diff --git a/.buildinfo b/.buildinfo index cb6e1df..8ab3ec0 100644 --- a/.buildinfo +++ b/.buildinfo @@ -1,4 +1,4 @@ # Sphinx build info version 1 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. -config: 00763591344d69343f6e7aff41b556d8 +config: f605d10148b9ecb01899162702e61712 tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/_images/haxby_data_57_0.png b/_images/haxby_data_57_0.png index 8ceb4d7..1d08631 100644 Binary files a/_images/haxby_data_57_0.png and b/_images/haxby_data_57_0.png differ diff --git a/_images/haxby_data_57_1.png b/_images/haxby_data_57_1.png index 209d09b..9f91713 100644 Binary files a/_images/haxby_data_57_1.png and b/_images/haxby_data_57_1.png differ diff --git a/_images/haxby_data_57_3.png b/_images/haxby_data_57_3.png index 1d08631..8ceb4d7 100644 Binary files a/_images/haxby_data_57_3.png and b/_images/haxby_data_57_3.png differ diff --git a/_images/haxby_data_57_4.png b/_images/haxby_data_57_4.png index 9f91713..209d09b 100644 Binary files a/_images/haxby_data_57_4.png and b/_images/haxby_data_57_4.png differ diff --git a/_images/mlp_decoding_26_0.png b/_images/mlp_decoding_26_0.png index 894ba07..81e0010 100644 Binary files a/_images/mlp_decoding_26_0.png and b/_images/mlp_decoding_26_0.png differ diff --git a/_images/mlp_decoding_28_0.png b/_images/mlp_decoding_28_0.png index 24bc22e..1029c53 100644 Binary files a/_images/mlp_decoding_28_0.png and b/_images/mlp_decoding_28_0.png differ diff --git a/_images/mlp_decoding_37_0.png b/_images/mlp_decoding_37_0.png index 0ceb079..cf1435a 100644 Binary files a/_images/mlp_decoding_37_0.png and b/_images/mlp_decoding_37_0.png differ diff --git a/_sources/intro.md b/_sources/intro.md index fc369d1..06a4e10 100644 --- a/_sources/intro.md +++ b/_sources/intro.md @@ -55,7 +55,7 @@ More information and their application can be found in the respective sections o :margin: 3 :class-body: text-center :class-header: bg-light text-center -:link: https://main-educational.github.io/brain_decoding/haxby_data.html +:link: https://main-educational.github.io/brain_encoding_decoding/haxby_data.html **An overview of the Haxby Dataset** ^^^ ```{image} https://main-educational.github.io/brain_encoding_decoding/_images/haxby_data_52_0.png @@ -290,4 +290,4 @@ The tutorial is rendered here using [Jupyter Book](https://github.com/jupyter/ju :filter: docname in docnames ``` --- - \ No newline at end of file + diff --git a/_sources/mlp_decoding.ipynb b/_sources/mlp_decoding.ipynb index eaa6aa8..ab60bb8 100644 --- a/_sources/mlp_decoding.ipynb +++ b/_sources/mlp_decoding.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "c9102cd6", + "id": "30ddaa2a", "metadata": {}, "source": [ "# Brain decoding with MLP\n", @@ -78,16 +78,16 @@ { "cell_type": "code", "execution_count": 1, - "id": "746fad47", + "id": "7ca4b86b", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/datasets/func.py:20: DeprecationWarning: Please use `MatReadError` from the `scipy.io.matlab` namespace, the `scipy.io.matlab.miobase` namespace is deprecated.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/datasets/func.py:20: DeprecationWarning: Please use `MatReadError` from the `scipy.io.matlab` namespace, the `scipy.io.matlab.miobase` namespace is deprecated.\n", " from scipy.io.matlab.miobase import MatReadError\n", - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/datasets/__init__.py:93: FutureWarning: Fetchers from the nilearn.datasets module will be updated in version 0.9 to return python strings instead of bytes and Pandas dataframes instead of Numpy arrays.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/datasets/__init__.py:93: FutureWarning: Fetchers from the nilearn.datasets module will be updated in version 0.9 to return python strings instead of bytes and Pandas dataframes instead of Numpy arrays.\n", " warn(\"Fetchers from the nilearn.datasets module will be \"\n" ] } @@ -118,7 +118,7 @@ }, { "cell_type": "markdown", - "id": "7ee2fd85", + "id": "d1958ee9", "metadata": {}, "source": [ "As an initial check, we'll have a look at the size of `X` and `y`:" @@ -127,7 +127,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "6cebd471", + "id": "37e220f1", "metadata": {}, "outputs": [ { @@ -150,7 +150,7 @@ }, { "cell_type": "markdown", - "id": "cf80a976", + "id": "28b10bab", "metadata": {}, "source": [ "So we have `1452` `time points`, with one `label` for the respective `stimulus percept` each, and for each `time point` we have `recordings` of `brain` activity obtained via `fMRI` across `675 voxels` (within the `VT` `mask`). We can also see that the `stimulus percept`s span `9` different `categories`.\n", @@ -161,7 +161,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "e77cf1de", + "id": "eec3894a", "metadata": {}, "outputs": [ { @@ -352,7 +352,7 @@ }, { "cell_type": "markdown", - "id": "1577f1bb", + "id": "e9b87166", "metadata": {}, "source": [ "## Training a model\n", @@ -363,7 +363,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "610f647d", + "id": "dcc852fc", "metadata": {}, "outputs": [], "source": [ @@ -373,7 +373,7 @@ }, { "cell_type": "markdown", - "id": "6c166c74", + "id": "62865604", "metadata": {}, "source": [ "With that, we can already build our `MLP`. Here, we are going to use [Tensorflow](https://www.tensorflow.org/) and [Keras](https://keras.io/). As with every other `ANN`, we need to `import` the respective components, here, the `model` and `layer` `type`. In our case we will use a [`Sequential` `model`](https://keras.io/guides/sequential_model/) and [`Dense`](https://keras.io/api/layers/core_layers/dense/) `layers`." @@ -382,33 +382,15 @@ { "cell_type": "code", "execution_count": 5, - "id": "0ff03555", + "id": "ab4b1dfc", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "2022-12-10 18:55:05.994707: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n", - "2022-12-10 18:55:05.994734: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/keras_preprocessing/image/utils.py:23: DeprecationWarning: NEAREST is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.NEAREST or Dither.NONE instead.\n", - " 'nearest': pil_image.NEAREST,\n", - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/keras_preprocessing/image/utils.py:24: DeprecationWarning: BILINEAR is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BILINEAR instead.\n", - " 'bilinear': pil_image.BILINEAR,\n", - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/keras_preprocessing/image/utils.py:25: DeprecationWarning: BICUBIC is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BICUBIC instead.\n", - " 'bicubic': pil_image.BICUBIC,\n", - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/keras_preprocessing/image/utils.py:28: DeprecationWarning: HAMMING is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.HAMMING instead.\n", - " if hasattr(pil_image, 'HAMMING'):\n", - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/keras_preprocessing/image/utils.py:30: DeprecationWarning: BOX is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BOX instead.\n", - " if hasattr(pil_image, 'BOX'):\n", - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/keras_preprocessing/image/utils.py:33: DeprecationWarning: LANCZOS is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.LANCZOS instead.\n", - " if hasattr(pil_image, 'LANCZOS'):\n" + "2023-05-22 08:57:18.709929: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n", + "2023-05-22 08:57:18.709957: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n" ] } ], @@ -419,7 +401,7 @@ }, { "cell_type": "markdown", - "id": "1d242d32", + "id": "8742f337", "metadata": {}, "source": [ "`````{admonition} A note regarding our MLP\n", @@ -430,7 +412,7 @@ }, { "cell_type": "markdown", - "id": "500e0b41", + "id": "3874b852", "metadata": {}, "source": [ "Initially, we need to create our, so far, `empty model`." @@ -439,17 +421,17 @@ { "cell_type": "code", "execution_count": 6, - "id": "cde05ea0", + "id": "7a91b4cf", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "2022-12-10 18:55:07.313462: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory\n", - "2022-12-10 18:55:07.313486: W tensorflow/stream_executor/cuda/cuda_driver.cc:269] failed call to cuInit: UNKNOWN ERROR (303)\n", - "2022-12-10 18:55:07.313508: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (fv-az359-603): /proc/driver/nvidia/version does not exist\n", - "2022-12-10 18:55:07.313922: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F FMA\n", + "2023-05-22 08:57:20.117988: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory\n", + "2023-05-22 08:57:20.118014: W tensorflow/stream_executor/cuda/cuda_driver.cc:269] failed call to cuInit: UNKNOWN ERROR (303)\n", + "2023-05-22 08:57:20.118035: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (fv-az1102-220): /proc/driver/nvidia/version does not exist\n", + "2023-05-22 08:57:20.118486: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F FMA\n", "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" ] } @@ -461,7 +443,7 @@ }, { "cell_type": "markdown", - "id": "2e94c9ca", + "id": "a976ef27", "metadata": {}, "source": [ "Next, we can add the `layers` to our `model`, starting with the `input layer`. Given this is a rather short introduction to the topic and does not focus on `ANN`s, we are going to set the `kernel initialization` and `activation function` to appropriate defaults (Please have a look at the [Introduction to deep learning session](https://main-educational.github.io/material.html#introduction-to-deep-learning-using-pytorch) for more information.)." @@ -470,7 +452,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "d25f7ef0", + "id": "45100224", "metadata": {}, "outputs": [], "source": [ @@ -479,7 +461,7 @@ }, { "cell_type": "markdown", - "id": "2cc9de13", + "id": "82209e59", "metadata": {}, "source": [ "As noted above, we are using `Dense` `layers` and as you can see, we set the `input dimensions` to `675`. You might have already notices that this is the number of `voxels` we have `data` from. Setting the `input dimension` according to the `data dimensions` is rather important is referred to as the [semantic gap](https://en.wikipedia.org/wiki/Semantic_gap): the transformation of `actions` & `percepts` conducted/perceived by `human`s into `computational representations`. For example, pictures are \"nothing\" but a huge `array` for a computer and what will be submitted to the input layer of an `ANN` (note: this also holds true for basically any other type of `data`). Here, our `MLP` receives the extracted `brain activity patterns` as `input` which are already in the right `array` format thanks to `nilearn`. Thus, always carefully think about what your `input` `data` entails and how it is structured to then setup your `input layer` accordingly.\n", @@ -490,7 +472,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "73547391", + "id": "44b2862d", "metadata": {}, "outputs": [], "source": [ @@ -499,7 +481,7 @@ }, { "cell_type": "markdown", - "id": "6451ddd9", + "id": "73ebbdf5", "metadata": {}, "source": [ "And because we are creating a very simple `MLP` with only three `layers`, we already add our `output layer`, using the `softmax` `activation function` given that we aim to `train` our `MLP` to `predict` the different `categories` that were perceived by the `participants` from their `brain activity patterns`." @@ -508,7 +490,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "19fbed8f", + "id": "bd718373", "metadata": {}, "outputs": [], "source": [ @@ -517,7 +499,7 @@ }, { "cell_type": "markdown", - "id": "f2cbcb2c", + "id": "9ff33442", "metadata": {}, "source": [ "To get a nice overview of our `ANN`, we can now use the `.summary()` `function`, which will provide us with the `model type`, `model parameters` and for each `layer`, the its `type`, `shape` and `parameters`." @@ -526,7 +508,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "223277ff", + "id": "73ac3bfb", "metadata": {}, "outputs": [ { @@ -641,7 +623,7 @@ }, { "cell_type": "markdown", - "id": "03ce8c44", + "id": "7f3b4e36", "metadata": {}, "source": [ "With that, we already created our `MLP` `architecture`, which is now ready to be `compiled`! Within this step, we will set the `optimizer`, `loss function` and `metric`, ie `components` that define how our `MLP` will `learn`." @@ -650,7 +632,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "e5ddf472", + "id": "20977d8e", "metadata": {}, "outputs": [], "source": [ @@ -659,7 +641,7 @@ }, { "cell_type": "markdown", - "id": "f72dd136", + "id": "17c892bb", "metadata": {}, "source": [ "Now it's to `train` our `MLP`. Thus, we have to `fit` it to our `data`, specifically only the `training` `data`. Here, we are going to provide a few more `hyperparameters` that will define how our `MLP` is going to `learn`. This entails the `batch size`, the `epochs` and `split` of `validation sets`. We will assign the respective output to a variable so that we can investigate our `MLP`'s `learning process`." @@ -668,7 +650,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "872f9bd1", + "id": "86034e09", "metadata": {}, "outputs": [ { @@ -683,15 +665,15 @@ "output_type": "stream", "text": [ "\r", - " 1/93 [..............................] - ETA: 34s - loss: 2.2317 - accuracy: 0.0000e+00" + " 1/93 [..............................] - ETA: 35s - loss: 2.2229 - accuracy: 0.1000" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "43/93 [============>.................] - ETA: 0s - loss: 1.8812 - accuracy: 0.3651 " + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + 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@@ -722,7 +704,7 @@ "output_type": "stream", "text": [ "\r", - " 1/93 [..............................] - ETA: 0s - loss: 1.1605 - accuracy: 0.6000" + " 1/93 [..............................] - ETA: 0s - loss: 1.5577 - accuracy: 0.4000" ] }, { @@ -730,7 +712,7 @@ "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "46/93 [=============>................] - ETA: 0s - loss: 1.2162 - accuracy: 0.5739" + "48/93 [==============>...............] - ETA: 0s - loss: 1.1682 - accuracy: 0.6000" ] }, { @@ -738,7 +720,7 @@ "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "92/93 [============================>.] - ETA: 0s - loss: 1.1021 - accuracy: 0.6185" + "92/93 [============================>.] - ETA: 0s - loss: 1.1279 - accuracy: 0.6217" ] }, { @@ -746,7 +728,7 @@ "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "93/93 [==============================] - 0s 1ms/step - loss: 1.0995 - accuracy: 0.6196 - val_loss: 1.1412 - val_accuracy: 0.6009\n" + "93/93 [==============================] - 0s 1ms/step - loss: 1.1285 - accuracy: 0.6207 - val_loss: 1.2011 - val_accuracy: 0.5665\n" ] }, { @@ -761,7 +743,7 @@ "output_type": "stream", "text": [ "\r", - " 1/93 [..............................] - ETA: 0s - loss: 0.6876 - accuracy: 0.8000" + " 1/93 [..............................] - ETA: 0s - loss: 0.3257 - accuracy: 1.0000" ] }, { @@ -769,7 +751,7 @@ "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "50/93 [===============>..............] - ETA: 0s - loss: 0.7936 - accuracy: 0.7480" + "47/93 [==============>...............] - ETA: 0s - loss: 0.8161 - accuracy: 0.7447" ] }, { @@ -777,7 +759,7 @@ "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "93/93 [==============================] - ETA: 0s - loss: 0.7539 - accuracy: 0.7608" + "93/93 [==============================] - ETA: 0s - loss: 0.8159 - accuracy: 0.7392" ] }, { @@ -785,7 +767,7 @@ "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "93/93 [==============================] - 0s 2ms/step - loss: 0.7539 - accuracy: 0.7608 - val_loss: 1.0137 - val_accuracy: 0.6395\n" + "93/93 [==============================] - 0s 1ms/step - loss: 0.8159 - accuracy: 0.7392 - val_loss: 1.0526 - val_accuracy: 0.6266\n" ] }, { @@ -800,7 +782,7 @@ "output_type": "stream", "text": [ "\r", - " 1/93 [..............................] - ETA: 0s - loss: 0.3305 - accuracy: 0.9000" + " 1/93 [..............................] - ETA: 0s - loss: 0.2624 - accuracy: 1.0000" ] }, { @@ -808,7 +790,7 @@ "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "48/93 [==============>...............] - ETA: 0s - loss: 0.5290 - accuracy: 0.8354" + "46/93 [=============>................] - ETA: 0s - loss: 0.5678 - accuracy: 0.8196" ] }, { @@ -816,7 +798,7 @@ "output_type": "stream", "text": [ 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+ " 1/93 [..............................] - ETA: 0s - loss: 0.2386 - accuracy: 1.0000" ] }, { @@ -847,7 +829,7 @@ "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "46/93 [=============>................] - ETA: 0s - loss: 0.3239 - accuracy: 0.9261" + "45/93 [=============>................] - ETA: 0s - loss: 0.3832 - accuracy: 0.8844" ] }, { @@ -855,7 +837,7 @@ "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "92/93 [============================>.] - ETA: 0s - loss: 0.3246 - accuracy: 0.9152" + "91/93 [============================>.] - ETA: 0s - loss: 0.3663 - accuracy: 0.8912" ] }, { @@ -863,7 +845,7 @@ "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "93/93 [==============================] - 0s 1ms/step - loss: 0.3232 - accuracy: 0.9159 - val_loss: 0.8900 - val_accuracy: 0.6867\n" + "93/93 [==============================] - 0s 2ms/step - loss: 0.3661 - accuracy: 0.8912 - val_loss: 0.8652 - val_accuracy: 0.7124\n" ] }, { @@ -878,7 +860,7 @@ "output_type": "stream", "text": [ "\r", - " 1/93 [..............................] - ETA: 0s - loss: 0.1384 - accuracy: 1.0000" + " 1/93 [..............................] - ETA: 0s - loss: 0.3449 - accuracy: 0.9000" ] }, { @@ -886,7 +868,7 @@ "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "46/93 [=============>................] - ETA: 0s - loss: 0.2379 - accuracy: 0.9326" + "46/93 [=============>................] - ETA: 0s - loss: 0.2661 - accuracy: 0.9261" ] }, { @@ -894,7 +876,7 @@ "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "90/93 [============================>.] - ETA: 0s - loss: 0.2275 - accuracy: 0.9333" + "91/93 [============================>.] - ETA: 0s - loss: 0.2419 - accuracy: 0.9363" ] }, { @@ -902,7 +884,7 @@ "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "93/93 [==============================] - 0s 2ms/step - loss: 0.2247 - accuracy: 0.9353 - val_loss: 0.8467 - val_accuracy: 0.7511\n" + "93/93 [==============================] - 0s 2ms/step - loss: 0.2418 - accuracy: 0.9364 - val_loss: 0.8276 - val_accuracy: 0.7554\n" ] }, { @@ -917,7 +899,7 @@ "output_type": "stream", "text": [ "\r", - " 1/93 [..............................] - ETA: 0s - loss: 0.0914 - accuracy: 1.0000" + " 1/93 [..............................] - ETA: 0s - loss: 0.1631 - accuracy: 1.0000" ] }, { @@ -925,7 +907,7 @@ "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "49/93 [==============>...............] - ETA: 0s - loss: 0.1526 - accuracy: 0.9531" + "47/93 [==============>...............] - ETA: 0s - loss: 0.1334 - accuracy: 0.9745" ] }, { @@ -933,7 +915,7 @@ "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "90/93 [============================>.] - ETA: 0s - loss: 0.1379 - accuracy: 0.9667" + "91/93 [============================>.] - ETA: 0s - loss: 0.1356 - accuracy: 0.9747" ] }, { @@ -941,7 +923,7 @@ "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "93/93 [==============================] - 0s 2ms/step - loss: 0.1385 - accuracy: 0.9677 - val_loss: 0.7506 - val_accuracy: 0.7554\n" + "93/93 [==============================] - 0s 2ms/step - loss: 0.1345 - accuracy: 0.9752 - val_loss: 0.7945 - val_accuracy: 0.7639\n" ] }, { @@ -956,7 +938,15 @@ "output_type": "stream", "text": [ "\r", - " 1/93 [..............................] - ETA: 0s - loss: 0.0513 - accuracy: 1.0000" + " 1/93 [..............................] - ETA: 0s - loss: 0.0720 - accuracy: 1.0000" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "48/93 [==============>...............] - ETA: 0s - loss: 0.0707 - accuracy: 0.9958" ] }, { @@ -964,7 +954,7 @@ "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "49/93 [==============>...............] - ETA: 0s - loss: 0.0621 - accuracy: 0.9939" + "92/93 [============================>.] - ETA: 0s - loss: 0.0737 - accuracy: 0.9946" ] }, { @@ -972,7 +962,7 @@ "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "93/93 [==============================] - 0s 1ms/step - loss: 0.0723 - accuracy: 0.9935 - val_loss: 0.7802 - val_accuracy: 0.7639\n" + "93/93 [==============================] - 0s 2ms/step - loss: 0.0738 - accuracy: 0.9946 - val_loss: 0.8261 - val_accuracy: 0.7468\n" ] }, { @@ -987,7 +977,7 @@ "output_type": "stream", "text": [ "\r", - " 1/93 [..............................] - ETA: 0s - loss: 0.0273 - accuracy: 1.0000" + " 1/93 [..............................] - ETA: 0s - loss: 0.1264 - accuracy: 0.9000" ] }, { @@ -995,7 +985,7 @@ "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "49/93 [==============>...............] - ETA: 0s - loss: 0.0394 - accuracy: 1.0000" + "48/93 [==============>...............] - ETA: 0s - loss: 0.0799 - accuracy: 0.9833" ] }, { @@ -1003,7 +993,7 @@ "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "93/93 [==============================] - ETA: 0s - loss: 0.0421 - accuracy: 0.9968" + "92/93 [============================>.] - ETA: 0s - loss: 0.0833 - accuracy: 0.9804" ] }, { @@ -1011,7 +1001,7 @@ "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "93/93 [==============================] - 0s 1ms/step - loss: 0.0421 - accuracy: 0.9968 - val_loss: 0.8278 - val_accuracy: 0.7639\n" + "93/93 [==============================] - 0s 1ms/step - loss: 0.0827 - accuracy: 0.9806 - val_loss: 0.8526 - val_accuracy: 0.7639\n" ] }, { @@ -1026,7 +1016,7 @@ "output_type": "stream", "text": [ "\r", - " 1/93 [..............................] - ETA: 0s - loss: 0.0250 - accuracy: 1.0000" + " 1/93 [..............................] - ETA: 0s - loss: 0.0169 - accuracy: 1.0000" ] }, { @@ -1034,7 +1024,7 @@ "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "48/93 [==============>...............] - ETA: 0s - loss: 0.0322 - accuracy: 0.9979" + "47/93 [==============>...............] - ETA: 0s - loss: 0.0302 - accuracy: 1.0000" ] }, { @@ -1042,7 +1032,7 @@ "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "93/93 [==============================] - 0s 1ms/step - loss: 0.0271 - accuracy: 0.9989 - val_loss: 0.7860 - val_accuracy: 0.7811\n" + "93/93 [==============================] - 0s 1ms/step - loss: 0.0321 - accuracy: 0.9989 - val_loss: 0.8581 - val_accuracy: 0.7639\n" ] } ], @@ -1053,7 +1043,7 @@ }, { "cell_type": "markdown", - "id": "309fee9c", + "id": "94de7ec1", "metadata": {}, "source": [ "This looks about and what we would expect the `learning process` to be: across `epochs`, the `loss` is decreasing and the `accuracy` is increasing. \n", @@ -1069,12 +1059,12 @@ { "cell_type": "code", "execution_count": 13, - "id": "e38ed77e", + "id": "ac8eeb81", "metadata": {}, "outputs": [ { "data": { - "image/png": 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UOt7pdHLNNdfQrl07/Pz86NKlC88995zn9YceeojXX3+dTz/9FMMwMAyDH3744aiPun788UcGDhyI3W4nNjaWKVOmUF1d7Xn9zDPP5LbbbuPuu+8mPDycmJgYHnrooRP/xp0gBZ8mIvLSSGzxNqoyq8h6P8vsckREWgy3202J02nK9nvdOX7rz3/+M7m5uXz//feefQcOHOCrr75i/PjxFBcXM3LkSBYtWsSaNWs477zzGDVqVK2+sL+nuLiYP/7xj3Tv3p1Vq1bx0EMPcdddd9U6xuVy0bp1az744AM2btzIAw88wL333sv7778PwF133cXo0aM577zzSE9PJz09naFDhx5xr/379zNy5EgGDBjA2rVreeGFF5g7dy6PPfZYreNef/11AgICWLZsGf/4xz945JFHGrw7S9Nvk/ISFl8L8TfFs/O+nex7bh/RV0RjGIbZZYmINHulLheBP/1kyr2LTz+dAKu1TseGhYVx/vnnM2/ePM455xwAPvzwQyIiIjjrrLOwWCwkJSV5jn/00UeZP38+n332Gbfccstxrz9v3jxcLhdz587F4XDQo0cP9u3bx4033ug5xtfXl4cfftjzebt27Vi6dCnvv/8+o0ePJjAwED8/PyoqKoiJiTnmvZ5//nkSEhKYOXMmhmHQtWtX0tLSuOeee3jggQewWGraXXr37s2DDz4IQKdOnZg5cyaLFi3i3HPPrdP37GSoxacJib0uFovDQvGqYgr/2zSW1hARkcYzfvx4PvroI89SS2+//TZjx47FYrFQXFzMXXfdRbdu3QgNDSUwMJBNmzbVucVn06ZN9O7dG4fD4dk3ZMiQI46bNWsW/fr1IzIyksDAQF566aU63+Pwew0ZMqTWH/DDhg2juLi41oTDvXv3rnVebGwsWVkN+9RDLT5NiC3CRtT4KDLmZrDvuX2EDAs5/kkiIvK7/C0Wik8/3bR7n4hRo0bhdrtZsGABAwYM4KeffuJf//oXUPOYaeHChfzzn/+kY8eO+Pn5cdlll1FZWVlv9b777rvcddddPPPMMwwZMoSgoCCefvppli1bVm/3OJyvr2+tzw3DOKKPU31T8GliWt/emoy5GWR/nE35nnIcbRzHP0lERI7JMIw6P24ym8Ph4E9/+hNvv/0227Zto0uXLvTt2xeAJUuWcOWVV3LJJZcANX12du3aVedrd+vWjTfffJPy8nJPq88vv/xS65glS5YwdOhQbrrpJs++7du31zrGZrPhdDqPe6+PPvoIt9vtafVZsmQJQUFBx1xpobHoUVcTE9grkNCzQ8EJ+2dpGQsREW8zfvx4FixYwCuvvML48eM9+zt16sTHH39MSkoKa9euZdy4cSfUOjJu3DgMw2DSpEls3LiRL774gn/+85+1junUqRMrV67k66+/JjU1lfvvv58VK1bUOiYxMZFff/2VLVu2kJOTQ1VV1RH3uummm9i7dy+33normzdv5tNPP+XBBx9k8uTJnv49ZlHwaYIOrdqePicdZ8nvp2oREWlZzj77bMLDw9myZQvjxo3z7H/22WcJCwtj6NChjBo1ihEjRnhag+oiMDCQ//znP6xbt47k5GTuu+8+nnrqqVrHXH/99fzpT39izJgxDBo0iNzc3FqtPwCTJk2iS5cu9O/fn8jISJYsWXLEveLj4/niiy9Yvnw5SUlJ3HDDDVxzzTX8/e9/P8HvRv0z3Ccy1q4FKCwsJCQkhIKCgia1YOnh3E43yzovo3xHOZ1nd/YsZCoiIsdXXl7Ozp07adeuXa2OvNK81df7qhafJsiwGsTfWjOh4b4Z+05oHggRERE5NgWfJir2qlisgVZKN5aS922e2eWIiIi0CAo+TZRPiA8xV9VMDqVV20VEROqHgk8TFn9rPBhwYMEBSreWml2OiIhIs6fg04T5d/InfGQ4APv/raHtIiInQv0jW5b6ej8VfJq4Q0PbM17NoLqg+jhHi4iI9eBkhfU5o7GY79D7aT3FySg1c3MTFzY8DP/u/pRuLCX91XQS7kgwuyQRkSbNx8cHf39/srOz8fX1NX3CPDl1LpeL7Oxs/P398fE5teiieXyagbQX00i9IRVHeweDUgdhWLVqu4jI76msrGTnzp0Nvu6TNB6LxUK7du2w2WyndB0Fn0byRkYGDouF0VFRJ3yus9TJ0tZLqc6rpuenPYm4MKIBKhQRaVlcLpced7UgNputXlrv9KirEXyUnc3EzZsJslrpGxhIR3//Ezrf6m8ldlIse/+xl33P7VPwERGpA4vFopmb5Qh68NkILmrVitNDQihyOhm7cSOVJ9H0Gn9zPFgh/7t8itcVN0CVIiIiLZ+CTyPwsVh4u1s3wn18WFVczNQdO074Go42DiIviQRg/wwNbRcRETkZCj6NJMHh4NWuXQF4dt8+FuTmnvA14m+vWb8r861MKnP03FpEROREKfg0ogsjIrgtvia8XLl5M/srKk7o/JBhIQT2C8RV7iL9pfSGKFFERKRFU/BpZP/o0IE+gYHkVFVxxaZNOE9gUJ1hGJ4JDfc/vx9XlYZpioiInAgFn0Zmt1h4t3t3AiwWfsjPZ9ru3Sd0ftToKHyjfancX0n2R9kNVKWIiEjLpOBjgi7+/jzfuTMAD+7axU/5+XU+12K3EH9jzeOy/c+pk7OIiMiJUPAxyYSYGP4SHY0LGLdpEweqqup8btwNcRg2g8JfCilcXthwRYqIiLQwCj4mmtWpE538/NhXUcHVmzfXeeVZW7SNqLE1M0Dve25fQ5YoIiLSoij4mCjIx4d3u3fHZhh8mpvLrP11f3R1qJNz9vvZVKSd2OgwERERb2Vq8Fm8eDGjRo0iLi4OwzD45JNP6nzukiVL8PHxoU+fPg1WX2PoGxTE0x06APDX7dtJKSqq03lBfYMIOS0Ed7WbtBfSGrJEERGRFsPU4FNSUkJSUhKzZs06ofPy8/OZMGEC55xzTgNV1rhujY9nVKtWVLrdjNm4keLq6jqdd2hCw7QX03CWOxuyRBERkRbB1OBz/vnn89hjj3HJJZec0Hk33HAD48aNY8iQIQ1UWeMyDINXu3Yl3mYjtayMW7ZurdN5ERdHYG9jpyq7iqx3shq4ShERkeav2fXxefXVV9mxYwcPPvig2aXUq1a+vszr3h0L8HpmJm9mZBz3HIuPpWbxUmo6Ode1c7SIiIi3albBZ+vWrUyZMoW33noLHx+fOp1TUVFBYWFhra2p+r/QUB5MTATgxtRUUktLj3tO7LWxWPwslKwtoWBxQQNXKCIi0rw1m+DjdDoZN24cDz/8MJ0PTv5XF9OmTSMkJMSzJSQkNGCVp+6+tm05IySEEpeLsRs3UuH6/WUpfMN9iZ4QDWhou4iIyPEY7ibyfMQwDObPn8/FF1981Nfz8/MJCwvDarV69rlcLtxuN1arlW+++Yazzz77iPMqKiqoOGwx0MLCQhISEigoKCA4OLjev476sL+igqQVK8itrub2+Himd+r0u8eXbCxhRY8VYIFB2wfhl+jXSJWKiIg0L82mxSc4OJh169aRkpLi2W644Qa6dOlCSkoKgwYNOup5drud4ODgWltTF2+381rXrgA8t38//8nJ+d3jA7oHEHZuGLhg/0wtYyEiInIspgaf4uJiT4gB2LlzJykpKezZsweAqVOnMmHCBAAsFgs9e/astUVFReFwOOjZsycBAQFmfRkN4o8REdzRumaSwqs2b2ZfefnvHn9oQsP0l9OpLq7bcHgRERFvY2rwWblyJcnJySQnJwMwefJkkpOTeeCBBwBIT0/3hCBv9GT79vQNDCS3uprxmzbh/J2nkuHnh+PXyQ9ngZPM1zMbsUoREZHmo8n08WkshYWFhISENOk+PofbWlpK31WrKHY6eSgx0TPq62j2zdzHtlu34dfZj4GbBmJYjMYrVEREpBloNn18vFUnf39mHxzF9siuXfyYn3/MY2MmxmANtlKWWsaBrw80UoUiIiLNh4JPMzA+OporY2JwAeM3biSnsvKox/kE+RB7TSygoe0iIiJHo+DTTPy7Y0e6+Pmxv7KSq7ZsOeYszfG3xIMBeV/nUbK5pJGrFBERadoUfJqJQB8f3u3eHbth8HluLjP2H33Yul97P1pd2AqA/TM0tF1ERORwCj7NSJ+gIJ7p2BGAv23fzqqioqMed2hoe8brGVTlVTVafSIiIk2dgk8zc1NcHBdHRFDldjN240aKqo+csyf0zFACegfgKnWRPjfdhCpFRESaJgWfZsYwDOZ26UKC3c62sjJuTE09or+PYRi0vq2m1Wf/zP24qn9/vS8RERFvoeDTDIX7+jKvWzcswNtZWbyReeSEhVHjovBp5UPF7gpyP8tt/CJFRESaIAWfZuq00FAePjiZ4U2pqWwpLa31utXPStz1cYCGtouIiByi4NOMTW3blrNCQyl1uRizYQPlTmet1+NvisfwMShYXEBRytE7QouIiHgTBZ9mzGoYvNWtGxG+vqwtKeFvO3bUet0ebyfyskgA9j+noe0iIiIKPs1cnN3O6127AjBz/34+zcmp9Xr87fEAZM7LpDLr6DM+i4iIeAsFnxZgZKtW/LV1zSiuqzZvZm95uee1kMEhBA0Mwl3pJu3FNLNKFBERaRIUfFqIJ9q3p39QEHnV1YzbtIlq1/+GsB+a0DDt+TRclRraLiIi3kvBp4WwWSy82707QVYrPxcU8Mju3Z7XIi+LxBZrozKjkqz3s0ysUkRExFwKPi1IBz8/XurcGYDHdu/m+7w8ACw2C/E31/T12f/c/mMucCoiItLSKfi0MGOjo7kmJgY3MH7TJrIrazo0x14Xi2E3KFpZROHSQnOLFBERMYmCTwv0XKdOdPP3J72ykombN+Nyu7FF2ogeHw1oQkMREfFeCj4tUIDVynvdu2M3DL48cIDp+2qCzqFOztkfZVO+t/z3LiEiItIiKfi0UL0CA/lXx44ATNmxgxWFhQT2DiT0zFBw1ozwEhER8TYKPi3YDXFx/Ckigiq3m7EbN1JYXe2Z0DDtpTScpc7jXEFERKRlUfBpwQzD4OUuXWhjt7OjvJzrU1Np9cdWONo5qD5QTebbR67qLiIi0pIp+LRwYb6+vNO9O1bg3awsXsvOJP6Wmlaffc/t09B2ERHxKgo+XmBoSAiPtmsHwC1bt5I3LhhLgIXSDaXkf5dvbnEiIiKNSMHHS9zTpg3Dw8Ioc7n4y95UQq/W0HYREfE+Cj5ewmIYvNm1K1G+vvxaUsKsK2o6Nud+nkvZ9jKTqxMREWkcCj5eJMZu541u3QB4qTSL1bcFghv2/VutPiIi4h0UfLzMiPBw7k5IAOCRi8vIiIaMVzKoLqw2uTIREZGGp+DjhR5r145BQUEUGE6eeNxCRamTjNcyzC5LRESkwSn4eCFfi4V3uncn2GplXQcXr10J+2bsw+3U0HYREWnZTA0+ixcvZtSoUcTFxWEYBp988snvHv/xxx9z7rnnEhkZSXBwMEOGDOHrr79unGJbmHZ+fszp0gWAeeNgSUg5uV/kmlyViIhIwzI1+JSUlJCUlMSsWbPqdPzixYs599xz+eKLL1i1ahVnnXUWo0aNYs2aNQ1cacs0OiqKSbGxuC3wxL2w9uU9ZpckIiLSoAx3E5m61zAM5s+fz8UXX3xC5/Xo0YMxY8bwwAMP1On4wsJCQkJCKCgoIDg4+CQqbVlKnU76/7KSTVVlDFgO357fj+BeQWaXJSIi0iCadR8fl8tFUVER4eHhZpfSbPlbrbyf1AN7NawYCI99m2p2SSIiIg2mWQeff/7znxQXFzN69OhjHlNRUUFhYWGtTWrrGRjIk76tAXi2VxFL9h4wuSIREZGG0WyDz7x583j44Yd5//33iYqKOuZx06ZNIyQkxLMlHJzDRmq77fT2DE/xwekDYzdsJL+qyuySRERE6l2zDD7vvvsu1157Le+//z7Dhw//3WOnTp1KQUGBZ9u7d28jVdm8WCwWZoa3IyYd9jmquW5zqlZuFxGRFqfZBZ933nmHq666infeeYcLLrjguMfb7XaCg4NrbXJ0ncbE8tAsK9Zq+CA3m7np6WaXJCIiUq9MDT7FxcWkpKSQkpICwM6dO0lJSWHPnpph1VOnTmXChAme4+fNm8eECRN45plnGDRoEBkZGWRkZFBQUGBG+S2OxW7hD+e05pq5NZ/ftm0bG0pKzC1KRESkHpkafFauXElycjLJyckATJ48meTkZM/Q9PT0dE8IAnjppZeorq7m5ptvJjY21rPdfvvtptTfEsXdGMfYj6H/CihzuRizYQNlTqfZZYmIiNSLJjOPT2PRPD7Ht+kvm9i0IJPr3rKQ6+/i+thYZh+c5VlERKQ5a3Z9fKThxd8eT3geTH3YhQG8mJ7OB1lZZpclIiJyyhR85AjB/YMJHhpMv+Vw/e6aVrFJW7aws6zM5MpEREROjYKPHFXr22smNBw7tZTBgUEUOJ1cvnEjVS6XyZWJiIicPAUfOaqISyKwt7bjTq9mxuZIQqxWlhUVcf/OnWaXJiIictIUfOSoLL4W4m6OA8B4Jos5Bzs3P7V3L98c0JIWIiLSPCn4yDHFTYrD4mehOKWYczfbuCGuJghdumEDD+3cSWF1tckVioiInBgFHzkm31a+RF8RDcC+5/bxbIcO/F9ICMVOJw/v3k37X37hn3v2aJ4fERFpNhR85Hcd6uSc80kO7K3k+z59+KB7d7r4+ZFbXc3fduyg47JlzN6/Xx2fRUSkyVPwkd8V0COAsOFh4IK0WWlYDIPLoqJYP2AAr3bpQhu7nbTKSm7cupWuy5fzVkYGTu+aE1NERJoRBR85rvjb4wFIfzkdZ0nNYy0fi4UrY2NJHTSIf3fsSLSvLzvKy/nL5s0krVjBJ9nZWt1dRESaHAUfOa5WI1vh19GP6vxqMt7IqPWa3WLhltat2T54MNPatSPUx4cNpaVcsmEDg1evZlFenklVi4iIHEnBR47LsBjE31rT6rN/xn7criNbcgKsVqa0bcvOQYO4r00b/C0WlhcVMXztWs5JSeGXgoLGLltEROQICj5SJzFXxmANslK6uZS8hcduxQn19eWx9u3ZMXgwt8XHYzMMvsvPZ8iaNVy0bh3riosbsWoREZHaFHykTnyCfYi5OgaoGdp+PNE2G8916kTqoEFcHRODBfgsN5eklSsZv3Ej20pLG7hiERGRIyn4SJ21vrU1GHDgywPkfJ5Tp3PaOhzM7dqVDQMGMDoyEjcwLyuLrsuXc/2WLewrL2/YokVERA6j4CN15tfBj9Z31Mzrs+XqLVRmVtb53K4BAbzXower+/VjZHg4TuCl9HQ6LlvGX7dtI7uy7tcSERE5WYbby8YcFxYWEhISQkFBAcHBwWaX0+w4y52sHriaknUlhF8QTq//9MIwjBO+zs/5+dy7cyc/Hez0HGi1Mrl1ayYnJBDi41PfZYuIiABq8ZETZHVY6fZ2Nwy7wYEFB0ibnXZS1zktNJQf+/Thq9696RsYSLHTySMHl8F4WstgiIhIA1GLj5yUvdP3sv3O7Vj8LPRb3Y+ArgEnfS23283HOTn8fedONh/s9Bxns3F/27ZcHRuLzaJ8LiIi9UPBR06K2+Xm1xG/kvdtHoF9A+m7tC8W26kFlGqXi7ezsnhw5052V1QA0N7h4OHERC6PjsZ6Eo/UREREDqc/peWkGBaDrq93xSfch+LVxex6aNcpX9PHYmFiTAxbBg1iZqdOWgZDRETqnVp85JRkf5TNhss2gAF9fuxD6Omh9XbtEqeTmfv389SePeRVVwMwICiIJ9q145ywsJPqVC0iIt5NwUdO2earN5Pxagb2tnYGrB2AT0j9jsrKr6rimX37+NfevZS4XACcFRrK4+3aMSQkpF7vJSIiLZuCj5yy6qJqVvZZSfmOcqKviKbbm90a5D6ZlZVM272bF9LSqDz4z3ZUq1Y81q4dvQMDG+SeIiLSsqiPj5wynyAfur3VDSyQ+VYmme9mNsh9om02pnfqxNZBg7jm4DIY/8nNpc/KlYzbuJGtWgZDRESOQ8FH6kXIkBDa/r0tAKk3pFK+p+GWomjjcPBy165sHDiQMQeXwXgnK4tuy5dznZbBEBGR36FHXVJvXFUu1py2hqLlRYSeGUrSt0kY1obvgJxSVMTfd+5kwYEDANgNg5vj45nSpg2RNluD319ERJoPtfhIvbH4Wuj2VjcsARbyf8hn77N7G+W+fYKC+Lx3b35OTub/QkKocLt5dt8+2i9bxoM7d1JwcESYiIiIWnyk3qW9nEbqpFQMX4O+y/oSlBzUaPd2u90szMvj3h07WFVcDEC4jw9T2rTh5vh4/K3WRqtFRESaHgUfqXdut5sNf9pAzic5+Hfzp9+qflj9GjdwuN1u5h9cBmPTwU7PsQeXwbhGy2CIiHgt/fSXemcYBp3ndMYWY6N0Uyk77t5hSg1/ioxk3YABvN61K4kOB+mVldy0dStdly/nzYwMnN6V+UVEBJODz+LFixk1ahRxcXEYhsEnn3xy3HN++OEH+vbti91up2PHjrz22msNXqecOFuEja6vdQVg/8z95H6Za0odVsNgQkwMWwYOZFanTsTYbOwsL2fC5s30WL6cGfv2kV9VZUptIiLS+EwNPiUlJSQlJTFr1qw6Hb9z504uuOACzjrrLFJSUrjjjju49tpr+frrrxu4UjkZ4SPCib81HoDNV22mMrvStFpsFgs3xcezfdAgnmrfnjAfH7aUlXH7tm3ELV3K1Zs3s7ywUGuBiYi0cE2mj49hGMyfP5+LL774mMfcc889LFiwgPXr13v2jR07lvz8fL766qs63Ud9fBqXs8zJqv6rKN1YSquLWtFzfs8mscZWYXU1b2Vm8kJaGutLSjz7+wYGckNcHJdHRRHoU79Lb4iIiPmaVR+fpUuXMnz48Fr7RowYwdKlS495TkVFBYWFhbU2aTxWPyvd3u6G4WuQ+2ku6S+nm10SAME+PtwUH8+v/fuzJDmZv0RHYzcMVhcXc11qKnFLl3JzairrDo4MExGRlqFZBZ+MjAyio6Nr7YuOjqawsJCysrKjnjNt2jRCQkI8W0JCQmOUKocJ6hNEuyfaAbDtjm2Ubm06S0sYhsHQkBDe6NaNfUOG8M8OHejo50eR08nzaWn0XrmS01av5q2MDMqdTrPLFRGRU9Ssgs/JmDp1KgUFBZ5t797GmVRPakuYnEDoWaG4Sl1sGr8JV5XL7JKOEGGz8deEBLYMHMi3SUlcFhmJj2GwpLCQv2zeTPzSpdy1bZvWBBMRacaaVfCJiYkhM7P2ApiZmZkEBwfj5+d31HPsdjvBwcG1Nml8hsWg6+td8Qn1oWhFEbsf3W12ScdkMQzOCQvjgx492DN4MI+1a0cbu50D1dU8s28fnZcv59y1a/koO5sqV9MLcCIicmzNKvgMGTKERYsW1dq3cOFChgwZYlJFciIcCQ46z+4MwO7Hd1OwpMDkio4v1m7nvrZt2TF4MP/p2ZMLwsMxgG/z8rhswwba/PIL9+/cyR4tjCoi0iyYGnyKi4tJSUkhJSUFqBmunpKSwp49e4Cax1QTJkzwHH/DDTewY8cO7r77bjZv3szzzz/P+++/z5133mlG+XISosZEEf2XaHDBpr9sorqweayjZTUM/hgRwee9e7Nz8GDua9OGaF9fMioreWz3btr98gsXrlvHF7m5mhhRRKQJM3U4+w8//MBZZ511xP6JEyfy2muvceWVV7Jr1y5++OGHWufceeedbNy4kdatW3P//fdz5ZVX1vmeGs5uvuqCalYkraBidwXRE6Pp9lo3s0s6KZUuF5/m5DA7LY3v8vM9+9va7VwXF8fVMTHE2O3mFSgiIkdoMvP4NBYFn6Yh/6d8Us5MARd0f787UX+OMrukU7KltJSX0tJ4NSODvIOrwfsYBn+KiOCGuDjODA1tEvMXiYh4OwUfMc2O+3aw54k9+IT50P/X/jhaO8wu6ZSVOZ18kJ3N7LQ0lh42Z1RnPz9uiItjYkwM4b6+JlYoIuLdFHzENK4qF2uGrqFoZRGh54SS9E0ShqXltIqsLS7mxbQ03szMpPjgHEAOi4XRkZHcGBfHoOBgtQKJiDQyBR8xVemWUlb2XYmr1EWHZzqQMLnlTTBZVF3NO1lZvJCWRsphM0EnBQRwQ1wc46OjCdLyGCIijULBR0yX9mIaqTekYtgM+q3oR2DvQLNLahBut5vlRUXMTkvj3awsyg/OARRotTI+Koob4uLoExRkcpUiIi2bgo+Yzu12s/6i9eT+J5eAngH0XdEXq8NqdlkNKq+qijcyM5mdlsbmw2aCHhQUxI3x8YyOjMTP2rK/ByIiZlDwkSahMquSFb1WUJVVRes7WtPxXx3NLqlRuN1uFhcU8ML+/Xyck0PVwf8dQ318uDImhutjY+kaEGBylSIiLYeCjzQZuV/ksu6CdQD0/qY34eeGm1xR48qsrOTV9HReTE9n12EzQZ8ZGsqNcXFcHBGBzdKsJlsXEWlyFHykSUm9OZW059OwxdkY8OsAfFt539Bvl9vNNwcO8EJaGp/n5nJoNbAoX1+uiY1lUmws7Y6xNp2IiPw+BR9pUpylTlb2XUnZljIi/hRBjw97ePWQ773l5bycns6c9HTSKysBMIDzwsO5IS6OC1q1wurF3x8RkROl4CNNTtGqIlYPXo272k2XV7oQe1Ws2SWZrsrl4vPcXGanpfFNXp5nf4LdzuVRUbRzOIiz24mz2Yiz24ny9cVHj8VERI6g4CNN0u4nd7Nz6k6sgVb6p/THr4Me7RyyrbSUl9LTeSU9ndzqoy/yagGibDZPEIr97ccHQ1KUzaYWIxHxKgo+0iS5nW5Szk6hYHEBwUOC6bO4DxYftWAcrsLl4qPsbH4qKCC9ooK0ykrSKytJr6jAWcdrWIDoY4SiwwNTpAKSiLQQCj7SZJXvLmdF7xU4C50kPpxI4gOJZpfULDjdbnKqqkirqCC9spK0g6HI8/nBjzMqKz0dp4/HCsTYbMQeJRQdCkqxdjuRvr5YFJBEpAlT8JEmLfPtTDZdsQmskPxzMiGDQ8wuqcVwut1kHxaEDrUY/TYoZZ5AQPIxDGIOBqLY34Siw0NSKwUkETGJgo80eRvHbSTrnSwcHRz0T+mPT6DWtWpM1S4XWYe3IB0elA4LSVlVVdT1h4mPYXhajA6FogS7nR4BAfQODKSN3e7Vo/lEpOGcVPB5/fXXiYiI4IILLgDg7rvv5qWXXqJ79+688847tG3btt4LrS8KPs1PVX4VK3uvpGJvBTHXxND15a5mlyRHUXVYQKr1aO03n2dVVR33WsFWK70CAugVGEjvgADPxyFazFVETtFJBZ8uXbrwwgsvcPbZZ7N06VKGDx/Ov/71Lz7//HN8fHz4+OOPG6LWeqHg0zzl/ZDH2rPXght6fNyDyEsizS5JTlKly0XmwZajw1uMdpaXs66khE2lpVQf48dSG7ud3oGB9AoIqAlEgYF09vPDV0P3RaSOTir4+Pv7s3nzZtq0acM999xDeno6b7zxBhs2bODMM88kOzu7IWqtFwo+zdf2e7az9x978Wnlw4BfB2CPs5tdkjSASpeLLaWlrCsp4dfi4pr/lpSwr6LiqMfbDINu/v7/C0QH/xtrs+lxmYgc4aTajQMDA8nNzaVNmzZ88803TJ48GQCHw0FZWVm9FihySLtH25H3TR7FKcVsvmozvb/sjWHRL7aWxmax0CswkF6BgYyLjvbsz6uqYl1JSa1AtK6khGKnk7UlJawtKal1nXAfnyNah3oGBBCgVe9FvNpJBZ9zzz2Xa6+9luTkZFJTUxk5ciQAGzZsIDExsT7rE/Gw2Cx0m9eNVX1XkfdNHvtn7qf1ba3NLksaSZivL/8XGsr/hYZ69rncbnYffER2eOtQamkpB6qr+SE/nx/y8z3HG0B7h+OI1qEOfn6ap0jES5zUo678/Hz+/ve/s3fvXm688UbOO+88AB588EFsNhv33XdfvRdaX/Soq/nbP2s/W2/ZimE36L+qPwE9AswuSZqYMqeTTb99XFZcTOYxOlb7WSw1I8oOdqQ+FIgibbZGrlxEGpqGs0uz43a7WXfBOg58eYCA3gH0W94Pi12dW+X4siorax6RHdY6tKGkhDLX0WcqirHZaj0q6x0QQDd/fxx6XCbSbJ1U8Pnqq68IDAzktNNOA2DWrFnMmTOH7t27M2vWLMLCwuq90Pqi4NMyVGRUsLLXSqpyqki4K4EOT3cwuyRpppxuN9vLyo5oHdpRXn7UeYmsQCd//yNah9o6HJqUUaQZOKng06tXL5566ilGjhzJunXrGDBgAJMnT+b777+na9euvPrqqw1Ra71Q8Gk5cj7LYf1F68GApG+TCDu76QZuaX5KnE42/Kbv0K/FxRw4xsKwQVYrPQMCSA4MZGxUFKeFhGhUmUgTdFLBJzAwkPXr15OYmMhDDz3E+vXr+fDDD1m9ejUjR44kIyOjIWqtFwo+LcuW67eQ/lI6tngbA9YNwDfM1+ySpAVzu92kH3xcdnjr0KbSUip/86O0q78/18bGMiE6Wn2FRJqQkxrVZbPZKC0tBeDbb79lwoQJAISHh1NYWFh/1YkcR8dnO5L/fT5lW8tIvSGV7u9211/Z0mAMw6hZb8xuZ0R4uGd/lctFalkZ64qL+TYvj3ezsthcWspd27czdccOLomIYFJsLGeHhelxmIjJTqrF58ILL6SyspJhw4bx6KOPsnPnTuLj4/nmm2+45ZZbSE1NbYha64VafFqewhWFrB6yGpzQ9Y2uxPwlxuySxMsVVVfzblYWc9LTWVFU5Nnf3uHgmthYroqJIdauCThFzHBSwWfPnj3cdNNN7N27l9tuu41rrrkGgDvvvBOn08mMGTPqvdD6ouDTMu16bBe77t+FNchK/7X98WvnZ3ZJIgCsLS5mTloab2VmUuB0AjUdpP/YqhWT4uI4LzxccwjJUWVUVLC6uJjVRUWsLykhwteXngEB9AwIoEdAAGG+erR/MjScXVoEt9PNmjPWULikkOBhwST/mIxh1S8TaTpKnU4+yM5mTloaSw7rEtDabufqmBiuiY2ljcNhYoViFrfbzd6KClYXFXmCzuriYtIrK3/3vHibjR4Hg9ChrbtmJz+ukw4+TqeTTz75hE2bNgHQo0cPLrzwQqxN/Buu4NNyle0sY2XSSpxFTto93o6297Y1uySRo9pYUsLL6em8kZFB7sFRYgYwIjycSbGxjGrVSguvtlAut5sdZWW1As7qoiLPv4PDWYBu/v70DQqiV0AAuVVVrC8pYX1JCbuPsXadAbRzOGqFoR4BAXTx98euf1PASQafbdu2MXLkSPbv30+XLl0A2LJlCwkJCSxYsIAOHU5sTpVZs2bx9NNPk5GRQVJSEv/+978ZOHDgMY+fPn06L7zwAnv27CEiIoLLLruMadOm4ajDX0sKPi1bxhsZbJ64GcPHIHlpMsH99R5L01XhcjE/O5s56el8d9jSGtG+vlwVG8s1MTF09Pc3r0A5JU63my2lpbUCzpriYgoPPvI8nI9h0DMggL6BgfQNCqJfYCC9AwPxP0ZjQmF1NRsPhqDDt2PNTm4FOvv71wpEPb10uZaTCj4jR47E7Xbz9ttvE35wZENubi5XXHEFFouFBQsW1Pla7733HhMmTGD27NkMGjSI6dOn88EHH7BlyxaioqKOOH7evHlcffXVvPLKKwwdOpTU1FSuvPJKxo4dy7PPPnvc+yn4tGxut5uNYzaS/UE2fp396L+6P9aApt0KKQKwrbSUuRkZvJqeXuuX11mhoUyKjeVPkZH6i70Jq3K52PibkJNSXEzpUWYFtxsGSQcDzqGg0zMgoF7e35zKSjaUlh4RiPKPMf+U3TDofrBV6PBA1MZub7EjZE8q+AQEBPDLL7/Qq1evWvvXrl3LsGHDKC4urvO1Bg0axIABA5g5cyYALpeLhIQEbr31VqZMmXLE8bfccgubNm1i0aJFnn1//etfWbZsGT///PNx76fg0/JVHahiRe8VVO6vJPb6WLrM7mJ2SSJ1VuVy8XluLnPS0/nqwAHP7NGtfHyYEBPDpNhYugVofTozlTudrCspqfW46tfi4iPmcgIIsFhIPizg9A0MpKu/f6M+ynS73aRVVh4RhjaWlBw1mEHNhJy/DUM9/P2JttmafSA6qXl87HY7RYcN0TykuLgY2wlM1FVZWcmqVauYOnWqZ5/FYmH48OEsXbr0qOcMHTqUt956i+XLlzNw4EB27NjBF198wV/+8pejHl9RUUHFYc9CNc9Qy+cb7ku317uxdvha0l9Mp9UFrYgYFWF2WSJ14muxcElkJJdERrKnvJxX0tOZm5HBvooK/rVvH//at49hwcFMiovjz5GRx3wUIvWjuLqatSUltVpyNpSUcOTDKgixWj3hpl9QEH2DgujYBB4lGYZBvN1O/G/mn3K53ewqLz8iEG0uLaXI6eSXwkJ++c3vzFY+Pkc8LmtuI8xOqsVnwoQJrF69mrlz53r64ixbtoxJkybRr18/XnvttTpdJy0tjfj4eP773/8yZMgQz/67776bH3/8kWXLlh31vBkzZnDXXXfhdruprq7mhhtu4IUXXjjqsQ899BAPP/zwEfvV4tPybfvrNvY9uw/fSF8GrBuALVqz50rz5HS7+erAAeakpfF5bq7nl26I1cr46GgmxcbSJyjI1BpbgvyqKlKKi1l1WEvOltLSo67ZFuHrS7/fPK5q53A0+9YQqGl13FpWVisMbSgpYVtZGUdvH2peI8xOKvjk5+czceJE/vOf/+B7MOVVVVVx0UUX8eqrrxIaGlqn65xM8Pnhhx8YO3Ysjz32GIMGDWLbtm3cfvvtTJo0ifvvv/+I44/W4pOQkKDg4wVcFS5WDVhFyboSwkeG0+vzXi3ih5J4t/SKCl7LyODl9HR2lJd79vcPCmJSbCyXR0UR5HNSjfleJbuykjW/GVm1/bDv5+HibbZaAadvYCDxLbgPzLGUOZ1sPkr/oT0nMMKsZ0AAnU0eYXZK8/hs27bNM5y9W7dudOzY8YTOr6ysxN/fnw8//JCLL77Ys3/ixInk5+fz6aefHnHO6aefzuDBg3n66ac9+9566y2uu+46iouLsRznm6k+Pt6leH0xq/qvwl3hptOsTsTfFG92SSL1wuV2831+Pi+lpTE/J4eqgz/KAywWxkZFMSkujoFBQV73y/m3Dq2v9ts5cvYe45d1osNRa2RVclAQ0Vpr7XcVHGOEWdYxRphF+vqSOXSoaf826/xnweTJk3/39e+//97zcV1GV0HNml/9+vVj0aJFnuDjcrlYtGgRt9xyy1HPKS0tPSLcHJo7yMvmYpQ6COwZSIenOrDtjm1s/+t2Qs8KJaCbOoZK82cxDM4JC+OcsDCyKyt5IzOTOWlpbCkrY25GBnMzMugVEMCk2FiuiI5uVn0wTlRRdTW7y8vZVV7O7ooKdpeXe7Yd5eVkH+MXcGc/v1otOcmBgYS34O9TQwnx8WFISAhDQkJq7c+urGTDUQJRV39/UwN5nVt8zjrrrLpd0DD47rvv6lzAe++9x8SJE3nxxRcZOHAg06dP5/3332fz5s1ER0czYcIE4uPjmTZtGlDTZ+fZZ5/lpZde8jzquvHGG+nXrx/vvffece+nFh/v43a5+fW8X8lbmEdg30D6Lu2LxaZhwdLyuN1ufi4oYE56Oh9kZ1N+cMSOw2LhsshIJsXGcnpISLNqBXK73eRUVdUEmd+EmkOf5x1jqPYhFqD7YXPk9A0MJCkwkGA9Emx0brebIqfT1O99k1iyYubMmZ4JDPv06cOMGTMYNGgQAGeeeSaJiYmeDtPV1dU8/vjjvPnmm+zfv5/IyEhGjRrF448/Xqe+RQo+3qkirYIVvVZQfaCaNlPa0H5ae7NLEmlQeVVVvJ2ZyZz0dH4tKfHs7+Lnx7WxsUyMiSGyCTzCcbrdpFdUHDXU7CovZ095+TGHXB8uzMeHtg4HiQ4Hbe122jocnq2bv79Gv4lHkwg+jUnBx3tlf5zNhks3gAF9vu9D6BmhZpck0uDcbjcrioqYk57OO5mZlBwMEb6GwcUREUyKjeWcsDAsDdQKVOlysfdgqNl1lNaavRUVVNfh11CszVYTZH4Tag59rg7dUlcKPuJVNl+zmYxXMrC3sdN/bX98Q/U8X7xHUXU172ZlMSc9nRWHzcXWzuHgmthYroqJIc5uP6FrFldXH/MR1O7yctIrK486HPxwViDhd0JNgt2OQy02Uk8UfMSrVBdVszJ5JeXby4m6PIpub3drVv0dROrL2uJi5qSl8VZmJgUH146yAhe0asWk2FjOb9UKC3DgYMfho4WaXeXlHDhO/xqo6WN0tFCTePDjOLvd9En+xHso+IjXKfilgDWnrQEnxN0cR6cZnTAs+qEr3qnU6eSD7GzmpKWx5LBZelv5+FDucnkejf2eEKvVE2gSf9Na09bhINLXV39gSJOh4CNeKW1OGqnXp4IboidE02VuFyw+Gukl3m1jSQkvp6fzRkYGuYe15ET7+h71EdShLUT9a6QZUfARr5U5L5NNEzaBEyIuiaD7O92x2BV+RCpcLlYXFRHu60sbux0/9a+RFkTBR7xazmc5bBi9AXeFm7Bzw+g5vyfWAP2QFxFpqfTnrXi1iAsj6L2gN5YAC3kL81g7Yi1V+Uef5VVERJo/BR/xemHnhJG0MAmfUB8KlxSy9qy1VGZXml2WiIg0AAUfESBkSAh9fuiDb5QvxSnFpPxfCuX7jr5Ss4iINF8KPiIHBSYFkvxTMvYEO6WbS1lz2hrKtpeZXZaIiNQjBR+Rw/h39if552T8OvpRsbuCNaevoXh9sdlliYhIPVHwEfkNRxsHfX7qQ0CvACrTK0k5I4XCFYXHP1FERJo8BR+Ro7DH2OnzQx+CBgVRfaCateesJf/HfLPLEhGRU6TgI3IMvuG+JC1MIvSsUJxFTn4971dyv8g1uywRETkFCj4iv8MnyIdeX/Si1R9b4Sp3sf6i9WS9n2V2WSIicpIUfESOw+qw0uPjHkSNjcJd7Wbj5RtJfyXd7LJEROQkKPiI1IHF10K3t7oROykWXLDlmi3snb7X7LJEROQEKfiI1JFhNej8YmcS7koAYPud29n1yC68bLk7EZFmTcFH5AQYhkH7f7Qn8dFEAHY9uIvtd21X+BERaSYUfEROkGEYJP49kY7PdQRg37P7SL0uFbdT4UdEpKlT8BE5Sa1va02XV7qABdJfTmfj+I24qlxmlyUiIr9DwUfkFMReFUv397pj+Bpkv5fN+kvW4yxzml2WiIgcg4KPyCmKuiyKnp/1xOJn4cCCA6wbuY7qomqzyxIRkaNQ8BGpB63Oa0Xvr3tjDbKS/0M+a89ZS1VuldlliYjIbyj4iNST0NNDSfouCZ9WPhStKCLlzBQq0ivMLktERA6j4CNSj4L7B5O8OBlbrI2S9SWsOX0NZbvKzC5LREQOUvARqWcB3QNI/jkZRzsH5dvLSTk9hdItpWaXJSIiKPiINAi/9n4k/5SMfzd/KvZVsOb0NRSlFJldloiI11PwEWkg9ng7fX7sQ2DfQKqyq0g5M4WC/xaYXZaIiFdT8BFpQLZIG32+60PIaSE4C5ysPXctB749YHZZIiJeq0kEn1mzZpGYmIjD4WDQoEEsX778d4/Pz8/n5ptvJjY2FrvdTufOnfniiy8aqVqRE+MT4kPvr3sTNiIMV6mLdResI/uTbLPLEhHxSqYHn/fee4/Jkyfz4IMPsnr1apKSkhgxYgRZWVlHPb6yspJzzz2XXbt28eGHH7JlyxbmzJlDfHx8I1cuUndWfyu9Pu1FxKURuCvdbLhsAxlvZZhdloiI1zHcJi8rPWjQIAYMGMDMmTMBcLlcJCQkcOuttzJlypQjjp89ezZPP/00mzdvxtfX94TvV1hYSEhICAUFBQQHB59y/SInwlXtYsu1W8h8PROATs93Iv5GhXYRkcZiaotPZWUlq1atYvjw4Z59FouF4cOHs3Tp0qOe89lnnzFkyBBuvvlmoqOj6dmzJ0888QRO59HXR6qoqKCwsLDWJmIWi4+Frq90Jf7WmrCz9aat7H5yt8lViYh4D1ODT05ODk6nk+jo6Fr7o6Ojycg4+mOAHTt28OGHH+J0Ovniiy+4//77eeaZZ3jssceOevy0adMICQnxbAkJCfX+dYicCMNi0PG5jrT9e1sAdk7dyY57d2By46uIiFcwvY/PiXK5XERFRfHSSy/Rr18/xowZw3333cfs2bOPevzUqVMpKCjwbHv37m3kikWOZBgG7R5tR/t/tAdgz7Q9bL11K26Xwo+ISEPyMfPmERERWK1WMjMza+3PzMwkJibmqOfExsbi6+uL1Wr17OvWrRsZGRlUVlZis9lqHW+327Hb7fVfvEg9aPO3NvgE+5B6Yypps9JwFjrp8koXLD7N7m8SEZFmwdSfrjabjX79+rFo0SLPPpfLxaJFixgyZMhRzxk2bBjbtm3D5XJ59qWmphIbG3tE6BFpDuKuj6PbW93ACplvZrJx9EZcFa7jnygiIifM9D8rJ0+ezJw5c3j99dfZtGkTN954IyUlJVx11VUATJgwgalTp3qOv/HGGzlw4AC33347qampLFiwgCeeeIKbb77ZrC9B5JRFj4um58c9MewGOfNzWDdqHc6So3fYFxGRk2fqoy6AMWPGkJ2dzQMPPEBGRgZ9+vThq6++8nR43rNnDxbL//JZQkICX3/9NXfeeSe9e/cmPj6e22+/nXvuucesL0GkXkRcGEHvBb1Zd9E68hbmsXbEWnp93gvf0BOftkFERI7O9Hl8Gpvm8ZGmruCXAtadv47q/GoC+wTS++ve2KL0GFdEpD6Y/qhLRGoLGRxCnx/64BvlS3FKMWv+bw3l+8rNLktEpEVQ8BFpggKTAkn+KRl7gp2yLWWsOW0NpdtKzS5LRKTZU/ARaaL8O/uT/HMyfp38qNhdQcrpKRSvLza7LBGRZk3BR6QJc7RxkPxTMgG9AqjMqCTljBQKV2jZFRGRk6XgI9LE2aJt9PmhD0GDgqg+UM3as9eS/2O+2WWJiDRLCj4izYBvuC9JC5MIPSsUZ7GTX8/7ldwvcs0uS0Sk2VHwEWkmfIJ86PVFL1qNaoWr3MX6i9aT9X6W2WWJiDQrCj4izYjVYaXHRz2IujwKd7WbjZdvJH1uutlliYg0Gwo+Is2MxddCtze7EXtdLLhgy7Vb2Hn/Tq3sLiJSBwo+Is2QYTXoPLszbaa0AWD3Y7vZcNkGqourTa5MRKRpU/ARaaYMw6D9tPZ0fa0rhq1mcdM1w9ZQvluzPIuIHIuCj0gzFzMxpmaJi2hfSn4tYdWAVeT/nG92WSIiTZKCj0gLEDIkhH4r+hGYHEhVdhVrz16rTs8iIkeh4CPSQjgSamZ5jvxzJO4qN1uu3cLWO7biqnaZXZqISJOh4CPSglgDrHR/rzuJjyQCsP+5/ay7YB1VeVXmFiYi0kQo+Ii0MIZhkHh/Ij0+7IHF30LeN3msHrya0i1a3V1ERMFHpIWKvDSS5CXJ2NvYKUstY9WgVRz45oDZZYmImErBR6QFC+oTRL8V/QgeFoyzwMmv5//K3ul7cbs12aGIeCcFH5EWzhZlo8+iPsRcFQMu2H7ndrZcuwVXhTo9i4j3UfAR8QIWu4Uuc7vQ4dkOYIGMVzJYO3wtlVmVZpcmItKoFHxEvIRhGCTcmUDvL3pjDbFS8HMBqwasonhtsdmliYg0GgUfES8TPiKcfsv64dfJj4o9Faweuprs+dlmlyUi0igUfES8kH8Xf/ou60vYuWG4Sl1s+NMGdj26S52eRaTFU/AR8VK+Yb70+qIX8bfHA7DrgV1sHLsRZ6nT5MpERBqOgo+IF7P4WOg0vROd53TG8DXIfj+bNaevoXyfVngXkZZJwUdEiLs2jqRFSfhG+FK8upjVA1ZT8EuB2WWJiNQ7BR8RASD09FD6ruhLQK8AKjMqSTkzhYw3M8wuS0SkXin4iIiHX6Ifyf9NptVFrXBXuNk8YTPb796O26lOzyLSMij4iEgtPoE+9Py4J23uawPA3qf3su6idVQXVptcmYjIqVPwEZEjGBaD9o+1p9s73bA4LBxYcKBmhfdtWuFdRJq3JhF8Zs2aRWJiIg6Hg0GDBrF8+fI6nffuu+9iGAYXX3xxwxYo4qWix0bT56c+2OJslG4qZfWg1eR9l2d2WSIiJ8304PPee+8xefJkHnzwQVavXk1SUhIjRowgKyvrd8/btWsXd911F6effnojVSrinYL7B9NvZT+CBgZRfaCatX9Yy/7n95tdlojISTE9+Dz77LNMmjSJq666iu7duzN79mz8/f155ZVXjnmO0+lk/PjxPPzww7Rv374RqxXxTvZYO31+7EP0FdHghK03byX1xlRcVVrhXUSaF1ODT2VlJatWrWL48OGefRaLheHDh7N06dJjnvfII48QFRXFNddcc9x7VFRUUFhYWGsTkRNndVjp+kZX2j/ZHgxIm53Gr3/4larcKrNLExGpM1ODT05ODk6nk+jo6Fr7o6Ojycg4+vwhP//8M3PnzmXOnDl1use0adMICQnxbAkJCadct4i3MgyDNve0oeenPbEGWsn/IZ9VA1ZRsqHE7NJEROrE9EddJ6KoqIi//OUvzJkzh4iIiDqdM3XqVAoKCjzb3r17G7hKkZYvYlQEfX/pi6O9g/Kd5awevJqc/+SYXZaIyHH5mHnziIgIrFYrmZmZtfZnZmYSExNzxPHbt29n165djBo1yrPP5arpY+Dj48OWLVvo0KFDrXPsdjt2u70BqhfxbgE9Aui7rC8b/7yR/B/yWX/RetpPa0/C3QkYhmF2eSIiR2Vqi4/NZqNfv34sWrTIs8/lcrFo0SKGDBlyxPFdu3Zl3bp1pKSkeLYLL7yQs846i5SUFD3GEmlktggbvb/pTdwNceCGHVN2sHnCZpzlWuFdRJomU1t8ACZPnszEiRPp378/AwcOZPr06ZSUlHDVVVcBMGHCBOLj45k2bRoOh4OePXvWOj80NBTgiP0i0jgsvhY6v9CZgF4BbL1tK5lvZVKaWkrPT3pij1Vrq4g0LaYHnzFjxpCdnc0DDzxARkYGffr04auvvvJ0eN6zZw8WS7PqiiTileJvise/qz8bLttA0fIiVg1YRc9PehLcP9js0kREPAy32+1Vqw8WFhYSEhJCQUEBwcH6gSxS38q2l7Fu1DpKN5VicVjo8moXosdGH/9EEZFGoKYUEalXfh386PtLX8IvCMdV7mLT5ZvY8fcduF1e9TeWiDRRCj4iUu98gn3o9WkvEv5WM+Bgz+N72HDpBqqLtcK7iJhLwUdEGoRhNejwjw50fb0rhs0g55Mc1gxdQ9muMrNLExEvpuAjIg0qZkIMfX7sg2+0LyXrSlg9YDX5i/PNLktEvJSCj4g0uJDBIfRb2Y/AvoFU5VSxdvha0l5OM7ssEfFCCj4i0igcrR0k/5RM5OhI3FVuUielsvX2rbiqtcK7iDQeBR8RaTRWfyvd3+1O4qOJAOyfsZ91I9dRlacV3kWkcSj4iEijMgyDxL8n0uOjHlj8LeQtzGP1oNWUbik1uzQR8QIKPiJiisg/RdL3v32xt7FTtrWMVQNWse2ubZSmKgCJSMPRzM0iYqrKrEo2XLqBgp8LPPtCzw4l7oY4Ii6KwGLT32ciUn8UfETEdG6nm9wvc0mbncaBLw7AwZ9KvlG+xF4TS+ykWPza+ZlbpIi0CAo+ItKklO8uJ/3ldNLnplOZXlmz04CwP4QRd0Mcrf7YCouPWoFE5OQo+IhIk+SqcpH7n1zSXkwj75s8z35bnI3Ya2OJvTYWR4LDxApFpDlS8BGRJq9sexlpc9LIeCWDquyDQ98t0OqCVsTdEEf4iHAMq2FukSLSLCj4iEiz4apwkfNJDmmz08j/Id+z397GTuykWGKvicUeazevQBFp8hR8RKRZKtlcQvpL6WS8lkF1Xs2q74aPQasLa1qBws4Jw7CoFUhEalPwEZFmzVnmJPvDbNJeTKNwSaFnv6ODg7jr4oi5KgZbpM3ECkWkKVHwEZEWo3h9MekvppPxRgbOQicAhq9B5KWRxF4fS+gZoRiGWoFEvJmCj4i0OM4SJ1nvZZE2O42iFUWe/X5d/Ii7Po6YiTH4hvuaWKGImEXBR0RatKLVRaS9mEbm25m4SmpWgjfsBlGjo4i7IY7gIcFqBRLxIgo+IuIVqguryZyXSdrsNErWlnj2B/QMIO6GOKKviMYnxMfECkWkMSj4iIhXcbvdFC2vaQXKejcLV1lNK5DF30LU5VHEXR9HUP8gtQKJtFAKPiLitaryq8h8M5O0F9Mo3fC/VeED+wYSd30cUeOi8AlUK5BIS6LgIyJez+12U7CkgLTZaWR/mI27oubHojXISvT4aOJuiCMwKdDkKkWkPij4iIgcpjKnkszXa1qByraWefYHDQqqaQUaE4XV32pihSJyKhR8RESOwu12k/99PmkvppEzPwd31cFWoBArMRNiiLs+joAeASZXKSInSsFHROQ4KjMrSX81nfSX0infWe7ZH3J6CHHXxxFxaQRWh1qBRJoDBR8RkTpyu9zkLcyraQX6LAdqJofGp5UPMVfGEHddHP6d/c0tUkR+l4KPiMhJqNhfQfrcdNLnpFOxr8KzP/TsUGImxBBxSQQ+wRoRJtLUKPiIiJwCV7WLA18eIO3FNA58cQAO/kS1OCy0GtWK6PHRhJ8XjsVuMbdQEQGgSfyfOGvWLBITE3E4HAwaNIjly5cf89g5c+Zw+umnExYWRlhYGMOHD//d40VEGpLFx0LEqAh6f96bwTsHk/hIIn5d/HCVu8j+IJv1F6/nv7H/Zct1W8j7IQ+3y6v+1hRpckxv8XnvvfeYMGECs2fPZtCgQUyfPp0PPviALVu2EBUVdcTx48ePZ9iwYQwdOhSHw8FTTz3F/Pnz2bBhA/Hx8ce9n1p8RKShud1uitcUkzkvk6x3sqhMq/S8Zou3EX15NFHjowhMCtQM0SKNzPTgM2jQIAYMGMDMmTMBcLlcJCQkcOuttzJlypTjnu90OgkLC2PmzJlMmDDhuMcr+IhIY3I73eT/mE/mvEyyP8zGWeD0vObfzZ/o8dFEjYvCr52fiVWKeA9TH3VVVlayatUqhg8f7tlnsVgYPnw4S5curdM1SktLqaqqIjw8/KivV1RUUFhYWGsTEWkshtUg7Owwur7claEZQ+nxcQ8iLo3AsBuUbipl5993sqz9MlYPXc3+WfupzK48/kVF5KSZGnxycnJwOp1ER0fX2h8dHU1GRkadrnHPPfcQFxdXKzwdbtq0aYSEhHi2hISEU65bRORkWB1WIi+JpOeHPRmWOYwur3QhbHgYWKBwaSFbb9nKf2P/y68jfyXjrQyqi6vNLlmkxWkSnZtP1pNPPsm7777L/PnzcTgcRz1m6tSpFBQUeLa9e/c2cpUiIkfyCfEh9qpYkhYmMWTfEDo824Gg/kHghANfHmDzXzbz36j/svHyjeR8noOrymV2ySItgqmTTERERGC1WsnMzKy1PzMzk5iYmN8995///CdPPvkk3377Lb179z7mcXa7HbvdXi/1iog0BHusnYQ7E0i4M4HSLaVkvpNJ1ttZlG0rI+vdLLLezcKnlQ9Rf44ianwUIUNDMCzqFC1yMkxt8bHZbPTr149FixZ59rlcLhYtWsSQIUOOed4//vEPHn30Ub766iv69+/fGKWKiDQK/y7+tHuoHQNTB9J3eV/ib4/HN9qX6txq0mankXJ6Cr+0/4UdU3dQvK7Y7HJFmh3TR3W99957TJw4kRdffJGBAwcyffp03n//fTZv3kx0dDQTJkwgPj6eadOmAfDUU0/xwAMPMG/ePIYNG+a5TmBgIIGBgce9n0Z1iUhz46p2kf99PplvZ5LzcQ7Oov+NDAvoFVAzMuzyKBxtjv7IX0T+x/TgAzBz5kyefvppMjIy6NOnDzNmzGDQoEEAnHnmmSQmJvLaa68BkJiYyO7du4+4xoMPPshDDz103Hsp+IhIc+Ysc5L7eS5Z87LI/SIXd+X/foSHnB5C1Lgoov4chW8rXxOrFGm6mkTwaUwKPiLSUlTlVZH9UTZZb2eR/2O+Z7kMw8cg/LxwosZHEXFhBFZ/rRwvcoiCj4hIC1C+r7ymI/S8LIrX/K/vjyXAQuQlkUSNjyJseBgWn2Y9mFfklCn4iIi0MCWbSsh8O5OseVmU7yz37PeN9CVqTBRR46IIHhys5TLEKyn4iIi0UG63m8JfCsmal0XWe1lUZVd5XnO0dxA9rma5jIBuASZWKdK4FHxERLyAq8pF3qI8st7OInt+Nq6S/02IGJgcSNS4KKIvj8Yer3nPpGVT8BER8TLOUic5n+WQNS+LA18ewF19qFc0hJ4RStT4KCIvjcQ3TCPDpOVR8BER8WJVuVVkf5hN5tuZFPxU4Nlv+BqEnBZC2B/CCP9DOIF9AjVbtLQICj4iIgJA+e6akWGZb2dSsq6k1mu+Eb6EDQ8j7NyazZGgyRKleVLwERGRI5SmlpK3MI8D3xwg//v8WrNFA/h39fe0BoWcEYJPoKlLP4rUmYKPiIj8LleVi8JlheQtzCPvmzwKlxfCYYvFG74GwUODCT83nLA/hBHUNwjDqsdi0jQp+IiIyAmpyqsi//t8DnxzgLxv8mrNFQTgE+5D2DlhNS1C54bjaKvHYtJ0KPiIiMgpKdtexoGFNSEo77s8nAW1H4v5dfYj7Nyax2KhZ4biE6zHYmIeBR8REak3rmoXRSuKyPsmjwMLD1D4SyEcloMMH4PgwcGE/aGmk3RQ/yAtoyGNSsFHREQaTHVBNfk/HHwstjCPsq1ltV73CfUh9OxQwv8QTti5Yfi19zOpUvEWCj4iItJoynaW1XSSXphH3rd5VOdX13rd0cHhCUGhZ4XiG6pJFKV+KfiIiIgp3E43RauKPK1Bhf8t/N8s0gBWCB4Y7OkkHTRIj8Xk1Cn4iIhIk1BdVPNY7ND8QWVbaj8WswZbCTv74CSKfwjDr4OfVpiXE6bgIyIiTVL5nvKaELTwQM1jsdzfPBZLdHg6SYedE6a1xaROFHxERKTJc7vcFK8p9jwWK/i5AHfVYb++LBA0IMgziWLw4GAsvnosJkdS8BERkWbHWeIk/8f/PRYr3Vha63VroJXQs0IJOzeMgJ4BONo6sCfYFYZEwUdERJq/iv0VNY/EDo4Yq8quOvIgC9jj7TgSHTVbW8f/Pk48GIxsCkYtnYKPiIi0KG6Xm+K1xeQtzCP/h3zKdpRRvqscd8Vxft0ZYIuz1QpDtcJRGwcWu4JRc6fgIyIiLZ7b5aYyq5LyXeWU7yqnYneF5+NDm6vcddzr2OJsR7QUHfrc3saO1c/aCF+NnAoFHxER8Xput5uq7KraYWj3b4JRaR2CUUxNi5G9rb12ODrYYmT1VzAym4KPiIjIcbjdbqpyqo4IQ4daj8p2luEqOX4w8o3yPWYfI0dbB9YABaOGpuAjIiJyitxuN9UHqo/ZWlS+qxxnkfO41/GN8K3d4fpQy1FbB76RvviE+mB1KBydCgUfERGRBuZ2u6nOrz4iDHkC0s5ynIXHD0YAht3AJ8QHn9DDtpCjf2wNsR6x3xpo9eoZrxV8REREmoCq/Kqjd7w+GI6q86qPf5G6sHDCYanW5yE+GNbmG5wUfERERJoBt8uNs8hJdX51zVZQ/b+Pf/v5YR87C/53Tq1FYE+BNch6YmHpNx+bOS2Aj2l3FhERkTozLIanxYW2J36+2+3GVeo6ZkA6Vlg6fL+rrKYDt7PIibPIScW+ihOuw97WzpBdQ078C6gnCj4iIiJewDAMrAFWrAFW7HH2k7qGq/LYwelYYenwz52FTnxCzY0eTSL4zJo1i6effpqMjAySkpL497//zcCBA495/AcffMD999/Prl276NSpE0899RQjR45sxIpFRES8j8VmwRZpwxZpO6nz3U43zrK6deJuKKbPvf3ee+8xefJkHnzwQVavXk1SUhIjRowgKyvrqMf/97//5fLLL+eaa65hzZo1XHzxxVx88cWsX7++kSsXERGRE2FYDXwCzW1zMb1z86BBgxgwYAAzZ84EwOVykZCQwK233sqUKVOOOH7MmDGUlJTw+eefe/YNHjyYPn36MHv27OPeT52bRUREvJepLT6VlZWsWrWK4cOHe/ZZLBaGDx/O0qVLj3rO0qVLax0PMGLEiGMeLyIiInKIqe1NOTk5OJ1OoqOja+2Pjo5m8+bNRz0nIyPjqMdnZGQc9fiKigoqKv7X67ywsPAUqxYREZHmyvQ+Pg1t2rRphISEeLaEhASzSxIRERGTmBp8IiIisFqtZGZm1tqfmZlJTEzMUc+JiYk5oeOnTp1KQUGBZ9u7d2/9FC8iIiLNjqnBx2az0a9fPxYtWuTZ53K5WLRoEUOGHH1yoyFDhtQ6HmDhwoXHPN5utxMcHFxrExEREe9k+jw+kydPZuLEifTv35+BAwcyffp0SkpKuOqqqwCYMGEC8fHxTJs2DYDbb7+dM844g2eeeYYLLriAd999l5UrV/LSSy+Z+WWIiIhIM2B68BkzZgzZ2dk88MADZGRk0KdPH7766itPB+Y9e/ZgsfyvYWro0KHMmzePv//979x777106tSJTz75hJ49e5r1JYiIiEgzYfo8Po1N8/iIiIh4rxY/qktERETkEAUfERER8RoKPiIiIuI1FHxERETEayj4iIiIiNdQ8BERERGv4XXD2d1uN0VFRQQFBWEYhtnliIiISCPyuuAjIiIi3kuPukRERMRrKPiIiIiI11DwEREREa9h+iKlTcmhjs8iIiLS/NRl4JKCz2GKiooICQkxuwwRERE5CXVZgFyjug7TkC0+hYWFJCQksHfvXq0K3wTo/Wha9H40LXo/mh69J3WjFp8TZBhGg/+DCg4O1j/aJkTvR9Oi96Np0fvR9Og9OXXq3CwiIiJeQ8FHREREvIaCTyOx2+08+OCD2O12s0sR9H40NXo/mha9H02P3pP6o87NIiIi4jXU4iMiIiJeQ8FHREREvIaCj4iIiHgNBR8RERHxGgo+jWDWrFkkJibicDgYNGgQy5cvN7skrzVt2jQGDBhAUFAQUVFRXHzxxWzZssXssuSgJ598EsMwuOOOO8wuxWvt37+fK664glatWuHn50evXr1YuXKl2WV5JafTyf3330+7du3w8/OjQ4cOPProo2hM0qlR8Glg7733HpMnT+bBBx9k9erVJCUlMWLECLKysswuzSv9+OOP3Hzzzfzyyy8sXLiQqqoq/vCHP1BSUmJ2aV5vxYoVvPjii/Tu3dvsUrxWXl4ew4YNw9fXly+//JKNGzfyzDPPEBYWZnZpXumpp57ihRdeYObMmWzatImnnnqKf/zjH/z73/82u7RmTcPZG9igQYMYMGAAM2fOBMDlcpGQkMCtt97KlClTTK5OsrOziYqK4scff+T//u//zC7HaxUXF9O3b1+ef/55HnvsMfr06cP06dPNLsvrTJkyhSVLlvDTTz+ZXYoAf/zjH4mOjmbu3LmefZdeeil+fn689dZbJlbWvKnFpwFVVlayatUqhg8f7tlnsVgYPnw4S5cuNbEyOaSgoACA8PBwkyvxbjfffDMXXHBBrf9XpPF99tln9O/fnz//+c9ERUWRnJzMnDlzzC7Law0dOpRFixaRmpoKwNq1a/n55585//zzTa6sedMipQ0oJycHp9NJdHR0rf3R0dFs3rzZpKrkEJfLxR133MGwYcPo2bOn2eV4rXfffZfVq1ezYsUKs0vxejt27OCFF15g8uTJ3HvvvaxYsYLbbrsNm83GxIkTzS7P60yZMoXCwkK6du2K1WrF6XTy+OOPM378eLNLa9YUfMRr3Xzzzaxfv56ff/7Z7FK81t69e7n99ttZuHAhDofD7HK8nsvlon///jzxxBMAJCcns379embPnq3gY4L333+ft99+m3nz5tGjRw9SUlK44447iIuL0/txChR8GlBERARWq5XMzMxa+zMzM4mJiTGpKgG45ZZb+Pzzz1m8eDGtW7c2uxyvtWrVKrKysujbt69nn9PpZPHixcycOZOKigqsVquJFXqX2NhYunfvXmtft27d+Oijj0yqyLv97W9/Y8qUKYwdOxaAXr16sXv3bqZNm6bgcwrUx6cB2Ww2+vXrx6JFizz7XC4XixYtYsiQISZW5r3cbje33HIL8+fP57vvvqNdu3Zml+TVzjnnHNatW0dKSopn69+/P+PHjyclJUWhp5ENGzbsiOkdUlNTadu2rUkVebfS0lIsltq/pq1WKy6Xy6SKWga1+DSwyZMnM3HiRPr378/AgQOZPn06JSUlXHXVVWaX5pVuvvlm5s2bx6effkpQUBAZGRkAhISE4OfnZ3J13icoKOiI/lUBAQG0atVK/a5McOeddzJ06FCeeOIJRo8ezfLly3nppZd46aWXzC7NK40aNYrHH3+cNm3a0KNHD9asWcOzzz7L1VdfbXZpzZqGszeCmTNn8vTTT5ORkUGfPn2YMWMGgwYNMrssr2QYxlH3v/rqq1x55ZWNW4wc1Zlnnqnh7Cb6/PPPmTp1Klu3bqVdu3ZMnjyZSZMmmV2WVyoqKuL+++9n/vz5ZGVlERcXx+WXX84DDzyAzWYzu7xmS8FHREREvIb6+IiIiIjXUPARERERr6HgIyIiIl5DwUdERES8hoKPiIiIeA0FHxEREfEaCj4iIiLiNRR8RER+xw8//IBhGOTn55tdiojUAwUfERER8RoKPiIiIuI1FHxEpElzuVxMmzaNdu3a4efnR1JSEh9++CHwv8dQCxYsoHfv3jgcDgYPHsz69etrXeOjjz6iR48e2O12EhMTeeaZZ2q9XlFRwT333ENCQgJ2u52OHTsyd+7cWsesWrWK/v374+/vz9ChQ49YxVxEmgcFHxFp0qZNm8Ybb7zB7Nmz2bBhA3feeSdXXHEFP/74o+eYv/3tbzzzzDOsWLGCyMhIRo0aRVVVFVATWEaPHs3YsWNZt24dDz30EPfffz+vvfaa5/wJEybwzjvvMGPGDDZt2sSLL75IYGBgrTruu+8+nnnmGVauXImPj49WyBZpprRIqYg0WRUVFYSHh/Ptt98yZMgQz/5rr72W0tJSrrvuOs466yzeffddxowZA8CBAwdo3bo1r732GqNHj2b8+PFkZ2fzzTffeM6/++67WbBgARs2bCA1NZUuXbqwcOFChg8ffkQNP/zwA2eddRbffvst55xzDgBffPEFF1xwAWVlZTgcjgb+LohIfVKLj4g0Wdu2baO0tJRzzz2XwMBAz/bGG2+wfft2z3GHh6Lw8HC6dOnCpk2bANi0aRPDhg2rdd1hw4axdetWnE4nKSkpWK1WzjjjjN+tpXfv3p6PY2NjAcjKyjrlr1FEGpeP2QWIiBxLcXExAAsWLCA+Pr7Wa3a7vVb4OVl+fn51Os7X19fzsWEYQE3/IxFpXtTiIyJNVvfu3bHb7ezZs4eOHTvW2hISEjzH/fLLL56P8/LySE1NpVu3bgB069aNJUuW1LrukiVL6Ny5M1arlV69euFyuWr1GRKRlkstPiLSZAUFBXHXXXdx55134nK5OO200ygoKGDJkiUEBwfTtm1bAB555BFatWpFdHQ09913HxEREVx88cUA/PWvf2XAgAE8+uijjBkzhqVLlzJz5kyef/55ABITE5k4cSJXX301M2bMICkpid27d5OVlcXo0aPN+tJFpIEo+IhIk/boo48SGRnJtGnT2LFjB6GhofTt25d7773X86jpySef5Pbbb2fr1q306dOH//znP9hsNgD69u3L+++/zwMPPMCjjz5KbGwsjzzyCFdeeaXnHi+88AL33nsvN910E7m5ubRp04Z7773XjC9XRBqYRnWJSLN1aMRVXl4eoaGhZpcjIs2A+viIiIiI11DwEREREa+hR10iIiLiNdTiIyIiIl5DwUdERES8hoKPiIiIeA0FHxEREfEaCj4iIiLiNRR8RERExGso+IiIiIjXUPARERERr6HgIyIiIl7j/wGRvDO/yqJrnwAAAABJRU5ErkJggg==\n", 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", 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" ] @@ -1105,7 +1095,7 @@ }, { "cell_type": "markdown", - "id": "5344347c", + "id": "c68185a8", "metadata": {}, "source": [ "And now the same for the `accuracy`." @@ -1114,12 +1104,12 @@ { "cell_type": "code", "execution_count": 14, - "id": "224d3e99", + "id": "2002732c", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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" ] @@ -1150,7 +1140,7 @@ }, { "cell_type": "markdown", - "id": "6bcb9178", + "id": "10a6d7e7", "metadata": {}, "source": [ "`````{admonition} How would you interpret these plots...\n", @@ -1161,7 +1151,7 @@ }, { "cell_type": "markdown", - "id": "8d64f4db", + "id": "0859ef0c", "metadata": {}, "source": [ "## Assessing performance\n", @@ -1172,7 +1162,7 @@ { "cell_type": "code", "execution_count": 15, - "id": "2f714e12", + "id": "6a2eac6b", "metadata": {}, "outputs": [ { @@ -1181,19 +1171,19 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.90 0.94 0.92 85\n", - " 1 0.97 0.99 0.98 88\n", - " 2 0.95 0.89 0.92 90\n", + " 0 0.84 0.91 0.87 85\n", + " 1 0.97 0.97 0.97 88\n", + " 2 0.99 0.88 0.93 90\n", " 3 0.99 0.96 0.97 81\n", - " 4 0.99 0.98 0.98 91\n", - " 5 0.97 0.98 0.98 471\n", - " 6 0.88 0.94 0.91 81\n", - " 7 0.97 0.96 0.96 90\n", - " 8 0.90 0.88 0.89 84\n", + " 4 0.99 0.95 0.97 91\n", + " 5 0.97 0.98 0.97 471\n", + " 6 0.87 0.96 0.91 81\n", + " 7 0.97 0.97 0.97 90\n", + " 8 0.93 0.88 0.90 84\n", "\n", - " accuracy 0.96 1161\n", - " macro avg 0.95 0.95 0.95 1161\n", - "weighted avg 0.96 0.96 0.96 1161\n", + " accuracy 0.95 1161\n", + " macro avg 0.94 0.94 0.94 1161\n", + "weighted avg 0.95 0.95 0.95 1161\n", "\n" ] } @@ -1206,7 +1196,7 @@ }, { "cell_type": "markdown", - "id": "0d3d84fe", + "id": "987f94ea", "metadata": {}, "source": [ "Why you might think: \"Oh, that's awesome, great performance.\", such outcomes are usually perceived as dangerously high and indicate that something is off... \n", @@ -1223,7 +1213,7 @@ { "cell_type": "code", "execution_count": 16, - "id": "dd3eb221", + "id": "d8ccb51a", "metadata": {}, "outputs": [ { @@ -1232,19 +1222,19 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.73 0.83 0.78 23\n", - " 1 0.76 0.65 0.70 20\n", - " 2 0.74 0.78 0.76 18\n", - " 3 0.93 0.93 0.93 27\n", - " 4 0.88 0.88 0.88 17\n", - " 5 0.91 0.91 0.91 117\n", - " 6 0.80 0.74 0.77 27\n", - " 7 1.00 0.94 0.97 18\n", - " 8 0.69 0.75 0.72 24\n", + " 0 0.61 0.74 0.67 23\n", + " 1 1.00 0.55 0.71 20\n", + " 2 0.69 0.61 0.65 18\n", + " 3 0.93 0.96 0.95 27\n", + " 4 1.00 0.82 0.90 17\n", + " 5 0.87 0.91 0.89 117\n", + " 6 0.67 0.81 0.73 27\n", + " 7 0.94 0.89 0.91 18\n", + " 8 0.68 0.62 0.65 24\n", "\n", - " accuracy 0.85 291\n", - " macro avg 0.83 0.82 0.82 291\n", - "weighted avg 0.85 0.85 0.85 291\n", + " accuracy 0.82 291\n", + " macro avg 0.82 0.77 0.78 291\n", + "weighted avg 0.83 0.82 0.82 291\n", "\n" ] } @@ -1256,7 +1246,7 @@ }, { "cell_type": "markdown", - "id": "3dda5435", + "id": "71ae8ebc", "metadata": {}, "source": [ "As you can see, the `scores`, ie `performance`, drops quite a bit. Do you know why and which you would report, e.g. in a `publication`?\n", @@ -1267,7 +1257,7 @@ { "cell_type": "code", "execution_count": 17, - "id": "e2cffd62", + "id": "2692b003", "metadata": {}, "outputs": [], "source": [ @@ -1280,7 +1270,7 @@ }, { "cell_type": "markdown", - "id": "a5e53ebb", + "id": "e0da23bd", "metadata": {}, "source": [ "After that, we can `plot` it for evaluation." @@ -1289,12 +1279,12 @@ { "cell_type": "code", "execution_count": 18, - "id": "8fe6c496", + "id": "a68fe426", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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" ] @@ -1325,7 +1315,7 @@ }, { "cell_type": "markdown", - "id": "8bf4bddc", + "id": "3e5f8334", "metadata": {}, "source": [ "Based on this outcome: how would you interpret the `confusion matrix`? Are some `categories` better `\"decodable\"` than others? Could even make such a statement?" @@ -1333,7 +1323,7 @@ }, { "cell_type": "markdown", - "id": "01c9b5bb", + "id": "532f1522", "metadata": {}, "source": [ "## Summary\n", @@ -1379,7 +1369,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.15" + "version": "3.8.16" }, "source_map": [ 14, diff --git a/_sources/svm_decoding.ipynb b/_sources/svm_decoding.ipynb index 8284a4a..3c47373 100644 --- a/_sources/svm_decoding.ipynb +++ b/_sources/svm_decoding.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "f26b83c7", + "id": "64a65eef", "metadata": {}, "source": [ "# Brain decoding with SVM\n", @@ -28,16 +28,16 @@ { "cell_type": "code", "execution_count": 1, - "id": "1aa08159", + "id": "41c0d0e5", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/datasets/func.py:20: DeprecationWarning: Please use `MatReadError` from the `scipy.io.matlab` namespace, the `scipy.io.matlab.miobase` namespace is deprecated.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/datasets/func.py:20: DeprecationWarning: Please use `MatReadError` from the `scipy.io.matlab` namespace, the `scipy.io.matlab.miobase` namespace is deprecated.\n", " from scipy.io.matlab.miobase import MatReadError\n", - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/datasets/__init__.py:93: FutureWarning: Fetchers from the nilearn.datasets module will be updated in version 0.9 to return python strings instead of bytes and Pandas dataframes instead of Numpy arrays.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/datasets/__init__.py:93: FutureWarning: Fetchers from the nilearn.datasets module will be updated in version 0.9 to return python strings instead of bytes and Pandas dataframes instead of Numpy arrays.\n", " warn(\"Fetchers from the nilearn.datasets module will be \"\n" ] } @@ -68,7 +68,7 @@ }, { "cell_type": "markdown", - "id": "dba5ebe4", + "id": "524e0217", "metadata": {}, "source": [ "Let's check the size of `X` and `y`:" @@ -77,7 +77,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "982931eb", + "id": "2594e77e", "metadata": {}, "outputs": [ { @@ -100,7 +100,7 @@ }, { "cell_type": "markdown", - "id": "2c47678d", + "id": "b6921f27", "metadata": {}, "source": [ "So we have 1452 time points, with one cognitive annotations each, and for each time point we have recordings of fMRI activity across 675 voxels. We can also see that the cognitive annotations span 9 different categories.\n", @@ -112,7 +112,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "f9d25f5f", + "id": "0e3a13a4", "metadata": {}, "outputs": [], "source": [ @@ -122,7 +122,7 @@ }, { "cell_type": "markdown", - "id": "f6a777d7", + "id": "03ea37b9", "metadata": {}, "source": [ "Now we can initialize a SVM classifier, and train it:" @@ -131,7 +131,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "b36f998b", + "id": "289da327", "metadata": {}, "outputs": [ { @@ -153,7 +153,7 @@ }, { "cell_type": "markdown", - "id": "7063e8e4", + "id": "5f220b81", "metadata": {}, "source": [ "## Assessing performance\n", @@ -163,7 +163,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "86e3bea3", + "id": "5f5a0061", "metadata": {}, "outputs": [ { @@ -197,7 +197,7 @@ }, { "cell_type": "markdown", - "id": "ae9503f6", + "id": "18003146", "metadata": {}, "source": [ "This is dangerously high. Let's check on the test set:" @@ -206,7 +206,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "cc234135", + "id": "3b78ccf4", "metadata": {}, "outputs": [ { @@ -239,7 +239,7 @@ }, { "cell_type": "markdown", - "id": "6f4822bf", + "id": "3301a609", "metadata": {}, "source": [ "We can have a look at the confusion matrix:" @@ -248,12 +248,12 @@ { "cell_type": "code", "execution_count": 7, - "id": "eefc593d", + "id": "1bf5b501", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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" ] @@ -283,7 +283,7 @@ }, { "cell_type": "markdown", - "id": "29cf874d", + "id": "c6d84e95", "metadata": {}, "source": [ "## Visualizing the weights\n", @@ -293,14 +293,14 @@ { "cell_type": "code", "execution_count": 8, - "id": "e1461d2a", + "id": "7c265744", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/scipy/ndimage/_measurements.py:305: DeprecationWarning: In future, it will be an error for 'np.bool_' scalars to be interpreted as an index\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/scipy/ndimage/_measurements.py:307: DeprecationWarning: In future, it will be an error for 'np.bool_' scalars to be interpreted as an index\n", " return _nd_image.find_objects(input, max_label)\n" ] }, @@ -349,7 +349,7 @@ "\" width=\"600\" height=\"366.0\" frameBorder=\"0\">" ], "text/plain": [ - "" + "" ] }, "execution_count": 8, @@ -370,7 +370,7 @@ }, { "cell_type": "markdown", - "id": "03fe2177", + "id": "42408ba7", "metadata": {}, "source": [ "## And now the easy way\n", @@ -380,7 +380,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "e8972928", + "id": "91097787", "metadata": {}, "outputs": [], "source": [ @@ -399,7 +399,7 @@ }, { "cell_type": "markdown", - "id": "ca888ca9", + "id": "4797351e", "metadata": {}, "source": [ "That's it !\n", @@ -409,7 +409,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "e1411d4b", + "id": "0a98f1bf", "metadata": {}, "outputs": [ { @@ -432,7 +432,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/scipy/ndimage/_measurements.py:305: DeprecationWarning: In future, it will be an error for 'np.bool_' scalars to be interpreted as an index\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/scipy/ndimage/_measurements.py:307: DeprecationWarning: In future, it will be an error for 'np.bool_' scalars to be interpreted as an index\n", " return _nd_image.find_objects(input, max_label)\n" ] }, @@ -472,7 +472,7 @@ " $( window ).on('load',function() {\n", " // Create brain slices\n", " var brain = brainsprite(\n", - " {"canvas": "3Dviewer", "sprite": "spriteImg", "nbSlice": {"X": 124, "Y": 256, "Z": 256}, "overlay": {"sprite": "overlayImg", "nbSlice": {"X": 124, "Y": 256, "Z": 256}, "opacity": 1}, "colorBackground": "#000000", "colorFont": "#FFFFFF", "crosshair": true, "affine": [[1.2000000476837158, 0.0, 0.0, -73.80000293254852], [0.0, 0.9375, 0.0, -119.53125], [0.0, 0.0, 0.9375, -119.53125], [0.0, 0.0, 0.0, 1.0]], "flagCoordinates": true, "title": "SVM weights for face", "flagValue": false, "numSlice": {"X": 34.014156036317985, "Y": 85.62060231484438, "Z": 123.15482641603873}, "colorMap": {"img": "colorMap", "min": -0.14514382183551788, "max": 0.14514382183551788}}\n", + " {"canvas": "3Dviewer", "sprite": "spriteImg", "nbSlice": {"X": 124, "Y": 256, "Z": 256}, "overlay": {"sprite": "overlayImg", "nbSlice": {"X": 124, "Y": 256, "Z": 256}, "opacity": 1}, "colorBackground": "#000000", "colorFont": "#FFFFFF", "crosshair": true, "affine": [[1.2000000476837158, 0.0, 0.0, -73.80000293254852], [0.0, 0.9375, 0.0, -119.53125], [0.0, 0.0, 0.9375, -119.53125], [0.0, 0.0, 0.0, 1.0]], "flagCoordinates": true, "title": "SVM weights for face", "flagValue": false, "numSlice": {"X": 34.01417601826262, "Y": 85.62059919447546, "Z": 123.15482506913908}, "colorMap": {"img": "colorMap", "min": -0.14514444768428802, "max": 0.14514444768428802}}\n", " );\n", " });\n", " </script>\n", @@ -481,7 +481,7 @@ "\" width=\"600\" height=\"366.0\" frameBorder=\"0\">" ], "text/plain": [ - "" + "" ] }, "execution_count": 10, @@ -501,7 +501,7 @@ }, { "cell_type": "markdown", - "id": "90971e02", + "id": "3d518b96", "metadata": {}, "source": [ "Note: the Decoder implements a one-vs-all strategy. Note that this is a better choice in general than one-vs-one.\n", @@ -513,14 +513,14 @@ { "cell_type": "code", "execution_count": 11, - "id": "a86b0221", + "id": "f5cfef88", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", " inv_max = dia_matrix((1. / max_connectivity, 0),\n" ] }, @@ -528,7 +528,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -536,7 +536,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -544,7 +544,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", " inv_max = dia_matrix((1. / max_connectivity, 0),\n" ] }, @@ -552,7 +552,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -560,7 +560,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -568,7 +568,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", " inv_max = dia_matrix((1. / max_connectivity, 0),\n" ] }, @@ -576,7 +576,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -584,7 +584,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -592,7 +592,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", " inv_max = dia_matrix((1. / max_connectivity, 0),\n" ] }, @@ -600,7 +600,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -608,7 +608,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -616,7 +616,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", " inv_max = dia_matrix((1. / max_connectivity, 0),\n" ] }, @@ -624,7 +624,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -632,7 +632,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -640,7 +640,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", " inv_max = dia_matrix((1. / max_connectivity, 0),\n" ] }, @@ -648,7 +648,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -656,7 +656,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -664,7 +664,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", " inv_max = dia_matrix((1. / max_connectivity, 0),\n" ] }, @@ -672,7 +672,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -680,7 +680,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -688,7 +688,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", " inv_max = dia_matrix((1. / max_connectivity, 0),\n" ] }, @@ -696,7 +696,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -704,7 +704,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -712,7 +712,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", " inv_max = dia_matrix((1. / max_connectivity, 0),\n" ] }, @@ -720,7 +720,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -728,7 +728,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -736,7 +736,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", " inv_max = dia_matrix((1. / max_connectivity, 0),\n" ] }, @@ -744,7 +744,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -752,7 +752,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -760,7 +760,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", " inv_max = dia_matrix((1. / max_connectivity, 0),\n" ] }, @@ -768,7 +768,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -776,7 +776,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -784,7 +784,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", " inv_max = dia_matrix((1. / max_connectivity, 0),\n" ] }, @@ -792,7 +792,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -800,7 +800,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -808,7 +808,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", " inv_max = dia_matrix((1. / max_connectivity, 0),\n" ] }, @@ -816,7 +816,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -824,7 +824,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -832,7 +832,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", " inv_max = dia_matrix((1. / max_connectivity, 0),\n" ] }, @@ -840,7 +840,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -848,7 +848,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -856,7 +856,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", " inv_max = dia_matrix((1. / max_connectivity, 0),\n" ] }, @@ -864,7 +864,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -872,7 +872,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -880,7 +880,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", " inv_max = dia_matrix((1. / max_connectivity, 0),\n" ] }, @@ -888,7 +888,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -896,7 +896,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -904,7 +904,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", " inv_max = dia_matrix((1. / max_connectivity, 0),\n" ] }, @@ -912,7 +912,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -920,7 +920,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -928,7 +928,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", " inv_max = dia_matrix((1. / max_connectivity, 0),\n" ] }, @@ -936,7 +936,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -944,7 +944,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -952,7 +952,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", " inv_max = dia_matrix((1. / max_connectivity, 0),\n" ] }, @@ -960,7 +960,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -968,7 +968,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -976,7 +976,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", " inv_max = dia_matrix((1. / max_connectivity, 0),\n" ] }, @@ -984,7 +984,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -992,7 +992,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -1000,7 +1000,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", " inv_max = dia_matrix((1. / max_connectivity, 0),\n" ] }, @@ -1008,7 +1008,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -1016,7 +1016,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -1024,7 +1024,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", " inv_max = dia_matrix((1. / max_connectivity, 0),\n" ] }, @@ -1032,7 +1032,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -1040,7 +1040,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -1048,7 +1048,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", " inv_max = dia_matrix((1. / max_connectivity, 0),\n" ] }, @@ -1056,7 +1056,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -1064,7 +1064,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -1072,7 +1072,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", " inv_max = dia_matrix((1. / max_connectivity, 0),\n" ] }, @@ -1080,7 +1080,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -1088,7 +1088,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -1096,7 +1096,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", " inv_max = dia_matrix((1. / max_connectivity, 0),\n" ] }, @@ -1104,7 +1104,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -1112,7 +1112,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -1120,7 +1120,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", " inv_max = dia_matrix((1. / max_connectivity, 0),\n" ] }, @@ -1128,7 +1128,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -1136,7 +1136,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -1144,7 +1144,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", " inv_max = dia_matrix((1. / max_connectivity, 0),\n" ] }, @@ -1152,7 +1152,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -1160,7 +1160,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -1168,7 +1168,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", " inv_max = dia_matrix((1. / max_connectivity, 0),\n" ] }, @@ -1176,7 +1176,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -1184,7 +1184,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -1192,7 +1192,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", " inv_max = dia_matrix((1. / max_connectivity, 0),\n" ] }, @@ -1200,7 +1200,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -1208,7 +1208,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -1216,7 +1216,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", " inv_max = dia_matrix((1. / max_connectivity, 0),\n" ] }, @@ -1224,7 +1224,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -1232,7 +1232,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -1240,7 +1240,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", " inv_max = dia_matrix((1. / max_connectivity, 0),\n" ] }, @@ -1248,7 +1248,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -1256,7 +1256,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -1264,7 +1264,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", " inv_max = dia_matrix((1. / max_connectivity, 0),\n" ] }, @@ -1272,7 +1272,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -1280,7 +1280,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -1288,7 +1288,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", " inv_max = dia_matrix((1. / max_connectivity, 0),\n" ] }, @@ -1296,7 +1296,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -1304,7 +1304,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -1312,7 +1312,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", " inv_max = dia_matrix((1. / max_connectivity, 0),\n" ] }, @@ -1320,7 +1320,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -1328,7 +1328,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -1336,7 +1336,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", " inv_max = dia_matrix((1. / max_connectivity, 0),\n" ] }, @@ -1344,7 +1344,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -1352,7 +1352,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -1360,7 +1360,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", " inv_max = dia_matrix((1. / max_connectivity, 0),\n" ] }, @@ -1368,7 +1368,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -1376,7 +1376,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -1384,7 +1384,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", " inv_max = dia_matrix((1. / max_connectivity, 0),\n" ] }, @@ -1392,7 +1392,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -1400,7 +1400,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -1408,7 +1408,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", " inv_max = dia_matrix((1. / max_connectivity, 0),\n" ] }, @@ -1416,7 +1416,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -1424,7 +1424,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -1432,7 +1432,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", " inv_max = dia_matrix((1. / max_connectivity, 0),\n" ] }, @@ -1440,7 +1440,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -1448,7 +1448,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -1456,7 +1456,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", " inv_max = dia_matrix((1. / max_connectivity, 0),\n" ] }, @@ -1464,7 +1464,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -1472,7 +1472,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -1480,7 +1480,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", " inv_max = dia_matrix((1. / max_connectivity, 0),\n" ] }, @@ -1488,7 +1488,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -1496,7 +1496,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -1504,7 +1504,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", " inv_max = dia_matrix((1. / max_connectivity, 0),\n" ] }, @@ -1512,7 +1512,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -1520,7 +1520,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -1528,7 +1528,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", " inv_max = dia_matrix((1. / max_connectivity, 0),\n" ] }, @@ -1536,7 +1536,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -1544,7 +1544,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -1552,7 +1552,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", " inv_max = dia_matrix((1. / max_connectivity, 0),\n" ] }, @@ -1560,7 +1560,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -1568,7 +1568,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -1576,7 +1576,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide\n", " inv_max = dia_matrix((1. / max_connectivity, 0),\n" ] }, @@ -1584,7 +1584,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -1592,7 +1592,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, @@ -1600,7 +1600,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/scipy/ndimage/_measurements.py:305: DeprecationWarning: In future, it will be an error for 'np.bool_' scalars to be interpreted as an index\n", + "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/scipy/ndimage/_measurements.py:307: DeprecationWarning: In future, it will be an error for 'np.bool_' scalars to be interpreted as an index\n", " return _nd_image.find_objects(input, max_label)\n" ] }, @@ -1622,7 +1622,7 @@ " <!-- the colormap -->\n", " <img id="colorMap" class="hidden" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAQAAAAABCAYAAAAxWXB3AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAA9hAAAPYQGoP6dpAAAAUUlEQVR4nGP8/////x8MDAz48E8ay//7w8DA8BuKfyGxqcGnhZkYdvyno2XU5fMyfGZgZWCAYzYk9mDnM7LS2XJqm8nCxMDAwIEHs9NYnoMBAFEChf4wrnHMAAAAAElFTkSuQmCC" alt="colormap">\n", " <!-- another sprite image, with an overlay-->\n", - " <img id="overlayImg" class="hidden" 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alt="overlay">\n", " </canvas>\n", " </div>\n", "\n", @@ -1640,7 +1640,7 @@ " $( window ).on('load',function() {\n", " // Create brain slices\n", " var brain = brainsprite(\n", - " {"canvas": "3Dviewer", "sprite": "spriteImg", "nbSlice": {"X": 124, "Y": 256, "Z": 256}, "overlay": {"sprite": "overlayImg", "nbSlice": {"X": 124, "Y": 256, "Z": 256}, "opacity": 1}, "colorBackground": "#000000", "colorFont": "#FFFFFF", "crosshair": true, "affine": [[1.2000000476837158, 0.0, 0.0, -73.80000293254852], [0.0, 0.9375, 0.0, -119.53125], [0.0, 0.0, 0.9375, -119.53125], [0.0, 0.0, 0.0, 1.0]], "flagCoordinates": true, "title": "SVM weights for face", "flagValue": false, "numSlice": {"X": 36.42768065372944, "Y": 84.07564436533885, "Z": 123.19057824995}, "colorMap": {"img": "colorMap", "min": -2.8867039680480957, "max": 2.8867039680480957}}\n", + " {"canvas": "3Dviewer", "sprite": "spriteImg", "nbSlice": {"X": 124, "Y": 256, "Z": 256}, "overlay": {"sprite": "overlayImg", "nbSlice": {"X": 124, "Y": 256, "Z": 256}, "opacity": 1}, "colorBackground": "#000000", "colorFont": "#FFFFFF", "crosshair": true, "affine": [[1.2000000476837158, 0.0, 0.0, -73.80000293254852], [0.0, 0.9375, 0.0, -119.53125], [0.0, 0.0, 0.9375, -119.53125], [0.0, 0.0, 0.0, 1.0]], "flagCoordinates": true, "title": "SVM weights for face", "flagValue": false, "numSlice": {"X": 36.45090678062532, "Y": 84.08648528871177, "Z": 123.19046272838872}, "colorMap": {"img": "colorMap", "min": -2.8215417861938477, "max": 2.8215417861938477}}\n", " );\n", " });\n", " </script>\n", @@ -1649,7 +1649,7 @@ "\" width=\"600\" height=\"366.0\" frameBorder=\"0\">" ], "text/plain": [ - "" + "" ] }, "execution_count": 11, @@ -1669,7 +1669,7 @@ }, { "cell_type": "markdown", - "id": "8bda1948", + "id": "62024c95", "metadata": {}, "source": [ "Note that the resulting accuracy is in general slightly higher:" @@ -1678,7 +1678,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "9fb28985", + "id": "9deab7b8", "metadata": {}, "outputs": [ { @@ -1706,7 +1706,7 @@ }, { "cell_type": "markdown", - "id": "13be67a5", + "id": "8f6f064f", "metadata": {}, "source": [ "## Exercises\n", @@ -1748,7 +1748,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.15" + "version": "3.8.16" }, "source_map": [ 14, diff --git a/_sphinx_design_static/design-style.4045f2051d55cab465a707391d5b2007.min.css b/_sphinx_design_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css similarity index 99% rename from _sphinx_design_static/design-style.4045f2051d55cab465a707391d5b2007.min.css rename to _sphinx_design_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css index 3225661..eb19f69 100644 --- a/_sphinx_design_static/design-style.4045f2051d55cab465a707391d5b2007.min.css +++ b/_sphinx_design_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css @@ -1 +1 @@ -.sd-bg-primary{background-color:var(--sd-color-primary) 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rgba(178, 206, 245, 0.62);--sd-color-tabs-underline-inactive: transparent;--sd-color-tabs-overline: rgb(222, 222, 222);--sd-color-tabs-underline: rgb(222, 222, 222);--sd-fontsize-tabs-label: 1rem} diff --git a/_static/__pycache__/__init__.cpython-38.pyc b/_static/__pycache__/__init__.cpython-38.pyc index 8b7b596..60ca8bf 100644 Binary files a/_static/__pycache__/__init__.cpython-38.pyc and b/_static/__pycache__/__init__.cpython-38.pyc differ diff --git a/_static/copybutton.js b/_static/copybutton.js index 02c5c82..2ea7ff3 100644 --- a/_static/copybutton.js +++ b/_static/copybutton.js @@ -20,7 +20,7 @@ const messages = { }, 'fr' : { 'copy': 'Copier', - 'copy_to_clipboard': 'Copié dans le presse-papier', + 'copy_to_clipboard': 'Copier dans le presse-papier', 'copy_success': 'Copié !', 'copy_failure': 'Échec de la copie', }, @@ -224,7 +224,7 @@ var copyTargetText = (trigger) => { var target = document.querySelector(trigger.attributes['data-clipboard-target'].value); // get filtered text - 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rgba(178, 206, 245, 0.62);--sd-color-tabs-underline-inactive: transparent;--sd-color-tabs-overline: rgb(222, 222, 222);--sd-color-tabs-underline: rgb(222, 222, 222);--sd-fontsize-tabs-label: 1rem} diff --git a/encoding.html b/encoding.html index a5de682..d1196bd 100644 --- a/encoding.html +++ b/encoding.html @@ -30,7 +30,7 @@ - + @@ -585,13 +585,13 @@

