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andycasey/ads | examples/monthly-institute-publications/stromlo.py | get_pdf | def get_pdf(article, debug=False):
"""
Download an article PDF from arXiv.
:param article:
The ADS article to retrieve.
:type article:
:class:`ads.search.Article`
:returns:
The binary content of the requested PDF.
"""
print('Retrieving {0}'.format(article))
i... | python | def get_pdf(article, debug=False):
"""
Download an article PDF from arXiv.
:param article:
The ADS article to retrieve.
:type article:
:class:`ads.search.Article`
:returns:
The binary content of the requested PDF.
"""
print('Retrieving {0}'.format(article))
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andycasey/ads | examples/monthly-institute-publications/stromlo.py | summarise_pdfs | def summarise_pdfs(pdfs):
"""
Collate the first page from each of the PDFs provided into a single PDF.
:param pdfs:
The contents of several PDF files.
:type pdfs:
list of str
:returns:
The contents of single PDF, which can be written directly to disk.
"""
# Ignore... | python | def summarise_pdfs(pdfs):
"""
Collate the first page from each of the PDFs provided into a single PDF.
:param pdfs:
The contents of several PDF files.
:type pdfs:
list of str
:returns:
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andycasey/ads | ads/metrics.py | MetricsQuery.execute | def execute(self):
"""
Execute the http request to the metrics service
"""
self.response = MetricsResponse.load_http_response(
self.session.post(self.HTTP_ENDPOINT, data=self.json_payload)
)
return self.response.metrics | python | def execute(self):
"""
Execute the http request to the metrics service
"""
self.response = MetricsResponse.load_http_response(
self.session.post(self.HTTP_ENDPOINT, data=self.json_payload)
)
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andycasey/ads | ads/base.py | _Singleton.get_info | def get_info(cls):
"""
Print all of the instantiated Singletons
"""
return '\n'.join(
[str(cls._instances[key]) for key in cls._instances]
) | python | def get_info(cls):
"""
Print all of the instantiated Singletons
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andycasey/ads | ads/base.py | APIResponse.load_http_response | def load_http_response(cls, http_response):
"""
This method should return an instantiated class and set its response
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"""
if not http_response.ok:
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andycasey/ads | ads/base.py | BaseQuery.token | def token(self):
"""
set the instance attribute `token` following the following logic,
stopping whenever a token is found. Raises NoTokenFound is no token
is found
- environment variables TOKEN_ENVIRON_VARS
- file containing plaintext as the contents in TOKEN_FILES
... | python | def token(self):
"""
set the instance attribute `token` following the following logic,
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- environment variables TOKEN_ENVIRON_VARS
- file containing plaintext as the contents in TOKEN_FILES
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andycasey/ads | ads/base.py | BaseQuery.session | def session(self):
"""
http session interface, transparent proxy to requests.session
"""
if self._session is None:
self._session = requests.session()
self._session.headers.update(
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"""
http session interface, transparent proxy to requests.session
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googledatalab/pydatalab | google/datalab/contrib/mlworkbench/commands/_ml.py | ml | def ml(line, cell=None):
"""Implements the datalab cell magic for MLWorkbench operations.
Args:
line: the contents of the ml command line.
Returns:
The results of executing the cell.
"""
parser = google.datalab.utils.commands.CommandParser(
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"""Implements the datalab cell magic for MLWorkbench operations.
Args:
line: the contents of the ml command line.
Returns:
The results of executing the cell.
"""
parser = google.datalab.utils.commands.CommandParser(
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googledatalab/pydatalab | google/datalab/ml/_metrics.py | Metrics.from_csv | def from_csv(input_csv_pattern, headers=None, schema_file=None):
"""Create a Metrics instance from csv file pattern.
Args:
input_csv_pattern: Path to Csv file pattern (with no header). Can be local or GCS path.
headers: Csv headers.
schema_file: Path to a JSON file containing BigQuery schema.... | python | def from_csv(input_csv_pattern, headers=None, schema_file=None):
"""Create a Metrics instance from csv file pattern.
