repository_name stringlengths 7 55 | func_path_in_repository stringlengths 4 223 | func_name stringlengths 1 134 | whole_func_string stringlengths 75 104k | language stringclasses 1
value | func_code_string stringlengths 75 104k | func_code_tokens listlengths 19 28.4k | func_documentation_string stringlengths 1 46.9k | func_documentation_tokens listlengths 1 1.97k | split_name stringclasses 1
value | func_code_url stringlengths 87 315 |
|---|---|---|---|---|---|---|---|---|---|---|
googledatalab/pydatalab | google/datalab/ml/_confusion_matrix.py | ConfusionMatrix.from_bigquery | def from_bigquery(sql):
"""Create a ConfusionMatrix from a BigQuery table or query.
Args:
sql: Can be one of:
A SQL query string.
A Bigquery table string.
A Query object defined with '%%bq query --name [query_name]'.
The query results or table must include "target", "p... | python | def from_bigquery(sql):
"""Create a ConfusionMatrix from a BigQuery table or query.
Args:
sql: Can be one of:
A SQL query string.
A Bigquery table string.
A Query object defined with '%%bq query --name [query_name]'.
The query results or table must include "target", "p... | [
"def",
"from_bigquery",
"(",
"sql",
")",
":",
"if",
"isinstance",
"(",
"sql",
",",
"bq",
".",
"Query",
")",
":",
"sql",
"=",
"sql",
".",
"_expanded_sql",
"(",
")",
"parts",
"=",
"sql",
".",
"split",
"(",
"'.'",
")",
"if",
"len",
"(",
"parts",
")"... | Create a ConfusionMatrix from a BigQuery table or query.
Args:
sql: Can be one of:
A SQL query string.
A Bigquery table string.
A Query object defined with '%%bq query --name [query_name]'.
The query results or table must include "target", "predicted" columns.
Returns:... | [
"Create",
"a",
"ConfusionMatrix",
"from",
"a",
"BigQuery",
"table",
"or",
"query",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/google/datalab/ml/_confusion_matrix.py#L80-L113 |
googledatalab/pydatalab | google/datalab/ml/_confusion_matrix.py | ConfusionMatrix.to_dataframe | def to_dataframe(self):
"""Convert the confusion matrix to a dataframe.
Returns:
A DataFrame with "target", "predicted", "count" columns.
"""
data = []
for target_index, target_row in enumerate(self._cm):
for predicted_index, count in enumerate(target_row):
data.append((self._l... | python | def to_dataframe(self):
"""Convert the confusion matrix to a dataframe.
Returns:
A DataFrame with "target", "predicted", "count" columns.
"""
data = []
for target_index, target_row in enumerate(self._cm):
for predicted_index, count in enumerate(target_row):
data.append((self._l... | [
"def",
"to_dataframe",
"(",
"self",
")",
":",
"data",
"=",
"[",
"]",
"for",
"target_index",
",",
"target_row",
"in",
"enumerate",
"(",
"self",
".",
"_cm",
")",
":",
"for",
"predicted_index",
",",
"count",
"in",
"enumerate",
"(",
"target_row",
")",
":",
... | Convert the confusion matrix to a dataframe.
Returns:
A DataFrame with "target", "predicted", "count" columns. | [
"Convert",
"the",
"confusion",
"matrix",
"to",
"a",
"dataframe",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/google/datalab/ml/_confusion_matrix.py#L115-L127 |
googledatalab/pydatalab | google/datalab/ml/_confusion_matrix.py | ConfusionMatrix.plot | def plot(self, figsize=None, rotation=45):
"""Plot the confusion matrix.
Args:
figsize: tuple (x, y) of ints. Sets the size of the figure
rotation: the rotation angle of the labels on the x-axis.
"""
fig, ax = plt.subplots(figsize=figsize)
plt.imshow(self._cm, interpolation='nearest',... | python | def plot(self, figsize=None, rotation=45):
"""Plot the confusion matrix.
Args:
figsize: tuple (x, y) of ints. Sets the size of the figure
rotation: the rotation angle of the labels on the x-axis.
"""
fig, ax = plt.subplots(figsize=figsize)
plt.imshow(self._cm, interpolation='nearest',... | [
"def",
"plot",
"(",
"self",
",",
"figsize",
"=",
"None",
",",
"rotation",
"=",
"45",
")",
":",
"fig",
",",
"ax",
"=",
"plt",
".",
"subplots",
"(",
"figsize",
"=",
"figsize",
")",
"plt",
".",
"imshow",
"(",
"self",
".",
"_cm",
",",
"interpolation",
... | Plot the confusion matrix.
Args:
figsize: tuple (x, y) of ints. Sets the size of the figure
rotation: the rotation angle of the labels on the x-axis. | [
"Plot",
"the",
"confusion",
"matrix",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/google/datalab/ml/_confusion_matrix.py#L129-L159 |
googledatalab/pydatalab | google/datalab/contrib/pipeline/composer/_api.py | Api.get_environment_details | def get_environment_details(zone, environment):
""" Issues a request to Composer to get the environment details.
Args:
zone: GCP zone of the composer environment
environment: name of the Composer environment
Returns:
A parsed result object.
Raises:
Exception if there is an error... | python | def get_environment_details(zone, environment):
""" Issues a request to Composer to get the environment details.
Args:
zone: GCP zone of the composer environment
environment: name of the Composer environment
Returns:
A parsed result object.
Raises:
Exception if there is an error... | [
"def",
"get_environment_details",
"(",
"zone",
",",
"environment",
")",
":",
"default_context",
"=",
"google",
".",
"datalab",
".",
"Context",
".",
"default",
"(",
")",
"url",
"=",
"(",
"Api",
".",
"_ENDPOINT",
"+",
"(",
"Api",
".",
"_ENVIRONMENTS_PATH_FORMA... | Issues a request to Composer to get the environment details.
Args:
zone: GCP zone of the composer environment
environment: name of the Composer environment
Returns:
A parsed result object.
Raises:
Exception if there is an error performing the operation. | [
"Issues",
"a",
"request",
"to",
"Composer",
"to",
"get",
"the",
"environment",
"details",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/google/datalab/contrib/pipeline/composer/_api.py#L24-L39 |
googledatalab/pydatalab | google/datalab/storage/_api.py | Api.buckets_delete | def buckets_delete(self, bucket):
"""Issues a request to delete a bucket.
Args:
bucket: the name of the bucket.
Raises:
Exception if there is an error performing the operation.
"""
url = Api._ENDPOINT + (Api._BUCKET_PATH % bucket)
google.datalab.utils.Http.request(url, method='DELET... | python | def buckets_delete(self, bucket):
"""Issues a request to delete a bucket.
Args:
bucket: the name of the bucket.
Raises:
Exception if there is an error performing the operation.
"""
url = Api._ENDPOINT + (Api._BUCKET_PATH % bucket)
google.datalab.utils.Http.request(url, method='DELET... | [
"def",
"buckets_delete",
"(",
"self",
",",
"bucket",
")",
":",
"url",
"=",
"Api",
".",
"_ENDPOINT",
"+",
"(",
"Api",
".",
"_BUCKET_PATH",
"%",
"bucket",
")",
"google",
".",
"datalab",
".",
"utils",
".",
"Http",
".",
"request",
"(",
"url",
",",
"metho... | Issues a request to delete a bucket.
Args:
bucket: the name of the bucket.
Raises:
Exception if there is an error performing the operation. | [
"Issues",
"a",
"request",
"to",
"delete",
"a",
"bucket",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/google/datalab/storage/_api.py#L72-L82 |
googledatalab/pydatalab | google/datalab/storage/_api.py | Api.buckets_get | def buckets_get(self, bucket, projection='noAcl'):
"""Issues a request to retrieve information about a bucket.
Args:
bucket: the name of the bucket.
projection: the projection of the bucket information to retrieve.
Returns:
A parsed bucket information dictionary.
Raises:
Excepti... | python | def buckets_get(self, bucket, projection='noAcl'):
"""Issues a request to retrieve information about a bucket.
Args:
bucket: the name of the bucket.
projection: the projection of the bucket information to retrieve.
Returns:
A parsed bucket information dictionary.
Raises:
Excepti... | [
"def",
"buckets_get",
"(",
"self",
",",
"bucket",
",",
"projection",
"=",
"'noAcl'",
")",
":",
"args",
"=",
"{",
"'projection'",
":",
"projection",
"}",
"url",
"=",
"Api",
".",
"_ENDPOINT",
"+",
"(",
"Api",
".",
"_BUCKET_PATH",
"%",
"bucket",
")",
"ret... | Issues a request to retrieve information about a bucket.
Args:
bucket: the name of the bucket.
projection: the projection of the bucket information to retrieve.
Returns:
A parsed bucket information dictionary.
Raises:
Exception if there is an error performing the operation. | [
"Issues",
"a",
"request",
"to",
"retrieve",
"information",
"about",
"a",
"bucket",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/google/datalab/storage/_api.py#L84-L97 |
googledatalab/pydatalab | google/datalab/storage/_api.py | Api.buckets_list | def buckets_list(self, projection='noAcl', max_results=0, page_token=None, project_id=None):
"""Issues a request to retrieve the list of buckets.
Args:
projection: the projection of the bucket information to retrieve.
max_results: an optional maximum number of objects to retrieve.
page_token:... | python | def buckets_list(self, projection='noAcl', max_results=0, page_token=None, project_id=None):
"""Issues a request to retrieve the list of buckets.
Args:
projection: the projection of the bucket information to retrieve.
max_results: an optional maximum number of objects to retrieve.
page_token:... | [
"def",
"buckets_list",
"(",
"self",
",",
"projection",
"=",
"'noAcl'",
",",
"max_results",
"=",
"0",
",",
"page_token",
"=",
"None",
",",
"project_id",
"=",
"None",
")",
":",
"if",
"max_results",
"==",
"0",
":",
"max_results",
"=",
"Api",
".",
"_MAX_RESU... | Issues a request to retrieve the list of buckets.
Args:
projection: the projection of the bucket information to retrieve.
max_results: an optional maximum number of objects to retrieve.
page_token: an optional token to continue the retrieval.
project_id: the project whose buckets should be ... | [
"Issues",
"a",
"request",
"to",
"retrieve",
"the",
"list",
"of",
"buckets",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/google/datalab/storage/_api.py#L99-L122 |
googledatalab/pydatalab | google/datalab/storage/_api.py | Api.object_download | def object_download(self, bucket, key, start_offset=0, byte_count=None):
"""Reads the contents of an object as text.
Args:
bucket: the name of the bucket containing the object.
key: the key of the object to be read.
start_offset: the start offset of bytes to read.
byte_count: the number... | python | def object_download(self, bucket, key, start_offset=0, byte_count=None):
"""Reads the contents of an object as text.
Args:
bucket: the name of the bucket containing the object.
key: the key of the object to be read.
start_offset: the start offset of bytes to read.
byte_count: the number... | [
"def",
"object_download",
"(",
"self",
",",
"bucket",
",",
"key",
",",
"start_offset",
"=",
"0",
",",
"byte_count",
"=",
"None",
")",
":",
"args",
"=",
"{",
"'alt'",
":",
"'media'",
"}",
"headers",
"=",
"{",
"}",
"if",
"start_offset",
">",
"0",
"or",... | Reads the contents of an object as text.
Args:
bucket: the name of the bucket containing the object.
key: the key of the object to be read.
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 con... | [
"Reads",
"the",
"contents",
"of",
"an",
"object",
"as",
"text",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/google/datalab/storage/_api.py#L124-L146 |
googledatalab/pydatalab | google/datalab/storage/_api.py | Api.object_upload | def object_upload(self, bucket, key, content, content_type):
"""Writes text content to the object.
Args:
bucket: the name of the bucket containing the object.
key: the key of the object to be written.
content: the text content to be written.
content_type: the type of text content.
R... | python | def object_upload(self, bucket, key, content, content_type):
"""Writes text content to the object.
Args:
bucket: the name of the bucket containing the object.
key: the key of the object to be written.
content: the text content to be written.
content_type: the type of text content.
R... | [
"def",
"object_upload",
"(",
"self",
",",
"bucket",
",",
"key",
",",
"content",
",",
"content_type",
")",
":",
"args",
"=",
"{",
"'uploadType'",
":",
"'media'",
",",
"'name'",
":",
"key",
"}",
"headers",
"=",
"{",
"'Content-Type'",
":",
"content_type",
"... | Writes text content to the object.
Args:
bucket: the name of the bucket containing the object.
key: the key of the object to be written.
content: the text content to be written.
content_type: the type of text content.
Raises:
Exception if the object could not be written to. | [
"Writes",
"text",
"content",
"to",
"the",
"object",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/google/datalab/storage/_api.py#L148-L164 |
googledatalab/pydatalab | google/datalab/storage/_api.py | Api.objects_list | def objects_list(self, bucket, prefix=None, delimiter=None, projection='noAcl', versions=False,
max_results=0, page_token=None):
"""Issues a request to retrieve information about an object.
Args:
bucket: the name of the bucket.
prefix: an optional key prefix.
delimiter: an ... | python | def objects_list(self, bucket, prefix=None, delimiter=None, projection='noAcl', versions=False,
max_results=0, page_token=None):
"""Issues a request to retrieve information about an object.
Args:
bucket: the name of the bucket.
prefix: an optional key prefix.
delimiter: an ... | [
"def",
"objects_list",
"(",
"self",
",",
"bucket",
",",
"prefix",
"=",
"None",
",",
"delimiter",
"=",
"None",
",",
"projection",
"=",
"'noAcl'",
",",
"versions",
"=",
"False",
",",
"max_results",
"=",
"0",
",",
"page_token",
"=",
"None",
")",
":",
"if"... | Issues a request to retrieve information about an object.
Args:
bucket: the name of the bucket.
prefix: an optional key prefix.
delimiter: an optional key delimiter.
projection: the projection of the objects to retrieve.
versions: whether to list each version of a file as a distinct o... | [
"Issues",
"a",
"request",
"to",
"retrieve",
"information",
"about",
"an",
"object",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/google/datalab/storage/_api.py#L215-L248 |
googledatalab/pydatalab | google/datalab/storage/_api.py | Api.objects_patch | def objects_patch(self, bucket, key, info):
"""Updates the metadata associated with an object.
Args:
bucket: the name of the bucket containing the object.
key: the key of the object being updated.
info: the metadata to update.
Returns:
A parsed object information dictionary.
Rai... | python | def objects_patch(self, bucket, key, info):
"""Updates the metadata associated with an object.
Args:
bucket: the name of the bucket containing the object.
key: the key of the object being updated.
info: the metadata to update.
Returns:
A parsed object information dictionary.
