def calc_metrics(self, data_gen, history, dataset, logs):
y_true = []
predictions = []
for i in range(data_gen.steps):
if self.verbose == 1:
print("\tdone {}/{}".format(i, data_gen.steps), end='\r')
if self.use_time:
([x, t], y) = next(data_gen)
outputs = self.model.predict([x, t],
batch_size=self.batch_size)
else:
(x, y) = next(data_gen)
outputs = self.model.predict(x, batch_size=self.batch_size)
if data_gen.target_repl:
y_true += list(y[0])
predictions += list(outputs[0])
else:
y_true += list(y)
predictions += list(outputs)
print('\n')
predictions = np.array(predictions)
ret = metrics.print_metrics_multilabel(y_true, predictions)
for k, v in ret.items():
logs[dataset + '_' + k] = v
history.append(ret)
def do_epoch(mode, epoch):
# mode is 'train' or 'test'
y_true = []
predictions = []
avg_loss = 0.0
sum_loss = 0.0
prev_time = time.time()
batches_per_epoch = network.get_batches_per_epoch(mode)
for i in range(0, batches_per_epoch):
step_data = network.step(mode)
prediction = step_data["prediction"]
answers = step_data["answers"]
current_loss = step_data["current_loss"]
log = step_data["log"]
avg_loss += current_loss
sum_loss += current_loss
for x in answers:
y_true.append(x)
for x in prediction:
predictions.append(x)
if ((i + 1) % args.log_every == 0):
cur_time = time.time()
print (" %sing: %d.%d / %d \t loss: %.3f \t avg_loss: %.3f \t"\
"%s \t time: %.2fs" % (mode, epoch, i * args.batch_size,
batches_per_epoch * args.batch_size,
current_loss, avg_loss / args.log_every,
log, cur_time - prev_time))
avg_loss = 0
prev_time = cur_time
if np.isnan(current_loss):
raise Exception ("current loss IS NaN. This should never happen :)")
sum_loss /= batches_per_epoch
print "\n %s loss = %.5f" % (mode, sum_loss)
metrics.print_metrics_multilabel(y_true, predictions)
return sum_loss
test_data_gen = utils.BatchGen(test_reader, discretizer,
normalizer, args.batch_size,
args.small_part, target_repl,
shuffle=False, return_names=True)
names = []
ts = []
labels = []
predictions = []
for i in range(test_data_gen.steps):
print "\rpredicting {} / {}".format(i, test_data_gen.steps),
ret = next(test_data_gen)
x = ret["data"][0]
y = ret["data"][1]
cur_names = ret["names"]
cur_ts = ret["ts"]
x = np.array(x)
pred = model.predict_on_batch(x)
predictions += list(pred)
labels += list(y)
names += list(cur_names)
ts += list(cur_ts)
metrics.print_metrics_multilabel(labels, predictions)
path = os.path.join("test_predictions", os.path.basename(args.load_state)) + ".csv"
utils.save_results(names, ts, predictions, labels, path)
else:
raise ValueError("Wrong value for args.mode")
continue
# Make sure only one file for this task
assert(not PRED_TASKS[matches[0]])
PRED_TASKS[matches[0]] = True
print("Evaluating {}".format(matches[0]))
match_pred, match_Y = read_file(os.path.join(indir, filename))
if merged_pred is None:
merged_pred = np.expand_dims(match_pred.copy(), axis=0)
merged_Y = np.expand_dims(match_Y.copy(), axis=0)
else:
merged_pred =np.concatenate((merged_pred, np.expand_dims(match_pred, axis=0)), axis=0)
merged_Y =np.concatenate((merged_Y, np.expand_dims(match_Y ,axis=0)), axis=0)
#print(merged_X.shape)
#print(merged_Y.shape)
metrics.print_metrics_binary(match_Y, match_pred)
print("----------------------------------------")
print("\n==========================================")
print("Evaluating all together:")
metrics.print_metrics_multilabel(merged_Y.T, merged_pred.T)
for key in PRED_TASKS:
if PRED_TASKS[key] != True:
print("WARNING: Data for task {} missing?".format(key))
header += "ave_auc_micro,ave_auc_macro,ave_auc_weighted,"
header += ','.join(["auc_%d" % i for i in range(NTASKS)])
resfile.write(header + "\n")
def write_results(resfile, ret):
resfile.write("%.6f,%.6f,%.6f,%.6f,%.6f,%.6f,%.6f,%.6f,%.6f," %
(ret['ave_prec_micro'], ret['ave_prec_macro'],
ret['ave_prec_weighted'], ret['ave_recall_micro'],
ret['ave_recall_macro'], ret['ave_recall_weighted'],
ret['ave_auc_micro'], ret['ave_auc_macro'],
ret['ave_auc_weighted']))
resfile.write(",".join(["%.6f" % x
for x in ret['auc_scores']]) + "\n")
print "\nAverage results on train"
