Code import - branch release/SIENTIAPDE-1645

docs/DB_CV022_WIT230 _double_date_column.csv

@@ -0,0 +1,33 @@
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docs/DB_CV022_WIT230.csv

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docs/scenarios.md

@@ -0,0 +1,66 @@
+# E2E Test Scenarios
+
+This document maps the workflow scenarios tested in the E2E suite to their corresponding JSON input files and expected behaviors.
+
+## Infrastructure (second-pass review)
+
+- **Containers:** PostgreSQL, MinIO, MongoDB, and Gitea via testcontainers; real clients and `Activities` code paths.
+- **MLflow:** `file://` tracking URI (real SDK, no remote server).
+- **Temporal:** `WorkflowEnvironment.start_time_skipping()` — official temporalio test runtime; workflows and activities are not stubbed.
+- **Logging/metrics:** `get_logger` + `MetricsController` (sientia_do); no `unittest.mock` for observability in `e2e/conftest.py`.
+- **Unit tests** under `tests/` may still use mocks where appropriate; that policy is separate from this E2E suite.
+
+## 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_linear_regression_with_scaler` | `02-linear-regression-with-scaler.json` | `TRAINING_SUCCESS` | Linear regression with `Standard Scaler`. |
+| `test_scenario_1_1_3_polynomial_regression_degree2_with_scaler` | `03-polynomial-regression-degree2.json` | `TRAINING_SUCCESS` | Polynomial regression (degree 2) with Standard Scaler. |
+| `test_scenario_1_1_4_polynomial_regression_degree3_with_scaler` | `04-polynomial-regression-degree3.json` | `TRAINING_SUCCESS` | Polynomial regression (degree 3) with Standard Scaler. |
+| `test_scenario_1_1_5_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_6_linear_regression_nan_interpolation` | `06-linear-regression-nan-interpolation.json` | `TRAINING_SUCCESS` | Linear regression with `nan_treatment='linear interpolation'`. |
+| `test_scenario_1_1_7_linear_regression_static_window_removal` | `07-linear-regression-static-window-removal.json` | `TRAINING_SUCCESS` | `rem_static_win=true` with default `static_threshold`. |
+| `test_scenario_1_1_8_linear_regression_with_limits` | `08-linear-regression-with-limits.json` | `TRAINING_SUCCESS` | `support_filters` with `min`/`max` per variable. |
+| `test_scenario_1_1_9_polynomial_degree2_scaler_and_lags` | `09-polynomial-degree2-with-scaler-and-lags.json` | `TRAINING_SUCCESS` | Polynomial (degree 2), Standard Scaler, and lags. |
+| `test_scenario_1_1_10_linear_regression_with_ar_opt_params` | `10-linear-regression-with-ar.json` | `TRAINING_SUCCESS` | `opt_params.include_ar=true` (placeholder for future AR behavior). |
+| `test_scenario_1_1_11_linear_regression_static_threshold_custom` | `11-linear-regression-static-threshold-custom.json` | `TRAINING_SUCCESS` | `rem_static_win=true` with custom `static_threshold`. |
+| `test_scenario_1_1_12_alternate_date_format_dd_mm_yyyy` | `12-angular-test-date-format.json` | `TRAINING_SUCCESS` | `date_column=DATA`, `dd/MM/yyyy` format, object `training_data_dd_mm_yyyy.csv`. |
+| `test_scenario_1_1_13_alternate_csv_narrow_date_window` | `13-angular-test-double-date-column.json` | `TRAINING_SUCCESS` | Same alternate CSV with a bounded `start_date`/`end_date` window. |
+| `test_scenario_1_1_14_polynomial_with_support_filters` | `14-angular-test-polynomial-support-filters.json` | `TRAINING_SUCCESS` | Polynomial (degree 4), scaler, `upper_line`/`lower_line` support filters. |
+| `test_scenario_1_1_15_linear_regression_custom_target_column_name` | `15-linear-regression-custom-target-column.json` | `TRAINING_SUCCESS` | Custom `target_variable` column name (not literal ``target``); Evidently/report columns must match. |
+| `test_scenario_1_1_16_naive_timestamp_header_column` | `16-linear-regression-naive-timestamp-header.json` | `TRAINING_SUCCESS` | `date_column`=`Timestamp`, naive CSV `training_data_timestamp_naive.csv`. |
+| `test_scenario_1_1_17_linear_regression_blank_timestamp_row_dropped` | `17-linear-regression-blank-timestamp-row.json` | `TRAINING_SUCCESS` | One empty timestamp cell; row dropped before index. |
+
+### 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) |
+| `test_scenario_2_1_8_missing_date_column` | Missing `date_column` | N/A (raises ValueError immediately) |
+| `test_scenario_2_1_9_whitespace_date_column` | `date_column = '   '` | `'date_column'` |
+
+## 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 directories matching `name_YYYYMMDD_HHMMSS_microseconds` are removed when older than retention. |
+| `test_scenario_3_1_3_cleanup_nonexistent_temp_path` | Target path does not exist. Handled gracefully without error. |

