atm.utilities module

Functions

base_64_to_object(b64str)	Inverse of object_to_base_64.
ensure_directory(directory)	Create directory if it doesn’t exist.
get_instance(class_, **kwargs)	Create an instance of the given class with required kwargs.
hash_dict(dictionary[, ignored_keys])	Hash a python dictionary to a hexadecimal string.
hash_nested_tuple(tup)	Hash a nested tuple to hexadecimal
hash_string(s)	Hash a string to hexadecimal
load_metrics(classifier, metrics_dir)	Load the performance metrics for a particular classifier
load_model(classifier, models_dir)	Load the Model object for a particular classifier
make_save_path(dir, classifier, suffix)	Generate the base save path for a classifier’s model and metrics files, based on the classifier’s dataset name and hyperparameters.
obj_has_method(obj, method)	http://stackoverflow.com/questions/34439/finding-what-methods-an-object-has
object_to_base_64(obj)	Pickle and base64-encode an object.
params_to_vectors(params, tunables)	Converts a list of parameter vectors (with metadata) into a numpy array ready for BTB tuning.
save_metrics(classifier, metrics_dir, metrics)	Save a JSON-serialized version of a set of performance metrics for a particular classifier.
save_model(classifier, models_dir, model)	Save a serialized version of a Model object for a particular classifier.
update_params(params, categoricals, constants)	Update params with categoricals and constants for the fitting proces.

atm.utilities.base_64_to_object(b64str)

Inverse of object_to_base_64. Decode base64-encoded string and then unpickle it.

atm.utilities.ensure_directory(directory)

Create directory if it doesn’t exist.

atm.utilities.get_instance(class_, **kwargs)

Create an instance of the given class with required kwargs.

The exact keyword arguments that the given class_ expects will be taken from kwargs and the rest will be ignored.

Parameters

• class_ (type) – class to instantiate

• **kwargs – keyword arguments

Returns

instance of specific class with the args that accepts.

atm.utilities.hash_dict(dictionary, ignored_keys=None)

Hash a python dictionary to a hexadecimal string. http://stackoverflow.com/questions/5884066/hashing-a-python-dictionary

atm.utilities.hash_nested_tuple(tup)

Hash a nested tuple to hexadecimal

atm.utilities.hash_string(s)

Hash a string to hexadecimal

atm.utilities.load_metrics(classifier, metrics_dir)

Load the performance metrics for a particular classifier

atm.utilities.load_model(classifier, models_dir)

Load the Model object for a particular classifier

atm.utilities.make_save_path(dir, classifier, suffix)

Generate the base save path for a classifier’s model and metrics files, based on the classifier’s dataset name and hyperparameters.

atm.utilities.obj_has_method(obj, method)

http://stackoverflow.com/questions/34439/finding-what-methods-an-object-has

atm.utilities.object_to_base_64(obj)

Pickle and base64-encode an object.

atm.utilities.params_to_vectors(params, tunables)

Converts a list of parameter vectors (with metadata) into a numpy array ready for BTB tuning.

Parameters

• params – list of hyperparameter vectors. Each vector is a dict mapping the names of parameters to those parameters’ values.

• tunables – list of HyperParameter metadata structures describing all the optimizable hyperparameters that should be in each vector. e.g.

• = [ (tunables) – (‘degree’, HyperParameter(‘int’, (2, 4))), (‘gamma’, HyperParameter(‘float_exp’, (1e-05, 1e5)))]

Returns

np.array of parameter vectors ready to be optimized by a

Gaussian Process (or what have you). vectors.shape = (len(params), len(tunables))

Return type

vectors

atm.utilities.save_metrics(classifier, metrics_dir, metrics)

Save a JSON-serialized version of a set of performance metrics for a particular classifier. The metrics will be stored at a path generated from the classifier’s attributes.

atm.utilities.save_model(classifier, models_dir, model)

Save a serialized version of a Model object for a particular classifier. The object will be stored at a path generated from the classifier’s attributes.

atm.utilities.update_params(params, categoricals, constants)

Update params with categoricals and constants for the fitting proces.

params: params proposed by the tuner

Examples of the format for SVM sigmoid hyperpartition:

categoricals = ((‘kernel’, ‘poly’),

(‘probability’, True), (‘_scale’, True))

constants = [(‘cache_size’, 15000)]