This notebook contains an excerpt from the Python Data Science Handbook by Jake VanderPlas; the content is available on GitHub.

The text is released under the CC-BY-NC-ND license, and code is released under the MIT license. If you find this content useful, please consider supporting the work by buying the book!

# Appendix: Figure Code¶

Many of the figures used throughout this text are created in-place by code that appears in print. In a few cases, however, the required code is long enough (or not immediately relevant enough) that we instead put it here for reference.

In [1]:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns

In [2]:
import os
if not os.path.exists('figures'):
os.makedirs('figures')


Figure Context

In [3]:
# Adapted from astroML: see http://www.astroml.org/book_figures/appendix/fig_broadcast_visual.html
import numpy as np
from matplotlib import pyplot as plt

#------------------------------------------------------------
# Draw a figure and axis with no boundary
fig = plt.figure(figsize=(6, 4.5), facecolor='w')
ax = plt.axes([0, 0, 1, 1], xticks=[], yticks=[], frameon=False)

def draw_cube(ax, xy, size, depth=0.4,
edges=None, label=None, label_kwargs=None, **kwargs):
"""draw and label a cube.  edges is a list of numbers between
1 and 12, specifying which of the 12 cube edges to draw"""
if edges is None:
edges = range(1, 13)

x, y = xy

if 1 in edges:
ax.plot([x, x + size],
[y + size, y + size], **kwargs)
if 2 in edges:
ax.plot([x + size, x + size],
[y, y + size], **kwargs)
if 3 in edges:
ax.plot([x, x + size],
[y, y], **kwargs)
if 4 in edges:
ax.plot([x, x],
[y, y + size], **kwargs)

if 5 in edges:
ax.plot([x, x + depth],
[y + size, y + depth + size], **kwargs)
if 6 in edges:
ax.plot([x + size, x + size + depth],
[y + size, y + depth + size], **kwargs)
if 7 in edges:
ax.plot([x + size, x + size + depth],
[y, y + depth], **kwargs)
if 8 in edges:
ax.plot([x, x + depth],
[y, y + depth], **kwargs)

if 9 in edges:
ax.plot([x + depth, x + depth + size],
[y + depth + size, y + depth + size], **kwargs)
if 10 in edges:
ax.plot([x + depth + size, x + depth + size],
[y + depth, y + depth + size], **kwargs)
if 11 in edges:
ax.plot([x + depth, x + depth + size],
[y + depth, y + depth], **kwargs)
if 12 in edges:
ax.plot([x + depth, x + depth],
[y + depth, y + depth + size], **kwargs)

if label:
if label_kwargs is None:
label_kwargs = {}
ax.text(x + 0.5 * size, y + 0.5 * size, label,
ha='center', va='center', **label_kwargs)

solid = dict(c='black', ls='-', lw=1,
label_kwargs=dict(color='k'))
dotted = dict(c='black', ls='-', lw=0.5, alpha=0.5,
label_kwargs=dict(color='gray'))
depth = 0.3

#------------------------------------------------------------
# Draw top operation: vector plus scalar
draw_cube(ax, (1, 10), 1, depth, [1, 2, 3, 4, 5, 6, 9], '0', **solid)
draw_cube(ax, (2, 10), 1, depth, [1, 2, 3, 6, 9], '1', **solid)
draw_cube(ax, (3, 10), 1, depth, [1, 2, 3, 6, 7, 9, 10], '2', **solid)

draw_cube(ax, (6, 10), 1, depth, [1, 2, 3, 4, 5, 6, 7, 9, 10], '5', **solid)
draw_cube(ax, (7, 10), 1, depth, [1, 2, 3, 6, 7, 9, 10, 11], '5', **dotted)
draw_cube(ax, (8, 10), 1, depth, [1, 2, 3, 6, 7, 9, 10, 11], '5', **dotted)

draw_cube(ax, (12, 10), 1, depth, [1, 2, 3, 4, 5, 6, 9], '5', **solid)
draw_cube(ax, (13, 10), 1, depth, [1, 2, 3, 6, 9], '6', **solid)
draw_cube(ax, (14, 10), 1, depth, [1, 2, 3, 6, 7, 9, 10], '7', **solid)

ax.text(5, 10.5, '+', size=12, ha='center', va='center')
ax.text(10.5, 10.5, '=', size=12, ha='center', va='center')
ax.text(1, 11.5, r'${\tt np.arange(3) + 5}$',
size=12, ha='left', va='bottom')

#------------------------------------------------------------
# Draw middle operation: matrix plus vector

# first block
draw_cube(ax, (1, 7.5), 1, depth, [1, 2, 3, 4, 5, 6, 9], '1', **solid)
draw_cube(ax, (2, 7.5), 1, depth, [1, 2, 3, 6, 9], '1', **solid)
draw_cube(ax, (3, 7.5), 1, depth, [1, 2, 3, 6, 7, 9, 10], '1', **solid)

draw_cube(ax, (1, 6.5), 1, depth, [2, 3, 4], '1', **solid)
draw_cube(ax, (2, 6.5), 1, depth, [2, 3], '1', **solid)
draw_cube(ax, (3, 6.5), 1, depth, [2, 3, 7, 10], '1', **solid)

draw_cube(ax, (1, 5.5), 1, depth, [2, 3, 4], '1', **solid)
draw_cube(ax, (2, 5.5), 1, depth, [2, 3], '1', **solid)
draw_cube(ax, (3, 5.5), 1, depth, [2, 3, 7, 10], '1', **solid)

# second block
draw_cube(ax, (6, 7.5), 1, depth, [1, 2, 3, 4, 5, 6, 9], '0', **solid)
draw_cube(ax, (7, 7.5), 1, depth, [1, 2, 3, 6, 9], '1', **solid)
draw_cube(ax, (8, 7.5), 1, depth, [1, 2, 3, 6, 7, 9, 10], '2', **solid)

draw_cube(ax, (6, 6.5), 1, depth, range(2, 13), '0', **dotted)
draw_cube(ax, (7, 6.5), 1, depth, [2, 3, 6, 7, 9, 10, 11], '1', **dotted)
draw_cube(ax, (8, 6.5), 1, depth, [2, 3, 6, 7, 9, 10, 11], '2', **dotted)

draw_cube(ax, (6, 5.5), 1, depth, [2, 3, 4, 7, 8, 10, 11, 12], '0', **dotted)
draw_cube(ax, (7, 5.5), 1, depth, [2, 3, 7, 10, 11], '1', **dotted)
draw_cube(ax, (8, 5.5), 1, depth, [2, 3, 7, 10, 11], '2', **dotted)

# third block
draw_cube(ax, (12, 7.5), 1, depth, [1, 2, 3, 4, 5, 6, 9], '1', **solid)
draw_cube(ax, (13, 7.5), 1, depth, [1, 2, 3, 6, 9], '2', **solid)
draw_cube(ax, (14, 7.5), 1, depth, [1, 2, 3, 6, 7, 9, 10], '3', **solid)

draw_cube(ax, (12, 6.5), 1, depth, [2, 3, 4], '1', **solid)
draw_cube(ax, (13, 6.5), 1, depth, [2, 3], '2', **solid)
draw_cube(ax, (14, 6.5), 1, depth, [2, 3, 7, 10], '3', **solid)

draw_cube(ax, (12, 5.5), 1, depth, [2, 3, 4], '1', **solid)
draw_cube(ax, (13, 5.5), 1, depth, [2, 3], '2', **solid)
draw_cube(ax, (14, 5.5), 1, depth, [2, 3, 7, 10], '3', **solid)

ax.text(5, 7.0, '+', size=12, ha='center', va='center')
ax.text(10.5, 7.0, '=', size=12, ha='center', va='center')
ax.text(1, 9.0, r'${\tt np.ones((3,\, 3)) + np.arange(3)}$',
size=12, ha='left', va='bottom')

#------------------------------------------------------------
# Draw bottom operation: vector plus vector, double broadcast

# first block
draw_cube(ax, (1, 3), 1, depth, [1, 2, 3, 4, 5, 6, 7, 9, 10], '0', **solid)
draw_cube(ax, (1, 2), 1, depth, [2, 3, 4, 7, 10], '1', **solid)
draw_cube(ax, (1, 1), 1, depth, [2, 3, 4, 7, 10], '2', **solid)

draw_cube(ax, (2, 3), 1, depth, [1, 2, 3, 6, 7, 9, 10, 11], '0', **dotted)
draw_cube(ax, (2, 2), 1, depth, [2, 3, 7, 10, 11], '1', **dotted)
draw_cube(ax, (2, 1), 1, depth, [2, 3, 7, 10, 11], '2', **dotted)

draw_cube(ax, (3, 3), 1, depth, [1, 2, 3, 6, 7, 9, 10, 11], '0', **dotted)
draw_cube(ax, (3, 2), 1, depth, [2, 3, 7, 10, 11], '1', **dotted)
draw_cube(ax, (3, 1), 1, depth, [2, 3, 7, 10, 11], '2', **dotted)