docs/test-scenarios/01-linear-regression-basic.json

@@ -0,0 +1,40 @@
+{
+  "_description": "Cenário básico de regressão linear sem scaler",
+  "experiment_run_id": 1001,
+  "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,
+  "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,
+    "interaction_only": false,
+    "scaler_name": "None"
+  },
+  "opt_params": {}
+}

docs/test-scenarios/02-linear-regression-with-scaler.json

@@ -0,0 +1,40 @@
+{
+  "_description": "Regressão linear com Standard Scaler habilitado",
+  "experiment_run_id": 1002,
+  "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,
+  "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,
+    "interaction_only": false,
+    "scaler_name": "Standard Scaler"
+  },
+  "opt_params": {}
+}

docs/test-scenarios/03-polynomial-regression-degree2.json

@@ -0,0 +1,40 @@
+{
+  "_description": "Regressão polinomial de grau 2 com scaler (obrigatório para evitar overflow)",
+  "experiment_run_id": 1003,
+  "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,
+  "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": null,
+    "end_date": null,
+    "support_filters": {},
+    "removed_intervals": []
+  },
+  "model_kwargs": {
+    "degree": 2,
+    "interaction_only": false,
+    "scaler_name": "Standard Scaler"
+  },
+  "opt_params": {}
+}

docs/test-scenarios/04-polynomial-regression-degree3.json

@@ -0,0 +1,40 @@
+{
+  "_description": "Regressão polinomial de grau 3 com scaler",
+  "experiment_run_id": 1004,
+  "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,
+  "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": null,
+    "end_date": null,
+    "support_filters": {},
+    "removed_intervals": []
+  },
+  "model_kwargs": {
+    "degree": 3,
+    "interaction_only": false,
+    "scaler_name": "Standard Scaler"
+  },
+  "opt_params": {}
+}

docs/test-scenarios/05-linear-regression-with-lags.json

@@ -0,0 +1,40 @@
+{
+  "_description": "Regressão linear com lags de treino e validação",
+  "experiment_run_id": 1005,
+  "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,
+  "random_state": 42,
+  "model_name": "Linear Regression",
+  "model_type": "linear_regression",
+  "data_model_kwargs": {
+    "lag_train": {
+      "303-WIT-200(Value)": 5
+    },
+    "lag_val": {
+      "303-WIT-200(Value)": 3
+    },
+    "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": "None"
+  },
+  "opt_params": {}
+}

docs/test-scenarios/06-linear-regression-nan-interpolation.json

@@ -0,0 +1,40 @@
+{
+  "_description": "Regressão linear com tratamento de NaN por interpolação linear",
+  "experiment_run_id": 1006,
+  "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,
+  "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": "linear interpolation",
+    "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": "None"
+  },
+  "opt_params": {}
+}

docs/test-scenarios/07-linear-regression-static-window-removal.json

@@ -0,0 +1,40 @@
+{
+  "_description": "Regressão linear com remoção de janelas estáticas",
+  "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,
+  "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,
+    "interaction_only": false,
+    "scaler_name": "None"
+  },
+  "opt_params": {}
+}

