def main():
print("Staring main...")
#define default parameters
interactions = [(3, (0, 1)), (4, (1, 2)), (5, (2, 0))]
thresh_mentions = 6
neg = 2 #negative samples per positive
rank = 5 #embedding dimensionality
#n_train = 287044
n_iter = 20
#choose a dot product to use. # can be
#multilinear
#multilinear_square_product
#generalised_multilinear_dot_product
dot_product = generalised_multilinear_dot_product
minibatch_size = 1000
learning_rate = 0.001
l2 = 0.0
eval_file_name = 'output.txt'
# use input arguments to define parameters (i.e. not using default above)
for x in sys.argv:
if len(x.split("__")) > 1:
arg = x.split("__")[1]
if "interactions" in x:
interactions = arg
print("Interactions not defined yet")
#TODO
elif "n_iter" in x:
n_iter = int(x.split("__")[1])
elif "thresh_mentions" in x:
thresh_mentions = int(arg)
elif "rank" in x:
rank = int(arg)
elif "n_train" in x:
n_train = int(arg)
elif "neg" in x:
neg = int(arg)
elif "minibatch" in x:
minibatch_size = int(arg)
elif "learning_rate" in x:
learning_rate = float(arg)
elif "L2" in x:
l2 = float(arg)
elif "eval_file_name" in x:
eval_file_name = str(arg)
print("Done parsing input arguments.")
# load data & dictionaries
train, String2Int, Int2String = dr.load_FB15K237_FB(
'train', None, None, interactions, thresh_mentions)
valid, String2Int, Int2String = dr.load_FB15K237_FB(
'valid', String2Int, Int2String, interactions)
test, String2Int, Int2String = dr.load_FB15K237_FB('test', String2Int,
Int2String,
interactions)
# in case not the full train data should be used
#train = train[:n_train]
n_train = len(train)
values = [1.0 for x in range(n_train)]
# sample some random negative validation tuples
n_test = len(valid)
valid_negative = np.random.randint(0, 1000,
[n_test, 3 + len(interactions)])
print("Done loading data.")
# initialise embeddings, reserve one extra entry (the first) for unknown
n_emb = len(Int2String.keys()) + 1
emb0 = np.random.normal(size=(n_emb, rank)) * 0.1
# initialise the norm scalers
norm_scalers = np.random.normal(size=[3 + len(interactions)]) * 0.1
# set other factorization inputs
optim = tf.train.AdamOptimizer(learning_rate=learning_rate)
scoring = lf.generalised_multilinear_dot_product_scorer
# factorize the train tuples, obtain all model parameters
# print("Starting factorisation...")
params = lf.factorize_tuples((train, values),
rank,
minibatch_size=minibatch_size,
emb0=emb0,
n_iter=n_iter,
negative_prop=neg,
loss_type="logistic",
tf_optim=optim,
scoring=scoring,
norm_scalers=norm_scalers)
#define two placeholders to feed in train and valid data for evaluation
inputs_train = tf.placeholder("int32", [n_train, len(train[0])])
inputs_valid = tf.placeholder("int32", [len(valid), len(train[0])])
# define prediction ops for train and valid set
pred_train = dot_product(params, inputs_train)
pred_valid = dot_product(params, inputs_valid)
# define the data feeders
feed1 = {inputs_train: train}
feed2 = {inputs_valid: valid_negative}
feed3 = {inputs_valid: valid}
print("Start evaluation...")
# obtain predictions for train, valid and negative validation set
with tf.Session() as sess:
sess.run(tf.initialize_all_variables())
print("Generating predictions... training data")
prediction_values = sigmoid(sess.run([pred_train], feed_dict=feed1)[0])
print("Generating predictions... validation data (negative)")
prediction_values2 = sigmoid(
sess.run([pred_valid], feed_dict=feed2)[0])
print("Generating predictions... validation data (positive)")
prediction_values3 = sigmoid(
sess.run([pred_valid], feed_dict=feed3)[0])
# evaluation: avg prediction among the different tuple sets
m1 = np.mean(prediction_values)
m2 = np.mean(prediction_values2)
m3 = np.mean(prediction_values3)
print("Avg Pos Train Prediction", m1)
print("Avg Random Neg Prediction", m2)
print("Avg Pos Test Prediction", m3)
