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The Digit Dataset in Scikit-learn

This dataset is made up of 1797 8x8 images. Each image, like the one shown below, is of a hand-written digit. In order to utilize an 8x8 figure like this, we’d have to first transform it into a feature vector with length 64.

See here for more information about this dataset.

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Version

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

Imports

In [2]:
import plotly.plotly as py
import plotly.graph_objs as go

from sklearn import datasets
import matplotlib.pyplot as plt
import numpy as np

Plot Digits Dataset

In [3]:
digits = datasets.load_digits()

Convert matplotlib colormap to plotly.

In [4]:
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
In [5]:
trace = go.Heatmap(z=digits.images[-1],
                   colorscale=matplotlib_to_plotly(plt.cm.gray_r, 
                                                   len(digits.images[-1])),
                   showscale=False,
                  )

layout = go.Layout(yaxis = dict(autorange='reversed'))
fig = go.Figure(data = [trace], layout=layout)
In [6]:
py.iplot(fig)
Out[6]:

License

Code source:

         Gaël Varoquaux

License:

        BSD 3 clause
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