t2ebm 0.1.2
A Natural Language Interface to Explainable Boosting Machines
Requires Python: >=3.6

# TalkToEBM
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<br/>

> ### A Natural Language Interface to Explainable Boosting Machines

<p align="center">
  <img src="images/landing.png" alt="drawing" width="900"/>
</p>

TalkToEBM is an open-source package that provides a natural language interface to [Explainable Boosting Machines (EBMs)](https://github.com/interpretml/interpret). With this package, you can convert the graphs of Explainable Boosting Machines to text and generate prompts for LLMs. We also have higher-level functions that directly ask the LLM to describe entire models. This package is under active development, so the current API is not guaranteed to stay stable.

Features:
- [x] Convert EBMs and their graphs to text that can be understood by LLMs. Includes confidence intervals.
- [x] Ask the LLM to describe and summarize individual graphs or entire models.
- [x] Modular approach that allows to write custom prompts - ask the LLM to perform any desired task with the EBM.
- [x] Automatic simplification of minor details in graphs to stay within the desired token limit.

# Installation

Python 3.8+ | Linux, Mac, Windows
```sh
pip install t2ebm
```

# High-Level API: Pass the EBM to the LLM

Here, we give an overview of high-level API functions that ask the LLM to describe graphs and LLMs. These functions take care of the prompts for you. The API Reference can be found [here](http://interpret.ml/TalkToEBM/api_reference.html).

We have trained an ```ExplainableBoostingClassifier``` on the Kaggle [Spaceship Titanic Dataset](https://www.kaggle.com/competitions/spaceship-titanic/overview).

```python
ebm = ExplainableBoostingClassifier(feature_names=feature_names)
ebm.fit(X_train, y_train)
```
Getting the LLM to describe a graph from the model is as simple as this. Let's describe the graph for feature 0, 'Home Planet'.

```python
import t2ebm

t2ebm.describe_graph('gpt-4-turbo-2024-04-09', ebm, 0)
```

> **GPT-4:** *The graph illustrates the effects of the categorical feature `HomePlanet` on a
dependent variable, as modeled by a Generalized Additive Model (GAM). The
feature `HomePlanet` includes three categories: "Earth," "Europa," and "Mars."
Passengers from Europa exhibit a notably high positive mean effect of 0.5678 on
the dependent variable, with a tight confidence interval ranging from 0.5116 to
0.624, suggesting a strong and consistent positive impact. In contrast, Earth
shows a negative mean effect of -0.3246 with the confidence interval from -0.354
to -0.2952, indicating a robust negative association. Mars, while also positive,
has a much milder effect of 0.1713, with its confidence interval spanning from
0.1256 to 0.2171. This pronounced disparity in the effects, especially the
negative impact associated with Earth, is surprising and could hint at
underlying socio-economic or contextual factors influencing these outcomes.
Understanding these patterns might require further investigation into the
dataset's characteristics, including possible biases or the nature of the
dependent variable.*

We can give the LLM more context by providing a description of the dataset and the outcome. This allows the LLM to talk about the spacetime anomaly.

```python
dataset_description = """ The description of the dataset from Kaggle """

y_axis_descripton = """The y-axis depicts contributions in log-odds towards the probability
that the passenger was transported to another dimension."""
```


```python
t2ebm.describe_graph('gpt-4-turbo-2024-04-09', 
                      ebm,
                      0, 
                      graph_description=y_axis_descripton,
                      dataset_description=dataset_description)  
```

