def mpi_target_dict(rank, mpi_shape, pbc_axes):
common.check_type('mpi_shape', mpi_shape, (list, tuple), int)
common.check_type('pbc_axes', pbc_axes, str)
mx, my, mz = mpi_shape
mpi_target_dict = {
'x-': None, 'x+': None, \
'y-': None, 'y+': None, \
'z-': None, 'z+': None}
mycoord = my_coord(rank, mpi_shape)
replace = lambda i, val: mycoord[:i] + [val] + mycoord[i + 1:]
coord_to_rank = lambda (i, j, k): i + j * mx + k * mx * my
for i, axis in zip([0, 1, 2], ['x', 'y', 'z']):
val = mycoord[i]
ms = mpi_shape[i]
if val > 0:
mpi_target_dict['%s-' % axis] = coord_to_rank(replace(i, val - 1))
elif val == 0 and axis in pbc_axes and ms != 1:
mpi_target_dict['%s-' % axis] = coord_to_rank(replace(i, ms - 1))
if val < ms - 1:
mpi_target_dict['%s+' % axis] = coord_to_rank(replace(i, val + 1))
elif val == ms - 1 and axis in pbc_axes and ms != 1:
mpi_target_dict['%s+' % axis] = coord_to_rank(replace(i, 0))
return mpi_target_dict
def sentiment_analysis(load_model, label_type, embs_convert_type,
label_type_folder, target_data_folder, save_folder):
check_type(label_type,
types_list=['tonality', 'toxicity'],
type_name='label')
check_type(embs_convert_type,
types_list=['mean', 'length_64'],
type_name='embeddings convert')
x_target, y_target = load_target_data(label_type=label_type,
convert_type=embs_convert_type,
data_folder=target_data_folder)
if not load_model:
x_source, x_source_test, y_source, y_source_test = load_source_data(
label_type=label_type,
label_data_folder=label_type_folder,
convert_type=embs_convert_type)
model = train_lstm(x_source=x_source,
y_source=y_source,
label_type=label_type,
convert_type=embs_convert_type,
save_folder=save_folder,
epochs=5)
predict(model=model, x=x_source_test, y=y_source_test, title='Source')
else:
model = load_lstm(label_type=label_type,
convert_type=embs_convert_type,
folder=save_folder)
y_pred = predict(model=model, x=x_target, y=y_target, title='Target')
return y_pred
def mpi_target_dict(rank, mpi_shape, pbc_axes):
common.check_type('mpi_shape', mpi_shape, (list, tuple), int)
common.check_type('pbc_axes', pbc_axes, str)
mx, my, mz = mpi_shape
mpi_target_dict = {
'x-': None, 'x+': None, \
'y-': None, 'y+': None, \
'z-': None, 'z+': None}
mycoord = my_coord(rank, mpi_shape)
replace = lambda i, val: mycoord[:i] + [val] + mycoord[i+1:]
coord_to_rank = lambda (i, j, k): i + j*mx + k*mx*my
for i, axis in zip([0, 1, 2], ['x', 'y', 'z']):
val = mycoord[i]
ms = mpi_shape[i]
if val > 0:
mpi_target_dict['%s-' % axis] = coord_to_rank(replace(i, val-1))
elif val == 0 and axis in pbc_axes and ms != 1:
mpi_target_dict['%s-' % axis] = coord_to_rank(replace(i, ms-1))
if val < ms-1:
mpi_target_dict['%s+' % axis] = coord_to_rank(replace(i, val+1))
elif val == ms-1 and axis in pbc_axes and ms != 1:
mpi_target_dict['%s+' % axis] = coord_to_rank(replace(i, 0))
return mpi_target_dict
def macro_replace_list(pt0, pt1):
"""
Return the replace string list correspond to macro
This is used to generate the cuda kernel from the template.
