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[Stack 13/27] Vectorize participant info computation (3-15x speedup) #2437
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9949b3a
Address Copilot review: clarify MAP estimate, centralize PSEUDO_COUNT…
jucor 50eeea1
Update plan: add stack cross-reference and GitHub PR numbers
jucor 01881b2
Vectorize _compute_participant_info_optimized for ~100x speedup
jucor dc37e2b
Add benchmark script for participant info vectorization
jucor 8dba645
Address Copilot review: drop unused cache, validate --runs, tighten t…
jucor 6138219
Remove old participant_stats() in favor of vectorized replacement
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| Original file line number | Diff line number | Diff line change |
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@@ -20,7 +20,7 @@ | |
| kmeans_sklearn, | ||
| calculate_silhouette_sklearn | ||
| ) | ||
| from polismath.pca_kmeans_rep.repness import conv_repness, participant_stats | ||
| from polismath.pca_kmeans_rep.repness import conv_repness | ||
| from polismath.pca_kmeans_rep.corr import compute_correlation | ||
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@@ -792,114 +792,101 @@ def _compute_participant_info_optimized(self, vote_matrix: pd.DataFrame, group_c | |
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| # OPTIMIZATION 3: Precompute group vote matrices and average votes | ||
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| # Precompute group vote matrices and their valid comment masks | ||
| group_vote_matrices = {} | ||
| # Precompute group average votes and valid comment masks | ||
| group_avg_votes = {} | ||
| group_valid_masks = {} | ||
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| for group_id, member_indices in group_member_indices.items(): | ||
| if len(member_indices) >= 3: # Only calculate for groups with enough members | ||
| # Extract the group vote matrix | ||
| group_vote_matrix = matrix_values[member_indices, :] | ||
| group_vote_matrices[group_id] = group_vote_matrix | ||
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| # Calculate average votes per comment for this group | ||
| group_avg_votes[group_id] = np.mean(group_vote_matrix, axis=0) | ||
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| # Precompute which comments have at least 3 votes from this group | ||
| group_valid_masks[group_id] = np.sum(group_vote_matrix != 0, axis=0) >= 3 | ||
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| # OPTIMIZATION 4: Use vectorized operations for participant stats | ||
| # VECTORIZED: Compute vote counts for ALL participants at once | ||
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| process_start = time.time() | ||
| batch_start = time.time() | ||
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| for p_idx, participant_id in enumerate(vote_matrix.index): | ||
| if p_idx >= matrix_values.shape[0]: | ||
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| n_agree_all = np.sum(matrix_values > 0, axis=1) # (N,) | ||
| n_disagree_all = np.sum(matrix_values < 0, axis=1) # (N,) | ||
| n_pass_all = np.sum(matrix_values == 0, axis=1) # (N,) | ||
| n_votes_all = n_agree_all + n_disagree_all # (N,) | ||
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| # Mask: participants with at least one real vote | ||
| has_votes = n_votes_all > 0 # (N,) bool | ||
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| # VECTORIZED: Compute per-group correlations for ALL participants at once | ||
| # Store as {group_id: corr_array} where corr_array is (N,) | ||
| group_corr_arrays = {} | ||
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| for group_id, member_indices in group_member_indices.items(): | ||
| if len(member_indices) < 3 or group_id not in group_avg_votes: | ||
| # All correlations default to 0.0 | ||
| group_corr_arrays[group_id] = np.zeros(participant_count) | ||
| continue | ||
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| # Print progress for large participant sets | ||
| if participant_count > 100 and p_idx % 100 == 0: | ||
| now = time.time() | ||
| elapsed = now - process_start | ||
| batch_time = now - batch_start | ||
| batch_start = now | ||
| percent = (p_idx / participant_count) * 100 | ||
| logger.info(f"Processed {p_idx}/{participant_count} participants ({percent:.1f}%) - " + | ||
| f"Elapsed: {elapsed:.2f}s, Batch: {batch_time:.4f}s") | ||
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| # Get participant votes | ||
| participant_votes = matrix_values[p_idx, :] | ||
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| # Count votes using vectorized operations | ||
| n_agree = np.sum(participant_votes > 0) | ||
| n_disagree = np.sum(participant_votes < 0) | ||
| n_pass = np.sum(participant_votes == 0) | ||
| n_votes = n_agree + n_disagree | ||
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| # Skip participants with no votes | ||
| if n_votes == 0: | ||
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| valid_mask = group_valid_masks[group_id] | ||
| n_valid = int(np.sum(valid_mask)) | ||
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| if n_valid < 3: | ||
| group_corr_arrays[group_id] = np.zeros(participant_count) | ||
| continue | ||
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| # Find participant's group using precomputed mapping | ||
| participant_group = ptpt_group_map.get(participant_id) | ||
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| # OPTIMIZATION 5: Efficient group correlation calculation | ||
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| # Calculate agreement with each group - optimized version | ||
