.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/demonstrate_07_inspect_predictions.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_07_inspect_predictions.py: ================================================================= Decision score and predictions based on the nudge mechanism ================================================================= .. GENERATED FROM PYTHON SOURCE LINES 8-11 .. code-block:: Python RANDOM_SEED = 42 VERBOSE = False .. GENERATED FROM PYTHON SOURCE LINES 12-18 .. code-block:: Python # generate train / test data from examples.utils.dataset import generate_train_data, generate_test_data X_train, y_train = generate_train_data(n_normal=100, n_abnormal=20, random_seed=RANDOM_SEED) X_test, y_test = generate_test_data(n_normal=100, n_abnormal=20, random_seed=RANDOM_SEED*2) .. GENERATED FROM PYTHON SOURCE LINES 19-23 .. code-block:: Python import matplotlib.pyplot as plt import numpy as np from pan import ParallelAnomalousNudge .. GENERATED FROM PYTHON SOURCE LINES 24-29 .. code-block:: Python # train PAN model = ParallelAnomalousNudge.with_derived_estimators(X_train, y_train, nu=.1, random_seed=RANDOM_SEED, verbose=VERBOSE) model.fit(X_train, y_train) .. raw:: html

ParallelAnomalousNudge(estimators={np.int64(0): SomRepresentationEstimator(nu=0.1,
                                                                               random_seed=42,
                                                                               som_representation=<minisom_representation.som_representation.SomRepresentation object at 0x7fc293d660c0>),
                                       np.int64(1): SomRepresentationEstimator(nu=0.1,
                                                                               random_seed=42,
                                                                               som_representation=<minisom_representation.som_representation.SomRepresentation object at 0x7fc2a37fd820>)},
                           nu=0.1, random_seed=42, scaler=StandardScaler())

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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.. GENERATED FROM PYTHON SOURCE LINES 30-71 .. code-block:: Python fig, axes = plt.subplots(2, 1, figsize=(10, 10), sharex=True, sharey=True) axes = axes.flatten() scores = model.score_samples(X_test) y_values = [np.ones_like(scores[y_test == 0]), np.ones_like(scores[y_test == 1])] ax = axes[0] ax.set_title(rf"Nudged scores and offset calculated based on $\nu={model.nu}$", loc="left") ax.text(0, .8, s="inlier", va="center", ha="center", fontsize=12, color="white", bbox=dict(color="seagreen", boxstyle='round,pad=.6'), zorder=4) ax.text(min(scores), .8, s="outlier", va="center", ha="center", fontsize=12, color="white", bbox=dict(color="indianred", boxstyle='round,pad=.6'), zorder=4) ax.violinplot(scores, positions=[1], vert=False) ax.scatter(scores[y_test == 0], y=y_values[0], marker="o", edgecolor="k", color="none", s=400, alpha=.75, label="True normal", zorder=2) ax.scatter(scores[y_test == 1], y=y_values[1], marker="x", color="crimson", s=200, alpha=.75, label="True anomaly", zorder=3) ax.axvline(model.offset_, 0, 1, color="k", linestyle="--") ax.text(model.offset_, 1.2, s=rf"$\text{{offset}}={model.offset_:.3}$", va="center", ha="center", fontsize=12, color="white", bbox=dict(color="black", boxstyle='round,pad=.6'), zorder=4) ax.legend() ax.margins(.1) scores = model.decision_function(X_test) predictions = model.predict(X_test) y_values = [np.ones_like(scores[predictions == 1]), np.ones_like(scores[predictions == -1])] ax = axes[1] ax.set_title(r"Decision scores (shifted nudged scores and offset)", loc="left") ax.text(-model.offset_, .8, s="Pred 1", va="center", ha="center", fontsize=12, color="white", bbox=dict(color="seagreen", boxstyle='round,pad=.6'), zorder=4) ax.text(min(scores), .8, s="Pred -1", va="center", ha="center", fontsize=12, color="white", bbox=dict(color="purple", boxstyle='round,pad=.6'), zorder=4) ax.violinplot(scores, positions=[1], vert=False) ax.scatter(scores[predictions == 1], y=y_values[0], marker="o", edgecolor="green", color="none", s=200, alpha=.75, label="Normal prediction", zorder=2) ax.scatter(scores[predictions == -1], y=y_values[1], marker="s", edgecolor="purple", color="none", s=200, alpha=.75, label="Abnormal prediction", zorder=3) ax.axvline(0, 0, 1, color="k", linestyle="--") ax.text(0, 1.2, s=r"$\text{decision boundary}=0$", va="center", ha="center", fontsize=12, color="white", bbox=dict(color="black", boxstyle='round,pad=.6'), zorder=4) ax.legend() ax.margins(.1) plt.yticks([]) plt.show() .. image-sg:: /auto_examples/images/sphx_glr_demonstrate_07_inspect_predictions_001.png :alt: Nudged scores and offset calculated based on $\nu=0.1$, Decision scores (shifted nudged scores and offset) :srcset: /auto_examples/images/sphx_glr_demonstrate_07_inspect_predictions_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none /home/runner/work/PAN/PAN/examples/demonstrate_07_inspect_predictions.py:41: MatplotlibDeprecationWarning: vert: bool was deprecated in Matplotlib 3.11 and will be removed in 3.13. Use orientation: {'vertical', 'horizontal'} instead. ax.violinplot(scores, positions=[1], vert=False) /home/runner/work/PAN/PAN/examples/demonstrate_07_inspect_predictions.py:57: MatplotlibDeprecationWarning: vert: bool was deprecated in Matplotlib 3.11 and will be removed in 3.13. Use orientation: {'vertical', 'horizontal'} instead. ax.violinplot(scores, positions=[1], vert=False) .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 0.327 seconds) .. _sphx_glr_download_auto_examples_demonstrate_07_inspect_predictions.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: demonstrate_07_inspect_predictions.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: demonstrate_07_inspect_predictions.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: demonstrate_07_inspect_predictions.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_