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googledatalab/pydatalab | datalab/stackdriver/commands/_monitoring.py | monitoring | def monitoring(line, cell=None):
"""Implements the monitoring cell magic for ipython notebooks.
Args:
line: the contents of the storage line.
Returns:
The results of executing the cell.
"""
parser = datalab.utils.commands.CommandParser(prog='monitoring', description=(
'Execute various Monitorin... | python | def monitoring(line, cell=None):
"""Implements the monitoring cell magic for ipython notebooks.
Args:
line: the contents of the storage line.
Returns:
The results of executing the cell.
"""
parser = datalab.utils.commands.CommandParser(prog='monitoring', description=(
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googledatalab/pydatalab | datalab/stackdriver/commands/_monitoring.py | _render_dataframe | def _render_dataframe(dataframe):
"""Helper to render a dataframe as an HTML table."""
data = dataframe.to_dict(orient='records')
fields = dataframe.columns.tolist()
return IPython.core.display.HTML(
datalab.utils.commands.HtmlBuilder.render_table(data, fields)) | python | def _render_dataframe(dataframe):
"""Helper to render a dataframe as an HTML table."""
data = dataframe.to_dict(orient='records')
fields = dataframe.columns.tolist()
return IPython.core.display.HTML(
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googledatalab/pydatalab | google/datalab/utils/commands/_job.py | _get_job_status | def _get_job_status(line):
"""magic used as an endpoint for client to get job status.
%_get_job_status <name>
Returns:
A JSON object of the job status.
"""
try:
args = line.strip().split()
job_name = args[0]
job = None
if job_name in _local_jobs:
job = _local_jobs[job_name]
... | python | def _get_job_status(line):
"""magic used as an endpoint for client to get job status.
%_get_job_status <name>
Returns:
A JSON object of the job status.
"""
try:
args = line.strip().split()
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googledatalab/pydatalab | google/datalab/storage/_object.py | ObjectMetadata.updated_on | def updated_on(self):
"""The updated timestamp of the object as a datetime.datetime."""
s = self._info.get('updated', None)
return dateutil.parser.parse(s) if s else None | python | def updated_on(self):
"""The updated timestamp of the object as a datetime.datetime."""
s = self._info.get('updated', None)
return dateutil.parser.parse(s) if s else None | [
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googledatalab/pydatalab | google/datalab/storage/_object.py | Object.delete | def delete(self, wait_for_deletion=True):
"""Deletes this object from its bucket.
Args:
wait_for_deletion: If True, we poll until this object no longer appears in
objects.list operations for this bucket before returning.
Raises:
Exception if there was an error deleting the object.
... | python | def delete(self, wait_for_deletion=True):
"""Deletes this object from its bucket.
Args:
wait_for_deletion: If True, we poll until this object no longer appears in
objects.list operations for this bucket before returning.
Raises:
Exception if there was an error deleting the object.
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googledatalab/pydatalab | google/datalab/storage/_object.py | Object.metadata | def metadata(self):
"""Retrieves metadata about the object.
Returns:
An ObjectMetadata instance with information about this object.
Raises:
Exception if there was an error requesting the object's metadata.
"""
if self._info is None:
try:
self._info = self._api.objects_get(... | python | def metadata(self):
"""Retrieves metadata about the object.
Returns:
An ObjectMetadata instance with information about this object.
Raises:
Exception if there was an error requesting the object's metadata.
"""
if self._info is None:
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googledatalab/pydatalab | google/datalab/storage/_object.py | Object.read_stream | def read_stream(self, start_offset=0, byte_count=None):
"""Reads the content of this object as text.
Args:
start_offset: the start offset of bytes to read.
byte_count: the number of bytes to read. If None, it reads to the end.
Returns:
The text content within the object.
Raises:
... | python | def read_stream(self, start_offset=0, byte_count=None):
"""Reads the content of this object as text.
Args:
start_offset: the start offset of bytes to read.
byte_count: the number of bytes to read. If None, it reads to the end.
Returns:
The text content within the object.
Raises:
... | [
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googledatalab/pydatalab | google/datalab/storage/_object.py | Object.read_lines | def read_lines(self, max_lines=None):
"""Reads the content of this object as text, and return a list of lines up to some max.
Args:
max_lines: max number of lines to return. If None, return all lines.
Returns:
The text content of the object as a list of lines.
