diff --git a/.buildinfo b/.buildinfo index 02a373a..9fe96a0 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: 0187fd5a56a7fa8e58d48ee8b0e02af9 +config: 616fd980f4e6a47c87c222a32bb198b0 tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/CNAME b/CNAME deleted file mode 100644 index 86c21a2..0000000 --- a/CNAME +++ /dev/null @@ -1 +0,0 @@ -www.qmlcode.org diff --git a/_sources/citation.rst.txt b/_sources/citation.rst.txt deleted file mode 100644 index ca2606c..0000000 --- a/_sources/citation.rst.txt +++ /dev/null @@ -1,15 +0,0 @@ -Citing use of QML ------------------ - -QML is freely available under the terms of the MIT open-source license. -However, we ask that you properly cite the relevant, underlying work in your published work. -For convenience we provide a list of citations here. - -Until the preprint is available from arXiv, please cite use of QML as: - -:: - - AS Christensen, FA Faber, B Huang, LA Bratholm, A Tkatchenko, KR Muller, OA von Lilienfeld (2017) "QML: A Python Toolkit for Quantum Machine Learning" https://github.com/qmlcode/qml - - - diff --git a/_sources/examples.rst.txt b/_sources/examples.rst.txt index c7e1549..3afd2be 100644 --- a/_sources/examples.rst.txt +++ b/_sources/examples.rst.txt @@ -1,7 +1,7 @@ Examples -------- -Generating representations using the ``Compound`` class +Generating representations using the ``Compound`` class: ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The following example demonstrates how to generate a representation via @@ -9,306 +9,36 @@ the ``qml.Compound`` class. .. code:: python + from qml import Compound + # Read in an xyz or cif file. water = Compound(xyz="water.xyz") # Generate a molecular coulomb matrices sorted by row norm. - water.generate_coulomb_matrix(size=5, sorting="row-norm") - - print(water.representation) + water.generate_coulomb_matrix(size=5, sort="row-norm") -Might print the following representation: + print water.coulomb_matrix -.. code:: - - [ 73.51669472 8.3593106 0.5 8.35237809 0.66066557 0.5 - 0. 0. 0. 0. 0. 0. 0. - 0. 0. ] - -Generating representations via the ``qml.representations`` module +Generating representations via the ``qml.representations`` module: ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. code:: python - import numpy as np from qml.representations import * - # Dummy coordinates for a water molecule - coordinates = np.array([[1.464, 0.707, 1.056], - [0.878, 1.218, 0.498], - [2.319, 1.126, 0.952]]) - - # Oxygen, Hydrogen, Hydrogen - nuclear_charges = np.array([8, 1, 1]) + # Dummy atomtypes and coordinates + atomtypes = ["O", "H", H"] + coordinates = np.array([1.464, 0.707, 1.056], + [0.878, 1.218, 0.498], + [2.319, 1.126, 0.952]) # Generate a molecular coulomb matrices sorted by row norm. - cm1 = generate_coulomb_matrix(nuclear_charges, coordinates, - size=5, sorting="row-norm") - print(cm1) - - -The resulting Coulomb-matrix for water: - -.. code:: - - [ 73.51669472 8.3593106 0.5 8.35237809 0.66066557 0.5 - 0. 0. 0. 0. 0. 0. 0. - 0. 0. ] - - - -.. code:: python + cm1 = generate_coulomb_matrix(atomtypes, coordinates, + size=5, sort="row-norm") + print cm1 # Generate all atomic coulomb matrices sorted by distance to # query atom. cm2 = generate_atomic_coulomb_matrix(atomtypes, coordinates, size=5, sort="distance") print cm2 - -.. code:: - - [[ 73.51669472 8.3593106 0.5 8.35237809 0.66066557 0.5 - 0. 0. 0. 0. 0. 0. - 0. 0. 0. ] - [ 0.5 8.3593106 73.51669472 0.66066557 8.35237809 0.5 - 0. 0. 0. 0. 0. 0. - 0. 0. 0. ] - [ 0.5 8.35237809 73.51669472 0.66066557 8.3593106 0.5 - 0. 0. 0. 0. 0. 0. - 0. 0. 0. ]] - - -Calculating a Gaussian kernel -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -The input for most of the kernels in QML is a numpy array, where the first dimension is the number of representations, and the second dimension is the size of each representation. An brief example is presented here, where ``compounds`` is a list of ``Compound()`` objects: - -.. code:: python - - import numpy as np - from qml.kernels import gaussian_kernel - - # Generate a numpy-array of the representation - X = np.array([c.representation for c in compounds]) - - # Kernel-width - sigma = 100.0 - - # Calculate the kernel-matrix - K = gaussian_kernel(X, X, sigma) - - -Calculating a Gaussian kernel using a local representation -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -The easiest way to calculate the kernel matrix using an explicit, local representation is via the wrappers module. Note that here the sigmas is a list of sigmas, and the result is a kernel for each sigma. The following examples currently work with the atomic coulomb matrix representation and the local SLATM representation: - -.. code:: python - - import numpy as np - from qml.kernels import get_local_kernels_gaussian - - # Assume the QM7 dataset is loaded into a list of Compound() - for compound in qm7: - - # Generate the desired representation for each compound - compound.generate_atomic_coulomb_matrix(size=23, sort="row-norm") - - # Make