CBRkit

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Customizable Case-Based Reasoning (CBR) toolkit for Python with a built-in API and CLI.

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CBRkit

CBRkit is a customizable and modular toolkit for Case-Based Reasoning (CBR) in Python. It provides a set of tools for loading cases and queries, defining similarity measures, and retrieving cases based on a query. The toolkit is designed to be flexible and extensible, allowing you to define custom similarity measures or use built-in ones. Retrieval pipelines are declared by composing these metrics, and the toolkit provides utility functions for applying them on a casebase. Additionally, it offers ready-to-use API and CLI interfaces for easy integration into your projects. The library is fully typed, enabling autocompletion and type checking in modern IDEs like VSCode and PyCharm.

To get started, we provide a demo project which contains a casebase and a predefined retriever. Further examples can be found in our tests and documentation. The following modules are part of CBRkit:

• cbrkit.loaders: Functions for loading cases and queries.
• cbrkit.sim: Similarity generator functions for common data types like strings and numbers.
• cbrkit.retrieval: Functions for defining and applying retrieval pipelines.
• cbrkit.typing: Generic type definitions for defining custom functions.

Installation

The library is available on PyPI, so you can install it with pip:

pip install cbrkit

It comes with several optional dependencies for certain tasks like NLP which can be installed with:

pip install cbrkit[EXTRA_NAME,...]

where EXTRA_NAME is one of the following:

• nlp: Standalone NLP tools levenshtein, nltk, openai, and spacy
• transformers: Advanced NLP tools based on pytorch and transformers
• cli: Command Line Interface (CLI)
• api: REST API Server
• all: All of the above

Loading Cases

The first step is to load cases and queries. We provide predefined functions for the most common formats like CSV, JSON, and XML. Additionally, CBRkit also integrates with pandas for loading data frames. The following example shows how to load cases and queries from a CSV file using pandas:

import pandas as pd
import cbrkit

df = pd.read_csv("path/to/cases.csv")
casebase = cbrkit.loaders.pandas(df)

When dealing with formats like JSON, the files can be loaded directly:

casebase = cbrkit.loaders.json("path/to/cases.json")

Defining Queries

CBRkit expects the type of the queries to match the type of the cases. You may define a single query directly in Python as follows

# for pandas
query = pd.Series({"name": "John", "age": 25})
# for json
query = {"name": "John", "age": 25}

If you have a collection of queries, you can load them using the same loader functions as for the cases.

 # for pandas
queries = cbrkit.loaders.pandas(pd.read_csv("path/to/queries.csv"))
# for json
queries = cbrkit.loaders.json("path/to/queries.json")

In case your query collection only contains a single entry, you can use the singleton function to extract it.

query = cbrkit.helpers.singleton(queries)

Similarity Measures and Aggregation

The next step is to define similarity measures for the cases and queries. It is possible to define custom measures, use built-in ones, or combine both.

Custom Similarity Measures

In CBRkit, a similarity measure is defined as a function that takes two arguments (a case and a query) and returns a similarity score: sim = f(x, y). It also supports pipeline-based similarity measures that are popular in NLP where a list of tuples is passed to the similarity measure: sims = f([(x1, y1), (x2, y2), ...]). This generic approach allows you to define custom similarity measures for your specific use case. For instance, the following function not only checks for strict equality, but also for partial matches (e.g., x = "blue" and y = "light blue"):

def color_similarity(x: str, y: str) -> float:
    if x == y:
        return 1.0
    elif x in y or y in x:
        return 0.5

    return 0.0

Please note: CBRkit inspects the signature of custom similarity functions to perform some checks. You need to make sure that the two parameters are named x and y, otherwise CBRkit will throw an error.

Built-in Similarity Measures

CBRkit also contains a selection of built-in similarity measures for the most common data types in the module cbrkit.sim. They are provided through generator functions that allow you to customize the behavior of the built-in measures. For example, an spacy-based embedding similarity measure can be obtained as follows:

semantic_similarity = cbrkit.sim.strings.spacy(model="en_core_web_lg")

Please note: Calling the function cbrkit.sim.strings.spacy returns a similarity function itself that has the same signature as the color_similarity function defined above.

An overview of all available similarity measures can be found in the module documentation.

