jjfraaa
/
AutoGL

 
			
							"""
Auto Classfier for Node Classification
"""
import time
import json

from copy import deepcopy

import torch
import numpy as np
import yaml

from .base import BaseClassifier
from ...module.feature import FEATURE_DICT
from ...module.model import MODEL_DICT
from ...module.train import TRAINER_DICT, get_feval
from ...module import BaseModel
from ..utils import Leaderboard, set_seed
from ...datasets import utils
from ...utils import get_logger

LOGGER = get_logger("NodeClassifier")


class AutoNodeClassifier(BaseClassifier):
    """
    Auto Multi-class Graph Node Classifier.

    Used to automatically solve the node classification problems.

    Parameters
    ----------
    feature_module: autogl.module.feature.BaseFeatureEngineer or str or None
        The (name of) auto feature engineer used to process the given dataset. Default ``deepgl``.
        Disable feature engineer by setting it to ``None``.

    graph_models: list of autogl.module.model.BaseModel or list of str
        The (name of) models to be optimized as backbone. Default ``['gat', 'gcn']``.

    hpo_module: autogl.module.hpo.BaseHPOptimizer or str or None
        The (name of) hpo module used to search for best hyper parameters. Default ``anneal``.
        Disable hpo by setting it to ``None``.

    ensemble_module: autogl.module.ensemble.BaseEnsembler or str or None
        The (name of) ensemble module used to ensemble the multi-models found. Default ``voting``.
        Disable ensemble by setting it to ``None``.

    max_evals: int (Optional)
        If given, will set the number eval times the hpo module will use.
        Only be effective when hpo_module is ``str``. Default ``None``.

    trainer_hp_space: list of dict (Optional)
        trainer hp space or list of trainer hp spaces configuration.
        If a single trainer hp is given, will specify the hp space of trainer for every model.
        If a list of trainer hp is given, will specify every model with corrsponding
        trainer hp space.
        Default ``None``.

    model_hp_spaces: list of list of dict (Optional)
        model hp space configuration.
        If given, will specify every hp space of every passed model. Default ``None``.

    size: int (Optional)
        The max models ensemble module will use. Default ``None``.

    device: torch.device or str
        The device where model will be running on. If set to ``auto``, will use gpu when available.
        You can also specify the device by directly giving ``gpu`` or ``cuda:0``, etc.
        Default ``auto``.
    """

    # pylint: disable=W0102

    def __init__(
        self,
        feature_module="deepgl",
        graph_models=["gat", "gcn"],
        hpo_module="anneal",
        ensemble_module="voting",
        max_evals=50,
        trainer_hp_space=None,
        model_hp_spaces=None,
        size=4,
        device="auto",
    ):

        super().__init__(
            feature_module=feature_module,
            graph_models=graph_models,
            hpo_module=hpo_module,
            ensemble_module=ensemble_module,
            max_evals=max_evals,
            trainer_hp_space=trainer_hp_space,
            model_hp_spaces=model_hp_spaces,
            size=size,
            device=device,
        )

        # data to be kept when fit
        self.data = None

    def _init_graph_module(
        self,
        graph_models,
        num_classes,
        num_features,
        *args,
        **kwargs,
    ) -> "AutoNodeClassifier":
        # load graph network module
        self.graph_model_list = []
        if isinstance(graph_models, list):
            for model in graph_models:
                if isinstance(model, str):
                    if model in MODEL_DICT:
                        self.graph_model_list.append(
                            MODEL_DICT[model](
                                num_classes=num_classes,
                                num_features=num_features,
                                *args,
                                **kwargs,
                                init=False,
                            )
                        )
                    else:
                        raise KeyError("cannot find model %s" % (model))
                elif isinstance(model, type) and issubclass(model, BaseModel):
                    self.graph_model_list.append(
                        model(
                            num_classes=num_classes,
                            num_features=num_features,
                            *args,
                            **kwargs,
                            init=False,
                        )
                    )
                elif isinstance(model, BaseModel):
                    # setup the hp of num_classes and num_features
                    model.set_num_classes(num_classes)
                    model.set_num_features(num_features)
                    self.graph_model_list.append(model)
                else:
                    raise KeyError("cannot find graph network %s." % (model))
        else:
            raise ValueError(
                "need graph network to be (list of) str or a BaseModel class/instance, get",
                graph_models,
                "instead.",
            )

