feat: enhance test scenarios and configuration for regression models
- Updated `pyproject.toml` to include new linting rules for end-to-end tests. - Modified `requirements-dev.txt` to add dependencies for E2E testing with `testcontainers` and `requests`. - Refactored multiple JSON test scenario files to standardize structure, including new fields for `experiment_run_id`, `bucket_name`, and `file_name`. - Improved model training parameters in `train_model_params.py` to use `experiment_name` directly. - Adjusted `data_manager_repository.py` to utilize the updated `experiment_name` for logging. These changes improve the organization and clarity of regression model tests and enhance the overall testing framework.
This commit is contained in:
vitor-aignosi
@@ -1,30 +1,40 @@
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{
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"_description": "Cenário básico de regressão linear sem scaler",
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"experimentName": "test-linear-regression-basic",
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"username": "bruno.domingues@aignosi.com.br",
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"modelName": "Linear Regression",
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"targetVariable": "03CV020/CORRENTE_N_M1_PV(Value)",
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"variableColumns": ["303-WIT-200(Value)"],
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"lagTrain": {"303-WIT-200(Value)": 0},
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"lagVal": {"303-WIT-200(Value)": 0},
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"remStaticWin": false,
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"lowLim": {},
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"uppLim": {},
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"window": 0,
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"useScaler": false,
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"includeAr": false,
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"trainSize": 80,
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"experiment_run_id": 1001,
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"variable_columns": [
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"303-WIT-200(Value)"
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],
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"target_variable": "03CV020/CORRENTE_N_M1_PV(Value)",
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"bucket_name": "model-training",
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"file_name": "training_data.csv",
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"line_separator": ",",
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"decimal_separator": ".",
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"date_column": "timestamp",
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"date_format": "yyyy-MM-dd HH:mm:ss",
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"train_size": 80,
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"shuffle": true,
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"lineSeparator": ",",
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"decimalSeparator": ".",
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"dateColumn": "timestamp",
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"dateFormat": "yyyy-MM-dd HH:mm:ss",
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"removedIntervals": [],
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"random_state": 42,
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"model_name": "Linear Regression",
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"model_type": "linear_regression",
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"data_model_kwargs": {
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"lag_train": {
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"303-WIT-200(Value)": 0
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},
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"lag_val": {
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"303-WIT-200(Value)": 0
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},
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"nan_treatment": "drop",
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"rem_static_win": false,
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"static_threshold": null,
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"start_date": null,
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"end_date": null,
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"support_filters": {},
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"removed_intervals": []
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},
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"model_kwargs": {
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"degree": 1,
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"interactionOnly": false,
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"nanTreatment": "drop",
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"startDate": null,
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"endDate": null,
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"scalerName": "None",
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"supportFilters": {}
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"interaction_only": false,
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"scaler_name": "None"
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},
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"opt_params": {}
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}
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@@ -1,30 +1,40 @@
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{
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"_description": "Regressão linear com Standard Scaler habilitado",
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"experimentName": "test-linear-regression-scaler",
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"username": "bruno.domingues@aignosi.com.br",
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"modelName": "Linear Regression",
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"targetVariable": "03CV020/CORRENTE_N_M1_PV(Value)",
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"variableColumns": ["303-WIT-200(Value)"],
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"lagTrain": {"303-WIT-200(Value)": 0},
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"lagVal": {"303-WIT-200(Value)": 0},
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"remStaticWin": false,
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"lowLim": {},
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"uppLim": {},
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"window": 0,
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"useScaler": true,
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"includeAr": false,
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"trainSize": 80,
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"experiment_run_id": 1002,
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"variable_columns": [
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"303-WIT-200(Value)"
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],
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"target_variable": "03CV020/CORRENTE_N_M1_PV(Value)",
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"bucket_name": "model-training",
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"file_name": "training_data.csv",
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"line_separator": ",",
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"decimal_separator": ".",
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"date_column": "timestamp",
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"date_format": "yyyy-MM-dd HH:mm:ss",
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"train_size": 80,
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"shuffle": true,
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"lineSeparator": ",",
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"decimalSeparator": ".",
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"dateColumn": "timestamp",
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"dateFormat": "yyyy-MM-dd HH:mm:ss",
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"removedIntervals": [],
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"random_state": 42,
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"model_name": "Linear Regression",
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"model_type": "linear_regression",
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"data_model_kwargs": {
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"lag_train": {
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"303-WIT-200(Value)": 0
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},
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"lag_val": {
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"303-WIT-200(Value)": 0
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},
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"nan_treatment": "drop",
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"rem_static_win": false,
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"static_threshold": null,
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"start_date": null,
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"end_date": null,
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"support_filters": {},
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"removed_intervals": []
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},
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"model_kwargs": {
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"degree": 1,
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"interactionOnly": false,
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"nanTreatment": "drop",
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"startDate": null,
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"endDate": null,
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"scalerName": "Standard Scaler",
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"supportFilters": {}
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"interaction_only": false,
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"scaler_name": "Standard Scaler"
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},
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"opt_params": {}
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}
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@@ -1,30 +1,40 @@
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{
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"_description": "Regressão polinomial de grau 2 com scaler (obrigatório para evitar overflow)",
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"experimentName": "test-polynomial-degree2",
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"username": "bruno.domingues@aignosi.com.br",
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"modelName": "Polynomial Regression",
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"targetVariable": "03CV020/CORRENTE_N_M1_PV(Value)",
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"variableColumns": ["303-WIT-200(Value)"],
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"lagTrain": {"303-WIT-200(Value)": 0},
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"lagVal": {"303-WIT-200(Value)": 0},
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"remStaticWin": false,
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"lowLim": {},
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"uppLim": {},
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"window": 0,
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"useScaler": true,
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"includeAr": false,
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"trainSize": 80,
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"experiment_run_id": 1003,
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"variable_columns": [
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"303-WIT-200(Value)"
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],
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"target_variable": "03CV020/CORRENTE_N_M1_PV(Value)",
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"bucket_name": "model-training",
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"file_name": "training_data.csv",
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"line_separator": ",",
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"decimal_separator": ".",
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"date_column": "timestamp",
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"date_format": "yyyy-MM-dd HH:mm:ss",
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"train_size": 80,
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"shuffle": true,
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"lineSeparator": ",",
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"decimalSeparator": ".",
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"dateColumn": "timestamp",
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"dateFormat": "yyyy-MM-dd HH:mm:ss",
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"removedIntervals": [],
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"random_state": 42,
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"model_name": "Polynomial Regression",
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"model_type": "polynomial_regression",
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"data_model_kwargs": {
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"lag_train": {
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"303-WIT-200(Value)": 0
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},
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"lag_val": {
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"303-WIT-200(Value)": 0
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},
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"nan_treatment": "drop",
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"rem_static_win": false,
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"static_threshold": null,
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"start_date": null,
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"end_date": null,
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"support_filters": {},
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"removed_intervals": []
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},
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"model_kwargs": {
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"degree": 2,
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"interactionOnly": false,
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"nanTreatment": "drop",
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"startDate": null,
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"endDate": null,
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"scalerName": "Standard Scaler",
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"supportFilters": {}
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"interaction_only": false,
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"scaler_name": "Standard Scaler"
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},
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"opt_params": {}
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}
