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@ericdatakelly
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Last active March 20, 2021 20:01
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def task_data():
"download the sample data"
return dict(actions=["wget https://docs.google.com/uc?id=1rkoE_xoUN7YMefnUcJ-LXAlI5gU8qYLv&export=download -O test_FA.nii.gz".split()], targets=["test_FA.nii.gz"])
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Other comments:\n",
"\n",
"I edited the text and left one comment in bold. The other comments are here:\n",
"\n",
"- Don't forget to follow the task list in [your issue](https://github.com/Quansight/content-marketing/issues/79), which includes alt text, high res images, etc.\n",
"- Move the final notebook to the content-creation repo\n",
"- I tried to make smaller paragraphs and sentences, which is better for blogging (most readers want to skim articles)\n",
"- If you're ok with the quote I added, let's be sure Brandon knows it's a quote that should stand out a bit\n",
"- Do a final check on spelling and capitalization, including library names, like NumPy (but when written as code, use lower case, like numpy.ndarray)\n",
"- Is there a link to the source of the data used in the example?\n",
"- Explain why this works in a sentence or two if possible: \"Now we can project the mean back onto one subject for visualization.\"\n",
"- Add comments to the code in some places, like when the mask is applied to highlight the white matter. Help the reader understand the steps.\n",
"- When you threshold the data to remove the gray matter (setting values to zero), explain why the image is not black in those parts. It shows shades of gray and preserves the contours of the brain instead.\n",
"- There's something gruesome about looking at eigen brains for fun. :) Is there another way to state it?\n",
"- Is it common to use superscripts in this kind of computational work? Would `VT` be better for a blog post than `Vᵀ`?\n",
"- Sum up what the reader should learn from the second brain example like you did in the first example.\n",
"- Is there a more intuitive way to explain what `a.indptr` does or what you have the simple explanation?\n",
"- Consider switching to the newer 'f' type print in python: \n",
"\n",
"`print(f\"The nonzero columns of row {i} {a.indices[a.indptr[i]:a.indptr[i+1]]}\")`\n",
"\n",
"- Did you intend to leave these lines in the cell?\n",
"\n",
"`\n",
" #print('compressed axes: %s compression ratio: %s ' %(axis, y.nbytes/(np.prod(y.shape)*y.dtype.itemsize) *100),\n",
" # 'shape of underlying matrix: ', y._compressed_shape)\n",
"`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Leveraging sparsity for efficient data manipulation: the GCXS sparse array format"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Across many areas of science, sparsity is ubiquitous. Whether in the form of sparse data arrays, sparse linear operators, or the results from a sparsity-promoting optimization, data in the form of sparse vectors, matrices, and tensors are commonplace in scientific computing. In this post, I will highlight the pydata/sparse package and the recently added GCXS sparse array format for working with large sparse data. For a more detailed demonstration of the GCXS sparse array format and operations, see the more technical section at the end of this post.\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Quick introduction to sparse data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src=\"mean_img.png\" width=\"500\" height=\"500\" align=\"left\"/> \n",
"<img src=\"empty_space.png\" width=\"600\" height=\"600\" align=\"center\"/>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Neuroimaging data is an interesting test case. MRI data is collected in a 3-dimensional grid and is often transformed into a standard space with specific dimensions. One standard, the Montreal Neurological Institute space (MNI152), has the dimensions (91, 109, 91). A brain in this space, however, typically occupies only a third of the full space, with the remainder of the array filled with zeros.\n",
"\n",
"When working with more dimensions like time or research subjects, these datasets can get quite large and be computationally expensive. Shown above on the left is an averaged fractional anisotropy (FA) image. The image on the right illustrates all of the remaining space, which is generally filled with zeros."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"identity (99% sparse) | symmetric (99% sparse) | random (99% sparse) \n",
"- | - | -\n",
"![alt](eye_matrix.png) | ![alt](symmetric2_matrix.png) | ![alt](random2.png) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"\n",
"Sparse data structures, by definition, consist mostly of the same repeated value, typically zero. In the three matricies shown above, only 1% of the entries have a nonzero (nz) value. This feature can be exploited so as to reduce storage and runtime; sparse data is highly compressible because we can get away with only storing the nonzero values. \n",
"\n",
