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BigCodeBench / BigCodeBench v0.1.0_hf ce1ecdcd-e838-56d4-91ef-1414918d4bcd

Problem

Answer published by the source. Consult the official source to check your work against its answer.

complete prompt

import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestClassifier


def task_func(
    feature_array,
    target_array,
    feature_names=["f1", "f2", "f3", "f4", "f5"],
    target_name="target",
    seed=None,
):
    """
    Shuffle the columns of a given numpy array and train a Random Forest Classifier on the shuffled data.

    Parameters:
    - feature_array (numpy.ndarray): 2D array containing the feature data with shape (n_samples, n_features).
    - target_array (numpy.ndarray): 1D array containing the target data with shape (n_samples,).
    - feature_names (list of str, optional): Names of the features corresponding to the columns in `feature_array`.
      Defaults to ['f1', 'f2', 'f3', 'f4', 'f5'].
    - target_name (str, optional): Name of the target column. Defaults to 'target'.
    - seed (int, optional): Seed for the random number generator to make shuffling reproducible. Defaults to None.

    Returns:
    sklearn.ensemble.RandomForestClassifier: A trained Random Forest Classifier on the shuffled feature data.

    Requirements:
    - numpy
    - pandas
    - sklearn

    Examples:
    >>> feature_array = np.array([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]])
    >>> target_array = np.array([0, 1])
    >>> clf = task_func(feature_array, target_array)
    >>> type(clf)
    <class 'sklearn.ensemble._forest.RandomForestClassifier'>
    """

instruct prompt

Shuffle the columns of a given numpy array and train a Random Forest Classifier on the shuffled data.
The function should output with:
    sklearn.ensemble.RandomForestClassifier: A trained Random Forest Classifier on the shuffled feature data.
You should write self-contained code starting with:

Code

import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
def task_func(
    feature_array,
    target_array,
    feature_names=["f1", "f2", "f3", "f4", "f5"],
    target_name="target",
    seed=None,
):

code prompt

Code

import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
def task_func(
    feature_array,
    target_array,
    feature_names=["f1", "f2", "f3", "f4", "f5"],
    target_name="target",
    seed=None,
):

entry point

task_func

libs

  • pandas
  • numpy
  • sklearn

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Source and history

task_id
ce1ecdcd-e838-56d4-91ef-1414918d4bcd
task_key
default--v0~2e1~2e0~5fhf--ce1ecdcd-e838-56d4-91ef-1414918d4bcd
task_revision_id
1
config / split
default / v0.1.0_hf

Official source

initial import