pyevoc.features.term_indices

Term-level AFE/AOE-style indices and EVOC quadrant assignment.

This module computes the term-level indicators used by PyEvoc to reconstruct computational Hierarchical Evocation Analysis (HEM) structures from token-level tables.

The module implements three analytical layers:

1. term-level statistics;

2. POS-specific AFE/AOE thresholds;

3. EVOC quadrant assignment.

It supports both user-level diffusion and document-level diffusion, preserves temporal information when available, and remains backward compatible with the earlier compute_term_indices interface.

class pyevoc.features.term_indices.SalienceConfig(alpha=0.5, max_rank_value=5.0, use_users_for_frequency=True, focal_upos=<factory>)

Bases: object

Configuration for term-level salience and diffusion indicators.

Parameters:

• alpha (float)

• max_rank_value (float)

• use_users_for_frequency (bool)

• focal_upos (set[str])

alpha: float = 0.5

max_rank_value: float = 5.0

use_users_for_frequency: bool = True

focal_upos: set[str]

pyevoc.features.term_indices.compute_term_statistics(tokens, *, term_col='term', upos_col='upos', user_col='user_id', doc_col='doc_id', time_col='time', r_pos_col='r_pos', r_str_col='r_str', term_type_col='term_type', config=SalienceConfig(), human_readable_quadrants=False, show_progress=True)

Compute term-level statistics, thresholds and quadrant summaries.

Parameters:

• tokens (DataFrame)

• term_col (str)

• upos_col (str)

• user_col (str)

• doc_col (str)

• time_col (str)

• r_pos_col (str)

• r_str_col (str)

• term_type_col (str)

• config (SalienceConfig)

• human_readable_quadrants (bool)

• show_progress (bool)

Return type:

tuple[DataFrame, DataFrame, DataFrame]

pyevoc.features.term_indices.compute_term_indices(tokens, *, term_col='term', upos_col='upos', user_col='user_id', doc_col='doc_id', r_pos_col='r_pos', r_str_col='r_str', config=SalienceConfig(), return_thresholds=False, return_quadrants=False)

Backward-compatible wrapper for term-level index computation.

Parameters:

• tokens (DataFrame)

• term_col (str)

• upos_col (str)

• user_col (str)

• doc_col (str)

• r_pos_col (str)

• r_str_col (str)

• config (SalienceConfig)

• return_thresholds (bool)

• return_quadrants (bool)

Return type:

DataFrame | tuple[DataFrame, DataFrame, DataFrame]

pyevoc.features.term_indices.term_statistics_summary(term_stats, *, upos_col='upos', quadrant_col='quadrant')

Return a compact summary of term-level statistics by UPOS and quadrant.

Parameters:

• term_stats (DataFrame)

• upos_col (str)

• quadrant_col (str)

Return type:

DataFrame