Miyawaki et al. 2008 Study

-
import numpy as np
+
import numpy as np
 
-import matplotlib.pyplot as plt 
-%matplotlib inline
+import matplotlib.pyplot as plt 
+%matplotlib inline
 
-import warnings
-warnings.filterwarnings("ignore")
+import warnings
+warnings.filterwarnings("ignore")
@@ -603,9 +603,9 @@

Loading the data
-
from nilearn.datasets import fetch_miyawaki2008
+
from nilearn.datasets import fetch_miyawaki2008
 
-dataset = fetch_miyawaki2008()
+dataset = fetch_miyawaki2008()
@@ -615,7 +615,28 @@

Loading the data
 ...done. (1 seconds, 0 min)
+
Downloaded 15974400 of 161069109 bytes (9.9%,    9.1s remaining)
+

+

+
Downloaded 39960576 of 161069109 bytes (24.8%,    6.1s remaining)
+

+

+
Downloaded 62767104 of 161069109 bytes (39.0%,    4.7s remaining)
+

+

+
Downloaded 84787200 of 161069109 bytes (52.6%,    3.6s remaining)
+

+

+
Downloaded 105357312 of 161069109 bytes (65.4%,    2.6s remaining)
+

+

+
Downloaded 128557056 of 161069109 bytes (79.8%,    1.5s remaining)
+

+

+
Downloaded 150183936 of 161069109 bytes (93.2%,    0.5s remaining)
+

+

+
 ...done. (8 seconds, 0 min)
 Extracting data from /home/runner/nilearn_data/miyawaki2008/18b67c55cebe5e71427c5ffdcfafd948/miyawaki2008.tgz...
 

