def get_predictions(config):
net_name = config[0]
hidden_activation, output_activation = activation_map[net_name]
# Pull datasets
b_predict_actions = "treatment_rnn" in net_name
b_use_actions_only = "rnn_action_inputs_only" in net_name
b_predict_censoring = "censor_rnn" in net_name
# Extract only relevant trajs and shift data
training_processed = core.get_processed_data(training_data,
scaling_data,
b_predict_actions,
b_use_actions_only,
b_predict_censoring)
validation_processed = core.get_processed_data(validation_data,
scaling_data,
b_predict_actions,
b_use_actions_only,
b_predict_censoring)
num_features = training_processed['scaled_inputs'].shape[
-1] # 4 if not b_use_actions_only else 3
num_outputs = training_processed['scaled_outputs'].shape[
-1] # 1 if not b_predict_actions else 3 # 5
# Unpack remaining variables
dropout_rate = config[1]
memory_multiplier = config[2] / num_features
num_epochs = config[3]
minibatch_size = config[4]
learning_rate = config[5]
max_norm = config[6]
model_folder = os.path.join(MODEL_ROOT, net_name)
means, outputs, _, _ = test(training_processed, validation_processed,
training_processed, tf_config, net_name,
expt_name, dropout_rate, num_features,
num_outputs, memory_multiplier, num_epochs,
minibatch_size, learning_rate, max_norm,
hidden_activation, output_activation,
model_folder)
return means, outputs
learning_rate = spec[4]
max_norm = spec[5]
hidden_activation, output_activation = activation_map[net_name]
model_folder = os.path.join(MODEL_ROOT, net_name)
train_preds, _, _, train_states = test(
training_processed,
validation_processed,
training_processed,
tf_config,
net_name,
expt_name,
dropout_rate,
num_features,
num_outputs,
memory_multiplier,
num_epochs,
minibatch_size,
learning_rate,
max_norm,
hidden_activation,
output_activation,
model_folder,
b_use_state_initialisation=False,
b_dump_all_states=True)
valid_preds, _, _, valid_states = test(
training_processed,
validation_processed,
validation_processed,
tf_config,
def generate_seq2seq_data(net_name,
chemo_coeff,
radio_coeff,
window_size=15):
# Setup the simulated datasets
b_load = True
b_save = True
pickle_map = core.get_cancer_sim_data(chemo_coeff,
radio_coeff,
b_load=b_load,
b_save=b_save,
window_size=window_size)
chemo_coeff = pickle_map['chemo_coeff']
radio_coeff = pickle_map['radio_coeff']
num_time_steps = pickle_map['num_time_steps']
training_data = pickle_map['training_data']
validation_data = pickle_map['validation_data']
test_data = pickle_map['test_data']
# Use scaling data only from the original
scale_map = core.get_cancer_sim_data(10,
10,
b_load=True,
b_save=True,
seed=100)
scaling_data = scale_map['scaling_data']
# Pull datasets
b_predict_actions = "treatment_rnn" in net_name
b_predict_censoring = "censor_rnn" in net_name
b_propensity_weight = "rnn_propensity_weighted" in net_name
b_use_actions_only = "rnn_action_inputs_only" in net_name
# checks
if b_predict_actions and b_predict_censoring:
raise ValueError(
"problem with RNN! RNN is both actions and censoring")
# Extract only relevant trajs and shift data
training_processed = core.get_processed_data(training_data,
scaling_data,
b_predict_actions,
b_use_actions_only,
b_predict_censoring)
validation_processed = core.get_processed_data(validation_data,
scaling_data,
b_predict_actions,
b_use_actions_only,
b_predict_censoring)
test_processed = core.get_processed_data(test_data, scaling_data,
b_predict_actions,
b_use_actions_only,
b_predict_censoring)
num_features = training_processed['scaled_inputs'].shape[
-1] # 4 if not b_use_actions_only else 3
num_outputs = training_processed['scaled_outputs'].shape[
-1] # 1 if not b_predict_actions else 3 # 5
# Load propensity weights if they exist
if b_propensity_weight:
