bump version

includes several aspects:

- harden forecast logic with additional error checks
- fix wrong behaviour
- ensure minimum data viability
- extrapolate for multiple data points into the future

fix #19

Co-authored-by: frasu
Reviewed-on: #21
Co-authored-by: foefl <f.foerster@d-opt.com>
Co-committed-by: foefl <f.foerster@d-opt.com>

pdm.lock

@@ -5,7 +5,7 @@
 groups = ["default", "dev", "lint", "nb", "tests"]
 strategy = ["inherit_metadata"]
 lock_version = "4.5.0"
-content_hash = "sha256:4931e32f8c146a72ad5b0a13c02485ea5ddc727de32fbe7c5e9314bbab05966c"
+content_hash = "sha256:545c39ef89d18d28a7bca4b08c93e6fb900c42612089300b867a4e0955acd6ab"
 
 [[metadata.targets]]
 requires_python = ">=3.11"
@@ -579,7 +579,7 @@ files = [
 
 [[package]]
 name = "dopt-basics"
-version = "0.1.2"
+version = "0.1.3"
 requires_python = ">=3.11"
 summary = "basic cross-project tools for Python-based d-opt projects"
 groups = ["default"]
@@ -587,8 +587,8 @@ dependencies = [
     "tzdata>=2025.1",
 ]
 files = [
-    {file = "dopt_basics-0.1.2-py3-none-any.whl", hash = "sha256:dae8b7e31197fb173d98c74ed6f227c3dceaadf980139f0852a7f031d2e78b84"},
+    {file = "dopt_basics-0.1.3-py3-none-any.whl", hash = "sha256:974c2b442e47f0f05e66ff821ae48a9b12f7b77a8a3bc06fe8ac232e2bc27608"},
-    {file = "dopt_basics-0.1.2.tar.gz", hash = "sha256:dc54942db95b0608fa44f7b612ee3247dad50d2538ad88a1697b3357a8b05634"},
+    {file = "dopt_basics-0.1.3.tar.gz", hash = "sha256:22ba30cbd385cb8929cb6a13fe01e253cd7d9617ef637e41609f2468691450e8"},
 ]
 
 [[package]]
@@ -2414,6 +2414,17 @@ files = [
     {file = "tinycss2-1.4.0.tar.gz", hash = "sha256:10c0972f6fc0fbee87c3edb76549357415e94548c1ae10ebccdea16fb404a9b7"},
 ]
 
+[[package]]
+name = "tomli-w"
+version = "1.2.0"
+requires_python = ">=3.9"
+summary = "A lil' TOML writer"
+groups = ["dev"]
+files = [
+    {file = "tomli_w-1.2.0-py3-none-any.whl", hash = "sha256:188306098d013b691fcadc011abd66727d3c414c571bb01b1a174ba8c983cf90"},
+    {file = "tomli_w-1.2.0.tar.gz", hash = "sha256:2dd14fac5a47c27be9cd4c976af5a12d87fb1f0b4512f81d69cce3b35ae25021"},
+]
+
 [[package]]
 name = "tomlkit"
 version = "0.13.2"

pyproject.toml

@@ -1,11 +1,11 @@
 [project]
 name = "delta-barth"
-version = "0.5.7dev1"
+version = "0.5.7"
 description = "workflows and pipelines for the Python-based Plugin of Delta Barth's ERP system"
 authors = [
     {name = "Florian Förster", email = "f.foerster@d-opt.com"},
 ]
-dependencies = ["scikit-learn>=1.6.1", "pandas>=2.2.3", "xgboost>=2.1.4", "joblib>=1.4.2", "typing-extensions>=4.12.2", "requests>=2.32.3", "pydantic>=2.10.6", "dopt-basics>=0.1.2", "SQLAlchemy>=2.0.39"]
+dependencies = ["scikit-learn>=1.6.1", "pandas>=2.2.3", "xgboost>=2.1.4", "joblib>=1.4.2", "typing-extensions>=4.12.2", "requests>=2.32.3", "pydantic>=2.10.6", "dopt-basics>=0.1.3", "SQLAlchemy>=2.0.39"]
 requires-python = ">=3.11"
 readme = "README.md"
 license = {text = "LicenseRef-Proprietary"}
@@ -44,7 +44,8 @@ filterwarnings = [
 ]
 markers = [
     "api_con_required: tests require an API connection (deselect with '-m \"not api_con_required\"')",
-    "new: to test only new tests, usually removed afterwards (deselect with '-m \"not quick\"')",
+    "new: to test only new tests, usually removed afterwards (deselect with '-m \"not new\"')",
+    "forecast: main components of forecast pipeline (deselect with '-m \"not forecast\"')"
 ]
 log_cli = true
 
