add 0.8

Author: Remi-Gau

0.8.0/_downloads/00a1bbd0443a134549688a8b5402105d/plot_multiscale_parcellations.py

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+"""
+Visualizing multiscale functional brain parcellations
+=====================================================
+
+This example shows how to download and fetch brain parcellations of
+multiple networks using :func:`nilearn.datasets.fetch_atlas_basc_multiscale_2015`
+and visualize them using plotting function :func:`nilearn.plotting.plot_roi`.
+
+We show here only three different networks of 'symmetric' version. For more
+details about different versions and different networks, please refer to its
+documentation.
+"""
+
+###############################################################################
+# Retrieving multiscale group brain parcellations
+# -----------------------------------------------
+
+# import datasets module and use `fetch_atlas_basc_multiscale_2015` function
+from nilearn import datasets
+
+parcellations = datasets.fetch_atlas_basc_multiscale_2015(version='sym')
+
+# We show here networks of 64, 197, 444
+networks_64 = parcellations['scale064']
+networks_197 = parcellations['scale197']
+networks_444 = parcellations['scale444']
+
+###############################################################################
+# Visualizing brain parcellations
+# -------------------------------
+
+# import plotting module and use `plot_roi` function, since the maps are in 3D
+from nilearn import plotting
+
+# The coordinates of all plots are selected automatically by itself
+# We manually change the colormap of our choice
+plotting.plot_roi(networks_64, cmap=plotting.cm.bwr,
+                  title='64 regions of brain clusters')
+
+plotting.plot_roi(networks_197, cmap=plotting.cm.bwr,
+                  title='197 regions of brain clusters')
+
+plotting.plot_roi(networks_444, cmap=plotting.cm.bwr_r,
+                  title='444 regions of brain clusters')
+
+plotting.show()

