FIX use solver="svd" in notebook 4

Author: TomDLT

setup.py

@@ -31,7 +31,7 @@
 ]
 
 extras_require = {
-    "docs": ["sphinx", "sphinx_gallery", "numpydoc"],
+    "docs": ["sphinx", "sphinx_gallery", "numpydoc", "nbformat"],
     "github": ["pytest"],
 }
 

tutorials/notebooks/shortclips/00_download_shortclips.ipynb

@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Download the data set\n\nIn this script, we download the data set from Wasabi or GIN. No account is\nrequired.\n\n## Cite this data set\n\nThis tutorial is based on publicly available data `published on GIN\n<https://gin.g-node.org/gallantlab/shortclips>`_. If you publish any work using\nthis data set, please cite the original publication [1]_, and the data set\n[2]_.\n"
+        "\n# Download the data set\n\nIn this script, we download the data set from Wasabi or GIN. No account is\nrequired.\n\n## Cite this data set\n\nThis tutorial is based on publicly available data [published on GIN](https://gin.g-node.org/gallantlab/shortclips). If you publish any work using\nthis data set, please cite the original publication [1]_, and the data set\n[2]_.\n"
       ]
     },
     {
@@ -89,7 +89,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.8.3"
+      "version": "3.10.9"
     }
   },
   "nbformat": 4,

tutorials/notebooks/shortclips/00_setup_colab.ipynb

@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Setup Google Colab\n\nIn this script, we setup a Google Colab environment. This script will only work\nwhen run from `Google Colab <https://colab.research.google.com/>`_). You can\nskip it if you run the tutorials on your machine.\n"
+        "\n# Setup Google Colab\n\nIn this script, we setup a Google Colab environment. This script will only work\nwhen run from [Google Colab](https://colab.research.google.com/)). You can\nskip it if you run the tutorials on your machine.\n"
       ]
     },
     {
@@ -132,7 +132,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.8.3"
+      "version": "3.10.9"
     }
   },
   "nbformat": 4,

tutorials/notebooks/shortclips/01_plot_explainable_variance.ipynb

@@ -177,7 +177,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Map to subject flatmap\n\nTo better understand the distribution of explainable variance, we map the\nvalues to the subject brain. This can be done with `pycortex\n<https://gallantlab.github.io/pycortex/>`_, which can create interactive 3D\nviewers to be displayed in any modern browser. ``pycortex`` can also display\nflattened maps of the cortical surface to visualize the entire cortical\nsurface at once.\n\nHere, we do not share the anatomical information of the subjects for privacy\nconcerns. Instead, we provide two mappers:\n\n- to map the voxels to a (subject-specific) flatmap\n- to map the voxels to the Freesurfer average cortical surface (\"fsaverage\")\n\nThe first mapper is 2D matrix of shape (n_pixels, n_voxels) that maps each\nvoxel to a set of pixel in a flatmap. The matrix is efficiently stored in a\n``scipy`` sparse CSR matrix. The function ``plot_flatmap_from_mapper``\nprovides an example of how to use the mapper and visualize the flatmap.\n\n"
+        "## Map to subject flatmap\n\nTo better understand the distribution of explainable variance, we map the\nvalues to the subject brain. This can be done with [pycortex](https://gallantlab.github.io/pycortex/), which can create interactive 3D\nviewers to be displayed in any modern browser. ``pycortex`` can also display\nflattened maps of the cortical surface to visualize the entire cortical\nsurface at once.\n\nHere, we do not share the anatomical information of the subjects for privacy\nconcerns. Instead, we provide two mappers:\n\n- to map the voxels to a (subject-specific) flatmap\n- to map the voxels to the Freesurfer average cortical surface (\"fsaverage\")\n\nThe first mapper is 2D matrix of shape (n_pixels, n_voxels) that maps each\nvoxel to a set of pixel in a flatmap. The matrix is efficiently stored in a\n``scipy`` sparse CSR matrix. The function ``plot_flatmap_from_mapper``\nprovides an example of how to use the mapper and visualize the flatmap.\n\n"
       ]
     },
     {
@@ -195,7 +195,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "This figure is a flattened map of the cortical surface. A number of regions\nof interest (ROIs) have been labeled to ease interpretation. If you have\nnever seen such a flatmap, we recommend taking a look at a `pycortex brain\nviewer <https://www.gallantlab.org/brainviewer/Deniz2019>`_, which displays\nthe brain in 3D. In this viewer, press \"I\" to inflate the brain, \"F\" to\nflatten the surface, and \"R\" to reset the view (or use the ``surface/unfold``\ncursor on the right menu). Press \"H\" for a list of all keyboard shortcuts.\nThis viewer should help you understand the correspondance between the flatten\nand the folded cortical surface of the brain.\n\n"
+        "This figure is a flattened map of the cortical surface. A number of regions\nof interest (ROIs) have been labeled to ease interpretation. If you have\nnever seen such a flatmap, we recommend taking a look at a [pycortex brain\nviewer](https://www.gallantlab.org/brainviewer/Deniz2019), which displays\nthe brain in 3D. In this viewer, press \"I\" to inflate the brain, \"F\" to\nflatten the surface, and \"R\" to reset the view (or use the ``surface/unfold``\ncursor on the right menu). Press \"H\" for a list of all keyboard shortcuts.\nThis viewer should help you understand the correspondance between the flatten\nand the folded cortical surface of the brain.\n\n"
       ]
     },
     {
@@ -337,7 +337,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.8.3"
+      "version": "3.10.9"
     }
   },
   "nbformat": 4,

