"""
Node classification Full Trainer Implementation
"""
from . import register_trainer
from .base import BaseNodeClassificationTrainer, EarlyStopping
import torch
from torch.optim.lr_scheduler import (
StepLR,
MultiStepLR,
ExponentialLR,
ReduceLROnPlateau,
)
import torch.nn.functional as F
from ..model import BaseEncoderMaintainer, BaseDecoderMaintainer, BaseAutoModel
from .evaluation import Evaluation, get_feval, Logloss
from typing import Callable, Iterable, Optional, Tuple, Type, Union
from copy import deepcopy
from ...utils import get_logger
from ...backend import DependentBackend
LOGGER = get_logger("node classification trainer")
[docs]@register_trainer("NodeClassificationFull")
class NodeClassificationFullTrainer(BaseNodeClassificationTrainer):
"""
The node classification trainer.
Used to automatically train the node classification problem.
Parameters
----------
model:
Models can be ``str``, ``autogl.module.model.BaseAutoModel``,
``autogl.module.model.encoders.BaseEncoderMaintainer`` or a tuple of (encoder, decoder)
if need to specify both encoder and decoder. Encoder can be ``str`` or
``autogl.module.model.encoders.BaseEncoderMaintainer``, and decoder can be ``str``
or ``autogl.module.model.decoders.BaseDecoderMaintainer``.
If only encoder is specified, decoder will be default to "logsoftmax"
num_features: int (Optional)
The number of features in dataset. default None
num_classes: int (Optional)
The number of classes. default None
optimizer: ``Optimizer`` of ``str``
The (name of) optimizer used to train and predict. default torch.optim.Adam
lr: ``float``
The learning rate of node classification task. default 1e-4
max_epoch: ``int``
The max number of epochs in training. default 100
early_stopping_round: ``int``
The round of early stop. default 100
weight_decay: ``float``
weight decay ratio, default 1e-4
device: ``torch.device`` or ``str``
The device where model will be running on.
init: ``bool``
If True(False), the model will (not) be initialized.
feval: (Sequence of) ``Evaluation`` or ``str``
The evaluation functions, default ``[LogLoss]``
loss: ``str``
The loss used. Default ``nll_loss``.
lr_scheduler_type: ``str`` (Optional)
The lr scheduler type used. Default None.
"""
def __init__(
self,
model: Union[Tuple[BaseEncoderMaintainer, BaseDecoderMaintainer], BaseEncoderMaintainer, BaseAutoModel, str] = None,
num_features: Optional[int] = None,
num_classes: Optional[int] = None,
optimizer: Union[str, Type[torch.optim.Optimizer]] = torch.optim.Adam,
lr: float = 1e-4,
max_epoch: int = 100,
early_stopping_round: int = 100,
weight_decay: float = 1e-4,
device: Union[torch.device, str] = "auto",
init: bool = False,
feval: Iterable[Type[Evaluation]] =[Logloss],
loss: Union[Callable, str] = "nll_loss",
lr_scheduler_type: Optional[str] = None,
**kwargs
):
if isinstance(model, Tuple):
encoder, decoder = model
elif isinstance(model, BaseAutoModel):
encoder, decoder = model, None
else:
encoder, decoder = model, "logsoftmax"
super().__init__(
encoder=encoder,
decoder=decoder,
num_features=num_features,
num_classes=num_classes,
device=device,
feval=feval,
loss=loss,
)
self.opt_received = optimizer
if isinstance(optimizer, str):
if optimizer.lower() == "adam": self.optimizer = torch.optim.Adam
elif optimizer.lower() == "sgd": self.optimizer = torch.optim.SGD
else: raise ValueError("Currently not support optimizer {}".format(optimizer))
elif isinstance(optimizer, type) and issubclass(optimizer, torch.optim.Optimizer):
self.optimizer = optimizer
else:
raise ValueError("Currently not support optimizer {}".format(optimizer))
self.lr_scheduler_type = lr_scheduler_type
self.lr = lr
self.max_epoch = max_epoch
self.early_stopping_round = early_stopping_round
self.kwargs = kwargs
self.weight_decay = weight_decay
self.early_stopping = EarlyStopping(
patience=early_stopping_round, verbose=False
)
self.valid_result = None
self.valid_result_prob = None
self.valid_score = None
self.pyg_dgl = DependentBackend.get_backend_name()
self.hyper_parameter_space = [
{
"parameterName": "max_epoch",
"type": "INTEGER",
"maxValue": 500,
"minValue": 10,
"scalingType": "LINEAR",
},
{
"parameterName": "early_stopping_round",
"type": "INTEGER",
"maxValue": 30,
"minValue": 10,
"scalingType": "LINEAR",
},
{
"parameterName": "lr",
"type": "DOUBLE",
"maxValue": 1e-1,
"minValue": 1e-4,
"scalingType": "LOG",
},
{
"parameterName": "weight_decay",
"type": "DOUBLE",
"maxValue": 1e-2,
"minValue": 1e-4,
"scalingType": "LOG",
},
]
self.hyper_parameters = {
"max_epoch": self.max_epoch,
"early_stopping_round": self.early_stopping_round,
"lr": self.lr,
"weight_decay": self.weight_decay,
}
if init is True:
self.initialize()
[docs] @classmethod
def get_task_name(cls):
"""
Derive the task name. (NodeClassification)
"""
return "NodeClassification"
def __train_only(self, data, train_mask=None):
data = data.to(self.device)
model = self._compose_model()
if train_mask is None:
if self.pyg_dgl == 'pyg':
mask = data.train_mask
elif self.pyg_dgl == 'dgl':
mask = data.ndata['train_mask']
else:
mask = train_mask
optimizer = self.optimizer(
model.parameters(),
lr=self.lr, weight_decay=self.weight_decay
)
# scheduler = StepLR(optimizer, step_size=100, gamma=0.1)
lr_scheduler_type = self.lr_scheduler_type
if type(lr_scheduler_type) == str and lr_scheduler_type == "steplr":
scheduler = StepLR(optimizer, step_size=100, gamma=0.1)
elif type(lr_scheduler_type) == str and lr_scheduler_type == "multisteplr":
scheduler = MultiStepLR(optimizer, milestones=[30, 80], gamma=0.1)
elif type(lr_scheduler_type) == str and lr_scheduler_type == "exponentiallr":
scheduler = ExponentialLR(optimizer, gamma=0.1)
elif (
type(lr_scheduler_type) == str and lr_scheduler_type == "reducelronplateau"
):
scheduler = ReduceLROnPlateau(optimizer, "min")
else:
scheduler = None
for epoch in range(1, self.max_epoch + 1):
model.train()
optimizer.zero_grad()
res = model(data)
if hasattr(F, self.loss):
if self.pyg_dgl == 'pyg':
loss = getattr(F, self.loss)(res[mask], data.y[mask])
elif self.pyg_dgl == 'dgl':
loss = getattr(F, self.loss)(res[mask], data.ndata['label'][mask])
else:
raise TypeError(
"PyTorch does not support loss type {}".format(self.loss)
)
loss.backward()
optimizer.step()
if self.lr_scheduler_type:
scheduler.step()
# TODO: move this to autogl.backend.utils
if self.pyg_dgl == 'pyg' and hasattr(data, "val_mask") and data.val_mask is not None:
val_mask = data.val_mask
elif self.pyg_dgl == 'dgl' and data.ndata.get('val_mask', None) is not None:
val_mask = data.ndata['val_mask']
else:
val_mask = None
if val_mask is not None:
if type(self.feval) is list:
feval = self.feval[0]
else:
feval = self.feval
val_loss = self.evaluate([data], mask=val_mask, feval=feval)
if feval.is_higher_better() is True:
val_loss = -val_loss
self.early_stopping(val_loss, model)
if self.early_stopping.early_stop:
LOGGER.debug("Early stopping at %d", epoch)
break
if self.pyg_dgl == "pyg" and hasattr(data, "val_mask") and data.val_mask is not None:
self.early_stopping.load_checkpoint(model)
elif self.pyg_dgl == 'dgl' and data.ndata.get('val_mask', None) is not None:
self.early_stopping.load_checkpoint(model)
@torch.no_grad()
def __predict_only(self, data, mask=None):
if isinstance(mask, str):
if self.pyg_dgl == 'pyg':
mask = getattr(data, f'{mask}_mask')
elif self.pyg_dgl == 'dgl':
mask = data.ndata[f'{mask}_mask']
model = self._compose_model()
model.to(self.device)
data = data.to(self.device)
model.eval()
res = model(data)
if mask is None:
return res
else:
return res[mask]
[docs] def train(self, dataset, keep_valid_result=True, train_mask=None):
"""
Train on the given dataset.
Parameters
----------
dataset: The node classification dataset used to be trained.
keep_valid_result: ``bool``
If True(False), save the validation result after training.
train_mask: The mask for training data
Returns
-------
self: ``autogl.train.NodeClassificationTrainer``
A reference of current trainer.
"""
data = dataset[0]
self.__train_only(data, train_mask)
if keep_valid_result:
if self.pyg_dgl == 'pyg':
val_mask = data.val_mask
elif self.pyg_dgl == 'dgl':
val_mask = data.ndata['val_mask']
else:
assert False
self.valid_result = self.__predict_only(data)[val_mask].max(1)[1]
self.valid_result_prob = self.__predict_only(data)[val_mask]
self.valid_score = self.evaluate(
dataset, mask=val_mask, feval=self.feval
)
[docs] def predict(self, dataset, mask=None):
"""
Predict on the given dataset using specified mask.
Parameters
----------
dataset: The node classification dataset used to be predicted.
mask: ``train``, ``val``, or ``test``.
The dataset mask.
Returns
-------
The prediction result.
"""
return self.predict_proba(dataset, mask=mask, in_log_format=True).max(1)[1]
[docs] def predict_proba(self, dataset, mask=None, in_log_format=False):
"""
Predict the probability on the given dataset using specified mask.
Parameters
----------
dataset: The node classification dataset used to be predicted.
mask: ``train``, ``val``, ``test``, or ``Tensor``.
The dataset mask.
in_log_format: ``bool``.
If True(False), the probability will (not) be log format.
Returns
-------
The prediction result.
"""
data = dataset[0]
data = data.to(self.device)
ret = self.__predict_only(data, mask)
if in_log_format is True:
return ret
else:
return torch.exp(ret)
[docs] def get_valid_predict(self):
# """Get the valid result."""
return self.valid_result
[docs] def get_valid_predict_proba(self):
# """Get the valid result (prediction probability)."""
return self.valid_result_prob
[docs] def get_valid_score(self, return_major=True):
"""
The function of getting the valid score.
Parameters
----------
return_major: ``bool``.
If True, the return only consists of the major result.
If False, the return consists of the all results.
Returns
-------
result: The valid score in training stage.
"""
if isinstance(self.feval, list):
if return_major:
return self.valid_score[0], self.feval[0].is_higher_better()
else:
return self.valid_score, [f.is_higher_better() for f in self.feval]
else:
return self.valid_score, self.feval.is_higher_better()
def __repr__(self) -> str:
import yaml
return yaml.dump(
{
"trainer_name": self.__class__.__name__,
"optimizer": self.optimizer,
"learning_rate": self.lr,
"max_epoch": self.max_epoch,
"early_stopping_round": self.early_stopping_round,
"encoder": repr(self.encoder),
"decoder": repr(self.decoder)
}
)
[docs] def evaluate(self, dataset, mask=None, feval=None):
"""
Evaluate on the given dataset.
Parameters
----------
dataset: The node classification dataset used to be evaluated.
mask: ``train``, ``val``, or ``test``.
The dataset mask.
feval: ``str``.
The evaluation method used in this function.
Returns
-------
res: The evaluation result on the given dataset.
"""
data = dataset[0]
data = data.to(self.device)
if isinstance(mask, str):
if self.pyg_dgl == 'pyg':
mask = getattr(data, f'{mask}_mask')
elif self.pyg_dgl == 'dgl':
mask = data.ndata[f'{mask}_mask']
if self.pyg_dgl == 'pyg': label = data.y
elif self.pyg_dgl == 'dgl': label = data.ndata['label']
if feval is None:
feval = self.feval
else:
feval = get_feval(feval)
y_pred_prob = self.predict_proba(dataset, mask)
y_true = label[mask] if mask is not None else label
if not isinstance(feval, list):
feval = [feval]
return_signle = True
else:
return_signle = False
res = []
for f in feval:
try:
res.append(f.evaluate(y_pred_prob, y_true))
except:
res.append(f.evaluate(y_pred_prob.cpu().numpy(), y_true.cpu().numpy()))
if return_signle:
return res[0]
return res
[docs] def duplicate_from_hyper_parameter(self, hp: dict, model=None, restricted=True):
"""
The function of duplicating a new instance from the given hyperparameter.
Parameters
----------
hp: ``dict``.
The hyperparameter used in the new instance. Should contain 3 keys "trainer", "encoder"
"decoder", with corresponding hyperparameters as values.
model:
Models can be ``str``, ``autogl.module.model.BaseAutoModel``,
``autogl.module.model.encoders.BaseEncoderMaintainer`` or a tuple of (encoder, decoder)
if need to specify both encoder and decoder. Encoder can be ``str`` or
``autogl.module.model.encoders.BaseEncoderMaintainer``, and decoder can be ``str``
or ``autogl.module.model.decoders.BaseDecoderMaintainer``.
restricted: ``bool``.
If False(True), the hyperparameter should (not) be updated from origin hyperparameter.
Returns
-------
self: ``autogl.train.NodeClassificationTrainer``
A new instance of trainer.
"""
if isinstance(model, Tuple):
encoder, decoder = model
elif isinstance(model, BaseAutoModel):
encoder, decoder = model, None
elif isinstance(model, BaseEncoderMaintainer):
encoder, decoder = model, self.decoder
elif model is None:
encoder, decoder = self.encoder, self.decoder
else:
raise TypeError("Cannot parse model with type", type(model))
hp_trainer = hp.get("trainer", {})
hp_encoder = hp.get("encoder", {})
hp_decoder = hp.get("decoder", {})
if not restricted:
origin_hp = deepcopy(self.hyper_parameters)
origin_hp.update(hp_trainer)
hp = origin_hp
else:
hp = hp_trainer
encoder = encoder.from_hyper_parameter(hp_encoder)
if isinstance(encoder, BaseEncoderMaintainer) and isinstance(decoder, BaseDecoderMaintainer):
decoder = decoder.from_hyper_parameter_and_encoder(hp_decoder, encoder)
ret = self.__class__(
model=(encoder, decoder),
num_features=self.num_features,
num_classes=self.num_classes,
optimizer=self.opt_received,
lr=hp["lr"],
max_epoch=hp["max_epoch"],
early_stopping_round=hp["early_stopping_round"],
device=self.device,
weight_decay=hp["weight_decay"],
feval=self.feval,
loss=self.loss,
lr_scheduler_type=self.lr_scheduler_type,
init=True,
**self.kwargs
)
return ret