Merge pull request #23 from Aignosi/fix/SIENTIAPDE-1579

Fix/SIENTIAPDE-1579: Corrigir bug durante o treinamento de certos arquivos no Soft Sensor
2026-02-19 09:34:34 -03:00
parent f0cdace820 14f3fbadbe
commit 8609245e77
29 changed files with 38714 additions and 125 deletions
--- a/_double_date_column.csv
+++ b/_double_date_column.csv
@@ -0,0 +1,33 @@
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--- a/docs/DB_CV022_WIT230.csv
+++ b/docs/DB_CV022_WIT230.csv
@@ -0,0 +1,647 @@
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--- a/docs/data-1749222138290.csv
+++ b/docs/data-1749222138290.csv
--- a/docs/test-scenarios/01-linear-regression-basic.json
+++ b/docs/test-scenarios/01-linear-regression-basic.json
@@ -17,6 +17,8 @@
  "shuffle": true,
  "lineSeparator": ",",
  "decimalSeparator": ".",
+  "dateColumn": "timestamp",
+  "dateFormat": "yyyy-MM-dd HH:mm:ss",
  "removedIntervals": [],
  "degree": 1,
  "interactionOnly": false,
--- a/docs/test-scenarios/02-linear-regression-with-scaler.json
+++ b/docs/test-scenarios/02-linear-regression-with-scaler.json
@@ -17,6 +17,8 @@
  "shuffle": true,
  "lineSeparator": ",",
  "decimalSeparator": ".",
+  "dateColumn": "timestamp",
+  "dateFormat": "yyyy-MM-dd HH:mm:ss",
  "removedIntervals": [],
  "degree": 1,
  "interactionOnly": false,
--- a/docs/test-scenarios/03-polynomial-regression-degree2.json
+++ b/docs/test-scenarios/03-polynomial-regression-degree2.json
@@ -17,6 +17,8 @@
  "shuffle": true,
  "lineSeparator": ",",
  "decimalSeparator": ".",
+  "dateColumn": "timestamp",
+  "dateFormat": "yyyy-MM-dd HH:mm:ss",
  "removedIntervals": [],
  "degree": 2,
  "interactionOnly": false,
--- a/docs/test-scenarios/04-polynomial-regression-degree3.json
+++ b/docs/test-scenarios/04-polynomial-regression-degree3.json
@@ -17,6 +17,8 @@
  "shuffle": true,
  "lineSeparator": ",",
  "decimalSeparator": ".",
+  "dateColumn": "timestamp",
+  "dateFormat": "yyyy-MM-dd HH:mm:ss",
  "removedIntervals": [],
  "degree": 3,
  "interactionOnly": false,
--- a/docs/test-scenarios/05-linear-regression-with-lags.json
+++ b/docs/test-scenarios/05-linear-regression-with-lags.json
@@ -17,6 +17,8 @@
  "shuffle": true,
  "lineSeparator": ",",
  "decimalSeparator": ".",
+  "dateColumn": "timestamp",
+  "dateFormat": "yyyy-MM-dd HH:mm:ss",
  "removedIntervals": [],
  "degree": 1,
  "interactionOnly": false,
--- a/docs/test-scenarios/06-linear-regression-nan-interpolation.json
+++ b/docs/test-scenarios/06-linear-regression-nan-interpolation.json
@@ -17,6 +17,8 @@
  "shuffle": true,
  "lineSeparator": ",",
  "decimalSeparator": ".",
+  "dateColumn": "timestamp",
+  "dateFormat": "yyyy-MM-dd HH:mm:ss",
  "removedIntervals": [],
  "degree": 1,
  "interactionOnly": false,
--- a/docs/test-scenarios/07-linear-regression-static-window-removal.json
+++ b/docs/test-scenarios/07-linear-regression-static-window-removal.json
@@ -17,6 +17,8 @@
  "shuffle": true,
  "lineSeparator": ",",
  "decimalSeparator": ".",
+  "dateColumn": "timestamp",
+  "dateFormat": "yyyy-MM-dd HH:mm:ss",
  "removedIntervals": [],
  "degree": 1,
  "interactionOnly": false,
--- a/docs/test-scenarios/08-linear-regression-with-limits.json
+++ b/docs/test-scenarios/08-linear-regression-with-limits.json
@@ -17,6 +17,8 @@
  "shuffle": true,
  "lineSeparator": ",",
  "decimalSeparator": ".",
+  "dateColumn": "timestamp",
+  "dateFormat": "yyyy-MM-dd HH:mm:ss",
  "removedIntervals": [],
  "degree": 1,
  "interactionOnly": false,
--- a/docs/test-scenarios/09-polynomial-degree2-with-scaler-and-lags.json
+++ b/docs/test-scenarios/09-polynomial-degree2-with-scaler-and-lags.json
@@ -17,6 +17,8 @@
  "shuffle": true,
  "lineSeparator": ",",
  "decimalSeparator": ".",
+  "dateColumn": "timestamp",
+  "dateFormat": "yyyy-MM-dd HH:mm:ss",
  "removedIntervals": [],
  "degree": 2,
  "interactionOnly": false,
--- a/docs/test-scenarios/10-linear-regression-with-ar.json
+++ b/docs/test-scenarios/10-linear-regression-with-ar.json
@@ -17,6 +17,8 @@
  "shuffle": true,
  "lineSeparator": ",",
  "decimalSeparator": ".",
+  "dateColumn": "timestamp",
+  "dateFormat": "yyyy-MM-dd HH:mm:ss",
  "removedIntervals": [],
  "degree": 1,
  "interactionOnly": false,
--- a/docs/test-scenarios/11-linear-regression-static-threshold-custom.json
+++ b/docs/test-scenarios/11-linear-regression-static-threshold-custom.json
@@ -18,6 +18,8 @@
  "shuffle": true,
  "lineSeparator": ",",
  "decimalSeparator": ".",
+  "dateColumn": "timestamp",
+  "dateFormat": "yyyy-MM-dd HH:mm:ss",
  "removedIntervals": [],
  "degree": 1,
  "interactionOnly": false,
--- a/docs/test-scenarios/12-angular-test-date-format.json
+++ b/docs/test-scenarios/12-angular-test-date-format.json
@@ -0,0 +1,31 @@
+{
+  "_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,
+  "shuffle": true,
+  "lineSeparator": ",",
+  "decimalSeparator": ".",
+  "dateColumn": "DATA",
+  "dateFormat": "dd/MM/yyyy HH:mm:ss",
+  "removedIntervals": [],
+  "degree": 1,
+  "interactionOnly": false,
+  "nanTreatment": "drop",
+  "startDate": "01/05/2022",
+  "endDate": "31/07/2022",
+  "scalerName": "None",
+  "supportFilters": {},
+  "staticThreshold": null
+}
--- a/docs/test-scenarios/13-angular-test-double-date-column.json
+++ b/docs/test-scenarios/13-angular-test-double-date-column.json
@@ -0,0 +1,31 @@
+{
+  "_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,
+  "shuffle": true,
+  "lineSeparator": ",",
+  "decimalSeparator": ".",
+  "dateColumn": "DATA",
+  "dateFormat": "dd/MM/yyyy HH:mm:ss",
+  "removedIntervals": [],
+  "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
+}
--- a/docs/test-scenarios/14-angular-test-polynomial-support-filters.json
+++ b/docs/test-scenarios/14-angular-test-polynomial-support-filters.json
@@ -0,0 +1,42 @@
+{
+  "_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,
+  "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": {
+    "303-WIT-200(Value)": {
+      "upper_line": {
+        "intercept": 40.400002,
+        "angle": 0
+      },
+      "lower_line": {
+        "intercept": 30.5,
+        "angle": 0
+      }
+    }
+  },
+  "staticThreshold": null
+}
--- a/model_manager/activities/experiment_tracking.py
+++ b/model_manager/activities/experiment_tracking.py
@@ -6,6 +6,8 @@ in the PostgreSQL database, extending the base Postgres activity with specialize
 methods for experiment management.
 """

+import enum
+
 from temporalio import activity, workflow

 with workflow.unsafe.imports_passed_through():
@@ -13,7 +15,6 @@ with workflow.unsafe.imports_passed_through():
    import traceback
    from collections.abc import Mapping
    from datetime import UTC, datetime
-    from enum import Enum
    from typing import Any

    from sientia_do.notifications.handlers import CoreNotificationHandler as NotificationHandler
@@ -24,7 +25,7 @@ with workflow.unsafe.imports_passed_through():
    from sqlalchemy import text


-class UpdateType(str, Enum):
+class UpdateType(enum.StrEnum):
    """Types of experiment run updates."""

