- class DuoSourceStage(c, input_glob, watch_directory=False, max_files=-1, file_type=<FileTypes.Auto: 0>, repeat=1, sort_glob=False, recursive=True, queue_max_size=128, batch_timeout=5.0)[source]
Bases:
morpheus.stages.input.autoencoder_source_stage.AutoencoderSourceStageSource stage is used to load Duo Authentication messages.
- Adds the following derived features:
locincrement: Increments every time a log contains a distinct city within a day.logcount: Tracks the number of logs generated by a user within a day.
- Parameters
- c
morpheus.config.Config - input_globstr
- watch_directorybool, default = False
- max_files: int, default = -1
- file_type
morpheus.common.FileTypes, default = ‘FileTypes.Auto’. - repeat: int, default = 1
- sort_globbool, default = False
- recursive: bool, default = True
- queue_max_size: int, default = 128
- batch_timeout: float, default = 5.0
Pipeline configuration instance.
Input glob pattern to match files to read. For example,
/input_dir/*.jsonwould read all files with the ‘json’ extension in the directory input_dir.The watch directory option instructs this stage to not close down once all files have been read. Instead it will read all files that match the ‘input_glob’ pattern, and then continue to watch the directory for additional files. Any new files that are added that match the glob will then be processed.
Max number of files to read. Useful for debugging to limit startup time. Default value of -1 is unlimited.
Indicates what type of file to read. Specifying ‘auto’ will determine the file type from the extension. Supported extensions: ‘json’, ‘csv’
How many times to repeat the dataset. Useful for extending small datasets in debugging.
If true the list of files matching
input_globwill be processed in sorted order.If true, events will be emitted for the files in subdirectories that match
input_glob.Maximum queue size to hold the file paths to be processed that match
input_glob.Timeout to retrieve batch messages from the queue.
- c
- Attributes
has_multi_input_portshas_multi_output_portsinput_countinput_portsis_builtnameoutput_portsunique_name
Indicates if this stage has multiple input ports.
Indicates if this stage has multiple output ports.
Return None for no max input count
Input ports to this stage.
Indicates if this stage has been built.
The name of the stage.
Output ports from this stage.
Unique name of stage.
Methods
batch_user_split(x, userid_column_name, ...)Creates a dataframe for each userid.
build(builder[, do_propagate])Build this stage.
can_build([check_ports])Determines if all inputs have been built allowing this node to be built.
change_columns(df)Removes characters (_,.,{,},:) from the names of the dataframe columns.
derive_features(df, feature_columns)Derives feature columns from the DUO (logs) source columns.
files_to_dfs_per_user(x, userid_column_name, ...)After loading the input batch of DUO logs into a dataframe, this method builds a dataframe for each set of userid rows in accordance with the specified filter condition.
Get all input stages to this stage.
Get all input senders to this stage.
Get all output stages from this stage.
Get all output receivers from this stage.
get_match_pattern(glob_split)Return a file match pattern
Stages which need to have columns inserted into the dataframe, should populate the
self._needed_columnsdictionary with mapping of column names tomorpheus.common.TypeId.join()Awaitable method that stages can implement this to perform cleanup steps when pipeline is stopped.
repeat_df(df, repeat_count)This function iterates over the same dataframe to extending small datasets in debugging with incremental updates to the
event_dtandeventTimecolumns.set_needed_columns(needed_columns)Sets the columns needed to perform preallocation.
stop()Stages can implement this to perform cleanup steps when pipeline is stopped.
Specifies whether this Stage is capable of creating C++ nodes.
- _build(builder, in_ports_streams)[source]
This function is responsible for constructing this stage’s internal
mrc.SegmentObjectobject. The input of this function contains the returned value from the upstream stage.The input values are the
mrc.Builderfor this stage and aStreamPairtuple which contain the inputmrc.SegmentObjectobject and the message data type.- Parameters
- builder
mrc.Builder - in_ports_streams
morpheus.pipeline.pipeline.StreamPair
mrc.Builderobject for the pipeline. This should be used to construct/attach the internalmrc.SegmentObject.List of tuples containing the input
mrc.SegmentObjectobject and the message data type.- builder
- Returns
typing.List[morpheus.pipeline.pipeline.StreamPair]
List of tuples containing the output
mrc.SegmentObjectobject from this stage and the message data type.
- _build_source(seg)[source]
Abstract method all derived Source classes should implement. Returns the same value as
build.- Returns
morpheus.pipeline.pipeline.StreamPair:
A tuple containing the output
mrc.SegmentObjectobject from this stage and the message data type.
- static batch_user_split(x, userid_column_name, userid_filter, datetime_column_name='event_dt')[source]
Creates a dataframe for each userid.
- Parameters
- xtyping.List[pd.DataFrame]
- userid_column_namestr
- userid_filterstr
- datetime_column_namestr
List of dataframes.
Name of a dataframe column used for categorization.
Only rows with the supplied userid are filtered.
Name of the dataframe column used to sort the rows.
- Returns
- user_dfstyping.Dict[str, pd.DataFrame]
Dataframes, each of which is associated with a single userid.
- build(builder, do_propagate=True)[source]
Build this stage.
- Parameters
- builder
mrc.Builder - do_propagatebool, optional
MRC segment for this stage.
Whether to propagate to build output stages, by default True.
- builder
- can_build(check_ports=False)[source]
Determines if all inputs have been built allowing this node to be built.
- Parameters
- check_portsbool, optional
Check if we can build based on the input ports, by default False.
- Returns
- bool
True if we can build, False otherwise.
- static change_columns(df)[source]
Removes characters (_,.,{,},:) from the names of the dataframe columns.
- Parameters
- df
pd.DataFrame
Dataframe that requires column renaming.
- df
- Returns
- df
pd.DataFrame
Dataframe with renamed columns.
- df
- static derive_features(df, feature_columns)[source]
Derives feature columns from the DUO (logs) source columns.
- Parameters
- dfpd.DataFrame
- feature_columnstyping.List[str]
Dataframe for deriving columns.
Names of columns that are need to be derived.
- Returns
- dftyping.List[pd.DataFrame]
Dataframe with actual and derived columns.
- static files_to_dfs_per_user(x, userid_column_name, feature_columns, userid_filter=None, repeat_count=1)[source]
After loading the input batch of DUO logs into a dataframe, this method builds a dataframe for each set of userid rows in accordance with the specified filter condition.
- Parameters
- xtyping.List[str]
- userid_column_namestr
- feature_columnstyping.List[str]
- userid_filterstr
- repeat_countstr
List of messages.
Name of the column used for categorization.
Feature column names.
Only rows with the supplied userid are filtered.
Number of times the given rows should be repeated.
- Returns
- df_per_usertyping.Dict[str, pd.DataFrame]
Dataframe per userid.
- get_all_input_stages()[source]
Get all input stages to this stage.
- Returns
- typing.List[
morpheus.pipeline.pipeline.StreamWrapper]
All input stages.
- typing.List[
- get_all_inputs()[source]
Get all input senders to this stage.
- Returns
- typing.List[
morpheus.pipeline.pipeline.Sender]
All input senders.
- typing.List[
- get_all_output_stages()[source]
Get all output stages from this stage.
- Returns
- typing.List[
morpheus.pipeline.pipeline.StreamWrapper]
All output stages.
- typing.List[
- get_all_outputs()[source]
Get all output receivers from this stage.
- Returns
- typing.List[
morpheus.pipeline.pipeline.Receiver]
All output receivers.
- typing.List[
- get_match_pattern(glob_split)[source]
Return a file match pattern
- get_needed_columns()[source]
Stages which need to have columns inserted into the dataframe, should populate the
self._needed_columnsdictionary with mapping of column names tomorpheus.common.TypeId. This will ensure that the columns are allocated and populated with null values.- property has_multi_input_ports: bool
Indicates if this stage has multiple input ports.
- Returns
- bool
True if stage has multiple input ports, False otherwise.
- property has_multi_output_ports: bool
Indicates if this stage has multiple output ports.
- Returns
- bool
True if stage has multiple output ports, False otherwise.
- property input_count: int
Return None for no max input count
- property input_ports: List[morpheus.pipeline.receiver.Receiver]
Input ports to this stage.
- Returns
- typing.List[
morpheus.pipeline.pipeline.Receiver]
Input ports to this stage.
- typing.List[
- property is_built: bool
Indicates if this stage has been built.
- Returns
- bool
True if stage is built, False otherwise.
- async join()[source]
Awaitable method that stages can implement this to perform cleanup steps when pipeline is stopped. Typically this is called after
stopduring a graceful shutdown, but may not be called if the pipeline is terminated on its own.- property name: str
The name of the stage. Used in logging. Each derived class should override this property with a unique name.
- Returns
- str
Name of a stage.
- property output_ports: List[morpheus.pipeline.sender.Sender]
Output ports from this stage.
- Returns
- typing.List[
morpheus.pipeline.pipeline.Sender]
Output ports from this stage.
- typing.List[
- static repeat_df(df, repeat_count)[source]
This function iterates over the same dataframe to extending small datasets in debugging with incremental updates to the
event_dtandeventTimecolumns.- Parameters
- dfpd.DataFrame
- repeat_countint
To be repeated dataframe.
Number of times the given dataframe should be repeated.
- Returns
- df_arraytyping.List[pd.DataFrame]
List of repeated dataframes.
- set_needed_columns(needed_columns)[source]
Sets the columns needed to perform preallocation. This should only be called by the Pipeline at build time. The needed_columns shoudl contain the entire set of columns needed by any other stage in this segment.
- stop()[source]
Stages can implement this to perform cleanup steps when pipeline is stopped.
- supports_cpp_node()[source]
Specifies whether this Stage is capable of creating C++ nodes. During the build phase, this value will be combined with
CppConfig.get_should_use_cpp()to determine whether or not a C++ node is created. This is an instance method to allow runtime decisions and derived classes to override base implementations.- property unique_name: str
Unique name of stage. Generated by appending stage id to stage name.
- Returns
- str
Unique name of stage.
