def __init__(self):
super(TrainData, self).__init__()
self.wvspace = WordVecSpaceMem(self.args.wvspace)
self.train_f = DiskArray(self.args.train_file,
shape=(self.get_shape(), ),
dtype=self.get_dtype())
self.words_f = open(self.args.words_file, 'w')
def _prepare_word_index_wvspace(self, dim, initialize=False, mode='r+'):
def J(x): return os.path.join(self.dirpath, x)
v_path = J('vectors')
m_path = J('magnitudes')
o_path = J('occurrences')
# FIXME: Support taking memmap array from diskarray
m_array = DiskArray(m_path, dtype='float32', mode=mode,
growby=self._growby, log=self.log)
o_array = DiskArray(o_path, dtype='uint64', mode=mode,
growby=self._growby, log=self.log)
if not initialize:
v_array = DiskArray(v_path, dtype='float32', mode=mode,
growby=self._growby, log=self.log)
vec_l = int(len(v_array)/dim)
v_array = v_array[:].reshape(vec_l, dim)
m_array = m_array[:]
o_array = o_array[:]
else:
v_array = DiskArray(v_path, shape=(0, dim), dtype='float32', mode=mode,
growby=self._growby, log=self.log)
wtoi = itow = None
if not self.sharding:
wtoi = DiskDict(J('wordtoindex'))
itow = DiskDict(J('indextoword'))
return v_array, o_array, m_array, wtoi, itow
def __init__(self,
in_dir,
outdir,
nvecs_per_shard=0,
shard_name="shard",
full_name="full"):
self.in_dir = in_dir
self.outdir = outdir
self.nvecs_per_shard = nvecs_per_shard
self.shard_name = shard_name
self.do_sharding = bool(self.nvecs_per_shard)
if self.do_sharding:
self.full_fpath = self.J(self.outdir, full_name)
os.makedirs(self.full_fpath)
map_itow = self.J(self.full_fpath, "indextoword")
self.itow = DiskDict(map_itow)
map_wtoi = self.J(self.full_fpath, "wordtoindex")
self.wtoi = DiskDict(map_wtoi)
self.mags = DiskArray(
self.J(self.full_fpath, "magnitudes"),
shape=(0, ),
dtype=np.float32,
growby=1000000,
)
self.occurs = DiskArray(
self.J(self.full_fpath, "occurrences"),
shape=(0, ),
dtype=np.uint64,
growby=1000000,
)
def _prepare_word_index_wvspace(self, dim, initialize=False):
v_dtype, o_dtype = self._make_dtype(dim)
def J(x):
return os.path.join(self.dirpath, x)
def S(f):
return os.stat(f).st_size
v_path = J('vectors')
o_path = J('occurrences')
nvecs = noccurs = 0
if not initialize:
nvecs = S(v_path) / np.dtype(v_dtype).itemsize
noccurs = S(o_path) / np.dtype(o_dtype).itemsize
v_array = DiskArray(v_path,
shape=(int(nvecs), ),
dtype=v_dtype,
growby=self._growby,
log=self.log)
o_array = DiskArray(o_path,
shape=(int(noccurs), ),
dtype=o_dtype,
growby=self._growby,
log=self.log)
w_index = DiskDict(J('wordtoindex'))
i_word = DiskDict(J('indextoword'))
return v_array, o_array, w_index, i_word
def load_data(self, fpath):
words = [line[:-1] for line in open(fpath)]
max_len = max(len(w)
for w in words) + 2 # adding 2 for start and end chars
nwords = len(words)
chars = list(sorted(list(set(list(''.join(words))))))
chars = [self.CHAR_NONE, self.CHAR_START, self.CHAR_END] + chars
charmap = {c: i for i, c in enumerate(chars)}
nchars = len(chars)
char_none = to_categorical(charmap[self.CHAR_NONE], num_classes=nchars)
data = DiskArray('866k_training_data.array',
shape=(nwords, max_len, nchars),
dtype=np.float32)
labels = DiskArraay('866k_labels_data.array',
shape=(nwords, nchars),
dtype=np.float32)
for i in range(nwords):
w = words[i][:-1]
last_char = words[i][-1]
w = '%s%s%s' % (self.CHAR_START, w, self.CHAR_END)
w = [to_categorical(charmap[x], num_classes=nchars) for x in w]
w = w + ([char_none] * (max_len - len(w)))
data[i] = w
labels[i] = to_categorical(charmap[last_char], num_classes=nchars)
data.flush()
labels.flush()
return data, labels
def run(self):
self.train_d = DiskArray(self.args.trainf, dtype=self._get_dtype())
self.test_d = DiskArray(self.args.testf, dtype=self._get_dtype())
model = self.init_model()
self.compile_model(model)
self.train_model(model)
self.test_model(model)
self.save_model(model)
def __init__(self):
super(GridScript, self).__init__()
self.train_d = DiskArray(self.args.train_d, dtype=self._get_dtype())
self.test_d = DiskArray(self.args.test_d, dtype=self._get_dtype())
self.csv = open(self.args.outf, 'w')
self.outf = csv.writer(self.csv)
self.outf.writerow([
'num of hidden layers', 'loss', 'activcation', 'optimizer',
'epochs', 'cohens_d', 'accuracy'
])
self.hyper_parameters = []
def __init__(self):
super(Generate_Histogram, self).__init__()
self.d = DiskArray(self.args.array, dtype=self._get_dtype())
self.psame = []
self.pnsame = []
csv_f = open(self.args.csv_f, 'w')
self.csv_f = csv.writer(csv_f)
def run(self):
self.train_d = DiskArray(self.args.trainf, dtype=self._get_dtype())
self.train_d = np.array([i for i in range(10000)], dtype=np.int)
model = self.init_model()
self.compile_model(model)
self.train_model(model)
self.test_model(model)
model.save(self.args.model)
def run(self):
fpath = self.args.text
max_len, nchars, nwords, charmap = self.get_char_to_int(fpath)
disk_array = DiskArray(self.args.out_f,
shape=(0, ),
dtype=[('vec', np.float32, 128)])
if not os.path.exists(self.args.training_data):
data, labels = self.load_data(max_len, nchars, nwords, charmap)
else:
data = DiskArray(self.args.training_data, dtype=np.float32)
labels = DiskArray(self.args.labels_data, dtype=np.float32)
if not os.path.exists(self.args.model_name):
model = self.create_model(128, max_len, nchars)
self.log.info('Started training the model')
history = model.fit(data[:],
labels[:],
epochs=self.args.epochs,
batch_size=128)
plt.plot(history.history['loss'])
plt.savefig(self.args.image_name)
else:
model = load_model(self.args.model_name)
model.save(self.args.model_name)
self.log.info('Accessing the layer weights')
new_model = Sequential()
new_model.add(LSTM(128, input_shape=(max_len, nchars), unroll=True))
weights = model.layers[0].get_weights()
new_model.set_weights(weights)
self.log.info('started predicting')
for word in open(fpath):
word = word.strip()
test_data, test_lables = self.get_test_data(
max_len, nchars, 1, [word], charmap)
p_out = new_model.predict(test_data)
disk_array.append((p_out[0], ))
disk_array.flush()
def test():
d = DiskArray(inp_f, dtype=[('vec', np.float32, 128)])
mapping = DiskDict(dict_file)
print('The given word is', mapping[str(index)])
vectors = d['vec']
vec = vectors[index].reshape(1, len(vectors[0]))
vectors_t = vectors.T
dists = np.dot(vec, vectors_t)
k_near = np.argsort(dists)[0]
words = []
for i in k_near:
words.append(mapping[str(i)])
return words
def run(self):
fpath = self.args.text
fpath_pickled = self.args.text + ".pickled"
max_len, nchars, nwords, words, charmap = self.get_char_to_int(fpath)
disk_array = DiskArray(self.args.out_f,
shape=(0, ),
dtype=[('vec', np.float32, 128)])
'''
if not os.path.exists(fpath_pickled):
data, labels = self.load_data(max_len, nchars, nwords, words, charmap)
pickle.dump((data, labels), open(fpath_pickled, 'wb'))
else:
data, labels = pickle.load(open(fpath_pickled, 'rb'))
'''
if not os.path.exists(self.args.model_name):
model = self.create_model(128, max_len, nchars)
#history = model.fit(data, labels, epochs=self.args.epochs, batch_size=128)
generator = self.generator(max_len, nchars, nwords, words, charmap,
2048)
model.fit_generator(generator,
steps_per_epoch=nwords / 2048,
epochs=self.args.epochs)
else:
model = load_model(self.args.model_name)
model.save(self.args.model_name)
if self.args.layer == 'lstm_layer':
self.log.info('Accessing the layer weights')
new_model = Sequential()
new_model.add(LSTM(128, input_shape=(max_len, nchars),
unroll=True))
weights = model.layers[0].get_weights()
new_model.set_weights(weights)
model_p = new_model
else:
model_p = model
self.log.info('started predicting')
for word in words:
test_data, test_lables = self.load_data(max_len, nchars, 1, [word],
charmap)
p_out = model_p.predict(test_data)
disk_array.append((p_out[0], ))
disk_array.flush()
def run(self):
model = load_model(
self.args.model_name,
custom_objects=dict(
_euclidean_distance=_euclidean_distance,
_eucl_dist_output_shape=_eucl_dist_output_shape))
test_d = DiskArray(self.args.test_f,
dtype=[('vec', np.float32, 300),
('vec', np.float32, 300), ('label', np.int)])
csv_f = open(self.args.csv_file, 'w')
csv_file = csv.writer(csv_f)
csv_file.writerow(['label', 'prediction'])
for i in len(test_d['vec']):
vec1 = test_d['vec1'][i]
vec2 = test_d['vec2'][i]
pred_val = self.get_prediction(vec1, vec2, model)
label = test_d['label'][i]
csv_file.writerow([label, pred_val])
def k_nearest(wvspace, disk_f, word):
wv = WordVecSpaceMem(wvspace)
da = DiskArray(disk_f, dtype=[('vec', np.float32, 300)])
index = wv.get_word_index(word)
result = wv.get_nearest(index, k=10)
print(wv.get_word_at_indices(result))
vec = da['vec'][index].reshape(1, 300)
vecs = da['vec']
#dist = distance.cdist(vec, vecs, 'cosine')
dist = distance.cdist(vec, vecs, 'euclidean')
#dist = np.dot(vec, vecs.T)
dist = pd.Series(dist[0])
res = dist.nsmallest(10).keys()
print('\n')
print(wv.get_word_at_indices(list(res)))
def k_nearest(wvspace, disk_f, words, metric, image_name):
f = open(words)
wv = WordVecSpaceMem(wvspace)
da = DiskArray(disk_f, dtype=[('vec', np.float32, 300)])
vecs = da['vec']
psame = []
pnsame = []
for line in f:
words = json.loads(line.strip())
index1 = wv.get_word_index(words[0])
index2 = wv.get_word_index(words[1])
if 'clinicaltrials' in words[0] or 'clinicaltrials' in words[1]:
continue
vec1 = vecs[index1].reshape(1, 300)
vec2 = vecs[index2].reshape(1, 300)
if metric == 'cosine':
vspace_dist = wv.get_distance(words[0], words[1])
tvspace_dist = distance.cosine(vec1, vec2)
else:
vspace_dist = wv.get_distance(words[0], words[1], metric='euclidean')
tvspace_dist = distance.euclidean(vec1, vec2)
if words[2] == 0:
psame.append(tvspace_dist)
else:
pnsame.append(tvspace_dist)
dm = (np.std(psame) + np.std(pnsame)) / 2
nm = abs(np.mean(psame) - np.mean(pnsame))
d = nm / dm
print('the cohens D distance is', d)
plt.hist(psame, bins=50, alpha=0.5, label='same points')
plt.hist(pnsame, bins=50, alpha=0.5, label='not same points')
plt.legend(loc='upper right')
plt.savefig(image_name)
def load_data(self, max_len, nchars, nwords, charmap):
char_none = to_categorical(charmap[self.CHAR_NONE], num_classes=nchars)
data = DiskArray(self.args.training_data,
shape=(nwords, max_len, nchars),
dtype=np.float32)
labels = DiskArray(self.args.labels_data,
shape=(nwords, nchars),
dtype=np.float32)
f = open(self.args.text)
for i, line in enumerate(f):
line = line.strip()
w = line[:-1]
last_char = line[-1]
w = '%s%s%s' % (self.CHAR_START, w, self.CHAR_END)
w = [to_categorical(charmap[x], num_classes=nchars) for x in w]
w = w + ([char_none] * (max_len - len(w)))
data[i] = w
labels[i] = to_categorical(charmap[last_char], num_classes=nchars)
self.log.info('generating vectors is done')
data.flush()
labels.flush()
return data, labels
def run(self):
fpath = self.args.text
max_len, nchars, nwords, words, charmap = self.get_char_to_int(fpath)
disk_array = DiskArray(self.args.out_f, shape=(0,), dtype=[('vec', np.float32, 108)])
model = load_model(self.args.model_name)
self.log.info('started predicting')
for word in words:
test_data, test_lables = self.load_data(max_len, nchars, 1, [word], charmap)
p_out = model.predict(test_data)
disk_array.append((p_out[0],))
disk_array.flush()
def __init__(self):
super(DistanceFunction, self).__init__()
self.test_d = DiskArray(self.args.testf, dtype=self._get_dtype())
import sys
from keras.Models import load_model
from wordvecspace import WordVecSpaceMem
from diskarray import DiskArray
def _euclidean_dis_loss(y_true, y_pred):
return K.sqrt(K.sum(K.square(y_pred - y_true), axis=0))
model = load_model(
sys.argv[1], custom_objects=dict(_euclidean_dis_loss=_euclidean_dis_loss))
out_f = DiskArray(sys.argv[2], dtype=[('vec', np.float32, 300)])
wv = WordVecSpaceMem(sys.argv[3])
def get_tras_vectors():
nvecs = len(wv.vectors)
for num in range(nvecs):
vec = wv.get_word_vector(num)
vec = vec.reshape(1, 300)
t_vec = model.predict(vec)
out_f.append((t_vec, ))
def disk_array_test():
timeout = 500
disks = 10
# Set up a disk model, disk array, and some files for testing purposes
test_disk = DiskModel(
2.5, # spin up time
30, # spin up energy
10, # spin down energy
3, # idle power
7, # read power
8, # write power
300 * units.MiB, # speed
0.003, # seek time
500 * units.GiB) # capacity
disk_array = DiskArray(0, test_disk, disks, test_disk, timeout)
file1 = FileInfo("file1", "/", FileType.TEXT, 1 * units.GiB)
file1_compressed_size = 300 * units.MiB
file2 = FileInfo("file2", "/", FileType.BINARY, 40 * units.MiB)
file2_compressed_size = 35 * units.MiB
# Tests
passed = True
# Write before the disks turn off
current_time = timeout / 2
disk_array.update_time(current_time)
time = disk_array.write(file1, file1_compressed_size)
energy = disk_array.get_energy_usage()
expected_time = test_disk.seek_time + (file1_compressed_size /
test_disk.speed)
expected_energy = (timeout / 2) * test_disk.idle_power * disks + \
expected_time * test_disk.write_power
if not floateq(time, expected_time):
passed = False
print "Failed write test 1 for time"
if not floateq(energy, expected_energy):
passed = False
print "Failed write test 1 for energy"
# Update the time to when most of the disks turn off
current_time += timeout / 2
disk_array.update_time(current_time)
energy = disk_array.get_energy_usage()
expected_energy += disks * (timeout / 2) * test_disk.idle_power + \
(disks - 1) * test_disk.spin_down_energy
if not floateq(energy, expected_energy):
passed = False
print "Failed test 2"
# Update the time so that the last disk turns off
current_time += timeout / 2
disk_array.update_time(current_time)
energy = disk_array.get_energy_usage()
expected_energy += (timeout / 2) * test_disk.idle_power + \
test_disk.spin_down_energy
if not floateq(energy, expected_energy):
passed = False
print "Failed test 3"
# Turn a disk back on to read
current_time += timeout * 10
disk_array.update_time(current_time)
time = disk_array.read(file1)
energy = disk_array.get_energy_usage()
read_time = file1_compressed_size / test_disk.speed
expected_time = test_disk.spin_up_time + read_time
expected_energy += test_disk.spin_up_energy + \
read_time * test_disk.read_power
if not floateq(time, expected_time):
passed = False
print "Failed read test 4 for time"
if not floateq(energy, expected_energy):
passed = False
print "Failed read test 4 for energy"
# Try to read a file that's not there
exception_occurred = False
try:
disk_array.read(file2)
except:
exception_occurred = True
if not exception_occurred:
passed = False
print "Failed read test for non-existant file"
# Try to allocate some cache disks
exception_occurred = False
try:
disk_array = DiskArray(1, test_disk, disks, test_disk, timeout)
except:
exception_occurred = True
if not exception_occurred:
passed = False
print "Failed read test for non-existant file"
# TODO: add a test where multiple disks are involved (one disk is still on
# from a previous operation while others are turned on for new operations)
if passed:
print "All disk array tests passed"
else:
print "Disk array tests FAILED"
import numpy as np
from keras.models import load_model
from keras import backend as K
from wordvecspace import WordVecSpaceMem
from diskarray import DiskArray
def _euclidean_dis_loss(y_true, y_pred):
return K.sqrt(K.sum(K.square(y_pred - y_true), axis=0))
model = load_model(
sys.argv[1], custom_objects=dict(_euclidean_dis_loss=_euclidean_dis_loss))
out_f = DiskArray(sys.argv[2], shape=(0, ), dtype=[('vec', np.float32, 300)])
wv = WordVecSpaceMem(sys.argv[3])
def get_tras_vectors():
for i in range(wv.nvecs):
word = wv.get_word_at_index(i)
vec = wv.get_word_vector(word, raise_exc=True)
vec = vec.reshape(1, 300)
t_vec = model.predict(vec)
out_f.append((t_vec[0], ))
get_tras_vectors()
out_f.flush()
import numpy as np
from diskarray import DiskArray
d = DiskArray('/tmp/pras.array', shape=(2, 2), dtype=np.float32)
a = np.ndarray((2, 2), dtype=np.float32)
d.extend(a)
print(d[:])
class GW2VectoWordVecSpaceFile(object):
"""
Abstraction that helps in converting word vector space data
(vectors and vocabulary) from Google Word2Vec format to
WordVecSpaceFile format.
"""
def __init__(self,
in_dir,
outdir,
nvecs_per_shard=0,
shard_name="shard",
full_name="full"):
self.in_dir = in_dir
self.outdir = outdir
self.nvecs_per_shard = nvecs_per_shard
self.shard_name = shard_name
self.do_sharding = bool(self.nvecs_per_shard)
if self.do_sharding:
self.full_fpath = self.J(self.outdir, full_name)
os.makedirs(self.full_fpath)
map_itow = self.J(self.full_fpath, "indextoword")
self.itow = DiskDict(map_itow)
map_wtoi = self.J(self.full_fpath, "wordtoindex")
self.wtoi = DiskDict(map_wtoi)
self.mags = DiskArray(
self.J(self.full_fpath, "magnitudes"),
shape=(0, ),
dtype=np.float32,
growby=1000000,
)
self.occurs = DiskArray(
self.J(self.full_fpath, "occurrences"),
shape=(0, ),
dtype=np.uint64,
growby=1000000,
)
def J(self, p1, p2):
return os.path.join(p1, p2)
def _iter_vecs(self, vfile, vocabfile):
for word, vec in vfile.iter_vectors():
vec = np.fromstring(vec, dtype="float32")
mag = np.linalg.norm(vec)
vec = vec / mag
_line = vocabfile.readline().split(" ")
word, occur = _line[0], int(_line[1])
yield vec, word, mag, occur
def _build_writer(self, vidx, dim):
if self.do_sharding:
shard_num = int(vidx / self.nvecs_per_shard)
shard_name = "{}{}".format(self.shard_name, shard_num)
fpath = self.J(self.outdir, shard_name)
return GWVecBinWriter(fpath, dim, sharding=True)
else:
return GWVecBinWriter(self.outdir, dim)
def _create_manifest(
self,
out_fpath,
nvecs,
dim,
N,
t_occur,
in_fpath,
m_info={},
full=False,
num_vecs=None,
nvps=None,
):
if full:
mfc = dict(
num_shards=N,
num_vectors=nvecs,
dimension=dim,
num_words=t_occur,
dt_creation=datetime.utcnow().isoformat(),
input_path=in_fpath,
manifest_info=m_info,
num_vecs_per_shard=self.nvecs_per_shard,
)
else:
mfc = dict(
num_shards=N,
num_vecs_in_shard=nvecs,
num_vecs=num_vecs,
num_vecs_per_shard=nvps,
dimension=dim,
num_words=t_occur,
dt_creation=datetime.utcnow().isoformat(),
input_path=in_fpath,
manifest_info=m_info,
)
fp = open(self.J(out_fpath, "manifest.json"), "w")
fp.write(json.dumps(mfc))
fp.close()
def _find_manifest_info(self, fpath):
m_file = self.J(fpath, "manifest.json")
c = {}
if os.path.isfile(m_file):
fp = open(m_file, "r")
c = json.loads(fp.read())
return c
def start(self):
inp_vec_f = open(self.J(self.in_dir, "vectors.bin"), "rb")
inp_vecs = GWVecBinReader(inp_vec_f)
dim = inp_vecs.dim
nvecs = inp_vecs.nvecs
vocab_file = open(self.J(self.in_dir, "vocab.txt"),
"r",
encoding="utf-8",
errors="ignore")
m_info = self._find_manifest_info(self.in_dir)
w = None
vecs = self._iter_vecs(inp_vecs, vocab_file)
N = self.nvecs_per_shard
if N:
num_shards = math.ceil(nvecs / N)
else:
num_shards = 1
t_occur = 0
count = -1
for index, (vec, word, mag, occur) in enumerate(vecs):
if self.do_sharding and index % N == 0:
if w:
count += 1
t_occur += s_occur
self._create_manifest(
w.outdir,
(index - (count * N)),
dim,
num_shards,
s_occur,
self.in_dir,
m_info,
num_vecs=nvecs,
nvps=N,
)
w.close()
w = None
if not w:
s_occur = 0
w = self._build_writer(index, dim)
if self.do_sharding:
self.wtoi[word] = index
self.itow[index] = word
self.mags.append(mag)
self.occurs.append(occur)
w.write(vec=vec, mag=mag, occur=occur)
else:
w.write(vec=vec, mag=mag, word=word, index=index, occur=occur)
s_occur += occur
if w:
w.close()
count += 1
t_occur += s_occur
self._create_manifest(
w.outdir,
(index - (count * N)),
dim,
num_shards,
s_occur,
self.in_dir,
m_info,
num_vecs=nvecs,
nvps=N,
)
if self.do_sharding:
self.wtoi.close()
self.itow.close()
self.mags.flush()
self.mags.close()
self.occurs.flush()
self.occurs.close()
self._create_manifest(
self.full_fpath,
nvecs,
dim,
num_shards,
t_occur,
self.in_dir,
m_info,
full=True,
)
class CorrectionalTraining(BaseScript):
VEC_DIM = 300
LABELS = [0, 1]
def __init__(self):
super(CorrectionalTraining, self).__init__()
self.train_f = DiskArray(self.args.train_f,
shape=(self.get_shape(), ),
dtype=self.get_dtype())
self.wv = WordVecSpaceMem(self.args.wvspace_f)
def get_user_token(self):
token = input("Enter the search token: ")
return token
def get_shape(self):
if not os.path.exists(self.args.train_f):
return 0
dtype = self.get_dtype()
shape = os.stat(self.args.train_f).st_size // np.dtype(dtype).itemsize
return shape
def get_nearest_token(self, token):
url = 'http://dev0.servers.deepcompute.com:8888/api/v1/get_k_nearest_cosine?word={}&k=10'.format(
token)
#url = 'http://dev0.servers.deepcompute.com:8888/api/v1/get_nn_model_k_nearest?word={}&k=10'.format(token)
response = requests.get(url)
response = response.json()
result = response.get('result')
return result
def get_user_label(self, token, nearest_token):
#name = nearest_token.get('name', '')
#nearest_token = nearest_token.get('word2', '')
name = token
'''
if not name:
name = nearest_token
'''
print('the nearest token is %s' % name)
label = input("Mark the distance between {} and {}: ".format(
token, nearest_token))
return int(label)
def get_token_vector(self, token, nearest_token):
token_vec = self.wv.get_word_vector(token)
nearest_tok_vec = self.wv.get_word_vector(nearest_token)
return token_vec, nearest_tok_vec
def append_label_to_diskarray(self, vec1, vec2, word1, word2, label):
self.train_f.append((vec1, vec2, word1, word2, label))
def get_dtype(self):
return [
('vec1', np.float32, self.VEC_DIM),
('vec2', np.float32, self.VEC_DIM),
('word1', 'S', self.VEC_DIM),
('word2', 'S', self.VEC_DIM),
('label', np.int),
]
def run(self):
try:
while True:
token = self.get_user_token()
nearest_tokens = self.get_nearest_token(token)
for nearest_token in nearest_tokens:
label = int(self.get_user_label(token, nearest_token))
if label not in self.LABELS:
continue
vec1, vec2 = self.get_token_vector(token, nearest_token)
self.append_label_to_diskarray(vec1, vec2, token,
nearest_token, label)
finally:
self.train_f.flush()
def define_args(self, parser):
parser.add_argument('train_f', help='diskarray train file')
parser.add_argument('wvspace_f', help='wvspace file')
def __init__(self):
super(CorrectionalTraining, self).__init__()
self.train_f = DiskArray(self.args.train_f,
shape=(self.get_shape(), ),
dtype=self.get_dtype())
self.wv = WordVecSpaceMem(self.args.wvspace_f)
class TrainData(BaseScript):
VEC_DIM = 300
def __init__(self):
super(TrainData, self).__init__()
self.wvspace = WordVecSpaceMem(self.args.wvspace)
self.train_f = DiskArray(self.args.train_file,
shape=(self.get_shape(), ),
dtype=self.get_dtype())
self.words_f = open(self.args.words_file, 'w')
#self.model = load_model(self.args.model)
def get_shape(self):
if not os.path.exists(self.args.train_f):
return 0
dtype = self.get_dtype()
shape = os.stat(
self.args.train_file).st_size // np.dtype(dtype).itemsize
return shape
def get_dtype(self):
return [
('vec1', np.float32, self.VEC_DIM),
('vec2', np.float32, self.VEC_DIM),
('label', np.int),
]
def get_random_point(self):
return random.randint(0, len(self.wvspace))
def near_pair(self):
index = self.get_random_point()
word1 = self.wvspace.get_word_at_index(index)
nearest = self.wvspace.get_nearest(word1, 10)
n_words = self.wvspace.get_word_at_indices(nearest)
word2 = n_words[1]
self.add_pair(word1, word2)
def add_pair(self, word1, word2):
vec1 = self.wvspace.get_word_vector(word1)
vec2 = self.wvspace.get_word_vector(word2)
diff_vec = abs(vec1 - vec2)
p_value = self.model.predict(vec1, vec2)
p_value = 0 if p_value < 3 else 1
self.train_f.append((vec1, vec2, p_value))
self.words_f(word1 + '<====>' + word2 + '<======>' + str(p_value))
def far_pair(self):
index1 = self.get_random_point()
word1 = self.wvspace.get_word_at_index(index)
index2 = self.get_random_point()
word2 = self.wvspace.get_word_at_index(index)
self.add_pair(word1, word2)
def run(self):
for i in range(self.args.n_samples):
word1, word2 = self.near_pair()
def define_args(self, parser):
parser.add_argument('train_file', metavar='training-file')
parser.add_argument('wvspace', metavar='vector-space')
parser.add_argument('words_file', metavar='words-file')
parser.add_argument('n_samples', metavar='num-of-pairs')
def __init__(self, actual_vspace, transformed_vspace):
self.wvspace = WordVecSpaceMem(actual_vspace)
self.t_vspace = DiskArray(transformed_vspace,
dtype=[('vec', np.float32, 300)])
def main():
'''
Main function: parses command line arguments, generates several objects,
and passes them all to the simulation. Then runs the simulation and writes
the results to an output file.
'''
# Set some defaults in case command line arguments are not supplied
DEFAULT_TRACE_FILE_NAME = "./trace"
DEFAULT_SPIN_DOWN_TIMEOUT = float("inf") # seconds
DEFAULT_COMPRESSION_THRESHOLD = 0.3 # compression ratio
DEFAULT_COMPRESSION_ALG = "g"
DEFAULT_OUTPUT_FILE_NAME = "output.csv"
# Generate a parser for the command line arguments
parser = argparse.ArgumentParser()
parser.add_argument("-f",
"--trace",
help="file name of trace file to execute",
default=DEFAULT_TRACE_FILE_NAME,
metavar="TRACE_FILE")
parser.add_argument("-t",
"--timeout",
help="spin down timeout for disks",
default=DEFAULT_SPIN_DOWN_TIMEOUT,
metavar="SPIN_DOWN_TIMEOUT")
parser.add_argument(
"-c",
"--compression_alg",
help=
"compression algorithm to use in the simulation (g = gzip, b = bzip2, 7 = 7z, l, = lzop, s = snappy, fx = gzip x times faster, gx = gzip with x times better compression)",
default=DEFAULT_COMPRESSION_ALG,
choices=[
"g", "b", "7", "l", "s", "f1", "f2", "f3", "f4", "f5", "f6", "f7",
"f8", "f9", "f10", "f11", "f12", "f13", "f14", "f15", "f16", "f17",
"f18", "f19", "f20", "g1", "g2", "g3", "g4", "g5"
])
group = parser.add_mutually_exclusive_group()
group.add_argument("-n",
"--none",
action="store_true",
help="do not compress any files")
group.add_argument("-a",
"--all",
action="store_true",
help="compress all files")
group.add_argument(
"-r",
"--compression_ratio",
help="compression ratio below which files will be compressed",
default=DEFAULT_COMPRESSION_THRESHOLD,
type=float,
metavar="COMPRESSION_RATIO")
parser.add_argument(
"-o",
"--output",
help="output file name (results will be appended to this file)",
default=DEFAULT_OUTPUT_FILE_NAME,
metavar="OUTPUT_FILE")
# Parse the command line arguments
args = parser.parse_args()
trace_file_name = args.trace
spin_down_timeout = args.timeout
output_file_name = args.output
if args.compression_alg == "g":
compression_alg = gzip_alg
elif args.compression_alg == "b":
compression_alg = bzip2_alg
elif args.compression_alg == "7":
compression_alg = sevenz_alg
elif args.compression_alg == "s":
compression_alg = snappy_alg
elif args.compression_alg == "l":
compression_alg = lzop_alg
elif args.compression_alg == "f1":
compression_alg = faster1_alg
elif args.compression_alg == "f2":
compression_alg = faster2_alg
elif args.compression_alg == "f3":
compression_alg = faster3_alg
elif args.compression_alg == "f4":
compression_alg = faster4_alg
elif args.compression_alg == "f5":
compression_alg = faster5_alg
elif args.compression_alg == "f6":
compression_alg = faster6_alg
elif args.compression_alg == "f7":
compression_alg = faster7_alg
elif args.compression_alg == "f8":
compression_alg = faster8_alg
elif args.compression_alg == "f9":
compression_alg = faster9_alg
elif args.compression_alg == "f10":
compression_alg = faster10_alg
elif args.compression_alg == "f11":
compression_alg = faster11_alg
elif args.compression_alg == "f12":
compression_alg = faster12_alg
elif args.compression_alg == "f13":
compression_alg = faster13_alg
elif args.compression_alg == "f14":
compression_alg = faster14_alg
elif args.compression_alg == "f15":
compression_alg = faster15_alg
elif args.compression_alg == "f16":
compression_alg = faster16_alg
elif args.compression_alg == "f17":
compression_alg = faster17_alg
elif args.compression_alg == "f18":
compression_alg = faster18_alg
elif args.compression_alg == "f19":
compression_alg = faster19_alg
elif args.compression_alg == "f20":
compression_alg = faster20_alg
elif args.compression_alg == "g1":
compression_alg = greater1_alg
elif args.compression_alg == "g2":
compression_alg = greater2_alg
elif args.compression_alg == "g3":
compression_alg = greater3_alg
elif args.compression_alg == "g4":
compression_alg = greater4_alg
elif args.compression_alg == "g5":
compression_alg = greater5_alg
if args.none:
selection_alg = NoCompressionSelectionAlgorithm()
compression_threshold = 0
elif args.all:
selection_alg = CompressEverythingSelectionAlgorithm()
compression_threshold = float("inf")
else:
compression_threshold = args.compression_ratio
selection_alg = ThresholdCompressionAlgorithm(compression_threshold,
compression_alg)
# The following parameters are hard coded
processor_model = xeonE52658v2
num_cache_disks = 0
cache_disk_model = siliconDriveA100ssd
num_passive_disks = 100
passive_disk_model = savvio10k6hd
trace = Trace(trace_file_name)
disk_array = DiskArray(num_cache_disks, cache_disk_model,
num_passive_disks, passive_disk_model,
spin_down_timeout)
sim = Simulation(trace, compression_alg, selection_alg, processor_model,
disk_array)
results = sim.run()
# Write the parameters and results to the output file. The file is written
# in CSV format, which is easy to parse and can be read by many spreadsheet
# applications.
# Do some calculations up front
average_read_time = 0
if results.read_count > 0:
average_read_time = results.total_read_time / results.read_count
average_write_time = 0
if results.write_count > 0:
average_write_time = results.total_write_time / results.write_count
total_energy_usage = results.processor_energy_usage + \
results.disk_energy_usage
# Open (or create) the file for appending.
# Technically there is a bug here because the file might spontaneously
# spring into existence between the time it is checked and the time it is
# opened, but there's no need to worry about that for non-production code.
file_exists = os.path.exists(output_file_name)
output_file = open(output_file_name, 'a')
# Write out the header, if needed
if not file_exists:
output_file.write("trace_file_name,compression_algorithm,"
"compression_threshold,spin_down_timeout,"
"total_read_time,read_count,avg_read_time,"
"total_write_time,write_count,avg_write_time,"
"processor_energy_used,disk_energy_used,"
"total_energy_used,total_capacity_used,"
"parse_error_occurred\n")
# Write out the input parameters
output_file.write(trace_file_name)
output_file.write(",")
output_file.write(compression_alg.name)
output_file.write(",")
output_file.write(str(compression_threshold))
output_file.write(",")
output_file.write(str(spin_down_timeout))
output_file.write(",")
# Write out the results
output_file.write(str(results.total_read_time))
output_file.write(",")
output_file.write(str(results.read_count))
output_file.write(",")
output_file.write(str(average_read_time))
output_file.write(",")
output_file.write(str(results.total_write_time))
output_file.write(",")
output_file.write(str(results.write_count))
output_file.write(",")
output_file.write(str(average_write_time))
output_file.write(",")
output_file.write(str(results.processor_energy_usage))
output_file.write(",")
output_file.write(str(results.disk_energy_usage))
output_file.write(",")
output_file.write(str(total_energy_usage))
output_file.write(",")
output_file.write(str(results.total_capacity_usage))
output_file.write(",")
output_file.write(str(results.parse_error_occurred))
output_file.write("\n")
# TODO: it might be nice to provide finer grained metrics: for example,
# energy used by reads vs write vs idle and separate out procesor time
# from disk time for reads and writes.
# Clean up
output_file.close()
