CNNClassifier#
- class CNNClassifier(n_epochs=2000, batch_size=16, kernel_size=7, avg_pool_size=3, n_conv_layers=2, callbacks=None, verbose=False, loss='categorical_crossentropy', metrics=None, random_state=None, activation='softmax', use_bias=True, optimizer=None, filter_sizes=None, padding='auto')[source]#
Time Convolutional Neural Network (CNN), as described in [1].
- Parameters:
- n_epochsint, default = 2000
the number of epochs to train the model
- batch_sizeint, default = 16
the number of samples per gradient update.
- kernel_sizeint, default = 7
the length of the 1D convolution window
- avg_pool_sizeint, default = 3
size of the average pooling windows
- n_conv_layersint, default = 2
the number of convolutional plus average pooling layers
- callbackslist of keras.callbacks, default = None
- verboseboolean, default = False
whether to output extra information
- lossstring, default=”categorical_crossentropy”
fit parameter for the keras model
- metricslist of strings, default=[“accuracy”],
- random_stateint or None, default=None
Seed for random number generation.
- activationstring or a tf callable, default=”softmax”
Activation function used in the output linear layer. List of available activation functions: https://keras.io/api/layers/activations/
- use_biasboolean, default = True
whether the layer uses a bias vector.
- optimizerkeras.optimizers object, default = Adam(lr=0.01)
specify the optimizer and the learning rate to be used.
- filter_sizesarray of shape (n_conv_layers) default = [6, 12]
- paddingstring, default = “auto”
Controls padding logic for the convolutional layers, i.e. whether
'valid'
and'same'
are passed to theConv1D
layer. - “auto”: as per original implementation,"same"
is passed ifinput_shape[0] < 60
in the input layer, and"valid"
otherwise.“valid”, “same”, and other values are passed directly to
Conv1D
- Attributes:
is_fitted
Whether
fit
has been called.
Notes
Adapted from the implementation from Fawaz et. al hfawaz/dl-4-tsc
References
[1]Zhao et. al, Convolutional neural networks for time series classification,
Journal of Systems Engineering and Electronics, 28(1):2017.
Examples
>>> from sktime.classification.deep_learning.cnn import CNNClassifier >>> from sktime.datasets import load_unit_test >>> X_train, y_train = load_unit_test(split="train") >>> X_test, y_test = load_unit_test(split="test") >>> cnn = CNNClassifier(n_epochs=20,batch_size=4) >>> cnn.fit(X_train, y_train) CNNClassifier(...)
Methods
build_model
(input_shape, n_classes, **kwargs)Construct a compiled, un-trained, keras model that is ready for training.
check_is_fitted
([method_name])Check if the estimator has been fitted.
clone
()Obtain a clone of the object with same hyper-parameters and config.
clone_tags
(estimator[, tag_names])Clone tags from another object as dynamic override.
create_test_instance
([parameter_set])Construct an instance of the class, using first test parameter set.
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 classifier to training data.
fit_predict
(X, y[, cv, change_state])Fit and predict labels for sequences in X.
fit_predict_proba
(X, y[, cv, change_state])Fit and predict labels probabilities for sequences in X.
get_class_tag
(tag_name[, tag_value_default])Get class tag value from class, with tag level inheritance from parents.
Get class tags from class, with tag level inheritance from parent classes.
Get config flags for self.
get_fitted_params
([deep])Get fitted parameters.
Get object's parameter defaults.
get_param_names
([sort])Get object's parameter names.
get_params
([deep])Get a dict of parameters values for this object.
get_tag
(tag_name[, tag_value_default, ...])Get tag value from instance, with tag level inheritance and overrides.
get_tags
()Get tags from instance, with tag level inheritance and overrides.
get_test_params
([parameter_set])Return testing parameter settings for the estimator.
Check if the object is composed of other BaseObjects.
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.
