Scikit-learn friendly library to explain, predict, and steer text models/data.
Also a bunch of utilities for getting started with text data.
Explainable modeling/steering
| Model | Reference | Output | Description |
|---|---|---|---|
| Tree-Prompt | 🗂️, 🔗, 📄, 📖, | Explanation + Steering |
Generates a tree of prompts to steer an LLM (Official) |
| iPrompt | 🗂️, 🔗, 📄, 📖 | Explanation + Steering |
Generates a prompt that explains patterns in data (Official) |
| AutoPrompt | ㅤㅤ🗂️, 🔗, 📄 | Explanation + Steering |
Find a natural-language prompt using input-gradients |
| D3 | 🗂️, 🔗, 📄, 📖 | Explanation | Explain the difference between two distributions |
| SASC | ㅤㅤ🗂️, 🔗, 📄 | Explanation | Explain a black-box text module using an LLM (Official) |
| Aug-Linear | 🗂️, 🔗, 📄, 📖 | 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 (Official) |
| QAEmb | 🗂️, 🔗, 📄, 📖 | Explainable embedding |
Generate interpretable embeddings by asking LLMs questions (Official) |
| KAN | 🗂️, 🔗, 📄, 📖 | Small network |
Fit 2-layer Kolmogorov-Arnold network |
📖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 😄
General utilities
| Model | Reference |
|---|---|
| 🗂️ LLM wrapper | Easily call different LLMs |
| 🗂️ Dataset wrapper | Download minimially processed huggingface datasets |
| 🗂️ Bag of Ngrams | Learn a linear model of ngrams |
| 🗂️ Linear Finetune | Finetune a single linear layer on top of LLM embeddings |
Installation: pip install imodelsx (or, for more control, clone and install from source)
Demos: see the demo notebooks
from imodelsx import TreePromptClassifier
import datasets
import numpy as np
from sklearn.tree import plot_tree
import matplotlib.pyplot as plt
# set up data
rng = np.random.default_rng(seed=42)
dset_train = datasets.load_dataset('rotten_tomatoes')['train']
dset_train = dset_train.select(rng.choice(
len(dset_train), size=100, replace=False))
dset_val = datasets.load_dataset('rotten_tomatoes')['validation']
dset_val = dset_val.select(rng.choice(
len(dset_val), size=100, replace=False))
# set up arguments
prompts = [
"This movie is",
" Positive or Negative? The movie was",
" The sentiment of the movie was",
" The plot of the movie was really",
" The acting in the movie was",
]
verbalizer = {0: " Negative.", 1: " Positive."}
checkpoint = "gpt2"
# fit model
m = TreePromptClassifier(
checkpoint=checkpoint,
prompts=prompts,
verbalizer=verbalizer,
cache_prompt_features_dir=None, # 'cache_prompt_features_dir/gp2',
)
m.fit(dset_train["text"], dset_train["label"])
# compute accuracy
preds = m.predict(dset_val['text'])
print('\nTree-Prompt acc (val) ->',
np.mean(preds == dset_val['label'])) # -> 0.7
# compare to accuracy for individual prompts
for i, prompt in enumerate(prompts):
print(i, prompt, '->', m.prompt_accs_[i]) # -> 0.65, 0.5, 0.5, 0.56, 0.51
# visualize decision tree
plot_tree(
m.clf_,
fontsize=10,
feature_names=m.feature_names_,
class_names=list(verbalizer.values()),
filled=True,
)
plt.show()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 stringsfrom imodelsx import explain_dataset_d3
hypotheses, hypothesis_scores = 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,
)Here, we explain a module rather than a dataset
from imodelsx import explain_module_sasc
# a toy module that responds to the length of a string
mod = lambda str_list: np.array([len(s) for s in str_list])
# a toy dataset where the longest strings are animals
text_str_list = ["red", "blue", "x", "1", "2", "hippopotamus", "elephant", "rhinoceros"]
explanation_dict = explain_module_sasc(
text_str_list,
mod,
ngrams=1,
)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 AugLinearClassifier, AugTreeClassifier, AugLinearRegressor, 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 = AugLinearClassifier(
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))import imodelsx
from sklearn.datasets import make_classification, make_regression
from sklearn.metrics import accuracy_score
import numpy as np
X, y = make_classification(n_samples=5000, n_features=5, n_informative=3)
model = imodelsx.KANClassifier(hidden_layer_size=64, device='cpu',
regularize_activation=1.0, regularize_entropy=1.0)
model.fit(X, y)
y_pred = model.predict(X)
print('Test acc', accuracy_score(y, y_pred))
# now try regression
X, y = make_regression(n_samples=5000, n_features=5, n_informative=3)
model = imodelsx.kan.KANRegressor(hidden_layer_size=64, device='cpu',
regularize_activation=1.0, regularize_entropy=1.0)
model.fit(X, y)
y_pred = model.predict(X)
print('Test correlation', np.corrcoef(y, y_pred.flatten())[0, 1])Easy-to-fit baselines that follow the sklearn API.
from imodelsx import LinearFinetuneClassifier, LinearNgramClassifier
# fit a simple one-layer finetune on top of LLM embeddings
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)Easy API for calling different language models with caching (much more lightweight than langchain).
import imodelsx.llm
# supports any huggingface checkpoint or openai checkpoint (including chat models)
llm = imodelsx.llm.get_llm(
checkpoint="gpt2-xl", # text-davinci-003, gpt-3.5-turbo, ...
CACHE_DIR=".cache",
)
out = llm("May the Force be")
llm("May the Force be") # when computing the same string again, uses the cacheAPI for loading huggingface datasets with basic preprocessing.
import imodelsx.data
dset, dataset_key_text = imodelsx.data.load_huggingface_dataset('ag_news')
# Ensures that dset has a split named 'train' and 'validation',
# and that the input data is contained for each split in a column given by {dataset_key_text}- 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