Tools / db_diag /example_generate.py
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import numpy as np
from typing import List
import heapq
import openai
# import editdistance
from gensim.models import bm25model
import json
from nltk import pos_tag
# from nltk.stem import WordNetLemmatizer
# from nltk.corpus import wordnet, stopwords
# from nltk.tokenize import word_tokenize
# import nltk
# nltk.download('stopwords')
# nltk.download('punkt')
# nltk.download('averaged_perceptron_tagger')
# nltk.download('wordnet')
# wnl = WordNetLemmatizer()
# corpus = []
# with open("/Users/4paradigm/Desktop/work/2023_05_22/root_causes_dbmind.jsonl", 'r') as f:
# data = json.load(f)
# corpus = [example["desc"] for example in data]
# metrics = [example["metrics"] for example in data]
# stop_words = set(stopwords.words('english'))
# preprocessed_corpus = []
# for c in corpus:
# word_tokens = word_tokenize(c)
# preprocessed_corpus.append([wnl.lemmatize(w,pos='n') for w in word_tokens if not w in stop_words])
# def embedding(input:str):
# response = openai.Embedding.create(
# input=input,
# model="text-embedding-ada-002"
# )
# embeddings = response['data'][0]['embedding']
# # print("\n-----\ntext:{}\n embeddings:{}\n-----\n".format(input, embeddings))
# return embeddings
# def euclidean_distance(target:List[float], sample:List[float]):
# """
# return the euclidean distance of two vectors
# """
# return np.sqrt(np.sum(np.square(np.asarray(target) - np.asarray(sample))))
# def cosine_distance(target:List[float], sample:List[float]):
# """
# return the euclidean distance of two vectors
# """
# return 1 - np.dot(target,sample)/(np.linalg.norm(target)*np.linalg.norm(sample))
# def linear_search(k:int, target:List[float], samples:List[List[float]]):
# """
# k: the top-k examples
# target: incoming metrics
# samples: examples
# """
# func_distance = cosine_distance
# # func_distance = cosine_distance
# dist = []
# for s in samples:
# dist.append(func_distance(target, s))
# index = heapq.nlargest(k, range(len(dist)), dist.__getitem__)
# return index
# THRESHOLD = 0.5
# def editdis_linear(k:int, target:List[str], samples:List[List[str]]):
# dist = []
# for sample in samples:
# dis = len(target)
# for t in target:
# dis_samples = [editdistance.eval(t, s)/max(len(t), len(s)) for s in sample]
# if min(dis_samples) < THRESHOLD:
# dis -= 1
# dist.append(dis)
# index = heapq.nsmallest(k, range(len(dist)), dist.__getitem__)
# return index
def simple_tok(sent: str):
return sent.split()
# def get_wordnet_pos(tag):
# if tag.startswith('J'):
# return wordnet.ADJ
# elif tag.startswith('V'):
# return wordnet.VERB
# elif tag.startswith('N'):
# return wordnet.NOUN
# elif tag.startswith('R'):
# return wordnet.ADV
# else:
# return None
def bm25(k, target: List[str], sample: List[List[str]]):
tok_corpus = sample
bm25 = bm25model(tok_corpus)
query = target
scores = bm25.get_scores(query)
best_docs = sorted(range(len(scores)), key=lambda i: scores[i], reverse=True)[:k]
best_docs_none_zero = []
for d in best_docs:
if scores[d] != 0:
best_docs_none_zero.append(d)
return best_docs_none_zero
# if __name__ == "__main__":
# nltk.download('stopwords')
# nltk.download('punkt')
# nltk.download('averaged_perceptron_tagger')
# nltk.download('wordnet')
# wnl = WordNetLemmatizer()
# corpus = []
# with open("/Users/4paradigm/Desktop/work/2023_05_22/root_causes_dbmind.jsonl", 'r') as f:
# data = json.load(f)
# corpus = [example["desc"] for example in data]
# metrics = [example["metrics"] for example in data]
# stop_words = set(stopwords.words('english'))
# preprocessed_corpus = []
# for c in corpus:
# word_tokens = word_tokenize(c)
# preprocessed_corpus.append([wnl.lemmatize(w,pos='n') for w in word_tokens if not w in stop_words])
# print(preprocessed_corpus)
# best_docs = bm25(3, "package drop rate threshold", preprocessed_corpus)
# for i, b in enumerate(best_docs):
# print(f"desc {i+1}: {corpus[b]}")
# print(f"metrics {i+1}: {metrics[b]}")
# print("\n\n\n\n")
# metrics_str = "the number of active sessions"
# examples = [
# "If there are many active sessions, high-concurrency sessions can consume significant amounts of memory, especially if they execute memory-intensive operations or hold large result sets in memory. This can lead to memory pressure, increased disk I/O, and potential out-of-memory errors if the system does not have enough available memory to handle the workload.",
# "If there are slow queries, they involve large result sets that need to be stored in memory before being sent back to the client application. If the query returns a substantial number of rows or the result set contains large data objects (e.g., images, documents), it can consume a significant amount of memory ...",
# ]
# metrics_embedding = embedding(metrics_str)
# examples_embedding = [embedding(e) for e in examples]
# metrics_embedding = [-0.0006464981124736369, 0.0011919541284441948, 0.012529219500720501, -0.015440601855516434, -0.00987129844725132, -0.005535051692277193, -0.039101578295230865, -0.0035553115885704756, -0.0023136925883591175, -0.035566817969083786, 0.018605446442961693, 0.012700477614998817, -0.003983456175774336, -0.008234074339270592, -0.020550934597849846, -0.01579681783914566, 0.014303450472652912, -0.00839163176715374, 0.02501733787357807, -0.02645590342581272, -0.022633429616689682, -0.003805347951129079, -0.025811973959207535, -0.0012887148186564445, -0.018947960808873177, -0.015522805973887444, 0.003020987380295992, -0.00863824225962162, -7.262400322360918e-05, -0.017509395256638527, 0.011645529419183731, -0.019893303513526917, -0.016070829704403877, -0.007624396588653326, -0.0179752167314291, 0.02253752388060093, -0.0007573875482194126, -0.005202811677008867, 0.02027692086994648, 0.01091939676553011, 0.023249957710504532, -0.0035107845906168222, -0.011967494152486324, 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# examples_embedding = [
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# ]
# index = linear_search(1,metrics_embedding,examples_embedding)
# print(index)
# str1 = "n_bad_tup"
# str2 = "many bad tuples"
# print(editdistance.eval('n_bad_tup', 'many_bad_tuples')/max(len(str1), len(str2)))