81 lines
2.4 KiB
Python
81 lines
2.4 KiB
Python
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#!/usr/bin/python
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# -*- coding: utf-8 -*-
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"""
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=========================================================
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Robust Scaling on Toy Data
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=========================================================
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Making sure that each Feature has approximately the same scale can be a
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crucial preprocessing step. However, when data contains outliers,
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:class:`StandardScaler <sklearn.preprocessing.StandardScaler>` can often
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be mislead. In such cases, it is better to use a scaler that is robust
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against outliers.
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Here, we demonstrate this on a toy dataset, where one single datapoint
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is a large outlier.
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"""
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from __future__ import print_function
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print(__doc__)
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# Code source: Thomas Unterthiner
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# License: BSD 3 clause
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import matplotlib.pyplot as plt
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import numpy as np
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from sklearn.preprocessing import StandardScaler, RobustScaler
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# Create training and test data
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np.random.seed(42)
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n_datapoints = 100
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C=[[0.9, 0.0],[0.0, 20.0]]
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mu1 = [100.0, -3.0]
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mu2 = [101.0, -3.0]
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X1 = np.random.multivariate_normal(mean=mu1, cov=C, size=n_datapoints)
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X2 = np.random.multivariate_normal(mean=mu2, cov=C, size=n_datapoints)
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Y_train = np.hstack([[-1]*n_datapoints, [1]*n_datapoints])
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X_train = np.vstack([X1, X2])
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X1 = np.random.multivariate_normal(mean=mu1, cov=C, size=n_datapoints)
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X2 = np.random.multivariate_normal(mean=mu2, cov=C, size=n_datapoints)
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Y_test = np.hstack([[-1]*n_datapoints, [1]*n_datapoints])
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X_test = np.vstack([X1, X2])
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X_train[0, 0] = -1000 # a fairly large outlier
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# Scale data
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s = StandardScaler()
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Xtr_s = s.fit_transform(X_train)
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Xte_s = s.transform(X_test)
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r = RobustScaler()
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Xtr_r = r.fit_transform(X_train)
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Xte_r = r.fit_transform(X_test)
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# Plot data (note we zoom in to the data center, so the outlier
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# data point can't be seen in this plot).
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fig, ax = plt.subplots(1, 2, figsize=(8, 4), sharex=True, sharey=True)
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ax[0].scatter(Xtr_s[:, 0], Xtr_s[:, 1], color=np.where(Y_train > 0, 'r', 'b'))
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ax[1].scatter(Xtr_r[:, 0], Xtr_r[:, 1], color=np.where(Y_train > 0, 'r', 'b'))
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ax[0].set_title("Training set after standard scaling")
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ax[1].set_title("Training set after robust scaling")
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ax[0].set_xlim(-3, 3)
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ax[0].set_ylim(-3, 3)
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plt.tight_layout()
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plt.show()
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# Classify using k-NN
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from sklearn.neighbors import KNeighborsClassifier
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knn = KNeighborsClassifier()
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knn.fit(Xtr_s, Y_train)
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acc_s = knn.score(Xte_s, Y_test)
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print("Testset accuracy using standard scaler: %.3f" % acc_s)
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knn.fit(Xtr_r, Y_train)
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acc_r = knn.score(Xte_r, Y_test)
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print("Testset accuracy using robust scaler: %.3f" % acc_r)
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