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CRPP_Recall.py
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CRPP_Recall.py
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import sys
import tensorflow as tf
import numpy as np
import math
from tensorflow.python import debug as tf_debug
processed = sys.argv[1]
rank=sys.argv[2]
def chunks(l, n):
"""Yield successive n-sized chunks from l."""
for i in xrange(0, len(l), n):
yield l[i:i + n]
with open(processed, "r") as f:
content = f.readlines()
f.close()
with open(rank, "r") as f2:
content2 = f2.readlines()
f2.close()
intervals=[]
givenrankintervals = []
no_intervals = 100
for line in content2:
tokens=line.split("\t")
t1=float(tokens[0])/10000
t2=float(tokens[1])/10000
givenrankintervals.append((t1,t2))
ranks = [int(x) for x in tokens[2:]]
#print ranks
L=len(givenrankintervals)
p=len(content)/7
tlast = givenrankintervals[L-1][1]
interval_size = tlast/no_intervals
marker = 0
for i in range(no_intervals):
intervals.append((marker, marker+interval_size))
marker = marker + interval_size
intervalcounts=[[0 for x in range(no_intervals)] for x in range(p)]
pred_intervalcounts=[[0 for x in range(no_intervals)] for x in range(p)]
test_t=[]
h_series = [[] for x in range(p)]
integral = [[] for x in range(p)]
num_epochs = 100
truncated_backprop_length = 15
echo_step = 3
batch_size = 5
batchk_placeholder = tf.placeholder(tf.float32, [batch_size, truncated_backprop_length])
batcht_placeholder = tf.placeholder(tf.float32, [batch_size, truncated_backprop_length])
init_h=tf.placeholder(tf.float32, (batch_size))
omega = tf.constant([0.01], shape=(batch_size,))
a_list = []
b_list = []
c_list = []
alpha_list = []
beta_list = []
gamma_list = []
t_series = [[] for x in range(p)]
k_series = [[] for x in range(p)]
G_loss = [[] for x in range(p)]
a = [[] for x in range(p)]
b = [[] for x in range(p)]
c = [[] for x in range(p)]
a1 = []
b1 = []
c1 = []
d1 = []
def greater(x, c):
return tf.nn.relu(tf.sign(x-c))
def lesser(x, c):
return tf.nn.relu(tf.sign(c-x))
l=0
for n in range(p):
alpha=tf.Variable([0.001 for x in range(batch_size)])
beta=tf.Variable([0.001 for x in range(batch_size)])
gamma=tf.Variable([0.001 for x in range(batch_size)])
a[n]=tf.Variable([0.001 for x in range(batch_size)])
b[n]=tf.Variable([0.001 for x in range(batch_size)])
c[n]=tf.Variable([0.001 for x in range(batch_size)])
str_k=content[l]
k=[float(ki) for ki in str_k.split(",")]
str_train=content[l+1].rstrip()
train_t=[float(ti)/10000 for ti in str_train.split(",")]
str_test=content[l+6].rstrip()
test_t=[float(ti)/10000 for ti in str_test.split(",")]
l=l+7
first_training_instance = train_t[0]
last_training_instance = train_t[-1]
first_training_interval = 0
last_training_interval = no_intervals - 1
while train_t[0]>intervals[first_training_interval][1]:
first_training_interval = first_training_interval+1
while train_t[-1]<intervals[last_training_interval][0]:
last_training_interval = last_training_interval-1
trainingintervals = intervals[:last_training_interval]
counter = 0
for i in range(len(train_t)):
while train_t[i]>intervals[counter][1]:
counter = counter+1
intervalcounts[n][counter] = intervalcounts[n][counter] + 1
for i in range(len(test_t)):
while counter<no_intervals and test_t[i]>intervals[counter][1]:
counter = counter+1
if counter==no_intervals:
break
intervalcounts[n][counter] = intervalcounts[n][counter] + 1
last_test_interval = no_intervals - 1
while intervalcounts[n][last_test_interval]==0:
last_test_interval = last_test_interval-1
#print intervalcounts[n][last_test_interval], intervals[last_test_interval]
no_training_intervals = last_training_interval-first_training_interval
