective: bool = False,
segment_aware: bool = True,
) -> Dict[str, Any]:
"""Full pipeline: load -> engineer -> validate -> train -> search -> write."""
if not XGB_AVAILABLE or not OPTUNA_AVAILABLE:
missing = []
if not XGB_AVAILABLE: missing.append("xgboost")
if not OPTUNA_AVAILABLE: missing.append("optuna")
return {"status": "error", "message": f"Missing: {', '.join(missing)}"}
from app.services.ml.ml_data_collector import get_collector
collector = get_collector()
records = collector.get_training_data(min_records=min_training_records)
if records is None:
count = collector.count_records()
return {
"status": "insufficient_data",
"message": f"{count} records - need >={min_training_records}.",
"records_available": count,
"records_needed": min_training_records,
}
records = self._add_lag_features(records)
X, y = self._prepare_data(records, ALL_FEATURE_COLS)
if X is None or len(X) == 0:
return {"status": "error", "message": "Data preparation failed."}
cv_results = self._cross_validate(X, y)
logger.info(f"[Hypertuner] CV: R2={cv_results['r2_score']:.3f}, MAE={cv_results['mae']:.2f}")
self._train_model(X, y, model_attr="_model")
self._latest_validation = cv_results
if segment_aware and len(records) >= 60:
peak_recs = [r for r in records if r.get("is_peak", 0) == 1]
offpeak_recs = [r for r in records if r.get("is_peak", 0) == 0]
if len(peak_recs) >= 20:
Xp, yp = self._prepare_data(peak_recs, ALL_FEATURE_COLS)
self._train_model(Xp, yp, model_attr="_peak_model")
if len(offpeak_recs) >= 20:
Xo, yo = self._prepare_data(offpeak_recs, ALL_FEATURE_COLS)
self._train_model(Xo, yo, model_attr="_offpeak_model")
baseline_stats = self._compute_baseline_stats(records)
self._latest_baseline = baseline_stats
context = context_override or self._get_current_context(records)
if multi_objective:
best_params, best_score, pareto = self._optuna_search_multi(context, n_trials)
self._pareto_frontier = pareto
else:
best_params, best_score = self._optuna_search_single(context, n_trials)
if best_params is None:
return {"status": "error", "message": "Optuna search failed."}
improvement = round(best_score - baseline_stats["avg_quality"], 2)
self._compute_feature_importance()
if cv_results["r2_score"] < 0.5:
return {
"status": "model_not_ready",
"message": f"R2={cv_results['r2_score']:.3f} too low.",
"validation": cv_results,
"training_rows": len(records),
"action_taken": "none - existing config preserved",
}
try:
from app.config.dynamic_config import get_config
get_config().set_bulk(best_params, source="ml_hypertuner")
except ImportError:
logger.info("[Hypertuner] DynamicConfig not available - params not written to config.")
self._save_report(best_params, best_score, len(records), n_trials, cv_results, baseline_stats)
return {
"status": "ok",
"best_params": best_params,
"best_predicted_quality": round(best_score, 2),
"training_rows": len(records),
"trials_run": n_trials,
"context_used": context,
"validation": cv_results,
"improvement_proof": {
"baseline_avg_quality": baseline_stats["avg_quality"],
"baseline_worst": baseline_stats["worst_quality"],
"baseline_best": baseline_stats["best_quality"],
"ml_predicted_quality": round(best_score, 2),
"predicted_improvement": improvement,
"verdict": (
"ML params significantly better" if improvement > 5 else
"Marginal improvement - keep collecting data" if improvement > 0 else
"No improvement - defaults may be near-optimal"
),
},
"feature_importance": self._feature_importance,
"top_trials": self._top_trials[:5],
"message": "Hyperparameters updated successfully.",
}
# ------------------------------------------------------------------
# Feature Engineering
# ------------------------------------------------------------------
def _add_lag_features(self, records: List[Dict]) -> List[Dict]:
scores = [float(r.get("quality_score", 0)) for r in records]
for i, r in enumerate(records):
window5 = scores[max(0, i - 5):i] if i > 0 else [scores[0]]
r["rolling_avg_quality_5"] = sum(window5) / len(window5)
r["quality_delta_10"] = (scores[i] - scores[max(0, i - 10)]) if i >= 10 else 0.0
return records
# ------------------------------------------------------------------
# Data Preparation
# ------------------------------------------------------------------
def _prepare_data(
