whylogs.app.logger
¶
Class and functions for whylogs logging
Module Contents¶
Classes¶
Class for logging whylogs statistics. |
Functions¶
|
Attributes¶
- whylogs.app.logger.SegmentTag¶
- whylogs.app.logger.Segment¶
- whylogs.app.logger._TAG_PREFIX = whylogs.tag.¶
- whylogs.app.logger._TAG_KEY = key¶
- whylogs.app.logger._TAG_VALUE = value¶
- whylogs.app.logger.logger¶
- class whylogs.app.logger.Logger(session_id: str, dataset_name: str, dataset_timestamp: Optional[datetime.datetime] = None, session_timestamp: Optional[datetime.datetime] = None, tags: Optional[Dict[str, str]] = None, metadata: Optional[Dict[str, str]] = None, writers: Optional[List[whylogs.app.writers.Writer]] = None, metadata_writer: Optional[whylogs.app.metadata_writer.MetadataWriter] = None, verbose: bool = False, with_rotation_time: Optional[str] = None, interval: int = 1, cache_size: int = 1, segments: Optional[Union[List[Segment], List[str], str]] = None, profile_full_dataset: bool = False, constraints: Optional[whylogs.core.statistics.constraints.DatasetConstraints] = None)¶
Class for logging whylogs statistics.
- Parameters
session_id – The session ID value. Should be set by the Session boject
dataset_name – The name of the dataset. Gets included in the DatasetProfile metadata and can be used in generated filenames.
dataset_timestamp – Optional. The timestamp that the logger represents
session_timestamp – Optional. The time the session was created
tags – Optional. Dictionary of key, value for aggregating data upstream
metadata – Optional. Dictionary of key, value. Useful for debugging (associated with every single dataset profile)
writers – Optional. List of Writer objects used to write out the data
metadata_writer – Optional. MetadataWriter object used to write non-profile information
with_rotation_time – Optional. Log rotation interval, consisting of digits with unit specification, e.g. 30s, 2h, d. units are seconds (“s”), minutes (“m”), hours, (“h”), or days (“d”) Output filenames will have a suffix reflecting the rotation interval.
interval – Deprecated: Interval multiplier for with_rotation_time, defaults to 1.
verbose – enable debug logging
cache_size – dataprofiles to cache
segments –
- Can be either:
Autosegmentation source, one of [“auto”, “local”]
List of tag key value pairs for tracking data segments
List of tag keys for which we will track every value
None, no segments will be used
profile_full_dataset – when segmenting dataset, an option to keep the full unsegmented profile of the dataset.
constraints – static assertions to be applied to streams and summaries.
- __enter__(self)¶
- __exit__(self, exc_type, exc_val, exc_tb)¶
- property profile(self) whylogs.core.DatasetProfile ¶
- Returns
the last backing dataset profile
- Return type
- tracking_checks(self)¶
- property segmented_profiles(self) Dict[str, whylogs.core.DatasetProfile] ¶
- Returns
the last backing dataset profile
- Return type
Dict[str, DatasetProfile]
- get_segment(self, segment: Segment) Optional[whylogs.core.DatasetProfile] ¶
- set_segments(self, segments: Union[List[Segment], List[str], str]) None ¶
- _retrieve_local_segments(self) Union[List[Segment], List[str], str] ¶
Retrieves local segments
- _intialize_profiles(self, dataset_timestamp: Optional[datetime.datetime] = datetime.datetime.now(datetime.timezone.utc)) None ¶
- _set_rotation(self, with_rotation_time: str = None)¶
- rotate_when(self, time)¶
- should_rotate(self)¶
- _rotate_time(self)¶
rotate with time add a suffix
- flush(self, rotation_suffix: Optional[str] = None)¶
Synchronously perform all remaining write tasks
- full_profile_check(self) bool ¶
returns a bool to determine if unsegmented dataset should be profiled.
- close(self) Optional[whylogs.core.DatasetProfile] ¶
Flush and close out the logger, outputs the last profile
- Returns
the result dataset profile. None if the logger is closed
- log(self, features: Optional[Dict[str, any]] = None, feature_name: Optional[str] = None, value: any = None, character_list: Optional[str] = None, token_method: Optional[Callable] = None)¶
Logs a collection of features or a single feature (must specify one or the other).
- Parameters
features – a map of key value feature for model input
feature_name – name of a single feature. Cannot be specified if ‘features’ is specified
value – value of as single feature. Cannot be specified if ‘features’ is specified
- log_segment_datum(self, feature_name, value, character_list: str = None, token_method: Optional[Callable] = None)¶
- log_metrics(self, targets, predictions, scores=None, model_type: whylogs.proto.ModelType = None, target_field=None, prediction_field=None, score_field=None)¶
- log_image(self, image, feature_transforms: Optional[List[Callable]] = None, metadata_attributes: Optional[List[str]] = METADATA_DEFAULT_ATTRIBUTES, feature_name: str = '')¶
API to track an image, either in PIL format or as an input path
- Parameters
feature_name – name of the feature
metadata_attributes – metadata attributes to extract for the images
feature_transforms – a list of callables to transform the input into metrics
- log_local_dataset(self, root_dir, folder_feature_name='folder_feature', image_feature_transforms=None, show_progress=False)¶
Log a local folder dataset It will log data from the files, along with structure file data like metadata, and magic numbers. If the folder has single layer for children folders, this will pick up folder names as a segmented feature
- Parameters
show_progress – showing the progress bar
image_feature_transforms – image transform that you would like to use with the image log
root_dir (str) – directory where dataset is located.
folder_feature_name (str, optional) – Name for the subfolder features, i.e. class, store etc.
- log_annotation(self, annotation_data)¶
Log structured annotation data ie. JSON like structures
- Parameters
annotation_data (Dict or List) – Description
- log_csv(self, filepath_or_buffer: Union[str, pathlib.Path, IO[AnyStr]], segments: Optional[Union[List[Segment], List[str]]] = None, profile_full_dataset: bool = False, **kwargs)¶
Log a CSV file. This supports the same parameters as :func`pandas.read_csv<pandas.read_csv>` function.
- Parameters
filepath_or_buffer – the path to the CSV or a CSV buffer
segments – define either a list of segment keys or a list of segments tags: [ {“key”:<featurename>,”value”: <featurevalue>},… ]
profile_full_dataset – when segmenting dataset, an option to keep the full unsegmented profile of the dataset
**kwargs – from pandas:read_csv
- log_dataframe(self, df, segments: Optional[Union[List[Segment], List[str]]] = None, profile_full_dataset: bool = False)¶
Generate and log a whylogs DatasetProfile from a pandas dataframe :param profile_full_dataset: when segmenting dataset, an option to keep the full unsegmented profile of the
dataset.
- Parameters
segments – specify the tag key value pairs for segments
df – the Pandas dataframe to log
- log_segments(self, data)¶
- log_segments_keys(self, data)¶
- log_fixed_segments(self, data)¶
- log_df_segment(self, df, segment: Segment)¶
- is_active(self)¶
Return the boolean state of the logger
- static _prefix_segment_tags(segment_key_values)¶
- whylogs.app.logger.hash_segment(seg: List[Dict]) str ¶