ret = metrics.print_metrics_multilabel(train_y, train_activations)
write_results(resfile, ret)
print "\nAverage results on val"
ret = metrics.print_metrics_multilabel(val_y, val_activations)
write_results(resfile, ret)
print "\nAverage results on test"
ret = metrics.print_metrics_multilabel(test_y, test_activations)
write_results(resfile, ret)
np.savetxt(os.path.join("activations", model_name + ".csv"),
test_activations,
delimiter=',')
print "==================== Done (penalty = %s, C = %f) ====================\n" % (
predictions = []
for i in range(test_data_gen.steps):
print "\rpredicting {} / {}".format(i, test_data_gen.steps),
ret = next(test_data_gen)
x = ret["data"][0]
y = ret["data"][1]
cur_names = ret["names"]
cur_ts = ret["ts"]
x = np.array(x)
pred = model.predict_on_batch(x)
predictions += list(pred)
labels += list(y)
names += list(cur_names)
ts += list(cur_ts)
ret = metrics.print_metrics_multilabel(labels, predictions)
with open("results.txt", "w") as resfile:
header = "ave_prec_micro,ave_prec_macro,ave_prec_weighted,"
header += "ave_recall_micro,ave_recall_macro,ave_recall_weighted,"
header += "ave_auc_micro,ave_auc_macro,ave_auc_weighted,"
header += ','.join(
["auc_%d" % i for i in range(args_dict['num_classes'])])
resfile.write(header + "\n")
resfile.write("%.6f,%.6f,%.6f,%.6f,%.6f,%.6f,%.6f,%.6f,%.6f," %
(ret['ave_prec_micro'], ret['ave_prec_macro'],
ret['ave_prec_weighted'], ret['ave_recall_micro'],
ret['ave_recall_macro'], ret['ave_recall_weighted'],
ret['ave_auc_micro'], ret['ave_auc_macro'],
ret['ave_auc_weighted']))
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--period',
type=str,
default='all',
help='specifies which period extract features from',
choices=[
'first4days', 'first8days', 'last12hours',
'first25percent', 'first50percent', 'all'
])
parser.add_argument('--features',
type=str,
default='all',
help='specifies what features to extract',
choices=['all', 'len', 'all_but_len'])
parser.add_argument('--grid-search',
dest='grid_search',
action='store_true')
parser.add_argument('--no-grid-search',
dest='grid_search',
action='store_false')
parser.set_defaults(grid_search=False)
parser.add_argument('--data',
type=str,
help='Path to the data of phenotyping task',
default=os.path.join(os.path.dirname(__file__),
'../../../data/phenotyping/'))
parser.add_argument(
'--output_dir',
type=str,
help='Directory relative which all output files are stored',
default='.')
args = parser.parse_args()
print(args)
if args.grid_search:
penalties = [
'l2', 'l2', 'l2', 'l2', 'l2', 'l2', 'l1', 'l1', 'l1', 'l1', 'l1'
]
coefs = [
1.0, 0.1, 0.01, 0.001, 0.0001, 0.00001, 1.0, 0.1, 0.01, 0.001,
0.0001
]
else:
penalties = ['l1']
coefs = [0.1]
train_reader = PhenotypingReader(
dataset_dir=os.path.join(args.data, 'train'),
listfile=os.path.join(args.data, 'train_listfile.csv'))
val_reader = PhenotypingReader(
dataset_dir=os.path.join(args.data, 'train'),
listfile=os.path.join(args.data, 'val_listfile.csv'))
test_reader = PhenotypingReader(
dataset_dir=os.path.join(args.data, 'test'),
listfile=os.path.join(args.data, 'test_listfile.csv'))
print('Reading data and extracting features ...')
(train_X, train_y, train_names,
train_ts) = read_and_extract_features(train_reader, args.period,
args.features)
train_y = np.array(train_y)
(val_X, val_y, val_names,
val_ts) = read_and_extract_features(val_reader, args.period,
args.features)
val_y = np.array(val_y)
(test_X, test_y, test_names,
test_ts) = read_and_extract_features(test_reader, args.period,
args.features)
test_y = np.array(test_y)
print("train set shape: {}".format(train_X.shape))
print("validation set shape: {}".format(val_X.shape))
print("test set shape: {}".format(test_X.shape))
print('Imputing missing values ...')
imputer = Imputer(missing_values=np.nan,
strategy='mean',
axis=0,
verbose=0,
copy=True)
imputer.fit(train_X)
train_X = np.array(imputer.transform(train_X), dtype=np.float32)
val_X = np.array(imputer.transform(val_X), dtype=np.float32)
test_X = np.array(imputer.transform(test_X), dtype=np.float32)
print('Normalizing the data to have zero mean and unit variance ...')
