mirror of
https://github.com/RYDE-WORK/lnp_ml.git
synced 2026-03-21 01:27:00 +08:00
201 lines
6.6 KiB
Makefile
201 lines
6.6 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 dataset for final training (interim -> processed/final, train:val=9:1, no test)
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.PHONY: data_final
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data_final: requirements
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$(PYTHON_INTERPRETER) scripts/process_data_final.py
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## Process external data for pretrain (external -> processed)
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.PHONY: data_pretrain
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data_pretrain: requirements
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$(PYTHON_INTERPRETER) scripts/process_external.py
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## Process CV data for cross-validation pretrain (external/all_amine_split_for_LiON -> processed/cv)
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.PHONY: data_pretrain_cv
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data_pretrain_cv: requirements
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$(PYTHON_INTERPRETER) scripts/process_external_cv.py
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## Process internal data with CV splitting (interim -> processed/cv)
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## Use SCAFFOLD_SPLIT=1 to enable amine-based scaffold splitting (default: random shuffle)
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SCAFFOLD_SPLIT_FLAG = $(if $(filter 1,$(SCAFFOLD_SPLIT)),--scaffold-split,)
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.PHONY: data_cv
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data_cv: requirements
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$(PYTHON_INTERPRETER) scripts/process_data_cv.py $(SCAFFOLD_SPLIT_FLAG)
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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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# 设备选择：使用 DEVICE=xxx 指定设备
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# 例如：make train DEVICE=cuda:0 或 make test_cv DEVICE=mps
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DEVICE_FLAG = $(if $(DEVICE),--device $(DEVICE),)
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## Pretrain on external data (delivery only)
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.PHONY: pretrain
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pretrain: requirements
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$(PYTHON_INTERPRETER) -m lnp_ml.modeling.pretrain main $(MPNN_FLAG) $(DEVICE_FLAG)
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## Evaluate pretrain model (delivery metrics)
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.PHONY: test_pretrain
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test_pretrain: requirements
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$(PYTHON_INTERPRETER) -m lnp_ml.modeling.pretrain test $(MPNN_FLAG) $(DEVICE_FLAG)
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## Pretrain with cross-validation (5-fold)
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.PHONY: pretrain_cv
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pretrain_cv: requirements
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$(PYTHON_INTERPRETER) -m lnp_ml.modeling.pretrain_cv main $(MPNN_FLAG) $(DEVICE_FLAG)
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## Evaluate CV pretrain models on test sets (auto-detects MPNN from checkpoint)
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.PHONY: test_pretrain_cv
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test_pretrain_cv: requirements
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$(PYTHON_INTERPRETER) -m lnp_ml.modeling.pretrain_cv test $(DEVICE_FLAG)
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## Train model (multi-task, from scratch)
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.PHONY: train
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train: requirements
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$(PYTHON_INTERPRETER) -m lnp_ml.modeling.train $(MPNN_FLAG) $(DEVICE_FLAG)
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## Finetune from pretrained checkpoint (use FREEZE_BACKBONE=1 to freeze backbone)
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.PHONY: finetune
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finetune: requirements
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$(PYTHON_INTERPRETER) -m lnp_ml.modeling.train --init-from-pretrain models/pretrain_delivery.pt $(FREEZE_FLAG) $(MPNN_FLAG) $(DEVICE_FLAG)
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## Final training using all data (train:val=9:1, no test set), with pretrained weights
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.PHONY: train_final
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train_final: requirements
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$(PYTHON_INTERPRETER) -m lnp_ml.modeling.train \
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--train-path data/processed/final/train.parquet \
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--val-path data/processed/final/val.parquet \
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--output-dir models/final \
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--init-from-pretrain models/pretrain_delivery.pt \
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$(FREEZE_FLAG) $(MPNN_FLAG) $(DEVICE_FLAG)
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## Finetune with cross-validation on internal data (5-fold, amine-based split) with pretrained weights
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.PHONY: finetune_cv
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finetune_cv: requirements
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$(PYTHON_INTERPRETER) -m lnp_ml.modeling.train_cv main --init-from-pretrain models/pretrain_delivery.pt $(FREEZE_FLAG) $(MPNN_FLAG) $(DEVICE_FLAG)
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## Train with cross-validation on internal data only (5-fold, amine-based split)
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.PHONY: train_cv
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train_cv: requirements
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$(PYTHON_INTERPRETER) -m lnp_ml.modeling.train_cv main $(FREEZE_FLAG) $(MPNN_FLAG) $(DEVICE_FLAG)
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## Evaluate CV finetuned models on test sets (auto-detects MPNN from checkpoint)
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.PHONY: test_cv
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test_cv: requirements
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$(PYTHON_INTERPRETER) -m lnp_ml.modeling.train_cv test $(DEVICE_FLAG)
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## Train with hyperparameter tuning
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.PHONY: tune
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tune: requirements
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$(PYTHON_INTERPRETER) -m lnp_ml.modeling.train --tune $(MPNN_FLAG) $(DEVICE_FLAG)
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## Run predictions
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.PHONY: predict
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predict: requirements
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$(PYTHON_INTERPRETER) -m lnp_ml.modeling.predict $(DEVICE_FLAG)
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## Test model on test set (with detailed metrics, auto-detects MPNN from checkpoint)
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.PHONY: test
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test: requirements
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$(PYTHON_INTERPRETER) -m lnp_ml.modeling.predict test $(DEVICE_FLAG)
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## Formulation optimization: find optimal LNP formulation for target organ
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## Usage: make optimize SMILES="CC(C)..." ORGAN=liver
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.PHONY: optimize
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optimize: requirements
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$(PYTHON_INTERPRETER) -m app.optimize --smiles "$(SMILES)" --organ $(ORGAN) $(DEVICE_FLAG)
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#################################################################################
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# Self Documenting Commands #
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#################################################################################
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.DEFAULT_GOAL := help
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define PRINT_HELP_PYSCRIPT
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import re, sys; \
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lines = '\n'.join([line for line in sys.stdin]); \
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matches = re.findall(r'\n## (.*)\n[\s\S]+?\n([a-zA-Z_-]+):', lines); \
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print('Available rules:\n'); \
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print('\n'.join(['{:25}{}'.format(*reversed(match)) for match in matches]))
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endef
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export PRINT_HELP_PYSCRIPT
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help:
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@$(PYTHON_INTERPRETER) -c "${PRINT_HELP_PYSCRIPT}" < $(MAKEFILE_LIST)
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