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0cd8655 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 | """Shared HVAC ML registry tables. Training/reference data only — never LIVE_BMS."""
from datetime import datetime
from sqlalchemy import Boolean, Column, DateTime, Float, ForeignKey, Integer, JSON, String, Text
from database.base import Base
class MLDatasetRegistryDB(Base):
__tablename__ = "ml_dataset_registry"
id = Column(String, primary_key=True)
name = Column(String, nullable=False)
source = Column(String, nullable=False, default="TRAINING_DATASET")
path = Column(String, nullable=False)
status = Column(String, nullable=False)
alias_of = Column(String, nullable=True)
notes = Column(Text, nullable=True)
created_at = Column(DateTime, default=datetime.utcnow)
updated_at = Column(DateTime, default=datetime.utcnow)
class MLDatasetFileDB(Base):
__tablename__ = "ml_dataset_files"
id = Column(String, primary_key=True)
dataset_id = Column(String, ForeignKey("ml_dataset_registry.id"), nullable=False)
file_path = Column(String, nullable=False)
file_name = Column(String, nullable=False)
format = Column(String, nullable=True)
size_bytes = Column(Integer, nullable=True)
row_count = Column(Integer, nullable=True)
columns_json = Column(JSON, nullable=True)
schema_json = Column(JSON, nullable=True)
class MLDatasetQualityDB(Base):
__tablename__ = "ml_dataset_quality"
id = Column(String, primary_key=True)
dataset_id = Column(String, ForeignKey("ml_dataset_registry.id"), nullable=False)
file_id = Column(String, ForeignKey("ml_dataset_files.id"), nullable=True)
missing_pct = Column(Float, nullable=True)
duplicate_rows = Column(Integer, nullable=True)
timestamp_valid = Column(Boolean, nullable=True)
numeric_valid_pct = Column(Float, nullable=True)
outlier_rate = Column(Float, nullable=True)
sampling_interval_seconds = Column(Float, nullable=True)
sample_rows = Column(Integer, nullable=True)
details_json = Column(JSON, nullable=True)
created_at = Column(DateTime, default=datetime.utcnow)
class MLDatasetOpportunityMapDB(Base):
__tablename__ = "ml_dataset_opportunity_map"
id = Column(String, primary_key=True)
dataset_id = Column(String, ForeignKey("ml_dataset_registry.id"), nullable=False)
opportunity_id = Column(String, nullable=False)
agent_id = Column(String, nullable=False)
file_name = Column(String, nullable=True)
feature_map = Column(JSON, nullable=False)
target_column = Column(String, nullable=True)
task_type = Column(String, nullable=False)
training_allowed = Column(Boolean, nullable=False, default=False)
status = Column(String, nullable=False)
notes = Column(Text, nullable=True)
class MLFeatureDefinitionDB(Base):
__tablename__ = "ml_feature_definitions"
id = Column(String, primary_key=True)
opportunity_id = Column(String, nullable=False)
feature_name = Column(String, nullable=False)
unit = Column(String, nullable=True)
min_value = Column(Float, nullable=True)
max_value = Column(Float, nullable=True)
required = Column(Boolean, nullable=False, default=False)
source_column = Column(String, nullable=True)
class MLTrainingRunDB(Base):
__tablename__ = "ml_training_runs"
id = Column(String, primary_key=True)
opportunity_id = Column(String, nullable=False)
dataset_id = Column(String, nullable=True)
map_id = Column(String, nullable=True)
status = Column(String, nullable=False)
algorithm = Column(String, nullable=True)
metrics_json = Column(JSON, nullable=True)
reason = Column(Text, nullable=True)
created_at = Column(DateTime, default=datetime.utcnow)
class MLModelRegistryDB(Base):
__tablename__ = "ml_model_registry"
id = Column(String, primary_key=True)
opportunity_id = Column(String, nullable=False)
agent_id = Column(String, nullable=False)
model_type = Column(String, nullable=False)
model_version = Column(String, nullable=False)
features_json = Column(JSON, nullable=True)
target_json = Column(JSON, nullable=True)
artifact_path = Column(String, nullable=True)
training_dataset_id = Column(String, nullable=True)
status = Column(String, nullable=False)
created_at = Column(DateTime, default=datetime.utcnow)
class MLModelMetricsDB(Base):
__tablename__ = "ml_model_metrics"
id = Column(String, primary_key=True)
model_id = Column(String, ForeignKey("ml_model_registry.id"), nullable=False)
split = Column(String, nullable=False)
metrics_json = Column(JSON, nullable=False)
class MLPredictionDB(Base):
__tablename__ = "ml_predictions"
id = Column(String, primary_key=True)
opportunity_id = Column(String, nullable=False)
equipment_id = Column(String, nullable=True)
building_id = Column(String, nullable=True)
model_id = Column(String, nullable=True)
input_json = Column(JSON, nullable=True)
prediction_json = Column(JSON, nullable=True)
confidence = Column(Float, nullable=True)
source = Column(String, nullable=False, default="ML_MODEL")
provenance = Column(String, nullable=False, default="MODEL PREDICTION")
status = Column(String, nullable=False)
created_at = Column(DateTime, default=datetime.utcnow)
class MLPredictionFeatureDB(Base):
__tablename__ = "ml_prediction_features"
id = Column(String, primary_key=True)
prediction_id = Column(String, ForeignKey("ml_predictions.id"), nullable=False)
feature = Column(String, nullable=False)
value = Column(Float, nullable=True)
importance = Column(Float, nullable=True)
class MLAgentPredictionDB(Base):
__tablename__ = "ml_agent_predictions"
id = Column(String, primary_key=True)
opportunity_id = Column(String, nullable=False)
agent_id = Column(String, nullable=False)
prediction_id = Column(String, ForeignKey("ml_predictions.id"), nullable=True)
recommendation_json = Column(JSON, nullable=True)
created_at = Column(DateTime, default=datetime.utcnow)
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