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input_csv_pattern: Path to Csv file pattern (with no header). Can be local or GCS path.
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googledatalab/pydatalab | google/datalab/ml/_metrics.py | Metrics.from_bigquery | def from_bigquery(sql):
"""Create a Metrics instance from a bigquery query or table.
Returns:
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Args:
sql: A BigQuery table name or a query.
"""
if isinstance(sql, bq.Query):
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Returns:
a Metrics instance.
Args:
sql: A BigQuery table name or a query.
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googledatalab/pydatalab | google/datalab/ml/_metrics.py | Metrics._get_data_from_csv_files | def _get_data_from_csv_files(self):
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all_df = []
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df = pd.concat(all_df, ignore_index=True)
return df | python | def _get_data_from_csv_files(self):
"""Get data from input csv files."""
all_df = []
for file_name in self._input_csv_files:
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googledatalab/pydatalab | google/datalab/ml/_metrics.py | Metrics._get_data_from_bigquery | def _get_data_from_bigquery(self, queries):
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all_df = []
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googledatalab/pydatalab | google/datalab/ml/_metrics.py | Metrics.accuracy | def accuracy(self):
"""Get accuracy numbers for each target and overall.
Returns:
A DataFrame with two columns: 'class' and 'accuracy'. It also contains the overall
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Raises:
Exception if the CSV headers do not include 'target' or 'predicted', or BigQuery
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googledatalab/pydatalab | google/datalab/ml/_metrics.py | Metrics.roc | def roc(self, num_thresholds, target_class, probability_column=None):
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Args:
num_thresholds: an integer. Number of thresholds.
target_class: a string indciating the target class, i.e. "daisy" in flower classification.
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num_thresholds: an integer. Number of thresholds.
target_class: a string indciating the target class, i.e. "daisy" in flower classification.
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googledatalab/pydatalab | google/datalab/ml/_metrics.py | Metrics.precision_recall | def precision_recall(self, num_thresholds, target_class, probability_column=None):
"""Get precision, recall values from evaluation results.
Args:
num_thresholds: an integer. Number of thresholds.
target_class: a string indciating the target class, i.e. "daisy" in flower classification.
probab... | python | def precision_recall(self, num_thresholds, target_class, probability_column=None):
"""Get precision, recall values from evaluation results.
Args:
num_thresholds: an integer. Number of thresholds.
target_class: a string indciating the target class, i.e. "daisy" in flower classification.
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googledatalab/pydatalab | google/datalab/ml/_metrics.py | Metrics.rmse | def rmse(self):
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Returns:
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googledatalab/pydatalab | google/datalab/ml/_metrics.py | Metrics.percentile_nearest | def percentile_nearest(self, percentile):
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Returns
The expanded SQL string of this UDF
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googledatalab/pydatalab | google/datalab/storage/_bucket.py | parse_name | def parse_name(name):
""" Parse a gs:// URL into the bucket and object names.
Args:
name: a GCS URL of the form gs://bucket or gs://bucket/object
Returns:
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""" Parse a gs:// URL into the bucket and object names.
Args:
name: a GCS URL of the form gs://bucket or gs://bucket/object
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googledatalab/pydatalab | google/datalab/storage/_bucket.py | Bucket.metadata | def metadata(self):
"""Retrieves metadata about the bucket.
Returns:
A BucketMetadata instance with information about this bucket.
Raises:
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Returns:
A BucketMetadata instance with information about this bucket.
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googledatalab/pydatalab | google/datalab/storage/_bucket.py | Bucket.object | def object(self, key):
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googledatalab/pydatalab | google/datalab/storage/_bucket.py | Bucket.delete | def delete(self):
"""Deletes the bucket.
Raises:
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"""
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"""Deletes the bucket.
