.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "examples/00_basic.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_examples_00_basic.py: Basic Usage of SiML =================== SiML facilitates machine learning processes, including preprocessing, learning, and prediction. We will cover the entire pipeline of a machine learning process using the gradient dataset example. .. GENERATED FROM PYTHON SOURCE LINES 13-15 Import necessary modules including :mod:`siml`. `FEMIO `_ is used to generate data. .. GENERATED FROM PYTHON SOURCE LINES 15-24 .. code-block:: default import pathlib import shutil import femio import numpy as np import siml .. GENERATED FROM PYTHON SOURCE LINES 25-26 Clean up old data if exists. .. GENERATED FROM PYTHON SOURCE LINES 26-33 .. code-block:: default shutil.rmtree('00_basic_data/raw', ignore_errors=True) shutil.rmtree('00_basic_data/interim', ignore_errors=True) shutil.rmtree('00_basic_data/preprocessed', ignore_errors=True) shutil.rmtree('00_basic_data/model', ignore_errors=True) shutil.rmtree('00_basic_data/inferred', ignore_errors=True) .. GENERATED FROM PYTHON SOURCE LINES 34-38 Data generation --------------- First, we define a function to generate data and call it to create the dataset. .. GENERATED FROM PYTHON SOURCE LINES 38-86 .. code-block:: default def generate_data(output_directory): # Generate a simple mesh n_x_element = np.random.randint(5, 10) n_y_element = np.random.randint(5, 10) n_z_element = 1 fem_data = femio.generate_brick( 'hex', n_x_element=n_x_element, n_y_element=n_y_element, n_z_element=n_z_element, x_length=n_x_element, y_length=n_y_element, z_length=n_z_element) # Generate scalar field phi and the gradient field associated to it scale = 1 / 5 nodes = np.copy(fem_data.nodes.data) nodes[:, -1] = 0. # Make pseudo 2D shift = np.random.rand(1, 3) / scale shift[:, -1] = 0 square_norm = .5 * np.linalg.norm(nodes - shift, axis=1)**2 phi = np.cos(square_norm * scale)[:, None] grad = - np.sin(square_norm * scale)[:, None] * scale * (nodes - shift) # Write data fem_data.nodal_data.update_data( fem_data.nodes.ids, {'phi': phi, 'grad': grad}, allow_overwrite=False) fem_data.write( 'ucd', output_directory / 'mesh.inp') return n_train_sample = 20 for i in range(n_train_sample): generate_data(pathlib.Path(f"00_basic_data/raw/train/{i}")) n_validation_sample = 5 for i in range(n_validation_sample): generate_data(pathlib.Path(f"00_basic_data/raw/validation/{i}")) n_test_data = 5 for i in range(n_validation_sample): generate_data(pathlib.Path(f"00_basic_data/raw/test/{i}")) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none Creating data: NODE Creating data: ELEMENT Creating data: phi Creating data: grad Start writing data File written in: 00_basic_data/raw/train/0/mesh.inp Creating data: NODE Creating data: ELEMENT Creating data: phi Creating data: grad Start writing data File written in: 00_basic_data/raw/train/1/mesh.inp Creating data: NODE Creating data: ELEMENT Creating data: phi Creating data: grad Start writing data File written in: 00_basic_data/raw/train/2/mesh.inp Creating data: NODE Creating data: ELEMENT Creating data: phi Creating data: grad Start writing data File written in: 00_basic_data/raw/train/3/mesh.inp Creating data: NODE Creating data: ELEMENT Creating data: phi Creating data: grad Start writing data File written in: 00_basic_data/raw/train/4/mesh.inp Creating data: NODE Creating data: ELEMENT Creating data: phi Creating data: grad Start writing data File written in: 00_basic_data/raw/train/5/mesh.inp Creating data: NODE Creating data: ELEMENT Creating data: phi Creating data: grad Start writing data File written in: 00_basic_data/raw/train/6/mesh.inp Creating data: NODE Creating data: ELEMENT Creating data: phi Creating data: grad Start writing data File written in: 00_basic_data/raw/train/7/mesh.inp Creating data: NODE Creating data: ELEMENT Creating data: phi Creating data: grad Start writing data File written in: 00_basic_data/raw/train/8/mesh.inp Creating data: NODE Creating data: ELEMENT Creating data: phi Creating data: grad Start writing data File written in: 00_basic_data/raw/train/9/mesh.inp Creating data: NODE Creating data: ELEMENT Creating data: phi Creating data: grad Start writing data File written in: 00_basic_data/raw/train/10/mesh.inp Creating data: NODE Creating data: ELEMENT Creating data: phi Creating data: grad Start writing data File written in: 00_basic_data/raw/train/11/mesh.inp Creating data: NODE Creating data: ELEMENT Creating data: phi Creating data: grad Start writing data File written in: 00_basic_data/raw/train/12/mesh.inp Creating data: NODE Creating data: ELEMENT Creating data: phi Creating data: grad Start writing data File written in: 00_basic_data/raw/train/13/mesh.inp Creating data: NODE Creating data: ELEMENT Creating data: phi Creating data: grad Start writing data File written in: 00_basic_data/raw/train/14/mesh.inp Creating data: NODE Creating data: ELEMENT Creating data: phi Creating data: grad Start writing data File written in: 00_basic_data/raw/train/15/mesh.inp Creating data: NODE Creating data: ELEMENT Creating data: phi Creating data: grad Start writing data File written in: 00_basic_data/raw/train/16/mesh.inp Creating data: NODE Creating data: ELEMENT Creating data: phi Creating data: grad Start writing data File written in: 00_basic_data/raw/train/17/mesh.inp Creating data: NODE Creating data: ELEMENT Creating data: phi Creating data: grad Start writing data File written in: 00_basic_data/raw/train/18/mesh.inp Creating data: NODE Creating data: ELEMENT Creating data: phi Creating data: grad Start writing data File written in: 00_basic_data/raw/train/19/mesh.inp Creating data: NODE Creating data: ELEMENT Creating data: phi Creating data: grad Start writing data File written in: 00_basic_data/raw/validation/0/mesh.inp Creating data: NODE Creating data: ELEMENT Creating data: phi Creating data: grad Start writing data File written in: 00_basic_data/raw/validation/1/mesh.inp Creating data: NODE Creating data: ELEMENT Creating data: phi Creating data: grad Start writing data File written in: 00_basic_data/raw/validation/2/mesh.inp Creating data: NODE Creating data: ELEMENT Creating data: phi Creating data: grad Start writing data File written in: 00_basic_data/raw/validation/3/mesh.inp Creating data: NODE Creating data: ELEMENT Creating data: phi Creating data: grad Start writing data File written in: 00_basic_data/raw/validation/4/mesh.inp Creating data: NODE Creating data: ELEMENT Creating data: phi Creating data: grad Start writing data File written in: 00_basic_data/raw/test/0/mesh.inp Creating data: NODE Creating data: ELEMENT Creating data: phi Creating data: grad Start writing data File written in: 00_basic_data/raw/test/1/mesh.inp Creating data: NODE Creating data: ELEMENT Creating data: phi Creating data: grad Start writing data File written in: 00_basic_data/raw/test/2/mesh.inp Creating data: NODE Creating data: ELEMENT Creating data: phi Creating data: grad Start writing data File written in: 00_basic_data/raw/test/3/mesh.inp Creating data: NODE Creating data: ELEMENT Creating data: phi Creating data: grad Start writing data File written in: 00_basic_data/raw/test/4/mesh.inp .. GENERATED FROM PYTHON SOURCE LINES 87-110 If the process finished successfully, the data should look as follows (visualization using `ParaView `_). .. image:: ../../examples/00_basic_fig/grad_train.png :width: 400 Here, we consider the task to predict the gradient field (arrows in the figure above) from the input of the scalar field (color map in the figure above). Data preprocessing ------------------ Here, we extract features from the generated dataset. Data generation and feature extraction is something SiML does not manage because the library does not know what simulation to run and what features to extract. Therefore, users should write some code for these two parts, although SiML (and FEMIO) can support it. Now, define a call-back function to extract features from the dataset. The function takes two arguments, :code:`femio.FEMData` object representing a sample in the dataset and :code:`pathlib.Path` object representing an output directory. .. GENERATED FROM PYTHON SOURCE LINES 110-140 .. code-block:: default def conversion_function(fem_data, raw_directory): node = fem_data.nodes.data phi = fem_data.nodal_data.get_attribute_data('phi') grad = fem_data.nodal_data.get_attribute_data('grad')[..., None] # Generate renormalized adjacency matrix based on Kipf and Welling 2016 nodal_adj = fem_data.calculate_adjacency_matrix_node() nodal_nadj = siml.prepost.normalize_adjacency_matrix(nodal_adj) # Generate IsoAM based on Horie et al. 2020 nodal_isoam_x, nodal_isoam_y, nodal_isoam_z = \ fem_data.calculate_spatial_gradient_adjacency_matrices( 'nodal', n_hop=1, moment_matrix=True) dict_data = { 'node': node, 'phi': phi, 'grad': grad, 'nodal_nadj': nodal_nadj, 'nodal_isoam_x': nodal_isoam_x, 'nodal_isoam_y': nodal_isoam_y, 'nodal_isoam_z': nodal_isoam_z, } return dict_data .. GENERATED FROM PYTHON SOURCE LINES 141-169 From here, SiML can manage most of the process. Please download `data.yml `_ file and place it in the :code:`00_basic_data` directory. SiML uses YAML files as setting files to control its behavior. Basically, each setting component can be omitted, and if so, the default setting will be adopted. The relevant contents of the YAML file are as follows. .. code-block:: yaml data: # Data directory setting raw: 00_basic_data/raw # Row data interim: 00_basic_data/interim # Extracted features preprocessed: 00_basic_data/preprocessed # Preprocessed data inferred: 