def test_dbscan_core_samples_toy():
X = [[0], [2], [3], [4], [6], [8], [10]]
n_samples = len(X)
for algorithm in ['brute', 'kd_tree', 'ball_tree']:
# Degenerate case: every sample is a core sample, either with its own
# cluster or including other close core samples.
core_samples, labels = dbscan(X, algorithm=algorithm, eps=1,
min_samples=1)
assert_array_equal(core_samples, np.arange(n_samples))
assert_array_equal(labels, [0, 1, 1, 1, 2, 3, 4])
# With eps=1 and min_samples=2 only the 3 samples from the denser area
# are core samples. All other points are isolated and considered noise.
core_samples, labels = dbscan(X, algorithm=algorithm, eps=1,
min_samples=2)
assert_array_equal(core_samples, [1, 2, 3])
assert_array_equal(labels, [-1, 0, 0, 0, -1, -1, -1])
# Only the sample in the middle of the dense area is core. Its two
# neighbors are edge samples. Remaining samples are noise.
core_samples, labels = dbscan(X, algorithm=algorithm, eps=1,
min_samples=3)
assert_array_equal(core_samples, [2])
assert_array_equal(labels, [-1, 0, 0, 0, -1, -1, -1])
# It's no longer possible to extract core samples with eps=1:
# everything is noise.
core_samples, labels = dbscan(X, algorithm=algorithm, eps=1,
min_samples=4)
assert_array_equal(core_samples, [])
assert_array_equal(labels, -np.ones(n_samples))
def test_dbscan_sparse():
core_sparse, labels_sparse = dbscan(sparse.lil_matrix(X),
eps=.8,
min_samples=10)
core_dense, labels_dense = dbscan(X, eps=.8, min_samples=10)
assert_array_equal(core_dense, core_sparse)
assert_array_equal(labels_dense, labels_sparse)
def test_dbscan_sparse():
core_sparse, labels_sparse = dbscan(sparse.lil_matrix(X), eps=.8,
min_samples=10, random_state=0)
core_dense, labels_dense = dbscan(X, eps=.8, min_samples=10,
random_state=0)
assert_array_equal(core_dense, core_sparse)
assert_array_equal(labels_dense, labels_sparse)
def test_boundaries():
# ensure min_samples is inclusive of core point
core, _ = dbscan([[0], [1]], eps=2, min_samples=2)
assert_in(0, core)
# ensure eps is inclusive of circumference
core, _ = dbscan([[0], [1], [1]], eps=1, min_samples=2)
assert_in(0, core)
core, _ = dbscan([[0], [1], [1]], eps=.99, min_samples=2)
assert_not_in(0, core)
def test_dbscan_sparse_precomputed():
D = pairwise_distances(X)
core_sparse, labels_sparse = dbscan(sparse.lil_matrix(D),
eps=.8,
min_samples=10,
metric='precomputed')
core_dense, labels_dense = dbscan(X, eps=.8, min_samples=10)
assert_array_equal(core_dense, core_sparse)
assert_array_equal(labels_dense, labels_sparse)
def test_dbscan_sparse_precomputed():
D = pairwise_distances(X)
nn = NearestNeighbors(radius=0.9).fit(X)
D_sparse = nn.radius_neighbors_graph(mode="distance")
# Ensure it is sparse not merely on diagonals:
assert D_sparse.nnz < D.shape[0] * (D.shape[0] - 1)
core_sparse, labels_sparse = dbscan(D_sparse, eps=0.8, min_samples=10, metric="precomputed")
core_dense, labels_dense = dbscan(D, eps=0.8, min_samples=10, metric="precomputed")
assert_array_equal(core_dense, core_sparse)
assert_array_equal(labels_dense, labels_sparse)
def test_dbscan_input_not_modified(use_sparse, metric):
# test that the input is not modified by dbscan
X = np.random.RandomState(0).rand(10, 10)
X = sparse.csr_matrix(X) if use_sparse else X
X_copy = X.copy()
dbscan(X, metric=metric)
if use_sparse:
assert_array_equal(X.toarray(), X_copy.toarray())
else:
assert_array_equal(X, X_copy)
def test_dbscan_sparse_precomputed():
D = pairwise_distances(X)
nn = NearestNeighbors(radius=.9).fit(X)
D_sparse = nn.radius_neighbors_graph(mode='distance')
# Ensure it is sparse not merely on diagonals:
assert D_sparse.nnz < D.shape[0] * (D.shape[0] - 1)
core_sparse, labels_sparse = dbscan(D_sparse,
eps=.8,
min_samples=10,
metric='precomputed')
core_dense, labels_dense = dbscan(D, eps=.8, min_samples=10,
metric='precomputed')
assert_array_equal(core_dense, core_sparse)
assert_array_equal(labels_dense, labels_sparse)
def test_dbscan_sparse_precomputed_different_eps():
# test that precomputed neighbors graph is filtered if computed with
# a radius larger than DBSCAN's eps.
lower_eps = 0.2
nn = NearestNeighbors(radius=lower_eps).fit(X)
D_sparse = nn.radius_neighbors_graph(X, mode='distance')
dbscan_lower = dbscan(D_sparse, eps=lower_eps, metric='precomputed')
higher_eps = lower_eps + 0.7
nn = NearestNeighbors(radius=higher_eps).fit(X)
D_sparse = nn.radius_neighbors_graph(X, mode='distance')
dbscan_higher = dbscan(D_sparse, eps=lower_eps, metric='precomputed')
assert_array_equal(dbscan_lower[0], dbscan_higher[0])
assert_array_equal(dbscan_lower[1], dbscan_higher[1])
def test_dbscan_balltree():