a big array with all the atomic representations - X = np.concatenate([mol.representation for mol in qm7]) - - # Make an array with the number of atoms in each compound - N = np.array([mol.natoms for mol in qm7]) - - # List of kernel-widths - sigmas = [50.0, 100.0, 200.0] - - # Calculate the kernel-matrix - K = get_local_kernels_gaussian(X, X, N, N, sigmas) - - print(K.shape) - -.. code:: - - (3, 7101, 7101) - -Note that ``mol.representation`` is just a 1D numpy array. - - -Generating the SLATM representation -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -The Spectrum of London and Axillrod-Teller-Muto potential (SLATM) representation requires additional input to reduce the size of the representation. -This input (the types of many-body terms) is generate via the ``get_slatm_mbtypes()`` function. The function takes a list of the nuclear charges for each molecule in the dataset as input. E.g.: - - -.. code:: python - - from qml.representations import get_slatm_mbtypes - - # Assume 'qm7' is a list of Compound() objects. - mbtypes = get_slatm_mbtypes([mol.nuclear_charges for compound in qm7]) - - # Assume the QM7 dataset is loaded into a list of Compound() - for compound in qm7: - - # Generate the desired representation for each compound - compound.generate_slatm(mbtypes, local=True) - -The ``local`` keyword in this example specifies that a local representation is produced. Alternatively the SLATM representation can be generate via the ``qml.representations`` module: - -.. code:: python - - from qml.representations import generate_slatm - - # Dummy coordinates - coordinates = ... - - # Dummy nuclear charges - nuclear_charges = ... - - # Dummy mbtypes - mbtypes = get_slatm_mbtypes( ... ) - - # Generate one representation - rep = generate_slatm(coordinates, nuclear_charges, mbtypes) - -Here ``coordinates`` is an Nx3 numpy array, and ``nuclear_charges`` is simply a list of charges. - -Generating the FCHL representation -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -The FCHL representation does not have an explicit representation in the form of a vector, and the kernel elements must be calculated analytically in a separate kernel function. -The syntax is analogous to the explicit representations (e.g. Coulomb matrix, BoB, SLATM, etc), but is handled by kernels from the separate ``qml.fchl`` module. - -The code below show three ways to create the input representations for the FHCL kernel functions. - -First using the ``Compound`` class: - -.. code:: python - - # Assume the dataset is loaded into a list of Compound() - for compound in mols: - - # Generate the desired representation for each compound, cut off in angstrom - compound.generate_fchl_representation(size=23, cut_off=10.0) - - # Make Numpy array of the representation, which can be parsed to the kernel - X = np.array([c.representation for c in mols]) - - -The dimensions of the array should be ``(number_molecules, size, 5, size)``, where ``size`` is the -size keyword used when generating the representations. - -In addition to using the ``Compound`` class to generate the representations, FCHL representations can also be generated via the ``qml.fchl.generate_fchl_representation()`` function, using similar notation to the functions in the ``qml.representations.*`` functions. - - -.. code:: python - - from qml.fchl import generate_representation - - # Dummy coordinates for a water molecule - coordinates = np.array([[1.464, 0.707, 1.056], - [0.878, 1.218, 0.498], - [2.319, 1.126, 0.952]]) - - # Oxygen, Hydrogen, Hydrogen - nuclear_charges = np.array([8, 1, 1]) - - rep = generate_representation(coordinates, nuclear_charges) - -To create the representation for a crystal, the notation is as follows: - - -.. code:: python - - from qml.fchl import generate_representation - - # Dummy fractional coordinates - fractional_coordinates = np.array( - [[ 0. , 0. , 0. ], - [ 0.75000042, 0.50000027, 0.25000015], - [ 0.15115386, 0.81961403, 0.33154037], - [ 0.51192691, 0.18038651, 0.3315404 ], - [ 0.08154025, 0.31961376, 0.40115401], - [ 0.66846017, 0.81961403, 0.48807366], - [ 0.08154025, 0.68038678, 0.76192703], - [ 0.66846021, 0.18038651, 0.84884672], - [ 0.23807355, 0.31961376, 0.91846033], - [ 0.59884657, 0.68038678, 0.91846033], - [ 0.50000031, 0. , 0.50000031], - [ 0.25000015, 0.50000027, 0.75000042]] - ) - - # Dummy nuclear charges - nuclear_charges = np.array( - [58, 58, 8, 8, 8, 8, 8, 8, 8, 8, 23, 23] - ) - - # Dummy unit cell - unit_cell = np.array( - [[ 3.699168, 3.699168, -3.255938], - [ 3.699168, -3.699168, 3.255938], - [-3.699168, -3.699168, -3.255938]] - ) - - # Generate the representation - rep = generate_representation(fractional_coordinates, nuclear_charges, - cell=unit_cell, neighbors=100, cut_distance=7.0) - - -The neighbors keyword is the max number of atoms with the cutoff-distance - -Generating the FCHL kernel -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -The following