    STATUS = 'status'
--- a/model_manager/sientia/models.py
+++ b/model_manager/sientia/models.py
@@ -17,6 +17,36 @@ NORMALIZATION = 'Normalization'
 FEATURE_CREATION = 'Feature Creation'
 LAG_CREATION = 'Lag Creation'

+# Allowed frontend date formats and their strftime equivalents (single source of truth)
+FRONTEND_DATE_FORMAT_TO_STRFTIME = {
+    'dd/MM/yyyy HH:mm:ss': '%d/%m/%Y %H:%M:%S',
+    'MM/dd/yyyy HH:mm:ss': '%m/%d/%Y %H:%M:%S',
+    'yyyy/MM/dd HH:mm:ss': '%Y/%m/%d %H:%M:%S',
+    'dd-MM-yyyy HH:mm:ss': '%d-%m-%Y %H:%M:%S',
+    'MM-dd-yyyy HH:mm:ss': '%m-%d-%Y %H:%M:%S',
+    'yyyy-MM-dd HH:mm:ss': '%Y-%m-%d %H:%M:%S',
+}
+ALLOWED_FRONTEND_DATE_FORMATS = frozenset(FRONTEND_DATE_FORMAT_TO_STRFTIME.keys())
+
+
+def validate_frontend_date_format(fmt: str | None) -> None:
+    """Raise ValueError if fmt is set and not one of the allowed frontend date formats."""
+    if not fmt or not fmt.strip():
+        return
+    if fmt not in ALLOWED_FRONTEND_DATE_FORMATS:
+        allowed = ', '.join(sorted(ALLOWED_FRONTEND_DATE_FORMATS))
+        raise ValueError(
+            f'Invalid date_format "{fmt}". Allowed formats: {allowed}'
+        )
+
+
+def _frontend_date_format_to_strftime(fmt: str | None) -> str | None:
+    """Convert front-end date format to Python strftime. Validates format; returns None for empty."""
+    if not fmt:
+        return None
+    validate_frontend_date_format(fmt)
+    return FRONTEND_DATE_FORMAT_TO_STRFTIME[fmt]
+

 class LinearRegressionModel(BaseEstimator, TransformerMixin):
    """
@@ -244,6 +274,7 @@ class DataPreprocessor(BaseEstimator, TransformerMixin):
        lag_transform: dict[str, int] | None = None,
        start_date: str | None = None,
        end_date: str | None = None,
+        date_format: str | None = None,
        removed_intervals: list[tuple[str, str]] | None = None,
        static_threshold: int | None = None,
        low_lim: dict[str, float] | None = None,
@@ -270,8 +301,9 @@ class DataPreprocessor(BaseEstimator, TransformerMixin):
                *Format: {'variable_name': lag}*
            lag_transform (dict): The lags for each variable to be applyed during transformation \\
                *Format: {'variable_name': lag}*
-            start_date (str): The start date for filtering data (format: 'YYYY-MM-DD HH:MM:SS')
-            end_date (str): The end date for filtering data (format: 'YYYY-MM-DD HH:MM:SS')
+            start_date (str): The start date for filtering data
+            end_date (str): The end date for filtering data
+            date_format (str | None): Frontend date format for start/end (e.g. dd/MM/yyyy HH:mm:ss or MM/dd/yyyy HH:mm:ss). When set, parsing matches the CSV date column.
            removed_intervals (list): List of tuples with intervals to remove from data \\
                *Format: [('start_date', 'end_date'), ...]*
            static_threshold (int): The number of repeated values to be considered as static
@@ -314,6 +346,7 @@ class DataPreprocessor(BaseEstimator, TransformerMixin):
        self.lag_transform = lag_transform if lag_transform else {}
        self.start_date = start_date
        self.end_date = end_date
+        self.date_format = date_format
        self.removed_intervals = removed_intervals if removed_intervals else []
        self.ar_var = ar_var
        self.self_operations = self_operations
@@ -443,10 +476,19 @@ class DataPreprocessor(BaseEstimator, TransformerMixin):
        return input_data

    def _parse_datetime(self, date_str: str | None) -> pd.Timestamp | None:
-        """Parse a date string to Timestamp, returning None on failure."""
+        """Parse a date string to Timestamp using date_format when set.
+
+        When date_format is set (e.g. dd/MM/yyyy HH:mm:ss or MM/dd/yyyy HH:mm:ss),
+        parsing matches the CSV date column so start_date/end_date filter correctly.
+        """
        if not date_str:
            return None
        try:
+            python_fmt = (
+                _frontend_date_format_to_strftime(self.date_format) if self.date_format else None
+            )
+            if python_fmt:
+                return pd.to_datetime(date_str, format=python_fmt)
            return pd.to_datetime(date_str)
        except (ValueError, TypeError):
            return None
--- a/model_manager/utils/models/experiment_status.py
+++ b/model_manager/utils/models/experiment_status.py
@@ -1,7 +1,7 @@
-from enum import Enum
+from enum import StrEnum


-class ExperimentStatus(str, Enum):
+class ExperimentStatus(StrEnum):
    """
    Status values for experiment run lifecycle.

--- a/model_manager/utils/models/train_model_params.py
+++ b/model_manager/utils/models/train_model_params.py
@@ -1,6 +1,8 @@
 from dataclasses import dataclass
 from typing import Any

+from model_manager.sientia.models import validate_frontend_date_format
+
 # Model name constants
 MODEL_LINEAR_REGRESSION = 'Linear Regression'
 MODEL_POLYNOMIAL_REGRESSION = 'Polynomial Regression'
@@ -34,6 +36,8 @@ class TrainModelParams:
        file_name (str): Name of the file in the MinIO bucket.
        line_separator (str): Line separator used in the CSV file.
        decimal_separator (str): Decimal separator used in the CSV file.
+        date_column (str | None): Name of the date/time column. If set with date_format, the column is parsed as datetime.
+        date_format (str | None): Format of the date column (e.g. dd/MM/yyyy HH:mm:ss). Used when date_column is set.
        train_size (int): Percentage of data to use for training (0-100).
        shuffle (bool): Whether to shuffle the data during train/test split.
        experiment_run_id (int): Unique identifier for the experiment run.
@@ -64,6 +68,8 @@ class TrainModelParams:
    file_name: str
    line_separator: str
    decimal_separator: str
+    date_column: str | None
+    date_format: str | None
    train_size: int
    shuffle: bool
    experiment_run_id: int
@@ -120,6 +126,8 @@ class TrainModelParams:
            decimal_separator=cls._check_none(
                data.get('decimal_separator'), str, 'decimal_separator'
            ),
+            date_column=data.get('date_column'),
+            date_format=data.get('date_format'),
            train_size=cls._check_none(data.get('train_size'), int, 'train_size'),
            shuffle=cls._check_none(data.get('shuffle'), bool, 'shuffle'),
            experiment_run_id=cls._check_none(
@@ -211,6 +219,7 @@ class TrainModelParams:
        self._validate_intervals_and_dates()
        self._validate_limits()
        self._validate_required_strings()
+        self._validate_date_format()

    def _validate_numeric_ranges(self) -> None:
        """Validate numeric parameters are within acceptable ranges."""
@@ -321,3 +330,8 @@ class TrainModelParams:

        if not self.experiment_name.strip():
            raise ValueError('experiment_name cannot be empty or whitespace')
+
+    def _validate_date_format(self) -> None:
+        """Validate date_format is one of the allowed frontend formats when set."""
+        if self.date_format:
+            validate_frontend_date_format(self.date_format)
--- a/model_manager/utils/repository/training_repository.py
+++ b/model_manager/utils/repository/training_repository.py
@@ -15,12 +15,103 @@ from sientia_do.operations.df_preprocessor import load_data
 from sientia_do.operations.normalization import MinMaxScaler, Z_Scaler

 from model_manager.sientia.metrics import mae, mse, r2
-from model_manager.sientia.models import DataPreprocessor, LinearRegressionModel
+from model_manager.sientia.models import (
+    DataPreprocessor,
+    LinearRegressionModel,
+)
+from model_manager.sientia.models import (
+    _frontend_date_format_to_strftime as _frontend_format_to_strftime,
+)
 from model_manager.sientia.utils import split_train_test
 from model_manager.utils.models.train_model_params import TrainModelParams
 from model_manager.utils.models.train_model_result import TrainModelResult


