def run():
results_dir = 'data/results/'
results_dir_input = results_dir + 'input/prepared/openml/'
results_dir_output = results_dir + 'output/openml/ablation/'
results_raw = load_pd.load(path=[
results_dir_input + 'openml_core.csv', results_dir_input +
'openml_autogluon_ablation.csv'
])
frameworks_1h = [
'autogluon_1h',
'autogluon_nostack_1h',
'autogluon_nobag_1h',
'autogluon_norepeatbag_1h',
'autogluon_nonn_1h',
# 'autogluon_noknn_1h',
]
frameworks_4h = [
'autogluon_4h',
'autogluon_nostack_4h',
'autogluon_nobag_4h',
'autogluon_norepeatbag_4h',
'autogluon_nonn_4h',
# 'autogluon_noknn_4h',
]
run_path_prefix_list = ['1h/', '4h/', 'combined/']
frameworks_compare_vs_all_list = [['autogluon_1h'], ['autogluon_4h'],
['autogluon_1h', 'autogluon_4h']]
frameworks_run_list = [
frameworks_1h, frameworks_4h, frameworks_1h + frameworks_4h
]
folds_to_keep_list = [[0], [0], [0]]
banned_datasets = []
num_runs = len(run_path_prefix_list)
for i in range(num_runs):
run_path_prefix = run_path_prefix_list[i]
frameworks_compare_vs_all = frameworks_compare_vs_all_list[i]
frameworks_run = frameworks_run_list[i]
folds_to_keep = folds_to_keep_list[i]
results_ranked, results_ranked_by_dataset, results_ranked_all, results_ranked_by_dataset_all, results_pairs_merged_dict = evaluate_results.evaluate(
results_raw=results_raw,
frameworks=frameworks_run,
banned_datasets=banned_datasets,
folds_to_keep=folds_to_keep,
columns_to_agg_extra=[
# TIME_INFER_S,
'acc',
'auc',
'logloss'
],
frameworks_compare_vs_all=frameworks_compare_vs_all,
output_dir=results_dir_output + run_path_prefix,
)
def run():
results_dir = 'data/results/'
results_dir_input = results_dir + 'input/prepared/openml/'
results_dir_output = results_dir + 'output/openml/core/'
results_raw = load_pd.load(path=results_dir_input + 'openml_core.csv')
frameworks_1h = [
'autogluon_1h',
'GCPTables_1h',
'H2OAutoML_1h',
'autosklearn_1h',
'TPOT_1h',
'AutoWEKA_1h',
]
frameworks_4h = [
'autogluon_4h',
'GCPTables_4h',
'H2OAutoML_4h',
'autosklearn_4h',
'TPOT_4h',
'AutoWEKA_4h',
]
run_path_prefix_list = ['1h/', '4h/']
frameworks_compare_vs_all_list = [['autogluon_1h'], ['autogluon_4h']]
frameworks_run_list = [frameworks_1h, frameworks_4h]
folds_to_keep_list = [[0], [0]]
banned_datasets = []
num_runs = len(run_path_prefix_list)
for i in range(num_runs):
run_path_prefix = run_path_prefix_list[i]
frameworks_compare_vs_all = frameworks_compare_vs_all_list[i]
frameworks_run = frameworks_run_list[i]
folds_to_keep = folds_to_keep_list[i]
results_ranked, results_ranked_by_dataset, results_ranked_all, results_ranked_by_dataset_all, results_pairs_merged_dict = evaluate_results.evaluate(
results_raw=results_raw,
frameworks=frameworks_run,
banned_datasets=banned_datasets,
folds_to_keep=folds_to_keep,
columns_to_agg_extra=[
# TIME_INFER_S,
'acc',
'auc',
'logloss'
],
frameworks_compare_vs_all=frameworks_compare_vs_all,
output_dir=results_dir_output + run_path_prefix,
)
def run():
results_dir = 'data/results/'
results_dir_input = results_dir + 'input/prepared/openml/'
results_dir_output = results_dir + 'output/openml/core_1h_vs_4h/'
results_raw = load_pd.load(path=results_dir_input + 'openml_core.csv')
frameworks = [
'autogluon',
'GCPTables',
'H2OAutoML',
'autosklearn',
'TPOT',
'AutoWEKA',
]
folds_to_keep = [0]
banned_datasets = []
full_results_pairs_merged_dict = {}
for framework in frameworks:
run_path_prefix = framework + '/'
framework_1h = framework + '_1h'
framework_4h = framework + '_4h'
results_ranked, results_ranked_by_dataset, results_ranked_all, results_ranked_by_dataset_all, results_pairs_merged_dict = evaluate_results.evaluate(
results_raw=results_raw,
frameworks=[framework_1h, framework_4h],
banned_datasets=banned_datasets,
folds_to_keep=folds_to_keep,
