morpheus.stages.postprocess.timeseries_stage.TimeSeriesStage#

class TimeSeriesStage( c, resolution='1 h', min_window='12 h', hot_start=False, cold_end=False, filter_percent=90.0, zscore_threshold=8.0, )[source]#

Bases: PassThruTypeMixin, SinglePortStage

Perform time series anomaly detection and add prediction.

Uses default resolution of “hour”.

Parameters:

cmorpheus.config.Config: Pipeline configuration instance.
resolutionstr, default = “1 h”: Time series resolution. Logs will be binned into groups of this size. Uses the pandas time delta format, i.e., ‘10m’ for 10 minutes.
min_windowstr, default = “12 h”: Minimum window on either side of a log necessary for calculation. Logs will be skipped during a warmup phase while this window is filled. Uses the pandas time delta format, i.e., ‘10m’ for 10 minutes.
hot_startbool, default = False, is_flag = True: This flag prevents the stage from ignoring messages during a warm up phase while the min_window is filled. Enabling ‘hot_start’ will run calculations on all messages even if the min_window is not satisfied on both sides, i.e., during startup or teardown. This is likely to increase the number of false positives but can be helpful for debugging and testing on small datasets.
cold_endbool, default = False, is_flag = True: This flag is the inverse of hot_start and prevents ignoring messages that don’t satisfy the min_window during the shutdown phase. This is likely to increase the number of false positives but can be helpful for debugging and testing on small datasets.
filter_percentfloat, default = 90.0, min=0.0, max=100.0: The percent of timeseries samples to remove from the inverse FFT for spectral density filtering.
zscore_thresholdfloat, default = 8.0, min=0.0: The z-score threshold required to flag datapoints. The value indicates the number of standard deviations from the mean that is required to be flagged. Increasing this value will decrease the number of detections.

Attributes:

has_multi_input_ports: Indicates if this stage has multiple input ports.
has_multi_output_ports: Indicates if this stage has multiple output ports.
input_ports: Input ports to this stage.
is_built: Indicates if this stage has been built.
is_pre_built: Indicates if this stage has been built.
name: The name of the stage.
output_ports: Output ports from this stage.
unique_name: Unique name of stage.

Methods

`accepted_types`()	Accepted input types for this stage are returned.
`build`(builder[, do_propagate])	Build this stage.
`can_build`([check_ports])	Determines if all inputs have been built allowing this node to be built.
`can_pre_build`([check_ports])	Determines if all inputs have been built allowing this node to be built.
`get_all_input_stages`()	Get all input stages to this stage.
`get_all_inputs`()	Get all input senders to this stage.
`get_all_output_stages`()	Get all output stages from this stage.
`get_all_outputs`()	Get all output receivers from this stage.
`get_needed_columns`()	Stages which need to have columns inserted into the dataframe, should populate the `self._needed_columns` dictionary with mapping of column names to `morpheus.common.TypeId`.
`join`()	Awaitable method that stages can implement this to perform cleanup steps when pipeline is stopped.
`start_async`()	This function is called along with on_start during stage initialization.
`stop`()	Stages can implement this to perform cleanup steps when pipeline is stopped.
`supported_execution_modes`()	Returns a tuple of supported execution modes of this stage.
`supports_cpp_node`()	Specifies whether this Stage is capable of creating C++ nodes.

compute_schema

_build(builder, input_nodes)[source]#

This function is responsible for constructing this stage’s internal mrc.SegmentObject object. The input of this function contains the returned value from the upstream stage.

The input values are the mrc.Builder for this stage and a list of parent nodes.

Parameters:

buildermrc.Builder: mrc.Builder object for the pipeline. This should be used to construct/attach the internal mrc.SegmentObject.
input_nodeslist[mrc.SegmentObject]: List containing the input mrc.SegmentObject objects.

Returns:

list[mrc.SegmentObject]: List of tuples containing the output mrc.SegmentObject object from this stage.

accepted_types()[source]#

Accepted input types for this stage are returned.

Returns:

typing.Tuple[ControlMessage]: Accepted input types.

build(builder, do_propagate=True)[source]#

Build this stage.

Parameters:

buildermrc.Builder: MRC segment for this stage.
do_propagatebool, optional: Whether to propagate to build output stages, by default True.

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.

can_pre_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.

compute_schema(schema)[source]#

Compute the schema for this stage based on the incoming schema from upstream stages.

Incoming schema and type information from upstream stages is available via the schema.input_schemas and schema.input_types properties.

Derived classes need to override this method, can set the output type(s) on schema by calling set_type for all output ports. For example a simple pass-thru stage might perform the following:

>>> for (port_idx, port_schema) in enumerate(schema.input_schemas):
...     schema.output_schemas[port_idx].set_type(port_schema.get_type())
>>>

If the port types in upstream_schema are incompatible the stage should raise a RuntimeError.

get_all_input_stages()[source]#

Get all input stages to this stage.

Returns:

list[morpheus.pipeline.pipeline.StageBase]: All input stages.

get_all_inputs()[source]#

Get all input senders to this stage.

Returns:

list[morpheus.pipeline.pipeline.Sender]: All input senders.

get_all_output_stages()[source]#

Get all output stages from this stage.

Returns:

list[morpheus.pipeline.pipeline.StageBase]: All output stages.

get_all_outputs()[source]#

Get all output receivers from this stage.

Returns:

list[morpheus.pipeline.pipeline.Receiver]: All output receivers.

get_needed_columns()[source]#: Stages which need to have columns inserted into the dataframe, should populate the self._needed_columns dictionary with mapping of column names to morpheus.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_ports: list[Receiver]#

Input ports to this stage.

Returns:

list[morpheus.pipeline.pipeline.Receiver]: Input ports to this stage.

property is_built: bool#

Indicates if this stage has been built.

Returns:

bool: True if stage is built, False otherwise.

property is_pre_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 stop during a graceful shutdown, but may not be called if the pipeline is terminated.

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[Sender]#

Output ports from this stage.

Returns:

list[morpheus.pipeline.pipeline.Sender]: Output ports from this stage.

async start_async()[source]#: This function is called along with on_start during stage initialization. Allows stages to utilize the asyncio loop if needed.

stop()[source]#: Stages can implement this to perform cleanup steps when pipeline is stopped.

supported_execution_modes()[source]#

Returns a tuple of supported execution modes of this stage. By default this returns (ExecutionMode.GPU,). Subclasses can override this method to specify different execution modes.

For most stages the values will be static, and this can be accomplished by making use of either the CpuOnlyMixin or GpuAndCpuMixin mixins.

However, complex stages may choose to make this decision at runtime, in which case this method should be overridden. directly within the stage class.

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.