self, records: List[Dict], feature_cols: List[str]
) -> Tuple[Optional[np.ndarray], Optional[np.ndarray]]:
try:
X_rows, y_vals = [], []
for rec in records:
row = []
for col in feature_cols:
try:
row.append(float(rec.get(col, 0) or 0))
except (TypeError, ValueError):
row.append(0.0)
X_rows.append(row)
y_vals.append(float(rec.get(LABEL_COL, 0)))
return (
np.array(X_rows, dtype=np.float32),
np.array(y_vals, dtype=np.float32),
)
except Exception as e:
logger.error(f"[Hypertuner] Data prep failed: {e}")
return None, None
# ------------------------------------------------------------------
# Model Training (warm-start capable)
# ------------------------------------------------------------------
def _train_model(self, X: np.ndarray, y: np.ndarray, model_attr: str = "_model") -> None:
kwargs = {
"n_estimators": 300, "max_depth": 5, "learning_rate": 0.04,
"subsample": 0.8, "colsample_bytree": 0.8,
"reg_alpha": 0.1, "reg_lambda": 1.0, "random_state": 42, "verbosity": 0,
}
existing = getattr(self, model_attr, None)
if existing is not None:
try:
m = xgb.XGBRegressor(n_estimators=50, **{k: v for k, v in kwargs.items() if k != "n_estimators"})
m.fit(X, y, xgb_model=existing.get_booster())
setattr(self, model_attr, m)
if model_attr == "_model":
self._model_trained_at = datetime.utcnow()
self._training_rows = len(X)
logger.info(f"[Hypertuner] XGBoost warm-updated ({model_attr}) - {len(X)} rows.")
return
except Exception:
pass
m = xgb.XGBRegressor(**kwargs)
m.fit(X, y)
setattr(self, model_attr, m)
if model_attr == "_model":
self._model_trained_at = datetime.utcnow()
self._training_rows = len(X)
logger.info(f"[Hypertuner] XGBoost trained ({model_attr}) - {len(X)} rows.")
# ------------------------------------------------------------------
# Cross Validation
# ------------------------------------------------------------------
def _cross_validate(self, X: np.ndarray, y: np.ndarray, k: int = 5) -> Dict:
if len(X) < k * 2:
split = max(1, int(len(X) * 0.8))
X_tr, X_te, y_tr, y_te = X[:split], X[split:], y[:split], y[split:]
if len(X_te) == 0:
return {"r2_score": 0.0, "mae": 99.0, "trust_level": "insufficient_data",
"trust_score": 0, "folds": 0}
m = xgb.XGBRegressor(n_estimators=100, max_depth=4, verbosity=0, random_state=42)
m.fit(X_tr, y_tr)
r2 = float(r2_score(y_te, m.predict(X_te)))
mae = float(mean_absolute_error(y_te, m.predict(X_te)))
folds_used = 1
else:
kf = KFold(n_splits=k, shuffle=True, random_state=42)
r2s, maes = [], []
for tr_idx, te_idx in kf.split(X):
m = xgb.XGBRegressor(n_estimators=100, max_depth=4, verbosity=0, random_state=42)
m.fit(X[tr_idx], y[tr_idx])
preds = m.predict(X[te_idx])
r2s.append(r2_score(y[te_idx], preds))
maes.append(mean_absolute_error(y[te_idx], preds))
r2, mae, folds_used = float(np.mean(r2s)), float(np.mean(maes)), k
trust_map = [(0.85, "excellent", 5), (0.75, "strong", 4),
(0.60, "good", 3), (0.50, "acceptable", 2)]
trust_level, trust_score = "poor - need more data", 1
for threshold, level, score in trust_map:
if r2 >= threshold:
trust_level, trust_score = level, score
break
return {
"r2_score": round(r2, 4),
"mae": round(mae, 3),
"folds": folds_used,
"trust_level": trust_level,
"trust_score": trust_score,
"interpretation": f"Predictions off by +/-{mae:.1f} pts (R2={r2:.2f}, trust={trust_level})",
}
# ------------------------------------------------------------------
# Optuna - Single Objective (persistent SQLite storage)
# ------------------------------------------------------------------
def _optuna_search_single(self, context: Dict, n_trials: int) -> Tuple[Optional[Dict], float]:
def objective(trial):
params = self._sample_params(trial)
if params.get("ideal_load", 6) > params.get("max_orders_per_rider", 12):
return 0.0
return self._predict_quality(context, params)
try:
study = optuna.create_study(
study_name="hypertuner_v1",
storage=f"sqlite:///{_STUDY_DB_PATH}",
direction="maximize",
load_if_exists=True,
sampler=optuna.samplers.TPESampler(seed=42),
)
study.optimize(objective, n_trials=n_trials, show_progress_bar=False)
best = study.best_trial
self._top_trials = [
{"params": t.params, "score": t.value}
for t in sorted(study.trials, key=lambda x: x.value or 0, reverse=True)[:10]
if t.value is not None
]
