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28,600 | tensorflow/cleverhans | cleverhans/attacks/bapp.py | initialize | def initialize(decision_function, sample, shape, clip_min, clip_max):
"""
Efficient Implementation of BlendedUniformNoiseAttack in Foolbox.
"""
success = 0
num_evals = 0
# Find a misclassified random noise.
while True:
random_noise = np.random.uniform(clip_min, clip_max, size=shape)
success = dec... | python | def initialize(decision_function, sample, shape, clip_min, clip_max):
"""
Efficient Implementation of BlendedUniformNoiseAttack in Foolbox.
"""
success = 0
num_evals = 0
# Find a misclassified random noise.
while True:
random_noise = np.random.uniform(clip_min, clip_max, size=shape)
success = dec... | [
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28,601 | tensorflow/cleverhans | cleverhans/attacks/bapp.py | geometric_progression_for_stepsize | def geometric_progression_for_stepsize(x, update, dist, decision_function,
current_iteration):
""" Geometric progression to search for stepsize.
Keep decreasing stepsize by half until reaching
the desired side of the boundary.
"""
epsilon = dist / np.sqrt(current... | python | def geometric_progression_for_stepsize(x, update, dist, decision_function,
current_iteration):
""" Geometric progression to search for stepsize.
Keep decreasing stepsize by half until reaching
the desired side of the boundary.
"""
epsilon = dist / np.sqrt(current... | [
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28,602 | tensorflow/cleverhans | cleverhans/attacks/bapp.py | select_delta | def select_delta(dist_post_update, current_iteration,
clip_max, clip_min, d, theta, constraint):
"""
Choose the delta at the scale of distance
between x and perturbed sample.
"""
if current_iteration == 1:
delta = 0.1 * (clip_max - clip_min)
else:
if constraint == 'l2':
delta... | python | def select_delta(dist_post_update, current_iteration,
clip_max, clip_min, d, theta, constraint):
"""
Choose the delta at the scale of distance
between x and perturbed sample.
"""
if current_iteration == 1:
delta = 0.1 * (clip_max - clip_min)
else:
if constraint == 'l2':
delta... | [
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28,603 | tensorflow/cleverhans | cleverhans/attacks/fast_feature_adversaries.py | FastFeatureAdversaries.attack_single_step | def attack_single_step(self, x, eta, g_feat):
"""
TensorFlow implementation of the Fast Feature Gradient. This is a
single step attack similar to Fast Gradient Method that attacks an
internal representation.
:param x: the input placeholder
:param eta: A tensor the same shape as x that holds the... | python | def attack_single_step(self, x, eta, g_feat):
"""
TensorFlow implementation of the Fast Feature Gradient. This is a
single step attack similar to Fast Gradient Method that attacks an
internal representation.
:param x: the input placeholder
:param eta: A tensor the same shape as x that holds the... | [
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28,604 | tensorflow/cleverhans | examples/nips17_adversarial_competition/dev_toolkit/sample_defenses/ens_adv_inception_resnet_v2/inception_resnet_v2.py | block35 | def block35(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=None):
"""Builds the 35x35 resnet block."""
with tf.variable_scope(scope, 'Block35', [net], reuse=reuse):
with tf.variable_scope('Branch_0'):
tower_conv = slim.conv2d(net, 32, 1, scope='Conv2d_1x1')
with tf.variable_scope('Branch_... | python | def block35(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=None):
"""Builds the 35x35 resnet block."""
with tf.variable_scope(scope, 'Block35', [net], reuse=reuse):
with tf.variable_scope('Branch_0'):
tower_conv = slim.conv2d(net, 32, 1, scope='Conv2d_1x1')
with tf.variable_scope('Branch_... | [
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28,605 | tensorflow/cleverhans | examples/nips17_adversarial_competition/dev_toolkit/sample_defenses/ens_adv_inception_resnet_v2/inception_resnet_v2.py | block17 | def block17(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=None):
"""Builds the 17x17 resnet block."""
with tf.variable_scope(scope, 'Block17', [net], reuse=reuse):
with tf.variable_scope('Branch_0'):
tower_conv = slim.conv2d(net, 192, 1, scope='Conv2d_1x1')
with tf.variable_scope('Branch... | python | def block17(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=None):
"""Builds the 17x17 resnet block."""
with tf.variable_scope(scope, 'Block17', [net], reuse=reuse):
with tf.variable_scope('Branch_0'):
tower_conv = slim.conv2d(net, 192, 1, scope='Conv2d_1x1')
with tf.variable_scope('Branch... | [
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28,606 | tensorflow/cleverhans | examples/nips17_adversarial_competition/dev_toolkit/sample_defenses/ens_adv_inception_resnet_v2/inception_resnet_v2.py | inception_resnet_v2 | def inception_resnet_v2(inputs, nb_classes=1001, is_training=True,
dropout_keep_prob=0.8,
reuse=None,
scope='InceptionResnetV2',
create_aux_logits=True,
num_classes=None):
"""Creates the Inception R... | python | def inception_resnet_v2(inputs, nb_classes=1001, is_training=True,
dropout_keep_prob=0.8,
reuse=None,
scope='InceptionResnetV2',
create_aux_logits=True,
num_classes=None):
"""Creates the Inception R... | [
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28,607 | tensorflow/cleverhans | examples/nips17_adversarial_competition/dev_toolkit/sample_defenses/ens_adv_inception_resnet_v2/inception_resnet_v2.py | inception_resnet_v2_arg_scope | def inception_resnet_v2_arg_scope(weight_decay=0.00004,
batch_norm_decay=0.9997,
batch_norm_epsilon=0.001):
"""Returns the scope with the default parameters for inception_resnet_v2.
Args:
weight_decay: the weight decay for weights variables.
... | python | def inception_resnet_v2_arg_scope(weight_decay=0.00004,
batch_norm_decay=0.9997,
batch_norm_epsilon=0.001):
"""Returns the scope with the default parameters for inception_resnet_v2.
Args:
weight_decay: the weight decay for weights variables.
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28,608 | tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/validation_tool/validate_and_copy_submissions.py | main | def main(args):
"""Validate all submissions and copy them into place"""
random.seed()
temp_dir = tempfile.mkdtemp()
logging.info('Created temporary directory: %s', temp_dir)
validator = SubmissionValidator(
source_dir=args.source_dir,
target_dir=args.target_dir,
temp_dir=temp_dir,
do_c... | python | def main(args):
"""Validate all submissions and copy them into place"""
random.seed()
temp_dir = tempfile.mkdtemp()
logging.info('Created temporary directory: %s', temp_dir)
validator = SubmissionValidator(
source_dir=args.source_dir,
target_dir=args.target_dir,
temp_dir=temp_dir,
do_c... | [
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28,609 | tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/validation_tool/validate_and_copy_submissions.py | ValidationStats._update_stat | def _update_stat(self, submission_type, increase_success, increase_fail):
"""Common method to update submission statistics."""
stat = self.stats.get(submission_type, (0, 0))
stat = (stat[0] + increase_success, stat[1] + increase_fail)
self.stats[submission_type] = stat | python | def _update_stat(self, submission_type, increase_success, increase_fail):
"""Common method to update submission statistics."""
stat = self.stats.get(submission_type, (0, 0))
stat = (stat[0] + increase_success, stat[1] + increase_fail)
self.stats[submission_type] = stat | [
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28,610 | tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/validation_tool/validate_and_copy_submissions.py | ValidationStats.log_stats | def log_stats(self):
"""Print statistics into log."""
logging.info('Validation statistics: ')
for k, v in iteritems(self.stats):
logging.info('%s - %d valid out of %d total submissions',
k, v[0], v[0] + v[1]) | python | def log_stats(self):
"""Print statistics into log."""
logging.info('Validation statistics: ')
for k, v in iteritems(self.stats):
logging.info('%s - %d valid out of %d total submissions',
k, v[0], v[0] + v[1]) | [
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28,611 | tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/validation_tool/validate_and_copy_submissions.py | SubmissionValidator.copy_submission_locally | def copy_submission_locally(self, cloud_path):
"""Copies submission from Google Cloud Storage to local directory.