Predicts labels probabilities for sequences in X.
reset
()Reset the object to a clean post-init state.
save
([path, serialization_format])Save serialized self to bytes-like object or to (.zip) file.
score
(X, y)Scores predicted labels against ground truth labels on X.
set_config
(**config_dict)Set config flags to given values.
set_params
(**params)Set the parameters of this object.
set_random_state
([random_state, deep, ...])Set random_state pseudo-random seed parameters for self.
set_tags
(**tag_dict)Set instance level tag overrides to given values.
summary
()Summary function to return the losses/metrics for model fit.
- build_model(input_shape, n_classes, **kwargs)[source]#
Construct a compiled, un-trained, keras model that is ready for training.
In sktime, time series are stored in numpy arrays of shape (d,m), where d is the number of dimensions, m is the series length. Keras/tensorflow assume data is in shape (m,d). This method also assumes (m,d). Transpose should happen in fit.
- Parameters:
- input_shapetuple
The shape of the data fed into the input layer, should be (m,d)
- n_classes: int
The number of classes, which becomes the size of the output layer
- Returns:
- outputa compiled Keras Model
- check_is_fitted(method_name=None)[source]#
Check if the estimator has been fitted.
Check if
_is_fitted
attribute is present andTrue
. Theis_fitted
attribute should be set toTrue
in calls to an object’sfit
method.If not, raises a
NotFittedError
.- Parameters:
- method_namestr, optional
Name of the method that called this function. If provided, the error message will include this information.
- Raises:
- NotFittedError
If the estimator has not been fitted yet.
- clone()[source]#
Obtain a clone of the object with same hyper-parameters and config.
A clone is a different object without shared references, in post-init state. This function is equivalent to returning
sklearn.clone
ofself
.Equivalent to constructing a new instance of
type(self)
, with parameters ofself
, that is,type(self)(**self.get_params(deep=False))
.If configs were set on
self
, the clone will also have the same configs as the original, equivalent to callingcloned_self.set_config(**self.get_config())
.Also equivalent in value to a call of
self.reset
, with the exception thatclone
returns a new object, instead of mutatingself
likereset
.- Raises:
- RuntimeError if the clone is non-conforming, due to faulty
__init__
.
- RuntimeError if the clone is non-conforming, due to faulty
- clone_tags(estimator, tag_names=None)[source]#
Clone tags from another object as dynamic override.
Every
scikit-base
compatible object has a dictionary of tags. Tags may be used to store metadata about the object, or to control behaviour of the object.Tags are key-value pairs specific to an instance
self
, they are static flags that are not changed after construction of the object.clone_tags
sets dynamic tag overrides from another object,estimator
.The
clone_tags
method should be called only in the__init__
method of an object, during construction, or directly after construction via__init__
.The dynamic tags are set to the values of the tags in
estimator
, with the names specified intag_names
.The default of
tag_names
writes all tags fromestimator
toself
.Current tag values can be inspected by
get_tags
orget_tag
.- Parameters:
- estimatorAn instance of :class:BaseObject or derived class
- tag_namesstr or list of str, default = None
Names of tags to clone. The default (
None
) clones all tags fromestimator
.
- Returns:
- self
Reference to
self
.
- classmethod create_test_instance(parameter_set='default')[source]#
Construct an instance of the class, using first test parameter set.
- 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
- classmethod create_test_instances_and_names(parameter_set='default')[source]#
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. The naming convention is
{cls.__name__}-{i}
if more than one instance, otherwise{cls.__name__}
- fit(X, y)[source]#
Fit time series classifier to training data.
- State change:
Changes state to “fitted”.
- Writes to self:
Sets self.is_fitted to True. Sets fitted model attributes ending in “_”.
- Parameters:
- Xsktime compatible time series panel data container of Panel scitype
time series to fit the estimator to.
