Merge pull request #90 from Hestia-Homes/sap-dev-model

Sap dev model

.github/workflows/Deploy.yml

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

modules/ml-pipeline/.dvc/.gitignore

@@ -1,3 +0,0 @@
-/config.local
-/tmp
-/cache

modules/ml-pipeline/.dvc/config

@@ -1,2 +0,0 @@
-['remote "myremote"']
-    url = /tmp/dvcstore

modules/ml-pipeline/.dvcignore

@@ -1,3 +0,0 @@
-# Add patterns of files dvc should ignore, which could improve
-# the performance. Learn more at
-# https://dvc.org/doc/user-guide/dvcignore

modules/ml-pipeline/.gto

@@ -1,2 +0,0 @@
-# .gto config file
-stages: [dev, stage, prod] # list of allowed Stages

modules/ml-pipeline/src/pipeline/2_build_model.py

@@ -67,7 +67,6 @@ def build_model(
     test_data: Union[pd.DataFrame, None] = None,
     pipeline_mode: bool = False,
 ):
-
     logger.info("--- Loading Data for build process ---")
 
     if train_data is None:

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

@@ -13,7 +13,7 @@ default:
       output_filepath: ./data/model/allmodels/
       problem_type: regression
       eval_metric: mean_squared_error #mean_absolute_error
-      time_limit: 4000
+      time_limit: 400
       presets: medium_quality
       excluded_model_types: ['KNN', 'RF']
       infer_limit: 0.05

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

@@ -9,11 +9,11 @@ Business Logic dict + functions
 
 def remove_starting_columns(df):
     keep_column_index = [
-        False if col_name.endswith("_STARTING") else True
+        False if col_name.endswith("_starting") else True
         for col_name in list(df.columns)
     ]
     keep_columns = df.columns[keep_column_index].to_list()
-    keep_columns.append("SAP_STARTING")
+    keep_columns.append("sap_starting")
     df = df[keep_columns]
     return df
 
@@ -22,7 +22,7 @@ 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)
+    df = df[df["floor_height_ending"] > 1.665].reset_index(drop=True)
     print("we in here")
     return df
 
@@ -30,13 +30,13 @@ def remove_floor_height_ending(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
+        df["total_floor_area_ending"] / df["number_habitable_rooms"] > 6.5
     ].reset_index(drop=True)
     return df
 
 
 def keep_flats(df):
-    df = df[df["PROPERTY_TYPE"] == "Flat"]
+    df = df[df["property_type"] == "Flat"]
     return df
 
 

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

@@ -12,9 +12,9 @@ def clip_predictions_to_minimum_value(
     predictions.name = "predictions"
     predictions_df = pd.concat([data, predictions], axis=1)
     # We expect all prediction to be atleast one point improvement
-    replace_index = predictions_df["SAP_STARTING"] + 1 > predictions_df["predictions"]
+    replace_index = predictions_df["sap_starting"] + 1 > predictions_df["predictions"]
     predictions_df.loc[replace_index, "predictions"] = (
-        predictions_df.loc[replace_index, "SAP_STARTING"] + minimum_value
+        predictions_df.loc[replace_index, "sap_starting"] + minimum_value
     )
 
     predictions_new = predictions_df["predictions"]

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

@@ -21,7 +21,8 @@ default:
     # data_filepath: s3://retrofit-data-dev/sap_change_model/dataset_with_differencing.parquet
     # data_filepath: s3://retrofit-data-dev/sap_change_model/floor_area_clean_test.parquet
     # data_filepath: s3://retrofit-data-dev/sap_change_model/dataset_without_differencing.parquet
-    data_filepath: s3://retrofit-data-dev/sap_change_model/dataset_test.parquet
+    # data_filepath: s3://retrofit-data-dev/sap_change_model/dataset_test.parquet
+    data_filepath: s3://retrofit-data-dev/sap_change_model/dataset_refactor.parquet
     train_proportion: 0.9
     output_train_filepath: ./data/prepared_data/train.parquet
     output_test_filepath: ./data/prepared_data/test.parquet
@@ -31,9 +32,9 @@ default:
     feature_processor_config:
       subsample_amount: null
       subsample_seed: 0
-      target: SAP_ENDING
+      target: sap_ending
-      identifier_columns: ["UPRN"]
+      identifier_columns: ["uprn"]
-      drop_columns: ["HEAT_DEMAND_CHANGE", "CARBON_CHANGE", "RDSAP_CHANGE", "HEAT_DEMAND_ENDING", "CARBON_ENDING"]
+      drop_columns: ["heat_demand_change", "carbon_change", "rdsap_change", "heat_demand_ending", "carbon_ending"]
       # retain_features: ["SAP_STARTING", "TOTAL_FLOOR_AREA_DIFF"]
       retain_features: null
 

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

@@ -10,17 +10,17 @@ stages:
     params:
       configs/settings.yaml:
         default.feature_processor.feature_processor_config.drop_columns:
-        - HEAT_DEMAND_CHANGE
+        - heat_demand_change
-        - CARBON_CHANGE
+        - carbon_change
-        - RDSAP_CHANGE
+        - rdsap_change
-        - HEAT_DEMAND_ENDING
+        - heat_demand_ending
-        - CARBON_ENDING
+        - carbon_ending
         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_config.target: sap_ending
         default.feature_processor.feature_processor_type: dataframe
-        default.prepare_data.data_filepath: s3://retrofit-data-dev/sap_change_model/dataset_test.parquet
+        default.prepare_data.data_filepath: s3://retrofit-data-dev/sap_change_model/dataset_refactor.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
@@ -29,20 +29,20 @@ stages:
     outs:
     - path: data/prepared_data/
       hash: md5
-      md5: 6bfdb621b608648c017bf2323f7b5052.dir
+      md5: 5d29397fcafe6b3dc4d51ffaf1e55239.dir
-      size: 37048968
+      size: 39303409
       nfiles: 2
   build_model:
     cmd: python 2_build_model.py
     deps:
     - path: 2_build_model.py
       hash: md5
-      md5: 7b79f280b8b0d5bc6f07669e7cc37c6a
+      md5: b824822475c222521516493e68eef9c5
-      size: 4150
+      size: 4149
     - path: data/prepared_data
       hash: md5
-      md5: 6bfdb621b608648c017bf2323f7b5052.dir
+      md5: 5d29397fcafe6b3dc4d51ffaf1e55239.dir
-      size: 37048968
+      size: 39303409
       nfiles: 2
     params:
       configs/build_model.yaml:
@@ -58,7 +58,7 @@ stages:
               output_filepath: ./data/model/allmodels/
               problem_type: regression
               eval_metric: mean_squared_error
-              time_limit: 4000
+              time_limit: 400
               presets: medium_quality
               excluded_model_types:
               - KNN
@@ -68,13 +68,13 @@ stages:
     outs:
     - path: data/model/
       hash: md5
-      md5: f2999107de7572ea5ff0f2d774fa83b8.dir
+      md5: 6265dafedf579905c31c676e81c2a9c7.dir
-      size: 424943352
+      size: 344212462
-      nfiles: 27
+      nfiles: 24
     - path: metrics/fit_metrics.json
       hash: md5
-      md5: 9537e7ebc2eb32b421a7cabd2005f00b
+      md5: 5cd6b92af1b1df753e20e9ea33629c4d
-      size: 223
+      size: 224
   generate_predictions:
     cmd: python 3_generate_predictions.py
     deps:
@@ -84,13 +84,13 @@ stages:
       size: 2464
     - path: data/model
       hash: md5
-      md5: f2999107de7572ea5ff0f2d774fa83b8.dir
+      md5: 6265dafedf579905c31c676e81c2a9c7.dir
-      size: 424943352
+      size: 344212462
-      nfiles: 27
+      nfiles: 24
     - path: data/prepared_data
       hash: md5
-      md5: 6bfdb621b608648c017bf2323f7b5052.dir
+      md5: 5d29397fcafe6b3dc4d51ffaf1e55239.dir
-      size: 37048968
+      size: 39303409
       nfiles: 2
     params:
       configs/settings.yaml:
@@ -102,8 +102,8 @@ stages:
     outs:
     - path: data/predictions/
       hash: md5
-      md5: f4439a56669f84bc51a9fcb4cd08353f.dir
+      md5: b130faf5117b06897b2deed97f5868ee.dir
-      size: 346539
+      size: 367038
       nfiles: 1
   generate_metrics:
     cmd: python 4_generate_metrics.py
@@ -114,13 +114,13 @@ stages:
       size: 3484
     - path: data/predictions
       hash: md5
-      md5: f4439a56669f84bc51a9fcb4cd08353f.dir
+      md5: b130faf5117b06897b2deed97f5868ee.dir
-      size: 346539
+      size: 367038
       nfiles: 1
     - path: data/prepared_data
       hash: md5
-      md5: 6bfdb621b608648c017bf2323f7b5052.dir
+      md5: 5d29397fcafe6b3dc4d51ffaf1e55239.dir
-      size: 37048968
+      size: 39303409
       nfiles: 2
     params:
       configs/settings.yaml:
@@ -130,7 +130,7 @@ stages:
     outs:
     - path: metrics/metrics.json
       hash: md5
-      md5: 357904cf106279be5a578e8faefa5d80
+      md5: 3900cc1697d6d7308728b3d5b3025f85
       size: 224
   startup_cleanup:
     cmd: python 0_startup_cleanup.py

modules/ml-pipeline/src/pipeline/eda.py

@@ -190,28 +190,35 @@ prediction_analysis_params = settings.prediction_analysis
 model = model_factory(build_model_params["model_type"])
 model.load_model(build_model_params["model_save_filepath"])
 dataclient_type = prediction_analysis_params["dataclient_type"]
-dataclient = dataclient_factory(
+# dataclient_type = 'aws-s3'
-    dataclient_type=dataclient_type,
+# dataclient = dataclient_factory(
-    dataclient_config=client_params[dataclient_type],
+#     dataclient_type=dataclient_type,
-)
+#     dataclient_config=client_params[dataclient_type],
+# )
+# data = dataclient.load_data("s3://retrofit-data-dev/sap_change_model/dataset.parquet")
 
 target = feature_process_params["feature_processor_config"]["target"]
 predictions_column_name = generate_predictions_params["predictions_column_name"]
 output_test_filepath = prepare_data_params["output_test_filepath"]
 predictions_output_filepath = generate_predictions_params["predictions_output_filepath"]
 
-test_df = dataclient.load_data(output_test_filepath)
+# score_data = dataclient.load_data("s3://retrofit-data-dev/carbon_change_predictions/51/2023-11-28T21:01:21.869339.parquet")
-predictions = dataclient.load_data(predictions_output_filepath)
+
+
+local_dataclient = dataclient_factory(
+    dataclient_type="local",
+    dataclient_config=client_params["local"],
+)
+test_df = local_dataclient.load_data(output_test_filepath)
+predictions = local_dataclient.load_data(predictions_output_filepath)
 mix_df = pd.concat([test_df.copy(), predictions], axis=1)
 mix_df["residual"] = abs(mix_df[predictions_column_name] - mix_df[target])
 mix_df = mix_df.sort_values("residual", ascending=False)
 
-cosine_similarity_df = mix_df[
+cosine_similarity_df = mix_df[mix_df.columns.difference(["predictions", "residual"])]
-    mix_df.columns.difference(["UPRN", "predictions", "residual", "SAP_ENDING"])
-]
 from sklearn.metrics.pairwise import cosine_similarity
 
-row_index = 20695
+row_index = 0
 
 from sklearn.preprocessing import LabelEncoder
 
@@ -224,8 +231,18 @@ cosine_similarity_df[object_columns.columns] = cosine_similarity_df[
 feature_vector = cosine_similarity_df.loc[[row_index]]
 
 cosine_similarity_df["cosine"] = cosine_similarity(cosine_similarity_df, feature_vector)
-
+similar_index = (
-similar_df = cosine_similarity_df.sort_values("cosine", ascending=False).head(5)
+    cosine_similarity_df.sort_values("cosine", ascending=False).head(15).index
-similar_index = similar_df.index
+)
 
 check_df = mix_df.loc[similar_index]
+
+columns_to_check = [
+    "LOW_ENERGY_LIGHTING_ENDING",
+    "walls_thermal_transmittance_ENDING",
+    "floor_thermal_transmittance_ENDING",
+    "roof_thermal_transmittance_ENDING",
+    "roof_insulation_thickness_ENDING",
+]
+
+cosine_similarity_df = mix_df[columns_to_check]

modules/ml-pipeline/src/pipeline/requirements/version_control/requirements.txt

@@ -1,4 +1,4 @@
-dvc==3.18.0
+dvc==3.36.0
-dvc-s3==2.23.0
+dvc-s3==3.0.1
-gto==1.0.4
+gto==1.6.1
-pyOpenSSL==23.2.0
+pyOpenSSL==23.3.0