@@ -73,7 +74,7 @@ directory = "reports/coverage"
 
 
 [tool.bumpversion]
-current_version = "0.5.7dev1"
+current_version = "0.5.7"
 parse = """(?x)
     (?P<major>0|[1-9]\\d*)\\.
     (?P<minor>0|[1-9]\\d*)\\.
@@ -145,6 +146,7 @@ dev = [
     "pdoc3>=0.11.5",
     "bump-my-version>=1.1.1",
     "nox>=2025.2.9",
+    "tomli-w>=1.2.0",
 ]
 nb = [
     "jupyterlab>=4.3.5",

src/delta_barth/analysis/forecast.py

@@ -8,7 +8,6 @@ from dataclasses import asdict
 from datetime import datetime as Datetime
 from typing import TYPE_CHECKING, Final, TypeAlias, cast
 
-import dopt_basics.datetime
 import numpy as np
 import pandas as pd
 import scipy.stats
@@ -16,7 +15,6 @@ import sqlalchemy as sql
 
 # --- new: for calculating timedelta
 from dateutil.relativedelta import relativedelta
-from dopt_basics.datetime import TimeUnitsTimedelta
 from sklearn.metrics import mean_absolute_error, r2_score
 from sklearn.model_selection import KFold, RandomizedSearchCV
 from xgboost import XGBRegressor
@@ -35,7 +33,6 @@ from delta_barth.constants import (
     DEFAULT_DB_ERR_CODE,
     DUMMY_DATA_PATH,
     FEATURES_SALES_PROGNOSIS,
-    SALES_BASE_NUM_DATAPOINTS_MONTHS,
     SALES_MIN_NUM_DATAPOINTS,
 )
 from delta_barth.errors import STATUS_HANDLER, wrap_result
@@ -210,17 +207,25 @@ def _process_sales(
     df_cust["jahr"] = df_cust[DATE_FEAT].dt.year
     df_cust["monat"] = df_cust[DATE_FEAT].dt.month
 
-    current_year = datetime.now().year
+    monthly_sum_data_only = df_cust.groupby(["jahr", "monat"])[SALES_FEAT].sum().reset_index()
-    current_month = datetime.now().month
+
+    current_year = datetime.datetime.now().year
+    current_month = datetime.datetime.now().month
     years = range(df_cust["jahr"].min(), current_year + 1)
 
-    old_monthly_sum = df_cust.groupby(["jahr", "monat"])[SALES_FEAT].sum().reset_index()
-
     all_month_year_combinations = pd.DataFrame(
-        [(year, month) for year in years for month in range(1, 13) if (year < current_year or (year == current_year and month <= current_month))], columns=["jahr", "monat"]
+        [
+            (year, month)
+            for year in years
+            for month in range(1, 13)
+            if (year < current_year or (year == current_year and month <= current_month))
+        ],
+        columns=["jahr", "monat"],
     )
 
-    monthly_sum = pd.merge(all_month_year_combinations, old_monthly_sum, on=["jahr", "monat"], how="left")
+    monthly_sum = pd.merge(
+        all_month_year_combinations, monthly_sum_data_only, on=["jahr", "monat"], how="left"
+    )
     monthly_sum[SALES_FEAT] = monthly_sum[SALES_FEAT].fillna(0)
     monthly_sum[DATE_FEAT] = (
         monthly_sum["monat"].astype(str) + "." + monthly_sum["jahr"].astype(str)
@@ -256,52 +261,24 @@ def _process_sales(
     best_score_r2: float | None = None
     best_start_year: int | None = None
     too_few_month_points: bool = True
-    
-    stride = dopt_basics.datetime.timedelta_from_val(365, TimeUnitsTimedelta.DAYS)
-    dates = cast(pd.DatetimeIndex, monthly_sum.index)
-    min_date = dates.min()
 
+    dates = cast(pd.DatetimeIndex, monthly_sum.index)
     # baseline: 3 years - 36 months
     starting_date = datetime.datetime.now() - relativedelta(months=36)
 