Raises:
Exception if the... | python | def read_lines(self, max_lines=None):
"""Reads the content of this object as text, and return a list of lines up to some max.
Args:
max_lines: max number of lines to return. If None, return all lines.
Returns:
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googledatalab/pydatalab | datalab/data/_csv.py | Csv.sample_to | def sample_to(self, count, skip_header_rows, strategy, target):
"""Sample rows from GCS or local file and save results to target file.
Args:
count: number of rows to sample. If strategy is "BIGQUERY", it is used as approximate number.
skip_header_rows: whether to skip first row when reading from so... | python | def sample_to(self, count, skip_header_rows, strategy, target):
"""Sample rows from GCS or local file and save results to target file.
Args:
count: number of rows to sample. If strategy is "BIGQUERY", it is used as approximate number.
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googledatalab/pydatalab | google/datalab/utils/facets/base_feature_statistics_generator.py | BaseFeatureStatisticsGenerator._ParseExample | def _ParseExample(self, example_features, example_feature_lists, entries,
index):
"""Parses data from an example, populating a dictionary of feature values.
Args:
example_features: A map of strings to tf.Features from the example.
example_feature_lists: A map of strings to tf.Fe... | python | def _ParseExample(self, example_features, example_feature_lists, entries,
index):
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example_features: A map of strings to tf.Features from the example.
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googledatalab/pydatalab | google/datalab/utils/facets/base_feature_statistics_generator.py | BaseFeatureStatisticsGenerator._GetEntries | def _GetEntries(self,
paths,
max_entries,
iterator_from_file,
is_sequence=False):
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Args:
paths: A list of the paths to the files to parse.
max_entries: The maximum number... | python | def _GetEntries(self,
paths,
max_entries,
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googledatalab/pydatalab | google/datalab/utils/facets/base_feature_statistics_generator.py | BaseFeatureStatisticsGenerator._GetTfRecordEntries | def _GetTfRecordEntries(self, path, max_entries, is_sequence,
iterator_options):
"""Extracts TFRecord examples into a dictionary of feature values.
Args:
path: The path to the TFRecord file(s).
max_entries: The maximum number of examples to load.
is_sequence: True if... | python | def _GetTfRecordEntries(self, path, max_entries, is_sequence,
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path: The path to the TFRecord file(s).
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googledatalab/pydatalab | datalab/storage/_api.py | Api.buckets_insert | def buckets_insert(self, bucket, project_id=None):
"""Issues a request to create a new bucket.
Args:
bucket: the name of the bucket.
project_id: the project to use when inserting the bucket.
Returns:
A parsed bucket information dictionary.
Raises:
Exception if there is an error ... | python | def buckets_insert(self, bucket, project_id=None):
"""Issues a request to create a new bucket.
Args:
bucket: the name of the bucket.
project_id: the project to use when inserting the bucket.
Returns:
A parsed bucket information dictionary.
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googledatalab/pydatalab | datalab/storage/_api.py | Api.objects_delete | def objects_delete(self, bucket, key):
"""Deletes the specified object.
Args:
bucket: the name of the bucket.
key: the key of the object within the bucket.
Raises:
Exception if there is an error performing the operation.
"""
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Args:
bucket: the name of the bucket.
key: the key of the object within the bucket.
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googledatalab/pydatalab | google/datalab/stackdriver/monitoring/_metric.py | MetricDescriptors.list | def list(self, pattern='*'):
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googledatalab/pydatalab | datalab/utils/commands/_utils.py | _get_data_from_empty_list | def _get_data_from_empty_list(source, fields='*', first_row=0, count=-1, schema=None):
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fields = get_field_list(fields, schema)
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config: the config to parse. Can be a tuple, list or dict.
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googledatalab/pydatalab | datalab/utils/commands/_utils.py | validate_config | def validate_config(config, required_keys, optional_keys=None):
""" Validate a config dictionary to make sure it includes all required keys
and does not include any unexpected keys.
Args:
config: the config to validate.
required_keys: the names of the keys that the config must have.
optional_keys... | python | def validate_config(config, required_keys, optional_keys=None):
""" Validate a config dictionary to make sure it includes all required keys
and does not include any unexpected keys.
Args:
config: the config to validate.
required_keys: the names of the keys that the config must have.
