A business rules engine
Installation
pip install retrack
Usage
import retrack
rule = retrack.from_json("rule.json")
result = await rule.execute(your_data_df)
Creating a rule/model
A rule is a set of conditions and actions that are executed when the conditions are met. The conditions are evaluated using the data passed to the runner. The actions are executed when the conditions are met.
Each rule is composed of many nodes. To see each node type, check the nodes folder.
To create a rule, you need to create a JSON file with the following structure:
{
"nodes": {
"node id": {
"id": "node id",
"data": {},
"inputs": {},
"outputs": {},
"name": "node name",
},
// ... more nodes
}
}
The nodes key is a dictionary of nodes. Each node has the following properties:
id: The node id. This is used to reference the node in theinputsandoutputsproperties.data: The node data. This is used as a metadata for the node.inputs: The node inputs. This is used to reference the node inputs.outputs: The node outputs. This is used to reference the node outputs.name: The node name. This is used to define the node type.
The inputs and outputs properties are dictionaries of node connections. Each connection has the following properties:
node: The node id that is connected to the current node.input: The input name of the connection that is connected to the current node. This is only used in theinputsproperty.output: The output name of the connection that is connected to the current node. This is only used in theoutputsproperty.
To see some examples, check the examples folder.
Creating a custom node
To create a custom node, you need to create a class that inherits from the BaseNode class. Each node is a pydantic model, so you can use pydantic features to create your custom node. To see the available features, check the pydantic documentation.
To create a custom node you need to define the inputs and outputs of the node. To do this, you need to define the inputs and outputs class attributes. Let's see an example of a custom node that has two inputs, sum them and return the result:
import retrack
import pydantic
import pandas as pd
import typing
class SumInputsModel(pydantic.BaseModel):
input_value_0: retrack.InputConnectionModel
input_value_1: retrack.InputConnectionModel
class SumOutputsModel(pydantic.BaseModel):
output_value: retrack.OutputConnectionModel
class SumNode(retrack.BaseNode):
inputs: SumInputsModel
outputs: SumOutputsModel
async def run(self, input_value_0: pd.Series,
input_value_1: pd.Series,
) -> typing.Dict[str, pd.Series]:
output_value = input_value_0.astype(float) + input_value_1.astype(float)
return {
"output_value": output_value,
}
After creating the custom node, you need to register it in the nodes registry and pass the registry to the parser. Let's see an example:
import retrack
# Register the custom node
custom_registry = retrack.nodes_registry()
custom_registry.register("sum", SumNode)
rule = retrack.from_json("rule.json", nodes_registry=custom_registry)
Contributing
Contributions are welcome! Please read the contributing guidelines first.
2 maintainers
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file retrack-3.7.0.tar.gz.
File metadata
- Download URL: retrack-3.7.0.tar.gz
- Upload date:
- Size: 27.5 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via:
poetry/1.4.2 CPython/3.12.13 Linux/6.17.0-1022-azure
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
3737977bcec441f51ca5857a84ff24a2e0fab63f3760dec4ee9fd72a439917db
|
|
| MD5 |
ee901678cad21061dc63c60c40da8185
|
|
| BLAKE2b-256 |
0b4c833c0de713b4fdd37d98118afc4b8f57c35fb3d3c806d6468e6f0694a009
|
File details
Details for the file retrack-3.7.0-py3-none-any.whl.
File metadata
- Download URL: retrack-3.7.0-py3-none-any.whl
- Upload date:
- Size: 43.3 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via:
poetry/1.4.2 CPython/3.12.13 Linux/6.17.0-1022-azure
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
9db31f2360766722bb2034f3fa8b28cf156d395aa966d591d78f7fcb09f7c589
|
|
| MD5 |
a4f03471087e2cb2ea2551e9f9d669fd
|
|
| BLAKE2b-256 |
6307fee1342873cff1027aa49ce0d1591ce1f8c61077364765cbd84a11f4b374
|
