Note
Click here to download the full example code
Optimization Info#
Tpcp is focused on “running” pipelines and less on the “optimization” step.
This is great for traditional algorithms and algorithms will complex return values, as you can easily store multiple
parameters as attributes on the object during run.
However, for optimization, you are limited to modifying input parameters. This works well in many cases, but sometimes, you need additional information from the optimization. For example, you might want to extract the loss decay of an iterative learning algorithms. This information is something that you wouldn’t want to store in the input parameters (usually).
For these cases tpcp provides the self_optimize_with_info method.
This is basically identical to self_optimize, but is expected to provide two return values: the optimized instance
AND an arbitrary additional object containing any information you like.
Methods that get optimizable pipelines as input (e.g. Optimize are aware of these method and
will call self_optimize_with_info if available and store the additional info as result objects.
The OptimizablePipeline base-class is implemented in a way that you only need to worry about
implementing either the self_optimize_with_info or the self_optimize method.
The other will be available automatically (the additional info will be NOTHING, if the method is not implemented).
If you are implementing a new Algorithm (instead of a pipeline), we don’t provide this additional support, but it is relatively simple to implement yourself.
In the following we will show how all of this works by expanding the QRS detection algorithm implemented in the other examples to return additional information from the optimization.
from typing import Dict, List, Tuple
import numpy as np
import pandas as pd
from sklearn.metrics import roc_curve
from typing_extensions import Self
from examples.algorithms.algorithms_qrs_detection_final import (
OptimizableQrsDetector,
QRSDetector,
match_events_with_reference,
)
from examples.datasets.datasets_final_ecg import ECGExampleData
from tpcp import HyperParameter, OptimizableParameter, OptimizablePipeline, Parameter, cf, make_optimize_safe
from tpcp.optimize import Optimize
In the algorithm class below, we basically reimplemented the OptimizableQrsDetector from the algorithm example.
However, instead of the self_optimize method, we implemented the self_optimize_with_info method and added
additional information from the threshold selection process to the output of the optimization.
To ensure interface compatibility with other algorithms, we also provided a self_optimize method, that simply calls
self_optimize_with_info under the hood.
class OptimizableQrsDetectorWithInfo(QRSDetector):
min_r_peak_height_over_baseline: OptimizableParameter[float]
r_peak_match_tolerance_s: HyperParameter[float]
def __init__(
self,
max_heart_rate_bpm: float = 200.0,
min_r_peak_height_over_baseline: float = 1.0,
r_peak_match_tolerance_s: float = 0.01,
high_pass_filter_cutoff_hz: float = 1,
):
self.r_peak_match_tolerance_s = r_peak_match_tolerance_s
super().__init__(
max_heart_rate_bpm=max_heart_rate_bpm,
min_r_peak_height_over_baseline=min_r_peak_height_over_baseline,
high_pass_filter_cutoff_hz=high_pass_filter_cutoff_hz,
)
@make_optimize_safe
def self_optimize_with_info(
self, ecg_data: List[pd.Series], r_peaks: List[pd.Series], sampling_rate_hz: float
) -> Tuple[Self, Dict[str, np.ndarray]]:
all_labels = []
all_peak_heights = []
for d, p in zip(ecg_data, r_peaks):
filtered = self._filter(d.to_numpy().flatten(), sampling_rate_hz)
# Find all potential peaks without the height threshold
potential_peaks = self._search_strategy(filtered, sampling_rate_hz, use_height=False)
# Determine the label for each peak, by matching them with our ground truth
labels = np.zeros(potential_peaks.shape)
matches = match_events_with_reference(
events=np.atleast_2d(potential_peaks).T,
reference=np.atleast_2d(p.to_numpy().astype(int)).T,
tolerance=self.r_peak_match_tolerance_s * sampling_rate_hz,
)
tp_matches = matches[(~np.isnan(matches)).all(axis=1), 0].astype(int)
labels[tp_matches] = 1
labels = labels.astype(bool)
all_labels.append(labels)
all_peak_heights.append(filtered[potential_peaks])
all_labels = np.hstack(all_labels)
all_peak_heights = np.hstack(all_peak_heights)
# We "brute-force" a good cutoff by testing a bunch of thresholds and then calculating the Youden Index for
# each.
fpr, tpr, thresholds = roc_curve(all_labels, all_peak_heights)
youden_index = tpr - fpr
# The best Youden index gives us a balance between sensitivity and specificity.
self.min_r_peak_height_over_baseline = thresholds[np.argmax(youden_index)]
# Here we create the additional infor object:
additional_info = {"all_youden_index": youden_index, "all_thresholds": thresholds}
return self, additional_info
def self_optimize(self, ecg_data: List[pd.Series], r_peaks: List[pd.Series], sampling_rate_hz: float) -> Self:
return self.self_optimize_with_info(ecg_data=ecg_data, r_peaks=r_peaks, sampling_rate_hz=sampling_rate_hz)[0]
To use this algorithm in an optimization, we need a pipeline to wrap it. Below we can find a reimplementation of the pipline from the “Optimizable Pipeline” example.
However, instead of implementing self_optimize method, we implemented the self_optimize_with_info method and
also called the self_optimize_with_info of our algorithm under the hood.
Note, that for pipelines, we don’t need to implement a dummy self_optimize method.
Our baseclass already takes care of that.
class MyPipeline(OptimizablePipeline[ECGExampleData]):
algorithm: Parameter[OptimizableQrsDetectorWithInfo]
algorithm__min_r_peak_height_over_baseline: OptimizableParameter[float]
r_peak_positions_: pd.Series
def __init__(self, algorithm: OptimizableQrsDetectorWithInfo = cf(OptimizableQrsDetectorWithInfo())):
self.algorithm = algorithm
@make_optimize_safe
def self_optimize_with_info(self, dataset: ECGExampleData, **kwargs):
ecg_data = [d.data["ecg"] for d in dataset]
r_peaks = [d.r_peak_positions_["r_peak_position"] for d in dataset]
# Note: We need to clone the algorithm instance, to make sure we don't leak any data between runs.
algo = self.algorithm.clone()
# Here we call the `self_optimize_with_info` method!
self.algorithm, additional_data = algo.self_optimize_with_info(ecg_data, r_peaks, dataset.sampling_rate_hz)
return self, additional_data
def run(self, datapoint: ECGExampleData):
# Note: We need to clone the algorithm instance, to make sure we don't leak any data between runs.
algo = self.algorithm.clone()
algo.detect(datapoint.data["ecg"], datapoint.sampling_rate_hz)
self.r_peak_positions_ = algo.r_peak_positions_
return self
Let’s test this class!
However, first we need some test data
from pathlib import Path
from sklearn.model_selection import train_test_split
try:
HERE = Path(__file__).parent
except NameError:
HERE = Path(".").resolve()
data_path = HERE.parent.parent / "example_data/ecg_mit_bih_arrhythmia/data"
example_data = ECGExampleData(data_path)
train_set, test_set = train_test_split(example_data, train_size=0.7, random_state=0)
# We only want a single dataset in the test set
test_set = test_set[0]
With the train data we can try out the optimization
optimized_pipe, info = MyPipeline().self_optimize_with_info(train_set)
info
{'all_youden_index': array([0.00000000e+00, 6.19540301e-05, 1.52512466e-05, 8.86714683e-02,
8.86247656e-02, 1.01263388e-01, 1.01216685e-01, 1.07040364e-01,
1.06993661e-01, 1.07489293e-01, 1.07442590e-01, 1.07752361e-01,
1.07705658e-01, 1.23751752e-01, 1.23705049e-01, 1.24076773e-01,
1.24030070e-01, 1.29915703e-01, 1.29869000e-01, 1.30302678e-01,
1.30255976e-01, 1.31185286e-01, 1.31138583e-01, 1.36714446e-01,
1.36667743e-01, 1.36915559e-01, 1.36868857e-01, 1.39161156e-01,
1.39114453e-01, 1.40973074e-01, 1.40926371e-01, 1.42227406e-01,
1.42180703e-01, 1.43729554e-01, 1.43682851e-01, 1.57932278e-01,
1.57885575e-01, 2.48152597e-01, 2.48105894e-01, 2.48477618e-01,
2.48430916e-01, 2.49917812e-01, 2.49871109e-01, 2.50180880e-01,
2.50134177e-01, 2.51744982e-01, 2.51698279e-01, 2.52441727e-01,
2.52395024e-01, 2.53324335e-01, 2.53230929e-01, 2.53850470e-01,
2.53803767e-01, 2.55290664e-01, 2.55243961e-01, 2.57969938e-01,
2.57923235e-01, 2.59410132e-01, 2.59363429e-01, 2.59797107e-01,
2.59750405e-01, 2.60493853e-01, 2.60447150e-01, 2.60633012e-01,
2.60586310e-01, 2.61949298e-01, 2.61902595e-01, 2.63017768e-01,
2.62971065e-01, 2.63838422e-01, 2.63791719e-01, 2.65278616e-01,
2.65185210e-01, 2.68035095e-01, 2.67988393e-01, 2.70652416e-01,
2.70605713e-01, 2.71473070e-01, 2.71426367e-01, 2.72665447e-01,
2.72618745e-01, 2.72742653e-01, 2.72695950e-01, 2.75917559e-01,
2.75870857e-01, 2.76924075e-01, 2.76877372e-01, 2.77806683e-01,
2.77759980e-01, 2.77883888e-01, 2.77837185e-01, 2.78456726e-01,
2.78410023e-01, 2.79339333e-01, 2.79292630e-01, 2.79478493e-01,
2.79431790e-01, 2.88353170e-01, 2.88306467e-01, 2.92023709e-01,
2.91977006e-01, 2.95880110e-01, 2.95833407e-01, 3.01223408e-01,
3.01176705e-01, 3.02415786e-01, 3.02369083e-01, 3.02740807e-01,
3.02694105e-01, 3.13164336e-01, 3.13117633e-01, 3.42297981e-01,
3.42251278e-01, 3.46588060e-01, 3.46541358e-01, 3.47470668e-01,
3.47423965e-01, 3.51265115e-01, 3.51218412e-01, 3.59644160e-01,
3.59597458e-01, 3.66350447e-01, 3.66303744e-01, 3.85571447e-01,
3.85524745e-01, 3.94941757e-01, 3.94895054e-01, 4.04374021e-01,
4.04327318e-01, 4.12691112e-01, 4.12644410e-01, 4.30982802e-01,
4.30936100e-01, 4.36264146e-01, 4.36217444e-01, 4.46006180e-01,
4.45959477e-01, 4.49243041e-01, 4.49196338e-01, 4.52851626e-01,
4.52804923e-01, 4.54167912e-01, 4.54121209e-01, 4.55793968e-01,
4.55747265e-01, 4.55995081e-01, 4.55948379e-01, 4.63073092e-01,
4.63026389e-01, 4.63212251e-01, 4.63165549e-01, 4.63599227e-01,
4.63552524e-01, 4.64667696e-01, 4.64620994e-01, 4.64744902e-01,
4.64698199e-01, 4.74177166e-01, 4.74130463e-01, 4.74378279e-01,
4.74331576e-01, 4.78172726e-01, 4.78126023e-01, 4.78187977e-01,
4.78141274e-01, 4.86071390e-01, 4.86024688e-01, 5.16320208e-01,
5.16273505e-01, 5.22840633e-01, 5.22793930e-01, 5.23165654e-01,
5.23118951e-01, 5.24729756e-01, 5.24683053e-01, 5.27966617e-01,
5.27919914e-01, 5.28911179e-01, 5.28864476e-01, 5.29917694e-01,
5.29870992e-01, 5.31357888e-01, 5.31311185e-01, 5.31744864e-01,
5.31698161e-01, 5.41796668e-01, 5.41749965e-01, 5.47511690e-01,
5.47464987e-01, 5.47526941e-01, 5.47480238e-01, 5.52622423e-01,
5.52575720e-01, 5.56231008e-01, 5.56184305e-01, 5.59343961e-01,
5.59297258e-01, 5.62394959e-01, 5.62348256e-01, 5.64206877e-01,
5.64160175e-01, 5.71470750e-01, 5.71424047e-01, 5.73344622e-01,
5.73297920e-01, 5.89158151e-01, 5.89111448e-01, 5.89235356e-01,
5.89188654e-01, 5.89808194e-01, 5.89761491e-01, 5.97257929e-01,
5.97211226e-01, 5.98760077e-01, 5.98713374e-01, 6.00571995e-01,
6.00478589e-01, 6.05001234e-01, 6.04954531e-01, 6.14805222e-01,
6.14758519e-01, 6.15440013e-01, 6.15393310e-01, 6.16384575e-01,
6.16337872e-01, 6.19373620e-01, 6.19326917e-01, 6.20689905e-01,
6.20643203e-01, 6.22563778e-01, 6.22517075e-01, 6.23508339e-01,
6.23461636e-01, 6.26063706e-01, 6.26017003e-01, 6.28866888e-01,
6.28820186e-01, 6.41210992e-01, 6.41164289e-01, 6.44200036e-01,
6.44153333e-01, 6.45454368e-01, 6.45407665e-01, 6.46027206e-01,
6.45980503e-01, 6.47777170e-01, 6.47730467e-01, 6.51943341e-01,
6.51896638e-01, 6.52392270e-01, 6.52345568e-01, 6.52841200e-01,
6.52794497e-01, 6.59733348e-01, 6.59686646e-01, 6.64147336e-01,
6.64100633e-01, 6.65277760e-01, 6.65231057e-01, 6.66779908e-01,
6.66733205e-01, 6.70326539e-01, 6.70279836e-01, 6.72757997e-01,
6.72711294e-01, 6.77233938e-01, 6.77187236e-01, 6.80532753e-01,
6.80486050e-01, 6.82096855e-01, 6.82050152e-01, 6.82236015e-01,
6.82189312e-01, 6.82313220e-01, 6.82266517e-01, 6.83195828e-01,
6.83149125e-01, 6.90335792e-01, 6.90289089e-01, 7.03547252e-01,
7.03500549e-01, 7.05173308e-01, 7.05126605e-01, 7.07480858e-01,
7.07434155e-01, 7.11151397e-01, 7.11104695e-01, 7.11662281e-01,
7.11615578e-01, 7.11677532e-01, 7.11630829e-01, 7.12126461e-01,
7.12079759e-01, 7.15673092e-01, 7.15579687e-01, 7.18057848e-01,
7.18011145e-01, 7.21418617e-01, 7.21371914e-01, 7.22734903e-01,
7.22688200e-01, 7.24422913e-01, 7.24376210e-01, 7.26234831e-01,
7.26188128e-01, 7.26250082e-01, 7.26203379e-01, 7.27132690e-01,
7.27085987e-01, 7.27395757e-01, 7.27349055e-01, 7.28464227e-01,
7.28417524e-01, 7.33745571e-01, 7.33698868e-01, 7.39336685e-01,
7.39289982e-01, 7.41644235e-01, 7.41597532e-01, 7.42464889e-01,
7.42418186e-01, 7.45330025e-01, 7.45283323e-01, 7.46646311e-01,
7.46599609e-01, 7.47095241e-01, 7.47048538e-01, 7.49154975e-01,
7.49108272e-01, 7.49294134e-01, 7.49247432e-01, 7.49371340e-01,
7.49324637e-01, 7.50068085e-01, 7.50021382e-01, 7.53986440e-01,
7.53939738e-01, 7.55054910e-01, 7.55008207e-01, 7.55194069e-01,
7.55147367e-01, 7.56200585e-01, 7.56153882e-01, 7.57083193e-01,
7.57036490e-01, 7.57408214e-01, 7.57361511e-01, 7.57485419e-01,
7.57438717e-01, 7.59854924e-01, 7.59808221e-01, 7.61790750e-01,
7.61744047e-01, 7.61867955e-01, 7.61821253e-01, 7.61883207e-01,
7.61836504e-01, 7.62394090e-01, 7.62347387e-01, 7.63090836e-01,
7.63044133e-01, 7.63539765e-01, 7.63493062e-01, 7.67829844e-01,
7.67783142e-01, 7.69765671e-01, 7.69718968e-01, 7.70772186e-01,
7.70725484e-01, 7.70973300e-01, 7.70926597e-01, 7.74457977e-01,
7.74364571e-01, 7.76347100e-01, 7.76300397e-01, 7.76424305e-01,
7.76377602e-01, 7.76935189e-01, 7.76888486e-01, 7.79862279e-01,
7.79815577e-01, 7.82169830e-01, 7.82123127e-01, 7.83857840e-01,
7.83811137e-01, 7.87032747e-01, 7.86986044e-01, 7.88410987e-01,
7.88364284e-01, 7.89541410e-01, 7.89494708e-01, 7.89556662e-01,
7.89509959e-01, 7.89633867e-01, 7.89587164e-01, 7.89834980e-01,
7.89788277e-01, 7.90160002e-01, 7.90113299e-01, 7.90485023e-01,
7.90438320e-01, 7.91801309e-01, 7.91754606e-01, 7.92436100e-01,
7.92389398e-01, 7.93008938e-01, 7.92962235e-01, 7.93581775e-01,
7.93535073e-01, 7.93906797e-01, 7.93860094e-01, 7.95285037e-01,
7.95238334e-01, 7.96725231e-01, 7.96678528e-01, 8.00333816e-01,
8.00287113e-01, 8.00349067e-01, 8.00302364e-01, 8.03090295e-01,
8.03043593e-01, 8.03291409e-01, 8.03198003e-01, 8.03259957e-01,
8.03213255e-01, 8.04204519e-01, 8.04157816e-01, 8.05087127e-01,
8.05040424e-01, 8.05598010e-01, 8.05551307e-01, 8.06542572e-01,
8.06495869e-01, 8.07177363e-01, 8.07130661e-01, 8.07564339e-01,
8.07517636e-01, 8.10119705e-01, 8.10073003e-01, 8.11435991e-01,
8.11389288e-01, 8.11451242e-01, 8.11404540e-01, 8.11652356e-01,
8.11558950e-01, 8.11930674e-01, 8.11883972e-01, 8.11945926e-01,
8.11899223e-01, 8.13571982e-01, 8.13525279e-01, 8.13587233e-01,
8.13540530e-01, 8.14098116e-01, 8.14051414e-01, 8.14175322e-01,
8.14128619e-01, 8.14190573e-01, 8.14143870e-01, 8.15073181e-01,
8.14979775e-01, 8.15165637e-01, 8.15118934e-01, 8.15180888e-01,
8.15134186e-01, 8.16806944e-01, 8.16760242e-01, 8.17193920e-01,
8.17147217e-01, 8.18758022e-01, 8.18711319e-01, 8.22490515e-01,
8.22443812e-01, 8.23620939e-01, 8.23574236e-01, 8.23760098e-01,
8.23713395e-01, 8.23837303e-01, 8.23790600e-01, 8.24038417e-01,
8.23991714e-01, 8.27770910e-01, 8.27724207e-01, 8.27786161e-01,
8.27739458e-01, 8.28792677e-01, 8.28745974e-01, 8.29737238e-01,
8.29690536e-01, 8.30433984e-01, 8.30387281e-01, 8.31936132e-01,
8.31889429e-01, 8.32880694e-01, 8.32833991e-01, 8.41321693e-01,
8.41274990e-01, 8.42204301e-01, 8.42157598e-01, 8.42405414e-01,