Can be in any mtype of
Panel
scitype, for instance:pd-multiindex: pd.DataFrame with columns = variables, index = pd.MultiIndex with first level = instance indices, second level = time indices
numpy3D: 3D np.array (any number of dimensions, equal length series) of shape [n_instances, n_dimensions, series_length]
or of any other supported
Panel
mtype
for list of mtypes, see
datatypes.SCITYPE_REGISTER
for specifications, see
examples/AA_datatypes_and_datasets.ipynb
Not all estimators support panels with multivariate or unequal length series, see the tag reference for details.
- ysktime compatible tabular data container, Table scitype
1D iterable, of shape [n_instances] or 2D iterable, of shape [n_instances, n_dimensions] class labels for fitting 0-th indices correspond to instance indices in X 1-st indices (if applicable) correspond to multioutput vector indices in X supported sktime types: np.ndarray (1D, 2D), pd.Series, pd.DataFrame
- Returns:
- selfReference to self.
- fit_predict(X, y, cv=None, change_state=True)[source]#
Fit and predict labels for sequences in X.
Convenience method to produce in-sample predictions and cross-validated out-of-sample predictions.
- Writes to self, if change_state=True:
Sets self.is_fitted to True. Sets fitted model attributes ending in “_”.
Does not update state if change_state=False.
- Parameters:
- Xsktime compatible time series panel data container of Panel scitype
time series to fit to and predict labels for.
Can be in any mtype of
Panel
scitype, for instance:pd-multiindex: pd.DataFrame with columns = variables, index = pd.MultiIndex with first level = instance indices, second level = time indices
numpy3D: 3D np.array (any number of dimensions, equal length series) of shape [n_instances, n_dimensions, series_length]
or of any other supported
Panel
mtype
for list of mtypes, see
datatypes.SCITYPE_REGISTER
for specifications, see
examples/AA_datatypes_and_datasets.ipynb
Not all estimators support panels with multivariate or unequal length series, see the tag reference for details.
- ysktime compatible tabular data container, Table scitype
1D iterable, of shape [n_instances] or 2D iterable, of shape [n_instances, n_dimensions] class labels for fitting 0-th indices correspond to instance indices in X 1-st indices (if applicable) correspond to multioutput vector indices in X supported sktime types: np.ndarray (1D, 2D), pd.Series, pd.DataFrame
- cvNone, int, or sklearn cross-validation object, optional, default=None
None : predictions are in-sample, equivalent to
fit(X, y).predict(X)
cv : predictions are equivalent to
fit(X_train, y_train).predict(X_test)
, where multipleX_train
,y_train
,X_test
are obtained fromcv
folds. returnedy
is union over all test fold predictions,cv
test folds must be non-intersectingint : equivalent to
cv=KFold(cv, shuffle=True, random_state=x)
, i.e., k-fold cross-validation predictions out-of-sample, and whererandom_state
x
is taken fromself
if exists, otherwisex=None
- change_statebool, optional (default=True)
if False, will not change the state of the classifier, i.e., fit/predict sequence is run with a copy, self does not change
if True, will fit self to the full X and y, end state will be equivalent to running fit(X, y)
- Returns:
- y_predsktime compatible tabular data container, of Table scitype
predicted class labels
1D iterable, of shape [n_instances], or 2D iterable, of shape [n_instances, n_dimensions].
0-th indices correspond to instance indices in X, 1-st indices (if applicable) correspond to multioutput vector indices in X.
1D np.npdarray, if y univariate (one dimension); otherwise, same type as y passed in fit
- fit_predict_proba(X, y, cv=None, change_state=True)[source]#
Fit and predict labels probabilities for sequences in X.
Convenience method to produce in-sample predictions and cross-validated out-of-sample predictions.
- Writes to self, if change_state=True:
Sets self.is_fitted to True. Sets fitted model attributes ending in “_”.
Does not update state if change_state=False.
- Parameters:
- Xsktime compatible time series panel data container of Panel scitype
time series to fit to and predict labels for.