 

@@ -626,7 +647,7 @@ 
Loading the data
 

-
dataset
+
dataset
 

 

 

@@ -749,17 +770,17 @@ 
Making a list of the necessary files
 

-
# Training data starts after the first 12 files
+
# Training data starts after the first 12 files
 
-fmri_random_runs_filenames = dataset.func[12:]
-stimuli_random_runs_filenames = dataset.label[12:]
+fmri_random_runs_filenames = dataset.func[12:]
+stimuli_random_runs_filenames = dataset.label[12:]
 

 

 

 

 

 

-
fmri_random_runs_filenames
+
fmri_random_runs_filenames
 

 

 

@@ -790,7 +811,7 @@ 
Making a list of the necessary files
 

-
stimuli_random_runs_filenames
+
stimuli_random_runs_filenames
 

 

 

@@ -826,13 +847,13 @@ 
fMRI data : Preprocessing, Masking and Data extraction
 

 

-
import numpy as np
-from nilearn.input_data import MultiNiftiMasker
+
import numpy as np
+from nilearn.input_data import MultiNiftiMasker
 
-masker = MultiNiftiMasker(mask_img=dataset.mask, detrend=True,
-                          standardize=True)
-masker.fit()
-fmri_data = masker.transform(fmri_random_runs_filenames)
+masker = MultiNiftiMasker(mask_img=dataset.mask, detrend=True,
+                          standardize=True)
+masker.fit()
+fmri_data = masker.transform(fmri_random_runs_filenames)
 

 

 

@@ -841,7 +862,7 @@ 
fMRI data : Preprocessing, Masking and Data extraction#samples x #voxels

 

 

-
print('# runs in fmri data = ',len(fmri_data))
+
print('# runs in fmri data = ',len(fmri_data))
 

 

 

@@ -853,7 +874,7 @@ 
fMRI data : Preprocessing, Masking and Data extraction
 

 

-
print('Dimensions of fmri data (#samples, #voxels) = ', fmri_data[0].shape)
+
print('Dimensions of fmri data (#samples, #voxels) = ', fmri_data[0].shape)
 

 

 

@@ -866,21 +887,21 @@ 
fMRI data : Preprocessing, Masking and Data extractionLet’s take a look at some of these BOLD response timecourses:

 

 

-
import matplotlib.pyplot as plt
+
import matplotlib.pyplot as plt
 
-runs = [5,10]
-voxels = [100,200]
+runs = [5,10]
+voxels = [100,200]
 
-num_voxels = len(voxels)
+num_voxels = len(voxels)
 
-plt.figure(figsize=(16,6*num_voxels))
+plt.figure(figsize=(16,6*num_voxels))
 
-for i in range(num_voxels):
-  plt.subplot(num_voxels, 1, i+1)
-  plt.xlabel('TR')
-  plt.ylabel('Normalized BOLD')
-  plt.title('Run {:d}, Voxel {:d}'.format(runs[i],voxels[i]))
-  plt.plot(fmri_data[runs[i]][:,voxels[i]]);
+for i in range(num_voxels):
+  plt.subplot(num_voxels, 1, i+1)
+  plt.xlabel('TR')
+  plt.ylabel('Normalized BOLD')
+  plt.title('Run {:d}, Voxel {:d}'.format(runs[i],voxels[i]))
+  plt.plot(fmri_data[runs[i]][:,voxels[i]]);
 

 

 

@@ -893,14 +914,14 @@ 
fMRI data : Preprocessing, Masking and Data extractionStimuli data: Data Extraction

 

 

-
stimulus_shape = (10, 10)
+
stimulus_shape = (10, 10)
 
-# We load the visual stimuli from csv files
-stimuli_data = []
-for stimulus_run in stimuli_random_runs_filenames:
-    stimuli_data.append(np.reshape(np.loadtxt(stimulus_run,
-                              dtype=np.int, delimiter=','),
-                              (-1,) + stimulus_shape, order='F'))
+# We load the visual stimuli from csv files
+stimuli_data = []
+for stimulus_run in stimuli_random_runs_filenames:
+    stimuli_data.append(np.reshape(np.loadtxt(stimulus_run,
+                              dtype=np.int, delimiter=','),
+                              (-1,) + stimulus_shape, order='F'))
 

 

 

@@ -909,7 +930,7 @@ 
Stimuli data: Data Extraction#samples x #voxels

 

 

-
print('# runs in stimuli data = ',len(stimuli_data))
+
print('# runs in stimuli data = ',len(stimuli_data))
 

 

 

@@ -921,7 +942,7 @@ 
Stimuli data: Data Extraction
 

-
print('Dimensions of stimuli data (#samples, image_height, image_width) = ', stimuli_data[0].shape)
+
print('Dimensions of stimuli data (#samples, image_height, image_width) = ', stimuli_data[0].shape)
 

 

 

@@ -934,27 +955,27 @@ 
Stimuli data: Data Extraction
 

-
import matplotlib.pyplot as plt
+
import matplotlib.pyplot as plt
 
-runs = [1,3,8]
-trials = [27,125,102]
+runs = [1,3,8]
+trials = [27,125,102]
 
-num_runs = len(runs)
-num_images = len(trials)
+num_runs = len(runs)
+num_images = len(trials)
 
-subplot_index = 1
+subplot_index = 1
 
-plt.figure(figsize=(4*num_images, 4*num_runs))
+plt.figure(figsize=(4*num_images, 4*num_runs))
 
-for i in range(num_runs):
-  for j in range(num_images):
-    plt.subplot(num_runs, num_images, subplot_index)
-    plt.imshow(stimuli_data[runs[i]][trials[j]], interpolation='nearest', cmap='gray')
-    plt.axis('off')
-    plt.title('Run {}, Stimulus Trial {}'.format(runs[i],trials[j]))
-    subplot_index += 1
+for i in range(num_runs):
+  for j in range(num_images):
+    plt.subplot(num_runs, num_images, subplot_index)
+    plt.imshow(stimuli_data[runs[i]][trials[j]], interpolation='nearest', cmap='gray')
+    plt.axis('off')
+    plt.title('Run {}, Stimulus Trial {}'.format(runs[i],trials[j]))
+    subplot_index += 1
 
-plt.subplots_adjust(wspace=0.5)
+plt.subplots_adjust(wspace=0.5)
 

 

 

@@ -974,12 +995,12 @@ 
Creating the input and target matrices for training
 

 

-
# Delay chosen based on the TR duration to expect a hemodynamic response
+
# Delay chosen based on the TR duration to expect a hemodynamic response
 
-delay_trials = 2
+delay_trials = 2
 
-fmri_data = np.vstack([fmri_run[delay_trials:] for fmri_run in fmri_data])
-stimuli_data = np.vstack([stimuli_run[:-delay_trials] for stimuli_run in stimuli_data]).astype(float)
+fmri_data = np.vstack([fmri_run[delay_trials:] for fmri_run in fmri_data])
+stimuli_data = np.vstack([stimuli_run[:-delay_trials] for stimuli_run in stimuli_data]).astype(float)
 

 

 

@@ -987,8 +1008,8 @@ 
Creating the input and target matrices for trainingShape of the feature (data) and target matrices

 

 

-
print('stimuli_data.shape =',stimuli_data.shape)
-print('fmri_data.shape =',fmri_data.shape)
+
print('stimuli_data.shape =',stimuli_data.shape)
+print('fmri_data.shape =',fmri_data.shape)
 

 

 

@@ -1003,11 +1024,11 @@ 
Creating the input and target matrices for training(10,10) dimensions need to be flattened into 100-dimensional vectors.

 

 

-
# Flatten the stimuli
+
# Flatten the stimuli
 
-stimuli_data = np.reshape(stimuli_data, (-1, stimulus_shape[0] * stimulus_shape[1]))
+stimuli_data = np.reshape(stimuli_data, (-1, stimulus_shape[0] * stimulus_shape[1]))
 
-print('stimuli_data.shape =',stimuli_data.shape)
+print('stimuli_data.shape =',stimuli_data.shape)
 

 

 

@@ -1066,8 +1087,8 @@ 
Cross-Validation

 

 

-
from sklearn.linear_model import Ridge
-from sklearn.model_selection import KFold
+
from sklearn.linear_model import Ridge
+from sklearn.model_selection import KFold
 

 

 

@@ -1077,49 +1098,51 @@ 
Training the model\(R^2\) scores of the predictions at each voxel and store the values for further analysis.

 

 

-
from sklearn.metrics import r2_score
+
from sklearn.metrics import r2_score
 
-cv = KFold(n_splits=10)
+cv = KFold(n_splits=10)
 
-scores = []
-for fold, (train, test) in enumerate(cv.split(X=stimuli_data)):
-    print('fold = [{:d}] :: #train samples = {:d}, #test samples = {:d}'.format(fold,len(train),len(test)))
+scores = []
+for fold, (train, test) in enumerate(cv.split(X=stimuli_data)):
+    print('fold = [{:d}] :: #train samples = {:d}, #test samples = {:d}'.format(fold,len(train),len(test)))
 
-    # create a ridge model
-    model = Ridge(alpha=100.)
+    # create a ridge model
+    model = Ridge(alpha=100.)
 
-    # train the Ridge model on the training set
-    model.fit(stimuli_data[train], fmri_data[train])
+    # train the Ridge model on the training set
+    model.fit(stimuli_data[train], fmri_data[train])
 
-    # and predict the fMRI activity for the test set
-    predictions = model.predict(stimuli_data[test])
+    # and predict the fMRI activity for the test set
+    predictions = model.predict(stimuli_data[test])
 
-    # we compute how much variance our encoding model explains in each voxel
-    scores.append(r2_score(fmri_data[test], predictions, multioutput='raw_values'))
+    # we compute how much variance our encoding model explains in each voxel
+    scores.append(r2_score(fmri_data[test], predictions, multioutput='raw_values'))
 

 

 

 

 
fold = [0] :: #train samples = 2574, #test samples = 286
-fold = [1] :: #train samples = 2574, #test samples = 286
+

+

+
fold = [1] :: #train samples = 2574, #test samples = 286
 

 

 
fold = [2] :: #train samples = 2574, #test samples = 286
-fold = [3] :: #train samples = 2574, #test samples = 286
 

 

-
fold = [4] :: #train samples = 2574, #test samples = 286
+
fold = [3] :: #train samples = 2574, #test samples = 286
+fold = [4] :: #train samples = 2574, #test samples = 286
 

 

 
fold = [5] :: #train samples = 2574, #test samples = 286
+fold = [6] :: #train samples = 2574, #test samples = 286
 

 

-
fold = [6] :: #train samples = 2574, #test samples = 286
-fold = [7] :: #train samples = 2574, #test samples = 286
+
fold = [7] :: #train samples = 2574, #test samples = 286
+fold = [8] :: #train samples = 2574, #test samples = 286
 

 

-
fold = [8] :: #train samples = 2574, #test samples = 286
-fold = [9] :: #train samples = 2574, #test samples = 286
+
fold = [9] :: #train samples = 2574, #test samples = 286
 

 

 

@@ -1131,14 +1154,14 @@ 
Analyzing the predictions
 

-
#Extract only the first run from the test set and predictions
+
#Extract only the first run from the test set and predictions
 
-actual = fmri_data[test[:len(test)//2]]
+actual = fmri_data[test[:len(test)//2]]
 
-predicted = predictions[:len(predictions)//2,:]
+predicted = predictions[:len(predictions)//2,:]
 
-print('   actual.shape (#TRs,#voxels) = ',actual.shape)
-print('predicted.shape (#TRs,#voxels) = ',predicted.shape)
+print('   actual.shape (#TRs,#voxels) = ',actual.shape)
+print('predicted.shape (#TRs,#voxels) = ',predicted.shape)
 

 

 

@@ -1153,22 +1176,22 @@ 
Analyzing the predictions
 

-
import matplotlib.pyplot as plt
+
import matplotlib.pyplot as plt
 
-voxels = [1951,2272,841]
+voxels = [1951,2272,841]
 
-num_voxels = len(voxels)
+num_voxels = len(voxels)
 
-plt.figure(figsize=(16,6*num_voxels))
+plt.figure(figsize=(16,6*num_voxels))
 
-for i in range(num_voxels):
-  plt.subplot(num_voxels, 1, i+1)
-  plt.xlabel('TR')
-  plt.ylabel('Normalized BOLD')
-  plt.title('Voxel {:d}'.format(voxels[i]))
-  plt.plot(actual[:,voxels[i]], label='actual')
-  plt.plot(predicted[:,voxels[i]], label='predicted')
-  plt.legend();
+for i in range(num_voxels):
+  plt.subplot(num_voxels, 1, i+1)
+  plt.xlabel('TR')
+  plt.ylabel('Normalized BOLD')
+  plt.title('Voxel {:d}'.format(voxels[i]))
+  plt.plot(actual[:,voxels[i]], label='actual')
+  plt.plot(predicted[:,voxels[i]], label='predicted')
+  plt.legend();
 

 

 

@@ -1180,21 +1203,21 @@ 
Analyzing the predictions

 

 

-
voxel = 1956
+
voxel = 1956
 
-plt.figure(figsize=(6,6))
-plt.ylabel('predicted BOLD')
-plt.xlabel('actual BOLD')
+plt.figure(figsize=(6,6))
+plt.ylabel('predicted BOLD')
+plt.xlabel('actual BOLD')
 
-lims = [
-    np.min([plt.xlim(), plt.ylim()]),  # min of both axes
-    np.max([plt.xlim(), plt.ylim()]),  # max of both axes
-]
+lims = [
+    np.min([plt.xlim(), plt.ylim()]),  # min of both axes
+    np.max([plt.xlim(), plt.ylim()]),  # max of both axes
+]
 
-# x=y line for reference
-plt.plot(lims, lims, '--', alpha=0.75, zorder=0)
+# x=y line for reference
+plt.plot(lims, lims, '--', alpha=0.75, zorder=0)
 
-plt.scatter(actual[:,voxel],predicted[:,voxel]);
+plt.scatter(actual[:,voxel],predicted[:,voxel]);
 

 

 

@@ -1206,15 +1229,15 @@ 
Scatter Plot of actual vs predicted values
 

-
from scipy.stats import pearsonr
+
from scipy.stats import pearsonr
 
-voxel = 1951
+voxel = 1951
 
-pearson_r, _  = pearsonr(actual[:,voxel],predicted[:,voxel])
-r2 = r2_score(actual[:,voxel],predicted[:,voxel])
+pearson_r, _  = pearsonr(actual[:,voxel],predicted[:,voxel])
+r2 = r2_score(actual[:,voxel],predicted[:,voxel])
 
-print('Pearson r at voxel {} = {:.3f}'.format(voxel,pearson_r))
-print('R2 score at voxel  {} = {:.3f}'.format(voxel,r2))
+print('Pearson r at voxel {} = {:.3f}'.format(voxel,pearson_r))
+print('R2 score at voxel  {} = {:.3f}'.format(voxel,r2))
 

 

 

@@ -1236,18 +1259,18 @@ 
Computation of the score map and generating the brain imagemasker object used to map the voxels to the data matrix is reused to inverse transform the score matrix to the brain volumne.

 

 

-
from nilearn.image import threshold_img
+
from nilearn.image import threshold_img
 
-# Average the score maps accross folds
-score_map = np.mean(scores, axis=0)
+# Average the score maps accross folds
+score_map = np.mean(scores, axis=0)
 
-# Ignore the negative score values 
-score_map[score_map < 0] = 0
+# Ignore the negative score values 
+score_map[score_map < 0] = 0
 
-# Bring the scores into the shape of the background brain
-score_map_img = masker.inverse_transform(score_map)
+# Bring the scores into the shape of the background brain
+score_map_img = masker.inverse_transform(score_map)
 
-thresholded_score_map_img = threshold_img(score_map_img, threshold=1e-6, copy=False)
+thresholded_score_map_img = threshold_img(score_map_img, threshold=1e-6, copy=False)
 

 

 

@@ -1255,7 +1278,7 @@ 
Computation of the score map and generating the brain imagescore_map represents the number of voxels for which the scores have been computed.

 

 

-
print('The score_map is of size = ',score_map.shape)
+
print('The score_map is of size = ',score_map.shape)
 

 

 

@@ -1268,10 +1291,10 @@ 
Computation of the score map and generating the brain image
 

-
best_score = np.max(score_map)
-best_voxel_ix = np.argmax(score_map)
+
best_score = np.max(score_map)
+best_voxel_ix = np.argmax(score_map)
 
-print('Best voxel {} with score {:.4f}'.format(best_voxel_ix,best_score))
+print('Best voxel {} with score {:.4f}'.format(best_voxel_ix,best_score))
 

 

 

@@ -1284,21 +1307,21 @@ 
Computation of the score map and generating the brain image
 

-
import pandas as pd
+
import pandas as pd
 
-# Number of top/bottom voxels to select
-k = 10
+# Number of top/bottom voxels to select
+k = 10
 
-# Pick the top voxel indexes and their scores
-top_voxels = np.argsort(score_map)[:-(k+1):-1]
-top_scores = score_map[top_voxels]
+# Pick the top voxel indexes and their scores
+top_voxels = np.argsort(score_map)[:-(k+1):-1]
+top_scores = score_map[top_voxels]
 
-# Organize the data into a dataframe for better visualization
-df = pd.DataFrame()
-df['voxel'] = top_voxels.astype(int)
-df['score'] = top_scores.round(2)
-df.index += 1
-df
+# Organize the data into a dataframe for better visualization
+df = pd.DataFrame()
+df['voxel'] = top_voxels.astype(int)
+df['score'] = top_scores.round(2)
+df.index += 1
+df
 

 

 

@@ -1385,11 +1408,11 @@ 
Histogram of the \(R^2\) s
 
We notice that only a small minority of voxels have higher \(R^2\) score and these are the voxels that are more predictive for the input features.

 

 

-
plt.figure(figsize=(6,6))
-plt.xlabel(r'$R^2$ value')
-plt.ylabel('#voxels')
-plt.title(r'Histogram of $R^2$ values across voxels')
-plt.hist(score_map);
+
plt.figure(figsize=(6,6))
+plt.xlabel(r'$R^2$ value')
+plt.ylabel('#voxels')
+plt.title(r'Histogram of $R^2$ values across voxels')
+plt.hist(score_map);
 

 

 

@@ -1404,11 +1427,11 @@ 
Plotting the score map brain image\(R^2\) scores in an interactive plot.

 

 

-
from nilearn import image
-from nilearn import plotting
+
from nilearn import image
+from nilearn import plotting
 
-plot = plotting.view_img(thresholded_score_map_img, draw_cross=False, symmetric_cmap=False, cmap=plotting.cm.black_red)
-plot
+plot = plotting.view_img(thresholded_score_map_img, draw_cross=False, symmetric_cmap=False, cmap=plotting.cm.black_red)
+plot
 

 

 

@@ -1458,34 +1481,34 @@ 
Plotting the score map brain image
 

-
from nilearn.plotting import plot_stat_map
-from nilearn.image import coord_transform
+
from nilearn.plotting import plot_stat_map
+from nilearn.image import coord_transform
 
-def index_to_xy_coord(x, y, z=10):
-    '''Transforms data index to coordinates of the background + offset'''
-    coords = coord_transform(x, y, z,
-                             affine=thresholded_score_map_img.affine)
-    return np.array(coords)[np.newaxis, :] + np.array([0, 1, 0])
+def index_to_xy_coord(x, y, z=10):
+    '''Transforms data index to coordinates of the background + offset'''
+    coords = coord_transform(x, y, z,
+                             affine=thresholded_score_map_img.affine)
+    return np.array(coords)[np.newaxis, :] + np.array([0, 1, 0])
 
 
-xy_indices_of_special_voxels = [(30, 10), (32, 10), (31, 9), (31, 10)]
+xy_indices_of_special_voxels = [(30, 10), (32, 10), (31, 9), (31, 10)]
 
-display = plot_stat_map(thresholded_score_map_img, bg_img=dataset.background,
-                        cut_coords=[-8], display_mode='z')
+display = plot_stat_map(thresholded_score_map_img, bg_img=dataset.background,
+                        cut_coords=[-8], display_mode='z')
 
-# creating a marker for each voxel and adding it to the statistical map
+# creating a marker for each voxel and adding it to the statistical map
 
-for i, (x, y) in enumerate(xy_indices_of_special_voxels):
-    display.add_markers(index_to_xy_coord(x, y), marker_color='none',
-                        edgecolor=['b', 'r', 'magenta', 'g'][i],
-                        marker_size=140, marker='s',
-                        facecolor='none', lw=4.5)
+for i, (x, y) in enumerate(xy_indices_of_special_voxels):
+    display.add_markers(index_to_xy_coord(x, y), marker_color='none',
+                        edgecolor=['b', 'r', 'magenta', 'g'][i],
+                        marker_size=140, marker='s',
+                        facecolor='none', lw=4.5)
 
 
-# Re-set figure size after construction so colorbar gets rescaled too
-fig = plt.gcf()
-fig.set_size_inches(8, 8)
-#fig.suptitle('Receptive fields of the marked voxels', fontsize=25)
+# Re-set figure size after construction so colorbar gets rescaled too
+fig = plt.gcf()
+fig.set_size_inches(8, 8)
+#fig.suptitle('Receptive fields of the marked voxels', fontsize=25)
 

 

 

@@ -1511,24 +1534,24 @@ 
Receptive fields from the Ridge model
 

-
import matplotlib.pyplot as plt
+
import matplotlib.pyplot as plt
 
 
-voxels = [1952,1424,1780,0]
+voxels = [1952,1424,1780,0]
 
-num_images = len(voxels)
+num_images = len(voxels)
 
-plt.figure(figsize=(6*num_images, 6))
+plt.figure(figsize=(6*num_images, 6))
 
-for j in range(num_images):
-    plt.subplot(1, num_images, j+1)
-    plt.imshow(model.coef_[voxels[j]].reshape(10,10))
-    plt.axis('off')
-    plt.title('Voxel {}'.format(voxels[j]))
-    plt.colorbar()
+for j in range(num_images):
+    plt.subplot(1, num_images, j+1)
+    plt.imshow(model.coef_[voxels[j]].reshape(10,10))
+    plt.axis('off')
+    plt.title('Voxel {}'.format(voxels[j]))
+    plt.colorbar()
 
-plt.subplots_adjust(wspace=0.25)
-plt.suptitle('Receptive fields of some random voxels predicted by the Ridge model');
+plt.subplots_adjust(wspace=0.25)
+plt.suptitle('Receptive fields of some random voxels predicted by the Ridge model');
 

 

 

@@ -1541,24 +1564,24 @@ 
Receptive fields from the Ridge model
 

-
import matplotlib.pyplot as plt
+
import matplotlib.pyplot as plt
 
 
-voxels = [1780, 1951, 2131, 1935] 
+voxels = [1780, 1951, 2131, 1935] 
 
-num_images = len(voxels)
+num_images = len(voxels)
 
-plt.figure(figsize=(6*num_images, 6))
+plt.figure(figsize=(6*num_images, 6))
 
-for j in range(num_images):
-    plt.subplot(1, num_images, j+1)
-    plt.imshow(model.coef_[voxels[j]].reshape(10,10))
-    plt.axis('off')
-    plt.title('Voxel {}'.format(voxels[j]))
-    plt.colorbar()
+for j in range(num_images):
+    plt.subplot(1, num_images, j+1)
+    plt.imshow(model.coef_[voxels[j]].reshape(10,10))
+    plt.axis('off')
+    plt.title('Voxel {}'.format(voxels[j]))
+    plt.colorbar()
 
-plt.subplots_adjust(wspace=0.25)
-plt.suptitle('Receptive fields of the 4 marked voxels predicted by the Ridge model');
+plt.subplots_adjust(wspace=0.25)
+plt.suptitle('Receptive fields of the 4 marked voxels predicted by the Ridge model');
 

 

 

@@ -1577,66 +1600,66 @@ 
Receptive fields from Lasso modelLassoLarsCV model from scikit-learn for this and plot the receptive fields of the 4 selected voxels. This procedure is very similar to the Ridge fit, but it is done automatically.

 

 

-
from sklearn.linear_model import LassoLarsCV
+
from sklearn.linear_model import LassoLarsCV
 
-# Automatically estimate the sparsity by cross-validation
-lasso = LassoLarsCV(max_iter=10)
+# Automatically estimate the sparsity by cross-validation
+lasso = LassoLarsCV(max_iter=10)
 
-# Index values of the voxels marked above
-voxels = [1780, 1951, 2131, 1935]
+# Index values of the voxels marked above
+voxels = [1780, 1951, 2131, 1935]
 
-# Mark the same pixel in each receptive field
-marked_pixel = (4, 2)
+# Mark the same pixel in each receptive field
+marked_pixel = (4, 2)
 
-from matplotlib import gridspec
-from matplotlib.patches import Rectangle
+from matplotlib import gridspec
+from matplotlib.patches import Rectangle
 
-fig = plt.figure(figsize=(12, 8))
-fig.suptitle('Receptive fields of the marked voxels', fontsize=25)
+fig = plt.figure(figsize=(12, 8))
+fig.suptitle('Receptive fields of the marked voxels', fontsize=25)
 
-# GridSpec allows us to do subplots with more control of the spacing
-gs = gridspec.GridSpec(2, 3)
+# GridSpec allows us to do subplots with more control of the spacing
+gs = gridspec.GridSpec(2, 3)
 
-# Fit the Lasso for each of the three voxels of the upper row
-for i, index in enumerate(voxels[:3]):
+# Fit the Lasso for each of the three voxels of the upper row
+for i, index in enumerate(voxels[:3]):
 
-    ax = plt.subplot(gs[0, i])
+    ax = plt.subplot(gs[0, i])
 
-    # Compute receptive field by fitting a lasso model for the encoding 
-    # Reshape the coefficients into the form of the original images
+    # Compute receptive field by fitting a lasso model for the encoding 
+    # Reshape the coefficients into the form of the original images
 
-    lasso_fit = lasso.fit(stimuli_data, fmri_data[:, index])
-    rf = lasso_fit.coef_.reshape((10, 10))
+    lasso_fit = lasso.fit(stimuli_data, fmri_data[:, index])
+    rf = lasso_fit.coef_.reshape((10, 10))
 
-    # add a black background
-    ax.imshow(np.zeros_like(rf), vmin=0., vmax=1., cmap='gray')
-    ax_im = ax.imshow(np.ma.masked_less(rf, 0.1), interpolation="nearest",
-                      cmap=['Blues', 'Greens', 'Reds'][i], vmin=0., vmax=0.75)
-    # add the marked pixel
-    ax.add_patch(Rectangle(
-        (marked_pixel[1] - .5, marked_pixel[0] - .5), 1, 1,
-        facecolor='none', edgecolor='r', lw=4))
-    plt.axis('off')
-    plt.colorbar(ax_im, ax=ax)
+    # add a black background
+    ax.imshow(np.zeros_like(rf), vmin=0., vmax=1., cmap='gray')
+    ax_im = ax.imshow(np.ma.masked_less(rf, 0.1), interpolation="nearest",
+                      cmap=['Blues', 'Greens', 'Reds'][i], vmin=0., vmax=0.75)
+    # add the marked pixel
+    ax.add_patch(Rectangle(
+        (marked_pixel[1] - .5, marked_pixel[0] - .5), 1, 1,
+        facecolor='none', edgecolor='r', lw=4))
+    plt.axis('off')
+    plt.colorbar(ax_im, ax=ax)
 
-# and then for the voxel at the bottom
+# and then for the voxel at the bottom
 
-gs.update(left=0., right=1., wspace=0.1)
-ax = plt.subplot(gs[1, 1])
-# Reshape the coefficients into the form of the original images
-lasso_fit = lasso.fit(stimuli_data, fmri_data[:, voxels[3]])
-rf = lasso_fit.coef_.reshape((10, 10))
+gs.update(left=0., right=1., wspace=0.1)
+ax = plt.subplot(gs[1, 1])
+# Reshape the coefficients into the form of the original images
+lasso_fit = lasso.fit(stimuli_data, fmri_data[:, voxels[3]])
+rf = lasso_fit.coef_.reshape((10, 10))
 
-ax.imshow(np.zeros_like(rf), vmin=0., vmax=1., cmap='gray')
-ax_im = ax.imshow(np.ma.masked_less(rf, 0.1), interpolation="nearest",
-                  cmap='RdPu', vmin=0., vmax=0.75)
+ax.imshow(np.zeros_like(rf), vmin=0., vmax=1., cmap='gray')
+ax_im = ax.imshow(np.ma.masked_less(rf, 0.1), interpolation="nearest",
+                  cmap='RdPu', vmin=0., vmax=0.75)
 
-# add the marked pixel
-ax.add_patch(Rectangle(
-    (marked_pixel[1] - .5, marked_pixel[0] - .5), 1, 1,
-    facecolor='none', edgecolor='r', lw=4))
-plt.axis('off')
-plt.colorbar(ax_im, ax=ax);
+# add the marked pixel
+ax.add_patch(Rectangle(
+    (marked_pixel[1] - .5, marked_pixel[0] - .5), 1, 1,
+    facecolor='none', edgecolor='r', lw=4))
+plt.axis('off')
+plt.colorbar(ax_im, ax=ax);
 

 

 

@@ -1661,9 +1684,9 @@ 
Data Preparation
 

-
# Training data is the first 12 files
+
# Training data is the first 12 files
 
-fmri_figure_runs_filenames, stimuli_figure_runs_filenames = dataset.func[:12], dataset.label[:12]
+fmri_figure_runs_filenames, stimuli_figure_runs_filenames = dataset.func[:12], dataset.label[:12]
 

 

 

@@ -1671,7 +1694,7 @@ 
Data Preparation
 

-
fmri_figure_data = masker.transform(fmri_figure_runs_filenames)
+
fmri_figure_data = masker.transform(fmri_figure_runs_filenames)
 

 

 

@@ -1679,41 +1702,41 @@ 
Data Preparation
 

-
stimulus_shape = (10, 10)
+
stimulus_shape = (10, 10)
 
-# We load the visual stimuli from csv files
-stimuli_figure_data = []
-for stimulus_run in stimuli_figure_runs_filenames:
-    stimuli_figure_data.append(np.reshape(np.loadtxt(stimulus_run,
-                              dtype=np.int, delimiter=','),
-                              (-1,) + stimulus_shape, order='F'))
+# We load the visual stimuli from csv files
+stimuli_figure_data = []
+for stimulus_run in stimuli_figure_runs_filenames:
+    stimuli_figure_data.append(np.reshape(np.loadtxt(stimulus_run,
+                              dtype=np.int, delimiter=','),
+                              (-1,) + stimulus_shape, order='F'))
 

 

 

 

 

 

-
import pylab as plt
+
import pylab as plt
 
-runs = [1,3,8]
-trials = [27,82,118]
+runs = [1,3,8]
+trials = [27,82,118]
 
-num_runs = len(runs)
-num_images = len(trials)
+num_runs = len(runs)
+num_images = len(trials)
 
-subplot_index = 1
+subplot_index = 1
 
-plt.figure(figsize=(4*num_images, 4*num_runs))
+plt.figure(figsize=(4*num_images, 4*num_runs))
 
-for i in range(num_runs):
-  for j in range(num_images):
-    plt.subplot(num_runs, num_images, subplot_index)
-    plt.imshow(stimuli_figure_data[runs[i]][trials[j]], interpolation='nearest', cmap='gray')
-    plt.axis('off')
-    plt.title('Run {}, Stimulus Trial {}'.format(runs[i],trials[j]))
-    subplot_index += 1
+for i in range(num_runs):
+  for j in range(num_images):
+    plt.subplot(num_runs, num_images, subplot_index)
+    plt.imshow(stimuli_figure_data[runs[i]][trials[j]], interpolation='nearest', cmap='gray')
+    plt.axis('off')
+    plt.title('Run {}, Stimulus Trial {}'.format(runs[i],trials[j]))
+    subplot_index += 1
 
-plt.subplots_adjust(wspace=0.5)
+plt.subplots_adjust(wspace=0.5)
 

 

 

@@ -1724,11 +1747,11 @@ 
Data Preparation
 

-
# Delay chosen based on the TR duration to expect a hemodynamic response
-delay_trials = 2
+
# Delay chosen based on the TR duration to expect a hemodynamic response
+delay_trials = 2
 
-fmri_figure_data = np.vstack([fmri_run[delay_trials:] for fmri_run in fmri_figure_data])
-stimuli_figure_data = np.vstack([stimuli_run[:-delay_trials] for stimuli_run in stimuli_figure_data]).astype(float)
+fmri_figure_data = np.vstack([fmri_run[delay_trials:] for fmri_run in fmri_figure_data])
+stimuli_figure_data = np.vstack([stimuli_run[:-delay_trials] for stimuli_run in stimuli_figure_data]).astype(float)
 

 

 

@@ -1736,12 +1759,12 @@ 
Data Preparation
 

-
# Flatten the stimuli
+
# Flatten the stimuli
 
-stimuli_figure_data = np.reshape(stimuli_figure_data, (-1, stimulus_shape[0] * stimulus_shape[1]))
+stimuli_figure_data = np.reshape(stimuli_figure_data, (-1, stimulus_shape[0] * stimulus_shape[1]))
 
-print('fmri_figure_data.shape =',fmri_figure_data.shape) 
-print('stimuli_figure_data.shape =',stimuli_figure_data.shape)
+print('fmri_figure_data.shape =',fmri_figure_data.shape) 
+print('stimuli_figure_data.shape =',stimuli_figure_data.shape)
 

 

 

@@ -1759,12 +1782,12 @@ 
Scoring the test set using the encoding model
 

-
from sklearn.metrics import r2_score
+
from sklearn.metrics import r2_score
 
-actual = fmri_figure_data
-predicted = model.predict(stimuli_figure_data)
+actual = fmri_figure_data
+predicted = model.predict(stimuli_figure_data)
 
-test_score_map = r2_score(actual, predicted, multioutput='raw_values')
+test_score_map = r2_score(actual, predicted, multioutput='raw_values')
 

 

 

@@ -1772,26 +1795,26 @@ 
Scoring the test set using the encoding model
 

-
from nilearn.image import threshold_img
+
from nilearn.image import threshold_img
 
-# Ignore the negative score values 
-test_score_map[test_score_map < 0] = 0
+# Ignore the negative score values 
+test_score_map[test_score_map < 0] = 0
 
-# Bring the scores into the shape of the background brain
-test_score_map_img = masker.inverse_transform(test_score_map)
+# Bring the scores into the shape of the background brain
+test_score_map_img = masker.inverse_transform(test_score_map)
 
-thresholded_test_score_map_img = threshold_img(test_score_map_img, threshold=1e-6, copy=False)
+thresholded_test_score_map_img = threshold_img(test_score_map_img, threshold=1e-6, copy=False)
 

 

 

 

 

 

-
from nilearn import image
-from nilearn import plotting
+
from nilearn import image
+from nilearn import plotting
 
-plot = plotting.view_img(thresholded_test_score_map_img, draw_cross=False, symmetric_cmap=False, cmap=plotting.cm.black_red)
-plot
+plot = plotting.view_img(thresholded_test_score_map_img, draw_cross=False, symmetric_cmap=False, cmap=plotting.cm.black_red)
+plot
 

 

 

@@ -1839,17 +1862,17 @@ 
Scoring the test set using the encoding model
 

-
from nilearn.plotting import plot_stat_map
-from nilearn.image import coord_transform
+
from nilearn.plotting import plot_stat_map
+from nilearn.image import coord_transform
 
-display = plot_stat_map(thresholded_test_score_map_img, bg_img=dataset.background,
-                        cut_coords=[-8], display_mode='z')
+display = plot_stat_map(thresholded_test_score_map_img, bg_img=dataset.background,
+                        cut_coords=[-8], display_mode='z')
 
-# creating a marker for each voxel and adding it to the statistical map
+# creating a marker for each voxel and adding it to the statistical map
 
-# Re-set figure size after construction so colorbar gets rescaled too
-fig = plt.gcf()
-fig.set_size_inches(8, 8)
+# Re-set figure size after construction so colorbar gets rescaled too
+fig = plt.gcf()
+fig.set_size_inches(8, 8)
 

 

 

diff --git a/gcn_decoding.html b/gcn_decoding.html
index 02ad528..2f2c5b1 100644
--- a/gcn_decoding.html
+++ b/gcn_decoding.html
@@ -30,7 +30,7 @@
     
     
     
-    
+    
     
   
 
@@ -378,43 +378,47 @@ 
Getting the data[HGF+01]. You can check An overview of the Haxby dataset section for more details on that dataset. Here we are going to quickly download it, and prepare it for machine learning applications with a set of predictive variable, the brain time series, and a dependent variable, the annotation on cognition.