00_basic_data/inferred # Predicted data conversion: # Feature extraction setting file_type: 'ucd' # File type to be read required_file_names: # Files to be regarded as data - '*.inp' As can be seen, the structure of the directory follows that of the `Cookiecutter Data Science `_. Now, generate a :class:`~siml.prepost.RawConverter` object by feeding the YAML file and perform feature extraction. .. GENERATED FROM PYTHON SOURCE LINES 169-177 .. code-block:: default settings_yaml = pathlib.Path('00_basic_data/data.yml') raw_converter = siml.preprocessing.converter.RawConverter.read_settings( settings_yaml, conversion_function=conversion_function) raw_converter.convert() .. rst-class:: sphx-glr-script-out Out: .. code-block:: none # process: 4 Searching: 00_basic_data/raw .. GENERATED FROM PYTHON SOURCE LINES 178-191 Next, perform preprocessing, e.g., scaling of the data. The relevant part of the YAML file is as follows. .. code-block:: yaml preprocess: # Data scaling setting node: std_scale # Standardization without subtraction of the mean phi: standardize # Standardization grad: std_scale nodal_nadj: identity # No scaling nodal_isoam_x: identity nodal_isoam_y: identity nodal_isoam_z: identity .. GENERATED FROM PYTHON SOURCE LINES 191-195 .. code-block:: default preprocessor = siml.preprocessing.ScalingConverter.read_settings(settings_yaml) preprocessor.fit_transform() .. GENERATED FROM PYTHON SOURCE LINES 196-234 Training -------- Then, we move on to the training. Please download `isogcn.yml `_ file and place it in the :code:`00_basic_data` directory. In the YAML file, the setting for the trainer is written as follows. .. code-block:: yaml trainer: output_directory: 00_basic_data/model # Output directory inputs: # Input data specification - name: phi # Input data name dim: 1 # phi's dimention support_input: # Support inputs e.g. adjacency matrix - nodal_isoam_x - nodal_isoam_y - nodal_isoam_z outputs: - name: grad # Output data name dim: 1 # gradient's dimention (the shape is in [n, 3, 1], so 1) prune: false n_epoch: 100 # The nmber of epochs log_trigger_epoch: 1 # The period to log the training stop_trigger_epoch: 5 # The period to condider early stopping seed: 0 # The rondom seed lazy: false # If true, data is read lazily rather than on-memory batch_size: 4 # The size of the batch num_workers: 0 # The number of processes to load data (0 means serial) figure_format: png # Format of the output figures (the default is pdf) In the same file, the setting for the machine learning model is also written. In this example, we use `IsoGCN `_ (Horie et al. ICLR 2021). We can try many machine learning trials with various training and model settings by editing the YAML file. .. GENERATED FROM PYTHON SOURCE LINES 234-244 .. code-block:: default isogcn_yaml = pathlib.Path('00_basic_data/isogcn.yml') train_main_setting = siml.setting.MainSetting.read_settings_yaml( isogcn_yaml ) trainer = siml.trainer.Trainer(train_main_setting) trainer.train() .. rst-class:: sphx-glr-script-out Out: .. code-block:: none Loading data 0%| | 0/20 [00:00}, {'dict_x': {'phi': array([[ 0.90399957], [ 0.9701677 ], [ 0.9694085 ], [ 0.8999687 ], [ 0.65681386], [ 0.11323014], [-0.6362584 ], [ 0.9703388 ], [ 0.99888253], [ 0.9987306 ], [ 0.9680396 ], [ 0.7920624 ], [ 0.3060756 ], [-0.4724284 ], [ 0.9697545 ], [ 0.9987659 ], [ 0.99860656], [ 0.96743345], [ 0.7905917 ], [ 0.30378473], [-0.47454718], [ 0.9008909 ], [ 0.968393 ], [ 0.9676121 ], [ 0.89679927], [ 0.6513553 ], [ 0.10605732], [-0.64180905], [ 0.65894264], [ 0.7933547 ], [ 0.79145634], [ 0.65189135], [ 0.30216578], [-0.29183832], [-0.89087164], [ 0.11673994], [ 0.30876532], [ 0.3058038 ], [ 0.10746255], [-0.2911624 ], [-0.7824271 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GENERATED FROM PYTHON SOURCE LINES 286-315 The predicted data is stored in :code:`00_basic_data/inferred/model_[date]/test` (:code:`[date]` depends on the date when you run this script.) The structure of the directory is as follows. :: 00_basic_data/inferred/model_[date] ├── log.csv # Summary file ├── settings.yml # Setting used to prediction (for reproducibility) └── test ├── 0 │   ├── grad.npy # Predicted gradient │   ├── mesh.inp # AVD UCD format file for visualization │   └── phi.npy # Input data ├── 1 │   ├── grad.npy │   ├── mesh.inp │   └── phi.npy . . . The predicted result will look as follows (left: ground truth, right: prediction). Looks good! .. image:: ../../examples/00_basic_fig/res.png :width: 400 .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 34.950 seconds) .. _sphx_glr_download_examples_00_basic.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: 00_basic.py <00_basic.py>` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: 00_basic.ipynb <00_basic.ipynb>` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_