Deploy CurbFlow AI Space with runtime artifacts

.gitignore

@@ -76,3 +76,24 @@ temp/
 !artifacts/**/.gitkeep
 !**/data/**/.gitkeep
 !**/artifacts/**/.gitkeep
+
+# Hosted demo runtime artifacts. These are aggregate/model outputs, not raw CSV.
+!curbflow-ai/artifacts/metrics/deep_metrics.json
+!curbflow-ai/artifacts/metrics/feature_importance.csv
+!curbflow-ai/artifacts/metrics/model_card.md
+!curbflow-ai/artifacts/metrics/ranker_metrics.json
+!curbflow-ai/artifacts/models/be_sthgt_model.pt
+!curbflow-ai/artifacts/models/model_metadata.json
+!curbflow-ai/artifacts/models/ranker_lgbm.txt
+!curbflow-ai/artifacts/models/scaler.pkl
+!curbflow-ai/artifacts/reports/eda_summary.json
+!curbflow-ai/data/interim/patrol_graph_edges.parquet
+!curbflow-ai/data/processed/coverage_audit.parquet
+!curbflow-ai/data/processed/demo_presets.json
+!curbflow-ai/data/processed/junction_basins.parquet
+!curbflow-ai/data/processed/patrol_graph_features.parquet
+!curbflow-ai/data/processed/patrol_myopia.parquet
+!curbflow-ai/data/processed/predictions.parquet
+!curbflow-ai/data/processed/recommendations.parquet
+!curbflow-ai/data/processed/zone_time_features.parquet
+!curbflow-ai/data/processed/zones.geojson

curbflow-ai/artifacts/metrics/deep_metrics.json

@@ -0,0 +1,346 @@
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+  "best_epoch": 4,
+  "device": "mps"
+}

curbflow-ai/artifacts/metrics/feature_importance.csv

@@ -0,0 +1,95 @@
+feature,importance_split,importance_gain
+p90_historical_pfdi_component,40,4850.999438762665
+station_neighbor_mean_pfdi,38,1521.3768200874329
+patrol_pagerank_y,9,665.5444493293762
+patrol_pagerank_x,10,621.7650051116943
+static_potential,22,548.0731182098389
+vehicle_graph_degree,16,542.5702066421509
+median_exposure_station_time,23,512.9751296043396
+geo_neighbor_mean_pfdi,15,473.05434560775757
+exposure_bias_correction_factor,19,430.33297061920166
+observed_pfdi,15,395.0472078323364
+lag_1_pfdi,17,390.3680896759033
+blindspot_risk,22,388.44132947921753
+bias_corrected_pfdi,15,359.4325895309448
+same_slot_21d_avg_pfdi,19,351.24954080581665
+patrol_myopia_station,13,326.09007358551025
+pattern_neighbor_mean_pfdi,8,325.86009979248047
+lag_2_pfdi,14,295.90729904174805
+rolling_1d_pfdi,10,292.76090240478516
+lag_8_pfdi,12,289.41020011901855
+corridor_pfdi,12,270.8675994873047
+lag_56_pfdi,13,267.11484813690186
+rolling_21d_pfdi,13,262.1382894515991
+growth_7d_vs_21d,13,257.3544006347656
+rolling_7d_pfdi,10,254.93850326538086
+evidence_quality_score_mean,12,251.0135383605957
+hour,10,237.48961114883423
+device_trust_mean,10,209.8597011566162
+junction_basin_pfdi,12,208.21852016448975
+same_slot_7d_avg_pfdi,10,194.72733879089355
+coverage_gap,9,185.49740028381348
+station_evidence_quality,8,181.9388189315796
+hidden_no_junction_spillover_impact,6,174.6709966659546
+repeat_pressure_mean,10,165.14962911605835
+user_trust_mean,8,163.84600830078125
+patrol_in_degree,6,144.04539680480957
+location_criticality_mean,5,138.5891990661621
+record_count,5,130.87249755859375
+repeat_vehicle_count,2,110.87239837646484
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+patrol_out_degree,5,98.36929178237915
+severe_violation_count,4,95.57019996643066
+patrol_route_coverage,4,85.57730102539062
+junction_basin_risk_component,3,77.16019821166992
+day_of_week,3,68.1058292388916
+corridor_recent_pfdi,3,67.18990135192871
+patrol_weighted_degree,2,51.41270065307617
+scita_success_rate,2,50.9822998046875
+station_hour_activity,2,50.38870048522949
+peak_priority,2,41.353200912475586
+large_vehicle_share_component,2,41.043901443481445
+repeat_vehicle_share,1,33.450199127197266
+transit_node,2,30.99472999572754
+validation_coverage,1,30.14739990234375
+persistence_score,1,28.8533992767334
+patrol_route_coverage_norm,1,27.156999588012695
+community_id,1,23.208999633789062
+main_road_parking_share,1,17.498600006103516
+same_vehicle_different_zone_6h_count,1,15.402600288391113
+exposure,1,15.068599700927734
+large_vehicle_share,1,14.42490005493164
+recurrence_component,1,13.88129997253418
+airport_zone,0,0.0
+commercial_market,0,0.0
+corridor_risk_component,0,0.0
+double_parking_share,0,0.0
+entertainment,0,0.0
+evening_peak_audit,0,0.0
+evening_severity_prior,0,0.0
+hidden_junction_spillover,0,0.0
+hidden_no_junction_spillover_count,0,0.0
+high_static_potential,0,0.0
+institutional,0,0.0
+is_active_graph_zone,0,0.0
+is_weekend,0,0.0
+location_criticality_component,0,0.0
+low_enforcement_visibility,0,0.0
+near_patrol_but_uncovered,0,0.0
+near_patrol_but_uncovered_flag,0,0.0
+patrol_expansion_opportunity_component,0,0.0
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+residential_layout,0,0.0
+same_vehicle_same_zone_6h_count,0,0.0
+scita_success_rate_norm,0,0.0
+station_hour_activity_norm,0,0.0
+type_correction_rate,0,0.0
+uncertainty,0,0.0
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curbflow-ai/artifacts/metrics/model_card.md

