Show Sidebar Hide Sidebar

# Nearest Neighbors Classification in Scikit-learn

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

#### New to Plotly?¶

Plotly's Python library is free and open source! Get started by downloading the client and reading the primer.
You can set up Plotly to work in online or offline mode, or in jupyter notebooks.
We also have a quick-reference cheatsheet (new!) to help you get started!

### 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
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]:
Still need help?