# ds1000 / 732 - 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 ``` # 732: DS-1000 Task ## Prompt Problem: I have two csr_matrix, c1 and c2. I want a new sparse matrix Feature = [c1, c2], that is, to stack c1 and c2 horizontally to get a new sparse matrix. To make use of sparse matrix's memory efficiency, I don't want results as dense arrays. But if I directly concatenate them this way, there's an error that says the matrix Feature is a list. And if I try this: Feature = csr_matrix(Feature) It gives the error: Traceback (most recent call last): File "yelpfilter.py", line 91, in <module> Feature = csr_matrix(Feature) File "c:\python27\lib\site-packages\scipy\sparse\compressed.py", line 66, in __init__ self._set_self( self.__class__(coo_matrix(arg1, dtype=dtype)) ) File "c:\python27\lib\site-packages\scipy\sparse\coo.py", line 185, in __init__ self.row, self.col = M.nonzero() TypeError: __nonzero__ should return bool or int, returned numpy.bool_ Any help would be appreciated! A: <code> from scipy import sparse c1 = sparse.csr_matrix([[0, 0, 1, 0], [2, 0, 0, 0], [0, 0, 0, 0]]) c2 = sparse.csr_matrix([[0, 3, 4, 0], [0, 0, 0, 5], [6, 7, 0, 8]]) </code> Feature = ... # 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