# second block
draw_cube(ax, (6, 3), 1, depth, [1, 2, 3, 4, 5, 6, 9], '0', **solid)
draw_cube(ax, (7, 3), 1, depth, [1, 2, 3, 6, 9], '1', **solid)
draw_cube(ax, (8, 3), 1, depth, [1, 2, 3, 6, 7, 9, 10], '2', **solid)

draw_cube(ax, (6, 2), 1, depth, range(2, 13), '0', **dotted)
draw_cube(ax, (7, 2), 1, depth, [2, 3, 6, 7, 9, 10, 11], '1', **dotted)
draw_cube(ax, (8, 2), 1, depth, [2, 3, 6, 7, 9, 10, 11], '2', **dotted)

draw_cube(ax, (6, 1), 1, depth, [2, 3, 4, 7, 8, 10, 11, 12], '0', **dotted)
draw_cube(ax, (7, 1), 1, depth, [2, 3, 7, 10, 11], '1', **dotted)
draw_cube(ax, (8, 1), 1, depth, [2, 3, 7, 10, 11], '2', **dotted)

# third block
draw_cube(ax, (12, 3), 1, depth, [1, 2, 3, 4, 5, 6, 9], '0', **solid)
draw_cube(ax, (13, 3), 1, depth, [1, 2, 3, 6, 9], '1', **solid)
draw_cube(ax, (14, 3), 1, depth, [1, 2, 3, 6, 7, 9, 10], '2', **solid)

draw_cube(ax, (12, 2), 1, depth, [2, 3, 4], '1', **solid)
draw_cube(ax, (13, 2), 1, depth, [2, 3], '2', **solid)
draw_cube(ax, (14, 2), 1, depth, [2, 3, 7, 10], '3', **solid)

draw_cube(ax, (12, 1), 1, depth, [2, 3, 4], '2', **solid)
draw_cube(ax, (13, 1), 1, depth, [2, 3], '3', **solid)
draw_cube(ax, (14, 1), 1, depth, [2, 3, 7, 10], '4', **solid)

ax.text(5, 2.5, '+', size=12, ha='center', va='center')
ax.text(10.5, 2.5, '=', size=12, ha='center', va='center')
ax.text(1, 4.5, r'${\tt np.arange(3).reshape((3,\, 1)) + np.arange(3)}$',
ha='left', size=12, va='bottom')

ax.set_xlim(0, 16)
ax.set_ylim(0.5, 12.5)



## Aggregation and Grouping¶

Figures from the chapter on aggregation and grouping

### Split-Apply-Combine¶

In [4]:
def draw_dataframe(df, loc=None, width=None, ax=None, linestyle=None,
textstyle=None):
loc = loc or [0, 0]
width = width or 1

x, y = loc

if ax is None:
ax = plt.gca()

ncols = len(df.columns) + 1
nrows = len(df.index) + 1

dx = dy = width / ncols

if linestyle is None:
linestyle = {'color':'black'}

if textstyle is None:
textstyle = {'size': 12}

textstyle.update({'ha':'center', 'va':'center'})

# draw vertical lines
for i in range(ncols + 1):
plt.plot(2 * [x + i * dx], [y, y + dy * nrows], **linestyle)

# draw horizontal lines
for i in range(nrows + 1):
plt.plot([x, x + dx * ncols], 2 * [y + i * dy], **linestyle)

# Create index labels
for i in range(nrows - 1):
plt.text(x + 0.5 * dx, y + (i + 0.5) * dy,
str(df.index[::-1][i]), **textstyle)

# Create column labels
for i in range(ncols - 1):
plt.text(x + (i + 1.5) * dx, y + (nrows - 0.5) * dy,
str(df.columns[i]), style='italic', **textstyle)

if df.index.name:
plt.text(x + 0.5 * dx, y + (nrows - 0.5) * dy,
str(df.index.name), style='italic', **textstyle)

# Insert data
for i in range(nrows - 1):
for j in range(ncols - 1):
plt.text(x + (j + 1.5) * dx,
y + (i + 0.5) * dy,
str(df.values[::-1][i, j]), **textstyle)