Args:
cloud_path: path of the submission in Google Cloud Storage
Returns:
name of the local file where submission is copied to
"""
local_path = os.path.join(self.download_d... | python | def copy_submission_locally(self, cloud_path):
"""Copies submission from Google Cloud Storage to local directory.
Args:
cloud_path: path of the submission in Google Cloud Storage
Returns:
name of the local file where submission is copied to
"""
local_path = os.path.join(self.download_d... | [
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28,612 | tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/validation_tool/validate_and_copy_submissions.py | SubmissionValidator.copy_submission_to_destination | def copy_submission_to_destination(self, src_filename, dst_subdir,
submission_id):
"""Copies submission to target directory.
Args:
src_filename: source filename of the submission
dst_subdir: subdirectory of the target directory where submission should
be... | python | def copy_submission_to_destination(self, src_filename, dst_subdir,
submission_id):
"""Copies submission to target directory.
Args:
src_filename: source filename of the submission
dst_subdir: subdirectory of the target directory where submission should
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28,613 | tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/validation_tool/validate_and_copy_submissions.py | SubmissionValidator.validate_and_copy_one_submission | def validate_and_copy_one_submission(self, submission_path):
"""Validates one submission and copies it to target directory.
Args:
submission_path: path in Google Cloud Storage of the submission file
"""
if os.path.exists(self.download_dir):
shutil.rmtree(self.download_dir)
os.makedirs(s... | python | def validate_and_copy_one_submission(self, submission_path):
"""Validates one submission and copies it to target directory.
Args:
submission_path: path in Google Cloud Storage of the submission file
"""
if os.path.exists(self.download_dir):
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28,614 | tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/validation_tool/validate_and_copy_submissions.py | SubmissionValidator.save_id_to_path_mapping | def save_id_to_path_mapping(self):
"""Saves mapping from submission IDs to original filenames.
This mapping is saved as CSV file into target directory.
"""
if not self.id_to_path_mapping:
return
with open(self.local_id_to_path_mapping_file, 'w') as f:
writer = csv.writer(f)
writer... | python | def save_id_to_path_mapping(self):
"""Saves mapping from submission IDs to original filenames.
This mapping is saved as CSV file into target directory.
"""
if not self.id_to_path_mapping:
return
with open(self.local_id_to_path_mapping_file, 'w') as f:
writer = csv.writer(f)
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28,615 | tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/validation_tool/validate_and_copy_submissions.py | SubmissionValidator.run | def run(self):
"""Runs validation of all submissions."""
cmd = ['gsutil', 'ls', os.path.join(self.source_dir, '**')]
try:
files_list = subprocess.check_output(cmd).split('\n')
except subprocess.CalledProcessError:
logging.error('Can''t read source directory')
all_submissions = [
... | python | def run(self):
"""Runs validation of all submissions."""
cmd = ['gsutil', 'ls', os.path.join(self.source_dir, '**')]
try:
files_list = subprocess.check_output(cmd).split('\n')
except subprocess.CalledProcessError:
logging.error('Can''t read source directory')
all_submissions = [
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28,616 | tensorflow/cleverhans | scripts/plot_success_fail_curve.py | main | def main(argv=None):
"""Takes the path to a directory with reports and renders success fail plots."""
report_paths = argv[1:]
fail_names = FLAGS.fail_names.split(',')
for report_path in report_paths:
plot_report_from_path(report_path, label=report_path, fail_names=fail_names)
pyplot.legend()
pyplot.x... | python | def main(argv=None):
"""Takes the path to a directory with reports and renders success fail plots."""
report_paths = argv[1:]
fail_names = FLAGS.fail_names.split(',')
for report_path in report_paths:
plot_report_from_path(report_path, label=report_path, fail_names=fail_names)
pyplot.legend()
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28,617 | tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/eval_lib/work_data.py | is_unclaimed | def is_unclaimed(work):
"""Returns True if work piece is unclaimed."""
if work['is_completed']:
return False
cutoff_time = time.time() - MAX_PROCESSING_TIME
if (work['claimed_worker_id'] and
work['claimed_worker_start_time'] is not None
and work['claimed_worker_start_time'] >= cutoff_time):
... | python | def is_unclaimed(work):
"""Returns True if work piece is unclaimed."""
if work['is_completed']:
return False
cutoff_time = time.time() - MAX_PROCESSING_TIME
if (work['claimed_worker_id'] and
work['claimed_worker_start_time'] is not None
and work['claimed_worker_start_time'] >= cutoff_time):
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28,618 | tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/eval_lib/work_data.py | WorkPiecesBase.write_all_to_datastore | def write_all_to_datastore(self):
"""Writes all work pieces into datastore.
Each work piece is identified by ID. This method writes/updates only those
work pieces which IDs are stored in this class. For examples, if this class
has only work pieces with IDs '1' ... '100' and datastore already contains
... | python | def write_all_to_datastore(self):
"""Writes all work pieces into datastore.
Each work piece is identified by ID. This method writes/updates only those
work pieces which IDs are stored in this class. For examples, if this class
has only work pieces with IDs '1' ... '100' and datastore already contains
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28,619 | tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/eval_lib/work_data.py | WorkPiecesBase.read_all_from_datastore | def read_all_from_datastore(self):
"""Reads all work pieces from the datastore."""
self._work = {}
client = self._datastore_client
parent_key = client.key(KIND_WORK_TYPE, self._work_type_entity_id)
for entity in client.query_fetch(kind=KIND_WORK, ancestor=parent_key):
work_id = entity.key.flat... | python | def read_all_from_datastore(self):
"""Reads all work pieces from the datastore."""
self._work = {}
client = self._datastore_client
parent_key = client.key(KIND_WORK_TYPE, self._work_type_entity_id)
for entity in client.query_fetch(kind=KIND_WORK, ancestor=parent_key):
work_id = entity.key.flat... | [
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28,620 | tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/eval_lib/work_data.py | WorkPiecesBase._read_undone_shard_from_datastore | def _read_undone_shard_from_datastore(self, shard_id=None):
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self._work = {}
client = self._datastore_client
parent_key = client.key(KIND_WORK_TYPE, self._work_type_entity_id)
filters = [('is_completed', '=', False)]
if sh... | python | def _read_undone_shard_from_datastore(self, shard_id=None):
"""Reads undone worke pieces which are assigned to shard with given id."""
self._work = {}
client = self._datastore_client
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filters = [('is_completed', '=', False)]
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28,621 | tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/eval_lib/work_data.py | WorkPiecesBase.read_undone_from_datastore | def read_undone_from_datastore(self, shard_id=None, num_shards=None):
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If shard_id and num_shards are specified then this method will attempt
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"""Reads undone work from the datastore.
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28,622 | tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/eval_lib/work_data.py | WorkPiecesBase.try_pick_piece_of_work | def try_pick_piece_of_work(self, worker_id, submission_id=None):
"""Tries pick next unclaimed piece of work to do.
Attempt to claim work piece is done using Cloud Datastore transaction, so
only one worker can claim any work piece at a time.
Args:
worker_id: ID of current worker
submission_... | python | def try_pick_piece_of_work(self, worker_id, submission_id=None):
"""Tries pick next unclaimed piece of work to do.
Attempt to claim work piece is done using Cloud Datastore transaction, so
only one worker can claim any work piece at a time.