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googledatalab/pydatalab | google/datalab/storage/_bucket.py | Buckets.contains | def contains(self, name):
"""Checks if the specified bucket exists.
Args:
name: the name of the bucket to lookup.
Returns:
True if the bucket exists; False otherwise.
Raises:
Exception if there was an error requesting information about the bucket.
"""
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"""Checks if the specified bucket exists.
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name: the name of the bucket to lookup.
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googledatalab/pydatalab | datalab/storage/_bucket.py | Bucket.create | def create(self, project_id=None):
"""Creates the bucket.
Args:
project_id: the project in which to create the bucket.
Returns:
The bucket.
Raises:
Exception if there was an error creating the bucket.
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Args:
project_id: the project in which to create the bucket.
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googledatalab/pydatalab | datalab/storage/_bucket.py | Buckets.create | def create(self, name):
"""Creates a new bucket.
Args:
name: a unique name for the new bucket.
Returns:
The newly created bucket.
Raises:
Exception if there was an error creating the bucket.
"""
return Bucket(name, context=self._context).create(self._project_id) | python | def create(self, name):
"""Creates a new bucket.
Args:
name: a unique name for the new bucket.
Returns:
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num_epochs=None,
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googledatalab/pydatalab | google/datalab/storage/commands/_storage.py | _gcs_list | def _gcs_list(args, _):
""" List the buckets or the contents of a bucket.
This command is a bit different in that we allow wildchars in the bucket name and will list
the buckets that match.
"""
target = args['objects']
project = args['project']
if target is None:
return _gcs_list_buckets(project, '*'... | python | def _gcs_list(args, _):
""" List the buckets or the contents of a bucket.
This command is a bit different in that we allow wildchars in the bucket name and will list
the buckets that match.
"""
target = args['objects']
project = args['project']
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googledatalab/pydatalab | solutionbox/ml_workbench/tensorflow/transform.py | parse_arguments | def parse_arguments(argv):
"""Parse command line arguments.
Args:
argv: list of command line arguments including program name.
Returns:
The parsed arguments as returned by argparse.ArgumentParser.
"""
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googledatalab/pydatalab | solutionbox/ml_workbench/tensorflow/transform.py | prepare_image_transforms | def prepare_image_transforms(element, image_columns):
"""Replace an images url with its jpeg bytes.
Args:
element: one input row, as a dict
image_columns: list of columns that are image paths
Return:
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googledatalab/pydatalab | solutionbox/ml_workbench/tensorflow/transform.py | decode_csv | def decode_csv(csv_string, column_names):
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csv_string: a csv string. May contain missing values "a,,c"
column_names: list of column names
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googledatalab/pydatalab | solutionbox/ml_workbench/tensorflow/transform.py | encode_csv | def encode_csv(data_dict, column_names):
"""Builds a csv string.
Args:
data_dict: dict of {column_name: 1 value}
column_names: list of column names
Returns:
A csv string version of data_dict
"""
import csv
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values = [str(data_dict[x]) for x in column_names]
str_buff = six.StringI... | python | def encode_csv(data_dict, column_names):
"""Builds a csv string.
Args:
data_dict: dict of {column_name: 1 value}
column_names: list of column names
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googledatalab/pydatalab | solutionbox/ml_workbench/tensorflow/transform.py | serialize_example | def serialize_example(transformed_json_data, info_dict):
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Args:
transformed_json_data: dict of transformed data.
info_dict: output of feature_transforms.get_transfrormed_feature_info()
Returns:
The serialized tf.example version of transformed_json_data.
"""
impor... | python | def serialize_example(transformed_json_data, info_dict):
"""Makes a serialized tf.example.
