Applied Machine Learning, Module 1: A simple classification task

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Applied Machine Learning, Module 1: A simple classification task

Import required modules and load data file

In [2]:

%matplotlib notebook

import numpy as np

import matplotlib.pyplot as plt

import pandas as pd

from sklearn.model_selection import train_test_split

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fruits = pd.read_table('readonly/fruit_data_with_colors.txt')

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In [3]:

fruits.head()

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Out[3]:

	fruit_label	fruit_name	fruit_subtype	mass	width	height	color_score
0	1	apple	granny_smith	192	8.4	7.3	0.55
1	1	apple	granny_smith	180	8.0	6.8	0.59
2	1	apple	granny_smith	176	7.4	7.2	0.60
3	2	mandarin	mandarin	86	6.2	4.7	0.80
4	2	mandarin	mandarin	84	6.0	4.6	0.79

In [4]:

# create a mapping from fruit label value to fruit name to make results easier to interpret

lookup_fruit_name = dict(zip(fruits.fruit_label.unique(), fruits.fruit_name.unique()))   

lookup_fruit_name

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Out[4]:

{1: 'apple', 2: 'mandarin', 3: 'orange', 4: 'lemon'}

The file contains the mass, height, and width of a selection of oranges, lemons and apples. The heights were measured along the core of the fruit. The widths were the widest width perpendicular to the height.

Examining the data

In [5]:

# plotting a scatter matrix

from matplotlib import cm

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X = fruits[['height', 'width', 'mass', 'color_score']]

y = fruits['fruit_label']

X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)

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cmap = cm.get_cmap('gnuplot')

scatter = pd.scatter_matrix(X_train, c= y_train, marker = 'o', s=40, hist_kwds={'bins':15}, figsize=(9,9), cmap=cmap)

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Figure 1

pan/zoom

In [6]:

# plotting a 3D scatter plot

from mpl_toolkits.mplot3d import Axes3D

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fig = plt.figure()

ax = fig.add_subplot(111, projection = '3d')

ax.scatter(X_train['width'], X_train['height'], X_train['color_score'], c = y_train, marker = 'o', s=100)

ax.set_xlabel('width')

ax.set_ylabel('height')

ax.set_zlabel('color_score')

plt.show()

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Figure 2

Stop Interaction

Create train-test split

In [7]:

# For this example, we use the mass, width, and height features of each fruit instance

X = fruits[['mass', 'width', 'height']]

y = fruits['fruit_label']

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# default is 75% / 25% train-test split

X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)

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Create classifier object

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from sklearn.neighbors import KNeighborsClassifier

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knn = KNeighborsClassifier(n_neighbors = 5)

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Train the classifier (fit the estimator) using the training data

In [9]:

knn.fit(X_train, y_train)

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Out[9]:

KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
           metric_params=None, n_jobs=1, n_neighbors=5, p=2,
           weights='uniform')

Estimate the accuracy of the classifier on future data, using the test data

In [11]:

knn.score(X_test, y_test)

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Out[11]:

0.53333333333333333

Use the trained k-NN classifier model to classify new, previously unseen objects

In [12]:

# first example: a small fruit with mass 20g, width 4.3 cm, height 5.5 cm

fruit_prediction = knn.predict([[20, 4.3, 5.5]])

lookup_fruit_name[fruit_prediction[0]]

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Out[12]:

'mandarin'

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# second example: a larger, elongated fruit with mass 100g, width 6.3 cm, height 8.5 cm

fruit_prediction = knn.predict([[100, 6.3, 8.5]])

lookup_fruit_name[fruit_prediction[0]]

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Out[13]:

'lemon'

In [16]:

# second example: a larger, elongated fruit with mass 100g, width 6.3 cm, height 8.5 cm

fruit_prediction = knn.predict([[20, 2, 12]])

lookup_fruit_name[fruit_prediction[0]]

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Out[16]:

'mandarin'

Plot the decision boundaries of the k-NN classifier

In [17]:

from adspy_shared_utilities import plot_fruit_knn

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plot_fruit_knn(X_train, y_train, 5, 'uniform')   # we choose 5 nearest neighbors

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Figure 3

How sensitive is k-NN classification accuracy to the choice of the 'k' parameter?

In [18]:

k_range = range(1,20)

scores = []

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for k in k_range:

    knn = KNeighborsClassifier(n_neighbors = k)

    knn.fit(X_train, y_train)

    scores.append(knn.score(X_test, y_test))

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plt.figure()

plt.xlabel('k')

plt.ylabel('accuracy')

plt.scatter(k_range, scores)

plt.xticks([0,5,10,15,20]);

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Figure 4

How sensitive is k-NN classification accuracy to the train/test split proportion?

In [20]:

t = [0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2]

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knn = KNeighborsClassifier(n_neighbors = 5)

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plt.figure()

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for s in t:

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    scores = []

    for i in range(1,1000):

        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 1-s)

        knn.fit(X_train, y_train)

        scores.append(knn.score(X_test, y_test))

    plt.plot(s, np.mean(scores), 'bo')

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plt.xlabel('Training set proportion (%)')

plt.ylabel('accuracy');

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Figure 5

x=0.689301 y=0.513783

In [ ]:

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