columns_to_agg_extra=[
# TIME_INFER_S,
'acc',
'auc',
'logloss'
],
frameworks_compare_vs_all=[framework_4h],
output_dir=results_dir_output + run_path_prefix,
)
full_results_pairs_merged_dict.update(results_pairs_merged_dict)
dfs = []
for framework in frameworks:
framework_1h = framework + '_1h'
framework_4h = framework + '_4h'
cur_df = full_results_pairs_merged_dict[framework_4h]
cur_df = cur_df[cur_df[FRAMEWORK] == framework_1h]
cur_columns = list(cur_df.columns)
cur_columns[1] = '> 4h'
cur_columns[2] = '< 4h'
cur_columns[3] = '= 4h'
cur_df.columns = cur_columns
dfs.append(cur_df)
df_final = pd.concat(dfs, ignore_index=True)
print(df_final)
save_pd.save(path=results_dir_output + 'pairwise/1h_vs_4h.csv',
df=df_final)
def run():
results_dir = 'data/results/'
results_dir_input = results_dir + 'input/prepared/openml/'
results_dir_output = results_dir + 'output/openml/orig_vs_core10fold/'
results_raw = load_pd.load(path=[
results_dir_input + 'openml_core.csv',
results_dir_input + 'openml_original.csv',
])
frameworks_1h = [
'H2OAutoML_1h',
'autosklearn_1h',
'TPOT_1h',
'AutoWEKA_1h',
]
frameworks_4h = [
'H2OAutoML_4h',
'autosklearn_4h',
'TPOT_4h',
'AutoWEKA_4h',
]
frameworks_run_list = [frameworks_1h, frameworks_4h]
folds = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
folds_to_keep_list = [folds, folds]
banned_datasets_list = [DATASETS_LARGE, []]
num_runs = len(frameworks_run_list)
full_results_pairs_merged_dict = {}
for i in range(num_runs):
frameworks_run = frameworks_run_list[i]
folds_to_keep = folds_to_keep_list[i]
banned_datasets = banned_datasets_list[i]
for framework in frameworks_run:
run_path_prefix = framework + '/'
orig_framework = 'orig_' + framework
results_ranked, results_ranked_by_dataset, results_ranked_all, results_ranked_by_dataset_all, results_pairs_merged_dict = evaluate_results.evaluate(
results_raw=results_raw,
frameworks=[framework, orig_framework],
banned_datasets=banned_datasets,
folds_to_keep=folds_to_keep,
columns_to_agg_extra=[
# TIME_INFER_S,
'acc',
'auc',
'logloss'
],
frameworks_compare_vs_all=[orig_framework],
output_dir=results_dir_output + run_path_prefix,
)
full_results_pairs_merged_dict.update(results_pairs_merged_dict)
dfs = []
frameworks_full = frameworks_1h + frameworks_4h
for framework in frameworks_full:
orig_framework = 'orig_' + framework
cur_df = full_results_pairs_merged_dict[orig_framework]
cur_df = cur_df[cur_df[FRAMEWORK] == framework]
cur_columns = list(cur_df.columns)
cur_columns[1] = '> Original'
cur_columns[2] = '< Original'
cur_columns[3] = '= Original'
cur_df.columns = cur_columns
dfs.append(cur_df)
df_final = pd.concat(dfs, ignore_index=True)
print(df_final)
save_pd.save(path=results_dir_output + 'pairwise/new_vs_old.csv', df=df_final)
def run():
results_dir = 'data/results/'
results_dir_input = results_dir + 'input/prepared/kaggle/'
output_prefix = 'output/kaggle/'
raw_kaggle_file = 'results_kaggle_wpercentile.csv'
results_raw = load_pd.load(path=[
results_dir_input + 'kaggle_core.csv',
])
# First generate datasets x frameworks raw data dumps:
metrics = ['LEADER_PERCENTILE', METRIC_SCORE]
dataset_order = [
'house-prices-advanced-regression-techniques',
'mercedes-benz-greener-manufacturing',
'santander-value-prediction-challenge', 'allstate-claims-severity',
'bnp-paribas-cardif-claims-management',
'santander-customer-transaction-prediction',
'santander-customer-satisfaction',
'porto-seguro-safe-driver-prediction', 'ieee-fraud-detection',
'walmart-recruiting-trip-type-classification',
'otto-group-product-classification-challenge'
]
dataset_order = [KAGGLE_ABBREVS[dat] for dat in dataset_order]
method_order = [
'AutoWEKA', 'autosklearn', 'TPOT', 'H2OAutoML', 'GCPTables',
'autogluon'
]
time_limits = ['4h', '8h']
