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test new data

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This commit is contained in:
Michael Duong 2023-12-22 10:28:56 +00:00
parent 598c1118f3
commit acdac3d8dc
5 changed files with 44 additions and 44 deletions
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2
modules/ml-pipeline/src/pipeline/configs/build_model.yaml
View file

@ -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

10
modules/ml-pipeline/src/pipeline/configs/feature_processor_logic.py
View file

@ -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

4
modules/ml-pipeline/src/pipeline/configs/post_prediction_logic.py
View file

@ -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"]

8
modules/ml-pipeline/src/pipeline/configs/settings.yaml
View file

@ -21,7 +21,7 @@ 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_refactor.parquet
train_proportion: 0.9
output_train_filepath: ./data/prepared_data/train.parquet
output_test_filepath: ./data/prepared_data/test.parquet
@ -31,9 +31,9 @@ default:
feature_processor_config:
subsample_amount: null
subsample_seed: 0
target: SAP_ENDING
identifier_columns: ["UPRN"]
drop_columns: ["HEAT_DEMAND_CHANGE", "CARBON_CHANGE", "RDSAP_CHANGE", "HEAT_DEMAND_ENDING", "CARBON_ENDING"]
target: sap_ending
identifier_columns: ["uprn"]
drop_columns: ["heat_demand_change", "carbon_change", "rdsap_change", "heat_demand_change", "carbon_ending"]
# retain_features: ["SAP_STARTING", "TOTAL_FLOOR_AREA_DIFF"]
retain_features: null

64
modules/ml-pipeline/src/pipeline/dvc.lock
View file

@ -10,17 +10,17 @@ stages:
params:
configs/settings.yaml:
default.feature_processor.feature_processor_config.drop_columns:
- HEAT_DEMAND_CHANGE
- CARBON_CHANGE
- RDSAP_CHANGE
- HEAT_DEMAND_ENDING
- CARBON_ENDING
- heat_demand_change
- carbon_change
- rdsap_change
- heat_demand_change
- 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
size: 37048968
md5: 3d1f4d54c7b520531e4f5ff5f33e34d8.dir
size: 40122363
nfiles: 2
build_model:
cmd: python 2_build_model.py
deps:
- path: 2_build_model.py
hash: md5
md5: 7b79f280b8b0d5bc6f07669e7cc37c6a
size: 4150
md5: b824822475c222521516493e68eef9c5
size: 4149
- path: data/prepared_data
hash: md5
md5: 6bfdb621b608648c017bf2323f7b5052.dir
size: 37048968
md5: 3d1f4d54c7b520531e4f5ff5f33e34d8.dir
size: 40122363
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
size: 424943352
nfiles: 27
md5: 6a737d44dae68be2e75d6edb7f04f3ca.dir
size: 334981921
nfiles: 24
- path: metrics/fit_metrics.json
hash: md5
md5: 9537e7ebc2eb32b421a7cabd2005f00b
size: 223
md5: 89ba30b943c911e24b13b4370db12d18
size: 225
generate_predictions:
cmd: python 3_generate_predictions.py
deps:
@ -84,13 +84,13 @@ stages:
size: 2464
- path: data/model
hash: md5
md5: f2999107de7572ea5ff0f2d774fa83b8.dir
size: 424943352
nfiles: 27
md5: 6a737d44dae68be2e75d6edb7f04f3ca.dir
size: 334981921
nfiles: 24
- path: data/prepared_data
hash: md5
md5: 6bfdb621b608648c017bf2323f7b5052.dir
size: 37048968
md5: 3d1f4d54c7b520531e4f5ff5f33e34d8.dir
size: 40122363
nfiles: 2
params:
configs/settings.yaml:
@ -102,8 +102,8 @@ stages:
outs:
- path: data/predictions/
hash: md5
md5: f4439a56669f84bc51a9fcb4cd08353f.dir
size: 346539
md5: c9a0ad3ef06f23d5d507bbec0ba86e98.dir
size: 362994
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
size: 346539
md5: c9a0ad3ef06f23d5d507bbec0ba86e98.dir
size: 362994
nfiles: 1
- path: data/prepared_data
hash: md5
md5: 6bfdb621b608648c017bf2323f7b5052.dir
size: 37048968
md5: 3d1f4d54c7b520531e4f5ff5f33e34d8.dir
size: 40122363
nfiles: 2
params:
configs/settings.yaml:
@ -130,8 +130,8 @@ stages:
outs:
- path: metrics/metrics.json
hash: md5
md5: 357904cf106279be5a578e8faefa5d80
size: 224
md5: fa40071006901c4335b5dbd567c9d9b3
size: 226
startup_cleanup:
cmd: python 0_startup_cleanup.py
deps: