SIENTIAPDE-1430: Introduce static_threshold parameter for static window removal.

This parameter allows customizing the threshold (1-1000) used when rem_static_win is enabled, defaulting to 1 if null.
Updates include parameter definition, business rule validation, repository logic for passing the threshold, documentation in README.md and PIPELINE_PARAMS_CHANGELOG.md, and new unit and integration tests.

PIPELINE_PARAMS_CHANGELOG.md

@@ -28,6 +28,7 @@ Este documento descreve as alterações nos parâmetros de entrada do pipeline T
 | `start_date` | `str \| null` | Data inicial para filtrar dados (formato: `"YYYY-MM-DD HH:MM:SS"`) | `null` |
 | `end_date` | `str \| null` | Data final para filtrar dados (formato: `"YYYY-MM-DD HH:MM:SS"`) | `null` |
 | `support_filters` | `dict \| null` | Filtros customizados por variável | `{}` |
+| `static_threshold` | `int \| null` | Threshold para remoção de janelas estáticas (1-1000). Só usado quando `rem_static_win` é `true`. | `1` |
 
 ---
 
@@ -79,6 +80,7 @@ Este documento descreve as alterações nos parâmetros de entrada do pipeline T
   },
   
   "rem_static_win": true,
+  "static_threshold": null,
   "low_lim": {"pressao": 0, "umidade": 0, "velocidade": 0},
   "upp_lim": {"pressao": 100, "umidade": 100, "velocidade": 50},
   "window": 0,
@@ -212,6 +214,7 @@ O sistema valida automaticamente:
 11. **`degree` vs `model_name`**: Se `model_name` = "Polynomial Regression", `degree` deve ser >= 2; se "Linear Regression", `degree` deve ser = 1
 12. **`removed_intervals`**: Cada elemento deve ser lista/tupla com pelo menos 2 elementos (start, end)
 13. **`target_variable`**: Não pode estar vazio
+14. **`static_threshold`**: Se `rem_static_win` = `true` e `static_threshold` tiver valor, deve estar entre 1 e 1000 (inclusive). Se `null`, assume valor `1`.
 
 ---
 

README.md

@@ -296,6 +296,7 @@ The **TrainModel** workflow orchestrates the complete ML model training pipeline
   "lag_train": {"feature1": 0, "feature2": 2},
   "lag_val": {"feature1": 0, "feature2": 1},
   "rem_static_win": false,
+  "static_threshold": null,
   "low_lim": {"feature1": 0.0, "feature2": 0.0},
   "upp_lim": {"feature1": 100.0, "feature2": 100.0},
   "window": 10,
@@ -359,6 +360,7 @@ The workflow validates comprehensive business rules beyond type checking:
 11. **model_name**: Must be 'Linear Regression' or 'Polynomial Regression'
 12. **Polynomial Regression requires Scaler**: Models with degree > 1 must have a scaler to prevent numerical overflow
 13. **Linear Regression requires degree 1**: Linear models must have degree = 1
+14. **static_threshold**: When `rem_static_win` is true and `static_threshold` has a value, it must be between 1 and 1000 (inclusive). If null, defaults to 1.
 
 ### Cleanup Files Workflow (`cleanup_files.py`)
 
@@ -977,6 +979,7 @@ Test scenarios are defined as JSON files in `docs/test-scenarios/`. Each scenari
 | `08-linear-regression-with-limits` | Linear regression with variable limits | `lowLim`/`uppLim` configuration |
 | `09-polynomial-degree2-with-scaler-and-lags` | Complete polynomial scenario | Scaler + lags + degree 2 |
 | `10-linear-regression-with-ar` | Linear regression with autoregressive variable | `includeAr: true` |
+| `11-linear-regression-static-threshold-custom` | Linear regression with custom static threshold | `staticThreshold: 100` |
 
 #### Scenario File Structure
 
@@ -991,6 +994,7 @@ Test scenarios are defined as JSON files in `docs/test-scenarios/`. Each scenari
   "lagTrain": {"feature1": 0, "feature2": 0},
   "lagVal": {"feature1": 0, "feature2": 0},
   "remStaticWin": false,
+  "staticThreshold": null,
   "lowLim": {},
   "uppLim": {},
   "window": 0,

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

@@ -0,0 +1,29 @@
+{
+  "_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,
+  "shuffle": true,
+  "lineSeparator": ",",
+  "decimalSeparator": ".",
+  "removedIntervals": [],
+  "degree": 1,
+  "interactionOnly": false,
+  "nanTreatment": "drop",
+  "startDate": null,
+  "endDate": null,
+  "scalerName": "None",
+  "supportFilters": {}
+}

model_manager/utils/models/train_model_params.py

@@ -47,6 +47,7 @@ class TrainModelParams:
         end_date (str | None): End date for filtering data.
         scaler_name (str): Name of the scaler to use ('Standard Scaler' or 'None').
         support_filters (dict): Custom support filters per variable.
+        static_threshold (int | None): Threshold for static window removal (1-1000). Only used when rem_static_win is True.
     """
 
     variable_columns: list[str]
@@ -76,6 +77,7 @@ class TrainModelParams:
     end_date: str | None
     scaler_name: str
     support_filters: dict
+    static_threshold: int | None
 