8.42358711e-01, 8.45580321e-01, 8.45533618e-01, 8.48631319e-01,
8.48584617e-01, 8.49575881e-01, 8.49529178e-01, 8.51573661e-01,
8.51526959e-01, 8.54067074e-01, 8.54020371e-01, 8.57551751e-01,
8.57505048e-01, 8.60912520e-01, 8.60865817e-01, 8.61795127e-01,
8.61748424e-01, 8.62182103e-01, 8.62135400e-01, 8.65604826e-01,
8.65558123e-01, 8.67230882e-01, 8.67184179e-01, 8.72388317e-01,
8.72341615e-01, 8.75872994e-01, 8.75826291e-01, 8.83012959e-01,
8.82966256e-01, 8.89471429e-01, 8.89424727e-01, 8.90168175e-01,
8.90121472e-01, 8.90431242e-01, 8.90384540e-01, 8.96146264e-01,
8.96099562e-01, 8.96595194e-01, 8.96548491e-01, 9.04106883e-01,
9.04060180e-01, 9.07963284e-01, 9.07916581e-01, 9.12005547e-01,
9.11958844e-01, 9.13569649e-01, 9.13522946e-01, 9.18789039e-01,
9.18742336e-01, 9.21716129e-01, 9.21669427e-01, 9.21979197e-01,
9.21932494e-01, 9.25525828e-01, 9.25479125e-01, 9.26780160e-01,
9.26733457e-01, 9.29645296e-01, 9.29598593e-01, 9.33997330e-01,
9.33950627e-01, 9.35437524e-01, 9.35390821e-01, 9.35948407e-01,
9.35901704e-01, 9.36149520e-01, 9.36102818e-01, 9.37094082e-01,
9.37047379e-01, 9.37171287e-01, 9.37077882e-01, 9.39679951e-01,
9.39633248e-01, 9.40934283e-01, 9.40887580e-01, 9.41321258e-01,
9.41274556e-01, 9.41584326e-01, 9.41537623e-01, 9.41723485e-01,
9.41676782e-01, 9.45332070e-01, 9.45285367e-01, 9.47515712e-01,
9.47469009e-01, 9.48770044e-01, 9.48723341e-01, 9.48785295e-01,
9.48738593e-01, 9.49543995e-01, 9.49497292e-01, 9.50116832e-01,
9.50070130e-01, 9.50503808e-01, 9.50457105e-01, 9.50828829e-01,
9.50782127e-01, 9.51153851e-01, 9.51107148e-01, 9.51231056e-01,
9.51184353e-01, 9.55706997e-01, 9.55660295e-01, 9.56837421e-01,
9.56790718e-01, 9.57472213e-01, 9.57425510e-01, 9.58107004e-01,
9.58060301e-01, 9.58617888e-01, 9.58571185e-01, 9.58633139e-01,
9.58586436e-01, 9.59205977e-01, 9.59159274e-01, 9.59469044e-01,
9.59422341e-01, 9.59732111e-01, 9.59685408e-01, 9.59809317e-01,
9.59762614e-01, 9.60382154e-01, 9.60335451e-01, 9.60521313e-01,
9.60474611e-01, 9.60598519e-01, 9.60551816e-01, 9.60613770e-01,
9.60567067e-01, 9.60629021e-01, 9.60582318e-01, 9.61449675e-01,
9.61402972e-01, 9.61712742e-01, 9.61666039e-01, 9.63462706e-01,
9.63369301e-01, 9.63431255e-01, 9.63384552e-01, 9.63446506e-01,
9.63353100e-01, 9.63477008e-01, 9.63430306e-01, 9.63863984e-01,
9.63817281e-01, 9.64127051e-01, 9.64080348e-01, 9.64328165e-01,
9.64281462e-01, 9.64591232e-01, 9.64544529e-01, 9.64916253e-01,
9.64869551e-01, 9.64993459e-01, 9.64900053e-01, 9.65085915e-01,
9.65039212e-01, 9.65410937e-01, 9.65364234e-01, 9.65859866e-01,
9.65813163e-01, 9.65999025e-01, 9.65952323e-01, 9.66262093e-01,
9.66215390e-01, 9.66587114e-01, 9.66540411e-01, 9.66602365e-01,
9.66555663e-01, 9.66803479e-01, 9.66756776e-01, 9.67004592e-01,
9.66957889e-01, 9.67143751e-01, 9.67097048e-01, 9.67159003e-01,
9.67112300e-01, 9.67422070e-01, 9.67328664e-01, 9.67452572e-01,
9.67359167e-01, 9.67421121e-01, 9.67374418e-01, 9.68241774e-01,
9.68195072e-01, 9.68318980e-01, 9.68272277e-01, 9.68767909e-01,
9.68721206e-01, 9.68907069e-01, 9.68813663e-01, 9.68875617e-01,
9.68828914e-01, 9.68890868e-01, 9.68844165e-01, 9.69091982e-01,
9.69045279e-01, 9.69355049e-01, 9.69261643e-01, 9.69323597e-01,
9.69276895e-01, 9.69772527e-01, 9.69725824e-01, 9.69787778e-01,
9.69741075e-01, 9.69988891e-01, 9.69942189e-01, 9.70251959e-01,
9.70205256e-01, 9.70267210e-01, 9.70220507e-01, 9.70530277e-01,
9.70483575e-01, 9.70545529e-01, 9.70498826e-01, 9.70622734e-01,
9.70576031e-01, 9.70699939e-01, 9.70653236e-01, 9.70839099e-01,
9.70792396e-01, 9.70854350e-01, 9.70807647e-01, 9.70993509e-01,
9.70946806e-01, 9.71008760e-01, 9.70868652e-01, 9.70930606e-01,
9.70883903e-01, 9.71007811e-01, 9.70961109e-01, 9.71085017e-01,
9.71038314e-01, 9.71224176e-01, 9.71177473e-01, 9.71301381e-01,
9.71254678e-01, 9.71626403e-01, 9.71579700e-01, 9.71703608e-01,
9.71656905e-01, 9.71780813e-01, 9.71734110e-01, 9.71858018e-01,
9.71624504e-01, 9.71686458e-01, 9.71639756e-01, 9.71949526e-01,
9.71809418e-01, 9.71871372e-01, 9.71824669e-01, 9.71948577e-01,
9.71901874e-01, 9.71963828e-01, 9.71917125e-01, 9.71979079e-01,
9.71932377e-01, 9.71994331e-01, 9.71900925e-01, 9.72954143e-01,
9.72907441e-01, 9.73031349e-01, 9.72984646e-01, 9.73046600e-01,
9.72906492e-01, 9.73030400e-01, 9.72936994e-01, 9.73184810e-01,
9.73138108e-01, 9.73447878e-01, 9.73401175e-01, 9.73648991e-01,
9.73462180e-01, 9.73771950e-01, 9.73398328e-01, 9.73460282e-01,
9.73413579e-01, 9.73537487e-01, 9.73444081e-01, 9.73691898e-01,
9.73505086e-01, 9.73628995e-01, 9.73582292e-01, 9.73706200e-01,
9.73659497e-01, 9.73845359e-01, 9.73705251e-01, 9.73829159e-01,
9.73689050e-01, 9.73812959e-01, 9.73766256e-01, 9.73828210e-01,
9.73781507e-01, 9.73905415e-01, 9.73858712e-01, 9.73982620e-01,
9.73935918e-01, 9.73997872e-01, 9.73951169e-01, 9.74013123e-01,
9.73919717e-01, 9.73981671e-01, 9.73794860e-01, 9.73918768e-01,
9.73872065e-01, 9.73995973e-01, 9.73902568e-01, 9.73964522e-01,
9.73917819e-01, 9.74041727e-01, 9.73948322e-01, 9.74010276e-01,
9.73823465e-01, 9.73885419e-01, 9.73792013e-01, 9.73853967e-01,
9.73807264e-01, 9.73931172e-01, 9.73791064e-01, 9.73853018e-01,
9.73806315e-01, 9.73868269e-01, 9.73447944e-01, 9.73633806e-01,
9.73353590e-01, 9.73415544e-01, 9.73368841e-01, 9.73492749e-01,
9.73352641e-01, 9.73414595e-01, 9.73367892e-01, 9.73553754e-01,
9.73507051e-01, 9.73630959e-01, 9.73397445e-01, 9.73645261e-01,
9.73598559e-01, 9.73660513e-01, 9.73613810e-01, 9.73675764e-01,
9.73348844e-01, 9.73410798e-01, 9.73270690e-01, 9.73332644e-01,
9.73099130e-01, 9.73223038e-01, 9.73176335e-01, 9.73238289e-01,
9.73098181e-01, 9.73160135e-01, 9.72973324e-01, 9.73035278e-01,
9.72988575e-01, 9.73050529e-01, 9.72770313e-01, 9.72832267e-01,
9.72738861e-01, 9.72800815e-01, 9.72707410e-01, 9.72769364e-01,
9.72489147e-01, 9.72551101e-01, 9.72270884e-01, 9.72332838e-01,
9.72146027e-01, 9.72207981e-01, 9.71180520e-01, 9.71304428e-01,
9.71117617e-01, 9.71179571e-01, 9.69965298e-01, 9.70027252e-01,
9.69980550e-01, 9.70042504e-01, 9.68921637e-01, 9.69045545e-01,
9.68998842e-01, 9.69060796e-01, 9.68967391e-01, 9.69029345e-01,
9.68982642e-01, 9.69044596e-01, 9.68997893e-01, 9.69059847e-01,
9.68826333e-01, 9.68888287e-01, 9.68841585e-01, 9.68903539e-01,
9.68763430e-01, 9.68825384e-01, 9.68638573e-01, 9.68700527e-01,
9.67626363e-01, 9.67688317e-01, 9.65960314e-01, 9.66084222e-01,
9.64496328e-01, 9.64558282e-01, 9.58160000e-01, 9.58221954e-01,
9.52197295e-01, 9.52259249e-01, 9.51231788e-01, 9.51293742e-01,
9.51200336e-01, 9.51262291e-01, 9.47899690e-01, 9.47961644e-01,
9.41423254e-01, 9.41485208e-01, 9.39523692e-01, 9.39585646e-01,
9.38184562e-01, 9.38246516e-01, 9.33249318e-01, 9.33311272e-01,
9.25978935e-01, 9.26040889e-01, 9.24406292e-01, 9.24468246e-01,
9.18910615e-01, 9.18972569e-01, 9.17898405e-01, 9.17960359e-01,
9.12916458e-01, 9.12978412e-01, 8.85703987e-01, 8.85765941e-01,
8.01374011e-01, 8.01435965e-01, 7.82568040e-01, 7.82629994e-01,
5.75222933e-01, 5.75284887e-01, 0.00000000e+00]), 'all_thresholds': array([ inf, 3.82557838, 3.70343786, 2.94218565, 2.942074 ,
2.8203026 , 2.82026647, 2.78245009, 2.782324 , 2.77971057,
2.7793436 , 2.77901207, 2.77884858, 2.6966796 , 2.69646795,
2.69351429, 2.69293252, 2.65556322, 2.65497563, 2.65093531,
2.65079644, 2.64342359, 2.6427749 , 2.57653608, 2.57650732,
2.57292632, 2.57236881, 2.51270735, 2.51209908, 2.45747118,
2.45537237, 2.38290977, 2.38233765, 2.26898769, 2.26876282,
2.11383814, 2.11369168, 1.85102953, 1.84930445, 1.84516188,
1.84442315, 1.8191622 , 1.8183331 , 1.81245478, 1.81042573,
1.77732863, 1.77693616, 1.76610969, 1.7641405 , 1.74665293,
1.74600351, 1.73458204, 1.73448568, 1.71982794, 1.7186537 ,
1.68146866, 1.67828692, 1.6674466 , 1.66723499, 1.66381917,
