Added ratio of common architectures for ranking.

run_all_ft_analysis.bat

@@ -27,3 +27,19 @@ python scripts/reports/analysis_freeze_natsbench_space.py --results-dir C:\\User
 
 python scripts/reports/analysis_freeze_natsbench_space.py --results-dir C:\\Users\\dedey\\Documents\\archaiphilly\\phillytools\\ft_fb256_ftlr0.1_fte5_ct256_ftt0.6 --out-dir C:\\Users\\dedey\\archai_experiment_reports
 python scripts/reports/analysis_freeze_natsbench_space.py --results-dir C:\\Users\\dedey\\Documents\\archaiphilly\\phillytools\\ft_fb256_ftlr0.1_fte10_ct256_ftt0.6 --out-dir C:\\Users\\dedey\\archai_experiment_reports
+
+
+python scripts/reports/analysis_regular_natsbench_space.py --results-dir C:\\Users\\dedey\\Documents\\archaiphilly\\phillytools\\nb_reg_b1024_e01 --out-dir C:\\Users\\dedey\\archai_experiment_reports
+python scripts/reports/analysis_regular_natsbench_space.py --results-dir C:\\Users\\dedey\\Documents\\archaiphilly\\phillytools\\nb_reg_b1024_e02 --out-dir C:\\Users\\dedey\\archai_experiment_reports
+python scripts/reports/analysis_regular_natsbench_space.py --results-dir C:\\Users\\dedey\\Documents\\archaiphilly\\phillytools\\nb_reg_b1024_e04 --out-dir C:\\Users\\dedey\\archai_experiment_reports
+python scripts/reports/analysis_regular_natsbench_space.py --results-dir C:\\Users\\dedey\\Documents\\archaiphilly\\phillytools\\nb_reg_b1024_e06 --out-dir C:\\Users\\dedey\\archai_experiment_reports
+python scripts/reports/analysis_regular_natsbench_space.py --results-dir C:\\Users\\dedey\\Documents\\archaiphilly\\phillytools\\nb_reg_b1024_e08 --out-dir C:\\Users\\dedey\\archai_experiment_reports
+python scripts/reports/analysis_regular_natsbench_space.py --results-dir C:\\Users\\dedey\\Documents\\archaiphilly\\phillytools\\nb_reg_b1024_e10 --out-dir C:\\Users\\dedey\\archai_experiment_reports
+
+python scripts/reports/analysis_regular_natsbench_space.py --results-dir C:\\Users\\dedey\\Documents\\archaiphilly\\phillytools\\nb_reg_b512_e01 --out-dir C:\\Users\\dedey\\archai_experiment_reports
+python scripts/reports/analysis_regular_natsbench_space.py --results-dir C:\\Users\\dedey\\Documents\\archaiphilly\\phillytools\\nb_reg_b512_e02 --out-dir C:\\Users\\dedey\\archai_experiment_reports
+python scripts/reports/analysis_regular_natsbench_space.py --results-dir C:\\Users\\dedey\\Documents\\archaiphilly\\phillytools\\nb_reg_b512_e04 --out-dir C:\\Users\\dedey\\archai_experiment_reports
+python scripts/reports/analysis_regular_natsbench_space.py --results-dir C:\\Users\\dedey\\Documents\\archaiphilly\\phillytools\\nb_reg_b512_e06 --out-dir C:\\Users\\dedey\\archai_experiment_reports
+python scripts/reports/analysis_regular_natsbench_space.py --results-dir C:\\Users\\dedey\\Documents\\archaiphilly\\phillytools\\nb_reg_b512_e08 --out-dir C:\\Users\\dedey\\archai_experiment_reports
+python scripts/reports/analysis_regular_natsbench_space.py --results-dir C:\\Users\\dedey\\Documents\\archaiphilly\\phillytools\\nb_reg_b512_e10 --out-dir C:\\Users\\dedey\\archai_experiment_reports
+

