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integrate parseWilkinsonFormula in ClassificationGAM #397

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Pasta-coder Feb 22, 2026
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FIX - sorting left-to-right hierarchy
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implement parseWilkinsonFormula in ClassificationGAM + removing redun…
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105 changes: 56 additions & 49 deletions inst/Classification/ClassificationGAM.m
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Original file line number Diff line number Diff line change
@@ -1,6 +1,7 @@
## Copyright (C) 2024 Ruchika Sonagote <ruchikasonagote2003@gmail.com>
## Copyright (C) 2024-2025 Andreas Bertsatos <abertsatos@biol.uoa.gr>
## Copyright (C) 2025 Swayam Shah <swayamshah66@gmail.com>
## Copyright (C) 2026 Jayant Chauhan <0001jayant@gmail.com>
##
## This file is part of the statistics package for GNU Octave.
##
Expand Down Expand Up @@ -222,9 +223,12 @@
##
## Model specification formula
##
## A character vector specifying the model formula in the form
## @qcode{"Y ~ terms"} where @qcode{Y} represents the response variable and
## @qcode{terms} specifies the predictor variables and interaction terms.
## A character vector specifying the model formula using standard Wilkinson
## notation in the form @qcode{"Y ~ terms"}. In addition to basic main
## effects and interactions (@code{+}, @code{:}), it fully supports
## advanced operators including crossing (@code{*}), nesting (@code{/}),
## power/limits (@code{^}), and deletion (@code{-}). The formula is
## evaluated internally via @code{parseWilkinsonFormula}.
## This property is read-only.
##
## @end deftp
Expand Down Expand Up @@ -518,9 +522,10 @@ function disp (this)
## @qcode{'symmetriclogit'}.
##
## @item @qcode{'Formula'} @tab @tab A character vector specifying the model
## formula in the form @qcode{"Y ~ terms"} where @qcode{Y} represents the
## response variable and @qcode{terms} specifies the predictor variables and
## interaction terms.
## formula using standard Wilkinson notation in the form @qcode{"Y ~ terms"}.
## In addition to basic main effects and interactions (@code{+}, @code{:}),
## it supports advanced operators including crossing (@code{*}), nesting
## (@code{/}), power/limits (@code{^}), and deletion (@code{-}).
##
## @item @qcode{'Interactions'} @tab @tab A logical matrix, a positive
## integer scalar, or the string @qcode{"all"} for defining the interactions
Expand Down Expand Up @@ -1276,52 +1281,38 @@ function savemodel (this, fname)

## Determine interactions from formula
function intMat = parseFormula (this)
intMat = [];
## Check formula for syntax
if (isempty (strfind (this.Formula, '~')))
error ("ClassificationGAM: invalid syntax in 'Formula'.");
try
schema = parseWilkinsonFormula (this.Formula, 'matrix');
catch ME
error ("ClassificationGAM: Invalid formula. %s", ME.message);
end_try_catch

termMat = schema.Terms;
varNames = schema.VariableNames;

if (! isempty (schema.ResponseIdx))
respIdx = schema.ResponseIdx;
varNames(respIdx) = [];
termMat(:, respIdx) = [];
endif
## Split formula and keep predictor terms
formulaParts = strsplit (this.Formula, '~');
## Check there is some string after '~'
if (numel (formulaParts) < 2)
error ("ClassificationGAM: no predictor terms in 'Formula'.");
endif
predictorString = strtrim (formulaParts{2});
if (isempty (predictorString))
error ("ClassificationGAM: no predictor terms in 'Formula'.");
endif
## Split additive terms (between + sign)
aterms = strtrim (strsplit (predictorString, '+'));
## Process all terms
for i = 1:numel (aterms)
## Find individual terms (string missing ':')
if (isempty (strfind (aterms(i), ':'){:}))
## Search PredictorNames to associate with column in X
sterms = strcmp (this.PredictorNames, aterms(i));
## Append to interactions matrix
intMat = [intMat; sterms];
else
## Split interaction terms (string contains ':')
mterms = strsplit (aterms{i}, ':');
## Add each individual predictor to interaction term vector
iterms = logical (zeros (1, this.NumPredictors));
for t = 1:numel (mterms)
iterms = iterms | strcmp (this.PredictorNames, mterms(t));
endfor
## Check that all predictors have been identified
if (sum (iterms) != t)
error (strcat ("ClassificationGAM: some predictors", ...
" have not been identified."));
endif
## Append to interactions matrix
intMat = [intMat; iterms];

intMat = zeros (rows (termMat), this.NumPredictors);

for i = 1:numel (varNames)
colIdx = find (strcmp (this.PredictorNames, varNames{i}));
if (isempty (colIdx))
error ("ClassificationGAM: Formula contains unknown predictor '%s'.", varNames{i});
endif
intMat(:, colIdx) = termMat(:, i);
endfor
## Check that all terms have been identified
if (! all (sum (intMat, 2) > 0))
error ("ClassificationGAM: some terms have not been identified.");
endif