@@ -0,0 +1,298 @@
+# Model Card: BE-STHGT + LightGBM Ensemble
+
+## Model Name
+
+```text
+BE-STHGT + LightGBM Ensemble
+```
+
+BE-STHGT is the **Bias-Exposure Spatio-Temporal Heterogeneous Graph Transformer**. It is ensembled with a LightGBM LambdaRank model and a rule-based blindspot score.
+
+## Intended Use
+
+The model is intended to support parking enforcement planning for Theme 1: Poor Visibility on Parking-Induced Congestion.
+
+Primary uses:
+
+- Rank zone-time windows for parking-induced flow disruption risk.
+- Separate observed hotspot priority from enforcement visibility gaps.
+- Identify blindspot audit candidates, especially in evidence-poor evening windows.
+- Support station-wise resource allocation across officers and tow units.
+- Provide aggregate operational intelligence for dashboards and planning.
+
+## Not Intended Use
+
+The model must not be used for:
+
+- Claiming exact traffic speed reduction.
+- Claiming measured congestion or measured travel-time loss.
+- Legal adjudication of individual violations.
+- Individual vehicle, device, or user monitoring.
+- Automated punitive action without human review.
+- Inferring that a no-challan zone has no illegal parking.
+- Treating evening blindspot outputs as validated evening predictions.
+
+## Inputs
+
+The model uses only the Theme 1 police parking violation CSV and derived features.
+
+Core row-level inputs:
+
+- `created_datetime`, parsed as UTC and converted to `Asia/Kolkata`.
+- `police_station`.
+- `junction_name`.
+- `location`.
+- `latitude`, `longitude`.
+- `violation_type`.
+- `vehicle_type`, `updated_vehicle_type`.
+- `validation_status`.
+- `data_sent_to_scita`.
+- `device_id` and `created_by_id` for aggregate evidence quality only.
+- `vehicle_number` for anonymized repeat-pressure features only.
+
+Derived inputs:
+
+- PFDI row obstruction features.
+- 300m grid zone assignments.
+- Zone x 3-hour IST window aggregates.
+- Evidence-quality trust scores.
+- Enforcement exposure and coverage-gap features.
+- Blindspot-risk features.
+- Hidden junction basin features.
+- Patrol myopia features.
+- Repeat-vehicle persistence features.
+- Road-corridor and place-type features.
+- Geo, station, temporal-pattern, repeat-vehicle, and patrol-transition graph features.
+- Lag and rolling PFDI features.
+
+Fields not used as outcome labels:
+
+- `closed_datetime`.
+- `action_taken_timestamp`.
+- `description`.
+
+## Outputs
+
+The ensemble writes `data/processed/predictions.parquet` with zone-window outputs including:
+
+- `predicted_count`.
+- `predicted_pfdi`.
+- `hotspot_probability`.
+- `q90_pfdi`.
+- `latent_risk`.
+- `exposure`.
+- `coverage_gap`.
+- `observed_risk_score`.
+- `blindspot_risk_score`.
+- `exploit_score`.
+- `explore_score`.
+- `deployment_priority_conservative`.
+- `deployment_priority_balanced`.
+- `deployment_priority_discovery`.
+- `recommended_action`.
+- `explanation_json`.
+
+Planner outputs are written to `data/processed/recommendations.parquet`.
+
+## Training Split
+
+Training uses chronological splitting only:
+
+```text
+train: first 70 percent of windows
+val:   next 15 percent of windows
+test:  final 15 percent of windows
+```
+
+No random split is used.
+
+The current fast-demo run produced:
+
+```text
+train rows: 36,647
+val rows:   6,593
+test rows:  6,436
+```
+
+Rank groups are formed by:
+
+```text
+police_station x window_start
+```
+
+## Model Components
+
+### BE-STHGT
+
+BE-STHGT models spatio-temporal zone risk with multiple graph relations:
+
+- Geographic adjacency.
+- Same-station adjacency.
+- Temporal-pattern similarity.
+- Repeat-vehicle graph.
+- Patrol-transition graph.
+- Learned adaptive adjacency.
+
+The key bias-exposure relationship is:
+
+```text
+observed_mu = latent_risk * exposure
+```
+
+This explicitly prevents low observed counts in low-exposure windows from being treated as strong proof of low risk.
+
+### LightGBM LambdaRank
+
+The ranker uses engineered zone-time, exposure, blindspot, graph, patrol, repeat-persistence, corridor, and place-type features. It optimizes ranking quality within station-window groups.
+
+### Rule Blindspot Score
+
+The rule component preserves operational blindspot signals where model training evidence is sparse, especially evening low-exposure windows.
+
+### Ensemble
+
+Configured ensemble weights:
+
+```text
+BE-STHGT rank score:     0.65
+LightGBM rank score:    0.25
+Rule blindspot score:   0.10
+```
+
+In fast-demo mode, BE-STHGT training is skipped and the available dashboard artifacts are generated using the LightGBM/rule path.
+
+## Metrics
+
+Current available metrics are from the fast-demo LightGBM LambdaRank component.
+
+Validation split:
+
+```text
+Precision@5:                  0.600
+Precision@10:                 0.700
+NDCG@5:                       0.635
+NDCG@10:                      0.649
+Station-wise Precision@5:     0.657
+Rows:                         6,593
+Groups:                       2,836
+```
+
+Test split:
+
+```text
+Precision@5:                  0.600
+Precision@10:                 0.800
+NDCG@5:                       0.723