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worker_id: ID of current worker
submission_... | [
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28,623 | tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/eval_lib/work_data.py | WorkPiecesBase.update_work_as_completed | def update_work_as_completed(self, worker_id, work_id, other_values=None,
error=None):
"""Updates work piece in datastore as completed.
Args:
worker_id: ID of the worker which did the work
work_id: ID of the work which was done
other_values: dictionary with addi... | python | def update_work_as_completed(self, worker_id, work_id, other_values=None,
error=None):
"""Updates work piece in datastore as completed.
Args:
worker_id: ID of the worker which did the work
work_id: ID of the work which was done
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28,624 | tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/eval_lib/work_data.py | WorkPiecesBase.compute_work_statistics | def compute_work_statistics(self):
"""Computes statistics from all work pieces stored in this class."""
result = {}
for v in itervalues(self.work):
submission_id = v['submission_id']
if submission_id not in result:
result[submission_id] = {
'completed': 0,
'num_er... | python | def compute_work_statistics(self):
"""Computes statistics from all work pieces stored in this class."""
result = {}
for v in itervalues(self.work):
submission_id = v['submission_id']
if submission_id not in result:
result[submission_id] = {
'completed': 0,
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28,625 | tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/eval_lib/work_data.py | AttackWorkPieces.init_from_adversarial_batches | def init_from_adversarial_batches(self, adv_batches):
"""Initializes work pieces from adversarial batches.
Args:
adv_batches: dict with adversarial batches,
could be obtained as AversarialBatches.data
"""
for idx, (adv_batch_id, adv_batch_val) in enumerate(iteritems(adv_batches)):
w... | python | def init_from_adversarial_batches(self, adv_batches):
"""Initializes work pieces from adversarial batches.
Args:
adv_batches: dict with adversarial batches,
could be obtained as AversarialBatches.data
"""
for idx, (adv_batch_id, adv_batch_val) in enumerate(iteritems(adv_batches)):
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28,626 | tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/eval_lib/work_data.py | DefenseWorkPieces.init_from_class_batches | def init_from_class_batches(self, class_batches, num_shards=None):
"""Initializes work pieces from classification batches.
Args:
class_batches: dict with classification batches, could be obtained
as ClassificationBatches.data
num_shards: number of shards to split data into,
if None ... | python | def init_from_class_batches(self, class_batches, num_shards=None):
"""Initializes work pieces from classification batches.
Args:
class_batches: dict with classification batches, could be obtained
as ClassificationBatches.data
num_shards: number of shards to split data into,
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28,627 | tensorflow/cleverhans | cleverhans/attacks/fast_gradient_method.py | FastGradientMethod.generate | def generate(self, x, **kwargs):
"""
Returns the graph for Fast Gradient Method adversarial examples.
:param x: The model's symbolic inputs.
:param kwargs: See `parse_params`
"""
# Parse and save attack-specific parameters
assert self.parse_params(**kwargs)
labels, _nb_classes = self.g... | python | def generate(self, x, **kwargs):
"""
Returns the graph for Fast Gradient Method adversarial examples.
:param x: The model's symbolic inputs.
:param kwargs: See `parse_params`
"""
# Parse and save attack-specific parameters
assert self.parse_params(**kwargs)
labels, _nb_classes = self.g... | [
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28,628 | tensorflow/cleverhans | cleverhans/experimental/certification/nn.py | load_network_from_checkpoint | def load_network_from_checkpoint(checkpoint, model_json, input_shape=None):
"""Function to read the weights from checkpoint based on json description.
Args:
checkpoint: tensorflow checkpoint with trained model to
verify
model_json: path of json file with model description of
the netwo... | python | def load_network_from_checkpoint(checkpoint, model_json, input_shape=None):
"""Function to read the weights from checkpoint based on json description.
Args:
checkpoint: tensorflow checkpoint with trained model to
verify
model_json: path of json file with model description of
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28,629 | tensorflow/cleverhans | cleverhans/experimental/certification/nn.py | NeuralNetwork.forward_pass | def forward_pass(self, vector, layer_index, is_transpose=False, is_abs=False):
"""Performs forward pass through the layer weights at layer_index.
Args:
vector: vector that has to be passed through in forward pass
layer_index: index of the layer
is_transpose: whether the weights of the layer h... | python | def forward_pass(self, vector, layer_index, is_transpose=False, is_abs=False):
"""Performs forward pass through the layer weights at layer_index.
Args:
vector: vector that has to be passed through in forward pass
layer_index: index of the layer
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28,630 | tensorflow/cleverhans | cleverhans/devtools/version.py | dev_version | def dev_version():
"""
Returns a hexdigest of all the python files in the module.
"""
md5_hash = hashlib.md5()
py_files = sorted(list_files(suffix=".py"))
if not py_files:
return ''
for filename in py_files:
with open(filename, 'rb') as fobj:
content = fobj.read()
md5_hash.update(conten... | python | def dev_version():
"""
Returns a hexdigest of all the python files in the module.
"""
md5_hash = hashlib.md5()
py_files = sorted(list_files(suffix=".py"))
if not py_files:
return ''
for filename in py_files:
with open(filename, 'rb') as fobj:
content = fobj.read()
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28,631 | tensorflow/cleverhans | cleverhans/experimental/certification/utils.py | initialize_dual | def initialize_dual(neural_net_params_object, init_dual_file=None,
random_init_variance=0.01, init_nu=200.0):
"""Function to initialize the dual variables of the class.
Args:
neural_net_params_object: Object with the neural net weights, biases
and types
init_dual_file: Path to fil... | python | def initialize_dual(neural_net_params_object, init_dual_file=None,
random_init_variance=0.01, init_nu=200.0):
"""Function to initialize the dual variables of the class.
Args:
neural_net_params_object: Object with the neural net weights, biases
and types
init_dual_file: Path to fil... | [
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28,632 | tensorflow/cleverhans | cleverhans/experimental/certification/utils.py | minimum_eigen_vector | def minimum_eigen_vector(x, num_steps, learning_rate, vector_prod_fn):
"""Computes eigenvector which corresponds to minimum eigenvalue.
Args:
x: initial value of eigenvector.
num_steps: number of optimization steps.
learning_rate: learning rate.
vector_prod_fn: function which takes x and returns pr... | python | def minimum_eigen_vector(x, num_steps, learning_rate, vector_prod_fn):
"""Computes eigenvector which corresponds to minimum eigenvalue.
Args:
x: initial value of eigenvector.
num_steps: number of optimization steps.
learning_rate: learning rate.
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28,633 | tensorflow/cleverhans | cleverhans/experimental/certification/utils.py | tf_lanczos_smallest_eigval | def tf_lanczos_smallest_eigval(vector_prod_fn,
matrix_dim,
initial_vector,
num_iter=1000,
max_iter=1000,
collapse_tol=1e-9,
dtype=tf.f... | python | def tf_lanczos_smallest_eigval(vector_prod_fn,
matrix_dim,
initial_vector,
num_iter=1000,
max_iter=1000,
collapse_tol=1e-9,
dtype=tf.f... | [
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28,634 | tensorflow/cleverhans | cleverhans/attacks/carlini_wagner_l2.py | CarliniWagnerL2.generate | def generate(self, x, **kwargs):
"""
Return a tensor that constructs adversarial examples for the given
input. Generate uses tf.py_func in order to operate over tensors.
:param x: A tensor with the inputs.
:param kwargs: See `parse_params`
"""
assert self.sess is not None, \
'Cannot... | python | def generate(self, x, **kwargs):
"""
Return a tensor that constructs adversarial examples for the given
input. Generate uses tf.py_func in order to operate over tensors.
:param x: A tensor with the inputs.