scaler = StandardScaler()
scaler.fit(train_X)
train_X = scaler.transform(train_X)
val_X = scaler.transform(val_X)
test_X = scaler.transform(test_X)
n_tasks = 25
result_dir = os.path.join(args.output_dir, 'results')
common_utils.create_directory(result_dir)
for (penalty, C) in zip(penalties, coefs):
model_name = '{}.{}.{}.C{}'.format(args.period, args.features, penalty,
C)
train_activations = np.zeros(shape=train_y.shape, dtype=float)
val_activations = np.zeros(shape=val_y.shape, dtype=float)
test_activations = np.zeros(shape=test_y.shape, dtype=float)
for task_id in range(n_tasks):
print('Starting task {}'.format(task_id))
logreg = LogisticRegression(penalty=penalty, C=C, random_state=42)
logreg.fit(train_X, train_y[:, task_id])
train_preds = logreg.predict_proba(train_X)
train_activations[:, task_id] = train_preds[:, 1]
val_preds = logreg.predict_proba(val_X)
val_activations[:, task_id] = val_preds[:, 1]
test_preds = logreg.predict_proba(test_X)
test_activations[:, task_id] = test_preds[:, 1]
with open(os.path.join(result_dir, 'train_{}.json'.format(model_name)),
'w') as f:
ret = metrics.print_metrics_multilabel(train_y, train_activations)
ret = {k: float(v) for k, v in ret.items() if k != 'auc_scores'}
json.dump(ret, f)
with open(os.path.join(result_dir, 'val_{}.json'.format(model_name)),
'w') as f:
ret = metrics.print_metrics_multilabel(val_y, val_activations)
ret = {k: float(v) for k, v in ret.items() if k != 'auc_scores'}
json.dump(ret, f)
with open(os.path.join(result_dir, 'test_{}.json'.format(model_name)),
'w') as f:
ret = metrics.print_metrics_multilabel(test_y, test_activations)
ret = {k: float(v) for k, v in ret.items() if k != 'auc_scores'}
json.dump(ret, f)
save_results(
test_names, test_ts, test_activations, test_y,
os.path.join(args.output_dir, 'predictions', model_name + '.csv'))
def calc_metrics(self, data_gen, history, dataset, logs):
ihm_y_true = []
decomp_y_true = []
los_y_true = []
pheno_y_true = []
ihm_pred = []
decomp_pred = []
los_pred = []
pheno_pred = []
for i in range(data_gen.steps):
if self.verbose == 1:
print("\tdone {}/{}".format(i, data_gen.steps), end='\r')
(X, y, los_y_reg) = data_gen.next(return_y_true=True)
outputs = self.model.predict(X, batch_size=self.batch_size)
ihm_M = X[1]
decomp_M = X[2]
los_M = X[3]
if not data_gen.target_repl: # no target replication
(ihm_p, decomp_p, los_p, pheno_p) = outputs
(ihm_t, decomp_t, los_t, pheno_t) = y
else: # target replication
(ihm_p, _, decomp_p, los_p, pheno_p, _) = outputs
(ihm_t, _, decomp_t, los_t, pheno_t, _) = y
los_t = los_y_reg # real value not the label
# ihm
for (m, t, p) in zip(ihm_M.flatten(), ihm_t.flatten(),
ihm_p.flatten()):
if np.equal(m, 1):
ihm_y_true.append(t)
ihm_pred.append(p)
# decomp
for (m, t, p) in zip(decomp_M.flatten(), decomp_t.flatten(),
decomp_p.flatten()):
if np.equal(m, 1):
decomp_y_true.append(t)
decomp_pred.append(p)
# los
if los_p.shape[-1] == 1: # regression
for (m, t, p) in zip(los_M.flatten(), los_t.flatten(),
los_p.flatten()):
if np.equal(m, 1):
los_y_true.append(t)
los_pred.append(p)
else: # classification
for (m, t, p) in zip(los_M.flatten(), los_t.flatten(),
los_p.reshape((-1, 10))):
if np.equal(m, 1):
los_y_true.append(t)
los_pred.append(p)
# pheno
for (t, p) in zip(pheno_t.reshape((-1, 25)),
pheno_p.reshape((-1, 25))):
pheno_y_true.append(t)
pheno_pred.append(p)
print('\n')
# ihm
print("\n ================= 48h mortality ================")
ihm_pred = np.array(ihm_pred)
ihm_pred = np.stack([1 - ihm_pred, ihm_pred], axis=1)
ret = metrics.print_metrics_binary(ihm_y_true, ihm_pred)
for k, v in ret.items():
logs[dataset + '_ihm_' + k] = v
# decomp
print("\n ================ decompensation ================")
decomp_pred = np.array(decomp_pred)
decomp_pred = np.stack([1 - decomp_pred, decomp_pred], axis=1)
ret = metrics.print_metrics_binary(decomp_y_true, decomp_pred)
for k, v in ret.items():
logs[dataset + '_decomp_' + k] = v
# los
print("\n ================ length of stay ================")
if self.partition == 'log':
los_pred = [metrics.get_estimate_log(x, 10) for x in los_pred]
ret = metrics.print_metrics_log_bins(los_y_true, los_pred)
if self.partition == 'custom':
los_pred = [metrics.get_estimate_custom(x, 10) for x in los_pred]
ret = metrics.print_metrics_custom_bins(los_y_true, los_pred)
if self.partition == 'none':