no_test_intervals = no_intervals - no_training_intervals
total_series_length = len(k)
num_batches = total_series_length//batch_size//truncated_backprop_length
k_series[n] = tf.unstack(batchk_placeholder, axis=1)
t_series[n] = tf.unstack(batcht_placeholder, axis=1)
# Forward pass
current_h = init_h
for i in range(len(k_series)):
current_k = k_series[n][i]
current_t = t_series[n][i]
numerator = -omega*current_t
denominator = current_k
frac = tf.truediv(numerator,denominator)
kernel = tf.exp(frac)
next_h = tf.exp((alpha*current_h + beta*kernel + gamma))
h_series[n].append(next_h)
current_h = next_h
G_loss[n] = tf.reduce_sum(tf.exp(a[n]*h_series[n][-1]+b[n]*t_series[n][-1]+c[n])/b[n] - tf.exp(a[n]*h_series[n][-1]+b[n]*t_series[n][0]+c[n])/b[n]) - sum([tf.reduce_sum(a[n]*h_series[n][-1]+b[n]*(t_series[n][k1+1]-t_series[n][k1])+c[n]) for k1 in range(len(t_series)-1)])
regularizer = tf.add_n([tf.nn.l2_loss(a[n]),tf.nn.l2_loss(b[n]),tf.nn.l2_loss(c[n]),tf.nn.l2_loss(alpha),tf.nn.l2_loss(beta),tf.nn.l2_loss(gamma)])
total_loss = G_loss[n]+regularizer
train_step = tf.train.AdagradOptimizer(0.3).minimize(total_loss)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
loss_list = []
h_list = []
last_h = np.ones((batch_size))
last_batchk = np.ones((batch_size, truncated_backprop_length))
last_batcht = np.ones((batch_size, truncated_backprop_length))
#sess = tf_debug.LocalCLIDebugWrapperSession(sess)
#sess.add_tensor_filter("has_inf_or_nan", tf_debug.has_inf_or_nan)
#writer = tf.summary.FileWriter("/home/supritam/Documents/aaai/code", sess.graph)
for epoch_idx in range(num_epochs):
_current_h = np.ones((batch_size))
#print("New data, epoch", epoch_idx)
for batch_idx in range(num_batches):
start_idx = batch_idx * batch_size * truncated_backprop_length
end_idx = start_idx + batch_size * truncated_backprop_length
batchk = list(chunks(k[start_idx:end_idx], truncated_backprop_length))
batcht = list(chunks(train_t[start_idx:end_idx], truncated_backprop_length))
#---debugging statements---
#print batchk, _current_h
_a, _b, _c, _total_loss, _train_step, _current_h, = sess.run(
[a[n], b[n], c[n], total_loss, train_step, current_h],
feed_dict={
batchk_placeholder:batchk,
batcht_placeholder:batcht,
init_h:_current_h,
})
last_h = _current_h
last_batchk = batchk
last_batcht = batcht
loss_list.append(_total_loss)
h_list.append(_current_h)
#if batch_idx%100 == 0:
#print("Step", batch_idx, "Loss", _total_loss)
#print("a", _a)
#print("b", _b)
#print("c", _c)
saved_a, saved_b, saved_c, saved_alpha, saved_beta, saved_gamma = sess.run([a[n], b[n], c[n], alpha, beta, gamma], feed_dict={
batchk_placeholder:last_batchk,
batcht_placeholder:last_batcht,
init_h:last_h,
})
a_list.append(saved_a)
b_list.append(saved_b)
c_list.append(saved_c)
alpha_list.append(saved_alpha)
beta_list.append(saved_beta)
gamma_list.append(saved_gamma)
for i in range(first_training_interval, last_training_interval+1):
t1, t2 = intervals[i]
pred_intervalcounts[n][i] = math.exp(sum(saved_a*last_h + saved_b*t2 + saved_c))/sum(saved_b) - math.exp(sum(saved_a*last_h + saved_b*t1 + saved_c))/sum(saved_b)
ADV_EPOCHS = 100
for counter in range(ADV_EPOCHS):
a1=[]
b1=[]
c1=[]
d1=[]
for i in range(p-1):
for j in range(i+1, p):
for n in range(last_training_interval+1):
predi= tf.exp(a[i]*h_series[i][-1]+b[i]*intervals[i][1]+c[i])/b[i] - (
tf.exp(a[i]*h_series[i][-1]+b[i]*intervals[i][0]+c[i])/b[i])
predj= tf.exp(a[j]*h_series[j][-1]+b[j]*intervals[j][1]+c[j])/b[j] - (
tf.exp(a[j]*h_series[j][-1]+b[j]*intervals[j][0]+c[j])/b[j])
diff = predi - predj
if intervalcounts[i][n] > intervalcounts[j][n]:
a1.append(greater(diff, 1))