example demonstrates how to calculate the local FCHL kernel elements between FCHL representations. ``X1`` and ``X2`` are numpy arrays with the shape ``(number_compounds,max_size, 5,neighbors)``, as generated in one of the previous examples. You MUST use the same, or larger, cut-off distance to generate the representation, as to calculate the kernel. - - -.. code:: python - - from qml.fchl import get_local_kernels - - # You can get kernels for multiple kernel-widths - sigmas = [2.5, 5.0, 10.0] - - # Calculate the kernel-matrices for each sigma - K = get_local_kernels(X1, X2, sigmas, cut_distance=10.0) - - print(K.shape) - - -As output you will get a kernel for each kernel-width. - -.. code:: - - (3, 100, 200) - - -In case ``X1`` and ``X2`` are identical, K will be symmetrical. This is handled by a separate function with exploits this symmetry (thus being twice as fast). - -.. code:: python - - from qml.fchl import get_local_symmetric_kernels - - # You can get kernels for multiple kernel-widths - sigmas = [2.5, 5.0, 10.0] - - # Calculate the kernel-matrices for each sigma - K = get_local_kernels(X1, sigmas, cut_distance=10.0) - - print(K.shape) - - -.. code:: - - (3, 100, 100) - -In addition to the local kernel, the FCHL module also provides kernels for atomic properties (e.g. chemical shifts, partial charges, etc). These have the name "atomic", rather than "local". - -.. code:: python - - from qml.fchl import get_atomic_kernels - from qml.fchl import get_atomic_symmetric_kernels - -The only difference between the local and atomic kernels is the shape of the input. -Since the atomic kernel outputs kernels with atomic resolution, the atomic input has the shape ``(number_atoms, 5, size)``. diff --git a/_sources/index.rst.txt b/_sources/index.rst.txt index ee4b235..630d2f4 100644 --- a/_sources/index.rst.txt +++ b/_sources/index.rst.txt @@ -3,7 +3,7 @@ You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. -|Build Status| |pypi| |doi| |Beta| +|Build Status| QML: A Python Toolkit for Quantum Machine Learning ================================================== @@ -13,8 +13,8 @@ properties of molecules and solids. QML is not a high-level framework where you can do ``model.train()``, but supplies the building blocks to carry out efficient and accurate machine learning on chemical compounds. As such, the goal is to provide usable and efficient implementations of -concepts such as representations and kernels. - +concepts such as representations and kernels, which can then be +implemented in other machine learning codes. Current list of contributors: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ @@ -24,34 +24,26 @@ Current list of contributors: - Bing Huang (University of Basel) - Lars A. Bratholm (University of Copenhagen) - Alexandre Tkatchenko (University of Luxembourg) -- Klaus-Robert Muller (Technische Universität Berlin/Korea University) +- Klaus-Robert Müller (Technische Universität Berlin/Korea University) - O. Anatole von Lilienfeld (University of Basel) -Code development -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -The QML code is developed through our GitHub repository: - -https://github.com/qmlcode/qml ------------------------------- - -Please add you code to QML by forking and making pull-requests to the "develop" branch. -Every now and then develop branch is pushed to the "master" branch and automatically deployed to PyPI, where the latest stable version is hosted. +1) Citing QML: +-------------- -See the "Installing QML" page for up-to-date installation instructions. +Until the preprint is available from arXiv, please cite this GitHub +repository as: +:: -Citing QML: --------------- + AS Christensen, F Faber, B Huang, LA Bratholm, A Tkatchenko, KR Müller, OA von Lilienfeld (2017) "QML: A Python Toolkit for Quantum Machine Learning" https://github.com/qmlcode/qml -Until the preprint is available from arXiv, please cite use of QML as: -:: +2) Installation +--------------- - AS Christensen, FA Faber, B Huang, LA Bratholm, A Tkatchenko, KR Muller, OA von Lilienfeld (2017) "QML: A Python Toolkit for Quantum Machine Learning" https://github.com/qmlcode/qml +Please go to the QML github repository at https://github.com/qmlcode/qml/ -For citation of the individual procedures of QML, please see the "Citing use of QML" section. .. toctree:: :maxdepth: 2 @@ -59,8 +51,6 @@ For citation of the individual procedures of QML, please see the "Citing use of :name: index installation - citation - tutorial examples @@ -71,20 +61,10 @@ For citation of the individual procedures of QML, please see the "Citing use of qml -License: +3) License: ----------- QML is freely available under the terms of the MIT license. .. |Build Status| image:: https://travis-ci.org/qmlcode/qml.svg?branch=master :target: https://travis-ci.org/qmlcode/qml - -.. |doi| image:: https://zenodo.org/badge/89045103.svg - :target: https://zenodo.org/badge/latestdoi/89045103 - -.. |Beta| image:: http://i.imgur.com/5fMAeek.jpg - :width: 90 px - :target: https://github.com/qmlcode/qml - -.. |Pypi| image:: https://badge.fury.io/py/qml.svg - :target: https://badge.fury.io/py/qml diff --git a/_sources/installation.rst.txt b/_sources/installation.rst.txt index 2134cea..918b7c5 100644 --- a/_sources/installation.rst.txt +++ b/_sources/installation.rst.txt @@ -1,18 +1,6 @@ -Installing QML +Installation --------------- -Installing prerequisite modules (for most Linux systems): - -.. code:: bash - - sudo apt-get install python-pip gfortran libblas-dev liblapack-dev git - -These should already be installed on most systems. The Intel compilers -and MKL math-libraries are supported as well (see section 1.3). - -Installing via ``pip`` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - The easiest way to install the stable version from the Python Package Index (PyPI) is using the official, built-in Python package manager, ``pip``: @@ -21,70 +9,12 @@ Index (PyPI) is using the official, built-in Python package manager, pip install qml --user -U -To use the Intel compiler, together with the MKL math library: - - -In addition to the stable version available from the official PyPI repository, you can install the most recent stable development snapshot directly from GitHub: - -.. code:: bash - - pip install git+https://github.com/qmlcode/qml@develop --user -U - -Use ``pip2 install ...`` or ``pip3 install ...`` to get the Python2 or -Python3 versions explicitly. QML supports both flavors. - -To uninstall simply use ``pip`` again. - -.. code:: bash - - pip uninstall qml - -Installing via with Intel compilers -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -If you have Intel compilers installed (2016 and newer), you can compile -QML with Ifort/MKL from PyPI using the following options: - -.. code:: bash - - pip install qml --user -U --global-option="build" --global-option="--compiler=intelem" --global-option="--fcompiler=intelem" - -Or alternatively the `develop` branch from GitHub: - -.. code:: bash - - pip install git+https://github.com/qmlcode/qml@develop --user -U --global-option="build" --global-option="--compiler=intelem" --global-option="--fcompiler=intelem" - - -Note on Apple/Mac support -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Installing QML requires a Fortran compiler. On MacOS you can install it -using ``brew``: - -.. code:: bash - - # Update brew - /usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)" - - # Install GCC - brew install gcc - -Note the Fortran compiler from brew (gfortran) unfortunately does not support OpenMP. -Therefore parallelism via OpenMP is disabled as default for MacOS systems. - -Additionally, we found that some users have multiple version of the ``as`` assembler - this might happen if you have GCC from e.g. brew and macports at the same time. Look for the following error: - - - ``FATAL:/opt/local/bin/../libexec/as/x86_64/as: I don't understand 'm' flag!`` - -If you experience this problems the setting the following path might fix the problem: +Alternatively, you can install the most recent stable development +snapshot directly from GitHub: .. code:: bash - export PATH=/usr/bin:$PATH + pip install git+https://github.com/qmlcode/qml --user -U -Report Bugs -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -Please report any bugs by opening an issue on GitHub: https://github.com/qmlcode/qml/issues +For more installation help, please see the github repository: https://github.com/qmlcode/qml/ diff --git a/_sources/qml.rst.txt b/_sources/qml.rst.txt index f89e239..64a24c8 100644 --- a/_sources/qml.rst.txt +++ b/_sources/qml.rst.txt @@ -1,5 +1,5 @@ -Python API documentation -======================== +qml package +=========== .. Submodules .. ---------- @@ -37,26 +37,36 @@ qml\.math module :undoc-members: :show-inheritance: -qml\.Compound class +qml\.compound module -------------------- -.. autoclass:: qml.Compound +.. automodule:: qml.compound :members: :undoc-members: :show-inheritance: -.. qml\.arad module -.. ---------------- -.. -.. .. automodule:: qml.arad -.. :members: -.. :show-inheritance: +qml\.arad module +---------------- + +.. automodule:: qml.arad + :members: + :undoc-members: + :show-inheritance: + +qml\.arad\_kernels module +------------------------- -qml\.fchl module +.. automodule:: qml.arad_kernels + :members: + :undoc-members: + :show-inheritance: + +qml\.data module ---------------- -.. automodule:: qml.fchl +.. automodule:: qml.data :members: + :undoc-members: :show-inheritance: @@ -68,14 +78,6 @@ qml\.wrappers module :undoc-members: :show-inheritance: - -.. qml\.data module -.. ---------------- -.. -.. .. automodule:: qml.data -.. :members: -.. :undoc-members: -.. :show-inheritance: .. Module contents .. --------------- diff --git a/_sources/tutorial.rst.txt b/_sources/tutorial.rst.txt deleted file mode 100644 index 5e6efc8..0000000 --- a/_sources/tutorial.rst.txt +++ /dev/null @@ -1,197 +0,0 @@ -QML Tutorial ------------------ - -This tutorial is a general introduction to kernel-ridge regression with QML. - -Theory -~~~~~~~~~~~ - -Regression model of some property, :math:`y`, for some system, :math:`\widetilde{\mathbf{X}}` - this could correspond to e.g. the atomization energy of a molecule: - - :math:`y\left(\widetilde{\mathbf{X}} \right) = \sum_i \alpha_i \ K\left( \widetilde{\mathbf{X}}, \mathbf{X}_i\right)` - -E.g. Using Gaussian kernel function with Frobenius norm: - - :math:`K_{ij} = K\left( \mathbf{X}_i, \mathbf{X}_j\right) = \exp\left( -\frac{\| \mathbf{X}_i - \mathbf{X}_j\|_2^2}{2\sigma^2}\right)` - -Regression coefficients are obtained through kernel matrix inversion and multiplication with reference labels - - :math:`\boldsymbol{\alpha} = (\mathbf{K} + \lambda \mathbf{I})^{-1} \mathbf{y}` - -Tutorial exercises -~~~~~~~~~~~~~~~~~~~~~ -Clone the following GIT repository to access the necessary scripts and QM7 dataset (atomization energies and relaxed geometries at PBE0/def2-TZVP level of theory) for ~7k GDB1-7 molecules. [#rupp]_ [#ruddigkeit]_ - -.. code:: bash - - git clone https://github.com/qmlcode/tutorial.git - -Additionally, the repository contains Python3 scripts with the solutions to each exercise. - -Exercise 1: Representations -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -In this exercise we use \qml~to generate the Coulomb matrix and Bag of bonds (BoB) representations. [#montavon]_ -In QML data can be parsed via the ``Compound`` class, which stores data and generates representations in Numpy's ndarray format. -If you run the code below, you will read in the file ``qm7/0001.xyz`` (a methane molecule) and generate a coulomb matrix representation (sorted by row-norm) and a BoB representation. - -.. code:: python - - import qml - - # Create the compound object mol from the file qm7/0001.xyz which happens to be methane - mol = qml.Compound(xyz="qm7/0001.xyz") - - # Generate and print a coulomb matrix for compound with 5 atoms - mol.generate_coulomb_matrix(size=5, sorting="row-norm") - print(mol.representation) - - # Generate and print BoB bags for compound containing C and H - mol.generate_bob(asize={"C":2, "H":5}) - print(mol.representation) - -The representations are simply stored as 1D-vectors. -Note the keyword ``size`` which is the largest number of atoms in a molecule occurring in test or training set. -Additionally, the coulomb matrix can take a sorting scheme as keyword, and the BoB representations requires the specifications of how many atoms of a certain type to make room for in the representations. - -Lastly, you can print the following properties which is read from the XYZ file: - -.. code:: python - - # Print other properties stored in the object - print(mol.coordinates) - print(mol.atomtypes) - print(mol.nuclear_charges) - print(mol.name) - print(mol.unit_cell) - -Exercise 2: Kernels -~~~~~~~~~~~~~~~~~~~~~ -In this exercise we generate a Gaussian kernel matrix, :math:`\mathbf{K}`, using the representations, :math:`\mathbf{X}`, which are generated similarly to the example in the previous exercise: - - :math:`K_{ij} = \exp\left( -\frac{\| \mathbf{X}_i - \mathbf{X}_j\|_2^2}{2\sigma^2}\right)` - -QML supplies functions to generate the most basic kernels (E.g. Gaussian, Laplacian). In the exercise below, we calculate a Gaussian kernel for the QM7 dataset. -In order to save time you can import the entire QM7 dataset as ``Compound`` objects from the file ``tutorial_data.py`` found in the tutorial GitHub repository. - -.. code:: python - - # Import QM7, already parsed to QML - from tutorial_data import compounds - - from qml.kernels import gaussian_kernel - - # For every compound generate a coulomb matrix or BoB - for mol in compounds: - - mol.generate_coulomb_matrix(size=23, sorting="row-norm") - # mol.generate_bob(size=23, asize={"O":3, "C":7, "N":3, "H":16, "S":1}) - - # Make a big 2D array with all the representations - X = np.array([mol.representation for mol in compounds]) - - # Print all representations - print(X) - - # Run on only a subset of the first 100 (for speed) - X = X[:100] - - # Define the kernel width - sigma = 1000.0 - - # K is also a Numpy array - K = gaussian_kernel(X, X, sigma) - - # Print the kernel - print K - - -Exercise 3: Regression -~~~~~~~~~~~~~~~~~~~~~~~~ -With the kernel matrix and representations sorted out in the previous two exercise, we can now solve the :math:`\boldsymbol{\alpha}` regression coefficients: - - :math:`\boldsymbol{\alpha} = (\mathbf{K} + \lambda \mathbf{I})^{-1} \mathbf{y}\label{eq:inv}` - -One of the most efficient ways of solving this equation is using a Cholesky-decomposition. -QML includes a function named ``cho_solve()`` to do this via the math module ``qml.math``. -In this step it is convenient to only use a subset of the full dataset as training data (see below). -The following builds on the code from the previous step. -To save time, you can import the PBE0/def2-TZVP atomization energies for the QM7 dataset from the