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BigCodeBench / BigCodeBench v0.1.0_hf b6032d38-0212-5555-a9bc-c85d541d91f5

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.preprocessing import StandardScaler


def task_func(P, T):
    """
    Calculate the product of matrix "P" and 3D tensor "T" then return dataframe of normalized results.

    This function performs matrix-tensor multiplication between a matrix "P" and a 3D tensor "T" using numpy.
    It checks if the shapes of P and T are compatible for multiplication, raising a ValueError if they are not.
    The function then normalizes the resulting 2D array using sklearn's StandardScaler. The final output
    is returned as a pandas DataFrame, with columns named feature_0, feature_1, ..., feature_n,
    where n is the number of features in the flattened result of the matrix-tensor multiplication.

    Parameters:
    - P (numpy.ndarray): The input matrix. Must not be empty.
    - T (numpy.ndarray): The input tensor. Must not be empty.

    Returns:
    pandas.DataFrame: A DataFrame with the normalized result.

    Requirements:
    - numpy
    - pandas
    - sklearn.preprocessing

    Example:
    >>> np.random.seed(0)
    >>> P = np.array([[6, 2, 7], [1, 1, 8], [8, 7, 1], [9, 6, 4], [2, 1, 1]])
    >>> T = np.random.rand(3, 5, 5)
    >>> result = task_func(P, T)
    >>> type(result)
    <class 'pandas.core.frame.DataFrame'>
    >>> result.head(2)
       feature_0  feature_1  feature_2  ...  feature_22  feature_23  feature_24
    0   0.214791   0.220904   1.697850  ...    1.768847   -1.759510   -0.003527
    1  -0.652336   1.064228  -0.707134  ...   -0.036116    1.002544   -0.813796
    <BLANKLINE>
    [2 rows x 25 columns]
    """

instruct prompt

Calculate the product of matrix "P" and 3D tensor "T" then return dataframe of normalized results. This function performs matrix-tensor multiplication between a matrix "P" and a 3D tensor "T" using numpy. It checks if the shapes of P and T are compatible for multiplication, raising a ValueError if they are not. The function then normalizes the resulting 2D array using sklearn's StandardScaler. The final output is returned as a pandas DataFrame, with columns named feature_0, feature_1, ..., feature_n, where n is the number of features in the flattened result of the matrix-tensor multiplication.
The function should output with:
    pandas.DataFrame: A DataFrame with the normalized result.
You should write self-contained code starting with:

Code

import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
def task_func(P, T):

code prompt

Code

import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
def task_func(P, T):

entry point

task_func

libs

  • pandas
  • numpy
  • sklearn

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Source and history

task_id
b6032d38-0212-5555-a9bc-c85d541d91f5
task_key
default--v0~2e1~2e0~5fhf--b6032d38-0212-5555-a9bc-c85d541d91f5
task_revision_id
1
config / split
default / v0.1.0_hf

Official source

initial import