1.66265832, 1.65184295, 1.65137835, 1.6504861 , 1.64939589,
1.63631954, 1.63608634, 1.62667492, 1.6265638 , 1.61984373,
1.61953213, 1.61034214, 1.60841267, 1.59141332, 1.59135861,
1.57477517, 1.57311128, 1.56784863, 1.56781236, 1.56222299,
1.56177292, 1.5615277 , 1.56112938, 1.54836107, 1.54834868,
1.54586096, 1.54535768, 1.54166149, 1.5415238 , 1.54115035,
1.5411388 , 1.53939579, 1.53928026, 1.53737686, 1.53720324,
1.53696605, 1.5369137 , 1.51737166, 1.51733414, 1.51156244,
1.51151847, 1.5058482 , 1.50560667, 1.4992319 , 1.49895466,
1.4973381 , 1.49728848, 1.49680808, 1.49678793, 1.48685761,
1.4868404 , 1.46332823, 1.46330189, 1.46039387, 1.46034866,
1.45976768, 1.45964813, 1.45711255, 1.4570676 , 1.45084292,
1.45081534, 1.44635881, 1.44634333, 1.43252731, 1.43235989,
1.4249837 , 1.42491962, 1.41716628, 1.41703196, 1.40956213,
1.40945521, 1.39358169, 1.39352731, 1.38868577, 1.38856911,
1.37897231, 1.37894062, 1.37544923, 1.37530062, 1.371638 ,
1.37159405, 1.37012799, 1.36987876, 1.36785537, 1.36780377,
1.36762469, 1.36750989, 1.35921131, 1.35918614, 1.35892724,
1.35879641, 1.35834045, 1.3581641 , 1.35689246, 1.35683036,
1.35675603, 1.35658806, 1.3449695 , 1.34492062, 1.34454382,
1.34446322, 1.33962769, 1.33957819, 1.33956742, 1.33950089,
1.32910081, 1.32908027, 1.28451325, 1.2843395 , 1.27323641,
1.27321097, 1.27238728, 1.27224228, 1.26970784, 1.26945415,
1.26370913, 1.26365353, 1.26196129, 1.26175542, 1.25988863,
1.25981328, 1.25754058, 1.25751213, 1.25640799, 1.25613685,
1.24065691, 1.24047563, 1.23175455, 1.23168287, 1.2313269 ,
1.23110531, 1.22269257, 1.22262297, 1.21646045, 1.21642907,
1.21159062, 1.21157299, 1.20654606, 1.20618608, 1.20328851,
1.20327127, 1.19307087, 1.19304479, 1.19039625, 1.1903346 ,
1.1694793 , 1.16937474, 1.16931868, 1.16919917, 1.16852717,
1.1685237 , 1.16048866, 1.16048261, 1.15888975, 1.15888371,
1.15719859, 1.15686527, 1.1520799 , 1.15203302, 1.14303115,
1.14298737, 1.14253623, 1.14250571, 1.14159804, 1.1415949 ,
1.13871869, 1.13870954, 1.13653078, 1.13649647, 1.13512337,
1.13511381, 1.13421743, 1.1341007 , 1.13184295, 1.13181107,
1.12932203, 1.12929242, 1.12035945, 1.12035425, 1.11784788,
1.11776482, 1.11632325, 1.11619945, 1.11590985, 1.11582998,
1.1144199 , 1.11439797, 1.11134006, 1.11122845, 1.11103901,
1.11098802, 1.11044349, 1.11043379, 1.10510776, 1.10503208,
1.10239814, 1.10233051, 1.1017279 , 1.10161173, 1.10064277,
1.10063671, 1.0980769 , 1.09801448, 1.09647131, 1.09645743,
1.09290406, 1.09279491, 1.09051218, 1.09045126, 1.08885437,
1.08875789, 1.08855991, 1.08855664, 1.08851413, 1.08848593,
1.08769603, 1.08765913, 1.08263579, 1.08249446, 1.07375172,
1.07373002, 1.07275922, 1.07270919, 1.0713031 , 1.07124633,
1.06855012, 1.06854607, 1.06814642, 1.06813638, 1.06813321,
1.06801285, 1.06741648, 1.06741356, 1.06514284, 1.0650941 ,
1.06329835, 1.06324538, 1.06106925, 1.06105992, 1.06031355,
1.0603068 , 1.05941026, 1.05932492, 1.05832529, 1.05831017,
1.05824411, 1.05821427, 1.05767542, 1.05765743, 1.05748705,
1.0574214 , 1.05668666, 1.05666404, 1.05275835, 1.05274152,
1.04813048, 1.04809599, 1.04618247, 1.0460922 , 1.04546659,
1.04538985, 1.04300093, 1.04294721, 1.04187907, 1.04182971,
1.04154616, 1.04144491, 1.03969332, 1.03969224, 1.03956863,
1.0394514 , 1.03937604, 1.0393111 , 1.03862453, 1.03856745,
1.03456277, 1.0345567 , 1.03393262, 1.03389013, 1.03371654,
1.03365245, 1.03275111, 1.03270477, 1.03203226, 1.032031 ,
1.03181089, 1.03176566, 1.03168584, 1.03165066, 1.03019743,
1.03015088, 1.02882299, 1.02866972, 1.02855439, 1.02852727,
1.02851534, 1.02844424, 1.02785266, 1.02783572, 1.02713685,
1.02710104, 1.02673965, 1.02673711, 1.02336653, 1.02332079,
1.02159723, 1.021587 , 1.02068661, 1.0206236 , 1.0203852 ,
1.02030698, 1.0173309 , 1.01720208, 1.01500154, 1.01491965,
1.01484806, 1.01483593, 1.01399961, 1.01392336, 1.01075976,
1.01070478, 1.00845234, 1.00842688, 1.00634991, 1.00602003,
1.00229234, 1.00205662, 1.00020597, 1.00017986, 0.99891037,
0.99887411, 0.99884444, 0.99873595, 0.99861254, 0.99856464,
0.99821125, 0.99818945, 0.99794761, 0.9978216 , 0.99727441,
0.99725162, 0.99580181, 0.99567839, 0.99509029, 0.99504531,
0.99458661, 0.9945848 , 0.99432725, 0.99412351, 0.9936942 ,
0.99368563, 0.99177834, 0.99173382, 0.9899686 , 0.9899361 ,
0.98464518, 0.98462125, 0.98454289, 0.98452069, 0.97951525,
0.97938638, 0.97915821, 0.97872533, 0.97872281, 0.97860977,
0.97704656, 0.97702392, 0.97541824, 0.97525394, 0.97408005,
0.97382923, 0.97236646, 0.97232604, 0.97161007, 0.9713012 ,
0.9710974 , 0.97082728, 0.96651514, 0.96632545, 0.96345172,
0.96344564, 0.96340662, 0.96336658, 0.96279613, 0.96254359,
0.96175349, 0.96170284, 0.9616607 , 0.96153871, 0.9579177 ,
0.95778477, 0.95767818, 0.95761271, 0.95614508, 0.95613962,
0.95584829, 0.95575495, 0.95543679, 0.95537128, 0.95370547,
0.95328636, 0.95268223, 0.95265827, 0.95261964, 0.95236633,
0.94847639, 0.94838579, 0.94656382, 0.94653863, 0.94204673,
0.94202674, 0.93405545, 0.93402455, 0.93054319, 0.93046595,
0.92988758, 0.92984985, 0.92982241, 0.92959247, 0.92918463,
0.92915776, 0.92155603, 0.92120089, 0.92118698, 0.92115569,
0.91851667, 0.91850813, 0.9160845 , 0.91587852, 0.91407055,
0.91376373, 0.91141424, 0.91138031, 0.90885957, 0.9087869 ,
0.89192196, 0.89185769, 0.89011777, 0.89011382, 0.88981681,
0.88964674, 0.88223642, 0.88217023, 0.87608721, 0.87585445,
0.87458083, 0.87451373, 0.87082195, 0.87065471, 0.86633224,
0.86617382, 0.86041355, 0.86035668, 0.85566435, 0.85553359,
0.85400699, 0.85382926, 0.85297244, 0.85295241, 0.84783928,
0.84775332, 0.84527466, 0.8452058 , 0.83736825, 0.83730929,
0.83145018, 0.83140077, 0.81958078, 0.81949127, 0.80841038,
0.80828759, 0.80696615, 0.80695282, 0.80637223, 0.8063226 ,
0.79746988, 0.79736956, 0.79645878, 0.79610352, 0.78487544,
0.78483116, 0.77803143, 0.7779771 , 0.77073243, 0.77052098,
0.76815736, 0.76814805, 0.75952639, 0.75933744, 0.75377991,
0.7537638 , 0.75349873, 0.75344439, 0.74899447, 0.74893122,
0.74752407, 0.74740191, 0.74275142, 0.74248021, 0.7359401 ,
0.73590682, 0.73294 , 0.73287211, 0.73188959, 0.731845 ,
0.73109145, 0.73088291, 0.72934919, 0.72927018, 0.72913392,
0.72904428, 0.72613004, 0.72596188, 0.72366922, 0.72347317,
0.72253149, 0.7224706 , 0.7221855 , 0.72197678, 0.72176066,
0.72173089, 0.71558356, 0.71555074, 0.71100395, 0.71095497,
0.70901417, 0.70897256, 0.70878428, 0.70850139, 0.70738402,
0.70721433, 0.70578037, 0.70577725, 0.70505623, 0.70505126,
0.70442012, 0.70433963, 0.70360878, 0.70360087, 0.70348527,
0.70344195, 0.69658778, 0.69656318, 0.69391896, 0.69385435,
0.69181593, 0.69146007, 0.69061568, 0.69055202, 0.68924453,
0.68882348, 0.68802755, 0.68799173, 0.68747934, 0.68746464,
0.68639005, 0.68625035, 0.68601778, 0.6859202 , 0.68553113,
0.68552224, 0.68364926, 0.68356245, 0.6828158 , 0.68272716,
0.68241768, 0.68182262, 0.68134824, 0.68119533, 0.68111281,
0.68089411, 0.67901509, 0.67881768, 0.67715718, 0.67676617,
0.67200944, 0.67186711, 0.6717779 , 0.67127497, 0.67075445,
0.67023658, 0.67018498, 0.67010033, 0.66885714, 0.66861262,
0.66798426, 0.667942 , 0.66671985, 0.66662631, 0.66594216,
0.66551798, 0.66452187, 0.66428268, 0.66416434, 0.66391563,
0.66331347, 0.66324723, 0.66132322, 0.66127354, 0.65977049,
0.65947404, 0.65865743, 0.65857733, 0.65823229, 0.65805309,
0.65656019, 0.6563066 , 0.65626032, 0.65612456, 0.65563928,
0.65553178, 0.65464861, 0.65452534, 0.65396304, 0.65382149,
0.65369281, 0.653397 , 0.65184375, 0.65130698, 0.65082814,
0.65054604, 0.65053239, 0.6499699 , 0.64747889, 0.64732049,
0.64671599, 0.64666635, 0.64548275, 0.6454153 , 0.64448791,
0.64416142, 0.64371965, 0.64354348, 0.64344275, 0.64300468,
0.64193997, 0.64171255, 0.64133969, 0.64116234, 0.64096289,