+NDCG@10:                      0.739
+Station-wise Precision@5:     0.723
+Rows:                         6,436
+Groups:                       2,715
+```
+
+Baseline comparisons are stored in:
+
+```text
+artifacts/metrics/ranker_metrics.json
+```
+
+BE-STHGT metrics are generated when deep training is run and saved to:
+
+```text
+artifacts/metrics/deep_metrics.json
+```
+
+## Baseline Comparison
+
+CurbFlow compares the ensemble against simple operational baselines so model value is not judged against an empty reference point.
+
+Baseline methods:
+
+- **Count-only baseline**: ranks zones by observed historical violation counts.
+- **Historical PFDI baseline**: ranks zones by lagged and same-slot historical PFDI.
+- **Rule-based blindspot baseline**: ranks audit candidates by static potential, coverage gap, and evening prior.
+- **LightGBM LambdaRank**: ranks station-window candidates using engineered tabular features.
+- **BE-STHGT deep model**: learns latent risk with exposure-aware observed intensity.
+- **BE-STHGT + LightGBM ensemble**: combines deep rank score, LightGBM rank score, and rule blindspot score.
+
+The fast-demo path always generates dashboard-ready baseline and LightGBM/rule artifacts. Deep-model comparison is included when `make train-deep` or the full pipeline with deep training has produced:
+
+```text
+artifacts/models/be_sthgt_model.pt
+artifacts/metrics/deep_metrics.json
+```
+
+Primary comparison metrics:
+
+- Precision@5 and Precision@10 for top deployment candidates.
+- NDCG@5 and NDCG@10 for ranked station-window quality.
+- Station-wise Precision@5 to avoid only optimizing city-wide aggregate performance.
+- MAE PFDI and WAPE count for calibrated risk/count outputs.
+
+Interpretation:
+
+- A heatmap-style count baseline is useful for visible hotspots but cannot distinguish no-problem zones from no-visibility zones.
+- The rule blindspot baseline protects sparse evening audit behavior from being erased by observed-count training labels.
+- The ensemble is intended to combine visible disruption evidence with blindspot discovery, not to claim measured traffic-speed improvement.
+
+## Known Limitations
+
+- The source dataset is an enforcement visibility dataset, not a complete record of all illegal parking.
+- PFDI is a proxy for parking-induced flow disruption, not measured traffic speed loss.
+- The dataset does not provide measured queue length, speed, travel time, or actual congestion outcomes.
+- `closed_datetime`, `action_taken_timestamp`, and `description` are fully null or unsuitable for outcome labels.
+- Evening records are very sparse; evening outputs should be interpreted as audit priorities.
+- Device and user fields support aggregate evidence-quality features only; raw IDs are not exposed.
+- Location text can be noisy, abbreviated, or incomplete.
+- Road-corridor and place-type extraction is text-derived and may be imperfect.
+- Repeat-vehicle intelligence depends on anonymized vehicle consistency in the CSV.
+- Fast-demo metrics do not represent a fully trained BE-STHGT model.
+
+## Bias Handling
+
+CurbFlow AI separates observed enforcement records from latent risk by modeling enforcement visibility.
+
+Bias-handling mechanisms:
+
+- Enforcement exposure score from devices, users, station activity, patrol coverage, SCITA success, and validation coverage.
+- Coverage gap score as `1 - exposure`.
+- Bias-corrected PFDI with conservative clipping to avoid inflating low-exposure windows too aggressively.
+- Evidence-quality scores with Bayesian smoothing for devices, users, and stations.
+- Patrol myopia index for station-level concentration and morning-heavy enforcement behavior.
+- Blindspot risk that uses low visibility as uncertainty and audit priority, not automatic hotspot proof.
+- Chronological split to avoid future leakage.
+- Repeat pressure computed from previous vehicle history only.
+
+## Evening Gap Handling
+
+Evening enforcement data is sparse. The model therefore treats evening zero-violation windows as evidence-poor audit windows, not safe zones.
+
+Evening handling includes:
+
+- Evening peak priority multiplier.
+- Evening severity prior for low-exposure windows.
+- Coverage-gap and uncertainty terms.
+- Blindspot explanations such as `evening_peak_audit` and `low_enforcement_visibility`.
+
+Evening blindspot outputs are operational audit recommendations, not validated evening predictions.
+
+## Privacy and Exposure Controls
+
+The API and frontend must not expose:
+
+- Raw vehicle numbers.
+- Raw device IDs.
+- Raw created-by user IDs.
+
+Allowed outputs are aggregate zone, station, corridor, graph, and planner features.
+
+## Guardrails
+
+- We do not claim exact traffic speed reduction.
+- We do not claim measured congestion.
+- PFDI is a proxy for parking-induced flow disruption.
+- No challan does not mean no illegal parking.
+- Evening outputs are blindspot audit priorities.
+- `validation_status` NaN is unknown confidence.
+- Repeat pressure uses past history only.
+- Train/test split is chronological.
+- ASTraM is not used in Theme 1.