tutorials/notebooks/shortclips/02_plot_ridge_regression.ipynb

@@ -406,7 +406,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.8.3"
+      "version": "3.7.12"
     }
   },
   "nbformat": 4,

tutorials/notebooks/shortclips/03_plot_wordnet_model.ipynb

@@ -653,7 +653,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.8.3"
+      "version": "3.7.12"
     }
   },
   "nbformat": 4,

tutorials/notebooks/shortclips/04_plot_hemodynamic_response.ipynb

@@ -235,7 +235,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "In the next cell we are plotting six lines. The subplot at the top shows the\nsimulated BOLD response, while the other subplots show the simulated feature\nat different delays. The effect of the delayer is clear: it creates multiple\ncopies of the original feature shifted forward in time by how many samples we\nrequested (in this case, from 0 to 4 samples, which correspond to 0, 2, 4, 6,\nand 8 s in time with a 2 s TR).\n\nWhen these delayed features are used to fit a voxelwise encoding model, the\nbrain response $y$ at time $t$ is simultaneously modeled by the\nfeature $x$ at times $t-0, t-2, t-4, t-6, t-8$. In the remaining\nof this example we will see that this method improves model prediction accuracy\nand it allows to account for the underlying shape of the hemodynamic response\nfunction.\n\n"
+        "In the next cell we are plotting six lines. The subplot at the top shows the\nsimulated BOLD response, while the other subplots show the simulated feature\nat different delays. The effect of the delayer is clear: it creates multiple\ncopies of the original feature shifted forward in time by how many samples we\nrequested (in this case, from 0 to 4 samples, which correspond to 0, 2, 4, 6,\nand 8 s in time with a 2 s TR).\n\nWhen these delayed features are used to fit a voxelwise encoding model, the\nbrain response $y$ at time $t$ is simultaneously modeled by the\nfeature $x$ at times $t-0, t-2, t-4, t-6, t-8$. In the remaining\nof this example we will see that this method improves model prediction\naccuracy and it allows to account for the underlying shape of the hemodynamic\nresponse function.\n\n"
       ]
     },
     {
@@ -253,7 +253,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Compare with a model without delays\n\nWe define here another model without feature delays (i.e. no ``Delayer``).\nBecause the BOLD signal is inherently slow due to the dynamics of\nneuro-vascular coupling, this model is unlikely to perform well.\n\nNote that if we remove the feature delays, we wil have more fMRI samples (3600) than\nnumber of features (1705). In this case, running a kernel version of ridge regression\nis computationally suboptimal. Thus, to create a model without delays we are using\n`RidgeCV` instead of `KernelRidgeCV`.\n\n"
+        "## Compare with a model without delays\n\nWe define here another model without feature delays (i.e. no ``Delayer``).\nBecause the BOLD signal is inherently slow due to the dynamics of\nneuro-vascular coupling, this model is unlikely to perform well.\n\nNote that if we remove the feature delays, we will have more fMRI samples\n(3600) than number of features (1705). In this case, running a kernel version\nof ridge regression is computationally suboptimal. Thus, to create a model\nwithout delays we are using `RidgeCV` instead of `KernelRidgeCV`.\n\n"
       ]
     },
     {
@@ -264,7 +264,7 @@
       },
       "outputs": [],
       "source": [
-        "pipeline_no_delay = make_pipeline(\n    StandardScaler(with_mean=True, with_std=False),\n    RidgeCV(\n        alphas=alphas, cv=cv,\n        solver_params=dict(n_targets_batch=500, n_alphas_batch=5,\n                           n_targets_batch_refit=100)),\n)\npipeline_no_delay"
+        "pipeline_no_delay = make_pipeline(\n    StandardScaler(with_mean=True, with_std=False),\n    RidgeCV(\n        alphas=alphas, cv=cv, solver=\"svd\",\n        solver_params=dict(n_targets_batch=500, n_alphas_batch=5,\n                           n_targets_batch_refit=100)),\n)\npipeline_no_delay"
       ]
     },
     {
@@ -352,7 +352,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.8.3"
+      "version": "3.10.9"
     }
   },
   "nbformat": 4,