:param kwargs: See `parse_params`
"""
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28,635 | tensorflow/cleverhans | cleverhans/attacks/carlini_wagner_l2.py | CWL2.attack | def attack(self, imgs, targets):
"""
Perform the L_2 attack on the given instance for the given targets.
If self.targeted is true, then the targets represents the target labels
If self.targeted is false, then targets are the original class labels
"""
r = []
for i in range(0, len(imgs), sel... | python | def attack(self, imgs, targets):
"""
Perform the L_2 attack on the given instance for the given targets.
If self.targeted is true, then the targets represents the target labels
If self.targeted is false, then targets are the original class labels
"""
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28,636 | tensorflow/cleverhans | examples/RL-attack/train.py | maybe_load_model | def maybe_load_model(savedir, container):
"""Load model if present at the specified path."""
if savedir is None:
return
state_path = os.path.join(os.path.join(savedir, 'training_state.pkl.zip'))
if container is not None:
logger.log("Attempting to download model from Azure")
found_model = container.... | python | def maybe_load_model(savedir, container):
"""Load model if present at the specified path."""
if savedir is None:
return
state_path = os.path.join(os.path.join(savedir, 'training_state.pkl.zip'))
if container is not None:
logger.log("Attempting to download model from Azure")
found_model = container.... | [
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28,637 | tensorflow/cleverhans | cleverhans_tutorials/__init__.py | check_installation | def check_installation(cur_file):
"""Warn user if running cleverhans from a different directory than tutorial."""
cur_dir = os.path.split(os.path.dirname(os.path.abspath(cur_file)))[0]
ch_dir = os.path.split(cleverhans.__path__[0])[0]
if cur_dir != ch_dir:
warnings.warn("It appears that you have at least tw... | python | def check_installation(cur_file):
"""Warn user if running cleverhans from a different directory than tutorial."""
cur_dir = os.path.split(os.path.dirname(os.path.abspath(cur_file)))[0]
ch_dir = os.path.split(cleverhans.__path__[0])[0]
if cur_dir != ch_dir:
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28,638 | tensorflow/cleverhans | examples/nips17_adversarial_competition/dataset/download_images.py | get_image | def get_image(row, output_dir):
"""Downloads the image that corresponds to the given row.
Prints a notification if the download fails."""
if not download_image(image_id=row[0],
url=row[1],
x1=float(row[2]),
y1=float(row[3]),
... | python | def get_image(row, output_dir):
"""Downloads the image that corresponds to the given row.
Prints a notification if the download fails."""
if not download_image(image_id=row[0],
url=row[1],
x1=float(row[2]),
y1=float(row[3]),
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28,639 | tensorflow/cleverhans | examples/nips17_adversarial_competition/dataset/download_images.py | download_image | def download_image(image_id, url, x1, y1, x2, y2, output_dir):
"""Downloads one image, crops it, resizes it and saves it locally."""
output_filename = os.path.join(output_dir, image_id + '.png')
if os.path.exists(output_filename):
# Don't download image if it's already there
return True
try:
# Downl... | python | def download_image(image_id, url, x1, y1, x2, y2, output_dir):
"""Downloads one image, crops it, resizes it and saves it locally."""
output_filename = os.path.join(output_dir, image_id + '.png')
if os.path.exists(output_filename):
# Don't download image if it's already there
return True
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28,640 | tensorflow/cleverhans | examples/robust_vision_benchmark/cleverhans_attack_example/utils.py | py_func_grad | def py_func_grad(func, inp, Tout, stateful=True, name=None, grad=None):
"""Custom py_func with gradient support
"""
# Need to generate a unique name to avoid duplicates:
rnd_name = 'PyFuncGrad' + str(np.random.randint(0, 1E+8))
tf.RegisterGradient(rnd_name)(grad)
g = tf.get_default_graph()
with g.gradie... | python | def py_func_grad(func, inp, Tout, stateful=True, name=None, grad=None):
"""Custom py_func with gradient support
"""
# Need to generate a unique name to avoid duplicates:
rnd_name = 'PyFuncGrad' + str(np.random.randint(0, 1E+8))
tf.RegisterGradient(rnd_name)(grad)
g = tf.get_default_graph()
with g.gradie... | [
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28,641 | tensorflow/cleverhans | cleverhans/plot/pyplot_image.py | get_logits_over_interval | def get_logits_over_interval(sess, model, x_data, fgsm_params,
min_epsilon=-10., max_epsilon=10.,
num_points=21):
"""Get logits when the input is perturbed in an interval in adv direction.
Args:
sess: Tf session
model: Model for which we wish to... | python | def get_logits_over_interval(sess, model, x_data, fgsm_params,
min_epsilon=-10., max_epsilon=10.,
num_points=21):
"""Get logits when the input is perturbed in an interval in adv direction.
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sess: Tf session
model: Model for which we wish to... | [
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28,642 | tensorflow/cleverhans | cleverhans/plot/pyplot_image.py | linear_extrapolation_plot | def linear_extrapolation_plot(log_prob_adv_array, y, file_name,
min_epsilon=-10, max_epsilon=10,
num_points=21):
"""Generate linear extrapolation plot.
Args:
log_prob_adv_array: Numpy array containing log probabilities
y: Tf placeholder for th... | python | def linear_extrapolation_plot(log_prob_adv_array, y, file_name,
min_epsilon=-10, max_epsilon=10,
num_points=21):
"""Generate linear extrapolation plot.
Args:
log_prob_adv_array: Numpy array containing log probabilities
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28,643 | backtrader/backtrader | contrib/utils/iqfeed-to-influxdb.py | IQFeedTool._send_cmd | def _send_cmd(self, cmd: str):
"""Encode IQFeed API messages."""
self._sock.sendall(cmd.encode(encoding='latin-1', errors='strict')) | python | def _send_cmd(self, cmd: str):
"""Encode IQFeed API messages."""
self._sock.sendall(cmd.encode(encoding='latin-1', errors='strict')) | [
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28,644 | backtrader/backtrader | contrib/utils/iqfeed-to-influxdb.py | IQFeedTool.iq_query | def iq_query(self, message: str):
"""Send data query to IQFeed API."""
end_msg = '!ENDMSG!'
recv_buffer = 4096
# Send the historical data request message and buffer the data
self._send_cmd(message)
chunk = ""
data = ""
while True:
chunk = sel... | python | def iq_query(self, message: str):
"""Send data query to IQFeed API."""
end_msg = '!ENDMSG!'
recv_buffer = 4096
# Send the historical data request message and buffer the data
self._send_cmd(message)
chunk = ""
data = ""
while True:
chunk = sel... | [
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28,645 | backtrader/backtrader | contrib/utils/iqfeed-to-influxdb.py | IQFeedTool.get_historical_minute_data | def get_historical_minute_data(self, ticker: str):
"""Request historical 5 minute data from DTN."""
start = self._start
stop = self._stop
if len(stop) > 4:
stop = stop[:4]
if len(start) > 4:
start = start[:4]
for year in range(int(start), int(st... | python | def get_historical_minute_data(self, ticker: str):
"""Request historical 5 minute data from DTN."""
