Python Machine Learning Essentials - Code Examples

Chapter 3 - A Tour of Machine Learning Classifiers Using Scikit-Learn

Note that the optional watermark extension is a small IPython notebook plugin that I developed to make the code reproducible. You can just skip the following line(s).

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%load_ext watermark
%watermark -a 'Sebastian Raschka' -u -d -v -p numpy,pandas,matplotlib,scikit-learn
Sebastian Raschka 
Last updated: 08/21/2015 

CPython 3.4.3
IPython 3.2.1

numpy 1.9.2
pandas 0.16.2
matplotlib 1.4.3
scikit-learn 0.16.1
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# to install watermark just uncomment the following line:


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from IPython.display import Image

Choosing a classification algorithm


First steps with scikit-learn

Loading the Iris dataset from scikit-learn. Here, the third column represents the petal length, and the fourth column the petal width of the flower samples. The classes are already converted to integer labels where 0=Iris-Setosa, 1=Iris-Versicolor, 2=Iris-Virginica.

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from sklearn import datasets
import numpy as np

iris = datasets.load_iris()
X =[:, [2, 3]]
y =

print('Class labels:', np.unique(y))
Class labels: [0 1 2]

Splitting data into 70% training and 30% test data:

In [4]:
from sklearn.cross_validation import train_test_split

X_train, X_test, y_train, y_test = train_test_split(
         X, y, test_size=0.3, random_state=0)

Standardizing the features:

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from sklearn.preprocessing import StandardScaler

sc = StandardScaler()
X_train_std = sc.transform(X_train)
X_test_std = sc.transform(X_test)

Training a perceptron via scikit-learn

Redefining the plot_decision_region function from chapter 2:

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from sklearn.linear_model import Perceptron

ppn = Perceptron(n_iter=40, eta0=0.1, random_state=0), y_train)
Perceptron(alpha=0.0001, class_weight=None, eta0=0.1, fit_intercept=True,
      n_iter=40, n_jobs=1, penalty=None, random_state=0, shuffle=True,
      verbose=0, warm_start=False)
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y_pred = ppn.predict(X_test_std)
print('Misclassified samples: %d' % (y_test != y_pred).sum())
Misclassified samples: 4
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from sklearn.metrics import accuracy_score

print('Accuracy: %.2f' % accuracy_score(y_test, y_pred))
Accuracy: 0.91
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from matplotlib.colors import ListedColormap
import matplotlib.pyplot as plt
%matplotlib inline

def plot_decision_regions(X, y, classifier, test_idx=None, resolution=0.02):

    # setup marker generator and color map
    markers = ('s', 'x', 'o', '^', 'v')
    colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')
    cmap = ListedColormap(colors[:len(np.unique(y))])

    # plot the decision surface
    x1_min, x1_max = X[:, 0].min() - 1, X[:, 0].max() + 1
    x2_min, x2_max = X[:, 1].min() - 1, X[:, 1].max() + 1
    xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution),
                         np.arange(x2_min, x2_max, resolution))
    Z = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T)
    Z = Z.reshape(xx1.shape)
    plt.contourf(xx1, xx2, Z, alpha=0.4, cmap=cmap)
    plt.xlim(xx1.min(), xx1.max())
    plt.ylim(xx2.min(), xx2.max())

    # plot all samples
    X_test, y_test = X[test_idx, :], y[test_idx]                               
    for idx, cl in enumerate(np.unique(y)):
        plt.scatter(x=X[y == cl, 0], y=X[y == cl, 1],
                    alpha=0.8, c=cmap(idx),
                    marker=markers[idx], label=cl)
    # highlight test samples
    if test_idx:
        X_test, y_test = X[test_idx, :], y[test_idx]   
        plt.scatter(X_test[:, 0], X_test[:, 1], c='', 
                alpha=1.0, linewidth=1, marker='o', 
                s=55, label='test set')

Training a perceptron model using the standardized training data:

