heatmap confusion

Tired Toucan answered on October 26, 2023 Popularity 6/10 Helpfulness 1/10

answer heatmap confusion

heatmap confusion

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import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt

def make_confusion_matrix(cf, group_names=None, categories='auto', count=True,
                          percent=True, cbar=True, xyticks=True, xyplotlabels=True,
                          sum_stats=True, figsize=None, cmap='Blues', title=None):
    """
    Generates a labeled confusion matrix using Seaborn's heatmap.
    
    Args:
    - cf (array-like): 2D array representing the confusion matrix.
    - group_names (list): List of labels to use for annotating the matrix.
    - categories (list or 'auto'): List of categories for axis labels.
    - count (bool): Whether to display counts in matrix boxes.
    - percent (bool): Whether to display percentage in matrix boxes.
    - cbar (bool): Whether to display a colorbar.
    - xyticks (bool): Whether to display x and y ticks.
    - xyplotlabels (bool): Whether to display x and y labels.
    - sum_stats (bool): Whether to display summary statistics.
    - figsize (tuple): Size of the figure.
    - cmap (str): Colormap to be used.
    - title (str): Title of the heatmap.
    
    Returns:
    - None: Displays a heatmap.
    """
    # Ensure matrix is square
    if cf.shape[0] != cf.shape[1]:
        raise ValueError("Confusion matrix should be a square matrix.")
    
    # Ensure group_names match matrix size if provided
    if group_names and len(group_names) != cf.size:
        raise ValueError("Length of group_names should match the size of the confusion matrix.")
    
    # Annotations for each box
    blanks = ['' for _ in range(cf.size)]
    
    group_labels = ["{}\n".format(value) for value in group_names] if group_names else blanks
    group_counts = ["{0:0.0f}\n".format(value) for value in cf.flatten()] if count else blanks
    group_percentages = ["{0:.2%}".format(value) for value in cf.flatten() / np.sum(cf)] if percent else blanks
    
    box_labels = [f"{v1}{v2}{v3}".strip() for v1, v2, v3 in zip(group_labels, group_counts, group_percentages)]
    box_labels = np.asarray(box_labels).reshape(cf.shape[0], cf.shape[1])

    # Summary statistics
    stats_text = ""
    if sum_stats:
        accuracy = np.trace(cf) / float(np.sum(cf))
        if len(cf) == 2:  # Binary classification metrics
            precision = cf[1, 1] / sum(cf[:, 1])
            recall = cf[1, 1] / sum(cf[1, :])
            f1_score = 2 * precision * recall / (precision + recall)
            stats_text = f"\n\nAccuracy={accuracy:.3f}\nPrecision={precision:.3f}\nRecall={recall:.3f}\nF1 Score={f1_score:.3f}"
        else:
            stats_text = f"\n\nAccuracy={accuracy:.3f}"

    # Visualization
    if not figsize:
        figsize = plt.rcParams.get('figure.figsize')

    plt.figure(figsize=figsize)
    sns.heatmap(cf, annot=box_labels, fmt="", cmap=cmap, cbar=cbar, xticklabels=categories, yticklabels=categories)
    
    if xyplotlabels:
        plt.ylabel('True label')
        plt.xlabel('Predicted label' + stats_text)
    else:
        plt.xlabel(stats_text)
    
    if title:
        plt.title(title)
    plt.show()

Popularity 6/10 Helpfulness 1/10 Language python

Source: chat.openai.com

Tags: heatmap python

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Contributed on Oct 26 2023

Tired Toucan

0 Answers Avg Quality 2/10

heatmap confusion

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