algomancy-scenario
Scenario modeling utilities for Algomancy: define algorithms and parameters, run scenarios against data, and compute KPIs.
Features
Scenariolifecycle with statuses (CREATED,QUEUED,PROCESSING,COMPLETE,FAILED)BaseAlgorithmand parameter classes to define pluggable algorithms- KPI framework (
BaseKPI) to compute metrics from algorithm results - Works with
algomancy-datadata sources and can be orchestrated from the GUI
Installation
pip install -e packages/algomancy-scenario
Requires Python >= 3.14.
Quick start
Define a simple algorithm and KPI, then run a Scenario:
from algomancy_scenario import (
Scenario, ScenarioStatus,
BaseAlgorithm, BaseParameterSet, BaseKPI,
)
from algomancy_data import DataSource, DataClassification
# Minimal parameters type
class ExampleParams(BaseParameterSet):
def serialize(self) -> dict:
return {"hello": "world"}
# Minimal algorithm
class ExampleAlgorithm(BaseAlgorithm):
def __init__(self):
super().__init__(name="Example", params=ExampleParams())
@staticmethod
def initialize_parameters() -> ExampleParams: # used by GUI tooling
return ExampleParams()
def run(self, data: DataSource) -> dict:
# do something with data and return a result dictionary
self.set_progress(100)
return {"count_tables": len(data.list_tables())}
# Minimal KPI
class CountTablesKPI(BaseKPI):
def __init__(self):
super().__init__(name="Tables", improvement_direction=None)
def compute_and_check(self, result: dict):
self.value = result["count_tables"]
# Prepare data
ds = DataSource(ds_type=DataClassification.MASTER_DATA, name="warehouse")
# Build and run scenario
scenario = Scenario(
tag="demo",
input_data=ds,
kpis={"tables": CountTablesKPI()},
algorithm=ExampleAlgorithm(),
)
scenario.process()
assert scenario.status == ScenarioStatus.COMPLETE
print("Tables KPI:", scenario.kpis["tables"].value)
Related docs and examples
- Example app demonstrates scenario wiring:
example/pages/ScenarioPageContent.py - Algorithm/KPI examples:
example/templates/algorithm/andexample/templates/kpi/
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