-    def get_index_date(
+    target_index, _ = next(
-        dates: pd.DatetimeIndex,
+        ((i, True) for i, date in enumerate(dates) if date >= starting_date),
-        starting_date: datetime.datetime | pd.Timestamp,
+        (len(dates) - 1, False),
-    ) -> tuple[pd.Timestamp, bool]:
+    )
-        target, succ = next(
-            ((date, True) for date in dates if date >= starting_date), (dates[-1], False)
-        )
-        return target, succ
 
-    first_date, succ = get_index_date(dates, starting_date)
+    for add_year, date_idx in enumerate(range(target_index, -1, -12)):
-    if not succ:
+        first_date = dates[date_idx]
-        # !! return early
-        ...
-
-    date_span = first_date - min_date
-    steps = date_span.days // stride.days
-
-    for step in range(steps + 1):
-        print("step: ", step)
-        target_date = first_date - step * stride
-        print("target date: ", target_date)
         split_date = dates[-6]
 
-        index_date, succ = get_index_date(dates, target_date)
-
-        if not succ:
-            break
-
-        if index_date >= split_date:
-            print("Skip because of date difference")
-            continue
-
         train = cast(
             pd.DataFrame,
-            monthly_sum.loc[index_date:split_date].copy(),  # type: ignore
+            monthly_sum.loc[first_date:split_date].copy(),  # type: ignore
         )
-        print(train)
-        print("Length train: ", len(train))
         test = cast(
             pd.DataFrame,
             monthly_sum.loc[split_date:].copy(),  # type: ignore
@@ -311,7 +288,7 @@ def _process_sales(
 
         # test set size fixed at 6 --> first iteration: baseline - 6 entries
         # for each new year 10 new data points (i.e., sales strictly positive) needed
-        if len(train[train[SALES_FEAT] > 0]) >= 30 + 10 * step:
+        if len(train[train[SALES_FEAT] > 0]) >= (base_num_data_points_months + 10 * add_year):
             too_few_month_points = False
 
             rand = RandomizedSearchCV(
@@ -334,12 +311,11 @@ def _process_sales(
                     best_params = cast(BestParametersXGBRegressor, rand.best_params_)
                     best_score_mae = error
                     best_score_r2 = cast(float, r2_score(y_test, y_pred))
-                    # --- new: use target_date for best_start_year
+                    # --- new: use first_date for best_start_year
-                    best_start_year = target_date.year
+                    best_start_year = first_date.year
                     # --- new: store best_estimator
                     best_estimator = copy.copy(rand.best_estimator_)
 
-    # ?? --- new: use best_estimator to calculate future values and store them in forecast
     if best_estimator is not None:
         X_future = pd.DataFrame(
             {"jahr": future_dates.year, "monat": future_dates.month}, index=future_dates
@@ -456,7 +432,7 @@ def pipeline_sales_forecast(
     pipe = _process_sales(
         pipe,
         min_num_data_points=SALES_MIN_NUM_DATAPOINTS,
-        base_num_data_points_months=SALES_BASE_NUM_DATAPOINTS_MONTHS,
+        base_num_data_points_months=SESSION.cfg.forecast.threshold_month_data_points,
     )
     if pipe.statistics is not None:
         res = _write_sales_forecast_stats_wrapped(pipe.statistics)

src/delta_barth/config.py

@@ -0,0 +1,43 @@
+from __future__ import annotations
+
+from pathlib import Path
+
+import dopt_basics.configs
+from pydantic import BaseModel
+
+
+class Config(BaseModel):
+    forecast: CfgForecast
+
+
+class CfgForecast(BaseModel):
+    threshold_month_data_points: int
+
+
+class LazyCfgLoader:
+    def __init__(
+        self,
+        cfg_path: Path,
+    ) -> None:
+        cfg_path = cfg_path.resolve()
+        assert cfg_path.exists(), f"config path {cfg_path} seems not to exist"
+        assert cfg_path.is_file(), f"config path {cfg_path} seems not to be a file"
+        self._path = cfg_path
+        self._cfg: Config | None = None
+
+    @property
+    def path(self) -> Path:
+        return self._path
+
+    def _load(self) -> Config:
+        cfg = dopt_basics.configs.load_toml(self.path)
+
+        return Config(**cfg)
+
+    def reload(self) -> None:
+        self._cfg = self._load()
+
+    def get(self) -> Config:
+        if self._cfg is None:
+            self._cfg = self._load()
+        return self._cfg

src/delta_barth/constants.py

@@ -5,6 +5,7 @@ from typing import Final
 from delta_barth.types import DualDict, HttpContentHeaders
 
