bakery-admin/bakery-ia
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

REFACTOR - Database logic

Browse Source
This commit is contained in:
Urtzi Alfaro
2025-08-08 09:08:41 +02:00
parent 0154365bfc
commit 488bb3ef93
113 changed files with 22842 additions and 6503 deletions
Download Patch File Download Diff File Expand all files Collapse all files

46
services/training/app/models/training.py
View File

@@ -6,7 +6,7 @@ Database models for training service
from sqlalchemy import Column, Integer, String, DateTime, Text, Boolean, JSON, Float
from sqlalchemy.dialects.postgresql import UUID, ARRAY
from shared.database.base import Base
from datetime import datetime
from datetime import datetime, timezone
import uuid
@@ -25,8 +25,8 @@ class ModelTrainingLog(Base):
current_step = Column(String(500), default="")
# Timestamps
start_time = Column(DateTime, default=datetime.now)
end_time = Column(DateTime, nullable=True)
start_time = Column(DateTime(timezone=True), default=lambda: datetime.now(timezone.utc))
end_time = Column(DateTime(timezone=True), nullable=True)
# Configuration and results
config = Column(JSON, nullable=True) # Training job configuration
@@ -34,8 +34,8 @@ class ModelTrainingLog(Base):
error_message = Column(Text, nullable=True)
# Metadata
created_at = Column(DateTime, default=datetime.now)
updated_at = Column(DateTime, default=datetime.now, onupdate=datetime.now)
created_at = Column(DateTime(timezone=True), default=lambda: datetime.now(timezone.utc))
updated_at = Column(DateTime(timezone=True), default=lambda: datetime.now(timezone.utc), onupdate=lambda: datetime.now(timezone.utc))
class ModelPerformanceMetric(Base):
"""
@@ -65,8 +65,8 @@ class ModelPerformanceMetric(Base):
evaluation_samples = Column(Integer, nullable=True)
# Metadata
measured_at = Column(DateTime, default=datetime.now)
created_at = Column(DateTime, default=datetime.now)
measured_at = Column(DateTime(timezone=True), default=lambda: datetime.now(timezone.utc))
created_at = Column(DateTime(timezone=True), default=lambda: datetime.now(timezone.utc))
class TrainingJobQueue(Base):
"""
@@ -94,8 +94,8 @@ class TrainingJobQueue(Base):
max_retries = Column(Integer, default=3)
# Metadata
created_at = Column(DateTime, default=datetime.now)
updated_at = Column(DateTime, default=datetime.now, onupdate=datetime.now)
created_at = Column(DateTime(timezone=True), default=lambda: datetime.now(timezone.utc))
updated_at = Column(DateTime(timezone=True), default=lambda: datetime.now(timezone.utc), onupdate=lambda: datetime.now(timezone.utc))
cancelled_by = Column(String, nullable=True)
class ModelArtifact(Base):
@@ -119,15 +119,15 @@ class ModelArtifact(Base):
compression = Column(String(50), nullable=True) # gzip, lz4, etc.
# Metadata
created_at = Column(DateTime, default=datetime.now)
expires_at = Column(DateTime, nullable=True) # For automatic cleanup
created_at = Column(DateTime(timezone=True), default=lambda: datetime.now(timezone.utc))
expires_at = Column(DateTime(timezone=True), nullable=True) # For automatic cleanup
class TrainedModel(Base):
__tablename__ = "trained_models"
# Primary identification
id = Column(String, primary_key=True, default=lambda: str(uuid.uuid4()))
tenant_id = Column(String, nullable=False, index=True)
# Primary identification - Updated to use UUID properly
id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
tenant_id = Column(UUID(as_uuid=True), nullable=False, index=True)
product_name = Column(String, nullable=False, index=True)
# Model information
@@ -154,13 +154,14 @@ class TrainedModel(Base):
is_active = Column(Boolean, default=True)
is_production = Column(Boolean, default=False)
# Timestamps
created_at = Column(DateTime, default=datetime.utcnow)
last_used_at = Column(DateTime)
# Timestamps - Updated to be timezone-aware with proper defaults
created_at = Column(DateTime(timezone=True), default=lambda: datetime.now(timezone.utc))
updated_at = Column(DateTime(timezone=True), default=lambda: datetime.now(timezone.utc), onupdate=lambda: datetime.now(timezone.utc))
last_used_at = Column(DateTime(timezone=True))
# Training data info
training_start_date = Column(DateTime)
training_end_date = Column(DateTime)
training_start_date = Column(DateTime(timezone=True))
training_end_date = Column(DateTime(timezone=True))
data_quality_score = Column(Float)
# Additional metadata
@@ -169,9 +170,9 @@ class TrainedModel(Base):
def to_dict(self):
return {
"id": self.id,
"model_id": self.id,
"tenant_id": self.tenant_id,
"id": str(self.id),
"model_id": str(self.id),
"tenant_id": str(self.tenant_id),
"product_name": self.product_name,
"model_type": self.model_type,
"model_version": self.model_version,
@@ -186,6 +187,7 @@ class TrainedModel(Base):
"is_active": self.is_active,
"is_production": self.is_production,
"created_at": self.created_at.isoformat() if self.created_at else None,
"updated_at": self.updated_at.isoformat() if self.updated_at else None,
"last_used_at": self.last_used_at.isoformat() if self.last_used_at else None,
"training_start_date": self.training_start_date.isoformat() if self.training_start_date else None,
"training_end_date": self.training_end_date.isoformat() if self.training_end_date else None,

