.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/02-multisite-data/01-plot_generate_multisite_data.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_02-multisite-data_01-plot_generate_multisite_data.py: Multisite data generation ========================= .. GENERATED FROM PYTHON SOURCE LINES 7-9 Imports ------- .. GENERATED FROM PYTHON SOURCE LINES 9-22 .. code-block:: Python import matplotlib.pyplot as plt import pandas as pd import seaborn as sns from uniharmony import verbosity from uniharmony.datasets import make_multisite_classification sns.set_theme(style="whitegrid") verbosity("warning") .. GENERATED FROM PYTHON SOURCE LINES 23-25 Data generation --------------- .. GENERATED FROM PYTHON SOURCE LINES 25-37 .. code-block:: Python # We will start with the function as default X, y, sites = make_multisite_classification() df = pd.DataFrame({"Class": y, "Site": sites}) print(f"X has {X.shape[0]} examples and {X.shape[1]} features") plt.figure(figsize=[10, 6]) plt.title("Class and site distribution") sns.countplot(df, x="Class", hue="Site") plt.grid(axis="y", color="black", alpha=0.5, linestyle="--") .. image-sg:: /auto_examples/02-multisite-data/images/sphx_glr_01-plot_generate_multisite_data_001.png :alt: Class and site distribution :srcset: /auto_examples/02-multisite-data/images/sphx_glr_01-plot_generate_multisite_data_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none X has 1000 examples and 10 features .. GENERATED FROM PYTHON SOURCE LINES 38-64 .. code-block:: Python # Let's increase the number of sites. # Note that the total number of samples is the same, but the distribution changed. fig, axes = plt.subplots(1, 3, figsize=[15, 5]) for idx, n_sites in enumerate([2, 4, 10]): X, y, sites = make_multisite_classification(n_sites=n_sites) print(f"For n_sites {n_sites}, X has {X.shape[0]} examples and {X.shape[1]} features") df_plot = pd.DataFrame( { "Class": y, "Site": sites, "Feature 1": X[:, 0], "Feature 2": X[:, 1], } ) sns.countplot(df_plot, x="Class", hue="Site", ax=axes[idx]) plt.title("Class and site distribution") axes[idx].set_title(f"Class and site distribution with {n_sites} sites") axes[idx].grid(axis="y", color="black", alpha=0.5, linestyle="--") plt.tight_layout() .. image-sg:: /auto_examples/02-multisite-data/images/sphx_glr_01-plot_generate_multisite_data_002.png :alt: Class and site distribution with 2 sites, Class and site distribution with 4 sites, Class and site distribution with 10 sites :srcset: /auto_examples/02-multisite-data/images/sphx_glr_01-plot_generate_multisite_data_002.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none For n_sites 2, X has 1000 examples and 10 features For n_sites 4, X has 1000 examples and 10 features For n_sites 10, X has 1000 examples and 10 features .. GENERATED FROM PYTHON SOURCE LINES 65-67 Plotting -------- .. GENERATED FROM PYTHON SOURCE LINES 67-89 .. code-block:: Python fig, axes = plt.subplots(1, 3, figsize=[15, 5]) for idx, n_classes in enumerate([2, 4, 10]): X, y, sites = make_multisite_classification(n_classes=n_classes) print(f"For n_classes {n_classes}, X has {X.shape[0]} examples and {X.shape[1]} features") df_plot = pd.DataFrame( { "Class": y, "Site": sites, "Feature 1": X[:, 0], "Feature 2": X[:, 1], } ) sns.countplot(df_plot, x="Class", hue="Site", ax=axes[idx]) axes[idx].set_title(f"Class and site distribution with {n_classes} classes") axes[idx].grid(axis="y", color="black", alpha=0.5, linestyle="--") plt.tight_layout() .. image-sg:: /auto_examples/02-multisite-data/images/sphx_glr_01-plot_generate_multisite_data_003.png :alt: Class and site distribution with 2 classes, Class and site distribution with 4 classes, Class and site distribution with 10 classes :srcset: /auto_examples/02-multisite-data/images/sphx_glr_01-plot_generate_multisite_data_003.