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Feature Agglomeration in Scikit-learn

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Version

In [1]:
import sklearn
sklearn.__version__
Out[1]:
'0.18'

Imports

In [2]:
print(__doc__)

import plotly.plotly as py
import plotly.graph_objs as go
from plotly import tools

import numpy as np
import matplotlib.pyplot as plt

from sklearn import datasets, cluster
from sklearn.feature_extraction.image import grid_to_graph
Automatically created module for IPython interactive environment

Calculations

In [3]:
digits = datasets.load_digits()
images = digits.images
X = np.reshape(images, (len(images), -1))
connectivity = grid_to_graph(*images[0].shape)

agglo = cluster.FeatureAgglomeration(connectivity=connectivity,
                                     n_clusters=32)

agglo.fit(X)
X_reduced = agglo.transform(X)

X_restored = agglo.inverse_transform(X_reduced)
images_restored = np.reshape(X_restored, images.shape)

Plot Result

In [4]:
fig = tools.make_subplots(rows=3, cols=4,
                          print_grid=False,
                         subplot_titles = ('','Original Data','','',
                                           '','Agglomerated Data','','',
                                           'Labels'),
                         specs=[[{}, {}, {}, {}], 
                                [{}, {}, {}, {}],
                                [None, {}, None, None]
                               ])

def matplotlib_to_plotly(cmap, pl_entries):
    h = 1.0/(pl_entries-1)
    pl_colorscale = []
    
    for k in range(pl_entries):
        C = map(np.uint8, np.array(cmap(k*h)[:3])*255)
        pl_colorscale.append([k*h, 'rgb'+str((C[0], C[1], C[2]))])
        
    return pl_colorscale


for i in range(4):
    original = go.Heatmap(z=images[i], showscale=False, 
                          colorscale=matplotlib_to_plotly(plt.cm.gray,
                                               len(images[i])))
    fig.append_trace(original, 1, i+1)
    
    agglomerated = go.Heatmap(z=images_restored[i], 
                              showscale=False, 
                              colorscale=matplotlib_to_plotly(plt.cm.gray,
                                               len(images_restored[i])))
    fig.append_trace(agglomerated , 2, i+1)

labels = go.Heatmap(z=np.reshape(agglo.labels_, images[0].shape),
                    showscale=False, 
                    colorscale=matplotlib_to_plotly(plt.cm.spectral,
                                                   len(np.reshape(agglo.labels_, images[0].shape))))
fig.append_trace(labels , 3, 2)

fig['layout'].update(height=900)

for i in map(str,range(1,10)):
    y = 'yaxis'+i
    x = 'xaxis'+i
    fig['layout'][y].update(autorange='reversed',
                               showticklabels=False, ticks='')
    fig['layout'][x].update(showticklabels=False, ticks='')
    
py.iplot(fig)
Out[4]:

License

Code source:

        Gaƫl Varoquaux

Modified for documentation by Jaques Grobler

License:

        BSD 3 clause
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