Rai... | [
"def",
"objects_patch",
"(",
"self",
",",
"bucket",
",",
"key",
",",
"info",
")",
":",
"url",
"=",
"Api",
".",
"_ENDPOINT",
"+",
"(",
"Api",
".",
"_OBJECT_PATH",
"%",
"(",
"bucket",
",",
"Api",
".",
"_escape_key",
"(",
"key",
")",
")",
")",
"return... | Updates the metadata associated with an object.
Args:
bucket: the name of the bucket containing the object.
key: the key of the object being updated.
info: the metadata to update.
Returns:
A parsed object information dictionary.
Raises:
Exception if there is an error performin... | [
"Updates",
"the",
"metadata",
"associated",
"with",
"an",
"object",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/google/datalab/storage/_api.py#L250-L264 |
googledatalab/pydatalab | google/datalab/storage/_api.py | Api.verify_permitted_to_read | def verify_permitted_to_read(gs_path):
"""Check if the user has permissions to read from the given path.
Args:
gs_path: the GCS path to check if user is permitted to read.
Raises:
Exception if user has no permissions to read.
"""
# TODO(qimingj): Storage APIs need to be modified to allo... | python | def verify_permitted_to_read(gs_path):
"""Check if the user has permissions to read from the given path.
Args:
gs_path: the GCS path to check if user is permitted to read.
Raises:
Exception if user has no permissions to read.
"""
# TODO(qimingj): Storage APIs need to be modified to allo... | [
"def",
"verify_permitted_to_read",
"(",
"gs_path",
")",
":",
"# TODO(qimingj): Storage APIs need to be modified to allow absence of project",
"# or credential on Objects. When that happens we can move the function",
"# to Objects class.",
"from",
".",
"import",
... | Check if the user has permissions to read from the given path.
Args:
gs_path: the GCS path to check if user is permitted to read.
Raises:
Exception if user has no permissions to read. | [
"Check",
"if",
"the",
"user",
"has",
"permissions",
"to",
"read",
"from",
"the",
"given",
"path",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/google/datalab/storage/_api.py#L272-L301 |
googledatalab/pydatalab | solutionbox/image_classification/mltoolbox/image/classification/_api.py | preprocess_async | def preprocess_async(train_dataset, output_dir, eval_dataset=None, checkpoint=None, cloud=None):
"""Preprocess data. Produce output that can be used by training efficiently.
Args:
train_dataset: training data source to preprocess. Can be CsvDataset or BigQueryDataSet.
If eval_dataset is None, the pipel... | python | def preprocess_async(train_dataset, output_dir, eval_dataset=None, checkpoint=None, cloud=None):
"""Preprocess data. Produce output that can be used by training efficiently.
Args:
train_dataset: training data source to preprocess. Can be CsvDataset or BigQueryDataSet.
If eval_dataset is None, the pipel... | [
"def",
"preprocess_async",
"(",
"train_dataset",
",",
"output_dir",
",",
"eval_dataset",
"=",
"None",
",",
"checkpoint",
"=",
"None",
",",
"cloud",
"=",
"None",
")",
":",
"with",
"warnings",
".",
"catch_warnings",
"(",
")",
":",
"warnings",
".",
"simplefilte... | Preprocess data. Produce output that can be used by training efficiently.
Args:
train_dataset: training data source to preprocess. Can be CsvDataset or BigQueryDataSet.
If eval_dataset is None, the pipeline will randomly split train_dataset into
train/eval set with 7:3 ratio.
output_dir: The ... | [
"Preprocess",
"data",
".",
"Produce",
"output",
"that",
"can",
"be",
"used",
"by",
"training",
"efficiently",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/solutionbox/image_classification/mltoolbox/image/classification/_api.py#L25-L52 |
googledatalab/pydatalab | solutionbox/image_classification/mltoolbox/image/classification/_api.py | preprocess | def preprocess(train_dataset, output_dir, eval_dataset=None, checkpoint=None, cloud=None):
"""Blocking version of preprocess_async(). The only difference is that it blocks the caller
until the job finishes, and it does not have a return value.
"""
with warnings.catch_warnings():
warnings.simplefilter("ig... | python | def preprocess(train_dataset, output_dir, eval_dataset=None, checkpoint=None, cloud=None):
"""Blocking version of preprocess_async(). The only difference is that it blocks the caller
until the job finishes, and it does not have a return value.
"""
with warnings.catch_warnings():
warnings.simplefilter("ig... | [
"def",
"preprocess",
"(",
"train_dataset",
",",
"output_dir",
",",
"eval_dataset",
"=",
"None",
",",
"checkpoint",
"=",
"None",
",",
"cloud",
"=",
"None",
")",
":",
"with",
"warnings",
".",
"catch_warnings",
"(",
")",
":",
"warnings",
".",
"simplefilter",
... | Blocking version of preprocess_async(). The only difference is that it blocks the caller
until the job finishes, and it does not have a return value. | [
"Blocking",
"version",
"of",
"preprocess_async",
"()",
".",
"The",
"only",
"difference",
"is",
"that",
"it",
"blocks",
"the",
"caller",
"until",
"the",
"job",
"finishes",
"and",
"it",
"does",
"not",
"have",
"a",
"return",
"value",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/solutionbox/image_classification/mltoolbox/image/classification/_api.py#L55-L63 |
googledatalab/pydatalab | solutionbox/image_classification/mltoolbox/image/classification/_api.py | train_async | def train_async(input_dir, batch_size, max_steps, output_dir, checkpoint=None, cloud=None):
"""Train model. The output can be used for batch prediction or online deployment.
Args:
input_dir: A directory path containing preprocessed results. Can be local or GCS path.
batch_size: size of batch used for train... | python | def train_async(input_dir, batch_size, max_steps, output_dir, checkpoint=None, cloud=None):
"""Train model. The output can be used for batch prediction or online deployment.
Args:
input_dir: A directory path containing preprocessed results. Can be local or GCS path.
batch_size: size of batch used for train... | [
"def",
"train_async",
"(",
"input_dir",
",",
"batch_size",
",",
"max_steps",
",",
"output_dir",
",",
"checkpoint",
"=",
"None",
",",
"cloud",
"=",
"None",
")",
":",
"with",
"warnings",
".",
"catch_warnings",
"(",
")",
":",
"warnings",
".",
"simplefilter",
... | Train model. The output can be used for batch prediction or online deployment.
Args:
input_dir: A directory path containing preprocessed results. Can be local or GCS path.
batch_size: size of batch used for training.
max_steps: number of steps to train.
output_dir: The output directory to use. Can be... | [
"Train",
"model",
".",
"The",
"output",
"can",
"be",
"used",
"for",
"batch",
"prediction",
"or",
"online",
"deployment",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/solutionbox/image_classification/mltoolbox/image/classification/_api.py#L66-L86 |
googledatalab/pydatalab | solutionbox/image_classification/mltoolbox/image/classification/_api.py | train | def train(input_dir, batch_size, max_steps, output_dir, checkpoint=None, cloud=None):
"""Blocking version of train_async(). The only difference is that it blocks the caller
until the job finishes, and it does not have a return value.
"""
with warnings.catch_warnings():
warnings.simplefilter("ignore")
... | python | def train(input_dir, batch_size, max_steps, output_dir, checkpoint=None, cloud=None):
"""Blocking version of train_async(). The only difference is that it blocks the caller
until the job finishes, and it does not have a return value.
"""
with warnings.catch_warnings():
warnings.simplefilter("ignore")
... | [
"def",
"train",
"(",
"input_dir",
",",
"batch_size",
",",
"max_steps",
",",
"output_dir",
",",
"checkpoint",
"=",
"None",
",",
"cloud",
"=",
"None",
")",
":",
"with",
"warnings",
".",
"catch_warnings",
"(",
")",
":",
"warnings",
".",
"simplefilter",
"(",
... | Blocking version of train_async(). The only difference is that it blocks the caller
until the job finishes, and it does not have a return value. | [
"Blocking",
"version",
"of",
"train_async",
"()",
".",
"The",
"only",
"difference",
"is",
"that",
"it",
"blocks",
"the",
"caller",
"until",
"the",
"job",
"finishes",
"and",
"it",
"does",
"not",
"have",
"a",
"return",
"value",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/solutionbox/image_classification/mltoolbox/image/classification/_api.py#L89-L97 |
googledatalab/pydatalab | solutionbox/image_classification/mltoolbox/image/classification/_api.py | predict | def predict(model, image_files, resize=False, show_image=True, cloud=None):
"""Predict using an model in a local or GCS directory (offline), or a deployed model (online).
Args:
model: if cloud is None, a local or GCS directory of a trained model. Otherwise, it specifies
a deployed model identified by m... | python | def predict(model, image_files, resize=False, show_image=True, cloud=None):
"""Predict using an model in a local or GCS directory (offline), or a deployed model (online).
Args:
model: if cloud is None, a local or GCS directory of a trained model. Otherwise, it specifies
a deployed model identified by m... | [
"def",
"predict",
"(",
"model",
",",
"image_files",
",",
"resize",
"=",
"False",
",",
"show_image",
"=",
"True",
",",
"cloud",
"=",
"None",
")",
":",
"print",
"(",
"'Predicting...'",
")",
"with",
"warnings",
".",
"catch_warnings",
"(",
")",
":",
"warning... | Predict using an model in a local or GCS directory (offline), or a deployed model (online).
Args:
model: if cloud is None, a local or GCS directory of a trained model. Otherwise, it specifies
a deployed model identified by model.version, such as "imagemodel.v1".
image_files: The paths to the image fi... | [
"Predict",
"using",
"an",
"model",
"in",
"a",
"local",
"or",
"GCS",
"directory",
"(",
"offline",
")",
"or",
"a",
"deployed",
"model",
"(",
"online",
")",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/solutionbox/image_classification/mltoolbox/image/classification/_api.py#L100-L123 |
googledatalab/pydatalab | solutionbox/image_classification/mltoolbox/image/classification/_api.py | batch_predict_async | def batch_predict_async(dataset, model_dir, output_csv=None, output_bq_table=None, cloud=None):
"""Batch prediction with an offline model.
Args:
dataset: CsvDataSet or BigQueryDataSet for batch prediction input. Can contain either
one column 'image_url', or two columns with another being 'label'.
m... | python | def batch_predict_async(dataset, model_dir, output_csv=None, output_bq_table=None, cloud=None):
"""Batch prediction with an offline model.
Args:
dataset: CsvDataSet or BigQueryDataSet for batch prediction input. Can contain either
one column 'image_url', or two columns with another being 'label'.
m... | [
"def",
"batch_predict_async",
"(",
"dataset",
",",
"model_dir",
",",
"output_csv",
"=",
"None",
",",
"output_bq_table",
"=",
"None",
",",
"cloud",
"=",
"None",
")",
":",
"with",
"warnings",
".",
"catch_warnings",
"(",
")",
":",
"warnings",
".",
"simplefilter... | Batch prediction with an offline model.
Args:
dataset: CsvDataSet or BigQueryDataSet for batch prediction input. Can contain either
one column 'image_url', or two columns with another being 'label'.
model_dir: The directory of a trained inception model. Can be local or GCS paths.
output_csv: The ... | [
"Batch",
"prediction",
"with",
"an",
"offline",
"model",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/solutionbox/image_classification/mltoolbox/image/classification/_api.py#L126-L156 |
googledatalab/pydatalab | solutionbox/image_classification/mltoolbox/image/classification/_api.py | batch_predict | def batch_predict(dataset, model_dir, output_csv=None, output_bq_table=None, cloud=None):
"""Blocking version of batch_predict_async(). The only difference is that it blocks the caller
until the job finishes, and it does not have a return value.
"""
with warnings.catch_warnings():
warnings.simplefilter("... | python | def batch_predict(dataset, model_dir, output_csv=None, output_bq_table=None, cloud=None):
"""Blocking version of batch_predict_async(). The only difference is that it blocks the caller
until the job finishes, and it does not have a return value.
"""
with warnings.catch_warnings():
warnings.simplefilter("... | [
"def",
"batch_predict",
"(",
"dataset",
",",
"model_dir",
",",
"output_csv",
"=",
"None",
",",
"output_bq_table",
"=",
"None",
",",
"cloud",
"=",
"None",
")",
":",
"with",
"warnings",
".",
"catch_warnings",
"(",
")",
":",
"warnings",
".",
"simplefilter",
"... | Blocking version of batch_predict_async(). The only difference is that it blocks the caller
until the job finishes, and it does not have a return value. | [
"Blocking",
"version",
"of",
"batch_predict_async",
"()",
".",
"The",
"only",
"difference",
"is",
"that",
"it",
"blocks",
"the",
"caller",
"until",
"the",
"job",
"finishes",
"and",
"it",
"does",
"not",
"have",
"a",
"return",
"value",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/solutionbox/image_classification/mltoolbox/image/classification/_api.py#L159-L167 |
googledatalab/pydatalab | google/datalab/bigquery/_job.py | Job._refresh_state | def _refresh_state(self):
""" Get the state of a job. If the job is complete this does nothing;
otherwise it gets a refreshed copy of the job resource.
"""
# TODO(gram): should we put a choke on refreshes? E.g. if the last call was less than
# a second ago should we return the cached value?
... | python | def _refresh_state(self):
""" Get the state of a job. If the job is complete this does nothing;
otherwise it gets a refreshed copy of the job resource.
"""
# TODO(gram): should we put a choke on refreshes? E.g. if the last call was less than
# a second ago should we return the cached value?
... | [
"def",
"_refresh_state",
"(",
"self",
")",
":",
"# TODO(gram): should we put a choke on refreshes? E.g. if the last call was less than",
"# a second ago should we return the cached value?",
"if",
"self",
".",
"_is_complete",
":",
"return",
"try",
":",
"response",
"=",
"self",
"... | Get the state of a job. If the job is complete this does nothing;
otherwise it gets a refreshed copy of the job resource. | [
"Get",
"the",
"state",
"of",
"a",
"job",
".",
"If",
"the",
"job",
"is",
"complete",
"this",
"does",
"nothing",
";",
"otherwise",
"it",
"gets",
"a",
"refreshed",
"copy",
"of",
"the",
"job",
"resource",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/google/datalab/bigquery/_job.py#L43-L70 |
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=(
'Execute various Monitorin... | [
"def",
"monitoring",
"(",
"line",
",",
"cell",
"=",
"None",
")",
":",
"parser",
"=",
"datalab",
".",
"utils",
".",
"commands",
".",
"CommandParser",
"(",
"prog",
"=",
"'monitoring'",
",",
"description",
"=",
"(",
"'Execute various Monitoring-related operations. ... | Implements the monitoring cell magic for ipython notebooks.