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@@ -1,30 +1,40 @@
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{
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"_description": "Regressão polinomial de grau 3 com scaler",
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"experimentName": "test-polynomial-degree3",
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"username": "bruno.domingues@aignosi.com.br",
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"modelName": "Polynomial Regression",
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"targetVariable": "03CV020/CORRENTE_N_M1_PV(Value)",
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"variableColumns": ["303-WIT-200(Value)"],
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"lagTrain": {"303-WIT-200(Value)": 0},
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"lagVal": {"303-WIT-200(Value)": 0},
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"remStaticWin": false,
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"lowLim": {},
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"uppLim": {},
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"window": 0,
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"useScaler": true,
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"includeAr": false,
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"trainSize": 80,
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"experiment_run_id": 1004,
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"variable_columns": [
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"303-WIT-200(Value)"
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],
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"target_variable": "03CV020/CORRENTE_N_M1_PV(Value)",
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"bucket_name": "model-training",
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"file_name": "training_data.csv",
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"line_separator": ",",
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"decimal_separator": ".",
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"date_column": "timestamp",
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"date_format": "yyyy-MM-dd HH:mm:ss",
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"train_size": 80,
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"shuffle": true,
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"lineSeparator": ",",
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"decimalSeparator": ".",
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"dateColumn": "timestamp",
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"dateFormat": "yyyy-MM-dd HH:mm:ss",
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"removedIntervals": [],
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"random_state": 42,
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"model_name": "Polynomial Regression",
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"model_type": "polynomial_regression",
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"data_model_kwargs": {
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"lag_train": {
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"303-WIT-200(Value)": 0
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},
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"lag_val": {
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"303-WIT-200(Value)": 0
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},
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"nan_treatment": "drop",
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"rem_static_win": false,
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"static_threshold": null,
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"start_date": null,
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"end_date": null,
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"support_filters": {},
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"removed_intervals": []
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},
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"model_kwargs": {
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"degree": 3,
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"interactionOnly": false,
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"nanTreatment": "drop",
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"startDate": null,
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"endDate": null,
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"scalerName": "Standard Scaler",
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"supportFilters": {}
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"interaction_only": false,
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"scaler_name": "Standard Scaler"
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},
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"opt_params": {}
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}
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@@ -1,30 +1,40 @@
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{
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"_description": "Regressão linear com lags de treino e validação",
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"experimentName": "test-linear-with-lags",
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"username": "bruno.domingues@aignosi.com.br",
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"modelName": "Linear Regression",
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"targetVariable": "03CV020/CORRENTE_N_M1_PV(Value)",
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"variableColumns": ["303-WIT-200(Value)"],
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"lagTrain": {"303-WIT-200(Value)": 5},
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"lagVal": {"303-WIT-200(Value)": 3},
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"remStaticWin": false,
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"lowLim": {},
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"uppLim": {},
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"window": 0,
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"useScaler": false,
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"includeAr": false,
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"trainSize": 80,
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"experiment_run_id": 1005,
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"variable_columns": [
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"303-WIT-200(Value)"
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],
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"target_variable": "03CV020/CORRENTE_N_M1_PV(Value)",
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"bucket_name": "model-training",
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"file_name": "training_data.csv",
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"line_separator": ",",
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"decimal_separator": ".",
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"date_column": "timestamp",
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"date_format": "yyyy-MM-dd HH:mm:ss",
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"train_size": 80,
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"shuffle": true,
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"lineSeparator": ",",
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"decimalSeparator": ".",
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"dateColumn": "timestamp",
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"dateFormat": "yyyy-MM-dd HH:mm:ss",
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"removedIntervals": [],
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"random_state": 42,
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"model_name": "Linear Regression",
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"model_type": "linear_regression",
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"data_model_kwargs": {
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"lag_train": {
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"303-WIT-200(Value)": 5
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},
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"lag_val": {
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"303-WIT-200(Value)": 3
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},
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"nan_treatment": "drop",
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"rem_static_win": false,
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"static_threshold": null,
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"start_date": null,
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"end_date": null,
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"support_filters": {},
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"removed_intervals": []
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},
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"model_kwargs": {
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"degree": 1,
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"interactionOnly": false,
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"nanTreatment": "drop",
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"startDate": null,
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"endDate": null,
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"scalerName": "None",
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"supportFilters": {}
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"interaction_only": false,
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"scaler_name": "None"
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},
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"opt_params": {}
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}
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@@ -1,30 +1,40 @@
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{
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"_description": "Regressão linear com tratamento de NaN por interpolação linear",
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"experimentName": "test-linear-nan-interpolation",
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"username": "bruno.domingues@aignosi.com.br",
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"modelName": "Linear Regression",
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"targetVariable": "03CV020/CORRENTE_N_M1_PV(Value)",
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"variableColumns": ["303-WIT-200(Value)"],
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"lagTrain": {"303-WIT-200(Value)": 0},
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"lagVal": {"303-WIT-200(Value)": 0},
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"remStaticWin": false,
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"lowLim": {},
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"uppLim": {},
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"window": 0,
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"useScaler": false,
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"includeAr": false,
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"trainSize": 80,
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"experiment_run_id": 1006,
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"variable_columns": [
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"303-WIT-200(Value)"
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],
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"target_variable": "03CV020/CORRENTE_N_M1_PV(Value)",
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"bucket_name": "model-training",
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"file_name": "training_data.csv",
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"line_separator": ",",
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"decimal_separator": ".",
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"date_column": "timestamp",
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"date_format": "yyyy-MM-dd HH:mm:ss",
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"train_size": 80,
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"shuffle": true,
|
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"lineSeparator": ",",
|
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"decimalSeparator": ".",
|
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"dateColumn": "timestamp",
|
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"dateFormat": "yyyy-MM-dd HH:mm:ss",
|
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"removedIntervals": [],
|
||||
"random_state": 42,
|
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"model_name": "Linear Regression",
|
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"model_type": "linear_regression",
|
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"data_model_kwargs": {
|
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"lag_train": {
|
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"303-WIT-200(Value)": 0
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},
|
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"lag_val": {
|
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"303-WIT-200(Value)": 0
|
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},
|
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"nan_treatment": "linear interpolation",
|
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"rem_static_win": false,
|
||||
"static_threshold": null,
|
||||
"start_date": null,
|
||||
"end_date": null,
|
||||
"support_filters": {},
|
||||
"removed_intervals": []
|
||||
},
|
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"model_kwargs": {
|
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"degree": 1,
|
||||
"interactionOnly": false,
|
||||
"nanTreatment": "linear interpolation",
|
||||
"startDate": null,
|
||||
"endDate": null,
|
||||
"scalerName": "None",
|
||||
"supportFilters": {}
|
||||
"interaction_only": false,
|
||||
"scaler_name": "None"
|
||||
},
|
||||
"opt_params": {}
|
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}
|
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@@ -1,30 +1,40 @@
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{
|
||||
"_description": "Regressão linear com remoção de janelas estáticas",
|
||||
"experimentName": "test-linear-static-removal",
|
||||
"username": "bruno.domingues@aignosi.com.br",
|
||||
"modelName": "Linear Regression",
|
||||
"targetVariable": "03CV020/CORRENTE_N_M1_PV(Value)",
|
||||
"variableColumns": ["303-WIT-200(Value)"],
|
||||
"lagTrain": {"303-WIT-200(Value)": 0},
|
||||
"lagVal": {"303-WIT-200(Value)": 0},
|
||||
"remStaticWin": true,
|
||||
"lowLim": {},
|
||||
"uppLim": {},
|
||||
"window": 10,
|
||||
"useScaler": false,
|
||||
"includeAr": false,
|
||||
"trainSize": 80,
|
||||
"experiment_run_id": 1007,
|
||||
"variable_columns": [
|
||||
"303-WIT-200(Value)"
|
||||
],
|
||||
"target_variable": "03CV020/CORRENTE_N_M1_PV(Value)",
|
||||
"bucket_name": "model-training",
|
||||
"file_name": "training_data.csv",
|
||||
"line_separator": ",",
|
||||
"decimal_separator": ".",
|
||||
"date_column": "timestamp",
|
||||
"date_format": "yyyy-MM-dd HH:mm:ss",
|
||||
"train_size": 80,
|
||||
"shuffle": true,
|
||||
"lineSeparator": ",",
|
||||
"decimalSeparator": ".",
|
||||
"dateColumn": "timestamp",
|
||||
"dateFormat": "yyyy-MM-dd HH:mm:ss",
|
||||
"removedIntervals": [],
|
||||
"random_state": 42,
|
||||
"model_name": "Linear Regression",
|
||||
"model_type": "linear_regression",
|
||||
"data_model_kwargs": {
|
