BaseDeepRegressor

class BaseDeepRegressor(batch_size=40, last_file_name='last_model')

Abstract base class for deep learning time series regression.

The base classifier provides a deep learning default method for _predict, and provides a new abstract method for building a model.

Parameters:

batch_sizeint, default = 40

training batch size for the model

last_file_namestr, default = “last_model”

The name of the file of the last model, used only if save_last_model_to_file is used

Attributes:

is_fitted

Whether fit has been called.

Methods

build_model(input_shape)	Construct a compiled, un-trained, keras model that is ready for training.
check_is_fitted()	Check if the estimator has been fitted.
clone()	Obtain a clone of the object with same hyper-parameters.
clone_tags(estimator[, tag_names])	clone/mirror tags from another estimator as dynamic override.
create_test_instance([parameter_set])	Construct Estimator instance if possible.
create_test_instances_and_names([parameter_set])	Create list of all test instances and a list of names for them.
fit(X, y)	Fit time series regressor to training data.
get_class_tag(tag_name[, tag_value_default])	Get tag value from estimator class (only class tags).
get_class_tags()	Get class tags from estimator class and all its parent classes.
get_fitted_params([deep])	Get fitted parameters.
get_param_defaults()	Get parameter defaults for the object.
get_param_names()	Get parameter names for the object.
get_params([deep])	Get parameters for this estimator.
get_tag(tag_name[, tag_value_default, ...])	Get tag value from estimator class and dynamic tag overrides.
get_tags()	Get tags from estimator class and dynamic tag overrides.
get_test_params([parameter_set])	Return testing parameter settings for the estimator.
is_composite()	Check if the object is composite.
load_from_path(serial)	Load object from file location.
load_from_serial(serial)	Load object from serialized memory container.
predict(X)	Predicts labels for sequences in X.
reset()	Reset the object to a clean post-init state.
save([path])	Save serialized self to bytes-like object or to (.zip) file.
save_last_model_to_file([file_path])	Save the last epoch of the trained deep learning model.
score(X, y)	Scores predicted labels against ground truth labels on X.
set_params(**params)	Set the parameters of this object.
set_tags(**tag_dict)	Set dynamic tags to given values.
summary()	Summary function to return the losses/metrics for model fit.

abstract build_model(input_shape)

Construct a compiled, un-trained, keras model that is ready for training.

Parameters:

input_shapetuple

The shape of the data fed into the input layer

Returns:

A compiled Keras Model

summary()

Summary function to return the losses/metrics for model fit.

Returns:

history: dict or None,

Dictionary containing model’s train/validation losses and metrics

save_last_model_to_file(file_path='./')

Save the last epoch of the trained deep learning model.

Parameters:

file_pathstr, default = “./”

The directory where the model will be saved

Returns:

None

check_is_fitted()

Check if the estimator has been fitted.

Raises:

NotFittedError

If the estimator has not been fitted yet.

clone()

Obtain a clone of the object with same hyper-parameters.

A clone is a different object without shared references, in post-init state. This function is equivalent to returning sklearn.clone of self. Equal in value to type(self)(**self.get_params(deep=False)).

Returns:

instance of type(self), clone of self (see above)

clone_tags(estimator, tag_names=None)

clone/mirror tags from another estimator as dynamic override.

Parameters:

estimatorestimator inheriting from :class:BaseEstimator

tag_namesstr or list of str, default = None

Names of tags to clone. If None then all tags in estimator are used as tag_names.

Returns:

Self

Reference to self.

Notes

Changes object state by setting tag values in tag_set from estimator as dynamic tags in self.

classmethod create_test_instance(parameter_set='default')

Construct Estimator instance if possible.

Parameters:

parameter_setstr, default=”default”

Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

Returns:

instanceinstance of the class with default parameters

Notes

get_test_params can return dict or list of dict. This function takes first or single dict that get_test_params returns, and constructs the object with that.

classmethod create_test_instances_and_names(parameter_set='default')

Create list of all test instances and a list of names for them.

Parameters:

parameter_setstr, default=”default”

Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

Returns:

objslist of instances of cls

i-th instance is cls(**cls.get_test_params()[i])

nameslist of str, same length as objs

i-th element is name of i-th instance of obj in tests convention is {cls.__name__}-{i} if more than one instance otherwise {cls.__name__}

parameter_setstr, default=”default”

Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

fit(X, y)

Fit time series regressor to training data.

Parameters:

X3D np.array (any number of channels, equal length series)

of shape [n_instances, n_channels, series_length]

or 2D np.array (univariate, equal length series)

of shape [n_instances, series_length]

or of any other supported input type

for list of mtypes, see datatypes.SCITYPE_REGISTER

y1D np.array of float, of shape [n_instances] - regression labels for fitting

indices correspond to instance indices in X

Returns:

selfReference to self.

Notes

Changes state by creating a fitted model that updates attributes ending in “_” and sets is_fitted flag to True.

classmethod get_class_tag(tag_name, tag_value_default=None)

Get tag value from estimator class (only class tags).

Parameters:

tag_namestr

Name of tag value.

tag_value_defaultany type

Default/fallback value if tag is not found.

Returns:

tag_value

Value of the tag_name tag in self. If not found, returns tag_value_default.

classmethod get_class_tags()

Get class tags from estimator class and all its parent classes.

Returns:

collected_tagsdict

Dictionary of tag name : tag value pairs. Collected from _tags class attribute via nested inheritance. NOT overridden by dynamic tags set by set_tags or mirror_tags.

get_fitted_params(deep=True)

Get fitted parameters.

State required:

Requires state to be “fitted”.