        # wrap all model_cls with specified trainer
        for i, model in enumerate(self.graph_model_list):
            if self._model_hp_spaces is not None:
                if self._model_hp_spaces[i] is not None:
                    model.hyper_parameter_space = self._model_hp_spaces[i]
            trainer = TRAINER_DICT["NodeClassification"](
                model=model,
                num_features=num_features,
                num_classes=num_classes,
                *args,
                **kwargs,
                init=False,
            )
            if self._trainer_hp_space is not None:
                if isinstance(self._trainer_hp_space[0], list):
                    current_hp_for_trainer = self._trainer_hp_space[i]
                else:
                    current_hp_for_trainer = self._trainer_hp_space
                trainer.hyper_parameter_space = (
                    current_hp_for_trainer + model.hyper_parameter_space
                )
            self.graph_model_list[i] = trainer

        return self

    # pylint: disable=arguments-differ
    def fit(
        self,
        dataset,
        time_limit=-1,
        inplace=False,
        train_split=None,
        val_split=None,
        balanced=True,
        evaluation_method="infer",
        seed=None,
    ) -> "AutoNodeClassifier":
        """
        Fit current solver on given dataset.

        Parameters
        ----------
        dataset: torch_geometric.data.dataset.Dataset
            The dataset needed to fit on. This dataset must have only one graph.

        time_limit: int
            The time limit of the whole fit process (in seconds). If set below 0,
            will ignore time limit. Default ``-1``.

        inplace: bool
            Whether we process the given dataset in inplace manner. Default ``False``.
            Set it to True if you want to save memory by modifying the given dataset directly.

        train_split: float or int (Optional)
            The train ratio (in ``float``) or number (in ``int``) of dataset. If you want to
            use default train/val/test split in dataset, please set this to ``None``.
            Default ``None``.

        val_split: float or int (Optional)
            The validation ratio (in ``float``) or number (in ``int``) of dataset. If you want
            to use default train/val/test split in dataset, please set this to ``None``.
            Default ``None``.

        balanced: bool
            Wether to create the train/valid/test split in a balanced way.
            If set to ``True``, the train/valid will have the same number of different classes.
            Default ``True``.

        evaluation_method: (list of) str or autogl.module.train.evaluation
            A (list of) evaluation method for current solver. If ``infer``, will automatically
            determine. Default ``infer``.

        seed: int (Optional)
            The random seed. If set to ``None``, will run everything at random.
            Default ``None``.

        Returns
        -------
        self: autogl.solver.AutoNodeClassifier
            A reference of current solver.
        """
        set_seed(seed)

        if time_limit < 0:
            time_limit = 3600 * 24
        time_begin = time.time()

        # initialize leaderboard
        if evaluation_method == "infer":
            if hasattr(dataset, "metric"):
                evaluation_method = [dataset.metric]
            else:
                num_of_label = dataset.num_classes
                if num_of_label == 2:
                    evaluation_method = ["auc"]
                else:
                    evaluation_method = ["acc"]
        assert isinstance(evaluation_method, list)
        evaluator_list = get_feval(evaluation_method)

        self.leaderboard = Leaderboard(
            [e.get_eval_name() for e in evaluator_list],
            {e.get_eval_name(): e.is_higher_better() for e in evaluator_list},
        )

        # set up the dataset
        if train_split is not None and val_split is not None:
            size = dataset.data.x.shape[0]
            if balanced:
                train_split = (
                    train_split if train_split > 1 else int(train_split * size)
                )
                val_split = val_split if val_split > 1 else int(val_split * size)
                utils.random_splits_mask_class(
                    dataset,
                    num_train_per_class=train_split // dataset.num_classes,
                    num_val_per_class=val_split // dataset.num_classes,
                    seed=seed,
                )
            else:
                train_split = train_split if train_split < 1 else train_split / size
                val_split = val_split if val_split < 1 else val_split / size
                utils.random_splits_mask(
                    dataset, train_ratio=train_split, val_ratio=val_split
                )
        else:
            assert hasattr(dataset.data, "train_mask") and hasattr(
                dataset.data, "val_mask"
            ), (
                "The dataset has no default train/val split! Please manually pass "
                "train and val ratio."
            )
            LOGGER.info("Use the default train/val/test ratio in given dataset")