Args:
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info_dict: output of feature_transforms.get_transfrormed_feature_info()
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googledatalab/pydatalab | solutionbox/ml_workbench/tensorflow/transform.py | preprocess | def preprocess(pipeline, args):
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2) replace image paths with base64 encoded image files
3) build a csv input string with images paths replaced with base64. This
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googledatalab/pydatalab | solutionbox/ml_workbench/tensorflow/transform.py | main | def main(argv=None):
"""Run Preprocessing as a Dataflow."""
args = parse_arguments(sys.argv if argv is None else argv)
temp_dir = os.path.join(args.output, 'tmp')
if args.cloud:
pipeline_name = 'DataflowRunner'
else:
pipeline_name = 'DirectRunner'
# Suppress TF warnings.
os.environ['TF_CPP_MI... | python | def main(argv=None):
"""Run Preprocessing as a Dataflow."""
args = parse_arguments(sys.argv if argv is None else argv)
temp_dir = os.path.join(args.output, 'tmp')
if args.cloud:
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googledatalab/pydatalab | solutionbox/ml_workbench/tensorflow/transform.py | TransformFeaturesDoFn.start_bundle | def start_bundle(self, element=None):
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g = tf.Graph()
session = tf.Session(graph=g)
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googledatalab/pydatalab | solutionbox/ml_workbench/tensorflow/transform.py | TransformFeaturesDoFn.process | def process(self, element):
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element: list of csv strings, representing one batch input to the TF graph.
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dict containing the transformed data. Results are un-batched. Sparse
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googledatalab/pydatalab | google/datalab/bigquery/_parser.py | Parser.parse_row | def parse_row(schema, data):
"""Parses a row from query results into an equivalent object.
Args:
schema: the array of fields defining the schema of the data.
data: the JSON row from a query result.
Returns:
The parsed row object.
"""
def parse_value(data_type, value):
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googledatalab/pydatalab | google/datalab/contrib/mlworkbench/_local_predict.py | _tf_load_model | def _tf_load_model(sess, model_dir):
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googledatalab/pydatalab | google/datalab/contrib/mlworkbench/_local_predict.py | _tf_predict | def _tf_predict(model_dir, input_csvlines):
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model_dir: directory that contains a saved model
input_csvlines: list of csv strings
Returns:
Dict in the form tensor_name:prediction_list. Note that the value is always
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googledatalab/pydatalab | google/datalab/contrib/mlworkbench/_local_predict.py | _download_images | def _download_images(data, img_cols):
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... | python | def _download_images(data, img_cols):
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googledatalab/pydatalab | google/datalab/contrib/mlworkbench/_local_predict.py | _get_predicton_csv_lines | def _get_predicton_csv_lines(data, headers, images):
"""Create CSV lines from list-of-dict data."""
if images:
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for img_col in images:
for d, im in zip(data, images[img_col]):
if im == '':
continue
im = im.copy()
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"""Create CSV lines from list-of-dict data."""
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for d, im in zip(data, images[img_col]):
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googledatalab/pydatalab | google/datalab/contrib/mlworkbench/_local_predict.py | _get_display_data_with_images | def _get_display_data_with_images(data, images):
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googledatalab/pydatalab | google/datalab/contrib/mlworkbench/_local_predict.py | get_model_schema_and_features | def get_model_schema_and_features(model_dir):
"""Get a local model's schema and features config.
Args:
model_dir: local or GCS path of a model.
Returns:
A tuple of schema (list) and features config (dict).
"""
schema_file = os.path.join(model_dir, 'assets.extra', 'schema.json')
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"""Get a local model's schema and features config.
Args:
model_dir: local or GCS path of a model.
Returns:
A tuple of schema (list) and features config (dict).
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googledatalab/pydatalab | google/datalab/contrib/mlworkbench/_local_predict.py | get_prediction_results | def get_prediction_results(model_dir_or_id, data, headers, img_cols=None,
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googledatalab/pydatalab | google/datalab/contrib/mlworkbench/_local_predict.py | get_probs_for_labels | def get_probs_for_labels(labels, prediction_results):
""" Given ML Workbench prediction results, get probs of each label for each instance.