# Tests the DBSCAN algorithm with balltree for neighbor calculation.
eps = 0.8
min_samples = 10
D = pairwise_distances(X)
core_samples, labels = dbscan(D, metric="precomputed", eps=eps, min_samples=min_samples)
# number of clusters, ignoring noise if present
n_clusters_1 = len(set(labels)) - int(-1 in labels)
assert_equal(n_clusters_1, n_clusters)
db = DBSCAN(p=2.0, eps=eps, min_samples=min_samples, algorithm="ball_tree")
labels = db.fit(X).labels_
n_clusters_2 = len(set(labels)) - int(-1 in labels)
assert_equal(n_clusters_2, n_clusters)
db = DBSCAN(p=2.0, eps=eps, min_samples=min_samples, algorithm="kd_tree")
labels = db.fit(X).labels_
n_clusters_3 = len(set(labels)) - int(-1 in labels)
assert_equal(n_clusters_3, n_clusters)
db = DBSCAN(p=1.0, eps=eps, min_samples=min_samples, algorithm="ball_tree")
labels = db.fit(X).labels_
n_clusters_4 = len(set(labels)) - int(-1 in labels)
assert_equal(n_clusters_4, n_clusters)
db = DBSCAN(leaf_size=20, eps=eps, min_samples=min_samples, algorithm="ball_tree")
labels = db.fit(X).labels_
n_clusters_5 = len(set(labels)) - int(-1 in labels)
assert_equal(n_clusters_5, n_clusters)
def angle_predict(hits,m,i,rz_shift,eps,weights):
aa = hits.a+m*(hits.r+0.000005*(hits.r**2))/1000*(i/2)/180*3.141
hits['f0'] = np.sin(aa)
hits['f1'] = np.cos(aa)
hits_b = hits[hits.type == 'b']['hit_id']
hits_c = hits[hits.type == 'c']['hit_id']
ss = StandardScaler()
X = ss.fit_transform(np.column_stack([hits.f0.values, hits.f1.values, hits.z1.values, hits.z2.values, hits.xr.values, hits.yr.values]))
X_b = np.multiply(np.vstack([X[ex-1] for ex in hits_b.values]),weights[0])
X_c = np.multiply(np.vstack([X[ex-1] for ex in hits_c.values]),weights[1])
Xw = np.zeros(X.shape)
Xw[hits_b.values-1] = X_b[range(len(hits_b.values))]
Xw[hits_c.values-1] = X_c[range(len(hits_c.values))]
eps = eps + (i*0.000005)
_,labels = dbscan(Xw, eps=eps, min_samples=1, algorithm='auto', n_jobs=4)
unique,reverse,count = np.unique(labels,return_counts=True,return_inverse=True)
c = count[reverse]
c[np.where(labels==0)]=0
if abs(rz_shift) < 0.1:
c[np.where(c>20)]=0
else:
c[np.where(c>8)]=0
return (labels,c)
def get_features(sub, cluster_size=10):
"""
Input: dataframe with hits long tracks
Output: array with features of long track
"""
hitst = sub.copy()
X = np.column_stack([
hitst.x.values, hitst.y.values, hitst.z.values,
hitst.track_id.values * 1000000
])
_, hitst['labels'] = dbscan(X,
eps=cluster_size,
min_samples=1,
algorithm='ball_tree',
metric='euclidean')
gp = hitst.groupby('track_id').agg({
'hit_id': 'count',
'labels': 'nunique',
'volume_id': 'min',
'x': ['min', 'max', 'var'],
'y': ['min', 'max', 'var'],
'z': ['min', 'max', 'var', 'mean']
})
gp.columns = ["".join(t) for t in gp.columns.ravel()]
gp = gp.rename(
columns={
'hit_idcount': 'nhits',
'labelsnunique': 'nclusters',
'volume_idmin': 'svolume'
}).reset_index()
gp['nhitspercluster'] = gp.nhits / gp.nclusters
return gp
def test_dbscan_callable():
# Tests the DBSCAN algorithm with a callable metric.
# Parameters chosen specifically for this task.
# Different eps to other test, because distance is not normalised.
eps = 0.8
min_samples = 10
# metric is the function reference, not the string key.
metric = distance.euclidean
# Compute DBSCAN
# parameters chosen for task
core_samples, labels = dbscan(X,
metric=metric,
eps=eps,
min_samples=min_samples,
algorithm='ball_tree')
# number of clusters, ignoring noise if present
n_clusters_1 = len(set(labels)) - int(-1 in labels)
assert_equal(n_clusters_1, n_clusters)
db = DBSCAN(metric=metric,
eps=eps,
min_samples=min_samples,
algorithm='ball_tree')
labels = db.fit(X).labels_
n_clusters_2 = len(set(labels)) - int(-1 in labels)
assert_equal(n_clusters_2, n_clusters)
def predict(self, dfh):
print("len(dfh) : {0}".format(len(dfh)))
if ("rt" not in dfh.columns):
dfh["rt"] = np.sqrt(dfh['x'].values**2 + dfh['y'].values**2)
z = dfh['z'].values
rt = dfh["rt"].values
r = np.sqrt(dfh['x']**2 + dfh['y']**2 + dfh['z']**2)
a0 = np.arctan2(dfh['y'].values, dfh['x'].values)
layer_id = dfh['layer_id'].values.astype(np.float32)
sys.stderr.write("dbscan for each parameters\n")
scan_labels_list = []
for (dj, di) in tqdm(product(self.djs, self.dis),
total=len(self.djs) * len(self.dis)):
ar = a0 + di * rt
zr = (z + dj) / rt * 0.1
if (self.param_type == 0):
params = [ar, zr]
elif (self.param_type == 1):
params = [np.sin(ar), np.cos(ar), zr * 10, 1 / (10 * zr)]
elif (self.param_type == 2):
params = [np.sin(ar), np.cos(ar), zr]
else:
raise RuntimeError("invalid param_type.")
if (self.weight is None):
w = np.array([1.0 for _ in params])
else:
w = np.array(self.weight)
ss = StandardScaler()
data1 = ss.fit_transform(np.column_stack(params))
data2 = w[np.newaxis, :] * data1
_, scan_label = dbscan(
data2,
eps=self.eps,
min_samples=1,
)
scan_labels_list.append(scan_label)
sys.stderr.write("clustering\n")
dfh["s1"] = dfh.hit_id
dfh["N1"] = 1
for scan_labels in scan_labels_list:
dfh["s2"] = scan_labels
dfh["N2"] = dfh.groupby('s2')['s2'].transform('count')
maxs1 = np.max(dfh.s1)
dfh.s1 = np.where((dfh.N2 > dfh.N1) & (dfh.N2 < 20),
dfh.s2 + maxs1, dfh.s1)
dfh['s1'] = dfh['s1'].astype('int64')
dfh['N1'] = dfh.groupby('s1')['s1'].transform('count')
labels = dfh['s1']
return labels
def predict(self, hits, weights):
x = hits.x.values
y = hits.y.values
z = self.rz_scale * hits.z.values
r = np.sqrt(x**2 + y**2)
d = np.sqrt(x**2 + y**2 + z**2)
a = np.arctan2(y, x)
zr = z / r
dr = d / r
hits['d'] = d
w0, w1, w2, w3, w4 = weights
ss = StandardScaler()
results = []
dzi = -0.00010
for step in [11]: #range(21): #0.00060/121/-60
dz = dzi + (step * 0.00001)
f0 = w0 * (a + (dz * z * np.sign(z)))
f1 = w1 * (zr)
f2 = w2 * (f0 / zr)
f3 = w3 * (1 / zr)
f4 = w4 * (f2 + f3)
X = ss.fit_transform(np.column_stack([f0, f1, f2, f3, f4]))
eps = self.eps - (abs(step - 10) * 0.000015)
_, labels = dbscan(X,
eps=eps,
min_samples=1,
algorithm='auto',
n_jobs=4)
unique, reverse, count = np.unique(labels,
return_counts=True,
return_inverse=True)
c = count[reverse]
c[np.where(labels == 0)] = 0
c[np.where(c > 20)] = 0
results.append((labels, c))
labels, counts = results[0]
for i in range(1, len(results)):
l, c = results[i]
idx = np.where((c - counts > 0))[0]
labels[idx] = l[idx] + labels.max()
counts[idx] = c[idx]
return labels
def find_labels(params):
hits, dz = params
a = hits['phi'].values
z = hits['z'].values
zr = hits['zr'].values
aa = a + np.sign(z) * dz * z
f0 = np.cos(aa)
f1 = np.sin(aa)
f2 = zr
X = StandardScaler().fit_transform(np.column_stack([f0, f1, f2]))
_, l = dbscan(X, eps=0.0045, min_samples=1, n_jobs=4)
return l + 1
def seed_tracks(event_id, df, start_d, end_d, ax):
seed = df.loc[df.d > start_d]
seed = seed.loc[seed.d < end_d]
N = len(seed)
p = seed[['particle_id']].values.astype(np.int64)
x, y, z, r, a, cosa, sina, phi = seed[[
'x', 'y', 'z', 'r', 'a', 'cosa', 'sina', 'phi'
]].values.astype(np.float32).T
particle_ids = np.unique(p)
particle_ids = particle_ids[particle_ids != 0]
num_particle_ids = len(particle_ids)
# do dbscan here =======================================
data = np.column_stack([a, z / r * 0.1])
_, l = dbscan(
data,
eps=0.01,
min_samples=1,
)
#print(len(truth))
#print(len(seed))
#print(len(submission))
#print(len(l))
seed['l'] = pd.Series(l, index=seed.index)
#print(seed)
submission = pd.DataFrame(columns=['event_id', 'hit_id', 'track_id'],
data=np.column_stack(([
int(event_id),
] * len(seed), seed.hit_id.values,
l))).astype(int)
score = score_event_fast(seed, submission)
print(score)
predicted_tracks, counts = np.unique(l, return_counts=True)
predicted_tracks = predicted_tracks[counts > 1]
for predicted_track in predicted_tracks[::100]:
track_hits = seed[seed.l == predicted_track]
ax.plot(xs=track_hits.a, ys=track_hits.r, zs=track_hits.z)
def get_base_partitioning(distance_matrix, eps=0.5, min_samples=2):
""" Gets the base partitioning from the distance matrix using DBScan
algorithm.
