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‎.gitignore

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target/
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**/*.rs.bk
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Cargo.lock
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.tox/
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build/
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dist/
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*.egg-info
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__pycache__/

‎.nojekyll

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‎_redirect.html

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<!DOCTYPE html>
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<html>
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<head>
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<meta charset="utf-8" />
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<meta http-equiv="X-UA-Compatible" content="IE=edge,chrome=1" />
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<meta http-equiv="refresh" content="0;URL=rascaline/index.html" />
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<body></body>
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‎featomic-torch-v0.6.0-rc1/.buildinfo

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# Sphinx build info version 1
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# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
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config: 03e11edcf884b4e6e1f62e583ec21792
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tags: 645f666f9bcd5a90fca523b33c5a78b7

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‎featomic-torch-v0.6.0-rc1/_downloads/081634a43959105f7f41a7682e76d405/property-selection.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"\n# Property Selection\n\n.. start-body\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"import chemfiles\nimport numpy as np\nfrom metatensor import Labels, MetatensorError, TensorBlock, TensorMap\nfrom skmatter.feature_selection import FPS\n\nfrom featomic import SoapPowerSpectrum"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"First we load the dataset with chemfiles\n\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"with chemfiles.Trajectory(\"dataset.xyz\") as trajectory:\n frames = [f for f in trajectory]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"and define the hyper parameters of the representation\n\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"HYPER_PARAMETERS = {\n \"cutoff\": {\n \"radius\": 5.0,\n \"smoothing\": {\"type\": \"ShiftedCosine\", \"width\": 0.5},\n },\n \"density\": {\n \"type\": \"Gaussian\",\n \"width\": 0.3,\n },\n \"basis\": {\n \"type\": \"TensorProduct\",\n \"max_angular\": 4,\n \"radial\": {\"type\": \"Gto\", \"max_radial\": 6},\n },\n}\n\ncalculator = SoapPowerSpectrum(**HYPER_PARAMETERS)\n\ndescriptor = calculator.compute(frames)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The selections for feature can be a set of ``Labels``, in which case the names\nof the labels must be a subset of the names of the properties produced by the\ncalculator. You can see the default set of names with:\n\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"print(\"property names:\", descriptor.property_names)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"We can use a subset of these names to define a selection. In this case, only\nproperties matching the labels in this selection will be used by featomic\n(here, only properties with ``l = 0`` will be used)\n\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"selection = Labels(\n names=[\"l\"],\n values=np.array([[0]]),\n)\nselected_descriptor = calculator.compute(frames, selected_properties=selection)\n\nselected_descriptor = selected_descriptor.keys_to_samples(\"center_type\")\nselected_descriptor = selected_descriptor.keys_to_properties(\n [\"neighbor_1_type\", \"neighbor_2_type\"]\n)\n\nproperties = selected_descriptor.block().properties"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"We expect to get `[0]` as the list of `l` properties\n\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"print(f\"we have the following angular components: {np.unique(properties['l'])}\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The previous selection method uses the same selection for all blocks. If you\ncan to use different selection for different blocks, you should use a\n``TensorMap`` to create your selection\n\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"selected_descriptor = calculator.compute(frames, selected_properties=selection)\ndescriptor_for_comparison = calculator.compute(\n frames, selected_properties=selected_descriptor\n)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The descriptor had 180 properties stored in the first block, the\nselected_descriptor had 36. So ``descriptor_for_comparison`` will also have 36\nproperties.\n\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"print(\"shape of first block initially:\", descriptor.block(0).values.shape)\nprint(\"shape of first block of reference:\", selected_descriptor.block(0).values.shape)\nprint(\n \"shape of first block after selection:\",\n descriptor_for_comparison.block(0).values.shape,\n)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The ``TensorMap`` format allows us to select different features within each\nblock, and then construct a general matrix of features. We can select the most\nsignificant features using FPS, which selects features based on the distance\nbetween them. The following code snippet selects the 10 most important\nfeatures in each block, then constructs a TensorMap containing this selection,\nand calculates the final matrix of features for it.\n\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"def fps_feature_selection(descriptor, n_to_select):\n \"\"\"\n Select ``n_to_select`` features block by block in the ``descriptor``, using\n Farthest Point Sampling to do the selection; and return a ``TensorMap`` with\n the right structure to be used as properties selection with featomic calculators\n \"\"\"\n blocks = []\n for block in descriptor:\n # create a separate FPS selector for each block\n fps = FPS(n_to_select=n_to_select)\n mask = fps.fit(block.values).get_support()\n selected_properties = Labels(\n names=block.properties.names,\n values=block.properties.values[mask],\n )\n # The only important data here is the properties, so we create empty\n # sets of samples and components.\n blocks.append(\n TensorBlock(\n values=np.empty((1, len(selected_properties))),\n samples=Labels.single(),\n components=[],\n properties=selected_properties,\n )\n )\n\n return TensorMap(descriptor.keys, blocks)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"We can then apply this function to subselect according to the data contained\nin a descriptor\n\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"selection = fps_feature_selection(descriptor, n_to_select=10)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"and use the selection with featomic, potentially running the calculation on a\ndifferent set of systems\n\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"selected_descriptor = calculator.compute(frames, selected_properties=selection)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Note that in this case it is no longer possible to have a single feature\nmatrix, because each block will have its own properties.\n\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"try:\n selected_descriptor.keys_to_samples(\"center_type\")\nexcept MetatensorError as err:\n print(err)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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".. end-body\n\n"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.7"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}

‎featomic-torch-v0.6.0-rc1/_downloads/157269325187c1e5cc5b17bff969fe70/profiling.py

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"""
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Profiling calculation
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=====================
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.. start-body
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"""
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import chemfiles
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import featomic
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from featomic import SoapPowerSpectrum
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def compute_soap(path):
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"""Compute SOAP power spectrum.
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This is the same code as the 'compute-soap' example
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"""
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with chemfiles.Trajectory(path) as trajectory:
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frames = [f for f in trajectory]
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HYPER_PARAMETERS = {
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"cutoff": {
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"radius": 5.0,
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"smoothing": {"type": "ShiftedCosine", "width": 0.5},
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},
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"density": {
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"type": "Gaussian",
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"width": 0.3,
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},
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"basis": {
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"type": "TensorProduct",
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"max_angular": 4,
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"radial": {"type": "Gto", "max_radial": 6},
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},
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}
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calculator = SoapPowerSpectrum(**HYPER_PARAMETERS)
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descriptor = calculator.compute(frames, gradients=["positions"])
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descriptor = descriptor.keys_to_samples("center_type")
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descriptor = descriptor.keys_to_properties(["neighbor_1_type", "neighbor_2_type"])
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return descriptor
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# %%
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#
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# Run the calculation with profiling enabled.
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with featomic.Profiler() as profiler:
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descriptor = compute_soap("dataset.xyz")
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# %%
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#
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# Display the recorded profiling data as table.
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print(profiler.as_short_table())
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# %%
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#
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# You can also save this data as json for future usage
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print(profiler.as_json())
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# %%
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#
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# .. end-body

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