+def _ensure_date_column_parsed(data: pd.DataFrame, params: TrainModelParams) -> pd.DataFrame:
+    """If date_column and date_format are set, parse the column as datetime to avoid comparison errors downstream."""
+    if not params.date_column or not params.date_format or params.date_column not in data.columns:
+        return data
+    try:
+        python_fmt = _frontend_format_to_strftime(params.date_format)
+        data = data.copy()
+        data[params.date_column] = pd.to_datetime(
+            data[params.date_column], format=python_fmt, errors='coerce'
+        )
+    except Exception as e:
+        raise ValueError(
+            f'Failed to parse date column "{params.date_column}" with format "{params.date_format}": {e}'
+        ) from e
+    return data
+
+
+def _single_variable_support_mask(
+    data_view: pd.DataFrame,
+    var_col: str,
+    target_variable: str,
+    config: dict,
+) -> np.ndarray | None:
+    """Compute keep mask for one variable's support lines; None if config is invalid or skipped."""
+    if var_col not in data_view.columns:
+        return None
+    upper = config.get('upper_line') or config.get('upperLine')
+    lower = config.get('lower_line') or config.get('lowerLine')
+    if not upper or not lower:
+        return None
+
+    x_vals = data_view[var_col].astype(float).to_numpy()
+    y_vals = data_view[target_variable].astype(float).to_numpy()
+    xmin, xmax = float(np.nanmin(x_vals)), float(np.nanmax(x_vals))
+    ymin, ymax = float(np.nanmin(y_vals)), float(np.nanmax(y_vals))
+    x_range = (xmax - xmin) if (xmax - xmin) != 0 else 1.0
+    y_range = (ymax - ymin) if (ymax - ymin) != 0 else 1.0
+    scale_ratio = y_range / x_range
+
+    b1 = float(upper.get('intercept', 0))
+    deg1 = float(upper.get('angle', 0))
+    b2 = float(lower.get('intercept', 0))
+    deg2 = float(lower.get('angle', 0))
+    m1 = np.tan(np.deg2rad(deg1)) * scale_ratio
+    m2 = np.tan(np.deg2rad(deg2)) * scale_ratio
+    y1 = m1 * x_vals + b1
+    y2 = m2 * x_vals + b2
+    lower_bound = np.minimum(y1, y2)
+    upper_bound = np.maximum(y1, y2)
+    return (y_vals >= lower_bound) & (y_vals <= upper_bound)
+
+
+def _apply_support_filters(
+    data_view: pd.DataFrame,
+    target_variable: str,
+    support_filters: dict,
+) -> pd.DataFrame:
+    """
+    Keep only rows where (var, target) lies between the two guide lines for each variable.
+
+    For each variable in support_filters, the condition is lower(x_var) <= target <= upper(x_var),
+    where lower/upper are the two lines (intercept + slope from angle, scaled by y_range/x_range).
+    Global mask is AND across all variables. Matches DEMO logic in template_01.py.
+
+    Args:
+        data_view: DataFrame after preprocessor transform.
+        target_variable: Name of the target column (y axis).
+        support_filters: Per-variable config with upper_line/lower_line, each {intercept, angle}.
+
+    Returns:
+        data_view filtered to rows satisfying all variable conditions; unchanged if support_filters empty.
+    """
+    if not support_filters or target_variable not in data_view.columns:
+        return data_view
+
+    combined_keep_mask = np.ones(len(data_view), dtype=bool)
+    n = len(data_view)
+    for var_col, config in support_filters.items():
+        keep_mask = _single_variable_support_mask(data_view, var_col, target_variable, config)
+        if keep_mask is not None and len(keep_mask) == n:
+            combined_keep_mask &= keep_mask
+
+    return data_view.loc[combined_keep_mask]
+
+
 class TrainingRepository:
    """
    Repository for machine learning model training operations.
@@ -67,15 +158,25 @@ class TrainingRepository:
            Exception: If data loading, preprocessing, or training fails
        """
        data = load_data(uploaded_file, params.line_separator, params.decimal_separator)
-
-        # Configure datetime index if timestamp column exists
-        # Required for TimeSeriesDiscontinuityAnalyzer (static window removal)
-        data = self._configure_datetime_index(data)
+        if data is None:
+            raise ValueError(
+                'Failed to load CSV data: load_data returned None. '
+                'Check file encoding, line separator and decimal separator.'
+            )
+        data = _ensure_date_column_parsed(data, params)
+        data = self._configure_datetime_index(data, params)

        process_data = self._init_data_preprocessor(params)
        process_data.fit(data)
        data_view = process_data.transform(data)

+        if params.support_filters:
+            data_view = _apply_support_filters(
+                data_view,
+                params.target_variable,
+                params.support_filters,
+            )
+
        if len(data_view) <= 0:
            raise ValueError('Data view is empty after transformation')

@@ -282,6 +383,7 @@ class TrainingRepository:
            lag_transform=params.lag_val,
            start_date=params.start_date,
            end_date=params.end_date,
+            date_format=params.date_format,
            removed_intervals=removed_intervals,
            static_threshold=self._get_static_threshold(params),
            low_lim=params.low_lim,
@@ -350,40 +452,41 @@ class TrainingRepository:
            'original_features': params.variable_columns,
        }

-    def _configure_datetime_index(self, data: pd.DataFrame) -> pd.DataFrame:
+    def _configure_datetime_index(
+        self, data: pd.DataFrame | None, params: TrainModelParams
+    ) -> pd.DataFrame:
        """
        Configure datetime index for the DataFrame.

-        This method attempts to identify a timestamp column and set it as the
-        DataFrame index with DatetimeIndex type. This is required for
-        TimeSeriesDiscontinuityAnalyzer (used in static window removal).
-
-        The method looks for common timestamp column names and converts the
-        first matching column to datetime, then sets it as the index.
-
-        Args:
-            data: Input DataFrame
-
-        Returns:
-            pd.DataFrame: DataFrame with DatetimeIndex if timestamp column found,
-                         otherwise returns original DataFrame unchanged
+        Guards against None to avoid 'NoneType' object has no attribute 'index' downstream.
+        Prefers params.date_column when set; otherwise looks for common timestamp column names.
        """
-        # If index is already DatetimeIndex, just ensure it's sorted
+        if data is None:
+            raise ValueError(
+                'Data is None after load_data. '
+                'Check file format, line separator and decimal separator.'
+            )
+        if not isinstance(data, pd.DataFrame):
+            raise TypeError(f'Expected DataFrame, got {type(data).__name__}')
+
        if isinstance(data.index, pd.DatetimeIndex):
            self.logger.info('DataFrame already has DatetimeIndex')
            return data.sort_index()

-        # Common timestamp column names
-        timestamp_columns = [
+        common_timestamp_columns = [
            'timestamp',
            'Timestamp',
            'TIMESTAMP',
            'date',
            'Date',
            'DATE',
+            'DATA',
            'datetime',
            'DateTime',
        ]
+        timestamp_columns = ([params.date_column] if params.date_column else []) + [
+            c for c in common_timestamp_columns if c != params.date_column
+        ]

        for col in timestamp_columns:
            if col in data.columns:
--- a/scripts/run_cleanup_test.py
+++ b/scripts/run_cleanup_test.py
@@ -14,18 +14,18 @@ import asyncio
 import os
 import sys
 from datetime import timedelta
-from typing import Any
-from dotenv import load_dotenv
 from pathlib import Path
+from typing import Any
+
+from dotenv import load_dotenv
 from temporalio.client import Client

-# Ensure project root is on PYTHONPATH when running directly
+# Ensure project root is on PYTHONPATH when running directly (must run before model_manager import)
 ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
 if ROOT_DIR not in sys.path:
    sys.path.insert(0, ROOT_DIR)

-from model_manager.workflows.cleanup_files import CleanupFiles
-
+from model_manager.workflows.cleanup_files import CleanupFiles  # noqa: E402

 # Carrega variáveis de ambiente do arquivo .env na raiz do projeto
 PROJECT_ROOT = Path(__file__).resolve().parent.parent
@@ -53,7 +53,7 @@ async def main(argv: list[str]) -> None:
    if argv:
        bucket_name = argv[0]

