mirror of
https://github.com/RYDE-WORK/lnp_ml.git
synced 2026-01-19 11:53:13 +08:00
132 lines
3.7 KiB
Makefile
132 lines
3.7 KiB
Makefile
#################################################################################
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# GLOBALS #
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#################################################################################
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PROJECT_NAME = lnp-ml
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PYTHON_VERSION = 3.8
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PYTHON_INTERPRETER = python
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#################################################################################
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# COMMANDS #
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#################################################################################
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## Install Python dependencies
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.PHONY: requirements
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requirements:
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pixi install
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## Delete all compiled Python files
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.PHONY: clean
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clean:
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find . -type f -name "*.py[co]" -delete
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find . -type d -name "__pycache__" -delete
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## Lint using ruff (use `make format` to do formatting)
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.PHONY: lint
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lint:
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ruff format --check
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ruff check
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## Format source code with ruff
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.PHONY: format
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format:
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ruff check --fix
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ruff format
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## Set up Python interpreter environment
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.PHONY: create_environment
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create_environment:
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@echo ">>> Pixi environment will be created when running 'make requirements'"
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@echo ">>> Activate with:\npixi shell"
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#################################################################################
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# PROJECT RULES #
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#################################################################################
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## Clean raw data (raw -> interim)
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.PHONY: clean_data
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clean_data: requirements
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$(PYTHON_INTERPRETER) scripts/data_cleaning.py
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## Process dataset (interim -> processed)
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.PHONY: data
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data: requirements
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$(PYTHON_INTERPRETER) scripts/process_data.py
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## Process external data for pretrain (external -> processed)
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.PHONY: data_pretrain
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data_pretrain: requirements
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$(PYTHON_INTERPRETER) scripts/process_external.py
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# MPNN 支持：使用 USE_MPNN=1 启用 MPNN encoder
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# 例如：make pretrain USE_MPNN=1
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MPNN_FLAG = $(if $(USE_MPNN),--use-mpnn,)
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# Backbone 冻结：使用 FREEZE_BACKBONE=1 冻结 backbone，只训练 heads
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# 例如：make finetune FREEZE_BACKBONE=1
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FREEZE_FLAG = $(if $(FREEZE_BACKBONE),--freeze-backbone,)
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## Pretrain on external data (delivery only)
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.PHONY: pretrain
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pretrain: requirements
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$(PYTHON_INTERPRETER) -m lnp_ml.modeling.pretrain $(MPNN_FLAG)
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## Train model (multi-task, from scratch)
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.PHONY: train
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train: requirements
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$(PYTHON_INTERPRETER) -m lnp_ml.modeling.train $(MPNN_FLAG)
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## Finetune from pretrained checkpoint (use FREEZE_BACKBONE=1 to freeze backbone)
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.PHONY: finetune
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finetune: requirements
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$(PYTHON_INTERPRETER) -m lnp_ml.modeling.train --init-from-pretrain models/pretrain_delivery.pt $(FREEZE_FLAG) $(MPNN_FLAG)
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## Train with hyperparameter tuning
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.PHONY: tune
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tune: requirements
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$(PYTHON_INTERPRETER) -m lnp_ml.modeling.train --tune $(MPNN_FLAG)
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## Run predictions
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.PHONY: predict
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predict: requirements
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$(PYTHON_INTERPRETER) -m lnp_ml.modeling.predict
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## Test model on test set (with detailed metrics)
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.PHONY: test
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test: requirements
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$(PYTHON_INTERPRETER) -m lnp_ml.modeling.predict test
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#################################################################################
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# Self Documenting Commands #
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#################################################################################
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.DEFAULT_GOAL := help
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define PRINT_HELP_PYSCRIPT
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import re, sys; \
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lines = '\n'.join([line for line in sys.stdin]); \
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matches = re.findall(r'\n## (.*)\n[\s\S]+?\n([a-zA-Z_-]+):', lines); \
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print('Available rules:\n'); \
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print('\n'.join(['{:25}{}'.format(*reversed(match)) for match in matches]))
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endef
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export PRINT_HELP_PYSCRIPT
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help:
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@$(PYTHON_INTERPRETER) -c "${PRINT_HELP_PYSCRIPT}" < $(MAKEFILE_LIST)
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