scikit-learn examples

kmeans.py

print(__doc__)

from time import time
import numpy as np
import pylab as pl

from sklearn import metrics
from sklearn.cluster import KMeans
from sklearn.datasets import load_digits
from sklearn.decomposition import PCA
from sklearn.preprocessing import scale

np.random.seed(42)

digits = load_digits()
data = scale(digits.data)

n_samples, n_features = data.shape
n_digits = len(np.unique(digits.target))
labels = digits.target

sample_size = 300

print("n_digits: %d, \t n_samples %d, \t n_features %d"
      % (n_digits, n_samples, n_features))


print(79 * '_')
print('% 9s' % 'init'
      '    time  inertia    homo   compl  v-meas     ARI AMI  silhouette')


def bench_k_means(estimator, name, data):
    t0 = time()
    estimator.fit(data)
    print('% 9s   %.2fs    %i   %.3f   %.3f   %.3f   %.3f   %.3f    %.3f'
          % (name, (time() - t0), estimator.inertia_,
             metrics.homogeneity_score(labels, estimator.labels_),
             metrics.completeness_score(labels, estimator.labels_),
             metrics.v_measure_score(labels, estimator.labels_),
             metrics.adjusted_rand_score(labels, estimator.labels_),
             metrics.adjusted_mutual_info_score(labels,  estimator.labels_),
             metrics.silhouette_score(data, estimator.labels_,
                                      metric='euclidean',
                                      sample_size=sample_size)))

bench_k_means(KMeans(init='k-means++', n_clusters=n_digits, n_init=10),
              name="k-means++", data=data)

bench_k_means(KMeans(init='random', n_clusters=n_digits, n_init=10),
              name="random", data=data)

# in this case the seeding of the centers is deterministic, hence we run the
# kmeans algorithm only once with n_init=1
pca = PCA(n_components=n_digits).fit(data)
bench_k_means(KMeans(init=pca.components_, n_clusters=n_digits, n_init=1),
              name="PCA-based",
              data=data)
print(79 * '_')

###############################################################################
# Visualize the results on PCA-reduced data

reduced_data = PCA(n_components=2).fit_transform(data)
kmeans = KMeans(init='k-means++', n_clusters=n_digits, n_init=10)
kmeans.fit(reduced_data)

# Step size of the mesh. Decrease to increase the quality of the VQ.
h = .02     # point in the mesh [x_min, m_max]x[y_min, y_max].

# Plot the decision boundary. For that, we will assign a color to each
x_min, x_max = reduced_data[:, 0].min() + 1, reduced_data[:, 0].max() - 1
y_min, y_max = reduced_data[:, 1].min() + 1, reduced_data[:, 1].max() - 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))

# Obtain labels for each point in mesh. Use last trained model.
Z = kmeans.predict(np.c_[xx.ravel(), yy.ravel()])

# Put the result into a color plot
Z = Z.reshape(xx.shape)
pl.figure(1)
pl.clf()
pl.imshow(Z, interpolation='nearest',
          extent=(xx.min(), xx.max(), yy.min(), yy.max()),
          cmap=pl.cm.Paired,
          aspect='auto', origin='lower')

pl.plot(reduced_data[:, 0], reduced_data[:, 1], 'k.', markersize=2)
# Plot the centroids as a white X
centroids = kmeans.cluster_centers_
pl.scatter(centroids[:, 0], centroids[:, 1],
           marker='x', s=169, linewidths=3,
           color='w', zorder=10)
pl.title('K-means clustering on the digits dataset (PCA-reduced data)\n'
         'Centroids are marked with white cross')
pl.xlim(x_min, x_max)
pl.ylim(y_min, y_max)
pl.xticks(())
pl.yticks(())
pl.show()

out.txt

bash-3.2$ python ./kmeans.py 
None
n_digits: 10, 	 n_samples 1797, 	 n_features 64
_______________________________________________________________________________
init    time  inertia    homo   compl  v-meas     ARI AMI  silhouette
k-means++   0.40s    69432   0.602   0.650   0.625   0.465   0.598    0.146
   random   0.34s    69694   0.669   0.710   0.689   0.553   0.666    0.147
PCA-based   0.05s    71207   0.612   0.686   0.647   0.499   0.608    0.130
_______________________________________________________________________________

svm.py

from sklearn.datasets import load_iris
from sklearn.svm import LinearSVC

iris = load_iris()

n_samples, n_features = iris.data.shape

n_samples
n_features

iris.target
list(iris.target_names)

X, y = iris.data, iris.target

clf = LinearSVC()
clf

clf = clf.fit(X, y)

clf.coef_
clf.intercept_ 

X_new = [[ 5.0,  3.6,  1.3,  0.25]]
l = clf.predict(X_new)
l

map(lambda x: iris.target_names[x], l)