.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/demonstrate_01_train_som_representation_estimator.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_demonstrate_01_train_som_representation_estimator.py: =================================================================== Training the SOM representation estimator =================================================================== .. GENERATED FROM PYTHON SOURCE LINES 8-11 .. code-block:: Python RANDOM_SEED = 42 VERBOSE = True .. GENERATED FROM PYTHON SOURCE LINES 12-17 .. code-block:: Python # generate train / test data from examples.utils.dataset import generate_train_data X_train, _ = generate_train_data(n_normal=100, n_abnormal=20, random_seed=RANDOM_SEED) .. GENERATED FROM PYTHON SOURCE LINES 18-21 .. code-block:: Python from pan import SomRepresentationEstimator from minisom_representation import SomRepresentation .. GENERATED FROM PYTHON SOURCE LINES 22-27 .. code-block:: Python # train with derived SOM representation estimator = SomRepresentationEstimator.with_derived_som_representation(X=X_train, random_seed=RANDOM_SEED, verbose=VERBOSE) estimator.fit(X_train) .. rst-class:: sphx-glr-script-out .. code-block:: none -------------- Based on 120 instances, the recommended SOM hyperparameters are the following: Total node count (M): 55 Recommended sides (d1 x d2): 8 x 8 Initial neighborhood radius (sigma): 4.0 -------------- [ 0 / 20 ] 0% - ? it/s [ 0 / 20 ] 0% - ? it/s [ 1 / 20 ] 5% - 0:00:00 left [ 2 / 20 ] 10% - 0:00:00 left [ 3 / 20 ] 15% - 0:00:00 left [ 4 / 20 ] 20% - 0:00:00 left [ 5 / 20 ] 25% - 0:00:00 left [ 6 / 20 ] 30% - 0:00:00 left [ 7 / 20 ] 35% - 0:00:00 left [ 8 / 20 ] 40% - 0:00:00 left [ 9 / 20 ] 45% - 0:00:00 left [ 10 / 20 ] 50% - 0:00:00 left [ 11 / 20 ] 55% - 0:00:00 left [ 12 / 20 ] 60% - 0:00:00 left [ 13 / 20 ] 65% - 0:00:00 left [ 14 / 20 ] 70% - 0:00:00 left [ 15 / 20 ] 75% - 0:00:00 left [ 16 / 20 ] 80% - 0:00:00 left [ 17 / 20 ] 85% - 0:00:00 left [ 18 / 20 ] 90% - 0:00:00 left [ 19 / 20 ] 95% - 0:00:00 left [ 20 / 20 ] 100% - 0:00:00 left Quantization Error: 0.0010 An SOM representation has been fitted as follows: ------------------------------------------------------- Hyperparameters of SOM: {'input_len': 2, 'x': 8, 'y': 8, 'sigma': np.float64(4.0), 'topology': 'rectangular', 'learning_rate': 0.5, 'decay_function': 'asymptotic_decay', 'sigma_decay_function': 'asymptotic_decay', 'neighborhood_function': 'gaussian', 'activation_distance': 'euclidean', 'random_seed': 42, 'num_iteration': 20, 'verbose': True} Quality of SOM: Quantization Error (QE): 0.0009728796386848607 Topographic Error (TE): 0.016666666666666666 An SOM representation estimator has been fitted as follows: ----------------------------------------------------------------- Hyperparameters: {'nu': 0.5, 'random_seed': 42, 'som_representation': , 'verbose': True} Learned parameters: Offset: -0.00047117853857801643 .. raw:: html

SomRepresentationEstimator(random_seed=42,
                               som_representation=<minisom_representation.som_representation.SomRepresentation object at 0x7fc2904b80e0>,
                               verbose=True)