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googledatalab/pydatalab | datalab/utils/commands/_utils.py | validate_config_must_have | def validate_config_must_have(config, required_keys):
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Args:
config: the config to validate.
required_keys: the list of possible keys that config must include.
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""" Validate a config dictionary to make sure it has all of the specified keys
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config: the config to validate.
required_keys: the list of possible keys that config must include.
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googledatalab/pydatalab | datalab/utils/commands/_utils.py | validate_config_has_one_of | def validate_config_has_one_of(config, one_of_keys):
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Args:
config: the config to validate.
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googledatalab/pydatalab | datalab/utils/commands/_utils.py | validate_config_value | def validate_config_value(value, possible_values):
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value: the config value to validate.
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googledatalab/pydatalab | datalab/utils/commands/_utils.py | validate_gcs_path | def validate_gcs_path(path, require_object):
""" Check whether a given path is a valid GCS path.
Args:
path: the config to check.
require_object: if True, the path has to be an object path but not bucket path.
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""" Check whether a given path is a valid GCS path.
Args:
path: the config to check.
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googledatalab/pydatalab | datalab/utils/commands/_utils.py | profile_df | def profile_df(df):
""" Generate a profile of data in a dataframe.
Args:
df: the Pandas dataframe.
"""
# The bootstrap CSS messes up the Datalab display so we tweak it to not have an effect.
# TODO(gram): strip it out rather than this kludge.
return IPython.core.display.HTML(
pandas_profiling.Pro... | python | def profile_df(df):
""" Generate a profile of data in a dataframe.
Args:
df: the Pandas dataframe.
"""
# The bootstrap CSS messes up the Datalab display so we tweak it to not have an effect.
# TODO(gram): strip it out rather than this kludge.
return IPython.core.display.HTML(
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googledatalab/pydatalab | solutionbox/structured_data/mltoolbox/_structured_data/_package.py | _package_to_staging | def _package_to_staging(staging_package_url):
"""Repackage this package from local installed location and copy it to GCS.
Args:
staging_package_url: GCS path.
"""
import google.datalab.ml as ml
# Find the package root. __file__ is under [package_root]/mltoolbox/_structured_data/this_file
... | python | def _package_to_staging(staging_package_url):
"""Repackage this package from local installed location and copy it to GCS.
Args:
staging_package_url: GCS path.
"""
import google.datalab.ml as ml
# Find the package root. __file__ is under [package_root]/mltoolbox/_structured_data/this_file
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googledatalab/pydatalab | solutionbox/structured_data/mltoolbox/_structured_data/_package.py | analyze | def analyze(output_dir, dataset, cloud=False, project_id=None):
"""Blocking version of analyze_async. See documentation of analyze_async."""
job = analyze_async(
output_dir=output_dir,
dataset=dataset,
cloud=cloud,
project_id=project_id)
job.wait()
print('Analyze: ' + str(job.state)) | python | def analyze(output_dir, dataset, cloud=False, project_id=None):
"""Blocking version of analyze_async. See documentation of analyze_async."""
job = analyze_async(
output_dir=output_dir,
dataset=dataset,
cloud=cloud,
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job.wait()
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googledatalab/pydatalab | solutionbox/structured_data/mltoolbox/_structured_data/_package.py | analyze_async | def analyze_async(output_dir, dataset, cloud=False, project_id=None):
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Produce analysis used by training. This can take a while, even for small
datasets. For small datasets, it may be faster to use local_analysis.
Args:
output_dir: The output directory... | python | def analyze_async(output_dir, dataset, cloud=False, project_id=None):
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googledatalab/pydatalab | solutionbox/structured_data/mltoolbox/_structured_data/_package.py | cloud_train | def cloud_train(train_dataset,
eval_dataset,
analysis_dir,
output_dir,
features,
model_type,
max_steps,
num_epochs,
train_batch_size,
eval_batch_size,
min_eval_... | python | def cloud_train(train_dataset,
eval_dataset,
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output_dir,
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max_steps,
num_epochs,
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eval_batch_size,
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googledatalab/pydatalab | solutionbox/structured_data/mltoolbox/_structured_data/_package.py | predict | def predict(data, training_dir=None, model_name=None, model_version=None, cloud=False):
"""Runs prediction locally or on the cloud.