#----------------------------------------------------------
# Draw figure

import pandas as pd
df = pd.DataFrame({'data': [1, 2, 3, 4, 5, 6]},
index=['A', 'B', 'C', 'A', 'B', 'C'])
df.index.name = 'key'

fig = plt.figure(figsize=(8, 6), facecolor='white')
ax = plt.axes([0, 0, 1, 1])

ax.axis('off')

draw_dataframe(df, [0, 0])

for y, ind in zip([3, 1, -1], 'ABC'):
split = df[df.index == ind]
draw_dataframe(split, [2, y])

sum = pd.DataFrame(split.sum()).T
sum.index = [ind]
sum.index.name = 'key'
sum.columns = ['data']
draw_dataframe(sum, [4, y + 0.25])

result = df.groupby(df.index).sum()
draw_dataframe(result, [6, 0.75])

style = dict(fontsize=14, ha='center', weight='bold')
plt.text(0.5, 3.6, "Input", **style)
plt.text(2.5, 4.6, "Split", **style)
plt.text(4.5, 4.35, "Apply (sum)", **style)
plt.text(6.5, 2.85, "Combine", **style)

plt.annotate('', (1.8, 3.6), (1.2, 2.8), arrowprops=arrowprops)
plt.annotate('', (1.8, 1.75), (1.2, 1.75), arrowprops=arrowprops)
plt.annotate('', (1.8, -0.1), (1.2, 0.7), arrowprops=arrowprops)

plt.annotate('', (3.8, 3.8), (3.2, 3.8), arrowprops=arrowprops)
plt.annotate('', (3.8, 1.75), (3.2, 1.75), arrowprops=arrowprops)
plt.annotate('', (3.8, -0.3), (3.2, -0.3), arrowprops=arrowprops)

plt.annotate('', (5.8, 2.8), (5.2, 3.6), arrowprops=arrowprops)
plt.annotate('', (5.8, 1.75), (5.2, 1.75), arrowprops=arrowprops)
plt.annotate('', (5.8, 0.7), (5.2, -0.1), arrowprops=arrowprops)

plt.axis('equal')
plt.ylim(-1.5, 5);

fig.savefig('figures/03.08-split-apply-combine.png')


## What Is Machine Learning?¶

In [5]:
# common plot formatting for below
def format_plot(ax, title):
ax.xaxis.set_major_formatter(plt.NullFormatter())
ax.yaxis.set_major_formatter(plt.NullFormatter())
ax.set_xlabel('feature 1', color='gray')
ax.set_ylabel('feature 2', color='gray')
ax.set_title(title, color='gray')


### Classification Example Figures¶

Figure context

The following code generates the figures from the Classification section.

In [6]:
from sklearn.datasets.samples_generator import make_blobs
from sklearn.svm import SVC

# create 50 separable points
X, y = make_blobs(n_samples=50, centers=2,
random_state=0, cluster_std=0.60)

# fit the support vector classifier model
clf = SVC(kernel='linear')
clf.fit(X, y)

# create some new points to predict
X2, _ = make_blobs(n_samples=80, centers=2,
random_state=0, cluster_std=0.80)
X2 = X2[50:]

# predict the labels
y2 = clf.predict(X2)


#### Classification Example Figure 1¶

In [7]:
# plot the data
fig, ax = plt.subplots(figsize=(8, 6))
point_style = dict(cmap='Paired', s=50)
ax.scatter(X[:, 0], X[:, 1], c=y, **point_style)

# format plot
format_plot(ax, 'Input Data')
ax.axis([-1, 4, -2, 7])

fig.savefig('figures/05.01-classification-1.png')


#### Classification Example Figure 2¶

In [8]:
# Get contours describing the model
xx = np.linspace(-1, 4, 10)
yy = np.linspace(-2, 7, 10)
xy1, xy2 = np.meshgrid(xx, yy)
Z = np.array([clf.decision_function([t])
for t in zip(xy1.flat, xy2.flat)]).reshape(xy1.shape)

# plot points and model
fig, ax = plt.subplots(figsize=(8, 6))
line_style = dict(levels = [-1.0, 0.0, 1.0],
linestyles = ['dashed', 'solid', 'dashed'],
colors = 'gray', linewidths=1)
ax.scatter(X[:, 0], X[:, 1], c=y, **point_style)
ax.contour(xy1, xy2, Z, **line_style)

# format plot
format_plot(ax, 'Model Learned from Input Data')
ax.axis([-1, 4, -2, 7])

fig.savefig('figures/05.01-classification-2.png')


#### Classification Example Figure 3¶

In [9]:
# plot the results
fig, ax = plt.subplots(1, 2, figsize=(16, 6))

ax[0].scatter(X2[:, 0], X2[:, 1], c='gray', **point_style)
ax[0].axis([-1, 4, -2, 7])

ax[1].scatter(X2[:, 0], X2[:, 1], c=y2, **point_style)
ax[1].contour(xy1, xy2, Z, **line_style)
ax[1].axis([-1, 4, -2, 7])

format_plot(ax[0], 'Unknown Data')
format_plot(ax[1], 'Predicted Labels')

fig.savefig('figures/05.01-classification-3.png')


### Regression Example Figures¶

Figure Context

The following code generates the figures from the regression section.

