.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/03-combat-based/06-plot_combatgam_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_06-plot_combatgam_with_mareos.py: Using ComBatGAM 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 ComBatGAM from uniharmony.datasets import load_MAREoS sns.set_theme(style="whitegrid") verbosity("debug") 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 38-57 .. 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 (ComBatGAM in this case) harm_model = ComBatGAM() .. rst-class:: sphx-glr-script-out .. code-block:: none 2026-05-18 13:04:40 [info ] MAREoS datasets already exist at: /home/runner/.cache/uniharmony/MAREoS 2026-05-18 13:04:40 [info ] Getting data file: /home/runner/.cache/uniharmony/MAREoS/public_datasets/eos_simple1_data.csv 2026-05-18 13:04:40 [info ] Getting data file: /home/runner/.cache/uniharmony/MAREoS/public_datasets/eos_simple2_data.csv 2026-05-18 13:04:40 [info ] Getting data file: /home/runner/.cache/uniharmony/MAREoS/public_datasets/eos_interaction1_data.csv 2026-05-18 13:04:40 [info ] Getting data file: /home/runner/.cache/uniharmony/MAREoS/public_datasets/eos_interaction2_data.csv 2026-05-18 13:04:40 [info ] Getting data file: /home/runner/.cache/uniharmony/MAREoS/public_datasets/true_simple1_data.csv 2026-05-18 13:04:40 [info ] Getting data file: /home/runner/.cache/uniharmony/MAREoS/public_datasets/true_simple2_data.csv 2026-05-18 13:04:40 [info ] Getting data file: /home/runner/.cache/uniharmony/MAREoS/public_datasets/true_interaction1_data.csv 2026-05-18 13:04:40 [info ] Getting data file: /home/runner/.cache/uniharmony/MAREoS/public_datasets/true_interaction2_data.csv .. GENERATED FROM PYTHON SOURCE LINES 58-75 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 77-159 .. 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 print(f"Running experiment: {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, ] ) # ComBatGAM (do not include target as covariate - avoiding data leakage) X_train_harm = harm_model.fit_transform(X=X_train, sites=site_train, smooth_covariates=y_train.reshape(-1, 1)) # 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, smooth_covariates=y_test.reshape(-1, 1)) 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"] = "ComBatGAM" results = pd.concat([unharmonized_results, neurocombat_results]) .. rst-class:: sphx-glr-script-out .. code-block:: none Running experiment: true_simple1 2026-05-18 13:04:40 [debug ] Fitting 2026-05-18 13:04:40 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:04:40 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:04:40 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:05:04 [debug ] Transforming 2026-05-18 13:05:04 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:05:04 [debug ] Transforming 2026-05-18 13:05:04 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:05:04 [debug ] Fitting 2026-05-18 13:05:04 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:05:04 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:05:04 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:05:26 [debug ] Transforming 2026-05-18 13:05:26 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:05:26 [debug ] Transforming 2026-05-18 13:05:26 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:05:26 [debug ] Fitting 2026-05-18 13:05:26 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:05:26 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:05:26 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:05:48 [debug ] Transforming 2026-05-18 13:05:48 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:05:48 [debug ] Transforming 2026-05-18 13:05:48 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:05:48 [debug ] Fitting 2026-05-18 13:05:48 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:05:48 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:05:48 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:06:10 [debug ] Transforming 2026-05-18 13:06:10 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:06:10 [debug ] Transforming 2026-05-18 13:06:10 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:06:10 [debug ] Fitting 2026-05-18 13:06:10 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:06:10 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:06:10 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:06:32 [debug ] Transforming 2026-05-18 13:06:32 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:06:32 [debug ] Transforming 2026-05-18 13:06:32 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:06:32 [debug ] Fitting 2026-05-18 13:06:32 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:06:32 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:06:32 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:06:54 [debug ] Transforming 2026-05-18 13:06:54 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:06:54 [debug ] Transforming 2026-05-18 13:06:54 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:06:54 [debug ] Fitting 2026-05-18 13:06:54 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:06:54 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:06:54 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:07:16 [debug ] Transforming 2026-05-18 13:07:16 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:07:16 [debug ] Transforming 2026-05-18 13:07:16 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:07:16 [debug ] Fitting 2026-05-18 13:07:16 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:07:16 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:07:16 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:07:38 [debug ] Transforming 2026-05-18 13:07:38 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:07:38 [debug ] Transforming 2026-05-18 13:07:38 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:07:38 [debug ] Fitting 2026-05-18 13:07:38 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:07:38 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:07:38 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:08:00 [debug ] Transforming 2026-05-18 13:08:00 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:08:00 [debug ] Transforming 2026-05-18 13:08:00 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:08:00 [debug ] Fitting 2026-05-18 13:08:00 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:08:00 