"There are many ways to store the nonzeros of a sparse data structure, which are manifested in sparse storage formats and are efficient for certain operations. For example, a hashmap is well-suited for storing pairs of indices and their corresponding nonzero values and allows for efficient writing and mutating, but is poorly-suited for mathematical operations. By contrast, the compressed sparse row (CSR) storage format is well-suited for fast mathematical operations and compresses nicely but offers extremely inefficient writing and mutating capabilities. \n",
"\n",
"There are many widely-used sparse matrix storage formats, many of which, however, do not generalize well to higher dimensional arrays. Some are listed below:\n",
"\n",
" - dictionary of keys (DOK)\n",
" - compressed sparse column (CSC)\n",
" - compressed sparse row (CSR)\n",
" - coordinate list (COO)\n",
" - list of lists (LIL)\n",
" - block compressed sparse row (BSR)\n",
" \n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Can we just use `scipy.sparse`?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the scientific Python ecosystem, scipy.sparse has long been the center piece of all sparse computation. It includes:\n",
"\n",
" - all of the above sparse matrix formats\n",
" - sparse linear system solvers (both direct and iterative)\n",
" - sparse eigen solvers (including support for the singular value decomposition)\n",
"\n",
"Additionaly, libraries that utilize sparse data such as scikit-learn rely on scipy.sparse. While being a mature and fast codebase, scipy.sparse emulates the numpy.matrix interface, which is restricted to two dimensions and is pending deprecation. As a result, scipy.sparse doesn't work well in places that numpy.ndarray does. It also doesn't play nicely with the many libraries that emulate the numpy.ndarray interface such as dask and xarray. \n",
"\n",
"Because some of these libraries play important roles in scaling out the scientific Python ecosystem to larger data and distributed computing platforms, the reliance on the numpy.matrix interface is holding scipy.sparse back.\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> scipy.sparse doesn't work well in places that numpy.ndarray does"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## `pydata/sparse` aims to be the solution"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In efforts to address the concerns of scipy.sparse, the pydata/sparse library implements n-dimensional sparse data structures that emulate the numpy.ndarray interface. Pydata/sparse does this by extending two sparse matrix formats and supporting common numpy operations: \n",
" - extends DOK for writing (uses a hashmap)\n",
" - extends COO for computing (uses Numpy arrays to store coordinates and nonzero values)\n",
" - element-wise operations\n",
" - reductions\n",
" - np.dot and dot-related operations\n",
" - compatible with Xarray and Dask\n",
"\n",
"The COO format, while fundamental, has several shortcomings in the way of compression and interoperability with the current users. Regarding compression, because COO stores a coordinate for each dimension of an array along with the nonzeros, as the number of dimensions increases, the storage requirements can quickly become unfavorable (O(ndim * nnz)). Also, most of the functions written inside scikit-learn and elsewhere work with compressed sparse row/column (CSR/CSC) matrices, not COO.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## GCXS format addresses shortcomings"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With these issues in mind, pydata/sparse recently released the GCXS format, inspired by the GCRS/GCCS formats of [Shaikh and Hasan 2015](https://ieeexplore.ieee.org/document/7237032/authors#authors). \n",
"\n",
"GCXS is an n-dimensional generalization of CSR/CSC. A main feature is that 2-D GCXS arrays are identical to CSR/CSC matrices (depending on which axis is compressed). This makes it so that libraries that use the CSR/CSC model internally, like scikit-learn, can easily be modified to work with GCXS arrays. GCXS is also capable of a high degree of compression."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### *Returning to neuroimagining data...*"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Because the neuroimaging data exceeds two dimensions, we can't use Scipy.sparse. When the density of an array grows too high, sparse array formats become less and less beneficial. Data with such high density (0.33) pushes the limits of what is useful for most sparse array formats. \n",
"\n",
"Let's import what we need and see how well we can do."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import sparse\n",
"import numpy\n",
"import pandas\n",
"import nibabel\n",
"import scipy.sparse\n",
"import nilearn.plotting\n",
"import warnings\n",
"warnings.filterwarnings('ignore')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" Here is a dataset with sixteen subjects:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"img = nibabel.load('test_FA.nii.gz')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As a quick data check, let's have a look at the fractional anisotropy (FA) values of our first subjects' brain."
