qsprpred.utils.testing package

Submodules

qsprpred.utils.testing.base module

class qsprpred.utils.testing.base.QSPRTestCase(methodName='runTest')[source]

Bases: 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.

countTestCases()

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

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.

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(): Hook method for deconstructing the test fixture after testing it.

classmethod tearDownClass(): Hook method for deconstructing the class fixture after running all tests in the class.

qsprpred.utils.testing.check_mixins module

class qsprpred.utils.testing.check_mixins.DataPrepCheckMixIn[source]

Bases: DescriptorCheckMixIn

Mixin for testing data preparation.

checkDescriptors(dataset: QSPRDataset, target_props: list[dict | qsprpred.tasks.TargetProperty])

Check if information about descriptors is consistent in the data set. Checks if calculators are consistent with the descriptors contained in the data set. This is tested also before and after serialization.

Parameters:

dataset (QSPRDataset) – The data set to check.
target_props (List of dicts or TargetProperty) – list of target properties

Raises:

AssertionError – If the consistency check fails.

checkFeatures(ds: QSPRDataset, expected_length: int)

Check if the feature names and the feature matrix of a data set is consistent with expected number of variables.

Parameters:

ds (QSPRDataset) – The data set to check.
expected_length (int) – The expected number of features.

Raises:

AssertionError – If the feature names or the feature matrix is not consistent

checkPrep(dataset, feature_calculators, split, feature_standardizer, feature_filter, data_filter, applicability_domain, expected_target_props)[source]: Check the consistency of the dataset after preparation.

class qsprpred.utils.testing.check_mixins.DescriptorCheckMixIn[source]

Bases: object

Mixin class for common descriptor checks.

checkDescriptors(dataset: QSPRDataset, target_props: list[dict | qsprpred.tasks.TargetProperty])[source]

Check if information about descriptors is consistent in the data set. Checks if calculators are consistent with the descriptors contained in the data set. This is tested also before and after serialization.

Parameters:

dataset (QSPRDataset) – The data set to check.
target_props (List of dicts or TargetProperty) – list of target properties

Raises:

AssertionError – If the consistency check fails.

checkFeatures(ds: QSPRDataset, expected_length: int)[source]

Check if the feature names and the feature matrix of a data set is consistent with expected number of variables.

Parameters:

ds (QSPRDataset) – The data set to check.
expected_length (int) – The expected number of features.

Raises:

AssertionError – If the feature names or the feature matrix is not consistent

class qsprpred.utils.testing.check_mixins.DescriptorInDataCheckMixIn[source]

Bases: DescriptorCheckMixIn

Mixin for testing descriptor sets in data sets.

checkDataSetContainsDescriptorSet(dataset, desc_set, prep_combo, target_props)[source]: Check if a descriptor set is in a data set.

checkDescriptors(dataset: QSPRDataset, target_props: list[dict | qsprpred.tasks.TargetProperty])

Check if information about descriptors is consistent in the data set. Checks if calculators are consistent with the descriptors contained in the data set. This is tested also before and after serialization.

Parameters:

dataset (QSPRDataset) – The data set to check.
target_props (List of dicts or TargetProperty) – list of target properties

Raises:

AssertionError – If the consistency check fails.

checkFeatures(ds: QSPRDataset, expected_length: int)

Check if the feature names and the feature matrix of a data set is consistent with expected number of variables.

Parameters:

ds (QSPRDataset) – The data set to check.
expected_length (int) – The expected number of features.

Raises:

AssertionError – If the feature names or the feature matrix is not consistent

static getDatSetName(desc_set, target_props)[source]: Get a unique name for a data set.

class qsprpred.utils.testing.check_mixins.ModelCheckMixIn[source]

Bases: object

This class holds the tests for the QSPRmodel class.

checkOptimization(model: QSPRModel, ds: QSPRDataset, optimizer: HyperparameterOptimization)[source]

fitTest(model: QSPRModel, ds: QSPRDataset)[source]

Test model fitting, optimization and evaluation.

Parameters:

model (QSPRModel) – The model to test.
ds (QSPRDataset) – The dataset to use for testing.

getParamGrid(model: QSPRModel, grid: str) → dict[source]

Get the parameter grid for a model.