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:08:00 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:08:22 [debug ] Transforming 2026-05-18 13:08:22 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:08:22 [debug ] Transforming 2026-05-18 13:08:22 [debug ] Setting up smoothing using B-Splines Running experiment: true_simple2 2026-05-18 13:08:22 [debug ] Fitting 2026-05-18 13:08:22 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:08:22 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:08:22 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:08:44 [debug ] Transforming 2026-05-18 13:08:44 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:08:44 [debug ] Transforming 2026-05-18 13:08:44 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:08:44 [debug ] Fitting 2026-05-18 13:08:44 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:08:44 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:08:44 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:09:07 [debug ] Transforming 2026-05-18 13:09:07 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:09:07 [debug ] Transforming 2026-05-18 13:09:07 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:09:07 [debug ] Fitting 2026-05-18 13:09:07 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:09:07 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:09:07 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:09:29 [debug ] Transforming 2026-05-18 13:09:29 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:09:29 [debug ] Transforming 2026-05-18 13:09:29 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:09:29 [debug ] Fitting 2026-05-18 13:09:29 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:09:29 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:09:29 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:09:52 [debug ] Transforming 2026-05-18 13:09:52 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:09:52 [debug ] Transforming 2026-05-18 13:09:52 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:09:52 [debug ] Fitting 2026-05-18 13:09:52 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:09:52 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:09:52 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:10:14 [debug ] Transforming 2026-05-18 13:10:14 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:10:14 [debug ] Transforming 2026-05-18 13:10:14 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:10:14 [debug ] Fitting 2026-05-18 13:10:14 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:10:14 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:10:14 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:10:36 [debug ] Transforming 2026-05-18 13:10:36 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:10:36 [debug ] Transforming 2026-05-18 13:10:36 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:10:36 [debug ] Fitting 2026-05-18 13:10:36 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:10:36 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:10:36 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:10:58 [debug ] Transforming 2026-05-18 13:10:58 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:10:58 [debug ] Transforming 2026-05-18 13:10:58 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:10:58 [debug ] Fitting 2026-05-18 13:10:58 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:10:58 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:10:58 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:11:19 [debug ] Transforming 2026-05-18 13:11:19 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:11:19 [debug ] Transforming 2026-05-18 13:11:19 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:11:19 [debug ] Fitting 2026-05-18 13:11:19 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:11:19 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:11:19 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:11:41 [debug ] Transforming 2026-05-18 13:11:41 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:11:41 [debug ] Transforming 2026-05-18 13:11:41 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:11:41 [debug ] Fitting 2026-05-18 13:11:41 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:11:41 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:11:41 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:12:03 [debug ] Transforming 2026-05-18 13:12:03 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:12:03 [debug ] Transforming 2026-05-18 13:12:03 [debug ] Setting up smoothing using B-Splines Running experiment: true_interaction1 2026-05-18 13:12:03 [debug ] Fitting 2026-05-18 13:12:03 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:12:03 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:12:03 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:12:24 [debug ] Transforming 2026-05-18 13:12:24 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:12:24 [debug ] Transforming 2026-05-18 13:12:24 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:12:24 [debug ] Fitting 2026-05-18 13:12:24 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:12:24 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:12:24 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:12:45 [debug ] Transforming 2026-05-18 13:12:45 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:12:45 [debug ] Transforming 2026-05-18 13:12:45 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:12:45 [debug ] Fitting 2026-05-18 13:12:45 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:12:45 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:12:45 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:13:06 [debug ] Transforming 2026-05-18 13:13:06 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:13:06 [debug ] Transforming 2026-05-18 13:13:06 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:13:06 [debug ] Fitting 2026-05-18 13:13:06 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:13:06 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:13:06 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:13:28 [debug ] Transforming 2026-05-18 13:13:28 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:13:28 [debug ] Transforming 2026-05-18 13:13:28 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:13:28 [debug ] Fitting 2026-05-18 13:13:28 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:13:28 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:13:28 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:13:49 [debug ] Transforming 2026-05-18 13:13:49 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:13:49 [debug ] Transforming 2026-05-18 13:13:49 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:13:49 [debug ] Fitting 2026-05-18 13:13:49 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:13:49 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:13:49 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:14:10 [debug ] Transforming 2026-05-18 13:14:10 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:14:10 [debug ] Transforming 2026-05-18 13:14:10 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:14:10 [debug ] Fitting 2026-05-18 13:14:10 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:14:10 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:14:10 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:14:32 [debug ] Transforming 2026-05-18 13:14:32 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:14:32 [debug ] Transforming 2026-05-18 13:14:32 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:14:32 [debug ] Fitting 2026-05-18 13:14:32 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:14:32 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:14:32 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:14:53 [debug ] Transforming 2026-05-18 13:14:53 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:14:53 [debug ] Transforming 2026-05-18 13:14:53 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:14:53 [debug ] Fitting 2026-05-18 13:14:53 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:14:53 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:14:53 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:15:14 [debug ] Transforming 2026-05-18 13:15:14 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:15:14 [debug ] Transforming 2026-05-18 13:15:14 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:15:14 [debug ] Fitting 2026-05-18 13:15:14 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:15:14 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:15:14 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:15:36 [debug ] Transforming 2026-05-18 13:15:36 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:15:36 [debug ] Transforming 2026-05-18 13:15:36 [debug ] Setting up smoothing using B-Splines Running experiment: true_interaction2 2026-05-18 13:15:36 [debug ] Fitting 2026-05-18 13:15:36 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:15:36 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:15:36 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:15:58 [debug ] Transforming 2026-05-18 13:15:58 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:15:58 [debug ] Transforming 2026-05-18 13:15:58 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:15:58 [debug ] Fitting 2026-05-18 13:15:58 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:15:58 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:15:58 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:16:19 [debug ] Transforming 2026-05-18 13:16:19 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:16:19 [debug ] Transforming 2026-05-18 13:16:19 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:16:19 [debug ] Fitting 2026-05-18 13:16:19 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:16:19 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:16:19 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:16:41 [debug ] Transforming 2026-05-18 13:16:41 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:16:41 [debug ] Transforming 2026-05-18 13:16:41 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:16:41 [debug ] Fitting 2026-05-18 13:16:41 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:16:41 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:16:41 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:17:03 [debug ] Transforming 2026-05-18 13:17:03 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:17:03 [debug ] Transforming 2026-05-18 13:17:03 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:17:03 [debug ] Fitting 2026-05-18 13:17:03 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:17:03 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:17:03 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:17:25 [debug ] Transforming 2026-05-18 13:17:25 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:17:25 [debug ] Transforming 2026-05-18 13:17:25 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:17:25 [debug ] Fitting 2026-05-18 13:17:25 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:17:25 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:17:25 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:17:46 [debug ] Transforming 2026-05-18 13:17:46 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:17:46 [debug ] Transforming 2026-05-18 13:17:46 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:17:46 [debug ] Fitting 2026-05-18 13:17:46 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:17:46 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:17:46 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:18:08 [debug ] Transforming 2026-05-18 13:18:08 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:18:08 [debug ] Transforming 2026-05-18 13:18:08 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:18:08 [debug ] Fitting 2026-05-18 13:18:08 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:18:08 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:18:08 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:18:29 [debug ] Transforming 2026-05-18 13:18:29 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:18:29 [debug ] Transforming 2026-05-18 13:18:29 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:18:29 [debug ] Fitting 2026-05-18 13:18:29 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:18:29 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:18:29 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:18:51 [debug ] Transforming 2026-05-18 13:18:51 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:18:51 [debug ] Transforming 2026-05-18 13:18:51 