0.8.0/_downloads/00b63a2f3c19f56521d9ac182410c025/plot_simulated_data.py

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+"""
+=================================================
+Example of pattern recognition on simulated data
+=================================================
+
+This example simulates data according to a very simple sketch of brain
+imaging data and applies machine learning techniques to predict output
+values.
+
+We use a very simple generating function to simulate data, as in `Michel
+et al. 2012 <http://dx.doi.org/10.1109/TMI.2011.2113378>`_ , a linear
+model with a random design matrix **X**:
+
+.. math::
+
+   \\mathbf{y} = \\mathbf{X} \\mathbf{w} + \\mathbf{e}
+
+* **w**: the weights of the linear model correspond to the predictive
+  brain regions. Here, in the simulations, they form a 3D image with 5, four
+  of which in opposite corners and one in the middle, as plotted below.
+
+* **X**: the design matrix corresponds to the observed fMRI data. Here
+  we simulate random normal variables and smooth them as in Gaussian
+  fields.
+
+* **e** is random normal noise.
+
+
+"""
+
+# Licence : BSD
+
+print(__doc__)
+
+from time import time
+
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy import linalg, ndimage
+
+from sklearn import linear_model, svm
+from sklearn.utils import check_random_state
+from sklearn.model_selection import KFold
+from sklearn.feature_selection import f_regression
+
+import nibabel
+
+from nilearn import decoding
+import nilearn.masking
+from nilearn.plotting import show
+
+
+##############################################################################
+# A function to generate data
+##############################################################################
+def create_simulation_data(snr=0, n_samples=2 * 100, size=12, random_state=1):
+    generator = check_random_state(random_state)
+    roi_size = 2  # size / 3
+    smooth_X = 1
+    # Coefs
+    w = np.zeros((size, size, size))
+    w[0:roi_size, 0:roi_size, 0:roi_size] = -0.6
+    w[-roi_size:, -roi_size:, 0:roi_size] = 0.5
+    w[0:roi_size, -roi_size:, -roi_size:] = -0.6
+    w[-roi_size:, 0:roi_size:, -roi_size:] = 0.5
+    w[(size - roi_size) // 2:(size + roi_size) // 2,
+    (size - roi_size) // 2:(size + roi_size) // 2,
+    (size - roi_size) // 2:(size + roi_size) // 2] = 0.5
+    w = w.ravel()
+    # Generate smooth background noise
+    XX = generator.randn(n_samples, size, size, size)
+    noise = []
+    for i in range(n_samples):
+        Xi = ndimage.filters.gaussian_filter(XX[i, :, :, :], smooth_X)
+        Xi = Xi.ravel()
+        noise.append(Xi)
+    noise = np.array(noise)
+    # Generate the signal y
+    y = generator.randn(n_samples)
+    X = np.dot(y[:, np.newaxis], w[np.newaxis])
+    norm_noise = linalg.norm(X, 2) / np.exp(snr / 20.)
+    noise_coef = norm_noise / linalg.norm(noise, 2)
+    noise *= noise_coef
+    snr = 20 * np.log(linalg.norm(X, 2) / linalg.norm(noise, 2))
+    print("SNR: %.1f dB" % snr)
+    # Mixing of signal + noise and splitting into train/test
+    X += noise
+    X -= X.mean(axis=-1)[:, np.newaxis]
+    X /= X.std(axis=-1)[:, np.newaxis]
+    X_test = X[n_samples // 2:, :]
+    X_train = X[:n_samples // 2, :]
+    y_test = y[n_samples // 2:]
+    y = y[:n_samples // 2]
+
+    return X_train, X_test, y, y_test, snr, w, size
+
+
+##############################################################################
+# A simple function to plot slices
+##############################################################################
+def plot_slices(data, title=None):
+    plt.figure(figsize=(5.5, 2.2))
+    vmax = np.abs(data).max()
+    for i in (0, 6, 11):
+        plt.subplot(1, 3, i // 5 + 1)
+        plt.imshow(data[:, :, i], vmin=-vmax, vmax=vmax,
+                   interpolation="nearest", cmap=plt.cm.RdBu_r)
+        plt.xticks(())
+        plt.yticks(())
+    plt.subplots_adjust(hspace=0.05, wspace=0.05, left=.03, right=.97, top=.9)
+    if title is not None:
+        plt.suptitle(title, y=.95)
+
+
+###############################################################################
+# Create data
+###############################################################################
+X_train, X_test, y_train, y_test, snr, coefs, size = \
+    create_simulation_data(snr=-10, n_samples=100, size=12)
+
+# Create masks for SearchLight. process_mask is the voxels where SearchLight
+# computation is performed. It is a subset of the brain mask, just to reduce
+# computation time.
+mask = np.ones((size, size, size), dtype=bool)
+mask_img = nibabel.Nifti1Image(mask.astype(int), np.eye(4))
+process_mask = np.zeros((size, size, size), dtype=bool)
+process_mask[:, :, 0] = True
+process_mask[:, :, 6] = True
+process_mask[:, :, 11] = True
+process_mask_img = nibabel.Nifti1Image(process_mask.astype(int), np.eye(4))
+
+coefs = np.reshape(coefs, [size, size, size])
+plot_slices(coefs, title="Ground truth")
+
+###############################################################################
+# Run different estimators
+###############################################################################
+#
+# We can now run different estimators and look at their prediction score,
+# as well as the feature maps that they recover. Namely, we will use
+#
+# * A support vector regression (`SVM
+#   <http://scikit-learn.org/stable/modules/svm.html>`_)
+#
+# * An `elastic-net
+#   <http://scikit-learn.org/stable/modules/linear_model.html#elastic-net>`_
+#
+# * A *Bayesian* ridge estimator, i.e. a ridge estimator that sets its
+#   parameter according to a metaprior
+#
+# * A ridge estimator that set its parameter by cross-validation
+#
+# Note that the `RidgeCV` and the `ElasticNetCV` have names ending in `CV`
+# that stands for `cross-validation`: in the list of possible `alpha`
+# values that they are given, they choose the best by cross-validation.
+
+estimators = [
+    ('bayesian_ridge', linear_model.BayesianRidge(normalize=True)),
+    ('enet_cv', linear_model.ElasticNetCV(alphas=[5, 1, 0.5, 0.1],
+                                          l1_ratio=0.05)),
+    ('ridge_cv', linear_model.RidgeCV(alphas=[100, 10, 1, 0.1], cv=5)),
+    ('svr', svm.SVR(kernel='linear', C=0.001)),
+    ('searchlight', decoding.SearchLight(mask_img,
+                                         process_mask_img=process_mask_img,
+                                         radius=2.7,
+                                         scoring='r2',
+                                         estimator=svm.SVR(kernel="linear"),
+                                         cv=KFold(n_splits=4),
+                                         verbose=1,
+                                         n_jobs=1,
+                                         )
+     )
+]
+
+###############################################################################
+# Run the estimators
+#
+# As the estimators expose a fairly consistent API, we can all fit them in
+# a for loop: they all have a `fit` method for fitting the data, a `score`
+# method to retrieve the prediction score, and because they are all linear
+# models, a `coef_` attribute that stores the coefficients **w** estimated
+
+for name, estimator in estimators:
+    t1 = time()
+    if name != "searchlight":
+        estimator.fit(X_train, y_train)
+    else:
+        X = nilearn.masking.unmask(X_train, mask_img)
+        estimator.fit(X, y_train)
+        del X
+    elapsed_time = time() - t1
+
+    if name != 'searchlight':
+        coefs = estimator.coef_
+        coefs = np.reshape(coefs, [size, size, size])
+        score = estimator.score(X_test, y_test)
+        title = '%s: prediction score %.3f, training time: %.2fs' % (
+            estimator.__class__.__name__, score,
+            elapsed_time)
+
+    else:  # Searchlight
+        coefs = estimator.scores_
+        title = '%s: training time: %.2fs' % (
+            estimator.__class__.__name__,
+            elapsed_time)
+
+    # We use the plot_slices function provided in the example to
+    # plot the results
+    plot_slices(coefs, title=title)
+
+    print(title)
+
+f_values, p_values = f_regression(X_train, y_train)
+p_values = np.reshape(p_values, (size, size, size))
+p_values = -np.log10(p_values)
+p_values[np.isnan(p_values)] = 0
+p_values[p_values > 10] = 10
+plot_slices(p_values, title="f_regress")
+
+show()
+
+###############################################################################
+# An exercice to go further
+###############################################################################
+#
+# As an exercice, you can use recursive feature elimination (RFE) with
+# the SVM
+#
+# Read the object's documentation to find out how to use RFE.
+#
+# **Performance tip**: increase the `step` parameter, or it will be very
+# slow.
+
+from sklearn.feature_selection import RFE