 

 

-
import os
-import warnings
-warnings.filterwarnings(action='once')
-from nilearn.input_data import NiftiMasker
+
import os
+import warnings
+warnings.filterwarnings(action='once')
+from nilearn.input_data import NiftiMasker
 
-from nilearn import datasets
+from nilearn import datasets
 
-# We are fetching the data for subject 4
-data_dir = os.path.join('..', 'data')
-sub_no = 4
-haxby_dataset = datasets.fetch_haxby(subjects=[sub_no], fetch_stimuli=True, data_dir=data_dir)
-func_file = haxby_dataset.func[0]
+# We are fetching the data for subject 4
+data_dir = os.path.join('..', 'data')
+sub_no = 4
+haxby_dataset = datasets.fetch_haxby(subjects=[sub_no], fetch_stimuli=True, data_dir=data_dir)
+func_file = haxby_dataset.func[0]
 
-# Standardizing
-mask_vt_file = haxby_dataset.mask_vt[0]
-masker = NiftiMasker(mask_img=mask_vt_file, standardize=True)
+# Standardizing
+mask_vt_file = haxby_dataset.mask_vt[0]
+masker = NiftiMasker(mask_img=mask_vt_file, standardize=True)
 
-# cognitive annotations
-import pandas as pd
-behavioral = pd.read_csv(haxby_dataset.session_target[0], delimiter=' ')
-X = masker.fit_transform(func_file)
-y = behavioral['labels']
+# cognitive annotations
+import pandas as pd
+behavioral = pd.read_csv(haxby_dataset.session_target[0], delimiter=' ')
+X = masker.fit_transform(func_file)
+y = behavioral['labels']
 

 

 

 

-
/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/datasets/func.py:20: DeprecationWarning: Please use `MatReadError` from the `scipy.io.matlab` namespace, the `scipy.io.matlab.miobase` namespace is deprecated.
+
/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/datasets/func.py:20: DeprecationWarning: Please use `MatReadError` from the `scipy.io.matlab` namespace, the `scipy.io.matlab.miobase` namespace is deprecated.
   from scipy.io.matlab.miobase import MatReadError
-/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/datasets/__init__.py:93: FutureWarning: Fetchers from the nilearn.datasets module will be updated in version 0.9 to return python strings instead of bytes and Pandas dataframes instead of Numpy arrays.
+/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/datasets/__init__.py:93: FutureWarning: Fetchers from the nilearn.datasets module will be updated in version 0.9 to return python strings instead of bytes and Pandas dataframes instead of Numpy arrays.
   warn("Fetchers from the nilearn.datasets module will be "
- ...done. (0 seconds, 0 min)
 

 

 
Dataset created in ../data/haxby2001
 
 Downloading data from https://www.nitrc.org/frs/download.php/7868/mask.nii.gz ...
-Downloading data from http://data.pymvpa.org/datasets/haxby2001/MD5SUMS ...
+

+

+
 ...done. (0 seconds, 0 min)
+

+

+
Downloading data from http://data.pymvpa.org/datasets/haxby2001/MD5SUMS ...
 

 

 
 ...done. (0 seconds, 0 min)
@@ -423,13 +427,25 @@ 
Getting the data
Downloading data from http://data.pymvpa.org/datasets/haxby2001/subj4-2010.01.14.tar.gz ...
 

 

-
Downloaded 120250368 of 329954386 bytes (36.4%,    1.7s remaining)
+
Downloaded 38535168 of 329954386 bytes (11.7%,    7.6s remaining)
+

+

+
Downloaded 92831744 of 329954386 bytes (28.1%,    5.2s remaining)
 

 

-
Downloaded 257130496 of 329954386 bytes (77.9%,    0.6s remaining)
+
Downloaded 136232960 of 329954386 bytes (41.3%,    4.3s remaining)
 

 

-
 ...done. (3 seconds, 0 min)
+
Downloaded 179044352 of 329954386 bytes (54.3%,    3.4s remaining)
+

+

+
Downloaded 226803712 of 329954386 bytes (68.7%,    2.3s remaining)
+

+

+
Downloaded 283230208 of 329954386 bytes (85.8%,    1.0s remaining)
+

+

+
 ...done. (7 seconds, 0 min)
 

 

 
Extracting data from ../data/haxby2001/622d4f5d4b8f14a567901606c924e90d/subj4-2010.01.14.tar.gz...
@@ -441,7 +457,7 @@ 
Getting the data
Downloading data from http://data.pymvpa.org/datasets/haxby2001/stimuli-2010.01.14.tar.gz ...
 

 

-
 ...done. (0 seconds, 0 min)
+
 ...done. (1 seconds, 0 min)
 Extracting data from ../data/haxby2001/5cd78c74b711572c7f41a5bddb69abca/stimuli-2010.01.14.tar.gz..... done.
 

 

@@ -450,10 +466,10 @@ 
Getting the data
 

-
categories = y.unique()
-print(categories)
-print('y:', y.shape)
-print('X:', X.shape)
+
categories = y.unique()
+print(categories)
+print('y:', y.shape)
+print('X:', X.shape)
 

 

 

@@ -482,24 +498,24 @@ 
Create brain graph for GCN
 

-
import warnings
-warnings.filterwarnings(action='once')
+
import warnings
+warnings.filterwarnings(action='once')
 
-import nilearn.connectome
+import nilearn.connectome
 
-# Estimating connectomes and save for pytorch to load
-corr_measure = nilearn.connectome.ConnectivityMeasure(kind="correlation")
-conn = corr_measure.fit_transform([X])[0]
+# Estimating connectomes and save for pytorch to load
+corr_measure = nilearn.connectome.ConnectivityMeasure(kind="correlation")
+conn = corr_measure.fit_transform([X])[0]
 
-n_regions_extracted = X.shape[-1]
-title = 'Correlation between %d regions' % n_regions_extracted
+n_regions_extracted = X.shape[-1]
+title = 'Correlation between %d regions' % n_regions_extracted
 
-print('Correlation matrix shape:',conn.shape)
+print('Correlation matrix shape:',conn.shape)
 
-# First plot the matrix
-from nilearn import plotting
-display = plotting.plot_matrix(conn, vmax=1, vmin=-1,
-                               colorbar=True, title=title)
+# First plot the matrix
+from nilearn import plotting
+display = plotting.plot_matrix(conn, vmax=1, vmin=-1,
+                               colorbar=True, title=title)
 

 

 

@@ -518,12 +534,12 @@ 
Create brain graph for GCN
 

-
import sys
-sys.path.append('../src')
-from graph_construction import make_group_graph
+
import sys
+sys.path.append('../src')
+from graph_construction import make_group_graph
 
-# make a graph for the subject
-graph = make_group_graph([conn], self_loops=False, k=8, symmetric=True)
+# make a graph for the subject
+graph = make_group_graph([conn], self_loops=False, k=8, symmetric=True)
 

 

 

@@ -535,16 +551,16 @@ 
Preparing the dataset for model training
 

-
# generate data
-import pandas as pd
-import numpy as np
+
# generate data
+import pandas as pd
+import numpy as np
 
-# cancatenate the same type of trials
-concat_bold = {}
-for label in categories:
-    cur_label_index = y.index[y == label].tolist()
-    curr_bold_seg = X[cur_label_index]    
-    concat_bold[label] = curr_bold_seg
+# cancatenate the same type of trials
+concat_bold = {}
+for label in categories:
+    cur_label_index = y.index[y == label].tolist()
+    curr_bold_seg = X[cur_label_index]    
+    concat_bold[label] = curr_bold_seg
 

 

 

@@ -557,39 +573,39 @@ 
Preparing the dataset for model traininglabel.csv.

 

 

-
# split the data by time window size and save to file
-window_length = 1
-dic_labels = {name: i for i, name in enumerate(categories)}
+
# split the data by time window size and save to file
+window_length = 1
+dic_labels = {name: i for i, name in enumerate(categories)}
 
-# set output paths
-split_path = os.path.join(data_dir, 'haxby_split_win/')
-if not os.path.exists(split_path):
-    os.makedirs(split_path)
-out_file = os.path.join(split_path, '{}_{:04d}.npy')
-out_csv = os.path.join(split_path, 'labels.csv')
+# set output paths
+split_path = os.path.join(data_dir, 'haxby_split_win/')
+if not os.path.exists(split_path):
+    os.makedirs(split_path)
+out_file = os.path.join(split_path, '{}_{:04d}.npy')
+out_csv = os.path.join(split_path, 'labels.csv')
 
-label_df = pd.DataFrame(columns=['label', 'filename'])
-for label, ts_data in concat_bold.items():
-    ts_duration = len(ts_data)
-    ts_filename = f"{label}_seg"
-    valid_label = dic_labels[label]
+label_df = pd.DataFrame(columns=['label', 'filename'])
+for label, ts_data in concat_bold.items():
+    ts_duration = len(ts_data)
+    ts_filename = f"{label}_seg"
+    valid_label = dic_labels[label]
 
-    # Split the timeseries
-    rem = ts_duration % window_length
-    n_splits = int(np.floor(ts_duration / window_length))
+    # Split the timeseries
+    rem = ts_duration % window_length
+    n_splits = int(np.floor(ts_duration / window_length))
 
-    ts_data = ts_data[:(ts_duration - rem), :]   
+    ts_data = ts_data[:(ts_duration - rem), :]   
 
-    for j, split_ts in enumerate(np.split(ts_data, n_splits)):
-        ts_output_file_name = out_file.format(ts_filename, j)
+    for j, split_ts in enumerate(np.split(ts_data, n_splits)):
+        ts_output_file_name = out_file.format(ts_filename, j)
 
-        split_ts = np.swapaxes(split_ts, 0, 1)
-        np.save(ts_output_file_name, split_ts)
+        split_ts = np.swapaxes(split_ts, 0, 1)
+        np.save(ts_output_file_name, split_ts)
 
-        curr_label = {'label': valid_label, 'filename': os.path.basename(ts_output_file_name)}
-        label_df = label_df.append(curr_label, ignore_index=True)
+        curr_label = {'label': valid_label, 'filename': os.path.basename(ts_output_file_name)}
+        label_df = label_df.append(curr_label, ignore_index=True)
         
-label_df.to_csv(out_csv, index=False)  
+label_df.to_csv(out_csv, index=False)  
 

 

 

@@ -601,38 +617,38 @@ 
Preparing the dataset for model trainingpytorch documentation.

 

 

-
# split dataset
-from gcn_windows_dataset import TimeWindowsDataset
+
# split dataset
+from gcn_windows_dataset import TimeWindowsDataset
 
-random_seed = 0
+random_seed = 0
 
-train_dataset = TimeWindowsDataset(
-    data_dir=split_path, 
-    partition="train", 
-    random_seed=random_seed, 
-    pin_memory=True, 
-    normalize=True,
-    shuffle=True)
+train_dataset = TimeWindowsDataset(
+    data_dir=split_path, 
+    partition="train", 
+    random_seed=random_seed, 
+    pin_memory=True, 
+    normalize=True,
+    shuffle=True)
 
-valid_dataset = TimeWindowsDataset(
-    data_dir=split_path, 
-    partition="valid", 
-    random_seed=random_seed, 
-    pin_memory=True, 
-    normalize=True,
-    shuffle=True)
+valid_dataset = TimeWindowsDataset(
+    data_dir=split_path, 
+    partition="valid", 
+    random_seed=random_seed, 
+    pin_memory=True, 
+    normalize=True,
+    shuffle=True)
 
-test_dataset = TimeWindowsDataset(
-    data_dir=split_path, 
-    partition="test", 
-    random_seed=random_seed, 
-    pin_memory=True, 
-    normalize=True,
-    shuffle=True)
+test_dataset = TimeWindowsDataset(
+    data_dir=split_path, 
+    partition="test", 
+    random_seed=random_seed, 
+    pin_memory=True, 
+    normalize=True,
+    shuffle=True)
 
-print("train dataset: {}".format(train_dataset))
-print("valid dataset: {}".format(valid_dataset))
-print("test dataset: {}".format(test_dataset))
+print("train dataset: {}".format(train_dataset))
+print("valid dataset: {}".format(valid_dataset))
+print("test dataset: {}".format(test_dataset))
 

 

 

@@ -649,18 +665,18 @@ 
Preparing the dataset for model training
 

-
import torch
-from torch.utils.data import DataLoader
+
import torch
+from torch.utils.data import DataLoader
 
-batch_size = 10
+batch_size = 10
 
-torch.manual_seed(random_seed)
-train_generator = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
-valid_generator = DataLoader(valid_dataset, batch_size=batch_size, shuffle=True)
-test_generator = DataLoader(test_dataset, batch_size=batch_size, shuffle=True)
-train_features, train_labels = next(iter(train_generator))
-print(f"Feature batch shape: {train_features.size()}; mean {torch.mean(train_features)}")
-print(f"Labels batch shape: {train_labels.size()}; mean {torch.mean(torch.Tensor.float(train_labels))}")
+torch.manual_seed(random_seed)
+train_generator = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
+valid_generator = DataLoader(valid_dataset, batch_size=batch_size, shuffle=True)
+test_generator = DataLoader(test_dataset, batch_size=batch_size, shuffle=True)
+train_features, train_labels = next(iter(train_generator))
+print(f"Feature batch shape: {train_features.size()}; mean {torch.mean(train_features)}")
+print(f"Labels batch shape: {train_labels.size()}; mean {torch.mean(torch.Tensor.float(train_labels))}")
 

 

 

@@ -683,15 +699,15 @@ 
Generating a GCN model
 

 

-
from gcn_model import GCN
+
from gcn_model import GCN
 
-gcn = GCN(graph.edge_index, 
-          graph.edge_attr, 
-          n_roi=X.shape[1],
-          batch_size=batch_size,
-          n_timepoints=window_length, 
-          n_classes=len(categories))
-gcn
+gcn = GCN(graph.edge_index, 
+          graph.edge_attr, 
+          n_roi=X.shape[1],
+          batch_size=batch_size,
+          n_timepoints=window_length, 
+          n_classes=len(categories))
+gcn
 

 

 

@@ -722,41 +738,41 @@ 
Train and evaluating the model
 

-
def train_loop(dataloader, model, loss_fn, optimizer):
-    size = len(dataloader.dataset)    
-
-    for batch, (X, y) in enumerate(dataloader):
-        # Compute prediction and loss
-        pred = model(X)
-        loss = loss_fn(pred, y)
-
-        # Backpropagation
-        optimizer.zero_grad()
-        loss.backward()
-        optimizer.step()
+
def train_loop(dataloader, model, loss_fn, optimizer):
+    size = len(dataloader.dataset)    
+
+    for batch, (X, y) in enumerate(dataloader):
+        # Compute prediction and loss
+        pred = model(X)
+        loss = loss_fn(pred, y)
+
+        # Backpropagation
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
         
-        loss, current = loss.item(), batch * dataloader.batch_size
+        loss, current = loss.item(), batch * dataloader.batch_size
 
-        correct = (pred.argmax(1) == y).type(torch.float).sum().item()
-        correct /= X.shape[0]
-        if (batch % 10 == 0) or (current == size):
-            print(f"#{batch:>5};\ttrain_loss: {loss:>0.3f};\ttrain_accuracy:{(100*correct):>5.1f}%\t\t[{current:>5d}/{size:>5d}]")
+        correct = (pred.argmax(1) == y).type(torch.float).sum().item()
+        correct /= X.shape[0]
+        if (batch % 10 == 0) or (current == size):
+            print(f"#{batch:>5};\ttrain_loss: {loss:>0.3f};\ttrain_accuracy:{(100*correct):>5.1f}%\t\t[{current:>5d}/{size:>5d}]")
 
         
-def valid_test_loop(dataloader, model, loss_fn):
-    size = len(dataloader.dataset)
-    loss, correct = 0, 0
+def valid_test_loop(dataloader, model, loss_fn):
+    size = len(dataloader.dataset)
+    loss, correct = 0, 0
 
-    with torch.no_grad():
-        for X, y in dataloader:
-            pred = model.forward(X)
-            loss += loss_fn(pred, y).item()
-            correct += (pred.argmax(1) == y).type(torch.float).sum().item()
+    with torch.no_grad():
+        for X, y in dataloader:
+            pred = model.forward(X)
+            loss += loss_fn(pred, y).item()
+            correct += (pred.argmax(1) == y).type(torch.float).sum().item()
 
-    loss /= size
-    correct /= size
+    loss /= size
+    correct /= size
 
-    return loss, correct
+    return loss, correct
 

 

 

@@ -766,15 +782,15 @@ 
Train and evaluating the modelCrossEntropyLoss and the optimizer to update the model is Adam.

 

 

-
loss_fn = torch.nn.CrossEntropyLoss()
-optimizer = torch.optim.Adam(gcn.parameters(), lr=1e-4, weight_decay=5e-4)
+
loss_fn = torch.nn.CrossEntropyLoss()
+optimizer = torch.optim.Adam(gcn.parameters(), lr=1e-4, weight_decay=5e-4)
 
-epochs = 25
-for t in range(epochs):
-    print(f"Epoch {t+1}/{epochs}\n-------------------------------")
-    train_loop(train_generator, gcn, loss_fn, optimizer)
-    loss, correct = valid_test_loop(valid_generator, gcn, loss_fn)
-    print(f"Valid metrics:\n\t avg_loss: {loss:>8f};\t avg_accuracy: {(100*correct):>0.1f}%")
+epochs = 25
+for t in range(epochs):
+    print(f"Epoch {t+1}/{epochs}\n-------------------------------")
+    train_loop(train_generator, gcn, loss_fn, optimizer)
+    loss, correct = valid_test_loop(valid_generator, gcn, loss_fn)
+    print(f"Valid metrics:\n\t avg_loss: {loss:>8f};\t avg_accuracy: {(100*correct):>0.1f}%")
 

 

 

@@ -836,7 +852,7 @@ 
Train and evaluating the model
#   50;	train_loss: 1.966;	train_accuracy: 20.0%		[  500/ 1016]
 

 

-
#   60;	train_loss: 1.559;	train_accuracy: 60.0%		[  600/ 1016]
+
#   60;	train_loss: 1.560;	train_accuracy: 60.0%		[  600/ 1016]
 

 

 
#   70;	train_loss: 1.871;	train_accuracy: 40.0%		[  700/ 1016]
@@ -852,7 +868,7 @@ 
Train and evaluating the model
Valid metrics:
-	 avg_loss: 0.155729;	 avg_accuracy: 48.6%
+	 avg_loss: 0.155730;	 avg_accuracy: 48.6%
 Epoch 3/25
 -------------------------------
 #    0;	train_loss: 1.833;	train_accuracy: 30.0%		[    0/ 1016]
@@ -926,7 +942,7 @@ 
Train and evaluating the model
Valid metrics:
-	 avg_loss: 0.123758;	 avg_accuracy: 61.0%
+	 avg_loss: 0.123757;	 avg_accuracy: 61.0%
 Epoch 5/25
 -------------------------------
 #    0;	train_loss: 1.023;	train_accuracy: 80.0%		[    0/ 1016]
@@ -963,7 +979,7 @@ 
Train and evaluating the model
Valid metrics:
-	 avg_loss: 0.106475;	 avg_accuracy: 65.5%
+	 avg_loss: 0.106478;	 avg_accuracy: 65.5%
 Epoch 6/25
 -------------------------------
 #    0;	train_loss: 0.945;	train_accuracy: 70.0%		[    0/ 1016]
@@ -1000,7 +1016,7 @@ 
Train and evaluating the model
Valid metrics:
-	 avg_loss: 0.100945;	 avg_accuracy: 68.3%
+	 avg_loss: 0.100944;	 avg_accuracy: 68.3%
 Epoch 7/25
 -------------------------------
 #    0;	train_loss: 0.399;	train_accuracy: 90.0%		[    0/ 1016]
@@ -1046,7 +1062,7 @@ 
Train and evaluating the model
#   10;	train_loss: 0.572;	train_accuracy: 80.0%		[  100/ 1016]
 

 

-
#   20;	train_loss: 0.549;	train_accuracy: 80.0%		[  200/ 1016]
+
#   20;	train_loss: 0.550;	train_accuracy: 80.0%		[  200/ 1016]
 

 

 
#   30;	train_loss: 0.561;	train_accuracy: 90.0%		[  300/ 1016]
@@ -1074,7 +1090,7 @@ 
Train and evaluating the model
Valid metrics:
-	 avg_loss: 0.082209;	 avg_accuracy: 71.7%
+	 avg_loss: 0.082211;	 avg_accuracy: 71.7%
 Epoch 9/25
 -------------------------------
 #    0;	train_loss: 0.449;	train_accuracy: 80.0%		[    0/ 1016]
@@ -1086,7 +1102,7 @@ 
Train and evaluating the model
#   20;	train_loss: 0.448;	train_accuracy: 90.0%		[  200/ 1016]
 

 

-
#   30;	train_loss: 0.685;	train_accuracy: 60.0%		[  300/ 1016]
+
#   30;	train_loss: 0.686;	train_accuracy: 60.0%		[  300/ 1016]
 

 

 
#   40;	train_loss: 0.447;	train_accuracy: 80.0%		[  400/ 1016]
@@ -1111,13 +1127,13 @@ 
Train and evaluating the model
Valid metrics:
-	 avg_loss: 0.083764;	 avg_accuracy: 70.7%
+	 avg_loss: 0.083768;	 avg_accuracy: 70.7%
 Epoch 10/25
 -------------------------------
 #    0;	train_loss: 0.336;	train_accuracy: 90.0%		[    0/ 1016]
 

 

-
#   10;	train_loss: 0.295;	train_accuracy: 90.0%		[  100/ 1016]
+
#   10;	train_loss: 0.296;	train_accuracy: 90.0%		[  100/ 1016]
 

 

 
#   20;	train_loss: 0.335;	train_accuracy: 90.0%		[  200/ 1016]
@@ -1148,7 +1164,7 @@ 
Train and evaluating the model
Valid metrics:
-	 avg_loss: 0.084388;	 avg_accuracy: 73.4%
+	 avg_loss: 0.084382;	 avg_accuracy: 73.4%
 Epoch 11/25
 -------------------------------
 #    0;	train_loss: 0.263;	train_accuracy: 90.0%		[    0/ 1016]
@@ -1185,7 +1201,7 @@ 
Train and evaluating the model
Valid metrics:
-	 avg_loss: 0.073709;	 avg_accuracy: 78.3%
+	 avg_loss: 0.073708;	 avg_accuracy: 78.3%
 Epoch 12/25
 -------------------------------
 #    0;	train_loss: 0.677;	train_accuracy: 80.0%		[    0/ 1016]
@@ -1194,7 +1210,7 @@ 
Train and evaluating the model
#   10;	train_loss: 0.326;	train_accuracy: 90.0%		[  100/ 1016]
 

 

-
#   20;	train_loss: 0.447;	train_accuracy: 80.0%		[  200/ 1016]
+
#   20;	train_loss: 0.446;	train_accuracy: 80.0%		[  200/ 1016]
 

 

 
#   30;	train_loss: 0.211;	train_accuracy: 90.0%		[  300/ 1016]
@@ -1222,7 +1238,7 @@ 
Train and evaluating the model
Valid metrics:
-	 avg_loss: 0.074502;	 avg_accuracy: 79.3%
+	 avg_loss: 0.074493;	 avg_accuracy: 79.3%
 Epoch 13/25
 -------------------------------
 #    0;	train_loss: 0.134;	train_accuracy:100.0%		[    0/ 1016]
@@ -1259,7 +1275,7 @@ 
Train and evaluating the model
Valid metrics:
-	 avg_loss: 0.074169;	 avg_accuracy: 79.3%
+	 avg_loss: 0.074170;	 avg_accuracy: 79.3%
 Epoch 14/25
 -------------------------------
 #    0;	train_loss: 0.193;	train_accuracy: 90.0%		[    0/ 1016]
@@ -1296,7 +1312,7 @@ 
Train and evaluating the model
Valid metrics:
-	 avg_loss: 0.087011;	 avg_accuracy: 76.6%
+	 avg_loss: 0.087010;	 avg_accuracy: 76.6%
 Epoch 15/25
 -------------------------------
 #    0;	train_loss: 0.092;	train_accuracy:100.0%		[    0/ 1016]
@@ -1333,7 +1349,7 @@ 
Train and evaluating the model
Valid metrics:
-	 avg_loss: 0.070469;	 avg_accuracy: 82.8%
+	 avg_loss: 0.070460;	 avg_accuracy: 82.8%
 Epoch 16/25
 -------------------------------
 #    0;	train_loss: 0.084;	train_accuracy:100.0%		[    0/ 1016]
@@ -1370,7 +1386,7 @@ 
Train and evaluating the model
Valid metrics:
-	 avg_loss: 0.080267;	 avg_accuracy: 79.7%
+	 avg_loss: 0.080264;	 avg_accuracy: 79.7%
 Epoch 17/25
 -------------------------------
 #    0;	train_loss: 0.017;	train_accuracy:100.0%		[    0/ 1016]
@@ -1407,7 +1423,7 @@ 
Train and evaluating the model
Valid metrics:
-	 avg_loss: 0.076710;	 avg_accuracy: 80.0%
+	 avg_loss: 0.076702;	 avg_accuracy: 80.0%
 Epoch 18/25
 -------------------------------
 #    0;	train_loss: 0.020;	train_accuracy:100.0%		[    0/ 1016]
@@ -1444,7 +1460,7 @@ 
Train and evaluating the model
Valid metrics:
-	 avg_loss: 0.083491;	 avg_accuracy: 82.4%
+	 avg_loss: 0.083478;	 avg_accuracy: 82.4%
 Epoch 19/25
 -------------------------------
 #    0;	train_loss: 0.039;	train_accuracy:100.0%		[    0/ 1016]
@@ -1456,7 +1472,7 @@ 
Train and evaluating the model
#   20;	train_loss: 0.006;	train_accuracy:100.0%		[  200/ 1016]
 

 

-
#   30;	train_loss: 0.247;	train_accuracy: 90.0%		[  300/ 1016]
+
#   30;	train_loss: 0.246;	train_accuracy: 90.0%		[  300/ 1016]
 

 

 
#   40;	train_loss: 0.137;	train_accuracy: 90.0%		[  400/ 1016]
@@ -1487,7 +1503,7 @@ 
Train and evaluating the model
#   10;	train_loss: 0.057;	train_accuracy:100.0%		[  100/ 1016]
+
#   10;	train_loss: 0.058;	train_accuracy:100.0%		[  100/ 1016]
 

 

 
#   20;	train_loss: 0.034;	train_accuracy:100.0%		[  200/ 1016]
@@ -1518,7 +1534,7 @@ 
Train and evaluating the model
Valid metrics:
-	 avg_loss: 0.096023;	 avg_accuracy: 78.6%
+	 avg_loss: 0.096033;	 avg_accuracy: 78.6%
 Epoch 21/25
 -------------------------------
 #    0;	train_loss: 0.383;	train_accuracy: 90.0%		[    0/ 1016]
@@ -1530,7 +1546,7 @@ 
Train and evaluating the model
#   20;	train_loss: 0.004;	train_accuracy:100.0%		[  200/ 1016]
 

 

-
#   30;	train_loss: 0.281;	train_accuracy: 90.0%		[  300/ 1016]
+
#   30;	train_loss: 0.282;	train_accuracy: 90.0%		[  300/ 1016]
 

 

 
#   40;	train_loss: 0.009;	train_accuracy:100.0%		[  400/ 1016]
@@ -1539,7 +1555,7 @@ 
Train and evaluating the model
#   50;	train_loss: 0.073;	train_accuracy:100.0%		[  500/ 1016]
 

 

-
#   60;	train_loss: 0.098;	train_accuracy: 90.0%		[  600/ 1016]
+
#   60;	train_loss: 0.097;	train_accuracy: 90.0%		[  600/ 1016]
 

 

 
#   70;	train_loss: 0.134;	train_accuracy:100.0%		[  700/ 1016]
@@ -1592,13 +1608,13 @@ 
Train and evaluating the model
Valid metrics:
-	 avg_loss: 0.093824;	 avg_accuracy: 79.0%
+	 avg_loss: 0.093829;	 avg_accuracy: 79.0%
 Epoch 23/25
 -------------------------------
 #    0;	train_loss: 0.110;	train_accuracy:100.0%		[    0/ 1016]
 

 

-
#   10;	train_loss: 0.100;	train_accuracy:100.0%		[  100/ 1016]
+
#   10;	train_loss: 0.101;	train_accuracy:100.0%		[  100/ 1016]
 

 

 
#   20;	train_loss: 0.002;	train_accuracy:100.0%		[  200/ 1016]
@@ -1625,11 +1641,11 @@ 
Train and evaluating the model
#   90;	train_loss: 0.008;	train_accuracy:100.0%		[  900/ 1016]
 

 

-
#  100;	train_loss: 0.034;	train_accuracy:100.0%		[ 1000/ 1016]
+
#  100;	train_loss: 0.035;	train_accuracy:100.0%		[ 1000/ 1016]
 

 

 
Valid metrics:
-	 avg_loss: 0.084973;	 avg_accuracy: 81.4%
+	 avg_loss: 0.084978;	 avg_accuracy: 81.4%
 Epoch 24/25
 -------------------------------
 #    0;	train_loss: 0.156;	train_accuracy: 90.0%		[    0/ 1016]
@@ -1666,7 +1682,7 @@ 
Train and evaluating the model
Valid metrics:
-	 avg_loss: 0.097458;	 avg_accuracy: 79.7%
+	 avg_loss: 0.097474;	 avg_accuracy: 79.7%
 Epoch 25/25
 -------------------------------
 #    0;	train_loss: 0.012;	train_accuracy:100.0%		[    0/ 1016]
@@ -1678,13 +1694,13 @@ 
Train and evaluating the model
#   20;	train_loss: 0.126;	train_accuracy:100.0%		[  200/ 1016]
 

 

-
#   30;	train_loss: 0.100;	train_accuracy: 90.0%		[  300/ 1016]
+
#   30;	train_loss: 0.100;	train_accuracy:100.0%		[  300/ 1016]
 

 

 
#   40;	train_loss: 0.037;	train_accuracy:100.0%		[  400/ 1016]
 

 

-
#   50;	train_loss: 0.033;	train_accuracy:100.0%		[  500/ 1016]
+
#   50;	train_loss: 0.032;	train_accuracy:100.0%		[  500/ 1016]
 

 

 
#   60;	train_loss: 0.012;	train_accuracy:100.0%		[  600/ 1016]
@@ -1703,7 +1719,7 @@ 
Train and evaluating the model
Valid metrics:
-	 avg_loss: 0.098673;	 avg_accuracy: 79.7%
+	 avg_loss: 0.098691;	 avg_accuracy: 79.7%
 

 

 

@@ -1711,15 +1727,15 @@ 
Train and evaluating the model
 

-
# results
-loss, correct = valid_test_loop(test_generator, gcn, loss_fn)
-print(f"Test metrics:\n\t avg_loss: {loss:>f};\t avg_accuracy: {(100*correct):>0.1f}%")
+
# results
+loss, correct = valid_test_loop(test_generator, gcn, loss_fn)
+print(f"Test metrics:\n\t avg_loss: {loss:>f};\t avg_accuracy: {(100*correct):>0.1f}%")
 

 

 

 

 
Test metrics:
-	 avg_loss: 0.103413;	 avg_accuracy: 77.4%
+	 avg_loss: 0.103379;	 avg_accuracy: 77.4%
 

 

 

diff --git a/genindex.html b/genindex.html
index aae4c5a..26e25ca 100644
--- a/genindex.html
+++ b/genindex.html
@@ -30,7 +30,7 @@
     
     
     
-    
+    
     
   
 
diff --git a/haxby_data.html b/haxby_data.html
index dc5ef1d..76e3521 100644
--- a/haxby_data.html
+++ b/haxby_data.html
@@ -30,7 +30,7 @@
     
     
     
-    
+    
     
   
 
@@ -409,13 +409,13 @@ 
Downloading & exploring the <
 
We are going to start with one subject, number 4. To get the data, we can simply use nilearn’s dataset module. At first, we need to import the respective module.

 

 

-
import os
-from nilearn import datasets
+
import os
+from nilearn import datasets
 

 

 

 

-
/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/datasets/__init__.py:93: FutureWarning: Fetchers from the nilearn.datasets module will be updated in version 0.9 to return python strings instead of bytes and Pandas dataframes instead of Numpy arrays.
+
/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/datasets/__init__.py:93: FutureWarning: Fetchers from the nilearn.datasets module will be updated in version 0.9 to return python strings instead of bytes and Pandas dataframes instead of Numpy arrays.
   warn("Fetchers from the nilearn.datasets module will be "
 

 

@@ -424,8 +424,8 @@ 
Downloading & exploring the <
 
Next, we get the data and going to save it in a directory called data. Depending on your machine and internet connection, this might take a minute or so.

 

 

-
data_dir = os.path.join('..', 'data')
-haxby_dataset = datasets.fetch_haxby(subjects=[4], fetch_stimuli=True, data_dir=data_dir)
+
data_dir = os.path.join('..', 'data')
+haxby_dataset = datasets.fetch_haxby(subjects=[4], fetch_stimuli=True, data_dir=data_dir)
 

 

 

@@ -433,7 +433,7 @@ 
Downloading & exploring the <
 
What do we have now? Lets have a look!

