The following are code examples for showing how to use . They are extracted from open source Python projects. You can vote up the examples you like or vote down the exmaples you don’t like. You can also save this page to your account.
Example 1
def save(self, filepath):
""" Save model parameters to file.
"""
all_params = OrderedDict([(param.name, param.get_value()) for param in self.params])
numpy.savez(filepath, **all_params)
print('Saved model to: {}'.format(filepath)) Example 2
def tf2npz(tf_path, export_folder=FAST):
vid_ids = []
labels = []
mean_rgb = []
mean_audio = []
tf_basename = os.path.basename(tf_path)
npz_basename = tf_basename[:-len('.tfrecord')] + '.npz'
isTrain = '/test' not in tf_path
for example in tf.python_io.tf_record_iterator(tf_path):
tf_example = tf.train.Example.FromString(example).features
vid_ids.append(tf_example.feature['video_id'].bytes_list.value[0].decode(encoding='UTF-8'))
if isTrain:
labels.append(np.array(tf_example.feature['labels'].int64_list.value))
mean_rgb.append(np.array(tf_example.feature['mean_rgb'].float_list.value).astype(np.float32))
mean_audio.append(np.array(tf_example.feature['mean_audio'].float_list.value).astype(np.float32))
save_path = export_folder + '/' + npz_basename
np.savez(save_path,
rgb=StandardScaler().fit_transform(np.array(mean_rgb)),
audio=StandardScaler().fit_transform(np.array(mean_audio)),
ids=np.array(vid_ids),
labels=labels
) Example 3
def run(self):
# generate dictionary of features
features = {'tokens':{'n_list':self.ngrams.split(), 'blackfeats':self.blackfeats.split(), 'mt':self.minimum_token_frequency}}
# format lines
documents = [[doc] for doc in format_tokdoc(self.in_tokenized().path,self.lowercase)]
# extract features
ft = featurizer.Featurizer(documents, features)
ft.fit_transform()
instances, vocabulary = ft.return_instances(['tokens'])
# write output
numpy.savez(self.out_features().path, data=instances.data, indices=instances.indices, indptr=instances.indptr, shape=instances.shape)
vocabulary = list(vocabulary)
with open(self.out_vocabulary().path,'w',encoding='utf-8') as vocab_out:
vocab_out.write('\n'.join(vocabulary))
# When the input is a directory with tokenized documents Example 4
def run(self):
# generate dictionary of features
features = {'tokens':{'n_list':self.ngrams.split(), 'blackfeats':self.blackfeats.split(), 'mt':self.minimum_token_frequency}}
# read in files and put in right format for featurizer
documents = []
for infile in sorted(listdir(self.in_tokdir().path),key=keyfunc):
documents.append(format_tokdoc(self.in_tokdir().path + '/' + infile,self.lowercase))
# extract features
ft = featurizer.Featurizer(documents, features) # to prevent ngrams across sentences, a featurizer is generated per document
ft.fit_transform()
instances, vocabulary = ft.return_instances(['tokens'])
# write output
numpy.savez(self.out_features().path, data=instances.data, indices=instances.indices, indptr=instances.indptr, shape=instances.shape)
with open(self.out_vocabulary().path,'w',encoding='utf-8') as vocab_out:
vocab_out.write('\n'.join(vocabulary))
# when the input is a file with frogged documents Example 5
def run(self):
# read in vectors
loader = numpy.load(self.in_vectors().path)
instances = sparse.csr_matrix((loader['data'], loader['indices'], loader['indptr']), shape = loader['shape'])
num_dimensions = instances.shape[1]
# generate vectorpopulation
random_vectorpopulation = ga_functions.random_vectorpopulation(num_dimensions, self.population_size)
numpy.savez(self.out_vectorpopulation().path, data=random_vectorpopulation.data, indices=random_vectorpopulation.indices, indptr=random_vectorpopulation.indptr, shape=random_vectorpopulation.shape)
# read in parameter options
with open(self.in_parameter_options().path) as infile:
lines = infile.read().rstrip().split('\n')
parameter_options = [[i for i in range(len(line.split()))] for line in lines]
# generate parameterpopulation
random_parameterpopulation = ga_functions.random_parameterpopulation(parameter_options, self.population_size)
numpy.savez(self.out_parameterpopulation().path, data=random_parameterpopulation.data, indices=random_parameterpopulation.indices, indptr=random_parameterpopulation.indptr, shape=random_parameterpopulation.shape)
################################################################################
###GA Iterator
################################################################################ Example 6
def savez_compressed(file, *args, **kwds):
"""
Save several arrays into a single file in compressed ``.npz`` format.