Args:
distance_matrix: a list of lists with the distances of references.
Returns:
A list of integers from 0 to k - 1, each one representing a block for the
reference represented by the index.
"""
labels = dbscan(np.array(distance_matrix), metric='precomputed', eps=eps,
min_samples=min_samples)
next_label = max(labels[1]) + 1
for i in range(len(labels[1])):
if labels[1][i] == -1:
labels[1][i] = next_label
next_label += 1
return labels[1].tolist(), number_of_clusters(labels[1])
def test_dbscan_similarity():
# Tests the DBSCAN algorithm with a similarity array.
# Parameters chosen specifically for this task.
eps = 0.15
min_samples = 10
# Compute similarities
D = distance.squareform(distance.pdist(X))
D /= np.max(D)
# Compute DBSCAN
core_samples, labels = dbscan(D, metric="precomputed", eps=eps, min_samples=min_samples)
# number of clusters, ignoring noise if present
n_clusters_1 = len(set(labels)) - (1 if -1 in labels else 0)
assert_equal(n_clusters_1, n_clusters)
db = DBSCAN(metric="precomputed", eps=eps, min_samples=min_samples)
labels = db.fit(D).labels_
n_clusters_2 = len(set(labels)) - int(-1 in labels)
assert_equal(n_clusters_2, n_clusters)
def test_dbscan_feature():
# Tests the DBSCAN algorithm with a feature vector array.
# Parameters chosen specifically for this task.
# Different eps to other test, because distance is not normalised.
eps = 0.8
min_samples = 10
metric = "euclidean"
# Compute DBSCAN
# parameters chosen for task
core_samples, labels = dbscan(X, metric=metric, eps=eps, min_samples=min_samples)
# number of clusters, ignoring noise if present
n_clusters_1 = len(set(labels)) - int(-1 in labels)
assert_equal(n_clusters_1, n_clusters)
db = DBSCAN(metric=metric, eps=eps, min_samples=min_samples)
labels = db.fit(X).labels_
n_clusters_2 = len(set(labels)) - int(-1 in labels)
assert_equal(n_clusters_2, n_clusters)
def test_dbscan_balltree():
# Tests the DBSCAN algorithm with balltree for neighbor calculation.
eps = 0.8
min_samples = 10
D = pairwise_distances(X)
core_samples, labels = dbscan(D,
metric="precomputed",
eps=eps,
min_samples=min_samples)
# number of clusters, ignoring noise if present
n_clusters_1 = len(set(labels)) - int(-1 in labels)
assert_equal(n_clusters_1, n_clusters)
db = DBSCAN(p=2.0, eps=eps, min_samples=min_samples, algorithm='ball_tree')
labels = db.fit(X).labels_
n_clusters_2 = len(set(labels)) - int(-1 in labels)
assert_equal(n_clusters_2, n_clusters)
db = DBSCAN(p=2.0, eps=eps, min_samples=min_samples, algorithm='kd_tree')
labels = db.fit(X).labels_
n_clusters_3 = len(set(labels)) - int(-1 in labels)
assert_equal(n_clusters_3, n_clusters)
db = DBSCAN(p=1.0, eps=eps, min_samples=min_samples, algorithm='ball_tree')
labels = db.fit(X).labels_
n_clusters_4 = len(set(labels)) - int(-1 in labels)
assert_equal(n_clusters_4, n_clusters)
db = DBSCAN(leaf_size=20,
eps=eps,
min_samples=min_samples,
algorithm='ball_tree')
labels = db.fit(X).labels_
n_clusters_5 = len(set(labels)) - int(-1 in labels)
assert_equal(n_clusters_5, n_clusters)
def test_dbscan_callable():
# Tests the DBSCAN algorithm with a callable metric.
# Parameters chosen specifically for this task.
# Different eps to other test, because distance is not normalised.
eps = 0.8
min_samples = 10
# metric is the function reference, not the string key.
metric = distance.euclidean
# Compute DBSCAN
# parameters chosen for task
core_samples, labels = dbscan(X, metric=metric, eps=eps, min_samples=min_samples, algorithm="ball_tree")
# number of clusters, ignoring noise if present
n_clusters_1 = len(set(labels)) - int(-1 in labels)
assert_equal(n_clusters_1, n_clusters)
db = DBSCAN(metric=metric, eps=eps, min_samples=min_samples, algorithm="ball_tree")
labels = db.fit(X).labels_
n_clusters_2 = len(set(labels)) - int(-1 in labels)
assert_equal(n_clusters_2, n_clusters)
def get_base_partitioning(distance_matrix, eps=0.5, min_samples=2):
""" Gets the base partitioning from the distance matrix using DBScan
algorithm.
Args:
distance_matrix: a list of lists with the distances of references.
Returns:
A list of integers from 0 to k - 1, each one representing a block for the
reference represented by the index.
