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lasts (Local Agnostic Subsequence-based Time Series explainer)

Introduction

lasts is a tool designed for explaining time series classifiers. It provides insights into time series models using local agnostic subsequence-based techniques. Due to the reliance on specific older package versions, the current installation method is exclusively through Conda using the provided environment.yml file. We aim to offer a more streamlined installation process in future updates.

Installation

Prerequisites

Steps

  1. Clone the Repository:

    git clone https://github.com/fspinna/lasts.git
cd lasts

  2. Create a Conda Environment from the environment.yml file:

    This file lists all the necessary dependencies for lasts. By creating a Conda environment from this file, you ensure all dependencies are properly installed and managed.

    conda env create -f environment.yml

  3. Activate the Environment:

    After creating the environment, activate it with the following command:

    conda activate lasts

  4. Verify Installation:

    Post-installation, your terminal prompt should reflect the active lasts environment:

    (lasts) user@hostname:~$

Sure! Let's simplify and streamline the provided code into a quick start guide.

Quick Start Guide for lasts

Get up and running with the Local Agnostic Subsequence-based Time Series Explainer (lasts) with this quick guide.

  1. Import necessary modules

    from lasts.blackboxes.loader import cached_blackbox_loader
from lasts.datasets.datasets import build_cbf
from lasts.autoencoders.variational_autoencoder import load_model
from lasts.utils import get_project_root, choose_z
from lasts.surrogates.shapelet_tree import ShapeletTree
from lasts.neighgen.counter_generator import CounterGenerator
from lasts.wrappers import DecoderWrapper
from lasts.surrogates.utils import generate_n_shapelets_per_size
from lasts.explainers.lasts import Lasts
import numpy as np

  2. Data preparation

    Set the random seed and prepare the dataset:

    random_state = 0
np.random.seed(random_state)
dataset_name = "cbf"

_, _, _, _, _, _, X_exp_train, y_exp_train, X_exp_val, y_exp_val, X_exp_test, y_exp_test = build_cbf(n_samples=600, random_state=random_state)

  3. Load the model and blackbox

    blackbox = cached_blackbox_loader("cbf_knn.joblib")
encoder, decoder, autoencoder = load_model(get_project_root() / "autoencoders" / "cached" / "vae" / "cbf" / "cbf_vae")

  4. Preparation for explanations

    Here, we choose the latent space representation for our instance and set up the counterfactual generator:

    i = 0
x = X_exp_test[i].ravel().reshape(1, -1, 1)
z_fixed = choose_z(x, encoder, decoder, n=1000, x_label=blackbox.predict(x)[0], blackbox=blackbox, check_label=True, mse=False)

neighgen = CounterGenerator(blackbox, DecoderWrapper(decoder), n_search=10000, ...)

n_shapelets_per_size = generate_n_shapelets_per_size(X_exp_train.shape[1])
surrogate = ShapeletTree(random_state=random_state, shapelet_model_kwargs={...})

  5. Initialize and fit the LASTS explainer

    lasts_ = Lasts(blackbox, encoder, DecoderWrapper(decoder), neighgen, surrogate, verbose=True, binarize_surrogate_labels=True, labels=["cylinder", "bell", "funnel"])

lasts_.fit(x, z_fixed)

  6. Generate and visualize explanations

    exp = lasts_.explain()

lasts_.plot("latent_space")
lasts_.plot("morphing_matrix")
lasts_.plot("counterexemplar_interpolation")
lasts_.plot("manifest_space")
lasts_.plot("saliency_map")
lasts_.plot("subsequences_heatmap")
lasts_.plot("rules")
lasts_.neighgen.plotter.plot_counterexemplar_shape_change()

Upcoming Features

We're dedicated to continually refining lasts. Look forward to enhancements and a more straightforward installation process in upcoming updates. For issues or feedback, kindly use the repository's "Issues" section.