318 lines
9.9 KiB
Python
318 lines
9.9 KiB
Python
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"""
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Comprehensive Input Validation Schemas
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Ensures all API inputs are properly validated before processing
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"""
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from pydantic import BaseModel, Field, validator, root_validator
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from typing import Optional, List, Dict, Any
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from datetime import datetime, timedelta
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from uuid import UUID
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import re
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class TrainingJobCreateRequest(BaseModel):
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"""Schema for creating a new training job"""
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tenant_id: UUID = Field(..., description="Tenant identifier")
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start_date: Optional[str] = Field(
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None,
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description="Training data start date (ISO format: YYYY-MM-DD)",
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example="2024-01-01"
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)
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end_date: Optional[str] = Field(
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None,
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description="Training data end date (ISO format: YYYY-MM-DD)",
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example="2024-12-31"
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)
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product_ids: Optional[List[UUID]] = Field(
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None,
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description="Specific products to train (optional, trains all if not provided)"
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)
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force_retrain: bool = Field(
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default=False,
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description="Force retraining even if recent models exist"
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)
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@validator('start_date', 'end_date')
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def validate_date_format(cls, v):
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"""Validate date is in ISO format"""
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if v is not None:
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try:
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datetime.fromisoformat(v)
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except ValueError:
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raise ValueError(f"Invalid date format: {v}. Use YYYY-MM-DD format")
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return v
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@root_validator
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def validate_date_range(cls, values):
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"""Validate date range is logical"""
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start = values.get('start_date')
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end = values.get('end_date')
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if start and end:
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start_dt = datetime.fromisoformat(start)
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end_dt = datetime.fromisoformat(end)
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if end_dt <= start_dt:
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raise ValueError("end_date must be after start_date")
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# Check reasonable range (max 3 years)
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if (end_dt - start_dt).days > 1095:
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raise ValueError("Date range cannot exceed 3 years (1095 days)")
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# Check not in future
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if end_dt > datetime.now():
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raise ValueError("end_date cannot be in the future")
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return values
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class Config:
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schema_extra = {
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"example": {
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"tenant_id": "123e4567-e89b-12d3-a456-426614174000",
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"start_date": "2024-01-01",
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"end_date": "2024-12-31",
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"product_ids": None,
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"force_retrain": False
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}
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}
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class ForecastRequest(BaseModel):
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"""Schema for generating forecasts"""
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tenant_id: UUID = Field(..., description="Tenant identifier")
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product_id: UUID = Field(..., description="Product identifier")
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forecast_days: int = Field(
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default=30,
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ge=1,
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le=365,
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description="Number of days to forecast (1-365)"
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)
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include_regressors: bool = Field(
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default=True,
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description="Include weather and traffic data in forecast"
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)
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confidence_level: float = Field(
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default=0.80,
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ge=0.5,
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le=0.99,
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description="Confidence interval (0.5-0.99)"
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)
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class Config:
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schema_extra = {
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"example": {
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"tenant_id": "123e4567-e89b-12d3-a456-426614174000",
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"product_id": "223e4567-e89b-12d3-a456-426614174000",
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"forecast_days": 30,
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"include_regressors": True,
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"confidence_level": 0.80
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}
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}
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class ModelEvaluationRequest(BaseModel):
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"""Schema for model evaluation"""
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tenant_id: UUID = Field(..., description="Tenant identifier")
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product_id: Optional[UUID] = Field(None, description="Specific product (optional)")
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evaluation_start_date: str = Field(..., description="Evaluation period start")
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evaluation_end_date: str = Field(..., description="Evaluation period end")
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@validator('evaluation_start_date', 'evaluation_end_date')
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def validate_date_format(cls, v):
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try:
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datetime.fromisoformat(v)
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except ValueError:
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raise ValueError(f"Invalid date format: {v}")
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return v
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@root_validator
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def validate_evaluation_period(cls, values):
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start = values.get('evaluation_start_date')
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end = values.get('evaluation_end_date')
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if start and end:
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start_dt = datetime.fromisoformat(start)
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end_dt = datetime.fromisoformat(end)
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if end_dt <= start_dt:
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raise ValueError("evaluation_end_date must be after evaluation_start_date")
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# Minimum 7 days for meaningful evaluation
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if (end_dt - start_dt).days < 7:
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raise ValueError("Evaluation period must be at least 7 days")
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return values
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class BulkTrainingRequest(BaseModel):
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"""Schema for bulk training operations"""
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tenant_ids: List[UUID] = Field(
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...,
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min_items=1,
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max_items=100,
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description="List of tenant IDs (max 100)"
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)
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start_date: Optional[str] = Field(None, description="Common start date")
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end_date: Optional[str] = Field(None, description="Common end date")