In [10]:
from sklearn.linear_model import LinearRegression

# Create some data for the regression
rng = np.random.RandomState(1)

X = rng.randn(200, 2)
y = np.dot(X, [-2, 1]) + 0.1 * rng.randn(X.shape[0])

# fit the regression model
model = LinearRegression()
model.fit(X, y)

# create some new points to predict
X2 = rng.randn(100, 2)

# predict the labels
y2 = model.predict(X2)


#### Regression Example Figure 1¶

In [11]:
# plot data points
fig, ax = plt.subplots()
points = ax.scatter(X[:, 0], X[:, 1], c=y, s=50,
cmap='viridis')

# format plot
format_plot(ax, 'Input Data')
ax.axis([-4, 4, -3, 3])

fig.savefig('figures/05.01-regression-1.png')


#### Regression Example Figure 2¶

In [12]:
from mpl_toolkits.mplot3d.art3d import Line3DCollection

points = np.hstack([X, y[:, None]]).reshape(-1, 1, 3)
segments = np.hstack([points, points])
segments[:, 0, 2] = -8

# plot points in 3D
fig = plt.figure()
ax.scatter(X[:, 0], X[:, 1], y, c=y, s=35,
cmap='viridis')
ax.scatter(X[:, 0], X[:, 1], -8 + np.zeros(X.shape[0]), c=y, s=10,
cmap='viridis')

# format plot
ax.patch.set_facecolor('white')
ax.view_init(elev=20, azim=-70)
ax.set_zlim3d(-8, 8)
ax.xaxis.set_major_formatter(plt.NullFormatter())
ax.yaxis.set_major_formatter(plt.NullFormatter())
ax.zaxis.set_major_formatter(plt.NullFormatter())
ax.set(xlabel='feature 1', ylabel='feature 2', zlabel='label')

# Hide axes (is there a better way?)
ax.w_xaxis.line.set_visible(False)
ax.w_yaxis.line.set_visible(False)
ax.w_zaxis.line.set_visible(False)
for tick in ax.w_xaxis.get_ticklines():
tick.set_visible(False)
for tick in ax.w_yaxis.get_ticklines():
tick.set_visible(False)
for tick in ax.w_zaxis.get_ticklines():
tick.set_visible(False)

fig.savefig('figures/05.01-regression-2.png')


#### Regression Example Figure 3¶

In [13]:
from matplotlib.collections import LineCollection

# plot data points
fig, ax = plt.subplots()
pts = ax.scatter(X[:, 0], X[:, 1], c=y, s=50,
cmap='viridis', zorder=2)

# compute and plot model color mesh
xx, yy = np.meshgrid(np.linspace(-4, 4),
np.linspace(-3, 3))
Xfit = np.vstack([xx.ravel(), yy.ravel()]).T
yfit = model.predict(Xfit)
zz = yfit.reshape(xx.shape)
ax.pcolorfast([-4, 4], [-3, 3], zz, alpha=0.5,
cmap='viridis', norm=pts.norm, zorder=1)

# format plot
format_plot(ax, 'Input Data with Linear Fit')
ax.axis([-4, 4, -3, 3])

fig.savefig('figures/05.01-regression-3.png')


#### Regression Example Figure 4¶

In [14]:
# plot the model fit
fig, ax = plt.subplots(1, 2, figsize=(16, 6))

ax[0].scatter(X2[:, 0], X2[:, 1], c='gray', s=50)
ax[0].axis([-4, 4, -3, 3])

ax[1].scatter(X2[:, 0], X2[:, 1], c=y2, s=50,
cmap='viridis', norm=pts.norm)
ax[1].axis([-4, 4, -3, 3])

# format plots
format_plot(ax[0], 'Unknown Data')
format_plot(ax[1], 'Predicted Labels')

fig.savefig('figures/05.01-regression-4.png')


### Clustering Example Figures¶

Figure context

The following code generates the figures from the clustering section.