return {k: best.params[k] for k in SEARCH_SPACE if k in best.params}, best.value
except Exception as e:
logger.error(f"[Hypertuner] Optuna single-obj failed: {e}", exc_info=True)
return None, 0.0
# ------------------------------------------------------------------
# Optuna - Multi Objective (quality + latency, NSGA-II)
# ------------------------------------------------------------------
def _optuna_search_multi(
self, context: Dict, n_trials: int
) -> Tuple[Optional[Dict], float, List[Dict]]:
def objective(trial):
params = self._sample_params(trial)
if params.get("ideal_load", 6) > params.get("max_orders_per_rider", 12):
return 0.0, 99.0
quality = self._predict_quality(context, params)
latency_proxy = float(params.get("search_time_limit_seconds", 5)) * 200.0
return quality, latency_proxy
try:
study = optuna.create_study(
study_name="hypertuner_multi_v1",
storage=f"sqlite:///{_STUDY_DB_PATH}",
directions=["maximize", "minimize"],
load_if_exists=True,
sampler=optuna.samplers.NSGAIISampler(seed=42),
)
study.optimize(objective, n_trials=n_trials, show_progress_bar=False)
pareto = [
{"params": t.params, "quality": t.values[0], "latency_proxy": t.values[1]}
for t in study.best_trials
]
if not pareto:
return None, 0.0, []
best_trial = max(pareto, key=lambda x: x["quality"])
return (
{k: best_trial["params"][k] for k in SEARCH_SPACE if k in best_trial["params"]},
best_trial["quality"],
pareto,
)
except Exception as e:
logger.error(f"[Hypertuner] Optuna multi-obj failed: {e}", exc_info=True)
return None, 0.0, []
def _sample_params(self, trial) -> Dict:
params = {}
for name, (p_type, lo, hi) in SEARCH_SPACE.items():
if p_type == "float":
params[name] = trial.suggest_float(name, lo, hi)
elif p_type == "int":
params[name] = trial.suggest_int(name, int(lo), int(hi))
return params
# ------------------------------------------------------------------
# Prediction
# ------------------------------------------------------------------
def _predict_quality(self, context: Dict, params: Dict) -> float:
if self._model is None:
return 0.0
combined = {
**context, **params,
"rolling_avg_quality_5": context.get("rolling_avg_quality_5", 50.0),
"quality_delta_10": context.get("quality_delta_10", 0.0),
}
row = []
for col in ALL_FEATURE_COLS:
try:
row.append(float(combined.get(col, 0) or 0))
except (TypeError, ValueError):
row.append(0.0)
is_peak = int(context.get("is_peak", 0))
model = (self._peak_model if is_peak else self._offpeak_model) or self._model
pred = float(model.predict(np.array([row], dtype=np.float32))[0])
return max(0.0, min(pred, 100.0))
# ------------------------------------------------------------------
# Feature Importance
# ------------------------------------------------------------------
def _compute_feature_importance(self) -> None:
if self._model is None:
return
try:
if SHAP_AVAILABLE:
from ml_data_collector import get_collector
records = get_collector().get_training_data(min_records=1) or []
records = self._add_lag_features(records[-200:])
X, _ = self._prepare_data(records, ALL_FEATURE_COLS)
if X is not None and len(X) > 0:
explainer = shap.TreeExplainer(self._model)
shap_values = np.abs(explainer.shap_values(X)).mean(axis=0)
total = max(shap_values.sum(), 1e-9)
self._feature_importance = dict(sorted(
{ALL_FEATURE_COLS[i]: round(float(shap_values[i] / total) * 100, 2)
for i in range(len(ALL_FEATURE_COLS))}.items(),
key=lambda x: x[1], reverse=True
))
return
except Exception:
pass
try:
scores = self._model.get_booster().get_fscore()
total = max(sum(scores.values()), 1)
self._feature_importance = dict(sorted(
{ALL_FEATURE_COLS[int(k[1:])]: round(v / total * 100, 2)
for k, v in scores.items()
if k.startswith("f") and k[1:].isdigit() and int(k[1:]) < len(ALL_FEATURE_COLS)
}.items(),
key=lambda x: x[1], reverse=True
))
except Exception as e:
logger.warning(f"[Hypertuner] Feature importance failed: {e}")
def get_feature_importance(self) -> Optional[Dict[str, float]]:
return self._feature_importance
# ------------------------------------------------------------------
# Context
# ------------------------------------------------------------------
def _get_current_context(self, records: List[Dict]) -> Dict:
now = datetime.utcnow()
recent = records[-20:]