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none For n_classes 2, X has 1000 examples and 10 features For n_classes 4, X has 1000 examples and 10 features For n_classes 10, X has 1000 examples and 10 features .. GENERATED FROM PYTHON SOURCE LINES 90-97 Variations ---------- Changing the class balance ^^^^^^^^^^^^^^^^^^^^^^^^^^ As default, the created problem is balanced accross sites, so all classes has the same number of examples in each site. We can control that using the ``balance_per_site`` argument of the function. .. GENERATED FROM PYTHON SOURCE LINES 99-127 .. code-block:: Python # In a binary classification problem, you need to set which # proportion of class 1 is present in that site. # Thus, the number of elements in the list must match the number of sites. fig, axes = plt.subplots(1, 3, figsize=[15, 5]) balance_per_site_list = [[0.5, 0.5], [0.3, 0.7], [0.1, 0.9]] for idx, balance_per_site in enumerate(balance_per_site_list): X, y, sites = make_multisite_classification(balance_per_site=balance_per_site) print(f"For n_classes {n_classes}, X has {X.shape[0]} examples and {X.shape[1]} features") df_plot = pd.DataFrame( { "Class": y, "Site": sites, "Feature 1": X[:, 0], "Feature 2": X[:, 1], } ) sns.countplot(df_plot, x="Class", hue="Site", ax=axes[idx]) axes[idx].set_title(f"Balance per classes {balance_per_site} ") axes[idx].grid(axis="y", color="black", alpha=0.5, linestyle="--") plt.tight_layout() # Now that we changed the balance, we have different occurence in different sites. Note that the total number of classes is still balanced, is the distribution accross sites that changed. .. image-sg:: /auto_examples/02-multisite-data/images/sphx_glr_01-plot_generate_multisite_data_004.png :alt: Balance per classes [0.5, 0.5] , Balance per classes [0.3, 0.7] , Balance per classes [0.1, 0.9] :srcset: /auto_examples/02-multisite-data/images/sphx_glr_01-plot_generate_multisite_data_004.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none For n_classes 10, X has 1000 examples and 10 features For n_classes 10, X has 1000 examples and 10 features For n_classes 10, X has 1000 examples and 10 features .. GENERATED FROM PYTHON SOURCE LINES 128-132 Changing the signal type ^^^^^^^^^^^^^^^^^^^^^^^^ The signal type is used to generate a ``base`` problem, using sklearn functions. From this generated problem, ``uniharmony`` adds an Effect of Site and noise on top. .. GENERATED FROM PYTHON SOURCE LINES 132-159 .. code-block:: Python fig, axes = plt.subplots(1, 5, figsize=[20, 7]) for idx, signal_type in enumerate(["linear", "moons", "circles", "blobs", "make_gaussian_quantiles"]): X, y, sites = make_multisite_classification( n_features=2, signal_type=signal_type, noise_strength=0.1, site_effect_strength=0, ) df_plot = pd.DataFrame( { "Class": y, "Site": sites, "Feature 1": X[:, 0], "Feature 2": X[:, 1], } ) sns.scatterplot(df_plot, x="Feature 1", y="Feature 2", hue="Class", ax=axes[idx]) axes[idx].set_title(f"Signal Type: {signal_type}") axes[idx].grid(axis="y", color="black", alpha=0.5, linestyle="--") plt.tight_layout() .. image-sg:: /auto_examples/02-multisite-data/images/sphx_glr_01-plot_generate_multisite_data_005.png :alt: Signal Type: linear, Signal Type: moons, Signal Type: circles, Signal Type: blobs, Signal Type: make_gaussian_quantiles :srcset: /auto_examples/02-multisite-data/images/sphx_glr_01-plot_generate_multisite_data_005.