`matchzoo.utils`¶

Submodules¶

Package Contents¶

matchzoo.utils.one_hot(indices:int, num_classes:int) → np.ndarray¶

Returns:	A one-hot encoded vector.

matchzoo.utils.TensorType¶

matchzoo.utils.list_recursive_concrete_subclasses(base)¶: List all concrete subclasses of base recursively.

matchzoo.utils.parse_loss(identifier:typing.Union[str, typing.Type[nn.Module], nn.Module], task:typing.Optional[str]=None) → nn.Module¶

Retrieves a torch Module instance.

Parameters:	identifier – loss identifier, one of - String: name of a loss - Torch Module subclass - Torch Module instance (it will be returned unchanged). task – Task type for determining specific loss.
Returns:	A `nn.Module` instance

Examples::

>>> from torch import nn
>>> from matchzoo.utils import parse_loss

Use str as loss:

>>> loss = parse_loss('mse')
>>> type(loss)
<class 'torch.nn.modules.loss.MSELoss'>

Use torch.nn.Module subclasses as loss:

>>> type(parse_loss(nn.MSELoss))
<class 'torch.nn.modules.loss.MSELoss'>

Use torch.nn.Module instances as loss:

>>> type(parse_loss(nn.MSELoss()))
<class 'torch.nn.modules.loss.MSELoss'>

matchzoo.utils.parse_activation(identifier:typing.Union[str, typing.Type[nn.Module], nn.Module]) → nn.Module¶

Retrieves a torch Module instance.

Parameters:	identifier – activation identifier, one of - String: name of a activation - Torch Modele subclass - Torch Module instance (it will be returned unchanged).
Returns:	A `nn.Module` instance

Examples::

>>> from torch import nn
>>> from matchzoo.utils import parse_activation

Use str as activation:

>>> activation = parse_activation('relu')
>>> type(activation)
<class 'torch.nn.modules.activation.ReLU'>

Use torch.nn.Module subclasses as activation:

>>> type(parse_activation(nn.ReLU))
<class 'torch.nn.modules.activation.ReLU'>

Use torch.nn.Module instances as activation:

>>> type(parse_activation(nn.ReLU()))
<class 'torch.nn.modules.activation.ReLU'>

matchzoo.utils.parse_metric(metric:typing.Union[str, typing.Type[BaseMetric], BaseMetric], task:str) → BaseMetric¶

Parse input metric in any form into a BaseMetric instance.

Parameters:	metric – Input metric in any form. task – Task type for determining specific metric.
Returns:	A `BaseMetric` instance

Examples::

>>> from matchzoo import metrics
>>> from matchzoo.utils import parse_metric

Use str as MatchZoo metrics:

>>> mz_metric = parse_metric('map', 'ranking')
>>> type(mz_metric)
<class 'matchzoo.metrics.mean_average_precision.MeanAveragePrecision'>

Use matchzoo.engine.BaseMetric subclasses as MatchZoo metrics:

>>> type(parse_metric(metrics.AveragePrecision, 'ranking'))
<class 'matchzoo.metrics.average_precision.AveragePrecision'>

Use matchzoo.engine.BaseMetric instances as MatchZoo metrics:

>>> type(parse_metric(metrics.AveragePrecision(), 'ranking'))
<class 'matchzoo.metrics.average_precision.AveragePrecision'>

matchzoo.utils.parse_optimizer(identifier:typing.Union[str, typing.Type[optim.Optimizer]]) → optim.Optimizer¶

Parse input metric in any form into a Optimizer class.

Parameters:	optimizer – Input optimizer in any form.
Returns:	A `Optimizer` class

Examples::

>>> from torch import optim
>>> from matchzoo.utils import parse_optimizer

Use str as optimizer:

>>> parse_optimizer('adam')
<class 'torch.optim.adam.Adam'>

Use torch.optim.Optimizer subclasses as optimizer:

>>> parse_optimizer(optim.Adam)
<class 'torch.optim.adam.Adam'>

class matchzoo.utils.AverageMeter¶

Bases: object

Computes and stores the average and current value.