start = self._start
stop = self._stop
if len(stop) > 4:
stop = stop[:4]
if len(start) > 4:
start = start[:4]
for year in range(int(start), int(st... | [
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28,646 | backtrader/backtrader | contrib/utils/iqfeed-to-influxdb.py | IQFeedTool.add_data_to_df | def add_data_to_df(self, data: np.array):
"""Build Pandas Dataframe in memory"""
col_names = ['high_p', 'low_p', 'open_p', 'close_p', 'volume', 'oi']
data = np.array(data).reshape(-1, len(col_names) + 1)
df = pd.DataFrame(data=data[:, 1:], index=data[:, 0],
co... | python | def add_data_to_df(self, data: np.array):
"""Build Pandas Dataframe in memory"""
col_names = ['high_p', 'low_p', 'open_p', 'close_p', 'volume', 'oi']
data = np.array(data).reshape(-1, len(col_names) + 1)
df = pd.DataFrame(data=data[:, 1:], index=data[:, 0],
co... | [
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28,647 | backtrader/backtrader | contrib/utils/iqfeed-to-influxdb.py | IQFeedTool.get_tickers_from_file | def get_tickers_from_file(self, filename):
"""Load ticker list from txt file"""
if not os.path.exists(filename):
log.error("Ticker List file does not exist: %s", filename)
tickers = []
with io.open(filename, 'r') as fd:
for ticker in fd:
tickers.a... | python | def get_tickers_from_file(self, filename):
"""Load ticker list from txt file"""
if not os.path.exists(filename):
log.error("Ticker List file does not exist: %s", filename)
tickers = []
with io.open(filename, 'r') as fd:
for ticker in fd:
tickers.a... | [
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28,648 | backtrader/backtrader | contrib/utils/influxdb-import.py | InfluxDBTool.write_dataframe_to_idb | def write_dataframe_to_idb(self, ticker):
"""Write Pandas Dataframe to InfluxDB database"""
cachepath = self._cache
cachefile = ('%s/%s-1M.csv.gz' % (cachepath, ticker))
if not os.path.exists(cachefile):
log.warn('Import file does not exist: %s' %
(cache... | python | def write_dataframe_to_idb(self, ticker):
"""Write Pandas Dataframe to InfluxDB database"""
cachepath = self._cache
cachefile = ('%s/%s-1M.csv.gz' % (cachepath, ticker))
if not os.path.exists(cachefile):
log.warn('Import file does not exist: %s' %
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28,649 | AirtestProject/Airtest | playground/win_ide.py | WindowsInIDE.connect | def connect(self, **kwargs):
"""
Connect to window and set it foreground
Args:
**kwargs: optional arguments
Returns:
None
"""
self.app = self._app.connect(**kwargs)
try:
self._top_window = self.app.top_window().wrapper_object... | python | def connect(self, **kwargs):
"""
Connect to window and set it foreground
Args:
**kwargs: optional arguments
Returns:
None
"""
self.app = self._app.connect(**kwargs)
try:
self._top_window = self.app.top_window().wrapper_object... | [
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28,650 | AirtestProject/Airtest | playground/win_ide.py | WindowsInIDE.get_rect | def get_rect(self):
"""
Get rectangle of app or desktop resolution
Returns:
RECT(left, top, right, bottom)
"""
if self.handle:
left, top, right, bottom = win32gui.GetWindowRect(self.handle)
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else:
... | python | def get_rect(self):
"""
Get rectangle of app or desktop resolution
Returns:
RECT(left, top, right, bottom)
"""
if self.handle:
left, top, right, bottom = win32gui.GetWindowRect(self.handle)
return RECT(left, top, right, bottom)
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28,651 | AirtestProject/Airtest | playground/win_ide.py | WindowsInIDE.snapshot | def snapshot(self, filename="tmp.png"):
"""
Take a screenshot and save it to `tmp.png` filename by default
Args:
filename: name of file where to store the screenshot
Returns:
display the screenshot
"""
if not filename:
filename = "tm... | python | def snapshot(self, filename="tmp.png"):
"""
Take a screenshot and save it to `tmp.png` filename by default
Args:
filename: name of file where to store the screenshot
Returns:
display the screenshot
"""
if not filename:
filename = "tm... | [
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28,652 | apple/turicreate | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/internal/decoder.py | _SimpleDecoder | def _SimpleDecoder(wire_type, decode_value):
"""Return a constructor for a decoder for fields of a particular type.
Args:
wire_type: The field's wire type.
decode_value: A function which decodes an individual value, e.g.
_DecodeVarint()
"""
def SpecificDecoder(field_number, is_repeated, ... | python | def _SimpleDecoder(wire_type, decode_value):
"""Return a constructor for a decoder for fields of a particular type.
Args:
wire_type: The field's wire type.
decode_value: A function which decodes an individual value, e.g.
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28,653 | apple/turicreate | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/internal/decoder.py | _ModifiedDecoder | def _ModifiedDecoder(wire_type, decode_value, modify_value):
"""Like SimpleDecoder but additionally invokes modify_value on every value
before storing it. Usually modify_value is ZigZagDecode.
"""
# Reusing _SimpleDecoder is slightly slower than copying a bunch of code, but
# not enough to make a significan... | python | def _ModifiedDecoder(wire_type, decode_value, modify_value):
"""Like SimpleDecoder but additionally invokes modify_value on every value
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28,654 | apple/turicreate | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/internal/decoder.py | _StructPackDecoder | def _StructPackDecoder(wire_type, format):
"""Return a constructor for a decoder for a fixed-width field.
Args:
wire_type: The field's wire type.
format: The format string to pass to struct.unpack().
"""
value_size = struct.calcsize(format)
local_unpack = struct.unpack
# Reusing _SimpleDeco... | python | def _StructPackDecoder(wire_type, format):
"""Return a constructor for a decoder for a fixed-width field.
Args:
wire_type: The field's wire type.
format: The format string to pass to struct.unpack().
"""
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28,655 | apple/turicreate | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/internal/decoder.py | _FloatDecoder | def _FloatDecoder():
"""Returns a decoder for a float field.
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"""
local_unpack = struct.unpack
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# bit, ... | python | def _FloatDecoder():
"""Returns a decoder for a float field.
This code works around a bug in struct.unpack for non-finite 32-bit
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local_unpack = struct.unpack
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# bit, ... | [
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28,656 | apple/turicreate | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/internal/decoder.py | _DoubleDecoder | def _DoubleDecoder():
"""Returns a decoder for a double field.
This code works around a bug in struct.unpack for not-a-number.
"""
local_unpack = struct.unpack
def InnerDecode(buffer, pos):
# We expect a 64-bit value in little-endian byte order. Bit 1 is the sign
# bit, bits 2-12 represent the exp... | python | def _DoubleDecoder():
"""Returns a decoder for a double field.
This code works around a bug in struct.unpack for not-a-number.
"""
local_unpack = struct.unpack
def InnerDecode(buffer, pos):
# We expect a 64-bit value in little-endian byte order. Bit 1 is the sign
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28,657 | apple/turicreate | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/internal/decoder.py | StringDecoder | def StringDecoder(field_number, is_repeated, is_packed, key, new_default):
"""Returns a decoder for a string field."""
local_DecodeVarint = _DecodeVarint
local_unicode = six.text_type
def _ConvertToUnicode(byte_str):
try:
return local_unicode(byte_str, 'utf-8')
except UnicodeDecodeError as e:
... | python | def StringDecoder(field_number, is_repeated, is_packed, key, new_default):
"""Returns a decoder for a string field."""
local_DecodeVarint = _DecodeVarint
local_unicode = six.text_type
def _ConvertToUnicode(byte_str):
try:
return local_unicode(byte_str, 'utf-8')
except UnicodeDecodeError as e:
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28,658 | apple/turicreate | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/internal/decoder.py | BytesDecoder | def BytesDecoder(field_number, is_repeated, is_packed, key, new_default):
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tag_bytes = encoder.TagBytes(field_number,
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"""Returns a decoder for a bytes field."""
local_DecodeVarint = _DecodeVarint
assert not is_packed
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tag_bytes = encoder.TagBytes(field_number,
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28,659 | apple/turicreate | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/internal/decoder.py | GroupDecoder | def GroupDecoder(field_number, is_repeated, is_packed, key, new_default):
"""Returns a decoder for a group field."""