In [15]:
from sklearn.datasets.samples_generator import make_blobs
from sklearn.cluster import KMeans

# create 50 separable points
X, y = make_blobs(n_samples=100, centers=4,
random_state=42, cluster_std=1.5)

# Fit the K Means model
model = KMeans(4, random_state=0)
y = model.fit_predict(X)


#### Clustering Example Figure 1¶

In [16]:
# plot the input data
fig, ax = plt.subplots(figsize=(8, 6))
ax.scatter(X[:, 0], X[:, 1], s=50, color='gray')

# format the plot
format_plot(ax, 'Input Data')

fig.savefig('figures/05.01-clustering-1.png')


#### Clustering Example Figure 2¶

In [17]:
# plot the data with cluster labels
fig, ax = plt.subplots(figsize=(8, 6))
ax.scatter(X[:, 0], X[:, 1], s=50, c=y, cmap='viridis')

# format the plot
format_plot(ax, 'Learned Cluster Labels')

fig.savefig('figures/05.01-clustering-2.png')


### Dimensionality Reduction Example Figures¶

Figure context

The following code generates the figures from the dimensionality reduction section.

#### Dimensionality Reduction Example Figure 1¶

In [18]:
from sklearn.datasets import make_swiss_roll

# make data
X, y = make_swiss_roll(200, noise=0.5, random_state=42)
X = X[:, [0, 2]]

# visualize data
fig, ax = plt.subplots()
ax.scatter(X[:, 0], X[:, 1], color='gray', s=30)

# format the plot
format_plot(ax, 'Input Data')

fig.savefig('figures/05.01-dimesionality-1.png')


#### Dimensionality Reduction Example Figure 2¶

In [19]:
from sklearn.manifold import Isomap

model = Isomap(n_neighbors=8, n_components=1)
y_fit = model.fit_transform(X).ravel()

# visualize data
fig, ax = plt.subplots()
pts = ax.scatter(X[:, 0], X[:, 1], c=y_fit, cmap='viridis', s=30)
cb = fig.colorbar(pts, ax=ax)

# format the plot
format_plot(ax, 'Learned Latent Parameter')
cb.set_ticks([])
cb.set_label('Latent Variable', color='gray')

fig.savefig('figures/05.01-dimesionality-2.png')


## Introducing Scikit-Learn¶

### Features and Labels Grid¶

The following is the code generating the diagram showing the features matrix and target array.

In [20]:
fig = plt.figure(figsize=(6, 4))
ax = fig.add_axes([0, 0, 1, 1])
ax.axis('off')
ax.axis('equal')

# Draw features matrix
ax.vlines(range(6), ymin=0, ymax=9, lw=1)
ax.hlines(range(10), xmin=0, xmax=5, lw=1)
font_prop = dict(size=12, family='monospace')
ax.text(-1, -1, "Feature Matrix ($X$)", size=14)
ax.text(0.1, -0.3, r'n_features $\longrightarrow$', **font_prop)
ax.text(-0.1, 0.1, r'$\longleftarrow$ n_samples', rotation=90,
va='top', ha='right', **font_prop)

# Draw labels vector
ax.vlines(range(8, 10), ymin=0, ymax=9, lw=1)
ax.hlines(range(10), xmin=8, xmax=9, lw=1)
ax.text(7, -1, "Target Vector ($y$)", size=14)
ax.text(7.9, 0.1, r'$\longleftarrow$ n_samples', rotation=90,
va='top', ha='right', **font_prop)

ax.set_ylim(10, -2)

fig.savefig('figures/05.02-samples-features.png')


## Hyperparameters and Model Validation¶

### Cross-Validation Figures¶

In [21]:
def draw_rects(N, ax, textprop={}):
for i in range(N):
ax.add_patch(plt.Rectangle((5. * i / N, i), 5. / N, 0.7, fc='lightgray'))
ax.text(5. * (i + 0.5) / N, i + 0.35,
"validation\nset", ha='center', va='center', **textprop)
ax.text(0, i + 0.35, "trial {0}".format(N - i),
ha='right', va='center', rotation=90, **textprop)
ax.set_xlim(-1, 6)
ax.set_ylim(-0.2, N + 0.2)


#### 2-Fold Cross-Validation¶

In [22]:
fig = plt.figure()
ax = fig.add_axes([0, 0, 1, 1])
ax.axis('off')
draw_rects(2, ax, textprop=dict(size=14))

fig.savefig('figures/05.03-2-fold-CV.png')


#### 5-Fold Cross-Validation¶

In [23]:
fig = plt.figure()
ax = fig.add_axes([0, 0, 1, 1])
ax.axis('off')
draw_rects(5, ax, textprop=dict(size=10))

fig.savefig('figures/05.03-5-fold-CV.png')


### Overfitting and Underfitting¶

In [24]:
import numpy as np

def make_data(N=30, err=0.8, rseed=1):
# randomly sample the data
rng = np.random.RandomState(rseed)
X = rng.rand(N, 1) ** 2
y = 10 - 1. / (X.ravel() + 0.1)
if err > 0:
y += err * rng.randn(N)
return X, y

