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# GLOBALS                                                                       #
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PROJECT_NAME = lnp-ml
PYTHON_VERSION = 3.8
PYTHON_INTERPRETER = python

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# COMMANDS                                                                      #
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## Install Python dependencies
.PHONY: requirements
requirements:
	pixi install




## Delete all compiled Python files
.PHONY: clean
clean:
	find . -type f -name "*.py[co]" -delete
	find . -type d -name "__pycache__" -delete


## Lint using ruff (use `make format` to do formatting)
.PHONY: lint
lint:
	ruff format --check
	ruff check

## Format source code with ruff
.PHONY: format
format:
	ruff check --fix
	ruff format





## Set up Python interpreter environment
.PHONY: create_environment
create_environment:
	
	@echo ">>> Pixi environment will be created when running 'make requirements'"
	
	@echo ">>> Activate with:\npixi shell"
	



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# PROJECT RULES                                                                 #
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## Clean raw data (raw -> interim)
.PHONY: clean_data
clean_data: requirements
	$(PYTHON_INTERPRETER) scripts/data_cleaning.py

## Process dataset (interim -> processed)
.PHONY: data
data: requirements
	$(PYTHON_INTERPRETER) scripts/process_data.py

## Process external data for pretrain (external -> processed)
.PHONY: data_pretrain
data_pretrain: requirements
	$(PYTHON_INTERPRETER) scripts/process_external.py

# MPNN 支持：使用 USE_MPNN=1 启用 MPNN encoder
# 例如：make pretrain USE_MPNN=1
MPNN_FLAG = $(if $(USE_MPNN),--use-mpnn,)

# Backbone 冻结：使用 FREEZE_BACKBONE=1 冻结 backbone，只训练 heads
# 例如：make finetune FREEZE_BACKBONE=1
FREEZE_FLAG = $(if $(FREEZE_BACKBONE),--freeze-backbone,)

## Pretrain on external data (delivery only)
.PHONY: pretrain
pretrain: requirements
	$(PYTHON_INTERPRETER) -m lnp_ml.modeling.pretrain $(MPNN_FLAG)

## Train model (multi-task, from scratch)
.PHONY: train
train: requirements
	$(PYTHON_INTERPRETER) -m lnp_ml.modeling.train $(MPNN_FLAG)

## Finetune from pretrained checkpoint (use FREEZE_BACKBONE=1 to freeze backbone)
.PHONY: finetune
finetune: requirements
	$(PYTHON_INTERPRETER) -m lnp_ml.modeling.train --init-from-pretrain models/pretrain_delivery.pt $(FREEZE_FLAG) $(MPNN_FLAG)

## Train with hyperparameter tuning
.PHONY: tune
tune: requirements
	$(PYTHON_INTERPRETER) -m lnp_ml.modeling.train --tune $(MPNN_FLAG)

## Run predictions
.PHONY: predict
predict: requirements
	$(PYTHON_INTERPRETER) -m lnp_ml.modeling.predict

## Test model on test set (with detailed metrics)
.PHONY: test
test: requirements
	$(PYTHON_INTERPRETER) -m lnp_ml.modeling.predict test


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# Self Documenting Commands                                                     #
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.DEFAULT_GOAL := help

define PRINT_HELP_PYSCRIPT
import re, sys; \
lines = '\n'.join([line for line in sys.stdin]); \
matches = re.findall(r'\n## (.*)\n[\s\S]+?\n([a-zA-Z_-]+):', lines); \
print('Available rules:\n'); \
print('\n'.join(['{:25}{}'.format(*reversed(match)) for match in matches]))
endef
export PRINT_HELP_PYSCRIPT

help:
	@$(PYTHON_INTERPRETER) -c "${PRINT_HELP_PYSCRIPT}" < $(MAKEFILE_LIST)