ret = metrics.print_metrics_regression(los_y_true, los_pred)
for k, v in ret.items():
logs[dataset + '_los_' + k] = v
# pheno
print("\n =================== phenotype ==================")
pheno_pred = np.array(pheno_pred)
ret = metrics.print_metrics_multilabel(pheno_y_true, pheno_pred)
for k, v in ret.items():
logs[dataset + '_pheno_' + k] = v
history.append(logs)
def do_epoch(mode, epoch):
# mode is 'train' or 'test'
ihm_predictions = []
ihm_answers = []
los_predictions = []
los_answers = []
ph_predictions = []
ph_answers = []
decomp_predictions = []
decomp_answers = []
avg_loss = 0.0
sum_loss = 0.0
prev_time = time.time()
batches_per_epoch = network.get_batches_per_epoch(mode)
for i in range(0, batches_per_epoch):
step_data = network.step(mode)
ihm_pred = step_data["ihm_prediction"]
los_pred = step_data["los_prediction"]
ph_pred = step_data["ph_prediction"]
decomp_pred = step_data["decomp_prediction"]
current_loss = step_data["loss"]
ihm_loss = step_data["ihm_loss"]
los_loss = step_data["los_loss"]
ph_loss = step_data["ph_loss"]
decomp_loss = step_data["decomp_loss"]
reg_loss = step_data["reg_loss"]
data = step_data["data"]
ihm_data = data[1]
ihm_mask = [x[1] for x in ihm_data]
ihm_label = [x[2] for x in ihm_data]
los_data = data[2]
los_mask = [x[0] for x in los_data]
los_label = [x[1] for x in los_data]
ph_data = data[3]
ph_label = ph_data
decomp_data = data[4]
decomp_mask = [x[0] for x in decomp_data]
decomp_label = [x[1] for x in decomp_data]
avg_loss += current_loss
sum_loss += current_loss
for (x, mask, y) in zip(ihm_pred, ihm_mask, ihm_label):
if (mask == 1):
ihm_predictions.append(x)
ihm_answers.append(y)
for (sx, smask, sy) in zip(los_pred, los_mask, los_label):
for (x, mask, y) in zip(sx, smask, sy):
if (mask == 1):
los_predictions.append(x)
los_answers.append(y)
for (x, y) in zip(ph_pred, ph_label):
ph_predictions.append(x)
ph_answers.append(y)
for (sx, smask, sy) in zip(decomp_pred, decomp_mask, decomp_label):
for (x, mask, y) in zip(sx, smask, sy):
if (mask == 1):
decomp_predictions.append(x)
decomp_answers.append(y)
if ((i + 1) % args.log_every == 0):
cur_time = time.time()
print " {}ing {}.{} / {} loss: {:8.4f} = {:1.2f} + {:8.2f} + {:1.2f} + "\
"{:1.2f} + {:.2f} avg_loss: {:6.4f} time: {:6.4f}".format(
mode, epoch, i * args.batch_size,
batches_per_epoch * args.batch_size,
float(current_loss),
float(ihm_loss), float(los_loss), float(ph_loss),
float(decomp_loss), float(reg_loss),
float(avg_loss / args.log_every),
float(cur_time - prev_time))
avg_loss = 0
prev_time = cur_time
if np.isnan(current_loss):
print "loss: {:6.4f} = {:1.2f} + {:8.2f} + {:1.2f} + {:1.2f} + {:.2f}".format(
float(current_loss), float(ihm_loss), float(los_loss),
float(ph_loss), float(decomp_loss), float(reg_loss))
raise Exception("current loss IS NaN. This should never happen :)")
sum_loss /= batches_per_epoch
print "\n %s loss = %.5f" % (mode, sum_loss)
eps = 1e-13
if args.ihm_C > eps:
print "\n ================= 48h mortality ================"
metrics.print_metrics_binary(ihm_answers, ihm_predictions)
if args.los_C > eps:
print "\n ================ length of stay ================"
if args.partition == 'log':
metrics.print_metrics_log_bins(los_answers, los_predictions)
else:
metrics.print_metrics_custom_bins(los_answers, los_predictions)
if args.ph_C > eps:
print "\n =================== phenotype =================="
metrics.print_metrics_multilabel(ph_answers, ph_predictions)
if args.decomp_C > eps:
print "\n ================ decompensation ================"
metrics.print_metrics_binary(decomp_answers, decomp_predictions)
return sum_loss
if args.los_C > 0:
print "\n ================ length of stay ================"
if args.partition == 'log':
los_pred = [metrics.get_estimate_log(x, 10) for x in los_pred]
los_ret = metrics.print_metrics_log_bins(los_y_true, los_pred)
if args.partition == 'custom':
los_pred = [metrics.get_estimate_custom(x, 10) for x in los_pred]
los_ret = metrics.print_metrics_custom_bins(los_y_true, los_pred)
if args.partition == 'none':
los_ret = metrics.print_metrics_regression(los_y_true, los_pred)
# pheno
if args.pheno_C > 0:
print "\n =================== phenotype =================="
pheno_pred = np.array(pheno_pred)
pheno_ret = metrics.print_metrics_multilabel(pheno_y_true, pheno_pred)
print "Saving the predictions in test_predictions/task directories ..."