The prediction results are like:
[
{'predicted': 'daisy', 'probability': 0.8, 'predicted_2': 'rose', 'probability_2': 0.1},
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""" Given ML Workbench prediction results, get probs of each label for each instance.
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googledatalab/pydatalab | google/datalab/contrib/mlworkbench/_local_predict.py | local_batch_predict | def local_batch_predict(model_dir, csv_file_pattern, output_dir, output_format, batch_size=100):
""" Batch Predict with a specified model.
It does batch prediction, saves results to output files and also creates an output
schema file. The output file names are input file names prepended by 'predict_results_'.
... | python | def local_batch_predict(model_dir, csv_file_pattern, output_dir, output_format, batch_size=100):
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googledatalab/pydatalab | google/datalab/ml/_job.py | Job._refresh_state | def _refresh_state(self):
""" Refresh the job info. """
self._info = self._api.projects().jobs().get(name=self._name).execute()
self._fatal_error = self._info.get('errorMessage', None)
state = str(self._info.get('state'))
self._is_complete = (state == 'SUCCEEDED' or state == 'FAILED') | python | def _refresh_state(self):
""" Refresh the job info. """
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googledatalab/pydatalab | google/datalab/ml/_job.py | Job.submit_training | def submit_training(job_request, job_id=None):
"""Submit a training job.
Args:
job_request: the arguments of the training job in a dict. For example,
{
'package_uris': 'gs://my-bucket/iris/trainer-0.1.tar.gz',
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'scale_tier': '... | python | def submit_training(job_request, job_id=None):
"""Submit a training job.
Args:
job_request: the arguments of the training job in a dict. For example,
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job_request: the arguments of the training job in a dict. For example,
{
'version_name': 'projects/my-project/models/my-model/versions/my-version',
'data_format': 'TEXT',
... | python | def submit_batch_prediction(job_request, job_id=None):
"""Submit a batch prediction job.
Args:
job_request: the arguments of the training job in a dict. For example,
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googledatalab/pydatalab | solutionbox/ml_workbench/xgboost/analyze.py | parse_arguments | def parse_arguments(argv):
"""Parse command line arguments.
Args:
argv: list of command line arguments, including program name.
Returns:
An argparse Namespace object.
Raises:
ValueError: for bad parameters
"""
parser = argparse.ArgumentParser(
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"""Parse command line arguments.
Args:
argv: list of command line arguments, including program name.
Returns:
An argparse Namespace object.
Raises:
ValueError: for bad parameters
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googledatalab/pydatalab | solutionbox/ml_workbench/xgboost/analyze.py | run_cloud_analysis | def run_cloud_analysis(output_dir, csv_file_pattern, bigquery_table, schema,
features):
"""Use BigQuery to analyze input date.
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Args:
output_dir: output folder
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googledatalab/pydatalab | solutionbox/image_classification/mltoolbox/image/classification/_inceptionlib.py | inception_v3_base | def inception_v3_base(inputs,
final_endpoint='Mixed_7c',
min_depth=16,
depth_multiplier=1.0,
scope=None):
"""Inception model from http://arxiv.org/abs/1512.00567.
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final_endpoint='Mixed_7c',
min_depth=16,
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googledatalab/pydatalab | solutionbox/image_classification/mltoolbox/image/classification/_inceptionlib.py | _reduced_kernel_size_for_small_input | def _reduced_kernel_size_for_small_input(input_tensor, kernel_size):
"""Define kernel size which is automatically reduced for small input.
If the shape of the input images is unknown at graph construction time this
function assumes that the input images are is large enough.
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input_tensor: input tenso... | python | def _reduced_kernel_size_for_small_input(input_tensor, kernel_size):
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googledatalab/pydatalab | solutionbox/image_classification/mltoolbox/image/classification/_inceptionlib.py | inception_v3_arg_scope | def inception_v3_arg_scope(weight_decay=0.00004,
stddev=0.1,
batch_norm_var_collection='moving_vars'):
"""Defines the default InceptionV3 arg scope.