 # ** config
+CFG_FILENAME: Final[str] = "dopt-cfg.toml"
 
 # ** lib path
 lib_path = Path(__file__).parent
@@ -63,4 +64,6 @@ FEATURES_SALES_PROGNOSIS: Final[frozenset[str]] = frozenset(
 # ** Pipelines
 # ** Forecast
 SALES_MIN_NUM_DATAPOINTS: Final[int] = 36
-SALES_BASE_NUM_DATAPOINTS_MONTHS: Final[int] = 36
+# !! now in config
+# TODO remove later till proven stable
+# SALES_BASE_NUM_DATAPOINTS_MONTHS: Final[int] = 36

src/delta_barth/dopt-cfg.toml

@@ -0,0 +1,2 @@
+[forecast]
+threshold_month_data_points = 28

src/delta_barth/logging.py

@@ -31,6 +31,8 @@ logger_status = logging.getLogger("delta_barth.status")
 logger_status.setLevel(logging.DEBUG)
 logger_session = logging.getLogger("delta_barth.session")
 logger_session.setLevel(logging.DEBUG)
+logger_config = logging.getLogger("delta_barth.config")
+logger_config.setLevel(logging.DEBUG)
 logger_management = logging.getLogger("delta_barth.management")
 logger_management.setLevel(logging.DEBUG)
 logger_wrapped_results = logging.getLogger("delta_barth.wrapped_results")

src/delta_barth/session.py

@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import shutil
 from pathlib import Path
 from typing import TYPE_CHECKING, Final
 
@@ -14,12 +15,19 @@ from delta_barth.api.common import (
     LoginResponse,
     validate_credentials,
 )
-from delta_barth.constants import API_CON_TIMEOUT, DB_ECHO
+from delta_barth.config import LazyCfgLoader
+from delta_barth.constants import (
+    API_CON_TIMEOUT,
+    CFG_FILENAME,
+    DB_ECHO,
+    LIB_PATH,
+)
 from delta_barth.errors import STATUS_HANDLER
 from delta_barth.logging import logger_session as logger
 from delta_barth.types import DelBarApiError, Status
 
 if TYPE_CHECKING:
+    from delta_barth.config import Config
     from delta_barth.types import ApiCredentials, HttpContentHeaders
 
 
@@ -41,6 +49,7 @@ class Session:
         base_headers: HttpContentHeaders,
         db_folder: str = "data",
         logging_folder: str = "logs",
+        cfg_folder: str = "config",
     ) -> None:
         self._setup: bool = False
         self._data_path: Path | None = None
@@ -49,6 +58,10 @@ class Session:
         self._db_engine: sql.Engine | None = None
         self._logging_dir: Path | None = None
         self._logging_folder = logging_folder
+        self._cfg_path: Path | None = None
+        self._cfg_folder = cfg_folder
+        self._cfg_loader: LazyCfgLoader | None = None
+        self._cfg: Config | None = None
         self._creds: ApiCredentials | None = None
         self._base_url: str | None = None
         self._headers = base_headers
@@ -59,6 +72,7 @@ class Session:
         # at this point: no logging configured
         assert not self._setup, "tried to setup session twice"
         self._setup_logging()
+        self._setup_config()
         self._setup_db_management()
         self._setup = True
         logger.info("[SESSION] Setup procedure successful")
@@ -68,6 +82,32 @@ class Session:
         assert self._data_path is not None, "accessed data path not set"
         return self._data_path
 
+    @property
+    def cfg_path(self) -> Path:
+        if self._cfg_path is not None and self._setup:
+            return self._cfg_path
+
+        root = (self.data_path / self._cfg_folder).resolve()
+        cfg_path = root / CFG_FILENAME
+        if not root.exists():
+            root.mkdir(parents=False)
+        self._cfg_path = cfg_path
+        return self._cfg_path
+
+    @property
+    def cfg(self) -> Config:
+        assert self._cfg is not None, "tried to access not set config from session"
+        return self._cfg
+
+    def _setup_config(self) -> None:
+        if not self.cfg_path.exists():
+            src_cfg = LIB_PATH / CFG_FILENAME
+            shutil.copyfile(src_cfg, self.cfg_path)
+
+        self._cfg_loader = LazyCfgLoader(self.cfg_path)
+        self._cfg = self._cfg_loader.get()
+        logger.info("[SESSION] Successfully read and setup config")
+
     @property
     def db_engine(self) -> sql.Engine:
         assert self._db_engine is not None, "accessed database engine not set"
@@ -78,10 +118,10 @@ class Session:
         if self._db_path is not None and self._setup:
             return self._db_path
 
