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Nearest Neighbors Classification in Scikit-learn

Sample usage of Nearest Neighbors classification. It will plot the decision boundaries for each class.

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Version

In [1]:
import sklearn
sklearn.__version__
Out[1]:
'0.18.1'

Imports

In [2]:
print(__doc__)

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

import numpy as np
from sklearn import neighbors, datasets
Automatically created module for IPython interactive environment

Calculations

In [3]:
n_neighbors = 15

# import some data to play with
iris = datasets.load_iris()
X = iris.data[:, :2]  # we only take the first two features. We could
                      # avoid this ugly slicing by using a two-dim dataset
y = iris.target

h = .02  # step size in the mesh

# Create color maps
cmap_light =[[0, '#FFAAAA'], [0.5, '#AAFFAA'], [1, '#AAAAFF']]
cmap_bold = [[0, '#FF0000'], [0.5, '#00FF00'], [1, '#0000FF']]

Plot Results

In [4]:
data = []
titles = []
i = 0

for weights in ['uniform', 'distance']:
    # we create an instance of Neighbours Classifier and fit the data.
    clf = neighbors.KNeighborsClassifier(n_neighbors, weights=weights)
    clf.fit(X, y)

    # Plot the decision boundary. For that, we will assign a color to each
    # point in the mesh [x_min, x_max]x[y_min, y_max].
    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
    x_ = np.arange(x_min, x_max, h)
    y_ = np.arange(y_min, y_max, h)
    xx, yy = np.meshgrid(x_, y_)
    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
    
    # Put the result into a color plot
    Z = Z.reshape(xx.shape)
    
    data.append([])
    p1 = go.Heatmap(x=x_, y=y_, z=Z,
                    showscale=False,
                    colorscale=cmap_light)
    
    # Plot also the training points
    p2 = go.Scatter(x=X[:, 0], y=X[:, 1], 
                    mode='markers',
                    marker=dict(color=X[:, 0],
                                colorscale=cmap_bold,
                                line=dict(color='black', width=1)))
    data[i].append(p1)
    data[i].append(p2) 
    titles.append("3-Class classification (k = %i, weights = '%s')"
              % (n_neighbors, weights))
    i+=1
In [5]:
fig = tools.make_subplots(rows=1, cols=2,
                          subplot_titles=tuple(titles), 
                          print_grid=False)

for i in range(0, len(data)):
    for j in range(0, len(data[i])):
        fig.append_trace(data[i][j], 1, i+1)

fig['layout'].update(height=700, hovermode='closest', 
                     showlegend=False)

                     
py.iplot(fig)
Out[5]:
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