Can be in any mtype of
Panel
scitype, for instance:pd-multiindex: pd.DataFrame with columns = variables, index = pd.MultiIndex with first level = instance indices, second level = time indices
numpy3D: 3D np.array (any number of dimensions, equal length series) of shape [n_instances, n_dimensions, series_length]
or of any other supported
Panel
mtype
for list of mtypes, see
datatypes.SCITYPE_REGISTER
for specifications, see
examples/AA_datatypes_and_datasets.ipynb
Not all estimators support panels with multivariate or unequal length series, see the tag reference for details.
- ysktime compatible tabular data container, Table scitype
1D iterable, of shape [n_instances] or 2D iterable, of shape [n_instances, n_dimensions] class labels for fitting 0-th indices correspond to instance indices in X 1-st indices (if applicable) correspond to multioutput vector indices in X supported sktime types: np.ndarray (1D, 2D), pd.Series, pd.DataFrame
- cvNone, int, or sklearn cross-validation object, optional, default=None
None : predictions are in-sample, equivalent to
fit(X, y).predict(X)
cv : predictions are equivalent to
fit(X_train, y_train).predict(X_test)
, where multipleX_train
,y_train
,X_test
are obtained fromcv
folds. returnedy
is union over all test fold predictions,cv
test folds must be non-intersectingint : equivalent to
cv=KFold(cv, shuffle=True, random_state=x)
, i.e., k-fold cross-validation predictions out-of-sample, and whererandom_state
x
is taken fromself
if exists, otherwisex=None
- change_statebool, optional (default=True)
if False, will not change the state of the classifier, i.e., fit/predict sequence is run with a copy, self does not change
if True, will fit self to the full X and y, end state will be equivalent to running fit(X, y)
- Returns:
- y_pred2D np.array of int, of shape [n_instances, n_classes]
predicted class label probabilities 0-th indices correspond to instance indices in X 1-st indices correspond to class index, in same order as in self.classes_ entries are predictive class probabilities, summing to 1
- classmethod get_class_tag(tag_name, tag_value_default=None)[source]#
Get class tag value from class, with tag level inheritance from parents.
Every
scikit-base
compatible object has a dictionary of tags. Tags may be used to store metadata about the object, or to control behaviour of the object.Tags are key-value pairs specific to an instance
self
, they are static flags that are not changed after construction of the object.The
get_class_tag
method is a class method, and retrieves the value of a tag taking into account only class-level tag values and overrides.It returns the value of the tag with name
tag_name
from the object, taking into account tag overrides, in the following order of descending priority:Tags set in the
_tags
attribute of the class.Tags set in the
_tags
attribute of parent classes,
in order of inheritance.
Does not take into account dynamic tag overrides on instances, set via
set_tags
orclone_tags
, that are defined on instances.To retrieve tag values with potential instance overrides, use the
get_tag
method instead.- 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 inself
. If not found, returnstag_value_default
.
- classmethod get_class_tags()[source]#
Get class tags from class, with tag level inheritance from parent classes.
Every
scikit-base
compatible object has a dictionary of tags. Tags may be used to store metadata about the object, or to control behaviour of the object.Tags are key-value pairs specific to an instance
self
, they are static flags that are not changed after construction of the object.The
get_class_tags
method is a class method, and retrieves the value of a tag taking into account only class-level tag values and overrides.It returns a dictionary with keys being keys of any attribute of
_tags
set in the class or any of its parent classes.Values are the corresponding tag values, with overrides in the following order of descending priority:
Tags set in the
_tags
attribute of the class.Tags set in the
_tags
attribute of parent classes,
in order of inheritance.
Instances can override these tags depending on hyper-parameters.
To retrieve tags with potential instance overrides, use the
get_tags
method instead.Does not take into account dynamic tag overrides on instances, set via
set_tags
orclone_tags
, that are defined on instances.For including overrides from dynamic tags, use
get_tags
.- Returns:
- collected_tagsdict
Dictionary of tag name : tag value pairs. Collected from
_tags
class attribute via nested inheritance. NOT overridden by dynamic tags set byset_tags
orclone_tags
.