docs/test-scenarios/08-linear-regression-with-limits.json

@@ -0,0 +1,45 @@
+{
+  "_description": "Regressão linear com limites inferior e superior para variáveis",
+  "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,
+  "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,
+    "interaction_only": false,
+    "scaler_name": "None"
+  },
+  "opt_params": {}
+}

docs/test-scenarios/09-polynomial-degree2-with-scaler-and-lags.json

@@ -0,0 +1,40 @@
+{
+  "_description": "Cenário completo: regressão polinomial grau 2 com scaler e lags",
+  "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,
+  "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,
+    "interaction_only": false,
+    "scaler_name": "Standard Scaler"
+  },
+  "opt_params": {}
+}

docs/test-scenarios/10-linear-regression-with-ar.json

@@ -0,0 +1,42 @@
+{
+  "_description": "Linear regression placeholder for autoregressive features; include_ar is reserved for future wrapper support (see opt_params).",
+  "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,
+  "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,
+    "interaction_only": false,
+    "scaler_name": "None"
+  },
+  "opt_params": {
+    "include_ar": true
+  }
+}

docs/test-scenarios/11-linear-regression-static-threshold-custom.json

@@ -0,0 +1,40 @@
+{
+  "_description": "Regressão linear com remoção de janelas estáticas e static_threshold customizado",
+  "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,
+  "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,
+    "interaction_only": false,
+    "scaler_name": "None"
+  },
+  "opt_params": {}
+}

docs/test-scenarios/12-angular-test-date-format.json

@@ -0,0 +1,40 @@
+{
+  "_description": "Alternate date column (DATA) and dd/MM/yyyy HH:mm:ss format; uses MinIO object training_data_dd_mm_yyyy.csv from E2E fixtures.",
+  "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_dd_mm_yyyy.csv",
+  "line_separator": ",",
+  "decimal_separator": ".",
+  "date_column": "DATA",
+  "date_format": "dd/MM/yyyy HH:mm:ss",
+  "train_size": 80,
+  "shuffle": true,
+  "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,
+    "interaction_only": false,
+    "scaler_name": "None"
+  },
+  "opt_params": {}
+}

docs/test-scenarios/13-angular-test-double-date-column.json

@@ -0,0 +1,40 @@
+{
+  "_description": "Same alternate CSV as scenario 12 (DATA + dd/MM/yyyy); narrow date window for regression coverage. Not a multi-date-column dataset.",
+  "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_dd_mm_yyyy.csv",
+  "line_separator": ",",
+  "decimal_separator": ".",
+  "date_column": "DATA",
+  "date_format": "dd/MM/yyyy HH:mm:ss",
+  "train_size": 80,
+  "shuffle": true,
+  "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": "31/05/2022 23:59:59",
+    "support_filters": {},
+    "removed_intervals": []
+  },
+  "model_kwargs": {
+    "degree": 1,
+    "interaction_only": false,
+    "scaler_name": "None"
+  },
+  "opt_params": {}
+}

docs/test-scenarios/14-angular-test-polynomial-support-filters.json

@@ -0,0 +1,51 @@
+{
+  "_description": "Cenário angular-test-01: regressão polinomial degree 4, scaler, support filters em 303-WIT-200",
+  "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,
+  "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 00:00:00",
+    "end_date": "2025-06-08 23:59:59",
+    "support_filters": {
+      "303-WIT-200(Value)": {
+        "upper_line": {
+          "intercept": 40.400002,
+          "angle": 0
+        },
+        "lower_line": {
+          "intercept": 30.5,
+          "angle": 0
+        }
+      }
+    },
+    "removed_intervals": []
+  },
+  "model_kwargs": {
+    "degree": 4,
+    "interaction_only": false,
+    "scaler_name": "Standard Scaler"
+  },
+  "opt_params": {}
+}

