initial model commit

.github/workflows/Deploy.yml

@@ -2,7 +2,7 @@ name: Sap Change Model Deploy
 
 on:
   push:
-    branches: [ sap-dev, sap-prod, heat-dev, heat-prod, carbon-dev, carbon-prod, lighting-dev, lighting-prod ]
+    branches: [ sap-dev, sap-prod, heat-dev, heat-prod, carbon-dev, carbon-prod, heatingkwh-dev, heatingkwh-prod]
 
 jobs:
   deploy:

.github/workflows/MLPipelinePostMerge.yml

@@ -13,7 +13,7 @@ on:
       - "sap-dev"
       - "heat-dev"
       - "carbon-dev"
-      - "lighting-dev"
+      - "heatingkwh-dev"
 
 permissions: write-all
 

.github/workflows/MLPipelinePullRequest.yml

@@ -5,7 +5,7 @@ on:
   #   branches:
   #     - "model-**"
   pull_request:
-    branches: ["sap-dev", "heat-dev", "carbon-dev", "lighting-dev"]
+    branches: ["sap-dev", "heat-dev", "carbon-dev", "heatingkwh-dev"]
   label:
     types: ["created", "edited"]
 

modules/ml-pipeline/src/pipeline/configs/feature_processor_logic.py

@@ -5,6 +5,18 @@ During the feature processor step, we can apply additional business logic and fe
 """
 Business Logic dict + functions
 """
+import pandas as pd
+import numpy as np
+import boto3
+import msgpack
+
+s3 = boto3.resource('s3')
+
+# Get the MessagePack data from S3
+obj = s3.Object("retrofit-data-dev", "cleaned_epc_data/cleaned.bson")
+cleaned = obj.get()['Body'].read()
+
+cleaned = msgpack.unpackb(cleaned, raw=False)
 
 
 def remove_starting_columns(df):
@@ -44,6 +56,107 @@ def keep_non_zero_rdsap(df):
     df = df[df["rdsap_change"] != 0]
     return df
 
+def add_features_from_code(df):
+
+    FEATURES = {
+        "heating_kwh": [
+            "lodgement-year", "lodgement-month", "current-energy-efficiency", "energy-consumption-current",
+            "heating-cost-current", "heating-cost-potential", "total-floor-area", "number-heated-rooms",
+            "mainheat-description", "mainheat-energy-eff", "main-fuel", "secondheat-description", "property-type",
+            "built-form", "mainheatcont-description", "hotwater-description", "hot-water-energy-eff",
+            "walls-energy-eff",
+            "roof-energy-eff", "windows-description", "windows-energy-eff", "floor-description", "flat-top-storey",
+            "flat-storey-count", "unheated-corridor-length", "solar-water-heating-flag", "mechanical-ventilation",
+            "low-energy-lighting", "environment-impact-current", "energy-tariff",
+            "county", "construction-age-band", "co2-emissions-current",
+        ],
+        "hot_water_kwh": [
+            "lodgement-year", "lodgement-month",
+            "current-energy-efficiency",
+            "energy-consumption-current",
+            "hot-water-cost-current",
+            "total-floor-area", "number-heated-rooms",
+            "hotwater-description", "hot-water-energy-eff", "main-fuel", "property-type", "built-form",
+            "co2-emissions-current",
+        ]
+    }
+    CATEGORICAL_COLUMNS = [
+        "lodgement-year", "lodgement-month", "main-fuel", "mainheat-description", "number-heated-rooms",
+        "number-habitable-rooms", "mainheat-energy-eff", "mainheatcont-description", "property-type", "built-form",
+        "construction-age-band", "secondheat-description", "hotwater-description", "hot-water-energy-eff",
+        "walls-description", "walls-energy-eff", "roof-description", "roof-energy-eff", "floor-description",
+        "county",
+        "windows-description", "windows-energy-eff", "flat-top-storey",
+        "flat-storey-count", "unheated-corridor-length", "solar-water-heating-flag", "mechanical-ventilation",
+        "low-energy-lighting", "environment-impact-current", "energy-tariff", "current-energy-rating"
+    ]
+
+    NUMERICAL_COLUMNS = list({
+            x for x in FEATURES["heating_kwh"] + FEATURES["hot_water_kwh"]
+            if x not in CATEGORICAL_COLUMNS
+        })
+    
+
+    """Performs feature engineering on the dataset."""
+    df["lodgement-date"] = pd.to_datetime(df["lodgement-date"])
+    df["lodgement-year"] = df["lodgement-date"].dt.year
+    df["lodgement-month"] = df["lodgement-date"].dt.month
+
+    # For walls, roof, floor description where we have average thermal transmittance, to avoid too many categories
+    # we group them
+    ranges = {
+        "lessthan 0.1": (0, 0.1),
+        "0.1 - 0.3": (0.1, 0.3),
+        "0.3 - 0.5": (0.3, 0.5),
+        "morethan 0.5": (0.5, 2.5),
+    }
+
+    # Generate the lookup table
+    thermal_transmittance_lookup_table = []
+    for i in range(1, 251):
+        value = i / 100
+        for label, (low, high) in ranges.items():
+            if low < value <= high:
+                thermal_transmittance_lookup_table.append({"from": value, "to": label})
+                break
+
+    # Convert to DataFrame for display
+    thermal_transmittance_lookup_table = pd.DataFrame(thermal_transmittance_lookup_table)
+    thermal_transmittance_lookup_table["from"] = thermal_transmittance_lookup_table["from"].astype(str)
+
+    # Apply the lookup table to the data
+    for feature in ["walls-description", "roof-description", "floor-description"]:
+        cleaned_df = pd.DataFrame(cleaned[feature])[["original_description", "thermal_transmittance"]]
+        # Round to 2 decimal places and convert to string
+        cleaned_df["thermal_transmittance"] = cleaned_df["thermal_transmittance"].round(2).astype(str)
+
+        df = df.merge(
+            cleaned_df,
+            how="left",
+            left_on=feature,
+            right_on="original_description",
+        )
+        # We now have the thermal transmittance in the data, which we can use to group with the lookup table
+        df = df.merge(
+            thermal_transmittance_lookup_table,
+            how="left",
+            left_on="thermal_transmittance",
+            right_on="from",
+        )
+        # Where "to" is populated, replace feature with to
+        df[feature] = np.where(
+            ~pd.isnull(df["to"]),
+            df["to"],
+            df[feature]
+        )
+        df = df.drop(columns=["original_description", "thermal_transmittance", "from", "to"])
+
+    # Convert data types
+    df[NUMERICAL_COLUMNS] = df[NUMERICAL_COLUMNS].apply(pd.to_numeric)
+    df[CATEGORICAL_COLUMNS] = df[CATEGORICAL_COLUMNS].astype(str)
+
+    return df
+
 
