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>

.gitignore

@@ -3,6 +3,7 @@ prototypes/
 data/
 reports/
 *.code-workspace
+docs/
 
 # credentials
 CREDENTIALS*

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-0.1.2.tar.gz", hash = "sha256:dc54942db95b0608fa44f7b612ee3247dad50d2538ad88a1697b3357a8b05634"},
+    {file = "dopt_basics-0.1.3-py3-none-any.whl", hash = "sha256:974c2b442e47f0f05e66ff821ae48a9b12f7b77a8a3bc06fe8ac232e2bc27608"},
+    {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 = "0.5.5"
+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 = "0.5.5"
+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",

scripts/bump_patch.ps1

@@ -0,0 +1,2 @@
+pdm run bump-my-version bump patch
+pdm run bump-my-version show current_version

src/delta_barth/analysis/forecast.py

@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import copy
 import datetime
 import math
 from collections.abc import Mapping, Set
@@ -11,6 +12,9 @@ import numpy as np
 import pandas as pd
 import scipy.stats
 import sqlalchemy as sql
+
+# --- new: for calculating timedelta
+from dateutil.relativedelta import relativedelta
 from sklearn.metrics import mean_absolute_error, r2_score
 from sklearn.model_selection import KFold, RandomizedSearchCV
 from xgboost import XGBRegressor
@@ -29,7 +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
@@ -183,16 +186,14 @@ def _process_sales(
     PipeResult
         _description_
     """
-    # cust_data: CustomerDataSalesForecast = CustomerDataSalesForecast()
-
     # filter data
     data = pipe.data
     assert data is not None, "processing not existing pipe result"
 
     DATE_FEAT: Final[str] = "buchungs_datum"
     SALES_FEAT: Final[str] = "betrag"
-    df_firma = data[(data["betrag"] > 0)]
-    df_cust = df_firma.copy()
+    df_filter = data[(data["betrag"] > 0)]
+    df_cust = df_filter.copy()
     df_cust = df_cust.sort_values(by=DATE_FEAT).reset_index()
     len_ds = len(df_cust)
 
@@ -206,7 +207,26 @@ def _process_sales(
     df_cust["jahr"] = df_cust[DATE_FEAT].dt.year
     df_cust["monat"] = df_cust[DATE_FEAT].dt.month
 
-    monthly_sum = df_cust.groupby(["jahr", "monat"])[SALES_FEAT].sum().reset_index()
+    monthly_sum_data_only = df_cust.groupby(["jahr", "monat"])[SALES_FEAT].sum().reset_index()
+
+    current_year = datetime.datetime.now().year
+    current_month = datetime.datetime.now().month
+    years = range(df_cust["jahr"].min(), current_year + 1)
+
+    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"],
+    )
+
+    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)
     )
@@ -215,13 +235,17 @@ def _process_sales(
 
     features = ["jahr", "monat"]
     target = SALES_FEAT
-    current_year = datetime.datetime.now().year
-    first_year = cast(int, df_cust["jahr"].min())
+
+    last_date = pd.to_datetime(datetime.datetime.now().strftime("%m.%Y"), format="%m.%Y")
+    future_dates = pd.date_range(
+        start=last_date + pd.DateOffset(months=1), periods=6, freq="MS"
+    )
+    forecast = pd.DataFrame({"datum": future_dates}).set_index("datum")
 
     # Randomized Search
     kfold = KFold(n_splits=5, shuffle=True)
     params: ParamSearchXGBRegressor = {
-        "n_estimators": scipy.stats.poisson(mu=1000),
+        "n_estimators": scipy.stats.poisson(mu=100),
         "learning_rate": [0.03, 0.04, 0.05],
         "max_depth": range(2, 9),
         "min_child_weight": range(1, 5),
@@ -231,26 +255,40 @@ def _process_sales(
         "early_stopping_rounds": [20, 50],
     }
 
+    best_estimator = None
     best_params: BestParametersXGBRegressor | None = None
     best_score_mae: float | None = float("inf")
     best_score_r2: float | None = None
     best_start_year: int | None = None
     too_few_month_points: bool = True
-    forecast: pd.DataFrame | None = None
 
-    for start_year in range(current_year - 4, first_year - 1, -1):
+    dates = cast(pd.DatetimeIndex, monthly_sum.index)
+    # baseline: 3 years - 36 months
+    starting_date = datetime.datetime.now() - relativedelta(months=36)
+
+    target_index, _ = next(
+        ((i, True) for i, date in enumerate(dates) if date >= starting_date),
+        (len(dates) - 1, False),
+    )
+
+    for add_year, date_idx in enumerate(range(target_index, -1, -12)):
+        first_date = dates[date_idx]
+        split_date = dates[-6]
+
         train = cast(
             pd.DataFrame,
-            monthly_sum[monthly_sum.index.year >= start_year].iloc[:-5].copy(),  # type: ignore
+            monthly_sum.loc[first_date:split_date].copy(),  # type: ignore
         )
         test = cast(
             pd.DataFrame,
-            monthly_sum[monthly_sum.index.year >= start_year].iloc[-5:].copy(),  # type: ignore
+            monthly_sum.loc[split_date:].copy(),  # type: ignore
         )
         X_train, X_test = train[features], test[features]
         y_train, y_test = train[target], test[target]
 
