benchmark.trainer

class SingleGraphLoader(args: Namespace, res_logger: ResLogger | None = None) → None

Bases: object

Loader for torch_geometric.data.Data object for one graph.

Parameters:

• args (Namespace) –

  Configuration arguments.

  • args.seed (int): Random seed.

  • args.data (str): Dataset name.

  • args.data_split (str): Index of dataset split.

• res_logger (ResLogger | None, default: None) – Logger for results.

args_out = ['in_channels', 'out_channels', 'multi', 'metric']

param = {'normg': ('float', (0.0, 1.0), {'step': 0.05}, <function SingleGraphLoader.<lambda>>)}

static available_datasets() → dict

get(args: Namespace) → Data

Load data based on parameters.

Parameters:

args.normg (float) – Generalized graph norm.

Updates:

• args.in_channels (int) – Number of input features.

• args.out_channels (int) – Number of output classes.

• args.multi (bool) – True for multi-label classification.

• args.metric (str) – Main metric name for evaluation.

class SingleGraphLoader_Trial(args: Namespace, res_logger: ResLogger | None = None) → None

Bases: SingleGraphLoader

Reuse necessary data for multiple runs.

get(args: Namespace) → Data

Load data based on parameters.

Parameters:

args.normg (float) – Generalized graph norm.

Updates:

• args.in_channels (int) – Number of input features.

• args.out_channels (int) – Number of output classes.

• args.multi (bool) – True for multi-label classification.

• args.metric (str) – Main metric name for evaluation.

update(args: Namespace, data: Data) → Data

Update data split for the next trial.

class ModelLoader(args: Namespace, res_logger: ResLogger | None = None) → None

Bases: object

Loader for torch.nn.Module object.

Parameters:

• args (Namespace) –

  Configuration arguments.

  • args.model (str): Model architecture name.

  • args.conv (str): Convolution layer name.

• res_logger (ResLogger | None, default: None) – Logger for results.

args_out = ['criterion']

param = {}

static available_models() → list[str]

static available_convs() → list[str]

static get_name(args: Namespace) → tuple[str]

Get model+conv name for logging path from argparse input without instantiation. Wrapper for pyg_spectral.nn.get_nn_name().

Parameters:

args (Namespace) –

Configuration arguments.

• args.model (str): Model architecture name.

• args.conv (str): Convolution layer name.

• other args specified in module name function.

Returns:

nn_name (tuple[str]) – Name strings (model_name, conv_name).

static get_trn(args: Namespace) → TrnBase

Get trainer class from model name.

Parameters:

args (Namespace) – Configuration arguments.

Returns:

trn (TrnBase) – Trainer class.

_resolve_import(args: Namespace) → tuple[str, str, dict]

get(args: Namespace) → tuple[Module, TrnBase]

Load model with specified arguments.

Parameters:

• args.num_hops (int) – Number of conv hops.

• args.in_layers (int) – Number of MLP layers before conv.

• args.out_layers (int) – Number of MLP layers after conv.

• args.in_channels (int) – Number of input features.

• args.out_channels (int) – Number of output classes.

• args.hidden_channels (int) – Number of hidden units.

• args.dropout_[lin/conv] (float) – Dropout rate for linear/conv.

Updates:

args.criterion (str) – Loss function name.

class ModelLoader_Trial(args: Namespace, res_logger: ResLogger | None = None) → None

Bases: ModelLoader

Reuse necessary data for multiple runs.

get(args: Namespace) → tuple[Module, TrnBase]

Load model with specified arguments.

Parameters:

• args.num_hops (int) – Number of conv hops.

• args.in_layers (int) – Number of MLP layers before conv.

• args.out_layers (int) – Number of MLP layers after conv.

• args.in_channels (int) – Number of input features.

• args.out_channels (int) – Number of output classes.

• args.hidden_channels (int) – Number of hidden units.

• args.dropout_[lin/conv] (float) – Dropout rate for linear/conv.

Updates:

args.criterion (str) – Loss function name.

update(args: Namespace, model: Module) → Module

class TrnBase_Trial(model: Module, data: Data, args: Namespace, res_logger: ResLogger | None = None, **kwargs)

Bases: TrnBase

Trainer supporting optuna.pruners in training.

split_hyperval(data: Data) → Data

clear()

train_val(split_train: list[str] = ['train'], split_val: list[str] = ['val']) → ResLogger

class TrnFullbatch(model: Module, data: Dataset, args: Namespace, **kwargs)

Bases: TrnBase

Fullbatch trainer class for node classification.

• Model forward input: separate edge index and node features.

• Run pipeline: train_val -> test.

Parameters:

• model (Module) – args for TrnBase.

• data (Dataset) – args for TrnBase.

• res_logger – args for TrnBase.

• args (Namespace) –

  args for TrnBase.

  • device (str): torch device.

  • metric (str): Metric for evaluation.

  • epoch (int): Number of training epochs.

  • lr_[lin/conv] (float): Learning rate for linear/conv.

  • wd_[lin/conv] (float): Weight decay for linear/conv.

  • patience (int): Patience for early stopping.

  • period (int): Period for checkpoint saving.

  • suffix (str): Suffix for checkpoint saving.

  • storage (str): Storage scheme for checkpoint saving.

  • logpath (Path): Path for logging.

  • multi (bool): True for multi-label classification.

  • in_channels (int): Number of data input features.

  • out_channels (int): Number of data output classes.

name: str = 'fb'

clear()

_fetch_data() → tuple[Data, dict]

Process the single graph data.

_fetch_input() → tuple

Process each sample of model input and label.

_learn_split(split: list = ['train']) → ResLogger

Actual train iteration on the given splits.

_eval_split(split: list = ['test']) → ResLogger

Actual test on the given splits.

test_deg() → ResLogger

Separate high/low degree subsets and evaluate.

run() → ResLogger

class TrnFullbatch_Trial(model: Module, data: Dataset, args: Namespace, **kwargs)

Bases: TrnFullbatch, TrnBase_Trial

Trainer supporting optuna.pruners in training.

run() → ResLogger

update(*args, **kwargs)

class TrnMinibatch(model: Module, data: Dataset, args: Namespace, **kwargs)

Bases: TrnBase

Minibatch trainer class for node classification.

• Model forward input: node embeddings.

• Run pipeline: propagate -> train_val -> test.

Parameters:

• args.batch (int) – Batch size.

• args.normf (int) – Embedding normalization.

• model (Module) – args for TrnBase.

• data (Dataset) – args for TrnBase.

• res_logger – args for TrnBase.

• args (Namespace) –

  args for TrnBase.

  • device (str): torch device.

  • metric (str): Metric for evaluation.

  • epoch (int): Number of training epochs.

  • lr_[lin/conv] (float): Learning rate for linear/conv.

  • wd_[lin/conv] (float): Weight decay for linear/conv.

  • patience (int): Patience for early stopping.

  • period (int): Period for checkpoint saving.

  • suffix (str): Suffix for checkpoint saving.

  • storage (str): Storage scheme for checkpoint saving.

  • logpath (Path): Path for logging.

  • multi (bool): True for multi-label classification.

  • in_channels (int): Number of data input features.

  • out_channels (int): Number of data output classes.

name: str = 'mb'

clear()

_fetch_data() → tuple

Process the single graph data.

_fetch_preprocess(embed: Tensor, label: Tensor, mask: dict) → dict

Call model preprocess for precomputation.

_fetch_input(split: str) → Generator

Process each sample of model input and label for training.

_learn_split(split: list = ['train']) → ResLogger

Actual train iteration on the given splits.

