65 lines
2.0 KiB
Python
65 lines
2.0 KiB
Python
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"""
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=======================================================
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Comparison of LDA and PCA 2D projection of Iris dataset
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=======================================================
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The Iris dataset represents 3 kind of Iris flowers (Setosa, Versicolour
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and Virginica) with 4 attributes: sepal length, sepal width, petal length
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and petal width.
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Principal Component Analysis (PCA) applied to this data identifies the
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combination of attributes (principal components, or directions in the
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feature space) that account for the most variance in the data. Here we
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plot the different samples on the 2 first principal components.
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Linear Discriminant Analysis (LDA) tries to identify attributes that
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account for the most variance *between classes*. In particular,
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LDA, in contrast to PCA, is a supervised method, using known class labels.
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"""
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import matplotlib.pyplot as plt
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from sklearn import datasets
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from sklearn.decomposition import PCA
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from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
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iris = datasets.load_iris()
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X = iris.data
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y = iris.target
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target_names = iris.target_names
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pca = PCA(n_components=2)
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X_r = pca.fit(X).transform(X)
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lda = LinearDiscriminantAnalysis(n_components=2)
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X_r2 = lda.fit(X, y).transform(X)
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# Percentage of variance explained for each components
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print(
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"explained variance ratio (first two components): %s"
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% str(pca.explained_variance_ratio_)
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)
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plt.figure()
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colors = ["navy", "turquoise", "darkorange"]
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lw = 2
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for color, i, target_name in zip(colors, [0, 1, 2], target_names):
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plt.scatter(
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X_r[y == i, 0], X_r[y == i, 1], color=color, alpha=0.8, lw=lw, label=target_name
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)
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plt.legend(loc="best", shadow=False, scatterpoints=1)
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plt.title("PCA of IRIS dataset")
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plt.figure()
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for color, i, target_name in zip(colors, [0, 1, 2], target_names):
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plt.scatter(
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X_r2[y == i, 0], X_r2[y == i, 1], alpha=0.8, color=color, label=target_name
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)
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plt.legend(loc="best", shadow=False, scatterpoints=1)
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plt.title("LDA of IRIS dataset")
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plt.show()
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