From 9ea0257f14fb07dabc9135e27804719855ca8b16 Mon Sep 17 00:00:00 2001 From: hurricane642 Date: Thu, 4 Aug 2022 16:14:14 +0200 Subject: [PATCH 1/2] added first version of the tutorial on the Bayesian optimization --- .../examples/Bayes_tutorial-librascal.ipynb | Bin 0 -> 944 bytes docs/source/examples/examples.rst | 1 + examples/Bayes_tutorial-librascal.ipynb | 36547 ++++++++++++++++ 3 files changed, 36548 insertions(+) create mode 100644 docs/source/examples/Bayes_tutorial-librascal.ipynb create mode 100644 examples/Bayes_tutorial-librascal.ipynb diff --git a/docs/source/examples/Bayes_tutorial-librascal.ipynb b/docs/source/examples/Bayes_tutorial-librascal.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..e2ec87c47d02c1218db0ebb2a05003674dfc3b44 GIT binary patch literal 944 zcmZ9Lze^io7{}i;iCF9qqo704P#n6PIdX}KE;a4dM$|4UIEZ?QC%$;Olia055Uho6 zE~RUS4i5bjIu(Zu4i1hE6`_>Up`eS@sqy=q&n4;u&%K}L$NRj``{PQkyD1L$J(5m z6OY(uxaC^Ka(v3`WZ!G)7i%Ta%CTjsYIpj1 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# function for the convergence plotting\n", + "plot_convergence(res)" + ] + }, + { + "cell_type": "markdown", + "id": "e9a6a5ff", + "metadata": {}, + "source": [ + "As you can see, this algorithm produces an optimal set of hyperparameters in 20 steps. The remaining iterations are carried out to make sure of the quality of the result obtained. At the same time, thanks to the cheapness of the acquisition function, this algorithm does not become too computationally heavy. 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"cell_type": "markdown", + "id": "3c96ebd1", + "metadata": {}, + "source": [ + "In the snippets below we extract the relevant properties for each ASE frame. We load the total potential energies and we use the `ASE.Atoms.arrays` methods to get the atomic forces. For more information on how to use ASE-related methods, check the [ASE Atoms documentation](https://wiki.fysik.dtu.dk/ase/ase/atoms.html)." + ] + }, { "cell_type": "code", "execution_count": 3, @@ -236,7 +244,7 @@ "id": "5476786b", "metadata": {}, "source": [ - "Build the GAP model:" + "Auxiliary function for selecting an array containing forces from a dataset. " ] }, { @@ -269,6 +277,22 @@ " return f" ] }, + { + "cell_type": "markdown", + "id": "126b2729", + "metadata": {}, + "source": [ + "## Build the GAP model" + ] + }, + { + "cell_type": "markdown", + "id": "3104368f", + "metadata": {}, + "source": [ + "The preliminary step towards building a GAP model is defining a training and a test set. The training set is built by random selection of $800$ of the available structures, while the remaining $200$ will represent the test set. The given function will allow us to get information for train and test sets for each optimization iteration (each time we use a new train-test partition, so that we do not get an optimized description for a particular partition)." + ] + }, { "cell_type": "code", "execution_count": 5, @@ -286,11 +310,6 @@ "# Number of structures to train the model with\n", "n = 800\n", "\n", - "global_species = set()\n", - "for frame in frames:\n", - " global_species.update(frame.get_atomic_numbers())\n", - "global_species = np.array(list(global_species))\n", - "\n", "np.random.seed(10)\n", "# Split our data in the train and test blocks\n", "def train_test():\n", @@ -315,10 +334,10 @@ }, { "cell_type": "markdown", - "id": "7a86ecdc", + "id": "c9778967", "metadata": {}, "source": [ - "Create a function to generate a Power Spectrum with defined hyperparameters:" + "At this point, we also extract the distinct atomic species present in our system, define the atomic baseline energy and extract the information regarding energies and forces in the train and test set structures, which we will need later on." ] }, { @@ -335,6 +354,11 @@ }, "outputs": [], "source": [ + "global_species = set()\n", + "for frame in frames:\n", + " global_species.update(frame.get_atomic_numbers())\n", + "global_species = np.array(list(global_species))\n", + "\n", "# Atomic energy baseline\n", "atom_energy_baseline = np.mean(energies)/(frames[0].get_global_number_of_atoms())\n", "energy_baseline = {int(species): atom_energy_baseline for species in global_species}\n", @@ -342,6 +366,16 @@ " return f\"{number:.