def testGaussian2():
all_X, all_y = testData()
xX = np.concatenate([all_X, all_y.reshape((len(all_y), 1))], axis=1)
xX = [list(x.ravel()) for x in xX]
np.random.shuffle(xX)
y = np.array(xX).T[2]
X = np.array(xX).T[:2].T
y_test, X_test = all_y[-10:], all_X[-10:]
params = {'bandwidth': np.logspace(-1, 1, 20)}
grid = GridSearchCV(KernelDensity(), params)
grid.fit(all_X)
bw = grid.best_estimator_.bandwidth
print 'Bandwidth {}'.format(bw)
myclassifier = hw7u.MyKNN(metric='gaussian', density=True, bandwidth=bw)
print 'start MyKNN'
myclassifier.fit(X, y)
#print 'start scikit'
#knnsci = skclassifier.fit(X, y)
print 'start my pred'
y_pred = myclassifier.predict(X_test)
#print 'start sk pred'
#y_sci = knnsci.score(X_test)
#print 'SciKit Accuracy: {} My Accuracy: {}'.format(accuracy_score(fix_y(y_test), fix_y(y_sci)), accuracy_score(fix_y(y_test), fix_y(y_pred)))
print 'My Accuracy: {}'.format(accuracy_score(hw7.fix_y(y_test), hw7.fix_y(y_pred)))
def tests_radius():
i = 0
j = 0
k = 10
X, y = testData()
#print X
X = np.concatenate([X, y.reshape((len(y), 1))], axis=1)
X = [list(x.ravel()) for x in X]
radius = [3, 5, 7]
radius = [1e-1,1e-2,1e-3] # for radius
metric = ['minkowski', 'cosine', 'gaussian', 'poly2']
ma = speedy.Kernel(ktype=metric[j]).compute
#ma = hw7u.Kernel(ktype=metric[j]).compute
print 'spam radius is {}'.format(radius[i])
clf = hw7u.MyKNN(radius=radius[i], metric=metric[j], outlier_label=-1)
skclf = RadiusNeighborsClassifier(radius=radius[i], algorithm='brute', metric="euclidean", p=2, outlier_label=.5)
all_folds = hw3u.partition_folds(X, k)
kf_train, kf_test = dl.get_train_and_test(all_folds, 0)
y, X = hw4u.split_truth_from_data(kf_train)
y_test, X_test = hw4u.split_truth_from_data(kf_test)
print 'start scikit'
knnsci = hw7u.KNN(classifier=skclf)
print 'start MyKNN'
knn = hw7u.KNN(classifier=clf)
print 'start sk pred'
y_sci = knnsci.predict(X_test, X, y)
print 'start my pred'
y_pred = knn.predict(X_test, X, y)
print y_pred
print 'SciKit Accuracy: {} My Accuracy: {}'.format(accuracy_score(hw7.fix_y(y_test), hw7.fix_y(y_sci)), accuracy_score(hw7.fix_y(y_test), hw7.fix_y(y_pred)))
def tests_density():
i = 0
j = 2
k = 10
X, y = testData()
print X
X = np.concatenate([X, y.reshape((len(y), 1))], axis=1)
X = [list(x.ravel()) for x in X]
radius = [3, 5, 7]
metric = ['minkowski', 'cosine', 'gaussian', 'poly2']
ma = hw7u.Kernel(ktype=metric[j]).compute
params = {'bandwidth': np.logspace(-1, 1, 20)}
grid = GridSearchCV(KernelDensity(), params)
grid.fit(X)
clf = hw7u.MyKNN(metric=metric[j], density=True)
bw = grid.best_estimator_.bandwidth
print("best bandwidth: {0}".format(bw))
# use the best estimator to compute the kernel density estimate
kde = grid.best_estimator_
skclf = KernelDensity(bandwidth=bw, kernel='gaussian')
skclf.fit(X[:-10], y[:-10])
print skclf.score_samples(X[-10:])
return
all_folds = hw3u.partition_folds(X, k)
kf_train, kf_test = dl.get_train_and_test(all_folds, 0)
y, X = hw4u.split_truth_from_data(kf_train)
y_test, X_test = hw4u.split_truth_from_data(kf_test)
print 'start scikit'
knnsci = hw7u.KNN(classifier=skclf)
print 'start MyKNN'
knn = hw7u.KNN(classifier=clf)
print 'start sk pred'
y_sci = knnsci.predict(X_test, X, y)
print 'start my pred'
y_pred = knn.predict(X_test, X, y)
print y_pred
print 'SciKit Accuracy: {} My Accuracy: {}'.format(accuracy_score(hw7.fix_y(y_test), hw7.fix_y(y_sci)), accuracy_score(hw7.fix_y(y_test), hw7.fix_y(y_pred)))
