topobench.data.preprocessor.preprocessor module#

Preprocessor for datasets.

class DataTransform(transform_name, **kwargs)#

Bases: BaseTransform

Abstract class to define a custom data lifting.

Parameters:

transform_namestr: The name of the transform to be used.
**kwargsdict: Additional arguments for the class. Should contain “transform_name”.

__init__(transform_name, **kwargs)#

forward(data)#

Forward pass of the lifting.

Parameters:

datatorch_geometric.data.Data: The input data to be lifted.

Returns:

torch_geometric.data.Data: The lifted data.

class DataloadDataset(data_lst)#

Bases: Dataset

Custom dataset to return all the values added to the dataset object.

Parameters:

data_lstlist[torch_geometric.data.Data]: List of torch_geometric.data.Data objects.

__init__(data_lst)#

get(idx)#

Get data object from data list.

Parameters:

idxint: Index of the data object to get.

Returns:

tuple: Tuple containing a list of all the values for the data and the corresponding keys.

len()#

Return the length of the dataset.

Returns:

int: Length of the dataset.

FileLock#: alias of UnixFileLock

class PreProcessor(dataset, data_dir, transforms_config=None, **kwargs)#

Bases: InMemoryDataset

Preprocessor for datasets.

Parameters:

datasetlist: List of data objects.
data_dirstr: Path to the directory containing the data.
transforms_configDictConfig, optional: Configuration parameters for the transforms (default: None).
**kwargsoptional: Optional additional arguments.

__init__(dataset, data_dir, transforms_config=None, **kwargs)#

instantiate_pre_transform(data_dir, transforms_config)#

Instantiate the pre-transforms.

Parameters:

data_dirstr: Path to the directory containing the data.
transforms_configDictConfig: Configuration parameters for the transforms.

Returns:

torch_geometric.transforms.Compose: Pre-transform object.

load(path)#

Load the dataset from the file path path.

Parameters:

pathstr: The path to the processed data.

load_dataset_splits(split_params)#

Load the dataset splits.

Parameters:

split_paramsdict: Parameters for loading the dataset splits.

Returns:

tuple: A tuple containing the train, validation, and test datasets.

process()#

Method that processes the data.

save_transform_parameters()#

Save the transform parameters.

set_processed_data_dir(pre_transforms_dict, data_dir, transforms_config)#

Set the processed data directory.

Parameters:

pre_transforms_dictdict: Dictionary containing the pre-transforms.
data_dirstr: Path to the directory containing the data.
transforms_configDictConfig: Configuration parameters for the transforms.

property processed_dir: str#

Return the path to the processed directory.

Returns:

str: Path to the processed directory.

property processed_file_names: str#

Return the name of the processed file.

Returns:

str: Name of the processed file.

class tqdm(*_, **__)#

Bases: Comparable

Decorate an iterable object, returning an iterator which acts exactly like the original iterable, but prints a dynamically updating progressbar every time a value is requested.

Parameters:

iterableiterable, optional

Iterable to decorate with a progressbar. Leave blank to manually manage the updates.

descstr, optional

Prefix for the progressbar.

totalint or float, optional

The number of expected iterations. If unspecified, len(iterable) is used if possible. If float(“inf”) or as a last resort, only basic progress statistics are displayed (no ETA, no progressbar). If gui is True and this parameter needs subsequent updating, specify an initial arbitrary large positive number, e.g. 9e9.

leavebool, optional

If [default: True], keeps all traces of the progressbar upon termination of iteration. If None, will leave only if position is 0.

fileio.TextIOWrapper or io.StringIO, optional

Specifies where to output the progress messages (default: sys.stderr). Uses file.write(str) and file.flush() methods. For encoding, see write_bytes.

ncolsint, optional

The width of the entire output message. If specified, dynamically resizes the progressbar to stay within this bound. If unspecified, attempts to use environment width. The fallback is a meter width of 10 and no limit for the counter and statistics. If 0, will not print any meter (only stats).

minintervalfloat, optional

Minimum progress display update interval [default: 0.1] seconds.