Args:
data: List of csv strings or a Pandas DataFrame that match the model schema.
training_dir: local path to the trained output folder.
model_name: deployed model name
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"""Runs prediction locally or on the cloud.
Args:
data: List of csv strings or a Pandas DataFrame that match the model schema.
training_dir: local path to the trained output folder.
model_name: deployed model name
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googledatalab/pydatalab | solutionbox/structured_data/mltoolbox/_structured_data/_package.py | local_predict | def local_predict(training_dir, data):
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Runs local prediction and returns the result in a Pandas DataFrame. For
running prediction on a large dataset or saving the results, run
local_batch_prediction or batch_prediction. Input data should fully match
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"""Runs local prediction on the prediction graph.
Runs local prediction and returns the result in a Pandas DataFrame. For
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googledatalab/pydatalab | solutionbox/structured_data/mltoolbox/_structured_data/_package.py | cloud_predict | def cloud_predict(model_name, model_version, data):
"""Use Online prediction.
Runs online prediction in the cloud and prints the results to the screen. For
running prediction on a large dataset or saving the results, run
local_batch_prediction or batch_prediction.
Args:
model_name: deployed model name
... | python | def cloud_predict(model_name, model_version, data):
"""Use Online prediction.
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googledatalab/pydatalab | solutionbox/structured_data/mltoolbox/_structured_data/_package.py | batch_predict | def batch_predict(training_dir, prediction_input_file, output_dir,
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cloud=False):
"""Blocking versoin of batch_predict.
See documentation of batch_prediction_async.
"""
job = batch_predict_async(
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mode, batch_size=16, shard_files=True, output_format='csv',
cloud=False):
"""Blocking versoin of batch_predict.
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googledatalab/pydatalab | solutionbox/structured_data/mltoolbox/_structured_data/_package.py | batch_predict_async | def batch_predict_async(training_dir, prediction_input_file, output_dir,
mode, batch_size=16, shard_files=True, output_format='csv', cloud=False):
"""Local and cloud batch prediction.
Args:
training_dir: The output folder of training.
prediction_input_file: csv file pattern to a fil... | python | def batch_predict_async(training_dir, prediction_input_file, output_dir,
mode, batch_size=16, shard_files=True, output_format='csv', cloud=False):
"""Local and cloud batch prediction.
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training_dir: The output folder of training.
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googledatalab/pydatalab | solutionbox/structured_data/mltoolbox/_structured_data/prediction/predict.py | make_prediction_pipeline | def make_prediction_pipeline(pipeline, args):
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googledatalab/pydatalab | solutionbox/structured_data/mltoolbox/_structured_data/prediction/predict.py | RunGraphDoFn.process | def process(self, element):
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element: list of strings, representing one batch input to the TF graph.
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Args:
element: list of strings, representing one batch input to the TF graph.
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googledatalab/pydatalab | solutionbox/structured_data/mltoolbox/_structured_data/prediction/predict.py | CSVCoder.encode | def encode(self, tf_graph_predictions):
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Args:
tf_graph_predictions: python dict.
Returns:
csv string.
"""
row = []
for col in self._header:
row.append(str(tf_graph_predictions[col]))
return ','.join(row) | python | def encode(self, tf_graph_predictions):
"""Encodes the graph json prediction into csv.
Args:
tf_graph_predictions: python dict.
Returns:
csv string.
"""
row = []
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googledatalab/pydatalab | google/datalab/stackdriver/monitoring/_query_metadata.py | QueryMetadata.as_dataframe | def as_dataframe(self, max_rows=None):
"""Creates a pandas dataframe from the query metadata.
Args:
max_rows: The maximum number of timeseries metadata to return. If None,
return all.
Returns:
A pandas dataframe containing the resource type, resource labels and
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"""Creates a pandas dataframe from the query metadata.
