.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/plot_age_regression.py"
.. LINE NUMBERS ARE GIVEN BELOW.
.. only:: html
.. note::
:class: sphx-glr-download-link-note
:ref:`Go to the end <sphx_glr_download_auto_examples_plot_age_regression.py>`
to download the full example code.
.. rst-class:: sphx-glr-example-title
.. _sphx_glr_auto_examples_plot_age_regression.py:
======================================
Predict age from white matter features
======================================
Predict subject age from white matter features. This example fetches the
Weston-Havens dataset described in Yeatman et al [1]_. This dataset contains
tractometry features from 77 subjects ages 6-50. The plots display the absolute
value of the mean regression coefficients (averaged across cross-validation
splits) for the mean diffusivity (MD) features.
Predictive performance for this example is quite poor. In a research setting,
one might have to ensemble a number of SGL estimators together and conduct a
more thorough search of the hyperparameter space. For more details, please see
[2]_.
.. [1] Jason D. Yeatman, Brian A. Wandell, & Aviv A. Mezer, "Lifespan
maturation and degeneration of human brain white matter" Nature
Communications, vol. 5:1, pp. 4932, 2014 DOI: 10.1038/ncomms5932
.. [2] Adam Richie-Halford, Jason Yeatman, Noah Simon, and Ariel Rokem
"Multidimensional analysis and detection of informative features in human
brain white matter" PLOS Computational Biology, 2021 DOI:
10.1371/journal.pcbi.1009136
.. GENERATED FROM PYTHON SOURCE LINES 27-37
.. code-block:: Python
import os.path as op
import matplotlib.pyplot as plt
import numpy as np
from sklearn.model_selection import cross_validate
from afqinsight import make_afq_regressor_pipeline
from afqinsight.datasets import download_weston_havens, load_afq_data
.. GENERATED FROM PYTHON SOURCE LINES 38-50
Fetch example data
------------------
The :func:`download_weston_havens` function download the data used in this
example and places it in the `~/.cache/afq-insight/weston_havens` directory.
If the directory does not exist, it is created. The data follows the format
expected by the :func:`load_afq_data` function: a file called `nodes.csv` that
contains AFQ tract profiles and a file called `subjects.csv` that contains
information about the subjects. The two files are linked through the
`subjectID` column that should exist in both of them. For more information
about this format, see also the `AFQ-Browser documentation
<https://yeatmanlab.github.io/AFQ-Browser/dataformat.html>`_ (items 2 and 3).
.. GENERATED FROM PYTHON SOURCE LINES 50-53
.. code-block:: Python
workdir = download_weston_havens()
.. rst-class:: sphx-glr-script-out
.. code-block:: none
subjects.csv: 0%| | 0.00/938 [00:00<?, ?iB/s]
subjects.csv: 3.31kiB [00:00, 11.4MiB/s]
nodes.csv: 0%| | 0.00/5.81M [00:00<?, ?iB/s]
nodes.csv: 8.36MiB [00:00, 83.6MiB/s]
nodes.csv: 16.3MiB [00:00, 87.7MiB/s]
.. GENERATED FROM PYTHON SOURCE LINES 54-59
Read in the data
----------------
Next, we read in the data. The :func:`load_afq_data` function expects a string
input that points to a directory that holds appropriately-shaped data and
returns variables that we will use below in our analysis of the data.
.. GENERATED FROM PYTHON SOURCE LINES 59-76
.. code-block:: Python
afqdata = load_afq_data(
fn_nodes=op.join(workdir, "nodes.csv"),
fn_subjects=op.join(workdir, "subjects.csv"),
dwi_metrics=["md", "fa"],
target_cols=["Age"],
)
# afqdata is a namedtuple. You can access it's fields using dot notation or by
# unpacking the tuple. To see all of the available fields use `afqdata._fields`
X = afqdata.X
y = afqdata.y
groups = afqdata.groups
feature_names = afqdata.feature_names
group_names = afqdata.group_names
subjects = afqdata.subjects
.. rst-class:: sphx-glr-script-out
.. code-block:: none
/opt/hostedtoolcache/Python/3.12.8/x64/lib/python3.12/site-packages/afqinsight/transform.py:144: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
features = interpolated.stack(["subjectID", "tractID", "metric"]).unstack(
.. GENERATED FROM PYTHON SOURCE LINES 77-80
Create an analysis pipeline
---------------------------
.. GENERATED FROM PYTHON SOURCE LINES 80-128
.. code-block:: Python
pipe = make_afq_regressor_pipeline(
imputer_kwargs={"strategy": "median"}, # Use median imputation
use_cv_estimator=True, # Automatically determine the best hyperparameters
scaler="standard", # Standard scale the features before regression
groups=groups,
verbose=0, # Be quiet!