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 160-187 .. code-block:: Python fig, axes = plt.subplots(1, 3, figsize=[15, 5]) for idx, signal_st in enumerate([0, 1, 5]): X, y, sites = make_multisite_classification( n_features=2, signal_strength=signal_st, noise_strength=0.1, site_effect_strength=0, ) df_plot = pd.DataFrame( { "Class": y, "Site": sites, "Feature 1": X[:, 0], "Feature 2": X[:, 1], } ) sns.scatterplot(df_plot, x="Feature 1", y="Feature 2", hue="Class", ax=axes[idx]) axes[idx].set_title(f"Signal Strength: {signal_st}") axes[idx].grid(axis="y", color="black", alpha=0.5, linestyle="--") plt.tight_layout() .. image-sg:: /auto_examples/02-multisite-data/images/sphx_glr_01-plot_generate_multisite_data_006.png :alt: Signal Strength: 0, Signal Strength: 1, Signal Strength: 5 :srcset: /auto_examples/02-multisite-data/images/sphx_glr_01-plot_generate_multisite_data_006.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none 2026-05-18 13:04:36 [warning ] signal_strength is 0. Adding a delta (1e-6) to signal_strength to avoid degenerate data. .. GENERATED FROM PYTHON SOURCE LINES 188-190 Effect of Site effect --------------------- .. GENERATED FROM PYTHON SOURCE LINES 190-220 .. code-block:: Python fig, axes = plt.subplots(1, 3, figsize=[15, 5]) for idx, site_st in enumerate([0, 1, 5]): X, y, sites = make_multisite_classification( n_sites=4, n_features=2, signal_strength=0.001, noise_strength=0, site_effect_strength=site_st, signal_type="circles", site_effect_homogeneous=True, ) df_plot = pd.DataFrame( { "Class": y, "Site": sites, "Feature 1": X[:, 0], "Feature 2": X[:, 1], } ) sns.scatterplot(df_plot, x="Feature 1", y="Feature 2", hue="Site", ax=axes[idx]) axes[idx].set_title(f"Effect of Site Strength: {site_st}") axes[idx].grid(axis="y", color="black", alpha=0.5, linestyle="--") plt.tight_layout() .. image-sg:: /auto_examples/02-multisite-data/images/sphx_glr_01-plot_generate_multisite_data_007.png :alt: Effect of Site Strength: 0, Effect of Site Strength: 1, Effect of Site Strength: 5 :srcset: /auto_examples/02-multisite-data/images/sphx_glr_01-plot_generate_multisite_data_007.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 221-223 Site effect homogeneous ----------------------- .. GENERATED FROM PYTHON SOURCE LINES 223-281 .. code-block:: Python fig, axes = plt.subplots(1, 3, figsize=[15, 5]) for idx, site_st in enumerate([0, 1, 5]): X, y, sites = make_multisite_classification( n_sites=10, n_features=2, signal_strength=0.001, noise_strength=0, site_effect_strength=site_st, signal_type="circles", noise=0.1, site_effect_homogeneous=True, ) df_plot = pd.DataFrame( { "Class": y, "Site": sites, "Feature 1": X[:, 0], "Feature 2": X[:, 1], } ) sns.scatterplot(df_plot, x="Feature 1", y="Feature 2", hue="Class", ax=axes[idx]) axes[idx].set_title(f"Effect of Site Strength: {site_st}") axes[idx].grid(axis="y", color="black", alpha=0.5, linestyle="--") plt.tight_layout() fig, axes = plt.subplots(1, 3, figsize=[15, 5]) for idx, site_st in enumerate([0, 1, 5]): X, y, sites = make_multisite_classification( n_sites=10, n_features=2, signal_strength=0.001, noise_strength=0, site_effect_strength=site_st, signal_type="circles", noise=0.1, site_effect_homogeneous=False, ) df_plot = pd.DataFrame( { "Class": y, "Site": sites, "Feature 