def main():
f = open('PCA results.txt', 'w')
channels = ( 'NDTV', 'TIMESNOW', 'CNNIBN', 'CNN', 'BBC' )
learn_methods = ({ 'class': KNeighborsClassifier, 'name': 'kNN',
'params': {'n_neighbors': 5}, 'dense_X': False },
{ 'class': LDA, 'name': 'LDA', 'params': {}, 'dense_X': True },
{ 'class': SVC, 'name': 'SVM', 'params': {}, 'dense_X': False },
{ 'class': RandomForestClassifier, 'name': 'Random forest',
'params': {'n_estimators': 50}, 'dense_X': False },
{ 'class': GradientBoostingClassifier,
'name': 'Gradient tree boosting',
'params': {'n_estimators': 100}, 'dense_X': True })
pca_n_features = []
for i in range(1,21):
pca_n_features.append(i*10)
pca_n_features.append(227)
timer = clock if platform == 'win32' else time
for channel in channels:
XBig, y = load_svmlight_file('../../Dataset/{}.txt'.format(channel))
XBig = VarianceThreshold().fit_transform(XBig)
print('Loaded {} dataset...'.format(channel))
reduction_rates = []
ps_times = []
scores = { 'kNN': [], 'LDA': [], 'SVM': [], 'Random forest': [],
'Gradient tree boosting': [] }
train_times = deepcopy(scores)
Xs_reduced = list()
print('Starting PCA...')
for n_features in pca_n_features:
start_time = timer()
pca = PCA(n_components = n_features)
Xs_reduced.append(pca.fit_transform(XBig.toarray()))
end_time = timer()
ps_times.append(end_time - start_time)
print('PCA finished')
for X in Xs_reduced:
print('Model dimension = {}'.format(X[0].shape[0]))
f.write('Model dimension = {}'.format(X[0].shape[0]))
for train, test in StratifiedKFold(y, 10):
X_train, X_test, y_train, y_test = (X[train], X[test],
y[train], y[test])
#X_train = X_train.toarray()
#X_test = X_test.toarray()
# print('Selecting prototypes...')
#start_time = timer()
#ps = PrototypeSelector(X_train, y_train.astype(np.int))
#X_train_red, y_train_red = ps.fcnn_reduce(int(argv[1]))
#end_time = timer()
#ps_times.append(end_time - start_time)
#reduction_rates.append(X_train_red.shape[0] / X_train.shape[0])
# print('{}% of {} instances selected in {} s.'.format(
# 100 * reduction_rates[-1], X_train.shape[0], ps_times[-1]))
for method in learn_methods:
method_class = method['class']
method_params = method['params']
print('Testing with {}...'.format(method['name']))
clf = method['class'](**method['params'])
#print('Training...')
start_time = timer()
clf.fit(X_train, y_train)
end_time = timer()
train_times[method['name']].append(end_time - start_time)
#print('Testing...')
scores[method['name']].append(clf.score(X_test, y_test))
for method in learn_methods:
mean_score = np.mean(scores[method['name']])
score_variance = np.var(scores[method['name']])
mean_train_time = np.mean(train_times[method['name']])
train_time_variance = np.var(train_times[method['name']])
print('{}, {}: Q = {}±{}, Ttr = {}±{}'.format(
channel, method['name'], mean_score, score_variance,
mean_train_time, train_time_variance))
f.write('{}, {}: Q = {}±{}, Ttr = {}±{}'.format(
channel, method['name'], mean_score, score_variance,
mean_train_time, train_time_variance))
f.close()