0.6409129 , 0.63952081, 0.63950827, 0.63902306, 0.6389233 ,
0.63810876, 0.63783852, 0.63612388, 0.63547062, 0.63525282,
0.6352358 , 0.63379831, 0.63365277, 0.63328088, 0.63308378,
0.63295313, 0.63290852, 0.63287906, 0.63286552, 0.63227 ,
0.63222855, 0.63221683, 0.63184702, 0.63122582, 0.63087174,
0.630557 , 0.63022362, 0.63011146, 0.63009744, 0.62971778,
0.62968969, 0.62932408, 0.62889603, 0.62797538, 0.62746714,
0.62719731, 0.62652844, 0.62395336, 0.62332224, 0.62276692,
0.62275731, 0.62205803, 0.62201743, 0.62137472, 0.62055958,
0.62018453, 0.62012921, 0.61888304, 0.61813255, 0.61765367,
0.61726459, 0.61717313, 0.61716811, 0.61702895, 0.61659888,
0.61659286, 0.61639966, 0.61627868, 0.61600207, 0.61276081,
0.61251471, 0.61229583, 0.61227858, 0.61224416, 0.61170973,
0.61081375, 0.61007074, 0.60960521, 0.60893997, 0.60807332,
0.60793793, 0.60691953, 0.60579938, 0.60138818, 0.5993801 ,
0.59928863, 0.59908586, 0.59791037, 0.5977756 , 0.59737943,
0.59573503, 0.59489916, 0.59489023, 0.59487381, 0.59473522,
0.59385356, 0.59284896, 0.5926476 , 0.59146026, 0.58956582,
0.58946917, 0.58939275, 0.58901627, 0.58871067, 0.58866832,
0.5881326 , 0.58799827, 0.58791072, 0.58785609, 0.58708307,
0.58648895, 0.58586351, 0.58498751, 0.58414999, 0.58376531,
0.58313602, 0.58264483, 0.58242003, 0.58207685, 0.58164475,
0.58117116, 0.58109385, 0.58020233, 0.58007321, 0.58001233,
0.57955434, 0.57878865, 0.5781294 , 0.57724563, 0.5771014 ,
0.57698526, 0.57695894, 0.57481346, 0.57309526, 0.5707037 ,
0.5703109 , 0.56986351, 0.56927792, 0.56755315, 0.56728743,
0.56726834, 0.56684715, 0.56684128, 0.56659215, 0.56464794,
0.56284241, 0.56283656, 0.56226535, 0.56224435, 0.56194802,
0.55939182, 0.55933155, 0.55828076, 0.55817749, 0.55459145,
0.55137933, 0.55071159, 0.55052289, 0.54967242, 0.54913999,
0.54759925, 0.54735771, 0.54720498, 0.54577005, 0.54359384,
0.54345838, 0.54165384, 0.54090694, 0.53980642, 0.53939542,
0.5384282 , 0.53820027, 0.53604474, 0.53531947, 0.53360005,
0.53271364, 0.52357718, 0.52207081, 0.51976737, 0.51943839,
0.50831462, 0.50815075, 0.50784908, 0.50754449, 0.49730647,
0.49701034, 0.49661627, 0.49645763, 0.49530115, 0.49518421,
0.49509224, 0.49497211, 0.49466723, 0.49448543, 0.49353372,
0.49333923, 0.49187409, 0.49169893, 0.48957056, 0.48943141,
0.488741 , 0.48860717, 0.48436627, 0.48351431, 0.4759083 ,
0.47570301, 0.46690634, 0.46679363, 0.44257288, 0.44252377,
0.42786575, 0.42765668, 0.42551195, 0.42546797, 0.42501634,
0.42499752, 0.41733453, 0.41724043, 0.40333304, 0.40315878,
0.39878492, 0.3987665 , 0.39650699, 0.39632832, 0.38491504,
0.38486632, 0.36865236, 0.36863028, 0.36407871, 0.36402918,
0.35090496, 0.35080663, 0.34914529, 0.3491028 , 0.33322126,
0.33313405, 0.27414564, 0.27394971, 0.19306236, 0.19293001,
0.17693419, 0.17684902, 0.08041446, 0.08037123, -2.03591419])}
optimized_pipe
MyPipeline(algorithm=OptimizableQrsDetectorWithInfo(high_pass_filter_cutoff_hz=1, max_heart_rate_bpm=200.0, min_r_peak_height_over_baseline=0.5816447455722318, r_peak_match_tolerance_s=0.01))
But we can also just call the auto-generated self_optimize method and don’t get the info:
optimized_pipe = MyPipeline().self_optimize(train_set)
optimized_pipe
MyPipeline(algorithm=OptimizableQrsDetectorWithInfo(high_pass_filter_cutoff_hz=1, max_heart_rate_bpm=200.0, min_r_peak_height_over_baseline=0.5816447455722318, r_peak_match_tolerance_s=0.01))
However, in most cases, we should just use the Optimize wrapper.
It will call the self_optimize_with_info method if available (you can force it to use self_optimize using the
optimize_with_info parameter) and then provide the additional info as attribute
optimizer = Optimize(MyPipeline()).optimize(train_set)
optimizer.optimization_info_
{'all_youden_index': array([0.00000000e+00, 6.19540301e-05, 1.52512466e-05, 8.86714683e-02,
8.86247656e-02, 1.01263388e-01, 1.01216685e-01, 1.07040364e-01,
1.06993661e-01, 1.07489293e-01, 1.07442590e-01, 1.07752361e-01,
1.07705658e-01, 1.23751752e-01, 1.23705049e-01, 1.24076773e-01,
1.24030070e-01, 1.29915703e-01, 1.29869000e-01, 1.30302678e-01,
1.30255976e-01, 1.31185286e-01, 1.31138583e-01, 1.36714446e-01,
1.36667743e-01, 1.36915559e-01, 1.36868857e-01, 1.39161156e-01,
1.39114453e-01, 1.40973074e-01, 1.40926371e-01, 1.42227406e-01,
1.42180703e-01, 1.43729554e-01, 1.43682851e-01, 1.57932278e-01,
1.57885575e-01, 2.48152597e-01, 2.48105894e-01, 2.48477618e-01,
2.48430916e-01, 2.49917812e-01, 2.49871109e-01, 2.50180880e-01,
2.50134177e-01, 2.51744982e-01, 2.51698279e-01, 2.52441727e-01,
2.52395024e-01, 2.53324335e-01, 2.53230929e-01, 2.53850470e-01,
2.53803767e-01, 2.55290664e-01, 2.55243961e-01, 2.57969938e-01,
2.57923235e-01, 2.59410132e-01, 2.59363429e-01, 2.59797107e-01,
2.59750405e-01, 2.60493853e-01, 2.60447150e-01, 2.60633012e-01,
2.60586310e-01, 2.61949298e-01, 2.61902595e-01, 2.63017768e-01,
2.62971065e-01, 2.63838422e-01, 2.63791719e-01, 2.65278616e-01,
2.65185210e-01, 2.68035095e-01, 2.67988393e-01, 2.70652416e-01,
2.70605713e-01, 2.71473070e-01, 2.71426367e-01, 2.72665447e-01,
2.72618745e-01, 2.72742653e-01, 2.72695950e-01, 2.75917559e-01,
2.75870857e-01, 2.76924075e-01, 2.76877372e-01, 2.77806683e-01,
2.77759980e-01, 2.77883888e-01, 2.77837185e-01, 2.78456726e-01,
2.78410023e-01, 2.79339333e-01, 2.79292630e-01, 2.79478493e-01,
2.79431790e-01, 2.88353170e-01, 2.88306467e-01, 2.92023709e-01,
2.91977006e-01, 2.95880110e-01, 2.95833407e-01, 3.01223408e-01,
3.01176705e-01, 3.02415786e-01, 3.02369083e-01, 3.02740807e-01,
3.02694105e-01, 3.13164336e-01, 3.13117633e-01, 3.42297981e-01,
3.42251278e-01, 3.46588060e-01, 3.46541358e-01, 3.47470668e-01,
3.47423965e-01, 3.51265115e-01, 3.51218412e-01, 3.59644160e-01,
3.59597458e-01, 3.66350447e-01, 3.66303744e-01, 3.85571447e-01,
3.85524745e-01, 3.94941757e-01, 3.94895054e-01, 4.04374021e-01,
4.04327318e-01, 4.12691112e-01, 4.12644410e-01, 4.30982802e-01,
4.30936100e-01, 4.36264146e-01, 4.36217444e-01, 4.46006180e-01,
4.45959477e-01, 4.49243041e-01, 4.49196338e-01, 4.52851626e-01,
4.52804923e-01, 4.54167912e-01, 4.54121209e-01, 4.55793968e-01,
4.55747265e-01, 4.55995081e-01, 4.55948379e-01, 4.63073092e-01,
4.63026389e-01, 4.63212251e-01, 4.63165549e-01, 4.63599227e-01,
4.63552524e-01, 4.64667696e-01, 4.64620994e-01, 4.64744902e-01,
4.64698199e-01, 4.74177166e-01, 4.74130463e-01, 4.74378279e-01,
4.74331576e-01, 4.78172726e-01, 4.78126023e-01, 4.78187977e-01,
4.78141274e-01, 4.86071390e-01, 4.86024688e-01, 5.16320208e-01,
5.16273505e-01, 5.22840633e-01, 5.22793930e-01, 5.23165654e-01,
5.23118951e-01, 5.24729756e-01, 5.24683053e-01, 5.27966617e-01,
5.27919914e-01, 5.28911179e-01, 5.28864476e-01, 5.29917694e-01,
5.29870992e-01, 5.31357888e-01, 5.31311185e-01, 5.31744864e-01,
5.31698161e-01, 5.41796668e-01, 5.41749965e-01, 5.47511690e-01,
5.47464987e-01, 5.47526941e-01, 5.47480238e-01, 5.52622423e-01,
5.52575720e-01, 5.56231008e-01, 5.56184305e-01, 5.59343961e-01,
5.59297258e-01, 5.62394959e-01, 5.62348256e-01, 5.64206877e-01,
5.64160175e-01, 5.71470750e-01, 5.71424047e-01, 5.73344622e-01,
5.73297920e-01, 5.89158151e-01, 5.89111448e-01, 5.89235356e-01,
5.89188654e-01, 5.89808194e-01, 5.89761491e-01, 5.97257929e-01,
5.97211226e-01, 5.98760077e-01, 5.98713374e-01, 6.00571995e-01,
6.00478589e-01, 6.05001234e-01, 6.04954531e-01, 6.14805222e-01,
6.14758519e-01, 6.15440013e-01, 6.15393310e-01, 6.16384575e-01,
6.16337872e-01, 6.19373620e-01, 6.19326917e-01, 6.20689905e-01,
6.20643203e-01, 6.22563778e-01, 6.22517075e-01, 6.23508339e-01,
6.23461636e-01, 6.26063706e-01, 6.26017003e-01, 6.28866888e-01,
6.28820186e-01, 6.41210992e-01, 6.41164289e-01, 6.44200036e-01,
6.44153333e-01, 6.45454368e-01, 6.45407665e-01, 6.46027206e-01,
6.45980503e-01, 6.47777170e-01, 6.47730467e-01, 6.51943341e-01,
