Module+1.py

# coding: utf-8

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# ## Applied Machine Learning, Module 1:  A simple classification task

# ### Import required modules and load data file

# In[1]:

get_ipython().magic('matplotlib notebook')
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.model_selection import train_test_split

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


# In[2]:

fruits.head()


# In[3]:

# 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


# 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[4]:

# plotting a scatter matrix
from matplotlib import cm

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)

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)


# In[5]:

# plotting a 3D scatter plot
from mpl_toolkits.mplot3d import Axes3D

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()


# ### Create train-test split

# In[6]:

# For this example, we use the mass, width, and height features of each fruit instance
X = fruits[['mass', 'width', 'height']]
y = fruits['fruit_label']

# default is 75% / 25% train-test split
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)


# ### Create classifier object

# In[7]:

from sklearn.neighbors import KNeighborsClassifier

knn = KNeighborsClassifier(n_neighbors = 5)


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

# In[8]:

knn.fit(X_train, y_train)


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

# In[9]:

knn.score(X_test, y_test)


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

# In[10]:

# 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]]


# In[11]:

# 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]]


# ### Plot the decision boundaries of the k-NN classifier

# In[14]:

from adspy_shared_utilities import plot_fruit_knn

plot_fruit_knn(X_train, y_train, 15, 'uniform')   # we choose 5 nearest neighbors


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

# In[15]:

k_range = range(1,20)
scores = []

for k in k_range:
    knn = KNeighborsClassifier(n_neighbors = k)
    knn.fit(X_train, y_train)
    scores.append(knn.score(X_test, y_test))

plt.figure()
plt.xlabel('k')
plt.ylabel('accuracy')
plt.scatter(k_range, scores)
plt.xticks([0,5,10,15,20]);


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

# In[16]:

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

knn = KNeighborsClassifier(n_neighbors = 5)

plt.figure()

for s in t:

    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')

plt.xlabel('Training set proportion (%)')
plt.ylabel('accuracy');


# In[ ]: