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# PCA example with Iris Data-set in Scikit-learn

Principal Component Analysis applied to the Iris dataset.

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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

import numpy as np
import matplotlib.pyplot as plt

from sklearn import decomposition
from sklearn import datasets

Automatically created module for IPython interactive environment


### Calculations¶

In [3]:
np.random.seed(5)

centers = [[1, 1], [-1, -1], [1, -1]]
X = iris.data
y = iris.target

pca = decomposition.PCA(n_components=3)
pca.fit(X)
X = pca.transform(X)


### Plot Results¶

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]:
# Reorder the labels to have colors matching the cluster results
y = np.choose(y, [1, 2, 0]).astype(np.float)

trace = go.Scatter3d(x=X[:, 0], y=X[:, 1], z=X[:, 2],
mode='markers',
marker=dict(color=y,
colorscale=matplotlib_to_plotly(plt.cm.spectral, 5),
line=dict(color='black', width=1))
)

layout = go.Layout(scene=
dict(
xaxis=dict(ticks='', showticklabels=False),
yaxis=dict(ticks='', showticklabels=False),
zaxis=dict(ticks='', showticklabels=False),
)
)

fig = go.Figure(data=[trace], layout=layout)

In [6]:
py.iplot(fig)

Out[6]:

Code source:

        GaĆ«l Varoquaux



        BSD 3 clause