.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/04-interpolation-based/04-plot_isi_advance_usage.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_04-interpolation-based_04-plot_isi_advance_usage.py: IntraSiteInterpolation advance usage ==================================== .. GENERATED FROM PYTHON SOURCE LINES 7-9 Global Maximum Balancing ------------------------ .. GENERATED FROM PYTHON SOURCE LINES 9-24 .. code-block:: Python # Balance all sites to the single largest class count found anywhere: import numpy as np from uniharmony.datasets import make_multisite_classification from uniharmony.interpolation import IntraSiteInterpolation X, y, sites = make_multisite_classification(balance_per_site=[0.3, 0.7]) isi = IntraSiteInterpolation(balance_strategy="global_max", interpolator="random", random_state=42) X_balanced, y_balanced = isi.fit_resample(X, y, sites=sites) print(f"Global target count: {isi.target_count_}") .. rst-class:: sphx-glr-script-out .. code-block:: none 2026-05-18 13:27:57 [info ] Overall class balance across sites: [np.float64(0.5), np.float64(0.5)] 2026-05-18 13:27:57 [debug ] Total Samples to generate 1000 2026-05-18 13:27:57 [debug ] Total Samples to generate per site [500 500] 2026-05-18 13:27:57 [info ] For site 0 2026-05-18 13:27:57 [info ] Generating 500 samples 2026-05-18 13:27:57 [debug ] Balance 0.3 for site 0 2026-05-18 13:27:57 [debug ] Site 0, site effect strength [3.0, 3.0] 2026-05-18 13:27:57 [info ] For site 1 2026-05-18 13:27:57 [info ] Generating 500 samples 2026-05-18 13:27:57 [debug ] Balance 0.7 for site 1 2026-05-18 13:27:57 [debug ] Site 1, site effect strength [3.0, 3.0] 2026-05-18 13:27:57 [info ] Generated 1000 samples across 2 sites 2026-05-18 13:27:57 [info ] Class distribution: [500 500] 2026-05-18 13:27:57 [info ] Site distribution: [500 500] 2026-05-18 13:27:57 [info ] [ISI] Starting fit_resample 2026-05-18 13:27:57 [debug ] [ISI] N target for global_max strategy = 350 2026-05-18 13:27:57 [info ] [ISI] Processing site 0 2026-05-18 13:27:57 [debug ] [ISI] For site 0, N target for per_site strategy = 350 2026-05-18 13:27:57 [info ] [ISI] Processing site 1 2026-05-18 13:27:57 [debug ] [ISI] For site 1, N target for per_site strategy = 350 Global target count: 350 .. GENERATED FROM PYTHON SOURCE LINES 25-30 Covariates ---------- Stratified Interpolation with Covariates ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. GENERATED FROM PYTHON SOURCE LINES 30-43 .. code-block:: Python # Preserve demographic distributions while balancing classes. # Synthetic samples are interpolated only between participants matching on all covariates: rng = np.random.default_rng(54) n_samples = 1000 X, y, sites = make_multisite_classification(n_samples=n_samples, balance_per_site=[0.3, 0.7]) sex = rng.integers(0, 2, (n_samples, 1)) age = rng.standard_normal((n_samples, 1)) * 10 + 50 isi = IntraSiteInterpolation(balance_strategy="per_site", random_state=42) X_balanced, y_balanced = isi.fit_resample( X, y, sites=sites, categorical_covariate=sex, continuous_covariate=age, n_bins_cont_cov=2 ) # Age binned with 5 bins .. rst-class:: sphx-glr-script-out .. code-block:: none 2026-05-18 13:27:57 [info ] Overall class balance across sites: [np.float64(0.5), np.float64(0.5)] 2026-05-18 13:27:57 [debug ] Total Samples to generate 1000 2026-05-18 13:27:57 [debug ] Total Samples to generate per site [500 500] 2026-05-18 13:27:57 [info ] For site 0 2026-05-18 13:27:57 [info ] Generating 500 samples 2026-05-18 13:27:57 [debug ] Balance 0.3 for site 0 2026-05-18 13:27:57 [debug ] Site 0, site effect strength [3.0, 3.0] 2026-05-18 13:27:57 [info ] For site 1 2026-05-18 13:27:57 [info ] Generating 500 samples 2026-05-18 13:27:57 [debug ] Balance 0.7 for site 1 2026-05-18 13:27:57 [debug ] Site 1, site effect strength [3.0, 3.0] 2026-05-18 13:27:57 [info ] Generated 1000 samples across 2 sites 2026-05-18 13:27:57 [info ] Class distribution: [500 500] 2026-05-18 13:27:57 [info ] Site distribution: [500 500] 2026-05-18 13:27:57 [info ] [ISI] Starting fit_resample 2026-05-18 13:27:57 [debug ] Using 1 categorical covariates 2026-05-18 13:27:57 [debug ] No tolerance specified, using exact matching 2026-05-18 13:27:57 [debug ] Using 1 continuous covariates with tolerance: [0.] 2026-05-18 13:27:57 [info ] [ISI] Processing site 0 2026-05-18 13:27:57 [debug ] [ISI] For site 0, N target for per_site strategy = 350 2026-05-18 13:27:57 [info ] [ISI] Processing site 1 2026-05-18 13:27:57 [debug ] [ISI] For site 1, N target for per_site strategy = 350 .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 1.062 seconds) .. _sphx_glr_download_auto_examples_04-interpolation-based_04-plot_isi_advance_usage.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: 04-plot_isi_advance_usage.ipynb <04-plot_isi_advance_usage.ipynb>` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: 04-plot_isi_advance_usage.py <04-plot_isi_advance_usage.py>` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: 04-plot_isi_advance_usage.zip <04-plot_isi_advance_usage.zip>` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_