-    print(f"Connecting to Temporal at {temporal_host} (namespace={temporal_namespace})...")
+    print(f'Connecting to Temporal at {temporal_host} (namespace={temporal_namespace})...')
    client = await Client.connect(
        target_host=temporal_host,
        namespace=temporal_namespace,
@@ -64,12 +64,14 @@ async def main(argv: list[str]) -> None:
        'bucket_name': bucket_name,
    }

-    workflow_id = f"cleanup-files-manual-{int(asyncio.get_event_loop().time())}"
+    workflow_id = f'cleanup-files-manual-{int(asyncio.get_event_loop().time())}'

-    print(f"Starting cleanup_files workflow once...\n"
-          f"  workflow_id = {workflow_id}\n"
-          f"  task_queue  = {task_queue}\n"
-          f"  bucket_name = {bucket_name}")
+    print(
+        f'Starting cleanup_files workflow once...\n'
+        f'  workflow_id = {workflow_id}\n'
+        f'  task_queue  = {task_queue}\n'
+        f'  bucket_name = {bucket_name}'
+    )

    handle = await client.start_workflow(
        CleanupFiles.run,
@@ -79,9 +81,9 @@ async def main(argv: list[str]) -> None:
        run_timeout=timedelta(minutes=10),
    )

-    print("Workflow started, waiting for completion...")
+    print('Workflow started, waiting for completion...')
    await handle.result()
-    print("cleanup_files workflow completed successfully.")
+    print('cleanup_files workflow completed successfully.')


 if __name__ == '__main__':  # pragma: no cover - manual utility script
--- a/scripts/run_training_test.py
+++ b/scripts/run_training_test.py
@@ -1,12 +1,20 @@
 #!/usr/bin/env python3
 """Utility script to trigger the training workflow end-to-end for testing.

-Steps performed:
+Steps performed (default):
 1. Upload the CSV test dataset to MinIO using the configured `mc` alias.
 2. Insert a new experiment_run record in Postgres and capture the generated ID.
 3. Trigger the Temporal `train_model` workflow with the correct payload.

-Prerequisites:
+Alternatives for local diagnosis (--local / --validate-only):
+- example: python scripts/run_training_test.py --scenario 01-linear-regression-basic --local --csv docs/test-model-data.csv
+- --validate-only: Validates scenario parameters only (no MinIO, Postgres, Temporal).
+- --local: Runs the same training pipeline locally (validate + load CSV + train +
+  after_train_calculation). Use to get full Python tracebacks for debugging.
+  Does not upload to MinIO, insert DB, or start Temporal.
+  By default skips MLflow save; use --local-save-mlflow to also test saving to MLflow.
+
+Prerequisites (default flow):
 - `mc` CLI configured with alias defined in MINIO_ALIAS.
 - PostgreSQL accessible with credentials in environment variables or defaults.
 - Temporal server reachable without TLS on TEMPORAL_HOST / TEMPORAL_NAMESPACE.
@@ -23,6 +31,7 @@ import subprocess
 import sys
 import uuid
 from datetime import datetime, timedelta
+from io import BytesIO
 from pathlib import Path

 import psycopg2
@@ -30,7 +39,6 @@ from dotenv import load_dotenv
 from psycopg2.extras import Json
 from temporalio import client

-
 # Carrega variáveis de ambiente do arquivo .env na raiz do projeto
 PROJECT_ROOT = Path(__file__).resolve().parent.parent
 ENV_PATH = PROJECT_ROOT / '.env'
@@ -97,6 +105,19 @@ def load_scenario(scenario_name: str) -> dict:
        return json.load(f)


+def _resolve_csv_path(csv_path: Path) -> Path:
+    """Resolve CSV path; if not found in project root, try docs/."""
+    if csv_path.is_absolute():
+        return csv_path
+    resolved = PROJECT_ROOT / csv_path
+    if resolved.exists():
+        return resolved
+    docs_path = PROJECT_ROOT / 'docs' / csv_path.name
+    if docs_path.exists():
+        return docs_path
+    return resolved
+
+
 def _ensure_source_file(path: Path) -> None:
    if not path.exists():
        raise FileNotFoundError(f'Test dataset not found at {path.resolve()}')
@@ -186,6 +207,8 @@ def build_workflow_payload(
        'file_name': file_name,
        'line_separator': request_data['lineSeparator'],
        'decimal_separator': request_data['decimalSeparator'],
+        'date_column': request_data.get('dateColumn'),
+        'date_format': request_data.get('dateFormat'),
        'removed_intervals': request_data['removedIntervals'],
        # New parameters
        'model_name': request_data.get('modelName', 'Linear Regression'),
@@ -196,6 +219,7 @@ def build_workflow_payload(
        'end_date': request_data.get('endDate'),
        'scaler_name': request_data.get('scalerName', 'None'),
        'support_filters': request_data.get('supportFilters', {}),
+        'static_threshold': request_data.get('staticThreshold'),
    }


@@ -220,6 +244,110 @@ async def trigger_temporal_workflow(workflow_input: dict) -> str:
    return workflow_id


+def _build_local_payload(request_data: dict, csv_path: Path) -> dict:
+    """Build workflow payload for local run (no real experiment_run_id)."""
+    return build_workflow_payload(
+        experiment_run_id=0,
+        file_name=csv_path.name,
+        request_data=request_data,
+    )
+
+
+def run_validate_only(scenario_name: str) -> dict:
+    """Validate scenario parameters only. No MinIO, Postgres, or Temporal.
+
+    Returns:
+        dict: {'success': bool, 'error': str | None, 'scenario': str}
+    """
+    from model_manager.utils.models.train_model_params import TrainModelParams
+
+    result = {'scenario': scenario_name, 'success': False, 'error': None}
+    try:
+        request_data = load_scenario(scenario_name)
+    except FileNotFoundError as exc:
+        result['error'] = str(exc)
+        return result
+
+    payload = _build_local_payload(request_data, Path('local.csv'))
+    try:
+        train_params = TrainModelParams.from_dict(payload)
+        train_params.validate_business_rules()
+        result['success'] = True
+    except (ValueError, TypeError, KeyError) as e:
+        result['error'] = str(e)
+    return result
+
+
+def run_local_pipeline(
+    scenario_name: str,
+    csv_path: Path,
+    save_mlflow: bool = False,
+) -> dict:
+    """Run the same training pipeline locally (validate + train + after_train).
+
+    Reads CSV from disk, runs TrainingRepository.train and after_train_calculation.
+    Optionally saves to MLflow if save_mlflow is True (requires MLflow env).
+
+    Returns:
+        dict: {'success': bool, 'error': str | None, 'scenario': str, ...}
+    """
+    from model_manager.utils.logger_helper import get_logger
+    from model_manager.utils.models.train_model_params import TrainModelParams
+    from model_manager.utils.repository.training_repository import TrainingRepository
+
+    result = {
+        'scenario': scenario_name,
+        'success': False,
+        'error': None,
+    }
+    try:
+        request_data = load_scenario(scenario_name)
+    except FileNotFoundError as exc:
+        result['error'] = str(exc)
+        return result
+
+    csv_path = _resolve_csv_path(csv_path)
+    _ensure_source_file(csv_path)
+    payload = _build_local_payload(request_data, csv_path)
+    try:
+        train_params = TrainModelParams.from_dict(payload)
+        train_params.validate_business_rules()
+    except (ValueError, TypeError, KeyError) as e:
+        result['error'] = f'Validation failed: {e}'
+        return result
+
+    logger = get_logger(__name__)
+    training_repository = TrainingRepository(logger)
+
+    with open(csv_path, 'rb') as f:
+        file_content = BytesIO(f.read())
+
+    try:
+        train_result = training_repository.train(file_content, train_params)
+        train_result = training_repository.after_train_calculation(train_params, train_result)
+    except Exception as e:
+        result['error'] = str(e)
+        raise  # re-raise so caller gets full traceback for diagnosis
+
+    if save_mlflow:
+        from model_manager.utils.connectors_config import build_mlflow_config
+        from model_manager.utils.repository.model_repository import ModelRepository
+
+        mlflow_config = build_mlflow_config()
+        model_repository = ModelRepository(
+            url=mlflow_config['url'],
+            username=mlflow_config['username'],
+            password=mlflow_config['password'],
+            logger=logger,
+        )
+        train_result = model_repository.save_model(train_result)
+
+    result['success'] = True
+    result['run_name'] = getattr(train_result, 'run_name', None)
+    result['run_dir'] = getattr(train_result, 'run_dir', None)
+    return result
+
+
 def parse_args() -> argparse.Namespace:
    """Parse command line arguments."""
    parser = argparse.ArgumentParser(
@@ -241,6 +369,15 @@ Examples:

  # Run all scenarios
  python scripts/run_training_test.py --all
+
+  # Validate scenario parameters only (no external services)
+  python scripts/run_training_test.py --scenario linear-regression-basic --validate-only
+
+  # Run training pipeline locally to diagnose errors (full traceback)
+  python scripts/run_training_test.py --scenario linear-regression-basic --local --csv docs/test-model-data.csv
+
+  # Local run and save to MLflow (requires MLflow env)
+  python scripts/run_training_test.py --scenario linear-regression-basic --local --local-save-mlflow
 """,
    )
    parser.add_argument(
@@ -268,6 +405,21 @@ Examples:
        action='store_true',
        help='Run all available test scenarios sequentially.',
    )
+    parser.add_argument(
+        '--validate-only',
+        action='store_true',
+        help='Only validate scenario parameters (no MinIO, Postgres, Temporal).',
+    )
+    parser.add_argument(
+        '--local',
+        action='store_true',
+        help='Run training pipeline locally (validate + train from CSV) to get full tracebacks.',
+    )
+    parser.add_argument(
+        '--local-save-mlflow',
+        action='store_true',
+        help='With --local, also save the model to MLflow (requires MLflow env).',
+    )
    return parser.parse_args()


@@ -293,16 +445,18 @@ def run_single_scenario(scenario_name: str, csv_path: Path) -> dict:
    # Load scenario
    try:
        experiment_request = load_scenario(scenario_name)
-        print(f"  Loaded scenario: {scenario_name}")
+        print(f'  Loaded scenario: {scenario_name}')
    except FileNotFoundError as exc:
        result['error'] = str(exc)
        return result

+    csv_path = _resolve_csv_path(csv_path)
+
    # Upload CSV to MinIO
    try:
        uploaded_file_name = upload_to_minio(csv_path)
        result['s3_object_name'] = uploaded_file_name
-        print(f"  Uploaded CSV to MinIO: {uploaded_file_name}")
+        print(f'  Uploaded CSV to MinIO: {uploaded_file_name}')
    except subprocess.CalledProcessError as exc:
        result['error'] = f'Failed to upload file to MinIO: {exc}'
        return result
@@ -314,7 +468,7 @@ def run_single_scenario(scenario_name: str, csv_path: Path) -> dict:
    try:
        experiment_run_id = insert_experiment_run(uploaded_file_name, experiment_request)
        result['experiment_run_id'] = experiment_run_id
-        print(f"  Created experiment_run with ID: {experiment_run_id}")
+        print(f'  Created experiment_run with ID: {experiment_run_id}')
    except psycopg2.Error as exc:
        result['error'] = f'Database error while inserting experiment_run: {exc}'
        return result
@@ -330,7 +484,7 @@ def run_single_scenario(scenario_name: str, csv_path: Path) -> dict:
        workflow_id = asyncio.run(trigger_temporal_workflow(workflow_payload))
        result['workflow_id'] = workflow_id
        result['success'] = True
-        print(f"  Workflow started: {workflow_id}")
+        print(f'  Workflow started: {workflow_id}')
    except Exception as exc:  # noqa: BLE001
        result['error'] = f'Failed to start Temporal workflow: {exc}'
        return result
@@ -350,29 +504,26 @@ def print_summary(results: list[dict]) -> None:
    print('\n' + '=' * 60)
    print('SUMMARY')
    print('=' * 60)
-    print(f"Total: {len(results)} | Passed: {len(passed)} | Failed: {len(failed)}")
+    print(f'Total: {len(results)} | Passed: {len(passed)} | Failed: {len(failed)}')
    print('=' * 60)

    if passed:
        print('\n✓ PASSED:')
        for r in passed:
-            print(f"  - {r['scenario']}")
+            print(f'  - {r["scenario"]}')

    if failed:
        print('\n✗ FAILED:')
        for r in failed:
-            print(f"  - {r['scenario']}")
+            print(f'  - {r["scenario"]}')
            if r['error']:
-                print(f"    Error: {r['error']}")
+                print(f'    Error: {r["error"]}')

    print()


-def main() -> None:
-    args = parse_args()
-
-    # List scenarios and exit if requested
-    if args.list:
+def _handle_list_scenarios() -> None:
+    """Print available scenarios and exit."""
    scenarios = list_available_scenarios()
    if scenarios:
        print('Available test scenarios:')
@@ -382,8 +533,9 @@ def main() -> None:
        print(f'No scenarios found in {TEST_SCENARIOS_DIR}')
    sys.exit(0)

-    # Run all scenarios if requested
-    if args.all:
+
+def _handle_run_all(args: argparse.Namespace) -> None:
+    """Run all scenarios and exit with appropriate code."""
    scenarios = list_available_scenarios()
    if not scenarios:
        print(f'No scenarios found in {TEST_SCENARIOS_DIR}', file=sys.stderr)
@@ -391,7 +543,6 @@ def main() -> None:

    print(f'Running {len(scenarios)} scenarios...\n')
    results = []
-
    for i, scenario in enumerate(scenarios, 1):
        print(f'[{i}/{len(scenarios)}] Running scenario: {scenario}')
        result = run_single_scenario(scenario, args.csv)
@@ -400,24 +551,53 @@ def main() -> None:
        print(f'[{i}/{len(scenarios)}] {status} {scenario}\n')

    print_summary(results)
-
-        # Exit with error code if any scenario failed
    failed_count = sum(1 for r in results if not r['success'])
    sys.exit(1 if failed_count > 0 else 0)

-    # Require scenario argument if not listing or running all
-    if not args.scenario:
-        print('Error: --scenario or --all is required. Use --list to see available scenarios.', file=sys.stderr)
-        sys.exit(1)

-    # Run single scenario
+def _handle_validate_only(args: argparse.Namespace) -> None:
+    """Validate scenario parameters only and exit."""
+    if not args.scenario:
+        print('Error: --scenario is required with --validate-only.', file=sys.stderr)
+        sys.exit(1)
+    result = run_validate_only(args.scenario)
+    if result['success']:
+        print(f'Validation OK: {result["scenario"]}')
+    else:
+        print(f'Validation failed: {result["error"]}', file=sys.stderr)
+        sys.exit(1)
+    sys.exit(0)
+
+
+def _handle_local(args: argparse.Namespace) -> None:
+    """Run local pipeline and exit."""
+    if not args.scenario:
+        print('Error: --scenario is required with --local.', file=sys.stderr)
+        sys.exit(1)
+    print(f'Running local pipeline: {args.scenario} (CSV: {args.csv})')
+    result = run_local_pipeline(
+        args.scenario,
+        args.csv,
+        save_mlflow=args.local_save_mlflow,
+    )
+    if not result['success']:
+        print(f'Error: {result["error"]}', file=sys.stderr)
+        sys.exit(1)
+    out = {'scenario': result['scenario'], 'success': True}
+    if result.get('run_name') is not None:
+        out['run_name'] = result['run_name']
+    if result.get('run_dir') is not None:
+        out['run_dir'] = result['run_dir']
+    print(json.dumps(out, indent=2))
+
+
+def _handle_single_scenario(args: argparse.Namespace) -> None:
+    """Run one scenario (MinIO + Postgres + Temporal) and print result."""
    print(f'Running scenario: {args.scenario}')
    result = run_single_scenario(args.scenario, args.csv)
-
    if not result['success']:
-        print(f"Error: {result['error']}", file=sys.stderr)
+        print(f'Error: {result["error"]}', file=sys.stderr)
        sys.exit(1)
-
    print(
        json.dumps(
            {
@@ -431,5 +611,31 @@ def main() -> None:
    )


+def main() -> None:
+    args = parse_args()
+
+    if args.list:
+        _handle_list_scenarios()
+
+    if args.all:
+        _handle_run_all(args)
+
+    if args.validate_only:
+        _handle_validate_only(args)
+
+    if args.local:
+        _handle_local(args)
+        return
+
+    if not args.scenario:
+        print(
+            'Error: --scenario or --all is required. Use --list to see available scenarios.',
+            file=sys.stderr,
+        )
+        sys.exit(1)
+
+    _handle_single_scenario(args)
+
+
 if __name__ == '__main__':
    main()
--- a/tests/sientia/test_models.py
+++ b/tests/sientia/test_models.py
@@ -7,11 +7,58 @@ import pandas as pd
 from pytest import raises

 from model_manager.sientia.models import (
+    ALLOWED_FRONTEND_DATE_FORMATS,
+    FRONTEND_DATE_FORMAT_TO_STRFTIME,
    DataPreprocessor,
    LinearRegressionModel,
+    _frontend_date_format_to_strftime,
+    validate_frontend_date_format,
 )