curbflow-ai/artifacts/metrics/ranker_metrics.json

@@ -0,0 +1,497 @@
+{
+  "model": "lightgbm_lambdarank",
+  "config": {
+    "train_fraction": 0.7,
+    "val_fraction": 0.15,
+    "objective": "lambdarank",
+    "metric": "ndcg",
+    "ndcg_eval_at": [
+      5,
+      10,
+      20
+    ],
+    "learning_rate": 0.05,
+    "num_leaves": 63,
+    "n_estimators": 800,
+    "feature_fraction": 0.85,
+    "bagging_fraction": 0.85,
+    "bagging_freq": 3,
+    "min_data_in_leaf": 20,
+    "early_stopping_rounds": 50,
+    "random_state": 42
+  },
+  "feature_columns": [
+    "airport_zone",
+    "bias_corrected_pfdi",
+    "blindspot_risk",
+    "commercial_market",
+    "community_id",
+    "corridor_pfdi",
+    "corridor_recent_pfdi",
+    "corridor_risk_component",
+    "coverage_gap",
+    "day_of_week",
+    "device_trust_mean",
+    "double_parking_share",
+    "entertainment",
+    "evening_peak_audit",
+    "evening_severity_prior",
+    "evidence_quality_score_mean",
+    "exposure",
+    "exposure_bias_correction_factor",
+    "geo_neighbor_mean_pfdi",
+    "growth_7d_vs_21d",
+    "hidden_junction_spillover",
+    "hidden_no_junction_spillover_count",
+    "hidden_no_junction_spillover_impact",
+    "high_static_potential",
+    "hour",
+    "institutional",
+    "is_active_graph_zone",
+    "is_weekend",
+    "junction_basin_pfdi",
+    "junction_basin_risk_component",
+    "lag_1_pfdi",
+    "lag_2_pfdi",
+    "lag_56_pfdi",
+    "lag_8_pfdi",
+    "large_vehicle_share",
+    "large_vehicle_share_component",
+    "location_criticality_component",
+    "location_criticality_mean",
+    "low_enforcement_visibility",
+    "main_road_parking_share",
+    "median_exposure_station_time",
+    "near_patrol_but_uncovered",
+    "near_patrol_but_uncovered_flag",
+    "observed_pfdi",
+    "p90_historical_pfdi_component",
+    "patrol_expansion_opportunity_component",
+    "patrol_in_degree",
+    "patrol_myopia_station",
+    "patrol_out_degree",
+    "patrol_pagerank_x",
+    "patrol_pagerank_y",
+    "patrol_route_coverage",
+    "patrol_route_coverage_norm",
+    "patrol_weighted_degree",
+    "pattern_neighbor_mean_pfdi",
+    "peak_priority",
+    "persistence_score",
+    "raw_impact",
+    "record_count",
+    "recurrence_component",
+    "religious_place",
+    "repeat_persistence_component",
+    "repeat_pressure_mean",
+    "repeat_vehicle_count",
+    "repeat_vehicle_share",
+    "residential_layout",
+    "rolling_1d_pfdi",
+    "rolling_21d_pfdi",
+    "rolling_7d_pfdi",
+    "same_slot_21d_avg_pfdi",
+    "same_slot_7d_avg_pfdi",
+    "same_vehicle_different_zone_6h_count",
+    "same_vehicle_same_zone_6h_count",
+    "scita_success_rate",
+    "scita_success_rate_norm",
+    "severe_violation_count",
+    "static_potential",
+    "station_evidence_quality",
+    "station_hour_activity",
+    "station_hour_activity_norm",
+    "station_neighbor_mean_pfdi",
+    "transit_node",
+    "two_wheeler_share",
+    "type_correction_rate",
+    "uncertainty",
+    "unique_created_by_user_count",
+    "unique_created_by_user_count_norm",
+    "unique_device_count",
+    "unique_device_count_norm",
+    "user_trust_mean",
+    "validation_coverage",
+    "validation_coverage_norm",
+    "vehicle_graph_degree",
+    "zero_window_weight"
+  ],
+  "feature_count": 94,
+  "row_counts": {
+    "train": 36647,
+    "val": 6593,
+    "test": 6436
+  },
+  "group_counts": {
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+    "val": 2836,
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+  "best_iteration": 9,
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+    }
+  ],
+  "top_features": [
+    {
+      "feature": "p90_historical_pfdi_component",
+      "importance_split": 40,
+      "importance_gain": 4850.999438762665
+    },
+    {
+      "feature": "station_neighbor_mean_pfdi",
+      "importance_split": 38,
+      "importance_gain": 1521.3768200874329
+    },
+    {
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curbflow-ai/artifacts/models/model_metadata.json

