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| from pydantic import BaseModel, Field | |
| class TrainRequest(BaseModel): | |
| """ | |
| API-level request model for the /train endpoint. | |
| Validates the incoming HTTP request body configuration for model training. | |
| """ | |
| model_name: str = Field( | |
| ..., | |
| description="Name of the scikit-learn model to train (e.g. 'LinearRegression', 'RandomForestClassifier').", | |
| example="LinearRegression" | |
| ) | |
| model_params: dict = Field( | |
| ..., | |
| description="Dictionary of key-value hyperparameter overrides matching the target scikit-learn estimator class.", | |
| example={"fit_intercept": True} | |
| ) | |
| type: str = Field( | |
| ..., | |
| description="Type of training task: either 'regression' or 'classification'.", | |
| example="regression" | |
| ) | |
| make_dataset: dict = Field( | |
| ..., | |
| description="Parameters for synthetic dataset generation using sklearn's make_regression or make_classification.", | |
| example={"n_samples": 100, "n_features": 2, "noise": 0.1} | |
| ) | |
| class Config: | |
| json_schema_extra = { | |
| "examples": [ | |
| { | |
| "model_name": "LinearRegression", | |
| "model_params": {"fit_intercept": True}, | |
| "type": "regression", | |
| "make_dataset": {"n_samples": 100, "n_features": 2, "noise": 0.1} | |
| }, | |
| { | |
| "model_name": "RandomForestClassifier", | |
| "model_params": {"n_estimators": 100, "max_depth": 5}, | |
| "type": "classification", | |
| "make_dataset": {"n_samples": 100, "n_features": 4, "n_informative": 2, "n_classes": 2} | |
| }, | |
| ] | |
| } | |