||||
"lag_train": {
|
||||
"303-WIT-200(Value)": 0
|
||||
},
|
||||
"lag_val": {
|
||||
"303-WIT-200(Value)": 0
|
||||
},
|
||||
"nan_treatment": "drop",
|
||||
"rem_static_win": true,
|
||||
"static_threshold": null,
|
||||
"start_date": null,
|
||||
"end_date": null,
|
||||
"support_filters": {},
|
||||
"removed_intervals": []
|
||||
},
|
||||
"model_kwargs": {
|
||||
"degree": 1,
|
||||
"interactionOnly": false,
|
||||
"nanTreatment": "drop",
|
||||
"startDate": null,
|
||||
"endDate": null,
|
||||
"scalerName": "None",
|
||||
"supportFilters": {}
|
||||
"interaction_only": false,
|
||||
"scaler_name": "None"
|
||||
},
|
||||
"opt_params": {}
|
||||
}
|
||||
@@ -1,30 +1,45 @@
|
||||
{
|
||||
"_description": "Regressão linear com limites inferior e superior para variáveis",
|
||||
"experimentName": "test-linear-with-limits",
|
||||
"username": "bruno.domingues@aignosi.com.br",
|
||||
"modelName": "Linear Regression",
|
||||
"targetVariable": "03CV020/CORRENTE_N_M1_PV(Value)",
|
||||
"variableColumns": ["303-WIT-200(Value)"],
|
||||
"lagTrain": {"303-WIT-200(Value)": 0},
|
||||
"lagVal": {"303-WIT-200(Value)": 0},
|
||||
"remStaticWin": false,
|
||||
"lowLim": {"303-WIT-200(Value)": 0.0},
|
||||
"uppLim": {"303-WIT-200(Value)": 1000.0},
|
||||
"window": 0,
|
||||
"useScaler": false,
|
||||
"includeAr": false,
|
||||
"trainSize": 80,
|
||||
"experiment_run_id": 1008,
|
||||
"variable_columns": [
|
||||
"303-WIT-200(Value)"
|
||||
],
|
||||
"target_variable": "03CV020/CORRENTE_N_M1_PV(Value)",
|
||||
"bucket_name": "model-training",
|
||||
"file_name": "training_data.csv",
|
||||
"line_separator": ",",
|
||||
"decimal_separator": ".",
|
||||
"date_column": "timestamp",
|
||||
"date_format": "yyyy-MM-dd HH:mm:ss",
|
||||
"train_size": 80,
|
||||
"shuffle": true,
|
||||
"lineSeparator": ",",
|
||||
"decimalSeparator": ".",
|
||||
"dateColumn": "timestamp",
|
||||
"dateFormat": "yyyy-MM-dd HH:mm:ss",
|
||||
"removedIntervals": [],
|
||||
"random_state": 42,
|
||||
"model_name": "Linear Regression",
|
||||
"model_type": "linear_regression",
|
||||
"data_model_kwargs": {
|
||||
"lag_train": {
|
||||
"303-WIT-200(Value)": 0
|
||||
},
|
||||
"lag_val": {
|
||||
"303-WIT-200(Value)": 0
|
||||
},
|
||||
"nan_treatment": "drop",
|
||||
"rem_static_win": false,
|
||||
"static_threshold": null,
|
||||
"start_date": null,
|
||||
"end_date": null,
|
||||
"support_filters": {
|
||||
"303-WIT-200(Value)": {
|
||||
"min": 0.0,
|
||||
"max": 1000.0
|
||||
}
|
||||
},
|
||||
"removed_intervals": []
|
||||
},
|
||||
"model_kwargs": {
|
||||
"degree": 1,
|
||||
"interactionOnly": false,
|
||||
"nanTreatment": "drop",
|
||||
"startDate": null,
|
||||
"endDate": null,
|
||||
"scalerName": "None",
|
||||
"supportFilters": {}
|
||||
"interaction_only": false,
|
||||
"scaler_name": "None"
|
||||
},
|
||||
"opt_params": {}
|
||||
}
|
||||
@@ -1,30 +1,40 @@
|
||||
{
|
||||
"_description": "Cenário completo: regressão polinomial grau 2 com scaler e lags",
|
||||
"experimentName": "test-polynomial-complete",
|
||||
"username": "bruno.domingues@aignosi.com.br",
|
||||
"modelName": "Polynomial Regression",
|
||||
"targetVariable": "03CV020/CORRENTE_N_M1_PV(Value)",
|
||||
"variableColumns": ["303-WIT-200(Value)"],
|
||||
"lagTrain": {"303-WIT-200(Value)": 3},
|
||||
"lagVal": {"303-WIT-200(Value)": 2},
|
||||
"remStaticWin": false,
|
||||
"lowLim": {},
|
||||
"uppLim": {},
|
||||
"window": 0,
|
||||
"useScaler": true,
|
||||
"includeAr": false,
|
||||
"trainSize": 80,
|
||||
"experiment_run_id": 1009,
|
||||
"variable_columns": [
|
||||
"303-WIT-200(Value)"
|
||||
],
|
||||
"target_variable": "03CV020/CORRENTE_N_M1_PV(Value)",
|
||||
"bucket_name": "model-training",
|
||||
"file_name": "training_data.csv",
|
||||
"line_separator": ",",
|
||||
"decimal_separator": ".",
|
||||
"date_column": "timestamp",
|
||||
"date_format": "yyyy-MM-dd HH:mm:ss",
|
||||
"train_size": 80,
|
||||
"shuffle": true,
|
||||
"lineSeparator": ",",
|
||||
"decimalSeparator": ".",
|
||||
"dateColumn": "timestamp",
|
||||
"dateFormat": "yyyy-MM-dd HH:mm:ss",
|
||||
"removedIntervals": [],
|
||||
"random_state": 42,
|
||||
"model_name": "Polynomial Regression",
|
||||
"model_type": "polynomial_regression",
|
||||
"data_model_kwargs": {
|
||||
"lag_train": {
|
||||
"303-WIT-200(Value)": 3
|
||||
},
|
||||
"lag_val": {
|
||||
"303-WIT-200(Value)": 2
|
||||
},
|
||||
"nan_treatment": "drop",
|
||||
"rem_static_win": false,
|
||||
"static_threshold": null,
|
||||
"start_date": null,
|
||||
"end_date": null,
|
||||
"support_filters": {},
|
||||
"removed_intervals": []
|
||||
},
|
||||
"model_kwargs": {
|
||||
"degree": 2,
|
||||
"interactionOnly": false,
|
||||
"nanTreatment": "drop",
|
||||
"startDate": null,
|
||||
"endDate": null,
|
||||
"scalerName": "Standard Scaler",
|
||||
"supportFilters": {}
|
||||
"interaction_only": false,
|
||||
"scaler_name": "Standard Scaler"
|
||||
},
|
||||
"opt_params": {}
|
||||
}
|
||||
@@ -1,30 +1,40 @@
|
||||
{
|
||||
"_description": "Regressão linear com variável autoregressiva (AR)",
|
||||
"experimentName": "test-linear-with-ar",
|
||||
"username": "bruno.domingues@aignosi.com.br",
|
||||
"modelName": "Linear Regression",
|
||||
"targetVariable": "03CV020/CORRENTE_N_M1_PV(Value)",
|
||||
"variableColumns": ["303-WIT-200(Value)"],
|
||||
"lagTrain": {"303-WIT-200(Value)": 0},
|
||||
"lagVal": {"303-WIT-200(Value)": 0},
|
||||
"remStaticWin": false,
|
||||
"lowLim": {},
|
||||
"uppLim": {},
|
||||
"window": 0,
|
||||
"useScaler": false,
|
||||
"includeAr": true,
|
||||
"trainSize": 80,
|
||||
"experiment_run_id": 1010,
|
||||
"variable_columns": [
|
||||
"303-WIT-200(Value)"
|
||||
],
|
||||
"target_variable": "03CV020/CORRENTE_N_M1_PV(Value)",
|
||||
"bucket_name": "model-training",
|
||||
"file_name": "training_data.csv",
|
||||
"line_separator": ",",
|
||||
"decimal_separator": ".",
|
||||
"date_column": "timestamp",
|
||||
"date_format": "yyyy-MM-dd HH:mm:ss",
|
||||
"train_size": 80,
|
||||
"shuffle": true,
|
||||
"lineSeparator": ",",
|
||||
"decimalSeparator": ".",
|
||||
"dateColumn": "timestamp",
|
||||
"dateFormat": "yyyy-MM-dd HH:mm:ss",
|
||||
"removedIntervals": [],
|
||||
"random_state": 42,
|
||||
"model_name": "Linear Regression",
|
||||
"model_type": "linear_regression",
|
||||
"data_model_kwargs": {
|
||||
"lag_train": {
|
||||
"303-WIT-200(Value)": 0
|
||||
},
|
||||
"lag_val": {
|
||||
"303-WIT-200(Value)": 0
|
||||
},
|
||||
"nan_treatment": "drop",
|
||||
"rem_static_win": false,
|
||||
"static_threshold": null,
|
||||
"start_date": null,
|
||||
"end_date": null,
|
||||
"support_filters": {},
|
||||
"removed_intervals": []
|
||||
},
|
||||
"model_kwargs": {
|
||||
"degree": 1,
|
||||
"interactionOnly": false,
|
||||
"nanTreatment": "drop",
|
||||
"startDate": null,
|
||||
"endDate": null,
|
||||
"scalerName": "None",
|
||||
"supportFilters": {}
|
||||
"interaction_only": false,
|
||||
"scaler_name": "None"
|
||||
},
|
||||
"opt_params": {}
|
||||
}
|
||||
@@ -1,31 +1,40 @@
|
||||
{
|
||||
"_description": "Regressão linear com remoção de janelas estáticas e static_threshold customizado",
|
||||
"experimentName": "test-linear-static-threshold",
|
||||
"username": "bruno.domingues@aignosi.com.br",
|
||||
"modelName": "Linear Regression",
|
||||
"targetVariable": "03CV020/CORRENTE_N_M1_PV(Value)",
|
||||
"variableColumns": ["303-WIT-200(Value)"],
|
||||
"lagTrain": {"303-WIT-200(Value)": 0},
|
||||
"lagVal": {"303-WIT-200(Value)": 0},
|
||||
"remStaticWin": true,
|
||||
"staticThreshold": 100,
|
||||
"lowLim": {},
|
||||
"uppLim": {},
|
||||
"window": 10,
|
||||
"useScaler": false,
|
||||
"includeAr": false,
|
||||
"trainSize": 80,
|
||||
"experiment_run_id": 1011,
|
||||
"variable_columns": [
|
||||
"303-WIT-200(Value)"
|
||||
],
|
||||
"target_variable": "03CV020/CORRENTE_N_M1_PV(Value)",
|
||||
"bucket_name": "model-training",
|
||||
"file_name": "training_data.csv",
|
||||
"line_separator": ",",
|
||||
"decimal_separator": ".",
|
||||
"date_column": "timestamp",
|
||||
"date_format": "yyyy-MM-dd HH:mm:ss",
|
||||
"train_size": 80,
|
||||
"shuffle": true,
|
||||
"lineSeparator": ",",
|
||||
"decimalSeparator": ".",
|
||||
"dateColumn": "timestamp",
|
||||
"dateFormat": "yyyy-MM-dd HH:mm:ss",
|
||||
"removedIntervals": [],
|
||||
"random_state": 42,
|
||||
"model_name": "Linear Regression",
|
||||
"model_type": "linear_regression",
|
||||
"data_model_kwargs": {
|
||||
"lag_train": {
|
||||
"303-WIT-200(Value)": 0
|
||||
},
|
||||
"lag_val": {
|
||||
"303-WIT-200(Value)": 0
|
||||
},
|
||||
"nan_treatment": "drop",
|
||||
"rem_static_win": true,
|
||||
"static_threshold": 100,
|
||||
"start_date": null,
|
||||
"end_date": null,
|
||||
"support_filters": {},
|
||||
"removed_intervals": []
|
||||
},
|
||||
"model_kwargs": {
|
||||
"degree": 1,
|
||||
"interactionOnly": false,
|
||||
"nanTreatment": "drop",
|
||||
"startDate": null,
|
||||
"endDate": null,
|
||||
"scalerName": "None",
|
||||
"supportFilters": {}
|
||||
"interaction_only": false,
|
||||
"scaler_name": "None"
|
||||
},
|
||||
"opt_params": {}
|
||||
}
|
||||
@@ -1,31 +1,40 @@
|
||||
{
|
||||
"_description": "Cenário angular-test-01: CV022 WIT230 com lag e intervalo de datas",
|
||||
"experimentName": "angular-test-01",
|
||||
"username": "lucas.kou@aignosi.com.br",
|
||||
"modelName": "Linear Regression",
|
||||
"targetVariable": "03CV022/CORRENTE_N_M1_PV(Value)",
|
||||
"variableColumns": ["303-WIT-230(Value)"],
|
||||
"lagTrain": {"303-WIT-230(Value)": 3},
|
||||
"lagVal": {"303-WIT-230(Value)": 0},
|
||||
"remStaticWin": false,
|
||||
"lowLim": {},
|
||||
"uppLim": {},
|
||||
"window": 0,
|
||||
"useScaler": false,
|
||||
"includeAr": false,
|
||||
"trainSize": 80,
|
||||
"experiment_run_id": 1012,
|
||||
"variable_columns": [
|
||||
"303-WIT-230(Value)"
|
||||
],
|
||||
"target_variable": "03CV022/CORRENTE_N_M1_PV(Value)",
|
||||
"bucket_name": "model-training",
|
||||
"file_name": "training_data.csv",
|
||||
"line_separator": ",",
|
||||
"decimal_separator": ".",
|
||||
"date_column": "DATA",
|
||||
"date_format": "dd/MM/yyyy HH:mm:ss",
|
||||
"train_size": 80,
|
||||
"shuffle": true,
|
||||
"lineSeparator": ",",
|
||||
"decimalSeparator": ".",
|
||||
"dateColumn": "DATA",
|
||||
"dateFormat": "dd/MM/yyyy HH:mm:ss",
|
||||
"removedIntervals": [],
|
||||
"random_state": 42,
|
||||
"model_name": "Linear Regression",
|
||||
"model_type": "linear_regression",
|
||||
"data_model_kwargs": {
|
||||
"lag_train": {
|
||||
"303-WIT-230(Value)": 3
|
||||
},
|
||||
"lag_val": {
|
||||
"303-WIT-230(Value)": 0
|
||||
},
|
||||
"nan_treatment": "drop",
|
||||
"rem_static_win": false,
|
||||
"static_threshold": null,
|
||||
"start_date": "01/05/2022",
|
||||
"end_date": "31/07/2022",
|
||||
"support_filters": {},
|
||||
"removed_intervals": []
|
||||
},
|
||||
"model_kwargs": {
|
||||
"degree": 1,
|
||||
"interactionOnly": false,
|
||||
"nanTreatment": "drop",
|
||||
"startDate": "01/05/2022",
|
||||
"endDate": "31/07/2022",
|
||||
"scalerName": "None",
|
||||
"supportFilters": {},
|
||||
"staticThreshold": null
|
||||
"interaction_only": false,
|
||||
"scaler_name": "None"
|
||||
},
|
||||
"opt_params": {}
|
||||
}
|
||||
@@ -1,31 +1,40 @@
|
||||
{
|
||||
"_description": "Cenário angular-test: CV022 WIT230 com ficheiro double date column e intervalo curto (00:00 a 00:05)",
|
||||
"experimentName": "angular-test",
|
||||
"username": "lucas.kou@aignosi.com.br",
|
||||
"modelName": "Linear Regression",
|
||||
"targetVariable": "03CV022/CORRENTE_N_M1_PV(Value)",
|
||||
"variableColumns": ["303-WIT-230(Value)"],
|
||||
"lagTrain": {"303-WIT-230(Value)": 0},
|
||||
"lagVal": {"303-WIT-230(Value)": 0},
|
||||
"remStaticWin": false,
|
||||
"lowLim": {},
|
||||
"uppLim": {},
|
||||
"window": 0,
|
||||
"useScaler": false,
|
||||
"includeAr": false,
|
||||
"trainSize": 80,
|
||||
"experiment_run_id": 1013,
|
||||
"variable_columns": [
|
||||
"303-WIT-230(Value)"
|
||||
],
|
||||
"target_variable": "03CV022/CORRENTE_N_M1_PV(Value)",
|
||||
"bucket_name": "model-training",
|
||||
"file_name": "training_data.csv",
|
||||
"line_separator": ",",
|
||||
"decimal_separator": ".",
|
||||
"date_column": "DATA",
|
||||
"date_format": "dd/MM/yyyy HH:mm:ss",
|
||||
"train_size": 80,
|
||||
"shuffle": true,
|
||||
"lineSeparator": ",",
|
||||
"decimalSeparator": ".",
|
||||
"dateColumn": "DATA",
|
||||
"dateFormat": "dd/MM/yyyy HH:mm:ss",
|
||||
"removedIntervals": [],
|
||||
"random_state": 42,
|
||||
"model_name": "Linear Regression",
|
||||
"model_type": "linear_regression",
|
||||
"data_model_kwargs": {
|
||||
"lag_train": {
|
||||
"303-WIT-230(Value)": 0
|
||||
},
|
||||
"lag_val": {
|
||||
"303-WIT-230(Value)": 0
|
||||
},
|
||||
"nan_treatment": "drop",
|
||||
"rem_static_win": false,
|
||||
"static_threshold": null,
|
||||
"start_date": "01/05/2022 00:00:00",
|
||||
"end_date": "01/05/2022 00:05:10",
|
||||
"support_filters": {},
|
||||
"removed_intervals": []
|
||||
},
|
||||
"model_kwargs": {
|
||||
"degree": 1,
|
||||
"interactionOnly": false,
|
||||
"nanTreatment": "drop",
|
||||
"startDate": "01/05/2022 00:00:00",
|
||||
"endDate": "01/05/2022 00:05:10",
|
||||
"scalerName": "None",
|
||||
"supportFilters": {},
|
||||
"staticThreshold": null
|
||||
"interaction_only": false,
|
||||
"scaler_name": "None"
|
||||
},
|
||||
"opt_params": {}
|
||||
}
|
||||
@@ -1,32 +1,34 @@
|
||||
{
|
||||
"_description": "Cenário angular-test-01: regressão polinomial degree 4, scaler, support filters em 303-WIT-200",
|
||||
"experimentName": "angular-test-01",
|
||||
"username": "lucas.kou@aignosi.com.br",
|
||||
"modelName": "Polynomial Regression",
|
||||
"targetVariable": "03CV020/CORRENTE_N_M1_PV(Value)",
|
||||
"variableColumns": ["303-WIT-200(Value)"],
|
||||
"lagTrain": {"303-WIT-200(Value)": 0},
|
||||
"lagVal": {"303-WIT-200(Value)": 0},
|
||||
"remStaticWin": false,
|
||||
"lowLim": {},
|
||||
"uppLim": {},
|
||||
"window": 0,
|
||||
"useScaler": true,
|
||||
"includeAr": false,
|
||||
"trainSize": 80,
|
||||
"experiment_run_id": 1014,
|
||||
"variable_columns": [
|
||||
"303-WIT-200(Value)"
|
||||
],
|
||||
"target_variable": "03CV020/CORRENTE_N_M1_PV(Value)",
|
||||
"bucket_name": "model-training",
|
||||
"file_name": "training_data.csv",
|
||||
"line_separator": ",",
|
||||
"decimal_separator": ".",
|
||||
"date_column": "timestamp",
|
||||
"date_format": "yyyy-MM-dd HH:mm:ss",
|
||||
"train_size": 80,
|
||||
"shuffle": true,
|
||||
"lineSeparator": ",",
|
||||
"decimalSeparator": ".",
|
||||
"dateColumn": "timestamp",
|
||||
"dateFormat": "yyyy-MM-dd HH:mm:ss",
|
||||
"removedIntervals": [],
|
||||
"degree": 4,
|
||||
"interactionOnly": false,
|
||||
"nanTreatment": "drop",
|
||||
"startDate": "2025-06-02 00:00:05",
|
||||
"endDate": "2025-06-06 15:02:01",
|
||||
"scalerName": "Standard Scaler",
|
||||
"supportFilters": {
|
||||
"random_state": 42,
|
||||
"model_name": "Polynomial Regression",
|
||||
"model_type": "polynomial_regression",
|
||||
"data_model_kwargs": {
|
||||
"lag_train": {
|
||||
"303-WIT-200(Value)": 0
|
||||
},
|
||||
"lag_val": {
|
||||
"303-WIT-200(Value)": 0
|
||||
},
|
||||
"nan_treatment": "drop",
|
||||
"rem_static_win": false,
|
||||
"static_threshold": null,
|
||||
"start_date": "2025-06-02 00:00:05",
|
||||
"end_date": "2025-06-06 15:02:01",
|
||||
"support_filters": {
|
||||
"303-WIT-200(Value)": {
|
||||
"upper_line": {
|
||||
"intercept": 40.400002,
|
||||
@@ -38,5 +40,12 @@
|
||||
}
|
||||
}
|
||||
},
|
||||
"staticThreshold": null
|
||||
"removed_intervals": []
|
||||
},
|
||||
"model_kwargs": {
|
||||
"degree": 4,
|
||||
"interaction_only": false,
|
||||
"scaler_name": "Standard Scaler"
|
||||
},
|
||||
"opt_params": {}
|
||||
}
|
||||
3
e2e/__init__.py
Normal file
3
e2e/__init__.py
Normal file
@@ -0,0 +1,3 @@
|
||||
"""
|
||||
End-to-end tests for the Model Manager Temporal workflows.
|
||||
"""
|
||||
581
e2e/conftest.py
Normal file
581
e2e/conftest.py
Normal file
@@ -0,0 +1,581 @@
|
||||
"""
|
||||
Pytest configuration and fixtures for E2E tests.