> **GPT-4:** *The graph from the Generalized Additive Model (GAM) showcases the contributions
of different home planets—Earth, Europa, and Mars—towards the log-odds of
passengers being transported to another dimension on the Spaceship Titanic.
Passengers from Europa exhibit a strong positive contribution to the likelihood
of being transported, with a mean log-odds contribution of 0.5678, indicating a
higher propensity for these individuals to experience the anomalous event.
Conversely, Earth shows a negative contribution (-0.3246 mean log-odds),
suggesting that passengers from Earth are less likely to be transported compared
to the baseline. Mars, while also showing a positive contribution (0.1713 mean
log-odds), has a lesser effect than Europa, but still indicates an increased
likelihood over Earth. This pattern could suggest unique environmental,
technological, or cultural factors on Europa and Mars that influence their
inhabitants' interactions with the spacetime anomaly encountered during the
voyage. The observed negative contribution from Earth might hint at more
effective protective measures or less vulnerability to such spacetime
disturbances. These findings highlight planet-based differences in
susceptibility to extraordinary events, revealing insights into interplanetary
travel and safety in this futuristic scenario.*

Instead of describing individual graphs, we can also ask the LLM to summarize the entire model. We can also ask for a summary in a given number of sentences.

```python
t2ebm.describe_ebm('gpt-4-turbo-2024-04-09',
                   ebm,
                   dataset_description=dataset_description, 
                   y_axis_description=y_axis_descripton) 
```

> **GPT-4:** *The Generalized Additive Model (GAM) used for analyzing the Spaceship Titanic
anomaly provides crucial insights into factors influencing the likelihood of
passengers being transported to an alternate dimension. Here’s a concise summary
of the most impactful features:  1. **CryoSleep**: This feature significantly
affects the outcome, with passengers in cryosleep more likely to be transported
(mean effect size 0.814) compared to those who are not (mean effect -0.447).
This suggests a unique interaction between the cryosleep state and the anomaly,
potentially due to the location or conditions of cryosleep chambers.  2.
**Spa**: Expenditures on spa services show a strong negative correlation with
the likelihood of transportation, particularly at higher spending levels. The
effect becomes extremely strong (below -5) at the highest expenditures,
indicating a protective factor potentially linked to socioeconomic status or
specific behaviors.  3. **VRDeck**: Similar to spa spending, expenditure on the
VRDeck is negatively correlated with the probability of transportation,
intensifying with higher spending. This suggests that engagement in VRDeck
amenities might be associated with safer areas or protective behaviors on the
ship.  4. **RoomService**: Initially, a slight increase in transportation
likelihood is observed at very low spending levels on room service, but it
shifts to a significant negative correlation as spending increases. High
expenditures on room service might correlate with safer locations on the ship. 5. **HomePlanet**: Passengers from Europa are much more likely to be transported
(mean effect 0.5678) compared to those from Earth (mean effect -0.3246) and Mars
(mean effect 0.1713). This indicates that planetary origin, reflecting differing
socio-economic or technological contexts, significantly influences
susceptibility to the anomaly.  6. **Cabin**: The cabin location, particularly
differences between Port and Starboard sides, significantly impacts the
likelihood of transportation. For instance, Starboard side cabins, especially on
specific decks (e.g., "C/S" with mean = 2.016), show higher positive effects.   7. **Destination**: The intended destination affects transportation likelihood,
with passengers destined for 55 Cancri e exhibiting a higher likelihood compared
to those heading to PSO J318.5-22 and TRAPPIST-1e. This might be influenced by
route or operational parameters specific to each destination.  The model
highlights the importance of understanding interactions between passenger states
(like cryosleep), cabin locations, spending on ship amenities, and origins in
assessing risks from spacetime anomalies. These factors play crucial roles in
the model's predictive accuracy and offer insights for enhancing safety and
design in future interstellar travel scenarios.*

# Low-Level API: Extract Graphs from the EBM and perform custom prompts

Here, we give an overview of low-level API functions that covert graphs to text. We also describe some basic prompt structures. You should use the functions described here if you want to write your own prompts based on the graphs of GAMs. The API Reference can be found [here](http://interpret.ml/TalkToEBM/api_reference.html).

```python
import t2ebm.graphs as graphs
```
We have a simple datastructure for graphs, and some useful operations on them

```python
graph = graphs.extract_graph(ebm, 9)  # feature 9, 'Spa'
graphs.plot_graph(graph)
```