- get_config()[source]#
Get config flags for self.
Configs are key-value pairs of
self
, typically used as transient flags for controlling behaviour.get_config
returns dynamic configs, which override the default configs.Default configs are set in the class attribute
_config
of the class or its parent classes, and are overridden by dynamic configs set viaset_config
.Configs are retained under
clone
orreset
calls.- Returns:
- config_dictdict
Dictionary of config name : config value pairs. Collected from _config class attribute via nested inheritance and then any overrides and new tags from _onfig_dynamic object attribute.
- get_fitted_params(deep=True)[source]#
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 objectif
deep=True
, also contains keys/value pairs of component parameters parameters of components are indexed as[componentname]__[paramname]
all parameters ofcomponentname
appear asparamname
with its valueif
deep=True
, also contains arbitrary levels of component recursion, e.g.,[componentname]__[componentcomponentname]__[paramname]
, etc
- classmethod get_param_defaults()[source]#
Get object’s parameter defaults.
- Returns:
- default_dict: dict[str, Any]
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(sort=True)[source]#
Get object’s parameter names.
- Parameters:
- sortbool, default=True
Whether to return the parameter names sorted in alphabetical order (True), or in the order they appear in the class
__init__
(False).
- Returns:
- param_names: list[str]
List of parameter names of
cls
. Ifsort=False
, in same order as they appear in the class__init__
. Ifsort=True
, alphabetically ordered.
- get_params(deep=True)[source]#
Get a dict of parameters values for this object.
- Parameters:
- deepbool, default=True
Whether to return parameters of components.
If
True
, will return adict
of parameter name : value for this object, including parameters of components (=BaseObject
-valued parameters).If
False
, will return adict
of parameter name : value for this object, but not include parameters of components.
- Returns:
- paramsdict with str-valued keys
Dictionary of parameters, paramname : paramvalue keys-value pairs include:
always: all parameters of this object, as via
get_param_names
values are parameter value for that key, of this object values are always identical to values passed at constructionif
deep=True
, also contains keys/value pairs of component parameters parameters of components are indexed as[componentname]__[paramname]
all parameters ofcomponentname
appear asparamname
with its valueif
deep=True
, also contains arbitrary levels of component recursion, e.g.,[componentname]__[componentcomponentname]__[paramname]
, etc
- get_tag(tag_name, tag_value_default=None, raise_error=True)[source]#
Get tag value from instance, with tag level inheritance and overrides.
Every
scikit-base
compatible object has a dictionary of tags. Tags may be used to store metadata about the object, or to control behaviour of the object.Tags are key-value pairs specific to an instance
self
, they are static flags that are not changed after construction of the object.The
get_tag
method retrieves the value of a single tag with nametag_name
from the instance, taking into account tag overrides, in the following order of descending priority:Tags set via
set_tags
orclone_tags
on the instance,
at construction of the instance.
Tags set in the
_tags
attribute of the class.Tags set in the
_tags
attribute of parent classes,
in order of inheritance.
- 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_valueAny
Value of the
tag_name
tag inself
. If not found, raises an error ifraise_error
is True, otherwise it returnstag_value_default
.
- Raises:
- ValueError, if
raise_error
isTrue
. The
ValueError
is then raised iftag_name
is not inself.get_tags().keys()
.
- ValueError, if
- get_tags()[source]#
Get tags from instance, with tag level inheritance and overrides.
Every
scikit-base
compatible object has a dictionary of tags. Tags may be used to store metadata about the object, or to control behaviour of the object.Tags are key-value pairs specific to an instance
self
, they are static flags that are not changed after construction of the object.The
get_tags
method returns a dictionary of tags, with keys being keys of any attribute of_tags
set in the class or any of its parent classes, or tags set viaset_tags
orclone_tags
.Values are the corresponding tag values, with overrides in the following order of descending priority:
Tags set via
set_tags
orclone_tags
on the instance,
at construction of the instance.
