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DRCRN_decoder.py
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# Original CRN: copyright of (c) 2020, Ioana Bica
# DR-CRN: copyright of (c) 2022, Jiebin Chu
import logging
import pickle
import numpy as np
from utils.evaluation_utils import get_processed_data, get_mse_at_follow_up_time, \
load_trained_model, write_results_to_file
from DR_CRN_model import DR_CRN_Model
def fit_DRCRN_decoder(dataset_train, dataset_val, model_name, model_dir,
encoder_hyperparams_file, decoder_hyperparams_file,
b_hyperparam_opt):
logging.info("Fitting DR-CRN decoder.")
_, length, num_covariates = dataset_train['current_covariates'].shape
num_treatments = dataset_train['current_treatments'].shape[-1]
num_outputs = dataset_train['outputs'].shape[-1]
num_inputs = dataset_train['current_covariates'].shape[-1] + dataset_train['current_treatments'].shape[-1]
params = {'num_treatments': num_treatments,
'num_covariates': num_covariates,
'num_outputs': num_outputs,
'max_sequence_length': length,
'num_epochs': 100}
hyperparams = dict()
num_simulations = 30
best_validation_mse = 1000000
with open(encoder_hyperparams_file, 'rb') as handle:
encoder_best_hyperparams = pickle.load(handle)
if b_hyperparam_opt:
logging.info("Performing hyperparameter optimization.")
for simulation in range(num_simulations):
logging.info("Simulation {} out of {}".format(simulation + 1, num_simulations))
# The first rnn hidden states in the decoder is initialized with the corresponding disentangled representations
# outputed by the encoder.
hyperparams['rnn_hidden_units'] = encoder_best_hyperparams['rnn_hidden_units']
hyperparams['dr_size'] = int(np.random.choice([0.5, 1.0, 2.0, 3.0, 4.0]) * num_inputs)
hyperparams['fc_hidden_units'] = int(np.random.choice([0.5, 1.0, 2.0, 3.0, 4.0]) * (hyperparams['dr_size']))
hyperparams['learning_rate'] = np.random.choice([0.01, 0.001, 0.0001])
hyperparams['batch_size'] = np.random.choice([256, 512, 1024])
hyperparams['rnn_keep_prob'] = np.random.choice([0.7, 0.8, 0.9])
logging.info("Current hyperparams used for training \n {}".format(hyperparams))
model = DR_CRN_Model(params, hyperparams, b_train_decoder=True)
model.train(dataset_train, dataset_val, model_name, model_dir)
validation_mse, _ = model.evaluate_predictions(dataset_val)
if (validation_mse < best_validation_mse):
logging.info(
"Updating best validation loss | Previous best validation loss: {} | Current best validation loss: {}".format(
best_validation_mse, validation_mse))
best_validation_mse = validation_mse
best_hyperparams = hyperparams.copy()
logging.info("Best hyperparams: \n {}".format(best_hyperparams))
write_results_to_file(decoder_hyperparams_file, best_hyperparams)
else:
# The rnn_hidden_units needs to be the same as the encoder dr_size.
logging.info("Using default hyperparameters")
# best_hyperparams = {
# 'dr_size': 12,
# 'rnn_keep_prob': 0.9,
# 'fc_hidden_units': 12,
# 'batch_size': 512,
# 'learning_rate': 0.001,
# # 'rnn_hidden_units': encoder_best_hyperparams['dr_size']}
# 'rnn_hidden_units': encoder_best_hyperparams['rnn_hidden_units']}
best_hyperparams = { #γ=5
'dr_size': 24,
'rnn_keep_prob': 0.9,
'fc_hidden_units': 12,
'batch_size': 1024,
'learning_rate': 0.001,
# 'rnn_hidden_units': encoder_best_hyperparams['dr_size']}
'rnn_hidden_units': encoder_best_hyperparams['rnn_hidden_units']}
# best_hyperparams = { # γ=3
# 'dr_size': 18,
# 'rnn_keep_prob': 0.9,
# 'fc_hidden_units': 72,
# 'batch_size': 512,
# 'learning_rate': 0.001,
# # 'rnn_hidden_units': encoder_best_hyperparams['dr_size']}
# 'rnn_hidden_units': encoder_best_hyperparams['rnn_hidden_units']}
# best_hyperparams = { # γ=1
# 'dr_size': 12,
# 'rnn_keep_prob': 0.9,
# 'fc_hidden_units': 12,
# 'batch_size': 512,
# 'learning_rate': 0.001,
# # 'rnn_hidden_units': encoder_best_hyperparams['dr_size']}
# 'rnn_hidden_units': encoder_best_hyperparams['rnn_hidden_units']}
write_results_to_file(decoder_hyperparams_file, best_hyperparams)
model = DR_CRN_Model(params, best_hyperparams, b_train_decoder=True)
model.train(dataset_train, dataset_val, model_name, model_dir)
def process_seq_data(data_map, states, projection_horizon):
"""
Split the sequences in the training data to train the decoder.
