commented out epc data reading code

recommendations/tests/test_data/heating_recommendations_data.py

@@ -1,18 +1,18 @@
-import random
-from pathlib import Path
-import inspect
-import pandas as pd
-
-# this can be used to get example data to build the test cases
-src_file_path = inspect.getfile(lambda: None)
-EPC_DIRECTORY = Path(src_file_path).parent / "local_data" / "all-domestic-certificates"
-epc_directories = [entry for entry in EPC_DIRECTORY.iterdir() if entry.is_dir()]
-directory = random.sample(epc_directories, 1)[0]
-data = pd.read_csv(directory / "certificates.csv", low_memory=False)
-# Rename the columns to the same format as the api returns
-data.columns = [c.replace("_", "-").lower() for c in data.columns]
-
-eg = data.sample(1).to_dict("records")[0]
+# import random
+# from pathlib import Path
+# import inspect
+# import pandas as pd
+#
+# # this can be used to get example data to build the test cases
+# src_file_path = inspect.getfile(lambda: None)
+# EPC_DIRECTORY = Path(src_file_path).parent / "local_data" / "all-domestic-certificates"
+# epc_directories = [entry for entry in EPC_DIRECTORY.iterdir() if entry.is_dir()]
+# directory = random.sample(epc_directories, 1)[0]
+# data = pd.read_csv(directory / "certificates.csv", low_memory=False)
+# # Rename the columns to the same format as the api returns
+# data.columns = [c.replace("_", "-").lower() for c in data.columns]
+#
+# eg = data.sample(1).to_dict("records")[0]
 
 testing_examples = [
     {
@@ -54,7 +54,12 @@ testing_examples = [
             'lodgement-datetime': '2014-09-04 09:22:45', 'tenure': 'owner-occupied',
             'fixed-lighting-outlets-count': 10.0, 'low-energy-fixed-light-count': 7.0, 'uprn': 100110195416.0,
             'uprn-source': 'Address Matched'
-        }
+        },
+        "kwh": {
 
+        },
+        "recommendation_descripptions": [
+
+        ]
     }
 ]

recommendations/tests/test_heating_recommendations.py

@@ -1,8 +1,10 @@
 import pandas as pd
-from utils.s3 import read_dataframe_from_s3_parquet
+import msgpack
+from utils.s3 import read_dataframe_from_s3_parquet, read_from_s3
 import pytest
 from backend.Property import Property
 from etl.epc.Record import EPCRecord
+from etl.bill_savings.KwhData import KwhData
 from recommendations.HeatingRecommender import HeatingRecommender
 from recommendations.tests.test_data.heating_recommendations_data import testing_examples
 
@@ -15,18 +17,32 @@ class TestHeatingRecommendations:
             bucket_name="retrofit-data-dev", file_key="sap_change_model/cleaning_dataset.parquet",
         )
 
+    @pytest.fixture
+    def cleaned(self):
+        df = read_from_s3(
+            s3_file_name="cleaned_epc_data/cleaned.bson",
+            bucket_name="retrofit-data-dev"
+        )
+
+        df = msgpack.unpackb(df, raw=False)
+        return df
+
+    @pytest.fixture
+    def kwh_client(self):
+        return KwhData(bucket="retrofit-data-dev", read_consumption_data=True)
+
     @pytest.mark.parametrize(
         "test_case",
         testing_examples
     )
-    def test_recommend(self, test_case, cleaning_data):
+    def test_recommend(self, test_case, cleaning_data, cleaned, kwh_client):
         """
         With this function, we test out multiple heating descriptions and check which recomendations
         we retrieve alongside them
         :return:
         """
 
-        epc_records = {"original_epc": test_case["epc"], "full_sap_epc": {}, "old_data": []}
+        epc_records = {"original_epc": test_case["epc"].copy(), "full_sap_epc": {}, "old_data": []}
 
         epc_record = EPCRecord(
             epc_records=epc_records,
@@ -38,8 +54,15 @@ class TestHeatingRecommendations:
             id=0,
             postcode=test_case["epc"]["postcode"],
             address=test_case["epc"]["address"],
-            epc_record=epc_record
+            epc_record=epc_record,
+            energy_assessment={
+                "condition": {},
+                "energy_assessment_is_newer": False
+            }
         )
+        # TODO: Implement me
+        kwh_predictions = test_case["kwhs"]
+        p.set_features(cleaned=cleaned, kwh_client=kwh_client, kwh_predictions=kwh_predictions)
 
         recommender = HeatingRecommender(property_instance=p)
         # Check they're empty