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 475.2x187.2 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"nilearn.plotting.plot_anat(img.slicer[...,0]);"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table><tbody><tr><th style=\"text-align: left\">Format</th><td style=\"text-align: left\">gcxs</td></tr><tr><th style=\"text-align: left\">Data Type</th><td style=\"text-align: left\">float64</td></tr><tr><th style=\"text-align: left\">Shape</th><td style=\"text-align: left\">(91, 109, 91, 16)</td></tr><tr><th style=\"text-align: left\">nnz</th><td style=\"text-align: left\">4597231</td></tr><tr><th style=\"text-align: left\">Density</th><td style=\"text-align: left\">0.31832229797624495</td></tr><tr><th style=\"text-align: left\">Read-only</th><td style=\"text-align: left\">True</td></tr><tr><th style=\"text-align: left\">Size</th><td style=\"text-align: left\">70.1M</td></tr><tr><th style=\"text-align: left\">Storage ratio</th><td style=\"text-align: left\">0.6</td></tr><tr><th style=\"text-align: left\">Compressed Axes</th><td style=\"text-align: left\">(3,)</td></tr></tbody></table>"
],
"text/plain": [
"<GCXS: shape=(91, 109, 91, 16), dtype=float64, nnz=4597231, fill_value=0.0, compressed_axes=(3,)>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = sparse._compressed.GCXS.from_numpy(img.get_data(), compressed_axes=[3])\n",
"data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The storage ratio (compressed/uncompressed) is 0.6. This means the storage requirements of this GCXS array are 60% of the resources required to store this same data in a numpy.ndarray. So even with the high data density (0.318) we are able to get substantial compression with GCXS. \n",
"\n",
"Because of the high density and the high number of dimensions, COO is much less suited to store the data. "
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table><tbody><tr><th style=\"text-align: left\">Format</th><td style=\"text-align: left\">coo</td></tr><tr><th style=\"text-align: left\">Data Type</th><td style=\"text-align: left\">float64</td></tr><tr><th style=\"text-align: left\">Shape</th><td style=\"text-align: left\">(91, 109, 91, 16)</td></tr><tr><th style=\"text-align: left\">nnz</th><td style=\"text-align: left\">4597231</td></tr><tr><th style=\"text-align: left\">Density</th><td style=\"text-align: left\">0.31832229797624495</td></tr><tr><th style=\"text-align: left\">Read-only</th><td style=\"text-align: left\">True</td></tr><tr><th style=\"text-align: left\">Size</th><td style=\"text-align: left\">175.4M</td></tr><tr><th style=\"text-align: left\">Storage ratio</th><td style=\"text-align: left\">1.6</td></tr></tbody></table>"
],
"text/plain": [
"<COO: shape=(91, 109, 91, 16), dtype=float64, nnz=4597231, fill_value=0.0>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.tocoo()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With a storage ratio of 1.6, using COO requires 160% of the storage requirements of a numpy.ndarray for this data.\n",
"\n",
"One common need in a diffusion-weighted imaging analysis is to separate the white matter from the gray matter. The goal is to define a white matter mask that we can use on all of our subjects. \n",
"\n",
"One simple way to do this is to take the mean across subjects and threshold the data. When we are working with fractional anisotropy values (FA), the white matter and gray matter are naturally separated by higher values in the white matter. Because we are interested in computing across subjects, we can compress the subjects' dimension and group all of the spatial dimensions together. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Just like if this were a NumPy array, we can take the mean along the third axis, corresponding to subjects:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"mean = data.mean(axis=3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can project the mean back onto one subject for visualization."
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 475.2x187.2 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Nifti1Image() requires min and max values passed in the header and dense data format\n",
"header = img.header\n",
"header['cal_min'] = mean.min()\n",
"header['cal_max'] = mean.max()\n",
"mean_dense = mean.todense()\n",
"mean_img = nibabel.Nifti1Image(mean_dense, header=header, affine=img.affine)\n",
"nilearn.plotting.plot_stat_map(mean_img, colorbar=False);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The white matter comes out very bright. We can threshold the data to fully separate the white matter from the gray."
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 475.2x187.2 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"mean_dense[mean_dense < .2] = 0\n",
"mean_img = nibabel.Nifti1Image(mean_dense, header=header, affine=img.affine)\n",
"nilearn.plotting.plot_stat_map(mean_img, colorbar=False);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### *What just happened?*"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this short demonstration we were able to use Sparse arrays instead of Numpy arrays without needing to learn a different interface, because Sparse copies the Numpy interface. Instead of computing the mean across every measurement we were able to compute over only the necessary measurements, while using only 60% of the memory required by Numpy."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## How about SVD?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pydata/sparse is primarily focused on providing sparse array objects. The objects are compatible with the algorithms in Scipy.sparse, which include direct and iterative sparse linear system solvers and iterative eigen solvers. \n",
"\n",
"The singular value decomposition is a critical procedure for a lot of data analytics applications. Using sparse arrays alongside scipy makes this procedure feel natural. Similar to generating \"eigen faces,\" with the SVD we can look at the low-rank structure of our data and visualize \"eigen brains\", possibly to denoise the data or examine structure, but mostly for fun.\n",
"\n",
"The singular value decomposition requires that we fold the sparse data into a new shape. Otherwise we raise an error."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"try: scipy.sparse.linalg.svds(data, 15)\n",
"except ValueError: ..."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A singular value decomposition can now operate on the folded 2-D form."
