def select_models():
vid = ca_data_utils.load_v_matrix().T[8:39992]
labels = ca_data_utils.load_labels()[8:39992]
X_train, X_test, y_train, y_test = train_test_split(
vid, labels, test_size=0.25) # random state?
classifiers = [
SVC(kernel="linear", C=0.025),
SVC(gamma=2, C=1),
RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1),
AdaBoostClassifier()
]
names = ['linear SVM', 'RBF SVM', 'Random Forest', 'AdaBoost']
train = []
test = []
for name, clf in zip(names, classifiers):
print('starting to train with ', name)
clf.fit(X_train, y_train)
print('---> calculating training set accuracy:')
train_accu = clf.score(X_train, y_train)
train.append(train_accu)
print('---> training set accuracy: ', train_accu)
print('---> calculating testing set accuracy:')
test_accu = clf.score(X_test, y_test)
test.append(test_accu)
print('---> testing set accuracy: ', test_accu)
np.save('../data/clf_results/clf_names', names)
np.save('../data/clf_results/train_accuracy', train)
np.save('../data/clf_results/test_accuracy', test)
def xgboost(gamma):
X = ca_data_utils.load_v_matrix().T[8:39992:5]
labels = ca_data_utils.load_labels()[8:39992:5]
clf = xgb.XGBClassifier(gamma=gamma, learning_rate=0.1)
total_len = len(labels)
cut = int(3 * total_len / 4)
clf.fit(X[:cut], labels[:cut])
score = clf.score(X[cut:], labels[cut:])
print(score)
def kernel_pca():
X = ca_data_utils.load_v_matrix().T[8:39992]
labels = ca_data_utils.load_labels()[8:39992]
kpca = KernelPCA(kernel="rbf",
fit_inverse_transform=True,
gamma=10,
n_jobs=20)
print('starting kpca')
X_kpca = kpca.fit_transform(X)
print('finished')
# pca = PCA()
# X_pca = pca.fit_transform(X)
sleep = labels == 1
wake1 = labels == 2
wake2 = labels == 3
plt.figure()
plt.subplot(1, 2, 1, aspect='equal')
plt.title("Original space")
plt.scatter(X[sleep, 0], X[sleep, 1], c="red", s=20, edgecolor='k')
plt.scatter(X[wake1, 0], X[wake1, 1], c="blue", s=20, edgecolor='k')
plt.scatter(X[wake2, 0], X[wake2, 1], c='green', s=20, edgecolors='k')
plt.xlabel("$x_1$")
plt.ylabel("$x_2$")
plt.subplot(1, 2, 2, aspect='equal')
plt.scatter(X_kpca[sleep, 0],
X_kpca[sleep, 1],
c="red",
s=20,
edgecolor='k')
plt.scatter(X_kpca[wake1, 0],
X_kpca[wake1, 1],
c="blue",
s=20,
edgecolor='k')
plt.scatter(X_kpca[wake2, 0],
X_kpca[wake2, 1],
c='green',
s=20,
edgecolor='k')
plt.title("Projection by KPCA")
plt.xlabel(r"1st principal component in space induced by $\phi$")
plt.ylabel("2nd component")
plt.tight_layout()
plt.savefig('../data/clf_results/kpca')
def tune_rbf_svm():
vid = ca_data_utils.load_v_matrix()[:, 9:39992]
labels = ca_data_utils.load_labels()[9:39992]
scaler = StandardScaler()
X = scaler.fit_transform(vid.T)
C_range = np.logspace(-1, 2, 4)
print('C range: ', C_range)
gamma_range = np.logspace(-3, 3, 7) * 1. / X.shape[1] #(-3,3,7)
print('gamma range: ', gamma_range)
param_grid = dict(gamma=gamma_range, C=C_range)
cv = StratifiedShuffleSplit(test_size=0.25, random_state=42)
grid = GridSearchCV(SVC(), param_grid=param_grid, cv=cv, n_jobs=20)
print('start to train...')
grid.fit(X, labels)
print("The best parameters are %s with a score of %0.2f" %
(grid.best_params_, grid.best_score_))
print('The results are:')
print(grid.cv_results_)
def load_data(step):
v = ca_data_utils.load_v_matrix().T[9:39992:step]
labels = ca_data_utils.load_labels()[9:39992:step]
return v, labels
param_grid = dict(gamma=gamma_range, C=C_range)
cv = StratifiedShuffleSplit(n_splits=5, test_size=0.2, random_state=42)
grid = GridSearchCV(SVC(), param_grid=param_grid, cv=cv)
print('start to train...')
grid.fit(X, labels)
print("The best parameters are %s with a score of %0.2f" %
(grid.best_params_, grid.best_score_))
print('The results are:')
print(grid.cv_results_)
if __name__ == '__main__':
vid = ca_data_utils.load_v_matrix().T[9:39992]
labels = ca_data_utils.load_labels()[9:39992]
for k in range(5, 11):
select_models(vid, labels, k)
# select_k(vid, labels, k)
# clf = SVC(gamma=0.001, C=10)
# scaler = StandardScaler()
# X = scaler.fit_transform(vid)
# X_train, X_test, y_train, y_test = train_test_split(X, labels, test_size=0.25)
# print('start to train')
# clf.fit(X_train, y_train)
# print('finally finished tada~~')
# y_pred = clf.predict(X)