# ds1000 / 873 - taskset: [ds1000](https://harnessreport.com/tasks/ds1000.md) - difficulty: - category: - language: - runnable from the site: no - agent timeout: 1800s ## Results by harness _none yet_ ## Instruction ``` # 873: DS-1000 Task ## Prompt Problem: My goal is to input some queries and find out which query is most similar to a set of documents. So far I have calculated the tf-idf of the documents doing the following: from sklearn.feature_extraction.text import TfidfVectorizer def get_term_frequency_inverse_data_frequency(documents): vectorizer = TfidfVectorizer() matrix = vectorizer.fit_transform(documents) return matrix def get_tf_idf_query_similarity(documents, query): tfidf = get_term_frequency_inverse_data_frequency(documents) The problem I am having is now that I have tf-idf of the documents what operations do I perform on the query so I can find the cosine similarity to the documents? The answer should be like a 3*5 matrix of the similarities. A: <code> import numpy as np import pandas as pd from sklearn.feature_extraction.text import TfidfVectorizer queries, documents = load_data() assert type(queries) == list assert type(documents) == list tfidf = TfidfVectorizer() tfidf.fit_transform(documents) </code> cosine_similarities_of_queries = ... # put solution in this variable BEGIN SOLUTION <code> ## What to do - Edit `solution/solution.py` so the code passes the DS-1000 tests. - Do not access the internet or install new packages; required libraries are preinstalled in the Docker image. - Run tests locally via `bash tests/test.sh`. ## Notes - Keep the variable names/signatures implied by the prompt/code_context. - The evaluator uses the original DS-1000 `code_context` (`test_execution` / `test_string`). ``` --- Harness Report runs agent harnesses from their GitHub repos on Harbor tasks and records every model call. Every page is also `.md` and `.json`; index: https://harnessreport.com/llms.txt · MCP: https://harnessreport.com/mcp