classification_utilities.py

from __future__ import print_function
import numpy as np


def display_cm(cm, labels, hide_zeros=False,
                             display_metrics=False):
    """Display confusion matrix with labels, along with
       metrics such as Recall, Precision and F1 score.
       Based on Zach Guo's print_cm gist at
       https://gist.github.com/zachguo/10296432
    """

    precision = np.diagonal(cm)/cm.sum(axis=0).astype('float')
    recall = np.diagonal(cm)/cm.sum(axis=1).astype('float')
    F1 = 2 * (precision * recall) / (precision + recall)
    
    precision[np.isnan(precision)] = 0
    recall[np.isnan(recall)] = 0
    F1[np.isnan(F1)] = 0
    
    total_precision = np.sum(precision * cm.sum(axis=1)) / cm.sum(axis=(0,1))
    total_recall = np.sum(recall * cm.sum(axis=1)) / cm.sum(axis=(0,1))
    total_F1 = np.sum(F1 * cm.sum(axis=1)) / cm.sum(axis=(0,1))
    #print total_precision
    
    columnwidth = max([len(x) for x in labels]+[5]) # 5 is value length
    empty_cell = " " * columnwidth
    # Print header
    print("    " + " Pred", end=' ')
    for label in labels: 
        print("%{0}s".format(columnwidth) % label, end=' ')
    print("%{0}s".format(columnwidth) % 'Total')
    print("    " + " True")
    # Print rows
    for i, label1 in enumerate(labels):
        print("    %{0}s".format(columnwidth) % label1, end=' ')
        for j in range(len(labels)): 
            cell = "%{0}d".format(columnwidth) % cm[i, j]
            if hide_zeros:
                cell = cell if float(cm[i, j]) != 0 else empty_cell
            print(cell, end=' ')
        print("%{0}d".format(columnwidth) % sum(cm[i,:]))
        
    if display_metrics:
        print()
        print("Precision", end=' ')
        for j in range(len(labels)):
            cell = "%{0}.2f".format(columnwidth) % precision[j]
            print(cell, end=' ')
        print("%{0}.2f".format(columnwidth) % total_precision)
        print("   Recall", end=' ')
        for j in range(len(labels)):
            cell = "%{0}.2f".format(columnwidth) % recall[j]
            print(cell, end=' ')
        print("%{0}.2f".format(columnwidth) % total_recall)
        print("       F1", end=' ')
        for j in range(len(labels)):
            cell = "%{0}.2f".format(columnwidth) % F1[j]
            print(cell, end=' ')
        print("%{0}.2f".format(columnwidth) % total_F1)
    
                  
def display_adj_cm(
        cm, labels, adjacent_facies, hide_zeros=False, 
        display_metrics=False):
    """This function displays a confusion matrix that counts 
       adjacent facies as correct.
    """
    adj_cm = np.copy(cm)
    
    for i in np.arange(0,cm.shape[0]):
        for j in adjacent_facies[i]:
            adj_cm[i][i] += adj_cm[i][j]
            adj_cm[i][j] = 0.0
        
    display_cm(adj_cm, labels, hide_zeros, 
                             display_metrics)