docs/test-scenarios/15-linear-regression-custom-target-column.json

@@ -0,0 +1,40 @@
+{
+  "_description": "Target column name is not ``target``; report/Evidently sections must use params.target_variable.",
+  "experiment_run_id": 1015,
+  "variable_columns": [
+    "303-WIT-200(Value)"
+  ],
+  "target_variable": "MY_CUSTOM_TARGET_COLUMN",
+  "bucket_name": "model-training",
+  "file_name": "training_data_custom_target.csv",
+  "line_separator": ",",
+  "decimal_separator": ".",
+  "date_column": "timestamp",
+  "date_format": "yyyy-MM-dd HH:mm:ss",
+  "train_size": 80,
+  "shuffle": true,
+  "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,
+    "interaction_only": false,
+    "scaler_name": "None"
+  },
+  "opt_params": {}
+}

docs/test-scenarios/16-linear-regression-naive-timestamp-header.json

@@ -0,0 +1,40 @@
+{
+  "_description": "Naive Timestamp column header; snake_case date_column/date_format and training_data_timestamp_naive.csv.",
+  "experiment_run_id": 1016,
+  "variable_columns": [
+    "303-WIT-200(Value)"
+  ],
+  "target_variable": "03CV020/CORRENTE_N_M1_PV(Value)",
+  "bucket_name": "model-training",
+  "file_name": "training_data_timestamp_naive.csv",
+  "line_separator": ",",
+  "decimal_separator": ".",
+  "date_column": "Timestamp",
+  "date_format": "yyyy-MM-dd HH:mm:ss",
+  "train_size": 80,
+  "shuffle": true,
+  "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,
+    "interaction_only": false,
+    "scaler_name": "None"
+  },
+  "opt_params": {}
+}

docs/test-scenarios/17-linear-regression-blank-timestamp-row.json

@@ -0,0 +1,40 @@
+{
+  "_description": "One CSV row has an empty timestamp; pipeline should drop it and continue training.",
+  "experiment_run_id": 1017,
+  "variable_columns": [
+    "303-WIT-200(Value)"
+  ],
+  "target_variable": "03CV020/CORRENTE_N_M1_PV(Value)",
+  "bucket_name": "model-training",
+  "file_name": "training_data_blank_timestamp_row.csv",
+  "line_separator": ",",
+  "decimal_separator": ".",
+  "date_column": "timestamp",
+  "date_format": "yyyy-MM-dd HH:mm:ss",
+  "train_size": 80,
+  "shuffle": true,
+  "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,
+    "interaction_only": false,
+    "scaler_name": "None"
+  },
+  "opt_params": {}
+}