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

.. GENERATED FROM PYTHON SOURCE LINES 28-38 .. code-block:: Python # train with SOM representation hyperparameters specified som_rep = SomRepresentation.with_derived_params( X_train, sigma=3.3, topology="rectangular", learning_rate=0.25, num_iteration=44, decay_function="asymptotic_decay", sigma_decay_function="asymptotic_decay", neighborhood_function='gaussian', activation_distance='euclidean', random_seed=RANDOM_SEED, verbose=VERBOSE ) estimator = SomRepresentationEstimator.from_som_representation(som_rep, nu=.01, random_seed=RANDOM_SEED, verbose=VERBOSE) estimator.fit(X_train) .. rst-class:: sphx-glr-script-out .. code-block:: none -------------- Based on 120 instances, the recommended SOM hyperparameters are the following: Total node count (M): 55 Recommended sides (d1 x d2): 8 x 8 Initial neighborhood radius (sigma): 4.0 -------------- [ 0 / 44 ] 0% - ? it/s [ 0 / 44 ] 0% - ? it/s [ 1 / 44 ] 2% - 0:00:00 left [ 2 / 44 ] 5% - 0:00:00 left [ 3 / 44 ] 7% - 0:00:00 left [ 4 / 44 ] 9% - 0:00:00 left [ 5 / 44 ] 11% - 0:00:00 left [ 6 / 44 ] 14% - 0:00:00 left [ 7 / 44 ] 16% - 0:00:00 left [ 8 / 44 ] 18% - 0:00:00 left [ 9 / 44 ] 20% - 0:00:00 left [ 10 / 44 ] 23% - 0:00:00 left [ 11 / 44 ] 25% - 0:00:00 left [ 12 / 44 ] 27% - 0:00:00 left [ 13 / 44 ] 30% - 0:00:00 left [ 14 / 44 ] 32% - 0:00:00 left [ 15 / 44 ] 34% - 0:00:00 left [ 16 / 44 ] 36% - 0:00:00 left [ 17 / 44 ] 39% - 0:00:00 left [ 18 / 44 ] 41% - 0:00:00 left [ 19 / 44 ] 43% - 0:00:00 left [ 20 / 44 ] 45% - 0:00:00 left [ 21 / 44 ] 48% - 0:00:00 left [ 22 / 44 ] 50% - 0:00:00 left [ 23 / 44 ] 52% - 0:00:00 left [ 24 / 44 ] 55% - 0:00:00 left [ 25 / 44 ] 57% - 0:00:00 left [ 26 / 44 ] 59% - 0:00:00 left [ 27 / 44 ] 61% - 0:00:00 left [ 28 / 44 ] 64% - 0:00:00 left [ 29 / 44 ] 66% - 0:00:00 left [ 30 / 44 ] 68% - 0:00:00 left [ 31 / 44 ] 70% - 0:00:00 left [ 32 / 44 ] 73% - 0:00:00 left [ 33 / 44 ] 75% - 0:00:00 left [ 34 / 44 ] 77% - 0:00:00 left [ 35 / 44 ] 80% - 0:00:00 left [ 36 / 44 ] 82% - 0:00:00 left [ 37 / 44 ] 84% - 0:00:00 left [ 38 / 44 ] 86% - 0:00:00 left [ 39 / 44 ] 89% - 0:00:00 left [ 40 / 44 ] 91% - 0:00:00 left [ 41 / 44 ] 93% - 0:00:00 left [ 42 / 44 ] 95% - 0:00:00 left [ 43 / 44 ] 98% - 0:00:00 left [ 44 / 44 ] 100% - 0:00:00 left Quantization Error: 0.0008 An SOM representation has been fitted as follows: ------------------------------------------------------- Hyperparameters of SOM: {'input_len': 2, 'x': 8, 'y': 8, 'sigma': 3.3, 'topology': 'rectangular', 'learning_rate': 0.25, 'decay_function': 'asymptotic_decay', 'sigma_decay_function': 'asymptotic_decay', 'neighborhood_function': 'gaussian', 'activation_distance': 'euclidean', 'random_seed': 42, 'num_iteration': 44, 'verbose': True} Quality of SOM: Quantization Error (QE): 0.0008264950770934449 Topographic Error (TE): 0.016666666666666666 An SOM representation estimator has been fitted as follows: ----------------------------------------------------------------- Hyperparameters: {'nu': 0.01, 'random_seed': 42, 'som_representation': , 'verbose': True} Learned parameters: Offset: -0.004763263703869526 .. raw:: html

SomRepresentationEstimator(nu=0.01, random_seed=42,
                               som_representation=<minisom_representation.som_representation.SomRepresentation object at 0x7fc2a826da00>,
                               verbose=True)

.. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 2.584 seconds) .. _sphx_glr_download_auto_examples_demonstrate_01_train_som_representation_estimator.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: demonstrate_01_train_som_representation_estimator.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: demonstrate_01_train_som_representation_estimator.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: demonstrate_01_train_som_representation_estimator.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_