"""
labels = dbscan(np.array(distance_matrix),
metric='precomputed',
eps=eps,
min_samples=min_samples)
next_label = max(labels[1]) + 1
for i in range(len(labels[1])):
if labels[1][i] == -1:
labels[1][i] = next_label
next_label += 1
return labels[1].tolist(), number_of_clusters(labels[1])
def test_dbscan_feature():
# Tests the DBSCAN algorithm with a feature vector array.
# Parameters chosen specifically for this task.
# Different eps to other test, because distance is not normalised.
eps = 0.8
min_samples = 10
metric = 'euclidean'
# Compute DBSCAN
# parameters chosen for task
core_samples, labels = dbscan(X, metric=metric, eps=eps,
min_samples=min_samples)
# number of clusters, ignoring noise if present
n_clusters_1 = len(set(labels)) - int(-1 in labels)
assert_equal(n_clusters_1, n_clusters)
db = DBSCAN(metric=metric, eps=eps, min_samples=min_samples)
labels = db.fit(X).labels_
n_clusters_2 = len(set(labels)) - int(-1 in labels)
assert_equal(n_clusters_2, n_clusters)
def test_dbscan_similarity():
# Tests the DBSCAN algorithm with a similarity array.
# Parameters chosen specifically for this task.
eps = 0.15
min_samples = 10
# Compute similarities
D = distance.squareform(distance.pdist(X))
D /= np.max(D)
# Compute DBSCAN
core_samples, labels = dbscan(D, metric="precomputed", eps=eps,
min_samples=min_samples)
# number of clusters, ignoring noise if present
n_clusters_1 = len(set(labels)) - (1 if -1 in labels else 0)
assert_equal(n_clusters_1, n_clusters)
db = DBSCAN(metric="precomputed", eps=eps, min_samples=min_samples)
labels = db.fit(D).labels_
n_clusters_2 = len(set(labels)) - int(-1 in labels)
assert_equal(n_clusters_2, n_clusters)
def img_association(network, propagate_loader, min_sample=4, eps=0,
rerank=False, k1=20, k2=6, intra_id_reinitialize=False):
network.eval()
print('Start Inference...')
features = []
global_labels = []
all_cams = []
with torch.no_grad():
for c, data in enumerate(propagate_loader):
images = data[0]
g_label = data[3]
cam = data[4]
embed_feat = network(images)
features.append(embed_feat.cpu())
global_labels.append(g_label)
all_cams.append(cam)
features = torch.cat(features, dim=0).numpy()
global_labels = torch.cat(global_labels, dim=0).numpy()
all_cams = torch.cat(all_cams, dim=0).numpy()
print(' features: shape= {}'.format(features.shape))
# if needed, average camera-style transferred image features
new_features = []
new_cams = []
for glab in np.unique(global_labels):
idx = np.where(global_labels == glab)[0]
new_features.append(np.mean(features[idx], axis=0))
new_cams.append(all_cams[idx])
new_features = np.array(new_features)
new_cams = np.array(new_cams).squeeze()
del features, all_cams
# compute distance W
new_features = new_features / np.linalg.norm(new_features, axis=1, keepdims=True) # l2-normalize
if rerank:
W = faiss_compute_jaccard_dist(torch.from_numpy(new_features), k1=k1, k2=k2)
else:
W = cdist(new_features, new_features, 'euclidean')
print(' distance matrix: shape= {}'.format(W.shape))
# self-similarity for association
print(' perform image grouping...')
_, updated_label = dbscan(W, eps=eps, min_samples=min_sample, metric='precomputed', n_jobs=8)
print(' eps in cluster: {:.3f}'.format(eps))
print(' updated_label: num_class= {}, {}/{} images are associated.'
.format(updated_label.max() + 1, len(updated_label[updated_label >= 0]), len(updated_label)))
if intra_id_reinitialize:
print('re-computing initialized intra-ID feature...')
intra_id_features = []
intra_id_labels = []
for cc in np.unique(new_cams):
percam_ind = np.where(new_cams == cc)[0]
percam_feature = new_features[percam_ind, :]
percam_label = updated_label[percam_ind]
percam_class_num = len(np.unique(percam_label[percam_label >= 0]))
percam_id_feature = np.zeros((percam_class_num, percam_feature.shape[1]), dtype=np.float32)
cnt = 0
for lbl in np.unique(percam_label):
if lbl >= 0:
ind = np.where(percam_label == lbl)[0]
id_feat = np.mean(percam_feature[ind], axis=0)
percam_id_feature[cnt, :] = id_feat
intra_id_labels.append(lbl)
cnt += 1
percam_id_feature = percam_id_feature / np.linalg.norm(percam_id_feature, axis=1, keepdims=True)
intra_id_features.append(torch.from_numpy(percam_id_feature))
return updated_label, intra_id_features
def copac(X,
k=10,
mu=5,
eps=0.5,
alpha=0.85,
metric='euclidean',
metric_params=None,
algorithm='auto',
leaf_size=30,
p=None,
n_jobs=1,
sample_weight=None):
"""Perform COPAC clustering from vector array.
Parameters
----------
X : array of shape (n_samples, n_features)
A feature array.
k : int, optional, default=10
Size of local neighborhood for local correlation dimensionality.
The paper suggests k >= 3 * n_features.
mu : int, optional, default=5
Minimum number of points in a copac with mu <= k.
eps : float, optional, default=0.5
Neighborhood predicate, so that neighbors are closer than `eps`.
alpha : float in ]0,1[, optional, default=0.85
Threshold of how much variance needs to be explained by Eigenvalues.
Assumed to be robust in range 0.8 <= alpha <= 0.9 [see Ref.]
metric : string, or callable
The metric to use when calculating distance between instances in a
feature array. If metric is a string or callable, it must be one of
the options allowed by sklearn.metrics.pairwise.pairwise_distances
for its metric parameter.
If metric is "precomputed", `X` is assumed to be a distance matrix and
must be square.
metric_params : dict, optional
Additional keyword arguments for the metric function.
algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, optional
The algorithm to be used by the scikit-learn NearestNeighbors module
to compute pointwise distances and find nearest neighbors.
See NearestNeighbors module documentation for details.
leaf_size : int, optional (default = 30)
Leaf size passed to BallTree or cKDTree. This can affect the speed
of the construction and query, as well as the memory required
to store the tree. The optimal value depends
on the nature of the problem.
p : float, optional
The power of the Minkowski metric to be used to calculate distance
between points.
n_jobs : int, optional, default=1
Number of parallel processes. Use all cores with n_jobs=-1.
sample_weight : None
Currently ignored
Returns
-------
labels : array [n_samples]
Cluster labels for each point. Noisy samples are given the label -1.
References
----------
Elke Achtert, Christian Bohm, Hans-Peter Kriegel, Peer Kroger,
A. Z. (n.d.). Robust, complete, and efficient correlation
clustering. In Proceedings of the Seventh SIAM International
Conference on Data Mining, April 26-28, 2007, Minneapolis,
Minnesota, USA (2007), pp. 413–418.