If keyword arguments are given, then filenames are taken from the keywords.
If arguments are passed in with no keywords, then stored file names are
arr_0, arr_1, etc.
Parameters
----------
file : str
File name of ``.npz`` file.
args : Arguments
Function arguments.
kwds : Keyword arguments
Keywords.
See Also
--------
numpy.savez : Save several arrays into an uncompressed ``.npz`` file format
numpy.load : Load the files created by savez_compressed.
"""
_savez(file, args, kwds, True) Example 7
def test_closing_fid(self):
# Test that issue #1517 (too many opened files) remains closed
# It might be a "weak" test since failed to get triggered on
# e.g. Debian sid of 2012 Jul 05 but was reported to
# trigger the failure on Ubuntu 10.04:
# http://projects.scipy.org/numpy/ticket/1517#comment:2
with temppath(suffix='.npz') as tmp:
np.savez(tmp, data='LOVELY LOAD')
# We need to check if the garbage collector can properly close
# numpy npz file returned by np.load when their reference count
# goes to zero. Python 3 running in debug mode raises a
# ResourceWarning when file closing is left to the garbage
# collector, so we catch the warnings. Because ResourceWarning
# is unknown in Python < 3.x, we take the easy way out and
# catch all warnings.
with warnings.catch_warnings():
warnings.simplefilter("ignore")
for i in range(1, 1025):
try:
np.load(tmp)["data"]
except Exception as e:
msg = "Failed to load data from a file: %s" % e
raise AssertionError(msg) Example 8
def test_npzfile_dict():
s = BytesIO()
x = np.zeros((3, 3))
y = np.zeros((3, 3))
np.savez(s, x=x, y=y)
s.seek(0)
z = np.load(s)
assert_('x' in z)
assert_('y' in z)
assert_('x' in z.keys())
assert_('y' in z.keys())
for f, a in z.items():
assert_(f in ['x', 'y'])
assert_equal(a.shape, (3, 3))
assert_(len(z.items()) == 2)
for f in z:
assert_(f in ['x', 'y'])
assert_('x' in z.keys()) Example 9
def test_load_refcount():
# Check that objects returned by np.load are directly freed based on
# their refcount, rather than needing the gc to collect them.
f = BytesIO()
np.savez(f, [1, 2, 3])
f.seek(0)
assert_(gc.isenabled())
gc.disable()
try:
gc.collect()
np.load(f)
# gc.collect returns the number of unreachable objects in cycles that
# were found -- we are checking that no cycles were created by np.load
n_objects_in_cycles = gc.collect()
finally:
gc.enable()
assert_equal(n_objects_in_cycles, 0) Example 10
def savez(file, *args, **kwds):
"""Saves one or more arrays into a file in uncompressed ``.npz`` format.
Arguments without keys are treated as arguments with automatic keys named
``arr_0``, ``arr_1``, etc. corresponding to the positions in the argument
list. The keys of arguments are used as keys in the ``.npz`` file, which
are used for accessing NpzFile object when the file is read by
:func:`cupy.load` function.
Args:
file (file or str): File or filename to save.
*args: Arrays with implicit keys.
**kwds: Arrays with explicit keys.
.. seealso:: :func:`numpy.savez`
"""
args = map(cupy.asnumpy, args)
for key in kwds:
kwds[key] = cupy.asnumpy(kwds[key])
numpy.savez(file, *args, **kwds) Example 11
def save_stored_rewards(self, file_name):
"""Saves the models stored rewards over time in a .npz file.
Args:
file_name: Name of the file that will be saved.