-        db_root = (self.data_path / self._db_folder).resolve()
+        root = (self.data_path / self._db_folder).resolve()
-        db_path = db_root / "dopt-data.db"
+        db_path = root / "dopt-data.db"
-        if not db_root.exists():
+        if not root.exists():
-            db_root.mkdir(parents=False)
+            root.mkdir(parents=False)
         self._db_path = db_path
         return self._db_path
 

tests/_test_data/dopt-cfg.toml

@@ -0,0 +1,2 @@
+[forecast]
+threshold_month_data_points = 28

tests/analysis/test_forecast.py

@@ -1,3 +1,4 @@
+import datetime
 from datetime import datetime as Datetime
 from unittest.mock import patch
 
@@ -255,6 +256,7 @@ def test_preprocess_sales_FailOnTargetFeature(
     assert pipe.results is None
 
 
+@pytest.mark.forecast
 def test_process_sales_Success(sales_data_real_preproc):
     data = sales_data_real_preproc.copy()
     pipe = PipeResult(data, STATUS_HANDLER.SUCCESS)
@@ -277,6 +279,7 @@ def test_process_sales_Success(sales_data_real_preproc):
     assert pipe.statistics.xgb_params is not None
 
 
+@pytest.mark.forecast
 def test_process_sales_FailTooFewPoints(sales_data_real_preproc):
     data = sales_data_real_preproc.copy()
     data = data.iloc[:20, :]
@@ -303,6 +306,7 @@ def test_process_sales_FailTooFewPoints(sales_data_real_preproc):
     assert pipe.statistics.xgb_params is None
 
 
+@pytest.mark.forecast
 def test_process_sales_FailTooFewMonthPoints(sales_data_real_preproc):
     data = sales_data_real_preproc.copy()
     pipe = PipeResult(data, STATUS_HANDLER.SUCCESS)
@@ -329,8 +333,19 @@ def test_process_sales_FailTooFewMonthPoints(sales_data_real_preproc):
     assert pipe.statistics.xgb_params is None
 
 
+@pytest.mark.forecast
 def test_process_sales_FailNoReliableForecast(sales_data_real_preproc):
-    data = sales_data_real_preproc.copy()
+    # prepare fake data
+    df = sales_data_real_preproc.copy()
+    f_dates = "buchungs_datum"
+    end = datetime.datetime.now()
+    start = df[f_dates].max()
+    fake_dates = pd.date_range(start, end, freq="MS")
+    fake_data = [(1234, 1014, 1024, 1000, 10, date) for date in fake_dates]
+    fake_df = pd.DataFrame(fake_data, columns=df.columns)
+    enhanced_df = pd.concat((df, fake_df), ignore_index=True)
+
+    data = enhanced_df.copy()
     data["betrag"] = 10000
     print(data["betrag"])
     data = data.iloc[:20000, :]
@@ -340,7 +355,7 @@ def test_process_sales_FailNoReliableForecast(sales_data_real_preproc):
         def __init__(self, *args, **kwargs) -> None:
             class Predictor:
                 def predict(self, *args, **kwargs):
-                    return np.array([1, 1, 1, 1])
+                    return np.array([1, 1, 1, 1], dtype=np.float64)
 
             self.best_estimator_ = Predictor()
 
@@ -354,7 +369,7 @@ def test_process_sales_FailNoReliableForecast(sales_data_real_preproc):
         pipe = fc._process_sales(
             pipe,
             min_num_data_points=1,
-            base_num_data_points_months=-100,
+            base_num_data_points_months=1,
         )
 
     assert pipe.status != STATUS_HANDLER.SUCCESS
@@ -415,27 +430,16 @@ def test_export_on_fail():
     assert res.status.description == status.description
 