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+is_linear=0
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+
+
+end of trees
+
+feature_importances:
+p90_historical_pfdi_component=40
+station_neighbor_mean_pfdi=38
+median_exposure_station_time=23
+blindspot_risk=22
+static_potential=22
+exposure_bias_correction_factor=19
+same_slot_21d_avg_pfdi=19
+lag_1_pfdi=17
+vehicle_graph_degree=16
+bias_corrected_pfdi=15
+geo_neighbor_mean_pfdi=15
+observed_pfdi=15
+lag_2_pfdi=14
+growth_7d_vs_21d=13
+lag_56_pfdi=13
+patrol_myopia_station=13
+rolling_21d_pfdi=13
+corridor_pfdi=12
+evidence_quality_score_mean=12
+junction_basin_pfdi=12
+lag_8_pfdi=12
+device_trust_mean=10
+hour=10
+patrol_pagerank_x=10
+repeat_pressure_mean=10
+rolling_1d_pfdi=10
+rolling_7d_pfdi=10
+same_slot_7d_avg_pfdi=10
+coverage_gap=9
+patrol_pagerank_y=9
+pattern_neighbor_mean_pfdi=8
+station_evidence_quality=8
+user_trust_mean=8
+hidden_no_junction_spillover_impact=6
+patrol_in_degree=6
+location_criticality_mean=5
+patrol_out_degree=5
+record_count=5
+patrol_route_coverage=4
+severe_violation_count=4
+corridor_recent_pfdi=3
+day_of_week=3
+junction_basin_risk_component=3
+two_wheeler_share=3
+large_vehicle_share_component=2
+patrol_weighted_degree=2
+peak_priority=2
+repeat_vehicle_count=2
+scita_success_rate=2
+station_hour_activity=2
+transit_node=2
+community_id=1
+exposure=1
+large_vehicle_share=1
+main_road_parking_share=1
+patrol_route_coverage_norm=1
+persistence_score=1
+recurrence_component=1
+repeat_vehicle_share=1
+same_vehicle_different_zone_6h_count=1
+validation_coverage=1
+
+parameters:
+[boosting: gbdt]
+[objective: lambdarank]
+[metric: ndcg]
+[tree_learner: serial]
+[device_type: cpu]
+[data_sample_strategy: bagging]
+[data: ]
+[valid: ]
+[num_iterations: 800]
+[learning_rate: 0.05]
+[num_leaves: 63]
+[num_threads: 8]
+[seed: 42]
+[deterministic: 0]
+[force_col_wise: 0]
+[force_row_wise: 0]
+[histogram_pool_size: -1]
+[max_depth: -1]
+[min_data_in_leaf: 20]
+[min_sum_hessian_in_leaf: 0.001]
+[bagging_fraction: 0.85]
+[pos_bagging_fraction: 1]
+[neg_bagging_fraction: 1]
+[bagging_freq: 3]
+[bagging_seed: 400]
+[bagging_by_query: 0]
+[feature_fraction: 0.85]
+[feature_fraction_bynode: 1]
+[feature_fraction_seed: 30056]
+[extra_trees: 0]
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+[early_stopping_round: 0]
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+[first_metric_only: 0]
+[max_delta_step: 0]
+[lambda_l1: 0]
+[lambda_l2: 0]
+[linear_lambda: 0]
+[min_gain_to_split: 0]
+[drop_rate: 0.1]
+[max_drop: 50]
+[skip_drop: 0.5]
+[xgboost_dart_mode: 0]
+[uniform_drop: 0]
+[drop_seed: 17869]
+[top_rate: 0.2]
+[other_rate: 0.1]
+[min_data_per_group: 100]
+[max_cat_threshold: 32]
+[cat_l2: 10]
+[cat_smooth: 10]
+[max_cat_to_onehot: 4]
+[top_k: 20]
+[monotone_constraints: ]
+[monotone_constraints_method: basic]
+[monotone_penalty: 0]
+[feature_contri: ]
+[forcedsplits_filename: ]
+[refit_decay_rate: 0.9]
+[cegb_tradeoff: 1]
+[cegb_penalty_split: 0]
+[cegb_penalty_feature_lazy: ]
+[cegb_penalty_feature_coupled: ]
+[path_smooth: 0]
+[interaction_constraints: ]
+[verbosity: -1]
+[saved_feature_importance_type: 0]
+[use_quantized_grad: 0]
+[num_grad_quant_bins: 4]
+[quant_train_renew_leaf: 0]
+[stochastic_rounding: 1]
+[linear_tree: 0]
+[max_bin: 255]
+[max_bin_by_feature: ]
+[min_data_in_bin: 3]
+[bin_construct_sample_cnt: 200000]
+[data_random_seed: 175]
+[is_enable_sparse: 1]
+[enable_bundle: 1]
+[use_missing: 1]
+[zero_as_missing: 0]
+[feature_pre_filter: 1]
+[pre_partition: 0]
+[two_round: 0]
+[header: 0]
+[label_column: ]
+[weight_column: ]
+[group_column: ]
+[ignore_column: ]
+[categorical_feature: ]
+[forcedbins_filename: ]
+[precise_float_parser: 0]
+[parser_config_file: ]
+[objective_seed: 16083]
+[num_class: 1]
+[is_unbalance: 0]
+[scale_pos_weight: 1]
+[sigmoid: 1]
+[boost_from_average: 1]
+[reg_sqrt: 0]
+[alpha: 0.9]
+[fair_c: 1]
+[poisson_max_delta_step: 0.7]
+[tweedie_variance_power: 1.5]
+[lambdarank_truncation_level: 30]
+[lambdarank_norm: 1]
+[label_gain: ]
+[lambdarank_position_bias_regularization: 0]
+[eval_at: 5,10,20]
+[multi_error_top_k: 1]
+[auc_mu_weights: ]
+[num_machines: 1]
+[local_listen_port: 12400]
+[time_out: 120]
+[machine_list_filename: ]
+[machines: ]
+[gpu_platform_id: -1]
+[gpu_device_id: -1]
+[gpu_use_dp: 0]
+[num_gpu: 1]
+
+end of parameters
+
+pandas_categorical:[]