81
services/training/app/models/training_models.py
View File

@@ -1,80 +1,11 @@
# services/training/app/models/training_models.py
"""
Database models for trained ML models
Legacy file - TrainedModel has been moved to training.py
This file is deprecated and should be removed after migration.
"""
from sqlalchemy import Column, String, DateTime, Float, Integer, Text, Boolean, JSON
from sqlalchemy.ext.declarative import declarative_base
from datetime import datetime
import uuid
# Import the actual model from the correct location
from .training import TrainedModel
Base = declarative_base()
class TrainedModel(Base):
__tablename__ = "trained_models"
# Primary identification
id = Column(String, primary_key=True, default=lambda: str(uuid.uuid4()))
tenant_id = Column(String, nullable=False, index=True)
product_name = Column(String, nullable=False, index=True)
# Model information
model_type = Column(String, default="prophet_optimized")
model_version = Column(String, default="1.0")
job_id = Column(String, nullable=False)
# File storage
model_path = Column(String, nullable=False) # Path to the .pkl file
metadata_path = Column(String) # Path to metadata JSON
# Training metrics
mape = Column(Float)
mae = Column(Float)
rmse = Column(Float)
r2_score = Column(Float)
training_samples = Column(Integer)
# Hyperparameters and features
hyperparameters = Column(JSON) # Store optimized parameters
features_used = Column(JSON) # List of regressor columns
# Model status
is_active = Column(Boolean, default=True)
is_production = Column(Boolean, default=False)
# Timestamps
created_at = Column(DateTime, default=datetime.utcnow)
last_used_at = Column(DateTime)
# Training data info
training_start_date = Column(DateTime)
training_end_date = Column(DateTime)
data_quality_score = Column(Float)
# Additional metadata
notes = Column(Text)
created_by = Column(String) # User who triggered training
def to_dict(self):
return {
"id": self.id,
"tenant_id": self.tenant_id,
"product_name": self.product_name,
"model_type": self.model_type,
"model_version": self.model_version,
"model_path": self.model_path,
"mape": self.mape,
"mae": self.mae,
"rmse": self.rmse,
"r2_score": self.r2_score,
"training_samples": self.training_samples,
"hyperparameters": self.hyperparameters,
"features_used": self.features_used,
"is_active": self.is_active,
"is_production": self.is_production,
"created_at": self.created_at.isoformat() if self.created_at else None,
"last_used_at": self.last_used_at.isoformat() if self.last_used_at else None,
"training_start_date": self.training_start_date.isoformat() if self.training_start_date else None,
"training_end_date": self.training_end_date.isoformat() if self.training_end_date else None,
"data_quality_score": self.data_quality_score
}
# For backward compatibility, re-export the model
__all__ = ["TrainedModel"]