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%matplotlib inline

X_combined_std = np.vstack((X_train_std, X_test_std))
y_combined = np.hstack((y_train, y_test))

plot_decision_regions(X=X_combined_std, y=y_combined, 
                      classifier=ppn, test_idx=range(105,150))
plt.xlabel('petal length [standardized]')
plt.ylabel('petal width [standardized]')
plt.legend(loc='upper left')

# plt.savefig('./figures/iris_perceptron_scikit.png', dpi=300)

Modeling class probabilities via logistic regression


Logistic regression intuition and conditional probabilities

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%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np

def sigmoid(z):
    return 1.0 / (1.0 + np.exp(-z))

z = np.arange(-7, 7, 0.1)
phi_z = sigmoid(z)

plt.plot(z, phi_z)
plt.axvline(0.0, color='k')
plt.ylim(-0.1, 1.1)
plt.ylabel('$\phi (z)$')

# y axis ticks and gridline
plt.yticks([0.0, 0.5, 1.0])
ax = plt.gca()

# plt.savefig('./figures/sigmoid.png', dpi=300)
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Image(filename='./images/03_03.png', width=500) 

Learning the weights of the logistic cost function

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def cost_1(z):
    return - np.log(sigmoid(z))
def cost_0(z):
    return - np.log(1 - sigmoid(z))

z = np.arange(-10, 10, 0.1)
phi_z = sigmoid(z)

c1 = [cost_1(x) for x in z]
plt.plot(phi_z, c1, label='J(w) if y=1')

c0 = [cost_0(x) for x in z]
plt.plot(phi_z, c0, linestyle='--', label='J(w) if y=0')

plt.ylim(0.0, 5.1)
plt.xlim([0, 1])
# plt.savefig('./figures/log_cost.png', dpi=300)

Training a logistic regression model with scikit-learn

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from sklearn.linear_model import LogisticRegression

lr = LogisticRegression(C=1000.0, random_state=0), y_train)

plot_decision_regions(X_combined_std, y_combined, 
                      classifier=lr, test_idx=range(105,150))
plt.xlabel('petal length [standardized]')
plt.ylabel('petal width [standardized]')
plt.legend(loc='upper left')
# plt.savefig('./figures/logistic_regression.png', dpi=300)
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array([[  2.05743774e-11,   6.31620264e-02,   9.36837974e-01]])

Tackling overfitting via regularization

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Image(filename='./images/03_06.png', width=700) 
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weights, params = [], []
for c in np.arange(-5, 5):
    lr = LogisticRegression(C=10**c, random_state=0), y_train)

weights = np.array(weights)
plt.plot(params, weights[:, 0], 
         label='petal length')
plt.plot(params, weights[:, 1], linestyle='--', 
         label='petal width')
plt.ylabel('weight coefficient')
plt.legend(loc='upper left')
# plt.savefig('./figures/regression_path.png', dpi=300)

Maximum margin classification with support vector machines

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Image(filename='./images/03_07.png', width=700) 

Maximum margin intuition


Dealing with the nonlinearly separable case using slack variables

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Image(filename='./images/03_08.png', width=600) 
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from sklearn.svm import SVC

svm = SVC(kernel='linear', C=1.0, random_state=0), y_train)

plot_decision_regions(X_combined_std, y_combined, 
                      classifier=svm, test_idx=range(105,150))
plt.xlabel('petal length [standardized]')
plt.ylabel('petal width [standardized]')
plt.legend(loc='upper left')
# plt.savefig('./figures/support_vector_machine_linear.png', dpi=300)

Alternative implementations in scikit-learn

Solving non-linear problems using a kernel SVM

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import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline

X_xor = np.random.randn(200, 2)
y_xor = np.logical_xor(X_xor[:, 0] > 0, X_xor[:, 1] > 0)
y_xor = np.where(y_xor, 1, -1)

plt.scatter(X_xor[y_xor==1, 0], X_xor[y_xor==1, 1], c='b', marker='x', label='1')
plt.scatter(X_xor[y_xor==-1, 0], X_xor[y_xor==-1, 1], c='r', marker='s', label='-1')

plt.xlim([-3, 3])
plt.ylim([-3, 3])
# plt.savefig('./figures/xor.png', dpi=300)
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Image(filename='./images/03_11.png', width=700)