Args:
weight_decay: The weight decay to use for regularizing the model.
stddev: The standard deviation o... | python | def inception_v3_arg_scope(weight_decay=0.00004,
stddev=0.1,
batch_norm_var_collection='moving_vars'):
"""Defines the default InceptionV3 arg scope.
Args:
weight_decay: The weight decay to use for regularizing the model.
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googledatalab/pydatalab | solutionbox/image_classification/mltoolbox/image/classification/_local.py | Local.preprocess | def preprocess(train_dataset, output_dir, eval_dataset, checkpoint):
"""Preprocess data locally."""
import apache_beam as beam
from google.datalab.utils import LambdaJob
from . import _preprocess
if checkpoint is None:
checkpoint = _util._DEFAULT_CHECKPOINT_GSURL
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"""Preprocess data locally."""
import apache_beam as beam
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from . import _preprocess
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googledatalab/pydatalab | solutionbox/image_classification/mltoolbox/image/classification/_local.py | Local.train | def train(input_dir, batch_size, max_steps, output_dir, checkpoint):
"""Train model locally."""
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if checkpoint is None:
checkpoint = _util._DEFAULT_CHECKPOINT_GSURL
labels = _util.get_labels(input_dir)
model = _model.Model(labels, 0.5, checkpoint)
... | python | def train(input_dir, batch_size, max_steps, output_dir, checkpoint):
"""Train model locally."""
from google.datalab.utils import LambdaJob
if checkpoint is None:
checkpoint = _util._DEFAULT_CHECKPOINT_GSURL
labels = _util.get_labels(input_dir)
model = _model.Model(labels, 0.5, checkpoint)
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googledatalab/pydatalab | solutionbox/image_classification/mltoolbox/image/classification/_local.py | Local.predict | def predict(model_dir, image_files, resize, show_image):
"""Predict using an model in a local or GCS directory."""
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images = _util.load_images(image_files, resize=resize)
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"""Predict using an model in a local or GCS directory."""
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googledatalab/pydatalab | solutionbox/image_classification/mltoolbox/image/classification/_local.py | Local.batch_predict | def batch_predict(dataset, model_dir, output_csv, output_bq_table):
"""Batch predict running locally."""
import apache_beam as beam
from google.datalab.utils import LambdaJob
from . import _predictor
if output_csv is None and output_bq_table is None:
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"""Batch predict running locally."""
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googledatalab/pydatalab | datalab/utils/_job.py | Job.result | def result(self):
""" Get the result for a job. This will block if the job is incomplete.
Returns:
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Raises:
An exception if the Job resulted in an exception.
"""
self.wait()
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raise self._fatal_error
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""" Get the result for a job. This will block if the job is incomplete.
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An exception if the Job resulted in an exception.
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googledatalab/pydatalab | datalab/utils/_job.py | Job._refresh_state | def _refresh_state(self):
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googledatalab/pydatalab | datalab/utils/_job.py | Job.wait | def wait(self, timeout=None):
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timeout: how long to wait before giving up (in seconds); default None which means no timeout.
Returns:
The Job
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""" Wait for the job to complete, or a timeout to happen.
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timeout: how long to wait before giving up (in seconds); default None which means no timeout.
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googledatalab/pydatalab | datalab/utils/_job.py | Job.state | def state(self):
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""" Describe the state of a Job.
Returns: A string describing the job's state.
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googledatalab/pydatalab | datalab/utils/_job.py | Job.wait_any | def wait_any(jobs, timeout=None):
""" Return when at least one of the specified jobs has completed or timeout expires.
Args:
jobs: a Job or list of Jobs to wait on.
timeout: a timeout in seconds to wait for. None (the default) means no timeout.
