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') # (x, y_processed, y) = data_gen.getitem(i, return_y_true=True) # pred = self.model.predict(x, batch_size=self.batch_size, verbose=self.verbose) # pass # aflanders: debug # if i == 0: # print(f"type(x): {type(x)} type(self.batch_size): {type(self.batch_size)}") # print(f"tf.executing_eagerly():{tf.executing_eagerly()}") # aflanders: debug #pred = self.model.predict(x, batch_size=self.batch_size, verbose=self.verbose) pred = self.model.predict(data_gen, batch_size=self.batch_size, verbose=self.verbose, steps=data_gen.steps, workers=self.workers, use_multiprocessing=True) # if isinstance(x, list) and len(x) == 2: # deep supervision # if pred.shape[-1] == 1: # regression # pred_flatten = pred.flatten() # else: # classification # pred_flatten = pred.reshape((-1, 10)) # for m, t, p in zip(x[1].flatten(), y.flatten(), pred_flatten): # if np.equal(m, 1): # y_true.append(t) # predictions.append(p) # else: y = data_gen.get_y(len(pred)) # if pred.shape[-1] == 1: # y_true += list(y.flatten()) # predictions += list(pred.flatten()) # else: y_true += list(y) predictions += list(pred) print('\n') if self.partition == 'log': predictions = [ metrics.get_estimate_log(x, 10) for x in predictions ] ret = metrics.print_metrics_log_bins(y_true, predictions) if self.partition == 'custom': predictions = [ metrics.get_estimate_custom(x, 10) for x in predictions ] ret = metrics.print_metrics_custom_bins(y_true, predictions) if self.partition == 'none': ret = metrics.print_metrics_regression(y_true, predictions) for k, v in ret.items(): logs[dataset + '_' + k] = v history.append(ret)
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_processed, y) = data_gen.next(return_y_true=True) pred = self.model.predict([x, t], batch_size=self.batch_size) else: (x, y_processed, y) = data_gen.next(return_y_true=True) pred = self.model.predict(x, batch_size=self.batch_size) if isinstance(x, list) and len(x) == 2: # deep supervision if pred.shape[-1] == 1: # regression pred_flatten = pred.flatten() else: # classification pred_flatten = pred.reshape((-1, 10)) for m, t, p in zip(x[1].flatten(), y.flatten(), pred_flatten): if np.equal(m, 1): y_true.append(t) predictions.append(p) else: if pred.shape[-1] == 1: y_true += list(y.flatten()) predictions += list(pred.flatten()) else: y_true += list(y) predictions += list(pred) print('\n') if self.partition == 'log': predictions = [ metrics.get_estimate_log(x, 10) for x in predictions ] ret = metrics.print_metrics_log_bins(y_true, predictions) if self.partition == 'custom': predictions = [ metrics.get_estimate_custom(x, 10) for x in predictions ] ret = metrics.print_metrics_custom_bins(y_true, predictions) if self.partition == 'none': ret = metrics.print_metrics_regression(y_true, predictions) for k, v in ret.items(): logs[dataset + '_' + k] = v history.append(ret)
predictions += list(pred) labels += list(y) names += list(cur_names) ts += list(cur_ts) if stochastic: aleatoric = [np.mean(x * (1. - x), axis=0) for x in predictions] epistemic = [np.var(x, axis=0) for x in predictions] predictions = [np.mean(x, axis=0) for x in predictions] if args.partition == 'log': predictions = [metrics.get_estimate_log(x, 10) for x in predictions] metrics.print_metrics_log_bins(labels, predictions) if args.partition == 'custom': predictions = [metrics.get_estimate_custom(x, 10) for x in predictions] metrics.print_metrics_custom_bins(labels, predictions) if args.partition == 'none': metrics.print_metrics_regression(labels, predictions) predictions = [x[0] for x in predictions] path = os.path.join( os.path.join(args.output_dir, "test_predictions", os.path.basename(args.load_state)) + ".csv") if stochastic: ee = np.mean(np.array(epistemic)) aa = np.mean(np.array(aleatoric)) print("Epistemic uncertainty =", ee) print("Aleatoric uncertainty =", aa) print("Uncertainty =", ee + aa) utils.save_results(names,