end_tag_bytes = encoder.TagBytes(field_number,
wire_format.WIRETYPE_END_GROUP)
end_tag_len = len(end_tag_bytes)
assert not is_packed
if is_repeated:
tag... | python | def GroupDecoder(field_number, is_repeated, is_packed, key, new_default):
"""Returns a decoder for a group field."""
end_tag_bytes = encoder.TagBytes(field_number,
wire_format.WIRETYPE_END_GROUP)
end_tag_len = len(end_tag_bytes)
assert not is_packed
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28,660 | apple/turicreate | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/internal/decoder.py | MapDecoder | def MapDecoder(field_descriptor, new_default, is_message_map):
"""Returns a decoder for a map field."""
key = field_descriptor
tag_bytes = encoder.TagBytes(field_descriptor.number,
wire_format.WIRETYPE_LENGTH_DELIMITED)
tag_len = len(tag_bytes)
local_DecodeVarint = _DecodeVarin... | python | def MapDecoder(field_descriptor, new_default, is_message_map):
"""Returns a decoder for a map field."""
key = field_descriptor
tag_bytes = encoder.TagBytes(field_descriptor.number,
wire_format.WIRETYPE_LENGTH_DELIMITED)
tag_len = len(tag_bytes)
local_DecodeVarint = _DecodeVarin... | [
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28,661 | apple/turicreate | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/internal/decoder.py | _SkipVarint | def _SkipVarint(buffer, pos, end):
"""Skip a varint value. Returns the new position."""
# Previously ord(buffer[pos]) raised IndexError when pos is out of range.
# With this code, ord(b'') raises TypeError. Both are handled in
# python_message.py to generate a 'Truncated message' error.
while ord(buffer[pos... | python | def _SkipVarint(buffer, pos, end):
"""Skip a varint value. Returns the new position."""
# Previously ord(buffer[pos]) raised IndexError when pos is out of range.
# With this code, ord(b'') raises TypeError. Both are handled in
# python_message.py to generate a 'Truncated message' error.
while ord(buffer[pos... | [
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28,662 | apple/turicreate | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/internal/decoder.py | _SkipLengthDelimited | def _SkipLengthDelimited(buffer, pos, end):
"""Skip a length-delimited value. Returns the new position."""
(size, pos) = _DecodeVarint(buffer, pos)
pos += size
if pos > end:
raise _DecodeError('Truncated message.')
return pos | python | def _SkipLengthDelimited(buffer, pos, end):
"""Skip a length-delimited value. Returns the new position."""
(size, pos) = _DecodeVarint(buffer, pos)
pos += size
if pos > end:
raise _DecodeError('Truncated message.')
return pos | [
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28,663 | apple/turicreate | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/internal/decoder.py | _SkipGroup | def _SkipGroup(buffer, pos, end):
"""Skip sub-group. Returns the new position."""
while 1:
(tag_bytes, pos) = ReadTag(buffer, pos)
new_pos = SkipField(buffer, pos, end, tag_bytes)
if new_pos == -1:
return pos
pos = new_pos | python | def _SkipGroup(buffer, pos, end):
"""Skip sub-group. Returns the new position."""
while 1:
(tag_bytes, pos) = ReadTag(buffer, pos)
new_pos = SkipField(buffer, pos, end, tag_bytes)
if new_pos == -1:
return pos
pos = new_pos | [
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28,664 | apple/turicreate | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/internal/decoder.py | _FieldSkipper | def _FieldSkipper():
"""Constructs the SkipField function."""
WIRETYPE_TO_SKIPPER = [
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wiretype_mask = wire_format.TAG_TYPE_M... | python | def _FieldSkipper():
"""Constructs the SkipField function."""
WIRETYPE_TO_SKIPPER = [
_SkipVarint,
_SkipFixed64,
_SkipLengthDelimited,
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wiretype_mask = wire_format.TAG_TYPE_M... | [
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28,665 | apple/turicreate | src/unity/python/turicreate/toolkits/classifier/decision_tree_classifier.py | DecisionTreeClassifier.predict | def predict(self, dataset, output_type='class', missing_value_action='auto'):
"""
A flexible and advanced prediction API.
The target column is provided during
:func:`~turicreate.decision_tree.create`. If the target column is in the
`dataset` it will be ignored.
Paramete... | python | def predict(self, dataset, output_type='class', missing_value_action='auto'):
"""
A flexible and advanced prediction API.
The target column is provided during
:func:`~turicreate.decision_tree.create`. If the target column is in the
`dataset` it will be ignored.
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28,666 | apple/turicreate | src/external/xgboost/subtree/rabit/tracker/rabit_tracker.py | Tracker.slave_envs | def slave_envs(self):
"""
get enviroment variables for slaves
can be passed in as args or envs
"""
if self.hostIP == 'dns':
host = socket.gethostname()
elif self.hostIP == 'ip':
host = socket.gethostbyname(socket.getfqdn())
else:
... | python | def slave_envs(self):
"""
get enviroment variables for slaves
can be passed in as args or envs
"""
if self.hostIP == 'dns':
host = socket.gethostname()
elif self.hostIP == 'ip':
host = socket.gethostbyname(socket.getfqdn())
else:
... | [
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28,667 | apple/turicreate | src/external/xgboost/subtree/rabit/tracker/rabit_tracker.py | Tracker.find_share_ring | def find_share_ring(self, tree_map, parent_map, r):
"""
get a ring structure that tends to share nodes with the tree
return a list starting from r
"""
nset = set(tree_map[r])
cset = nset - set([parent_map[r]])
if len(cset) == 0:
return [r]
rlst... | python | def find_share_ring(self, tree_map, parent_map, r):
"""
get a ring structure that tends to share nodes with the tree
return a list starting from r
"""
nset = set(tree_map[r])
cset = nset - set([parent_map[r]])
if len(cset) == 0:
return [r]
rlst... | [
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28,668 | apple/turicreate | src/external/xgboost/subtree/rabit/tracker/rabit_tracker.py | Tracker.get_ring | def get_ring(self, tree_map, parent_map):
"""
get a ring connection used to recover local data
"""
assert parent_map[0] == -1
rlst = self.find_share_ring(tree_map, parent_map, 0)
assert len(rlst) == len(tree_map)
ring_map = {}
nslave = len(tree_map)
... | python | def get_ring(self, tree_map, parent_map):
"""
get a ring connection used to recover local data
"""
assert parent_map[0] == -1
rlst = self.find_share_ring(tree_map, parent_map, 0)
assert len(rlst) == len(tree_map)
ring_map = {}
nslave = len(tree_map)
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28,669 | apple/turicreate | src/external/xgboost/subtree/rabit/tracker/rabit_tracker.py | Tracker.get_link_map | def get_link_map(self, nslave):
"""
get the link map, this is a bit hacky, call for better algorithm
to place similar nodes together
"""
tree_map, parent_map = self.get_tree(nslave)
ring_map = self.get_ring(tree_map, parent_map)
rmap = {0 : 0}
k = 0
... | python | def get_link_map(self, nslave):
"""
get the link map, this is a bit hacky, call for better algorithm
to place similar nodes together
"""
tree_map, parent_map = self.get_tree(nslave)
ring_map = self.get_ring(tree_map, parent_map)
rmap = {0 : 0}
k = 0
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28,670 | apple/turicreate | deps/src/boost_1_68_0/tools/build/src/tools/msvc.py | maybe_rewrite_setup | def maybe_rewrite_setup(toolset, setup_script, setup_options, version, rewrite_setup='off'):
"""
Helper rule to generate a faster alternative to MSVC setup scripts.
