def run():
print(datetime.datetime.now(), sys.argv[0], " begin")
submission = pd.read_csv("01_merge.submission.csv")
model = extension.RemoveOutliersByQuadric()
path_to_input = os.path.join(path_to_trackml, "train_1")
for event_id, hits, truth in load_dataset(path_to_input,
parts=["hits", "truth"],
skip=0,
nevents=1):
submission = extension.extend(submission, hits)
score = score_event(truth, submission)
print("step1, score: %0.5f" % (score))
submission = extension.extend(submission, hits)
score = score_event(truth, submission)
print("step2, score: %0.5f" % (score))
submission = extension.extend(submission, hits)
score = score_event(truth, submission)
print("step3, score: %0.5f" % (score))
submission = extension.extend(submission, hits)
score = score_event(truth, submission)
print("step4, score: %0.5f" % (score))
submission = model.run(submission, hits)
score = score_event(truth, submission)
print("step5, score: %0.5f" % (score))
print(datetime.datetime.now(), sys.argv[0], " end")
def run():
max_istep = 4
full_submission_list = [
pd.read_csv("step{0}.submission.csv".format(istep))
for istep in range(max_istep + 1)
]
good_submission_list = [
pd.read_csv("step{0}_good.submission.csv".format(istep))
for istep in range(max_istep)
]
path_to_input = os.path.join(path_to_trackml, "train_1")
for event_id, hits, truth in load_dataset(path_to_input,
parts=["hits", "truth"],
skip=0,
nevents=1):
for mi in range(max_istep + 1):
submission_list = [full_submission_list[mi]] + [
good_submission_list[i] for i in range(mi)
]
submission = pd.concat(submission_list)
score0 = score_event(truth, submission)
print("step = {0}, score = {1}".format(mi, score0))
if (mi == max_istep):
for i in range(5):
submission = extension.extend(submission, hits)
score0 = score_event(truth, submission)
print("with extension = {0}, score = {1}".format(
i + 1, score0))
def run():
f = open("08.log", "w")
f.write("extention of 07 results.\n")
path_to_input = os.path.join(path_to_trackml, "test")
old_submission = pd.read_csv("07_test_UHBO_submission.csv")
sys.stderr.write("load data\n")
for event_id, hits in load_dataset(path_to_input, parts=["hits"]):
submission = extension.extend(old_submission, hits)
submission.to_csv("08_ext07_submission.csv", index=None)
f.close()
def run():
path_to_input = os.path.join(path_to_trackml, "train_1")
nevents = 1
old_submission = pd.read_csv("02.csv")
sys.stderr.write("load data\n")
for event_id, hits, truth in load_dataset(path_to_input,
parts=["hits", "truth"],
skip=0,
nevents=nevents):
submission = extension.extend(old_submission, hits)
submission.to_csv("09.csv", index=None)
score = score_event(truth, submission)
print("")
print("score: %0.5f" % (score))
def run():
print(datetime.datetime.now(), sys.argv[0], " begin")
submission = pd.read_csv("01_merge.submission.csv")
path_to_input = os.path.join(path_to_trackml, "train_1")
for event_id, hits, truth in load_dataset(path_to_input,
parts=["hits", "truth"],
skip=0,
nevents=1):
for i in range(5):
submission = extension.extend(submission, hits)
score = score_event(truth, submission)
print("step%d, score: %0.5f" % (i + 1, score))
print(datetime.datetime.now(), sys.argv[0], " end")
def run():
f = open("10.log", "w")
f.write("extention many times\n")
path_to_input = os.path.join(path_to_trackml, "train_1")
nevents = 1
submission = pd.read_csv("09.csv")
sys.stderr.write("load data\n")
for event_id, hits, truth in load_dataset(path_to_input,
parts=["hits", "truth"],
skip=0,
nevents=nevents):
for i in range(5):
submission = extension.extend(submission, hits)
submission.to_csv("10_{0}.csv".format(i), index=None)
score = score_event(truth, submission)
f.write("i={0} score={1}\n".format(i, score))
def calc_one(istep, submission0, event_id, hits, output_dir, mk_cand, merger, th_len, num_extend):
candidates_dir = os.path.join(output_dir, "candidates{0}".format(istep))
mk_cand.output_dir = candidates_dir
merger.candidates_output_dir = candidates_dir
if(submission0 is None):
hits0 = hits
else:
hits0 = submission0.merge(hits, on="hit_id")[hits.columns]
# -- make candidate --
print(datetime.datetime.now(), "step{0} make_candidates".format(istep))
mk_cand.run(event_id, hits0)
# -- merge --
print(datetime.datetime.now(), "step{0} merge".format(istep))
submission = merger.run(event_id, hits0)
# -- extend --
print(datetime.datetime.now(), "step{0} extend {1} times".format(istep, num_extend))
for i in range(num_extend):
submission = extend(submission, hits0)
# -- separate outlier --
print(datetime.datetime.now(), "step{0} compute outlier".format(istep, num_extend))
tscore = score_track.score_by_length(submission, hits)
outlier_mask = tscore < th_len
submission_outlier = submission[outlier_mask]
submission_good = submission[~outlier_mask]
print("# of outlier: ", len(submission_outlier))
print("# of good: ", len(submission_good))
# -- save --
submission_outlier.to_csv(
os.path.join(output_dir,
"step{0}_outlier.submission.csv".format(istep)), index=None)
submission_good.to_csv(
os.path.join(output_dir,
"step{0}_good.submission.csv".format(istep)), index=None)
submission.to_csv(
os.path.join(output_dir,
"step{0}.submission.csv".format(istep)), index=None)
return (submission_good, submission_outlier)
def fit(dfh, y=None):
dfh["s1"] = dfh.hit_id
dfh["N1"] = 1
dfh['r'] = np.sqrt(dfh['x'].values**2 + dfh['y'].values**2 +
dfh['z'].values**2)
dfh['rt'] = np.sqrt(dfh['x'].values**2 + dfh['y'].values**2)
dfh['a0'] = np.arctan2(dfh['y'].values, dfh['x'].values)
mm = 1
for ii in tqdm(range(self.niter), total=self.niter):
# unroll helices
mm = mm * (-1)
dfh['z1'] = (dfh['z'].values + dj) / dfh['rt'].values
dfh['z2'] = (dfh['z'].values + dj) / dfh['r'].values
dfh["a1"] = dfh.a0 + mm * (self.coef_rt * dfh.rt.values
) / 1000.0 * (ii / 2) / 180.0 * np.pi
dfh["sina1"] = np.sin(dfh["a1"].values)
dfh["cosa1"] = np.cos(dfh["a1"].values)
# scaling
ss = StandardScaler()
dfs = ss.fit_transform(dfh[self.features].values)
dfs[:, :] = dfs[:, :] * self.weight[np.newaxis, :]
# clustering
res = DBSCAN(eps=self.eps0,
min_samples=1,
metric='euclidean',
n_jobs=4).fit(dfs).labels_
dfh["s2"] = res
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"]
submission = create_one_event_submission(0, dfh, labels)
for i in range(self.nextend):
submission = extend(submission, dfh)
self.submission = submission
return self
