BigCodeBench / BigCodeBench v0.1.0_hf 37d1cdc0-90d3-55eb-90da-12814f0d2f72

complete prompt

import pandas as pd
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA


def task_func(data_matrix, n_components=2):
    """
    Apply PCA with n_components components to a 2D data matrix, calculate the mean value of each component, and then return the cumulative explained variance of the components in a plot.
    - The function returns a dataframe with columns 'Component 1', 'Component 2', ... etc.
    - Each row of the dataframe correspond to a row of the original matrix mapped in the PCA space.
    - The dataframe should also include a column 'Mean' which is the average value of each component value per row
    - Create a plot of the cumulative explained variance.
        - the xlabel should be 'Number of Components' and the ylabel 'Cumulative Explained Variance'

    Parameters:
    data_matrix (numpy.array): The 2D data matrix.

    Returns:
    tuple:
        - pandas.DataFrame: A DataFrame containing the PCA transformed data and the mean of each component.
        - matplotlib.axes._axes.Axes: A plot showing the cumulative explained variance of the components.

    Requirements:
    - pandas
    - matplotlib.pyplot
    - sklearn.decomposition

    Example:
    >>> import numpy as np
    >>> data = np.array([[6, 8, 1, 3, 4], [-1, 0, 3, 5, 1]])
    >>> df, ax = task_func(data)
    >>> print(df["Mean"])
    0    2.850439
    1   -2.850439
    Name: Mean, dtype: float64
    """

instruct prompt

Apply PCA with n_components components to a 2D data matrix, calculate the mean value of each component, and then return the cumulative explained variance of the components in a plot. - The function returns a dataframe with columns 'Component 1', 'Component 2', ... etc. - Each row of the dataframe correspond to a row of the original matrix mapped in the PCA space. - The dataframe should also include a column 'Mean' which is the average value of each component value per row - Create a plot of the cumulative explained variance. - the xlabel should be 'Number of Components' and the ylabel 'Cumulative Explained Variance'
The function should output with:
    tuple:
    pandas.DataFrame: A DataFrame containing the PCA transformed data and the mean of each component.
    matplotlib.axes._axes.Axes: A plot showing the cumulative explained variance of the components.
You should write self-contained code starting with:

Code

import pandas as pd
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
def task_func(data_matrix, n_components=2):

code prompt

Code

import pandas as pd
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
def task_func(data_matrix, n_components=2):

entry point

task_func

libs

• pandas
• matplotlib
• sklearn