        # feature engineering
        if self.feature_module is not None:
            dataset = self.feature_module.fit_transform(dataset, inplace=inplace)

        self.dataset = dataset
        assert self.dataset[0].x is not None, (
            "Does not support fit on non node-feature dataset!"
            " Please add node features to dataset or specify feature engineers that generate"
            " node features."
        )

        # initialize graph networks
        self._init_graph_module(
            self.gml,
            num_features=self.dataset[0].x.shape[1],
            num_classes=dataset.num_classes,
            feval=evaluator_list,
            device=self.runtime_device,
            loss="cross_entropy" if not hasattr(dataset, "loss") else dataset.loss,
        )

        # train the models and tune hpo
        result_valid = []
        names = []
        for idx, model in enumerate(self.graph_model_list):
            time_for_each_model = (time_limit - time.time() + time_begin) / (
                len(self.graph_model_list) - idx
            )
            if self.hpo_module is None:
                model.initialize()
                model.train(self.dataset, True)
                optimized = model
            else:
                optimized, _ = self.hpo_module.optimize(
                    trainer=model, dataset=self.dataset, time_limit=time_for_each_model
                )
            # to save memory, all the trainer derived will be mapped to cpu
            optimized.to(torch.device("cpu"))
            name = optimized.get_name_with_hp() + "_idx%d" % (idx)
            names.append(name)
            performance_on_valid, _ = optimized.get_valid_score(return_major=False)
            result_valid.append(optimized.get_valid_predict_proba().cpu().numpy())
            self.leaderboard.insert_model_performance(
                name,
                dict(
                    zip(
                        [e.get_eval_name() for e in evaluator_list],
                        performance_on_valid,
                    )
                ),
            )
            self.trained_models[name] = optimized

        # fit the ensemble model
        if self.ensemble_module is not None:
            performance = self.ensemble_module.fit(
                result_valid,
                self.dataset[0].y[self.dataset[0].val_mask].cpu().numpy(),
                names,
                evaluator_list,
                n_classes=dataset.num_classes,
            )
            self.leaderboard.insert_model_performance(
                "ensemble",
                dict(zip([e.get_eval_name() for e in evaluator_list], performance)),
            )

        return self

    def fit_predict(
        self,
        dataset,
        time_limit=-1,
        inplace=False,
        train_split=None,
        val_split=None,
        balanced=True,
        evaluation_method="infer",
        use_ensemble=True,
        use_best=True,
        name=None,
    ) -> np.ndarray:
        """
        Fit current solver on given dataset and return the predicted value.

        Parameters
        ----------
        dataset: torch_geometric.data.dataset.Dataset
            The dataset needed to fit on. This dataset must have only one graph.

        time_limit: int
            The time limit of the whole fit process (in seconds).
            If set below 0, will ignore time limit. Default ``-1``.

        inplace: bool
            Whether we process the given dataset in inplace manner. Default ``False``.
            Set it to True if you want to save memory by modifying the given dataset directly.

        train_split: float or int (Optional)
            The train ratio (in ``float``) or number (in ``int``) of dataset. If you want to
            use default train/val/test split in dataset, please set this to ``None``.
            Default ``None``.

        val_split: float or int (Optional)
            The validation ratio (in ``float``) or number (in ``int``) of dataset. If you want
            to use default train/val/test split in dataset, please set this to ``None``.
            Default ``None``.

        balanced: bool
            Wether to create the train/valid/test split in a balanced way.
            If set to ``True``, the train/valid will have the same number of different classes.
            Default ``False``.

        evaluation_method: (list of) str or autogl.module.train.evaluation
            A (list of) evaluation method for current solver. If ``infer``, will automatically
            determine. Default ``infer``.

        use_ensemble: bool
            Whether to use ensemble to do the predict. Default ``True``.

        use_best: bool
            Whether to use the best single model to do the predict. Will only be effective when
            ``use_ensemble`` is ``False``.
            Default ``True``.

        name: str or None
            The name of model used to predict. Will only be effective when ``use_ensemble`` and
            ``use_best`` both are ``False``.
            Default ``None``.