Args:
line: the contents of the storage line.
Returns:
The results of executing the cell. | [
"Implements",
"the",
"monitoring",
"cell",
"magic",
"for",
"ipython",
"notebooks",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/datalab/stackdriver/commands/_monitoring.py#L27-L73 |
googledatalab/pydatalab | datalab/stackdriver/commands/_monitoring.py | _list_metric_descriptors | def _list_metric_descriptors(args, _):
"""Lists the metric descriptors in the project."""
project_id = args['project']
pattern = args['type'] or '*'
descriptors = gcm.MetricDescriptors(project_id=project_id)
dataframe = descriptors.as_dataframe(pattern=pattern)
return _render_dataframe(dataframe) | python | def _list_metric_descriptors(args, _):
"""Lists the metric descriptors in the project."""
project_id = args['project']
pattern = args['type'] or '*'
descriptors = gcm.MetricDescriptors(project_id=project_id)
dataframe = descriptors.as_dataframe(pattern=pattern)
return _render_dataframe(dataframe) | [
"def",
"_list_metric_descriptors",
"(",
"args",
",",
"_",
")",
":",
"project_id",
"=",
"args",
"[",
"'project'",
"]",
"pattern",
"=",
"args",
"[",
"'type'",
"]",
"or",
"'*'",
"descriptors",
"=",
"gcm",
".",
"MetricDescriptors",
"(",
"project_id",
"=",
"pro... | Lists the metric descriptors in the project. | [
"Lists",
"the",
"metric",
"descriptors",
"in",
"the",
"project",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/datalab/stackdriver/commands/_monitoring.py#L76-L82 |
googledatalab/pydatalab | datalab/stackdriver/commands/_monitoring.py | _list_resource_descriptors | def _list_resource_descriptors(args, _):
"""Lists the resource descriptors in the project."""
project_id = args['project']
pattern = args['type'] or '*'
descriptors = gcm.ResourceDescriptors(project_id=project_id)
dataframe = descriptors.as_dataframe(pattern=pattern)
return _render_dataframe(dataframe) | python | def _list_resource_descriptors(args, _):
"""Lists the resource descriptors in the project."""
project_id = args['project']
pattern = args['type'] or '*'
descriptors = gcm.ResourceDescriptors(project_id=project_id)
dataframe = descriptors.as_dataframe(pattern=pattern)
return _render_dataframe(dataframe) | [
"def",
"_list_resource_descriptors",
"(",
"args",
",",
"_",
")",
":",
"project_id",
"=",
"args",
"[",
"'project'",
"]",
"pattern",
"=",
"args",
"[",
"'type'",
"]",
"or",
"'*'",
"descriptors",
"=",
"gcm",
".",
"ResourceDescriptors",
"(",
"project_id",
"=",
... | Lists the resource descriptors in the project. | [
"Lists",
"the",
"resource",
"descriptors",
"in",
"the",
"project",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/datalab/stackdriver/commands/_monitoring.py#L85-L91 |
googledatalab/pydatalab | datalab/stackdriver/commands/_monitoring.py | _list_groups | def _list_groups(args, _):
"""Lists the groups in the project."""
project_id = args['project']
pattern = args['name'] or '*'
groups = gcm.Groups(project_id=project_id)
dataframe = groups.as_dataframe(pattern=pattern)
return _render_dataframe(dataframe) | python | def _list_groups(args, _):
"""Lists the groups in the project."""
project_id = args['project']
pattern = args['name'] or '*'
groups = gcm.Groups(project_id=project_id)
dataframe = groups.as_dataframe(pattern=pattern)
return _render_dataframe(dataframe) | [
"def",
"_list_groups",
"(",
"args",
",",
"_",
")",
":",
"project_id",
"=",
"args",
"[",
"'project'",
"]",
"pattern",
"=",
"args",
"[",
"'name'",
"]",
"or",
"'*'",
"groups",
"=",
"gcm",
".",
"Groups",
"(",
"project_id",
"=",
"project_id",
")",
"datafram... | Lists the groups in the project. | [
"Lists",
"the",
"groups",
"in",
"the",
"project",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/datalab/stackdriver/commands/_monitoring.py#L94-L100 |
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(
datalab.utils.commands.HtmlBuilder.render_table(data, fields)) | [
"def",
"_render_dataframe",
"(",
"dataframe",
")",
":",
"data",
"=",
"dataframe",
".",
"to_dict",
"(",
"orient",
"=",
"'records'",
")",
"fields",
"=",
"dataframe",
".",
"columns",
".",
"tolist",
"(",
")",
"return",
"IPython",
".",
"core",
".",
"display",
... | Helper to render a dataframe as an HTML table. | [
"Helper",
"to",
"render",
"a",
"dataframe",
"as",
"an",
"HTML",
"table",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/datalab/stackdriver/commands/_monitoring.py#L103-L108 |
googledatalab/pydatalab | google/datalab/utils/commands/_job.py | html_job_status | def html_job_status(job_name, job_type, refresh_interval, html_on_running, html_on_success):
"""create html representation of status of a job (long running operation).
Args:
job_name: the full name of the job.
job_type: type of job. Can be 'local' or 'cloud'.
refresh_interval: how often should the clie... | python | def html_job_status(job_name, job_type, refresh_interval, html_on_running, html_on_success):
"""create html representation of status of a job (long running operation).
Args:
job_name: the full name of the job.
job_type: type of job. Can be 'local' or 'cloud'.
refresh_interval: how often should the clie... | [
"def",
"html_job_status",
"(",
"job_name",
",",
"job_type",
",",
"refresh_interval",
",",
"html_on_running",
",",
"html_on_success",
")",
":",
"_HTML_TEMPLATE",
"=",
"\"\"\"\n <div class=\"jobstatus\" id=\"%s\">\n </div>\n <script>\n require(['datalab/job', 'datalab/ele... | create html representation of status of a job (long running operation).
Args:
job_name: the full name of the job.
job_type: type of job. Can be 'local' or 'cloud'.
refresh_interval: how often should the client refresh status.
html_on_running: additional html that the job view needs to include on job ... | [
"create",
"html",
"representation",
"of",
"status",
"of",
"a",
"job",
"(",
"long",
"running",
"operation",
")",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/google/datalab/utils/commands/_job.py#L34-L57 |
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()
job_name = args[0]
job = None
if job_name in _local_jobs:
job = _local_jobs[job_name]
... | [
"def",
"_get_job_status",
"(",
"line",
")",
":",
"try",
":",
"args",
"=",
"line",
".",
"strip",
"(",
")",
".",
"split",
"(",
")",
"job_name",
"=",
"args",
"[",
"0",
"]",
"job",
"=",
"None",
"if",
"job_name",
"in",
"_local_jobs",
":",
"job",
"=",
... | magic used as an endpoint for client to get job status.
%_get_job_status <name>
Returns:
A JSON object of the job status. | [
"magic",
"used",
"as",
"an",
"endpoint",
"for",
"client",
"to",
"get",
"job",
"status",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/google/datalab/utils/commands/_job.py#L61-L89 |
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 | [
"def",
"updated_on",
"(",
"self",
")",
":",
"s",
"=",
"self",
".",
"_info",
".",
"get",
"(",
"'updated'",
",",
"None",
")",
"return",
"dateutil",
".",
"parser",
".",
"parse",
"(",
"s",
")",
"if",
"s",
"else",
"None"
] | The updated timestamp of the object as a datetime.datetime. | [
"The",
"updated",
"timestamp",
"of",
"the",
"object",
"as",
"a",
"datetime",
".",
"datetime",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/google/datalab/storage/_object.py#L69-L72 |
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.
... | [
"def",
"delete",
"(",
"self",
",",
"wait_for_deletion",
"=",
"True",
")",
":",
"if",
"self",
".",
"exists",
"(",
")",
":",
"try",
":",
"self",
".",
"_api",
".",
"objects_delete",
"(",
"self",
".",
"_bucket",
",",
"self",
".",
"_key",
")",
"except",
... | 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. | [
"Deletes",
"this",
"object",
"from",
"its",
"bucket",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/google/datalab/storage/_object.py#L147-L172 |
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:
try:
self._info = self._api.objects_get(... | [
"def",
"metadata",
"(",
"self",
")",
":",
"if",
"self",
".",
"_info",
"is",
"None",
":",
"try",
":",
"self",
".",
"_info",
"=",
"self",
".",
"_api",
".",
"objects_get",
"(",
"self",
".",
"_bucket",
",",
"self",
".",
"_key",
")",
"except",
"Exceptio... | 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. | [
"Retrieves",
"metadata",
"about",
"the",
"object",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/google/datalab/storage/_object.py#L175-L188 |
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:
... | [
"def",
"read_stream",
"(",
"self",
",",
"start_offset",
"=",
"0",
",",
"byte_count",
"=",
"None",
")",
":",
"try",
":",
"return",
"self",
".",
"_api",
".",
"object_download",
"(",
"self",
".",
"_bucket",
",",
"self",
".",
"_key",
",",
"start_offset",
"... | 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:
Exception if there was an error requesting the object's conten... | [
"Reads",
"the",
"content",
"of",
"this",
"object",
"as",
"text",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/google/datalab/storage/_object.py#L190-L205 |
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:
The text content of the object as a list of lines.
Raises:
Exception if the... | [
"def",
"read_lines",
"(",
"self",
",",
"max_lines",
"=",
"None",
")",
":",
"if",
"max_lines",
"is",
"None",
":",
"return",
"self",
".",
"read_stream",
"(",
")",
".",
"split",
"(",
"'\\n'",
")",
"max_to_read",
"=",
"self",
".",
"metadata",
".",
"size",
... | 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 there was an error requesting the object's conte... | [
"Reads",
"the",
"content",
"of",
"this",
"object",
"as",
"text",
"and",
"return",
"a",
"list",
"of",
"lines",
"up",
"to",
"some",
"max",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/google/datalab/storage/_object.py#L217-L243 |
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.
skip_header_rows: whether to skip first row when reading from so... | [
"def",
"sample_to",
"(",
"self",
",",
"count",
",",
"skip_header_rows",
",",
"strategy",
",",
"target",
")",
":",
"# TODO(qimingj) Add unit test",
"# Read data from source into DataFrame.",
"if",
"sys",
".",
"version_info",
".",
"major",
">",
"2",
":",
"xrange",
"... | 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 source.
strategy: can be "LOCAL" or "BIGQUERY". If local, the sampl... | [
"Sample",
"rows",
"from",
"GCS",
"or",
"local",
"file",
"and",
"save",
"results",
"to",
"target",
"file",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/datalab/data/_csv.py#L129-L183 |
googledatalab/pydatalab | google/datalab/utils/facets/base_feature_statistics_generator.py | BaseFeatureStatisticsGenerator.ProtoFromTfRecordFiles | def ProtoFromTfRecordFiles(self,
files,
max_entries=10000,
features=None,
is_sequence=False,
iterator_options=None):
"""Creates a feature statistics proto from a set of TF... | python | def ProtoFromTfRecordFiles(self,
files,
max_entries=10000,
features=None,
is_sequence=False,
iterator_options=None):
"""Creates a feature statistics proto from a set of TF... | [
"def",
"ProtoFromTfRecordFiles",
"(",
"self",
",",
"files",
",",
"max_entries",
"=",
"10000",
",",
"features",
"=",
"None",
",",
"is_sequence",
"=",
"False",
",",
"iterator_options",
"=",
"None",
")",
":",
"datasets",
"=",
"[",
"]",
"for",
"entry",
"in",
... | Creates a feature statistics proto from a set of TFRecord files.
Args:
files: A list of dicts describing files for each dataset for the proto.
Each
entry contains a 'path' field with the path to the TFRecord file on
disk
and a 'name' field to identify the dataset in th... | [
"Creates",
"a",
"feature",
"statistics",
"proto",
"from",
"a",
"set",
"of",
"TFRecord",
"files",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/google/datalab/utils/facets/base_feature_statistics_generator.py#L39-L72 |
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):
"""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... | [
"def",
"_ParseExample",
"(",
"self",
",",
"example_features",
",",
"example_feature_lists",
",",
"entries",
",",
"index",
")",
":",
"features_seen",
"=",
"set",
"(",
")",
"for",
"feature_list",
",",
"is_feature",
"in",
"zip",
"(",
"[",
"example_features",
",",... | 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.FeatureLists from the
example.
entries: A dictionary of all features parsed thus far and arrays o... | [
"Parses",
"data",
"from",
"an",
"example",
"populating",
"a",
"dictionary",
"of",
"feature",
"values",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/google/datalab/utils/facets/base_feature_statistics_generator.py#L74-L160 |
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):
"""Extracts examples into a dictionary of feature values.
Args:
paths: A list of the paths to the files to parse.
max_entries: The maximum number... | python | def _GetEntries(self,
paths,
max_entries,
iterator_from_file,
is_sequence=False):
"""Extracts examples into a dictionary of feature values.
Args:
paths: A list of the paths to the files to parse.
max_entries: The maximum number... | [
"def",
"_GetEntries",
"(",
"self",
",",
"paths",
",",
"max_entries",
",",
"iterator_from_file",
",",
"is_sequence",
"=",
"False",
")",
":",
"entries",
"=",
"{",
"}",
"index",
"=",
"0",
"for",
"filepath",
"in",
"paths",
":",
"reader",
"=",
"iterator_from_fi... | Extracts examples into a dictionary of feature values.
Args:
paths: A list of the paths to the files to parse.
max_entries: The maximum number of examples to load.
iterator_from_file: A method that takes a file path string and returns an
iterator to the examples in that file.
is_s... | [
"Extracts",
"examples",
"into",
"a",
"dictionary",
"of",
"feature",
"values",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/google/datalab/utils/facets/base_feature_statistics_generator.py#L162-L200 |
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,
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... | [
"def",
"_GetTfRecordEntries",
"(",
"self",
",",
"path",
",",
"max_entries",
",",
"is_sequence",
",",
"iterator_options",
")",
":",
"return",
"self",
".",
"_GetEntries",
"(",
"[",
"path",
"]",
",",
"max_entries",
",",
"partial",
"(",
"tf",
".",
"python_io",
... | 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 the input data from 'path' are tf.SequenceExamples,
False if tf.Examples. Defaults to false.
ite... | [
"Extracts",
"TFRecord",
"examples",
"into",
"a",
"dictionary",
"of",
"feature",
"values",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/google/datalab/utils/facets/base_feature_statistics_generator.py#L202-L223 |
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.
