{"kind":"task","effective_mode":"full","benchmark":{"kind":"benchmark","effective_mode":"full","slug":"ds-1000","formal_name":"DS-1000","introduction":"DS-1000 builds 1,000 data-science problems from real StackOverflow questions across seven libraries including NumPy, Pandas and Matplotlib. The problems are perturbed so that recalling the original answer does not solve them.","introduction_ja":"","introduction_en":"","category":"Category not supplied","task_count":null,"acquisition_status":"Acquisition status not supplied","official_url":"https://ds1000-code-gen.github.io/","indexing_mode":"index","profile":{"resources":[],"task_format":"","scoring":"","metric":"","size":"","answer_access":"","license":"","citation":"","maintainer":"","released":"","why_hard":"","related":[],"history":"","construction":"","limitations":"","saturation":"","scores":[]}},"task_id":"dae7a8cf-99b4-5cfc-a0cb-0aa3ebbb2260","task_key":"default--test--911","task_revision_id":"1","upstream_id":"911","short_description":"I am trying to run an Elastic Net regression but get the following error:…","config":"default","split":"test","body":"{\"library\":\"Sklearn\",\"prompt\":\"Problem:\\n\\nI am trying to run an Elastic Net regression but get the following error: NameError: name 'sklearn' is not defined... any help is greatly appreciated!\\n\\n    # ElasticNet Regression\\n\\n    from sklearn import linear_model\\n    import statsmodels.api as sm\\n\\n    ElasticNet = sklearn.linear_model.ElasticNet() # create a lasso instance\\n    ElasticNet.fit(X_train, y_train) # fit data\\n\\n    # print(lasso.coef_)\\n    # print (lasso.intercept_) # print out the coefficients\\n\\n    print (\\\"R^2 for training set:\\\"),\\n    print (ElasticNet.score(X_train, y_train))\\n\\n    print ('-'*50)\\n\\n    print (\\\"R^2 for test set:\\\"),\\n    print (ElasticNet.score(X_test, y_test))\\n\\nA:\\n\\ncorrected code\\n<code>\\nimport numpy as np\\nimport pandas as pd\\nfrom sklearn import linear_model\\nimport statsmodels.api as sm\\nX_train, y_train, X_test, y_test = load_data()\\nassert type(X_train) == np.ndarray\\nassert type(y_train) == np.ndarray\\nassert type(X_test) == np.ndarray\\nassert type(y_test) == np.ndarray\\n</code>\\ntraining_set_score, test_set_score = ... # put solution in these variables\\nBEGIN SOLUTION\\n<code>\\n\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://ds1000-code-gen.github.io/","history":"initial import","indexing_mode":"index","subproblems":[],"grids":[]}