 # def keep_ending_columns(df):
 #     ending_column_index = [ col_name.endswith("_ENDING") for col_name in list(df.columns)]
@@ -54,6 +167,7 @@ def keep_non_zero_rdsap(df):
 #     return df
 
 business_logic = {
+    "add_features_from_code": add_features_from_code
     # "keep_non_zero_rdsap": keep_non_zero_rdsap,
     # "keep_flats": keep_flats,
     # "remove_minimum_habitable_room_size": remove_minimum_habitable_room_size,

modules/ml-pipeline/src/pipeline/configs/settings.yaml

@@ -22,7 +22,8 @@ default:
     # data_filepath: s3://retrofit-data-dev/sap_change_model/2024-05-25-08-36-36/dataset_rooms.parquet
     # data_filepath: s3://retrofit-data-dev/sap_change_model/2024-05-26-10-31-39/dataset_rooms.parquet
     # data_filepath: s3://retrofit-data-dev/sap_change_model/2024-05-28-19-08-25/dataset_rooms.parquet
-    data_filepath: s3://retrofit-data-dev/sap_change_model/2024-07-03-23-11-39/dataset_rooms.parquet
+    # data_filepath: s3://retrofit-data-dev/sap_change_model/2024-07-03-23-11-39/dataset_rooms.parquet
+    data_filepath: s3://retrofit-data-dev/energy_consumption/2024-07-08/energy_consumption_dataset.parquet
     train_proportion: 0.9
     output_train_filepath: ./data/prepared_data/train.parquet
     output_test_filepath: ./data/prepared_data/test.parquet
@@ -32,15 +33,16 @@ default:
     feature_processor_config:
       subsample_amount: null
       subsample_seed: 0
-      target: lighting_cost_ending
+      target: heating_kwh
       identifier_columns: ["uprn"]
       # drop_columns: ["heat_demand_change", "carbon_change", "rdsap_change", "heat_demand_ending", "carbon_ending", "days_to_starting", "days_to_ending"]
-      drop_columns: [
-        "sap_ending", "heat_demand_change", "carbon_change", "rdsap_change", "heat_demand_ending", "carbon_ending", 
-        "heating_cost_ending", "hot_water_cost_ending",
-        # "days_to_starting", "days_to_ending",
-        'number_habitable_rooms_starting', 'number_habitable_rooms_ending', 'number_heated_rooms_starting', 'number_heated_rooms_ending',
-        'number_habitable_rooms', 'number_heated_rooms']
+      drop_columns: []
+      # [
+      #   "sap_ending", "heat_demand_change", "carbon_change", "rdsap_change", "heat_demand_ending", "carbon_ending", 
+      #   "heating_cost_ending", "hot_water_cost_ending",
+      #   # "days_to_starting", "days_to_ending",
+      #   'number_habitable_rooms_starting', 'number_habitable_rooms_ending', 'number_heated_rooms_starting', 'number_heated_rooms_ending',
+      #   'number_habitable_rooms', 'number_heated_rooms']
       retain_features: null
       # retain_features: ['uprn', 'sap_starting', 'hot_water_energy_eff_ending',
       #  'mainheat_energy_eff_ending', 'constituency', 'roof_energy_eff_ending',

