{"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":"1d3bccca-543d-52c8-b058-11d081e2f0e1","task_key":"default--test--848","task_revision_id":"1","upstream_id":"848","short_description":"Given the following example:","config":"default","split":"test","body":"{\"library\":\"Sklearn\",\"prompt\":\"Problem:\\n\\nGiven the following example:\\n\\nfrom sklearn.feature_selection import SelectKBest\\nfrom sklearn.linear_model import LogisticRegression\\nfrom sklearn.pipeline import Pipeline\\nimport pandas as pd\\n\\npipe = Pipeline(steps=[\\n    ('select', SelectKBest(k=2)),\\n    ('clf', LogisticRegression())]\\n)\\n\\npipe.fit(data, target)\\nI would like to get intermediate data state in scikit learn pipeline corresponding to 'select' output (after fit_transform on 'select' but not LogisticRegression). Or to say things in another way, it would be the same than to apply\\n\\nSelectKBest(k=2).fit_transform(data, target)\\nAny ideas to do that?\\n\\nA:\\n\\n<code>\\nimport numpy as np\\nfrom sklearn.feature_selection import SelectKBest\\nfrom sklearn.linear_model import LogisticRegression\\nfrom sklearn.pipeline import Pipeline\\nimport pandas as pd\\n\\ndata, target = load_data()\\n\\npipe = Pipeline(steps=[\\n    ('select', SelectKBest(k=2)),\\n    ('clf', LogisticRegression())]\\n)\\n</code>\\nselect_out = ... # put solution in this variable\\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":[]}