maxintervalfloat, optional

Maximum progress display update interval [default: 10] seconds. Automatically adjusts miniters to correspond to mininterval after long display update lag. Only works if dynamic_miniters or monitor thread is enabled.

minitersint or float, optional

Minimum progress display update interval, in iterations. If 0 and dynamic_miniters, will automatically adjust to equal mininterval (more CPU efficient, good for tight loops). If > 0, will skip display of specified number of iterations. Tweak this and mininterval to get very efficient loops. If your progress is erratic with both fast and slow iterations (network, skipping items, etc) you should set miniters=1.

asciibool or str, optional

If unspecified or False, use unicode (smooth blocks) to fill the meter. The fallback is to use ASCII characters “ 123456789#”.

disablebool, optional

Whether to disable the entire progressbar wrapper [default: False]. If set to None, disable on non-TTY.

unitstr, optional

String that will be used to define the unit of each iteration [default: it].

unit_scalebool or int or float, optional

If 1 or True, the number of iterations will be reduced/scaled automatically and a metric prefix following the International System of Units standard will be added (kilo, mega, etc.) [default: False]. If any other non-zero number, will scale total and n.

dynamic_ncolsbool, optional

If set, constantly alters ncols and nrows to the environment (allowing for window resizes) [default: False].

smoothingfloat, optional

Exponential moving average smoothing factor for speed estimates (ignored in GUI mode). Ranges from 0 (average speed) to 1 (current/instantaneous speed) [default: 0.3].

bar_formatstr, optional

Specify a custom bar string formatting. May impact performance. [default: ‘{l_bar}{bar}{r_bar}’], where l_bar=’{desc}: {percentage:3.0f}%|’ and r_bar=’| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, ‘

‘{rate_fmt}{postfix}]’

Possible vars: l_bar, bar, r_bar, n, n_fmt, total, total_fmt,: percentage, elapsed, elapsed_s, ncols, nrows, desc, unit, rate, rate_fmt, rate_noinv, rate_noinv_fmt, rate_inv, rate_inv_fmt, postfix, unit_divisor, remaining, remaining_s, eta.

Note that a trailing “: “ is automatically removed after {desc} if the latter is empty.

initialint or float, optional

The initial counter value. Useful when restarting a progress bar [default: 0]. If using float, consider specifying {n:.3f} or similar in bar_format, or specifying unit_scale.

positionint, optional

Specify the line offset to print this bar (starting from 0) Automatic if unspecified. Useful to manage multiple bars at once (eg, from threads).

postfixdict or *, optional

Specify additional stats to display at the end of the bar. Calls set_postfix(**postfix) if possible (dict).

unit_divisorfloat, optional

[default: 1000], ignored unless unit_scale is True.

write_bytesbool, optional

Whether to write bytes. If (default: False) will write unicode.

lock_argstuple, optional

Passed to refresh for intermediate output (initialisation, iterating, and updating).

nrowsint, optional

The screen height. If specified, hides nested bars outside this bound. If unspecified, attempts to use environment height. The fallback is 20.

colourstr, optional

Bar colour (e.g. ‘green’, ‘#00ff00’).

delayfloat, optional

Don’t display until [default: 0] seconds have elapsed.

guibool, optional

WARNING: internal parameter - do not use. Use tqdm.gui.tqdm(…) instead. If set, will attempt to use matplotlib animations for a graphical output [default: False].

Returns:

outdecorated iterator.

classmethod external_write_mode(file=None, nolock=False)#: Disable tqdm within context and refresh tqdm when exits. Useful when writing to standard output stream

classmethod get_lock()#: Get the global lock. Construct it if it does not exist.

classmethod pandas(**tqdm_kwargs)#

Registers the current tqdm class with: pandas.core. ( frame.DataFrame | series.Series | groupby.(generic.)DataFrameGroupBy | groupby.(generic.)SeriesGroupBy ).progress_apply

A new instance will be created every time progress_apply is called, and each instance will automatically close() upon completion.

