def test_synthetic_circles(self):
print('''
two concentric circles
''')
N = 10**3
X, y = make_circles(n_samples=N, noise=1.0)
k = len(np.unique(y))
X_incomplete = create_incomplete_matrix(X)
labels, _, X_hat = kmeans_missing(X_incomplete, k)
sklearn_mse = ((X - X_hat)**2).mean()
score = metrics.homogeneity_completeness_v_measure(labels, y)
print(f'sklearn mse: {sklearn_mse}')
print(f'sklearn scores: {score}')
displacements = np.nan_to_num(X_incomplete)
spans = np.nan_to_num(X_incomplete)
spans[spans == 0] = 1
spans[spans != 1] = 0
L = SetOfLines(spans, displacements, np.ones(N), np.ones(N))
config = ParameterConfig()
## data
m = 100 # coreset size ~ reduction ratio
tau = 1e-2
config.a_b_approx_minimum_number_of_lines = 100 # constant 100, line 2, algo 2 BI-CRITERIA
config.sample_size_for_a_b_approx = int(
m * 1.05) # |S| >= m, line 3 of algo 2
# note: there'll be a O(|S|^2) cost while computing algo 1
config.farthest_to_centers_rate_in_a_b_approx = 4 / 11 # opp of 7/11, line 6, algo 2 BI-CRITERIA
config.number_of_remains_multiply_factor = int(
math.log(N)
) // k # this is `b` in algo 2, other paper, set as random here - how to calculate it?
config.closest_to_median_rate = (1 - tau) / (
2 * k) # refer line 4, algo 1, other paper
config.median_sample_size = int(
N * 0.05) # size of q_i, line 3, algo 2, other paper
config.max_sensitivity_multiply_factor = 100 # for outliers in coresets
config.number_of_remains = 20
SAMPLE_SIZE = 50 # keep it < 100, works fast
ITER = 5
klines_mse = np.zeros(ITER)
scores = [[]] * ITER
for i in range(ITER):
print(f'Running KLines iter {i+1} of {ITER}')
X_klines, kl_labels = customStreamer(L, k, m, SAMPLE_SIZE, config)
klines_mse[i] = ((X - X_klines)**2).mean()
scores[i] = metrics.homogeneity_completeness_v_measure(
kl_labels, y)
print(f"Klines MSE: {klines_mse.mean()}")
print(f"Klines scores: {np.array(scores).mean(axis=0)}")
assert sklearn_mse / klines_mse.mean() > 0.5
def test_benchmark_Chainlink(self):
print('Clustering Chainlink.npz')
npzfile = np.load('data/Chainlink.npz')
X, y = npzfile['X'], npzfile['y']
(N, _), k = X.shape, np.unique(y).shape[0]
print(f'#Datapoints {N}')
X_incomplete = create_incomplete_matrix(X)
labels, _, X_hat = kmeans_missing(X_incomplete, k)
sklearn_mse = ((X - X_hat)**2).mean()
score = metrics.homogeneity_completeness_v_measure(labels, y)
print(f'MSE sklearn: {sklearn_mse}')
print(f'MSE scores/measures: {score}')
displacements = np.nan_to_num(X_incomplete)
spans = np.nan_to_num(X_incomplete)
spans[spans == 0] = 1
spans[spans != 1] = 0
L = SetOfLines(spans, displacements, np.ones(N), np.ones(N))
config = ParameterConfig()
## data
m = 60 # coreset size ~ reduction ratio
tau = 1e-2
config.a_b_approx_minimum_number_of_lines = 40 # constant 100, line 2, algo 2 BI-CRITERIA
config.sample_size_for_a_b_approx = int(
m * 1.05) # |S| >= m, line 3 of algo 2
# note: there'll be a O(|S|^2) cost while computing algo 1
config.farthest_to_centers_rate_in_a_b_approx = 4 / 11 # opp of 7/11, line 6, algo 2 BI-CRITERIA
config.number_of_remains_multiply_factor = int(
math.log(N)
) // k # this is `b` in algo 2, other paper, set as random here - how to calculate it?
config.closest_to_median_rate = (1 - tau) / (
2 * k) # refer line 4, algo 1, other paper
config.median_sample_size = int(
N * 0.05) # size of q_i, line 3, algo 2, other paper
config.max_sensitivity_multiply_factor = 100 # for outliers in coresets
config.number_of_remains = 20
SAMPLE_SIZE = 50
ITER = 5
klines_mse = np.zeros(ITER)
scores = [[]] * ITER
for i in range(ITER):
print(f'Running KLines iter {i+1} of {ITER}')
X_klines, kl_labels = customStreamer(L, k, m, SAMPLE_SIZE, config)
klines_mse[i] = ((X - X_klines)**2).mean()
scores[i] = metrics.homogeneity_completeness_v_measure(
kl_labels, y)
print(f"Klines MSE: {klines_mse.mean()}")
print(f"Scores: {np.array(scores).mean(axis=0)}")
assert sklearn_mse / klines_mse.mean() > 0.8
def test_scores(self):
data = load_iris()
X = data.data
y = data.target
k = len(np.unique(y))
X_incomplete = create_incomplete_matrix(X)
# X is the complete data matrix
# X_incomplete has the same values as X except a subset have been replace with NaN
labels, _, X_hat = kmeans_missing(X_incomplete, k)
metrics.homogeneity_completeness_v_measure(labels, y)
klines_mse_sklearn = ((X - X_hat)**2).mean()
# ## Clustering using KLines
displacements = np.nan_to_num(X_incomplete)
N, _ = X_incomplete.shape
spans = np.nan_to_num(X_incomplete)
spans[spans==0] = 1
spans[spans!=1] = 0
L = SetOfLines(spans, displacements, np.ones(N), np.ones(N))
config = ParameterConfig()
## data
k = 3
m = min(int(N*0.1), 100) # coreset size ~ reduction ratio
tau = 1e-3
config.a_b_approx_minimum_number_of_lines = min(int(N*0.1), 100) # constant 100, line 2, algo 2 BI-CRITERIA
config.sample_size_for_a_b_approx = int(m*1.05) # |S| >= m, line 3 of algo 2
# note: there'll be a O(|S|^2) cost while computing algo 1
config.farthest_to_centers_rate_in_a_b_approx = 0.25 # opp of 7/11, line 6, algo 2 BI-CRITERIA
config.number_of_remains_multiply_factor = int(math.log(N))//k # this is `b` in algo 2, other paper, set as random here - how to calculate it?
config.closest_to_median_rate = (1-tau)/(2*k) # refer line 4, algo 1, other paper
config.median_sample_size = int(N*0.05) # size of q_i, line 3, algo 2, other paper
config.max_sensitivity_multiply_factor = 2 # for outliers in coresets
config.number_of_remains = min(int(N*0.05), 20)
def util():
_, B, _ = CorsetForKMeansForLines(config).coreset(L, k, m, True)
# reduce the coreset of random centers
cwc = CoresetForWeightedCenters(config)
MAX_ITER = 5
while MAX_ITER > 0:
B = cwc.coreset(B, k, m)
if B.get_size() <= k:
MAX_ITER = 0
MAX_ITER -= 1
X_klines = L.get_projected_centers(B)
kl_labels = L.get_indices_clusters(B)
return X_klines, kl_labels
klines_mse = []
scores = []
ITER = 10
for i in range(ITER):
X_klines, kl_labels = util()
klines_mse.append(((X - X_klines)**2).mean())
scores.append(metrics.homogeneity_completeness_v_measure(kl_labels, y))
assert klines_mse_sklearn/np.array(klines_mse).mean() < 0.6