+class TestFrontendDateFormatToStrftime:
+    """Tests for _frontend_date_format_to_strftime (models module)."""
+
+    def test_none_or_empty_returns_none(self):
+        """None or empty string returns None."""
+        assert _frontend_date_format_to_strftime(None) is None
+        assert _frontend_date_format_to_strftime('') is None
+
+    def test_all_six_allowed_formats_convert_correctly(self):
+        """All allowed frontend formats map to expected strftime."""
+        for frontend_fmt, strftime_fmt in FRONTEND_DATE_FORMAT_TO_STRFTIME.items():
+            assert _frontend_date_format_to_strftime(frontend_fmt) == strftime_fmt
+
+    def test_invalid_format_raises(self):
+        """Invalid format raises ValueError with allowed list in message."""
+        with raises(ValueError, match='Invalid date_format'):
+            _frontend_date_format_to_strftime('yyyy-MM-dd')
+        with raises(ValueError, match='Allowed formats'):
+            _frontend_date_format_to_strftime('invalid')
+
+
+class TestValidateFrontendDateFormat:
+    """Tests for validate_frontend_date_format."""
+
+    def test_none_or_empty_does_not_raise(self):
+        """None or empty string does not raise."""
+        validate_frontend_date_format(None)
+        validate_frontend_date_format('')
+        validate_frontend_date_format('   ')
+
+    def test_allowed_formats_do_not_raise(self):
+        """All allowed formats pass validation."""
+        for fmt in ALLOWED_FRONTEND_DATE_FORMATS:
+            validate_frontend_date_format(fmt)
+
+    def test_invalid_format_raises(self):
+        """Invalid format raises ValueError."""
+        with raises(ValueError, match='Invalid date_format'):
+            validate_frontend_date_format('yyyy-MM-dd')
+        with raises(ValueError, match='Invalid date_format'):
+            validate_frontend_date_format('custom-bad-format')
+
+
 class _IterableWithContains:
    def __init__(self, iterable, contains_values):
        self._iterable = iterable
--- a/tests/utils/models/test_train_model_params.py
+++ b/tests/utils/models/test_train_model_params.py
@@ -555,6 +555,27 @@ def test_validate_business_rules_valid_start_and_end_date(valid_train_params_dic
    params.validate_business_rules()  # Should not raise


+def test_validate_business_rules_invalid_date_format(valid_train_params_dict):
+    """Test validate_business_rules raises when date_format is not allowed."""
+    from model_manager.utils.models.train_model_params import TrainModelParams
+
+    params = TrainModelParams.from_dict(valid_train_params_dict)
+    params.date_format = 'yyyy-MM-dd'
+
+    with pytest.raises(ValueError, match='Invalid date_format'):
+        params.validate_business_rules()
+
+
+def test_validate_business_rules_valid_date_format(valid_train_params_dict):
+    """Test validate_business_rules accepts allowed date_format."""
+    from model_manager.utils.models.train_model_params import TrainModelParams
+
+    params = TrainModelParams.from_dict(valid_train_params_dict)
+    params.date_format = 'yyyy-MM-dd HH:mm:ss'
+
+    params.validate_business_rules()  # Should not raise
+
+
 def test_validate_business_rules_polynomial_regression_valid(valid_train_params_dict):
    """Test validate_business_rules accepts valid Polynomial Regression config."""
    from model_manager.utils.models.train_model_params import TrainModelParams
--- a/tests/utils/models/test_train_model_result.py
+++ b/tests/utils/models/test_train_model_result.py
@@ -41,6 +41,8 @@ def sample_params():
        scaler_name='Standard Scaler',
        support_filters={},
        static_threshold=None,
+        date_column=None,
+        date_format=None,
    )


--- a/tests/utils/repository/test_model_repository.py
+++ b/tests/utils/repository/test_model_repository.py
@@ -344,9 +344,14 @@ def test_create_run_directory_success(

    result = repo._create_run_directory('/tmp/reports', 'test_run')  # noqa: S108

-    expected_path = os.path.join('/tmp/reports/temp', 'test_run_20240101_120000_123456')  # noqa: S108
-    assert result == expected_path
-    mock_makedirs.assert_called_once_with(expected_path, exist_ok=True)
+    expected_path = os.path.normpath(
+        os.path.join('/tmp/reports', 'temp', 'test_run_20240101_120000_123456')  # noqa: S108
+    )
+    assert os.path.normpath(result) == expected_path
+    mock_makedirs.assert_called_once()
+    call_path = mock_makedirs.call_args[0][0]
+    assert os.path.normpath(call_path) == expected_path
+    assert mock_makedirs.call_args[1] == {'exist_ok': True}


@patch('model_manager.utils.repository.model_repository.ModelServing')
--- a/tests/utils/repository/test_training_repository.py
+++ b/tests/utils/repository/test_training_repository.py
@@ -10,7 +10,12 @@ import pytest
 from model_manager.sientia.models import LinearRegressionModel
 from model_manager.utils.models.train_model_params import TrainModelParams
 from model_manager.utils.models.train_model_result import TrainModelResult
-from model_manager.utils.repository.training_repository import TrainingRepository
+from model_manager.utils.repository.training_repository import (
+    TrainingRepository,
+    _apply_support_filters,
+    _ensure_date_column_parsed,
+    _frontend_format_to_strftime,
+)


@pytest.fixture
@@ -57,6 +62,8 @@ def sample_params():
        scaler_name='None',
        support_filters={},
        static_threshold=None,
+        date_column=None,
+        date_format=None,
    )


@@ -139,6 +146,8 @@ class TestExtractModelEquation:
            scaler_name='None',
            support_filters={},
            static_threshold=None,
+            date_column=None,
+            date_format=None,
        )

        # Mock model with single coefficient
@@ -585,7 +594,7 @@ class TestTrain:
 """
        return BytesIO(csv_content.encode('utf-8'))

-    @patch.object(TrainingRepository, '_configure_datetime_index', lambda self, df: df)
+    @patch.object(TrainingRepository, '_configure_datetime_index', lambda self, df, params: df)
    @patch('model_manager.utils.repository.training_repository.split_train_test')
    @patch('model_manager.utils.repository.training_repository.load_data')
    def test_train_basic_workflow(
@@ -631,7 +640,7 @@ class TestTrain:
        # Verify split was called
        assert mock_split_train_test.called

-    @patch.object(TrainingRepository, '_configure_datetime_index', lambda self, df: df)
+    @patch.object(TrainingRepository, '_configure_datetime_index', lambda self, df, params: df)
    @patch('model_manager.utils.repository.training_repository.split_train_test')
    @patch('model_manager.utils.repository.training_repository.load_data')
    def test_train_with_scaler(
@@ -661,7 +670,7 @@ class TestTrain:
        assert result is not None
        assert result.scaler_dict is not None

-    @patch.object(TrainingRepository, '_configure_datetime_index', lambda self, df: df)
+    @patch.object(TrainingRepository, '_configure_datetime_index', lambda self, df, params: df)
    @patch('model_manager.utils.repository.training_repository.split_train_test')
    @patch('model_manager.utils.repository.training_repository.load_data')
    def test_train_with_shuffle_enabled(
@@ -692,7 +701,7 @@ class TestTrain:
        call_kwargs = mock_split_train_test.call_args[1]
        assert call_kwargs['shuffle'] is True

-    @patch.object(TrainingRepository, '_configure_datetime_index', lambda self, df: df)
+    @patch.object(TrainingRepository, '_configure_datetime_index', lambda self, df, params: df)
    @patch('model_manager.utils.repository.training_repository.split_train_test')
    @patch('model_manager.utils.repository.training_repository.load_data')
    def test_train_with_different_train_size(
@@ -723,7 +732,7 @@ class TestTrain:
        call_kwargs = mock_split_train_test.call_args[1]
        assert call_kwargs['train_size'] == 0.7

