SVM.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Nov 27 19:03:26 2016
SVM for digits classification
@author: sig
"""
# %% load
from sklearn.datasets import load_digits
from sklearn.cross_validation import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.svm import LinearSVC
from sklearn.metrics import classification_report
# %%
digits = load_digits()
digits.data.shape
# %%
X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target, \
                                                    test_size = 0.25, \
                                                    random_state = 33)
y_train.shape
y_test.shape
# %% 
ss = StandardScaler()
X_train = ss.fit_transform(X_train)
X_test = ss.transform(X_test)
# %%
lsvc = LinearSVC()
lsvc.fit(X_train, y_train)
y_predict = lsvc.predict(X_test)
# %%
print('The Accuracy of Linear SVC is', lsvc.score(X_test, y_test))
print(classification_report(y_test, y_predict, \
                            target_names = digits.target_names.astype(str)))