Global Similarity and Aggregation

When dealing with cases that are not represented through elementary data types like strings, we need to aggregate individual measures to obtain a global similarity score. We provide a predefined aggregator that transforms a list of similarities into a single score. It can be used with custom and/or built-in measures.

similarities = [0.8, 0.6, 0.9]
aggregator = cbrkit.sim.aggregator(pooling="mean")
global_similarity = aggregator(similarities)

For the common use case of attribute-value based data, CBRkit provides a predefined global similarity measure that can be used as follows:

cbrkit.sim.attribute_value(
    attributes={
        "price": cbrkit.sim.numbers.linear(),
        "color": color_similarity # custom measure
        ...
    },
    aggregator=cbrkit.sim.aggregator(pooling="mean"),
)

The attribute_value function lets you define measures for each attribute of the cases/queries as well as the aggregation function. It also allows to use custom measures like the color_similarity function defined above.

Please note: The custom measure is not executed (i.e., there are no parenthesis at the end), but instead passed as a reference to the attribute_value function.

You may even nest similarity functions to create measures for object-oriented cases:

cbrkit.sim.attribute_value(
    attributes={
        "manufacturer": cbrkit.sim.attribute_value(
            attributes={
                "name": cbrkit.sim.strings.spacy(model="en_core_web_lg"),
                "country": cbrkit.sim.strings.levenshtein(),
            },
            aggregator=cbrkit.sim.aggregator(pooling="mean"),
        ),
        "color": color_similarity # custom measure
        ...
    },
    aggregator=cbrkit.sim.aggregator(pooling="mean"),
)

Retrieval

The final step is to retrieve cases based on the loaded queries. The cbrkit.retrieval module provides utility functions for this purpose. You first build a retrieval pipeline by specifying a global similarity function and optionally a limit for the number of retrieved cases.

retriever = cbrkit.retrieval.build(
    cbrkit.sim.attribute_value(...),
    limit=10
)

This retriever can then be applied on a casebase to retrieve cases for a given query.

result = cbrkit.retrieval.apply(casebase, query, retriever)

Our result has the following attributes:

• similarities: A dictionary containing the similarity scores for each case.
• ranking A list of case indices sorted by their similarity score.
• casebase The casebase containing only the retrieved cases (useful for downstream tasks).

Combining Multiple Retrieval Pipelines

In some cases, it is useful to combine multiple retrieval pipelines, for example when applying the MAC/FAC pattern where a cheap pre-filter is applied to the whole casebase before a more expensive similarity measure is applied on the remaining cases. To use this pattern, first create the corresponding retrievers using the builder:

retriever1 = cbrkit.retrieval.build(..., min_similarity=0.5, limit=20)
retriever2 = cbrkit.retrieval.build(..., limit=10)

Then apply all of them sequentially by passing them as a list or tuple to the apply function:

result = cbrkit.retrieval.apply(casebase, query, (retriever1, retriever2))

The result has the following two attributes:

• final: Result of the last retriever in the list.
• steps: A list of results for each retriever in the list.

Both final and each entry in steps have the same attributes as discussed previously. The returned result also has these entries which are an alias for the corresponding entries in final (i.e., result.ranking == result.final.ranking).

REST API and CLI

In order to use the built-in API and CLI, you need to define a retriever in a Python module using the function cbrkit.retrieval.build(). For example, the file ./retriever_module.py could contain the following code:

import cbrkit

custom_retriever = cbrkit.retrieval.build(
    cbrkit.sim.attribute_value(...),
    limit=10
)

Our custom retriever can then be specified for the API/CLI using standard Python module syntax: retriever_module:custom_retriever.

CLI

When installing with the cli extra, CBRkit provides a command line interface that can be started with the following command:

cbrkit retrieve PATH_TO_CASEBASE PATH_TO_QUERY retriever_module:custom_retriever

It will then print the retrieval results to the console, so you could pipe the output to a file or another command.

API

When installing with the api extra, CBRkit provides a REST API server that can be started with the following command:

cbrkit serve retriever_module:custom_retriever

It offers a single endpoint /retrieve that accepts POST requests with a JSON body with the following structure:

{
  "casebase": {
    "case1": ...,
    "case2": ...
  },
  "queries": {
    "query1": ...,
    "query2": ...
  }
}

The server will return a JSON object containing the retrieval results for each query.