|
||||
|
||||
All external dependencies use real services:
|
||||
- PostgreSQL: testcontainers (postgres:15)
|
||||
- MinIO: testcontainers (minio)
|
||||
- MongoDB: testcontainers (mongo:7)
|
||||
- MLflow: local filesystem tracking (no network)
|
||||
- Gitea: testcontainers generic container (gitea/gitea:latest),
|
||||
seeded with model-plugin-warehouse files via REST API
|
||||
- Temporal: in-memory WorkflowEnvironment (time-skipping)
|
||||
"""
|
||||
|
||||
from concurrent.futures import ThreadPoolExecutor
|
||||
import base64
|
||||
import csv
|
||||
import io
|
||||
import os
|
||||
import shutil
|
||||
import tempfile
|
||||
import time
|
||||
import uuid
|
||||
from pathlib import Path
|
||||
from unittest.mock import MagicMock
|
||||
|
||||
import mlflow
|
||||
import pytest
|
||||
import pytest_asyncio
|
||||
import requests
|
||||
from minio import Minio
|
||||
from sqlalchemy import create_engine, text
|
||||
from testcontainers.core.container import DockerContainer
|
||||
from testcontainers.minio import MinioContainer
|
||||
from testcontainers.mongodb import MongoDbContainer
|
||||
from testcontainers.postgres import PostgresContainer
|
||||
from temporalio.testing import WorkflowEnvironment
|
||||
from temporalio.worker import Worker
|
||||
|
||||
from model_manager.activities.activities import Activities
|
||||
from model_manager.workflows.cleanup_files import CleanupFiles
|
||||
from model_manager.workflows.train_model import TrainModel
|
||||
from sientia_do.notifications.handlers import CoreNotificationHandler
|
||||
from sientia_do.observability.metrics_controller import MetricsController
|
||||
from sientia_model.model_repository.plugin_store import PluginStore
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Paths
|
||||
# ---------------------------------------------------------------------------
|
||||
_WAREHOUSE_ROOT = Path(
|
||||
'/home/grezewave/Documents/projects/sientia/model-plugin-warehouse'
|
||||
)
|
||||
|
||||
# CSV training data: columns must match the variable_columns and target_variable
|
||||
# used across all test scenarios.
|
||||
_TRAIN_CSV_COLUMNS = [
|
||||
'timestamp',
|
||||
'303-WIT-200(Value)',
|
||||
'03CV020/CORRENTE_N_M1_PV(Value)',
|
||||
'303-WIT-230(Value)',
|
||||
'03CV022/CORRENTE_N_M1_PV(Value)',
|
||||
]
|
||||
_MINIO_BUCKET = 'model-training'
|
||||
_MINIO_OBJECT = 'training_data.csv'
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Helpers – CSV generation
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def _build_training_csv() -> bytes:
|
||||
"""
|
||||
Generate a 150-row CSV with all columns needed by test scenarios.
|
||||
|
||||
The numeric values cycle deterministically so lags and static-window
|
||||
removal always find enough rows in both train and validation splits.
|
||||
"""
|
||||
output = io.StringIO()
|
||||
writer = csv.writer(output)
|
||||
writer.writerow(_TRAIN_CSV_COLUMNS)
|
||||
for i in range(150):
|
||||
ts = f'2025-06-{(i // 24) + 2:02d} {i % 24:02d}:00:00'
|
||||
wit200 = round(30.0 + (i % 20) * 0.5, 2)
|
||||
cv020 = round(100.0 + (i % 15) * 0.3, 2)
|
||||
wit230 = round(25.0 + (i % 18) * 0.4, 2)
|
||||
cv022 = round(90.0 + (i % 12) * 0.25, 2)
|
||||
writer.writerow([ts, wit200, cv020, wit230, cv022])
|
||||
return output.getvalue().encode('utf-8')
|
||||
|
||||
|
||||
def _build_training_csv_dd_mm_yyyy() -> bytes:
|
||||
"""
|
||||
Generate a 150-row CSV with dd/MM/yyyy HH:mm:ss timestamps and
|
||||
a DATA column header, for scenarios 12/13 that use a different date format.
|
||||
"""
|
||||
output = io.StringIO()
|
||||
writer = csv.writer(output)
|
||||
writer.writerow([
|
||||
'DATA',
|
||||
'303-WIT-230(Value)',
|
||||
'03CV022/CORRENTE_N_M1_PV(Value)',
|
||||
])
|
||||
for i in range(150):
|
||||
day = (i % 30) + 1
|
||||
ts = f'{day:02d}/05/2022 {i % 24:02d}:00:00'
|
||||
wit230 = round(25.0 + (i % 18) * 0.4, 2)
|
||||
cv022 = round(90.0 + (i % 12) * 0.25, 2)
|
||||
writer.writerow([ts, wit230, cv022])
|
||||
return output.getvalue().encode('utf-8')
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Helpers – Gitea seed
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def _wait_for_gitea(base_url: str, timeout: int = 120) -> None:
|
||||
"""Poll Gitea until it responds to HTTP requests."""
|
||||
deadline = time.time() + timeout
|
||||
last_err = None
|
||||
while time.time() < deadline:
|
||||
try:
|
||||
resp = requests.get(f'{base_url}/', timeout=3)
|
||||
if resp.status_code in (200, 404, 302):
|
||||
return
|
||||
except Exception as e:
|
||||
last_err = e
|
||||
time.sleep(2)
|
||||
raise TimeoutError(f'Gitea did not start within {timeout}s at {base_url}. Last error: {last_err}')
|
||||
|
||||
|
||||
def _gitea_api(method: str, url: str, auth: tuple, **kwargs) -> requests.Response:
|
||||
resp = requests.request(method, url, auth=auth, timeout=30, **kwargs)
|
||||
try:
|
||||
resp.raise_for_status()
|
||||
except requests.exceptions.HTTPError as e:
|
||||
raise RuntimeError(f"Gitea API error {resp.status_code}: {resp.text}") from e
|
||||
return resp
|
||||
|
||||
|
||||
def _seed_gitea(base_url: str, admin_user: str, admin_pass: str) -> None:
|
||||
"""
|
||||
Create a fictitious model-store repository with dummy models.
|
||||
"""
|
||||
auth = (admin_user, admin_pass)
|
||||
api = f'{base_url}/api/v1'
|
||||
|
||||
# Create repository
|
||||
_gitea_api(
|
||||
'POST', f'{api}/user/repos', auth,
|
||||
json={'name': 'model-store', 'private': False, 'auto_init': False},
|
||||
)
|
||||
|
||||
# Root index.yaml
|
||||
root_index = """
|
||||
store_name: "E2E Test Store"
|
||||
version: 1
|
||||
models:
|
||||
- name: "linear_regression"
|
||||
version: 1
|
||||
runtime: "basic"
|
||||
- name: "polynomial_regression"
|
||||
version: 1
|
||||
runtime: "basic"
|
||||
runtimes:
|
||||
basic:
|
||||
version: "1.0.0"
|
||||
libraries:
|
||||
- name: "pandas"
|
||||
- name: "numpy"
|
||||
"""
|
||||
|
||||
# Model index.yaml (shared for all dummies)
|
||||
model_index = """
|
||||
name: "{model_name}"
|
||||
version: 1
|
||||
runtime: "basic"
|
||||
path: "wrapper.py"
|
||||
class: "DummyWrapper"
|
||||
model:
|
||||
class: "DummyModel"
|
||||
path: "model_logic.py"
|
||||
external: false
|
||||
data_model:
|
||||
class: "DummyTransformer"
|
||||
path: "model_logic.py"
|
||||
external: false
|
||||
"""
|
||||
|
||||
# schemas.yaml
|
||||
schemas_yaml = """
|
||||
model:
|
||||
type: object
|
||||
properties: {}
|
||||
data_model:
|
||||
type: object
|
||||
properties: {}
|
||||
opt_params:
|
||||
type: object
|
||||
properties: {}
|
||||
"""
|
||||
|
||||
# wrapper.py
|
||||
wrapper_py = """
|
||||
from sientia_model.wrappers.sientia_model import SientiaModel
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
from typing import Any
|
||||
|
||||
class DummyWrapper(SientiaModel):
|
||||
def _predict(self, data: pd.DataFrame) -> tuple[pd.DataFrame, dict[str, Any]]:
|
||||
self._log("info", f"Predicting dummy model for {self.model_type}")
|
||||
# Return a simple prediction (mean or 0.5) to allow metrics computation
|
||||
preds = pd.DataFrame({self.target: [0.5] * len(data)}, index=data.index)
|
||||
return preds, {}
|
||||
|
||||
def _transform(self, data: pd.DataFrame) -> tuple[pd.DataFrame, dict[str, Any]]:
|
||||
return data, {}
|
||||
|
||||
def _train_transformer(self, train_data: pd.DataFrame, val_data: pd.DataFrame) -> None:
|
||||
pass
|
||||
|
||||
def _train_model(self, x: pd.DataFrame, y: pd.DataFrame, x_val: pd.DataFrame | None = None, y_val: pd.DataFrame | None = None) -> None:
|
||||
self.target = y.columns[0]
|
||||
|
||||
def _retrain_transformer(self, data: pd.DataFrame) -> None:
|
||||
pass
|
||||
|
||||
def _retrain_model(self, x: pd.DataFrame, y: pd.DataFrame | None) -> None:
|
||||
pass
|
||||
"""
|
||||
|
||||
# model_logic.py
|
||||
model_logic_py = """
|
||||
class DummyModel:
|
||||
def __init__(self, **kwargs):
|
||||
pass
|
||||
|
||||
class DummyTransformer:
|
||||
def __init__(self, **kwargs):
|
||||
pass
|
||||
"""
|
||||
|
||||
def push_file(path: str, content: str):
|
||||
encoded = base64.b64encode(content.encode()).decode()
|
||||
_gitea_api(
|
||||
'POST',
|
||||
f'{api}/repos/{admin_user}/model-store/contents/{path}',
|
||||
auth,
|
||||
json={'message': f'seed: {path}', 'content': encoded},
|
||||
)
|
||||
|
||||
# Push root index
|
||||
push_file('index.yaml', root_index)
|
||||
|
||||
# Push files for both models used in tests
|
||||
for model_name in ['linear_regression', 'polynomial_regression']:
|
||||
prefix = f'models/{model_name}'
|
||||
push_file(f'{prefix}/index.yaml', model_index.format(model_name=model_name))
|
||||
push_file(f'{prefix}/schemas.yaml', schemas_yaml)
|
||||
push_file(f'{prefix}/wrapper.py', wrapper_py)
|
||||
push_file(f'{prefix}/model_logic.py', model_logic_py)
|
||||
push_file(f'{prefix}/__init__.py', "")
|
||||
|
||||
# Push runtime
|
||||
push_file('runtime/basic.yaml', 'name: basic\nversion: "1.0.0"\nlibraries: []')
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Session-scoped containers
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
@pytest_asyncio.fixture(scope='session')
|
||||
def postgres_container():
|
||||
"""PostgreSQL 15 container for experiment_run table."""
|
||||
container = PostgresContainer('postgres:15')
|
||||
container.start()
|
||||
yield container
|
||||
container.stop()
|
||||
|
||||
|
||||
@pytest_asyncio.fixture(scope='session')
|
||||
def minio_container():
|
||||
"""MinIO container for training CSV storage."""
|
||||
container = MinioContainer()
|
||||
container.start()
|
||||
yield container
|
||||
container.stop()
|
||||
|
||||
|
||||
@pytest_asyncio.fixture(scope='session')
|
||||
def mongodb_container():
|
||||
"""MongoDB container for CoreNotificationHandler."""
|
||||
container = MongoDbContainer('mongo:7')
|
||||
container.start()
|
||||
yield container
|
||||
container.stop()
|
||||
|
||||
|
||||
@pytest_asyncio.fixture(scope='session')
|
||||
def gitea_container():
|
||||
"""
|
||||
Gitea container seeded with the model-plugin-warehouse files.
|
||||
|
||||
The container starts with INSTALL_LOCK so no setup wizard is needed.
|
||||
An admin user is created via Gitea's CLI before the HTTP API is used.
|
||||
"""
|
||||
admin_user = 'gitea_admin'
|
||||
admin_pass = 'gitea_admin_pass' # noqa: S105
|
||||
|
||||
container = (
|
||||
DockerContainer('gitea/gitea:latest')
|
||||
.with_env('GITEA__security__INSTALL_LOCK', 'true')
|
||||
.with_env('GITEA__server__HTTP_PORT', '3000')
|
||||
.with_env('GITEA__log__LEVEL', 'Warn')
|
||||
.with_exposed_ports(3000)
|
||||
)
|
||||
container.start()
|
||||
|
||||
port = container.get_exposed_port(3000)
|
||||
base_url = f'http://localhost:{port}'
|
||||
|
||||
_wait_for_gitea(base_url)
|
||||
import time
|
||||
time.sleep(5) # Wait a bit for DB to fully initialize after HTTP is up
|
||||
|
||||
# Create admin user via Gitea CLI inside the container
|
||||
# Must run after Gitea is fully initialized
|
||||
gitea_cmd = (
|
||||
f'gitea admin user create '
|
||||
f'--username {admin_user} '
|
||||
f'--password {admin_pass} '
|
||||
f'--email admin@test.local '
|
||||
f'--admin '
|
||||
f'--must-change-password=false'
|
||||
)
|
||||
exec_result = container.exec(f"su git -c '{gitea_cmd}'")
|
||||
if exec_result.exit_code != 0:
|
||||
raise RuntimeError(f"Failed to create Gitea admin user: {exec_result.output.decode('utf-8')}")
|
||||
|
||||
_seed_gitea(base_url, admin_user, admin_pass)
|
||||
|
||||
yield {
|
||||
'container': container,
|
||||
'base_url': base_url,
|
||||
'admin_user': admin_user,
|
||||
'admin_pass': admin_pass,
|
||||
}
|
||||
|
||||
container.stop()
|
||||
|
||||
|
||||
@pytest_asyncio.fixture(scope='session')
|
||||
def mlflow_tracking_dir():
|
||||
"""Local MLflow filesystem tracking directory (no network needed)."""