Tags set in the
_tags
attribute of the class.Tags set in the
_tags
attribute of parent classes,
in order of inheritance.
- 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')[source]#
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. For classifiers, a “default” set of parameters should be provided for general testing, and a “results_comparison” set for comparing against previously recorded results if the general set does not produce suitable probabilities to compare against.
- 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)
orMyClass(**params[i])
creates a valid test instance.create_test_instance
uses the first (or only) dictionary inparams
.
- is_composite()[source]#
Check if the object is composed of other BaseObjects.
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 an object has any parameters whose values are
BaseObject
descendant instances.
- property is_fitted[source]#
Whether
fit
has been called.Inspects object’s
_is_fitted` attribute that should initialize to ``False
during object construction, and be set to True in calls to an object’s fit method.- Returns:
- bool
Whether the estimator has been fit.
- classmethod load_from_path(serial)[source]#
Load object from file location.
- Parameters:
- serialName of the zip file.
- Returns:
- deserialized self resulting in output at
path
, ofcls.save(path)
- deserialized self resulting in output at
- classmethod load_from_serial(serial)[source]#
Load object from serialized memory container.
- Parameters:
- serial: 1st element of output of ``cls.save(None)``
This is a tuple of size 3. The first element represents pickle-serialized instance. The second element represents h5py-serialized
keras
model. The third element represent pickle-serialized history of.fit()
.
- Returns:
- Deserialized self resulting in output
serial
, ofcls.save(None)
- Deserialized self resulting in output
- predict(X)[source]#
Predicts labels for sequences in X.
- Parameters:
- Xsktime compatible time series panel data container of Panel scitype
time series to predict labels for.
Can be in any mtype of
Panel
scitype, for instance:pd-multiindex: pd.DataFrame with columns = variables, index = pd.MultiIndex with first level = instance indices, second level = time indices
numpy3D: 3D np.array (any number of dimensions, equal length series) of shape [n_instances, n_dimensions, series_length]
or of any other supported
Panel
mtype
for list of mtypes, see
datatypes.SCITYPE_REGISTER
for specifications, see
examples/AA_datatypes_and_datasets.ipynb
Not all estimators support panels with multivariate or unequal length series, see the tag reference for details.
- Returns:
- y_predsktime compatible tabular data container, of Table scitype
predicted class labels
1D iterable, of shape [n_instances], or 2D iterable, of shape [n_instances, n_dimensions].
0-th indices correspond to instance indices in X, 1-st indices (if applicable) correspond to multioutput vector indices in X.
1D np.npdarray, if y univariate (one dimension); otherwise, same type as y passed in fit
- predict_proba(X)[source]#
Predicts labels probabilities for sequences in X.
- Parameters:
- Xsktime compatible time series panel data container of Panel scitype
time series to predict labels for.
Can be in any mtype of
Panel
scitype, for instance:pd-multiindex: pd.DataFrame with columns = variables, index = pd.MultiIndex with first level = instance indices, second level = time indices
numpy3D: 3D np.array (any number of dimensions, equal length series) of shape [n_instances, n_dimensions, series_length]
or of any other supported
Panel
mtype
for list of mtypes, see
datatypes.SCITYPE_REGISTER
for specifications, see
examples/AA_datatypes_and_datasets.ipynb
Not all estimators support panels with multivariate or unequal length series, see the tag reference for details.
- Returns:
- y_pred2D np.array of int, of shape [n_instances, n_classes]
predicted class label probabilities 0-th indices correspond to instance indices in X 1-st indices correspond to class index, in same order as in self.classes_ entries are predictive class probabilities, summing to 1
- reset()[source]#
Reset the object to a clean post-init state.