docs/train-model-workflow-io-diff-main-vs-current-branch.md

@@ -0,0 +1,417 @@
+# Train Model Workflow IO Diff (`main` vs current branch)
+
+Base comparison: `git diff main...HEAD`
+Workflow analyzed: `train_model`
+
+## 1) Executive overview
+
+This branch introduces a structural refactor of the training stack and a contract update for workflow input/output.
+
+Main impacts:
+
+- The old in-house training stack (`TrainingRepository`, `ModelRepository`, `StorageRepository`, `model_manager.sientia.models`) was replaced by:
+  - `DataManagerRepository` (data prep + metrics + report generation)
+  - `SientiaModel` wrapper from plugin store (`sientia_model`)
+  - `SientiaMLflowRepository` (MLflow integration)
+  - `MinioRepository` (storage integration)
+- Input contract moved from many fixed legacy ML params to a plugin/wrapper-oriented schema (`model_type`, `*_kwargs`, `model_metadata`, optional `val_file_name`).
+- Workflow return changed from `None` to a serializable result object (`dict[str, Any] | None`) containing training execution metadata.
+- Queue naming and worker bootstrap architecture now depend on runtime (`train_model-<runtime>-queue`).
+
+---
+
+## 2) Input contract diff (before vs now)
+
+### 2.1 Previous contract (`main`)
+
+`TrainModelParams` in `main` required a large set of explicit fields for the old preprocessing/model pipeline, focused only in linear regression model:
+
+- Core:
+  - `experiment_run_id`, `variable_columns`, `target_variable`
+  - `bucket_name`, `file_name`, `line_separator`, `decimal_separator`
+  - `train_size`, `shuffle`
+- Legacy preprocessing/model fields focused only in linear regression model (required in `from_dict`):
+  - `lag_train`, `lag_val`
+  - `rem_static_win`, `low_lim`, `upp_lim`, `window`
+  - `use_scaler`, `include_ar`, `scaler_name`
+  - `removed_intervals`, `start_date`, `end_date`, `nan_treatment`
+  - `degree`, `interaction_only`
+  - `experiment_name`, `model_name`
+  - `support_filters` (optional dict), `static_threshold` (optional int)
+
+Validation was strongly tied to this structure (lag ranges, limits consistency, polynomial/scaler constraints, etc.).
+
+### 2.2 Current contract (this branch)
+
+`TrainModelParams` now supports a plugin-driven schema and wrapper kwargs:
+
+- Kept/mandatory core fields:
+  - `experiment_run_id` (now accepts numeric string too; coerced to int)
+  - `variable_columns`, `target_variable`
+  - `bucket_name`, `file_name`, `line_separator`, `decimal_separator`
+  - `train_size`, `shuffle`
+  - `model_name`
+  - `model_type`
+  - `data_model_kwargs`, `model_kwargs`, `opt_params` (required as dict by current `from_dict`)
+- New/updated fields:
+  - `random_state` (default `42`)
+  - `val_file_name` (optional explicit validation file)
+  - `model_id` (currently optional, but needs discussion, since the model metadata in MongoDB should be created before the model training)
+- Removed from required input contract:
+  - `lag_train`, `lag_val`, `rem_static_win`, `low_lim`, `upp_lim`, `window`
+  - `use_scaler`, `include_ar`
+  - `degree`, `interaction_only`, `nan_treatment`
+  - `start_date`, `end_date`, `scaler_name`
+  - `removed_intervals`, `support_filters`, `static_threshold`
+- Parameters internally derived:
+  - `model_metadata` model type info from plugin store.
+  - `run_name` is internally derived from experiment name and datetime.
+  - `experiment_name` is internally derived from `model_name`.
+
+### 2.3 Validation behavior changes
+
+Before:
+- Validation was mostly hardcoded business checks tied to legacy linear/polynomial stack.
+
+Now:
+- Validation still checks core constraints (`train_size`, non-empty strings, etc.), but model-specific validation moved to JSON Schema driven checks, using OpenAPI/JSON Schema definitions from plugin store:
+  - `model_metadata.schemas.components.schemas.data_model`
+  - `model_metadata.schemas.components.schemas.model`
+  - `model_metadata.schemas.components.schemas.opt_params`
+- `model_metadata` is now a required semantic dependency for `validate_business_rules()`.
+- Date format validation remains, but allowed formats are defined locally in `train_model_params.py`.
