import seaborn as sns
import scipy.stats as sst
from flowcat import utils, io_functions
NAME = "result_analysis_removeedge"
RESULTS = {
"path": utils.URLPath("output"),
"names": ["classifier_ungated", "classifier_gated_removeedge"],
}
OUTPUT = utils.URLPath(f"output/{NAME}")
LOGGER = utils.setup_logging(utils.URLPath(f"logs/{NAME}_{utils.create_stamp()}"), NAME)
def get_result_dirs(path: utils.URLPath, names: list):
"""Get result directories for individual iterations from given path and names"""
result_dirs = {
name: Metrics(list(map(Result, path.glob(f"./{name}*")))) for name in names
}
return result_dirs
@dataclass
class Result:
path: utils.URLPath
@property
def json_results(self):
LOGGER.info("Tube 1 %s", sample)
data = sample.get_data()
LOGGER.info(data)
LOGGER.info(
f"{data.data.shape}, {data.data.min(axis=1)}, {data.data.max(axis=1)}")
SEED = None
OUTPUT = utils.URLPath(f"output/{NAME}")
LOGDIR = utils.URLPath(f"logs/{NAME}_{utils.create_stamp()}")
INPUT = {
"data": utils.URLPath("output/ungated/data"),
"meta": utils.URLPath("output/samples/meta.json.gz"),
}
LOGGER = utils.setup_logging(LOGDIR, NAME)
set_seed(SEED)
dataset = io_functions.load_case_collection(INPUT["data"], INPUT["meta"])
check_dataset(dataset)
train, test = dataset.create_split(0.9)
io_functions.save_json(train.labels, OUTPUT / "train_ids.json")
io_functions.save_json(test.labels, OUTPUT / "test_ids.json")
reference = train.sample(1)
LOGGER.info("Reference dataset: %s", reference)
LOGGER.info("Reference labels: %s", reference.labels)
model = flowcat.FlowCat()