Examples

>>> am = AverageMeter()
>>> am.update(1)
>>> am.avg
1.0
>>> am.update(val=2.5, n=2)
>>> am.avg
2.0

avg¶: Get avg.

reset(self)¶: Reset AverageMeter.

update(self, val, n=1)¶: Update value.

class matchzoo.utils.Timer¶

Bases: object

Computes elapsed time.

time¶: Return time.

reset(self)¶: Reset timer.

resume(self)¶: Resume.

stop(self)¶: Stop.

class matchzoo.utils.EarlyStopping(patience:typing.Optional[int]=None, should_decrease:bool=None, key:typing.Any=None)¶

EarlyStopping stops training if no improvement after a given patience.

Parameters:	patience – Number fo events to wait if no improvement and then stop the training. should_decrease – The way to judge the best so far. key – Key of metric to be compared.

best_so_far¶: Returns best so far.

is_best_so_far¶: Returns true if it is the best so far.

should_stop_early¶: Returns true if improvement has stopped for long enough.

state_dict(self)¶: A Trainer can use this to serialize the state.

load_state_dict(self, state_dict:typing.Dict[str, typing.Any])¶: Hydrate a early stopping from a serialized state.

update(self, result:list)¶: Call function.

matchzoo.utils.get_file(fname:str=None, origin:str=None, untar:bool=False, extract:bool=False, md5_hash:typing.Any=None, file_hash:typing.Any=None, hash_algorithm:str='auto', archive_format:str='auto', cache_subdir:typing.Union[Path, str]='data', cache_dir:typing.Union[Path, str]=matchzoo.USER_DATA_DIR, verbose:int=1) → str¶

Downloads a file from a URL if it not already in the cache.

By default the file at the url origin is downloaded to the cache_dir ~/.matchzoo/datasets, placed in the cache_subdir data, and given the filename fname. The final location of a file example.txt would therefore be ~/.matchzoo/datasets/data/example.txt.

Files in tar, tar.gz, tar.bz, and zip formats can also be extracted. Passing a hash will verify the file after download. The command line programs shasum and sha256sum can compute the hash.

Parameters:	fname – Name of the file. If an absolute path /path/to/file.txt is specified the file will be saved at that location. origin – Original URL of the file. untar – Deprecated in favor of ‘extract’. Boolean, whether the file should be decompressed. md5_hash – Deprecated in favor of ‘file_hash’. md5 hash of the file for verification. file_hash – The expected hash string of the file after download. The sha256 and md5 hash algorithms are both supported. cache_subdir – Subdirectory under the cache dir where the file is saved. If an absolute path /path/to/folder is specified the file will be saved at that location. hash_algorithm – Select the hash algorithm to verify the file. options are ‘md5’, ‘sha256’, and ‘auto’. The default ‘auto’ detects the hash algorithm in use. archive_format – Archive format to try for extracting the file. Options are ‘auto’, ‘tar’, ‘zip’, and None. ‘tar’ includes tar, tar.gz, and tar.bz files. The default ‘auto’ is [‘tar’, ‘zip’]. None or an empty list will return no matches found. cache_dir – Location to store cached files, when None it defaults to the [matchzoo.USER_DATA_DIR](~/.matchzoo/datasets). verbose – Verbosity mode, 0 (silent), 1 (verbose), 2 (semi-verbose)
Papram extract:	True tries extracting the file as an Archive, like tar or zip.
Returns:	Path to the downloaded file.

matchzoo.utils._hash_file(fpath, algorithm='sha256', chunk_size=65535)¶

Calculates a file sha256 or md5 hash.

Parameters:	fpath – path to the file being validated algorithm – hash algorithm, one of ‘auto’, ‘sha256’, or ‘md5’. The default ‘auto’ detects the hash algorithm in use. chunk_size – Bytes to read at a time, important for large files.
Returns:	The file hash.

matchzoo.utils¶

Submodules¶

Package Contents¶

`matchzoo.utils`¶