Parameters:

model (QSPRModel) – The model to get the parameter grid for.
grid (str) – The grid type to get the parameter grid for.

Returns:

The parameter grid.

Return type:

dict

property gridFile

predictorTest(model: QSPRModel, dataset: QSPRDataset, comparison_model: QSPRModel | None = None, expect_equal_result=True, **pred_kwargs)[source]

Test model predictions.

Checks if the shape of the predictions is as expected and if the predictions of the predictMols function are consistent with the predictions of the predict/predictProba functions. Also checks if the predictions of the model are the same as the predictions of the comparison model if given.

Parameters:

model (QSPRModel) – The model to make predictions with.
dataset (QSPRDataset) – The dataset to make predictions for.
comparison_model (QSPRModel) – another model to compare the predictions with.
expect_equal_result (bool) – Whether the expected result should be equal or not equal to the predictions of the comparison model.
**pred_kwargs – Extra keyword arguments to pass to the predictor’s predictMols method.

class qsprpred.utils.testing.check_mixins.MonitorsCheckMixIn[source]

Bases: ModelDataSetsPathMixIn, ModelCheckMixIn

baseMonitorTest(monitor: BaseMonitor, monitor_type: Literal['hyperparam', 'crossval', 'test', 'fit'], neural_net: bool)[source]: Test the base monitor.

checkOptimization(model: QSPRModel, ds: QSPRDataset, optimizer: HyperparameterOptimization)

clearGenerated(): Remove the directories that are used for testing.

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

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

fileMonitorTest(monitor: FileMonitor, monitor_type: Literal['hyperparam', 'crossval', 'test', 'fit'], neural_net: bool)[source]: Test if the correct files are generated

fitTest(model: QSPRModel, ds: QSPRDataset)

Test model fitting, optimization and evaluation.

Parameters:

model (QSPRModel) – The model to test.
ds (QSPRDataset) – The dataset to use for testing.

classmethod getAllDescriptors()

Return a list of (ideally) all available descriptor sets. For now they need to be added manually to the list below.

TODO: would be nice to create the list automatically by implementing a descriptor set registry that would hold all installed descriptor sets.

Returns:: list of DescriptorCalculator 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()

Makes a list of default descriptor calculators that can be used in tests. It creates a calculator with only morgan fingerprints and rdkit descriptors, but also one with them both to test behaviour with multiple descriptor sets. Override this method if you want to test with other descriptor sets and calculator combinations.

Returns:: list of created DescriptorCalculator objects
Return type:: list

static getDefaultPrep(): Return a dictionary with default preparation settings.

getParamGrid(model: QSPRModel, grid: str) → dict

Get the parameter grid for a model.

Parameters:

model (QSPRModel) – The model to get the parameter grid for.
grid (str) – The grid type to get the parameter grid for.

Returns:

The parameter grid.

Return type:

dict

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

property gridFile

listMonitorTest(monitor: ListMonitor, monitor_type: Literal['hyperparam', 'crossval', 'test', 'fit'], neural_net: bool)[source]

predictorTest(model: QSPRModel, dataset: QSPRDataset, comparison_model: QSPRModel | None = None, expect_equal_result=True, **pred_kwargs)

Test model predictions.

Checks if the shape of the predictions is as expected and if the predictions of the predictMols function are consistent with the predictions of the predict/predictProba functions. Also checks if the predictions of the model are the same as the predictions of the comparison model if given.

Parameters:

model (QSPRModel) – The model to make predictions with.
dataset (QSPRDataset) – The dataset to make predictions for.
comparison_model (QSPRModel) – another model to compare the predictions with.
expect_equal_result (bool) – Whether the expected result should be equal or not equal to the predictions of the comparison model.
**pred_kwargs – Extra keyword arguments to pass to the predictor’s predictMols method.

runMonitorTest(model, data, monitor_type, test_method, nerual_net, *args, **kwargs)[source]

setUpPaths(): Set up the test environment.

tearDown(): Remove all files and directories that are used for testing.