+
+### 2.4 Input loading pipeline changes in workflow
+
+Before:
+- `validate_train_params` directly consumed workflow input.
+
+Now:
+1. `load_model_metadata` runs first (fetches model index/schema from plugin store and injects `model_metadata`).
+2. `validate_train_params` runs with enriched payload.
+
+This means IO preprocessing now depends on plugin-store metadata resolution before final validation.
+
+---
+
+## 3) Output contract diff (before vs now)
+
+### 3.1 Workflow return (`train_model.run`)
+
+Before (`main`):
+- Return type: `None`
+- Workflow side effects were persisted mainly via DB status updates and MLflow artifacts.
+
+Now:
+- Return type: `dict[str, Any] | None`
+- Workflow returns the training activity summary when successful.
+
+### 3.2 Activity-level training result payload
+
+Before (from `Training.train_model` in `main` path):
+- Returned minimal dict:
+  - `run_name`
+  - `run_dir`
+
+Now:
+- Returns extended dict:
+  - `run_name`
+  - `experiment_name`
+  - `run_id`
+  - `run_dir`
+
+### 3.3 Persistence map by destination (DB, MLflow, MinIO, local filesystem)
+
+This section maps where each artifact/metadata goes, in which format, and how that changed from `main`.
+
+#### 3.3.1 PostgreSQL (`experiment_run` table)
+
+## Before (`main`)
+
+- Update path: `update_experiment_run` activity with `UpdateType.MODEL_SAVED`.
+- Persisted on success:
+  - `status` transition to `TRAINING_SUCCESS`
+  - `run_name` (MLflow run identifier used by current implementation)
+- Persisted on failures:
+  - `status` transition to validation/training error statuses
+  - `error_message`
+
+## Now (current branch)
+
+- Same update path and status/error behavior.
+- Even though train activity now returns more metadata (`run_id`, `experiment_name`), current workflow update for `MODEL_SAVED` still forwards mainly `run_name`.
+- Practical effect:
+  - DB remains status-centric and run-name-centric
+  - richer identifiers exist in workflow return payload, not fully mirrored to DB columns in current flow
+
+#### 3.3.2 MLflow (tracking server/artifact store)
+
+## Before (`main`)
+
+- Persistence orchestration lived in `ModelRepository.save_model()` + `_save_run()`.
+- Typical persisted content:
+  - model params (many legacy params such as lags, limits, scaler config, removed intervals)
+  - regression metrics (`MSE`, `R2`, `MAE`)
+  - model objects:
+    - `data_model`
+    - `prediction_model`
+  - artifacts:
+    - `report.html`
+    - `train_data.csv`
+    - `test_data.csv`
+    - optional `model_equation.json`
+- Run naming:
+  - computed by querying existing runs and appending sequence (`<experiment>-<n>` style)
+
+## Now (current branch)
+
+- Persistence orchestrated in `Training._persist_training_artifacts()` and MLflow run context is opened by `SientiaMLflowRepository.start_run(...)`.
+- Persisted content now:
+  - model wrapper itself via `wrapper.store_model(name=train_params.model_name)`
+  - regression metrics also logged as MLflow params via `mlflow.log_param(...)`:
+    - `mse_val`
+    - `mae_val`
+    - `r2_val`
+  - artifacts explicitly logged with `mlflow.log_artifact(...)`:
+    - `report.html`
+    - `train_data.csv`
+    - `test_data.csv`
+  - metrics are computed before save (`mse_val`, `mae_val`, `r2_val`) and persisted in the run as params
+- Run identifiers now exposed back to workflow:
+  - `experiment_name`
+  - `run_name`
+  - `run_id`
+- Notable behavioral change:
+  - `wrapper._input_example` is cleared (`None`) before storing model.
+
+#### 3.3.3 MinIO object storage
+
+## Before (`main`)
+
+- Read path:
+  - single source object downloaded via `StorageRepository.fetch_file(bucket_name, file_name)`
+- Write path:
+  - training workflow did not write generated outputs to MinIO in this code path
+  - generated artifacts were persisted to MLflow, not uploaded back to MinIO
+- Location:
+  - source data in input bucket/key provided by workflow input (`bucket_name` + `file_name`)
+
+## Now (current branch)
+
+- Read path migrated to `MinioRepository.download_file(...)`.
+- Supports two input objects:
+  - mandatory training object: `bucket_name` + `file_name`
