# ds1000 / 911 - 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 ``` # 911: DS-1000 Task ## Prompt Problem: I am trying to run an Elastic Net regression but get the following error: NameError: name 'sklearn' is not defined... any help is greatly appreciated! # ElasticNet Regression from sklearn import linear_model import statsmodels.api as sm ElasticNet = sklearn.linear_model.ElasticNet() # create a lasso instance ElasticNet.fit(X_train, y_train) # fit data # print(lasso.coef_) # print (lasso.intercept_) # print out the coefficients print ("R^2 for training set:"), print (ElasticNet.score(X_train, y_train)) print ('-'*50) print ("R^2 for test set:"), print (ElasticNet.score(X_test, y_test)) A: corrected code <code> import numpy as np import pandas as pd from sklearn import linear_model import statsmodels.api as sm X_train, y_train, X_test, y_test = load_data() assert type(X_train) == np.ndarray assert type(y_train) == np.ndarray assert type(X_test) == np.ndarray assert type(y_test) == np.ndarray </code> training_set_score, test_set_score = ... # put solution in these variables 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