Prime dilation slower on some cases about convst

[baraline]

When using the benchmark script (3cv) on the Rock dataset, we notice a very high standard deviation for RDST Ensemble Prime:

n_timestamps	RDST Prime	RDST Ensemble Prime	RDST	RDST Ensemble	Rocket	MultiRocket	DrCIF	TDE	STC	HC2	RDST Prime_std	RDST Ensemble Prime_std	RDST_std	RDST Ensemble_std	Rocket_std	MultiRocket_std	DrCIF_std	TDE_std	STC_std	HC2_std
474	1.0630292184650898	25.052939302287996	1.4401322677731514	4.770994765684009	9.273215881548822	23.353670642711222	49.786722905002534	77.79590922128409	80.43357326462865	7884.829313875176	0.004903493449091911	16.0698274159804	0.01821163296699524	3.447608422487974	0.021983223967254162	0.48081249091774225	7.353978189639747	18.233609943650663	0.43430908396840096	3.822393278591335
948	2.377154231071472	25.898940067738295	3.0750416861847043	3.0481695402413607	13.261304871179163	24.991533936932683	55.76158287934959	108.96176979038864	101.37790459487587	7960.439127580263	0.006226222962141037	16.065012263134122	0.06886849086731672	0.03800296410918236	0.03066807147115469	0.5928529351949692	7.1618244629353285	12.518209223635495	3.9045204231515527	0.2534934086725116
1422	3.4417717000469565	27.04298056382686	5.201300728134811	8.40116765908897	18.263901693746448	25.862254047766328	64.07761400006711	119.52752438280731	102.72296567447484	7970.518767527305	0.011878960765898228	15.908938153646886	0.006814070977270603	3.253817331045866	0.9779286533594131	0.002123715355992317	7.804655771702528	24.37082715984434	5.647393397986889	11.92994621861726
1896	4.6401264341548085	28.24125813692808	8.04301328677684	11.572580952197313	21.337463438510895	27.698388851247728	67.51543310005218	150.7656864784658	111.24626373499632	8078.189195295796	0.08419013861566782	15.739565890282393	0.008268513716757298	4.111701520159841	0.04452386498451233	0.7664412679150701	9.207885961048305	18.048286149278283	0.7535054516047239	3.2518416047096252
2370	6.071481054648757	27.885636082850397	11.334959764964879	13.973823537118733	25.491070554591715	29.93132807407528	77.2411882840097	177.13270619604737	122.8379875915125	8132.091950537637	0.0660868901759386	15.293029426597059	0.4737072614952922	2.480648464523256	0.006583829410374165	0.9237984782084823	9.473532510921359	0.10296206641942263	0.4684769967570901	11.067898195236921

A possible cause would an issue with numba not caching the function correctly, and having to recompile some before each new step of validation for RDST Ensemble Prime. This may also be true for RDST Ensemble.

The problem also appears in UCR cross validation run, (only) the first dataset had a high standard deviation for timing, despite a first run on a synthetic dataset before.

[baraline]

Code to reproduce the issue :

from convst.classifiers import R_DST_Ensemble
from convst.utils.dataset_utils import load_sktime_dataset_split
from sktime.classification.kernel_based import RocketClassifier
from timeit import default_timer as timer
import pandas as pd

def time_pipe(pipeline, X_train, y_train, X_test):
    t0 = timer()
    pipeline.fit(X_train, y_train)
    pipeline.predict(X_test)
    t1 = timer()
    return t1-t0


X_train, X_test, y_train, y_test, _ = load_sktime_dataset_split('GunPoint')
rdst = R_DST_Ensemble(n_shapelets_per_estimator=1, n_jobs=-1)
rkt = RocketClassifier(rocket_transform='minirocket', n_jobs=-1)
df = pd.DataFrame()

for i in range(10):
    df.loc[i,'rdst'] = time_pipe(rdst, X_train, y_train, X_test)
    df.loc[i,'rkt'] = time_pipe(rkt, X_train, y_train, X_test)

Results in

        rdst       rkt
0  49.011466  0.335395
1   0.110011  0.351882
2   0.090319  0.370477
3   0.106159  0.346266
4  10.088280  0.368425
5   0.120686  0.353858
6   0.147703  0.352415
7   0.127688  0.335258
8   0.127785  0.354415
9   8.584705  0.341209

If Rocket is not called, the results are similar. The problem would then come from some numba function that need to be compiled again after some runs.

[baraline]

Fixed by #36