/
test_event_4v.py
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/
test_event_4v.py
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import click
import numpy as np
import logging
import pickle
from sklearn.preprocessing import RobustScaler
from sklearn.utils import check_random_state
from recnn.recnn import event_baseline_predict
logging.basicConfig(level=logging.INFO,
format="[%(asctime)s %(levelname)s] %(message)s")
@click.command()
@click.argument("filename_train")
@click.argument("filename_test")
@click.argument("filename_model")
@click.argument("n_events_train")
@click.argument("n_events_test")
@click.argument("filename_output")
@click.option("--n_particles_per_event", default=10)
@click.option("--random_state", default=1)
def test(filename_train,
filename_test,
filename_model,
n_events_train,
n_events_test,
filename_output,
n_particles_per_event=10,
random_state=1):
# Initialization
n_events_train = int(n_events_train)
n_events_test = int(n_events_test)
logging.info("Calling with...")
logging.info("\tfilename_train = %s" % filename_train)
logging.info("\tfilename_test = %s" % filename_test)
logging.info("\tfilename_model = %s" % filename_model)
logging.info("\tn_events_train = %d" % n_events_train)
logging.info("\tn_events_test = %d" % n_events_test)
logging.info("\tfilename_output = %s" % filename_output)
logging.info("\tn_particles_per_event = %d" % n_particles_per_event)
logging.info("\trandom_state = %d" % random_state)
rng = check_random_state(random_state)
# Make data
logging.info("Loading train data + preprocessing...")
fd = open(filename_train, "rb")
X = []
y = []
for i in range(n_events_train):
v_i, y_i = pickle.load(fd)
v_i = v_i[:n_particles_per_event]
X.append(v_i)
y.append(y_i)
y = np.array(y)
fd.close()
logging.info("\tfilename = %s" % filename_train)
logging.info("\tX size = %d" % len(X))
logging.info("\ty size = %d" % len(y))
# Building scalers
logging.info("Building scalers...")
tf_features = RobustScaler().fit(
np.vstack([features for features in X]))
X = None
y = None
# Loading test data
logging.info("Loading test data + preprocessing...")
fd = open(filename_test, "rb")
X = []
y = []
for i in range(n_events_test):
v_i, y_i = pickle.load(fd)
v_i = v_i[:n_particles_per_event]
X.append(v_i)
y.append(y_i)
y = np.array(y)
fd.close()
logging.info("\tfilename = %s" % filename_train)
logging.info("\tX size = %d" % len(X))
logging.info("\ty size = %d" % len(y))
# Scaling
logging.info("Scaling...")
for i in range(len(X)):
X[i] = tf_features.transform(X[i])
if len(X[i]) < n_particles_per_event:
X[i] = np.vstack([X[i],
np.zeros((n_particles_per_event - len(X[i]), 4))])
# Testing
logging.info("Testing...")
predict = event_baseline_predict
fd = open(filename_model, "rb")
params = pickle.load(fd)
fd.close()
all_y_pred = []
for start in range(0, len(y), 1000):
y_pred = predict(params, X[start:start+1000],
n_particles_per_event=n_particles_per_event)
all_y_pred.append(y_pred)
y_pred = np.concatenate(all_y_pred)
# Save
output = np.hstack((y.reshape(-1, 1),
y_pred.reshape(-1, 1)))
fd = open(filename_output, "wb")
pickle.dump(output, fd, protocol=2)
fd.close()
if __name__ == "__main__":
test()