reasoner

The reasoner module provides functions to perform reasoning tasks. It takes one or more .vada file paths to files with Vadalog rules and applies logical reasoning, optionally including time mesuring, explanations and persist the results flags.

Functions

def reason(vada_file_paths, params=None, measure_time=False, to_explain=False, to_persist=False)

Parameters:

vada_file_paths (str or list): The path(s) to the .vada file(s) to be used for reasoning. This can be a single string for one file or a list of strings for multiple files.
params (dict, optional): A dictionary of additional parameters to customize the reasoning process.
measure_time (bool, optional): Whether to measure the time taken for the reasoning process.
to_explain (bool, optional): Whether perform a reasoning task whith results to be explained.
to_persist (bool, optional): Whether to persist the virtual kg or let it be ephemeral.

Returns:

A dictionary representing the virtual knowledge graph resulting from the reasoning process.

Raises: Exception in the following cases:

duplicate .vada filenames are found in the provided file paths.
there is an error while opening a .vada file.

Example

import prometheux_chain as pmtx
import os

# Define the path to the .vada file to be used for reasoning
os.environ['PMTX_TOKEN'] = 'my_pmtx_token'

# Perform reasoning on the .vada file
virtual_kg = pmtx.reason(
    vada_file_paths="min_distance_from_city.vada",
    params={"filter": "New York", "min_distance": 100.0},
    to_explain=True,
    to_persist=True
)

A possible set of Vadalog rules in the min_distance_from_city.vada file is as follows:

% Define the input predicate, marking the entry point for reasoning.
@input("distance"). 

% Bind the predicate `distance` to a CSV file containing city distances.
% - `csv multiline=true`: Allows multi-line CSV files.
% - `useHeaders=true`: Indicates the CSV file includes column headers.
% - `"disk/data"`: Directory location of the CSV file.
% - `"city_distance.csv"`: The CSV file containing distances between cities.
@bind("distance", "csv multiline=true, useHeaders=true", "disk/data", "city_distance.csv"). 

% Define a rule to calculate the minimum distance:
% - `From`: Starting city.
% - `To`: Destination city.
% - `D`: Distance between `From` and `To`.
% - `${filter}`: Placeholder for the starting city (to be replaced dynamically).
% - `${min_distance}`: Placeholder for the minimum distance constraint.
min_distance(From, To) :- distance(From, To, D), From == ${filter}, D > ${min_distance}.

% Define the schema for the `distance` predicate:
% - `From`: City of origin (string).
% - `To`: Destination city (string).
% - `D`: Distance between the two cities (double).
@model("distance", "['From:string','To:string','D:double']", "The city [From] is distant [D] miles from the city [To]"). 

% Define the schema for the `min_distance` predicate:
% - `From`: Starting city (string).
% - `To`: Destination city (string).
@model("min_distance", "['From:string','To:string']", "The constraint of minimum distance is respected between the two cities [From] and [To]"). 

% Specify `min_distance` as the output predicate of the reasoning process.
@output("min_distance").

Functions​

Functions