palladium package

Submodules

palladium.R module

palladium.cache module

The cache module provides caching utilities in order to provide faster access to data which is needed repeatedly. The disk cache (diskcache) which is primarily used during development when loading data from the local harddisk is faster than querying a remote database.

class palladium.cache.abstractcache(compute_key=None, ignore=False)

Bases: object

An abstract class for providing basic functionality for caching function calls. It contains the handling of keys used for caching objects.

palladium.cache.compute_key_attrs(attrs)
class palladium.cache.diskcache(*args, filename_tmpl=None, **kwargs)

Bases: palladium.cache.abstractcache

The disk cache stores results of function calls as pickled files to disk. Usually used during development and evaluation to save costly DB interactions in repeated calls with the same data.

Note: Should changes to the database or to your functions require you to purge existing cached values, then those cache files are found in the location defined in filename_tmpl.

class palladium.cache.picklediskcache(*args, filename_tmpl=None, **kwargs)

Bases: palladium.cache.diskcache

Same as diskcache, except that standard pickle is used instead of joblib’s pickle functionality.

dump(value, filename)
load(filename)

palladium.config module

class palladium.config.ComponentHandler(config)

Bases: object

finish()
class palladium.config.Config

Bases: dict

A dictionary that represents the app’s configuration.

Tries to send a more user friendly message in case of KeyError.

class palladium.config.CopyHandler(configs)

Bases: object

class palladium.config.PythonHandler(config)

Bases: object

palladium.config.get_config(**extra)
palladium.config.initialize_config(**extra)
palladium.config.process_config(*configs, handlers0=<function _handlers_phase0>, handlers1=<function _handlers_phase1>, handlers2=<function _handlers_phase2>)

palladium.dataset module

DatasetLoader implementations.

class palladium.dataset.EmptyDatasetLoader

Bases: palladium.interfaces.DatasetLoader

This DatasetLoader can be used if no actual data should be loaded. Returns a (None, None) tuple.

class palladium.dataset.SQL(url, sql, target_column=None, ndarray=True, **kwargs)

Bases: palladium.interfaces.DatasetLoader

A DatasetLoader that uses pandas.io.sql.read_sql() to load data from an SQL database. Supports all databases that SQLAlchemy has support for.

class palladium.dataset.ScheduledDatasetLoader(impl, update_cache_rrule)

Bases: palladium.interfaces.DatasetLoader

A DatasetLoader that loads periodically data into RAM to make it available to the prediction server inside the process_store.

ScheduledDatasetLoader wraps another DatasetLoader class that it uses to do the actual loading of the data.

An update_cache_rrule is used to define how often data should be loaded anew.

This class’ read() read method never calls the underlying dataset loader. It will only ever fetch the data from the in-memory cache.

cache = {'process_metadata': {}}
initialize_component(config)
update_cache(*args, **kwargs)
class palladium.dataset.Table(path, target_column=None, ndarray=True, **kwargs)

Bases: palladium.interfaces.DatasetLoader

A DatasetLoader that uses pandas.io.parsers.read_table() to load data from a file or URL.

palladium.eval module

Utilities for testing the performance of a trained model.

palladium.eval.list(model_persister)
palladium.eval.list_cmd(argv=['-T', '-b', 'readthedocs', '-d', '_build/doctrees-readthedocs', '-D', 'language=en', '.', '_build/html'])

List information about available models.

Uses the ‘model_persister’ from the configuration to display a list of models and their metadata.

Usage:
pld-list [options]
Options:
-h –help Show this screen.
palladium.eval.test(dataset_loader_test, model_persister, scoring=None, model_version=None)
palladium.eval.test_cmd(argv=['-T', '-b', 'readthedocs', '-d', '_build/doctrees-readthedocs', '-D', 'language=en', '.', '_build/html'])

Test a model.

Uses ‘dataset_loader_test’ and ‘model_persister’ from the configuration to load a test dataset to test the accuracy of a trained model with.

Usage:
pld-test [options]
Options:

-h –help Show this screen.

--model-version=<version>
 The version of the model to be tested. If not specified, the newest model will be used.

palladium.fit module

palladium.interfaces module

Interfaces defining the behaviour of Palladium’s components.

class palladium.interfaces.CrossValidationGenerator

Bases: object

A CrossValidationGenerator provides train/test indices to split data in train and validation sets.

