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def make_plots(self, which, options: dict): for item in which: if item == "draft-learning-curve": # TODO: outdated (-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