trainModelWithMonitoring(model: ~qsprpred.models.model.QSPRModel, ds: ~qsprpred.data.tables.qspr.QSPRDataset, hyperparam_monitor: ~qsprpred.models.monitors.HyperparameterOptimizationMonitor, crossval_monitor: ~qsprpred.models.monitors.AssessorMonitor, test_monitor: ~qsprpred.models.monitors.AssessorMonitor, fit_monitor: ~qsprpred.models.monitors.FitMonitor) -> (<class 'qsprpred.models.monitors.HyperparameterOptimizationMonitor'>, <class 'qsprpred.models.monitors.AssessorMonitor'>, <class 'qsprpred.models.monitors.AssessorMonitor'>, <class 'qsprpred.models.monitors.FitMonitor'>)[source]

validate_split(dataset): Check if the split has the data it should have after splitting.

qsprpred.utils.testing.path_mixins module

class qsprpred.utils.testing.path_mixins.DataSetsPathMixIn[source]

Bases: PathMixIn

Mix-in class that provides a small and large testing data set and some common preparation settings to use in tests.

clearGenerated(): Remove the directories that are used for testing.

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)[source]

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)[source]

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

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

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)[source]

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

classmethod getAllDescriptors()[source]

Return a list of (ideally) all available descriptor sets. For now they need to be added manually to the list below.

TODO: would be nice to create the list automatically by implementing a descriptor set registry that would hold all installed descriptor sets.

Returns:: list of DescriptorCalculator objects
Return type:: list

getBigDF()[source]

Get a large data frame for testing purposes.

Returns:: a pandas.DataFrame containing the dataset
Return type:: pd.DataFrame

classmethod getDataPrepGrid()[source]

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()[source]

Makes a list of default descriptor calculators that can be used in tests. It creates a calculator with only morgan fingerprints and rdkit descriptors, but also one with them both to test behaviour with multiple descriptor sets. Override this method if you want to test with other descriptor sets and calculator combinations.

Returns:: list of created DescriptorCalculator objects
Return type:: list

static getDefaultPrep()[source]: Return a dictionary with default preparation settings.

classmethod getPrepCombos()[source]

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()[source]

Get a small data frame for testing purposes.

Returns:: a pandas.DataFrame containing the dataset
Return type:: pd.DataFrame

setUpPaths()[source]: Create the directories that are used for testing.

tearDown(): Remove all files and directories that are used for testing.

validate_split(dataset)[source]: Check if the split has the data it should have after splitting.

class qsprpred.utils.testing.path_mixins.ModelDataSetsPathMixIn[source]

Bases: DataSetsPathMixIn

This class sets up the datasets for the model tests.

clearGenerated(): Remove the directories that are used for testing.

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

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

classmethod getAllDescriptors()

Return a list of (ideally) all available descriptor sets. For now they need to be added manually to the list below.

TODO: would be nice to create the list automatically by implementing a descriptor set registry that would hold all installed descriptor sets.

Returns:: list of DescriptorCalculator 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()

Makes a list of default descriptor calculators that can be used in tests. It creates a calculator with only morgan fingerprints and rdkit descriptors, but also one with them both to test behaviour with multiple descriptor sets. Override this method if you want to test with other descriptor sets and calculator combinations.

Returns:: list of created DescriptorCalculator objects
Return type:: list

static getDefaultPrep(): Return a dictionary with default preparation settings.

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

setUpPaths()[source]: Set up the test environment.

tearDown(): Remove all files and directories that are used for testing.

validate_split(dataset): Check if the split has the data it should have after splitting.

class qsprpred.utils.testing.path_mixins.PathMixIn[source]

Bases: object

Mix-in class that provides paths to test files and directories and handles their creation and deletion.

Variables:: generatedPath (str) – path to the directory where generated files are stored, this directory is created before and cleared after each test

clearGenerated()[source]: Remove the directories that are used for testing.

setUpPaths()[source]: Create the directories that are used for testing.

tearDown()[source]: Remove all files and directories that are used for testing.

qsprpred.utils.testing package

Submodules

qsprpred.utils.testing.base module

qsprpred.utils.testing.check_mixins module

qsprpred.utils.testing.path_mixins module

Module contents