file ``tutorial_data.py``. -This has been sorted to match the ordering of the representations generated in the previous exercise. -Extend your code from the previous step with the code below: - -.. code:: python - - from qml.math import cho_solve - from tutorial_data import energy_pbe0 - - # Assign 1000 first molecules to the training set - X_training = X[:1000] - Y_training = energy_pbe0[:1000] - - sigma = 4000.0 - K = gaussian_kernel(X_training, X_training, sigma) - print(K) - - # Add a small lambda to the diagonal of the kernel matrix - K[np.diag_indices_from(K)] += 1e-8 - - # Use the built-in Cholesky-decomposition to solve - alpha = cho_solve(K, Y_training) - - print(alpha) - - -Exercise 4: Prediction -~~~~~~~~~~~~~~~~~~~~~~~~ -With the :math:`\boldsymbol{\alpha}` regression coefficients from the previous step, we have (successfully) trained the machine, and we are now ready to do predictions for other compounds. -This is done using the following equation: - - :math:`y\left(\widetilde{\mathbf{X}} \right) = \sum_i \alpha_i \ K\left( \widetilde{\mathbf{X}}, \mathbf{X}_i\right)` - -In this step we further divide the dataset into a training and a test set. Try using the last 1000 entries as test set. - -.. code:: python - - # Assign 1000 last molecules to the test set - X_test = X[-1000:] - Y_test = energy_pbe0[-1000:] - - # calculate a kernel matrix between test and training data, using the same sigma - Ks = gaussian_kernel(X_test, X_training, sigma) - - # Make the predictions - Y_predicted = np.dot(Ks, alpha) - - # Calculate mean-absolute-error (MAE): - print np.mean(np.abs(Y_predicted - Y_test)) - -Exercise 5: Learning curves -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -Repeat the prediction from Exercise 2.4 with training set sizes of 1000, 2000, and 4000 molecules. -Note the MAE for every training size. -Plot a learning curve of the MAE versus the training set size. -Generate a learning curve for the Gaussian and Laplacian kernels, as well using the coulomb matrix and bag-of-bonds representations. -Which combination gives the best learning curve? Note you will have to adjust the kernel width (sigma) underway. - - -Exercise 6: Delta learning -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -A powerful technique in machine learning is the delta learning approach. Instead of predicting the PBE0/def2-TZVP atomization energies, we shall try to predict the difference between DFTB3 (a semi-empirical quantum method) and PBE0 atomization energies. -Instead of importing the ``energy_pbe0`` data, you can import the ``energy_delta`` and use this instead - -.. code:: python - - from tutorial_data import energy_delta - - Y_training = energy_delta[:1000] - Y_test = energy_delta[-1000:] - -Finally re-draw one of the learning curves from the previous exercise, and note how the prediction improves. - - -References -~~~~~~~~~~~~~ - -.. [#rupp] Rupp et al, Phys Rev Letters, 2012. -.. [#ruddigkeit] Ruddigkeit et al, J Chem Inf Model, 2012. -.. 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100644 --- a/_static/doctools.js +++ b/_static/doctools.js @@ -4,7 +4,7 @@ * * Sphinx JavaScript utilities for all documentation. * - * :copyright: Copyright 2007-2018 by the Sphinx team, see AUTHORS. + * :copyright: Copyright 2007-2017 by the Sphinx team, see AUTHORS. * :license: BSD, see LICENSE for details. * */ @@ -45,7 +45,7 @@ jQuery.urlencode = encodeURIComponent; * it will always return arrays of strings for the value parts. */ jQuery.getQueryParameters = function(s) { - if (typeof s === 'undefined') + if (typeof s == 'undefined') s = document.location.search; var parts = s.substr(s.indexOf('?') + 1).split('&'); var result = {}; @@ -66,55 +66,29 @@ jQuery.getQueryParameters = function(s) { * span elements with the given class name. */ jQuery.fn.highlightText = function(text, className) { - function highlight(node, addItems) { - if (node.nodeType === 3) { + function highlight(node) { + if (node.nodeType == 3) { var val = node.nodeValue; var pos = val.toLowerCase().indexOf(text); - if (pos >= 0 && - !jQuery(node.parentNode).hasClass(className) && - !jQuery(node.parentNode).hasClass("nohighlight")) { - var span; - var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg"); - if (isInSVG) { - span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); - } else { - span = document.createElement("span"); - span.className = className; - } + if (pos >= 0 && !jQuery(node.parentNode).hasClass(className)) { + var span = document.createElement("span"); + span.className = className; span.appendChild(document.createTextNode(val.substr(pos, text.length))); node.parentNode.insertBefore(span, node.parentNode.insertBefore( document.createTextNode(val.substr(pos + text.length)), node.nextSibling)); node.nodeValue = val.substr(0, pos); - if (isInSVG) { - var bbox = span.getBBox(); - var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect"); - rect.x.baseVal.value = bbox.x; - rect.y.baseVal.value = bbox.y; - rect.width.baseVal.value = bbox.width; - rect.height.baseVal.value = bbox.height; - rect.setAttribute('class', className); - var parentOfText = node.parentNode.parentNode; - addItems.push({ - "parent": node.parentNode, - "target": rect}); - } } } else if (!jQuery(node).is("button, select, textarea")) { jQuery.each(node.childNodes, function() { - highlight(this, addItems); + highlight(this); }); } } - var addItems = []; - var result = this.each(function() { - highlight(this, addItems); + return this.each(function() { + highlight(this); }); - for (var i = 0; i < addItems.length; ++i) { - jQuery(addItems[i].parent).before(addItems[i].target); - } - return result; }; /* @@ -157,21 +131,21 @@ var Documentation = { * i18n support */ TRANSLATIONS : {}, - PLURAL_EXPR : function(n) { return n === 1 ? 0 : 1; }, + PLURAL_EXPR : function(n) { return n == 1 ? 0 : 1; }, LOCALE : 'unknown', // gettext and ngettext don't access this so that the functions // can safely bound to a different name (_ = Documentation.gettext) gettext : function(string) { var translated = Documentation.TRANSLATIONS[string]; - if (typeof translated === 'undefined') + if (typeof translated == 'undefined') return string; - return (typeof translated === 'string') ? translated : translated[0]; + return (typeof translated == 'string') ? translated : translated[0]; }, ngettext : function(singular, plural, n) { var translated = Documentation.TRANSLATIONS[singular]; - if (typeof translated === 'undefined') + if (typeof translated == 'undefined') return (n == 1) ? singular : plural; return translated[Documentation.PLURALEXPR(n)]; }, @@ -206,7 +180,7 @@ var Documentation = { * see: https://bugzilla.mozilla.org/show_bug.cgi?id=645075 */ fixFirefoxAnchorBug : function() { - if (document.location.hash && $.browser.mozilla) + if (document.location.hash) window.setTimeout(function() { document.location.href += ''; }, 10); @@ -242,7 +216,7 @@ var Documentation = { var src = $(this).attr('src'); var idnum = $(this).attr('id').substr(7); 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a/_static/fonts/RobotoSlab/roboto-slab-v7-regular.woff2 b/_static/fonts/RobotoSlab/roboto-slab-v7-regular.woff2 deleted file mode 100644 index f2c76e5..0000000 Binary files a/_static/fonts/RobotoSlab/roboto-slab-v7-regular.woff2 and /dev/null differ diff --git a/_static/fonts/fontawesome-webfont.eot b/_static/fonts/fontawesome-webfont.eot index e9f60ca..c7b00d2 100644 Binary files a/_static/fonts/fontawesome-webfont.eot and b/_static/fonts/fontawesome-webfont.eot differ diff --git a/_static/fonts/fontawesome-webfont.svg b/_static/fonts/fontawesome-webfont.svg index 855c845..8b66187 100644 --- a/_static/fonts/fontawesome-webfont.svg +++ b/_static/fonts/fontawesome-webfont.svg @@ -1,2671 +1,685 @@ - - -Created by FontForge 20120731 at Mon Oct 24 17:37:40 2016 - By ,,, -Copyright Dave Gandy 2016. All rights reserved. - + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_static/fonts/fontawesome-webfont.ttf b/_static/fonts/fontawesome-webfont.ttf index 35acda2..f221e50 100644 Binary files a/_static/fonts/fontawesome-webfont.ttf and b/_static/fonts/fontawesome-webfont.ttf differ diff --git a/_static/fonts/fontawesome-webfont.woff b/_static/fonts/fontawesome-webfont.woff index 400014a..6e7483c 100644 Binary files a/_static/fonts/fontawesome-webfont.woff and b/_static/fonts/fontawesome-webfont.woff differ diff --git a/_static/fonts/fontawesome-webfont.woff2 b/_static/fonts/fontawesome-webfont.woff2 deleted file mode 100644 index 4d13fc6..0000000 Binary files a/_static/fonts/fontawesome-webfont.woff2 and /dev/null differ diff --git a/_static/jquery-3.2.1.js b/_static/jquery-3.1.0.js similarity index 94% rename from _static/jquery-3.2.1.js rename to _static/jquery-3.1.0.js index d2d8ca4..f2fc274 100644 --- a/_static/jquery-3.2.1.js +++ b/_static/jquery-3.1.0.js @@ -1,15 +1,16 @@ +/*eslint-disable no-unused-vars*/ /*! - * jQuery JavaScript Library v3.2.1 + * jQuery JavaScript Library v3.1.0 * https://jquery.com/ * * Includes Sizzle.js * https://sizzlejs.com/ * - * Copyright JS Foundation and other contributors + * Copyright jQuery Foundation and other contributors * Released under the MIT license * https://jquery.org/license * - * Date: 2017-03-20T18:59Z + * Date: 2016-07-07T21:44Z */ ( function( global, factory ) { @@ -82,13 +83,13 @@ var support = {}; doc.head.appendChild( script ).parentNode.removeChild( script ); } /* global Symbol */ -// Defining this global in .eslintrc.json would create a danger of using the global +// Defining this global in .eslintrc would create a danger of using the global // unguarded in another place, it seems safer to define global only for this module var - version = "3.2.1", + version = "3.1.0", // Define a local copy of jQuery jQuery = function( selector, context ) { @@ -128,14 +129,13 @@ jQuery.fn = jQuery.prototype = { // Get the Nth element in the matched element set OR // Get the whole matched element set as