lace.Engine

class lace.Engine(core_engine: CoreEngine)

The cross-categorization model with states and data.

__init__(core_engine: CoreEngine) → None

Create a new Engine with its internal representation.

In general, you will use Engine.from_df or Engine.load instead.

Properties

codebook	Return the codebook.
columns	A list of the column names appearing in their order in the table.
ftypes	A dictionary mapping column names to feature types.
index	The string row names of the engine.
n_cols	The number of columns in the table.
n_rows	The number of rows in the table.
n_states	The number of states (independent Markov chains).
shape	A tuple containing the number of rows and the number of columns in the table.
engine

Methods

append_columns(cols[, metadata, cat_cutoff, ...])	Append new columns to the Engine.
append_rows(rows)	Append new rows to the table.
clustermap(fn_name, *[, indices, ...])	Generate a clustermap of a pairwise function.
column_assignment(state_ix)	Return the assignment of columns to views.
del_column(col)	Delete a given column.
depprob(col_pairs)	Compute the dependence probability between pairs of columns.
diagnostics([name])	Get convergence diagnostics.
draw(row, col[, n])	Draw data from the distribution of a specific cell in the table.
edit_cell(row, col, value)	Edit the value of a cell in the table.
entropy(cols[, n_mc_samples])	Estimate the entropy or joint entropy of one or more features.
feature_params(col[, state_ixs])	Get the component parameters for a given column.
flatten_columns()	Flatten the column assignment.
from_df(df[, codebook, n_states, id_offset, ...])	Create a new Engine from a DataFrame.
ftype(col)	Get the feature type of a column.
impute(col[, rows, with_uncertainty])	Impute (predict) the value of a cell(s) in the lace table.
inconsistency(values[, given])	Compute inconsistency.
load(path)	Load an Engine from a path.
logp(values[, given, state_ixs, scaled])	Compute the log likelihood.
mi(col_pairs[, n_mc_samples, mi_type])	Compute the mutual information between pairs of columns.
novelty(row[, wrt])	Compute the novelty of a row.
pairwise_fn(fn_name[, indices])	Compute a function for a set of pairs of rows or columns.
predict(target[, given, state_ixs, ...])	Predict a single target from a conditional distribution.
row_assignments(state_ix)	Return the assignment of rows to categories for each view.
rowsim(row_pairs[, wrt, col_weighted])	Compute the row similarity between pairs of rows.
save(path)	Save the Engine metadata to path.
seed(rng_seed)	Set the state of the random number generator (RNG).
simulate(cols[, given, n, include_given])	Simulate data from a conditional distribution.
surprisal(col, *[, rows, values, state_ixs])	Compute the surprisal of a values in specific cells.
update(n_iters, *[, timeout, checkpoint, ...])	Update the Engine by advancing the Markov chains.
variability(target[, given, state_ixs])	Return the variability of a conditional distribution.