curbflow-ai/artifacts/models/scaler.pkl

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+oid sha256:cc3c76c3a6fd95c16e36877c0190956bef9addf88ae90dd8e39c702291640546
+size 2727

curbflow-ai/artifacts/reports/eda_summary.json

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+    "btp042 - minsk square junction (cto)": 2044,
+    "btp038 - mysore bank junction": 2021,
+    "btp023 - mahalaxmi layout entrance": 1807,
+    "btp108 - tagore park junction": 1801,
+    "btp043 - upparpet junction": 1786,
+    "btp048 - shanthala junction": 1593,
+    "btp104 - medical college circle": 1508,
+    "btp182 - 19th main junction, rajajinagar": 1390,
+    "btp011 - rr kalyana mantapa, dr. rajkumar road": 1208,
+    "btp008 - navarang theatre": 1190,
+    "btp022 - marenahalli junction, vijayanagar": 1157,
+    "btp135 - uco bank junction": 1112,
+    "btp026 - 1st block junction, rajajinagar": 1086,
+    "btp069 - mc circle": 1075,
+    "btp063 - siddalingaiah circle": 1060,
+    "btp074 - leprosy hospital junction": 1047,
+    "btp205 - modi hospital junction": 1045,
+    "btp061 - bhashyam circle": 1030,
+    "btp096 - gali anjaneya temple": 972,
+    "btp010 - rajajinagar entrance": 957,
+    "btp208 - havanur circle": 812,
+    "btp172 - nanjappa circle": 776,
+    "btp095 - satellite bus stand, mysore road": 775,
+    "btp028 - vijayanagar bus stand junction": 754,
+    "btp054 - shivananda circle": 751,
+    "btp148 - 17th main, doopanahalli bus stop": 745,
+    "btp071 - prasanna junction": 721,
+    "btp206 - shankara mutt circle, vijayanagar": 719,
+    "btp118 - basappa circle": 653,
+    "btp204 - esi hospital, rajajinagar": 638,
+    "btp024 - nps, vijayanagar": 632,
+    "btp130 - gd mara junction": 631,
+    "btp192 - urvashi theater": 625,
+    "btp189 - new diagonal road, jayanagar": 602,
+    "btp137 - madiwala traffic ps junction": 596,
+    "btp170 - dr. bheem rao palace, chord road": 573,
+    "btp186 - 9th cross, 4th main, jayanagar": 546,
+    "btp123 - 10th cross, wilson garden": 535,
+    "btp068 - veeresh theatre junction": 504,
+    "btp226 - 33rd cross, 11th main, jayanagar": 492,
+    "btp076 - hudson circle": 455,
+    "btp210 - 50th cross, 12th main , rajajinagar": 455,
+    "btp215 - vanivillas junction": 434,
+    "btp062 - basava mantapa, dr. rajkumar road": 425,
+    "btp017 - attiguppe circle": 422,
+    "btp101 - blood bank circle, kh road": 410,
+    "btp067 - jai muni rao circle": 409,
+    "btp018 - dhobi ghat junction": 382,
+    "btp041 - gopal gowda circle": 378,
+    "btp167 - jc road junction": 372,
+    "btp140 - madiwala check post": 368,
+    "btp163 - 16th main road, btm layout": 364,
+    "btp115 - sahasra hospital, jayanagar": 362,
+    "btp136 - hosur-tavarkere road junction": 360,
+    "btp219 - canara bank junction": 358,
+    "btp218 - syndicate bank junction": 351,
+    "btp099 - cash pharmacy, residency road": 349,
+    "btp155 - katriguppe junction": 339,
