junte/ML
1
0
Fork 0
mirror of https://github.com/Hestia-Homes/ML.git synced 2026-06-08 11:17:25 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions

Merge pull request #107 from Hestia-Homes/carbon-dev-model

Browse source 
Carbon dev model
This commit is contained in:
KhalimCK 2024-03-28 16:21:52 +00:00 • committed by GitHub
commit c7edb7c611
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
22 changed files with 119 additions and 87 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

4
deployment/.dockerignore Normal file
View file

@ -0,0 +1,4 @@
modules/ml-pipeline/src/pipeline/data/predictions*
modules/ml-pipeline/src/pipeline/data/prepared_data*
modules/ml-pipeline/src/pipeline/data/model/allmodels*
modules/ml-pipeline/src/pipeline/metrics*

2
deployment/Dockerfile.prediction.lambda
View file

@ -9,7 +9,7 @@ ARG RUNTIME_ENVIRONMENT
ENV RUNTIME_ENVIRONMENT=${RUNTIME_ENVIRONMENT}
# Install necessary build tools - required to test locally
RUN yum install -y gcc python3-devel
RUN yum install -y gcc python3-devel gcc-c++
# Install python packages
COPY modules/ml-pipeline/src/pipeline/requirements/predictions/requirements.txt ./requirements.txt

3
modules/ml-pipeline/.dvc/.gitignore vendored
View file

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

2
modules/ml-pipeline/.dvc/config
View file

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

3
modules/ml-pipeline/.dvcignore
View file

@ -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

2
modules/ml-pipeline/.gto
View file

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

4
modules/ml-pipeline/src/.dockerignore Normal file
View file

@ -0,0 +1,4 @@
pipeline/data/predictions*
pipeline/data/prepared_data/train.parquet*
pipeline/data/model/allmodels*
pipeline/metrics*

1
modules/ml-pipeline/src/pipeline/.gitignore vendored
View file

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

1
modules/ml-pipeline/src/pipeline/4_generate_metrics.py
View file

@ -33,7 +33,6 @@ predictions_output_filepath = generate_predictions_params["predictions_output_fi
predictions_column_name = generate_predictions_params["predictions_column_name"]
metrics_output_filepath = generate_metrics_params["metrics_output_filepath"]
logger.info(f"--- Initiate MLModel ---")
model = model_factory(build_model_params["model_type"])

2
modules/ml-pipeline/src/pipeline/configs/analysis.yaml
View file

@ -13,4 +13,4 @@ default:
dataclient_type: local
nshap_samples: 100 # how many samples to use to approximate each Shapely value, larger values will be slower
n_val: 30 # how many datapoints from validation data should we interpret predictions for, larger values will be slower
row_index: [0, 10, 20] # index of an example datapoint
row_index: [20695, 50243, 7653] # index of an example datapoint

1
modules/ml-pipeline/src/pipeline/configs/build_model.yaml
View file

@ -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}

2
modules/ml-pipeline/src/pipeline/configs/feature_processor_logic.py
View file

@ -73,7 +73,7 @@ business_logic = {
"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
"keep_non_negative_carbon_ending": keep_non_negative_carbon_ending,
# "remove_starting_columns": remove_starting_columns
# "keep_ENDING_COLUMNS": keep_ending_columns
}

2
modules/ml-pipeline/src/pipeline/configs/post_prediction_logic.py
View file

@ -1,6 +1,7 @@
"""
After predictions, we may want to apply some post processing to the predictions
"""
import pandas as pd
@ -11,6 +12,7 @@ def clip_predictions_to_minimum_value(
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["predictions"] > predictions_df["carbon_starting"]

14
modules/ml-pipeline/src/pipeline/configs/settings.yaml
View file

@ -18,12 +18,8 @@ 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.parquet
data_filepath: s3://retrofit-datalake-dev/dataset_with0perm_all.parquet
train_proportion: 1
data_filepath: s3://retrofit-data-dev/sap_change_model/2024-03-22-18-56-53/dataset_rooms.parquet
train_proportion: 0.9
output_train_filepath: ./data/prepared_data/train.parquet
output_test_filepath: ./data/prepared_data/test.parquet
@ -34,7 +30,11 @@ default:
subsample_seed: 0
target: carbon_ending
identifier_columns: ["uprn"]
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"]
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(

