Source code for autogl.module.train.node_classification_full

"""
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