Args:
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googledatalab/pydatalab | solutionbox/image_classification/mltoolbox/image/classification/_util.py | get_train_eval_files | def get_train_eval_files(input_dir):
"""Get preprocessed training and eval files."""
data_dir = _get_latest_data_dir(input_dir)
train_pattern = os.path.join(data_dir, 'train*.tfrecord.gz')
eval_pattern = os.path.join(data_dir, 'eval*.tfrecord.gz')
train_files = file_io.get_matching_files(train_pattern)
eval... | python | def get_train_eval_files(input_dir):
"""Get preprocessed training and eval files."""
data_dir = _get_latest_data_dir(input_dir)
train_pattern = os.path.join(data_dir, 'train*.tfrecord.gz')
eval_pattern = os.path.join(data_dir, 'eval*.tfrecord.gz')
train_files = file_io.get_matching_files(train_pattern)
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googledatalab/pydatalab | solutionbox/image_classification/mltoolbox/image/classification/_util.py | get_labels | def get_labels(input_dir):
"""Get a list of labels from preprocessed output dir."""
data_dir = _get_latest_data_dir(input_dir)
labels_file = os.path.join(data_dir, 'labels')
with file_io.FileIO(labels_file, 'r') as f:
labels = f.read().rstrip().split('\n')
return labels | python | def get_labels(input_dir):
"""Get a list of labels from preprocessed output dir."""
data_dir = _get_latest_data_dir(input_dir)
labels_file = os.path.join(data_dir, 'labels')
with file_io.FileIO(labels_file, 'r') as f:
labels = f.read().rstrip().split('\n')
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googledatalab/pydatalab | solutionbox/image_classification/mltoolbox/image/classification/_util.py | override_if_not_in_args | def override_if_not_in_args(flag, argument, args):
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if flag not in args:
args.extend([flag, argument]) | python | def override_if_not_in_args(flag, argument, args):
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googledatalab/pydatalab | solutionbox/image_classification/mltoolbox/image/classification/_util.py | loss | def loss(loss_value):
"""Calculates aggregated mean loss."""
total_loss = tf.Variable(0.0, False)
loss_count = tf.Variable(0, False)
total_loss_update = tf.assign_add(total_loss, loss_value)
loss_count_update = tf.assign_add(loss_count, 1)
loss_op = total_loss / tf.cast(loss_count, tf.float32)
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"""Calculates aggregated mean loss."""
total_loss = tf.Variable(0.0, False)
loss_count = tf.Variable(0, False)
total_loss_update = tf.assign_add(total_loss, loss_value)
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googledatalab/pydatalab | solutionbox/image_classification/mltoolbox/image/classification/_util.py | accuracy | def accuracy(logits, labels):
"""Calculates aggregated accuracy."""
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correct = tf.reduce_sum(tf.cast(is_correct, tf.int32))
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"""Calculates aggregated accuracy."""
is_correct = tf.nn.in_top_k(logits, labels, 1)
correct = tf.reduce_sum(tf.cast(is_correct, tf.int32))
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googledatalab/pydatalab | solutionbox/image_classification/mltoolbox/image/classification/_util.py | check_dataset | def check_dataset(dataset, mode):
"""Validate we have a good dataset."""
names = [x['name'] for x in dataset.schema]
types = [x['type'] for x in dataset.schema]
if mode == 'train':
if (set(['image_url', 'label']) != set(names) or any(t != 'STRING' for t in types)):
raise ValueError('Invalid dataset. ... | python | def check_dataset(dataset, mode):
"""Validate we have a good dataset."""
names = [x['name'] for x in dataset.schema]
types = [x['type'] for x in dataset.schema]
if mode == 'train':
if (set(['image_url', 'label']) != set(names) or any(t != 'STRING' for t in types)):
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googledatalab/pydatalab | solutionbox/image_classification/mltoolbox/image/classification/_util.py | get_sources_from_dataset | def get_sources_from_dataset(p, dataset, mode):
"""get pcollection from dataset."""
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import csv
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check_dataset(dataset, mode)
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source_list = []
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"""get pcollection from dataset."""
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import csv
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googledatalab/pydatalab | solutionbox/image_classification/mltoolbox/image/classification/_util.py | decode_and_resize | def decode_and_resize(image_str_tensor):
"""Decodes jpeg string, resizes it and returns a uint8 tensor."""
# These constants are set by Inception v3's expectations.
height = 299
width = 299
channels = 3
image = tf.image.decode_jpeg(image_str_tensor, channels=channels)
# Note resize expects a batch_size,... | python | def decode_and_resize(image_str_tensor):
"""Decodes jpeg string, resizes it and returns a uint8 tensor."""
