90 lines
2.6 KiB
Python
90 lines
2.6 KiB
Python
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"""
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================
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The Iris Dataset
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================
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This data sets consists of 3 different types of irises'
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(Setosa, Versicolour, and Virginica) petal and sepal
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length, stored in a 150x4 numpy.ndarray
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The rows being the samples and the columns being:
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Sepal Length, Sepal Width, Petal Length and Petal Width.
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The below plot uses the first two features.
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See `here <https://en.wikipedia.org/wiki/Iris_flower_data_set>`_ for more
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information on this dataset.
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"""
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# Code source: Gaël Varoquaux
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# Modified for documentation by Jaques Grobler
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# License: BSD 3 clause
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# %%
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# Loading the iris dataset
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# ------------------------
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from sklearn import datasets
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iris = datasets.load_iris()
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# %%
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# Scatter Plot of the Iris dataset
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# --------------------------------
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import matplotlib.pyplot as plt
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_, ax = plt.subplots()
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scatter = ax.scatter(iris.data[:, 0], iris.data[:, 1], c=iris.target)
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ax.set(xlabel=iris.feature_names[0], ylabel=iris.feature_names[1])
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_ = ax.legend(
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scatter.legend_elements()[0], iris.target_names, loc="lower right", title="Classes"
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)
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# %%
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# Each point in the scatter plot refers to one of the 150 iris flowers
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# in the dataset, with the color indicating their respective type
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# (Setosa, Versicolour, and Virginica).
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# You can already see a pattern regarding the Setosa type, which is
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# easily identifiable based on its short and wide sepal. Only
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# considering these 2 dimensions, sepal width and length, there's still
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# overlap between the Versicolor and Virginica types.
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# %%
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# Plot a PCA representation
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# -------------------------
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# Let's apply a Principal Component Analysis (PCA) to the iris dataset
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# and then plot the irises across the first three PCA dimensions.
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# This will allow us to better differentiate between the three types!
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# unused but required import for doing 3d projections with matplotlib < 3.2
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import mpl_toolkits.mplot3d # noqa: F401
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from sklearn.decomposition import PCA
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fig = plt.figure(1, figsize=(8, 6))
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ax = fig.add_subplot(111, projection="3d", elev=-150, azim=110)
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X_reduced = PCA(n_components=3).fit_transform(iris.data)
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ax.scatter(
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X_reduced[:, 0],
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X_reduced[:, 1],
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X_reduced[:, 2],
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c=iris.target,
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s=40,
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)
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ax.set_title("First three PCA dimensions")
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ax.set_xlabel("1st Eigenvector")
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ax.xaxis.set_ticklabels([])
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ax.set_ylabel("2nd Eigenvector")
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ax.yaxis.set_ticklabels([])
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ax.set_zlabel("3rd Eigenvector")
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ax.zaxis.set_ticklabels([])
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plt.show()
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# %%
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# PCA will create 3 new features that are a linear combination of the
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# 4 original features. In addition, this transform maximizes the variance.
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# With this transformation, we see that we can identify each species using
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# only the first feature (i.e. first eigenvalues).
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