pipeline_verbosity=False, # No really, be quiet!
tuning_strategy="bayes", # Use BayesSearchCV to determine optimal hyperparameters
n_bayes_iter=10, # Consider only this many points in hyperparameter space
cv=3, # Use three CV splits to evaluate each hyperparameter combination
l1_ratio=[0.0, 1.0], # Explore the entire range of ``l1_ratio``
eps=5e-2, # This is the ratio of the smallest to largest ``alpha`` value
tol=1e-2, # Set a lenient convergence tolerance just for this example
)
# ``pipe`` is a scikit-learn pipeline and can be used in other scikit-learn
# functions. For example, here we are doing 5-fold cross-validation using scikit
# learn's :func:`cross_validate` function.
scores = cross_validate(
pipe, X, y, cv=5, return_train_score=True, return_estimator=True
)
print(f"Mean train score: {np.mean(scores['train_score']):5.3f}")
print(f"Mean test score: {np.mean(scores['test_score']):5.3f}")
print(f"Mean fit time: {np.mean(scores['fit_time']):5.2f}s")
print(f"Mean score time: {np.mean(scores['score_time']):5.2f}s")
mean_coefs = np.mean(
np.abs([est.named_steps["estimate"].coef_ for est in scores["estimator"]]), axis=0
)
fig, ax = plt.subplots(1, 1, figsize=(8, 8))
_ = ax.plot(mean_coefs[1800:], color="black", lw=2)
_ = ax.set_xlim(0, 1800)
colors = plt.get_cmap("tab20").colors
for grp, grp_name, color in zip(groups[:18], group_names[18:], colors):
_ = ax.axvspan(grp.min(), grp.max() + 1, color=color, alpha=0.8, label=grp_name[1])
box = ax.get_position()
ax.set_position([box.x0, box.y0 + box.height * 0.375, box.width, box.height * 0.625])
_ = ax.legend(loc="upper center", bbox_to_anchor=(0.5, -0.125), ncol=3)
_ = ax.set_ylabel(r"$\hat{\beta}$", fontsize=16)
_ = ax.set_xlabel("Group principal component", fontsize=16)
_ = ax.set_title("Group Principal Regression Coefficients (MD only)", fontsize=18)
.. image-sg:: /auto_examples/images/sphx_glr_plot_age_regression_001.png
:alt: Group Principal Regression Coefficients (MD only)
:srcset: /auto_examples/images/sphx_glr_plot_age_regression_001.png
:class: sphx-glr-single-img
.. rst-class:: sphx-glr-script-out
.. code-block:: none
Mean train score: 0.856
Mean test score: -0.406
Mean fit time: 8.94s
Mean score time: 0.00s
.. rst-class:: sphx-glr-timing
**Total running time of the script:** (0 minutes 46.222 seconds)
.. _sphx_glr_download_auto_examples_plot_age_regression.py:
.. only:: html
.. container:: sphx-glr-footer sphx-glr-footer-example
.. container:: sphx-glr-download sphx-glr-download-jupyter
:download:`Download Jupyter notebook: plot_age_regression.ipynb <plot_age_regression.ipynb>`
.. container:: sphx-glr-download sphx-glr-download-python
:download:`Download Python source code: plot_age_regression.py <plot_age_regression.py>`
.. container:: sphx-glr-download sphx-glr-download-zip
:download:`Download zipped: plot_age_regression.zip <plot_age_regression.zip>`
.. only:: html
.. rst-class:: sphx-glr-signature
`Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_