{digits}f}\"" ] }, + { + "cell_type": "markdown", + "id": "7a86ecdc", + "metadata": {}, + "source": [ + "Now we proceed with the actual calculation of the SOAP vectors of our training set. We need to specify an hyperparameters dictionary, which `librascal` uses to compute the structural features. The meaning of each hyperparameter is reported in [the `SphericalInvariants` documentation](https://lab-cosmo.github.io/librascal/reference/python.html#rascal.representations.SphericalInvariants). \n", + "\n", + "Since we have to redefine SOAP each iteration, we will also make it as a function. " + ] + }, { "cell_type": "code", "execution_count": 7, @@ -388,6 +422,94 @@ " return soap" ] }, + { + "cell_type": "markdown", + "id": "0f00c580", + "metadata": {}, + "source": [ + "The following snippet computes the descriptors and the managers for each frame, which will represent the expensive part of the calculation, in terms of memory usage.\n", + "\n", + "NOTE: the `librascal` structure manager only works if the atoms have been wrapped within the cell provided in the input file using e.g. `ase.Atoms.wrap()`, or one of the structure preprocessors provided in `rascal.neighbourlist.structure_manager` module (useful especially for non-periodic structures)." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "5576b0a1", + "metadata": {}, + "outputs": [], + "source": [ + "def soap_calculation(frames_train, sigma, cutoff, width):\n", + " managers = []\n", + " for f in frames_train:\n", + " f.wrap(eps=1e-18)\n", + "\n", + " soap = SOAP_definition(sigma, cutoff, width, False)\n", + " managers = soap.transform(frames_train)\n", + " return soap, managers" + ] + }, + { + "cell_type": "markdown", + "id": "3197ebd8", + "metadata": {}, + "source": [ + "Next, we define a function to calculate the ideal sparse set, kernels to calculate, and create the model itself. You can read more about exactly how to do this in the first example in this documentation. We use the CUR method again, and use it to implement Sparse Kernel Ridge Regression (SparseKRR)." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "6000123a", + "metadata": {}, + "outputs": [], + "source": [ + "def model_definition(soap, managers, sigma, cutoff, width, frames_train, f_train, y_train):\n", + " n_sparse = {1:50, 8:100}\n", + " compressor = CURFilter(soap, n_sparse, act_on='sample per species')\n", + " X_sparse = compressor.select_and_filter(managers)\n", + "\n", + " zeta = 2\n", + "\n", + " soap = SOAP_definition(sigma, cutoff, width, True)\n", + " kernel = Kernel(soap, name='GAP', zeta=zeta, target_type='Structure', kernel_type='Sparse')\n", + "\n", + " KNM = compute_KNM(tqdm(frames_train, leave=True, desc=\"Computing kernel matrix\"), X_sparse, kernel, soap)\n", + "\n", + " model = train_gap_model(kernel, frames_train, KNM, X_sparse, y_train, energy_baseline, \n", + " grad_train=-f_train, lambdas=[1e-12, 1e-12], jitter=1e-13)\n", + " return soap, model" + ] + }, + { + "cell_type": "markdown", + "id": "4cb434e6", + "metadata": {}, + "source": [ + "And a small function that will use our model to predict the properties of molecules. " + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "9fa68eba", + "metadata": {}, + "outputs": [], + "source": [ + "def prediction(soap, model, frames_test):\n", + " y_pred = []\n", + "\n", + " for f in frames_test:\n", + " positions = f.get_positions()\n", + " f.set_positions(positions+[1,1,1])\n", + " f.wrap(eps=1e-18)\n", + " m = soap.transform(f)\n", + " y_pred.append(model.predict(m))\n", + "\n", + " y_pred = np.array(y_pred).flatten()\n", + " return y_pred" + ] + }, { "cell_type": "markdown", "id": "2884d7c2", @@ -400,7 +522,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 17, "id": "cbc90eca", "metadata": { "execution": { @@ -414,12 +536,12 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "40c12a16138a48eab95c5b5088fd32aa", + "model_id": "7ab8170a37534856bb0302616b2cd6f7", "version_major": 2, "version_minor": 0 }, "text/plain": [ - " 0%| | 0/100 [00:00