.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/applications/plot_outlier_rejections.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_applications_plot_outlier_rejections.py: =============================================================== Customized sampler to implement an outlier rejections estimator =============================================================== This example illustrates the use of a custom sampler to implement an outlier rejections estimator. It can be used easily within a pipeline in which the number of samples can vary during training, which usually is a limitation of the current scikit-learn pipeline. .. GENERATED FROM PYTHON SOURCE LINES 12-40 .. code-block:: Python # Authors: Guillaume Lemaitre # License: MIT import matplotlib.pyplot as plt import numpy as np from sklearn.datasets import make_blobs, make_moons from sklearn.ensemble import IsolationForest from sklearn.linear_model import LogisticRegression from sklearn.metrics import classification_report from imblearn import FunctionSampler from imblearn.pipeline import make_pipeline print(__doc__) rng = np.random.RandomState(42) def plot_scatter(X, y, title): """Function to plot some data as a scatter plot.""" plt.figure() plt.scatter(X[y == 1, 0], X[y == 1, 1], label="Class #1") plt.scatter(X[y == 0, 0], X[y == 0, 1], label="Class #0") plt.legend() plt.title(title) .. GENERATED FROM PYTHON SOURCE LINES 41-43 Toy data generation ############################################################################# .. GENERATED FROM PYTHON SOURCE LINES 45-47 We are generating some non Gaussian data set contaminated with some unform noise. .. GENERATED FROM PYTHON SOURCE LINES 47-64 .. code-block:: Python moons, _ = make_moons(n_samples=500, noise=0.05) blobs, _ = make_blobs( n_samples=500, centers=[(-0.75, 2.25), (1.0, 2.0)], cluster_std=0.25 ) outliers = rng.uniform(low=-3, high=3, size=(500, 2)) X_train = np.vstack([moons, blobs, outliers]) y_train = np.hstack( [ np.ones(moons.shape[0], dtype=np.int8), np.zeros(blobs.shape[0], dtype=np.int8), rng.randint(0, 2, size=outliers.shape[0], dtype=np.int8), ] ) plot_scatter(X_train, y_train, "Training dataset") .. image-sg:: /auto_examples/applications/images/sphx_glr_plot_outlier_rejections_001.png :alt: Training dataset :srcset: /auto_examples/applications/images/sphx_glr_plot_outlier_rejections_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 65-66 We will generate some cleaned test data without outliers. .. GENERATED FROM PYTHON SOURCE LINES 66-78 .. code-block:: Python moons, _ = make_moons(n_samples=50, noise=0.05) blobs, _ = make_blobs( n_samples=50, centers=[(-0.75, 2.25), (1.0, 2.0)], cluster_std=0.25 ) X_test = np.vstack([moons, blobs]) y_test = np.hstack( [np.ones(moons.shape[0], dtype=np.int8), np.zeros(blobs.shape[0], dtype=np.int8)] ) plot_scatter(X_test, y_test, "Testing dataset") .. image-sg:: /auto_examples/applications/images/sphx_glr_plot_outlier_rejections_002.png :alt: Testing dataset :srcset: /auto_examples/applications/images/sphx_glr_plot_outlier_rejections_002.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 79-81 How to use the :class:`~imblearn.FunctionSampler` ############################################################################# .. GENERATED FROM PYTHON SOURCE LINES 83-88 We first define a function which will use :class:`~sklearn.ensemble.IsolationForest` to eliminate some outliers from our dataset during training. The function passed to the :class:`~imblearn.FunctionSampler` will be called when using the method ``fit_resample``. .. GENERATED FROM PYTHON SOURCE LINES 88-102 .. code-block:: Python def outlier_rejection(X, y): """This will be our function used to resample our dataset.""" model = IsolationForest(max_samples=100, contamination=0.4, random_state=rng) model.fit(X) y_pred = model.predict(X) return X[y_pred == 1], y[y_pred == 1] reject_sampler = FunctionSampler(func=outlier_rejection) X_inliers, y_inliers = reject_sampler.fit_resample(X_train, y_train) plot_scatter(X_inliers, y_inliers, "Training data without outliers") .. image-sg:: /auto_examples/applications/images/sphx_glr_plot_outlier_rejections_003.png :alt: Training data without outliers :srcset: /auto_examples/applications/images/sphx_glr_plot_outlier_rejections_003.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 103-105 Integrate it within a pipeline ############################################################################# .. GENERATED FROM PYTHON SOURCE LINES 107-109 By elimnating outliers before the training, the classifier will be less affected during the prediction. .. GENERATED FROM PYTHON SOURCE LINES 109-122 .. code-block:: Python pipe = make_pipeline( FunctionSampler(func=outlier_rejection), LogisticRegression(solver="lbfgs", multi_class="auto", random_state=rng), ) y_pred = pipe.fit(X_train, y_train).predict(X_test) print(classification_report(y_test, y_pred)) clf = LogisticRegression(solver="lbfgs", multi_class="auto", random_state=rng) y_pred = clf.fit(X_train, y_train).predict(X_test) print(classification_report(y_test, y_pred)) plt.show() .. rst-class:: sphx-glr-script-out .. code-block:: none precision recall f1-score support 0 1.00 1.00 1.00 50 1 1.00 1.00 1.00 50 accuracy 1.00 100 macro avg 1.00 1.00 1.00 100 weighted avg 1.00 1.00 1.00 100 precision recall f1-score support 0 0.88 1.00 0.93 50 1 1.00 0.86 0.92 50 accuracy 0.93 100 macro avg 0.94 0.93 0.93 100 weighted avg 0.94 0.93 0.93 100 .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 1.905 seconds) **Estimated memory usage:** 11 MB .. _sphx_glr_download_auto_examples_applications_plot_outlier_rejections.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_outlier_rejections.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_outlier_rejections.py ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_