Parameters:

tqdm_kwargsarguments for the tqdm instance

References

<https://stackoverflow.com/questions/18603270/ progress-indicator-during-pandas-operations-python>

Examples

>>> import pandas as pd
>>> import numpy as np
>>> from tqdm import tqdm
>>> from tqdm.gui import tqdm as tqdm_gui
>>>
>>> df = pd.DataFrame(np.random.randint(0, 100, (100000, 6)))
>>> tqdm.pandas(ncols=50)  # can use tqdm_gui, optional kwargs, etc
>>> # Now you can use `progress_apply` instead of `apply`
>>> df.groupby(0).progress_apply(lambda x: x**2)

classmethod set_lock(lock)#: Set the global lock.

classmethod wrapattr(stream, method, total=None, bytes=True, **tqdm_kwargs)#

stream : file-like object. method : str, “read” or “write”. The result of read() and

the first argument of write() should have a len().

>>> with tqdm.wrapattr(file_obj, "read", total=file_obj.size) as fobj:
...     while True:
...         chunk = fobj.read(chunk_size)
...         if not chunk:
...             break

classmethod write(s, file=None, end='\n', nolock=False)#: Print a message via tqdm (without overlap with bars).

static format_interval(t)#

Formats a number of seconds as a clock time, [H:]MM:SS

Parameters:

tint: Number of seconds.

Returns:

outstr: [H:]MM:SS

static format_meter(n, total, elapsed, ncols=None, prefix='', ascii=False, unit='it', unit_scale=False, rate=None, bar_format=None, postfix=None, unit_divisor=1000, initial=0, colour=None, **extra_kwargs)#

Return a string-based progress bar given some parameters

Parameters:

nint or float

Number of finished iterations.

totalint or float

The expected total number of iterations. If meaningless (None), only basic progress statistics are displayed (no ETA).

elapsedfloat

Number of seconds passed since start.

ncolsint, optional

The width of the entire output message. If specified, dynamically resizes {bar} to stay within this bound [default: None]. If 0, will not print any bar (only stats). The fallback is {bar:10}.

prefixstr, optional

Prefix message (included in total width) [default: ‘’]. Use as {desc} in bar_format string.

asciibool, optional or str, optional

If not set, use unicode (smooth blocks) to fill the meter [default: False]. The fallback is to use ASCII characters “ 123456789#”.

unitstr, optional

The iteration unit [default: ‘it’].

unit_scalebool or int or float, optional

If 1 or True, the number of iterations will be printed with an appropriate SI metric prefix (k = 10^3, M = 10^6, etc.) [default: False]. If any other non-zero number, will scale total and n.

ratefloat, optional

Manual override for iteration rate. If [default: None], uses n/elapsed.

bar_formatstr, optional

‘{rate_fmt}{postfix}]’

Possible vars: l_bar, bar, r_bar, n, n_fmt, total, total_fmt,: percentage, elapsed, elapsed_s, ncols, nrows, desc, unit, rate, rate_fmt, rate_noinv, rate_noinv_fmt, rate_inv, rate_inv_fmt, postfix, unit_divisor, remaining, remaining_s, eta.

Note that a trailing “: “ is automatically removed after {desc} if the latter is empty.

postfix*, optional

Similar to prefix, but placed at the end (e.g. for additional stats). Note: postfix is usually a string (not a dict) for this method, and will if possible be set to postfix = ‘, ‘ + postfix. However other types are supported (#382).

unit_divisorfloat, optional

[default: 1000], ignored unless unit_scale is True.

initialint or float, optional

The initial counter value [default: 0].

colourstr, optional

Bar colour (e.g. ‘green’, ‘#00ff00’).

Returns:

outFormatted meter and stats, ready to display.

static format_num(n)#

Intelligent scientific notation (.3g).

Parameters:

nint or float or Numeric: A Number.

Returns:

outstr: Formatted number.

static format_sizeof(num, suffix='', divisor=1000)#

Formats a number (greater than unity) with SI Order of Magnitude prefixes.