# do batch ranking evaluation, compute MRR and HITS@10 on valid set.
h = 20
#h=len(valid)
#params[0][0,:] = np.zeros([params[0].shape[1]])
MRR, H10 = ranking_evaluation(valid[:h], prediction_values3[:h],
interactions, 2, String2Int, Int2String,
params, dot_product)
print("MRR:", MRR)
print("HITS@10:", H10)
print("Writing results to file " + eval_file_name)
with open(eval_file_name, 'w') as f:
for x in sys.argv:
f.write(x + "\n")
f.write("---------\n")
f.write("MRR: " + str(MRR) + "\n")
f.write("HITS@10: " + str(H10) + "\n")
f.write("Mean train score:" + str(m1) + "\n")
f.write("Mean val score (neg):" + str(m2) + "\n")
f.write("Mean val score (pos):" + str(m3) + "\n")
print("EOF.")
thresh_mentions = 5
neg = 2 #negative samples per positive
rank = 5 #embedding dimensionality
n_train = 287044
n_iter = 20
#choose a dot product to use. # can be
#multilinear
#multilinear_square_product
#generalised_multilinear_dot_product
dot_product = generalised_multilinear_dot_product
# load data & dictionaries
train, String2Int, Int2String = dr.load_FB15K237_FB('train', None, None,
interactions, thresh_mentions)
valid, String2Int, Int2String = dr.load_FB15K237_FB('valid', String2Int,
Int2String, interactions)
test, String2Int, Int2String = dr.load_FB15K237_FB('test', String2Int,
Int2String, interactions)
# in case not the full train data should be used
train = train[:n_train]
values = [1.0 for x in range(n_train)]
# sample some random negative validation tuples
n_test = len(valid)
valid_negative = np.random.randint(0, 1000 ,[n_test,3+len(interactions)])
def main():
print("Staring main...")
#define default parameters
interactions = [(3,(0,1)), (4,(1,2)), (5,(2,0))]
thresh_mentions = 6
neg = 2 #negative samples per positive
rank = 5 #embedding dimensionality
#n_train = 287044
n_iter = 20
#choose a dot product to use. # can be
#multilinear
#multilinear_square_product
#generalised_multilinear_dot_product
dot_product = generalised_multilinear_dot_product
minibatch_size=1000
learning_rate = 0.001
l2 = 0.0
eval_file_name = 'output.txt'
# use input arguments to define parameters (i.e. not using default above)
for x in sys.argv:
if len( x.split("__") ) > 1:
arg = x.split("__")[1]
if "interactions" in x:
interactions = arg
print("Interactions not defined yet")
#TODO
elif "n_iter" in x:
n_iter = int(x.split("__")[1])
elif "thresh_mentions" in x:
thresh_mentions = int(arg)
elif "rank" in x:
rank = int(arg)
elif "n_train" in x:
n_train = int(arg)
elif "neg" in x:
neg = int(arg)
elif "minibatch" in x:
minibatch_size = int(arg)
elif "learning_rate" in x:
learning_rate = float(arg)
elif "L2" in x:
l2 = float(arg)
elif "eval_file_name" in x:
eval_file_name = str(arg)
print("Done parsing input arguments.")
# load data & dictionaries
train, String2Int, Int2String = dr.load_FB15K237_FB('train', None, None,
interactions, thresh_mentions)
valid, String2Int, Int2String = dr.load_FB15K237_FB('valid', String2Int,
Int2String, interactions)
test, String2Int, Int2String = dr.load_FB15K237_FB('test', String2Int,
Int2String, interactions)
# in case not the full train data should be used
#train = train[:n_train]
n_train = len(train)
values = [1.0 for x in range(n_train)]
# sample some random negative validation tuples
n_test = len(valid)
valid_negative = np.random.randint(0, 1000 ,[n_test,3+len(interactions)])
print("Done loading data.")