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:18:51 [debug ] Fitting 2026-05-18 13:18:51 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:18:51 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:18:51 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:19:13 [debug ] Transforming 2026-05-18 13:19:13 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:19:13 [debug ] Transforming 2026-05-18 13:19:13 [debug ] Setting up smoothing using B-Splines Running experiment: eos_simple1 2026-05-18 13:19:13 [debug ] Fitting 2026-05-18 13:19:13 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:19:13 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:19:13 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:19:18 [debug ] Transforming 2026-05-18 13:19:18 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:19:18 [debug ] Transforming 2026-05-18 13:19:18 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:19:18 [debug ] Fitting 2026-05-18 13:19:18 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:19:18 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:19:18 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:19:22 [debug ] Transforming 2026-05-18 13:19:22 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:19:22 [debug ] Transforming 2026-05-18 13:19:22 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:19:22 [debug ] Fitting 2026-05-18 13:19:22 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:19:22 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:19:22 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:19:27 [debug ] Transforming 2026-05-18 13:19:27 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:19:27 [debug ] Transforming 2026-05-18 13:19:27 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:19:27 [debug ] Fitting 2026-05-18 13:19:27 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:19:27 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:19:27 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:19:31 [debug ] Transforming 2026-05-18 13:19:31 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:19:31 [debug ] Transforming 2026-05-18 13:19:31 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:19:31 [debug ] Fitting 2026-05-18 13:19:31 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:19:31 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:19:31 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:19:36 [debug ] Transforming 2026-05-18 13:19:36 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:19:36 [debug ] Transforming 2026-05-18 13:19:36 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:19:36 [debug ] Fitting 2026-05-18 13:19:36 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:19:36 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:19:36 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:19:40 [debug ] Transforming 2026-05-18 13:19:40 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:19:40 [debug ] Transforming 2026-05-18 13:19:40 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:19:40 [debug ] Fitting 2026-05-18 13:19:40 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:19:40 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:19:40 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:19:45 [debug ] Transforming 2026-05-18 13:19:45 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:19:45 [debug ] Transforming 2026-05-18 13:19:45 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:19:45 [debug ] Fitting 2026-05-18 13:19:45 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:19:45 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:19:45 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:19:49 [debug ] Transforming 2026-05-18 13:19:49 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:19:49 [debug ] Transforming 2026-05-18 13:19:49 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:19:49 [debug ] Fitting 2026-05-18 13:19:49 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:19:49 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:19:49 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:19:54 [debug ] Transforming 2026-05-18 13:19:54 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:19:54 [debug ] Transforming 2026-05-18 13:19:54 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:19:54 [debug ] Fitting 2026-05-18 13:19:54 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:19:54 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:19:54 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:19:58 [debug ] Transforming 2026-05-18 13:19:58 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:19:58 [debug ] Transforming 2026-05-18 13:19:58 [debug ] Setting up smoothing using B-Splines Running experiment: eos_simple2 2026-05-18 13:19:58 [debug ] Fitting 2026-05-18 13:19:58 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:19:58 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:19:58 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:20:20 [debug ] Transforming 2026-05-18 13:20:20 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:20:20 [debug ] Transforming 2026-05-18 13:20:20 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:20:20 [debug ] Fitting 2026-05-18 13:20:20 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:20:20 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:20:20 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:20:42 [debug ] Transforming 2026-05-18 13:20:42 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:20:42 [debug ] Transforming 2026-05-18 13:20:42 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:20:42 [debug ] Fitting 2026-05-18 13:20:42 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:20:42 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:20:42 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:21:04 [debug ] Transforming 2026-05-18 13:21:04 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:21:04 [debug ] Transforming 2026-05-18 13:21:04 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:21:04 [debug ] Fitting 2026-05-18 13:21:04 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:21:04 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:21:04 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:21:26 [debug ] Transforming 2026-05-18 13:21:26 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:21:26 [debug ] Transforming 2026-05-18 13:21:26 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:21:26 [debug ] Fitting 2026-05-18 13:21:26 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:21:26 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:21:26 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:21:48 [debug ] Transforming 2026-05-18 13:21:48 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:21:48 [debug ] Transforming 2026-05-18 13:21:48 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:21:48 [debug ] Fitting 2026-05-18 13:21:48 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:21:48 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:21:48 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:22:10 [debug ] Transforming 2026-05-18 13:22:10 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:22:10 [debug ] Transforming 2026-05-18 13:22:10 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:22:10 [debug ] Fitting 2026-05-18 13:22:10 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:22:10 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:22:10 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:22:32 [debug ] Transforming 2026-05-18 13:22:32 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:22:32 [debug ] Transforming 2026-05-18 13:22:32 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:22:32 [debug ] Fitting 2026-05-18 13:22:32 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:22:32 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:22:32 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:22:54 [debug ] Transforming 2026-05-18 13:22:54 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:22:54 [debug ] Transforming 2026-05-18 13:22:54 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:22:54 [debug ] Fitting 2026-05-18 13:22:54 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:22:54 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:22:54 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:23:16 [debug ] Transforming 2026-05-18 13:23:16 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:23:16 [debug ] Transforming 2026-05-18 13:23:16 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:23:16 [debug ] Fitting 2026-05-18 13:23:16 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:23:16 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:23:16 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:23:38 [debug ] Transforming 2026-05-18 13:23:38 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:23:38 [debug ] Transforming 2026-05-18 13:23:38 [debug ] Setting up smoothing using B-Splines Running experiment: eos_interaction1 2026-05-18 13:23:38 [debug ] Fitting 2026-05-18 13:23:38 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:23:38 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:23:38 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:23:59 [debug ] Transforming 2026-05-18 13:23:59 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:23:59 [debug ] Transforming 2026-05-18 13:23:59 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:24:00 [debug ] Fitting 2026-05-18 13:24:00 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:24:00 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:24:00 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:24:21 [debug ] Transforming 2026-05-18 13:24:21 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:24:21 [debug ] Transforming 2026-05-18 13:24:21 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:24:21 [debug ] Fitting 2026-05-18 13:24:21 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:24:21 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:24:21 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:24:42 [debug ] Transforming 2026-05-18 13:24:42 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:24:42 [debug ] Transforming 2026-05-18 13:24:42 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:24:42 [debug ] Fitting 2026-05-18 13:24:42 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:24:42 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:24:42 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:25:03 [debug ] Transforming 2026-05-18 13:25:03 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:25:03 [debug ] Transforming 2026-05-18 13:25:03 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:25:03 [debug ] Fitting 2026-05-18 13:25:03 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:25:03 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:25:03 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:25:24 [debug ] Transforming 2026-05-18 13:25:24 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:25:24 [debug ] Transforming 2026-05-18 13:25:24 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:25:24 [debug ] Fitting 2026-05-18 13:25:24 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:25:24 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:25:24 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:25:45 [debug ] Transforming 2026-05-18 13:25:45 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:25:45 [debug ] Transforming 2026-05-18 13:25:45 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:25:45 [debug ] Fitting 2026-05-18 13:25:45 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:25:45 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:25:45 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:26:06 [debug ] Transforming 2026-05-18 13:26:06 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:26:06 [debug ] Transforming 2026-05-18 13:26:06 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:26:06 [debug ] Fitting 2026-05-18 13:26:06 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:26:06 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:26:06 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:26:27 [debug ] Transforming 2026-05-18 13:26:27 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:26:28 [debug ] Transforming 2026-05-18 13:26:28 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:26:28 [debug ] Fitting 2026-05-18 13:26:28 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:26:28 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:26:28 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:26:49 [debug ] Transforming 2026-05-18 13:26:49 