6.51896638e-01, 6.52392270e-01, 6.52345568e-01, 6.52841200e-01,
6.52794497e-01, 6.59733348e-01, 6.59686646e-01, 6.64147336e-01,
6.64100633e-01, 6.65277760e-01, 6.65231057e-01, 6.66779908e-01,
6.66733205e-01, 6.70326539e-01, 6.70279836e-01, 6.72757997e-01,
6.72711294e-01, 6.77233938e-01, 6.77187236e-01, 6.80532753e-01,
6.80486050e-01, 6.82096855e-01, 6.82050152e-01, 6.82236015e-01,
6.82189312e-01, 6.82313220e-01, 6.82266517e-01, 6.83195828e-01,
6.83149125e-01, 6.90335792e-01, 6.90289089e-01, 7.03547252e-01,
7.03500549e-01, 7.05173308e-01, 7.05126605e-01, 7.07480858e-01,
7.07434155e-01, 7.11151397e-01, 7.11104695e-01, 7.11662281e-01,
7.11615578e-01, 7.11677532e-01, 7.11630829e-01, 7.12126461e-01,
7.12079759e-01, 7.15673092e-01, 7.15579687e-01, 7.18057848e-01,
7.18011145e-01, 7.21418617e-01, 7.21371914e-01, 7.22734903e-01,
7.22688200e-01, 7.24422913e-01, 7.24376210e-01, 7.26234831e-01,
7.26188128e-01, 7.26250082e-01, 7.26203379e-01, 7.27132690e-01,
7.27085987e-01, 7.27395757e-01, 7.27349055e-01, 7.28464227e-01,
7.28417524e-01, 7.33745571e-01, 7.33698868e-01, 7.39336685e-01,
7.39289982e-01, 7.41644235e-01, 7.41597532e-01, 7.42464889e-01,
7.42418186e-01, 7.45330025e-01, 7.45283323e-01, 7.46646311e-01,
7.46599609e-01, 7.47095241e-01, 7.47048538e-01, 7.49154975e-01,
7.49108272e-01, 7.49294134e-01, 7.49247432e-01, 7.49371340e-01,
7.49324637e-01, 7.50068085e-01, 7.50021382e-01, 7.53986440e-01,
7.53939738e-01, 7.55054910e-01, 7.55008207e-01, 7.55194069e-01,
7.55147367e-01, 7.56200585e-01, 7.56153882e-01, 7.57083193e-01,
7.57036490e-01, 7.57408214e-01, 7.57361511e-01, 7.57485419e-01,
7.57438717e-01, 7.59854924e-01, 7.59808221e-01, 7.61790750e-01,
7.61744047e-01, 7.61867955e-01, 7.61821253e-01, 7.61883207e-01,
7.61836504e-01, 7.62394090e-01, 7.62347387e-01, 7.63090836e-01,
7.63044133e-01, 7.63539765e-01, 7.63493062e-01, 7.67829844e-01,
7.67783142e-01, 7.69765671e-01, 7.69718968e-01, 7.70772186e-01,
7.70725484e-01, 7.70973300e-01, 7.70926597e-01, 7.74457977e-01,
7.74364571e-01, 7.76347100e-01, 7.76300397e-01, 7.76424305e-01,
7.76377602e-01, 7.76935189e-01, 7.76888486e-01, 7.79862279e-01,
7.79815577e-01, 7.82169830e-01, 7.82123127e-01, 7.83857840e-01,
7.83811137e-01, 7.87032747e-01, 7.86986044e-01, 7.88410987e-01,
7.88364284e-01, 7.89541410e-01, 7.89494708e-01, 7.89556662e-01,
7.89509959e-01, 7.89633867e-01, 7.89587164e-01, 7.89834980e-01,
7.89788277e-01, 7.90160002e-01, 7.90113299e-01, 7.90485023e-01,
7.90438320e-01, 7.91801309e-01, 7.91754606e-01, 7.92436100e-01,
7.92389398e-01, 7.93008938e-01, 7.92962235e-01, 7.93581775e-01,
7.93535073e-01, 7.93906797e-01, 7.93860094e-01, 7.95285037e-01,
7.95238334e-01, 7.96725231e-01, 7.96678528e-01, 8.00333816e-01,
8.00287113e-01, 8.00349067e-01, 8.00302364e-01, 8.03090295e-01,
8.03043593e-01, 8.03291409e-01, 8.03198003e-01, 8.03259957e-01,
8.03213255e-01, 8.04204519e-01, 8.04157816e-01, 8.05087127e-01,
8.05040424e-01, 8.05598010e-01, 8.05551307e-01, 8.06542572e-01,
8.06495869e-01, 8.07177363e-01, 8.07130661e-01, 8.07564339e-01,
8.07517636e-01, 8.10119705e-01, 8.10073003e-01, 8.11435991e-01,
8.11389288e-01, 8.11451242e-01, 8.11404540e-01, 8.11652356e-01,
8.11558950e-01, 8.11930674e-01, 8.11883972e-01, 8.11945926e-01,
8.11899223e-01, 8.13571982e-01, 8.13525279e-01, 8.13587233e-01,
8.13540530e-01, 8.14098116e-01, 8.14051414e-01, 8.14175322e-01,
8.14128619e-01, 8.14190573e-01, 8.14143870e-01, 8.15073181e-01,
8.14979775e-01, 8.15165637e-01, 8.15118934e-01, 8.15180888e-01,
8.15134186e-01, 8.16806944e-01, 8.16760242e-01, 8.17193920e-01,
8.17147217e-01, 8.18758022e-01, 8.18711319e-01, 8.22490515e-01,
8.22443812e-01, 8.23620939e-01, 8.23574236e-01, 8.23760098e-01,
8.23713395e-01, 8.23837303e-01, 8.23790600e-01, 8.24038417e-01,
8.23991714e-01, 8.27770910e-01, 8.27724207e-01, 8.27786161e-01,
8.27739458e-01, 8.28792677e-01, 8.28745974e-01, 8.29737238e-01,
8.29690536e-01, 8.30433984e-01, 8.30387281e-01, 8.31936132e-01,
8.31889429e-01, 8.32880694e-01, 8.32833991e-01, 8.41321693e-01,
8.41274990e-01, 8.42204301e-01, 8.42157598e-01, 8.42405414e-01,
8.42358711e-01, 8.45580321e-01, 8.45533618e-01, 8.48631319e-01,
8.48584617e-01, 8.49575881e-01, 8.49529178e-01, 8.51573661e-01,
8.51526959e-01, 8.54067074e-01, 8.54020371e-01, 8.57551751e-01,
8.57505048e-01, 8.60912520e-01, 8.60865817e-01, 8.61795127e-01,
8.61748424e-01, 8.62182103e-01, 8.62135400e-01, 8.65604826e-01,
8.65558123e-01, 8.67230882e-01, 8.67184179e-01, 8.72388317e-01,
8.72341615e-01, 8.75872994e-01, 8.75826291e-01, 8.83012959e-01,
8.82966256e-01, 8.89471429e-01, 8.89424727e-01, 8.90168175e-01,
8.90121472e-01, 8.90431242e-01, 8.90384540e-01, 8.96146264e-01,
8.96099562e-01, 8.96595194e-01, 8.96548491e-01, 9.04106883e-01,
9.04060180e-01, 9.07963284e-01, 9.07916581e-01, 9.12005547e-01,
9.11958844e-01, 9.13569649e-01, 9.13522946e-01, 9.18789039e-01,
9.18742336e-01, 9.21716129e-01, 9.21669427e-01, 9.21979197e-01,
9.21932494e-01, 9.25525828e-01, 9.25479125e-01, 9.26780160e-01,
9.26733457e-01, 9.29645296e-01, 9.29598593e-01, 9.33997330e-01,
9.33950627e-01, 9.35437524e-01, 9.35390821e-01, 9.35948407e-01,
9.35901704e-01, 9.36149520e-01, 9.36102818e-01, 9.37094082e-01,
9.37047379e-01, 9.37171287e-01, 9.37077882e-01, 9.39679951e-01,
9.39633248e-01, 9.40934283e-01, 9.40887580e-01, 9.41321258e-01,
9.41274556e-01, 9.41584326e-01, 9.41537623e-01, 9.41723485e-01,
9.41676782e-01, 9.45332070e-01, 9.45285367e-01, 9.47515712e-01,
9.47469009e-01, 9.48770044e-01, 9.48723341e-01, 9.48785295e-01,
9.48738593e-01, 9.49543995e-01, 9.49497292e-01, 9.50116832e-01,
9.50070130e-01, 9.50503808e-01, 9.50457105e-01, 9.50828829e-01,
9.50782127e-01, 9.51153851e-01, 9.51107148e-01, 9.51231056e-01,
9.51184353e-01, 9.55706997e-01, 9.55660295e-01, 9.56837421e-01,
9.56790718e-01, 9.57472213e-01, 9.57425510e-01, 9.58107004e-01,
9.58060301e-01, 9.58617888e-01, 9.58571185e-01, 9.58633139e-01,
9.58586436e-01, 9.59205977e-01, 9.59159274e-01, 9.59469044e-01,
9.59422341e-01, 9.59732111e-01, 9.59685408e-01, 9.59809317e-01,
9.59762614e-01, 9.60382154e-01, 9.60335451e-01, 9.60521313e-01,
9.60474611e-01, 9.60598519e-01, 9.60551816e-01, 9.60613770e-01,
9.60567067e-01, 9.60629021e-01, 9.60582318e-01, 9.61449675e-01,
9.61402972e-01, 9.61712742e-01, 9.61666039e-01, 9.63462706e-01,
9.63369301e-01, 9.63431255e-01, 9.63384552e-01, 9.63446506e-01,
9.63353100e-01, 9.63477008e-01, 9.63430306e-01, 9.63863984e-01,
9.63817281e-01, 9.64127051e-01, 9.64080348e-01, 9.64328165e-01,
9.64281462e-01, 9.64591232e-01, 9.64544529e-01, 9.64916253e-01,
9.64869551e-01, 9.64993459e-01, 9.64900053e-01, 9.65085915e-01,
9.65039212e-01, 9.65410937e-01, 9.65364234e-01, 9.65859866e-01,
9.65813163e-01, 9.65999025e-01, 9.65952323e-01, 9.66262093e-01,
9.66215390e-01, 9.66587114e-01, 9.66540411e-01, 9.66602365e-01,
9.66555663e-01, 9.66803479e-01, 9.66756776e-01, 9.67004592e-01,
9.66957889e-01, 9.67143751e-01, 9.67097048e-01, 9.67159003e-01,
9.67112300e-01, 9.67422070e-01, 9.67328664e-01, 9.67452572e-01,
9.67359167e-01, 9.67421121e-01, 9.67374418e-01, 9.68241774e-01,
9.68195072e-01, 9.68318980e-01, 9.68272277e-01, 9.68767909e-01,
9.68721206e-01, 9.68907069e-01, 9.68813663e-01, 9.68875617e-01,
9.68828914e-01, 9.68890868e-01, 9.68844165e-01, 9.69091982e-01,
9.69045279e-01, 9.69355049e-01, 9.69261643e-01, 9.69323597e-01,
9.69276895e-01, 9.69772527e-01, 9.69725824e-01, 9.69787778e-01,
9.69741075e-01, 9.69988891e-01, 9.69942189e-01, 9.70251959e-01,
9.70205256e-01, 9.70267210e-01, 9.70220507e-01, 9.70530277e-01,
9.70483575e-01, 9.70545529e-01, 9.70498826e-01, 9.70622734e-01,
9.70576031e-01, 9.70699939e-01, 9.70653236e-01, 9.70839099e-01,
9.70792396e-01, 9.70854350e-01, 9.70807647e-01, 9.70993509e-01,
9.70946806e-01, 9.71008760e-01, 9.70868652e-01, 9.70930606e-01,