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [],
"source": [
"folded = sparse._compressed.GCXS(\n",
" (data.data, data.indices, data.indptr),\n",
" shape=data._compressed_shape[::-1], \n",
" compressed_axes=[1])\n",
"\n",
"U, S, Vᵀ = scipy.sparse.linalg.svds(folded, 15)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The columns of U hold the dominant spatial patterns of the data. We can reshape these vectors and project them onto the brain data:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 525.6x187.2 with 5 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 525.6x187.2 with 5 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 525.6x187.2 with 5 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for i in range(3):\n",
" image = U[:,i].reshape((91,109,91))\n",
" header['cal_min'], header['cal_max'] = image.min(), image.max()\n",
" brain = nibabel.Nifti1Image(image, header=header, affine=img.affine)\n",
" nilearn.plotting.plot_stat_map(brain)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### *What just happened?*"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**What did we learn from the last example? What should I see in the images? Add at least a sentence or two.**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Conclusion"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Problems involving sparsity are pervasive in scientific computing. Efficiently storing and manipulating large sparse arrays requires thoughtful storage formats. In an ecosystem where more and more libraries emulate and interact with NumPy, it's important that sparse arrays be compatable, especially with libraries like Dask, for predictablity of behavior and scalability. Pydata/sparse arrays offer these features. \n",
"\n",
"Each storage format offers different advantages. Because GCXS is identical to CSR/CSC for two-dimensional arrays, it is primed to work effectively in all of the libraries that expect CSR/CSC. It also excels in the same areas as CSR/CSC; namely in fast matrix multiplication. Above and beyond this, GCXS offers good compression for storage of high-dimensional arrays."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# A Closer Look at GCXS Operations"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Understanding how GCXS works requires first understanding how the CSR/CSC model works for matrices. For a CSR matrix, we store three arrays: the nonzero data, the indices corresponding the columns of each of the nonzero data, and an array of integers that point to where each row begins in the indices array.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Matrix indexing"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here we make a 5x5 CSR matrix"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"a = sparse.random((5,5), density=.2, format='gcxs')"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table><tbody><tr><th style=\"text-align: left\">Format</th><td style=\"text-align: left\">gcxs</td></tr><tr><th style=\"text-align: left\">Data Type</th><td style=\"text-align: left\">float64</td></tr><tr><th style=\"text-align: left\">Shape</th><td style=\"text-align: left\">(5, 5)</td></tr><tr><th style=\"text-align: left\">nnz</th><td style=\"text-align: left\">5</td></tr><tr><th style=\"text-align: left\">Density</th><td style=\"text-align: left\">0.2</td></tr><tr><th style=\"text-align: left\">Read-only</th><td style=\"text-align: left\">True</td></tr><tr><th style=\"text-align: left\">Size</th><td style=\"text-align: left\">128</td></tr><tr><th style=\"text-align: left\">Storage ratio</th><td style=\"text-align: left\">0.6</td></tr><tr><th style=\"text-align: left\">Compressed Axes</th><td style=\"text-align: left\">(0,)</td></tr></tbody></table>"
],
"text/plain": [
"<GCXS: shape=(5, 5), dtype=float64, nnz=5, fill_value=0.0, compressed_axes=(0,)>"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[0. , 0. , 0. , 0. , 0. ],\n",
" [0. , 0. , 0. , 0. , 0. ],\n",
" [0. , 0. , 0.13671017, 0. , 0. ],\n",
" [0. , 0. , 0. , 0.65074899, 0. ],\n",
" [0.76342748, 0.53543005, 0. , 0. , 0.00682385]])"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a.todense()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As we can see, the data is sorted in row-major ordering:"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0.13671017, 0.65074899, 0.76342748, 0.53543005, 0.00682385])"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a.data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The indices store which columns have nonzeros:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([2, 3, 0, 1, 4])"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a.indices"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The index pointer array tells us which rows are paired with the columns in 'indices'"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0, 0, 0, 1, 2, 5])"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a.indptr"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The columns stored in 'indices' for the i-th row correspond to a.indices[a.indptr[i]:a.indptr[i+1]]"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The nonzero columns of row 0 []\n",
