qsprpred.extra.data.sampling package

Submodules

qsprpred.extra.data.sampling.splits module

Different splitters to create train and tests for evalutating QSPR model performance.

To add a new data splitter: * Add a datasplit subclass for your new splitter

class qsprpred.extra.data.sampling.splits.LeaveTargetsOut(targets: list[str], dataset: PCMDataSet | None = None)[source]

Bases: DataSplit

Creates a leave target out splitter.

Parameters:

  • targets (list) – the identifiers of the targets to leave out as test set

  • dataset (PCMDataset) – a PCMDataset instance to split

getDataSet()

Get the data set attached to this object.

Raises:

ValueError – If no data set is attached to this object.

property hasDataSet: bool

Indicates if this object has a data set attached to it.

setDataSet(dataset: MoleculeDataTable)
split(X, y)[source]

Split the given data into one or multiple train/test subsets.

These classes handle partitioning of a feature matrix by returning an generator of train and test indices. It is compatible with the approach taken in the sklearn package (see sklearn.model_selection._BaseKFold). This can be used for both cross-validation or a one time train/test split.

Parameters:

  • X (np.ndarray | pd.DataFrame) – the input data matrix

  • y (np.ndarray | pd.DataFrame | pd.Series) – the target variable(s)

Returns:

an generator over the generated subsets represented as a tuple of (train_indices, test_indices) where the indices are the row indices of the input data matrix X (note that these are integer indices, rather than a pandas index!)

splitDataset(dataset: QSPRDataset)
class qsprpred.extra.data.sampling.splits.PCMSplit(splitter: DataSplit, dataset: PCMDataSet | None = None)[source]

Bases: DataSplit

Splits a dataset into train and test set such that the subsets are balanced with respect to each of the protein targets.

This is done with https://github.com/sohviluukkonen/gbmt-splits, linear programming of initial clusters (random-, scaffold- or cluster-based) to get a balanced split.

Variables:

  • dataset (PCMDataSet) – The dataset to split.

  • splitter (DataSplit) – The splitter to use on the initial clusters.

getDataSet()

Get the data set attached to this object.

Raises:

ValueError – If no data set is attached to this object.

property hasDataSet: bool

Indicates if this object has a data set attached to it.

setDataSet(dataset: MoleculeDataTable)
split(X, y) → Iterable[tuple[list[int], list[int]]][source]

Split the PCM dataset into train and test set such that the subsets are balanced with respect to the protein targets and there is not data leakage between the train and test set.

Converts the PCM dataset into a multi-task dataset with protein targets as columns and uses the given splitter to split the multi-task dataset.

Parameters:

  • X (np.ndarray | pd.DataFrame) – the input data matrix

  • y (np.ndarray | pd.DataFrame | pd.Series) – the target variable(s)

Returns:

an generator over the generated subsets represented as a tuple of (train_indices, test_indices) where the indices are the row indices of the input data matrix X (note that these are integer indices, rather than a pandas index!)

splitDataset(dataset: QSPRDataset)
class qsprpred.extra.data.sampling.splits.TemporalPerTarget(year_col: str, split_years: dict[str, int], firts_year_per_compound: bool = True, dataset: PCMDataSet | None = None)[source]

Bases: DataSplit

Creates a temporal split that is consistent across targets.

Parameters:

  • year_col (str) – the name of the column in the dataframe that contains the year information

  • split_years (dict[str,int]) – a dictionary with target keys as keys and split years as values

  • firts_year_per_compound (bool) – if True, the first year a compound appears in the dataset is used for all targets

  • dataset (PCMDataset) – a PCMDataset instance to split

getDataSet()

Get the data set attached to this object.

Raises:

ValueError – If no data set is attached to this object.

property hasDataSet: bool

Indicates if this object has a data set attached to it.

setDataSet(dataset: MoleculeDataTable)
split(X, y) → Iterable[tuple[list[int], list[int]]][source]

Split the given data into one or multiple train/test subsets.

These classes handle partitioning of a feature matrix by returning an generator of train and test indices. It is compatible with the approach taken in the sklearn package (see sklearn.model_selection._BaseKFold). This can be used for both cross-validation or a one time train/test split.