        Returns
        -------
        result: np.ndarray
            An array of shape ``(N,)``, where ``N`` is the number of test nodes. The prediction
            on given dataset.
        """
        self.fit(
            dataset=dataset,
            time_limit=time_limit,
            inplace=inplace,
            train_split=train_split,
            val_split=val_split,
            balanced=balanced,
            evaluation_method=evaluation_method,
        )
        return self.predict(
            dataset=dataset,
            inplaced=inplace,
            inplace=inplace,
            use_ensemble=use_ensemble,
            use_best=use_best,
            name=name,
        )

    def predict_proba(
        self,
        dataset=None,
        inplaced=False,
        inplace=False,
        use_ensemble=True,
        use_best=True,
        name=None,
        mask="test",
    ) -> np.ndarray:
        """
        Predict the node probability.

        Parameters
        ----------
        dataset: torch_geometric.data.dataset.Dataset or None
            The dataset needed to predict. If ``None``, will use the processed dataset passed
            to ``fit()`` instead. Default ``None``.

        inplaced: bool
            Whether the given dataset is processed. Only be effective when ``dataset``
            is not ``None``. If you pass the dataset to ``fit()`` with ``inplace=True``, and
            you pass the dataset again to this method, you should set this argument to ``True``.
            Otherwise ``False``. Default ``False``.

        inplace: bool
            Whether we process the given dataset in inplace manner. Default ``False``. Set it to
            True if you want to save memory by modifying the given dataset directly.

        use_ensemble: bool
            Whether to use ensemble to do the predict. Default ``True``.

        use_best: bool
            Whether to use the best single model to do the predict. Will only be effective when
            ``use_ensemble`` is ``False``. Default ``True``.

        name: str or None
            The name of model used to predict. Will only be effective when ``use_ensemble`` and
            ``use_best`` both are ``False``. Default ``None``.

        mask: str
            The data split to give prediction on. Default ``test``.

        Returns
        -------
        result: np.ndarray
            An array of shape ``(N,C,)``, where ``N`` is the number of test nodes and ``C`` is
            the number of classes. The prediction on given dataset.
        """
        if dataset is None:
            dataset = self.dataset
            assert dataset is not None, (
                "Please execute fit() first before" " predicting on remembered dataset"
            )
        elif not inplaced and self.feature_module is not None:
            dataset = self.feature_module.transform(dataset, inplace=inplace)

        if use_ensemble:
            LOGGER.info("Ensemble argument on, will try using ensemble model.")

        if not use_ensemble and use_best:
            LOGGER.info(
                "Ensemble argument off and best argument on, will try using best model."
            )

        if (use_ensemble and self.ensemble_module is not None) or (
            not use_best and name == "ensemble"
        ):
            # we need to get all the prediction of every model trained
            predict_result = []
            names = []
            for model_name in self.trained_models:
                predict_result.append(
                    self._predict_proba_by_name(dataset, model_name, mask)
                )
                names.append(model_name)
            return self.ensemble_module.ensemble(predict_result, names)

        if use_ensemble and self.ensemble_module is None:
            LOGGER.warning(
                "Cannot use ensemble because no ensebmle module is given."
                "Will use best model instead."
            )

        if use_best or (use_ensemble and self.ensemble_module is None):
            # just return the best model we have found
            name = self.leaderboard.get_best_model()
            return self._predict_proba_by_name(dataset, name, mask)

        if name is not None:
            # return model performance by name
            return self._predict_proba_by_name(dataset, name, mask)

        LOGGER.error(
            "No model name is given while ensemble and best arguments are off."
        )
        raise ValueError(
            "You need to specify a model name if you do not want use ensemble and best model."
        )

    def _predict_proba_by_name(self, dataset, name, mask="test"):
        self.trained_models[name].to(self.runtime_device)
        predicted = (
            self.trained_models[name].predict_proba(dataset, mask=mask).cpu().numpy()
        )
        self.trained_models[name].to(torch.device("cpu"))
        return predicted

    def predict(
        self,
        dataset=None,
        inplaced=False,
        inplace=False,
        use_ensemble=True,
        use_best=True,
        name=None,
        mask="test",
    ) -> np.ndarray:
        """
        Predict the node class number.