"The nonzero columns of row 1 []\n",
"The nonzero columns of row 2 [2]\n",
"The nonzero columns of row 3 [3]\n",
"The nonzero columns of row 4 [0 1 4]\n"
]
}
],
"source": [
"for i in range(5):\n",
" print(\"The nonzero columns of row %s \" %(i), a.indices[a.indptr[i]:a.indptr[i+1]])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The CSC data structure uses the same idea but stores the data in column-major ordering:"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table><tbody><tr><th style=\"text-align: left\">Format</th><td style=\"text-align: left\">gcxs</td></tr><tr><th style=\"text-align: left\">Data Type</th><td style=\"text-align: left\">float64</td></tr><tr><th style=\"text-align: left\">Shape</th><td style=\"text-align: left\">(5, 5)</td></tr><tr><th style=\"text-align: left\">nnz</th><td style=\"text-align: left\">5</td></tr><tr><th style=\"text-align: left\">Density</th><td style=\"text-align: left\">0.2</td></tr><tr><th style=\"text-align: left\">Read-only</th><td style=\"text-align: left\">True</td></tr><tr><th style=\"text-align: left\">Size</th><td style=\"text-align: left\">128</td></tr><tr><th style=\"text-align: left\">Storage ratio</th><td style=\"text-align: left\">0.6</td></tr><tr><th style=\"text-align: left\">Compressed Axes</th><td style=\"text-align: left\">(1,)</td></tr></tbody></table>"
],
"text/plain": [
"<GCXS: shape=(5, 5), dtype=float64, nnz=5, fill_value=0.0, compressed_axes=(1,)>"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b = a.change_compressed_axes([1])\n",
"b"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[0. , 0. , 0. , 0. , 0. ],\n",
" [0. , 0. , 0. , 0. , 0. ],\n",
" [0. , 0. , 0.13671017, 0. , 0. ],\n",
" [0. , 0. , 0. , 0.65074899, 0. ],\n",
" [0.76342748, 0.53543005, 0. , 0. , 0.00682385]])"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b.todense()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now the data is sorted in column-major form:"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0.76342748, 0.53543005, 0.13671017, 0.65074899, 0.00682385])"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b.data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The 'indices' array now stores the rows of the nonzero values:"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([4, 4, 2, 3, 4])"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b.indices"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And now the index pointer array tells us which of the rows in 'indices' correspond to which column:"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0, 1, 2, 3, 4, 5])"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b.indptr"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The nonzero rows of column 0 [4]\n",
"The nonzero rows of column 1 [4]\n",
"The nonzero rows of column 2 [2]\n",
"The nonzero rows of column 3 [3]\n",
"The nonzero rows of column 4 [4]\n"
]
}
],
"source": [
"for i in range(5):\n",
" print(\"The nonzero rows of column %s \" %(i), b.indices[b.indptr[i]:b.indptr[i+1]])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For GCXS, CSR and CSC can be thought of as the same data structure, but with a different compressed axis. For CSR the first axis (rows) are compressed whereas for CSC the second axis (columns) are compressed."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(0,)"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a.compressed_axes"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(1,)"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b.compressed_axes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Transposing and reshaping"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The way we can work with arrays of higher dimensions using this kind of scheme is by transposing axes and reshaping the original array into a two-dimensional array and applying the same methods. We transpose the axes to put the compressed axes first and the reshape the array to two dimensions with the resulting shape: (product of compressed axes, product of uncompressed axes). For a 3x4x5 array, if we compress the first axis then we will store a CSR matrix with a shape of (3, 4x5=20)."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"x = sparse.random((3,4,5), density=.2, format='gcxs')"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(3, 20)"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x._compressed_shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If we compress the second axis, then we transpose the array and reshape it into a (4, 3x5=15) matrix."