"""
training_epochs = len(self.rewards_batched) * self.output_every_nth
filename = os.path.join(self.output_dir,
file_name + '-' + str(training_epochs))
np.savez(filename,
train_rewards=self.rewards_batched,
train_music_theory_rewards=self.music_theory_rewards_batched,
train_note_rnn_rewards=self.note_rnn_rewards_batched,
eval_rewards=self.eval_avg_reward,
eval_music_theory_rewards=self.eval_avg_music_theory_reward,
eval_note_rnn_rewards=self.eval_avg_note_rnn_reward,
target_val_list=self.target_val_list) Example 12
def test(self):
self.model.best_params = [(x.name, x.get_value()) for x in
self.model.params]
numpy.savez(self.state['prefix'] + '_best_params',
**dict(self.model.best_params))
self.state['best_params_pos'] = self.step
if self.test_data is not None:
rvals = self.model.validate(self.test_data)
else:
rvals = []
msg = '>>> Test'
pos = self.step // self.state['validFreq']
for k, v in rvals:
msg = msg + ' ' + k + ':%6.3f ' % v
self.timings['test' + k][pos] = float(v)
self.state['test' + k] = float(v)
print msg
self.state['testtime'] = float(time.time()-self.start_time)/60. Example 13
def save(self):
start = time.time()
print "Saving the model..."
# ignore keyboard interrupt while saving
s = signal.signal(signal.SIGINT, signal.SIG_IGN)
numpy.savez(self.state['prefix']+'timing.npz',
**self.timings)
if self.state['overwrite']:
self.model.save(self.state['prefix']+'model.npz')
else:
self.model.save(self.state['prefix'] +
'model%d.npz' % self.save_iter)
cPickle.dump(self.state, open(self.state['prefix']+'state.pkl', 'w'))
self.save_iter += 1
signal.signal(signal.SIGINT, s)
print "Model saved, took {}".format(time.time() - start)
# FIXME Example 14
def test(self):
self.model.best_params = [(x.name, x.get_value()) for x in
self.model.params]
numpy.savez(self.state['prefix'] + '_best_params',
**dict(self.model.best_params))
self.state['best_params_pos'] = self.step
if self.test_data is not None:
rvals = self.model.validate(self.test_data)
else:
rvals = []
msg = '>>> Test'
pos = self.step // self.state['validFreq']
for k, v in rvals:
msg = msg + ' ' + k + ':%6.3f ' % v
self.timings['test' + k][pos] = float(v)
self.state['test' + k] = float(v)
print msg
self.state['testtime'] = float(time.time()-self.start_time)/60. Example 15
def save_total_power(data,times,SCADA_faults,filename):
total_power = np.array([])
new_times = np.array([])
percent_active = np.array([])
for time in np.unique(times):
state_fault = SCADA_faults[times == time]
fault_mask = [state_fault == 2,state_fault == 1]
fault_mask = reduce(np.logical_or,fault_mask)
total_power = np.append(total_power,np.sum(data[times == time]))
new_times = np.append(new_times,time)
percent_active = np.append(percent_active,float(np.sum(fault_mask))/float(len(fault_mask)))
total_dictionary = {}
total_dictionary['total_power'] = total_power
total_dictionary['time'] = new_times
total_dictionary['percent_active'] = percent_active
file_path = os.path.normpath('%s/FormattedData/%s' % (os.getcwd(),filename))
np.savez(file_path,**total_dictionary) Example 16
def main():
args = check_argv()
print datetime.datetime.now()
print "Reading HTK features from directory:", args.htk_dir
npz_dict = {}
n_feat_files = 0
for feat_fn in glob.glob(path.join(args.htk_dir, "*." + args.extension)):
hlist_output = shell("HList -r " + feat_fn)
features = [
[float(i) for i in line.split(" ") if i != ""] for line in
hlist_output.split("\n") if line != ""
]
key = path.splitext(path.split(feat_fn)[-1])[0]
npz_dict[key] = np.array(features)
n_feat_files += 1
print "Read", n_feat_files, "feature files"
print "Writing Numpy archive:", args.npz_fn
np.savez(args.npz_fn, **npz_dict)
print datetime.datetime.now() Example 17
def main():
args = check_argv()
print("Reading:", args.mat_fn)
mat = tables.open_file(args.mat_fn)
n_audio = mat.root.files_train[0].shape[0]
print("No. audio files:", n_audio)
filenames = []
for i_audio in xrange(n_audio):
filenames.append("".join([chr(i[0]) for i in mat.root.files_train[0][i_audio][0]]))
audio_keys = [path.splitext(path.split(i)[-1])[0] for i in filenames]
features_dict = {}
for i_audio in xrange(n_audio):
features = mat.root.F_train_iter[0][i_audio][0]
features_dict[audio_keys[i_audio].replace("_", "-")] = features.T
print("Writing:", args.npz_fn)
np.savez(args.npz_fn, **features_dict) Example 18
def cleanup(self,*args, **kwargs):
from theanompi.models.lstm import zipp, unzip, get_minibatches_idx, pred_error
if self.best_p is not None:
zipp(self.best_p, self.tparams)
else:
self.best_p = unzip(self.tparams)
self.use_noise.set_value(0.)
