RandomIntervalSpectralForest

class sktime.classification.interval_based.RandomIntervalSpectralForest(n_estimators=200, min_interval=16, acf_lag=100, acf_min_values=4, n_jobs=None, random_state=None)[source]

Random Interval Spectral Forest (RISE) from [1]

Overview:

Input: n series length m
for each tree
    sample a random intervals
    take the ACF and PS over this interval, and concatenate features
    build tree on new features
ensemble the trees through averaging probabilities.

Need to have a minimum interval for each tree This is from the python github.

Parameters
  • n_estimators (int, optional (default=200)) – The number of trees in the forest.

  • min_interval (int, optional (default=16)) – The minimum width of an interval.

  • acf_lag (int, optional (default=100)) – The maximum number of autocorrelation terms to use.

  • acf_min_values (int, optional (default=4)) – Never use fewer than this number of terms to find a correlation.

  • n_jobs (int or None, optional (default=None)) – The number of jobs to run in parallel for both fit and predict. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors.

  • random_state (int, RandomState instance or None, optional (default=None)) – If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.

n_classes[source]

The number of classes, extracted from the data.

Type

int

classifiers[source]
Type

array of shape = [n_estimators] of DecisionTree classifiers

intervals[source]

Stores indexes of start and end points for all classifiers.

Type

array of shape = [n_estimators][2]

Notes

..[1] Jason Lines, Sarah Taylor and Anthony Bagnall, “Time Series Classification with HIVE-COTE: The Hierarchical Vote Collective of Transformation-Based Ensembles”,

ACM Transactions on Knowledge and Data Engineering, 12(5): 2018

https://dl.acm.org/doi/10.1145/3182382 Java implementation https://github.com/uea-machine-learning/tsml/blob/master/src/main/java/tsml/ classifiers/frequency_based/RISE.java

__init__(n_estimators=200, min_interval=16, acf_lag=100, acf_min_values=4, n_jobs=None, random_state=None)[source]

Initialize self. See help(type(self)) for accurate signature.