"""
X = check_array(X)
n, d = X.shape
y = -np.ones(n, dtype=np.int)
if n_jobs == -1:
n_jobs = cpu_count()
# Calculating M^ just once requires more memory, but saves computation
lambda_ = np.zeros(n, dtype=int)
M_hat = list()
# Get nearest neighbors
nn = NearestNeighbors(n_neighbors=k,
metric=metric,
algorithm=algorithm,
leaf_size=leaf_size,
metric_params=metric_params,
p=p,
n_jobs=n_jobs)
nn.fit(X)
knns = nn.kneighbors(return_distance=False)
for P, knn in enumerate(knns):
N_P = X[knn]
# Correlation copac covariance matrix
Sigma = np.cov(N_P[:, :], rowvar=False, ddof=0)
# Decompose spsd matrix, and sort Eigenvalues descending
E, V = LA.eigh(Sigma)
E = np.sort(E)[::-1]
# Local correlation dimension
explanation_portion = np.cumsum(E) / E.sum()
lambda_P = np.searchsorted(explanation_portion, alpha, side='left')
lambda_P += 1
lambda_[P] = lambda_P
# Correlation distance matrix
E_hat = (np.arange(1, d + 1) > lambda_P).astype(int)
M_hat.append(V @ np.diag(E_hat) @ V.T)
# Group points by corr. dim.
argsorted = np.argsort(lambda_)
edges, _ = np.histogram(lambda_[argsorted], bins=np.arange(1, d + 2))
Ds = np.split(argsorted, np.cumsum(edges))
# Loop over partitions according to local corr. dim.
max_label = 0
used_y = np.zeros_like(y, dtype=int)
for D in Ds:
n_D = D.shape[0]
cdist_P = -np.ones(n_D * (n_D - 1) // 2, dtype=np.float)
cdist_Q = -np.ones((n_D, n_D), dtype=np.float)
start = 0
# Calculate triu part of distance matrix
for i in range(0, n_D - 1):
p = D[i]
# Vectorized inner loop
q = D[i + 1:n_D]
stop = start + n_D - i - 1
cdist_P[start:stop] = _cdist(X[p], X[q], M_hat[p])
start = stop
# Calculate tril part of distance matrix
for i in range(1, n_D):
q = D[i]
p = D[0:i]
cdist_Q[i, :i] = _cdist(X[q], X[p], M_hat[q])
# Extract tril to 1D array
# TODO simplify...
cdist_Q = cdist_Q.T[np.triu_indices_from(cdist_Q, k=1)]
cdist = np.block([[cdist_P], [cdist_Q]])
# Square root of the higher value of cdist_P, cdist_Q
cdist = np.sqrt(cdist.max(axis=0))
# Perform DBSCAN with full distance matrix
cdist = squareform(cdist)
clust = dbscan(X=cdist,
eps=eps,
min_samples=mu,
metric='precomputed',
n_jobs=n_jobs)
_, labels = clust
# Each DBSCAN run is unaware of previous ones,
# so we need to keep track of previous copac IDs
y_D = labels + max_label
new_labels = np.unique(labels[labels >= 0]).size
max_label += new_labels
# Set copac labels in `y`
y[D] = y_D
used_y[D] += 1
assert np.all(used_y == 1), "Not all samples were handled exactly once!"
return y
def test_weighted_dbscan():
# ensure sample_weight is validated
assert_raises(ValueError, dbscan, [[0], [1]], sample_weight=[2])
assert_raises(ValueError, dbscan, [[0], [1]], sample_weight=[2, 3, 4])
# ensure sample_weight has an effect
assert_array_equal([], dbscan([[0], [1]], sample_weight=None,
min_samples=6)[0])
assert_array_equal([], dbscan([[0], [1]], sample_weight=[5, 5],
min_samples=6)[0])
assert_array_equal([0], dbscan([[0], [1]], sample_weight=[6, 5],
min_samples=6)[0])
assert_array_equal([0, 1], dbscan([[0], [1]], sample_weight=[6, 6],
min_samples=6)[0])
# points within eps of each other:
assert_array_equal([0, 1], dbscan([[0], [1]], eps=1.5,
sample_weight=[5, 1], min_samples=6)[0])
# and effect of non-positive and non-integer sample_weight:
assert_array_equal([], dbscan([[0], [1]], sample_weight=[5, 0],
eps=1.5, min_samples=6)[0])
assert_array_equal([0, 1], dbscan([[0], [1]], sample_weight=[5.9, 0.1],
eps=1.5, min_samples=6)[0])
assert_array_equal([0, 1], dbscan([[0], [1]], sample_weight=[6, 0],
eps=1.5, min_samples=6)[0])
assert_array_equal([], dbscan([[0], [1]], sample_weight=[6, -1],
eps=1.5, min_samples=6)[0])
# for non-negative sample_weight, cores should be identical to repetition
rng = np.random.RandomState(42)
sample_weight = rng.randint(0, 5, X.shape[0])
core1, label1 = dbscan(X, sample_weight=sample_weight)
assert_equal(len(label1), len(X))
X_repeated = np.repeat(X, sample_weight, axis=0)
core_repeated, label_repeated = dbscan(X_repeated)
core_repeated_mask = np.zeros(X_repeated.shape[0], dtype=bool)
core_repeated_mask[core_repeated] = True
core_mask = np.zeros(X.shape[0], dtype=bool)
core_mask[core1] = True
assert_array_equal(np.repeat(core_mask, sample_weight), core_repeated_mask)
# sample_weight should work with precomputed distance matrix
D = pairwise_distances(X)
core3, label3 = dbscan(D, sample_weight=sample_weight,
metric='precomputed')
assert_array_equal(core1, core3)
assert_array_equal(label1, label3)
# sample_weight should work with estimator
est = DBSCAN().fit(X, sample_weight=sample_weight)
core4 = est.core_sample_indices_
label4 = est.labels_
assert_array_equal(core1, core4)
assert_array_equal(label1, label4)
est = DBSCAN()
label5 = est.fit_predict(X, sample_weight=sample_weight)
core5 = est.core_sample_indices_
assert_array_equal(core1, core5)
assert_array_equal(label1, label5)
assert_array_equal(label1, est.labels_)
def study_dbscan_for_tracklet_seeding():
## load an event ---
event_id = '000001029'
data_dir = '/root/share/project/kaggle/cern/data/__download__/train_100_events'
#detectors = pd.read_csv('/root/share/project/kaggle/cern/data/__download__/detectors.csv')
