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submit.py
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submit.py
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"""Submit.py is the main script for running inference on the test set and creating a submission."""
import os
import warnings
from pathlib import Path
import hydra
from distributed import Client
from hydra.core.config_store import ConfigStore
from omegaconf import DictConfig
from src.config.submit_config import SubmitConfig
from src.logging_utils.logger import logger
from src.logging_utils.section_separator import print_section_separator
from src.utils.make_submission import make_submission
from src.utils.setup import setup_config, setup_pipeline, setup_test_data
warnings.filterwarnings("ignore", category=UserWarning)
# Makes hydra give full error messages
os.environ["HYDRA_FULL_ERROR"] = "1"
# Set up the config store, necessary for type checking of config yaml
cs = ConfigStore.instance()
cs.store(name="base_submit", node=SubmitConfig)
@hydra.main(version_base=None, config_path="conf", config_name="submit")
# TODO(Jeffrey): Use SubmitConfig instead of DictConfig
def run_submit(cfg: DictConfig) -> None:
"""Run the main script for submitting the predictions."""
# Print section separator
print_section_separator("Q2 Detect Kelp States - Submit")
output_dir = Path(hydra.core.hydra_config.HydraConfig.get().runtime.output_dir)
# Set up logging
import coloredlogs
coloredlogs.install()
# Check for missing keys in the config file
setup_config(cfg)
# Preload the pipeline and save it to HTML
print_section_separator("Setup pipeline")
model_pipeline = setup_pipeline(cfg, output_dir, is_train=False)
# Load the test data
X, filenames = setup_test_data(cfg.raw_data_path)
# Predict on the test data
logger.info("Now transforming the pipeline...")
predictions = model_pipeline.transform(X)
# Make submission
if predictions is not None:
make_submission(output_dir, predictions, filenames)
else:
raise ValueError("Predictions are None")
if __name__ == "__main__":
# Run with dask client, which will automatically close if there is an error
with Client() as client:
logger.info(f"Client: {client}")
run_submit()