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(4, 15)"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x.change_compressed_axes([1])._compressed_shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can also compress multiple axes:"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(15, 4)"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x.change_compressed_axes([0,2])._compressed_shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Compression"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Different choices in compressed axes affects the degree of compression and the speed of certain operations, especially indexing. Like CSR/CSC, the storage demands are determined by the size of the compressed axes and the number of nonzeros. \n",
"\n",
"Because the storage demands of a GCXS array are not dependent on the number of dimensions of the array, GCXS compresses well even with high dimensional arrays. The best compression is achieved by compressing the smallest axis. \n",
"\n",
"Let's several compression options and see how well they work:"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>compressed_axes</th>\n",
" <th>percent_compression</th>\n",
" <th>compressed_shape</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>[0]</td>\n",
" <td>30.003429</td>\n",
" <td>(5, 35000)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>[1]</td>\n",
" <td>30.012000</td>\n",
" <td>(20, 8750)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>[2]</td>\n",
" <td>30.020571</td>\n",
" <td>(35, 5000)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>[3]</td>\n",
" <td>30.029143</td>\n",
" <td>(50, 3500)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>[0, 1]</td>\n",
" <td>30.057714</td>\n",
" <td>(100, 1750)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>[0, 2]</td>\n",
" <td>30.100571</td>\n",
" <td>(175, 1000)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>[0, 3]</td>\n",
" <td>30.143429</td>\n",
" <td>(250, 700)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>[1, 2]</td>\n",
" <td>30.400571</td>\n",
" <td>(700, 250)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>[1, 3]</td>\n",
" <td>30.572000</td>\n",
" <td>(1000, 175)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>[2, 3]</td>\n",
" <td>31.000571</td>\n",
" <td>(1750, 100)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>[0, 1, 2]</td>\n",
" <td>32.000571</td>\n",
" <td>(3500, 50)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>[0, 2, 3]</td>\n",
" <td>35.000571</td>\n",
" <td>(8750, 20)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>[1, 2, 3]</td>\n",
" <td>50.000571</td>\n",
" <td>(35000, 5)</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" compressed_axes percent_compression compressed_shape\n",
"0 [0] 30.003429 (5, 35000)\n",
"1 [1] 30.012000 (20, 8750)\n",
"2 [2] 30.020571 (35, 5000)\n",
"3 [3] 30.029143 (50, 3500)\n",
"4 [0, 1] 30.057714 (100, 1750)\n",
"5 [0, 2] 30.100571 (175, 1000)\n",
"6 [0, 3] 30.143429 (250, 700)\n",
"7 [1, 2] 30.400571 (700, 250)\n",
"8 [1, 3] 30.572000 (1000, 175)\n",
"9 [2, 3] 31.000571 (1750, 100)\n",
"10 [0, 1, 2] 32.000571 (3500, 50)\n",
"11 [0, 2, 3] 35.000571 (8750, 20)\n",
"12 [1, 2, 3] 50.000571 (35000, 5)"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y = sparse.random((5,20,35,50), density=.15, format='gcxs')\n",
"axes = ([0], [1], [2], [3], [0,1], \n",
" [0,2], [0,3], [1,2], \n",
" [1,3], [2,3], [0,1,2],\n",
" [0,2,3], [1,2,3])\n",
"percent_stored = []\n",
"shapes = []\n",
"for axis in axes:\n",
" y = y.change_compressed_axes(axis)\n",
" percent_stored.append(y.nbytes/(numpy.prod(y.shape)*y.dtype.itemsize) *100)\n",
" shapes.append(y._compressed_shape)\n",
" #print('compressed axes: %s compression ratio: %s ' %(axis, y.nbytes/(np.prod(y.shape)*y.dtype.itemsize) *100),\n",
" # 'shape of underlying matrix: ', y._compressed_shape)\n",
"df = pandas.DataFrame()\n",
"df['compressed_axes'] = axes\n",
"df['percent_compression'] = percent_stored\n",
"df['compressed_shape'] = shapes\n",
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As we can see, compressing the three largest axes together gets the worst compression. Nonetheless, in this example the worst compression still results in storing only half of the bytes needed for the equivelant dense array. Also, with this number of dimensions, the compression will usually be much better than COO:"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"75.0"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y.tocoo().nbytes/(numpy.prod(y.shape)*y.dtype.itemsize) *100"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"GCXS arrays compress well and look and behave like NumPy arrays. GCXS currently supports the following operations:\n",
" - indexing\n",
" - multiplication\n",
" - dot\n",
" - tensordot\n",
" - matmul\n",
" - element-wise operations (including broadcasting)\n",
" - np.sin\n",
" - np.sqrt\n",
" - np.add\n",
" - np.log\n",
" - ...\n",
" - reductions\n",
" - min\n",
" - max\n",
" - sum\n",
" - prod\n",
" - mean\n",
" - std\n",
" - var\n",
" - ..."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
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"version": "3.8.5"
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},
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git+https://github.com/pydata/sparse@master
scipy
numpy
pandas
nibabel
nilearn
doit
matplotlib
pytest
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