results_raw2 = results_raw.drop(METRIC_ERROR, axis=1).copy()
results_raw2['LEADER_PERCENTILE'] = 1 - results_raw2[
'LEADER_PERCENTILE'] # convert to actual percentile
results_raw2.rename(columns={'LEADER_PERCENTILE': METRIC_ERROR},
inplace=True)
# loss_df = generate_charts.compute_dataset_framework_df(results_raw) # values = losses
percentile_df = generate_charts.compute_dataset_framework_df(results_raw2)
for time_limit in time_limits:
methods_t = [meth + "_" + time_limit for meth in method_order]
df_time = percentile_df[[DATASET] + methods_t].copy()
df_time[DATASET] = df_time[DATASET].map(KAGGLE_ABBREVS)
df_ordered = df_time.set_index(DATASET)
df_ordered = df_ordered.reindex(dataset_order)
# df_ordered.reset_index(inplace=True)
# df_ordered.rename(columns={'dataset': 'Dataset'},inplace=True)
df_ordered.rename(columns=NOTIME_NAMES, inplace=True)
save_pd.save(path=results_dir + output_prefix + time_limit +
"/datasetsXframeworks.csv",
df=df_ordered)
textable_file = results_dir + output_prefix + time_limit + "/allpercentiles.tex"
tex_table.tex_table(df_ordered,
textable_file,
bold='max',
nan_char=" x ",
max_digits=5)
# Next do pairwise comparisons:
num_frameworks = 6
valid_frameworks = [
'autogluon_4h',
'GCPTables_4h',
'autosklearn_4h',
'H2OAutoML_4h',
'TPOT_4h',
'AutoWEKA_4h',
'autogluon_8h',
'GCPTables_8h',
'H2OAutoML_8h',
'autosklearn_8h',
'TPOT_8h',
'AutoWEKA_8h',
]
frameworks_compare_vs_all_list = [
'autogluon_4h', 'autogluon_8h', 'autogluon_4h', 'autogluon_8h'
]
results_dir_output_list = [
'4h/', '8h/', 'allVautogluon_4h/', 'allVautogluon_8h/'
]
results_dir_output_list = [
results_dir + output_prefix + name for name in results_dir_output_list
]
framework_compare_ind_list = [ # list of lists, each corresponding to indices of valid_frameworks that should be compared in a single table.
list(range(num_frameworks)),
list(range(num_frameworks, num_frameworks * 2)),
range(num_frameworks * 2),
range(num_frameworks * 2),
]
for i in range(len(results_dir_output_list)):
results_dir_output = results_dir_output_list[i]
frameworks_to_compare = [
valid_frameworks[j] for j in framework_compare_ind_list[i]
]
framework_compare_vs_all = frameworks_compare_vs_all_list[i]
results_ranked, results_ranked_by_dataset, results_ranked_all, results_ranked_by_dataset_all, results_pairs_merged_dict = evaluate_results.evaluate(
results_raw=results_raw,
frameworks=frameworks_to_compare,
banned_datasets=[],
folds_to_keep=None,
frameworks_compare_vs_all=[framework_compare_vs_all],
output_dir=results_dir_output,
columns_to_agg_extra=['LEADER_PERCENTILE'],
)
textab = tex_pairwise_table(results_dir_output,
framework_compare_vs_all)
# Generate plots:
producePlots(time_limits, results_dir, raw_kaggle_file)
def run():
results_dir = 'data/results/'
results_dir_input = results_dir + 'input/prepared/openml/'
results_dir_output = results_dir + 'output/openml/accuracy/'
results_raw = load_pd.load(
path=[
results_dir_input + 'openml_core.csv',
results_dir_input + 'openml_autopilot.csv'
],
worker_count=1
)
valid_frameworks = [
'autogluon_1h',
'GCPTables_1h',
'H2OAutoML_1h',
'autosklearn_1h',
'TPOT_1h',
'AutoWEKA_1h',
'AutoPilot_1h',
]
results_raw[METRIC_SCORE] = results_raw['acc']
results_raw[METRIC_ERROR] = 1 - results_raw[METRIC_SCORE]
run_path_prefix = '1h/'
banned_datasets = []
folds_to_keep = [0]
results_ranked, results_ranked_by_dataset, results_ranked_all, results_ranked_by_dataset_all, results_pairs_merged_dict = evaluate_results.evaluate(
results_raw=results_raw,
frameworks=valid_frameworks,
banned_datasets=banned_datasets,
folds_to_keep=folds_to_keep,
columns_to_agg_extra=[
# TIME_INFER_S,
'acc',
],
frameworks_compare_vs_all=['autogluon_1h', 'AutoPilot_1h'],
output_dir=results_dir_output + run_path_prefix,
)