CrossValidationGenerator corresponds to the cross validation generator interface of scikit-learn.

class palladium.interfaces.DatasetLoader

Bases: object

A DatasetLoader is responsible for loading datasets for use in training and evaluation.

class palladium.interfaces.DatasetLoaderMeta(name, bases, attrs, **kwargs)

Bases: abc.ABCMeta

class palladium.interfaces.Model

Bases: Dummy

A Model can be fit() to data and can be used to predict() data.

Model corresponds to the estimators interface of scikit-learn.

fit(X, y=None)

Fit to data array X and possibly a target array y.

Returns:self
predict(X, **kw)

Predict classes for data array X with shape n x m.

Some models may accept additional keyword arguments.

Returns:A numpy array of length n with the predicted classes (for classification problems) or numeric values (for regression problems).
Raises:May raise a PredictError to indicate that some condition made it impossible to deliver a prediction.
predict_proba(X, **kw)

Predict probabilities for data array X with shape n x m.

Returns:A numpy array of length n x c with a list class probabilities per sample.
Raises:NotImplementedError if not applicable.
class palladium.interfaces.ModelPersister

Bases: object

activate(version)

Set the model with the given version to be the active one.

Implies that any previously active model becomes inactive.

Parameters:version (str) – The version of the model that’s activated.
Raises:LookupError if no model with given version exists.
delete(version)

Delete the model with the given version from the database.

Parameters:version (str) – The version of the model that’s activated.
Raises:LookupError if no model with given version exists.
list_models()

List metadata of all available models.

Returns:A list of dicts, with each dict containing information about one of the available models. Each dict is guaranteed to contain the version key, which is the same version number that ModelPersister.read() accepts for loading specific models.
list_properties()

List properties of ModelPersister itself.

Returns:A dictionary of key and value pairs, where both keys and values are of type str. Properties will usually include active-model and db-version entries.
read(version=None)

Returns a Model instance.

Parameters:version (str) – version may be used to read a specific version of a model. If version is None, returns the active model.
Returns:The model object.
Raises:LookupError if no model was available.
upgrade(from_version=None, to_version='n/a')

Upgrade the underlying database to the latest version.

Newer versions of Palladium may require changes to the ModelPersister‘s database. This method provides an opportunity to run the necessary upgrade steps.

It’s the ModelPersister‘s responsibility to keep track of the Palladium version that was used to create and upgrade its database, and thus to determine the upgrade steps necessary.

write(model)

Persists a Model and returns a new version number.

It is the ModelPersister‘s responsibility to annotate the ‘version’ information onto the model before it is saved.

The new model will initially be inactive. Use ModelPersister.activate() to activate the model.

Returns:The new model’s version identifier.
class palladium.interfaces.ModelPersisterMeta(name, bases, attrs, **kwargs)

Bases: abc.ABCMeta

exception palladium.interfaces.PredictError(error_message, error_code=-1)

Bases: Exception

Raised by Model.predict() to indicate that some condition made it impossible to deliver a prediction.

class palladium.interfaces.PredictService

Bases: object

Responsible for producing the output for the ‘/predict’ HTTP endpoint.

palladium.interfaces.annotate(obj, metadata=None)

palladium.julia module

class palladium.julia.AbstractModel(fit_func, predict_func, fit_kwargs=None, predict_kwargs=None, encode_labels=False)

Bases: palladium.interfaces.Model

fit(X, y)
predict(X)
class palladium.julia.ClassificationModel(fit_func, predict_func, fit_kwargs=None, predict_kwargs=None, encode_labels=False)

Bases: palladium.julia.AbstractModel

score(X, y)
palladium.julia.make_bridge()

palladium.persistence module

ModelPersister implementations.

class palladium.persistence.CachedUpdatePersister(impl, update_cache_rrule=None, check_version=True)

Bases: palladium.interfaces.ModelPersister

A ModelPersister that serves as a caching decorator around another ~palladium.interfaces.ModelPersister object.

Calls to read() will look up a model from the global process_store, i.e. there is never any actual loading involved when calling read().

To fill the process_store cache periodically using the return value of the underlying ModelPersister‘s read method, a dictionary containing keyword arguments to dateutil.rrule.rrule may be passed. The cache will then be filled periodically according to that recurrence rule.