 

 

-
haxby_dataset
+
haxby_dataset
 

 

 

@@ -448,54 +448,54 @@ 
Downloading & exploring the <
  'mask_house_little': ['../data/haxby2001/subj4/mask8_house_vt.nii.gz'],
  'mask': '../data/haxby2001/mask.nii.gz',
  'description': b'Haxby 2001 results\n\n\nNotes\n-----\nResults from a classical fMRI study that investigated the differences between\nthe neural correlates of face versus object processing in the ventral visual\nstream. Face and object stimuli showed widely distributed and overlapping\nresponse patterns.\n\nContent\n-------\nThe "simple" dataset includes\n    :\'func\': Nifti images with bold data\n    :\'session_target\': Text file containing session data\n    :\'mask\': Nifti images with employed mask\n    :\'session\': Text file with condition labels\n\n\nThe full dataset additionally includes\n    :\'anat\': Nifti images with anatomical image\n    :\'func\': Nifti images with bold data\n    :\'mask_vt\': Nifti images with mask for ventral visual/temporal cortex\n    :\'mask_face\': Nifti images with face-reponsive brain regions\n    :\'mask_house\': Nifti images with house-reponsive brain regions\n    :\'mask_face_little\': Spatially more constrained version of the above\n    :\'mask_house_little\': Spatially more constrained version of the above\n\n\nReferences\n----------\nFor more information see:\nPyMVPA provides a tutorial using this dataset :\nhttp://www.pymvpa.org/tutorial.html\n\nMore information about its structure :\nhttp://dev.pymvpa.org/datadb/haxby2001.html\n\n\n`Haxby, J., Gobbini, M., Furey, M., Ishai, A., Schouten, J.,\nand Pietrini, P. (2001). Distributed and overlapping representations of\nfaces and objects in ventral temporal cortex. Science 293, 2425-2430.`\n\n\nLicence: unknown.\n',
- 'stimuli': {'chairs': ['../data/haxby2001/stimuli/chairs/d23a.jpg',
-   '../data/haxby2001/stimuli/chairs/d23b.jpg',
-   '../data/haxby2001/stimuli/chairs/d23c.jpg',
-   '../data/haxby2001/stimuli/chairs/d23d.jpg',
-   '../data/haxby2001/stimuli/chairs/d25a.jpg',
-   '../data/haxby2001/stimuli/chairs/d25b.jpg',
-   '../data/haxby2001/stimuli/chairs/d25c.jpg',
-   '../data/haxby2001/stimuli/chairs/d25d.jpg',
-   '../data/haxby2001/stimuli/chairs/d30a.jpg',
-   '../data/haxby2001/stimuli/chairs/d30b.jpg',
-   '../data/haxby2001/stimuli/chairs/d30c.jpg',
-   '../data/haxby2001/stimuli/chairs/d30d.jpg',
-   '../data/haxby2001/stimuli/chairs/d37a.jpg',
-   '../data/haxby2001/stimuli/chairs/d37b.jpg',
-   '../data/haxby2001/stimuli/chairs/d37c.jpg',
-   '../data/haxby2001/stimuli/chairs/d37d.jpg',
-   '../data/haxby2001/stimuli/chairs/d38a.jpg',
-   '../data/haxby2001/stimuli/chairs/d38b.jpg',
-   '../data/haxby2001/stimuli/chairs/d38c.jpg',
-   '../data/haxby2001/stimuli/chairs/d38d.jpg',
-   '../data/haxby2001/stimuli/chairs/d39a.jpg',
-   '../data/haxby2001/stimuli/chairs/d39b.jpg',
-   '../data/haxby2001/stimuli/chairs/d39c.jpg',
-   '../data/haxby2001/stimuli/chairs/d39d.jpg',
-   '../data/haxby2001/stimuli/chairs/d62a.jpg',
-   '../data/haxby2001/stimuli/chairs/d62b.jpg',
-   '../data/haxby2001/stimuli/chairs/d62c.jpg',
-   '../data/haxby2001/stimuli/chairs/d62d.jpg',
-   '../data/haxby2001/stimuli/chairs/d63a.jpg',
-   '../data/haxby2001/stimuli/chairs/d63b.jpg',
-   '../data/haxby2001/stimuli/chairs/d63c.jpg',
-   '../data/haxby2001/stimuli/chairs/d63d.jpg',
-   '../data/haxby2001/stimuli/chairs/d67a.jpg',
-   '../data/haxby2001/stimuli/chairs/d67b.jpg',
-   '../data/haxby2001/stimuli/chairs/d67c.jpg',
-   '../data/haxby2001/stimuli/chairs/d67d.jpg',
-   '../data/haxby2001/stimuli/chairs/d79a.jpg',
-   '../data/haxby2001/stimuli/chairs/d79b.jpg',
-   '../data/haxby2001/stimuli/chairs/d79c.jpg',
-   '../data/haxby2001/stimuli/chairs/d79d.jpg',
-   '../data/haxby2001/stimuli/chairs/d85a.jpg',
-   '../data/haxby2001/stimuli/chairs/d85b.jpg',
-   '../data/haxby2001/stimuli/chairs/d85c.jpg',
-   '../data/haxby2001/stimuli/chairs/d85d.jpg',
-   '../data/haxby2001/stimuli/chairs/d9a.jpg',
-   '../data/haxby2001/stimuli/chairs/d9b.jpg',
-   '../data/haxby2001/stimuli/chairs/d9c.jpg',
-   '../data/haxby2001/stimuli/chairs/d9d.jpg'],
+ 'stimuli': {'houses': ['../data/haxby2001/stimuli/houses/house1.1.jpg',
+   '../data/haxby2001/stimuli/houses/house1.2.jpg',
+   '../data/haxby2001/stimuli/houses/house1.3.jpg',
+   '../data/haxby2001/stimuli/houses/house1.4.jpg',
+   '../data/haxby2001/stimuli/houses/house10.1.jpg',
+   '../data/haxby2001/stimuli/houses/house10.2.jpg',
+   '../data/haxby2001/stimuli/houses/house10.3.jpg',
+   '../data/haxby2001/stimuli/houses/house10.4.jpg',
+   '../data/haxby2001/stimuli/houses/house11.1.jpg',
+   '../data/haxby2001/stimuli/houses/house11.2.jpg',
+   '../data/haxby2001/stimuli/houses/house11.3.jpg',
+   '../data/haxby2001/stimuli/houses/house11.4.jpg',
+   '../data/haxby2001/stimuli/houses/house12.1.jpg',
+   '../data/haxby2001/stimuli/houses/house12.2.jpg',
+   '../data/haxby2001/stimuli/houses/house12.3.jpg',
+   '../data/haxby2001/stimuli/houses/house12.4.jpg',
+   '../data/haxby2001/stimuli/houses/house2.1.jpg',
+   '../data/haxby2001/stimuli/houses/house2.2.jpg',
+   '../data/haxby2001/stimuli/houses/house2.3.jpg',
+   '../data/haxby2001/stimuli/houses/house2.4.jpg',
+   '../data/haxby2001/stimuli/houses/house3.1.jpg',
+   '../data/haxby2001/stimuli/houses/house3.2.jpg',
+   '../data/haxby2001/stimuli/houses/house3.3.jpg',
+   '../data/haxby2001/stimuli/houses/house3.4.jpg',
+   '../data/haxby2001/stimuli/houses/house4.1.jpg',
+   '../data/haxby2001/stimuli/houses/house4.2.jpg',
+   '../data/haxby2001/stimuli/houses/house4.3.jpg',
+   '../data/haxby2001/stimuli/houses/house4.4.jpg',
+   '../data/haxby2001/stimuli/houses/house5.1.jpg',
+   '../data/haxby2001/stimuli/houses/house5.2.jpg',
+   '../data/haxby2001/stimuli/houses/house5.3.jpg',
+   '../data/haxby2001/stimuli/houses/house5.4.jpg',
+   '../data/haxby2001/stimuli/houses/house6.1.jpg',
+   '../data/haxby2001/stimuli/houses/house6.2.jpg',
+   '../data/haxby2001/stimuli/houses/house6.3.jpg',
+   '../data/haxby2001/stimuli/houses/house6.4.jpg',
+   '../data/haxby2001/stimuli/houses/house7.1.jpg',
+   '../data/haxby2001/stimuli/houses/house7.2.jpg',
+   '../data/haxby2001/stimuli/houses/house7.3.jpg',
+   '../data/haxby2001/stimuli/houses/house7.4.jpg',
+   '../data/haxby2001/stimuli/houses/house8.1.jpg',
+   '../data/haxby2001/stimuli/houses/house8.2.jpg',
+   '../data/haxby2001/stimuli/houses/house8.3.jpg',
+   '../data/haxby2001/stimuli/houses/house8.4.jpg',
+   '../data/haxby2001/stimuli/houses/house9.1.jpg',
+   '../data/haxby2001/stimuli/houses/house9.2.jpg',
+   '../data/haxby2001/stimuli/houses/house9.3.jpg',
+   '../data/haxby2001/stimuli/houses/house9.4.jpg'],
   'scissors': ['../data/haxby2001/stimuli/scissors/scissor1.1.jpg',
    '../data/haxby2001/stimuli/scissors/scissor1.2.jpg',
    '../data/haxby2001/stimuli/scissors/scissor1.3.jpg',
@@ -544,150 +544,6 @@ 
Downloading & exploring the <
    '../data/haxby2001/stimuli/scissors/scissor9.2.jpg',
    '../data/haxby2001/stimuli/scissors/scissor9.3.jpg',
    '../data/haxby2001/stimuli/scissors/scissor9.4.jpg'],
-  'bottles': ['../data/haxby2001/stimuli/bottles/bottle1.1.jpg',
-   '../data/haxby2001/stimuli/bottles/bottle1.2.jpg',
-   '../data/haxby2001/stimuli/bottles/bottle1.3.jpg',
-   '../data/haxby2001/stimuli/bottles/bottle1.4.jpg',
-   '../data/haxby2001/stimuli/bottles/bottle10.1.jpg',
-   '../data/haxby2001/stimuli/bottles/bottle10.2.jpg',
-   '../data/haxby2001/stimuli/bottles/bottle10.3.jpg',
-   '../data/haxby2001/stimuli/bottles/bottle10.4.jpg',
-   '../data/haxby2001/stimuli/bottles/bottle11.1.jpg',
-   '../data/haxby2001/stimuli/bottles/bottle11.2.jpg',
-   '../data/haxby2001/stimuli/bottles/bottle11.3.jpg',
-   '../data/haxby2001/stimuli/bottles/bottle11.4.jpg',
-   '../data/haxby2001/stimuli/bottles/bottle12.1.jpg',
-   '../data/haxby2001/stimuli/bottles/bottle12.2.jpg',
-   '../data/haxby2001/stimuli/bottles/bottle12.3.jpg',
-   '../data/haxby2001/stimuli/bottles/bottle12.4.jpg',
-   '../data/haxby2001/stimuli/bottles/bottle2.1.jpg',
-   '../data/haxby2001/stimuli/bottles/bottle2.2.jpg',
-   '../data/haxby2001/stimuli/bottles/bottle2.3.jpg',
-   '../data/haxby2001/stimuli/bottles/bottle2.4.jpg',
-   '../data/haxby2001/stimuli/bottles/bottle3.1.jpg',
-   '../data/haxby2001/stimuli/bottles/bottle3.2.jpg',
-   '../data/haxby2001/stimuli/bottles/bottle3.3.jpg',
-   '../data/haxby2001/stimuli/bottles/bottle3.4.jpg',
-   '../data/haxby2001/stimuli/bottles/bottle4.1.jpg',
-   '../data/haxby2001/stimuli/bottles/bottle4.2.jpg',
-   '../data/haxby2001/stimuli/bottles/bottle4.3.jpg',
-   '../data/haxby2001/stimuli/bottles/bottle4.4.jpg',
-   '../data/haxby2001/stimuli/bottles/bottle5.1.jpg',
-   '../data/haxby2001/stimuli/bottles/bottle5.2.jpg',
-   '../data/haxby2001/stimuli/bottles/bottle5.3.jpg',
-   '../data/haxby2001/stimuli/bottles/bottle5.4.jpg',
-   '../data/haxby2001/stimuli/bottles/bottle6.1.jpg',
-   '../data/haxby2001/stimuli/bottles/bottle6.2.jpg',
-   '../data/haxby2001/stimuli/bottles/bottle6.3.jpg',
-   '../data/haxby2001/stimuli/bottles/bottle6.4.jpg',
-   '../data/haxby2001/stimuli/bottles/bottle7.1.jpg',
-   '../data/haxby2001/stimuli/bottles/bottle7.2.jpg',
-   '../data/haxby2001/stimuli/bottles/bottle7.3.jpg',
-   '../data/haxby2001/stimuli/bottles/bottle7.4.jpg',
-   '../data/haxby2001/stimuli/bottles/bottle8.1.jpg',
-   '../data/haxby2001/stimuli/bottles/bottle8.2.jpg',
-   '../data/haxby2001/stimuli/bottles/bottle8.3.jpg',
-   '../data/haxby2001/stimuli/bottles/bottle8.4.jpg',
-   '../data/haxby2001/stimuli/bottles/bottle9.1.jpg',
-   '../data/haxby2001/stimuli/bottles/bottle9.2.jpg',
-   '../data/haxby2001/stimuli/bottles/bottle9.3.jpg',
-   '../data/haxby2001/stimuli/bottles/bottle9.4.jpg'],
-  'cats': ['../data/haxby2001/stimuli/cats/MISTY3.jpg',
-   '../data/haxby2001/stimuli/cats/MISTY4.jpg',
-   '../data/haxby2001/stimuli/cats/MISTY5.jpg',
-   '../data/haxby2001/stimuli/cats/MISTY6.jpg',
-   '../data/haxby2001/stimuli/cats/SPOTZ1.jpg',
-   '../data/haxby2001/stimuli/cats/SPOTZ4.jpg',
-   '../data/haxby2001/stimuli/cats/SPOTZ5.jpg',
-   '../data/haxby2001/stimuli/cats/SPOTZ8.jpg',
-   '../data/haxby2001/stimuli/cats/brenda1.jpg',
-   '../data/haxby2001/stimuli/cats/brenda2.jpg',
-   '../data/haxby2001/stimuli/cats/brenda4.jpg',
-   '../data/haxby2001/stimuli/cats/brenda5.jpg',
-   '../data/haxby2001/stimuli/cats/bugs4.jpg',
-   '../data/haxby2001/stimuli/cats/bugs5.jpg',
-   '../data/haxby2001/stimuli/cats/bugs7.jpg',
-   '../data/haxby2001/stimuli/cats/bugs8.jpg',
-   '../data/haxby2001/stimuli/cats/lucky12.jpg',
-   '../data/haxby2001/stimuli/cats/lucky13.jpg',
-   '../data/haxby2001/stimuli/cats/lucky4.jpg',
-   '../data/haxby2001/stimuli/cats/lucky7.jpg',
-   '../data/haxby2001/stimuli/cats/majellan1.jpg',
-   '../data/haxby2001/stimuli/cats/majellan2.jpg',
-   '../data/haxby2001/stimuli/cats/majellan3.jpg',
-   '../data/haxby2001/stimuli/cats/majellan4.jpg',
-   '../data/haxby2001/stimuli/cats/mickey1.jpg',
-   '../data/haxby2001/stimuli/cats/mickey2.jpg',
-   '../data/haxby2001/stimuli/cats/mickey3.jpg',
-   '../data/haxby2001/stimuli/cats/mickey4.jpg',
-   '../data/haxby2001/stimuli/cats/orange1.jpg',
-   '../data/haxby2001/stimuli/cats/orange2.jpg',
-   '../data/haxby2001/stimuli/cats/orange3.jpg',
-   '../data/haxby2001/stimuli/cats/orange4.jpg',
-   '../data/haxby2001/stimuli/cats/pepper1.jpg',
-   '../data/haxby2001/stimuli/cats/pepper2.jpg',
-   '../data/haxby2001/stimuli/cats/pepper3.jpg',
-   '../data/haxby2001/stimuli/cats/pepper5.jpg',
-   '../data/haxby2001/stimuli/cats/robo1.jpg',
-   '../data/haxby2001/stimuli/cats/robo2.jpg',
-   '../data/haxby2001/stimuli/cats/robo4.jpg',
-   '../data/haxby2001/stimuli/cats/robo5.jpg',
-   '../data/haxby2001/stimuli/cats/stripes2.jpg',
-   '../data/haxby2001/stimuli/cats/stripes3.jpg',
-   '../data/haxby2001/stimuli/cats/stripes5.jpg',
-   '../data/haxby2001/stimuli/cats/stripes6.jpg',
-   '../data/haxby2001/stimuli/cats/wookie6.jpg',
-   '../data/haxby2001/stimuli/cats/wookie7.jpg',
-   '../data/haxby2001/stimuli/cats/wookie8.jpg',
-   '../data/haxby2001/stimuli/cats/wookie9.jpg'],
-  'houses': ['../data/haxby2001/stimuli/houses/house1.1.jpg',
-   '../data/haxby2001/stimuli/houses/house1.2.jpg',
-   '../data/haxby2001/stimuli/houses/house1.3.jpg',
-   '../data/haxby2001/stimuli/houses/house1.4.jpg',
-   '../data/haxby2001/stimuli/houses/house10.1.jpg',
-   '../data/haxby2001/stimuli/houses/house10.2.jpg',
-   '../data/haxby2001/stimuli/houses/house10.3.jpg',
-   '../data/haxby2001/stimuli/houses/house10.4.jpg',
-   '../data/haxby2001/stimuli/houses/house11.1.jpg',
-   '../data/haxby2001/stimuli/houses/house11.2.jpg',
-   '../data/haxby2001/stimuli/houses/house11.3.jpg',
-   '../data/haxby2001/stimuli/houses/house11.4.jpg',
-   '../data/haxby2001/stimuli/houses/house12.1.jpg',
-   '../data/haxby2001/stimuli/houses/house12.2.jpg',
-   '../data/haxby2001/stimuli/houses/house12.3.jpg',
-   '../data/haxby2001/stimuli/houses/house12.4.jpg',
-   '../data/haxby2001/stimuli/houses/house2.1.jpg',
-   '../data/haxby2001/stimuli/houses/house2.2.jpg',
-   '../data/haxby2001/stimuli/houses/house2.3.jpg',
-   '../data/haxby2001/stimuli/houses/house2.4.jpg',
-   '../data/haxby2001/stimuli/houses/house3.1.jpg',
-   '../data/haxby2001/stimuli/houses/house3.2.jpg',
-   '../data/haxby2001/stimuli/houses/house3.3.jpg',
-   '../data/haxby2001/stimuli/houses/house3.4.jpg',
-   '../data/haxby2001/stimuli/houses/house4.1.jpg',
-   '../data/haxby2001/stimuli/houses/house4.2.jpg',
-   '../data/haxby2001/stimuli/houses/house4.3.jpg',
-   '../data/haxby2001/stimuli/houses/house4.4.jpg',
-   '../data/haxby2001/stimuli/houses/house5.1.jpg',
-   '../data/haxby2001/stimuli/houses/house5.2.jpg',
-   '../data/haxby2001/stimuli/houses/house5.3.jpg',
-   '../data/haxby2001/stimuli/houses/house5.4.jpg',
-   '../data/haxby2001/stimuli/houses/house6.1.jpg',
-   '../data/haxby2001/stimuli/houses/house6.2.jpg',
-   '../data/haxby2001/stimuli/houses/house6.3.jpg',
-   '../data/haxby2001/stimuli/houses/house6.4.jpg',
-   '../data/haxby2001/stimuli/houses/house7.1.jpg',
-   '../data/haxby2001/stimuli/houses/house7.2.jpg',
-   '../data/haxby2001/stimuli/houses/house7.3.jpg',
-   '../data/haxby2001/stimuli/houses/house7.4.jpg',
-   '../data/haxby2001/stimuli/houses/house8.1.jpg',
-   '../data/haxby2001/stimuli/houses/house8.2.jpg',
-   '../data/haxby2001/stimuli/houses/house8.3.jpg',
-   '../data/haxby2001/stimuli/houses/house8.4.jpg',
-   '../data/haxby2001/stimuli/houses/house9.1.jpg',
-   '../data/haxby2001/stimuli/houses/house9.2.jpg',
-   '../data/haxby2001/stimuli/houses/house9.3.jpg',
-   '../data/haxby2001/stimuli/houses/house9.4.jpg'],
   'controls': [('scrambled_bottles',
     ['../data/haxby2001/stimuli/controls/scrambled_bottles/scrambled_bottle1.1.jpg',
      '../data/haxby2001/stimuli/controls/scrambled_bottles/scrambled_bottle1.2.jpg',
@@ -1023,53 +879,6 @@ 
Downloading & exploring the <
      '../data/haxby2001/stimuli/controls/scrambled_shoes/scrambled_shoev2.jpg',
      '../data/haxby2001/stimuli/controls/scrambled_shoes/scrambled_shoev3.jpg',
      '../data/haxby2001/stimuli/controls/scrambled_shoes/scrambled_shoev4.jpg'])],
-  'shoes': ['../data/haxby2001/stimuli/shoes/shoea1.jpg',
-   '../data/haxby2001/stimuli/shoes/shoea2.jpg',
-   '../data/haxby2001/stimuli/shoes/shoea3.jpg',
-   '../data/haxby2001/stimuli/shoes/shoea5.jpg',
-   '../data/haxby2001/stimuli/shoes/shoeb1.jpg',
-   '../data/haxby2001/stimuli/shoes/shoeb2.jpg',
-   '../data/haxby2001/stimuli/shoes/shoeb4.jpg',
-   '../data/haxby2001/stimuli/shoes/shoec1.jpg',
-   '../data/haxby2001/stimuli/shoes/shoec2.jpg',
-   '../data/haxby2001/stimuli/shoes/shoec3.jpg',
-   '../data/haxby2001/stimuli/shoes/shoec5.jpg',
-   '../data/haxby2001/stimuli/shoes/shoed1.jpg',
-   '../data/haxby2001/stimuli/shoes/shoed2.jpg',
-   '../data/haxby2001/stimuli/shoes/shoed3.jpg',
-   '../data/haxby2001/stimuli/shoes/shoed5.jpg',
-   '../data/haxby2001/stimuli/shoes/shoee1.jpg',
-   '../data/haxby2001/stimuli/shoes/shoee2.jpg',
-   '../data/haxby2001/stimuli/shoes/shoee3.jpg',
-   '../data/haxby2001/stimuli/shoes/shoee5.jpg',
-   '../data/haxby2001/stimuli/shoes/shoef1.jpg',
-   '../data/haxby2001/stimuli/shoes/shoef2.jpg',
-   '../data/haxby2001/stimuli/shoes/shoef3.jpg',
-   '../data/haxby2001/stimuli/shoes/shoef5.jpg',
-   '../data/haxby2001/stimuli/shoes/shoeg1.jpg',
-   '../data/haxby2001/stimuli/shoes/shoeg2.jpg',
-   '../data/haxby2001/stimuli/shoes/shoeg3.jpg',
-   '../data/haxby2001/stimuli/shoes/shoeg4.jpg',
-   '../data/haxby2001/stimuli/shoes/shoeh1.jpg',
-   '../data/haxby2001/stimuli/shoes/shoeh2.jpg',
-   '../data/haxby2001/stimuli/shoes/shoeh3.jpg',
-   '../data/haxby2001/stimuli/shoes/shoeh4.jpg',
-   '../data/haxby2001/stimuli/shoes/shoei1.jpg',
-   '../data/haxby2001/stimuli/shoes/shoei2.jpg',
-   '../data/haxby2001/stimuli/shoes/shoei3.jpg',
-   '../data/haxby2001/stimuli/shoes/shoei4.jpg',
-   '../data/haxby2001/stimuli/shoes/shoep1.jpg',
-   '../data/haxby2001/stimuli/shoes/shoep2.jpg',
-   '../data/haxby2001/stimuli/shoes/shoep3.jpg',
-   '../data/haxby2001/stimuli/shoes/shoep4.jpg',
-   '../data/haxby2001/stimuli/shoes/shoeu1.jpg',
-   '../data/haxby2001/stimuli/shoes/shoeu2.jpg',
-   '../data/haxby2001/stimuli/shoes/shoeu3.jpg',
-   '../data/haxby2001/stimuli/shoes/shoeu4.jpg',
-   '../data/haxby2001/stimuli/shoes/shoev1.jpg',
-   '../data/haxby2001/stimuli/shoes/shoev2.jpg',
-   '../data/haxby2001/stimuli/shoes/shoev3.jpg',
-   '../data/haxby2001/stimuli/shoes/shoev4.jpg'],
   'faces': ['../data/haxby2001/stimuli/faces/Annie_1.jpg',
    '../data/haxby2001/stimuli/faces/Annie_2.jpg',
    '../data/haxby2001/stimuli/faces/Annie_3.jpg',
@@ -1117,7 +926,198 @@ 
Downloading & exploring the <
    '../data/haxby2001/stimuli/faces/Wallace_1.jpg',
    '../data/haxby2001/stimuli/faces/Wallace_2.jpg',
    '../data/haxby2001/stimuli/faces/Wallace_3.jpg',
-   '../data/haxby2001/stimuli/faces/Wallace_4.jpg']}}
+   '../data/haxby2001/stimuli/faces/Wallace_4.jpg'],
+  'shoes': ['../data/haxby2001/stimuli/shoes/shoea1.jpg',
+   '../data/haxby2001/stimuli/shoes/shoea2.jpg',
+   '../data/haxby2001/stimuli/shoes/shoea3.jpg',
+   '../data/haxby2001/stimuli/shoes/shoea5.jpg',
+   '../data/haxby2001/stimuli/shoes/shoeb1.jpg',
+   '../data/haxby2001/stimuli/shoes/shoeb2.jpg',
+   '../data/haxby2001/stimuli/shoes/shoeb4.jpg',
+   '../data/haxby2001/stimuli/shoes/shoec1.jpg',
+   '../data/haxby2001/stimuli/shoes/shoec2.jpg',
+   '../data/haxby2001/stimuli/shoes/shoec3.jpg',
+   '../data/haxby2001/stimuli/shoes/shoec5.jpg',
+   '../data/haxby2001/stimuli/shoes/shoed1.jpg',
+   '../data/haxby2001/stimuli/shoes/shoed2.jpg',
+   '../data/haxby2001/stimuli/shoes/shoed3.jpg',
+   '../data/haxby2001/stimuli/shoes/shoed5.jpg',
+   '../data/haxby2001/stimuli/shoes/shoee1.jpg',
+   '../data/haxby2001/stimuli/shoes/shoee2.jpg',
+   '../data/haxby2001/stimuli/shoes/shoee3.jpg',
+   '../data/haxby2001/stimuli/shoes/shoee5.jpg',
+   '../data/haxby2001/stimuli/shoes/shoef1.jpg',
+   '../data/haxby2001/stimuli/shoes/shoef2.jpg',
+   '../data/haxby2001/stimuli/shoes/shoef3.jpg',
+   '../data/haxby2001/stimuli/shoes/shoef5.jpg',
+   '../data/haxby2001/stimuli/shoes/shoeg1.jpg',
+   '../data/haxby2001/stimuli/shoes/shoeg2.jpg',
+   '../data/haxby2001/stimuli/shoes/shoeg3.jpg',
+   '../data/haxby2001/stimuli/shoes/shoeg4.jpg',
+   '../data/haxby2001/stimuli/shoes/shoeh1.jpg',
+   '../data/haxby2001/stimuli/shoes/shoeh2.jpg',
+   '../data/haxby2001/stimuli/shoes/shoeh3.jpg',
+   '../data/haxby2001/stimuli/shoes/shoeh4.jpg',
+   '../data/haxby2001/stimuli/shoes/shoei1.jpg',
+   '../data/haxby2001/stimuli/shoes/shoei2.jpg',
+   '../data/haxby2001/stimuli/shoes/shoei3.jpg',
+   '../data/haxby2001/stimuli/shoes/shoei4.jpg',
+   '../data/haxby2001/stimuli/shoes/shoep1.jpg',
+   '../data/haxby2001/stimuli/shoes/shoep2.jpg',
+   '../data/haxby2001/stimuli/shoes/shoep3.jpg',
+   '../data/haxby2001/stimuli/shoes/shoep4.jpg',
+   '../data/haxby2001/stimuli/shoes/shoeu1.jpg',
+   '../data/haxby2001/stimuli/shoes/shoeu2.jpg',
+   '../data/haxby2001/stimuli/shoes/shoeu3.jpg',
+   '../data/haxby2001/stimuli/shoes/shoeu4.jpg',
+   '../data/haxby2001/stimuli/shoes/shoev1.jpg',
+   '../data/haxby2001/stimuli/shoes/shoev2.jpg',
+   '../data/haxby2001/stimuli/shoes/shoev3.jpg',
+   '../data/haxby2001/stimuli/shoes/shoev4.jpg'],
+  'cats': ['../data/haxby2001/stimuli/cats/MISTY3.jpg',
+   '../data/haxby2001/stimuli/cats/MISTY4.jpg',
+   '../data/haxby2001/stimuli/cats/MISTY5.jpg',
+   '../data/haxby2001/stimuli/cats/MISTY6.jpg',
+   '../data/haxby2001/stimuli/cats/SPOTZ1.jpg',
+   '../data/haxby2001/stimuli/cats/SPOTZ4.jpg',
+   '../data/haxby2001/stimuli/cats/SPOTZ5.jpg',
+   '../data/haxby2001/stimuli/cats/SPOTZ8.jpg',
+   '../data/haxby2001/stimuli/cats/brenda1.jpg',
+   '../data/haxby2001/stimuli/cats/brenda2.jpg',
+   '../data/haxby2001/stimuli/cats/brenda4.jpg',
+   '../data/haxby2001/stimuli/cats/brenda5.jpg',
+   '../data/haxby2001/stimuli/cats/bugs4.jpg',
+   '../data/haxby2001/stimuli/cats/bugs5.jpg',
+   '../data/haxby2001/stimuli/cats/bugs7.jpg',
+   '../data/haxby2001/stimuli/cats/bugs8.jpg',
+   '../data/haxby2001/stimuli/cats/lucky12.jpg',
+   '../data/haxby2001/stimuli/cats/lucky13.jpg',
+   '../data/haxby2001/stimuli/cats/lucky4.jpg',
+   '../data/haxby2001/stimuli/cats/lucky7.jpg',
+   '../data/haxby2001/stimuli/cats/majellan1.jpg',
+   '../data/haxby2001/stimuli/cats/majellan2.jpg',
+   '../data/haxby2001/stimuli/cats/majellan3.jpg',
+   '../data/haxby2001/stimuli/cats/majellan4.jpg',
+   '../data/haxby2001/stimuli/cats/mickey1.jpg',
+   '../data/haxby2001/stimuli/cats/mickey2.jpg',
+   '../data/haxby2001/stimuli/cats/mickey3.jpg',
+   '../data/haxby2001/stimuli/cats/mickey4.jpg',
+   '../data/haxby2001/stimuli/cats/orange1.jpg',
+   '../data/haxby2001/stimuli/cats/orange2.jpg',
+   '../data/haxby2001/stimuli/cats/orange3.jpg',
+   '../data/haxby2001/stimuli/cats/orange4.jpg',
+   '../data/haxby2001/stimuli/cats/pepper1.jpg',
+   '../data/haxby2001/stimuli/cats/pepper2.jpg',
+   '../data/haxby2001/stimuli/cats/pepper3.jpg',
+   '../data/haxby2001/stimuli/cats/pepper5.jpg',
+   '../data/haxby2001/stimuli/cats/robo1.jpg',
+   '../data/haxby2001/stimuli/cats/robo2.jpg',
+   '../data/haxby2001/stimuli/cats/robo4.jpg',
+   '../data/haxby2001/stimuli/cats/robo5.jpg',
+   '../data/haxby2001/stimuli/cats/stripes2.jpg',
+   '../data/haxby2001/stimuli/cats/stripes3.jpg',
+   '../data/haxby2001/stimuli/cats/stripes5.jpg',
+   '../data/haxby2001/stimuli/cats/stripes6.jpg',
+   '../data/haxby2001/stimuli/cats/wookie6.jpg',
+   '../data/haxby2001/stimuli/cats/wookie7.jpg',
+   '../data/haxby2001/stimuli/cats/wookie8.jpg',
+   '../data/haxby2001/stimuli/cats/wookie9.jpg'],
+  'bottles': ['../data/haxby2001/stimuli/bottles/bottle1.1.jpg',
+   '../data/haxby2001/stimuli/bottles/bottle1.2.jpg',
+   '../data/haxby2001/stimuli/bottles/bottle1.3.jpg',
+   '../data/haxby2001/stimuli/bottles/bottle1.4.jpg',
+   '../data/haxby2001/stimuli/bottles/bottle10.1.jpg',
+   '../data/haxby2001/stimuli/bottles/bottle10.2.jpg',
+   '../data/haxby2001/stimuli/bottles/bottle10.3.jpg',
+   '../data/haxby2001/stimuli/bottles/bottle10.4.jpg',
+   '../data/haxby2001/stimuli/bottles/bottle11.1.jpg',
+   '../data/haxby2001/stimuli/bottles/bottle11.2.jpg',
+   '../data/haxby2001/stimuli/bottles/bottle11.3.jpg',
+   '../data/haxby2001/stimuli/bottles/bottle11.4.jpg',
+   '../data/haxby2001/stimuli/bottles/bottle12.1.jpg',
+   '../data/haxby2001/stimuli/bottles/bottle12.2.jpg',
+   '../data/haxby2001/stimuli/bottles/bottle12.3.jpg',
+   '../data/haxby2001/stimuli/bottles/bottle12.4.jpg',
+   '../data/haxby2001/stimuli/bottles/bottle2.1.jpg',
+   '../data/haxby2001/stimuli/bottles/bottle2.2.jpg',
+   '../data/haxby2001/stimuli/bottles/bottle2.3.jpg',
+   '../data/haxby2001/stimuli/bottles/bottle2.4.jpg',
+   '../data/haxby2001/stimuli/bottles/bottle3.1.jpg',
+   '../data/haxby2001/stimuli/bottles/bottle3.2.jpg',
+   '../data/haxby2001/stimuli/bottles/bottle3.3.jpg',
+   '../data/haxby2001/stimuli/bottles/bottle3.4.jpg',
+   '../data/haxby2001/stimuli/bottles/bottle4.1.jpg',
+   '../data/haxby2001/stimuli/bottles/bottle4.2.jpg',
+   '../data/haxby2001/stimuli/bottles/bottle4.3.jpg',
+   '../data/haxby2001/stimuli/bottles/bottle4.4.jpg',
+   '../data/haxby2001/stimuli/bottles/bottle5.1.jpg',
+   '../data/haxby2001/stimuli/bottles/bottle5.2.jpg',
+   '../data/haxby2001/stimuli/bottles/bottle5.3.jpg',
+   '../data/haxby2001/stimuli/bottles/bottle5.4.jpg',
+   '../data/haxby2001/stimuli/bottles/bottle6.1.jpg',
+   '../data/haxby2001/stimuli/bottles/bottle6.2.jpg',
+   '../data/haxby2001/stimuli/bottles/bottle6.3.jpg',
+   '../data/haxby2001/stimuli/bottles/bottle6.4.jpg',
+   '../data/haxby2001/stimuli/bottles/bottle7.1.jpg',
+   '../data/haxby2001/stimuli/bottles/bottle7.2.jpg',
+   '../data/haxby2001/stimuli/bottles/bottle7.3.jpg',
+   '../data/haxby2001/stimuli/bottles/bottle7.4.jpg',
+   '../data/haxby2001/stimuli/bottles/bottle8.1.jpg',
+   '../data/haxby2001/stimuli/bottles/bottle8.2.jpg',
+   '../data/haxby2001/stimuli/bottles/bottle8.3.jpg',
+   '../data/haxby2001/stimuli/bottles/bottle8.4.jpg',
+   '../data/haxby2001/stimuli/bottles/bottle9.1.jpg',
+   '../data/haxby2001/stimuli/bottles/bottle9.2.jpg',
+   '../data/haxby2001/stimuli/bottles/bottle9.3.jpg',
+   '../data/haxby2001/stimuli/bottles/bottle9.4.jpg'],
+  'chairs': ['../data/haxby2001/stimuli/chairs/d23a.jpg',
+   '../data/haxby2001/stimuli/chairs/d23b.jpg',
+   '../data/haxby2001/stimuli/chairs/d23c.jpg',
+   '../data/haxby2001/stimuli/chairs/d23d.jpg',
+   '../data/haxby2001/stimuli/chairs/d25a.jpg',
+   '../data/haxby2001/stimuli/chairs/d25b.jpg',
+   '../data/haxby2001/stimuli/chairs/d25c.jpg',
+   '../data/haxby2001/stimuli/chairs/d25d.jpg',
+   '../data/haxby2001/stimuli/chairs/d30a.jpg',
+   '../data/haxby2001/stimuli/chairs/d30b.jpg',
+   '../data/haxby2001/stimuli/chairs/d30c.jpg',
+   '../data/haxby2001/stimuli/chairs/d30d.jpg',
+   '../data/haxby2001/stimuli/chairs/d37a.jpg',
+   '../data/haxby2001/stimuli/chairs/d37b.jpg',
+   '../data/haxby2001/stimuli/chairs/d37c.jpg',
+   '../data/haxby2001/stimuli/chairs/d37d.jpg',
+   '../data/haxby2001/stimuli/chairs/d38a.jpg',
+   '../data/haxby2001/stimuli/chairs/d38b.jpg',
+   '../data/haxby2001/stimuli/chairs/d38c.jpg',
+   '../data/haxby2001/stimuli/chairs/d38d.jpg',
+   '../data/haxby2001/stimuli/chairs/d39a.jpg',
+   '../data/haxby2001/stimuli/chairs/d39b.jpg',
+   '../data/haxby2001/stimuli/chairs/d39c.jpg',
+   '../data/haxby2001/stimuli/chairs/d39d.jpg',
+   '../data/haxby2001/stimuli/chairs/d62a.jpg',
+   '../data/haxby2001/stimuli/chairs/d62b.jpg',
+   '../data/haxby2001/stimuli/chairs/d62c.jpg',
+   '../data/haxby2001/stimuli/chairs/d62d.jpg',
+   '../data/haxby2001/stimuli/chairs/d63a.jpg',
+   '../data/haxby2001/stimuli/chairs/d63b.jpg',
+   '../data/haxby2001/stimuli/chairs/d63c.jpg',
+   '../data/haxby2001/stimuli/chairs/d63d.jpg',
+   '../data/haxby2001/stimuli/chairs/d67a.jpg',
+   '../data/haxby2001/stimuli/chairs/d67b.jpg',
+   '../data/haxby2001/stimuli/chairs/d67c.jpg',
+   '../data/haxby2001/stimuli/chairs/d67d.jpg',
+   '../data/haxby2001/stimuli/chairs/d79a.jpg',
+   '../data/haxby2001/stimuli/chairs/d79b.jpg',
+   '../data/haxby2001/stimuli/chairs/d79c.jpg',
+   '../data/haxby2001/stimuli/chairs/d79d.jpg',
+   '../data/haxby2001/stimuli/chairs/d85a.jpg',
+   '../data/haxby2001/stimuli/chairs/d85b.jpg',
+   '../data/haxby2001/stimuli/chairs/d85c.jpg',
+   '../data/haxby2001/stimuli/chairs/d85d.jpg',
+   '../data/haxby2001/stimuli/chairs/d9a.jpg',
+   '../data/haxby2001/stimuli/chairs/d9b.jpg',
+   '../data/haxby2001/stimuli/chairs/d9c.jpg',
+   '../data/haxby2001/stimuli/chairs/d9d.jpg']}}
 

 

 

@@ -1145,14 +1145,14 @@ 
Neuroimaging filesnilearn, we can either load and then plot it or directly plot it. Here we are going to do the first option as it will allow us to check the properties of the image.