kf_train_sorted = get_minibatches_idx(len(self.train[0]), self.model_options['batch_size'])
train_err = pred_error(self.f_pred, self.prepare_data, self.train, kf_train_sorted)
valid_err = pred_error(self.f_pred, self.prepare_data, self.valid, kf_valid)
test_err = pred_error(self.f_pred, self.prepare_data, self.test, kf_test)
if self.rank==0: print( 'Train ', train_err, 'Valid ', valid_err, 'Test ', test_err )
if saveto:
numpy.savez(self.model_options['saveto'], train_err=train_err,
valid_err=valid_err, test_err=test_err,
history_errs=self.history_errs, **self.best_p)
# print('The code run for %d epochs, with %f sec/epochs' % (
# (self.eidx + 1), (end_time - start_time) / (1. * (self.eidx + 1))))
# print( ('Training took %.1fs' %
# (end_time - start_time)), file=sys.stderr) Example 19
def test_error_bars_stacked2(cmdopt, data_gen):
output = skh_plt.hist([data_gen[0], data_gen[1]], bins=20, histtype='barstacked',
weights=[data_gen[2], data_gen[2]], errorbars=True, err_return=True,
normed=True, scale=2)
if cmdopt == "generate":
with open(answer_dir+'/answers_error_bars_stacked2.npz', 'wb') as f:
np.savez(f, bc=output[0], be=output[1], berr=output[2])
plt.title('test_error_bars_stacked2')
plt.show()
elif cmdopt == "test":
answers = np.load(answer_dir+'/answers_error_bars_stacked2.npz')
assert(np.all(output[0] == answers['bc']))
assert(np.all(output[1] == answers['be']))
assert(np.all(output[2] == answers['berr'])) Example 20
def test_error_bars_stacked3(cmdopt, data_gen):
output = skh_plt.hist([data_gen[0], data_gen[1]], bins=20, histtype='step', stacked=True,
weights=[data_gen[2], data_gen[2]], errorbars=True, err_return=True,
normed=True, scale=2)
if cmdopt == "generate":
with open(answer_dir+'/answers_error_bars_stacked3.npz', 'wb') as f:
np.savez(f, bc=output[0], be=output[1], berr=output[2])
plt.title('test_error_bars_stacked2')
plt.show()
elif cmdopt == "test":
answers = np.load(answer_dir+'/answers_error_bars_stacked3.npz')
assert(np.all(output[0] == answers['bc']))
assert(np.all(output[1] == answers['be']))
assert(np.all(output[2] == answers['berr'])) Example 21
def test_ratio_plot(cmdopt, data_gen):
output = skh_plt.ratio_plot(dict(x=data_gen[0], errorbars=True, histtype='marker'),
dict(x=data_gen[1], weights=data_gen[2], errorbars=True))
if cmdopt == "generate":
with open(answer_dir+'/answers_ratio_plot.npz', 'wb') as f:
np.savez(f, bc1=output[1][0], be1=output[1][1],
bc2=output[2][0], be2=output[2][1])
output[0][0].set_title('test_ratio_plot')
plt.show()
elif cmdopt == "test":
answers = np.load(answer_dir+'/answers_ratio_plot.npz')
assert(np.all(output[1][0] == answers['bc1']))
assert(np.all(output[1][1] == answers['be1']))
assert(np.all(output[2][0] == answers['bc2']))
assert(np.all(output[2][1] == answers['be2'])) Example 22
def test_ratio_plot_log(cmdopt, data_gen):
output = skh_plt.ratio_plot(dict(x=data_gen[0], errorbars=True, histtype='marker', log=True,
err_x=False),
dict(x=data_gen[1], weights=data_gen[2], errorbars=True),