Parameters:

numfloat: Number ( >= 1) to format.
suffixstr, optional: Post-postfix [default: ‘’].
divisorfloat, optional: Divisor between prefixes [default: 1000].

Returns:

outstr: Number with Order of Magnitude SI unit postfix.

static status_printer(file)#: Manage the printing and in-place updating of a line of characters. Note that if the string is longer than a line, then in-place updating may not work (it will print a new line at each refresh).

__init__(iterable=None, desc=None, total=None, leave=True, file=None, ncols=None, mininterval=0.1, maxinterval=10.0, miniters=None, ascii=None, disable=False, unit='it', unit_scale=False, dynamic_ncols=False, smoothing=0.3, bar_format=None, initial=0, position=None, postfix=None, unit_divisor=1000, write_bytes=False, lock_args=None, nrows=None, colour=None, delay=0.0, gui=False, **kwargs)#

clear(nolock=False)#: Clear current bar display.

close()#: Cleanup and (if leave=False) close the progressbar.

display(msg=None, pos=None)#

Use self.sp to display msg in the specified pos.

Consider overloading this function when inheriting to use e.g.: self.some_frontend(**self.format_dict) instead of self.sp.

Parameters:

msgstr, optional. What to display (default: repr(self)).
posint, optional. Position to moveto: (default: abs(self.pos)).

moveto(n)#

refresh(nolock=False, lock_args=None)#

Force refresh the display of this bar.

Parameters:

nolockbool, optional: If True, does not lock. If [default: False]: calls acquire() on internal lock.
lock_argstuple, optional: Passed to internal lock’s acquire(). If specified, will only display() if acquire() returns True.

reset(total=None)#

Resets to 0 iterations for repeated use.

Consider combining with leave=True.

Parameters:

totalint or float, optional. Total to use for the new bar.

set_description(desc=None, refresh=True)#

Set/modify description of the progress bar.

Parameters:

descstr, optional
refreshbool, optional: Forces refresh [default: True].

set_description_str(desc=None, refresh=True)#: Set/modify description without ‘: ‘ appended.

set_postfix(ordered_dict=None, refresh=True, **kwargs)#

Set/modify postfix (additional stats) with automatic formatting based on datatype.

Parameters:

ordered_dictdict or OrderedDict, optional
refreshbool, optional: Forces refresh [default: True].
kwargsdict, optional

set_postfix_str(s='', refresh=True)#: Postfix without dictionary expansion, similar to prefix handling.

unpause()#: Restart tqdm timer from last print time.

update(n=1)#

Manually update the progress bar, useful for streams such as reading files. E.g.: >>> t = tqdm(total=filesize) # Initialise >>> for current_buffer in stream: … … … t.update(len(current_buffer)) >>> t.close() The last line is highly recommended, but possibly not necessary if t.update() will be called in such a way that filesize will be exactly reached and printed.

Parameters:

nint or float, optional: Increment to add to the internal counter of iterations [default: 1]. If using float, consider specifying {n:.3f} or similar in bar_format, or specifying unit_scale.

Returns:

outbool or None: True if a display() was triggered.

property format_dict#: Public API for read-only member access.

monitor = None#

monitor_interval = 10#

ensure_serializable(obj)#

Ensure that the object is serializable.

Parameters:

objobject: Object to ensure serializability.

Returns:

object: Object that is serializable.

load_inductive_splits(dataset, parameters)#

Load multiple-graph datasets with the specified split.

Parameters:

datasettorch_geometric.data.Dataset: Graph dataset.
parametersDictConfig: Configuration parameters.

Returns:

list:: List containing the train, validation, and test splits.

load_transductive_splits(dataset, parameters)#

Load the graph dataset with the specified split.

Parameters:

datasettorch_geometric.data.Dataset: Graph dataset.
parametersDictConfig: Configuration parameters.

Returns:

list:: List containing the train, validation, and test splits.

make_hash(o)#

Make a hash from a dictionary, list, tuple or set to any level, that contains only other hashable types.

Parameters:

odict, list, tuple, set: Object to hash.

Returns:

int: Hash of the object.