     @classmethod
     def from_dict(cls, data: dict[str, Any]) -> 'TrainModelParams':
@@ -138,6 +140,7 @@ class TrainModelParams:
             scaler_name=cls._check_none(data.get('scaler_name'), str, 'scaler_name'),
             support_filters=cls._check_type(data.get('support_filters'), dict, 'support_filters')
             or {},
+            static_threshold=cls._check_type(data.get('static_threshold'), int, 'static_threshold'),
         )
 
     @staticmethod
@@ -228,6 +231,13 @@ class TrainModelParams:
         if self.window < 0:
             raise ValueError(f'window must be non-negative, got {self.window}')
 
+        # Validate static_threshold only when rem_static_win is True and value is provided
+        if self.rem_static_win and self.static_threshold is not None:
+            if not 1 <= self.static_threshold <= 1000:
+                raise ValueError(
+                    f'static_threshold must be between 1 and 1000, got {self.static_threshold}'
+                )
+
     def _validate_model_params(self) -> None:
         """Validate model-related parameters."""
         if self.degree < 1:

model_manager/utils/repository/model_repository.py

@@ -206,7 +206,10 @@ class ModelRepository:
             self.model_serving.log_param('lag_train', data.params.lag_train)
             self.model_serving.log_param('lag_transform', data.params.lag_val)
             self.model_serving.log_param(
-                'static_threshold', 1 if data.params.rem_static_win else None
+                'static_threshold',
+                (data.params.static_threshold if data.params.static_threshold is not None else 1)
+                if data.params.rem_static_win
+                else None,
             )
             self.model_serving.log_param('lower_limits', data.params.low_lim)
             self.model_serving.log_param('upper_limits', data.params.upp_lim)

model_manager/utils/repository/training_repository.py

@@ -269,7 +269,9 @@ class TrainingRepository:
             start_date=params.start_date,
             end_date=params.end_date,
             removed_intervals=removed_intervals,
-            static_threshold=1 if params.rem_static_win else None,
+            static_threshold=(params.static_threshold if params.static_threshold is not None else 1)
+            if params.rem_static_win
+            else None,
             low_lim=params.low_lim,
             upp_lim=params.upp_lim,
             scaler_name=params.scaler_name,

tests/utils/models/test_train_model_params.py

@@ -34,6 +34,7 @@ def valid_train_params_dict():
         'end_date': None,
         'scaler_name': 'Standard Scaler',
         'support_filters': {},
+        'static_threshold': None,
     }
 
 
@@ -564,3 +565,96 @@ def test_validate_business_rules_polynomial_regression_valid(valid_train_params_
     params = TrainModelParams.from_dict(valid_train_params_dict)
 
