Instantly share code, notes, and snippets.

# tmramalho/Digits analysis

Created April 12, 2015 18:22
Show Gist options
• Save tmramalho/ea938b7958d2803227f5 to your computer and use it in GitHub Desktop.
Factor analysis and PCA blog post
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters. Learn more about bidirectional Unicode characters
 import numpy as np import matplotlib.pyplot as plt from sklearn.decomposition import PCA, FactorAnalysis, SparsePCA from sklearn import datasets dset = datasets.load_digits() x = dset.data y = dset.target model = PCA(n_components=2) x_reduced = model.fit_transform(x) plt.figure(figsize=(3*1.618,3)) plt.scatter(x_reduced[:, 0], x_reduced[:, 1], c=y, cmap=plt.cm.Set1) # plt.savefig('digits_PCA.png') print np.dot(model.components_[0], model.components_[1]) print model.noise_variance_ v = model.transform(x) v_samples = np.random.multivariate_normal(np.mean(v, axis=0), np.cov(v.T), size=100) x_samples = model.inverse_transform(v_samples) n_row = 5 n_col = 20 plt.figure(figsize=(10, 2.5)) for i in xrange(n_row*n_col): comp = x_samples[i] plt.subplot(n_row, n_col, i + 1) vmax = max(comp.max(), -comp.min()) plt.imshow(comp.reshape([8,8]), cmap=plt.cm.gray, interpolation='nearest') plt.xticks(()) plt.yticks(()) plt.tight_layout(h_pad=0, w_pad=0) plt.savefig('gen_PCA.png') model = FactorAnalysis(n_components=2) x_reduced = model.fit_transform(x) plt.figure(figsize=(3*1.618,3)) plt.scatter(x_reduced[:, 0], x_reduced[:, 1], c=y, cmap=plt.cm.Set1) # plt.savefig('digits_FA.png') print np.dot(model.components_[0], model.components_[1]) print model.noise_variance_ model = SparsePCA(n_components=10) x_reduced = model.fit_transform(x) plt.figure(figsize=(3*1.618,3)) plt.scatter(x_reduced[:, 0], x_reduced[:, 1], c=y, cmap=plt.cm.Set1) plt.savefig('digits_SPCA.png') print np.dot(model.components_[0], model.components_[1]) n_row = 5 n_col = 2 plt.figure(figsize=(6, 15)) for i in xrange(n_row*n_col): comp = model.components_[i] plt.subplot(n_row, n_col, i + 1) vmax = max(comp.max(), -comp.min()) plt.imshow(comp.reshape([8,8]), cmap=plt.cm.gray, interpolation='nearest') plt.xticks(()) plt.yticks(()) plt.tight_layout() plt.savefig('digits_SPCA_rec.png')
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters. Learn more about bidirectional Unicode characters
 import numpy as np import matplotlib.pyplot as plt from scipy import linalg from sklearn.decomposition import PCA, FactorAnalysis from sklearn.covariance import ShrunkCovariance, LedoitWolf from sklearn.cross_validation import cross_val_score from sklearn.grid_search import GridSearchCV from sklearn import datasets iris = datasets.load_iris() x = iris.data y = iris.target model = PCA(n_components=2) x_reduced = model.fit_transform(x) plt.figure(figsize=(3*1.618,3)) plt.scatter(x_reduced[:, 0], x_reduced[:, 1], c=y, cmap=plt.cm.Paired) plt.savefig('iris_PCA.png') print np.dot(model.components_[0], model.components_[1]) print model.noise_variance_ model = PCA(n_components=1) x_reduced = model.fit_transform(x) y_rand = np.random.normal(scale=0.1, size=x_reduced.shape) plt.figure(figsize=(3*1.618,1)) plt.scatter(x_reduced[:, 0], y_rand, c=y, cmap=plt.cm.Paired) plt.ylim(-1,1) plt.yticks([-1,1]) plt.setp(plt.gca().get_yticklabels(), visible=False) plt.tight_layout() plt.savefig('iris_PCA_hist.png') model = FactorAnalysis(n_components=2) x_reduced = model.fit_transform(x) plt.figure(figsize=(3*1.618,3)) plt.scatter(x_reduced[:, 0], x_reduced[:, 1], c=y, cmap=plt.cm.Paired) plt.savefig('iris_FA.png') print np.dot(model.components_[0], model.components_[1]) print model.noise_variance_ model = FactorAnalysis(n_components=1) x_reduced = model.fit_transform(x) y_rand = np.random.normal(scale=0.1, size=x_reduced.shape) plt.figure(figsize=(3*1.618,1)) plt.scatter(x_reduced[:, 0], y_rand, c=y, cmap=plt.cm.Paired) plt.ylim(-1,1) plt.yticks([-1,1]) plt.setp(plt.gca().get_yticklabels(), visible=False) plt.tight_layout() plt.savefig('iris_FA_hist.png') print model.noise_variance_