add statistics logging to sales forecast pipeline

src/delta_barth/analysis/forecast.py

@@ -1,9 +1,10 @@
 from __future__ import annotations
 
 import datetime
+import math
 from collections.abc import Mapping, Set
 from datetime import datetime as Datetime
-from typing import TYPE_CHECKING, Final, cast
+from typing import TYPE_CHECKING, Final, TypeAlias, cast
 
 import numpy as np
 import pandas as pd
@@ -34,12 +35,15 @@ from delta_barth.types import (
     DualDict,
     ParamSearchXGBRegressor,
     PipeResult,
+    SalesForecastStatistics,
 )
 
 if TYPE_CHECKING:
     from delta_barth.api.common import Session
     from delta_barth.types import Status
 
+ForecastPipe: TypeAlias = PipeResult[SalesPrognosisResultsExport, SalesForecastStatistics]
+
 
 def _parse_api_resp_to_df(
     resp: SalesPrognosisResponse,
@@ -110,7 +114,7 @@ def _preprocess_sales(
     resp: SalesPrognosisResponse,
     feature_map: Mapping[str, str],
     target_features: Set[str],
-) -> PipeResult[SalesPrognosisResultsExport]:
+) -> ForecastPipe:
     """n = 1
 
     Parameters
@@ -127,7 +131,7 @@ def _preprocess_sales(
     PipeResult
         _description_
     """
-    pipe: PipeResult[SalesPrognosisResultsExport] = PipeResult(None, STATUS_HANDLER.SUCCESS)
+    pipe: ForecastPipe = PipeResult(None, STATUS_HANDLER.SUCCESS)
     res = _parse_api_resp_to_df_wrapped(resp)
 
     if res.status != STATUS_HANDLER.SUCCESS:
@@ -149,10 +153,10 @@ def _preprocess_sales(
 
 
 def _process_sales(
-    pipe: PipeResult[SalesPrognosisResultsExport],
+    pipe: ForecastPipe,
     min_num_data_points: int,
     base_num_data_points_months: int,
-) -> PipeResult[SalesPrognosisResultsExport]:
+) -> ForecastPipe:
     """n = 1
     Input-Data:
     fields: ["artikel_refid", "firma_refid", "betrag", "menge", "buchungs_datum"]
@@ -182,9 +186,13 @@ def _process_sales(
     df_firma = data[(data["betrag"] > 0)]
     df_cust = df_firma.copy()
     df_cust = df_cust.sort_values(by=DATE_FEAT).reset_index()
+    len_ds = len(df_cust)
 
-    if len(df_cust) < min_num_data_points:
+    if len_ds < min_num_data_points:
-        pipe.fail(STATUS_HANDLER.pipe_states.TOO_FEW_POINTS)
+        status = STATUS_HANDLER.pipe_states.TOO_FEW_POINTS
+        pipe.fail(status)
+        stats = SalesForecastStatistics(status.code, status.description, len_ds)
+        pipe.stats(stats)
         return pipe
 
     df_cust["jahr"] = df_cust[DATE_FEAT].dt.year
@@ -216,8 +224,8 @@ def _process_sales(
     }
 
     best_params: BestParametersXGBRegressor | None = None
-    best_score_mae: float = float("inf")
+    best_score_mae: float | None = float("inf")
-    best_score_r2: float = 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
@@ -252,7 +260,6 @@ def _process_sales(
             y_pred = rand.best_estimator_.predict(X_test)  # type: ignore
 
             if len(np.unique(y_pred)) != 1:
-                # pp(y_pred)
                 error = cast(float, mean_absolute_error(y_test, y_pred))
                 if error < best_score_mae:
                     best_params = cast(BestParametersXGBRegressor, rand.best_params_)
@@ -263,31 +270,44 @@ def _process_sales(
                     forecast = test.copy()
                     forecast.loc[:, "vorhersage"] = y_pred
 