Parameters:

  • X (np.ndarray | pd.DataFrame) – the input data matrix

  • y (np.ndarray | pd.DataFrame | pd.Series) – the target variable(s)

Returns:

an generator over the generated subsets represented as a tuple of (train_indices, test_indices) where the indices are the row indices of the input data matrix X (note that these are integer indices, rather than a pandas index!)

splitDataset(dataset: QSPRDataset)

qsprpred.extra.data.sampling.tests module

class qsprpred.extra.data.sampling.tests.TestPCMSplitters(methodName='runTest')[source]

Bases: DataSetsMixInExtras, TestCase

Create an instance of the class that will use the named test method when executed. Raises a ValueError if the instance does not have a method with the specified name.

classmethod addClassCleanup(function, /, *args, **kwargs)

Same as addCleanup, except the cleanup items are called even if setUpClass fails (unlike tearDownClass).

addCleanup(function, /, *args, **kwargs)

Add a function, with arguments, to be called when the test is completed. Functions added are called on a LIFO basis and are called after tearDown on test failure or success.

Cleanup items are called even if setUp fails (unlike tearDown).

addTypeEqualityFunc(typeobj, function)

Add a type specific assertEqual style function to compare a type.

This method is for use by TestCase subclasses that need to register their own type equality functions to provide nicer error messages.

Parameters:

  • typeobj – The data type to call this function on when both values are of the same type in assertEqual().

  • function – The callable taking two arguments and an optional msg= argument that raises self.failureException with a useful error message when the two arguments are not equal.

assertAlmostEqual(first, second, places=None, msg=None, delta=None)

Fail if the two objects are unequal as determined by their difference rounded to the given number of decimal places (default 7) and comparing to zero, or by comparing that the difference between the two objects is more than the given delta.

Note that decimal places (from zero) are usually not the same as significant digits (measured from the most significant digit).

If the two objects compare equal then they will automatically compare almost equal.

assertCountEqual(first, second, msg=None)

Asserts that two iterables have the same elements, the same number of times, without regard to order.

self.assertEqual(Counter(list(first)),

Counter(list(second)))

Example:

  • [0, 1, 1] and [1, 0, 1] compare equal.

  • [0, 0, 1] and [0, 1] compare unequal.

assertDictEqual(d1, d2, msg=None)
assertEqual(first, second, msg=None)

Fail if the two objects are unequal as determined by the ‘==’ operator.

assertFalse(expr, msg=None)

Check that the expression is false.

assertGreater(a, b, msg=None)

Just like self.assertTrue(a > b), but with a nicer default message.

assertGreaterEqual(a, b, msg=None)

Just like self.assertTrue(a >= b), but with a nicer default message.

assertIn(member, container, msg=None)

Just like self.assertTrue(a in b), but with a nicer default message.

assertIs(expr1, expr2, msg=None)

Just like self.assertTrue(a is b), but with a nicer default message.

assertIsInstance(obj, cls, msg=None)

Same as self.assertTrue(isinstance(obj, cls)), with a nicer default message.

assertIsNone(obj, msg=None)

Same as self.assertTrue(obj is None), with a nicer default message.

assertIsNot(expr1, expr2, msg=None)

Just like self.assertTrue(a is not b), but with a nicer default message.

assertIsNotNone(obj, msg=None)

Included for symmetry with assertIsNone.

assertLess(a, b, msg=None)

Just like self.assertTrue(a < b), but with a nicer default message.

assertLessEqual(a, b, msg=None)

Just like self.assertTrue(a <= b), but with a nicer default message.

assertListEqual(list1, list2, msg=None)

A list-specific equality assertion.

Parameters:

  • list1 – The first list to compare.

  • list2 – The second list to compare.

  • msg – Optional message to use on failure instead of a list of differences.

assertLogs(logger=None, level=None)

Fail unless a log message of level level or higher is emitted on logger_name or its children. If omitted, level defaults to INFO and logger defaults to the root logger.

This method must be used as a context manager, and will yield a recording object with two attributes: output and records. At the end of the context manager, the output attribute will be a list of the matching formatted log messages and the records attribute will be a list of the corresponding LogRecord objects.