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parallel: bool = Field(
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default=True,
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description="Execute training jobs in parallel"
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)
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@validator('tenant_ids')
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def validate_unique_tenants(cls, v):
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if len(v) != len(set(v)):
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raise ValueError("Duplicate tenant IDs not allowed")
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return v
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class HyperparameterOverride(BaseModel):
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"""Schema for manual hyperparameter override"""
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changepoint_prior_scale: Optional[float] = Field(
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None, ge=0.001, le=0.5,
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description="Flexibility of trend changes"
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)
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seasonality_prior_scale: Optional[float] = Field(
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None, ge=0.01, le=10.0,
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description="Strength of seasonality"
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)
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holidays_prior_scale: Optional[float] = Field(
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None, ge=0.01, le=10.0,
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description="Strength of holiday effects"
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)
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seasonality_mode: Optional[str] = Field(
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None,
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description="Seasonality mode",
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regex="^(additive|multiplicative)$"
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)
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daily_seasonality: Optional[bool] = None
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weekly_seasonality: Optional[bool] = None
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yearly_seasonality: Optional[bool] = None
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class Config:
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schema_extra = {
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"example": {
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"changepoint_prior_scale": 0.05,
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"seasonality_prior_scale": 10.0,
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"holidays_prior_scale": 10.0,
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"seasonality_mode": "additive",
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"daily_seasonality": False,
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"weekly_seasonality": True,
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"yearly_seasonality": True
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}
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}
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class AdvancedTrainingRequest(TrainingJobCreateRequest):
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"""Extended training request with advanced options"""
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hyperparameter_override: Optional[HyperparameterOverride] = Field(
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None,
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description="Manual hyperparameter settings (skips optimization)"
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)
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enable_cross_validation: bool = Field(
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default=True,
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description="Enable cross-validation during training"
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)
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cv_folds: int = Field(
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default=3,
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ge=2,
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le=10,
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description="Number of cross-validation folds"
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)
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optimization_trials: Optional[int] = Field(
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None,
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ge=5,
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le=100,
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description="Number of hyperparameter optimization trials (overrides defaults)"
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)
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save_diagnostics: bool = Field(
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default=False,
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description="Save detailed diagnostic plots and metrics"
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)
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class DataQualityCheckRequest(BaseModel):
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"""Schema for data quality validation"""
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tenant_id: UUID = Field(..., description="Tenant identifier")
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start_date: str = Field(..., description="Check period start")
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end_date: str = Field(..., description="Check period end")
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product_ids: Optional[List[UUID]] = Field(
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None,
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description="Specific products to check"
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)
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include_recommendations: bool = Field(
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default=True,
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description="Include improvement recommendations"
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)
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@validator('start_date', 'end_date')
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def validate_date(cls, v):
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try:
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datetime.fromisoformat(v)
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except ValueError:
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raise ValueError(f"Invalid date format: {v}")
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return v
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class ModelQueryParams(BaseModel):
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"""Query parameters for model listing"""
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tenant_id: Optional[UUID] = None
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product_id: Optional[UUID] = None
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is_active: Optional[bool] = None
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is_production: Optional[bool] = None
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model_type: Optional[str] = Field(None, regex="^(prophet|prophet_optimized|lstm|arima)$")
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min_accuracy: Optional[float] = Field(None, ge=0.0, le=1.0)
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created_after: Optional[datetime] = None
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created_before: Optional[datetime] = None
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limit: int = Field(default=100, ge=1, le=1000)
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offset: int = Field(default=0, ge=0)
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class Config:
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schema_extra = {
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"example": {
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"tenant_id": "123e4567-e89b-12d3-a456-426614174000",
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"is_active": True,
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"is_production": True,
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"limit": 50,
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"offset": 0
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}
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}
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def validate_uuid(value: str) -> UUID:
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"""Validate and convert string to UUID"""
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try:
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return UUID(value)
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except (ValueError, AttributeError):
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raise ValueError(f"Invalid UUID format: {value}")
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def validate_date_string(value: str) -> datetime:
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"""Validate and convert date string to datetime"""
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try:
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return datetime.fromisoformat(value)
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except ValueError:
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raise ValueError(f"Invalid date format: {value}. Use ISO format (YYYY-MM-DD)")
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def validate_positive_integer(value: int, field_name: str = "value") -> int:
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"""Validate positive integer"""
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if value <= 0:
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raise ValueError(f"{field_name} must be positive, got {value}")
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return value
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def validate_probability(value: float, field_name: str = "value") -> float:
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"""Validate probability value (0.0-1.0)"""
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if not 0.0 <= value <= 1.0:
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raise ValueError(f"{field_name} must be between 0.0 and 1.0, got {value}")
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return value
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