def test_get_mlp_params(): n_mot = [6, 7, 8] vect_size = [23, 5, 7] n_hidden = 14 dwin = 5 window = LookUpTrain(dwin, n_mot, vect_size, n_hidden, n_out=1) window.initialize() window.get_Params()
def build_lookup(repo, output_dico, filename_load):
dwin = 9
with closing(open(os.path.join(repo, output_dico), 'rb')) as f:
dico = pickle.load(f)
n_mot = [len(dico[i]) for i in dico.keys()]
vect_size = [20, 10, 5, 5]
n_hidden = 25
x = T.imatrix('x')
t_nlp = LookUpTrain(dwin, n_mot, vect_size, n_hidden)
t_nlp.initialize()
t_nlp.load(repo, filename_load)
lookup = theano.function(inputs=[x], outputs=t_nlp.embedding(x), allow_input_downcast=True)
return lookup
def test_embedding(): n_mot = [6, 7, 8] vect_size = [23, 5, 7] n_hidden = 14 dwin = 5 window = LookUpTrain(dwin, n_mot, vect_size, n_hidden, n_out=1) window.initialize() x = T.itensor3() x_value = np.zeros((2, 3, dwin)).astype(int) for p in range(2): for i in range(3): for q in range(dwin): x_value[p,i,q] = np.random.randint(n_mot[i]) f = theano.function([x], window.embedding(x), allow_input_downcast=True) print f(x_value).shape
def test_unsupTraining(): n_mot = [6, 7, 8] vect_size = [23, 5, 7] n_hidden = 14 dwin = 5 window = LookUpTrain(dwin, n_mot, vect_size, n_hidden, n_out=1) window.initialize() x = T.itensor3() xc = T.itensor3() y = window.score(x, xc) x_value = np.zeros((2, 3, dwin)).astype(int) xc_value = np.zeros((2, 3, dwin)).astype(int) for p in range(2): for i in range(3): for q in range(dwin): x_value[p,i,q] = np.random.randint(n_mot[i]) xc_value[p,i,q] = np.random.randint(n_mot[i]) f = theano.function([x,xc], y, allow_input_downcast=True) result = f(x_value, xc_value) print result
def test_cost(): n_mot = [6, 7, 8] vect_size = [23, 5, 7] n_hidden = 14 dwin = 5 window = LookUpTrain(dwin, n_mot, vect_size, n_hidden, n_out=1) window.initialize() x = T.itensor3() y = T.imatrix() cost = window.cost(x, y) x_value = np.zeros((2, 3, dwin)).astype(int) y_value = np.zeros((2,)).astype(int) for p in range(2): for i in range(3): for q in range(dwin): x_value[p,i,q] = np.random.randint(n_mot[i]) y_value[p] = np.random.randint(2) f = theano.function([x,y], cost, allow_input_downcast=True) return result = f(x_value, xc_value) print result
def unsupervised_training(learning_rate, decay_rate, epochs, repo, output_dico,
database_name):
dwin = 9
with closing(open(os.path.join(repo, output_dico), 'rb')) as f:
dico = pickle.load(f)
n_mot = [len(dico[i]) for i in dico.keys()]
vect_size = [20, 10, 5, 5]
n_hidden = 100
x = T.itensor3('x')
xc = T.itensor3('x')
y = T.ivector('y')
#xc = T.itensor3('xc')
t_nlp = LookUpTrain(dwin, n_mot, vect_size, n_hidden)
t_nlp.initialize()
cost = T.mean(t_nlp.cost(x, y))
error = T.mean(t_nlp.errors(x, y))
params = getParams(t_nlp, x)
for p, i in zip(params, range(len(params))):
p.name += '_' + str(i)
#calcul du gradient avec RMSProp
updates = []
caches = {}
grad_params = T.grad(cost, params)
for param, grad_param in zip(params, grad_params):
if not caches.has_key(param.name):
caches[param.name] = shared_floatx(param.get_value() * 0.,
"cache_" + param.name)
# update rule
update_cache = decay_rate*caches[param.name]\
+ (1 - decay_rate)*grad_param**2
update_param = param - learning_rate * grad_param / T.sqrt(
update_cache + 1e-8)
updates.append((caches[param.name], update_cache))
updates.append((param, update_param))
train_model = theano.function(inputs=[x, y],
outputs=cost,
updates=updates,
allow_input_downcast=True)
valid_model = theano.function(inputs=[x, y],
outputs=cost,
allow_input_downcast=True)
test_model = theano.function(inputs=[x, y],
outputs=error,
allow_input_downcast=True)
data_path = os.path.join(repo, database_name)
with closing(open(data_path, 'rb')) as f:
data, data_c = pickle.load(f)
data = numpy.asarray(data).astype(int)
labels = numpy.asarray(data_c).astype(int)
# test : reduce data
data = data
data_c = data_c
# reading by minibatch
batch_size = 15
n_sample = data.shape[0] / batch_size
# 80% of the data will go into the training set
n_train = (int)(n_sample * 0.8)
y_value = numpy.zeros((2 * batch_size), dtype=int)
y_value[batch_size:] = 1 + y_value[batch_size:]
index_filename = 0
saving = "params_savings_bis_v4_"
#t_nlp.load(repo, (saving+str(95)))
#index_filename = 96
#saving = "params_savings_bis"
for epoch in range(4):
train_cost = []
valid_cost = []
index_valid = n_train
for minibatch_index in range(n_train):
correct_sentences = data[minibatch_index *
batch_size:(minibatch_index + 1) *
batch_size, :, :]
incorrect_sentences = data_c[minibatch_index *
batch_size:(minibatch_index + 1) *
batch_size, :, :]
sentences = numpy.concatenate(
[incorrect_sentences, correct_sentences], axis=0)
train_value = train_model(sentences, y_value)
if minibatch_index % 10 == 0:
train_cost = []
for minibatch_train in range(n_train):
correct_sentences = data[minibatch_train *
batch_size:(minibatch_train + 1) *
batch_size, :, :]
incorrect_sentences = data_c[minibatch_train *
batch_size:(minibatch_train +
1) *
batch_size, :, :]
sentences = numpy.concatenate(
[incorrect_sentences, correct_sentences], axis=0)
train_value = valid_model(sentences, y_value)
train_cost.append(train_value)
print "Train : " + str(numpy.mean(train_cost) * 100)
valid_cost = []
for minibatch_valid in range(n_train, n_sample):
correct_sentences = data[minibatch_valid *
batch_size:(minibatch_valid + 1) *
batch_size, :, :]
incorrect_sentences = data_c[minibatch_valid *
batch_size:(minibatch_valid +
1) *
batch_size, :, :]
sentences = numpy.concatenate(
[incorrect_sentences, correct_sentences], axis=0)
valid_value = test_model(sentences, y_value)
#import pdb
#pdb.set_trace()
valid_cost.append(valid_value)
print "Valid : " + str(
numpy.mean(valid_value) *
100) + " in : " + (saving + str(index_filename))
t_nlp.save(repo, (saving + str(index_filename)))
index_filename += 1
def training_Hollande(repo, output_dico, learning_rate, decay_rate, filenames):
#########
# MODEL #
#########
dwin = 20
with closing(open(os.path.join(repo, output_dico), 'rb')) as f:
dico = pickle.load(f)
n_mot = [len(dico[i]) for i in dico.keys()]
vect_size = [100, 10, 5, 5]
n_hidden = [100, 50]
t_nlp = LookUpTrain(dwin, n_mot, vect_size, n_hidden, n_out=2)
t_nlp.initialize()
#t_nlp.load(repo, filename_load)
x = T.itensor3('x')
y = T.ivector('y')
cost = T.mean(t_nlp.cost(x, y))
error = T.mean(t_nlp.errors(x,y))
params = getParams(t_nlp, x)
updates, _ = Adam(cost, params, learning_rate)
"""
for p, i in zip(params, range(len(params))):
p.name+='_'+str(i)
#calcul du gradient avec RMSProp
updates = []
caches = {}
grad_params = T.grad(cost, params)
for param, grad_param in zip(params, grad_params):
if not caches.has_key(param.name):
caches[param.name] = shared_floatx(param.get_value() * 0.,
"cache_"+param.name)
# update rule
update_cache = decay_rate*caches[param.name]\
+ (1 - decay_rate)*grad_param**2
update_param = param - learning_rate*grad_param/T.sqrt(update_cache + 1e-8)
updates.append((caches[param.name], update_cache))
updates.append((param, update_param))
"""
train_model = theano.function(inputs=[x,y], outputs=cost, updates=updates,
allow_input_downcast=True)
valid_model = theano.function(inputs=[x, y], outputs=cost, allow_input_downcast=True)
test_model = theano.function(inputs=[x, y], outputs=error, allow_input_downcast=True)
predict = theano.function(inputs=[x], outputs=t_nlp.predict(x), allow_input_downcast=True)
predict_confidency = theano.function(inputs=[x], outputs=t_nlp.predict_confidency(x)[0], allow_input_downcast=True)
index = 0
y_value = []
x_value = []
with closing(open(os.path.join(repo, output_dico), 'rb')) as f:
dico = pickle.load(f)
for filename in filenames:
lines, _ = get_input_from_files(repo, [filename], dico)
for line in lines:
x_value.append(line)
y_value.append(index)
if index ==0:
index+=1
y_value = np.asarray(y_value, dtype=int)
# balance the samples
x_value_0 = [ x_value[i] for i in range(np.argmax(y_value))]# put the 0
y_value_0 = [ y_value[i] for i in range(np.argmax(y_value))]# put the 0
indexes = np.random.permutation(y_value.shape[0] - np.argmax(y_value))[:np.argmax(y_value)]
x_value_1 = [x_value[i+np.argmax(y_value)] for i in indexes]# balance the numbers
y_value_1 = [y_value[i+np.argmax(y_value)] for i in indexes]# balance the numbers
pos_percentage = (int) (len(y_value_0)*0.8)
neg_percentage = (int) (len(y_value_1)*0.8)
other_pos_percentage = (len(y_value_0) - pos_percentage)/2
other_neg_percentage = (len(y_value_1) - neg_percentage)/2
pos_permut = np.random.permutation(len(y_value_0))
neg_permut = np.random.permutation(len(y_value_1))
x_train = [x_value_0[i] for i in pos_permut[:pos_percentage]] + [x_value_1[i] for i in neg_permut[:neg_percentage]]
x_valid = [x_value_0[i] for i in pos_permut[pos_percentage:pos_percentage+other_pos_percentage]] + \
[x_value_1[i] for i in neg_permut[neg_percentage:neg_percentage+other_neg_percentage]]
x_test = [x_value_0[i] for i in pos_permut[pos_percentage+other_pos_percentage:]] + \
[x_value_1[i] for i in neg_permut[neg_percentage+other_neg_percentage:]]
y_train = [y_value_0[i] for i in pos_permut[:pos_percentage]] + [y_value_1[i] for i in neg_permut[:neg_percentage]]
y_valid = [y_value_0[i] for i in pos_permut[pos_percentage:pos_percentage+other_pos_percentage]] + \
[y_value_1[i] for i in neg_permut[neg_percentage:neg_percentage+other_neg_percentage]]
y_test = [y_value_0[i] for i in pos_permut[pos_percentage+other_pos_percentage:]] + \
[y_value_1[i] for i in neg_permut[neg_percentage+other_neg_percentage:]]
index_train = np.random.permutation(len(y_train))
batch_size = 32
index_valid = np.random.permutation(len(y_valid))
index_test = np.random.permutation(len(y_test))
x_train_ = [x_train[i].astype(int) for i in index_train]
x_valid_ = [x_valid[i].astype(int) for i in index_valid]
x_test_ = [x_test[i].astype(int) for i in index_test]
y_train_ = [y_train[i] for i in index_train]
y_valid_ = [y_valid[i] for i in index_valid]
y_test_ = [y_test[i] for i in index_test]
paddings = [ [], [], [], []]
for i in range(dwin/2):
for i in xrange(4):
paddings[i].append(dico[i]['PARSING'])
paddings = np.asarray(paddings)
#paddings = paddings.reshape((1, paddings.shape[0], paddings.shape[1]))
x_train_ = [add_padding(elem, paddings) for elem in x_train_]
x_valid_ = [add_padding(elem, paddings) for elem in x_valid_]
x_test_ = [add_padding(elem, paddings) for elem in x_test_]
x_train=[]; x_valid=[]; x_test=[]
y_train=[]; y_valid=[]; y_test=[]
for elem, label in zip(x_train_, y_train_):
for i in range(elem.shape[1] -dwin):
x_train.append(elem[:,i:i+dwin])
y_train.append(label)
for elem, label in zip(x_valid_, y_valid_):
for i in range(elem.shape[1] -dwin):
x_valid.append(elem[:,i:i+dwin])
y_valid.append(label)
for elem, label in zip(x_test_, y_test_):
for i in range(elem.shape[1] -dwin):
x_test.append(elem[:,i:i+dwin])
y_test.append(label)
index_train = np.random.permutation(len(y_train))
index_valid = np.random.permutation(len(y_valid))
index_test = np.random.permutation(len(y_test))
x_train = [x_train[i].astype(int) for i in index_train]
x_valid = [x_valid[i].astype(int) for i in index_valid]
x_test = [x_test[i].astype(int) for i in index_test]
y_train = [y_train[i] for i in index_train]
y_valid = [y_valid[i] for i in index_valid]
y_test = [y_test[i] for i in index_test]
n_train = len(y_train)/batch_size
n_valid = len(y_valid)/batch_size
n_test = len(y_test)/batch_size
print (n_train, n_valid, n_test)
print (1.*sum(y_valid))/len(y_valid)
print (1.*sum(y_test))/len(y_test)
print "#############################"
saving ='JADT_2_Fev_H_G_'
index_filename=0
epochs = 10 # number of iterations on the corpus
for epoch in range(epochs):
index_valid = n_train
for minibatch_index in range(n_train):
sentence = x_train[minibatch_index*batch_size:(minibatch_index+1)*batch_size]
y_value = y_train[minibatch_index*batch_size:(minibatch_index+1)*batch_size]
#before = valid_model(sentence, y_value)
train_value = train_model(sentence, y_value)
#after = valid_model(sentence, y_value)
#print before - after
if True:
train_cost=[]
for minibatch_train in range(n_train):
sentence = x_train[minibatch_train*batch_size:(minibatch_train+1)*batch_size]
y_value = y_train[minibatch_train*batch_size:(minibatch_train+1)*batch_size]
train_value = valid_model(sentence, y_value)
train_cost.append(train_value)
print "Train : "+str(np.mean(train_cost)*100)
valid_cost=[]
predictions=[]
for minibatch_valid in range(n_valid):
y_value = y_valid[minibatch_valid*batch_size:(minibatch_valid+1)*batch_size]
sentence = x_valid[minibatch_valid*batch_size:(minibatch_valid+1)*batch_size]
valid_value = test_model(sentence, y_value)
valid_cost.append(valid_value)
print "Valid : "+str(np.mean(valid_cost)*100)+" in : "+(saving+str(index_filename))
test_cost=[]
for minibatch_test in range(n_test):
sentence = x_test[minibatch_test*batch_size:(minibatch_test+1)*batch_size]
y_value = y_test[minibatch_test*batch_size:(minibatch_test+1)*batch_size]
test_value = test_model(sentence, y_value)
test_cost.append(test_value)
print "Test : "+str(np.mean(test_cost)*100)
index_filename+=1
t_nlp.save(repo, saving)
return
#### parcourir le test : take the 10 most accurate sentence ###
#### parcourir le test : take the 10 less accurate sentence ###
scores = []
for index in range(len(y_test)):
x_value=x_test[index:index+1]
scores.append(predict_confidency(x_value))
right = [x_test[i] for i in np.argsort(scores)[::-1][:20]]
false = [x_test[i] for i in np.argsort(scores)[:20]]
print scores[:10]
with closing(open('data/sentence/relevant_sentence_H_G', 'wb')) as f:
pickle.dump([right, false], f, protocol=pickle.HIGHEST_PROTOCOL)