svm.py

# coding= UTF-8
#
# Author: Fing
# Date  : 2017-12-03
#

import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC

# Load data from numpy file
X = np.load('data/feat.npy')
y = np.load('data/label.npy').ravel()

# Split data into training and test subsets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=0)

# Simple SVM
print('fitting...')
clf = SVC(C=20.0, gamma=0.00001)
clf.fit(X_train, y_train)
acc = clf.score(X_test, y_test)
print("acc=%0.3f" % acc)

# Grid search for best parameters
# Set the parameters by cross-validation
# tuned_parameters = [{'kernel': ['rbf'], 'gamma': [1e-3, 1e-4, 1e-5],
#                      'C': [1, 10 ,20,30,40,50]}]
#                     #  ,
#                     # {'kernel': ['linear'], 'C': [1, 10, 100, 1000]}]

# scores = ['precision', 'recall']

# for score in scores:
#     print("# Tuning hyper-parameters for %s" % score)
#     print('')

#     clf = GridSearchCV(SVC(), tuned_parameters, cv=5,
#                        scoring='%s_macro' % score)
#     clf.fit(X_train, y_train)

#     print("Best parameters set found on development set:")
#     print('')
#     print(clf.best_params_)
#     print('')
#     print("Grid scores on development set:")
#     print('')
#     means = clf.cv_results_['mean_test_score']
#     stds = clf.cv_results_['std_test_score']
#     for mean, std, params in zip(means, stds, clf.cv_results_['params']):
#         print("%0.3f (+/-%0.03f) for %r"
#               % (mean, std * 2, params))
#     print('')

#     print("Detailed classification report:")
#     print('')
#     print("The model is trained on the full development set.")
#     print("The scores are computed on the full evaluation set.")
#     print('')
#     y_true, y_pred = y_test, clf.predict(X_test)
#     print(classification_report(y_true, y_pred))
#     print('')