particles = pd.read_csv(data_dir + '/event%s-particles.csv' % event_id)
hits = pd.read_csv(data_dir + '/event%s-hits.csv' % event_id)
truth = pd.read_csv(data_dir + '/event%s-truth.csv' % event_id)
#cells = pd.read_csv(data_dir + '/event%s-cells.csv'%event_id)
truth = truth.merge(hits, on=['hit_id'], how='left')
truth = truth.merge(particles, on=['particle_id'], how='left')
#--------------------------------------------------------
df = truth.copy()
df = df.assign(r=np.sqrt(df.x**2 + df.y**2))
df = df.assign(d=np.sqrt(df.x**2 + df.y**2 + df.z**2))
df = df.assign(a=np.arctan2(df.y, df.x))
df = df.assign(cosa=np.cos(df.a))
df = df.assign(sina=np.sin(df.a))
df = df.assign(phi=np.arctan2(df.z, df.r))
df = df.assign(momentum=np.sqrt(df.px**2 + df.py**2 + df.pz**2))
df.loc[df.particle_id == 0, 'momentum'] = 0
#df = df.loc[df.z>500] # consider dataset subset
#df = df.loc[df.r<50 ] ## 0.04397/0.04750 (0.92569)
df = df.loc[df.z > 500]
df = df.loc[(df.r > 50) & (df.r < 100)] ## 0.05259/0.05808 (0.90551)
#df = df.loc[df.z>500]
#df = df.loc[df.r<100] ## 0.09417/0.10557 (0.89195)
#df = df.loc[(df.a>0) & (df.a<0.5)]
#df = df.loc[(df.a>0) & (df.a<1)]
# df = df.loc[df.z>500] # consider dataset subset
# df = df.loc[(df.r>50) & (df.r<100)]
#df = df.loc[(df.z>0) &(df.z<500)]
#df = df.loc[df.r<200 ]
#df = df.loc[(df.a>0) & (df.a<0.5)]
#df = df.loc[(df.z>df.r)]
#df = df.loc[(df.r>50) & (df.r<100) ]
#-------------------------------------------------------
N = len(df)
layer_id = df['layer_id'].values.astype(np.float32)
momentum = df['momentum'].values.astype(np.float32)
p = df[['particle_id']].values.astype(np.int64)
x, y, z, r, a, cosa, sina, phi = df[[
'x', 'y', 'z', 'r', 'a', 'cosa', 'sina', 'phi'
]].values.astype(np.float32).T
particle_ids = np.unique(p)
particle_ids = particle_ids[particle_ids != 0]
num_particle_ids = len(particle_ids)
# do xxx =======================================
#color = plt.cm.hsv( (z-z.min()) / (z.max()-z.min()))
color = plt.cm.hsv(
(layer_id - layer_id.min()) / (layer_id.max() + 1 - layer_id.min()))
plot3d_particles(ax3d1, particle_ids, p, a, r, z, z)
ax3d1.scatter(a, r, z, c=color, s=64, edgecolors='none')
#plt.show()
dj = 0
di = 0
EPS = 1e-12
#if 1:
candidates = []
for dj in np.arange(-20, 20 + EPS, 10):
for di in np.arange(-0.003, 0.003 + EPS, 0.00025):
ar = a + di * r
zr = (z + dj) / r * 0.1
data2 = np.column_stack([ar, zr])
_, l = dbscan(
data2,
eps=0.0025,
min_samples=1,
)
track_ids = np.unique(l)
track_ids = track_ids[track_ids != 0]
neigbour = [np.where(l == t)[0] for t in track_ids]
unique, inverse, c = np.unique(l,
return_counts=True,
return_inverse=True)
unique = unique[unique != 0]
c = c[inverse]
c[l == 0] = 0
for u in unique:
candidate = np.where(l == u)[0]
candidates.append(candidate)
#---
#<todo>
#fix angle discontinunity problem here ...
#-----
#sort
count = np.array([len(candidate) for candidate in candidates])
sort = np.argsort(-count)
candidates = [candidates[s] for s in sort]
#show
max_label = 1
label = np.zeros(N, np.int32)
count = np.zeros(N, np.int32)
for candidate in candidates:
n = candidate
L = len(n)
#print(L)
#---- filtering (secret sauce) ----------
#if L<3: continue
n = n[np.argsort(np.fabs(z[n]))]
layer_id0 = layer_id[n[:-1]]
layer_id1 = layer_id[n[1:]]
ld = layer_id1 - layer_id0
if np.any(ld > 2): continue
m = count[n].max()
if L < m: continue
#---- filtering ----------------------
count[n] = L
label[n] = max_label
max_label += 1
## show:
if L >= 3:
#c = np.random.uniform(0,1,3)#[0,0,0]
c = [0, 0, 0]
#ax3d1.clear()
#plot_particles(ax3d1, particle_ids, p, a,r,zr, z)
#ax3d1.scatter(ar, r, zr, c=color, s=64, edgecolors='none')
ax3d1.plot(a[n],
r[n],
z[n],
'.-',
color=c,
markersize=5,
linewidth=1)
#ax3d1.plot(a[[n[0],n[-1]]],r[[n[0],n[-1]]],zr[[n[0],n[-1]]],'-', color=[1,0,0], markersize=5, linewidth=1)
#plt.pause(0.01)
#plt.waitforbuttonpress(-1)
#plt.show()
##-###################################################################################3
submission = pd.DataFrame(columns=['event_id', 'hit_id', 'track_id'],
data=np.column_stack(([
int(event_id),
] * len(df), df.hit_id.values,
label))).astype(int)
score1 = score_event(df, submission)
score2, results = cpmp_fast_score(df, submission)
#print results
max_score = df.weight.sum()
print('max_score = df.weight.sum() = %0.5f' % max_score)
print('score1= %0.5f (%0.5f)' % (score1 * max_score, score1))
print('score2= %0.5f (%0.5f)' % (score2, score2 / max_score))
plt.show()
print('end')
exit(0)
def study_dbscan_for_tracklet_seeding():
## load an event ---
event_id = '000001029'
path_to_train = "data/train_1"
particles = pd.read_csv(path_to_train +
'/event%s-particles.csv' % event_id)
hits = pd.read_csv(path_to_train + '/event%s-hits.csv' % event_id)
truth = pd.read_csv(path_to_train + '/event%s-truth.csv' % event_id)
#cells = pd.read_csv(path_to_train + '/event%s-cells.csv'%event_id)
truth = truth.merge(hits, on=['hit_id'], how='left')
truth = truth.merge(particles, on=['particle_id'], how='left')
#--------------------------------------------------------
df = truth.copy()
df = df.assign(r=np.sqrt(df.x**2 + df.y**2))
