def train(CFR, sess, train_step, D, I_valid, D_test, logfile, i_exp):
""" Trains a CFR model on supplied data """
''' Train/validation split '''
n = D['x'].shape[0]
I = range(n); I_train = list(set(I)-set(I_valid))
n_train = len(I_train)
''' Compute treatment probability'''
p_treated = np.mean(D['t'][I_train,:])
''' Set up loss feed_dicts'''
dict_factual = {CFR.x: D['x'][I_train,:], CFR.t: D['t'][I_train,:], CFR.y_: D['yf'][I_train,:], \
CFR.do_in: 1.0, CFR.do_out: 1.0, CFR.r_alpha: FLAGS.p_alpha, \
CFR.r_lambda: FLAGS.p_lambda, CFR.p_t: p_treated}
if FLAGS.val_part > 0:
dict_valid = {CFR.x: D['x'][I_valid,:], CFR.t: D['t'][I_valid,:], CFR.y_: D['yf'][I_valid,:], \
CFR.do_in: 1.0, CFR.do_out: 1.0, CFR.r_alpha: FLAGS.p_alpha, \
CFR.r_lambda: FLAGS.p_lambda, CFR.p_t: p_treated}
if D['HAVE_TRUTH']:
dict_cfactual = {CFR.x: D['x'][I_train,:], CFR.t: 1-D['t'][I_train,:], CFR.y_: D['ycf'][I_train,:], \
CFR.do_in: 1.0, CFR.do_out: 1.0}
''' Initialize TensorFlow variables '''
sess.run(tf.global_variables_initializer())
''' Set up for storing predictions '''
preds_train = []
preds_test = []
''' Compute losses '''
losses = []
obj_loss, f_error, imb_err = sess.run([CFR.tot_loss, CFR.pred_loss, CFR.imb_dist],\
feed_dict=dict_factual)
cf_error = np.nan
if D['HAVE_TRUTH']:
cf_error = sess.run(CFR.pred_loss, feed_dict=dict_cfactual)
valid_obj = np.nan; valid_imb = np.nan; valid_f_error = np.nan;
if FLAGS.val_part > 0:
valid_obj, valid_f_error, valid_imb = sess.run([CFR.tot_loss, CFR.pred_loss, CFR.imb_dist],\
feed_dict=dict_valid)
losses.append([obj_loss, f_error, cf_error, imb_err, valid_f_error, valid_imb, valid_obj])
objnan = False
reps = []
reps_test = []
''' Train for multiple iterations '''
for i in range(FLAGS.iterations):
''' Fetch sample '''
I = random.sample(range(0, n_train), FLAGS.batch_size)
x_batch = D['x'][I_train,:][I,:]
t_batch = D['t'][I_train,:][I]
y_batch = D['yf'][I_train,:][I]
if __DEBUG__:
M = sess.run(cfr.pop_dist(CFR.x, CFR.t), feed_dict={CFR.x: x_batch, CFR.t: t_batch})
log(logfile, 'Median: %.4g, Mean: %.4f, Max: %.4f' % (np.median(M.tolist()), np.mean(M.tolist()), np.amax(M.tolist())))
''' Do one step of gradient descent '''
if not objnan:
sess.run(train_step, feed_dict={CFR.x: x_batch, CFR.t: t_batch, \
CFR.y_: y_batch, CFR.do_in: FLAGS.dropout_in, CFR.do_out: FLAGS.dropout_out, \
CFR.r_alpha: FLAGS.p_alpha, CFR.r_lambda: FLAGS.p_lambda, CFR.p_t: p_treated})
''' Project variable selection weights '''
if FLAGS.varsel:
wip = simplex_project(sess.run(CFR.weights_in[0]), 1)
sess.run(CFR.projection, feed_dict={CFR.w_proj: wip})
''' Compute loss every N iterations '''
if i % FLAGS.output_delay == 0 or i==FLAGS.iterations-1:
obj_loss,f_error,imb_err = sess.run([CFR.tot_loss, CFR.pred_loss, CFR.imb_dist],
feed_dict=dict_factual)
rep = sess.run(CFR.h_rep_norm, feed_dict={CFR.x: D['x'], CFR.do_in: 1.0})
rep_norm = np.mean(np.sqrt(np.sum(np.square(rep), 1)))
cf_error = np.nan
if D['HAVE_TRUTH']:
cf_error = sess.run(CFR.pred_loss, feed_dict=dict_cfactual)
valid_obj = np.nan; valid_imb = np.nan; valid_f_error = np.nan;
if FLAGS.val_part > 0:
valid_obj, valid_f_error, valid_imb = sess.run([CFR.tot_loss, CFR.pred_loss, CFR.imb_dist], feed_dict=dict_valid)
losses.append([obj_loss, f_error, cf_error, imb_err, valid_f_error, valid_imb, valid_obj])
loss_str = str(i) + '\tObj: %.3f,\tF: %.3f,\tCf: %.3f,\tImb: %.2g,\tVal: %.3f,\tValImb: %.2g,\tValObj: %.2f' \
% (obj_loss, f_error, cf_error, imb_err, valid_f_error, valid_imb, valid_obj)
if FLAGS.loss == 'log':
y_pred = sess.run(CFR.output, feed_dict={CFR.x: x_batch, \
CFR.t: t_batch, CFR.do_in: 1.0, CFR.do_out: 1.0})
y_pred = 1.0*(y_pred > 0.5)
acc = 100*(1 - np.mean(np.abs(y_batch - y_pred)))
loss_str += ',\tAcc: %.2f%%' % acc
log(logfile, loss_str)
if np.isnan(obj_loss):
log(logfile,'Experiment %d: Objective is NaN. Skipping.' % i_exp)
objnan = True
''' Compute predictions every M iterations '''
if (FLAGS.pred_output_delay > 0 and i % FLAGS.pred_output_delay == 0) or i==FLAGS.iterations-1:
y_pred_f = sess.run(CFR.output, feed_dict={CFR.x: D['x'], \
CFR.t: D['t'], CFR.do_in: 1.0, CFR.do_out: 1.0})
y_pred_cf = sess.run(CFR.output, feed_dict={CFR.x: D['x'], \
CFR.t: 1-D['t'], CFR.do_in: 1.0, CFR.do_out: 1.0})
preds_train.append(np.concatenate((y_pred_f, y_pred_cf),axis=1))
if D_test is not None:
y_pred_f_test = sess.run(CFR.output, feed_dict={CFR.x: D_test['x'], \
CFR.t: D_test['t'], CFR.do_in: 1.0, CFR.do_out: 1.0})
y_pred_cf_test = sess.run(CFR.output, feed_dict={CFR.x: D_test['x'], \
CFR.t: 1-D_test['t'], CFR.do_in: 1.0, CFR.do_out: 1.0})
preds_test.append(np.concatenate((y_pred_f_test, y_pred_cf_test),axis=1))
if FLAGS.save_rep and i_exp == 1:
reps_i = sess.run([CFR.h_rep], feed_dict={CFR.x: D['x'], \
CFR.do_in: 1.0, CFR.do_out: 0.0})
reps.append(reps_i)
if D_test is not None:
reps_test_i = sess.run([CFR.h_rep], feed_dict={CFR.x: D_test['x'], \
CFR.do_in: 1.0, CFR.do_out: 0.0})
reps_test.append(reps_test_i)
return losses, preds_train, preds_test, reps, reps_test
def run_descriptive_stats(self, x_batch, t_batch):
m_statistics = self.sess.run(cfr.pop_dist(self.CFR.x, self.CFR.t),
feed_dict={self.CFR.x: x_batch, self.CFR.t: t_batch})
return m_statistics