tomicapretto

tomicapretto

import numpy as np
import matplotlib.pyplot as plt
import polars as pl

from scipy import stats
from scipy.optimize import minimize, minimize_scalar
from scipy import special

from abc import ABC, abstractmethod

tomicapretto

# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
#                                         Utils                                         #
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
ltail <- function(l) {
  l[[length(l)]]
}


lappend <- function(l, object) {
  l[[length(l) + 1]] <- object

tomicapretto

tomicapretto

# Will be useful when we work with non-linear terms in Bambi.
# This is the first step.
# 1. Split top level terms DONE
# 2. Detect terms where non-linear parameters play a role TO DO
# 3. Manipulate the expression in terms where non-linear parameters play a role
#    to pass only the 'predictor' to `formulae`, but keep the expression to be used later TO DO

import ast
from functools import singledispatch

tomicapretto

tomicapretto

tomicapretto

tomicapretto

tomicapretto

import keyword
keywords = keyword.kwlist.copy()

kwrds_n = len(keywords)
rows_n = 7
cols_n = kwrds_n // rows_n if kwrds_n % rows_n == 0 else (kwrds_n // rows_n) + 1

keywords_list = [
    [
        keywords[rows_n * j + i]