scripts/reports/analysis_freeze_natsbench_space.py

@@ -31,66 +31,11 @@ from collections import namedtuple
 
 from archai.common import utils
 from archai.common.ordereddict_logger import OrderedDictLogger
-from analysis_utils import epoch_nodes, fix_yaml, remove_seed_part, group_multi_runs, collect_epoch_nodes, EpochStats, FoldStats, stat2str, get_epoch_stats, get_summary_text, get_details_text, plot_epochs, write_report
+from analysis_utils import epoch_nodes, parse_a_job, fix_yaml, remove_seed_part, group_multi_runs, collect_epoch_nodes, EpochStats, FoldStats, stat2str, get_epoch_stats, get_summary_text, get_details_text, plot_epochs, write_report
 
 import re
 
 
-def find_valid_log(subdir:str)->str:
-    # originally log should be in base folder of eval or search
-    logs_filepath_og = os.path.join(str(subdir), 'log.yaml')
-    if os.path.isfile(logs_filepath_og):
-        return logs_filepath_og
-    else:
-        # look in the 'dist' folder for any yaml file
-        dist_folder = os.path.join(str(subdir), 'dist')
-        for f in os.listdir(dist_folder):
-            if f.endswith(".yaml"):
-                return os.path.join(dist_folder, f)
-
-
-def parse_a_job(job_dir:str)->Dict:
-     if job_dir.is_dir():
-
-        storage = {}
-        for subdir in job_dir.iterdir():
-            if not subdir.is_dir():
-                continue
-            # currently we expect that each job was ExperimentRunner job which should have
-            # _search or _eval folders
-            if subdir.stem.endswith('_search'):
-                sub_job = 'search'
-            elif subdir.stem.endswith('_eval'):
-                sub_job = 'eval'
-            else:
-                raise RuntimeError(f'Sub directory "{subdir}" in job "{job_dir}" must '
-                                'end with either _search or _eval which '
-                                'should be the case if ExperimentRunner was used.')
-
-            logs_filepath = find_valid_log(subdir)
-            # if no valid logfile found, ignore this job as it probably 
-            # didn't finish or errored out or is yet to run
-            if not logs_filepath:
-                continue
-
-            config_used_filepath = os.path.join(subdir, 'config_used.yaml')
-
-            if os.path.isfile(logs_filepath):
-                fix_yaml(logs_filepath)
-                key = job_dir.name + subdir.name + ':' + sub_job
-                # parse log
-                with open(logs_filepath, 'r') as f:
-                    data = yaml.load(f, Loader=yaml.Loader)                    
-                # parse config used
-                with open(config_used_filepath, 'r') as f:
-                    confs = yaml.load(f, Loader=yaml.Loader)
-                storage[key] = (data, confs)
-            
-        return storage
-
-def myfunc(x):
-    return x*x
-
 def main():
     parser = argparse.ArgumentParser(description='Report creator')
     parser.add_argument('--results-dir', '-d', type=str,
@@ -176,6 +121,8 @@ def main():
             logs.pop(key)
 
 
+    all_arch_ids = []
+
     all_reg_evals = []
     
     all_naswotrain_evals = []
@@ -243,8 +190,6 @@ def main():
                 all_cond_time_last.append(cond_duration)
                 all_partial_time_last.append(freeze_duration)
                 
-                
-
                 # naswotrain
                 # --------------
                 naswotrain_top1 = logs[key]['naswotrain_evaluate']['eval_arch']['eval_train']['naswithouttraining']
@@ -255,6 +200,10 @@ def main():
                 reg_eval_top1 = logs[key]['regular_evaluate']['regtrainingtop1']
                 all_reg_evals.append(reg_eval_top1)
 
+                # record the arch id
+                # --------------------
+                all_arch_ids.append(confs[key]['nas']['eval']['natsbench']['arch_index'])
+                    
             except KeyError as err:
                 print(f'KeyError {err} not in {key}!')
 
@@ -275,6 +224,7 @@ def main():
     assert len(all_reg_evals) == len(all_freeze_flops_last)
     assert len(all_reg_evals) == len(all_cond_flops_last)
     assert len(all_reg_evals) == len(all_freeze_time_last)
+    assert len(all_reg_evals) == len(all_arch_ids)
     