Results in setting
self
to the state it had directly after the constructor call, with the same hyper-parameters. Config values set byset_config
are also retained.A
reset
call deletes any object attributes, except:hyper-parameters = arguments of
__init__
written toself
, e.g.,self.paramname
whereparamname
is an argument of__init__
object attributes containing double-underscores, i.e., the string “__”. For instance, an attribute named “__myattr” is retained.
config attributes, configs are retained without change. That is, results of
get_config
before and afterreset
are equal.
Class and object methods, and class attributes are also unaffected.
Equivalent to
clone
, with the exception thatreset
mutatesself
instead of returning a new object.After a
self.reset()
call,self
is equal in value and state, to the object obtained after a constructor call``type(self)(**self.get_params(deep=False))``.- Returns:
- self
Instance of class reset to a clean post-init state but retaining the current hyper-parameter values.
- save(path=None, serialization_format='pickle')[source]#
Save serialized self to bytes-like object or to (.zip) file.
Behaviour: if
path
is None, returns an in-memory serialized self ifpath
is a file, stores the zip with that name at the location. The contents of the zip file are: _metadata - contains class of self, i.e., type(self). _obj - serialized self. This class uses the default serialization (pickle). keras/ - model, optimizer and state stored inside this directory. history - serialized history object.- 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. For eg:
path=”estimator” then a zip file
estimator.zip
will be made at cwd. path=”/home/stored/estimator” then a zip fileestimator.zip
will be stored in/home/stored/
.- serialization_formatstr, default = “pickle”
Module to use for serialization. The available options are present under
sktime.base._base.SERIALIZATION_FORMATS
. Note that non-default formats might require installation of other soft dependencies.
- Returns:
- if
path
is None - in-memory serialized self - if
path
is file location - ZipFile with reference to the file
- if
- score(X, y) float [source]#
Scores predicted labels against ground truth labels on X.
- Parameters:
- Xsktime compatible time series panel data container of Panel scitype
time series to score predicted labels for.
Can be in any mtype of
Panel
scitype, for instance:pd-multiindex: pd.DataFrame with columns = variables, index = pd.MultiIndex with first level = instance indices, second level = time indices
numpy3D: 3D np.array (any number of dimensions, equal length series) of shape [n_instances, n_dimensions, series_length]
or of any other supported
Panel
mtype
for list of mtypes, see
datatypes.SCITYPE_REGISTER
for specifications, see
examples/AA_datatypes_and_datasets.ipynb
Not all estimators support panels with multivariate or unequal length series, see the tag reference for details.
- ysktime compatible tabular data container, Table scitype
1D iterable, of shape [n_instances] or 2D iterable, of shape [n_instances, n_dimensions] class labels for fitting 0-th indices correspond to instance indices in X 1-st indices (if applicable) correspond to multioutput vector indices in X supported sktime types: np.ndarray (1D, 2D), pd.Series, pd.DataFrame
- Returns:
- float, accuracy score of predict(X) vs y
- set_config(**config_dict)[source]#
Set config flags to given values.
- Parameters:
- config_dictdict
Dictionary of config name : config value pairs. Valid configs, values, and their meaning is listed below:
- displaystr, “diagram” (default), or “text”
how jupyter kernels display instances of self
“diagram” = html box diagram representation
“text” = string printout
- print_changed_onlybool, default=True
whether printing of self lists only self-parameters that differ from defaults (False), or all parameter names and values (False). Does not nest, i.e., only affects self and not component estimators.
