"""
Node classification Het Trainer Implementation
"""
from . import register_trainer
from .base import BaseNodeClassificationHetTrainer, EarlyStopping
import torch
from torch.optim.lr_scheduler import (
StepLR,
MultiStepLR,
ExponentialLR,
ReduceLROnPlateau,
)
import torch.nn.functional as F
from ..model import BaseAutoModel
from .evaluation import get_feval, Logloss
from typing import Union
from copy import deepcopy
from sklearn.metrics import f1_score
from ...utils import get_logger
from ...backend import DependentBackend
LOGGER = get_logger("node classification het trainer")
def score(logits, labels):
_, indices = torch.max(logits, dim=1)
prediction = indices.long().cpu().numpy()
labels = labels.cpu().numpy()
accuracy = (prediction == labels).sum() / len(prediction)
micro_f1 = f1_score(labels, prediction, average='micro')
macro_f1 = f1_score(labels, prediction, average='macro')
return accuracy, micro_f1, macro_f1
[docs]@register_trainer("NodeClassificationHet")
class NodeClassificationHetTrainer(BaseNodeClassificationHetTrainer):
"""
The heterogeneous node classification trainer.
Parameters
----------
model: ``autogl.module.model.BaseAutoModel``
Currently Heterogeneous trainer doesn't support decoupled model setting.
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[BaseAutoModel, str] = None,
dataset = None,
num_features=None,
num_classes=None,
optimizer=torch.optim.AdamW,
lr=1e-4,
max_epoch=100,
early_stopping_round=100,
weight_decay=1e-4,
device="auto",
init=False,
feval=[Logloss],
loss="nll_loss",
lr_scheduler_type=None,
*args,
**kwargs
):
super().__init__(
model,
dataset,
num_features,
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.args = args
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()
def _initialize(self):
self.encoder.initialize()
[docs] @classmethod
def get_task_name(cls):
"""
Get task name ("NodeClassificationHet")
"""
return "NodeClassificationHet"
def _train_only(self, dataset, train_mask="train"):
G = dataset[0].to(self.device)
field = dataset.schema["target_node_type"]
labels = G.nodes[field].data['label'].to(self.device)
train_mask = self._get_mask(dataset, train_mask).to(self.device)
val_mask = self._get_mask(dataset, "val").to(self.device)
model = self.encoder.model.to(self.device)
optimizer = self.optimizer(
model.parameters(), lr=self.lr, weight_decay=self.weight_decay
)
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):
model.train()
optimizer.zero_grad()
logits = model(G)
if hasattr(F, self.loss):
loss = getattr(F, self.loss)(logits[train_mask], labels[train_mask])
else:
raise TypeError(
"PyTorch does not support loss type {}".format(self.loss)
)
loss.backward()
optimizer.step()
if self.lr_scheduler_type:
scheduler.step()
if val_mask is not None:
if type(self.feval) is list:
feval = self.feval[0]
else:
feval = self.feval
val_loss = self.evaluate(dataset, 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 val_mask is not None:
self.early_stopping.load_checkpoint(model)
def _predict_only(self, dataset, mask=None):
model = self.encoder.model.to(self.device)
model.eval()
G = dataset[0].to(self.device)
with torch.no_grad():
res = model(G)
if mask is None:
return res
else:
return res[mask]
[docs] def train(self, dataset, keep_valid_result=True, train_mask="train"):
"""
The function of training on the given dataset and keeping valid result.
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.
"""
self._train_only(dataset, train_mask)
G = dataset[0].to(self.device)
if keep_valid_result:
# generate labels
val_mask = G.nodes[dataset.schema["target_node_type"]].data["val_mask"]
self.valid_result = self._predict_only(dataset)[val_mask].max(1)[1]
self.valid_result_prob = self._predict_only(dataset)[val_mask]
self.valid_score = self.evaluate(
dataset, mask=val_mask, feval=self.feval
)
# print(self.valid_score)
[docs] def predict(self, dataset, mask="test"):
"""
The function of predicting on the given dataset.
Parameters
----------
dataset: The node classification dataset used to be predicted.
mask: ``train``, ``val``, or ``test``.
The dataset mask.
Returns
-------
The prediction result of ``predict_proba``.
"""
return self.predict_proba(dataset, mask=mask, in_log_format=True).max(1)[1]
[docs] def predict_proba(self, dataset, mask="test", in_log_format=False):
"""
The function of predicting the probability on the given dataset.
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.
"""
G = dataset[0].to(self.device)
if mask in ["train", "val", "test"]:
mask = G.nodes[dataset.schema["target_node_type"]].data[f"{mask}_mask"]
ret = self._predict_only(dataset, 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,
"model": repr(self.model.model),
}
)
def _get_mask(self, dataset, mask):
if mask in ["train", "val", "test"]:
return dataset[0].nodes[dataset.schema["target_node_type"]].data[f"{mask}_mask"]
return mask
[docs] def evaluate(self, dataset, mask='val', feval = None):
"""
The function of training on the given dataset and keeping valid result.
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.
"""
G = dataset[0].to(self.device)
mask = self._get_mask(dataset, mask)
label = G.nodes[dataset.schema["target_node_type"]].data['label'].to(self.device)
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 to(self, new_device):
self.device = new_device
if self.model is not None:
self.model.to(self.device)
[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 2 keys "trainer", "encoder"
with corresponding hyperparameters as values.
model: ``autogl.module.model.BaseAutoModel``
Currently Heterogeneous trainer doesn't support decoupled model setting.
If only encoder is specified, decoder will be default to "logsoftmax"
restricted: ``bool``.
If False(True), the hyperparameter should (not) be updated from origin hyperparameter.
Returns
-------
self: ``autogl.train.NodeClassificationTrainer``
A new instance of trainer.
"""
trainer_hp = hp["trainer"]
model_hp = hp["encoder"]
if not restricted:
origin_hp = deepcopy(self.hyper_parameters)
origin_hp.update(trainer_hp)
trainer_hp = origin_hp
if model is None:
model = self.model
model = model.from_hyper_parameter(model_hp)
ret = self.__class__(
model=model,
dataset=self._dataset,
num_features=self.num_features,
num_classes=self.num_classes,
optimizer=self.opt_received,
lr=trainer_hp["lr"],
max_epoch=trainer_hp["max_epoch"],
early_stopping_round=trainer_hp["early_stopping_round"],
device=self.device,
weight_decay=trainer_hp["weight_decay"],
feval=self.feval,
loss=self.loss,
lr_scheduler_type=self.lr_scheduler_type,
init=True,
*self.args,
**self.kwargs
)
return ret