-        if len(train) >= (base_num_data_points_months + 10 * (current_year - 4 - start_year)):
+        # 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]) >= (base_num_data_points_months + 10 * add_year):
             too_few_month_points = False
 
             rand = RandomizedSearchCV(
@@ -273,13 +311,21 @@ 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))
-                    best_start_year = start_year
-                    print("executed")
-                    forecast = test.copy()
-                    forecast.loc[:, "vorhersage"] = y_pred
+                    # --- new: use first_date for best_start_year
+                    best_start_year = first_date.year
+                    # --- new: store best_estimator
+                    best_estimator = copy.copy(rand.best_estimator_)
+
+    if best_estimator is not None:
+        X_future = pd.DataFrame(
+            {"jahr": future_dates.year, "monat": future_dates.month}, index=future_dates
+        )
+        y_future = best_estimator.predict(X_future)  # type: ignore
+        forecast["vorhersage"] = y_future
+        forecast["jahr"] = forecast.index.year  # type: ignore
+        forecast["monat"] = forecast.index.month  # type: ignore
+        forecast = forecast.reset_index(drop=True)
 
-    if forecast is not None:
-        forecast = forecast.drop(SALES_FEAT, axis=1).reset_index(drop=True)
     best_score_mae = best_score_mae if not math.isinf(best_score_mae) else None
 
     if too_few_month_points:
@@ -295,7 +341,9 @@ def _process_sales(
         pipe.stats(stats)
         return pipe
 
-    assert forecast is not None, "forecast is None, but was attempted to be returned"
+    assert "vorhersage" in forecast.columns, (
+        "forecast does not contain prognosis values, but was attempted to be returned"
+    )
     status = STATUS_HANDLER.SUCCESS
     pipe.success(forecast, status)
     stats = SalesForecastStatistics(
@@ -384,7 +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
@@ -15,9 +16,9 @@ assert dummy_data_pth.exists(), f"dummy data path not found: {dummy_data_pth}"
 DUMMY_DATA_PATH: Final[Path] = dummy_data_pth
 
 # ** logging
-ENABLE_LOGGING: Final[bool] = False
+ENABLE_LOGGING: Final[bool] = True
 LOGGING_TO_FILE: Final[bool] = True
-LOGGING_TO_STDERR: Final[bool] = True
+LOGGING_TO_STDERR: Final[bool] = False
 LOG_FILENAME: Final[str] = "dopt-delbar.log"
 
 # ** databases
@@ -40,7 +41,7 @@ class KnownDelBarApiErrorCodes(enum.Enum):
 
 
 # ** API
-API_CON_TIMEOUT: Final[float] = 1.0  # secs to response
+API_CON_TIMEOUT: Final[float] = 10.0  # secs to response
 # ** API response parsing
 # ** column mapping [API-Response --> Target-Features]
 COL_MAP_SALES_PROGNOSIS: Final[DualDict[str, str]] = DualDict(
@@ -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()
-        db_path = db_root / "dopt-data.db"
-        if not db_root.exists():
-            db_root.mkdir(parents=False)
+        root = (self.data_path / self._db_folder).resolve()
+        db_path = root / "dopt-data.db"
+        if not root.exists():
+            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(
-        "delta_barth.analysis.forecast.get_sales_prognosis_data",
-    ) as mock:
-        mock.return_value = exmpl_api_sales_prognosis_resp, STATUS_HANDLER.SUCCESS
-        with patch("delta_barth.analysis.forecast.SESSION", session):
-            result = fc.pipeline_sales_forecast(None)  # type: ignore
-    print(result)
-    assert result.status == STATUS_HANDLER.SUCCESS
-    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
+    with (
+        patch(
+            "delta_barth.analysis.forecast.get_sales_prognosis_data",
+        ) as get_mock,
+        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
         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_sales_prognosis_pipeline(exmpl_api_sales_prognosis_resp, session):
-    with patch(
-        "delta_barth.analysis.forecast.get_sales_prognosis_data",
-    ) as mock:
-        mock.return_value = (exmpl_api_sales_prognosis_resp, STATUS_HANDLER.SUCCESS)
-        with patch("delta_barth.pipelines.SESSION", session):
-            json_export = pl.pipeline_sales_forecast(None, None)
+def test_write_performance_metrics_FailStartingTime(session):
+    pipe_name = "test_pipe"
+    t_start = 30_000_000_000
+    t_end = 20_000_000_000
+
+    with patch("delta_barth.pipelines.SESSION", session):
+        with pytest.raises(ValueError):
+            _ = 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"