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seeding.py
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seeding.py
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import os
#os.environ['CUDA_VISIBLE_DEVICES'] = '0' #'3,2' #'3,2,1,0'
## other library ---
from trackml_utils import score_event_fast
from sklearn.cluster.dbscan_ import dbscan
from sklearn.preprocessing import StandardScaler
from sklearn.manifold import TSNE
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
##--------------------------------------------------------
def cpmp_fast_score(truth, submission):
truth = truth[['hit_id', 'particle_id', 'weight']].merge(submission, how='left', on='hit_id')
df = truth.groupby(['track_id', 'particle_id']).hit_id.count().to_frame('count_both').reset_index()
truth = truth.merge(df, how='left', on=['track_id', 'particle_id'])
df1 = df.groupby(['particle_id']).count_both.sum().to_frame('count_particle').reset_index()
truth = truth.merge(df1, how='left', on='particle_id')
df1 = df.groupby(['track_id']).count_both.sum().to_frame('count_track').reset_index()
truth = truth.merge(df1, how='left', on='track_id')
truth.count_both *= 2
score = truth[(truth.count_both > truth.count_particle) & (truth.count_both > truth.count_track)].weight.sum()
results = truth
return score, results
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)
## kdtree = KDTree(data)
## shows nearest neighours
# <todo> ....
# main #################################################################
if __name__ == '__main__':
print( '%s: calling main function ... ' % os.path.basename(__file__))
study_dbscan_for_tracklet_seeding()
print('\nsucess!')
# ffmpeg -f image2 -pattern_type glob -r 33 -i "iterations/*.png" -c:v libx264 iterations.mp4
# convert *.png animated.gif
#