sktime.classification.shapelet_based.mrseql.mrseql

class sktime.classification.shapelet_based.mrseql.mrseql.AdaptedSFA(N, w, a)[source]

Bases: object

SFA adaptation for Mr-SEQL. This code uses a different alphabet for each Fourier coefficient in the output of SFA.

fit(train_x)[source]
timeseries2SFAseq(ts)[source]
class sktime.classification.shapelet_based.mrseql.mrseql.MrSEQLClassifier(seql_mode='fs', symrep='sax', custom_config=None)[source]

Bases: sktime.classification.base.BaseClassifier

Time Series Classification with multiple symbolic representations and SEQL (Mr-SEQL)

@article{mrseql, author = {Le Nguyen, Thach and Gsponer, Severin and Ilie, Iulia and O’reilly, Martin and Ifrim, Georgiana}, title = {Interpretable Time Series Classification Using Linear Models and Multi-resolution Multi-domain Symbolic Representations}, journal = {Data Mining and Knowledge Discovery}, volume = {33}, number = {4}, year = {2019}, }

Overview: Mr-SEQL is a time series classifier that learn from multiple symbolic representations of multiple resolutions and multiple domains. Currently, Mr-SEQL supports both SAX and SFA representations.

Parameters
  • seql_mode (str, either 'clf' or 'fs'. In the 'clf' mode, Mr-SEQL is an ensemble of SEQL models while in the 'fs' mode Mr-SEQL trains a logistic regression model with features extracted by SEQL from symbolic representations of time series.) –

  • symrep (list or tuple, should contains only 'sax' or 'sfa' or both. The symbolic representations to be used to transform the input time series.) –

  • custom_config (dict, customized parameters for the symbolic transformation. If defined, symrep will be ignored.) –

capabilities = {'missing_values': False, 'multivariate': False, 'unequal_length': False}[source]
fit(X, y)[source]

Fit the model according to the given training time series data. :param X: :type X: Time series data. :param y: :type y: Target vector relative to X.

Returns

Fitted estimator.

Return type

self

map_sax_model(ts)[source]

For interpretation. Returns vectors of weights with the same length of the input time series. The weight of each point implies its contribution in the classification decision regarding the class.

Parameters

ts (A single time series.) –

Returns

weighted_ts

Return type

ndarray of (number of classes, length of time series)

Note

Only supports univariate time series and SAX features.

predict(X)[source]

Predict class labels for samples in X. :param X: :type X: time series data.

Returns

C – Predicted class label per sample.

Return type

array

predict_proba(X)[source]

If seql_mode is set to ‘fs’, it returns the estimation by sklearn logistic regression model. Otherwise (seql_mode == ‘clf’), it returns normalized probability estimated with one-versus-all method.

Parameters

X (time series data.) –

Returns

T – Returns the probability of the sample for each class in the model, where classes are ordered as they are in self.classes_.

Return type

array-like of shape (n_samples, n_classes)

class sktime.classification.shapelet_based.mrseql.mrseql.PySAX[source]

Bases: object

Wrapper of SAX C++ implementation.

map_weighted_patterns()[source]
timeseries2SAX()[source]
timeseries2SAXseq()[source]
class sktime.classification.shapelet_based.mrseql.mrseql.PySEQL[source]

Bases: object

Wrapper of SEQL C++ implementation.

learn()[source]
class sktime.classification.shapelet_based.mrseql.mrseql.SEQLCLF[source]

Bases: object

SEQL with multiple symbolic representations of time series.

fit(mr_seqs, labels)[source]
get_sequence_features()[source]
is_binary()[source]
predict_proba(mr_seqs)[source]