-    @patch.object(TrainingRepository, '_configure_datetime_index', lambda self, df: df)
+    @patch.object(TrainingRepository, '_configure_datetime_index', lambda self, df, params: df)
    @patch('model_manager.utils.repository.training_repository.split_train_test')
    @patch('model_manager.utils.repository.training_repository.load_data')
    def test_train_raises_on_empty_data_after_transform(
@@ -744,7 +753,7 @@ class TestTrain:
        with pytest.raises(ValueError, match='Data view is empty after transformation'):
            training_repo.train(sample_csv_data, sample_params)

-    @patch.object(TrainingRepository, '_configure_datetime_index', lambda self, df: df)
+    @patch.object(TrainingRepository, '_configure_datetime_index', lambda self, df, params: df)
    @patch('model_manager.utils.repository.training_repository.split_train_test')
    @patch('model_manager.utils.repository.training_repository.load_data')
    def test_train_logs_success(
@@ -781,7 +790,7 @@ class TestTrain:
            'Model trained successfully' in str(call) for call in mock_logger.info.call_args_list
        )

-    @patch.object(TrainingRepository, '_configure_datetime_index', lambda self, df: df)
+    @patch.object(TrainingRepository, '_configure_datetime_index', lambda self, df, params: df)
    @patch('model_manager.utils.repository.training_repository.split_train_test')
    @patch('model_manager.utils.repository.training_repository.load_data')
    def test_train_with_custom_separators(
@@ -812,7 +821,7 @@ class TestTrain:
        # Verify load_data was called with custom separators
        mock_load_data.assert_called_once_with(sample_csv_data, ';', ',')

-    @patch.object(TrainingRepository, '_configure_datetime_index', lambda self, df: df)
+    @patch.object(TrainingRepository, '_configure_datetime_index', lambda self, df, params: df)
    @patch('model_manager.utils.repository.training_repository.split_train_test')
    @patch('model_manager.utils.repository.training_repository.load_data')
    def test_train_result_contains_all_fields(
@@ -862,19 +871,55 @@ class TestConfigureDatetimeIndex:
        """Create a TrainingRepository instance."""
        return TrainingRepository(logger=mock_logger)

-    def test_configure_datetime_index_already_datetime(self, training_repo):
+    @pytest.fixture
+    def datetime_params(self):
+        """Minimal params for _configure_datetime_index (date_column/date_format can be None)."""
+        return TrainModelParams(
+            experiment_run_id=1,
+            experiment_name='test',
+            target_variable='y',
+            variable_columns=['x'],
+            lag_train={'x': 0},
+            lag_val={'x': 0},
+            rem_static_win=False,
+            low_lim={},
+            upp_lim={},
+            window=0,
+            use_scaler=False,
+            include_ar=False,
+            train_size=80,
+            shuffle=True,
+            bucket_name='b',
+            file_name='f.csv',
+            line_separator=',',
+            decimal_separator='.',
+            removed_intervals=[],
+            model_name='Linear Regression',
+            degree=1,
+            interaction_only=False,
+            nan_treatment='drop',
+            start_date=None,
+            end_date=None,
+            scaler_name='None',
+            support_filters={},
+            static_threshold=None,
+            date_column=None,
+            date_format=None,
+        )
+
+    def test_configure_datetime_index_already_datetime(self, training_repo, datetime_params):
        """Test _configure_datetime_index when index is already DatetimeIndex."""
        data = pd.DataFrame(
            {'var1': [1, 2, 3], 'var2': [4, 5, 6]},
            index=pd.to_datetime(['2023-01-01', '2023-01-02', '2023-01-03']),
        )

-        result = training_repo._configure_datetime_index(data)
+        result = training_repo._configure_datetime_index(data, datetime_params)

        assert isinstance(result.index, pd.DatetimeIndex)
        assert len(result) == 3

-    def test_configure_datetime_index_with_timestamp_column(self, training_repo):
+    def test_configure_datetime_index_with_timestamp_column(self, training_repo, datetime_params):
        """Test _configure_datetime_index with 'timestamp' column."""
        data = pd.DataFrame(
            {
@@ -884,12 +929,12 @@ class TestConfigureDatetimeIndex:
            }
        )

-        result = training_repo._configure_datetime_index(data)
+        result = training_repo._configure_datetime_index(data, datetime_params)

        assert isinstance(result.index, pd.DatetimeIndex)
        assert 'timestamp' not in result.columns

-    def test_configure_datetime_index_with_date_column(self, training_repo):
+    def test_configure_datetime_index_with_date_column(self, training_repo, datetime_params):
        """Test _configure_datetime_index with 'date' column."""
        data = pd.DataFrame(
            {
@@ -898,12 +943,12 @@ class TestConfigureDatetimeIndex:
            }
        )

-        result = training_repo._configure_datetime_index(data)
+        result = training_repo._configure_datetime_index(data, datetime_params)

        assert isinstance(result.index, pd.DatetimeIndex)
        assert 'date' not in result.columns

-    def test_configure_datetime_index_with_datetime_column(self, training_repo):
+    def test_configure_datetime_index_with_datetime_column(self, training_repo, datetime_params):
        """Test _configure_datetime_index with 'datetime' column."""
        data = pd.DataFrame(
            {
@@ -912,12 +957,12 @@ class TestConfigureDatetimeIndex:
            }
        )

-        result = training_repo._configure_datetime_index(data)
+        result = training_repo._configure_datetime_index(data, datetime_params)

        assert isinstance(result.index, pd.DatetimeIndex)
        assert 'datetime' not in result.columns

-    def test_configure_datetime_index_first_column_datetime(self, training_repo):
+    def test_configure_datetime_index_first_column_datetime(self, training_repo, datetime_params):
        """Test _configure_datetime_index when first column looks like datetime."""
        data = pd.DataFrame(
            {
@@ -926,13 +971,13 @@ class TestConfigureDatetimeIndex:
            }
        )

-        result = training_repo._configure_datetime_index(data)
+        result = training_repo._configure_datetime_index(data, datetime_params)

        assert isinstance(result.index, pd.DatetimeIndex)
        assert 'my_date' not in result.columns

    @pytest.mark.filterwarnings('ignore::UserWarning')
-    def test_configure_datetime_index_no_timestamp_column(self, training_repo):
+    def test_configure_datetime_index_no_timestamp_column(self, training_repo, datetime_params):
        """Test _configure_datetime_index when no timestamp column found."""
        data = pd.DataFrame(
            {
@@ -941,14 +986,14 @@ class TestConfigureDatetimeIndex:
            }
        )

-        result = training_repo._configure_datetime_index(data)
+        result = training_repo._configure_datetime_index(data, datetime_params)

        # Should return original data unchanged (no valid datetime columns)
        assert 'var1' in result.columns
        assert 'var2' in result.columns

    @pytest.mark.filterwarnings('ignore::UserWarning')
-    def test_configure_datetime_index_invalid_timestamp_column(self, training_repo):
+    def test_configure_datetime_index_invalid_timestamp_column(self, training_repo, datetime_params):
        """Test _configure_datetime_index with invalid timestamp values."""
        data = pd.DataFrame(
            {
@@ -957,13 +1002,13 @@ class TestConfigureDatetimeIndex:
            }
        )

-        result = training_repo._configure_datetime_index(data)
+        result = training_repo._configure_datetime_index(data, datetime_params)

        # Should skip invalid column and try first column
        assert 'var1' in result.columns

    @pytest.mark.filterwarnings('ignore::UserWarning')
-    def test_configure_datetime_index_invalid_first_column(self, training_repo):
+    def test_configure_datetime_index_invalid_first_column(self, training_repo, datetime_params):
        """Test _configure_datetime_index when first column is not datetime."""
        data = pd.DataFrame(
            {
@@ -972,13 +1017,13 @@ class TestConfigureDatetimeIndex:
            }
        )

-        result = training_repo._configure_datetime_index(data)
+        result = training_repo._configure_datetime_index(data, datetime_params)

        # Should return original data unchanged
        assert 'var1' in result.columns
        assert 'var2' in result.columns

-    def test_configure_datetime_index_first_column_all_nan(self, training_repo):
+    def test_configure_datetime_index_first_column_all_nan(self, training_repo, datetime_params):
        """Test _configure_datetime_index when first column has all NaN values."""
        data = pd.DataFrame(
            {
@@ -987,7 +1032,7 @@ class TestConfigureDatetimeIndex:
            }
        )

-        result = training_repo._configure_datetime_index(data)
+        result = training_repo._configure_datetime_index(data, datetime_params)