# ihm
ihm_path = os.path.join("test_predictions/ihm", os.path.basename(args.load_state)) + ".csv"
ihm_utils.save_results(ihm_names, ihm_pred, ihm_y_true, ihm_path)
# decomp
decomp_path = os.path.join("test_predictions/decomp", os.path.basename(args.load_state)) + ".csv"
decomp_utils.save_results(decomp_names, decomp_ts, decomp_pred, decomp_y_true, decomp_path)
# los
los_path = os.path.join("test_predictions/los", os.path.basename(args.load_state)) + ".csv"
los_utils.save_results(los_names, los_ts, los_pred, los_y_true, los_path)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--period', type=str, default='all', help='specifies which period extract features from',
choices=['first4days', 'first8days', 'last12hours', 'first25percent', 'first50percent', 'all'])
parser.add_argument('--features', type=str, default='all', help='specifies what features to extract',
choices=['all', 'len', 'all_but_len'])
args = parser.parse_args()
print(args)
# penalties = ['l2', 'l2', 'l2', 'l2', 'l2', 'l2', 'l1', 'l1', 'l1', 'l1', 'l1']
# Cs = [1.0, 0.1, 0.01, 0.001, 0.0001, 0.00001, 1.0, 0.1, 0.01, 0.001, 0.0001]
penalties = ['l1']
Cs = [0.1]
train_reader = PhenotypingReader(dataset_dir='../../../data/phenotyping/train/',
listfile='../../../data/phenotyping/train_listfile.csv')
val_reader = PhenotypingReader(dataset_dir='../../../data/phenotyping/train/',
listfile='../../../data/phenotyping/val_listfile.csv')
test_reader = PhenotypingReader(dataset_dir='../../../data/phenotyping/test/',
listfile='../../../data/phenotyping/test_listfile.csv')
print('Reading data and extracting features ...')
(train_X, train_y, train_names, train_ts) = read_and_extract_features(train_reader, args.period, args.features)
train_y = np.array(train_y)
(val_X, val_y, val_names, val_ts) = read_and_extract_features(val_reader, args.period, args.features)
val_y = np.array(val_y)
(test_X, test_y, test_names, test_ts) = read_and_extract_features(test_reader, args.period, args.features)
test_y = np.array(test_y)
print("train set shape: {}".format(train_X.shape))
print("validation set shape: {}".format(val_X.shape))
print("test set shape: {}".format(test_X.shape))
print('Imputing missing values ...')
imputer = Imputer(missing_values=np.nan, strategy='mean', axis=0, verbose=0, copy=True)
imputer.fit(train_X)
train_X = np.array(imputer.transform(train_X), dtype=np.float32)
val_X = np.array(imputer.transform(val_X), dtype=np.float32)
test_X = np.array(imputer.transform(test_X), dtype=np.float32)
print('Normalizing the data to have zero mean and unit variance ...')
