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update to autogluon 1.4

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This commit is contained in:
Michael Duong 2025-11-02 17:26:40 +00:00
parent 2c735737a8
commit 43aacd80be
8 changed files with 40 additions and 38 deletions
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1
deployment/Dockerfile.prediction.lambda
View file

@ -3,6 +3,7 @@ FROM public.ecr.aws/lambda/python:3.12
# Set the working directory
WORKDIR ${LAMBDA_TASK_ROOT}
ENV PYTHONPATH="${PYTHONPATH}:${LAMBDA_TASK_ROOT}"
ENV MPLCONFIGDIR="${LAMBDA_TASK_ROOT}/tmp/matplotlib"
# Environment variables
ARG RUNTIME_ENVIRONMENT

9
modules/ml-pipeline/Makefile
View file

@ -1,7 +1,7 @@
export PYENV_ROOT=$(HOME)/.pyenv
export PATH := $(PYENV_ROOT)/bin:$(PATH)
PYTHON_VERSION ?= 3.12.12
CONDA_ENV=dev_env_pipeline_1
CONDA_ENV=dev_env_pipeline
CONDA_ACTIVATE=source $$(conda info --base)/etc/profile.d/conda.sh ; conda deactivate ; conda activate
.PHONY: init
@ -16,10 +16,11 @@ dev-conda:
${CONDA_ACTIVATE} ${CONDA_ENV} && \
which pip && \
pip install --upgrade pip && \
pip install -r src/pipeline/requirements/training/requirements-dev.txt && \
pip install -r src/pipeline/requirements/version_control/requirements.txt && \
pip install uv && \
uv pip install -r src/pipeline/requirements/training/requirements-dev.txt && \
uv pip install -r src/pipeline/requirements/version_control/requirements.txt && \
pre-commit install && \
pip install ipykernel
uv pip install ipykernel
echo "TO ACTIVATE ENVIRONMENT, USE THE FOLLOWING COMMAND"
echo "conda activate ${CONDA_ENV}"

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

@ -14,7 +14,7 @@ default:
output_filepath: ./data/model/allmodels/
problem_type: regression
eval_metric: mean_squared_error #mean_absolute_error
time_limit: 180
time_limit: 1800
presets: medium_quality
excluded_model_types: ['RF', 'CAT', 'NN_TORCH', 'KNN', 'XT']
infer_limit: 0.0005