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height = 299
width = 299
channels = 3
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googledatalab/pydatalab | solutionbox/image_classification/mltoolbox/image/classification/_util.py | resize_image | def resize_image(image_str_tensor):
"""Decodes jpeg string, resizes it and re-encode it to jpeg."""
image = decode_and_resize(image_str_tensor)
image = tf.image.encode_jpeg(image, quality=100)
return image | python | def resize_image(image_str_tensor):
"""Decodes jpeg string, resizes it and re-encode it to jpeg."""
image = decode_and_resize(image_str_tensor)
image = tf.image.encode_jpeg(image, quality=100)
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googledatalab/pydatalab | solutionbox/image_classification/mltoolbox/image/classification/_util.py | load_images | def load_images(image_files, resize=True):
"""Load images from files and optionally resize it."""
images = []
for image_file in image_files:
with file_io.FileIO(image_file, 'r') as ff:
images.append(ff.read())
if resize is False:
return images
# To resize, run a tf session so we can reuse 'dec... | python | def load_images(image_files, resize=True):
"""Load images from files and optionally resize it."""
images = []
for image_file in image_files:
with file_io.FileIO(image_file, 'r') as ff:
images.append(ff.read())
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googledatalab/pydatalab | solutionbox/image_classification/mltoolbox/image/classification/_util.py | process_prediction_results | def process_prediction_results(results, show_image):
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googledatalab/pydatalab | solutionbox/image_classification/mltoolbox/image/classification/_util.py | repackage_to_staging | def repackage_to_staging(output_path):
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# Find the package root. __file__ is under [package_root]/mltoolbox/image/classification.
package_root = os.path.join(os.path.dirname(__file__), '../../../')
# We deploy... | python | def repackage_to_staging(output_path):
"""Repackage it from local installed location and copy it to GCS."""
import google.datalab.ml as ml
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# TODO(rajivpb): Rename this var correctly.
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googledatalab/pydatalab | google/datalab/ml/_dataset.py | BigQueryDataSet.sample | def sample(self, n):
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n: number of sampled counts. Note that the number of counts returned is approximated.
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googledatalab/pydatalab | google/datalab/ml/_dataset.py | TransformedDataSet.size | def size(self):
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googledatalab/pydatalab | google/datalab/stackdriver/monitoring/_group.py | Groups.list | def list(self, pattern='*'):
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googledatalab/pydatalab | datalab/data/_sql_statement.py | SqlStatement._find_recursive_dependencies | def _find_recursive_dependencies(sql, values, code, resolved_vars, resolving_vars=None):
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googledatalab/pydatalab | datalab/data/_sql_statement.py | SqlStatement.format | def format(sql, args=None):
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googledatalab/pydatalab | datalab/utils/commands/_modules.py | pymodule | def pymodule(line, cell=None):
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parser.add_argument('-n', '--name',
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retur... | python | def pymodule(line, cell=None):
"""Creates and subsequently auto-imports a python module.
"""
parser = _commands.CommandParser.create('pymodule')
parser.add_argument('-n', '--name',
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googledatalab/pydatalab | google/datalab/utils/_utils.py | compare_datetimes | def compare_datetimes(d1, d2):
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d1: first datetime.
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googledatalab/pydatalab | google/datalab/utils/_utils.py | pick_unused_port | def pick_unused_port():
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Returns:
An unused port.
"""
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""" get an unused port on the VM.
Returns:
An unused port.
"""
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
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googledatalab/pydatalab | google/datalab/utils/_utils.py | is_http_running_on | def is_http_running_on(port):
""" Check if an http server runs on a given port.
Args:
The port to check.
Returns:
True if it is used by an http server. False otherwise.
"""
try:
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conn.close()
return True
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""" Check if an http server runs on a given port.
Args:
The port to check.
Returns:
True if it is used by an http server. False otherwise.
"""
try:
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googledatalab/pydatalab | google/datalab/utils/_utils.py | save_project_id | def save_project_id(project_id):
""" Save project id to config file.
Args:
project_id: the project_id to save.
"""
# Try gcloud first. If gcloud fails (probably because it does not exist), then
# write to a config file.
try:
subprocess.call(['gcloud', 'config', 'set', 'project', project_id])
exce... | python | def save_project_id(project_id):
""" Save project id to config file.
Args:
project_id: the project_id to save.
"""
# Try gcloud first. If gcloud fails (probably because it does not exist), then
# write to a config file.
try:
subprocess.call(['gcloud', 'config', 'set', 'project', project_id])
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googledatalab/pydatalab | google/datalab/utils/_utils.py | get_default_project_id | def get_default_project_id():
""" Get default project id from config or environment var.