 

 

-
from nilearn.image import load_img
+
from nilearn.image import load_img
 

 

 

 

 

 

-
anat_image = load_img(haxby_dataset.anat)
+
anat_image = load_img(haxby_dataset.anat)
 

 

 

@@ -1160,7 +1160,7 @@ 
Neuroimaging filesimage, including the header

 

 

-
print(anat_image.header)
+
print(anat_image.header)
 

 

 

@@ -1216,7 +1216,7 @@ 
Neuroimaging filesdata.

 

 

-
anat_image.dataobj
+
anat_image.dataobj
 

 

 

@@ -1534,7 +1534,7 @@ 
Neuroimaging files
 

-
anat_image.dataobj.shape
+
anat_image.dataobj.shape
 

 

 

@@ -1547,19 +1547,19 @@ 
Neuroimaging filesnumpy array that has the same dimensions as our image and the data reflect values for a given voxel. So far so good but how does it actually look? We can make use of one of nilearn’s many plotting functions.

 

 

-
from nilearn import plotting
+
from nilearn import plotting
 

 

 

 

 

 

-
plotting.plot_anat(anat_image)
+
plotting.plot_anat(anat_image)
 

 

 

 

-
<nilearn.plotting.displays.OrthoSlicer at 0x7f2371a7f9a0>
+
<nilearn.plotting.displays.OrthoSlicer at 0x7fcc2be50220>
 

 

 _images/haxby_data_19_1.png
@@ -1568,16 +1568,16 @@ 
Neuroimaging filesinteractive plot:

 

 

-
plotting.view_img(anat_image, symmetric_cmap=False, cmap='Greys_r', colorbar=False)
+
plotting.view_img(anat_image, symmetric_cmap=False, cmap='Greys_r', colorbar=False)
 

 

 

 

-
/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/plotting/html_stat_map.py:217: FutureWarning: Default resolution of the MNI template will change from 2mm to 1mm in version 0.10.0
+
/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/plotting/html_stat_map.py:217: FutureWarning: Default resolution of the MNI template will change from 2mm to 1mm in version 0.10.0
   bg_img = load_mni152_template()
 

 

-
/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/image/resampling.py:531: UserWarning: Casting data from int32 to float32
+
/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/image/resampling.py:531: UserWarning: Casting data from int32 to float32
   warnings.warn("Casting data from %s to %s" % (data.dtype.name, aux))
 

 

@@ -1625,7 +1625,7 @@ 
Neuroimaging filesfunctional image. That is loading the image:

 

 

-
func_image = load_img(haxby_dataset.func)
+
func_image = load_img(haxby_dataset.func)
 

 

 

@@ -1633,7 +1633,7 @@ 
Neuroimaging filesheader:

 

 

-
print(func_image.header)
+
print(func_image.header)
 

 

 

@@ -1689,332 +1689,35 @@ 
Neuroimaging filesdata:

 

 

-
func_image.get_data()
+
func_image.get_data()
 

 

 

 

-
/tmp/ipykernel_2784/1789311566.py:1: DeprecationWarning: get_data() is deprecated in favor of get_fdata(), which has a more predictable return type. To obtain get_data() behavior going forward, use numpy.asanyarray(img.dataobj).
-
-* deprecated from version: 3.0
-* Will raise <class 'nibabel.deprecator.ExpiredDeprecationError'> as of version: 5.0
-  func_image.get_data()
-

-

-
array([[[[ 0,  0,  0, ...,  0,  0,  0],
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-         [ 0,  0,  0, ...,  0,  0,  0],
-         ...,
-         [ 0,  0,  0, ...,  0,  0,  0],
-         [ 0,  0,  0, ...,  0,  0,  0],
-         [ 0,  0,  0, ...,  0,  0,  0]],
-
-        [[10, 11, 10, ...,  0,  0,  0],
-         [16, 28, 14, ...,  0,  0,  0],
-         [17, 23, 28, ...,  0,  0,  0],
-         ...,
-         [ 0,  0,  0, ..., 13,  7,  8],
-         [ 0,  0,  0, ..., 21, 18, 13],
-         [ 0,  0,  0, ...,  0,  0,  0]],
-
-        [[ 4, 11, 11, ...,  0,  0,  0],
-         [17, 26, 16, ...,  0,  0,  0],
-         [35, 32, 35, ...,  0,  0,  0],
-         ...,
-         [ 0,  0,  0, ..., 24, 14, 11],
-         [ 0,  0,  0, ..., 22, 24, 23],
-         [ 0,  0,  0, ...,  0,  0,  0]],
-
-        ...,
-
-        [[ 0,  0,  0, ...,  0,  0,  0],
-         [10, 11, 17, ...,  0,  0,  0],
-         [21, 14, 29, ...,  0,  0,  0],
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-         [ 0,  0,  0, ..., 10, 14, 10],
-         [ 0,  0,  0, ...,  4,  7,  4],
-         [ 0,  0,  0, ...,  0,  0,  0]],
-
-        [[ 0,  0,  0, ...,  0,  0,  0],
-         [ 8, 13, 15, ...,  0,  0,  0],
-         [23, 21, 31, ...,  0,  0,  0],
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-         [ 0,  0,  0, ...,  9,  7, 11],
-         [ 0,  0,  0, ...,  6,  3,  3],
-         [ 0,  0,  0, ...,  0,  0,  0]],
-
-        [[ 0,  0,  0, ...,  0,  0,  0],
-         [10, 28, 21, ...,  0,  0,  0],
-         [17, 35, 36, ...,  0,  0,  0],
-         ...,
-         [ 0,  0,  0, ..., 12, 15, 13],
-         [ 0,  0,  0, ...,  7, 13,  9],
-         [ 0,  0,  0, ...,  0,  0,  0]]],
-
-
-       [[[ 0,  0,  0, ..., 18, 24, 13],
-         [ 0,  0,  0, ..., 20, 24, 24],
-         [ 0,  0,  0, ..., 34, 33, 25],
-         ...,
-         [15, 24, 18, ...,  0,  0,  0],
-         [ 7,  9, 11, ...,  0,  0,  0],
-         [ 0,  0,  0, ...,  0,  0,  0]],
-
-        [[10,  9, 14, ...,  7, 24, 25],
-         [14, 14, 14, ..., 17, 28, 26],
-         [12, 14, 21, ..., 15, 17, 35],
-         ...,
-         [ 8, 16, 18, ..., 19,  8,  9],
-         [ 9,  7, 10, ..., 12,  9, 17],
-         [ 0,  0,  0, ...,  0,  0,  0]],
-
-        [[ 8,  6, 10, ..., 20, 23, 25],
-         [12,  6, 14, ..., 22, 24, 21],
-         [22, 18, 16, ..., 21, 18, 43],
-         ...,
-         [20, 28, 17, ..., 11, 26, 16],
-         [ 9, 12,  8, ..., 14, 14,  8],
-         [ 0,  0,  0, ...,  0,  0,  0]],
-
-        ...,
-
-        [[ 0,  0,  0, ..., 23, 18, 12],
-         [20, 23, 32, ..., 28, 37, 14],
-         [30, 22, 32, ..., 24, 37, 11],
-         ...,
-         [22, 17, 23, ..., 22, 10, 17],
-         [22, 19, 15, ...,  5,  4,  6],
-         [ 7,  9, 12, ...,  0,  0,  0]],
-
-        [[ 0,  0,  0, ..., 12, 24, 17],
-         [17, 14, 17, ..., 32, 43, 25],
-         [20,  9, 17, ..., 36, 37, 20],
-         ...,
-         [19, 34, 20, ..., 13, 15, 10],
-         [20, 33, 21, ...,  6, 10,  6],
-         [17, 22, 18, ...,  0,  0,  0]],
-
-        [[ 0,  0,  0, ...,  0,  0,  0],
-         [18, 22, 19, ...,  0,  0,  0],
-         [29, 39, 24, ...,  0,  0,  0],
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-         [ 0,  0,  0, ..., 15, 16, 20],
-         [ 0,  0,  0, ...,  6,  7,  9],
-         [ 0,  0,  0, ...,  0,  0,  0]]],
-
-
-       [[[ 0,  0,  0, ..., 21, 29, 28],
-         [ 0,  0,  0, ..., 23, 34, 29],
-         [ 0,  0,  0, ..., 22, 49, 29],
-         ...,
-         [18, 15, 21, ...,  0,  0,  0],
-         [12,  3, 10, ...,  0,  0,  0],
-         [ 0,  0,  0, ...,  0,  0,  0]],
-
-        [[21, 23, 16, ..., 13,  8, 11],
-         [28, 23, 11, ..., 17, 20, 19],
-         [29, 35, 19, ...,  7, 21, 55],
-         ...,
-         [25, 23, 14, ..., 12, 17, 35],
-         [15,  6, 10, ..., 22, 18, 13],
-         [ 0,  0,  0, ...,  0,  0,  0]],
-
-        [[12, 16, 17, ..., 16, 14, 11],
-         [20, 15, 16, ..., 11,  7, 14],
-         [32, 44, 11, ...,  5, 18, 40],
-         ...,
-         [31, 34, 23, ...,  2, 16, 18],
-         [18, 12, 20, ...,  9, 10, 19],
-         [ 0,  0,  0, ...,  0,  0,  0]],
-
-        ...,
-
-        [[ 0,  0,  0, ..., 24, 26, 44],
-         [20, 21, 23, ..., 21, 28, 45],
-         [21, 24, 20, ..., 22, 31, 28],
-         ...,
-         [14, 27, 21, ..., 19, 17, 32],
-         [20, 29, 22, ...,  4, 11, 16],
-         [11, 17, 12, ...,  0,  0,  0]],
-
-        [[ 0,  0,  0, ..., 23, 25, 38],
-         [21, 20, 21, ..., 24, 24, 46],
-         [20, 13, 20, ..., 23, 16, 31],
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-         [20, 30, 18, ..., 12, 18, 21],
-         [15, 21, 15, ...,  0,  0,  0]],
-
-        [[ 0,  0,  0, ...,  0,  0,  0],
-         [14, 13, 20, ...,  0,  0,  0],
-         [19, 12, 23, ...,  0,  0,  0],
-         ...,
-         [ 0,  0,  0, ..., 31, 32, 46],
-         [ 0,  0,  0, ..., 13, 19, 19],
-         [ 0,  0,  0, ...,  0,  0,  0]]],
-
-
-       ...,
-
-
-       [[[ 0,  0,  0, ...,  0,  0,  0],
-         [ 0,  0,  0, ..., 32, 13, 20],
-         [ 0,  0,  0, ..., 15, 22, 41],
-         ...,
-         [26, 37, 20, ...,  0,  0,  0],
-         [16, 18, 17, ...,  0,  0,  0],
-         [ 0,  0,  0, ...,  0,  0,  0]],
-
-        [[23, 26, 34, ...,  0,  0,  0],
-         [37, 28, 31, ..., 43, 30, 41],
-         [37, 28, 34, ..., 12, 20, 27],
-         ...,
-         [26, 21, 20, ..., 22, 17, 20],
-         [22, 11, 24, ..., 25, 19, 10],
-         [ 0,  0,  0, ...,  1,  0,  0]],
-
-        [[12, 10, 28, ...,  0, 24,  0],
-         [31, 18, 23, ..., 31, 32, 29],
-         [25, 23, 19, ..., 20, 30, 33],
-         ...,
-         [34, 26, 22, ..., 30, 15, 35],
-         [32, 17, 18, ..., 18, 18, 20],
-         [ 0,  0,  0, ...,  0,  0,  0]],
-
-        ...,
-
-        [[ 0,  0,  0, ..., 20, 12,  7],
-         [31, 24, 34, ..., 33, 16, 19],
-         [36, 26, 37, ..., 22, 24, 26],
-         ...,
-         [18, 28, 19, ..., 19, 21, 21],
-         [22, 23, 19, ...,  8,  9,  9],
-         [ 9, 13, 15, ...,  0,  0,  0]],
+
---------------------------------------------------------------------------
+ExpiredDeprecationError                   Traceback (most recent call last)
+Cell In[14], line 1
+----> 1 func_image.get_data()
 
-        [[ 0,  0,  0, ..., 27, 22, 10],
-         [34, 20, 29, ..., 34, 16, 22],
-         [35, 20, 32, ..., 23, 20, 38],
-         ...,
-         [32, 40, 18, ..., 18, 21, 17],
-         [29, 32, 12, ...,  5, 12, 12],
-         [12, 20, 13, ...,  0,  0,  0]],
-
-        [[ 0,  0,  0, ...,  0,  0,  0],
-         [31, 26, 29, ...,  0,  0,  0],
-         [32, 30, 24, ...,  0,  0,  0],
-         ...,
-         [ 0,  0,  0, ..., 29, 19, 21],
-         [ 0,  0,  0, ..., 14,  4, 11],
-         [ 0,  0,  0, ...,  0,  0,  0]]],
-
-
-       [[[ 0,  0,  0, ...,  0,  0,  0],
-         [ 0,  0,  0, ..., 32, 19, 29],
-         [ 0,  0,  0, ..., 27, 16, 32],
-         ...,
-         [20, 27, 21, ...,  0,  0,  0],
-         [14, 20, 17, ...,  0,  0,  0],
-         [ 0,  0,  0, ...,  0,  0,  0]],
-
-        [[23, 24, 18, ...,  0,  0,  0],
-         [39, 24, 20, ..., 25, 26, 32],
-         [29, 40, 32, ..., 33, 17, 28],
-         ...,
-         [17, 18, 20, ..., 31,  9, 38],
-         [10, 10, 13, ..., 16, 18, 11],
-         [ 0,  0,  0, ...,  0,  0,  0]],
-
-        [[16, 16, 13, ...,  0, 10,  0],
-         [24, 26, 19, ..., 29, 18, 29],
-         [27, 32, 20, ..., 24, 20, 25],
-         ...,
-         [21, 12, 16, ..., 13, 11, 25],
-         [14,  9, 16, ..., 24, 11,  8],
-         [ 0,  0,  0, ...,  0,  0,  0]],
-
-        ...,
-
-        [[ 0,  0,  0, ..., 18, 16, 14],
-         [11, 16, 13, ..., 22, 16, 16],
-         [19, 23, 11, ..., 13, 14, 17],
-         ...,
-         [24, 28, 14, ..., 24, 11,  6],
-         [20, 18, 10, ..., 16, 11,  2],
-         [10,  8,  7, ...,  0,  0,  0]],
-
-        [[ 0,  0,  0, ..., 17, 24, 28],
-         [16, 13, 16, ..., 12, 30, 26],
-         [27, 22, 18, ..., 16, 31, 26],
-         ...,
-         [21, 31, 16, ..., 18, 20, 17],
-         [22, 31, 15, ..., 11, 17, 12],
-         [11, 21, 15, ...,  0,  0,  0]],
+File /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nibabel/deprecator.py:208, in Deprecator.__call__.<locals>.deprecator.<locals>.deprecated_func(*args, **kwargs)
+    205 @functools.wraps(func)
+    206 def deprecated_func(*args: P.args, **kwargs: P.kwargs) -> T:
+    207     if until and self.is_bad_version(until):
+--> 208         raise exception(message)
+    209     warnings.warn(message, warning, stacklevel=2)
+    210     return func(*args, **kwargs)
 
-        [[ 0,  0,  0, ...,  0,  0,  0],
-         [18, 24, 19, ...,  0,  0,  0],
-         [26, 16, 18, ...,  0,  0,  0],
-         ...,
-         [ 0,  0,  0, ..., 17, 18, 16],
-         [ 0,  0,  0, ..., 13, 17,  9],
-         [ 0,  0,  0, ...,  0,  0,  0]]],
-
-
-       [[[ 0,  0,  0, ...,  0,  0,  0],
-         [ 0,  0,  0, ..., 26, 14, 19],
-         [ 0,  0,  0, ..., 30, 21, 21],
-         ...,
-         [22, 24, 12, ...,  0,  0,  0],
-         [ 7, 14,  6, ...,  0,  0,  0],
-         [ 0,  0,  0, ...,  0,  0,  0]],
-
-        [[ 0,  0,  0, ...,  0,  0,  0],
-         [ 0,  0,  0, ..., 35,  9, 20],
-         [ 0,  0,  0, ..., 24, 18, 20],
-         ...,
-         [25, 20, 18, ...,  0,  0,  0],
-         [10, 10,  7, ...,  0,  0,  0],
-         [ 0,  0,  0, ...,  0,  0,  0]],
-
-        [[ 0,  0,  0, ...,  0,  7,  0],
-         [ 0,  0,  0, ..., 20, 24, 11],
-         [ 0,  0,  0, ..., 16, 30, 21],
-         ...,
-         [28, 17, 17, ...,  0,  0,  0],
-         [16, 10, 12, ...,  0,  0,  0],
-         [ 0,  0,  0, ...,  0,  0,  0]],
+ExpiredDeprecationError: get_data() is deprecated in favor of get_fdata(), which has a more predictable return type. To obtain get_data() behavior going forward, use numpy.asanyarray(img.dataobj).
 
-        ...,
-
-        [[ 0,  0,  0, ..., 14, 20, 16],
-         [ 0,  0,  0, ..., 19, 29, 15],
-         [ 0,  0,  0, ..., 18, 29, 21],
-         ...,
-         [22, 19, 24, ...,  0,  0,  0],
-         [11, 13, 13, ...,  0,  0,  0],
-         [ 6,  6,  9, ...,  0,  0,  0]],
-
-        [[ 0,  0,  0, ..., 13, 12, 13],
-         [ 0,  0,  0, ..., 17, 12, 16],
-         [ 0,  0,  0, ..., 15, 15, 25],
-         ...,
-         [24, 24, 23, ...,  0,  0,  0],
-         [ 9, 16, 17, ...,  0,  0,  0],
-         [ 5,  5,  9, ...,  0,  0,  0]],
-
-        [[ 0,  0,  0, ...,  0,  0,  0],
-         [ 0,  0,  0, ...,  0,  0,  0],
-         [ 0,  0,  0, ...,  0,  0,  0],
-         ...,
-         [ 0,  0,  0, ...,  0,  0,  0],
-         [ 0,  0,  0, ...,  0,  0,  0],
-         [ 0,  0,  0, ...,  0,  0,  0]]]], dtype=int16)
+* deprecated from version: 3.0
+* Raises <class 'nibabel.deprecator.ExpiredDeprecationError'> as of version: 5.0
 

 

 

 

 

 

-
func_image.dataobj.shape
+
func_image.dataobj.shape
 

 

 

@@ -2031,14 +1734,14 @@ 
Neuroimaging files4D image, that is brain volumes acquired over time (the 4th dimension), we need to adapt the plotting a bit. More precisely, we need to either plot a 3D image at a given time point or e.g. compute the mean image over time and plot that. The latter might be more informative and additional shows you how easy this can be done using nilearn’s image functions. Thus, we, at first, import the respective function and compute the mean image:

 

 

-
from nilearn.image import mean_img
+
from nilearn.image import mean_img
 

 

 

 

 

 

-
func_image_mean = mean_img(func_image)
+
func_image_mean = mean_img(func_image)
 

 

 

@@ -2046,7 +1749,7 @@ 
Neuroimaging filesdata:

 

 

-
func_image_mean.dataobj.shape
+
func_image_mean.dataobj.shape
 

 

 

@@ -2059,12 +1762,12 @@ 
Neuroimaging files
 

-
plotting.plot_epi(func_image_mean, cmap='magma')
+
plotting.plot_epi(func_image_mean, cmap='magma')
 

 

 

 

-
<nilearn.plotting.displays.OrthoSlicer at 0x7f237135b8e0>
+
<nilearn.plotting.displays.OrthoSlicer at 0x7fde97f6da60>
 

 

 _images/haxby_data_36_1.png
@@ -2073,7 +1776,7 @@ 
Neuroimaging filesinteractive plots.

 

 

-
plotting.view_img(func_image_mean, cmap='magma', symmetric_cmap=False)
+
plotting.view_img(func_image_mean, cmap='magma', symmetric_cmap=False)
 

 

 

@@ -2122,7 +1825,7 @@ 
Neuroimaging filesneuroimaging file we need to check are the (binary) masks, so let’s do it for one example mask: the ventral temporal cortex. This mask has been generated as part of the Haxby et al. (2001) study [HGF+01], and highlights a part of the brain specialized in the processing of visual information, and which contains areas sensitive to different types of image categories [GSW14] . As with the types before, we can load,

 

 

-
vt_mask = load_img(haxby_dataset.mask_vt)
+
vt_mask = load_img(haxby_dataset.mask_vt)
 

 

 

@@ -2130,7 +1833,7 @@ 
Neuroimaging filesinspect

 

 

-
print(vt_mask.header)
+
print(vt_mask.header)
 

 

 

@@ -2185,12 +1888,12 @@ 
Neuroimaging files
 

-
vt_mask.get_data()
+
vt_mask.get_data()
 

 

 

 

-
/tmp/ipykernel_2784/2751075843.py:1: DeprecationWarning: get_data() is deprecated in favor of get_fdata(), which has a more predictable return type. To obtain get_data() behavior going forward, use numpy.asanyarray(img.dataobj).
+
/var/folders/61/0lj9r7px3k52gv9yfyx6ky300000gn/T/ipykernel_23749/2751075843.py:1: DeprecationWarning: get_data() is deprecated in favor of get_fdata(), which has a more predictable return type. To obtain get_data() behavior going forward, use numpy.asanyarray(img.dataobj).
 
 * deprecated from version: 3.0
 * Will raise <class 'nibabel.deprecator.ExpiredDeprecationError'> as of version: 5.0
@@ -2510,7 +2213,7 @@ 
Neuroimaging files
 

-
vt_mask.dataobj.shape
+
vt_mask.dataobj.shape
 

 

 

@@ -2523,13 +2226,13 @@ 
Neuroimaging filesvisualize it (Here, we are going to plot it as an overlay on the anatomical image).

 

 

-
plotting.plot_roi(vt_mask, bg_img=anat_image,
-                  cmap='Paired')
+
plotting.plot_roi(vt_mask, bg_img=anat_image,
+                  cmap='Paired')
 

 

 

 

-
<nilearn.plotting.displays.OrthoSlicer at 0x7f2370dd07c0>
+
<nilearn.plotting.displays.OrthoSlicer at 0x7fde17f88fd0>
 

 

 _images/haxby_data_46_1.png
@@ -2544,15 +2247,15 @@ 
Labels and stimulus annotationstutorial dataset, this information is included in the session_target file. Using pandas we can easily load and inspect this file:

 

 

-
import pandas as pd
-stimulus_annotations = pd.read_csv(haxby_dataset.session_target[0], delimiter=' ')
+
import pandas as pd
+stimulus_annotations = pd.read_csv(haxby_dataset.session_target[0], delimiter=' ')
 

 

 

 

 

 

-
stimulus_annotations.head(n=40)
+
stimulus_annotations.head(n=40)
 

 

 

@@ -2787,14 +2490,14 @@ 
Labels and stimulus annotations
 

-
import seaborn as sns
-import matplotlib.pyplot as plt
-
-ax = sns.scatterplot(x=stimulus_annotations.index, y=stimulus_annotations['labels'], 
-                     hue=stimulus_annotations['labels'], legend=False, palette='colorblind')
-plt.title('Categories shown across time')
-ax.set_xlabel('Time point/fMRI scan')
-sns.despine(offset=5)
+
import seaborn as sns
+import matplotlib.pyplot as plt
+
+ax = sns.scatterplot(x=stimulus_annotations.index, y=stimulus_annotations['labels'], 
+                     hue=stimulus_annotations['labels'], legend=False, palette='colorblind')
+plt.title('Categories shown across time')
+ax.set_xlabel('Time point/fMRI scan')
+sns.despine(offset=5)
 

 

 

@@ -2848,29 +2551,29 @@ 
Bonus: checking the stimulinilearn.image.mean_img to extract the average brain volume.

 

 

-
import matplotlib.pyplot as plt
+
import matplotlib.pyplot as plt
 
-from nilearn import datasets
-from nilearn.plotting import show
+from nilearn import datasets
+from nilearn.plotting import show
 
-stimulus_information = haxby_dataset.stimuli
+stimulus_information = haxby_dataset.stimuli
 
-for stim_type in stimulus_information:
-  # skip control images, there are too many
-  if stim_type != 'controls':
+for stim_type in stimulus_information:
+  # skip control images, there are too many
+  if stim_type != 'controls':
 
-     file_names = stimulus_information[stim_type]
-     file_names = file_names[0:16]
-     fig, axes = plt.subplots(4, 4)
-     fig.suptitle(stim_type)
+     file_names = stimulus_information[stim_type]
+     file_names = file_names[0:16]
+     fig, axes = plt.subplots(4, 4)
+     fig.suptitle(stim_type)
 
-     for img_path, ax in zip(file_names, axes.ravel()):
-         ax.imshow(plt.imread(img_path), cmap=plt.cm.gray)
+     for img_path, ax in zip(file_names, axes.ravel()):
+         ax.imshow(plt.imread(img_path), cmap=plt.cm.gray)
 
-     for ax in axes.ravel():
-         ax.axis("off")
+     for ax in axes.ravel():
+         ax.axis("off")
 
-show()
+show()
 

 

 

@@ -2887,20 +2590,20 @@ 
Bonus: checking the stimuliimage category, a number of scrambled images were also presented.

 

 

-
for stim_num in range(len(stimulus_information['controls'])):
-    stim_type = stimulus_information['controls'][stim_num][0]
-    file_names = stimulus_information['controls'][stim_num][1]  
-    file_names = file_names[0:16]
-    fig, axes = plt.subplots(4, 4)
-    fig.suptitle(stim_type)
+
for stim_num in range(len(stimulus_information['controls'])):
+    stim_type = stimulus_information['controls'][stim_num][0]
+    file_names = stimulus_information['controls'][stim_num][1]  
+    file_names = file_names[0:16]
+    fig, axes = plt.subplots(4, 4)
+    fig.suptitle(stim_type)
 
-    for img_path, ax in zip(file_names, axes.ravel()):
-     ax.imshow(plt.imread(img_path), cmap=plt.cm.gray)
+    for img_path, ax in zip(file_names, axes.ravel()):
+     ax.imshow(plt.imread(img_path), cmap=plt.cm.gray)
 
-    for ax in axes.ravel():
-     ax.axis("off")
+    for ax in axes.ravel():
+     ax.axis("off")
 
-show()
+show()
 

 

 

diff --git a/intro.html b/intro.html
index 61e3382..506b480 100644
--- a/intro.html
+++ b/intro.html
@@ -30,7 +30,7 @@
     
     
     
-    
+    
     
   
 
@@ -339,7 +339,7 @@ 
Brain decoding vs. encoding
 
Explore this tutorial 

 

-

+

 

 

 
Brain decoding with SVM

@@ -411,8 +411,8 @@ 
Setup
 
After making sure you have a working python installation, you need to get the content that is going to presented during the tutorial. In more detail, this is done via interactive jupyter notebooks which you can obtain by following the steps below:

 
    
 
  1. Clone/download this repository to your machine and navigate to the directory.
    
-
    git clone https://github.com/main-educational/brain_encoding_decoding.git
-cd brain_encoding_decoding
+
    git clone https://github.com/main-educational/brain_encoding_decoding.git
+cd brain_encoding_decoding
 
    
 
    
 
    2. 
@@ -420,24 +420,24 @@ 
    Setup
 (and for all your projects, that’s a good practice).
 To do this, run the following commands in your terminal, it will create the
 environment in a folder named env_tuto:
    
-
    python3 -m venv env_tuto
+
    python3 -m venv env_tuto
 
    
 
    
 
    Then the following command will activate the environment:
    
-
    source env_tuto/bin/activate
+
    source env_tuto/bin/activate
 
    
 
    
 
    Finally, you can install the required libraries:
    
-
    pip install -r binder/requirements.txt
+
    pip install -r binder/requirements.txt
 
    
 
    
 
 
  2. Navigate to the content of the jupyter book:
    
-
    cd content/
+
    cd content/
 
    
 
    
 
    Now that you are all set, you can run the notebooks with the command:
    
-
    jupyter notebook
+
    jupyter notebook
 
    
 
    
 
    Click on the .md files. They will be rendered as jupyter notebooks 🎉
    
@@ -462,21 +462,21 @@ 
    Instructors
 
    
-card-img-top
+
 
    
 
    
 Peer Herholz
    
 
    
 
    
 
    
-card-img-top
+
 
    
 
    
 Shima Rastegarnia
    
 
    
 
    
 
    
-card-img-top
+
 
    
 
    
 Bertrand Thirion
    
@@ -485,21 +485,21 @@ 
    Instructors
 
    
-card-img-top
+
 
    
 
    
 Isil Bilgin
    
 
    
 
    
 
    
-card-img-top
+
 
    
 
    
 Alexandre Pasquiou
    
 
    
 
    
 
    
-card-img-top
+
 
    
 
    
 Pravish Sainath
    
@@ -528,7 +528,8 @@ 
    References
-
    
+
+
    
 
    
 
    
 
    
diff --git a/mlp_decoding.html b/mlp_decoding.html
index 09fa4f4..a107436 100644
--- a/mlp_decoding.html
+++ b/mlp_decoding.html
@@ -30,7 +30,7 @@
     
     
     
-    
+    
     
   
 
@@ -394,34 +394,34 @@ 
    Getting the data[HGF+01] again. You can check the section An overview of the Haxby dataset for more details on that dataset. Here we are going to quickly download and prepare it for machine learning applications with a set of predictive variables, the brain time series X, and a dependent variable, the respective cognitive processes/function/percepts y.
    
 
    
 
    
-
    import os
-import warnings
-warnings.filterwarnings(action='once')
+
    import os
+import warnings
+warnings.filterwarnings(action='once')
 
-from nilearn import datasets
-# We are fetching the data for subject 4
-data_dir = os.path.join('..', 'data')
-sub_no = 4
-haxby_dataset = datasets.fetch_haxby(subjects=[sub_no], fetch_stimuli=True, data_dir=data_dir)
-func_file = haxby_dataset.func[0]
+from nilearn import datasets
+# We are fetching the data for subject 4
+data_dir = os.path.join('..', 'data')
+sub_no = 4
+haxby_dataset = datasets.fetch_haxby(subjects=[sub_no], fetch_stimuli=True, data_dir=data_dir)
+func_file = haxby_dataset.func[0]
 
-# mask the data
-from nilearn.input_data import NiftiMasker
-mask_filename = haxby_dataset.mask_vt[0]
-masker = NiftiMasker(mask_img=mask_filename, standardize=True, detrend=True)
-X = masker.fit_transform(func_file)
+# mask the data
+from nilearn.input_data import NiftiMasker
+mask_filename = haxby_dataset.mask_vt[0]
+masker = NiftiMasker(mask_img=mask_filename, standardize=True, detrend=True)
+X = masker.fit_transform(func_file)
 
-# cognitive annotations
-import pandas as pd
-behavioral = pd.read_csv(haxby_dataset.session_target[0], delimiter=' ')
-y = behavioral['labels']
+# cognitive annotations
+import pandas as pd
+behavioral = pd.read_csv(haxby_dataset.session_target[0], delimiter=' ')
+y = behavioral['labels']
 
    
 
    
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/datasets/func.py:20: DeprecationWarning: Please use `MatReadError` from the `scipy.io.matlab` namespace, the `scipy.io.matlab.miobase` namespace is deprecated.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/datasets/func.py:20: DeprecationWarning: Please use `MatReadError` from the `scipy.io.matlab` namespace, the `scipy.io.matlab.miobase` namespace is deprecated.
   from scipy.io.matlab.miobase import MatReadError
-/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/datasets/__init__.py:93: FutureWarning: Fetchers from the nilearn.datasets module will be updated in version 0.9 to return python strings instead of bytes and Pandas dataframes instead of Numpy arrays.
+/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/datasets/__init__.py:93: FutureWarning: Fetchers from the nilearn.datasets module will be updated in version 0.9 to return python strings instead of bytes and Pandas dataframes instead of Numpy arrays.
   warn("Fetchers from the nilearn.datasets module will be "
 
    
 
    
@@ -430,10 +430,10 @@ 
    Getting the dataX and y:
    
 
    
 
    
-
    categories = y.unique()
-print(categories)
-print(y.shape)
-print(X.shape)
+
    categories = y.unique()
+print(categories)
+print(y.shape)
+print(X.shape)
 
    
 
    
 
    
@@ -450,14 +450,14 @@ 
    Getting the datacategories into a one-hot encoder:
    
 
    
 
    
-
    # creating instance of one-hot-encoder
-from sklearn.preprocessing import OneHotEncoder
-import numpy as np
-enc = OneHotEncoder(handle_unknown='ignore')
-y_onehot = enc.fit_transform(np.array(y).reshape(-1, 1))
-# turn the sparse matrix into a pandas dataframe
-y = pd.DataFrame(y_onehot.toarray())
-display(y[:10])
+
    # creating instance of one-hot-encoder
+from sklearn.preprocessing import OneHotEncoder
+import numpy as np
+enc = OneHotEncoder(handle_unknown='ignore')
+y_onehot = enc.fit_transform(np.array(y).reshape(-1, 1))
+# turn the sparse matrix into a pandas dataframe
+y = pd.DataFrame(y_onehot.toarray())
+display(y[:10])
 
    
 
    
 
    
@@ -622,8 +622,8 @@ 
    Training a modeltutorials, one of the most important aspects of machine learning is the split between train and tests. MLPs are no exception to that and thus we need to split our dataset accordingly. We will keep 20% of the time points as test, and then set up a 10 fold cross validation for training/validation.
    
 
    
 
    
-
    from sklearn.model_selection import train_test_split
-X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)   
+
    from sklearn.model_selection import train_test_split
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)   
 
    
 
    
 
    
@@ -631,28 +631,14 @@ 
    Training a modelMLP. Here, we are going to use Tensorflow and Keras. As with every other ANN, we need to import the respective components, here, the model and layer type. In our case we will use a Sequential model and Dense layers.
    
 
    
 
    
-
    from keras.models import Sequential
-from keras.layers import Dense
+
    from keras.models import Sequential
+from keras.layers import Dense
 
    
 
    
 
    
 
    
-
    2022-12-10 18:55:05.994707: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
-2022-12-10 18:55:05.994734: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
-
    
-
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/keras_preprocessing/image/utils.py:23: DeprecationWarning: NEAREST is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.NEAREST or Dither.NONE instead.
-  'nearest': pil_image.NEAREST,
-/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/keras_preprocessing/image/utils.py:24: DeprecationWarning: BILINEAR is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BILINEAR instead.
-  'bilinear': pil_image.BILINEAR,
-/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/keras_preprocessing/image/utils.py:25: DeprecationWarning: BICUBIC is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BICUBIC instead.
-  'bicubic': pil_image.BICUBIC,
-/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/keras_preprocessing/image/utils.py:28: DeprecationWarning: HAMMING is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.HAMMING instead.
-  if hasattr(pil_image, 'HAMMING'):
-/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/keras_preprocessing/image/utils.py:30: DeprecationWarning: BOX is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BOX instead.
-  if hasattr(pil_image, 'BOX'):
-/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/keras_preprocessing/image/utils.py:33: DeprecationWarning: LANCZOS is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.LANCZOS instead.
-  if hasattr(pil_image, 'LANCZOS'):
+
    2023-05-22 08:57:18.709929: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
+2023-05-22 08:57:18.709957: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
 
    
 
    
 
    
@@ -664,16 +650,16 @@ 
    Training a modelempty model.
    
 
    
 
    
-
    # number of unique conditions that we have
-model_mlp = Sequential()
+
    # number of unique conditions that we have
+model_mlp = Sequential()
 
    
 
    
 
    
 
    
-
    2022-12-10 18:55:07.313462: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory
-2022-12-10 18:55:07.313486: W tensorflow/stream_executor/cuda/cuda_driver.cc:269] failed call to cuInit: UNKNOWN ERROR (303)
-2022-12-10 18:55:07.313508: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (fv-az359-603): /proc/driver/nvidia/version does not exist
-2022-12-10 18:55:07.313922: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA
+
    2023-05-22 08:57:20.117988: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory
+2023-05-22 08:57:20.118014: W tensorflow/stream_executor/cuda/cuda_driver.cc:269] failed call to cuInit: UNKNOWN ERROR (303)
+2023-05-22 08:57:20.118035: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (fv-az1102-220): /proc/driver/nvidia/version does not exist
+2023-05-22 08:57:20.118486: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA
 To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
 
    
 
    
@@ -682,7 +668,7 @@ 
    Training a modellayers to our model, starting with the input layer. Given this is a rather short introduction to the topic and does not focus on ANNs, we are going to set the kernel initialization and activation function to appropriate defaults (Please have a look at the Introduction to deep learning session for more information.).
    
 
    
 
    
-
    model_mlp.add(Dense(50 , input_dim = 675, kernel_initializer="uniform", activation = 'relu'))
+
    model_mlp.add(Dense(50 , input_dim = 675, kernel_initializer="uniform", activation = 'relu'))
 
    
 
    
 
    
@@ -691,7 +677,7 @@ 
    Training a modelhidden layer.
    
