benchmarks.wiki

benchmarks.wiki / Public workspace

BigCodeBench / BigCodeBench v0.1.0_hf c94498ab-2aee-5c80-8019-7c7676de0805

Problem

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

complete prompt

from random import randint, seed
import pandas as pd
from sklearn.linear_model import LinearRegression


# Constants
TEAMS = ['Team A', 'Team B', 'Team C', 'Team D', 'Team E']
PENALTY_COST = 1000  # in dollars


def task_func(goals, penalties, rng_seed=None):
    """
    Simulates football match results with random goals and penalties for multiple teams,
    and trains a linear regression model to predict penalty costs from goals.

    Parameters:
    - goals (int): Maximum number of goals a team can score in a match.
    - penalties (int): Maximum number of penalties a team can receive in a match.
    - rng_seed (int, optional): Seed for the random number generator to ensure reproducibility. Defaults to None.

    Returns:
    - tuple:
        - pd.DataFrame: Contains 'Team', 'Goals', and 'Penalty Cost' columns.
        - LinearRegression: Trained model to predict 'Penalty Cost' based on 'Goals'.

    Requirements:
    - pandas
    - sklearn.linear_model
    - random

    Example:
    >>> df, model = task_func(5, 3, rng_seed=42)
    >>> predictions = model.predict([[2], [3]])
    >>> print(predictions)
    [706.89655172 439.65517241]
    """

instruct prompt

Simulates football match results with random goals and penalties for multiple teams, and trains a linear regression model to predict penalty costs from goals.
The function should output with:
    tuple:
    pd.DataFrame: Contains 'Team', 'Goals', and 'Penalty Cost' columns.
    LinearRegression: Trained model to predict 'Penalty Cost' based on 'Goals'.
You should write self-contained code starting with:

Code

from random import randint, seed
import pandas as pd
from sklearn.linear_model import LinearRegression
# Constants
TEAMS = ['Team A', 'Team B', 'Team C', 'Team D', 'Team E']
PENALTY_COST = 1000  # in dollars
def task_func(goals, penalties, rng_seed=None):

code prompt

Code

from random import randint, seed
import pandas as pd
from sklearn.linear_model import LinearRegression
# Constants
TEAMS = ['Team A', 'Team B', 'Team C', 'Team D', 'Team E']
PENALTY_COST = 1000  # in dollars
def task_func(goals, penalties, rng_seed=None):

entry point

task_func

libs

  • pandas
  • random
  • sklearn

Discussion

Discussion

No discussion posts on this page yet. Share a minimal failing example, an algorithm with its complexity, or a reproducible command and result. Use the posting template.

See answer Answer published by the source

Artifacts

Code, notes and reproducible work shared by participants. Files are served from a separate origin.

No artifacts on this page yet. Share reproducible code or notes in a contribution. Share a minimal failing example, an algorithm with its complexity, or a reproducible command and result. Use the posting template.

Source and history

task_id
c94498ab-2aee-5c80-8019-7c7676de0805
task_key
default--v0~2e1~2e0~5fhf--c94498ab-2aee-5c80-8019-7c7676de0805
task_revision_id
1
config / split
default / v0.1.0_hf

Official source

initial import