Example:

with self.assertLogs('foo', level='INFO') as cm:
    logging.getLogger('foo').info('first message')
    logging.getLogger('foo.bar').error('second message')
self.assertEqual(cm.output, ['INFO:foo:first message',
                             'ERROR:foo.bar:second message'])
assertMultiLineEqual(first, second, msg=None)

Assert that two multi-line strings are equal.

assertNoLogs(logger=None, level=None)

Fail unless no log messages of level level or higher are emitted on logger_name or its children.

This method must be used as a context manager.

assertNotAlmostEqual(first, second, places=None, msg=None, delta=None)

Fail if the two objects are equal as determined by their difference rounded to the given number of decimal places (default 7) and comparing to zero, or by comparing that the difference between the two objects is less than the given delta.

Note that decimal places (from zero) are usually not the same as significant digits (measured from the most significant digit).

Objects that are equal automatically fail.

assertNotEqual(first, second, msg=None)

Fail if the two objects are equal as determined by the ‘!=’ operator.

assertNotIn(member, container, msg=None)

Just like self.assertTrue(a not in b), but with a nicer default message.

assertNotIsInstance(obj, cls, msg=None)

Included for symmetry with assertIsInstance.

assertNotRegex(text, unexpected_regex, msg=None)

Fail the test if the text matches the regular expression.

assertRaises(expected_exception, *args, **kwargs)

Fail unless an exception of class expected_exception is raised by the callable when invoked with specified positional and keyword arguments. If a different type of exception is raised, it will not be caught, and the test case will be deemed to have suffered an error, exactly as for an unexpected exception.

If called with the callable and arguments omitted, will return a context object used like this:

with self.assertRaises(SomeException):
    do_something()

An optional keyword argument ‘msg’ can be provided when assertRaises is used as a context object.

The context manager keeps a reference to the exception as the ‘exception’ attribute. This allows you to inspect the exception after the assertion:

with self.assertRaises(SomeException) as cm:
    do_something()
the_exception = cm.exception
self.assertEqual(the_exception.error_code, 3)
assertRaisesRegex(expected_exception, expected_regex, *args, **kwargs)

Asserts that the message in a raised exception matches a regex.

Parameters:

  • expected_exception – Exception class expected to be raised.

  • expected_regex – Regex (re.Pattern object or string) expected to be found in error message.

  • args – Function to be called and extra positional args.

  • kwargs – Extra kwargs.

  • msg – Optional message used in case of failure. Can only be used when assertRaisesRegex is used as a context manager.

assertRegex(text, expected_regex, msg=None)

Fail the test unless the text matches the regular expression.

assertSequenceEqual(seq1, seq2, msg=None, seq_type=None)

An equality assertion for ordered sequences (like lists and tuples).

For the purposes of this function, a valid ordered sequence type is one which can be indexed, has a length, and has an equality operator.

Parameters:

  • seq1 – The first sequence to compare.

  • seq2 – The second sequence to compare.

  • seq_type – The expected datatype of the sequences, or None if no datatype should be enforced.

  • msg – Optional message to use on failure instead of a list of differences.

assertSetEqual(set1, set2, msg=None)

A set-specific equality assertion.

Parameters:

  • set1 – The first set to compare.

  • set2 – The second set to compare.

  • msg – Optional message to use on failure instead of a list of differences.

assertSetEqual uses ducktyping to support different types of sets, and is optimized for sets specifically (parameters must support a difference method).

assertTrue(expr, msg=None)

Check that the expression is true.

assertTupleEqual(tuple1, tuple2, msg=None)

A tuple-specific equality assertion.

Parameters:

  • tuple1 – The first tuple to compare.

  • tuple2 – The second tuple to compare.

  • msg – Optional message to use on failure instead of a list of differences.

assertWarns(expected_warning, *args, **kwargs)

Fail unless a warning of class warnClass is triggered by the callable when invoked with specified positional and keyword arguments. If a different type of warning is triggered, it will not be handled: depending on the other warning filtering rules in effect, it might be silenced, printed out, or raised as an exception.

If called with the callable and arguments omitted, will return a context object used like this:

with self.assertWarns(SomeWarning):
    do_something()

An optional keyword argument ‘msg’ can be provided when assertWarns is used as a context object.