tutorials/notebooks/shortclips/05_plot_motion_energy_model.ipynb

@@ -341,7 +341,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.8.3"
+      "version": "3.7.12"
     }
   },
   "nbformat": 4,

tutorials/notebooks/shortclips/06_plot_banded_ridge_model.ipynb

@@ -510,7 +510,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.8.3"
+      "version": "3.7.12"
     }
   },
   "nbformat": 4,

tutorials/notebooks/shortclips/07_extract_motion_energy.ipynb

@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Extract motion energy features from the stimuli\n\nThis script describes how to extract motion-energy features from the stimuli.\n\n.. Note:: The public data set already contains precomputed motion-energy.\n    Therefore, you do not need to run this script to fit motion-energy models\n    in other part of this tutorial.\n\n*Motion-energy features:* Motion-energy features result from filtering a video\nstimulus with spatio-temporal Gabor filters. A pyramid of filters is used to\ncompute the motion-energy features at multiple spatial and temporal scales.\nMotion-energy features were introduced in [1]_.\n\nThe motion-energy extraction is performed by the package `pymoten\n<https://github.com/gallantlab/pymoten>`_. Check the pymoten `gallery of\nexamples <https://gallantlab.github.io/pymoten/auto_examples/index.html>`_ for\nvisualizing motion-energy filters, and for pymoten API usage examples.\n\n## Running time\nExtracting motion energy is a bit longer than the other examples. It typically\ntakes a couple hours to run.\n"
+        "\n# Extract motion energy features from the stimuli\n\nThis script describes how to extract motion-energy features from the stimuli.\n\n.. Note:: The public data set already contains precomputed motion-energy.\n    Therefore, you do not need to run this script to fit motion-energy models\n    in other part of this tutorial.\n\n*Motion-energy features:* Motion-energy features result from filtering a video\nstimulus with spatio-temporal Gabor filters. A pyramid of filters is used to\ncompute the motion-energy features at multiple spatial and temporal scales.\nMotion-energy features were introduced in [1]_.\n\nThe motion-energy extraction is performed by the package [pymoten](https://github.com/gallantlab/pymoten). Check the pymoten [gallery of\nexamples](https://gallantlab.github.io/pymoten/auto_examples/index.html) for\nvisualizing motion-energy filters, and for pymoten API usage examples.\n\n## Running time\nExtracting motion energy is a bit longer than the other examples. It typically\ntakes a couple hours to run.\n"
       ]
     },
     {
@@ -136,7 +136,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.8.3"
+      "version": "3.10.9"
     }
   },
   "nbformat": 4,