 
    
 
    
-
    model_mlp.add(Dense(30, kernel_initializer="uniform", activation = 'relu'))
+
    model_mlp.add(Dense(30, kernel_initializer="uniform", activation = 'relu'))
 
    
 
    
 
    
@@ -699,7 +685,7 @@ 
    Training a modelMLP with only three layers, we already add our output layer, using the softmax activation function given that we aim to train our MLP to predict the different categories that were perceived by the participants from their brain activity patterns.
    
 
    
 
    
-
    model_mlp.add(Dense(len(categories), activation = 'softmax'))
+
    model_mlp.add(Dense(len(categories), activation = 'softmax'))
 
    
 
    
 
    
@@ -707,7 +693,7 @@ 
    Training a modelANN, we can now use the .summary() function, which will provide us with the model type, model parameters and for each layer, the its type, shape and parameters.
    
 
    
 
    
-
    model_mlp.summary()
+
    model_mlp.summary()
 
    
 
    
 
    
@@ -762,7 +748,7 @@ 
    Training a modelMLP architecture, which is now ready to be compiled! Within this step, we will set the optimizer, loss function and metric, ie components that define how our MLP will learn.
    
 
    
 
    
-
    model_mlp.compile(optimizer = 'adam', loss = 'categorical_crossentropy', metrics = ['accuracy'])
+
    model_mlp.compile(optimizer = 'adam', loss = 'categorical_crossentropy', metrics = ['accuracy'])
 
    
 
    
 
    
@@ -770,8 +756,8 @@ 
    Training a modeltrain our MLP. Thus, we have to fit it to our data, specifically only the training data. Here, we are going to provide a few more hyperparameters that will define how our MLP is going to learn. This entails the batch size, the epochs and split of validation sets. We will assign the respective output to a variable so that we can investigate our MLP’s learning process.
    
 
    
 
    
-
    history = model_mlp.fit(X_train, y_train, batch_size = 10,
-                             epochs = 10, validation_split = 0.2)
+
    history = model_mlp.fit(X_train, y_train, batch_size = 10,
+                             epochs = 10, validation_split = 0.2)
 
    
 
    
 
    
@@ -779,173 +765,177 @@ 
    Training a model
    Epoch 1/10
 
    
 
    
-
     1/93 [..............................] - ETA: 34s - loss: 2.2317 - accuracy: 0.0000e+00
+
     1/93 [..............................] - ETA: 35s - loss: 2.2229 - accuracy: 0.1000
 
    
 
    
-
    
-43/93 [============>.................] - ETA: 0s - loss: 1.8812 - accuracy: 0.3651     
+
    
+42/93 [============>.................] - ETA: 0s - loss: 1.9819 - accuracy: 0.3429 
 
    
 
    
 
    
-89/93 [===========================>..] - ETA: 0s - loss: 1.6774 - accuracy: 0.4213
+87/93 [===========================>..] - ETA: 0s - loss: 1.7037 - accuracy: 0.4138
 
    
 
    
 
    
-93/93 [==============================] - 1s 3ms/step - loss: 1.6612 - accuracy: 0.4278 - val_loss: 1.4732 - val_accuracy: 0.4592
+93/93 [==============================] - 1s 3ms/step - loss: 1.6752 - accuracy: 0.4224 - val_loss: 1.4664 - val_accuracy: 0.4506
 
    
 
    
 
    Epoch 2/10
 
    
 
    
-
     1/93 [..............................] - ETA: 0s - loss: 1.1605 - accuracy: 0.6000
+
     1/93 [..............................] - ETA: 0s - loss: 1.5577 - accuracy: 0.4000
 
    
 
    
 
    
-46/93 [=============>................] - ETA: 0s - loss: 1.2162 - accuracy: 0.5739
+48/93 [==============>...............] - ETA: 0s - loss: 1.1682 - accuracy: 0.6000
 
    
 
    
 
    
-92/93 [============================>.] - ETA: 0s - loss: 1.1021 - accuracy: 0.6185
+92/93 [============================>.] - ETA: 0s - loss: 1.1279 - accuracy: 0.6217
 
    
 
    
 
    
-93/93 [==============================] - 0s 1ms/step - loss: 1.0995 - accuracy: 0.6196 - val_loss: 1.1412 - val_accuracy: 0.6009
+93/93 [==============================] - 0s 1ms/step - loss: 1.1285 - accuracy: 0.6207 - val_loss: 1.2011 - val_accuracy: 0.5665
 
    
 
    
 
    Epoch 3/10
 
    
 
    
-
     1/93 [..............................] - ETA: 0s - loss: 0.6876 - accuracy: 0.8000
+
     1/93 [..............................] - ETA: 0s - loss: 0.3257 - accuracy: 1.0000
 
    
 
    
 
    
-50/93 [===============>..............] - ETA: 0s - loss: 0.7936 - accuracy: 0.7480
+47/93 [==============>...............] - ETA: 0s - loss: 0.8161 - accuracy: 0.7447
 
    
 
    
 
    
-93/93 [==============================] - ETA: 0s - loss: 0.7539 - accuracy: 0.7608
+93/93 [==============================] - ETA: 0s - loss: 0.8159 - accuracy: 0.7392
 
    
 
    
 
    
-93/93 [==============================] - 0s 2ms/step - loss: 0.7539 - accuracy: 0.7608 - val_loss: 1.0137 - val_accuracy: 0.6395
+93/93 [==============================] - 0s 1ms/step - loss: 0.8159 - accuracy: 0.7392 - val_loss: 1.0526 - val_accuracy: 0.6266
 
    
 
    
 
    Epoch 4/10
 
    
 
    
-
     1/93 [..............................] - ETA: 0s - loss: 0.3305 - accuracy: 0.9000
+
     1/93 [..............................] - ETA: 0s - loss: 0.2624 - accuracy: 1.0000
 
    
 
    
 
    
-48/93 [==============>...............] - ETA: 0s - loss: 0.5290 - accuracy: 0.8354
+46/93 [=============>................] - ETA: 0s - loss: 0.5678 - accuracy: 0.8196
 
    
 
    
 
    
-93/93 [==============================] - ETA: 0s - loss: 0.5242 - accuracy: 0.8362
+88/93 [===========================>..] - ETA: 0s - loss: 0.5670 - accuracy: 0.8330
 
    
 
    
 
    
-93/93 [==============================] - 0s 1ms/step - loss: 0.5242 - accuracy: 0.8362 - val_loss: 0.9145 - val_accuracy: 0.7082
+93/93 [==============================] - 0s 2ms/step - loss: 0.5575 - accuracy: 0.8394 - val_loss: 0.9237 - val_accuracy: 0.6781
 
    
 
    
 
    Epoch 5/10
 
    
 
    
-
     1/93 [..............................] - ETA: 0s - loss: 0.3873 - accuracy: 0.9000
+
     1/93 [..............................] - ETA: 0s - loss: 0.2386 - accuracy: 1.0000
 
    
 
    
 
    
-46/93 [=============>................] - ETA: 0s - loss: 0.3239 - accuracy: 0.9261
+45/93 [=============>................] - ETA: 0s - loss: 0.3832 - accuracy: 0.8844
 
    
 
    
 
    
-92/93 [============================>.] - ETA: 0s - loss: 0.3246 - accuracy: 0.9152
+91/93 [============================>.] - ETA: 0s - loss: 0.3663 - accuracy: 0.8912
 
    
 
    
 
    
-93/93 [==============================] - 0s 1ms/step - loss: 0.3232 - accuracy: 0.9159 - val_loss: 0.8900 - val_accuracy: 0.6867
+93/93 [==============================] - 0s 2ms/step - loss: 0.3661 - accuracy: 0.8912 - val_loss: 0.8652 - val_accuracy: 0.7124
 
    
 
    
 
    Epoch 6/10
 
    
 
    
-
     1/93 [..............................] - ETA: 0s - loss: 0.1384 - accuracy: 1.0000
+
     1/93 [..............................] - ETA: 0s - loss: 0.3449 - accuracy: 0.9000
 
    
 
    
 
    
-46/93 [=============>................] - ETA: 0s - loss: 0.2379 - accuracy: 0.9326
+46/93 [=============>................] - ETA: 0s - loss: 0.2661 - accuracy: 0.9261
 
    
 
    
 
    
-90/93 [============================>.] - ETA: 0s - loss: 0.2275 - accuracy: 0.9333
+91/93 [============================>.] - ETA: 0s - loss: 0.2419 - accuracy: 0.9363
 
    
 
    
 
    
-93/93 [==============================] - 0s 2ms/step - loss: 0.2247 - accuracy: 0.9353 - val_loss: 0.8467 - val_accuracy: 0.7511
+93/93 [==============================] - 0s 2ms/step - loss: 0.2418 - accuracy: 0.9364 - val_loss: 0.8276 - val_accuracy: 0.7554
 
    
 
    
 
    Epoch 7/10
 
    
 
    
-
     1/93 [..............................] - ETA: 0s - loss: 0.0914 - accuracy: 1.0000
+
     1/93 [..............................] - ETA: 0s - loss: 0.1631 - accuracy: 1.0000
 
    
 
    
 
    
-49/93 [==============>...............] - ETA: 0s - loss: 0.1526 - accuracy: 0.9531
+47/93 [==============>...............] - ETA: 0s - loss: 0.1334 - accuracy: 0.9745
 
    
 
    
 
    
-90/93 [============================>.] - ETA: 0s - loss: 0.1379 - accuracy: 0.9667
+91/93 [============================>.] - ETA: 0s - loss: 0.1356 - accuracy: 0.9747
 
    
 
    
 
    
-93/93 [==============================] - 0s 2ms/step - loss: 0.1385 - accuracy: 0.9677 - val_loss: 0.7506 - val_accuracy: 0.7554
+93/93 [==============================] - 0s 2ms/step - loss: 0.1345 - accuracy: 0.9752 - val_loss: 0.7945 - val_accuracy: 0.7639
 
    
 
    
 
    Epoch 8/10
 
    
 
    
-
     1/93 [..............................] - ETA: 0s - loss: 0.0513 - accuracy: 1.0000
+
     1/93 [..............................] - ETA: 0s - loss: 0.0720 - accuracy: 1.0000
+
    
+
    
+
    
+48/93 [==============>...............] - ETA: 0s - loss: 0.0707 - accuracy: 0.9958
 
    
 
    
 
    
-49/93 [==============>...............] - ETA: 0s - loss: 0.0621 - accuracy: 0.9939
+92/93 [============================>.] - ETA: 0s - loss: 0.0737 - accuracy: 0.9946
 
    
 
    
 
    
-93/93 [==============================] - 0s 1ms/step - loss: 0.0723 - accuracy: 0.9935 - val_loss: 0.7802 - val_accuracy: 0.7639
+93/93 [==============================] - 0s 2ms/step - loss: 0.0738 - accuracy: 0.9946 - val_loss: 0.8261 - val_accuracy: 0.7468
 
    
 
    
 
    Epoch 9/10
 
    
 
    
-
     1/93 [..............................] - ETA: 0s - loss: 0.0273 - accuracy: 1.0000
+
     1/93 [..............................] - ETA: 0s - loss: 0.1264 - accuracy: 0.9000
 
    
 
    
 
    
-49/93 [==============>...............] - ETA: 0s - loss: 0.0394 - accuracy: 1.0000
+48/93 [==============>...............] - ETA: 0s - loss: 0.0799 - accuracy: 0.9833
 
    
 
    
 
    
-93/93 [==============================] - ETA: 0s - loss: 0.0421 - accuracy: 0.9968
+92/93 [============================>.] - ETA: 0s - loss: 0.0833 - accuracy: 0.9804
 
    
 
    
 
    
-93/93 [==============================] - 0s 1ms/step - loss: 0.0421 - accuracy: 0.9968 - val_loss: 0.8278 - val_accuracy: 0.7639
+93/93 [==============================] - 0s 1ms/step - loss: 0.0827 - accuracy: 0.9806 - val_loss: 0.8526 - val_accuracy: 0.7639
 
    
 
    
 
    Epoch 10/10
 
    
 
    
-
     1/93 [..............................] - ETA: 0s - loss: 0.0250 - accuracy: 1.0000
+
     1/93 [..............................] - ETA: 0s - loss: 0.0169 - accuracy: 1.0000
 
    
 
    
 
    
-48/93 [==============>...............] - ETA: 0s - loss: 0.0322 - accuracy: 0.9979
+47/93 [==============>...............] - ETA: 0s - loss: 0.0302 - accuracy: 1.0000
 
    
 
    
 
    
-93/93 [==============================] - 0s 1ms/step - loss: 0.0271 - accuracy: 0.9989 - val_loss: 0.7860 - val_accuracy: 0.7811
+93/93 [==============================] - 0s 1ms/step - loss: 0.0321 - accuracy: 0.9989 - val_loss: 0.8581 - val_accuracy: 0.7639
 
    
 
    
 
    
@@ -958,19 +948,19 @@ 
    Training a modelloss and accuracy in the training and validation sets respectively. Let’s start with the loss.
    
 
    
 
    
-
    import matplotlib.pyplot as plt
-import seaborn as sns
+
    import matplotlib.pyplot as plt
+import seaborn as sns
 
-plt.plot(history.history['loss'], color='m')
-plt.plot(history.history['val_loss'], color='c')
-plt.title('MLP loss')
-plt.ylabel('loss')
-plt.xlabel('epoch')
-plt.legend(['train', 'validation'], loc = 'upper right')
+plt.plot(history.history['loss'], color='m')
+plt.plot(history.history['val_loss'], color='c')
+plt.title('MLP loss')
+plt.ylabel('loss')
+plt.xlabel('epoch')
+plt.legend(['train', 'validation'], loc = 'upper right')
 
-sns.despine(offset=5)
+sns.despine(offset=5)
 
-plt.show()
+plt.show()
 
    
 
    
 
    
@@ -981,19 +971,19 @@ 
    Training a modelaccuracy.
    
 
    
 
    
-
    import matplotlib.pyplot as plt
-import seaborn as sns
+
    import matplotlib.pyplot as plt
+import seaborn as sns
 
-plt.plot(history.history['accuracy'], color='m')
-plt.plot(history.history['val_accuracy'], color='c')
-plt.title('MLP accuracy')
-plt.ylabel('accuracy')
-plt.xlabel('epoch')
-plt.legend(['train', 'validation'], loc = 'upper left')
+plt.plot(history.history['accuracy'], color='m')
+plt.plot(history.history['val_accuracy'], color='c')
+plt.title('MLP accuracy')
+plt.ylabel('accuracy')
+plt.xlabel('epoch')
+plt.legend(['train', 'validation'], loc = 'upper left')
 
-sns.despine(offset=5)
+sns.despine(offset=5)
 
-plt.show()
+plt.show()
 
    
 
    
 
    
@@ -1011,28 +1001,28 @@ 
    Assessing performanceAfter evaluating the training of our MLP, we of course also need to evaluate its (predictive) performance. Here, this refers to the accuracy of our MLP’s outcomes, ie its predictions. We already saw this in the above plots and during the training across epochs but let’s check the accuracy of the prediction on the training set again:
    
 
    
 
    
-
    from sklearn.metrics import classification_report
-y_train_pred = model_mlp.predict(X_train)
-print(classification_report(y_train.values.argmax(axis = 1), y_train_pred.argmax(axis=1)))
+
    from sklearn.metrics import classification_report
+y_train_pred = model_mlp.predict(X_train)
+print(classification_report(y_train.values.argmax(axis = 1), y_train_pred.argmax(axis=1)))
 
    
 
    
 
    
 
    
 
                  precision    recall  f1-score   support
 
-           0       0.90      0.94      0.92        85
-           1       0.97      0.99      0.98        88
-           2       0.95      0.89      0.92        90
+           0       0.84      0.91      0.87        85
+           1       0.97      0.97      0.97        88
+           2       0.99      0.88      0.93        90
            3       0.99      0.96      0.97        81
-           4       0.99      0.98      0.98        91
-           5       0.97      0.98      0.98       471
-           6       0.88      0.94      0.91        81
-           7       0.97      0.96      0.96        90
-           8       0.90      0.88      0.89        84
+           4       0.99      0.95      0.97        91
+           5       0.97      0.98      0.97       471
+           6       0.87      0.96      0.91        81
+           7       0.97      0.97      0.97        90
+           8       0.93      0.88      0.90        84
 
-    accuracy                           0.96      1161
-   macro avg       0.95      0.95      0.95      1161
-weighted avg       0.96      0.96      0.96      1161
+    accuracy                           0.95      1161
+   macro avg       0.94      0.94      0.94      1161
+weighted avg       0.95      0.95      0.95      1161
 
    
 
    
 
    
@@ -1045,27 +1035,27 @@ 
    Assessing performanceLuckily, we did split our dataset into independent training and test sets. So, let’s check our MLP’s performance on the test set:
    
 
    
 
    
-
    y_test_pred = model_mlp.predict(X_test)
-print(classification_report(y_test.values.argmax(axis = 1), y_test_pred.argmax(axis=1)))
+
    y_test_pred = model_mlp.predict(X_test)
+print(classification_report(y_test.values.argmax(axis = 1), y_test_pred.argmax(axis=1)))
 
    
 
    
 
    
 
    
 
                  precision    recall  f1-score   support
 
-           0       0.73      0.83      0.78        23
-           1       0.76      0.65      0.70        20
-           2       0.74      0.78      0.76        18
-           3       0.93      0.93      0.93        27
-           4       0.88      0.88      0.88        17
-           5       0.91      0.91      0.91       117
-           6       0.80      0.74      0.77        27
-           7       1.00      0.94      0.97        18
-           8       0.69      0.75      0.72        24
+           0       0.61      0.74      0.67        23
+           1       1.00      0.55      0.71        20
+           2       0.69      0.61      0.65        18
+           3       0.93      0.96      0.95        27
+           4       1.00      0.82      0.90        17
+           5       0.87      0.91      0.89       117
+           6       0.67      0.81      0.73        27
+           7       0.94      0.89      0.91        18
+           8       0.68      0.62      0.65        24
 
-    accuracy                           0.85       291
-   macro avg       0.83      0.82      0.82       291
-weighted avg       0.85      0.85      0.85       291
+    accuracy                           0.82       291
+   macro avg       0.82      0.77      0.78       291
+weighted avg       0.83      0.82      0.82       291
 
    
 
    
 
    
@@ -1074,11 +1064,11 @@ 
    Assessing performanceBeside checking the overall scores, there are other options to further evaluate our MLP’s (or basically any other model’s) performance. One of the most commonly used ones is called confusion matrix (which you most likely have seen before in this course). A confusion matrix displays how often a given sample was predicted as a certain label, thus, for example, providing insights into differentiability, etc. . To implement this, we initially have to compute the confusion matrix:
    
 
    
 
    
-
    import numpy as np
-from sklearn.metrics import confusion_matrix
+
    import numpy as np
+from sklearn.metrics import confusion_matrix
 
-cm_svm = confusion_matrix(y_test.values.argmax(axis = 1), y_test_pred.argmax(axis=1))
-model_conf_matrix = cm_svm.astype('float') / cm_svm.sum(axis = 1)[:, np.newaxis]
+cm_svm = confusion_matrix(y_test.values.argmax(axis = 1), y_test_pred.argmax(axis=1))
+model_conf_matrix = cm_svm.astype('float') / cm_svm.sum(axis = 1)[:, np.newaxis]
 
    
 
    
 
    
@@ -1086,19 +1076,19 @@ 
    Assessing performanceAfter that, we can plot it for evaluation.
    
 
    
 
    
-
    import pandas as pd
-import seaborn as sns
+
    import pandas as pd
+import seaborn as sns
 
-df_cm = pd.DataFrame(model_conf_matrix, index = categories,
-                     columns = categories)
+df_cm = pd.DataFrame(model_conf_matrix, index = categories,
+                     columns = categories)
 
-plt.figure(figsize = (10,7))
-sns.heatmap(df_cm, annot = True, cmap = 'Blues', square = True)
-plt.xticks(rotation = 45)
-plt.title('MLP decoding results - confusion matrix' , fontsize = 15, fontweight = 'bold')
-plt.xlabel("true labels", fontsize = 14, fontweight = 'bold')
-plt.ylabel("predicted labels", fontsize = 14, fontweight = 'bold')
-plt.show()
+plt.figure(figsize = (10,7))
+sns.heatmap(df_cm, annot = True, cmap = 'Blues', square = True)
+plt.xticks(rotation = 45)
+plt.title('MLP decoding results - confusion matrix' , fontsize = 15, fontweight = 'bold')
+plt.xlabel("true labels", fontsize = 14, fontweight = 'bold')
+plt.ylabel("predicted labels", fontsize = 14, fontweight = 'bold')
+plt.show()
 
    
 
    
 
    
diff --git a/reports/haxby_data.log b/reports/haxby_data.log
new file mode 100644
index 0000000..496c04f
--- /dev/null
+++ b/reports/haxby_data.log
@@ -0,0 +1,44 @@
+Traceback (most recent call last):
+  File "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/jupyter_cache/executors/utils.py", line 51, in single_nb_execution
+    executenb(
+  File "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nbclient/client.py", line 1204, in execute
+    return NotebookClient(nb=nb, resources=resources, km=km, **kwargs).execute()
+  File "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nbclient/util.py", line 84, in wrapped
+    return just_run(coro(*args, **kwargs))
+  File "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nbclient/util.py", line 62, in just_run
+    return loop.run_until_complete(coro)
+  File "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/asyncio/base_events.py", line 616, in run_until_complete
+    return future.result()
+  File "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nbclient/client.py", line 663, in async_execute
+    await self.async_execute_cell(
+  File "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nbclient/client.py", line 965, in async_execute_cell
+    await self._check_raise_for_error(cell, cell_index, exec_reply)
+  File "/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nbclient/client.py", line 862, in _check_raise_for_error
+    raise CellExecutionError.from_cell_and_msg(cell, exec_reply_content)
+nbclient.exceptions.CellExecutionError: An error occurred while executing the following cell:
+------------------
+func_image.get_data()
+------------------
+
+---------------------------------------------------------------------------
+ExpiredDeprecationError                   Traceback (most recent call last)
+Cell In[14], line 1
+----> 1 func_image.get_data()
+
+File /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nibabel/deprecator.py:208, in Deprecator.__call__..deprecator..deprecated_func(*args, **kwargs)
+    205 @functools.wraps(func)
+    206 def deprecated_func(*args: P.args, **kwargs: P.kwargs) -> T:
+    207     if until and self.is_bad_version(until):
+--> 208         raise exception(message)
+    209     warnings.warn(message, warning, stacklevel=2)
+    210     return func(*args, **kwargs)
+
+ExpiredDeprecationError: get_data() is deprecated in favor of get_fdata(), which has a more predictable return type. To obtain get_data() behavior going forward, use numpy.asanyarray(img.dataobj).
+
+* deprecated from version: 3.0
+* Raises  as of version: 5.0
+ExpiredDeprecationError: get_data() is deprecated in favor of get_fdata(), which has a more predictable return type. To obtain get_data() behavior going forward, use numpy.asanyarray(img.dataobj).
+
+* deprecated from version: 3.0
+* Raises  as of version: 5.0
+
diff --git a/search.html b/search.html
index 8ff7455..a5649ff 100644
--- a/search.html
+++ b/search.html
@@ -30,7 +30,7 @@
     
     
     
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+    
     
   
 
diff --git a/searchindex.js b/searchindex.js
index 6917ade..24d5317 100644
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\ No newline at end of file
diff --git a/svm_decoding.html b/svm_decoding.html
index 33bc29c..3b45dd5 100644
--- a/svm_decoding.html
+++ b/svm_decoding.html
@@ -30,7 +30,7 @@
     
     
     
-    
+    
     
   
 
@@ -365,34 +365,34 @@ 
    Getting the data[HGF+01]. You can check section An overview of the Haxby dataset for more details on that dataset. Here we are going to quickly download it, and prepare it for machine learning applications with a set of predictive variable, the brain time series X, and a dependent variable, the annotation on cognition y.
    
 
    
 
    
-
    import os
-import warnings
-warnings.filterwarnings(action='once')
+
    import os
+import warnings
+warnings.filterwarnings(action='once')
 
-from nilearn import datasets
-# We are fetching the data for subject 4
-data_dir = os.path.join('..', 'data')
-sub_no = 4
-haxby_dataset = datasets.fetch_haxby(subjects=[sub_no], fetch_stimuli=True, data_dir=data_dir)
-func_file = haxby_dataset.func[0]
+from nilearn import datasets
+# We are fetching the data for subject 4
+data_dir = os.path.join('..', 'data')
+sub_no = 4
+haxby_dataset = datasets.fetch_haxby(subjects=[sub_no], fetch_stimuli=True, data_dir=data_dir)
+func_file = haxby_dataset.func[0]
 
-# mask the data
-from nilearn.input_data import NiftiMasker
-mask_filename = haxby_dataset.mask_vt[0]
-masker = NiftiMasker(mask_img=mask_filename, standardize=True, detrend=True)
-X = masker.fit_transform(func_file)
+# mask the data
+from nilearn.input_data import NiftiMasker
+mask_filename = haxby_dataset.mask_vt[0]
+masker = NiftiMasker(mask_img=mask_filename, standardize=True, detrend=True)
+X = masker.fit_transform(func_file)
 
-# cognitive annotations
-import pandas as pd
-behavioral = pd.read_csv(haxby_dataset.session_target[0], delimiter=' ')
-y = behavioral['labels']
+# cognitive annotations
+import pandas as pd
+behavioral = pd.read_csv(haxby_dataset.session_target[0], delimiter=' ')
+y = behavioral['labels']
 
    
 
    
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/datasets/func.py:20: DeprecationWarning: Please use `MatReadError` from the `scipy.io.matlab` namespace, the `scipy.io.matlab.miobase` namespace is deprecated.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/datasets/func.py:20: DeprecationWarning: Please use `MatReadError` from the `scipy.io.matlab` namespace, the `scipy.io.matlab.miobase` namespace is deprecated.
   from scipy.io.matlab.miobase import MatReadError
-/opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/datasets/__init__.py:93: FutureWarning: Fetchers from the nilearn.datasets module will be updated in version 0.9 to return python strings instead of bytes and Pandas dataframes instead of Numpy arrays.
+/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/datasets/__init__.py:93: FutureWarning: Fetchers from the nilearn.datasets module will be updated in version 0.9 to return python strings instead of bytes and Pandas dataframes instead of Numpy arrays.
   warn("Fetchers from the nilearn.datasets module will be "
 
    
 
    
@@ -401,10 +401,10 @@ 
    Getting the dataX and y:
    
 
    
 
    
-
    categories = y.unique()
-print(categories)
-print(y.shape)
-print(X.shape)
+
    categories = y.unique()
+print(categories)
+print(y.shape)
+print(X.shape)
 
    
 
    
 
    
@@ -424,8 +424,8 @@ 
    Training a model
 
    
-
    from sklearn.model_selection import train_test_split
-X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)   
+
    from sklearn.model_selection import train_test_split
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)   
 
    
 
    
 
    
@@ -433,9 +433,9 @@ 
    Training a model
 
    
-
    from sklearn.svm import SVC
-model_svm = SVC(random_state=0, kernel='linear', C=1)
-model_svm.fit(X_train, y_train)
+
    from sklearn.svm import SVC
+model_svm = SVC(random_state=0, kernel='linear', C=1)
+model_svm.fit(X_train, y_train)
 
    
 
    
 
    
@@ -451,9 +451,9 @@ 
    Assessing performanceLet’s check the accuracy of the prediction on the training set:
    
 
    
 
    
-
    from sklearn.metrics import classification_report
-y_train_pred = model_svm.predict(X_train)
-print(classification_report(y_train, y_train_pred))
+
    from sklearn.metrics import classification_report
+y_train_pred = model_svm.predict(X_train)
+print(classification_report(y_train, y_train_pred))
 
    
 
    
 
    
@@ -480,8 +480,8 @@ 
    Assessing performanceThis is dangerously high. Let’s check on the test set:
    
 
    
 
    
-
    y_test_pred = model_svm.predict(X_test)
-print(classification_report(y_test, y_test_pred))
+
    y_test_pred = model_svm.predict(X_test)
+print(classification_report(y_test, y_test_pred))
 
    
 
    
 
    
@@ -508,18 +508,18 @@ 
    Assessing performanceWe can have a look at the confusion matrix:
    
 
    
 
    
-
    # confusion matrix
-import sys
-import numpy as np
-from sklearn.metrics import confusion_matrix
-sys.path.append('../src')
-import visualization
-cm_svm = confusion_matrix(y_test, y_test_pred)
-model_conf_matrix = cm_svm.astype('float') / cm_svm.sum(axis=1)[:, np.newaxis]
+
    # confusion matrix
+import sys
+import numpy as np
+from sklearn.metrics import confusion_matrix
+sys.path.append('../src')
+import visualization
+cm_svm = confusion_matrix(y_test, y_test_pred)
+model_conf_matrix = cm_svm.astype('float') / cm_svm.sum(axis=1)[:, np.newaxis]
 
-visualization.conf_matrix(model_conf_matrix,
-                          categories,
-                          title='SVM decoding results on Haxby')
+visualization.conf_matrix(model_conf_matrix,
+                          categories,
+                          title='SVM decoding results on Haxby')
 
    
 
    
 
    
@@ -533,19 +533,19 @@ 
    Visualizing the weightsFinally we can visualize the weights of the (linear) classifier to see which brain region seem to impact most the decision, for example for faces:
    
 
    
 
    
-
    from nilearn import plotting
-# first row of coef_ is comparing the first pair of class labels
-# with 9 classes, there are 9 * 8 / 2 distinct
-coef_img = masker.inverse_transform(model_svm.coef_[0, :])
-plotting.view_img(
-    coef_img, bg_img=haxby_dataset.anat[0],
-    title="SVM weights", dim=-1, resampling_interpolation='nearest'
-)
+
    from nilearn import plotting
+# first row of coef_ is comparing the first pair of class labels
+# with 9 classes, there are 9 * 8 / 2 distinct
+coef_img = masker.inverse_transform(model_svm.coef_[0, :])
+plotting.view_img(
+    coef_img, bg_img=haxby_dataset.anat[0],
+    title="SVM weights", dim=-1, resampling_interpolation='nearest'
+)
 
    
 
    
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/scipy/ndimage/_measurements.py:305: DeprecationWarning: In future, it will be an error for 'np.bool_' scalars to be interpreted as an index
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/scipy/ndimage/_measurements.py:307: DeprecationWarning: In future, it will be an error for 'np.bool_' scalars to be interpreted as an index
   return _nd_image.find_objects(input, max_label)
 
    
 
    
@@ -596,17 +596,17 @@ 
    And now the easy wayWe can use the high-level Decoder object from Nilearn. See Decoder object for details. It reduces model specification and fit to two lines of code:
    
 
    
 
    
-
    from nilearn.decoding import Decoder
-# Specify the classifier to the decoder object.
-# With the decoder we can input the masker directly.
-# We are using the svc_l1 here because it is intra subject.
-#
-# cv=5 means that we use 5-fold cross-validation
-#
-# As a scoring scheme, one can use f1, accuracy or ROC-AUC
-#
-decoder = Decoder(estimator='svc', cv=5, mask=mask_filename, scoring='f1') 
-decoder.fit(func_file, y)
+
    from nilearn.decoding import Decoder
+# Specify the classifier to the decoder object.
+# With the decoder we can input the masker directly.
+# We are using the svc_l1 here because it is intra subject.
+#
+# cv=5 means that we use 5-fold cross-validation
+#
+# As a scoring scheme, one can use f1, accuracy or ROC-AUC
+#
+decoder = Decoder(estimator='svc', cv=5, mask=mask_filename, scoring='f1') 
+decoder.fit(func_file, y)
 
    
 
    
 
    
@@ -615,13 +615,13 @@ 
    And now the easy way
 
    
 
    
-
    print('F1 scores')
-for category in categories:
-    print(category, '\t\t    {:.2f}'.format(np.mean(decoder.cv_scores_[category])))
-plotting.view_img(
-    decoder.coef_img_['face'], bg_img=haxby_dataset.anat[0],
-    title="SVM weights for face", dim=-1, resampling_interpolation='nearest'
-)
+
    print('F1 scores')
+for category in categories:
+    print(category, '\t\t    {:.2f}'.format(np.mean(decoder.cv_scores_[category])))
+plotting.view_img(
+    decoder.coef_img_['face'], bg_img=haxby_dataset.anat[0],
+    title="SVM weights for face", dim=-1, resampling_interpolation='nearest'
+)
 
    
 
    
 
    
@@ -638,7 +638,7 @@ 
    And now the easy way
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/scipy/ndimage/_measurements.py:305: DeprecationWarning: In future, it will be an error for 'np.bool_' scalars to be interpreted as an index
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/scipy/ndimage/_measurements.py:307: DeprecationWarning: In future, it will be an error for 'np.bool_' scalars to be interpreted as an index
   return _nd_image.find_objects(input, max_label)
 
    
 
    
@@ -675,7 +675,7 @@ 
    And now the easy wayGetting more meaningful weight maps with Frem
 
    
-
    from nilearn.decoding import FREMClassifier
-frem = FREMClassifier(estimator='svc', cv=5, mask=mask_filename, scoring='f1')
-frem.fit(func_file, y)
-plotting.view_img(
-    frem.coef_img_['face'], bg_img=haxby_dataset.anat[0],
-    title="SVM weights for face", dim=-1, resampling_interpolation='nearest'
-)
+
    from nilearn.decoding import FREMClassifier
+frem = FREMClassifier(estimator='svc', cv=5, mask=mask_filename, scoring='f1')
+frem.fit(func_file, y)
+plotting.view_img(
+    frem.coef_img_['face'], bg_img=haxby_dataset.anat[0],
+    title="SVM weights for face", dim=-1, resampling_interpolation='nearest'
+)
 
    
 
    
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
   inv_max = dia_matrix((1. / max_connectivity, 0),
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
   inv_max = dia_matrix((1. / max_connectivity, 0),
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
   inv_max = dia_matrix((1. / max_connectivity, 0),
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
   inv_max = dia_matrix((1. / max_connectivity, 0),
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
   inv_max = dia_matrix((1. / max_connectivity, 0),
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
   inv_max = dia_matrix((1. / max_connectivity, 0),
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
   inv_max = dia_matrix((1. / max_connectivity, 0),
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
   inv_max = dia_matrix((1. / max_connectivity, 0),
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
   inv_max = dia_matrix((1. / max_connectivity, 0),
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
   inv_max = dia_matrix((1. / max_connectivity, 0),
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
   inv_max = dia_matrix((1. / max_connectivity, 0),
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
   inv_max = dia_matrix((1. / max_connectivity, 0),
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
   inv_max = dia_matrix((1. / max_connectivity, 0),
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
   inv_max = dia_matrix((1. / max_connectivity, 0),
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
   inv_max = dia_matrix((1. / max_connectivity, 0),
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
   inv_max = dia_matrix((1. / max_connectivity, 0),
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
   inv_max = dia_matrix((1. / max_connectivity, 0),
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
   inv_max = dia_matrix((1. / max_connectivity, 0),
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
   inv_max = dia_matrix((1. / max_connectivity, 0),
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
   inv_max = dia_matrix((1. / max_connectivity, 0),
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
   inv_max = dia_matrix((1. / max_connectivity, 0),
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
   inv_max = dia_matrix((1. / max_connectivity, 0),
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
   inv_max = dia_matrix((1. / max_connectivity, 0),
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
   inv_max = dia_matrix((1. / max_connectivity, 0),
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
   inv_max = dia_matrix((1. / max_connectivity, 0),
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
   inv_max = dia_matrix((1. / max_connectivity, 0),
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
   inv_max = dia_matrix((1. / max_connectivity, 0),
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
   inv_max = dia_matrix((1. / max_connectivity, 0),
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
   inv_max = dia_matrix((1. / max_connectivity, 0),
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
   inv_max = dia_matrix((1. / max_connectivity, 0),
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
   inv_max = dia_matrix((1. / max_connectivity, 0),
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
   inv_max = dia_matrix((1. / max_connectivity, 0),
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
   inv_max = dia_matrix((1. / max_connectivity, 0),
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
   inv_max = dia_matrix((1. / max_connectivity, 0),
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
   inv_max = dia_matrix((1. / max_connectivity, 0),
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
   inv_max = dia_matrix((1. / max_connectivity, 0),
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
   inv_max = dia_matrix((1. / max_connectivity, 0),
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
   inv_max = dia_matrix((1. / max_connectivity, 0),
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
   inv_max = dia_matrix((1. / max_connectivity, 0),
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
   inv_max = dia_matrix((1. / max_connectivity, 0),
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
   inv_max = dia_matrix((1. / max_connectivity, 0),
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
   inv_max = dia_matrix((1. / max_connectivity, 0),
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
   inv_max = dia_matrix((1. / max_connectivity, 0),
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
   inv_max = dia_matrix((1. / max_connectivity, 0),
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/nilearn/regions/rena_clustering.py:205: RuntimeWarning: divide by zero encountered in true_divide
   inv_max = dia_matrix((1. / max_connectivity, 0),
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
   warnings.warn(
 
    
 
    
-
    /opt/hostedtoolcache/Python/3.8.15/x64/lib/python3.8/site-packages/scipy/ndimage/_measurements.py:305: DeprecationWarning: In future, it will be an error for 'np.bool_' scalars to be interpreted as an index
+
    /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/scipy/ndimage/_measurements.py:307: DeprecationWarning: In future, it will be an error for 'np.bool_' scalars to be interpreted as an index
   return _nd_image.find_objects(input, max_label)
 
    
 
    
@@ -1260,7 +1260,7 @@ 
    Getting more meaningful weight maps with Frem
 
    
-
    print('F1 scoreswith FREM')
-for category in categories:
-    print(category, '\t\t    {:.2f}'.format(np.mean(decoder.cv_scores_[category])))
+
    print('F1 scoreswith FREM')
+for category in categories:
+    print(category, '\t\t    {:.2f}'.format(np.mean(decoder.cv_scores_[category])))