     params.validate_business_rules()  # Should not raise
+
+
+def test_validate_business_rules_static_threshold_valid(valid_train_params_dict):
+    """Test validate_business_rules accepts valid static_threshold when rem_static_win is True."""
+    from model_manager.utils.models.train_model_params import TrainModelParams
+
+    valid_train_params_dict['rem_static_win'] = True
+    valid_train_params_dict['static_threshold'] = 500
+    params = TrainModelParams.from_dict(valid_train_params_dict)
+
+    params.validate_business_rules()  # Should not raise
+
+
+def test_validate_business_rules_static_threshold_min_valid(valid_train_params_dict):
+    """Test validate_business_rules accepts static_threshold = 1."""
+    from model_manager.utils.models.train_model_params import TrainModelParams
+
+    valid_train_params_dict['rem_static_win'] = True
+    valid_train_params_dict['static_threshold'] = 1
+    params = TrainModelParams.from_dict(valid_train_params_dict)
+
+    params.validate_business_rules()  # Should not raise
+
+
+def test_validate_business_rules_static_threshold_max_valid(valid_train_params_dict):
+    """Test validate_business_rules accepts static_threshold = 1000."""
+    from model_manager.utils.models.train_model_params import TrainModelParams
+
+    valid_train_params_dict['rem_static_win'] = True
+    valid_train_params_dict['static_threshold'] = 1000
+    params = TrainModelParams.from_dict(valid_train_params_dict)
+
+    params.validate_business_rules()  # Should not raise
+
+
+def test_validate_business_rules_static_threshold_below_min(valid_train_params_dict):
+    """Test validate_business_rules raises error when static_threshold < 1."""
+    from model_manager.utils.models.train_model_params import TrainModelParams
+
+    valid_train_params_dict['rem_static_win'] = True
+    valid_train_params_dict['static_threshold'] = 0
+    params = TrainModelParams.from_dict(valid_train_params_dict)
+
+    with pytest.raises(ValueError, match='static_threshold must be between 1 and 1000, got 0'):
+        params.validate_business_rules()
+
+
+def test_validate_business_rules_static_threshold_above_max(valid_train_params_dict):
+    """Test validate_business_rules raises error when static_threshold > 1000."""
+    from model_manager.utils.models.train_model_params import TrainModelParams
+
+    valid_train_params_dict['rem_static_win'] = True
+    valid_train_params_dict['static_threshold'] = 1001
+    params = TrainModelParams.from_dict(valid_train_params_dict)
+
+    with pytest.raises(ValueError, match='static_threshold must be between 1 and 1000, got 1001'):
+        params.validate_business_rules()
+
+
+def test_validate_business_rules_static_threshold_none_when_rem_static_win_true(
+    valid_train_params_dict,
+):
+    """Test validate_business_rules accepts None static_threshold when rem_static_win is True."""
+    from model_manager.utils.models.train_model_params import TrainModelParams
+
+    valid_train_params_dict['rem_static_win'] = True
+    valid_train_params_dict['static_threshold'] = None
+    params = TrainModelParams.from_dict(valid_train_params_dict)
+
+    params.validate_business_rules()  # Should not raise - None is allowed
+
+
+def test_validate_business_rules_static_threshold_ignored_when_rem_static_win_false(
+    valid_train_params_dict,
+):
+    """Test validate_business_rules ignores static_threshold when rem_static_win is False."""
+    from model_manager.utils.models.train_model_params import TrainModelParams
+
+    valid_train_params_dict['rem_static_win'] = False
+    valid_train_params_dict['static_threshold'] = 5000  # Invalid value, but should be ignored
+    params = TrainModelParams.from_dict(valid_train_params_dict)
+
+    params.validate_business_rules()  # Should not raise - validation skipped
+
+
+def test_from_dict_static_threshold_type_error(valid_train_params_dict):
+    """Test from_dict raises TypeError when static_threshold has wrong type."""
+    from model_manager.utils.models.train_model_params import TrainModelParams
+
+    valid_train_params_dict['static_threshold'] = 'not_an_int'
+
+    with pytest.raises(TypeError, match='static_threshold must be of type int, but got str'):
+        TrainModelParams.from_dict(valid_train_params_dict)

tests/utils/models/test_train_model_result.py

@@ -40,6 +40,7 @@ def sample_params():
         end_date=None,
         scaler_name='Standard Scaler',
         support_filters={},
+        static_threshold=None,
     )
 
 

tests/utils/repository/test_model_repository.py

@@ -55,6 +55,8 @@ def mock_train_result():
     result.params.include_ar = False
     result.params.train_size = 80
     result.params.removed_intervals = []
+    result.params.rem_static_win = True
+    result.params.static_threshold = None
     result.run_name = 'test_run'
     result.run_dir = '/tmp/test_run'  # noqa: S108
     result.report_path = '/tmp/test_run/report.html'  # noqa: S108

tests/utils/repository/test_training_repository.py

@@ -56,6 +56,7 @@ def sample_params():
         end_date=None,
         scaler_name='None',
         support_filters={},
+        static_threshold=None,
     )
 
 
@@ -137,6 +138,7 @@ class TestExtractModelEquation:
             end_date=None,
             scaler_name='None',
             support_filters={},
+            static_threshold=None,
         )
 
         # Mock model with single coefficient
@@ -221,12 +223,33 @@ class TestInitDataPreprocessor:
         assert preprocessor.ar_var is None
 
     def test_init_preprocessor_with_static_removal(self, training_repo, sample_params):
-        """Test preprocessor with static window removal enabled."""
+        """Test preprocessor with static window removal enabled and no static_threshold."""
         sample_params.rem_static_win = True
+        sample_params.static_threshold = None
         preprocessor = training_repo._init_data_preprocessor(sample_params)
 
         assert preprocessor.static_threshold == 1
 
+    def test_init_preprocessor_with_static_removal_custom_threshold(
+        self, training_repo, sample_params
+    ):
+        """Test preprocessor with static window removal and custom static_threshold."""
+        sample_params.rem_static_win = True
+        sample_params.static_threshold = 500
+        preprocessor = training_repo._init_data_preprocessor(sample_params)
+
+        assert preprocessor.static_threshold == 500
+
+    def test_init_preprocessor_without_static_removal_ignores_threshold(
+        self, training_repo, sample_params
+    ):
+        """Test preprocessor without static removal ignores static_threshold."""
+        sample_params.rem_static_win = False
+        sample_params.static_threshold = 500
+        preprocessor = training_repo._init_data_preprocessor(sample_params)
+
+        assert preprocessor.static_threshold is None
+
     def test_init_preprocessor_lag_configuration(self, training_repo, sample_params):
         """Test preprocessor lag configuration."""
         sample_params.lag_train = {'var1': 5, 'var2': 5, 'var3': 5}