_eval_split(split: list = ['test']) → ResLogger

Actual test on the given splits.

preprocess(*args, **kwargs)

run() → ResLogger

class TrnMinibatch_Trial(model: Module, data: Dataset, args: Namespace, **kwargs)

Bases: TrnMinibatch, TrnBase_Trial

Trainer supporting optuna.pruners in training. Lazy calling precomputation.

preprocess(*args, **kwargs)

run() → ResLogger

update(model: Module, data: Data, args: Namespace, res_logger: ResLogger | None = None, **kwargs)

benchmark.trainer.base

Author: nyLiao File Created: 2024-03-03

class TrnBase(model: Module, data: Data, args: Namespace, res_logger: ResLogger | None = None, **kwargs)

Bases: object

Base trainer class for general pipelines and tasks.

Parameters:

• model (Module) – Pytorch model to be trained.

• data (Data) – PyG style data.

• res_logger (ResLogger | None, default: None) – Logger for results.

• args (Namespace) –

  Configuration arguments.

  • device (str): torch device.

  • metric (str): Metric for evaluation.

  • criterion (set): Loss function in torch.nn.

  • epoch (int): Number of training epochs.

  • lr_[lin/conv] (float): Learning rate for linear/conv.

  • wd_[lin/conv] (float): Weight decay for linear/conv.

  • patience (int): Patience for early stopping.

  • period (int): Period for checkpoint saving.

  • suffix (str): Suffix for checkpoint saving.

  • storage (str): Storage scheme for checkpoint saving.

  • logpath (Path): Path for logging.

  • multi (bool): True for multi-label classification.

  • in_channels (int): Number of data input features.

  • out_channels (int): Number of data output classes.

setup_optimizer()

Set up the optimizer and scheduler.

clear()

Clear self cache.

run()

Run the training process.

name: str

param = {'lr_conv': ('float', (1e-05, 0.5), {'log': True}, <function TrnBase.<lambda>>), 'lr_lin': ('float', (1e-05, 0.5), {'log': True}, <function TrnBase.<lambda>>), 'wd_conv': ('float', (1e-07, 0.001), {'log': True}, <function TrnBase.<lambda>>), 'wd_lin': ('float', (1e-07, 0.001), {'log': True}, <function TrnBase.<lambda>>)}

setup_optimizer()

clear()

static _log_memory(split: str | None = None, row: int = 0)

train_val(*args, **kwargs)

test(*args, **kwargs)

_fetch_data()

Process the single graph data.

_fetch_input() → tuple

Process each sample of model input and label.

_learn_split(split: list[str] = ['train']) → ResLogger

Actual train iteration on the given splits.

_eval_split(split: list[str]) → ResLogger

Actual test on the given splits.

run() → ResLogger

class TrnBase_Trial(model: Module, data: Data, args: Namespace, res_logger: ResLogger | None = None, **kwargs)

Bases: TrnBase

Trainer supporting optuna.pruners in training.

split_hyperval(data: Data) → Data

clear()

train_val(split_train: list[str] = ['train'], split_val: list[str] = ['val']) → ResLogger

benchmark.trainer.regression

class TrnRegression(model: Module, data: Dataset, args: Namespace, **kwargs)

Bases: TrnFullbatch

name: str = 'regression'

_fetch_input() → tuple

Process each sample of model input and label.

class RegressionLoader(args: Namespace, res_logger: ResLogger | None = None) → None

Bases: object

Loader for regression learning datas.

_resolve_import(args: Namespace) → tuple[str, str, dict]

get(args: Namespace) → Data

Load data based on parameters.

benchmark.trainer.load_data

Author: nyLiao File Created: 2024-02-26

class SingleGraphLoader(args: Namespace, res_logger: ResLogger | None = None) → None

Bases: object

Loader for torch_geometric.data.Data object for one graph.

Parameters:

• args (Namespace) –

  Configuration arguments.

  • args.seed (int): Random seed.

  • args.data (str): Dataset name.

  • args.data_split (str): Index of dataset split.

• res_logger (ResLogger | None, default: None) – Logger for results.

args_out = ['in_channels', 'out_channels', 'multi', 'metric']

param = {'normg': ('float', (0.0, 1.0), {'step': 0.05}, <function SingleGraphLoader.<lambda>>)}

static available_datasets() → dict

get(args: Namespace) → Data

Load data based on parameters.

Parameters:

args.normg (float) – Generalized graph norm.

Updates:

• args.in_channels (int) – Number of input features.

• args.out_channels (int) – Number of output classes.

• args.multi (bool) – True for multi-label classification.

• args.metric (str) – Main metric name for evaluation.

class SingleGraphLoader_Trial(args: Namespace, res_logger: ResLogger | None = None) → None

Bases: SingleGraphLoader

Reuse necessary data for multiple runs.

get(args: Namespace) → Data

Load data based on parameters.

Parameters:

args.normg (float) – Generalized graph norm.

Updates:

• args.in_channels (int) – Number of input features.

• args.out_channels (int) – Number of output classes.

• args.multi (bool) – True for multi-label classification.

• args.metric (str) – Main metric name for evaluation.

update(args: Namespace, data: Data) → Data

Update data split for the next trial.

benchmark.trainer.load_metric

Author: nyLiao File Created: 2024-03-03

class ResCollection(metrics: Metric | Sequence[Metric] | Dict[str, Metric], *additional_metrics: Metric, prefix: str | None = None, postfix: str | None = None, compute_groups: bool | List[List[str]] = True) → None

Bases: MetricCollection

compute() → list[tuple[str, Any, Callable]]

Wrap compute output to ResLogger style.

metric_loader(args: Namespace) → MetricCollection

Loader for torchmetrics.Metric object.

Parameters:

args (Namespace) –

Configuration arguments.

• args.multi (bool): True for multi-label classification.

• args.out_channels (int): Number of output classes/labels.

benchmark.trainer.load_model

Author: nyLiao File Created: 2024-02-26

class ModelLoader(args: Namespace, res_logger: ResLogger | None = None) → None

Bases: object

Loader for torch.nn.Module object.

Parameters:

• args (Namespace) –

  Configuration arguments.

  • args.model (str): Model architecture name.

  • args.conv (str): Convolution layer name.

• res_logger (ResLogger | None, default: None) – Logger for results.

args_out = ['criterion']

param = {}

static available_models() → list[str]

static available_convs() → list[str]

static get_name(args: Namespace) → tuple[str]

Get model+conv name for logging path from argparse input without instantiation. Wrapper for pyg_spectral.nn.get_nn_name().

Parameters:

args (Namespace) –

Configuration arguments.

• args.model (str): Model architecture name.

• args.conv (str): Convolution layer name.

• other args specified in module name function.

Returns:

nn_name (tuple[str]) – Name strings (model_name, conv_name).

static get_trn(args: Namespace) → TrnBase

Get trainer class from model name.

Parameters:

args (Namespace) – Configuration arguments.

Returns:

trn (TrnBase) – Trainer class.

_resolve_import(args: Namespace) → tuple[str, str, dict]

get(args: Namespace) → tuple[Module, TrnBase]

Load model with specified arguments.

Parameters:

• args.num_hops (int) – Number of conv hops.

• args.in_layers (int) – Number of MLP layers before conv.

• args.out_layers (int) – Number of MLP layers after conv.

• args.in_channels (int) – Number of input features.

• args.out_channels (int) – Number of output classes.

• args.hidden_channels (int) – Number of hidden units.

• args.dropout_[lin/conv] (float) – Dropout rate for linear/conv.

Updates:

args.criterion (str) – Loss function name.

class ModelLoader_Trial(args: Namespace, res_logger: ResLogger | None = None) → None

Bases: ModelLoader

Reuse necessary data for multiple runs.

get(args: Namespace) → tuple[Module, TrnBase]

Load model with specified arguments.

Parameters:

• args.num_hops (int) – Number of conv hops.

• args.in_layers (int) – Number of MLP layers before conv.

• args.out_layers (int) – Number of MLP layers after conv.

• args.in_channels (int) – Number of input features.

• args.out_channels (int) – Number of output classes.

• args.hidden_channels (int) – Number of hidden units.

• args.dropout_[lin/conv] (float) – Dropout rate for linear/conv.

Updates:

args.criterion (str) – Loss function name.

update(args: Namespace, model: Module) → Module