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A list of the jobs that have now co... | python | def wait_any(jobs, timeout=None):
""" Return when at least one of the specified jobs has completed or timeout expires.
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googledatalab/pydatalab | datalab/utils/_job.py | Job.wait_all | def wait_all(jobs, timeout=None):
""" Return when at all of the specified jobs have completed or timeout expires.
Args:
jobs: a Job or list of Jobs to wait on.
timeout: a timeout in seconds to wait for. None (the default) means no timeout.
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""" Return when at all of the specified jobs have completed or timeout expires.
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googledatalab/pydatalab | solutionbox/image_classification/mltoolbox/image/classification/_trainer.py | Evaluator.evaluate | def evaluate(self, num_eval_batches=None):
"""Run one round of evaluation, return loss and accuracy."""
num_eval_batches = num_eval_batches or self.num_eval_batches
with tf.Graph().as_default() as graph:
self.tensors = self.model.build_eval_graph(self.eval_data_paths,
... | python | def evaluate(self, num_eval_batches=None):
"""Run one round of evaluation, return loss and accuracy."""
num_eval_batches = num_eval_batches or self.num_eval_batches
with tf.Graph().as_default() as graph:
self.tensors = self.model.build_eval_graph(self.eval_data_paths,
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googledatalab/pydatalab | solutionbox/image_classification/mltoolbox/image/classification/_trainer.py | Trainer.run_training | def run_training(self):
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googledatalab/pydatalab | solutionbox/image_classification/mltoolbox/image/classification/_trainer.py | Trainer.log | def log(self, session):
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googledatalab/pydatalab | solutionbox/image_classification/mltoolbox/image/classification/_trainer.py | Trainer.eval | def eval(self, session):
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"""Runs evaluation loop."""
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googledatalab/pydatalab | google/datalab/ml/_feature_slice_view.py | FeatureSliceView._get_lantern_format | def _get_lantern_format(self, df):
""" Feature slice view browser expects data in the format of:
{"metricValues": {"count": 12, "accuracy": 1.0}, "feature": "species:Iris-setosa"}
{"metricValues": {"count": 11, "accuracy": 0.72}, "feature": "species:Iris-versicolor"}
...
This f... | python | def _get_lantern_format(self, df):
""" Feature slice view browser expects data in the format of:
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{"metricValues": {"count": 12, "accuracy": 1.0}, "feature": "species:Iris-setosa"}
{"metricValues": {"count": 11, "accuracy": 0.72}, "feature": "species:Iris-versicolor"}
...
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googledatalab/pydatalab | google/datalab/ml/_feature_slice_view.py | FeatureSliceView.plot | def plot(self, data):
""" Plots a featire slice view on given data.
Args:
data: Can be one of:
A string of sql query.
A sql query module defined by "%%sql --module module_name".
A pandas DataFrame.
Regardless of data type, it must include the following columns:
... | python | def plot(self, data):
""" Plots a featire slice view on given data.
Args:
data: Can be one of:
A string of sql query.
A sql query module defined by "%%sql --module module_name".
A pandas DataFrame.
Regardless of data type, it must include the following columns:
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googledatalab/pydatalab | solutionbox/structured_data/mltoolbox/_structured_data/preprocess/cloud_preprocess.py | parse_arguments | def parse_arguments(argv):
"""Parse command line arguments.
Args:
argv: list of command line arguments, includeing programe name.
Returns:
An argparse Namespace object.
Raises:
ValueError: for bad parameters
"""
parser = argparse.ArgumentParser(
description='Runs Preprocessing on struct... | python | def parse_arguments(argv):
"""Parse command line arguments.
Args:
argv: list of command line arguments, includeing programe name.
Returns:
An argparse Namespace object.
Raises:
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googledatalab/pydatalab | solutionbox/structured_data/mltoolbox/_structured_data/preprocess/cloud_preprocess.py | parse_table_name | def parse_table_name(bigquery_table):
"""Giving a string a:b.c, returns b.c.