"""
common.check_type('pt0', pt0, (list, tuple), int)
common.check_type('pt1', pt1, (list, tuple), int)
x0, y0, z0 = pt0
x1, y1, z1 = pt1
snx = abs(x1 - x0) + 1
sny = abs(y1 - y0) + 1
snz = abs(z1 - z0) + 1
nmax = snx * sny * snz
xid, yid, zid = x0, y0, z0
if x0 == x1 and y0 == y1 and z0 == z1:
pass
elif x0 != x1 and y0 == y1 and z0 == z1:
xid = '(gid + %d)' % x0
elif x0 == x1 and y0 != y1 and z0 == z1:
yid = '(gid + %d)' % y0
elif x0 == x1 and y0 == y1 and z0 != z1:
zid = '(gid + %d)' % z0
elif x0 != x1 and y0 != y1 and z0 == z1:
xid = '(gid/%d + %d)' % (sny, x0)
yid = '(gid%%%d + %d)' % (sny, y0)
elif x0 == x1 and y0 != y1 and z0 != z1:
yid = '(gid/%d + %d)' % (snz, y0)
zid = '(gid%%%d + %d)' % (snz, z0)
elif x0 != x1 and y0 == y1 and z0 != z1:
xid = '(gid/%d + %d)' % (snz, x0)
zid = '(gid%%%d + %d)' % (snz, z0)
elif x0 != x1 and y0 != y1 and z0 != z1:
xid = '(gid/%d + %d)' % (sny*snz, x0)
yid = '((gid/%d)%%%d + %d)' % (snz, sny, y0)
zid = '(gid%%%d + %d)' % (snz, z0)
return [str(nmax), str(xid), str(yid), str(zid)]
def macro_replace_list(pt0, pt1):
"""
Return the replace string list correspond to macro
This is used to generate the opencl kernel from the template.
"""
common.check_type('pt0', pt0, (list, tuple), int)
common.check_type('pt1', pt1, (list, tuple), int)
x0, y0, z0 = pt0
x1, y1, z1 = pt1
snx = abs(x1 - x0) + 1
sny = abs(y1 - y0) + 1
snz = abs(z1 - z0) + 1
nmax = snx * sny * snz
xid, yid, zid = x0, y0, z0
if x0 == x1 and y0 == y1 and z0 == z1:
pass
elif x0 != x1 and y0 == y1 and z0 == z1:
xid = '(gid + %d)' % x0
elif x0 == x1 and y0 != y1 and z0 == z1:
yid = '(gid + %d)' % y0
elif x0 == x1 and y0 == y1 and z0 != z1:
zid = '(gid + %d)' % z0
elif x0 != x1 and y0 != y1 and z0 == z1:
xid = '(gid/%d + %d)' % (sny, x0)
yid = '(gid%%%d + %d)' % (sny, y0)
elif x0 == x1 and y0 != y1 and z0 != z1:
yid = '(gid/%d + %d)' % (snz, y0)
zid = '(gid%%%d + %d)' % (snz, z0)
elif x0 != x1 and y0 == y1 and z0 != z1:
xid = '(gid/%d + %d)' % (snz, x0)
zid = '(gid%%%d + %d)' % (snz, z0)
elif x0 != x1 and y0 != y1 and z0 != z1:
xid = '(gid/%d + %d)' % (sny * snz, x0)
yid = '((gid/%d)%%%d + %d)' % (snz, sny, y0)
zid = '(gid%%%d + %d)' % (snz, z0)
return [str(nmax), str(xid), str(yid), str(zid)]
def accum_sub_ns_dict(mpi_shape, ndev, dnx_list, ny_list, nz_list):
common.check_type('mpi_shape', mpi_shape, (tuple, list), int)
common.check_type('ndev', ndev, int)
common.check_type('dnx_list', dnx_list, (tuple, list), int)
common.check_type('ny_list', ny_list, (tuple, list), int)
common.check_type('nz_list', nz_list, (tuple, list), int)
mx, my, mz = mpi_shape
snx_list = []
strip_dnx_list = []
for mi in xrange(mx):
sub_dnx_list = dnx_list[mi * ndev:(mi + 1) * ndev]
snx_list.append(sum(sub_dnx_list) - ndev + 1)
strip_dnx_list.extend([nx - 1 for nx in sub_dnx_list])
strip_dnx_list[-1] += 1
accum_sub_ns_dict = { \
'x': np.add.accumulate([0] + snx_list), \
'y': np.add.accumulate([0] + ny_list), \
'z': np.add.accumulate([0] + nz_list), \
'dx': np.add.accumulate([0] + strip_dnx_list) }
return accum_sub_ns_dict
def accum_sub_ns_dict(mpi_shape, ndev, dnx_list, ny_list, nz_list):
common.check_type('mpi_shape', mpi_shape, (tuple, list), int)
common.check_type('ndev', ndev, int)
common.check_type('dnx_list', dnx_list, (tuple, list), int)
common.check_type('ny_list', ny_list, (tuple, list), int)
common.check_type('nz_list', nz_list, (tuple, list), int)
mx, my, mz = mpi_shape
snx_list = []
strip_dnx_list = []