- warningsstr, “on” (default), or “off”
whether to raise warnings, affects warnings from sktime only
“on” = will raise warnings from sktime
“off” = will not raise warnings from sktime
- backend:parallelstr, optional, default=”None”
backend to use for parallelization when broadcasting/vectorizing, one of
“None”: executes loop sequentally, simple list comprehension
“loky”, “multiprocessing” and “threading”: uses
joblib.Parallel
“joblib”: custom and 3rd party
joblib
backends, e.g.,spark
“dask”: uses
dask
, requiresdask
package in environment
- backend:parallel:paramsdict, optional, default={} (no parameters passed)
additional parameters passed to the parallelization backend as config. Valid keys depend on the value of
backend:parallel
:“None”: no additional parameters,
backend_params
is ignored“loky”, “multiprocessing” and “threading”: default
joblib
backends any valid keys forjoblib.Parallel
can be passed here, e.g.,n_jobs
, with the exception ofbackend
which is directly controlled bybackend
. Ifn_jobs
is not passed, it will default to-1
, other parameters will default tojoblib
defaults.“joblib”: custom and 3rd party
joblib
backends, e.g.,spark
. Any valid keys forjoblib.Parallel
can be passed here, e.g.,n_jobs
,backend
must be passed as a key ofbackend_params
in this case. Ifn_jobs
is not passed, it will default to-1
, other parameters will default tojoblib
defaults.“dask”: any valid keys for
dask.compute
can be passed, e.g.,scheduler
- Returns:
- selfreference to self.
Notes
Changes object state, copies configs in config_dict to self._config_dynamic.
- set_params(**params)[source]#
Set the parameters of this object.
The method works on simple skbase objects as well as on composite objects. Parameter key strings
<component>__<parameter>
can be used for composites, i.e., objects that contain other objects, to access<parameter>
in the component<component>
. The string<parameter>
, without<component>__
, can also be used if this makes the reference unambiguous, e.g., there are no two parameters of components with the name<parameter>
.- Parameters:
- **paramsdict
BaseObject parameters, keys must be
<component>__<parameter>
strings.__
suffixes can alias full strings, if unique among get_params keys.
- Returns:
- selfreference to self (after parameters have been set)
- set_random_state(random_state=None, deep=True, self_policy='copy')[source]#
Set random_state pseudo-random seed parameters for self.
Finds
random_state
named parameters viaself.get_params
, and sets them to integers derived fromrandom_state
viaset_params
. These integers are sampled from chain hashing viasample_dependent_seed
, and guarantee pseudo-random independence of seeded random generators.Applies to
random_state
parameters inself
, depending onself_policy
, and remaining component objects if and only ifdeep=True
.Note: calls
set_params
even ifself
does not have arandom_state
, or none of the components have arandom_state
parameter. Therefore,set_random_state
will reset anyscikit-base
object, even those without arandom_state
parameter.- Parameters:
- random_stateint, RandomState instance or None, default=None
Pseudo-random number generator to control the generation of the random integers. Pass int for reproducible output across multiple function calls.
- deepbool, default=True
Whether to set the random state in skbase object valued parameters, i.e., component estimators.
If False, will set only
self
’srandom_state
parameter, if exists.If True, will set
random_state
parameters in component objects as well.
- self_policystr, one of {“copy”, “keep”, “new”}, default=”copy”
“copy” :
self.random_state
is set to inputrandom_state
“keep” :
self.random_state
is kept as is“new” :
self.random_state
is set to a new random state,
derived from input
random_state
, and in general different from it
- Returns:
- selfreference to self
- set_tags(**tag_dict)[source]#
Set instance level tag overrides to given values.
Every
scikit-base
compatible object has a dictionary of tags. Tags may be used to store metadata about the object, or to control behaviour of the object.Tags are key-value pairs specific to an instance
self
, they are static flags that are not changed after construction of the object.set_tags
sets dynamic tag overrides to the values as specified intag_dict
, with keys being the tag name, and dict values being the value to set the tag to.The
set_tags
method should be called only in the__init__
method of an object, during construction, or directly after construction via__init__
.Current tag values can be inspected by
get_tags
orget_tag
.- Parameters:
- **tag_dictdict
Dictionary of tag name: tag value pairs.
- Returns:
- Self
Reference to self.