def _make_term_column_builders(terms, num_column_counts, cat_levels_contrasts):
# Sort each term into a bucket based on the set of numeric factors it
# contains:
term_buckets = OrderedDict()
bucket_ordering = []
for term in terms:
num_factors = []
for factor in term.factors:
if factor in num_column_counts:
num_factors.append(factor)
bucket = frozenset(num_factors)
if bucket not in term_buckets:
bucket_ordering.append(bucket)
term_buckets.setdefault(bucket, []).append(term)
# Special rule: if there is a no-numerics bucket, then it always comes
# first:
if frozenset() in term_buckets:
bucket_ordering.remove(frozenset())
bucket_ordering.insert(0, frozenset())
term_to_column_builders = {}
new_term_order = []
# Then within each bucket, work out which sort of contrasts we want to use
# for each term to avoid redundancy
for bucket in bucket_ordering:
bucket_terms = term_buckets[bucket]
# Sort by degree of interaction
bucket_terms.sort(key=lambda t: len(t.factors))
new_term_order += bucket_terms
used_subterms = set()
for term in bucket_terms:
column_builders = []
factor_codings = pick_contrasts_for_term(term, num_column_counts, used_subterms)
# Construct one _ColumnBuilder for each subterm
for factor_coding in factor_codings:
builder_factors = []
num_columns = {}
cat_contrasts = {}
# In order to preserve factor ordering information, the
# coding_for_term just returns dicts, and we refer to
# the original factors to figure out which are included in
# each subterm, and in what order
for factor in term.factors:
# Numeric factors are included in every subterm
if factor in num_column_counts:
builder_factors.append(factor)
num_columns[factor] = num_column_counts[factor]
elif factor in factor_coding:
builder_factors.append(factor)
levels, contrast = cat_levels_contrasts[factor]
# This is where the default coding is set to
# Treatment:
coded = code_contrast_matrix(factor_coding[factor], levels, contrast, default=Treatment)
cat_contrasts[factor] = coded
column_builder = _ColumnBuilder(builder_factors, num_columns, cat_contrasts)
column_builders.append(column_builder)
term_to_column_builders[term] = column_builders
return new_term_order, term_to_column_builders
def _make_subterm_infos(terms,
num_column_counts,
cat_levels_contrasts):
# Sort each term into a bucket based on the set of numeric factors it
# contains:
term_buckets = OrderedDict()
bucket_ordering = []
for term in terms:
num_factors = []
for factor in term.factors:
if factor in num_column_counts:
num_factors.append(factor)
bucket = frozenset(num_factors)
if bucket not in term_buckets:
bucket_ordering.append(bucket)
term_buckets.setdefault(bucket, []).append(term)
# Special rule: if there is a no-numerics bucket, then it always comes
# first:
if frozenset() in term_buckets:
bucket_ordering.remove(frozenset())
bucket_ordering.insert(0, frozenset())
term_to_subterm_infos = OrderedDict()
new_term_order = []
# Then within each bucket, work out which sort of contrasts we want to use
# for each term to avoid redundancy
for bucket in bucket_ordering:
bucket_terms = term_buckets[bucket]
# Sort by degree of interaction
bucket_terms.sort(key=lambda t: len(t.factors))
new_term_order += bucket_terms
used_subterms = set()
for term in bucket_terms:
subterm_infos = []
factor_codings = pick_contrasts_for_term(term,
num_column_counts,
used_subterms)
# Construct one SubtermInfo for each subterm
for factor_coding in factor_codings:
subterm_factors = []
contrast_matrices = {}
subterm_columns = 1
# In order to preserve factor ordering information, the
# coding_for_term just returns dicts, and we refer to
# the original factors to figure out which are included in
# each subterm, and in what order
for factor in term.factors:
# Numeric factors are included in every subterm
if factor in num_column_counts:
subterm_factors.append(factor)
subterm_columns *= num_column_counts[factor]
elif factor in factor_coding:
subterm_factors.append(factor)
levels, contrast = cat_levels_contrasts[factor]
# This is where the default coding is set to
# Treatment:
coded = code_contrast_matrix(factor_coding[factor],
levels, contrast,
default=Treatment)
contrast_matrices[factor] = coded
subterm_columns *= coded.matrix.shape[1]
subterm_infos.append(SubtermInfo(subterm_factors,
contrast_matrices,
subterm_columns))
term_to_subterm_infos[term] = subterm_infos
assert new_term_order == list(term_to_subterm_infos)
return term_to_subterm_infos
