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BigCodeBench / BigCodeBench v0.1.0_hf 0e8ed6c9-e87d-55c7-81ec-abd975e01095

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.linear_model import LinearRegression
import matplotlib.pyplot as plt

def task_func(df):
    """
    Performs linear regression on a DataFrame using 'date' (converted to ordinal) as the predictor for 'value'. It plots both the original and 
    predicted values, showcasing the linear relationship.

    Parameters:
        df (DataFrame): DataFrame containing 'group', 'date' (in datetime format), and 'value' columns.

    Returns:
        tuple: Consists of the LinearRegression model, the predictions array, and the matplotlib Axes object of the plot.
               The Axes object will have a title 'Value vs Date (Linear Regression Prediction)', 
               x-axis labeled as 'Date (ordinal)', and y-axis labeled as 'Value'.

    Raises:
        ValueError: If 'df' is not a valid DataFrame, lacks the required columns, or if 'date' column is not in datetime format.

    Requirements:
        - pandas
        - sklearn
        - matplotlib

    Example:
        >>> df = pd.DataFrame({
        ...     "group": ["A", "A", "A", "B", "B"],
        ...     "date": pd.to_datetime(["2022-01-02", "2022-01-13", "2022-02-01", "2022-02-23", "2022-03-05"]),
        ...     "value": [10, 20, 16, 31, 56],
        ... })
        >>> model, predictions, ax = task_func(df)
        >>> plt.show()  # Displays the plot with original and predicted values
    """

instruct prompt

Performs linear regression on a DataFrame using 'date' (converted to ordinal) as the predictor for 'value'. It plots both the original and predicted values, showcasing the linear relationship.
The function should raise the exception for: ValueError: If 'df' is not a valid DataFrame, lacks the required columns, or if 'date' column is not in datetime format.
The function should output with:
    tuple: Consists of the LinearRegression model, the predictions array, and the matplotlib Axes object of the plot.
    The Axes object will have a title 'Value vs Date (Linear Regression Prediction)',
    x-axis labeled as 'Date (ordinal)', and y-axis labeled as 'Value'.
You should write self-contained code starting with:

Code

import pandas as pd
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
def task_func(df):

code prompt

Code

import pandas as pd
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
def task_func(df):

entry point

task_func

libs

  • pandas
  • matplotlib
  • sklearn

Discussion

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Source and history

task_id
0e8ed6c9-e87d-55c7-81ec-abd975e01095
task_key
default--v0~2e1~2e0~5fhf--0e8ed6c9-e87d-55c7-81ec-abd975e01095
task_revision_id
3
config / split
default / v0.1.0_hf

Official source

initial import