scaler = StandardScaler()
scaler.fit(train_X)
train_X = scaler.transform(train_X)
val_X = scaler.transform(val_X)
test_X = scaler.transform(test_X)
n_tasks = 25
common_utils.create_directory('results')
for (penalty, C) in zip(penalties, Cs):
model_name = '{}.{}.{}.C{}'.format(args.period, args.features, penalty, C)
train_activations = np.zeros(shape=train_y.shape, dtype=float)
val_activations = np.zeros(shape=val_y.shape, dtype=float)
test_activations = np.zeros(shape=test_y.shape, dtype=float)
for task_id in range(n_tasks):
print('Starting task {}'.format(task_id))
logreg = LogisticRegression(penalty=penalty, C=C, random_state=42)
logreg.fit(train_X, train_y[:, task_id])
train_preds = logreg.predict_proba(train_X)
train_activations[:, task_id] = train_preds[:, 1]
val_preds = logreg.predict_proba(val_X)
val_activations[:, task_id] = val_preds[:, 1]
test_preds = logreg.predict_proba(test_X)
test_activations[:, task_id] = test_preds[:, 1]
with open(os.path.join('results', 'train_{}.json'.format(model_name)), 'w') as f:
ret = metrics.print_metrics_multilabel(train_y, train_activations)
ret = {k: float(v) for k, v in ret.items() if k != 'auc_scores'}
json.dump(ret, f)
with open(os.path.join('results', 'val_{}.json'.format(model_name)), 'w') as f:
ret = metrics.print_metrics_multilabel(val_y, val_activations)
ret = {k: float(v) for k, v in ret.items() if k != 'auc_scores'}
json.dump(ret, f)
with open(os.path.join('results', 'test_{}.json'.format(model_name)), 'w') as f:
ret = metrics.print_metrics_multilabel(test_y, test_activations)
ret = {k: float(v) for k, v in ret.items() if k != 'auc_scores'}
json.dump(ret, f)
save_results(test_names, test_ts, test_activations, test_y, os.path.join('predictions', model_name + '.csv'))
def main():
parser = argparse.ArgumentParser()
parser.add_argument('prediction', type=str)
parser.add_argument('--test_listfile',
type=str,
default='../data/phenotyping/test/listfile.csv')
parser.add_argument('--n_iters', type=int, default=10000)
parser.add_argument('--save_file', type=str, default='pheno_results.json')
args = parser.parse_args()
pred_df = pd.read_csv(args.prediction,
index_col=False,
dtype={'period_length': np.float32})
test_df = pd.read_csv(args.test_listfile,
index_col=False,
dtype={'period_length': np.float32})
n_tasks = 25
labels_cols = ["label_{}".format(i) for i in range(1, n_tasks + 1)]
test_df.columns = list(test_df.columns[:2]) + labels_cols
df = test_df.merge(pred_df,
left_on='stay',
right_on='stay',
how='left',
suffixes=['_l', '_r'])
assert (df['pred_1'].isnull().sum() == 0)
assert (df['period_length_l'].equals(df['period_length_r']))
for i in range(1, n_tasks + 1):
assert (df['label_{}_l'.format(i)].equals(df['label_{}_r'.format(i)]))
metrics = [('Macro ROC AUC', 'ave_auc_macro'),
('Micro ROC AUC', 'ave_auc_micro'),
('Weighted ROC AUC', 'ave_auc_weighted')]
data = np.zeros((df.shape[0], 50))
for i in range(1, n_tasks + 1):
data[:, i - 1] = df['pred_{}'.format(i)]
data[:, 25 + i - 1] = df['label_{}_l'.format(i)]
results = dict()
results['n_iters'] = args.n_iters
ret = print_metrics_multilabel(data[:, 25:], data[:, :25], verbose=0)
for (m, k) in metrics:
results[m] = dict()
results[m]['value'] = ret[k]
results[m]['runs'] = []
for i in range(1, n_tasks + 1):
m = 'ROC AUC of task {}'.format(i)
results[m] = dict()
results[m]['value'] = print_metrics_binary(data[:, 25 + i - 1],
data[:, i - 1],
verbose=0)['auroc']
results[m]['runs'] = []
for iteration in range(args.n_iters):
cur_data = sk_utils.resample(data, n_samples=len(data))
ret = print_metrics_multilabel(cur_data[:, 25:],
cur_data[:, :25],
verbose=0)
for (m, k) in metrics:
results[m]['runs'].append(ret[k])
for i in range(1, n_tasks + 1):
m = 'ROC AUC of task {}'.format(i)
cur_auc = print_metrics_binary(cur_data[:, 25 + i - 1],
cur_data[:, i - 1],
verbose=0)['auroc']
results[m]['runs'].append(cur_auc)
reported_metrics = [m for m, k in metrics]
reported_metrics += [
'ROC AUC of task {}'.format(i) for i in range(1, n_tasks + 1)
]
for m in reported_metrics:
runs = results[m]['runs']
results[m]['mean'] = np.mean(runs)
results[m]['median'] = np.median(runs)
results[m]['std'] = np.std(runs)
results[m]['2.5% percentile'] = np.percentile(runs, 2.5)
results[m]['97.5% percentile'] = np.percentile(runs, 97.5)
del results[m]['runs']
print "Saving the results (including task specific metrics) in {} ...".format(
args.save_file)
with open(args.save_file, 'w') as f:
json.dump(results, f)
print "Printing the summary of results (task specific metrics are skipped) ..."
