def process_data_shared(**options):
from csevo.processor.DataProcessor import DataProcessor
output_dir = options.get("output_dir", Macros.data_dir / "models-data")
task = options.get("task")
years = Utils.get_option_as_list(options, "years")
eval_settings = Utils.get_option_as_list(options, "eval_settings")
dp = DataProcessor()
dp.process_shared(output_dir, years, eval_settings, task)
return
def make_plots(**options):
from csevo.Plot import Plot
which = Utils.get_option_as_list(options, "which")
plot_maker = Plot()
plot_maker.make_plots(which, options)
return
def make_tables(**options):
from csevo.Table import Table
which = Utils.get_option_as_list(options, "which")
table_maker = Table()
table_maker.make_tables(which, options)
return
def make_plots(self, which, options: dict):
for item in which:
if item == "draft-learning-curve":
# TODO: outdated (->remove)
training_log_path = Path(options.get("training-log-path"))
output_name = options.get("output-name")
self.make_plot_draft_learning_curve(training_log_path, output_name)
elif item == "models-results-metrics-dist":
task = options["task"]
models = Utils.get_option_as_list(options, "models", self.TASK_2_MODELS.get(task))
metrics = Utils.get_option_as_list(options, "metrics", self.TASK_2_METRICS.get(task))
exps = Utils.get_option_as_list(options, "exps", self.EXPS)
self.plot_models_results_metrics_dist(task, models, metrics, exps)
elif item == "models-results-variance-dist":
task = options["task"]
models = Utils.get_option_as_list(options, "models", self.TASK_2_MODELS.get(task))
metrics = Utils.get_option_as_list(options, "metrics", self.TASK_2_METRICS.get(task))
exps = Utils.get_option_as_list(options, "exps", self.EXPS)
self.plot_models_results_variance_dist(task, models, metrics, exps)
elif item == "num-data-evolution":
self.plot_num_data_evolution(
Utils.get_option_as_list(options, "years", self.EVO_YEARS),
)
else:
self.logger.warning(f"Unknown plot {item}")
# end if
# end for
return
def run_models(**options):
from csevo.ml.TACCRunner import TACCRunner
work_dir = Path(options.get("work_dir", Macros.data_dir / "models-work"))
mode = options.get("mode", Macros.train)
models = Utils.get_option_as_list(options, "models")
exps = Utils.get_option_as_list(options, "exps")
trials = Utils.get_option_as_list(options, "trials")
timeout = options.get("timeout")
beg = options.get("beg", 0)
cnt = options.get("cnt", -1)
local = Utils.get_option_as_boolean(options, "local")
runner = TACCRunner(work_dir)
if not local:
runner.run_models(mode, models, exps, trials, timeout, beg, cnt)
else:
runner.run_models_local(mode, models, exps, trials, timeout, beg, cnt)
return