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The Iris Dataset in Scikit-learn

This data sets consists of 3 different types of irises’ (Setosa, Versicolour, and Virginica) petal and sepal length, stored in a 150x4 numpy.ndarray

The rows being the samples and the columns being: Sepal Length, Sepal Width, Petal Length and Petal Width.

The below plot uses the first two features. See here for more information on 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
from sklearn.decomposition import PCA
import numpy as np

Calculations

In [3]:
# import some data to play with
iris = datasets.load_iris()
X = iris.data[:, :2]  # we only take the first two features.
Y = iris.target

x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5

Plot the Training Points

In [4]:
trace = go.Scatter(x=X[:, 0],
                   y=X[:, 1],
                   mode='markers',
                   marker=dict(color=np.random.randn(150),
                               size=10,
                               colorscale='Viridis',
                               showscale=False))

layout = go.Layout(title='Training Points',
                   xaxis=dict(title='Sepal length',
                            showgrid=False),
                   yaxis=dict(title='Sepal width',
                            showgrid=False),
                  )
 
fig = go.Figure(data=[trace], layout=layout)
In [5]:
py.iplot(fig)
Out[5]:

Plot the first three PCA dimensions

In [6]:
X_reduced = PCA(n_components=3).fit_transform(iris.data)
In [7]:
trace = go.Scatter3d(x=X_reduced[:, 0], 
                     y=X_reduced[:, 1], 
                     z=X_reduced[:, 2],
                     mode='markers',
                     marker=dict(
                         size=6,
                         color=np.random.randn(150),
                         colorscale='Viridis',   
                         opacity=0.8)
                    )
layout=go.Layout(title='First three PCA directions',
                 scene=dict(
                         xaxis=dict(title='1st eigenvector'),
                         yaxis=dict(title='2nd eigenvector'),
                         zaxis=dict(title='3rd eigenvector'))
                 )
fig = go.Figure(data=[trace], layout=layout)
In [8]:
py.iplot(fig)
Out[8]:

License

Code source:

        Gaël Varoquaux

License:

        BSD 3 clause
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