9.70883903e-01, 9.71007811e-01, 9.70961109e-01, 9.71085017e-01,
9.71038314e-01, 9.71224176e-01, 9.71177473e-01, 9.71301381e-01,
9.71254678e-01, 9.71626403e-01, 9.71579700e-01, 9.71703608e-01,
9.71656905e-01, 9.71780813e-01, 9.71734110e-01, 9.71858018e-01,
9.71624504e-01, 9.71686458e-01, 9.71639756e-01, 9.71949526e-01,
9.71809418e-01, 9.71871372e-01, 9.71824669e-01, 9.71948577e-01,
9.71901874e-01, 9.71963828e-01, 9.71917125e-01, 9.71979079e-01,
9.71932377e-01, 9.71994331e-01, 9.71900925e-01, 9.72954143e-01,
9.72907441e-01, 9.73031349e-01, 9.72984646e-01, 9.73046600e-01,
9.72906492e-01, 9.73030400e-01, 9.72936994e-01, 9.73184810e-01,
9.73138108e-01, 9.73447878e-01, 9.73401175e-01, 9.73648991e-01,
9.73462180e-01, 9.73771950e-01, 9.73398328e-01, 9.73460282e-01,
9.73413579e-01, 9.73537487e-01, 9.73444081e-01, 9.73691898e-01,
9.73505086e-01, 9.73628995e-01, 9.73582292e-01, 9.73706200e-01,
9.73659497e-01, 9.73845359e-01, 9.73705251e-01, 9.73829159e-01,
9.73689050e-01, 9.73812959e-01, 9.73766256e-01, 9.73828210e-01,
9.73781507e-01, 9.73905415e-01, 9.73858712e-01, 9.73982620e-01,
9.73935918e-01, 9.73997872e-01, 9.73951169e-01, 9.74013123e-01,
9.73919717e-01, 9.73981671e-01, 9.73794860e-01, 9.73918768e-01,
9.73872065e-01, 9.73995973e-01, 9.73902568e-01, 9.73964522e-01,
9.73917819e-01, 9.74041727e-01, 9.73948322e-01, 9.74010276e-01,
9.73823465e-01, 9.73885419e-01, 9.73792013e-01, 9.73853967e-01,
9.73807264e-01, 9.73931172e-01, 9.73791064e-01, 9.73853018e-01,
9.73806315e-01, 9.73868269e-01, 9.73447944e-01, 9.73633806e-01,
9.73353590e-01, 9.73415544e-01, 9.73368841e-01, 9.73492749e-01,
9.73352641e-01, 9.73414595e-01, 9.73367892e-01, 9.73553754e-01,
9.73507051e-01, 9.73630959e-01, 9.73397445e-01, 9.73645261e-01,
9.73598559e-01, 9.73660513e-01, 9.73613810e-01, 9.73675764e-01,
9.73348844e-01, 9.73410798e-01, 9.73270690e-01, 9.73332644e-01,
9.73099130e-01, 9.73223038e-01, 9.73176335e-01, 9.73238289e-01,
9.73098181e-01, 9.73160135e-01, 9.72973324e-01, 9.73035278e-01,
9.72988575e-01, 9.73050529e-01, 9.72770313e-01, 9.72832267e-01,
9.72738861e-01, 9.72800815e-01, 9.72707410e-01, 9.72769364e-01,
9.72489147e-01, 9.72551101e-01, 9.72270884e-01, 9.72332838e-01,
9.72146027e-01, 9.72207981e-01, 9.71180520e-01, 9.71304428e-01,
9.71117617e-01, 9.71179571e-01, 9.69965298e-01, 9.70027252e-01,
9.69980550e-01, 9.70042504e-01, 9.68921637e-01, 9.69045545e-01,
9.68998842e-01, 9.69060796e-01, 9.68967391e-01, 9.69029345e-01,
9.68982642e-01, 9.69044596e-01, 9.68997893e-01, 9.69059847e-01,
9.68826333e-01, 9.68888287e-01, 9.68841585e-01, 9.68903539e-01,
9.68763430e-01, 9.68825384e-01, 9.68638573e-01, 9.68700527e-01,
9.67626363e-01, 9.67688317e-01, 9.65960314e-01, 9.66084222e-01,
9.64496328e-01, 9.64558282e-01, 9.58160000e-01, 9.58221954e-01,
9.52197295e-01, 9.52259249e-01, 9.51231788e-01, 9.51293742e-01,
9.51200336e-01, 9.51262291e-01, 9.47899690e-01, 9.47961644e-01,
9.41423254e-01, 9.41485208e-01, 9.39523692e-01, 9.39585646e-01,
9.38184562e-01, 9.38246516e-01, 9.33249318e-01, 9.33311272e-01,
9.25978935e-01, 9.26040889e-01, 9.24406292e-01, 9.24468246e-01,
9.18910615e-01, 9.18972569e-01, 9.17898405e-01, 9.17960359e-01,
9.12916458e-01, 9.12978412e-01, 8.85703987e-01, 8.85765941e-01,
8.01374011e-01, 8.01435965e-01, 7.82568040e-01, 7.82629994e-01,
5.75222933e-01, 5.75284887e-01, 0.00000000e+00]), 'all_thresholds': array([ inf, 3.82557838, 3.70343786, 2.94218565, 2.942074 ,
2.8203026 , 2.82026647, 2.78245009, 2.782324 , 2.77971057,
2.7793436 , 2.77901207, 2.77884858, 2.6966796 , 2.69646795,
2.69351429, 2.69293252, 2.65556322, 2.65497563, 2.65093531,
2.65079644, 2.64342359, 2.6427749 , 2.57653608, 2.57650732,
2.57292632, 2.57236881, 2.51270735, 2.51209908, 2.45747118,
2.45537237, 2.38290977, 2.38233765, 2.26898769, 2.26876282,
2.11383814, 2.11369168, 1.85102953, 1.84930445, 1.84516188,
1.84442315, 1.8191622 , 1.8183331 , 1.81245478, 1.81042573,
1.77732863, 1.77693616, 1.76610969, 1.7641405 , 1.74665293,
1.74600351, 1.73458204, 1.73448568, 1.71982794, 1.7186537 ,
1.68146866, 1.67828692, 1.6674466 , 1.66723499, 1.66381917,
1.66265832, 1.65184295, 1.65137835, 1.6504861 , 1.64939589,
1.63631954, 1.63608634, 1.62667492, 1.6265638 , 1.61984373,
1.61953213, 1.61034214, 1.60841267, 1.59141332, 1.59135861,
1.57477517, 1.57311128, 1.56784863, 1.56781236, 1.56222299,
1.56177292, 1.5615277 , 1.56112938, 1.54836107, 1.54834868,
1.54586096, 1.54535768, 1.54166149, 1.5415238 , 1.54115035,
1.5411388 , 1.53939579, 1.53928026, 1.53737686, 1.53720324,
1.53696605, 1.5369137 , 1.51737166, 1.51733414, 1.51156244,
1.51151847, 1.5058482 , 1.50560667, 1.4992319 , 1.49895466,
1.4973381 , 1.49728848, 1.49680808, 1.49678793, 1.48685761,
1.4868404 , 1.46332823, 1.46330189, 1.46039387, 1.46034866,
1.45976768, 1.45964813, 1.45711255, 1.4570676 , 1.45084292,
1.45081534, 1.44635881, 1.44634333, 1.43252731, 1.43235989,
1.4249837 , 1.42491962, 1.41716628, 1.41703196, 1.40956213,
1.40945521, 1.39358169, 1.39352731, 1.38868577, 1.38856911,
1.37897231, 1.37894062, 1.37544923, 1.37530062, 1.371638 ,
1.37159405, 1.37012799, 1.36987876, 1.36785537, 1.36780377,
1.36762469, 1.36750989, 1.35921131, 1.35918614, 1.35892724,
1.35879641, 1.35834045, 1.3581641 , 1.35689246, 1.35683036,
1.35675603, 1.35658806, 1.3449695 , 1.34492062, 1.34454382,
1.34446322, 1.33962769, 1.33957819, 1.33956742, 1.33950089,
1.32910081, 1.32908027, 1.28451325, 1.2843395 , 1.27323641,
1.27321097, 1.27238728, 1.27224228, 1.26970784, 1.26945415,
1.26370913, 1.26365353, 1.26196129, 1.26175542, 1.25988863,
1.25981328, 1.25754058, 1.25751213, 1.25640799, 1.25613685,
1.24065691, 1.24047563, 1.23175455, 1.23168287, 1.2313269 ,
1.23110531, 1.22269257, 1.22262297, 1.21646045, 1.21642907,
1.21159062, 1.21157299, 1.20654606, 1.20618608, 1.20328851,
1.20327127, 1.19307087, 1.19304479, 1.19039625, 1.1903346 ,
1.1694793 , 1.16937474, 1.16931868, 1.16919917, 1.16852717,
1.1685237 , 1.16048866, 1.16048261, 1.15888975, 1.15888371,
1.15719859, 1.15686527, 1.1520799 , 1.15203302, 1.14303115,
1.14298737, 1.14253623, 1.14250571, 1.14159804, 1.1415949 ,
1.13871869, 1.13870954, 1.13653078, 1.13649647, 1.13512337,
1.13511381, 1.13421743, 1.1341007 , 1.13184295, 1.13181107,
1.12932203, 1.12929242, 1.12035945, 1.12035425, 1.11784788,
1.11776482, 1.11632325, 1.11619945, 1.11590985, 1.11582998,
1.1144199 , 1.11439797, 1.11134006, 1.11122845, 1.11103901,
1.11098802, 1.11044349, 1.11043379, 1.10510776, 1.10503208,
1.10239814, 1.10233051, 1.1017279 , 1.10161173, 1.10064277,
1.10063671, 1.0980769 , 1.09801448, 1.09647131, 1.09645743,
1.09290406, 1.09279491, 1.09051218, 1.09045126, 1.08885437,
1.08875789, 1.08855991, 1.08855664, 1.08851413, 1.08848593,
1.08769603, 1.08765913, 1.08263579, 1.08249446, 1.07375172,
1.07373002, 1.07275922, 1.07270919, 1.0713031 , 1.07124633,
1.06855012, 1.06854607, 1.06814642, 1.06813638, 1.06813321,
1.06801285, 1.06741648, 1.06741356, 1.06514284, 1.0650941 ,
1.06329835, 1.06324538, 1.06106925, 1.06105992, 1.06031355,
1.0603068 , 1.05941026, 1.05932492, 1.05832529, 1.05831017,
1.05824411, 1.05821427, 1.05767542, 1.05765743, 1.05748705,
1.0574214 , 1.05668666, 1.05666404, 1.05275835, 1.05274152,
1.04813048, 1.04809599, 1.04618247, 1.0460922 , 1.04546659,
1.04538985, 1.04300093, 1.04294721, 1.04187907, 1.04182971,
1.04154616, 1.04144491, 1.03969332, 1.03969224, 1.03956863,
1.0394514 , 1.03937604, 1.0393111 , 1.03862453, 1.03856745,
1.03456277, 1.0345567 , 1.03393262, 1.03389013, 1.03371654,
1.03365245, 1.03275111, 1.03270477, 1.03203226, 1.032031 ,
1.03181089, 1.03176566, 1.03168584, 1.03165066, 1.03019743,
1.03015088, 1.02882299, 1.02866972, 1.02855439, 1.02852727,
1.02851534, 1.02844424, 1.02785266, 1.02783572, 1.02713685,