-                    # pp(best_params)
-                    # pp(best_score_mae)
-                    # pp(best_score_r2)
-                    # pp(best_start_year)
     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
-    # TODO log metrics
 
     if too_few_month_points:
-        pipe.fail(STATUS_HANDLER.pipe_states.TOO_FEW_MONTH_POINTS)
+        status = STATUS_HANDLER.pipe_states.TOO_FEW_MONTH_POINTS
+        pipe.fail(status)
+        stats = SalesForecastStatistics(status.code, status.description, len_ds)
+        pipe.stats(stats)
         return pipe
     elif best_params is None:
-        pipe.fail(STATUS_HANDLER.pipe_states.NO_RELIABLE_FORECAST)
+        status = STATUS_HANDLER.pipe_states.NO_RELIABLE_FORECAST
+        pipe.fail(status)
+        stats = SalesForecastStatistics(status.code, status.description, len_ds)
+        pipe.stats(stats)
         return pipe
 
     assert forecast is not None, "forecast is None, but was attempted to be returned"
-    pipe.success(forecast, STATUS_HANDLER.SUCCESS)
+    status = STATUS_HANDLER.SUCCESS
+    pipe.success(forecast, status)
+    stats = SalesForecastStatistics(
+        status.code,
+        status.description,
+        len_ds,
+        score_mae=best_score_mae,
+        score_r2=best_score_r2,
+        best_start_year=best_start_year,
+        XGB_params=best_params,
+    )
+    pipe.stats(stats)
+
     return pipe
 
 
 def _postprocess_sales(
-    pipe: PipeResult[SalesPrognosisResultsExport],
+    pipe: ForecastPipe,
     feature_map: Mapping[str, str],
-) -> PipeResult[SalesPrognosisResultsExport]:
+) -> ForecastPipe:
     data = pipe.data
     assert data is not None, "processing not existing pipe result"
     # convert features back to original naming
@@ -393,7 +413,7 @@ def pipeline_sales_dummy(
     data_pth = DUMMY_DATA_PATH / "exmp_sales_prognosis_output.pkl"
     assert data_pth.exists(), "sales forecast dummy data not existent"
     data = pd.read_pickle(data_pth)
-    pipe: PipeResult[SalesPrognosisResultsExport] = PipeResult(None, STATUS_HANDLER.SUCCESS)
+    pipe: ForecastPipe = PipeResult(None, STATUS_HANDLER.SUCCESS)
     res = _parse_df_to_results_wrapped(data)
 
     if res.status != STATUS_HANDLER.SUCCESS:

src/delta_barth/types.py

@@ -14,6 +14,7 @@ __all__ = ["DualDict"]
 # ** Pipeline state management
 StatusDescription: t.TypeAlias = tuple[str, int, str]
 R = t.TypeVar("R", bound="ExportResponse")
+S = t.TypeVar("S", bound="Statistics")
 
 
 class IError(t.Protocol):
@@ -47,10 +48,11 @@ class DataPipeStates:
 
 
 @dataclass(slots=True)
-class PipeResult(t.Generic[R]):
+class PipeResult(t.Generic[R, S]):
     data: pd.DataFrame | None
     status: Status
     results: R | None = None
+    statistics: S | None = None
 
     def success(
         self,
@@ -77,6 +79,12 @@ class PipeResult(t.Generic[R]):
         self.status = response.status
         self.results = response
 
+    def stats(
+        self,
+        statistics: S,
+    ) -> None:
+        self.statistics = statistics
+
 
 JsonExportResponse = t.NewType("JsonExportResponse", str)
 JsonResponse = t.NewType("JsonResponse", str)
@@ -121,6 +129,11 @@ HttpContentHeaders = t.TypedDict(
 )
 
 
+# ** statistics
+class Statistics:
+    pass
+
+
 # ** forecasts
 @dataclass(slots=True)
 class CustomerDataSalesForecast:
@@ -140,6 +153,17 @@ class ParamSearchXGBRegressor(t.TypedDict):
     early_stopping_rounds: Sequence[int]
 
 
+@dataclass(slots=True, eq=False)
+class SalesForecastStatistics(Statistics):
+    status_code: int
+    status_dscr: str
+    length_dataset: int
+    score_mae: float | None = None
+    score_r2: float | None = None
+    best_start_year: int | None = None
+    XGB_params: BestParametersXGBRegressor | None = None
+
+
 class BestParametersXGBRegressor(t.TypedDict):
     n_estimators: int
     learning_rate: float

tests/analysis/test_forecast.py

@@ -1,4 +1,3 @@
-import importlib
 from datetime import datetime as Datetime
 from unittest.mock import patch
 
@@ -7,7 +6,6 @@ import pandas as pd
 import pytest
 from pydantic import ValidationError
 