In [25]:
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LinearRegression
from sklearn.pipeline import make_pipeline

def PolynomialRegression(degree=2, **kwargs):
return make_pipeline(PolynomialFeatures(degree),
LinearRegression(**kwargs))


In [26]:
X, y = make_data()
xfit = np.linspace(-0.1, 1.0, 1000)[:, None]
model1 = PolynomialRegression(1).fit(X, y)
model20 = PolynomialRegression(20).fit(X, y)

fig, ax = plt.subplots(1, 2, figsize=(16, 6))

ax[0].scatter(X.ravel(), y, s=40)
ax[0].plot(xfit.ravel(), model1.predict(xfit), color='gray')
ax[0].axis([-0.1, 1.0, -2, 14])
ax[0].set_title('High-bias model: Underfits the data', size=14)

ax[1].scatter(X.ravel(), y, s=40)
ax[1].plot(xfit.ravel(), model20.predict(xfit), color='gray')
ax[1].axis([-0.1, 1.0, -2, 14])
ax[1].set_title('High-variance model: Overfits the data', size=14)

fig.savefig('figures/05.03-bias-variance.png')


In [27]:
fig, ax = plt.subplots(1, 2, figsize=(16, 6))

X2, y2 = make_data(10, rseed=42)

ax[0].scatter(X.ravel(), y, s=40, c='blue')
ax[0].plot(xfit.ravel(), model1.predict(xfit), color='gray')
ax[0].axis([-0.1, 1.0, -2, 14])
ax[0].set_title('High-bias model: Underfits the data', size=14)
ax[0].scatter(X2.ravel(), y2, s=40, c='red')
ax[0].text(0.02, 0.98, "training score: $R^2$ = {0:.2f}".format(model1.score(X, y)),
ha='left', va='top', transform=ax[0].transAxes, size=14, color='blue')
ax[0].text(0.02, 0.91, "validation score: $R^2$ = {0:.2f}".format(model1.score(X2, y2)),
ha='left', va='top', transform=ax[0].transAxes, size=14, color='red')

ax[1].scatter(X.ravel(), y, s=40, c='blue')
ax[1].plot(xfit.ravel(), model20.predict(xfit), color='gray')
ax[1].axis([-0.1, 1.0, -2, 14])
ax[1].set_title('High-variance model: Overfits the data', size=14)
ax[1].scatter(X2.ravel(), y2, s=40, c='red')
ax[1].text(0.02, 0.98, "training score: $R^2$ = {0:.2g}".format(model20.score(X, y)),
ha='left', va='top', transform=ax[1].transAxes, size=14, color='blue')
ax[1].text(0.02, 0.91, "validation score: $R^2$ = {0:.2g}".format(model20.score(X2, y2)),
ha='left', va='top', transform=ax[1].transAxes, size=14, color='red')

fig.savefig('figures/05.03-bias-variance-2.png')


#### Validation Curve¶

In [28]:
x = np.linspace(0, 1, 1000)
y1 = -(x - 0.5) ** 2
y2 = y1 - 0.33 + np.exp(x - 1)

fig, ax = plt.subplots()
ax.plot(x, y2, lw=10, alpha=0.5, color='blue')
ax.plot(x, y1, lw=10, alpha=0.5, color='red')

ax.text(0.15, 0.2, "training score", rotation=45, size=16, color='blue')
ax.text(0.2, -0.05, "validation score", rotation=20, size=16, color='red')

ax.text(0.02, 0.1, r'$\longleftarrow$ High Bias', size=18, rotation=90, va='center')
ax.text(0.98, 0.1, r'$\longleftarrow$ High Variance $\longrightarrow$', size=18, rotation=90, ha='right', va='center')
ax.text(0.48, -0.12, 'Best$\\longrightarrow$\nModel', size=18, rotation=90, va='center')

ax.set_xlim(0, 1)
ax.set_ylim(-0.3, 0.5)

ax.set_xlabel(r'model complexity $\longrightarrow$', size=14)
ax.set_ylabel(r'model score $\longrightarrow$', size=14)

ax.xaxis.set_major_formatter(plt.NullFormatter())
ax.yaxis.set_major_formatter(plt.NullFormatter())

ax.set_title("Validation Curve Schematic", size=16)

fig.savefig('figures/05.03-validation-curve.png')