 
-@patch("delta_barth.analysis.forecast.SALES_BASE_NUM_DATAPOINTS_MONTHS", 1)
 def test_pipeline_sales_forecast_SuccessDbWrite(exmpl_api_sales_prognosis_resp, session):
-    with patch(
+    with (
-        "delta_barth.analysis.forecast.get_sales_prognosis_data",
+        patch(
-    ) as mock:
+            "delta_barth.analysis.forecast.get_sales_prognosis_data",
-        mock.return_value = exmpl_api_sales_prognosis_resp, STATUS_HANDLER.SUCCESS
+        ) as get_mock,
-        with patch("delta_barth.analysis.forecast.SESSION", session):
+        patch("delta_barth.analysis.forecast.SESSION", session) as sess_mock,
-            result = fc.pipeline_sales_forecast(None)  # type: ignore
+    ):
-    print(result)
+        get_mock.return_value = exmpl_api_sales_prognosis_resp, STATUS_HANDLER.SUCCESS
-    assert result.status == STATUS_HANDLER.SUCCESS
+        sess_mock.cfg.forecast.threshold_month_data_points = 1
-    assert len(result.response.daten) > 0
-
-
-@patch("delta_barth.analysis.forecast.SALES_BASE_NUM_DATAPOINTS_MONTHS", 1)
-def test_pipeline_sales_forecast_FailDbWrite(exmpl_api_sales_prognosis_resp):
-    with patch(
-        "delta_barth.analysis.forecast.get_sales_prognosis_data",
-    ) as mock:
-        mock.return_value = exmpl_api_sales_prognosis_resp, STATUS_HANDLER.SUCCESS
         result = fc.pipeline_sales_forecast(None)  # type: ignore
-    print(result)
     assert result.status == STATUS_HANDLER.SUCCESS
     assert len(result.response.daten) > 0
 

tests/conftest.py

@@ -8,6 +8,7 @@ from unittest.mock import patch
 
 import pandas as pd
 import pytest
+import tomli_w
 
 import delta_barth.session
 from delta_barth.api.requests import SalesPrognosisResponse
@@ -33,6 +34,28 @@ def api_base_url(credentials) -> str:
     return credentials["base_url"]
 
 
+@pytest.fixture(scope="session")
+def pth_dummy_cfg() -> Path:
+    pwd = Path.cwd()
+    assert "barth" in pwd.parent.name.lower(), "not in project root directory"
+    data_pth = pwd / "./tests/_test_data/dopt-cfg.toml"
+    assert data_pth.exists(), "file to dummy CFG not found"
+    return data_pth
+
+
+@pytest.fixture(scope="function")
+def pth_cfg(pth_dummy_cfg, tmp_path) -> Path:
+    with open(pth_dummy_cfg, "rb") as file:
+        cfg_data = tomllib.load(file)
+
+    target = tmp_path / "dummy_cfg.toml"
+    target.touch()
+    with open(target, "wb") as file:
+        tomli_w.dump(cfg_data, file)
+
+    return target
+
+
 @pytest.fixture(scope="session")
 def sales_data_real() -> pd.DataFrame:
     pwd = Path.cwd()

tests/test_config.py

@@ -0,0 +1,40 @@
+import tomllib
+
+import tomli_w
+
+from delta_barth import config
+
+
+def test_CfgLoader_Init(pth_cfg):
+    loader = config.LazyCfgLoader(pth_cfg)
+
+    assert loader.path == pth_cfg
+    assert loader._cfg is None
+
+
+def test_CfgLoader_Get(pth_cfg):
+    loader = config.LazyCfgLoader(pth_cfg)
+
+    parsed_cfg = loader.get()
+    assert isinstance(parsed_cfg, config.Config)
+    assert parsed_cfg.forecast.threshold_month_data_points == 28
+
+
+def test_CfgLoader_Reload(pth_cfg):
+    loader = config.LazyCfgLoader(pth_cfg)
+
+    parsed_cfg = loader.get()
+    assert isinstance(parsed_cfg, config.Config)
+    assert parsed_cfg.forecast.threshold_month_data_points == 28
+    # modify config and reload
+    with open(pth_cfg, "rb") as file:
+        cfg_data = tomllib.load(file)
+    cfg_data["forecast"]["threshold_month_data_points"] = 30
+    with open(pth_cfg, "wb") as file:
+        tomli_w.dump(cfg_data, file)
+
+    assert parsed_cfg.forecast.threshold_month_data_points == 28
+    loader.reload()
+    parsed_cfg = loader.get()
+    assert isinstance(parsed_cfg, config.Config)
+    assert parsed_cfg.forecast.threshold_month_data_points == 30

tests/test_pipelines.py

@@ -1,17 +1,15 @@
-import importlib
 import json
 from unittest.mock import patch
 
 import pytest
 import sqlalchemy as sql
 
-import delta_barth.pipelines
 from delta_barth import databases as db
 from delta_barth import pipelines as pl
 from delta_barth.errors import STATUS_HANDLER
 
 
-def test_write_performance_metrics(session):
+def test_write_performance_metrics_Success(session):
     pipe_name = "test_pipe"
     t_start = 20_000_000_000
     t_end = 30_000_000_000
@@ -33,14 +31,31 @@ def test_write_performance_metrics(session):
     assert metrics.execution_duration == 10
 
 
-@patch("delta_barth.analysis.forecast.SALES_BASE_NUM_DATAPOINTS_MONTHS", 1)
+def test_write_performance_metrics_FailStartingTime(session):
-def test_sales_prognosis_pipeline(exmpl_api_sales_prognosis_resp, session):
+    pipe_name = "test_pipe"
-    with patch(
+    t_start = 30_000_000_000
-        "delta_barth.analysis.forecast.get_sales_prognosis_data",
+    t_end = 20_000_000_000
-    ) as mock:
+
-        mock.return_value = (exmpl_api_sales_prognosis_resp, STATUS_HANDLER.SUCCESS)
+    with patch("delta_barth.pipelines.SESSION", session):
-        with patch("delta_barth.pipelines.SESSION", session):
+        with pytest.raises(ValueError):
-            json_export = pl.pipeline_sales_forecast(None, None)
+            _ = pl._write_performance_metrics(
+                pipeline_name=pipe_name,
+                time_start=t_start,
+                time_end=t_end,
+            )
+
+
+def test_sales_prognosis_pipeline(exmpl_api_sales_prognosis_resp, session, monkeypatch):
+    with (
+        patch(
+            "delta_barth.analysis.forecast.get_sales_prognosis_data",
+        ) as get_mock,
+        patch("delta_barth.pipelines.SESSION", session),
+        patch("delta_barth.analysis.forecast.SESSION", session) as sess_mock,
+    ):
+        get_mock.return_value = (exmpl_api_sales_prognosis_resp, STATUS_HANDLER.SUCCESS)
+        sess_mock.cfg.forecast.threshold_month_data_points = 1
+        json_export = pl.pipeline_sales_forecast(None, None)
 
     assert isinstance(json_export, str)
     parsed_resp = json.loads(json_export)
@@ -59,7 +74,6 @@ def test_sales_prognosis_pipeline(exmpl_api_sales_prognosis_resp, session):
     assert metrics.execution_duration > 0
 
 
-@pytest.mark.new
 def test_sales_prognosis_pipeline_dummy(session):
     with patch("delta_barth.pipelines.SESSION", session):
         json_export = pl.pipeline_sales_forecast_dummy(None, None)

tests/test_session.py

@@ -62,8 +62,27 @@ def test_session_setup_db_management(tmp_path):
     assert db_path.exists()
 
 
+def test_session_setup_config(tmp_path, pth_cfg):
+    str_path = str(tmp_path)
+    foldername: str = "cfg_test"
+    target_cfg_dir = tmp_path / foldername
+    session = delta_barth.session.Session(HTTP_BASE_CONTENT_HEADERS, cfg_folder=foldername)
+    session.set_data_path(str_path)
+    cfg_path = session.cfg_path
+    assert cfg_path.parent.exists()
+    assert cfg_path.parent == target_cfg_dir
+    assert not cfg_path.exists()
+    session.setup()
+    cfg_path2 = session.cfg_path
+    assert cfg_path2 == cfg_path
+    assert session._cfg is not None
+    assert cfg_path.exists()
+    assert session.cfg.forecast.threshold_month_data_points == 28
+
+
 @patch("delta_barth.logging.ENABLE_LOGGING", True)
 @patch("delta_barth.logging.LOGGING_TO_FILE", True)
+@patch("delta_barth.logging.LOGGING_TO_STDERR", True)
 def test_session_setup_logging(tmp_path):
     str_path = str(tmp_path)
     foldername: str = "logging_test"