"""
outputs = data_map['outputs']
sequence_lengths = data_map['sequence_lengths']
active_entries = data_map['active_entries']
current_treatments = data_map['current_treatments']
previous_treatments = data_map['previous_treatments']
current_covariates = data_map['current_covariates']
num_patients, num_time_steps, num_features = outputs.shape
num_seq2seq_rows = num_patients * num_time_steps
seq2seq_state_inits = np.zeros((num_seq2seq_rows, states[0].shape[-1]))
seq2seq_state_inits1 = np.zeros((num_seq2seq_rows, states[1].shape[-1]))
seq2seq_previous_treatments = np.zeros((num_seq2seq_rows, projection_horizon, previous_treatments.shape[-1]))
seq2seq_current_treatments = np.zeros((num_seq2seq_rows, projection_horizon, current_treatments.shape[-1]))
seq2seq_current_covariates = np.zeros((num_seq2seq_rows, projection_horizon, current_covariates.shape[-1]))
seq2seq_outputs = np.zeros((num_seq2seq_rows, projection_horizon, outputs.shape[-1]))
seq2seq_active_entries = np.zeros((num_seq2seq_rows, projection_horizon, active_entries.shape[-1]))
seq2seq_sequence_lengths = np.zeros(num_seq2seq_rows)
total_seq2seq_rows = 0 # we use this to shorten any trajectories later
for i in range(num_patients):
sequence_length = int(sequence_lengths[i])
for t in range(1, sequence_length): # shift outputs back by 1
seq2seq_state_inits[total_seq2seq_rows, :] = states[0][i, t - 1, :] # previous state output
seq2seq_state_inits1[total_seq2seq_rows, :] = states[1][i, t - 1, :] # previous state output
max_projection = min(projection_horizon, sequence_length - t)
seq2seq_active_entries[total_seq2seq_rows, :max_projection, :] = active_entries[i, t:t + max_projection, :]
seq2seq_previous_treatments[total_seq2seq_rows, :max_projection, :] = previous_treatments[i,
t - 1:t + max_projection - 1, :]
seq2seq_current_treatments[total_seq2seq_rows, :max_projection, :] = current_treatments[i,
t:t + max_projection, :]
seq2seq_outputs[total_seq2seq_rows, :max_projection, :] = outputs[i, t:t + max_projection, :]
seq2seq_sequence_lengths[total_seq2seq_rows] = max_projection
seq2seq_current_covariates[total_seq2seq_rows, :max_projection, :] = current_covariates[i,
t:t + max_projection, :]
total_seq2seq_rows += 1
# Filter everything shorter
seq2seq_state_inits = seq2seq_state_inits[:total_seq2seq_rows, :] # 556057,SIZE
seq2seq_state_inits1 = seq2seq_state_inits1[:total_seq2seq_rows, :]
seq2seq_previous_treatments = seq2seq_previous_treatments[:total_seq2seq_rows, :, :] #556057, 5, 4
seq2seq_current_treatments = seq2seq_current_treatments[:total_seq2seq_rows, :, :]
seq2seq_current_covariates = seq2seq_current_covariates[:total_seq2seq_rows, :, :] #556057, 5, 2
seq2seq_outputs = seq2seq_outputs[:total_seq2seq_rows, :, :] #556057, 5, 1
seq2seq_active_entries = seq2seq_active_entries[:total_seq2seq_rows, :, :]
seq2seq_sequence_lengths = seq2seq_sequence_lengths[:total_seq2seq_rows]
# Package outputs
seq2seq_data_map = {
'init_state': [seq2seq_state_inits,seq2seq_state_inits1],
'previous_treatments': seq2seq_previous_treatments,
'current_treatments': seq2seq_current_treatments,
'current_covariates': seq2seq_current_covariates,
'outputs': seq2seq_outputs,
'sequence_lengths': seq2seq_sequence_lengths,
'active_entries': seq2seq_active_entries,
'unscaled_outputs': seq2seq_outputs * data_map['output_stds'] + data_map['output_means'],
'output_means': data_map['output_means'],
'output_stds': data_map['output_stds'],
}
return seq2seq_data_map
def process_counterfactual_seq_test_data(test_data, data_map, states, projection_horizon):
sequence_lengths = test_data['sequence_lengths']
outputs = data_map['outputs']
current_treatments = data_map['current_treatments']
previous_treatments = data_map['previous_treatments']
current_covariates = data_map['current_covariates']
num_patient_points = outputs.shape[0]
sequence_lengths = sequence_lengths - 1
seq2seq_state_inits = np.zeros((num_patient_points, states[0].shape[-1]))
seq2seq_state_inits1 = np.zeros((num_patient_points, states[1].shape[-1]))
seq2seq_previous_treatments = np.zeros((num_patient_points, projection_horizon, previous_treatments.shape[-1]))
seq2seq_current_treatments = np.zeros((num_patient_points, projection_horizon, current_treatments.shape[-1]))
seq2seq_current_covariates = np.zeros((num_patient_points, projection_horizon, current_covariates.shape[-1]))
seq2seq_outputs = np.zeros((num_patient_points, projection_horizon, outputs.shape[-1]))
