# ds1000 / 931 - 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 ``` # 931: DS-1000 Task ## Prompt Problem: I am using python and scikit-learn to find cosine similarity between item descriptions. A have a df, for example: items description 1fgg abcd ty 2hhj abc r 3jkl r df I did following procedures: 1) tokenizing each description 2) transform the corpus into vector space using tf-idf 3) calculated cosine distance between each description text as a measure of similarity. distance = 1 - cosinesimilarity(tfidf_matrix) My goal is to have a similarity matrix of items like this and answer the question like: "What is the similarity between the items 1ffg and 2hhj : 1fgg 2hhj 3jkl 1ffg 1.0 0.8 0.1 2hhj 0.8 1.0 0.0 3jkl 0.1 0.0 1.0 How to get this result? Thank you for your time. A: <code> import numpy as np import pandas as pd import sklearn from sklearn.feature_extraction.text import TfidfVectorizer df = load_data() tfidf = TfidfVectorizer() </code> cosine_similarity_matrix = ... # 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