avg_orders = sum(r.get("num_orders", 0) for r in recent) / max(len(recent), 1)
avg_riders = sum(r.get("num_riders", 0) for r in recent) / max(len(recent), 1)
recent_scores = [float(r.get("quality_score", 0)) for r in recent]
rolling_avg5 = sum(recent_scores[-5:]) / max(len(recent_scores[-5:]), 1)
delta10 = (recent_scores[-1] - recent_scores[-11]) if len(recent_scores) >= 11 else 0.0
return {
"hour": now.hour,
"day_of_week": now.weekday(),
"is_peak": int(now.hour in (7, 8, 9, 12, 13, 18, 19, 20)),
"num_orders": round(avg_orders),
"num_riders": round(avg_riders),
"rolling_avg_quality_5": round(rolling_avg5, 2),
"quality_delta_10": round(delta10, 2),
}
def _compute_baseline_stats(self, records: List[Dict]) -> Dict:
scores = [float(r.get("quality_score", 0)) for r in records if r.get("quality_score")]
if not scores:
return {"avg_quality": 0.0, "best_quality": 0.0, "worst_quality": 0.0}
return {
"avg_quality": round(sum(scores) / len(scores), 2),
"best_quality": round(max(scores), 2),
"worst_quality": round(min(scores), 2),
"sample_size": len(scores),
}
# ------------------------------------------------------------------
# Model Info
# ------------------------------------------------------------------
def get_model_info(self) -> Dict[str, Any]:
baseline = self._latest_baseline
if baseline is None:
try:
from ml_data_collector import get_collector
records = get_collector().get_training_data(min_records=1)
if records:
baseline = self._compute_baseline_stats(records)
except Exception:
pass
return {
"model_trained": self._model is not None,
"trained_at": self._model_trained_at.isoformat() if self._model_trained_at else None,
"training_rows": self._training_rows,
"peak_model_trained": self._peak_model is not None,
"offpeak_model_trained": self._offpeak_model is not None,
"features": ALL_FEATURE_COLS,
"validation": self._latest_validation,
"baseline": baseline,
"search_space": {k: {"type": v[0], "low": v[1], "high": v[2]} for k, v in SEARCH_SPACE.items()},
"feature_importance": self._feature_importance,
"top_trials": self._top_trials[:10],
"pareto_frontier_size": len(self._pareto_frontier),
}
# ------------------------------------------------------------------
# Report I/O
# ------------------------------------------------------------------
def _save_report(self, best_params, best_score, training_rows,
n_trials, cv_results, baseline_stats) -> None:
try:
os.makedirs(_REPORT_DIR, exist_ok=True)
report = {
"timestamp": datetime.utcnow().isoformat(),
"training_rows": training_rows,
"n_trials": n_trials,
"best_predicted_quality": round(best_score, 2),
"best_params": best_params,
"validation": cv_results or {},
"baseline_stats": baseline_stats or {},
"feature_importance": self._feature_importance or {},
"top_trials": self._top_trials[:10],
"pareto_frontier": self._pareto_frontier[:20],
}
path = os.path.join(_REPORT_DIR, f"tuning_{datetime.utcnow().strftime('%Y%m%d_%H%M%S')}.json")
with open(path, "w") as f:
json.dump(report, f, indent=2)
logger.info(f"[Hypertuner] Report -> {path}")
except Exception as e:
logger.warning(f"[Hypertuner] Report save failed: {e}")
def _load_latest_report(self) -> None:
try:
if not os.path.isdir(_REPORT_DIR):
return
files = sorted([f for f in os.listdir(_REPORT_DIR) if f.endswith(".json")], reverse=True)
if not files:
return
with open(os.path.join(_REPORT_DIR, files[0])) as f:
report = json.load(f)
self._latest_validation = report.get("validation")
self._latest_baseline = report.get("baseline_stats")
self._training_rows = report.get("training_rows", 0)
self._feature_importance = report.get("feature_importance")
self._top_trials = report.get("top_trials", [])
self._pareto_frontier = report.get("pareto_frontier", [])
ts = report.get("timestamp")
if ts:
self._model_trained_at = datetime.fromisoformat(ts)
logger.info(f"[Hypertuner] Restored state from {files[0]}")
except Exception as e:
logger.warning(f"[Hypertuner] Load latest report failed: {e}")
# ---------------------------------------------------------------------------
# Module-level singleton
# ---------------------------------------------------------------------------
_tuner: Optional[MLHypertuner] = None
def get_hypertuner() -> MLHypertuner:
global _tuner
if _tuner is None:
_tuner = MLHypertuner()
return _tuner