93
modules/ml-pipeline/src/pipeline/dvc.lock
View file

@ -1,5 +1,16 @@
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:
@ -15,34 +26,43 @@ stages:
- 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
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: 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
default.prepare_data.output_train_filepath: ./data/prepared_data/train.parquet
default.prepare_data.train_proportion: 1
default.prepare_data.train_proportion: 0.9
outs:
- path: data/prepared_data/
hash: md5
md5: 44737880f5437e23143479a7818a3136.dir
size: 36064622
md5: 824541f44e6538d2ef10e9d754c79743.dir
size: 36691842
nfiles: 2
build_model:
cmd: python 2_build_model.py
deps:
- path: 2_build_model.py
hash: md5
md5: 090bfb7dbaff39f45784b7fe332a9b8e
size: 4819
md5: 7231450b78920b0c5e7c6bada496b24a
size: 4820
- path: data/prepared_data
hash: md5
md5: 44737880f5437e23143479a7818a3136.dir
size: 36064622
md5: 824541f44e6538d2ef10e9d754c79743.dir
size: 36691842
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: 7b74ae1174ae2c7fab03ee0ce0a8ae71.dir
size: 3877514
md5: 5a3091120d3497fa00b994d91bc7e5eb.dir
size: 3664806
nfiles: 1
- path: data/model/
hash: md5
md5: d2ebaa73a894387f85083c49e58637bc.dir
size: 798349514
nfiles: 32
md5: 074da8dcfa515b9f3d082b21c7d76616.dir
size: 721558897
nfiles: 31
- path: metrics/fit_metrics.json
hash: md5
md5: 51c9c678bbd19bc9f7e16f0bf5df3fef
size: 229
md5: 728a49dcef5a98182325df455f929a33
size: 225
generate_predictions:
cmd: python 3_generate_predictions.py
deps:
@ -94,13 +116,13 @@ stages:
size: 2464
- path: data/model
hash: md5
md5: d2ebaa73a894387f85083c49e58637bc.dir
size: 798349514
nfiles: 32
md5: 074da8dcfa515b9f3d082b21c7d76616.dir
size: 721558897
nfiles: 31
- path: data/prepared_data
hash: md5
md5: 44737880f5437e23143479a7818a3136.dir
size: 36064622
md5: 824541f44e6538d2ef10e9d754c79743.dir
size: 36691842
nfiles: 2
params:
configs/settings.yaml:
@ -112,25 +134,25 @@ stages:
outs:
- path: data/predictions/
hash: md5
md5: ac0a698f14fb9002b337b1b163997333.dir
size: 638033
md5: 680f51234d214d4cab9e6a064c75fc5d.dir
size: 499546
nfiles: 1
generate_metrics:
cmd: python 4_generate_metrics.py
deps:
- path: 4_generate_metrics.py
hash: md5
md5: d09a80dd55f1f69e2a832b1991b3c406
size: 3485
md5: 4fedb86d89d528f0a6597934ba3890a0
size: 3484
- path: data/predictions
hash: md5
md5: ac0a698f14fb9002b337b1b163997333.dir
size: 638033
md5: 680f51234d214d4cab9e6a064c75fc5d.dir
size: 499546
nfiles: 1
- path: data/prepared_data
hash: md5
md5: 44737880f5437e23143479a7818a3136.dir
size: 36064622
md5: 824541f44e6538d2ef10e9d754c79743.dir
size: 36691842
nfiles: 2
params:
configs/settings.yaml:
@ -140,16 +162,5 @@ stages:
outs:
- path: metrics/metrics.json
hash: md5
md5: 47aa4601e71a93163d2cc1b85d0eda91
size: 228
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: 67b7ab30a4b0839d20bc6eb0c84e4dd1
size: 226

39
modules/ml-pipeline/src/pipeline/eda.py
View file

@ -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=dataclient_type,
dataclient_config=client_params[dataclient_type],
)
# dataclient_type = 'aws-s3'
# dataclient = dataclient_factory(
# 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)
predictions = dataclient.load_data(predictions_output_filepath)
# score_data = dataclient.load_data("s3://retrofit-data-dev/carbon_change_predictions/51/2023-11-28T21:01:21.869339.parquet")
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[
mix_df.columns.difference(["predictions", "residual", "SAP_ENDING"])
]
cosine_similarity_df = mix_df[mix_df.columns.difference(["predictions", "residual"])]
from sklearn.metrics.pairwise import cosine_similarity
row_index = 58199
row_index = 0
from sklearn.preprocessing import LabelEncoder
@ -225,7 +232,17 @@ feature_vector = cosine_similarity_df.loc[[row_index]]
cosine_similarity_df["cosine"] = cosine_similarity(cosine_similarity_df, feature_vector)
similar_index = (
cosine_similarity_df.sort_values("cosine", ascending=False).head(5).index
cosine_similarity_df.sort_values("cosine", ascending=False).head(15).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]

0
modules/ml-pipeline/src/pipeline/example.py
View file

6
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
dynaconf==3.2.0
pandas==2.1.4
autogluon==1.0.0
dynaconf==3.2.1
pyarrow==13.0.0
pre-commit==3.3.3

6
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
dynaconf==3.2.0
pandas==2.1.4
autogluon==1.0.0
dynaconf==3.2.1
pyarrow==13.0.0
PyYAML==6.0.1

9
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
dynaconf==3.2.0
alibi==0.9.4
pandas==2.1.4
autogluon==1.0.0
ray==2.6.3
dynaconf==3.2.1
alibi==0.9.5
shap==0.42.1
pyarrow==13.0.0
pre-commit==3.3.3

6
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
dynaconf==3.2.0
pandas==2.1.4
autogluon==1.0.0
dynaconf==3.2.1