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Update docs dependencies; re-run notebooks

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Alinson S. Xavier
2025-06-10 12:28:39 -05:00
parent e0b4181579
commit a306f0df26
10 changed files with 388 additions and 302 deletions
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38
docs/guide/collectors.ipynb
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@@ -14,7 +14,7 @@
"\n",
"## HDF5 Format\n",
"\n",
"MIPLearn stores all training data in [HDF5](HDF5) (Hierarchical Data Format, Version 5) files. The HDF format was originally developed by the [National Center for Supercomputing Applications][NCSA] (NCSA) for storing and organizing large amounts of data, and supports a variety of data types, including integers, floating-point numbers, strings, and arrays. Compared to other formats, such as CSV, JSON or SQLite, the HDF5 format provides several advantages for MIPLearn, including:\n",
"MIPLearn stores all training data in [HDF5][HDF5] (Hierarchical Data Format, Version 5) files. The HDF format was originally developed by the [National Center for Supercomputing Applications][NCSA] (NCSA) for storing and organizing large amounts of data, and supports a variety of data types, including integers, floating-point numbers, strings, and arrays. Compared to other formats, such as CSV, JSON or SQLite, the HDF5 format provides several advantages for MIPLearn, including:\n",
"\n",
"- *Storage of multiple scalars, vectors and matrices in a single file* --- This allows MIPLearn to store all training data related to a given problem instance in a single file, which makes training data easier to store, organize and transfer.\n",
"- *High-performance partial I/O* --- Partial I/O allows MIPLearn to read a single element from the training data (e.g. value of the optimal solution) without loading the entire file to memory or reading it from beginning to end, which dramatically improves performance and reduces memory requirements. This is especially important when processing a large number of training data files.\n",
@@ -58,21 +58,21 @@
"x1 = 1\n",
"x2 = hello world\n",
"x3 = [1 2 3]\n",
"x4 = [[0.37454012 0.9507143 0.7319939 ]\n",
" [0.5986585 0.15601864 0.15599452]\n",
" [0.05808361 0.8661761 0.601115 ]]\n",
"x5 = (3, 2)\t0.6803075671195984\n",
" (2, 3)\t0.4504992663860321\n",
" (0, 4)\t0.013264961540699005\n",
" (2, 0)\t0.9422017335891724\n",
" (2, 4)\t0.5632882118225098\n",
" (1, 2)\t0.38541650772094727\n",
" (1, 1)\t0.015966251492500305\n",
" (0, 3)\t0.2308938205242157\n",
" (4, 4)\t0.24102546274662018\n",
" (3, 1)\t0.6832635402679443\n",
" (1, 3)\t0.6099966764450073\n",
" (3, 0)\t0.83319491147995\n"
"x4 = [[0.37454012 0.95071431 0.73199394]\n",
" [0.59865848 0.15601864 0.15599452]\n",
" [0.05808361 0.86617615 0.60111501]]\n",
"x5 = (3, 2)\t0.6803075385877797\n",
" (2, 3)\t0.450499251969543\n",
" (0, 4)\t0.013264961159866528\n",
" (2, 0)\t0.9422017556848528\n",
" (2, 4)\t0.5632882178455393\n",
" (1, 2)\t0.3854165025399161\n",
" (1, 1)\t0.015966252220214194\n",
" (0, 3)\t0.230893825622149\n",
" (4, 4)\t0.24102546602601171\n",
" (3, 1)\t0.6832635188254582\n",
" (1, 3)\t0.6099966577826209\n",
" (3, 0)\t0.8331949117361643\n"
]
}
],
@@ -108,12 +108,6 @@
" print(\"x5 =\", h5.get_sparse(\"x5\"))"
]
},
{
"cell_type": "markdown",
"id": "50441907",
"metadata": {},
"source": []
},
{
"cell_type": "markdown",
"id": "d0000c8d",