        # Should return original data unchanged (first column has no valid values)
        assert 'first_col' in result.columns
@@ -1072,3 +1117,201 @@ class TestExtractModelEquationPolynomial:

        assert 'equation_string' in result
        assert 'latex_equation' in result
+
+
+# ============================================================================
+# Tests for _frontend_format_to_strftime and _ensure_date_column_parsed
+# ============================================================================
+
+
+class TestFrontendFormatToStrftime:
+    """Tests for _frontend_format_to_strftime (alias from models)."""
+
+    def test_empty_or_none_returns_none(self):
+        """Empty string or None returns None (same behavior as models module)."""
+        assert _frontend_format_to_strftime('') is None
+        assert _frontend_format_to_strftime(None) is None
+
+    def test_allowed_formats_convert_correctly(self):
+        """Allowed frontend formats convert to strftime via mapper."""
+        assert _frontend_format_to_strftime('dd/MM/yyyy HH:mm:ss') == '%d/%m/%Y %H:%M:%S'
+        assert _frontend_format_to_strftime('yyyy-MM-dd HH:mm:ss') == '%Y-%m-%d %H:%M:%S'
+        assert _frontend_format_to_strftime('MM/dd/yyyy HH:mm:ss') == '%m/%d/%Y %H:%M:%S'
+
+    def test_invalid_format_raises(self):
+        """Invalid date format raises ValueError."""
+        with pytest.raises(ValueError, match='Invalid date_format'):
+            _frontend_format_to_strftime('yyyy-MM-dd')
+
+
+class TestEnsureDateColumnParsed:
+    """Tests for _ensure_date_column_parsed."""
+
+    @pytest.fixture
+    def date_params(self):
+        """Params with date_column and date_format set."""
+        return TrainModelParams(
+            experiment_run_id=1,
+            experiment_name='test',
+            target_variable='y',
+            variable_columns=['x'],
+            lag_train={'x': 0},
+            lag_val={'x': 0},
+            rem_static_win=False,
+            low_lim={},
+            upp_lim={},
+            window=0,
+            use_scaler=False,
+            include_ar=False,
+            train_size=80,
+            shuffle=True,
+            bucket_name='b',
+            file_name='f.csv',
+            line_separator=',',
+            decimal_separator='.',
+            removed_intervals=[],
+            model_name='Linear Regression',
+            degree=1,
+            interaction_only=False,
+            nan_treatment='drop',
+            start_date=None,
+            end_date=None,
+            scaler_name='None',
+            support_filters={},
+            static_threshold=None,
+            date_column='ts',
+            date_format='yyyy-MM-dd HH:mm:ss',
+        )
+
+    def test_returns_unchanged_when_no_date_column(self, date_params):
+        """When params.date_column is None, data is returned unchanged."""
+        date_params.date_column = None
+        date_params.date_format = None
+        data = pd.DataFrame({'ts': ['2023-01-01'], 'x': [1]})
+        result = _ensure_date_column_parsed(data, date_params)
+        pd.testing.assert_frame_equal(result, data)
+
+    def test_returns_unchanged_when_column_missing(self, date_params):
+        """When date_column not in data columns, data is returned unchanged."""
+        data = pd.DataFrame({'other': [1], 'x': [2]})
+        result = _ensure_date_column_parsed(data, date_params)
+        pd.testing.assert_frame_equal(result, data)
+
+    def test_parses_column_with_format(self, date_params):
+        """When date_column and date_format set, column is parsed as datetime."""
+        data = pd.DataFrame({
+            'ts': ['2023-01-01 10:00:00', '2023-06-15 14:30:00'],
+            'x': [1, 2],
+        })
+        result = _ensure_date_column_parsed(data, date_params)
+        assert result['ts'].dtype == 'datetime64[ns]'
+        assert result['ts'].iloc[0].year == 2023
+        assert result['ts'].iloc[0].month == 1
+        assert result['ts'].iloc[1].month == 6
+
+    def test_invalid_values_coerced_to_nat(self, date_params):
+        """Invalid date strings are coerced to NaT when format is set."""
+        date_params.date_format = 'yyyy-MM-dd HH:mm:ss'
+        data = pd.DataFrame({
+            'ts': ['2023-01-01 00:00:00', 'not-a-date', '2023-12-31 00:00:00'],
+            'x': [1, 2, 3],
+        })
+        result = _ensure_date_column_parsed(data, date_params)
+        assert pd.isna(result['ts'].iloc[1])
+        assert result['ts'].iloc[0].year == 2023
+        assert result['ts'].iloc[2].month == 12
+
+
+# ============================================================================
+# Tests for _apply_support_filters
+# ============================================================================
+
+
+class TestApplySupportFilters:
+    """Tests for _apply_support_filters function."""
+
+    def test_empty_support_filters_returns_unchanged(self):
+        """When support_filters is empty, data_view is returned unchanged."""
+        data = pd.DataFrame({'x': [1, 2, 3], 'target': [10, 20, 30]})
+        result = _apply_support_filters(data, 'target', {})
+        pd.testing.assert_frame_equal(result, data)
+
+    def test_target_not_in_columns_returns_unchanged(self):
+        """When target_variable is not in data_view columns, return unchanged."""
+        data = pd.DataFrame({'x': [1, 2, 3], 'y': [10, 20, 30]})
+        result = _apply_support_filters(data, 'target', {'x': {}})
+        pd.testing.assert_frame_equal(result, data)
+
+    def test_variable_not_in_columns_skipped(self):
+        """When a filter variable is not in data_view, that variable is skipped."""
+        data = pd.DataFrame({'x': [1, 2, 3], 'target': [10, 20, 30]})
+        support_filters = {
+            'missing_var': {
+                'upper_line': {'intercept': 100, 'angle': 10},
+                'lower_line': {'intercept': 0, 'angle': -10},
+            },
+        }
+        result = _apply_support_filters(data, 'target', support_filters)
+        pd.testing.assert_frame_equal(result, data)
+
+    def test_snake_case_upper_lower_line(self):
+        """Support filters with upper_line/lower_line (snake_case) filter rows."""
+        data = pd.DataFrame({
+            'x': [1.0, 2.0, 3.0, 4.0],
+            'target': [2.0, 4.0, 6.0, 8.0],
+        })
+        support_filters = {
+            'x': {
+                'upper_line': {'intercept': 1.0, 'angle': 50},
+                'lower_line': {'intercept': -1.0, 'angle': -50},
+            },
+        }
+        result = _apply_support_filters(data, 'target', support_filters)
+        assert len(result) <= 4
+        assert list(result.columns) == ['x', 'target']
+
+    def test_camel_case_upper_lower_line(self):
+        """Support filters with upperLine/lowerLine (camelCase) are accepted."""
+        data = pd.DataFrame({
+            'x': [1.0, 2.0, 3.0],
+            'target': [1.0, 2.0, 3.0],
+        })
+        support_filters = {
+            'x': {
+                'upperLine': {'intercept': 2, 'angle': 5},
+                'lowerLine': {'intercept': 0, 'angle': -5},
+            },
+        }
+        result = _apply_support_filters(data, 'target', support_filters)
+        assert len(result) <= 3
+        assert list(result.columns) == ['x', 'target']
+
+    def test_two_variables_ands_masks(self):
+        """Two variables apply AND of both masks."""
+        data = pd.DataFrame({
+            'a': [1.0, 2.0, 3.0],
+            'b': [1.0, 2.0, 3.0],
+            'target': [2.0, 2.0, 2.0],
+        })
+        support_filters = {
+            'a': {
+                'upper_line': {'intercept': 10, 'angle': 45},
+                'lower_line': {'intercept': -10, 'angle': -45},
+            },
+            'b': {
+                'upper_line': {'intercept': 10, 'angle': 45},
+                'lower_line': {'intercept': -10, 'angle': -45},
+            },
+        }
+        result = _apply_support_filters(data, 'target', support_filters)
+        assert len(result) <= 3
+        assert list(result.columns) == ['a', 'b', 'target']
+
+    def test_missing_upper_or_lower_skips_variable(self):
+        """If upper_line or lower_line is missing, that variable is skipped."""
+        data = pd.DataFrame({'x': [1, 2, 3], 'target': [10, 20, 30]})
+        support_filters = {
+            'x': {'upper_line': {'intercept': 100, 'angle': 0}},
+        }
+        result = _apply_support_filters(data, 'target', support_filters)
+        pd.testing.assert_frame_equal(result, data)