<img src="images/Spaceship%20Titanic_21_0.png" alt="drawing" width="400"/>

The graphs learned by EBMs can contain many small details. We can simplify them to reduce the number of tokens. There is a parameter to control the degree of simplification.

```python
t2ebm.graphs.plot_graph(t2ebm.graphs.simplify_graph(graph, min_variation_per_cent=0.041))
```

<img src="images/Spaceship%20Titanic_24_0.png" alt="drawing" width="400"/>


The package also supports categorical and boolean features

```python
graphs.plot_graph(graphs.extract_graph(ebm, 0)) # home planet
```

<img src="images/Spaceship%20Titanic_22_0.png" alt="drawing" width="400"/>

We have to convert graphs to text before passing them to the LLM

```python
graph_as_text = graphs.graph_to_text(graph, max_tokens=1000)
print(graph_as_text)
```

>    This graph represents a continuous-valued feature. The keys are intervals that represent ranges where the function predicts the same value.
>    
>    Feature Name: Spa
>    Feature Type: continuous
>    Means: {"(0.0, 36.5)": 0.517, "(36.5, 117.5)": 0.212, ..., "(3978.0, 18572.0)": -5.917}
> 
>    Lower Bounds (95%-Confidence Interval): {"(0.0, 36.5)": 0.068, "(36.5, 117.5)": 0.049, ..., "(3978.0, 18572.0)": -6.956}
> > 
>    Upper Bounds (95%-Confidence Interval): {"(0.0, 36.5)": 0.966, "(36.5, 117.5)": 0.374, ..., "(3978.0, 18572.0)": -4.877}
    

Given the textual representation of the graph, you can start to write your own prompts. Usually, the first prompt will ask the LLM to describe the graph. 

```python
t2ebm.prompts.describe_graph(graph_as_text,
                             graph_description="{The y-axis on the graph depicts how cool the graph is.}",
                             dataset_description="{The dataset is great!}",
                             task_description="{Please summarize the graph. Pay special attention to run 'pip install t2ebm'}",)
```

```
Below is the graph of a Generalized Additive Model (GAM). The graph is presented as a JSON object with keys representing the x-axis and values representing the y-axis. For continuous features, the keys are intervals that represent ranges where the function predicts the same value. For categorical features, each key represents a possible value that the feature can take.
    
The graph is provided in the following format:
    - The name of the feature depicted in the graph
    - The type of the feature (continuous, categorical, or boolean)
    - Mean values
    - Lower bounds of confidence interval (optional)
    - Upper bounds of confidence interval (optional)

Here is the graph:

This graph represents categorical feature. Each key represents a possible value that the feature can take.

Feature Name: HomePlanet
Feature Type: categorical
Means: {"Earth": -0.3246, "Europa": 0.5678, "Mars": 0.1713}
Lower Bounds (95%-Confidence Interval): {"Earth": -0.354, "Europa": 0.5116, "Mars": 0.1256}
Upper Bounds (95%-Confidence Interval): {"Earth": -0.2952, "Europa": 0.624, "Mars": 0.2171}


{The y-axis on the graph depicts how cool the graph is.}

Here is a description of the dataset that the model was trained on:

{The dataset is great!}

{Please summarize the graph. Pay special attention to run 'pip install t2ebm'}
```

# Citation

If you use this software in your research, please consider citing our research papers.

```bib
@inproceedings{bordt2024talktoebm,
  author    = {Sebastian Bordt, Ben Lengerich, Harsha Nori, Rich Caruana},
  title     = {Data Science with LLMs and Interpretable Models},
  booktitle = {XAI4Sci Workshop at AAAI-24},
  year      = {2024}
 }
```

```bib
@inproceedings{lengerich2023llms,
  author    = {Benjamin J. Lengerich, Sebastian Bordt, Harsha Nori, Mark E. Nunnally, Yin Aphinyanaphongs, Manolis Kellis, and Rich Caruana},
  title     = {LLMs Understand Glass-Box Models, Discover Surprises, and Suggest Repairs},
  booktitle = {arxiv},
  year      = {2023}
 }
```