Time Series Classification Experiments

In this notebook we will show how to run a simple time series classification experiment using the tsml-eval package. Time series classification is the task of predicting a class label for a given time series. This is a common task in many domains, such as medical and industrial settings.

​

import numpy as np
import pandas as pd
from aeon.benchmarking import get_estimator_results
from aeon.classification import DummyClassifier
from aeon.datasets import load_classification
from aeon.visualisation import plot_critical_difference
from sklearn.metrics import accuracy_score
from tsml.datasets import load_minimal_chinatown

from tsml_eval.evaluation.storage import load_classifier_results
from tsml_eval.experiments import (
    experiments,
    get_classifier_by_name,
    run_classification_experiment,
)

X_train, y_train = load_minimal_chinatown(split="train")
X_test, y_test = load_minimal_chinatown(split="test")

# set_classifier can be used to find various classifiers by string, but
# any aeon, tsml or sklearn classifier can be used in the experiments function
classifier = get_classifier_by_name("DummyClassifier")

# record memory usage every 0.1 seconds, just here for notebook speed
# does not need to be changed for usage
experiments.MEMRECORD_INTERVAL = 0.1

run_classification_experiment(
    X_train,
    y_train,
    X_test,
    y_test,
    classifier,
    "./generated_results/",
    dataset_name="Chinatown",
    resample_id=0,
)

A function is also available to load the dataset as well as run an experiment, see load_and_run_classification_experiment in tsml_eval.experiments.

Both experiment functions will output a results file in the {results_dir}/{classifier_name}/Predictions/{dataset_name}/ directory. These files can be loaded individually, or used as a collection in the evaluation module. See the evaluation notebook for more details.

cr = load_classifier_results(
    "./generated_results/DummyClassifier/Predictions/Chinatown/testResample0.csv"
)
print(cr.predictions)
print(cr.accuracy)
print(cr.balanced_accuracy)
print(cr.auroc_score)
print(cr.log_loss)

[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
0.5
0.5
0.5
0.6931471805599453

A common use-case is wanting to compare a new algorithm against provided benchmark results. The tsml group stores their publication results and provides an aeon function to load them. An example of this is shown below for classification.

clf = DummyClassifier()
datasets = ["ArrowHead", "GunPoint", "ItalyPowerDemand", "Chinatown", "BeetleFly"]

# find accuracy for each of our datasets on our estimator
results = {}
for d in datasets:
    train_X, train_y = load_classification(d, split="train")
    test_X, test_y = load_classification(d, split="test")
    clf.fit(train_X, train_y)
    y_pred = clf.predict(test_X)
    results[d] = accuracy_score(test_y, y_pred)

results

{'ArrowHead': 0.3942857142857143,
 'GunPoint': 0.49333333333333335,
 'ItalyPowerDemand': 0.49854227405247814,
 'Chinatown': 0.27405247813411077,
 'BeetleFly': 0.5}

benchmarks = ["STC", "DrCIF", "HC2"]
res = get_estimator_results(
    datasets=datasets, estimators=benchmarks, task="classification", measure="accuracy"
)
res

{'STC': {'ArrowHead': 0.7828571428571428,
  'GunPoint': 1.0,
  'ItalyPowerDemand': 0.9620991253644317,
  'Chinatown': 0.9737609329446064,
  'BeetleFly': 0.95},
 'DrCIF': {'ArrowHead': 0.84,
  'GunPoint': 0.9933333333333332,
  'ItalyPowerDemand': 0.9689018464528668,
  'Chinatown': 0.9854227405247812,
  'BeetleFly': 0.9},
 'HC2': {'ArrowHead': 0.8685714285714285,
  'GunPoint': 1.0,
  'ItalyPowerDemand': 0.9698736637512148,
  'Chinatown': 0.9825072886297376,
  'BeetleFly': 0.95}}

res["Dummy"] = results
table = pd.DataFrame(res)
table

	STC	DrCIF	HC2	Dummy
ArrowHead	0.782857	0.840000	0.868571	0.394286
GunPoint	1.000000	0.993333	1.000000	0.493333
ItalyPowerDemand	0.962099	0.968902	0.969874	0.498542
Chinatown	0.973761	0.985423	0.982507	0.274052
BeetleFly	0.950000	0.900000	0.950000	0.500000

plt, _ = plot_critical_difference(np.array(table), list(table.columns))
plt.show()

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Generated using nbsphinx. The Jupyter notebook can be found here.