implement new sales forecast pipeline

tests/analysis/test_forecast.py

@@ -2,6 +2,7 @@ import importlib
 from datetime import datetime as Datetime
 from unittest.mock import patch
 
+import numpy as np
 import pandas as pd
 import pytest
 from pydantic import ValidationError
@@ -114,16 +115,6 @@ def test_parse_api_resp_to_df_empty():
     assert all(col in features for col in df.columns)
 
 
-# def test_parse_df_to_api_resp_ValidData(valid_df):
-#     ret = fc._parse_df_to_api_resp(valid_df)
-#     assert len(ret.daten) > 0
-
-
-# def test_parse_df_to_api_resp_InvalidData(invalid_df):
-#     with pytest.raises(ValidationError):
-#         _ = fc._parse_df_to_api_resp(invalid_df)
-
-
 def test_parse_df_to_results_ValidData(valid_results):
     ret = fc._parse_df_to_results(valid_results)
     assert len(ret.daten) > 0
@@ -171,9 +162,12 @@ def test_preprocess_sales_FailOnTargetFeature(
 
 def test_process_sales_Success(sales_data_real_preproc):
     data = sales_data_real_preproc.copy()
-    # fc._preprocess_sales_per_customer()
     pipe = PipeResult(data, STATUS_HANDLER.SUCCESS)
-    pipe = fc._process_sales(pipe)
+    pipe = fc._process_sales(
+        pipe,
+        min_num_data_points=36,
+        base_num_data_points_months=1,
+    )
 
     assert pipe.status == STATUS_HANDLER.SUCCESS
     assert pipe.data is not None
@@ -183,9 +177,12 @@ def test_process_sales_Success(sales_data_real_preproc):
 def test_process_sales_FailTooFewPoints(sales_data_real_preproc):
     data = sales_data_real_preproc.copy()
     data = data.iloc[:20, :]
-    # fc._preprocess_sales_per_customer()
     pipe = PipeResult(data, STATUS_HANDLER.SUCCESS)
-    pipe = fc._process_sales(pipe)
+    pipe = fc._process_sales(
+        pipe,
+        min_num_data_points=36,
+        base_num_data_points_months=36,
+    )
 
     assert pipe.status != STATUS_HANDLER.SUCCESS
     assert pipe.status == STATUS_HANDLER.pipe_states.TOO_FEW_POINTS
@@ -193,6 +190,55 @@ def test_process_sales_FailTooFewPoints(sales_data_real_preproc):
     assert pipe.results is None
 
 
+def test_process_sales_FailTooFewMonthPoints(sales_data_real_preproc):
+    data = sales_data_real_preproc.copy()
+    pipe = PipeResult(data, STATUS_HANDLER.SUCCESS)
+    pipe = fc._process_sales(
+        pipe,
+        min_num_data_points=36,
+        base_num_data_points_months=36,
+    )
+
+    assert pipe.status != STATUS_HANDLER.SUCCESS
+    assert pipe.status == STATUS_HANDLER.pipe_states.TOO_FEW_MONTH_POINTS
+    assert pipe.data is None
+    assert pipe.results is None
+
+
+def test_process_sales_FailNoReliableForecast(sales_data_real_preproc):
+    data = sales_data_real_preproc.copy()
+    data["betrag"] = 10000
+    print(data["betrag"])
+    data = data.iloc[:20000, :]
+    pipe = PipeResult(data, STATUS_HANDLER.SUCCESS)
+
+    class PatchSearchCV:
+        def __init__(self, *args, **kwargs) -> None:
+            class Predictor:
+                def predict(self, *args, **kwargs):
+                    return np.array([1, 1, 1, 1])
+
+            self.best_estimator_ = Predictor()
+
+        def fit(*args, **kwargs):
+            pass
+
+    with patch(
+        "delta_barth.analysis.forecast.RandomizedSearchCV",
+        new=PatchSearchCV,
+    ):
+        pipe = fc._process_sales(
+            pipe,
+            min_num_data_points=1,
+            base_num_data_points_months=-100,
+        )
+
+    assert pipe.status != STATUS_HANDLER.SUCCESS
+    assert pipe.status == STATUS_HANDLER.pipe_states.NO_RELIABLE_FORECAST
+    assert pipe.data is None
+    assert pipe.results is None
+
+
 def test_postprocess_sales_Success(
     valid_results,
 ):
@@ -234,17 +280,18 @@ 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_prognosis(exmpl_api_sales_prognosis_resp):
     def mock_request(*args, **kwargs):  # pragma: no cover
         return exmpl_api_sales_prognosis_resp, STATUS_HANDLER.SUCCESS
 
     with patch(
-        "delta_barth.api.requests.get_sales_prognosis_data",
-        new=mock_request,
-    ):
-        importlib.reload(delta_barth.analysis.forecast)
+        "delta_barth.analysis.forecast.get_sales_prognosis_data",
+        # new=mock_request,
+    ) as mock:
+        mock.return_value = exmpl_api_sales_prognosis_resp, STATUS_HANDLER.SUCCESS
         result = fc.pipeline_sales(None)  # type: ignore
-
+    print(result)
     assert result.status == STATUS_HANDLER.SUCCESS
     assert len(result.response.daten) > 0
 

tests/test_errors.py

@@ -92,7 +92,7 @@ def test_status_handler_raise_for_status_Success(status_hdlr):
 
 def test_status_handler_raise_for_status_PredefinedErrors(status_hdlr):
     # data related errors (predefined)
-    err_status = status_hdlr.pipe_states.BAD_QUALITY
+    err_status = status_hdlr.pipe_states.NO_RELIABLE_FORECAST
     err_descr = err_status.description
     with pytest.raises(errors.UDataProcessingError):
         try:

tests/test_pipelines.py

@@ -9,6 +9,7 @@ from delta_barth import pipelines as pl
 from delta_barth.errors import STATUS_HANDLER
 
 
+@patch("delta_barth.analysis.forecast.SALES_BASE_NUM_DATAPOINTS_MONTHS", 1)
 def test_sales_prognosis_pipeline(exmpl_api_sales_prognosis_resp):
     with patch(
         "delta_barth.analysis.forecast.get_sales_prognosis_data",