def run(filename):
w_a1 = 0.90291
w_xy_rt = 0.010809
w_z1 = 0.357996
w_z2 = 0.229602
c_rt1 = 1.330075
c_rt2 = 1.92522
model = models.UnrollingHelicesRt2(
dbscan_features=["sina1", "cosa1", "z1", "z2", "x_rt", "y_rt"],
dbscan_weight=[w_a1, w_a1, w_z1, w_z2, w_xy_rt, w_xy_rt])
model.coef_rt1 = c_rt1
model.coef_rt2 = c_rt2
model.niter = 150
path_to_input = os.path.join(path_to_trackml, "test")
dataset_submission = []
for event_id, hits in load_dataset(path_to_input, parts=["hits"]):
labels = model.predict(hits)
one_submission = create_one_event_submission(event_id, hits, labels)
dataset_submission.append(one_submission)
submission = pd.concat(dataset_submission)
submission.to_csv(filename, index=None)
def run(filename):
model = models.UnrollingHelicesRt2(
dbscan_features=["sina1", "cosa1", "z1", "z2", "x_rt", "y_rt"],
dbscan_weight=[1.0, 1.0, 0.75, 0.2, 0.05, 0.05])
model.niter = 150
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):
def Fun4BO(w_a1, w_z1, w_z2, w_xy_rt, c_rt1, c_rt2):
model.dbscan_weight[0] = w_a1
model.dbscan_weight[1] = w_a1
model.dbscan_weight[2] = w_z1
model.dbscan_weight[3] = w_z2
model.dbscan_weight[4] = w_xy_rt
model.dbscan_weight[5] = w_xy_rt
model.coef_rt1 = c_rt1
model.coef_rt2 = c_rt2
labels = model.predict(hits)
one_submission = create_one_event_submission(
event_id, hits, labels)
score = score_event(truth, one_submission)
return score
print("Bayesian Optimization")
opt = BayesianOptimization(Fun4BO, {
"w_a1": (0.9, 1.2),
"w_z1": (0.3, 0.7),
"w_z2": (0.1, 0.4),
"w_xy_rt": (0.01, 0.2),
"c_rt1": (0.5, 1.5),
"c_rt2": (0.1, 5.0)
},
verbose=True)
opt.maximize(
init_points=3,
n_iter=100, #
acq="ucb",
kappa=2.576)
# [string]
labels = opt.res["max"]["max_params"].keys()
# [dict(string, [float])]
params = opt.res["all"]["params"]
len_params = len(params)
data_dic = {}
for label in labels:
val = [opt.res["max"]["max_params"][label]]
for i in range(len_params):
val.append(params[i][label])
data_dic[label] = val
data_dic["value"] = [opt.res["max"]["max_val"]
] + opt.res["all"]["values"]
data_dic["label"] = ["max"] + [str(x) for x in range(len_params)]
df = pd.DataFrame(data_dic)
df.to_csv(filename, index=None)
def run(filename):
model = models.UnrollingHelicesRt2()
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):
def Fun4BO(w1, w2, w3, niter):
model.dbscan_weight[0] = w1
model.dbscan_weight[1] = w1
model.dbscan_weight[2] = w2
model.dbscan_weight[3] = w3
model.niter = int(niter)
labels = model.predict(hits)
one_submission = create_one_event_submission(event_id, hits, labels)
score = score_event(truth, one_submission)
return score
print("Bayesian Optimization")
opt = BayesianOptimization(Fun4BO,
{"w1": (0.9, 1.2),
"w2": (0.3, 0.7),
"w3": (0.1, 0.4),
"niter": (140, 190)}, #(140, 190)
verbose = True)
opt.maximize(init_points = 3,
n_iter = 100, #
acq = "ucb",
kappa = 2.576)
# [string]
labels = opt.res["max"]["max_params"].keys()
# [dict(string, [float])]
params = opt.res["all"]["params"]
len_params = len(params)
data_dic = {}
for label in labels:
val = [opt.res["max"]["max_params"][label]]
for i in range(len_params):
val.append(params[i][label])
data_dic[label] = val
data_dic["value"] = [opt.res["max"]["max_val"]] + opt.res["all"]["values"]
data_dic["label"] = ["max"] + [str(x) for x in range(len_params)]
df = pd.DataFrame(data_dic)
df.to_csv(filename, label=None)
def run(filename):
model = models.UnrollingHelicesRt2(
dbscan_features=["sina1", "cosa1", "z1", "z2", "x_rt", "y_rt"],
dbscan_weight=[1.0, 1.0, 0.75, 0.2, 0.05, 0.05])
model.niter = 150
nevents = 1
niter_bo = 40
path_to_input = os.path.join(path_to_trackml, "train_1")
event_id_list = []
hits_list = []
truth_list = []
for event_id, hits, truth in load_dataset(path_to_input,
parts=["hits", "truth"],
nevents=nevents):
event_id_list.append(event_id)
hits_list.append(hits)
truth_list.append(truth)
def Fun4BO(w_a1, w_z1, w_z2, w_xy_rt, eps0):
model.dbscan_weight[0] = w_a1
model.dbscan_weight[1] = w_a1
model.dbscan_weight[2] = w_z1
model.dbscan_weight[3] = w_z2
model.dbscan_weight[4] = w_xy_rt
model.dbscan_weight[5] = w_xy_rt
model.eps0 = eps0
score_list = []
for (hits, truth) in zip(hits_list, truth_list):
labels = model.predict(hits)
one_submission = create_one_event_submission(
event_id, hits, labels)
score = score_event(truth, one_submission)
score_list.append(score)
return np.sum(score_list) / len(score_list)
print("Bayesian Optimization")
opt = BayesianOptimization(Fun4BO, {
"w_a1": (0.7, 1.2),
"w_z1": (0.3, 0.7),
"w_z2": (0.1, 0.4),
"w_xy_rt": (0.002, 0.2),
"eps0": (0.001, 0.01)
},
verbose=True)
opt.maximize(
init_points=3,
n_iter=niter_bo, #
acq="ucb",
kappa=2.576)
# [string]
labels = opt.res["max"]["max_params"].keys()
# [dict(string, [float])]
params = opt.res["all"]["params"]
len_params = len(params)
data_dic = {}
for label in labels:
val = [opt.res["max"]["max_params"][label]]
for i in range(len_params):
val.append(params[i][label])
data_dic[label] = val
data_dic["value"] = [opt.res["max"]["max_val"]] + opt.res["all"]["values"]
data_dic["label"] = ["max"] + [str(x) for x in range(len_params)]
df = pd.DataFrame(data_dic)
df.to_csv(filename, index=None)