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train_event_4v.py
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train_event_4v.py
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import autograd as ag
import click
import copy
import numpy as np
import logging
import pickle
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_auc_score
from sklearn.preprocessing import RobustScaler
from sklearn.utils import check_random_state
from recnn.recnn import log_loss
from recnn.recnn import adam
from recnn.recnn import event_baseline_init
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_model")
@click.argument("n_events")
@click.option("--n_features_rnn", default=4)
@click.option("--n_hidden_rnn", default=40)
@click.option("--n_epochs", default=20)
@click.option("--batch_size", default=64)
@click.option("--step_size", default=0.0005)
@click.option("--decay", default=0.9)
@click.option("--n_particles_per_event", default=10)
@click.option("--random_state", default=1)
def train(filename_train,
filename_model,
n_events,
n_features_rnn=4,
n_hidden_rnn=40,
n_epochs=5,
batch_size=64,
step_size=0.01,
decay=0.7,
n_particles_per_event=10,
random_state=1):
# Initialization
n_events = int(n_events)
logging.info("Calling with...")
logging.info("\tfilename_train = %s" % filename_train)
logging.info("\tfilename_model = %s" % filename_model)
logging.info("\tn_events = %d" % n_events)
logging.info("\tn_features_rnn = %d" % n_features_rnn)
logging.info("\tn_hidden_rnn = %d" % n_hidden_rnn)
logging.info("\tn_epochs = %d" % n_epochs)
logging.info("\tbatch_size = %d" % batch_size)
logging.info("\tstep_size = %f" % step_size)
logging.info("\tdecay = %f" % decay)
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 data + preprocessing...")
fd = open(filename_train, "rb")
# training file is assumed to be formatted a sequence of pickled pairs
# (e_i, y_i), where e_i is a list of (phi, eta, pt, mass) tuples.
X = []
y = []
for i in range(n_events):
v_i, y_i = pickle.load(fd)
v_i = v_i[:n_particles_per_event] # truncate to the top particles
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))
# Preprocessing
logging.info("Preprocessing...")
tf_features = RobustScaler().fit(
np.vstack([features for features in X]))
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))])
# Split into train+test
logging.info("Splitting into train and validation...")
X_train, X_valid, y_train, y_valid = train_test_split(X, y,
test_size=1000,
stratify=y,
random_state=rng)
# Training
logging.info("Training...")
predict = event_baseline_predict
init = event_baseline_init
trained_params = init(n_features_rnn, n_hidden_rnn,
random_state=rng)
n_batches = int(np.ceil(len(X_train) / batch_size))
best_score = [-np.inf] # yuck, but works
best_params = [trained_params]
def loss(X, y, params):
y_pred = predict(params, X,
n_particles_per_event=n_particles_per_event)
l = log_loss(y, y_pred).mean()
return l
def objective(params, iteration):
rng = check_random_state(iteration)
start = rng.randint(len(X_train) - batch_size)
idx = slice(start, start+batch_size)
return loss(X_train[idx], y_train[idx], params)
def callback(params, iteration, gradient):
if iteration % 25 == 0:
roc_auc = roc_auc_score(y_valid,
predict(params, X_valid,
n_particles_per_event=n_particles_per_event))
if roc_auc > best_score[0]:
best_score[0] = roc_auc
best_params[0] = copy.deepcopy(params)
fd = open(filename_model, "wb")
pickle.dump(best_params[0], fd)
fd.close()
logging.info(
"%5d\t~loss(train)=%.4f\tloss(valid)=%.4f"
"\troc_auc(valid)=%.4f\tbest_roc_auc(valid)=%.4f" % (
iteration,
loss(X_train[:5000], y_train[:5000], params),
loss(X_valid, y_valid, params),
roc_auc,
best_score[0]))
for i in range(n_epochs):
logging.info("epoch = %d" % i)
logging.info("step_size = %.4f" % step_size)
trained_params = adam(ag.grad(objective),
trained_params,
step_size=step_size,
num_iters=1 * n_batches,
callback=callback)
step_size = step_size * decay
if __name__ == "__main__":
train()