for i in range(1, n_tasks + 1):
m = 'ROC AUC of task {}'.format(i)
del results[m]
print results
test_data_gen = utils.BatchGen(test_reader, discretizer,
normalizer, args.batch_size,
args.small_part, target_repl,
shuffle=False, return_names=True)
names = []
ts = []
labels = []
predictions = []
for i in range(test_data_gen.steps):
print("predicting {} / {}".format(i, test_data_gen.steps), end='\r')
ret = next(test_data_gen)
x = ret["data"][0]
y = ret["data"][1]
cur_names = ret["names"]
cur_ts = ret["ts"]
x = np.array(x)
pred = model.predict_on_batch(x)
predictions += list(pred)
labels += list(y)
names += list(cur_names)
ts += list(cur_ts)
metrics.print_metrics_multilabel(labels, predictions, stochastic=stochastic)
path = os.path.join(args.output_dir, "test_predictions", os.path.basename(args.load_state)) + ".csv"
utils.save_results(names, ts, predictions, labels, path, stochastic=stochastic)
else:
raise ValueError("Wrong value for args.mode")
def main():
parser = argparse.ArgumentParser()
parser.add_argument('prediction', type=str)
parser.add_argument('--test_listfile', type=str, default='../data/phenotyping/test/listfile.csv')
parser.add_argument('--n_iters', type=int, default=10000)
parser.add_argument('--save_file', type=str, default='pheno_results.json')
args = parser.parse_args()
pred_df = pd.read_csv(args.prediction, index_col=False, dtype={'period_length': np.float32})
test_df = pd.read_csv(args.test_listfile, index_col=False, dtype={'period_length': np.float32})
n_tasks = 25
labels_cols = ["label_{}".format(i) for i in range(1, n_tasks + 1)]
test_df.columns = list(test_df.columns[:2]) + labels_cols
df = test_df.merge(pred_df, left_on='stay', right_on='stay', how='left', suffixes=['_l', '_r'])
assert (df['pred_1'].isnull().sum() == 0)
assert (df['period_length_l'].equals(df['period_length_r']))
for i in range(1, n_tasks + 1):
assert (df['label_{}_l'.format(i)].equals(df['label_{}_r'.format(i)]))
metrics = [('Macro ROC AUC', 'ave_auc_macro'),
('Micro ROC AUC', 'ave_auc_micro'),
('Weighted ROC AUC', 'ave_auc_weighted')]
data = np.zeros((df.shape[0], 50))
for i in range(1, n_tasks + 1):
data[:, i - 1] = df['pred_{}'.format(i)]
data[:, 25 + i - 1] = df['label_{}_l'.format(i)]
results = dict()
results['n_iters'] = args.n_iters
ret = print_metrics_multilabel(data[:, 25:], data[:, :25], verbose=0)
for (m, k) in metrics:
results[m] = dict()
results[m]['value'] = ret[k]
results[m]['runs'] = []
for i in range(1, n_tasks + 1):
m = 'ROC AUC of task {}'.format(i)
results[m] = dict()
results[m]['value'] = print_metrics_binary(data[:, 25 + i - 1], data[:, i - 1], verbose=0)['auroc']
results[m]['runs'] = []
for iteration in range(args.n_iters):
cur_data = sk_utils.resample(data, n_samples=len(data))
ret = print_metrics_multilabel(cur_data[:, 25:], cur_data[:, :25], verbose=0)
for (m, k) in metrics:
results[m]['runs'].append(ret[k])
for i in range(1, n_tasks + 1):
m = 'ROC AUC of task {}'.format(i)
cur_auc = print_metrics_binary(cur_data[:, 25 + i - 1], cur_data[:, i - 1], verbose=0)['auroc']
results[m]['runs'].append(cur_auc)
reported_metrics = [m for m, k in metrics]
reported_metrics += ['ROC AUC of task {}'.format(i) for i in range(1, n_tasks + 1)]
for m in reported_metrics:
runs = results[m]['runs']
results[m]['mean'] = np.mean(runs)
results[m]['median'] = np.median(runs)
results[m]['std'] = np.std(runs)
results[m]['2.5% percentile'] = np.percentile(runs, 2.5)
results[m]['97.5% percentile'] = np.percentile(runs, 97.5)
del results[m]['runs']
print "Saving the results (including task specific metrics) in {} ...".format(args.save_file)
with open(args.save_file, 'w') as f:
json.dump(results, f)
print "Printing the summary of results (task specific metrics are skipped) ..."
