imodelsx 0.21

Library to explain a dataset in natural language.

Library to explain a dataset in natural language.

📖 demo notebooks

Model	Reference	Output	Description
iPrompt	📖, 🗂️, 🔗, 📄	Explanation	Generates a prompt that explains patterns in data (Official)
D3	📖, 🗂️, 🔗, 📄	Explanation	Explain the difference between two distributions
AutoPrompt	⠀⠀⠀🗂️, 🔗, 📄	Explanation	Find a natural-language prompt using input-gradients (⌛ In progress)
Aug-GAM	📖, 🗂️, 🔗, 📄	Linear model	Fit better linear model using an LLM to extract embeddings (Official)
Aug-Tree	📖, 🗂️, 🔗, 📄	Decision tree	Fit better decision tree using an LLM to expand features (⌛ In progress)
Linear Finetune	📖, 🗂️, ⠀⠀	Black-box model	Finetune a single linear layer on top of LLM embeddings

📖Demo notebooks   🗂️ Doc   🔗 Reference code   📄 Research paper
⌛ We plan to support other interpretable algorithms like RLPrompt, CBMs, and NBDT. If you want to contribute an algorithm, feel free to open a PR 😄

Quickstart

Installation: pip install imodelsx (or, for more control, clone and install from source)

Demos: see the demo notebooks

iPrompt

from imodelsx import explain_dataset_iprompt, get_add_two_numbers_dataset

# get a simple dataset of adding two numbers
input_strings, output_strings = get_add_two_numbers_dataset(num_examples=100)
for i in range(5):
    print(repr(input_strings[i]), repr(output_strings[i]))

# explain the relationship between the inputs and outputs
# with a natural-language prompt string
prompts, metadata = explain_dataset_iprompt(
    input_strings=input_strings,
    output_strings=output_strings,
    checkpoint='EleutherAI/gpt-j-6B', # which language model to use
    num_learned_tokens=3, # how long of a prompt to learn
    n_shots=3, # shots per example
    n_epochs=15, # how many epochs to search
    verbose=0, # how much to print
    llm_float16=True, # whether to load the model in float_16
)
--------
prompts is a list of found natural-language prompt strings

D3 (DescribeDistributionalDifferences)

import imodelsx
hypotheses, hypothesis_scores = imodelsx.explain_dataset_d3(
    pos=positive_samples, # List[str] of positive examples
    neg=negative_samples, # another List[str]
    num_steps=100,
    num_folds=2,
    batch_size=64,
)

Aug-models

Use these just a like a scikit-learn model. During training, they fit better features via LLMs, but at test-time they are extremely fast and completely transparent.

from imodelsx import AugGAMClassifier, AugTreeClassifier, AugGAMRegressor, AugTreeRegressor
import datasets
import numpy as np

# set up data
dset = datasets.load_dataset('rotten_tomatoes')['train']
dset = dset.select(np.random.choice(len(dset), size=300, replace=False))
dset_val = datasets.load_dataset('rotten_tomatoes')['validation']
dset_val = dset_val.select(np.random.choice(len(dset_val), size=300, replace=False))

# fit model
m = AugGAMClassifier(
    checkpoint='textattack/distilbert-base-uncased-rotten-tomatoes',
    ngrams=2, # use bigrams
)
m.fit(dset['text'], dset['label'])

# predict
preds = m.predict(dset_val['text'])
print('acc_val', np.mean(preds == dset_val['label']))

# interpret
print('Total ngram coefficients: ', len(m.coefs_dict_))
print('Most positive ngrams')
for k, v in sorted(m.coefs_dict_.items(), key=lambda item: item[1], reverse=True)[:8]:
    print('\t', k, round(v, 2))
print('Most negative ngrams')
for k, v in sorted(m.coefs_dict_.items(), key=lambda item: item[1])[:8]:
    print('\t', k, round(v, 2))

Linear finetune

An easy-to-fit baseline that follows the same API.

# fit a simple one-layer finetune
m = LinearFinetuneClassifier(
    checkpoint='distilbert-base-uncased',
)
m.fit(dset['text'], dset['label'])
preds = m.predict(dset_val['text'])
acc = (preds == dset_val['label']).mean()
print('validation acc', acc)

Related work

• imodels package (JOSS 2021 github) - interpretable ML package for concise, transparent, and accurate predictive modeling (sklearn-compatible).
• Adaptive wavelet distillation (NeurIPS 2021 pdf, github) - distilling a neural network into a concise wavelet model
• Transformation importance (ICLR 2020 workshop pdf, github) - using simple reparameterizations, allows for calculating disentangled importances to transformations of the input (e.g. assigning importances to different frequencies)
• Hierarchical interpretations (ICLR 2019 pdf, github) - extends CD to CNNs / arbitrary DNNs, and aggregates explanations into a hierarchy
• Interpretation regularization (ICML 2020 pdf, github) - penalizes CD / ACD scores during training to make models generalize better
• PDR interpretability framework (PNAS 2019 pdf) - an overarching framewwork for guiding and framing interpretable machine learning