160 lines
4.9 KiB
Python
160 lines
4.9 KiB
Python
"""
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=====================
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Classifier comparison
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=====================
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A comparison of several classifiers in scikit-learn on synthetic datasets.
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The point of this example is to illustrate the nature of decision boundaries
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of different classifiers.
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This should be taken with a grain of salt, as the intuition conveyed by
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these examples does not necessarily carry over to real datasets.
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Particularly in high-dimensional spaces, data can more easily be separated
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linearly and the simplicity of classifiers such as naive Bayes and linear SVMs
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might lead to better generalization than is achieved by other classifiers.
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The plots show training points in solid colors and testing points
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semi-transparent. The lower right shows the classification accuracy on the test
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set.
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"""
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# Code source: Gaël Varoquaux
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# Andreas Müller
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# Modified for documentation by Jaques Grobler
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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 matplotlib.colors import ListedColormap
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from sklearn.datasets import make_circles, make_classification, make_moons
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from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
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from sklearn.ensemble import AdaBoostClassifier, RandomForestClassifier
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from sklearn.gaussian_process import GaussianProcessClassifier
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from sklearn.gaussian_process.kernels import RBF
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from sklearn.inspection import DecisionBoundaryDisplay
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from sklearn.model_selection import train_test_split
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from sklearn.naive_bayes import GaussianNB
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from sklearn.neighbors import KNeighborsClassifier
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from sklearn.neural_network import MLPClassifier
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from sklearn.pipeline import make_pipeline
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from sklearn.preprocessing import StandardScaler
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from sklearn.svm import SVC
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from sklearn.tree import DecisionTreeClassifier
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names = [
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"Nearest Neighbors",
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"Linear SVM",
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"RBF SVM",
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"Gaussian Process",
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"Decision Tree",
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"Random Forest",
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"Neural Net",
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"AdaBoost",
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"Naive Bayes",
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"QDA",
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]
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classifiers = [
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KNeighborsClassifier(3),
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SVC(kernel="linear", C=0.025, random_state=42),
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SVC(gamma=2, C=1, random_state=42),
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GaussianProcessClassifier(1.0 * RBF(1.0), random_state=42),
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DecisionTreeClassifier(max_depth=5, random_state=42),
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RandomForestClassifier(
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max_depth=5, n_estimators=10, max_features=1, random_state=42
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),
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MLPClassifier(alpha=1, max_iter=1000, random_state=42),
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AdaBoostClassifier(algorithm="SAMME", random_state=42),
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GaussianNB(),
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QuadraticDiscriminantAnalysis(),
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]
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X, y = make_classification(
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n_features=2, n_redundant=0, n_informative=2, random_state=1, n_clusters_per_class=1
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)
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rng = np.random.RandomState(2)
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X += 2 * rng.uniform(size=X.shape)
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linearly_separable = (X, y)
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datasets = [
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make_moons(noise=0.3, random_state=0),
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make_circles(noise=0.2, factor=0.5, random_state=1),
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linearly_separable,
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]
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figure = plt.figure(figsize=(27, 9))
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i = 1
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# iterate over datasets
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for ds_cnt, ds in enumerate(datasets):
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# preprocess dataset, split into training and test part
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X, y = ds
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X_train, X_test, y_train, y_test = train_test_split(
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X, y, test_size=0.4, random_state=42
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)
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x_min, x_max = X[:, 0].min() - 0.5, X[:, 0].max() + 0.5
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y_min, y_max = X[:, 1].min() - 0.5, X[:, 1].max() + 0.5
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# just plot the dataset first
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cm = plt.cm.RdBu
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cm_bright = ListedColormap(["#FF0000", "#0000FF"])
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ax = plt.subplot(len(datasets), len(classifiers) + 1, i)
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if ds_cnt == 0:
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ax.set_title("Input data")
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# Plot the training points
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ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright, edgecolors="k")
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# Plot the testing points
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ax.scatter(
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X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.6, edgecolors="k"
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)
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ax.set_xlim(x_min, x_max)
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ax.set_ylim(y_min, y_max)
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ax.set_xticks(())
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ax.set_yticks(())
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i += 1
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# iterate over classifiers
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for name, clf in zip(names, classifiers):
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ax = plt.subplot(len(datasets), len(classifiers) + 1, i)
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clf = make_pipeline(StandardScaler(), clf)
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clf.fit(X_train, y_train)
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score = clf.score(X_test, y_test)
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DecisionBoundaryDisplay.from_estimator(
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clf, X, cmap=cm, alpha=0.8, ax=ax, eps=0.5
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)
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# Plot the training points
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ax.scatter(
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X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright, edgecolors="k"
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)
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# Plot the testing points
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ax.scatter(
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X_test[:, 0],
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X_test[:, 1],
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c=y_test,
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cmap=cm_bright,
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edgecolors="k",
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alpha=0.6,
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)
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ax.set_xlim(x_min, x_max)
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ax.set_ylim(y_min, y_max)
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ax.set_xticks(())
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ax.set_yticks(())
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if ds_cnt == 0:
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ax.set_title(name)
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ax.text(
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x_max - 0.3,
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y_min + 0.3,
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("%.2f" % score).lstrip("0"),
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size=15,
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horizontalalignment="right",
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)
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i += 1
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plt.tight_layout()
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plt.show()
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