Returns: the project id if available, or None.
"""
# Try getting default project id from gcloud. If it fails try config.json.
try:
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""" Get default project id from config or environment var.
Returns: the project id if available, or None.
"""
# Try getting default project id from gcloud. If it fails try config.json.
try:
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googledatalab/pydatalab | google/datalab/utils/_utils.py | _construct_context_for_args | def _construct_context_for_args(args):
"""Construct a new Context for the parsed arguments.
Args:
args: the dictionary of magic arguments.
Returns:
A new Context based on the current default context, but with any explicitly
specified arguments overriding the default's config.
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"""Construct a new Context for the parsed arguments.
Args:
args: the dictionary of magic arguments.
Returns:
A new Context based on the current default context, but with any explicitly
specified arguments overriding the default's config.
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googledatalab/pydatalab | google/datalab/utils/_utils.py | python_portable_string | def python_portable_string(string, encoding='utf-8'):
"""Converts bytes into a string type.
Valid string types are retuned without modification. So in Python 2, type str
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In Python 3, type bytes is converted to type str (unicode)
"""
if isinstance(string, six.string_types):
... | python | def python_portable_string(string, encoding='utf-8'):
"""Converts bytes into a string type.
Valid string types are retuned without modification. So in Python 2, type str
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googledatalab/pydatalab | datalab/storage/commands/_storage.py | _storage_list_buckets | def _storage_list_buckets(project, pattern):
""" List all storage buckets that match a pattern. """
data = [{'Bucket': 'gs://' + bucket.name, 'Created': bucket.metadata.created_on}
for bucket in datalab.storage.Buckets(project_id=project)
if fnmatch.fnmatch(bucket.name, pattern)]
return datala... | python | def _storage_list_buckets(project, pattern):
""" List all storage buckets that match a pattern. """
data = [{'Bucket': 'gs://' + bucket.name, 'Created': bucket.metadata.created_on}
for bucket in datalab.storage.Buckets(project_id=project)
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googledatalab/pydatalab | datalab/storage/commands/_storage.py | _storage_list_keys | def _storage_list_keys(bucket, pattern):
""" List all storage keys in a specified bucket that match a pattern. """
data = [{'Name': item.metadata.name,
'Type': item.metadata.content_type,
'Size': item.metadata.size,
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for item in _storage... | python | def _storage_list_keys(bucket, pattern):
""" List all storage keys in a specified bucket that match a pattern. """
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'Size': item.metadata.size,
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googledatalab/pydatalab | google/datalab/bigquery/_api.py | Api.tables_list | def tables_list(self, dataset_name, max_results=0, page_token=None):
"""Issues a request to retrieve a list of tables.
Args:
dataset_name: the name of the dataset to enumerate.
max_results: an optional maximum number of tables to retrieve.
page_token: an optional token to continue the retriev... | python | def tables_list(self, dataset_name, max_results=0, page_token=None):
"""Issues a request to retrieve a list of tables.
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dataset_name: the name of the dataset to enumerate.
max_results: an optional maximum number of tables to retrieve.
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googledatalab/pydatalab | solutionbox/ml_workbench/tensorflow/trainer/feature_transforms.py | _bag_of_words | def _bag_of_words(x):
"""Computes bag of words weights
Note the return type is a float sparse tensor, not a int sparse tensor. This
is so that the output types batch tfidf, and any downstream transformation
in tf layers during training can be applied to both.
"""
def _bow(x):
"""Comptue BOW weights.
