.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/03-combat-based/03-plot_neurocombat_with_mareos.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_03-combat-based_03-plot_neurocombat_with_mareos.py: Using NeuroComBat with MAREoS dataset ===================================== .. GENERATED FROM PYTHON SOURCE LINES 7-9 Imports ------- .. GENERATED FROM PYTHON SOURCE LINES 9-30 .. code-block:: Python import warnings import matplotlib.pyplot as plt import pandas as pd import seaborn as sns from sklearn.ensemble import RandomForestClassifier from sklearn.exceptions import ConvergenceWarning from sklearn.linear_model import LogisticRegression from sklearn.metrics import balanced_accuracy_score from uniharmony import verbosity from uniharmony.combat import NeuroComBat from uniharmony.datasets import load_MAREoS sns.set_theme(style="whitegrid") verbosity("warning") warnings.filterwarnings(action="ignore", category=ConvergenceWarning) .. GENERATED FROM PYTHON SOURCE LINES 31-38 Data loading ------------ Load MAREoS benchmark dataset - ``datasets = load_MAREoS()`` loads simulated neuroimaging benchmark data. - The dataset contains multiple scenarios (``true`` vs ``eos`` effects; ``simple`` vs ``interaction``; example 1/2). .. GENERATED FROM PYTHON SOURCE LINES 38-56 .. code-block:: Python # Load the MAREoS dataset (made for benchmarking harmonisation methods) datasets = load_MAREoS() # Define the different effects, effect types, and examples to iterate over effects = ["true", "eos"] effect_types = ["simple", "interaction"] effect_examples = ["1", "2"] random_state = 23 # Assign an empty list to each key in the results dictionary unharmonized_results = [] neurocombat_results = [] # Define the harmonisation model to use (NeuroComBat in this case) harm_model = NeuroComBat() .. GENERATED FROM PYTHON SOURCE LINES 57-74 Experiments ----------- - Iterates all combinations: - ``effect`` = ``true`` or ``eos`` - ``effect_type`` = ``simple`` or ``interaction`` - ``example`` = ``1`` or ``2`` - For each combination: - Choose classifier: logistic regression for simple; random forest for interaction. - Extract data: ``X``, ``y``, ``sites``, ``folds``. - Do leave-one-fold-out cross-validation: - train on folds != current fold - test on fold == current fold - Train baseline classifier on unharmonized training data and compute balanced accuracy on raw test. - Harmonize training with ``NeuroComBat.fit_transform(...)``, then train classifier, transform test, compute balanced accuracy. - Collect results into two lists and then into DataFrames. .. GENERATED FROM PYTHON SOURCE LINES 76-158 .. code-block:: Python for effect in effects: for e_types in effect_types: if e_types == "interaction": clf = RandomForestClassifier(n_estimators=10, random_state=random_state) elif e_types == "simple": clf = LogisticRegression(random_state=random_state) for e_example in effect_examples: example = effect + "_" + e_types + e_example data = datasets[example] sites = data["sites"] X = data["X"] folds = data["folds"] folds = pd.Series(folds) sites = data["sites"] target = data["y"] covars = target.ravel().reshape(-1, 1) for fold in folds.unique(): # Train Data X = data["X"].copy() y = data["y"].copy() sites = data["sites"].copy() # Train Target X_train = X[data["folds"] != fold] site_train = sites[data["folds"] != fold] y_train = y[data["folds"] != fold] # Test data X_test = X[data["folds"] == fold] site_test = sites[data["folds"] == fold] # Test target y_test = y[data["folds"] == fold] # Unharmonized baseline model clf.fit(X_train, y_train) unharmonized_results.append( [ balanced_accuracy_score(y_true=y_test, y_pred=clf.predict(X=X_test)), fold, effect, e_types, e_example, example, ] ) # neuroComBat (do not include target as covariate - avoiding data leakage) X_train_harm = harm_model.fit_transform(X=X_train, sites=site_train) # Fit the model with the harmonized train clf.fit(X_train_harm, y_train) # harmonize the test data X_test_harm = harm_model.transform(X=X_test, sites=site_test) neurocombat_results.append( [ balanced_accuracy_score(y_true=y_test, y_pred=clf.predict(X=X_test_harm)), fold, effect, e_types, e_example, example, ] ) # Results to dataframe unharmonized_results = pd.DataFrame(data=unharmonized_results, columns=["bACC", "Fold", "Effect", "Type", "Example", "Name"]) unharmonized_results["Method"] = "Unharmonized Baseline" neurocombat_results = pd.DataFrame(data=neurocombat_results, columns=["bACC", "Fold", "Effect", "Type", "Example", "Name"]) neurocombat_results["Method"] = "neuroComBat" results = pd.concat([unharmonized_results, neurocombat_results]) .. GENERATED FROM PYTHON SOURCE LINES 159-161 Plotting -------- .. GENERATED FROM PYTHON SOURCE LINES 161-192 .. code-block:: Python fig, ax = plt.subplots(1, 1, figsize=[15, 7]) harm_methods = [ "NeuroComBat", "Unharmonized Baseline", ] sns.swarmplot(data=results, x="Name", y="bACC", hue="Method", hue_order=harm_methods, dodge=True, ax=ax) sns.boxplot( data=results, color="w", zorder=1, x="Name", y="bACC", hue="Method", hue_order=harm_methods, dodge=True, ax=ax, palette=["w"] * len(harm_methods), ) handles, labels = ax.get_legend_handles_labels() ax.legend(handles[: len(harm_methods)], labels[: len(harm_methods)]) ax.axhline(0.5, lw=2, color="k", ls="--", alpha=0.7, label="Chance level") plt.grid(axis="y") plt.grid(axis="y") plt.xticks(rotation=45) plt.show() .. image-sg:: /auto_examples/03-combat-based/images/sphx_glr_03-plot_neurocombat_with_mareos_001.png :alt: 03 plot neurocombat with mareos :srcset: /auto_examples/03-combat-based/images/sphx_glr_03-plot_neurocombat_with_mareos_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 193-198 .. admonition:: Take-home message NeuroComBat removes the ``eos`` site effect while preserving the "true" signal. The plot and results demonstrate the method’s ability to reduce spurious site-related variation and maintain true biological effect performance. .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 8.066 seconds) .. _sphx_glr_download_auto_examples_03-combat-based_03-plot_neurocombat_with_mareos.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: 03-plot_neurocombat_with_mareos.ipynb <03-plot_neurocombat_with_mareos.ipynb>` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: 03-plot_neurocombat_with_mareos.py <03-plot_neurocombat_with_mareos.py>` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: 03-plot_neurocombat_with_mareos.zip <03-plot_neurocombat_with_mareos.zip>` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_