64 lines
1.9 KiB
Python
64 lines
1.9 KiB
Python
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"""
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===============
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Incremental PCA
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===============
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Incremental principal component analysis (IPCA) is typically used as a
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replacement for principal component analysis (PCA) when the dataset to be
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decomposed is too large to fit in memory. IPCA builds a low-rank approximation
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for the input data using an amount of memory which is independent of the
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number of input data samples. It is still dependent on the input data features,
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but changing the batch size allows for control of memory usage.
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This example serves as a visual check that IPCA is able to find a similar
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projection of the data to PCA (to a sign flip), while only processing a
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few samples at a time. This can be considered a "toy example", as IPCA is
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intended for large datasets which do not fit in main memory, requiring
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incremental approaches.
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"""
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# Authors: Kyle Kastner
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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.datasets import load_iris
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from sklearn.decomposition import PCA, IncrementalPCA
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iris = load_iris()
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X = iris.data
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y = iris.target
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n_components = 2
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ipca = IncrementalPCA(n_components=n_components, batch_size=10)
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X_ipca = ipca.fit_transform(X)
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pca = PCA(n_components=n_components)
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X_pca = pca.fit_transform(X)
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colors = ["navy", "turquoise", "darkorange"]
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for X_transformed, title in [(X_ipca, "Incremental PCA"), (X_pca, "PCA")]:
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plt.figure(figsize=(8, 8))
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for color, i, target_name in zip(colors, [0, 1, 2], iris.target_names):
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plt.scatter(
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X_transformed[y == i, 0],
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X_transformed[y == i, 1],
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color=color,
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lw=2,
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label=target_name,
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)
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if "Incremental" in title:
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err = np.abs(np.abs(X_pca) - np.abs(X_ipca)).mean()
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plt.title(title + " of iris dataset\nMean absolute unsigned error %.6f" % err)
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else:
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plt.title(title + " of iris dataset")
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plt.legend(loc="best", shadow=False, scatterpoints=1)
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plt.axis([-4, 4, -1.5, 1.5])
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plt.show()
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