... | python | def _bag_of_words(x):
"""Computes bag of words weights
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"""
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googledatalab/pydatalab | solutionbox/ml_workbench/tensorflow/trainer/feature_transforms.py | csv_header_and_defaults | def csv_header_and_defaults(features, schema, stats, keep_target):
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target_name = get_target_name(features)
if keep_target and not target_name:
raise ValueError('Cannot find target transform')
csv_header = []
record_defaults = []
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googledatalab/pydatalab | solutionbox/ml_workbench/tensorflow/trainer/feature_transforms.py | build_csv_serving_tensors_for_transform_step | def build_csv_serving_tensors_for_transform_step(analysis_path,
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schema,
stats,
keep_target):
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googledatalab/pydatalab | solutionbox/ml_workbench/tensorflow/trainer/feature_transforms.py | build_csv_serving_tensors_for_training_step | def build_csv_serving_tensors_for_training_step(analysis_path,
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googledatalab/pydatalab | solutionbox/ml_workbench/tensorflow/trainer/feature_transforms.py | build_csv_transforming_training_input_fn | def build_csv_transforming_training_input_fn(schema,
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googledatalab/pydatalab | solutionbox/ml_workbench/tensorflow/trainer/feature_transforms.py | build_tfexample_transfored_training_input_fn | def build_tfexample_transfored_training_input_fn(schema,
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googledatalab/pydatalab | solutionbox/ml_workbench/tensorflow/trainer/feature_transforms.py | image_feature_engineering | def image_feature_engineering(features, feature_tensors_dict):
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features: features dict
feature_tensors_dict: dict of feature-name: tensor
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engineered_features = {}
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if name in feature... | python | def image_feature_engineering(features, feature_tensors_dict):
"""Add a hidden layer on image features.
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googledatalab/pydatalab | solutionbox/ml_workbench/tensorflow/trainer/feature_transforms.py | read_vocab_file | def read_vocab_file(file_path):
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file_path: Each line of the vocab is in the form "token,example_count"
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... | python | def read_vocab_file(file_path):
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googledatalab/pydatalab | google/datalab/kernel/__init__.py | load_ipython_extension | def load_ipython_extension(shell):
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shell - (NotebookWebApplication): handle to the Notebook interactive shell instance.
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Args:
shell - (NotebookWebApplication): handle to the Notebook interactive shell instance.
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googledatalab/pydatalab | datalab/data/_sql_module.py | SqlModule.get_sql_statement_with_environment | def get_sql_statement_with_environment(item, args=None):
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googledatalab/pydatalab | google/datalab/bigquery/_utils.py | parse_dataset_name | def parse_dataset_name(name, project_id=None):
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name: the name to parse, or a tuple, dictionary or array containing the parts.
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googledatalab/pydatalab | google/datalab/bigquery/_utils.py | parse_table_name | def parse_table_name(name, project_id=None, dataset_id=None):
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name: the name to parse, or a tuple, dictionary or array containing the parts.
project_id: the expected project ID. If the name does not contain a project ID,
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googledatalab/pydatalab | google/datalab/contrib/mlworkbench/_prediction_explainer.py | PredictionExplainer._make_text_predict_fn | def _make_text_predict_fn(self, labels, instance, column_to_explain):
"""Create a predict_fn that can be used by LIME text explainer. """
def _predict_fn(perturbed_text):
predict_input = []
for x in perturbed_text:
instance_copy = dict(instance)
i... | python | def _make_text_predict_fn(self, labels, instance, column_to_explain):
"""Create a predict_fn that can be used by LIME text explainer. """
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googledatalab/pydatalab | google/datalab/contrib/mlworkbench/_prediction_explainer.py | PredictionExplainer._make_image_predict_fn | def _make_image_predict_fn(self, labels, instance, column_to_explain):
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googledatalab/pydatalab | google/datalab/contrib/mlworkbench/_prediction_explainer.py | PredictionExplainer._get_unique_categories | def _get_unique_categories(self, df):
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categories = []
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categories = []
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] | d9031901d5bca22fe0d5925d204e6698df9852e1 | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/google/datalab/contrib/mlworkbench/_prediction_explainer.py#L92-L101 | train |
googledatalab/pydatalab | google/datalab/contrib/mlworkbench/_prediction_explainer.py | PredictionExplainer._preprocess_data_for_tabular_explain | def _preprocess_data_for_tabular_explain(self, df, categories):
"""Get preprocessed training set in numpy array, and categorical names from raw training data.
LIME tabular explainer requires a training set to know the distribution of numeric and
categorical values. The training set has to be nu... | python | def _preprocess_data_for_tabular_explain(self, df, categories):
"""Get preprocessed training set in numpy array, and categorical names from raw training data.
LIME tabular explainer requires a training set to know the distribution of numeric and
categorical values. The training set has to be nu... | [
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LIME tabular explainer requires a training set to know the distribution of numeric and
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] | d9031901d5bca22fe0d5925d204e6698df9852e1 | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/google/datalab/contrib/mlworkbench/_prediction_explainer.py#L103-L128 | train |
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