fixed merge conflict

modules/ml-pipeline/src/README.md

@@ -1,3 +1,3 @@
-# The generic reproducible ML-pipeline
+# The generic reproducible ML-pipeline!
 
 Pipeline required to build a model to produce an output, that gets hashed via DVC

modules/ml-pipeline/src/pipeline/.gitignore

@@ -1,3 +1,4 @@
 
 # Ignore dynaconf secret files
 .secrets.*
+example.py

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

@@ -18,30 +18,44 @@ def remove_starting_columns(df):
     return df
 
 
-def remove_floor_height_ending(df):
-    # df.describe(percentiles=[0.005,0.99])['FLOOR_HEIGHT_ENDING']
-    # shows bottom 0.5 percentile is 1.665
-    # So keep anything above this
-    df = df[df["floor_height_ending"] > 1.665].reset_index(drop=True)
-    print("we in here")
+def keep_negative_heat_change(df):
+    df = df[df["heat_demand_change"] < 0]
     return df
 
 
-def remove_minimum_habitable_room_size(df):
-    # Need minimum of 6.5m per habitable room
-    df = df[
-        df["total_floor_area_ending"] / df["number_habitable_rooms"] > 6.5
-    ].reset_index(drop=True)
+def keep_non_negative_carbon_ending(df):
+    df = df[df["carbon_ending"] > 0]
     return df
 
 
-def keep_flats(df):
-    df = df[df["property_type"] == "Flat"]
+def keep_negative_carbon_change(df):
+    df = df[df["carbon_change"] < 0]
     return df
 
 
-def keep_non_zero_rdsap(df):
-    df = df[df["rdsap_change"] != 0]
+# TODO: Move to ETL pipeline
+def remove_unreasonable_habitable_rooms(df):
+    """
+    Assumption is that proportion of floor area to habitable rooms should be at least 6.5m2
+    """
+    minimum_room_size_index = (
+        df["total_floor_area_ending"] / df["number_habitable_rooms"] >= 6.5
+    )
+    df = df[minimum_room_size_index]
+    return df
+
+
+def remove_top_1_percent_heat_demand(df):
+    # threshold_value = df.describe(percentiles=[0.99])['HEAT_DEMAND_STARTING']['99%']
+    threshold_value = 860
+    df = df[df["heat_demand_starting"] < threshold_value]
+    return df
+
+
+def remove_top_1_percent_carbon(df):
+    # threshold_value = df.describe(percentiles=[0.99])['CARBON_STARTING']['99%']
+    threshold_value = 18
+    df = df[df["carbon_starting"] < threshold_value]
     return df
 
 
@@ -54,10 +68,12 @@ def keep_non_zero_rdsap(df):
 #     return df
 
 business_logic = {
-    # "keep_non_zero_rdsap": keep_non_zero_rdsap,
-    # "keep_flats": keep_flats,
-    # "remove_minimum_habitable_room_size": remove_minimum_habitable_room_size,
-    # "remove_floor_height_ending": remove_floor_height_ending
+    "remove_unreasonable_habitable_rooms": remove_unreasonable_habitable_rooms,
+    "keep_negative_heat_change": keep_negative_heat_change,
+    "keep_negative_carbon_change": keep_negative_carbon_change,
+    "remove_top_1_percent_heat_demand": remove_top_1_percent_heat_demand,
+    "remove_top_1_percent_carbon": remove_top_1_percent_carbon,
+    "keep_non_negative_carbon_ending": keep_non_negative_carbon_ending,
     # "remove_starting_columns": remove_starting_columns
     # "keep_ENDING_COLUMNS": keep_ending_columns
 }

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

@@ -1,23 +1,24 @@
 """
 After predictions, we may want to apply some post processing to the predictions
 """
+
 import pandas as pd
 
 
 def clip_predictions_to_minimum_value(
-    data: pd.DataFrame, predictions: pd.Series, minimum_value: int = 0
+    data: pd.DataFrame,
+    predictions: pd.Series,
 ) -> pd.Series:
 
     series_name = predictions.name
     predictions.name = "predictions"
+    predictions = predictions.astype(data["carbon_starting"].dtype)
     predictions_df = pd.concat([data, predictions], axis=1)
     # We expect all prediction to be atleast one point improvement
-    replace_index = (
-        predictions_df["sap_starting"] + minimum_value > predictions_df["predictions"]
-    )
-    predictions_df.loc[replace_index, "predictions"] = (
-        predictions_df.loc[replace_index, "sap_starting"] + minimum_value
-    )
+    replace_index = predictions_df["predictions"] > predictions_df["carbon_starting"]
+    predictions_df.loc[replace_index, "predictions"] = predictions_df.loc[
+        replace_index, "carbon_starting"
+    ]
 
     predictions_new = predictions_df["predictions"]
     predictions_new.name = series_name

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

@@ -31,13 +31,14 @@ default:
     feature_processor_config:
       subsample_amount: null
       subsample_seed: 0
-      target: sap_ending
+      target: carbon_ending
       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: ["heat_demand_change", "carbon_change", "rdsap_change", "heat_demand_ending", "sap_ending"]
       drop_columns: [
-        "heat_demand_change", "carbon_change", "rdsap_change", "heat_demand_ending", "carbon_ending", "days_to_starting", "days_to_ending",
+        "heat_demand_change", "carbon_change", "rdsap_change", "heat_demand_ending", "sap_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: ["SAP_STARTING", "TOTAL_FLOOR_AREA_DIFF"]
       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

@@ -1,190 +0,0 @@
-schema: '2.0'
-stages:
-  startup_cleanup:
-    cmd: python 0_startup_cleanup.py
-    deps:
-    - path: 0_startup_cleanup.py
-      hash: md5
-      md5: b1b12f6b6393fbf8b83d23684df0a3d4
-      size: 1220
-    params:
-      configs/settings.yaml:
-        default.startup_cleanup.artefacts: ./data
-        default.startup_cleanup.metrics: ./metrics
-  prepare_data:
-    cmd: python 1_prepare_data.py
-    deps:
-    - path: 1_prepare_data.py
-      hash: md5
-      md5: 11a3b8bfdfe199ab7ecc39ccc5652649
-      size: 4298
-    params:
-      configs/settings.yaml:
-        default.feature_processor.feature_processor_config.drop_columns:
-        - heat_demand_change
-        - carbon_change
-        - rdsap_change
-        - heat_demand_ending
-        - carbon_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
-        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: sap_ending
-        default.feature_processor.feature_processor_type: dataframe
-        default.prepare_data.data_filepath:
-          s3://retrofit-data-dev/sap_change_model/2024-05-28-19-08-25/dataset_rooms.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
-        default.prepare_data.output_train_filepath: ./data/prepared_data/train.parquet
-        default.prepare_data.train_proportion: 0.9
-    outs:
-    - path: data/prepared_data/
-      hash: md5
-      md5: 80c9e138146a1d96b9d16091c207e2e8.dir
-      size: 45056059
-      nfiles: 2
-  build_model:
-    cmd: python 2_build_model.py
-    deps:
-    - path: 2_build_model.py
-      hash: md5
-      md5: 7231450b78920b0c5e7c6bada496b24a
-      size: 4820
-    - path: data/prepared_data
-      hash: md5
-      md5: 80c9e138146a1d96b9d16091c207e2e8.dir
-      size: 45056059
-      nfiles: 2
-    params:
-      configs/build_model.yaml:
-        default:
-          build_model:
-            model_type: AutogluonAutoML
-            model_save_filepath: ./data/model/optimised/
-            fit_metrics_filepath: ./metrics/fit_metrics.json
-            fit_predictions_filepath: ./data/fit_predictions/predictions.parquet
-            SKLearnLinearRegression:
-            SKLearnSVMRegression:
-              kernel: linear
-            AutogluonAutoML:
-              output_filepath: ./data/model/allmodels/
-              problem_type: regression
-              eval_metric: mean_squared_error
-              time_limit: 1800
-              presets: medium_quality
-              excluded_model_types:
-              - RF
-              - CAT
-              - NN_TORCH
-              - KNN
-              - XT
-              infer_limit: 0.05
-              infer_limit_batch_size: 10000
-              ag_args_ensemble:
-                num_folds_parallel: 2
-    outs:
-    - path: data/fit_predictions/
-      hash: md5
-      md5: d9c9afc05e8780db47c0548b19bf7d19.dir
-      size: 3349989
-      nfiles: 1
-    - path: data/model/
-      hash: md5
-      md5: 13c3100e1486c27a83a8a47491077842.dir
-      size: 773523079
-      nfiles: 36
-    - path: metrics/fit_metrics.json
-      hash: md5
-      md5: 2ff70a2a45813e1bcdf2ea3aa8e07d4a
-      size: 224
-  generate_predictions:
-    cmd: python 3_generate_predictions.py
-    deps:
-    - path: 3_generate_predictions.py
-      hash: md5
-      md5: 0a70ad4dfe99414a75d1261c75a177b9
-      size: 2464
-    - path: data/model
-      hash: md5
-      md5: 13c3100e1486c27a83a8a47491077842.dir
-      size: 773523079
-      nfiles: 36
-    - path: data/prepared_data
-      hash: md5
-      md5: 80c9e138146a1d96b9d16091c207e2e8.dir
-      size: 45056059
-      nfiles: 2
-    params:
-      configs/settings.yaml:
-        default.generate_predictions.input_dataclient_type: local
-        default.generate_predictions.output_dataclient_type: local
-        default.generate_predictions.predictions_column_name: predictions
-        default.generate_predictions.predictions_output_filepath: ./data/predictions/predictions.parquet
-        default.generate_predictions.test_data_filepath: ./data/prepared_data/test.parquet
-    outs:
-    - path: data/predictions/
-      hash: md5
-      md5: 5d07bcebf3160a72bb18dfd79106e85c.dir
-      size: 463197
-      nfiles: 1
-  generate_metrics:
-    cmd: python 4_generate_metrics.py
-    deps:
-    - path: 4_generate_metrics.py
-      hash: md5
-      md5: 4fedb86d89d528f0a6597934ba3890a0
-      size: 3484
-    - path: data/predictions
-      hash: md5
-      md5: 5d07bcebf3160a72bb18dfd79106e85c.dir
-      size: 463197
-      nfiles: 1
-    - path: data/prepared_data
-      hash: md5
-      md5: 80c9e138146a1d96b9d16091c207e2e8.dir
-      size: 45056059
-      nfiles: 2
-    params:
-      configs/settings.yaml:
-        default.generate_metrics.dataclient_type: local
-        default.generate_metrics.metrics_output_filepath: ./metrics/metrics.json
-        default.generate_metrics.metrics_type: Regression
-    outs:
-    - path: metrics/metrics.json
-      hash: md5
-      md5: 3e08df02fd5c5d094bcf936e1338d596
-      size: 223
-  generate_scenerio_metrics:
-    cmd: python 5_generate_scenarios.py
-    deps:
-    - path: 5_generate_scenarios.py
-      hash: md5
-      md5: 40506749fefd926d47c60ff5b16db307
-      size: 5337
-    params:
-      configs/scenarios.yaml:
-        default.scenarios:
-          input_dataclient_type: aws-s3
-          output_dataclient_type: local
-          scenario_data_filepaths:
-          - s3://retrofit-data-dev/scenario_data/28-05-2024-19-22-41/recommendations_scoring_data.parquet
-          comparison_output_filepath: ./metrics/scenario_table.md
-          metrics_output_filepath: ./metrics/scenario_metrics.md
-    outs:
-    - path: metrics/scenario_metrics.md
-      hash: md5
-      md5: fa4d6d7bbd7818613800da5f8f37ea96
-      size: 363
-    - path: metrics/scenario_table.md
-      hash: md5
-      md5: d6baf100a1623cc2467c2f8221d314c9
-      size: 2133