From 29e5d0c0ed1e6c79a5779e520033127b59150841 Mon Sep 17 00:00:00 2001 From: nkumar-bdaii Date: Wed, 10 Apr 2024 19:50:52 -0400 Subject: [PATCH] good to go! --- scripts/analyze_results_directory.py | 7 +++++-- scripts/plotting/create_active_sampler_learning_plots.py | 6 ++++++ 2 files changed, 11 insertions(+), 2 deletions(-) diff --git a/scripts/analyze_results_directory.py b/scripts/analyze_results_directory.py index 2107187b9d..da360658ea 100644 --- a/scripts/analyze_results_directory.py +++ b/scripts/analyze_results_directory.py @@ -108,8 +108,11 @@ def create_raw_dataframe( run_data_defaultdict.update(config) else: run_data_defaultdict = outdata - (env, approach, seed, excluded_predicates, included_options, - experiment_id, online_learning_cycle) = filepath[8:-4].split("__") + try: + (env, approach, seed, excluded_predicates, included_options, + experiment_id, online_learning_cycle) = filepath[8:-4].split("__") + except ValueError: + import ipdb; ipdb.set_trace() if not excluded_predicates: excluded_predicates = "none" run_data = dict( diff --git a/scripts/plotting/create_active_sampler_learning_plots.py b/scripts/plotting/create_active_sampler_learning_plots.py index 6d1169dad2..553e7bec60 100644 --- a/scripts/plotting/create_active_sampler_learning_plots.py +++ b/scripts/plotting/create_active_sampler_learning_plots.py @@ -160,18 +160,24 @@ def _derive_per_task_average(metric: str, lambda v: "grid_row-planning_progress_explore_original" in v)), ("No Feature Eng.", "blue", lambda df: df["EXPERIMENT_ID"].apply( lambda v: "grid_row-planning_progress_explore_no_feature_engineering" in v)), + ("No Weight Sharing", "red", lambda df: df["EXPERIMENT_ID"].apply( + lambda v: "grid_row-planning_progress_explore_no_weight_sharing" in v)), ], "Ball and Cup Sticky Table": [ ("Original", "green", lambda df: df["EXPERIMENT_ID"].apply( lambda v: "sticky_table-planning_progress_explore_original" in v)), ("No Feature Eng.", "blue", lambda df: df["EXPERIMENT_ID"].apply( lambda v: "sticky_table-planning_progress_explore_no_feature_engineering" in v)), + ("No Weight Sharing", "red", lambda df: df["EXPERIMENT_ID"].apply( + lambda v: "sticky_table-planning_progress_explore_no_weight_sharing" in v)), ], "Cleanup Playroom": [ ("Original", "green", lambda df: df["EXPERIMENT_ID"].apply( lambda v: "spot_sweeping_sim-planning_progress_explore_original" in v)), ("No Feature Eng.", "blue", lambda df: df["EXPERIMENT_ID"].apply( lambda v: "spot_sweeping_sim-planning_progress_explore_no_feature_engineering" in v)), + ("No Weight Sharing", "red", lambda df: df["EXPERIMENT_ID"].apply( + lambda v: "spot_sweeping_sim-planning_progress_explore_no_weight_sharing" in v)), ], }