seq2seq_active_entries = np.zeros((num_patient_points, projection_horizon, 1))
seq2seq_sequence_lengths = np.zeros(num_patient_points)
for i in range(num_patient_points):
seq_length = int(sequence_lengths[i])
seq2seq_state_inits[i] = states[0][i, seq_length - 1]
seq2seq_state_inits1[i] = states[1][i, seq_length - 1]
seq2seq_active_entries[i] = np.ones(shape=(projection_horizon, 1))
seq2seq_previous_treatments[i] = previous_treatments[i, seq_length - 1:seq_length + projection_horizon - 1, :]
seq2seq_current_treatments[i] = current_treatments[i, seq_length:seq_length + projection_horizon, :]
seq2seq_outputs[i] = outputs[i, seq_length: seq_length + projection_horizon, :]
seq2seq_sequence_lengths[i] = projection_horizon
seq2seq_current_covariates[i] = np.repeat([current_covariates[i, seq_length - 1]], projection_horizon, axis=0) # 为什么是repeat
# Package outputs
seq2seq_data_map = {
'init_state': [seq2seq_state_inits,seq2seq_state_inits1],
'previous_treatments': seq2seq_previous_treatments,
'current_treatments': seq2seq_current_treatments,
'current_covariates': seq2seq_current_covariates,
'outputs': seq2seq_outputs,
'sequence_lengths': seq2seq_sequence_lengths,
'active_entries': seq2seq_active_entries,
'unscaled_outputs': seq2seq_outputs * data_map['output_stds'] + data_map['output_means'],
'output_means': data_map['output_means'],
'output_stds': data_map['output_stds'],
'patient_types': test_data['patient_types'],
'patient_ids_all_trajectories': test_data['patient_ids_all_trajectories'],
'patient_current_t': test_data['patient_current_t']
}
return seq2seq_data_map
def test_DRCRN_decoder(pickle_map, max_projection_horizon, projection_horizon, models_dir,
encoder_model_name, encoder_hyperparams_file,
decoder_model_name, decoder_hyperparams_file,
b_decoder_hyperparm_tuning):
training_data = pickle_map['training_data']
validation_data = pickle_map['validation_data']
scaling_data = pickle_map['scaling_data']
training_processed = get_processed_data(training_data, scaling_data)
validation_processed = get_processed_data(validation_data, scaling_data)
encoder_model = load_trained_model(validation_processed, encoder_hyperparams_file, encoder_model_name, models_dir)
training_dr_states = encoder_model.get_disentangled_reps(training_processed)
validation_dr_states = encoder_model.get_disentangled_reps(validation_processed)
training_seq_processed = process_seq_data(training_processed, training_dr_states, max_projection_horizon)
validation_seq_processed = process_seq_data(validation_processed, validation_dr_states, max_projection_horizon)
# if b_decoder_hyperparm_tuning:
fit_DRCRN_decoder(dataset_train=training_seq_processed, dataset_val=validation_seq_processed,
model_dir=models_dir,
model_name=decoder_model_name, encoder_hyperparams_file=encoder_hyperparams_file,
decoder_hyperparams_file=decoder_hyperparams_file, b_hyperparam_opt=b_decoder_hyperparm_tuning)
# else:
# pass
test_data_seq_actions = pickle_map['test_data_seq']
test_processed = get_processed_data(pickle_map['test_data_seq'], scaling_data)
encoder_model = load_trained_model(test_processed, encoder_hyperparams_file, encoder_model_name,
models_dir)
test_dr_states = encoder_model.get_disentangled_reps(test_processed)
test_dr_outputs = encoder_model.get_predictions(test_processed)
test_seq_processed = process_counterfactual_seq_test_data(test_data_seq_actions, test_processed, test_dr_states,
projection_horizon)
DRCRN_deocoder = load_trained_model(test_seq_processed, decoder_hyperparams_file, decoder_model_name, models_dir,
b_decoder_model=True)
seq_predictions = DRCRN_deocoder.get_autoregressive_sequence_predictions(test_data_seq_actions, test_processed,
test_dr_states, test_dr_outputs,
projection_horizon)
seq_predictions = seq_predictions * test_seq_processed['output_stds'] + test_seq_processed['output_means']
# During the simulation some trajectories in the test set have nan values. These were removed when
# computing the test metric. This only happens for the test set where we generate counterfactuals under different
# treatment plans.
nan_idx = np.unique(np.where(np.isnan(test_seq_processed['unscaled_outputs']))[0])
not_nan = np.array([i for i in range(seq_predictions.shape[0]) if i not in nan_idx])
mse = get_mse_at_follow_up_time(seq_predictions[not_nan], test_seq_processed['unscaled_outputs'][not_nan],
test_seq_processed['active_entries'][not_nan])
rmse = np.sqrt(mse[projection_horizon - 1]) / 1150 * 100 # Max tumour volume = 1150
return rmse