     # Freeze training results at last epoch        
     freeze_tau, freeze_p_value = kendalltau(all_reg_evals, all_freeze_evals_last)
@@ -396,7 +346,8 @@ def main():
     spe_freeze_top_percents = []
     spe_naswot_top_percents = []
     top_percents = []
-    for top_percent in range(2, 101, 2):
+    top_percent_range = range(2, 101, 2)
+    for top_percent in top_percent_range:
         top_percents.append(top_percent)
         num_to_keep = int(ma.floor(len(reg_freezelast_naswot_evals) * top_percent * 0.01))
         top_percent_evals = reg_freezelast_naswot_evals[:num_to_keep]
@@ -433,6 +384,39 @@ def main():
     savename = os.path.join(out_dir, f'freeze_train_duration_top_archs.png')
     plt.savefig(savename, dpi=plt.gcf().dpi, bbox_inches='tight')
 
+    # how much overlap in top x% of architectures between method and groundtruth
+    # ----------------------------------------------------------------------------
+    arch_id_reg_evals =  [(arch_id, reg_eval) for arch_id, reg_eval in zip(all_arch_ids, all_reg_evals)]
+    arch_id_freezetrain_evals = [(arch_id, freeze_eval) for arch_id, freeze_eval in zip(all_arch_ids, all_freeze_evals_last)]
+    arch_id_naswot_evals = [(arch_id, naswot_eval) for arch_id, naswot_eval in zip(all_arch_ids, all_naswotrain_evals)]
+
+    arch_id_reg_evals.sort(key=lambda x: x[1], reverse=True)
+    arch_id_freezetrain_evals.sort(key=lambda x: x[1], reverse=True)
+    arch_id_naswot_evals.sort(key=lambda x: x[1], reverse=True)
+
+    assert len(arch_id_reg_evals) == len(arch_id_freezetrain_evals)
+    assert len(arch_id_reg_evals) == len(arch_id_naswot_evals)
+
+    top_percents = []
+    freezetrain_ratio_common = []
+    naswot_ratio_common = []
+    for top_percent in top_percent_range:
+        top_percents.append(top_percent)
+        num_to_keep = int(ma.floor(len(arch_id_reg_evals) * top_percent * 0.01))
+        top_percent_arch_id_reg_evals = arch_id_reg_evals[:num_to_keep]
+        top_percent_arch_id_freezetrain_evals = arch_id_freezetrain_evals[:num_to_keep]
+        top_percent_arch_id_naswot_evals = arch_id_naswot_evals[:num_to_keep]
+
+        # take the set of arch_ids in each method and find overlap with top archs
+        set_reg = set([x[0] for x in top_percent_arch_id_reg_evals])
+        set_ft = set([x[0] for x in top_percent_arch_id_freezetrain_evals])
+        ft_num_common = len(set_reg.intersection(set_ft))
+        freezetrain_ratio_common.append(ft_num_common/num_to_keep)
+
+        set_naswot = set([x[0] for x in top_percent_arch_id_naswot_evals])
+        naswot_num_common = len(set_reg.intersection(set_naswot))
+        naswot_ratio_common.append(naswot_num_common/num_to_keep)
+    
     # save raw data for other aggregate plots over experiments
     raw_data_dict = {}
     raw_data_dict['top_percents'] = top_percents
@@ -440,6 +424,9 @@ def main():
     raw_data_dict['spe_naswot'] = spe_naswot_top_percents
     raw_data_dict['freeze_times_avg'] = top_percent_freeze_times_avg
     raw_data_dict['freeze_times_std'] = top_percent_freeze_times_std
+    raw_data_dict['freeze_ratio_common'] = freezetrain_ratio_common
+    raw_data_dict['naswot_ratio_common'] = naswot_ratio_common
+
 
     savename = os.path.join(out_dir, 'raw_data.yaml')
     with open(savename, 'w') as f:

scripts/reports/analysis_regular_natsbench_space.py

@@ -30,34 +30,10 @@ from multiprocessing import Pool
 
 from archai.common import utils
 from archai.common.ordereddict_logger import OrderedDictLogger
-from analysis_utils import epoch_nodes, fix_yaml, remove_seed_part, group_multi_runs, collect_epoch_nodes, EpochStats, FoldStats, stat2str, get_epoch_stats, get_summary_text, get_details_text, plot_epochs, write_report
+from analysis_utils import epoch_nodes, parse_a_job, fix_yaml, remove_seed_part, group_multi_runs, collect_epoch_nodes, EpochStats, FoldStats, stat2str, get_epoch_stats, get_summary_text, get_details_text, plot_epochs, write_report
 
 import re
 
-def parse_a_job(job_dir:str)->OrderedDict:
-     if job_dir.is_dir():
-        for subdir in job_dir.iterdir():
-            if not subdir.is_dir():
-                continue
-            # currently we expect that each job was ExperimentRunner job which should have
-            # _search or _eval folders
-            if subdir.stem.endswith('_search'):
-                sub_job = 'search'
-            elif subdir.stem.endswith('_eval'):
-                sub_job = 'eval'
-            else:
-                raise RuntimeError(f'Sub directory "{subdir}" in job "{job_dir}" must '
-                                'end with either _search or _eval which '
-                                'should be the case if ExperimentRunner was used.')
-
-            logs_filepath = os.path.join(str(subdir), 'log.yaml')
-            if os.path.isfile(logs_filepath):
-                fix_yaml(logs_filepath)
-                with open(logs_filepath, 'r') as f:
-                    key = job_dir.name + ':' + sub_job
-                    data = yaml.load(f, Loader=yaml.Loader)
-                return (key, data)
-
 
 def main():
     parser = argparse.ArgumentParser(description='Report creator')
@@ -82,21 +58,56 @@ def main():
 
     # get list of all structured logs for each job
     logs = {}
+    confs = {}
     job_dirs = list(results_dir.iterdir())
 
-    # parallel parssing of yaml logs
+    # # test single job parsing for debugging
+    # # WARNING: very slow, just use for debugging
+    # for job_dir in job_dirs:
+    #     a = parse_a_job(job_dir)
+
+    # parallel parsing of yaml logs
     with Pool(18) as p:
         a = p.map(parse_a_job, job_dirs)
 
-    for key, data in a:
+    for storage in a:
-        logs[key] = data
+        for key, val in storage.items():
-
+            logs[key] = val[0]
+            confs[key] = val[1]
                    
     # examples of accessing logs
     # best_test = logs[key]['eval_arch']['eval_train']['best_test']['top1']
     # best_train = logs[key]['eval_arch']['eval_train']['best_train']['top1']
 
+    # remove all search jobs
+    for key in list(logs.keys()):
+        if 'search' in key:
+            logs.pop(key)
 
+    # remove all arch_ids which did not finish
+    # remove all arch_ids which did not finish
+    for key in list(logs.keys()):
+        to_delete = False
+
+        # it might have died early
+        if 'eval_arch' not in list(logs[key].keys()):
+            to_delete = True
+ 
+        if 'regular_evaluate' not in list(logs[key].keys()):
+            to_delete = True
+        
+        if to_delete:
+            print(f'arch id {key} did not finish. removing from calculations.')
+            logs.pop(key)
+            continue
+
+        if 'eval_train' not in list(logs[key]['eval_arch'].keys()):
+            print(f'arch id {key} did not finish. removing from calculations.')
+            logs.pop(key)
+            continue
+
+
+    all_arch_ids = []
     all_reg_evals = []
     all_short_reg_evals = []
     all_short_reg_time = []
@@ -121,6 +132,10 @@ def main():
                 reg_eval_top1 = logs[key]['regular_evaluate']['regtrainingtop1']
                 all_reg_evals.append(reg_eval_top1)
 
+                # record the arch id
+                # --------------------
+                all_arch_ids.append(confs[key]['nas']['eval']['natsbench']['arch_index'])
+                                
             except KeyError as err:
                 print(f'KeyError {err} not in {key}!')
 
@@ -160,7 +175,8 @@ def main():
 
     spe_shortreg_top_percents = []
     top_percents = []
-    for top_percent in range(2, 101, 2):
+    top_percent_range = range(2, 101, 2) 
+    for top_percent in top_percent_range:
         top_percents.append(top_percent)
         num_to_keep = int(ma.floor(len(reg_shortreg_evals) * top_percent * 0.01))
         top_percent_evals = reg_shortreg_evals[:num_to_keep]
@@ -201,12 +217,37 @@ def main():
     with open(results_savename, 'a') as f:
         f.write(f'Avg. Shortened Training Runtime: {avg_shortreg_runtime:.03f}, stderr {stderr_shortreg_runtime:.03f} \n')
 
