def test_eval(self):
with self.test_session() as sess:
x = Normal(0.0, 0.1)
x_ph = tf.placeholder(tf.float32, [])
y = Normal(x_ph, 0.1)
self.assertLess(x.eval(), 5.0)
self.assertLess(x.eval(sess), 5.0)
self.assertLess(x.eval(feed_dict={x_ph: 100.0}), 5.0)
self.assertGreater(y.eval(feed_dict={x_ph: 100.0}), 5.0)
self.assertGreater(y.eval(sess, feed_dict={x_ph: 100.0}), 5.0)
self.assertRaises(tf.errors.InvalidArgumentError, y.eval)
self.assertRaises(tf.errors.InvalidArgumentError, y.eval, sess)
D = np.float32(irt_data.values)
model.init_inference(data=D,n_iter=niter)
model.fit()
# generate output files #
# output ability
ability = pd.DataFrame(index=irt_data.columns)
ability['ability'] = tf.nn.sigmoid(model.qtheta.distribution.loc).eval()
ability.loc['stddev'] = ability.ability.std()
ability.to_csv(result_path+'/irt_ability_vi_'+partial_save_name+'.csv')
# output difficulty and discrimination
if args.fixed_a:
discrimination = a.eval()
else:
discrimination = model.qa.loc.eval()
difficulty = tf.nn.sigmoid(model.qdelta.distribution.loc).eval()
if not dataset in ['fashion','mnist']:
#if not args.fixed_a:
fig = vs.plot_parameters(xtest.values[:,:-1], difficulty, discrimination)
fig.savefig(result_path+'/irt_parameters_vi_'+partial_save_name+'.pdf')
parameters = pd.DataFrame(index=irt_data.index)
parameters['difficulty'] = difficulty
parameters['discrimination'] = discrimination
parameters.to_csv(result_path+'/irt_parameters_vi_'+partial_save_name+'.csv',index=False)
# visualize correlation between difficulty and response
def save(arr,xdata,ydata):
tf.reset_default_graph()
trainSetNumber = round(FLAGS.T* 0.8)
x_train = xdata[:trainSetNumber]
y_train = ydata[:trainSetNumber]
x_test = xdata[trainSetNumber:]
y_test = ydata[trainSetNumber:]
x_train = np.asarray(x_train)
x_test = np.asarray(x_test)
x_train = np.asarray(x_train)
x_test = np.asarray(x_test)
# print(x_test)
# print(y_test)
pos = 0
name = arr[pos]
pos +=1
H1 = int(arr[pos])
pos+=1
H2 = int(arr[pos])
pos+=1
param1 = float(arr[pos])
pos += 1
param2 = float(arr[pos])
graph1 = tf.Graph()
with graph1.as_default():
with tf.name_scope("model"):
W_0 = Normal(loc=tf.zeros([FLAGS.D, H1]), scale=param1*tf.ones([FLAGS.D,H1 ]),name="W_0")
W_1 = Normal(loc=tf.zeros([H1, H2]), scale=param2*tf.ones([H1, H2]), name="W_1")
W_2 = Normal(loc=tf.zeros([H2, FLAGS.O]), scale=param2*tf.ones([H2, FLAGS.O]), name="W_2")
b_0 = Normal(loc=tf.zeros(H1), scale=param1 *tf.ones(H1), name="b_0")
b_1 = Normal(loc=tf.zeros(H2), scale=param2* tf.ones(H2), name="b_1")
b_2 = Normal(loc=tf.zeros(FLAGS.O), scale=param2* tf.ones(FLAGS.O), name="b_2")
X = tf.placeholder(tf.float32, [trainSetNumber, FLAGS.D], name="X")
y = Normal(loc=neural_network(x_train,W_0, W_1, W_2, b_0, b_1, b_2, trainSetNumber), scale=0.1*tf.ones([trainSetNumber,FLAGS.O]), name="y")
with tf.variable_scope("posterior",reuse=tf.AUTO_REUSE):
with tf.variable_scope("qW_0",reuse=tf.AUTO_REUSE):
loc = tf.get_variable("loc", [FLAGS.D, H1])
scale = param1*tf.nn.softplus(tf.get_variable("scale", [FLAGS.D, H1]))
qW_0 = Normal(loc=loc, scale=scale)
with tf.variable_scope("qW_1",reuse=tf.AUTO_REUSE):
loc = tf.get_variable("loc", [H1, H2])