We used to call MSVC setup scripts directly in every action, however in
newer MSVC versions (10.0+) they make long-lasting registry querie... | python | def maybe_rewrite_setup(toolset, setup_script, setup_options, version, rewrite_setup='off'):
"""
Helper rule to generate a faster alternative to MSVC setup scripts.
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28,671 | apple/turicreate | src/unity/python/turicreate/toolkits/classifier/_classifier.py | create | def create(dataset, target, features=None, validation_set = 'auto',
verbose=True):
"""
Automatically create a suitable classifier model based on the provided
training data.
To use specific options of a desired model, use the ``create`` function
of the corresponding model.
Parameters
... | python | def create(dataset, target, features=None, validation_set = 'auto',
verbose=True):
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Automatically create a suitable classifier model based on the provided
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28,672 | apple/turicreate | src/unity/python/turicreate/data_structures/gframe.py | GFrame.add_column | def add_column(self, data, column_name="", inplace=False):
"""
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28,673 | apple/turicreate | src/unity/python/turicreate/data_structures/gframe.py | GFrame.add_columns | def add_columns(self, data, column_names=None, inplace=False):
"""
Adds columns to the SFrame. The number of elements in all columns must
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28,674 | apple/turicreate | src/unity/python/turicreate/data_structures/gframe.py | GFrame.remove_column | def remove_column(self, column_name, inplace=False):
"""
Removes the column with the given name from the SFrame.
If inplace == False (default) this operation does not modify the
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... | python | def remove_column(self, column_name, inplace=False):
"""
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28,675 | apple/turicreate | src/unity/python/turicreate/data_structures/gframe.py | GFrame.swap_columns | def swap_columns(self, column_name_1, column_name_2, inplace=False):
"""
Swaps the columns with the given names.
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... | python | def swap_columns(self, column_name_1, column_name_2, inplace=False):
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28,676 | apple/turicreate | src/unity/python/turicreate/data_structures/gframe.py | GFrame.rename | def rename(self, names, inplace=False):
"""
Rename the columns using the 'names' dict. This changes the names of
the columns given as the keys and replaces them with the names given as
the values.
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current ... | python | def rename(self, names, inplace=False):
"""
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28,677 | apple/turicreate | src/unity/python/turicreate/data_structures/gframe.py | GFrame.num_rows | def num_rows(self):
"""
Returns the number of rows.
Returns
-------
out : int
Number of rows in the SFrame.
"""
if self._is_vertex_frame():
return self.__graph__.summary()['num_vertices']
elif self._is_edge_frame():
ret... | python | def num_rows(self):
"""
Returns the number of rows.
Returns
-------
out : int
Number of rows in the SFrame.
"""
if self._is_vertex_frame():
return self.__graph__.summary()['num_vertices']
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28,678 | apple/turicreate | src/unity/python/turicreate/data_structures/gframe.py | GFrame.column_names | def column_names(self):
"""
Returns the column names.
Returns
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out : list[string]
Column names of the SFrame.
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return self.__graph__.__proxy__.get_vertex_fields()
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Returns
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Column names of the SFrame.
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28,679 | apple/turicreate | src/unity/python/turicreate/data_structures/gframe.py | GFrame.column_types | def column_types(self):
"""
Returns the column types.
Returns
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out : list[type]
Column types of the SFrame.
"""
if self.__type__ == VERTEX_GFRAME:
return self.__graph__.__proxy__.get_vertex_field_types()
elif self.__type__ =... | python | def column_types(self):
"""
Returns the column types.
Returns
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out : list[type]
Column types of the SFrame.
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28,680 | apple/turicreate | src/unity/python/turicreate/toolkits/regression/_regression.py | create | def create(dataset, target, features=None, validation_set = 'auto',
verbose=True):
"""
Automatically create a suitable regression model based on the provided
training data.
To use specific options of a desired model, use the ``create`` function
of the corresponding model.
Parameters
... | python | def create(dataset, target, features=None, validation_set = 'auto',
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"""
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28,681 | apple/turicreate | src/unity/python/turicreate/meta/asttools/mutators/prune_mutator.py | removable | def removable(self, node):
'''
node is removable only if all of its children are as well.
'''
throw_away = []
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if self.mode == 'exclusive':
return all(throw_away)
elif self.mode == 'inclusive':
ret... | python | def removable(self, node):
'''
node is removable only if all of its children are as well.
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throw_away = []
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28,682 | apple/turicreate | src/unity/python/turicreate/meta/asttools/mutators/prune_mutator.py | PruneVisitor.reduce | def reduce(self, body):
'''
remove nodes from a list
'''
i = 0
while i < len(body):
stmnt = body[i]
if self.visit(stmnt):
body.pop(i)
else:
i += 1 | python | def reduce(self, body):
'''
remove nodes from a list
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i = 0
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body.pop(i)
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28,683 | apple/turicreate | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/symbol_database.py | SymbolDatabase.RegisterMessage | def RegisterMessage(self, message):
"""Registers the given message type in the local database.
Calls to GetSymbol() and GetMessages() will return messages registered here.
Args:
message: a message.Message, to be registered.
Returns:
The provided message.
"""
desc = message.DESCRI... | python | def RegisterMessage(self, message):
"""Registers the given message type in the local database.
Calls to GetSymbol() and GetMessages() will return messages registered here.
Args:
message: a message.Message, to be registered.
Returns:
The provided message.
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28,684 | apple/turicreate | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/symbol_database.py | SymbolDatabase.GetMessages | def GetMessages(self, files):
# TODO(amauryfa): Fix the differences with MessageFactory.
"""Gets all registered messages from a specified file.
Only messages already created and registered will be returned; (this is the
case for imported _pb2 modules)
But unlike MessageFactory, this version also re... | python | def GetMessages(self, files):
# TODO(amauryfa): Fix the differences with MessageFactory.
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28,685 | apple/turicreate | src/unity/python/turicreate/toolkits/evaluation.py | _check_prob_and_prob_vector | def _check_prob_and_prob_vector(predictions):
"""
Check that the predictionsa are either probabilities of prob-vectors.
"""
from .._deps import numpy
ptype = predictions.dtype
import array
if ptype not in [float, numpy.ndarray, array.array, int]:
err_msg = "Input `predictions` must... | python | def _check_prob_and_prob_vector(predictions):
"""
Check that the predictionsa are either probabilities of prob-vectors.
"""
from .._deps import numpy
ptype = predictions.dtype
import array
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28,686 | apple/turicreate | src/unity/python/turicreate/toolkits/evaluation.py | _supervised_evaluation_error_checking | def _supervised_evaluation_error_checking(targets, predictions):
"""
Perform basic error checking for the evaluation metrics. Check
types and sizes of the inputs.
"""
_raise_error_if_not_sarray(targets, "targets")
_raise_error_if_not_sarray(predictions, "predictions")
if (len(targets) != len... | python | def _supervised_evaluation_error_checking(targets, predictions):
"""
Perform basic error checking for the evaluation metrics. Check
types and sizes of the inputs.
"""
_raise_error_if_not_sarray(targets, "targets")
_raise_error_if_not_sarray(predictions, "predictions")
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28,687 | apple/turicreate | src/unity/python/turicreate/toolkits/evaluation.py | max_error | def max_error(targets, predictions):
r"""
Compute the maximum absolute deviation between two SArrays.
Parameters
----------
targets : SArray[float or int]
An Sarray of ground truth target values.
predictions : SArray[float or int]
The prediction that corresponds to each target ... | python | def max_error(targets, predictions):
r"""
Compute the maximum absolute deviation between two SArrays.
Parameters
----------
targets : SArray[float or int]
An Sarray of ground truth target values.
predictions : SArray[float or int]
The prediction that corresponds to each target ... | [
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28,688 | apple/turicreate | src/unity/python/turicreate/toolkits/evaluation.py | rmse | def rmse(targets, predictions):
r"""
Compute the root mean squared error between two SArrays.