logx=True, ratio_range=(0, 10))
if cmdopt == "generate":
with open(answer_dir+'/answers_ratio_plot_log.npz', 'wb') as f:
np.savez(f, bc1=output[1][0], be1=output[1][1],
bc2=output[2][0], be2=output[2][1])
output[0][0].set_title('test_ratio_plot_log')
plt.show()
elif cmdopt == "test":
answers = np.load(answer_dir+'/answers_ratio_plot_log.npz')
assert(np.all(output[1][0] == answers['bc1']))
assert(np.all(output[1][1] == answers['be1']))
assert(np.all(output[2][0] == answers['bc2']))
assert(np.all(output[2][1] == answers['be2'])) Example 23
def write_dndlnm(outfile, dndlnm, z, mass, clobber=False):
"""
Write the halo mass distribution data into file in NumPy's ".npz"
format, which packs the ``dndlnm``, ``z``, and ``mass`` arrays.
Parameters
----------
outfile : str
The output file to store the dndlnm data, in ".npz" format.
dndlnm : 2D float `~numpy.ndarray`
Shape: (len(z), len(mass))
Differential mass function in terms of natural log of M.
Unit: [Mpc^-3] (the little "h" is folded into the values)
z : 1D float `~numpy.ndarray`
Redshifts where the halo mass distribution is calculated.
mass : 1D float `~numpy.ndarray`
(Logarithmic-distributed) masses points.
Unit: [Msun] (the little "h" is folded into the values)
clobber : bool, optional
Whether to overwrite the existing output file?
"""
_create_dir(outfile)
_check_existence(outfile, clobber=clobber, remove=True)
np.savez(outfile, dndlnm=dndlnm, z=z, mass=mass) Example 24
def main():
parser = argparse.ArgumentParser()
parser.add_argument("envid")
parser.add_argument("outfile")
parser.add_argument("--gymdir")
args = parser.parse_args()
if args.gymdir:
sys.path.insert(0, args.gymdir)
import gym
from gym import utils
print utils.colorize("gym directory: %s"%path.dirname(gym.__file__), "yellow")
env = gym.make(args.envid)
agent = RandomAgent(env.action_space)
alldata = {}
for i in xrange(2):
np.random.seed(i)
data = rollout(env, agent, env.spec.max_episode_steps)
for (k, v) in data.items():
alldata["%i-%s"%(i, k)] = v
np.savez(args.outfile, **alldata) Example 25
def savez_compressed(file, *args, **kwds):
"""
Save several arrays into a single file in compressed ``.npz`` format.
If keyword arguments are given, then filenames are taken from the keywords.
If arguments are passed in with no keywords, then stored file names are
arr_0, arr_1, etc.
Parameters
----------
file : str
File name of ``.npz`` file.
args : Arguments
Function arguments.
kwds : Keyword arguments
Keywords.
See Also
--------
numpy.savez : Save several arrays into an uncompressed ``.npz`` file format
numpy.load : Load the files created by savez_compressed.
"""
_savez(file, args, kwds, True) Example 26
def test_closing_fid(self):
# Test that issue #1517 (too many opened files) remains closed
# It might be a "weak" test since failed to get triggered on
# e.g. Debian sid of 2012 Jul 05 but was reported to
# trigger the failure on Ubuntu 10.04:
# http://projects.scipy.org/numpy/ticket/1517#comment:2
with temppath(suffix='.npz') as tmp:
np.savez(tmp, data='LOVELY LOAD')