Args:
bigquery_table: full table name project_id:dataset:table
Returns:
dataset:table
Raises:
ValueError: if a, b, or c contain the character ':'.
"""
id_name = bigquery_table.split(':')
if len(id_name) != 2:
... | python | def parse_table_name(bigquery_table):
"""Giving a string a:b.c, returns b.c.
Args:
bigquery_table: full table name project_id:dataset:table
Returns:
dataset:table
Raises:
ValueError: if a, b, or c contain the character ':'.
"""
id_name = bigquery_table.split(':')
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googledatalab/pydatalab | solutionbox/structured_data/mltoolbox/_structured_data/preprocess/cloud_preprocess.py | run_numerical_analysis | def run_numerical_analysis(table, schema_list, args):
"""Find min/max values for the numerical columns and writes a json file.
Args:
table: Reference to FederatedTable (if bigquery_table is false) or a
regular Table (otherwise)
schema_list: Bigquery schema json object
args: the command line arg... | python | def run_numerical_analysis(table, schema_list, args):
"""Find min/max values for the numerical columns and writes a json file.
Args:
table: Reference to FederatedTable (if bigquery_table is false) or a
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googledatalab/pydatalab | solutionbox/structured_data/mltoolbox/_structured_data/preprocess/cloud_preprocess.py | run_categorical_analysis | def run_categorical_analysis(table, schema_list, args):
"""Find vocab values for the categorical columns and writes a csv file.
The vocab files are in the from
label1
label2
label3
...
Args:
table: Reference to FederatedTable (if bigquery_table is false) or a
regular Table (otherwise)
sc... | python | def run_categorical_analysis(table, schema_list, args):
"""Find vocab values for the categorical columns and writes a csv file.
The vocab files are in the from
label1
label2
label3
...
Args:
table: Reference to FederatedTable (if bigquery_table is false) or a
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googledatalab/pydatalab | solutionbox/structured_data/mltoolbox/_structured_data/preprocess/cloud_preprocess.py | run_analysis | def run_analysis(args):
"""Builds an analysis file for training.
Uses BiqQuery tables to do the analysis.
Args:
args: command line args
Raises:
ValueError if schema contains unknown types.
"""
import google.datalab.bigquery as bq
if args.bigquery_table:
table = bq.Table(args.bigquery_table)... | python | def run_analysis(args):
"""Builds an analysis file for training.
Uses BiqQuery tables to do the analysis.
Args:
args: command line args
Raises:
ValueError if schema contains unknown types.
"""
import google.datalab.bigquery as bq
if args.bigquery_table:
table = bq.Table(args.bigquery_table)... | [
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googledatalab/pydatalab | google/datalab/_context.py | Context.set_project_id | def set_project_id(self, project_id):
""" Set the project_id for the context. """
self._project_id = project_id
if self == Context._global_context:
du.save_project_id(self._project_id) | python | def set_project_id(self, project_id):
""" Set the project_id for the context. """
self._project_id = project_id
if self == Context._global_context:
du.save_project_id(self._project_id) | [
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googledatalab/pydatalab | google/datalab/_context.py | Context.default | def default():
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googledatalab/pydatalab | google/datalab/ml/_confusion_matrix.py | ConfusionMatrix.from_csv | def from_csv(input_csv, headers=None, schema_file=None):
"""Create a ConfusionMatrix from a csv file.
Args:
input_csv: Path to a Csv file (with no header). Can be local or GCS path.
headers: Csv headers. If present, it must include 'target' and 'predicted'.
schema_file: Path to a JSON file co... | python | def from_csv(input_csv, headers=None, schema_file=None):
"""Create a ConfusionMatrix from a csv file.
Args:
input_csv: Path to a Csv file (with no header). Can be local or GCS path.
headers: Csv headers. If present, it must include 'target' and 'predicted'.
schema_file: Path to a JSON file co... | [
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