modules/ml-pipeline/src/pipeline/dvc.lock

@@ -20,28 +20,14 @@ stages:
       size: 4298
     params:
       configs/settings.yaml:
-        default.feature_processor.feature_processor_config.drop_columns:
-        - sap_ending
-        - heat_demand_change
-        - carbon_change
-        - rdsap_change
-        - heat_demand_ending
-        - carbon_ending
-        - heating_cost_ending
-        - hot_water_cost_ending
-        - number_habitable_rooms_starting
-        - number_habitable_rooms_ending
-        - number_heated_rooms_starting
-        - number_heated_rooms_ending
-        - number_habitable_rooms
-        - number_heated_rooms
+        default.feature_processor.feature_processor_config.drop_columns: []
         default.feature_processor.feature_processor_config.retain_features:
         default.feature_processor.feature_processor_config.subsample_amount:
         default.feature_processor.feature_processor_config.subsample_seed: 0
-        default.feature_processor.feature_processor_config.target: lighting_cost_ending
+        default.feature_processor.feature_processor_config.target: heating_kwh
         default.feature_processor.feature_processor_type: dataframe
         default.prepare_data.data_filepath: 
-          s3://retrofit-data-dev/sap_change_model/2024-07-03-23-11-39/dataset_rooms.parquet
+          s3://retrofit-data-dev/energy_consumption/2024-07-08/energy_consumption_dataset.parquet
         default.prepare_data.input_dataclient_type: aws-s3
         default.prepare_data.output_dataclient_type: local
         default.prepare_data.output_test_filepath: ./data/prepared_data/test.parquet
@@ -50,8 +36,8 @@ stages:
     outs:
     - path: data/prepared_data/
       hash: md5
-      md5: 0f11a02cf75c0421757c0b26184cec33.dir
-      size: 48971227
+      md5: c162c4b80826f42b33cce656446460f3.dir
+      size: 23784411
       nfiles: 2
   build_model:
     cmd: python 2_build_model.py
@@ -62,8 +48,8 @@ stages:
       size: 4820
     - path: data/prepared_data
       hash: md5
-      md5: 0f11a02cf75c0421757c0b26184cec33.dir
-      size: 48971227
+      md5: c162c4b80826f42b33cce656446460f3.dir
+      size: 23784411
       nfiles: 2
     params:
       configs/build_model.yaml:
@@ -95,18 +81,18 @@ stages:
     outs:
     - path: data/fit_predictions/
       hash: md5
-      md5: 36c41f88681ab90668c17ce63fd9c318.dir
-      size: 3444201
+      md5: c6925db5c6b2ff0f95e97aed727462a1.dir
+      size: 726994
       nfiles: 1
     - path: data/model/
       hash: md5
-      md5: bb9c3f1538e02e20e918ec36a0b7546f.dir
-      size: 754271944
-      nfiles: 37
+      md5: f8cd16b81139a2ed1f40009204b5bb67.dir
+      size: 557447134
+      nfiles: 36
     - path: metrics/fit_metrics.json
       hash: md5
-      md5: 16ae1efa8ac48d8ed978bb3fa67be64a
-      size: 225
+      md5: 35cc792ba9bda3755928561c512aed3c
+      size: 214
   generate_predictions:
     cmd: python 3_generate_predictions.py
     deps:
@@ -116,13 +102,13 @@ stages:
       size: 2464
     - path: data/model
       hash: md5
-      md5: bb9c3f1538e02e20e918ec36a0b7546f.dir
-      size: 754271944
-      nfiles: 37
+      md5: f8cd16b81139a2ed1f40009204b5bb67.dir
+      size: 557447134
+      nfiles: 36
     - path: data/prepared_data
       hash: md5
-      md5: 0f11a02cf75c0421757c0b26184cec33.dir
-      size: 48971227
+      md5: c162c4b80826f42b33cce656446460f3.dir
+      size: 23784411
       nfiles: 2
     params:
       configs/settings.yaml:
@@ -134,8 +120,8 @@ stages:
     outs:
     - path: data/predictions/
       hash: md5
-      md5: 50909a5b19c2551410e921dc9a92bef7.dir
-      size: 480359
+      md5: 4edc571b115ec0b8be1d2689e97ff36f.dir
+      size: 77479
       nfiles: 1
   generate_metrics:
     cmd: python 4_generate_metrics.py
@@ -146,13 +132,13 @@ stages:
       size: 3484
     - path: data/predictions
       hash: md5
-      md5: 50909a5b19c2551410e921dc9a92bef7.dir
-      size: 480359
+      md5: 4edc571b115ec0b8be1d2689e97ff36f.dir
+      size: 77479
       nfiles: 1
     - path: data/prepared_data
       hash: md5
-      md5: 0f11a02cf75c0421757c0b26184cec33.dir
-      size: 48971227
+      md5: c162c4b80826f42b33cce656446460f3.dir
+      size: 23784411
       nfiles: 2
     params:
       configs/settings.yaml:
@@ -162,8 +148,8 @@ stages:
     outs:
     - path: metrics/metrics.json
       hash: md5
-      md5: d74767b34a1042c9ab0e3d6535791be6
-      size: 224
+      md5: 32811206191a4b2f24234d1f94b80b70
+      size: 222
   generate_scenerio_metrics:
     cmd: python 5_generate_scenarios.py
     deps: