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run carbon model with new data

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This commit is contained in:
Michael Duong 2024-03-27 23:10:36 +00:00
parent 96eb3904e2
commit c0dc934be6
13 changed files with 119 additions and 87 deletions
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4
MODEL_REGISTRY.md
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@ -8,9 +8,9 @@
"active": true
},
"sap": {
"version": "v0.5.0",
"version": "v0.4.0",
"stage": {
"dev": "v0.5.0"
"dev": "v0.4.0"
},
"registered": true,
"active": true

2
modules/ml-pipeline/src/README.md
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@ -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

1
modules/ml-pipeline/src/pipeline/.gitignore vendored
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@ -1,3 +1,4 @@
# Ignore dynaconf secret files
.secrets.*
example.py

1
modules/ml-pipeline/src/pipeline/configs/build_model.yaml
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@ -19,3 +19,4 @@ default:
excluded_model_types: ['RF', 'FASTAI', 'CAT', 'NN_TORCH', 'KNN', 'XT']
infer_limit: 0.05
infer_limit_batch_size: 10000
ag_args_ensemble: {'num_folds_parallel': 2}

54
modules/ml-pipeline/src/pipeline/configs/feature_processor_logic.py
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@ -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
}

15
modules/ml-pipeline/src/pipeline/configs/post_prediction_logic.py
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@ -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

15
modules/ml-pipeline/src/pipeline/configs/settings.yaml
View file

@ -18,12 +18,7 @@ default:
prepare_data:
input_dataclient_type: aws-s3
output_dataclient_type: local
# 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.parquet
data_filepath: s3://retrofit-datalake-dev/dataset_with0perm_all.parquet
data_filepath: s3://retrofit-data-dev/sap_change_model/2024-03-22-18-56-53/dataset_rooms.parquet
train_proportion: 1
output_train_filepath: ./data/prepared_data/train.parquet
output_test_filepath: ./data/prepared_data/test.parquet
@ -33,9 +28,13 @@ 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"]
# 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", "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

4
modules/ml-pipeline/src/pipeline/core/MLModels.py
View file

@ -25,7 +25,7 @@ def model_factory(model_type: str) -> MLModel:
models = {
"SKLearnLinearRegression": SKLearnLinearRegression(),
"SKLearnSVMRegression": SKLearnSVMRegression(),
"AutogluonAutoML": AutogluonAutoML()
"AutogluonAutoML": AutogluonAutoML(),
# ADD OTHER MODELS HERE
}
@ -151,6 +151,7 @@ class AutogluonAutoML:
"excluded_model_types",
"infer_limit",
"infer_limit_batch_size",
"ag_args_ensemble",
]
def load_model(self, path: Union[Path, str]) -> None:
@ -207,6 +208,7 @@ class AutogluonAutoML:
excluded_model_types=model_hyperparameters["excluded_model_types"],
infer_limit=model_hyperparameters["infer_limit"],
infer_limit_batch_size=model_hyperparameters["infer_limit_batch_size"],
ag_args_ensemble=model_hyperparameters["ag_args_ensemble"],
)
def predict(

