diff --git a/analyze_frequency.py b/analyze_frequency.py new file mode 100644 index 0000000..c018105 --- /dev/null +++ b/analyze_frequency.py @@ -0,0 +1,236 @@ +#!/usr/bin/env python3 +""" +Analyze frequency distribution in pinyin_char_statistics.json +""" + +import json +import sys +import os +import math +from collections import defaultdict +from pathlib import Path + +def main(): + # Path to the JSON file + json_path = Path("src/model/assets/pinyin_char_statistics.json") + if not json_path.exists(): + print(f"Error: File not found: {json_path}") + sys.exit(1) + + print(f"Loading {json_path}...") + with open(json_path, 'r', encoding='utf-8') as f: + data = json.load(f) + + print(f"Timestamp: {data.get('timestamp')}") + print(f"Total characters: {data.get('total_characters')}") + print(f"Total pinyins: {data.get('total_pinyins')}") + print(f"Valid input character count: {data.get('valid_input_character_count')}") + + pairs = data.get('pairs', {}) + print(f"Number of pairs: {len(pairs)}") + + # Extract counts and IDs + counts = [] + id_to_count = {} + char_to_count = {} + for key, pair in pairs.items(): + try: + char_id = pair.get('id') + count = pair.get('count') + char = pair.get('char', '') + if char_id is not None and count is not None: + counts.append(count) + id_to_count[char_id] = count + if char: + char_to_count[char] = count + except (ValueError, TypeError) as e: + print(f"Warning: Could not parse pair {key}: {e}") + continue + + if not counts: + print("No valid count data found.") + return + + # Basic statistics + min_count = min(counts) + max_count = max(counts) + total_count = sum(counts) + mean_count = total_count / len(counts) + + # Sort counts for percentiles + sorted_counts = sorted(counts) + n = len(sorted_counts) + + # Percentiles + p10 = sorted_counts[int(0.1 * n)] + p25 = sorted_counts[int(0.25 * n)] + p50 = sorted_counts[int(0.5 * n)] + p75 = sorted_counts[int(0.75 * n)] + p90 = sorted_counts[int(0.9 * n)] + p99 = sorted_counts[int(0.99 * n)] + + # Variance and std dev + variance = sum((x - mean_count) ** 2 for x in counts) / n + std_dev = math.sqrt(variance) + + print("\n=== BASIC STATISTICS ===") + print(f"Min frequency: {min_count}") + print(f"Max frequency: {max_count}") + print(f"Mean frequency: {mean_count:.2f}") + print(f"Standard deviation: {std_dev:.2f}") + print(f"Total frequency sum: {total_count}") + print(f"Number of entries: {n}") + + print("\n=== PERCENTILES ===") + print(f"10th percentile: {p10}") + print(f"25th percentile: {p25}") + print(f"50th percentile (median): {p50}") + print(f"75th percentile: {p75}") + print(f"90th percentile: {p90}") + print(f"99th percentile: {p99}") + + # Find IDs with min and max counts + min_ids = [id for id, count in id_to_count.items() if count == min_count] + max_ids = [id for id, count in id_to_count.items() if count == max_count] + + print(f"\nIDs with min frequency ({min_count}): {min_ids}") + print(f"IDs with max frequency ({max_count}): {max_ids}") + + # Check if IDs are assigned in frequency order + # Compute correlation between ID and count + ids = list(id_to_count.keys()) + id_counts = [id_to_count[id] for id in ids] + + # Sort by ID and check if counts are decreasing + sorted_by_id = sorted(ids) + counts_by_id = [id_to_count[id] for id in sorted_by_id] + + # Calculate monotonicity: count of times count decreases as ID increases + decreases = 0 + increases = 0 + for i in range(1, len(counts_by_id)): + if counts_by_id[i] < counts_by_id[i-1]: + decreases += 1 + elif counts_by_id[i] > counts_by_id[i-1]: + increases += 1 + + print(f"\n=== ID ORDER ANALYSIS ===") + print(f"Total pairs: {len(counts_by_id)}") + print(f"Decreases as ID increases: {decreases} times") + print(f"Increases as ID increases: {increases} times") + print(f"Percentage decreasing: {decreases/(len(counts_by_id)-1)*100:.2f}%") + + # Check if IDs are roughly sorted by frequency + # Compute Spearman rank correlation (simplified) + sorted_by_count = sorted(ids, key=lambda x: id_to_count[x], reverse=True) + rank_by_id = {id: i for i, id in enumerate(sorted_by_id)} + rank_by_count = {id: i for i, id in enumerate(sorted_by_count)} + + # Average rank difference + rank_diffs = [abs(rank_by_id[id] - rank_by_count[id]) for id in ids] + avg_rank_diff = sum(rank_diffs) / len(rank_diffs) + max_rank_diff = max(rank_diffs) + + print(f"Average rank difference between ID order and frequency order: {avg_rank_diff:.2f}") + print(f"Maximum rank difference: {max_rank_diff}") + + # Analyze specific ID range 5000-5500 + print("\n=== ANALYSIS OF ID RANGE 5000-5500 ===") + range_counts = [] + range_ids = [] + for id in range(5000, 5501): + if id in id_to_count: + range_counts.append(id_to_count[id]) + range_ids.append(id) + + if range_counts: + range_min = min(range_counts) + range_max = max(range_counts) + range_mean = sum(range_counts) / len(range_counts) + range_sorted = sorted(range_counts) + range_n = len(range_counts) + range_p10 = range_sorted[int(0.1 * range_n)] if range_n > 0 else 0 + range_p50 = range_sorted[int(0.5 * range_n)] if range_n > 0 else 0 + range_p90 = range_sorted[int(0.9 * range_n)] if range_n > 0 else 0 + + print(f"IDs in range 5000-5500: {len(range_counts)}") + print(f"Min frequency in range: {range_min}") + print(f"Max frequency in range: {range_max}") + print(f"Mean frequency in range: {range_mean:.2f}") + print(f"10th percentile in range: {range_p10}") + print(f"50th percentile in range: {range_p50}") + print(f"90th percentile in range: {range_p90}") + + # Find IDs with min frequency in this range + min_in_range_ids = [id for id in range_ids if id_to_count[id] == range_min] + print(f"IDs with min frequency in range: {min_in_range_ids[:10]}{'...' if len(min_in_range_ids) > 10 else ''}") + else: + print("No IDs found in range 5000-5500") + + # Histogram of frequencies (log bins) + print("\n=== FREQUENCY DISTRIBUTION (LOG BINS) ===") + if max_count > 0: + log_min = math.log10(min_count) if min_count > 0 else 0 + log_max = math.log10(max_count) + num_bins = 20 + bin_edges = [10**(log_min + i*(log_max-log_min)/num_bins) for i in range(num_bins+1)] + + hist = [0] * num_bins + for count in counts: + if count > 0: + log_val = math.log10(count) + bin_idx = min(int((log_val - log_min) / (log_max - log_min) * num_bins), num_bins-1) + hist[bin_idx] += 1 + + print("Log-scale histogram (count range -> frequency count):") + for i in range(num_bins): + if hist[i] > 0: + lower = bin_edges[i] + upper = bin_edges[i+1] + print(f" {lower:.2e} - {upper:.2e}: {hist[i]} entries") + + # Check for zero or near-zero frequencies + zero_count = sum(1 for c in counts if c == 0) + low_count = sum(1 for c in counts if 0 < c <= 10) + very_low_count = sum(1 for c in counts if 0 < c <= 100) + + print(f"\n=== LOW FREQUENCY ANALYSIS ===") + print(f"Entries with zero frequency: {zero_count}") + print(f"Entries with frequency <= 10: {low_count}") + print(f"Entries with frequency <= 100: {very_low_count}") + + # Find the actual min frequency (excluding zeros if any) + non_zero_counts = [c for c in counts if c > 0] + if non_zero_counts: + actual_min = min(non_zero_counts) + print(f"Actual min frequency (non-zero): {actual_min}") + actual_min_ids = [id for id, count in id_to_count.items() if count == actual_min] + print(f"IDs with actual min frequency: {actual_min_ids[:10]}{'...' if len(actual_min_ids) > 10 else ''}") + + # Summary for smoothing algorithm design + print("\n=== SUMMARY FOR SMOOTHING ALGORITHM DESIGN ===") + print(f"Frequency range spans {max_count/min_count if min_count>0 else 'inf'}:1 ratio") + print(f"Most entries ({p50}) have frequency around {p50}") + print(f"Top 10% of entries have frequency > {p90}") + print(f"Bottom 10% of entries have frequency < {p10}") + print(f"ID order is {'roughly' if decreases > increases else 'not'} sorted by frequency") + + # Save detailed data for further analysis + output_file = "frequency_analysis_results.txt" + with open(output_file, 'w', encoding='utf-8') as f: + f.write("Frequency Analysis Results\n") + f.write("="*50 + "\n") + f.write(f"Min frequency: {min_count}\n") + f.write(f"Max frequency: {max_count}\n") + f.write(f"Mean frequency: {mean_count:.2f}\n") + f.write(f"Standard deviation: {std_dev:.2f}\n") + f.write(f"10th percentile: {p10}\n") + f.write(f"50th percentile: {p50}\n") + f.write(f"90th percentile: {p90}\n") + f.write(f"IDs in range 5000-5500 min: {range_min if 'range_min' in locals() else 'N/A'}\n") + f.write(f"IDs in range 5000-5500 max: {range_max if 'range_max' in locals() else 'N/A'}\n") + + print(f"\nDetailed results saved to {output_file}") + +if __name__ == "__main__": + main() diff --git a/analyze_range.py b/analyze_range.py new file mode 100644 index 0000000..0df339d --- /dev/null +++ b/analyze_range.py @@ -0,0 +1,115 @@ +#!/usr/bin/env python3 +""" +Analyze specific ID ranges in pinyin_char_statistics.json +""" + +import json +import sys +from pathlib import Path + +def main(): + json_path = Path("src/model/assets/pinyin_char_statistics.json") + with open(json_path, 'r', encoding='utf-8') as f: + data = json.load(f) + + pairs = data.get('pairs', {}) + + # Build ID to count mapping + id_to_count = {} + for key, pair in pairs.items(): + char_id = pair.get('id') + count = pair.get('count') + if char_id is not None and count is not None: + id_to_count[char_id] = count + + # Analyze range 5000-5500 in detail + print("ID range 5000-5500 detailed analysis:") + print("ID\tCount\tChar\tPinyin") + + range_data = [] + for id in range(5000, 5501): + if id in id_to_count: + # Find the pair to get char and pinyin + for key, pair in pairs.items(): + if pair.get('id') == id: + char = pair.get('char', '') + pinyin = pair.get('pinyin', '') + count = pair.get('count', 0) + range_data.append((id, count, char, pinyin)) + if id % 100 == 0: # Print every 100th for overview + print(f"{id}\t{count}\t{char}\t{pinyin}") + break + + # Print min and max in range + if range_data: + min_item = min(range_data, key=lambda x: x[1]) + max_item = max(range_data, key=lambda x: x[1]) + print(f"\nMin in range: ID {min_item[0]}, count {min_item[1]}, char '{min_item[2]}', pinyin '{min_item[3]}'") + print(f"Max in range: ID {max_item[0]}, count {max_item[1]}, char '{max_item[2]}', pinyin '{max_item[3]}'") + + # Check if frequencies are monotonic in this range + counts = [item[1] for item in range_data] + increasing = all(counts[i] <= counts[i+1] for i in range(len(counts)-1)) + decreasing = all(counts[i] >= counts[i+1] for i in range(len(counts)-1)) + print(f"Monotonic in range: increasing={increasing}, decreasing={decreasing}") + + # Check for frequency plateaus + from collections import Counter + freq_count = Counter(counts) + most_common = freq_count.most_common(5) + print(f"Most common frequencies in range: {most_common}") + + # Analyze the tail (IDs with frequency 1) + print("\n\nAnalysis of frequency=1 entries:") + freq_one_ids = [id for id, count in id_to_count.items() if count == 1] + print(f"Number of entries with frequency=1: {len(freq_one_ids)}") + if freq_one_ids: + print(f"ID range of frequency=1: {min(freq_one_ids)} to {max(freq_one_ids)}") + print(f"First 10 IDs: {freq_one_ids[:10]}") + print(f"Last 10 IDs: {freq_one_ids[-10:]}") + + # Check if they're contiguous + sorted_ids = sorted(freq_one_ids) + contiguous = all(sorted_ids[i] + 1 == sorted_ids[i+1] for i in range(len(sorted_ids)-1)) + print(f"Are they contiguous IDs? {contiguous}") + + # Sample some characters + print("\nSample characters with frequency=1:") + sample_count = 0 + for key, pair in pairs.items(): + if pair.get('count') == 1 and sample_count < 10: + print(f" ID {pair.get('id')}: char '{pair.get('char')}', pinyin '{pair.get('pinyin')}'") + sample_count += 1 + + # Check overall ID-frequency ordering + print("\n\nOverall ID-frequency ordering analysis:") + all_ids = sorted(id_to_count.keys()) + all_counts = [id_to_count[id] for id in all_ids] + + # Count monotonic segments + non_increasing_segments = 0 + current_segment_length = 1 + for i in range(1, len(all_counts)): + if all_counts[i] <= all_counts[i-1]: + current_segment_length += 1 + else: + if current_segment_length > 1: + non_increasing_segments += 1 + current_segment_length = 1 + if current_segment_length > 1: + non_increasing_segments += 1 + + print(f"Total IDs: {len(all_ids)}") + print(f"Non-increasing segments: {non_increasing_segments}") + + # Check for frequency plateaus overall + from collections import Counter + overall_freq_count = Counter(all_counts) + plateaus = [(freq, count) for freq, count in overall_freq_count.items() if count > 1] + plateaus_sorted = sorted(plateaus, key=lambda x: x[1], reverse=True)[:10] + print(f"Top 10 frequency plateaus (freq: count of IDs sharing that freq):") + for freq, count in plateaus_sorted: + print(f" {freq}: {count} IDs") + +if __name__ == "__main__": + main() diff --git a/comprehensive_analysis.py b/comprehensive_analysis.py new file mode 100644 index 0000000..25ca936 --- /dev/null +++ b/comprehensive_analysis.py @@ -0,0 +1,177 @@ +#!/usr/bin/env python3 +""" +Comprehensive frequency distribution analysis +""" + +import json +import sys +import math +from collections import Counter +from pathlib import Path + +def main(): + json_path = Path("src/model/assets/pinyin_char_statistics.json") + with open(json_path, 'r', encoding='utf-8') as f: + data = json.load(f) + + pairs = data.get('pairs', {}) + + # Extract counts + counts = [] + for key, pair in pairs.items(): + count = pair.get('count') + if count is not None: + counts.append(count) + + n = len(counts) + print(f"Total entries: {n}") + + # Sort descending for rank-frequency analysis + counts_sorted_desc = sorted(counts, reverse=True) + + # Basic statistics + min_count = min(counts) + max_count = max(counts) + mean_count = sum(counts) / n + + # Percentiles + percentiles = [0.01, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95, 0.99] + print("\n=== PERCENTILE DISTRIBUTION ===") + for p in percentiles: + idx = int(p * n) + value = counts_sorted_desc[idx] + print(f"{p*100:5.1f}%: {value:>12} (rank ~{idx})") + + # Cumulative distribution + print("\n=== CUMULATIVE DISTRIBUTION ===") + thresholds = [1, 2, 3, 5, 10, 20, 50, 100, 200, 500, 1000, 2000, 5000, 10000, 20000, 50000, 100000, 200000, 500000, 1000000, 5000000, 10000000, 50000000, 100000000, 500000000] + for thresh in thresholds: + if thresh > max_count: + break + below = sum(1 for c in counts if c <= thresh) + above = sum(1 for c in counts if c >= thresh) + print(f"Count <= {thresh:10}: {below:6} entries ({below/n*100:5.1f}%)") + # print(f"Count >= {thresh:10}: {above:6} entries ({above/n*100:5.1f}%)") + + # Check min_count=109 parameter + print("\n=== ANALYSIS OF THRESHOLD 109 ===") + below_109 = sum(1 for c in counts if c < 109) + at_or_above_109 = sum(1 for c in counts if c >= 109) + print(f"Entries with count < 109: {below_109} ({below_109/n*100:.1f}%)") + print(f"Entries with count >= 109: {at_or_above_109} ({at_or_above_109/n*100:.1f}%)") + + # If 109 is a threshold, what's the actual min among those >= 109? + counts_ge_109 = [c for c in counts if c >= 109] + if counts_ge_109: + actual_min_ge_109 = min(counts_ge_109) + print(f"Actual min frequency among those >= 109: {actual_min_ge_109}") + + # Rank-frequency analysis (Zipf's law) + print("\n=== RANK-FREQUENCY ANALYSIS (Top 100) ===") + print("Rank\tFrequency\tlog(rank)\tlog(freq)") + for rank in range(1, 101): + freq = counts_sorted_desc[rank-1] + print(f"{rank}\t{freq}\t{math.log(rank):.3f}\t{math.log(freq):.3f}") + + # Frequency spectrum (how many distinct frequencies) + freq_counter = Counter(counts) + print(f"\n=== FREQUENCY SPECTRUM ===") + print(f"Distinct frequency values: {len(freq_counter)}") + + # Most common frequencies + print("\nTop 20 most common frequencies (plateau sizes):") + for freq, freq_count in freq_counter.most_common(20): + print(f" Frequency {freq}: {freq_count} entries") + + # Analyze ID ranges + print("\n=== ID RANGE ANALYSIS ===") + # Build ID to count mapping + id_to_count = {} + for key, pair in pairs.items(): + char_id = pair.get('id') + count = pair.get('count') + if char_id is not None and count is not None: + id_to_count[char_id] = count + + ranges = [ + (0, 100, "Top 100 IDs"), + (100, 500, "IDs 100-500"), + (500, 1000, "IDs 500-1000"), + (1000, 2000, "IDs 1000-2000"), + (2000, 5000, "IDs 2000-5000"), + (5000, 5500, "IDs 5000-5500 (user mentioned)"), + (5500, 6000, "IDs 5500-6000"), + (10000, 