Crate lace

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A fast, extensible probabilistic cross-categorization engine.

Lace is a probabilistic cross-categorization engine written in rust with an optional interface to python. Unlike traditional machine learning methods, which learn some function mapping inputs to outputs, Lace learns a joint probability distribution over your dataset, which enables users to…

predict or compute likelihoods of any number of features conditioned on any number of other features
identify, quantify, and attribute uncertainty from variance in the data, epistemic uncertainty in the model, and missing features
determine which variables are predictive of which others
determine which records/rows are similar to which others on the whole or given a specific context
simulate and manipulate synthetic data
work natively with missing data and make inferences about missingness (missing not-at-random)
work with continuous and categorical data natively, without transformation
identify anomalies, errors, and inconsistencies within the data
edit, backfill, and append data without retraining

and more, all in one place, without any explicit model building.

§Design

Lace learns a probabilistic model of tabular data using cross-categorization. The general steps to operation are

Create a prelude::Codebook which describes your data. One can be autogenerated but it is best to check it before use.
Create an prelude::Engine with your data and codebook.
Train the prelude::Engine and monitor the model likelihood for convergence.
Ask questions via the prelude::OracleT implementation of prelude::Engine to explore your data.

§Example

(For a complete tutorial, see the Lace Book)

The following example uses the pre-trained animals example dataset. Each row represents an animal and each column represents a feature of that animal. The feature is present if the cell value is 1 and is absent if the value is 0.

First, we create an oracle and import some enums that allow us to call out some of the row and column indices in plain English.

use lace::prelude::*;
use lace::examples::Example;

let oracle = Example::Animals.oracle().unwrap();

Let’s ask about the statistical dependence between whether something swims and is fast or has flippers. We expect that something swimming is more indicative of whether it swims than whether something is fast, therefore we expect the dependence between swims and flippers to be higher.

let depprob_fast = oracle.depprob(
    "swims",
    "fast",
).unwrap();

let depprob_flippers = oracle.depprob(
    "swims",
    "flippers",
).unwrap();

assert!(depprob_flippers > depprob_fast);

We have the same expectation of mutual information. Mutual information requires more input from the user. We need to know what type of mutual information, and how many samples to take if we need to estimate the mutual information.

let mut rng = rand::thread_rng();

let mi_fast = oracle.mi(
    "swims",
    "fast",
    1000,
    MiType::Iqr,
).unwrap();

let mi_flippers = oracle.mi(
    "swims",
    "flippers",
    1000,
    MiType::Iqr,
).unwrap();

assert!(mi_flippers > mi_fast);

We can likewise ask about the similarity between rows – in this case, animals.

let wrt: Option<&[usize]> = None;
let rowsim_wolf = oracle.rowsim(
    "wolf",
    "chihuahua",
    wrt,
    RowSimilarityVariant::ViewWeighted,
).unwrap();

let rowsim_rat = oracle.rowsim(
    "rat",
    "chihuahua",
    wrt,
    RowSimilarityVariant::ViewWeighted,
).unwrap();

assert!(rowsim_rat > rowsim_wolf);

And we can add context to similarity.

let context = vec!["swims"];
let rowsim_otter = oracle.rowsim(
    "beaver",
    "otter",
    Some(&context),
    RowSimilarityVariant::ViewWeighted,
).unwrap();

let rowsim_dolphin = oracle.rowsim(
    "beaver",
    "dolphin",
    Some(&context),
    RowSimilarityVariant::ViewWeighted,
).unwrap();

§Feature flags

formats: create Engines and Codebooks from IPC, CSV, JSON, and Parquet data files
bencher: Build benchmarking utilities
ctrc_handler: enables and update handler than captures Ctrl+C

Re-exports§

pub use config::EngineUpdateConfig;

Modules§

cc
codebook
config
consts
data: Data loaders and utilities
defaults: Default values
error
examples
metadata
misc: Misc, generally useful helper functions
optimize: Function optimization utilities
prelude: Common import for general use.
stats
update_handler
utils

Structs§

DatalessOracle: An oracle without data for sensitive data applications
Engine: The engine runs states in parallel
EngineBuilder: Builds Engines
InsertDataActions: Describes table-extending actions taken when inserting data
Metadata
MiComponents: Holds the components required to compute mutual information
Oracle: Oracle answers questions
ParseError
Row: A list of data for insertion into a certain row
StateDiagnostics: Stores some diagnostic info in the State at every iteration
Value: A datum for insertion into a certain column
WriteMode: Defines how/where data may be inserted, which day may and may not be overwritten, and whether data may extend the domain

Enums§

AppendStrategy: Defines the behavior of the data table when new rows are appended
BuildEngineError
Category
ConditionalEntropyType: The variant on conditional entropy to compute
Datum: Represents the types of data lace can work with
FType: Feature type
Given: Describes a the conditions (or not) on a conditional distribution
InsertMode: Defines insert data behavior – where data may be inserted.
MiType: Mutual Information Type
NameOrIndex: Holds a String name or a usize index
OverwriteMode: Defines which data may be overwritten
RowSimilarityVariant: The variant of row similarity to compute
StateTransition: MCMC transitions in the State
SummaryStatistics
SupportExtension: Describes the support extension action taken
TableIndex

Traits§

ColumnIndex: Trait defining items that can converted into a usize column index
HasData: Returns and summarizes data
HasStates: Returns references to crosscat states
OracleT
RowIndex: Trait defining an item that can be converted into a row index

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