Raises:
Exception if there is an error ... | [
"def",
"buckets_insert",
"(",
"self",
",",
"bucket",
",",
"project_id",
"=",
"None",
")",
":",
"args",
"=",
"{",
"'project'",
":",
"project_id",
"if",
"project_id",
"else",
"self",
".",
"_project_id",
"}",
"data",
"=",
"{",
"'name'",
":",
"bucket",
"}",
... | 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 performing the operation. | [
"Issues",
"a",
"request",
"to",
"create",
"a",
"new",
"bucket",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/datalab/storage/_api.py#L54-L69 |
googledatalab/pydatalab | datalab/storage/_api.py | Api.objects_copy | def objects_copy(self, source_bucket, source_key, target_bucket, target_key):
"""Updates the metadata associated with an object.
Args:
source_bucket: the name of the bucket containing the source object.
source_key: the key of the source object being copied.
target_bucket: the name of the buck... | python | def objects_copy(self, source_bucket, source_key, target_bucket, target_key):
"""Updates the metadata associated with an object.
Args:
source_bucket: the name of the bucket containing the source object.
source_key: the key of the source object being copied.
target_bucket: the name of the buck... | [
"def",
"objects_copy",
"(",
"self",
",",
"source_bucket",
",",
"source_key",
",",
"target_bucket",
",",
"target_key",
")",
":",
"url",
"=",
"Api",
".",
"_ENDPOINT",
"+",
"(",
"Api",
".",
"_OBJECT_COPY_PATH",
"%",
"(",
"source_bucket",
",",
"Api",
".",
"_es... | Updates the metadata associated with an object.
Args:
source_bucket: the name of the bucket containing the source object.
source_key: the key of the source object being copied.
target_bucket: the name of the bucket that will contain the copied object.
target_key: the key of the copied objec... | [
"Updates",
"the",
"metadata",
"associated",
"with",
"an",
"object",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/datalab/storage/_api.py#L165-L180 |
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.
"""
url = Api._ENDPOINT + (Api._OBJECT_PATH % (bucket, Api._... | python | 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.
"""
url = Api._ENDPOINT + (Api._OBJECT_PATH % (bucket, Api._... | [
"def",
"objects_delete",
"(",
"self",
",",
"bucket",
",",
"key",
")",
":",
"url",
"=",
"Api",
".",
"_ENDPOINT",
"+",
"(",
"Api",
".",
"_OBJECT_PATH",
"%",
"(",
"bucket",
",",
"Api",
".",
"_escape_key",
"(",
"key",
")",
")",
")",
"datalab",
".",
"ut... | 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. | [
"Deletes",
"the",
"specified",
"object",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/datalab/storage/_api.py#L182-L193 |
googledatalab/pydatalab | datalab/storage/_api.py | Api.objects_get | def objects_get(self, bucket, key, projection='noAcl'):
"""Issues a request to retrieve information about an object.
Args:
bucket: the name of the bucket.
key: the key of the object within the bucket.
projection: the projection of the object to retrieve.
Returns:
A parsed object inf... | python | def objects_get(self, bucket, key, projection='noAcl'):
"""Issues a request to retrieve information about an object.
Args:
bucket: the name of the bucket.
key: the key of the object within the bucket.
projection: the projection of the object to retrieve.
Returns:
A parsed object inf... | [
"def",
"objects_get",
"(",
"self",
",",
"bucket",
",",
"key",
",",
"projection",
"=",
"'noAcl'",
")",
":",
"args",
"=",
"{",
"}",
"if",
"projection",
"is",
"not",
"None",
":",
"args",
"[",
"'projection'",
"]",
"=",
"projection",
"url",
"=",
"Api",
".... | Issues a request to retrieve information about an object.
Args:
bucket: the name of the bucket.
key: the key of the object within the bucket.
projection: the projection of the object to retrieve.
Returns:
A parsed object information dictionary.
Raises:
Exception if there is an... | [
"Issues",
"a",
"request",
"to",
"retrieve",
"information",
"about",
"an",
"object",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/datalab/storage/_api.py#L195-L212 |
googledatalab/pydatalab | datalab/storage/_api.py | Api.verify_permitted_to_read | def verify_permitted_to_read(gs_path):
"""Check if the user has permissions to read from the given path.
Args:
gs_path: the GCS path to check if user is permitted to read.
Raises:
Exception if user has no permissions to read.
"""
# TODO(qimingj): Storage APIs need to be modified to allo... | python | def verify_permitted_to_read(gs_path):
"""Check if the user has permissions to read from the given path.
Args:
gs_path: the GCS path to check if user is permitted to read.
Raises:
Exception if user has no permissions to read.
"""
# TODO(qimingj): Storage APIs need to be modified to allo... | [
"def",
"verify_permitted_to_read",
"(",
"gs_path",
")",
":",
"# TODO(qimingj): Storage APIs need to be modified to allow absence of project",
"# or credential on Items. When that happens we can move the function",
"# to Items class.",
"from",
".",
"import",
"_bu... | Check if the user has permissions to read from the given path.
Args:
gs_path: the GCS path to check if user is permitted to read.
Raises:
Exception if user has no permissions to read. | [
"Check",
"if",
"the",
"user",
"has",
"permissions",
"to",
"read",
"from",
"the",
"given",
"path",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/datalab/storage/_api.py#L270-L299 |
googledatalab/pydatalab | datalab/bigquery/_query_job.py | QueryJob.wait | def wait(self, timeout=None):
""" Wait for the job to complete, or a timeout to happen.
This is more efficient than the version in the base Job class, in that we can
use a call that blocks for the poll duration rather than a sleep. That means we
shouldn't block unnecessarily long and can also pol... | python | def wait(self, timeout=None):
""" Wait for the job to complete, or a timeout to happen.
This is more efficient than the version in the base Job class, in that we can
use a call that blocks for the poll duration rather than a sleep. That means we
shouldn't block unnecessarily long and can also pol... | [
"def",
"wait",
"(",
"self",
",",
"timeout",
"=",
"None",
")",
":",
"poll",
"=",
"30",
"while",
"not",
"self",
".",
"_is_complete",
":",
"try",
":",
"query_result",
"=",
"self",
".",
"_api",
".",
"jobs_query_results",
"(",
"self",
".",
"_job_id",
",",
... | Wait for the job to complete, or a timeout to happen.
This is more efficient than the version in the base Job class, in that we can
use a call that blocks for the poll duration rather than a sleep. That means we
shouldn't block unnecessarily long and can also poll less.
Args:
timeout: how ... | [
"Wait",
"for",
"the",
"job",
"to",
"complete",
"or",
"a",
"timeout",
"to",
"happen",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/datalab/bigquery/_query_job.py#L63-L99 |
googledatalab/pydatalab | datalab/bigquery/_query_job.py | QueryJob.results | def results(self):
""" Get the table used for the results of the query. If the query is incomplete, this blocks.
Raises:
Exception if we timed out waiting for results or the query failed.
"""
self.wait()
if self.failed:
raise Exception('Query failed: %s' % str(self.errors))
return s... | python | def results(self):
""" Get the table used for the results of the query. If the query is incomplete, this blocks.
Raises:
Exception if we timed out waiting for results or the query failed.
"""
self.wait()
if self.failed:
raise Exception('Query failed: %s' % str(self.errors))
return s... | [
"def",
"results",
"(",
"self",
")",
":",
"self",
".",
"wait",
"(",
")",
"if",
"self",
".",
"failed",
":",
"raise",
"Exception",
"(",
"'Query failed: %s'",
"%",
"str",
"(",
"self",
".",
"errors",
")",
")",
"return",
"self",
".",
"_table"
] | Get the table used for the results of the query. If the query is incomplete, this blocks.
Raises:
Exception if we timed out waiting for results or the query failed. | [
"Get",
"the",
"table",
"used",
"for",
"the",
"results",
"of",
"the",
"query",
".",
"If",
"the",
"query",
"is",
"incomplete",
"this",
"blocks",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/datalab/bigquery/_query_job.py#L102-L111 |
googledatalab/pydatalab | google/datalab/stackdriver/monitoring/_metric.py | MetricDescriptors.list | def list(self, pattern='*'):
"""Returns a list of metric descriptors that match the filters.
Args:
pattern: An optional pattern to further filter the descriptors. This can
include Unix shell-style wildcards. E.g. ``"compute*"``,
``"*cpu/load_??m"``.
Returns:
A list of Metri... | python | def list(self, pattern='*'):
"""Returns a list of metric descriptors that match the filters.
Args:
pattern: An optional pattern to further filter the descriptors. This can
include Unix shell-style wildcards. E.g. ``"compute*"``,
``"*cpu/load_??m"``.
Returns:
A list of Metri... | [
"def",
"list",
"(",
"self",
",",
"pattern",
"=",
"'*'",
")",
":",
"if",
"self",
".",
"_descriptors",
"is",
"None",
":",
"self",
".",
"_descriptors",
"=",
"self",
".",
"_client",
".",
"list_metric_descriptors",
"(",
"filter_string",
"=",
"self",
".",
"_fi... | Returns a list of metric descriptors that match the filters.
Args:
pattern: An optional pattern to further filter the descriptors. This can
include Unix shell-style wildcards. E.g. ``"compute*"``,
``"*cpu/load_??m"``.
Returns:
A list of MetricDescriptor objects that match the f... | [
"Returns",
"a",
"list",
"of",
"metric",
"descriptors",
"that",
"match",
"the",
"filters",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/google/datalab/stackdriver/monitoring/_metric.py#L45-L60 |
googledatalab/pydatalab | google/datalab/stackdriver/monitoring/_metric.py | MetricDescriptors.as_dataframe | def as_dataframe(self, pattern='*', max_rows=None):
"""Creates a pandas dataframe from the descriptors that match the filters.
Args:
pattern: An optional pattern to further filter the descriptors. This can
include Unix shell-style wildcards. E.g. ``"compute*"``,
``"*/cpu/load_??m"``.
... | python | def as_dataframe(self, pattern='*', max_rows=None):
"""Creates a pandas dataframe from the descriptors that match the filters.
Args:
pattern: An optional pattern to further filter the descriptors. This can
include Unix shell-style wildcards. E.g. ``"compute*"``,
``"*/cpu/load_??m"``.
... | [
"def",
"as_dataframe",
"(",
"self",
",",
"pattern",
"=",
"'*'",
",",
"max_rows",
"=",
"None",
")",
":",
"data",
"=",
"[",
"]",
"for",
"i",
",",
"metric",
"in",
"enumerate",
"(",
"self",
".",
"list",
"(",
"pattern",
")",
")",
":",
"if",
"max_rows",
... | Creates a pandas dataframe from the descriptors that match the filters.
Args:
pattern: An optional pattern to further filter the descriptors. This can
include Unix shell-style wildcards. E.g. ``"compute*"``,
``"*/cpu/load_??m"``.
max_rows: The maximum number of descriptors to return... | [
"Creates",
"a",
"pandas",
"dataframe",
"from",
"the",
"descriptors",
"that",
"match",
"the",
"filters",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/google/datalab/stackdriver/monitoring/_metric.py#L62-L84 |
googledatalab/pydatalab | datalab/data/_utils.py | get_default_query_from_module | def get_default_query_from_module(module):
""" Given a %%sql module return the default (last) query for the module.
Args:
module: the %%sql module.
Returns:
The default query associated with this module.
"""
if isinstance(module, types.ModuleType):
return module.__dict__.get(_SQL_MODULE_LAST, No... | python | def get_default_query_from_module(module):
""" Given a %%sql module return the default (last) query for the module.
Args:
module: the %%sql module.
Returns:
The default query associated with this module.
"""
if isinstance(module, types.ModuleType):
return module.__dict__.get(_SQL_MODULE_LAST, No... | [
"def",
"get_default_query_from_module",
"(",
"module",
")",
":",
"if",
"isinstance",
"(",
"module",
",",
"types",
".",
"ModuleType",
")",
":",
"return",
"module",
".",
"__dict__",
".",
"get",
"(",
"_SQL_MODULE_LAST",
",",
"None",
")",
"return",
"None"
] | Given a %%sql module return the default (last) query for the module.
Args:
module: the %%sql module.
Returns:
The default query associated with this module. | [
"Given",
"a",
"%%sql",
"module",
"return",
"the",
"default",
"(",
"last",
")",
"query",
"for",
"the",
"module",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/datalab/data/_utils.py#L27-L38 |
googledatalab/pydatalab | datalab/data/_utils.py | _next_token | def _next_token(sql):
""" This is a basic tokenizer for our limited purposes.
It splits a SQL statement up into a series of segments, where a segment is one of:
- identifiers
- left or right parentheses
- multi-line comments
- single line comments
- white-space sequences
- string literals
- consecutiv... | python | def _next_token(sql):
""" This is a basic tokenizer for our limited purposes.
It splits a SQL statement up into a series of segments, where a segment is one of:
- identifiers
- left or right parentheses
- multi-line comments
- single line comments
- white-space sequences
- string literals
- consecutiv... | [
"def",
"_next_token",
"(",
"sql",
")",
":",
"i",
"=",
"0",
"# We use def statements here to make the logic more clear. The start_* functions return",
"# true if i is the index of the start of that construct, while the end_* functions",
"# return true if i point to the first character beyond th... | This is a basic tokenizer for our limited purposes.
It splits a SQL statement up into a series of segments, where a segment is one of:
- identifiers
- left or right parentheses
- multi-line comments
- single line comments
- white-space sequences
- string literals
- consecutive strings of characters that... | [
"This",
"is",
"a",
"basic",
"tokenizer",
"for",
"our",
"limited",
"purposes",
".",
"It",
"splits",
"a",
"SQL",
"statement",
"up",
"into",
"a",
"series",
"of",
"segments",
"where",
"a",
"segment",
"is",
"one",
"of",
":",
"-",
"identifiers",
"-",
"left",
... | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/datalab/data/_utils.py#L41-L137 |
googledatalab/pydatalab | datalab/bigquery/_schema.py | Schema._from_dataframe | def _from_dataframe(dataframe, default_type='STRING'):
"""
Infer a BigQuery table schema from a Pandas dataframe. Note that if you don't explicitly set
the types of the columns in the dataframe, they may be of a type that forces coercion to
STRING, so even though the fields in the dataframe themse... | python | def _from_dataframe(dataframe, default_type='STRING'):
"""
Infer a BigQuery table schema from a Pandas dataframe. Note that if you don't explicitly set
the types of the columns in the dataframe, they may be of a type that forces coercion to
STRING, so even though the fields in the dataframe themse... | [
"def",
"_from_dataframe",
"(",
"dataframe",
",",
"default_type",
"=",
"'STRING'",
")",
":",
"type_mapping",
"=",
"{",
"'i'",
":",
"'INTEGER'",
",",
"'b'",
":",
"'BOOLEAN'",
",",
"'f'",
":",
"'FLOAT'",
",",
"'O'",
":",
"'STRING'",
",",
"'S'",
":",
"'STRIN... | Infer a BigQuery table schema from a Pandas dataframe. Note that if you don't explicitly set
the types of the columns in the dataframe, they may be of a type that forces coercion to
STRING, so even though the fields in the dataframe themselves may be numeric, the type in the
derived schema may not be.... | [
"Infer",
"a",
"BigQuery",
"table",
"schema",
"from",
"a",
"Pandas",
"dataframe",
".",
"Note",
"that",
"if",
"you",
"don",
"t",
"explicitly",
"set",
"the",
"types",
"of",
"the",
"columns",
"in",
"the",
"dataframe",
"they",
"may",
"be",
"of",
"a",
"type",
... | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/datalab/bigquery/_schema.py#L92-L124 |
googledatalab/pydatalab | datalab/bigquery/_schema.py | Schema._from_dict_record | def _from_dict_record(data):
"""
Infer a BigQuery table schema from a dictionary. If the dictionary has entries that
are in turn OrderedDicts these will be turned into RECORD types. Ideally this will
be an OrderedDict but it is not required.