+    # how much overlap in top x% of architectures between method and groundtruth
+    # ----------------------------------------------------------------------------
+    arch_id_reg_evals =  [(arch_id, reg_eval) for arch_id, reg_eval in zip(all_arch_ids, all_reg_evals)]
+    arch_id_shortreg_evals = [(arch_id, shortreg_eval) for arch_id, shortreg_eval in zip(all_arch_ids, all_short_reg_evals)]
+
+    arch_id_reg_evals.sort(key=lambda x: x[1], reverse=True)
+    arch_id_shortreg_evals.sort(key=lambda x: x[1], reverse=True)
+
+    assert len(arch_id_reg_evals) == len(arch_id_shortreg_evals)
+    
+    top_percents = []
+    shortreg_ratio_common = []
+    for top_percent in top_percent_range:
+        top_percents.append(top_percent)
+        num_to_keep = int(ma.floor(len(arch_id_reg_evals) * top_percent * 0.01))
+        top_percent_arch_id_reg_evals = arch_id_reg_evals[:num_to_keep]
+        top_percent_arch_id_shortreg_evals = arch_id_shortreg_evals[:num_to_keep]
+        
+        # take the set of arch_ids in each method and find overlap with top archs
+        set_reg = set([x[0] for x in top_percent_arch_id_reg_evals])
+        set_ft = set([x[0] for x in top_percent_arch_id_shortreg_evals])
+        ft_num_common = len(set_reg.intersection(set_ft))
+        shortreg_ratio_common.append(ft_num_common/num_to_keep)
+
     # save raw data for other aggregate plots over experiments
     raw_data_dict = {}
     raw_data_dict['top_percents'] = top_percents
     raw_data_dict['spe_shortreg'] = spe_shortreg_top_percents
     raw_data_dict['shortreg_times_avg'] = top_percent_shortreg_times_avg
     raw_data_dict['shortreg_times_std'] = top_percent_shortreg_times_std
+    raw_data_dict['shortreg_ratio_common'] = shortreg_ratio_common
 
     savename = os.path.join(out_dir, 'raw_data.yaml')
     with open(savename, 'w') as f:

scripts/reports/analysis_utils.py

@@ -300,3 +300,56 @@ def write_report(template_filename:str, **kwargs)->None:
     with open(outfilepath, 'w', encoding='utf-8') as f:
         f.write(report)
     print(f'report written to: {outfilepath}')
+
+
+def find_valid_log(subdir:str)->str:
+    # originally log should be in base folder of eval or search
+    logs_filepath_og = os.path.join(str(subdir), 'log.yaml')
+    if os.path.isfile(logs_filepath_og):
+        return logs_filepath_og
+    else:
+        # look in the 'dist' folder for any yaml file
+        dist_folder = os.path.join(str(subdir), 'dist')
+        for f in os.listdir(dist_folder):
+            if f.endswith(".yaml"):
+                return os.path.join(dist_folder, f)
+
+
+def parse_a_job(job_dir:str)->Dict:
+     if job_dir.is_dir():
+
+        storage = {}
+        for subdir in job_dir.iterdir():
+            if not subdir.is_dir():
+                continue
+            # currently we expect that each job was ExperimentRunner job which should have
+            # _search or _eval folders
+            if subdir.stem.endswith('_search'):
+                sub_job = 'search'
+            elif subdir.stem.endswith('_eval'):
+                sub_job = 'eval'
+            else:
+                raise RuntimeError(f'Sub directory "{subdir}" in job "{job_dir}" must '
+                                'end with either _search or _eval which '
+                                'should be the case if ExperimentRunner was used.')
+
+            logs_filepath = find_valid_log(subdir)
+            # if no valid logfile found, ignore this job as it probably 
+            # didn't finish or errored out or is yet to run
+            if not logs_filepath:
+                continue
+
+            config_used_filepath = os.path.join(subdir, 'config_used.yaml')
+
+            if os.path.isfile(logs_filepath):
+                fix_yaml(logs_filepath)
+                key = job_dir.name + subdir.name + ':' + sub_job
+                # parse log
+                with open(logs_filepath, 'r') as f:
+                    data = yaml.load(f, Loader=yaml.Loader)                    
+                # parse config used
+                with open(config_used_filepath, 'r') as f:
+                    confs = yaml.load(f, Loader=yaml.Loader)
+                storage[key] = (data, confs)
+            
+        return storage

scripts/reports/proxynas_plots/cross_exp_plots.py

@@ -73,7 +73,19 @@ def main():
                 'ft_fb512_ftlr0.1_fte5_ct256_ftt0.6']
 