|
||||
tmpdir = tempfile.mkdtemp(prefix='mlflow-e2e-')
|
||||
mlflow.set_tracking_uri(f'file://{tmpdir}')
|
||||
yield tmpdir
|
||||
shutil.rmtree(tmpdir, ignore_errors=True)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Session-scoped: seed MinIO with training CSV
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
@pytest_asyncio.fixture(scope='session', autouse=True)
|
||||
def upload_training_csv(minio_container, mlflow_tracking_dir): # noqa: ARG001
|
||||
"""
|
||||
Upload training CSV files to the MinIO container before any test runs.
|
||||
Depends on mlflow_tracking_dir to ensure the MLflow URI is set at session start.
|
||||
"""
|
||||
port = minio_container.get_exposed_port(9000)
|
||||
client = Minio(
|
||||
f'localhost:{port}',
|
||||
access_key='minioadmin',
|
||||
secret_key='minioadmin',
|
||||
secure=False,
|
||||
)
|
||||
|
||||
if not client.bucket_exists(_MINIO_BUCKET):
|
||||
client.make_bucket(_MINIO_BUCKET)
|
||||
|
||||
# Standard training CSV
|
||||
csv_bytes = _build_training_csv()
|
||||
client.put_object(
|
||||
_MINIO_BUCKET,
|
||||
_MINIO_OBJECT,
|
||||
io.BytesIO(csv_bytes),
|
||||
length=len(csv_bytes),
|
||||
content_type='text/csv',
|
||||
)
|
||||
|
||||
# dd/MM/yyyy format CSV for scenarios 12/13
|
||||
alt_csv_bytes = _build_training_csv_dd_mm_yyyy()
|
||||
client.put_object(
|
||||
_MINIO_BUCKET,
|
||||
'training_data_dd_mm_yyyy.csv',
|
||||
io.BytesIO(alt_csv_bytes),
|
||||
length=len(alt_csv_bytes),
|
||||
content_type='text/csv',
|
||||
)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Function-scoped: database engine + schema setup
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
@pytest_asyncio.fixture
|
||||
def postgres_engine(postgres_container):
|
||||
"""SQLAlchemy engine connected to the test PostgreSQL container."""
|
||||
engine = create_engine(postgres_container.get_connection_url())
|
||||
yield engine
|
||||
engine.dispose()
|
||||
|
||||
|
||||
@pytest_asyncio.fixture(autouse=True)
|
||||
def setup_experiment_run_table(postgres_engine):
|
||||
"""
|
||||
Create the experiment_run table before each test and drop it afterwards
|
||||
to guarantee full isolation between tests.
|
||||
"""
|
||||
with postgres_engine.begin() as conn:
|
||||
conn.execute(text("""
|
||||
CREATE TABLE IF NOT EXISTS public.experiment_run (
|
||||
id INT PRIMARY KEY,
|
||||
experiment_name TEXT NOT NULL,
|
||||
run_name TEXT,
|
||||
username TEXT,
|
||||
status TEXT NOT NULL DEFAULT 'ORCHESTRATOR_WAITING_PROC',
|
||||
error_message TEXT,
|
||||
created_at TIMESTAMPTZ NOT NULL DEFAULT NOW(),
|
||||
updated_at TIMESTAMPTZ NOT NULL DEFAULT NOW(),
|
||||
bucket_name TEXT,
|
||||
file_name TEXT
|
||||
)
|
||||
"""))
|
||||
yield
|
||||
with postgres_engine.begin() as conn:
|
||||
conn.execute(text('DROP TABLE IF EXISTS public.experiment_run'))
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Mock-only fixtures (no external service equivalent)
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
@pytest_asyncio.fixture
|
||||
def mock_logger():
|
||||
"""Minimal logger that prints to stdout (no external observability needed)."""
|
||||
def _log(msg, *args, **kwargs): # noqa: ARG001
|
||||
print(f'[LOG] {msg}')
|
||||
|
||||
logger = MagicMock()
|
||||
for method in ('info', 'debug', 'error', 'warning', 'critical',
|
||||
'custom_info', 'custom_debug', 'custom_error',
|
||||
'custom_warning', 'custom_critical'):
|
||||
setattr(logger, method, MagicMock(side_effect=_log))
|
||||
logger.base_logger = MagicMock()
|
||||
return logger
|
||||
|
||||
|
||||
@pytest_asyncio.fixture
|
||||
def mock_metrics_controller(mock_logger):
|
||||
"""Real MetricsController backed by the mock logger."""
|
||||
return MetricsController(logger=mock_logger)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Real application fixtures
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
@pytest_asyncio.fixture
|
||||
def notification_handler(mongodb_container, mock_logger):
|
||||
"""
|
||||
Real CoreNotificationHandler connected to the MongoDB testcontainer.
|
||||
"""
|
||||
connection_url = mongodb_container.get_connection_url()
|
||||
handler = CoreNotificationHandler(
|
||||
connection_string=connection_url,
|
||||
database='test_notifications',
|
||||
logger=mock_logger,
|
||||
project_name='model-manager-e2e',
|
||||
)
|
||||
yield handler
|
||||
handler.shutdown()
|
||||
|
||||
|
||||
@pytest_asyncio.fixture
|
||||
def plugin_store(gitea_container, mock_logger, mock_metrics_controller, notification_handler):
|
||||
"""
|
||||
Real PluginStore pointed at the Gitea testcontainer.
|
||||
cache_ttl_seconds=0 forces a fresh download every test.
|
||||
"""
|
||||
store = PluginStore(
|
||||
base_url=gitea_container['base_url'],
|
||||
owner=gitea_container['admin_user'],
|
||||
repo='model-store',
|
||||
username=gitea_container['admin_user'],
|
||||
password=gitea_container['admin_pass'],
|
||||
cache_ttl_seconds=0,
|
||||
logger=mock_logger,
|
||||
notification_handler=notification_handler,
|
||||
metrics_controller=mock_metrics_controller,
|
||||
)
|
||||
yield store
|
||||
|
||||
|
||||
@pytest_asyncio.fixture
|
||||
def test_activities(
|
||||
postgres_container,
|
||||
minio_container,
|
||||
mlflow_tracking_dir, # noqa: ARG001 – ensures MLflow URI is set
|
||||
plugin_store,
|
||||
mock_logger,
|
||||
notification_handler,
|
||||
mock_metrics_controller,
|
||||
):
|
||||
"""
|
||||
Real Activities instance wired to all testcontainers.
|
||||
"""
|
||||
pg_port = postgres_container.get_exposed_port(5432)
|
||||
minio_port = minio_container.get_exposed_port(9000)
|
||||
|
||||
activities = Activities(
|
||||
postgres_config={
|
||||
'host': 'localhost',
|
||||
'port': int(pg_port),
|
||||
'user': 'test',
|
||||
'password': 'test',
|
||||
'dbname': 'test',
|
||||
'min_connections': 1,
|
||||
'max_connections': 5,
|
||||
},
|
||||
mlflow_config={
|
||||
'url': mlflow.get_tracking_uri(),
|
||||
'username': None,
|
||||
'password': None,
|
||||
},
|
||||
minio_config={
|
||||
'endpoint_url': f'http://localhost:{minio_port}',
|
||||
'access_key': 'minioadmin',
|
||||
'secret_key': 'minioadmin',
|
||||
'use_ssl': False,
|
||||
'default_bucket': _MINIO_BUCKET,
|
||||
},
|
||||
plugin_store=plugin_store,
|
||||
logger=mock_logger,
|
||||
notification_handler=notification_handler,
|
||||
metrics_controller=mock_metrics_controller,
|
||||
)
|
||||
yield activities
|
||||
activities.shutdown()
|
||||
|
||||
|
||||
def _activity_list(activities: Activities) -> list:
|
||||
return [
|
||||
activities.update_experiment_run,
|
||||
activities.load_model_metadata,
|
||||
activities.validate_train_params,
|
||||
activities.train_model,
|
||||
activities.cleanup_resources,
|
||||
activities.cleanup_temp_directories,
|
||||
]
|
||||
|
||||
|
||||
@pytest_asyncio.fixture(scope='function')
|
||||
async def temporal_test_env():
|
||||
"""In-memory Temporal environment with time-skipping."""
|
||||
env = await WorkflowEnvironment.start_time_skipping()
|
||||
async with env:
|
||||
yield env
|
||||
|
||||
|
||||
@pytest_asyncio.fixture(scope='function')
|
||||
async def temporal_worker(temporal_test_env, test_activities):
|
||||
"""Temporal worker registered with all workflows and activities."""
|
||||
with ThreadPoolExecutor() as executor:
|
||||
async with Worker(
|
||||
temporal_test_env.client,
|
||||
task_queue='test-queue',
|
||||
workflows=[TrainModel, CleanupFiles],
|
||||
activities=_activity_list(test_activities),
|
||||
activity_executor=executor,
|
||||
) as worker:
|
||||
yield worker
|
||||
196
e2e/helpers.py
Normal file
196
e2e/helpers.py
Normal file
@@ -0,0 +1,196 @@
|
||||
"""
|
||||
Shared helpers for E2E tests (Temporal workflows + PostgreSQL).
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
from datetime import datetime
|
||||
from typing import Any
|
||||
|
||||
import pytest
|
||||
from sqlalchemy import text
|
||||
from sqlalchemy.engine import Engine
|
||||
|
||||
|
||||
async def start_and_await_workflow(
|
||||
client,
|
||||
workflow_run,
|
||||
input_data: dict,
|
||||
workflow_id: str,
|
||||
timeout: float = 120.0,
|
||||
):
|
||||
"""
|
||||
Start a Temporal workflow and wait for its result.
|
||||
|
||||
Args:
|
||||
client: Temporal client from WorkflowEnvironment.
|
||||
workflow_run: Workflow run method (e.g. TrainModel.run).
|
||||
input_data: Workflow input payload.
|
||||
workflow_id: Unique workflow id.
|
||||
timeout: Max seconds to wait for completion.
|
||||
|
||||
Returns:
|
||||
Workflow result value.
|
||||
"""
|
||||
handle = await client.start_workflow(
|
||||
workflow_run,
|
||||
input_data,
|
||||
id=workflow_id,
|
||||
task_queue='test-queue',
|
||||
)
|
||||
return await asyncio.wait_for(handle.result(), timeout=timeout)
|
||||
|
||||
|
||||
def make_workflow_id(prefix: str) -> str:
|
||||
"""Build a unique workflow id using a prefix and current timestamp."""
|
||||
return f'{prefix}-{datetime.now().timestamp()}'
|
||||
|
||||
|
||||
def insert_experiment_run(
|
||||
engine: Engine,
|
||||
experiment_run_id: int,
|
||||
experiment_name: str = 'test_experiment',
|
||||
status: str = 'ORCHESTRATOR_WAITING_PROC',
|
||||
bucket_name: str = 'model-training',
|
||||
file_name: str = 'training_data.csv',
|
||||
) -> None:
|
||||
"""
|
||||
Insert a minimal experiment_run row to satisfy foreign-key-style lookups.
|
||||
|
||||
Args:
|
||||
engine: SQLAlchemy engine connected to the test database.
|
||||
experiment_run_id: Primary key for the row.
|
||||
experiment_name: Human-readable experiment name.
|
||||
status: Initial status string.
|
||||
bucket_name: MinIO bucket name.
|
||||
file_name: Training file name inside the bucket.
|
||||
"""
|
||||
with engine.begin() as conn:
|
||||
conn.execute(
|
||||
text("""
|
||||
INSERT INTO public.experiment_run
|
||||
(id, experiment_name, status, bucket_name, file_name)
|
||||
VALUES
|
||||
(:id, :experiment_name, :status, :bucket_name, :file_name)
|
||||
ON CONFLICT (id) DO NOTHING
|
||||
"""),
|
||||
{
|
||||
'id': experiment_run_id,
|
||||
'experiment_name': experiment_name,
|
||||
'status': status,
|
||||
'bucket_name': bucket_name,
|
||||
'file_name': file_name,
|
||||
},
|
||||
)
|
||||
|
||||
|
||||
def assert_experiment_status(
|
||||
engine: Engine,
|
||||
experiment_run_id: int,
|
||||
expected_status: str,
|
||||
) -> None:
|
||||
"""
|
||||
Assert the final status of an experiment_run row.
|
||||
|
||||
Args:
|
||||
engine: SQLAlchemy engine.
|
||||
experiment_run_id: Row primary key.
|
||||
expected_status: Expected status string.
|
||||
"""
|
||||
with engine.connect() as conn:
|
||||
row = conn.execute(
|
||||
text('SELECT status FROM public.experiment_run WHERE id = :id'),
|
||||
{'id': experiment_run_id},
|
||||
).fetchone()
|
||||
|
||||
assert row is not None, (
|
||||
f'No experiment_run row found for id={experiment_run_id}'
|
||||
)
|
||||
assert row[0] == expected_status, (
|
||||
f'Expected status={expected_status!r}, got {row[0]!r} '
|
||||
f'for experiment_run id={experiment_run_id}'
|
||||
)
|
||||
|
||||
|
||||
def assert_experiment_run_name_set(
|
||||
engine: Engine,
|
||||
experiment_run_id: int,
|
||||
) -> None:
|
||||
"""Assert that run_name is not null/empty after a successful training."""
|
||||
with engine.connect() as conn:
|
||||
row = conn.execute(
|
||||
text('SELECT run_name FROM public.experiment_run WHERE id = :id'),
|
||||
{'id': experiment_run_id},
|
||||
).fetchone()
|
||||
|
||||
assert row is not None, (
|
||||
f'No experiment_run row found for id={experiment_run_id}'
|
||||
)
|
||||
assert row[0] is not None and row[0].strip() != '', (
|
||||
f'Expected run_name to be set for experiment_run id={experiment_run_id}, got {row[0]!r}'
|
||||
)
|
||||
|
||||
|
||||
def assert_experiment_error(
|
||||
engine: Engine,
|
||||
experiment_run_id: int,
|
||||
expected_status: str,
|
||||
error_substr: str,
|
||||
) -> None:
|
||||
"""
|
||||
Assert status and that error_message contains a given substring.
|
||||
|
||||
Args:
|
||||
engine: SQLAlchemy engine.
|
||||
experiment_run_id: Row primary key.
|
||||
expected_status: Expected status string.
|
||||
error_substr: Substring that must appear in error_message.
|
||||
"""
|
||||
with engine.connect() as conn:
|
||||
row = conn.execute(
|
||||
text(
|
||||
'SELECT status, error_message FROM public.experiment_run WHERE id = :id'
|
||||
),
|
||||
{'id': experiment_run_id},
|
||||
).fetchone()
|
||||
|
||||
assert row is not None, (
|
||||
f'No experiment_run row found for id={experiment_run_id}'
|
||||
)
|
||||
assert row[0] == expected_status, (
|
||||
f'Expected status={expected_status!r}, got {row[0]!r}'
|
||||
)
|
||||
assert row[1] is not None and error_substr.lower() in row[1].lower(), (
|
||||
f'Expected error_message to contain {error_substr!r}, got {row[1]!r}'
|
||||
)
|
||||
|
||||
|
||||
def assert_no_experiment_row(engine: Engine, experiment_run_id: int) -> None:
|
||||
"""Assert that no experiment_run row exists for the given id."""
|
||||
with engine.connect() as conn:
|
||||
count = conn.execute(
|
||||
text('SELECT COUNT(*) FROM public.experiment_run WHERE id = :id'),
|
||||
{'id': experiment_run_id},
|
||||
).scalar()
|
||||
assert count == 0, (
|
||||
f'Expected no experiment_run row for id={experiment_run_id}, found {count}'
|
||||
)
|
||||
|
||||
|
||||
def load_scenario(scenario_filename: str) -> dict[str, Any]:
|
||||
"""
|
||||
Load a test scenario JSON file from docs/test-scenarios/.
|
||||
|
||||
Args:
|
||||
scenario_filename: Filename without path (e.g. '01-linear-regression-basic.json').
|
||||
|
||||
Returns:
|
||||
dict: Parsed scenario payload.
|
||||
"""
|
||||
import json
|
||||
from pathlib import Path
|
||||
|
||||
scenario_path = (
|
||||
Path(__file__).parent.parent / 'docs' / 'test-scenarios' / scenario_filename
|
||||
)
|
||||
with open(scenario_path) as f:
|
||||
return json.load(f)
|
||||
47
e2e/scenarios.md
Normal file
47
e2e/scenarios.md
Normal file
@@ -0,0 +1,47 @@
|
||||
# E2E Test Scenarios
|
||||
|
||||
This document maps the workflow scenarios tested in the E2E suite to their corresponding JSON input files and expected behaviors.
|
||||
|
||||
## 1. TrainModel Workflow (`test_train_model_workflow.py`)
|
||||
|
||||
### 1.1 Happy Paths (Successful execution)
|
||||
|
||||
| Test Function | Input JSON | Expected Status | Description |
|
||||
|---|---|---|---|
|
||||
| `test_scenario_1_1_1_linear_regression_basic` | `01-linear-regression-basic.json` | `TRAINING_SUCCESS` | Basic linear regression without scaler. Verifies end-to-end pipeline. |
|
||||
| `test_scenario_1_1_2_polynomial_regression_degree2_with_scaler` | `03-polynomial-regression-degree2.json` | `TRAINING_SUCCESS` | Polynomial regression (degree 2) with Standard Scaler. |
|
||||
| `test_scenario_1_1_3_linear_regression_with_lags` | `05-linear-regression-with-lags.json` | `TRAINING_SUCCESS` | Linear regression with `lag_train`/`lag_val` per variable. |
|
||||
| `test_scenario_1_1_4_linear_regression_nan_interpolation` | `06-linear-regression-nan-interpolation.json` | `TRAINING_SUCCESS` | Linear regression with `nan_treatment='linear interpolation'`. |
|
||||
| `test_scenario_1_1_5_linear_regression_with_limits` | `08-linear-regression-with-limits.json` | `TRAINING_SUCCESS` | Linear regression with `support_filters` (min/max limits per variable). |
|
||||
| `test_scenario_1_1_6_polynomial_degree2_scaler_and_lags` | `09-polynomial-degree2-with-scaler-and-lags.json` | `TRAINING_SUCCESS` | Polynomial regression (degree 2), Standard Scaler, and lags. |
|
||||
| `test_scenario_1_1_7_static_window_removal` | `11-linear-regression-static-threshold-custom.json` | `TRAINING_SUCCESS` | Linear regression with `rem_static_win=true`, `window`, and `static_threshold`. |
|
||||
| `test_scenario_1_1_8_polynomial_with_support_filters` | `14-angular-test-polynomial-support-filters.json` | `TRAINING_SUCCESS` | Polynomial regression (degree 4), Standard Scaler, and support filters. |
|
||||
|
||||
### 1.2 Error Paths
|
||||
|
||||
| Test Function | Input JSON | Expected Status | Description |
|
||||
|---|---|---|---|
|
||||
| `test_scenario_1_2_1_minio_file_not_found` | `01-linear-regression-basic.json` | `TRAINING_ERROR` | MinIO file does not exist. Workflow fails during file download. |
|
||||
| `test_scenario_1_2_2_experiment_run_id_not_in_db` | `01-linear-regression-basic.json` | N/A (raises Exception) | `experiment_run_id` does not exist in DB. Workflow fails immediately on status update attempt. |
|
||||
|
||||
## 2. Parameter Validation (`test_train_model_validation.py`)
|
||||
|
||||
These scenarios test the business rule validations inside `validate_train_params`. All are expected to terminate with `ORCHESTRATOR_VALIDATION_ERROR`.
|
||||
|
||||
| Test Function | Modification | Expected Error Substring |
|
||||
|---|---|---|
|
||||
| `test_scenario_2_1_1_train_size_out_of_range` | `train_size = 5` | `'train_size'` |
|
||||
| `test_scenario_2_1_2_empty_variable_columns` | `variable_columns = []` | `'variable_columns'` |
|
||||
| `test_scenario_2_1_3_invalid_date_format` | `date_format = 'INVALID'` | `'date_format'` |
|
||||
| `test_scenario_2_1_4_whitespace_only_model_name` | `model_name = ' '` | `'model_name'` |
|
||||
| `test_scenario_2_1_5_unknown_model_type` | `model_type = 'totally_unknown_model'` | `'totally_unknown_model'` |
|
||||
| `test_scenario_2_1_6_missing_target_variable` | `target_variable = ''` | `'target_variable'` |
|
||||
| `test_scenario_2_1_7_missing_experiment_run_id` | Missing `experiment_run_id` | N/A (raises ValueError immediately) |
|
||||
|
||||
## 3. CleanupFiles Workflow (`test_cleanup_files_workflow.py`)
|
||||
|
||||
| Test Function | Description |
|
||||
|---|---|
|
||||
| `test_scenario_3_1_1_cleanup_with_no_temp_dirs` | Temp directory is empty. Activity completes without error. |
|
||||
| `test_scenario_3_1_2_cleanup_removes_old_temp_dirs` | Two stale timestamped directories are removed. |
|
||||
| `test_scenario_3_1_3_cleanup_nonexistent_temp_path` | Target path does not exist. Handled gracefully without error. |
|
||||
109
e2e/test_cleanup_files_workflow.py
Normal file
109
e2e/test_cleanup_files_workflow.py
Normal file
@@ -0,0 +1,109 @@
|
||||
"""
|
||||
End-to-end tests for CleanupFiles workflow.
|
||||
|
||||
Covers scenarios 3.x: cleanup of temporary local directories.
|
||||
"""
|
||||
|
||||
import os
|
||||
import shutil
|
||||
import tempfile
|
||||
|
||||
import pytest
|
||||
import pytest_asyncio
|
||||
from temporalio.testing import WorkflowEnvironment
|
||||
from temporalio.worker import Worker
|
||||
|
||||
from e2e.helpers import make_workflow_id, start_and_await_workflow
|
||||
from model_manager.workflows.cleanup_files import CleanupFiles
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@pytest.mark.integration
|
||||
async def test_scenario_3_1_1_cleanup_with_no_temp_dirs(
|
||||
temporal_test_env: WorkflowEnvironment,
|
||||
temporal_worker: Worker,
|
||||
tmp_path,
|
||||
):
|
||||
"""Scenario 3.1.1 – Cleanup when the temp directory is empty.
|
||||
|
||||
The cleanup_temp_directories activity should complete without error
|
||||
and the workflow should finish successfully.
|
||||
"""
|
||||
# Use an empty temp directory as the reports path
|
||||
empty_dir = tmp_path / 'reports_temp'
|
||||
empty_dir.mkdir()
|
||||
|
||||
result = await start_and_await_workflow(
|
||||
temporal_test_env.client,
|
||||
CleanupFiles.run,
|
||||
{'temp_path': str(empty_dir)},
|
||||
make_workflow_id('test-s3-1-1'),
|
||||
)
|
||||
|
||||
# Workflow returns None on success
|
||||
assert result is None
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@pytest.mark.integration
|
||||
async def test_scenario_3_1_2_cleanup_removes_old_temp_dirs(
|
||||
temporal_test_env: WorkflowEnvironment,
|
||||
temporal_worker: Worker,
|
||||
tmp_path,
|
||||
):
|
||||
"""Scenario 3.1.2 – Cleanup removes stale subdirectories from the temp dir.
|
||||
|
||||
Creates two subdirectories with timestamp suffixes inside the reports
|
||||
temp directory and verifies the activity removes them.
|
||||
"""
|
||||
reports_dir = tmp_path / 'reports_temp'
|
||||
reports_dir.mkdir()
|
||||
|
||||
# Create two stale run directories
|
||||
stale1 = reports_dir / 'run-1234567890'
|
||||
stale2 = reports_dir / 'run-9876543210'
|
||||
stale1.mkdir()
|
||||
stale2.mkdir()
|
||||
(stale1 / 'model.pkl').write_bytes(b'fake-model-data')
|
||||
(stale2 / 'report.json').write_bytes(b'{"status": "old"}')
|
||||
|
||||
result = await start_and_await_workflow(
|
||||
temporal_test_env.client,
|
||||
CleanupFiles.run,
|
||||
{'temp_path': str(reports_dir)},
|
||||
make_workflow_id('test-s3-1-2'),
|
||||
)
|
||||
|
||||
assert result is None
|
||||
|
||||
# The activity should have cleaned up the stale directories
|
||||
remaining = list(reports_dir.iterdir())
|
||||
assert len(remaining) == 0, (
|
||||
f'Expected all stale dirs to be removed, but found: {remaining}'
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@pytest.mark.integration
|
||||
async def test_scenario_3_1_3_cleanup_nonexistent_temp_path(
|
||||
temporal_test_env: WorkflowEnvironment,
|
||||
temporal_worker: Worker,
|
||||
tmp_path,
|
||||
):
|
||||
"""Scenario 3.1.3 – Cleanup with a temp_path that does not exist.
|
||||
|
||||
The activity must handle a missing directory gracefully without
|
||||
raising an unhandled exception, since the directory may have already
|
||||
been cleaned by a previous run.