if intervalcounts[i][n] < intervalcounts[j][n]:
b1.append(greater(diff, 1))
if intervalcounts[i][n] > intervalcounts[j][n]:
c1.append(lesser(diff, -1))
if intervalcounts[i][n] < intervalcounts[j][n]:
d1.append(lesser(diff, -1))
a1 = tf.reduce_sum(a1)
b1 = tf.reduce_sum(b1)
c1 = tf.reduce_sum(c1)
d1 = tf.reduce_sum(d1)
#Precision = a1/(a1+b1)
Recall = a1/(b1+d1)
l = 0
for n in range(p):
alpha=tf.Variable([0.001 for x in range(batch_size)])
beta=tf.Variable([0.001 for x in range(batch_size)])
gamma=tf.Variable([0.001 for x in range(batch_size)])
a[n]=tf.Variable([0.001 for x in range(batch_size)])
b[n]=tf.Variable([0.001 for x in range(batch_size)])
c[n]=tf.Variable([0.001 for x in range(batch_size)])
str_k=content[l]
k=[float(ki) for ki in str_k.split(",")]
str_train=content[l+1].rstrip()
train_t=[float(ti)/10000 for ti in str_train.split(",")]
str_test=content[l+6].rstrip()
test_t=[float(ti)/10000 for ti in str_test.split(",")]
l=l+7
first_training_instance = train_t[0]
last_training_instance = train_t[-1]
first_training_interval = 0
last_training_interval = no_intervals - 1
while train_t[0]>intervals[first_training_interval][1]:
first_training_interval = first_training_interval+1
while train_t[-1]<intervals[last_training_interval][0]:
last_training_interval = last_training_interval-1
trainingintervals = intervals[:last_training_interval]
counter = 0
for i in range(len(train_t)):
while train_t[i]>intervals[counter][1]:
counter = counter+1
intervalcounts[n][counter] = intervalcounts[n][counter] + 1
for i in range(len(test_t)):
while counter<no_intervals and test_t[i]>intervals[counter][1]:
counter = counter+1
if counter==no_intervals:
break
intervalcounts[n][counter] = intervalcounts[n][counter] + 1
last_test_interval = no_intervals - 1
while intervalcounts[n][last_test_interval]==0:
last_test_interval = last_test_interval-1
#print intervalcounts[n][last_test_interval], intervals[last_test_interval]
no_training_intervals = last_training_interval-first_training_interval
no_test_intervals = no_intervals - no_training_intervals
total_series_length = len(k)
num_batches = total_series_length//batch_size//truncated_backprop_length
regularizer = tf.add_n([tf.nn.l2_loss(a[n]),tf.nn.l2_loss(b[n]),tf.nn.l2_loss(c[n]),tf.nn.l2_loss(alpha),tf.nn.l2_loss(beta),tf.nn.l2_loss(gamma)])
D_loss = Recall
G_loss[n] = tf.reduce_sum(tf.exp(a[n]*h_series[n][-1]+b[n]*t_series[n][-1]+c[n])/b[n] - tf.exp(a[n]*h_series[n][-1]+b[n]*t_series[n][0]+c[n])/b[n]) - sum([tf.reduce_sum(a[n]*h_series[n][-1]+b[n]*(t_series[n][k1+1]-t_series[n][k1])+c[n]) for k1 in range(len(t_series)-1)])
adv_loss = tf.reduce_sum(D_loss) + tf.reduce_sum(G_loss[n])
total_loss = adv_loss+regularizer
train_step = tf.train.AdagradOptimizer(0.3).minimize(total_loss)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
loss_list = []
h_list = []
last_h = np.ones((batch_size))
last_batchk = np.ones((batch_size, truncated_backprop_length))
last_batcht = np.ones((batch_size, truncated_backprop_length))
#sess = tf_debug.LocalCLIDebugWrapperSession(sess)
#sess.add_tensor_filter("has_inf_or_nan", tf_debug.has_inf_or_nan)
#writer = tf.summary.FileWriter("/home/supritam/Documents/aaai/code", sess.graph)
for epoch_idx in range(num_epochs):
_current_h = np.ones((batch_size))
#print("New data, epoch", epoch_idx)
for batch_idx in range(num_batches):
start_idx = batch_idx * batch_size * truncated_backprop_length
end_idx = start_idx + batch_size * truncated_backprop_length
batchk = list(chunks(k[start_idx:end_idx], truncated_backprop_length))
batcht = list(chunks(train_t[start_idx:end_idx], truncated_backprop_length))
#---debugging statements---
#print batchk, _current_h
_a, _b, _c, _total_loss, _train_step, _current_h, = sess.run(
[a[n], b[n], c[n], total_loss, train_step, current_h],
feed_dict={
batchk_placeholder:batchk,
batcht_placeholder:batcht,
init_h:_current_h,
})
last_h = _current_h
last_batchk = batchk
last_batcht = batcht
#if batch_idx%100 == 0:
#print("Step", batch_idx, "Loss", _total_loss)
#print("a", _a)
#print("b", _b)
#print("c", _c)
saved_a, saved_b, saved_c, saved_alpha, saved_beta, saved_gamma = sess.run([a[n], b[n], c[n], alpha, beta, gamma], feed_dict={
batchk_placeholder:last_batchk,
batcht_placeholder:last_batcht,
init_h:last_h,
})
for i in range(first_training_interval, last_training_interval+1):
t1, t2 = intervals[i]
pred_intervalcounts[n][i] = math.exp(saved_a*last_h + saved_b*t2 + saved_c)/saved_b - math.exp(saved_a*last_h + saved_b*t1 + saved_c)/saved_b
for n in range(p):
cumulative = 0
cumulative_pred = 0
for i in range(first_training_interval, last_training_interval):
t1, t2 = intervals[i]
actual = intervalcounts[n][i]
cumulative = cumulative+actual
pred = pred_intervalcounts[n][i]
cumulative_pred = cumulative_pred+pred
trainAPE = trainAPE + abs(cumulative_pred-cumulative)/cumulative
#print "Dataset:", dataset, "Interval:", t1, t2, "MAPE:", trainAPE/(i+1)
trainMAPE = trainAPE/no_training_intervals
#print "Training MAPE:", trainMAPE
testAPE = trainAPE
for i in range(last_training_interval, last_test_interval+1):
#for i in range(last_training_interval, no_intervals):
t1, t2 = intervals[i]
actual = intervalcounts[n][i]
cumulative = cumulative+actual
pred = pred_intervalcounts[n][i]
cumulative_pred = cumulative_pred+pred
#print "Ntrue:", cumulative, "Npred:", cumulative_pred
testAPE = testAPE + abs(cumulative_pred-cumulative)/cumulative
#print "Dataset:", dataset, "Interval:", t1, t2, "MAPE:", testAPE/(i+1)
testMAPE = testAPE/no_intervals
#print "Test MAPE:", testMAPE
MAPEsum = MAPEsum + testMAPE
for x in range(rankintervals):
rankcounts[n][x] = sum(intervalcounts[n][last_training_interval+x*no_test_intervals/rankintervals:last_training_interval+(x+1)*no_test_intervals/rankintervals])
pred_rankcounts[n][x] = sum(pred_intervalcounts[n][last_training_interval+x*no_test_intervals/rankintervals:last_training_interval+(x+1)*no_test_intervals/rankintervals])
#print rankcounts[n]
#print pred_rankcounts[n]
#print
#print str(n)+"-"*50
#print
#with open("output_MAPE.txt", "a") as out:
#out.write(dataset + "\t" + names[n] + "\t" + str(float(testMAPE)) + "\n")
def convert_to_ranks(l):
aux = [(l[i], i) for i in range(len(l))]
aux2 = sorted(aux, key=lambda x:x[0], reverse=True)
rl = list(l)
for i in range(len(l)):
x = aux2[i]
rl[x[1]] = i
return rl
def SRCC(l1, l2):
rl1 = convert_to_ranks(l1)
rl2 = convert_to_ranks(l2)
return np.corrcoef(rl1, rl2)[0][1]
r = []
for x in range(rankintervals):
ranklist = convert_to_ranks([rankcounts[n][x] for n in range(p)])
pred_ranklist = convert_to_ranks([pred_rankcounts[n][x] for n in range(p)])
print ranklist, pred_ranklist
#r.append(np.corrcoef(ranklist, pred_ranklist)[0][1])
val = SRCC([rankcounts[n][x] for n in range(p)], [pred_rankcounts[n][x] for n in range(p)])
r.append(val)
#print "Dataset:", dataset, "Interval:", intervals[x*(no_intervals-1)/rankintervals][1], intervals[(x+1)*(no_intervals-1)/rankintervals][1], "SRCC:", val
r2 = []
for x in range(last_training_interval+1,no_intervals+1):
r2.append(SRCC([intervalcounts[n][x] for n in range(p)], [pred_intervalcounts[n][x] for n in range(p)]))
print r
meanSRCC = sum(r)/rankintervals
meanSRCC2 = sum(r2)/no_test_intervals
print "Mean MAPE:", MAPEsum/p, "Mean SRCC:", meanSRCC2, "No. of intervals:", no_intervals