        Parameters
        ----------
        dataset: torch_geometric.data.dataset.Dataset or None
            The dataset needed to predict. If ``None``, will use the processed dataset passed
            to ``fit()`` instead. Default ``None``.

        inplaced: bool
            Whether the given dataset is processed. Only be effective when ``dataset``
            is not ``None``. If you pass the dataset to ``fit()`` with ``inplace=True``,
            and you pass the dataset again to this method, you should set this argument
            to ``True``. Otherwise ``False``. Default ``False``.

        inplace: bool
            Whether we process the given dataset in inplace manner. Default ``False``.
            Set it to True if you want to save memory by modifying the given dataset directly.

        use_ensemble: bool
            Whether to use ensemble to do the predict. Default ``True``.

        use_best: bool
            Whether to use the best single model to do the predict. Will only be effective
            when ``use_ensemble`` is ``False``. Default ``True``.

        name: str or None
            The name of model used to predict. Will only be effective when ``use_ensemble``
            and ``use_best`` both are ``False``. Default ``None``.

        mask: str
            The data split to give prediction on. Default ``test``.

        Returns
        -------
        result: np.ndarray
            An array of shape ``(N,)``, where ``N`` is the number of test nodes.
            The prediction on given dataset.
        """
        proba = self.predict_proba(
            dataset, inplaced, inplace, use_ensemble, use_best, name, mask
        )
        return np.argmax(proba, axis=1)

    @classmethod
    def from_config(cls, path_or_dict, filetype="auto") -> "AutoNodeClassifier":
        """
        Load solver from config file.

        You can use this function to directly load a solver from predefined config dict
        or config file path. Currently, only support file type of ``json`` or ``yaml``,
        if you pass a path.

        Parameters
        ----------
        path_or_dict: str or dict
            The path to the config file or the config dictionary object

        filetype: str
            The filetype the given file if the path is specified. Currently only support
            ``json`` or ``yaml``. You can set to ``auto`` to automatically detect the file
            type (from file name). Default ``auto``.

        Returns
        -------
        solver: autogl.solver.AutoGraphClassifier
            The solver that is created from given file or dictionary.
        """
        assert filetype in ["auto", "yaml", "json"], (
            "currently only support yaml file or json file type, but get type "
            + filetype
        )
        if isinstance(path_or_dict, str):
            if filetype == "auto":
                if path_or_dict.endswith(".yaml"):
                    filetype = "yaml"
                elif path_or_dict.endswith(".json"):
                    filetype = "json"
                else:
                    LOGGER.error(
                        "cannot parse the type of the given file name, "
                        "please manually set the file type"
                    )
                    raise ValueError(
                        "cannot parse the type of the given file name, "
                        "please manually set the file type"
                    )
            if filetype == "yaml":
                path_or_dict = yaml.load(
                    open(path_or_dict, "r").read(), Loader=yaml.FullLoader
                )
            else:
                path_or_dict = json.load(open(path_or_dict, "r"))

        path_or_dict = deepcopy(path_or_dict)
        solver = cls(None, [], None, None)
        fe_list = path_or_dict.pop("feature", [{"name": "deepgl"}])
        if fe_list is not None:
            fe_list_ele = []
            for feature_engineer in fe_list:
                name = feature_engineer.pop("name")
                if name is not None:
                    fe_list_ele.append(FEATURE_DICT[name](**feature_engineer))
            if fe_list_ele != []:
                solver.set_feature_module(fe_list_ele)

        models = path_or_dict.pop("models", {"gcn": None, "gat": None})
        model_list = list(models.keys())
        model_hp_space = [models[m] for m in model_list]
        trainer_space = path_or_dict.pop("trainer", None)

        if model_hp_space:
            # parse lambda function
            for space in model_hp_space:
                if space is not None:
                    for keys in space:
                        if "cutFunc" in keys and isinstance(keys["cutFunc"], str):
                            keys["cutFunc"] = eval(keys["cutFunc"])

        if trainer_space:
            for space in trainer_space:
                if (
                    isinstance(space, dict)
                    and "cutFunc" in space
                    and isinstance(space["cutFunc"], str)
                ):
                    space["cutFunc"] = eval(space["cutFunc"])
                elif space is not None:
                    for keys in space:
                        if "cutFunc" in keys and isinstance(keys["cutFunc"], str):
                            keys["cutFunc"] = eval(keys["cutFunc"])

        solver.set_graph_models(model_list, trainer_space, model_hp_space)

        hpo_dict = path_or_dict.pop("hpo", {"name": "anneal"})
        name = hpo_dict.pop("name")
        solver.set_hpo_module(name, **hpo_dict)

        ensemble_dict = path_or_dict.pop("ensemble", {"name": "voting"})
        name = ensemble_dict.pop("name")
        solver.set_ensemble_module(name, **ensemble_dict)

        return solver