Args:
data: The dict to infer a schema from.
Re... | python | def _from_dict_record(data):
"""
Infer a BigQuery table schema from a dictionary. If the dictionary has entries that
are in turn OrderedDicts these will be turned into RECORD types. Ideally this will
be an OrderedDict but it is not required.
Args:
data: The dict to infer a schema from.
Re... | [
"def",
"_from_dict_record",
"(",
"data",
")",
":",
"return",
"[",
"Schema",
".",
"_get_field_entry",
"(",
"name",
",",
"value",
")",
"for",
"name",
",",
"value",
"in",
"list",
"(",
"data",
".",
"items",
"(",
")",
")",
"]"
] | Infer a BigQuery table schema from a dictionary. If the dictionary has entries that
are in turn OrderedDicts these will be turned into RECORD types. Ideally this will
be an OrderedDict but it is not required.
Args:
data: The dict to infer a schema from.
Returns:
A list of dictionaries conta... | [
"Infer",
"a",
"BigQuery",
"table",
"schema",
"from",
"a",
"dictionary",
".",
"If",
"the",
"dictionary",
"has",
"entries",
"that",
"are",
"in",
"turn",
"OrderedDicts",
"these",
"will",
"be",
"turned",
"into",
"RECORD",
"types",
".",
"Ideally",
"this",
"will",... | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/datalab/bigquery/_schema.py#L164-L176 |
googledatalab/pydatalab | datalab/bigquery/_schema.py | Schema._from_list_record | def _from_list_record(data):
"""
Infer a BigQuery table schema from a list of values.
Args:
data: The list of values.
Returns:
A list of dictionaries containing field 'name' and 'type' entries, suitable for use in a
BigQuery Tables resource schema.
"""
return [Schema._get_... | python | def _from_list_record(data):
"""
Infer a BigQuery table schema from a list of values.
Args:
data: The list of values.
Returns:
A list of dictionaries containing field 'name' and 'type' entries, suitable for use in a
BigQuery Tables resource schema.
"""
return [Schema._get_... | [
"def",
"_from_list_record",
"(",
"data",
")",
":",
"return",
"[",
"Schema",
".",
"_get_field_entry",
"(",
"'Column%d'",
"%",
"(",
"i",
"+",
"1",
")",
",",
"value",
")",
"for",
"i",
",",
"value",
"in",
"enumerate",
"(",
"data",
")",
"]"
] | Infer a BigQuery table schema from a list of values.
Args:
data: The list of values.
Returns:
A list of dictionaries containing field 'name' and 'type' entries, suitable for use in a
BigQuery Tables resource schema. | [
"Infer",
"a",
"BigQuery",
"table",
"schema",
"from",
"a",
"list",
"of",
"values",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/datalab/bigquery/_schema.py#L179-L189 |
googledatalab/pydatalab | datalab/bigquery/_schema.py | Schema._from_record | def _from_record(data):
"""
Infer a BigQuery table schema from a list of fields or a dictionary. The typeof the elements
is used. For a list, the field names are simply 'Column1', 'Column2', etc.
Args:
data: The list of fields or dictionary.
Returns:
A list of dictionaries containing fi... | python | def _from_record(data):
"""
Infer a BigQuery table schema from a list of fields or a dictionary. The typeof the elements
is used. For a list, the field names are simply 'Column1', 'Column2', etc.
Args:
data: The list of fields or dictionary.
Returns:
A list of dictionaries containing fi... | [
"def",
"_from_record",
"(",
"data",
")",
":",
"if",
"isinstance",
"(",
"data",
",",
"dict",
")",
":",
"return",
"Schema",
".",
"_from_dict_record",
"(",
"data",
")",
"elif",
"isinstance",
"(",
"data",
",",
"list",
")",
":",
"return",
"Schema",
".",
"_f... | Infer a BigQuery table schema from a list of fields or a dictionary. The typeof the elements
is used. For a list, the field names are simply 'Column1', 'Column2', etc.
Args:
data: The list of fields or dictionary.
Returns:
A list of dictionaries containing field 'name' and 'type' entries, suita... | [
"Infer",
"a",
"BigQuery",
"table",
"schema",
"from",
"a",
"list",
"of",
"fields",
"or",
"a",
"dictionary",
".",
"The",
"typeof",
"the",
"elements",
"is",
"used",
".",
"For",
"a",
"list",
"the",
"field",
"names",
"are",
"simply",
"Column1",
"Column2",
"et... | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/datalab/bigquery/_schema.py#L192-L208 |
googledatalab/pydatalab | datalab/bigquery/_schema.py | Schema.from_data | def from_data(source):
"""Infers a table/view schema from its JSON representation, a list of records, or a Pandas
dataframe.
Args:
source: the Pandas Dataframe, a dictionary representing a record, a list of heterogeneous
data (record) or homogeneous data (list of records) from which to i... | python | def from_data(source):
"""Infers a table/view schema from its JSON representation, a list of records, or a Pandas
dataframe.
Args:
source: the Pandas Dataframe, a dictionary representing a record, a list of heterogeneous
data (record) or homogeneous data (list of records) from which to i... | [
"def",
"from_data",
"(",
"source",
")",
":",
"if",
"isinstance",
"(",
"source",
",",
"pandas",
".",
"DataFrame",
")",
":",
"bq_schema",
"=",
"Schema",
".",
"_from_dataframe",
"(",
"source",
")",
"elif",
"isinstance",
"(",
"source",
",",
"list",
")",
":",... | Infers a table/view schema from its JSON representation, a list of records, or a Pandas
dataframe.
Args:
source: the Pandas Dataframe, a dictionary representing a record, a list of heterogeneous
data (record) or homogeneous data (list of records) from which to infer the schema, or
... | [
"Infers",
"a",
"table",
"/",
"view",
"schema",
"from",
"its",
"JSON",
"representation",
"a",
"list",
"of",
"records",
"or",
"a",
"Pandas",
"dataframe",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/datalab/bigquery/_schema.py#L230-L279 |
googledatalab/pydatalab | datalab/bigquery/_schema.py | Schema.find | def find(self, name):
""" Get the index of a field in the flattened list given its (fully-qualified) name.
Args:
name: the fully-qualified name of the field.
Returns:
The index of the field, if found; else -1.
"""
for i in range(0, len(self)):
if self[i].name == name:
retu... | python | def find(self, name):
""" Get the index of a field in the flattened list given its (fully-qualified) name.
Args:
name: the fully-qualified name of the field.
Returns:
The index of the field, if found; else -1.
"""
for i in range(0, len(self)):
if self[i].name == name:
retu... | [
"def",
"find",
"(",
"self",
",",
"name",
")",
":",
"for",
"i",
"in",
"range",
"(",
"0",
",",
"len",
"(",
"self",
")",
")",
":",
"if",
"self",
"[",
"i",
"]",
".",
"name",
"==",
"name",
":",
"return",
"i",
"return",
"-",
"1"
] | Get the index of a field in the flattened list given its (fully-qualified) name.
Args:
name: the fully-qualified name of the field.
Returns:
The index of the field, if found; else -1. | [
"Get",
"the",
"index",
"of",
"a",
"field",
"in",
"the",
"flattened",
"list",
"given",
"its",
"(",
"fully",
"-",
"qualified",
")",
"name",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/datalab/bigquery/_schema.py#L308-L319 |
googledatalab/pydatalab | datalab/utils/commands/_commands.py | CommandParser.create_args | def create_args(line, namespace):
""" Expand any meta-variable references in the argument list. """
args = []
# Using shlex.split handles quotes args and escape characters.
for arg in shlex.split(line):
if not arg:
continue
if arg[0] == '$':
var_name = arg[1:]
if var... | python | def create_args(line, namespace):
""" Expand any meta-variable references in the argument list. """
args = []
# Using shlex.split handles quotes args and escape characters.
for arg in shlex.split(line):
if not arg:
continue
if arg[0] == '$':
var_name = arg[1:]
if var... | [
"def",
"create_args",
"(",
"line",
",",
"namespace",
")",
":",
"args",
"=",
"[",
"]",
"# Using shlex.split handles quotes args and escape characters.",
"for",
"arg",
"in",
"shlex",
".",
"split",
"(",
"line",
")",
":",
"if",
"not",
"arg",
":",
"continue",
"if",... | Expand any meta-variable references in the argument list. | [
"Expand",
"any",
"meta",
"-",
"variable",
"references",
"in",
"the",
"argument",
"list",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/datalab/utils/commands/_commands.py#L49-L64 |
googledatalab/pydatalab | datalab/utils/commands/_commands.py | CommandParser.parse | def parse(self, line, namespace=None):
"""Parses a line into a dictionary of arguments, expanding meta-variables from a namespace. """
try:
if namespace is None:
ipy = IPython.get_ipython()
namespace = ipy.user_ns
args = CommandParser.create_args(line, namespace)
return self.pa... | python | def parse(self, line, namespace=None):
"""Parses a line into a dictionary of arguments, expanding meta-variables from a namespace. """
try:
if namespace is None:
ipy = IPython.get_ipython()
namespace = ipy.user_ns
args = CommandParser.create_args(line, namespace)
return self.pa... | [
"def",
"parse",
"(",
"self",
",",
"line",
",",
"namespace",
"=",
"None",
")",
":",
"try",
":",
"if",
"namespace",
"is",
"None",
":",
"ipy",
"=",
"IPython",
".",
"get_ipython",
"(",
")",
"namespace",
"=",
"ipy",
".",
"user_ns",
"args",
"=",
"CommandPa... | Parses a line into a dictionary of arguments, expanding meta-variables from a namespace. | [
"Parses",
"a",
"line",
"into",
"a",
"dictionary",
"of",
"arguments",
"expanding",
"meta",
"-",
"variables",
"from",
"a",
"namespace",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/datalab/utils/commands/_commands.py#L66-L76 |
googledatalab/pydatalab | datalab/utils/commands/_commands.py | CommandParser.subcommand | def subcommand(self, name, help):
"""Creates a parser for a sub-command. """
if self._subcommands is None:
self._subcommands = self.add_subparsers(help='commands')
return self._subcommands.add_parser(name, description=help, help=help) | python | def subcommand(self, name, help):
"""Creates a parser for a sub-command. """
if self._subcommands is None:
self._subcommands = self.add_subparsers(help='commands')
return self._subcommands.add_parser(name, description=help, help=help) | [
"def",
"subcommand",
"(",
"self",
",",
"name",
",",
"help",
")",
":",
"if",
"self",
".",
"_subcommands",
"is",
"None",
":",
"self",
".",
"_subcommands",
"=",
"self",
".",
"add_subparsers",
"(",
"help",
"=",
"'commands'",
")",
"return",
"self",
".",
"_s... | Creates a parser for a sub-command. | [
"Creates",
"a",
"parser",
"for",
"a",
"sub",
"-",
"command",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/datalab/utils/commands/_commands.py#L78-L82 |
googledatalab/pydatalab | datalab/utils/commands/_utils.py | render_dictionary | def render_dictionary(data, headers=None):
""" Return a dictionary list formatted as a HTML table.
Args:
data: the dictionary list
headers: the keys in the dictionary to use as table columns, in order.
"""
return IPython.core.display.HTML(_html.HtmlBuilder.render_table(data, headers)) | python | def render_dictionary(data, headers=None):
""" Return a dictionary list formatted as a HTML table.
Args:
data: the dictionary list
headers: the keys in the dictionary to use as table columns, in order.
"""
return IPython.core.display.HTML(_html.HtmlBuilder.render_table(data, headers)) | [
"def",
"render_dictionary",
"(",
"data",
",",
"headers",
"=",
"None",
")",
":",
"return",
"IPython",
".",
"core",
".",
"display",
".",
"HTML",
"(",
"_html",
".",
"HtmlBuilder",
".",
"render_table",
"(",
"data",
",",
"headers",
")",
")"
] | Return a dictionary list formatted as a HTML table.
Args:
data: the dictionary list
headers: the keys in the dictionary to use as table columns, in order. | [
"Return",
"a",
"dictionary",
"list",
"formatted",
"as",
"a",
"HTML",
"table",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/datalab/utils/commands/_utils.py#L63-L70 |
googledatalab/pydatalab | datalab/utils/commands/_utils.py | render_text | def render_text(text, preformatted=False):
""" Return text formatted as a HTML
Args:
text: the text to render
preformatted: whether the text should be rendered as preformatted
"""
return IPython.core.display.HTML(_html.HtmlBuilder.render_text(text, preformatted)) | python | def render_text(text, preformatted=False):
""" Return text formatted as a HTML
Args:
text: the text to render
preformatted: whether the text should be rendered as preformatted
"""
return IPython.core.display.HTML(_html.HtmlBuilder.render_text(text, preformatted)) | [
"def",
"render_text",
"(",
"text",
",",
"preformatted",
"=",
"False",
")",
":",
"return",
"IPython",
".",
"core",
".",
"display",
".",
"HTML",
"(",
"_html",
".",
"HtmlBuilder",
".",
"render_text",
"(",
"text",
",",
"preformatted",
")",
")"
] | Return text formatted as a HTML
Args:
text: the text to render
preformatted: whether the text should be rendered as preformatted | [
"Return",
"text",
"formatted",
"as",
"a",
"HTML"
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/datalab/utils/commands/_utils.py#L73-L80 |
googledatalab/pydatalab | datalab/utils/commands/_utils.py | get_field_list | def get_field_list(fields, schema):
""" Convert a field list spec into a real list of field names.
For tables, we return only the top-level non-RECORD fields as Google charts
can't handle nested data.
"""
# If the fields weren't supplied get them from the schema.
if isinstance(fields, list):
re... | python | def get_field_list(fields, schema):
""" Convert a field list spec into a real list of field names.
For tables, we return only the top-level non-RECORD fields as Google charts
can't handle nested data.
"""
# If the fields weren't supplied get them from the schema.
if isinstance(fields, list):
re... | [
"def",
"get_field_list",
"(",
"fields",
",",
"schema",
")",
":",
"# If the fields weren't supplied get them from the schema.",
"if",
"isinstance",
"(",
"fields",
",",
"list",
")",
":",
"return",
"fields",
"if",
"isinstance",
"(",
"fields",
",",
"basestring",
")",
... | Convert a field list spec into a real list of field names.
For tables, we return only the top-level non-RECORD fields as Google charts
can't handle nested data. | [
"Convert",
"a",
"field",
"list",
"spec",
"into",
"a",
"real",
"list",
"of",
"field",
"names",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/datalab/utils/commands/_utils.py#L83-L96 |
googledatalab/pydatalab | datalab/utils/commands/_utils.py | _get_cols | def _get_cols(fields, schema):
""" Get column metadata for Google Charts based on field list and schema. """
typemap = {
'STRING': 'string',
'INT64': 'number',
'INTEGER': 'number',
'FLOAT': 'number',
'FLOAT64': 'number',
'BOOL': 'boolean',
'BOOLEAN': 'boolean',
'DATE': 'date',
'T... | python | def _get_cols(fields, schema):
""" Get column metadata for Google Charts based on field list and schema. """
typemap = {
'STRING': 'string',
'INT64': 'number',
'INTEGER': 'number',
'FLOAT': 'number',
'FLOAT64': 'number',
'BOOL': 'boolean',
'BOOLEAN': 'boolean',
'DATE': 'date',
'T... | [
"def",
"_get_cols",
"(",
"fields",
",",
"schema",
")",
":",
"typemap",
"=",
"{",
"'STRING'",
":",
"'string'",
",",
"'INT64'",
":",
"'number'",
",",
"'INTEGER'",
":",
"'number'",
",",
"'FLOAT'",
":",
"'number'",
",",
"'FLOAT64'",
":",
"'number'",
",",
"'B... | Get column metadata for Google Charts based on field list and schema. | [
"Get",
"column",
"metadata",
"for",
"Google",
"Charts",
"based",
"on",
"field",
"list",
"and",
"schema",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/datalab/utils/commands/_utils.py#L99-L125 |
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):
""" Helper function for _get_data that handles empty lists. """
fields = get_field_list(fields, schema)
return {'cols': _get_cols(fields, schema), 'rows': []}, 0 | python | def _get_data_from_empty_list(source, fields='*', first_row=0, count=-1, schema=None):
""" Helper function for _get_data that handles empty lists. """
fields = get_field_list(fields, schema)
return {'cols': _get_cols(fields, schema), 'rows': []}, 0 | [
"def",
"_get_data_from_empty_list",
"(",
"source",
",",
"fields",
"=",
"'*'",
",",
"first_row",
"=",
"0",
",",
"count",
"=",
"-",
"1",
",",
"schema",
"=",
"None",
")",
":",
"fields",
"=",
"get_field_list",
"(",
"fields",
",",
"schema",
")",
"return",
"... | Helper function for _get_data that handles empty lists. | [
"Helper",
"function",
"for",
"_get_data",
"that",
"handles",
"empty",
"lists",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/datalab/utils/commands/_utils.py#L128-L131 |
googledatalab/pydatalab | datalab/utils/commands/_utils.py | _get_data_from_table | def _get_data_from_table(source, fields='*', first_row=0, count=-1, schema=None):
""" Helper function for _get_data that handles BQ Tables. """
if not source.exists():
return _get_data_from_empty_list(source, fields, first_row, count)
if schema is None:
schema = source.schema
fields = get_field_list(fie... | python | def _get_data_from_table(source, fields='*', first_row=0, count=-1, schema=None):
""" Helper function for _get_data that handles BQ Tables. """
if not source.exists():
return _get_data_from_empty_list(source, fields, first_row, count)
if schema is None:
schema = source.schema
fields = get_field_list(fie... | [
"def",
"_get_data_from_table",
"(",
"source",
",",
"fields",
"=",
"'*'",
",",
"first_row",
"=",
"0",
",",
"count",
"=",
"-",
"1",
",",
"schema",
"=",
"None",
")",
":",
"if",
"not",
"source",
".",
"exists",
"(",
")",
":",
"return",
"_get_data_from_empty... | Helper function for _get_data that handles BQ Tables. | [
"Helper",
"function",
"for",
"_get_data",
"that",
"handles",
"BQ",
"Tables",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/datalab/utils/commands/_utils.py#L176-L185 |
googledatalab/pydatalab | datalab/utils/commands/_utils.py | get_data | def get_data(source, fields='*', env=None, first_row=0, count=-1, schema=None):
""" A utility function to get a subset of data from a Table, Query, Pandas dataframe or List.
Args:
source: the source of the data. Can be a Table, Pandas DataFrame, List of dictionaries or
lists, or a string, in which case... | python | def get_data(source, fields='*', env=None, first_row=0, count=-1, schema=None):
""" A utility function to get a subset of data from a Table, Query, Pandas dataframe or List.
Args:
source: the source of the data. Can be a Table, Pandas DataFrame, List of dictionaries or
lists, or a string, in which case... | [
"def",
"get_data",
"(",
"source",
",",
"fields",
"=",
"'*'",
",",
"env",
"=",
"None",
",",
"first_row",
"=",
"0",
",",
"count",
"=",
"-",
"1",
",",
"schema",
"=",
"None",
")",
":",
"ipy",
"=",
"IPython",
".",
"get_ipython",
"(",
")",
"if",
"env",... | A utility function to get a subset of data from a Table, Query, Pandas dataframe or List.
Args:
source: the source of the data. Can be a Table, Pandas DataFrame, List of dictionaries or
lists, or a string, in which case it is expected to be the name of a table in BQ.
fields: a list of fields that we ... | [
"A",
"utility",
"function",
"to",
"get",
"a",
"subset",
"of",
"data",
"from",
"a",
"Table",
"Query",
"Pandas",
"dataframe",
"or",
"List",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/datalab/utils/commands/_utils.py#L188-L244 |
googledatalab/pydatalab | datalab/utils/commands/_utils.py | handle_magic_line | def handle_magic_line(line, cell, parser, namespace=None):
""" Helper function for handling magic command lines given a parser with handlers set. """
args = parser.parse(line, namespace)
if args:
try:
return args.func(vars(args), cell)
except Exception as e:
sys.stderr.write(str(e))
sys.... | python | def handle_magic_line(line, cell, parser, namespace=None):
""" Helper function for handling magic command lines given a parser with handlers set. """
args = parser.parse(line, namespace)
if args:
try:
return args.func(vars(args), cell)
except Exception as e:
sys.stderr.write(str(e))
sys.... | [
"def",
"handle_magic_line",
"(",
"line",
",",
"cell",
",",
"parser",
",",
"namespace",
"=",
"None",
")",
":",
"args",
"=",
"parser",
".",
"parse",
"(",
"line",
",",
"namespace",
")",
"if",
"args",
":",
"try",
":",
"return",
"args",
".",
"func",
"(",
... | Helper function for handling magic command lines given a parser with handlers set. | [
"Helper",
"function",
"for",
"handling",
"magic",
"command",
"lines",
"given",
"a",
"parser",
"with",
"handlers",
"set",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/datalab/utils/commands/_utils.py#L247-L257 |
googledatalab/pydatalab | datalab/utils/commands/_utils.py | expand_var | def expand_var(v, env):
""" If v is a variable reference (for example: '$myvar'), replace it using the supplied
env dictionary.
Args:
v: the variable to replace if needed.
env: user supplied dictionary.
Raises:
Exception if v is a variable reference but it is not found in env.
"""
if len(v... | python | def expand_var(v, env):
""" If v is a variable reference (for example: '$myvar'), replace it using the supplied
env dictionary.
Args:
v: the variable to replace if needed.
env: user supplied dictionary.
Raises:
Exception if v is a variable reference but it is not found in env.
"""
if len(v... | [
"def",
"expand_var",
"(",
"v",
",",
"env",
")",
":",
"if",
"len",
"(",
"v",
")",
"==",
"0",
":",
"return",
"v",
"# Using len() and v[0] instead of startswith makes this Unicode-safe.",
"if",
"v",
"[",
"0",
"]",
"==",
"'$'",
":",
"v",
"=",
"v",
"[",
"1",
... | If v is a variable reference (for example: '$myvar'), replace it using the supplied
env dictionary.
Args:
v: the variable to replace if needed.
env: user supplied dictionary.
Raises:
Exception if v is a variable reference but it is not found in env. | [
"If",
"v",
"is",
"a",
"variable",
"reference",
"(",
"for",
"example",
":",
"$myvar",
")",
"replace",
"it",
"using",
"the",
"supplied",
"env",
"dictionary",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/datalab/utils/commands/_utils.py#L260-L281 |
googledatalab/pydatalab | datalab/utils/commands/_utils.py | replace_vars | def replace_vars(config, env):
""" Replace variable references in config using the supplied env dictionary.
Args:
config: the config to parse. Can be a tuple, list or dict.
env: user supplied dictionary.
Raises:
Exception if any variable references are not found in env.
"""
if isinstance(config,... | python | def replace_vars(config, env):
""" Replace variable references in config using the supplied env dictionary.
Args:
config: the config to parse. Can be a tuple, list or dict.
env: user supplied dictionary.
Raises:
Exception if any variable references are not found in env.
"""
if isinstance(config,... | [
"def",
"replace_vars",
"(",
"config",
",",
"env",
")",
":",
"if",
"isinstance",
"(",
"config",
",",
"dict",
")",
":",
"for",
"k",
",",
"v",
"in",
"list",
"(",
"config",
".",
"items",
"(",
")",
")",
":",
"if",
"isinstance",
"(",
"v",
",",
"dict",
... | Replace variable references in config using the supplied env dictionary.
Args:
config: the config to parse. Can be a tuple, list or dict.
env: user supplied dictionary.
Raises:
Exception if any variable references are not found in env. | [
"Replace",
"variable",
"references",
"in",
"config",
"using",
"the",
"supplied",
"env",
"dictionary",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/datalab/utils/commands/_utils.py#L284-L310 |
googledatalab/pydatalab | datalab/utils/commands/_utils.py | parse_config | def parse_config(config, env, as_dict=True):
""" Parse a config from a magic cell body. This could be JSON or YAML. We turn it into
a Python dictionary then recursively replace any variable references using the supplied
env dictionary.
"""
if config is None:
return None
stripped = config.strip(... | python | def parse_config(config, env, as_dict=True):
""" Parse a config from a magic cell body. This could be JSON or YAML. We turn it into
a Python dictionary then recursively replace any variable references using the supplied
env dictionary.
"""
if config is None:
return None
stripped = config.strip(... | [
"def",
"parse_config",
"(",
"config",
",",
"env",
",",
"as_dict",
"=",
"True",
")",
":",
"if",
"config",
"is",
"None",
":",
"return",
"None",
"stripped",
"=",
"config",
".",
"strip",
"(",
")",
"if",
"len",
"(",
"stripped",
")",
"==",
"0",
":",
"con... | Parse a config from a magic cell body. This could be JSON or YAML. We turn it into
a Python dictionary then recursively replace any variable references using the supplied
env dictionary. | [
"Parse",
"a",
"config",
"from",
"a",
"magic",
"cell",
"body",
".",
"This",
"could",
"be",
"JSON",
"or",
"YAML",
".",
"We",
"turn",
"it",
"into",
"a",
"Python",
"dictionary",
"then",
"recursively",
"replace",
"any",
"variable",
"references",
"using",
"the",... | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/datalab/utils/commands/_utils.py#L313-L333 |
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.
optional_keys... | [
"def",
"validate_config",
"(",
"config",
",",
"required_keys",
",",
"optional_keys",
"=",
"None",
")",
":",
"if",
"optional_keys",
"is",
"None",
":",
"optional_keys",
"=",
"[",
"]",
"if",
"not",
"isinstance",
"(",
"config",
",",
"dict",
")",
":",
"raise",
... | 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: the names of the keys that the config can have.
Raises:
Excep... | [
"Validate",
"a",
"config",
"dictionary",
"to",
"make",
"sure",
"it",
"includes",
"all",
"required",
"keys",
"and",
"does",
"not",
"include",
"any",
"unexpected",
"keys",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/datalab/utils/commands/_utils.py#L336-L357 |
googledatalab/pydatalab | datalab/utils/commands/_utils.py | validate_config_must_have | def validate_config_must_have(config, required_keys):
""" Validate a config dictionary to make sure it has all of the specified keys
Args:
config: the config to validate.
required_keys: the list of possible keys that config must include.
Raises:
Exception if the config does not have any of them.
"... | python | def validate_config_must_have(config, required_keys):
""" Validate a config dictionary to make sure it has all of the specified keys
Args:
config: the config to validate.
required_keys: the list of possible keys that config must include.
Raises:
Exception if the config does not have any of them.
"... | [
"def",
"validate_config_must_have",
"(",
"config",
",",
"required_keys",
")",
":",
"missing_keys",
"=",
"set",
"(",
"required_keys",
")",
"-",
"set",
"(",
"config",
")",
"if",
"len",
"(",
"missing_keys",
")",
">",
"0",
":",
"raise",
"Exception",
"(",
"'Inv... | Validate a config dictionary to make sure it has all of the specified keys
Args:
config: the config to validate.
required_keys: the list of possible keys that config must include.
Raises:
Exception if the config does not have any of them. | [
"Validate",
"a",
"config",
"dictionary",
"to",
"make",
"sure",
"it",
"has",
"all",
"of",
"the",
"specified",
"keys"
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/datalab/utils/commands/_utils.py#L360-L372 |
googledatalab/pydatalab | datalab/utils/commands/_utils.py | validate_config_has_one_of | def validate_config_has_one_of(config, one_of_keys):
""" Validate a config dictionary to make sure it has one and only one
key in one_of_keys.
Args:
config: the config to validate.
one_of_keys: the list of possible keys that config can have one and only one.
Raises:
Exception if the config doe... | python | def validate_config_has_one_of(config, one_of_keys):
""" Validate a config dictionary to make sure it has one and only one
key in one_of_keys.
Args:
config: the config to validate.
one_of_keys: the list of possible keys that config can have one and only one.
