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Splits#

thunder.layout.split.Split #

Bases: Layout

Source code in thunder/layout/split.py 
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class Split(Layout):
    def __init__(self, split: SplitType, entries: Sequence, *args: Any, names: Optional[Sequence[str]] = None,
                 **kwargs: Any):
        """
        Splits data according to split function.
        Parameters
        ----------
        split: Callable
            Split function, or a sklearn splitter.
        entries: Sequence
            Series of ids or torch Dataset or Connectome Layer.
        args: Any
            args for split.
        names: Optional[Sequence[str]]
            Names of folds, e.g. 'train', 'val', test'
        kwargs: Any
            kwargs for split.
        Examples
        ----------
        ```python
        from sklearn.model_selection import KFold

        ids = [0, 1, ...]
        layout = Split(KFold(3), ids, names=["train", "test"])
        ```
        """
        if not callable(split):
            if not hasattr(split, 'split'):
                raise TypeError(f'Expected either a function, or a sklearn splitter, got {type(split)!r}')
            split = split.split

        ids = entries_to_ids(entries)
        # TODO: safer way to unify types
        splits = [tuple(map(jsonify, xs)) for xs in split(ids, *args, **kwargs)]
        if names is not None:
            # TODO
            assert len(set(names)) == len(names)
            assert len(splits[0]) == len(names)

        self.entries = entries
        self.splits = splits
        self.names = names
        self.fold: Optional[int] = None

    def __getitem__(self, item: int):
        return self._subset(item)

    def __getattr__(self, name: str):
        if self.names is None:
            raise AttributeError(name)
        return self._subset(self.names.index(name))

    def _subset(self, idx):
        # TODO
        assert self.fold is not None
        return entries_subset(self.entries, self.splits[self.fold][idx])

    def build(self, experiment: Path, config: Config):
        config.dump(experiment / 'experiment.config')
        name = experiment.name
        for fold, split in enumerate(self.splits):
            folder = experiment / f'fold_{fold}'
            folder.mkdir()
            save(split, folder / 'split.json')

            local = config.copy().update(ExpName=f'{name}({fold})', GroupName=name)
            local.dump(folder / 'experiment.config')
            yield Node(name=str(fold))

    def load(self, experiment: Path, node: Optional[Node]) -> Tuple[Config, Path, Dict[str, Any]]:
        folder = experiment / f'fold_{node.name}'
        return Config.load(folder / 'experiment.config'), folder, {
            'fold': int(node.name),
            'split': tuple(load(folder / 'split.json')),
        }

    def set(self, fold: int, split: Optional[Sequence[Sequence]] = None):
        self.fold = fold
        if split is None:
            warnings.warn('No reference split provided. Your results might be inconsistent!', UserWarning)
        else:
            if split != self.splits[fold]:
                # TODO: consistency error?
                raise ValueError

build(experiment, config) #

Source code in thunder/layout/split.py 
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def build(self, experiment: Path, config: Config):
    config.dump(experiment / 'experiment.config')
    name = experiment.name
    for fold, split in enumerate(self.splits):
        folder = experiment / f'fold_{fold}'
        folder.mkdir()
        save(split, folder / 'split.json')

        local = config.copy().update(ExpName=f'{name}({fold})', GroupName=name)
        local.dump(folder / 'experiment.config')
        yield Node(name=str(fold))

load(experiment, node) #

Source code in thunder/layout/split.py 
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def load(self, experiment: Path, node: Optional[Node]) -> Tuple[Config, Path, Dict[str, Any]]:
    folder = experiment / f'fold_{node.name}'
    return Config.load(folder / 'experiment.config'), folder, {
        'fold': int(node.name),
        'split': tuple(load(folder / 'split.json')),
    }

set(fold, split=None) #

Source code in thunder/layout/split.py 
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def set(self, fold: int, split: Optional[Sequence[Sequence]] = None):
    self.fold = fold
    if split is None:
        warnings.warn('No reference split provided. Your results might be inconsistent!', UserWarning)
    else:
        if split != self.splits[fold]:
            # TODO: consistency error?
            raise ValueError

thunder.layout.split.SingleSplit #

Bases: Layout

Source code in thunder/layout/split.py 
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class SingleSplit(Layout):
    def __init__(self, entries: Sequence, *, shuffle: bool = True,
                 random_state: Union[np.random.RandomState, int, None] = 0,
                 **sizes: Union[int, float]):
        """
        Creates single fold experiment, with custom number of sets.
        Parameters
        ----------
        entries: Sequence
            Sequence of ids or
        shuffle: bool
            Whether to shuffle entries.
        random_state : Union[np.random.RandomState, int, None]
        sizes: Union[int, float]
            Size of each split.
        Examples
        ----------
        ```python
        ids = [...]
        layout = SingleSplit(ids, train=0.7, val=0.1, test=0.2)
        ```
        """
        if not isinstance(random_state, np.random.RandomState):
            random_state = np.random.RandomState(random_state)

        ids = entries_to_ids(entries)
        self.entries = entries
        self.split = dict(zip_equal(sizes.keys(), multi_split(
            ids, list(sizes.values()), shuffle=shuffle, random_state=random_state
        )))

    def __getattr__(self, name: str):
        if name not in self.split:
            raise AttributeError(name)
        return entries_subset(self.entries, self.split[name])

    def build(self, experiment: Path, config: Config):
        config.dump(experiment / 'experiment.config')
        name = experiment.name
        save(self.split, experiment / 'split.json')

        local = config.copy().update(ExpName=name, GroupName=name)
        local.dump(experiment / 'experiment.config')
        return []

    def load(self, experiment: Path, node: Optional[Node]) -> Tuple[Config, Path, Dict[str, Any]]:
        return Config.load(experiment / 'experiment.config'), experiment, {
            'split': load(experiment / 'split.json'),
        }

    def set(self, split: Optional[Dict[str, Sequence]] = None):
        if split is None:
            warnings.warn('No reference split provided. Your results might be inconsistent!', UserWarning)
        else:
            if split != self.split:
                # TODO: consistency error?
                raise ValueError

build(experiment, config) #

Source code in thunder/layout/split.py 
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def build(self, experiment: Path, config: Config):
    config.dump(experiment / 'experiment.config')
    name = experiment.name
    save(self.split, experiment / 'split.json')

    local = config.copy().update(ExpName=name, GroupName=name)
    local.dump(experiment / 'experiment.config')
    return []

load(experiment, node) #

Source code in thunder/layout/split.py 
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def load(self, experiment: Path, node: Optional[Node]) -> Tuple[Config, Path, Dict[str, Any]]:
    return Config.load(experiment / 'experiment.config'), experiment, {
        'split': load(experiment / 'split.json'),
    }

set(split=None) #

Source code in thunder/layout/split.py 
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def set(self, split: Optional[Dict[str, Sequence]] = None):
    if split is None:
        warnings.warn('No reference split provided. Your results might be inconsistent!', UserWarning)
    else:
        if split != self.split:
            # TODO: consistency error?
            raise ValueError