code: import sys
  import csv
  import numpy as np
  import gpflow
  import os
  import pandas as pd
  import h5py
  from sklearn.model_selection import train_test_split
  import tensorflow as tf
  from scipy.cluster.vq import kmeans
  tf.set_random_seed(1234)
  import pickle
  import argparse
  import PKutils
  
  def train_model(**kwargs):
  	npseed = kwargs['npseed']
  	iters = kwargs['iterations']
  	gpu = kwargs['gpu']
  	numInducingPoints = kwargs['IP']
  	print("npseed: " + str(npseed))
  	print("iterations: " + str(iters))
  	print("gpu: " + str(gpu))
  	print("IPs: " + str(numInducingPoints))
  
  	os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
  	os.environ["CUDA_VISIBLE_DEVICES"]=str(gpu)
  
  	print("Loading Data ....")
  	Vnoswitchdf = pd.read_csv("LastSwitchThatTrial.csv")
  	result = Vnoswitchdf["result"]
  	testtrim = Vnoswitchdf[["goalieypos","ball_xpos","ball_ypos","goalie_yvel","ball_yvel","opp","tslc","subID"]]
  	cputrialsdf = testtrim[testtrim["opp"]==0] #trials against computer goalie
  	cputrialsdf_result = result.loc[cputrialsdf.index]
  	humantrialsdf = testtrim[testtrim["opp"]==1]
  	humantrialsdf_result = result.loc[humantrialsdf.index]
  	humantrialsdf["subID"] = humantrialsdf["subID"].astype('int')
  	goalie1trialsdf = humantrialsdf[humantrialsdf["subID"]<50]
  	goalie1trialsdf_result = humantrialsdf_result.loc[goalie1trialsdf.index]
  	goalie2trialsdf = humantrialsdf[humantrialsdf["subID"]>=50]
  	goalie2trialsdf_result = humantrialsdf_result.loc[goalie2trialsdf.index]
  	del goalie2trialsdf["subID"]
  	del goalie1trialsdf["subID"]
  	del cputrialsdf["subID"]
  
  	# Train the GPs
  	X_train, X_test = train_test_split(goalie1trialsdf, test_size=0.2, random_state=1)
  	y_train, y_test = train_test_split(goalie1trialsdf_result, test_size=0.2, random_state=1)
  	optimizer = 'Adam'
  	mb = 256
  
  	np.random.seed(npseed)
  	Ms = numInducingPoints
  	X = np.array(X_train, dtype=float) 
  	Y = np.array(y_train, dtype=float)
  	Y = np.expand_dims(Y,axis=-1)
  	Z = kmeans(X_train, Ms, iter=1)[0]
  	Z = np.array(Z, dtype=float)
  	dimsize = X.shape[1]
  	kernel = gpflow.kernels.RBF(input_dim=dimsize, ARD=True)
  
  	m = gpflow.models.SVGP(
  	    X,Y, kern=kernel,
  	    likelihood=gpflow.likelihoods.Bernoulli(), Z=Z, minibatch_size=mb)
  	m.feature.set_trainable(True)
  
  	global_step = tf.get_variable("global_step", (), tf.int32, tf.zeros_initializer(), trainable=False)
  	learning_rate = 0.001 #adam default
  
  	experstring = 'Vnoswitch_goalie1_iters' + str(iters) + '_inducingpts' + str(numInducingPoints) + '_' + "_npseed" + str(npseed)
  	fw = tf.summary.FileWriter("Vnoswitchtrain_logs/{}".format(experstring), m.graph)
  
  	#define summary scalars for examination in tensorboard
  	tf.summary.scalar("likelihood", m._build_likelihood())
  	tf.summary.scalar("lengthscales_goalieposy", tf.gather(m.kern.lengthscales._constrained_tensor, 0))
  	tf.summary.scalar("lengthscales_shooterposx", tf.gather(m.kern.lengthscales._constrained_tensor, 1))
  	tf.summary.scalar("lengthscales_shooterposy", tf.gather(m.kern.lengthscales._constrained_tensor, 2))
  	tf.summary.scalar("lengthscales_goalievely", tf.gather(m.kern.lengthscales._constrained_tensor, 3))
  	tf.summary.scalar("lengthscales_shootervely", tf.gather(m.kern.lengthscales._constrained_tensor, 4))
  	tf.summary.scalar("lengthscales_opp", tf.gather(m.kern.lengthscales._constrained_tensor, 5))
  	tf.summary.scalar("lengthscales_timesincelastchange", tf.gather(m.kern.lengthscales._constrained_tensor, 6))
  
  	mysum = tf.summary.merge_all()
  	def loss_callback(summary):
  	    fw.add_summary(summary, loss_callback.iter)
  	    loss_callback.iter += 1
  	loss_callback.iter=0
  	print("Training Model...")
  	gpflow.train.AdamOptimizer(learning_rate).minimize(m, maxiter=iters, var_list=[global_step], global_step=global_step, summary_op=mysum, file_writer=fw)
  
  	#save model
  	param_dict = {p[0].full_name.replace('SGPR', 'SGPU'): p[1] for p in zip(m.trainable_parameters, m.read_trainables())}
  	with open('VnoswitchGPs/goalie1noswitchVmodel_'+str(numInducingPoints)+'IP_np'+str(npseed)+ '_iters' + str(iters) + '.pickle', 'wb') as handle:
  	    pickle.dump(param_dict, handle, protocol=pickle.HIGHEST_PROTOCOL)
  	m.as_pandas_table().to_pickle('VnoswitchGPs/goalie1modelparams_'+str(numInducingPoints)+'IP_np'+str(npseed) + '_iters'+str(iters))
  
  	print("goalie1 Value GP Complete")
  	
  if __name__ == "__main__":
  	parser = argparse.ArgumentParser()
  	parser.add_argument('--npseed',default=1, type=int)
  	parser.add_argument('--iterations',default=100000,type=int)
  	parser.add_argument('--gpu',default=0, type=int)
  	parser.add_argument('--IP', default=500, type=int)
  	args = parser.parse_args()
  	train_model(**vars(args))
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