# We need to check if the garbage collector can properly close
# numpy npz file returned by np.load when their reference count
# goes to zero. Python 3 running in debug mode raises a
# ResourceWarning when file closing is left to the garbage
# collector, so we catch the warnings. Because ResourceWarning
# is unknown in Python < 3.x, we take the easy way out and
# catch all warnings.
with warnings.catch_warnings():
warnings.simplefilter("ignore")
for i in range(1, 1025):
try:
np.load(tmp)["data"]
except Exception as e:
msg = "Failed to load data from a file: %s" % e
raise AssertionError(msg) Example 27
def test_npzfile_dict():
s = BytesIO()
x = np.zeros((3, 3))
y = np.zeros((3, 3))
np.savez(s, x=x, y=y)
s.seek(0)
z = np.load(s)
assert_('x' in z)
assert_('y' in z)
assert_('x' in z.keys())
assert_('y' in z.keys())
for f, a in z.items():
assert_(f in ['x', 'y'])
assert_equal(a.shape, (3, 3))
assert_(len(z.items()) == 2)
for f in z:
assert_(f in ['x', 'y'])
assert_('x' in z.keys()) Example 28
def test_load_refcount():
# Check that objects returned by np.load are directly freed based on
# their refcount, rather than needing the gc to collect them.
f = BytesIO()
np.savez(f, [1, 2, 3])
f.seek(0)
assert_(gc.isenabled())
gc.disable()
try:
gc.collect()
np.load(f)
# gc.collect returns the number of unreachable objects in cycles that
# were found -- we are checking that no cycles were created by np.load
n_objects_in_cycles = gc.collect()
finally:
gc.enable()
assert_equal(n_objects_in_cycles, 0) Example 29
def save(self, path):
savedir = smartutils.create_folder(pjoin(path, type(self).__name__))
hyperparameters = {'version': 2,
'image_shape': self.image_shape,
'nb_channels': self.nb_channels,
'ordering_seed': self.ordering_seed,
'use_mask_as_input': self.use_mask_as_input,
'hidden_activation': self.hidden_activation,
'has_convnet': self.has_convnet,
'has_fullnet': self.has_fullnet}
smartutils.save_dict_to_json_file(pjoin(savedir, "meta.json"), {"name": self.__class__.__name__})
smartutils.save_dict_to_json_file(pjoin(savedir, "hyperparams.json"), hyperparameters)
# Save residual parameters for the projection shortcuts.
np.savez(pjoin(savedir, "params.npz"), *self.parameters) Example 30
def __len__(self):
return self.D
# def save(self, savedir):
# state = {"version": 1,
# "seed": self.seed,
# "use_mask_as_input": self.use_mask_as_input,
# "batch_size": self.batch_size,
# "shared_batch_count": self.shared_batch_count.get_value(),
# "rng": pickle.dumps(self.rng),
# "shared_batch_mask": self._shared_mask_o_lt_d.get_value(),
# }
# np.savez(pjoin(savedir, 'mini_batch_scheduler_with_autoregressive_mask.npz'), **state)
# def load(self, loaddir):
# state = np.load(pjoin(loaddir, 'mini_batch_scheduler_with_autoregressive_mask.npz'))
# self.batch_size = state["batch_size"]
# self.shared_batch_count.set_value(state["shared_batch_count"])
# self.rng = pickle.loads(state["rng"])
# self._shared_mask_o_lt_d.set_value(state["shared_batch_mask"]) Example 31
def writeToFile(self, filename, saveOpts=False):
'''
write the distortion coeffs to file
saveOpts --> Whether so save calibration options (and not just results)
'''
try:
if not filename.endswith('.%s' % self.ftype):
filename += '.%s' % self.ftype
s = {'coeffs': self.coeffs}
if saveOpts:
s['opts'] = self.opts
# else:
# s['opts':{}]
np.savez(filename, **s)
return filename
except AttributeError:
raise Exception(
'need to calibrate camera before calibration can be saved to file') Example 32
def _save_model(self, bleu_score):
if self._is_valid_to_save(bleu_score):
model = ModelInfo(bleu_score, self.config['saveto'])
# Manage n-best model list first
if len(self.best_models) >= self.track_n_models:
old_model = self.best_models[0]
if old_model.path and os.path.isfile(old_model.path):
logger.info("Deleting old model %s" % old_model.path)
os.remove(old_model.path)
self.best_models.remove(old_model)
self.best_models.append(model)
self.best_models.sort(key=operator.attrgetter('bleu_score'))
# Save the model here
s = signal.signal(signal.SIGINT, signal.SIG_IGN)
logger.info("Saving new model {}".format(model.path))
numpy.savez(
model.path, **self.main_loop.model.get_parameter_dict())
numpy.savez(
os.path.join(self.config['saveto'], 'val_bleu_scores.npz'),
bleu_scores=self.val_bleu_curve)
signal.signal(signal.SIGINT, s) Example 33
def save_training(self, filename):
"""
Save traning set and labels of current model
:param filename: filename of new data.npz, it will be saved in 'train/'
"""
np.savez(os.path.join(TRAIN_DATA, filename),
train_set=self.train_set,
train_labels=self.train_labels) Example 34
def end(self, _session):
np.savez(self.params["file"], **self._beam_accum) Example 35
def end(self, _session):
scores_path = os.path.join(self.params["output_dir"],
"attention_scores.npz")
np.savez(scores_path, *self._attention_scores_accum)
tf.logging.info("Wrote %s", scores_path) Example 36
def save_path(self, path):
self._model.save(path)
self._feature_scaler.save_path(path.with_suffix('.feature_scaler'))
with open(path.with_suffix('.pessimism'), 'wb') as f:
np.savez(
f,
aim_pessimism_factor=self._aim_pessimism_factor,
accuracy_pessimism_factor=self._accuracy_pessimism_factor,
) Example 37
def save_path(self, path):
with open(path, 'wb') as f:
np.savez(
f,
ndim=len(self._axes) + 1,
mean=self.mean,
std=self.std,
) Example 38
def dataset_to_file(dataset, ngrams, filename='dataset'):
"""Save a dataset to a file.
Args:
dataset (:class:`np.ndarray`): the dataset to save (built with :func:`dataset_tools.build_dataset`)
ngrams (list of strings): the ngrams used to compute the features
filename (string): the filename without extension (will be .npz)
"""
num_samples, num_entries, num_features = dataset.shape
# We rehaspe the ndarray from 3D to 2D in order to write it into a text file
# Each line of the file will correspond to one cited paper
# Therefore, on each there will be the `num_entries` sets of features
dataset_sp = sparse.csr_matrix(dataset.reshape(num_samples*num_entries, num_features))
np.savez(filename, num_entries=np.array([num_entries]), data=dataset_sp.data, indices=dataset_sp.indices,
indptr=dataset_sp.indptr, shape=dataset_sp.shape, ngrams=ngrams) Example 39
def save_sparse_csr(filename,array):
np.savez(filename,data = array.data ,indices=array.indices,
indptr =array.indptr, shape=array.shape ) Example 40
def secure_numpy_save(params_dict, path):
"""Try saving into a temporary file and then move."""
try:
dirname = os.path.dirname(path)
with tempfile.NamedTemporaryFile(delete=False, dir=dirname) as temp:
numpy.savez(temp, **params_dict)
shutil.move(temp.name, path)
except Exception as e:
# if "temp" in locals():
# os.remove(temp.name)
logger.error(" Error {0}".format(str(e))) Example 41
def _save_params(self, model, params):
# Rename accordingly for blocks compatibility
params_to_save = dict(
(k.replace('/', '-'), v) for k, v in params.items())
numpy.savez(model.path, **params_to_save) Example 42
def _save_bleu_scores(self):
numpy.savez(
os.path.join(
self.saveto,
'val_bleu_scores{}_{}.npz'.format(self.enc_id, self.dec_id)),
bleu_scores=self.val_bleu_curve) Example 43
def encode_npz(subvol):
"""
This file format is unrelated to np.savez
We are just saving as .npy and the compressing
using zlib.
The .npy format contains metadata indicating
shape and dtype, instead of np.tobytes which doesn't
contain any metadata.