10500, "IDs 10000-10500"), + (15000, 15500, "IDs 15000-15500"), + (19000, 19500, "IDs 19000-19500 (before freq=1)"), + (19499, 20647, "IDs with freq=1"), + ] + + for start, end, label in ranges: + range_counts = [id_to_count[id] for id in range(start, end) if id in id_to_count] + if range_counts: + min_c = min(range_counts) + max_c = max(range_counts) + mean_c = sum(range_counts) / len(range_counts) + median_c = sorted(range_counts)[len(range_counts)//2] + print(f"{label} ({len(range_counts)} entries): min={min_c}, max={max_c}, mean={mean_c:.1f}, median={median_c}") + + # Check if IDs are perfectly sorted by frequency + print("\n=== ID ORDER VERIFICATION ===") + all_ids = sorted(id_to_count.keys()) + all_counts = [id_to_count[id] for id in all_ids] + + # Check for any violations of non-increasing order + violations = 0 + for i in range(1, len(all_counts)): + if all_counts[i] > all_counts[i-1]: + violations += 1 + if violations <= 5: + print(f"Violation at ID {all_ids[i]}: {all_counts[i]} > {all_counts[i-1]} (ID {all_ids[i-1]})") + + print(f"Total violations of non-increasing order: {violations}") + + # Check if equal frequencies are grouped together + print("\n=== FREQUENCY GROUPING ANALYSIS ===") + current_freq = None + group_start = None + group_sizes = [] + + for i, (id, count) in enumerate(zip(all_ids, all_counts)): + if count != current_freq: + if current_freq is not None: + group_sizes.append((current_freq, group_start, all_ids[i-1], i - group_start)) + current_freq = count + group_start = i + + # Last group + if current_freq is not None: + group_sizes.append((current_freq, group_start, all_ids[-1], len(all_ids) - group_start)) + + # Sort groups by size + group_sizes.sort(key=lambda x: x[3], reverse=True) + print("Top 10 largest frequency groups (plateaus):") + for freq, start_id_idx, end_id, size in group_sizes[:10]: + start_id = all_ids[start_id_idx] + print(f" Frequency {freq}: IDs {start_id}-{end_id} ({size} entries)") + + # Summary for smoothing algorithm + print("\n=== SMOOTHING ALGORITHM IMPLICATIONS ===") + print("1. IDs are perfectly sorted by frequency (non-increasing).") + print(f"2. Frequency range: {min_count} to {max_count} (ratio {max_count/min_count:.1e}:1).") + print(f"3. {below_109} entries ({below_109/n*100:.1f}%) have frequency < 109.") + print(f"4. Median frequency: {counts_sorted_desc[n//2]}.") + print(f"5. 90% of entries have frequency <= {counts_sorted_desc[int(0.9*n)]}.") + print(f"6. Top 1% of entries have frequency >= {counts_sorted_desc[int(0.01*n)]}.") + print("7. Large frequency plateaus exist (many IDs share same frequency).") + print("8. Smoothing should handle extreme frequency ratios (1:5e8).") + + # Save data for plotting + with open("rank_freq.csv", "w") as f: + f.write("rank,frequency\n") + for rank, freq in enumerate(counts_sorted_desc, 1): + f.write(f"{rank},{freq}\n") + print("\nRank-frequency data saved to rank_freq.csv") + +if __name__ == "__main__": + main() diff --git a/docs/WORD_BREAK_DESIGN.md b/docs/WORD_BREAK_DESIGN.md new file mode 100644 index 0000000..9fb4519 --- /dev/null +++ b/docs/WORD_BREAK_DESIGN.md @@ -0,0 +1,168 @@ +# 破词训练设计文档 + +## 背景 + +输入法用户在实际使用中通常是**逐词输入**的，而非逐字输入。例如输入"那边的特别漂亮的女孩是我的表姐"时，用户可能分词为： + +``` +那边 / 的 / 特别 / 漂亮 / 的 / 女孩 / 是 / 我 / 的 / 表姐 +``` + +但为了增强模型的泛化能力，需要模拟用户**从词中间断开**的情况。例如用户可能只输入了"漂"就开始选字"亮"。 + +## 破词概念 + +### 术语定义 + +| 术语 | 说明 | +|------|------| +| 整词输入 | 用户输入完整词的拼音，如"piaoliang" | +| 破词输入 | 用户只输入词的部分拼音，如"piao" | +| 前缀 | 光标前的已确认文本 | +| 拼音 | 当前待选字的拼音（可能不完整） | +| 后缀 | 光标后的原文内容 | + +### 场景示例 + +以词"漂亮"为例： + +**整词模式（90%概率）：** +``` +光标前: 那边的特别 +拼音: piaoliang +预测: 漂 → 亮 +``` + +**破词模式（10%概率）：** +``` +光标前: 那边的特别漂 +拼音: liang +预测: 亮 +``` + +## 实现方案 + +### 分词策略 + +使用 jieba 分词器进行词语边界识别： + +```python +import jieba +words = list(jieba.cut(text, HMM=False)) +# "那边的特别漂亮的女孩是我的表姐。" +# → ['那边', '的', '特别', '漂亮', '的', '女孩', '是', '我', '的', '表姐', '。'] +``` + +### 两阶段样本生成 + +每个词生成样本时分为两个阶段： + +#### Phase 1：前缀/整词阶段 + +- **整词（90%）**：`prefix_positions = 整个词的所有字符` +- **破词前缀（10%）**：`prefix_positions = 词的前 break_pos 个字符` + +```python +if should_break: + break_pos = random.randint(1, word_len_chars - 1) # 随机破开位置 +else: + break_pos = word_len_chars # 整词 +``` + +#### Phase 2：破词续接阶段（仅当破词时） + +当破词发生时，从断点位置开始继续采样： + +```python +if should_break and break_pos < word_len_chars: + cont_start = char_positions[break_pos] + # 从断点开始采样后续字符 + target_len = random_choice(1-8) # 采样长度 + cont_positions = [cont_start, ...] # 后续字符位置 +``` + +### 样本结构 + +每个字符生成一个训练样本，包含： + +| 字段 | 说明 | 示例 | +|------|------|------| +| `part1` (prefix) | 光标前文本 | "那边的特别漂" | +| `part2` (pinyin) | 当前字拼音 | "liang" | +| `part3` (suffix) | 光标后文本 | "亮的女孩是我的表姐" | +| `part4` | 专有词提示 | "漂亮\|特别" | +| `label` | 目标汉字ID | 1234 | +| `history_slot_ids` | 历史已确认字 | [0, 0, 0, 0, 0, 0, 0, 0] | + +### 拼音增强策略 + +根据 `py_style_weight` 参数，拼音有以下三种形式： + +| 形式 | 概率 | 示例 | +|------|------|------| +| 完整拼音 | 75% (9/12) | "piaoliang" | +| 仅声母 | 16.7% (2/12) | "pl" (通过 to_initials) | +| 仅首字母 | 8.3% (1/12) | "p" | + +参数配置：`py_style_weight=(9, 2, 1)` + +## 破词概率控制 + +### word_break_prob 参数 + +控制每个词从中间断开的概率，默认为 **10%**： + +```python +self.word_break_prob = 0.10 # 10%概率从词中间破开 +``` + +### 破词位置分布 + +对于长度为 N 的词，破开位置 `break_pos` 的分布： + +```python +break_pos = random.randint(1, N - 1) +``` + +- 2字词：break_pos = 1（100%在第1字后破开） +- 3字词：break_pos = 1 或 2（各50%） +- 4字词：break_pos = 1, 2, 或 3（各33%） + +## 数据分布预期 + +### 理想分布 + +| 类别 | 预期比例 | +|------|----------| +| 单字样本 | ~15% | +| 2字词整词 | ~30% | +| 3字词整词 | ~20% | +| 破词样本 | ~10% | +| 其他 | ~25% | + +### 拼音不完整率 + +由于 `py_style_weight=(9, 2, 1)`： + +- 声母（initials）：~16.7% +- 首字母：~8.3% +- **总计不完整**：~25% + +## 代码实现位置 + +主要实现文件：`src/model/dataset.py` + +| 函数/类 | 行号 | 功能 | +|---------|------|------| +| `segment_text()` | ~30 | jieba分词 | +| `build_word_boundaries()` | ~35 | 建立词边界映射 | +| `PinyinInputDataset.__iter__()` | ~280 | 核心迭代逻辑 | +| `get_mask_pinyin()` | ~215 | 拼音加强处理 | +| `_add_word_samples()` | ~240 | 样本构建 | + +## 注意事项 + +1. **破词仅针对多字词**：单字词（如"的"、“是”）不会破词 +2. **破词保持语义完整**：破词后仍能根据上下文预测正确汉字 +3. **历史槽位模拟逐步确认**：同一词内已确认的字会填入 `history_slot_ids` +4. **10% EOS标记**：词尾有10%概率追加ID=0表示句子结束 diff --git a/frequency_analysis_results.txt b/frequency_analysis_results.txt new file mode 100644 index 0000000..50094ce --- /dev/null +++ b/frequency_analysis_results.txt @@ -0,0 +1,11 @@ +Frequency Analysis Results +================================================== +Min frequency: 1 +Max frequency: 494748360 +Mean frequency: 560007.90 +Standard deviation: 5730144.34 +10th percentile: 3 +50th percentile: 93 +90th percentile: 331538 +IDs in range 5000-5500 min: 5594 +IDs in range 5000-5500 max: 9569 diff --git 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PinyinInputDataset, sample_size: int = 10000): +_id2char_cache = {} + + +def get_char_by_id(query_engine: QueryEngine, char_id: int) -> str: + if char_id == 0: + return "" + if char_id not in _id2char_cache: + info = query_engine.query_by_id(char_id) + _id2char_cache[char_id] = info.char if info else f"" + return _id2char_cache[char_id] + + +def analyze_label_distribution( + dataset: PinyinInputDataset, + sample_size: int = 10000, + query_engine: QueryEngine = None, +): """分析 label 在指定区间的分布""" target_ranges = [ (0, 10), @@ -63,13 +80,23 @@ def analyze_label_distribution(dataset: PinyinInputDataset, sample_size: int = 1 in_target_range = True if len(all_examples) < 200: + label_char = ( + get_char_by_id(query_engine, label) if query_engine else f"" + ) + history_chars = ( + [get_char_by_id(query_engine, hid) for hid in history] + if query_engine + else history + ) all_examples.append( { "label": label, + "label_char": label_char, "prefix": prefix, "suffix": suffix, "pinyin": pinyin, "history": history, + "history_chars": history_chars, "part4": part4, } ) @@ -96,12 +123,13 @@ def analyze_label_distribution(dataset: PinyinInputDataset, sample_size: int = 1 random.shuffle(all_examples) for idx, ex in enumerate(all_examples[:20], 1): print(f"\n样本 {idx}:") - print(f" Label: {ex['label']}") + print(f" Label: {ex['label']} ({ex['label_char']})") print(f" Part4: {ex['part4']}") print(f" 光标前: {ex['prefix']}") print(f" 光标后: {ex['suffix']}") print(f" 拼音: {ex['pinyin']}") print(f" 历史槽位: {ex['history']}") + print(f" 历史汉字: {ex['history_chars']}") def main(): @@ -127,7 +155,9 @@ def main(): help="数据集路径 (本地文件或HuggingFace路径)", ) parser.add_argument("--sample_size", type=int, default=10000, help="采样大小") - parser.add_argument("--max_workers", type=int, default=-1, help="DataLoader workers") + parser.add_argument( + "--max_workers", type=int, default=-1, help="DataLoader workers" + ) args = parser.parse_args() print(f"加载数据集: {args.data_path}") @@ -148,7 +178,11 @@ def main(): print(" 3. 如果是 HuggingFace 数据集，路径应该正确") return - analyze_label_distribution(dataset, sample_size=args.sample_size) + query_engine = QueryEngine() + query_engine.load() + analyze_label_distribution( + dataset, sample_size=args.sample_size, query_engine=query_engine + ) if __name__ == "__main__": diff --git a/src/model/dataset.py b/src/model/dataset.py index 3f8d928..60125bc 100644 --- a/src/model/dataset.py +++ b/src/model/dataset.py @@ -62,10 +62,13 @@ class PinyinInputDataset(IterableDataset): max_iter_length=1e6, max_seq_length=128, text_field: str = "text", - py_style_weight=(90, 2, 1), + py_style_weight=(9, 2, 1), shuffle_buffer_size: int = 100000, retention_ratio: float = 0.8, length_weights={1: 10, 2: 50, 3: 50, 4: 40, 5: 15, 6: 10, 7: 5, 8: 2}, + merge_short_words_prob: float = 0.5, + merge_max_short_words: int = 3, + merge_max_total_chars: int = 6, ): # 频率调整参数 (可根据需要调整) self.drop_start_freq = 10_000_000 @@ -76,6 +79,9 @@ class PinyinInputDataset(IterableDataset): self.word_break_prob = 0.10 self.cont_length_probs = [0.05, 0.16, 0.30, 0.20, 0.12, 0.08, 0.05, 0.04] self._history_weights = [0.2, 0.2, 0.2, 0.9, 1.2, 1.8, 2.5, 3.5, 4.0] + self.merge_short_words_prob = merge_short_words_prob + self.merge_max_short_words = merge_max_short_words + self.merge_max_total_chars = merge_max_total_chars jieba.initialize() @@ -259,8 +265,10 @@ class PinyinInputDataset(IterableDataset): repeats = max(1, int(base_repeats * weight)) history = labels[:label_idx] - len_h = len(history) - history.extend([0] * (8 - len_h)) + if len(history) > 8: + history = history[-8:] + else: + history.extend([0] * (8 - len(history))) sample_dict = { "input_ids": encoded["input_ids"], @@ -291,7 +299,12 @@ class PinyinInputDataset(IterableDataset): if worker_id >= num_workers: return - worker_dataset = self.dataset.shard(num_shards=num_workers, index=worker_id) + try: + worker_dataset = self.dataset.shard( + num_shards=num_workers, index=worker_id + ) + except (IndexError, ValueError): + worker_dataset = self.dataset total_quota = int(self.max_iter_length) base_quota = total_quota // num_workers @@ -321,17 +334,58 @@ class PinyinInputDataset(IterableDataset): word_boundaries = build_word_boundaries(words) pinyin_list = self.generate_pinyin(text) - for word_start, word_end in word_boundaries: + idx = 0 + while idx < len(word_boundaries): + word_start, word_end = word_boundaries[idx] + char_positions = [] for i in range(word_start, word_end): if self.query_engine.is_chinese_char(text[i]): char_positions.append(i) if not char_positions: + idx += 1 continue word_len_chars = len(char_positions) + merge_end_idx = idx + 1 + if word_len_chars <= 2: + accumulated_positions = list(char_positions) + accumulated_count = 1 + next_idx = idx + 1 + + while next_idx < len(word_boundaries): + ns, ne = word_boundaries[next_idx] + next_positions = [] + for i in range(ns, ne): + if self.query_engine.is_chinese_char(text[i]): + next_positions.append(i) + next_len = len(next_positions) + + if next_len == 0 or next_len > 2: + break + if ( + len(accumulated_positions) + next_len + > self.merge_max_total_chars + ): + break + if accumulated_count + 1 > self.merge_max_short_words: + break + if random.random() > self.merge_short_words_prob: + break + + accumulated_positions.extend(next_positions) + accumulated_count += 1 + next_idx += 1 + + if accumulated_count > 1: + char_positions = accumulated_positions + word_len_chars = len(char_positions) + merge_end_idx = next_idx + word_start = word_boundaries[idx][0] + word_end = word_boundaries[next_idx - 1][1] + should_break = ( word_len_chars > 1 and random.random() < self.word_break_prob ) @@ -364,6 +418,7 @@ class PinyinInputDataset(IterableDataset): logger.error( f"e: {e}, (text, pinyin): {prefix_text} - {prefix_pinyin}" ) + idx = merge_end_idx continue # 整词末尾 10% 概率追加 EOS（破词前缀不加） @@ -448,6 +503,7 @@ class PinyinInputDataset(IterableDataset): logger.error( f"e: {e}, (text, pinyin): {cont_text} - {cont_pinyin}" ) + idx = merge_end_idx continue # 续接末尾 10% 概率追加 EOS @@ -486,6 +542,8 @@ class PinyinInputDataset(IterableDataset): pinyin_ids_cont, ) + idx = merge_end_idx + # 处理shuffle buffer - 单缓冲区半保留方案 if len(batch_samples) >= self.shuffle_buffer_size: indices = np.random.permutation(len(batch_samples)) diff --git a/src/model/inspect_preprocessed.py b/src/model/inspect_preprocessed.py new file mode 100644 index 0000000..80a7746 --- /dev/null +++ b/src/model/inspect_preprocessed.py @@ -0,0 +1,401 @@ +#!/usr/bin/env python3 +""" +预处理数据质量分析脚本 + +功能： +1. 统计 labels 的分布（出现次数、比例，最大/最小，未出现标签数） +2. 随机抽样还原为人类可读文本，导出为 CSV 文件 + +用法： + python -m model.inspect_preprocessed --data-dir /path/to/preprocessed/train + python -m model.inspect_preprocessed --data-dir /path/to/preprocessed/train --num-samples 50 --output samples.csv +""" + +import argparse +import csv +import json +import random +from collections import Counter +from pathlib import Path + +import numpy as np +from loguru import logger +from rich.console import Console +from rich.table import Table +from tqdm import tqdm + +from .char_info import CharInfo +from .dataset import CHAR_TO_ID +from .preprocessed_dataset import PreProcessedDataset +from .query import QueryEngine + +ID_TO_CHAR = {v: k for k, v in CHAR_TO_ID.items()} + + +def decode_pinyin_ids(pinyin_ids: list) -> str: + """将 pinyin_ids 还原为拼音字符串""" + chars = [] + for pid in pinyin_ids: + if pid == 0: + break + chars.append(ID_TO_CHAR.get(pid, "?")) + return "".join(chars) + + +def decode_history(history_ids: list, query_engine: QueryEngine) -> str: + """将 history_slot_ids 还原为文字""" + parts = [] + for hid in history_ids: + if hid == 0: + parts.append("") + else: + info = query_engine.query_by_id(hid) + if info is not None: + parts.append(f"{info.char}({info.pinyin})") + else: + parts.append(f"") + return " | ".join(parts) + + +def analyze_labels(dataset: PreProcessedDataset, max_shards: int = 0): + """统计 labels 分布，带进度条""" + logger.info("正在统计 labels 分布...") + counter = Counter() + total = 0 + + num_shards = dataset._num_shards if dataset._is_sharded else 1 + effective_shards = min(num_shards, max_shards) if max_shards > 0 else num_shards + + pbar = tqdm(range(effective_shards), desc="统计 labels", unit="shard") + + for shard_idx in pbar: + if dataset._is_sharded: + shard_data = dict(np.load(dataset.data_dir / f"shard_{shard_idx:06d}.npz")) + labels = shard_data["labels"].astype(np.int64) + else: + labels = dataset.labels[:].astype(np.int64) + + unique, counts = np.unique(labels, return_counts=True) + for uid, cnt in zip(unique, counts): + counter[int(uid)] += cnt + total += len(labels) + + if dataset._is_sharded: + del shard_data + + return counter, total + + +def decode_sample(sample: dict, tokenizer, query_engine: QueryEngine) -> dict: + """将一个样本还原为人类可读格式""" + input_ids = ( + sample["input_ids"].tolist() + if hasattr(sample["input_ids"], "tolist") + else sample["input_ids"] + ) + token_type_ids = ( + sample["token_type_ids"].tolist() + if hasattr(sample["token_type_ids"], "tolist") + else sample["token_type_ids"] + ) + labels = ( + sample["labels"].item() + if hasattr(sample["labels"], "item") + else sample["labels"] + ) + history_ids = ( + sample["history_slot_ids"].tolist() + if hasattr(sample["history_slot_ids"], "tolist") + else sample["history_slot_ids"] + ) + pinyin_ids = ( + sample["pinyin_ids"].tolist() + if hasattr(sample["pinyin_ids"], "tolist") + else sample["pinyin_ids"] + ) + + # 还原 token 文本 + token_text = tokenizer.decode(input_ids, skip_special_tokens=False) + + # 找到 token_type_ids 切换点，分离 sentence A 和 sentence B + sep_positions = [i for i, tid in enumerate(token_type_ids) if tid == 1] + if sep_positions: + sep_start = sep_positions[0] + sent_a_ids = [ + tid + for tid, tt in zip(input_ids[:sep_start], token_type_ids[:sep_start]) + if tt == 0 + ] + sent_b_ids = [tid for tid, tt in zip(input_ids, token_type_ids) if tt == 1] + else: + sent_a_ids = input_ids + sent_b_ids = [] + + context_text = tokenizer.decode(sent_a_ids, skip_special_tokens=True) + suffix_text = ( + tokenizer.decode(sent_b_ids, skip_special_tokens=True) if sent_b_ids else "" + ) + + pinyin_str = decode_pinyin_ids(pinyin_ids) + label_info = query_engine.query_by_id(labels) + history_str = decode_history(history_ids, query_engine) + + return { + "context": context_text, + "suffix": suffix_text, + "pinyin": pinyin_str, + "label_id": labels, + "label_char": f"{label_info.char}({label_info.pinyin})" + if label_info + else f"", + "label_count": label_info.count if label_info else 0, + "history": history_str, + "full_tokens": token_text, + } + + +def main(): + console = Console() + + parser = argparse.ArgumentParser(description="预处理数据质量分析") + parser.add_argument( + "--data-dir", + type=str, + required=True, + help="预处理数据目录（train/ 或 eval/）", + ) + parser.add_argument( + "--num-samples", + type=int, + default=50, + help="随机抽样的样本数量（默认50）", + ) + parser.add_argument( + "--output", + type=str, + default=None, + help="CSV 输出文件路径（默认: /samples.csv）", + ) + parser.add_argument( + "--max-shards", + type=int, + default=0, + help="统计 labels 时最多读取的分片数（0=全部）", + ) + parser.add_argument( + "--seed", + type=int, + default=42, + help="随机种子", + ) + parser.add_argument( + "--top-k", + type=int, + default=30, + help="显示出现次数最多和最少的标签数量", + ) + + args = parser.parse_args() + + random.seed(args.seed) + np.random.seed(args.seed) + + if args.output is None: + args.output = str(Path(args.data_dir) / "samples.csv") + + # 加载数据集 + logger.info(f"加载数据集: {args.data_dir}") + dataset = PreProcessedDataset(args.data_dir) + console.print(f"[bold cyan]数据集: {len(dataset):,} 个样本[/bold cyan]") + if dataset._is_sharded: + console.print( + f" 分片数: {dataset._num_shards}, 每分片: {dataset._shard_size:,} 样本" + ) + console.print() + + # 加载 QueryEngine + logger.info("加载 QueryEngine...") + query_engine = QueryEngine() + query_engine.load() + + # 加载 Tokenizer + logger.info("加载 Tokenizer...") + from importlib.resources import files as pkg_files + from modelscope import AutoTokenizer + + tokenizer = AutoTokenizer.from_pretrained( + Path(str(pkg_files(__package__))) / "assets" / "tokenizer" + ) + + # ====== 1. Labels 分布分析 ====== + console.print("[bold yellow]====== Labels 分布分析 ======[/bold yellow]") + counter, total = analyze_labels(dataset, max_shards=args.max_shards) + + # 获取词表总大小 + vocab_size = len(query_engine._id_to_info) # 不含 EOS (id=0) + + appeared_ids = set(counter.keys()) + all_ids = set(range(0, vocab_size + 1)) # +1 包含 id=0 (EOS) + missing_ids = all_ids - appeared_ids + + console.print(f"\n总样本数: {total:,}") + console.print(f"词表大小: {vocab_size + 1:,} (含 EOS)") + console.print(f"唯一标签数: {len(counter):,}") + console.print( + f"EOS (id=0) 出现次数: {counter.get(0, 0):,} ({counter.get(0, 0) / total * 100:.2f}%)" + ) + console.print( + f"[bold red]未出现的标签数: {len(missing_ids):,} / {vocab_size + 1:,} ({len(missing_ids) / (vocab_size + 1) * 100:.2f}%)[/bold red]" + ) + + most_common = counter.most_common(args.top_k) + least_common = ( + counter.most_common()[: -args.top_k - 1 : -1] + if len(counter) > args.top_k + else counter.most_common() + ) + + # 最多标签表 + table_top = Table( + title=f"出现次数最多的 {args.top_k} 个标签", + show_header=True, + header_style="bold magenta", + ) + table_top.add_column("排名", style="cyan", width=6) + table_top.add_column("ID", style="green", width=8) + table_top.add_column("字符(拼音)", style="yellow", width=20) + table_top.add_column("频次", style="red", width=12) + table_top.add_column("占比", style="blue", width=10) + + for rank, (label_id, count) in enumerate(most_common, 1): + info = query_engine.query_by_id(label_id) + label_str = f"{info.char}({info.pinyin})" if info else f"" + pct = count / total * 100 + table_top.add_row( + str(rank), str(label_id), label_str, f"{count:,}", f"{pct:.3f}%" + ) + console.print(table_top) + + # 最少标签表 + table_bottom = Table( + title=f"出现次数最少的 {min(args.top_k, len(counter))} 个标签", + show_header=True, + header_style="bold magenta", + ) + table_bottom.add_column("排名", style="cyan", width=6) + table_bottom.add_column("ID", style="green", width=8) + table_bottom.add_column("字符(拼音)", style="yellow", width=20) + table_bottom.add_column("频次", style="red", width=12) + table_bottom.add_column("占比", style="blue", width=10) + + for rank, (label_id, count) in enumerate(least_common, 1): + info = query_engine.query_by_id(label_id) + label_str = f"{info.char}({info.pinyin})" if info else f"" + pct = count / total * 100 + table_bottom.add_row( + str(rank), str(label_id), label_str, f"{count:,}", f"{pct:.6f}%" + ) + console.print(table_bottom) + + # 频次分布概览 + table_dist = Table( + title="频次分布概览", show_header=True, header_style="bold magenta" + ) + table_dist.add_column("频次区间", style="cyan") + table_dist.add_column("标签数", style="green") + table_dist.add_column("占总标签数比例", style="yellow") + + bins = [ + (1, 10), + (11, 100), + (101, 1000), + (1001, 10000), + (10001, 100000), + (100001, 1000000), + (1000001, float("inf")), + ] + for lo, hi in bins: + count_in_bin = sum(1 for c in counter.values() if lo <= c <= hi) + if count_in_bin > 0: + hi_str = str(int(hi)) if hi != float("inf") else "∞" + table_dist.add_row( + f"{lo}-{hi_str}", + f"{count_in_bin:,}", + f"{count_in_bin / len(counter) * 100:.1f}%", + ) + # 未出现 + if len(missing_ids) > 0: + table_dist.add_row( + "未出现", + f"{len(missing_ids):,}", + f"{len(missing_ids) / (vocab_size + 1) * 100:.1f}%", + ) + console.print(table_dist) + + # ====== 2. 随机抽样还原 → CSV ====== + num_samples = min(args.num_samples, len(dataset)) + console.print( + f"\n[bold yellow]====== 随机抽样还原 ({num_samples} 个样本) → {args.output} ======[/bold yellow]" + ) + + indices = random.sample(range(len(dataset)), num_samples) + + csv_path = Path(args.output) + csv_path.parent.mkdir(parents=True, exist_ok=True) + + csv_headers = [ + "index", + "pinyin", + "label_char", + "label_id", + "label_count", + "context", + "suffix", + "history", + "full_tokens", + ] + + with open(csv_path, "w", encoding="utf-8", newline="") as f: + writer = csv.writer(f) + writer.writerow(csv_headers) + + for i, idx in enumerate(tqdm(indices, desc="解码样本", unit="sample")): + sample = dataset[idx] + decoded = decode_sample(sample, tokenizer, query_engine) + writer.writerow( + [ + idx, + decoded["pinyin"], + decoded["label_char"], + decoded["label_id"], + decoded["label_count"], + decoded["context"], + decoded["suffix"], + decoded["history"], + decoded["full_tokens"], + ] + ) + + console.print( + f"[bold green]✓ 已导出 {num_samples} 个样本到 {csv_path}[/bold green]" + ) + + # 打印前 5 个样本的概要 + console.print(f"\n[bold cyan]前 {min(5, num_samples)} 个样本概览:[/bold cyan]") + with open(csv_path, "r", encoding="utf-8") as f: + reader = csv.DictReader(f) + for i, row in enumerate(reader): + if i >= 5: + break + console.print( + f" [{i + 1}] 拼音={row['pinyin']} 目标={row['label_char']} " + f"上下文={row['context'][:50]}..." + ) + + console.print("\n[bold green]分析完成[/bold green]") + + +app = main + +if __name__ == "__main__": + main() diff --git a/src/model/preprocess.py b/src/model/preprocess.py new file mode 100644 index 0000000..f5d8a6e --- /dev/null +++ b/src/model/preprocess.py @@ -0,0 +1,335 @@ +#!/usr/bin/env python3 +""" +预处理脚本：将 PinyinInputDataset 的输出转换为分片压缩 .npz 文件。 + +采用分片流式写入，内存峰值固定为 shard_size 级别，不随总样本数增长。 +每个分片使用 np.savez_compressed 保存（zlib 压缩），GPU 服务器无需解压到硬盘。 + +用法： + python -m model.preprocess \ + --train-data-path "some/hf_dataset" \ + --eval-data-path "some/hf_dataset" \ + --output-dir ./preprocessed \ + --num-train-samples 5000000 \ + --num-eval-samples 8192 + +生成目录结构： + output_dir/ + train/ + metadata.json + shard_000.npz (5M样本, 6个字段, zlib压缩) + shard_001.npz + ... + eval/ + metadata.json + shard_000.npz + ... +""" + +import argparse +import gc +import json +from pathlib import Path +from typing import Dict, List + +import numpy as np +import torch +from loguru import logger +from rich.console import Console +from torch.utils.data import DataLoader +from tqdm import tqdm + +from .dataset import PinyinInputDataset +from .trainer import collate_fn, worker_init_fn + +FIELDS = [ + "input_ids", + "token_type_ids", + "attention_mask", + "labels", + "history_slot_ids", + "pinyin_ids", +] + + +def _extract_batch(batch: dict, take: int) -> Dict[str, np.ndarray]: + """从 DataLoader batch 中提取指定数量的样本，转为 int16 numpy 数组""" + result = {} + for f in FIELDS: + tensor = batch[f][:take] + arr = tensor.numpy().astype(np.int16) + if f == "labels" and arr.ndim > 1 and arr.shape[-1] == 1: + arr = arr.squeeze(-1) + result[f] = arr + return result + + +def collect_samples( + dataloader: DataLoader, + num_samples: int, + output_dir: Path, + split_name: str, + max_seq_length: int = 128, + shard_size: int = 5_000_000, +) -> int: + """ + 分片流式收集样本，每累积 shard_size 个样本保存为一个压缩 .npz 分片。 + + 内存峰值 = shard_size × 每样本字节数（约578字节 @ shard_size=5M → 约2.9GB） + """ + split_dir = output_dir / split_name + split_dir.mkdir(parents=True, exist_ok=True) + + shard_buffers: Dict[str, List[np.ndarray]] = {f: [] for f in FIELDS} + shard_count = 0 + shard_idx = 0 + total = 0 + + pbar = tqdm(total=num_samples, desc=f"Processing {split_name}", unit="samples") + + for batch in dataloader: + batch_size = batch["input_ids"].size(0) + remaining = num_samples - total + if remaining <= 0: + break + take = min(batch_size, remaining) + + extracted = _extract_batch(batch, take) + for f in FIELDS: + shard_buffers[f].append(extracted[f]) + + shard_count += take + total += take + pbar.update(take) + + if shard_count >= shard_size: + merged = {} + for f in FIELDS: + merged[f] = np.concatenate(shard_buffers[f], axis=0) + np.savez_compressed(split_dir / f"shard_{shard_idx:06d}.npz", **merged) + logger.debug(f"Saved {split_name} shard {shard_idx}: {shard_count} samples") + + shard_idx += 1 + shard_buffers = {f: [] for f in FIELDS} + shard_count = 0 + del merged + gc.collect() + + if total >= num_samples: + break + + # 写入最后一个不满的分片 + if shard_count > 0: + merged = {} + for f in FIELDS: + merged[f] = np.concatenate(shard_buffers[f], axis=0) + np.savez_compressed(split_dir / f"shard_{shard_idx:06d}.npz", **merged) + logger.debug(f"Saved {split_name} shard {shard_idx}: {shard_count} samples") + shard_idx += 1 + + pbar.close() + + actual_count = min(total, num_samples) + num_shards = shard_idx + + metadata = { + "num_samples": actual_count, + "max_seq_length": max_seq_length, + "dtype": "int16", + "fields": FIELDS, + "shard_size": shard_size, + "num_shards": num_shards, + } + with open(split_dir / "metadata.json", "w", encoding="utf-8") as fp: + json.dump(metadata, fp, indent=2, ensure_ascii=False) + + total_size = sum( + f.stat().st_size for f in split_dir.iterdir() if f.suffix == ".npz" + ) + logger.info( + f"{split_name}: {actual_count} samples in {num_shards} shards, " + f"{total_size / (1024**3):.2f} GB (compressed)" + ) + + return actual_count + + +def main(): + console = Console() + + parser = argparse.ArgumentParser(description="预处理数据集为分片压缩npz文件") + parser.add_argument( + "--train-data-path", + type=str, + required=True, + help="训练数据集路径（HuggingFace格式）", + ) + parser.add_argument( + "--eval-data-path", + type=str, + required=True, + help="评估数据集路径（HuggingFace格式）", + ) + parser.add_argument("--output-dir", type=str, required=True, help="输出目录") + parser.add_argument( + "--num-train-samples", type=int, required=True, help="训练集样本数量" + ) + parser.add_argument( + "--num-eval-samples", type=int, required=True, help="评估集样本数量" + ) + parser.add_argument("--batch-size", type=int, default=128, help="批大小") + parser.add_argument( + "--num-workers", type=int, default=2, help="DataLoader worker数量" + ) + parser.add_argument("--max-seq-length", type=int, default=128, help="最大序列长度") + parser.add_argument("--seed", type=int, default=42, help="随机种子") + parser.add_argument( + "--shard-size", + type=int, + default=5_000_000, + help="分片大小（样本数），控制内存峰值（默认500万，约2.9GB/分片未压缩）", + ) + parser.add_argument( + "--py-style-weight", + type=str, + default="9,2,1", + help="拼音风格权重（逗号分隔）", + ) + parser.add_argument( + "--shuffle-buffer-size", + type=int, + default=2000000, + help="数据集shuffle缓冲区大小", + ) + parser.add_argument( + "--length-weights", + type=str, + default="1:10,2:50,3:50,4:40,5:15,6:10,7:5,8:2", + help="词长权重", + ) + + args = parser.parse_args() + + torch.manual_seed(args.seed) + np.random.seed(args.seed) + + py_style_weight = tuple(int(x) for x in args.py_style_weight.split(",")) + length_weights = { + int(k): int(v) + for k, v in (item.split(":") for item in args.length_weights.split(",")) + } + + output_dir = Path(args.output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + + train_max_iter = args.num_train_samples * 5 + eval_max_iter = args.num_eval_samples * 5 + + shard_mem_gb = args.shard_size * 578 / (1024**3) + console.print("[bold cyan]=== 数据预处理 ===[/bold cyan]") + console.print(f"训练集目标: {args.num_train_samples:,} 样本") + console.print(f"评估集目标: {args.num_eval_samples:,} 样本") + console.print(f"输出目录: {output_dir}") + console.print(f"数据类型: int16") + console.print( + f"分片大小: {args.shard_size:,} 样本 (约 {shard_mem_gb:.1f} GB/分片 未压缩)" + ) + console.print() + + num_train_workers = args.num_workers + num_eval_workers = max(1, args.num_workers // 2) + + console.print("[bold cyan]创建训练数据集...[/bold cyan]") + train_dataset = PinyinInputDataset( + data_path=args.train_data_path, + max_workers=num_train_workers, + max_iter_length=train_max_iter, + max_seq_length=args.max_seq_length, + text_field="text", + py_style_weight=py_style_weight, + shuffle_buffer_size=100, + length_weights=length_weights, + ) + + train_dataloader = DataLoader( + train_dataset, + batch_size=args.batch_size, + num_workers=num_train_workers, + pin_memory=False, + worker_init_fn=worker_init_fn, + collate_fn=collate_fn, + prefetch_factor=2, + persistent_workers=True if num_train_workers > 0 else False, + ) + + console.print("[bold cyan]创建评估数据集...[/bold cyan]") + eval_dataset = PinyinInputDataset( + data_path=args.eval_data_path, + max_workers=num_eval_workers, + max_iter_length=eval_max_iter, + max_seq_length=args.max_seq_length, + text_field="text", + py_style_weight=py_style_weight, + shuffle_buffer_size=100, + length_weights=length_weights, + ) + + eval_dataloader = DataLoader( + eval_dataset, + batch_size=args.batch_size, + num_workers=num_eval_workers, + pin_memory=False, + worker_init_fn=worker_init_fn, + collate_fn=collate_fn, + prefetch_factor=2, + persistent_workers=True if num_eval_workers > 0 else False, + ) + + logger.info("开始收集训练数据...") + train_count = collect_samples( + train_dataloader, + args.num_train_samples, + output_dir, + "train", + args.max_seq_length, + args.shard_size, + ) + + if train_count < args.num_train_samples: + logger.warning( + f"训练集样本不足: 目标 {args.num_train_samples}, 实际 {train_count}" + ) + + logger.info("开始收集评估数据...") + eval_count = collect_samples( + eval_dataloader, + args.num_eval_samples, + output_dir, + "eval", + args.max_seq_length, + args.shard_size, + ) + + if eval_count < args.num_eval_samples: + logger.warning( + f"评估集样本不足: 目标 {args.num_eval_samples}, 实际 {eval_count}" + ) + + console.print("\n[bold green]=== 预处理完成 ===[/bold green]") + console.print(f"训练集: {train_count:,} 样本") + console.print(f"评估集: {eval_count:,} 样本") + console.print(f"输出目录: {output_dir}") + + for split in ["train", "eval"]: + split_dir = output_dir / split + if split_dir.exists(): + total_size = sum( + f.stat().st_size for f in split_dir.iterdir() if f.suffix == ".npz" + ) + console.print(f"{split}/: {total_size / (1024**3):.2f} GB (compressed)") + + +app = main + +if __name__ == "__main__": + main() diff --git a/src/model/preprocessed_dataset.py b/src/model/preprocessed_dataset.py new file mode 100644 index 0000000..c61ac9d --- /dev/null +++ b/src/model/preprocessed_dataset.py @@ -0,0 +1,188 @@ +""" +预处理数据集加载器 + +支持两种格式： +1. 分片压缩格式（.npz）：从压缩文件中按需加载分片，LRU 缓存最多持有 max_cache_shards 个分片 +2. 单体格式（.npy）：向后兼容，使用 mmap 零拷贝加载 + +GPU 服务器仅需存放压缩后的 .npz 文件，无需解压到硬盘。 +""" + +import gc +import json +from collections import OrderedDict +from pathlib import Path +from typing import Dict, Optional + +import numpy as np +import torch +from loguru import logger +from torch.utils.data import Dataset + +FIELDS = [ + "input_ids", + "token_type_ids", + "attention_mask", + "labels", + "history_slot_ids", + "pinyin_ids", +] + + +def is_preprocessed_data(path: str) -> bool: + """判断路径是否为预处理数据目录""" + p = Path(path) + return p.is_dir() and (p / "metadata.json").exists() + + +class _ShardCache: + """LRU 缓存，管理按需加载的 .npz 分片，最多持有 max_size 个分片""" + + def __init__(self, max_size: int = 2): + self.max_size = max_size + self._cache: OrderedDict[int, Dict[str, np.ndarray]] = OrderedDict() + + def get(self, shard_idx: int, loader_fn) -> Dict[str, np.ndarray]: + if shard_idx in self._cache: + self._cache.move_to_end(shard_idx) + return self._cache[shard_idx] + + data = loader_fn(shard_idx) + self._cache[shard_idx] = data + self._cache.move_to_end(shard_idx) + + while len(self._cache) > self.max_size: + evicted_key, evicted_data = self._cache.popitem(last=False) + del evicted_data + gc.collect() + + return data + + def clear(self): + self._cache.clear() + gc.collect() + + +class PreProcessedDataset(Dataset): + """ + 预处理数据集加载器，自动检测数据格式： + + - 分片压缩格式（metadata.json 含 shard_size 字段）： + 从 .npz 分片按需加载，LRU 缓存控制内存 + - 单体格式（向后兼容）： + mmap 零拷贝加载 .npy 文件 + + 所有数据以 int16 存储，读取时转为 torch.long (int64)。 + """ + + def __init__(self, data_dir: str, max_cache_shards: int = 2): + self.data_dir = Path(data_dir) + + with open(self.data_dir / "metadata.json", "r", encoding="utf-8") as f: + self.metadata = json.load(f) + + self.num_samples = self.metadata["num_samples"] + self.max_seq_length = self.metadata["max_seq_length"] + self._shard_size: Optional[int] = self.metadata.get("shard_size") + self._num_shards: Optional[int] = self.metadata.get("num_shards") + + if self._shard_size is not None and self._num_shards is not None: + self._is_sharded = True + self._cache = _ShardCache(max_size=max_cache_shards) + logger.info( + f"Loaded sharded dataset: {self.num_samples:,} samples, " + f"{self._num_shards} shards, shard_size={self._shard_size:,}" + ) + else: + self._is_sharded = False + self._load_single_files() + logger.info( + f"Loaded single-file dataset: {self.num_samples:,} samples (mmap)" + ) + + def _load_single_files(self): + """向后兼容：加载单体 .npy 文件（mmap 模式）""" + self.input_ids = np.load(self.data_dir / "input_ids.npy", mmap_mode="r") + self.token_type_ids = np.load( + self.data_dir / "token_type_ids.npy", mmap_mode="r" + ) + self.attention_mask = np.load( + self.data_dir / "attention_mask.npy", mmap_mode="r" + ) + self.labels = np.load(self.data_dir / "labels.npy", mmap_mode="r") + self.history_slot_ids = np.load( + self.data_dir / "history_slot_ids.npy", mmap_mode="r" + ) + self.pinyin_ids = np.load(self.data_dir / "pinyin_ids.npy", mmap_mode="r") + + def _load_shard(self, shard_idx: int) -> Dict[str, np.ndarray]: + """加载一个 .npz 分片到内存""" + shard_path = self.data_dir / f"shard_{shard_idx:06d}.npz" + data = dict(np.load(shard_path)) + for key in data: + data[key] = data[key].astype(np.int64) + return data + + def __len__(self) -> int: + return self.num_samples + + def __getitem__(self, idx: int) -> dict: + if not 0 <= idx < self.num_samples: + raise IndexError( + f"Index {idx} out of range for dataset with {self.num_samples} samples" + ) + + if self._is_sharded: + shard_idx = idx // self._shard_size + local_idx = idx % self._shard_size + shard_data = self._cache.get(shard_idx, self._load_shard) + return { + "input_ids": torch.from_numpy( + shard_data["input_ids"][local_idx].copy() + ), + "token_type_ids": torch.from_numpy( + shard_data["token_type_ids"][local_idx].copy() + ), + "attention_mask": torch.from_numpy( + shard_data["attention_mask"][local_idx].copy() + ), + "labels": torch.tensor( + shard_data["labels"][local_idx], dtype=torch.long + ), + "history_slot_ids": torch.from_numpy( + shard_data["history_slot_ids"][local_idx].copy() + ), + "pinyin_ids": torch.from_numpy( + shard_data["pinyin_ids"][local_idx].copy() + ), + } + else: + return { + "input_ids": torch.from_numpy(self.input_ids[idx].astype(np.int64)), + "token_type_ids": torch.from_numpy( + self.token_type_ids[idx].astype(np.int64) + ), + "attention_mask": torch.from_numpy( + self.attention_mask[idx].astype(np.int64) + ), + "labels": torch.tensor(self.labels[idx], dtype=torch.long), + "history_slot_ids": torch.from_numpy( + self.history_slot_ids[idx].astype(np.int64) + ), + "pinyin_ids": torch.from_numpy(self.pinyin_ids[idx].astype(np.int64)), + } + + +def preprocessed_collate_fn(batch): + """ + 预处理数据的 collate 函数。 + 不含 string 字段（prefix/suffix/pinyin），仅处理 tensor 字段。 + """ + return { + "input_ids": torch.stack([item["input_ids"] for item in batch]), + "token_type_ids": torch.stack([item["token_type_ids"] for item in batch]), + "attention_mask": torch.stack([item["attention_mask"] for item in batch]), + "labels": torch.stack([item["labels"] for item in batch]), + "history_slot_ids": torch.stack([item["history_slot_ids"] for item in batch]), + "pinyin_ids": torch.stack([item["pinyin_ids"] for item in batch]), + } diff --git a/src/model/trainer.py b/src/model/trainer.py index 1509618..1c27cfd 100644 --- a/src/model/trainer.py +++ b/src/model/trainer.py @@ -28,6 +28,11 @@ from torch.utils.data import DataLoader from torch.utils.tensorboard import SummaryWriter from .dataset import PinyinInputDataset +from .preprocessed_dataset import ( + PreProcessedDataset, + is_preprocessed_data, + preprocessed_collate_fn, +) # 导入模型和数据 from .model import InputMethodEngine @@ -97,7 +102,12 @@ class Trainer: """ self.model = model self.train_dataloader = train_dataloader - self.eval_dataloader = list([i for i in eval_dataloader]) + if isinstance(eval_dataloader, DataLoader) and not isinstance( + eval_dataloader.dataset, torch.utils.data.IterableDataset + ): + self.eval_dataloader = eval_dataloader + else: + self.eval_dataloader = list([i for i in eval_dataloader]) self.output_dir = Path(output_dir) self.num_epochs = num_epochs self.learning_rate = learning_rate @@ -1003,7 +1013,7 @@ def collate_fn(batch: List[Dict[str, Any]]) -> Dict[str, Any]: # Typer CLI应用 def create_dataloader( - dataset: PinyinInputDataset, + dataset, batch_size: int, num_workers: int = 2, pin_memory: bool = True, @@ -1011,20 +1021,23 @@ def create_dataloader( max_iter_length: Optional[int] = None, ) -> Any: """ - 创建数据加载器，优先使用DataLoader2，如果不可用则回退到DataLoader。 - 专门针对流式数据集优化。 + 创建数据加载器，自动识别数据集类型。 - Args: - dataset: PinyinInputDataset实例 - batch_size: 批次大小 - num_workers: worker数量（对于流式数据集建议为2） - pin_memory: 是否固定内存 - shuffle: 是否打乱（流式数据集内部处理打乱） - max_iter_length: 最大迭代长度，用于计算总步数 - - Returns: - 数据加载器实例 + - PinyinInputDataset（IterableDataset）：使用流式加载 + - PreProcessedDataset（map-style）：使用标准加载，支持 shuffle """ + if isinstance(dataset, PreProcessedDataset): + logger.info(f"📊 使用预处理数据集，样本数: {len(dataset)}") + return DataLoader( + dataset, + batch_size=batch_size, + shuffle=shuffle, + num_workers=num_workers, + pin_memory=pin_memory, + collate_fn=preprocessed_collate_fn, + persistent_workers=True if num_workers > 0 else False, + ) + logger.info(f"📊 使用标准DataLoader，worker数量: {num_workers}") dataloader = DataLoader( dataset, @@ -1164,12 +1177,82 @@ def train( config_table.add_row("训练", "混合精度", str(mixed_precision)) config_table.add_row("其他", "自动恢复", str(auto_resume)) - console.print(config_table) # 创建输出目录 output_path = Path(output_dir) output_path.mkdir(parents=True, exist_ok=True) + # 检测数据类型并创建数据加载器 + console.print("[bold cyan]正在创建数据加载器...[/bold cyan]") + + is_train_preprocessed = is_preprocessed_data(train_data_path) + is_eval_preprocessed = is_preprocessed_data(eval_data_path) + + if is_train_preprocessed: + train_dataset = PreProcessedDataset(train_data_path) + total_steps = (len(train_dataset) // batch_size) * num_epochs + train_dataloader = create_dataloader( + dataset=train_dataset, + batch_size=batch_size, + num_workers=num_workers, + pin_memory=torch.cuda.is_available(), + shuffle=True, + ) + config_table.add_row("数据", "训练数据类型", "预处理数据") + else: + train_dataset = PinyinInputDataset( + data_path=train_data_path, + max_workers=-1, + max_iter_length=max_iter_length, + max_seq_length=max_seq_len, + text_field="text", + py_style_weight=(9, 2, 1), + shuffle_buffer_size=2000000, + length_weights={1: 10, 2: 50, 3: 50, 4: 40, 5: 15, 6: 10, 7: 5, 8: 2}, + ) + total_steps = int(max_iter_length * num_epochs / batch_size) + train_dataloader = create_dataloader( + dataset=train_dataset, + batch_size=batch_size, + num_workers=num_workers, + pin_memory=torch.cuda.is_available(), + max_iter_length=max_iter_length, + ) + config_table.add_row("数据", "训练数据类型", "流式数据") + + if is_eval_preprocessed: + eval_dataset = PreProcessedDataset(eval_data_path) + eval_dataloader = create_dataloader( + dataset=eval_dataset, + batch_size=batch_size, + num_workers=2, + pin_memory=torch.cuda.is_available(), + shuffle=False, + ) + config_table.add_row("数据", "评估数据类型", "预处理数据") + else: + eval_dataset = PinyinInputDataset( + data_path=eval_data_path, + max_workers=-1, + max_iter_length=batch_size * 64, + max_seq_length=max_seq_len, + text_field="text", + py_style_weight=(9, 2, 1), + shuffle_buffer_size=2000000, + length_weights={1: 10, 2: 50, 3: 50, 4: 40, 5: 15, 6: 10, 7: 5, 8: 2}, + ) + eval_dataloader = create_dataloader( + dataset=eval_dataset, + batch_size=batch_size, + num_workers=2, + pin_memory=torch.cuda.is_available(), + max_iter_length=batch_size * 64, + ) + config_table.add_row("数据", "评估数据类型", "流式数据") + + config_table.add_row("数据", "总步数", str(total_steps)) + console.print(config_table) + # 保存配置 config = { "train_data_path": train_data_path, @@ -1202,6 +1285,9 @@ def train( "auto_resume": auto_resume, "max_iter_length": max_iter_length, "compile": compile, + "is_train_preprocessed": is_train_preprocessed, + "is_eval_preprocessed": is_eval_preprocessed, + "total_steps": total_steps, } config_file = output_path / "training_config.json" @@ -1210,52 +1296,6 @@ def train( logger.info(f"Configuration saved to {config_file}") - # 创建数据加载器 - console.print("[bold cyan]正在创建数据加载器...[/bold cyan]") - - # 训练数据集 - train_dataset = PinyinInputDataset( - data_path=train_data_path, - max_workers=-1, # 自动选择worker数量 - max_iter_length=max_iter_length, - max_seq_length=max_seq_len, - text_field="text", - py_style_weight=(9, 2, 1), - shuffle_buffer_size=2000000, - length_weights={1: 10, 2: 50, 3: 50, 4: 40, 5: 15, 6: 10, 7: 5, 8: 2}, - ) - - # 训练数据加载器 - # 注意：PinyinInputDataset是IterableDataset，所以不能使用shuffle参数 - # 多worker配置：每个worker处理数据集的一个分片，由dataset.__iter__中的shard处理 - train_dataloader = create_dataloader( - dataset=train_dataset, - batch_size=batch_size, - num_workers=num_workers, - pin_memory=torch.cuda.is_available(), - max_iter_length=max_iter_length, - ) - - # 评估数据集（使用相同的设置，但可以调整参数） - eval_dataset = PinyinInputDataset( - data_path=eval_data_path, - max_workers=-1, - max_iter_length=batch_size * 64, # 评估集较小 - max_seq_length=max_seq_len, - text_field="text", - py_style_weight=(9, 2, 1), - shuffle_buffer_size=2000000, - length_weights={1: 10, 2: 50, 3: 50, 4: 40, 5: 15, 6: 10, 7: 5, 8: 2}, - ) - - eval_dataloader = create_dataloader( - dataset=eval_dataset, - batch_size=batch_size, - num_workers=2, # 评估使用较少的worker - pin_memory=torch.cuda.is_available(), - max_iter_length=batch_size * 64, - ) - console.print("[bold cyan]正在创建模型...[/bold cyan]") model = InputMethodEngine( vocab_size=vocab_size, @@ -1279,7 +1319,7 @@ def train( model=model, train_dataloader=train_dataloader, eval_dataloader=eval_dataloader, - total_steps=int(max_iter_length * num_epochs / batch_size), + total_steps=total_steps, output_dir=output_dir, num_epochs=num_epochs, learning_rate=learning_rate, diff --git a/test_dataset.py b/test_dataset.py index d27244d..6984dcb 100644 --- a/test_dataset.py +++ b/test_dataset.py @@ -89,7 +89,7 @@ train_dataset = PinyinInputDataset( dataloader = DataLoader( train_dataset, batch_size=512, - num_workers=2, + num_workers=16, worker_init_fn=worker_init_fn, collate_fn=collate_fn, prefetch_factor=2, # 减少预取以避免内存问题 diff --git a/visualize_distribution.py b/visualize_distribution.py new file mode 100644 index 0000000..2cd7be9 --- /dev/null +++ b/visualize_distribution.py @@ -0,0 +1,146 @@ +#!/usr/bin/env python3 +""" +Visualize frequency distribution with ASCII plots +""" + +import json +import math +import sys +from pathlib import Path + +def ascii_histogram(data, bins=20, width=60): + """Create ASCII histogram""" + if not data: + return "" + + min_val = min(data) + max_val = max(data) + + # Use log bins for wide range + if max_val / min_val > 1000: + log_min = math.log10(min_val) if min_val > 0 else 0 + log_max = math.log10(max_val) + bin_edges = [10**(log_min + i*(log_max-log_min)/bins) for i in range(bins+1)] + hist = [0] * bins + for val in data: + if val > 0: + log_val = math.log10(val) + bin_idx = min(int((log_val - log_min) / (log_max - log_min) * bins), bins-1) + hist[bin_idx] += 1 + bin_labels = [f"{bin_edges[i]:.1e}-{bin_edges[i+1]:.1e}" for i in range(bins)] + else: + bin_width = (max_val - min_val) / bins + bin_edges = [min_val + i*bin_width for i in range(bins+1)] + hist = [0] * bins + for val in data: + bin_idx = min(int((val - min_val) / (max_val - min_val) * bins), bins-1) + hist[bin_idx] += 1 + bin_labels = [f"{bin_edges[i]:.1f}-{bin_edges[i+1]:.1f}" for i in range(bins)] + + max_count = max(hist) + result = [] + for i in range(bins): + if hist[i] == 0: + continue + bar = '#' * int(hist[i] / max_count * width) + result.append(f"{bin_labels[i]:20} | {bar} {hist[i]}") + + return "\n".join(result) + +def main(): + json_path = Path("src/model/assets/pinyin_char_statistics.json") + with open(json_path, 'r', encoding='utf-8') as f: + data = json.load(f) + + pairs = data.get('pairs', {}) + counts = [pair.get('count', 0) for pair in pairs.values() if pair.get('count') is not None] + + print("FREQUENCY DISTRIBUTION ANALYSIS") + print("="*60) + + print("\n1. ASCII Histogram (log bins):") + print(ascii_histogram(counts, bins=20, width=60)) + + # Rank-frequency plot in ASCII + print("\n2. Rank-Frequency Relationship (Top 50):") + counts_sorted_desc = sorted(counts, reverse=True) + max_freq = counts_sorted_desc[0] + max_rank = 50 + + for rank in range(1, max_rank + 1): + freq = counts_sorted_desc[rank-1] + bar_length = int(math.log(freq) / math.log(max_freq) * 40) + bar = '#' * bar_length + print(f"Rank {rank:3}: {freq:12} {bar}") + + # ID vs Frequency plot (sampled) + print("\n3. ID vs Frequency (sampled every 500 IDs):") + # Build ID to count mapping + id_to_count = {} + for key, pair in pairs.items(): + char_id = pair.get('id') + count = pair.get('count') + if char_id is not None and count is not None: + id_to_count[char_id] = count + + all_ids = sorted(id_to_count.keys()) + max_id = all_ids[-1] + + print("ID Frequency log10(freq)") + for id in range(0, max_id + 1, 500): + if id in id_to_count: + freq = id_to_count[id] + log_freq = math.log10(freq) if freq > 0 else 0 + bar = '#' * int(log_freq / math.log10(max_freq) * 40) + print(f"{id:6} {freq:10} {log_freq:6.2f} {bar}") + + # Zipf's law fit + print("\n4. Zipf's Law Analysis:") + print(" Rank * Frequency ≈ constant for Zipf's law") + print(" Top 10 ranks:") + for rank in range(1, 11): + freq = counts_sorted_desc[rank-1] + product = rank * freq + print(f" Rank {rank}: {freq:12} rank*freq = {product:.3e}") + + # Check if product is roughly constant + products = [(rank+1) * counts_sorted_desc[rank] for rank in range(10)] + avg_product = sum(products) / len(products) + std_product = math.sqrt(sum((p - avg_product)**2 for p in products) / len(products)) + print(f" Average product (ranks 2-11): {avg_product:.3e} ± {std_product:.3e}") + print(f" Coefficient of variation: {std_product/avg_product*100:.1f}%") + + # Frequency spectrum + from collections import Counter + freq_counter = Counter(counts) + print("\n5. Frequency Spectrum (how many entries have each frequency):") + print(" Frequency Count Cumulative") + cum = 0 + for freq in sorted(freq_counter.keys())[:20]: + count = freq_counter[freq] + cum += count + print(f" {freq:10} {count:6} {cum:6}") + + # Summary statistics + print("\n6. Key Statistics:") + n = len(counts) + print(f" Total entries: {n}") + print(f" Min frequency: {min(counts)}") + print(f" Max frequency: {max(counts)}") + print(f" Ratio max/min: {max(counts)/min(counts):.2e}") + + percentiles = [0.01, 0.1, 0.5, 0.9, 0.99] + for p in percentiles: + idx = int(p * n) + value = counts_sorted_desc[idx] + print(f" {p*100:5.1f}th percentile: {value:12} (rank ~{idx})") + + # Save data for external plotting + with open("id_vs_freq.csv", "w") as f: + f.write("id,frequency\n") + for id in sorted(id_to_count.keys()): + f.write(f"{id},{id_to_count[id]}\n") + print("\nData saved to id_vs_freq.csv for external plotting") + +if __name__ == "__main__": + main()