91
modules/ml-pipeline/src/pipeline/dvc.lock
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@ -1,12 +1,23 @@
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: 1793a35e71751d3c84f9affc67ecb9a8
size: 4296
md5: 11a3b8bfdfe199ab7ecc39ccc5652649
size: 4298
params:
configs/settings.yaml:
default.feature_processor.feature_processor_config.drop_columns:
@ -14,13 +25,22 @@ stages:
- carbon_change
- rdsap_change
- heat_demand_ending
- carbon_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
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: carbon_ending
default.feature_processor.feature_processor_type: dataframe
default.prepare_data.data_filepath: s3://retrofit-datalake-dev/dataset_with0perm_all.parquet
default.prepare_data.data_filepath:
s3://retrofit-data-dev/sap_change_model/2024-03-22-18-56-53/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
@ -29,8 +49,8 @@ stages:
outs:
- path: data/prepared_data/
hash: md5
md5: 84fa631bd02686b052d6a7144eafd38e.dir
size: 43859225
md5: 35d7daa8144434e188ba3b1da4bcf328.dir
size: 33946500
nfiles: 2
build_model:
cmd: python 2_build_model.py
@ -41,8 +61,8 @@ stages:
size: 4820
- path: data/prepared_data
hash: md5
md5: 84fa631bd02686b052d6a7144eafd38e.dir
size: 43859225
md5: 35d7daa8144434e188ba3b1da4bcf328.dir
size: 33946500
nfiles: 2
params:
configs/build_model.yaml:
@ -70,21 +90,23 @@ stages:
- 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: ede187e9d0bffdef054f573f3c2bd222.dir
size: 3578590
md5: 19d033f5abfa9b064c3e52815e607ced.dir
size: 3927492
nfiles: 1
- path: data/model/
hash: md5
md5: b2ad0b538dc4aef0de3d431fc9c40c4f.dir
size: 814720415
nfiles: 31
md5: f159d40353b01ffdcf1b1b490c019f1f.dir
size: 787748148
nfiles: 32
- path: metrics/fit_metrics.json
hash: md5
md5: c45b84f12971a0156e4f3d85d3e725f5
size: 218
md5: e69d56ab9d82f23f2aa66001bd9bebbc
size: 229
generate_predictions:
cmd: python 3_generate_predictions.py
deps:
@ -94,13 +116,13 @@ stages:
size: 2464
- path: data/model
hash: md5
md5: b2ad0b538dc4aef0de3d431fc9c40c4f.dir
size: 814720415
nfiles: 31
md5: f159d40353b01ffdcf1b1b490c019f1f.dir
size: 787748148
nfiles: 32
- path: data/prepared_data
hash: md5
md5: 84fa631bd02686b052d6a7144eafd38e.dir
size: 43859225
md5: 35d7daa8144434e188ba3b1da4bcf328.dir
size: 33946500
nfiles: 2
params:
configs/settings.yaml:
@ -112,8 +134,8 @@ stages:
outs:
- path: data/predictions/
hash: md5
md5: 5e60ca251af51de6fef3d0c659f8bb27.dir
size: 627416
md5: 50d0c76fc56c6290babeff1c84750316.dir
size: 651956
nfiles: 1
generate_metrics:
cmd: python 4_generate_metrics.py
@ -124,13 +146,13 @@ stages:
size: 3484
- path: data/predictions
hash: md5
md5: 5e60ca251af51de6fef3d0c659f8bb27.dir
size: 627416
md5: 50d0c76fc56c6290babeff1c84750316.dir
size: 651956
nfiles: 1
- path: data/prepared_data
hash: md5
md5: 84fa631bd02686b052d6a7144eafd38e.dir
size: 43859225
md5: 35d7daa8144434e188ba3b1da4bcf328.dir
size: 33946500
nfiles: 2
params:
configs/settings.yaml:
@ -140,16 +162,5 @@ stages:
outs:
- path: metrics/metrics.json
hash: md5
md5: 033efa4d4044b6b6fc92dd37194727fa
size: 225
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
md5: 542b982d6aa9fe0fdb89611e4299cb1e
size: 228

4
modules/ml-pipeline/src/pipeline/requirements/predictions/requirements-dev.txt
View file

@ -1,7 +1,7 @@
joblib==1.3.2
boto3==1.28.17
pandas==1.5.3
autogluon==0.8.2
pandas==2.1.4
autogluon==1.0.0
dynaconf==3.2.0
pyarrow==13.0.0
pre-commit==3.3.3

4
modules/ml-pipeline/src/pipeline/requirements/predictions/requirements.txt
View file

@ -1,7 +1,7 @@
joblib==1.3.2
boto3==1.28.17
pandas==1.5.3
autogluon==0.8.2
pandas==2.1.4
autogluon==1.0.0
dynaconf==3.2.0
pyarrow==13.0.0
PyYAML==6.0.1

7
modules/ml-pipeline/src/pipeline/requirements/training/requirements-dev.txt
View file

@ -1,9 +1,10 @@
joblib==1.3.2
boto3==1.28.17
pandas==1.5.3
autogluon==0.8.2
pandas==2.1.4
autogluon==1.0.0
ray==2.6.3
dynaconf==3.2.0
alibi==0.9.4
alibi==0.9.5
shap==0.42.1
pyarrow==13.0.0
pre-commit==3.3.3

4
modules/ml-pipeline/src/pipeline/requirements/training/requirements.txt
View file

@ -1,4 +1,4 @@
boto3==1.28.41
pandas==1.5.3
autogluon==0.8.2
pandas==2.1.4
autogluon==1.0.0
dynaconf==3.2.0