machine_learning_2.py

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scikit-learn classifiers
Based on code here and here

import numpy as np
import matplotlib.pyplot as plt
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Gaussian Naive Bayes: Simple example 


    

  

  

  

    

      

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Create a couple of numpy arrays

X contains the feature values

    

    

      
X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])

    

  

  

  

    

      

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Y contains the target values

    

    

      
Y = np.array([1, 1, 1, 2, 2, 2])

    

  

  

  

    

      

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data plotted and points colored by target value

    

    

      
plt.scatter(X[:, 0], X[:, 1], c=Y)

    

  

  

  

    

      

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feature values for new data

    

    

      
new_obs = np.array([[-0.8, -1], [1, 0.8], [1, -0.5]])

    

  

  

  

    

      

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new data plotted and given a different color (no target value)

    

    

      
plt.scatter(new_obs[:, 0], new_obs[:, 1])
plt.show()

    

  

  

  

    

      

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Import Gaussian naive bayesian classifier

    

    

      
from sklearn.naive_bayes import GaussianNB

    

  

  

  

    

      

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Create an instance of the classifier

    

    

      
clf = GaussianNB()

    

  

  

  

    

      

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Fit the classifier to the data

    

    

      
clf.fit(X, Y)

    

  

  

  

    

      

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Make predictions on new feature values based on the fit

    

    

      
predictions = clf.predict(new_obs)
print("Predicted targets (labels) for new data:", predictions, "\n")

    

  

  

  

    

      

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Extend feature and target vectors with new data and predicted targets

    

    

      
new_X = np.append(X, new_obs, axis=0)
new_Y = np.append(Y, predictions)

    

  

  

  

    

      

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Create new scatterplot with new data points colored according to their
predicted classification

    

    

      
plt.scatter(new_X[:, 0], new_X[:, 1], c=new_Y)
plt.show()

    

  

  

  

    

      

        #
      

      
 Gaussian Naive Bayes: Example with Iris dataset 

    

    

      
from sklearn import datasets

iris = datasets.load_iris()

gnb = GaussianNB()
gnb.fit(iris.data, iris.target)
y_pred = gnb.predict(iris.data)

print("Iris dataset predictions based on GaussianNB classifier")
for pred, targ in zip(y_pred, iris.target):
    print('Predicted:', pred, 'Target:', targ)

print("\nNumber of mislabeled points out of a total %d points : %d"
      % (iris.data.shape[0], (iris.target != y_pred).sum()))

    

  

  

  

    

      

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 k-nearest-neighbor classifier: Example with Iris dataset 

    

    

      

    

  

  

  

    

      

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Split iris dataset into data and target arrays

    

    

      
X = iris.data
y = iris.target

    

  

  

  

    

      

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Split iris data into train and test data for the purposes of cross validation
using numpy.random.permutation to split the data randomly.

Run code several times for different randomly assigned train and

test samples.

    

    

      
indices = np.random.permutation(len(X))

    

  

  

  

    

      

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Assign all but the last 10 randomly permuted indices for training

    

    

      
X_train = X[indices[:-10]]
y_train = y[indices[:-10]]

    

  

  

  

    

      

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Assign the remaining 10 indices for testing

    

    

      
X_test  = X[indices[-10:]]
y_test  = y[indices[-10:]]

    

  

  

  

    

      

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Create and fit a nearest-neighbor classifier

    

    

      
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier()
knn.fit(X_train, y_train)

    

  

  

  

    

      

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Predict the target for the test data

    

    

      
pred = knn.predict(X_test)
print('\n\nPredicted values for test data based on KNN classifier:\n',
      pred, '\n')

    

  

  

  

    

      

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The true targets for the test data

    

    

      
print('True values for test data:\n', y_test, '\n')

    

  

  

  

    

      

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Use the classifier's .score method for measuring the accuracy of predictions

    

    

      
score = knn.score(X_test, y_test)
print('Accuracy is', score)