The context manager keeps a reference to the first matching warning as the ‘warning’ attribute; similarly, the ‘filename’ and ‘lineno’ attributes give you information about the line of Python code from which the warning was triggered. This allows you to inspect the warning after the assertion:

with self.assertWarns(SomeWarning) as cm:
    do_something()
the_warning = cm.warning
self.assertEqual(the_warning.some_attribute, 147)
assertWarnsRegex(expected_warning, expected_regex, *args, **kwargs)

Asserts that the message in a triggered warning matches a regexp. Basic functioning is similar to assertWarns() with the addition that only warnings whose messages also match the regular expression are considered successful matches.

Parameters:

  • expected_warning – Warning class expected to be triggered.

  • expected_regex – Regex (re.Pattern object or string) expected to be found in error message.

  • args – Function to be called and extra positional args.

  • kwargs – Extra kwargs.

  • msg – Optional message used in case of failure. Can only be used when assertWarnsRegex is used as a context manager.

clearGenerated()

Remove the directories that are used for testing.

countTestCases()
createLargeMultitaskDataSet(name='QSPRDataset_multi_test', target_props=[{'name': 'HBD', 'task': <TargetTasks.MULTICLASS: 'MULTICLASS'>, 'th': [-1, 1, 2, 100]}, {'name': 'CL', 'task': <TargetTasks.REGRESSION: 'REGRESSION'>}], preparation_settings=None, random_state=42)

Create a large dataset for testing purposes.

Parameters:

  • name (str) – name of the dataset

  • target_props (List of dicts or TargetProperty) – list of target properties

  • preparation_settings (dict) – dictionary containing preparation settings

  • random_state (int) – random state to use for splitting and shuffling

Returns:

a QSPRDataset object

Return type:

QSPRDataset

createLargeTestDataSet(name='QSPRDataset_test_large', target_props=[{'name': 'CL', 'task': <TargetTasks.REGRESSION: 'REGRESSION'>}], preparation_settings=None, random_state=42, n_jobs=1, chunk_size=None)

Create a large dataset for testing purposes.

Parameters:

  • name (str) – name of the dataset

  • target_props (List of dicts or TargetProperty) – list of target properties

  • random_state (int) – random state to use for splitting and shuffling

  • preparation_settings (dict) – dictionary containing preparation settings

Returns:

a QSPRDataset object

Return type:

QSPRDataset

createPCMDataSet(name: str = 'QSPRDataset_test_pcm', target_props: list[qsprpred.tasks.TargetProperty] | list[dict] = [{'name': 'pchembl_value_Median', 'task': <TargetTasks.REGRESSION: 'REGRESSION'>}], preparation_settings: dict | None = None, protein_col: str = 'accession', random_state: int | None = None)

Create a small dataset for testing purposes.

Parameters:

  • name (str, optional) – name of the dataset. Defaults to “QSPRDataset_test”.

  • target_props (list[TargetProperty] | list[dict], optional) – target properties.

  • preparation_settings (dict | None, optional) – preparation settings. Defaults to None.

  • protein_col (str, optional) – name of the column with protein accessions. Defaults to “accession”.

  • random_state (int, optional) – random seed to use in the dataset. Defaults to None

Returns:

a QSPRDataset object

Return type:

QSPRDataset

createSmallTestDataSet(name='QSPRDataset_test_small', target_props=[{'name': 'CL', 'task': <TargetTasks.REGRESSION: 'REGRESSION'>}], preparation_settings=None, random_state=42)

Create a small dataset for testing purposes.

Parameters:

  • name (str) – name of the dataset

  • target_props (List of dicts or TargetProperty) – list of target properties

  • random_state (int) – random state to use for splitting and shuffling

  • preparation_settings (dict) – dictionary containing preparation settings

Returns:

a QSPRDataset object

Return type:

QSPRDataset

createTestDataSetFromFrame(df, name='QSPRDataset_test', target_props=[{'name': 'CL', 'task': <TargetTasks.REGRESSION: 'REGRESSION'>}], random_state=None, prep=None, n_jobs=1, chunk_size=None)

Create a dataset for testing purposes from the given data frame.

Parameters:

  • df (pd.DataFrame) – data frame containing the dataset

  • name (str) – name of the dataset

  • target_props (List of dicts or TargetProperty) – list of target properties

  • random_state (int) – random state to use for splitting and shuffling

  • prep (dict) – dictionary containing preparation settings

Returns:

a QSPRDataset object

Return type:

QSPRDataset

debug()

Run the test without collecting errors in a TestResult

defaultTestResult()
classmethod doClassCleanups()

Execute all class cleanup functions. Normally called for you after tearDownClass.

doCleanups()

Execute all cleanup functions. Normally called for you after tearDown.

classmethod enterClassContext(cm)

Same as enterContext, but class-wide.

enterContext(cm)

Enters the supplied context manager.

If successful, also adds its __exit__ method as a cleanup function and returns the result of the __enter__ method.

fail(msg=None)

Fail immediately, with the given message.

failureException

alias of AssertionError

classmethod getAllDescriptors() → list[qsprpred.data.descriptors.sets.DescriptorSet]

Return a list of all available molecule descriptor sets.

Returns:

list of MoleculeDescriptorSet objects

Return type:

list

classmethod getAllProteinDescriptors() → list[qsprpred.extra.data.descriptors.sets.ProteinDescriptorSet]

Return a list of all available protein descriptor sets.

Returns:

list of ProteinDescriptorSet objects

Return type:

list

getBigDF()

Get a large data frame for testing purposes.

Returns:

a pandas.DataFrame containing the dataset

Return type:

pd.DataFrame

classmethod getDataPrepGrid()

Return a list of many possible combinations of descriptor calculators, splits, feature standardizers, feature filters and data filters. Again, this is not exhaustive, but should cover a lot of cases.

Returns:

a generator that yields tuples of all possible combinations as stated above, each tuple is defined as: (descriptor_calculator, split, feature_standardizer, feature_filters, data_filters)

Return type:

grid

classmethod getDefaultCalculatorCombo()

Return the default descriptor calculator combo.

static getDefaultPrep()

Return a dictionary with default preparation settings.

classmethod getMSAProvider(out_dir: str)
getPCMDF() → DataFrame

Return a test dataframe with PCM data.

Returns:

dataframe with PCM data

Return type:

pd.DataFrame

getPCMSeqProvider() → Callable[[list[str]], tuple[dict[str, str], dict[str, dict]]]

Return a function that provides sequences for given accessions.

Returns:

function that provides sequences for given accessions

Return type:

Callable[[list[str]], tuple[dict[str, str], dict[str, dict]]]

getPCMTargetsDF() → DataFrame

Return a test dataframe with PCM targets and their sequences.

Returns:

dataframe with PCM targets and their sequences

Return type:

pd.DataFrame

classmethod getPrepCombos()

Return a list of all possible preparation combinations as generated by getDataPrepGrid as well as their names. The generated list can be used to parameterize tests with the given named combinations.

Returns:

list of `list`s of all possible combinations of preparation

Return type:

list

getSmallDF()

Get a small data frame for testing purposes.

Returns:

a pandas.DataFrame containing the dataset

Return type:

pd.DataFrame

id()
longMessage = True
maxDiff = 640
run(result=None)
setUp()[source]

Hook method for setting up the test fixture before exercising it.

classmethod setUpClass()

Hook method for setting up class fixture before running tests in the class.

setUpPaths()

Create the directories that are used for testing.

shortDescription()

Returns a one-line description of the test, or None if no description has been provided.

The default implementation of this method returns the first line of the specified test method’s docstring.

skipTest(reason)

Skip this test.

subTest(msg=<object object>, **params)

Return a context manager that will return the enclosed block of code in a subtest identified by the optional message and keyword parameters. A failure in the subtest marks the test case as failed but resumes execution at the end of the enclosed block, allowing further test code to be executed.

tearDown()

Remove all files and directories that are used for testing.

classmethod tearDownClass()

Hook method for deconstructing the class fixture after running all tests in the class.

testLeaveTargetOut()[source]
testPCMSplit = None
testPCMSplitRandomShuffle()[source]
testPCMSplit_0(**kw)
testPCMSplit_1(**kw)
testPCMSplit_2(**kw)
testPerTargetTemporal()[source]
validate_split(dataset)

Check if the split has the data it should have after splitting.

Module contents