notebook.community

Edit and run

Support vector machine in Python using libsvm example of features

Stackoverflow questions by Usi Usi: http://stackoverflow.com/questions/30991592/support-vector-machine-in-python-using-libsvm-example-of-features

Step 0: Install dependencies

You need to install the following libraries:

  • pandas
  • scikit-learn

From command line:

pip install pandas
pip install scikit-learn

Step 1: Load the data

We will use pandas to load our data. pandas is a library for easily loading data. For illustration, we first save sample data to a csv and then load it.

We will train the SVM with train.csv and get test labels with test.csv





In [76]:



    


import pandas as pd

train_data_contents = """
class_label,distance_from_beginning,distance_from_end,contains_digit,capitalized
B,1,10,1,0
M,10,1,0,1
C,2,3,0,1
S,23,2,0,0
N,12,0,0,1"""


with open('train.csv', 'w') as output:
    output.write(train_data_contents)
    
train_dataframe = pd.read_csv('train.csv')

Step 2: Process the data

We will convert our dataframe into numpy arrays which is a format that scikit-learn understands.

We need to convert the labels "B", "M", "C",... to numbers also because svm does not understand strings.

Then we will train a linear svm with the data





In [78]:



    


import numpy as np

train_labels = train_dataframe.class_label
labels = list(set(train_labels))
train_labels = np.array([labels.index(x) for x in train_labels])
train_features = train_dataframe.iloc[:,1:]
train_features = np.array(train_features)

print "train labels: "
print train_labels
print 
print "train features:"
print train_features



    


















    






train labels: 
[1 2 0 3 4]

train features:
[[ 1 10  1  0]
 [10  1  0  1]
 [ 2  3  0  1]
 [23  2  0  0]
 [12  0  0  1]]

We see here that the length of train_labels (5) exactly matches how many rows we have in trainfeatures. Each item in train_labels corresponds to a row.

Step 3: Train the SVM





In [65]:



    


from sklearn import svm
classifier = svm.SVC()
classifier.fit(train_features, train_labels)



    


















    
Out[65]:








SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,
  kernel='rbf', max_iter=-1, probability=False, random_state=None,
  shrinking=True, tol=0.001, verbose=False)

Step 4: Evaluate the SVM on some testing data





In [75]:



    


test_data_contents = """
class_label,distance_from_beginning,distance_from_end,contains_digit,capitalized
B,1,10,1,0
M,10,1,0,1
C,2,3,0,1
S,23,2,0,0
N,12,0,0,1
"""

with open('test.csv', 'w') as output:
    output.write(test_data_contents)

test_dataframe = pd.read_csv('test.csv')

train_labels = test_dataframe.class_label
labels = list(set(train_labels))
test_labels = np.array([labels.index(x) for x in train_labels])

test_features = test_dataframe.iloc[:,1:]
test_features = np.array(test_features)

results = classifier.predict(test_features)
num_correct = (results == test_labels).sum()
recall = num_correct / len(test_labels)
print "model accuracy (%): ", recall * 100, "%"



    


















    






model accuracy (%):  100 %

Content source: julenka/ipython-notebooks

Similar notebooks:

notebook.community | gallery | about