[2/6] Integrate adjust_influence into calculate_influence#6
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…as kwargs
Replaces the hardcoded NEG_NEUROTRANSMITTERS module constant with two
explicit constructor arguments so that the library no longer pre-empts
the user's neurotransmitter sign assignment:
- inhibitory_nts: pre-neuron top_nt values to negate when signed=True
(required when signed=True; raises ValueError otherwise).
- excluded_nts: pre-neuron top_nt values to drop entirely from W,
independent of signed=True/False. Useful for transmitter classes
whose net sign at a given target depends on the receptor mix and so
cannot be assigned a single sign safely.
Adds lambda_max as a constructor argument (default 0.99 for backwards
compatibility). _normalize_W now always rescales to lambda_max exactly
rather than only capping when the natural eigenvalue exceeds it, so the
parameter is a true control knob over leading-mode amplification rather
than just a stability ceiling. The amplification of the leading mode in
(I - W_rescaled)^-1 is 1 / (1 - lambda_max), so 0.99 gives ~100x and
0.5 gives ~2x.
Surfaces syn_weight_measure ('count' or 'norm') as a constructor
argument and changes the default from 'norm' to 'count'. Fixes a
pre-existing bug in _create_sparse_W: the signed=True path negated the
'count' column unconditionally, but the matrix was populated from the
column named by syn_weight_measure (default 'norm'), so the signed flag
silently produced the same matrix as signed=False. The negation now
applies to the column actually consumed. An inline comment notes that
flipping signs on 'norm' breaks the column-sums-to-1 interpretation, so
'count' is the more natural choice in signed mode.
Sign preservation: _build_influence_dataframe now keeps the real part
of the steady-state vector in signed mode rather than always taking the
magnitude, so net-inhibited targets carry a negative score.
Validates lambda_max in (0, 1) and syn_weight_measure in {'count',
'norm'}. When signed=True or excluded_nts is set, the SQLite meta
table must include a 'top_nt' column or _create_sparse_W raises.
calculate_influence now returns both the raw influence column and the three log-compressed adjusted_influence columns by default. Users can compare adjusted vs unadjusted scores from a single call rather than having to import adjust_influence and post-process the output themselves; opt out with adjust=False. The log compression is parameterised via two new kwargs on calculate_influence: adjust_const (the exp(-c) junk-node floor /+c shift, default 24) and adjust_signif (rounding, default 6). adjust_influence is added as a module-level function so advanced workflows can still post-process aggregated DataFrames (e.g. summing per-(target_class, seed_class) across multiple seeds before log compression in a worked example). Its output is three columns: - adjusted_influence = sign(x) * (log(max(|x|, exp(-const))) + const) - adjusted_influence_norm_by_targets (divides by n_targets per group) - adjusted_influence_norm_by_sources_and_targets (divides by n_sources * n_targets per group) The function dispatches on the presence of 'target' and 'seed' columns: when present it groups and sums per (target, seed); when absent it treats each row as its own group, which is the case for the DataFrame calculate_influence builds. Sign is preserved, so signed-mode input yields signed-mode output.
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Integrate adjust_influence into calculate_influence
calculate_influence now returns both the raw influence column and the
three log-compressed adjusted_influence columns by default. Users can
compare adjusted vs unadjusted scores from a single call rather than
having to import adjust_influence and post-process the output
themselves; opt out with adjust=False.
The log compression is parameterised via two new kwargs on
calculate_influence: adjust_const (the exp(-c) junk-node floor /+c
shift, default 24) and adjust_signif (rounding, default 6).
adjust_influence is added as a module-level function so advanced
workflows can still post-process aggregated DataFrames (e.g. summing
per-(target_class, seed_class) across multiple seeds before log
compression in a worked example). Its output is three columns:
n_sources * n_targets per group)
The function dispatches on the presence of 'target' and 'seed' columns:
when present it groups and sums per (target, seed); when absent it
treats each row as its own group, which is the case for the DataFrame
calculate_influence builds. Sign is preserved, so signed-mode input
yields signed-mode output.