def main(args):
"""Main function of RL-LIM for synthetic data experiments.
Args:
args: data_name, train_no, probe_no, test_no,
seed, hyperparameters, network parameters
"""
# Problem specification
problem = args.problem
# The ratio between training and probe datasets
train_rate = args.train_rate
probe_rate = args.probe_rate
dict_rate = {'train': train_rate, 'probe': probe_rate}
# Random seed
seed = args.seed
# Network parameters
parameters = dict()
parameters['hidden_dim'] = args.hidden_dim
parameters['iterations'] = args.iterations
parameters['num_layers'] = args.num_layers
parameters['batch_size'] = args.batch_size
parameters['batch_size_inner'] = args.batch_size_inner
parameters['lambda'] = args.hyper_lambda
# Checkpoint file name
checkpoint_file_name = args.checkpoint_file_name
# Number of sample explanations
n_exp = args.n_exp
# Loads data
data_loading.load_facebook_data(dict_rate, seed)
print('Finished data loading.')
# Preprocesses data
# Normalization methods: either 'minmax' or 'standard'
normalization = args.normalization
# Extracts features and labels & normalizes features
x_train, y_train, x_probe, _, x_test, y_test, col_names = \
data_loading.preprocess_data(normalization,
'train.csv', 'probe.csv', 'test.csv')
print('Finished data preprocess.')
# Trains black-box model
# Initializes black-box model
if problem == 'regression':
bb_model = lightgbm.LGBMRegressor()
elif problem == 'classification':
bb_model = lightgbm.LGBMClassifier()
# Trains black-box model
bb_model = bb_model.fit(x_train, y_train)
print('Finished black-box model training.')
# Constructs auxiliary datasets
if problem == 'regression':
y_train_hat = bb_model.predict(x_train)
y_probe_hat = bb_model.predict(x_probe)
elif problem == 'classification':
y_train_hat = bb_model.predict_proba(x_train)[:, 1]
y_probe_hat = bb_model.predict_proba(x_probe)[:, 1]
print('Finished auxiliary dataset construction.')
# Trains interpretable baseline
# Defines baseline
baseline = linear_model.Ridge(alpha=1)
# Trains baseline model
baseline.fit(x_train, y_train_hat)
print('Finished interpretable baseline training.')
# Trains instance-wise weight estimator
# Defines locally interpretable model
interp_model = linear_model.Ridge(alpha=1)
# Initializes RL-LIM
rllim_class = rllim.Rllim(x_train, y_train_hat, x_probe, y_probe_hat,
parameters, interp_model, baseline,
checkpoint_file_name)
# Trains RL-LIM
rllim_class.rllim_train()
print('Finished instance-wise weight estimator training.')
# Interpretable inference
# Trains locally interpretable models and output
# instance-wise explanations (test_coef) and
# interpretable predictions (test_y_fit)
test_y_fit, test_coef = rllim_class.rllim_interpreter(
x_train, y_train_hat, x_test, interp_model)
print('Finished instance-wise predictions and local explanations.')
# Overall performance
mae = rllim_metrics.overall_performance_metrics(y_test,
test_y_fit,
metric='mae')
print('Overall performance of RL-LIM in terms of MAE: ' +
str(np.round(mae, 4)))
# Black-box model predictions
y_test_hat = bb_model.predict(x_test)
# Fidelity in terms of MAE
mae = rllim_metrics.fidelity_metrics(y_test_hat, test_y_fit, metric='mae')
print('Fidelity of RL-LIM in terms of MAE: ' + str(np.round(mae, 4)))
# Fidelity in terms of R2 Score
r2 = rllim_metrics.fidelity_metrics(y_test_hat, test_y_fit, metric='r2')
print('Fidelity of RL-LIM in terms of R2 Score: ' + str(np.round(r2, 4)))
# Instance-wise explanations
# Local explanations of n_exp samples
local_explanations = test_coef[:n_exp, :]
final_col_names = np.concatenate((np.asarray(['intercept']), col_names),
axis=0)
pd.DataFrame(data=local_explanations,
index=range(n_exp),
columns=final_col_names)
def main(args):
"""Main function of RL-LIM for synthetic data experiments.
Args:
args: data_name, train_no, probe_no, test_no,
seed, hyperparameters, network parameters
"""
# Inputs
data_name = args.data_name
# The number of training, probe and testing samples
train_no = args.train_no
probe_no = args.probe_no
test_no = args.test_no
dim_no = args.dim_no
dict_no = {
'train': train_no,
'probe': probe_no,
'test': test_no,
'dim': dim_no
}
# Random seed
seed = args.seed
# Network parameters
parameters = dict()
parameters['hidden_dim'] = args.hidden_dim
parameters['iterations'] = args.iterations
parameters['num_layers'] = args.num_layers
parameters['batch_size'] = args.batch_size
parameters['batch_size_inner'] = args.batch_size_inner
parameters['lambda'] = args.hyper_lambda
# Checkpoint file name
checkpoint_file_name = args.checkpoint_file_name
# Loads data
x_train, y_train, x_probe, y_probe, x_test, y_test, c_test = \
data_loading.load_synthetic_data(data_name, dict_no, seed)
print('Finish ' + str(data_name) + ' data loading')
# Trains interpretable baseline
# Defins baseline
baseline = linear_model.Ridge(alpha=1)
# Trains interpretable baseline model
baseline.fit(x_train, y_train)
print('Finished interpretable baseline training.')
# Trains instance-wise weight estimator
# Defines locally interpretable model
interp_model = linear_model.Ridge(alpha=1)
# Initializes RL-LIM
rllim_class = rllim.Rllim(x_train, y_train, x_probe, y_probe, parameters,
interp_model, baseline, checkpoint_file_name)
# Trains RL-LIM
rllim_class.rllim_train()
print('Finished instance-wise weight estimator training.')
# Interpretable inference
# Trains locally interpretable models and output
# instance-wise explanations (test_coef)
# and interpretable predictions (test_y_fit)
test_y_fit, test_coef = \
rllim_class.rllim_interpreter(x_train, y_train, x_test, interp_model)
print('Finished interpretable predictions and local explanations.')
# Fidelity
mae = rllim_metrics.fidelity_metrics(y_test, test_y_fit, metric='mae')
print('fidelity of RL-LIM in terms of MAE: ' + str(np.round(mae, 4)))
# Absolute Weight Differences (AWD) between ground truth local dynamics and
# estimated local dynamics by RL-LIM
awd = rllim_metrics.awd_metric(c_test, test_coef)
print('AWD of RL-LIM: ' + str(np.round(awd, 4)))
# Fidelity plot
rllim_metrics.plot_result(x_test,
data_name,
y_test,
test_y_fit,
c_test,
test_coef,
metric='mae',
criteria='Fidelity')
# AWD plot
rllim_metrics.plot_result(x_test,
data_name,
y_test,
test_y_fit,
c_test,
test_coef,
metric='mae',
criteria='AWD')