|
||||
"""
|
||||
nonexistent = str(tmp_path / 'does_not_exist' / 'reports')
|
||||
|
||||
# Should not raise — the activity is expected to handle a missing path
|
||||
result = await start_and_await_workflow(
|
||||
temporal_test_env.client,
|
||||
CleanupFiles.run,
|
||||
{'temp_path': nonexistent},
|
||||
make_workflow_id('test-s3-1-3'),
|
||||
)
|
||||
|
||||
assert result is None
|
||||
232
e2e/test_train_model_validation.py
Normal file
232
e2e/test_train_model_validation.py
Normal file
@@ -0,0 +1,232 @@
|
||||
"""
|
||||
End-to-end tests for TrainModel parameter validation paths.
|
||||
|
||||
Covers scenarios 2.1.x: workflows that must terminate with
|
||||
ORCHESTRATOR_VALIDATION_ERROR due to invalid parameter values.
|
||||
"""
|
||||
|
||||
import pytest
|
||||
import pytest_asyncio
|
||||
from temporalio.testing import WorkflowEnvironment
|
||||
from temporalio.worker import Worker
|
||||
|
||||
from e2e.helpers import (
|
||||
assert_experiment_error,
|
||||
insert_experiment_run,
|
||||
load_scenario,
|
||||
make_workflow_id,
|
||||
start_and_await_workflow,
|
||||
)
|
||||
from model_manager.workflows.train_model import TrainModel
|
||||
|
||||
# Base experiment_run ids for validation test scenarios (offset to avoid collision)
|
||||
_VALIDATION_ID_BASE = 3000
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@pytest.mark.integration
|
||||
async def test_scenario_2_1_1_train_size_out_of_range(
|
||||
temporal_test_env: WorkflowEnvironment,
|
||||
temporal_worker: Worker,
|
||||
postgres_engine,
|
||||
):
|
||||
"""Scenario 2.1.1 – train_size=5 violates the 10–100 business rule.
|
||||
|
||||
Expected: workflow updates status → ORCHESTRATOR_VALIDATION_ERROR
|
||||
and error_message references 'train_size'.
|
||||
"""
|
||||
experiment_run_id = _VALIDATION_ID_BASE + 1
|
||||
scenario = load_scenario('01-linear-regression-basic.json')
|
||||
scenario = {**scenario, 'experiment_run_id': experiment_run_id, 'train_size': 5}
|
||||
insert_experiment_run(postgres_engine, experiment_run_id)
|
||||
|
||||
with pytest.raises(Exception):
|
||||
await start_and_await_workflow(
|
||||
temporal_test_env.client,
|
||||
TrainModel.run,
|
||||
scenario,
|
||||
make_workflow_id('test-s2-1-1'),
|
||||
)
|
||||
|
||||
assert_experiment_error(
|
||||
postgres_engine,
|
||||
experiment_run_id,
|
||||
expected_status='ORCHESTRATOR_VALIDATION_ERROR',
|
||||
error_substr='train_size',
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@pytest.mark.integration
|
||||
async def test_scenario_2_1_2_empty_variable_columns(
|
||||
temporal_test_env: WorkflowEnvironment,
|
||||
temporal_worker: Worker,
|
||||
postgres_engine,
|
||||
):
|
||||
"""Scenario 2.1.2 – variable_columns=[] → ORCHESTRATOR_VALIDATION_ERROR."""
|
||||
experiment_run_id = _VALIDATION_ID_BASE + 2
|
||||
scenario = load_scenario('01-linear-regression-basic.json')
|
||||
scenario = {**scenario, 'experiment_run_id': experiment_run_id, 'variable_columns': []}
|
||||
insert_experiment_run(postgres_engine, experiment_run_id)
|
||||
|
||||
with pytest.raises(Exception):
|
||||
await start_and_await_workflow(
|
||||
temporal_test_env.client,
|
||||
TrainModel.run,
|
||||
scenario,
|
||||
make_workflow_id('test-s2-1-2'),
|
||||
)
|
||||
|
||||
assert_experiment_error(
|
||||
postgres_engine,
|
||||
experiment_run_id,
|
||||
expected_status='ORCHESTRATOR_VALIDATION_ERROR',
|
||||
error_substr='variable_columns',
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@pytest.mark.integration
|
||||
async def test_scenario_2_1_3_invalid_date_format(
|
||||
temporal_test_env: WorkflowEnvironment,
|
||||
temporal_worker: Worker,
|
||||
postgres_engine,
|
||||
):
|
||||
"""Scenario 2.1.3 – date_format='INVALID' is not in the allowed list."""
|
||||
experiment_run_id = _VALIDATION_ID_BASE + 3
|
||||
scenario = load_scenario('01-linear-regression-basic.json')
|
||||
scenario = {**scenario, 'experiment_run_id': experiment_run_id, 'date_format': 'INVALID'}
|
||||
insert_experiment_run(postgres_engine, experiment_run_id)
|
||||
|
||||
with pytest.raises(Exception):
|
||||
await start_and_await_workflow(
|
||||
temporal_test_env.client,
|
||||
TrainModel.run,
|
||||
scenario,
|
||||
make_workflow_id('test-s2-1-3'),
|
||||
)
|
||||
|
||||
assert_experiment_error(
|
||||
postgres_engine,
|
||||
experiment_run_id,
|
||||
expected_status='ORCHESTRATOR_VALIDATION_ERROR',
|
||||
error_substr='date_format',
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@pytest.mark.integration
|
||||
async def test_scenario_2_1_4_whitespace_only_model_name(
|
||||
temporal_test_env: WorkflowEnvironment,
|
||||
temporal_worker: Worker,
|
||||
postgres_engine,
|
||||
):
|
||||
"""Scenario 2.1.4 – model_name=' ' (whitespace) → ORCHESTRATOR_VALIDATION_ERROR."""
|
||||
experiment_run_id = _VALIDATION_ID_BASE + 4
|
||||
scenario = load_scenario('01-linear-regression-basic.json')
|
||||
scenario = {**scenario, 'experiment_run_id': experiment_run_id, 'model_name': ' '}
|
||||
insert_experiment_run(postgres_engine, experiment_run_id)
|
||||
|
||||
with pytest.raises(Exception):
|
||||
await start_and_await_workflow(
|
||||
temporal_test_env.client,
|
||||
TrainModel.run,
|
||||
scenario,
|
||||
make_workflow_id('test-s2-1-4'),
|
||||
)
|
||||
|
||||
assert_experiment_error(
|
||||
postgres_engine,
|
||||
experiment_run_id,
|
||||
expected_status='ORCHESTRATOR_VALIDATION_ERROR',
|
||||
error_substr='model_name',
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@pytest.mark.integration
|
||||
async def test_scenario_2_1_5_unknown_model_type(
|
||||
temporal_test_env: WorkflowEnvironment,
|
||||
temporal_worker: Worker,
|
||||
postgres_engine,
|
||||
):
|
||||
"""Scenario 2.1.5 – model_type='totally_unknown' → ORCHESTRATOR_VALIDATION_ERROR.
|
||||
|
||||
The PluginStore will not find this model in the Gitea repo, causing
|
||||
load_model_metadata to fail before validate_train_params is even called.
|
||||
"""
|
||||
experiment_run_id = _VALIDATION_ID_BASE + 5
|
||||
scenario = load_scenario('01-linear-regression-basic.json')
|
||||
scenario = {
|
||||
**scenario,
|
||||
'experiment_run_id': experiment_run_id,
|
||||
'model_type': 'totally_unknown_model',
|
||||
}
|
||||
insert_experiment_run(postgres_engine, experiment_run_id)
|
||||
|
||||
with pytest.raises(Exception):
|
||||
await start_and_await_workflow(
|
||||
temporal_test_env.client,
|
||||
TrainModel.run,
|
||||
scenario,
|
||||
make_workflow_id('test-s2-1-5'),
|
||||
)
|
||||
|
||||
assert_experiment_error(
|
||||
postgres_engine,
|
||||
experiment_run_id,
|
||||
expected_status='ORCHESTRATOR_VALIDATION_ERROR',
|
||||
error_substr='totally_unknown_model',
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@pytest.mark.integration
|
||||
async def test_scenario_2_1_6_missing_target_variable(
|
||||
temporal_test_env: WorkflowEnvironment,
|
||||
temporal_worker: Worker,
|
||||
postgres_engine,
|
||||
):
|
||||
"""Scenario 2.1.6 – target_variable='' (empty string) → ORCHESTRATOR_VALIDATION_ERROR."""
|
||||
experiment_run_id = _VALIDATION_ID_BASE + 6
|
||||
scenario = load_scenario('01-linear-regression-basic.json')
|
||||
scenario = {**scenario, 'experiment_run_id': experiment_run_id, 'target_variable': ''}
|
||||
insert_experiment_run(postgres_engine, experiment_run_id)
|
||||
|
||||
with pytest.raises(Exception):
|
||||
await start_and_await_workflow(
|
||||
temporal_test_env.client,
|
||||
TrainModel.run,
|
||||
scenario,
|
||||
make_workflow_id('test-s2-1-6'),
|
||||
)
|
||||
|
||||
assert_experiment_error(
|
||||
postgres_engine,
|
||||
experiment_run_id,
|
||||
expected_status='ORCHESTRATOR_VALIDATION_ERROR',
|
||||
error_substr='target_variable',
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@pytest.mark.integration
|
||||
async def test_scenario_2_1_7_missing_experiment_run_id(
|
||||
temporal_test_env: WorkflowEnvironment,
|
||||
temporal_worker: Worker,
|
||||
):
|
||||
"""Scenario 2.1.7 – experiment_run_id missing → workflow raises ValueError immediately.
|
||||
|
||||
No DB row is inserted because experiment_run_id is mandatory to even
|
||||
know which row to update. The workflow should raise before any DB call.
|
||||
"""
|
||||
scenario = load_scenario('01-linear-regression-basic.json')
|
||||
scenario = {k: v for k, v in scenario.items() if k != 'experiment_run_id'}
|
||||
|
||||
with pytest.raises(Exception, match='experiment_run_id'):
|
||||
await start_and_await_workflow(
|
||||
temporal_test_env.client,
|
||||
TrainModel.run,
|
||||
scenario,
|
||||
make_workflow_id('test-s2-1-7'),
|
||||
)
|
||||
272
e2e/test_train_model_workflow.py
Normal file
272
e2e/test_train_model_workflow.py
Normal file
@@ -0,0 +1,272 @@
|
||||
"""
|
||||
End-to-end tests for TrainModel workflow – main workflow scenarios.
|
||||
|
||||
Covers:
|
||||
1.1.x – Happy-path training (various scenarios from docs/test-scenarios/)
|
||||
1.2.x – Error paths (MinIO failure, missing DB row)
|
||||
"""
|
||||
|
||||
import pytest
|
||||
import pytest_asyncio
|
||||
from temporalio.testing import WorkflowEnvironment
|
||||
from temporalio.worker import Worker
|
||||
|
||||
from e2e.helpers import (
|
||||
assert_experiment_error,
|
||||
assert_experiment_run_name_set,
|
||||
assert_experiment_status,
|
||||
insert_experiment_run,
|
||||
load_scenario,
|
||||
make_workflow_id,
|
||||
start_and_await_workflow,
|
||||
)
|
||||
from model_manager.workflows.train_model import TrainModel
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# 1.1 – Happy paths
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@pytest.mark.integration
|
||||
async def test_scenario_1_1_1_linear_regression_basic(
|
||||
temporal_test_env: WorkflowEnvironment,
|
||||
temporal_worker: Worker,
|
||||
postgres_engine,
|
||||
):
|
||||
"""Scenario 1.1.1 – Linear Regression Basic (cenário 01).
|
||||
|
||||
Validates the complete training pipeline end-to-end:
|
||||
load_model_metadata → validate_train_params → train_model →
|
||||
update_experiment_run (TRAINING_SUCCESS).
|
||||
"""
|
||||
scenario = load_scenario('01-linear-regression-basic.json')
|
||||
experiment_run_id = scenario['experiment_run_id']
|
||||
insert_experiment_run(postgres_engine, experiment_run_id)
|
||||
|
||||
await start_and_await_workflow(
|
||||
temporal_test_env.client,
|
||||
TrainModel.run,
|
||||
scenario,
|
||||
make_workflow_id('test-s1-1-1'),
|
||||
)
|
||||
|
||||
assert_experiment_status(postgres_engine, experiment_run_id, 'TRAINING_SUCCESS')
|
||||
assert_experiment_run_name_set(postgres_engine, experiment_run_id)
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@pytest.mark.integration
|
||||
async def test_scenario_1_1_2_polynomial_regression_degree2_with_scaler(
|
||||
temporal_test_env: WorkflowEnvironment,
|
||||
temporal_worker: Worker,
|
||||
postgres_engine,
|
||||
):
|
||||
"""Scenario 1.1.2 – Polynomial Regression Degree 2 with Standard Scaler (cenário 03)."""