Parameters:

deepbool, default=True

Whether to return fitted parameters of components.

• If True, will return a dict of parameter name : value for this object, including fitted parameters of fittable components (= BaseEstimator-valued parameters).

• If False, will return a dict of parameter name : value for this object, but not include fitted parameters of components.

Returns:

fitted_paramsdict with str-valued keys

Dictionary of fitted parameters, paramname : paramvalue keys-value pairs include:

• always: all fitted parameters of this object, as via get_param_names values are fitted parameter value for that key, of this object

• if deep=True, also contains keys/value pairs of component parameters parameters of components are indexed as [componentname]__[paramname] all parameters of componentname appear as paramname with its value

• if deep=True, also contains arbitrary levels of component recursion, e.g., [componentname]__[componentcomponentname]__[paramname], etc

classmethod get_param_defaults()

Get parameter defaults for the object.

Returns:

default_dict: dict with str keys

keys are all parameters of cls that have a default defined in __init__ values are the defaults, as defined in __init__

classmethod get_param_names()

Get parameter names for the object.

Returns:

param_names: list of str, alphabetically sorted list of parameter names of cls

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict

Parameter names mapped to their values.

get_tag(tag_name, tag_value_default=None, raise_error=True)

Get tag value from estimator class and dynamic tag overrides.

Parameters:

tag_namestr

Name of tag to be retrieved

tag_value_defaultany type, optional; default=None

Default/fallback value if tag is not found

raise_errorbool

whether a ValueError is raised when the tag is not found

Returns:

tag_value

Value of the tag_name tag in self. If not found, returns an error if raise_error is True, otherwise it returns tag_value_default.

Raises:

ValueError if raise_error is True i.e. if tag_name is not in self.get_tags(

).keys()

get_tags()

Get tags from estimator class and dynamic tag overrides.

Returns:

collected_tagsdict

Dictionary of tag name : tag value pairs. Collected from _tags class attribute via nested inheritance and then any overrides and new tags from _tags_dynamic object attribute.

classmethod get_test_params(parameter_set='default')

Return testing parameter settings for the estimator.

Parameters:

parameter_setstr, default=”default”

Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

Returns:

paramsdict or list of dict, default = {}

Parameters to create testing instances of the class Each dict are parameters to construct an “interesting” test instance, i.e., MyClass(**params) or MyClass(**params[i]) creates a valid test instance. create_test_instance uses the first (or only) dictionary in params

is_composite()

Check if the object is composite.

A composite object is an object which contains objects, as parameters. Called on an instance, since this may differ by instance.

Returns:

composite: bool, whether self contains a parameter which is BaseObject

property is_fitted

Whether fit has been called.

classmethod load_from_path(serial)

Load object from file location.

Parameters:

serialresult of ZipFile(path).open(“object)

Returns:

deserialized self resulting in output at path, of cls.save(path)

classmethod load_from_serial(serial)

Load object from serialized memory container.

Parameters:

serial1st element of output of cls.save(None)

Returns:

deserialized self resulting in output serial, of cls.save(None)

predict(X) → ndarray

Predicts labels for sequences in X.

Parameters:

X3D np.array (any number of channels, equal length series)

of shape [n_instances, n_channels, series_length]

or 2D np.array (univariate, equal length series)

of shape [n_instances, series_length]

or of any other supported input type

for list of mtypes, see datatypes.SCITYPE_REGISTER

Returns:

y1D np.array of float, of shape [n_instances] - predicted regression labels

indices correspond to instance indices in X

reset()

Reset the object to a clean post-init state.

Equivalent to sklearn.clone but overwrites self. After self.reset() call, self is equal in value to type(self)(**self.get_params(deep=False))

Detail behaviour: removes any object attributes, except:

hyper-parameters = arguments of __init__ object attributes containing double-underscores, i.e., the string “__”

runs __init__ with current values of hyper-parameters (result of get_params)

Not affected by the reset are: object attributes containing double-underscores class and object methods, class attributes

save(path=None)

Save serialized self to bytes-like object or to (.zip) file.

Behaviour: if path is None, returns an in-memory serialized self if path is a file location, stores self at that location as a zip file

saved files are zip files with following contents: _metadata - contains class of self, i.e., type(self) _obj - serialized self. This class uses the default serialization (pickle).

Parameters:

pathNone or file location (str or Path)

if None, self is saved to an in-memory object if file location, self is saved to that file location. If:

path=”estimator” then a zip file estimator.zip will be made at cwd. path=”/home/stored/estimator” then a zip file estimator.zip will be stored in /home/stored/.

Returns:

if path is None - in-memory serialized self

if path is file location - ZipFile with reference to the file

score(X, y) → float

Scores predicted labels against ground truth labels on X.

Parameters:

X3D np.array (any number of channels, equal length series)

of shape [n_instances, n_channels, series_length]

or 2D np.array (univariate, equal length series)

of shape [n_instances, series_length]

or of any other supported type

for list of mtypes, see datatypes.SCITYPE_REGISTER

y1D np.array of float, of shape [n_instances] - regression labels (gnr truth)

indices correspond to instance indices in X

Returns:

float, R-squared score of predict(X) vs y

set_params(**params)

Set the parameters of this object.

The method works on simple estimators as well as on nested objects. The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**paramsdict

BaseObject parameters

Returns:

selfreference to self (after parameters have been set)

set_tags(**tag_dict)

Set dynamic tags to given values.

Parameters:

tag_dictdict

Dictionary of tag name : tag value pairs.

Returns:

Self

Reference to self.

Notes

Changes object state by settting tag values in tag_dict as dynamic tags in self.