+  - optional validation object: same `bucket_name` + `val_file_name`
+- Write path:
+  - still no artifact upload to MinIO in this workflow path
+  - report/CSV outputs continue to flow to MLflow artifacts
+- Location details:
+  - bucket resolved from payload (`bucket_name`)
+  - object key exactly from payload (`file_name`, optional `val_file_name`)
+  - default bucket in env/config is `MINIO_DEFAULT_BUCKET`, but runtime payload can override via `bucket_name`
+
+#### 3.3.4 Local filesystem (ephemeral runtime workspace)
+
+## Before (`main`)
+
+- Temporary run dir created under reports root using run name + timestamp suffix.
+- Artifacts generated locally in that directory:
+  - `report.html`
+  - `train_data.csv`
+  - `test_data.csv`
+  - optional `model_equation.json`
+- After MLflow logging, cleanup activity removed temp directory.
+
+## Now (current branch)
+
+- Temporary run dir managed by `DataManagerRepository` under runtime reports root (`.../reports/temp/<run_name>`).
+- Same artifact family generated locally:
+  - `report.html`
+  - `train_data.csv`
+  - `test_data.csv`
+  - optional `model_equation.json` (for `linear_regression`)
+- Cleanup behavior is now tolerant:
+  - cleanup runs in guarded `finally`
+  - training success is not reverted if cleanup later fails
+
+#### 3.3.5 Quick matrix (before vs now)
+
+- **Postgres**
+  - before: status + run_name + errors
+  - now: same persisted shape; workflow return contains extra IDs
+- **MLflow**
+  - before: legacy model objects + params/metrics + report/data artifacts
+  - now: wrapper-based model persistence + `mse_val`/`mae_val`/`r2_val` as params + report/data artifacts + run_id exposed
+- **MinIO**
+  - before: reads 1 CSV input object
+  - now: reads 1 or 2 CSV input objects (train + optional validation), still no output upload
+- **Local temp**
+  - before: generated artifacts, then cleanup
+  - now: generated artifacts, then best-effort cleanup (non-blocking for success result)
+
+### 3.4 Cleanup behavior impact on output semantics
+
+Before:
+- Cleanup was called directly after training result; failures propagated straightforwardly.
+
+Now:
+- Cleanup is in a guarded `finally`.
+- If training succeeded but cleanup fails, workflow warns and does not rollback success semantics.
+- Effective output semantics: successful training result can be returned even if temp cleanup fails.
+
+---
+
+## 4) Detailed field mapping (old -> new)
+
+## Kept (or equivalent role)
+
+- `experiment_run_id` -> kept (broader accepted types: int or numeric string)
+- `variable_columns` -> kept
+- `target_variable` -> kept
+- `bucket_name` -> kept
+- `file_name` -> kept
+- `line_separator` -> kept
+- `decimal_separator` -> kept
+- `date_column` -> required (snake_case key; must exist in CSV)
+- `date_format` -> optional in payload; omitted/null/blank resolves to default `yyyy-MM-dd HH:mm:ss`
+- `train_size` -> kept
+- `shuffle` -> kept
+- `model_name` -> kept (now less coupled to legacy model enum)
+
+## Added
+
+- `model_type` (primary selector for plugin wrapper/index lookup)
+- `data_model_kwargs`
+- `model_kwargs`
+- `opt_params`
+- `val_file_name` (optional second dataset input)
+- `model_id` (optional metadata)
+- `model_metadata` (loaded/required for schema validation)
+- `random_state` (explicit split reproducibility control)
+
+## Removed from new required contract
+
+- `lag_train`, `lag_val`
+- `rem_static_win`, `static_threshold`
+- `low_lim`, `upp_lim`
+- `window`
+- `use_scaler`, `include_ar`
+- `degree`, `interaction_only`
+- `nan_treatment`
+- `start_date`, `end_date`
+- `scaler_name`
+- `removed_intervals`
+- `support_filters`
+- `experiment_name` (no longer required as top-level client input)
+
+---
+
+## 5) Internal architecture update notes
+
+### 5.1 Repository layer redesign
+
+Removed:
+- `model_manager/utils/repository/model_repository.py`
+- `model_manager/utils/repository/training_repository.py`
+- `model_manager/utils/repository/storage_repository.py`
+
+Added:
+- `model_manager/utils/repository/data_manager_repository.py`
+
+Interpretation:
+- Data preprocessing/report/metrics responsibilities were consolidated into `DataManagerRepository`.
