def run(model, exp, terms, save_freq=5, data=None):
exp_dir = ds.get_path_to_dataset(pb.Experiment.Dataset.Name(exp.dataset))
save_dir = os.path.join(exp_dir, exp.description)
##
# Load data from .npz archive created by invoking
# deep_learning/utils/archive.py
##
if data:
x_train, y_train, x_test, y_test = data
x_train, x_test = tr.transform(x_train, x_test)
else:
h_file, (x_train, y_train, x_test, y_test) = ds.load_dataset(
pb.Experiment.Dataset.Name(exp.dataset),
exp.coordinates + '/transformed')
data = x_train, y_train, x_test, y_test
exp_file_name = exp.description + '.exp'
# Start training
train_length = x_train.shape[0]
num_batches = int(ceil(train_length / exp.batch_size))
valid = Validator(exp, terms)
eTimes = np.array([])
valid._clock = clock()
model.summary()
while valid.check():
t = clock()
if valid._num_epochs:
print("Epoch {}/{}".format(valid.epochs + 1, valid._num_epochs))
else:
print("Epoch {}".format(valid.epochs + 1))
bETA = 0
bTimes = np.array([])
#print("\t Training: ")
for b in xrange(num_batches):
bt = clock()
# Update progress bar
progress(b, num_batches, exp.batch_size, bETA)
# Train on a batch
x_batch = x_train[b * exp.batch_size:b * exp.batch_size +
exp.batch_size, :]
y_batch = y_train[b * exp.batch_size:b * exp.batch_size +
exp.batch_size, :]
model.train_on_batch(x_batch, y_batch)
bTimes = np.append(bTimes, clock() - bt)
bETA = np.median(bTimes) * (num_batches - b - 1)
# Finish progress bar
progress(num_batches,
num_batches,
exp.batch_size,
0,
end='\n',
time=clock() - t)
# Calculate stats and add the epoch results to the experiment object
epoch = exp.results.add()
timer = clock()
print("Evaluating Train")
epoch.train_loss, epoch.train_accuracy = model.evaluate_generator(
((x_train[i * exp.batch_size:(i + 1) * exp.batch_size],
y_train[i * exp.batch_size:(i + 1) * exp.batch_size])
for i in xrange(num_batches)),
num_batches,
max_q_size=min((num_batches // 2, 10)))
#print("Finished {:.2f}s".format(clock()-timer))
timer = clock()
print("Evaluating Test")
epoch.test_loss, epoch.test_accuracy = model.evaluate_generator(
((x_test[i * exp.batch_size:(i + 1) * exp.batch_size],
y_test[i * exp.batch_size:(i + 1) * exp.batch_size])
for i in xrange(int(ceil(x_test.shape[0] / exp.batch_size)))),
int(ceil(x_test.shape[0] / exp.batch_size)),
max_q_size=min(
(int(ceil(x_test.shape[0] / exp.batch_size)) // 2, 10)))
#print("Finished {:.2f}s".format(clock() - timer))
timer = clock()
print("Calculating Sig")
epoch.s_b = st.significance(model, data)
#print("Finished {:.2f}".format(clock() - timer))
#timer = clock()
#print("Calculating AUC {:.2f}".format(clock()))
#epoch.auc = st.AUC(model, data, experiment_epoch=epoch)
#print("Finished {:.2f}".format(clock() - timer))
timer = clock()
for r in st.num_of_each_cell(model, data):
epoch.matrix.add().columns.extend(r)
print("Making CFM")
matrix = st.confusion_matrix(model, data, offset='\t ')
#print("Finished {:.2f}".format(clock() - timer))
epoch.num_seconds = clock() - t
timer = clock()
print("Getting output")
output = st.get_output_distro(model, data)
epoch.output.background.extend(output["background"])
epoch.output.signal.extend(output["signal"])
#print("Finished {:.2f}".format(clock() - timer))
# Print statistics
print("\t Train Accuracy: {:.3f}\tTest Accuracy: {:.3f}".format(
epoch.train_accuracy, epoch.test_accuracy))
if valid.update_w():
print("\t Slope: {:.5f} (test_accuracy / second)".format(
valid.slope))
print("\t Time this epoch: {:.2f}s".format(epoch.num_seconds), end='')
if valid._num_epochs:
eTimes = np.append(eTimes, epoch.num_seconds)
print("\tFinal ETA: {}".format(
convert_seconds(
np.median(eTimes) * (valid._num_epochs - valid.epochs))))
else:
print()
print("\t Significance (S/sqrt(B)): {:.2f}".format(epoch.s_b))
print("\t Area Under the Curve (efficiency): {:.3f}".format(epoch.auc))
print(matrix)
# Saves the model
if (len(exp.results) % save_freq) == 0:
save(model, exp, save_dir, exp_file_name)
print("\t ", end='')
sys.stdout.flush()
exp.end_date_time = str(datetime.datetime.now())
exp.total_time = valid.time
print("\n" + valid.failed)
print("Total Time: {}".format(convert_seconds(valid.time)))
save(model, exp, save_dir, exp_file_name)
print("\t ", end='')
h_file.close()