| group_agreements = {} | ||
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| for group_id, member_indices in group_member_indices.items(): | ||
| if len(member_indices) < 3: | ||
| # Skip groups with too few members | ||
| group_agreements[group_id] = 0.0 | ||
| continue | ||
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| if group_id not in group_avg_votes or group_id not in group_valid_masks: | ||
| group_agreements[group_id] = 0.0 | ||
| continue | ||
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| # Use precomputed data | ||
| g_votes = group_avg_votes[group_id] | ||
| valid_mask = group_valid_masks[group_id] | ||
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| if np.sum(valid_mask) >= 3: # At least 3 valid comments | ||
| # Extract only valid comment votes | ||
| p_votes = participant_votes[valid_mask] | ||
| g_votes_valid = g_votes[valid_mask] | ||
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| # Fast correlation calculation | ||
| p_std = np.std(p_votes) | ||
| g_std = np.std(g_votes_valid) | ||
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| if p_std > 0 and g_std > 0: | ||
| # Use numpy's built-in correlation (faster and more numerically stable) | ||
| correlation = np.corrcoef(p_votes, g_votes_valid)[0, 1] | ||
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| if not np.isnan(correlation): | ||
| group_agreements[group_id] = correlation | ||
| else: | ||
| group_agreements[group_id] = 0.0 | ||
| else: | ||
| group_agreements[group_id] = 0.0 | ||
| else: | ||
| group_agreements[group_id] = 0.0 | ||
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| # Store participant stats | ||
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| # P: all participants' votes on valid comments — (N, n_valid) | ||
| P = matrix_values[:, valid_mask] | ||
| # g: group average on valid comments — (n_valid,) | ||
| g = group_avg_votes[group_id][valid_mask] | ||
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| p_mean = P.mean(axis=1) # (N,) | ||
| g_mean = g.mean() # scalar | ||
| p_std = P.std(axis=1) # (N,) | ||
| g_std = g.std() # scalar | ||
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| if g_std == 0: | ||
| group_corr_arrays[group_id] = np.zeros(participant_count) | ||
| continue | ||
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| # Pearson correlation: (mean(P*g) - mean(P)*mean(g)) / (std(P)*std(g)) | ||
| cross_mean = (P @ g) / n_valid # (N,) | ||
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| # np.where evaluates both branches; suppress divide-by-zero for p_std==0 | ||
| with np.errstate(invalid='ignore', divide='ignore'): | ||
| corr = np.where( | ||
| p_std > 0, | ||
| (cross_mean - p_mean * g_mean) / (p_std * g_std), | ||
| 0.0, | ||
| ) | ||
| corr = np.nan_to_num(corr, nan=0.0) | ||
| group_corr_arrays[group_id] = corr | ||
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| # Assemble result dicts (zero computation — just indexing) | ||
| group_ids = list(group_member_indices.keys()) | ||
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| for p_idx, participant_id in enumerate(vote_matrix.index): | ||
| if not has_votes[p_idx]: | ||
| continue | ||
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| result['stats'][participant_id] = { | ||
| 'n_agree': int(n_agree), | ||
| 'n_disagree': int(n_disagree), | ||
| 'n_pass': int(n_pass), | ||
| 'n_votes': int(n_votes), | ||
| 'group': participant_group, | ||
| 'group_correlations': group_agreements | ||
| 'n_agree': int(n_agree_all[p_idx]), | ||
| 'n_disagree': int(n_disagree_all[p_idx]), | ||
| 'n_pass': int(n_pass_all[p_idx]), | ||
| 'n_votes': int(n_votes_all[p_idx]), | ||
| 'group': ptpt_group_map.get(participant_id), | ||
| 'group_correlations': { | ||
| gid: float(group_corr_arrays[gid][p_idx]) | ||
| for gid in group_ids | ||
| } | ||
| } | ||
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| total_time = time.time() - start_time | ||
| process_time = time.time() - process_start | ||
| logger.info(f"Participant stats completed in {total_time:.2f}s (preparation: {prep_time:.2f}s, processing: {process_time:.2f}s)") | ||
| logger.info(f"Processed {len(result['stats'])} participants with {len(group_clusters)} groups") | ||
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| return result | ||
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| def _compute_participant_info(self) -> None: | ||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
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@@ -10,14 +10,13 @@ | |
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| from polismath.pca_kmeans_rep.pca import pca_project_dataframe | ||
| from polismath.pca_kmeans_rep.clusters import cluster_dataframe, Cluster | ||
| from polismath.pca_kmeans_rep.repness import conv_repness, participant_stats | ||
| from polismath.pca_kmeans_rep.repness import conv_repness | ||
| from polismath.pca_kmeans_rep.corr import compute_correlation | ||
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| __all__ = [ | ||
| 'pca_project_dataframe', | ||
| 'cluster_dataframe', | ||
| 'Cluster', | ||
| 'conv_repness', | ||
| 'participant_stats', | ||
| 'compute_correlation', | ||
| ] | ||
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This cross-reference table hard-codes stack positions like
Stack 8/10and10/10, but this PR is titledStack 11/25, so the doc becomes stale as soon as the stack grows. Suggestion: avoid fixed denominators (e.g., useStack 8/Stack 10), or reference the canonical stack file (.claude/STACK) and keep this table focused on PR numbers ↔ GitHub PR links rather than mutable stack indices.