+- Training/model persistence shifted to wrapper + plugin store + MLflow repository integrations.
+
+### 5.2 Model engine abstraction migration
+
+Before:
+- Strong coupling to local classes in `model_manager.sientia.models` and custom preprocessing/model objects in `TrainModelResult`.
+
+Now:
+- Training uses `SientiaModel` wrapper dynamically obtained by `plugin_store.get_model(model_type=...)`.
+- Contract is wrapper-driven (`train`, `transform`, `predict`, `store_model`).
+- The codebase removed `model_manager/sientia/models.py`, `model_serving.py`, and `utils.py`, indicating full migration to externalized model runtime abstraction.
+
+### 5.3 Worker/runtime architecture changes
+
+- New `prepare_worker.py` centralizes worker setup and autoscaling parameters.
+- Queue names are now runtime-derived:
+  - `train_model-<runtime>-queue`
+  - `cleanup_files-<runtime>-queue`
+- `worker.py` now installs runtime via plugin store (`plugin_store.install_runtime(runtime_name=...)`) before starting workers.
+- This introduces environment/runtime-aware deployment and model packaging behavior.
+
+### 5.4 Synchronous activity and tracking adjustments
+
+- `experiment_tracking` migrated from async postgres helper to sync postgres client path (`postgres_sync`).
+- Several activities switched to sync method signatures.
+- Error handling in workflow and DB status update paths is more defensive (secondary failures while persisting error status are logged and do not mask primary failure cause).
+
+### 5.5 `TrainModelResult` shape update
+
+Before:
+- Stored classic split artifacts (`x_train`, `x_test`, `y_train`, `y_test`) + concrete preprocessing/model objects (`process_data`, `regr`, `scaler_dict`).
+
+Now:
+- Stores `train_data`, `val_data` and prediction DataFrames, plus tracking identifiers (`experiment_name`, `run_id`).
+- Result object is less tied to internal estimator classes and more aligned with serializable workflow/model-store integration.
+
+---
+
+## 6) Net IO compatibility assessment
+
+## Input compatibility
+
+Not backward compatible with old payloads without adaptation.
+
+Key reasons:
+- Legacy required fields removed/ignored by new path.
+- New required fields introduced (`model_type`, `*_kwargs` dicts, runtime metadata flow dependency).
+- Validation pipeline now expects model metadata semantics.
+
+## Output compatibility
+
+Behavior changed:
+- Workflow now returns a result object (previously `None`).
+- Training summary includes `experiment_name` and `run_id` in addition to `run_name` and `run_dir`.
+- DB update still centered on `run_name`; callers relying only on DB may not see all new output info unless workflow return is consumed.
+
+---
+
+## 7) Practical migration guidance (client side)
+
+To call `train_model` in this branch:
+
+1. Send snake_case payload aligned to new `TrainModelParams`.
+2. Always provide:
+   - `model_name` slugified model name (ex.: `test_model_name or test-model-name`)
+   - `model_type`
+   - `data_model_kwargs` (dict)
+   - `model_kwargs` (dict)
+   - `opt_params` (dict)
+3. Keep `experiment_run_id` numeric (int or numeric string).
+4. Use runtime queue naming consistent with worker runtime:
+   - `train_model-<runtime>-queue`
+5. If you need explicit validation split file, send `val_file_name`; otherwise split uses `train_size`/`shuffle`/`random_state`.
+
+---
+
+## 8) Source references used for this document
+
+Primary diffs:
+- `model_manager/workflows/train_model.py`
+- `model_manager/utils/models/train_model_params.py`
+- `model_manager/utils/models/train_model_result.py`
+- `model_manager/activities/training.py`
+- `model_manager/activities/activities.py`
+- `model_manager/activities/experiment_tracking.py`
+- `model_manager/utils/repository/data_manager_repository.py`
+- `model_manager/utils/repository/model_repository.py` (removed)
+- `model_manager/utils/repository/training_repository.py` (removed)
+- `model_manager/utils/repository/storage_repository.py` (removed)
+- `model_manager/worker/worker.py`
+- `model_manager/worker/prepare_worker.py`
+- `README.md`
+- `input-sample.md`
+- `scripts/run_training_test.py`
+