56
modules/ml-pipeline/src/pipeline/dvc.lock
View file

@ -61,8 +61,8 @@ stages:
outs:
- path: data/prepared_data/
hash: md5
md5: 02b2c25e488f75c4a676540c127b8930.dir
size: 45890160
md5: 2feba8772c240b507eb900934efcb8ca.dir
size: 46064555
nfiles: 3
build_model:
cmd: python 2_build_model.py
@ -73,8 +73,8 @@ stages:
size: 4820
- path: data/prepared_data
hash: md5
md5: 02b2c25e488f75c4a676540c127b8930.dir
size: 45890160
md5: 2feba8772c240b507eb900934efcb8ca.dir
size: 46064555
nfiles: 3
params:
configs/build_model.yaml:
@ -91,7 +91,7 @@ stages:
output_filepath: ./data/model/allmodels/
problem_type: regression
eval_metric: mean_squared_error
time_limit: 180
time_limit: 1800
presets: medium_quality
excluded_model_types:
- RF
@ -107,18 +107,18 @@ stages:
outs:
- path: data/fit_predictions/
hash: md5
md5: 7f9a534daf824434262bee89e2ee2cfd.dir
size: 3475064
md5: 29036f4f42b1fdcab7f9e40a87f38a8c.dir
size: 3474783
nfiles: 1
- path: data/model/
hash: md5
md5: c67bb2e8b24d9c574bc7c522ac3d66b9.dir
size: 414148418
nfiles: 24
md5: 77cab231e3d51bbebbae5a7af310c18a.dir
size: 791390619
nfiles: 34
- path: metrics/fit_metrics.json
hash: md5
md5: 7763f689b46c38ec8f0cc605deac4c2a
size: 221
md5: 4f39064fb6b31c7c879299621bcea28d
size: 224
generate_predictions:
cmd: python 3_generate_predictions.py
deps:
@ -128,13 +128,13 @@ stages:
size: 2464
- path: data/model
hash: md5
md5: c67bb2e8b24d9c574bc7c522ac3d66b9.dir
size: 414148418
nfiles: 24
md5: 77cab231e3d51bbebbae5a7af310c18a.dir
size: 791390619
nfiles: 34
- path: data/prepared_data
hash: md5
md5: 02b2c25e488f75c4a676540c127b8930.dir
size: 45890160
md5: 2feba8772c240b507eb900934efcb8ca.dir
size: 46064555
nfiles: 3
params:
configs/settings.yaml:
@ -148,8 +148,8 @@ stages:
outs:
- path: data/predictions/
hash: md5
md5: 2d9353f60e16d4f85dd4a08a71dce548.dir
size: 483856
md5: 8dfa69b48586da6b0ef33a6fbedb7c4a.dir
size: 484314
nfiles: 1
generate_metrics:
cmd: python 4_generate_metrics.py
@ -160,13 +160,13 @@ stages:
size: 3484
- path: data/predictions
hash: md5
md5: 2d9353f60e16d4f85dd4a08a71dce548.dir
size: 483856
md5: 8dfa69b48586da6b0ef33a6fbedb7c4a.dir
size: 484314
nfiles: 1
- path: data/prepared_data
hash: md5
md5: 02b2c25e488f75c4a676540c127b8930.dir
size: 45890160
md5: 2feba8772c240b507eb900934efcb8ca.dir
size: 46064555
nfiles: 3
params:
configs/settings.yaml:
@ -176,8 +176,8 @@ stages:
outs:
- path: metrics/metrics.json
hash: md5
md5: 8a52e3a0047c68b9de5c371a1d406f73
size: 224
md5: bf980dad2dc5b97651546b0b755419ae
size: 223
generate_scenerio_metrics:
cmd: python 5_generate_scenarios.py
deps:
@ -197,9 +197,9 @@ stages:
outs:
- path: metrics/scenario_metrics.md
hash: md5
md5: 666f73f6fdb49484737f1a7edd798727
size: 363
md5: 05e2cce8e61d5005398659e9f3465cd6
size: 356
- path: metrics/scenario_table.md
hash: md5
md5: 71c9fcb9ec304353aba0d7f5c58ca8b2
md5: 92446d2f3836c6f790d06e3b268b05f3
size: 872

2
modules/ml-pipeline/src/pipeline/requirements/predictions/requirements-dev.txt
View file

@ -1,7 +1,7 @@
joblib==1.5.2
boto3==1.40.61
pandas==2.2.3
autogluon.tabular[all]==1.3
autogluon.tabular[all]==1.4
dynaconf==3.2.12
pyarrow==22.0.0
pre-commit==4.3.0

2
modules/ml-pipeline/src/pipeline/requirements/predictions/requirements.txt
View file

@ -1,7 +1,7 @@
joblib==1.5.2
boto3==1.40.61
pandas==2.2.3
autogluon.tabular[all]==1.3
autogluon.tabular[all]==1.4
dynaconf==3.2.12
pyarrow==22.0.0
PyYAML==6.0.3

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

@ -1,10 +1,10 @@
joblib==1.5.2
boto3==1.40.61
pandas==2.2.3
autogluon.tabular[all]==1.3
autogluon.tabular[all]==1.4
ray==2.44.1
dynaconf==3.2.12
alibi==0.5.5
shap==0.49.1
pyarrow==22.0.0
pyarrow
pre-commit==4.3.0

2
modules/ml-pipeline/src/pipeline/requirements/training/requirements.txt
View file

@ -1,4 +1,4 @@
boto3==1.40.61
pandas==2.2.3
autogluon.tabular[all]==1.3
autogluon.tabular[all]==1.4
dynaconf==3.2.12