# initialise embeddings, reserve one extra entry (the first) for unknown
n_emb = len(Int2String.keys()) + 1
emb0 = np.random.normal(size=(n_emb, rank)) * 0.1
# initialise the norm scalers
norm_scalers = np.random.normal(size = [3+len(interactions)]) * 0.1
# set other factorization inputs
optim = tf.train.AdamOptimizer(learning_rate=learning_rate)
scoring = lf.generalised_multilinear_dot_product_scorer
# factorize the train tuples, obtain all model parameters
# print("Starting factorisation...")
params = lf.factorize_tuples((train, values), rank, minibatch_size=minibatch_size,
emb0=emb0, n_iter=n_iter,
negative_prop = neg,
loss_type = "logistic",
tf_optim=optim, scoring=scoring,
norm_scalers = norm_scalers)
#define two placeholders to feed in train and valid data for evaluation
inputs_train = tf.placeholder("int32", [n_train, len(train[0])])
inputs_valid = tf.placeholder("int32", [len(valid), len(train[0])])
# define prediction ops for train and valid set
pred_train = dot_product(params, inputs_train)
pred_valid = dot_product(params, inputs_valid)
# define the data feeders
feed1 = {inputs_train: train}
feed2 = {inputs_valid: valid_negative}
feed3 = {inputs_valid: valid}
print("Start evaluation...")
# obtain predictions for train, valid and negative validation set
with tf.Session() as sess:
sess.run(tf.initialize_all_variables())
print("Generating predictions... training data")
prediction_values = sigmoid( sess.run([pred_train], feed_dict=feed1)[0] )
print("Generating predictions... validation data (negative)")
prediction_values2 = sigmoid( sess.run([pred_valid], feed_dict=feed2)[0] )
print("Generating predictions... validation data (positive)")
prediction_values3 = sigmoid( sess.run([pred_valid], feed_dict=feed3)[0] )
# evaluation: avg prediction among the different tuple sets
m1 = np.mean(prediction_values)
m2 = np.mean(prediction_values2)
m3 = np.mean(prediction_values3)
print ("Avg Pos Train Prediction", m1)
print ("Avg Random Neg Prediction", m2)
print ("Avg Pos Test Prediction", m3)
# do batch ranking evaluation, compute MRR and HITS@10 on valid set.
h = 20
#h=len(valid)
#params[0][0,:] = np.zeros([params[0].shape[1]])
MRR, H10 = ranking_evaluation(valid[:h], prediction_values3[:h],
interactions, 2, String2Int, Int2String, params, dot_product)
print ("MRR:", MRR)
print ("HITS@10:", H10)
print("Writing results to file " + eval_file_name)
with open(eval_file_name, 'w') as f:
for x in sys.argv:
f.write(x+"\n")
f.write("---------\n")
f.write("MRR: "+ str(MRR)+"\n")
f.write("HITS@10: "+ str(H10)+"\n")
f.write("Mean train score:" + str(m1)+"\n")
f.write("Mean val score (neg):" + str(m2)+"\n")
f.write("Mean val score (pos):" + str(m3)+"\n")
print("EOF.")
interactions = [(3, (0, 1)), (4, (1, 2))]
#interactions = []
thresh_mentions = 5
neg = 2 #negative samples per positive
rank = 5 #embedding dimensionality
n_train = 287044
n_iter = 20
#choose a dot product to use. # can be
#multilinear
#multilinear_square_product
#generalised_multilinear_dot_product
dot_product = generalised_multilinear_dot_product
# load data & dictionaries
train, String2Int, Int2String = dr.load_FB15K237_FB(
'train', None, None, interactions, thresh_mentions)
valid, String2Int, Int2String = dr.load_FB15K237_FB(
'valid', String2Int, Int2String, interactions)
test, String2Int, Int2String = dr.load_FB15K237_FB('test', String2Int,
Int2String,
interactions)
# in case not the full train data should be used
train = train[:n_train]
values = [1.0 for x in range(n_train)]
# sample some random negative validation tuples
n_test = len(valid)
valid_negative = np.random.randint(0, 1000,
[n_test, 3 + len(interactions)])
