pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "delta-barth"
-version = "0.5.7dev1"
+version = "0.5.7dev2"
 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"},
@@ -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 = "0.5.7dev2"
 parse = """(?x)
     (?P<major>0|[1-9]\\d*)\\.
     (?P<minor>0|[1-9]\\d*)\\.

src/delta_barth/analysis/forecast.py

@@ -208,17 +208,25 @@ 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
-    current_month = datetime.now().month
+    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)
 
-    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)
@@ -256,27 +264,22 @@ def _process_sales(
     too_few_month_points: bool = True
 
     dates = cast(pd.DatetimeIndex, monthly_sum.index)
-    # print("dates: ", dates)
     # baseline: 3 years - 36 months
     starting_date = datetime.datetime.now() - relativedelta(months=36)
 
-    target_index, succ = next(
-        ((i, True) for i, date in enumerate(dates) if date >= starting_date), (len(dates) - 1, False)
+    target_index, _ = next(
+        ((i, True) for i, date in enumerate(dates) if date >= starting_date),
+        (len(dates) - 1, False),
     )
-    # print("start idx: ", target_index, "length dates: ", len(dates))
 
-    for add_year, date_idx in enumerate(range(start_index, -1, -12)):
-        # print("date_idx: ", date_idx)
+    for add_year, date_idx in enumerate(range(target_index, -1, -12)):
         first_date = dates[date_idx]
-        # print("first date: ", first_date)
         split_date = dates[-6]
 
         train = cast(
             pd.DataFrame,
             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
@@ -286,7 +289,7 @@ 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 * add_year:
+        if len(train[train[SALES_FEAT] > 0]) >= (base_num_data_points_months + 10 * add_year):
             too_few_month_points = False
 
             rand = RandomizedSearchCV(
@@ -314,7 +317,6 @@ def _process_sales(
                     # --- 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

tests/analysis/test_forecast.py

@@ -1,4 +1,6 @@
+import datetime
 from datetime import datetime as Datetime
+from pathlib import Path
 from unittest.mock import patch
 
 import numpy as np
@@ -255,6 +257,7 @@ 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 +280,7 @@ 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 +307,7 @@ 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 +334,19 @@ 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 +356,7 @@ 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 +370,7 @@ 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