RQA2 ML Pipeline Example

This example shows how to use the RQA2_ml pipeline to:

• build a feature table from simulated dynamical systems,

• benchmark supervised classifiers, and

• benchmark unsupervised clustering methods.

The full runnable script is available in:

docs/examples/rqa2_ml_pipeline_example.py

Quick Walkthrough

If you are brand new, think of this example as:

1. Generate a few example time series.

2. Turn each series into a set of numbers (RQA features).

3. Train a classifier and try clustering.

from SMdRQA.RQA2 import RQA2_ml, RQA2_simulators
import numpy as np

sim = RQA2_simulators(seed=42)
battery = sim.generate_test_battery()

signals, labels = [], []
for name, data in battery.items():
    x = data['x']
    seg_len = len(x) // 4
    for i in range(4):
        signals.append(x[i * seg_len:(i + 1) * seg_len])
        labels.append(name)

ml = RQA2_ml()
features = ml.build_feature_table(
    signals,
    labels=labels,
    window_size=100,
    window_step=20,
    window_stats=('mean', 'median', 'mode'),
)

X = features.drop(columns=['id', 'label'])
y = features['label']

supervised, best_model = ml.supervised_benchmark(X, y, cv=3)
print(supervised)

unsupervised, cluster_labels = ml.unsupervised_benchmark(
    X, n_clusters=len(set(labels)))
print(unsupervised)

Expected Output

You should see:

• A table of supervised performance metrics (accuracy and macro-F1)

• A table of unsupervised silhouette scores

• Cluster label arrays for each method

Adjust the window_size and window_step to trade off temporal resolution and computational cost.