Raises:
Exception if the config doe... | [
"def",
"validate_config_has_one_of",
"(",
"config",
",",
"one_of_keys",
")",
":",
"intersection",
"=",
"set",
"(",
"config",
")",
".",
"intersection",
"(",
"one_of_keys",
")",
"if",
"len",
"(",
"intersection",
")",
">",
"1",
":",
"raise",
"Exception",
"(",
... | Validate a config dictionary to make sure it has one and only one
key in one_of_keys.
Args:
config: the config to validate.
one_of_keys: the list of possible keys that config can have one and only one.
Raises:
Exception if the config does not have any of them, or multiple of them. | [
"Validate",
"a",
"config",
"dictionary",
"to",
"make",
"sure",
"it",
"has",
"one",
"and",
"only",
"one",
"key",
"in",
"one_of_keys",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/datalab/utils/commands/_utils.py#L375-L390 |
googledatalab/pydatalab | datalab/utils/commands/_utils.py | validate_config_value | def validate_config_value(value, possible_values):
""" Validate a config value to make sure it is one of the possible values.
Args:
value: the config value to validate.
possible_values: the possible values the value can be
Raises:
Exception if the value is not one of possible values.
"""
if valu... | python | def validate_config_value(value, possible_values):
""" Validate a config value to make sure it is one of the possible values.
Args:
value: the config value to validate.
possible_values: the possible values the value can be
Raises:
Exception if the value is not one of possible values.
"""
if valu... | [
"def",
"validate_config_value",
"(",
"value",
",",
"possible_values",
")",
":",
"if",
"value",
"not",
"in",
"possible_values",
":",
"raise",
"Exception",
"(",
"'Invalid config value \"%s\". Possible values are '",
"'%s'",
"%",
"(",
"value",
",",
"', '",
".",
"join",... | Validate a config value to make sure it is one of the possible values.
Args:
value: the config value to validate.
possible_values: the possible values the value can be
Raises:
Exception if the value is not one of possible values. | [
"Validate",
"a",
"config",
"value",
"to",
"make",
"sure",
"it",
"is",
"one",
"of",
"the",
"possible",
"values",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/datalab/utils/commands/_utils.py#L393-L405 |
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.
Raises:
Exception if the path is invalid
"""
bucket, key = datalab.storage._bucket.par... | python | 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.
Raises:
Exception if the path is invalid
"""
bucket, key = datalab.storage._bucket.par... | [
"def",
"validate_gcs_path",
"(",
"path",
",",
"require_object",
")",
":",
"bucket",
",",
"key",
"=",
"datalab",
".",
"storage",
".",
"_bucket",
".",
"parse_name",
"(",
"path",
")",
"if",
"bucket",
"is",
"None",
":",
"raise",
"Exception",
"(",
"'Invalid GCS... | 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.
Raises:
Exception if the path is invalid | [
"Check",
"whether",
"a",
"given",
"path",
"is",
"a",
"valid",
"GCS",
"path",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/datalab/utils/commands/_utils.py#L421-L435 |
googledatalab/pydatalab | datalab/utils/commands/_utils.py | parse_control_options | def parse_control_options(controls, variable_defaults=None):
""" Parse a set of control options.
Args:
controls: The dictionary of control options.
variable_defaults: If the controls are for a Query with variables, then this is the
default variable values defined in the Query module. The options in... | python | def parse_control_options(controls, variable_defaults=None):
""" Parse a set of control options.
Args:
controls: The dictionary of control options.
variable_defaults: If the controls are for a Query with variables, then this is the
default variable values defined in the Query module. The options in... | [
"def",
"parse_control_options",
"(",
"controls",
",",
"variable_defaults",
"=",
"None",
")",
":",
"controls_html",
"=",
"''",
"control_defaults",
"=",
"{",
"}",
"control_ids",
"=",
"[",
"]",
"div_id",
"=",
"_html",
".",
"Html",
".",
"next_id",
"(",
")",
"i... | Parse a set of control options.
Args:
controls: The dictionary of control options.
variable_defaults: If the controls are for a Query with variables, then this is the
default variable values defined in the Query module. The options in the controls
parameter can override these but if a variabl... | [
"Parse",
"a",
"set",
"of",
"control",
"options",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/datalab/utils/commands/_utils.py#L438-L550 |
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(
pandas_profiling.Pro... | [
"def",
"profile_df",
"(",
"df",
")",
":",
"# 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",
".",... | Generate a profile of data in a dataframe.
Args:
df: the Pandas dataframe. | [
"Generate",
"a",
"profile",
"of",
"data",
"in",
"a",
"dataframe",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/datalab/utils/commands/_utils.py#L675-L684 |
googledatalab/pydatalab | solutionbox/structured_data/mltoolbox/_structured_data/_package.py | _assert_gcs_files | def _assert_gcs_files(files):
"""Check files starts wtih gs://.
Args:
files: string to file path, or list of file paths.
"""
if sys.version_info.major > 2:
string_type = (str, bytes) # for python 3 compatibility
else:
string_type = basestring # noqa
if isinstance(files, string_type):
fi... | python | def _assert_gcs_files(files):
"""Check files starts wtih gs://.
Args:
files: string to file path, or list of file paths.
"""
if sys.version_info.major > 2:
string_type = (str, bytes) # for python 3 compatibility
else:
string_type = basestring # noqa
if isinstance(files, string_type):
fi... | [
"def",
"_assert_gcs_files",
"(",
"files",
")",
":",
"if",
"sys",
".",
"version_info",
".",
"major",
">",
"2",
":",
"string_type",
"=",
"(",
"str",
",",
"bytes",
")",
"# for python 3 compatibility",
"else",
":",
"string_type",
"=",
"basestring",
"# noqa",
"if... | Check files starts wtih gs://.
Args:
files: string to file path, or list of file paths. | [
"Check",
"files",
"starts",
"wtih",
"gs",
":",
"//",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/solutionbox/structured_data/mltoolbox/_structured_data/_package.py#L67-L84 |
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
... | [
"def",
"_package_to_staging",
"(",
"staging_package_url",
")",
":",
"import",
"google",
".",
"datalab",
".",
"ml",
"as",
"ml",
"# Find the package root. __file__ is under [package_root]/mltoolbox/_structured_data/this_file",
"package_root",
"=",
"os",
".",
"path",
".",
"abs... | Repackage this package from local installed location and copy it to GCS.
Args:
staging_package_url: GCS path. | [
"Repackage",
"this",
"package",
"from",
"local",
"installed",
"location",
"and",
"copy",
"it",
"to",
"GCS",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/solutionbox/structured_data/mltoolbox/_structured_data/_package.py#L87-L105 |
googledatalab/pydatalab | solutionbox/structured_data/mltoolbox/_structured_data/_package.py | _wait_and_kill | def _wait_and_kill(pid_to_wait, pids_to_kill):
""" Helper function.
Wait for a process to finish if it exists, and then try to kill a list of
processes.
Used by local_train
Args:
pid_to_wait: the process to wait for.
pids_to_kill: a list of processes to kill after the process of pid_to_... | python | def _wait_and_kill(pid_to_wait, pids_to_kill):
""" Helper function.
Wait for a process to finish if it exists, and then try to kill a list of
processes.
Used by local_train
Args:
pid_to_wait: the process to wait for.
pids_to_kill: a list of processes to kill after the process of pid_to_... | [
"def",
"_wait_and_kill",
"(",
"pid_to_wait",
",",
"pids_to_kill",
")",
":",
"# cloud workers don't have psutil",
"import",
"psutil",
"if",
"psutil",
".",
"pid_exists",
"(",
"pid_to_wait",
")",
":",
"psutil",
".",
"Process",
"(",
"pid",
"=",
"pid_to_wait",
")",
"... | Helper function.
Wait for a process to finish if it exists, and then try to kill a list of
processes.
Used by local_train
Args:
pid_to_wait: the process to wait for.
pids_to_kill: a list of processes to kill after the process of pid_to_wait finishes. | [
"Helper",
"function",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/solutionbox/structured_data/mltoolbox/_structured_data/_package.py#L108-L129 |
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,
project_id=project_id)
job.wait()
print('Analyze: ' + str(job.state)) | [
"def",
"analyze",
"(",
"output_dir",
",",
"dataset",
",",
"cloud",
"=",
"False",
",",
"project_id",
"=",
"None",
")",
":",
"job",
"=",
"analyze_async",
"(",
"output_dir",
"=",
"output_dir",
",",
"dataset",
"=",
"dataset",
",",
"cloud",
"=",
"cloud",
",",... | Blocking version of analyze_async. See documentation of analyze_async. | [
"Blocking",
"version",
"of",
"analyze_async",
".",
"See",
"documentation",
"of",
"analyze_async",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/solutionbox/structured_data/mltoolbox/_structured_data/_package.py#L136-L144 |
googledatalab/pydatalab | solutionbox/structured_data/mltoolbox/_structured_data/_package.py | analyze_async | def analyze_async(output_dir, dataset, cloud=False, project_id=None):
"""Analyze data locally or in the cloud with BigQuery.
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):
"""Analyze data locally or in the cloud with BigQuery.
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... | [
"def",
"analyze_async",
"(",
"output_dir",
",",
"dataset",
",",
"cloud",
"=",
"False",
",",
"project_id",
"=",
"None",
")",
":",
"import",
"google",
".",
"datalab",
".",
"utils",
"as",
"du",
"with",
"warnings",
".",
"catch_warnings",
"(",
")",
":",
"warn... | Analyze data locally or in the cloud with BigQuery.
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 to use.
dataset: only CsvDataSet is supported currently.
cloud: If... | [
"Analyze",
"data",
"locally",
"or",
"in",
"the",
"cloud",
"with",
"BigQuery",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/solutionbox/structured_data/mltoolbox/_structured_data/_package.py#L147-L168 |
googledatalab/pydatalab | solutionbox/structured_data/mltoolbox/_structured_data/_package.py | train_async | def train_async(train_dataset,
eval_dataset,
analysis_dir,
output_dir,
features,
model_type,
max_steps=5000,
num_epochs=None,
train_batch_size=100,
eval_batch_size=16,
... | python | def train_async(train_dataset,
eval_dataset,
analysis_dir,
output_dir,
features,
model_type,
max_steps=5000,
num_epochs=None,
train_batch_size=100,
eval_batch_size=16,
... | [
"def",
"train_async",
"(",
"train_dataset",
",",
"eval_dataset",
",",
"analysis_dir",
",",
"output_dir",
",",
"features",
",",
"model_type",
",",
"max_steps",
"=",
"5000",
",",
"num_epochs",
"=",
"None",
",",
"train_batch_size",
"=",
"100",
",",
"eval_batch_size... | Train model locally or in the cloud.
Local Training:
Args:
train_dataset: CsvDataSet
eval_dataset: CsvDataSet
analysis_dir: The output directory from local_analysis
output_dir: Output directory of training.
features: file path or features object. Example:
{
"col_A": {"trans... | [
"Train",
"model",
"locally",
"or",
"in",
"the",
"cloud",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/solutionbox/structured_data/mltoolbox/_structured_data/_package.py#L216-L360 |
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,
analysis_dir,
output_dir,
features,
model_type,
max_steps,
num_epochs,
train_batch_size,
eval_batch_size,
min_eval_... | [
"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_frequency",
",",
"top_n"... | Train model using CloudML.
See local_train() for a description of the args.
Args:
config: A CloudTrainingConfig object.
job_name: Training job name. A default will be picked if None. | [
"Train",
"model",
"using",
"CloudML",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/solutionbox/structured_data/mltoolbox/_structured_data/_package.py#L456-L543 |
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
... | python | 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
... | [
"def",
"predict",
"(",
"data",
",",
"training_dir",
"=",
"None",
",",
"model_name",
"=",
"None",
",",
"model_version",
"=",
"None",
",",
"cloud",
"=",
"False",
")",
":",
"if",
"cloud",
":",
"if",
"not",
"model_version",
"or",
"not",
"model_name",
":",
... | 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
model_version: depoyed model version
cloud: bool. If False, does local prediction and ... | [
"Runs",
"prediction",
"locally",
"or",
"on",
"the",
"cloud",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/solutionbox/structured_data/mltoolbox/_structured_data/_package.py#L550-L592 |
googledatalab/pydatalab | solutionbox/structured_data/mltoolbox/_structured_data/_package.py | local_predict | def local_predict(training_dir, data):
"""Runs local prediction on the prediction graph.
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
the schem... | python | def local_predict(training_dir, data):
"""Runs local prediction on the prediction graph.
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
the schem... | [
"def",
"local_predict",
"(",
"training_dir",
",",
"data",
")",
":",
"# from . import predict as predict_module",
"from",
".",
"prediction",
"import",
"predict",
"as",
"predict_module",
"# Save the instances to a file, call local batch prediction, and return it",
"tmp_dir",
"=",
... | Runs local prediction on the prediction graph.
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
the schema that was used at training, except the targ... | [
"Runs",
"local",
"prediction",
"on",
"the",
"prediction",
"graph",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/solutionbox/structured_data/mltoolbox/_structured_data/_package.py#L595-L668 |
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.
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
... | [
"def",
"cloud_predict",
"(",
"model_name",
",",
"model_version",
",",
"data",
")",
":",
"import",
"google",
".",
"datalab",
".",
"ml",
"as",
"ml",
"if",
"isinstance",
"(",
"data",
",",
"pd",
".",
"DataFrame",
")",
":",
"# write the df to csv.",
"string_buffe... | 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
model_version: depoyed model version
data: List of... | [
"Use",
"Online",
"prediction",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/solutionbox/structured_data/mltoolbox/_structured_data/_package.py#L671-L715 |
googledatalab/pydatalab | solutionbox/structured_data/mltoolbox/_structured_data/_package.py | batch_predict | def batch_predict(training_dir, prediction_input_file, output_dir,
mode, batch_size=16, shard_files=True, output_format='csv',
cloud=False):
"""Blocking versoin of batch_predict.
See documentation of batch_prediction_async.
"""
job = batch_predict_async(
training_dir=t... | python | def batch_predict(training_dir, prediction_input_file, output_dir,
mode, batch_size=16, shard_files=True, output_format='csv',
cloud=False):
"""Blocking versoin of batch_predict.
See documentation of batch_prediction_async.
"""
job = batch_predict_async(
training_dir=t... | [
"def",
"batch_predict",
"(",
"training_dir",
",",
"prediction_input_file",
",",
"output_dir",
",",
"mode",
",",
"batch_size",
"=",
"16",
",",
"shard_files",
"=",
"True",
",",
"output_format",
"=",
"'csv'",
",",
"cloud",
"=",
"False",
")",
":",
"job",
"=",
... | Blocking versoin of batch_predict.