df = df.assign(d=np.sqrt(df.x**2 + df.y**2 + df.z**2))
df = df.assign(a=np.arctan2(df.y, df.x))
df = df.assign(cosa=np.cos(df.a))
df = df.assign(sina=np.sin(df.a))
df = df.assign(phi=np.arctan2(df.z, df.r))
df = df.assign(momentum=np.sqrt(df.px**2 + df.py**2 + df.pz**2))
df.loc[df.particle_id == 0, 'momentum'] = 0
df = df.loc[df.z > 500] # consider dataset subset
df = df.loc[df.r < 50]
N = len(df)
#-------------------------------------------------------
momentum = df[['momentum']].values.astype(np.float32)
p = df[['particle_id']].values.astype(np.int64)
x, y, z, r, a, cosa, sina, phi = df[[
'x', 'y', 'z', 'r', 'a', 'cosa', 'sina', 'phi'
]].values.astype(np.float32).T
particle_ids = np.unique(p)
particle_ids = particle_ids[particle_ids != 0]
num_particle_ids = len(particle_ids)
# do dbscan here =======================================
data = np.column_stack([a, z / r * 0.1])
_, l = dbscan(
data,
eps=0.01,
min_samples=1,
)
submission = pd.DataFrame(columns=['event_id', 'hit_id', 'track_id'],
data=np.column_stack(([
int(event_id),
] * len(df), df.hit_id.values, l))).astype(int)
#score1 = score_event(df, submission)
#print(df)
#print(submission)
score2, results = cpmp_fast_score(df, submission)
#print results
#max_score = df.weight.sum()
#print('max_score = df.weight.sum() = %0.5f'%max_score)
#print('score1= %0.5f (%0.5f)'%(score1*max_score,score1))
#print('score2= %0.5f (%0.5f)'%(score2,score2/max_score))
## analyse the results here =============================
d0, d1 = data.T
track_ids = np.unique(l)
track_ids = track_ids[track_ids != 0]
num_track_ids = len(track_ids)
fig = plt.figure(figsize=(8, 8))
ax = fig.add_subplot(111)
fig.patch.set_facecolor('white')
fig1 = plt.figure(figsize=(8, 8))
ax1 = fig1.add_subplot(111)
ax1 = Axes3D(fig1)
fig1.patch.set_facecolor('white')
def show_ax():
ax1.set_xlabel('a', fontsize=16)
ax1.set_ylabel('r', fontsize=16)
ax1.set_zlabel('z', fontsize=16)
ax.set_xlabel('a', fontsize=16)
ax.set_ylabel('z/r', fontsize=16)
# ax.grid()
# ax.set_aspect('equal', 'box')
plt.show()
## 0. show data:
if False:
ax.clear()
ax1.clear()
ax.plot(d0,
d1,
'.',
color=[0.75, 0.75, 0.75],
markersize=3,
linewidth=0)
ax1.plot(a,
r,
z,
'.',
color=[0.75, 0.75, 0.75],
markersize=3,
linewidth=0)
show_ax()
## 1. show GT:
if True:
ax.clear()
ax1.clear()
ax.plot(d0,
d1,
'.',
color=[0.75, 0.75, 0.75],
markersize=3,
linewidth=0)
ax1.plot(a,
r,
z,
'.',
color=[0.75, 0.75, 0.75],
markersize=3,
linewidth=0)
ax.set_title('Ground truth')
ax1.set_title('Ground truth')
ax1.set_xlabel('a', fontsize=16)
ax1.set_ylabel('r', fontsize=16)
ax1.set_zlabel('z', fontsize=16)
ax.set_xlabel('a', fontsize=16)
ax.set_ylabel('z/r', fontsize=16)
for n in range(0, num_particle_ids, 1):
particle_id = particle_ids[n]
t = np.where(p == particle_id)[0]
#if momentum[t[0]]<min_momentum: continue
t = t[np.argsort(np.fabs(z[t]))]
if np.fabs(a[t[0]] - a[t[-1]]) > 1: continue
d = ((x[t[0]] - x[t[-1]])**2 + (y[t[0]] - y[t[-1]])**2 +
(z[t[0]] - z[t[-1]])**2)**0.5
if d < 10: continue
###print(n, particle_id)
color = np.random.uniform(0, 1, (3))
#ax.clear()
#ax1.clear()
ax.plot(data[t, 0],
data[t, 1],
'.',
color=color,
markersize=5,
linewidth=0)
ax1.plot(a[t],
r[t],
z[t],
'.-',
color=color,
markersize=5,
linewidth=1)
#ax1.plot(a[h],r[h], z[h], 'o', color=[0,0,0], markersize=8, linewidth=1, mfc='none')
#ax1.view_init(0, (ax_n*3)%360)
#ax_n += 1
#fig1.savefig('/root/share/project/kaggle/cern/results/yy/%05d.png'%ax_n)
#plt.pause(0.01)
#plt.waitforbuttonpress(-1)
#show_ax()
## 2. show dbscan prediction:
if True:
fig_ = plt.figure(figsize=(8, 8))
ax_ = fig_.add_subplot(111, )
fig_.patch.set_facecolor('white')
fig1_ = plt.figure(figsize=(8, 8))
ax1_ = fig1_.add_subplot(111, projection='3d')
fig1_.patch.set_facecolor('white')
ax_.clear()
ax1_.clear()
ax_.plot(d0,
d1,
'.',
color=[0.75, 0.75, 0.75],
markersize=3,
linewidth=0)
ax1_.plot(a,
r,
z,
'.',
color=[0.75, 0.75, 0.75],
markersize=3,
linewidth=0)
ax.set_title('DBSCAN Prediction')
ax1.set_title('DBSCAN Prediction')
ax1_.set_xlabel('a', fontsize=16)
ax1_.set_ylabel('r', fontsize=16)
ax1_.set_zlabel('z', fontsize=16)
ax_.set_xlabel('a', fontsize=16)
ax_.set_ylabel('z/r', fontsize=16)
for n in range(0, num_track_ids, 1):
track_id = track_ids[n]
t = np.where(l == track_id)[0]
#if momentum[t[0]]<min_momentum: continue
t = t[np.argsort(np.fabs(z[t]))]
if np.fabs(a[t[0]] - a[t[-1]]) > 1: continue
d = ((x[t[0]] - x[t[-1]])**2 + (y[t[0]] - y[t[-1]])**2 +
(z[t[0]] - z[t[-1]])**2)**0.5
if d < 10: continue
###print(n, track_id)
color = np.random.uniform(0, 1, (3))
#ax.clear()
#ax1.clear()
ax_.plot(data[t, 0],
data[t, 1],
'.',
color=color,
markersize=5,
linewidth=0)
ax1_.plot(a[t],
r[t],
z[t],
'.-',
color=color,
markersize=5,
linewidth=1)
#ax1.plot(a[h],r[h], z[h], 'o', color=[0,0,0], markersize=8, linewidth=1, mfc='none')
#ax1.view_init(0, (ax_n*3)%360)
#ax_n += 1
#fig1.savefig('/root/share/project/kaggle/cern/results/yy/%05d.png'%ax_n)
#plt.pause(0.01)
#plt.waitforbuttonpress(-1)
#show_ax()
#plt.show()
################################################################################################
