def run(filename):
model = models.UnrollingHelices(use_outlier=False,
dbscan_features = ["sina1", "cosa1", "z1", "x1", "x2", "x_y", "x_rt", "y_rt"],
dbscan_weight = [1.0, 1.0, 0.75, 0.5, 0.5, 0.2, 0.2, 0.2])
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_x1, w_x2, w_x_y, w_xy_rt, niter):
model.dbscan_weight[0] = w_a1
model.dbscan_weight[1] = w_a1
model.dbscan_weight[2] = w_z1
model.dbscan_weight[3] = w_x1
model.dbscan_weight[4] = w_x2
model.dbscan_weight[5] = w_x_y
model.dbscan_weight[6] = w_xy_rt
model.dbscan_weight[7] = w_xy_rt
model.iter_size_helix = 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, 0.8),
"w3": (0.1, 0.6),
"w4": (0.1, 0.6),
"w5": (0.1, 0.6),
"w6": (0.1, 0.6),
"niter": (140, 190)}, #(140, 190)
verbose = True)
opt.maximize(init_points = 3,
n_iter = 20,
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)
import sys
import models
import train
if __name__ == "__main__":
model = models.UnrollingHelices(use_outlier=False, iter_size_helix=100)
train.run(model, "train_1", sys.argv[0].split(".")[0], 1)
import os
import sys
import models
import train
if __name__ == "__main__":
model = models.UnrollingHelices(
use_outlier=False,
iter_size_helix=100,
dbscan_features=[
"sina1", "cosa1", "z1", "x1", "x2", "x_y", "x_rt", "y_rt"
],
dbscan_weight=[1.0, 1.0, 0.75, 0.5, 0.5, 0.1, 0.1, 0.1])
train.run(model,
"train_1",
path_to_out=os.path.join("out", sys.argv[0].split(".")[0]),
1)
import os
import sys
import models
import train
if __name__ == "__main__":
model = models.UnrollingHelices(
use_outlier=False,
iter_size_helix=100,
dz0=-0.00070,
stepdz=+0.00001,
dbscan_features=["sina1", "cosa1", "z1", "x1", "x2"],
dbscan_weight=[1.0, 1.0, 0.75, 0.5, 0.5])
train.run(model,
"train_1",
path_to_out=os.path.join("out", sys.argv[0].split(".")[0]),
1)