Download openevolve-fixed/examples/ADRS/llm_sql/utils.py from zhouk777/evolve: direct link, hf CLI and curl.
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https://huggingface.co/datasets/zhouk777/evolve/resolve/main/openevolve-fixed/examples/ADRS/llm_sql/utils.py
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2.76 kB
| from concurrent.futures import ThreadPoolExecutor | |
| import pandas as pd | |
| from typing import List, Tuple | |
| class TrieNode: | |
| def __init__(self): | |
| self.children = {} | |
| self.end_of_word = False | |
| class Trie: | |
| def __init__(self): | |
| self.root = TrieNode() | |
| def insert(self, word): | |
| node = self.root | |
| for char in word: | |
| if char not in node.children: | |
| node.children[char] = TrieNode() | |
| node = node.children[char] | |
| node.end_of_word = True | |
| def longest_common_prefix(self, word): | |
| node = self.root | |
| common_prefix_length = 0 | |
| for char in word: | |
| if char in node.children: | |
| common_prefix_length += len(char) | |
| node = node.children[char] | |
| else: | |
| break | |
| return common_prefix_length | |
| def calculate_length(value): | |
| val = 0 | |
| if isinstance(value, bool): | |
| val = 4 # length of 'True' or 'False' | |
| elif isinstance(value, (int, float)): | |
| val = len(str(value)) | |
| elif isinstance(value, str): | |
| val = len(value) | |
| else: | |
| val = 0 | |
| return val**2 | |
| def evaluate_df_prefix_hit_cnt(df: pd.DataFrame) -> Tuple[int, int]: | |
| """ | |
| Function to evaluate the prefix hit count of a DataFrame | |
| """ | |
| def max_overlap(trie, row_string): | |
| return min(len(row_string), trie.longest_common_prefix(row_string)) | |
| trie = Trie() | |
| total_prefix_hit_count = 0 | |
| total_string_length = 0 | |
| def process_row(index, row): | |
| nonlocal total_string_length | |
| row_string = "".join(row.fillna("").astype(str).values) # No spaces between columns | |
| total_string_length += len(row_string) | |
| row_prefix_hit_count = max_overlap(trie, row_string) | |
| trie.insert(row_string) | |
| return row_prefix_hit_count | |
| with ThreadPoolExecutor() as executor: | |
| results = executor.map(process_row, df.index, [row for _, row in df.iterrows()]) | |
| total_prefix_hit_count = sum(results) | |
| total_prefix_hit_rate = total_prefix_hit_count / total_string_length | |
| assert total_prefix_hit_count <= total_string_length | |
| print(f"Total string length: {total_string_length}") | |
| no_cache_pricing = 2.5 / 5 # per 1M if not cached | |
| cache_pricing = 1.25 / 5 # per 1M if cached | |
| cached_tokens_pricing = total_prefix_hit_count * cache_pricing / 1e6 | |
| non_cached_tokens_pricing = (total_string_length - total_prefix_hit_count) * no_cache_pricing / 1e6 | |
| print( | |
| f"Cached tokens pricing = {round(cached_tokens_pricing,2)}, Non-cached tokens pricing = {round(non_cached_tokens_pricing,2)}, total pricing = {round(cached_tokens_pricing + non_cached_tokens_pricing,2)}" | |
| ) | |
| return total_prefix_hit_count, total_prefix_hit_rate * 100 |