# analysis ...
## <to be updated> ...
results = results.assign(
detected=(results.count_both > results.count_particle)
& (results.count_both > results.count_track))
detected = results.loc[results.detected == True]
missed = results.loc[(results.detected == False) &
(results.count_track < results.count_particle * 0.5)]
fp = results.loc[(results.detected == False)
& (results.count_track > results.count_particle * 0.5)]
detected = np.unique(detected.particle_id.values)
missed = np.unique(missed.particle_id.values)
fp = np.unique(fp.track_id.values)
detected = detected[detected != 0]
missed = missed[missed != 0]
fp = fp[fp != 0]
num_detected = len(detected)
num_missed = len(missed)
num_fp = len(fp)
#shows detected tracks
for (p, q) in [(p, detected), (p, missed), (l, fp)]:
fig_ = plt.figure(figsize=(8, 8))
ax_ = fig_.add_subplot(111, )
fig_.patch.set_facecolor('white')
fig1_ = plt.figure(figsize=(8, 8))
ax1_ = fig1_.add_subplot(111, projection='3d')
fig1_.patch.set_facecolor('white')
ax_.clear()
ax1_.clear()
ax_.plot(d0,
d1,
'.',
color=[0.75, 0.75, 0.75],
markersize=3,
linewidth=0)
ax1_.plot(a,
r,
z,
'.',
color=[0.75, 0.75, 0.75],
markersize=3,
linewidth=0)
ax1_.set_xlabel('a', fontsize=16)
ax1_.set_ylabel('r', fontsize=16)
ax1_.set_zlabel('z', fontsize=16)
ax_.set_xlabel('a', fontsize=16)
ax_.set_ylabel('z/r', fontsize=16)
for n in range(0, len(q), 1):
t = np.where(p == q[n])[0]
#if momentum[t[0]]<min_momentum: continue
t = t[np.argsort(np.fabs(z[t]))]
if np.fabs(a[t[0]] - a[t[-1]]) > 1: continue
d = ((x[t[0]] - x[t[-1]])**2 + (y[t[0]] - y[t[-1]])**2 +
(z[t[0]] - z[t[-1]])**2)**0.5
if d < 10: continue
##print(n, track_id)
color = np.random.uniform(0, 1, (3))
#ax.clear()
#ax1.clear()
ax_.plot(data[t, 0],
data[t, 1],
'.',
color=color,
markersize=5,
linewidth=0)
ax1_.plot(a[t],
r[t],
z[t],
'.-',
color=color,
markersize=5,
linewidth=1)
#plt.pause(0.01)
plt.show()
zz = 0
exit(0)
def make_data(
a,
zr,
z,
my_layer_id,
p,
# a_limit=(1.0,3.0), zr_limit=(4.0,7.0),
a_limit=(1.0, 2.0),
zr_limit=(4.0, 5.0),
depth=6):
a0, a1 = a_limit
zr0, zr1 = zr_limit
idx = np.where((a >= a0) & (a < a1) & (zr >= zr0) & (zr < zr1))[0]
aa, zzr, zz = a[idx], zr[idx], z[idx] / 1000
ll = my_layer_id[idx]
pp = p[idx]
data3 = np.column_stack((aa, zzr, zz))
L = len(data3)
pairs = []
for d in range(depth - 1):
i0 = np.where(ll == d)[0]
i1 = np.where(ll == d + 1)[0]
L0 = len(i0)
L1 = len(i1)
if L0 == 0: continue
if L1 == 0: continue
q0 = data3[i0]
q1 = data3[i1]
qq0 = np.repeat(q0.reshape(L0, 1, 3), L1, axis=1).reshape(-1, 3)
qq1 = np.repeat(q1.reshape(1, L1, 3), L0, axis=0).reshape(-1, 3)
ii0 = np.repeat(i0.reshape(L0, 1), L1, axis=1).reshape(-1, 1)
ii1 = np.repeat(i1.reshape(1, L1), L0, axis=0).reshape(-1, 1)
unit = qq1 - qq0
unit = unit / np.sqrt((unit**2).sum(1, keepdims=True))
ii = np.zeros((L0 * L1, 1), np.int32)
pair = np.concatenate((ii0, ii1, ii, qq0, qq1, unit), 1)
pairs.append(pair)
P = len(pairs)
M = 0
for p in pairs:
dM = len(p)
p[:, 2] = np.arange(M, M + dM)
M += dM
distance = np.full((M, M), 100, np.float32) #INF
for d in range(P - 1):
for a in pairs[d]:
ai0, ai1, ai = a[:3].astype(np.int32)
ap, aq, aunit = np.split(a[3:], 3)
if ((np.fabs(aunit[0]) > 0.25) | (np.fabs(aunit[1]) > 0.25)):
continue
b = pairs[d + 1]
i = (np.where((b[:, 0] == ai1)))[0]
bi = (b[:, 2][i]).astype(np.int32)
dis = np.sqrt(((b[:, -3:][i] - aunit)**2).sum(1))
distance[ai, bi] = dis
print('dbscan')
_, l = dbscan(distance, eps=0.080, min_samples=1, metric='precomputed')
cluster_id = np.unique(l + 1)
#cluster_id = cluster_id[cluster_id!=0]
num_cluster_id = len(cluster_id)
## draw clustering results -----------------------------------
print('draw clustering results')
pairs_flat = np.vstack(pairs)
AX3d1.clear()
AX3d1.scatter(aa,
zzr,
zz,
c=plt.cm.gnuplot(ll / depth),
s=16,
edgecolors='none')
plot3d_particles(AX3d1,
aa,
zzr,
zz,
zz,
pp,
subsample=1,
color=[0, 0, 0],
linewidth=4)
for id in cluster_id:
#AX3d1.clear()
#AX3d1.scatter(aa, zzr, zz, c=plt.cm.gnuplot( ll/depth ), s=16, edgecolors='none')
t = np.where(l == id)
t0 = pairs_flat[t, 0].astype(np.int32).reshape(-1)
t1 = pairs_flat[t, 1].astype(np.int32).reshape(-1)
t = np.unique(np.concatenate((t0, t1)))
#if len(t0)<3: continue
color = np.random.uniform(0, 1, 3)
#AX3d1.plot(aa[t0], zzr[t0], zz[t0],'.-', color=color, markersize=15) #edgecolors=
#AX3d1.plot(aa[t1], zzr[t1], zz[t1],'.-', color=color, markersize=15)
AX3d1.plot(aa[t], zzr[t], zz[t], '.-', color=color, markersize=15)
#plt.pause(0.01)
#plt.waitforbuttonpress(-1)
plt.show()
return 0
def test_dbscan_badargs(args):
# Test bad argument values: these should all raise ValueErrors
with pytest.raises(ValueError):
dbscan(X, **args)
def predict(self, dfh):
print("size(dfh): {0}".format(len(dfh)))
if ("rt" not in dfh.columns):
dfh["rt"] = np.sqrt(dfh['x'].values**2 + dfh['y'].values**2)
z = dfh['z'].values
rt = dfh["rt"].values
a0 = np.arctan2(dfh['y'].values, dfh['x'].values)
layer_id = dfh['layer_id'].values.astype(np.float32)
sys.stderr.write("dbscan for each (z,a) shifting\n")
scan_labels = []
for (dj, di) in tqdm(product(self.djs, self.dis),
total=len(self.djs) * len(self.dis)):
ar = a0 + di * rt
zr = (z + dj) / rt * 0.1
data2 = np.column_stack([ar, zr])
_, scan_label = dbscan(
data2,
eps=0.0025,
min_samples=1,
)
scan_labels.append(scan_label)
sys.stderr.write("make candidates\n")
candidates = []
for scan_label in tqdm(scan_labels):
l = scan_label
unique = np.unique(l)
for u in unique:
candidate = np.where(l == u)[0]
candidates.append(candidate)
print("# of candidates : {0}".format(len(candidates)))
count = np.array([len(candidate) for candidate in candidates])
sort = np.argsort(-count)
candidates = [candidates[s] for s in sort]
max_label = 1
N = len(dfh)
label = np.zeros(N, np.int32)
count = np.zeros(N, np.int32)
sys.stderr.write("calculate clustering label from candidates\n")
for candidate in tqdm(candidates):
n = candidate
L = len(n)
n = n[np.argsort(np.fabs(z[n]))]
layer_id0 = layer_id[n[:-1]]
layer_id1 = layer_id[n[1:]]
ld = layer_id1 - layer_id0
if np.any(ld > 2): continue
m = count[n].max()
if L < m: continue
count[n] = L
label[n] = max_label
max_label += 1
return label