1.02710104, 1.02673965, 1.02673711, 1.02336653, 1.02332079,
1.02159723, 1.021587 , 1.02068661, 1.0206236 , 1.0203852 ,
1.02030698, 1.0173309 , 1.01720208, 1.01500154, 1.01491965,
1.01484806, 1.01483593, 1.01399961, 1.01392336, 1.01075976,
1.01070478, 1.00845234, 1.00842688, 1.00634991, 1.00602003,
1.00229234, 1.00205662, 1.00020597, 1.00017986, 0.99891037,
0.99887411, 0.99884444, 0.99873595, 0.99861254, 0.99856464,
0.99821125, 0.99818945, 0.99794761, 0.9978216 , 0.99727441,
0.99725162, 0.99580181, 0.99567839, 0.99509029, 0.99504531,
0.99458661, 0.9945848 , 0.99432725, 0.99412351, 0.9936942 ,
0.99368563, 0.99177834, 0.99173382, 0.9899686 , 0.9899361 ,
0.98464518, 0.98462125, 0.98454289, 0.98452069, 0.97951525,
0.97938638, 0.97915821, 0.97872533, 0.97872281, 0.97860977,
0.97704656, 0.97702392, 0.97541824, 0.97525394, 0.97408005,
0.97382923, 0.97236646, 0.97232604, 0.97161007, 0.9713012 ,
0.9710974 , 0.97082728, 0.96651514, 0.96632545, 0.96345172,
0.96344564, 0.96340662, 0.96336658, 0.96279613, 0.96254359,
0.96175349, 0.96170284, 0.9616607 , 0.96153871, 0.9579177 ,
0.95778477, 0.95767818, 0.95761271, 0.95614508, 0.95613962,
0.95584829, 0.95575495, 0.95543679, 0.95537128, 0.95370547,
0.95328636, 0.95268223, 0.95265827, 0.95261964, 0.95236633,
0.94847639, 0.94838579, 0.94656382, 0.94653863, 0.94204673,
0.94202674, 0.93405545, 0.93402455, 0.93054319, 0.93046595,
0.92988758, 0.92984985, 0.92982241, 0.92959247, 0.92918463,
0.92915776, 0.92155603, 0.92120089, 0.92118698, 0.92115569,
0.91851667, 0.91850813, 0.9160845 , 0.91587852, 0.91407055,
0.91376373, 0.91141424, 0.91138031, 0.90885957, 0.9087869 ,
0.89192196, 0.89185769, 0.89011777, 0.89011382, 0.88981681,
0.88964674, 0.88223642, 0.88217023, 0.87608721, 0.87585445,
0.87458083, 0.87451373, 0.87082195, 0.87065471, 0.86633224,
0.86617382, 0.86041355, 0.86035668, 0.85566435, 0.85553359,
0.85400699, 0.85382926, 0.85297244, 0.85295241, 0.84783928,
0.84775332, 0.84527466, 0.8452058 , 0.83736825, 0.83730929,
0.83145018, 0.83140077, 0.81958078, 0.81949127, 0.80841038,
0.80828759, 0.80696615, 0.80695282, 0.80637223, 0.8063226 ,
0.79746988, 0.79736956, 0.79645878, 0.79610352, 0.78487544,
0.78483116, 0.77803143, 0.7779771 , 0.77073243, 0.77052098,
0.76815736, 0.76814805, 0.75952639, 0.75933744, 0.75377991,
0.7537638 , 0.75349873, 0.75344439, 0.74899447, 0.74893122,
0.74752407, 0.74740191, 0.74275142, 0.74248021, 0.7359401 ,
0.73590682, 0.73294 , 0.73287211, 0.73188959, 0.731845 ,
0.73109145, 0.73088291, 0.72934919, 0.72927018, 0.72913392,
0.72904428, 0.72613004, 0.72596188, 0.72366922, 0.72347317,
0.72253149, 0.7224706 , 0.7221855 , 0.72197678, 0.72176066,
0.72173089, 0.71558356, 0.71555074, 0.71100395, 0.71095497,
0.70901417, 0.70897256, 0.70878428, 0.70850139, 0.70738402,
0.70721433, 0.70578037, 0.70577725, 0.70505623, 0.70505126,
0.70442012, 0.70433963, 0.70360878, 0.70360087, 0.70348527,
0.70344195, 0.69658778, 0.69656318, 0.69391896, 0.69385435,
0.69181593, 0.69146007, 0.69061568, 0.69055202, 0.68924453,
0.68882348, 0.68802755, 0.68799173, 0.68747934, 0.68746464,
0.68639005, 0.68625035, 0.68601778, 0.6859202 , 0.68553113,
0.68552224, 0.68364926, 0.68356245, 0.6828158 , 0.68272716,
0.68241768, 0.68182262, 0.68134824, 0.68119533, 0.68111281,
0.68089411, 0.67901509, 0.67881768, 0.67715718, 0.67676617,
0.67200944, 0.67186711, 0.6717779 , 0.67127497, 0.67075445,
0.67023658, 0.67018498, 0.67010033, 0.66885714, 0.66861262,
0.66798426, 0.667942 , 0.66671985, 0.66662631, 0.66594216,
0.66551798, 0.66452187, 0.66428268, 0.66416434, 0.66391563,
0.66331347, 0.66324723, 0.66132322, 0.66127354, 0.65977049,
0.65947404, 0.65865743, 0.65857733, 0.65823229, 0.65805309,
0.65656019, 0.6563066 , 0.65626032, 0.65612456, 0.65563928,
0.65553178, 0.65464861, 0.65452534, 0.65396304, 0.65382149,
0.65369281, 0.653397 , 0.65184375, 0.65130698, 0.65082814,
0.65054604, 0.65053239, 0.6499699 , 0.64747889, 0.64732049,
0.64671599, 0.64666635, 0.64548275, 0.6454153 , 0.64448791,
0.64416142, 0.64371965, 0.64354348, 0.64344275, 0.64300468,
0.64193997, 0.64171255, 0.64133969, 0.64116234, 0.64096289,
0.6409129 , 0.63952081, 0.63950827, 0.63902306, 0.6389233 ,
0.63810876, 0.63783852, 0.63612388, 0.63547062, 0.63525282,
0.6352358 , 0.63379831, 0.63365277, 0.63328088, 0.63308378,
0.63295313, 0.63290852, 0.63287906, 0.63286552, 0.63227 ,
0.63222855, 0.63221683, 0.63184702, 0.63122582, 0.63087174,
0.630557 , 0.63022362, 0.63011146, 0.63009744, 0.62971778,
0.62968969, 0.62932408, 0.62889603, 0.62797538, 0.62746714,
0.62719731, 0.62652844, 0.62395336, 0.62332224, 0.62276692,
0.62275731, 0.62205803, 0.62201743, 0.62137472, 0.62055958,
0.62018453, 0.62012921, 0.61888304, 0.61813255, 0.61765367,
0.61726459, 0.61717313, 0.61716811, 0.61702895, 0.61659888,
0.61659286, 0.61639966, 0.61627868, 0.61600207, 0.61276081,
0.61251471, 0.61229583, 0.61227858, 0.61224416, 0.61170973,
0.61081375, 0.61007074, 0.60960521, 0.60893997, 0.60807332,
0.60793793, 0.60691953, 0.60579938, 0.60138818, 0.5993801 ,
0.59928863, 0.59908586, 0.59791037, 0.5977756 , 0.59737943,
0.59573503, 0.59489916, 0.59489023, 0.59487381, 0.59473522,
0.59385356, 0.59284896, 0.5926476 , 0.59146026, 0.58956582,
0.58946917, 0.58939275, 0.58901627, 0.58871067, 0.58866832,
0.5881326 , 0.58799827, 0.58791072, 0.58785609, 0.58708307,
0.58648895, 0.58586351, 0.58498751, 0.58414999, 0.58376531,
0.58313602, 0.58264483, 0.58242003, 0.58207685, 0.58164475,
0.58117116, 0.58109385, 0.58020233, 0.58007321, 0.58001233,
0.57955434, 0.57878865, 0.5781294 , 0.57724563, 0.5771014 ,
0.57698526, 0.57695894, 0.57481346, 0.57309526, 0.5707037 ,
0.5703109 , 0.56986351, 0.56927792, 0.56755315, 0.56728743,
0.56726834, 0.56684715, 0.56684128, 0.56659215, 0.56464794,
0.56284241, 0.56283656, 0.56226535, 0.56224435, 0.56194802,
0.55939182, 0.55933155, 0.55828076, 0.55817749, 0.55459145,
0.55137933, 0.55071159, 0.55052289, 0.54967242, 0.54913999,
0.54759925, 0.54735771, 0.54720498, 0.54577005, 0.54359384,
0.54345838, 0.54165384, 0.54090694, 0.53980642, 0.53939542,
0.5384282 , 0.53820027, 0.53604474, 0.53531947, 0.53360005,
0.53271364, 0.52357718, 0.52207081, 0.51976737, 0.51943839,
0.50831462, 0.50815075, 0.50784908, 0.50754449, 0.49730647,
0.49701034, 0.49661627, 0.49645763, 0.49530115, 0.49518421,
0.49509224, 0.49497211, 0.49466723, 0.49448543, 0.49353372,
0.49333923, 0.49187409, 0.49169893, 0.48957056, 0.48943141,
0.488741 , 0.48860717, 0.48436627, 0.48351431, 0.4759083 ,
0.47570301, 0.46690634, 0.46679363, 0.44257288, 0.44252377,
0.42786575, 0.42765668, 0.42551195, 0.42546797, 0.42501634,
0.42499752, 0.41733453, 0.41724043, 0.40333304, 0.40315878,
0.39878492, 0.3987665 , 0.39650699, 0.39632832, 0.38491504,
0.38486632, 0.36865236, 0.36863028, 0.36407871, 0.36402918,
0.35090496, 0.35080663, 0.34914529, 0.3491028 , 0.33322126,
0.33313405, 0.27414564, 0.27394971, 0.19306236, 0.19293001,
0.17693419, 0.17684902, 0.08041446, 0.08037123, -2.03591419])}
optimizer.optimized_pipeline_
MyPipeline(algorithm=OptimizableQrsDetectorWithInfo(high_pass_filter_cutoff_hz=1, max_heart_rate_bpm=200.0, min_r_peak_height_over_baseline=0.5816447455722318, r_peak_match_tolerance_s=0.01))
As Optimize is aware of this and stores the info as a result attribute, the information is also available in the
output of a cross validation.
Further Notes#
Sometimes it might be a good idea to provide separate implementation of self_optimize and self_optimize_with_info.
This might be required, when collecting and calculating the additional info creates a relevant computational overhead.
However, you should make sure, that the two methods return the same optimization result otherwise.
Total running time of the script: ( 0 minutes 3.910 seconds)
Estimated memory usage: 8 MB