cutoff = 1-i*(1/datapoints)
e_b[i], e_s[i] = efficiencies(model, data, cutoff)[:,1]
if experiment_epoch:
point = experiment_epoch.curve.add()
point.signal = e_s[i]
point.background = e_b[i]
point.cutoff = cutoff
if save:
plt.plot(e_b, e_s)
plt.title("Efficiency Curve")
plt.ylabel("Signal Efficiency")
plt.xlabel("Background Inefficiency")
plt.savefig(save, format="png")
return trapz(e_s,e_b)
# ""
def confusion_matrix(model, data, offset='', **kwargs):
eff = efficiencies(model, data, **kwargs)
return MATRIX.format(offset, *(eff*100).flatten())
if __name__ == "__main__":
from deep_learning.trainNN import load_model
model = load_model("ttHLep/U_Optimal")
x_train, y_train, x_test, y_test = ds.load_dataset("ttHLep", "Unsorted")
x_train, x_test = tr.transform(x_train, x_test)
data = (x_train, y_train, x_test, y_test)
print significance(model, data)
print AUC(model, data)
print confusion_matrix(model, data)
print confusion_matrix(model, data, over_rows=False)
def run(model, exp, terms, save_freq=5, data=None):
exp_dir = ds.get_path_to_dataset(pb.Experiment.Dataset.Name(exp.dataset))
save_dir = os.path.join(exp_dir, exp.description)
##
# Load data from .npz archive created by invoking
# deep_learning/utils/archive.py
##
if data:
x_train, y_train, x_test, y_test = data
x_train, x_test = tr.transform(x_train, x_test)
else:
h_file, (x_train, y_train, x_test, y_test) = ds.load_dataset(pb.Experiment.Dataset.Name(exp.dataset), exp.coordinates)
x_train, x_test = tr.transform(x_train, x_test)
data = x_train, y_train, x_test, y_test
exp_file_name = exp.description + '.exp'
train_length = x_train.shape[0]
num_batches = int(ceil(train_length / exp.batch_size))
valid = Validator(exp, terms)
eTimes = np.array([])
valid._clock = clock()
model.summary()
while valid.check():
t = clock()
if valid._num_epochs:
print("Epoch {}/{}".format(valid.epochs+1, valid._num_epochs))
else:
print("Epoch {}".format(valid.epochs+1))
bETA = 0
bTimes = np.array([])
for b in xrange(num_batches):
bt = clock()
# Update progress bar
progress(b, num_batches, exp.batch_size, bETA)
# Train on a batch
model.train_on_batch(x_train[b*exp.batch_size:b*exp.batch_size+exp.batch_size, :],
y_train[b*exp.batch_size:b*exp.batch_size+exp.batch_size, :])
bTimes = np.append(bTimes, clock()-bt)
bETA = np.median(bTimes)*(num_batches-b-1)
# Finish progress bar
progress(num_batches, num_batches, exp.batch_size, 0, end='\n')
# Calculate stats and add the epoch results to the experiment object
epoch = exp.results.add()
epoch.train_loss, epoch.train_accuracy = model.evaluate_generator(((x_train[i*exp.batch_size:(i+1)*exp.batch_size],
y_train[i*exp.batch_size:(i+1)*exp.batch_size]) for i in xrange(int(ceil(x_test.shape[0]/exp.batch_size)))),
int(ceil(x_test.shape[0]/exp.batch_size)))
epoch.test_loss, epoch.test_accuracy = model.evaluate_generator(((x_test[i*exp.batch_size:(i+1)*exp.batch_size],
y_test[i*exp.batch_size:(i+1)*exp.batch_size]) for i in xrange(num_batches)),
num_batches)
epoch.s_b = st.significance(model, data)
epoch.auc = st.AUC(model, data, experiment_epoch=epoch)
for r in st.num_of_each_cell(model, data):
epoch.matrix.add().columns.extend(r)
matrix = st.confusion_matrix(model, data, offset='\t ')
epoch.num_seconds = clock() - t
# Print statistics
print("\t Train Accuracy: {:.3f}\tTest Accuracy: {:.3f}".format(epoch.train_accuracy, epoch.test_accuracy))
if valid.update_w():
print("\t Slope: {:.5f} (test_accuracy / second)".format(valid.slope))
print("\t Time this epoch: {:.2f}s".format(epoch.num_seconds), end='')
if valid._num_epochs:
eTimes = np.append(eTimes, epoch.num_seconds)
print("\tFinal ETA: {}".format(convert_seconds(np.median(eTimes) * (valid._num_epochs - valid.epochs))))
else:
print()
print("\t Significance (S/sqrt(B)): {:.2f}".format(epoch.s_b))
print("\t Area Under the Curve (efficiency): {:.3f}".format(epoch.auc))
print(matrix)
if (len(exp.results) % save_freq) == 0:
save(model, exp, save_dir, exp_file_name)
print("\t Saved the model\n")
sys.stdout.flush()
exp.end_date_time = str(datetime.datetime.now())
exp.total_time = valid.time
print("\n"+valid.failed)
print("Total Time: {}".format(convert_seconds(valid.time)))
save(model, exp, save_dir, exp_file_name, graph=True)
h_file.close()