#### Learning Curve¶

In [29]:
N = np.linspace(0, 1, 1000)
y1 = 0.75 + 0.2 * np.exp(-4 * N)
y2 = 0.7 - 0.6 * np.exp(-4 * N)

fig, ax = plt.subplots()
ax.plot(x, y1, lw=10, alpha=0.5, color='blue')
ax.plot(x, y2, lw=10, alpha=0.5, color='red')

ax.text(0.2, 0.88, "training score", rotation=-10, size=16, color='blue')
ax.text(0.2, 0.5, "validation score", rotation=30, size=16, color='red')

ax.text(0.98, 0.45, r'Good Fit $\longrightarrow$', size=18, rotation=90, ha='right', va='center')
ax.text(0.02, 0.57, r'$\longleftarrow$ High Variance $\longrightarrow$', size=18, rotation=90, va='center')

ax.set_xlim(0, 1)
ax.set_ylim(0, 1)

ax.set_xlabel(r'training set size $\longrightarrow$', size=14)
ax.set_ylabel(r'model score $\longrightarrow$', size=14)

ax.xaxis.set_major_formatter(plt.NullFormatter())
ax.yaxis.set_major_formatter(plt.NullFormatter())

ax.set_title("Learning Curve Schematic", size=16)

fig.savefig('figures/05.03-learning-curve.png')


## Gaussian Naive Bayes¶

### Gaussian Naive Bayes Example¶

Figure Context

In [30]:
from sklearn.datasets import make_blobs
X, y = make_blobs(100, 2, centers=2, random_state=2, cluster_std=1.5)

fig, ax = plt.subplots()

ax.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='RdBu')
ax.set_title('Naive Bayes Model', size=14)

xlim = (-8, 8)
ylim = (-15, 5)

xg = np.linspace(xlim[0], xlim[1], 60)
yg = np.linspace(ylim[0], ylim[1], 40)
xx, yy = np.meshgrid(xg, yg)
Xgrid = np.vstack([xx.ravel(), yy.ravel()]).T

for label, color in enumerate(['red', 'blue']):
P = np.exp(-0.5 * (Xgrid - mu) ** 2 / std ** 2).prod(1)
Pm = np.ma.masked_array(P, P < 0.03)
ax.pcolorfast(xg, yg, Pm.reshape(xx.shape), alpha=0.5,
cmap=color.title() + 's')
ax.contour(xx, yy, P.reshape(xx.shape),
levels=[0.01, 0.1, 0.5, 0.9],
colors=color, alpha=0.2)

ax.set(xlim=xlim, ylim=ylim)

fig.savefig('figures/05.05-gaussian-NB.png')


## Linear Regression¶

### Gaussian Basis Functions¶

Figure Context

In [31]:
from sklearn.pipeline import make_pipeline
from sklearn.linear_model import LinearRegression

from sklearn.base import BaseEstimator, TransformerMixin

class GaussianFeatures(BaseEstimator, TransformerMixin):
"""Uniformly-spaced Gaussian Features for 1D input"""

def __init__(self, N, width_factor=2.0):
self.N = N
self.width_factor = width_factor

@staticmethod
def _gauss_basis(x, y, width, axis=None):
arg = (x - y) / width
return np.exp(-0.5 * np.sum(arg ** 2, axis))

def fit(self, X, y=None):
# create N centers spread along the data range
self.centers_ = np.linspace(X.min(), X.max(), self.N)
self.width_ = self.width_factor * (self.centers_[1] - self.centers_[0])
return self

def transform(self, X):
return self._gauss_basis(X[:, :, np.newaxis], self.centers_,
self.width_, axis=1)

rng = np.random.RandomState(1)
x = 10 * rng.rand(50)
y = np.sin(x) + 0.1 * rng.randn(50)
xfit = np.linspace(0, 10, 1000)

gauss_model = make_pipeline(GaussianFeatures(10, 1.0),
LinearRegression())
gauss_model.fit(x[:, np.newaxis], y)
yfit = gauss_model.predict(xfit[:, np.newaxis])

gf = gauss_model.named_steps['gaussianfeatures']
lm = gauss_model.named_steps['linearregression']

fig, ax = plt.subplots()

for i in range(10):
selector = np.zeros(10)
selector[i] = 1
Xfit = gf.transform(xfit[:, None]) * selector
yfit = lm.predict(Xfit)
ax.fill_between(xfit, yfit.min(), yfit, color='gray', alpha=0.2)

ax.scatter(x, y)
ax.plot(xfit, gauss_model.predict(xfit[:, np.newaxis]))
ax.set_xlim(0, 10)
ax.set_ylim(yfit.min(), 1.5)

fig.savefig('figures/05.06-gaussian-basis.png')