Parameters
----------
targets : SArray[float or int]
An Sarray of ground truth target values.
predictions : SArray[float or int]
The prediction that corresponds to each target value.
... | python | def rmse(targets, predictions):
r"""
Compute the root mean squared error between two SArrays.
Parameters
----------
targets : SArray[float or int]
An Sarray of ground truth target values.
predictions : SArray[float or int]
The prediction that corresponds to each target value.
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28,689 | apple/turicreate | src/unity/python/turicreate/toolkits/evaluation.py | confusion_matrix | def confusion_matrix(targets, predictions):
r"""
Compute the confusion matrix for classifier predictions.
Parameters
----------
targets : SArray
Ground truth class labels (cannot be of type float).
predictions : SArray
The prediction that corresponds to each target value.
... | python | def confusion_matrix(targets, predictions):
r"""
Compute the confusion matrix for classifier predictions.
Parameters
----------
targets : SArray
Ground truth class labels (cannot be of type float).
predictions : SArray
The prediction that corresponds to each target value.
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28,690 | apple/turicreate | src/unity/python/turicreate/toolkits/evaluation.py | auc | def auc(targets, predictions, average='macro', index_map=None):
r"""
Compute the area under the ROC curve for the given targets and predictions.
Parameters
----------
targets : SArray
An SArray containing the observed values. For binary classification,
the alpha-numerically first ca... | python | def auc(targets, predictions, average='macro', index_map=None):
r"""
Compute the area under the ROC curve for the given targets and predictions.
Parameters
----------
targets : SArray
An SArray containing the observed values. For binary classification,
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28,691 | apple/turicreate | deps/src/boost_1_68_0/status/boost_check_library.py | check_library.get_library_meta | def get_library_meta(self):
'''
Fetches the meta data for the current library. The data could be in
the superlib meta data file. If we can't find the data None is returned.
'''
parent_dir = os.path.dirname(self.library_dir)
if self.test_file_exists(os.path.join(self.libra... | python | def get_library_meta(self):
'''
Fetches the meta data for the current library. The data could be in
the superlib meta data file. If we can't find the data None is returned.
'''
parent_dir = os.path.dirname(self.library_dir)
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28,692 | apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/converters/xgboost/_tree.py | convert | def convert(model, feature_names = None, target = 'target', force_32bit_float = True):
"""
Convert a trained XGBoost model to Core ML format.
Parameters
----------
decision_tree : Booster
A trained XGboost tree model.
feature_names: [str] | str
Names of input features that will... | python | def convert(model, feature_names = None, target = 'target', force_32bit_float = True):
"""
Convert a trained XGBoost model to Core ML format.
Parameters
----------
decision_tree : Booster
A trained XGboost tree model.
feature_names: [str] | str
Names of input features that will... | [
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28,693 | apple/turicreate | src/unity/python/turicreate/toolkits/_feature_engineering/_feature_engineering.py | Transformer.fit | def fit(self, data):
"""
Fit a transformer using the SFrame `data`.
Parameters
----------
data : SFrame
The data used to fit the transformer.
Returns
-------
self (A fitted version of the object)
See Also
--------
tra... | python | def fit(self, data):
"""
Fit a transformer using the SFrame `data`.
Parameters
----------
data : SFrame
The data used to fit the transformer.
Returns
-------
self (A fitted version of the object)
See Also
--------
tra... | [
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28,694 | apple/turicreate | src/unity/python/turicreate/toolkits/_tree_model_mixin.py | TreeModelMixin.extract_features | def extract_features(self, dataset, missing_value_action='auto'):
"""
For each example in the dataset, extract the leaf indices of
each tree as features.
For multiclass classification, each leaf index contains #num_class
numbers.
The returned feature vectors can be used... | python | def extract_features(self, dataset, missing_value_action='auto'):
"""
For each example in the dataset, extract the leaf indices of
each tree as features.
For multiclass classification, each leaf index contains #num_class
numbers.
The returned feature vectors can be used... | [
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28,695 | apple/turicreate | src/unity/python/turicreate/toolkits/_tree_model_mixin.py | TreeModelMixin._extract_features_with_missing | def _extract_features_with_missing(self, dataset, tree_id = 0,
missing_value_action = 'auto'):
"""
Extract features along with all the missing features associated with
a dataset.
Parameters
----------
dataset: bool
Dataset on which to make predict... | python | def _extract_features_with_missing(self, dataset, tree_id = 0,
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"""
Extract features along with all the missing features associated with
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28,696 | apple/turicreate | src/unity/python/turicreate/toolkits/classifier/nearest_neighbor_classifier.py | _sort_topk_votes | def _sort_topk_votes(x, k):
"""
Sort a dictionary of classes and corresponding vote totals according to the
votes, then truncate to the highest 'k' classes.
"""
y = sorted(x.items(), key=lambda x: x[1], reverse=True)[:k]
return [{'class': i[0], 'votes': i[1]} for i in y] | python | def _sort_topk_votes(x, k):
"""
Sort a dictionary of classes and corresponding vote totals according to the
votes, then truncate to the highest 'k' classes.
"""
y = sorted(x.items(), key=lambda x: x[1], reverse=True)[:k]
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28,697 | apple/turicreate | src/unity/python/turicreate/toolkits/classifier/nearest_neighbor_classifier.py | _construct_auto_distance | def _construct_auto_distance(features, column_types):
"""
Construct a composite distance function for a set of features, based on the
types of those features.
NOTE: This function is very similar to
`:func:_nearest_neighbors.choose_auto_distance`. The function is separate
because the auto-distan... | python | def _construct_auto_distance(features, column_types):
"""
Construct a composite distance function for a set of features, based on the
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types of those features.
NOTE: This function is very similar to
`:func:_nearest_neighbors.choose_auto_distance`. The function is separate
because the auto-distance logic different than for each nearest
neighbors-based toolk... | [
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] | 74514c3f99e25b46f22c6e02977fe3da69221c2e | https://github.com/apple/turicreate/blob/74514c3f99e25b46f22c6e02977fe3da69221c2e/src/unity/python/turicreate/toolkits/classifier/nearest_neighbor_classifier.py#L42-L108 |
28,698 | apple/turicreate | src/unity/python/turicreate/toolkits/classifier/nearest_neighbor_classifier.py | NearestNeighborClassifier._load_version | def _load_version(cls, state, version):
"""
A function to load a previously saved NearestNeighborClassifier model.
Parameters
----------
unpickler : GLUnpickler
A GLUnpickler file handler.
version : int
Version number maintained by the class writ... | python | def _load_version(cls, state, version):
"""
A function to load a previously saved NearestNeighborClassifier model.
Parameters
----------
unpickler : GLUnpickler
A GLUnpickler file handler.
version : int
Version number maintained by the class writ... | [
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unpickler : GLUnpickler
A GLUnpickler file handler.
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Version number maintained by the class writer. | [
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28,699 | apple/turicreate | src/unity/python/turicreate/toolkits/classifier/nearest_neighbor_classifier.py | NearestNeighborClassifier.evaluate | def evaluate(self, dataset, metric='auto', max_neighbors=10, radius=None):
"""
Evaluate the model's predictive accuracy. This is done by predicting the
target class for instances in a new dataset and comparing to known
target values.
Parameters
----------
dataset... | python | def evaluate(self, dataset, metric='auto', max_neighbors=10, radius=None):
"""
Evaluate the model's predictive accuracy. This is done by predicting the
target class for instances in a new dataset and comparing to known
target values.
Parameters
----------
dataset... | [
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dataset : SFrame
Dataset of new observations. Must include columns with the same
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