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:26:49 [debug ] Transforming 2026-05-18 13:26:49 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:26:49 [debug ] Fitting 2026-05-18 13:26:49 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:26:49 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:26:49 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:27:10 [debug ] Transforming 2026-05-18 13:27:10 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:27:10 [debug ] Transforming 2026-05-18 13:27:10 [debug ] Setting up smoothing using B-Splines Running experiment: eos_interaction2 2026-05-18 13:27:10 [debug ] Fitting 2026-05-18 13:27:10 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:27:10 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:27:10 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:27:14 [debug ] Transforming 2026-05-18 13:27:14 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:27:14 [debug ] Transforming 2026-05-18 13:27:14 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:27:14 [debug ] Fitting 2026-05-18 13:27:14 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:27:14 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:27:14 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:27:19 [debug ] Transforming 2026-05-18 13:27:19 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:27:19 [debug ] Transforming 2026-05-18 13:27:19 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:27:19 [debug ] Fitting 2026-05-18 13:27:19 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:27:19 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:27:19 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:27:23 [debug ] Transforming 2026-05-18 13:27:23 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:27:23 [debug ] Transforming 2026-05-18 13:27:23 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:27:23 [debug ] Fitting 2026-05-18 13:27:23 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:27:23 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:27:23 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:27:27 [debug ] Transforming 2026-05-18 13:27:27 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:27:27 [debug ] Transforming 2026-05-18 13:27:27 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:27:28 [debug ] Fitting 2026-05-18 13:27:28 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:27:28 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:27:28 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:27:32 [debug ] Transforming 2026-05-18 13:27:32 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:27:32 [debug ] Transforming 2026-05-18 13:27:32 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:27:32 [debug ] Fitting 2026-05-18 13:27:32 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:27:32 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:27:32 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:27:36 [debug ] Transforming 2026-05-18 13:27:36 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:27:36 [debug ] Transforming 2026-05-18 13:27:36 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:27:36 [debug ] Fitting 2026-05-18 13:27:36 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:27:36 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:27:36 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:27:41 [debug ] Transforming 2026-05-18 13:27:41 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:27:41 [debug ] Transforming 2026-05-18 13:27:41 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:27:41 [debug ] Fitting 2026-05-18 13:27:41 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:27:41 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:27:41 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:27:45 [debug ] Transforming 2026-05-18 13:27:45 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:27:45 [debug ] Transforming 2026-05-18 13:27:45 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:27:45 [debug ] Fitting 2026-05-18 13:27:45 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:27:45 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:27:45 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:27:49 [debug ] Transforming 2026-05-18 13:27:49 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:27:50 [debug ] Transforming 2026-05-18 13:27:50 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:27:50 [debug ] Fitting 2026-05-18 13:27:50 [info ] If you intend to build a machine learning (ML) model,then make sure that you DO *NOT* preserve the ML model's target as covariate. You will be required to provide the covariate also at transform time, and this will produce data leakage. If you are performing a statistical analysis and want to preserve a variable of interest, then it is correct to specify it as covariate. 2026-05-18 13:27:50 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:27:50 [debug ] Final formula for smoothing: y ~ x0 + x1 + x2 + x3 + x4 + x5 + x6 + x7 - 1 2026-05-18 13:27:54 [debug ] Transforming 2026-05-18 13:27:54 [debug ] Setting up smoothing using B-Splines 2026-05-18 13:27:54 [debug ] Transforming 2026-05-18 13:27:54 [debug ] Setting up smoothing using B-Splines .. GENERATED FROM PYTHON SOURCE LINES 160-162 Plotting -------- .. GENERATED FROM PYTHON SOURCE LINES 162-193 .. code-block:: Python fig, ax = plt.subplots(1, 1, figsize=[15, 7]) harm_methods = [ "ComBatGAM", "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_06-plot_combatgam_with_mareos_001.png :alt: 06 plot combatgam with mareos :srcset: /auto_examples/03-combat-based/images/sphx_glr_06-plot_combatgam_with_mareos_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 194-199 .. admonition:: Take-home message ComBatGAM 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:** (23 minutes 14.802 seconds) .. _sphx_glr_download_auto_examples_03-combat-based_06-plot_combatgam_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: 06-plot_combatgam_with_mareos.ipynb <06-plot_combatgam_with_mareos.ipynb>` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: 06-plot_combatgam_with_mareos.py <06-plot_combatgam_with_mareos.py>` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: 06-plot_combatgam_with_mareos.zip <06-plot_combatgam_with_mareos.zip>` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_