 
-    shortreg_exp_list = ['nb_reg_b1024_e10', 'nb_reg_b1024_e20', 'nb_reg_b1024_e30']
+    shortreg_exp_list = ['nb_reg_b1024_e01', \
+                         'nb_reg_b1024_e02', \
+                         'nb_reg_b1024_e04', \
+                         'nb_reg_b1024_e06', \
+                         'nb_reg_b1024_e08', \
+                         'nb_reg_b1024_e10', \
+                         'nb_reg_b512_e01', \
+                         'nb_reg_b512_e02', \
+                         'nb_reg_b512_e04', \
+                         'nb_reg_b512_e06', \
+                         'nb_reg_b512_e08', \
+                         'nb_reg_b512_e10' ]
+                        
 
     # parse raw data from all processed experiments
     data = parse_raw_data(exp_folder, exp_list)
@@ -84,6 +96,7 @@ def main():
     colors = [cmap(i) for i in np.linspace(0, 1, len(exp_list)*2)]
     linestyles = ['solid', 'dashdot', 'dotted', 'dashed']
     markers = ['.', 'v', '^', '<', '>', '1', 's', 'p', '*', '+', 'x', 'X', 'D', 'd']
+    mathy_markers = ["$a$", "$b$", "$c$", "$d$", "$e$", "$f$", "$g$", "$h$", "$i$", "$j$", "$k$", "$l$", "$m$", "$n$", "$o$", "$p$", "$q$", "$r$", "$s$", "$t$", "$u$", "$v$", "$w$", "$x$", "$y$", "$z$"]
     
     cc = cycler(color=colors) * cycler(linestyle=linestyles) * cycler(marker=markers)
 
@@ -112,8 +125,10 @@ def main():
             break
 
     # plot shortreg data
+    counter = 0
     for i, key in enumerate(shortreg_data.keys()):
-        plt.plot(shortreg_data[key]['top_percents'], shortreg_data[key]['spe_shortreg'], marker = '8', mfc='green', ms=10)
+        plt.plot(shortreg_data[key]['top_percents'], shortreg_data[key]['spe_shortreg'], marker = mathy_markers[counter], mfc='green', ms=10)
+        counter += 1
         legend_labels.append(key)
     
         # annotate the shortreg data points with time information
@@ -166,23 +181,35 @@ def main():
         tp_info[tp] = this_tp_info
 
     # now plot each top percent
-    cc = cycler(marker=markers)
+    # markers the same size as number of freezetrain experiments
+    
     fig, axs = plt.subplots(5, 10)
     handles = None
     labels = None
     for tp_key, ax in zip(tp_info.keys(), axs.flat):
+        counter = 0
+        counter_reg = 0
         for exp in tp_info[tp_key].keys():
             duration = tp_info[tp_key][exp][0]
             spe = tp_info[tp_key][exp][1]
-            #ax.set_prop_cycle(cc)
+
-            ax.scatter(duration, spe, label=exp)
+            if 'ft_fb' in exp:
+                marker = markers[counter]
+                counter += 1
+            elif 'nb_reg' in exp:
+                marker = mathy_markers[counter_reg]
+                counter_reg += 1
+            else:
+                raise NotImplementedError
+
+            ax.scatter(duration, spe, label=exp, marker=marker)
             ax.set_title(str(tp_key))
             #ax.set(xlabel='Duration (s)', ylabel='SPE')
             ax.set_ylim([0, 1])
             
     
     handles, labels = axs.flat[-1].get_legend_handles_labels()
-    fig.legend(handles, labels, loc='upper center')
+    fig.legend(handles, labels, loc='center right')
     
     plt.show()
 
@@ -191,7 +218,6 @@ def main():
     # ------------------------------------------------------------
     plt.clf()
     time_legend_labels = []
-
     for key in data.keys():
         plt.errorbar(data[key]['top_percents'], data[key]['freeze_times_avg'], yerr=np.array(data[key]['freeze_times_std'])/2, marker='*', mfc='red', ms=5)    
         time_legend_labels.append(key + '_freezetrain')