See documentation of batch_prediction_async. | [
"Blocking",
"versoin",
"of",
"batch_predict",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/solutionbox/structured_data/mltoolbox/_structured_data/_package.py#L722-L739 |
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.
Args:
training_dir: The output folder of training.
prediction_input_file: csv file pattern to a fil... | [
"def",
"batch_predict_async",
"(",
"training_dir",
",",
"prediction_input_file",
",",
"output_dir",
",",
"mode",
",",
"batch_size",
"=",
"16",
",",
"shard_files",
"=",
"True",
",",
"output_format",
"=",
"'csv'",
",",
"cloud",
"=",
"False",
")",
":",
"import",
... | Local and cloud batch prediction.
Args:
training_dir: The output folder of training.
prediction_input_file: csv file pattern to a file. File must be on GCS if
running cloud prediction
output_dir: output location to save the results. Must be a GSC path if
running cloud prediction.
mode... | [
"Local",
"and",
"cloud",
"batch",
"prediction",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/solutionbox/structured_data/mltoolbox/_structured_data/_package.py#L742-L777 |
googledatalab/pydatalab | solutionbox/structured_data/mltoolbox/_structured_data/_package.py | local_batch_predict | def local_batch_predict(training_dir, prediction_input_file, output_dir, mode, batch_size,
shard_files, output_format):
"""See batch_predict"""
# from . import predict as predict_module
from .prediction import predict as predict_module
if mode == 'evaluation':
model_dir = os.path.jo... | python | def local_batch_predict(training_dir, prediction_input_file, output_dir, mode, batch_size,
shard_files, output_format):
"""See batch_predict"""
# from . import predict as predict_module
from .prediction import predict as predict_module
if mode == 'evaluation':
model_dir = os.path.jo... | [
"def",
"local_batch_predict",
"(",
"training_dir",
",",
"prediction_input_file",
",",
"output_dir",
",",
"mode",
",",
"batch_size",
",",
"shard_files",
",",
"output_format",
")",
":",
"# from . import predict as predict_module",
"from",
".",
"prediction",
"import",
"pre... | See batch_predict | [
"See",
"batch_predict"
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/solutionbox/structured_data/mltoolbox/_structured_data/_package.py#L780-L807 |
googledatalab/pydatalab | solutionbox/structured_data/mltoolbox/_structured_data/_package.py | cloud_batch_predict | def cloud_batch_predict(training_dir, prediction_input_file, output_dir, mode, batch_size,
shard_files, output_format):
"""See batch_predict"""
# from . import predict as predict_module
from .prediction import predict as predict_module
if mode == 'evaluation':
model_dir = os.path.jo... | python | def cloud_batch_predict(training_dir, prediction_input_file, output_dir, mode, batch_size,
shard_files, output_format):
"""See batch_predict"""
# from . import predict as predict_module
from .prediction import predict as predict_module
if mode == 'evaluation':
model_dir = os.path.jo... | [
"def",
"cloud_batch_predict",
"(",
"training_dir",
",",
"prediction_input_file",
",",
"output_dir",
",",
"mode",
",",
"batch_size",
",",
"shard_files",
",",
"output_format",
")",
":",
"# from . import predict as predict_module",
"from",
".",
"prediction",
"import",
"pre... | See batch_predict | [
"See",
"batch_predict"
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/solutionbox/structured_data/mltoolbox/_structured_data/_package.py#L810-L842 |
googledatalab/pydatalab | solutionbox/structured_data/mltoolbox/_structured_data/prediction/predict.py | parse_arguments | def parse_arguments(argv):
"""Parse command line arguments.
Args:
argv: includes the script's name.
Returns:
argparse object
"""
parser = argparse.ArgumentParser(
description='Runs Prediction inside a beam or Dataflow job.')
# cloud options
parser.add_argument('--project-id',
... | python | def parse_arguments(argv):
"""Parse command line arguments.
Args:
argv: includes the script's name.
Returns:
argparse object
"""
parser = argparse.ArgumentParser(
description='Runs Prediction inside a beam or Dataflow job.')
# cloud options
parser.add_argument('--project-id',
... | [
"def",
"parse_arguments",
"(",
"argv",
")",
":",
"parser",
"=",
"argparse",
".",
"ArgumentParser",
"(",
"description",
"=",
"'Runs Prediction inside a beam or Dataflow job.'",
")",
"# cloud options",
"parser",
".",
"add_argument",
"(",
"'--project-id'",
",",
"help",
"... | Parse command line arguments.
Args:
argv: includes the script's name.
Returns:
argparse object | [
"Parse",
"command",
"line",
"arguments",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/solutionbox/structured_data/mltoolbox/_structured_data/prediction/predict.py#L30-L109 |
googledatalab/pydatalab | solutionbox/structured_data/mltoolbox/_structured_data/prediction/predict.py | make_prediction_pipeline | def make_prediction_pipeline(pipeline, args):
"""Builds the prediction pipeline.
Reads the csv files, prepends a ',' if the target column is missing, run
prediction, and then prints the formated results to a file.
Args:
pipeline: the pipeline
args: command line args
"""
# DF bug: DF does not work... | python | def make_prediction_pipeline(pipeline, args):
"""Builds the prediction pipeline.
Reads the csv files, prepends a ',' if the target column is missing, run
prediction, and then prints the formated results to a file.
Args:
pipeline: the pipeline
args: command line args
"""
# DF bug: DF does not work... | [
"def",
"make_prediction_pipeline",
"(",
"pipeline",
",",
"args",
")",
":",
"# DF bug: DF does not work with unicode strings",
"predicted_values",
",",
"errors",
"=",
"(",
"pipeline",
"|",
"'Read CSV Files'",
">>",
"beam",
".",
"io",
".",
"ReadFromText",
"(",
"str",
... | Builds the prediction pipeline.
Reads the csv files, prepends a ',' if the target column is missing, run
prediction, and then prints the formated results to a file.
Args:
pipeline: the pipeline
args: command line args | [
"Builds",
"the",
"prediction",
"pipeline",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/solutionbox/structured_data/mltoolbox/_structured_data/prediction/predict.py#L349-L373 |
googledatalab/pydatalab | solutionbox/structured_data/mltoolbox/_structured_data/prediction/predict.py | RunGraphDoFn.process | def process(self, element):
"""Run batch prediciton on a TF graph.
Args:
element: list of strings, representing one batch input to the TF graph.
"""
import collections
import apache_beam as beam
num_in_batch = 0
try:
assert self._session is not None
feed_dict = collectio... | python | def process(self, element):
"""Run batch prediciton on a TF graph.
Args:
element: list of strings, representing one batch input to the TF graph.
"""
import collections
import apache_beam as beam
num_in_batch = 0
try:
assert self._session is not None
feed_dict = collectio... | [
"def",
"process",
"(",
"self",
",",
"element",
")",
":",
"import",
"collections",
"import",
"apache_beam",
"as",
"beam",
"num_in_batch",
"=",
"0",
"try",
":",
"assert",
"self",
".",
"_session",
"is",
"not",
"None",
"feed_dict",
"=",
"collections",
".",
"de... | Run batch prediciton on a TF graph.
Args:
element: list of strings, representing one batch input to the TF graph. | [
"Run",
"batch",
"prediciton",
"on",
"a",
"TF",
"graph",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/solutionbox/structured_data/mltoolbox/_structured_data/prediction/predict.py#L165-L218 |
googledatalab/pydatalab | solutionbox/structured_data/mltoolbox/_structured_data/prediction/predict.py | CSVCoder.encode | def encode(self, tf_graph_predictions):
"""Encodes the graph json prediction into csv.
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 = []
for col in self._header:
row.append(str(tf_graph_predictions[col]))
return ','.join(row) | [
"def",
"encode",
"(",
"self",
",",
"tf_graph_predictions",
")",
":",
"row",
"=",
"[",
"]",
"for",
"col",
"in",
"self",
".",
"_header",
":",
"row",
".",
"append",
"(",
"str",
"(",
"tf_graph_predictions",
"[",
"col",
"]",
")",
")",
"return",
"','",
"."... | Encodes the graph json prediction into csv.
Args:
tf_graph_predictions: python dict.
Returns:
csv string. | [
"Encodes",
"the",
"graph",
"json",
"prediction",
"into",
"csv",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/solutionbox/structured_data/mltoolbox/_structured_data/prediction/predict.py#L251-L264 |
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
me... | python | 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
me... | [
"def",
"as_dataframe",
"(",
"self",
",",
"max_rows",
"=",
"None",
")",
":",
"max_rows",
"=",
"len",
"(",
"self",
".",
"_timeseries_list",
")",
"if",
"max_rows",
"is",
"None",
"else",
"max_rows",
"headers",
"=",
"[",
"{",
"'resource'",
":",
"ts",
".",
"... | 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
metric labels. Each row in this dataframe correspo... | [
"Creates",
"a",
"pandas",
"dataframe",
"from",
"the",
"query",
"metadata",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/google/datalab/stackdriver/monitoring/_query_metadata.py#L53-L92 |
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)
eval... | [
"def",
"get_train_eval_files",
"(",
"input_dir",
")",
":",
"data_dir",
"=",
"_get_latest_data_dir",
"(",
"input_dir",
")",
"train_pattern",
"=",
"os",
".",
"path",
".",
"join",
"(",
"data_dir",
",",
"'train*.tfrecord.gz'",
")",
"eval_pattern",
"=",
"os",
".",
... | Get preprocessed training and eval files. | [
"Get",
"preprocessed",
"training",
"and",
"eval",
"files",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/solutionbox/image_classification/mltoolbox/image/classification/_util.py#L53-L60 |
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')
return labels | [
"def",
"get_labels",
"(",
"input_dir",
")",
":",
"data_dir",
"=",
"_get_latest_data_dir",
"(",
"input_dir",
")",
"labels_file",
"=",
"os",
".",
"path",
".",
"join",
"(",
"data_dir",
",",
"'labels'",
")",
"with",
"file_io",
".",
"FileIO",
"(",
"labels_file",
... | Get a list of labels from preprocessed output dir. | [
"Get",
"a",
"list",
"of",
"labels",
"from",
"preprocessed",
"output",
"dir",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/solutionbox/image_classification/mltoolbox/image/classification/_util.py#L63-L69 |
googledatalab/pydatalab | solutionbox/image_classification/mltoolbox/image/classification/_util.py | read_examples | def read_examples(input_files, batch_size, shuffle, num_epochs=None):
"""Creates readers and queues for reading example protos."""
files = []
for e in input_files:
for path in e.split(','):
files.extend(file_io.get_matching_files(path))
thread_count = multiprocessing.cpu_count()
# The minimum numbe... | python | def read_examples(input_files, batch_size, shuffle, num_epochs=None):
"""Creates readers and queues for reading example protos."""
files = []
for e in input_files:
for path in e.split(','):
files.extend(file_io.get_matching_files(path))
thread_count = multiprocessing.cpu_count()
# The minimum numbe... | [
"def",
"read_examples",
"(",
"input_files",
",",
"batch_size",
",",
"shuffle",
",",
"num_epochs",
"=",
"None",
")",
":",
"files",
"=",
"[",
"]",
"for",
"e",
"in",
"input_files",
":",
"for",
"path",
"in",
"e",
".",
"split",
"(",
"','",
")",
":",
"file... | Creates readers and queues for reading example protos. | [
"Creates",
"readers",
"and",
"queues",
"for",
"reading",
"example",
"protos",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/solutionbox/image_classification/mltoolbox/image/classification/_util.py#L72-L117 |
googledatalab/pydatalab | solutionbox/image_classification/mltoolbox/image/classification/_util.py | override_if_not_in_args | def override_if_not_in_args(flag, argument, args):
"""Checks if flags is in args, and if not it adds the flag to args."""
if flag not in args:
args.extend([flag, argument]) | python | def override_if_not_in_args(flag, argument, args):
"""Checks if flags is in args, and if not it adds the flag to args."""
if flag not in args:
args.extend([flag, argument]) | [
"def",
"override_if_not_in_args",
"(",
"flag",
",",
"argument",
",",
"args",
")",
":",
"if",
"flag",
"not",
"in",
"args",
":",
"args",
".",
"extend",
"(",
"[",
"flag",
",",
"argument",
"]",
")"
] | Checks if flags is in args, and if not it adds the flag to args. | [
"Checks",
"if",
"flags",
"is",
"in",
"args",
"and",
"if",
"not",
"it",
"adds",
"the",
"flag",
"to",
"args",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/solutionbox/image_classification/mltoolbox/image/classification/_util.py#L120-L123 |
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)
return [tot... | python | 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)
return [tot... | [
"def",
"loss",
"(",
"loss_value",
")",
":",
"total_loss",
"=",
"tf",
".",
"Variable",
"(",
"0.0",
",",
"False",
")",
"loss_count",
"=",
"tf",
".",
"Variable",
"(",
"0",
",",
"False",
")",
"total_loss_update",
"=",
"tf",
".",
"assign_add",
"(",
"total_l... | Calculates aggregated mean loss. | [
"Calculates",
"aggregated",
"mean",
"loss",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/solutionbox/image_classification/mltoolbox/image/classification/_util.py#L126-L133 |
googledatalab/pydatalab | solutionbox/image_classification/mltoolbox/image/classification/_util.py | accuracy | def accuracy(logits, labels):
"""Calculates aggregated accuracy."""
is_correct = tf.nn.in_top_k(logits, labels, 1)
correct = tf.reduce_sum(tf.cast(is_correct, tf.int32))
incorrect = tf.reduce_sum(tf.cast(tf.logical_not(is_correct), tf.int32))
correct_count = tf.Variable(0, False)
incorrect_count = tf.Variab... | python | def accuracy(logits, labels):
"""Calculates aggregated accuracy."""
is_correct = tf.nn.in_top_k(logits, labels, 1)
correct = tf.reduce_sum(tf.cast(is_correct, tf.int32))
incorrect = tf.reduce_sum(tf.cast(tf.logical_not(is_correct), tf.int32))
correct_count = tf.Variable(0, False)
incorrect_count = tf.Variab... | [
"def",
"accuracy",
"(",
"logits",
",",
"labels",
")",
":",
"is_correct",
"=",
"tf",
".",
"nn",
".",
"in_top_k",
"(",
"logits",
",",
"labels",
",",
"1",
")",
"correct",
"=",
"tf",
".",
"reduce_sum",
"(",
"tf",
".",
"cast",
"(",
"is_correct",
",",
"t... | Calculates aggregated accuracy. | [
"Calculates",
"aggregated",
"accuracy",
"."
] | train | https://github.com/googledatalab/pydatalab/blob/d9031901d5bca22fe0d5925d204e6698df9852e1/solutionbox/image_classification/mltoolbox/image/classification/_util.py#L136-L147 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.