# raise NotImplementedError("Propensity weights will be added later")
if net_name == 'rnn_propensity_weighted_den_only':
propensity_weights = np.load(
os.path.join(MODEL_ROOT, "propensity_scores_den_only.npy"))
elif net_name == "rnn_propensity_weighted_logistic":
propensity_weights = np.load(
os.path.join(MODEL_ROOT, "propensity_scores.npy"))
tmp = np.load(
os.path.join(MODEL_ROOT, "propensity_scores_logistic.npy"))
propensity_weights = tmp[:propensity_weights.shape[0], :, :]
else:
propensity_weights = np.load(
os.path.join(MODEL_ROOT, "propensity_scores.npy"))
training_processed['propensity_weights'] = propensity_weights
logging.info(
"Loading basic network to generate states: {}".format(net_name))
if net_name not in encoder_specifications:
raise ValueError(
"Can't find term in hyperparameter specifications")
spec = encoder_specifications[net_name]
logging.info("Using specifications for {}: {}".format(net_name, spec))
dropout_rate = spec[0]
memory_multiplier = spec[1] / num_features
num_epochs = spec[2]
minibatch_size = spec[3]
learning_rate = spec[4]
max_norm = spec[5]
hidden_activation, output_activation = activation_map[net_name]
model_folder = os.path.join(MODEL_ROOT, net_name)
train_preds, _, _, train_states = test(
training_processed,
validation_processed,
training_processed,
tf_config,
net_name,
expt_name,
dropout_rate,
num_features,
num_outputs,
memory_multiplier,
num_epochs,
minibatch_size,
learning_rate,
max_norm,
hidden_activation,
output_activation,
model_folder,
b_use_state_initialisation=False,
b_dump_all_states=True)
valid_preds, _, _, valid_states = test(
training_processed,
validation_processed,
validation_processed,
tf_config,
net_name,
expt_name,
dropout_rate,
num_features,
num_outputs,
memory_multiplier,
num_epochs,
minibatch_size,
learning_rate,
max_norm,
hidden_activation,
output_activation,
model_folder,
b_use_state_initialisation=False,
b_dump_all_states=True)
test_preds, _, _, test_states = test(training_processed,
validation_processed,
test_processed,
tf_config,
net_name,
expt_name,
dropout_rate,
num_features,
num_outputs,
memory_multiplier,
num_epochs,
minibatch_size,
learning_rate,
max_norm,
hidden_activation,
output_activation,
model_folder,
b_use_state_initialisation=False,
b_dump_all_states=True)
# Repackage inputs here
training_processed = process_seq_data(
training_processed,
train_states,
num_features_to_include=num_outputs)
validation_processed = process_seq_data(
validation_processed,
valid_states,
num_features_to_include=num_outputs)
test_processed = process_seq_data(test_processed,
test_states,
num_features_to_include=num_outputs)
return training_processed, validation_processed, test_processed
logging.info("Using specifications for {}: {}".format(
seq_net_name, spec))
dropout_rate = spec[0]
memory_multiplier = spec[
1] / num_features # hack to recover correct size
num_epochs = spec[2]
minibatch_size = spec[3]
learning_rate = spec[4]
max_norm = spec[5]
hidden_activation, output_activation = activation_map[net_name]
_, _, mse, _ \
= test(training_processed, validation_processed, test_processed, tf_config,
seq_net_name, expt_name, dropout_rate, num_features, num_outputs,
memory_multiplier, num_epochs, minibatch_size, learning_rate, max_norm,
hidden_activation, output_activation, model_folder,
b_use_state_initialisation=True, b_dump_all_states=False,
b_mse_by_time=True,
b_use_memory_adapter=b_apply_memory_adapter)
mse = mse.flatten()
for proj_idx in range(mse.shape[0]):
if proj_idx > 4:
break
if proj_idx not in projection_map[seq_net_name]:
projection_map[seq_net_name][proj_idx] = \
pd.DataFrame([], index=[i for i in range(max_coeff + 1)],
columns=[i for i in range(max_coeff + 1)])