## Remove intercept row (all zeros)
## ClassificationGAM handles the intercept internally during fitting,
## so we strip out the explicit intercept term if the parser included it.
interceptRow = (sum (intMat, 2) == 0);
intMat(interceptRow, :) = [];

intMat = logical (intMat);
endfunction

## Fit the model
Expand Down Expand Up @@ -1634,6 +1625,22 @@ function savemodel (this, fname)
%! assert (CVMdl.KFold == 3)
%! assert (class (CVMdl.Trained{1}), "CompactClassificationGAM")
%! assert (CVMdl.CrossValidatedModel, "ClassificationGAM")
%!test
%! ## Test advanced Wilkinson notation parsing in ClassificationGAM
%! X = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
%! Y = [0; 0; 1; 1];
%!
%! ## The * operator should automatically expand to main effects + interaction
%! a = ClassificationGAM (X, Y, 'Formula', 'Y ~ x1 * x2');
%!
%! assert (class (a), "ClassificationGAM");
%! assert (a.NumPredictors, 3);
%!
%! ## Verify the IntMatrix correctly captured x1, x2, and x1:x2 in MATLAB order
%! expected_int = logical ([1, 0, 0;
%! 0, 1, 0;
%! 1, 1, 0]);
%! assert (a.IntMatrix, expected_int);

## Test input validation for crossval method
%!error<ClassificationGAM.crossval: Name-Value arguments must be in pairs.> ...
Expand Down
70 changes: 47 additions & 23 deletions inst/parseWilkinsonFormula.m
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Original file line number Diff line number Diff line change
Expand Up @@ -201,7 +201,7 @@
else
lhs_vars = resolve_lhs_symbolic (lhs_str);
endif

## build the required output.
varargout{1} = run_equation_builder (lhs_vars, rhs_terms);

Expand Down Expand Up @@ -519,14 +519,14 @@
args_str_parts = {};
for k = 1:length (node.args)
arg_res = run_expander (node.args{k}, mode);

if (! isempty (arg_res) && ! isempty (arg_res{1}))
args_str_parts{end+1} = arg_res{1}{1};
args_str_parts{end+1} = arg_res{1}{1};
else
args_str_parts{end+1} = '';
endif
endfor

full_term = sprintf ("%s(%s)", node.name, strjoin (args_str_parts, ','));
result = {{full_term}};
else
Expand Down Expand Up @@ -829,14 +829,20 @@
terms_mat(i, idx) = 1;
endfor

## sorting : order by order.
## sorting : order by order (ascending), then by variable sequence (descending)
term_orders = sum (terms_mat, 2);
M = [term_orders, terms_mat];

## Create unique rows first
[~, unique_idx] = unique (M, 'rows');
terms_mat = terms_mat (unique_idx, :);
M = M (unique_idx, :);

## Create the direction vector: [1, -2, -3, -4, ...]
sort_dirs = [1, -(2:size(M, 2))];

[~, sort_idx] = sortrows ([sum(terms_mat, 2), terms_mat]);
## Sort using the direction vector
[~, sort_idx] = sortrows (M, sort_dirs);
schema.Terms = terms_mat (sort_idx, :);

endfunction
Expand Down Expand Up @@ -1140,14 +1146,14 @@
## process RHS
rhs_tokens = run_lexer (rhs_str);
[rhs_tree, ~] = run_parser (rhs_tokens);

wrapper.type = 'OPERATOR';
wrapper.value = '~';
wrapper.left = [];
wrapper.right = rhs_tree;

expanded = run_expander (wrapper, mode);

## extract the terms.
if (isstruct (expanded) && isfield (expanded, 'model'))
rhs_terms = expanded.model;
Expand All @@ -1164,22 +1170,22 @@
for i = 1:length (parts)
p = strtrim (parts{i});
if (isempty (p)), continue; endif

range_parts = strsplit (p, '-');

if (length (range_parts) == 2)
s_str = strtrim (range_parts{1});
e_str = strtrim (range_parts{2});