+    "btp126 - dairy circle": 334,
+    "btp109 - dewan madhava rao junction": 309,
+    "btp117 - 9th cross, kanakapura road": 307,
+    "btp100 - richmond circle": 304,
+    "btp220 - puttanna junction": 294,
+    "btp188 - 27th cross, 4th main, jayanagar": 294,
+    "btp225 - geetha junction": 287,
+    "btp197 - 9th cross, 24th main, jp nagar": 284,
+    "btp091 - sirsi circle": 263,
+    "btp009 - prakash nagar junction": 262,
+    "btp180 - swagath poornima theatre": 261,
+    "btp214 - 5th main chamrajpet junction": 252,
+    "btp120 - lalbagh main gate junction": 244,
+    "btp078 - nr square junction": 244,
+    "btp037 - kr circle": 239,
+    "btp034 - lrde junction": 217,
+    "btp184 - lalbagh west gate": 215,
+    "btp207 - 8th main road basaveshwara nagar": 213,
+    "btp033 - sophia high school, sankey road": 208,
+    "btp098 - nayandahalli junction": 206,
+    "btp047 - vidhan soudha metro station": 200,
+    "btp119 - minerva circle": 199,
+    "btp052 - cid carlton house junction": 198,
+    "btp176 - rajalakshmi kalyana mantapa, jp nagar": 196,
+    "btp075 - police corner": 188
+  },
+  "vehicle_type_counts": {
+    "scooter": 94856,
+    "car": 88870,
+    "motor cycle": 40811,
+    "passenger auto": 37813,
+    "maxi-cab": 11372,
+    "lgv": 8255,
+    "goods auto": 2934,
+    "moped": 2199,
+    "private bus": 1633,
+    "van": 1466,
+    "tempo": 1368,
+    "bus (bmtc/ksrtc)": 1281,
+    "hgv": 1144,
+    "lorry/goods vehicle": 1122,
+    "jeep": 913,
+    "others": 895,
+    "tourist bus": 379,
+    "school vehicle": 378,
+    "tanker": 260,
+    "factory bus": 238,
+    "mini lorry": 199,
+    "tractor": 64
+  },
+  "validation_status_breakdown": {
+    "including_unknown": {
+      "unknown": 125254,
+      "approved": 115400,
+      "rejected": 49754,
+      "created1": 7044,
+      "processing": 678,
+      "duplicate": 320
+    },
+    "non_unknown_only": {
+      "approved": 115400,
+      "rejected": 49754,
+      "created1": 7044,
+      "processing": 678,
+      "duplicate": 320
+    }
+  },
+  "data_sent_to_scita_rate": 0.8574066007706483,
+  "device_user_counts": {
+    "device_id_unique": 3070,
+    "device_id_non_null_rows": 298450,
+    "created_by_id_unique": 2666,
+    "created_by_id_non_null_rows": 298445
+  },
+  "top_zone_concentration": {
+    "available": false,
+    "top_10_zone_share": null,
+    "note": "Top-zone concentration requires zoning artifacts and will be populated after zone assignment."
+  },
+  "patrol_myopia": {
+    "available": false,
+    "note": "Patrol myopia requires zone_id and is populated after zone assignment."
+  },
+  "interpretation_warnings": [
+    "This dataset is an enforcement visibility dataset, not a complete record of all illegal parking.",
+    "No challan should not be interpreted as no illegal parking.",
+    "Evening windows are evidence-poor and should be handled as blind-spot audit windows."
+  ]
+}