1": X[:, 0], "Feature 2": X[:, 1], } ) sns.scatterplot(df_plot, x="Feature 1", y="Feature 2", hue="Class", ax=axes[idx]) axes[idx].set_title(f"Effect of Site Strength: {site_st}") axes[idx].grid(axis="y", color="black", alpha=0.5, linestyle="--") plt.tight_layout() .. rst-class:: sphx-glr-horizontal * .. image-sg:: /auto_examples/02-multisite-data/images/sphx_glr_01-plot_generate_multisite_data_008.png :alt: Effect of Site Strength: 0, Effect of Site Strength: 1, Effect of Site Strength: 5 :srcset: /auto_examples/02-multisite-data/images/sphx_glr_01-plot_generate_multisite_data_008.png :class: sphx-glr-multi-img * .. image-sg:: /auto_examples/02-multisite-data/images/sphx_glr_01-plot_generate_multisite_data_009.png :alt: Effect of Site Strength: 0, Effect of Site Strength: 1, Effect of Site Strength: 5 :srcset: /auto_examples/02-multisite-data/images/sphx_glr_01-plot_generate_multisite_data_009.png :class: sphx-glr-multi-img .. GENERATED FROM PYTHON SOURCE LINES 282-284 Noise effect ------------ .. GENERATED FROM PYTHON SOURCE LINES 284-315 .. code-block:: Python verbosity("warning") fig, axes = plt.subplots(1, 3, figsize=[15, 5]) for idx, noise_st in enumerate([0.01, 0.1, 0.5]): X, y, sites = make_multisite_classification( n_features=2, signal_strength=1, noise_strength=noise_st, site_effect_strength=0, ) df_plot = pd.DataFrame( { "Class": y, "Site": sites, "Feature 1": X[:, 0], "Feature 2": X[:, 1], } ) sns.scatterplot(df_plot, x="Feature 1", y="Feature 2", hue="Class", ax=axes[idx]) axes[idx].set_title(f"Noise Strength: {noise_st}") axes[idx].grid(axis="y", color="black", alpha=0.5, linestyle="--") axes[idx].set_xlim(-5, 5) axes[idx].set_ylim(-5, 5) plt.tight_layout() .. image-sg:: /auto_examples/02-multisite-data/images/sphx_glr_01-plot_generate_multisite_data_010.png :alt: Noise Strength: 0.01, Noise Strength: 0.1, Noise Strength: 0.5 :srcset: /auto_examples/02-multisite-data/images/sphx_glr_01-plot_generate_multisite_data_010.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 316-339 .. code-block:: Python fig, axes = plt.subplots(1, 3, figsize=[15, 5]) for idx, strength in enumerate([0, 1, 50]): X, y, sites = make_multisite_classification( n_sites=3, n_features=2, signal_strength=4, site_effect_strength=strength, signal_type="blobs", random_state=23 ) df_plot = pd.DataFrame( { "Class": y, "Site": sites, "Feature 1": X[:, 0], "Feature 2": X[:, 1], } ) sns.scatterplot(df_plot, x="Feature 1", y="Feature 2", hue="Site", ax=axes[idx]) axes[idx].set_title(f"Signal Strength: {strength}") axes[idx].grid(axis="y", color="black", alpha=0.5, linestyle="--") plt.tight_layout() .. image-sg:: /auto_examples/02-multisite-data/images/sphx_glr_01-plot_generate_multisite_data_011.png :alt: Signal Strength: 0, Signal Strength: 1, Signal Strength: 50 :srcset: /auto_examples/02-multisite-data/images/sphx_glr_01-plot_generate_multisite_data_011.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 6.779 seconds) .. _sphx_glr_download_auto_examples_02-multisite-data_01-plot_generate_multisite_data.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: 01-plot_generate_multisite_data.ipynb <01-plot_generate_multisite_data.ipynb>` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: 01-plot_generate_multisite_data.py <01-plot_generate_multisite_data.py>` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: 01-plot_generate_multisite_data.zip <01-plot_generate_multisite_data.zip>` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_