run_expt.py
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6
F401: 'csv' imported but unused
(and 5 similar)
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'csv' imported but unused:
2 import csv -
'sys' imported but unused:
4 import sys -
'graph_tool' imported but unused:
9 import graph_tool -
'pandas as pd' imported but unused:
12 import pandas as pd -
'torch.nn' imported but unused:
15 import torch.nn as nn -
'torchvision' imported but unused:
16 import torchvision
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- 1 F841: local variable 'e' is assigned to but never used
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51
E501: line too long (112 > 79 characters)
(and 29 similar)
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23 from utils import set_seed, Logger, BatchLogger, log_config, initialize_wandb, close_wandb, ParseKwargs, load, \ -
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35 parser.add_argument('-d', '--dataset', choices=gds.supported_datasets, required=True) -
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36 parser.add_argument('-a', '--algorithm', choices=supported.algorithms, required=True) -
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40 help='Convenience parameter that scales all dataset splits down to the specified fraction, ' -
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41 'for development purposes. Note that this also scales the test set down, so the reported ' -
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46 parser.add_argument('--resume', type=parse_bool, const=True, nargs='?', default=False) -
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50 help='Identifies how the train/val/test split is constructed. Choices are dataset-specific.') -
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51 parser.add_argument('--dataset_kwargs', nargs='*', action=ParseKwargs, default={}) -
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52 parser.add_argument('--download', default=False, type=parse_bool, const=True, nargs='?', -
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53 help='If true, tries to downloads the dataset if it does not exist in root_dir.') -
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57 parser.add_argument('--loader_kwargs', nargs='*', action=ParseKwargs, default={}) -
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59 parser.add_argument('--uniform_over_groups', type=parse_bool, const=True, nargs='?') -
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60 parser.add_argument('--distinct_groups', type=parse_bool, const=True, nargs='?') -
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63 parser.add_argument('--eval_loader', choices=['standard'], default='standard') -
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66 parser.add_argument('--model_kwargs', nargs='*', action=ParseKwargs, default={}, -
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67 help='keyword arguments for model initialization passed as key1=value1 key2=value2') -
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71 parser.add_argument('--loss_kwargs', nargs='*', action=ParseKwargs, default={}, -
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72 help='keyword arguments for loss initialization passed as key1=value1 key2=value2') -
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90 choices=['cycle_graph', 'path_graph', 'complete_graph', 'binomial_tree']) -
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95 parser.add_argument('--val_metric_decreasing', type=parse_bool, const=True, nargs='?') -
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103 parser.add_argument('--optimizer_kwargs', nargs='*', action=ParseKwargs, default={}) -
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107 parser.add_argument('--scheduler_kwargs', nargs='*', action=ParseKwargs, default={}) -
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108 parser.add_argument('--scheduler_metric_split', choices=['train', 'val'], default='val') -
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112 parser.add_argument('--eval_only', type=parse_bool, const=True, nargs='?', default=False) -
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113 parser.add_argument('--process_outputs_function', choices=supported.process_outputs_functions) -
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114 parser.add_argument('--evaluate_all_splits', type=parse_bool, const=True, nargs='?', default=True) -
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117 help='If eval_only is set, then eval_epoch allows you to specify evaluating at a particular epoch. By default, it evaluates the best epoch by validation performance.') -
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120 parser.add_argument('--random_split', type=parse_bool, const=True, nargs='?', default=False) -
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125 help='The directory where [dataset]/data can be found (or should be downloaded to, if it does not exist).') -
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129 parser.add_argument('--save_best', type=parse_bool, const=True, nargs='?', default=True) -
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130 parser.add_argument('--save_last', type=parse_bool, const=True, nargs='?', default=True) -
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131 parser.add_argument('--save_pred', type=parse_bool, const=True, nargs='?', default=True) -
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132 parser.add_argument('--no_group_logging', type=parse_bool, const=True, nargs='?') -
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133 parser.add_argument('--use_wandb', type=parse_bool, const=True, nargs='?', default=False) -
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134 parser.add_argument('--progress_bar', type=parse_bool, const=True, nargs='?', default=False) -
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144 if config.algorithm in {'deepCORAL', 'FLAG', 'GCL'} or config.model in {'gin_10_layers', 'cheb'} : -
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145 raise ValueError('For Jiuhai\'s experiments, these are too slow, kill.') -
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154 if (config.algorithm == 'MLDG' or config.algorithm == 'FLAG') and config.dataset != 'SBM-Isolation' : -
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182 config.device = torch.device("cuda:" + str(config.device)) if torch.cuda.is_available() else torch.device("cpu") -
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198 logger = Logger(os.path.join(config.log_dir, f'{config.dataset}_{config.algorithm}_{config.parameter}_{config.model}_seed-{config.seed}.txt'), mode) -
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240 datasets[split]['dataset'] = full_dataset.get_subset(split, frac=config.default_frac) -
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242 datasets[split]['dataset'] = full_dataset.get_subset(split, frac=1.0) -
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269 os.path.join(config.log_dir, f'{config.dataset}_{config.algorithm}_{config.parameter}_{config.model}_seed-{config.seed}_{split}_eval.csv'), mode=mode, use_wandb=(config.use_wandb and verbose)) -
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271 os.path.join(config.log_dir, f'{config.dataset}_{config.algorithm}_{config.parameter}_{config.model}_seed-{config.seed}_{split}_algo.csv'), mode=mode, use_wandb=(config.use_wandb and verbose)) -
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275 if config.no_group_logging and full_dataset.is_classification and full_dataset.y_size == 1 and full_dataset.n_classes <= 10: -
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300 save_path = model_prefix.with_name(model_prefix.name + 'epoch-last_model.pth') -
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304 for file in os.listdir(config.log_dir) if file.endswith('.pth')] -
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307 save_path = model_prefix.with_name(model_prefix.name + f'epoch-{latest_epoch}_model.pth') -
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311 logger.write(f'Resuming from epoch {epoch_offset} with best val metric {best_val_metric}') -
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330 eval_model_path = model_prefix.with_name(model_prefix.name + 'epoch-best_model.pth') -
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332 eval_model_path = model_prefix.with_name(model_prefix.name + f'epoch-{config.eval_epoch}_model.pth')
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8
E203: whitespace before ':'
- 142 if config.jiuhai :
- 143 if config.dataset in {'ogb-molpcba', 'ogbg-ppa'} :
- 144 if config.algorithm in {'deepCORAL', 'FLAG', 'GCL'} or config.model in {'gin_10_layers', 'cheb'} :
- 147 if config.algorithm == 'MLDG' :
- 150 if config.model == 'cheb' and config.algorithm == 'GCL' :
- 154 if (config.algorithm == 'MLDG' or config.algorithm == 'FLAG') and config.dataset != 'SBM-Isolation' :
- 156 if config.algorithm == 'DANN' or config.algorithm == 'DANN-G' :
- 158 if config.algorithm == 'IRM' :
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7
E303: too many blank lines (3)
(and 1 similar)
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Too many blank lines (3):
30 def main(): -
Too many blank lines (2):
45 # Resume -
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177 # For the 3wlgnn model, we need to set batch_size to 1 -
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196 if not os.path.exists(config.log_dir): -
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201 # Record config -
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286 ## Initialize algorithm -
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296 if not config.eval_only:
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- 5 W293: blank line contains whitespace
- 4 E266: too many leading '#' for block comment
- 3 E128: continuation line under-indented for visual indent
- 1 E712: comparison to False should be 'if cond is False:' or 'if not cond:'