"""
fileobj = io.BytesIO()
if len(subvol.shape) == 3:
subvol = np.expand_dims(subvol, 0)
np.save(fileobj, subvol)
cdz = zlib.compress(fileobj.getvalue())
return cdz Example 44
def save_np(reader, output_filename):
ts, data = reader.read_stack()
np.savez(output_filename, timestamps=ts, data=data) Example 45
def write_test_file(self, variable='v', check=False):
data, metadata = self.build_test_data(variable)
metadata_array = np.array(sorted(metadata.items()))
np.savez(self.test_file, data=data, metadata=metadata_array)
if check:
data1, metadata1 = read_test_file(self.test_file)
assert metadata == metadata1, "%s != %s" % (metadata, metadata1)
assert data.shape == data1.shape == (505, 2), \
"%s, %s, (505, 2)" % (data.shape, data1.shape)
assert (data == data1).all()
assert metadata["n"] == 505 Example 46
def _write_file_contents(self, data, metadata):
# we explicitly set the dtype to ensure roundtrips preserve file contents exactly
max_metadata_length = max(chain([len(k) for k in metadata.keys()],
[len(str(v)) for v in metadata.values()]))
if PY2:
dtype = "S%d" % max_metadata_length
else:
dtype = "U%d" % max_metadata_length
metadata_array = numpy.array(sorted(metadata.items()), dtype)
numpy.savez(self.filename, data=data, metadata=metadata_array) Example 47
def write_test_file(self, variable='v', check=False):
data, metadata = self.build_test_data(variable)
metadata_array = np.array(sorted(metadata.items()))
np.savez(self.test_file, data=data, metadata=metadata_array)
if check:
data1, metadata1 = read_test_file(self.test_file)
assert metadata == metadata1, "%s != %s" % (metadata, metadata1)
assert data.shape == data1.shape == (505, 2), \
"%s, %s, (505, 2)" % (data.shape, data1.shape)
assert (data == data1).all()
assert metadata["n"] == 505 Example 48
def _write_file_contents(self, data, metadata):
# we explicitly set the dtype to ensure roundtrips preserve file contents exactly
max_metadata_length = max(chain([len(k) for k in metadata.keys()],
[len(str(v)) for v in metadata.values()]))
if PY2:
dtype = "S%d" % max_metadata_length
else:
dtype = "U%d" % max_metadata_length
metadata_array = numpy.array(sorted(metadata.items()), dtype)
numpy.savez(self.filename, data=data, metadata=metadata_array) Example 49
def save_model(self, file_name, directory): """Saves a checkpoint of the model and a .npz file with stored rewards. Args: file_name: String name to use for the checkpoint and rewards files. Defaults to self.model_name if None is provided. """ if self.verbose: print "Saving model..." save_dir = directory + file_name os.mkdir(save_dir) directory = save_dir + '/' save_loc = os.path.join(directory, file_name + '.ckpt') training_epochs = len(self.training_val_results) * self.accuracy_logged_every_n self.saver.save(self.session, save_loc, global_step=training_epochs) npz_name = os.path.join(directory, file_name + '-' + str(training_epochs)) if not self.print_per_task: np.savez(npz_name, training_val_results=self.training_val_results, l2_beta=self.l2_beta, dropout=self.dropout, hidden_sizes_shared=self.hidden_sizes_shared, hidden_size_task=self.hidden_size_task)
Example 50
def save_model(self, file_name, directory): """Saves a checkpoint of the model and a .npz file with stored rewards. Args: file_name: String name to use for the checkpoint and rewards files. Defaults to self.model_name if None is provided. """ if self.verbose: print "Saving model..." save_dir = directory + file_name os.mkdir(save_dir) directory = save_dir + '/' save_loc = os.path.join(directory, file_name + '.ckpt') training_epochs = len(self.training_val_results) * self.accuracy_logged_every_n self.saver.save(self.session, save_loc, global_step=training_epochs) npz_name = os.path.join(directory, file_name + '-' + str(training_epochs)) if not self.print_per_task: np.savez(npz_name, training_val_results=self.training_val_results, l2_beta=self.l2_beta, dropout=self.dropout, hidden_sizes_shared=self.hidden_sizes_shared, hidden_size_task=self.hidden_size_task) else: np.savez(npz_name, training_val_results=self.training_val_results, training_val_results_per_task=self.training_val_results_per_task, l2_beta=self.l2_beta, dropout=self.dropout, hidden_sizes_shared=self.hidden_sizes_shared, hidden_size_task=self.hidden_size_task)
