.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "generated/sphinx_gallery_examples/1-pool-classification/plot-SubSamplingWrapper-Sub-sampling_Wrapper.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_generated_sphinx_gallery_examples_1-pool-classification_plot-SubSamplingWrapper-Sub-sampling_Wrapper.py: Sub-sampling Wrapper ==================== .. GENERATED FROM PYTHON SOURCE LINES 7-8 **Idea:** The Sub-sampling Wrapper creates a subset of candidates before computing their utilities. This is useful when the number of available candidates is too large and a small subset of candidates is sufficient to select a good batch for labeling. The number of candidates can be controlled using `max_candidates` which supports an absolute number or a fraction of the available candidates. Additionally, `exclude_non_subsample` provides an option to mask all candidates that were not included in the subsample. This can further improve the runtime for query strategies that utilize all available unlabeled data in their selection. .. GENERATED FROM PYTHON SOURCE LINES 10-20 | **Google Colab Note**: If the notebook fails to run after installing the needed packages, try to restart the runtime (Ctrl + M) under Runtime -> Restart session. .. image:: https://colab.research.google.com/assets/colab-badge.svg :target: https://colab.research.google.com/github/scikit-activeml/scikit-activeml.github.io/blob/gh-pages/latest/generated/sphinx_gallery_notebooks//1-pool-classification/plot-SubSamplingWrapper-Sub-sampling_Wrapper.ipynb | **Notebook Dependencies** | Uncomment the following cell to install all dependencies for this tutorial. .. GENERATED FROM PYTHON SOURCE LINES 20-23 .. code-block:: Python # !pip install scikit-activeml .. GENERATED FROM PYTHON SOURCE LINES 24-129 .. code-block:: Python import numpy as np from matplotlib import pyplot as plt, animation from sklearn.datasets import make_blobs from sklearn.model_selection import train_test_split from skactiveml.utils import MISSING_LABEL, labeled_indices from skactiveml.visualization import plot_utilities, plot_decision_boundary from skactiveml.classifier import ParzenWindowClassifier from skactiveml.pool import SubSamplingWrapper, UncertaintySampling random_state = np.random.RandomState(0) # Build a dataset. X_true, y_clusters = make_blobs( n_samples=400, n_features=2, centers=[[0, 1], [-3, 0.5], [-1, -1], [2, 1], [1, -0.5]], cluster_std=0.7, random_state=random_state, ) y_true = y_clusters % 2 X_pool, X_test, y_pool, y_test = train_test_split( X_true, y_true, test_size=0.25, random_state=random_state ) X = X_pool y = np.full(shape=y_pool.shape, fill_value=MISSING_LABEL) # Initialise the classifier. clf = ParzenWindowClassifier(classes=[0, 1], random_state=random_state) # Initialise the query strategy. qs = SubSamplingWrapper( query_strategy=UncertaintySampling(), max_candidates=0.5 ) # Preparation for plotting. fig, ax = plt.subplots() feature_bound = [ [min(X[:, 0]), min(X[:, 1])], [max(X[:, 0]), max(X[:, 1])] ] artists = [] # Active learning cycle: n_cycles = 20 for c in range(n_cycles): # Fit the classifier with current labels. clf.fit(X, y) # Query the next sample(s). query_idx = qs.query(X=X, y=y, clf=clf) # Capture the current plot state. coll_old = list(ax.collections) title = ax.text( 0.5, 1.05, f"Decision boundary after acquiring {c} labels\n" f"Test Accuracy: {clf.score(X_test, y_test):.4f}", size=plt.rcParams["axes.titlesize"], ha="center", transform=ax.transAxes, ) # Update plot with utility values, samples, and decision boundary. X_labeled = X[labeled_indices(y)] ax = plot_utilities( qs, X=X, y=y, clf=clf, candidates=X, res=25, feature_bound=feature_bound, ax=ax, ) ax.scatter( X[:, 0], X[:, 1], c=y_pool, cmap="coolwarm", marker=".", zorder=2 ) ax.scatter( X_labeled[:, 0], X_labeled[:, 1], c="grey", alpha=0.8, marker=".", s=300, ) ax = plot_decision_boundary(clf, feature_bound, ax=ax) ax.set_xlabel('Feature 1') ax.set_ylabel('Feature 2') coll_new = list(ax.collections) coll_new.append(title) artists.append([x for x in coll_new if x not in coll_old]) # Update labels based on query. y[query_idx] = y_pool[query_idx] ani = animation.ArtistAnimation(fig, artists, interval=1000, blit=True) .. container:: sphx-glr-animation .. raw:: html
.. GENERATED FROM PYTHON SOURCE LINES 130-131 .. image:: ../../examples/pool_classification_legend.png .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 5.696 seconds) .. _sphx_glr_download_generated_sphinx_gallery_examples_1-pool-classification_plot-SubSamplingWrapper-Sub-sampling_Wrapper.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot-SubSamplingWrapper-Sub-sampling_Wrapper.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot-SubSamplingWrapper-Sub-sampling_Wrapper.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot-SubSamplingWrapper-Sub-sampling_Wrapper.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_