[s_tok] = regexp (s_str, '^([a-zA-Z_]\w*)(\d+)$', 'tokens');
[e_tok] = regexp (e_str, '^([a-zA-Z_]\w*)(\d+)$', 'tokens');

if (! isempty (s_tok) && ! isempty (e_tok))
prefix = s_tok{1}{1};
s_num = str2double (s_tok{1}{2});
e_prefix = e_tok{1}{1};
e_num = str2double (e_tok{1}{2});

if (strcmp (prefix, e_prefix) && s_num <= e_num)
for n = s_num:e_num
vars{end+1} = sprintf ("%s%d", prefix, n);
Expand All @@ -1203,7 +1209,7 @@
for i = 1:length (rhs_terms)
t = rhs_terms{i};
if (isempty (t))
term_strs{end+1} = '';
term_strs{end+1} = '';
else
if (length (t) == 1 && any (strfind (t{1}, "(")))
term_strs{end+1} = t{1};
Expand All @@ -1229,13 +1235,13 @@
rhs_parts{end+1} = sprintf ("%s*%s", coeff, t_str);
endif
endfor

full_rhs = strjoin (rhs_parts, ' + ');
if (isempty (full_rhs)), full_rhs = '0'; endif
lines{end+1} = sprintf ("%s = %s", lhs_vars{k}, full_rhs);
endfor

eq_list = string (lines');
eq_list = string (lines');
endfunction

%!demo
Expand Down Expand Up @@ -1271,7 +1277,7 @@

%!demo
%!
%! ## Interaction Effects :
%! ## Interaction Effects :
%! ## We analyze Relief Score based on Drug Type and Dosage Level.
%! ## The '*' operator expands to the main effects PLUS the interaction term.
%! ## Categorical variables are automatically created.
Expand All @@ -1287,11 +1293,11 @@

%!demo
%!
%! ## Polynomial Regression :
%! ## Polynomial Regression :
%! ## Uses the power operator (^) to model non-linear relationships.
%! Distance = [20; 45; 80; 125];
%! Speed = [30; 50; 70; 90];
%! Speed_2 = Speed .^ 2;
%! Speed_2 = Speed .^ 2;
%! t = table (Distance, Speed, Speed_2, 'VariableNames', {'Distance', 'Speed', 'Speed^2'});
%!
%! formula = 'Distance ~ Speed^2';
Expand All @@ -1316,7 +1322,7 @@

%!demo
%!
%! ## Explicit Nesting :
%! ## Explicit Nesting :
%! ## The parser also supports the explicit 'B(A)' syntax, which means
%! ## 'B is nested within A'. This is equivalent to the interaction 'A:B'
%! ## but often used to denote random effects or specific hierarchy.
Expand All @@ -1327,7 +1333,7 @@

%!demo
%!
%! ## Excluding Terms :
%! ## Excluding Terms :
%! ## Demonstrates building a complex model and then simplifying it.
%! ## We define a full 3-way interaction (A*B*C) but explicitly remove the
%! ## three-way term (A:B:C) using the minus operator.
Expand All @@ -1338,7 +1344,7 @@

%!demo
%!
%! ## Repeated Measures :
%! ## Repeated Measures :
%! ## This allows predicting multiple outcomes simultaneously.
%! ## The range operator '-' selects all variables between 'T1' and 'T3'
%! ## as the response matrix Y.
Expand Down Expand Up @@ -1659,6 +1665,24 @@
%! eq = parseWilkinsonFormula ('y ~ A - A', 'equation');
%! expected = string('y = c1');
%! assert (isequal (eq, expected));
%!test
%! ## Verify parseWilkinsonFormula schema matches MATLAB fitlm sorting
%! formula = 'Y ~ x1 * x2 * x3';
%! schema = parseWilkinsonFormula(formula, 'matrix');
%!
%! ## Expected Octave Binary Matrix (8 rows, 4 columns)
%! ## Columns are sorted alphabetically by the parser: {'Y', 'x1', 'x2', 'x3'}
%! expected_terms = [0, 0, 0, 0; ## (Intercept)
%! 0, 1, 0, 0; ## x1
%! 0, 0, 1, 0; ## x2
%! 0, 0, 0, 1; ## x3
%! 0, 1, 1, 0; ## x1:x2
%! 0, 1, 0, 1; ## x1:x3
%! 0, 0, 1, 1; ## x2:x3
%! 0, 1, 1, 1]; ## x1:x2:x3
%!
%! assert(schema.VariableNames, {'Y', 'x1', 'x2', 'x3'});
%! assert(schema.Terms, expected_terms);
%!error <Input formula string is required> parseWilkinsonFormula ()
%!error <Unknown mode> parseWilkinsonFormula ('y ~ x', 'invalid_mode')
%!error <Unexpected End Of Formula> parseWilkinsonFormula ('', 'parse')
Expand Down