If no update_cache_rrule is used, the CachedUpdatePersister will call once and remember the return value of the underlying ModelPersister‘s read method during initialization.

activate(version)
cache = {'process_metadata': {}}
delete(version)
initialize_component(config)
list_models()
list_properties()
read(*args, **kwargs)
update_cache(*args, **kwargs)
upgrade(from_version=None, to_version='n/a')
write(model)
class palladium.persistence.Database(url, poolclass=None, chunk_size=104857600, table_postfix='')

Bases: palladium.interfaces.ModelPersister

A ModelPersister that pickles models into an SQL database.

DBModelChunkClass(Base)
DBModelClass(Base)
PropertyClass(Base)
activate(version)
create_orm_classes()
delete(version)
list_models()
list_properties()
read(version=None)
upgrade(from_version=None, to_version='n/a')
upgrade_steps = <palladium.persistence.UpgradeSteps object>
write(model)
class palladium.persistence.DatabaseCLOB(url, poolclass=None, chunk_size=104857600, table_postfix='')

Bases: palladium.persistence.Database

A ModelPersister derived from Database, with only the slight difference of using CLOB instead of BLOB to store the pickle data.

Use when BLOB is not available.

DBModelChunkClass(Base)
read(version=None)
write(model)
class palladium.persistence.File(path)

Bases: palladium.persistence.FileLike

A ModelPersister that pickles models onto the file system, into a given directory.

read(version=None)
write(model)
class palladium.persistence.FileIO

Bases: palladium.persistence.FileLikeIO

exists(path)
open(path, mode='r')
remove(path)
class palladium.persistence.FileLike(path, io)

Bases: palladium.interfaces.ModelPersister

A ModelPersister that pickles models through file-like handles.

An argument io is used to access low level file handle operations.

activate(version)
delete(version)
list_models()
list_properties()
read(version=None)
upgrade(from_version=None, to_version='n/a')
upgrade_steps = <palladium.persistence.UpgradeSteps object>
write(model)
class palladium.persistence.FileLikeIO

Bases: object

Used by FileLike to access low level file handle operations.

exists(path)

Test whether a path exists

For normal files, the implementation is:

`python return os.path.exists(path) `

open(path, mode='r')

Return a file handle

For normal files, the implementation is:

`python return open(path, mode) `

remove(path)

Remove a file

For normal files, the implementation is:

`python os.remove(path) `

class palladium.persistence.Rest(url, auth)

Bases: palladium.persistence.FileLike

read(version=None)
write(model)
class palladium.persistence.RestIO(auth)

Bases: palladium.persistence.FileLikeIO

exists(path)
open(path, mode='r')
remove(path)
class palladium.persistence.UpgradeSteps

Bases: object

add(version)
run(persister, from_version, to_version)

palladium.server module

palladium.util module

Assorted utilties.

palladium.util.Partial(func, **kwargs)

Allows the use of partially applied functions in the configuration.

class palladium.util.PluggableDecorator(decorator_config_name)

Bases: object

class palladium.util.ProcessStore(*args, **kwargs)

Bases: collections.UserDict

class palladium.util.RruleThread(func, rrule, sleep_between_checks=60)

Bases: threading.Thread

Calls a given function in intervals defined by given recurrence rules (from datetuil.rrule).

run()
palladium.util.apply_kwargs(func, **kwargs)

Call func with kwargs, but only those kwargs that it accepts.

palladium.util.args_from_config(func)

Decorator that injects parameters from the configuration.

palladium.util.get_metadata(error_code=0, error_message=None, status='OK')
palladium.util.memory_usage_psutil()

Return the current process memory usage in MB.

palladium.util.resolve_dotted_name(dotted_name)
palladium.util.run_job(func, **params)
palladium.util.session_scope(session)

Provide a transactional scope around a series of operations.

palladium.util.timer(log=None, message=None)
palladium.util.upgrade(model_persister, from_version=None, to_version=None)
palladium.util.upgrade_cmd(argv=['-T', '-b', 'readthedocs', '-d', '_build/doctrees-readthedocs', '-D', 'language=en', '.', '_build/html'])

Upgrade the database to the latest version.

Usage:
pld-ugprade [options]
Options:
--from=<v> Upgrade from a specific version, overriding the version stored in the database.
--to=<v> Upgrade to a specific version instead of the latest version.

-h –help Show this screen.

palladium.util.version_cmd(argv=['-T', '-b', 'readthedocs', '-d', '_build/doctrees-readthedocs', '-D', 'language=en', '.', '_build/html'])

Print the version number of Palladium.

Usage:
pld-version [options]
Options:
-h –help Show this screen.

palladium.wsgi module

Module contents