## decomp if args.decomp_C > 0: print "\n ================ decompensation ================" decomp_pred = np.array(decomp_pred) decomp_pred = np.stack([1-decomp_pred, decomp_pred], axis=1) decomp_ret = metrics.print_metrics_binary(decomp_y_true, decomp_pred) ## los 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
write_results(resfile, ret) train_predictions = np.array([metrics.get_estimate_custom(x, nbins) for x in train_activations]) val_predictions = np.array([metrics.get_estimate_custom(x, nbins) for x in val_activations]) test_predictions = np.array([metrics.get_estimate_custom(x, nbins) for x in test_activations]) with open(os.path.join("cf_activations", model_name + ".txt"), "w") as actfile: for (x, y) in zip(test_predictions, test_actual): actfile.write("%.6f %.6f\n" % (x, y)) with open(os.path.join("cf_results", model_name + ".txt"), "w") as resfile: resfile.write("mad, mse, mape, kappa\n") print "Scores on train set" ret = metrics.print_metrics_custom_bins(train_actual, train_predictions) resfile.write("%.6f,%.6f,%.6f,%.6f\n" % ( ret['mad'], ret['mse'], ret['mape'], ret['kappa'])) print "Scores on val set" ret = metrics.print_metrics_custom_bins(val_actual, val_predictions) resfile.write("%.6f,%.6f,%.6f,%.6f\n" % ( ret['mad'], ret['mse'], ret['mape'], ret['kappa'])) print "Scores on test set"
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)
# decomp if args.decomp_C > 0: print "\n ================ decompensation ================" decomp_pred = np.array(decomp_pred) decomp_ret = metrics.print_metrics_binary(decomp_y_true, decomp_pred) # los 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)
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 length-of-stay task', default=os.path.join(os.path.dirname(__file__), '../../../data/length-of-stay/')) 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 = ['l2'] coefs = [0.00001] train_reader = LengthOfStayReader(dataset_dir=os.path.join(args.data, 'train'), listfile=os.path.join(args.data, 'train_listfile.csv')) val_reader = LengthOfStayReader(dataset_dir=os.path.join(args.data, 'train'), listfile=os.path.join(args.data, 'val_listfile.csv')) test_reader = LengthOfStayReader(dataset_dir=os.path.join(args.data, 'test'), listfile=os.path.join(args.data, 'test_listfile.csv')) print('Reading data and extracting features ...') n_train = min(100000, train_reader.get_number_of_examples()) n_val = min(100000, val_reader.get_number_of_examples()) (train_X, train_y, train_actual, train_names, train_ts) = read_and_extract_features( train_reader, n_train, args.period, args.features) (val_X, val_y, val_actual, val_names, val_ts) = read_and_extract_features( val_reader, n_val, args.period, args.features) (test_X, test_y, test_actual, test_names, test_ts) = read_and_extract_features( test_reader, test_reader.get_number_of_examples(), args.period, args.features) 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) result_dir = os.path.join(args.output_dir, 'cf_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_bins): 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] train_predictions = np.array([metrics.get_estimate_custom(x, n_bins) for x in train_activations]) val_predictions = np.array([metrics.get_estimate_custom(x, n_bins) for x in val_activations]) test_predictions = np.array([metrics.get_estimate_custom(x, n_bins) for x in test_activations]) with open(os.path.join(result_dir, 'train_{}.json'.format(model_name)), 'w') as f: ret = metrics.print_metrics_custom_bins(train_actual, train_predictions) ret = {k: float(v) for k, v in ret.items()} json.dump(ret, f) with open(os.path.join(result_dir, 'val_{}.json'.format(model_name)), 'w') as f: ret = metrics.print_metrics_custom_bins(val_actual, val_predictions) ret = {k: float(v) for k, v in ret.items()} json.dump(ret, f) with open(os.path.join(result_dir, 'test_{}.json'.format(model_name)), 'w') as f: ret = metrics.print_metrics_custom_bins(test_actual, test_predictions) ret = {k: float(v) for k, v in ret.items()} json.dump(ret, f) save_results(test_names, test_ts, test_predictions, test_actual, os.path.join(args.output_dir, 'cf_predictions', model_name + '.csv'))
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 = ['l2'] Cs = [0.00001] train_reader = LengthOfStayReader(dataset_dir='../../../data/length-of-stay/train/', listfile='../../../data/length-of-stay/train_listfile.csv') val_reader = LengthOfStayReader(dataset_dir='../../../data/length-of-stay/train/', listfile='../../../data/length-of-stay/val_listfile.csv') test_reader = LengthOfStayReader(dataset_dir='../../../data/length-of-stay/test/', listfile='../../../data/length-of-stay/test_listfile.csv') print('Reading data and extracting features ...') n_train = min(100000, train_reader.get_number_of_examples()) n_val = min(100000, val_reader.get_number_of_examples()) (train_X, train_y, train_actual, train_names, train_ts) = read_and_extract_features( train_reader, n_train, args.period, args.features) (val_X, val_y, val_actual, val_names, val_ts) = read_and_extract_features( val_reader, n_val, args.period, args.features) (test_X, test_y, test_actual, test_names, test_ts) = read_and_extract_features( test_reader, test_reader.get_number_of_examples(), args.period, args.features) 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) common_utils.create_directory('cf_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_bins): 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] train_predictions = np.array([metrics.get_estimate_custom(x, n_bins) for x in train_activations]) val_predictions = np.array([metrics.get_estimate_custom(x, n_bins) for x in val_activations]) test_predictions = np.array([metrics.get_estimate_custom(x, n_bins) for x in test_activations]) with open(os.path.join('cf_results', 'train_{}.json'.format(model_name)), 'w') as f: ret = metrics.print_metrics_custom_bins(train_actual, train_predictions) ret = {k: float(v) for k, v in ret.items()} json.dump(ret, f) with open(os.path.join('cf_results', 'val_{}.json'.format(model_name)), 'w') as f: ret = metrics.print_metrics_custom_bins(val_actual, val_predictions) ret = {k: float(v) for k, v in ret.items()} json.dump(ret, f) with open(os.path.join('cf_results', 'test_{}.json'.format(model_name)), 'w') as f: ret = metrics.print_metrics_custom_bins(test_actual, test_predictions) ret = {k: float(v) for k, v in ret.items()} json.dump(ret, f) save_results(test_names, test_ts, test_predictions, test_actual, os.path.join('cf_predictions', model_name + '.csv'))
def process_one_chunk(mode, chunk_index): assert (mode == "train" or mode == "test") if (mode == "train"): reader = train_reader if (mode == "test"): reader = val_reader (data, ts, ys, header) = utils.read_chunk(reader, chunk_size) data = utils.preprocess_chunk(data, ts, discretizer, normalizer) if (mode == "train"): network.set_datasets((data, ys), None) if (mode == "test"): network.set_datasets(None, (data, ys)) network.shuffle_train_set() y_true = [] predictions = [] avg_loss = 0.0 sum_loss = 0.0 prev_time = time.time() n_batches = network.get_batches_per_epoch(mode) for i in range(0, n_batches): step_data = network.step(mode) prediction = step_data["prediction"] answers = step_data["answers"] current_loss = step_data["current_loss"] current_loss_mse = step_data["loss_mse"] current_loss_reg = step_data["loss_reg"] 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 = %.3f + %.3f \t avg_loss: %.3f \t"\ "%s \t time: %.2fs" % (mode, chunk_index, i * args.batch_size, n_batches * args.batch_size, current_loss, current_loss_mse, current_loss_reg, 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 /= n_batches print "\n %s loss = %.5f" % (mode, sum_loss) if args.network in ['lstm', 'lstm_log']: metrics.print_metrics_regression(y_true, predictions) if args.network == 'lstm_cf_log': metrics.print_metrics_log_bins(y_true, predictions) if args.network == 'lstm_cf_custom': metrics.print_metrics_custom_bins(y_true, predictions) return sum_loss
predictions.append(x) 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 if args.network in ['lstm', 'lstm_log']: metrics.print_metrics_regression(y_true, predictions) if args.network == 'lstm_cf_log': metrics.print_metrics_log_bins(y_true, predictions) if args.network == 'lstm_cf_custom': metrics.print_metrics_custom_bins(y_true, predictions) with open("activations.txt", "w") as fout: fout.write("prediction, y_true") for (x, y) in zip(predictions, y_true): fout.write("%.6f, %.6f\n" % (x, y)) else: raise Exception("unknown mode")
for i in range(test_data_gen.steps): print "\rpredicting {} / {}".format(i, test_data_gen.steps), ret = test_data_gen.next(return_y_true=True) (x, y_processed, y) = ret["data"] 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) if args.partition == 'log': predictions = [metrics.get_estimate_log(x, 10) for x in predictions] metrics.print_metrics_log_bins(labels, predictions) if args.partition == 'custom': predictions = [metrics.get_estimate_custom(x, 10) for x in predictions] metrics.print_metrics_custom_bins(labels, predictions) if args.partition == 'none': metrics.print_metrics_regression(labels, predictions) predictions = [x[0] for x in 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")
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
raise Exception("current loss IS NaN. This should never happen :)") 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: