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BigCodeBench / BigCodeBench v0.1.0_hf 1c284408-3bcb-58e7-8770-c155fe7b952a

Problem

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

complete prompt

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix
import numpy as np
import matplotlib.pyplot as plt


def task_func(feature: pd.Series, target: pd.Series) -> (np.ndarray, plt.Axes):
    """
    Train a logistic regression model on one feature and evaluate its performance using a confusion matrix plot.
    The function takes a feature and a target series, splits them into training and testing sets, trains the logistic
    regression model, predicts the target for the test set, and plots the confusion matrix.

    Parameters:
    feature (pd.Series): Series representing the single feature for the logistic regression model.
    target (pd.Series): Series representing the target variable.

    Returns:
    (np.ndarray, plt.Axes): A tuple containing the confusion matrix and the matplotlib Axes object of the confusion matrix plot.

    Requirements:
    - pandas
    - sklearn.model_selection.train_test_split
    - sklearn.linear_model.LogisticRegression
    - sklearn.metrics.confusion_matrix
    - numpy
    - matplotlib.pyplot

    Example:
    >>> feature = pd.Series(np.random.rand(1000)) # Feature data
    >>> target = pd.Series(np.random.randint(0, 2, size=1000)) # Target data (binary)
    >>> cm, ax = task_func(feature, target)
    >>> ax.get_title()
    'Confusion Matrix'
    """

instruct prompt

Train a logistic regression model on one feature and evaluate its performance using a confusion matrix plot. The function takes a feature and a target series, splits them into training and testing sets, trains the logistic regression model, predicts the target for the test set, and plots the confusion matrix.
The function should output with:
    (np.ndarray, plt.Axes): A tuple containing the confusion matrix and the matplotlib Axes object of the confusion matrix plot.
You should write self-contained code starting with:

Code

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix
import numpy as np
import matplotlib.pyplot as plt
def task_func(feature: pd.Series, target: pd.Series) -> (np.ndarray, plt.Axes):

code prompt

Code

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix
import numpy as np
import matplotlib.pyplot as plt
def task_func(feature: pd.Series, target: pd.Series) -> (np.ndarray, plt.Axes):

entry point

task_func

libs

  • pandas
  • numpy
  • matplotlib
  • sklearn

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Source and history

task_id
1c284408-3bcb-58e7-8770-c155fe7b952a
task_key
default--v0~2e1~2e0~5fhf--1c284408-3bcb-58e7-8770-c155fe7b952a
task_revision_id
1
config / split
default / v0.1.0_hf

Official source

initial import