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 ajoblib.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.
-
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