scale = param2*tf.nn.softplus(tf.get_variable("scale", [H1, H2]))
qW_1 = Normal(loc=loc, scale=scale)
with tf.variable_scope("qW_2",reuse=tf.AUTO_REUSE):
loc = tf.get_variable("loc", [H2, FLAGS.O])
scale = param2*tf.nn.softplus(tf.get_variable("scale", [H2, FLAGS.O]))
qW_2 = Normal(loc=loc, scale=scale)
with tf.variable_scope("qb_0",reuse=tf.AUTO_REUSE):
loc = tf.get_variable("loc", [H1])
scale =param1 * tf.nn.softplus(tf.get_variable("scale", [H1]))
qb_0 = Normal(loc=loc, scale=scale)
with tf.variable_scope("qb_1",reuse=tf.AUTO_REUSE):
loc = tf.get_variable("loc", [H2])
scale =param2 * tf.nn.softplus(tf.get_variable("scale", [H2]))
qb_1 = Normal(loc=loc, scale=scale)
with tf.variable_scope("qb_2",reuse=tf.AUTO_REUSE):
loc = tf.get_variable("loc", [FLAGS.O])
scale =param2 * tf.nn.softplus(tf.get_variable("scale", [FLAGS.O]))
qb_2 = Normal(loc=loc, scale=scale)
#inference
with tf.Session(graph=graph1) as sess:
# Set up the inference method, mapping the prior to the posterior variables
inference = ed.KLqp({W_0: qW_0, b_0: qb_0,W_1: qW_1, b_1: qb_1,W_2: qW_2, b_2: qb_2}, data={X: x_train, y: y_train})
# Set up the adam optimizer
global_step = tf.Variable(0, trainable=False)
starter_learning_rate = 0.1
learning_rate = tf.train.exponential_decay(starter_learning_rate, global_step,100, 0.3, staircase=True)
optimizer = tf.train.AdamOptimizer(learning_rate)
# Run the inference method
pos += 1
iter1 = arr[pos]
inference.run(n_iter=iter1,optimizer=optimizer ,n_samples=5)
#Run the test data through the neural network
infered = neural_network(x_test, qW_0, qW_1, qW_2, qb_0, qb_1, qb_2, len(x_test))
inferedList = infered.eval()
#Accuracy checks on the data (The test data)
# In order to work with PPC and other metrics, it must be a random variables
# Normal creates this random varaibles by sampling from the poterior with a normal distribution
NormalTest =Normal(loc=neural_network(x_test, qW_0, qW_1, qW_2, qb_0, qb_1, qb_2,len(x_test)), scale=0.1*tf.ones([len(x_test),FLAGS.O]), name="y_other")
NormalTestList = NormalTest.eval()
# Change the graph so that the posterior point to the output
y_post = ed.copy(NormalTest, {W_0: qW_0, b_0: qb_0,W_1: qW_1, b_1: qb_1,W_2: qW_2, b_2: qb_2})
X = tf.placeholder(tf.float32, [len(x_test), FLAGS.D], name="X")
y_test_tensor = tf.convert_to_tensor(y_test)
MSE = ed.evaluate('mean_squared_error', data={X: x_test, NormalTest: y_test_tensor})
MAE =ed.evaluate('mean_absolute_error', data={X: x_test, NormalTest: y_test_tensor})
# PPC calculation
PPCMean = ed.ppc(lambda xs, zs: tf.reduce_mean(xs[y_post]), data={y_post: y_test, X:x_test}, latent_vars={W_0: qW_0, b_0: qb_0,W_1: qW_1, b_1: qb_1,W_2: qW_2, b_2: qb_2}, n_samples=5)
# Change the graph again, this is done to do epistemic uncertainty calculations
posterior = ed.copy(NormalTest, dict_swap={W_0: qW_0.mean(), b_0: qb_0.mean(),W_1: qW_1.mean(), b_1: qb_1.mean(),W_2: qW_2.mean(), b_2: qb_2.mean()})
Y_post1 = sess.run(posterior.sample(len(x_test)), feed_dict={X: x_test, posterior: y_test})
mean_prob_over_samples=np.mean(Y_post1, axis=0) ## prediction means
prediction_variances = np.apply_along_axis(predictive_entropy, axis=1, arr=mean_prob_over_samples)
# Run analysis on test data, to see how many records were correct
classes, actualClass, cor, firsts, seconds, thirds, fails, perCorrect = Analysis(inferedList, y_test)
# Save the model through TF saver
saver = tf.train.Saver()
dir_path = os.path.dirname(os.path.realpath(__file__))
save_path = saver.save(sess, dir_path +"/"+name+"/model.ckpt")
print("Model saved in path: %s" % save_path)
file = open(dir_path+"/"+name +"/"+name+".csv",'w')
file.write("MSE = " + str(MSE))
file.write("\nMAE = " + str(MAE))
file.write("\nPPC mean = " + str(PPCMean))
file.write("; Predicted First;Predicted Second; Predicted Third; Predicted Fail \n")
classNames = ['First','Second', 'Third', 'Fail']
for x in range(len(firsts)):
file.write(classNames[x] + ";" + str(firsts[x]) + ";" + str(seconds[x])+ ";" + str(thirds[x])+ ";" + str(fails[x]) + "\n")
file.write("Num;Class 1;Class 2;Class 3;Class 4;Epi;Predicted Class;Correct Class\n ")
for x in range(len(inferedList)):
line = str(x)
for i in range(len(inferedList[x])):
line += ";" + str(round(inferedList[x][i],2))
line += ";" + str(round(prediction_variances[x],2)) + ";" + str(classes[x]+1) + ";" + str(actualClass[x]+1) + "\n"
file.write(line)
file.close()
return perCorrect
plt.scatter(x_train[:,0], np.log(mu[:,which_species]));
for _ in range(20):
plt.scatter(x_train[:,0], np.log(qyhat.eval()[:,which_species]), alpha=0.1, c="darkred", s=10);
# In[ ]:
plt.hist(qz.stddev().eval(), bins=50);
# In[ ]:
# Approximate posterior distributions of z, given x. Should be no big (low-frequency?) trends or gaps
for i in range(n_z):
for _ in range(25):
plt.scatter(x_train[:,0], qz.eval()[:,i], alpha=0.1, c="darkred", s=5)
plt.show()
# In[ ]:
nn = 1000
xx = 0
qxx = 0
for _ in range(nn):
xx += yhat.eval() / nn
qxx += qyhat.eval() / nn
# Predicted mean versus x
plt.scatter(x_train[:,0], np.log(mu)[:,which_species])
plt.scatter(x_train[:,0], np.log(xx)[:,which_species], s=10, alpha=0.5, c="black");
def main():
try:
if not ('.csv' in args.input): raise Exception('input_format')
if not ('.pkl' in args.output): raise Exception('output_format')
with open(args.input, 'rb') as input:
# DATA
reader = csv.reader(input, delimiter=';')
reader.next()
n = 0
xn = []
for track in reader:
print('Track {}'.format(n))
track = format_track(track[0])
xn.append(track)
n += 1
xn = np.asarray(xn) # N x D
xn = xn.T # D x N
D = len(xn)
N = len(xn[0])
# MODEL
ds = tf.contrib.distributions
sigma = ed.models.Gamma(1.0, 1.0)
alpha = ed.models.Gamma(tf.ones([K]), tf.ones([K]))
w = Normal(mu=tf.zeros([D, K]),
sigma=tf.reshape(tf.tile(alpha, [D]), [D, K]))
z = Normal(mu=tf.zeros([K, N]), sigma=tf.ones([K, N]))
mu = Normal(mu=tf.zeros([D]), sigma=tf.ones([D]))
x = Normal(mu=tf.matmul(w, z) +
tf.transpose(tf.reshape(tf.tile(mu, [N]), [N, D])),
sigma=sigma * tf.ones([D, N]))
# INFERENCE
qalpha = ed.models.TransformedDistribution(
distribution=ed.models.NormalWithSoftplusSigma(
mu=tf.Variable(tf.random_normal([K])),
sigma=tf.Variable(tf.random_normal([K]))),
bijector=ds.bijector.Exp(),
name='qalpha')
qw = Normal(mu=tf.Variable(tf.random_normal([D, K])),
sigma=tf.nn.softplus(
tf.Variable(tf.random_normal([D, K]))))
qz = Normal(mu=tf.Variable(tf.random_normal([K, N])),
sigma=tf.nn.softplus(
tf.Variable(tf.random_normal([K, N]))))
data_mean = np.mean(xn, axis=1).astype(np.float32, copy=False)
qmu = Normal(mu=tf.Variable(data_mean + tf.random_normal([D])),
sigma=tf.nn.softplus(
tf.Variable(tf.random_normal([D]))))
qsigma = ed.models.TransformedDistribution(
distribution=ed.models.NormalWithSoftplusSigma(
mu=tf.Variable(0.0), sigma=tf.Variable(1.0)),
bijector=ds.bijector.Exp(),
name='qsigma')
inference = ed.KLqp(
{
alpha: qalpha,
w: qw,
z: qz,
mu: qmu,
sigma: qsigma
},
data={x: xn})
inference.run(n_iter=N_ITERS, n_samples=N_SAMPLES)
alphas = tf.exp(qalpha.distribution.mean()).eval()
alphas.sort()
# mean_alphas = np.mean(alphas)
print('Alphas: {}'.format(alphas))
points = qz.eval()
xn_new = []
for i in range(len(alphas)):
# if alphas[i] > (mean_alphas * 1.2):
xn_new.append(points[i])
xn_new = np.asarray(xn_new).T
# Normalization
maxs = np.max(xn_new, axis=0)
mins = np.min(xn_new, axis=0)
rng = maxs - mins
high = 100.0
low = 0.0
xn_new = high - (((high - low) * (maxs - xn_new)) / rng)
print('New points: {}'.format(xn_new))
print('Number of points: {}'.format(len(xn_new)))
print('Point dimensions: {}'.format(len(xn_new[0])))
with open(args.output, 'w') as output:
pkl.dump({'xn': np.array(xn_new)}, output)
except IOError:
print('File not found!')
except Exception as e:
if e.args[0] == 'input_format': print('Input must be a CSV file')
elif e.args[0] == 'output_format': print('Output must be a PKL file')
else:
print('Unexpected error: {}'.format(sys.exc_info()[0]))
raise
class BayesianRegression:
def __init__(self, in_dim=1, n_classes=2):
"""
Bayesian Logistic regression based on Edward lib (http://edwardlib.org).
y = W * x + b
:param in_dim:
:param n_classes:
"""
self.in_dim = in_dim
self.n_classes = n_classes
self.X = tf.placeholder(tf.float32, [None, self.in_dim])
self.W = Normal(loc=tf.zeros([self.in_dim, self.n_classes]),
scale=tf.ones([self.in_dim, self.n_classes]))
self.b = Normal(loc=tf.zeros(self.n_classes),
scale=tf.ones(self.n_classes))
h = tf.matmul(self.X, self.W) + self.b
self.y = Normal(loc=tf.sigmoid(-h), scale=0.1)
self.qW = Normal(loc=tf.get_variable("qW/loc",
[self.in_dim, self.n_classes]),
scale=tf.nn.softplus(
tf.get_variable("qW/scale",
[self.in_dim, self.n_classes])))
self.qb = Normal(loc=tf.get_variable("qb/loc", [self.n_classes]),
scale=tf.nn.softplus(
tf.get_variable("qb/scale", [self.n_classes])))
def infer(self, X, y, n_samples=5, n_iter=250):
inference = ed.KLqp({
self.W: self.qW,
self.b: self.qb,
},
data={
self.y: y,
self.X: X
})
inference.run(n_samples=n_samples, n_iter=n_iter)
def predict(self, X):
self.qW_mean = self.qW.mean().eval()
self.qb_mean = self.qb.mean().eval()
h = tf.matmul(X, self.qW_mean) + self.qb_mean
return tf.sigmoid(-h).eval()
def sample_boudary(self, X):
qW = self.qW.eval()
qb = self.qb.eval()
w = -qW[0][0] / qW[1][0]
b = (0.5 - qb[0]) / qW[0][0]
return w, b
def predict_std(self, X):
self.qW_stddev = self.qW.stddev().eval()
self.qb_stddev = self.qb.stddev().eval()
h = tf.matmul(X, self.qW_stddev) + self.qb_stddev
return tf.sigmoid(-h).eval()
def get_coef(self):
return self.qW.mean().eval().T[0]