for mi in xrange(mx):
sub_dnx_list = dnx_list[mi*ndev:(mi+1)*ndev]
snx_list.append( sum(sub_dnx_list) - ndev + 1 )
strip_dnx_list.extend( [nx-1 for nx in sub_dnx_list] )
strip_dnx_list[-1] += 1
accum_sub_ns_dict = { \
'x': np.add.accumulate([0] + snx_list), \
'y': np.add.accumulate([0] + ny_list), \
'z': np.add.accumulate([0] + nz_list), \
'dx': np.add.accumulate([0] + strip_dnx_list) }
return accum_sub_ns_dict
def check_mpi_shape(size, mpi_shape):
common.check_type('mpi_shape', mpi_shape, (list, tuple), int)
assert size == reduce(lambda x, y: x*y, mpi_shape), \
'MPI size %d is not matched with the mpi_shape %s.' % (size, repr(mpi_shape))
def my_coord(rank, mpi_shape):
common.check_type('mpi_shape', mpi_shape, (list, tuple), int)
mx, my, mz = mpi_shape
return [rank % mx, rank / mx % my, rank / (mx * my)]
def lda_analysis(load_model, lda_model_type, data_folder, results_folder,
csv_file_name, mallet_download_folder):
print("\nLDA analysis")
check_type(lda_model_type, ['mallet', 'lda'], 'lda model')
# Downloads
print('\nDownloads')
nltk.download('stopwords')
if not os.path.exists(os.path.join(mallet_download_folder,
'mallet-2.0.8')):
dload.save_unzip("http://mallet.cs.umass.edu/dist/mallet-2.0.8.zip",
mallet_download_folder)
mallet_path = os.path.join(mallet_download_folder, 'mallet-2.0.8', 'bin',
'mallet')
os.environ.update(
{'MALLET_HOME': os.path.join(mallet_download_folder, 'mallet-2.0.8')})
# Load data
data = pd.read_csv(os.path.join(data_folder, csv_file_name))
texts_original = data['text'].values.tolist()
tonality = data['tonality'].values.tolist()
# tonality = [change_class_label(value) for value in tonality]
toxicity = data['toxicity'].values.tolist()
# Preprocess texts
texts_processed = preprocessing(texts_original)
# Create dictionary
id2word = corpora.Dictionary(texts_processed)
# Get term document frequency
corpus = [id2word.doc2bow(text) for text in texts_processed]
# Get optimal model
if not load_model:
model = get_optimal_model(results_folder=results_folder,
corpus=corpus,
id2word=id2word,
lda_model_type=lda_model_type,
texts=texts_processed,
mallet_path=mallet_path)
save_lda_model(lda_model=model,
save_path=os.path.join(results_folder,
lda_model_type + '_model.bin'))
else:
model = load_lda_model(
model_path=os.path.join(results_folder, lda_model_type +
'_model.bin'))
# Find dominant topic in each text
topic_nums, topic_keywords = get_dominant_topic_df(
lda_model=model,
model_type=lda_model_type,
corpus=corpus,
texts=texts_original)
# Save to excel-file
df_result = pd.DataFrame({
'texts': texts_original,
'tonality': tonality,
'toxicity': toxicity,
'dominant_topic': topic_nums,
'topic_keywords': topic_keywords
})
df_result.to_excel(os.path.join(results_folder,
'results_' + lda_model_type + '.xlsx'),
index=False)
# Distribution of tonality and toxicity by topics
plot_label_by_topic(df=df_result,
label_name='tonality',
model_type=lda_model_type,
results_folder=results_folder)
plot_label_by_topic(df=df_result,
label_name='toxicity',
model_type=lda_model_type,
results_folder=results_folder)
def check_mpi_shape(size, mpi_shape):
common.check_type('mpi_shape', mpi_shape, (list, tuple), int)
assert size == reduce(lambda x, y: x*y, mpi_shape), \
'MPI size %d is not matched with the mpi_shape %s.' % (size, repr(mpi_shape))
def my_coord(rank, mpi_shape):
common.check_type('mpi_shape', mpi_shape, (list, tuple), int)
mx, my, mz = mpi_shape
return [rank%mx, rank/mx%my, rank/(mx*my)]