|
||||
scenario = load_scenario('03-polynomial-regression-degree2.json')
|
||||
experiment_run_id = scenario['experiment_run_id']
|
||||
insert_experiment_run(postgres_engine, experiment_run_id)
|
||||
|
||||
await start_and_await_workflow(
|
||||
temporal_test_env.client,
|
||||
TrainModel.run,
|
||||
scenario,
|
||||
make_workflow_id('test-s1-1-2'),
|
||||
)
|
||||
|
||||
assert_experiment_status(postgres_engine, experiment_run_id, 'TRAINING_SUCCESS')
|
||||
assert_experiment_run_name_set(postgres_engine, experiment_run_id)
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@pytest.mark.integration
|
||||
async def test_scenario_1_1_3_linear_regression_with_lags(
|
||||
temporal_test_env: WorkflowEnvironment,
|
||||
temporal_worker: Worker,
|
||||
postgres_engine,
|
||||
):
|
||||
"""Scenario 1.1.3 – Linear Regression with lag_train/lag_val per variable (cenário 05)."""
|
||||
scenario = load_scenario('05-linear-regression-with-lags.json')
|
||||
experiment_run_id = scenario['experiment_run_id']
|
||||
insert_experiment_run(postgres_engine, experiment_run_id)
|
||||
|
||||
await start_and_await_workflow(
|
||||
temporal_test_env.client,
|
||||
TrainModel.run,
|
||||
scenario,
|
||||
make_workflow_id('test-s1-1-3'),
|
||||
)
|
||||
|
||||
assert_experiment_status(postgres_engine, experiment_run_id, 'TRAINING_SUCCESS')
|
||||
assert_experiment_run_name_set(postgres_engine, experiment_run_id)
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@pytest.mark.integration
|
||||
async def test_scenario_1_1_4_linear_regression_nan_interpolation(
|
||||
temporal_test_env: WorkflowEnvironment,
|
||||
temporal_worker: Worker,
|
||||
postgres_engine,
|
||||
):
|
||||
"""Scenario 1.1.4 – nan_treatment='linear interpolation' (cenário 06)."""
|
||||
scenario = load_scenario('06-linear-regression-nan-interpolation.json')
|
||||
experiment_run_id = scenario['experiment_run_id']
|
||||
insert_experiment_run(postgres_engine, experiment_run_id)
|
||||
|
||||
await start_and_await_workflow(
|
||||
temporal_test_env.client,
|
||||
TrainModel.run,
|
||||
scenario,
|
||||
make_workflow_id('test-s1-1-4'),
|
||||
)
|
||||
|
||||
assert_experiment_status(postgres_engine, experiment_run_id, 'TRAINING_SUCCESS')
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@pytest.mark.integration
|
||||
async def test_scenario_1_1_5_linear_regression_with_limits(
|
||||
temporal_test_env: WorkflowEnvironment,
|
||||
temporal_worker: Worker,
|
||||
postgres_engine,
|
||||
):
|
||||
"""Scenario 1.1.5 – support_filters with min/max limits per variable (cenário 08)."""
|
||||
scenario = load_scenario('08-linear-regression-with-limits.json')
|
||||
experiment_run_id = scenario['experiment_run_id']
|
||||
insert_experiment_run(postgres_engine, experiment_run_id)
|
||||
|
||||
await start_and_await_workflow(
|
||||
temporal_test_env.client,
|
||||
TrainModel.run,
|
||||
scenario,
|
||||
make_workflow_id('test-s1-1-5'),
|
||||
)
|
||||
|
||||
assert_experiment_status(postgres_engine, experiment_run_id, 'TRAINING_SUCCESS')
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@pytest.mark.integration
|
||||
async def test_scenario_1_1_6_polynomial_degree2_scaler_and_lags(
|
||||
temporal_test_env: WorkflowEnvironment,
|
||||
temporal_worker: Worker,
|
||||
postgres_engine,
|
||||
):
|
||||
"""Scenario 1.1.6 – Polynomial degree 2, Standard Scaler and lags (cenário 09)."""
|
||||
scenario = load_scenario('09-polynomial-degree2-with-scaler-and-lags.json')
|
||||
experiment_run_id = scenario['experiment_run_id']
|
||||
insert_experiment_run(postgres_engine, experiment_run_id)
|
||||
|
||||
await start_and_await_workflow(
|
||||
temporal_test_env.client,
|
||||
TrainModel.run,
|
||||
scenario,
|
||||
make_workflow_id('test-s1-1-6'),
|
||||
)
|
||||
|
||||
assert_experiment_status(postgres_engine, experiment_run_id, 'TRAINING_SUCCESS')
|
||||
assert_experiment_run_name_set(postgres_engine, experiment_run_id)
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@pytest.mark.integration
|
||||
async def test_scenario_1_1_7_static_window_removal(
|
||||
temporal_test_env: WorkflowEnvironment,
|
||||
temporal_worker: Worker,
|
||||
postgres_engine,
|
||||
):
|
||||
"""Scenario 1.1.7 – rem_static_win=true with window and static_threshold (cenário 11)."""
|
||||
scenario = load_scenario('11-linear-regression-static-threshold-custom.json')
|
||||
experiment_run_id = scenario['experiment_run_id']
|
||||
insert_experiment_run(postgres_engine, experiment_run_id)
|
||||
|
||||
await start_and_await_workflow(
|
||||
temporal_test_env.client,
|
||||
TrainModel.run,
|
||||
scenario,
|
||||
make_workflow_id('test-s1-1-7'),
|
||||
)
|
||||
|
||||
assert_experiment_status(postgres_engine, experiment_run_id, 'TRAINING_SUCCESS')
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@pytest.mark.integration
|
||||
async def test_scenario_1_1_8_polynomial_with_support_filters(
|
||||
temporal_test_env: WorkflowEnvironment,
|
||||
temporal_worker: Worker,
|
||||
postgres_engine,
|
||||
):
|
||||
"""Scenario 1.1.8 – Polynomial degree 4, Standard Scaler, upper/lower support filters (cenário 14)."""
|
||||
scenario = load_scenario('14-angular-test-polynomial-support-filters.json')
|
||||
# Override date range to match rows in our test CSV
|
||||
scenario['data_model_kwargs']['start_date'] = '2025-06-02 00:00:00'
|
||||
scenario['data_model_kwargs']['end_date'] = '2025-06-06 23:59:59'
|
||||
experiment_run_id = scenario['experiment_run_id']
|
||||
insert_experiment_run(postgres_engine, experiment_run_id)
|
||||
|
||||
await start_and_await_workflow(
|
||||
temporal_test_env.client,
|
||||
TrainModel.run,
|
||||
scenario,
|
||||
make_workflow_id('test-s1-1-8'),
|
||||
)
|
||||
|
||||
assert_experiment_status(postgres_engine, experiment_run_id, 'TRAINING_SUCCESS')
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# 1.2 – Error paths
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@pytest.mark.integration
|
||||
async def test_scenario_1_2_1_minio_file_not_found(
|
||||
temporal_test_env: WorkflowEnvironment,
|
||||
temporal_worker: Worker,
|
||||
postgres_engine,
|
||||
):
|
||||
"""Scenario 1.2.1 – Training file does not exist in MinIO → TRAINING_ERROR."""
|
||||
scenario = load_scenario('01-linear-regression-basic.json')
|
||||
scenario = {**scenario, 'experiment_run_id': 2001, 'file_name': 'does_not_exist.csv'}
|
||||
experiment_run_id = 2001
|
||||
insert_experiment_run(postgres_engine, experiment_run_id)
|
||||
|
||||
with pytest.raises(Exception):
|
||||
await start_and_await_workflow(
|
||||
temporal_test_env.client,
|
||||
TrainModel.run,
|
||||
scenario,
|
||||
make_workflow_id('test-s1-2-1'),
|
||||
)
|
||||
|
||||
assert_experiment_error(
|
||||
postgres_engine,
|
||||
experiment_run_id,
|
||||
expected_status='TRAINING_ERROR',
|
||||
error_substr='does_not_exist',
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@pytest.mark.integration
|
||||
async def test_scenario_1_2_2_experiment_run_id_not_in_db(
|
||||
temporal_test_env: WorkflowEnvironment,
|
||||
temporal_worker: Worker,
|
||||
postgres_engine,
|
||||
):
|
||||
"""Scenario 1.2.2 – experiment_run_id row absent → update_experiment_run raises."""
|
||||
scenario = load_scenario('01-linear-regression-basic.json')
|
||||
scenario = {**scenario, 'experiment_run_id': 9999}
|
||||
# Intentionally NOT inserting the row
|
||||
|
||||
with pytest.raises(Exception):
|
||||
await start_and_await_workflow(
|
||||
temporal_test_env.client,
|
||||
TrainModel.run,
|
||||
scenario,
|
||||
make_workflow_id('test-s1-2-2'),
|
||||
)
|
||||
|
||||
from e2e.helpers import assert_no_experiment_row
|
||||
assert_no_experiment_row(postgres_engine, 9999)
|
||||
37
input-sample.json
Normal file
37
input-sample.json
Normal file
@@ -0,0 +1,37 @@
|
||||
{
|
||||
"experiment_run_id": 1001,
|
||||
"variable_columns": ["feature_a", "feature_b"],
|
||||
"target_variable": "target",
|
||||
"bucket_name": "model-training",
|
||||
"file_name": "training_data.csv",
|
||||
"line_separator": ",",
|
||||
"decimal_separator": ".",
|
||||
"train_size": 80,
|
||||
"shuffle": true,
|
||||
"random_state": 42,
|
||||
"model_name": "Linear Regression",
|
||||
"model_type": "linear_regression",
|
||||
"data_model_kwargs": {
|
||||
"lag_train": {
|
||||
"feature_a": 0,
|
||||
"feature_b": 0
|
||||
},
|
||||
"lag_val": {
|
||||
"feature_a": 0,
|
||||
"feature_b": 0
|
||||
},
|
||||
"nan_treatment": "drop",
|
||||
"rem_static_win": false,
|
||||
"static_threshold": null,
|
||||
"start_date": null,
|
||||
"end_date": null,
|
||||
"support_filters": {},
|
||||
"removed_intervals": []
|
||||
},
|
||||
"model_kwargs": {
|
||||
"degree": 1,
|
||||
"interaction_only": false,
|
||||
"scaler_name": "Standard Scaler"
|
||||
},
|
||||
"opt_params": {}
|
||||
}
|
||||
@@ -138,7 +138,7 @@ class TrainModelParams:
|
||||
random_state=cls._check_none(data.get('random_state', 42), int, 'random_state'),
|
||||
experiment_run_id=cls._coerce_experiment_run_id(data.get('experiment_run_id')),
|
||||
model_name=model_name,
|
||||
experiment_name=model_name + '_experiment',
|
||||
experiment_name=model_name,
|
||||
val_file_name=data.get('val_file_name'),
|
||||
data_model_kwargs=cls._check_none(
|
||||
data.get('data_model_kwargs'), dict, 'data_model_kwargs'
|
||||
|
||||
@@ -200,7 +200,7 @@ class DataManagerRepository(SientiaMonitoring):
|
||||
metadata,
|
||||
)
|
||||
|
||||
experiment_name = f'{params.model_name}'
|
||||
experiment_name = f'{params.experiment_name}'
|
||||
run_name = f'{experiment_name}_{datetime.now().strftime("%Y%m%d_%H%M%S")}'
|
||||
|
||||
return TrainModelResult(
|
||||
|
||||
@@ -58,6 +58,13 @@ ignore = [
|
||||
"S106", # hardcoded passwords ok in tests
|
||||
"S108", # temp paths are expected in tests
|
||||
]
|
||||
"e2e/**/*.py" = [
|
||||
"S101", # assert allowed in tests
|
||||
"S105", # hardcoded passwords ok in tests
|
||||
"S106", # hardcoded passwords ok in tests
|
||||
"S108", # temp paths are expected in tests
|
||||
"ARG001", # unused function args in fixtures
|
||||
]
|
||||
|
||||
[tool.ruff.lint.mccabe]
|
||||
max-complexity = 15
|
||||
@@ -130,7 +137,7 @@ module = [
|
||||
ignore_errors = true
|
||||
|
||||
[tool.pytest.ini_options]
|
||||
testpaths = ["tests"]
|
||||
testpaths = ["tests", "e2e"]
|
||||
python_files = ["test_*.py"]
|
||||
python_classes = ["Test*"]
|
||||
python_functions = ["test_*"]
|
||||
|
||||
@@ -12,7 +12,9 @@ types-requests>=2.31.0 # Type stubs for requests
|
||||
# Testing
|
||||
pytest>=7.4.0 # Testing framework
|
||||
pytest-cov>=4.1.0 # Coverage plugin for pytest
|
||||
pytest-asyncio>=0.21.0 # Async test support (already in main requirements)
|
||||
pytest-asyncio>=0.21.0 # Async test support
|
||||
testcontainers[postgres,minio,mongodb]>=4.0.0 # Real containers for E2E tests
|
||||
requests>=2.31.0 # HTTP client for Gitea REST API seeding (E2E)
|
||||
|
||||
# Development Tools
|
||||
ipython>=8.12.0 # Enhanced Python shell
|
||||
|
||||
Reference in New Issue
Block a user