# 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. |