a clean array get: function( num ) { + return num != null ? - // Return all the elements in a clean array - if ( num == null ) { - return slice.call( this ); - } + // Return just the one element from the set + ( num < 0 ? this[ num + this.length ] : this[ num ] ) : - // Return just the one element from the set - return num < 0 ? this[ num + this.length ] : this[ num ]; + // Return all the elements in a clean array + slice.call( this ); }, // Take an array of elements and push it onto the stack @@ -236,11 +236,11 @@ jQuery.extend = jQuery.fn.extend = function() { // Recurse if we're merging plain objects or arrays if ( deep && copy && ( jQuery.isPlainObject( copy ) || - ( copyIsArray = Array.isArray( copy ) ) ) ) { + ( copyIsArray = jQuery.isArray( copy ) ) ) ) { if ( copyIsArray ) { copyIsArray = false; - clone = src && Array.isArray( src ) ? src : []; + clone = src && jQuery.isArray( src ) ? src : []; } else { clone = src && jQuery.isPlainObject( src ) ? src : {}; @@ -279,6 +279,8 @@ jQuery.extend( { return jQuery.type( obj ) === "function"; }, + isArray: Array.isArray, + isWindow: function( obj ) { return obj != null && obj === obj.window; }, @@ -353,6 +355,10 @@ jQuery.extend( { return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase ); }, + nodeName: function( elem, name ) { + return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase(); + }, + each: function( obj, callback ) { var length, i = 0; @@ -537,14 +543,14 @@ function isArrayLike( obj ) { } var Sizzle = /*! - * Sizzle CSS Selector Engine v2.3.3 + * Sizzle CSS Selector Engine v2.3.0 * https://sizzlejs.com/ * * Copyright jQuery Foundation and other contributors * Released under the MIT license * http://jquery.org/license * - * Date: 2016-08-08 + * Date: 2016-01-04 */ (function( window ) { @@ -690,7 +696,7 @@ var i, // CSS string/identifier serialization // https://drafts.csswg.org/cssom/#common-serializing-idioms - rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g, + rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\x80-\uFFFF\w-]/g, fcssescape = function( ch, asCodePoint ) { if ( asCodePoint ) { @@ -717,7 +723,7 @@ var i, disabledAncestor = addCombinator( function( elem ) { - return elem.disabled === true && ("form" in elem || "label" in elem); + return elem.disabled === true; }, { dir: "parentNode", next: "legend" } ); @@ -1003,54 +1009,26 @@ function createButtonPseudo( type ) { * @param {Boolean} disabled true for :disabled; false for :enabled */ function createDisabledPseudo( disabled ) { - - // Known :disabled false positives: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable + // Known :disabled false positives: + // IE: *[disabled]:not(button, input, select, textarea, optgroup, option, menuitem, fieldset) + // not IE: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable return function( elem ) { - // Only certain elements can match :enabled or :disabled - // https://html.spec.whatwg.org/multipage/scripting.html#selector-enabled - // https://html.spec.whatwg.org/multipage/scripting.html#selector-disabled - if ( "form" in elem ) { - - // Check for inherited disabledness on relevant non-disabled elements: - // * listed form-associated elements in a disabled fieldset - // https://html.spec.whatwg.org/multipage/forms.html#category-listed - // https://html.spec.whatwg.org/multipage/forms.html#concept-fe-disabled - // * option elements in a disabled optgroup - // https://html.spec.whatwg.org/multipage/forms.html#concept-option-disabled - // All such elements have a "form" property. - if ( elem.parentNode && elem.disabled === false ) { - - // Option elements defer to a parent optgroup if present - if ( "label" in elem ) { - if ( "label" in elem.parentNode ) { - return elem.parentNode.disabled === disabled; - } else { - return elem.disabled === disabled; - } - } - - // Support: IE 6 - 11 - // Use the isDisabled shortcut property to check for disabled fieldset ancestors - return elem.isDisabled === disabled || + // Check form elements and option elements for explicit disabling + return "label" in elem && elem.disabled === disabled || + "form" in elem && elem.disabled === disabled || - // Where there is no isDisabled, check manually - /* jshint -W018 */ - elem.isDisabled !== !disabled && - disabledAncestor( elem ) === disabled; - } - - return elem.disabled === disabled; + // Check non-disabled form elements for fieldset[disabled] ancestors + "form" in elem && elem.disabled === false && ( + // Support: IE6-11+ + // Ancestry is covered for us + elem.isDisabled === disabled || - // Try to winnow out elements that can't be disabled before trusting the disabled property. - // Some victims get caught in our net (label, legend, menu, track), but it shouldn't - // even exist on them, let alone have a boolean value. - } else if ( "label" in elem ) { - return elem.disabled === disabled; - } - - // Remaining elements are neither :enabled nor :disabled - return false; + // Otherwise, assume any non-