for i in range(1, n_tasks + 1):
m = 'ROC AUC of task {}'.format(i)
del results[m]
print results
# Find specific files within the directory for this task
task_str_path = retrieve_matching_file(task_prefix, args.str_path)
task_ustr_path = retrieve_matching_file(task_prefix, args.ustr_path)
task_test_str_path = retrieve_matching_file(task_prefix, args.test_str_path)
task_test_ustr_path = retrieve_matching_file(task_prefix, args.test_ustr_path)
print("\n\n-----------------------\nFitting Model For {}\n".format(task_prefix))
labels, preds = load_fit_save(args.mode,
task_str_path,
task_ustr_path,
task_test_str_path,
task_test_ustr_path,
args.outdir,
out_filename_prefix=task_prefix)
if merged_pred is None:
merged_pred = np.expand_dims(preds, axis=1)
merged_Y = np.expand_dims(labels, axis=1)
else:
merged_pred = np.concatenate((merged_pred, np.expand_dims(preds, axis=1)), axis=1)
merged_Y = np.concatenate((merged_Y, np.expand_dims(labels, axis=1)), axis=1)
print('\n============================')
print("Overall performance:")
metrics.print_metrics_multilabel(merged_Y, merged_pred)
if args.los_C > 0:
print "\n ================ length of stay ================"
if args.partition == 'log':
los_pred = [metrics.get_estimate_log(x, 10) for x in los_pred]
los_ret = metrics.print_metrics_log_bins(los_y_true, los_pred)
if args.partition == 'custom':
los_pred = [metrics.get_estimate_custom(x, 10) for x in los_pred]
los_ret = metrics.print_metrics_custom_bins(los_y_true, los_pred)
if args.partition == 'none':
los_ret = metrics.print_metrics_regression(los_y_true, los_pred)
## pheno
if args.pheno_C > 0:
print "\n =================== phenotype =================="
pheno_pred = np.array(pheno_pred)
pheno_ret = metrics.print_metrics_multilabel(pheno_y_true, pheno_pred)
# TODO: save activations if needed
elif args.mode == 'test_single':
# ensure that the code uses test_reader
del train_reader
del val_reader
del train_data_gen
del val_data_gen
# Testing ihm
from mimic3benchmark.readers import InHospitalMortalityReader
from mimic3models.in_hospital_mortality.utils import read_chunk
from mimic3models import nn_utils
if ((i + 1) % args.log_every == 0):
cur_time = time.time()
print (" testing: %d / %d \t loss: %.3f \t avg_loss: %.3f \t"\
" time: %.2fs" % ((i+1) * args.batch_size,
n_batches * args.batch_size, current_loss,
avg_loss / args.log_every, cur_time - prev_time))
avg_loss = 0
prev_time = cur_time
if np.isnan(current_loss):
raise Exception("current loss IS NaN. This should never happen :)")
sum_loss /= n_batches
print "\n test loss = %.5f" % sum_loss
ret = metrics.print_metrics_multilabel(y_true, predictions)
with open("results.txt", "w") as resfile:
header = "ave_prec_micro,ave_prec_macro,ave_prec_weighted,"
header += "ave_recall_micro,ave_recall_macro,ave_recall_weighted,"
header += "ave_auc_micro,ave_auc_macro,ave_auc_weighted,"
header += ','.join(["auc_%d" % i for i in range(NTASKS)])
resfile.write(header + "\n")
resfile.write("%.6f,%.6f,%.6f,%.6f,%.6f,%.6f,%.6f,%.6f,%.6f," %
(ret['ave_prec_micro'], ret['ave_prec_macro'],
ret['ave_prec_weighted'], ret['ave_recall_micro'],
ret['ave_recall_macro'], ret['ave_recall_weighted'],
ret['ave_auc_micro'], ret['ave_auc_macro'],
ret['ave_auc_weighted']))
resfile.write(",".join(["%.6f" % x for x in ret['auc_scores']]) + "\n")
sum_loss /= batches_per_epoch
print "\n %s loss = %.5f" % (args.mode, sum_loss)
eps = 1e-13
if args.ihm_C > eps:
print "\n ================= 48h mortality ================"
metrics.print_metrics_binary(ihm_answers, ihm_predictions)
if args.los_C > eps:
print "\n ================ length of stay ================"
if args.partition == 'log':
metrics.print_metrics_log_bins(los_answers, los_predictions)
else:
metrics.print_metrics_custom_bins(los_answers, los_predictions)
if args.ph_C > eps:
print "\n =================== phenotype =================="
metrics.print_metrics_multilabel(ph_answers, ph_predictions)
if args.decomp_C > eps:
print "\n ================ decompensation ================"
metrics.print_metrics_binary(decomp_answers, decomp_predictions)
with open("los_activations.txt", "w") as fout:
fout.write("prediction, y_true")
for (x, y) in zip(los_predictions, los_answers):
fout.write("%.6f, %.6f\n" % (x, y))
else:
raise Exception("unknown mode")