-import delta_barth.analysis.forecast
 from delta_barth.analysis import forecast as fc
 from delta_barth.api.requests import SalesPrognosisResponse, SalesPrognosisResponseEntry
 from delta_barth.errors import STATUS_HANDLER
@@ -160,6 +158,7 @@ def test_preprocess_sales_FailOnTargetFeature(
     assert pipe.results is None
 
 
+@pytest.mark.new
 def test_process_sales_Success(sales_data_real_preproc):
     data = sales_data_real_preproc.copy()
     pipe = PipeResult(data, STATUS_HANDLER.SUCCESS)
@@ -172,8 +171,17 @@ def test_process_sales_Success(sales_data_real_preproc):
     assert pipe.status == STATUS_HANDLER.SUCCESS
     assert pipe.data is not None
     assert pipe.results is None
+    assert pipe.statistics is not None
+    assert pipe.statistics.status_code == STATUS_HANDLER.SUCCESS.code
+    assert pipe.statistics.status_dscr == STATUS_HANDLER.SUCCESS.description
+    assert pipe.statistics.length_dataset is not None
+    assert pipe.statistics.score_mae is not None
+    assert pipe.statistics.score_r2 is not None
+    assert pipe.statistics.best_start_year is not None
+    assert pipe.statistics.XGB_params is not None
 
 
+@pytest.mark.new
 def test_process_sales_FailTooFewPoints(sales_data_real_preproc):
     data = sales_data_real_preproc.copy()
     data = data.iloc[:20, :]
@@ -188,8 +196,19 @@ def test_process_sales_FailTooFewPoints(sales_data_real_preproc):
     assert pipe.status == STATUS_HANDLER.pipe_states.TOO_FEW_POINTS
     assert pipe.data is None
     assert pipe.results is None
+    assert pipe.statistics is not None
+    assert pipe.statistics.status_code == STATUS_HANDLER.pipe_states.TOO_FEW_POINTS.code
+    assert (
+        pipe.statistics.status_dscr == STATUS_HANDLER.pipe_states.TOO_FEW_POINTS.description
+    )
+    assert pipe.statistics.length_dataset is not None
+    assert pipe.statistics.score_mae is None
+    assert pipe.statistics.score_r2 is None
+    assert pipe.statistics.best_start_year is None
+    assert pipe.statistics.XGB_params is None
 
 
+@pytest.mark.new
 def test_process_sales_FailTooFewMonthPoints(sales_data_real_preproc):
     data = sales_data_real_preproc.copy()
     pipe = PipeResult(data, STATUS_HANDLER.SUCCESS)
@@ -203,8 +222,20 @@ def test_process_sales_FailTooFewMonthPoints(sales_data_real_preproc):
     assert pipe.status == STATUS_HANDLER.pipe_states.TOO_FEW_MONTH_POINTS
     assert pipe.data is None
     assert pipe.results is None
+    assert pipe.statistics is not None
+    assert pipe.statistics.status_code == STATUS_HANDLER.pipe_states.TOO_FEW_MONTH_POINTS.code
+    assert (
+        pipe.statistics.status_dscr
+        == STATUS_HANDLER.pipe_states.TOO_FEW_MONTH_POINTS.description
+    )
+    assert pipe.statistics.length_dataset is not None
+    assert pipe.statistics.score_mae is None
+    assert pipe.statistics.score_r2 is None
+    assert pipe.statistics.best_start_year is None
+    assert pipe.statistics.XGB_params is None
 
 
+@pytest.mark.new
 def test_process_sales_FailNoReliableForecast(sales_data_real_preproc):
     data = sales_data_real_preproc.copy()
     data["betrag"] = 10000
@@ -237,6 +268,17 @@ def test_process_sales_FailNoReliableForecast(sales_data_real_preproc):
     assert pipe.status == STATUS_HANDLER.pipe_states.NO_RELIABLE_FORECAST
     assert pipe.data is None
     assert pipe.results is None
+    assert pipe.statistics is not None
+    assert pipe.statistics.status_code == STATUS_HANDLER.pipe_states.NO_RELIABLE_FORECAST.code
+    assert (
+        pipe.statistics.status_dscr
+        == STATUS_HANDLER.pipe_states.NO_RELIABLE_FORECAST.description
+    )
+    assert pipe.statistics.length_dataset is not None
+    assert pipe.statistics.score_mae is None
+    assert pipe.statistics.score_r2 is None
+    assert pipe.statistics.best_start_year is None
+    assert pipe.statistics.XGB_params is None
 
 
 def test_postprocess_sales_Success(