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+  "source_predictions": "data/processed/predictions.parquet",
+  "source_training_table": "data/processed/model_training_table.parquet",
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+    "end_window": "2024-04-08T15:00:00+05:30",
+    "method": "final 15 percent of chronological prediction windows"
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+      "id": "morning_known_hotspot",
+      "label": "Morning known hotspot",
+      "button_label": "Load Morning Hotspot Demo",
+      "window_start": "2024-03-20T09:00:00+05:30",
+      "police_station": "rajajinagar",
+      "zone_id": "4826_28040",
+      "mode": "conservative",
+      "officers": 8,
+      "tow_units": 2,
+      "purpose": "Show an observed high-confidence hotspot from a high-record morning/midday test-period window.",
+      "selection_reason": "Selected from the final 15 percent chronological test period by high observed risk among high-record morning/midday rows.",
+      "metrics": {
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+      "label": "Evening blindspot",
+      "button_label": "Load Evening Blindspot Demo",
+      "window_start": "2024-04-06T18:00:00+05:30",
+      "police_station": "city market",
+      "zone_id": "4810_28050",
+      "mode": "discovery",
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+      "tow_units": 2,
+      "purpose": "Show low enforcement visibility and high blindspot audit priority in an evening test-period window.",
+      "selection_reason": "Selected from the final 15 percent chronological test period by highest blindspot risk in evening windows.",
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+      "id": "balanced_planner",
+      "label": "Balanced planner",
+      "button_label": "Load Balanced Planner Demo",
+      "window_start": "2024-04-02T09:00:00+05:30",
+      "police_station": "malleshwaram",
+      "zone_id": "4823_28041",
+      "mode": "balanced",
+      "officers": 20,
+      "tow_units": 4,
+      "purpose": "Show 70/30 exploit/explore allocation with 20 officers and 4 tow units.",
+      "selection_reason": "Selected station-window with 17 candidate zones and 17 planner recommendations.",
+      "metrics": {
+        "record_count": 11,
+        "observed_risk_score": 87.04317084797829,
+        "blindspot_risk_score": 4.619527856557067,
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+  ]
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